{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Example on how to configure a case using mom6-tools\n",
    "\n",
    "This is a very simple example showing how to read `diag_config.yaml`, create a case instance, compute a climatology and visualize the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case Information\n",
    "- **Case:**\n",
    "  - **CASEROOT**: Path to the root directory of the case:  \n",
    "    `/glade/work/gmarques/cesm.cases/G/g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079/`\n",
    "  - **OCN_DIAG_ROOT**: Directory for ocean diagnostics files:  \n",
    "    `ncfiles/`\n",
    "  - **SNAME**: Identifier or short name of the case:  \n",
    "    `\"079\"`\n",
    "\n",
    "---\n",
    "\n",
    "### Average Dates\n",
    "- **Avg:**\n",
    "  - **start_date**: Start date for averaging data:  \n",
    "    `'0031-01-01'`\n",
    "  - **end_date**: End date for averaging data:  \n",
    "    `'0062-01-01'`\n",
    "\n",
    "---\n",
    "\n",
    "### CESM History File Naming Conventions\n",
    "- **Fnames:**\n",
    "  - **rho2**: Format for density files:  \n",
    "    `.mom6.h.rho2.????-??.nc`\n",
    "  - **z**: Format for depth files:  \n",
    "    `.mom6.h.z.????-??.nc`\n",
    "  - **native**: Format for native files:  \n",
    "    `.mom6.h.native.????-??.nc`\n",
    "  - **sfc**: Surface files naming convention:  \n",
    "    `.mom6.h.sfc.????-??.nc`\n",
    "  - **static**: Static files naming convention:  \n",
    "    `.mom6.h.static.nc`\n",
    "  - **geom**: Ocean geometry files naming convention:  \n",
    "    `.mom6.h.ocean_geometry.nc`\n",
    "\n",
    "---\n",
    "\n",
    "### Transport Sections\n",
    "- **Transports:**\n",
    "  - **sections**: List of sections where transports are computed, including observational estimates where applicable:\n",
    "    - **`h.Agulhas_Section`**: Uses `umo` component, range `[129.8, 143.6]`\n",
    "    - **`h.Barents_Opening`**: Uses `vmo` component, range `[2.0]`\n",
    "    - **`h.Bering_Strait`**: Uses `vmo` component, range `[0.7, 1.1]`\n",
    "    - *(Additional sections follow similar structure)*\n",
    "\n",
    "---\n",
    "\n",
    "### Ocean Catalog Path\n",
    "- **oce_cat**: Path to the ocean-related datasets catalog:  \n",
    "  `/glade/u/home/gmarques/libs/oce-catalogs/reference-datasets.yml`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mom6_tools.m6toolbox import cime_xmlquery\n",
    "import yaml, os\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib\n",
    "from mom6_tools import m6toolbox\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the yaml file\n",
    "diag_config_yml_path = \"diag_config.yml\"\n",
    "diag_config_yml = yaml.load(open(diag_config_yml_path,'r'), Loader=yaml.Loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "caseroot = diag_config_yml['Case']['CASEROOT']\n",
    "casename = cime_xmlquery(caseroot, 'CASE')\n",
    "DOUT_S = cime_xmlquery(caseroot, 'DOUT_S')\n",
    "if DOUT_S:\n",
    "  OUTDIR = cime_xmlquery(caseroot, 'DOUT_S_ROOT')+'/ocn/hist/'\n",
    "else:\n",
    "  OUTDIR = cime_xmlquery(caseroot, 'RUNDIR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output directory is: /glade/derecho/scratch/gmarques/archive/g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079/ocn/hist/\n"
     ]
    }
   ],
   "source": [
    "print('Output directory is:', OUTDIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casename is: g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079\n"
     ]
    }
   ],
   "source": [
    "print('Casename is:', casename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an empty class object\n",
    "class args:\n",
    "  pass\n",
    "\n",
    "args.static = casename+diag_config_yml['Fnames']['static']\n",
    "args.native = casename+diag_config_yml['Fnames']['native']\n",
    "args.geom =   casename+diag_config_yml['Fnames']['geom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR 1: PROJ: proj_create_from_database: Open of /glade/work/gmarques/conda-envs/mom6-tools/share/proj failed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MOM6 grid successfully loaded... \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load the grid\n",
    "from mom6_tools.MOM6grid import MOM6grid\n",
    "\n",
    "geom_file = OUTDIR+'/'+args.geom\n",
    "if os.path.exists(geom_file):\n",
    "  grd = MOM6grid(OUTDIR+'/'+args.static, geom_file, xrformat=True)\n",
    "else:\n",
    "  grd = MOM6grid(OUTDIR+'/'+args.static, xrformat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_jobqueue.PBSCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
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       "                Launch dashboard in JupyterLab\n",
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       "<Client: 'tcp://128.117.208.64:37721' processes=0 threads=0, memory=0 B>"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# request 6 Dask workers\n",
    "from ncar_jobqueue import NCARCluster\n",
    "from dask.distributed import Client\n",
    "\n",
