data_access.ipynb

{
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   "source": [
    "import dask\n",
    "import intake\n",
    "import pandas as pd\n",
    "import xarray\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4eea6a-0f29-4806-8043-de07bbaea272",
   "metadata": {},
   "source": [
    "**Read in catalog and initialize dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "905569e2-3441-4fa6-a7b7-854133a22a56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CORA-V1-fort.63', 'CORA-V1-maxele.63', 'CORA-V1-fort.64', 'CORA-V1-500m-grid-1979-2022']\n"
     ]
    }
   ],
   "source": [
    "catalog = intake.open_catalog(\"s3://noaa-nos-cora-pds/CORA_intake.yml\",storage_options={'anon':True})\n",
    "print(list(catalog))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0ef1f09-d84b-4ff2-b2db-92961dddf802",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (mesh: 1, node: 1813443, nele: 3564104, nvertex: 3, nbou: 186,\n",
       "                  nope: 1, neta_dim: 186, nvel_dim: 62972, time: 385704)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1979-01-01 ... 2022-12-31T23:00:00\n",
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       "    y            (node) float64 dask.array&lt;chunksize=(160000,), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: mesh, node, nele, nvertex, nbou, nope,\n",
       "                                neta_dim, nvel_dim\n",
       "Data variables: (12/14)\n",
       "    adcirc_mesh  (mesh) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    depth        (node) float64 dask.array&lt;chunksize=(160000,), meta=np.ndarray&gt;\n",
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       "    ibtype       (nbou) int32 dask.array&lt;chunksize=(186,), meta=np.ndarray&gt;\n",
       "    ibtypee      (nope) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    max_nvdll    float64 ...\n",
       "    ...           ...\n",
       "    nbvv         (nvel_dim) int32 dask.array&lt;chunksize=(62972,), meta=np.ndarray&gt;\n",
       "    neta         float64 ...\n",
       "    nvdll        (nope) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    nvel         float64 ...\n",
       "    nvell        (nbou) int32 dask.array&lt;chunksize=(186,), meta=np.ndarray&gt;\n",
       "    zeta         (time, node) float64 dask.array&lt;chunksize=(240, 160000), meta=np.ndarray&gt;\n",
       "Attributes: (12/49)\n",
       "    Conventions:        UGRID-0.9.0\n",
       "    NCO:                netCDF Operators version 4.7.5 (Homepage = http://nco...\n",
       "    a00:                0.0\n",
       "    agrid:              NOMAD mesh v1e MSL - fixed bahamas bathy\n",
       "    b00:                1.0\n",
       "    c00:                0.0\n",
       "    ...                 ...\n",
       "    slam0:              265.5\n",
       "    source:             RENCI_HPC\n",
       "    statim:             0.0\n",
       "    tau0:               -3.0\n",
       "    title:              NOAA-Reanalysis-Post\n",
       "    version:            noaa.stofs.2d.glo.v1.1.0r2-21-g7052115</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-202d95de-a90b-4acd-b319-2da0cd89daac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-202d95de-a90b-4acd-b319-2da0cd89daac' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>mesh</span>: 1</li><li><span>node</span>: 1813443</li><li><span>nele</span>: 3564104</li><li><span>nvertex</span>: 3</li><li><span>nbou</span>: 186</li><li><span>nope</span>: 1</li><li><span>neta_dim</span>: 186</li><li><span>nvel_dim</span>: 62972</li><li><span class='xr-has-index'>time</span>: 385704</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a2568616-6b68-4617-9516-8093d97ef906' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2568616-6b68-4617-9516-8093d97ef906' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1979-01-01 ... 