ocean/runoff_budget.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb84e7f4-7626-44a1-8bd6-97c397975bdd",
   "metadata": {},
   "source": [
    "# Runoff\n",
    "\n",
    "see model-agnostic tutorial:\n",
    "https://github.com/COSIMA/cosima-recipes/blob/main/Tutorials/Model_Agnostic_Analysis.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b496fe34-0709-4eb8-ab03-cefe4a7421da",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import intake\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import cf_xarray as cfxr\n",
    "import pint_xarray\n",
    "from pint import application_registry as ureg\n",
    "import cf_xarray.units\n",
    "import cftime\n",
    "import xgcm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cmocean as cm\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cft\n",
    "\n",
    "from dask.distributed import Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1958a5c5-1503-4547-a181-00bde2a54eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "xr.set_options(keep_attrs=True); # cf_xarray works best when xarray keeps attributes by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4077122-37c7-41ee-bf1a-a0ce7516fe42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-7bd3ed4e-7fad-11ef-979a-000007a9fe80</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/proxy/8787/status\" }'>\n",
       "                Launch dashboard in JupyterLab\n",
       "            </button>\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">221b0e78</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/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> 14\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 63.00 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-1a112c30-fdeb-4278-8b63-b9c2aca688e2</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:33171\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=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 14\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> 63.00 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://127.0.0.1:42739\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41535/status\" target=\"_blank\">/proxy/41535/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36581\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-qxuk6c7a\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://127.0.0.1:33801\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42949/status\" target=\"_blank\">/proxy/42949/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39113\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-__9pbfun\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://127.0.0.1:33911\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38653/status\" target=\"_blank\">/proxy/38653/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40521\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-o23zmv7h\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://127.0.0.1:36283\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46761/status\" target=\"_blank\">/proxy/46761/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46799\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-m6hfc6cy\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://127.0.0.1:34757\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/45891/status\" target=\"_blank\">/proxy/45891/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45663\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-c2nkibwu\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://127.0.0.1:44341\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42551/status\" target=\"_blank\">/proxy/42551/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36309\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-5uzuhjcj\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://127.0.0.1:41519\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/42429/status\" target=\"_blank\">/proxy/42429/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41165\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-nrzvo147\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://127.0.0.1:33049\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40605/status\" target=\"_blank\">/proxy/40605/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38197\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-rwtauave\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://127.0.0.1:44287\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39709/status\" target=\"_blank\">/proxy/39709/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39969\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-7h8jckya\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://127.0.0.1:38409\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/37259/status\" target=\"_blank\">/proxy/37259/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45819\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-g6hf1di_\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://127.0.0.1:43933\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40697/status\" target=\"_blank\">/proxy/40697/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34643\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-vtlzgp1q\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://127.0.0.1:39029\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/37881/status\" target=\"_blank\">/proxy/37881/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34225\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-c_57ohwj\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://127.0.0.1:36839\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39453/status\" target=\"_blank\">/proxy/39453/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41823\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-891oedwq\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://127.0.0.1:40003\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40239/status\" target=\"_blank\">/proxy/40239/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 4.50 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40455\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> /jobfs/125872743.gadi-pbs/dask-scratch-space/worker-22bpgj8x\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:33171' processes=14 threads=14, memory=63.00 GiB>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(threads_per_worker=1)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f242f296-8844-42d9-9fed-5d8fd1d14ea3",
   "metadata": {},
   "source": [
    "## JRA55do data\n",
    "\n",
    "Need access to `gdata/qv56` and `gdata/vk83`\n",
    "\n",
    "#### RYF data\n",
    "\n",
    "from [`config.yaml`](https://github.com/ACCESS-NRI/access-om3-configs/blob/dev-1deg_jra55do_ryf/config.yaml#L35C7-L35C62) and[`drof.streams.xml`](https://github.com/ACCESS-NRI/access-om3-configs/blob/dev-1deg_jra55do_ryf/drof.streams.xml)\n",
    "- `/g/data/vk83/configurations/inputs/JRA-55/RYF/v1-4/data/RYF.friver.1990_1991.nc`\n",
    "- `/g/data/vk83/configurations/inputs/JRA-55/RYF/v1-4/data/RYF.licalvf.1990_1991.nc`\n",
    "\n",
    "#### IAF data\n",
    "from [`config.yaml`](https://github.com/ACCESS-NRI/access-om3-configs/blob/dev-1deg_jra55do_iaf/config.yaml) and[`drof.streams.xml`](https://github.com/ACCESS-NRI/access-om3-configs/blob/dev-1deg_jra55do_iaf/drof.streams.xml)\n",
    "- `/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-4-0/land/day/friver/gr/v20190429/friver_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-4-0_gr_*.nc`\n",
    "- `/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-4-0/landIce/day/licalvf/gr/v20190429/licalvf_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-4-0_gr_*.nc`\n",
    "\n",
    "#### Cell areas (`areacello`)\n",
    "`/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-4-0/ocean/fx/areacello/gr/v20190429/areacello_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-4-0_gr.nc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "acfa3d91-bdeb-419c-bf51-f694a5b6900c",
   "metadata": {},
   "outputs": [],
   "source": [
    "jra55dopaths = {\n",
    "    'areacello': '/g/data/qv56/replicas/input4MIPs/CMIP6/OMIP/MRI/MRI-JRA55-do-1-4-0/ocean/fx/areacello/gr/v20190429/areacello_input4MIPs_atmosphericState_OMIP_MRI-JRA55-do-1-4-0_gr.nc',\n",
    "    'friver': '/g/data/vk83/configurations/inputs/JRA-55/RYF/v1-4/data/RYF.friver.1990_1991.nc',\n",
    "    'licalvf': '/g/data/vk83/configurations/inputs/JRA-55/RYF/v1-4/data/RYF.licalvf.1990_1991.nc'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a312a1bb-89a3-4a3b-9609-456f33653e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pint.util:Redefining 'degrees_north' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degrees_N' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degreesN' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degree_north' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degree_N' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degreeN' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degrees_east' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degrees_E' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degreesE' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degree_east' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degree_E' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'degreeE' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
      "WARNING:pint.util:Redefining 'gpm' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n"
     ]
    }
   ],
   "source": [
    "jra55do = xr.open_mfdataset([ v for v in jra55dopaths.values() ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c4a9d1c-379b-4a78-b50b-d38960c65fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3GB\n",
       "Dimensions:    (lat: 720, bnds: 2, lon: 1440, time: 365)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "  * lon        (lon) float64 12kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
       "  * time       (time) object 3kB 1900-01-01 00:00:00 ... 1900-12-31 00:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lat_bnds   (lat, bnds) float64 12kB dask.array&lt;chunksize=(720, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (lon, bnds) float64 23kB dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;\n",
       "    areacello  (lat, lon) float32 4MB dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;\n",
       "    friver     (time, lat, lon) float32 2GB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    licalvf    (time, lat, lon) float32 2GB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/34)\n",
       "    Conventions:         CF-1.7 CMIP-6.2\n",
       "    activity_id:         input4MIPs\n",
       "    comment:             Based on JRA-55 reanalysis (1958-01 to 2019-01)\n",
       "    contact:             Hiroyuki Tsujino (htsujino@mri-jma.go.jp)\n",
       "    creation_date:       2019-03-08T11:48:52Z\n",
       "    data_specs_version:  01.00.29\n",
       "    ...                  ...\n",
       "    target_mip:          OMIP\n",
       "    title:               MRI JRA55-do 1.4.0 dataset prepared for input4MIPs\n",
       "    tracking_id:         hdl:21.14100/29996f42-a99e-4762-8f11-24888d857e61\n",
       "    variable_id:         areacello\n",
       "    license:             OMIP boundary condition data produced by MRI is lice...\n",
       "    cmor_version:        3.4.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-de3af096-07d1-4e83-94cf-e98d9941eae8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de3af096-07d1-4e83-94cf-e98d9941eae8' 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'>lat</span>: 720</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>time</span>: 365</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-827e4b38-b1c7-4fe8-8349-dcaf3bf86e8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-827e4b38-b1c7-4fe8-8349-dcaf3bf86e8b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-9f516f94-4ec0-46b5-b1b5-0f20582a95b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f516f94-4ec0-46b5-b1b5-0f20582a95b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6854ec2e-382c-4e03-b938-a743c4b178b5' class='xr-var-data-in' type='checkbox'><label for='data-6854ec2e-382c-4e03-b938-a743c4b178b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-7943745d-d92e-4654-a1d3-47a52419e95c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7943745d-d92e-4654-a1d3-47a52419e95c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4577ad4-a869-4498-b0e3-a81d41384ffb' class='xr-var-data-in' type='checkbox'><label for='data-a4577ad4-a869-4498-b0e3-a81d41384ffb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02])</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'>object</div><div class='xr-var-preview xr-preview'>1900-01-01 00:00:00 ... 1900-12-...</div><input id='attrs-132ff855-361d-44a5-95da-d709b6b11987' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-132ff855-361d-44a5-95da-d709b6b11987' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c36863b-538a-4156-86df-d307450066a0' class='xr-var-data-in' type='checkbox'><label for='data-9c36863b-538a-4156-86df-d307450066a0' 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>cartesian_axis :</span></dt><dd>T</dd><dt><span>modulo :</span></dt><dd> </dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1900, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 1, 3, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(1900, 12, 29, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 12, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4c1be1e-ce85-4cad-8e15-16e23f5b6d21' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4c1be1e-ce85-4cad-8e15-16e23f5b6d21' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(720, 2), meta=np.ndarray&gt;</div><input id='attrs-76839865-bfc9-4375-aee0-e50d99b0760a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76839865-bfc9-4375-aee0-e50d99b0760a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bc3ba55-2734-49b9-b3d2-1509467c444f' class='xr-var-data-in' type='checkbox'><label for='data-8bc3ba55-2734-49b9-b3d2-1509467c444f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.25 kiB </td>\n",
       "                        <td> 11.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (720, 2) </td>\n",
       "                        <td> (720, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">720</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;</div><input id='attrs-291fc9a5-8a9d-4c70-b9c7-fd0b7268b4d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-291fc9a5-8a9d-4c70-b9c7-fd0b7268b4d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb37ad8a-c009-4f2e-84da-50b75e3203c7' class='xr-var-data-in' type='checkbox'><label for='data-cb37ad8a-c009-4f2e-84da-50b75e3203c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 22.50 kiB </td>\n",
       "                        <td> 22.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1440, 2) </td>\n",
       "                        <td> (1440, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1440</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacello</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;</div><input id='attrs-0f288313-e0aa-4d6f-80b9-7802e0f5d30c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f288313-e0aa-4d6f-80b9-7802e0f5d30c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac6bbca2-2a65-45a7-bfad-f6be2807a029' class='xr-var-data-in' type='checkbox'><label for='data-ac6bbca2-2a65-45a7-bfad-f6be2807a029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Ocean Variables</dd><dt><span>comment :</span></dt><dd>Horizontal area of ocean grid cells</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>cell_methods :</span></dt><dd>area: sum</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> 3.96 MiB </td>\n",
       "                        <td> 3.96 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (720, 1440) </td>\n",
       "                        <td> (720, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">720</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>friver</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-22fb6679-fbdc-4b1d-b529-58ba477cfa4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22fb6679-fbdc-4b1d-b529-58ba477cfa4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4260d87b-5ef9-4e8e-b03c-574919784e0f' class='xr-var-data-in' type='checkbox'><label for='data-4260d87b-5ef9-4e8e-b03c-574919784e0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_into_sea_water_from_rivers</dd><dt><span>long_name :</span></dt><dd>Water Flux into Sea Water from Rivers</dd><dt><span>comment :</span></dt><dd>computed as the river flux of liquid water into the ocean divided by the area of the ocean portion of the grid cell, input4MIPs_table_comment: computed as the river flux of water into the ocean divided by the area of the ocean portion of the grid cell</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</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> 1.41 GiB </td>\n",
       "                        <td> 3.96 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 720, 1440) </td>\n",
       "                        <td> (1, 720, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 365 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"205\" height=\"135\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"60\" x2=\"35\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"61\" />\n",
       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"62\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"63\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"65\" />\n",
       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"66\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"67\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"69\" />\n",
       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"70\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"71\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"73\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" />\n",
       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"77\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"78\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"79\" />\n",
       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"81\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"82\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"83\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.26481292860946,25.26481292860946 35.26481292860946,85.26481292860946 10.0,60.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"133\" y2=\"3\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"152\" y2=\"22\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 155.26481292860944,25.26481292860946 35.26481292860946,25.26481292860946\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"85\" x2=\"155\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"35.26481292860946,25.26481292860946 155.26481292860944,25.26481292860946 155.26481292860944,85.26481292860946 35.26481292860946,85.26481292860946\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"95.264813\" y=\"105.264813\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"175.264813\" y=\"55.264813\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,175.264813,55.264813)\">720</text>\n",
       "  <text x=\"12.632406\" y=\"92.632406\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.632406,92.632406)\">365</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>licalvf</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-b6e2fd08-cdc3-4b25-b004-84d2b4dc760a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6e2fd08-cdc3-4b25-b004-84d2b4dc760a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34f701e9-a899-44cb-8bd4-c2bd7b5d3bec' class='xr-var-data-in' type='checkbox'><label for='data-34f701e9-a899-44cb-8bd4-c2bd7b5d3bec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>land_ice_specific_mass_flux_due_to_calving</dd><dt><span>long_name :</span></dt><dd>Land Ice Calving Flux</dd><dt><span>comment :</span></dt><dd>Computed as the flux of solid ice into the ocean divided by the area of the land portion of the grid cell</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean where ice_sheet</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</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> 1.41 GiB </td>\n",
       "                        <td> 3.96 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 720, 1440) </td>\n",
       "                        <td> (1, 720, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 365 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"205\" height=\"135\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"60\" x2=\"35\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"61\" />\n",
       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"62\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"63\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"65\" />\n",
       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"66\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"67\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"69\" />\n",
       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"70\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"71\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"73\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" />\n",
       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"77\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"78\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"79\" />\n",
       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"81\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"82\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"83\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.26481292860946,25.26481292860946 35.26481292860946,85.26481292860946 10.0,60.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"133\" y2=\"3\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"152\" y2=\"22\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 155.26481292860944,25.26481292860946 35.26481292860946,25.26481292860946\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"85\" x2=\"155\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"85\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"35.26481292860946,25.26481292860946 155.26481292860944,25.26481292860946 155.26481292860944,85.26481292860946 35.26481292860946,85.26481292860946\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"95.264813\" y=\"105.264813\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"175.264813\" y=\"55.264813\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,175.264813,55.264813)\">720</text>\n",
       "  <text x=\"12.632406\" y=\"92.632406\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.632406,92.632406)\">365</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-845d3656-363d-471c-a48c-10edc2948616' class='xr-section-summary-in' type='checkbox'  ><label for='section-845d3656-363d-471c-a48c-10edc2948616' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-324d69a0-0621-43a2-a002-935d7522192e' class='xr-index-data-in' type='checkbox'/><label for='index-324d69a0-0621-43a2-a002-935d7522192e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,\n",
       "       -87.875, -87.625,\n",
       "       ...\n",
       "        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,\n",
       "        89.625,  89.875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ae68b4ca-685d-4b78-adfe-4205dafe5bc7' class='xr-index-data-in' type='checkbox'/><label for='index-ae68b4ca-685d-4b78-adfe-4205dafe5bc7' 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([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,\n",
       "         2.125,   2.375,\n",
       "       ...\n",
       "       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
       "       359.625, 359.875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</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-498b644e-72de-47a8-be51-83305a2051ab' class='xr-index-data-in' type='checkbox'/><label for='index-498b644e-72de-47a8-be51-83305a2051ab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1900-01-01 00:00:00, 1900-01-02 00:00:00, 1900-01-03 00:00:00,\n",
       "             1900-01-04 00:00:00, 1900-01-05 00:00:00, 1900-01-06 00:00:00,\n",
       "             1900-01-07 00:00:00, 1900-01-08 00:00:00, 1900-01-09 00:00:00,\n",
       "             1900-01-10 00:00:00,\n",
       "             ...\n",
       "             1900-12-22 00:00:00, 1900-12-23 00:00:00, 1900-12-24 00:00:00,\n",
       "             1900-12-25 00:00:00, 1900-12-26 00:00:00, 1900-12-27 00:00:00,\n",
       "             1900-12-28 00:00:00, 1900-12-29 00:00:00, 1900-12-30 00:00:00,\n",
       "             1900-12-31 00:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=365, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32703c3a-cf48-4301-b4ca-068c2bf2692b' class='xr-section-summary-in' type='checkbox'  ><label for='section-32703c3a-cf48-4301-b4ca-068c2bf2692b' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>input4MIPs</dd><dt><span>comment :</span></dt><dd>Based on JRA-55 reanalysis (1958-01 to 2019-01)</dd><dt><span>contact :</span></dt><dd>Hiroyuki Tsujino (htsujino@mri-jma.go.jp)</dd><dt><span>creation_date :</span></dt><dd>2019-03-08T11:48:52Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>dataset_category :</span></dt><dd>atmosphericState</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>further_info_url :</span></dt><dd>http://climate.mri-jma.go.jp/~htsujino/jra55do.html</dd><dt><span>grid :</span></dt><dd>0.25x0.25 degree latitude x longitude</dd><dt><span>grid_label :</span></dt><dd>gr</dd><dt><span>history :</span></dt><dd>2019-03-08T11:48:52Z; CMOR rewrote data to be consistent with input4MIPs, CMIP6, and CF-1.7 CMIP-6.2 standards</dd><dt><span>institution :</span></dt><dd>Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan</dd><dt><span>institution_id :</span></dt><dd>MRI</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>25 km</dd><dt><span>product :</span></dt><dd>reanalysis</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>references :</span></dt><dd>Tsujino et al., 2018: JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do), Ocean Modelling, 130(1), pp 79-139. https://doi.org/10.1016/j.ocemod.2018.07.002</dd><dt><span>region :</span></dt><dd>global_ocean</dd><dt><span>release_year :</span></dt><dd>2019</dd><dt><span>source :</span></dt><dd>MRI JRA55-do 1.4.0: Atmospheric state generated for OMIP based on the JRA-55 reanalysis</dd><dt><span>source_description :</span></dt><dd>Atmospheric state and terrestrial runoff datasets produced by MRI for the OMIP experiment of CMIP6</dd><dt><span>source_id :</span></dt><dd>MRI-JRA55-do-1-4-0</dd><dt><span>source_type :</span></dt><dd>satellite_blended</dd><dt><span>source_version :</span></dt><dd>1.4.0</dd><dt><span>table_id :</span></dt><dd>input4MIPs_Ofx</dd><dt><span>table_info :</span></dt><dd>Creation Date:(06 March 2019) MD5:b14ca57e92343d575b84b8ab3624b3ed</dd><dt><span>target_mip :</span></dt><dd>OMIP</dd><dt><span>title :</span></dt><dd>MRI JRA55-do 1.4.0 dataset prepared for input4MIPs</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/29996f42-a99e-4762-8f11-24888d857e61</dd><dt><span>variable_id :</span></dt><dd>areacello</dd><dt><span>license :</span></dt><dd>OMIP boundary condition data produced by MRI is licensed under a Creative Commons Attribution-[NonCommercial-]ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing input4MIPs output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 3GB\n",
       "Dimensions:    (lat: 720, bnds: 2, lon: 1440, time: 365)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "  * lon        (lon) float64 12kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
       "  * time       (time) object 3kB 1900-01-01 00:00:00 ... 1900-12-31 00:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lat_bnds   (lat, bnds) float64 12kB dask.array<chunksize=(720, 2), meta=np.ndarray>\n",
       "    lon_bnds   (lon, bnds) float64 23kB dask.array<chunksize=(1440, 2), meta=np.ndarray>\n",
       "    areacello  (lat, lon) float32 4MB dask.array<chunksize=(720, 1440), meta=np.ndarray>\n",
       "    friver     (time, lat, lon) float32 2GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    licalvf    (time, lat, lon) float32 2GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "Attributes: (12/34)\n",
       "    Conventions:         CF-1.7 CMIP-6.2\n",
       "    activity_id:         input4MIPs\n",
       "    comment:             Based on JRA-55 reanalysis (1958-01 to 2019-01)\n",
       "    contact:             Hiroyuki Tsujino (htsujino@mri-jma.go.jp)\n",
       "    creation_date:       2019-03-08T11:48:52Z\n",
       "    data_specs_version:  01.00.29\n",
       "    ...                  ...\n",
       "    target_mip:          OMIP\n",
       "    title:               MRI JRA55-do 1.4.0 dataset prepared for input4MIPs\n",
       "    tracking_id:         hdl:21.14100/29996f42-a99e-4762-8f11-24888d857e61\n",
       "    variable_id:         areacello\n",
       "    license:             OMIP boundary condition data produced by MRI is lice...\n",
       "    cmor_version:        3.4.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jra55do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd670cd0-ae79-478b-b2b7-9d75f0c94d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "jra55doGrid = xgcm.Grid(jra55do,\n",
    "                        periodic=['X'],\n",
    "                        metrics = { ('X', 'Y'): ['areacello']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef3af1a6-dd89-4778-a7f3-535e4b552c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x153553b3ece0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jra55doGrid.integrate(jra55do.friver, ['X', 'Y']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9de0502-506b-49fa-a02d-7036b72dcac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15355380b760>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jra55doGrid.integrate(jra55do.licalvf, ['X', 'Y']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2ed9fc-a7a9-41e4-8550-2d642a473dc3",
   "metadata": {},
   "source": [
    "## ACCESS-OM3 data\n",
    "`access-om3.mom6.h.native_1900_01.nc` contains\n",
    "```\n",
    "\tfloat total_frunoff(time, scalar_axis) ;\n",
    "\t\ttotal_frunoff:units = \"kg s-1\" ;\n",
    "\t\ttotal_frunoff:long_name = \"Area integrated frozen runoff (calving) & iceberg melt into ocean\" ;\n",
    "\t\ttotal_frunoff:cell_methods = \"time: mean\" ;\n",
    "\tfloat total_lrunoff(time, scalar_axis) ;\n",
    "\t\ttotal_lrunoff:units = \"kg s-1\" ;\n",
    "\t\ttotal_lrunoff:long_name = \"Area integrated liquid runoff into ocean\" ;\n",
    "\t\ttotal_lrunoff:cell_methods = \"time: mean\" ;\n",
    "\tfloat friver(time, yh, xh) ;\n",
    "\t\tfriver:units = \"kg m-2 s-1\" ;\n",
    "\t\tfriver:long_name = \"Water Flux into Sea Water From Rivers\" ;\n",
    "\t\tfriver:cell_methods = \"area:mean yh:mean xh:mean time: mean\" ;\n",
    "\t\tfriver:cell_measures = \"area: areacello\" ;\n",
    "\t\tfriver:standard_name = \"water_flux_into_sea_water_from_rivers\" ;\n",
    "\tfloat ficeberg(time, yh, xh) ;\n",
    "\t\tficeberg:units = \"kg m-2 s-1\" ;\n",
    "\t\tficeberg:long_name = \"Water Flux into Seawater from Icebergs\" ;\n",
    "\t\tficeberg:cell_methods = \"area:mean yh:mean xh:mean time: mean\" ;\n",
    "\t\tficeberg:cell_measures = \"area: areacello\" ;\n",
    "\t\tficeberg:standard_name = \"water_flux_into_sea_water_from_icebergs\" ;\n",
    "\t\ttotal_frunoff:long_name = \"Area integrated frozen runoff (calving) & iceberg melt into ocean\" ;\n",
    "\tfloat net_massin(time, yh, xh) ;\n",
    "\t\tnet_massin:units = \"kg m-2 s-1\" ;\n",
    "\t\tnet_massin:long_name = \"Net mass entering ocean due to precip, runoff, ice melt\" ;\n",
    "\t\tnet_massin:cell_methods = \"area:mean yh:mean xh:mean time: mean\" ;\n",
    "\t\tnet_massin:cell_measures = \"area: areacello\" ;\n",
    "\tfloat net_massout(time, yh, xh) ;\n",
    "\t\tnet_massout:units = \"kg m-2 s-1\" ;\n",
    "\t\tnet_massout:long_name = \"Net mass leaving the ocean due to evaporation, seaice formation\" ;\n",
    "\t\tnet_massout:cell_methods = \"area:mean yh:mean xh:mean time: mean\" ;\n",
    "\t\tnet_massout:cell_measures = \"area: areacello\" ;\n",
    "\tfloat masso(time, scalar_axis) ;\n",
    "\t\tmasso:units = \"kg\" ;\n",
    "\t\tmasso:long_name = \"Mass of liquid ocean\" ;\n",
    "\t\tmasso:cell_methods = \"time: mean\" ;\n",
    "\t\tmasso:standard_name = \"sea_water_mass\" ;\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51198acb-474c-49df-91e9-fbcdf05915da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PATH='/scratch/v45/aek156/access-om3/archive/MOM6-CICE6-1deg_jra55do_ryf.iss138/'\n",
    "# PATH='/scratch/v45/aek156/access-om3/archive/MOM6-CICE6-1deg_jra55do_ryf.testAug2024/'\n",
    "PATH='/scratch/v45/aek156/access-om3/archive/MOM6-CICE6-1deg_jra55do_ryf.runoff/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "392504fd-35a0-4027-b33a-ae0ecf158be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from https://github.com/COSIMA/cosima-recipes/blob/main/Tutorials/ACCESS-NRI_Intake_Catalog.ipynb\n",
    "# Try passing the following argument to your to_dask or to_dataset_dict call:\n",
    "# See the xarray documentation on Reading multi-file datasets for more details about these arguments.\n",
    "# https://docs.xarray.dev/en/stable/user-guide/io.html#reading-multi-file-datasets\n",
    "\n",
    "xarray_combine_by_coords_kwargs=dict(\n",
    "    compat=\"override\",\n",
    "    data_vars=\"minimal\",\n",
    "    coords=\"minimal\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a0d8147-5a23-4c7f-b420-a3b51d25a5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# datastore = intake.open_esm_datastore(PATH+'intake_datastore.json', columns_with_iterables=['variable'])\n",
