Prueva_GLORYS_Lee_dask.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1df62f37-e43c-4f83-9107-5cebfdbacca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # numerical library\n",
    "import xarray as xr  # netCDF library\n",
    "\n",
    "import matplotlib.pyplot as plt  # plotting library\n",
    "import cartopy  # Map projections libary\n",
    "import cartopy.crs as ccrs  # Projections list\n",
    "\n",
    "import cftime\n",
    "\n",
    "import dask\n",
    "import dask.array as da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cf1ca1b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 42061 instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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;\">53e4e59a</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:42061/status\" target=\"_blank\">http://127.0.0.1:42061/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 14\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 112\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 28.00 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-4bad04a9-995d-47de-8ca1-cfc564d2a097</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:37479\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 14\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:42061/status\" target=\"_blank\">http://127.0.0.1:42061/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 112\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> 28.00 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://127.0.0.1:46249\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43647/status\" target=\"_blank\">http://127.0.0.1:43647/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41163\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> /tmp/dask-scratch-space/worker-yu3fiw43\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://127.0.0.1:39915\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:41393/status\" target=\"_blank\">http://127.0.0.1:41393/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38791\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> /tmp/dask-scratch-space/worker-p1bwch5x\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://127.0.0.1:42243\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43093/status\" target=\"_blank\">http://127.0.0.1:43093/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35157\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> /tmp/dask-scratch-space/worker-vw4nb3xt\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://127.0.0.1:33357\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42509/status\" target=\"_blank\">http://127.0.0.1:42509/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35853\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> /tmp/dask-scratch-space/worker-p6srqfxi\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://127.0.0.1:41545\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40715/status\" target=\"_blank\">http://127.0.0.1:40715/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39055\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> /tmp/dask-scratch-space/worker-nm1w022b\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://127.0.0.1:34579\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44327/status\" target=\"_blank\">http://127.0.0.1:44327/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45099\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> /tmp/dask-scratch-space/worker-l86fhtl6\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://127.0.0.1:43163\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34375/status\" target=\"_blank\">http://127.0.0.1:34375/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38261\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> /tmp/dask-scratch-space/worker-liyld9al\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://127.0.0.1:36973\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:37113/status\" target=\"_blank\">http://127.0.0.1:37113/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41229\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> /tmp/dask-scratch-space/worker-o7_7ppny\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://127.0.0.1:35833\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40231/status\" target=\"_blank\">http://127.0.0.1:40231/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41743\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> /tmp/dask-scratch-space/worker-mknopjy7\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: 9</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:45363\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34407/status\" target=\"_blank\">http://127.0.0.1:34407/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39145\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> /tmp/dask-scratch-space/worker-q05jydhr\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: 10</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:33573\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35417/status\" target=\"_blank\">http://127.0.0.1:35417/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37807\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> /tmp/dask-scratch-space/worker-8ii3a5_x\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: 11</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:40639\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:38195/status\" target=\"_blank\">http://127.0.0.1:38195/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36231\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> /tmp/dask-scratch-space/worker-ppymaf4t\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: 12</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37977\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43783/status\" target=\"_blank\">http://127.0.0.1:43783/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34615\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> /tmp/dask-scratch-space/worker-rafk_6py\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: 13</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:34297\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:45765/status\" target=\"_blank\">http://127.0.0.1:45765/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 2.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41473\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> /tmp/dask-scratch-space/worker-q25i2m5o\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>"
      ],
      "text/plain": [
       "LocalCluster(53e4e59a, 'tcp://127.0.0.1:37479', workers=14, threads=112, memory=28.00 GiB)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 16:25:34,103 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:37977 (pid=47177) exceeded 95% memory budget. Restarting...\n",
      "2024-05-22 16:25:34,180 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:37977' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'original-open_dataset-thetao-57b54290831e294e385c642e4de78ec0', ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 249, 7), ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 1, 0), ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 0, 1), ('mean_chunk-e685339d3701554a09a7509c7f472588', 249, 4, 2), 'original-open_dataset-thetao-6538973c236ad73450d78a9e92f744ed', ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 2, 1), 'original-open_dataset-thetao-255b70d118e9c4de8d2b029d629fe997', 'original-open_dataset-thetao-3d228aef7c275efd383ce0616d89649e', ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 0, 0), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 249, 6), 'original-open_dataset-thetao-5aebae588783486d2d85e4caa1ad6490', ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 1, 2), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 249, 4, 1, 0), 'original-open_dataset-thetao-9f84d9332aeff4f856ea628e4c8a7394', ('mean_chunk-e685339d3701554a09a7509c7f472588', 249, 4, 1), ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 2, 0), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 249, 5), ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 0, 2), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 249, 4, 0, 0), ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 1, 1), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 249, 4, 2, 0), 'original-open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', ('mean_combine-partial-0df9f2f99890de9d66cd1e46fc9b496e', 249, 4, 1), 'original-open_dataset-thetao-9321cafa7e86e45d473be45cbc2f8fdd', ('mean_chunk-e685339d3701554a09a7509c7f472588', 249, 4, 3), ('open_dataset-thetao-cbc4116b19f120fefdbad0515b39fed0', 0, 1, 2, 2), 'original-open_dataset-thetao-111b85891ec16b3f9f1fb2b54d45aaa5'} (stimulus_id='handle-worker-cleanup-1716395134.1788976')\n",
      "2024-05-22 16:25:34,253 - distributed.nanny - WARNING - Restarting worker\n",
      "2024-05-22 16:25:34,941 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:34579 (pid=47149) exceeded 95% memory budget. Restarting...\n",
      "2024-05-22 16:25:35,016 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:34579' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'original-open_dataset-thetao-ac5f9e0941da651a16794a4e0314e3f9', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 250, 4, 4, 0), ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 0, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 250, 4, 6, 0), ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 2, 0), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 250, 4, 0, 1), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 250, 4, 8, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 250, 4, 10, 0), 'original-open_dataset-thetao-9b72ea722181eb5606bd83e8005df1f9', 'original-open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 1, 1), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 250, 5), 'original-open_dataset-thetao-2a4df4517a02463ac2ef37cac2dfec29', ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 250, 4, 2, 1), 'original-open_dataset-thetao-5626ca6da4e4730940dbd0105adc1ab5', ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 0, 2), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 250, 4, 5, 0), ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 2, 2), ('mean_chunk-e685339d3701554a09a7509c7f472588', 250, 4, 3), ('mean_combine-partial-0df9f2f99890de9d66cd1e46fc9b496e', 250, 4, 1), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 250, 4, 0, 0), 'original-open_dataset-thetao-af503447e8417883c64747c0e246b9f0', ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 250, 7), ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 1, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 250, 4, 7, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 250, 4, 9, 0), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 250, 4, 1, 1), ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 0, 1), ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 2, 1), 'original-open_dataset-thetao-518b0a9fa6ef6baf3d6b3dac8869661e', ('mean_chunk-e685339d3701554a09a7509c7f472588', 250, 4, 2), 'original-open_dataset-thetao-f4e962230faac0d1af8a6c15cdabde8d', ('open_dataset-thetao-8e7b7be78fb332ff203c339f7a84009f', 0, 1, 1, 2), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 250, 6), 'original-open_dataset-thetao-66f4245645ccc5355c0c621de94ead23'} (stimulus_id='handle-worker-cleanup-1716395135.0150347')\n",
      "2024-05-22 16:25:35,132 - distributed.nanny - WARNING - Restarting worker\n",
      "2024-05-22 16:25:42,088 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:36973 (pid=47157) exceeded 95% memory budget. Restarting...\n",
      "2024-05-22 16:25:42,254 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:36973' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'original-open_dataset-thetao-ee595e3798c1ad2bc8dbc7e1c37ee42d', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 2), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 0, 0), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 2, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 2, 2), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 5), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 0), 'original-open_dataset-thetao-bafde426849c1785dcd622c5a4be27e0', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 2), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 4, 0), 'original-open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', ('mean_combine-partial-0df9f2f99890de9d66cd1e46fc9b496e', 248, 6, 1), 'original-open_dataset-thetao-a5c7157d1f5869ef3ee73b23db4483ea', ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 1, 1), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 1), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 3, 0), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 10, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 5, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 1, 2), 'original-open_dataset-thetao-cd13dd7732c1851dbc297707f641ac2b', ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 7), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 8, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 1), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 4), ('mean_chunk-e685339d3701554a09a7509c7f472588', 248, 6, 3), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 2), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 1, 0), 'original-open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 7, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 0), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 0, 1), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 2, 1), 'original-open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', ('mean_chunk-e685339d3701554a09a7509c7f472588', 248, 6, 2), 'original-open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 1), 'original-open_dataset-thetao-b2e39c43fb9e58617d8c5c19744eca35', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 0), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 9, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 6, 0)} (stimulus_id='handle-worker-cleanup-1716395142.2522144')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 16:25:42,328 - distributed.nanny - WARNING - Restarting worker\n",
      "2024-05-22 16:25:42,846 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:43163 (pid=47153) exceeded 95% memory budget. Restarting...\n",
      "2024-05-22 16:25:43,031 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:43163' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {('mean_combine-partial-0df9f2f99890de9d66cd1e46fc9b496e', 251, 6, 1), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 0, 1), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 251, 4), 'original-open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 251, 7), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 1, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 0, 2), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 251, 6, 2, 0), 'original-open_dataset-thetao-b7a23595f19772611520eb96329369de', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 1, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 1, 2), ('mean_chunk-e685339d3701554a09a7509c7f472588', 251, 6, 1), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 3, 0), 'original-open_dataset-thetao-1a6453bb9915ae437201b2038b23eb46', 'original-open_dataset-thetao-3a25163db8b9903af198c5473bde4858', ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 2, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 2, 1), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 0, 0), 'original-open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 0, 1), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 1, 2), 'original-open_dataset-thetao-80edfc04f9061783525f21ac8afd4d32', 'original-open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 1, 1), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 251, 6, 1, 0), ('mean_chunk-e685339d3701554a09a7509c7f472588', 251, 6, 3), 'original-open_dataset-thetao-a47f7596af6fffea9f26c470238d47bd', ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 2, 2), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 2, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 0, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 0, 2), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 2, 0), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 251, 5), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 1, 1), 'original-open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 1, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 2, 1), ('mean_chunk-e685339d3701554a09a7509c7f472588', 251, 6, 2), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 2, 2)} (stimulus_id='handle-worker-cleanup-1716395143.0292814')\n",
