{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example using dask-gateway with a JupyterHub sandbox, intake-esm (on S3 NetCDF)\n",
    "\n",
    "\n",
    "Author: A.Radhakrishnan, Feb 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "using t2.xlarge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import xarray as xr\n",
    "import intake,yaml\n",
    "import intake_esm\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from dask_gateway import Gateway\n",
    "\n",
    "def launchDask():\n",
    "    #TODO try, except\n",
    "    gateway = Gateway()\n",
    "    clusters = gateway.list_clusters()\n",
    "    clusters\n",
    "    if len(clusters) >0:\n",
    "        cluster = gateway.connect(clusters[0].name)\n",
    "        print(\"using existing connection\")\n",
    "        print(cluster)\n",
    "    else:\n",
    "        from dask_gateway import GatewayCluster\n",
    "        cluster = GatewayCluster()\n",
    "    return(cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LAUNCH DASK CLUSTER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using existing connection\n",
      "GatewayCluster<pdub.08aa64e1f7134720b507fd602d19393a, status=running>\n"
     ]
    }
   ],
   "source": [
    "cluster = launchDask()\n",
    "#%pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "VBox(children=(HTML(value='<h2>GatewayCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n<style scoped>\\n    …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster ## see the cluster settings below, we will scale it up in a couple of cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_w = 0\n",
    "max_w = 30\n",
    "\n",
    "if(min_w > 30):\n",
    "    print(\"Let's restrict the max workers to 30 for now\")\n",
    "    min_w = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>gateway://traefik-pdub-dask-gateway.pdub:80/pdub.08aa64e1f7134720b507fd602d19393a</li>\n",
       "  <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/pdub.08aa64e1f7134720b507fd602d19393a/status' target='_blank'>/services/dask-gateway/clusters/pdub.08aa64e1f7134720b507fd602d19393a/status</a></li>\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>0</li>\n",
       "  <li><b>Cores: </b>0</li>\n",
       "  <li><b>Memory: </b>0 B</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
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       "<Client: 'tls://192.168.52.141:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from distributed import Client\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MONITOR THE DASHBOARD from link above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADAPTIVELY SCALE CLUSTER, so when nothing is going on we use minimum nodes in the setting below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.adapt(minimum=min_w, maximum=max_w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now use our intake_esm catalog and the intake_esm API for data exploration and search.\n",
    "Then use xarray and matplotlib as needed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Catalog: \n",
    "\n",
    "esgf-world.json is the ESM collections spec file for the netCDF data in the S3 bucket esgf-world. The cell below points to the most updated catalog (updated periodically using a CatalogBuilder API)\n",
    "\n",
    "Other examples can be found here as well:\n",
    "\n",
    "You can refer to https://github.com/aradhakrishnanGFDL/gfdl-aws-analysis/tree/community/esm-collection-spec-examples for the most recent catalogs\n",
    "More examples can be found in https://github.com/aradhakrishnanGFDL/gfdl-aws-analysis/tree/community/examples \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_url = \"https://cmip6-nc.s3.us-east-2.amazonaws.com/esgf-world.json\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = intake.open_esm_datastore(col_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examples to just search for what we want from the catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "expname_filter = ['historical']\n",
    "table_id_filter = 'Omon'\n",
    "model_filter = 'GFDL-ESM4'\n",
    "variable_id_filter = \"thetao\"\n",
    "ens_filter = \"r1i1p1f1\"\n",
    "#version_filter = \"v20190726\"\n",
    "grid_label=\"gn\"\n",
    "cat = col.search(grid_label=\"gn\",experiment_id=expname_filter, mip_table=table_id_filter,model=model_filter,variable=variable_id_filter,version=\"v20190726\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'project.institute.model.experiment_id.mip_table'\n"
     ]
    },
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   "source": [
    "dset_dict = cat.to_dataset_dict(cdf_kwargs={'chunks': {'time': 1}},storage_options={'anon':True})"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "ds = dset_dict['CMIP6.NOAA-GFDL.GFDL-ESM4.historical.Omon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;thetao&#x27; (ensemble_member: 1, time: 1980, lev: 35, y: 576, x: 720)&gt;\n",
       "dask.array&lt;broadcast_to, shape=(1, 1980, 35, 576, 720), dtype=float32, chunksize=(1, 1, 35, 576, 720), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    lat              (y, x) float32 dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;\n",
