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    "# Equatorial Upperocean model vs. obs comparison\n",
    "\n",
    "The goal of this notebook is the following:\n",
    "\n",
    "1) serve as an example of how to post-process CESM/MOM6 output;\n",
    "\n",
    "2) create time averages of T, S and VEL fields and compared agains observations (PHC2 and Johnson et al, 2002);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": [
      "Basemap module not found. Some regional plots may not function properly\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from mom6_tools.MOM6grid import MOM6grid\n",
    "from mom6_tools.m6toolbox import shiftgrid\n",
    "from mom6_tools.m6plot import yzcompare, yzplot\n",
    "from mom6_tools.m6toolbox import cime_xmlquery\n",
    "from mom6_tools import m6toolbox\n",
    "from ncar_jobqueue import NCARCluster\n",
    "from dask.distributed import Client\n",
    "import yaml, intake, os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from IPython.display import display, Markdown, Latex"
   ]
  },
  {
   "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": [
    {
     "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",
      "Casename is: g.e30_a03c.GJRAv4.TL319_t232_wgx3_hycom1_N75.2024.079\n"
     ]
    }
   ],
   "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')\n",
    "\n",
    "print('Output directory is:', OUTDIR)\n",
    "print('Casename is:', casename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following parameters must be set accordingly\n",
    "######################################################\n",
    "\n",
    "# create an empty class object\n",
    "class args:\n",
    "  pass\n",
    "\n",
    "# load avg dates\n",
    "avg = diag_config_yml['Avg']\n",
    "\n",
    "args.start_date = avg['start_date']\n",
    "args.end_date = avg['end_date']\n",
    "args.casename = casename\n",
    "args.obs = \"woa-2018-tx2_3v2-annual-all\"\n",
    "args.monthly = casename+diag_config_yml['Fnames']['z']\n",
    "args.static = casename+diag_config_yml['Fnames']['static']\n",
    "args.geom =   casename+diag_config_yml['Fnames']['geom']\n",
    "args.savefigs = False\n",
    "args.nw = 6 # requesting 6 workers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MOM6 grid successfully loaded... \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# read grid info\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": 6,
   "metadata": {},
   "outputs": [],
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    "parallel = False\n",
    "if args.nw  > 1:\n",
    "  parallel = True\n",
    "  cluster = NCARCluster()\n",
    "  cluster.scale(args.nw)\n",
    "  client = Client(cluster)\n",
    "  client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "# Compute the climatology dataset\n",
    "#dset_climo = climo.stage()\n",
    "variables = ['thetao', 'so', 'uo', 'h', 'z_i']\n",
    "def preprocess(ds):\n",
    "    ''' Compute yearly averages and return the dataset with variables'''\n",
    "    return ds[variables]\n",
    "\n",
    "ds = xr.open_mfdataset(OUTDIR+'/'+args.monthly, \\\n",
    "         parallel=True, data_vars='minimal', \\\n",
    "         coords='minimal', compat='override', preprocess=preprocess)"
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       "  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: 103GB\n",
       "Dimensions:  (time: 732, z_l: 34, yh: 480, xh: 540, xq: 540, z_i: 35)\n",
       "Coordinates:\n",
       "  * z_i      (z_i) float64 280B 0.0 5.0 15.0 25.0 ... 5.25e+03 5.75e+03 6.25e+03\n",
       "  * xq       (xq) float64 4kB -286.3 -285.7 -285.0 -284.3 ... 71.67 72.33 73.0\n",
       "  * yh       (yh) float64 4kB -81.56 -81.46 -81.36 -81.26 ... 87.65 87.71 87.74\n",
       "  * z_l      (z_l) float64 272B 2.5 10.0 20.0 32.5 ... 5e+03 5.5e+03 6e+03\n",
       "  * time     (time) object 6kB 0001-01-16 12:00:00 ... 0061-12-16 12:00:00\n",
       "  * xh       (xh) float64 4kB -286.7 -286.0 -285.3 -284.7 ... 71.33 72.0 72.67\n",
       "Data variables:\n",
       "    thetao   (time, z_l, yh, xh) float32 26GB dask.array&lt;chunksize=(1, 34, 480, 540), meta=np.ndarray&gt;\n",
       "    so       (time, z_l, yh, xh) float32 26GB dask.array&lt;chunksize=(1, 34, 480, 540), meta=np.ndarray&gt;\n",
       "    uo       (time, z_l, yh, xq) float32 26GB dask.array&lt;chunksize=(1, 34, 480, 540), meta=np.ndarray&gt;\n",
       "    h        (time, z_l, yh, xh) float32 26GB dask.array&lt;chunksize=(1, 34, 480, 540), 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-aebf4f8e-d06c-4f1e-a5fd-3bd6b3f5b9d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aebf4f8e-d06c-4f1e-a5fd-3bd6b3f5b9d4' 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>: 732</li><li><span class='xr-has-index'>z_l</span>: 34</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'>z_i</span>: 35</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f002e71-b62b-4e18-a503-fd95fdfaa57f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f002e71-b62b-4e18-a503-fd95fdfaa57f' 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'>z_i</span></div><div class='xr-var-dims'>(z_i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.0 15.0 ... 5.75e+03 6.25e+03</div><input id='attrs-a7630cb8-1dc9-40b4-833d-edd1da6e2bf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7630cb8-1dc9-40b4-833d-edd1da6e2bf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b052c6c-1277-461e-9dc4-0f103bd4596a' class='xr-var-data-in' type='checkbox'><label for='data-7b052c6c-1277-461e-9dc4-0f103bd4596a' 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>meters</dd><dt><span>long_name :</span></dt><dd>Depth at interface</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([0.000e+00, 5.000e+00, 1.500e+01, 2.500e+01, 4.000e+01, 6.250e+01,\n",