    "datastore = intake.open_esm_datastore(PATH+'intake_esm_ds.json', columns_with_iterables=['variable'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b6fde70-7c28-46c5-be50-de79d5b055b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>realm</th>\n",
       "      <th>variable</th>\n",
       "      <th>frequency</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>variable_long_name</th>\n",
       "      <th>variable_standard_name</th>\n",
       "      <th>variable_cell_methods</th>\n",
       "      <th>variable_units</th>\n",
       "      <th>filename</th>\n",
       "      <th>file_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>seaIce</td>\n",
       "      <td>[time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-01-01, 00:00:00</td>\n",
       "      <td>1900-02-01, 00:00:00</td>\n",
       "      <td>['time interval endpoints', 'category maximum ...</td>\n",
       "      <td>['', '', '', '', '', '', '', '', '', '', '', '...</td>\n",
       "      <td>['time: mean', '', '', '', '', '', '', '', '',...</td>\n",
       "      <td>['days since 1900-01-01 00:00:00', 'm', '1', '...</td>\n",
       "      <td>access-om3.cice.1900-01.nc</td>\n",
       "      <td>access_om3_cice_XXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>seaIce</td>\n",
       "      <td>[time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-02-01, 00:00:00</td>\n",
       "      <td>1900-03-01, 00:00:00</td>\n",
       "      <td>['time interval endpoints', 'category maximum ...</td>\n",
       "      <td>['', '', '', '', '', '', '', '', '', '', '', '...</td>\n",
       "      <td>['time: mean', '', '', '', '', '', '', '', '',...</td>\n",
       "      <td>['days since 1900-01-01 00:00:00', 'm', '1', '...</td>\n",
       "      <td>access-om3.cice.1900-02.nc</td>\n",
       "      <td>access_om3_cice_XXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>seaIce</td>\n",
       "      <td>[time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-03-01, 00:00:00</td>\n",
       "      <td>1900-04-01, 00:00:00</td>\n",
       "      <td>['time interval endpoints', 'category maximum ...</td>\n",
       "      <td>['', '', '', '', '', '', '', '', '', '', '', '...</td>\n",
       "      <td>['time: mean', '', '', '', '', '', '', '', '',...</td>\n",
       "      <td>['days since 1900-01-01 00:00:00', 'm', '1', '...</td>\n",
       "      <td>access-om3.cice.1900-03.nc</td>\n",
       "      <td>access_om3_cice_XXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>seaIce</td>\n",
       "      <td>[time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-04-01, 00:00:00</td>\n",
       "      <td>1900-05-01, 00:00:00</td>\n",
       "      <td>['time interval endpoints', 'category maximum ...</td>\n",
       "      <td>['', '', '', '', '', '', '', '', '', '', '', '...</td>\n",
       "      <td>['time: mean', '', '', '', '', '', '', '', '',...</td>\n",
       "      <td>['days since 1900-01-01 00:00:00', 'm', '1', '...</td>\n",
       "      <td>access-om3.cice.1900-04.nc</td>\n",
       "      <td>access_om3_cice_XXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>seaIce</td>\n",
       "      <td>[time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-05-01, 00:00:00</td>\n",
       "      <td>1900-06-01, 00:00:00</td>\n",
       "      <td>['time interval endpoints', 'category maximum ...</td>\n",
       "      <td>['', '', '', '', '', '', '', '', '', '', '', '...</td>\n",
       "      <td>['time: mean', '', '', '', '', '', '', '', '',...</td>\n",
       "      <td>['days since 1900-01-01 00:00:00', 'm', '1', '...</td>\n",
       "      <td>access-om3.cice.1900-05.nc</td>\n",
       "      <td>access_om3_cice_XXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[uo, vo, KE, h, thetao, so, agessc, rhopot0, v...</td>\n",
       "      <td>1mon</td>\n",
       "      <td>1900-08-01, 00:00:00</td>\n",
       "      <td>1900-09-01, 00:00:00</td>\n",
       "      <td>['Sea Water X Velocity', 'Sea Water Y Velocity...</td>\n",
       "      <td>['sea_water_x_velocity', 'sea_water_y_velocity...</td>\n",
       "      <td>['z_l:mean yh:mean xq:point time: mean', 'z_l:...</td>\n",
       "      <td>['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...</td>\n",
       "      <td>access-om3.mom6.h.z1900_08.nc</td>\n",
       "      <td>access_om3_mom6_h_zXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[uo, vo, KE, h, thetao, so, agessc, rhopot0, v...</td>\n",
       "      <td>1mon</td>\n",
       "      <td>1900-09-01, 00:00:00</td>\n",
       "      <td>1900-10-01, 00:00:00</td>\n",
       "      <td>['Sea Water X Velocity', 'Sea Water Y Velocity...</td>\n",
       "      <td>['sea_water_x_velocity', 'sea_water_y_velocity...</td>\n",
       "      <td>['z_l:mean yh:mean xq:point time: mean', 'z_l:...</td>\n",
       "      <td>['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...</td>\n",
       "      <td>access-om3.mom6.h.z1900_09.nc</td>\n",
       "      <td>access_om3_mom6_h_zXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[uo, vo, KE, h, thetao, so, agessc, rhopot0, v...</td>\n",
       "      <td>1mon</td>\n",
       "      <td>1900-10-01, 00:00:00</td>\n",
       "      <td>1900-11-01, 00:00:00</td>\n",
       "      <td>['Sea Water X Velocity', 'Sea Water Y Velocity...</td>\n",
       "      <td>['sea_water_x_velocity', 'sea_water_y_velocity...</td>\n",
       "      <td>['z_l:mean yh:mean xq:point time: mean', 'z_l:...</td>\n",
       "      <td>['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...</td>\n",
       "      <td>access-om3.mom6.h.z1900_10.nc</td>\n",
       "      <td>access_om3_mom6_h_zXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[uo, vo, KE, h, thetao, so, agessc, rhopot0, v...</td>\n",
       "      <td>1mon</td>\n",
       "      <td>1900-11-01, 00:00:00</td>\n",
       "      <td>1900-12-01, 00:00:00</td>\n",
       "      <td>['Sea Water X Velocity', 'Sea Water Y Velocity...</td>\n",
       "      <td>['sea_water_x_velocity', 'sea_water_y_velocity...</td>\n",
       "      <td>['z_l:mean yh:mean xq:point time: mean', 'z_l:...</td>\n",
       "      <td>['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...</td>\n",
       "      <td>access-om3.mom6.h.z1900_11.nc</td>\n",
       "      <td>access_om3_mom6_h_zXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[uo, vo, KE, h, thetao, so, agessc, rhopot0, v...</td>\n",
       "      <td>1mon</td>\n",
       "      <td>1900-12-01, 00:00:00</td>\n",
       "      <td>1901-01-01, 00:00:00</td>\n",
       "      <td>['Sea Water X Velocity', 'Sea Water Y Velocity...</td>\n",
       "      <td>['sea_water_x_velocity', 'sea_water_y_velocity...</td>\n",
       "      <td>['z_l:mean yh:mean xq:point time: mean', 'z_l:...</td>\n",
       "      <td>['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...</td>\n",
       "      <td>access-om3.mom6.h.z1900_12.nc</td>\n",
       "      <td>access_om3_mom6_h_zXXXX_XX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 path   realm  \\\n",
       "0   /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  seaIce   \n",
       "1   /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  seaIce   \n",
       "2   /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  seaIce   \n",
       "3   /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  seaIce   \n",
       "4   /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  seaIce   \n",
       "..                                                ...     ...   \n",
       "92  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...   ocean   \n",
       "93  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...   ocean   \n",
       "94  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...   ocean   \n",
       "95  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...   ocean   \n",
       "96  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...   ocean   \n",
       "\n",
       "                                             variable frequency  \\\n",
       "0   [time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...      1day   \n",
       "1   [time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...      1day   \n",
       "2   [time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...      1day   \n",
       "3   [time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...      1day   \n",
       "4   [time_bounds, NCAT, VGRDi, VGRDs, VGRDb, VGRDa...      1day   \n",
       "..                                                ...       ...   \n",
       "92  [uo, vo, KE, h, thetao, so, agessc, rhopot0, v...      1mon   \n",
       "93  [uo, vo, KE, h, thetao, so, agessc, rhopot0, v...      1mon   \n",
       "94  [uo, vo, KE, h, thetao, so, agessc, rhopot0, v...      1mon   \n",
       "95  [uo, vo, KE, h, thetao, so, agessc, rhopot0, v...      1mon   \n",
       "96  [uo, vo, KE, h, thetao, so, agessc, rhopot0, v...      1mon   \n",
       "\n",
       "              start_date              end_date  \\\n",
       "0   1900-01-01, 00:00:00  1900-02-01, 00:00:00   \n",
       "1   1900-02-01, 00:00:00  1900-03-01, 00:00:00   \n",
       "2   1900-03-01, 00:00:00  1900-04-01, 00:00:00   \n",
       "3   1900-04-01, 00:00:00  1900-05-01, 00:00:00   \n",
       "4   1900-05-01, 00:00:00  1900-06-01, 00:00:00   \n",
       "..                   ...                   ...   \n",
       "92  1900-08-01, 00:00:00  1900-09-01, 00:00:00   \n",
       "93  1900-09-01, 00:00:00  1900-10-01, 00:00:00   \n",
       "94  1900-10-01, 00:00:00  1900-11-01, 00:00:00   \n",
       "95  1900-11-01, 00:00:00  1900-12-01, 00:00:00   \n",
       "96  1900-12-01, 00:00:00  1901-01-01, 00:00:00   \n",
       "\n",
       "                                   variable_long_name  \\\n",
       "0   ['time interval endpoints', 'category maximum ...   \n",
       "1   ['time interval endpoints', 'category maximum ...   \n",
       "2   ['time interval endpoints', 'category maximum ...   \n",
       "3   ['time interval endpoints', 'category maximum ...   \n",
       "4   ['time interval endpoints', 'category maximum ...   \n",
       "..                                                ...   \n",
       "92  ['Sea Water X Velocity', 'Sea Water Y Velocity...   \n",
       "93  ['Sea Water X Velocity', 'Sea Water Y Velocity...   \n",
       "94  ['Sea Water X Velocity', 'Sea Water Y Velocity...   \n",
       "95  ['Sea Water X Velocity', 'Sea Water Y Velocity...   \n",
       "96  ['Sea Water X Velocity', 'Sea Water Y Velocity...   \n",
       "\n",
       "                               variable_standard_name  \\\n",
       "0   ['', '', '', '', '', '', '', '', '', '', '', '...   \n",
       "1   ['', '', '', '', '', '', '', '', '', '', '', '...   \n",
       "2   ['', '', '', '', '', '', '', '', '', '', '', '...   \n",
       "3   ['', '', '', '', '', '', '', '', '', '', '', '...   \n",
       "4   ['', '', '', '', '', '', '', '', '', '', '', '...   \n",
       "..                                                ...   \n",
       "92  ['sea_water_x_velocity', 'sea_water_y_velocity...   \n",
       "93  ['sea_water_x_velocity', 'sea_water_y_velocity...   \n",
       "94  ['sea_water_x_velocity', 'sea_water_y_velocity...   \n",
       "95  ['sea_water_x_velocity', 'sea_water_y_velocity...   \n",
       "96  ['sea_water_x_velocity', 'sea_water_y_velocity...   \n",
       "\n",
       "                                variable_cell_methods  \\\n",
       "0   ['time: mean', '', '', '', '', '', '', '', '',...   \n",
       "1   ['time: mean', '', '', '', '', '', '', '', '',...   \n",
       "2   ['time: mean', '', '', '', '', '', '', '', '',...   \n",
       "3   ['time: mean', '', '', '', '', '', '', '', '',...   \n",
       "4   ['time: mean', '', '', '', '', '', '', '', '',...   \n",
       "..                                                ...   \n",
       "92  ['z_l:mean yh:mean xq:point time: mean', 'z_l:...   \n",
       "93  ['z_l:mean yh:mean xq:point time: mean', 'z_l:...   \n",
       "94  ['z_l:mean yh:mean xq:point time: mean', 'z_l:...   \n",
       "95  ['z_l:mean yh:mean xq:point time: mean', 'z_l:...   \n",
       "96  ['z_l:mean yh:mean xq:point time: mean', 'z_l:...   \n",
       "\n",
       "                                       variable_units  \\\n",
       "0   ['days since 1900-01-01 00:00:00', 'm', '1', '...   \n",
       "1   ['days since 1900-01-01 00:00:00', 'm', '1', '...   \n",
       "2   ['days since 1900-01-01 00:00:00', 'm', '1', '...   \n",
       "3   ['days since 1900-01-01 00:00:00', 'm', '1', '...   \n",
       "4   ['days since 1900-01-01 00:00:00', 'm', '1', '...   \n",
       "..                                                ...   \n",
       "92  ['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...   \n",
       "93  ['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...   \n",
       "94  ['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...   \n",
       "95  ['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...   \n",
       "96  ['m s-1', 'm s-1', 'm2 s-2', 'm', 'degC', 'psu...   \n",
       "\n",
       "                         filename                     file_id  \n",
       "0      access-om3.cice.1900-01.nc     access_om3_cice_XXXX_XX  \n",
       "1      access-om3.cice.1900-02.nc     access_om3_cice_XXXX_XX  \n",
       "2      access-om3.cice.1900-03.nc     access_om3_cice_XXXX_XX  \n",
       "3      access-om3.cice.1900-04.nc     access_om3_cice_XXXX_XX  \n",
       "4      access-om3.cice.1900-05.nc     access_om3_cice_XXXX_XX  \n",
       "..                            ...                         ...  \n",
       "92  access-om3.mom6.h.z1900_08.nc  access_om3_mom6_h_zXXXX_XX  \n",
       "93  access-om3.mom6.h.z1900_09.nc  access_om3_mom6_h_zXXXX_XX  \n",
       "94  access-om3.mom6.h.z1900_10.nc  access_om3_mom6_h_zXXXX_XX  \n",
       "95  access-om3.mom6.h.z1900_11.nc  access_om3_mom6_h_zXXXX_XX  \n",
       "96  access-om3.mom6.h.z1900_12.nc  access_om3_mom6_h_zXXXX_XX  \n",
       "\n",
       "[97 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datastore.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee3ca2cc-80f2-4cf3-8c26-0c44af0e4b68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>realm</th>\n",
       "      <th>variable</th>\n",
       "      <th>frequency</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>variable_long_name</th>\n",
       "      <th>variable_standard_name</th>\n",
       "      <th>variable_cell_methods</th>\n",
       "      <th>variable_units</th>\n",
       "      <th>filename</th>\n",
       "      <th>file_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-01-01, 00:00:00</td>\n",
       "      <td>1900-02-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_01.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-02-01, 00:00:00</td>\n",
       "      <td>1900-03-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_02.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-03-01, 00:00:00</td>\n",
       "      <td>1900-04-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_03.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-04-01, 00:00:00</td>\n",
       "      <td>1900-05-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_04.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-05-01, 00:00:00</td>\n",
       "      <td>1900-06-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_05.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                path  realm  \\\n",
       "0  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "1  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "2  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "3  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "4  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "\n",
       "                                            variable frequency  \\\n",
       "0  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "1  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "2  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "3  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "4  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "\n",
       "             start_date              end_date  \\\n",
       "0  1900-01-01, 00:00:00  1900-02-01, 00:00:00   \n",
       "1  1900-02-01, 00:00:00  1900-03-01, 00:00:00   \n",
       "2  1900-03-01, 00:00:00  1900-04-01, 00:00:00   \n",
       "3  1900-04-01, 00:00:00  1900-05-01, 00:00:00   \n",
       "4  1900-05-01, 00:00:00  1900-06-01, 00:00:00   \n",
       "\n",
       "                                  variable_long_name  \\\n",
       "0  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "1  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "2  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "3  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "4  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "\n",
       "                              variable_standard_name  \\\n",
       "0  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "1  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "2  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "3  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "4  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "\n",
       "                               variable_cell_methods  \\\n",
       "0  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "1  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "2  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "3  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "4  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "\n",
       "                                      variable_units  \\\n",
       "0  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "1  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "2  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "3  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "4  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "\n",
       "                                   filename  \\\n",
       "0  access-om3.mom6.h.native_daily1900_01.nc   \n",
       "1  access-om3.mom6.h.native_daily1900_02.nc   \n",
       "2  access-om3.mom6.h.native_daily1900_03.nc   \n",
       "3  access-om3.mom6.h.native_daily1900_04.nc   \n",
       "4  access-om3.mom6.h.native_daily1900_05.nc   \n",
       "\n",
       "                                 file_id  \n",
       "0  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "1  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "2  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "3  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "4  access_om3_mom6_h_native_dailyXXXX_XX  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datastore.search(path='.*access-om3\\.mom6\\.h\\.native.*',\n",
    "                 frequency='1day').df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af5422b9-9dd5-44cb-909e-c4ed70e63d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"['area:mean yh:mean xh:mean time: mean', 'area:mean yh:mean xh:mean time: mean', 'time: mean', 'time: mean', '', '', '', '']\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datastore.search(path='.*access-om3\\.mom6\\.h\\.native.*',\n",
    "                 frequency='1day').df.head()['variable_cell_methods'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02f34230-7af0-4996-9fca-45d57f93049c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>intake_esm_ds catalog with 1 dataset(s) from 12 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>realm</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_date</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_date</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_long_name</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_standard_name</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_cell_methods</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_units</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datastore.search(path='.*access-om3\\.mom6\\.h\\.native.*',\n",
    "                 frequency='1day',\n",
    "                 variable_cell_methods='.*time: mean.*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "332e9935-fdec-4c5c-8f59-3c28837134e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# see https://github.com/COSIMA/cosima-recipes/blob/main/Tutorials/ACCESS-NRI_Intake_Catalog.ipynb\n",
    "static = datastore.search(path='.*output000.*',\n",
    "                          frequency='fx'\n",
    "                         ).to_dask(xarray_combine_by_coords_kwargs=xarray_combine_by_coords_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "848071c4-821b-487f-bf57-fd24d170597b",
   "metadata": {},
   "outputs": [],
   "source": [
    "areacello = static['areacello']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c319336d-94a6-4d17-8f95-1def8837b07a",
   "metadata": {},
   "source": [
    "see https://xgcm.readthedocs.io/en/latest/xgcm-examples/03_MOM6.html#xgcm-grid-definition\n",
    "\n",
    "ACCESS-OM3 uses a non-symmetric memory layout, i.e. all fields have the same i and j sizes. See\n",
    "https://mom6.readthedocs.io/en/main/api/generated/pages/Horizontal_Indexing.html?highlight=symmetric#declaration-of-variables\n",
    "\n",
    "`MOM_parameter_doc.layout`:\n",
    "```\n",
    "!SYMMETRIC_MEMORY_ = False      !   [Boolean]\n",
    "                                ! If defined, the velocity point data domain includes every face of the\n",
    "                                ! thickness points. In other words, some arrays are larger than others,\n",
    "                                ! depending on where they are on the staggered grid.  Also, the starting index\n",
    "                                ! of the velocity-point arrays is usually 0, not 1. This can only be set at\n",
    "                                ! compile time.```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5908ee1c-1411-4054-a72a-c5cf3ff14d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for non-symmetric \n",
    "# SYMMETRIC_MEMORY_ = False\n",
    "# see https://xgcm.readthedocs.io/en/latest/xgcm-examples/03_MOM6.html#xgcm-grid-definition\n",
    "# and https://xgcm.readthedocs.io/en/latest/grid_metrics.html#Using-metrics-with-xgcm\n",
    "grid = xgcm.Grid(static,\n",
    "                 coords={'X': {'center': 'xh', 'right': 'xq'},\n",
    "                         'Y': {'center': 'yh', 'right': 'yq'},},\n",
    "                         # 'Z': { 'inner': 'zl', 'outer': 'zi'}},\n",
    "                 metrics = {\n",
    "                        ('X',): ['dxt', 'dxCu', 'dxCv'], # X distances\n",
    "                        ('Y',): ['dyt', 'dyCu', 'dyCv'], # Y distances\n",
    "                        ('X', 'Y'): ['areacello', 'areacello_cu', 'areacello_cv', 'areacello_bu'] # Areas\n",
    "                        },\n",
    "                 periodic=['X'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f90ca3e9-fbf3-46db-9569-a7d7f3f6f48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 315MB\n",
       "Dimensions:        (time: 365, yh: 300, xh: 360, scalar_axis: 1, nv: 2)\n",
       "Coordinates:\n",
       "  * xh             (xh) float64 3kB -279.5 -278.5 -277.5 ... 77.5 78.5 79.5\n",
       "  * yh             (yh) float64 2kB -77.88 -77.63 -77.38 ... 88.87 89.32 89.77\n",
       "  * time           (time) object 3kB 1900-01-01 12:00:00 ... 1900-12-31 12:00:00\n",
       "  * nv             (nv) float64 16B 1.0 2.0\n",
       "  * scalar_axis    (scalar_axis) float64 8B 0.0\n",
       "Data variables:\n",
       "    friver         (time, yh, xh) float32 158MB dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
       "    ficeberg       (time, yh, xh) float32 158MB dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;\n",
       "    total_frunoff  (time, scalar_axis) float32 1kB dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;\n",
       "    total_lrunoff  (time, scalar_axis) float32 1kB dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;\n",
       "    average_T1     (time) datetime64[ns] 3kB dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;\n",
       "    average_T2     (time) datetime64[ns] 3kB dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;\n",
       "    average_DT     (time) timedelta64[ns] 3kB dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;\n",
       "    time_bnds      (time, nv) object 6kB dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "Attributes: (12/16)\n",
       "    NumFilesInSet:                            1\n",
       "    title:                                    MOM6 diagnostic fields table\n",
       "    associated_files:                         areacello: access-om3.mom6.h.st...\n",
       "    grid_type:                                regular\n",
       "    grid_tile:                                N/A\n",
       "    intake_esm_vars:                          [&#x27;friver&#x27;, &#x27;ficeberg&#x27;, &#x27;total_f...\n",
       "    ...                                       ...\n",
       "    intake_esm_attrs:variable_standard_name:  [&#x27;water_flux_into_sea_water_fro...\n",
       "    intake_esm_attrs:variable_cell_methods:   [&#x27;area:mean yh:mean xh:mean tim...\n",
       "    intake_esm_attrs:variable_units:          [&#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;k...\n",
       "    intake_esm_attrs:file_id:                 access_om3_mom6_h_native_dailyX...\n",
       "    intake_esm_attrs:_data_format_:           netcdf\n",
       "    intake_esm_dataset_key:                   access_om3_mom6_h_native_dailyX...</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-56c68705-ed5a-43e9-9154-6c72ca23a4cc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-56c68705-ed5a-43e9-9154-6c72ca23a4cc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>yh</span>: 300</li><li><span class='xr-has-index'>xh</span>: 360</li><li><span class='xr-has-index'>scalar_axis</span>: 1</li><li><span class='xr-has-index'>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8a328e88-a83f-4108-a89b-973df3f2b4af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a328e88-a83f-4108-a89b-973df3f2b4af' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xh</span></div><div class='xr-var-dims'>(xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.5 -278.5 -277.5 ... 78.5 79.5</div><input id='attrs-125f9a79-d82a-460c-9305-653ef64af255' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-125f9a79-d82a-460c-9305-653ef64af255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcbf2b7a-89e8-4fce-8e82-2471fa4bc31a' class='xr-var-data-in' type='checkbox'><label for='data-bcbf2b7a-89e8-4fce-8e82-2471fa4bc31a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>h point nominal longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.5, -278.5, -277.5, ...,   77.5,   78.5,   79.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yh</span></div><div class='xr-var-dims'>(yh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... 89.32 89.77</div><input id='attrs-1c6fd3de-4835-4b6c-afdd-80aff68dfd1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c6fd3de-4835-4b6c-afdd-80aff68dfd1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38067984-1dfe-403d-af39-9af34d55ce94' class='xr-var-data-in' type='checkbox'><label for='data-38067984-1dfe-403d-af39-9af34d55ce94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>h point nominal latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.876623, -77.629713, -77.381707, ...,  88.872933,  89.324006,\n",
       "        89.774476])</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'>object</div><div class='xr-var-preview xr-preview'>1900-01-01 12:00:00 ... 1900-12-...</div><input id='attrs-bd33a729-fc35-4516-b225-dbc482d98fb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd33a729-fc35-4516-b225-dbc482d98fb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fa08bf6-14f6-4527-ba01-cf6cc514112d' class='xr-var-data-in' type='checkbox'><label for='data-0fa08bf6-14f6-4527-ba01-cf6cc514112d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1900, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(1900, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nv</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-474c2431-dda9-4eee-bf1c-c160e31eef00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-474c2431-dda9-4eee-bf1c-c160e31eef00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34704216-8fc2-4599-8eb4-6f7c6dc4e22b' class='xr-var-data-in' type='checkbox'><label for='data-34704216-8fc2-4599-8eb4-6f7c6dc4e22b' 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>vertex number</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scalar_axis</span></div><div class='xr-var-dims'>(scalar_axis)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-3dee1a50-f836-4a10-ab86-4235fb558928' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dee1a50-f836-4a10-ab86-4235fb558928' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce7b4c04-3181-4390-951d-872b87a2fa1c' class='xr-var-data-in' type='checkbox'><label for='data-ce7b4c04-3181-4390-951d-872b87a2fa1c' 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>none</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e2cd8b9-a32f-4c14-b06f-eafa86fe3b37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e2cd8b9-a32f-4c14-b06f-eafa86fe3b37' class='xr-section-summary' >Data variables: <span>(8)</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>friver</span></div><div class='xr-var-dims'>(time, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-ba2af841-820a-4a95-a487-8cbacebd6886' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba2af841-820a-4a95-a487-8cbacebd6886' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f790ac8-dece-4a49-a8a1-8d497347eb5e' class='xr-var-data-in' type='checkbox'><label for='data-3f790ac8-dece-4a49-a8a1-8d497347eb5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>Water Flux into Sea Water From Rivers</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>water_flux_into_sea_water_from_rivers</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> 150.38 MiB </td>\n",
       "                        <td> 421.88 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 300, 360) </td>\n",
       "                        <td> (1, 300, 360) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 365 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"248\" height=\"219\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"98\" x2=\"80\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"102\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"105\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"109\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"113\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"117\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"120\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"124\" />\n",
       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"128\" />\n",
       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"131\" />\n",
       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"135\" />\n",
       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"139\" />\n",
       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"143\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"146\" />\n",
       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"150\" />\n",
       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"154\" />\n",
       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"158\" />\n",
       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"161\" />\n",
       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"165\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,169.21837228041903 10.0,98.63013698630138\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"128\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"132\" y2=\"3\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"135\" y2=\"7\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"139\" y2=\"11\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"143\" y2=\"14\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"146\" y2=\"18\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"150\" y2=\"22\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"154\" y2=\"25\" />\n",
       "  <line x1=\"39\" y1=\"29\" x2=\"157\" y2=\"29\" />\n",
       "  <line x1=\"43\" y1=\"33\" x2=\"161\" y2=\"33\" />\n",
       "  <line x1=\"47\" y1=\"37\" x2=\"165\" y2=\"37\" />\n",
       "  <line x1=\"50\" y1=\"40\" x2=\"169\" y2=\"40\" />\n",
       "  <line x1=\"54\" y1=\"44\" x2=\"172\" y2=\"44\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"176\" y2=\"48\" />\n",
       "  <line x1=\"61\" y1=\"51\" x2=\"180\" y2=\"51\" />\n",
       "  <line x1=\"65\" y1=\"55\" x2=\"184\" y2=\"55\" />\n",
       "  <line x1=\"69\" y1=\"59\" x2=\"187\" y2=\"59\" />\n",
       "  <line x1=\"73\" y1=\"63\" x2=\"191\" y2=\"63\" />\n",
       "  <line x1=\"76\" y1=\"66\" x2=\"195\" y2=\"66\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"198\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"128\" y1=\"0\" x2=\"198\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 128.35616438356166,0.0 198.9443996776793,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"198\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"169\" x2=\"198\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"198\" y1=\"70\" x2=\"198\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 198.9443996776793,70.58823529411765 198.9443996776793,169.21837228041903 80.58823529411765,169.21837228041903\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"139.766317\" y=\"189.218372\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
       "  <text x=\"218.944400\" y=\"119.903304\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,218.944400,119.903304)\">300</text>\n",
