examples/notebooks/viewer/xcube-viewer-in-jl.ipynb

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    "# xcube JupyterLab Integration\n",
    "\n",
    "This Notebook demonstrates how to use the features of the xcube JupyterLab integration.\n",
    "The Notebook demonstrates two scenarios how xcube Viewer is utilized in JupyterLab.\n",
    "In particular, we open xcube Viewer for any `xarray.Dataset` instances \n",
    "\n",
    "1. opened or otherwise created in this Notebook (in-memory datasets);\n",
    "2. persisted in this Notebooks's workspace or from other sources (saved datasets).\n",
    "\n",
    "For this to work, the following requirements must be satisfied:\n",
    "\n",
    "* xcube version must be >= 0.13\n",
    "* JupyterLab version must be 3.4.x\n",
    "* The xcube JupyterLab extension [xcube-jl-ext](https://github.com/dcs4cop/xcube-jl-ext) \n",
    "  must be installed and enabled.\n",
    "\n",
    "If you run JupyterLab remotely, e.g., with JupyterHub, then also\n",
    "\n",
    "* the [jupyter-proxy](https://jupyter-server-proxy.readthedocs.io/en/latest/#) \n",
    "  server extension must be installed and enabled.\n",
    "  "
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   "execution_count": 1,
   "id": "539dd106-ffb9-4ed7-99c4-7241a9798547",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "from xcube.webapi.viewer import Viewer\n",
    "from xcube.core.store import new_data_store\n",
    "from xcube.core.select import select_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028285fc-7b3f-478f-98fe-17a2f8ed05ce",
   "metadata": {},
   "source": [
    "We create some datasets so we have something to show. We use the xcube datastore framework here to open the dataset, but it could also be opened by other means, e.g., `xr.open_dataset()`, provided it has variables with dimensions [\"time\", \"y\", \"x\"] or [\"y\", \"x\"]. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71a28392-d820-46df-9041-2f78d33113e7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "['esdc-8d-0.083deg-184x270x270-2.1.1.zarr',\n",
       " 'esdc-8d-0.083deg-1x2160x4320-2.1.1.zarr',\n",
       " 'esdc-8d-0.25deg-184x90x90-2.1.1.zarr',\n",
       " 'esdc-8d-0.25deg-1x720x1440-2.1.1.zarr']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store = new_data_store(\"s3\", root=\"esdl-esdc-v2.1.1\", storage_options={\"anon\": True})\n",
    "store.list_data_ids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24b7759f-44b2-4638-91f1-8d218c8fc527",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = store.open_data('esdc-8d-0.083deg-1x2160x4320-2.1.1.zarr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32becbac-0361-4640-b8bf-677179c316d6",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (time: 138, lat: 72, lon: 96, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                 (lat) float64 53.96 53.88 53.79 ... 48.21 48.12 48.04\n",
       "    lat_bnds            (lat, bnds) float64 dask.array&lt;chunksize=(72, 2), meta=np.ndarray&gt;\n",
       "  * lon                 (lon) float64 6.042 6.125 6.208 ... 13.79 13.87 13.96\n",
       "    lon_bnds            (lon, bnds) float64 dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;\n",
       "  * time                (time) datetime64[ns] 2016-01-05 ... 2018-12-31\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array&lt;chunksize=(138, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    air_temperature_2m  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 72, 96), meta=np.ndarray&gt;\n",
       "    cfc                 (time, lat, lon) float32 dask.array&lt;chunksize=(1, 72, 96), meta=np.ndarray&gt;\n",
       "    evaporation         (time, lat, lon) float32 dask.array&lt;chunksize=(1, 72, 96), meta=np.ndarray&gt;\n",
       "    ozone               (time, lat, lon) float32 dask.array&lt;chunksize=(1, 72, 96), meta=np.ndarray&gt;\n",
       "Attributes: (12/35)\n",
       "    Metadata_conventions :     Unidata Dataset Discovery v1.0\n",
       "    acknowledgment:            The ESDL team acknowledges all data providers! \n",
       "    chunking:                  1x2160x4320\n",
       "    comment:                   none. \n",
       "    contributor_name:          Max Planck Institute for Biogeochemistry\n",
       "    contributor_role:          ESDL Science Lead \n",
       "    ...                        ...\n",
       "    summary:                   This data set contains a data cube of Earth Sy...\n",
       "    time_coverage_duration:    P40Y\n",
       "    time_coverage_end:         31.12.2018\n",
       "    time_coverage_resolution:  P8D\n",
       "    time_coverage_start:       05.01.1979\n",