2022-12-31T23:00:00</div><input id='attrs-4a70a3f1-d98b-4509-b5fc-c49ecfc13d86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a70a3f1-d98b-4509-b5fc-c49ecfc13d86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-757c5b66-32ef-4f5a-9100-9b6e6a2bdf03' class='xr-var-data-in' type='checkbox'><label for='data-757c5b66-32ef-4f5a-9100-9b6e6a2bdf03' 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>model time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T02:00:00.000000000&#x27;, ..., &#x27;2022-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-31T22:00:00.000000000&#x27;, &#x27;2022-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(160000,), meta=np.ndarray&gt;</div><input id='attrs-bd9caf8f-f997-4f53-b8f9-cbc2d4ed5a99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd9caf8f-f997-4f53-b8f9-cbc2d4ed5a99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52671167-8ed9-400e-9501-5e4c468112c4' class='xr-var-data-in' type='checkbox'><label for='data-52671167-8ed9-400e-9501-5e4c468112c4' 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</dd><dt><span>positive :</span></dt><dd>east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.84 MiB </td>\n",
       "                        <td> 1.22 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1813443,) </td>\n",
       "                        <td> (160000,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 2 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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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(160000,), meta=np.ndarray&gt;</div><input id='attrs-0a057d16-a726-4632-9d47-e1c27e68b5bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a057d16-a726-4632-9d47-e1c27e68b5bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5b5311-5165-447f-9fc6-3dce7e12d54b' class='xr-var-data-in' type='checkbox'><label for='data-7c5b5311-5165-447f-9fc6-3dce7e12d54b' 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</dd><dt><span>positive :</span></dt><dd>north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (160000,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8581f73e-55e6-4a66-b301-c2c53367f46c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8581f73e-55e6-4a66-b301-c2c53367f46c' class='xr-section-summary' >Data variables: <span>(14)</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>adcirc_mesh</span></div><div class='xr-var-dims'>(mesh)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c51cf5bb-56c7-4b28-b516-6d1eedf65023' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c51cf5bb-56c7-4b28-b516-6d1eedf65023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-425e6c36-46b2-4404-be85-55eca9f7c457' class='xr-var-data-in' type='checkbox'><label for='data-425e6c36-46b2-4404-be85-55eca9f7c457' 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>cf_role :</span></dt><dd>mesh_topology</dd><dt><span>face_node_connectivity :</span></dt><dd>element</dd><dt><span>long_name :</span></dt><dd>mesh_topology</dd><dt><span>node_coordinates :</span></dt><dd>x y</dd><dt><span>topology_dimension :</span></dt><dd>2</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> 4 B </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1,) </td>\n",
       "                        <td> (1,) </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\"> int32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(160000,), meta=np.ndarray&gt;</div><input id='attrs-70cc0a73-a214-4287-8a01-45ab3d48c1e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70cc0a73-a214-4287-8a01-45ab3d48c1e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ada73d79-d5fa-46e0-8421-08f4e39010ad' class='xr-var-data-in' type='checkbox'><label for='data-ada73d79-d5fa-46e0-8421-08f4e39010ad' 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>location :</span></dt><dd>node</dd><dt><span>long_name :</span></dt><dd>distance  below geoid</dd><dt><span>mesh :</span></dt><dd>adcirc_mesh</dd><dt><span>standard_name :</span></dt><dd>depth below geoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.84 MiB </td>\n",
       "                        <td> 1.22 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1813443,) </td>\n",
       "                        <td> (160000,) </td>\n",
       "                    </tr>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>element</span></div><div class='xr-var-dims'>(nele, nvertex)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3564104, 3), meta=np.ndarray&gt;</div><input id='attrs-814c1da9-79ee-409b-a885-7dd82d1969a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-814c1da9-79ee-409b-a885-7dd82d1969a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37288c1-18b8-43f8-b708-17620915c390' class='xr-var-data-in' type='checkbox'><label for='data-d37288c1-18b8-43f8-b708-17620915c390' 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>cf_role :</span></dt><dd>face_node_connectivity</dd><dt><span>long_name :</span></dt><dd>element</dd><dt><span>start_index :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 40.79 MiB </td>\n",
