{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e0af68b-a8da-4c43-8d6a-3db9a5e24be3",
   "metadata": {},
   "source": [
    "# Generically access model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7649cd37-68cf-44f4-b6b8-7ba94bc31eae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import cf_xarray\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import xcmocean\n",
    "import cmocean.cm as cmo\n",
    "import extract_model as em"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c399b1c-f0b5-4256-a9f3-4b50fd7c91ae",
   "metadata": {},
   "source": [
    "## ROMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad6b6ec4-fdcc-4459-a24f-cd9055953239",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 371)\n",
       "Coordinates:\n",
       "    Cs_r        (s_rho) float64 dask.array&lt;chunksize=(30,), meta=np.ndarray&gt;\n",
       "    lon_rho     (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(191, 371), meta=np.ndarray&gt;\n",
       "    hc          float64 20.0\n",
       "    h           (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(191, 371), meta=np.ndarray&gt;\n",
       "    lat_rho     (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(191, 371), meta=np.ndarray&gt;\n",
       "    Vtransform  int32 2\n",
       "  * ocean_time  (ocean_time) datetime64[ns] 2001-08-01 2001-08-08\n",
       "  * s_rho       (s_rho) float64 -0.9833 -0.95 -0.9167 ... -0.05 -0.01667\n",
       "  * xi_rho      (xi_rho) int64 0 1 2 3 4 5 6 7 ... 364 365 366 367 368 369 370\n",
       "  * eta_rho     (eta_rho) int64 0 1 2 3 4 5 6 7 ... 184 185 186 187 188 189 190\n",
       "    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 dask.array&lt;chunksize=(1, 30, 191, 371), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    salt        (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array&lt;chunksize=(1, 30, 191, 371), meta=np.ndarray&gt;\n",
       "    zeta        (ocean_time, eta_rho, xi_rho) float32 dask.array&lt;chunksize=(1, 191, 371), meta=np.ndarray&gt;\n",
       "Attributes: (12/34)\n",
       "    file:              ../output_20yr_obc/2001/ocean_his_0015.nc\n",
       "    format:            netCDF-4/HDF5 file\n",
       "    Conventions:       CF-1.4\n",
       "    type:              ROMS/TOMS history file\n",
       "    title:             TXLA ROMS hindcast run with dyes and oxygen\n",
       "    rst_file:          ../output_20yr_obc/2001/ocean_rst.nc\n",
       "    ...                ...\n",
       "    compiler_flags:    -heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free\n",
       "    tiling:            010x012\n",
       "    history:           Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /cop...\n",
       "    ana_file:          /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux...\n",
       "    CPP_options:       TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOE...\n",
       "    NCO:               netCDF Operators version 4.7.6-alpha04 (Homepage = htt...</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-3550c7e6-687e-429a-88b5-a2883260f982' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3550c7e6-687e-429a-88b5-a2883260f982' 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'>ocean_time</span>: 2</li><li><span class='xr-has-index'>s_rho</span>: 30</li><li><span class='xr-has-index'>eta_rho</span>: 191</li><li><span class='xr-has-index'>xi_rho</span>: 371</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e70faf9b-3916-49d7-a62f-140bf274d5cb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e70faf9b-3916-49d7-a62f-140bf274d5cb' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30,), meta=np.ndarray&gt;</div><input id='attrs-dcac64ba-26d0-4c83-9f4f-d04135ac0d6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcac64ba-26d0-4c83-9f4f-d04135ac0d6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8e657cc-762a-44bb-8364-8a71f364ada9' class='xr-var-data-in' type='checkbox'><label for='data-c8e657cc-762a-44bb-8364-8a71f364ada9' 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>S-coordinate stretching curves at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>field :</span></dt><dd>Cs_r, scalar</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> 240 B </td>\n",
       "                        <td> 240 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30,) </td>\n",
       "                        <td> (30,) </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\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"82\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\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>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(191, 371), meta=np.ndarray&gt;</div><input id='attrs-df76adc8-f12e-4c37-9228-66d30bbe3c0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df76adc8-f12e-4c37-9228-66d30bbe3c0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853f8537-778d-4cd6-8b36-ae0047cf1022' class='xr-var-data-in' type='checkbox'><label for='data-853f8537-778d-4cd6-8b36-ae0047cf1022' 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>longitude of RHO-points</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>field :</span></dt><dd>lon_rho, scalar</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> 553.60 kiB </td>\n",
       "                        <td> 553.60 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (191, 371) </td>\n",
       "                        <td> (191, 371) </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\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"111\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "\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>hc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.0</div><input id='attrs-9917663f-496e-4849-a91f-b7c7c0856e3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9917663f-496e-4849-a91f-b7c7c0856e3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05daf474-a66c-47a2-8452-419402887e0d' class='xr-var-data-in' type='checkbox'><label for='data-05daf474-a66c-47a2-8452-419402887e0d' 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>S-coordinate parameter, critical depth</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array(20.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(191, 371), meta=np.ndarray&gt;</div><input id='attrs-2e7f9a21-7378-469e-acc1-3c4c0b975293' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e7f9a21-7378-469e-acc1-3c4c0b975293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e065521-876b-469c-95da-3d00dbaf3874' class='xr-var-data-in' type='checkbox'><label for='data-9e065521-876b-469c-95da-3d00dbaf3874' 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>bathymetry at RHO-points</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>field :</span></dt><dd>bath, scalar</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> 553.60 kiB </td>\n",
       "                        <td> 553.60 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (191, 371) </td>\n",
       "                        <td> (191, 371) </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\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"111\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"81.778976\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >371</text>\n",
       "  <text x=\"140.000000\" y=\"30.889488\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.889488)\">191</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(191, 371), meta=np.ndarray&gt;</div><input id='attrs-d0d7a2c1-545f-4105-8f4c-0b6fd1fdcf66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0d7a2c1-545f-4105-8f4c-0b6fd1fdcf66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e7cf4a-67d5-429c-8567-263cf2af704a' class='xr-var-data-in' type='checkbox'><label for='data-99e7cf4a-67d5-429c-8567-263cf2af704a' 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>latitude of RHO-points</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>field :</span></dt><dd>lat_rho, scalar</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> 553.60 kiB </td>\n",
       "                        <td> 553.60 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (191, 371) </td>\n",
       "                        <td> (191, 371) </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\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"111\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "\n",
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       "\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>Vtransform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-c326969e-8258-4528-a506-f47a8e79e2de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c326969e-8258-4528-a506-f47a8e79e2de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba1fe7cf-fed5-4da6-a967-201e568ea26d' class='xr-var-data-in' type='checkbox'><label for='data-ba1fe7cf-fed5-4da6-a967-201e568ea26d' 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>vertical terrain-following transformation equation</dd></dl></div><div class='xr-var-data'><pre>array(2, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-08-01 2001-08-08</div><input id='attrs-c1d7aec1-afe3-4c43-9073-c9e3d616d2cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1d7aec1-afe3-4c43-9073-c9e3d616d2cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-092ee040-9679-4f08-a02f-e3a6a7f0e461' class='xr-var-data-in' type='checkbox'><label for='data-092ee040-9679-4f08-a02f-e3a6a7f0e461' 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 since initialization</dd><dt><span>field :</span></dt><dd>time, scalar, series</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2001-08-01T00:00:00.000000000&#x27;, &#x27;2001-08-08T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>s_rho</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.9833 -0.95 ... -0.05 -0.01667</div><input id='attrs-06fd5c2d-10fe-411d-8513-2086fc22b84b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06fd5c2d-10fe-411d-8513-2086fc22b84b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03e2b3e6-d37f-4035-9b2f-3fdd35529b39' class='xr-var-data-in' type='checkbox'><label for='data-03e2b3e6-d37f-4035-9b2f-3fdd35529b39' 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>S-coordinate at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,\n",
       "       -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,\n",
       "       -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,\n",
       "       -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,\n",
       "       -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xi_rho</span></div><div class='xr-var-dims'>(xi_rho)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 366 367 368 369 370</div><input id='attrs-d6e11ead-4ca9-43c7-9c42-c849b98c3bdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6e11ead-4ca9-43c7-9c42-c849b98c3bdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cf10c6d-2508-46b1-8033-b8d97b6ec3c4' class='xr-var-data-in' type='checkbox'><label for='data-1cf10c6d-2508-46b1-8033-b8d97b6ec3c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 368, 369, 370])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eta_rho</span></div><div class='xr-var-dims'>(eta_rho)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 186 187 188 189 190</div><input id='attrs-e96ccb0f-d7e6-48d5-90d6-1fef3280a8f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e96ccb0f-d7e6-48d5-90d6-1fef3280a8f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d3d1b43-20e1-4757-9e05-4bf541e11830' class='xr-var-data-in' type='checkbox'><label for='data-5d3d1b43-20e1-4757-9e05-4bf541e11830' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_rho</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 30, 191, 371), meta=np.ndarray&gt;</div><input id='attrs-b156efd5-a59f-4ffd-a615-6b5bfa6ce558' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b156efd5-a59f-4ffd-a615-6b5bfa6ce558' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6553badf-de7c-484d-8171-f39408b6f598' class='xr-var-data-in' type='checkbox'><label for='data-6553badf-de7c-484d-8171-f39408b6f598' 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>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2be5838a-1be9-4772-ba66-fd5495351d86' class='xr-section-summary-in' type='checkbox'  ><label for='section-2be5838a-1be9-4772-ba66-fd5495351d86' 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>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a182768-e032-4909-9bd3-ea6e5f1d9584' class='xr-index-data-in' type='checkbox'/><label for='index-5a182768-e032-4909-9bd3-ea6e5f1d9584' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2001-08-01&#x27;, &#x27;2001-08-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;ocean_time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>s_rho</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-671efac7-808d-4cd1-8369-9c17fb09bff4' class='xr-index-data-in' type='checkbox'/><label for='index-671efac7-808d-4cd1-8369-9c17fb09bff4' 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(Float64Index([  -0.9833333333333333,                 -0.95,\n",
       "                -0.9166666666666666,   -0.8833333333333333,\n",
       "                              -0.85,   -0.8166666666666667,\n",
       "                -0.7833333333333333,                 -0.75,\n",
       "                -0.7166666666666667,   -0.6833333333333333,\n",
       "                              -0.65,   -0.6166666666666667,\n",
       "                -0.5833333333333334,                 -0.55,\n",
       "                -0.5166666666666666,  -0.48333333333333334,\n",
       "                              -0.45,   -0.4166666666666667,\n",
       "                -0.3833333333333333,                 -0.35,\n",
       "               -0.31666666666666665,   -0.2833333333333333,\n",
       "                              -0.25,  -0.21666666666666667,\n",
       "               -0.18333333333333332,                 -0.15,\n",
       "               -0.11666666666666667,  -0.08333333333333333,\n",