      "2024-05-22 16:25:43,143 - distributed.nanny - WARNING - Restarting worker\n",
      "2024-05-22 16:25:59,892 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:37509 (pid=47444) exceeded 95% memory budget. Restarting...\n",
      "2024-05-22 16:25:59,946 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:37509' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {('mean_combine-partial-0df9f2f99890de9d66cd1e46fc9b496e', 251, 6, 1), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 0, 1), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 251, 4), 'original-open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 251, 7), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 10, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 1, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 1, 2), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 251, 6, 1, 1), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 3, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 2, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 0, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 1, 1), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 251, 6, 1, 0), ('mean_chunk-e685339d3701554a09a7509c7f472588', 251, 6, 3), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 2, 2), 'original-open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 9, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 0, 0), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 0, 2), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 251, 6, 0, 1), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 2, 0), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 251, 5), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 1, 0), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 251, 6, 2, 1), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 1, 2, 1), ('mean_agg-aggregate-mean_chunk-e685339d3701554a09a7509c7f472588', 251, 6, 2), ('open_dataset-thetao-e74c739671758165eaddfed29d2f33e3', 0, 0, 2, 2), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 251, 6, 8, 0)} (stimulus_id='handle-worker-cleanup-1716395159.9447048')\n",
      "2024-05-22 16:26:00,151 - distributed.nanny - WARNING - Restarting worker\n",
      "2024-05-22 16:26:51,995 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:36755 (pid=47482) exceeded 95% memory budget. Restarting...\n",
      "2024-05-22 16:26:52,133 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:36755' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'original-open_dataset-thetao-57b54290831e294e385c642e4de78ec0', ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 2, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 2), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 2, 2), ('mean_chunk-e685339d3701554a09a7509c7f472588', 248, 6, 1), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 5), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 2), 'original-open_dataset-thetao-2a4df4517a02463ac2ef37cac2dfec29', 'original-open_dataset-thetao-b66d0f054223d931bff26b82b3e83131', 'original-open_dataset-thetao-7034e75dc66b315b2d71af818d156283', ('mean_combine-partial-0df9f2f99890de9d66cd1e46fc9b496e', 248, 6, 1), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 3, 5, 7), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 1), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 1, 2), 'original-open_dataset-thetao-eaad17d752969284ad145c2d3b82fac6', 'original-open_dataset-thetao-ee212160b603538beeab3ea63fd00aef', ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 7), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 1), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 4), ('mean_chunk-e685339d3701554a09a7509c7f472588', 248, 6, 3), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 2), 'original-open_dataset-thetao-22e79e3c0d537a886441c9bc2b6d44b9', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 3, 7, 7), 'original-open_dataset-thetao-34201edf89c58c747489fb0d8abeb876', 'original-open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 0), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 1, 0), ('mean_chunk-e685339d3701554a09a7509c7f472588', 248, 6, 2), 'original-open_dataset-thetao-3b421ae827cb6975776d4e3f57e48ce4', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 1), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 3, 6, 7), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 0), 'original-open_dataset-thetao-b508d41e13d1c89c45d83d1e363b713b', ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 0, 0)} (stimulus_id='handle-worker-cleanup-1716395212.1315954')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 16:26:52,282 - distributed.nanny - WARNING - Restarting worker\n",
      "2024-05-22 16:27:05,898 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:43823 (pid=47504) exceeded 95% memory budget. Restarting...\n",
      "2024-05-22 16:27:06,020 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 3, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,022 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 1, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,024 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 3, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,025 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,026 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 3, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,027 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 3, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,028 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 3, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,030 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 3, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,031 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 3, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,033 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 1, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,034 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 2, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,035 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 1, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,036 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 0, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,038 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 3, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,039 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 0, 3, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,040 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 3, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,041 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 1, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,043 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 0, 0, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,044 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 1, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,045 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 1, 3, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,046 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 2, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,047 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,048 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 3, 3, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,050 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 0, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,051 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 0, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,052 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 0, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,054 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 2, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,055 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,056 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 2, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,056 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 0, 3, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,057 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 1, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,064 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 1, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,065 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 2, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,066 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 3, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,067 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 0, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,067 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 0, 3, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,069 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 2, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,069 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 2, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,070 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 3, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,071 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 0, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,072 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 2, 3, 0) marked as failed because 4 workers died while trying to run it\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 16:27:06,073 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 3, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,073 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 1, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,074 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 2, 0, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,077 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 0, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,077 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 0, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,078 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 3, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,080 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 0, 2, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,081 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 0, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,082 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 3, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,083 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 3, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,084 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 3, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,085 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 1, 3, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,086 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 2, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,087 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 1, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,088 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 1, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,089 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 0, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,091 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,092 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,093 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,095 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 1, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,096 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 1, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,101 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 2, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,104 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 3, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,106 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 1, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,108 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 2, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,110 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 1, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,112 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 1, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,113 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-57b54290831e294e385c642e4de78ec0', 0, 0, 2, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,115 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 0, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,118 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 1, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,119 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-2a4df4517a02463ac2ef37cac2dfec29', 0, 2, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,121 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 0, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,123 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 2, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,125 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 2, 2, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,126 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 2, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,128 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 2, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,130 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 3, 0, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,132 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 3, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,134 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 2, 3, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,136 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 0, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,138 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-57b54290831e294e385c642e4de78ec0', 0, 3, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,140 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 0, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,141 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 0, 0, 3) marked as failed because 4 workers died while trying to run it\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 16:27:06,144 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-57b54290831e294e385c642e4de78ec0', 0, 2, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,145 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 0, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,147 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 3, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,149 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 3, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,151 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,153 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 0, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,155 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-2a4df4517a02463ac2ef37cac2dfec29', 0, 1, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,157 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 3, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,158 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 2, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,160 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-2a4df4517a02463ac2ef37cac2dfec29', 0, 0, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,162 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 1, 3, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,167 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 2, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,168 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-2a4df4517a02463ac2ef37cac2dfec29', 0, 2, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,170 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,171 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,172 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,173 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,175 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 0, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,176 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 2, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,177 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 0, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,178 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 2, 3, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,179 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 3, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,181 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 0, 2, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,182 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 0, 3, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,183 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 3, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,184 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 0, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,186 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 0, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,187 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 0, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,188 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b7a23595f19772611520eb96329369de', 0, 3, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,189 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 0, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,190 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 1, 2, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,191 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 3, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,192 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 1, 1, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,194 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 1, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,195 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 2, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,196 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 3, 0, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,197 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 1, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,198 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 2, 2, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,199 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 1, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,200 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 1, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,201 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 0, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,202 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 1, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,203 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 2, 0, 3) marked as failed because 4 workers died while trying to run it\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 16:27:06,204 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', 0, 2, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,205 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', 0, 2, 2, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,206 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 3, 3, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,208 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 2, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,209 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 1, 0, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,209 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 0, 1, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,210 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-1513e7447810c4ada5fb19395915b28f', 0, 0, 3, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,211 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 0, 0, 1) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,212 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 3, 0, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,213 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 3, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,214 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', 0, 3, 0, 0) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,215 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 2, 1, 3) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,216 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-9af69639f841c51ba0ca1fc22801b2d0', 0, 1, 3, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,217 - distributed.scheduler - ERROR - Task ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 2, 1, 2) marked as failed because 4 workers died while trying to run it\n",
      "2024-05-22 16:27:06,219 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:43823' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'original-open_dataset-thetao-57b54290831e294e385c642e4de78ec0', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 2), ('mean_agg-aggregate-mean_chunk-e685339d3701554a09a7509c7f472588', 248, 6, 2), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 8, 0), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 5), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 0), 'original-open_dataset-thetao-b7a23595f19772611520eb96329369de', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 1, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 2), 'original-open_dataset-thetao-2a4df4517a02463ac2ef37cac2dfec29', 'original-open_dataset-thetao-7034e75dc66b315b2d71af818d156283', ('mean_combine-partial-0df9f2f99890de9d66cd1e46fc9b496e', 248, 6, 1), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 1, 1), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 1), 'original-open_dataset-thetao-7f121d4f2ce9bf86bace41d86620f43d', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 3, 0), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 10, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 5, 0), 'original-open_dataset-thetao-eaad17d752969284ad145c2d3b82fac6', 'original-open_dataset-thetao-ee212160b603538beeab3ea63fd00aef', ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 7), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 1), ('mean_chunk-d151c5b2c78da836ff31e62adb5e7057', 248, 4), ('mean_chunk-e685339d3701554a09a7509c7f472588', 248, 6, 3), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 2), 'original-open_dataset-thetao-34201edf89c58c747489fb0d8abeb876', 'original-open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 7, 0), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 0, 0), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 0, 1), ('mean_combine-partial-766cab73f09b9a74964b9aef47e86c6d', 248, 6, 2, 1), 'original-open_dataset-thetao-78eb6fd0b82378fb46aeae0a785e6004', ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 0, 0), 'original-open_dataset-thetao-3b421ae827cb6975776d4e3f57e48ce4', 'original-open_dataset-thetao-d7c70203e0b5cd03cbcba0571853aeb5', ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 1, 1), ('open_dataset-thetao-511a8a409fe2cb68a6f916bdeb44b4cf', 0, 1, 2, 0), ('concatenate-rechunk-merge-rechunk-split-mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 9, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 2, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 4, 0), ('mean_chunk-a030e5a606be3408bfacd5fedc50c729', 248, 6, 6, 0)} (stimulus_id='handle-worker-cleanup-1716395226.0191674')\n",
      "2024-05-22 16:27:06,537 - distributed.nanny - WARNING - Restarting worker\n"
     ]
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "# Setup a local cluster.\n",
    "# By default this sets up 1 worker per core\n",
    "client = Client(memory_limit=\"2 GiB\")\n",
    "client.cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f793e5e4-9b74-44d0-b7c7-ac69cfe73851",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some defaults:\n",
    "plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4eb855b5-d1a2-4ea6-80a1-1ce1277be1d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir='/data/shareddata/Climatologias/Glorys/cmems_mod_glo_phy_my_0.083_P1M-m/NC/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2132fa6f-c131-4258-aedb-2660613be4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = []\n",
    "for iy in range(2000,2021):\n",
    "    for im in range(1,13):\n",
    "        files.append(data_dir+\"mercatorglorys12v1_gl12_mean_%04d%02d.nc\"%(iy,im))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ef86b3",
   "metadata": {},
   "source": [
    "https://docs.coiled.io/blog/xarray-at-scale.html\n",
    "\n",
    "https://gist.github.com/fmaussion/48a33deae4fdee1f9433ec58c8956ac6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60d9c106",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "58e37429",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = xr.open_mfdataset(files,chunks={'time': 100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c48e104c-74f0-42da-b07f-59e2c92d6adb",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4TB\n",
       "Dimensions:    (longitude: 4320, latitude: 2041, depth: 50, time: 252)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9\n",
       "  * latitude   (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0\n",
       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * time       (time) datetime64[ns] 2kB 2000-01-16T12:00:00 ... 2020-12-16T1...\n",
       "Data variables:\n",
       "    mlotst     (time, latitude, longitude) float64 18GB dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;\n",
       "    zos        (time, latitude, longitude) float64 18GB dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;\n",
       "    bottomT    (time, latitude, longitude) float64 18GB dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;\n",
       "    sithick    (time, latitude, longitude) float64 18GB dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;\n",
       "    siconc     (time, latitude, longitude) float64 18GB dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;\n",
       "    usi        (time, latitude, longitude) float64 18GB dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;\n",
       "    vsi        (time, latitude, longitude) float64 18GB dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;\n",
       "    thetao     (time, depth, latitude, longitude) float64 889GB dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;\n",
       "    so         (time, depth, latitude, longitude) float64 889GB dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;\n",
       "    uo         (time, depth, latitude, longitude) float64 889GB dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;\n",
       "    vo         (time, depth, latitude, longitude) float64 889GB dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;\n",
       "Attributes: (12/14)\n",
       "    title:                         Monthly mean fields for product GLOBAL_REA...\n",
       "    references:                    http://marine.copernicus.eu\n",
       "    credit:                        E.U. Copernicus Marine Service Information...\n",
       "    licence:                       http://marine.copernicus.eu/services-portf...\n",
       "    contact:                       servicedesk.cmems@mercator-ocean.eu\n",
       "    producer:                      CMEMS - Global Monitoring and Forecasting ...\n",
       "    ...                            ...\n",
       "    area:                          GLOBAL\n",
       "    product:                       GLOBAL_REANALYSIS_001_030\n",
       "    dataset:                       global-reanalysis-001-030-monthly\n",
       "    source:                        MERCATOR GLORYS12V1\n",
       "    product_user_manual:           http://marine.copernicus.eu/documents/PUM/...\n",
       "    quality_information_document:  http://marine.copernicus.eu/documents/QUID...</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-f04864a5-45c2-4eec-bc0e-119ddada66ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f04864a5-45c2-4eec-bc0e-119ddada66ad' 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'>longitude</span>: 4320</li><li><span class='xr-has-index'>latitude</span>: 2041</li><li><span class='xr-has-index'>depth</span>: 50</li><li><span class='xr-has-index'>time</span>: 252</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fd928854-097b-4874-884f-84ac5ef7c027' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd928854-097b-4874-884f-84ac5ef7c027' 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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.8 179.9</div><input id='attrs-95085e98-2a5b-4071-9d56-8b267c951246' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95085e98-2a5b-4071-9d56-8b267c951246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a1afe98-0e77-456d-afda-4d61d112d15b' class='xr-var-data-in' type='checkbox'><label for='data-9a1afe98-0e77-456d-afda-4d61d112d15b' 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>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>179.91667</dd><dt><span>step :</span></dt><dd>0.08332825</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-180.     , -179.91667, -179.83333, ...,  179.75   ,  179.83333,\n",
       "        179.91667], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-80.0 -79.92 -79.83 ... 89.92 90.0</div><input id='attrs-2779703b-f77d-45ff-b1c0-d4b10634f9c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2779703b-f77d-45ff-b1c0-d4b10634f9c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a27e5da7-a8ce-46bf-8900-20ed8fe74dfd' class='xr-var-data-in' type='checkbox'><label for='data-a27e5da7-a8ce-46bf-8900-20ed8fe74dfd' 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>valid_min :</span></dt><dd>-80.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>step :</span></dt><dd>0.08333588</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-80.      , -79.916664, -79.833336, ...,  89.833336,  89.916664,\n",
       "        90.      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494 1.541 ... 5.275e+03 5.728e+03</div><input id='attrs-529cd76b-1eb2-4635-9cba-b70a6f5c7046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-529cd76b-1eb2-4635-9cba-b70a6f5c7046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcdab5c9-5890-4026-977c-031d30b3a190' class='xr-var-data-in' type='checkbox'><label for='data-fcdab5c9-5890-4026-977c-031d30b3a190' 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>valid_min :</span></dt><dd>0.494025</dd><dt><span>valid_max :</span></dt><dd>5727.917</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([4.940250e-01, 1.541375e+00, 2.645669e+00, 3.819495e+00, 5.078224e+00,\n",
