code/get_zg_CMIP6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.dkrz.de/blog/2020/dask-dashboard.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import dask\n",
    "from dask.distributed import Client, LocalCluster\n",
    "\n",
    "dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-24 21:51:22,738 - distributed.diskutils - INFO - Found stale lock file and directory '/scratch/b/b381258/dask-worker-space/worker-cia3dy0n', purging\n"
     ]
    }
   ],
   "source": [
    "with dask.config.set({\"distributed.scheduler.worker-saturation\": 1.0}):\n",
    "    #client = Client()\n",
    "    client = Client(LocalCluster(local_directory='/scratch/b/b381258/')) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-5579bd1e-bafa-11ee-85ce-080038c02de1</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/8787/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">475e176a</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/8787/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 9\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 54\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 100.00 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-c291f20a-4dd1-4c03-8033-7566a8f8f89a</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:38127\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 9\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/8787/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 54\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 100.00 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37135\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/38525/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/38525/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36409\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-_axus4zf\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:35815\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/41169/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/41169/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34691\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-388kr8m5\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:36559\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/44785/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/44785/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35207\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-fjs4e5rp\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37069\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/44321/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/44321/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39699\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-x9g3x4df\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:39539\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/33997/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/33997/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37149\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-c4bez6xn\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:40375\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/35957/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/35957/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44859\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-ze0l4p05\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37919\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/33585/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/33585/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33765\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-oug_q_il\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:39029\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/36509/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/36509/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46753\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-vlo207ik\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 8</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:33801\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/user/b381258/levante-spawner-preset//proxy/42695/status\" target=\"_blank\">/user/b381258/levante-spawner-preset//proxy/42695/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 11.11 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40999\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /scratch/b/b381258/dask-worker-space/worker-x98holqt\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:38127' processes=9 threads=54, memory=100.00 GiB>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import griddata  \n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import intake\n",
    "import xarray as xr\n",
    "import sys\n",
    "import pandas as pd\n",
    "import tempfile\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Architecture:        x86_64\n",
      "CPU op-mode(s):      32-bit, 64-bit\n",
      "Byte Order:          Little Endian\n",
      "CPU(s):              256\n",
      "On-line CPU(s) list: 0-255\n",
      "Thread(s) per core:  2\n",
      "Core(s) per socket:  64\n",
      "Socket(s):           2\n",
      "NUMA node(s):        8\n",
      "Vendor ID:           AuthenticAMD\n",
      "CPU family:          25\n",
      "Model:               1\n",
      "Model name:          AMD EPYC 7763 64-Core Processor\n",
      "Stepping:            1\n",
      "CPU MHz:             2450.000\n",
      "CPU max MHz:         3529.0520\n",
      "CPU min MHz:         1500.0000\n",
      "BogoMIPS:            4900.18\n",
      "Virtualization:      AMD-V\n",
      "L1d cache:           32K\n",
      "L1i cache:           32K\n",
      "L2 cache:            512K\n",
      "L3 cache:            32768K\n",
      "NUMA node0 CPU(s):   0-15,128-143\n",
      "NUMA node1 CPU(s):   16-31,144-159\n",
      "NUMA node2 CPU(s):   32-47,160-175\n",
      "NUMA node3 CPU(s):   48-63,176-191\n",
      "NUMA node4 CPU(s):   64-79,192-207\n",
      "NUMA node5 CPU(s):   80-95,208-223\n",
      "NUMA node6 CPU(s):   96-111,224-239\n",
      "NUMA node7 CPU(s):   112-127,240-255\n",
      "Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\n"
     ]
    }
   ],
   "source": [
    "!lscpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages/intake_esm/cat.py:264: DtypeWarning: Columns (21,22,23) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<p><strong>/work/ik1017/Catalogs/dkrz_cmip6_disk catalog with 17246 dataset(s) from 6132163 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>1164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_range</th>\n",
       "      <td>41465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>6132163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opendap_url</th>\n",
       "      <td>6132163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>project</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulation_id</th>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_reduction</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_name</th>\n",
       "      <td>1129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>units</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>realm</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level_type</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_min</th>\n",
       "      <td>24464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_max</th>\n",
       "      <td>35016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>format</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uri</th>\n",
       "      <td>6132163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "root_path = '/work/ik1017/Catalogs/'\n",
    "col_url = f\"{root_path}mistral-cmip6.json\"\n",
    "col_url = f\"{root_path}dkrz_cmip6_disk.json\"\n",
    "col = intake.open_esm_datastore(col_url)\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "models = []\n",
    "models =  ['CanESM5']#, 'MPI-ESM1-2-LR']#,\n",
    "#models = ['CNRM-CM6-1']#'IPSL-CM6A-LR']#, 'MPI-ESM1-2-LR'] # 'EC-Earth3' not working, \n",
    "#models = ['MPI-ESM1-2-HR', 'MIROC6', 'UKESM1-0-LL']\n",
    "#models = ['MPI-ESM1-2-HR']\n",
    "models = ['MIROC6']\n",
    "#models = ['UKESM1-0-LL']\n",
    "#models = ['INM-CM5-0']\n",
    "#models =  ['CanESM5']\n",
    "#models = ['IPSL-CM6A-LR']\n",
    " # NIMS-KMA.UKESM1-0 has one 1 ensemble member\n",
    "    \n",
    "sel_var = 'ua'# 'zg'\n",
    "query = dict(experiment_id='historical', table_id='day', \n",
    "             variable_id=sel_var, source_id=models[:])#,grid_label='gn') # ssp585\n",
    "cat = col.search(**query)\n",
    "df = cat.df\n",
    "k = df['activity_id'][0]+'_'+df['source_id'][0]+'_'+df['experiment_id'][0]+'_'+df['table_id'][0]+'_'+df['grid_label'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>/work/ik1017/Catalogs/dkrz_cmip6_disk catalog with 1 dataset(s) from 1614 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>version</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_range</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>1614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opendap_url</th>\n",
       "      <td>1614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>project</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simulation_id</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_reduction</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_name</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>units</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>realm</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level_type</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_min</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_max</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>format</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uri</th>\n",
       "      <td>1614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       18500101-18501231\n",
       "1       18510101-18511231\n",
       "2       18520101-18521231\n",
       "3       18530101-18531231\n",
       "4       18540101-18541231\n",
       "              ...        \n",
       "1609    20100101-20101231\n",
       "1610    20110101-20111231\n",
       "1611    20120101-20121231\n",
       "1612    20130101-20131231\n",
       "1613    20140101-20141231\n",
       "Name: time_range, Length: 1614, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['time_range']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_18500101-18501231.nc',\n",
       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_18510101-18511231.nc',\n",
       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_18520101-18521231.nc',\n",
       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_18530101-18531231.nc',\n",
       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_18540101-18541231.nc',\n",
       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_18550101-18551231.nc',\n",
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       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_20020101-20021231.nc',\n",
       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_20030101-20031231.nc',\n",
       " '/work/ik1017/CMIP6/data/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/day/ua/gn/v20191016/ua_day_MIROC6_historical_r8i1p1f1_gn_20040101-20041231.nc']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7547147fae2d48abb167d8561e72388f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "165\n",
      "165\n",
      "139\n",
      "165\n",
      "165\n",
      "165\n",
      "165\n",
      "165\n",
      "155\n",
      "165\n"
     ]
    }
   ],
   "source": [
    "ls = []\n",
    "member_ls = df.member_id.unique()\n",
    "#member_ls = member_ls[~np.isin(member_ls, ['r2i1p1f1','r8i1p1f1'])] for miroc6\n",
    "for ens in tqdm(member_ls):\n",
    "    mask = df.member_id == ens\n",
    "    infiles = list(df[mask]['uri'].values)\n",
    "    print(len(infiles))\n",
    "    #ls.append(infiles)\n",
    "    #if models[0] == 'IPSL-CM6A-LR': \n",
    "    #    ls.append([infiles[-1]])\n",
    "    #else:\n",
    "    ls.append(infiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f65acb8c2b0e41f493eb956d724884bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/35 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n",
      "17\n"
     ]
    },
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       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 60225, member_id: 35, bnds: 2, plev: 8, lat: 64, lon: 128)\n",
       "Coordinates:\n",
       "  * time       (time) object 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "  * plev       (plev) float64 1e+05 8.5e+04 7e+04 5e+04 ... 1e+04 5e+03 1e+03\n",
       "  * lat        (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86\n",