    "cluster = NCARCluster()\n",
    "cluster.scale(6)\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.44 s, sys: 51.8 ms, total: 1.49 s\n",
      "Wall time: 27 s\n"
     ]
    }
   ],
   "source": [
    "%time ds = xr.open_mfdataset(OUTDIR+'/'+casename+'.mom6.h.sfc.000?-??.nc', \\\n",
    "         parallel=True, data_vars='minimal', chunks={'time': 12},\\\n",
    "         coords='minimal', compat='override')"
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   "execution_count": 10,
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       "}\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: 41GB\n",
       "Dimensions:       (time: 3285, yh: 480, xh: 540, xq: 540, yq: 480, nbnd: 2)\n",
       "Coordinates:\n",
       "  * xh            (xh) float64 4kB -286.7 -286.0 -285.3 ... 71.33 72.0 72.67\n",
       "  * yh            (yh) float64 4kB -81.56 -81.46 -81.36 ... 87.65 87.71 87.74\n",
       "  * time          (time) object 26kB 0001-01-01 12:00:00 ... 0009-12-31 12:00:00\n",
       "  * nbnd          (nbnd) float64 16B 1.0 2.0\n",
       "  * xq            (xq) float64 4kB -286.3 -285.7 -285.0 ... 71.67 72.33 73.0\n",
       "  * yq            (yq) float64 4kB -81.51 -81.41 -81.31 ... 87.68 87.73 87.74\n",
       "Data variables: (12/16)\n",
       "    SSH           (time, yh, xh) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    tos           (time, yh, xh) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    sos           (time, yh, xh) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    SSU           (time, yh, xq) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    SSV           (time, yq, xh) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    mass_wt       (time, yh, xh) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    ...            ...\n",
       "    mlotst        (time, yh, xh) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    oml           (time, yh, xh) float32 3GB dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;\n",
       "    average_T1    (time) object 26kB dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;\n",
       "    average_T2    (time) object 26kB dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;\n",
       "    average_DT    (time) timedelta64[ns] 26kB dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;\n",
       "    time_bounds   (time, nbnd) object 53kB dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    NumFilesInSet:     1\n",
       "    title:             MOM6 diagnostic fields table for CESM case: g.e30_a03c...\n",
       "    associated_files:  areacello: g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N7...\n",
       "    grid_type:         regular\n",
       "    grid_tile:         N/A</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-74deba28-f2b3-4529-a901-fb3b499bac42' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74deba28-f2b3-4529-a901-fb3b499bac42' 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>: 3285</li><li><span class='xr-has-index'>yh</span>: 480</li><li><span class='xr-has-index'>xh</span>: 540</li><li><span class='xr-has-index'>xq</span>: 540</li><li><span class='xr-has-index'>yq</span>: 480</li><li><span class='xr-has-index'>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-887430b1-6254-4f55-b6c0-c25f28f288fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-887430b1-6254-4f55-b6c0-c25f28f288fb' class='xr-section-summary' >Coordinates: <span>(6)</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'>-286.7 -286.0 -285.3 ... 72.0 72.67</div><input id='attrs-33446c67-e387-42ce-9459-c23f71d0f222' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33446c67-e387-42ce-9459-c23f71d0f222' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5baef1d7-597e-43ac-a5c3-dacbff4471bc' class='xr-var-data-in' type='checkbox'><label for='data-5baef1d7-597e-43ac-a5c3-dacbff4471bc' 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([-286.666667, -286.      , -285.333333, ...,   71.333333,   72.      ,\n",
       "         72.666667])</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'>-81.56 -81.46 ... 87.71 87.74</div><input id='attrs-efb120a9-c5a4-4994-8b53-92dd2194518a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efb120a9-c5a4-4994-8b53-92dd2194518a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccef4202-be85-48e3-afd1-0fcc1888faab' class='xr-var-data-in' type='checkbox'><label for='data-ccef4202-be85-48e3-afd1-0fcc1888faab' 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([-81.558125, -81.459689, -81.360114, ...,  87.645456,  87.708781,\n",
