{
 "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": 64,
   "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": [
    "#%pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "from dask_gateway import Gateway\n",
    "gateway = Gateway()\n",
    "clusters = gateway.list_clusters()\n",
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(clusters) >0:\n",
    "    cluster = gateway.connect(clusters[0].name)\n",
    "    print(\"using existing connection\")\n",
    "else:\n",
    "    from dask_gateway import GatewayCluster\n",
    "    cluster = GatewayCluster()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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-dub-dask-gateway.dub:80/dub.f4d1317d5e284345abf2367572deac53</li>\n",
       "  <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/dub.f4d1317d5e284345abf2367572deac53/status' target='_blank'>/services/dask-gateway/clusters/dub.f4d1317d5e284345abf2367572deac53/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>"
      ],
      "text/plain": [
       "<Client: 'tls://192.168.27.46:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from distributed import Client\n",
    "\n",
    "client = Client(cluster)\n",
    "client\n",
    "\n",
    "#cluster.adapt(active=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b89e11c43e34e3e81a44f1ef44caa18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h2>Cluster Options</h2>'), GridBox(children=(HTML(value=\"<p style='font-weight: bo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options = gateway.cluster_options()  \n",
    "options\n",
    "#DO NOT CHANGE THIS FOR NOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Try this first if you're trying it for the first time. cluster.scale(2)\n",
    "cluster.adapt(minimum=0, maximum=6)  # scale between 0 and 6 workers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install intake_esm intake netcdf4 %pip install dask_gateway"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "esgf-world.json is the ESM collections spec file for the netCDF data in the S3 bucket esgf-world. \n",
    "The catalog is updated on an on-demand basis for now. \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": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_url = \"https://cmip6-nc.s3.us-east-2.amazonaws.com/esgf-world.json\" \n",
    "#col_url = \"https://raw.githubusercontent.com/aradhakrishnanGFDL/gfdl-aws-analysis/community/esm-collection-spec-examples/esgf-world.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = intake.open_esm_datastore(col_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project</th>\n",
       "      <th>institute</th>\n",
       "      <th>model</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>modeling_realm</th>\n",
       "      <th>mip_table</th>\n",
       "      <th>ensemble_member</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>variable</th>\n",
       "      <th>temporal subset</th>\n",
       "      <th>version</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>ps</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CFmon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>ta</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lmon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>rh</td>\n",
       "      <td>185002-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>ps</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>v20200310</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CFmon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>ta</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>v20200316</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424087</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>atmos</td>\n",
       "      <td>Amon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gr</td>\n",
       "      <td>rlds</td>\n",
       "      <td>201501-210012</td>\n",
       "      <td>v20200417</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424088</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>atmos</td>\n",
       "      <td>Amon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gr</td>\n",
       "      <td>rsds</td>\n",
       "      <td>201501-210012</td>\n",
       "      <td>v20200417</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424089</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>atmos</td>\n",
       "      <td>Amon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gr</td>\n",
       "      <td>ta</td>\n",
       "      <td>201501-210012</td>\n",
       "      <td>v20200417</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424090</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Omon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>thetao</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>v20200220</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424091</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>THU</td>\n",
       "      <td>CIESM</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Omon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>thetao</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>v20200220</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>424092 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       project institute    model   experiment_id frequency modeling_realm  \\\n",
       "0        CMIP6   AS-RCEC  TaiESM1  histSST-piNTCF       NaN            NaN   \n",
       "1        CMIP6   AS-RCEC  TaiESM1  histSST-piNTCF       NaN            NaN   \n",
       "2        CMIP6   AS-RCEC  TaiESM1  histSST-piNTCF       NaN            NaN   \n",
       "3        CMIP6   AS-RCEC  TaiESM1         histSST       NaN            NaN   \n",
       "4        CMIP6   AS-RCEC  TaiESM1         histSST       NaN            NaN   \n",
       "...        ...       ...      ...             ...       ...            ...   \n",