       "       8.750e+01, 1.125e+02, 1.375e+02, 1.750e+02, 2.250e+02, 2.750e+02,\n",
       "       3.500e+02, 4.500e+02, 5.500e+02, 6.500e+02, 7.500e+02, 8.500e+02,\n",
       "       9.500e+02, 1.050e+03, 1.150e+03, 1.250e+03, 1.350e+03, 1.450e+03,\n",
       "       1.625e+03, 1.875e+03, 2.250e+03, 2.750e+03, 3.250e+03, 3.750e+03,\n",
       "       4.250e+03, 4.750e+03, 5.250e+03, 5.750e+03, 6.250e+03])</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-2aa8872f-02d8-42e0-86b0-812600a69419' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2aa8872f-02d8-42e0-86b0-812600a69419' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99dc9b55-6f7a-4c08-898f-f97eeb16739d' class='xr-var-data-in' type='checkbox'><label for='data-99dc9b55-6f7a-4c08-898f-f97eeb16739d' 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'>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-5bbf5e26-a2fd-4034-ac8d-46e642e19000' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bbf5e26-a2fd-4034-ac8d-46e642e19000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0c2201a-64f4-4d55-acd6-599629a25824' class='xr-var-data-in' type='checkbox'><label for='data-f0c2201a-64f4-4d55-acd6-599629a25824' 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'>z_l</span></div><div class='xr-var-dims'>(z_l)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 5.5e+03 6e+03</div><input id='attrs-3ed75e62-ecbb-4037-933b-cd633c246cc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ed75e62-ecbb-4037-933b-cd633c246cc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d281339-e72e-4f95-bf08-19a2d6e42a59' class='xr-var-data-in' type='checkbox'><label for='data-2d281339-e72e-4f95-bf08-19a2d6e42a59' 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>meters</dd><dt><span>long_name :</span></dt><dd>Depth at cell center</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>z_i</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03])</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'>0001-01-16 12:00:00 ... 0061-12-...</div><input id='attrs-f0497458-5269-411a-b3c3-bcfed190ee70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0497458-5269-411a-b3c3-bcfed190ee70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6798d73c-3e12-4cb9-bcb0-7db08a8b0e9b' class='xr-var-data-in' type='checkbox'><label for='data-6798d73c-3e12-4cb9-bcb0-7db08a8b0e9b' 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, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 3, 16, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(61, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(61, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(61, 12, 16, 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'>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-ba2f3167-0055-474c-a944-5a1e1813e022' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba2f3167-0055-474c-a944-5a1e1813e022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41677554-06c0-47f9-8cbf-34d469b32d7e' class='xr-var-data-in' type='checkbox'><label for='data-41677554-06c0-47f9-8cbf-34d469b32d7e' 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></ul></div></li><li class='xr-section-item'><input id='section-ca10a625-ed70-4cd1-b4fe-973a7c096aad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca10a625-ed70-4cd1-b4fe-973a7c096aad' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(time, z_l, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 34, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-3bda4c57-32a7-42bc-b9ae-ed3cdcbb460d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bda4c57-32a7-42bc-b9ae-ed3cdcbb460d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1624f56e-c24d-40d3-b80c-0d92e1d9b6b5' class='xr-var-data-in' type='checkbox'><label for='data-1624f56e-c24d-40d3-b80c-0d92e1d9b6b5' 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</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>cell_methods :</span></dt><dd>area:mean z_l:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>volume: volcello 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>sea_water_potential_temperature</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> 24.03 GiB </td>\n",
       "                        <td> 33.62 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (732, 34, 480, 540) </td>\n",
       "                        <td> (1, 34, 480, 540) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 732 chunks in 1465 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=\"538\" height=\"148\" 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=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so</span></div><div class='xr-var-dims'>(time, z_l, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 34, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-97be72bf-c2b0-4399-9a38-2eb14949c7ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97be72bf-c2b0-4399-9a38-2eb14949c7ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2afcf456-ff4a-4700-a62c-d335727a969b' class='xr-var-data-in' type='checkbox'><label for='data-2afcf456-ff4a-4700-a62c-d335727a969b' 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</dd><dt><span>long_name :</span></dt><dd>Sea Water Salinity</dd><dt><span>cell_methods :</span></dt><dd>area:mean z_l:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>volume: volcello 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>sea_water_salinity</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.03 GiB </td>\n",