       "    lon              (y, x) float32 dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;\n",
       "  * time             (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * x                (x) float64 -299.8 -299.2 -298.8 ... 58.75 59.25 59.75\n",
       "  * y                (y) float64 -77.91 -77.72 -77.54 ... 89.47 89.68 89.89\n",
       "  * lev              (lev) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03\n",
       "  * ensemble_member  (ensemble_member) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "Attributes:\n",
       "    long_name:      Sea Water Potential Temperature\n",
       "    units:          degC\n",
       "    cell_methods:   area: mean where sea time: mean\n",
       "    cell_measures:  area: areacello volume: volcello\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    original_name:  thetao</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'thetao'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>ensemble_member</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lev</span>: 35</li><li><span class='xr-has-index'>y</span>: 576</li><li><span class='xr-has-index'>x</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9bb07d32-a1a9-4e0f-a336-22a5b9613521' class='xr-array-in' type='checkbox' checked><label for='section-9bb07d32-a1a9-4e0f-a336-22a5b9613521' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 35, 576, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 114.96 GB </td> <td> 58.06 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1, 1980, 35, 576, 720) </td> <td> (1, 1, 35, 576, 720) </td></tr>\n",
       "    <tr><th> Count </th><td> 5949 Tasks </td><td> 1980 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-44221ceb-93a5-401c-91a7-5fc9a070d232' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44221ceb-93a5-401c-91a7-5fc9a070d232' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-28bbcbd8-1786-4a6a-97ec-8a7e893163c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28bbcbd8-1786-4a6a-97ec-8a7e893163c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dca7b61d-a4ed-4e35-a58f-4caec170b4a3' class='xr-var-data-in' type='checkbox'><label for='data-dca7b61d-a4ed-4e35-a58f-4caec170b4a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.66 MB </td> <td> 1.66 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (576, 720) </td> <td> (576, 720) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-d8f95dd3-57bf-4811-9ca6-fb24905268ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8f95dd3-57bf-4811-9ca6-fb24905268ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-032fd290-4375-442c-bef3-84d9fb8f5c87' class='xr-var-data-in' type='checkbox'><label for='data-032fd290-4375-442c-bef3-84d9fb8f5c87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.66 MB </td> <td> 1.66 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (576, 720) </td> <td> (576, 720) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-90a4c676-8b54-48cc-bb6e-5c0fa71d729d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90a4c676-8b54-48cc-bb6e-5c0fa71d729d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-479754a0-6f52-41e7-a633-d7639ba01599' class='xr-var-data-in' type='checkbox'><label for='data-479754a0-6f52-41e7-a633-d7639ba01599' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>description :</span></dt><dd>Temporal mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.2 ... 59.25 59.75</div><input id='attrs-5f586198-a091-4f84-8962-5fc6a0ee6ed6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f586198-a091-4f84-8962-5fc6a0ee6ed6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e9430c-19d3-4b1c-a2a9-d5eeed7bffb7' class='xr-var-data-in' type='checkbox'><label for='data-10e9430c-19d3-4b1c-a2a9-d5eeed7bffb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-299.75, -299.25, -298.75, ...,   58.75,   59.25,   59.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.91 -77.72 ... 89.68 89.89</div><input id='attrs-72399bb3-7043-471d-9022-8f5cdaac040a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72399bb3-7043-471d-9022-8f5cdaac040a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05a359f2-246e-484c-8064-1ab02fe17d75' class='xr-var-data-in' type='checkbox'><label for='data-05a359f2-246e-484c-8064-1ab02fe17d75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-77.907938, -77.723813, -77.539688, ...,  89.472   ,  89.6832  ,\n",