       "                        <td> 40.79 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (3564104, 3) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">3564104</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ibtype</span></div><div class='xr-var-dims'>(nbou)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(186,), meta=np.ndarray&gt;</div><input id='attrs-c632c034-c1c4-4589-8a54-629af7452212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c632c034-c1c4-4589-8a54-629af7452212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ad9f519-c5a1-464b-8dbe-f34e78603eda' class='xr-var-data-in' type='checkbox'><label for='data-2ad9f519-c5a1-464b-8dbe-f34e78603eda' 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>type of normal flow (discharge) boundary</dd><dt><span>units :</span></dt><dd>nondimensional</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> 744 B </td>\n",
       "                        <td> 744 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (186,) </td>\n",
       "                        <td> (186,) </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\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\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>ibtypee</span></div><div class='xr-var-dims'>(nope)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-a43159ec-574b-4e0c-94b8-148fd1de0fb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a43159ec-574b-4e0c-94b8-148fd1de0fb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12846191-cba3-4a64-b46c-096f181095d4' class='xr-var-data-in' type='checkbox'><label for='data-12846191-cba3-4a64-b46c-096f181095d4' 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>elevation boundary type</dd><dt><span>units :</span></dt><dd>nondimensional</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> 4 B </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1,) </td>\n",
       "                        <td> (1,) </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\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" 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=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_nvdll</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d0ffe44-1d6f-47eb-9757-177f4c670004' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d0ffe44-1d6f-47eb-9757-177f4c670004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf931814-9077-4478-8974-c288f33710ef' class='xr-var-data-in' type='checkbox'><label for='data-bf931814-9077-4478-8974-c288f33710ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_nvell</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4d38d45-b6dc-4f65-8eae-c62083da20f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4d38d45-b6dc-4f65-8eae-c62083da20f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce7875e5-5982-4358-91f7-24ae0bce3519' class='xr-var-data-in' type='checkbox'><label for='data-ce7875e5-5982-4358-91f7-24ae0bce3519' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbdv</span></div><div class='xr-var-dims'>(neta_dim)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(186,), meta=np.ndarray&gt;</div><input id='attrs-7ca8a149-d76e-490f-9e7d-13db80431254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ca8a149-d76e-490f-9e7d-13db80431254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-469f75ea-2aa8-405b-acc5-0f9988bdf9bb' class='xr-var-data-in' type='checkbox'><label for='data-469f75ea-2aa8-405b-acc5-0f9988bdf9bb' 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>node numbers on each elevation specified boundary segment</dd><dt><span>units :</span></dt><dd>nondimensional</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> 744 B </td>\n",
       "                        <td> 744 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (186,) </td>\n",