       "       6.440614e+00, 7.929560e+00, 9.572997e+00, 1.140500e+01, 1.346714e+01,\n",
       "       1.581007e+01, 1.849556e+01, 2.159882e+01, 2.521141e+01, 2.944473e+01,\n",
       "       3.443415e+01, 4.034405e+01, 4.737369e+01, 5.576429e+01, 6.580727e+01,\n",
       "       7.785385e+01, 9.232607e+01, 1.097293e+02, 1.306660e+02, 1.558507e+02,\n",
       "       1.861256e+02, 2.224752e+02, 2.660403e+02, 3.181274e+02, 3.802130e+02,\n",
       "       4.539377e+02, 5.410889e+02, 6.435668e+02, 7.633331e+02, 9.023393e+02,\n",
       "       1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,\n",
       "       2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,\n",
       "       3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03, 5.727917e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2020-12-...</div><input id='attrs-b5f15fcc-1ba0-4066-9680-fbe238728ff3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5f15fcc-1ba0-4066-9680-fbe238728ff3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f180d1a-52eb-451a-9018-31eec2005f5c' class='xr-var-data-in' type='checkbox'><label for='data-2f180d1a-52eb-451a-9018-31eec2005f5c' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Time (hours since 1950-01-01)</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-03-16T12:00:00.000000000&#x27;, ..., &#x27;2020-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-16T00:00:00.000000000&#x27;, &#x27;2020-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d61c0e6-ed59-48ef-a7da-3cab07c3f9a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d61c0e6-ed59-48ef-a7da-3cab07c3f9a8' class='xr-section-summary' >Data variables: <span>(11)</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>mlotst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;</div><input id='attrs-36755065-f4d3-4e74-ac47-d4dd16ab92fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36755065-f4d3-4e74-ac47-d4dd16ab92fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3ec39b1-9b7b-4997-ae2f-29649c1b64c2' class='xr-var-data-in' type='checkbox'><label for='data-b3ec39b1-9b7b-4997-ae2f-29649c1b64c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Density ocean mixed layer thickness</dd><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>8881</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 16.55 GiB </td>\n",
       "                        <td> 7.48 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (252, 2041, 4320) </td>\n",
       "                        <td> (1, 681, 1440) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4032 chunks in 757 graph layers </td>\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>zos</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;</div><input id='attrs-4a5f8d9c-a1b6-41da-b275-7a9531e3c526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a5f8d9c-a1b6-41da-b275-7a9531e3c526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8005d26-fd58-4ecc-acca-c9d042de3bd5' class='xr-var-data-in' type='checkbox'><label for='data-d8005d26-fd58-4ecc-acca-c9d042de3bd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>valid_min :</span></dt><dd>-6382</dd><dt><span>valid_max :</span></dt><dd>4883</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 16.55 GiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 681, 1440) </td>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bottomT</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;</div><input id='attrs-a4c27430-e11b-4e54-be9b-870328a82f4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4c27430-e11b-4e54-be9b-870328a82f4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0655d3f0-2e9a-41b3-beb8-9cc395bef85a' class='xr-var-data-in' type='checkbox'><label for='data-0655d3f0-2e9a-41b3-beb8-9cc395bef85a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea floor potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature_at_sea_floor</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>valid_min :</span></dt><dd>-32639</dd><dt><span>valid_max :</span></dt><dd>18498</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vsi</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 681, 1440), meta=np.ndarray&gt;</div><input id='attrs-f99a2a89-8816-4a14-b7c5-fcb73c6c31a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f99a2a89-8816-4a14-b7c5-fcb73c6c31a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4ebf5b4-f9ed-40d6-8585-75d1d4097dfa' class='xr-var-data-in' type='checkbox'><label for='data-f4ebf5b4-f9ed-40d6-8585-75d1d4097dfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea ice northward velocity</dd><dt><span>standard_name :</span></dt><dd>northward_sea_ice_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>valid_min :</span></dt><dd>-32637</dd><dt><span>valid_max :</span></dt><dd>24967</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea_ice</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 681, 1440) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <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>thetao</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;</div><input id='attrs-ca991021-ce24-434d-8514-6e2a5aceca67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca991021-ce24-434d-8514-6e2a5aceca67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36fb5d63-116f-4b59-91eb-1d47f18abd52' class='xr-var-data-in' type='checkbox'><label for='data-36fb5d63-116f-4b59-91eb-1d47f18abd52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>valid_min :</span></dt><dd>-32764</dd><dt><span>valid_max :</span></dt><dd>18468</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 827.73 GiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;</div><input id='attrs-b23eeb96-234f-4fc7-84e9-bd54406d6ac7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b23eeb96-234f-4fc7-84e9-bd54406d6ac7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7becff04-696c-42de-807e-4596ab9f8cb4' class='xr-var-data-in' type='checkbox'><label for='data-7becff04-696c-42de-807e-4596ab9f8cb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>27353</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                <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> 827.73 GiB </td>\n",
       "                        <td> 13.11 MiB </td>\n",
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       "                    \n",
       "                    <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",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;</div><input id='attrs-861393f5-d3b1-4964-bf9b-ced69600cb9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-861393f5-d3b1-4964-bf9b-ced69600cb9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e368dcf1-b1af-448a-bc53-9fd8bd903a3b' class='xr-var-data-in' type='checkbox'><label for='data-e368dcf1-b1af-448a-bc53-9fd8bd903a3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Northward velocity</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>valid_min :</span></dt><dd>-3234</dd><dt><span>valid_max :</span></dt><dd>3332</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef2057c1-8f24-4f25-b6ab-95a81f3ac687' class='xr-section-summary-in' type='checkbox'  ><label for='section-ef2057c1-8f24-4f25-b6ab-95a81f3ac687' class='xr-section-summary' >Indexes: <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-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-68cdaec1-8fd9-4e77-a83d-72d9b1ed9d5e' class='xr-index-data-in' type='checkbox'/><label for='index-68cdaec1-8fd9-4e77-a83d-72d9b1ed9d5e' 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([            -180.0, -179.9166717529297, -179.8333282470703,\n",
       "                  -179.75, -179.6666717529297, -179.5833282470703,\n",
       "                   -179.5, -179.4166717529297, -179.3333282470703,\n",
       "                  -179.25,\n",
       "       ...\n",
       "        179.1666717529297,             179.25,  179.3333282470703,\n",
       "        179.4166717529297,              179.5,  179.5833282470703,\n",
       "        179.6666717529297,             179.75,  179.8333282470703,\n",
       "        179.9166717529297],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=4320))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bf450f50-3f46-4813-8707-dd525464c214' class='xr-index-data-in' type='checkbox'/><label for='index-bf450f50-3f46-4813-8707-dd525464c214' 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([             -80.0, -79.91666412353516, -79.83333587646484,\n",
       "                   -79.75, -79.66666412353516, -79.58333587646484,\n",
       "                    -79.5, -79.41666412353516, -79.33333587646484,\n",
       "                   -79.25,\n",
       "       ...\n",
       "                    89.25,  89.33333587646484,  89.41666412353516,\n",
       "                     89.5,  89.58333587646484,  89.66666412353516,\n",
       "                    89.75,  89.83333587646484,  89.91666412353516,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=2041))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85118aa1-8b2b-4530-a4aa-2b065fd1d77d' class='xr-index-data-in' type='checkbox'/><label for='index-85118aa1-8b2b-4530-a4aa-2b065fd1d77d' 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([0.49402499198913574,  1.5413750410079956,  2.6456689834594727,\n",
       "        3.8194949626922607,   5.078224182128906,   6.440614223480225,\n",
       "          7.92956018447876,   9.572997093200684,  11.404999732971191,\n",
       "        13.467140197753906,  15.810070037841797,  18.495559692382812,\n",
       "        21.598819732666016,  25.211410522460938,  29.444730758666992,\n",
       "         34.43415069580078,  40.344051361083984,   47.37369155883789,\n",
       "         55.76428985595703,   65.80726623535156,   77.85385131835938,\n",
       "          92.3260726928711,  109.72930145263672,  130.66600036621094,\n",
       "        155.85069274902344,  186.12559509277344,  222.47520446777344,\n",
       "         266.0403137207031,   318.1274108886719,   380.2130126953125,\n",
       "         453.9377136230469,   541.0889282226562,   643.5667724609375,\n",
       "         763.3331298828125,   902.3392944335938,    1062.43994140625,\n",
       "            1245.291015625,     1452.2509765625,  1684.2840576171875,\n",
       "        1941.8929443359375,   2225.077880859375,        2533.3359375,\n",
       "         2865.702880859375,   3220.820068359375,   3597.031982421875,\n",
       "          3992.48388671875,    4405.22412109375,      4833.291015625,\n",
       "           5274.7841796875,     5727.9169921875],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><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-2766ef9f-2875-4ed0-b399-ff9251811aba' class='xr-index-data-in' type='checkbox'/><label for='index-2766ef9f-2875-4ed0-b399-ff9251811aba' 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(DatetimeIndex([&#x27;2000-01-16 12:00:00&#x27;, &#x27;2000-02-15 12:00:00&#x27;,\n",
       "               &#x27;2000-03-16 12:00:00&#x27;, &#x27;2000-04-16 00:00:00&#x27;,\n",
       "               &#x27;2000-05-16 12:00:00&#x27;, &#x27;2000-06-16 00:00:00&#x27;,\n",
       "               &#x27;2000-07-16 12:00:00&#x27;, &#x27;2000-08-16 12:00:00&#x27;,\n",
       "               &#x27;2000-09-16 00:00:00&#x27;, &#x27;2000-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-03-16 12:00:00&#x27;, &#x27;2020-04-16 00:00:00&#x27;,\n",
       "               &#x27;2020-05-16 12:00:00&#x27;, &#x27;2020-06-16 00:00:00&#x27;,\n",
       "               &#x27;2020-07-16 12:00:00&#x27;, &#x27;2020-08-16 12:00:00&#x27;,\n",
       "               &#x27;2020-09-16 00:00:00&#x27;, &#x27;2020-10-16 12:00:00&#x27;,\n",
       "               &#x27;2020-11-16 00:00:00&#x27;, &#x27;2020-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=252, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d50fb78-e458-4e01-aae0-6c372e10bc76' class='xr-section-summary-in' type='checkbox'  ><label for='section-8d50fb78-e458-4e01-aae0-6c372e10bc76' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>credit :</span></dt><dd>E.U. Copernicus Marine Service Information (CMEMS)</dd><dt><span>licence :</span></dt><dd>http://marine.copernicus.eu/services-portfolio/service-commitments-and-licence/</dd><dt><span>contact :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu</dd><dt><span>producer :</span></dt><dd>CMEMS - Global Monitoring and Forecasting Centre</dd><dt><span>institution :</span></dt><dd>Mercator Ocean</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>area :</span></dt><dd>GLOBAL</dd><dt><span>product :</span></dt><dd>GLOBAL_REANALYSIS_001_030</dd><dt><span>dataset :</span></dt><dd>global-reanalysis-001-030-monthly</dd><dt><span>source :</span></dt><dd>MERCATOR GLORYS12V1</dd><dt><span>product_user_manual :</span></dt><dd>http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-030.pdf</dd><dt><span>quality_information_document :</span></dt><dd>http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-030.pdf</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 4TB\n",
       "Dimensions:    (longitude: 4320, latitude: 2041, depth: 50, time: 252)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9\n",
       "  * latitude   (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0\n",
       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * time       (time) datetime64[ns] 2kB 2000-01-16T12:00:00 ... 2020-12-16T1...\n",
       "Data variables:\n",
       "    mlotst     (time, latitude, longitude) float64 18GB dask.array<chunksize=(1, 681, 1440), meta=np.ndarray>\n",
       "    zos        (time, latitude, longitude) float64 18GB dask.array<chunksize=(1, 681, 1440), meta=np.ndarray>\n",
       "    bottomT    (time, latitude, longitude) float64 18GB dask.array<chunksize=(1, 681, 1440), meta=np.ndarray>\n",
       "    sithick    (time, latitude, longitude) float64 18GB dask.array<chunksize=(1, 681, 1440), meta=np.ndarray>\n",
       "    siconc     (time, latitude, longitude) float64 18GB dask.array<chunksize=(1, 681, 1440), meta=np.ndarray>\n",
       "    usi        (time, latitude, longitude) float64 18GB dask.array<chunksize=(1, 681, 1440), meta=np.ndarray>\n",
       "    vsi        (time, latitude, longitude) float64 18GB dask.array<chunksize=(1, 681, 1440), meta=np.ndarray>\n",
       "    thetao     (time, depth, latitude, longitude) float64 889GB dask.array<chunksize=(1, 7, 341, 720), meta=np.ndarray>\n",
       "    so         (time, depth, latitude, longitude) float64 889GB dask.array<chunksize=(1, 7, 341, 720), meta=np.ndarray>\n",
       "    uo         (time, depth, latitude, longitude) float64 889GB dask.array<chunksize=(1, 7, 341, 720), meta=np.ndarray>\n",
       "    vo         (time, depth, latitude, longitude) float64 889GB dask.array<chunksize=(1, 7, 341, 720), meta=np.ndarray>\n",
       "Attributes: (12/14)\n",
       "    title:                         Monthly mean fields for product GLOBAL_REA...\n",
       "    references:                    http://marine.copernicus.eu\n",
       "    credit:                        E.U. Copernicus Marine Service Information...\n",
       "    licence:                       http://marine.copernicus.eu/services-portf...\n",
       "    contact:                       servicedesk.cmems@mercator-ocean.eu\n",
       "    producer:                      CMEMS - Global Monitoring and Forecasting ...\n",
       "    ...                            ...\n",
       "    area:                          GLOBAL\n",
       "    product:                       GLOBAL_REANALYSIS_001_030\n",
       "    dataset:                       global-reanalysis-001-030-monthly\n",
       "    source:                        MERCATOR GLORYS12V1\n",
       "    product_user_manual:           http://marine.copernicus.eu/documents/PUM/...\n",
       "    quality_information_document:  http://marine.copernicus.eu/documents/QUID..."