       "  * lon        (lon) float64 0.0 2.812 5.625 8.438 ... 348.8 351.6 354.4 357.2\n",
       "  * member_id  (member_id) object &#x27;r10i1p1f1&#x27; &#x27;r10i1p2f1&#x27; ... &#x27;r9i1p2f1&#x27;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (member_id, time, bnds) object dask.array&lt;chunksize=(1, 4015, 2), meta=np.ndarray&gt;\n",
       "    lat_bnds   (time, member_id, lat, bnds) float64 dask.array&lt;chunksize=(4015, 1, 64, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (time, member_id, lon, bnds) float64 dask.array&lt;chunksize=(4015, 1, 128, 2), meta=np.ndarray&gt;\n",
       "    ua         (member_id, time, plev, lat, lon) float32 dask.array&lt;chunksize=(1, 4015, 1, 64, 128), meta=np.ndarray&gt;\n",
       "Attributes: (12/53)\n",
       "    CCCma_model_hash:            55f484f90aff0e32c5a8e92a42c6b9ae7ffe6224\n",
       "    CCCma_parent_runid:          rc3.1-pictrl\n",
       "    CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "    CCCma_runid:                 rc3.1-his10\n",
       "    Conventions:                 CF-1.7 CMIP-6.2\n",
       "    YMDH_branch_time_in_child:   1850:01:01:00\n",
       "    ...                          ...\n",
       "    tracking_id:                 hdl:21.14100/a986bc3f-c258-4197-9c3f-137b9ff...\n",
       "    variable_id:                 ua\n",
       "    variant_label:               r10i1p1f1\n",
       "    version:                     v20190429\n",
       "    license:                     CMIP6 model data produced by The Government ...\n",
       "    cmor_version:                3.4.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-aeb3d86c-3ef6-429f-9b64-80de2fab28ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aeb3d86c-3ef6-429f-9b64-80de2fab28ab' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60225</li><li><span class='xr-has-index'>member_id</span>: 35</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>plev</span>: 8</li><li><span class='xr-has-index'>lat</span>: 64</li><li><span class='xr-has-index'>lon</span>: 128</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5c33fd95-ef59-4113-a46c-655c0eefb7d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c33fd95-ef59-4113-a46c-655c0eefb7d6' class='xr-section-summary' >Coordinates: <span>(5)</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 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'>1850-01-01 12:00:00 ... 2014-12-...</div><input id='attrs-3177d14b-a5ef-4bcc-9dd3-fc201bf85391' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3177d14b-a5ef-4bcc-9dd3-fc201bf85391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c233af44-a996-44a1-9c37-903aa0570bf1' class='xr-var-data-in' type='checkbox'><label for='data-c233af44-a996-44a1-9c37-903aa0570bf1' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05 8.5e+04 7e+04 ... 5e+03 1e+03</div><input id='attrs-70e8281f-6c43-4a2b-8646-1fe45455be8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70e8281f-6c43-4a2b-8646-1fe45455be8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e951f8a-d262-4ed5-b6eb-ca9df9f3c1ea' class='xr-var-data-in' type='checkbox'><label for='data-8e951f8a-d262-4ed5-b6eb-ca9df9f3c1ea' 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>units :</span></dt><dd>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  85000.,  70000.,  50000.,  25000.,  10000.,   5000.,   1000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.86 -85.1 -82.31 ... 85.1 87.86</div><input id='attrs-5196814f-5bb5-47dd-9104-799f0d660706' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5196814f-5bb5-47dd-9104-799f0d660706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5801a652-ae40-40c5-8f92-41a577012016' class='xr-var-data-in' type='checkbox'><label for='data-5801a652-ae40-40c5-8f92-41a577012016' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.863799, -85.096527, -82.312913, -79.525607, -76.7369  , -73.947515,\n",
       "       -71.157752, -68.367756, -65.577607, -62.787352, -59.99702 , -57.206632,\n",
       "       -54.4162  , -51.625734, -48.835241, -46.044727, -43.254195, -40.463648,\n",
       "       -37.67309 , -34.882521, -32.091944, -29.30136 , -26.510769, -23.720174,\n",
       "       -20.929574, -18.138971, -15.348365, -12.557756,  -9.767146,  -6.976534,\n",
       "        -4.185921,  -1.395307,   1.395307,   4.185921,   6.976534,   9.767146,\n",
       "        12.557756,  15.348365,  18.138971,  20.929574,  23.720174,  26.510769,\n",
       "        29.30136 ,  32.091944,  34.882521,  37.67309 ,  40.463648,  43.254195,\n",
       "        46.044727,  48.835241,  51.625734,  54.4162  ,  57.206632,  59.99702 ,\n",
       "        62.787352,  65.577607,  68.367756,  71.157752,  73.947515,  76.7369  ,\n",
       "        79.525607,  82.312913,  85.096527,  87.863799])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.812 5.625 ... 354.4 357.2</div><input id='attrs-507402d8-2ab2-4e83-99b7-48d5b962b2e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-507402d8-2ab2-4e83-99b7-48d5b962b2e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e19f037-1bf8-4a17-80a6-82a124a8030d' class='xr-var-data-in' type='checkbox'><label for='data-9e19f037-1bf8-4a17-80a6-82a124a8030d' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,\n",
       "        19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,\n",
       "        39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,\n",
       "        59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,\n",
       "        78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,\n",
       "        98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,\n",
       "       118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,\n",
       "       137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,\n",
       "       157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,\n",
       "       177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,\n",
       "       196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,\n",
       "       216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,\n",
       "       236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,\n",
       "       255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,\n",
       "       275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,\n",
       "       295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,\n",
       "       315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,\n",
       "       334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,\n",
       "       354.375 , 357.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r10i1p1f1&#x27; ... &#x27;r9i1p2f1&#x27;</div><input id='attrs-8bf483ba-12cd-4b00-b78a-07ff221e9593' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bf483ba-12cd-4b00-b78a-07ff221e9593' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac8dda93-893c-4ceb-9ca8-a19bdbd811dd' class='xr-var-data-in' type='checkbox'><label for='data-ac8dda93-893c-4ceb-9ca8-a19bdbd811dd' 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;r10i1p1f1&#x27;, &#x27;r10i1p2f1&#x27;, &#x27;r11i1p1f1&#x27;, &#x27;r12i1p1f1&#x27;, &#x27;r13i1p1f1&#x27;,\n",
       "       &#x27;r14i1p1f1&#x27;, &#x27;r15i1p1f1&#x27;, &#x27;r16i1p1f1&#x27;, &#x27;r17i1p1f1&#x27;, &#x27;r18i1p1f1&#x27;,\n",
       "       &#x27;r19i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r1i1p2f1&#x27;, &#x27;r20i1p1f1&#x27;, &#x27;r21i1p1f1&#x27;,\n",
       "       &#x27;r22i1p1f1&#x27;, &#x27;r23i1p1f1&#x27;, &#x27;r24i1p1f1&#x27;, &#x27;r25i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;,\n",
       "       &#x27;r2i1p2f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r3i1p2f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r4i1p2f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r5i1p2f1&#x27;, &#x27;r6i1p1f1&#x27;, &#x27;r6i1p2f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r7i1p2f1&#x27;, &#x27;r8i1p1f1&#x27;,\n",
       "       &#x27;r8i1p2f1&#x27;, &#x27;r9i1p1f1&#x27;, &#x27;r9i1p2f1&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9dc13550-d769-430e-a878-7cd6c7a4ed40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9dc13550-d769-430e-a878-7cd6c7a4ed40' class='xr-section-summary' >Data variables: <span>(4)</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>time_bnds</span></div><div class='xr-var-dims'>(member_id, time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 4015, 2), meta=np.ndarray&gt;</div><input id='attrs-7637bd91-b713-4d11-8b68-5abc251cc78c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7637bd91-b713-4d11-8b68-5abc251cc78c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caba4060-5462-42f2-9f44-dcd4f28f18f6' class='xr-var-data-in' type='checkbox'><label for='data-caba4060-5462-42f2-9f44-dcd4f28f18f6' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 32.16 MiB </td>\n",
       "                        <td> 62.73 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (35, 60225, 2) </td>\n",
       "                        <td> (1, 4015, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 595 chunks in 1803 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, member_id, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4015, 1, 64, 2), meta=np.ndarray&gt;</div><input id='attrs-9687dfc2-d794-415f-a5d9-6a2e49355491' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9687dfc2-d794-415f-a5d9-6a2e49355491' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d6eacdb-99cb-4450-a5fa-510c946881bb' class='xr-var-data-in' type='checkbox'><label for='data-2d6eacdb-99cb-4450-a5fa-510c946881bb' 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'><table>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.01 GiB </td>\n",
       "                        <td> 3.92 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (60225, 35, 64, 2) </td>\n",
       "                        <td> (4015, 1, 64, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 595 chunks in 1820 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, member_id, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4015, 1, 128, 2), meta=np.ndarray&gt;</div><input id='attrs-25167c64-988f-404d-a97d-d19384966d41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25167c64-988f-404d-a97d-d19384966d41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82c89c99-197d-4552-ac24-02b0e05062f9' class='xr-var-data-in' type='checkbox'><label for='data-82c89c99-197d-4552-ac24-02b0e05062f9' 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'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.02 GiB </td>\n",
       "                        <td> 7.84 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ua</span></div><div class='xr-var-dims'>(member_id, time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 4015, 1, 64, 128), meta=np.ndarray&gt;</div><input id='attrs-92329a23-2f6b-48fd-9b89-b84b7d8bce53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92329a23-2f6b-48fd-9b89-b84b7d8bce53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b42683c-f49c-44c3-9e14-377b4eceb22a' class='xr-var-data-in' type='checkbox'><label for='data-6b42683c-f49c-44c3-9e14-377b4eceb22a' 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>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>long_name :</span></dt><dd>Eastward Wind</dd><dt><span>comment :</span></dt><dd>Bin 4 saves to one daily save., CMIP_table_comment: Zonal wind (positive in a eastward direction).</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>original_name :</span></dt><dd>U</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-05-02T08:14:15Z altered by CMOR: Reordered dimensions, original order: lat lon plev time. 2019-05-02T08:14:15Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 514.62 GiB </td>\n",
       "                        <td> 125.47 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (35, 60225, 8, 64, 128) </td>\n",
       "                        <td> (1, 4015, 1, 64, 128) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4760 chunks in 1803 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-30b53b9a-cde4-4fbc-8d30-b51a2b47fd45' class='xr-section-summary-in' type='checkbox'  ><label for='section-30b53b9a-cde4-4fbc-8d30-b51a2b47fd45' class='xr-section-summary' >Indexes: <span>(5)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe1e9fde-45c9-48e6-90da-de90fd26c961' class='xr-index-data-in' type='checkbox'/><label for='index-fe1e9fde-45c9-48e6-90da-de90fd26c961' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-01 12:00:00, 1850-01-02 12:00:00, 1850-01-03 12:00:00,\n",
       "             1850-01-04 12:00:00, 1850-01-05 12:00:00, 1850-01-06 12:00:00,\n",
       "             1850-01-07 12:00:00, 1850-01-08 12:00:00, 1850-01-09 12:00:00,\n",
       "             1850-01-10 12:00:00,\n",