       "                        <td> (186,) </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\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >186</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbvv</span></div><div class='xr-var-dims'>(nvel_dim)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(62972,), meta=np.ndarray&gt;</div><input id='attrs-98f67a33-f36f-4ea1-b29a-048d6c3f6e3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98f67a33-f36f-4ea1-b29a-048d6c3f6e3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128e8b1c-d357-4116-b756-ca3f44c5c087' class='xr-var-data-in' type='checkbox'><label for='data-128e8b1c-d357-4116-b756-ca3f44c5c087' 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>node numbers on normal flow boundary segment</dd><dt><span>units :</span></dt><dd>nondimensional</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> 245.98 kiB </td>\n",
       "                        <td> 245.98 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (62972,) </td>\n",
       "                        <td> (62972,) </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\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >62972</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7dc0269-a635-487a-886a-3427359bfb0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7dc0269-a635-487a-886a-3427359bfb0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6311d9f5-eead-495c-8984-ae1f8e6a7329' class='xr-var-data-in' type='checkbox'><label for='data-6311d9f5-eead-495c-8984-ae1f8e6a7329' 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>total number of elevation specified boundary nodes</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nvdll</span></div><div class='xr-var-dims'>(nope)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-a8511878-5f83-4df9-9eff-04819d2dd043' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8511878-5f83-4df9-9eff-04819d2dd043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53181982-5a5a-4a49-9940-371d410cc7c5' class='xr-var-data-in' type='checkbox'><label for='data-53181982-5a5a-4a49-9940-371d410cc7c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of nodes in each elevation specified boundary segment</dd><dt><span>units :</span></dt><dd>nondimensional</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> 4 B </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1,) </td>\n",
       "                        <td> (1,) </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\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" 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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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nvel</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e00ce40-3d7d-48c5-b91d-5b49e24d6940' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e00ce40-3d7d-48c5-b91d-5b49e24d6940' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dea53f9b-e91c-4d4b-9bcc-84aedaa5a196' class='xr-var-data-in' type='checkbox'><label for='data-dea53f9b-e91c-4d4b-9bcc-84aedaa5a196' 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>total number of normal flow specified boundary nodes including both the front and back nodes on internal barrier boundaries</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nvell</span></div><div class='xr-var-dims'>(nbou)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(186,), meta=np.ndarray&gt;</div><input id='attrs-aa410bad-f27c-4166-adc5-0bb2b113310f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa410bad-f27c-4166-adc5-0bb2b113310f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b2a9620-da51-45c7-b408-e41cc6f7f9e1' class='xr-var-data-in' type='checkbox'><label for='data-1b2a9620-da51-45c7-b408-e41cc6f7f9e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of nodes in each normal flow specified boundary segment</dd><dt><span>units :</span></dt><dd>nondimensional</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> 744 B </td>\n",
       "                        <td> 744 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (186,) </td>\n",
       "                        <td> (186,) </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\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta</span></div><div class='xr-var-dims'>(time, node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(240, 160000), meta=np.ndarray&gt;</div><input id='attrs-f0132e36-db59-4783-8392-7bf752684da2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0132e36-db59-4783-8392-7bf752684da2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41046edb-2dac-41f6-bc4a-6e4273abd9e6' class='xr-var-data-in' type='checkbox'><label for='data-41046edb-2dac-41f6-bc4a-6e4273abd9e6' 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>location :</span></dt><dd>node</dd><dt><span>long_name :</span></dt><dd>water surface elevation above geoid</dd><dt><span>mesh :</span></dt><dd>adcirc_mesh</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.09 TiB </td>\n",