       "                        <td> 33.62 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 34, 480, 540) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <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>uo</span></div><div class='xr-var-dims'>(time, z_l, yh, xq)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 34, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-bb6b346a-9a10-4073-bef7-e81ed3328ee1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb6b346a-9a10-4073-bef7-e81ed3328ee1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-520f87b2-01f5-41e7-b782-cab6fe11455b' class='xr-var-data-in' type='checkbox'><label for='data-520f87b2-01f5-41e7-b782-cab6fe11455b' 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 Water X Velocity</dd><dt><span>cell_methods :</span></dt><dd>z_l:mean yh:mean xq:point time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_water_x_velocity</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> 24.03 GiB </td>\n",
       "                        <td> 33.62 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 34, 480, 540) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 732 chunks in 1465 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>(time, z_l, yh, xh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 34, 480, 540), meta=np.ndarray&gt;</div><input id='attrs-1263c9be-cf6c-4693-a82a-3cfba1deb60b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1263c9be-cf6c-4693-a82a-3cfba1deb60b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2f0e131-bb44-4e8e-8208-d1541af049f3' class='xr-var-data-in' type='checkbox'><label for='data-b2f0e131-bb44-4e8e-8208-d1541af049f3' 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>Layer Thickness</dd><dt><span>cell_methods :</span></dt><dd>area:mean z_l:sum yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>volume: volcello 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> 24.03 GiB </td>\n",
       "                        <td> 33.62 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (732, 34, 480, 540) </td>\n",
       "                        <td> (1, 34, 480, 540) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 732 chunks in 1465 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",
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       "  <text x=\"190.014555\" y=\"108.703079\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,190.014555,108.703079)\">34</text>\n",
       "</svg>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-95ee9056-56dc-45ad-b1c3-d9fcebd1cfa0' class='xr-section-summary-in' type='checkbox'  ><label for='section-95ee9056-56dc-45ad-b1c3-d9fcebd1cfa0' 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>z_i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f647e9ca-6225-4b00-8213-b33a9bb78627' class='xr-index-data-in' type='checkbox'/><label for='index-f647e9ca-6225-4b00-8213-b33a9bb78627' 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,    5.0,   15.0,   25.0,   40.0,   62.5,   87.5,  112.5,  137.5,\n",
       "        175.0,  225.0,  275.0,  350.0,  450.0,  550.0,  650.0,  750.0,  850.0,\n",
       "        950.0, 1050.0, 1150.0, 1250.0, 1350.0, 1450.0, 1625.0, 1875.0, 2250.0,\n",
       "       2750.0, 3250.0, 3750.0, 4250.0, 4750.0, 5250.0, 5750.0, 6250.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;z_i&#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-9551729d-6248-42cf-a6c8-bbb7760881d9' class='xr-index-data-in' type='checkbox'/><label for='index-9551729d-6248-42cf-a6c8-bbb7760881d9' 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>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5352cef5-b3fd-4760-8847-f674b926ad95' class='xr-index-data-in' type='checkbox'/><label for='index-5352cef5-b3fd-4760-8847-f674b926ad95' 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>z_l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-87c3ea1f-938b-41a9-b9e9-01730085c781' class='xr-index-data-in' type='checkbox'/><label for='index-87c3ea1f-938b-41a9-b9e9-01730085c781' 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([   2.5,   10.0,   20.0,   32.5,  51.25,   75.0,  100.0,  125.0, 156.25,\n",
       "        200.0,  250.0,  312.5,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1537.5, 1750.0, 2062.5, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;z_l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a67b359-b47b-4eef-91dc-8df957dbd848' class='xr-index-data-in' type='checkbox'/><label for='index-7a67b359-b47b-4eef-91dc-8df957dbd848' 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([0001-01-16 12:00:00, 0001-02-15 00:00:00, 0001-03-16 12:00:00,\n",
       "             0001-04-16 00:00:00, 0001-05-16 12:00:00, 0001-06-16 00:00:00,\n",
       "             0001-07-16 12:00:00, 0001-08-16 12:00:00, 0001-09-16 00:00:00,\n",
       "             0001-10-16 12:00:00,\n",
       "             ...\n",
       "             0061-03-16 12:00:00, 0061-04-16 00:00:00, 0061-05-16 12:00:00,\n",
       "             0061-06-16 00:00:00, 0061-07-16 12:00:00, 0061-08-16 12:00:00,\n",
       "             0061-09-16 00:00:00, 0061-10-16 12:00:00, 0061-11-16 00:00:00,\n",
       "             0061-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=732, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><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-735aeb5f-c7f4-4982-9fa1-ba268b80b782' class='xr-index-data-in' type='checkbox'/><label for='index-735aeb5f-c7f4-4982-9fa1-ba268b80b782' 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></ul></div></li><li class='xr-section-item'><input id='section-0eaecc53-2076-4526-9fc5-76c9da74d198' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0eaecc53-2076-4526-9fc5-76c9da74d198' 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: 103GB\n",
       "Dimensions:  (time: 732, z_l: 34, yh: 480, xh: 540, xq: 540, z_i: 35)\n",
       "Coordinates:\n",
       "  * z_i      (z_i) float64 280B 0.0 5.0 15.0 25.0 ... 5.25e+03 5.75e+03 6.25e+03\n",
       "  * xq       (xq) float64 4kB -286.3 -285.7 -285.0 -284.3 ... 71.67 72.33 73.0\n",