       "                              -0.05, -0.016666666666666666],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;s_rho&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xi_rho</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f01b667f-9411-42ce-8f7b-593dfc4e699d' class='xr-index-data-in' type='checkbox'/><label for='index-f01b667f-9411-42ce-8f7b-593dfc4e699d' 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(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            361, 362, 363, 364, 365, 366, 367, 368, 369, 370],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;xi_rho&#x27;, length=371))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>eta_rho</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-089f2ac0-58c3-4b22-8c4c-7173fa9ea954' class='xr-index-data-in' type='checkbox'/><label for='index-089f2ac0-58c3-4b22-8c4c-7173fa9ea954' 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(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            181, 182, 183, 184, 185, 186, 187, 188, 189, 190],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;eta_rho&#x27;, length=191))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93cfd3bf-1c4c-47cd-8c32-e27b594687fd' class='xr-section-summary-in' type='checkbox'  ><label for='section-93cfd3bf-1c4c-47cd-8c32-e27b594687fd' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>file :</span></dt><dd>../output_20yr_obc/2001/ocean_his_0015.nc</dd><dt><span>format :</span></dt><dd>netCDF-4/HDF5 file</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>type :</span></dt><dd>ROMS/TOMS history file</dd><dt><span>title :</span></dt><dd>TXLA ROMS hindcast run with dyes and oxygen</dd><dt><span>rst_file :</span></dt><dd>../output_20yr_obc/2001/ocean_rst.nc</dd><dt><span>his_base :</span></dt><dd>../output_20yr_obc/2001/ocean_his</dd><dt><span>avg_base :</span></dt><dd>../output_20yr_obc/2001/ocean_avg</dd><dt><span>dia_base :</span></dt><dd>../output_20yr_obc/2001/ocean_dia</dd><dt><span>sta_file :</span></dt><dd>ocean_sta.nc</dd><dt><span>grd_file :</span></dt><dd>../inputs/grd/txla2_grd_v4_test_lcut_hglo_wtype.nc</dd><dt><span>ini_file :</span></dt><dd>../output_20yr_obc/2000/ocean_rst.nc</dd><dt><span>frc_file_01 :</span></dt><dd>../inputs/2001/txla_bulk_ERAI_2001.nc</dd><dt><span>frc_file_02 :</span></dt><dd>../inputs/2001/txla_flx_ICOADS_AVHRR_SST_2001.nc, ../inputs/2002/txla_flx_ICOADS_AVHRR_SST_2002.nc</dd><dt><span>bry_file_01 :</span></dt><dd>../inputs/2001/txla2_bry_2001_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_bry_2002_glo_ReAna_v4_o2woa.nc</dd><dt><span>clm_file_01 :</span></dt><dd>../inputs/2001/txla2_clm_2001_01_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_02_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_03_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_04_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_05_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_06_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_07_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_08_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_09_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_10_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_11_glo_ReAna_v4_o2woa.nc, ../inputs/2001/txla2_clm_2001_12_glo_ReAna_v4_o2woa.nc, ../inputs/2002/txla2_clm_2002_01_glo_ReAna_v4_o2woa.nc</dd><dt><span>script_file :</span></dt><dd></dd><dt><span>spos_file :</span></dt><dd>/scratch/user/d.kobashi/inputs/stations.in</dd><dt><span>NLM_LBC :</span></dt><dd>\n",
       "EDGE:    WEST   SOUTH  EAST   NORTH  \n",
       "zeta:    Che    Che    Che    Clo    \n",
       "ubar:    Shc    Shc    Shc    Clo    \n",
       "vbar:    Shc    Shc    Shc    Clo    \n",
       "u:       Rad    Rad    Rad    Clo    \n",
       "v:       Rad    Rad    Rad    Clo    \n",
       "temp:    Rad    Rad    Rad    Clo    \n",
       "salt:    Rad    Rad    Rad    Clo    \n",
       "dye_01:  Gra    Gra    Gra    Clo    \n",
       "dye_02:  Rad    Rad    Rad    Clo    \n",
       "dye_03:  Rad    Rad    Rad    Clo    \n",
       "dye_04:  Rad    Rad    Rad    Clo    \n",
       "tke:     Gra    Gra    Gra    Clo</dd><dt><span>svn_url :</span></dt><dd>https:://myroms.org/svn/src</dd><dt><span>svn_rev :</span></dt><dd></dd><dt><span>code_dir :</span></dt><dd>/scratch/user/d.kobashi/source_code/COAWST/COAWST.r960-dev</dd><dt><span>header_dir :</span></dt><dd>/home/d.kobashi/TXLA_ROMS_reana/work_20yr_obc</dd><dt><span>header_file :</span></dt><dd>txla2.h</dd><dt><span>os :</span></dt><dd>Linux</dd><dt><span>cpu :</span></dt><dd>x86_64</dd><dt><span>compiler_system :</span></dt><dd>ifort</dd><dt><span>compiler_command :</span></dt><dd>/software/easybuild/software/impi/5.0.1.035-iccifort-2015.0.090/bin64/mpiifort</dd><dt><span>compiler_flags :</span></dt><dd>-heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free</dd><dt><span>tiling :</span></dt><dd>010x012</dd><dt><span>history :</span></dt><dd>Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /copano/d1/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc --cnk_dmn ocean_time,4 --cnk_dmn eta_rho,8 --cnk_dmn eta_u,8 --cnk_dmn eta_v,8 --cnk_dmn eta_psi,8 --cnk_dmn xi_rho,16 --cnk_dmn xi_u,16 --cnk_dmn xi_v,16 --cnk_dmn xi_psi,16 --cnk_dmn s_rho,2 --cnk_dmn s_w,2 --output /copano/d2/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc\n",
       "ROMS/TOMS, Version 3.7, Monday - July 18, 2016 - 10:38:26 PM</dd><dt><span>ana_file :</span></dt><dd>/home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_sponge.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_nudgcoef.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_stflux.h</dd><dt><span>CPP_options :</span></dt><dd>TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOEF, ANA_SPFLUX, ANA_SPONGE, ASSUMED_SHAPE, AVERAGES, BULK_FLUXES, CURVGRID, DEFLATE, DIAGNOSTICS_TS, DIAGNOSTICS_UV, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, RADIATION_2D, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SPLINES, SPHERICAL, STATIONS, T_PASSIVE, TS_MPDATA, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_VIS2, VAR_RHO_2D, VISC_GRID, WTYPE_GRID</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.6-alpha04 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 371)\n",
       "Coordinates:\n",
       "    Cs_r        (s_rho) float64 dask.array<chunksize=(30,), meta=np.ndarray>\n",
       "    lon_rho     (eta_rho, xi_rho) float64 dask.array<chunksize=(191, 371), meta=np.ndarray>\n",
       "    hc          float64 20.0\n",
       "    h           (eta_rho, xi_rho) float64 dask.array<chunksize=(191, 371), meta=np.ndarray>\n",
       "    lat_rho     (eta_rho, xi_rho) float64 dask.array<chunksize=(191, 371), meta=np.ndarray>\n",
       "    Vtransform  int32 2\n",
       "  * ocean_time  (ocean_time) datetime64[ns] 2001-08-01 2001-08-08\n",
       "  * s_rho       (s_rho) float64 -0.9833 -0.95 -0.9167 ... -0.05 -0.01667\n",
       "  * xi_rho      (xi_rho) int64 0 1 2 3 4 5 6 7 ... 364 365 366 367 368 369 370\n",
       "  * eta_rho     (eta_rho) int64 0 1 2 3 4 5 6 7 ... 184 185 186 187 188 189 190\n",
       "    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 dask.array<chunksize=(1, 30, 191, 371), meta=np.ndarray>\n",
       "Data variables:\n",
       "    salt        (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 30, 191, 371), meta=np.ndarray>\n",
       "    zeta        (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 191, 371), meta=np.ndarray>\n",
       "Attributes: (12/34)\n",
       "    file:              ../output_20yr_obc/2001/ocean_his_0015.nc\n",
       "    format:            netCDF-4/HDF5 file\n",
       "    Conventions:       CF-1.4\n",
       "    type:              ROMS/TOMS history file\n",
       "    title:             TXLA ROMS hindcast run with dyes and oxygen\n",
       "    rst_file:          ../output_20yr_obc/2001/ocean_rst.nc\n",
       "    ...                ...\n",
       "    compiler_flags:    -heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free\n",
       "    tiling:            010x012\n",
       "    history:           Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /cop...\n",
       "    ana_file:          /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux...\n",
       "    CPP_options:       TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOE...\n",
       "    NCO:               netCDF Operators version 4.7.6-alpha04 (Homepage = htt..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open an example dataset from xarray's tutorials\n",
    "ds = xr.tutorial.open_dataset('ROMS_example.nc', chunks={'ocean_time': 1})\n",
    "# normally could run the `preprocess` code as part of reading in the dataset\n",
    "# but with the tutorial model output, run it separately:\n",
    "ds = em.preprocess(ds)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e5afe4-36d6-4ca3-a4cc-4449bf80cf41",
   "metadata": {},
   "source": [
    "Note that the preprocessing code sets up a ROMS dataset so that it can be used with `cf-xarray`. For example, axis and coordinate variables have been identified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3419dd9f-0ceb-48c0-95f5-df8ffc9a273f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes: * X: ['xi_rho']\n",
       "           * Y: ['eta_rho']\n",
       "           * Z: ['s_rho']\n",
       "           * T: ['ocean_time']\n",
       "\n",
       "- CF Coordinates:   longitude: ['lon_rho']\n",
       "                    latitude: ['lat_rho']\n",
       "                    vertical: ['z_rho']\n",
       "                  * time: ['ocean_time']\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   latitude: ['lat_rho']\n",
       "                    longitude: ['lon_rho']\n",
       "                  * ocean_s_coordinate_g2: ['s_rho']\n",
       "                  * time: ['ocean_time']\n",
       "\n",
       "- Bounds:   n/a\n",
       "\n",
       "Data Variables:\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   sea_surface_elevation: ['zeta']\n",
       "                    sea_water_practical_salinity: ['salt']\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8afe42e-3338-4994-beb3-f341008f0523",
   "metadata": {},
   "source": [
    "Variable to use, by standard_name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40a1ae3f-710e-467c-912d-454516492bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "zeta = 'sea_surface_elevation'\n",
    "salt = 'sea_water_practical_salinity'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce882f3b-16d3-447c-b749-aad1dfa78806",
   "metadata": {},
   "source": [
    "### Subset numerical domain\n",
    "\n",
    "Use `.em.sub_grid()` to narrow the model area down using a bounding box on a Dataset which respects the horizontal structure of multiple grids. Currently only is relevant for ROMS models but will run on any ROMS model or models with a single longitude/latitude set of coordinates.\n",
    "\n",
    "Resulting area of model will not be exactly the bounding box if the domain is curvilinear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0fa140a9-ab12-4882-baa2-784ce7c9024e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbfa182a7c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_sub = ds.em.sub_grid([-92, 27, -90, 29])\n",
    "ds_sub.cf[zeta].cf.isel(T=0).cf.plot(x='longitude', y='latitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760cb68f-9764-4e5e-a438-992e04e8c26a",
   "metadata": {},
   "source": [
    "Note that this is an unusual ROMS Dataset because it has only one horizontal grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6af6565-b215-45b6-977e-38aa2600ff4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (ocean_time: 2, s_rho: 30, eta_rho: 100, xi_rho: 144)\n",
       "Coordinates:\n",
       "    Cs_r        (s_rho) float64 dask.array&lt;chunksize=(30,), meta=np.ndarray&gt;\n",
       "    lon_rho     (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(100, 144), meta=np.ndarray&gt;\n",
       "    hc          float64 20.0\n",
       "    h           (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(100, 144), meta=np.ndarray&gt;\n",
       "    lat_rho     (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(100, 144), meta=np.ndarray&gt;\n",
       "    Vtransform  int32 2\n",
       "  * ocean_time  (ocean_time) datetime64[ns] 2001-08-01 2001-08-08\n",
       "  * s_rho       (s_rho) float64 -0.9833 -0.95 -0.9167 ... -0.05 -0.01667\n",
       "  * xi_rho      (xi_rho) int64 98 99 100 101 102 103 ... 236 237 238 239 240 241\n",
       "  * eta_rho     (eta_rho) int64 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
       "    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 dask.array&lt;chunksize=(1, 30, 100, 144), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    salt        (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array&lt;chunksize=(1, 30, 100, 144), meta=np.ndarray&gt;\n",
       "    zeta        (ocean_time, eta_rho, xi_rho) float32 dask.array&lt;chunksize=(1, 100, 144), meta=np.ndarray&gt;\n",
       "Attributes: (12/34)\n",
       "    file:              ../output_20yr_obc/2001/ocean_his_0015.nc\n",
       "    format:            netCDF-4/HDF5 file\n",
       "    Conventions:       CF-1.4\n",
       "    type:              ROMS/TOMS history file\n",
       "    title:             TXLA ROMS hindcast run with dyes and oxygen\n",
       "    rst_file:          ../output_20yr_obc/2001/ocean_rst.nc\n",
       "    ...                ...\n",
       "    compiler_flags:    -heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free\n",
       "    tiling:            010x012\n",
       "    history:           Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /cop...\n",
       "    ana_file:          /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux...\n",
       "    CPP_options:       TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOE...\n",
       "    NCO:               netCDF Operators version 4.7.6-alpha04 (Homepage = htt...</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-f85d0294-42f1-4c9f-bda9-e849ce19ade8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f85d0294-42f1-4c9f-bda9-e849ce19ade8' 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'>ocean_time</span>: 2</li><li><span class='xr-has-index'>s_rho</span>: 30</li><li><span class='xr-has-index'>eta_rho</span>: 100</li><li><span class='xr-has-index'>xi_rho</span>: 144</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1a6c35e9-19ec-455d-86f7-d522143baaa0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a6c35e9-19ec-455d-86f7-d522143baaa0' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30,), meta=np.ndarray&gt;</div><input id='attrs-86829e8c-7a1b-4545-a801-f4fbb4102a5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86829e8c-7a1b-4545-a801-f4fbb4102a5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-030a595d-823f-4438-b403-b1757b1bd8db' class='xr-var-data-in' type='checkbox'><label for='data-030a595d-823f-4438-b403-b1757b1bd8db' 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>S-coordinate stretching curves at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>field :</span></dt><dd>Cs_r, scalar</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 240 B </td>\n",
       "                        <td> 240 B </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float64 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>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 144), meta=np.ndarray&gt;</div><input id='attrs-e4420076-a264-4b49-b3f8-3d357db72d78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4420076-a264-4b49-b3f8-3d357db72d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fcc5111-692f-4cc4-812f-fc8be4bc2f72' class='xr-var-data-in' type='checkbox'><label for='data-7fcc5111-692f-4cc4-812f-fc8be4bc2f72' 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>longitude of RHO-points</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>field :</span></dt><dd>lon_rho, scalar</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> 112.50 kiB </td>\n",
       "                        <td> 112.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 144) </td>\n",