       "        89.8944  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-42e09875-c693-4ff3-8a08-c1daa98a23d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42e09875-c693-4ff3-8a08-c1daa98a23d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55c95fe5-ee61-4cea-942b-2488f955f4b4' class='xr-var-data-in' type='checkbox'><label for='data-55c95fe5-ee61-4cea-942b-2488f955f4b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ocean model level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>description :</span></dt><dd>generic ocean model vertical coordinate (nondimensional or dimensional)</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ensemble_member</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-bdbd6df4-53c8-4280-b879-c57cd7dfc200' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bdbd6df4-53c8-4280-b879-c57cd7dfc200' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1a51ab2-3404-4429-b1d7-31f8bb813dd7' class='xr-var-data-in' type='checkbox'><label for='data-b1a51ab2-3404-4429-b1d7-31f8bb813dd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68c2f7f1-7479-4673-af47-f0bdb020e74b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68c2f7f1-7479-4673-af47-f0bdb020e74b' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>original_name :</span></dt><dd>thetao</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'thetao' (ensemble_member: 1, time: 1980, lev: 35, y: 576, x: 720)>\n",
       "dask.array<broadcast_to, shape=(1, 1980, 35, 576, 720), dtype=float32, chunksize=(1, 1, 35, 576, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat              (y, x) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>\n",
       "    lon              (y, x) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>\n",
       "  * time             (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * x                (x) float64 -299.8 -299.2 -298.8 ... 58.75 59.25 59.75\n",
       "  * y                (y) float64 -77.91 -77.72 -77.54 ... 89.47 89.68 89.89\n",
       "  * lev              (lev) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03\n",
       "  * ensemble_member  (ensemble_member) <U8 'r1i1p1f1'\n",
       "Attributes:\n",
       "    long_name:      Sea Water Potential Temperature\n",
       "    units:          degC\n",
       "    cell_methods:   area: mean where sea time: mean\n",
       "    cell_measures:  area: areacello volume: volcello\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    original_name:  thetao"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thetao = ds.thetao\n",
    "thetao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;thetao&#x27; (ensemble_member: 1, time: 1980, lev: 35, y: 576, x: 720)&gt;\n",
       "dask.array&lt;broadcast_to, shape=(1, 1980, 35, 576, 720), dtype=float32, chunksize=(1, 1, 35, 576, 720), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    lat              (y, x) float32 dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;\n",
       "    lon              (y, x) float32 dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;\n",
       "  * time             (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * x                (x) float64 -299.8 -299.2 -298.8 ... 58.75 59.25 59.75\n",
       "  * y                (y) float64 -77.91 -77.72 -77.54 ... 89.47 89.68 89.89\n",
       "  * lev              (lev) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03\n",
       "  * ensemble_member  (ensemble_member) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "Attributes:\n",
       "    long_name:      Sea Water Potential Temperature\n",
       "    units:          degC\n",
       "    cell_methods:   area: mean where sea time: mean\n",
       "    cell_measures:  area: areacello volume: volcello\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    original_name:  thetao</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'thetao'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>ensemble_member</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lev</span>: 35</li><li><span class='xr-has-index'>y</span>: 576</li><li><span class='xr-has-index'>x</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-651d32a1-a727-48eb-ba52-8b0f6ad904b0' class='xr-array-in' type='checkbox' checked><label for='section-651d32a1-a727-48eb-ba52-8b0f6ad904b0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 35, 576, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 114.96 GB </td> <td> 58.06 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1, 1980, 35, 576, 720) </td> <td> (1, 1, 35, 576, 720) </td></tr>\n",
       "    <tr><th> Count </th><td> 5949 Tasks </td><td> 1980 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-fea389c6-65d3-400c-9bce-fe2941499aa8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fea389c6-65d3-400c-9bce-fe2941499aa8' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-97ffd5b4-ec84-4372-9fa8-5e81436aa4f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97ffd5b4-ec84-4372-9fa8-5e81436aa4f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ff08230-e906-40d5-8d0f-3e0c750bda23' class='xr-var-data-in' type='checkbox'><label for='data-1ff08230-e906-40d5-8d0f-3e0c750bda23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.66 MB </td> <td> 1.66 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (576, 720) </td> <td> (576, 720) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(576, 720), meta=np.ndarray&gt;</div><input id='attrs-022f0c62-5a59-4907-a753-100d1a7b1220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-022f0c62-5a59-4907-a753-100d1a7b1220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8453971-6711-4b33-9d02-528319992287' class='xr-var-data-in' type='checkbox'><label