       "                        <td> 292.97 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (385704, 1813443) </td>\n",
       "                        <td> (240, 160000) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 19296 chunks in 2 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",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e071465-fc8d-4aa0-98e0-693d02fcd0a6' class='xr-section-summary-in' type='checkbox'  ><label for='section-1e071465-fc8d-4aa0-98e0-693d02fcd0a6' class='xr-section-summary' >Indexes: <span>(1)</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-eb71b66c-f9dd-4d9b-a5bf-a027cd40724e' class='xr-index-data-in' type='checkbox'/><label for='index-eb71b66c-f9dd-4d9b-a5bf-a027cd40724e' 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;1979-01-01 00:00:00&#x27;, &#x27;1979-01-01 01:00:00&#x27;,\n",
       "               &#x27;1979-01-01 02:00:00&#x27;, &#x27;1979-01-01 03:00:00&#x27;,\n",
       "               &#x27;1979-01-01 04:00:00&#x27;, &#x27;1979-01-01 05:00:00&#x27;,\n",
       "               &#x27;1979-01-01 06:00:00&#x27;, &#x27;1979-01-01 07:00:00&#x27;,\n",
       "               &#x27;1979-01-01 08:00:00&#x27;, &#x27;1979-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-12-31 14:00:00&#x27;, &#x27;2022-12-31 15:00:00&#x27;,\n",
       "               &#x27;2022-12-31 16:00:00&#x27;, &#x27;2022-12-31 17:00:00&#x27;,\n",
       "               &#x27;2022-12-31 18:00:00&#x27;, &#x27;2022-12-31 19:00:00&#x27;,\n",
       "               &#x27;2022-12-31 20:00:00&#x27;, &#x27;2022-12-31 21:00:00&#x27;,\n",
       "               &#x27;2022-12-31 22:00:00&#x27;, &#x27;2022-12-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=385704, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20d7ad60-7236-4bcc-9747-bf452435cca9' class='xr-section-summary-in' type='checkbox'  ><label for='section-20d7ad60-7236-4bcc-9747-bf452435cca9' class='xr-section-summary' >Attributes: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>UGRID-0.9.0</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>a00 :</span></dt><dd>0.0</dd><dt><span>agrid :</span></dt><dd>NOMAD mesh v1e MSL - fixed bahamas bathy</dd><dt><span>b00 :</span></dt><dd>1.0</dd><dt><span>c00 :</span></dt><dd>0.0</dd><dt><span>cf :</span></dt><dd>0.002</dd><dt><span>comments :</span></dt><dd>NOAA-Reanalysis.post</dd><dt><span>contact :</span></dt><dd>Brian_Blanton@Renci.Org</dd><dt><span>convention :</span></dt><dd>CF-UGRID_0.90</dd><dt><span>cori :</span></dt><dd>0.0</dd><dt><span>creation_date :</span></dt><dd>2023-09-29 14:31:49 -04:00</dd><dt><span>description :</span></dt><dd>Reanalysis-NOAA        ! 32 CHARACTER ALPHANUMERIC RUN DESCRIPTION</dd><dt><span>dramp :</span></dt><dd>15.0</dd><dt><span>dt :</span></dt><dd>2.0</dd><dt><span>eslm :</span></dt><dd>-5.0</dd><dt><span>fort.15 :</span></dt><dd>==== Input File Parameters (below) ====</dd><dt><span>grid_type :</span></dt><dd>Triangular</dd><dt><span>h0 :</span></dt><dd>0.1</dd><dt><span>history :</span></dt><dd>Fri Jan 19 15:55:59 2024: ncatted -O -a base_date,time,d,, /data/workdisk/fort.63_1979.nc\n",
       "RENCI</dd><dt><span>host :</span></dt><dd>ht-ncfs.cluster</dd><dt><span>ics :</span></dt><dd>2</dd><dt><span>ihot :</span></dt><dd>0</dd><dt><span>institution :</span></dt><dd>RENCI</dd><dt><span>model :</span></dt><dd>ADCIRC</dd><dt><span>modification_date :</span></dt><dd>2023-09-29 14:31:49 -04:00</dd><dt><span>nbfr :</span></dt><dd>10</dd><dt><span>ncor :</span></dt><dd>1</dd><dt><span>nolibf :</span></dt><dd>1</dd><dt><span>nolica :</span></dt><dd>1</dd><dt><span>nolicat :</span></dt><dd>1</dd><dt><span>nolifa :</span></dt><dd>2</dd><dt><span>nramp :</span></dt><dd>1</dd><dt><span>ntif :</span></dt><dd>10</dd><dt><span>ntip :</span></dt><dd>1</dd><dt><span>nwp :</span></dt><dd>7</dd><dt><span>nws :</span></dt><dd>-12</dd><dt><span>references :</span></dt><dd>http://www.renci.org, http://renci.org/reanalysis</dd><dt><span>reftim :</span></dt><dd>0.0</dd><dt><span>rnday :</span></dt><dd>400.0</dd><dt><span>rundes :</span></dt><dd>Reanalysis-NOAA        ! 32 CHARACTER ALPHANUMERIC RUN DESCRIPTION</dd><dt><span>runid :</span></dt><dd>(p)adcirc 56.dev       ! 24 CHARACTER ALPANUMERIC RUN IDENTIFICATION</dd><dt><span>sfea0 :</span></dt><dd>29.000000000000004</dd><dt><span>slam0 :</span></dt><dd>265.5</dd><dt><span>source :</span></dt><dd>RENCI_HPC</dd><dt><span>statim :</span></dt><dd>0.0</dd><dt><span>tau0 :</span></dt><dd>-3.0</dd><dt><span>title :</span></dt><dd>NOAA-Reanalysis-Post</dd><dt><span>version :</span></dt><dd>noaa.stofs.2d.glo.v1.1.0r2-21-g7052115</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (mesh: 1, node: 1813443, nele: 3564104, nvertex: 3, nbou: 186,\n",