       "                        <td> (100, 144) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"133\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\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>hc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.0</div><input id='attrs-c4546625-a30a-43e3-9205-30c926639005' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4546625-a30a-43e3-9205-30c926639005' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5ea8522-8c34-46d4-acb7-d80417371067' class='xr-var-data-in' type='checkbox'><label for='data-d5ea8522-8c34-46d4-acb7-d80417371067' 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>S-coordinate parameter, critical depth</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array(20.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 144), meta=np.ndarray&gt;</div><input id='attrs-353c4145-c86e-46e6-8eba-757813b53dfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-353c4145-c86e-46e6-8eba-757813b53dfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c333c56-17c8-483b-b32a-b050eca2c6f4' class='xr-var-data-in' type='checkbox'><label for='data-2c333c56-17c8-483b-b32a-b050eca2c6f4' 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>bathymetry at RHO-points</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>field :</span></dt><dd>bath, scalar</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> 112.50 kiB </td>\n",
       "                        <td> 112.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 144) </td>\n",
       "                        <td> (100, 144) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"133\" 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",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,83.33333333333334 0.0,83.33333333333334\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"103.333333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >144</text>\n",
       "  <text x=\"140.000000\" y=\"41.666667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.666667)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 144), meta=np.ndarray&gt;</div><input id='attrs-0b63673e-31c3-4a46-9e82-f901a8c37b06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b63673e-31c3-4a46-9e82-f901a8c37b06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b6c6f51-2e50-47cc-8448-3d62e6abf81e' class='xr-var-data-in' type='checkbox'><label for='data-0b6c6f51-2e50-47cc-8448-3d62e6abf81e' 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>latitude of RHO-points</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>field :</span></dt><dd>lat_rho, scalar</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> 112.50 kiB </td>\n",
       "                        <td> 112.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 144) </td>\n",
       "                        <td> (100, 144) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"133\" 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=\"83\" x2=\"120\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,83.33333333333334 0.0,83.33333333333334\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"103.333333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >144</text>\n",
       "  <text x=\"140.000000\" y=\"41.666667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.666667)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Vtransform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-7ca2d6db-9549-42eb-b6a6-3669192df24b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ca2d6db-9549-42eb-b6a6-3669192df24b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3534d002-d77d-4ec7-88bc-8532fda18acb' class='xr-var-data-in' type='checkbox'><label for='data-3534d002-d77d-4ec7-88bc-8532fda18acb' 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>vertical terrain-following transformation equation</dd></dl></div><div class='xr-var-data'><pre>array(2, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-08-01 2001-08-08</div><input id='attrs-c28320ec-4023-4584-bce5-92e6be47c3b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c28320ec-4023-4584-bce5-92e6be47c3b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52fc5f74-2d25-41a0-8d53-b230ff40b33c' class='xr-var-data-in' type='checkbox'><label for='data-52fc5f74-2d25-41a0-8d53-b230ff40b33c' 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 since initialization</dd><dt><span>field :</span></dt><dd>time, scalar, series</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2001-08-01T00:00:00.000000000&#x27;, &#x27;2001-08-08T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>s_rho</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.9833 -0.95 ... -0.05 -0.01667</div><input id='attrs-510c0001-9161-4045-980c-31c205fdeb49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-510c0001-9161-4045-980c-31c205fdeb49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2342a800-09a7-4d34-85bf-3bbe7b9a7c75' class='xr-var-data-in' type='checkbox'><label for='data-2342a800-09a7-4d34-85bf-3bbe7b9a7c75' 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>S-coordinate at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,\n",
       "       -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,\n",
       "       -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,\n",
       "       -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,\n",
       "       -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xi_rho</span></div><div class='xr-var-dims'>(xi_rho)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>98 99 100 101 ... 238 239 240 241</div><input id='attrs-be3a6e0e-bd21-43a6-bc8a-320f8fb9d5a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be3a6e0e-bd21-43a6-bc8a-320f8fb9d5a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97aa9782-bc32-4601-88bf-2d5289852c09' class='xr-var-data-in' type='checkbox'><label for='data-97aa9782-bc32-4601-88bf-2d5289852c09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,\n",
       "       210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,\n",
       "       224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
       "       238, 239, 240, 241])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eta_rho</span></div><div class='xr-var-dims'>(eta_rho)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 94 95 96 97 98 99</div><input id='attrs-eb287c09-09ed-4fc8-beb2-21ab4f356f0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb287c09-09ed-4fc8-beb2-21ab4f356f0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5088809-36fe-4142-9056-634a3494eb6e' class='xr-var-data-in' type='checkbox'><label for='data-d5088809-36fe-4142-9056-634a3494eb6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_rho</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 30, 100, 144), meta=np.ndarray&gt;</div><input id='attrs-0bb54729-5ac2-48c9-8301-f6df873cc5c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bb54729-5ac2-48c9-8301-f6df873cc5c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9c919f0-9c11-4d0e-9e8b-9a3ca7112376' class='xr-var-data-in' type='checkbox'><label for='data-c9c919f0-9c11-4d0e-9e8b-9a3ca7112376' 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>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 29 graph layers </td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.30 MiB </td>\n",
       "                        <td> 1.65 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2, 30, 100, 144) </td>\n",
       "                        <td> (1, 30, 100, 144) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 4 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><li class='xr-var-item'><div class='xr-var-name'><span>zeta</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 144), meta=np.ndarray&gt;</div><input id='attrs-093cdf7c-3d03-44d8-9395-c20c36fd9435' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-093cdf7c-3d03-44d8-9395-c20c36fd9435' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9077cd11-7d0f-4882-a5fd-278bc6b3d444' class='xr-var-data-in' type='checkbox'><label for='data-9077cd11-7d0f-4882-a5fd-278bc6b3d444' 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>free-surface</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>field :</span></dt><dd>free-surface, scalar, series</dd><dt><span>standard_name :</span></dt><dd>sea_surface_elevation</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 112.50 kiB </td>\n",
       "                        <td> 56.25 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 4 graph layers </td>\n",
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       "                        <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-ac27cd2e-6d09-43c3-a09c-fd049a0f3e5c' class='xr-section-summary-in' type='checkbox'  ><label for='section-ac27cd2e-6d09-43c3-a09c-fd049a0f3e5c' 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>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1e1b86a-a25c-40b0-a3a8-35e1b8ab20dc' class='xr-index-data-in' type='checkbox'/><label for='index-c1e1b86a-a25c-40b0-a3a8-35e1b8ab20dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2001-08-01&#x27;, &#x27;2001-08-08&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;ocean_time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>s_rho</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3bd488e7-656f-440a-8bb4-73774088f5c7' class='xr-index-data-in' type='checkbox'/><label for='index-3bd488e7-656f-440a-8bb4-73774088f5c7' 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(Float64Index([  -0.9833333333333333,                 -0.95,\n",
       "                -0.9166666666666666,   -0.8833333333333333,\n",
       "                              -0.85,   -0.8166666666666667,\n",
       "                -0.7833333333333333,                 -0.75,\n",
       "                -0.7166666666666667,   -0.6833333333333333,\n",
       "                              -0.65,   -0.6166666666666667,\n",
       "                -0.5833333333333334,                 -0.55,\n",
       "                -0.5166666666666666,  -0.48333333333333334,\n",
       "                              -0.45,   -0.4166666666666667,\n",
       "                -0.3833333333333333,                 -0.35,\n",
       "               -0.31666666666666665,   -0.2833333333333333,\n",
       "                              -0.25,  -0.21666666666666667,\n",
       "               -0.18333333333333332,                 -0.15,\n",
       "               -0.11666666666666667,  -0.08333333333333333,\n",
       "                              -0.05, -0.016666666666666666],\n",
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       "EDGE:    WEST   SOUTH  EAST   NORTH  \n",
       "zeta:    Che    Che    Che    Clo    \n",
       "ubar:    Shc    Shc    Shc    Clo    \n",
       "vbar:    Shc    Shc    Shc    Clo    \n",
       "u:       Rad    Rad    Rad    Clo    \n",
       "v:       Rad    Rad    Rad    Clo    \n",
       "temp:    Rad    Rad    Rad    Clo    \n",
       "salt:    Rad    Rad    Rad    Clo    \n",
       "dye_01:  Gra    Gra    Gra    Clo    \n",
       "dye_02:  Rad    Rad    Rad    Clo    \n",
       "dye_03:  Rad    Rad    Rad    Clo    \n",
       "dye_04:  Rad    Rad    Rad    Clo    \n",
       "tke:     Gra    Gra    Gra    Clo</dd><dt><span>svn_url :</span></dt><dd>https:://myroms.org/svn/src</dd><dt><span>svn_rev :</span></dt><dd></dd><dt><span>code_dir :</span></dt><dd>/scratch/user/d.kobashi/source_code/COAWST/COAWST.r960-dev</dd><dt><span>header_dir :</span></dt><dd>/home/d.kobashi/TXLA_ROMS_reana/work_20yr_obc</dd><dt><span>header_file :</span></dt><dd>txla2.h</dd><dt><span>os :</span></dt><dd>Linux</dd><dt><span>cpu :</span></dt><dd>x86_64</dd><dt><span>compiler_system :</span></dt><dd>ifort</dd><dt><span>compiler_command :</span></dt><dd>/software/easybuild/software/impi/5.0.1.035-iccifort-2015.0.090/bin64/mpiifort</dd><dt><span>compiler_flags :</span></dt><dd>-heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free</dd><dt><span>tiling :</span></dt><dd>010x012</dd><dt><span>history :</span></dt><dd>Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /copano/d1/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc --cnk_dmn ocean_time,4 --cnk_dmn eta_rho,8 --cnk_dmn eta_u,8 --cnk_dmn eta_v,8 --cnk_dmn eta_psi,8 --cnk_dmn xi_rho,16 --cnk_dmn xi_u,16 --cnk_dmn xi_v,16 --cnk_dmn xi_psi,16 --cnk_dmn s_rho,2 --cnk_dmn s_w,2 --output /copano/d2/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc\n",
       "ROMS/TOMS, Version 3.7, Monday - July 18, 2016 - 10:38:26 PM</dd><dt><span>ana_file :</span></dt><dd>/home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_sponge.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_nudgcoef.h, /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_stflux.h</dd><dt><span>CPP_options :</span></dt><dd>TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOEF, ANA_SPFLUX, ANA_SPONGE, ASSUMED_SHAPE, AVERAGES, BULK_FLUXES, CURVGRID, DEFLATE, DIAGNOSTICS_TS, DIAGNOSTICS_UV, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, RADIATION_2D, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SPLINES, SPHERICAL, STATIONS, T_PASSIVE, TS_MPDATA, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_VIS2, VAR_RHO_2D, VISC_GRID, WTYPE_GRID</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.6-alpha04 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:     (ocean_time: 2, s_rho: 30, eta_rho: 100, xi_rho: 144)\n",
       "Coordinates:\n",
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       "    lon_rho     (eta_rho, xi_rho) float64 dask.array<chunksize=(100, 144), meta=np.ndarray>\n",
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       "    h           (eta_rho, xi_rho) float64 dask.array<chunksize=(100, 144), meta=np.ndarray>\n",
       "    lat_rho     (eta_rho, xi_rho) float64 dask.array<chunksize=(100, 144), meta=np.ndarray>\n",
       "    Vtransform  int32 2\n",
       "  * ocean_time  (ocean_time) datetime64[ns] 2001-08-01 2001-08-08\n",
       "  * s_rho       (s_rho) float64 -0.9833 -0.95 -0.9167 ... -0.05 -0.01667\n",
       "  * xi_rho      (xi_rho) int64 98 99 100 101 102 103 ... 236 237 238 239 240 241\n",
       "  * eta_rho     (eta_rho) int64 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
       "    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 dask.array<chunksize=(1, 30, 100, 144), meta=np.ndarray>\n",
       "Data variables:\n",
       "    salt        (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 30, 100, 144), meta=np.ndarray>\n",
       "    zeta        (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 100, 144), meta=np.ndarray>\n",
       "Attributes: (12/34)\n",
       "    file:              ../output_20yr_obc/2001/ocean_his_0015.nc\n",
       "    format:            netCDF-4/HDF5 file\n",
       "    Conventions:       CF-1.4\n",
       "    type:              ROMS/TOMS history file\n",
       "    title:             TXLA ROMS hindcast run with dyes and oxygen\n",
       "    rst_file:          ../output_20yr_obc/2001/ocean_rst.nc\n",
       "    ...                ...\n",
       "    compiler_flags:    -heap-arrays -fp-model fast -mt_mpi -ip -O3 -msse2 -free\n",
       "    tiling:            010x012\n",
       "    history:           Tue Jul 24 11:04:43 2018: /opt/nco/ncks -D 4 -t 8 /cop...\n",
       "    ana_file:          /home/d.kobashi/TXLA_ROMS_reana/Functionals/ana_btflux...\n",
       "    CPP_options:       TXLA2, ANA_BPFLUX, ANA_BSFLUX, ANA_BTFLUX, ANA_NUDGCOE...\n",
       "    NCO:               netCDF Operators version 4.7.6-alpha04 (Homepage = htt..."
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    "### Subset to a horizontal box\n",
    "\n",
    "Use `.em.sub_bbox()` to narrow the model area down using a bounding box on either a Dataset or DataArray. There is no expectation of multiple horizontal grids having the \"correct\" relationship to each other.\n",
    "\n",
    "#### Dataset\n",
    "\n",
    "In the case of a Dataset, all map-based variables are filtered using the same bounding box."