       "  <text x=\"35.294118\" y=\"153.924255\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,153.924255)\">365</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ficeberg</span></div><div class='xr-var-dims'>(time, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-b5b6a1f8-5cec-4e8d-8c70-3e7368e6f5f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5b6a1f8-5cec-4e8d-8c70-3e7368e6f5f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c56b7392-5a0d-4cfb-b0bd-07367c6e82d2' class='xr-var-data-in' type='checkbox'><label for='data-c56b7392-5a0d-4cfb-b0bd-07367c6e82d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>Water Flux into Seawater from Icebergs</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>water_flux_into_sea_water_from_icebergs</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> 150.38 MiB </td>\n",
       "                        <td> 421.88 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 300, 360) </td>\n",
       "                        <td> (1, 300, 360) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 365 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"248\" height=\"219\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"98\" x2=\"80\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"102\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"105\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"109\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"113\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"117\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"120\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"124\" />\n",
       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"128\" />\n",
       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"131\" />\n",
       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"135\" />\n",
       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"139\" />\n",
       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"143\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"146\" />\n",
       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"150\" />\n",
       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"154\" />\n",
       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"158\" />\n",
       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"161\" />\n",
       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"165\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,169.21837228041903 10.0,98.63013698630138\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"128\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"132\" y2=\"3\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"135\" y2=\"7\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"139\" y2=\"11\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"143\" y2=\"14\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"146\" y2=\"18\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"150\" y2=\"22\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"154\" y2=\"25\" />\n",
       "  <line x1=\"39\" y1=\"29\" x2=\"157\" y2=\"29\" />\n",
       "  <line x1=\"43\" y1=\"33\" x2=\"161\" y2=\"33\" />\n",
       "  <line x1=\"47\" y1=\"37\" x2=\"165\" y2=\"37\" />\n",
       "  <line x1=\"50\" y1=\"40\" x2=\"169\" y2=\"40\" />\n",
       "  <line x1=\"54\" y1=\"44\" x2=\"172\" y2=\"44\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"176\" y2=\"48\" />\n",
       "  <line x1=\"61\" y1=\"51\" x2=\"180\" y2=\"51\" />\n",
       "  <line x1=\"65\" y1=\"55\" x2=\"184\" y2=\"55\" />\n",
       "  <line x1=\"69\" y1=\"59\" x2=\"187\" y2=\"59\" />\n",
       "  <line x1=\"73\" y1=\"63\" x2=\"191\" y2=\"63\" />\n",
       "  <line x1=\"76\" y1=\"66\" x2=\"195\" y2=\"66\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"198\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"128\" y1=\"0\" x2=\"198\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 128.35616438356166,0.0 198.9443996776793,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"198\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"169\" x2=\"198\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"198\" y1=\"70\" x2=\"198\" y2=\"169\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 198.9443996776793,70.58823529411765 198.9443996776793,169.21837228041903 80.58823529411765,169.21837228041903\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"139.766317\" y=\"189.218372\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
       "  <text x=\"218.944400\" y=\"119.903304\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,218.944400,119.903304)\">300</text>\n",
       "  <text x=\"35.294118\" y=\"153.924255\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,153.924255)\">365</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_frunoff</span></div><div class='xr-var-dims'>(time, scalar_axis)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</div><input id='attrs-78f8a0e7-58a0-44c5-8162-051679ec0cab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78f8a0e7-58a0-44c5-8162-051679ec0cab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b200c0e8-be87-4504-9c8a-a08630c3901d' class='xr-var-data-in' type='checkbox'><label for='data-b200c0e8-be87-4504-9c8a-a08630c3901d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg s-1</dd><dt><span>long_name :</span></dt><dd>Area integrated frozen runoff (calving) &amp; iceberg melt into ocean</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</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> 1.43 kiB </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 1) </td>\n",
       "                        <td> (1, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 365 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"24\" x2=\"25\" y2=\"24\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"25\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
       "  <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n",
       "  <line x1=\"0\" y1=\"69\" x2=\"25\" y2=\"69\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"81\" x2=\"25\" y2=\"81\" />\n",
       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">365</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_lrunoff</span></div><div class='xr-var-dims'>(time, scalar_axis)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</div><input id='attrs-312a00dc-1e74-4cd2-8696-50bfc9f17f31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-312a00dc-1e74-4cd2-8696-50bfc9f17f31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4033ea4a-3617-41b0-b114-9861cfb12a3f' class='xr-var-data-in' type='checkbox'><label for='data-4033ea4a-3617-41b0-b114-9861cfb12a3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg s-1</dd><dt><span>long_name :</span></dt><dd>Area integrated liquid runoff into ocean</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</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> 1.43 kiB </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 1) </td>\n",
       "                        <td> (1, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 365 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"24\" x2=\"25\" y2=\"24\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"25\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
       "  <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n",
       "  <line x1=\"0\" y1=\"69\" x2=\"25\" y2=\"69\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"81\" x2=\"25\" y2=\"81\" />\n",
       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">365</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;</div><input id='attrs-55c5f7e8-8611-4063-99d1-1aab139aa9f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55c5f7e8-8611-4063-99d1-1aab139aa9f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c12bce2-9623-4600-a5b0-120e7074012d' class='xr-var-data-in' type='checkbox'><label for='data-6c12bce2-9623-4600-a5b0-120e7074012d' 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>Start time for average period</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> 2.85 kiB </td>\n",
       "                        <td> 248 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365,) </td>\n",
       "                        <td> (31,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n",
       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
       "  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"25\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"109\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >365</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_T2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;</div><input id='attrs-8601ab7e-408c-48b3-a995-b95a0848ae0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8601ab7e-408c-48b3-a995-b95a0848ae0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1ba3b4-4e15-4297-85ac-daebceb60156' class='xr-var-data-in' type='checkbox'><label for='data-ff1ba3b4-4e15-4297-85ac-daebceb60156' 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>End time for average period</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> 2.85 kiB </td>\n",
       "                        <td> 248 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365,) </td>\n",
       "                        <td> (31,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n",
       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
       "  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"25\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"109\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >365</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>average_DT</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;</div><input id='attrs-0d404563-a288-4e5d-8eb9-013490b96a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d404563-a288-4e5d-8eb9-013490b96a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81cfc37b-51fe-4b90-9c3d-ca62ee3ae776' class='xr-var-data-in' type='checkbox'><label for='data-81cfc37b-51fe-4b90-9c3d-ca62ee3ae776' 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>Length of average period</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> 2.85 kiB </td>\n",
       "                        <td> 248 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365,) </td>\n",
       "                        <td> (31,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> timedelta64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n",
       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
       "  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"25\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"109\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >365</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-a9230aa9-47bd-46bb-aec9-7773ed079371' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9230aa9-47bd-46bb-aec9-7773ed079371' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10a95db0-1431-4d8d-affb-c61de3625cb8' class='xr-var-data-in' type='checkbox'><label for='data-10a95db0-1431-4d8d-affb-c61de3625cb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time axis boundaries</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> 5.70 kiB </td>\n",
       "                        <td> 16 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 365 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"24\" x2=\"25\" y2=\"24\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"25\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
       "  <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n",
       "  <line x1=\"0\" y1=\"69\" x2=\"25\" y2=\"69\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"81\" x2=\"25\" y2=\"81\" />\n",
       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">365</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-349c9112-1446-4619-b601-af6df3824920' class='xr-section-summary-in' type='checkbox'  ><label for='section-349c9112-1446-4619-b601-af6df3824920' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-744ebd84-3d92-4a7b-b5e0-51affaedf867' class='xr-index-data-in' type='checkbox'/><label for='index-744ebd84-3d92-4a7b-b5e0-51affaedf867' 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([-279.5, -278.5, -277.5, -276.5, -275.5, -274.5, -273.5, -272.5, -271.5,\n",
       "       -270.5,\n",
       "       ...\n",
       "         70.5,   71.5,   72.5,   73.5,   74.5,   75.5,   76.5,   77.5,   78.5,\n",
       "         79.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-32af0945-6f45-45b8-9d35-2de17d55b516' class='xr-index-data-in' type='checkbox'/><label for='index-32af0945-6f45-45b8-9d35-2de17d55b516' 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([-77.87662337662337, -77.62971340045422, -77.38170670967813,\n",
       "       -77.13229193169715, -76.88053325031572, -76.62612553775392,\n",
       "       -76.36814128806307,  -76.1062857353073,  -75.8396437697329,\n",
       "        -75.5679350353177,\n",
       "       ...\n",
       "        85.71622733564506,  86.16718145415204,  86.61861799525872,\n",
       "        87.06938121176299,  87.52064932837074,  87.97126714724561,\n",
       "        88.42241336735029,  88.87293318844945,   89.3240056291542,\n",
       "        89.77447612988227],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=300))</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-d7dd9988-3543-409a-9327-7dc09872fb03' class='xr-index-data-in' type='checkbox'/><label for='index-d7dd9988-3543-409a-9327-7dc09872fb03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1900-01-01 12:00:00, 1900-01-02 12:00:00, 1900-01-03 12:00:00,\n",
       "             1900-01-04 12:00:00, 1900-01-05 12:00:00, 1900-01-06 12:00:00,\n",
       "             1900-01-07 12:00:00, 1900-01-08 12:00:00, 1900-01-09 12:00:00,\n",
       "             1900-01-10 12:00:00,\n",
       "             ...\n",
       "             1900-12-22 12:00:00, 1900-12-23 12:00:00, 1900-12-24 12:00:00,\n",
       "             1900-12-25 12:00:00, 1900-12-26 12:00:00, 1900-12-27 12:00:00,\n",
       "             1900-12-28 12:00:00, 1900-12-29 12:00:00, 1900-12-30 12:00:00,\n",
       "             1900-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=365, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nv</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f46c3a69-fa89-4477-b1b4-7eec0d9a10bd' class='xr-index-data-in' type='checkbox'/><label for='index-f46c3a69-fa89-4477-b1b4-7eec0d9a10bd' 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([1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;nv&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>scalar_axis</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7fada71-10b5-4225-8b3f-a39fc0902347' class='xr-index-data-in' type='checkbox'/><label for='index-f7fada71-10b5-4225-8b3f-a39fc0902347' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;scalar_axis&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d0403ad-c74a-47e0-93ae-0badd587ec54' class='xr-section-summary-in' type='checkbox'  ><label for='section-5d0403ad-c74a-47e0-93ae-0badd587ec54' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>NumFilesInSet :</span></dt><dd>1</dd><dt><span>title :</span></dt><dd>MOM6 diagnostic fields table</dd><dt><span>associated_files :</span></dt><dd>areacello: access-om3.mom6.h.static.nc</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;friver&#x27;, &#x27;ficeberg&#x27;, &#x27;total_frunoff&#x27;, &#x27;total_lrunoff&#x27;, &#x27;average_T1&#x27;, &#x27;average_T2&#x27;, &#x27;average_DT&#x27;, &#x27;time_bnds&#x27;]</dd><dt><span>intake_esm_attrs:realm :</span></dt><dd>ocean</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>friver,ficeberg,total_frunoff,total_lrunoff,average_T1,average_T2,average_DT,time_bnds</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>1day</dd><dt><span>intake_esm_attrs:variable_long_name :</span></dt><dd>[&#x27;Water Flux into Sea Water From Rivers&#x27;, &#x27;Water Flux into Seawater from Icebergs&#x27;, &#x27;Area integrated frozen runoff (calving) &amp; iceberg melt into ocean&#x27;, &#x27;Area integrated liquid runoff into ocean&#x27;, &#x27;Start time for average period&#x27;, &#x27;End time for average period&#x27;, &#x27;Length of average period&#x27;, &#x27;time axis boundaries&#x27;]</dd><dt><span>intake_esm_attrs:variable_standard_name :</span></dt><dd>[&#x27;water_flux_into_sea_water_from_rivers&#x27;, &#x27;water_flux_into_sea_water_from_icebergs&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;]</dd><dt><span>intake_esm_attrs:variable_cell_methods :</span></dt><dd>[&#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;area:mean yh:mean xh:mean time: mean&#x27;, &#x27;time: mean&#x27;, &#x27;time: mean&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;]</dd><dt><span>intake_esm_attrs:variable_units :</span></dt><dd>[&#x27;kg m-2 s-1&#x27;, &#x27;kg m-2 s-1&#x27;, &#x27;kg s-1&#x27;, &#x27;kg s-1&#x27;, &#x27;days since 0001-01-01 00:00:00&#x27;, &#x27;days since 0001-01-01 00:00:00&#x27;, &#x27;days&#x27;, &#x27;days since 0001-01-01 00:00:00&#x27;]</dd><dt><span>intake_esm_attrs:file_id :</span></dt><dd>access_om3_mom6_h_native_dailyXXXX_XX</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>access_om3_mom6_h_native_dailyXXXX_XX.1day</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 315MB\n",
       "Dimensions:        (time: 365, yh: 300, xh: 360, scalar_axis: 1, nv: 2)\n",
       "Coordinates:\n",
       "  * xh             (xh) float64 3kB -279.5 -278.5 -277.5 ... 77.5 78.5 79.5\n",
       "  * yh             (yh) float64 2kB -77.88 -77.63 -77.38 ... 88.87 89.32 89.77\n",
       "  * time           (time) object 3kB 1900-01-01 12:00:00 ... 1900-12-31 12:00:00\n",
       "  * nv             (nv) float64 16B 1.0 2.0\n",
       "  * scalar_axis    (scalar_axis) float64 8B 0.0\n",
       "Data variables:\n",
       "    friver         (time, yh, xh) float32 158MB dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
       "    ficeberg       (time, yh, xh) float32 158MB dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
       "    total_frunoff  (time, scalar_axis) float32 1kB dask.array<chunksize=(1, 1), meta=np.ndarray>\n",
       "    total_lrunoff  (time, scalar_axis) float32 1kB dask.array<chunksize=(1, 1), meta=np.ndarray>\n",
       "    average_T1     (time) datetime64[ns] 3kB dask.array<chunksize=(31,), meta=np.ndarray>\n",
       "    average_T2     (time) datetime64[ns] 3kB dask.array<chunksize=(31,), meta=np.ndarray>\n",
       "    average_DT     (time) timedelta64[ns] 3kB dask.array<chunksize=(31,), meta=np.ndarray>\n",
       "    time_bnds      (time, nv) object 6kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "Attributes: (12/16)\n",
       "    NumFilesInSet:                            1\n",
       "    title:                                    MOM6 diagnostic fields table\n",
       "    associated_files:                         areacello: access-om3.mom6.h.st...\n",
       "    grid_type:                                regular\n",
       "    grid_tile:                                N/A\n",
       "    intake_esm_vars:                          ['friver', 'ficeberg', 'total_f...\n",
       "    ...                                       ...\n",
       "    intake_esm_attrs:variable_standard_name:  ['water_flux_into_sea_water_fro...\n",
       "    intake_esm_attrs:variable_cell_methods:   ['area:mean yh:mean xh:mean tim...\n",
       "    intake_esm_attrs:variable_units:          ['kg m-2 s-1', 'kg m-2 s-1', 'k...\n",
       "    intake_esm_attrs:file_id:                 access_om3_mom6_h_native_dailyX...\n",
       "    intake_esm_attrs:_data_format_:           netcdf\n",
       "    intake_esm_dataset_key:                   access_om3_mom6_h_native_dailyX..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datastore.search(path='.*access-om3\\.mom6\\.h\\.native.*',\n",
    "                          frequency='1day',\n",
    "                          variable_cell_methods='.*time: mean.*'\n",
    "                         ).to_dask(xarray_combine_by_coords_kwargs=xarray_combine_by_coords_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3bd435d-6878-4d77-a745-4d5c03976c0d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>realm</th>\n",
       "      <th>variable</th>\n",
       "      <th>frequency</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>variable_long_name</th>\n",
       "      <th>variable_standard_name</th>\n",
       "      <th>variable_cell_methods</th>\n",
       "      <th>variable_units</th>\n",
       "      <th>filename</th>\n",
       "      <th>file_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-01-01, 00:00:00</td>\n",
       "      <td>1900-02-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_01.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-02-01, 00:00:00</td>\n",
       "      <td>1900-03-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_02.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-03-01, 00:00:00</td>\n",
       "      <td>1900-04-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_03.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-04-01, 00:00:00</td>\n",
       "      <td>1900-05-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_04.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...</td>\n",
       "      <td>ocean</td>\n",
       "      <td>[friver, ficeberg, total_frunoff, total_lrunof...</td>\n",
       "      <td>1day</td>\n",
       "      <td>1900-05-01, 00:00:00</td>\n",
       "      <td>1900-06-01, 00:00:00</td>\n",
       "      <td>['Water Flux into Sea Water From Rivers', 'Wat...</td>\n",
       "      <td>['water_flux_into_sea_water_from_rivers', 'wat...</td>\n",
       "      <td>['area:mean yh:mean xh:mean time: mean', 'area...</td>\n",
       "      <td>['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...</td>\n",
       "      <td>access-om3.mom6.h.native_daily1900_05.nc</td>\n",
       "      <td>access_om3_mom6_h_native_dailyXXXX_XX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                path  realm  \\\n",
       "0  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "1  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "2  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "3  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "4  /home/156/aek156/payu/MOM6-CICE6-1deg_jra55do_...  ocean   \n",
       "\n",
       "                                            variable frequency  \\\n",
       "0  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "1  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "2  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "3  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "4  [friver, ficeberg, total_frunoff, total_lrunof...      1day   \n",
       "\n",
       "             start_date              end_date  \\\n",
       "0  1900-01-01, 00:00:00  1900-02-01, 00:00:00   \n",
       "1  1900-02-01, 00:00:00  1900-03-01, 00:00:00   \n",
       "2  1900-03-01, 00:00:00  1900-04-01, 00:00:00   \n",
       "3  1900-04-01, 00:00:00  1900-05-01, 00:00:00   \n",
       "4  1900-05-01, 00:00:00  1900-06-01, 00:00:00   \n",
       "\n",
       "                                  variable_long_name  \\\n",
       "0  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "1  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "2  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "3  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "4  ['Water Flux into Sea Water From Rivers', 'Wat...   \n",
       "\n",
       "                              variable_standard_name  \\\n",
       "0  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "1  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "2  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "3  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "4  ['water_flux_into_sea_water_from_rivers', 'wat...   \n",
       "\n",
       "                               variable_cell_methods  \\\n",
       "0  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "1  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "2  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "3  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "4  ['area:mean yh:mean xh:mean time: mean', 'area...   \n",
       "\n",
       "                                      variable_units  \\\n",
       "0  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "1  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "2  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "3  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "4  ['kg m-2 s-1', 'kg m-2 s-1', 'kg s-1', 'kg s-1...   \n",
       "\n",
       "                                   filename  \\\n",
       "0  access-om3.mom6.h.native_daily1900_01.nc   \n",
       "1  access-om3.mom6.h.native_daily1900_02.nc   \n",
       "2  access-om3.mom6.h.native_daily1900_03.nc   \n",
       "3  access-om3.mom6.h.native_daily1900_04.nc   \n",
       "4  access-om3.mom6.h.native_daily1900_05.nc   \n",
       "\n",
       "                                 file_id  \n",
       "0  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "1  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "2  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "3  access_om3_mom6_h_native_dailyXXXX_XX  \n",
       "4  access_om3_mom6_h_native_dailyXXXX_XX  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datastore.search(variable='friver', frequency='1day').df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a3db0e89-7d21-46e0-8a13-d30795f7c543",
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = ['total_lrunoff', 'friver', 'total_frunoff', 'ficeberg',\n",
    "          # 'net_massin', 'net_massout', 'masso'\n",
    "         ]\n",
    "data = { k: datastore.search(variable=k, frequency='1day', variable_cell_methods='.*time: mean.*').to_dask(\n",
    "    xarray_combine_by_coords_kwargs=xarray_combine_by_coords_kwargs) for k in fields }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "522f0d9f-4e02-4abe-ba7e-c0c02db4bf6f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAHUCAYAAAAqbPllAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAACKKUlEQVR4nOzdd3hU1drG4d+k90YIEAgl9E4ggHSxAGJHBUXpIKgogliwoCiK5aBYjhUERUAsgA0VBOk19CotkEASIAnppM3s7w80x3y0TEjYmeS5r2uuY/bsveeZoynvrLXeZTEMw0BERERERERKjZPZAURERERERMo7FV4iIiIiIiKlTIWXiIiIiIhIKVPhJSIiIiIiUspUeImIiIiIiJQyFV4iIiIiIiKlTIWXiIiIiIhIKVPhJSIiIiIiUspUeImIiIiIiJQyFV4iIlLmrFixAovFwooVK8yOcsVq167N4MGDzY5xQV9//TWtWrXCw8OD0NBQHn/8cTIyMop8/fvvv0+jRo1wd3enTp06TJo0iby8vFJMLCLiuFzMDiAiIlKeLVy4ED8/P7NjnGfOnDk88MADDB8+nHfeeYcDBw7w9NNPs3fvXpYsWXLZ61999VVeeOEFnnnmGXr06MHmzZt5/vnnOXHiBJ9++ulVeAciIo7FYhiGYXYIERGRf1uxYgXdu3fnzz//5Nprr73i+509exYPDw8sFsuVhysHrFYrYWFhNG/enN9//73g+Ny5c7n//vtZvHgxN91000WvT0pKokaNGgwcOJBPPvmk4Phrr73G888/z+7du2nSpEmpvgcREUejqYYiInJZp0+f5sEHHyQsLAx3d3cqV65Mp06d+OOPPwqd99tvv3H99dfj7++Pl5cXjRs3ZsqUKQXPR0VFce+991K7dm08PT2pXbs29913H8eOHbtshqJeO2vWLCwWC0uWLGHo0KFUrlwZLy8vcnJyivReJ02aRPv27QkKCsLPz4/WrVszY8YM/v055Zo1a3B1dWX8+PEXfO0ZM2YUHPv/Uw1tNhuTJ0+mYcOGeHp6EhAQQIsWLXj33XeLlK8kbNiwgfj4eIYMGVLo+D333IOPjw8LFy685PW//fYb2dnZ510/ZMgQDMNg0aJFJR1ZRMThaaqhiIhc1oABA9i6dSuvvvoqDRo0ICUlha1bt5KUlFRwzowZMxgxYgTdunXj448/JiQkhAMHDrB79+6Cc44ePUrDhg259957CQoKIj4+no8++oi2bduyd+9egoODL5rB3muHDh3KzTffzOzZs8nMzMTV1bVI7/Xo0aOMHDmSmjVrAueKlEcffZQTJ04wceJEADp37szkyZN55pln6Nq1K7fddht79uzhkUce4YEHHmDYsGEXvf+bb77JSy+9xPPPP0/Xrl3Jy8tj//79pKSkXDZbfn5+kd6Ds7PzJUf3/vl30qJFi0LHXV1dadSoUaF/Z5e6vnnz5oWOV6tWjeDg4MteLyJSIRkiIiKX4ePjYzz++OMXfT49Pd3w8/MzOnfubNhstiLfNz8/38jIyDC8vb2Nd999t+D4n3/+aQDGn3/+afe1M2fONABj4MCBRc5xMVar1cjLyzNefvllo1KlSoXem81mM3r37m0EBAQYu3fvNpo0aWI0atTIyMjIKHSPWrVqGYMGDSr4+pZbbjFatWpVrDxAkR4zZ8685H1effVVAzDi4+PPe65Hjx5GgwYNLnn9iBEjDHd39ws+16BBA6NHjx5Ffk8iIhWFRryuwKpVq3jrrbfYsmUL8fHxLFy4kDvuuMOue3zzzTe89tprHDhwgMqVKzN69GiefPLJ0gksIlJM7dq1Y9asWVSqVIkbbriBNm3aFBpBWrduHWlpaTz88MOXHGnJyMjglVde4fvvv+fo0aNYrdaC5/bt23fJDPZee9ddd9nzFgssX76c1157jc2bN5OWllbouVOnTlGlShUALBYLX375JREREURGRmKxWNi4cSPe3t6XvH+7du345ZdfePjhh7n99tvp0KFDkZtvbN68uUjn1alTp0jnXezfVVHWwl3qHK2lExE5nwqvK5CZmUnLli0ZMmRIsX7B//rrr9x///28//779OjRg3379jF8+HA8PT0ZPXp0KSQWESme+fPnM3nyZKZPn84LL7yAj48Pd955J2+++SZVq1bl9OnTANSoUeOS9+nfvz/Lli3jhRdeoG3btvj5+WGxWOjduzdnz54t0WurVatm9/vctGkTPXr04Nprr+Wzzz6jRo0auLm5sWjRIl599dXzXqdSpUrcdttt/Pe//+XOO+88b+rdhUyYMAFvb2+++uorPv74Y5ydnenatStvvPEGkZGRl7y2VatWRXofzs7Ol3y+UqVKwLkmGf8Ukv9ITk4mKCjostdnZ2eTlZWFl5fXede3adOmSDlFRCoUs4fcygvAWLhwYaFjOTk5xpNPPmmEhoYaXl5eRrt27QpNm7nvvvuMu+++u9A177zzjlGjRg27puqIiFxNx44dM95//33D29vb6Nmzp2EYhvH7778bgDF37tyLXpeSkmJYLBbjpZdeKnQ8OzvbcHZ2LjQd7/9PNbTn2n+mGm7evNnu9zZ27FjDw8PDOHv2bKHjzz33nAEY0dHRhY4vWbLEcHJyMtq1a2cAxnfffXfePf//VMN/O3PmjPHtt98a9evXN4KCgozMzMxL5qOEphquXbvWAIyvv/660PG8vDzDx8fHGDFixCWvnzNnjgEYGzZsKHQ8Pj7eAIxXX331kteLiFREGvEqRUOGDOHo0aN8/fXXhIaGsnDhQnr16sWuXbuoX78+OTk5531S6OnpyfHjxzl27Bi1a9c2J7iIyCXUrFmT0aNHs2zZMtauXQtAx44d8ff35+OPP+bee++94FQzi8WCYRi4u7sXOj59+vRC0wYv5EqutYfFYsHFxaXQiNHZs2eZPXv2eefGx8fzwAMP0K1bN5YuXUqfPn0YNmwYrVu3LvJUv4CAAO6++25OnDjB448/ztGjRy/Zhr2kphq2b9+eatWqMWvWLPr161dw/LvvviMjI4M+ffpc8vpevXrh4eHBrFmzaN++fcHxf7o62jvtXkSkIlDhVUoOHz7MvHnzOH78OKGhoQCMHz+e3377jZkzZ/Laa6/Rs2dPxo4dy+DBg+nevTuHDh1i2rRpwLlf6Cq8RKQsSE1NpXv37vTv359GjRrh6+vL5s2b+e233wr+QPfx8WHq1KkMHz6cG264gREjRlClShUOHTrEjh07+OCDD/Dz86Nr16689dZbBAcHU7t2bVauXMmMGTMICAi4ZIYrudYeN998M2+//Tb9+/fnwQcfJCkpif/85z/nFXxWq5X77rsPi8XC3LlzcXZ2ZtasWbRq1Yp+/fqxZs0a3NzcLvgat956K82aNSMyMpLKlStz7Ngxpk2bRq1atahfv/4l811uKmJROTs78+abbzJgwABGjhzJfffdx8GDB3nqqae48cYb6dWrV8G5K1eu5Prrr2fixIkFXR2DgoJ4/vnneeGFFwgKCirYQPmll15i+PDh2sNLRORCzB5yKy/4f1MNv/nmGwMwvL29Cz1cXFyMvn37GoZxriPWU089ZXh4eBjOzs5GYGCg8dJLLxmAsXHjRpPeiYhIYdnZ2caoUaOMFi1aGH5+foanp6fRsGFD48UXXzxvatzixYuNbt26Gd7e3oaXl5fRpEkT44033ih4/vjx48Zdd91lBAYGGr6+vkavXr2M3bt3nzcd70JdDYt67ZVMNTQMw/j888+Nhg0bGu7u7kZ4eLgxZcoUY8aMGYWmGj733HOGk5OTsWzZskLXrlu3znBxcTHGjBlTcOz/55s6darRsWNHIzg42HBzczNq1qxpDBs2zDh69Gix8l6JuXPnGi1atDDc3NyMqlWrGo899piRnp5e6Jx//l28+OKL513/7rvvGg0aNCh4Hy+++KKRm5t7ldKLiDgWi2H8a0dIKTaLxVKoq+H8+fO5//772bNnz3mLnH18fKhatWrB11arlYSEBCpXrsyyZcvo3bs3J0+eJCQk5Gq+BRERERERKSWaalhKIiIisFqtnDp1ii5dulzyXGdnZ6pXrw7AvHnz6NChg4ouEREREZFyRIXXFcjIyODQoUMFX0dHR7N9+3aCgoJo0KAB999/PwMHDmTq1KlERESQmJjI8uXLad68Ob179yYxMZHvvvuOa6+9luzsbGbOnMm3337LypUrTXxXIiLlU35+/iWfd3JywsnJ6SqlERGRika/Ya5AVFQUERERREREADBu3DgiIiIKFh/PnDmTgQMH8sQTT9CwYUNuu+02Nm7cSFhYWME9vvjiCyIjI+nUqRN79uxhxYoVtGvXzpT3IyJSXh09ehRXV9dLPl5++WWzY4qISDmmNV4iIlLu5ebmsnPnzkueExoaWtCFVkREpKSp8BIRERERESllmmooIiIiIiJSytRcoxhsNhtxcXH4+vpisVjMjiMiIiIiIiYxDIP09HRCQ0Mv2aRJhVcxxMXFFWqQISIiIiIiFVtsbCw1atS46PMqvIrB19cXOPd/rp+fn8lpRERERETELGlpaYSFhRXUCBejwqsY/ple6Ofnp8JLREREREQuuwRJzTVERERERERKmQovERERERGRUqbCS0REREREpJSp8BIRERERESllKrxERERERERKmQovERERERGRUqbCS0REREREpJSp8BIRERERESllKrxERERERERKmQovERERERGRUqbCS0REREREpJSp8BIRERERESllKrxERERERERKmQovERGRcmxTdDIDP9/Ewm3HzY4iIlKhuZgdQEREREqe1Wbw5u/7+XTVEQwD1h5KpIqfBx3rBpsdTUSkQtKIl4iISDn08crDfLLyXNFVJ9gbq81g9NxtHD+TZXY0EZEKSYWXiIhIOfNXQjrv/nEQgMl3NOPXMV1oGupHcmYuLyzabXI6EZGKSYWXiIhIOZKRk8/4b3eQa7VxfaMQ7m9fEw9XZ96/LwInC/z512l2Hk8xO6aISIWjwktERKSc2B6bws3vrWbXiVT8PV15rU9zLBYLAOGVfbitZSgA7y8/ZGZMEZEKSYWXiIhIObD7RCp9P17PsaQsQv09+HxwJFX8PAqdM/q6elgssHTvSfbFp5mUVESkYlLhJSIi4uDO5loZ8/U2cq02utQP5tcxXWlTK+i88+qF+NK7eTUA3ll64GrHFBGp0FR4iYiIlDGHTmXw8crDpGXnXfZcwzB4dfFeDp/OJMTXnffujcDfy/Wi5z9+fX2cLLBk70m2HEsuydgiInIJKrxERETKkIycfAbM2Mjrv+5n4IxNpF+i+MrJt/L09zv5akMMAFP7tiTQ2+2S969fxZd72oQBMGXxfgzDKLnwIiJyUSq8REREypD//P4X8anZwLlmGYM+30RyZu55551My+beTzfwTdRxnCww8ZYmdKlfuUivMfbGBni4OhF17Ay/7zlZovlFROTCVHiJiIiUEdtjU/hi/VEAnuvdGD8PF7bGpHDTu6tYdzix4Lwtx85wy/tr2BaTgr+nK7OGtGNo5zpFfp2q/h4M+/v8l3/aQ2ZOfom+DxEROZ8KLxERkTLi7aUHMAzoE1GdEV3D+WZUB+pW9uZkWg73T9/I1CV/MXdjDPd+up7T6Tk0rOLLj6M70bVB0Ua6/u2R7vUIC/IkLjWbqUvUaENEpLRZDE3utltaWhr+/v6kpqbi5+dndhwRESkH4lLO0umN5RgGrHqyOzUreQGQlZvPpB/3Mj8qttD5vZpWZWrflni7uxT7NVceOM2gzzdhsUCIrztOFgsfPdCGVmEBV/JWREQqlKLWBhrxEhERKQMWbD2OYcA14UEFRReAl5sLb9zdgvfvi8DX3QWLBcb3aMBHD7S+oqILoFuDyvSJqI5hwMm0HOJTs3n6u53kWW1X+nZEROT/ubKf2CIiInLFDMPg2y3HAQo6Dv5/t7YMpWPdSqSczaNuZZ8Se+3X72rBve1q4mSBB2dv4a+T6UxfHc1D19YtsdcQERGNeImIiJhuU3Qyx5Ky8HF34abmVS96XiUf9xItugDcXJxoVyeIyNpBPNu7MQDvLjvA8TNZJfo6IiIVnQovERERE+2NS2Ps/O0A3Ny8Gl5u5k1Guat1ddrVDiI7z8bs9cdMyyEiUh6p8BIRETGBzWYwf3MMd3+8jrjUbMKDvRlzQ31TM1ksFkZ0DQfgm6hYcvKtpuYRESlPtMZLRETkKktIzWb03K1EHTsDQKd6lfiwfxv8vVxNTgbdG1ammr8H8anZ/LY7gdtbVTc7kohIuaARLxERkasoJ9/KyNlRRB07g5ebM8/f3JgvhrQrE0UXgIuzE/e2rQnAnI0xJqcRESk/VHiJiIhcRS//tJcdx1MJ8HJl8WNdGN4lHBfnsvXruF/bMJydLGyKTmZPXKrZcUREyoWy9ZNeRESkHPt8TTRzNsZgscC0fq2oHextdqQLqurvQe/m1QCY/PM+DMMwOZGIiONT4SUiInIVfLHuKC//vBeA8T0acm3DEJMTXdpTPRvi7uLE+iNJ/LY7wew4IiIOT801REREiskwDGatO0rUsTMEebnRJNSPW1uG4uP+v1+vZzJzeW3xvoINkh++ti4PO8DmxGFBXozsVpf3lh1k8i/76N4oBA9XZ7NjiYg4LBVeIiIixWAYBpN+2susdUcLHZ/88156Nq3KNeGV2J+QzoJtx0nJysNigUe712PsjQ2wWCzmhLbTQ93q8l1ULCdSzvLJyiOmt7sXEXFkFkMTt+2WlpaGv78/qamp+Pn5mR1HRERM8Nbv+/nvn4cBGNk1HCcnC7/vSeDI6czzzm1QxYcpfZrTplbQ1Y55xX7aEcej87bh4erEsieupXqAp9mRRETKlKLWBhrxEhERsdPRxEw+WnGu6Hrtzub0b3+u/fpTPRuy+egZVvx1is1Hkwnx9eCuNtXpWr9ymetcWFS3tKjG7A3H2BSdzGuL9/Hf/q3NjiQi4pBUeImIiNjp09VHsBnnNhv+p+gCsFgstKsTRLs6jjeydTEWi4WXbm3KLe+v5ped8QzrfIbWNQPNjiUi4nAc8+M3ERERk5xKz+a7vxtljOpW9ptklIQmoX7c3aYGAG8vOWByGhERx6TCS0RExA6z1h4lN99GRM2AcjWydTmPXlcfV2cLaw4lsv5wktlxREQcjgovERGRIkrPzmP2hmPAudEuR+lOWBLCgrzo1zYMgKlL/tKmyiIidlLhJSIiUkRzN8aQnp1P3cre3Ni4itlxrrrR3evj7uJE1LEzzN8ce8Fz0rPzyMm3XvZeCanZ7IlL5VjS+V0gRUTKIzXXEBERKYKcfCsz1kQDMLJbXZycKs5o1z+q+nswvkdDXl28j1d+3kvHusHUrORV8PzaQ4kMnbUZw4AWNfx57Pr6dG1Q+bz7vL/sIFOX/m+t2KhudXm6V8MKNYIoIhWPRrxERESKYNG2E5xKz6Gqnwd3tKpudhzTDO1ch3Z1gsjMtfLo19tIzMgB4NCpdEZ9tYWcfBu5VhtRx84wcvYWohMLj2htPJLE23+cK7qCfdwB+HjlYd7546CmL4pIuabCS0RE5DK2x6bw2uL9AAzrXAc3l4r769PZycLUe1ri6+7CjtgUbnp3NRMW7OK+zzaSnp1Pm1qBLB3blfZ1gjibZ+Xx+dvJs9oASMvOY9w3OzAM6BtZg6jnb2DiLU0AeG/ZQe75eD1rDiaqABORcsli6Keb3Yq6O7WIiDi+tYcSefDLKDJzrbQKC2DeiGvwdHM2O5bpDpxM55E5Wzl4KqPgWK1KXix4qCOVfNyJSzlLr2mrSMvOp3fzqgzqUJsXf9zD/oR0agZ5sXhMF3zcz614mLEmmjd+3U/u3wVam1qB3NqiGgdOZeDr4cLD19bD39PVlPcpInI5Ra0NVHgVgwovEZHyL99q4/3lh3h/+UFsBnSsW4nPBkbi7a7l0f84m2vl87XRpGTlElEzkK4NKhcUUwC/7orn4blb+fdfGsE+bswc3I7mNfwL3SshNZuPVx5m7qYYcvNthZ6rXcmLjwe0oVFV/c4VkbJHhVcpUuElIlK+JWbk8Ojcbaw/cm6/qrvb1GDyHc3wcNVIl722x6bw6i972Xz0DG1qBfLf/q2p6u9x0fNPpmXz2aojHDiVQcMqPizelcCJlLP4uLvw2+NdqBHoddFrRUTMoMKrFKnwEhEpv7bGnOHhr7aSkJaNt5szr/Vpzu0VuJlGSTAMg6NJWdQM8sLZzm6QZzJzGTxrMztiU7ihcRWmD4ospZQiIsVT1Nqg4q4OFhER+RfDMJi94Rj9PllPQlo2dSt788PoTiq6SoDFYqFOsLfdRRdAoLcb/7m7BS5OFv7Yd5Kle0+WQkIRkdKnwktERCo8m81g0k97eWHRbvKsBjc1q8oPoztTL8TX7GgC1K/iy4iu4QC89OMesnLzTU4kImI/FV4iIlKhWW0Gzy7cxax1RwF45qZGfHh/60JNIsR8j11Xn+oBnpxIOcu7yw6aHUdExG4qvEREpEL7aMUhvt4ci5MF/nNPS0Z1q4vFYv+UOCldnm7OvHx7UwBmrI7mr4R0kxOJiNhHhZeIiFRYcSln+eDPQwC8dmdz7m5Tw+REcinXN65CjyZVyLcZPL9olzZaFhGHosJLREQqrCm/7ic7z0bb2oH0axtmdhwpghdva4qnqzObj57h190JZscRESkyFV4iIlIhRR1N5qcdcVgs8OKtTTW90EFUD/Dkwb8bbbz+635y8q0mJxIRKRoVXiIiUiFNXXIAgH6RYTSr7m9yGrHHg13DqezrTkxyFrPXHzM7johIkajwEhGRCmdTdDLrjyTh6mzh0evrmx1H7OTt7sL4Hg0A+HDFYfKsNpMTiYhcngovERGpcN5ddm606+42YVQP8DQ5jRTHXa1rEOzjRnJmLmsPJZodR0Tkshy68Fq1ahW33noroaGhWCwWFi1aVORr165di4uLC61atSq1fCIiUvasP5zE2kNJuDhZePjaumbHkWJycXaid/NqAPy0I97kNCIil+fQhVdmZiYtW7bkgw8+sOu61NRUBg4cyPXXX19KyUREpCzKys3nmQU7AejXNoywIC+TE8mVuLVlKABL9iSQnacmGyJStrmYHeBK3HTTTdx00012Xzdy5Ej69++Ps7OzXaNkIiLi2N76/S+OJWVRzd+Dp29qZHYcuUJtagZSzd+D+NRsVh44Tc+mVc2OJCJyUQ494lUcM2fO5PDhw7z44otFviYnJ4e0tLRCDxERcSzrDiUya91RAF6/qwV+Hq7mBpIr5uRk4ZYW56Yb/rgjzuQ0IiKXVqEKr4MHD/LMM88wZ84cXFyKPtg3ZcoU/P39Cx5hYdpkU0TEkZxKz+axr7djGHBfuzC6NahsdiQpIbe3qg6cm254IuWsyWlERC6uwhReVquV/v37M2nSJBo0aGDXtRMmTCA1NbXgERsbW0opRUSkpOVbbYyZt53EjBwaVvFl4i1NzY4kJahZdX86hFciz2rw6crDZscREbkoh17jZY/09HSioqLYtm0bo0ePBsBms2EYBi4uLixZsoTrrrvugte6u7vj7u5+NeOKiEgJMAyDiT/uYf2RJLzcnPnv/a3xdHM2O5aUsEevq8f6I0nM2xzLI9fVI8TXw+xIIiLnqTAjXn5+fuzatYvt27cXPEaNGkXDhg3Zvn077du3NzuiiIiUsA9XHGbuxhgsFni7byvqhfiYHUlKQYe6lYioGUBuvo3pq6PNjiMickEOPeKVkZHBoUOHCr6Ojo5m+/btBAUFUbNmTSZMmMCJEyf48ssvcXJyolmzZoWuDwkJwcPD47zjIiLi2HLyrUz+eR+zNxwD4KVbm9KrmTrelVcWi4XR3esx7Isovt4Uw9gbGmhkU0TKHIce8YqKiiIiIoKIiAgAxo0bR0REBBMnTgQgPj6emJgYMyOKiMhVtu5wInf+d11B0TW+RwMGdaxtbigpdd0bhlAj0JO07Hx+2aUNlUWk7LEYhmGYHcLRpKWl4e/vT2pqKn5+fmbHERERICE1m2cX7mL5/lMABHi58k6/VnRvGGJyMrla/vvnId76/S9a1wxgwcOdzI4jIhVEUWsDhx7xEhERAfhtdwI9p61i+f5TuDpbGNShFkvHdlPRVcHcE1kDFycLW2NS2J+gPTdFpGxR4SUiIg7t551xPDRnC6ln82he3Z9fx3Rh0u3NqOyrbrQVTYivBzc2qQLAnA1aaiAiZYsKLxERcVh//nWKsfPPbYzcN7IG3z/UkXohvmbHEhMNuKYWAPOjYklIzTY5jYjI/6jwEhERh3TgZDqPzNlKntXglhbVmNKnBW4u+rVW0XWoW4l2tYPIzbfxwZ8HzY4jIlJAv6FERMThpJ7NY+TsLWTlWukQXom3+7bC2clidiwpAywWC0/0aADA/M2xxCZnmZxIROQcFV4iIuJQbDaDsfO3E52YSfUATz7oH6GRLimkfXglutQPJs9q8M7SA2bHEREBVHiJiIiDmbbsIMv3n8LdxYlPBrShko+aaMj5xvdoCMCCbSfYEZtibhgREVR4iYiIA/l9TwLvLTu3bmdKn+Y0q+5vciIpq1qGBdAnojoAL/+8F21bKiJmU+ElIiIOYfXB0zw6bxsAgzvWpk/rGiYnkrLuqV6N8HR1ZsuxM/y4I87sOCJSwanwEhGRMm/94SSGfxFFbr6NG5tU4bmbG5sdSRxAVX8PHr62LgCTftpLYkaOyYlEpCJT4SUiImVaTFIWo77aQk6+jesahfBB/whcnfXrS4rmwW7hNKrqS3JmLhN/2G12HBGpwPSbS0REyqys3HwenB1F6tk8WoYF8OH9rXF3cTY7ljgQdxdn/nNPS5ydLCzelcDcjTHAue6YyZm5JqcTkYrExewAIiIiF2IYBk9+t5P9CekE+7jzyQNt8HBV0SX2a1bdn0evq8e0Pw7y7MJdHEvKZNXBRPbFp9GzaRWe7NmQeiG+ZscUkXJOI14iIlImfbrqCL/sjMfFycJHD7Smqr+H2ZHEgY25vj5DOtUG4JNVR9gXnwbA73tOctO7q9lyLNnEdCJSEajwEhGRMueXnfG88dt+AF68tQltaweZnEgcncViYeItTRh7QwMCvVwZ2qkO3z/UgWvCg8izGry2eL9azotIqbIY+iljt7S0NPz9/UlNTcXPz8/sOCIi5UZ2npXXf93PrHVHAegbWYM37mqBxWIxN5iUW6fSsun61p9k59n4fHAk1zWqYnYkEXEwRa0NNOIlIiKmMwyD3/ck0OOdVQVF14Ndw3n1zuYquqRUhfh5MKhjbQDe+v0ANps+jxaR0lGkwistLa20c4iISAWVZ7Xx7MLdjJy9hZjkLKr4uTNjUCTP9m6stvFyVYzqWhdfdxf2xacVFP4iIiWtSL/RAgMDOXXqFADXXXcdKSkppZlJREQqiPTsPIbO2sy8TTFYLPDQtXVZ/sS1XN9Y073k6gn0duPJXg0BeP3X/ew+kWpyIhEpj4pUePn4+JCUlATAihUryMvLK9VQIiJS/p3JzOX+6RtZfTARLzdnPhsQydO9GuHtrp1O5OobcE0tejSpQq7Vxui5W8nIyTc7koiUM0X67XbDDTfQvXt3GjduDMCdd96Jm5vbBc9dvnx5yaUTEZFy6XR6Dg9M38hfJ9MJ8nbjiyHtaF7D3+xYUoFZLBbevLsFe95bw9GkLJ5fuIt3+rXSGkMRKTFFKry++uorvvjiCw4fPszKlStp2rQpXl5epZ1NRETKoaSMHO6fvoEDJzMI8XVnzvD21K+izWvFfAFebrx7byv6fbqBRdvj6FQvmHsiw8yOJSLlRJHayaelpRW0RuzevTsLFy4kICCgtLOVWWonLyJSPMmZufT/bAP7E9Kp4ufO1w92oE6wt9mxRAr575+HeOv3v/B0dWbFk9dSxU+bd4vIxZVoO/l/N9fQkLuIiBRHSlYuD0zfyP6EdCr7ujN3xDUquqRMeqhbXSJqBnA2z8rMtUfNjiMi5YTdzTVWrlyp5hoiImKXpIwcBszYxN74NIJ93Jg3oj11K/uYHUvkgpycLDxybT0A5mw4Rnq2/u4RkStnd3MNwzDUXENERIrsyOkMBs/cTExyFpW83Zg74hrqhWhNl5Rt1zUKoV6ID4dOZTBvUwwPdq1rdiQRcXBqriEiIqUmITWbuz9eT3JmLmFBnswc3I56IRrpkrLPycnCg13Deeq7ncxYE80D19TCy01bHYhI8RWpuca/qbmGmmuIiBSFYRgMnbWZP/86TeNqfswe1o5gH3ezY4kUWU6+le5vrSAuNZshnWrz4q1NzY4kImVQiTbX+Lc///yzoOgyDAM76zYREakgvt96gj//Oo2bixPv39dKRZc4HHcXZ17t0xyAWeuOsik62eREIuLI7C68AL788kuaN2+Op6cnnp6etGjRgtmzZ5d0NhERcVAJqdlM+mkPAGNvaKA1XeKwujcMoW9kDQwDnvxuBxk5+WZHEhEHZXfh9fbbb/PQQw/Ru3dvvvnmG+bPn0+vXr0YNWoU77zzTmlkFBERB2IYBs8u3EV6dj4ta/gzoksdsyOJXJHnbm5CqL8Hx5KyeG7hLs32EZFisXuNV506dZg0aRIDBw4sdPyLL77gpZdeIjo6ukQDlkVa4yUicnELth5n3Dc7cHN24ufHOtOgika7xPFFHU2m36cbsNoM3rirOf3a1jQ7koiUEaW2xis+Pp6OHTued7xjx47Ex8fbezsRESlHkjJymPTTXgDG3FBfRZeUG5G1g3iiRwMAJv+8j+w8q8mJRMTR2F141atXj2+++ea84/Pnz6d+/folEkpERBzTG7/tJ/VsHo2r+fFg13Cz44iUqFFd6xLq70F6Tj4rD5w2O46IOBi7N6SYNGkS/fr1Y9WqVXTq1AmLxcKaNWtYtmzZBQsyERGpGLYcS+abqOMATL6jGa7OxerfJFJmOTlZ6N28GtPXRPPzznh6Nq1qdiQRcSB2/1a866672LhxI8HBwSxatIgFCxYQHBzMpk2buPPOO0sjo4iIlHGGYfDij+e6GPaNrEGbWoEmJxIpHbe0DAVg2b6TnM3VdEMRKbpibcHepk0bvvrqq5LOIiIiDmr94SR2n0jDy82Zp3s1MjuOSKlpWcOfGoGeHD9zlj//OkXv5tXMjiQiDqJYhZfNZuPQoUOcOnUKm81W6LmuXbuWSDAREXEcn689CsBdrWtQSRslSzlmsVi4uUU1Pll5hJ93xqnwEpEis7vw2rBhA/379+fYsWPn7WNhsViwWjXsLiJSkRxLymTZ/pMADO5U29wwIlfBrS1C+WTlEf7Ye4pTadmE+HmYHUlEHIDda7xGjRpFZGQku3fvJjk5mTNnzhQ8kpOTSyOjiIiUYV+sO4ZhQLcGlalb2cfsOCKlrll1fyJrBZJrtTFz3VGz44iIg7C78Dp48CCvvfYajRs3JiAgAH9//0IPERGpODJy8vk2KhaAIRrtkgpkZLe6AHy14Rjp2XkmpxERR2B34dW+fXsOHTpUGllERMTBfL/lOOk5+YRX9qZr/cpmxxG5aq5vFELdyt6kZ+fz9aZYs+OIiAOwe43Xo48+yhNPPEFCQgLNmzfH1dW10PMtWrQosXAiIlJ22WwGs/6eZjWkY22cnCzmBhK5ipycLIzsWpenvt/JzLXRDO1cB2d9D4jIJdhdeN11110ADB06tOCYxWLBMAw11xARqUBWHjhNdGImvh4u9Gldw+w4Ilfdba1CmfLrPuJSs1m+/xQ3NqlidiQRKcPsLryio6NLI4eIiDiYGWvO/T7oFxmGt3uxdicRcWgers70jQzjk1VH+GrDMRVeInJJdv+mrFWrVmnkEBERB7LlWDJrDiXi4mRhUMfaZscRMU3/9jX5ZNURVh08zbGkTGpV8jY7koiUUXYXXl9++eUlnx84cGCxw4iIiGN4Z+lBAO5uU4OwIC+T04iYp1Ylb7o1qMzKA6eZuzGGCb0bmx1JRMoouwuvMWPGFPo6Ly+PrKws3Nzc8PLyUuElIlLObT76v9GuR7rXMzuOiOnub1+TlQdO8/3WEzzZsyEuznY3jRaRCsDunwz/3jD5zJkzZGRk8Ndff9G5c2fmzZtXGhlFRKSMMAyDqUv+AuCeyDCNdokA3RuFEOTtRmJGDqsPJZodR0TKqBL5SKZ+/fq8/vrr542GiYhI+bLywGk2HEnGzdmJ0ddptEsEwNXZidtahgKwcOsJk9OISFlVYmPhzs7OxMXFldTtRESkjLHZDF7/dT8AgzrWonqAp8mJRMqOOyOqA/D7ngTSs/NMTiMiZZHda7x+/PHHQl8bhkF8fDwffPABnTp1KrFgIiJStvyw4wT7E9Lx9XDh4Ws12iXyby1q+FO3sjeHT2fy6+4E+kaGmR1JRMoYuwuvO+64o9DXFouFypUrc9111zF16tQLXvPee+/ZHWzIkCH4+vrafZ2IiJS8jJz8gtGuh66tS6C3m8mJRMoWi8VCn9Y1eOv3v/gu6rgKLxE5j92Fl81ms/tFHn/8cWrUqIGzs3ORzo+NjeWWW25R4SUiUka8v+wgJ9NyqBnkxdBOdcyOI1Im3dW6Bm8vPcCmo8kcPJlO/Sr6O0ZE/seuwisvL4+GDRvy888/06RJE7teKCoqipCQkCKdq4JLRKTsOHgynRlrogF46bYmeLgW7UM0kYqmqr8H1zUKYenek8zbFMvEW+37W0lEyje7mmu4urqSk5ODxWKx60VefPFFfHx8inz+s88+S1BQkF2vISIiJS87z8rj87eTbzO4oXEVrmtUxexIImVa//Y1Afh+63Gy86wmpxGRssTuroaPPvoob7zxBvn5+UW+5sUXX8TLq+h7vUyYMIGAgAB7o4mISAkyDINnF+xiT1waQd5uvHx7U7MjiZR5XetXpnqAJ6ln81i8K97sOCJShti9xmvjxo0sW7aMJUuW0Lx5c7y9vQs9v2DBghILJyIi5vls9REWbDuBs5OFD/pHEKr28SKX5exk4b52YfxnyQHmboyhT+saZkcSkTLC7hGvgIAA7rrrLnr27EloaCj+/v6FHsW1b98+wsPDi329iIiUnG+jYnlt8bkuhs/2bkzHusEmJxJxHH0jw3BxshB17AwHTqabHUdEygi7R7xmzpxZGjnIzc3l2LFjpXJvEREpuiV7EnhmwS4AHuwaztBOtc0NJOJgQvw8uKFxFX7bk8DcjTG8dJum6YpIMQqv4ho3btwlnz99+vRVSiIiIhez/nASo+dtw2ozuKdNDSbc1Mjuhkoicq7Jxm97Evh+63Ge7tUITzd1AxWp6K5a4fXuu+/SqlUr/Pz8Lvh8RkbG1YoiIiIXsOt4KiO+jCI330aPJlWY0qe5ii6RYupcL5iwIE9ik8/y88447tGGyiIV3lUrvOrXr8/YsWN54IEHLvj89u3badOmzdWKIyIi/3L4dAaDZm4iIyefDuGVeO++CFyc7V4GLCJ/c3KycG/bmrz1+1/M3RSjwktE7G+uUVxt2rRhy5YtF33eYrFgGMbViiMiUuJy822cTMs2O4bd4lLOMmD6RpIzc2le3Z9PB7bRJskiJeCeyBq4OFnYFpPCvvg0s+OIiMmuWuE1depUHn/88Ys+37JlS2w229WKIyJSouJTz3LTu6voMGUZ0YmZZscpsuTMXAbM2Ehcajbhwd7MGtIWXw9Xs2OJlAshvh70aHpu0/G5G2NMTiMiZivSVMP33nuvyDd87LHHLni8atWqRb6HiIgj2RefxvAvojiRchaAgyfTqRPsfZmrzJeWnceQmZs4fDqTav4ezB7enko+7mbHEilX+rerxeJdCSzadoIJvRvh5XbVVnmISBlTpO/+d955p9DXp0+fJisri4CAAABSUlLw8vIiJCTkooXXhTz88MO8/PLLBAcXb3+YVatW8dZbb7Flyxbi4+NZuHAhd9xxx0XPX7BgAR999BHbt28nJyeHpk2b8tJLL9GzZ89ivb6IVFyn0rL5fU8Ci7bHseXYmULPpZ7NMylV0Z1Kz2bQ55vZF59GoJcrs4e1o7o2SBYpcR3rVqJWJS+OJWXx4/Y47m1X0+xIImKSIk01jI6OLni8+uqrtGrVin379pGcnExycjL79u2jdevWvPLKK3a9+FdffUVaWvHnPGdmZtKyZUs++OCDIp2/atUqbrzxRhYvXsyWLVvo3r07t956K9u2bSt2BhGpWI6fyeKxedtoP2UZL/ywhy3HzuDsZKFn0yp0rFsJgLTsfJNTXtruE6nc9dE69sWnEezjzlfD21MvxNfsWCLlkpOThfvbnyu2vlx/TOvZRSowi2HnT4C6devy3XffERERUej4li1buPvuu4mOji7yvXx9fdmxYwfh4eH2RLggi8Vy2RGvC2natCn9+vVj4sSJRb4mLS0Nf39/UlNTL9oeX0TKF8Mw+GLdUV77dT+5+efWo0bUDKB3s2rc3iqUED8Pnl+0i682xDDm+vqMvbGByYnPZxgGM9ce5fVf95NrtVEzyIvZw9pRq1LZnxYp4shSsnJp/9oycvJtfDeqA5G1g8yOJCIlqKi1gd0TjePj48nLO38ajdVq5eTJk/bezlQ2m4309HSCgi79AzAnJ4ecnJyCr69klE5EHE9uvo0Xf9zNvE2xwLmpQ8/2bkyz6v6FzvP7uylFWrZ5Uw0zc/LJzrMS4OWGs9P/9uBKzszlyW93sGz/KQBubFKFN+9qQaC3m1lRRSqMAC837mhVnflRscxad1SFl0gFZXfhdf311zNixAhmzJhBmzZtsFgsREVFMXLkSG644Qa77pWenm7vy5eoqVOnkpmZSd++fS953pQpU5g0adJVSiUiZUm+1caj87by+56TWCzw7E2NGd6lzgU3Fvbz/LvwOnv1phrm5ttYtu8k3289zuajZwrWl1ksEOjlRpC3G7n5NuJSzpJvM3BzceL5mxsz4Jpa2hxZ5Coa2LEW86Ni+W13AifTsqni52F2JBG5yuxuJ//5559TvXp12rVrh4eHB+7u7rRv355q1aoxffr0y16/detWdu3aVfD1Dz/8wB133MGzzz5Lbm6uvXGKbd68ebz00kvMnz+fkJCQS547YcIEUlNTCx6xsbFXKaWImMlqM3ji2x38vuckbs5OfDYgkhFdwy9asFzNES/DMFi69yQ3vrOSh+Zs5Y99pwo19TCMc6Nch05lEJOcRb7NoEEVHxY93ImBHWqr6BK5ypqG+tO2diD5NoPP1xZ9WYaIlB92j3hVrlyZxYsXc+DAAfbv349hGDRu3JgGDYq2nmHkyJE888wzNG/enCNHjnDvvfdy55138u2335KVlcW0adPsjWS3+fPnM2zYML799tsijdK5u7vj7q4WyyIVzZTF+/hhexwuThY+vL81NzSpcsnz/TzP/UhNK+WuhunZeTz9/U4W70oAINjHjbvbhHFz82rUCvbC09WZM1m5JGfmkpyRi7OThZqVvKji64GTkwouEbOM6laXzUej+HLdMUZ0CSdY2zeIVCjF3kyiQYMGRS62/u3AgQO0atUKgG+//ZauXbsyd+5c1q5dy7333lvqhde8efMYOnQo8+bN4+abby7V1xIRx/XN5limrzn3qfTUvi0vW3TBv0e8Sm+q4f6ENB76aivRiZm4OlsY1jmc0dfVw8e98I/zEF8PQnw1lUmkLLmuUQgtaviz83gqn606woTejc2OJCJXUZEKr3HjxvHKK6/g7e3NuHHjLnnu22+/fcnnDcPAZjvXEeyPP/7glltuASAsLIzExMSixCmQkZHBoUOHCr6Ojo5m+/btBAUFUbNmTSZMmMCJEyf48ssvgXNF18CBA3n33Xe55pprSEg492mxp6cn/v7+F3wNEal4Nh9N5rlF56ZEj7m+Pre3ql6k6/63xqt0RrwWbD3Oswt3kZ1nI9Tfg//e35qImoGl8loiUvIsFguP31CfobOi+HL9MUZ01aiXSEVSpMJr27ZtBZ0ML7XnVVHWDERGRjJ58mRuuOEGVq5cyUcffQScK5qqVLn8J8r/FhUVRffu3Qu+/qcoHDRoELNmzSI+Pp6YmJiC5z/55BPy8/N55JFHeOSRRwqO/3O+iEhschYjZ28hz2rQu3lVxlxfv8jX+nn8PdWwhNd45eRbefmnvczZeO7nWZf6wbx7bwRB6kgo4nC6NwyhZVgAO2JT+GTlYZ67uYnZkUTkKrF7H68rtXPnTu6//35iYmIYN24cL774IgCPPvooSUlJzJ0792rGKRbt4yVSPmXm5HPXR+vYn5BO01A/vh3VAS+3os/ITszIIXLyH1gscPjV3iWyniohNZuRs6PYcTwViwUeu64+j11fv1CreBFxLH/+dYohMzfj4erEqqe6a1qwiIMrtX28rlSLFi0KdTX8x1tvvYWzs/PVjiMiUmDKr/vYn5BOZV93pg+KtKvoAvD9e8TLMCA9Jx//v6ceFldiRg79p2/gyOlMArxcmdavFdc2vHQXVhEp+65tUJlWYQFsj03hk5VHeOEWjXqJVAR2F17du3e/5JTC5cuXFyuIh4c+7RER86w+eJqvNpybyvdO31ZU8/e0+x7uLs54uDqRnWcj7WzeFRVeqVl5DJixiSOnM6ke4MnXD15DWJBXse8nImXHP2u9Bs/czFcbjjGyazgh2tdLpNyzex+vVq1a0bJly4JHkyZNyM3NZevWrTRv3rw0MoqIlKq07Dye+m4nAAM71KJz/eBi36sk9vLKzMlnyKxN7ItPI9jHna+Gt1fRJVLOdGtQmdY1A8jJt/HOHwfNjiMiV4HdI17vvPPOBY+/9NJLZGRkXHEgEZGr7eWf9hKfmk2tSl48c1OjK7qXn6crp9JzSDtbvJby2XlWHpwdxdaYFPw9XflqeDvqBHtfUSYRKXssFgsTejfmno/XM39zDIM71qZhVV+zY4lIKbJ7xOtiHnjgAT7//POSup2IyFWxbN9JvttyHIsFpt7T0u51Xf/flXQ2zM6zMuLLKNYeSsLbzZkvhrajUVU18BEpr9rWDqJX06rYjHNrTEWkfCuxwmv9+vVapyUiDiU1K49nFpxr9jOiSziRtYOu+J7F3csrKzef4V9EsfpgIl5uzswY3JZWYQFXnEdEyrZnbmqEq7OFFX+dZvaGY2bHEZFSZPdHu3369Cn0tWEYxMfHExUVxQsvvHDZ6y+2AbPFYsHDw4N69epx++23ExR05X8AiYhcypRf93E6PYe6lb0Zd2ODErnnP2u8Uu0ovBIzchg2azM7jqfi7ebMzCHtaFdHPwNFKoLawd6Mub4+/1lygIk/7CbQy5VbWoSaHUtESoHdhZe/v3+hr52cnGjYsCEvv/wyPXr0uOz127ZtY+vWrVitVho2bIhhGBw8eBBnZ2caNWrEhx9+yBNPPMGaNWto0kTtVUWkdGyKTubrzbEAvH5XCzxcS2Y7Cz/Pf6YaFm2N19HETAbN3MSxpCwCvVyZMbgtrWsGlkgWEXEMj3Svx8m0HGZvOMbY+dtxc3aiR9OqZscSkRJmd+E1c+bMK3rBf0azZs6cWbDBWFpaGsOGDaNz586MGDGC/v37M3bsWH7//fcrei0RkQvJybcyYcG5Lob3tQujbQlMMfxHQVfDIox4bY9NYdiszSRl5hIW5MkXQ9oRXtmnxLKIiGOwWCy8dFtTUs/m8eOOOB6Zu5UP+remp4ovkXKlxNZ4FdVbb73FK6+8UmhXZz8/P1566SXefPNNvLy8mDhxIlu2bLna0USkgvh4xREOn84k2MedZ3o1LtF7F6zxukRzDcMwmL3+KPd+up6kzFyaV/dnwUOdVHSJVGDOThbe7tuS21qGkmc1eHTuNo4mZpodS0RKkN0jXoGBgZfcQPnfkpOTzzuWmprKqVOnzptGePr0adLS0gAICAggNzfX3mgiIpd1+HQG//3zEAATb22Cv1fxNzm+kP+NeF14qmFqVh5jv9nO8v2nALiuUQjv3xeBt/uVdVMUEcfn4uzE231bkpSZw9pDSby37CBv92tldiwRKSF2/6Z/4YUXmDx5Mj179qRDhw7AuY6Gv//+Oy+88MJlm2LcfvvtDB06lKlTp9K2bVssFgubNm1i/Pjx3HHHHQBs2rSJBg1KZqG7iMg/DMPguYW7yLXa6NagMre2qFbir/G/NV7nj3gdTcxk6BebOXI6EzcXJybc1IhBHWrj5FS0D7NEpPxzcXbimV6NufWDNSzcfoKHrq1L/Sra30ukPLC78Fq7di0vv/wyo0ePLjj22GOP8cEHH/DHH3+waNGiS17/ySefMHbsWO69917y8899Iuzi4sKgQYN4++23AWjUqBHTp0+3N5qIyCX9uCOODUeS8XB1YvIdzYo8em+Pi63xOnQqnb6fbCA5M5dQfw8+GxRJ01D/C91CRCq45jX86dm0Cr/vOcm0Pw7y3/tbmx1JREqAxTAMw54LfHx82L59O/Xq1St0/ODBg0RERJCRkVGk+2RkZHDkyBEMw6Bu3br4+DjO2oa0tDT8/f1JTU0ttFZNRMqurNx8rvvPShLSsnnixgY8en39Unmd7bEp3PHftVQP8GTtM9cBEJucxT0frychLZtm1f34fFBbQvy076GIXNz+hDRuenc1hgFLxnalgUa9RMqsotYGdjfXqFSpEgsXLjzv+KJFi6hUqdJlr1+2bBlwroBr0aIFLVu2LCi6PvjgA3vjiIgUyYd/HiYhLZuwIE9GdA0vtdfx8/h7quHfI16HT2dw//SNJKRlUz/Eh9lD26voEpHLalTVj55NznU1/HjlYZPTiEhJsHuq4aRJkxg2bBgrVqwoWOO1YcMGfvvttyJND7zrrrtYunQpbdu2LXR82rRpTJw4sdAURhGRknAsKZNPVx0B4Pmbm5TYnl0X4v93V8P0nHzWHEzk4TlbSMvOJyzIk9nD2hPo7VZqry0i5ctD19bltz0J/Lg9jnE3NqBGoJfZkUTkCtg94jV48GDWrVtHQEAACxYs4Pvvv8ff35+1a9cyePDgy17/zjvv0Lt3b/bu3Vtw7D//+Q8vvvgiv/zyi71xREQua/Iv+8i12uhSP5geTaqU6mv5evyvS+KQWZtIy86ndc0AFj7ciar+GukSkaJrGRZAp3qVyLcZfPb3h0ci4riK1b+4ffv2zJkzp1gvOGTIEJKSkujRowdr1qxh/vz5vPbaa/z666907NixWPcUEbmYlQdOs3TvSVycLLx4a5NSaajxb24uTni6OnM2z0qe1eCGxiF80L91qY6yiUj59fC19Vh7KImvN8fy0LX19AGOiAMrUuGVlpZWsFDsn722LqYozSbGjx9PUlISkZGRWK1WlixZQvv27YsSRUSkyHLzbUz6aQ8AgzrWpl7I1VmcHujlytlUK53qVVLRJSJXpGPdSrStHcjmo2d4d9lBpvRpbnYkESmmIhVegYGBxMfHExISQkBAwAU/MTYMA4vFgtVqPe+5995777xj1apVw8vLi65du7Jx40Y2btwInGtNLyJSEr5cf5QjpzMJ9nFjzA2l08XwQp6+qRHbYlJ4smdDFV0ickUsFgtP9WrEPR+v55uoWIZ3qUPdyo7TCVpE/qdI7eRXrlxJp06dcHFxYeXKlZc8t1u3bucdq1OnTtHCWCwcOVL25zCrnbxI2XcqPZvr/rOSjJx83ryrBX3bhpkdSUSk2IbN2syy/ae4qVlVPnqgjdlxRORfilobFGnE69/F1IUKq8uJjo62+xoRkSvx1m9/kZGTT4sa/tzdpobZcURErsiTvRry51+n+HV3Akv3nuTGUm4UJCIlr0iF186dO4t8wxYtWhQ7jIhISdifkMZ3W48D8NJtTXFyKt2GGiIipa1RVT9GdAnnk1VHeHbhLtrWDiTAS9tTiDiSIhVerVq1wmKxcLlZiRdb4zVu3DheeeUVvL29ixRqwoQJPPnkkwQFBRXpfBGRf3vzt78wDOjdvCqtawaaHUdEpESMvbEBf+w7yeHTmbz8017e7tfK7EgiYocirfE6duxYkW9Yq1at8445OzuTkJBA5cqVi3QPPz8/tm/fTnh4eJFf92rSGi+RsmtTdDJ9P1mPs5OFpWO7Eq5F6CJSjmyNOcPdH63DZsD0gZHcoCmHIqYr0TVeFyqm7GEYBg0aNCjy/jmZmZlX9HoiUnH95/e/AOjXNkxFl4iUO61rBjK8SzifFkw5DMLfy/XyF4qI6Yq1gbK9Zs6cafc1VaroExwRsU/U0WQ2HU3G1dnCY9ddvfbxIiJX07i/pxweOZ3JpJ/38HbfVmZHEpEiuCqF16BBg67Gy4hIBffxysMA9ImoQVV/D5PTiIiUDg9XZ966uyV3f7yOBVtPcHPzalzfWB9Yi5R1TmYHEBEpCQdOpvPHvlNYLPBgt7K5PlREpKS0qRXI8M7n9kl9duEuUrPyTE4kIpejwktEyoUP/zwEQI8mVairtV0iUgE80aMh4cHenEzL4ZVf9podR0QuQ4WXiDi8ncdTWLQ9DoBHutczOY2IyNXh4erMW/e0wGKB77YcZ/n+k2ZHEpFLKNIar8DAwCJ3JExOTr6iQCIi9jAMg5d/OvdJb5+I6rSoEWBuIBGRq6hNrSCGdarD9DXRTFiwiyVjg/D3VJdDkbKoSIXXtGnTCv45KSmJyZMn07NnTzp06ADA+vXr+f3333nhhRdKJaSIyMX8vDOeqGNn8HB14sleDc2OIyJy1Y3v2ZDl+09xJDGTyT/v5a17WpodSUQuoEgbKP/bXXfdRffu3Rk9enSh4x988AF//PEHixYtuuT1mZmZvP766yxbtoxTp05hs9kKPX/kyBF74phCGyiLlA3xqWfp/e5qzmTlMeb6+oy9sYHZkURETBF1NJl7PlmPYcDMwW3p3ijE7EgiFUZRawO7Cy8fHx+2b99OvXqF11EcPHiQiIgIMjIyLnn9fffdx8qVKxkwYADVqlU7bwrjmDFj7IljChVeIuaz2gzu+2wDm6KTaRrqx4KHO+Lu4mx2LBER07zy815mrImmqp8Hv4/tqimHIldJUWsDu/fxqlSpEgsXLuTJJ58sdHzRokVUqlTpstf/+uuv/PLLL3Tq1MnelxYRKfD20r/YFJ2Mt5szH/RvraJLRCq88T3OTTmMTszk1V/28ubdmnIoUpbYXXhNmjSJYcOGsWLFioI1Xhs2bOC3335j+vTpl70+MDCQoKAg+5OKiPzth+0n+O+f5zZLfq1Pc+oEe5ucSETEfJ5uzrx5dwvu+Xg930Qd5/ZW1elUL9jsWCLyN7vbyQ8ePJh169YREBDAggUL+P777/H392ft2rUMHjz4ste/8sorTJw4kaysrOLkFZEKblvMGZ78bicAo7rV5fZW1U1OJCJSdrStHcTADrUAmLBgF1m5+SYnEpF/2L3G60pFRERw+PBhDMOgdu3auLoWnn+8devWqxmnWLTGS8QccSlnue2DtSRm5HBD4yp8OqANTk5F2+pCRKSiyMjJp8fbK4lLzWbANbV45Y5mZkcSKddKbY0XwOHDh5k5cyZHjhxh2rRphISE8NtvvxEWFkbTpk0vee0dd9xRnJcUkQouMyefYV9EkZiRQ6Oqvky7t5WKLhGRC/Bxd+H1u1ow8PNNzN5wjE71gunVrKrZsUQqPLtHvFauXMlNN91Ep06dWLVqFfv27SM8PJw333yTTZs28d1335VW1jJDI14iV5fNZjDqqy0s2XuSYB83Fj3SiRqBXmbHEhEp06Ys3scnq47g5+HC4jFd9HNTpJQUtTawe43XM888w+TJk1m6dClubm4Fx7t378769euLl1ZE5BKmLv2LJXtP4ubsxCcDIvXHg4hIETzRoyEtwwJIy85nzNfbybfaLn+RiJQauwuvXbt2ceedd553vHLlyiQlJV32eqvVyn/+8x/atWtH1apVCQoKKvQQEfm3hduOF3QwfP2u5rSpFWhyIhERx+Dm4sT790bg6+7ClmNnmPbHQbMjiVRodhdeAQEBxMfHn3d827ZtVK9++e5ikyZN4u2336Zv376kpqYybtw4+vTpg5OTEy+99JK9cUSkHNty7AxPf78LgIeurUuf1jVMTiQi4lhqVvLi9btaAPDfFYdYeyjR5EQiFZfdhVf//v15+umnSUhIwGKxYLPZWLt2LePHj2fgwIGXvX7OnDl89tlnjB8/HhcXF+677z6mT5/OxIkT2bBhQ7HehIiUP7HJWYycHUVuvo0eTarwZI+GZkcSEXFIN7eoxn3tamIY8Pj87SRm5JgdSaRCsrvwevXVV6lZsybVq1cnIyODJk2a0LVrVzp27Mjzzz9/2esTEhJo3rw5AD4+PqSmpgJwyy238Msvv9gbR0TKoRMpZ7n30w0kZuTSuJof7/RTB0MRkSsx8ZYmNKjiw+n0HMZ9swOb7aruJiQiFKPwcnV1Zc6cORw8eJBvvvmGr776iv379zN79mycnZ0ve32NGjUKpirWq1ePJUuWALB582bc3d3tjSMi5Uxcyln6f7aBEylnqRPszRdD2uLtXqydL0RE5G+ebs580L81Hq5OrDpwmi/WHzU7kkiFY3fh9fLLL5OVlUV4eDh33303ffv2pX79+pw9e5aXX375stffeeedLFu2DIAxY8bwwgsvUL9+fQYOHMjQoUPtfwciUm4cTczkno/Xcywpi7AgT+aOaE+In4fZsUREyoUGVXx5rndjAN787S+OJWWanEikYrF7Hy9nZ2fi4+MJCQkpdDwpKYmQkBCsVqtdATZs2MC6deuoV68et912m13XmkX7eImUvOjETPp9sp5T6TnUCfZmzvD2hAZ4mh1LRKRcsdkM+k/fwIYjyVwTHsTc4ddoKrfIFSpqbWD3/B3DMLBYzv8G3bFjR7HawV9zzTVcc801dl8nIuXH8TNZ3P/ZBk6l59Cwii+zh7cjxFcjXSIiJc3JycKbd7Wk57RVbDiSzDdRsdzbrqbZsUQqhCJPNQwMDCQoKAiLxUKDBg0K7b3l7+/PjTfeSN++fYt0r9mzZ9OpUydCQ0M5duwYANOmTeOHH34o3rsQEYeVkpXLA9M3EpeaTXhlb+aMaK+iS0SkFNWs5MUTPRoA8Ppv+0nOzDU5kUjFUOQRr2nTpmEYBkOHDmXSpEn4+/sXPOfm5kbt2rXp0KHDZe/z0UcfMXHiRB5//HFeffXVgqmJAQEBTJs2jdtvv70Yb0NEHJHNZvDENzs4mpRFjUBP5g6/hmAfNdkRESltgzvW5vutJ9gXn8aUxft4656WZkcSKffsXuO1cuVKOnbsiKura7FesEmTJrz22mvccccd+Pr6smPHDsLDw9m9ezfXXnstiYllf2M/rfESKRkfrjjEm7/9hZuLEwsf7kjTUP/LXyQiIiVia8wZ7vpoHYYBXw1rT+f6wWZHEnFIRa0N7O5q2K1bt4Ki6+zZs6SlpRV6XE50dDQRERHnHXd3dyczU911RCqK3SdSmbrkAACv3N5URZeIyFXWumYgA66pBcD4b3eQkqUphyKlye7CKysri9GjRxMSEoKPjw+BgYGFHpdTp04dtm/fft7xX3/9lSZNmtgbR0QcUL7VxoQFu7DaDG5uXo2+kWFmRxIRqZCeuakR4cHeJKRl88IPe8yOI1Ku2V14PfnkkyxfvpwPP/wQd3d3pk+fzqRJkwgNDeXLL78s0vWPPPII8+fPxzAMNm3axKuvvsqzzz7Lk08+Waw3ISKOZda6o+w6kYqfhwsv3tbkgp1SRUSk9Hm5ufB2v1Y4O1n4aUccP2w/YXYkkXLL7jVeNWvW5Msvv+Taa6/Fz8+PrVu3Uq9ePWbPns28efNYvHjxZe/x2WefMXnyZGJjYwGoXr06L730EsOGDSveu7jKtMZLpPjmbYph4g+7ybMavN6nudoYi4iUAe/+cZB3/jiAr4cLvz/eVfsoitih1NZ4JScnU6dOHQD8/PxITk4GoHPnzqxatapI9xgxYgTHjh3j1KlTJCQkEBsb6zBFl4gUT1zKWZ78dgcTFuwiz2pwW8tQTTEUESkjHulel5ZhAaRn5zP+2x3Y+bm8iBSB3YVXeHg4R48eBc51KPzmm28A+OmnnwgICLjs9dHR0Rw8eBCA4OBgQkJCADh48GDBfUWk/IhLOcvzi3Zx7Vsr+HbLcQDG92jAu/e2wslJUwxFRMoCF2cnpvVrhaerM+sOJ/H7npNmRxIpd+wuvIYMGcKOHTsAmDBhQsFar7FjxxZpjdbgwYNZt27decc3btzI4MGD7Y0jImXUiZSzPLdwF93e+pOvNsSQa7VxTXgQ34zswOjr6mtdl4hIGVMn2Jthnc/Napq65C+sNo16iZQku9d4/X8xMTFERUVRt25dWra8/OZ7/14X9m+HDh0iMjKSlJSUK4lzVWiNl8jFGYbBtD8O8uGKQ+RZz/146RBeiTE31Oea8EompxMRkUtJPZtHlzeWk5adzzv9WnJnRA2zI4mUeaWyxisvL4/u3btz4MCBgmM1a9akT58+RSq6ACwWC+np6ecdT01NxWq12hNHRMqYnHwrY77ezrvLDpJnNehYtxLzH7yGeQ9eo6JLRMQB+Hu6MuraugC8s/Qgufk2kxOJlB92FV6urq7s3r37iqYIdenShSlTphQqsqxWK1OmTKFz587Fvq+ImMtqM3j4q638uCMOFycLb97dgrkjrqG9Ci4REYcyuGNtgn3ciUnO4puoWLPjiJQbdk81fOKJJ3B1deX1118v1gvu3buXrl27EhAQQJcuXQBYvXo1aWlpLF++nGbNmhXrvleTphqKnG/yz3uZviYadxcnZgxqS+f6wWZHEhGRYpq1NpqXftpLiK87q57qjoers9mRRMqsotYGLvbeODc3l+nTp7N06VIiIyPx9vYu9Pzbb799yeubNGnCzp07+eCDD9ixYweenp4MHDiQ0aNHExQUZG8cETGZYRhMXx3N9DXRAEzt21JFl4iIg7uvfU0+Wx3NiZSzfLn+KA92rWt2JBGHZ/eIV/fu3S9+M4uF5cuXX3Gosk4jXiLn5FltvPTjHuZsjAFg7A0NGHNDfZNTiYhISfgmKpanvttJoJcrq57qjq+Hq9mRRMqkUhvx+vPPP68oGEBKSgozZsxg3759WCwWmjRpwtChQ/H397/ie4vI1ZGdZ2X03K38se8UFgs806sRD3YNNzuWiIiUkD4R1fl45WGOnM5kxppoHr+hgdmRRBya3ft4Xal/Ws+/8847JCcnk5iYyNtvv03dunXZunWrXfdatWoVt956K6GhoVgsFhYtWnTZa1auXEmbNm3w8PAgPDycjz/+uJjvRKTiysrNZ9gXm/lj3yncXZz45IE2jOxWV3tziYiUIy7OToy78VyxNX11NMmZuSYnEnFsdo943XnnnRf848piseDh4UG9evXo378/DRs2vOD1Y8eO5bbbbuOzzz7DxeXcy+fn5zN8+HAef/xxVq1aVeQsmZmZtGzZkiFDhnDXXXdd9vzo6Gh69+7NiBEj+Oqrr1i7di0PP/wwlStXLtL1IgJp2XkMnbmZqGNn8HJzZsagtnSoq86FIiLlUe9m1WhS7TB749P4eOVhnu3d2OxIIg7L7jVegwcPZtGiRQQEBNCmTRsMw2Dbtm2kpKTQo0cPduzYwdGjR1m2bBmdOnU673pPT0+2bdtGo0aNCh3fu3cvkZGRZGVlFe+NWCwsXLiQO+6446LnPP300/z444/s27ev4NioUaPYsWMH69evL/JraY2XVFRJGTkMmrmJ3SfS8PNwYdbQdrSuGWh2LBERKUV/7j/FkFmbcXdxYtVT3ani52F2JJEypVQ2UAaoWrUq/fv358iRI3z//fcsWLCAw4cP88ADD1C3bl327dvHoEGDePrppy94vZ+fHzExMecdj42NxdfX1944dlm/fj09evQodKxnz55ERUWRl5d30etycnJIS0sr9BCpaP5KSOf2/65l94k0grzdmPfgNSq6REQqgGsbViayViA5+TbeX37Q7DgiDsvuwmvGjBk8/vjjODn971InJyceffRRPv30UywWC6NHj2b37t0XvL5fv34MGzaM+fPnExsby/Hjx/n6668ZPnw49913X/HfSREkJCRQpUqVQseqVKlCfn4+iYmJF71uypQp+Pv7FzzCwsJKNadIWbP+cBJ9PlzL8TNnqVXJi29GdqBpqJrhiIhUBBaLhfE9zy0h+XpTLDFJxZudJFLR2V145efns3///vOO79+/H6vVCoCHh8dFF9n/5z//oU+fPgwcOJDatWtTq1YtBg8ezN13380bb7xhbxy7/f9c/8y0vFRTgAkTJpCamlrwiI3VLu5Scaw7lMiQWZvIzLXSvk4Qix7uRL0QH7NjiYjIVXRNeCW61A8m32YwbdkBs+OIOCS7m2sMGDCAYcOG8eyzz9K2bVssFgubNm3itddeY+DAgcC5zoFNmza94PVubm68++67TJkyhcOHD2MYBvXq1cPLy+vK3kkRVK1alYSEhELHTp06hYuLC5UqXbw5gLu7O+7u7qUdT6RMOZtrZfrqI3zw5yFy8m1c27AyHz/QBg9XZ7OjiYiICcb3aMjqg4n8uD2OJ3s2pJq/p9mRRByK3YXXO++8Q5UqVXjzzTc5efIkcG663tixYwvWdfXo0YNevXpd8j5eXl40b968GJGLr0OHDvz000+Fji1ZsoTIyEhcXbUpoMg/jiVl0v+zjZxIOQvADY1D+O/9rXF3UdElIlJRtQwLoH2dIDZGJ/PFumM8c1Ojy18kIgXs7mr4b/80mTCrs19GRgaHDh0CICIigrfffpvu3bsTFBREzZo1mTBhAidOnODLL78EzrWTb9asGSNHjmTEiBGsX7+eUaNGMW/ePLvayauroZRnqWfz6PPhWg6fzqR6gCdP39SIW1tU0x5dIiLC0r0nGfFlFH4eLqyfcD3e7nZ/hi9S7pRaV0M4t87rjz/+YN68eQV/jMXFxZGRkVG8tMUUFRVFREQEERERAIwbN46IiAgmTpwIQHx8fKEOinXq1GHx4sWsWLGCVq1a8corr/Dee+9pDy+Rv2XnWXl4zhYOn86kmr8HCx/uyG0tQ1V0iYgIANc3CqF2JS/SsvP5bstxs+OIOBS7R7yOHTtGr169iImJIScnhwMHDhAeHs7jjz9OdnY2H3/8cWllLTM04iXlUWZOPg/OjmLtoSS83Jz5blRHmoTqv28RESnsy/VHmfjDHmpX8mLZE9fi7KQP56RiK7URrzFjxhAZGcmZM2fw9Pzfoso777yTZcuWFS+tiJgqJimL/p9tYO2hJLzdnJk+KFJFl4iIXNDdbWrg7+nK0aQslu07aXYcEYdh98TcNWvWsHbtWtzc3Aodr1WrFidOnCjSPQ4fPsy0adPYt28fFouFxo0bM2bMGOrWrWtvHBG5Arn5Nr7eHMPrv+4nK9dKgJcrs4a0o1VYgNnRRESkjPJyc+G+djX5eOVhZqyJpkfTqmZHEnEIdo942Wy2gv26/u348eP4+vpe9vrff/+dJk2asGnTJlq0aEGzZs3YuHEjTZs2ZenSpfbGEZFiMAyDrzfFcO1bfzLxhz1k5VppVyeIn0Z3VtElIiKXNahjLVycLGyMTmb3iVSz44g4BLvXePXr1w9/f38+/fRTfH192blzJ5UrV+b222+nZs2azJw585LXR0RE0LNnT15//fVCx5955hmWLFnC1q1b7X8XV5nWeIkji0s5y9Pf72T1wUQAKvu6M7p7PQZcUwsnzdMXEZEiGvP1Nn7YHsedEdV5p18rs+OImKaotYHdhVdcXBzdu3fH2dmZgwcPEhkZycGDBwkODmbVqlWEhIRc8noPDw927dpF/fr1Cx0/cOAALVq0IDs72544plDhJY5qzcFERs/bSkpWHu4uTozv0ZABHWppU2QREbHbzuMp3PbBWlycLKx5+jqq+nuYHUnEFEWtDexe4xUaGsr27dv5+uuv2bJlCzabjWHDhnH//fcXarZxMZUrV2b79u3nFV7bt2+/bNEmIsU3c200r/y8F5sBzav7M+3eVtSt7GN2LBERcVAtagTQrk4Qm6KT+WL9UZ7upQ2VRS7F7sJr1apVdOzYkSFDhjBkyJCC4/n5+axatYquXbte8voRI0bw4IMPcuTIETp27IjFYmHNmjW88cYbPPHEE/a/AxG5rI9XHub1X/cD57pRTb6jmUa5RETkig3rXIdN0cnM3RjDo9fVw8tNGyqLXIzdUw2dnZ2Jj48/b3QqKSmJkJCQCzbe+DfDMJg2bRpTp04lLi4OODeK9uSTT/LYY485xEatmmoojuTfRdfjN9RnzPX1HeL7TEREyj6rzeC6qSs4lpTFy7c3ZWCH2mZHErnqSm0fL8MwLvhHW1JSEt7e3pe93mKxMHbsWI4fP05qaiqpqakcP36cMWPG6I9BkRI2b1NMQdE17sYGPH5DA32fiYhIiXF2sjC0Ux0APlpxmOy8S38AL1KRFXk8uE+fPsC5wmnw4MG4u7sXPGe1Wtm5cycdO3a87H2uu+46FixYQEBAQKH282lpadxxxx0sX77cnvwichFL9iTw3MJdADx0bV0eu77+Za4QERGxX7+2YXy04jDxqdnM2xTDkL8LMREprMgjXv7+/vj7+2MYBr6+vgVf+/v7U7VqVR588EG++uqry95nxYoV5Obmnnc8Ozub1atX25deRC7oVFo2T363E5sB97YN46meDc2OJCIi5ZSHqzOPXl8PgP/+eZis3HyTE4mUTUUe8fpnf67atWszfvz4Ik0r/LedO3cW/PPevXtJSEgo+NpqtfLbb79RvXp1u+4pIuczDIPnFu0m9Wwezar78codzTS9UERESlXfyDA+WXmEmOQsZqyO5lHNshA5j93NNYrLycmp4I+/C72kp6cn77//PkOHDr0aca6ImmtIWfbD9hOM+Xo7rs4Wfnq0M42q6r9REREpff/8/nF3ceL3x7tSO9i+D+lFHFWpNdc4efIkAwYMIDQ0FBcXF5ydnQs9LiY6OprDhw9jGAabNm0iOjq64HHixAnS0tIcougSKctOp+fw4o97AHj0uvoqukRE5Kq5rWUonesFk5Nv47lFuy74QbtIRWb3ZguDBw8mJiaGF154gWrVqhV5ClOtWrUAsNls9r6kiBSBYRi8sGg3KVl5NKnmx0PX1jU7koiIVCAWi4VX72xGj3dWsfZQEnM2xvDANbXMjiVSZthdeK1Zs4bVq1fTqlWrYr/ogQMHWLFiBadOnTqvEJs4cWKx7ytSkf24I47f9iTg4mThrXta4Ops94C2iIjIFalVyZsnejTgtcX7eenHPdQJ9qZTvWCzY4mUCXYXXmFhYVc0dPzZZ5/x0EMPERwcTNWqVQuNmFksFhVeIsXwV0I6Exacax3/cPd6NA31NzmRiIhUVCO6hLMnLo0ftscx6qstLHy4I/VCfC9/oUg5Z3dzjSVLljB16lQ++eQTateubfcL1qpVi4cffpinn37a7mvLCjXXkLIkJSuX2z5YS0xyFp3qVeKLIe1w0WiXiIiYKDvPygPTNxJ17AxhQZ4sergTlXzcL3+hiAMqam1gd+EVGBhIVlYW+fn5eHl54erqWuj55OTkS17v5+fH9u3bCQ8Pt+dlyxQVXlJWWG0Gg2duYvXBRGoEevLT6M4EeruZHUtERISkjBzu/HAdMclZtKkVyJzh7fFwvXgjNhFHVdTawO6phtOmTbuSXNxzzz0sWbKEUaNGXdF9RATe+v0vVh9MxMPViU8HRKroEhGRMqOSjzufD25Lnw/XsuXYGZ76bifv3ttKe0tKhWV34TVo0KAresF69erxwgsvsGHDBpo3b37eiNljjz12RfcXqSh+2hHHxysPA/Dm3S1pEqrRVxERKVvqhfjw8QNtGPj5Jn7cEUftYG/G3djA7FgipijSVMO0tLSCYbO0tLRLnnu5qXd16tS5eBiLhSNHjlwujuk01VDMtjcujbs+WsfZPCsju4Uz4abGZkcSERG5qG82x/LU9zsBmHhLE4Z2vvjfgyKOpkSnGgYGBhIfH09ISAgBAQEXHCI2DAOLxYLVar3kvaKjo4vykiJyEUkZOTw4O4qzeVa61A/mqZ6NzI4kIiJySX3bhnH8TBbvLT/Eyz/vJSffpv0mpcIpUuG1fPlygoKCAPjzzz9LNZCIXFxcylkGzNjI8TNnqRnkxfv3ReDspLnyIiJS9o29sQEWi4V3lx3kjd/2k5yZw4SbGuOk32NSQdjd1bA4xo0bxyuvvIK3tzfjxo275Llvv/12ace5YppqKFeTzWZw8FQGy/afZPb6Y8SnZhPq78Hs4e2pW9nH7HgiIiJ2+WTlYab8uh+AW1pUY2rflri7qNuhOK5S62pYHNu2bSMvL6/gny9GXW5EIN9qY9XB02yMTmZnbCq7T6SSnpNf8Hx4sDezh7eneoCniSlFRESKZ2S3uoT4ufPUdzv5eWc8p9Nz+HRAJP5erpe/WMSBXZURr/JGI15SGk6n5zB7/VG+3hzLqfScQs95uDrRrk4lejatwq0tQ/Hz0C8nERFxbGsPJTJq9hbSc/JpUMWHWUPaEaoPFcUBldoGyqLCS0pWbHIWn6w6zDdRx8nNtwFQyduNHk2r0irMnxY1Aqgf4oOLs5PJSUVERErWvvg0hszcTEJaNlX83Jk1pB2Nq+lvK3EsKrxKkQov+f+SMnJYdfA0WblWnCwWWtTwp3FVv0suGP4rIZ2PVhzip53xWG3nvg1bhgUwoksdejSpipuLCi0RESn/4lLOMnjmJg6czMDH3YVPBrShU71gs2OJFJkKr1Kkwkv+cTItm8m/7OO33fHkWQt/KwV5u9EhvBIRNQMwDMjOs3I2z8rp9Bz2J6Sz60RqwbldG1Tm4Wvr0r5OkNY6iohIhZN6No+Rs6PYcCQZV2cLb97dgjsjapgdS6RISrXw+u677/jmm2+IiYkhNze30HNbt261P62DUeElAMfPZHH/9I0cS8oCoGmoHzUCPcnKtbLl2Bmyci+9p53FAjc1q8pD3erRvIb/1YgsIiJSZuXkWxn/7U5+2hEHwMu3N2Vgh9rmhhIpglLravjee+/x3HPPMWjQIH744QeGDBnC4cOH2bx5M4888sgVhRZxFCfTsun3yQZOpJwlLMiTD/u3KVQ85Vlt7IhNYc2hRA6cTMfN2QlPN2fcXZzx93SlYVVfWtTwp0agl4nvQkREpOxwd3Hm3X6tCPZxY+bao0z8YQ8pWXk8el09zQaRcsHuEa9GjRrx4osvct999+Hr68uOHTsIDw9n4sSJJCcn88EHH5RW1jJDI17y9Hc7mR8VS3iwN3NGtKeav7owiYiIlATDMHhn6QHeW34IgB5NqvCfvi3V0VfKrKLWBnav3o+JiaFjx44AeHp6kp6eDsCAAQOYN29eMeOKOI6jiZl8t/U4AG/d01JFl4iISAmyWCyM69GQV+9shpuzE0v2nuT2D9ayPyHN7GgiV8Tuwqtq1aokJSUBUKtWLTZs2ABAdHQ06tMhFcF7yw5itRl0b1iZNrUCzY4jIiJSLt3fvhbfjOpAqL8H0YmZ3PHftfyyM97sWCLFZnfhdd111/HTTz8BMGzYMMaOHcuNN95Iv379uPPOO0s8oEhZcvh0Bgu3nwBg3I0NTU4jIiJSvrUKC+Dnx7rQpX4w2Xk2Hp23lcW7VHyJY7J7jZfNZsNms+Hicq4vxzfffMOaNWuoV68eo0aNws3NrVSCliVa41VxPb9oF19tiOGGxiFMH9TW7DgiIiIVgtVm8Mz3O/l2y3FcnCx8MqAN1zeuYnYsEUD7eJUqFV4VU+rZPK55bRln86zMG3ENHepWMjuSiIhIhWG1GYydv50fd8Th5uzEjMGRdKlf2exYIqXXXANg9erVPPDAA3To0IETJ85Nu5o9ezZr1qwpXloRB/BtVCxn86w0qurLNeFBZscRERGpUJydLEzt25KeTauQa7Ux4ssoNkUnmx1LpMjsLry+//57evbsiaenJ9u2bSMnJweA9PR0XnvttRIPKFIWWG0GX6w/CsDgjrW1n4iIiIgJXJ2deO++CK5tWJnsPBvDvtjMXwnpZscSKRK7C6/Jkyfz8ccf89lnn+Hq+r/9FDp27MjWrVtLNJxIWbEpOpnY5LP4e7pye6vqZscRERGpsNxdnPn4gTZE1gokPTufQZ9vIi7lrNmxRC7L7sLrr7/+omvXrucd9/PzIyUlpSQyiZQ5G6PPbaHQrUFlPN2cTU4jIiJSsXm4OjN9UCT1QnxISMtm5OwtZOdZzY4lckl2F17VqlXj0KFD5x1fs2YN4eHhJRJKpKzZfPTcHPK2dbS2S0REpCwI8HJj1pC2BHq5sutEKpN+2mt2JJFLsrvwGjlyJGPGjGHjxo1YLBbi4uKYM2cO48eP5+GHHy6NjCKmyrPa2HosBYB2tVV4iYiIlBU1Ar2Ydm8EFgvM2xTDgq3HzY4kclEu9l7w1FNPkZqaSvfu3cnOzqZr1664u7szfvx4Ro8eXRoZRUy1Jy6Ns3lW/D1dqR/iY3YcERER+ZduDSoz5vr6TPvjIBN/2EPb2kGEBXmZHUvkPHaNeFmtVlauXMkTTzxBYmIimzZtYsOGDZw+fZpXXnmltDKKmGrz361qI2sF4uSkboYiIiJlzaPX1adNrUAycvJ54psdWG3aplbKHrsKL2dnZ3r27ElqaipeXl5ERkbSrl07fHw0CiDl1yat7xIRESnTnJ0svNO3Fd5uzmw6msysdUfNjiRyHrvXeDVv3pwjR46URhaRMsdmM4j6p/DS+i4REZEyq2YlL567uQkAby/5i4TUbJMTiRRmd+H16quvMn78eH7++Wfi4+NJS0sr9BApTw6dzuBMVh7uLk40r+5vdhwRERG5hHvbhhFRM4DMXCuTf1GXQylb7G6u0atXLwBuu+02LJb/rXcxDAOLxYLVqj0UpPxYfTARgHZ1gnBzsftzChEREbmKnJwsvHJ7M277YA0/74ynf7tEOtYLNjuWCFCMwuvPP/8sjRwiZdKqA6cB6Fq/sslJREREpCiaVfdnwDW1+GL9Mab8up8fHumk5lhSJthdeHXr1u2iz23fvv1KsoiUKdl5VjZGJwHQpYE+LRMREXEUj11fn++3nmDXiVR+2RXPrS1DzY4kYv8ar/8vNTWVDz/8kNatW9OmTZuSyCRSJmw5dobsPBshvu40rOJrdhwREREpoko+7ozoEg7A1CV/kWe1mZxI5AoKr+XLl/PAAw9QrVo13n//fXr37k1UVFRJZhMx1T/TDLvUr1xoPaOIiIiUfcO71CHYx42jSVl8G3Xc7Dgi9hVex48fZ/LkyYSHh3PfffcRGBhIXl4e33//PZMnTyYiIqK0copcdav+bqzRVdMMRUREHI63uwsPX1sPgP/+eUijXmK6IhdevXv3pkmTJuzdu5f333+fuLg43n///dLMJmKaU+nZ7Is/tz1CZ3VDEhERcUj929ck2MedEylnWbBVo15iriIXXkuWLGH48OFMmjSJm2++GWdn59LMJWKqNX+PdjWr7kclH3eT04iIiEhxeLg6M6rbubVeH2jUS0xW5MJr9erVpKenExkZSfv27fnggw84ffp0aWYTMc0/+3epjbyIiIhj69++JpW83YhNPstPO+LMjiMVWJELrw4dOvDZZ58RHx/PyJEj+frrr6levTo2m42lS5eSnp5emjlFrhqbzWD1wf811hARERHH5eXmwtDOdQD4ZOURDMMwOZFUVHZ3NfTy8mLo0KGsWbOGXbt28cQTT/D6668TEhLCbbfdVhoZRa6qfQlpJGbk4uXmTJtagWbHERERkSv0QPtaeLs589fJdFb8pRlbYo4r2serYcOGvPnmmxw/fpx58+aVVCYRU606cG6aYYfwSri5XPFWdyIiImIyfy9X+revCcDHKw+bnEYqqhL5q9LZ2Zk77riDH3/8sSRuJ2Kq/00zVDdDERGR8mJo5zq4OlvYGJ3MjtgUs+NIBaSP80X+JTMnn6ijZwDo2kDru0RERMqLav6e3NoiFIBZ646aG0YqJBVeIv+y6sBpcq02alXyok6wt9lxREREpAQN7lQbgJ93xnEqLdvcMFLhqPAS+Zcle08C0KNJFSwWi8lpREREpCS1qBFAm1qB5FkN5myMMTuOVDAqvET+lme1sXz/KQBubFLV5DQiIiJSGgZ3rA3AnI0x5ORbzQ0jFUq5KLw+/PBD6tSpg4eHB23atGH16tWXPH/OnDm0bNkSLy8vqlWrxpAhQ0hKSrpKaaWs2hydTOrZPIK83dRGXkREpJzq1awqVf08SMzI4Zed8WbHkQrE4Quv+fPn8/jjj/Pcc8+xbds2unTpwk033URMzIWHj9esWcPAgQMZNmwYe/bs4dtvv2Xz5s0MHz78KieXsuafaYY3NA7B2UnTDEVERMojV2cnBnSoBcDMtUe1obJcNQ5feL399tsMGzaM4cOH07hxY6ZNm0ZYWBgfffTRBc/fsGEDtWvX5rHHHqNOnTp07tyZkSNHEhUVdZWTS1litRks/bvw0jRDERGR8u3etmG4uTix60QqW2POmB1HKgiHLrxyc3PZsmULPXr0KHS8R48erFu37oLXdOzYkePHj7N48WIMw+DkyZN899133HzzzRd9nZycHNLS0go9pHxZuvckJ1LO4u/pqv27REREyrlKPu7c0epca/mZa4+aG0YqDIcuvBITE7FarVSpUqXQ8SpVqpCQkHDBazp27MicOXPo168fbm5uVK1alYCAAN5///2Lvs6UKVPw9/cveISFhZXo+xDzfbb6CAAPXFMTD1dnk9OIiIhIaRvcsQ4Av+5OID71rMlppCJw6MLrH/+/7bdhGBdtBb53714ee+wxJk6cyJYtW/jtt9+Ijo5m1KhRF73/hAkTSE1NLXjExsaWaH4x15ZjyWw5dgY3ZycG/d3pSERERMq3JqF+tK8ThNVm8NWGY2bHkQrAxewAVyI4OBhnZ+fzRrdOnTp13ijYP6ZMmUKnTp148sknAWjRogXe3t506dKFyZMnU61atfOucXd3x93dveTfgJQJH688N9p1Z0R1Qnw9TE4jIiIiV8uQTrXZGJ3M3I0xPHpdfc16kVLl0CNebm5utGnThqVLlxY6vnTpUjp27HjBa7KysnByKvy2nZ3PfZOpq03Fs/rgaZbuPYnFAiO61jE7joiIiFxFNzSuQvUAT85k5fHj9jiz40g559CFF8C4ceOYPn06n3/+Ofv27WPs2LHExMQUTB2cMGECAwcOLDj/1ltvZcGCBXz00UccOXKEtWvX8thjj9GuXTtCQ0PNehtiguw8K88v2g3AoA61qRfia3IiERERuZpcnJ0Y+E9r+XVqLS+ly6GnGgL069ePpKQkXn75ZeLj42nWrBmLFy+mVq1z30Tx8fGF9vQaPHgw6enpfPDBBzzxxBMEBARw3XXX8cYbb5j1FsQk7y07yLGkLKr6efBEjwZmxxERERET9Gsbxjt/HGBffBqbopNpH17J7EhSTlkMlfZ2S0tLw9/fn9TUVPz8/MyOI8Ww+Wgy/T5Zj82ATwa0oWdT7d0lIiJSUT27cBdzN8bQq2lVPh7Qxuw44mCKWhs4/FRDEXulZuUxZt42bAb0aV1dRZeIiEgFN/jvrsZL9iZw/EyWuWGk3FLhJRWKYRg8s2AncanZ1K7kxcu3NzM7koiIiJisQRVfOtcLxmbAbLWWl1KiwksqlK83x/Lr7gRcnS28d18EPu4Ov8xRRERESsA/o15fb4olKzff3DBSLqnwkgrj4Ml0Jv20B4AnezakRY0AcwOJiIhImdG9UQg1g7xIPZvHom1qLS8lT4WXVAjZeVYenbeN7DwbXeoHM7xzuNmRREREpAxxdrIw6O9Rr1nrotVaXkqcCi+pEKYs3sf+hHSCfdyY2rclTk4WsyOJiIhIGXNPZA283Zw5cDKDdYeTzI4j5YwKLyn3luxJ4Iv15xbKvnVPS0J8PUxOJCIiImWRn4crd7epAcDMtUfNDSPljgovKdf2xacxdv52AIZ1rkP3hiHmBhIREZEybeDf0w2X7T/JsaRMc8NIuaLCS8qtU+nZDJu1mcxcKx3CK/F0r0ZmRxIREZEyrm5lH7o1qIxhwJfr1VpeSo4KLymXzuZaGfHlFuJSswkP9uajB1rj5qL/3EVEROTyhnSqDcA3m2PJyFFreSkZ+ktUyh2bzeCJb7ezIzaFAC9XPh/clgAvN7NjiYiIiIPoWr8y4cHepOfks2DrcbPjSDmhwkvKnalL/2LxrnObJH/yQBtqB3ubHUlEREQciJOThcF/j3pNXx1NntVmbiApF1R4SbnybVQs//3zMACv92lB+/BKJicSERERR3R3mxoE+7gRk5zFt1Ea9ZIrp8JLyo0NR5J4duEuAEZ3r8ddf7eDFREREbGXl5sLj3SvB8B7yw6SnWc1OZE4OhVeUi5EJ2Yy6qst5FkNbm5ejXE3NjA7koiIiDi4/u1rUj3Ak4S0bGarw6FcIRVe4vBSs/IYOmszKVl5tAoLYGrfljg5WcyOJSIiIg7O3cWZMTfUB86Nep1MyzY5kTgyFV7i0AzD4IlvdxCdmEn1AE8+GxiJh6uz2bFERESknLirdQ1ahQWQnpPPpJ/2mB1HHJgKL3Fos9Yd5Y99J3FzduKTAW2o7OtudiQREREpR5ydLEzp0xxnJwuLdyWwdO9JsyOJg1LhJQ5re2wKry3eB8CzvRvRrLq/yYlERESkPGpczY/hXeoAMO6b7exPSDM5kTgiFV7ikE6lZTNydhR5VoOeTaswqGNtsyOJiIhIOTb2hga0qx1EenY+A2dsIjY5y+xI4mBUeInDyc6zMvKrLZxMy6F+iA//uaclFouaaYiIiEjp8XB15rOBkTSs4sup9BwGfb6J5Mxcs2OJA1HhJQ4lO8/KyNlb2BaTgr+nK58NjMTXw9XsWCIiIlIB+Hu58sXQdlQP8ORIYiZDZm0mKzff7FjiIFR4icNIzszloa+2sPLAaTz//tSpdrC32bFERESkAqnq78EXQ9sR4OXKjtgUnvx2J4ZhmB1LHIAKLynzDMNg9vqjXPvWn/z512ncXZyYMSiSdnWCzI4mIiIiFVC9EB9mDIrExcnCL7vi+WpjjNmRxAGo8JIy75NVR3jhhz2kZefTuJofc0dcQ8d6wWbHEhERkQqsTa0gnrmpEQCv/LyX3SdSTU4kZZ0KLynTFm47zuu/7gfgiRsb8POjnWlTK9DkVCIiIiIwrHMdbmgcQm6+jdFzt5KenWd2JCnDVHhJmbU3Lo2nvtsJwIgudXj0+vo4O6l7oYiIiJQNFouF/9zTklB/D44mZTFhwS6t95KLUuElZVJOvpVx32wnz2pwQ+MQJtzU2OxIIiIiIucJ8HLj/f6tcXGy8PPOeOZu0novuTAVXlImTfvjIPsT0gnyduP1u1rgpJEuERERKaPa1ArkyZ4NAZj00172xqWZnEjKIhVeUuZ8GxXLRysOA/Danc0J9nE3OZGIiIjIpY3oEs51jc6t93pk7lYycrS/lxSmwkvKlF93xfP09+fWdQ3vXIdezaqanEhERETk8pycLEy9pyXV/D2ITszkWa33kv9HhZeUGb/uiufReduwGdAvMoznbta6LhEREXEcgd5uvH9fBM5OFn7cEcfXm2PNjiRliAovKRN+2hHH6HnbyLcZ3NYylNf6NMdi0bouERERcSyRtYMY3+Pceq+Xftyj/b2kgAovMd2ff51i7PztWG0Gd7WuwTv9WqltvIiIiDiskV3D6d6wMjn5NkbO3kJiRo7ZkaQMUOElptpy7AwPf7WVfJvBHa1CeevuFiq6RERExKE5OVmY1i+COsHenEg5y8NfbSU7z2p2LDGZCi8xhWEYfL0phv6fbeBsnpVuDSrz1j0t1TZeREREygV/L1c+G9gGH3cXNh1NZtDnm0g9m2d2LDGRCi+56rYcS2bwzM08s2AXOfk2ujeszIf3t8bVWf85ioiISPlRL8SX6YMi8XV3YWN0Mvd8vI4NR5LMjiUmsRjqc2m3tLQ0/P39SU1Nxc/Pz+w4DuN0eg7PL9rF73tOAuDsZGF8j4aM7BqukS4REREpt/bGpTFo5iZOp59b69W9YWWe6tWIxtX0d2R5UNTaQIVXMajwsk9Wbj7fbTnOO0sPcCYrDxcnC3e3qcHIbnWpE+xtdjwRERGRUnc6PYf3lx9k7sYY8m0GFgvc0iKUQR1q0aZWoLo5OzAVXqVIhdflZedZ+XF7HOsOJ7LiwGlSss7NaW5czY+p97SkSaj+fxMREZGK52hiJv9Z8hc/74wvOBYe7M2NTapwY5MqRNQMVKMxB6PCqxSp8Lq0jUeSePr7nRxNyio4VjPIi2Gd63Bfu5q4uWgtl4iIiFRsu0+k8uX6o/y4I47sPFvB8UreblzfOIQbm1SlS/1gPFydTUwpRaHCqxSp8LqwfKuNt5ce4MMVhwGo6ufBPZE1uCa8EteEV9KnNyIiIiL/T3p2Hiv+Os3SvSf5869TpGfnFzwX7OPOkE61GdihFr4eriamlEtR4VWKVHj9j81mkHI2j03RScxad5QNR5IBuK9dGBN6N8ZPPyREREREiiQ338am6GT+2HeSX3fHczLtXDOOGoGevHtvBG1qBZqcUC5EhVcpquiFV26+jfmbY/hsdTTHz2Rh+9d/QV5uzrx+VwtuaxlqXkARERERB5dntfHj9jje+eMAx8+cxdnJwtO9GjKiS7gacZQxKrxKUUUtvA6eTGfBthMs3HqChLTsQs+FV/ama/3KDOhQi7qVfUxKKCIiIlK+pGXn8cKi3fywPQ6Au9vU4NU7m+HuorVfZUVRawOXq5hJHNDp9Bx+3BHHwm3H2X0ireB4iK87o6+rR6+mVfHzdNXCTxEREZFS4Ofhyrv3RtC6ZiCTftrDd1uOE5OUxUcPtKaSj7vZ8cQOGvEqhvI+4nU218qSvQks3HaC1QcTsf49l9DFycK1DUPo07o61zUKUbElIiIichWtPHCa0XO2kp6TT41AT6YPiqRR1fL3t6ij0VTDUlReC69jSZnM2xTLvE0xpJ7NKzjeKiyAPq2rc0uLUIK83UxMKCIiIlKxHTqVztBZUcQkZ+Hh6sTrfVpwR0R1s2NVaCq8SlF5KLwMw+D4mbPsOJ7CjtgU1hxKYl/8/6YS1gj0pE9Ede6IqE641myJiIiIlBnJmbmM+Xobqw8mAjCwQy2ev7mJ9ko1iQqvUuRohZfNZrAtNoVtMWfYE5dGTHIW0YmZJGfmFjrP2clCh/BKDOxQi+sbV9G+WyIiIiJllNVmMO2PA7y//BBwbobSh/e3JjTA0+RkFY8Kr1LkSIXXqgOneeO3/eyJSzvvOVdnC42r+dGyRgARNQPo3jCEQE0lFBEREXEYy/ef5PGvt5OWnU+Qtxvv3xdBp3rBZseqUFR4lSJHKLxOpWfz4g97+HV3AgDebs50rBdMi+r+1KnsTa0gbxpU9VErUhEREREHF5OUxaivtrA3Pg2LBfq3q8n4Hg31gfpVosKrFJW1wis5M5cDJ9OJTz1LQmoOO4+nsOZQIunZ+bg4WRjUsTaPdK+nxhgiIiIi5VR2npUXf9jD/KhYAPw8XBjQoRb3tq1JFT8Prf8qRSq8SlFZKbw2HEli9NxtJGbkXPD5pqF+vHV3S5qEml8cioiIiEjp23AkiUk/7S3UNA0g2MeN1jUDaVs7iDa1A2le3R9XZ8csxgzDYPeJNMIre+Ptbv62xCq8SlFZKbwOnUrnhrdXAVAzyIuwIE8q+7jTsKofrWsGEFk7SA0yRERERCoYq81g6d6TfLb6CFtjznChv/Yr+7ozuns97m0XVuaXnhiGQWzyWbbFnmHDkWSW7z/JybQc/tu/NTe3qGZ2PBVepamsFF75Vhu749KoH+JTJqp9ERERESlbrDaDjOx8Dp3OIOpoMpuPniHqWDIpWef2bK0e4MmYG+rTJ6I6LsUcAcvMyWdjdBK7T6RxKj2bnDwbIX7u1Aj0olFVX+qG+ODr7oLFcvkBAZvNICY5i33xaexLSGdvXCrbY1NIzCjcjdvLzZknezZkSKc6xcpcklR4laKyUniJiIiIiNgrN9/G/KhY3l92kFPp55asBPu4071hZa5rFELn+sH4erhe9Po8q42E1Gx2nUjl+y3HWXXwNHnWS5cUbi5OVPZxp5KPG8E+7lT6u/dA6tk8TqZlE5eaTVZOPrlW2wXv5epsoWmoPxE1A+jWoDLXhFfCw7VsjNSp8CpFKrxERERExNFl51mZvf4YH608XGh/V1dnCyG+Hni6OZOdZyUzJx+LxYKTxcLZ3Hwyc63n3SssyJN2tStRPcADd1dnTqZlE52Yyb749Iv2I7gYNxcnGlTxoXFVv3NbH4UF0DTUr8wUWv+fCq9SpMJLRERERMqL3Hwbm48ms3z/KZbtO8nRpKzLXuPm7ESNQE96NqvKXa2rUy/E96LnZuXmk5SRS2JGzv/+9+9Cz8/Dhcq+HtQI9MTXwwVXZydCfN2LPe3RDCq8SpEKLxEREREpr2KTs0jOzCUr14qHqxM+7i4YnFsv5uXmjI+7C4FebjipiRtQ9NpAHRlERERERKRAWJAXYUFeZscodxxnDE9ERERERMRBqfASEREREREpZSq8RERERERESpkKLxERERERkVKmwktERERERKSUqfASEREREREpZeWi8Prwww+pU6cOHh4etGnThtWrV1/y/JycHJ577jlq1aqFu7s7devW5fPPP79KaUVEREREpKJx+H285s+fz+OPP86HH35Ip06d+OSTT7jpppvYu3cvNWvWvOA1ffv25eTJk8yYMYN69epx6tQp8vPzr3JyERERERGpKCyGYRhmh7gS7du3p3Xr1nz00UcFxxo3bswdd9zBlClTzjv/t99+49577+XIkSMEBQUV6zWLuju1iIiIiIiUb0WtDRx6qmFubi5btmyhR48ehY736NGDdevWXfCaH3/8kcjISN58802qV69OgwYNGD9+PGfPnr3o6+Tk5JCWllboISIiIiIiUlQOPdUwMTERq9VKlSpVCh2vUqUKCQkJF7zmyJEjrFmzBg8PDxYuXEhiYiIPP/wwycnJF13nNWXKFCZNmlTi+UVEREREpGJw6BGvf1gslkJfG4Zx3rF/2Gw2LBYLc+bMoV27dvTu3Zu3336bWbNmXXTUa8KECaSmphY8YmNjS/w9iIiIiIhI+eXQI17BwcE4OzufN7p16tSp80bB/lGtWjWqV6+Ov79/wbHGjRtjGAbHjx+nfv36513j7u6Ou7t7yYYXEREREZEKw6FHvNzc3GjTpg1Lly4tdHzp0qV07Njxgtd06tSJuLg4MjIyCo4dOHAAJycnatSoUap5RURERESkYnLoES+AcePGMWDAACIjI+nQoQOffvopMTExjBo1Cjg3TfDEiRN8+eWXAPTv359XXnmFIUOGMGnSJBITE3nyyScZOnQonp6eRXrNfxpBqsmGiIiIiEjF9k9NcLlm8Q5fePXr14+kpCRefvll4uPjadasGYsXL6ZWrVoAxMfHExMTU3C+j48PS5cu5dFHHyUyMpJKlSrRt29fJk+eXOTXTE9PByAsLKxk34yIiIiIiDik9PT0QsuZ/j+H38fLDDabjbi4OHx9fS/axONqSEtLIywsjNjYWO0nJlLG6PtTxPHo+1bEcZSl71fDMEhPTyc0NBQnp4uv5HL4ES8zlLX1YH5+fqb/ByciF6bvTxHHo+9bEcdRVr5fLzXS9Q+Hbq4hIiIiIiLiCFR4iYiIiIiIlDIVXg7M3d2dF198UXuMiZRB+v4UcTz6vhVxHI74/armGiIiIiIiIqVMI14iIiIiIiKlTIWXiIiIiIhIKVPhJSIiIiIiUspUeImIiIiIiJQyFV4lbNWqVdx6662EhoZisVhYtGhRoedPnjzJ4MGDCQ0NxcvLi169enHw4MFC5+Tk5PDoo48SHByMt7c3t912G8ePHy90zpkzZxgwYAD+/v74+/szYMAAUlJSLptv165ddOvWDU9PT6pXr87LL7/Mv/urxMfH079/fxr+X3t3H1Nl/cZx/HM8HfIQeIBC4EjB5gNlFpptgOmsrJTFopGNJUvtj7ZaNd1iLVdt2taDG7PoQdfDxLIt1gbUKsaqDXIuQsXDEhzQVHQVD1lARMVBuH5//Ooswvz5w3M4HHm/trNx7nPd33N9v+Pavtfum5uMDM2YMUObN2+e6FIAU1Kk12hdXZ0cDse4V2tr64TXBJjqIr1uJen111/XNddcI7fbrYyMDL377rsTWgtgqpusen3uuee0bNkyRUdHKy4u7rzzC+demMYryAYHB5WZmanXXntt3GdmprvvvlvHjx/XRx99JJ/Pp7S0NN12220aHBwMxG3evFlVVVUqLy/X/v379euvvyovL08jIyOBmHXr1qmpqUk1NTWqqalRU1OT7r///nPm9ssvv+j222+X1+vVwYMH9eqrr6qkpEQ7duwIxAwNDSkxMVFPPfWUMjMzg7AiwNQS6TX6l7a2NnV2dgZe8+fPv4BVAaa2SK/bXbt2acuWLdq6dataWlq0bds2PfLII/r444+DsDrA1DJZ9er3+3Xvvffq4YcfPu/cwr4XNoSMJKuqqgq8b2trM0nW3NwcOHbmzBlLSEiwt956y8zM+vr6zOVyWXl5eSDm+++/txkzZlhNTY2ZmR09etQk2ddffx2Iqa+vN0nW2tr6r/ns3LnTPB6P/fHHH4FjL7zwgnm9XhsdHR0Xv3LlStu0adP/PW8gUkRijdbW1pok6+3tvaC5A5EqEus2JyfHiouLx5y3adMmu+mmmyawAkDkCFW9/l1ZWZl5PJ7zyifce2GueE2ioaEhSdLMmTMDx5xOp6KiorR//35JUmNjo4aHh3XHHXcEYrxerxYtWqSvvvpKklRfXy+Px6OsrKxATHZ2tjweTyDmbOrr67Vy5cox/2hu9erV+uGHH9TR0RGUOQKRLJJqdMmSJUpJSdGqVatUW1s78UkDES4S6nZoaGhMfpLkdrt14MABDQ8PT3DmQOQJVr1OVLj3wjRek+jqq69WWlqatmzZot7eXvn9fr344ovq6upSZ2enJKmrq0tRUVGKj48fc25SUpK6uroCMbNnzx43/uzZswMxZ9PV1aWkpKRx4/71GTDdRUKNpqSk6M0331RFRYUqKyuVkZGhVatWad++fROfOBDBIqFuV69erbfffluNjY0yMx06dEi7d+/W8PCwTp8+PfHJAxEmWPU6UeHeC9N4TSKXy6WKigq1t7crISFB0dHRqqurU25urpxO5znPNTM5HI7A+7//fLaYa6+9VjExMYqJiVFubu6/nmd//jHh2cYDpptIqNGMjAw9+OCDuuGGG5STk6OdO3fqzjvvVElJycQmDUS4SKjbZ555Rrm5ucrOzpbL5VJ+fr42btwoSf8zR+BiEsx6/V+m4l74kpB/A8ZYunSpmpqa1N/fL7/fr8TERGVlZenGG2+UJCUnJ8vv96u3t3dMp9/T06Nly5YFYrq7u8eN/eOPPwa69urq6sDtC263O3DeP7v5np4eSRrX/QPTVSTWaHZ2tt57772JThmIeFO9bt1ut3bv3q033nhD3d3dgSvXsbGxuuKKK4K5FMCUF4x6PR9TcS/MFa8w8Xg8SkxM1LfffqtDhw4pPz9f0n9/GV0ulz7//PNAbGdnp5qbmwO/bDk5Oerv79eBAwcCMQ0NDerv7w/EpKWlad68eZo3b57mzJkTOG/fvn3y+/2B8z777DN5vV6lp6eHespARImkGvX5fEpJSQna3IFINdXr1uVyKTU1VU6nU+Xl5crLy9OMGWzFMD1dSL2ejym5Fw7aYzpgZmYDAwPm8/nM5/OZJNuxY4f5fD47efKkmZl98MEHVltba8eOHbMPP/zQ0tLSrKCgYMwYDz30kKWmptoXX3xhhw8ftltvvdUyMzPtzJkzgZg1a9bY9ddfb/X19VZfX2/XXXed5eXlnTO3vr4+S0pKsvvuu8+OHDlilZWVNmvWLCspKRkT91f+S5cutXXr1pnP57OWlpYgrRAQXpFeoy+99JJVVVVZe3u7NTc325NPPmmSrKKiIoirBEwtkV63bW1ttnfvXmtvb7eGhgYrLCy0hIQEO3HiRPAWCZgiJqteT548aT6fz7Zt22YxMTGB7xwYGPjX3MK9F6bxCrK/HvX8z9eGDRvMzKy0tNRSU1PN5XLZVVddZU8//bQNDQ2NGeP333+3Rx991BISEsztdlteXp6dOnVqTMxPP/1kRUVFFhsba7GxsVZUVHRej5f+5ptvbMWKFXbppZdacnKybd26ddzjM8+Wf1pa2oUsCzBlRHqNbt++3ebOnWszZ860+Ph4W758uX366acXvC7AVBbpdXv06FFbvHixud1umzVrluXn55/zEfVAJJuset2wYcNZv6e2tvac+YVzL+z4c3AAAAAAQIhwYzEAAAAAhBiNFwAAAACEGI0XAAAAAIQYjRcAAAAAhBiNFwAAAACEGI0XAAAAAIQYjRcAAAAAhBiNFwAAAACEGI0XAAD/UFdXJ4fDob6+vnCnAgC4SDjMzMKdBAAA4XTzzTdr8eLFevnllyVJfr9fP//8s5KSkuRwOMKbHADgonBJuBMAAGCqiYqKUnJycrjTAABcRLjVEAAwrW3cuFFffvmlSktL5XA45HA4tGfPnjG3Gu7Zs0dxcXH65JNPlJGRoejoaK1du1aDg4N65513lJ6ervj4eD322GMaGRkJjO33+/XEE09ozpw5uuyyy5SVlaW6urrwTBQAEFZc8QIATGulpaVqb2/XokWL9Oyzz0qSWlpaxsX99ttveuWVV1ReXq6BgQEVFBSooKBAcXFxqq6u1vHjx3XPPfdo+fLlKiwslCQ98MAD6ujoUHl5ubxer6qqqrRmzRodOXJE8+fPn9R5AgDCi8YLADCteTweRUVFKTo6OnB7YWtr67i44eFh7dq1S3PnzpUkrV27Vnv37lV3d7diYmK0cOFC3XLLLaqtrVVhYaGOHTum999/X9999528Xq8kqbi4WDU1NSorK9Pzzz8/eZMEAIQdjRcAAOchOjo60HRJUlJSktLT0xUTEzPmWE9PjyTp8OHDMjMtWLBgzDhDQ0O6/PLLJydpAMCUQeMFAMB5cLlcY947HI6zHhsdHZUkjY6Oyul0qrGxUU6nc0zc35s1AMD0QOMFAJj2oqKixjwUIxiWLFmikZER9fT0aMWKFUEdGwAQeXiqIQBg2ktPT1dDQ4M6Ojp0+vTpwFWrC7FgwQIVFRVp/fr1qqys1IkTJ3Tw4EFt375d1dXVQcgaABBJaLwAANNecXGxnE6nFi5cqMTERJ06dSoo45aVlWn9+vV6/PHHlZGRobvuuksNDQ268sorgzI+ACByOMzMwp0EAAAAAFzMuOIFAAAAACFG4wUAAAAAIUbjBQAAAAAhRuMFAAAAACFG4wUAAAAAIUbjBQAAAAAhRuMFAAAAACFG4wUAAAAAIUbjBQAAAAAhRuMFAAAAACFG4wUAAAAAIfYfI5kHmba4c0cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAHPCAYAAABDfQs9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAACCrklEQVR4nOzdd3hTZf8G8Psk3XuXLjpp2aW07L2HgBORvQVRQBFU1FdFERQFcbwMQUD2UEBUQPYulJaWVXb3pjPdIzm/P6r17Y+VlKYnae/PdeXSnpyT3OlFIN88z/N9BFEURRAREREREZHWyKQOQEREREREVN+x8CIiIiIiItIyFl5ERERERERaxsKLiIiIiIhIy1h4ERERERERaRkLLyIiIiIiIi1j4UVERERERKRlLLyIiIiIiIi0jIUXERERERGRlrHwIiIiIiIi0jIWXk/h1KlTGDp0KFxdXSEIAvbu3avxY+zcuRNt2rSBmZkZPD098dVXX9V+UCIiIiIikhQLr6dQWFiIwMBA/PDDDzW6/sCBAxg9ejSmT5+Oa9euYcWKFVi2bFmNH4+IiIiIiHSTIIqiKHWI+kAQBOzZswfPPfdc1bGysjJ8+OGH2LJlC3Jzc9GyZUt8+eWX6NmzJwBg1KhRKC8vx65du6quWb58OZYuXYqEhAQIglDHr4KIiIiIiLSBI15aNHHiRJw9exbbt2/HlStXMHz4cAwcOBB37twBAJSWlsLExKTaNaampkhKSkJ8fLwUkYmIiIiISAtYeGnJvXv3sG3bNuzatQvdunWDr68v5s6di65du2L9+vUAgAEDBmD37t04evQoVCoVbt++jeXLlwMAUlNTJUxPRERERES1yUDqAPXVpUuXIIoi/P39qx0vLS2Fvb09AGDq1Km4d+8ehgwZgvLyclhZWWH27Nn45JNPIJfLpYhNRERERERawMJLS1QqFeRyOSIiIh4ooiwsLABUrgv78ssvsWjRIqSlpcHR0RFHjx4FAHh5edV1ZCIiIiIi0hIWXloSFBQEpVKJjIwMdOvW7bHnyuVyuLm5AQC2bduGTp06wcnJqS5iEhERERFRHWDh9RQKCgpw9+7dqp9jY2MRFRUFOzs7+Pv7Y/To0Rg3bhyWLl2KoKAgZGZm4tixY2jVqhUGDx6MzMxM/PLLL+jZsydKSkqwfv167Nq1CydPnpTwVRERERERUW1jO/mncOLECfTq1euB4+PHj8eGDRtQXl6OhQsXYuPGjUhOToa9vT06deqEBQsWoFWrVsjMzMTQoUNx9epViKKITp064fPPP0eHDh0keDVERERERKQtLLyIiIiIiIi0jO3kiYiIiIiItIyFFxERERERkZaxuUYNqFQqpKSkwNLSEoIgSB2HiIiIiIgkIooi8vPz4erqCpns0eNaLLxqICUlBR4eHlLHICIiIiIiHZGYmAh3d/dH3s/CqwYsLS0BVP5yraysJE5DRERERERSUSgU8PDwqKoRHoWFVw38M73QysqKhRcRERERET1xCRKbaxAREREREWkZCy8iIiIiIiItY+FFRERERESkZSy8iIiIiIiItIyFFxERERERkZax8CIiIiIiItIyFl5ERERERERaxsKLiIiIiIhIy1h4ERERERERaRkLLyIiIiIiIi1j4UVERERERKRlLLyIiIiIiIi0jIUXERERERGRlrHwIiIiqsfCYrMxbl0Y9kQmSR2FiKhBM5A6ABEREdU+pUrEkr9u4sdTMRBF4OzdTDhbmaCzr4PU0YiIGiSOeBEREdVDq07ew+qTlUWXt4M5lCoRb2yNRFJOkdTRiIgaJBZeRERE9cyttHx8e+QOAGDhcy1xYHY3tHC1QnZhGf6z95rE6YiIGiYWXkRERPVIQWkF5u66jDKlCn2aOmF0h8YwMZTj+5FBkAnA8Vv3cSUpV+qYREQNDgsvIiKieiIqMRfPfHcaV5PzYG1qiEUvtIIgCAAAH0cLDAt0BQB8f+yulDGJiBokFl5ERET1wLXkPLy8OhTxWUVwtTbBugkhcLYyqXbOG739IAjA4eh03EhVSJSUiKhhYuFFRESk54rLlJi9PRJlFSp0a+KAA7O7I9jT7oHz/JwsMbiVCwDgm8O36zomEVGDxsKLiIhIx9zNKMCqk/egKCl/4rmiKOLz/dG4d78QTpbG+O6VIFibGT7y/Df7NIFMAA5FpyMiPrs2YxMR0WOw8CIiItIhBaUVGPvTBXxx4CbG/RSG/McUX6UVSrz76xVsPp8AAFj6ciBszY0e+/hNnC0xPNgDALB4/02Iolh74YmI6JFYeBEREemQr/+6hdS8EgCVzTLGrwtDdmHZA+elK0rwyo/nsTM8CTIB+GhIc3Rr4qjWc7zVzx8mhjKEx+fgr+vptZqfiIgejoUXERGRjohKzMXPoXEAgA8GN4OViQEuJeRi0LencO5eZtV5EfE5GPL9GUQm5MLa1BAbJrbHpK7eaj9PI2sTTP77/E9/v47C0opafR1ERPQgFl5EREQ6Ytnh2xBF4IUgN0zt7oOd0zvB19Ec6YpSjF57AUsP3cLWCwl45cdQ3M8vRYCzJfa90QXd/dUb6fpfr/fyg4edKVLySrD0EBttEBFpmyBycrfGFAoFrK2tkZeXBysrK6njEBFRPZCSW4wuXx6DKAKn5vVCY3szAEBRWQUW7IvGjvDEaucPbNEIS18OhLmxQY2f8+Tt+xi/LgyCADhZGkMmCFg5JhhtPGye5qUQETUo6tYGHPEiIiLSAbsvJUEUgY4+dlVFFwCYGRngy5da4/uRQbA0NoAgAHP7+2PlmLZPVXQBQA9/R7wQ5AZRBNIVpUjNK8G7v1xBuVL1tC+HiIj+n6f7G5uIiIiemiiK2BWRBABVHQf/v6GBrujsa4/c4nL4OlrU2nN/8WJrvNK+MWQC8OqmCNxKz8fa07F4radvrT0HERFxxIuIiEhyYbHZiM8qgoWxAQa1avTI8+wtjGu16AIAIwMZ2nvbIcTLDu8PbgYA+PbobSTlFNXq8xARNXQsvIiIiCQUnaLAWzuiAADPtHKBmZF0k1FebOuG9l52KClXYVNovGQ5iIjqIxZeREREElCpROy4mICXVp1DSl4JfBzMMbtvE0kzCYKAqd19AAA7wxNRWqGUNA8RUX3CNV5ERER1LC2vBG9svYTw+BwAQBc/e6wYFQxrM0OJkwG9AhzhYm2C1LwSHLyWhmfbuEkdiYioXuCIFxERUR0qrVBi2qZwhMfnwMxIjg+faYafJ7bXiaILAAzkMrzSrjEAYMuFBInTEBHVHyy8iIiI6tCnv0fjclIebMwMsX9WN0zp5gMDuW79czyinQfkMgFhsdm4npIndRwionpBt/6mJyIiqsfWnYnFlgsJEARg+Yg28HIwlzrSQzWyNsHgVi4AgIV/3IAoihInIiLSfyy8iIiI6sDP5+Lw6R/RAIC5/QPQM8BJ4kSP986AABgbyBAak4WD19KkjkNEpPfYXIOIiKiGRFHEhnNxCI/PgZ2ZEZq7WmFooCssjP/95zWnsAyL9t+o2iB5Rk9fzNCDzYk97MwwrYcvvjt6Bwv/vIFeTZ1gYiiXOhYRkd5i4UVERFQDoihiwe/R2HAurtrxhX9EY0CLRujoY4+bafnYHZmE3KJyCAIws5cf3urnD0EQpAmtodd6+OKX8EQk5xZj9ckYydvdExHpM0HkxG2NKRQKWFtbIy8vD1ZWVlLHISIiCXz110389/g9AMC07j6QyQT8dT0NMfcLHzjX39kCi19ohWBPu7qO+dR+v5yCmdsiYWIow9G3e8LNxlTqSEREOkXd2oAjXkRERBqKyyzEyhOVRdei51thVIfK9uvvDAjAxbgcnLiVgYtx2XCyMsFLbd3RrYmDznUuVNeQ1i7YdD4eYbHZWLT/Bv47qq3UkYiI9BILLyIiIg39eDoGKrFys+F/ii4AEAQB7b3t0N5b/0a2HkUQBHwytAWGfH8af15JxeSuOWjb2FbqWEREekc/v34jIiKSSEZ+CX75u1HG9B663ySjNjR3tcJLwe4AgGWHbkuchohIP7HwIiIi0sCGs3Eoq1AhqLFNvRrZepKZvZvAUC7gzN1MhN7LkjoOEZHeYeFFRESkpvyScmw6Hw+gcrRLX7oT1gYPOzOMaOcBAFh66BY3VSYi0hALLyIiIjVtvZCA/JIK+Dqao18zZ6nj1Lk3ejWBsYEM4fE52HEx8aHn5JeUo7RC+cTHSssrwfWUPMRnPdgFkoioPmJzDSIiIjWUVijx05lYAMC0Hr6QyRrOaNc/GlmbYG7/AHy+/wY++yManX0d0NjerOr+s3czMWnDRYgi0NrdGrP6NEF3f8cHHuf7o3ew9PC/a8Wm9/DFuwMDGtQIIhE1PBzxIiIiUsPeyGRk5JeikZUJnmvjJnUcyUzq6o323nYoLFNi5vZIZBaUAgDuZuRj+uYIlFaoUKZUITw+B9M2RSA2s/qI1oWYLCw7Ull0OVgYAwBWnbyHb47c4fRFIqrXWHgRERE9QVRiLhbtvwkAmNzVG0YGDfefT7lMwNLhgbA0NsDlxFwM+vY05u++ipFrLiC/pALBnrY4/FZ3dPC2Q3G5Em/uiEK5UgUAUJSUY87OyxBF4OUQd4R/2BcfDWkOAPju6B0MXxWKM3cyWYARUb0kiPzbTWPq7k5NRET67+zdTLy6MRyFZUq08bDBtqkdYWoklzqW5G6n5+P1LZdwJ6Og6pinvRl2v9YZ9hbGSMktxsDlp6AoqcDgVo0wvpMXPt53HTfT8tHYzgz7Z3eDhXHlioefzsTiywM3UfZ3gRbsaYuhrV1wO6MAliYGmNHTD9amhpK8TiKiJ1G3NmDhVQMsvIiI6r8KpQrfH7uL74/dgUoEOvvaY824EJgbc3n0P4rLlFh3Nha5RWUIamyL7v6OVcUUABy4mooZWy/hfz9pOFgYYf2E9mjlbl3tsdLySrDq5D1sDUtAWYWq2n1e9mZYNTYYTRvx31wi0j0svLSIhRcRUf2WWVCKmVsjERpTuV/VS8HuWPhcS5gYcqRLU1GJufj8z2hcjMtBsKct/juqLRpZmzzy/HRFCdacisHtjAIEOFtg/9U0JOcWw8LYAAff7AZ3W7NHXktEJAUWXlrEwouIqP66lJCDGZsvIU1RAnMjORa90ArPNuBmGrVBFEXEZRWhsZ0Z5Bp2g8wpLMOEDRdxOTEXfZs5Y+34EC2lJCKqGXVrg4a7OpiIiOh/iKKITefjMWJ1KNIUJfB1NMdvb3Rh0VULBEGAt4O5xkUXANiaG+Hrl1rDQCbgyI10HI5O10JCIiLtY+FFREQNnkolYsHv0fjP3msoV4oY1LIRfnujK/ycLKWORgCaOFtiancfAMAn+66jqKxC4kRERJpj4UVERA2aUiXi/T1XseFcHADgvUFNsWJ022pNIkh6s3o3gZuNKZJzi/Ht0TtSxyEi0hgLLyIiatBWnriL7RcTIROAr4cHYnoPXwiC5lPiSLtMjeT49NkWAICfTsfiVlq+xImIiDTDwouIiBqslNxi/HD8LgBg0fOt8FKwu8SJ6HH6NHNG/+bOqFCJ+HDvVW60TER6hYUXERE1WIsP3ERJuQrtvGwxop2H1HFIDR8PawFTQzkuxuXgwLU0qeMQEamNhRcRETVI4XHZ+P1yCgQB+HhoC04v1BNuNqZ49e9GG18cuInSCqXEiYiI1MPCi4iIGqSlh24DAEaEeKClm7XEaUgTr3b3gaOlMRKyi7ApNF7qOEREamHhRUREDU5YbDZCY7JgKBcws08TqeOQhsyNDTC3vz8AYMWJeyhXqiRORET0ZCy8iIiowfn2aOVo10vBHnCzMZU4DdXEi23d4WBhhOzCMpy9myl1HCKiJ9LrwuvUqVMYOnQoXF1dIQgC9u7dq/a1Z8+ehYGBAdq0aaO1fEREpHtC72Xh7N0sGMgEzOjpK3UcqiEDuQyDW7kAAH6/nCpxGiKiJ9PrwquwsBCBgYH44YcfNLouLy8P48aNQ58+fbSUjIiIdFFRWQXe230FADCinQc87MwkTkRPY2igKwDg0PU0lJSzyQYR6TYDqQM8jUGDBmHQoEEaXzdt2jSMGjUKcrlco1EyIiLSb1/9dQvxWUVwsTbBu4OaSh2HnlJwY1u4WJsgNa8EJ2/fx4AWjaSORET0SHo94lUT69evx7179/Dxxx+rfU1paSkUCkW1GxER6ZdzdzOx4VwcAOCLF1vDysRQ2kD01GQyAUNaV0433Hc5ReI0RESP16AKrzt37uC9997Dli1bYGCg/mDf4sWLYW1tXXXz8OAmm0RE+iQjvwSztkdBFIGR7T3Qw99R6khUS55t4wagcrphcm6xxGmIiB6twRReSqUSo0aNwoIFC+Dv76/RtfPnz0deXl7VLTExUUspiYiotlUoVZi9LQqZBaUIcLbER0NaSB2JalFLN2t08rFHuVLEjyfvSR2HiOiR9HqNlyby8/MRHh6OyMhIvPHGGwAAlUoFURRhYGCAQ4cOoXfv3g+91tjYGMbGxnUZl4iIaoEoivho33WExmTBzEiO/45uC1MjudSxqJbN7O2H0JgsbLuYiNd7+8HJ0kTqSERED2gwI15WVla4evUqoqKiqm7Tp09HQEAAoqKi0KFDB6kjEhFRLVtx4h62XkiAIADLXm4DPycLqSORFnTytUdQYxuUVaiw9nSs1HGIiB5Kr0e8CgoKcPfu3aqfY2NjERUVBTs7OzRu3Bjz589HcnIyNm7cCJlMhpYtW1a73snJCSYmJg8cJyIi/VZaocTCP25g0/l4AMAnQ1tgYEt2vKuvBEHAG738MPnncGwPS8Bbff05sklEOkevR7zCw8MRFBSEoKAgAMCcOXMQFBSEjz76CACQmpqKhIQEKSMSEVEdO3cvEy+sOFdVdM3t74/xnb2kDUVa1yvACe62plCUVODPq9xQmYh0jyCKoih1CH2jUChgbW2NvLw8WFlZSR2HiIgApOWV4P09V3HsZgYAwMbMEN+MaINeAU4SJ6O68t/jd/HVX7fQtrENds/oInUcImog1K0N9HrEi4iICAAOXkvDgOWncOxmBgzlAsZ38sTht3qw6Gpghoe4w0Am4FJCLm6mcc9NItItLLyIiEiv/XElBa9tiUBecTlauVnjwOxuWPBsSzhashttQ+NkaYJ+zZ0BAFvOc6kBEekWFl5ERKS3jt/KwFs7KjdGfjnEHb++1hl+TpZSxyIJje3oCQDYEZ6ItLwSidMQEf2LhRcREeml2+n5eH3LJZQrRQxp7YLFL7SGkQH/WWvoOvnao72XHcoqVPjh+B2p4xARVeG/UEREpHfyissxbVMEisqU6ORjj2Uvt4FcJkgdi3SAIAh4u78/AGDHxUQkZhdJnIiIqBILLyIi0isqlYi3dkQhNrMQbjam+GFUEEe6qJoOPvbo1sQB5UoR3xy+LXUcIiIALLyIiEjPLD96B8duZsDYQIbVY4Nhb8EmGvSguf0DAAC7I5NxOTFX2jBERHjKwqugoAAKhaLajYiISFv+up6G745WrttZ/EIrtHSzljgR6apADxu8EOQGAPj0j2hw21IikprGhVdsbCyeeeYZmJubw9raGra2trC1tYWNjQ1sbW21kZGIiAin79zHzG2RAIAJnb3wQlt3iRORrntnYFOYGsoREZ+DfZdTpI5DRA2cgaYXjB49GgCwbt06ODs7QxC4mJmIiLQr9F4WpvwcjrIKFfo1d8YHzzSTOhLpgUbWJpjR0xdLD9/Ggt+j0cXPAQ6cmkpEEtG48Lpy5QoiIiIQEBCgjTxERETVJGQVYfrmCJRWqNC7qRN+GBUEQzmXKJN6Xu3hgz+vpuJmWj4++u0aVowOljoSETVQGv/L1a5dOyQmJmojCxERUTVFZRV4dVM48orLEehhgxWj28LYQC51LNIjxgZyfD08EHKZgP1X07D1QgKAyu6Y2YVlEqcjooZE4xGvtWvXYvr06UhOTkbLli1haGhY7f7WrVvXWjgiImq4RFHEvF+u4GZaPhwsjLF6TDBMDFl0keZaulljZm8/LD9yB+/vuYr4rEKcupOJG6kKDGjhjHkDAuDnZCl1TCKq5zQuvO7fv4979+5h4sSJVccEQYAoihAEAUqlslYDEhFRw/TjqRj8eSUVBjIBK8e0RSNrE6kjkR6b3acJ8orLsf5sHFafiqk6/tf1dBy7mYHtr3ZEsKedhAmJqL7TuPCaNGkSgoKCsG3bNjbXICIirfjzSiq+PHgTAPDx0OZo58UPxPR0BEHAR0Oaw8bUCBvOxeL5IHc807oRvvrrFs7HZGPR/pv4ZXonfq4hIq0RRA03tjA3N8fly5fh5+enrUw6T6FQwNraGnl5ebCyspI6DhFRvVFSrsQXB25iw7k4AMDLIe748sXW/DBMWpOhKEH3r46jpFyFdRNC0Lups9SRiEjPqFsbaNxco3fv3rh8+fJThSMiIvpfoijir+tp6P/Nqaqi69XuPvj8+VYsukirnKxMML6zFwDgq79uQ6XiRstEpB0aTzUcOnQo3nrrLVy9ehWtWrV6oLnGsGHDai0cERHVf+VKFT767Tq2hVV2m3O2Msai51uhTzOOPFDdmN7dF1vPJ+BGqgIbzsVhUldvqSMRUT2k8VRDmezRg2QNpbkGpxoSEdWO/JJyzNhyCafvZEIQgOk9fPFGLz+YG2v8vSDRU9kYGoePfrsOI7kMu2d0Rks3a6kjEZGe0NpUQ5VK9chbQyi6iIioduQUlmH02gs4fScTZkZyrBkbgncHNmXRRZIY29ET/Zs7o0ypwhtbL6GgtELqSERUz2hceBERET2t+/mleOXH87iSlAc7cyPseLUT+jbn1EKSjiAIWPJSa7jZmCIuqwgf7rkKDScFERE9Vo0Kr5MnT2Lo0KHw8/NDkyZNMGzYMJw+fbq2sxERUT2UVVCK0WvP41Z6PpwsjbHj1Y5o5c5pXSQ9GzMjfPtKG8hlAvZGpeCXiCSpIxFRPaJx4bV582b07dsXZmZmmDVrFt544w2YmpqiT58+2Lp1qzYyEhFRPZH99/TC2+kFcLYyxo5pndDE2VLqWERVQrzsMKefPwDgo9+uI11RInEiIqovNG6u0axZM7z66qt46623qh1ftmwZ1qxZgxs3btRqQF3E5hpERJrLLSrDqDUXEJ2qgKOlMba/2hG+jhZSxyJ6gEol4sVV5xCZkIvpPXzx3qCmUkciIh2mteYaMTExGDp06APHhw0bhtjYWE0fjoiIGoCsglKM/SkM0akKOFgYYdvUDiy6SGfJZAJe7+kHANhyPh75JeUSJyKi+kDjwsvDwwNHjx594PjRo0fh4eFRK6GIiKj+iLlfgOdXnMPV5DzYmxth69SO8HPi9ELSbb2bOsHPyQL5pRVVe8wRET0NjXv2vv3225g1axaioqLQuXNnCIKAM2fOYMOGDfj222+1kZGIiPRUWl4JXloViuzCMnjYmWL9hPbwc+JIF+k+mUzAq9198M4vV/DTmViM7egFUyO51LGISI9pXHi99tpraNSoEZYuXYqdO3cCqFz3tWPHDjz77LO1HpCIiPSTKIqYv/sKsgvL0MzFCpsmt4eDhbHUsYjU9mwbVyw/fBspeSVY8tdNfDy0hdSRiEiPaVR4VVRU4PPPP8ekSZNw5swZbWUiIqJ64NdLyTh+6z6MDGT4fmQbFl2kd4wN5Pj8hVaYuP4iNpyLw6CWLmjvbSd1LCLSUxqt8TIwMMBXX30FpVKprTxERFQPpOWVYMHv1wEAb/X155ou0lu9Apzwcog7RBGY98tlFJRWSB2JiPSUxs01+vbtixMnTmghChER1QeiKOL9PVeRX1KBQHdrTO3mLXUkoqfywTPN4WptgvisInyw5yo03ImHiAhADdZ4DRo0CPPnz8e1a9cQHBwMc3PzavcPGzas1sIREZH+2ROZjGM3M2Akl+Gr4YEwkGv8HR+RTrE2NcR3I4Mw4sfz+C0qBZ197TGiXWOpYxGRntF4A2WZ7NH/gAqC0CCmIXIDZSKih8sqKEXvpSeRV1yOeQMC8HovP6kjEdWaFSfuYsnBW7A0NsDFD/vCxJBdDolIixsoq1SqR94aQtFFRESP9uXBm8grLkczFyu82t1H6jhEtWp6d1+4Wpsgv7QCJ2/flzoOEekZtQuvxo0bIysrq+rnH374AQqFQiuhiIhI/0TEZ2NneBIAYOFzLWHIKYZUz8hkAga3cgEA/HElVeI0RKRv1P5XMSkpqdqI1vvvv4/MzEythCIiIv0iiiI+3lfZxfDlEHcEe9pKnIhIO4YEugIAjt5IR3EZZ/oQkfpq/HUkO/oQEdE/Qu9l4VqyAmZGcrw7sKnUcYi0JtDdGu62pigqU+L4rQyp4xCRHuE8ECIiemrrzsYBAF5s6w57bpRM9ZggCHim9T/TDVMkTkNE+kSjdvJr166FhYUFAKCiogIbNmyAg4NDtXNmzZpVe+mIiEjnxWcV4ujNdADAhC5e0oYhqgNDW7ti9ckYHInOQIaiBE5WJlJHIiI9oHY7eS8vLwiC8PgHEwTExMTUSjBdxnbyRET/+vT3aKw7G4se/o74eVJ7qeMQ1YmXVp5DeHwOXuvpy+m1RA2curWB2iNecXFxtZGLiIjqkYLSCuwKTwQATORoFzUg03r4InxjODafj8eMnr6wNDGUOhIR6Tiu8SIiohr7NSIJ+aUV8HE0R/cmjlLHIaozfZo6wdfRHPklFdgelih1HCLSAyy8iIioRlQqERvOxQEAJnb2gkz2+OnoRPWJTCZgWndfAMD6s7FQqtjtmYgej4UXERHVyMnb9xGbWQhLEwO80NZd6jhEdW5YG1fYmhkiJa8Ex26ytTwRPR4LLyIiqpGfzsQCAEaEeMDcWKMmuUT1gomhHC+HeAAANp+PlzgNEek6jQqviooK/Pzzz0hLS9NWHiIi0gMR8dk4czcTBjIB4zt7SR2HSDKjOjQGAJy6cx/xWYUSpyEiXaZR4WVgYIDXXnsNpaWl2spDRER64JvDdwAALwW7w8POTOI0RNLxtDdHD39HiCKw9UKC1HGISIdpPNWwQ4cOiIqK0kIUIiLSBxfj/h3ter2Xn9RxiCQ3+u9Rr18vJaNCqZI4DRHpKo0n5c+YMQNz5sxBYmIigoODYW5uXu3+1q1b11o4IiLSLaIoYtmh2wCA4SEeHO0iAtCrqRPszI2QWVCK03cz0SvASepIRKSDNC68RowYAQCYNWtW1TFBECCKIgRBgFKprL10RESkU07evo/QmCwYyWV4ozdHu4gAwFAuw7BAV2w4F4c9l5JZeBHRQ2lceMXGxmojBxER6TiVSsQXB24CAMZ39oSbjanEiYh0x/NBbthwLg5/XU9Dfkk5LE0MpY5ERDpG48LL09NT4yfZt2+fxtf069cPpqb8R52ISFf8djkZN9PyYWligBk9OdpF9L9au1vD19Ec9+4X4sC1tKo280RE/6jRxiubNm3CqlWrEBsbi9DQUHh6emL58uXw9vbGs88++8D5zz33nEaPLwgC7ty5Ax8fn5rEIyKiWlZQWlE12vVaT1/YmhtJnIhItwiCgBfauuOrv27hl/AkFl5E9ACNuxquXLkSc+bMweDBg5Gbm1u1psvGxgbLly9/5HVpaWlQqVRq3czMuFibiEiXfH/0DtIVpWhsZ4ZJXbyljkOkk15s6w65TEBYXDbupOdLHYeIdIzGhdf333+PNWvW4IMPPoBcLq86HhISgqtXrz70mvHjx2s0bXDMmDGwsrLSNBoREWnBnfR8/HSmcn3vJ8Oaw8RQ/oQriBqmRtYm6N20srHGtrBEidMQka7RuPCKjY1FUFDQA8eNjY1RWPjwHdvXr18PS0tLtZ9j5cqVcHBw0DQaERHVspJyJd7cEYUKlYi+zZzRu6mz1JGIdNqoqj29klBSzk7PRPQvjQsvb2/vh26gfODAATRv3rw2MhERkQ4QRRHv776K6ykK2Jkb4dNnW0gdiUjndW/iCDcbU+QVl2P/1VSp4xCRDtG48Jo3bx5ef/117NixA6IoIiwsDJ9//jnef/99zJs3r8ZB7t27h969e9f4eiIiql1rTsdgd2Qy5DIBP4wKgivbxxM9kVwm4JV2lY01tl5IkDgNEekSjbsaTpw4ERUVFXjnnXdQVFSEUaNGwc3NDd9++y1eeeWVGgcpKCjAyZMna3w9ERHVnl3hiVi0v7KL4fuDm6GzL6d/E6nr5XYeWH70DsLjc3A7PR/+zuovtyCi+qtG7eSnTp2KqVOnIjMzEyqVCk5OT96h/bvvvnvs/cnJyTWJQkREtezQ9TS8t7uyWdKr3X0wqYuXtIGI9IyzlQn6NnPCX9fTsfVCAj4Zxmm6RAQIoiiKmlzQu3dv7N69GzY2NtWOKxQKPPfcczh27NhDr5PJZHBxcYGR0cP3fikrK0NaWlpVe3pdplAoYG1tjby8PHZfJKJ6JfReFsavD0NZhQrDg92x5KXWEARB6lhEeufk7fsYvy4MliYGCHu/L0yN2A2UqL5StzbQeMTrxIkTKCsre+B4SUkJTp8+/cjrPD098eWXX+Lll19+6P1RUVEIDg7WNA4REdWSq0l5mLoxHGUVKvRv7ozFL7Ri0UVUQ938HOBhZ4rE7GL8cSUFw7mhMlGDp3bhdeXKlar/j46ORlpaWtXPSqUSBw8ehJub2yOvDw4ORkRExCMLL0EQoOHgGxER1ZJ79wswfn0YCkor0MnHHt+NDIKBXOP+S0T0N5lMwCvtGuOrv25ha1gCCy8iUr/watOmDQRBgCAID+0+aGpqiu+///6R13/66acoKip65P3NmzdHbGysunGIiHROWYUKOUVlcLYykTqKRlJyizF27QVkF5ahlZs1fhwXzE2SiWrB8BB3fHP4NiITcnEjVYFmLlyeQNSQqV14xcbGQhRF+Pj4ICwsDI6OjlX3GRkZwcnJCXL5o/+hftIeX4aGhvD09FQ3DhGRTknNK8aYtRcQm1mIo2/3hLeDudSR1JJdWIaxP11ASl4JfBzMsWFiO1iaGEodi6hecLI0Qf8Wzth/NQ1bLyTgs+daSh2JiCSk9jwST09PeHl5QaVSISQkBJ6enlU3FxeXxxZdj/LFF18gNzdX4+uIiHTJjVQFXloZinv3C6ESgTvp+VJHUouipBwT14fh3v1CuFibYNOUDrC3MJY6FlG9Mqp95ZfKeyOTUVRWIXEaIpJSjSfwR0dH4+DBg9i3b1+1myYWLVqE7OzsmkbAqVOnMHToULi6ukIQBOzdu/ex5+/evRv9+vWDo6MjrKys0KlTJ/z11181fn4iargyFCXYFBqHF1eew6BvTyM5t7jqvrzicgmTqScjvwQjVp/H5aQ82JoZYtPk9nDjBslEta6zrz087c2QX1qBfVEpUschIglp3NUwJiYGzz//PK5evVqtIcY/na80aQf/tM00CgsLERgYiIkTJ+LFF1984vmnTp1Cv379sGjRItjY2GD9+vUYOnQoLly4gKCgoKfKQkQNQ1JOEZYcvIXfr6Tgn7/C5DIBfZs5Ib+kAufuZUFRotvfal9LzsNrWyKQmF0MBwtj/DypHfycuMErkTbIZAJGtW+MxQduYmNoPEa082C3UKIGSuPCa/bs2fD29saRI0eq1ntlZWXh7bffxtdff62NjI80aNAgDBo0SO3zly9fXu3nRYsW4bfffsPvv//OwouIHksURfx8Lg6LDtxEWYUKABDU2AaDW7rg2TaucLIywYd7r1YWXjo64iWKItafjcMXB26iTKlCYzszbJrcHp72+rEejUhfvRzigWWHbyM6VYGI+ByEeNlJHYmIJKBx4RUaGopjx47B0dERMpkMMpkMXbt2xeLFizFr1ixERkaq/VjR0dGPbUGvbSqVCvn5+bCze/xfgKWlpSgtLa36WaFQaDsaEemQsgoVPt53DdvCEgFUTh16f3AztHSzrnae1d9NKRQl0hVehaUVKClXwsbMCHLZv9+qZxeWYd6uyzh6MwMA0K+5M5a82Bq25g/f1J6Iao+tuRGea+OGHeGJ2HAujoUXUQOlceGlVCphYWEBAHBwcEBKSgoCAgLg6emJW7duPfH6xMRECIIAd3d3eHh4ICwsDFu3bkXz5s3x6quvav4KnsLSpUtRWFj4yL3F/rF48WIsWLCgjlIRkS6pUKowc9sl/HU9HYIAvD+oGaZ0837oVCEr078Lr+K6m2pYVqHC0Rvp+PVSEi7G5VStLxMEwNbMCHbmRiirUCEltxgVKhFGBjJ8+EwzjO3oyelORHVoXGdP7AhPxMFraUhXlOjdthNE9PQ0bq7RsmXLqs2UO3TogCVLluDs2bP49NNP4ePj88TrR40ahePHjwMA0tLS0K9fP4SFheH999/Hp59+qmmcGtu2bRs++eQT7NixA05OTo89d/78+cjLy6u6JSYm1lFKIpKSUiXi7V2X8df1dBjJZVgzNgRTu/s8smCpyxEvURRxODod/b45ide2XMKRGxnVmnqIYuUo192MAiRkF6FCJcLf2QJ7Z3TBuE5eLLqI6lgLV2u087JFhUrEurPct5SoIdJ4xOvDDz9EYWEhAGDhwoUYMmQIunXrBnt7e+zYseOJ11+7dg3t27cHAOzcuRMtW7bE2bNncejQIUyfPh0fffSRppE0tmPHDkyePBm7du1C3759n3i+sbExjI3ZYpmooVm8/wZ+i0qBgUzAitFt0be582PPtzKt/CtV22u88kvK8e6vV7D/ahoAwMHCCC8Fe+CZVi7wdDCDqaEcOUVlyC4sQ3ZBGeQyAY3tzeBsaQKZjAUXkVSm9/DFxbhwbDwXj6ndfODA7RuIGhS1C68jR46gS5cuGDBgQNUxHx8fREdHIzs7G7a2tmp9g1peXl5VxBw5cgTDhg0DADRt2hSpqama5tfYtm3bMGnSJGzbtg3PPPOM1p+PiPTTzouJWHum8lvppS8HPrHoAv53xEt7Uw1vpinw2uZLiM0shKFcwOSuPnijtx8sjKv/de5kaQInS05lItIlvZs6obW7Na4k5WHNqRjMH9xM6khEVIfUnmrYv39/2Nraonv37vj4449x4sQJlJWVAQDs7OzUnrbSokULrFq1CqdPn8bhw4cxcOBAAEBKSgrs7e01Cl9QUICoqChERUUBAGJjYxEVFYWEhAQAlVMEx40bV3X+tm3bMG7cOCxduhQdO3ZEWloa0tLSkJeXp9HzElH9djEuGx/svQoAmN2nCZ5to14ToH/XeGlnxGv3pSQ899+ziM0shKu1CXZO64T3BjV9oOgiIt0kCALe7NsEALAxNB6ZBaVPuIKI6hO1C6/ExESsWbMG/v7+2Lx5M3r37g0bGxv06dMHCxcuxNmzZ1FR8eRveb/88kusXr0aPXv2xMiRIxEYGAgA2LdvX9UURHWFh4cjKCioqhX8nDlzEBQUVDVdMTU1taoIA4DVq1ejoqICr7/+OlxcXKpus2fP1uh5iaj+SswuwrRNEShXihjcqhFm92mi9rVWJn9PNazlNV6lFUp8sOcq5uy8jJJyFbo1ccAfs7ohqLFtrT4PEWlfrwAnBHrYoLhcidUn70kdh4jqkCDWcBfjxMREHD9+HCdOnMCJEycQHx8PMzMz5OfnP/FapVIJhUIBW9t/PzTExcXBzMzsiY0udIFCoYC1tTXy8vJgZWUldRwiqiWFpRV4ceU53EzLRwtXK+ya3glmRuqPJmUWlCJk4REIAnDv88G1sp4qLa8E0zaF43JSHgQBmNW7CWb1aVKtVTwR6ZfjtzIwcf1FmBjKcOqdXpwWTKTn1K0Najw/xcPDA126dKna4yorKwtKpVKta+VyebWiCwC8vLxqGoWIqFYsPnADN9Py4WhpjLXjQzQqugDA8u8RL1EE8ksrYP331MOayiwoxai15xFzvxA2ZoZYPqINegbo/pdTRPR4Pf0d0cbDBlGJuVh9Mgb/GdJc6khEVAc0aicfExODdevWYezYsXB3d0fbtm2xe/dutGzZEgcOHEBOTo62chIRadXpO/ex+Xzl1ORvXm4DF2tTjR/D2EAOE8PKv1afdp1XXlE5xv4Uhpj7hXCzMcXvb3Rl0UVUT/zvWq/N5+ORoSiROBER1QW1v8719PSEQqFA165d0b17d8ycORPBwcGQy+XazEdEpHWKknK880vl/oTjOnmiaxOHGj+WlYkhSspLn2qdV2FpBSZuCMONVAUcLIyxeUoHeNiZ1fjxiEj39PB3RNvGNriUkItvjtzB4hdaSR2JiLRM7RGv0tLKzjuCIEAul0Mul0Mm03j/ZSIinfPp79FIzSuBp70Z3hvU9Kke69/OhjVrKV9SrsSrm8JxKSEX1qaG2DylPbwdzJ8qExHpHkEQqtrJ77iYgFtpT14jT0T6Te3KKS0tDaGhoRg8eDAuXLiAZ555Bra2thgyZAi+/vprXLx4ESqVSptZiYhq3dEb6fglIgmCACwdHqjxuq7/72k6G5aUKzF1YzjO3s2CuZEcP09qj6aN2MCHqL5q52WHgS0aQSVWrjElovpNoyGrpk2bYvr06dixY0e1QiwsLAx9+/aFnZ3dEx8jKSkJBQUFDxwvLy/HqVOnNIlDRPRU8orK8d7uyv26pnbzQYjXk/8Oe5Ka7uVVVFaBKT+H4/SdTJgZyfHThHZo42Hz1HmISLe9N6gpDOUCTty6j03n46WOQ0RaVOO5gunp6bhy5QquXLmCy5cvIz8/v2o64sOkpqaiffv28PT0hI2NDcaPH1+tAMvOzkavXr1qGoeISGOLD9zA/fxS+DqaY04//1p5TCuTysIrT4PCK7OgFCN/PI8zdzNhbiTHhont0dFHsw3liUg/eTmYV+0X+NFv1/DHlRSJExGRtqhdeGVkZGDnzp2YMWMGmjVrBldXV4wfPx7R0dF45ZVXcOzYMeTm5j7y+vfeew9yuRwXLlzAwYMHER0djZ49e1brhFjDLcWIiDQWFpuN7RcTAQBfvNgaJoa10yjIyvSfqYbqrfGKyyzEiyvP4XJSHmzNDLFpSge09376kTci0h+v9/LD2I6eEEXgrR1ROHQ9TepIRKQFai9maNSoEQwNDRESEoIXX3wRPXv2RJcuXWBqql7L5SNHjmDPnj0ICQkBAHTr1g0jRoxA7969cfToUQCVC02JiLSttEKJ+bsruxiObO+BdrUwxfAf/4x4qTPVMCoxF5M3XERWYRk87Ezx88T28HG0qLUsRKQfBEHAJ8NaIK+4HPsup+D1rZfww6i2GNCikdTRiKgWqV14HThwAF27doW5ec26a+Xl5VXbNNnY2Bi//PILhg8fjl69emHz5s01elwiIk2tOhGDe/cL4WBhjPcGNqvVx65a4/WY5hqiKGLz+Xh8vv8GSspVaOVmjXUT2sHR0rhWsxCR/pDLBCx7ORAAsO9yCmZujcSht7rDi11NieoNtacaDhgwoMZFFwD4+PjgypUr1Y4ZGBhg165d8PHxwZAhQ2r82ERE6rp3vwD/PX4XAPDR0OawNjOs1cf/d8Tr4VMN84rKMfnncPznt+soKVehd1MnbH+1I4suIoKBXIZlLweii589ypQqfHf0jtSRiKgW1dlGXIMGDcKPP/74wPF/iq82bdrUVRQiaqBEUcQHe66iTKlCD39HDG3tUuvP8e8arwdHvOIyC/H8yrM4djMDRgYyfDy0OdaOC4G58dO1sCei+sNALqsaid8TlYw76dzfi6i+qLPC6/PPP8euXbseep+BgQF2796NmJiYuopDRA3QvsspOB+TDRNDGRY+11Ir60oftcbrbkY+Xlh5DjH3C+FqbYI9MzpjYhdvyGRc20pE1bVyt8aAFs4QRWD5EY56EdUXdVZ4GRgYwMqq+kagZ8+erWpBL5fL4enpWVdxiKiBKSqrwOL9NwEAr/f0g4edmVae5581Xvn/09UwMbsIY9aGIbuwDC3drLD39S5o4WqtlecnovrhrX7+EATgz6upuM1RL6J6oc4Kr4cZNGgQkpOTpYxARA3EiuP3kKYogYedKaZ299Ha81iZ/D3V8O8Rr3v3CzB67QWkKUrQxMkCmyZ1gJOVidaen4jqh6aNrDCgeWVXw1Un70mchohqQ40WFhQWFuLkyZNISEhAWVlZtftmzZql9uNw3y4iqgvxWYX48VTlVOYPn2lea3t2PYz1PyNepRU4cycTM7ZEQFFSAQ87U2ya3AG25kZae24iql9e6+mLg9fTsC8qBXP6+cPdVjsj9URUNzQuvCIjIzF48GAUFRWhsLAQdnZ2yMzMhJmZGZycnDQqvIiI6sLCP2+gTKlCtyYO6N/cWavPZWnyb5fEiRvCUK4U0baxDX4cFwIHC3YuJCL1BXrYoIufPc7ezcKaUzFY8GxLqSMR0VPQeKrhW2+9haFDhyI7OxumpqY4f/484uPjERwcjK+//lqjx1q9ejWcnbX7IYiIGraTt+/jcHQ6DGQCPh7aXOsbtRsZyGD694hauVJE32ZO2Dq1I4suIqqRGT39AADbLyYiLa9E4jRE9DQ0LryioqLw9ttvQy6XQy6Xo7S0FB4eHliyZAnef/99jR5r1KhRT7U3GBHR45RVqLDg9+sAgPGdveDnZFknz2v7995gXfzs8cOotlqd2khE9VtnX3u087JFaYUK33JfLyK9pvFUQ0NDw6pvjJ2dnZGQkIBmzZrB2toaCQkJT7y+pKQE33//PY4fP46MjAyoVKpq91+6dEnTSERED7UxNA4x9wvhYGGE2X2b1NnzvjuoKSITcjFvQACLLiJ6KoIg4J2BTTF8VSh2hidiSjdv+DpaSB2LiGpA48IrKCgI4eHh8Pf3R69evfDRRx8hMzMTmzZtQqtWrZ54/aRJk3D48GG89NJLaN++vdan/RBRw5SRX1K1/807A5pW7a9VF55t44Zn27jV2fMRUf3WzssOfZo64ejNDHz91y2sHBMsdSQiqgGNC69FixYhP79yP4nPPvsM48ePx2uvvQY/Pz+sX7/+idf/+eef2L9/P7p06aJ5WiIiNX118BYKSivQ2t0aLwW7Sx2HiOipzBsYgOO3MnDgWhoOR6ejn5YbBRFR7dO48AoJCan6f0dHR+zfv1+j693c3GBpWTfrLIioYbqZpsAvl5IAAJ8MawGZjCPrRKTfmjaywtRuPlh9Kgbv77mKdl62sDHj9hRE+qRGGyhXVFTgyJEjWL16ddXoV0pKCgoKCp547dKlS/Huu+8iPj6+Jk9NRPRESw7egigCg1s1QtvGtlLHISKqFW/184evoznu55fi09+jpY5DRBrSuPCKj49Hq1at8Oyzz+L111/H/fv3AQBLlizB3Llzn3h9SEgISkpK4OPjA0tLS9jZ2VW7ERE9jbDYbBy7mQG5TMDc/gFSxyEiqjUmhnJ8NTwQMgHYHZmMI9HpUkciIg1oPNVw9uzZCAkJweXLl2Fvb191/Pnnn8eUKVOeeP3IkSORnJyMRYsWwdnZmc01iKhWff3XLQDAiHYe8GHnLyKqZ9o2tsWUbj74sWrKoR2szequeRAR1ZzGhdeZM2dw9uxZGBlVn1fs6emJ5OTkJ15/7tw5hIaGIjAwUNOnJiJ6rPC4bITFZcNQLmBW77prH09EVJfm9PPHkRvpiLlfiAV/XMeyl9tIHYmI1KDxVEOVSgWlUvnA8aSkJLWaZjRt2hTFxcWaPi0R0ROtOnkPAPBCkDsaWZtInIaISDtMDOX46qVACAKw+1Iyjt7glEMifaBx4dWvXz8sX7686mdBEFBQUICPP/4YgwcPfuL1X3zxBd5++22cOHECWVlZUCgU1W5ERDVxOz0fR25kQBCAV3v4SB2HiEirgj1tMaWrNwDg/T1XkVdULnEiInoSQRRFUZMLUlJS0KtXL8jlcty5cwchISG4c+cOHBwccOrUKTg5OT32epmsstb7/2u7RFGEIAgPHU3TNQqFAtbW1sjLy4OVlZXUcYgIwJvbI7E3KgUDWjhj9diQJ19ARKTnSsqVGPztacRkFuKlYHd8PZzLOIikoG5toPEaL1dXV0RFRWH79u2IiIiASqXC5MmTMXr0aJiamj7x+uPHj2v6lEREj3UlKRd7o1IAAK/38pM4DRFR3ajsctgaL60KxS8RSRjcqhF6N+XGykS6SuMRL+KIF5EuEUURw1eFIjw+By8EuWHZiDZSRyIiqlML/4jG2jOxcLYyxqG3esDalF0OieqSurWB2mu87t69i4iIiGrHjh49il69eqF9+/ZYtGhRzdMSEdXQH1dSER6fAxNDGeYN5L5dRNTwzB0QAB8Hc6QrSrHwD26sTKSr1C685s2bh71791b9HBsbi6FDh8LIyAidOnXC4sWLqzXdICLSttS8Ynz02zUAwLTuvnCxfvJ0ZyKi+sbEUI4lL7WGIAC7IpJw/GaG1JGI6CHULrzCw8OrdS3csmUL/P398ddff+Hbb7/F8uXLsWHDBm1kJCJ6gFIlYvb2KOQUlaOFqxVm9PKVOhIRkWRCvOwwqUtll8P5u68ir5hdDol0jdqFV2ZmJtzd3at+Pn78OIYOHVr1c8+ePREXF1er4YiIHmXZ4VsIi82GuZEcP4xqC2MDudSRiIgkNbd/ALwdzJGmKMHnf3LKIZGuUbvwsrOzQ2pqKoDKTZTDw8PRoUOHqvvLysrAPh1EVBd+i0rGf49Xbpa86IVW8HYwlzgREZH0TI0qpxwCwM7wJJy9mylxIiL6X2q3k+/Rowc+++wzrFixArt27YJKpUKvXr2q7o+OjoaXl9cTHycoKOiBPbyAyn29TExM4OfnhwkTJlR7bCKif0Qm5GDeL1cAANN7+OLZNm4SJyIi0h3tvOwwrpMnNobGY/7uqzj4ZjeYGWm8exARaYHaI16ff/45bty4AS8vL7z77rtYsmQJzM3//ZZ506ZN6N279xMfZ+DAgYiJiYG5uTl69eqFnj17wsLCAvfu3UO7du2QmpqKvn374rfffqvZKyKieisltxhTN0agrEKFvs2c8c4AdjEkIvr/3hnYFK7WJkjILsLi/TeljkNEf9NoH6/y8nJER0fD0dERrq6u1e67fPky3N3dYW9v/9jHmDp1Kho3boz//Oc/1Y4vXLgQ8fHxWLNmDT7++GP8+eefCA8P1+Cl1B3u40VU9wpLK/DSqlDcSFWgaSNL/PJaZ1gY81tcIqKHOXX7PsatCwMArBoTjIEtG0mciKj+Urc2qPMNlK2trREREQE/P79qx+/evYvg4GDk5eXh5s2baNeuHfLz8+symtpYeBHVLZVKxPTNETgUnQ4HCyPsfb0L3G3NpI5FRKTTFu+/gdWnYmBlYoD9s7vx700iLan1DZRri4mJCc6dO/fA8XPnzsHExARAZfMOY2Pjuo5GRDpq6eFbOBSdDiO5DKvHhvDDAxGRGt7uH4BADxsoSiowe3sUKpQqqSMRNWh1Pk9n5syZmD59OiIiItCuXTsIgoCwsDCsXbsW77//PgDgr7/+QlBQUF1HIyIdtCcyqaqD4RcvtkKwp63EiYiI9IORgQzfvxKEZ747jYj4HCw/cgdzuTaWSDJ1PtUQqNx8+YcffsCtW7cAAAEBAZg5cyZGjRoFACguLq7qcqiLONWQqG5ExOdg5JrzKKtQ4bWevnh3YFOpIxER6Z0/r6Ti9a2XIAjA5skd0MXPQepIRPWKzq7xehxRFB/aal7XsPAi0r7E7CI8v+IsMgvK0L+5M1aNCYZMpvt/PxAR6aL5u69iW1gCHC2NcWB2NzhYcEkHUW3R6hqv3NxcLF26FFOmTMHUqVOxbNky5OXlqXXt4sWLH3pcqVRWjXgRUcOWnFuMV348j8yCMjRzscI3I9qw6CIiegofDWkOf2cL3M8vxZydl6FS6cz37kQNhsaFV3h4OHx9ffHNN98gOzsbmZmZ+Oabb+Dr64tLly498frly5fjxx9/rHZMqVTilVdeQVRUlKZxiKieScktxqg155GcWwxvB3P8PLEdzNk2nojoqZgayfHDqLYwMZTh1O37+Dk0TupIRA2OxoXXW2+9hWHDhiEuLg67d+/Gnj17EBsbiyFDhuDNN9984vX79+/Hu+++i507dwKo3Bts+PDhuH79Oo4fP67xCyCi+iMusxDDV4UiPqsIHnam2Dq1A5ysdHOtJxGRvvF3tsQHg5sBAJYcvIX4rEKJExE1LBqv8TI1NUVkZCSaNq2+yD06OhohISEoKip64mOcOHECzz77LDZu3IiffvoJ9+7dw7Fjx+Ds7KxZeolwjRdR7YvNLMSI1aHIyC+Ft4M5tkzpAFcbU6ljERHVKyqViFFrz+N8TDY6+thh65SOnMpN9JS0tsbLysoKCQkJDxxPTEyEpaWlWo/Rs2dPbNq0CS+99BLi4uJw8uRJvSm6iKj2JeUUYfSa88jIL0WAsyV2TOvIoouISAtkMgFLXgyEqaEc52OysTM8UepIRA2GxgsnRowYgcmTJ+Prr79G586dIQgCzpw5g3nz5mHkyJEPveaFF1546HFHR0fY2Njg1VdfrTq2e/duTSMRkR7LLSrDmLUXkJJXAh9Hc2yZ2oHdtoiItKixvRne7u+PhX/ewBcHb6J/i0awMzeSOhZRvadx4fX1119DEASMGzcOFRUVAABDQ0O89tpr+OKLLx56jbW19UOPDxgwQNOnJ6J6RKUS8fbOy4jLKoK7rSm2TunIoouIqA5M6OyFXy8l40aqAov338BXwwOljkRU79V4H6+ioiLcu3cPoijCz88PZmZmtZ1NZ3GNF1HtWHHiLpYcvAUjAxn2zOiMFq4P/5KGiIhq36WEHLy48hxEsXJj5a5NuLEyUU1obY3XpEmTkJ+fDzMzM7Rq1QqtW7eGmZkZCgsLMWnSpKcKTUQNx7XkPCw9dBsA8NmzLVh0ERHVsbaNbTG2oycAYO6uy8gtKpM4EVH9pnHh9fPPP6O4uPiB48XFxdi4ceNDr2nbti1ycnLUfo6uXbsiOTlZ02hEpCcqlCrM330VSpWIZ1q54OUQD6kjERE1SO8NagofB3OkKUrwn9+uSx2HqF5Te42XQqGAKIoQRRH5+fkwMfl3bx2lUon9+/fDycnpoddGRUXh8uXLsLOzU+u5oqKiUFpaqm40ItIzG87F4WpyHqxMDPDxsOYQBLYyJiKSgpmRAZaNaIMXV57D75dT0LeZE55t4yZ1LKJ6Se3Cy8bGBoIgQBAE+Pv7P3C/IAhYsGDBI6/v06cP1F1Oxg9hRPXXtrAEfHnwJgDg/cHN4GTJDZKJiKTUxsMGs3o3wTdHbuPDvdfQzsuOW3oQaYHahdfx48chiiJ69+6NX3/9tdrolZGRETw9PeHq6vrQa2NjYzUO5u7urvE1RKS7UnKL8c3h29gVkQQAGBboyimGREQ64vVevjh2KwOXE3Mxd9dlbJnSgV+EE9UyjbsaxsfHw8PDAzKZxsvD6g12NSRSX0puMVacuIudF5NQplQBAOb298frvfz4jzoRkQ6JzSzE4G9Po7hciVVjgjGwZSOpIxHpBXVrA4338fL09ERubi7CwsKQkZEBlUpV7f5x48ZpnpaI6p3k3GKsOH4XO8MTUa6s/H6no48d5vQLQHtv9dZ7EhFR3fF2MMfkrt744fhdLD10C/2aO0Mu4xdkRLVF48Lr999/x+jRo1FYWAhLS8tq31j/s7EyETVcoihi+ZE7WHHiblXB1cnHHrP7NkFHH3uJ0xER0eNM7e6DjaFxuJNRgH2Xk/F8EJd+ENUWjecLvv3221V7eeXm5iInJ6fqlp2drY2MRKQnSiuUmL09Ct8evYNypYjOvvbY8WpHbHu1I4suIiI9YG1qiOk9fQEA3xy+g7IK1ROuICJ1aVx4JScnY9asWTAzM9NGHiLSU0qViBmbL2Hf5RQYyAQseak1tk7tiA4suIiI9MqEzl5wsDBGQnYRdoYnSh2HqN7QuPAaMGAAwsPDa/yEiYmJSEpKqvo5LCwMb775Jn788ccaPyYRSW/x/hs4ejMDxgYybJjYnh0LiYj0lJmRAd7oVTnq9d3ROygpV0qciKh+0HiN1zPPPIN58+YhOjoarVq1gqGhYbX7hw0b9tjrR40ahVdffRVjx45FWloa+vXrhxYtWmDz5s1IS0vDRx99pGkkIpKQKIpYezoWa89Ubhux9OVAdG3iIHEqIiJ6GiM7NMaa07FIzi3GxtA4vNrdV+pIRHpP43byj2sjLwgClMrHfytia2uL8+fPIyAgAN999x127NiBs2fP4tChQ5g+fTpiYmI0iSMJtpMnqlSuVOGTfdex5UICAOCtvv6Y3beJxKmIiKg27AxPxDu/XIGtmSFOvdMLliaGT76IqAFStzbQeKqhSqV65O1JRRcAlJeXw9jYGABw5MiRqhGypk2bIjU1VdM4RCSRknIlXtscgS0XEiAIwPxBTTGrj5/UsYiIqJa8EOQGH0dz5BSV46e/ZzUQUc3V+S7ILVq0wKpVq3D69GkcPnwYAwcOBACkpKTA3l6zRfinTp3C0KFD4erqCkEQsHfv3idec/LkSQQHB8PExAQ+Pj5YtWpVTV4GUYNWVFaByT9fxJEblWu6Vo8JxrQevtwQmYioHjGQyzCnnz8AYO3pWGQXlkmciEi/qbXG67vvvsOrr74KExMTfPfdd489d9asWY+9/8svv8Tzzz+Pr776CuPHj0dgYCAAYN++fWjfvr2asSsVFhYiMDAQEydOxIsvvvjE82NjYzF48GBMnToVmzdvxtmzZzFjxgw4OjqqdT0RAYqSckxafxHh8TkwM5Ljp/Ht0MmXnQuJiOqjwS1d0NzlHqJTFVh18h7eH9xM6khEekutNV7e3t4IDw+Hvb09vL29H/1ggvDYNVqiKCIhIQG2trZQKpWwtbWtui8uLg5mZmZwcnLS8CX8+9x79uzBc88998hz3n33Xezbtw83btyoOjZ9+nRcvnwZoaGhaj8X13hRQ5VVUIrx68NwLVkBKxMDbJjUHm0b2z75QiIi0lvHb2Zg4oaLMDaQ4dQ7veBsZSJ1JCKdom5toNaIV2xs7EP/X1OiKKJJkya4fv06mjSpvgDfy8urxo+rrtDQUPTv37/asQEDBuCnn35CeXn5Ax0a/1FaWorS0tKqnxUKhVZzEumiW2n5mPzzRSTlFMPO3AibJrdHC1drqWMREZGW9QxwRIinLcLjc/D9sTtY+FwrqSMR6aU6XeMlk8nQpEkTZGVl1eXTVklLS4Ozs3O1Y87OzqioqEBmZuYjr1u8eDGsra2rbh4e3J+IGpbQe1l4YcVZJOUUw9PeDDundWLRRUTUQAiCgLkDAgAA28MSkZBVJHEiIv1U5801lixZgnnz5uHatWt1/dQA8MDi/39mWj6uKcD8+fORl5dXdUtM5C7u1HCcu5uJiRvCUFimRAdvO+yd0QV+ThZSxyIiojrU0cce3Zo4oEIlYvnR21LHIdJLGm+g/LTGjBmDoqIiBAYGwsjICKamptXuz87O1tpzN2rUCGlpadWOZWRkwMDA4LEdFY2Njata4BM1FMVlSqw9HYMfjt9FaYUKPQMcsWpMMEwM5VJHIyIiCcztH4DTdzKxLyoF8wYEwMXa9MkXEVGVOi+8li9fXtdPWaVTp074/fffqx07dOgQQkJCHrm+i6ghis8qxKg1F5CcWwwA6NvMCf8d3RbGBiy6iIgaqkAPG3TwtsOF2Gz8fC4e7w1qKnUkIr1S54XX+PHja+2xCgoKcPfu3aqfY2NjERUVBTs7OzRu3Bjz589HcnIyNm7cCKCyg+EPP/yAOXPmYOrUqQgNDcVPP/2Ebdu21VomIn2XV1yOSRsuIjm3GG42pnh3UFMMbe3CPbqIiAhTuvngQmw2tl6Ix8zefjA3rvOPkkR6S+M1Xv/5z3+gVCofOJ6Xl4eRI0eq9Rj37t3Dhx9+iJEjRyIjIwMAcPDgQVy/fl2jLOHh4QgKCkJQUBAAYM6cOQgKCsJHH30EAEhNTUVCQkLV+d7e3ti/fz9OnDiBNm3a4LPPPsN3333HPbyI/lZSrsSMLRG4d78QLtYm2DOjM4YFurLoIiIiAECfpk7wsjeDoqQCv0QkSR2HSK+otY/X//L09ISLiwu2bNkCX19fAMCJEycwbtw4uLm5PXE/rJMnT2LQoEHo0qULTp06hRs3bsDHxwdLlixBWFgYfvnll5q/mjrCfbyoPiosrcCrm8Jx9m4WzIzk+GV6ZzR35Z9vIiKqbmNoHD767Tq87M1w9O2ekMv45Rw1bOrWBhqPeF25cgVeXl5o06YN1qxZg3nz5qF///6YMGECzpw588Tr33vvPSxcuBCHDx+GkZFR1fFevXpptIkxEdWehKwijFpzHmfvZsHcSI6140NYdBER0UO9FOwOa1NDxGUV4eiNdKnjEOkNjSfmWltbY/v27fjggw8wbdo0GBgY4MCBA+jTp49a11+9ehVbt2594Lijo6Nk+3sRNVRlFSpsv5iALw7cRFGZEjZmhtgwsT3aeNhIHY2IiHSUmZEBRrZvjFUn7+GnM7Ho36KR1JGI9EKN9vH6/vvv8c0332DkyJHw8fHBrFmzcPnyZbWutbGxQWpq6gPHIyMj4ebmVpM4RKQhURSxPSwBPb86jo9+u46iMiXae9vh9ze6sugiIqInGt/ZEwYyARdis3EtOU/qOER6QePCa9CgQViwYAE2btyILVu2IDIyEt27d0fHjh2xZMmSJ14/atQovPvuu0hLS4MgCFCpVDh79izmzp2LcePG1ehFEJH6UnKLMW5dGN7bfRUpeSVwtDTGgmEtsH1qR3jYmUkdj4iI9ICLtSmeae0CAPjpTKzEaYj0g8bNNfr164eff/4Zrq6u1Y7/+eefmDJlykNHs/5XeXk5JkyYgO3bt0MURRgYGECpVGLUqFHYsGED5HLd3yeIzTVIX525k4k3tl1CblE5jA1kmNs/AGM7eXJTZCIi0tiVpFwM++EsDGQCzrzbG42sTaSORCQJdWsDjQuvx8nMzISDg4Na5967dw+RkZFQqVQICgpCkyZNaiuG1rHwIn20/mwsPvsjGioRaOVmjeWvtIGvo4XUsYiISI+9vDoUYbHZeK2nL94dyA2VqWFStzao1V3v1Cm6Tp48iR49esDX17eqHT0Radeqk/fwxYGbACq7US18riVHuYiI6KlN7uqNsNhsbL2QgJm9/WBmxA2ViR5F4zVeMpkMcrn8kbcn6devHxo3boz33nsP165dq1FoIlLf/xZdb/Ztgq9eas2ii4iIakXfZs7wtDdDXnE5N1QmegKNv5bYs2dPtZ/Ly8sRGRmJn3/+GQsWLHji9SkpKdi+fTu2bduGJUuWoGXLlhgzZgxGjRoFd3d3TeMQ0WNsC0uoKrrm9PPHrD76M6WXiIh0n1wmYFIXb3y87zpWnriHl0M8+OUe0SPU2hqvrVu3YseOHfjtt9/UviY2NhZbt27Ftm3bcPPmTXTv3h3Hjh2rjThaxTVepA8OXU/D9M0RUIng3HsiItKaknIlen51AmmKEnw8tDkmdvGWOhJRnVK3NqjRPl4P06FDBxw5ckSja7y9vfHee+/hiy++QKtWrXDy5MnaikPUoGUoSjDvlytQicAr7TzwzoAAqSMREVE9ZWIox8w+fgCA/x6/h6KyCokTEemmWim8iouL8f3332s0VfDs2bOYMWMGXFxcMGrUKLRo0QJ//PFHbcQhatBEUcQHe68hr7gcLd2s8NlzLSEIgtSxiIioHns5xAON7cyQWVCKn05zXy+ih9F4jZetrW21D3GiKCI/Px9mZmbYvHnzE69///33sW3bNqSkpKBv375Yvnw5nnvuOZiZceNWotqw73IKDkenw1Au4OvhgTCU19rANhER0UMZymV4u78/Zm+Pwg/H72JooCu8HMyljkWkUzQuvL755ptqhZdMJoOjoyM6dOgAW1vbJ15/4sQJzJ07FyNGjFB7zy8iUs/9/FJ8vO86AGBm7yZo2ohrEImIqG4MC3TFrvAknLmbiQ/2XsXmyR0444Lof9TqBsoNBZtrkC4SRRGvbb6Eg9fT0NzFCr+90YWjXUREVKfiswrR/5tTKK1QYeFzLTGmo6fUkYi0rlY3UL5y5YraT9y6desHju3btw+DBg2CoaEh9u3b99jrhw0bpvZzEdG/9l1OwcHraTCQCfhqeGsWXUREVOc87c3xdn9/LNp/E5/suw5vB3N08eMMJyJAzREvmUwGQRDwpFMFQYBSqXzo9WlpaXBycoJM9ugPg4+6XtdwxIt0za20fDy/4iyKypSY1acJ5vTzlzoSERE1UKIo4s0dUfgtKgWWJgbYM6Mz/JwspY5FpDW1OuIVG/t03WlUKtVD/5+Inl5uURmmbgxHUZkSXfzsMau3n9SRiIioARMEAV++2BrJOcUIj8/BxA0XsXdGF9hbGEsdjUhSOrXGKzk5GW5ublLHeCKOeJGuUKpETFgfhtN3MuFua4rf3+gKW3MjqWMREREhq6AUz684h4TsIgR72mLLlA4wMZRLHYuo1tX6Bsrdu3dHbm5u1c/79u1DcXHxU4X8R1paGmbOnAk/P35TT6SJr/66hdN3MmFiKMOPY0NYdBERkc6wtzDGugntYGVigIj4HLzzy5UnLlshqs/ULrzOnDmDsrKyqp/HjBmD1NRUtZ8oNzcXo0ePhqOjI1xdXfHdd99BpVLho48+go+PD86fP49169Zplp6oAfv9cgpWnbwHAFjyUiCau3L0lYiIdIufkwVWjQmGgUzAvssp+ObIHakjEUmmxm3PNP3G4v3338epU6cwfvx42NnZ4a233sKQIUNw5swZHDhwABcvXsTIkSNrGoeoQYlOUeCdXyq7jU7r4YNhga4SJyIiInq4zn4OWPR8KwDAd0fvYN2Zp+sdQKSv6qzf9J9//on169fj66+/xr59+yCKIvz9/XHs2DH06NGjrmIQ6b2sglK8uikcxeVKdGvigHcGNJU6EhER0WO93M6jqvnTp39EY+WJexInIqp7anU1/Mdff/0Fa2trAJXdCY8ePYpr165VO+dR+3ClpKSgefPmAAAfHx+YmJhgypQpNclM1GCl5BZj7E8XkJRTjMZ2Zvh+ZBDkMkHqWERERE/0Vj9/CIKAb4/ewZcHbyK7sBTzBzWDjP+OUQOhUeE1fvz4aj9Pmzat2s+P24dLpVLB0NCw6me5XA5zc3NNnp6oQVKpRNzJKMDRm+nYFBqP1LwSuFqbYP3EdrAxYzMNIiLSD4Ig4K1+/jAzkmPxgZtYczoWqXklWPpyIIwN2O2Q6j+1C6+n3X9LFEVMmDABxsaVeziUlJRg+vTpDxRfu3fvfqrnIdJ3FUoVTt25jwux2biSmIdryXnIL62out/HwRybpnSAm42phCmJiIhqZloPXzhZGeOdX67gjyupuJ9fih/HhsDazPDJFxPpMY1GvJ7G/x8tGzNmTF09NZFeuJ9fik2hcdh+MREZ+aXV7jMxlKG9tz0GtHDG0EBXWJnwHyciItJfzwe5w8nSBNM3ReBCbDaGrz6HDRPbw5VfKlI9plMbKOsLbqBMtSkxuwirT93DzvAklFVUjizbmxuhf4tGaONhjdbuNmjiZAEDeZ31wiEiIqoTN1IVmLj+ItIUJXC2MsaGie3RzIWfrUi/qFsbsPCqARZe9P9lFZTi1J37KCpTQiYIaO1ujWaNrB67YPhWWj5WnriL36+kQqmqfBsGethgajdv9G/eCEYGLLSIiKj+S8ktxoT1YbidXgALYwOsHhuMLn4OUsciUhsLLy1i4UX/SFeUYOGfN3DwWirKldXfSnbmRujkY4+gxjYQRaCkXIniciXu55fiZlo+ribnVZ3b3d8RM3r6ooO3HQSB3Z2IiKhhySsux7RN4Tgfkw1DuYAlL7XG80HuUsciUotWCi+lUokzZ86gdevWsLW1rZWg+oiFFwFAUk4RRq+9gPisIgBAC1cruNuaoqhMiYj4HBSVPbzD5z8EARjUshFe6+GHVu7WdRGZiIhIZ5VWKDF31xX8fjkFAPDpsy0wrpOXtKGI1KBubaBRcw25XI4BAwbgxo0bDbrwIkpXlGDE6vNIzi2Gh50pVowKrlY8lStVuJyYizN3M3E7PR9GchlMjeQwNpDD2tQQAY0s0drdGu62ZhK+CiIiIt1hbCDHtyPawMHCCOvPxuGj364jt6gcM3v7cTYI1QsadzVs1aoVYmJi4O3trY08RHph2aHbSM4tho+DObZM7QAX6+pdmAzlMoR42SHEy06ihERERPpHJhPw0ZDmsDQ2wHfH7mLZ4du4lpyHr18OZEdf0nsar97//PPPMXfuXPzxxx9ITU2FQqGodiOq7+IyC/HLpSQAwFfDAx8ouoiIiKjmBEHAnP4B+Pz5ljCSy3AoOh3P/nAWN9P4OZP0m8bNNWSyf2u1/x32FUURgiBAqXz8upb6gGu8GrY5O6KwOzIZvQIcsX5ie6njEBER1VtRibmYsTkCKXklMDGUYenwNnimtYvUsYiq0coaLwA4fvz4UwUj0mf37hdgT1QyAGBOvwCJ0xAREdVvbTxs8Mesbpi9PRKn72Ri5rZLEIS2GNyKxRfpH40Lrx49emgjB5FeWH82FqII9G3mxE6EREREdcDO3AgbJrbHe79ewa6IJMzaFgljAxn6NHOWOhqRRmq0Q+vp06cxZswYdO7cGcnJld/+b9q0CWfOnKnVcES6JK+4HL9GVP55n9zVR+I0REREDYdcJuCLF1tjWKArKlQiXtt8Cafv3Jc6FpFGNC68fv31VwwYMACmpqa4dOkSSktLAQD5+flYtGhRrQck0hW7whNRXK5E00aW6OjDboVERER1SS4TsPTlQAxo4YwypQpTN4YjLDZb6lhEatO48Fq4cCFWrVqFNWvWwNDw37aenTt3xqVLl2o1HJGuUKpE/BwaBwCY0NmL+4kQERFJwFAuw3cjg9AzwBEl5SpM/vkibqXlSx2LSC0aF163bt1C9+7dHzhuZWWF3Nzc2shEpHPCYrORmF0Ma1NDPNvGTeo4REREDZaxgRyrxgQjxNMW+SUVGL8uDCm5xVLHInoijQsvFxcX3L1794HjZ86cgY8P171Q/XQhNgsA0MPfEaZGconTEBERNWwmhnKsHR8CPycLpClKMG1TBErK6/+WRqTfNC68pk2bhtmzZ+PChQsQBAEpKSnYsmUL5s6dixkzZmgjI5HkLsZVziFv5821XURERLrAxswIGya2g62ZIa4m52HB79FSRyJ6LI3byb/zzjvIy8tDr169UFJSgu7du8PY2Bhz587FG2+8oY2MRJIqV6pwKT4XANDei4UXERGRrnC3NcPyV4IwYX0YtoUloJ2XLV5o6y51LKKHqlE7+c8//xyZmZkICwvD+fPncf/+fXz22We1nY1IJ1xPUaC4XAlrU0M0cbKQOg4RERH9jx7+jpjdpwkA4KPfriMxu0jiREQPp3HhNWnSJOTn58PMzAwhISFo3749LCwsUFhYiEmTJmkjI5GkLv7dqjbE0xYyGbsZEhER6ZqZvZsg2NMWBaUVeHvnZShVotSRiB6gceH1888/o7j4wc4xxcXF2LhxY62EItIlYVzfRUREpNPkMgHfvNwG5kZyhMVlY8O5OKkjET1A7cJLoVAgLy8PoigiPz8fCoWi6paTk4P9+/fDyclJm1mJ6pxKJSL8n8KL67uIiIh0VmN7M3zwTHMAwLJDt5CWVyJxIqLq1G6uYWNjA0EQIAgC/P39H7hfEAQsWLCgVsMRSe3u/QLkFJXD2ECGVm7WUschIiKix3ilnQd2RSQiMiEXC/+Mxg+j2kodiaiK2oXX8ePHIYoievfujV9//RV2dv9++29kZARPT0+4urpqJSSRVE7fyQQAtPe2g5FBjXrREBERUR2RyQR89mxLDPvhDP64kopR7TPR2c9B6lhEADQovHr06AEAiI2NhYeHB2Qyfgil+u/U7fsAgO5NHCVOQkREROpo6WaNsR098XNoPBYfuInfXu/C5likEzTex8vT0xMAUFRUhISEBJSVlVW7v3Xr1rWTjEhiJeVKXIjNAgB08+e3ZURERPpiVp8m+PVSMq4m5+HPq6kYGshZWSQ9jQuv+/fvY+LEiThw4MBD71cqlU8dikgXRMTnoKRcBSdLYwQ4W0odh4iIiNRkb2GMqd188M2R21h66BYGtmwEQzlna5G0NP4T+OabbyInJwfnz5+HqakpDh48iJ9//hlNmjTBvn37tJGRSBL/TDPs1sQRgsApCkRERPpkSjdvOFgYIS6rCLvCk6SOQ6R54XXs2DF88803aNeuHWQyGTw9PTFmzBgsWbIEixcv1kZGIkmc+ruxRndOMyQiItI75sYGmNHTDwDw3+N3Ua5USZyIGjqNC6/CwsKq/brs7Oxw/37lqECrVq1w6dKl2k1HJJGM/BLcSFUAALqyGxIREZFeGtWhMRwsjJGcW4zdlzjqRdLSuPAKCAjArVu3AABt2rTB6tWrkZycjFWrVsHFxaXWAxJJ4czfo10t3axgb2EscRoiIiKqCRNDOab38AEA/MBRL5JYjdZ4paamAgA+/vhjHDx4EI0bN8Z3332HRYsW1XpAIin8s38X28gTERHpt1EdGsPe3AiJ2cX4/XKK1HGoAVO7q+Hdu3fh5+eH0aNHVx0LCgpCXFwcbt68icaNG8PBgVOySP+pVCJO3/m3sQYRERHpLzMjA0zq6o2v/rqF1Sdj8HyQG5tmkSTUHvHy9/eHh4cHxo0bhw0bNiAuLg4AYGZmhrZt27LoonrjRpoCmQVlMDOSI9jTVuo4RERE9JTGdPCEuZEct9LzceLWfanjUAOlduF18uRJTJs2DSkpKXj99dfh6+sLb29vTJ48GZs3b0ZycrI2cxLVmVO3K6cZdvKxh5EB9/wgIiLSd9ZmhhjVoTEAYNXJexKnoYZK7U+V3bp1w4cffogjR44gNzcXx48fx8SJExEbG4tXX30VjRs3RkBAgDazEtWJf6cZchSXiIiovpjU1RuGcgEXYrNxOTFX6jjUANXo63xDQ0N0794d8+bNw/z58zFjxgxYWFjg7t27tZ2PqE4VllYgPC4HANDdn+u7iIiI6gsXa1MMbe0KANhwLk7aMNQgaVR4lZSU4NixY/jPf/6Dbt26wdbWFrNmzUJBQQFWrlyJhIQEbeUkqhOnbt9HmVIFT3szeDuYSx2HiIiIatGELl4AgD+upCBDUSJtGGpw1O5q2KNHD1y8eBG+vr7o3r07Zs6ciR49esDZ2Vmb+Yjq1KHodABA/+bO7HhERERUz7R2t0Gwpy0i4nOw5UIC3urnL3UkakDUHvE6d+4cHBwc0KtXL/Tp0we9e/dm0UX1SrlShWM3MwAA/Zo3kjgNERERacOEzl4AgC0XElBaoZQ2DDUoahdeubm5+PHHH2FmZoYvv/wSbm5uaNWqFd544w388ssvuH9futacK1asgLe3N0xMTBAcHIzTp08/9vwtW7YgMDAQZmZmcHFxwcSJE5GVlVVHaUlXXYzNRl5xOezMjdhGnoiIqJ4a2LIRGlmZILOgFH9eSZU6DjUgahde5ubmGDhwIL744gtcuHABmZmZWLJkCczMzLBkyRK4u7ujZcuW2sz6UDt27MCbb76JDz74AJGRkejWrRsGDRr0yPVmZ86cwbhx4zB58mRcv34du3btwsWLFzFlypQ6Tk665p9phn2bOUEu4zRDIiKi+shQLsPYTp4AgPVn4yCKosSJqKGo8SZF5ubmsLOzg52dHWxtbWFgYIAbN27UZja1LFu2DJMnT8aUKVPQrFkzLF++HB4eHli5cuVDzz9//jy8vLwwa9YseHt7o2vXrpg2bRrCw8PrODnpEqVKxOG/Cy9OMyQiIqrfXmnnASMDGa4m5+FSQo7UcaiBULvwUqlUCAsLw5IlSzBo0CDY2Nigc+fOWLFiBRo1aoT//ve/iImJ0WbWB5SVlSEiIgL9+/evdrx///44d+7cQ6/p3LkzkpKSsH//foiiiPT0dPzyyy945plnHvk8paWlUCgU1W5UvxyOTkdybjGsTQ25fxcREVE9Z29hjOfaVLaWX382Ttow1GCo3dXQxsYGhYWFcHFxQc+ePbFs2TL06tULvr6+2sz3WJmZmVAqlQ80+XB2dkZaWtpDr+ncuTO2bNmCESNGoKSkBBUVFRg2bBi+//77Rz7P4sWLsWDBglrNTrplzenKLw3GdGwME0O5xGmIiIhI2yZ09sbO8CQcuJaG1LxiuFibSh2J6jm1R7y++uor3LhxA0lJSdi8eTOmTJkiadH1v/5/229RFB/ZCjw6OhqzZs3CRx99hIiICBw8eBCxsbGYPn36Ix9//vz5yMvLq7olJibWan6SVkR8NiLic2Akl2H8352OiIiIqH5r7mqFDt52UKpEbD4fL3UcagDUHvGaNm2aNnPUiIODA+Ry+QOjWxkZGY9sdb948WJ06dIF8+bNAwC0bt0a5ubm6NatGxYuXAgXF5cHrjE2NoaxsXHtvwDSCatOVo52PR/kBidLE4nTEBERUV2Z2MULF2KzsfVCAmb2bsJZL6RVNW6uoQuMjIwQHByMw4cPVzt++PBhdO7c+aHXFBUVQSar/rLl8so3GbvaNDyn79zH4eh0CAIwtbu31HGIiIioDvVt5gw3G1PkFJVjX1SK1HGontPrwgsA5syZg7Vr12LdunW4ceMG3nrrLSQkJFRNHZw/fz7GjRtXdf7QoUOxe/durFy5EjExMTh79ixmzZqF9u3bw9XVVaqXQRIoKVfiw73XAADjO3nBz8lS4kRERERUlwzkMoz7p7X8ObaWJ+1Se6qhrhoxYgSysrLw6aefIjU1FS1btsT+/fvh6Vn5JkpNTa22p9eECROQn5+PH374AW+//TZsbGzQu3dvfPnll1K9BJLId0fvID6rCI2sTPB2f3+p4xAREZEERrTzwDdHbuNGqgJhsdno4GMvdSSqpwSRpb3GFAoFrK2tkZeXBysrK6njUA1cjMvGiNWhUInA6rHBGNCCe3cRERE1VO/vuYqtFxIwsEUjrBobLHUc0jPq1gZ6P9WQSFN5ReWYvS0SKhF4oa0biy4iIqIGbsLfXY0PRachKadI2jBUb7HwogZFFEW8t/sKUvJK4GVvhk+fbSl1JCIiIpKYv7Mluvo5QCUCm9hanrSEhRc1KNsvJuLAtTQYygV8NzIIFsZ6v8yRiIiIasE/o17bwxJRVFYhbRiql1h4UYNxJz0fC36/DgCYNyAArd1tpA1EREREOqNXUyc0tjNDXnE59kaytTzVPhZe1CCUlCsxc1skSspV6NbEAVO6+kgdiYiIiHSIXCZg/N+jXhvOxbK1PNU6Fl7UICzefwM30/LhYGGEpS8HQiYTpI5EREREOmZ4iDvMjeS4nV6Ac/eypI5D9QwLL6r3Dl1Pw8+hlQtlvxoeCCdLE4kTERERkS6yMjHES8HuAID1Z+OkDUP1DgsvqtdupCrw1o4oAMDkrt7oFeAkbSAiIiLSaeP+nm549GY64rMKpQ1D9QoLL6q3MvJLMHnDRRSWKdHJxx7vDmwqdSQiIiLScb6OFujh7whRBDaGsrU81R4WXlQvFZcpMXVjBFLySuDjYI6VY9rCyIB/3ImIiOjJJnbxAgDsvJiIglK2lqfawU+iVO+oVCLe3hWFy4m5sDEzxLoJ7WBjZiR1LCIiItIT3Zs4wsfBHPmlFdh9KUnqOFRPsPCiemfp4VvYf7Vyk+TVY4Lh5WAudSQiIiLSIzKZgAl/j3qtPR2LcqVK2kBUL7DwonplV3gi/nv8HgDgixdao4OPvcSJiIiISB+9FOwOBwsjJGQXYVc4R73o6bHwonrjfEwW3t9zFQDwRi8/vPh3O1giIiIiTZkZGeD1Xn4AgO+O3kFJuVLiRKTvWHhRvRCbWYjpmyNQrhTxTCsXzOnnL3UkIiIi0nOjOjSGm40p0hQl2MQOh/SUWHiR3ssrKsekDReRW1SONh42WPpyIGQyQepYREREpOeMDeSY3bcJgMpRr3RFicSJSJ+x8CK9Jooi3t51GbGZhXCzMcWacSEwMZRLHYuIiIjqiRfbuqONhw3ySyuw4PfrUschPcbCi/TahnNxOHIjHUZyGVaPDYajpbHUkYiIiKgekcsELH6hFeQyAfuvpuFwdLrUkUhPsfAivRWVmItF+28AAN4f3BQt3awlTkRERET1UTMXK0zp5g0AmLMzCjfTFBInIn3Ewov0UoaiBNM2haNcKWJAC2eM7+wldSQiIiKqx97q64/2XnbIL6nAuJ/CkJhdJHUk0jMsvEjvlJQrMW1zBNIVpWjiZIGvhwdCENhMg4iIiLTHxFCONeNCEOBsiYz8UoxfF4bswjKpY5EeYeFFeqWkXIlpmyIQmZALa1NDrBkXAksTQ6ljERERUQNgbWaInye1h5uNKWIyCzFxw0UUlVVIHYv0BAsv0hvZhWV4bXMETt6+D9O/v3XycjCXOhYRERE1II2sTfDzpPawMTPE5cRczNt1BaIoSh2L9AALL9J5oihiU2gcen51HMdv3YexgQw/jQ9Be287qaMRERFRA+TnZIGfxofAQCbgz6up2HwhQepIpAdYeJHOW30qBv/57ToUJRVo5mKFrVM7orOfg9SxiIiIqAEL9rTDe4OaAgA++yMa15LzJE5Euo6FF+m0PZFJ+OLATQDA2/388cfMrgj2tJU4FREREREwuas3+jZzQlmFCm9svYT8knKpI5EOY+FFOis6RYF3frkCAJjazRsz+zSBXMbuhURERKQbBEHA18MD4WptgrisIszffZXrveiRWHiRTiqtUGLOziiUK0X0beaE+YOaSR2JiIiI6AE2Zkb4flRbGMgE/HElFVvDuN6LHo6FF+mk5Ufu4GZaPuzMjfDFi60h40gXERER6ahgT1vMGxAAAFjwezSiUxQSJyJdxMKLdM6u8ESsPHEPALDo+VZwsDCWOBERERHR403t5oPeTSvXe72+9RIKSrm/F1XHwot0yoGrqXj318p1XVO6emNgy0YSJyIiIiJ6MplMwNLhgXCxNkFsZiHe53ov+n9YeJHOOHA1FTO3RUIlAiNCPPDBM1zXRURERPrD1twI348MglwmYN/lFGy/mCh1JNIhLLxIJ/x+OQVvbItEhUrEsEBXLHqhFQSB67qIiIhIv4R42WFu/8r1Xp/su879vagKCy+S3PFbGXhrRxSUKhEvtnXHNyPasG08ERER6a1p3X3QK8ARpRUqTNsUgcyCUqkjkQ5g4UWSiojPwYzNl1ChEvFcG1d89VJrFl1ERESk12QyActHBMHbwRzJucWYsfkSSsqVUsciibHwIkmIoojtYQkYteY8isuV6OHviK+GB7JtPBEREdUL1maGWDMuGBbGBgiLy8b4dWHIKy6XOhZJiIUX1bmI+GxMWH8R7+2+itIKFXoFOGLF6LYwlPOPIxEREdUffk6WWDs+BJbGBrgQm43hq87hfEyW1LFIIoLIPpcaUygUsLa2Rl5eHqysrKSOozfu55fiw71X8df1dACAXCZgbv8ATOvuw5EuIiIiqreiUxQYvz4M9/Mr13r1CnDEOwObopkLP0fWB+rWBiy8aoCFl2aKyirwS0QSvjl8GzlF5TCQCXgp2B3TevjC28Fc6nhEREREWnc/vxTfH7uDrRcSUKESIQjAkNauGN/JE8GetuzmrMdYeGkRC68nKylXYl9UCs7dy8SJ2/eRW1Q5p7mZixWWDg9Ec1f+3oiIiKjhicssxNeHbuGPK6lVx3wczNGvuTP6NXdGUGNbNhrTMyy8tIiF1+NdiMnCu79eQVxWUdWxxnZmmNzVGyPbN4aRAddyERERUcN2LTkPG0PjsO9yCkrKVVXH7c2N0KeZE/o1b4RuTRxgYiiXMCWpg4WXFrHwergKpQrLDt/GihP3AACNrEwwPMQdHX3s0dHHnt/eEBEREf0/+SXlOHHrPg5Hp+P4rQzkl1RU3edgYYyJXbwwrpMnLE0MJUxJj8PCS4tYeP1LpRKRW1yOsNgsbDgXh/Mx2QCAke09MH9wM1jxLwkiIiIitZRVqBAWm40jN9Jx4Foq0hWVzTjcbU3x7StBCPa0lTghPQwLLy1q6IVXWYUKOy4mYM3pWCTlFEH1P3+CzIzk+OLF1hgW6CpdQCIiIiI9V65UYV9UCr45chtJOcWQywS8OzAAU7v5sBGHjmHhpUUNtfC6k56P3ZHJ2HMpGWmKkmr3+Tiao3sTR4zt5AlfRwuJEhIRERHVL4qScvxn7zX8FpUCAHgp2B2fP98SxgZc+6Ur1K0NDOowE+mh+/ml2Hc5BXsik3AtWVF13MnSGG/09sPAFo1gZWrIhZ9EREREWmBlYohvXwlC28a2WPD7dfwSkYSErCKsHNMW9hbGUscjDXDEqwbq+4hXcZkSh6LTsCcyGafvZEL591xCA5mAngFOeKGtG3o3dWKxRURERFSHTt6+jze2XEJ+aQXcbU2xdnwImjaqf59F9Q2nGmpRfS284rMKsS0sEdvCEpBXXF51vI2HDV5o64YhrV1hZ24kYUIiIiKihu1uRj4mbQhHQnYRTAxl+OKF1nguyE3qWA0aCy8tqg+FlyiKSMopxuWkXFxOzMWZu1m4kfrvVEJ3W1O8EOSG54Lc4MM1W0REREQ6I7uwDLO3R+L0nUwAwLhOnvjwmebcK1UiLLy0SN8KL5VKRGRiLiITcnA9RYGE7CLEZhYiu7Cs2nlymYBOPvYY18kTfZo5c98tIiIiIh2lVIlYfuQ2vj92F0DlDKUVo9vC1cZU4mQNDwsvLdKnwuvU7fv48uBNXE9RPHCfoVxAMxcrBLrbIKixDXoFOMGWUwmJiIiI9Maxm+l4c3sUFCUVsDM3wvcjg9DFz0HqWA0KCy8t0ofCKyO/BB//dh0HrqUBAMyN5Ojs54DWbtbwdjSHp505/BtZsBUpERERkZ5LyCrC9M0RiE5VQBCAUe0bY27/AH6hXkdYeGmRrhVe2YVluJ2ej9S8YqTlleJKUi7O3M1EfkkFDGQCxnf2wuu9/NgYg4iIiKieKilX4uPfrmNHeCIAwMrEAGM7eeKVdo3hbGXC9V9axMJLi3Sl8Dofk4U3tkYis6D0ofe3cLXCVy8Formr9MUhEREREWnf+ZgsLPg9ulrTNABwsDBC28a2aOdlh2AvW7Rys4ahXD+LMVEUcS1ZAR9Hc5gbS78tMQsvLdKVwutuRj76LjsFAGhsZwYPO1M4WhgjoJEV2ja2QYiXHRtkEBERETUwSpWIw9HpWHM6BpcScvCwT/uOlsZ4o5cfXmnvofNLT0RRRGJ2MSITc3A+JhvHbqYjXVGK/45qi2dau0gdj4WXNulK4VWhVOFaigJNnCx0otonIiIiIt2iVIkoKKnA3fsFCI/LxsW4HITHZyO3qHLPVjcbU8zu2wQvBLnBoIYjYIWlFbgQm4VryQpk5JegtFwFJytjuNuaoWkjS/g6WcDS2ACC8OQBAZVKREJ2EW6kKnAjLR/RKXmISsxFZkH1btxmRnLMGxCAiV28a5S5NrHw0iJdKbyIiIiIiDRVVqHCjvBEfH/0DjLyK5esOFgYo1eAI3o3dULXJg6wNDF85PXlShXS8kpwNTkPv0Yk4dSd+yhXPr6kMDKQwdHCGPYWRnCwMIb9370H8orLka4oQUpeCYpKK1CmVD30sQzlAlq4WiOosQ16+Duio489TAx1Y6SOhZcWsfAiIiIiIn1XUq7EptB4rDx5r9r+roZyAU6WJjA1kqOkXInC0goIggCZIKC4rAKFZcoHHsvDzhTtvezhZmMCY0M50hUliM0sxI3U/Ef2I3gUIwMZ/J0t0KyRVeXWRx42aOFqpTOF1v/HwkuLWHgRERERUX1RVqHCxbhsHLuZgaM30hGXVfTEa4zkMrjbmmJAy0Z4sa0b/JwsH3luUVkFsgrKkFlQ+u9//y70rEwM4GhpAndbU1iaGMBQLoOTpXGNpz1KgYWXFrHwIiIiIqL6KjG7CNmFZSgqU8LEUAYLYwOIqFwvZmYkh4WxAWzNjCBjEzcA6tcG7MhARERERERVPOzM4GFnJnWMekd/xvCIiIiIiIj0FAsvIiIiIiIiLWPhRUREREREpGUsvIiIiIiIiLSMhRcREREREZGWsfAiIiIiIiLSsnpReK1YsQLe3t4wMTFBcHAwTp8+/djzS0tL8cEHH8DT0xPGxsbw9fXFunXr6igtERERERE1NHq/j9eOHTvw5ptvYsWKFejSpQtWr16NQYMGITo6Go0bN37oNS+//DLS09Px008/wc/PDxkZGaioqKjj5ERERERE1FAIoiiKUod4Gh06dEDbtm2xcuXKqmPNmjXDc889h8WLFz9w/sGDB/HKK68gJiYGdnZ2NXpOdXenJiIiIiKi+k3d2kCvpxqWlZUhIiIC/fv3r3a8f//+OHfu3EOv2bdvH0JCQrBkyRK4ubnB398fc+fORXFx8SOfp7S0FAqFotqNiIiIiIhIXXo91TAzMxNKpRLOzs7Vjjs7OyMtLe2h18TExODMmTMwMTHBnj17kJmZiRkzZiA7O/uR67wWL16MBQsW1Hp+IiIiIiJqGPR6xOsfgiBU+1kUxQeO/UOlUkEQBGzZsgXt27fH4MGDsWzZMmzYsOGRo17z589HXl5e1S0xMbHWXwMREREREdVfej3i5eDgALlc/sDoVkZGxgOjYP9wcXGBm5sbrK2tq441a9YMoigiKSkJTZo0eeAaY2NjGBsb1254IiIiIiJqMPR6xMvIyAjBwcE4fPhwteOHDx9G586dH3pNly5dkJKSgoKCgqpjt2/fhkwmg7u7u1bzEhERERFRw6TXI14AMGfOHIwdOxYhISHo1KkTfvzxRyQkJGD69OkAKqcJJicnY+PGjQCAUaNG4bPPPsPEiROxYMECZGZmYt68eZg0aRJMTU3Ves5/GkGyyQYRERERUcP2T03wpGbxel94jRgxAllZWfj000+RmpqKli1bYv/+/fD09AQApKamIiEhoep8CwsLHD58GDNnzkRISAjs7e3x8ssvY+HChWo/Z35+PgDAw8Ojdl8MERERERHppfz8/GrLmf4/vd/HSwoqlQopKSmwtLR8ZBOPuqBQKODh4YHExETuJ0akY/j+JNI/fN8S6Q9der+Kooj8/Hy4urpCJnv0Si69H/GSgq6tB7OyspL8DxwRPRzfn0T6h+9bIv2hK+/Xx410/UOvm2sQERERERHpAxZeREREREREWsbCS48ZGxvj448/5h5jRDqI708i/cP3LZH+0Mf3K5trEBERERERaRlHvIiIiIiIiLSMhRcREREREZGWsfAiIiIiIiLSMhZeREREREREWsbCq5adOnUKQ4cOhaurKwRBwN69e6vdn56ejgkTJsDV1RVmZmYYOHAg7ty5U+2c0tJSzJw5Ew4ODjA3N8ewYcOQlJRU7ZycnByMHTsW1tbWsLa2xtixY5Gbm/vEfFevXkWPHj1gamoKNzc3fPrpp/jf/iqpqakYNWoUAgICIJPJ8Oabb9b0V0Gkk/T9PXrixAkIgvDA7ebNmzX+nRDpOn1/3wLAf//7XzRr1gympqYICAjAxo0ba/S7INJ1dfV+/fzzz9G5c2eYmZnBxsZG7XxSfhZm4VXLCgsLERgYiB9++OGB+0RRxHPPPYeYmBj89ttviIyMhKenJ/r27YvCwsKq8958803s2bMH27dvx5kzZ1BQUIAhQ4ZAqVRWnTNq1ChERUXh4MGDOHjwIKKiojB27NjHZlMoFOjXrx9cXV1x8eJFfP/99/j666+xbNmyqnNKS0vh6OiIDz74AIGBgbXwGyHSLfr+Hv3HrVu3kJqaWnVr0qTJU/xWiHSbvr9vV65cifnz5+OTTz7B9evXsWDBArz++uv4/fffa+G3Q6Rb6ur9WlZWhuHDh+O1115TO5vkn4VF0hoA4p49e6p+vnXrlghAvHbtWtWxiooK0c7OTlyzZo0oiqKYm5srGhoaitu3b686Jzk5WZTJZOLBgwdFURTF6OhoEYB4/vz5qnNCQ0NFAOLNmzcfmWfFihWitbW1WFJSUnVs8eLFoqurq6hSqR44v0ePHuLs2bM1ft1E+kIf36PHjx8XAYg5OTlP9dqJ9JU+vm87deokzp07t9p1s2fPFrt06VKD3wCR/tDW+/V/rV+/XrS2tlYrj9SfhTniVYdKS0sBACYmJlXH5HI5jIyMcObMGQBAREQEysvL0b9//6pzXF1d0bJlS5w7dw4AEBoaCmtra3To0KHqnI4dO8La2rrqnIcJDQ1Fjx49qm00N2DAAKSkpCAuLq5WXiORPtOn92hQUBBcXFzQp08fHD9+vOYvmkjP6cP7trS0tFo+ADA1NUVYWBjKy8tr+MqJ9E9tvV9rSurPwiy86lDTpk3h6emJ+fPnIycnB2VlZfjiiy+QlpaG1NRUAEBaWhqMjIxga2tb7VpnZ2ekpaVVnePk5PTA4zs5OVWd8zBpaWlwdnZ+4HH/uY+oodOH96iLiwt+/PFH/Prrr9i9ezcCAgLQp08fnDp1quYvnEiP6cP7dsCAAVi7di0iIiIgiiLCw8Oxbt06lJeXIzMzs+YvnkjP1Nb7taak/izMwqsOGRoa4tdff8Xt27dhZ2cHMzMznDhxAoMGDYJcLn/staIoQhCEqp//9/8fdk6LFi1gYWEBCwsLDBo06JHXiX8vJnzY4xE1NPrwHg0ICMDUqVPRtm1bdOrUCStWrMAzzzyDr7/+umYvmkjP6cP79j//+Q8GDRqEjh07wtDQEM8++ywmTJgAAE/MSFSf1Ob79Ul08bOwgdafgaoJDg5GVFQU8vLyUFZWBkdHR3To0AEhISEAgEaNGqGsrAw5OTnVKv2MjAx07ty56pz09PQHHvv+/ftVVfv+/furpi+YmppWXff/q/mMjAwAeKD6J2qo9PE92rFjR2zevLmmL5lI7+n6+9bU1BTr1q3D6tWrkZ6eXjVybWlpCQcHh9r8VRDpvNp4v6pDFz8Lc8RLItbW1nB0dMSdO3cQHh6OZ599FkDlH0ZDQ0McPny46tzU1FRcu3at6g9bp06dkJeXh7CwsKpzLly4gLy8vKpzPD094efnBz8/P7i5uVVdd+rUKZSVlVVdd+jQIbi6usLLy0vbL5lIr+jTezQyMhIuLi619tqJ9JWuv28NDQ3h7u4OuVyO7du3Y8iQIZDJ+FGMGqaneb+qQyc/C9damw4SRVEU8/PzxcjISDEyMlIEIC5btkyMjIwU4+PjRVEUxZ07d4rHjx8X7927J+7du1f09PQUX3jhhWqPMX36dNHd3V08cuSIeOnSJbF3795iYGCgWFFRUXXOwIEDxdatW4uhoaFiaGio2KpVK3HIkCGPzZabmys6OzuLI0eOFK9evSru3r1btLKyEr/++utq5/2TPzg4WBw1apQYGRkpXr9+vZZ+Q0TS0vf36DfffCPu2bNHvH37tnjt2jXxvffeEwGIv/76ay3+loh0i76/b2/duiVu2rRJvH37tnjhwgVxxIgRop2dnRgbG1t7vyQiHVFX79f4+HgxMjJSXLBggWhhYVH1nPn5+Y/MJvVnYRZeteyfVs///zZ+/HhRFEXx22+/Fd3d3UVDQ0OxcePG4ocffiiWlpZWe4zi4mLxjTfeEO3s7ERTU1NxyJAhYkJCQrVzsrKyxNGjR4uWlpaipaWlOHr0aLXaS1+5ckXs1q2baGxsLDZq1Ej85JNPHmif+bD8np6eT/NrIdIZ+v4e/fLLL0VfX1/RxMREtLW1Fbt27Sr++eefT/17IdJl+v6+jY6OFtu0aSOampqKVlZW4rPPPvvYFvVE+qyu3q/jx49/6PMcP378sfmk/Cws/P3gREREREREpCWcWExERERERKRlLLyIiIiIiIi0jIUXERERERGRlrHwIiIiIiIi0jIWXkRERERERFrGwouIiIiIiEjLWHgRERERERFpGQsvIiKi/+fEiRMQBAG5ublSRyEionqCGygTEVGD17NnT7Rp0wbLly8HAJSVlSE7OxvOzs4QBEHacEREVC8YSB2AiIhI1xgZGaFRo0ZSxyAionqEUw2JiKhBmzBhAk6ePIlvv/0WgiBAEARs2PB/7dwxS3JtHIDxKyQHEzIiAiMQIgVpaWqpoa2pwQSHQOgDNCV9gIagsZZGi4acaglpq1WilggiqCRakrYoqFDfLV553uGB15NPj9dvO4ebP/c9XtyHs9PyqeHOzg6xWIyjoyNSqRSRSIRsNsvr6yu7u7skEgkGBgZYXl6mXq9/zf74+GB1dZWRkRH6+vqYmpri9PS0MweVJHWUN16SpK62ubnJzc0NExMTrK2tAXB1dfXLure3N7a2tiiVSry8vJDJZMhkMsRiMcrlMnd3dywsLDA9PU0ulwNgaWmJarVKqVQiHo9zeHjI3Nwcl5eXjI+Pf+s5JUmdZXhJkrpaf38/4XCYSCTy9Xnh9fX1L+s+Pz/Z3t5mbGwMgGw2y97eHk9PT0SjUdLpNLOzs5ycnJDL5bi9vWV/f5/Hx0fi8TgAhUKB4+NjisUi6+vr33dISVLHGV6SJP2GSCTyFV0Aw8PDJBIJotFoy7tarQbAxcUFzWaTZDLZMuf9/Z3BwcHv2bQk6Y9heEmS9Bt6e3tbnnt6ev7zXaPRAKDRaBAKhTg/PycUCrWs+3esSZK6g+ElSep64XC45acY7TA5OUm9XqdWqzEzM9PW2ZKkn8e/GkqSul4ikaBSqVCtVnl+fv66tfo/kskki4uL5PN5Dg4OuL+/5+zsjI2NDcrlcht2LUn6SQwvSVLXKxQKhEIh0uk0Q0NDPDw8tGVusVgkn8+zsrJCKpVifn6eSqXC6OhoW+ZLkn6Onmaz2ez0JiRJkiTpb+aNlyRJkiQFzPCSJEmSpIAZXpIkSZIUMMNLkiRJkgJmeEmSJElSwAwvSZIkSQqY4SVJkiRJATO8JEmSJClghpckSZIkBczwkiRJkqSAGV6SJEmSFDDDS5IkSZIC9g9bVZLWTJw6pwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAAHPCAYAAAAS6TheAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAACdFElEQVR4nOzdeVyU9fYH8M8zM6yyKyjIjuKKSO77lpqaWlqmaC65L5madfWmmZX5y25qZS65b6WVS1ZW7iAuICguuSIgCKggMuzbzPP7AxhFRRkEnlk+79eL142ZZzlzVZgz3+85RxBFUQQRERERERHpPJnUARAREREREVH5MIEjIiIiIiLSE0zgiIiIiIiI9AQTOCIiIiIiIj3BBI6IiIiIiEhPMIEjIiIiIiLSE0zgiIiIiIiI9AQTOCIiIiIiIj3BBI6IiIiIiEhPMIEjIiIiIiLSE0zgdEBwcDD69+8PFxcXCIKAvXv3anX+J598AkEQnviqUaNG1QRMRERERESSYAKnA7KysuDv748VK1ZU6PzZs2cjKSmp1Ffjxo3x5ptvVnKkREREREQkJSZwOqBPnz74/PPPMWjQoKc+n5+fjw8//BB169ZFjRo10KZNGxw7dkzzvJWVFerUqaP5unv3Li5fvoyxY8dW0ysgIiIiIqLqoJA6AHq+MWPGIDY2Fjt27ICLiwv27NmDV155BRcvXkT9+vWfOH7dunXw9fVFp06dJIiWiIiIiIiqClfgdNzNmzfx008/4ZdffkGnTp3g4+OD2bNno2PHjti4ceMTx+fl5WH79u1cfSMiIiIiMkBcgdNxZ8+ehSiK8PX1LfV4Xl4eatas+cTxu3fvRkZGBkaOHFldIRIRERERUTVhAqfj1Go15HI5IiIiIJfLSz1nZWX1xPHr1q3Dq6++ijp16lRXiEREREREVE2YwOm4gIAAqFQq3Lt377k1bTExMTh69Cj27dtXTdEREREREVF1YgKnAzIzMxEVFaX5PiYmBpGRkXBwcICvry+GDx+OkSNH4uuvv0ZAQABSUlJw5MgR+Pn5oW/fvprzNmzYAGdnZ/Tp00eKl0FERERERFVMEEVRlDoIY3fs2DF069bticdHjRqFTZs2oaCgAJ9//jm2bNmChIQE1KxZE+3atcPChQvh5+cHoGirpYeHB0aOHIlFixZV90sgIiIiIqJqwASOiIiIiIhIT3CMABERERERkZ5gAkdERERERKQn2MREImq1GomJibC2toYgCFKHQ0REREREEhFFERkZGXBxcYFM9uw1NiZwEklMTISbm5vUYRARERERkY6Ij4+Hq6vrM49hAicRa2trAEV/SDY2NhJHQ0REREREUklPT4ebm5smR3gWJnASKdk2aWNjwwSOiIiIiIjKVVrFJiZERERERER6ggkcERERERGRnmACR0REREREpCeYwBEREREREekJJnBERERERER6ggkcERERERGRntDrBC44OBj9+/eHi4sLBEHA3r17y33uiRMnoFAo0Lx581KPFxQU4NNPP4WPjw/Mzc3h7++Pv//+u9QxixcvRqtWrWBtbQ0nJye89tpruHbtWiW8IiIiIiIiorLpdQKXlZUFf39/rFixQqvzlEolRo4ciR49ejzx3Lx587BmzRp89913uHz5MiZNmoTXX38d586d0xwTFBSEqVOn4vTp0zh48CAKCwvRq1cvZGVlvfBrIiIiIiIiKosgiqIodRCVQRAE7NmzB6+99tpzjx06dCjq168PuVyOvXv3IjIyUvOci4sLPvroI0ydOlXz2GuvvQYrKyts27btqddLTk6Gk5MTgoKC0Llz53LFm56eDltbWyiVSg7yJiIiIiIyYtrkBnq9AlcRGzduxM2bN7FgwYKnPp+Xlwdzc/NSj1lYWCAkJKTMayqVSgCAg4NDmcfk5eUhPT291BcREREREZE2jCqBu3HjBubMmYPt27dDoVA89ZjevXtj6dKluHHjBtRqNQ4ePIjffvsNSUlJTz1eFEXMmjULHTt2RNOmTcu89+LFi2Fra6v5cnNzq5TXRERERERExsNoEjiVSoXAwEAsXLgQvr6+ZR73zTffoH79+mjYsCFMTU0xbdo0jBkzBnK5/KnHT5s2DRcuXMBPP/30zPvPnTsXSqVS8xUfH/9Cr4eIiIiIiIzP05ehDFBGRgbCw8Nx7tw5TJs2DQCgVqshiiIUCgUOHDiA7t27w9HREXv37kVubi7u378PFxcXzJkzB15eXk9c891338W+ffsQHBwMV1fXZ97fzMwMZmZmVfLaiIiIiIjIOBhNAmdjY4OLFy+WemzlypU4cuQIfv311ycSNHNzc9StWxcFBQXYtWsXhgwZonlOFEW8++672LNnD44dO/bU5I6MU0ZuAWb9fB45+Sp41aqBt1q5oWldW6nDIiIiIiIDodcJXGZmJqKiojTfx8TEIDIyEg4ODnB3d8fcuXORkJCALVu2QCaTPVGj5uTkBHNz81KPh4aGIiEhAc2bN0dCQgI++eQTqNVqfPjhh5pjpk6dih9//BG//fYbrK2tcefOHQCAra0tLCwsqvhVky77ITgaBy/fBQCERKXg6LV7ODa7KxRyo9mtTERERERVSK/fVYaHhyMgIAABAQEAgFmzZiEgIAAff/wxACApKQlxcXFaXTM3Nxfz5s1D48aN8frrr6Nu3boICQmBnZ2d5phVq1ZBqVSia9eucHZ21nzt3Lmz0l4b6Z/7mXnYEBIDAJjYxRv2lia4/SAH//x7V+LIiIiIiMhQGMwcOH3DOXCG5/M/LmNdSAz86tpi37QOWHbwOr49EgV/NzvsndIegiBIHSIRERER6SDOgSOqZneUudhy+hYA4P1evhAEAW+384SpQobz8WkIv/VA4giJiIiIyBAwgSOqBN8duYH8QjVaezqgi68jAMDR2gyDAuoCANYGR0sZHhEREREZCCZwRC8o7n42dp4pmus3u3eDUlslx3Uq6lB68MpdxKRkSRIfERERERkOJnBEL2j54esoVIvo7OuI1l4OpZ6r52SN7g2dIIrA+hCuwhERERHRi2ECR/QCbtzNwJ5zCQCA2b18n3pMySrcrxG38SArv9piIyIiIiLDwwSO6AUsPXgdogj0blIbzVztnnpMO++aaFrXBrkFamwrbnRCRERERFQRTOCIKuhSghJ/XboDQQDe79WgzOMEQcD4Tt4AgM2nYpFboKquEImIiIjIwDCBI6qg/x24BgAY6O8C39rWzzy2r58znG3NkZKZj98iE6ojPCIiIiIyQEzgiCogPDYVx64lQy4TMOPlp9e+PcpELsM7HYpq4dYej4FaLVZ1iERERERkgJjAEWlJFEV89U/R6tuQlq7wrFWjXOe91doNVmYKRN3LRND15KoMkfTc/cw8KHMKpA6DiIiIdBATOCIthUSlIDQmFaZyGd7tXr/c59mYm2BoKzcAwNrjHClAT5eckYeey4Lx6nfHuVJLRERET2ACR6QFURTxv+LVtxFtPeBiZ6HV+WM6ekEuE3Dy5n1cSlBWRYik51YH3URqVj7iU3NQyASOiIiIHsMEjkgLBy/fxfnbSliayjGlm4/W59e1s0A/P2cAwDquwtFj7qbnlho1IYIJHBEREZXGBI6onNRqEV8fuA4AGNPBE7WszCp0nZKRAn9cSEKSMqfS4iP9t+rYTeQVqjXfi8zfiIiI6DFM4IjK6fcLibh2NwPW5gpM6KT96lsJP1dbtPV2QKFaxKYTsZUXIOm1JGUOfgyNK/WYmhkcERERPYYJHFE5FKjUWHawaPVtYmdv2FqavND1SlbhfgyNQ0Yuuw0S8P3RKOSr1PB3tdU8xvyNiIiIHscEjqgcdkXcRuz9bNSsYYoxxfPcXkS3Bk7wdqyBjLxC7DwTXwkRkj67/SBb8/dgVq8Gmse5AkdERESPYwJH9Bx5hSp8e/gGAGByVx/UMFO88DVlMkGzCrfxRCwKVernnEGG7PujUShQiehQrybaejtoHmf6RkRERI9jAkf0HD+GxiFRmYs6NuYY0daj0q77ekBd1KxhioS0HOy/dKfSrkv6Je5+Nn4Jvw0AmPmyL2SCoHlOZF5PREREj2ECR/QM2fmF+P5oFADg3R71YG4ir7Rrm5vI8Xa7ooRwbXA0RG6XM0rfHbmBQrWIzr6OaOnpAOGR5zhGgIiIiB7HBI7oGTadjEVKZj7cHSwxpKVbpV//7bYeMFPIcDFBidCY1Eq/Pum22JQs7D6XAACY+XJ9ACi1Asc53kRERPQ4JnBEZVDmFGBNUNGw7Zk968NEXvn/XGpamWFwC1cAHOxtjL49cgMqtYhuDRwR4G4PAHgkf2MTEyIiInoCEziiMqw/Hg1lTgHqO1lhgH/dKrvP2I5FXS0PXbmHm8mZVXYf0i03kzOxt2T1raev5nHh0Ro45m9ERET0GCZwRE9xPzMP60NiAADv9/KFXCY854yK83G0wsuNagMA1h2PqbL7kG759vANqEXg5Ua10czVrtRzJTkc6yKJiIjocUzgiJ5i1bGbyMpXwa+uLXo3qVPl9xvfqWgVbvfZ27ifmVfl9yNp3bibgX3nEwEAM4pr3x5VUgfH9I2IiIgexwSO6DF3lLnYcvoWgKLVt0e3tFWV1l4OaOZqi7xCNbYW35sM1/LDNyCKwCtN6qBpXdsnni9Z8GUNHBERET2OCRzRY747cgP5hWq08rRHF1/HarmnIDwc7L3l1C3kFqiq5b5U/a7eScefF5IAADN6Prn6BgBC8TAB5m9ERET0OCZwRI+Iu5+NnWfiAQCzezWoltW3En2a1kFdOwukZuVj99mEarsvVa/lB28AAPo1c0bDOjZPPUbgChwRERGVgQkc0SOWH76OQrWITvVroY13zWq9t0Iuw5gOngCAdSHRUHMImMG5lKDE3//egSAAM3o8ffUNeLSJSTUFRkRERHqDCRxRsah7GZq27rN7NZAkhqGt3WFtrkB0chaOXL0nSQxUdZYfKlp9G+Dvgvq1rcs8TtPEhAkcERERPYYJHFGxpQevQy0CvRrXhr+bnSQxWJkpENjaHQCwloO9DcqF22k4dOUuZAIw/RmrbwBQsnGXWyiJiIjocUzgiFC0tW3/xaKtbe9LtPpWYnQHTyhkAkJjUnHhdpqksVDlWXbwOgDgtYC68HG0euaxHCNAREREZWECRwTg6wPXAAAD/V3QoE7ZW9uqg7OtBfr7uwAA1nKwt0E4G/cAR68lQy4TML37s1ffADYxISIiorIxgSOjFx6bqnlzPeNlX6nDAQCMKx7svf9iEhLSciSOhl5Uyerb4JfqwrNWjeceL7AGjoiIiMrABI6MmiiK+OqfotW3IS1dy/Xmujo0cbFFh3o1oVKL2BjCVTh9diY2FcdvpEAhE/BuOVbfgIeDvEVmcERERPQYJnBk1EKiUhAakwpTuazcb66ry7jiwd47zsQjPbdA4miookpW395s6QY3B8tynVOyAsdJEkRERPQ4JnBktERRxP+KV9+Gt3WHi52FxBGV1tXXEfWdrJCZV4gdYXFSh0MVcOrmfZy8eR8mcgHTutcr93maFTi2MSEiIqLHMIEjo3Xw8l2cv62EhYkcU7qW/811dREEAeOLV+E2nohFgUotcUSkDVEUsexQ0erb0FbuqKvVBwTFK3D8IyciIqLHMIEjo6RWi1havLVtTAdPOFqbSRzR0w0McEEtKzMkKXPx54UkqcMhLZy8eR9hMakwVcgwpZuPVudyBY6IiIjKwgSOjNLvFxJx9U4GrM0VmNhZuzfX1clMIceodh4AigZ7s6mFfhDFhx8QBLZ2h7OtdttzZexCSURERGVgAkdGp1ClxvJDNwAAEzp5w9bSROKInm1EWw+Ym8jwb2I6TkXflzocKofgGymIuPUAZgoZpnTV/gMCQdOFspIDIyIiIr3HBI6Mzq6ztxGTkgWHGqYY09FL6nCey76GKd5s4QYAWBscLXE09DyPrr6NaOsBJxtzra8h03ShZAZHREREpTGBI6OSV6jCN8Wrb1O6+sDKTCFxROUztqMXBAE4ei0ZUfcypA6HnuHotXs4H58GcxMZJnV5se25TOCIiIjocUzgyKj8GBqHRGUu6tiYY0RbD6nDKTfPWjXQq3FtAMC64xzsraseXX0b1a7izXFkxT+Zmb4RERHR45jAkdHIzi/E90ejAADv9qgHcxO5xBFpp2SkwO6zCUjOyJM4Gnqag5fv4lJCOixN5ZjQ2bvC1xFQ0sSEKRwRERGVxgSOjMamk7FIycyHu4MlhrR0kzocrbXwsEdzNzvkq9TYeipW6nDoMWq1iGXF23NHt/dETauKj6aQsYkJERERlUGvE7jg4GD0798fLi4uEAQBe/fuLfe5J06cgEKhQPPmzUs9XlBQgE8//RQ+Pj4wNzeHv78//v777yfOX7lyJby8vGBubo4WLVrg+PHjL/hqqCopcwqwJqioAciMl+vDRK5/f/UFQdCs6mw9fQs5+SqJI6JHHbh8B1eS0mFlptCsllbUwyYmlREZERERGRL9exf7iKysLPj7+2PFihVanadUKjFy5Ej06NHjiefmzZuHNWvW4LvvvsPly5cxadIkvP766zh37pzmmJ07d2LGjBn46KOPcO7cOXTq1Al9+vRBXFzcC78mqhrrj0dDmVOA+k5WGNi8rtThVFjvJnXg5mCBB9kF+PXsbanDoWJqtYhlB4tW397p4An7GqYvdkHNChwzOCIiIipNrxO4Pn364PPPP8egQYO0Om/ixIkIDAxEu3btnnhu69at+O9//4u+ffvC29sbkydPRu/evfH1119rjlm6dCnGjh2LcePGoVGjRli+fDnc3NywatWqF35NVPnuZ+ZhfUhR449ZPX0hL9mfpofkMgHvdCgafbAhJAYqLtHohP2XknDtbtFg+LEdX2z1DeAKHBEREZVNrxO4iti4cSNu3ryJBQsWPPX5vLw8mJuXnttkYWGBkJAQAEB+fj4iIiLQq1evUsf06tULJ0+eLPO+eXl5SE9PL/VF1WN10E1k5avQtK4NXmlaR+pwXtiQlm6wMVcgJiULh67clToco6dSi5rB8OM6Vs5g+JKPGLgCR0RERI8zqgTuxo0bmDNnDrZv3w6F4unzv3r37o2lS5fixo0bUKvVOHjwIH777TckJSUBAFJSUqBSqVC7du1S59WuXRt37twp896LFy+Gra2t5svNTf+aaOiju+m52HLqFgDg/V4NIAj6u/pWooaZAsOLRyCsO87B3lL740Iiou5lwtbCBGM6elbKNUtW4Ji+ERER0eOMJoFTqVQIDAzEwoUL4evrW+Zx33zzDerXr4+GDRvC1NQU06ZNw5gxYyCXl245/3giIIriM5ODuXPnQqlUar7i4+Nf7AVRuXx35AbyCtVo6WGPrr6OUodTaUa394SJXMCZ2Ac4F/dA6nCMVqFKrRkMP6GzN2zMX3z1DQBKfpRwkDcRERE9zmgSuIyMDISHh2PatGlQKBRQKBT49NNPcf78eSgUChw5cgQA4OjoiL179yIrKwu3bt3C1atXYWVlBS+vorqjWrVqQS6XP7Hadu/evSdW5R5lZmYGGxubUl9UteLuZ2NHWFGi/EFvw1h9K1HbxhwD/IuasXCwt3R+i0xEdEoW7C1NMKq9Z6Vdt+TvKvM3IiIiepzRJHA2Nja4ePEiIiMjNV+TJk1CgwYNEBkZiTZt2pQ63tzcHHXr1kVhYSF27dqFgQMHAgBMTU3RokULHDx4sNTxBw8eRPv27avt9dDzLT98HYVqEZ3q10Ib75pSh1PpxnUq+lDhr0tJiE/Nljga41OgUuPbI0WrbxO7+MDK7OnbsitCxhU4IiIiKkPlveOQQGZmJqKiojTfx8TEIDIyEg4ODnB3d8fcuXORkJCALVu2QCaToWnTpqXOd3Jygrm5eanHQ0NDkZCQgObNmyMhIQGffPIJ1Go1PvzwQ80xs2bNwttvv42WLVuiXbt2+OGHHxAXF4dJkyZV/Yumcom6l4G95xIAALN7NZA4mqrRyNkGnerXwvEbKdhwIgYL+jeROiSjsudsAm7dz0bNGqYY2c6jUq9dsljM9I2IiIgep9cJXHh4OLp166b5ftasWQCAUaNGYdOmTUhKStJ6Nltubi7mzZuH6OhoWFlZoW/fvti6dSvs7Ow0x7z11lu4f/8+Pv30UyQlJaFp06bYv38/PDwq900cVdzSg9ehFoFejWvD381O6nCqzPhO3jh+IwU7z8RjRg/fSumASM+XX/hw9W1yVx9Ymlbuj1JNExOuwBEREdFjBJHvECSRnp4OW1tbKJVK1sNVsksJSrz6XQgEAfj7vc5oUMda6pCqjCiK6PPNcVy9k4H/vNIQk7v6SB2SUfgxNA7/3XMRjtZmCP6gGyxM5c8/SQsDV4Tg/G0l1o1siZcbl11bS0RERIZBm9zAaGrgyHh8feAaAGCAv4tBJ29AUbOLsR2LauE2nYxBfqFa4ogMX16hCiuKV9+mdPWp9OQNeKSJSaVfmYiIiPQdEzgyKBG3UnH0WjLkMgEzXy57XIQhGdDcBU7WZribnoffzydKHY7B+/lMPBKVuahtY4Zhrd2r5B4cI0BERERlYQJHBkMURXz1T9Hq25stXOFZq4bEEVUPM4Vc08J+7fFo1k1VodwCFVYcLWqcNLVbPZibVP7qG/BoDVyVXJ6IiIj0GBM4Mhgnou7jdHQqTOUyvNujvtThVKvhbdxhaSrH1TsZOBF1X+pwDNZPYXG4m54HZ1tzvNXKrcruUzJGgMk4ERERPY4JHBkEURTxVXHtW2Abd9S1s5A4ouplZ2mKIS2LEoofjkdLHI1hyslXYeWxmwCAad3rwUxRNatvACCANXBERET0dEzgyCAcvHwX5+PTYGEix9Ru9aQORxLvdPCCTACCryfj2p0MqcMxONtDbyE5Iw917SzwZouqW30DWANHREREZWMCR3pPrRax9OB1AMCYDp5wtDaTOCJpuNe0xCtN6wAA1nEVrlJl5BZgdVDR6tv0HvVgqqjaH50PE7gqvQ0RERHpISZwpPd+v5CIq3cyYG2uwMTOxj0HbVwnbwDA3sgE3EvPlTgaw/HZH5eRkpkPz5qWGPSSa5Xfj4O8iYiIqCxM4EivFarUWH6oaCbXhE7esLU0kTgiab3kbo8WHvYoUInYfCpW6nAMwqHLd/Fz+G0IArDkDX+YyKv+x6agaWJS5bciIiIiPcMEjvTarrO3EZOSBYcaphhTPNDa2I0vXoXbdjoO2fmFEkej36LuZeA/uy4AKPr/tbWXQ7XcV7MCxzYmRERE9BgmcKS38gpV+PZw0UyuKV19YGWmkDgi3dCzcW141LSEMqcAv4TfljocvXUpQYkha07jflY+GjvbYFbP6hsMLxQncGp1td2SiIiI9ESF3/Heu3cP9+7dg/qxdxjNmjV74aCIyuOn0DgkpOWgto0ZRrT1kDocnSGXCRjb0Qsf//Yv1ofEYERbD8hLBotRuUTcSsXojWeQkVuIZq622DymdZUN7X6akj8trr8RERHR47RO4CIiIjBq1ChcuXJFU2AvCAJEUYQgCFCpVJUeJNHjsvMLseJoUVfAd7vXr9Y31/rgjRauWHrwOuJSs3Hw8h280tRZ6pD0xomoFIzbHI6cAhVaezpg/eiWsDav3tpKGccIEBERURm03kI5ZswY+Pr64uTJk4iOjkZMTEyp/yWqDptP3kJKZh7cHCw0A6zpIUtTBUa0KVqV/CGY/y7L69Dluxiz6QxyClToVL8WNr/TutqTN+DhFkp2oSQiIqLHab0CFxMTg927d6NePeMclkzSS39kJteMHr5VPpNLX41s74EfgqNxNi4NEbdS0cKjehpw6Kt95xMxa2ckCtUiejepjW+HBcBMIc3KroxdKImIiKgMWr/z7dGjB86fP18VsRCVy7rgaChzClDPyQqvBdSVOhyd5WRtjtcCXAAAa4NjJI5Gt+0Ii8N7O86hUC3i9YC6+D7wJcmStyLFTUyYwBEREdFjtF6BW7duHUaNGoVLly6hadOmMDEpvb1owIABlRYc0ePuZ+ZhfUhRMvJ+T18253iOcZ288XP4bfxz+Q5u3c+CR80aUoekc9aHxOCzPy4DAIa3ccdnA5tCJvHfK80KHNuYEBER0WO0TuBOnjyJkJAQ/PXXX088xyYmVNVWB91EVr4KTeva4JWmdaQOR+f51rZGF19HBF1PxoaQGCwc2FTqkHSGKIpYcSQKXx+8DgCY0Nkbc/s01NSfSalkDhxX4IiIiOhxWm+hnD59Ot5++20kJSVBrVaX+mLyRlXpbnoutpy6BQB4v1cDnXijrQ8mdC4a7P1z+G2kZedLHI1uEEUR//f3VU3yNqunr84kbwCgCYNFcERERPQYrRO4+/fvY+bMmahdu3ZVxENUpu+O3EBeoRotPezR1ddR6nD0RnufmmjkbIOcAhW2h8ZJHY7k1GoR83+7hDVBRd055/VrhOk96utM8gZwBY6IiIjKpnUCN2jQIBw9erQqYiEqU3xqNnaExQMAZvfm6ps2BEHA+E5eAIBNJ2ORV2i8K+WFKjVm/3Ie207HQRCAxYP8MK6Tt9RhPYlz4IiIiKgMWtfA+fr6Yu7cuQgJCYGfn98TTUymT59eacERlVh+6AYK1SI61a+Ftt41pQ5H77zazAVL/r6GO+m5+C0y0Shn5+UVqvDeT5H4+987kMsELB3ij4HNdbOLqUwzB07iQIiIiEjnVKgLpZWVFYKCghAUFFTqOUEQmMBRpYu6l4E9524DKKp9I+2ZKmQY3cET//fXVaw/HoM3W7ga1SpmTr4KE7dFIPh6MkzlMqwIDECvJrrbBKfkT4YrcERERPS4Cg3yJqpOyw7egFoEejaujeZudlKHo7eGtXbHd4dv4NrdDATfSEEXI6kjzMgtwNhN4QiLTYWFiRw/jGyBTvV1+7VzOgYRERGVResauEeJogiRnxBTFbqUoMSfF5MgCMD7vXylDkev2VqY4K1W7gCAtcHREkdTPR5k5WPEulCExabC2kyBrWNb63zyBjzaxIQ/X4mIiKi0CiVwW7ZsgZ+fHywsLGBhYYFmzZph69atlR0bEb4+cA0AMMDfBQ3r2Egcjf4b08ETMgEIiUrB5cR0qcOpUvcycjH0h9M4f1sJe0sT/DShLVp6OkgdVvmUDPJm/kZERESP0TqBW7p0KSZPnoy+ffvi559/xs6dO/HKK69g0qRJWLZsWVXESEYq4lYqjl5LhlwmYObLXH2rDG4Olujj5wwAWBdiuKtwtx9kY8jqU7h2NwNO1mb4eWI7NK1rK3VY5cYxAkRERFQWrWvgvvvuO6xatQojR47UPDZw4EA0adIEn3zyCWbOnFmpAZJxEkURX/1TtPr2ZgtXeNaqIXFEhmNCJ2/8eSEJ+yIT8WHvhqhjay51SJUqJiULw9eeRqIyF3XtLPDj+DbwqKlff380c7zBDI6IiIhK03oFLikpCe3bt3/i8fbt2yMpKalSgiI6EXUfp6NTYSqX4d0e9aUOx6D4u9mhtacDCtUiNp2MlTqcSnX1TjreXH0KicpceDvWwK+T2+ld8gZwjAARERGVTesErl69evj555+feHznzp2oX59vtOnFiaKIr4pr3wLbuKOunYXEERmeccWDvX8MvYXMvEKJo6kc5+PTMPSH00jJzEMjZxv8PLEdnG318+9OyYQHNfdQEhER0WO03kK5cOFCvPXWWwgODkaHDh0gCAJCQkJw+PDhpyZ2RNo6dOUezsenwcJEjqnd6kkdjkF6uVFteNWqgZiULPx8Jh7vdPSSOqQXEhp9H2M3hyMzrxDN3eyweUxr2FqaSB1WhZXM6GP6RkRERI/TegVu8ODBCAsLQ61atbB3717s3r0btWrVQlhYGF5//fWqiJGMiFotajpPju7gCUdrM4kjMkwymYCxxUnbhhMxKFSpJY6o4o5du4dRG8OQmVeIdt41sW1cG71O3oCHc+A4RoCIiIgep9UKXEFBASZMmID58+dj27ZtVRUTGbE/Libh6p0MWJspMLGzt9ThGLTBL7li6cHruP0gB//8exf9mjlLHZLW/rqYhOk7zqFAJaJ7QyesHP4SzE3kUof1wgSOESAiIqIyaLUCZ2Jigj179lRVLGTkClVqLDt4HQAwvrM37CxNJY7IsFmYyjGirQcA4Ifj0RD1LFvYffY2pv54FgUqEf38nLF6RAuDSN6AR5uY6NefCREREVU9rbdQvv7669i7d28VhELGbtfZ24hJyYJDDVO9r8nSFyPbecBUIcP5+DSE33ogdTjltvX0Lcz6+TzUYtGYiW+HBcBUofWPM531cIwAERERUWlaNzGpV68ePvvsM5w8eRItWrRAjRqlW3RPnz690oIj45FXqMK3h6MAAJO7+MDKTOu/mlQBtazMMPiluvgpLB5rg6PRytNB6pCea03QTSz+6yoAYHR7T3z8amPISorGDISgGeTNFI6IiIhK0/pd8rp162BnZ4eIiAhERESUek4QBCZwVCE/hcYhIS0HtW3M8HY7D6nDMSpjO3rjp7B4HLxyFzEpWfDS0aHpoihi6cHr+O5IUaI/tZsPZvdqoEl2DIlmjADzNyIiInpMuRK49PR02NjYAABiYmKqNCAyPtn5hVhx9CYA4N3u9Q2mjklf1HOyQveGTjhy9R7Wh0Tj89f8pA7pCaIo4tM/LmPjiVgAwIevNMCUroY7YoKDvImIiKgs5Soasbe3x7179wAA3bt3R1paWlXGREZm88lbSMnMg5uDBYa0dJM6HKM0vlNRx89fI24jNStf4mhKU6lFzNl1UZO8LRzQxKCTN+DhGAE2MSEiIqLHlSuBs7Kywv379wEAx44dQ0FBQZUGRcYjPbcAq4OKVt9m9PA1qEYU+qSttwOa1rVBboEa207fkjocjQKVGu/tOIed4fGQCcBXbzTDqPaeUodV5TjIm4iIiMpSri2UL7/8Mrp164ZGjRoBKOpEaWr69BbvR44cqbzoyOCtOx4DZU4B6jlZ4bWAulKHY7QEQcD4Tt54b0cktpyKxYTO3pJvZc0tUGHaj2dx6Mo9mMgFfDM0AH399G9WXUVoauBYBEdERESPKVcCt23bNmzevBk3b95EUFAQmjRpAktLy6qOjQxcalY+1h+PBgDM6ukLuYF1EtQ3ff2c8eVfV5GozMVvkQl4q5W7ZLFk5RViwtZwnIi6DzOFDKtHtEC3hk6SxVPdBHAFjoiIiJ6uXAmchYUFJk2aBAAIDw/Hl19+CTs7u6qMi4zA6qCbyMpXoYmLDV5pUkfqcIyeiVyGMR28sGj/Faw9HoM3W7hJ0p5fmVOAMRvDcDYuDTVM5Vg3qhXa+dSs9jikJNN0oWQKR0RERKVpPUbg6NGjWt8kPT1d63NKul6SYbqbnovNJ2MBALN7NTC4OV766q3Wbvjm8A1E3ctE0PXkal/1up+Zh7fXh+FyUjpszBXY/E5rBLjbV2sMukDQNDGRNg4iIiLSPdUyLdnOzk6rWU2CIOD69evw9vauwqhISt8duYG8QjVaeNijawNHqcOhYjbmJhjW2g1rj8fgh+Doak3g7ihzMXzdadxMzkItK1NsHdsGjZyN84Och2MEmMERERFRadWSwAHAr7/+CgcHh+ceJ4oi+vbtWw0RkVTiU7OxIyweAAx2ELM+G93BCxtOxOJU9H1cSlCiaV3bKr9nfGo2AtedRnxqDpxtzbFtXBv4OFpV+X11Vcm/CfYwISIiosdVSwLn4eGBzp07o2bN8tWxeHt7w8TEpIqjIqksP3QDhWoRnerXMrraJn1Q184C/fycse98ItYdj8byoQFVer+oexkYvi4Ud9Pz4FHTEtvGtoGbg3E3SSr5SENkGxMiIiJ6jFZDtwoLC7Fw4ULEx8drdZOYmJhyJ28AcOnSJbi5PX+gc3BwMPr37w8XFxcIgoC9e/eW+x4nTpyAQqFA8+bNn3hu+fLlaNCgASwsLODm5oaZM2ciNzdX83xhYSHmzZsHLy8vWFhYwNvbG59++inUanW572+sopMzsefcbQDA+70aSBwNlaVksPfvF5KQmJZTZfe5lKDEkDWncTc9D/WdrPDLxHZGn7wBD7dQcgWOiIiIHqdVAqdQKPDVV19BpVJVVTxaycrKgr+/P1asWKHVeUqlEiNHjkSPHj2eeG779u2YM2cOFixYgCtXrmD9+vXYuXMn5s6dqznmyy+/xOrVq7FixQpcuXIFS5YswVdffYXvvvvuhV+Tofvr0h2oRaCLryOau9lJHQ6Vwc/VFm29HaBSi9hU3GymskXceoBha08jNSsfTevaYOfEdnCyMa+Se+kbNjEhIiKismiVwAFFQ72PHTtW6YHcvXsXn376qVbn9OnTB59//jkGDRqk1XkTJ05EYGAg2rVr98Rzp06dQocOHRAYGAhPT0/06tULw4YNQ3h4eKljBg4ciH79+sHT0xNvvPEGevXqVeoYerrT0fcBAN3YuETnlazC/RQah4zcgkq99smoFLy9PhQZuYVo6WGPH8e3hUMN00q9hz6TaRI4ZnBERERUmtYJXJ8+fTB37lzMnj0bP/30E/bt21fqq6Lu3LmDhQsXVvj88tq4cSNu3ryJBQsWPPX5jh07IiIiAmFhYQCA6Oho7N+/H/369St1zOHDh3H9+nUAwPnz5xESEvLM5it5eXlIT08v9WVsClRqRNx6AABoy9o3ndetgRN8HGsgI68QO89ot236WQ5fuYvRm84gO1+FTvVrYcvY1rAxZ83rox42MWECR0RERKVp3cRk8uTJAIClS5c+8ZwgCGVur7xw4cIzr3vt2jVtQ9HajRs3MGfOHBw/fhwKxdNf+tChQ5GcnIyOHTtCFEUUFhZi8uTJmDNnjuaY//znP1AqlWjYsCHkcjlUKhUWLVqEYcOGlXnvxYsXV0uCqssuJiiRna+CnaUJfJ2spQ6HnkMmEzCukzfm7r6IjSdiMbq9JxRyrT/zKeX384mYuTMShWoRPRvXxorAAJgp5JUUseHgFkoiIiIqi9YJXEUbdTRv3hyCIDx1S1DJ41XZTl6lUiEwMBALFy6Er69vmccdO3YMixYtwsqVK9GmTRtERUXhvffeg7OzM+bPnw8A2LlzJ7Zt24Yff/wRTZo0QWRkJGbMmAEXFxeMGjXqqdedO3cuZs2apfk+PT29XI1aDEnJ9sk2Xg4c3K0nXg+oi//9cw0JaTnYf+kOBvi7VPhaO8/EYc7uixBFYGBzF/zvTX+YvGBCaKjYxISIiIjK8kJjBHJzc2FuXr6mAzVr1sSXX3751MYhAPDvv/+if//+LxLOM2VkZCA8PBznzp3DtGnTABQlo6IoQqFQ4MCBA+jevTvmz5+Pt99+G+PGjQMA+Pn5ISsrCxMmTMBHH30EmUyGDz74AHPmzMHQoUM1x9y6dQuLFy8uM4EzMzODmZlZlb0+fRAanQoAaOPF7ZP6wtxEjpHtPLHs0HWsDY5G/2bOFfqgZUNIDD794zIAYFhrd3z+WlPImcSXiWMEiIiIqCxaJ3AqlQpffPEFVq9ejbt37+L69evw9vbG/Pnz4enpibFjxz71vBYtWiAxMREeHh5PfT4tLa1KC/ZtbGxw8eLFUo+tXLkSR44cwa+//govLy8AQHZ2NmSy0qsCcrkcoihq4ivrGI4RKFuhSo3w2KIErq03Ezh9MqKtO1Yei8LFBCVCY1K1+vMTRRErjkTh64NF9aLjO3nhv30bcXj7c5SswHELJRERET1O6/1LixYtwqZNm7BkyRKYmj7sGufn54d169aVed7EiRPh6elZ5vPu7u7YuHGjVrFkZmYiMjISkZGRAIrmzUVGRiIuLg5A0bbFkSNHAgBkMhmaNm1a6svJyQnm5uZo2rQpatSoAQDo378/Vq1ahR07diAmJgYHDx7E/PnzMWDAAMjlcs0xixYtwp9//onY2Fjs2bMHS5cuxeuvv65V/MbkUmI6svJVsLUwQcM6rH/TJzWtzDC4hSsAYN3x6HKfJ4oi/u/vq5rkbcbL9Zm8lZPALpRERERUBq1X4LZs2YIffvgBPXr0wKRJkzSPN2vWDFevXi3zvOclN/b29mVuPyxLeHg4unXrpvm+pMZs1KhR2LRpE5KSkjTJXHnNmzcPgiBg3rx5SEhIgKOjoyZhK/Hdd99h/vz5mDJlCu7duwcXFxdMnDgRH3/8sVb3MiahxfVvrTxZ/6aPxnb0wk9hcTh05R6i7mWinpPVM49Xq0Us2Pcvtp6+BQD4qG8jjO/sXR2hGgSBNXBERERUBkHU8iNeCwsLXL16FR4eHrC2tsb58+fh7e2Ny5cvo3Xr1sjMzCz3tU6cOIGWLVsaZW1Yeno6bG1toVQqYWNjI3U4VW7MxjAcvZaMef0aYVwnvpHXR+M2h+PQlbsY1todiwf5lXlcoUqND3ddwO6zCRAEYNFrfghs416Nkeq/kprBV5s5Y0XgS1KHQ0RERFVMm9xA6y2UTZo0wfHjx594/JdffkFAQIBW1+rTpw8SEhK0DYH0TFH9W/H8N9a/6a0JxStou8/eRkpm3lOPyStUYdqP57D7bALkMgHLhjRn8lYBmkHe0oZBREREOkjrLZQLFizA22+/jYSEBKjVauzevRvXrl3Dli1b8Mcff2h1LdZ3GIfLSenIyCuEtbkCjZwNf7XRULXytIe/qy3O31Zi66lbmNmz9DiOnHwVJm2LQND1ZJjKZfguMAC9m9SRKFr9VrLNmD8jiYiI6HFar8D1798fO3fuxP79+yEIAj7++GNcuXIFv//+O3r27FkVMZKeKxkf0NrTga3j9ZggCJrtr1tP30JugUrzXEZuAUZtDEPQ9WRYmMixfnRLJm8vQDNGgPkbERERPaZCc+B69+6N3r17v/DN16xZg9q1a7/wdUi3lQzw5vZJ/denaR3UtbNAQloOdp9NQGAbd6Rl52PUhjCcv62EtZkCG8a0QitPB6lD1WsPm5gwgyMiIqLStF6B8/b2xv379594PC0tDd7e2jWnCAwMhEqlwt69e3HlyhVtQyE9oFKLCCue/9bGm2/q9Z1CLsM7HYtmJq47Ho276bl4a81pnL+thL2lCX4c35bJWyV4OEZA2jiIiIhI92idwMXGxkKlUj3xeF5eXrkakgwZMgQrVqwAAOTk5KBly5YYMmQImjVrhl27dmkbDum4K0npyMgthLWZAo1Z/2YQ3mrlBmtzBaJTsvDK8mBcu5sBR2sz7JzYDn6utlKHZxBkHCNAREREZSj3Fsp9+/Zp/vuff/6Bre3DN2oqlQqHDx9+5qDuEsHBwfjoo48AAHv27IEoikhLS8PmzZvx+eefY/DgwVqET7quZPtkS097KORaf15AOsjKTIHANu5YExSNB9kFqGtnge3j2sCzVg2pQzMYD2vgmMERERFRaeVO4F577TUARbUZjw/cNjExgaenJ77++uvnXkepVMLBoWiL1d9//43BgwfD0tIS/fr1wwcffKBF6KQPThc3MGH9m2EZ094LO8Li4Whthi3vtIaLnYXUIRmUkhU4pm9ERET0uHIncGq1GgDg5eWFM2fOoFatWhW6oZubG06dOgUHBwf8/fff2LFjBwDgwYMHMDc3r9A1STep1SLOaOrfmMAZkjq25jg9twcUcgEmXFmtdCU1cGxiQkRERI/TugtlTEzMC91wxowZGD58OKysrODh4YGuXbsCKNpa6efn90LXJt1y5U46lDkFqGEqR1MX1r8ZGgtTudQhGKySLpTM34iIiOhxFRojkJWVhaCgIMTFxSE/P7/Uc9OnT3/muVOmTEGbNm0QFxeHnj17QiYr+vTe29sbn3/+eUXCIR1VMv+tpacD69+ItCDjChxVogKVGhcTlLAwkaMRm0kREek9rRO4c+fOoW/fvsjOzkZWVhYcHByQkpICS0tLODk5PTeBA4AWLVqgRYsWpR7r16+ftqGQjuP8N6KKETjvnl6AKIq4cS8TITdScCIqBaej7yMrv6h7tL+bHca090RfP2eYKvjBGhGRPtI6gZs5cyb69++PVatWwc7ODqdPn4aJiQlGjBiB9957rypiJD2k5vw3ogqTcZA3aSlJmYOQGyk4efM+QqJSkJyRV+p5O0sTZOepcD4+DTN2RmLR/isIbO2O4W3c4WTD+nMiIn2idQIXGRmJNWvWQC6XQy6XIy8vD97e3liyZAlGjRqFQYMGVUWcpGeu3c1AWnYBLE3l8KvL2WBEFVHcO4roCcqcApyOvo8TUSkIiUpBdHJWqefNTWRo5emAjvVqoUO9WmjsbIPU7Hz8FBqHbaG3cDc9D98cvoGVx6LQ188Zo9t7IsDdXqJXQ0RE2tA6gTMxMdEU2NeuXRtxcXFo1KgRbG1tERcXV+kBkn4KLd4+2cLDnl0KibT0cIwAV+CoSF6hCmdvpWkStgu300oNepcJQDNXO3SsVwvt69XES+72MDcp3WiolpUZ3u1RH5O6+uDvS3ew6WQsIm49wG+RifgtMhH+bnYY3d4Dff2cYaZgkyIiIl2ldQIXEBCA8PBw+Pr6olu3bvj444+RkpKCrVu3soskaYTGcP4bUUU93EIpcSAkGbVaxOWkdE3CdiY2FbkFpZdkvR1raFbY2nrXhK2FSbmubSKXob+/C/r7u+DibSU2nYzF7+cTcT4+DTN3pmHRn1cR2MYdI7i9kohIJ2mdwH3xxRfIyMgAAHz22WcYNWoUJk+ejHr16mHjxo2VHiDpH1EUH0ngWP9GpC1NExMmcEYlPjUbIcUJ28moFDzILij1fC0rM3SsVxMdipM2FzuLF76nn6stvh7ij7l9G2JHWBy2ni7aXvnt4RtYeTQK/ZpxeyURka7ROoFr2bKl5r8dHR2xf//+cp1XUFCAjz76CLt374aDgwMmT56MMWPGaJ6/e/cuXFxcoFKptA2JdMyNe5lIzcqHuYkMfnXtpA6HSO9wjIBxSM3Kx6nipiMnolIQl5pd6vkapnK09X6YsPnWttKUMFS2WlZmmNa9PiZ28cE//97BphOxCH90e6WrLUZ38OT2SiIiHaB1Ard27Vp07doV9evX1+q8RYsWYcuWLZg9ezbS0tIwc+ZMnD59GmvWrNEcI/LNikEoGR/Q0sOBbaqJKqSkBo4MSU6+CmdiUzXbIi8npZca1q6QCQhwt0OHerXQsV4t+LvZVXsNsYlchlebueDVZi64lFC0vXJfZCLO31Zi5s7zWPTnFQS28eD2SiIiCWmdwH399deYNGkSateujS5duqBr167o0qULGjZs+Mzztm/fjnXr1uHVV18FAIwZMwZ9+vTBmDFjsGHDBgCosk8WqXqVDPBu48Xtk0QVwRU4w