{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DFS2 - Export to NetCDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install mikeio xarray netcdf4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mikeio\n",
    "import xarray as xr\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = mikeio.read(\"../tests/testdata/gebco_sound.dfs2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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.Elevation.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest approach is to use the `Dataset.to_xarray()` or `DataArray.to_xarray()` method, if no custom information is neeeded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 1, x: 216, y: 264)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2020-05-15T11:04:52\n",
       "  * y          (y) float64 55.2 55.21 55.21 55.21 ... 56.29 56.29 56.29 56.3\n",
       "  * x          (x) float64 12.2 12.21 12.21 12.21 ... 13.09 13.09 13.09 13.1\n",
       "Data variables:\n",
       "    Elevation  (time, y, x) float32 -8.0 -8.0 -8.0 -8.0 -9.0 ... nan nan nan nan</pre><div class='xr-wrap' hidden><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-e8e1ebb4-24c0-4936-96b2-089b8488d11e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e8e1ebb4-24c0-4936-96b2-089b8488d11e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>x</span>: 216</li><li><span class='xr-has-index'>y</span>: 264</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b53eb8a8-18f1-4fbf-bc47-d537f939829b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b53eb8a8-18f1-4fbf-bc47-d537f939829b' 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'>2020-05-15T11:04:52</div><input id='attrs-3808f71a-266e-4c9b-9ffd-0668922b012c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3808f71a-266e-4c9b-9ffd-0668922b012c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78235655-d137-4579-9ac2-3cc83c6db6ff' class='xr-var-data-in' type='checkbox'><label for='data-78235655-d137-4579-9ac2-3cc83c6db6ff' 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>array([&#x27;2020-05-15T11:04:52.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>55.2 55.21 55.21 ... 56.29 56.3</div><input id='attrs-0f4db733-1a8c-4670-93dc-0b0566a707bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f4db733-1a8c-4670-93dc-0b0566a707bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-357e4c23-ded7-4b13-bd2e-8e09e1dc4286' class='xr-var-data-in' type='checkbox'><label for='data-357e4c23-ded7-4b13-bd2e-8e09e1dc4286' 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>array([55.202083, 55.20625 , 55.210417, ..., 56.289583, 56.29375 , 56.297917])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>12.2 12.21 12.21 ... 13.09 13.1</div><input id='attrs-1d8f5edd-ddc2-4524-9f96-82f7fd0756c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d8f5edd-ddc2-4524-9f96-82f7fd0756c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a8abf7-6101-4c3e-8dc6-b98773e14dce' class='xr-var-data-in' type='checkbox'><label for='data-90a8abf7-6101-4c3e-8dc6-b98773e14dce' 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>array([12.202083, 12.20625 , 12.210417, ..., 13.089583, 13.09375 , 13.097917])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-725ceda6-cf0e-4951-8d38-e45605614069' class='xr-section-summary-in' type='checkbox'  checked><label for='section-725ceda6-cf0e-4951-8d38-e45605614069' class='xr-section-summary' >Data variables: <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-var-name'><span>Elevation</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.0 -8.0 -8.0 -8.0 ... nan nan nan</div><input id='attrs-c4a11927-f4ec-4fea-9722-616d8e3c37b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a11927-f4ec-4fea-9722-616d8e3c37b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d32c56ee-5d10-4276-abd9-18063427852f' class='xr-var-data-in' type='checkbox'><label for='data-d32c56ee-5d10-4276-abd9-18063427852f' 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>name :</span></dt><dd>Elevation</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>eumType :</span></dt><dd>EUMType.Total_Water_Depth</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.meter</dd></dl></div><div class='xr-var-data'><pre>array([[[ -8.,  -8.,  -8., ..., -37., -38., -38.],\n",
       "        [ -7.,  -7.,  -7., ..., -38., -38., -38.],\n",
       "        [ -5.,  -6.,  -6., ..., -37., -38., -38.],\n",
       "        ...,\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-31., -30., -31., ...,  nan,  nan,  nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a63ff474-3842-4b8e-b881-a67608f57bdf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a63ff474-3842-4b8e-b881-a67608f57bdf' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:    (time: 1, x: 216, y: 264)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2020-05-15T11:04:52\n",
       "  * y          (y) float64 55.2 55.21 55.21 55.21 ... 56.29 56.29 56.29 56.3\n",
       "  * x          (x) float64 12.2 12.21 12.21 12.21 ... 13.09 13.09 13.09 13.1\n",
       "Data variables:\n",
       "    Elevation  (time, y, x) float32 -8.0 -8.0 -8.0 -8.0 -9.0 ... nan nan nan nan"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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  {
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Elevation&#x27; (time: 1, y: 264, x: 216)&gt;\n",
       "array([[[ -8.,  -8.,  -8., ..., -37., -38., -38.],\n",