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   "execution_count": 7,
   "id": "a87bf886-cde2-4a4d-a052-47fa6f7d87bb",
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       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salt&#x27; (eta_rho: 100, xi_rho: 144)&gt;\n",
       "dask.array&lt;getitem, shape=(100, 144), dtype=float32, chunksize=(100, 144), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    s_rho       float64 -0.01667\n",
       "  * xi_rho      (xi_rho) int64 98 99 100 101 102 103 ... 236 237 238 239 240 241\n",
       "  * eta_rho     (eta_rho) int64 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
       "Attributes:\n",
       "    long_name:      salinity\n",
       "    time:           ocean_time\n",
       "    field:          salinity, scalar, series\n",
       "    standard_name:  sea_water_practical_salinity</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'salt'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>eta_rho</span>: 100</li><li><span class='xr-has-index'>xi_rho</span>: 144</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b56b925b-d8ef-4dde-a67e-b4d4cb352dcd' class='xr-array-in' type='checkbox' checked><label for='section-b56b925b-d8ef-4dde-a67e-b4d4cb352dcd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(100, 144), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 56.25 kiB </td>\n",
       "                        <td> 56.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 144) </td>\n",
       "                        <td> (100, 144) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"133\" 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=\"83\" x2=\"120\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"83\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,83.33333333333334 0.0,83.33333333333334\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"103.333333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >144</text>\n",
       "  <text x=\"140.000000\" y=\"41.666667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.666667)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-045e17a4-1c8c-4c91-a6e4-f8c7b68d876d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-045e17a4-1c8c-4c91-a6e4-f8c7b68d876d' 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>ocean_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-08-01</div><input id='attrs-c7298994-9d8f-4f45-82be-02317c502cc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7298994-9d8f-4f45-82be-02317c502cc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b00ef435-d163-4bce-a515-7d57b0f443da' class='xr-var-data-in' type='checkbox'><label for='data-b00ef435-d163-4bce-a515-7d57b0f443da' 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 since initialization</dd><dt><span>field :</span></dt><dd>time, scalar, series</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2001-08-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.01667</div><input id='attrs-615d3e8b-d13a-4f0f-9047-5b4c787829fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-615d3e8b-d13a-4f0f-9047-5b4c787829fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d46886c8-12fd-40d8-9b2d-19ac250a9288' class='xr-var-data-in' type='checkbox'><label for='data-d46886c8-12fd-40d8-9b2d-19ac250a9288' 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>S-coordinate at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(-0.01666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xi_rho</span></div><div class='xr-var-dims'>(xi_rho)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>98 99 100 101 ... 238 239 240 241</div><input id='attrs-e83cb05a-84cd-4cc4-b513-7b47136ae687' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e83cb05a-84cd-4cc4-b513-7b47136ae687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-734f0a91-bee8-49df-b7aa-3cd383f479c5' class='xr-var-data-in' type='checkbox'><label for='data-734f0a91-bee8-49df-b7aa-3cd383f479c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,\n",
       "       210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,\n",
       "       224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
       "       238, 239, 240, 241])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eta_rho</span></div><div class='xr-var-dims'>(eta_rho)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 94 95 96 97 98 99</div><input id='attrs-d9d2c8b7-00d4-48d7-ad50-84c6a870124c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d2c8b7-00d4-48d7-ad50-84c6a870124c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dace200-f7ed-4a8b-bf58-3af85e99ecc8' class='xr-var-data-in' type='checkbox'><label for='data-2dace200-f7ed-4a8b-bf58-3af85e99ecc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-403966bf-a759-4cd7-ae48-87b22211e293' class='xr-section-summary-in' type='checkbox'  ><label for='section-403966bf-a759-4cd7-ae48-87b22211e293' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xi_rho</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e81ad61-4142-4021-ac8d-36db5f127132' class='xr-index-data-in' type='checkbox'/><label for='index-7e81ad61-4142-4021-ac8d-36db5f127132' 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(Int64Index([ 98,  99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
       "            ...\n",
       "            232, 233, 234, 235, 236, 237, 238, 239, 240, 241],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;xi_rho&#x27;, length=144))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>eta_rho</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3287b5d5-94e2-4df1-9d53-16930d5e582d' class='xr-index-data-in' type='checkbox'/><label for='index-3287b5d5-94e2-4df1-9d53-16930d5e582d' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "            85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;eta_rho&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3cffdd9b-23ec-408b-9fd9-c29c2a42cb0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3cffdd9b-23ec-408b-9fd9-c29c2a42cb0b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>field :</span></dt><dd>salinity, scalar, series</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'salt' (eta_rho: 100, xi_rho: 144)>\n",
       "dask.array<getitem, shape=(100, 144), dtype=float32, chunksize=(100, 144), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    s_rho       float64 -0.01667\n",
       "  * xi_rho      (xi_rho) int64 98 99 100 101 102 103 ... 236 237 238 239 240 241\n",
       "  * eta_rho     (eta_rho) int64 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
       "Attributes:\n",
       "    long_name:      salinity\n",
       "    time:           ocean_time\n",
       "    field:          salinity, scalar, series\n",
       "    standard_name:  sea_water_practical_salinity"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.em.sub_bbox([-92, 27, -90, 29], drop=True).cf[salt].cf.isel(T=0).cf.sel(Z=0, method='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512e50c4-1e6f-4df5-99cc-cef703ec2390",
   "metadata": {},
   "source": [
    "#### DataArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8188713-3a0a-4812-8378-3445b9be846f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbf81ce3fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.cf[salt].em.sub_bbox([-92, 27, -90, 29], drop=True).cf.isel(T=0, Z=-1).cf.plot(x='longitude', y='latitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0481704a-5725-4c49-81dd-cdfa8f3d75e9",
   "metadata": {},
   "source": [
    "### grid point (interpolation and selecting nearest)\n",
    "\n",
    "Interpolate to a single existing horizontal grid point (and any additional depth and time values for that location) and compare it with method selecting the nearest point to demonstrate we get the same value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47bfcf88-1ca5-4a16-8450-b738e1cdf875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.18 s, sys: 164 ms, total: 4.34 s\n",
      "Wall time: 4.36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "varname = salt\n",
    "\n",
    "# Set up a single lon/lat location\n",
    "j, i = 50, 10\n",
    "longitude = float(ds.cf[varname].cf['longitude'][j,i])\n",
    "latitude = float(ds.cf[varname].cf['latitude'][j,i])\n",
    "\n",
    "# Interpolation\n",
    "da_out = ds.cf[varname].em.interp2d(longitude, latitude)\n",
    "\n",
    "# Selection of nearest location in 2D\n",
    "da_check = ds.cf[varname].em.sel2dcf(longitude=longitude, latitude=latitude).squeeze()\n",
    "\n",
    "assert np.allclose(da_out, da_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c839605-8a99-4f4d-9d57-5917229cb0bc",
   "metadata": {},
   "source": [
    "You could also select a time and/or depth index or interpolate in time and/or depth at the same time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea0036e2-b0eb-4563-886f-9c7efb98ea57",
   "metadata": {},
   "outputs": [
    {
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       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salt&#x27; ()&gt;\n",
       "dask.array&lt;getitem, shape=(), dtype=float32, chunksize=(), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    s_rho       float64 -0.9833\n",
       "    Cs_r        float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "    hc          float64 20.0\n",
       "    Vtransform  int32 2\n",
       "    lat         float64 28.23\n",
       "    lon         float64 -93.59\n",
       "    z_rho       float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    long_name:      salinity\n",
       "    time:           ocean_time\n",
       "    field:          salinity, scalar, series\n",
       "    standard_name:  sea_water_practical_salinity</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'salt'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-217d20fd-8e80-457e-a967-e6fc1f8110f6' class='xr-array-in' type='checkbox' checked><label for='section-217d20fd-8e80-457e-a967-e6fc1f8110f6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4 B </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 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",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-ec821208-bd00-4834-9a1b-e99c2fc85b6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec821208-bd00-4834-9a1b-e99c2fc85b6f' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ocean_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-08-01</div><input id='attrs-11d69b69-4b5a-4d7e-b405-1aa8d9a89ec2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11d69b69-4b5a-4d7e-b405-1aa8d9a89ec2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa7c7214-cbaf-4113-833b-b0a35daab471' class='xr-var-data-in' type='checkbox'><label for='data-aa7c7214-cbaf-4113-833b-b0a35daab471' 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 since initialization</dd><dt><span>field :</span></dt><dd>time, scalar, series</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2001-08-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.9833</div><input id='attrs-d55bf6a5-36e1-42de-b21d-09713cbc321f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d55bf6a5-36e1-42de-b21d-09713cbc321f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86bd7c4b-da43-4f07-a25a-a29cc2663d51' class='xr-var-data-in' type='checkbox'><label for='data-86bd7c4b-da43-4f07-a25a-a29cc2663d51' 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>S-coordinate at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(-0.98333333)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-9f7a5016-d3c7-45a4-a507-bde40bf9680a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f7a5016-d3c7-45a4-a507-bde40bf9680a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c96883-7591-4eea-879f-8027c0b2a51f' class='xr-var-data-in' type='checkbox'><label for='data-91c96883-7591-4eea-879f-8027c0b2a51f' 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>S-coordinate stretching curves at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>field :</span></dt><dd>Cs_r, scalar</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> 8 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.0</div><input id='attrs-efc76912-fef4-4787-8e67-07cca8eb5854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efc76912-fef4-4787-8e67-07cca8eb5854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf3c4877-20c0-4cc3-8a2b-cbf83a92c360' class='xr-var-data-in' type='checkbox'><label for='data-cf3c4877-20c0-4cc3-8a2b-cbf83a92c360' 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>S-coordinate parameter, critical depth</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array(20.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Vtransform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-b1124aab-221b-4f9e-8263-18bd6b5aa025' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1124aab-221b-4f9e-8263-18bd6b5aa025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f402e36-9c55-47d6-81ca-55672591dc14' class='xr-var-data-in' type='checkbox'><label for='data-0f402e36-9c55-47d6-81ca-55672591dc14' 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>vertical terrain-following transformation equation</dd></dl></div><div class='xr-var-data'><pre>array(2, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.23</div><input id='attrs-dbb083cd-3d09-42ac-b42e-3be9e6a8d958' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbb083cd-3d09-42ac-b42e-3be9e6a8d958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55340828-428f-468a-82c1-dd9c946b68d9' class='xr-var-data-in' type='checkbox'><label for='data-55340828-428f-468a-82c1-dd9c946b68d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array(28.22602702)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-93.59</div><input id='attrs-b34765f9-ef63-4f62-bcec-e11623c10bc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b34765f9-ef63-4f62-bcec-e11623c10bc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d81b5bd-5e26-4ea2-96fe-17c689a84862' class='xr-var-data-in' type='checkbox'><label for='data-0d81b5bd-5e26-4ea2-96fe-17c689a84862' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array(-93.5927727)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-e78814da-c501-4b47-92d3-fe723286014e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e78814da-c501-4b47-92d3-fe723286014e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52fc2ee7-7a60-4db8-b941-23ee35f47375' class='xr-var-data-in' type='checkbox'><label for='data-52fc2ee7-7a60-4db8-b941-23ee35f47375' 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>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 8 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 33 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
       "        \n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0228e4fb-99b4-4dd8-a649-119b6595986e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0228e4fb-99b4-4dd8-a649-119b6595986e' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-c891a41b-3d29-4844-a688-2c73538a8319' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c891a41b-3d29-4844-a688-2c73538a8319' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>field :</span></dt><dd>salinity, scalar, series</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'salt' ()>\n",
       "dask.array<getitem, shape=(), dtype=float32, chunksize=(), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    s_rho       float64 -0.9833\n",
       "    Cs_r        float64 dask.array<chunksize=(), meta=np.ndarray>\n",