       "        87.740555])</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'>0001-01-01 12:00:00 ... 0009-12-...</div><input id='attrs-fb7aadda-0d0a-4b56-9e82-86c7032bb546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb7aadda-0d0a-4b56-9e82-86c7032bb546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-181ae221-d5af-4007-bb41-d0d546e99217' class='xr-var-data-in' type='checkbox'><label for='data-181ae221-d5af-4007-bb41-d0d546e99217' 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>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(9, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(9, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(9, 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'>nbnd</span></div><div class='xr-var-dims'>(nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-f1068dc7-02e9-45ae-8e37-6cf87f26d967' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1068dc7-02e9-45ae-8e37-6cf87f26d967' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd94614d-130c-46bd-a45f-aa2bd744495a' class='xr-var-data-in' type='checkbox'><label for='data-fd94614d-130c-46bd-a45f-aa2bd744495a' 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>bounds</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'>xq</span></div><div class='xr-var-dims'>(xq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-286.3 -285.7 -285.0 ... 72.33 73.0</div><input id='attrs-38962742-c72d-471a-8e96-9562e0816886' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38962742-c72d-471a-8e96-9562e0816886' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8313063-1956-4148-9805-124015bea3d1' class='xr-var-data-in' type='checkbox'><label for='data-c8313063-1956-4148-9805-124015bea3d1' 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>q point nominal longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-286.333333, -285.666667, -285.      , ...,   71.666667,   72.333333,\n",
       "         73.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yq</span></div><div class='xr-var-dims'>(yq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.51 -81.41 ... 87.73 87.74</div><input id='attrs-a95d00c1-5ab8-4ffe-a8af-f209fd4f1046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a95d00c1-5ab8-4ffe-a8af-f209fd4f1046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba69d5e-7702-48a1-bfbb-18382635fb2c' class='xr-var-data-in' type='checkbox'><label for='data-5ba69d5e-7702-48a1-bfbb-18382635fb2c' 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>q point nominal latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.509049, -81.410045, -81.309895, ...,  87.681   ,  87.728654,\n",
       "        87.744508])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bac68987-16c4-4eef-ae42-6f9d48c106b9' class='xr-section-summary-in' type='checkbox'  ><label for='section-bac68987-16c4-4eef-ae42-6f9d48c106b9' class='xr-section-summary' >Data variables: <span>(16)</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>SSH</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=(12, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-f6629f74-da87-4a00-bbbb-0fa515fb31fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6629f74-da87-4a00-bbbb-0fa515fb31fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e148bed3-66fb-44f6-91c8-9d0ef83a6165' class='xr-var-data-in' type='checkbox'><label for='data-e148bed3-66fb-44f6-91c8-9d0ef83a6165' 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>m</dd><dt><span>long_name :</span></dt><dd>Sea Surface Height</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></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> 3.17 GiB </td>\n",
       "                        <td> 11.87 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3285, 480, 540) </td>\n",
       "                        <td> (12, 480, 540) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 324 chunks in 217 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=\"171\" height=\"161\" 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=\"40\" x2=\"80\" y2=\"111\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"44\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"48\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"51\" />\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.17 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSU</span></div><div class='xr-var-dims'>(time, yh, xq)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-7b06cd29-3b66-4cd9-9846-5e6a345fa58b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b06cd29-3b66-4cd9-9846-5e6a345fa58b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dab5568-c43a-4f84-befe-ae8f42e461ab' class='xr-var-data-in' type='checkbox'><label for='data-0dab5568-c43a-4f84-befe-ae8f42e461ab' 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>m s-1</dd><dt><span>long_name :</span></dt><dd>Sea Surface Zonal Velocity</dd><dt><span>cell_methods :</span></dt><dd>yh:mean xq:point time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>interp_method :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.17 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSV</span></div><div class='xr-var-dims'>(time, yq, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-8a3d3c25-ca5d-4240-bf89-b6112b77aa63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a3d3c25-ca5d-4240-bf89-b6112b77aa63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-215134cb-4c63-4f4c-be13-3742be225bb0' class='xr-var-data-in' type='checkbox'><label for='data-215134cb-4c63-4f4c-be13-3742be225bb0' 