       "  * yh       (yh) float64 4kB -81.56 -81.46 -81.36 -81.26 ... 87.65 87.71 87.74\n",
       "  * z_l      (z_l) float64 272B 2.5 10.0 20.0 32.5 ... 5e+03 5.5e+03 6e+03\n",
       "  * time     (time) object 6kB 0001-01-16 12:00:00 ... 0061-12-16 12:00:00\n",
       "  * xh       (xh) float64 4kB -286.7 -286.0 -285.3 -284.7 ... 71.33 72.0 72.67\n",
       "Data variables:\n",
       "    thetao   (time, z_l, yh, xh) float32 26GB dask.array<chunksize=(1, 34, 480, 540), meta=np.ndarray>\n",
       "    so       (time, z_l, yh, xh) float32 26GB dask.array<chunksize=(1, 34, 480, 540), meta=np.ndarray>\n",
       "    uo       (time, z_l, yh, xq) float32 26GB dask.array<chunksize=(1, 34, 480, 540), meta=np.ndarray>\n",
       "    h        (time, z_l, yh, xh) float32 26GB dask.array<chunksize=(1, 34, 480, 540), 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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34.8 ms, sys: 772 μs, total: 35.5 ms\n",
      "Wall time: 75.2 ms\n"
     ]
    }
   ],
   "source": [
    "%time ds_sel = ds.sel(time=slice(args.start_date, args.end_date)).sel(yh=slice(-10,10)).isel(z_l=slice(0,14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load WOA18 data\n",
    "catalog = intake.open_catalog(diag_config_yml['oce_cat'])\n",
    "woa18 = catalog[args.obs].to_dask()\n",
    "woa18['xh'] = grd['xh']\n",
    "woa18['yh'] = grd['yh']\n",
    "obs_label = catalog[args.obs].metadata['prefix']+' '+str(catalog[args.obs].metadata['version'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    UPTS        (ZDEP1_50, YLAT11_101, XLON) float32 182kB dask.array&lt;chunksize=(50, 91, 10), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    history:  FERRET V5.41    1-Oct-02</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-31b219ef-52b7-4830-bca2-2a59175c1b36' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31b219ef-52b7-4830-bca2-2a59175c1b36' 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'>XLON</span>: 10</li><li><span class='xr-has-index'>XLONedges</span>: 11</li><li><span class='xr-has-index'>YLAT11_101</span>: 91</li><li><span class='xr-has-index'>ZDEP1_50</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c46d259a-ee09-4bb2-837c-9a9e830fe655' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c46d259a-ee09-4bb2-837c-9a9e830fe655' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>XLON</span></div><div class='xr-var-dims'>(XLON)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>143.0 156.0 165.0 ... 250.0 265.0</div><input id='attrs-76b7f4e0-00eb-48c0-9bd4-17eba08cb09a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76b7f4e0-00eb-48c0-9bd4-17eba08cb09a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82f56bc8-c108-4348-8eb5-a08d37db32cf' class='xr-var-data-in' type='checkbox'><label for='data-82f56bc8-c108-4348-8eb5-a08d37db32cf' 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>point_spacing :</span></dt><dd>uneven</dd><dt><span>edges :</span></dt><dd>XLONedges</dd></dl></div><div class='xr-var-data'><pre>array([143., 156., 165., 180., 190., 205., 220., 235., 250., 265.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>XLONedges</span></div><div class='xr-var-dims'>(XLONedges)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>136.5 149.5 160.5 ... 257.5 272.5</div><input id='attrs-5c25d068-e533-408e-8969-3eaab3f99630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c25d068-e533-408e-8969-3eaab3f99630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-101369cb-2e32-4bf6-af96-151293a579fb' class='xr-var-data-in' type='checkbox'><label for='data-101369cb-2e32-4bf6-af96-151293a579fb' 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>edges :</span></dt><dd> </dd></dl></div><div class='xr-var-data'><pre>array([136.5, 149.5, 160.5, 172.5, 185. , 197.5, 212.5, 227.5, 242.5, 257.5,\n",
       "       272.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>YLAT11_101</span></div><div class='xr-var-dims'>(YLAT11_101)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-8.0 -7.8 -7.6 ... 9.6 9.8 10.0</div><input id='attrs-8fe447a2-6af5-4d07-88d8-814b15136075' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fe447a2-6af5-4d07-88d8-814b15136075' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-383942bc-2edd-48b1-945e-ced1b67878e2' class='xr-var-data-in' type='checkbox'><label for='data-383942bc-2edd-48b1-945e-ced1b67878e2' 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>point_spacing :</span></dt><dd>even</dd></dl></div><div class='xr-var-data'><pre>array([-8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6, -6.4, -6.2, -6. , -5.8,\n",
       "       -5.6, -5.4, -5.2, -5. , -4.8, -4.6, -4.4, -4.2, -4. , -3.8, -3.6, -3.4,\n",
       "       -3.2, -3. , -2.8, -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2, -1. ,\n",
       "       -0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,\n",
       "        1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ,  3.2,  3.4,  3.6,  3.8,\n",
       "        4. ,  4.2,  4.4,  4.6,  4.8,  5. ,  5.2,  5.4,  5.6,  5.8,  6. ,  6.2,\n",
       "        6.4,  6.6,  6.8,  7. ,  7.2,  7.4,  7.6,  7.8,  8. ,  8.2,  8.4,  8.6,\n",
       "        8.8,  9. ,  9.2,  9.4,  9.6,  9.8, 10. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ZDEP1_50</span></div><div class='xr-var-dims'>(ZDEP1_50)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 25.0 ... 475.0 485.0 495.0</div><input id='attrs-118553a6-0dac-4d70-a702-5ac38627c978' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-118553a6-0dac-4d70-a702-5ac38627c978' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c86c541f-a6bc-48ac-b51a-73c35dc5053f' class='xr-var-data-in' type='checkbox'><label for='data-c86c541f-a6bc-48ac-b51a-73c35dc5053f' 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>METER</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>point_spacing :</span></dt><dd>even</dd></dl></div><div class='xr-var-data'><pre>array([  5.,  15.,  25.,  35.,  45.,  55.,  65.,  75.,  85.,  95., 105., 115.,\n",