       "    hc          float64 20.0\n",
       "    Vtransform  int32 2\n",
       "    lat         float64 28.23\n",
       "    lon         float64 -93.59\n",
       "    z_rho       float64 dask.array<chunksize=(), meta=np.ndarray>\n",
       "Attributes:\n",
       "    long_name:      salinity\n",
       "    time:           ocean_time\n",
       "    field:          salinity, scalar, series\n",
       "    standard_name:  sea_water_practical_salinity"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select time index and depth index\n",
    "ds.cf[varname].em.interp2d(longitude, latitude, iT=0, iZ=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b61a5ed7-eb4f-49a9-a726-b7d24b27d61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes: * X: ['xi_rho']\n",
       "           * Y: ['eta_rho']\n",
       "           * Z: ['s_rho']\n",
       "           * T: ['ocean_time']\n",
       "\n",
       "- CF Coordinates:   longitude: ['lon_rho']\n",
       "                    latitude: ['lat_rho']\n",
       "                    vertical: ['z_rho']\n",
       "                  * time: ['ocean_time']\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   latitude: ['lat_rho']\n",
       "                    longitude: ['lon_rho']\n",
       "                  * ocean_s_coordinate_g2: ['s_rho']\n",
       "                  * time: ['ocean_time']\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.cf[varname].cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20dde6c2-faf1-43be-91ef-34e19bf161cc",
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       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salt&#x27; ()&gt;\n",
       "dask.array&lt;dask_aware_interpnd, shape=(), dtype=float32, chunksize=(), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    s_rho       float64 -0.4475\n",
       "    Cs_r        float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "    hc          float64 20.0\n",
       "    Vtransform  int32 2\n",
       "    lat         float64 28.23\n",
       "    lon         float64 -93.59\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    z_rho       int64 -10\n",
       "Attributes:\n",
       "    long_name:      salinity\n",
       "    time:           ocean_time\n",
       "    field:          salinity, scalar, series\n",
       "    standard_name:  sea_water_practical_salinity</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'salt'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-161b3c07-44e2-4bac-b6ba-99da7f01a18b' class='xr-array-in' type='checkbox' checked><label for='section-161b3c07-44e2-4bac-b6ba-99da7f01a18b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4 B </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 17 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",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-d08260ef-8315-4f40-b97a-7887c12fe9e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d08260ef-8315-4f40-b97a-7887c12fe9e3' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>s_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4475</div><input id='attrs-f0c8c179-9556-4fb8-b35b-d646f08e9805' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0c8c179-9556-4fb8-b35b-d646f08e9805' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4ece02a-ef76-4ab5-9c20-9ad1e070d339' class='xr-var-data-in' type='checkbox'><label for='data-e4ece02a-ef76-4ab5-9c20-9ad1e070d339' 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>S-coordinate at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(-0.44745145)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-c4eedfe4-ac2c-4ef7-a020-f376a954aa47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4eedfe4-ac2c-4ef7-a020-f376a954aa47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92f89c4c-b2f2-4dc5-ace1-c1226c4d6108' class='xr-var-data-in' type='checkbox'><label for='data-92f89c4c-b2f2-4dc5-ace1-c1226c4d6108' 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>S-coordinate stretching curves at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>field :</span></dt><dd>Cs_r, scalar</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> 8 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.0</div><input id='attrs-80e94211-1312-4c85-8fb1-af72f471ca02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80e94211-1312-4c85-8fb1-af72f471ca02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5d96cb9-7616-48d9-ace8-0053efe4a8c1' class='xr-var-data-in' type='checkbox'><label for='data-e5d96cb9-7616-48d9-ace8-0053efe4a8c1' 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>S-coordinate parameter, critical depth</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array(20.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Vtransform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-66a8a794-fcb5-4f5c-a627-daea03dff91f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66a8a794-fcb5-4f5c-a627-daea03dff91f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60efa44c-77b5-4dc1-8857-d4a39545572f' class='xr-var-data-in' type='checkbox'><label for='data-60efa44c-77b5-4dc1-8857-d4a39545572f' 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>vertical terrain-following transformation equation</dd></dl></div><div class='xr-var-data'><pre>array(2, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.23</div><input id='attrs-49d7fa42-e7fa-48fd-8744-fa883e080391' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d7fa42-e7fa-48fd-8744-fa883e080391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c43fd191-b536-42b4-9c36-26641f14cf42' class='xr-var-data-in' type='checkbox'><label for='data-c43fd191-b536-42b4-9c36-26641f14cf42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array(28.22602702)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-93.59</div><input id='attrs-889c52d3-b84e-4f7c-9a73-c012d5ef2d37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-889c52d3-b84e-4f7c-9a73-c012d5ef2d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fde2b31-74d0-4ef9-b438-1d096ed3152c' class='xr-var-data-in' type='checkbox'><label for='data-7fde2b31-74d0-4ef9-b438-1d096ed3152c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array(-93.5927727)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocean_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-08-01</div><input id='attrs-dbb90e08-3a81-4cb9-8ae3-ed4ced5f4ac3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbb90e08-3a81-4cb9-8ae3-ed4ced5f4ac3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-555999b8-00ff-47b5-a5d6-507add76e87c' class='xr-var-data-in' type='checkbox'><label for='data-555999b8-00ff-47b5-a5d6-507add76e87c' 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 since initialization</dd><dt><span>field :</span></dt><dd>time, scalar, series</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2001-08-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-10</div><input id='attrs-5dbe5ce3-6bae-4b02-bcb2-e34114732937' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dbe5ce3-6bae-4b02-bcb2-e34114732937' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ea4b212-f0f0-466e-b02b-becd1cac2c33' class='xr-var-data-in' type='checkbox'><label for='data-1ea4b212-f0f0-466e-b02b-becd1cac2c33' 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>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><pre>array(-10)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f8cb32c-d0d8-4380-9a48-c23057ad9fb1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f8cb32c-d0d8-4380-9a48-c23057ad9fb1' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ba46e829-2552-4ed9-9cc7-53114158b29f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba46e829-2552-4ed9-9cc7-53114158b29f' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>field :</span></dt><dd>salinity, scalar, series</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'salt' ()>\n",
       "dask.array<dask_aware_interpnd, shape=(), dtype=float32, chunksize=(), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    s_rho       float64 -0.4475\n",
       "    Cs_r        float64 dask.array<chunksize=(), meta=np.ndarray>\n",
       "    hc          float64 20.0\n",
       "    Vtransform  int32 2\n",
       "    lat         float64 28.23\n",
       "    lon         float64 -93.59\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    z_rho       int64 -10\n",
       "Attributes:\n",
       "    long_name:      salinity\n",
       "    time:           ocean_time\n",
       "    field:          salinity, scalar, series\n",
       "    standard_name:  sea_water_practical_salinity"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interpolate to time value and depth value\n",
    "ds.cf[varname].em.interp2d(longitude, latitude, T=ds.cf['T'][0], Z=-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe576fe2-8e14-4ada-8df8-9d424a8fd5f3",
   "metadata": {},
   "source": [
    "The interpolation is faster the second time the regridder is used — it is saved by the `extract_model` accessor and reused if the lon/lat locations to be interpolated to are the same. Here we interpolate to salinity and it is faster than it was the first time it was used for interpolation the sea surface elevation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d27f585-ad2c-4484-b127-46f1d8593ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 956 ms, sys: 34.3 ms, total: 990 ms\n",
      "Wall time: 1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "varname = zeta\n",
    "\n",
    "# Set up a single lon/lat location\n",
    "j, i = 50, 10\n",
    "longitude = float(ds.cf[varname].cf['longitude'][j,i])\n",
    "latitude = float(ds.cf[varname].cf['latitude'][j,i])\n",
    "\n",
    "# Interpolation\n",
    "da_out = ds.cf[varname].em.interp2d(longitude, latitude)\n",
    "\n",
    "# Selection of nearest location in 2D\n",
    "da_check = ds.cf[varname].em.sel2dcf(longitude=longitude, latitude=latitude).squeeze()\n",
    "\n",
    "assert np.allclose(da_out, da_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88324efb-1231-4a00-99d4-a195f251e95a",
   "metadata": {},
   "source": [
    "### not grid point\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe8a3e9-147a-47e9-ac64-555391fc812c",
   "metadata": {},
   "source": [
    "#### inside domain  (interpolation and selecting nearest)\n",
    "\n",
    "For a selected location that is not a grid point (so we can't check it exactly), we show here both interpolating to that location horizontally and selecting the nearest point to that location.\n",
    "\n",
    "The square in the right hand side plot shows the nearest point selected using `.em.sel2d()` and the circle shows the interpolated value at the exact selected location using `.em.interp2d()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ce15ce3-7752-4ee8-832f-1103fe4f742a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbfc254cb50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varname = zeta\n",
    "\n",
    "# sel\n",
    "longitude = -91.49\n",
    "latitude = 28.510\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "# Interpolation\n",
    "da_out = ds.cf[varname].em.interp2d(longitude, latitude, iT=iT, iZ=iZ)\n",
    "\n",
    "# Selection of nearest location in 2D\n",
    "da_sel = ds.cf[varname].em.sel2dcf(longitude=longitude, latitude=latitude, distances_name=\"distance\").cf.isel(T=iT).squeeze()\n",
    "\n",
    "# Plot\n",
    "cmap = ds.cf[varname].cmo.seq\n",
    "dacheck = ds.cf[varname].cf.isel(isel)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15,5))\n",
    "\n",
    "dacheck.cmo.cfplot(ax=axes[0], x='longitude', y='latitude')\n",
    "axes[0].scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n",
    "           vmin=dacheck.min().values, vmax=dacheck.max().values, cmap=cmap, edgecolors='k')\n",
    "\n",
    "# make smaller area of model to show\n",
    "# want model output only within the box defined by these lat/lon values\n",
    "dacheck_min = dacheck.em.sub_bbox([-91.52, 28.49, -91.49, 28.525], drop=True)\n",
    "dacheck_min.cmo.cfplot(ax=axes[1], x='longitude', y='latitude')\n",
    "# interpolation\n",
    "axes[1].scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n",
    "           vmin=dacheck_min.min().values, vmax=dacheck_min.max().values, \n",
    "                cmap=cmap, edgecolors='k')\n",
    "# selection\n",
    "axes[1].scatter(da_sel.cf['longitude'], da_sel.cf['latitude'], s=50, c=da_sel.cf[varname], \n",
    "           vmin=dacheck_min.min().values, vmax=dacheck_min.max().values, \n",
    "                cmap=cmap, edgecolors='k', marker='s')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d087f455",
   "metadata": {},
   "source": [
    "We input the extra keyword argument `distances_name` into the call `ds.cf[varname].em.sel2dcf` in order to also return the distance between the requested location and the returned model location. This value is shown here in km:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bf5c008",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
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       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;distance&#x27; ()&gt;\n",
       "array(0.51134)\n",
       "Coordinates:\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    xi_rho      int64 136\n",
       "    eta_rho     int64 54\n",
       "    lon_rho     float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "    hc          float64 20.0\n",
       "    h           float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "    Vtransform  int32 2\n",