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>m s-1</dd><dt><span>long_name :</span></dt><dd>Sea Surface Meridional Velocity</dd><dt><span>cell_methods :</span></dt><dd>yq:point xh:mean time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>interp_method :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.17 GiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_wt</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=(12, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-fc59b29a-8c66-4cf3-89e0-ae670e4b1a7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc59b29a-8c66-4cf3-89e0-ae670e4b1a7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e669f9f2-eff8-45f9-a461-757ceca2f4e1' class='xr-var-data-in' type='checkbox'><label for='data-e669f9f2-eff8-45f9-a461-757ceca2f4e1' 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</dd><dt><span>long_name :</span></dt><dd>The column mass for calculating mass-weighted average properties</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></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 3.17 GiB </td>\n",
       "                        <td> 11.87 MiB </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>opottempmint</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=(12, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-ed0fc121-c980-4ab9-9360-22301cb5255b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed0fc121-c980-4ab9-9360-22301cb5255b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ef60898-b9f3-45cb-9195-2f6b4965fb91' class='xr-var-data-in' type='checkbox'><label for='data-2ef60898-b9f3-45cb-9195-2f6b4965fb91' 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>degC kg m-2</dd><dt><span>long_name :</span></dt><dd>integral_wrt_depth_of_product_of_sea_water_density_and_potential_temperature</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>Depth integrated density times potential temperature</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.17 GiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somint</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=(12, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-17f54e77-b269-46f3-8b2f-d265034ae0eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17f54e77-b269-46f3-8b2f-d265034ae0eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f6388a-9d64-4cbd-8dec-3148540db877' class='xr-var-data-in' type='checkbox'><label for='data-30f6388a-9d64-4cbd-8dec-3148540db877' 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>psu kg m-2</dd><dt><span>long_name :</span></dt><dd>integral_wrt_depth_of_product_of_sea_water_density_and_salinity</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>Depth integrated density times salinity</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Bytes </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mlotst</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=(12, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-87ab326d-987f-4685-b916-37f3d8d2a942' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87ab326d-987f-4685-b916-37f3d8d2a942' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56e8dfc4-eadf-4db8-8bea-38bf208f8427' class='xr-var-data-in' type='checkbox'><label for='data-56e8dfc4-eadf-4db8-8bea-38bf208f8427' 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>m</dd><dt><span>long_name :</span></dt><dd>Ocean Mixed Layer Thickness Defined by Sigma T</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>ocean_mixed_layer_thickness_defined_by_sigma_t</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.17 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (12, 480, 540) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</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'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;</div><input id='attrs-f7e75228-dd7f-4328-abaf-31802fc01dac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7e75228-dd7f-4328-abaf-31802fc01dac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa51d37e-8357-4efb-8132-f23455fb9fe7' class='xr-var-data-in' type='checkbox'><label for='data-aa51d37e-8357-4efb-8132-f23455fb9fe7' 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",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 25.66 kiB </td>\n",
       "                        <td> 96 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (12,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 25.66 kiB </td>\n",
       "                        <td> 96 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (12,) </td>\n",
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       "  <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=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
       "  <line x1=\"113\" y1=\"0\" x2=\"113\" 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:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3285</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=(12,), meta=np.ndarray&gt;</div><input id='attrs-8aa60583-fe09-4ddf-8af5-b7634da123ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8aa60583-fe09-4ddf-8af5-b7634da123ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef37aa35-506e-4166-87c4-15f429cfd064' class='xr-var-data-in' type='checkbox'><label for='data-ef37aa35-506e-4166-87c4-15f429cfd064' 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> 25.66 kiB </td>\n",