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "93a57697-c09c-4416-afad-8a3c73e84140",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "nanmean() got an unexpected keyword argument 'dims'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[53], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnearest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlat\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlon\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mcompute()  \n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/core/_aggregations.py:509\u001b[0m, in \u001b[0;36mDatasetAggregations.mean\u001b[0;34m(self, dim, skipna, keep_attrs, **kwargs)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m    428\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    429\u001b[0m     dim: Dims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    434\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m    435\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    436\u001b[0m \u001b[38;5;124;03m    Reduce this Dataset's data by applying ``mean`` along some dimension(s).\u001b[39;00m\n\u001b[1;32m    437\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[38;5;124;03m        da       float64 8B nan\u001b[39;00m\n\u001b[1;32m    508\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    510\u001b[0m \u001b[43m        \u001b[49m\u001b[43mduck_array_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    511\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[43m        \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    513\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeep_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    515\u001b[0m \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\u001b[1;32m    516\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/core/dataset.py:6858\u001b[0m, in \u001b[0;36mDataset.reduce\u001b[0;34m(self, func, dim, keep_attrs, keepdims, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m   6841\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   6842\u001b[0m             \u001b[38;5;66;03m# Some reduction functions (e.g. std, var) need to run on variables\u001b[39;00m\n\u001b[1;32m   6843\u001b[0m             \u001b[38;5;66;03m# that don't have the reduce dims: PR5393\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   6851\u001b[0m             \u001b[38;5;66;03m# the former is often more efficient\u001b[39;00m\n\u001b[1;32m   6852\u001b[0m             \u001b[38;5;66;03m# keep single-element dims as list, to support Hashables\u001b[39;00m\n\u001b[1;32m   6853\u001b[0m             reduce_maybe_single \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   6854\u001b[0m                 \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   6855\u001b[0m                 \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(reduce_dims) \u001b[38;5;241m==\u001b[39m var\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;129;01mand\u001b[39;00m var\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   6856\u001b[0m                 \u001b[38;5;28;01melse\u001b[39;00m reduce_dims\n\u001b[1;32m   6857\u001b[0m             )\n\u001b[0;32m-> 6858\u001b[0m             variables[name] \u001b[38;5;241m=\u001b[39m \u001b[43mvar\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   6859\u001b[0m \u001b[43m                \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6860\u001b[0m \u001b[43m                \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreduce_maybe_single\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6861\u001b[0m \u001b[43m                \u001b[49m\u001b[43mkeep_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6862\u001b[0m \u001b[43m                \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6863\u001b[0m \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\u001b[1;32m   6864\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6866\u001b[0m coord_names \u001b[38;5;241m=\u001b[39m {k \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoords \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m variables}\n\u001b[1;32m   6867\u001b[0m indexes \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_indexes\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m variables}\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/core/variable.py:1643\u001b[0m, in \u001b[0;36mVariable.reduce\u001b[0;34m(self, func, dim, axis, keep_attrs, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m   1636\u001b[0m keep_attrs_ \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   1637\u001b[0m     _get_keep_attrs(default\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;28;01mif\u001b[39;00m keep_attrs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m keep_attrs\n\u001b[1;32m   1638\u001b[0m )\n\u001b[1;32m   1640\u001b[0m \u001b[38;5;66;03m# Noe that the call order for Variable.mean is\u001b[39;00m\n\u001b[1;32m   1641\u001b[0m \u001b[38;5;66;03m#    Variable.mean -> NamedArray.mean -> Variable.reduce\u001b[39;00m\n\u001b[1;32m   1642\u001b[0m \u001b[38;5;66;03m#    -> NamedArray.reduce\u001b[39;00m\n\u001b[0;32m-> 1643\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1644\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\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\n\u001b[1;32m   1645\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1647\u001b[0m \u001b[38;5;66;03m# return Variable always to support IndexVariable\u001b[39;00m\n\u001b[1;32m   1648\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Variable(\n\u001b[1;32m   1649\u001b[0m     result\u001b[38;5;241m.\u001b[39mdims, result\u001b[38;5;241m.\u001b[39m_data, attrs\u001b[38;5;241m=\u001b[39mresult\u001b[38;5;241m.\u001b[39m_attrs \u001b[38;5;28;01mif\u001b[39;00m keep_attrs_ \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1650\u001b[0m )\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/namedarray/core.py:889\u001b[0m, in \u001b[0;36mNamedArray.reduce\u001b[0;34m(self, func, dim, axis, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m         data \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, axis\u001b[38;5;241m=\u001b[39maxis, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    888\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 889\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\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\u001b[1;32m    891\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(data, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m, ()) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m    892\u001b[0m     dims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/core/duck_array_ops.py:649\u001b[0m, in \u001b[0;36mmean\u001b[0;34m(array, axis, skipna, **kwargs)\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _to_pytimedelta(mean_timedeltas, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mus\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m+\u001b[39m offset\n\u001b[1;32m    648\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 649\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_mean\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\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/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/core/duck_array_ops.py:416\u001b[0m, in \u001b[0;36m_create_nan_agg_method.<locals>.f\u001b[0;34m(values, axis, skipna, **kwargs)\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m    415\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll-NaN slice encountered\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 416\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\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\u001b[1;32m    417\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m    418\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_duck_dask_array(values):\n",
      "\u001b[0;31mTypeError\u001b[0m: nanmean() got an unexpected keyword argument 'dims'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a3a7c54c-f42b-42c1-afbe-4acd4387b213",
   "metadata": {},
   "outputs": [],
   "source": [
    "GL = data.drop_vars(\"mlotst\").drop_vars(\"zos\")\n",
    "GL = GL.drop_vars(\"sithick\").drop_vars(\"siconc\")\n",
    "GL = GL.drop_vars(\"usi\").drop_vars(\"vsi\").drop_vars(\"uo\").drop_vars(\"vo\").drop_vars(\"bottomT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f8ed626f-6a35-4ce0-9e5b-67f2621951d3",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2TB\n",
       "Dimensions:    (longitude: 4320, latitude: 2041, depth: 50, time: 252)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9\n",
       "  * latitude   (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0\n",
       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * time       (time) datetime64[ns] 2kB 2000-01-16T12:00:00 ... 2020-12-16T1...\n",
       "Data variables:\n",
       "    thetao     (time, depth, latitude, longitude) float64 889GB dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;\n",
       "    so         (time, depth, latitude, longitude) float64 889GB dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;\n",
       "Attributes: (12/14)\n",
       "    title:                         Monthly mean fields for product GLOBAL_REA...\n",
       "    references:                    http://marine.copernicus.eu\n",
       "    credit:                        E.U. Copernicus Marine Service Information...\n",
       "    licence:                       http://marine.copernicus.eu/services-portf...\n",
       "    contact:                       servicedesk.cmems@mercator-ocean.eu\n",
       "    producer:                      CMEMS - Global Monitoring and Forecasting ...\n",
       "    ...                            ...\n",
       "    area:                          GLOBAL\n",
       "    product:                       GLOBAL_REANALYSIS_001_030\n",
       "    dataset:                       global-reanalysis-001-030-monthly\n",
       "    source:                        MERCATOR GLORYS12V1\n",
       "    product_user_manual:           http://marine.copernicus.eu/documents/PUM/...\n",
       "    quality_information_document:  http://marine.copernicus.eu/documents/QUID...</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-3bbfc998-87b6-43b7-9805-cf6f7cca6ee5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3bbfc998-87b6-43b7-9805-cf6f7cca6ee5' 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'>longitude</span>: 4320</li><li><span class='xr-has-index'>latitude</span>: 2041</li><li><span class='xr-has-index'>depth</span>: 50</li><li><span class='xr-has-index'>time</span>: 252</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1ddca98c-907f-46b4-8014-3047235d2ccd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ddca98c-907f-46b4-8014-3047235d2ccd' 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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.8 179.9</div><input id='attrs-483c1273-9385-4f7f-86ea-4e94ced90ba3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-483c1273-9385-4f7f-86ea-4e94ced90ba3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d095b454-3918-4378-b3d6-7d9ece6f1b56' class='xr-var-data-in' type='checkbox'><label for='data-d095b454-3918-4378-b3d6-7d9ece6f1b56' 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>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>179.91667</dd><dt><span>step :</span></dt><dd>0.08332825</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-180.     , -179.91667, -179.83333, ...,  179.75   ,  179.83333,\n",
       "        179.91667], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-80.0 -79.92 -79.83 ... 89.92 90.0</div><input id='attrs-90c44b46-3113-4152-a673-786142a2c0c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90c44b46-3113-4152-a673-786142a2c0c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02f82cb0-267e-463d-a63c-8faa0333e63e' class='xr-var-data-in' type='checkbox'><label for='data-02f82cb0-267e-463d-a63c-8faa0333e63e' 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>valid_min :</span></dt><dd>-80.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>step :</span></dt><dd>0.08333588</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-80.      , -79.916664, -79.833336, ...,  89.833336,  89.916664,\n",
       "        90.      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494 1.541 ... 5.275e+03 5.728e+03</div><input id='attrs-b22e23ea-7b7b-427c-9404-7794218c92d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b22e23ea-7b7b-427c-9404-7794218c92d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53190e02-6b45-45a9-9969-2fe5ca8e782e' class='xr-var-data-in' type='checkbox'><label for='data-53190e02-6b45-45a9-9969-2fe5ca8e782e' 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>valid_min :</span></dt><dd>0.494025</dd><dt><span>valid_max :</span></dt><dd>5727.917</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([4.940250e-01, 1.541375e+00, 2.645669e+00, 3.819495e+00, 5.078224e+00,\n",
       "       6.440614e+00, 7.929560e+00, 9.572997e+00, 1.140500e+01, 1.346714e+01,\n",
       "       1.581007e+01, 1.849556e+01, 2.159882e+01, 2.521141e+01, 2.944473e+01,\n",
       "       3.443415e+01, 4.034405e+01, 4.737369e+01, 5.576429e+01, 6.580727e+01,\n",
       "       7.785385e+01, 9.232607e+01, 1.097293e+02, 1.306660e+02, 1.558507e+02,\n",