       "    lat_rho     float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    units:    km</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'distance'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-682361f8-8579-4a48-bc61-7809d591924a' class='xr-array-in' type='checkbox' checked><label for='section-682361f8-8579-4a48-bc61-7809d591924a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.5113</span></div><div class='xr-array-data'><pre>array(0.51134)</pre></div></div></li><li class='xr-section-item'><input id='section-3f6bfbdd-72a2-46e2-94b9-a58143e02ad7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f6bfbdd-72a2-46e2-94b9-a58143e02ad7' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ocean_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2001-08-01</div><input id='attrs-e0bec279-5730-4213-bb78-ec0fba6ae613' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0bec279-5730-4213-bb78-ec0fba6ae613' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af6f2cad-bc8f-45c1-9e1b-c1f145afc4f7' class='xr-var-data-in' type='checkbox'><label for='data-af6f2cad-bc8f-45c1-9e1b-c1f145afc4f7' 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 since initialization</dd><dt><span>field :</span></dt><dd>time, scalar, series</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2001-08-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xi_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>136</div><input id='attrs-959b3f2e-6496-4a9c-82c4-1c49c35bcdc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-959b3f2e-6496-4a9c-82c4-1c49c35bcdc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13106512-7667-4b9e-8078-552f2ac732ab' class='xr-var-data-in' type='checkbox'><label for='data-13106512-7667-4b9e-8078-552f2ac732ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(136)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>54</div><input id='attrs-862af1fb-f56b-494b-860c-07de1bdcda08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-862af1fb-f56b-494b-860c-07de1bdcda08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9e6c497-bb55-4197-88d7-038c249ed3e3' class='xr-var-data-in' type='checkbox'><label for='data-c9e6c497-bb55-4197-88d7-038c249ed3e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(54)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-2546c19d-7231-4a03-a76f-c92597b4f484' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2546c19d-7231-4a03-a76f-c92597b4f484' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d7d16f1-46ba-49d4-b86c-d776c6b6d1bb' class='xr-var-data-in' type='checkbox'><label for='data-1d7d16f1-46ba-49d4-b86c-d776c6b6d1bb' 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>longitude of RHO-points</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>field :</span></dt><dd>lon_rho, scalar</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> 8 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.0</div><input id='attrs-70d3ff86-4a85-4e33-b5e8-7e78b4ae00e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70d3ff86-4a85-4e33-b5e8-7e78b4ae00e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-682c6bcf-8446-4e73-9d75-50c36aaa9b2e' class='xr-var-data-in' type='checkbox'><label for='data-682c6bcf-8446-4e73-9d75-50c36aaa9b2e' 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>S-coordinate parameter, critical depth</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array(20.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-edb1b5bb-529b-4ace-970d-78423b9e41fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edb1b5bb-529b-4ace-970d-78423b9e41fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-614f8962-ffb9-414a-8c1a-78c89113453d' class='xr-var-data-in' type='checkbox'><label for='data-614f8962-ffb9-414a-8c1a-78c89113453d' 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>bathymetry at RHO-points</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>field :</span></dt><dd>bath, scalar</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> 8 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Vtransform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-bdb7af12-b683-4773-b0f0-80ddde0e3238' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdb7af12-b683-4773-b0f0-80ddde0e3238' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa70c421-69a9-482f-b685-4fa91a556f9c' class='xr-var-data-in' type='checkbox'><label for='data-fa70c421-69a9-482f-b685-4fa91a556f9c' 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>vertical terrain-following transformation equation</dd></dl></div><div class='xr-var-data'><pre>array(2, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-bfa21984-091d-4a7d-b34d-747ec251f4f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfa21984-091d-4a7d-b34d-747ec251f4f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-672c20e2-ec4e-42a5-9d5e-5e5bdc375750' class='xr-var-data-in' type='checkbox'><label for='data-672c20e2-ec4e-42a5-9d5e-5e5bdc375750' 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>latitude of RHO-points</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>field :</span></dt><dd>lat_rho, scalar</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> 8 B </td>\n",
       "                        <td> 8 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-de68c71e-9c9a-44c2-9985-206f528559f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de68c71e-9c9a-44c2-9985-206f528559f7' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0b5f4d1c-b9ed-4509-a679-41addb7adee5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b5f4d1c-b9ed-4509-a679-41addb7adee5' 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>units :</span></dt><dd>km</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'distance' ()>\n",
       "array(0.51134)\n",
       "Coordinates:\n",
       "    ocean_time  datetime64[ns] 2001-08-01\n",
       "    xi_rho      int64 136\n",
       "    eta_rho     int64 54\n",
       "    lon_rho     float64 dask.array<chunksize=(), meta=np.ndarray>\n",
       "    hc          float64 20.0\n",
       "    h           float64 dask.array<chunksize=(), meta=np.ndarray>\n",
       "    Vtransform  int32 2\n",
       "    lat_rho     float64 dask.array<chunksize=(), meta=np.ndarray>\n",
       "Attributes:\n",
       "    units:    km"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_sel[\"distance\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9c9c29-ffb8-4c57-b3a9-98f3380492b1",
   "metadata": {},
   "source": [
    "#### outside domain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a2deb6-91d1-47f8-b1bf-d990f180f5f0",
   "metadata": {},
   "source": [
    "Don't extrapolate\n",
    "\n",
    "This is commented out since it purposefully raises an error:\n",
    "> ValueError: Longitude outside of available domain. Use extrap=True to extrapolate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee4d93c5-8b7e-498e-acc9-1daa42c2f252",
   "metadata": {},
   "outputs": [],
   "source": [
    "# varname = zeta\n",
    "\n",
    "# # sel\n",
    "# longitude = -166\n",
    "# latitude = 48\n",
    "# sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# # isel\n",
    "# iZ = 0\n",
    "# iT = 0\n",
    "# isel = dict(Z=iZ, T=iT)\n",
    "\n",
    "# da_out = ds.cf[varname].em.interp2d(longitude, latitude, iT=iT, iZ=iZ, extrap=False)\n",
    "\n",
    "# da_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49c6d41-7c51-4661-911a-79923c4ef307",
   "metadata": {},
   "source": [
    "Extrapolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53dcd122-f08e-49c9-ba59-c9fdfdbb7a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbfa1e57700>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varname = zeta\n",
    "\n",
    "# sel\n",
    "longitude = -89\n",
    "latitude = 28.3\n",
    "sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.cf[varname].em.interp2d(longitude, latitude, iT=iT, iZ=iZ, extrap=True)\n",
    "\n",
    "# plot\n",
    "cmap = ds.cf[varname].cmo.seq\n",
    "dacheck = ds.cf[varname].cf.isel(isel)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "dacheck.cmo.cfplot(ax=ax, x='longitude', y='latitude')\n",
    "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n",
    "           vmin=dacheck.min().values, vmax=dacheck.max().values, cmap=cmap, edgecolors='k')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f58f562-ac41-4bc8-889d-59bcedf2183f",
   "metadata": {},
   "source": [
    "### points (locstream, interpolation)\n",
    "\n",
    "Interpolate to unstructured pairs of lon/lat locations instead of grids of lon/lat locations, using `locstream`. Choose grid points so that we can check the accuracy of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf607c66-1880-4f19-855d-40882e6d768c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "varname = zeta\n",
    "\n",
    "# sel\n",
    "# this creates 12 pairs of lon/lat points that \n",
    "# align with grid points so we can check the \n",
    "# interpolation\n",
    "longitude = ds.cf[varname].cf['longitude'].isel(eta_rho=60, xi_rho=slice(None,None,10))\n",
    "latitude = ds.cf[varname].cf['latitude'].isel(eta_rho=60, xi_rho=slice(None,None,10))\n",
    "sel = dict(X=longitude.xi_rho, Y=longitude.eta_rho)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.cf[varname].em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=True)\n",
    "\n",
    "# check\n",
    "da_check = ds.cf[varname].cf.isel(isel).cf.sel(sel)\n",
    "\n",
    "assert np.allclose(da_out, da_check, equal_nan=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199b045b-a2b4-4489-ba35-5cc12d811ed7",
   "metadata": {},
   "source": [
    "It is not currently possible to interpolate in depth with both more than one time and location. \n",
    "\n",
    "This cell is commented out because it purposefully returns an error:\n",
    "> NotImplementedError: Currently it is not possible to interpolate in depth with more than 1 other (time) dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af347ab7-4848-4465-a564-ff7028245746",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.cf[salt].em.interp2d(longitude, latitude, Z=-10, locstream=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb41f85-9599-43fc-9b3f-118b2c20bc90",
   "metadata": {},
   "source": [
    "### grid of known locations (interpolation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f08d63a0-80e4-4060-ad24-0580fb334367",
   "metadata": {},
   "outputs": [],
   "source": [
    "varname = zeta\n",
    "\n",
    "# sel\n",
    "longitude = ds.cf[varname].cf['longitude'][:-50:20,:-200:100]\n",
    "latitude = ds.cf[varname].cf['latitude'][:-50:20,:-200:100]\n",
    "sel = dict(X=longitude.xi_rho, Y=longitude.eta_rho)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.cf[varname].em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=False)\n",
    "\n",
    "# check\n",
    "da_check = ds.cf[varname].cf.sel(sel).cf.isel(isel)\n",
    "\n",
    "assert np.allclose(da_out, da_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010b474f-8a94-49b8-92e7-2da5632e756a",
   "metadata": {},
   "source": [
    "### grid of new locations (interpolation, regridding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7e8738e3-ddff-44ed-afe6-90e1b217f9bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbf8254f970>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varname = zeta\n",
    "\n",
    "# sel\n",
    "longitude = np.linspace(ds.cf[varname].cf['longitude'].min(), ds.cf[varname].cf['longitude'].max(), 30)\n",
    "latitude = np.linspace(ds.cf[varname].cf['latitude'].min(), ds.cf[varname].cf['latitude'].max(), 30)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.cf[varname].em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=False, extrap=False, extrap_val=np.nan)\n",
    "\n",
    "# plot\n",
    "cmap = cmo.delta\n",
    "dacheck = ds.cf[varname].cf.isel(isel)\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(10,4))\n",
    "dacheck.cmo.cfplot(ax=axes[0], x='longitude', y='latitude')\n",
    "da_out.cmo.cfplot(ax=axes[1], x='longitude', y='latitude')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ed35fe-cd31-44b0-90d2-423dc14f3393",
   "metadata": {},
   "source": [
    "## HYCOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9870855d-79a3-48e0-82ff-bfbc5c6e563b",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;water_u&#x27; (time: 2, lat: 1251, lon: 626)&gt;\n",
       "array([[[        nan,         nan,         nan, ...,  0.002     ,\n",
       "          0.008     ,  0.011     ],\n",
       "        [        nan,         nan,         nan, ..., -0.022     ,\n",
       "         -0.013     , -0.008     ],\n",
       "        [        nan,         nan,         nan, ..., -0.056     ,\n",
       "         -0.044     , -0.028     ],\n",
       "        ...,\n",
       "        [-0.24200001, -0.23400001, -0.231     , ...,  0.19800001,\n",
       "          0.163     ,  0.142     ],\n",
       "        [-0.2       , -0.194     , -0.18800001, ...,  0.142     ,\n",
       "          0.11800001,  0.109     ],\n",
       "        [-0.163     , -0.155     , -0.148     , ...,  0.097     ,\n",
       "          0.08800001,  0.09200001]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,  0.024     ,\n",
       "          0.028     ,  0.028     ],\n",
       "        [        nan,         nan,         nan, ..., -0.006     ,\n",
       "          0.001     ,  0.006     ],\n",
       "        [        nan,         nan,         nan, ..., -0.042     ,\n",
       "         -0.031     , -0.015     ],\n",
       "        ...,\n",
       "        [-0.22900002, -0.21900001, -0.20500001, ...,  0.132     ,\n",
       "          0.094     ,  0.07600001],\n",
       "        [-0.177     , -0.17300001, -0.16600001, ...,  0.081     ,\n",
       "          0.057     ,  0.053     ],\n",
       "        [-0.127     , -0.128     , -0.126     , ...,  0.043     ,\n",
       "          0.035     ,  0.043     ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat       (lat) float64 -20.0 -19.96 -19.92 -19.88 ... 29.92 29.96 30.0\n",
       "  * lon       (lon) float64 140.0 140.1 140.2 140.2 ... 189.8 189.8 189.9 190.0\n",
       "    depth     float64 0.0\n",
       "  * time      (time) datetime64[ns] 2023-01-21T12:00:00 2023-01-21T15:00:00\n",
       "    time_run  (time) datetime64[ns] 2023-01-21T12:00:00 2023-01-21T12:00:00\n",
       "Attributes:\n",
       "    _CoordinateAxes:  time_run time depth lat lon\n",
       "    units:            m/s\n",
       "    long_name:        Eastward Water Velocity\n",
       "    standard_name:    eastward_sea_water_velocity\n",
       "    NAVO_code:        17</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'water_u'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>lat</span>: 1251</li><li><span class='xr-has-index'>lon</span>: 626</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-18a0b59b-ccc4-4159-9127-ed850937a634' class='xr-array-in' type='checkbox' checked><label for='section-18a0b59b-ccc4-4159-9127-ed850937a634' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan ... 0.187 0.129 0.075 0.043 0.035 0.043</span></div><div class='xr-array-data'><pre>array([[[        nan,         nan,         nan, ...,  0.002     ,\n",
       "          0.008     ,  0.011     ],\n",
       "        [        nan,         nan,         nan, ..., -0.022     ,\n",
       "         -0.013     , -0.008     ],\n",
       "        [        nan,         nan,         nan, ..., -0.056     ,\n",
       "         -0.044     , -0.028     ],\n",
       "        ...,\n",
       "        [-0.24200001, -0.23400001, -0.231     , ...,  0.19800001,\n",
       "          0.163     ,  0.142     ],\n",
       "        [-0.2       , -0.194     , -0.18800001, ...,  0.142     ,\n",
       "          0.11800001,  0.109     ],\n",
       "        [-0.163     , -0.155     , -0.148     , ...,  0.097     ,\n",
       "          0.08800001,  0.09200001]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,  0.024     ,\n",
       "          0.028     ,  0.028     ],\n",
       "        [        nan,         nan,         nan, ..., -0.006     ,\n",
       "          0.001     ,  0.006     ],\n",
       "        [        nan,         nan,         nan, ..., -0.042     ,\n",
       "         -0.031     , -0.015     ],\n",
       "        ...,\n",
       "        [-0.22900002, -0.21900001, -0.20500001, ...,  0.132     ,\n",
       "          0.094     ,  0.07600001],\n",
       "        [-0.177     , -0.17300001, -0.16600001, ...,  0.081     ,\n",
       "          0.057     ,  0.053     ],\n",
       "        [-0.127     , -0.128     , -0.126     , ...,  0.043     ,\n",
       "          0.035     ,  0.043     ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-04ee64ec-093e-44f9-9512-1afb9265a07a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04ee64ec-093e-44f9-9512-1afb9265a07a' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-20.0 -19.96 -19.92 ... 29.96 30.0</div><input id='attrs-c4c8d34f-fd11-4409-ac3a-c5f92a1a622b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4c8d34f-fd11-4409-ac3a-c5f92a1a622b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8f0f32e-e77f-4e7c-8768-fff1f9029324' class='xr-var-data-in' type='checkbox'><label for='data-e8f0f32e-e77f-4e7c-8768-fff1f9029324' 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>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>NAVO_code :</span></dt><dd>1</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([-20.      , -19.959999, -19.92    , ...,  29.92    ,  29.959999,\n",