       "        [ -7.,  -7.,  -7., ..., -38., -38., -38.],\n",
       "        [ -5.,  -6.,  -6., ..., -37., -38., -38.],\n",
       "        ...,\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-31., -30., -31., ...,  nan,  nan,  nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-05-15T11:04:52\n",
       "  * y        (y) float64 55.2 55.21 55.21 55.21 55.22 ... 56.29 56.29 56.29 56.3\n",
       "  * x        (x) float64 12.2 12.21 12.21 12.21 12.22 ... 13.09 13.09 13.09 13.1\n",
       "Attributes:\n",
       "    name:     Elevation\n",
       "    units:    meter\n",
       "    eumType:  EUMType.Total_Water_Depth\n",
       "    eumUnit:  EUMUnit.meter</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Elevation'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>y</span>: 264</li><li><span class='xr-has-index'>x</span>: 216</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-78807859-b165-4c36-afb2-4fa80c87faca' class='xr-array-in' type='checkbox' checked><label for='section-78807859-b165-4c36-afb2-4fa80c87faca' 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>-8.0 -8.0 -8.0 -8.0 -9.0 -9.0 -9.0 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[ -8.,  -8.,  -8., ..., -37., -38., -38.],\n",
       "        [ -7.,  -7.,  -7., ..., -38., -38., -38.],\n",
       "        [ -5.,  -6.,  -6., ..., -37., -38., -38.],\n",
       "        ...,\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-31., -30., -31., ...,  nan,  nan,  nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-966b366f-fb90-4880-9327-09fe24a484a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-966b366f-fb90-4880-9327-09fe24a484a0' 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'>2020-05-15T11:04:52</div><input id='attrs-cc59ac3a-a1f3-4112-9e26-dfb32157b20f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc59ac3a-a1f3-4112-9e26-dfb32157b20f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa87a94-20e6-4ddf-9645-9ce5638cdaed' class='xr-var-data-in' type='checkbox'><label for='data-faa87a94-20e6-4ddf-9645-9ce5638cdaed' 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>array([&#x27;2020-05-15T11:04:52.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>55.2 55.21 55.21 ... 56.29 56.3</div><input id='attrs-347ead12-1cf1-4b63-ae63-31d71d044e51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-347ead12-1cf1-4b63-ae63-31d71d044e51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f56ed510-e1be-4d37-abd4-4553edb3e75f' class='xr-var-data-in' type='checkbox'><label for='data-f56ed510-e1be-4d37-abd4-4553edb3e75f' 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>array([55.202083, 55.20625 , 55.210417, ..., 56.289583, 56.29375 , 56.297917])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>12.2 12.21 12.21 ... 13.09 13.1</div><input id='attrs-f5e0211d-7341-4e71-9fb6-50a04f3b28bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5e0211d-7341-4e71-9fb6-50a04f3b28bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94be74aa-ccfc-415f-aac0-313b16e9f150' class='xr-var-data-in' type='checkbox'><label for='data-94be74aa-ccfc-415f-aac0-313b16e9f150' 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>array([12.202083, 12.20625 , 12.210417, ..., 13.089583, 13.09375 , 13.097917])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-51ff7b49-bf0f-4aae-bfec-b94832742dc0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51ff7b49-bf0f-4aae-bfec-b94832742dc0' 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>name :</span></dt><dd>Elevation</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>eumType :</span></dt><dd>EUMType.Total_Water_Depth</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.meter</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'Elevation' (time: 1, y: 264, x: 216)>\n",
       "array([[[ -8.,  -8.,  -8., ..., -37., -38., -38.],\n",
       "        [ -7.,  -7.,  -7., ..., -38., -38., -38.],\n",
       "        [ -5.,  -6.,  -6., ..., -37., -38., -38.],\n",
       "        ...,\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "        [-31., -30., -31., ...,  nan,  nan,  nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-05-15T11:04:52\n",
       "  * y        (y) float64 55.2 55.21 55.21 55.21 55.22 ... 56.29 56.29 56.29 56.3\n",
       "  * x        (x) float64 12.2 12.21 12.21 12.21 12.22 ... 13.09 13.09 13.09 13.1\n",
       "Attributes:\n",
       "    name:     Elevation\n",
       "    units:    meter\n",
       "    eumType:  EUMType.Total_Water_Depth\n",
       "    eumUnit:  EUMUnit.meter"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr_da = ds.Elevation.to_xarray()\n",
    "xr_da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save it as a NetCDF file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr_da.to_netcdf(\"gebco_std.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customized NetCDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Time-invariant file -> remove time dimension\n",
    "2. Rename y, x to lat, lon\n",
    "3. Lowercase names\n",
    "4. Add other metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ds.geometry.x\n",
    "y = ds.geometry.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = {}\n",
    "\n",
    "spdims = [\"lat\", \"lon\"]\n",
    "\n",
    "if len(ds.time) > 1:\n",
    "    dims = [\"t\"] + spdims\n",
    "    coords = {\"t\": ds.time}\n",
    "else:\n",
    "    dims = spdims\n",
    "    coords = {}\n",
    "    \n",