       "             ...\n",
       "             2014-12-22 12:00:00, 2014-12-23 12:00:00, 2014-12-24 12:00:00,\n",
       "             2014-12-25 12:00:00, 2014-12-26 12:00:00, 2014-12-27 12:00:00,\n",
       "             2014-12-28 12:00:00, 2014-12-29 12:00:00, 2014-12-30 12:00:00,\n",
       "             2014-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=60225, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-75b0e923-9436-4394-9be2-bcdb24a39d49' class='xr-index-data-in' type='checkbox'/><label for='index-75b0e923-9436-4394-9be2-bcdb24a39d49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0, 85000.0, 70000.0, 50000.0, 25000.0, 10000.0, 5000.0, 1000.0], dtype=&#x27;float64&#x27;, name=&#x27;plev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-781d6e20-8f7d-4c8a-a27e-3417ec4e5472' class='xr-index-data-in' type='checkbox'/><label for='index-781d6e20-8f7d-4c8a-a27e-3417ec4e5472' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.86379883923273,  -85.09652698831745,  -82.31291294788636,\n",
       "        -79.52560657265951,   -76.7368996803684,  -73.94751515398974,\n",
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       "        -4.185920533189158, -1.3953069108194975,  1.3953069108194975,\n",
       "         4.185920533189158,   6.976533553948642,   9.767145559195578,\n",
       "         12.55775611523069,  15.348364759491508,  18.138970990239372,\n",
       "         20.92957425448953,   23.72017393353477,  26.510769325211022,\n",
       "        29.301359621762764,  32.091943881744044,    34.8825209937735,\n",
       "         37.67308962904537,   40.46364817811508,   43.25419466535099,\n",
       "        46.044726631101724,   48.83524096625064,  51.625733674938296,\n",
       "         54.41619952608627,    57.2066315276433,  59.997020108491355,\n",
       "         62.78735179896313,   65.57760701082788,   68.36775610831324,\n",
       "         71.15775201158739,   73.94751515398974,    76.7368996803684,\n",
       "         79.52560657265951,   82.31291294788636,   85.09652698831745,\n",
       "         87.86379883923273],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2a24371d-d271-41ae-b497-dcdecea9cac1' class='xr-index-data-in' type='checkbox'/><label for='index-2a24371d-d271-41ae-b497-dcdecea9cac1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0.0,   2.8125,    5.625,   8.4375,    11.25,  14.0625,   16.875,\n",
       "        19.6875,     22.5,  25.3125,\n",
       "       ...\n",
       "        331.875, 334.6875,    337.5, 340.3125,  343.125, 345.9375,   348.75,\n",
       "       351.5625,  354.375, 357.1875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=128))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dd2e7c2c-cf41-4d32-9160-dd408c85b60c' class='xr-index-data-in' type='checkbox'/><label for='index-dd2e7c2c-cf41-4d32-9160-dd408c85b60c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1p1f1&#x27;, &#x27;r10i1p2f1&#x27;, &#x27;r11i1p1f1&#x27;, &#x27;r12i1p1f1&#x27;, &#x27;r13i1p1f1&#x27;,\n",
       "       &#x27;r14i1p1f1&#x27;, &#x27;r15i1p1f1&#x27;, &#x27;r16i1p1f1&#x27;, &#x27;r17i1p1f1&#x27;, &#x27;r18i1p1f1&#x27;,\n",
       "       &#x27;r19i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r1i1p2f1&#x27;, &#x27;r20i1p1f1&#x27;, &#x27;r21i1p1f1&#x27;,\n",
       "       &#x27;r22i1p1f1&#x27;, &#x27;r23i1p1f1&#x27;, &#x27;r24i1p1f1&#x27;, &#x27;r25i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;,\n",
       "       &#x27;r2i1p2f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r3i1p2f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r4i1p2f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r5i1p2f1&#x27;, &#x27;r6i1p1f1&#x27;, &#x27;r6i1p2f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r7i1p2f1&#x27;, &#x27;r8i1p1f1&#x27;,\n",
       "       &#x27;r8i1p2f1&#x27;, &#x27;r9i1p1f1&#x27;, &#x27;r9i1p2f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c75b695-c146-415d-8510-07a314696054' class='xr-section-summary-in' type='checkbox'  ><label for='section-3c75b695-c146-415d-8510-07a314696054' class='xr-section-summary' >Attributes: <span>(53)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CCCma_model_hash :</span></dt><dd>55f484f90aff0e32c5a8e92a42c6b9ae7ffe6224</dd><dt><span>CCCma_parent_runid :</span></dt><dd>rc3.1-pictrl</dd><dt><span>CCCma_pycmor_hash :</span></dt><dd>33c30511acc319a98240633965a04ca99c26427e</dd><dt><span>CCCma_runid :</span></dt><dd>rc3.1-his10</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>YMDH_branch_time_in_child :</span></dt><dd>1850:01:01:00</dd><dt><span>YMDH_branch_time_in_parent :</span></dt><dd>5651:01:01:00</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>Spin-up documentation</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>1387365.0</dd><dt><span>contact :</span></dt><dd>ec.cccma.info-info.ccmac.ec@canada.ca</dd><dt><span>creation_date :</span></dt><dd>2019-05-02T08:14:15Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CCCma.CanESM5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-05-02T08:14:15Z ;rewrote data to be consistent with CMIP for variable ua found in table day.;\n",
       "Output from $runid</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada</dd><dt><span>institution_id :</span></dt><dd>CCCma</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>500 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CanESM5</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01 0:0:0.0</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>Geophysical Model Development Special issue on CanESM5 (https://www.geosci-model-dev.net/special_issues.html)</dd><dt><span>source :</span></dt><dd>CanESM5 (2019): \n",
       "aerosol: interactive\n",
       "atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa)\n",
       "atmosChem: specified oxidants for aerosols\n",
       "land: CLASS3.6/CTEM1.2\n",
       "landIce: specified ice sheets\n",
       "ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m)\n",
       "ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry\n",
       "seaIce: LIM2</dd><dt><span>source_id :</span></dt><dd>CanESM5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>day</dd><dt><span>table_info :</span></dt><dd>Creation Date:(20 February 2019) MD5:374fbe5a2bcca535c40f7f23da271e49</dd><dt><span>title :</span></dt><dd>CanESM5 output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/a986bc3f-c258-4197-9c3f-137b9ffd0c63</dd><dt><span>variable_id :</span></dt><dd>ua</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20190429</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by The Government of Canada (Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada) is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https:///pcmdi.llnl.gov/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 60225, member_id: 35, bnds: 2, plev: 8, lat: 64, lon: 128)\n",
       "Coordinates:\n",
       "  * time       (time) object 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "  * plev       (plev) float64 1e+05 8.5e+04 7e+04 5e+04 ... 1e+04 5e+03 1e+03\n",
       "  * lat        (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86\n",
       "  * lon        (lon) float64 0.0 2.812 5.625 8.438 ... 348.8 351.6 354.4 357.2\n",
       "  * member_id  (member_id) object 'r10i1p1f1' 'r10i1p2f1' ... 'r9i1p2f1'\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (member_id, time, bnds) object dask.array<chunksize=(1, 4015, 2), meta=np.ndarray>\n",
       "    lat_bnds   (time, member_id, lat, bnds) float64 dask.array<chunksize=(4015, 1, 64, 2), meta=np.ndarray>\n",
       "    lon_bnds   (time, member_id, lon, bnds) float64 dask.array<chunksize=(4015, 1, 128, 2), meta=np.ndarray>\n",
       "    ua         (member_id, time, plev, lat, lon) float32 dask.array<chunksize=(1, 4015, 1, 64, 128), meta=np.ndarray>\n",
       "Attributes: (12/53)\n",
       "    CCCma_model_hash:            55f484f90aff0e32c5a8e92a42c6b9ae7ffe6224\n",
       "    CCCma_parent_runid:          rc3.1-pictrl\n",
       "    CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "    CCCma_runid:                 rc3.1-his10\n",
       "    Conventions:                 CF-1.7 CMIP-6.2\n",
       "    YMDH_branch_time_in_child:   1850:01:01:00\n",
       "    ...                          ...\n",
       "    tracking_id:                 hdl:21.14100/a986bc3f-c258-4197-9c3f-137b9ff...\n",
       "    variable_id:                 ua\n",
       "    variant_label:               r10i1p1f1\n",
       "    version:                     v20190429\n",
       "    license:                     CMIP6 model data produced by The Government ...\n",
       "    cmor_version:                3.4.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls = []\n",
    "member_ls = df.member_id.unique()\n",
    "#member_ls = member_ls[~np.isin(member_ls, ['r2i1p1f1','r8i1p1f1'])] for miroc6\n",
    "for ens in tqdm(member_ls):\n",
    "    mask = df.member_id == ens\n",
    "    infiles = list(df[mask]['uri'].values)\n",
    "    print(len(infiles))\n",
    "    #ls.append(infiles)\n",
    "    #if models[0] == 'IPSL-CM6A-LR': \n",
    "    #    ls.append([infiles[-1]])\n",
    "    #else:\n",
    "    ls.append(infiles)\n",
    "    #print(infiles)\n",
    "    \n",
    "with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
    "    ds = xr.open_mfdataset(\n",
    "        ls, concat_dim = ['member_id','time'], \n",
    "        parallel = True, \n",
    "        combine='nested', \n",
    "        engine = 'netcdf4',\n",
    "        chunks = chunk_dict[models[0]],#, time = 730)\n",
    "        #**kwargsuse_cftime = False\n",
    "    )\n",
    "    ds['member_id'] = member_ls\n",
    "    \n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 13140, member_id: 35, bnds: 2)\n",
       "Coordinates:\n",
       "  * time       (time) object 1979-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "    plev       float64 1e+03\n",
       "    lat        float64 60.0\n",
       "  * member_id  (member_id) object &#x27;r10i1p1f1&#x27; &#x27;r10i1p2f1&#x27; ... &#x27;r9i1p2f1&#x27;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (member_id, time, bnds) object dask.array&lt;chunksize=(1, 730, 2), meta=np.ndarray&gt;\n",
       "    lat_bnds   (time, member_id, bnds) float64 dask.array&lt;chunksize=(730, 1, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (time, member_id, bnds) float64 dask.array&lt;chunksize=(730, 1, 2), meta=np.ndarray&gt;\n",
       "    ua         (member_id, time) float32 dask.array&lt;chunksize=(1, 730), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-44de1516-0d13-4c34-ad0f-cd8749fb245e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-44de1516-0d13-4c34-ad0f-cd8749fb245e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 13140</li><li><span class='xr-has-index'>member_id</span>: 35</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-681ed2fb-9fc6-4a38-b275-feca107eb858' class='xr-section-summary-in' type='checkbox'  checked><label for='section-681ed2fb-9fc6-4a38-b275-feca107eb858' class='xr-section-summary' >Coordinates: <span>(4)</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 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'>1979-01-01 12:00:00 ... 2014-12-...</div><input id='attrs-971e22ed-609f-4dd0-a387-9a1b102369de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-971e22ed-609f-4dd0-a387-9a1b102369de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c1d8cc6-a80e-4549-b862-42a3820cf9d6' class='xr-var-data-in' type='checkbox'><label for='data-0c1d8cc6-a80e-4549-b862-42a3820cf9d6' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1979, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-b36334b0-868c-4b1c-b718-c118da73677f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b36334b0-868c-4b1c-b718-c118da73677f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-046fab0a-ff5f-4c71-bc9d-ec4469d367ba' class='xr-var-data-in' type='checkbox'><label for='data-046fab0a-ff5f-4c71-bc9d-ec4469d367ba' 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>units :</span></dt><dd>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array(1000.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-4bb6f54b-2928-418c-9bd3-935abf34fab2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bb6f54b-2928-418c-9bd3-935abf34fab2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4648588-f7de-42a6-9215-ce31bb5524e6' class='xr-var-data-in' type='checkbox'><label for='data-e4648588-f7de-42a6-9215-ce31bb5524e6' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array(59.99702011)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r10i1p1f1&#x27; ... &#x27;r9i1p2f1&#x27;</div><input id='attrs-ce4f8936-9c7d-4b10-8da7-630aa7873969' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce4f8936-9c7d-4b10-8da7-630aa7873969' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4a37fb3-72ac-42cd-b6b0-4e5a61755609' class='xr-var-data-in' type='checkbox'><label for='data-a4a37fb3-72ac-42cd-b6b0-4e5a61755609' 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;r10i1p1f1&#x27;, &#x27;r10i1p2f1&#x27;, &#x27;r11i1p1f1&#x27;, &#x27;r12i1p1f1&#x27;, &#x27;r13i1p1f1&#x27;,\n",