       "424087   CMIP6       THU    CIESM          ssp585       mon          atmos   \n",
       "424088   CMIP6       THU    CIESM          ssp585       mon          atmos   \n",
       "424089   CMIP6       THU    CIESM          ssp585       mon          atmos   \n",
       "424090   CMIP6       THU    CIESM          ssp585       mon          ocean   \n",
       "424091   CMIP6       THU    CIESM          ssp585       mon          ocean   \n",
       "\n",
       "       mip_table ensemble_member grid_label variable temporal subset  \\\n",
       "0         AERmon        r1i1p1f1         gn       ps   185001-201412   \n",
       "1          CFmon        r1i1p1f1         gn       ta   185001-201412   \n",
       "2           Lmon        r1i1p1f1         gn       rh   185002-201412   \n",
       "3         AERmon        r1i1p1f1         gn       ps   185001-201412   \n",
       "4          CFmon        r1i1p1f1         gn       ta   185001-201412   \n",
       "...          ...             ...        ...      ...             ...   \n",
       "424087      Amon        r1i1p1f1         gr     rlds   201501-210012   \n",
       "424088      Amon        r1i1p1f1         gr     rsds   201501-210012   \n",
       "424089      Amon        r1i1p1f1         gr       ta   201501-210012   \n",
       "424090      Omon        r1i1p1f1         gn   thetao   201501-206412   \n",
       "424091      Omon        r1i1p1f1         gn   thetao   206501-210012   \n",
       "\n",
       "          version                                               path  \n",
       "0       v20200318  s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "1       v20200318  s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "2       v20200318  s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "3       v20200310  s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "4       v20200316  s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "...           ...                                                ...  \n",
       "424087  v20200417  s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...  \n",
       "424088  v20200417  s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...  \n",
       "424089  v20200417  s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...  \n",
       "424090  v20200220  s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...  \n",
       "424091  v20200220  s3://esgf-world/CMIP6/ScenarioMIP/THU/CIESM/ss...  \n",
       "\n",
       "[424092 rows x 13 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Examples to just search for what we want from the catalog\n",
    "expname_filter = ['historical']\n",
    "table_id_filter = 'Amon'\n",
    "model_filter = 'GFDL-ESM4'\n",
    "variable_id_filter = \"tas\"\n",
    "ens_filter = \"r1i1p1f1\"\n",
    "version_filter = \"v20190726\"\n",
    "cat = col.search(experiment_id=expname_filter, mip_table=table_id_filter,model=model_filter,variable=variable_id_filter,version=\"v20190726\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    }
   ],
   "source": [
    "dset_dict = cat.to_dataset_dict(cdf_kwargs={'chunks': {'time': 1}},storage_options={'anon':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "hxr_gfdl_esm4 = dset_dict[\"CMIP6.NOAA-GFDL.GFDL-ESM4.historical.Amon\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project</th>\n",
       "      <th>institute</th>\n",
       "      <th>model</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>modeling_realm</th>\n",
       "      <th>mip_table</th>\n",
       "      <th>ensemble_member</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>variable</th>\n",
       "      <th>temporal subset</th>\n",
       "      <th>version</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-ESM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>mon</td>\n",
       "      <td>atmos</td>\n",
       "      <td>Amon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gr1</td>\n",
       "      <td>tas</td>\n",
       "      <td>185001-194912</td>\n",
       "      <td>v20190726</td>\n",
       "      <td>s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-ESM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>mon</td>\n",
       "      <td>atmos</td>\n",
       "      <td>Amon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gr1</td>\n",
       "      <td>tas</td>\n",
       "      <td>195001-201412</td>\n",
       "      <td>v20190726</td>\n",
       "      <td>s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  project  institute      model experiment_id frequency modeling_realm  \\\n",
       "0   CMIP6  NOAA-GFDL  GFDL-ESM4    historical       mon          atmos   \n",
       "1   CMIP6  NOAA-GFDL  GFDL-ESM4    historical       mon          atmos   \n",
       "\n",
       "  mip_table ensemble_member grid_label variable temporal subset    version  \\\n",
       "0      Amon        r1i1p1f1        gr1      tas   185001-194912  v20190726   \n",
       "1      Amon        r1i1p1f1        gr1      tas   195001-201412  v20190726   \n",
       "\n",
       "                                                path  \n",
       "0  s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4...  \n",
       "1  s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4...  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "! GLOBAL MEAN \n",
    "let hgtas = tas[x=@ave,y=@ave,d=1,l=529:780@ave]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas = hxr_gfdl_esm4.tas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 160 ms, sys: 18.3 ms, total: 178 ms\n",
      "Wall time: 9.03 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "%time hgtas2d = tas.isel(time=1).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.close()\n",
    "client.close()\n",
    "#you can also set an auto close when you create a cluster , refer dask gateway docs"
   ]
  }
 ],
 "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
}