6BSi7iYoCxK2G6kIOLWA+SrStexNahtXZSw1a+J1l41YWWm9a/lKtO0ri3+96Y/5vQp2l657XQc7qTnarZX9vVzxugOnghws+PvbyKiaqT1b4qrV6/izp07OHr0KIKCgrBs2TJMmTIFjo6O6Nq1K3bs2PHU8xISEtC0aVPN9z4+Pjh27Bi6d++Ot99+G0uWLKn4qyCdUVT/VjzA24cNTIgqQmATE70kiiJiUrI0K2ynbt5Hem5hqWOcbc01jUfa16sJJ2vdX8V6fHvl5pOxOBP7APvOJ2Lf+UQ0c7XF6Pae6NeM2yuJiKqDIL7AvsWsrCyEhIRgx44d2LZtG0RRRGFh4VOP9fb2xtq1a9GjR49SjycmJqJbt25wd3fHkSNHjKYGLj09Hba2tlAqlbCxsZE6nEoTdS8DLy8NhplChguf9OIvc6IKOHzlLsZuDoe/qy1+m9ZR6nDoGe5l5OJk1MM6tiRlbqnnrc0VaO9TU5O0edWqYRCrVZrtlecTkV9YtKpYy8q0aDh4Ww/U5vZKIiKtaJMbaL0C99dffyEoKAjHjh3D+fPn0aRJE3Tu3Bm7du1Cp06dyjyve/fu+PHHH59I4FxcXHDkyBF07dpV21BIB50q3j7ZwsOeyRtRBXGMgO7KzCtEWMx9hNwoGqJ97W5GqedN5TK08LBHx/pFCVtTFxsoDHAWZsn2yrl9GmLHmXhsPXWraHvlkSisPHYTfYqHg7/kzu2VRESVTesErl+/fnB0dMT777+Pf/75B7a2tuU6b/78+bh69epTn6tbty6Cg4Nx4MABbcMhHVMywLuNF7dPElVY8ftdDvKWXoFKjcj4NITcKFphi4xPQ+EjmbUgAE1cbIo6RfrUQitPB1iYGs+HVzWtzDC1Wz1M6OyNA//exaaTMTgT+wC/n0/E7+cT4Ve3aHvlq/7cXklEVFm03kK5fPlyBAcH4/jx45DL5ZpGJl27dkWjRo2qKk6DY4hbKEVRRKtFh5GSmYedE9qiDYd4E1VI0PVkjNoQhsbONtj/Xtk7G6jyiaKI63czNVsiQ6PvIyu/9NZ+dwdLTafIdj414VDDVKJoddOlBCU2n4zFb49trxzW2h0juL2SiOiptMkNXqgG7uLFiwgKCsLRo0fx+++/o2bNmkhKSir3+TY2NoiMjIS3t3dFQ9BbhpjA3UzORI+vg2CqkOHCgl4wN+GnrUQVEXw9GSM3hKGRsw3+YgJX5RLTcnCiOGELibqPlMy8Us/bW5qgfXHC1sGnFtxrWkoUqX65n5mHHWfise30LU1toEImFG+v9MBL7vbcXklEVKxKa+BKnDt3DseOHcPRo0dx/PhxqNVquLq6anUNzn0zLCXz315yt2PyRvQCSmrg+DOyaqXnFmDc5nCExaSWetzcRIbWXjXRwadoiHZjZxvIZEw0tFWyvXJiZ28cuHwXm07EIiw2ldsriYhekNYJ3IABAxASEoL09HQ0b94cXbt2xYQJE9C5c2eDWUmiink4/41bJ4lehMA5cFVOpRbx7o/nEBaTCpkANHO103SKfMnDjglFJVLIZejr54y+fs74N7Foe+XeyERcTFDi/V/O44v9VxDYxh3D23igji23VxIRPY/WCZyvr2+lJWwjRoxg0mcgRFHUrMC1Ze0b0QspSeCYv1WdL/++iqDryTA3keHnie3QzNVO6pCMQhMXWyx5wx9z+jTCT2Fxmu2V3x2JwqpjN/FK0zoY08GT2yuJiJ5B6wTuf//7X6XdfNWqVZV2LZJW7P1s3MvIg6lchgB3O6nDIdJrD8cIMIOrCjfuZuCH4GgAwP/e9GfyJgGHGqZP3V75x4Uk/HEhCU3r2mB0ey+82syZW/KJiB5ToRq4rKwsBAUFIS4uDvn5+aWemz59+nPPDwsLw7Fjx3Dv3j2o1epSzy1durQiIZHESlbfmrux/o3oRZWsOzB9qxpripO3Xo1r49VmLhJHY9yetr3yt8hEXEpIx+yS7ZXF3Su5vZKIqIjWCdy5c+fQt29fZGdnIysrCw4ODkhJSYGlpSWcnJyem8B98cUXmDdvHho0aIDatWuX2iLB7RL6K1SzfdJB4kiI9F9JwwwuwFW+JGUOfotMAABM6uojcTT0qEe3V+44E4etp4q2V644GoVVQcXbK9t7ooUHt1cSkXHTOoGbOXMm+vfvj1WrVsHOzg6nT5+GiYkJRowYgffee++553/zzTfYsGEDRo8eXZF4SQeJoojQ4i5unP1G9OI0K3DM4CrdhpAYFKhEtPZywEvu9lKHQ0/hUMMUU7rWw4RO3jh4+S42noxFWEwq/ryQhD+Lt1eOaueJ/v4u3PFBREZJpu0JkZGReP/99yGXyyGXy5GXlwc3NzcsWbIE//3vf59/Q5kMHTp0qFCwpJviUrORpMyFiVzgGyKiSiBoauAkDsTAKHMK8GNoHABgUhfjmz+qbxRyGfr4OePnie2wf3onvNXSDWYKGS4lpOODXy+g/f8dwVf/XEWSMkfqUImIqpXWCZyJiYnmzUXt2rURF1f0y9DW1lbz388yc+ZMfP/999relnRYyfgAf1c7WJjy01CiF8UxAlVj2+lbyMpXoUFta3Rr4CR1OKSFxi42+PKNZjg1twc+fKUBXGzNkZqVj++P3kTHL49i6o9ncSY2lavWRGQUtN5CGRAQgPDwcPj6+qJbt274+OOPkZKSgq1bt8LPz++558+ePRv9+vWDj48PGjduDBMTk1LP7969W9uQSGIcH0BUuR4O8pY4EAOSW6DCxhOxAIAJnb1ZQ6WnHt9euelkLEIf2V7ZxMUGo9p7YgC3VxKRAdN6Be6LL76As7MzAOCzzz5DzZo1MXnyZNy7dw8//PDDc89/9913cfToUfj6+qJmzZqwtbUt9UX6pXT9GxuYEFUGmWYOHDO4yrL7bAJSMvPgYmuOAc3ZeVLflWyv3PnY9sp/E9PxYfH2yu+PRkHNfchEZIC0XoFr2bKl5r8dHR2xf/9+rc7fsmULdu3ahX79+ml7a9JBtx/kICEtBwqZgBYerH8jqgxCcRsTvvWsHCq1iLXHi0YHvNPRCyZyrT+7JB1Wsr1yTp+G2HEmHttO30JCWg6++uca/k1UYumQ5lyNIyKDUqHfYoWFhTh06BDWrFmDjIwMAEBiYiIyMzOfe66DgwN8fNi62VCUbJ9s5moLS9MKjRUkosewBq5yHfj3DmJSsmBrYYJhrd2lDoeqiH0NU0zu6oOgD7ri/wb5wUQuYP/FO3h7fSjSsvOffwEiIj2hdQJ369Yt+Pn5YeDAgZg6dSqSk5MBAEuWLMHs2bOfe/4nn3yCBQsWIDs7W/toSeecLm5gwvo3osojaLZQShuHIRBFEauDbgIARrbzQA0zftBk6BRyGYa2dsfmd1rD2kyBM7EPMHjVScSn8n0HERkGrRO49957Dy1btsSDBw9gYWGhefz111/H4cOHn3v+t99+i7/++gu1a9eGn58fXnrppVJfpF9CY4pW4Dj/jajyyDhGoNKcjk7F+dtKmClkGNXeU+pwqBq196mFXya3g7OtOW4mZ2HQqpO4lKCUOiwiohem9UeRISEhOHHiBExNTUs97uHhgYSEhOee/9prr2l7S9JRtx9k4/aDHMhlAlqy/o2o0ghsYlJpSlbf3mzpilpWZhJHQ9WtYR0b7J7SHmM2nsHVOxkYsuYUVg5/CV05RoKI9JjWCZxarYZKpXri8du3b8Pa2vq55y9YsEDbW5KOKpn/5lfXltuSiCqRZoyAxHHouytJ6Qi6ngyZAIzvxMHdxsrZ1gI/T2qHSVsjcPLmfYzdHI7Fr/thSCs3qUMjIqoQrbdQ9uzZE8uXL9d8LwgCMjMzsWDBAvTt27cyYyMdx/lvRFVDxiYmlWJN8epbHz9neNSsIXE0JCUbcxNsGtMarzV3gUot4sNdF7D80HWuchORXtI6gVu2bBmCgoLQuHFj5ObmIjAwEJ6enkhISMCXX35ZFTGSjuL8N6KqwkHeL+r2g2z8fiEJADCpMzsfE2CqkGHZW80xpWvR34flh27gP7suoEClljgyIiLtaL3vzcXFBZGRkdixYwciIiKgVqsxduxYDB8+vFRTEzJsiWk5iEvNhkwA69+IKhlX4F7cuuMxUKlFdKhXE36utlKHQzpCEAR8+EpDONtZYMFvl/Bz+G3cTc/DyuEvsRSAiPRGhebAWVhYYMyYMVixYgVWrlyJcePGSZK8BQcHo3///nBxcYEgCNi7d2+5zz1x4gQUCgWaN2/+xHPLly9HgwYNYGFhATc3N8ycORO5ubmljklISMCIESNQs2ZNWFpaonnz5oiIiHjBV6Q/SrpP+tW1hbW5icTREBkWQdPFRNo49NWDrHzsPBMPAJjI1Td6irfbemDN2y1hbiJD0PVkvPXDKdzLyH3+iUREOqDcCVxUVNQTCcrhw4fRrVs3tG7dGl988UWlB/c8WVlZ8Pf3x4oVK7Q6T6lUYuTIkejRo8cTz23fvh1z5szBggULcOXKFaxfvx47d+7E3LlzNcc8ePAAHTp0gImJCf766y9cvnwZX3/9Nezs7F70JemNkgYmHB9AVPm4Avditpy6hZwCFRo726BT/VpSh0M6qmfj2vhpfFs41DDFpYR0DFp5ElH3MqUOi4joucq9X+CDDz5A06ZN0aJFCwBATEwM+vfvj06dOqFZs2ZYvHgxLC0tMWPGjGdeZ9asWU99XBAEmJubo169ehg4cCAcHJ5fV9WnTx/06dOnvC9BY+LEiQgMDIRcLn9i1e7UqVPo0KEDAgMDAQCenp4YNmwYwsLCNMd8+eWXcHNzw8aNGzWPeXp6ah2HPnvYwIT1b0SVTQDnwFVUTr4Km0/FAgAmdvF+uJpJ9BQB7vbYPbk9Rm0Mw6372Ri86iTWjWqJVp783UZEuqvcK3Dh4eGlukxu374dvr6++Oeff/DNN99g+fLl2LRp03Ovc+7cOaxfvx4//PADgoKCcOzYMaxduxbr16/H4cOHMWvWLNSrVw+XL1+u0At6no0bN+LmzZtljjPo2LEjIiIiNAlbdHQ09u/fj379+mmO2bdvH1q2bIk333wTTk5OCAgIwNq1a59537y8PKSnp5f60ld3lLmIvV9c/8ZfckSV7uEOSmZw2volIh6pWflwtbdAPz9nqcMhPeBZqwZ2T24Pfzc7KHMKMHxdKP66mCR1WEREZSp3ApeSkgJXV1fN90ePHkX//v0133ft2hWxsbHPvc7AgQPx8ssvIzExERERETh79iwSEhLQs2dPDBs2DAkJCejcuTNmzpyp3Ssphxs3bmDOnDnYvn07FIqnLz4OHToUn332GTp27AgTExP4+PigW7dumDNnjuaY6OhorFq1CvXr18c///yDSZMmYfr06diyZUuZ9168eDFsbW01X25u+jt/pqT+rYmLLWxY/0ZU6WQyrsBVRKFKjbXHowEUzX1TyCtU5k1GqKaVGXaMb4uXGzkhv1CNKT+exYaQGKnDIiJ6qnL/dnNwcEBSUtEnUmq1GuHh4WjTpo3m+fz8/HLNU/nqq6/w2WefwcbGRvOYjY0NPvnkEyxZsgSWlpb4+OOPK70hiEqlQmBgIBYuXAhfX98yjzt27BgWLVqElStX4uzZs9i9ezf++OMPfPbZZ5pj1Go1XnrpJXzxxRcICAjAxIkTMX78eKxatarM686dOxdKpVLzFR8fX6mvrzqdLql/8+LqG1FV0Gz6YwKnlf2X7iA+NQf2liYY0lJ/PyQjaViYyrF6RAuMaOsOUQQ+/eMyPv/jMtT8JIWIdEy5a+C6dOmCzz77DCtXrsQvv/wCtVqNbt26aZ6/fPlyuerAlEol7t27h8aNG5d6PDk5WbOt0M7ODvn5+eUNrVwyMjIQHh6Oc+fOYdq0aQCKEjFRFKFQKHDgwAF0794d8+fPx9tvv41x48YBAPz8/JCVlYUJEybgo48+gkwmg7Oz8xPxN2rUCLt27Srz/mZmZjAzM6vU1ySVUA7wJqpSMqFkBY5vHMtLFEXN4O5R7T1hYSqXOCLSRwq5DJ8NbAoXOwss+fsa1oXEICk9F1+/6Q9zE/6dIiLdUO4EbtGiRejZsyc8PT0hk8nw7bffokaNGprnt27diu7duz/3OgMHDsQ777yDr7/+Gq1atYIgCAgLC8Ps2bPx2muvAQDCwsKeuUpWETY2Nrh48WKpx1auXIkjR47g119/hZeXFwAgOzsbMlnphUm5XA5RFDUrjB06dMC1a9dKHXP9+nV4eHhUasy66F56LqJTsiAIQCuuwBFVCU4R0F5IVAr+TUyHhYkco9p5Sh0O6TFBEDClaz0425rjw18v4M8LSUjOyMPat1vC1pJlA0QkvXIncF5eXrhy5QouX74MR0dHuLi4lHp+4cKFpWrkyrJmzRrMnDkTQ4cORWFhYVEQCgVGjRqFpUuXAgAaNmyIdevWPfdamZmZiIqK0nwfExODyMhIODg4wN3dHXPnzkVCQgK2bNkCmUyGpk2bljrfyckJ5ubmpR7v378/li5dioCAALRp0wZRUVGYP38+BgwYALm86NO3mTNnon379vjiiy8wZMgQhIWF4YcffsAPP/zw3Jj13emYou2TjZ1tYGvBX2REVUHgGAGtrQkqqn17q5Ub7GuYShwNGYLXA1zhZG2OSVsjEBaTisGrT2LTmFZwtbeUOjQiMnLlTuAAwMTEBP7+/k99rqzHH2dlZYW1a9di2bJliI6OhiiK8PHxgZWVleaYpw3Xfprw8PBS2zhLRhSMGjUKmzZtQlJSEuLi4sp1rRLz5s2DIAiYN28eEhIS4OjoiP79+2PRokWaY1q1aoU9e/Zg7ty5+PTTT+Hl5YXly5dj+PDhWt1LH5Vsn2zjxe2TRFWlZIwA87fyuXhbiZCoFMhlAsZ29JI6HDIgHerVws+T2mHMxjOIupeJQStPYuOYVmjiYit1aERkxASxPJ1HKtHhw4efOkAbAFasWKGpTzN06enpsLW1hVKpLNXQRdf1+PoYbiZn4Ye3W6BXkzpSh0NkkO5n5qHF54cAADGL+3KW2XNM/fEs/ryQhNeau2D50ACpwyEDlKTMwegNZ3DtbgZqmMqxakQLdPZ1lDosIjIg2uQG1d5jefDgwThz5swTjy9fvhz//e9/qzsc0kJyRh5uJhfVv7Vm/RtRlZE9krCxAd6z3bqfpZnZNaGzj8TRkKFytrXAz5PaoZ13TWTlq/DOpjP4NeK21GERkZGq9gRu2bJl6Nu3b6lB3f/73/+wYMEC/Pnnn9UdDmmhZP5bwzo2sLNkjQlRVXl0wa2aN0nonbXHo6EWgS6+jmjsoj+7GUj/2FqYYNM7rTDA3wWFahGzfzmP7w7f4L9RIqp2WtXAVYYxY8bg/v376NWrF0JCQrBz50588cUX+Ouvv9C+ffvqDoe0EMr5b0TVQuAKXLmkZObhl/CiVZCJXbwljoaMgZlCjuVvNYeLnQVWB93E1wevI1GZg88GNuXgeCKqNhVK4NLS0rB+/XpcuXIFgiCgUaNGGDt2LGxty1fUO3v2bNy/fx8tW7aESqXCgQMHSg0FJ910WjP/jQkcUVUqtQLHYQJl2nwyFnmFavi72qId51JSNZHJBMzp0xAuduZYsO9f/BQWjzvKXKwIfAk1zKr9c3EiMkJa/6QJDw9H7969YWFhgdatW0MURSxbtgxffPEFDhw4gJdeeumJc7799tsnHnN2doalpSU6d+6M0NBQhIaGAgCmT59egZdBVS0lMw837mUCAFqzAyVRlXq0Bo67s54uK68QW07dAgBM7OLDRi9U7Ua280RtG3NM/+kcjl5LxrC1p7F+VCs4WptJHRoRGTitu1B26tQJ9erVw9q1a6FQFOV/hYWFGDduHKKjoxEcHPzEOSVDsp8bjCAgOjpam3D0lr51odx/MQlTtp9Fg9rW+GdmZ6nDITJoWXmFaLLgHwDA5U97w9KUn+o/bn1IDD774zI8a1ri8PtdIZcxgSNpRNx6gHGbz+BBdgHcHCyweUxreDtaPf9EIqJHaJMbVGgF7tHkDSgaxP3hhx+iZcuWTz0nJiZG29uQjgnl9kmiasMVuGcrUKmx/njRh33jO3szeSNJtfCwx67J7TF64xnEpWZj8KqTWDeqJVp48PclEVUNrStubWxsnjocOz4+HtbW1pUSFOme0JjiBiasMyGqco/uBlQzg3vC7+cTkajMRS0rUwx+yVXqcIjg7WiF3VPaw9/VFg+yCxC4NhR/X7ojdVhEZKC0TuDeeustjB07Fjt37kR8fDxu376NHTt2YNy4cRg2bNhTz5k1axaysrLKfY+5c+ciNTVV29CoiqRm5ePqnQwAnP9GVB1KNzGhR4miiDVBRatvYzp4wdxELnFEREVqWZnhpwlt0aOhE/IK1Zi8PQKbT8ZKHRYRGSCtE7j//e9/GDRoEEaOHAlPT094eHhg9OjReOONN/Dll18+9ZxvvvkG2dnZ5b7H999/j7S0NG1DoyoSVrz6Vt/JCrWsWJxNVNVKbaFUSxiIDjp2LRnX7maghqkcI9p4SB0OUSmWpgqsebsFhrV2hygCC/b9i8X7r0DNeSBEVIm0roEzNTXFN998g8WLF+PmzZsQRRH16tWDpaVlmeeIoghfX99ydwnTZrWOqt7D8QHcPklUHR79SckxAqWtDroJABjW2h22liYSR0P0JIVchi9ebwpXewt89c81rAmORqIyF/97sxnMFFwxJqIXp3UC98477+Cbb76BtbU1/Pz8NI9nZWXh3XffxYYNG544Z+PGjVoHVrt2ba3PoarxsP6N2yeJqoOMg7yf6lzcA4TGpEIhEzC2U/m6GxNJQRAETO1WD3VszPGfXRfw+/lE3EvPxQ8jW8LWgh88ENGL0XqMgFwuR1JSEpycnEo9npKSgjp16qCwsLBSAzRU+jJGIC07HwGfHYQoAmc+epnzbYiqgSiK8Jq7HwAQPu9lbl0uNmlrBP7+9w4Gv+SKr4f4Sx0OUbkcv5GMydvOIjOvEL61rbBxTGvUtbOQOiwi0jHa5AblroFLT0+HUqmEKIrIyMhAenq65uvBgwfYv3//E0kd6b+wmFSIIuDjWIPJG1E1EThG4Ak3kzPxz+Wirn6TunhLHA1R+XWq74ifJ7ZDbRszXL+biUErT+ByYrrUYRGRHiv3Fko7OzsIggBBEODr6/vE84IgYOHChZUaHEnvdHTR9knWvxFVL5lQtH1Sy00SBmttcDREEXi5kRPq1+bIGtIvjV1ssHtKB4zeEIYb9zIxZM0prB7RAh3r15I6NCLSQ+VO4I4ePQpRFNG9e3fs2rULDg4P66FMTU3h4eEBFxeXKgmSpBMaU9TAhPPfiKqXIAiAyBYmAHAvPRe7zyYAACZ28ZE4GqKKqWtngV8ntceEreEIjUnF6I1hWPJGMwziLEMi0lK5E7guXboAAGJiYuDm5gaZTOsJBKRnlNkFuJxUtM2jLee/EVUrmQCowEHeALDhRCzyVWq08LBHK0/+LCL9ZWtpgi1jW2P2L0WNTWb9fB5JylxM6epT7k7dRERad6H08PBAWloawsLCcO/ePajVpYcUjRw5Uqvrpaen48iRI2jQoAEaNWqkbThUhc7EFtW/edeqAScbc6nDITIqAgQAotHXwGXkFmD76VsAgImdWftG+s9MIcc3bzWHi6051gRH46t/riExLQcLBzSBQs4Px4no+bRO4H7//XcMHz4cWVlZsLa2LvWJkSAIz03ghgwZgs6dO2PatGnIyclBy5YtERsbC1EUsWPHDgwePFj7V0FVomT+G8cHEFW/kh+txr4C92NoHDLyCuHjWAMvN+J4GTIMMpmAuX0bwdnWHAv/uIztoXG4m56Lb4cFwNJU67dmRGRktP6o5/3338c777yDjIwMpKWl4cGDB5qv1NTU554fHByMTp06AQD27NkDURSRlpaGb7/9Fp9//rn2r4CqTMn8NzYwIap+JQmcMedveYUqbDgRAwCY2NkHMhm3mJFhGd3BC6uGt4CZQoZDV+5h2NpQpGTmSR0WEek4rRO4hIQETJ8+HZaWlhW6oVKp1DRA+fvvvzF48GBYWlqiX79+uHHjRoWuSZUvPbcA/yYqAQBtvJjAEVW3kmHexpzA/XYuEXfT81DbxgwDA9gkiwzTK03r4MfxbWBnaYLz8WkYvOokYlKypA6LiHSY1glc7969ER4eXuEburm54dSpU8jKysLff/+NXr16AQAePHgAc3PWWemK8NhUqEXAs6Yl6tjyz4WoupUkcMa6hVKtFrEm+CYA4J0OXjBTyCWOiKjqtPBwwK7J7eHmYIFb97MxeNVJnI17IHVYRKSjtN5o3a9fP3zwwQe4fPky/Pz8YGJiUur5AQMGPPP8GTNmYPjw4bCysoKHhwe6du0KoGhrpZ+fn7bhUBUpmf/G1TciaZRsFjTO9A04dOUubiZnwdpMgcA27lKHQ1TlfBytsHtyB7yz6QwuJigRuPY0vh0agF5N6kgdGhHpGK0TuPHjxwMAPv300yeeEwQBKpXqmedPmTIFrVu3Rnx8PHr27KkZR+Dt7c0aOB0SWtzApK0PG5gQScHYm5isCY4GAAxv6wFrc5PnHE1kGBytzbBjQltM+/Esjl5LxqRtEVg4oAnebucpdWhEpEO0TuAeHxtQES1btkTLli1LPdavX78Xvi5VjozcAlxKLJr/xhU4ImkIRlwDdyY2FRG3HsBULsM7HTylDoeoWtUwU2DtyJaY/9sl/BQWj/m//YuEtFx82LsBG/kQEYAKJHAvatasWU99XBAEmJubo169ehg4cKCm0QlVv/BbD6BSi3B3sISLnYXU4RAZJZmmC6XxZXBrgopq3wa9VJczKMkoKeQyfPG6H1xsLfD1wetYHXQTScocLHmjGetBiah8Cdy3336LCRMmwNzcHN9+++0zj50+ffoznz937hzOnj0LlUqFBg0aQBRF3LhxA3K5HA0bNsTKlSvx/vvvIyQkBI0bNy7/K6FKE6qpf2MSTSQVQdPEROJAqtn1uxk4dOUeBAEYz8HdZMQEQcC7Peqjjq055u6+iN8iE3EvPQ+r324BWwtuKyYyZuVK4JYtW4bhw4fD3Nwcy5YtK/M4QRCem8CVrK5t3LgRNjY2AID09HSMHTsWHTt2xPjx4xEYGIiZM2fin3/+0eKlUGUpGeDN+W9E0tGswBlZG5MfimvfejWuDR9HK4mjIZLemy3dUNvGHJO3ReBU9H0MWX0KG8e04g4ZIiMmiNW8P6du3bo4ePDgE6tr//77L3r16oWEhAScPXsWvXr1QkpKSnWGVq3S09Nha2sLpVKpSWR1QVZeIZotPACVWkTIf7rB1b5i8/6I6MW0WnQIyRl52D+9Exq76M7PiKqUpMxB5yVHUaASsWdKewS420sdEpHOuJSgxJhNZ5CckYc6NubY9E4rNKxjHD8biIyBNrmB1nPgXpRSqcS9e/eeeDw5ORnp6UWNM+zs7JCfn1/doREe1r+52lsweSOS0MMxAsazArchJAYFKhGtvRyYvBE9pmldW+yZ0h71nKxwJz0Xb646hZNRhvtBNxGVrdoTuIEDB+Kdd97Bnj17cPv2bSQkJGDPnj0YO3YsXnvtNQBAWFgYfH19qzs0wsPxAew+SSQtmZF1oVRmF+DH0DgAwOQuPhJHQ6SbXO0t8eukdmjt6YCMvEKM2hiGPeduSx0WEVWzak/g1qxZgx49emDo0KHw8PCAu7s7hg4dih49emD16tUAgIYNG2LdunXVHRrh0fo3NjAhkpKg6UIpbRzVZVvoLWTlq9CgtjW6NnCUOhwinWVnaYotY1ujn58zClQiZu48j8X7r6BQ9eJjnohIP1TrGAGVSoWIiAh8+eWXWLZsGaKjoyGKInx8fGBl9bBYvXnz5tUZFhXLzi/EhdtKAGxgQiQ1maYLpeFncLkFKmw8EQMAmNjFW9OBk4ieztxEju+GBcDVwQJrgqKxJjgaF24rsfQtfzjbsrkJkaGr1hU4uVyO3r17Q6lUwsrKCs2aNYO/v3+p5I2kE3HrAQrVIlxszeFqz18ARLrAGBK4XWdvIyUzHy625ujv7yJ1OER6QSYTMLdPI6wIDIClqRynou+j97Jg/BaZIHVoRFTFtE7g5s+fD5VK9cTjSqUSw4YNe+75fn5+iI6O1va2VA1K5r+19a7JT8CJJCYr/uls6OmbSi1ibfHogLGdvGEir/ad/UR67dVmLvj93Y7wd7VFem4h3tsRiWk/nkVaNpvBERkqrX9TbtmyBR06dMDNmzc1jx07dgx+fn6IjY197vmLFi3C7Nmz8ccffyApKQnp6emlvkg6JfVvbVj/RiS5h01MDDuF++ffO4i9nw1bCxMMbeUmdThEesnH0Qq/Tm6PGS/Xh1wm4I8LSei9PBjB15OlDo2IqoDWCdyFCxfg6emJ5s2bY+3atfjggw/Qq1cvjB49GiEhIc89/5VXXsH58+cxYMAAuLq6wt7eHvb29rCzs4O9PdtGSyUnX4Xzt9MAsP6NSBdoxggYcP4miiLWBBV9GDiynQdqmFVrWTaRQTGRyzDjZV/smtwe3rVq4G56HkZuCMOC3y4hJ//JnVNEpL+0/m1pa2uLHTt24KOPPsLEiROhUCjw119/oUePHuU6/+jRo1oHSVXvbNwDFKhE1LExh7sD578RSe1hExOJA6lCp6Lv4/xtJcwUMoxq7yl1OEQGobmbHf6c3gmL/7qCLaduYfOpWzgelYJlQ5rD381O6vCIqBJU6OPO7777DsuWLcOwYcMQERGB6dOn48cff4S/v/9zz+3SpUtFbklVLPSR8QGsfyPSAZoxAoabwa0JKqp9e7OlK2pZmUkcDZHhsDCV49OBTdGjUW188Mt5RCdnYdCqk5jevT6mdvOBgrWmRHpN63/Bffr0wcKFC7FlyxZs374d586dQ+fOndG2bVssWbKkXNc4fvw4RowYgfbt2yMhoahb0tatW8u1BZOqxuniBiZtuH2SSCcY+grc5cR0BF1PhkwAxnfyljocIoPUxdcRB2Z2Rr9mzlCpRSw7dB2DV59CdHKm1KER0QvQOoErLCzEhQsX8MYbbwAALCwssGrVKvz6669YtmzZc8/ftWsXevfuDQsLC5w9exZ5eXkAgIyMDHzxxRfahkOVILdAhcj4NACsfyPSFQ9r4Awzg/shuKj2rY+fMzxq1pA4GiLDZWdpihXDAvDN0OawNlfgfHwa+n57HFtP3zLYny9Ehk7rBO7gwYNwcXlyTk+/fv1w8eLF557/+eefY/Xq1Vi7di1MTEw0j7dv3x5nz57VNhyqBOfi0pCvUsPJ2gyeNVn/RqQLNF0oJY6jKsSnZuP3C0kAgEmdfSSOhsjwCYKAgc3r4p8ZndGhXk3kFqgxf+8ljN54BnfTc6UOj4i0VKmboGvVqvXcY65du4bOnTs/8biNjQ3S0tIqMxwqp9Oa+jfOfyPSFSX/FA1xkPf6kBio1CI61KsJP1dbqcMhMhoudhbY+k4bfPxqY5gpZAi6nozey4PxZ/EHKkSkH7RO4GQyGeRyeZlfz+Ps7IyoqKgnHg8JCYG3N+sgpNDZtxbGd/JCv2bOUodCRMUEzRw4iQOpZA+y8rHzTDwAYFIXrr4RVTeZTMA7Hb3wx7sd0bSuDdKyCzD1x7OYuTMSypwCqcMjonLQugvlnj17Sn1fUFCAc+fOYfPmzVi4cOFzz584cSLee+89bNiwAYIgIDExEadOncLs2bPx8ccfaxsOVYIWHg5o4cHh3US6RGagK3BbTt1CToEKTVxs0LHe83dtEFHVqF/bGrsnd8C3h29g5bEo7DmXgNDo+/jfm/5oz3+bRDpN6wRu4MCBTzz2xhtvoEmTJti5cyfGjh37zPM//PBDKJVKdOvWDbm5uejcuTPMzMwwe/ZsTJs2TdtwiIgMUskWSkNK33LyVdh8KhYAMLGLD7dsE0nMVCHD7N4N0K2hE2b9HIlb97MRuC4UYzt64YPeDWBu8vydVURU/SqtBq5NmzY4dOhQuY5dtGgRUlJSEBYWhtOnTyM5ORmfffZZZYVCRKT3NE1MDGgF7peIeKRm5cPNwQJ9m9aROhwiKtbCwx77p3dCYBt3AEV1qv2/C8GlBKXEkRHR01RKApeTk4PvvvsOrq6u5T7H0tIStWvXhouLC6ysrCp03+DgYPTv3x8uLi4QBAF79+4t97knTpyAQqFA8+bNn3hu+fLlaNCgASwsLODm5oaZM2ciN/fpXZoWL14MQRAwY8aMCr0GIqKnKVmbUqslDaPSFKrU+CG4aHD3+E7eHCRMpGNqmCnwxet+2DC6JWpZmeHGvUy8vvIEvj8aBZWhDqQk0lNa/wa1t7eHg4OD5sve3h7W1tbYsGEDvvrqq+eeX1hYiPnz58PW1haenp7w8PCAra0t5s2bh4IC7Ypns7Ky4O/vjxUrVmh1nlKpxMiRI9GjR48nntu+fTvmzJmDBQsW4MqVK1i/fj127tyJuXPnPnHsmTNn8MMPP6BZs2Za3Z+I6HkEAxsj8OfFJNx+kAOHGqZ4s4Wb1OEQURm6N6yNf2Z0Qu8mtVGgEvHVP9cwZM0p3LqfJXVoRFRM6xq4ZcuWlapbkMlkcHR0RJs2bWBvb//c86dNm4Y9e/ZgyZIlaNeuHQDg1KlT+OSTT5CSkoLVq1eXO5Y+ffqgT58+2r4ETJw4EYGBgZDL5U+s2p06dQodOnRAYGAgAMDT0xPDhg1DWFhYqeMyMzMxfPhwrF27Fp9//rnWMRARPYshNTERRRFrgopW30a184SFKetqiHRZTSszrB7RArvOJuCTff8i4tYD9PnmOOa/2hhDW7mxfpUMxoQt4RABzO7VAA3qWEsdTrlpncCNHj36hW74008/YceOHaUSr2bNmsHd3R1Dhw7VKoGriI0bN+LmzZvYtm3bUxOvjh07Ytu2bQgLC0Pr1q0RHR2N/fv3Y9SoUaWOmzp1Kvr164eXX365XAlcXl4e8vLyNN+np6e/+IshIoNlSGMEjt9IweWkdFiYyDGynYfU4RBROQiCgDdauKKttwPe//k8QmNSMXf3RRy6fBeLB/vBydpc6hCJXkhugQrHriUjX6XG3D4NpQ5HK+VK4C5cuFDuCz5vO6G5uTk8PT2feNzT0xOmpqblvk9F3LhxA3PmzMHx48ehUDz9pQ8dOhTJycno2LEjRFFEYWEhJk+ejDlz5miO2bFjB86ePYszZ86U+96LFy8u15gFIiLg4QqcITQxWRN8EwDwVis32Neo2p/zRFS5XO0t8dP4tlgfEoOv/rmGw1fv4ZXlx/HF6354hc2ISI+di0tDvkoNJ2szeNWqIXU4WilXAte8eXMIgvDcNxKCIEClUj3zmKlTp+Kzzz7Dxo0bYWZmBqBodWrRokVVOkZApVIhMDAQCxcuhK+vb5nHHTt2DIsWLcLKlSvRpk0bREVF4b333oOzszPmz5+P+Ph4vPfeezhw4ADMzcv/6dPcuXMxa9Yszffp6elwc2MdCBE9nQDDqIG7eFuJE1H3IZcJGNfJS+pwiKgCZDIB4zt7o5NvLczceR5XktIxaVsE3mjhigX9G8Pa3ETqEIm0djr6PgCgjXdNvdsWXK4ELiYm5oVuMmjQoFLfHzp0CK6urvD39wcAnD9/Hvn5+U9tKlJZMjIyEB4ejnPnzmkSRbVaDVEUoVAocODAAXTv3h3z58/H22+/jXHjxgEA/Pz8kJWVhQkTJuCjjz5CREQE7t27hxYtWmiurVKpEBwcjBUrViAvLw9y+ZP1HWZmZpqElYjoeQQDqYFbXbz61r+ZM1ztLSWOhoheRMM6Ntg7tT2WHbyBNcE38WvEbZy6eR9Lh/ijjXdNqcMj0kpoTFEC19bbQeJItFeuBM7D48VqFmxtbUt9P3jw4FLfV8dKlI2NDS5evFjqsZUrV+LIkSP49ddf4eVV9MlwdnY2ZLLSzTnlcjlEUYQoiujRo8cT1xkzZgwaNmyI//znP09N3oiItPUwgZM2jhdx634W/rqYBKBocDcR6T8zhRxz+jRE9+Lh37cf5GDo2tOY0Mkbs3r5wkzB90Gk+3ILVDgblwYAaOOlfx8+lLuJSefOnbFv3z7Y2dkBAPbt24eePXvCwsLiuedu3LixwgE+S2ZmJqKiojTfx8TEIDIyEg4ODnB3d8fcuXORkJCALVu2QCaToWnTpqXOd3Jygrm5eanH+/fvj6VLlyIgIECzhXL+/PkYMGAA5HI5rK2tn7hOjRo1ULNmzSceJyKqKEMY5L32eDTUItDF1xGNnG2kDoeIKlFrLwf8PaMzPvv9MnaGx2NNcDSCridj2VvN+e+ddN75+DTkF6pRy8oMPo76Vf8GaJHAhYSEID8/X/P9iBEjEBkZCW9vb61vWlhYiGPHjuHmzZsIDAyEtbU1EhMTYWNjo9VQ7/DwcHTr1k3zfUmN2ahRo7Bp0yYkJSUhLi5Oq9jmzZsHQRAwb948JCQkwNHREf3798eiRYu0ug4R0YuQ6XkXypTMPPwSfhsAMImrb0QGycpMgS/faIYejZwwd/dFXL2TgYErTuD9Xr4Y18kbcpl+1RWR8QiNSQUAtPF20Lv6NwAQxHJ+vCuTyXDnzh04OTkBAKytrXH+/HmtE7hbt27hlVdeQVxcHPLy8nD9+nV4e3tjxowZyM3NrfIxAroiPT0dtra2UCqVsLHhJ1VEVNrb60Nx/EYKlr3lj9cDXKUOR2tfH7iG745Ewd/VFnundtDLX5BEVH4pmXmYs+siDl25CwBo7emAr4f4w82Bta+kewLXnsbJm/fx2WtN8XZb3Rhvo01uIHvms1XgvffeQ8uWLfHgwYNS2y9ff/11HD58uLrDISLSSSUJj1otcSAVkJVXiC2nbgEoWn1j8kZk+GpZmWHtyBb4crAfapjKERabij7fHMcv4fF6vRWcDE9eoQpn4x4AANp66V8DE0DLQd7//POPpiGJWq3G4cOHcenSpVLHDBgw4JnXCAkJwYkTJ56Y+ebh4YGEhARtwiEiMlglKY8+vu3ZcSYeypwCeNWqgV5NOCeKyFgIgoC3WrmjnXctzPo5EuG3HuCDXy/g4OW7WDzIDzWt2I2bpHfhthK5BWrUrGGKek7lL93SJVolcKNGjSr1/cSJE0t9X545cGq1+qnH3L59G9bW1tqEQ0RksGR6OkagQKXG+uPRAIDxrIEhMkruNS2xc2I7/BAcjaUHr+HA5bs4G/cAXw5uhh6NaksdHhm5UM38N/2sfwO02EKpVquf+/W85A0AevbsieXLl2u+FwQBmZmZWLBgAfr27VuhF0FEZGgEPe1C+fv5RCQqc1HLygyDXqordThEJBG5TMDkrj7YO7UDGtS2RkpmPsZuDsfc3ReQlVcodXhkxE5HFzUwaavHswurvQZu2bJlCAoKQuPGjZGbm4vAwEB4enoiISEBX375ZXWHQ0Skk0oWrvQpfxNFEWuCilbfxnTwhLkJ50ERGbsmLrb4bVoHjO/kBUEAfgqLR59vjiPiVqrUoZERyi9UI+JWUf2bPs5/K6HVFsrK4OLigsjISOzYsQMRERFQq9UYO3Yshg8fXq6ZckRExkDTxESPErhj15Jx7W4GapjKMUJHunoRkfTMTeT4qF9jdG9YG7N/OY+41Gy8ufoUJnf1wXs9fGGqqPb1BDJSFxPSkFOggr2lCerraf0bIEECBwAWFhYYM2YMxowZI8XtiYh03sMmJvqTwa0KugkACGzjDlsLE4mjISJd086nJv6a0Qmf7PsXu88m4PujN3HsWtHwb9/a7INAVa9k+2Qbr5qQ6XGNdrV/5LF48WJs2LDhicc3bNjALZRERMVkerYCdzbuAcJiUmEiF/BORy+pwyEiHWVjboKlQ5pj1fCXYG9pgn8T0/HqdyFYdzwaan35gUd66/QjDUz0mVYJnEqlQlBQEB48eFDhG65ZswYNGzZ84vEmTZoYzRBvIqLn0TTG0pMiuDXFq28Dm9eFsy23wxPRs/Xxc8Y/MzqjWwNH5Beq8fmfVzB8XSgS0nKkDo0MVIHqYf2bPjcwAbRM4ORyOXr37o20tLQK3/DOnTtwdnZ+4nFHR0ckJSVV+LpERIZEn1bgbiZn4sDluwCAiZ29JY6GiPSFk405NoxuhUWvN4WFiRynou/jlWXB2HPutt514CXddzFBiex8FewsTdBAz7fsar2F0s/PD9HR0RW+oZubG06cOPHE4ydOnICLi0uFr0tEZFD0aA7c2uBoiCLwciMn1NfzX4pEVL0EQcDwNh7Y/14nBLjbISOvEDN3nsfUH8/iQVa+1OGRAQktrn9r7emg1/VvQAUSuEWLFmH27Nn4448/kJSUhPT09FJfzzNu3DjMmDEDGzduxK1bt3Dr1i1s2LABM2fOxPjx4yv0IoiIDI1MMwdO4kCe4156LnafTQAATOriI3E0RKSvvGrVwC8T2+H9nr5QyATsv3gHvZcH49i1e1KHRgYiNKak/k2/t08CFehC+corrwAABgwYUGp6uSiKEAThucO8P/zwQ6SmpmLKlCnIzy/6ZMXc3Bz/+c9/MHfuXG3DISIySDI9WYHbcCIW+So1WnjYo6WnfheFE5G0FHIZ3u1RH10bOGHGznO4mZyF0RvPYERbd/y3byNYmkrSPJ0MQKFKjTMxJQO89f93ldb/Eo4ePfpCNxQEAV9++SXmz5+PK1euwMLCAvXr14eZmdkLXZeIyJDow+aO9NwCbD99CwBX34io8vi52uLP6Z3w5d9XsfFELLadjsOJqPtYOsQfAe72UodHeujfxHRk5atgY65Awzo2UofzwrRO4Lp06VIpN7ayskKrVq0q5VpERIbmYRMT3V2B+yk0Dhl5hajnZIUeDZ2kDoeIDIi5iRwL+jdBj4a18cGv5xGTkoU3Vp/ClK4+mNqtHsxN5FKHSHqkZHxAa6+akOt5/RtQwUHex48fx5o1axAdHY1ffvkFdevWxdatW+Hl5YWOHTs+9ZxBgwaV69q7d++uSEhERIal+PeLruZveYUqrA+JAQBM6Oyt9wXhRKSbOtavhb/f64yP913Cb5GJ+O5IFHZF3Macvo3Qv5lzqXIeorKEGtD2SaACTUx27dqF3r17w8LCAmfPnkVeXh4AICMjA1988UWZ59na2pbri4iIdH+MwG/nEnEvIw+1bcwwsDk7CBNR1bG1NME3QwOwavhLqGtngURlLqb/dA5vrD6F8/FpUodHOk6lFh+pf9P/BiZABVbgPv/8c6xevRojR47Ejh07NI+3b98en376aZnnbdy4sWIREhEZoZLPlHVxC6VaLWJ1cNHg7rEdvWCm4FYmIqp6ffyc0a2hE9YGR2PlsZuIuPUAA78/gUEv1cWHvRuijq251CGSDrqcmI6MvEJYmyvQyFn/69+ACqzAXbt2DZ07d37icRsbmxca8E1ERA/JdHhb0KErdxGdnAVrcwWGtXaXOhwiMiLmJnK826M+jn3QFYNfcgUA7D6bgG7/O4ZvD99AbsGzu6GT8Smpf2vl6WAQ9W9ABRI4Z2dnREVFPfF4SEgIvL29KyUoIiJjJyv+6azWsT2UoihidVDR6tuIth6wNjeROCIiMka1bczx9RB//Da1A1p42COnQIWlB6+j+/+OYd/5RIg6uHuBpFEy/81Q6t+ACiRwEydOxHvvvYfQ0FAIgoDExERs374ds2fPxpQpU6oiRiIiI1Q8yFviKB4XfusBzsalwVQuw5gOnlKHQ0RGzt/NDr9OaofvhgWwPo6eoFKLmgYmbbwMo/4NqEAN3IcffgilUolu3bohNzcXnTt3hpmZGWbPno1p06ZVRYxEREZHVwd5rz5WtPo2uEVdOFmz3oSIpCcIAvr7u6Bn49qsj6NSriSlIyO3EFZmCjRxMYz6N6ACK3AAsGjRIqSkpCAsLAynT59GcnIyPvvss8qOjYjIaAk6OEbg+t0MHL56D4IAjO/ELfNEpFtYH0ePK1l9a+lpD4W8QmmPTtL6lbzzzjvIyMiApaUlWrZsidatW8PKygpZWVl45513qiJGIiKjU9LERJfqONYERQMAejeuA29HK4mjISJ6OtbHUYmSBiaGMj6ghNYJ3ObNm5GTk/PE4zk5OdiyZUulBEVEZOwejhGQNAyNxLQc/BaZAACY2IWrb0Sk+8qqjxu86iQiWR9n8NRqEWGa+jfDaWACaJHApaenQ6lUQhRFZGRkID09XfP14MED7N+/H05OTlUZKxGR0RBKVuB0pI3JhpAYFKpFtPFyQIC7vdThEBGVS0l93OH3u+D9nr6wMJHjbFwaXvv+BGbtjMQdZa7UIVIVuXonA8qcAtQwlaNpXVupw6lU5W5iYmdnB0EQIAgCfH19n3heEAQsXLiwUoMjIjJWJVsodWEFTpldgJ/C4gAAk7r6SBwNEZH2SurjhrRyw5K/r2HX2dvYfS4Bf126g8ldfTC+kzcsTOVSh0mVqGR8QAtPB5gYUP0boEUCd/ToUYiiiO7du2PXrl1wcHi4FGlqagoPDw+4uLhUSZBERMZGl5qYbAu9hax8FRrWsUZXX0epwyEiqrCS+riR7Tzw6R+XEXHrAZYevI4dYXH4T5+GGODvotkBQfrtYf2bYW2fBLRI4Lp06QIAiImJgZubG2Qyw8pkiYh0iUyTwEmbweUWqLDxRAyAoto3vrEhIkNQUh/3x4Uk/N9fV5GQloP3dkRi88lYfNy/CZq72UkdIr2A0vVvhtXABKjAHDgPDw8AQHZ2NuLi4pCfn1/q+WbNmlVOZERERuxhDZy0dp29jZTMfNS1s8CrzbjLgogMx6Pz49YdL5ofV1IfNyigLj58hfPj9NX1exl4kF0ACxM5mrkaVv0bUIEELjk5GWPGjMFff/311OdVKs7YICJ6USULXWoJi+BUahFrg4tGB4zt6GVwNQREREBRfdy07vXxZkvWxxmK0OiH898M8XeX1q9oxowZePDgAU6fPg0LCwv8/fff2Lx5M+rXr499+/ZVRYxEREZHgPRNTP759w5i72fD1sIEb7Vyky4QIqJqUFIft29aB7R8ZH5cj6+P4bfIBMm3tFP5ldS/Gdr4gBJaJ3BHjhzBsmXL0KpVK8hkMnh4eGDEiBFYsmQJFi9eXBUxEhEZHU0NnESbKEVRxOqgmwCAUe08UMNM6w0bRER6qZmrHX55bH7cezsiOT9OT4jiw/o3QxvgXULrBC4rK0sz783BwQHJyckAAD8/P5w9e7ZyoyMiMlIlYwSk+sD3VPR9XLithJlChpHtPaUJgohIIo/Oj5vdyxeWppwfpy+i7mXiflY+zE1kaOZqJ3U4VULrBK5Bgwa4du0aAKB58+ZYs2YNEhISsHr1ajg7O1d6gERExkiQuAvl6qCi2rchLd1Qy8pMkhiIiKRWUh93dHZXDH7JFQCw+1wCuv3vGL49fAM5+ez9oGtKtk+28LCHqcLw6t+ACtbAJSUlAQAWLFiAv//+G+7u7vj222/xxRdfVHqARETGSJBwkPflxHQEX0+GTADGd/Ku/gCIiHQM6+P0x2kDHh9QotxFDVFRUahXrx6GDx+ueSwgIACxsbG4evUq3N3dUatWrSoJkojI2JRMW5OiBm5NcFHtW18/Z7jXtKz2+xMR6aqS+jjOj9NNoigiVDPA23ATuHKvwPn6+sLNzQ0jR47Epk2bEBsbCwCwtLTESy+9xOSNiKgSySRagYtPzcYfF4p2WUzq4lO9Nyci0gOsj9NdN5OzkJKZDzOFDP5uhjf/rUS5E7igoCBMnDgRiYmJmDp1Knx8fODl5YWxY8di27ZtSEhIqMo4iYiMysMauOq97/qQGKjUIjrWq4WmdQ33lx8R0Yt6Vn3cN4dYHyeFkvq3l9ztYaYw3Nl95U7gOnXqhHnz5uHQoUNIS0vD0aNHMWbMGMTExGDChAlwd3dHgwYNqjJWIiKjIZOgiUlKZh52nokHwNU3IqLyelp93LJDrI+TQmhJ/Zu3Yc5/K1Gh1iwmJibo3LkzPvjgA8ydOxdTpkyBlZUVoqKiKjs+IiKj9LCJSfX94l997CZyClRo5mqLDvUMt3aAiKgqcH6ctERR1KzAGXL9G6BlApebm4sjR45g/vz56NSpE+zt7TF9+nRkZmZi1apViIuLq6o4iYiMSnVvobybnoutp28BAGb19NUkkEREVH6sj5NOTEoWkjPyYKqQGXwzmXJ3oezSpQvOnDkDHx8fdO7cGe+++y66dOmC2rVrV2V8RERGqbqbmHx/NAp5hWq08LBHF1/H6rkpEZGBKqmPe7OlG7765xp+jbiN3ecS8NelO5jUxQcTOnvDwtRwa7SkcDq6aPtkczc7mJsY9v+35V6BO3nyJGrVqoVu3bqhR48e6N69O5M3IqIqUp1jBG4/yMZPYUU7KN7vxdU3IqLKUtvGHP97k/Vx1SE0xji2TwJaJHBpaWn44YcfYGlpiS+//BJ169aFn58fpk2bhl9//RXJyclVGScRkVEpWYGrjt/tK45EoUAlor1PTbT34UgYIqLKVlIftyKQ9XFVoVT9m5dhNzABtEjgatSogVdeeQX/93//h9DQUKSkpGDJkiWwtLTEkiVL4OrqiqZNm1ZlrERERkOopi6UsSlZ+CXiNoCi1TciIqoagiDg1Wasj6sKt+5n4256HkzlMgS420sdTpWrUBdKoCihc3BwgIODA+zt7aFQKHDlypXKjO25goOD0b9/f7i4uEAQBOzdu7fc5544cQIKhQLNmzd/4rnly5ejQYMGsLCwgJubG2bOnInc3If/qBYvXoxWrVrB2toaTk5OeO2113Dt2rVKeEVEREWEaqqB+/bwDajUIro2cEQLD8P/1JKISGqPzo97owXnx1WGku2T/m62RlFbWO4ETq1WIywsDEuWLEGfPn1gZ2eH9u3bY+XKlahTpw6+//57REdHV2WsT8jKyoK/vz9WrFih1XlKpRIjR45Ejx49nnhu+/btmDNnDhYsWIArV65g/fr12LlzJ+bOnas5JigoCFOnTsXp06dx8OBBFBYWolevXsjKynrh10REBDycA1eVYwSi7mVgT2QCgKLOk0REVH2eVR/3+/lEqcPTKyUNTIyh/g3QogulnZ0dsrKy4OzsjK5du2Lp0qXo1q0bfHykG/bap08f9OnTR+vzJk6ciMDAQMjl8idW7U6dOoUOHTogMDAQAODp6Ylhw4YhLCxMc8zff/9d6pyNGzfCyckJERER6Ny5s/YvhIjoMQ+bmFSdZYduQBSBXo1ro5mrXRXeiYiIylJSH/fnxSQs3n8VCWk5ePenczgXl4aP+jWCXMbGUs8iiiJCi+vf2ngZRwJX7hW4r776CleuXMHt27exbds2jBs3TtLkraI2btyImzdvYsGCBU99vmPHjoiIiNAkbNHR0di/fz/69etX5jWVSiUAwMGh7O1HeXl5SE9PL/VFRFQWmaykiUnVpHCXE9Px54UkAMBMrr4REUnq0fq46d3rAQA2nIjBlO0RKFCpJY5Ot8Wn5iBRmQsTuYCXPOykDqdalHsFbuLEiVUZR7W4ceMG5syZg+PHj0OhePpLHzp0KJKTk9GxY0eIoojCwkJMnjwZc+bMeerxoihi1qxZ6Nix4zObuCxevBgLFy6slNdBRIZPswJXRUtwyw5dBwC82swZjZxtquYmRESkFXMTOWb1aoD6ta3x/i/n8c+/d/Hf3Rex5I1mHPFShtPF9W/NXO1gaVru1EavVbiJib5RqVQIDAzEwoUL4etb9qfNx44dw6JFi7By5UqcPXsWu3fvxh9//IHPPvvsqcdPmzYNFy5cwE8//fTM+8+dOxdKpVLzFR8f/0Kvh4gM28MmJpWfwZ2PT8PBy3chE4AZL3P1jYhI1/T3d8GaES0gE4BfIm5jxZEoqUPSWZrxAd7G04jLONJUABkZGQgPD8e5c+cwbdo0AEWNWURRhEKhwIEDB9C9e3fMnz8fb7/9NsaNGwcA8PPzQ1ZWFiZMmICPPvoIMtnDnPfdd9/Fvn37EBwcDFdX12fe38zMDGZmZlX3AonIoDwcI1D51/76YNHq22sBdVHPyaryb0BERC+sW0MnfDqwKebtvYSvD16Hq4MFXg949vtNYxRa3MDEWOrfACNK4GxsbHDx4sVSj61cuRJHjhzBr7/+Ci8vLwBAdnZ2qSQNAORyOURR1NSiiKKId999F3v27MGxY8c05xIRVRZZFY0ROBObiuDryVDIBLzXo37lXpyIiCrViLYeiE/NxprgaHz46wU421oYTafF8ohPzUZCWg4UMgEtPAx//lsJvU7gMjMzERX1cEk5JiYGkZGRcHBwgLu7O+bOnYuEhARs2bIFMpnsiRo1JycnmJubl3q8f//+WLp0KQICAtCmTRtERUVh/vz5GDBgAOTyorkSU6dOxY8//ojffvsN1tbWuHPnDgDA1tYWFhYW1fDKicjQPayBq9wM7usDRTMr32zpCo+aNSr12kREVPn+80pD3H6Qgz8vJmHClnDsntIe9ZyspQ5LJ4TGFK2++bnaooaZXqc1WtHrVxoeHo5u3bppvp81axYAYNSoUdi0aROSkpIQFxen1TXnzZsHQRAwb948JCQkwNHREf3798eiRYs0x6xatQoA0LVr11Lnbty4EaNHj67YiyEiekTJClxlpm8no1JwOjoVpnIZpnXn6hsRkT6QyQR8PcQfd9JzEXHrAUZvPIM9UzrA0ZqlOaeNbHxACUGsqh7V9Ezp6emwtbWFUqmEjQ07wBFRab+Ex+ODXy+gawNHbBrT+oWvJ4oiBq86ibNxaRjVzgMLB5bdNZeIiHRPalY+Bq08gdj72fB3tcWOCe1gYSqXOixJdVpyBPGpOdg0phW6NnCSOpwXok1uYDRdKImI9ElJF8rK+ojt2PVknI1Lg5lChqnd6lXORYmIqNo41DDFxjGtYW9pgvO3lXhvxzmoKrtQWo8kpOUgPjUHcpmAlp7G04ESYAJHRKSTiud4V8oYAVEUsfRAUefJke084GRj/sLXJCKi6udVqwbWjmwJU4UMBy7fxed/XpY6JMmEFm+fbFrXFlZGVP8GMIEjItJJlTmv9cDlu7iYoISlqRyTuvhU3oWJiKjatfR0wNdv+gMANp6IxYaQGIkjkkbJ+IC2Xsa1+gYwgSMi0kmyShrkrVY/XH17p4MXalqx6J2ISN/193fBnD4NAQCf/XkZB/69I3FE1e90TMkAb+NqYAIwgSMi0mlq9Yud/+fFJFy7mwFrcwXGd/KunKCIiEhyEzt7I7CNO0QRmL7jHM7Hp0kdUrVJUubg1v1syASgpafxzH8rwQSOiEgHPRwjUPEVuEKVGssOFa2+je/kDVtLk0qJjYiIpCcIAj4d0ARdfB2RW6DG2M1nEJ+aLXVY1aJk+2TTurawNje+321M4IiIdNDDLZQVv8ZvkYmITs6CnaUJxnTwrJzAiIhIZyjkMnw//CU0drZBSmY+xmw6A2V2gdRhVbnQmJL5b8ZX/wYwgSMi0kmaJiYVTOAKVGosP1y0+japi49RfkJJRGQMrMwU2DC6FZxtzRF1LxMTt4Ujr1AldVhV6nRJAxMjrH8DmMAREemkFx0j8Ev4bcSn5qCWlSlGtvOoxMiIiEjX1LE1x4bRrWBlpsDp6FTM2XURYmUNEtUxd9NzEZOSBUGA0c1/K8EEjohIJ5XUwGkvt0CF747cAABM6VoPlqbGNR+HiMgYNXK2wcrhL0EuE7DnXAKWHbohdUhV4nTx/LcmLjawtTDO3SVM4IiIdNCLrMDtCItDkjIXdWzMEdjGvZIjIyIiXdXZ1xGLXmsKAPj28A38HB4vcUSVLzSmaPtkGy/j3D4JMIEjItJJQgWbmOTkq/D9sZsAgGnd68HcRF7ZoRERkQ4b2todU7v5AAD+u/siQm6kSBxR5SpZgTPWBiYAEzgiIp0k0zQx0S6D23o6FskZeXC1t8CQlm6VHxgREem82b0aYGBzFxSqRUzeFoFrdzKkDqlS3MvIRXRyUf1bayZwRESkSyoyRiAzrxCrg6IBANN71Iepgj/iiYiMkSAIWPJGM7T2dEBGXiHGbAzD3fRcqcN6YWHF2ycb1rGBnaWpxNFIh7/diYh0UfEKnDaDvDediEFqVj68atXAoIC6VRQYERHpAzOFHD+MbAFvxxpIVOZi7OYzyMorlDqsF1KyfbKtt/GuvgFM4IiIdJJmBU5dvuOVOQX4Ibho9W3Gy/WhkPPHOxGRsbOzNMWm0a1Rs4YpLiWk492fzqFQVc5fLDooNJoNTAAmcEREOknbOd7rj0cjPbcQvrWt8Gozl6oKi4iI9Ix7TUusG9USZgoZjly9h09+/1cvZ8SlZObhxr1MAMbdwARgAkdEpJNKVuDK80s2NSsfG07EAgBmvuwLuaYDChERERDgbo9vhjaHIADbTsdh3fEYqUPS2sP6N2vY1zDe+jeACRwRkU4StJgDtyb4JjLzCtHExQa9m9Sp4siIiEgfvdLUGR/1bQQAWLT/CvZfTJI4Iu08rH8z7u2TABM4IiKdVJLAPS9/u5eRi80nYwEAs3r6QsbVNyIiKsPYjl4Y1c4DADBzZyQibj2QOKLye1j/ZtzbJwEmcEREOunhGIFnZ3Crjt1EboEazd3s0L2hU3WERkREekoQBHzcvwlebuSEvEI1xm8JR2xKltRhPVdqVj6u3S2aZWfM899KMIEjItJB5WlikqTMwfbTcQCKhrYKAlffiIjo2eQyAd8OC4BfXVukZuVjzKYzeJCVL3VYzxQWU7R90re2FWpamUkcjfSYwBER6aCSrZDPWoBbcSQK+So1Wns5oEM91gQQEVH5WJoqsH50S9S1s0BMShYmbA1HboFK6rDKdJrjA0phAkdEpIM0K3BlZHDxqdnYeSYeAPB+T1+uvhERkVacrM2xcUwrWJsrcCb2AWb/ch5qtW6OF2ADk9KYwBER6SBBUwP39Oe/PXwDhWoRnerXQhv+QiMiogrwrW2NNSNaQCET8MeFJHx14JrUIT0hLZv1b49jAkdEpIOeNUYgOjkTu87eBlDUeZKIiKii2terhf8b3AxAUWOsH0PjJI6otNCYVIgiUM/JCo7WrH8DmMAREemkh4O8n3zum8M3oBaBHg2dEOBuX82RERGRoXmjhStmvFwfADD/t0s4du2exBE9xPEBT2ICR0Skg2SaOXClM7hrdzKw73wiAGAmV9+IiKiSvNejPga9VBcqtYip28/icmK61CEBYP3b0zCBIyLSQUJxG5PHF+CWH7oOUQT6NK2DpnVtqz8wIiIySIIg4P8GNUM775rIylfhnU1nkKTMkTQmZXYBrtwpSiTbeHMFrgQTOCIiHfS0GrhLCUr8dekOBIGrb0REVPlMFTKsfrsF6jtZ4U56LsZsPIOM3ALJ4gmLLap/83asASdrc8ni0DVM4IiIdJCg2UL58LFlB68DAAb6u8C3trUEURER0f+3d+/BUZV5Gsef7k4n6ZCkQ0wINAkJgxAVFBDcAMIi4gBZbjOoyxg2gKOjuMqKDmORBQqBYRhrWBRlYL0Bio7RLS7uKKI4RUQlAgnJykUucgtgQkSSEIIkIXn3D01LDNfQpPvA91PVVab7Pef83lO8ZT/9nnPeq53b5dTi+29TbESIdhSV69G/5am6ptYvtWz48fJJ1n+rjwAHAAHI/rNlBDYXlOgfO4rlsNv0+F3MvgEArpz45mF6dUx3uZwOrdv1raau3HrOdUmvpC/21d3/xuWTZyLAAUAAsv3sISZ1s29339pabWOa+assAMA14pb4KL1wX1fZbVLmpoNakLWnSY9f9n2190EqPMCkPgIcAAQg7zIC+uEJXJ/uPiqnw6bxd7b3b2EAgGvGXTfFadrQjpKkv3y4U+/mH26yY+fsP6ZaI7WNaaa4SO5/OxMBDgACkP2Mh5jM/eiH2beRtyUoITrMj1UBAK41Y3ol6YHebSVJf/ifL7Vx37EmOe6Gfaz/di4EOAAISD8kuNKT1dq4/5iCg+x6rB+zbwCApjf5X27UoI4tVVVTq4eW5mjPtyeu+DG9DzDh/rcGCHAAEIDqZuDq/FtKolq6uYQEAND07Habnh3ZRV0SolR6slr3L96koycqr9jxyk9Va8vhMkk8gfJsCHAAEIBstp8SnMvp0CN3tPNjNQCAa50r2KFXxnRXQrRLBcdO6sHXcnSquuaKHCvnQIlqjdQmOkyeKNcVOYaVEeAAIACdOQM3pleSYiNC/FcMAACSYsJDtOT+f5Lb5VT+wVJNyMxXba3vlxf4Yi/LB5wPAQ4AApDb5VSww66I0CA9/M+/8Hc5AABIktrFhuul9G4Kdti1eluRZn/wlc+PsWFv3QNMuHzybAhwABCAosKC9c64nnr30dvVvFmwv8sBAMAr5RfX6S/33iJJevnTfXo9e7/P9n2i8vRP978xA3dWBDgACFBdEqL0i9hwf5cBAEADw7u01h8GJkuSnv7fbfp4+xGf7Df3QIlqao3im7sU35ylc86GAAcAAADgkv37He00snuCao00/q08bTlUdtn7/On+Ny6fPBcCHAAAAIBLZrPZ9Mdfd1Kf9jH6vrpGv31tkw6VnLysfXrXf2MB73MiwAEAAABoFKfDrgWjbtUNLSP0bXmlfrtkk8q+r27Uvk5WndaXP87iMQN3bgQ4AAAAAI0WEerUorG3KS4yRLuOnNAjb+Sq6nTtJe8n90CJTtcatY5yKSGa+9/OhQAHAAAA4LJ4olxaNPY2NQt2aP2e75SxfIuMubQ14uruf+Ppk+dn6QC3bt06DR06VB6PRzabTStXrrzobT///HMFBQWpS5cuDT577rnnlJycLJfLpYSEBD3xxBM6depUvTYLFixQ27ZtFRoaqm7duunTTz+9zN4AAAAA1tXR49b8UbfKYbdp2eZDev4fX1/S9nXrv/Vg/bfzsnSAq6ioUOfOnTV//vxL2q6srEyjR49W//79G3z25ptvatKkSZo2bZq++uorvfrqq3r77beVkZHhbfP2229rwoQJmjx5svLy8tSnTx+lpqaqoKDgsvsEAAAAWFW/5BaaObyTJOnZj3dpWe6hi9ru+6oa/d+hUknMwF1IkL8LuBypqalKTU295O0efvhhpaWlyeFwNJi1y87O1u233660tDRJUlJSku677z5t3LjR22bu3Ll64IEH9OCDD0r6Ycbuww8/1MKFCzV79uzGdwgAAACwuLSUNio4dlL//ckeTVr+pVpFhapXu5jzbrO5oETVNUat3KFqw/1v52XpGbjGWLx4sfbs2aNp06ad9fPevXsrNzfXG9j27t2rVatWafDgwZKkqqoq5ebmasCAAfW2GzBggNavX3/O41ZWVur48eP1XgAAAMDV6KmByRp8SytV1xg9vDRXu4+Un7f9mcsH2Gy2pijRsq6pALd7925NmjRJb775poKCzj75+Jvf/EYzZ85U79695XQ61a5dO/Xr10+TJk2SJB09elQ1NTWKi4urt11cXJyKiorOeezZs2fL7XZ7XwkJCb7rGAAAABBA7Hab/uvezuqW2Fzlp05r7OJNKi4/dc72X9Td/8byARd0zQS4mpoapaWlafr06erQocM522VlZWnWrFlasGCBNm/erOXLl+u9997TzJkz67X7+S8Dxpjz/lqQkZGhsrIy7+vgwYOX1yEAAAAggIU6HXp5dHe1jWmmw6Xf68HXcnSy6nSDdqeqa5R/sFSSlEKAuyBL3wN3KcrLy5WTk6O8vDw99thjkqTa2loZYxQUFKSPPvpId955p6ZOnar09HTv/W0333yzKioq9NBDD2ny5MmKiYmRw+FoMNtWXFzcYFbuTCEhIQoJCblyHQQAAAACTHSzYC0ee5t+veBzfXmoTP/xVr5eTO8mh/2niY/NBSWqqqlVXGSIkq7j/rcLuWZm4CIjI7Vlyxbl5+d7X+PGjVNycrLy8/OVkpIiSTp58qTs9vqnxeFwyBgjY4yCg4PVrVs3rVmzpl6bNWvWqFevXk3WHwAAAMAKkmKa6ZUx3RUcZNfHXx3RzPe211sjrm75gJS213H/20Ww9AzciRMn9PXXP60vsW/fPuXn5ys6Olpt2rRRRkaGDh8+rNdff112u12dOnWqt32LFi0UGhpa7/2hQ4dq7ty56tq1q1JSUvT1119r6tSpGjZsmBwOhyTpySefVHp6urp3766ePXvqpZdeUkFBgcaNG9c0HQcAAAAspFtitJ791y569G+btWT9fiVEh+mB3m0l/bSAN/e/XRxLB7icnBz169fP+/eTTz4pSRozZoyWLFmiwsLCS16bbcqUKbLZbJoyZYoOHz6s2NhYDR06VLNmzfK2GTlypL777jvNmDFDhYWF6tSpk1atWqXExETfdAwAAAC4ygy+pZUOldyg2R/s0B/f367WUS7dkRyrPO/9b6z/djFs5sz5SzSZ48ePy+12q6ysTJGRkf4uBwAAALjijDGasnKr3txQoFCnXU/c1UGzP9ih2IgQbfzP/tfsJZSXkg2umXvgAAAAAPiXzWbT9GEd1S85VqeqazX7gx2SWP/tUhDgAAAAADSZIIdd89NuVUfPTzNN3P928QhwAAAAAJpUs5AgLRp7m1pHuRTssKtvh1h/l2QZln6ICQAAAABriosM1eoJfVRSUa2EaNZ/u1gEOAAAAAB+ERHqVESo099lWAqXUAIAAACARRDgAAAAAMAiCHAAAAAAYBEEOAAAAACwCAIcAAAAAFgEAQ4AAAAALIIABwAAAAAWQYADAAAAAIsgwAEAAACARRDgAAAAAMAiCHAAAAAAYBEEOAAAAACwCAIcAAAAAFgEAQ4AAAAALCLI3wVcq4wxkqTjx4/7uRIAAAAA/lSXCeoywvkQ4PykvLxckpSQkODnSgAAAAAEgvLycrnd7vO2sZmLiXnwudraWn3zzTeKiIiQzWbzay3Hjx9XQkKCDh48qMjISL/WAqA+xidgPYxbwDoCZbwaY1ReXi6PxyO7/fx3uTED5yd2u13x8fH+LqOeyMhI/kcDBCjGJ2A9jFvAOgJhvF5o5q0ODzEBAAAAAIsgwAEAAACARRDgoJCQEE2bNk0hISH+LgXAzzA+Aeth3ALWYcXxykNMAAAAAMAimIEDAAAAAIsgwAEAAACARRDgAAAAAMAiCHAAAAAAYBEEuAC1bt06DR06VB6PRzabTStXrqz3+ZEjRzR27Fh5PB6FhYVp0KBB2r17d702lZWVGj9+vGJiYtSsWTMNGzZMhw4dqtempKRE6enpcrvdcrvdSk9PV2lp6QXr27Jli/r27SuXy6XWrVtrxowZOvN5OIWFhUpLS1NycrLsdrsmTJjQ2FMBBByrj8+srCzZbLYGrx07djT6nACBzurjVpL++te/6sYbb5TL5VJycrJef/31Rp0LwAqaaszOmjVLvXr1UlhYmKKioi66Pn9+FybABaiKigp17txZ8+fPb/CZMUa/+tWvtHfvXr377rvKy8tTYmKi7rrrLlVUVHjbTZgwQStWrFBmZqY+++wznThxQkOGDFFNTY23TVpamvLz87V69WqtXr1a+fn5Sk9PP29tx48f1y9/+Ut5PB5t2rRJL7zwgubMmaO5c+d621RWVio2NlaTJ09W586dfXBGgMBh9fFZZ+fOnSosLPS+2rdvfxlnBQhsVh+3CxcuVEZGhp5++mlt27ZN06dP16OPPqq///3vPjg7QOBpqjFbVVWle++9V4888shF1+b378IGAU+SWbFihffvnTt3Gklm69at3vdOnz5toqOjzcsvv2yMMaa0tNQ4nU6TmZnpbXP48GFjt9vN6tWrjTHGbN++3UgyX3zxhbdNdna2kWR27NhxznoWLFhg3G63OXXqlPe92bNnG4/HY2praxu079u3r3n88ccvud+AFVhxfK5du9ZIMiUlJZfVd8CqrDhue/bsaSZOnFhvu8cff9zcfvvtjTgDgLVcqTF7psWLFxu3231R9fj7uzAzcBZUWVkpSQoNDfW+53A4FBwcrM8++0ySlJubq+rqag0YMMDbxuPxqFOnTlq/fr0kKTs7W263WykpKd42PXr0kNvt9rY5m+zsbPXt27fegocDBw7UN998o/379/ukj4BVWWl8du3aVa1atVL//v21du3axncasDgrjNvKysp69UmSy+XSxo0bVV1d3cieA9bkqzHbWP7+LkyAs6AbbrhBiYmJysjIUElJiaqqqvTnP/9ZRUVFKiwslCQVFRUpODhYzZs3r7dtXFycioqKvG1atGjRYP8tWrTwtjmboqIixcXFNdhv3WfAtcwK47NVq1Z66aWXtGzZMi1fvlzJycnq37+/1q1b1/iOAxZmhXE7cOBAvfLKK8rNzZUxRjk5OVq0aJGqq6t19OjRxncesCBfjdnG8vd3YQKcBTmdTi1btky7du1SdHS0wsLClJWVpdTUVDkcjvNua4yRzWbz/n3mf5+tTceOHRUeHq7w8HClpqaeczvz402bZ9sfcC2xwvhMTk7W7373O916663q2bOnFixYoMGDB2vOnDmN6zRgcVYYt1OnTlVqaqp69Oghp9Op4cOHa+zYsZJ0wRqBq40vx+yFBOJ34aArfgRcEd26dVN+fr7KyspUVVWl2NhYpaSkqHv37pKkli1bqqqqSiUlJfV+eSguLlavXr28bY4cOdJg399++633V4RVq1Z5L81wuVze7X7+60JxcbEkNfg1ArgWWXF89ujRQ2+88UZjuwxYXqCPW5fLpUWLFunFF1/UkSNHvDPpERERiomJ8eWpACzBF2P2YgTid2Fm4CzO7XYrNjZWu3fvVk5OjoYPHy7ph3/UTqdTa9as8bYtLCzU1q1bvf9oe/bsqbKyMm3cuNHbZsOGDSorK/O2SUxM1PXXX6/rr79erVu39m63bt06VVVVebf76KOP5PF4lJSUdKW7DFiGlcZnXl6eWrVq5bO+A1YV6OPW6XQqPj5eDodDmZmZGjJkiOx2vs7h2nU5Y/ZiBOR3YZ89DgU+VV5ebvLy8kxeXp6RZObOnWvy8vLMgQMHjDHGvPPOO2bt2rVmz549ZuXKlSYxMdGMGDGi3j7GjRtn4uPjzccff2w2b95s7rzzTtO5c2dz+vRpb5tBgwaZW265xWRnZ5vs7Gxz8803myFDhpy3ttLSUhMXF2fuu+8+s2XLFrN8+XITGRlp5syZU69dXf3dunUzaWlpJi8vz2zbts1HZwjwH6uPz2effdasWLHC7Nq1y2zdutVMmjTJSDLLli3z4VkCAovVx+3OnTvN0qVLza5du8yGDRvMyJEjTXR0tNm3b5/vThIQQJpqzB44cMDk5eWZ6dOnm/DwcO8xy8vLz1mbv78LE+ACVN1jvn/+GjNmjDHGmHnz5pn4+HjjdDpNmzZtzJQpU0xlZWW9fXz//ffmscceM9HR0cblcpkhQ4aYgoKCem2+++47M2rUKBMREWEiIiLMqFGjLurR4l9++aXp06ePCQkJMS1btjRPP/10g8emnq3+xMTEyzktQECw+vh85plnTLt27UxoaKhp3ry56d27t3n//fcv+7wAgczq43b79u2mS5cuxuVymcjISDN8+PDzLk0AWF1TjdkxY8ac9Thr1649b33+/C5s+3HnAAAAAIAAx0XTAAAAAGARBDgAAAAAsAgCHAAAAABYBAEOAAAAACyCAAcAAAAAFkGAAwAAAACLIMABAAAAgEUQ4AAAuEKysrJks9lUWlrq71IAAFcJFvIGAMBH7rjjDnXp0kXPPfecJKmqqkrHjh1TXFycbDabf4sDAFwVgvxdAAAAV6vg4GC1bNnS32UAAK4iXEIJAIAPjB07Vp988onmzZsnm80mm82mJUuW1LuEcsmSJYqKitJ7772n5ORkhYWF6Z577lFFRYVee+01JSUlqXnz5ho/frxqamq8+66qqtJTTz2l1q1bq1mzZkpJSVFWVpZ/OgoA8Ctm4AAA8IF58+Zp165d6tSpk2bMmCFJ2rZtW4N2J0+e1PPPP6/MzEyVl5drxIgRGjFihKKiorRq1Srt3btXd999t3r37q2RI0dKku6//37t379fmZmZ8ng8WrFihQYNGqQtW7aoffv2TdpPAIB/EeAAAPABt9ut4OBghYWFeS+b3LFjR4N21dXVWrhwodq1aydJuueee7R06VIdOXJE4eHhuummm9SvXz+tXbtWI0eO1J49e/TWW2/p0KFD8ng8kqSJEydq9erVWrx4sf70pz81XScBAH5HgAMAoAmFhYV5w5skxcXFKSkpSeHh4fXeKy4uliRt3rxZxhh16NCh3n4qKyt13XXXNU3RAICAQYADAKAJOZ3Oen/bbLazvldbWytJqq2tlcPhUG5urhwOR712Z4Y+AMC1gQAHAICPBAcH13v4iC907dpVNTU1Ki4uVp8+fXy6bwCA9fAUSgAAfCQpKUkbNmzQ/v37dfToUe8s2uXo0KGDRo0apdGjR2v58uXat2+fNm3apGeeeUarVq3yQdUAACshwAEA4CMTJ06Uw+HQTTfdpNjYWBUUFPhkv4sXL9bo0aP1+9//XsnJyRo2bJg2bNighIQEn+wfAGAdNmOM8XcRAAAAAIALYwYOAAAAACyCAAcAAAAAFkGAAwAAAACLIMABAAAAgEUQ4AAAAADAIghwAAAAAGARBDgAAAAAsAgCHAAAAABYBAEOAAAAACyCAAcAAAAAFkGAAwAAAACLIMABAAAAgEX8P/+yYZEQ4RbtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k, d in data.items():\n",