       "       &#x27;r14i1p1f1&#x27;, &#x27;r15i1p1f1&#x27;, &#x27;r16i1p1f1&#x27;, &#x27;r17i1p1f1&#x27;, &#x27;r18i1p1f1&#x27;,\n",
       "       &#x27;r19i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r1i1p2f1&#x27;, &#x27;r20i1p1f1&#x27;, &#x27;r21i1p1f1&#x27;,\n",
       "       &#x27;r22i1p1f1&#x27;, &#x27;r23i1p1f1&#x27;, &#x27;r24i1p1f1&#x27;, &#x27;r25i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;,\n",
       "       &#x27;r2i1p2f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r3i1p2f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r4i1p2f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r5i1p2f1&#x27;, &#x27;r6i1p1f1&#x27;, &#x27;r6i1p2f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r7i1p2f1&#x27;, &#x27;r8i1p1f1&#x27;,\n",
       "       &#x27;r8i1p2f1&#x27;, &#x27;r9i1p1f1&#x27;, &#x27;r9i1p2f1&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9809c8d-e0a5-409c-9b72-a5c8fd1a2276' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9809c8d-e0a5-409c-9b72-a5c8fd1a2276' class='xr-section-summary' >Data variables: <span>(4)</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>time_bnds</span></div><div class='xr-var-dims'>(member_id, time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 730, 2), meta=np.ndarray&gt;</div><input id='attrs-ace63e20-d3d0-4691-b8e4-81b0876e8a7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ace63e20-d3d0-4691-b8e4-81b0876e8a7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19ee44c8-3b24-4b46-bb53-474c540f995d' class='xr-var-data-in' type='checkbox'><label for='data-19ee44c8-3b24-4b46-bb53-474c540f995d' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.02 MiB </td>\n",
       "                        <td> 57.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (35, 13140, 2) </td>\n",
       "                        <td> (1, 3650, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 175 chunks in 1830 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.02 MiB </td>\n",
       "                        <td> 57.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13140, 35, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 175 chunks in 1822 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >13140</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">35</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-494f0474-c623-457a-9e61-dd9e1d6ef256' class='xr-section-summary-in' type='checkbox'  ><label for='section-494f0474-c623-457a-9e61-dd9e1d6ef256' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3b13f169-f12f-4b26-a9c8-3379dd2e07e7' class='xr-index-data-in' type='checkbox'/><label for='index-3b13f169-f12f-4b26-a9c8-3379dd2e07e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1979-01-01 12:00:00, 1979-01-02 12:00:00, 1979-01-03 12:00:00,\n",
       "             1979-01-04 12:00:00, 1979-01-05 12:00:00, 1979-01-06 12:00:00,\n",
       "             1979-01-07 12:00:00, 1979-01-08 12:00:00, 1979-01-09 12:00:00,\n",
       "             1979-01-10 12:00:00,\n",
       "             ...\n",
       "             2014-12-22 12:00:00, 2014-12-23 12:00:00, 2014-12-24 12:00:00,\n",
       "             2014-12-25 12:00:00, 2014-12-26 12:00:00, 2014-12-27 12:00:00,\n",
       "             2014-12-28 12:00:00, 2014-12-29 12:00:00, 2014-12-30 12:00:00,\n",
       "             2014-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=13140, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e5d90a7b-4ed6-461d-a57e-ac4fd476d0c1' class='xr-index-data-in' type='checkbox'/><label for='index-e5d90a7b-4ed6-461d-a57e-ac4fd476d0c1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1p1f1&#x27;, &#x27;r10i1p2f1&#x27;, &#x27;r11i1p1f1&#x27;, &#x27;r12i1p1f1&#x27;, &#x27;r13i1p1f1&#x27;,\n",
       "       &#x27;r14i1p1f1&#x27;, &#x27;r15i1p1f1&#x27;, &#x27;r16i1p1f1&#x27;, &#x27;r17i1p1f1&#x27;, &#x27;r18i1p1f1&#x27;,\n",
       "       &#x27;r19i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r1i1p2f1&#x27;, &#x27;r20i1p1f1&#x27;, &#x27;r21i1p1f1&#x27;,\n",
       "       &#x27;r22i1p1f1&#x27;, &#x27;r23i1p1f1&#x27;, &#x27;r24i1p1f1&#x27;, &#x27;r25i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;,\n",
       "       &#x27;r2i1p2f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r3i1p2f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r4i1p2f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r5i1p2f1&#x27;, &#x27;r6i1p1f1&#x27;, &#x27;r6i1p2f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r7i1p2f1&#x27;, &#x27;r8i1p1f1&#x27;,\n",
       "       &#x27;r8i1p2f1&#x27;, &#x27;r9i1p1f1&#x27;, &#x27;r9i1p2f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3d387f2-eb5d-4d6a-b9d7-1f6dcd6d1ef6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f3d387f2-eb5d-4d6a-b9d7-1f6dcd6d1ef6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 13140, member_id: 35, bnds: 2)\n",
       "Coordinates:\n",
       "  * time       (time) object 1979-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "    plev       float64 1e+03\n",
       "    lat        float64 60.0\n",
       "  * member_id  (member_id) object 'r10i1p1f1' 'r10i1p2f1' ... 'r9i1p2f1'\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (member_id, time, bnds) object dask.array<chunksize=(1, 730, 2), meta=np.ndarray>\n",
       "    lat_bnds   (time, member_id, bnds) float64 dask.array<chunksize=(730, 1, 2), meta=np.ndarray>\n",
       "    lon_bnds   (time, member_id, bnds) float64 dask.array<chunksize=(730, 1, 2), meta=np.ndarray>\n",
       "    ua         (member_id, time) float32 dask.array<chunksize=(1, 730), meta=np.ndarray>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_sel = ds.sel(\n",
    "    plev = 1000,\n",
    "    time = slice('1979', '2014')\n",
    ").sel(lat = 60, method = 'nearest').mean('lon')\n",
    "ds_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/work/bd1022/b381258/CMIP6/including_April/CMIP_CanESM5_historical_day_gn_ua_1979-2014.nc\n"
     ]
    }
   ],
   "source": [
    "ds_sel_winter = ds_sel[[sel_var]].sel(time = np.isin(ds_sel.time.dt.month, [11,12,1,2,3,4])).squeeze().reset_coords(drop = True)\n",
    "#sys.exit()\n",
    "output = f'{outpath}CMIP6/including_April/{k}_{sel_var}_1979-2014.nc'\n",
    "print(output)\n",
    "ds_sel_winter.to_netcdf(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "directory = tempfile.gettempdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# https://intake-esm.readthedocs.io/en/stable/how-to/modify-catalog.html\n",
    "cat.serialize(directory='./catalogs/', name=f\"my_catalog_subset_{models[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: -c: line 0: syntax error near unexpected token `('\n",
      "/bin/bash: -c: line 0: `cat /home/b/b381258/mistral_home/notebooks/('file', 'local'):///home/b/b381258/mistral_home/notebooks/catalogs/my_catalog_subset_INM-CM5-0.json'\n"
     ]
    }
   ],
   "source": [
    "!cat /home/b/b381258/mistral_home/notebooks/('file', 'local'):///home/b/b381258/mistral_home/notebooks/catalogs/my_catalog_subset_INM-CM5-0.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat: {directory}/my_catalog_subset.json: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!cat {directory}/my_catalog_subset.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'directory' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cat\u001b[38;5;241m.\u001b[39mserialize(directory\u001b[38;5;241m=\u001b[39m\u001b[43mdirectory\u001b[49m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmy_catalog_subset\u001b[39m\u001b[38;5;124m\"\u001b[39m, catalog_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'directory' is not defined"
     ]
    }
   ],
   "source": [
    "cat.serialize(directory=directory, name=\"my_catalog_subset\", catalog_type=\"file\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "activity_id,institution_id,source_id,experiment_id,member_id,table_id,variable_id,grid_label,dcpp_init_year,version,time_range,path,opendap_url,project,simulation_id,grid_id,frequency,time_reduction,long_name,units,realm,level_type,time_min,time_max,format,uri\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r10i1p1f2,day,zg,gn,,v20191213,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_18500101-18991230.nc,CMIP6,r10i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r10i1p1f2,day,zg,gn,,v20191213,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_19000101-19491230.nc,CMIP6,r10i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r10i1p1f2,day,zg,gn,,v20191213,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_19500101-19991230.nc,CMIP6,r10i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r10i1p1f2,day,zg,gn,,v20191213,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_20000101-20141230.nc,CMIP6,r10i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r10i1p1f2/day/zg/gn/v20191213/zg_day_UKESM1-0-LL_historical_r10i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r11i1p1f2,day,zg,gn,,v20191209,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_18500101-18991230.nc,CMIP6,r11i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r11i1p1f2,day,zg,gn,,v20191209,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_19000101-19491230.nc,CMIP6,r11i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r11i1p1f2,day,zg,gn,,v20191209,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_19500101-19991230.nc,CMIP6,r11i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r11i1p1f2,day,zg,gn,,v20191209,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_20000101-20141230.nc,CMIP6,r11i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r11i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r11i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r12i1p1f2,day,zg,gn,,v20191210,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_18500101-18991230.nc,CMIP6,r12i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r12i1p1f2,day,zg,gn,,v20191210,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_19000101-19491230.nc,CMIP6,r12i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r12i1p1f2,day,zg,gn,,v20191210,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_19500101-19991230.nc,CMIP6,r12i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r12i1p1f2,day,zg,gn,,v20191210,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_20000101-20141230.nc,CMIP6,r12i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r12i1p1f2/day/zg/gn/v20191210/zg_day_UKESM1-0-LL_historical_r12i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r16i1p1f2,day,zg,gn,,v20191209,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_18500101-18991230.nc,CMIP6,r16i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r16i1p1f2,day,zg,gn,,v20191209,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_19000101-19491230.nc,CMIP6,r16i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r16i1p1f2,day,zg,gn,,v20191209,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_19500101-19991230.nc,CMIP6,r16i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r16i1p1f2,day,zg,gn,,v20191209,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_20000101-20141230.nc,CMIP6,r16i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r16i1p1f2/day/zg/gn/v20191209/zg_day_UKESM1-0-LL_historical_r16i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r17i1p1f2,day,zg,gn,,v20191115,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_18500101-18991230.nc,CMIP6,r17i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r17i1p1f2,day,zg,gn,,v20191115,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_19000101-19491230.nc,CMIP6,r17i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r17i1p1f2,day,zg,gn,,v20191115,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_19500101-19991230.nc,CMIP6,r17i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r17i1p1f2,day,zg,gn,,v20191115,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_20000101-20141230.nc,CMIP6,r17i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r17i1p1f2/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r17i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r18i1p1f2,day,zg,gn,,v20191119,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_18500101-18991230.nc,CMIP6,r18i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r18i1p1f2,day,zg,gn,,v20191119,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_19000101-19491230.nc,CMIP6,r18i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r18i1p1f2,day,zg,gn,,v20191119,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