    "coords[\"lon\"] = xr.DataArray(x, dims=\"lon\", attrs={\"standard_name\" : \"longitude\", \"units\" : \"degrees_east\"})\n",
    "coords[\"lat\"] = xr.DataArray(y, dims=\"lat\", attrs={\"standard_name\" : \"latitude\", \"units\" : \"degrees_north\"})\n",
    "\n",
    "for da in ds:\n",
    "    name = da.name.lower()\n",
    "    res[name] = xr.DataArray(np.squeeze(da.to_numpy()), dims=dims, \n",
    "    attrs={'name': name,\n",
    "            # TODO add standard name from https://cfconventions.org/standard-names.html\n",
    "            'units': da.unit.name,\n",
    "            'eumType' : da.type,\n",
    "            'eumUnit' : da.unit})        \n",
    "\n",
    "xr_ds = xr.Dataset(res, coords=coords, attrs={'title': 'Converted from Dfs2 by MIKE IO'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 264, lon: 216)\n",
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       "  * lon        (lon) float64 12.2 12.21 12.21 12.21 ... 13.09 13.09 13.09 13.1\n",
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       "    elevation  (lat, lon) float32 -8.0 -8.0 -8.0 -8.0 -9.0 ... nan nan nan nan\n",
       "Attributes:\n",
       "    title:    Converted from Dfs2 by MIKE IO</pre><div class='xr-wrap' hidden><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-19f76913-c7a4-42b1-8a26-268ff96ad206' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19f76913-c7a4-42b1-8a26-268ff96ad206' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 264</li><li><span class='xr-has-index'>lon</span>: 216</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a36e60ba-58aa-4534-8e70-1b3246b0e453' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a36e60ba-58aa-4534-8e70-1b3246b0e453' class='xr-section-summary' >Coordinates: <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-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'>12.2 12.21 12.21 ... 13.09 13.1</div><input id='attrs-b9c519a3-4567-4bd4-b890-96cfdf4b3c73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9c519a3-4567-4bd4-b890-96cfdf4b3c73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15ba159d-f140-4cac-b9a5-50d042588eca' class='xr-var-data-in' type='checkbox'><label for='data-15ba159d-f140-4cac-b9a5-50d042588eca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([12.202083, 12.20625 , 12.210417, ..., 13.089583, 13.09375 , 13.097917])</pre></div></li><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'>55.2 55.21 55.21 ... 56.29 56.3</div><input id='attrs-cbbbc6aa-8310-4306-8ce1-5e13b3e3e96b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbbbc6aa-8310-4306-8ce1-5e13b3e3e96b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8af27086-7e37-422d-a15a-dc0e42221ee1' class='xr-var-data-in' type='checkbox'><label for='data-8af27086-7e37-422d-a15a-dc0e42221ee1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([55.202083, 55.20625 , 55.210417, ..., 56.289583, 56.29375 , 56.297917])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14346e76-69c6-4564-a44b-92100112d47f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14346e76-69c6-4564-a44b-92100112d47f' class='xr-section-summary' >Data variables: <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-var-name'><span>elevation</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.0 -8.0 -8.0 -8.0 ... nan nan nan</div><input id='attrs-219c2857-5328-4e3f-8774-b6f9d7759556' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-219c2857-5328-4e3f-8774-b6f9d7759556' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aec58b80-12e4-4168-b138-bce6e9d2c28a' class='xr-var-data-in' type='checkbox'><label for='data-aec58b80-12e4-4168-b138-bce6e9d2c28a' 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>name :</span></dt><dd>elevation</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>eumType :</span></dt><dd>EUMType.Total_Water_Depth</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.meter</dd></dl></div><div class='xr-var-data'><pre>array([[ -8.,  -8.,  -8., ..., -37., -38., -38.],\n",
       "       [ -7.,  -7.,  -7., ..., -38., -38., -38.],\n",
       "       [ -5.,  -6.,  -6., ..., -37., -38., -38.],\n",
       "       ...,\n",
       "       [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "       [-30., -30., -30., ...,  nan,  nan,  nan],\n",
       "       [-31., -30., -31., ...,  nan,  nan,  nan]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e1025d9-7ee1-4ce1-bb0d-725a3baf18a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e1025d9-7ee1-4ce1-bb0d-725a3baf18a0' 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>title :</span></dt><dd>Converted from Dfs2 by MIKE IO</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 264, lon: 216)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 12.2 12.21 12.21 12.21 ... 13.09 13.09 13.09 13.1\n",
       "  * lat        (lat) float64 55.2 55.21 55.21 55.21 ... 56.29 56.29 56.29 56.3\n",
       "Data variables:\n",
       "    elevation  (lat, lon) float32 -8.0 -8.0 -8.0 -8.0 -9.0 ... nan nan nan nan\n",
       "Attributes:\n",
       "    title:    Converted from Dfs2 by MIKE IO"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xr_ds.elevation.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr_ds.to_netcdf(\"gebco.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.remove(\"gebco_std.nc\")\n",
    "os.remove(\"gebco.nc\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f4041ee05ab07c15354d6207e763f17a216c3f5ccf08906343c2b4fd3fa7a6fb"
  },
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}