    "    plt.figure(figsize=(10,5))\n",
    "    dat = d[k]\n",
    "    if 'depth' in dat.cf.coords: # https://xgcm.readthedocs.io/en/latest/grid_metrics.html?highlight=average#Grid-aware-(weighted)-average\n",
    "        dat = grid.integrate(dat, ['X', 'Y', 'Z'])\n",
    "    elif 'longitude' in dat.cf.coords: # https://xgcm.readthedocs.io/en/latest/grid_metrics.html?highlight=average#Grid-aware-(weighted)-average\n",
    "        dat = grid.integrate(dat, ['X', 'Y'])\n",
    "    dat.load()\n",
    "    dat.plot()\n",
    "    # dat.cf.resample(time='1YE').mean('time').plot()\n",
    "    # dat.cf.rolling(time=365, center=True).mean('time').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b613ef3-b4ce-4712-9765-584dec36785a",
   "metadata": {},
   "source": [
    "## Comparing JRA55do inputs with MOM6 outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "acbce4be-2329-46fb-b919-601dc918ca26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x153550eb6410>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data['total_lrunoff']['total_lrunoff'].plot(label='MOM6 total_runoff')\n",
    "jra55doGrid.integrate(jra55do.friver, ['X', 'Y']).plot(label='JRA55do friver')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "284b0292-9856-450a-97dc-7ede4ebf8b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1535537ad8d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAHPCAYAAABDfQs9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAABXJUlEQVR4nO3deVxUZf//8fewirK4hCyJK2q5pKZ9XVLTNLcyyxYtc8nu7qxs0bj1trK0zbpb1H5WlqmZVuQdanZrLpVompoLlikumaYpSGaCKwic3x/IOAMDzMAcYOD1fDzOgznXuc45nxkYnfdcZ7EYhmEIAAAAAGAar7IuAAAAAAAqOoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgVQLr1q1T//79FRkZKYvFoiVLlri0/qRJk2SxWPJN1apVM6dgAAAAAGWC4FUCZ8+eVatWrTRjxoxirR8TE6OkpCS7qVmzZrrrrrvcXCkAAACAskTwKoG+ffvqpZde0sCBAx0uz8jI0Lhx43TllVeqWrVqat++veLj463LAwMDFR4ebp2OHz+u3bt364EHHiilZwAAAACgNPiUdQEV2f33369Dhw4pNjZWkZGRWrx4sfr06aOdO3eqcePG+fp/+OGHatKkibp06VIG1QIAAAAwCyNeJjlw4IA+++wz/fe//1WXLl3UqFEjxcTEqHPnzpo7d26+/unp6frkk08Y7QIAAAAqIEa8TLJ9+3YZhqEmTZrYtaenp6tWrVr5+i9atEinT5/WsGHDSqtEAAAAAKWE4GWS7OxseXt7a9u2bfL29rZbFhgYmK//hx9+qFtuuUXh4eGlVSIAAACAUkLwMkmbNm2UlZWllJSUIs/ZOnjwoNasWaOlS5eWUnUAAAAAShPBqwTOnDmjX3/91Tp/8OBB7dixQzVr1lSTJk00ZMgQDRs2TG+++abatGmjEydO6LvvvlPLli3Vr18/63pz5sxRRESE+vbtWxZPAwAAAIDJLIZhGGVdhKeKj49X9+7d87UPHz5cH330kS5evKiXXnpJH3/8sY4ePapatWqpY8eOmjx5slq2bCkp55DEevXqadiwYXr55ZdL+ykAAAAAKAUELwAAAAAwGZeTBwAAAACTEbwAAAAAwGRcXKMYsrOzdezYMQUFBclisZR1OQAAAADKiGEYOn36tCIjI+XlVfC4FsGrGI4dO6aoqKiyLgMAAABAOXHkyBHVqVOnwOUEr2IICgqSlPPiBgcHl3E1AAAAAMpKWlqaoqKirBmhIASvYsg9vDA4OJjgBQAAAKDIU5C4uAYAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyTw6eE2aNEkWi8VuCg8PL7B/fHx8vv4Wi0V79uwpxaoBAAAAVDY+ZV1ASTVv3lzffPONdd7b27vIdfbu3avg4GDrfGhoqCm1AQAAAIBUAYKXj49PoaNcjtSuXVvVq1c3pyBUHNlZkmFIXt6SxVLW1cARw8j5PWVnSsaln9lZl9tyJyPbZt6mn5GnX/alflkZl39mZUhZFy9NGVL2Rfv5rIuX2mz6ZV+Umt4stb6nrF+hiiM7S8pMlzIv5PzMSreZz7Bvz87K+Z1bJyPPfHbO777Q5Zem3H8HClru9GQ4qKugGouo38tX8vGTvG0mH3/J21fyvvTTx9/B8tzHue0F9bVZbvvYy6MPkgGAMufxwWv//v2KjIyUv7+/2rdvr1deeUUNGzYsdJ02bdrowoULatasmZ599ll179690P7p6elKT0+3zqelpbmldijnA0Xuh9vMjJwPTQU+vnj5g5X1ce66BT12dTu5H7TTcz7gWFkuBTAvyeJ96bF3zgcR67yXgzbbZV552rwvhzpH/QvbX75t2czbLcuzvu1289Zi8ZJkOA4ihQabvAHGZrndOpn26+ULQ1lF78tuncw8v6NyZs9yKbSpdOW1ZV1JyRiX/ibsAs6FS++XS2En0yYEZdn0sQ1EDtdxIkTltmdnlvUrAYt3noDnV0AALEnAcxQki9qPf86/YwBQzlkMwzDKuoji+vrrr3Xu3Dk1adJEx48f10svvaQ9e/Zo165dqlWrVr7+e/fu1bp169S2bVulp6dr/vz5mjlzpuLj49W1a9cC9zNp0iRNnjw5X3tqaqrdIYseLTtLung+5wPOxXPSxUs/Hc6fvzxlnncwf+HSByYngk5WRlk/c1RUFm/JyyfnA1nuT2ubT04ozX1s19f70gc635yfXr6XH3vbPvbLWcf6YdDm8f5V0q/fSLWbS/+Mz/nQWJTsbPv3hd1oW+4XChcLXm73xUFue3rhfXIDUFa6HIao3GXlLeBavCSfgJzX1afKpQ/h/jk/ffzzfMlgufzFh8Mpd7l3EcvzTNYvRpzZfkHbdrC+l6O+Nn1kuTzqmvffUtsvjqzLL14OsNa/CSfXzb5Y1r9p51m8Cglpbgx4xRlV5KgJoMJLS0tTSEhIkdnAo4NXXmfPnlWjRo00btw4jR071ql1+vfvL4vFoqVLlxbYx9GIV1RUVNkHr7N/Sb+tcTIMnS8gTF1aXl4CkPUb1YL+I3Pwn2NB/zHaPS7g0JnC/pP19sv5zzJ3RMd62FHW5UOGrIcOZdksyy6d/vmWZRdQqwv9LV7OBRNru23/ggJO3nW87fsWd1uOgpXttsryg87Zv6R3/k86d0IKvVryDbAJP3kD1KUPuUZW2dXrCi/fy2HHOlW59P5x0G4NRc6skzdEFbDM2+MP1vAMuUck2IW2IgJegcszitiWo+WOvrSzWddjWEow2ufr+EgJu8e5/x56FdLmk2c7Xs61Odx37r/XhbXZbodDVFFM2dmXP8NmnL30Wfbc5c+3V0RLNeqXdZWSnA9eFep/r2rVqqlly5bav3+/0+t06NBBCxYsKLSPv7+//P39S1qe+508IMU94P7t+lTJmXyrSr6XfvpUyfnwmDv52D627XtpWe5/Mg6/TbT9tjDPN4ccLoKKoFot6eY3pP+OkP5MLN42rKNpNu+f3NE2uw9qziy3ed/ZtluDTRWb0aO8gchmGef5VC4Wy+XgW94YRv4RYIehzpmAV9ByJ0YSC1pXtt9pG5cPv62sHIUx2y/iXGrLc4h8vtFhy+XR4cJGju1Gm/Muc7C9QrdpcWJ/efrb9s23T5ufDpc5eh6XapBsvni0+QIyb1uB8870sdj9sM4Yhk0wcvQzT1uGgzbbfkW9Z3q/InV8tPA+5UyFCl7p6elKTExUly5dnF4nISFBERERJlZloqq1pPpdnA9DBQUn23mfKnywAtyh+e1SYLh09s88oShvAMoTknz8c0aVeB8CBbNYLn0h4MRhvKUt97zIAkfwihrhyxP+8h4NYT3fNW+b7REOmQ6OenCizbqdwtryPM49aqLQ1yRLysqSPGRgH+WQ9XNt1cufXQNqlHVVLvPo4BUTE6P+/furbt26SklJ0UsvvaS0tDQNHz5ckjRhwgQdPXpUH3/8sSRp2rRpql+/vpo3b66MjAwtWLBAcXFxiouLK8unUXy1Gkkj/lfWVQAoSL2OZV0BgNJmsVw+H9SvWllXUzqsV+20DWWZ9oe62wW4bCfbCgh62TbrGNmSbK/8adj/VN6rkhp51nO0bt7t5u1X0Hbzbrug7aqImhw8jwK3a9hvN3e01cgz6mrXVsC8M32sXR2sY7HYfKlvG5Jsfvo5aMsbqKz9ql4eHKggX0Z6dPD6448/dM899+jEiRMKDQ1Vhw4dtGnTJtWrV0+SlJSUpMOHD1v7Z2RkKCYmRkePHlVAQICaN2+uZcuWqV+/fmX1FAAAADybxXLp3EuP/lgJmK5CXVyjtDh7Ah0AAACAis3ZbFAxxu0AAAAAoBwjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJvPo4DVp0iRZLBa7KTw8vNB11q5dq7Zt26pKlSpq2LChZs6cWUrVAgAAAKisfMq6gJJq3ry5vvnmG+u8t7d3gX0PHjyofv366cEHH9SCBQu0YcMGPfLIIwoNDdUdd9xRGuUCAAAAqIQ8Pnj5+PgUOcqVa+bMmapbt66mTZsmSbr66qu1detWvfHGGwQvAAAAAKbx6EMNJWn//v2KjIxUgwYNNHjwYP32228F9t24caN69epl19a7d29t3bpVFy9eLHC99PR0paWl2U0AAAAA4CyPDl7t27fXxx9/rJUrV2rWrFlKTk5Wp06d9Ndffznsn5ycrLCwMLu2sLAwZWZm6sSJEwXuZ8qUKQoJCbFOUVFRbn0eAAAAACo2jw5effv21R133KGWLVuqZ8+eWrZsmSRp3rx5Ba5jsVjs5g3DcNhua8KECUpNTbVOR44ccUP1AAAAACoLjz/Hy1a1atXUsmVL7d+/3+Hy8PBwJScn27WlpKTIx8dHtWrVKnC7/v7+8vf3d2utAAAAACoPjx7xyis9PV2JiYmKiIhwuLxjx45avXq1XduqVavUrl07+fr6lkaJAAAAACohjw5eMTExWrt2rQ4ePKjNmzfrzjvvVFpamoYPHy4p5xDBYcOGWfuPGjVKv//+u8aOHavExETNmTNHs2fPVkxMTFk9BQAAAACVgEcfavjHH3/onnvu0YkTJxQaGqoOHTpo06ZNqlevniQpKSlJhw8ftvZv0KCBli9frjFjxuidd95RZGSk3n77bS4lDwAAAMBUFiP36hJwWlpamkJCQpSamqrg4OCyLgcAAABAGXE2G3j0oYYAAAAA4AkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyl4NXdnZ2ge2HDx8ucUEAAAAAUNE4HbzS0tJ09913q1q1agoLC9Pzzz+vrKws6/I///xTDRo0MKVIAAAAAPBkPs52nDhxon766SfNnz9fp06d0ksvvaRt27Zp0aJF8vPzkyQZhmFaoQAAAADgqZwe8VqyZInef/993XnnnfrHP/6hbdu26cSJE+rfv7/S09MlSRaLxbRCAQAAAMBTOR28Tpw4oXr16lnna9WqpdWrV+v06dPq16+fzp07Z0qBAAAAAODpnA5eUVFRSkxMtGsLCgrSqlWrdP78ed1+++1uLw4AAAAAKgKng1evXr00d+7cfO2BgYFauXKlqlSp4tbCAAAAAKCicPriGpMnT9axY8ccLgsKCtI333yjbdu2ua0wAAAAAKgonB7xqlGjhpo3b27X9scff1jv6xUYGKgbbrjBvdUBAAAAQAXg8g2UbTVr1kyHDh1yUykAAAAAUDGVKHiVp/t2TZkyRRaLRU8++WSBfeLj42WxWPJNe/bsKb1CAQAAAFQ6Tp/jVZ5t2bJFH3zwga655hqn+u/du1fBwcHW+dDQULNKAwAAAICSjXg9/fTTqlmzprtqKZYzZ85oyJAhmjVrlmrUqOHUOrVr11Z4eLh18vb2NrlKAAAAAJVZiYLXhAkTVL16dTeVUjyPPvqobr75ZvXs2dPpddq0aaOIiAj16NFDa9asKbJ/enq60tLS7CYAAAAAcJbLhxoahqEvvvhCa9asUUpKivWqhrkWLVrktuKKEhsbq+3bt2vLli1O9Y+IiNAHH3ygtm3bKj09XfPnz1ePHj0UHx+vrl27FrjelClTNHnyZHeVDQAAAKCSsRguXiHj8ccf1wcffKDu3bsrLCxMFovFbrmjmyyb4ciRI2rXrp1WrVqlVq1aSZK6deum1q1ba9q0aU5vp3///rJYLFq6dGmBfdLT05Wenm6dT0tLU1RUlFJTU+3OFQMAAABQuaSlpSkkJKTIbODyiNeCBQu0aNEi9evXr0QFltS2bduUkpKitm3bWtuysrK0bt06zZgxQ+np6U6du9WhQwctWLCg0D7+/v7y9/cvcc0AAAAAKieXg1dISIgaNmxoRi0u6dGjh3bu3GnXdv/99+uqq67S+PHjnb5gRkJCgiIiIswoEQAAAAAkFSN4TZo0SZMnT9acOXMUEBBgRk1OCQoKUosWLezaqlWrplq1alnbJ0yYoKNHj+rjjz+WJE2bNk3169dX8+bNlZGRoQULFiguLk5xcXGlXj8AAACAysPl4HXXXXfps88+U+3atVW/fn35+vraLd++fbvbiiuppKQkHT582DqfkZGhmJgYHT16VAEBAWrevLmWLVtW5odNAgAAAKjYXL64xt133601a9bozjvvdHhxjeeff96tBZZHzp5ABwAAAKBiM+3iGsuWLdPKlSvVuXPnEhUIAAAAAJWFyzdQjoqKYpQHAAAAAFzgcvB68803NW7cOB06dMiEcgAAAACg4nH5UMP77rtP586dU6NGjVS1atV8F9c4efKk24oDAAAAgIrA5eA1bdo0E8oAAAAAgIrL5eA1fPhwM+oAAAAAgArL5XO8AAAAAACuIXgBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJnP5qoa33367LBZLvnaLxaIqVaooOjpa9957r5o2beqWAgEAAADA07k84hUSEqLvvvtO27dvtwawhIQEfffdd8rMzNTnn3+uVq1aacOGDW4vFgAAAAA8kcsjXuHh4br33ns1Y8YMeXnl5Lbs7Gw98cQTCgoKUmxsrEaNGqXx48dr/fr1bi8YAAAAADyNxTAMw5UVQkNDtWHDBjVp0sSufd++ferUqZNOnDihnTt3qkuXLjp16pQ7ay030tLSFBISotTUVAUHB5d1OQAAAADKiLPZwOVDDTMzM7Vnz5587Xv27FFWVpYkqUqVKg7PAwMAAACAysjlQw2HDh2qBx54QE8//bSuu+46WSwW/fjjj3rllVc0bNgwSdLatWvVvHlztxcLAAAAAJ7I5eA1depUhYWF6T//+Y+OHz8uSQoLC9OYMWM0fvx4SVKvXr3Up08f91YKAAAAAB7K5XO8bKWlpUlSpTvPiXO8AAAAAEgmnuO1YMEC6+Pg4GC7jf/rX/9ydXMAAAAAUOG5HLxGjx6t//3vf/nax4wZYxfKAAAAAAA5XA5esbGxuu+++7Ru3Tpr22OPPaaFCxdqzZo1bi0OAAAAACoCl4NXnz59NHPmTN12223aunWrHnnkES1atEhr1qzRVVddZUaNAAAAAODRXL6qoSQNHjxYf//9tzp37qzQ0FCtXbtW0dHR7q4NAAAAACoEp4LX2LFjHbbXrl1bbdq00bvvvmtte+utt9xTGQAAAABUEE4Fr4SEBIftjRo1UlpamnW5xWJxX2UAAAAAUEE4Fby4aAYAAAAAFJ/LF9cAAAAAALjGqeA1cOBApaWlOb3RIUOGKCUlpdhFAQAAAEBFYjEMwyiqk7e3t/bt26fQ0NAiN2gYhqKiorRjxw41bNjQLUWWN2lpaQoJCVFqaqqCg4PLuhwAAAAAZcTZbODUOV6GYahJkyZuKw4AAAAAKhPTLq5x5ZVXurwOAAAAAFRETgWvG264wew6SmzKlCl6+umn9cQTT2jatGkF9lu7dq3Gjh2rXbt2KTIyUuPGjdOoUaNKr1AAAAAAlU6FuKrhli1b9MEHH+iaa64ptN/BgwfVr18/denSRQkJCXr66af1+OOPKy4urpQqBQAAAFAZeXzwOnPmjIYMGaJZs2apRo0ahfadOXOm6tatq2nTpunqq6/WP/7xD40cOVJvvPFGKVULAAAAoDLy+OD16KOP6uabb1bPnj2L7Ltx40b16tXLrq13797aunWrLl68aFaJAAAAACo5p87xKq9iY2O1fft2bdmyxan+ycnJCgsLs2sLCwtTZmamTpw4oYiICIfrpaenKz093Trvyj3NAAAAAMDlEa9du3YVuGzFihUlKsYVR44c0RNPPKEFCxaoSpUqTq9nsVjs5nNvY5a33daUKVMUEhJinaKioopXNAAAAIBKyeXg1a5dO/2///f/7NrS09M1evRo3X777W4rrCjbtm1TSkqK2rZtKx8fH/n4+Gjt2rV6++235ePjo6ysrHzrhIeHKzk52a4tJSVFPj4+qlWrVoH7mjBhglJTU63TkSNH3P58AAAAAFRcLh9q+Mknn+if//ynli9frrlz5yo5OVn33nuvJGnDhg1uL7AgPXr00M6dO+3a7r//fl111VUaP368vL29863TsWNHffXVV3Ztq1atUrt27eTr61vgvvz9/eXv7++ewgEAAABUOi6PeA0cOFA///yzMjMz1aJFC3Xs2FHdunXTtm3bdO2115pRo0NBQUFq0aKF3VStWjXVqlVLLVq0kJQzUjVs2DDrOqNGjdLvv/+usWPHKjExUXPmzNHs2bMVExNTanUDAAAAqHyKdVXDrKwsZWRkKCsrS1lZWQoPDy+XI0JJSUk6fPiwdb5BgwZavny54uPj1bp1a7344ot6++23dccdd5RhlQAAAAAqOouRe3UJJ8XGxurhhx9Wly5dNHv2bO3YsUP333+/6tWrp/nz56thw4Zm1VpupKWlKSQkRKmpqQoODi7rcgAAAACUEWezgcsjXg888IBeeeUVLV26VKGhobrpppu0c+dOXXnllWrdunVJagYAAACACsnli2ts375dTZs2tWurUaOGFi5cqPnz57utMAAAAACoKFw+1BAcaggAAAAgh7PZwOURr5EjRxa6fM6cOa5uEgAAAAAqNJeD199//203f/HiRf3yyy86deqUbrzxRrcVBgAAAAAVhcvBa/HixfnasrOz9cgjj1SKKxoCAAAAgKuKdR+vfBvx8tKYMWM0depUd2wOAAAAACoUtwQvSTpw4IAyMzPdtTkAAAAAqDBcPtRw7NixdvOGYSgpKUnLli3T8OHD3VYYAAAAAFQULgevhIQEu3kvLy+FhobqzTffLPKKhwAAAABQGbkcvNasWWNGHQAAAABQYbntHC8AAAAAgGNOjXi1adNGFovFqQ1u3769RAUBAAAAQEXjVPC67bbbTC4DAAAAFUVWVpYuXrxY1mUAbuHr6ytvb+8Sb8diGIbhhnoqlbS0NIWEhCg1NVXBwcFlXQ4AAEC5YBiGkpOTderUqbIuBXCr6tWrKzw83OFRgM5mA6cvrjFnzhwNGTJE/v7+xasWAAAAFVpu6Kpdu7aqVq3q9KkqQHllGIbOnTunlJQUSVJERESxt+V08HrwwQd1yy23qHbt2pKkyMhI/fDDD6pfv36xdw4AAICKISsryxq6atWqVdblAG4TEBAgSUpJSVHt2rWLfdih01c1zHtE4unTp5WdnV2snQIAAKBiyT2nq2rVqmVcCeB+uX/XJTl3kcvJAwAAwG04vBAVkTv+rp0OXhaLxW6HeecBAAAAAI65dKhhkyZNVLNmTdWsWVNnzpxRmzZtrPO5EwAAAICKadKkSWrdurVL/cPCwmSxWLRkyRLT6nLWuXPndMcddyg4OFgWi0WnTp1y2GYGp4PX3LlzNW3aNE2dOlVTp07V3LlzNX36dOt87gQAAAB4ihEjRshisWjUqFH5lj3yyCOyWCwaMWKEXfuRI0f0wAMPKDIyUn5+fqpXr56eeOIJ/fXXX3b9unXrJovFoldffTXftvv16yeLxaJJkybZtScmJurWW29VSEiIgoKC1KFDBx0+fLjQ+otzz93irueKxMRETZ48We+//76SkpLUt29fU/fnjHnz5un777/XDz/8oKSkJIWEhDhsM4PTVzUcPny4KQUAAAAAZSkqKkqxsbGaOnWq9Qp2Fy5c0Geffaa6deva9f3tt9/UsWNHNWnSRJ999pkaNGigXbt26V//+pe+/vprbdq0ye4osKioKM2dO1f//ve/rW3Hjh3Td999l+/S5AcOHFDnzp31wAMPaPLkyQoJCVFiYqKqVKli4rM3z4EDByRJAwYMKPAUpYyMDPn5+ZVqTVdffbVatGhRaJsZuLgGAAAAKrVrr71WdevW1aJFi6xtixYtUlRUlNq0aWPX99FHH5Wfn59WrVqlG264QXXr1lXfvn31zTff6OjRo3rmmWfs+t9yyy3666+/tGHDBmvbRx99pF69ellv05TrmWeeUb9+/fSf//xHbdq0UcOGDXXzzTfn65dr0qRJmjdvnr788kvr9Rfi4+MlSTt37tSNN96ogIAA1apVS//85z915syZItcbP368mjRpoqpVq6phw4aaOHFisa7kN2nSJPXv31+S5OXlZQ1euSNtU6ZMUWRkpJo0aSJJDg9FrF69uj766CNJ0qFDh2SxWLRo0SJ1795dVatWVatWrbRx40a7deLi4tS8eXP5+/urfv36evPNN63LunXrpjfffFPr1q2TxWJRt27dHLaZheAFAAAAtzMMQ+cyMstkynsbJGfcf//9mjt3rnV+zpw5GjlypF2fkydPauXKlXrkkUesI2O5wsPDNWTIEH3++ed2+/fz89OQIUPstv3RRx/l23Z2draWLVumJk2aqHfv3qpdu7bat29f6HlRMTExuvvuu9WnTx8lJSUpKSlJnTp10rlz59SnTx/VqFFDW7Zs0X//+1998803Gj16dKHrSVJQUJA++ugj7d69W9OnT9esWbOKdTpRTEyM9Tnn7iPXt99+q8TERK1evVr/+9//XNruM888o5iYGO3YsUNNmjTRPffco8zMTEnStm3bdPfdd2vw4MHauXOnJk2apIkTJ1rD26JFi/Tggw+qY8eOSkpK0qJFixy2mcXpQw0BAAAAZ52/mKVmz60sk33vfqG3qvq59jF36NChmjBhgnVkZcOGDYqNjbWOBEnS/v37ZRiGrr76aofbuPrqq/X333/rzz//tBuleuCBB9S5c2dNnz5d27ZtU2pqqm6++Wa787tSUlJ05swZvfrqq3rppZf02muvacWKFRo4cKDWrFmjG264Id/+AgMDFRAQoPT0dIWHh1vb582bp/Pnz+vjjz9WtWrVJEkzZsxQ//799dprryksLMzhepL07LPPWh/Xr19fTz31lD7//HONGzfOpdczMDBQ1atXl6R8+6hWrZo+/PDDYh1iGBMTo5tvvlmSNHnyZDVv3ly//vqrrrrqKr311lvq0aOHJk6cKElq0qSJdu/erddff10jRoxQzZo1VbVqVfn5+dnV5KjNDAQvAAAAVHpXXHGFbr75Zs2bN0+GYejmm2/WFVdc4dI2cke68p7PdM0116hx48b64osvtGbNGg0dOlS+vr52fbKzsyXlnA81ZswYSVLr1q31ww8/aObMmQ6DV0ESExPVqlUra+iSpOuvv17Z2dnau3evwsLCClz3iy++0LRp0/Trr7/qzJkzyszMVHBwsNP7dkbLli2LfV7XNddcY32ce45cSkqKrrrqKiUmJmrAgAF2/a+//npNmzZNWVlZ8vb2Ln7RblBqwevnn392eZ1mzZrJx4dsCAAA4GkCfL21+4XeZbbv4hg5cqT1cLx33nkn3/Lo6GhZLBbt3r3b4RUB9+zZoxo1ajgMbCNHjtQ777yj3bt368cff8y3/IorrpCPj4+aNWtm13711Vdr/fr1Lj0PwzAKvJhFYffh3bRpkwYPHqzJkyerd+/eCgkJUWxsrN15Uu5gGwht68p7iKijc8tsA2vuc8kNrY6ed3EOOzWLy6lm7NixDtstFouqVKmi6OhoDRgwIN89vVq3bu3wBS2Il5eX9u3bp4YNG7paIgAAAMqYxWJx+XC/stanTx9lZGRIknr3zh8aa9WqpZtuuknvvvuuxowZY3eeV3Jysj755BMNGzbMYbi59957FRMTo1atWuULV1LOuWDXXXed9u7da9e+b98+1atXr8Ca/fz8lJWVZdfWrFkzzZs3T2fPnrWGnA0bNsjLy8t6MQtH623YsEH16tWzu0DI77//XuC+3Sk0NNTuPLD9+/fr3LlzLm2jWbNm+ULqDz/8oCZNmpT5aJdUjOCVkJCg7du3KysrS02bNpVhGNq/f7+8vb111VVX6d1339VTTz2l9evX5/uj2rx5s0JDQ4vch2EYpl/OEQAAALDl7e2txMRE62NHZsyYoU6dOql379566aWX7C4nf+WVV+rll192uF6NGjWUlJSU7xBDW//61780aNAgde3aVd27d9eKFSv01Vdf2Z1nllf9+vW1cuVK7d27V7Vq1VJISIiGDBmi559/XsOHD9ekSZP0559/6rHHHtPQoUOthxk6Wi86OlqHDx9WbGysrrvuOi1btkyLFy928tUrmRtvvFEzZsxQhw4dlJ2drfHjxxf6Wjny1FNP6brrrtOLL76oQYMGaePGjZoxY4beffddk6p2jctXNRwwYIB69uypY8eOadu2bdq+fbuOHj2qm266Sffcc4+OHj2qrl27Wo9NzXXDDTcoOjpa9erVK3KqX7++unbtmu9qMQAAAICZgoODCz2nqXHjxtq6dasaNWqkQYMGqVGjRvrnP/+p7t27a+PGjfmO+rJVvXp1h4fZ5br99ts1c+ZM/ec//1HLli314YcfKi4uTp07dy5wnQcffFBNmzZVu3btFBoaqg0bNqhq1apauXKlTp48qeuuu0533nmnevTooRkzZhS6Xu75ZaNHj7aeX5Z7oQqzvfnmm4qKilLXrl2to4NVq1Z1aRvXXnutFi5cqNjYWLVo0ULPPfecXnjhhXw3wC4rFsPFAx+vvPJKrV69Ot9o1q5du9SrVy8dPXpU27dvV69evXTixAm3FltepKWlKSQkRKmpqW4/2RAAAMATXbhwQQcPHlSDBg089oa/QEEK+/t2Nhu4POKVmpqqlJSUfO1//vmn0tLSJOWk+dzjYwEAAACgsivWoYYjR47U4sWL9ccff+jo0aNavHixHnjgAevVXX788UfriXuuOHLkSL6byRXmvffe0zXXXGMdEu7YsaO+/vrrAvvHx8db785tO+3Zs8flWgEAAIDKLDAwsMDp+++/L+vyyh2XL67x/vvva8yYMRo8eLD1LtE+Pj4aPny49a7WV111lT788EOXizl58qTmzZunOXPmONW/Tp06evXVVxUdHS0p52ZxAwYMUEJCgpo3b17genv37rUbBnTmgh8AAAAALtuxY0eBy6688srSK8RDuBy8AgMDNWvWLE2dOlW//fabDMNQo0aNFBgYaO3TunVrh+suXbq00G3/9ttvLtXSv39/u/mXX35Z7733njZt2lRo8Kpdu7b1TtoAAAAAXJc7+AHnFPvmCoGBgXZ3jnbGbbfdVuS9vAq7qVthsrKy9N///ldnz55Vx44dC+3bpk0bXbhwQc2aNdOzzz6r7t27F9o/PT1d6enp1vncc9kAAAAAwBkun+N19uxZTZw4UZ06dVJ0dLQaNmxoNxUmIiJCcXFxys7Odjht377d5Sewc+dOBQYGyt/fX6NGjdLixYsd3pQud/8ffPCB4uLitGjRIjVt2lQ9evTQunXrCt3HlClTFBISYp2ioqJcrhMAAABA5eXyiNc//vEPrV27VkOHDlVERIRLI1Rt27bV9u3brRfhyKuo0TBHmjZtqh07dujUqVOKi4vT8OHDtXbtWofhq2nTpmratKl1vmPHjjpy5IjeeOMNde3atcB9TJgwQWPHjrXOp6WlEb4AAAAAOM3l4PX1119r2bJluv76613e2b/+9S+dPXu2wOXR0dFas2aNS9v08/OzHl/arl07bdmyRdOnT9f777/v1PodOnTQggULCu3j7+8vf39/l+oCAAAAgFwuB68aNWoUekfuwnTp0qXQ5dWqVdMNN9xQrG3nMgzD7nysoiQkJCgiIqJE+wQAAACAwrh8jteLL76o5557TufOnXNLAZ999lmho2CFefrpp/X999/r0KFD2rlzp5555hnFx8dryJAhknIOERw2bJi1/7Rp07RkyRLt379fu3bt0oQJExQXF6fRo0e75bkAAAAAZsu9N+2pU6eKvY1JkybZXYl8xIgRBZ4O5I7tO2PJkiWKjo6Wt7e3nnzySbfVUl64HLzefPNNrVy5UmFhYWrZsqWuvfZau8lVDz30kI4fP+7yepJ0/PhxDR061HqRjM2bN2vFihW66aabJElJSUk6fPiwtX9GRoZiYmJ0zTXXqEuXLlq/fr2WLVumgQMHFmv/AAAA8Hy2oWPEiBGyWCyyWCzy8fFR3bp19fDDD+vvv//Ot9758+etR4OdP38+3/Ju3bpZt5U7DR482K5P/fr18/X597//bcrzLMz06dP10Ucflfp+bT300EO68847deTIEb344otlWosZXD7U0J1JWJLLF9OwNXv27EKX5/3jGTdunMaNG1fs/QEAAKDi69Onj+bOnavMzEzt3r1bI0eO1KlTp/TZZ5/Z9YuLi1OLFi1kGIYWLVpkPerK1oMPPqgXXnjBOh8QEJCvzwsvvKAHH3zQOm97f9zSEhISUur7tHXmzBmlpKSod+/eioyMLNNazOJy8Hr++efNqAMAAAAoF/z9/RUeHi5JqlOnjgYNGuRwNGj27Nm67777ZBiGZs+e7TB4Va1a1bqtggQFBRXaZ/ny5XryySd15MgRdejQQcOHD8/XJy4uTs8995x+/fVXRURE6LHHHtNTTz1VxDO9bMSIETp16pSWLFkiScrOztbrr7+uWbNm6ciRIwoLC9NDDz2kZ555RpI0fvx4LV68WH/88YfCw8M1ZMgQPffcc/L19c237ZUrV2rAgAFKTk5W9erVre2PP/64fvrpJ02ePNl6X90bb7xRkrRmzRp169bN6fo9gcuHGrrD+fPnreeIff3117p48aKmTZumlStXlkU5AAAAcDfDkDLOls1UgiOq8vrtt9+0YsWKfIHiwIED2rhxo+6++27dfffd+uGHH/Tbb7/lW/+TTz7RFVdcoebNmysmJkanT5/O1+e1115TrVq11Lp1a7388svKyMiwLjty5IgGDhyofv36aceOHfrHP/6R71DEbdu26e6779bgwYO1c+dOTZo0SRMnTizRoYMTJkzQa6+9pokTJ2r37t369NNPFRYWZl0eFBSkjz76SLt379b06dM1a9YsTZ061eG2evbsqerVqysuLs7alpWVpYULF2rIkCHq1KmT9u7dKyknQCYlJalTp07Frr28cmrEq2bNmtq3b5+uuOIK1ahRo9B7d508ebLI7Q0YMEADBw7UqFGj1KJFC1111VXy9fXViRMn9NZbb+nhhx92/hkAAACg/Ll4TnqljA4Ze/qY5Fet2Kv/73//U2BgoLKysnThwgVJ0ltvvWXXZ86cOerbt69q1KghKefwxDlz5uill16y9hkyZIgaNGig8PBw/fLLL5owYYJ++uknrV692trniSee0LXXXqsaNWroxx9/1IQJE3Tw4EF9+OGHkqT33ntPDRs21NSpU2WxWNS0aVPt3LlTr732mnUbb731lnr06KGJEydKkpo0aaLdu3fr9ddf14gRI1x+/qdPn9b06dM1Y8YM6+hao0aN1LlzZ2ufZ5991vq4fv36euqpp/T55587PK3H29tbgwYN0qeffqoHHnhAkvTtt9/q77//1l133SU/Pz/Vrl1bUk7uKGqE0FM5FbymTp2qoKAg62NXbprsyPbt262J+IsvvlBYWJgSEhKsQ6QELwAAAJSV7t2767333tO5c+f04Ycfat++fXrsscesy7OysjRv3jxNnz7d2nbfffdpzJgxmjx5sry9vSXJ7rytFi1aqHHjxmrXrp22b99uvSjdmDFjrH2uueYa1ahRQ3feead1FCwxMVEdOnSw+/zdsWNHu3oTExM1YMAAu7brr79e06ZNU1ZWlrUeZyUmJio9PV09evQosM8XX3yhadOm6ddff9WZM2eUmZmp4ODgAvsPGTJEHTt21LFjxxQZGalPPvlE/fr1swbXysCp4GV7HGlxUnNe586dswa5VatWaeDAgfLy8lKHDh30+++/l3j7AAAAKGO+VXNGnspq3yVQrVo1RUdHS5Lefvttde/eXZMnT7ZeaW/lypU6evSoBg0aZLdeVlaWVq1apb59+zrc7rXXXitfX1/t37+/wKuBd+jQQZL066+/qlatWk5diM4wjHwDIyW5gJ2jC4DY2rRpkwYPHqzJkyerd+/eCgkJUWxsrN58880C1/m///s/NWrUSLGxsXr44Ye1ePFizZ07t9g1eiKXz/Hq3r27Zs+erdTU1GLvNDo6WkuWLNGRI0e0cuVK9erVS5KUkpJSaFIGAACAh7BYcg73K4uphEdn5fX888/rjTfe0LFjOUFy9uzZGjx4sHbs2GE3DRkypNCrbu/atUsXL15UREREgX0SEhIkydqnWbNm2rRpk12fvPPNmjXT+vXr7dp++OEHNWnSxOXRLklq3LixAgIC9O233zpcvmHDBtWrV0/PPPOM2rVrp8aNGzs1eHLvvffqk08+0VdffSUvLy/dfPPNLtfmyVwOXi1bttSzzz6r8PBw3XHHHVqyZIndCYDOeO655xQTE6P69eurffv21uHSVatWqU2bNq6WBAAAAJimW7duat68uV555RX9+eef+uqrrzR8+HC1aNHCbho+fLiWLl2qP//8UwcOHNALL7ygrVu36tChQ1q+fLnuuusutWnTRtdff70kaePGjZo6dap27NihgwcPauHChXrooYd06623qm7dupKkUaNG6cCBAxo7dqz27t2rTz/9NN9FM5566il9++23evHFF7Vv3z7NmzdPM2bMUExMTLGeb5UqVTR+/HiNGzdOH3/8sQ4cOKBNmzZZQ2V0dLQOHz6s2NhYHThwQG+//bYWL15c5HaHDBmi7du36+WXX9add96pKlWqFKs+T+Vy8Hr77bd19OhRffnllwoKCtLw4cMVHh6uf/7zn1q7dq1T27jzzjt1+PBhbd26VStWrLC29+jRo8CroQAAAABmyM7Olo9P4WfgjB07VrNmzdK7776ratWqOTz/qXv37goKCtL8+fPl5+enb7/9Vr1791bTpk31+OOPq1evXvrmm2+so1D+/v76/PPP1a1bNzVr1kzPPfecHnzwQbv7hdWtW1dxcXH66quv1KpVK82cOVOvvPKK3X6vvfZaLVy4ULGxsWrRooWee+45vfDCCyU6RWjixIl66qmn9Nxzz+nqq6/WoEGDlJKSIinnQnljxozR6NGj1bp1a/3www/WC3sUpnHjxrruuuv0888/O7z0fkVnMUpyAKikCxcu6KuvvtLLL7+snTt3Kisry121lVtpaWkKCQlRamoqh0YCAAAo5zPhwYMH1aBBA48byejTp4+io6M1Y8aMsi4F5VRhf9/OZgOXb6BsKzk5WbGxsVqwYIF+/vlnXXfddSXZHAAAAFBq/v77b/3www+Kj4/XqFGjyrocVHAuB6+0tDTFxcXp008/VXx8vBo2bKh7771XsbGx1qu/AAAAAOXdyJEjtWXLFj311FP5LscOuJvLwSssLEw1atTQ3XffrVdeeYVRLgAAAHgkZy4IAbiLy8Hryy+/VM+ePeXl5fJ1OQAAAACgUnI5eOXecwsAAAAA4BynglebNm3y3Q27INu3by9RQQAAAPBc2dnZZV0C4Hbu+Lt2KnjddtttJd4RAAAAKi4/Pz95eXnp2LFjCg0NlZ+fn9Nf3APllWEYysjI0J9//ikvLy/5+fkVe1slvo9XZcR9vAAAAPLLyMhQUlKSzp07V9alAG5VtWpVRUREOAxepXIfLwAAACCXn5+f6tatq8zMTGVlZZV1OYBbeHt7y8fHp8QjuC4Hr6ysLE2dOlULFy7U4cOHlZGRYbf85MmTJSoIAAAAnstiscjX11e+vr5lXQpQrrh8TfjJkyfrrbfe0t13363U1FSNHTtWAwcOlJeXlyZNmmRCiQAAAADg2VwOXp988olmzZqlmJgY+fj46J577tGHH36o5557Tps2bTKjRgAAAADwaC4Hr+TkZLVs2VKSFBgYqNTUVEnSLbfcomXLlrm3OgAAAACoAFwOXnXq1FFSUpIkKTo6WqtWrZIkbdmyRf7+/u6tDgAAAAAqAJeD1+23365vv/1WkvTEE09o4sSJaty4sYYNG6aRI0e6vUAAAAAA8HQlvo/X5s2btWHDBkVHR+vWW291V13lGvfxAgAAACCV4n282rdvr/bt25d0MwAAAABQYTl9qOG2bdvUvXt3paWl5VuWmpqq7t2766effnJrcQAAAABQETgdvN58803deOONDofPQkJC1LNnT73++utuLQ4AAAAAKgKng9fmzZs1YMCAApffeuut+uGHH9xSFAAAAABUJE4Hr6NHjyooKKjA5YGBgdbLzAMAAAAALnM6eIWGhmrv3r0FLt+zZ4+uuOIKtxQFAAAAABWJ08GrZ8+eevnllx0uMwxDr7zyinr27Om2wgAAAACgonA6eD377LPauXOn2rdvr4ULF+qnn37Szz//rM8//1zt27fXzp079cwzz5hZaz7vvfeerrnmGgUHBys4OFgdO3bU119/Xeg6a9euVdu2bVWlShU1bNhQM2fOLKVqAQAAAFRWTt/Hq1GjRvrmm280YsQIDR48WBaLRVLOaFezZs20evVqRUdHm1aoI3Xq1NGrr75q3e+8efM0YMAAJSQkqHnz5vn6Hzx4UP369dODDz6oBQsWaMOGDXrkkUcUGhqqO+64o1RrBwAAAFB5WAzDMFxdaceOHdq/f78Mw1CTJk3UunVrE0ornpo1a+r111/XAw88kG/Z+PHjtXTpUiUmJlrbRo0apZ9++kkbN250eh/O3p0aAAAAQMXmbDZwesTLVuvWrctV2JKkrKws/fe//9XZs2fVsWNHh302btyoXr162bX17t1bs2fP1sWLF+Xr6+twvfT0dKWnp1vnHd1EGgAAAAAK4vQ5XuXVzp07FRgYKH9/f40aNUqLFy9Ws2bNHPZNTk5WWFiYXVtYWJgyMzN14sSJAvcxZcoUhYSEWKeoqCi3PgcAAAAAFZvHB6+mTZtqx44d2rRpkx5++GENHz5cu3fvLrB/7rlpuXKPtMzbbmvChAlKTU21TkeOHHFP8QAAAAAqhWIdalie+Pn5WS+u0a5dO23ZskXTp0/X+++/n69veHi4kpOT7dpSUlLk4+OjWrVqFbgPf39/+fv7u7dwAAAAAJWGx4945WUYht35WLY6duyo1atX27WtWrVK7dq1K/D8LgAAAAAoqWIFr++//1733XefOnbsqKNHj0qS5s+fr/Xr17u1uKI8/fTT+v7773Xo0CHrfcTi4+M1ZMgQSTmHCA4bNszaf9SoUfr99981duxYJSYmas6cOZo9e7ZiYmJKtW4AAAAAlYvLwSsuLk69e/dWQECAEhISrKNLp0+f1iuvvOL2Agtz/PhxDR06VE2bNlWPHj20efNmrVixQjfddJMkKSkpSYcPH7b2b9CggZYvX674+Hi1bt1aL774ot5++23u4QUAAADAVC7fx6tNmzYaM2aMhg0bpqCgIP30009q2LChduzYoT59+uQ7h6oi4j5eAAAAACTns4HLI1579+5V165d87UHBwfr1KlTrm4OAAAAACo8l4NXRESEfv3113zt69evV8OGDd1SFAAAAABUJC4Hr4ceekhPPPGENm/eLIvFomPHjumTTz5RTEyMHnnkETNqBAAAAACP5vJ9vMaNG6fU1FR1795dFy5cUNeuXeXv76+YmBiNHj3ajBoBAAAAwKO5fHGNXOfOndPu3buVnZ2tZs2aKTAw0N21lVtcXAMAAACA5Hw2cHnEKzU1VVlZWapZs6batWtnbT958qR8fHwIIgAAAACQh8vneA0ePFixsbH52hcuXKjBgwe7pSgAAAAAqEhcDl6bN29W9+7d87V369ZNmzdvdktRAAAAAFCRuBy80tPTlZmZma/94sWLOn/+vFuKAgAAAICKxOXgdd111+mDDz7I1z5z5ky1bdvWLUUBAAAAQEXi8sU1Xn75ZfXs2VM//fSTevToIUn69ttvtWXLFq1atcrtBQIAAACAp3N5xOv666/Xxo0bFRUVpYULF+qrr75SdHS0fv75Z3Xp0sWMGgEAAADAoxX7Pl6VGffxAgAAACCZcB+vtLQ0p/oRRAAAAADAntPBq3r16rJYLAUuNwxDFotFWVlZbikMAAAAACoKp4PXmjVrzKwDAAAAACosp4PXDTfcYGYdAAAAAFBhuXxVQwAAAACAawheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMmcuqrhwIEDnd7gokWLil0MAAAAAFRETo14hYSEWKfg4GB9++232rp1q3X5tm3b9O233yokJMS0QgEAAADAUzk14jV37lzr4/Hjx+vuu+/WzJkz5e3tLUnKysrSI488ouDgYHOqBAAAAAAPZjEMw3BlhdDQUK1fv15Nmza1a9+7d686deqkv/76y60FlkdpaWkKCQlRamoqYRMAAACoxJzNBi5fXCMzM1OJiYn52hMTE5Wdne3q5gAAAACgwnPqUENb999/v0aOHKlff/1VHTp0kCRt2rRJr776qu6//363FwgAAAAAns7l4PXGG28oPDxcU6dOVVJSkiQpIiJC48aN01NPPeX2AgEAAADA07l8jpettLQ0Sap05zlxjhcAAAAAyfls4PKIly1CBwAAAAAUzeWLaxw/flxDhw5VZGSkfHx85O3tbTcBAAAAAOy5POI1YsQIHT58WBMnTlRERIQsFosZdQEAAABAheFy8Fq/fr2+//57tW7d2oRyXDNlyhQtWrRIe/bsUUBAgDp16qTXXnst3z3GbMXHx6t79+752hMTE3XVVVeZWS4AAACASsrlQw2joqJUgutxuNXatWv16KOPatOmTVq9erUyMzPVq1cvnT17tsh19+7dq6SkJOvUuHHjUqgYAAAAQGXk8ojXtGnT9O9//1vvv/++6tevb0JJzluxYoXd/Ny5c1W7dm1t27ZNXbt2LXTd2rVrq3r16iZWBwAAAAA5XA5egwYN0rlz59SoUSNVrVpVvr6+dstPnjzptuJclZqaKkmqWbNmkX3btGmjCxcuqFmzZnr22WcdHn6YKz09Xenp6db53MvoAwAAAIAzijXiVR4ZhqGxY8eqc+fOatGiRYH9IiIi9MEHH6ht27ZKT0/X/Pnz1aNHD8XHxxc4SjZlyhRNnjzZrNIBAAAAVHAluoFyefLoo49q2bJlWr9+verUqePSuv3795fFYtHSpUsdLnc04hUVFcUNlAEAAIBKrlRuoHz+/HldvHjRrq0sgshjjz2mpUuXat26dS6HLknq0KGDFixYUOByf39/+fv7l6REAAAAAJWYy1c1PHv2rEaPHq3atWsrMDBQNWrUsJtKk2EYGj16tBYtWqTvvvtODRo0KNZ2EhISFBER4ebqAAAAACCHyyNe48aN05o1a/Tuu+9q2LBheuedd3T06FG9//77evXVV82osUCPPvqoPv30U3355ZcKCgpScnKyJCkkJEQBAQGSpAkTJujo0aP6+OOPJeWco1a/fn01b95cGRkZWrBggeLi4hQXF1eqtQMAAACoPFwOXl999ZU+/vhjdevWTSNHjlSXLl0UHR2tevXq6ZNPPtGQIUPMqNOh9957T5LUrVs3u/a5c+dqxIgRkqSkpCQdPnzYuiwjI0MxMTE6evSoAgIC1Lx5cy1btkz9+vUrrbIBAAAAVDIuX1wjMDBQu3btUr169VSnTh0tWrRI//d//6eDBw+qZcuWOnPmjFm1lhvOnkAHAAAAoGJzNhu4fI5Xw4YNdejQIUlSs2bNtHDhQkk5I2HckBgAAAAA8nM5eN1///366aefJOWcP/Xuu+/K399fY8aM0b/+9S+3FwgAAAAAnq7E9/E6fPiwtm7dqkaNGqlVq1buqqtc41BDAAAAAJKJhxrmVbduXQ0cOFA1a9bUyJEjS7o5AAAAAKhwShy8cp08eVLz5s1z1+YAAAAAoMJwW/ACAAAAADhG8AIAAAAAkxG8AAAAAMBkPs52HDhwYKHLT506VdJaAAAAAKBCcjp4hYSEFLl82LBhJS4IAAAAACoap4PX3LlzzawDAAAAACoszvECAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAk3l08JoyZYquu+46BQUFqXbt2rrtttu0d+/eItdbu3at2rZtqypVqqhhw4aaOXNmKVQLAAAAoLLy6OC1du1aPfroo9q0aZNWr16tzMxM9erVS2fPni1wnYMHD6pfv37q0qWLEhIS9PTTT+vxxx9XXFxcKVYOAAAAoDKxGIZhlHUR7vLnn3+qdu3aWrt2rbp27eqwz/jx47V06VIlJiZa20aNGqWffvpJGzdudGo/aWlpCgkJUWpqqoKDg91SOwAAAADP42w28OgRr7xSU1MlSTVr1iywz8aNG9WrVy+7tt69e2vr1q26ePGiw3XS09OVlpZmNwEAAACAsypM8DIMQ2PHjlXnzp3VokWLAvslJycrLCzMri0sLEyZmZk6ceKEw3WmTJmikJAQ6xQVFeXW2gEAAABUbBUmeI0ePVo///yzPvvssyL7WiwWu/ncoy3ztueaMGGCUlNTrdORI0dKXjAAAACASsOnrAtwh8cee0xLly7VunXrVKdOnUL7hoeHKzk52a4tJSVFPj4+qlWrlsN1/P395e/v77Z6AQAAAFQuHj3iZRiGRo8erUWLFum7775TgwYNilynY8eOWr16tV3bqlWr1K5dO/n6+ppVKgAAAIBKzKOD16OPPqoFCxbo008/VVBQkJKTk5WcnKzz589b+0yYMEHDhg2zzo8aNUq///67xo4dq8TERM2ZM0ezZ89WTExMWTwFAAAAAJWARwev9957T6mpqerWrZsiIiKs0+eff27tk5SUpMOHD1vnGzRooOXLlys+Pl6tW7fWiy++qLffflt33HFHWTwFAAAAAJVAhbqPV2nhPl4AAAAApEp6Hy8AAAAAKI8IXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACbz6OC1bt069e/fX5GRkbJYLFqyZEmh/ePj42WxWPJNe/bsKZ2CAQAAAFRKPmVdQEmcPXtWrVq10v3336877rjD6fX27t2r4OBg63xoaKgZ5QEAAACAJA8PXn379lXfvn1dXq927dqqXr26+wsCAAAAAAc8+lDD4mrTpo0iIiLUo0cPrVmzpsj+6enpSktLs5sAAAAAwFkePeLlqoiICH3wwQdq27at0tPTNX/+fPXo0UPx8fHq2rVrgetNmTJFkydPLsVKnXf6wkXtO35GhmEoK9tQtiFlG4ayL80bl+YLWpbTnjvpUruj/pceX2rPMhz0yzYutdtuV5fWyb/MkCRDMpTTbtg8vrTo0uNLyyUZl9aznc9lFLAt2/0oz7q225LdvE0defZls0tZLMo5V1CSl5dkkcWuzWJRzjLLpXbr8pzHBa3jZfNYssjLwTrK0y/3sXL75VlHl/bjZVOHV84O8u3TYlOvkfPiXX7NDBXwOuV//fL9Dh0sy91+toPfbWF/I678fTjzt+HK34Vd37zbk9QiMliPdI9Wk7Cgot7CxWb7u8jOW4fNY9vX1f51znl/2v5+s223mW3/2mUbeV4bm8dO79+6naL3n/e1LXL/Nr9v63Zstpv7e87OU5/j51Hw77YwlsKWFbZQl97TJVi/8HULXrmozRa236LXLWS/ljzbsFx+BS4vs+Trl/vvkm2jxWZfdv0cbMe2Nmf3lztncWF/uQsvL7fYrFvw/mTz/Bzuz67NZiXra1P4/mS3XgG12C53tI5sHtu9rpZ87fZ9LQ56Onjd8m3XQd8itlXQ9goot8i+Lu3Pwetb0PZcqV0u9s37t1Do/kryD0wJ5H5uzP1cmJVtMxmGsrN16WdOW2aeftmGocjqAboi0L9M6i8Oi2H7ydWDWSwWLV68WLfddptL6/Xv318Wi0VLly4tsE96errS09Ot82lpaYqKilJqaqrduWJlYeOBv3TPrE1lWgOA/CwWKSyoSv5QZ/PYNkzkDZ95v5zIGzwAADBLUaHRtr2gIOjooSFZg5M7vDiguYZ2rO+WbZVEWlqaQkJCiswGlWrEy5EOHTpowYIFhfbx9/eXv3/5TNMBft6qW7OqvL1yvq3ytljkZbHIyytnxMP28eVlOe0561jknbeftf3SNrxy1vO2Wffy+pe2e6mPw33aLMvdtpfl8rd41m/p7EaJ7L/Bs7bZrpfb7vAbO/tt2W7H2ruAbeXdT07v/Nty9G17cb6hL3gd+5Gdyx/E7T+E5z6W8n5jf+mbftl+yL/8IV7W9e1HTmxHt7INm+eeZ0Qt7++moN+jXT+bb20L2kb+31/hfycF/Y1YRxkL3Xbevw3n/y4cP3cpPTNbC7cc0de/JCs57YJzb+QyZLHkGe2U/evmled372Wxf428bF43r3yv6eVtXd5HnpFVmxpks037WpwbQbbY1CcVXmu+v5E89eV93o5GTWwVFoZzR9sL6VDsxUV9d1r4umWz39yFdkchOGrLV6f9ERG5deTtZzv6ad/u3P5s63N2f7kztstst53/OTmur7D9ycHzK2p/l/dh2Kxb8HO3/7Ua+dpsF9s9B7v2wpfLwfaK2lbeeoverqPX1/naUdjrXNSL5L4X0dsr57Om96XPnLmfI729Ln8OreLr7bb9lYZKH7wSEhIUERFR1mUUW+uo6lo3rntZlwHARvemtXXs1HmdPJshKX8IzA0Wsg0LyhtAbEKIg4CRN4DIYh8WbANI3uCT2w8AUDhHIVlyHOSKCo15213p60pIzfvlhjO12+/DcT2OQ7PztefysQlOuV/qe+drq5j/R3l08Dpz5ox+/fVX6/zBgwe1Y8cO1axZU3Xr1tWECRN09OhRffzxx5KkadOmqX79+mrevLkyMjK0YMECxcXFKS4urqyeAoAKKrJ6gCKrB5R1GQCAErA7tK7ALFAxQwLcz6OD19atW9W9++XRnrFjx0qShg8fro8++khJSUk6fPiwdXlGRoZiYmJ09OhRBQQEqHnz5lq2bJn69etX6rUDAAAAqDwqzMU1SpOzJ9ABAAAAqNiczQaV8j5eAAAAAFCaCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJvMp6wI8kWEYkqS0tLQyrgQAAABAWcrNBLkZoSAEr2I4ffq0JCkqKqqMKwEAAABQHpw+fVohISEFLrcYRUUz5JOdna1jx44pKChIFoulTGtJS0tTVFSUjhw5ouDg4DKtBYA93p+A5+F9C3iG8vReNQxDp0+fVmRkpLy8Cj6TixGvYvDy8lKdOnXKugw7wcHBZf5HB8Ax3p+A5+F9C3iG8vJeLWykKxcX1wAAAAAAkxG8AAAAAMBkBC8P5+/vr+eff17+/v5lXQqAPHh/Ap6H9y3gGTzxvcrFNQAAAADAZIx4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeJlg3bp16t+/vyIjI2WxWLRkyRK75cePH9eIESMUGRmpqlWrqk+fPtq/f79dn/T0dD322GO64oorVK1aNd166636448/7Pr8/fffGjp0qEJCQhQSEqKhQ4fq1KlTRda3c+dO3XDDDQoICNCVV16pF154QbbXWElKStK9996rpk2bysvLS08++WRxXwqg3PH092d8fLwsFku+ac+ePcV+TYDyztPft5L0zjvv6Oqrr1ZAQICaNm2qjz/+uFivBVDeldb79eWXX1anTp1UtWpVVa9e3en6yvJzMMHLBGfPnlWrVq00Y8aMfMsMw9Btt92m3377TV9++aUSEhJUr1499ezZU2fPnrX2e/LJJ7V48WLFxsZq/fr1OnPmjG655RZlZWVZ+9x7773asWOHVqxYoRUrVmjHjh0aOnRoobWlpaXppptuUmRkpLZs2aL/9//+n9544w299dZb1j7p6ekKDQ3VM888o1atWrnhFQHKD09/f+bau3evkpKSrFPjxo1L8KoA5Zunv2/fe+89TZgwQZMmTdKuXbs0efJkPfroo/rqq6/c8OoA5UtpvV8zMjJ011136eGHH3a6tjL/HGzAVJKMxYsXW+f37t1rSDJ++eUXa1tmZqZRs2ZNY9asWYZhGMapU6cMX19fIzY21trn6NGjhpeXl7FixQrDMAxj9+7dhiRj06ZN1j4bN240JBl79uwpsJ53333XCAkJMS5cuGBtmzJlihEZGWlkZ2fn63/DDTcYTzzxhMvPG/AEnvj+XLNmjSHJ+Pvvv0v03AFP5Ynv244dOxoxMTF26z3xxBPG9ddfX4xXAPAcZr1fbc2dO9cICQlxqp6y/hzMiFcpS09PlyRVqVLF2ubt7S0/Pz+tX79ekrRt2zZdvHhRvXr1svaJjIxUixYt9MMPP0iSNm7cqJCQELVv397ap0OHDgoJCbH2cWTjxo264YYb7G4217t3bx07dkyHDh1yy3MEPJUnvT/btGmjiIgI9ejRQ2vWrCn+kwY8nCe8b9PT0+3qk6SAgAD9+OOPunjxYjGfOeB53PV+La6y/hxM8CplV111lerVq6cJEybo77//VkZGhl599VUlJycrKSlJkpScnCw/Pz/VqFHDbt2wsDAlJydb+9SuXTvf9mvXrm3t40hycrLCwsLybTd3GVCZecL7MyIiQh988IHi4uK0aNEiNW3aVD169NC6deuK/8QBD+YJ79vevXvrww8/1LZt22QYhrZu3ao5c+bo4sWLOnHiRPGfPOBh3PV+La6y/hxM8Cplvr6+iouL0759+1SzZk1VrVpV8fHx6tu3r7y9vQtd1zAMWSwW67ztY0d9mjdvrsDAQAUGBqpv374FrmdcOqHQ0faAysQT3p9NmzbVgw8+qGuvvVYdO3bUu+++q5tvvllvvPFG8Z404OE84X07ceJE9e3bVx06dJCvr68GDBigESNGSFKRNQIViTvfr0Upj5+DfUzfA/Jp27atduzYodTUVGVkZCg0NFTt27dXu3btJEnh4eHKyMjQ33//bZf2U1JS1KlTJ2uf48eP59v2n3/+aU3uy5cvtx7CEBAQYF0vb6JPSUmRpHzfAACVkSe+Pzt06KAFCxYU9ykDHq+8v28DAgI0Z84cvf/++zp+/Lh15DooKEhXXHGFO18KoNxzx/vVGeXxczAjXmUoJCREoaGh2r9/v7Zu3aoBAwZIyvmD9PX11erVq619k5KS9Msvv1j/4Dp27KjU1FT9+OOP1j6bN29WamqqtU+9evUUHR2t6OhoXXnlldb11q1bp4yMDOt6q1atUmRkpOrXr2/2UwY8hie9PxMSEhQREeG25w54qvL+vvX19VWdOnXk7e2t2NhY3XLLLfLy4qMYKqeSvF+dUS4/B7vtMh2wOn36tJGQkGAkJCQYkoy33nrLSEhIMH7//XfDMAxj4cKFxpo1a4wDBw4YS5YsMerVq2cMHDjQbhujRo0y6tSpY3zzzTfG9u3bjRtvvNFo1aqVkZmZae3Tp08f45prrjE2btxobNy40WjZsqVxyy23FFrbqVOnjLCwMOOee+4xdu7caSxatMgIDg423njjDbt+ufW3bdvWuPfee42EhARj165dbnqFgLLj6e/PqVOnGosXLzb27dtn/PLLL8a///1vQ5IRFxfnxlcJKF88/X27d+9eY/78+ca+ffuMzZs3G4MGDTJq1qxpHDx40H0vElBOlNb79ffffzcSEhKMyZMnG4GBgdZ9nj59usDayvpzMMHLBLmXe847DR8+3DAMw5g+fbpRp04dw9fX16hbt67x7LPPGunp6XbbOH/+vDF69GijZs2aRkBAgHHLLbcYhw8ftuvz119/GUOGDDGCgoKMoKAgY8iQIU5dYvrnn382unTpYvj7+xvh4eHGpEmT8l1C01H99erVK8nLApQLnv7+fO2114xGjRoZVapUMWrUqGF07tzZWLZsWYlfF6A88/T37e7du43WrVsbAQEBRnBwsDFgwIBCL1EPeLLSer8OHz7c4X7WrFlTaH1l+TnYcmnjAAAAAACTcGAxAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAADkER8fL4vFolOnTpV1KQCACoIbKAMAKr1u3bqpdevWmjZtmiQpIyNDJ0+eVFhYmCwWS9kWBwCoEHzKugAAAMobPz8/hYeHl3UZAIAKhEMNAQCV2ogRI7R27VpNnz5dFotFFotFH330kd2hhh999JGqV6+u//3vf2ratKmqVq2qO++8U2fPntW8efNUv3591ahRQ4899piysrKs287IyNC4ceN05ZVXqlq1amrfvr3i4+PL5okCAMoUI14AgEpt+vTp2rdvn1q0aKEXXnhBkrRr1658/c6dO6e3335bsbGxOn36tAYOHKiBAweqevXqWr58uX777Tfdcccd6ty5swYNGiRJuv/++3Xo0CHFxsYqMjJSixcvVp8+fbRz5041bty4VJ8nAKBsEbwAAJVaSEiI/Pz8VLVqVevhhXv27MnX7+LFi3rvvffUqFEjSdKdd96p+fPn6/jx4woMDFSzZs3UvXt3rVmzRoMGDdKBAwf02Wef6Y8//lBkZKQkKSYmRitWrNDcuXP1yiuvlN6TBACUOYIXAABOqFq1qjV0SVJYWJjq16+vwMBAu7aUlBRJ0vbt22UYhpo0aWK3nfT0dNWqVat0igYAlBsELwAAnODr62s3b7FYHLZlZ2dLkrKzs+Xt7a1t27bJ29vbrp9tWAMAVA4ELwBApefn52d3UQx3aNOmjbKyspSSkqIuXbq4ddsAAM/DVQ0BAJVe/fr1tXnzZh06dEgnTpywjlqVRJMmTTRkyBANGzZMixYt0sGDB7Vlyxa99tprWr58uRuqBgB4EoIXAKDSi4mJkbe3t5o1a6bQ0FAdPnzYLdudO3euhg0bpqeeekpNmzbVrbfeqs2bNysqKsot2wcAeA6LYRhGWRcBAAAAABUZI14AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJ/j/0Y/QTMtV0NwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data['total_frunoff']['total_frunoff'].plot(label='MOM6 total_frunoff')\n",
    "jra55doGrid.integrate(jra55do.licalvf, ['X', 'Y']).plot(label='JRA55do licalvf')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad595739-b4bd-46aa-a851-01e7828a5367",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.04/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: divide by zero encountered in log\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x153551731cf0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NB: missing Black Sea!\n",
    "\n",
    "np.log(data['friver']['friver'].isel(time=0)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0fa71a70-43d3-49f4-8ee1-eb61161c80e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.04/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: divide by zero encountered in log\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x153550bb5db0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(jra55do.friver.isel(time=0)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e2981c06-f2db-4ac1-930d-b07089edd9ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.04/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: divide by zero encountered in log\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x153551325540>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(data['ficeberg']['ficeberg'].isel(time=0)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "82960812-0c45-4236-acb6-e2a7ab1665a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.04/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: divide by zero encountered in log\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x153550ba81c0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(jra55do.licalvf.isel(time=0)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bc3e4e98-4b56-4729-9200-4ba06cd30159",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check area-integral of friver equals total_lrunoff\n",
    "plt.figure(figsize=(10,5))\n",
    "for i, (k, d) in enumerate({ k: data[k] for k in ['total_lrunoff', 'friver'] }.items()):\n",
    "    dat = d[k]\n",
    "    if 'depth' in dat.cf.coords: # https://xgcm.readthedocs.io/en/latest/grid_metrics.html?highlight=average#Grid-aware-(weighted)-average\n",
    "        dat = grid.integrate(dat, ['X', 'Y', 'Z'])\n",
    "    elif 'longitude' in dat.cf.coords: # https://xgcm.readthedocs.io/en/latest/grid_metrics.html?highlight=average#Grid-aware-(weighted)-average\n",
    "        dat = grid.integrate(dat, ['X', 'Y'])\n",
    "    dat.load()\n",
    "    dat.plot(linewidth=2*(2-i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2119d34a-5b30-4e09-a965-40f4bce18f62",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check area-integral of ficeberg equals total_frunoff\n",
    "plt.figure(figsize=(10,5))\n",
    "for i, (k, d) in enumerate({ k: data[k] for k in ['total_frunoff', 'ficeberg'] }.items()):\n",
    "    dat = d[k]\n",
    "    if 'depth' in dat.cf.coords: # https://xgcm.readthedocs.io/en/latest/grid_metrics.html?highlight=average#Grid-aware-(weighted)-average\n",
    "        dat = grid.integrate(dat, ['X', 'Y', 'Z'])\n",
    "    elif 'longitude' in dat.cf.coords: # https://xgcm.readthedocs.io/en/latest/grid_metrics.html?highlight=average#Grid-aware-(weighted)-average\n",
    "        dat = grid.integrate(dat, ['X', 'Y'])\n",
    "    dat.load()\n",
    "    dat.plot(linewidth=2*(2-i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eb5becb-1781-4366-b899-142b7a348645",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3-24.04] *",
   "language": "python",
   "name": "conda-env-analysis3-24.04-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}