       "       125., 135., 145., 155., 165., 175., 185., 195., 205., 215., 225., 235.,\n",
       "       245., 255., 265., 275., 285., 295., 305., 315., 325., 335., 345., 355.,\n",
       "       365., 375., 385., 395., 405., 415., 425., 435., 445., 455., 465., 475.,\n",
       "       485., 495.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17c00efe-4ef6-4a2e-ab33-d9f4d8d87f69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17c00efe-4ef6-4a2e-ab33-d9f4d8d87f69' class='xr-section-summary' >Data variables: <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>POTEMPM</span></div><div class='xr-var-dims'>(ZDEP1_50, YLAT11_101, XLON)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 91, 10), meta=np.ndarray&gt;</div><input id='attrs-bae232a8-603c-4c02-9211-ff2f46bd19e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bae232a8-603c-4c02-9211-ff2f46bd19e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3837eff8-923c-4b92-9df1-dbbeb58e9c42' class='xr-var-data-in' type='checkbox'><label for='data-3837eff8-923c-4b92-9df1-dbbeb58e9c42' 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>Potential Temperature (If &gt; 16 obs)</dd><dt><span>history :</span></dt><dd>From meanfit2</dd><dt><span>units :</span></dt><dd>Degrees C</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> 177.73 kiB </td>\n",
       "                        <td> 177.73 kiB </td>\n",
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       "                        <td> (50, 91, 10) </td>\n",
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       "                    <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",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SALINITYM</span></div><div class='xr-var-dims'>(ZDEP1_50, YLAT11_101, XLON)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 91, 10), meta=np.ndarray&gt;</div><input id='attrs-9e5aafa1-ac51-45ee-8fd8-0716fe06e2b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e5aafa1-ac51-45ee-8fd8-0716fe06e2b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d87e5e2-62fa-4948-99ca-fd29a08a0697' class='xr-var-data-in' type='checkbox'><label for='data-9d87e5e2-62fa-4948-99ca-fd29a08a0697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Salinity (If &gt; 16 obs)</dd><dt><span>history :</span></dt><dd>From meanfit2</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 177.73 kiB </td>\n",
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       "                        <td> (50, 91, 10) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\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>SIGMAM</span></div><div class='xr-var-dims'>(ZDEP1_50, YLAT11_101, XLON)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 91, 10), meta=np.ndarray&gt;</div><input id='attrs-f3456c82-b11d-4190-b8db-b59a4acd205d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3456c82-b11d-4190-b8db-b59a4acd205d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be1633e0-4215-485f-85f7-4a1ac40559f0' class='xr-var-data-in' type='checkbox'><label for='data-be1633e0-4215-485f-85f7-4a1ac40559f0' 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>Potential Density (If &gt; 16 obs)</dd><dt><span>history :</span></dt><dd>From meanfit2</dd><dt><span>units :</span></dt><dd>kg/m3</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 177.73 kiB </td>\n",
       "                        <td> 177.73 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 91, 10) </td>\n",
       "                        <td> (50, 91, 10) </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",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UM</span></div><div class='xr-var-dims'>(ZDEP1_50, YLAT11_101, XLON)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 91, 10), meta=np.ndarray&gt;</div><input id='attrs-4c7a7bc3-6ee9-43d4-8186-84bc706f045b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c7a7bc3-6ee9-43d4-8186-84bc706f045b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4384f029-d061-4cb4-8eae-53df470c1de2' class='xr-var-data-in' type='checkbox'><label for='data-4384f029-d061-4cb4-8eae-53df470c1de2' 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>U component of velocity</dd><dt><span>history :</span></dt><dd>From meanfit2</dd><dt><span>units :</span></dt><dd>cm/s</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",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 177.73 kiB </td>\n",
       "                        <td> 177.73 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (50, 91, 10) </td>\n",
       "                        <td> (50, 91, 10) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSPTS</span></div><div class='xr-var-dims'>(ZDEP1_50, YLAT11_101, XLON)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 91, 10), meta=np.ndarray&gt;</div><input id='attrs-9ea2d904-7a4e-4f8e-b1f4-04087e4941af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ea2d904-7a4e-4f8e-b1f4-04087e4941af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e8f0549-d58b-4f88-bd7c-14d328291595' class='xr-var-data-in' type='checkbox'><label for='data-7e8f0549-d58b-4f88-bd7c-14d328291595' 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>Number of CTD data pts used for T and S</dd><dt><span>history :</span></dt><dd>From meanfit2</dd><dt><span>units :</span></dt><dd>no unit</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 177.73 kiB </td>\n",
       "                        <td> 177.73 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (50, 91, 10) </td>\n",