_19500101-19991230.nc,CMIP6,r18i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r18i1p1f2,day,zg,gn,,v20191119,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_20000101-20141230.nc,CMIP6,r18i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r18i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r18i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r19i1p1f2,day,zg,gn,,v20191119,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_18500101-18991230.nc,CMIP6,r19i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r19i1p1f2,day,zg,gn,,v20191119,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_19000101-19491230.nc,CMIP6,r19i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r19i1p1f2,day,zg,gn,,v20191119,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_19500101-19991230.nc,CMIP6,r19i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r19i1p1f2,day,zg,gn,,v20191119,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_20000101-20141230.nc,CMIP6,r19i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r19i1p1f2/day/zg/gn/v20191119/zg_day_UKESM1-0-LL_historical_r19i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r1i1p1f2,day,zg,gn,,v20190627,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_18500101-18991230.nc,CMIP6,r1i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r1i1p1f2,day,zg,gn,,v20190627,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_19000101-19491230.nc,CMIP6,r1i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r1i1p1f2,day,zg,gn,,v20190627,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_19500101-19991230.nc,CMIP6,r1i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r1i1p1f2,day,zg,gn,,v20190627,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_20000101-20141230.nc,CMIP6,r1i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/day/zg/gn/v20190627/zg_day_UKESM1-0-LL_historical_r1i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r2i1p1f2,day,zg,gn,,v20190708,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_18500101-18991230.nc,CMIP6,r2i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r2i1p1f2,day,zg,gn,,v20190708,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_19000101-19491230.nc,CMIP6,r2i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r2i1p1f2,day,zg,gn,,v20190708,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_19500101-19991230.nc,CMIP6,r2i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r2i1p1f2,day,zg,gn,,v20190708,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_20000101-20141230.nc,CMIP6,r2i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r2i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r2i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r3i1p1f2,day,zg,gn,,v20190708,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_18500101-18991230.nc,CMIP6,r3i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r3i1p1f2,day,zg,gn,,v20190708,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_19000101-19491230.nc,CMIP6,r3i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r3i1p1f2,day,zg,gn,,v20190708,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_19500101-19991230.nc,CMIP6,r3i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r3i1p1f2,day,zg,gn,,v20190708,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_20000101-20141230.nc,CMIP6,r3i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r3i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r3i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r4i1p1f2,day,zg,gn,,v20190708,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_18500101-18991230.nc,CMIP6,r4i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r4i1p1f2,day,zg,gn,,v20190708,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_19000101-19491230.nc,CMIP6,r4i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r4i1p1f2,day,zg,gn,,v20190708,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_19500101-19991230.nc,CMIP6,r4i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r4i1p1f2,day,zg,gn,,v20190708,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_20000101-20141230.nc,CMIP6,r4i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r4i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r4i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r5i1p1f3,day,zg,gn,,v20191115,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_18500101-18991230.nc,CMIP6,r5i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r5i1p1f3,day,zg,gn,,v20191115,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_19000101-19491230.nc,CMIP6,r5i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r5i1p1f3,day,zg,gn,,v20191115,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_19500101-19991230.nc,CMIP6,r5i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r5i1p1f3,day,zg,gn,,v20191115,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_20000101-20141230.nc,CMIP6,r5i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r5i1p1f3/day/zg/gn/v20191115/zg_day_UKESM1-0-LL_historical_r5i1p1f3_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r6i1p1f3,day,zg,gn,,v20191113,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_18500101-18991230.nc,CMIP6,r6i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r6i1p1f3,day,zg,gn,,v20191113,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_19000101-19491230.nc,CMIP6,r6i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r6i1p1f3,day,zg,gn,,v20191113,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_19500101-19991230.nc,CMIP6,r6i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r6i1p1f3,day,zg,gn,,v20191113,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_20000101-20141230.nc,CMIP6,r6i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r6i1p1f3/day/zg/gn/v20191113/zg_day_UKESM1-0-LL_historical_r6i1p1f3_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r7i1p1f3,day,zg,gn,,v20191011,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_18500101-18991230.nc,CMIP6,r7i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r7i1p1f3,day,zg,gn,,v20191011,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_19000101-19491230.nc,CMIP6,r7i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r7i1p1f3,day,zg,gn,,v20191011,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_19500101-19991230.nc,CMIP6,r7i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r7i1p1f3,day,zg,gn,,v20191011,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_20000101-20141230.nc,CMIP6,r7i1p1f3,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r7i1p1f3/day/zg/gn/v20191011/zg_day_UKESM1-0-LL_historical_r7i1p1f3_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r8i1p1f2,day,zg,gn,,v20190708,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_18500101-18991230.nc,CMIP6,r8i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r8i1p1f2,day,zg,gn,,v20190708,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_19000101-19491230.nc,CMIP6,r8i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r8i1p1f2,day,zg,gn,,v20190708,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_19500101-19991230.nc,CMIP6,r8i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r8i1p1f2,day,zg,gn,,v20190708,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_20000101-20141230.nc,CMIP6,r8i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r8i1p1f2/day/zg/gn/v20190708/zg_day_UKESM1-0-LL_historical_r8i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r9i1p1f2,day,zg,gn,,v20191015,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_18500101-18991230.nc,CMIP6,r9i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r9i1p1f2,day,zg,gn,,v20191015,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_19000101-19491230.nc,CMIP6,r9i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r9i1p1f2,day,zg,gn,,v20191015,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_19500101-19991230.nc,CMIP6,r9i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,MOHC,UKESM1-0-LL,historical,r9i1p1f2,day,zg,gn,,v20191015,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_20000101-20141230.nc,CMIP6,r9i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r9i1p1f2/day/zg/gn/v20191015/zg_day_UKESM1-0-LL_historical_r9i1p1f2_gn_20000101-20141230.nc\n",
      "CMIP,NIMS-KMA,UKESM1-0-LL,historical,r14i1p1f2,day,zg,gn,,v20201019,18500101-18991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_18500101-18991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_18500101-18991230.nc,CMIP6,r14i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,18500101.0,18991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_18500101-18991230.nc\n",
      "CMIP,NIMS-KMA,UKESM1-0-LL,historical,r14i1p1f2,day,zg,gn,,v20201019,19000101-19491230,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_19000101-19491230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_19000101-19491230.nc,CMIP6,r14i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19000101.0,19491230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_19000101-19491230.nc\n",
      "CMIP,NIMS-KMA,UKESM1-0-LL,historical,r14i1p1f2,day,zg,gn,,v20201019,19500101-19991230,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_19500101-19991230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_19500101-19991230.nc,CMIP6,r14i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,19500101.0,19991230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_19500101-19991230.nc\n",
      "CMIP,NIMS-KMA,UKESM1-0-LL,historical,r14i1p1f2,day,zg,gn,,v20201019,20000101-20141230,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_20000101-20141230.nc,http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_20000101-20141230.nc,CMIP6,r14i1p1f2,No entry,day, mean,Geopotential Height,m,atmos,,20000101.0,20141230,netcdf,/work/ik1017/CMIP6/data/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/day/zg/gn/v20201019/zg_day_UKESM1-0-LL_historical_r14i1p1f2_gn_20000101-20141230.nc\n"
     ]
    }
   ],
   "source": [
    "!cat {directory}/my_catalog_subset.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "chunk_dict = {\n",
    "    'UKESM1-0-LL':  dict(plev = 1, time = 1800),\n",
    "    'MIROC6': {},\n",
    "    'MPI-ESM1-2-HR': dict(plev = 1),\n",
    "    'CanESM5': dict(plev = 1),\n",
    "    'CNRM-CM6-1': dict(plev = 1, time = 2000),\n",
    "    'IPSL-CM6A-LR': dict(plev = 1, time = 3000),\n",
    "    'INM-CM5-0': dict(plev = 1),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "https://easy.gems.dkrz.de/Processing/Intake/find_files.html#using-time-ranges\n",
    "--time_range 2020-02-01 2020-02-03\n",
    "df = hitlist.df[\n",
    "            (hitlist.df[\"time_min\"] <= tr[1]) * (hitlist.df[\"time_max\"] >= tr[0])\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     19500101.0\n",
       "1     19750101.0\n",
       "2     20000101.0\n",
       "3     19500101.0\n",
       "4     19750101.0\n",
       "         ...    \n",
       "73    19750101.0\n",
       "74    20000101.0\n",
       "75    19500101.0\n",
       "76    19750101.0\n",
       "77    20000101.0\n",
       "Name: time_min, Length: 78, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"time_min\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
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    },
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     "name": "stdout",
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     "text": [
      "33\n",
      "33\n",
      "33\n",
      "33\n",
      "33\n",
      "33\n",
      "33\n",
      "33\n",
      "33\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "ls = []\n",
    "member_ls = df.member_id.unique()\n",
    "for ens in tqdm(member_ls):\n",
    "    mask = df.member_id == ens\n",
    "    infiles = list(df[mask]['uri'].values)\n",
    "    print(len(infiles))\n",
    "    #ls.append(infiles)\n",
    "    if models[0] == 'IPSL-CM6A-LR': \n",
    "        ls.append([infiles[-1]])\n",
    "    else:\n",
    "        ls.append(infiles)\n",
    "    #print(infiles)\n",
    "    \n",
    "with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
    "    ds = xr.open_mfdataset(\n",
    "        ls, concat_dim = ['member_id','time'], \n",
    "        parallel = True, \n",
    "        combine='nested', \n",
    "        engine = 'netcdf4',\n",
    "        chunks = chunk_dict[models[0]],#, time = 730)\n",
    "        #**kwargsuse_cftime = False\n",
    "    )\n",
    "    ds['member_id'] = member_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 60225, member_id: 10, bnds: 2, plev: 8, lat: 120, lon: 180)\n",
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       "  * time       (time) object 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "  * plev       (plev) float64 1e+05 8.5e+04 7e+04 5e+04 ... 1e+04 5e+03 1e+03\n",