       "                        <td> 96 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3285,) </td>\n",
       "                        <td> (12,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 324 chunks in 217 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=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
       "  <line x1=\"113\" y1=\"0\" x2=\"113\" 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:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3285</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_bounds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-834c8508-c1ae-49a2-bb77-e75735786bad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-834c8508-c1ae-49a2-bb77-e75735786bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c384a936-e94f-467c-968d-b86ac99e40e6' class='xr-var-data-in' type='checkbox'><label for='data-c384a936-e94f-467c-968d-b86ac99e40e6' 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 interval endpoints</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> 51.33 kiB </td>\n",
       "                        <td> 192 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3285, 2) </td>\n",
       "                        <td> (12, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 324 chunks in 217 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=\"25\" x2=\"25\" y2=\"25\" />\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=\"82\" x2=\"25\" y2=\"82\" />\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)\">3285</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-46e23dbc-3c53-427e-afcc-4644662eb614' class='xr-section-summary-in' type='checkbox'  ><label for='section-46e23dbc-3c53-427e-afcc-4644662eb614' class='xr-section-summary' >Indexes: <span>(6)</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-1579a6bb-08c1-4937-98c8-0a967527c19e' class='xr-index-data-in' type='checkbox'/><label for='index-1579a6bb-08c1-4937-98c8-0a967527c19e' 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([-286.66666666666663,              -286.0, -285.33333333333337,\n",
       "       -284.66666666666663,              -284.0, -283.33333333333326,\n",
       "       -282.66666666666674,              -282.0, -281.33333333333326,\n",
       "       -280.66666666666674,\n",
       "       ...\n",
       "         66.66666666666652,   67.33333333333326,                68.0,\n",
       "         68.66666666666652,   69.33333333333326,                70.0,\n",
       "         70.66666666666652,   71.33333333333326,                72.0,\n",
       "         72.66666666666674],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=540))</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-c00ad71b-0604-40a0-a97a-40e4aa81c776' class='xr-index-data-in' type='checkbox'/><label for='index-c00ad71b-0604-40a0-a97a-40e4aa81c776' 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([-81.55812493176025, -81.45968892243141, -81.36011371758804,\n",
       "       -81.25938643762682, -81.15749406809196, -81.05442345864881,\n",
       "       -80.95016132206449, -80.84469423319594,  -80.7380086279867,\n",
       "       -80.63009080247244,\n",
       "       ...\n",
       "        86.50487294857186,  86.72368442615951,  86.92807916262758,\n",
       "        87.11580151651526,  87.28427233645957,  87.43064950723284,\n",
       "        87.55197961937547,  87.64545609807651,  87.70878090686321,\n",
       "        87.74055512340995],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=480))</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-272ab011-e332-4522-98bd-e03d2abe7e01' class='xr-index-data-in' type='checkbox'/><label for='index-272ab011-e332-4522-98bd-e03d2abe7e01' 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([0001-01-01 12:00:00, 0001-01-02 12:00:00, 0001-01-03 12:00:00,\n",
       "             0001-01-04 12:00:00, 0001-01-05 12:00:00, 0001-01-06 12:00:00,\n",
       "             0001-01-07 12:00:00, 0001-01-08 12:00:00, 0001-01-09 12:00:00,\n",
       "             0001-01-10 12:00:00,\n",
       "             ...\n",
       "             0009-12-22 12:00:00, 0009-12-23 12:00:00, 0009-12-24 12:00:00,\n",
       "             0009-12-25 12:00:00, 0009-12-26 12:00:00, 0009-12-27 12:00:00,\n",
       "             0009-12-28 12:00:00, 0009-12-29 12:00:00, 0009-12-30 12:00:00,\n",
       "             0009-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=3285, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nbnd</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00c3b883-81cd-49b7-b7b3-b01008df5574' class='xr-index-data-in' type='checkbox'/><label for='index-00c3b883-81cd-49b7-b7b3-b01008df5574' 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;nbnd&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9d113d69-782f-4ef0-955a-fe9674aa7064' class='xr-index-data-in' type='checkbox'/><label for='index-9d113d69-782f-4ef0-955a-fe9674aa7064' 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([-286.33333333333337, -285.66666666666663,              -285.0,\n",