       "                        <td> (50, 91, 10) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\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>UPTS</span></div><div class='xr-var-dims'>(ZDEP1_50, YLAT11_101, XLON)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 91, 10), meta=np.ndarray&gt;</div><input id='attrs-054f83ef-cc57-46e6-bcfe-890f5470bc04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-054f83ef-cc57-46e6-bcfe-890f5470bc04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c22ea6d0-51b0-45c9-9a9d-400799225294' class='xr-var-data-in' type='checkbox'><label for='data-c22ea6d0-51b0-45c9-9a9d-400799225294' 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>Number of ADCP data pts used for U</dd><dt><span>history :</span></dt><dd>From meanfit2</dd><dt><span>units :</span></dt><dd>no unit</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 177.73 kiB </td>\n",
       "                        <td> 177.73 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 91, 10) </td>\n",
       "                        <td> (50, 91, 10) </td>\n",
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       "                    <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",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc86657d-88bf-4757-aa1c-b595f060eb05' class='xr-section-summary-in' type='checkbox'  ><label for='section-dc86657d-88bf-4757-aa1c-b595f060eb05' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>XLON</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1da5fa2-ec83-42e8-be41-deed719aaef0' class='xr-index-data-in' type='checkbox'/><label for='index-c1da5fa2-ec83-42e8-be41-deed719aaef0' 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([143.0, 156.0, 165.0, 180.0, 190.0, 205.0, 220.0, 235.0, 250.0, 265.0], dtype=&#x27;float64&#x27;, name=&#x27;XLON&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>XLONedges</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-29f16f16-436a-4bc0-b95f-f590d8543ed4' class='xr-index-data-in' type='checkbox'/><label for='index-29f16f16-436a-4bc0-b95f-f590d8543ed4' 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([136.5, 149.5, 160.5, 172.5, 185.0, 197.5, 212.5, 227.5, 242.5, 257.5,\n",
       "       272.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;XLONedges&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>YLAT11_101</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64ff2f0c-b0ee-47c8-9d78-8175594e86fb' class='xr-index-data-in' type='checkbox'/><label for='index-64ff2f0c-b0ee-47c8-9d78-8175594e86fb' 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([               -8.0,                -7.8,                -7.6,\n",
       "                      -7.4,  -7.199999999999999,                -7.0,\n",
       "                      -6.8,                -6.6,                -6.4,\n",
       "        -6.199999999999999,                -6.0,                -5.8,\n",
       "                      -5.6, -5.3999999999999995,  -5.199999999999999,\n",
       "                      -5.0,                -4.8,                -4.6,\n",
       "       -4.3999999999999995,  -4.199999999999999,                -4.0,\n",
       "                      -3.8, -3.5999999999999996, -3.3999999999999995,\n",
       "       -3.1999999999999993,                -3.0,                -2.8,\n",
       "       -2.5999999999999996, -2.3999999999999995, -2.1999999999999993,\n",
       "                      -2.0,  -1.799999999999999, -1.5999999999999996,\n",
       "       -1.4000000000000004, -1.1999999999999993,                -1.0,\n",
       "       -0.7999999999999989, -0.5999999999999996, -0.3999999999999986,\n",
       "       -0.1999999999999993,                 0.0, 0.20000000000000107,\n",
       "       0.40000000000000036,  0.6000000000000014,  0.8000000000000007,\n",
       "                       1.0,   1.200000000000001,  1.4000000000000004,\n",
       "        1.6000000000000014,  1.8000000000000007,                 2.0,\n",
       "         2.200000000000001,  2.4000000000000004,  2.6000000000000014,\n",
       "        2.8000000000000007,                 3.0,   3.200000000000001,\n",
       "        3.4000000000000004,  3.6000000000000014,  3.8000000000000007,\n",
       "                       4.0,   4.200000000000001,                 4.4,\n",
       "         4.600000000000001,   4.800000000000001,                 5.0,\n",
       "         5.200000000000001,                 5.4,   5.600000000000001,\n",
       "         5.800000000000001,                 6.0,   6.199999999999999,\n",
       "         6.400000000000002,   6.600000000000001,   6.800000000000001,\n",
       "                       7.0,   7.199999999999999,   7.400000000000002,\n",
       "         7.600000000000001,   7.800000000000001,                 8.0,\n",
       "                       8.2,   8.400000000000002,   8.600000000000001,\n",
       "                       8.8,                 9.0,   9.200000000000003,\n",
       "         9.400000000000002,   9.600000000000001,                 9.8,\n",
       "                      10.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;YLAT11_101&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ZDEP1_50</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac51872d-947d-44ba-bc1d-0b9cdfe22ae3' class='xr-index-data-in' type='checkbox'/><label for='index-ac51872d-947d-44ba-bc1d-0b9cdfe22ae3' 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([  5.0,  15.0,  25.0,  35.0,  45.0,  55.0,  65.0,  75.0,  85.0,  95.0,\n",
       "       105.0, 115.0, 125.0, 135.0, 145.0, 155.0, 165.0, 175.0, 185.0, 195.0,\n",
       "       205.0, 215.0, 225.0, 235.0, 245.0, 255.0, 265.0, 275.0, 285.0, 295.0,\n",
       "       305.0, 315.0, 325.0, 335.0, 345.0, 355.0, 365.0, 375.0, 385.0, 395.0,\n",