       "        30.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>140.0 140.1 140.2 ... 189.9 190.0</div><input id='attrs-3d83424a-2353-4df7-97ab-5b4e46ea4e04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d83424a-2353-4df7-97ab-5b4e46ea4e04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11b15f7d-f4d3-4b8a-abc9-2a1afa958fc0' class='xr-var-data-in' type='checkbox'><label for='data-11b15f7d-f4d3-4b8a-abc9-2a1afa958fc0' 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>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>modulo :</span></dt><dd>360 degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>NAVO_code :</span></dt><dd>2</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([140.      , 140.079956, 140.160034, ..., 189.839966, 189.919983,\n",
       "       190.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-b1e6af5e-4bf9-4472-9605-e2c2688c4360' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1e6af5e-4bf9-4472-9605-e2c2688c4360' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa51c622-b0c8-4d0a-befc-b33aed2a1f42' class='xr-var-data-in' type='checkbox'><label for='data-fa51c622-b0c8-4d0a-befc-b33aed2a1f42' 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>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>NAVO_code :</span></dt><dd>5</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-21T12:00:00 2023-01-21T1...</div><input id='attrs-380dc3e3-f5bb-43f0-a76b-34ac4091f496' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-380dc3e3-f5bb-43f0-a76b-34ac4091f496' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78373e0c-bfb4-4aba-a3ae-6fa4be614a22' class='xr-var-data-in' type='checkbox'><label for='data-78373e0c-bfb4-4aba-a3ae-6fa4be614a22' 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>Forecast time for ForecastModelRunCollection</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-21T12:00:00.000000000&#x27;, &#x27;2023-01-21T15:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_run</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-21T12:00:00 2023-01-21T1...</div><input id='attrs-0cfd601b-2895-420a-a8e5-0fe24f738cd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cfd601b-2895-420a-a8e5-0fe24f738cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a5dc7ee-a6f4-4de0-9d63-3ea361580d12' class='xr-var-data-in' type='checkbox'><label for='data-7a5dc7ee-a6f4-4de0-9d63-3ea361580d12' 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>run times for coordinate = time</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-21T12:00:00.000000000&#x27;, &#x27;2023-01-21T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3162153-79f9-4969-9710-95317b3e24e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-f3162153-79f9-4969-9710-95317b3e24e0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bf927f04-c7e3-4c7b-af3b-20f61c96a7ab' class='xr-index-data-in' type='checkbox'/><label for='index-bf927f04-c7e3-4c7b-af3b-20f61c96a7ab' 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(Float64Index([              -20.0, -19.959999084472656, -19.920000076293945,\n",
       "                -19.8799991607666,  -19.84000015258789, -19.799999237060547,\n",
       "              -19.760000228881836, -19.719999313354492,  -19.68000030517578,\n",
       "              -19.639999389648438,\n",
       "              ...\n",
       "               29.639999389648438,   29.68000030517578,  29.719999313354492,\n",
       "               29.760000228881836,  29.799999237060547,   29.84000015258789,\n",
       "                 29.8799991607666,  29.920000076293945,  29.959999084472656,\n",
       "                             30.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=1251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3ce9d10-0d7b-4e00-b06a-d62361709dd3' class='xr-index-data-in' type='checkbox'/><label for='index-a3ce9d10-0d7b-4e00-b06a-d62361709dd3' 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(Float64Index([             140.0,  140.0799560546875,  140.1600341796875,\n",
       "                140.239990234375,   140.320068359375,  140.4000244140625,\n",
       "                 140.47998046875,    140.56005859375,  140.6400146484375,\n",
       "                140.719970703125,\n",
       "              ...\n",
       "                189.280029296875,  189.3599853515625, 189.44000244140625,\n",
       "                 189.52001953125,  189.5999755859375, 189.67999267578125,\n",
       "                189.760009765625,  189.8399658203125, 189.91998291015625,\n",
       "                           190.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=626))</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-d7c086e1-c78f-42e7-9e87-7ec6d6be9ebb' class='xr-index-data-in' type='checkbox'/><label for='index-d7c086e1-c78f-42e7-9e87-7ec6d6be9ebb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-01-21 12:00:00&#x27;, &#x27;2023-01-21 15:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14e9b30f-91ab-4ef5-8a93-d4d3aadffae3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14e9b30f-91ab-4ef5-8a93-d4d3aadffae3' 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>_CoordinateAxes :</span></dt><dd>time_run time depth lat lon</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>long_name :</span></dt><dd>Eastward Water Velocity</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>NAVO_code :</span></dt><dd>17</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'water_u' (time: 2, lat: 1251, lon: 626)>\n",
       "array([[[        nan,         nan,         nan, ...,  0.002     ,\n",
       "          0.008     ,  0.011     ],\n",
       "        [        nan,         nan,         nan, ..., -0.022     ,\n",
       "         -0.013     , -0.008     ],\n",
       "        [        nan,         nan,         nan, ..., -0.056     ,\n",
       "         -0.044     , -0.028     ],\n",
       "        ...,\n",
       "        [-0.24200001, -0.23400001, -0.231     , ...,  0.19800001,\n",
       "          0.163     ,  0.142     ],\n",
       "        [-0.2       , -0.194     , -0.18800001, ...,  0.142     ,\n",
       "          0.11800001,  0.109     ],\n",
       "        [-0.163     , -0.155     , -0.148     , ...,  0.097     ,\n",
       "          0.08800001,  0.09200001]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,  0.024     ,\n",
       "          0.028     ,  0.028     ],\n",
       "        [        nan,         nan,         nan, ..., -0.006     ,\n",
       "          0.001     ,  0.006     ],\n",
       "        [        nan,         nan,         nan, ..., -0.042     ,\n",
       "         -0.031     , -0.015     ],\n",
       "        ...,\n",
       "        [-0.22900002, -0.21900001, -0.20500001, ...,  0.132     ,\n",
       "          0.094     ,  0.07600001],\n",
       "        [-0.177     , -0.17300001, -0.16600001, ...,  0.081     ,\n",
       "          0.057     ,  0.053     ],\n",
       "        [-0.127     , -0.128     , -0.126     , ...,  0.043     ,\n",
       "          0.035     ,  0.043     ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat       (lat) float64 -20.0 -19.96 -19.92 -19.88 ... 29.92 29.96 30.0\n",
       "  * lon       (lon) float64 140.0 140.1 140.2 140.2 ... 189.8 189.8 189.9 190.0\n",
       "    depth     float64 0.0\n",
       "  * time      (time) datetime64[ns] 2023-01-21T12:00:00 2023-01-21T15:00:00\n",
       "    time_run  (time) datetime64[ns] 2023-01-21T12:00:00 2023-01-21T12:00:00\n",
       "Attributes:\n",
       "    _CoordinateAxes:  time_run time depth lat lon\n",
       "    units:            m/s\n",
       "    long_name:        Eastward Water Velocity\n",
       "    standard_name:    eastward_sea_water_velocity\n",
       "    NAVO_code:        17"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# url = ['http://tds.hycom.org/thredds/dodsC/GLBy0.08/latest']\n",
    "# ds = xr.open_mfdataset(url, preprocess=em.preprocess, drop_variables='tau')\n",
    "# ds.isel(time=slice(0,2)).sel(lat=slice(-20, 30), lon=slice(140,190)).to_netcdf('hycom.nc')\n",
    "# ds = xr.open_mfdataset('hycom.nc', preprocess=em.preprocess)\n",
    "\n",
    "url = 'http://tds.hycom.org/thredds/dodsC/GLBy0.08/latest'\n",
    "ds = xr.open_dataset(url, drop_variables='tau')[\"water_u\"].isel(time=slice(0,2), depth=0).sel(lat=slice(-20, 30), lon=slice(140,190))\n",
    "ds = em.preprocess(ds)\n",
    "ds = ds.load()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ddf75afa-5a40-4488-b5d5-876a31b474ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes: * X: ['lon']\n",
       "           * Y: ['lat']\n",
       "             Z: ['depth']\n",
       "           * T: ['time']\n",
       "\n",
       "- CF Coordinates: * longitude: ['lon']\n",
       "                  * latitude: ['lat']\n",
       "                    vertical: ['depth']\n",
       "                  * time: ['time']\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   depth: ['depth']\n",
       "                    forecast_reference_time: ['time_run']\n",
       "                  * latitude: ['lat']\n",
       "                  * longitude: ['lon']\n",
       "                  * time: ['time']\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79341d1-492e-4896-8c49-aa8db06a7aba",
   "metadata": {},
   "source": [
    "### grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c796884-ee95-4410-8a6c-fbc455041932",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "# sel\n",
    "longitude = float(ds.cf['X'][100])\n",
    "latitude = float(ds.cf['Y'][150])\n",
    "sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = None\n",
    "# isel = dict(Z=iZ)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ)\n",
    "\n",
    "# check\n",
    "da_check = ds.cf.sel(sel)#.cf.isel(isel)\n",
    "\n",
    "assert np.allclose(da_out, da_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f3ca75-f2af-43bf-9f8c-0818b7072d2a",
   "metadata": {},
   "source": [
    "### not grid point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf72205-aec3-42dc-b77f-b12a931be41c",
   "metadata": {},
   "source": [
    "#### inside domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ea47f0dc-a5ed-46f4-81c0-2044856c3f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbf824e8550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# sel\n",
    "longitude = 155\n",
    "latitude = 5\n",
    "sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ)\n",
    "\n",
    "# plot\n",
    "cmap = cmo.delta\n",
    "dacheck = ds.cf.isel(isel)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "dacheck.cmo.plot(ax=ax)\n",
    "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n",
    "           vmin=dacheck.min().values, vmax=dacheck.max().values, cmap=cmap, edgecolors='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2abd653-de42-44f9-8350-6827657dae47",
   "metadata": {},
   "source": [
    "#### outside domain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbf2d59-939b-409b-b1aa-2e447b62a896",
   "metadata": {},
   "source": [
    "Don't extrapolate\n",
    "\n",
    "This purposefully raises an error so is commented out:\n",
    "> ValueError: Longitude outside of available domain. Use extrap=True to extrapolate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ed6261a-3bcf-495d-8c5e-cb9dc257fa62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # sel\n",
    "# longitude = -166\n",
    "# latitude = 48\n",
    "# sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# # isel\n",
    "# iZ = None\n",
    "# iT = 0\n",
    "# isel = dict(T=iT)\n",
    "\n",
    "# da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ, extrap=False)\n",
    "\n",
    "# da_out = em.select(**kwargs)\n",
    "# da_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78762933-aefe-4097-8866-1aca3148c662",
   "metadata": {},
   "source": [
    "Extrapolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f1232129-055f-4ea0-86ab-9ca129edfb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(138.0, 190.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# sel\n",
    "longitude = 139\n",
    "latitude = 0\n",
    "sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ, extrap=True)\n",
    "\n",
    "# plot\n",
    "cmap = cmo.delta\n",
    "dacheck = ds.cf.isel(isel)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "dacheck.cmo.plot(ax=ax)\n",
    "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n",
    "           vmin=dacheck.min().values, vmax=dacheck.max().values, cmap=cmap, edgecolors='k')\n",
    "\n",
    "ax.set_xlim(138,190)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bce0f5-0be3-4f9d-a097-c7f39e7f8788",
   "metadata": {},
   "source": [
    "### points (locstream)\n",
    "\n",
    "Unstructured pairs of lon/lat locations instead of grids of lon/lat locations, using `locstream`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f6609d67-8217-4a7e-ab2a-31e2646ea840",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "# sel\n",
    "# this creates 12 pairs of lon/lat points that \n",
    "# align with grid points so we can check the \n",
    "# interpolation\n",
    "longitude = ds.cf['X'][::40].values\n",
    "latitude = ds.cf['Y'][::80].values\n",
    "# selecting individual lon/lat locations with advanced xarray indexing\n",
    "sel = dict(longitude=xr.DataArray(longitude, dims=\"pts\"), latitude=xr.DataArray(latitude, dims=\"pts\"))\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=True)\n",
    "\n",
    "# check\n",
    "da_check = ds.cf.isel(isel).cf.sel(sel)\n",
    "\n",
    "assert np.allclose(da_out, da_check, equal_nan=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aee17b6-55f7-43bf-b1ce-0040fe6506a0",
   "metadata": {},
   "source": [
    "### grid of known locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e16ce5d8-d126-43f0-bed7-08661a04e5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# sel\n",
    "longitude = ds.cf['X'][100::500]\n",
    "latitude = ds.cf['Y'][100::500]\n",
    "sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = None\n",
    "# isel = dict(Z=iZ)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=False)\n",
    "\n",
    "# check\n",
    "da_check = ds.cf.sel(sel)#.cf.isel(isel)\n",
    "\n",
    "assert np.allclose(da_out, da_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc123ada-7da4-4a43-8da0-5b905dd61e19",
   "metadata": {},
   "source": [
    "### grid of new locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f18dd5b0-be7d-4dc6-83fc-92ed8bff2f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbfc349bee0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# sel\n",
    "longitude = np.linspace(ds.cf['X'].min(), ds.cf['X'].max(), 30)\n",
    "latitude = np.linspace(ds.cf['Y'].min(), ds.cf['Y'].max(), 30)\n",
    "sel = dict(longitude=longitude, latitude=latitude)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=False)\n",
    "# kwargs = dict(da, longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n",
    "\n",
    "# da_out = em.select(**kwargs)\n",
    "\n",
    "# plot\n",
    "cmap = cmo.delta\n",
    "dacheck = ds.cf.isel(isel)\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(10,4))\n",
    "dacheck.cmo.plot(ax=axes[0])\n",