       "       -284.33333333333337, -283.66666666666674,              -283.0,\n",
       "       -282.33333333333326, -281.66666666666674,              -281.0,\n",
       "       -280.33333333333326,\n",
       "       ...\n",
       "                      67.0,   67.66666666666652,   68.33333333333326,\n",
       "                      69.0,   69.66666666666652,   70.33333333333326,\n",
       "                      71.0,   71.66666666666652,   72.33333333333326,\n",
       "                      73.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xq&#x27;, length=540))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b76e4b6c-bda4-4eca-abdc-9ba3a3a523da' class='xr-index-data-in' type='checkbox'/><label for='index-b76e4b6c-bda4-4eca-abdc-9ba3a3a523da' 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([-81.50904852679943,  -81.4100445212757, -81.30989489722798,\n",
       "       -81.20858670775536, -81.10610687059054, -81.00244216707667,\n",
       "       -80.89757924115176,   -80.791504598341, -80.68420460475764,\n",
       "        -80.5756654861134,\n",
       "       ...\n",
       "        86.61595285044066,  86.82781694882009,  87.02417832140226,\n",
       "        87.20261611085037,   87.3604070055495,  87.49462829656471,\n",
       "        87.60235982679546,   87.6810004121276,   87.7286543648068,\n",
       "        87.74450779376627],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yq&#x27;, length=480))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-926f0566-cad7-4495-80c2-a1e102cc0dc7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-926f0566-cad7-4495-80c2-a1e102cc0dc7' class='xr-section-summary' >Attributes: <span>(5)</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 for CESM case: g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079</dd><dt><span>associated_files :</span></dt><dd>areacello: g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079.mom6.h.static.nc</dd><dt><span>grid_type :</span></dt><dd>regular</dd><dt><span>grid_tile :</span></dt><dd>N/A</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 41GB\n",
       "Dimensions:       (time: 3285, yh: 480, xh: 540, xq: 540, yq: 480, nbnd: 2)\n",
       "Coordinates:\n",
       "  * xh            (xh) float64 4kB -286.7 -286.0 -285.3 ... 71.33 72.0 72.67\n",
       "  * yh            (yh) float64 4kB -81.56 -81.46 -81.36 ... 87.65 87.71 87.74\n",
       "  * time          (time) object 26kB 0001-01-01 12:00:00 ... 0009-12-31 12:00:00\n",
       "  * nbnd          (nbnd) float64 16B 1.0 2.0\n",
       "  * xq            (xq) float64 4kB -286.3 -285.7 -285.0 ... 71.67 72.33 73.0\n",
       "  * yq            (yq) float64 4kB -81.51 -81.41 -81.31 ... 87.68 87.73 87.74\n",
       "Data variables: (12/16)\n",
       "    SSH           (time, yh, xh) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    tos           (time, yh, xh) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    sos           (time, yh, xh) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    SSU           (time, yh, xq) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    SSV           (time, yq, xh) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    mass_wt       (time, yh, xh) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    ...            ...\n",
       "    mlotst        (time, yh, xh) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    oml           (time, yh, xh) float32 3GB dask.array<chunksize=(12, 480, 540), meta=np.ndarray>\n",
       "    average_T1    (time) object 26kB dask.array<chunksize=(12,), meta=np.ndarray>\n",
       "    average_T2    (time) object 26kB dask.array<chunksize=(12,), meta=np.ndarray>\n",
       "    average_DT    (time) timedelta64[ns] 26kB dask.array<chunksize=(12,), meta=np.ndarray>\n",
       "    time_bounds   (time, nbnd) object 53kB dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    NumFilesInSet:     1\n",
       "    title:             MOM6 diagnostic fields table for CESM case: g.e30_a03c...\n",
       "    associated_files:  areacello: g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N7...\n",
       "    grid_type:         regular\n",
       "    grid_tile:         N/A"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1024x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Plot mean SST\n",
    "from mom6_tools.m6plot import xyplot\n",
    "\n",
    "area = np.ma.masked_where(grd.wet == 0, np.ma.masked_invalid(grd.areacello))\n",
    "\n",
    "dummy1 = np.ma.masked_where(grd.wet == 0, ds.tos.mean('time'))\n",
    "\n",
    "xyplot(dummy1, grd.geolon, grd.geolat, area , suptitle='Mean SST', clim=(-2,32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (mom6-tools)",
   "language": "python",
   "name": "mom6-tools"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}