       "  * lat        (lat) float64 -89.25 -87.75 -86.25 -84.75 ... 86.25 87.75 89.25\n",
       "  * lon        (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * member_id  (member_id) object &#x27;r10i1p1f1&#x27; &#x27;r1i1p1f1&#x27; ... &#x27;r9i1p1f1&#x27;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (member_id, time, bnds) object dask.array&lt;chunksize=(1, 1825, 2), meta=np.ndarray&gt;\n",
       "    lat_bnds   (time, member_id, lat, bnds) float64 dask.array&lt;chunksize=(1825, 1, 120, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (time, member_id, lon, bnds) float64 dask.array&lt;chunksize=(1825, 1, 180, 2), meta=np.ndarray&gt;\n",
       "    zg         (member_id, time, plev, lat, lon) float32 dask.array&lt;chunksize=(1, 1825, 1, 120, 180), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  381060.0\n",
       "    contact:                Evgeny Volodin (volodinev@gmail.com)\n",
       "    ...                     ...\n",
       "    title:                  INM-CM5-0 output prepared for CMIP6\n",
       "    tracking_id:            hdl:21.14100/c2ef1014-8125-4905-b0fe-db72adc75cf8\n",
       "    variable_id:            zg\n",
       "    variant_label:          r10i1p1f1\n",
       "    license:                CMIP6 model data produced by Lawrence Livermore P...\n",
       "    cmor_version:           3.4.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e32e34f2-105d-4440-8888-93794e205fef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e32e34f2-105d-4440-8888-93794e205fef' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60225</li><li><span class='xr-has-index'>member_id</span>: 10</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>plev</span>: 8</li><li><span class='xr-has-index'>lat</span>: 120</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0040f569-afee-4a4f-9335-93a09188bc09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0040f569-afee-4a4f-9335-93a09188bc09' class='xr-section-summary' >Coordinates: <span>(5)</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 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'>1850-01-01 12:00:00 ... 2014-12-...</div><input id='attrs-cf6d850e-2a8f-49d2-9ed7-5d58fe1379ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf6d850e-2a8f-49d2-9ed7-5d58fe1379ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3675f1cb-8d76-4228-a23a-19dbdbf0dcf5' class='xr-var-data-in' type='checkbox'><label for='data-3675f1cb-8d76-4228-a23a-19dbdbf0dcf5' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05 8.5e+04 7e+04 ... 5e+03 1e+03</div><input id='attrs-fbf581fc-66bd-4144-84d6-830f9e39e021' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbf581fc-66bd-4144-84d6-830f9e39e021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb212f35-dafa-486b-a0d7-75aaabdd2512' class='xr-var-data-in' type='checkbox'><label for='data-fb212f35-dafa-486b-a0d7-75aaabdd2512' 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>units :</span></dt><dd>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  85000.,  70000.,  50000.,  25000.,  10000.,   5000.,   1000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.25 -87.75 ... 87.75 89.25</div><input id='attrs-6c75cb82-8e42-4727-994d-1e3e2911cdcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c75cb82-8e42-4727-994d-1e3e2911cdcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-295f4b6d-04fd-4c2d-b58d-d8b50ceb8655' class='xr-var-data-in' type='checkbox'><label for='data-295f4b6d-04fd-4c2d-b58d-d8b50ceb8655' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.25, -87.75, -86.25, -84.75, -83.25, -81.75, -80.25, -78.75, -77.25,\n",
       "       -75.75, -74.25, -72.75, -71.25, -69.75, -68.25, -66.75, -65.25, -63.75,\n",
       "       -62.25, -60.75, -59.25, -57.75, -56.25, -54.75, -53.25, -51.75, -50.25,\n",
       "       -48.75, -47.25, -45.75, -44.25, -42.75, -41.25, -39.75, -38.25, -36.75,\n",
       "       -35.25, -33.75, -32.25, -30.75, -29.25, -27.75, -26.25, -24.75, -23.25,\n",
       "       -21.75, -20.25, -18.75, -17.25, -15.75, -14.25, -12.75, -11.25,  -9.75,\n",
       "        -8.25,  -6.75,  -5.25,  -3.75,  -2.25,  -0.75,   0.75,   2.25,   3.75,\n",
       "         5.25,   6.75,   8.25,   9.75,  11.25,  12.75,  14.25,  15.75,  17.25,\n",
       "        18.75,  20.25,  21.75,  23.25,  24.75,  26.25,  27.75,  29.25,  30.75,\n",
       "        32.25,  33.75,  35.25,  36.75,  38.25,  39.75,  41.25,  42.75,  44.25,\n",
       "        45.75,  47.25,  48.75,  50.25,  51.75,  53.25,  54.75,  56.25,  57.75,\n",
       "        59.25,  60.75,  62.25,  63.75,  65.25,  66.75,  68.25,  69.75,  71.25,\n",
       "        72.75,  74.25,  75.75,  77.25,  78.75,  80.25,  81.75,  83.25,  84.75,\n",
       "        86.25,  87.75,  89.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-1d151562-d2a7-4a7d-aa2e-49274292b6ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d151562-d2a7-4a7d-aa2e-49274292b6ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e28b90e-5897-4f61-a255-4e5717b66df4' class='xr-var-data-in' type='checkbox'><label for='data-0e28b90e-5897-4f61-a255-4e5717b66df4' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r10i1p1f1&#x27; ... &#x27;r9i1p1f1&#x27;</div><input id='attrs-24f76a3b-5bcd-44ca-a02f-6a86ecb75945' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24f76a3b-5bcd-44ca-a02f-6a86ecb75945' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c73bdb28-f451-4729-bcae-6060fd203abc' class='xr-var-data-in' type='checkbox'><label for='data-c73bdb28-f451-4729-bcae-6060fd203abc' 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;r10i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r6i1p1f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r8i1p1f1&#x27;, &#x27;r9i1p1f1&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4bb0fcdf-b3ee-4aab-95a0-b732146ff780' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4bb0fcdf-b3ee-4aab-95a0-b732146ff780' class='xr-section-summary' >Data variables: <span>(4)</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>time_bnds</span></div><div class='xr-var-dims'>(member_id, time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1825, 2), meta=np.ndarray&gt;</div><input id='attrs-60d12071-ec8c-4ecb-bf42-44517d0dc77c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60d12071-ec8c-4ecb-bf42-44517d0dc77c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95b67179-7038-48be-ac35-07f0874dc427' class='xr-var-data-in' type='checkbox'><label for='data-95b67179-7038-48be-ac35-07f0874dc427' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 9.19 MiB </td>\n",
       "                        <td> 28.52 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (10, 60225, 2) </td>\n",
       "                        <td> (1, 1825, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 330 chunks in 1024 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.08 GiB </td>\n",
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       "                        <td> 1.62 GiB </td>\n",
       "                        <td> 5.01 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zg</span></div><div class='xr-var-dims'>(member_id, time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1825, 1, 120, 180), meta=np.ndarray&gt;</div><input id='attrs-73f1871e-2971-4374-8de4-a888c5b72e43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73f1871e-2971-4374-8de4-a888c5b72e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e01a3df9-5e98-4e5d-ac9a-562ac99918fb' class='xr-var-data-in' type='checkbox'><label for='data-e01a3df9-5e98-4e5d-ac9a-562ac99918fb' 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>standard_name :</span></dt><dd>geopotential_height</dd><dt><span>long_name :</span></dt><dd>Geopotential Height</dd><dt><span>comment :</span></dt><dd>Geopotential is the sum of the specific gravitational potential energy relative to the geoid and the specific centripetal potential energy. Geopotential height is the geopotential divided by the standard acceleration due to gravity. It is numerically similar to the altitude (or geometric height) and not to the quantity with standard name height, which is relative to the surface.</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>original_name :</span></dt><dd>zg</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 1 day)</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 387.69 GiB </td>\n",
       "                        <td> 150.38 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (10, 60225, 8, 120, 180) </td>\n",
       "                        <td> (1, 1825, 1, 120, 180) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2640 chunks in 1024 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-24adc33b-cd87-456e-8f6b-30cb28cc6aa4' class='xr-section-summary-in' type='checkbox'  ><label for='section-24adc33b-cd87-456e-8f6b-30cb28cc6aa4' class='xr-section-summary' >Indexes: <span>(5)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0710483-bbb6-4c46-bf88-d8ef29efa46d' class='xr-index-data-in' type='checkbox'/><label for='index-a0710483-bbb6-4c46-bf88-d8ef29efa46d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-01 12:00:00, 1850-01-02 12:00:00, 1850-01-03 12:00:00,\n",
       "             1850-01-04 12:00:00, 1850-01-05 12:00:00, 1850-01-06 12:00:00,\n",
       "             1850-01-07 12:00:00, 1850-01-08 12:00:00, 1850-01-09 12:00:00,\n",
       "             1850-01-10 12:00:00,\n",
       "             ...\n",
       "             2014-12-22 12:00:00, 2014-12-23 12:00:00, 2014-12-24 12:00:00,\n",
       "             2014-12-25 12:00:00, 2014-12-26 12:00:00, 2014-12-27 12:00:00,\n",
       "             2014-12-28 12:00:00, 2014-12-29 12:00:00, 2014-12-30 12:00:00,\n",
       "             2014-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=60225, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4f020bc5-432c-4664-9b7b-3209efe1c22e' class='xr-index-data-in' type='checkbox'/><label for='index-4f020bc5-432c-4664-9b7b-3209efe1c22e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0, 85000.0, 70000.0, 50000.0, 25000.0, 10000.0, 5000.0, 1000.0], dtype=&#x27;float64&#x27;, name=&#x27;plev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-832ec9d2-ca5c-406f-a413-ec4d02f81e90' class='xr-index-data-in' type='checkbox'/><label for='index-832ec9d2-ca5c-406f-a413-ec4d02f81e90' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.25, -87.75, -86.25, -84.75, -83.25, -81.75, -80.25, -78.75, -77.25,\n",
       "       -75.75,\n",
       "       ...\n",
       "        75.75,  77.25,  78.75,  80.25,  81.75,  83.25,  84.75,  86.25,  87.75,\n",
       "        89.25],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-daec1c7a-d363-4eee-bb36-c69bf9dee998' class='xr-index-data-in' type='checkbox'/><label for='index-daec1c7a-d363-4eee-bb36-c69bf9dee998' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-183ba970-252f-4e98-9f9f-51b98b5b7b00' class='xr-index-data-in' type='checkbox'/><label for='index-183ba970-252f-4e98-9f9f-51b98b5b7b00' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r6i1p1f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r8i1p1f1&#x27;, &#x27;r9i1p1f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe10e7a9-a766-4a0d-a16e-a0065ac3f03d' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe10e7a9-a766-4a0d-a16e-a0065ac3f03d' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>381060.0</dd><dt><span>contact :</span></dt><dd>Evgeny Volodin (volodinev@gmail.com)</dd><dt><span>creation_date :</span></dt><dd>2019-07-10T11:36:08Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.INM.INM-CM5-0.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>gs2x1.5</dd><dt><span>grid_label :</span></dt><dd>gr1</dd><dt><span>history :</span></dt><dd>2019-07-10T11:36:07Z ;rewrote data to be consistent with CMIP for variable hur found in table day.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Institute for Numerical Mathematics, Russian Academy of Science, Moscow 119991, Russia</dd><dt><span>institution_id :</span></dt><dd>INM</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>INM-CM5-0</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01</dd><dt><span>parent_variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>RJNAMM 2018, 367-374. DOI: 10.1515/rnam-2018-0032</dd><dt><span>run_variant :</span></dt><dd>standard</dd><dt><span>source :</span></dt><dd>INM-CM5-0 (2016): \n",
       "aerosol: INM-AER1\n",
       "atmos: INM-AM5-0 (2x1.5; 180 x 120 longitude/latitude; 73 levels; top level sigma = 0.0002)\n",
       "atmosChem: none\n",
       "land: INM-LND1\n",
       "landIce: none\n",
       "ocean: INM-OM5 (North Pole shifted to 60N, 90E. 0.5x0.25; 720 x 720 longitude/latitude; 40 levels; vertical sigma coordinate)\n",
       "ocnBgchem: none\n",