for='data-f8453971-6711-4b33-9d02-528319992287' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>cell_methods :</span></dt><dd>y: point x: point</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.66 MB </td> <td> 1.66 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (576, 720) </td> <td> (576, 720) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-f758d4a7-6109-4fa5-b41b-2fcd28beb923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f758d4a7-6109-4fa5-b41b-2fcd28beb923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc3b6bcc-cdf4-4f21-8c46-af147680ab84' class='xr-var-data-in' type='checkbox'><label for='data-bc3b6bcc-cdf4-4f21-8c46-af147680ab84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>description :</span></dt><dd>Temporal mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.2 ... 59.25 59.75</div><input id='attrs-83041764-6a54-4e01-b1b0-e8918c9d020e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83041764-6a54-4e01-b1b0-e8918c9d020e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a1d8de7-ea43-46c9-9281-ac49e3602da2' class='xr-var-data-in' type='checkbox'><label for='data-6a1d8de7-ea43-46c9-9281-ac49e3602da2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-299.75, -299.25, -298.75, ...,   58.75,   59.25,   59.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.91 -77.72 ... 89.68 89.89</div><input id='attrs-104f256a-fb7b-4af1-b764-794156972989' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-104f256a-fb7b-4af1-b764-794156972989' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c890960-3344-4a56-87af-2ca9bd23baf3' class='xr-var-data-in' type='checkbox'><label for='data-8c890960-3344-4a56-87af-2ca9bd23baf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-77.907938, -77.723813, -77.539688, ...,  89.472   ,  89.6832  ,\n",
       "        89.8944  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-da8a5e09-9da9-4882-9489-80b7b71d275f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da8a5e09-9da9-4882-9489-80b7b71d275f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-664a01f8-2c97-440d-b554-c0a046d1754b' class='xr-var-data-in' type='checkbox'><label for='data-664a01f8-2c97-440d-b554-c0a046d1754b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ocean model level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>description :</span></dt><dd>generic ocean model vertical coordinate (nondimensional or dimensional)</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ensemble_member</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-3ccd3791-f0db-463b-ae10-ecfbc4d668cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ccd3791-f0db-463b-ae10-ecfbc4d668cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e296159-696f-4686-8fb6-1fa58b7e9602' class='xr-var-data-in' type='checkbox'><label for='data-3e296159-696f-4686-8fb6-1fa58b7e9602' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-024253b3-336b-4a50-b169-d536907677ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-024253b3-336b-4a50-b169-d536907677ba' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>original_name :</span></dt><dd>thetao</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'thetao' (ensemble_member: 1, time: 1980, lev: 35, y: 576, x: 720)>\n",
       "dask.array<broadcast_to, shape=(1, 1980, 35, 576, 720), dtype=float32, chunksize=(1, 1, 35, 576, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat              (y, x) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>\n",
       "    lon              (y, x) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>\n",
       "  * time             (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * x                (x) float64 -299.8 -299.2 -298.8 ... 58.75 59.25 59.75\n",
       "  * y                (y) float64 -77.91 -77.72 -77.54 ... 89.47 89.68 89.89\n",
       "  * lev              (lev) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03\n",
       "  * ensemble_member  (ensemble_member) <U8 'r1i1p1f1'\n",
       "Attributes:\n",
       "    long_name:      Sea Water Potential Temperature\n",
       "    units:          degC\n",
       "    cell_methods:   area: mean where sea time: mean\n",
       "    cell_measures:  area: areacello volume: volcello\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    original_name:  thetao"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.thetao "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.06 s, sys: 125 ms, total: 1.19 s\n",
      "Wall time: 1min 50s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f693d7b6ca0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thetao_mean = thetao.sel(lev=2.5).mean(dim='time')\n",
    "%time thetao_mean.plot(figsize=[10,8], vmin=-2, vmax=38, cmap='gist_ncar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()\n",
    "cluster.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:notebook] *",
   "language": "python",
   "name": "conda-env-notebook-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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}