       "       1.861256e+02, 2.224752e+02, 2.660403e+02, 3.181274e+02, 3.802130e+02,\n",
       "       4.539377e+02, 5.410889e+02, 6.435668e+02, 7.633331e+02, 9.023393e+02,\n",
       "       1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,\n",
       "       2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,\n",
       "       3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03, 5.727917e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2020-12-...</div><input id='attrs-03ad7001-7db5-4b09-a2c4-fd17c5ed367b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03ad7001-7db5-4b09-a2c4-fd17c5ed367b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ed549b9-3650-4b56-9639-e02be1decffe' class='xr-var-data-in' type='checkbox'><label for='data-8ed549b9-3650-4b56-9639-e02be1decffe' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Time (hours since 1950-01-01)</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-03-16T12:00:00.000000000&#x27;, ..., &#x27;2020-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-16T00:00:00.000000000&#x27;, &#x27;2020-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3904507e-d9ca-43b8-b46b-b56c8d32c1d2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3904507e-d9ca-43b8-b46b-b56c8d32c1d2' class='xr-section-summary' >Data variables: <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-var-name'><span>thetao</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;</div><input id='attrs-00884c27-43b2-429e-a1e4-cd1150408ab4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00884c27-43b2-429e-a1e4-cd1150408ab4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba1e26e-67d7-418c-8e4d-1aa5d893d47f' class='xr-var-data-in' type='checkbox'><label for='data-0ba1e26e-67d7-418c-8e4d-1aa5d893d47f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>valid_min :</span></dt><dd>-32764</dd><dt><span>valid_max :</span></dt><dd>18468</dd><dt><span>cell_methods :</span></dt><dd>area: mean</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> 827.73 GiB </td>\n",
       "                        <td> 13.11 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (252, 50, 2041, 4320) </td>\n",
       "                        <td> (1, 7, 341, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 310464 chunks in 757 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>so</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 7, 341, 720), meta=np.ndarray&gt;</div><input id='attrs-50c6d81e-35c6-469a-86d2-b19c45ce131b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50c6d81e-35c6-469a-86d2-b19c45ce131b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11bcd58c-624b-4ea8-bf5e-ada8115ba030' class='xr-var-data-in' type='checkbox'><label for='data-11bcd58c-624b-4ea8-bf5e-ada8115ba030' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>27353</dd><dt><span>cell_methods :</span></dt><dd>area: mean</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> 827.73 GiB </td>\n",
       "                        <td> 13.11 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (252, 50, 2041, 4320) </td>\n",
       "                        <td> (1, 7, 341, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 310464 chunks in 757 graph layers </td>\n",
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       "                    <tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-4366f0d7-3f7e-4cae-ae82-c31acd2d38c1' class='xr-section-summary-in' type='checkbox'  ><label for='section-4366f0d7-3f7e-4cae-ae82-c31acd2d38c1' class='xr-section-summary' >Indexes: <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-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8f7617bb-ca1c-4e0e-8d7f-34353905a00a' class='xr-index-data-in' type='checkbox'/><label for='index-8f7617bb-ca1c-4e0e-8d7f-34353905a00a' 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([            -180.0, -179.9166717529297, -179.8333282470703,\n",
       "                  -179.75, -179.6666717529297, -179.5833282470703,\n",
       "                   -179.5, -179.4166717529297, -179.3333282470703,\n",
       "                  -179.25,\n",
       "       ...\n",
       "        179.1666717529297,             179.25,  179.3333282470703,\n",
       "        179.4166717529297,              179.5,  179.5833282470703,\n",
       "        179.6666717529297,             179.75,  179.8333282470703,\n",
       "        179.9166717529297],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=4320))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-704cedb3-d21d-4b5f-83f6-b1de191c9080' class='xr-index-data-in' type='checkbox'/><label for='index-704cedb3-d21d-4b5f-83f6-b1de191c9080' 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([             -80.0, -79.91666412353516, -79.83333587646484,\n",
       "                   -79.75, -79.66666412353516, -79.58333587646484,\n",
       "                    -79.5, -79.41666412353516, -79.33333587646484,\n",
       "                   -79.25,\n",
       "       ...\n",
       "                    89.25,  89.33333587646484,  89.41666412353516,\n",
       "                     89.5,  89.58333587646484,  89.66666412353516,\n",
       "                    89.75,  89.83333587646484,  89.91666412353516,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=2041))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c800e06a-582c-4093-a694-1578edc05cab' class='xr-index-data-in' type='checkbox'/><label for='index-c800e06a-582c-4093-a694-1578edc05cab' 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([0.49402499198913574,  1.5413750410079956,  2.6456689834594727,\n",
       "        3.8194949626922607,   5.078224182128906,   6.440614223480225,\n",
       "          7.92956018447876,   9.572997093200684,  11.404999732971191,\n",
       "        13.467140197753906,  15.810070037841797,  18.495559692382812,\n",
       "        21.598819732666016,  25.211410522460938,  29.444730758666992,\n",
       "         34.43415069580078,  40.344051361083984,   47.37369155883789,\n",
       "         55.76428985595703,   65.80726623535156,   77.85385131835938,\n",
       "          92.3260726928711,  109.72930145263672,  130.66600036621094,\n",
       "        155.85069274902344,  186.12559509277344,  222.47520446777344,\n",
       "         266.0403137207031,   318.1274108886719,   380.2130126953125,\n",
       "         453.9377136230469,   541.0889282226562,   643.5667724609375,\n",
       "         763.3331298828125,   902.3392944335938,    1062.43994140625,\n",
       "            1245.291015625,     1452.2509765625,  1684.2840576171875,\n",
       "        1941.8929443359375,   2225.077880859375,        2533.3359375,\n",
       "         2865.702880859375,   3220.820068359375,   3597.031982421875,\n",
       "          3992.48388671875,    4405.22412109375,      4833.291015625,\n",
       "           5274.7841796875,     5727.9169921875],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><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-8da12d4b-fa13-46f8-8f04-cbe28de023d2' class='xr-index-data-in' type='checkbox'/><label for='index-8da12d4b-fa13-46f8-8f04-cbe28de023d2' 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(DatetimeIndex([&#x27;2000-01-16 12:00:00&#x27;, &#x27;2000-02-15 12:00:00&#x27;,\n",
       "               &#x27;2000-03-16 12:00:00&#x27;, &#x27;2000-04-16 00:00:00&#x27;,\n",
       "               &#x27;2000-05-16 12:00:00&#x27;, &#x27;2000-06-16 00:00:00&#x27;,\n",
       "               &#x27;2000-07-16 12:00:00&#x27;, &#x27;2000-08-16 12:00:00&#x27;,\n",
       "               &#x27;2000-09-16 00:00:00&#x27;, &#x27;2000-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-03-16 12:00:00&#x27;, &#x27;2020-04-16 00:00:00&#x27;,\n",
       "               &#x27;2020-05-16 12:00:00&#x27;, &#x27;2020-06-16 00:00:00&#x27;,\n",
       "               &#x27;2020-07-16 12:00:00&#x27;, &#x27;2020-08-16 12:00:00&#x27;,\n",
       "               &#x27;2020-09-16 00:00:00&#x27;, &#x27;2020-10-16 12:00:00&#x27;,\n",
       "               &#x27;2020-11-16 00:00:00&#x27;, &#x27;2020-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=252, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adc5a28d-196c-4f75-a5a3-33cfd85933c0' class='xr-section-summary-in' type='checkbox'  ><label for='section-adc5a28d-196c-4f75-a5a3-33cfd85933c0' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>credit :</span></dt><dd>E.U. Copernicus Marine Service Information (CMEMS)</dd><dt><span>licence :</span></dt><dd>http://marine.copernicus.eu/services-portfolio/service-commitments-and-licence/</dd><dt><span>contact :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu</dd><dt><span>producer :</span></dt><dd>CMEMS - Global Monitoring and Forecasting Centre</dd><dt><span>institution :</span></dt><dd>Mercator Ocean</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>area :</span></dt><dd>GLOBAL</dd><dt><span>product :</span></dt><dd>GLOBAL_REANALYSIS_001_030</dd><dt><span>dataset :</span></dt><dd>global-reanalysis-001-030-monthly</dd><dt><span>source :</span></dt><dd>MERCATOR GLORYS12V1</dd><dt><span>product_user_manual :</span></dt><dd>http://marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-030.pdf</dd><dt><span>quality_information_document :</span></dt><dd>http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-030.pdf</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2TB\n",
       "Dimensions:    (longitude: 4320, latitude: 2041, depth: 50, time: 252)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9\n",
       "  * latitude   (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0\n",
       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * time       (time) datetime64[ns] 2kB 2000-01-16T12:00:00 ... 2020-12-16T1...\n",
       "Data variables:\n",
       "    thetao     (time, depth, latitude, longitude) float64 889GB dask.array<chunksize=(1, 7, 341, 720), meta=np.ndarray>\n",
       "    so         (time, depth, latitude, longitude) float64 889GB dask.array<chunksize=(1, 7, 341, 720), meta=np.ndarray>\n",
       "Attributes: (12/14)\n",
       "    title:                         Monthly mean fields for product GLOBAL_REA...\n",
       "    references:                    http://marine.copernicus.eu\n",
       "    credit:                        E.U. Copernicus Marine Service Information...\n",
       "    licence:                       http://marine.copernicus.eu/services-portf...\n",
       "    contact:                       servicedesk.cmems@mercator-ocean.eu\n",
       "    producer:                      CMEMS - Global Monitoring and Forecasting ...\n",
       "    ...                            ...\n",
       "    area:                          GLOBAL\n",
       "    product:                       GLOBAL_REANALYSIS_001_030\n",
       "    dataset:                       global-reanalysis-001-030-monthly\n",
       "    source:                        MERCATOR GLORYS12V1\n",
       "    product_user_manual:           http://marine.copernicus.eu/documents/PUM/...\n",
       "    quality_information_document:  http://marine.copernicus.eu/documents/QUID..."
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cb61952d-c3e6-4db2-8441-ae597c9dcd16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/client.py:3161: UserWarning: Sending large graph of size 49.59 MiB.\n",
      "This may cause some slowdown.\n",
      "Consider scattering data ahead of time and using futures.\n",
      "  warnings.warn(\n",
      "2024-05-22 16:25:33,833 - distributed.worker.memory - WARNING - Worker is at 84% memory usage. Pausing worker.  Process memory: 1.68 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:25:34,731 - distributed.worker.memory - WARNING - Worker is at 82% memory usage. Pausing worker.  Process memory: 1.65 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:25:39,848 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 1.60 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:25:40,376 - distributed.worker.memory - WARNING - Worker is at 73% memory usage. Resuming worker. Process memory: 1.46 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:25:40,870 - distributed.worker.memory - WARNING - Worker is at 85% memory usage. Pausing worker.  Process memory: 1.71 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:25:41,313 - distributed.worker.memory - WARNING - Worker is at 72% memory usage. Resuming worker. Process memory: 1.45 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:25:41,991 - distributed.worker.memory - WARNING - Worker is at 91% memory usage. Pausing worker.  Process memory: 1.83 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:25:43,012 - distributed.diskutils - ERROR - Failed to remove '/tmp/dask-scratch-space/worker-liyld9al/storage/%28%27mean_chunk-a030e5a606be3408bfacd5fedc50c729%27%2C%20251%2C%206%2C%201%2C%200%29#32' (failed in <built-in function unlink>): [Errno 2] No such file or directory: '%28%27mean_chunk-a030e5a606be3408bfacd5fedc50c729%27%2C%20251%2C%206%2C%201%2C%200%29#32'\n",
      "2024-05-22 16:25:43,012 - distributed.diskutils - ERROR - Failed to remove '/tmp/dask-scratch-space/worker-liyld9al/storage/%28%27open_dataset-thetao-e74c739671758165eaddfed29d2f33e3%27%2C%200%2C%201%2C%200%2C%200%29#28' (failed in <built-in function unlink>): [Errno 2] No such file or directory: '%28%27open_dataset-thetao-e74c739671758165eaddfed29d2f33e3%27%2C%200%2C%201%2C%200%2C%200%29#28'\n",