    "da_out.cmo.plot(ax=axes[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3ff659-563d-4a23-8475-4cf75531ce0d",
   "metadata": {},
   "source": [
    "## POM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29f33ead-285c-4dfc-bab1-2aac93a36191",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;zeta&#x27; (time: 2, ny: 25, nx: 61)&gt;\n",
       "array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 0.5127469, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 0.5261942, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "    lon       (ny, nx) float32 -79.79 -79.73 -79.67 ... -76.19 -76.13 -76.07\n",
       "    lat       (ny, nx) float32 43.14 43.14 43.15 43.15 ... 44.22 44.22 44.22\n",
       "  * time      (time) datetime64[ns] 2018-01-01T00:59:45 2018-01-01T02:00:13.1...\n",
       "    time_run  (time) datetime64[ns] 2018-01-01T00:59:45 2018-01-01T00:59:45\n",
       "  * nx        (nx) int64 0 1 2 3 4 5 6 7 8 9 ... 51 52 53 54 55 56 57 58 59 60\n",
       "  * ny        (ny) int64 0 1 2 3 4 5 6 7 8 9 ... 15 16 17 18 19 20 21 22 23 24\n",
       "Attributes:\n",
       "    units:          meters\n",
       "    long_name:      Height Above Model Sea Level\n",
       "    reference:      model_sea_level\n",
       "    standard_name:  sea_surface_elevation</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'zeta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>ny</span>: 25</li><li><span class='xr-has-index'>nx</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-67548861-89d9-4854-adca-67bbcabb785f' class='xr-array-in' type='checkbox' checked><label for='section-67548861-89d9-4854-adca-67bbcabb785f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 0.5127469, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 0.5261942, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-278864bd-d61a-4dd2-bd82-52b9656277c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-278864bd-d61a-4dd2-bd82-52b9656277c6' 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>lon</span></div><div class='xr-var-dims'>(ny, nx)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-79.79 -79.73 ... -76.13 -76.07</div><input id='attrs-efa5d3d7-162a-4128-94cb-cafe02fe6c1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efa5d3d7-162a-4128-94cb-cafe02fe6c1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700414d4-3a31-49c2-9bfb-b521a1327a9a' class='xr-var-data-in' type='checkbox'><label for='data-700414d4-3a31-49c2-9bfb-b521a1327a9a' 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>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([[-79.7885, -79.7271, -79.6656, ..., -76.2239, -76.1625, -76.101 ],\n",
       "       [-79.7899, -79.7284, -79.6669, ..., -76.2226, -76.1611, -76.0995],\n",
       "       [-79.7912, -79.7297, -79.6681, ..., -76.2212, -76.1597, -76.0981],\n",
       "       ...,\n",
       "       [-79.8183, -79.7558, -79.6933, ..., -76.1943, -76.1318, -76.0694],\n",
       "       [-79.8196, -79.7571, -79.6946, ..., -76.193 , -76.1305, -76.0679],\n",
       "       [-79.8209, -79.7584, -79.6958, ..., -76.1916, -76.1291, -76.0665]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(ny, nx)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>43.14 43.14 43.15 ... 44.22 44.22</div><input id='attrs-64aed18b-6cc5-49e4-b065-3e250d4b4cae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64aed18b-6cc5-49e4-b065-3e250d4b4cae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-692a7eaf-5d2e-4384-a5b0-0ad04dc07141' class='xr-var-data-in' type='checkbox'><label for='data-692a7eaf-5d2e-4384-a5b0-0ad04dc07141' 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>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([[43.14354, 43.1445 , 43.14544, ..., 43.14364, 43.14264, 43.14162],\n",
       "       [43.18853, 43.1895 , 43.19042, ..., 43.18863, 43.18763, 43.18661],\n",
       "       [43.23352, 43.23448, 43.23541, ..., 43.23362, 43.23262, 43.23159],\n",
       "       ...,\n",
       "       [44.13329, 44.13425, 44.13518, ..., 44.13338, 44.13239, 44.13136],\n",
       "       [44.17828, 44.17924, 44.18017, ..., 44.17837, 44.17738, 44.17635],\n",
       "       [44.22327, 44.22423, 44.22516, ..., 44.22336, 44.22237, 44.22134]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01T00:59:45 2018-01-01T0...</div><input id='attrs-d2a725e2-2301-45b1-8977-bc8381b9a83b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2a725e2-2301-45b1-8977-bc8381b9a83b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae0d25bf-4cdf-4b69-a604-f44f4f5f6816' class='xr-var-data-in' type='checkbox'><label for='data-ae0d25bf-4cdf-4b69-a604-f44f4f5f6816' 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>Forecast time for ForecastModelRunCollection</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:59:45.000000000&#x27;, &#x27;2018-01-01T02:00:13.126800000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_run</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01T00:59:45 2018-01-01T0...</div><input id='attrs-dca0934e-51d1-478f-94a3-39f96c924a55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dca0934e-51d1-478f-94a3-39f96c924a55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-985ab310-e450-4e17-a18e-d2fc5da9ddeb' class='xr-var-data-in' type='checkbox'><label for='data-985ab310-e450-4e17-a18e-d2fc5da9ddeb' 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>run times for coordinate = time</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:59:45.000000000&#x27;, &#x27;2018-01-01T00:59:45.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nx</span></div><div class='xr-var-dims'>(nx)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 55 56 57 58 59 60</div><input id='attrs-1b9646c9-fe1d-43c4-a99d-3a7099ed3f17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b9646c9-fe1d-43c4-a99d-3a7099ed3f17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-269dbd2c-29c4-4e77-a84a-df410cfe3dc5' class='xr-var-data-in' type='checkbox'><label for='data-269dbd2c-29c4-4e77-a84a-df410cfe3dc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ny</span></div><div class='xr-var-dims'>(ny)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 19 20 21 22 23 24</div><input id='attrs-67681fec-49dd-4c09-907c-ccfd3e5dcdba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67681fec-49dd-4c09-907c-ccfd3e5dcdba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d43f94e-3060-4f06-88e2-8b5f5510d0d6' class='xr-var-data-in' type='checkbox'><label for='data-6d43f94e-3060-4f06-88e2-8b5f5510d0d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9373f210-146b-4d96-a5be-057b654e22ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-9373f210-146b-4d96-a5be-057b654e22ff' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6a2bd0e8-bdf7-4d0c-bdf7-05b176c4406d' class='xr-index-data-in' type='checkbox'/><label for='index-6a2bd0e8-bdf7-4d0c-bdf7-05b176c4406d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2018-01-01 00:59:45&#x27;, &#x27;2018-01-01 02:00:13.126800&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>nx</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e06b0053-f30b-4451-b280-ac6434fa2a54' class='xr-index-data-in' type='checkbox'/><label for='index-e06b0053-f30b-4451-b280-ac6434fa2a54' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;nx&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ny</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bad8ffc2-eafe-45cc-a083-5a9c3afc2d9c' class='xr-index-data-in' type='checkbox'/><label for='index-bad8ffc2-eafe-45cc-a083-5a9c3afc2d9c' 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(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;ny&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18cdbdb9-beb2-4fed-a2e9-4f2e1b5b36c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18cdbdb9-beb2-4fed-a2e9-4f2e1b5b36c9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>meters</dd><dt><span>long_name :</span></dt><dd>Height Above Model Sea Level</dd><dt><span>reference :</span></dt><dd>model_sea_level</dd><dt><span>standard_name :</span></dt><dd>sea_surface_elevation</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'zeta' (time: 2, ny: 25, nx: 61)>\n",
       "array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 0.5127469, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 0.5261942, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "    lon       (ny, nx) float32 -79.79 -79.73 -79.67 ... -76.19 -76.13 -76.07\n",
       "    lat       (ny, nx) float32 43.14 43.14 43.15 43.15 ... 44.22 44.22 44.22\n",
       "  * time      (time) datetime64[ns] 2018-01-01T00:59:45 2018-01-01T02:00:13.1...\n",
       "    time_run  (time) datetime64[ns] 2018-01-01T00:59:45 2018-01-01T00:59:45\n",
       "  * nx        (nx) int64 0 1 2 3 4 5 6 7 8 9 ... 51 52 53 54 55 56 57 58 59 60\n",
       "  * ny        (ny) int64 0 1 2 3 4 5 6 7 8 9 ... 15 16 17 18 19 20 21 22 23 24\n",
       "Attributes:\n",
       "    units:          meters\n",
       "    long_name:      Height Above Model Sea Level\n",
       "    reference:      model_sea_level\n",
       "    standard_name:  sea_surface_elevation"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    url = \"https://www.ncei.noaa.gov/thredds/dodsC/model-loofs-agg/Aggregated_LOOFS_Fields_Forecast_best.ncd\"\n",
    "    # url = ['https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/LOOFS/fmrc/Aggregated_7_day_LOOFS_Fields_Forecast_best.ncd']\n",
    "    # ds = xr.open_mfdataset(url, preprocess=em.preprocess, chunks=None)\n",
    "    ds= xr.open_dataset(url)\n",
    "    ds = em.utils.preprocess_pom(ds, interp_vertical=False)\n",
    "except OSError:\n",
    "    import pandas as pd\n",
    "    today = pd.Timestamp.today()\n",
    "    url = [today.strftime('https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LOOFS/MODELS/%Y/%m/%d/glofs.loofs.fields.nowcast.%Y%m%d.t00z.nc'),\n",
    "           today.strftime('https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/LOOFS/MODELS/%Y/%m/%d/glofs.loofs.fields.nowcast.%Y%m%d.t06z.nc')]\n",
    "    ds = xr.open_mfdataset(url, preprocess=em.preprocess)\n",
    "\n",
    "ds = ds[\"zeta\"].isel(time=slice(0,2)).load()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "20fe7d43-d333-461b-a3a5-98c7dfaeb107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes: * X: ['nx']\n",
       "           * Y: ['ny']\n",
       "           * T: ['time']\n",
       "             Z: n/a\n",
       "\n",
       "- CF Coordinates:   longitude: ['lon']\n",
       "                    latitude: ['lat']\n",
       "                  * time: ['time']\n",
       "                    vertical: n/a\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   forecast_reference_time: ['time_run']\n",
       "                    latitude: ['lat']\n",
       "                    longitude: ['lon']\n",
       "                  * time: ['time']\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a77b08f-8ae3-4b68-90e5-3b4f5e6187f5",
   "metadata": {},
   "source": [
    "### grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5093c10b-de10-41a6-8e86-b9e2955b4e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 143 ms, sys: 2.23 ms, total: 146 ms\n",
      "Wall time: 145 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Set up a single lon/lat location\n",
    "j, i = 10, 10\n",
    "longitude = float(ds.cf['longitude'][j,i])\n",
    "latitude = float(ds.cf['latitude'][j,i])\n",
    "\n",
    "# Select-by-index a time index and no vertical index (zeta has none)\n",
    "# also lon/lat by index\n",
    "Z = None\n",
    "iT = 0\n",
    "isel = dict(T=iT, X=i, Y=j)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=Z)\n",
    "\n",
    "# check work\n",
    "da_check = ds.cf.isel(isel)\n",
    "\n",
    "assert np.allclose(da_out, da_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be4e70-803d-4f7a-83f1-ad2cbb1799c3",
   "metadata": {},
   "source": [
    "This is faster the second time the regridder is used — it is saved by the `extract_model` accessor and reused if the lon/lat locations to be interpolated to are the same."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2fb123-b87a-435b-b197-ce45b444006a",
   "metadata": {},
   "source": [
    "### not grid point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5cbd61d-23a7-486b-85fb-bd3d87b84748",
   "metadata": {},
   "source": [
    "#### inside domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0da47f83-0033-4ec9-a823-fbbc9d8132eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbfc2c23a90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# sel\n",
    "longitude = -78.0\n",
    "latitude = 43.6\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 1\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ)\n",
    "\n",
    "# plot\n",
    "cmap = ds.cmo.seq\n",
    "dacheck = ds.cf.isel(isel)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "dacheck.cmo.cfplot(ax=ax, x='longitude', y='latitude')\n",
    "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n",
    "           vmin=dacheck.min().values, vmax=dacheck.max().values, cmap=cmap, edgecolors='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a71f88f-1e67-42f2-8804-0b9ff205b8c7",
   "metadata": {},
   "source": [
    "### points (locstream)\n",
    "\n",
    "Unstructured pairs of lon/lat locations instead of grids of lon/lat locations, using `locstream`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "292f4a64-a3ff-4e66-8bc7-d84776d2af6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "# sel\n",
    "# this creates 12 pairs of lon/lat points that \n",
    "# align with grid points so we can check the \n",
    "# interpolation\n",
    "longitude = ds.cf['longitude'].cf.isel(Y=20, X=slice(None, None, 10))\n",
    "latitude = ds.cf['latitude'].cf.isel(Y=20, X=slice(None, None, 10))\n",
    "sel = dict(X=longitude.cf['X'], Y=longitude.cf['Y'])\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 0\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=True)\n",
    "\n",
    "# check\n",
    "da_check = ds.cf.isel(isel).cf.sel(sel)\n",
    "\n",
    "assert np.allclose(da_out, da_check, equal_nan=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb939dc-c2dd-49ad-8c6c-bff63650c6df",
   "metadata": {},
   "source": [
    "### grid of new locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a508b682-4dd1-477b-a94f-2c3efa4eaa3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbf82a61cd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# sel\n",
    "longitude = np.linspace(ds.cf['longitude'].min(), ds.cf['longitude'].max(), 15)\n",
    "latitude = np.linspace(ds.cf['latitude'].min(), ds.cf['latitude'].max(), 15)\n",
    "\n",
    "# isel\n",
    "iZ = None\n",
    "iT = 1\n",
    "isel = dict(T=iT)\n",
    "\n",
    "da_out = ds.em.interp2d(longitude, latitude, iT=iT, iZ=iZ, locstream=False, extrap=False, extrap_val=np.nan)\n",
    "\n",
    "# plot\n",
    "cmap = cmo.delta\n",
    "dacheck = ds.cf.isel(isel)\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(10,4))\n",
    "dacheck.cmo.cfplot(ax=axes[0], x='longitude', y='latitude')\n",
    "da_out.cmo.cfplot(ax=axes[1], x='longitude', y='latitude')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07d49c2e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 ('extract_model_docs')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "2550ba5b6be2a0d45a8f2250e82dc8cc77257ff4ba16039f1d5283ab11cac183"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}