       "       405.0, 415.0, 425.0, 435.0, 445.0, 455.0, 465.0, 475.0, 485.0, 495.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;ZDEP1_50&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9d21691-bfb2-4c31-be7a-c38bfda7ed18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e9d21691-bfb2-4c31-be7a-c38bfda7ed18' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>FERRET V5.41    1-Oct-02</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 1MB\n",
       "Dimensions:     (XLON: 10, XLONedges: 11, YLAT11_101: 91, ZDEP1_50: 50)\n",
       "Coordinates:\n",
       "  * XLON        (XLON) float64 80B 143.0 156.0 165.0 180.0 ... 235.0 250.0 265.0\n",
       "  * XLONedges   (XLONedges) float64 88B 136.5 149.5 160.5 ... 242.5 257.5 272.5\n",
       "  * YLAT11_101  (YLAT11_101) float64 728B -8.0 -7.8 -7.6 -7.4 ... 9.6 9.8 10.0\n",
       "  * ZDEP1_50    (ZDEP1_50) float64 400B 5.0 15.0 25.0 35.0 ... 475.0 485.0 495.0\n",
       "Data variables:\n",
       "    POTEMPM     (ZDEP1_50, YLAT11_101, XLON) float32 182kB dask.array<chunksize=(50, 91, 10), meta=np.ndarray>\n",
       "    SALINITYM   (ZDEP1_50, YLAT11_101, XLON) float32 182kB dask.array<chunksize=(50, 91, 10), meta=np.ndarray>\n",
       "    SIGMAM      (ZDEP1_50, YLAT11_101, XLON) float32 182kB dask.array<chunksize=(50, 91, 10), meta=np.ndarray>\n",
       "    UM          (ZDEP1_50, YLAT11_101, XLON) float32 182kB dask.array<chunksize=(50, 91, 10), meta=np.ndarray>\n",
       "    TSPTS       (ZDEP1_50, YLAT11_101, XLON) float32 182kB dask.array<chunksize=(50, 91, 10), meta=np.ndarray>\n",
       "    UPTS        (ZDEP1_50, YLAT11_101, XLON) float32 182kB dask.array<chunksize=(50, 91, 10), meta=np.ndarray>\n",
       "Attributes:\n",
       "    history:  FERRET V5.41    1-Oct-02"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load johnson_pmel\n",
    "johnson =catalog['eq-uvts-johnson'].to_dask()\n",
    "johnson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time averaging...\n"
     ]
    }
   ],
   "source": [
    "print('Time averaging...')\n",
    "# compute annual mean and then average in time\n",
    "ds_ann = m6toolbox.weighted_temporal_mean_vars(ds_sel)\n",
    "thetao = ds_ann.thetao.mean('time')\n",
    "so = ds_ann.so.mean('time')\n",
    "uo = ds_ann.uo.mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting equatorial data...\n"
     ]
    }
   ],
   "source": [
    "print('Selecting equatorial data...')\n",
    "# select Equatorial region\n",
    "grd_eq = grd.sel(yh=slice(-10,10))\n",
    "# find point closest to eq. and select data\n",
    "j = np.abs( grd_eq.geolat[:,0].values - 0. ).argmin()\n",
    "temp_eq = np.ma.masked_invalid(thetao.isel(yh=j).values)\n",
    "salt_eq = np.ma.masked_invalid(so.isel(yh=j).values)\n",
    "u_eq    = np.ma.masked_invalid(uo.isel(yh=j).values)\n",
    "#e_eq    = np.ma.masked_invalid(eta.isel(yh=j).values)\n",
    "thetao_obs_eq = np.ma.masked_invalid(woa18.thetao.sel(yh=slice(-10,10)).isel(yh=j).isel(z_l=slice(0,14)).values)\n",
    "salt_obs_eq = np.ma.masked_invalid(woa18.so.sel(yh=slice(-10,10)).isel(yh=j).isel(z_l=slice(0,14)).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ds.yh.values\n",
    "zz = ds.z_i[0:15].values\n",
    "x = ds.xh.values\n",
    "[X, Z] = np.meshgrid(x, zz)\n",
    "z = 0.5 * ( Z[:-1] + Z[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving netCDF files...\n"
     ]
    }
   ],
   "source": [
    "print('Saving netCDF files...')\n",
    "if not os.path.isdir('ncfiles'):\n",
    "      os.system('mkdir -p ncfiles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataarays and saving data\n",
    "temp_eq_da = xr.DataArray(temp_eq, dims=['zl','xh'],\n",
    "                          coords={'zl' : z[:,0], 'xh' : x[:]}).rename('temp_eq')\n",
    "\n",
    "attrs = {'casename': args.casename}\n",
    "m6toolbox.add_global_attrs(temp_eq_da,attrs)\n",
    "temp_eq_da.to_netcdf('ncfiles/'+str(args.casename)+'_temp_eq.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "salt_eq_da = xr.DataArray(salt_eq, dims=['zl','xh'],\n",
    "                       coords={'zl' : z[:,0], 'xh' : x[:]}).rename('salt_eq')\n",
    "m6toolbox.add_global_attrs(salt_eq_da,attrs)\n",
    "salt_eq_da.to_netcdf('ncfiles/'+str(args.casename)+'_salt_eq.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close(); cluster.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(14,16))\n",
    "yzcompare(temp_eq , thetao_obs_eq, x, -Z,\n",
    "        title1 = 'model temperature',\n",
    "        title2 = 'observed temperature ({})'.format(obs_label), axis=ax,\n",
    "        suptitle=casename + ', averaged '+str(args.start_date)+ ' to ' +str(args.end_date),\n",
    "        extend='neither', dextend='neither', clim=(6,31.), dlim=(-5,5), dcolormap=plt.cm.bwr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,16))\n",
    "yzcompare(salt_eq , salt_obs_eq, x, -Z,\n",
    "        title1 = 'model salinity',\n",
    "        title2 = 'observed salinity ({})'.format(obs_label), axis=ax,\n",
    "        suptitle=casename + ', averaged '+str(args.start_date)+ ' to ' +str(args.end_date),\n",
    "        extend='neither', dextend='neither', clim=(33.5,37.), dlim=(-1,1), dcolormap=plt.cm.bwr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,8))\n",
    "yzplot(temp_eq, x, -Z, axis=ax, clim=(5,31), landcolor=[0., 0., 0.], ignore=np.nan)\n",
    "cs1 = ax.contour( x + 0*z, -z, temp_eq,  colors='k',); plt.clabel(cs1,fmt='%2.1f', fontsize=14)\n",
    "plt.ylim(-400,0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,8))\n",
    "yzplot(thetao_obs_eq, x, -Z, axis=ax, clim=(5,31))\n",
    "cs1 = ax.contour( x + 0*z, -z, thetao_obs_eq,  colors='k',); plt.clabel(cs1,fmt='%2.1f', fontsize=14)\n",
    "ax.set_ylim(-400,0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift model data to compare against obs\n",