      "2024-05-22 16:25:59,649 - distributed.worker.memory - WARNING - Worker is at 83% memory usage. Pausing worker.  Process memory: 1.68 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:05,616 - distributed.worker.memory - WARNING - Worker is at 83% memory usage. Pausing worker.  Process memory: 1.67 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:06,420 - distributed.worker.memory - WARNING - Worker is at 74% memory usage. Resuming worker. Process memory: 1.48 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:13,743 - distributed.worker.memory - WARNING - Worker is at 82% memory usage. Pausing worker.  Process memory: 1.66 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:14,884 - distributed.worker.memory - WARNING - Worker is at 73% memory usage. Resuming worker. Process memory: 1.48 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:26,667 - distributed.worker.memory - WARNING - Worker is at 83% memory usage. Pausing worker.  Process memory: 1.66 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:26,803 - distributed.worker.memory - WARNING - Worker is at 74% memory usage. Resuming worker. Process memory: 1.48 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:42,425 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 1.61 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:42,427 - distributed.worker.memory - WARNING - Worker is at 78% memory usage. Resuming worker. Process memory: 1.57 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:42,527 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker.  Process memory: 1.64 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:42,627 - distributed.worker.memory - WARNING - Worker is at 79% memory usage. Resuming worker. Process memory: 1.59 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:26:50,910 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker.  Process memory: 1.63 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:05,615 - distributed.worker.memory - WARNING - Worker is at 82% memory usage. Pausing worker.  Process memory: 1.65 GiB -- Worker memory limit: 2.00 GiB\n"
     ]
    },
    {
     "ename": "KilledWorker",
     "evalue": "Attempted to run task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 2, 1, 3) on 4 different workers, but all those workers died while running it. The last worker that attempt to run the task was tcp://127.0.0.1:43823. Inspecting worker logs is often a good next step to diagnose what went wrong. For more information see https://distributed.dask.org/en/stable/killed.html.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKilledWorker\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[56], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m , \u001b[38;5;241m1\u001b[39m , figsize \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m14\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mGL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m        \u001b[49m\u001b[43mGL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthetao\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlongitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mslice\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2000\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m ax\u001b[38;5;241m.\u001b[39mgrid(linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.9\u001b[39m);\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1724\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1481\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1482\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1483\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1721\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1723\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1724\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1725\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1726\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/matplotlib/axes/_base.py:489\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(xy) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    488\u001b[0m     x \u001b[38;5;241m=\u001b[39m _check_1d(xy[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m--> 489\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[43m_check_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxy\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    491\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m index_of(xy[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/matplotlib/cbook.py:1351\u001b[0m, in \u001b[0;36m_check_1d\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1349\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convert scalars to 1D arrays; pass-through arrays as is.\"\"\"\u001b[39;00m\n\u001b[1;32m   1350\u001b[0m \u001b[38;5;66;03m# Unpack in case of e.g. Pandas or xarray object\u001b[39;00m\n\u001b[0;32m-> 1351\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43m_unpack_to_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1352\u001b[0m \u001b[38;5;66;03m# plot requires `shape` and `ndim`.  If passed an\u001b[39;00m\n\u001b[1;32m   1353\u001b[0m \u001b[38;5;66;03m# object that doesn't provide them, then force to numpy array.\u001b[39;00m\n\u001b[1;32m   1354\u001b[0m \u001b[38;5;66;03m# Note this will strip unit information.\u001b[39;00m\n\u001b[1;32m   1355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m   1356\u001b[0m         \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m   1357\u001b[0m         \u001b[38;5;28mlen\u001b[39m(x\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m):\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/matplotlib/cbook.py:2316\u001b[0m, in \u001b[0;36m_unpack_to_numpy\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   2313\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[1;32m   2314\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mto_numpy\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   2315\u001b[0m     \u001b[38;5;66;03m# Assume that any to_numpy() method actually returns a numpy array\u001b[39;00m\n\u001b[0;32m-> 2316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   2318\u001b[0m     xtmp \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mvalues\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/core/dataarray.py:801\u001b[0m, in \u001b[0;36mDataArray.to_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_numpy\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m    791\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    792\u001b[0m \u001b[38;5;124;03m    Coerces wrapped data to numpy and returns a numpy.ndarray.\u001b[39;00m\n\u001b[1;32m    793\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    799\u001b[0m \u001b[38;5;124;03m    DataArray.data\u001b[39;00m\n\u001b[1;32m    800\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 801\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/namedarray/core.py:828\u001b[0m, in \u001b[0;36mNamedArray.to_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    826\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Coerces wrapped data to numpy and returns a numpy.ndarray\"\"\"\u001b[39;00m\n\u001b[1;32m    827\u001b[0m \u001b[38;5;66;03m# TODO an entrypoint so array libraries can choose coercion method?\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/namedarray/pycompat.py:111\u001b[0m, in \u001b[0;36mto_numpy\u001b[0;34m(data, **kwargs)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_chunked_array(data):\n\u001b[1;32m    110\u001b[0m     chunkmanager \u001b[38;5;241m=\u001b[39m get_chunked_array_type(data)\n\u001b[0;32m--> 111\u001b[0m     data, \u001b[38;5;241m*\u001b[39m_ \u001b[38;5;241m=\u001b[39m \u001b[43mchunkmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\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\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, array_type(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcupy\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n\u001b[1;32m    113\u001b[0m     data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mget()\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/namedarray/daskmanager.py:86\u001b[0m, in \u001b[0;36mDaskManager.compute\u001b[0;34m(self, *data, **kwargs)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mdata: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any\n\u001b[1;32m     83\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[np\u001b[38;5;241m.\u001b[39mndarray[Any, _DType_co], \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]:\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compute\n\u001b[0;32m---> 86\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdata\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/opt/conda/envs/ocean/lib/python3.11/site-packages/dask/base.py:661\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m    658\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[1;32m    660\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m shorten_traceback():\n\u001b[0;32m--> 661\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\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\u001b[1;32m    663\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/client.py:2232\u001b[0m, in \u001b[0;36mClient._gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m   2230\u001b[0m         exc \u001b[38;5;241m=\u001b[39m CancelledError(key)\n\u001b[1;32m   2231\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2232\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exception\u001b[38;5;241m.\u001b[39mwith_traceback(traceback)\n\u001b[1;32m   2233\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m   2234\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[0;31mKilledWorker\u001b[0m: Attempted to run task ('open_dataset-thetao-b0722a0b7ce0109a4806cc79457b3d2a', 0, 2, 1, 3) on 4 different workers, but all those workers died while running it. The last worker that attempt to run the task was tcp://127.0.0.1:43823. Inspecting worker logs is often a good next step to diagnose what went wrong. For more information see https://distributed.dask.org/en/stable/killed.html."
     ]
    },
    {
     "data": {
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AgETEHgAAAIBExB4AAACARMQeAAAAgETEHgAAAIBExB4AAACARMQeAAAAgETEHgAAAIBExB4AAACARMQeAAAAgETEHgAAAIBExB4AAACARMQeAAAAgETEHgAAAIBExB4AAACARMQeAAAAgESGFHs2bNgQ06dPj8rKyqitrY2dO3d+7v7t27dHbW1tVFZWxnXXXRfPPffckIYFAAAA4POVHHuamppi1apVsW7dumhra4sFCxbEwoULo729fdD9hw4dikWLFsWCBQuira0tHn744Vi5cmW89tprX3h4AAAAAPorK4qiKOWCuXPnxuzZs2Pjxo19azNmzIglS5ZEY2PjgP0PPvhgbN26NQ4cONC31tDQEO+8807s2bPnvH5nT09PVFdXR3d3d1RVVZUyLgAAAMAlaySaR3kpm0+cOBGtra3x0EMP9Vuvr6+P3bt3D3rNnj17or6+vt/abbfdFps2bYpPP/00xo0bN+Ca3t7e6O3t7Xve3d0dEZ/9FwAAAACQxZnWUeK9OJ+rpNjT1dUVp06dipqamn7rNTU10dnZOeg1nZ2dg+4/efJkdHV1xaRJkwZc09jYGI899tiA9alTp5YyLgAAAMCocOzYsaiurh6Wn1VS7DmjrKys3/OiKAasnWv/YOtnrF27NtasWdP3/He/+11MmzYt2tvbh+2FA/+rp6cnpk6dGocPH/ZWSRgBzhiMLGcMRpYzBiOru7s7rr322rjyyiuH7WeWFHsmTpwYY8eOHXAXz9GjRwfcvXPG1VdfPej+8vLymDBhwqDXVFRUREVFxYD16upq/+MCI6iqqsoZgxHkjMHIcsZgZDljMLLGjBnSF6YP/rNK2Tx+/Piora2NlpaWfustLS1RV1c36DXz5s0bsH/btm0xZ86cQT+vBwAAAIChKzkbrVmzJp5//vnYvHlzHDhwIFavXh3t7e3R0NAQEZ+9BWv58uV9+xsaGuL999+PNWvWxIEDB2Lz5s2xadOmuP/++4fvVQAAAAAQEUP4zJ5ly5bFsWPHYv369dHR0REzZ86M5ubmmDZtWkREdHR0RHt7e9/+6dOnR3Nzc6xevTqeffbZmDx5cjz11FPxve9977x/Z0VFRTz66KODvrUL+OKcMRhZzhiMLGcMRpYzBiNrJM5YWTGc3+0FAAAAwEU1fJ/+AwAAAMBFJ/YAAAAAJCL2AAAAACQi9gAAAAAkcsnEng0bNsT06dOjsrIyamtrY+fOnZ+7f/v27VFbWxuVlZVx3XXXxXPPPXeBJoXRqZQz9vrrr8ett94aX/3qV6OqqirmzZsXP//5zy/gtDD6lPp37Iy33347ysvL49vf/vbIDgijXKlnrLe3N9atWxfTpk2LioqK+PrXvx6bN2++QNPC6FPqGduyZUvMmjUrLr/88pg0aVLce++9cezYsQs0LYweO3bsiMWLF8fkyZOjrKws3nzzzXNeMxy945KIPU1NTbFq1apYt25dtLW1xYIFC2LhwoX9vsL9/zp06FAsWrQoFixYEG1tbfHwww/HypUr47XXXrvAk8PoUOoZ27FjR9x6663R3Nwcra2tcdNNN8XixYujra3tAk8Oo0OpZ+yM7u7uWL58eXz3u9+9QJPC6DSUM7Z06dL4t3/7t9i0aVP813/9V7z88svxp3/6pxdwahg9Sj1ju3btiuXLl8eKFSvi3XffjVdeeSV+8YtfxH333XeBJ4dL38cffxyzZs2KZ5555rz2D1fvuCS+en3u3Lkxe/bs2LhxY9/ajBkzYsmSJdHY2Dhg/4MPPhhbt26NAwcO9K01NDTEO++8E3v27LkgM8NoUuoZG8w3v/nNWLZsWTzyyCMjNSaMWkM9Y3fccUdcf/31MXbs2HjzzTdj3759F2BaGH1KPWNvvfVW3HHHHXHw4MG48sorL+SoMCqVesb+6Z/+KTZu3Bjvvfde39rTTz8dTzzxRBw+fPiCzAyjUVlZWbzxxhuxZMmSs+4Zrt5x0e/sOXHiRLS2tkZ9fX2/9fr6+ti9e/eg1+zZs2fA/ttuuy327t0bn3766YjNCqPRUM7YHzp9+nQcP37cPzDDIIZ6xl544YV477334tFHHx3pEWFUG8oZ27p1a8yZMyeeeOKJuOaaa+KGG26I+++/P37/+99fiJFhVBnKGaurq4sjR45Ec3NzFEURH3zwQbz66qtx++23X4iRIbXh6h3lwz1Yqbq6uuLUqVNRU1PTb72mpiY6OzsHvaazs3PQ/SdPnoyurq6YNGnSiM0Lo81Qztgf+vGPfxwff/xxLF26dCRGhFFtKGfs17/+dTz00EOxc+fOKC+/6H+K4ZI2lDN28ODB2LVrV1RWVsYbb7wRXV1d8f3vfz8+/PBDn9sDf2AoZ6yuri62bNkSy5Yti//5n/+JkydPxl/91V/F008/fSFGhtSGq3dc9Dt7zigrK+v3vCiKAWvn2j/YOvCZUs/YGS+//HL86Ec/iqamprjqqqtGajwY9c73jJ06dSruvPPOeOyxx+KGG264UOPBqFfK37HTp09HWVlZbNmyJW688cZYtGhRPPnkk/Hiiy+6uwfOopQztn///li5cmU88sgj0draGm+99VYcOnQoGhoaLsSokN5w9I6L/n8nTpw4McaOHTugGh89enRAzTrj6quvHnR/eXl5TJgwYcRmhdFoKGfsjKamplixYkW88sorccstt4zkmDBqlXrGjh8/Hnv37o22trb44Q9/GBGf/YtpURRRXl4e27Zti5tvvvmCzA6jwVD+jk2aNCmuueaaqK6u7lubMWNGFEURR44cieuvv35EZ4bRZChnrLGxMebPnx8PPPBARER861vfiiuuuCIWLFgQjz/+uHdawBcwXL3jot/ZM378+KitrY2WlpZ+6y0tLVFXVzfoNfPmzRuwf9u2bTFnzpwYN27ciM0Ko9FQzljEZ3f03HPPPfHSSy95/zV8jlLPWFVVVfzyl7+Mffv29T0aGhriG9/4Ruzbty/mzp17oUaHUWEof8fmz58fv/3tb+Ojjz7qW/vVr34VY8aMiSlTpozovDDaDOWMffLJJzFmTP9/lRw7dmxE/O8dCMDQDFvvKC4B//qv/1qMGzeu2LRpU7F///5i1apVxRVXXFH893//d1EURfHQQw8Vd911V9/+gwcPFpdffnmxevXqYv/+/cWmTZuKcePGFa+++urFeglwSSv1jL300ktFeXl58eyzzxYdHR19j9/97ncX6yXAJa3UM/aHHn300WLWrFkXaFoYfUo9Y8ePHy+mTJlS/PVf/3Xx7rvvFtu3by+uv/764r777rtYLwEuaaWesRdeeKEoLy8vNmzYULz33nvFrl27ijlz5hQ33njjxXoJcMk6fvx40dbWVrS1tRURUTz55JNFW1tb8f777xdFMXK945KIPUVRFM8++2wxbdq0Yvz48cXs2bOL7du39/1nd999d/Gd73yn3/7/+I//KP78z/+8GD9+fPG1r32t2Lhx4wWeGEaXUs7Yd77znSIiBjzuvvvuCz84jBKl/h37v8QeOLdSz9iBAweKW265pbjsssuKKVOmFGvWrCk++eSTCzw1jB6lnrGnnnqq+LM/+7PisssuKyZNmlT8zd/8TXHkyJELPDVc+v793//9c//daqR6R1lRuM8OAAAAIIuL/pk9AAAAAAwfsQcAAAAgEbEHAAAAIBGxBwAAACARsQcAAAAgEbEHAAAAIBGxBwAAACARsQcAAAAgEbEHAAAAIBGxBwAAACARsQcAAAAgEbEHAAAAIJH/B6pbnT6ZUBV3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 16:27:09,712 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 1.61 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:10,074 - distributed.worker.memory - WARNING - Worker is at 77% memory usage. Resuming worker. Process memory: 1.54 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:10,498 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 1.62 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:10,637 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 1.61 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:10,854 - distributed.worker.memory - WARNING - Worker is at 77% memory usage. Resuming worker. Process memory: 1.54 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:10,940 - distributed.worker.memory - WARNING - Worker is at 76% memory usage. Resuming worker. Process memory: 1.52 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:13,635 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 1.61 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:13,709 - distributed.worker.memory - WARNING - Worker is at 76% memory usage. Resuming worker. Process memory: 1.53 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:15,964 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker.  