       "seaIce: INM-ICE1</dd><dt><span>source_id :</span></dt><dd>INM-CM5-0</dd><dt><span>source_type :</span></dt><dd>AOGCM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>day</dd><dt><span>table_info :</span></dt><dd>Creation Date:(20 February 2019) MD5:951084b632bd52c3f6224e495b1cb65e</dd><dt><span>title :</span></dt><dd>INM-CM5-0 output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/c2ef1014-8125-4905-b0fe-db72adc75cf8</dd><dt><span>variable_id :</span></dt><dd>zg</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by Lawrence Livermore PCMDI is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https:///pcmdi.llnl.gov/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 60225, member_id: 10, bnds: 2, plev: 8, lat: 120, lon: 180)\n",
       "Coordinates:\n",
       "  * time       (time) object 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "  * plev       (plev) float64 1e+05 8.5e+04 7e+04 5e+04 ... 1e+04 5e+03 1e+03\n",
       "  * lat        (lat) float64 -89.25 -87.75 -86.25 -84.75 ... 86.25 87.75 89.25\n",
       "  * lon        (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * member_id  (member_id) object 'r10i1p1f1' 'r1i1p1f1' ... 'r9i1p1f1'\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (member_id, time, bnds) object dask.array<chunksize=(1, 1825, 2), meta=np.ndarray>\n",
       "    lat_bnds   (time, member_id, lat, bnds) float64 dask.array<chunksize=(1825, 1, 120, 2), meta=np.ndarray>\n",
       "    lon_bnds   (time, member_id, lon, bnds) float64 dask.array<chunksize=(1825, 1, 180, 2), meta=np.ndarray>\n",
       "    zg         (member_id, time, plev, lat, lon) float32 dask.array<chunksize=(1, 1825, 1, 120, 180), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  381060.0\n",
       "    contact:                Evgeny Volodin (volodinev@gmail.com)\n",
       "    ...                     ...\n",
       "    title:                  INM-CM5-0 output prepared for CMIP6\n",
       "    tracking_id:            hdl:21.14100/c2ef1014-8125-4905-b0fe-db72adc75cf8\n",
       "    variable_id:            zg\n",
       "    variant_label:          r10i1p1f1\n",
       "    license:                CMIP6 model data produced by Lawrence Livermore P...\n",
       "    cmor_version:           3.4.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selection and calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "A = 6374.0e3       # Earth radius\n",
    "DEGRAD = np.pi/180. # conversion of degrees to radians\n",
    "RADDEG = 180./np.pi # conversion of radians to degrees \n",
    "\n",
    "#optimized\n",
    "def sph_to_car(field, lons, lats, xypoints,resolution):\n",
    "    nlons = len(lons)\n",
    "    nlats = len(lats)\n",
    "    xyvals = []\n",
    "    \"\"\"for ilon in range(nlons): # -1s needed?\n",
    "        for ilat in range(nlats):\n",
    "            xyvals.append(field[ilat,ilon])\"\"\"\n",
    "    xyvals = field.reshape(nlons*nlats, order='F')\n",
    "\n",
    "    if resolution == 'full':       \n",
    "        cart_x_points = -1.+np.arange(nlons)/(0.5*nlons)             \n",
    "        cart_y_points = -1.+np.arange(nlons)/(0.5*nlons)\n",
    "    elif resolution == 'low':\n",
    "        cart_x_points = -1.+np.arange(50)/(0.5*50)\n",
    "        cart_y_points = -1.+np.arange(50)/(0.5*50)\n",
    "    else:\n",
    "        raise ValueError()\n",
    "    \n",
    "    cart_gridx, cart_gridy = np.meshgrid(cart_x_points,cart_y_points)\n",
    "\n",
    "    field_cart = griddata(xypoints, np.array(xyvals), (cart_gridx,cart_gridy), \\\n",
    "                        method='linear')  # Might want to change to cubic etc?\n",
    "                              \n",
    "    return field_cart, cart_x_points, cart_y_points  \n",
    "#the same\n",
    "def isolate_vortex(field_cart, edge, field_type='GPH'):\n",
    "    \"\"\"\n",
    "    Replace vortex into region (outside) equal to field on vortex edge, and \n",
    "    region (inside) with values of vortex. \n",
    "    For GPH vortex is less than surrounding, for PV it is greater. \n",
    "    \"\"\"\n",
    "    if field_type == 'GPH':\n",
    "        field_cart[np.where(field_cart > edge)] = edge\n",
    "        field_cart[np.isnan(field_cart)] = edge # set NaN regions to edge \n",
    "    elif field_type == 'PV':\n",
    "        field_cart[np.where(field_cart < edge)] = edge\n",
    "        field_cart[np.isnan(field_cart)] = edge # set NaN regions to edge\n",
    "    else:\n",
    "        raise ValueError() # maybe want more specific error type\n",
    "\n",
    "    return field_cart\n",
    "\n",
    "def moment_integrate2(vtx_field, x, y,edge):\n",
    "    \"\"\"\n",
    "    Performs moment diagnostic calculations on cartesian field\n",
    "    \"\"\"\n",
    "\n",
    "    # x and y are cartesian gridpoints; vtx_field is cartesian field\n",
    "    # edge is value on vortex edge \n",
    "\n",
    "    box_length = 2*A/len(x)\n",
    "    box_area = box_length**2\n",
    "\n",
    "    # Set up moment diagnostics\n",
    "    M00 = 0\n",
    "    M10 = 0\n",
    "    M01 = 0\n",
    "    Marea = 0\n",
    "    # Integrate over vortex\n",
    "          \n",
    "    M00 = np.sum(np.abs(vtx_field-edge)*(x[:,np.newaxis]**0)*(y[np.newaxis,:]**0))\n",
    "    M10 = np.sum(np.abs(vtx_field-edge)*(x[:,np.newaxis]**1)*(y[np.newaxis,:]**0))\n",
    "    M01 = np.sum(np.abs(vtx_field-edge)*(x[:,np.newaxis]**0)*(y[np.newaxis,:]**1))\n",
    "    Marea = np.sum(np.abs(vtx_field-edge)*(x[:,np.newaxis]**0)*(y[np.newaxis,:]**0)*box_area)\n",
    "            \n",
    "    # Calculate centroid \n",
    "    centx = M10/M00\n",
    "    centy = M01/M00\n",
    "\n",
    "    # Convert back to polar coordinates \n",
    "    R = centx**2 + centy**2\n",
    "    latcent = np.arcsin((1-R)/(1+R))*RADDEG\n",
    "    loncent = np.arctan(centx/centy)*RADDEG\n",
    "    \n",
    "    # Set up relative moment diagnostics \n",
    "    J11 = np.sum(np.abs(vtx_field-edge)*((x[:,np.newaxis]-centx)**1)*((y[np.newaxis,:]-centy)**1))\n",
    "    J20 = np.sum(np.abs(vtx_field-edge)*((x[:,np.newaxis]-centx)**2)*((y[np.newaxis,:]-centy)**0))\n",
    "    J02 = np.sum(np.abs(vtx_field-edge)*((x[:,np.newaxis]-centx)**0)*((y[np.newaxis,:]-centy)**2))\n",
    "    J22 = np.sum(np.abs(vtx_field-edge)*((x[:,np.newaxis]-centx)**2)*((y[np.newaxis,:]-centy)**2))\n",
    "    J40 = np.sum(np.abs(vtx_field-edge)*((x[:,np.newaxis]-centx)**4)*((y[np.newaxis,:]-centy)**0))\n",
    "    J04 = np.sum(np.abs(vtx_field-edge)*((x[:,np.newaxis]-centx)**0)*((y[np.newaxis,:]-centy)**4))  \n",
    "\n",
    "    \n",
    "      # Calculate angle between x-axis and major axis of ellipse                  \n",
    "    angle = 0.5*np.arctan((2*J11)/(J20-J02))*RADDEG\n",
    "    \n",
    "    # Calculate aspect ratio\n",
    "    ar = np.sqrt(abs(( (J20+J02) + np.sqrt(4*(J11**2)+(J20-J02)**2) ) / \\\n",
    "                               ( (J20+J02) - np.sqrt(4*(J11**2)+(J20-J02)**2) ))) \n",
    "    \n",
    "    objective_area = Marea/edge \n",
    "    \n",
    "    # Calculate excess kurtosis \n",
    "    kurtosis = M00 * (J40+2*J22+J04)/((J20+J02)**2) \\\n",
    "                         - (2./3.)*( (3*(ar**4)+2*(ar**2)+3) / (((ar**2)+1)**2) ) \n",
    "    \n",
    "    return angle, ar, objective_area, kurtosis, latcent, loncent \n",
    "\n",
    "def calc_moments(field, lats, lons, xypoints, hemisphere='NH', field_type='GPH', \\\n",
    "                 edge=3.02e4, resolution='full'):\n",
    "    field_cart, x, y = sph_to_car(field,lons,lats,xypoints,resolution)\n",
    "    field_vtx = isolate_vortex(field_cart, edge, field_type)\n",
    "   \n",
    "    #angle, aspect_ratio, objective_area, kurtosis, latcent, loncent = moment_integrate2(field_vtx, x, y,edge)\n",
    "        \n",
    "    #return {'aspect_ratio':aspect_ratio, 'centroid_latitude': latcent, 'centroid'}\n",
    "    values = moment_integrate2(field_vtx, x, y, edge)\n",
    "    return values\n",
    "                 \n",
    "def calc_xypoints(lons, lats):\n",
    "    DEGRAD = np.pi/180.\n",
    "    lons = lons * DEGRAD\n",
    "    lats = lats * DEGRAD\n",
    "    x = (np.cos(lons[:,np.newaxis])*np.cos(lats))/ (1. + np.sin(lats[np.newaxis,:]))               \n",
    "    y = (np.sin(lons[:,np.newaxis])*np.cos(lats[np.newaxis,:]))/(1. + np.sin(lats[np.newaxis,:]))\n",
    "    nlon_nlat = lons.shape[0]*lats.shape[0]\n",
    "    xypoints = np.stack([x.reshape(nlon_nlat),y.reshape(nlon_nlat)], axis = 1)\n",
    "    return xypoints\n",
    "                 \n",
    "def moments_fce(arr, edge=3.02e4):\n",
    "    moments = calc_moments(arr, lats, lons, xypoints, 'NH','GPH', edge)\n",
    "    return np.array(moments) #np.array([moments['aspect_ratio'], moments['centroid_latitude']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/work/bd1022/b381258/CMIP6/including_April/CMIP_INM-CM5-0_historical_day_gr1_moments@10hPa.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:100: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:100: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:90: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:99: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:78: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:90: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:99: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
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      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:90: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:99: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:100: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:90: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:99: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:100: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:90: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:99: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_378318/698317998.py:100: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "user = 'b381258'\n",
    "outpath=f'/work/bd1022/{user}/'\n",
    "winter_month_ls = [11,12,1,2,3,4]\n",
    "\n",
    "\n",
    "\n",
    "ds_sel = ds['zg'].sel(\n",
    "    plev = 1000,#[10000,5000,1000],\n",
    "    time =slice('1979-01-01',None), #slice('1920-01-01',None),#time = slice(None,'2100-12-31'),\n",
    "    lat = slice(20,None)\n",
    ")\n",
    "ds_sel_winter = ds_sel.sel(\n",
    "time = np.isin(\n",
    "ds_sel.time.dt.month, \n",
    "winter_month_ls\n",
    "))\n",
    "\n",
    "#output = f'{outpath}CMIP6/{k}_zg@10and100hPa.nc'\n",
    "\n",
    "\n",
    "lons = ds_sel.lon.values\n",
    "lats = ds_sel.lat.values\n",
    "xypoints = calc_xypoints(lons,lats)\n",
    "\n",
    "temp = ds_sel_winter.sel(lat = 60, method = 'nearest').mean(['lon','time'])#.load() # \n",
    "#print(temp)\n",
    "#sys.exit()\n",
    "dask_gufun_dict = dict(output_sizes = dict(vars = 6))\n",
    "moments_xr = xr.apply_ufunc(moments_fce, ds_sel_winter, temp, \\\n",
    "                        input_core_dims=[['lat', 'lon'], []], \\\n",
    "                        output_core_dims=[['vars']], \\\n",
    "                        output_dtypes = [float], vectorize=True, \\\n",
    "                        dask = 'parallelized',\\\n",
    "                        dask_gufunc_kwargs = dask_gufun_dict)#.compute()\n",
    "\n",
    "\n",
    "moments_xr['vars'] = ['angle', 'aspect_ratio', 'objective_area', 'kurtosis', 'centroid_latitude', 'centroid_longitude']#.reset_coords()\n",
    "moments_ds = moments_xr.to_dataset(dim = 'vars') #.reset_coords('vars')\n",
    "\n",
    "\n",
    "#sys.exit()\n",
    "output = f'{outpath}CMIP6/including_April/{k}_moments@10hPa.nc'\n",
    "print(output)\n",
    "\n",
    "\n",
    "moments_ds.to_netcdf(output)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'kerchunk.hdf'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mkerchunk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhdf\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'kerchunk.hdf'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-24 17:11:00,753 - distributed.nanny - WARNING - Worker process still alive after 3.1999990844726565 seconds, killing\n",
      "2024-01-24 17:11:00,754 - distributed.nanny - WARNING - Worker process still alive after 3.1999995422363288 seconds, killing\n",
      "2024-01-24 17:11:00,755 - distributed.nanny - WARNING - Worker process still alive after 3.1999995422363288 seconds, killing\n",
      "2024-01-24 17:11:00,755 - distributed.nanny - WARNING - Worker process still alive after 3.1999995422363288 seconds, killing\n",
      "2024-01-24 17:11:00,756 - distributed.nanny - WARNING - Worker process still alive after 3.1999995422363288 seconds, killing\n"
     ]
    }
   ],
   "source": [
    "import kerchunk.hdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;aspect_ratio&#x27; (member_id: 17, time: 365)&gt;\n",