    "tmp, lonh = shiftgrid(thetao.xh[-1].values, thetao[0,0,:].values, ds.thetao.xh.values)\n",
    "tmp, lonq = shiftgrid(uo.xq[-1].values, uo[0,0,:].values, uo.xq.values)\n",
    "\n",
    "thetao['xh'].values[:] = lonh\n",
    "so['xh'].values[:] = lonh\n",
    "uo['xq'].values[:] = lonq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y and z from obs \n",
    "y_obs = johnson.YLAT11_101.values\n",
    "zz = np.arange(0,510,10)\n",
    "[Y, Z_obs] = np.meshgrid(y_obs, zz)\n",
    "z_obs = 0.5 * ( Z_obs[0:-1,:] + Z_obs[1:,] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y and z from model\n",
    "y_model = thetao.yh.values\n",
    "z = ds.z_i[0:15].values\n",
    "[Y, Z_model] = np.meshgrid(y_model, z)\n",
    "z_model = 0.5 * ( Z_model[0:-1,:] + Z_model[1:,:] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "longitudes = [143., 156., 165., 180., 190., 205., 220., 235., 250., 265.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temperature\n",
    "for l in longitudes:\n",
    "    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16,8))\n",
    "    dummy_model = np.ma.masked_invalid(thetao.sel(xh=l, method='nearest').values)\n",
    "    dummy_obs = np.ma.masked_invalid(johnson.POTEMPM.sel(XLON=l, method='nearest').values)\n",
    "    yzplot(dummy_model, y_model, -Z_model, clim=(9,29), axis=ax1, zlabel='Depth', ylabel='Latitude', title=str(dcase.casename))\n",
    "    cs1 = ax1.contour( y_model + 0*z_model, -z_model, dummy_model, levels=np.arange(0,30,2), colors='k',); plt.clabel(cs1,fmt='%3.1f', fontsize=14)\n",
    "    ax1.set_ylim(-400,0)\n",
    "    yzplot(dummy_obs, y_obs, -Z_obs, clim=(9,29), axis=ax2, zlabel='Depth', ylabel='Latitude', title='Johnson et al (2002)')\n",
    "    cs2 = ax2.contour( y_obs + 0*z_obs, -z_obs, dummy_obs, levels=np.arange(0,30,2), colors='k',); plt.clabel(cs2,fmt='%3.1f', fontsize=14)\n",
    "    ax2.set_ylim(-400,0)\n",
    "    plt.suptitle('Temperature [C] @ '+str(l)+ ', averaged between '+str(args.start_date)+' and '+str(args.end_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in longitudes:\n",
    "    # Salt\n",
    "    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16,8))\n",
    "    dummy_model = np.ma.masked_invalid(so.sel(xh=l, method='nearest').values)\n",
    "    dummy_obs = np.ma.masked_invalid(johnson.SALINITYM.sel(XLON=l, method='nearest').values)\n",
    "    yzplot(dummy_model, y_model, -Z_model, clim=(32,36), axis=ax1, zlabel='Depth', ylabel='Latitude', title=str(dcase.casename))\n",
    "    cs1 = ax1.contour( y_model + 0*z_model, -z_model, dummy_model, levels=np.arange(32,36,0.5), colors='k',); plt.clabel(cs1,fmt='%3.1f', fontsize=14)\n",
    "    ax1.set_ylim(-400,0)\n",
    "    yzplot(dummy_obs, y_obs, -Z_obs, clim=(32,36), axis=ax2, zlabel='Depth', ylabel='Latitude', title='Johnson et al (2002)')\n",
    "    cs2 = ax2.contour( y_obs + 0*z_obs, -z_obs, dummy_obs, levels=np.arange(32,36,0.5), colors='k',); plt.clabel(cs2,fmt='%3.1f', fontsize=14)\n",
    "    ax2.set_ylim(-400,0)\n",
    "    plt.suptitle('Salinity [psu] @ '+str(l)+ ', averaged between '+str(args.start_date)+' and '+str(args.end_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in longitudes:\n",
    "    # uo\n",
    "    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16,8))\n",
    "    dummy_model = np.ma.masked_invalid(uo.sel(xq=l, method='nearest').values)\n",
    "    dummy_obs = np.ma.masked_invalid(johnson.UM.sel(XLON=l, method='nearest').values)\n",
    "    yzplot(dummy_model, y_model, -Z_model, clim=(-1,1), axis=ax1, zlabel='Depth', ylabel='Latitude', title=str(dcase.casename))\n",
    "    cs1 = ax1.contour( y_model + 0*z_model, -z_model, dummy_model, levels=np.arange(-1,1,0.1), colors='k',); plt.clabel(cs1,fmt='%3.1f', fontsize=14)\n",
    "    ax1.set_ylim(-400,0)\n",
    "    yzplot(dummy_obs, y_obs, -Z_obs, clim=(-1,1), axis=ax2, zlabel='Depth', ylabel='Latitude', title='Johnson et al (2002)')\n",
    "    cs2 = ax2.contour( y_obs + 0*z_obs, -z_obs, dummy_obs, levels=np.arange(-1,1,0.1), colors='k',); plt.clabel(cs2,fmt='%3.1f', fontsize=14)\n",
    "    ax2.set_ylim(-400,0)\n",
    "    plt.suptitle('Eastward velocity [m/s] @ '+str(l)+ ', averaged between '+str(args.start_date)+' and '+str(args.end_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_obs = johnson.XLON.values\n",
    "[X_obs, Z_obs] = np.meshgrid(x_obs, zz)\n",
    "z_obs = 0.5 * ( Z_obs[:-1,:] + Z_obs[1:,:] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_model = so.xh.values\n",
    "z = ds.z_i[0:15].values\n",
    "[X, Z_model] = np.meshgrid(x_model, z)\n",
    "z_model = 0.5 * ( Z_model[:-1,:] + Z_model[1:,:] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16,8))\n",
    "dummy_obs = np.ma.masked_invalid(johnson.UM.sel(YLAT11_101=0).values)\n",
    "dummy_model = np.ma.masked_invalid(uo.sel(yh=0, method='nearest').values)\n",
    "\n",
    "yzplot(dummy_model, x_model, -Z_model, clim=(-0.4,1.2), axis=ax1, landcolor=[0., 0., 0.], title=str(dcase.casename), ylabel='Longitude', yunits=r'$^o$E' )\n",
    "cs1 = ax1.contour( x_model + 0*z_model, -z_model, dummy_model,  levels=np.arange(-1.2,1.2,0.1), colors='k',); plt.clabel(cs1,fmt='%2.1f', fontsize=14)\n",
    "ax1.set_xlim(143,265); ax1.set_ylim(-400,0)\n",
    "yzplot(dummy_obs, x_obs, -Z_obs, clim=(-0.4,1.2), axis=ax2, title='Johnson et al (2002)', ylabel='Longitude', yunits=r'$^o$E' )\n",
    "cs1 = ax2.contour( x_obs + 0*z_obs, -z_obs, dummy_obs,  levels=np.arange(-1.2,1.2,0.1), colors='k',); plt.clabel(cs1,fmt='%2.1f', fontsize=14)\n",
    "ax2.set_xlim(143,265); ax2.set_ylim(-400,0)\n",
    "plt.suptitle('Eastward velocity [m/s] along the Equatorial Pacific, averaged between '+str(args.start_date)+' and '+str(args.end_date))"
   ]
  }
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