Process memory: 1.63 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:16,298 - distributed.worker.memory - WARNING - Worker is at 76% memory usage. Resuming worker. Process memory: 1.53 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:17,919 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 1.20 GiB -- Worker memory limit: 2.00 GiB\n",
      "2024-05-22 16:27:21,435 - distributed.worker - ERROR - Failed to communicate with scheduler during heartbeat.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/core.py\", line 1620, in connect\n",
      "    return await self._connect(addr=addr, timeout=timeout)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/core.py\", line 1564, in _connect\n",
      "    comm = await connect(\n",
      "           ^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/comm/core.py\", line 377, in connect\n",
      "    handshake = await comm.read()\n",
      "                ^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/comm/tcp.py\", line 225, in read\n",
      "    frames_nosplit_nbytes_bin = await stream.read_bytes(fmt_size)\n",
      "                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "asyncio.exceptions.CancelledError\n",
      "\n",
      "The above exception was the direct cause of the following exception:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/core.py\", line 1611, in connect\n",
      "    async with asyncio.timeout(math.inf) as scope:\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/asyncio/timeouts.py\", line 115, in __aexit__\n",
      "    raise TimeoutError from exc_val\n",
      "TimeoutError\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/worker.py\", line 1252, in heartbeat\n",
      "    response = await retry_operation(\n",
      "               ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/utils_comm.py\", line 452, in retry_operation\n",
      "    return await retry(\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/utils_comm.py\", line 431, in retry\n",
      "    return await coro()\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/core.py\", line 1392, in send_recv_from_rpc\n",
      "    comm = await self.pool.connect(self.addr)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/envs/ocean/lib/python3.11/site-packages/distributed/core.py\", line 1632, in connect\n",
      "    raise CommClosedError(reason)\n",
      "distributed.comm.core.CommClosedError: ConnectionPool closing.\n",
      "2024-05-22 16:27:23,620 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 1.27 GiB -- Worker memory limit: 2.00 GiB\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1 , 1 , figsize = (14,8))\n",
    "ax.plot(GL.time,\n",
    "        GL.thetao.mean(dim='latitude').mean(dim='longitude').sel(depth=slice(0,2000)).mean('depth'))\n",
    "ax.grid(linestyle='-', linewidth=.9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69290d2b-0369-4d1d-b504-1c214596e650",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cb98ffa5-2a44-49a0-a32d-f76201d8205e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mGL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./Data/GLORYS_Global_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mGL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrftime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mGL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrftime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/core/dataset.py:2298\u001b[0m, in \u001b[0;36mDataset.to_netcdf\u001b[0;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)\u001b[0m\n\u001b[1;32m   2295\u001b[0m     encoding \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   2296\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_netcdf\n\u001b[0;32m-> 2298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore  # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[1;32m   2299\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2302\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2303\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2304\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2306\u001b[0m \u001b[43m    \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2307\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2308\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultifile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   2309\u001b[0m \u001b[43m    \u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2310\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/backends/api.py:1348\u001b[0m, in \u001b[0;36mto_netcdf\u001b[0;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)\u001b[0m\n\u001b[1;32m   1345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m multifile:\n\u001b[1;32m   1346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m writer, store\n\u001b[0;32m-> 1348\u001b[0m writes \u001b[38;5;241m=\u001b[39m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(target, BytesIO):\n\u001b[1;32m   1351\u001b[0m     store\u001b[38;5;241m.\u001b[39msync()\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/backends/common.py:297\u001b[0m, in \u001b[0;36mArrayWriter.sync\u001b[0;34m(self, compute, chunkmanager_store_kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunkmanager_store_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    295\u001b[0m     chunkmanager_store_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 297\u001b[0m delayed_store \u001b[38;5;241m=\u001b[39m \u001b[43mchunkmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    302\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflush\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    303\u001b[0m \u001b[43m    \u001b[49m\u001b[43mregions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mchunkmanager_store_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msources \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtargets \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/xarray/namedarray/daskmanager.py:249\u001b[0m, in \u001b[0;36mDaskManager.store\u001b[0;34m(self, sources, targets, **kwargs)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstore\u001b[39m(\n\u001b[1;32m    242\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    243\u001b[0m     sources: Any \u001b[38;5;241m|\u001b[39m Sequence[Any],\n\u001b[1;32m    244\u001b[0m     targets: Any,\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    246\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    247\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m store\n\u001b[0;32m--> 249\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43msources\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtargets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    252\u001b[0m \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\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/dask/array/core.py:1236\u001b[0m, in \u001b[0;36mstore\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   1234\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m compute:\n\u001b[1;32m   1235\u001b[0m     store_dsk \u001b[38;5;241m=\u001b[39m HighLevelGraph(layers, dependencies)\n\u001b[0;32m-> 1236\u001b[0m     \u001b[43mcompute_as_if_collection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mArray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore_dsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_keys\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\u001b[1;32m   1237\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/dask/base.py:369\u001b[0m, in \u001b[0;36mcompute_as_if_collection\u001b[0;34m(cls, dsk, keys, scheduler, get, **kwargs)\u001b[0m\n\u001b[1;32m    367\u001b[0m schedule \u001b[38;5;241m=\u001b[39m get_scheduler(scheduler\u001b[38;5;241m=\u001b[39mscheduler, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mcls\u001b[39m, get\u001b[38;5;241m=\u001b[39mget)\n\u001b[1;32m    368\u001b[0m dsk2 \u001b[38;5;241m=\u001b[39m optimization_function(\u001b[38;5;28mcls\u001b[39m)(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 369\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\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/opt/conda/envs/ocean/lib/python3.11/site-packages/dask/threaded.py:90\u001b[0m, in \u001b[0;36mget\u001b[0;34m(dsk, keys, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pool, multiprocessing\u001b[38;5;241m.\u001b[39mpool\u001b[38;5;241m.\u001b[39mPool):\n\u001b[1;32m     88\u001b[0m         pool \u001b[38;5;241m=\u001b[39m MultiprocessingPoolExecutor(pool)\n\u001b[0;32m---> 90\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mget_async\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_max_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m    \u001b[49m\u001b[43mget_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_thread_get_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpack_exception\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpack_exception\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     98\u001b[0m \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\u001b[1;32m     99\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;66;03m# Cleanup pools associated to dead threads\u001b[39;00m\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pools_lock:\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/dask/local.py:501\u001b[0m, in \u001b[0;36mget_async\u001b[0;34m(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwaiting\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mready\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrunning\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    500\u001b[0m     fire_tasks(chunksize)\n\u001b[0;32m--> 501\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key, res_info, failed \u001b[38;5;129;01min\u001b[39;00m \u001b[43mqueue_get\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mresult():\n\u001b[1;32m    502\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m failed:\n\u001b[1;32m    503\u001b[0m             exc, tb \u001b[38;5;241m=\u001b[39m loads(res_info)\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/site-packages/dask/local.py:138\u001b[0m, in \u001b[0;36mqueue_get\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mqueue_get\u001b[39m(q):\n\u001b[0;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/queue.py:171\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    170\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qsize():\n\u001b[0;32m--> 171\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnot_empty\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be a non-negative number\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/ocean/lib/python3.11/threading.py:327\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:    \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 327\u001b[0m         \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    328\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "GL.to_netcdf('./Data/GLORYS_Global_'+str(GL.time[0].dt.strftime('%Y-%m').values)+'_'+str(GL.time[-1].dt.strftime('%Y-%m').values)+'.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4594277d-f81f-4162-bd64-36a871472b5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "ocean",
   "language": "python",
   "name": "ocean"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
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