       "                  nope: 1, neta_dim: 186, nvel_dim: 62972, time: 385704)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1979-01-01 ... 2022-12-31T23:00:00\n",
       "    x            (node) float64 dask.array<chunksize=(160000,), meta=np.ndarray>\n",
       "    y            (node) float64 dask.array<chunksize=(160000,), meta=np.ndarray>\n",
       "Dimensions without coordinates: mesh, node, nele, nvertex, nbou, nope,\n",
       "                                neta_dim, nvel_dim\n",
       "Data variables: (12/14)\n",
       "    adcirc_mesh  (mesh) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    depth        (node) float64 dask.array<chunksize=(160000,), meta=np.ndarray>\n",
       "    element      (nele, nvertex) int32 dask.array<chunksize=(3564104, 3), meta=np.ndarray>\n",
       "    ibtype       (nbou) int32 dask.array<chunksize=(186,), meta=np.ndarray>\n",
       "    ibtypee      (nope) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    max_nvdll    float64 ...\n",
       "    ...           ...\n",
       "    nbvv         (nvel_dim) int32 dask.array<chunksize=(62972,), meta=np.ndarray>\n",
       "    neta         float64 ...\n",
       "    nvdll        (nope) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    nvel         float64 ...\n",
       "    nvell        (nbou) int32 dask.array<chunksize=(186,), meta=np.ndarray>\n",
       "    zeta         (time, node) float64 dask.array<chunksize=(240, 160000), meta=np.ndarray>\n",
       "Attributes: (12/49)\n",
       "    Conventions:        UGRID-0.9.0\n",
       "    NCO:                netCDF Operators version 4.7.5 (Homepage = http://nco...\n",
       "    a00:                0.0\n",
       "    agrid:              NOMAD mesh v1e MSL - fixed bahamas bathy\n",
       "    b00:                1.0\n",
       "    c00:                0.0\n",
       "    ...                 ...\n",
       "    slam0:              265.5\n",
       "    source:             RENCI_HPC\n",
       "    statim:             0.0\n",
       "    tau0:               -3.0\n",
       "    title:              NOAA-Reanalysis-Post\n",
       "    version:            noaa.stofs.2d.glo.v1.1.0r2-21-g7052115"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = catalog['CORA-V1-fort.63'].to_dask()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e2424c8-eb83-45c3-a6b3-e8fcd287d2e0",
   "metadata": {},
   "source": [
    "<br/>**Create the kdtree to be used for fast location lookups**<br/>\n",
    "This function uses the lat/lon values defined in ds to create a kdtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c8f353b-1ad6-44ca-b813-027f416f0c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run setup_kdtree.ipynb\n",
    "tree, areas, e, x_vals, y_vals = define_kd_tree(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8febdea7-745d-43cc-a2fb-d704f47f6d19",
   "metadata": {},
   "source": [
    "<br/>**Define the locations to extract data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "791d145d-0f5f-4244-95ec-82c3d45b725c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of locations= 2\n"
     ]
    }
   ],
   "source": [
    "d = {\"lat\": [30.123363, 30.128206], \"lon\": [-81.67663, -81.64425]}\n",
    "df = pd.DataFrame(data=d)\n",
    "print(\"Number of locations=\", len(df.lon))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc277484-c279-482f-bf5e-4daab5b47654",
   "metadata": {},
   "source": [
    "<br/>**Determines the nodes for each point that should be extracted**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5fd2abc-5741-4418-835c-2c84b4d7380f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run find_node_triangle.ipynb # where find_triangle is defined\n",
    "\n",
    "nodes = len(df.lon)\n",