       "dask.array&lt;getitem, shape=(17, 365), dtype=float64, chunksize=(1, 185), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time       (time) object 1979-01-01 12:00:00 ... 1981-01-05 12:00:00\n",
       "    plev       float64 1e+03\n",
       "  * member_id  (member_id) object &#x27;r10i1p1f2&#x27; &#x27;r11i1p1f2&#x27; ... &#x27;r14i1p1f2&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'aspect_ratio'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 17</li><li><span class='xr-has-index'>time</span>: 365</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e5f1283d-8a3f-4251-b7da-6eea7a6c7ac5' class='xr-array-in' type='checkbox' checked><label for='section-e5f1283d-8a3f-4251-b7da-6eea7a6c7ac5' 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, 180), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 48.48 kiB </td>\n",
       "                        <td> 1.45 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (17, 365) </td>\n",
       "                        <td> (1, 185) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 34 chunks in 253 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
       "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"34\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"34\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,34.066259112396764 0.0,34.066259112396764\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"54.066259\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >365</text>\n",
       "  <text x=\"140.000000\" y=\"17.033130\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.033130)\">17</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-a5e1af2c-0f99-4d60-bc01-f3d4aa13ae19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5e1af2c-0f99-4d60-bc01-f3d4aa13ae19' class='xr-section-summary' >Coordinates: <span>(3)</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 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'>1979-01-01 12:00:00 ... 1981-01-...</div><input id='attrs-8b2620f1-adbe-4d93-9b8d-68a5bcdd5527' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b2620f1-adbe-4d93-9b8d-68a5bcdd5527' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00f20f66-7f04-4df4-8334-474417666945' class='xr-var-data-in' type='checkbox'><label for='data-00f20f66-7f04-4df4-8334-474417666945' 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([cftime.Datetime360Day(1979, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1979, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1979, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.Datetime360Day(1981, 1, 3, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1981, 1, 4, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1981, 1, 5, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-fa0465d3-d68f-4fbf-a19c-34448aa7cfb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa0465d3-d68f-4fbf-a19c-34448aa7cfb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1731f1d-983f-4508-a97f-d553e9e69ea9' class='xr-var-data-in' type='checkbox'><label for='data-c1731f1d-983f-4508-a97f-d553e9e69ea9' 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(1000.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r10i1p1f2&#x27; ... &#x27;r14i1p1f2&#x27;</div><input id='attrs-fa0c3d10-b968-4155-b116-1bae542ce9d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa0c3d10-b968-4155-b116-1bae542ce9d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5809ae0a-6ec4-40db-be76-bd252f67a355' class='xr-var-data-in' type='checkbox'><label for='data-5809ae0a-6ec4-40db-be76-bd252f67a355' 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;r10i1p1f2&#x27;, &#x27;r11i1p1f2&#x27;, &#x27;r12i1p1f2&#x27;, &#x27;r16i1p1f2&#x27;, &#x27;r17i1p1f2&#x27;,\n",
       "       &#x27;r18i1p1f2&#x27;, &#x27;r19i1p1f2&#x27;, &#x27;r1i1p1f2&#x27;, &#x27;r2i1p1f2&#x27;, &#x27;r3i1p1f2&#x27;,\n",
       "       &#x27;r4i1p1f2&#x27;, &#x27;r5i1p1f3&#x27;, &#x27;r6i1p1f3&#x27;, &#x27;r7i1p1f3&#x27;, &#x27;r8i1p1f2&#x27;, &#x27;r9i1p1f2&#x27;,\n",
       "       &#x27;r14i1p1f2&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-979df870-c242-47cc-a0dc-e6e079640264' class='xr-section-summary-in' type='checkbox'  ><label for='section-979df870-c242-47cc-a0dc-e6e079640264' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-895933a1-23f0-4edf-a845-23dde1307c6d' class='xr-index-data-in' type='checkbox'/><label for='index-895933a1-23f0-4edf-a845-23dde1307c6d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1979-01-01 12:00:00, 1979-01-02 12:00:00, 1979-01-03 12:00:00,\n",
       "             1979-01-04 12:00:00, 1979-01-05 12:00:00, 1979-01-06 12:00:00,\n",
       "             1979-01-07 12:00:00, 1979-01-08 12:00:00, 1979-01-09 12:00:00,\n",
       "             1979-01-10 12:00:00,\n",
       "             ...\n",
       "             1980-12-26 12:00:00, 1980-12-27 12:00:00, 1980-12-28 12:00:00,\n",
       "             1980-12-29 12:00:00, 1980-12-30 12:00:00, 1981-01-01 12:00:00,\n",
       "             1981-01-02 12:00:00, 1981-01-03 12:00:00, 1981-01-04 12:00:00,\n",
       "             1981-01-05 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=365, calendar=&#x27;360_day&#x27;, freq=&#x27;None&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-75fcffa7-add0-4896-9930-bce0535439dc' class='xr-index-data-in' type='checkbox'/><label for='index-75fcffa7-add0-4896-9930-bce0535439dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1p1f2&#x27;, &#x27;r11i1p1f2&#x27;, &#x27;r12i1p1f2&#x27;, &#x27;r16i1p1f2&#x27;, &#x27;r17i1p1f2&#x27;,\n",
       "       &#x27;r18i1p1f2&#x27;, &#x27;r19i1p1f2&#x27;, &#x27;r1i1p1f2&#x27;, &#x27;r2i1p1f2&#x27;, &#x27;r3i1p1f2&#x27;,\n",
       "       &#x27;r4i1p1f2&#x27;, &#x27;r5i1p1f3&#x27;, &#x27;r6i1p1f3&#x27;, &#x27;r7i1p1f3&#x27;, &#x27;r8i1p1f2&#x27;, &#x27;r9i1p1f2&#x27;,\n",
       "       &#x27;r14i1p1f2&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68334b0d-fa94-4aa0-aabc-ba650c12d3e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68334b0d-fa94-4aa0-aabc-ba650c12d3e4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'aspect_ratio' (member_id: 17, time: 365)>\n",
       "dask.array<getitem, shape=(17, 365), dtype=float64, chunksize=(1, 185), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time       (time) object 1979-01-01 12:00:00 ... 1981-01-05 12:00:00\n",
       "    plev       float64 1e+03\n",
       "  * member_id  (member_id) object 'r10i1p1f2' 'r11i1p1f2' ... 'r14i1p1f2'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moments_ds['aspect_ratio'][:,:365]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffe0e883d60>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "moments_ds['aspect_ratio'][0,:365].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fff9ec61390>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "moments_ds['aspect_ratio'].sel(member_id = 'r9i1p1f2')[:365].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffe0f867a60>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "moments_ds['aspect_ratio'][-1,:365].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:100: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:78: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:100: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:72: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/tmp/ipykernel_173444/698317998.py:73: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Plotting requires coordinates to be numeric, boolean, or dates of type numpy.datetime64, datetime.datetime, cftime.datetime or pandas.Interval. Received data of type object instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[65], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmoments_ds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maspect_ratio\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m365\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages/xarray/plot/accessor.py:47\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(dataarray_plot\u001b[38;5;241m.\u001b[39mplot, assigned\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__doc__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__annotations__\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m---> 47\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdataarray_plot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_da\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages/xarray/plot/dataarray_plot.py:306\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m     plotfunc \u001b[38;5;241m=\u001b[39m hist\n\u001b[1;32m    304\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m ax\n\u001b[0;32m--> 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplotfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdarray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages/xarray/plot/dataarray_plot.py:1565\u001b[0m, in \u001b[0;36m_plot2d.<locals>.newplotfunc\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   1562\u001b[0m xplt, xlab_extra \u001b[38;5;241m=\u001b[39m _resolve_intervals_2dplot(xvalnp, plotfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m   1563\u001b[0m yplt, ylab_extra \u001b[38;5;241m=\u001b[39m _resolve_intervals_2dplot(yvalnp, plotfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[0;32m-> 1565\u001b[0m \u001b[43m_ensure_plottable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxplt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myplt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mzval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1567\u001b[0m cmap_params, cbar_kwargs \u001b[38;5;241m=\u001b[39m _process_cmap_cbar_kwargs(\n\u001b[1;32m   1568\u001b[0m     plotfunc,\n\u001b[1;32m   1569\u001b[0m     zval\u001b[38;5;241m.\u001b[39mdata,\n\u001b[1;32m   1570\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mlocals\u001b[39m(),\n\u001b[1;32m   1571\u001b[0m     _is_facetgrid\u001b[38;5;241m=\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_is_facetgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m   1572\u001b[0m )\n\u001b[1;32m   1574\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontour\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m plotfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m:\n\u001b[1;32m   1575\u001b[0m     \u001b[38;5;66;03m# extend is a keyword argument only for contour and contourf, but\u001b[39;00m\n\u001b[1;32m   1576\u001b[0m     \u001b[38;5;66;03m# passing it to the colorbar is sufficient for imshow and\u001b[39;00m\n\u001b[1;32m   1577\u001b[0m     \u001b[38;5;66;03m# pcolormesh\u001b[39;00m\n",
      "File \u001b[0;32m/sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages/xarray/plot/utils.py:693\u001b[0m, in \u001b[0;36m_ensure_plottable\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[1;32m    689\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\n\u001b[1;32m    690\u001b[0m         _valid_numpy_subdtype(np\u001b[38;5;241m.\u001b[39masarray(x), numpy_types)\n\u001b[1;32m    691\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _valid_other_type(np\u001b[38;5;241m.\u001b[39masarray(x), other_types)\n\u001b[1;32m    692\u001b[0m     ):\n\u001b[0;32m--> 693\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    694\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlotting requires coordinates to be numeric, boolean, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    695\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor dates of type numpy.datetime64, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    696\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatetime.datetime, cftime.datetime or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    697\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpandas.Interval. Received data of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39masarray(x)\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    698\u001b[0m         )\n\u001b[1;32m    699\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _valid_other_type(np\u001b[38;5;241m.\u001b[39masarray(x), cftime_datetime_types):\n\u001b[1;32m    700\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m nc_time_axis_available:\n\u001b[1;32m    701\u001b[0m             \u001b[38;5;66;03m# Register cftime datetypes to matplotlib.units.registry,\u001b[39;00m\n\u001b[1;32m    702\u001b[0m             \u001b[38;5;66;03m# otherwise matplotlib will raise an error:\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: Plotting requires coordinates to be numeric, boolean, or dates of type numpy.datetime64, datetime.datetime, cftime.datetime or pandas.Interval. Received data of type object instead."
     ]
    }
   ],
   "source": [
    "moments_ds['aspect_ratio'][:,:365].plot()"
   ]
  }
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