    "t_indices = np.zeros(shape=(nodes), dtype=int)\n",
    "weights = np.zeros(shape=((nodes, 3)), dtype=float)\n",
    "triangle_arr = np.zeros(shape=((nodes, 3)), dtype=int)\n",
    "\n",
    "for i in range(0, nodes):\n",
    "    t_indices[i] = find_triangle(x_vals, y_vals, e, df.lat[i], df.lon[i])\n",
    "\n",
    "    triangle_arr[i, :] = e[t_indices[i]]\n",
    "    x_dist = df.lon[i] - x_vals[triangle_arr[i, :]]\n",
    "    y_dist = df.lat[i] - y_vals[triangle_arr[i, :]]\n",
    "    dist = np.sqrt(x_dist * x_dist + y_dist * y_dist)\n",
    "    if np.any(dist == 0):\n",
    "\n",
    "        weights[i, :] = np.where(dist == 0, 1, 0)\n",
    "    else:\n",
    "        weights[i, :] = 1 / dask.array.sqrt(x_dist * x_dist + y_dist * y_dist)\n",
    "\n",
    "\n",
    "unique_nodes = np.unique(triangle_arr)\n",
    "mapped_triangle = np.searchsorted(unique_nodes, triangle_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06a4d03-6cf7-4d4a-8380-1485edbc66e7",
   "metadata": {},
   "source": [
    "<br/>**Extract a timeseries at each location**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18a5aa45-261c-4132-b3d8-d1ed5395cc5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.3 s, sys: 8.01 s, total: 23.3 s\n",
      "Wall time: 1min 58s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "start_t = \"1980-01-01\"\n",
    "end_t = \"1980-02-01\"\n",
    "var_name = \"zeta\"\n",
    "\n",
    "zeta_tslice = ds[var_name].sel(time=slice(start_t, end_t), node=unique_nodes).compute()\n",
    "\n",
    "num_ts = len(zeta_tslice)\n",
    "nodes = len(mapped_triangle)\n",
    "\n",
    "t = np.zeros((num_ts, nodes, 3), dtype=float)\n",
    "\n",
    "for t_i in range(0, 3):\n",
    "    t[:, :, t_i] = zeta_tslice[:, mapped_triangle[:, t_i]] * weights[:, t_i]\n",
    "\n",
    "zeta_interp = np.sum(t, axis=2) / np.sum(weights, axis=1)\n",
    "\n",
    "plot_arr = xarray.DataArray(\n",
    "    data=zeta_interp,\n",
    "    coords=dict(lon=([\"node\"], df.lon), lat=([\"node\"], df.lat), time=zeta_tslice.time),\n",
    "    dims=[\"time\", \"node\"],\n",
    ")\n",
    "\n",
    "plot_arr[:, 0].plot()\n",
    "\n",
    "del zeta_tslice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e719b3-a133-40af-a769-364b4cecd63f",
   "metadata": {},
   "source": [
    "**Initializes the grid plotting function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b215ab39-61ae-4d8c-b773-3b29039bad48",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run plot_grid.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6793e8ed-3639-4445-af0c-c807a306fb8f",
   "metadata": {},
   "source": [
    "<br/> **Plot water level data from a specific date/time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24b94fee-aa49-4840-a5ab-1039dacfaaf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "sel_time = \"1980-03-08 19:00\"\n",
    "\n",
    "gridded_data = ds[var_name].sel(time=sel_time).compute()\n",
    "\n",
    "plot_gridded_output(f\"Data at {sel_time}\", gridded_data, \"turbo\", 500, 350)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98dfab42-a17c-42e0-89a2-90e24cc07619",
   "metadata": {},
   "source": [
    "<br/>**Compute and plot the max water level over the selected timeframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9a7d781-876a-4bc2-9bfd-82ba76bb5145",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "catalog = intake.open_catalog(\"s3://ncdis-ra/ncdis_intake.yml\")\n",
    "ds = catalog[\"NCDIS-WaterLevel-1979-2021\"].to_dask()\n",
    "start_t = \"1985-01-01\"\n",
    "end_t = \"1985-02-01\"\n",
    "\n",
    "zeta_sel = ds[\"zeta\"].sel(time=slice(start_t, end_t))\n",
    "grid_max = zeta_sel.max(dim=\"time\").load()\n",
    "plot_gridded_output(\"Maximum water level at each point\", grid_max, \"turbo\", 500, 350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "661f7105-001f-4595-bf95-b2173505a326",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "start_t = \"1985-01-01\"\n",
    "end_t = \"1985-02-01\"\n",
    "\n",
    "v_sel = ds[var_name].sel(time=slice(start_t, end_t))\n",
    "grid_max = v_sel.max(dim=\"time\").load()\n",
    "plot_gridded_output(f\"Maximum {var_name} at each point\", grid_max, \"turbo\", 500, 350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77546fed-22cb-4655-8f2f-7a7a57c00f9a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}