       "    title:                     My ESDC Subset</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-1cad3302-0766-4fcd-9741-2da1c2f059eb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cad3302-0766-4fcd-9741-2da1c2f059eb' 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>: 138</li><li><span class='xr-has-index'>lat</span>: 72</li><li><span class='xr-has-index'>lon</span>: 96</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57000b43-4eb2-4d45-b847-960705294b41' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57000b43-4eb2-4d45-b847-960705294b41' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.96 53.88 53.79 ... 48.12 48.04</div><input id='attrs-be642e06-8521-444b-ac75-85e9eaa39fd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be642e06-8521-444b-ac75-85e9eaa39fd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-401c97fc-eea4-4a74-aab2-0e1629d58d3d' class='xr-var-data-in' type='checkbox'><label for='data-401c97fc-eea4-4a74-aab2-0e1629d58d3d' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</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'><pre>array([53.958333, 53.875   , 53.791667, 53.708333, 53.625   , 53.541667,\n",
       "       53.458333, 53.375   , 53.291667, 53.208333, 53.125   , 53.041667,\n",
       "       52.958333, 52.875   , 52.791667, 52.708333, 52.625   , 52.541667,\n",
       "       52.458333, 52.375   , 52.291667, 52.208333, 52.125   , 52.041667,\n",
       "       51.958333, 51.875   , 51.791667, 51.708333, 51.625   , 51.541667,\n",
       "       51.458333, 51.375   , 51.291667, 51.208333, 51.125   , 51.041667,\n",
       "       50.958333, 50.875   , 50.791667, 50.708333, 50.625   , 50.541667,\n",
       "       50.458333, 50.375   , 50.291667, 50.208333, 50.125   , 50.041667,\n",
       "       49.958333, 49.875   , 49.791667, 49.708333, 49.625   , 49.541667,\n",
       "       49.458333, 49.375   , 49.291667, 49.208333, 49.125   , 49.041667,\n",
       "       48.958333, 48.875   , 48.791667, 48.708333, 48.625   , 48.541667,\n",
       "       48.458333, 48.375   , 48.291667, 48.208333, 48.125   , 48.041667])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(72, 2), meta=np.ndarray&gt;</div><input id='attrs-7b6a7b91-cd25-4623-aa85-f8063c183b0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b6a7b91-cd25-4623-aa85-f8063c183b0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dc21f63-9a64-4c9f-9178-92b4e7afb501' class='xr-var-data-in' type='checkbox'><label for='data-8dc21f63-9a64-4c9f-9178-92b4e7afb501' 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></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> 1.12 kiB </td>\n",
       "                        <td> 1.12 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (72, 2) </td>\n",
       "                        <td> (72, 2) </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",
       "        <svg width=\"80\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"30\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"15.465911\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"50.931823\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,50.931823,60.000000)\">72</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></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'>6.042 6.125 6.208 ... 13.87 13.96</div><input id='attrs-93a55a4f-dd70-41f6-ae00-b726a30b5fa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93a55a4f-dd70-41f6-ae00-b726a30b5fa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6d81442-d79c-43a6-a2af-f3f01c30ae61' class='xr-var-data-in' type='checkbox'><label for='data-d6d81442-d79c-43a6-a2af-f3f01c30ae61' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</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'><pre>array([ 6.041667,  6.125   ,  6.208333,  6.291667,  6.375   ,  6.458333,\n",
       "        6.541667,  6.625   ,  6.708333,  6.791667,  6.875   ,  6.958333,\n",
       "        7.041667,  7.125   ,  7.208333,  7.291667,  7.375   ,  7.458333,\n",
       "        7.541667,  7.625   ,  7.708333,  7.791667,  7.875   ,  7.958333,\n",
       "        8.041667,  8.125   ,  8.208333,  8.291667,  8.375   ,  8.458333,\n",
       "        8.541667,  8.625   ,  8.708333,  8.791667,  8.875   ,  8.958333,\n",
       "        9.041667,  9.125   ,  9.208333,  9.291667,  9.375   ,  9.458333,\n",
       "        9.541667,  9.625   ,  9.708333,  9.791667,  9.875   ,  9.958333,\n",
       "       10.041667, 10.125   , 10.208333, 10.291667, 10.375   , 10.458333,\n",
       "       10.541667, 10.625   , 10.708333, 10.791667, 10.875   , 10.958333,\n",
       "       11.041667, 11.125   , 11.208333, 11.291667, 11.375   , 11.458333,\n",
       "       11.541667, 11.625   , 11.708333, 11.791667, 11.875   , 11.958333,\n",
       "       12.041667, 12.125   , 12.208333, 12.291667, 12.375   , 12.458333,\n",
       "       12.541667, 12.625   , 12.708333, 12.791667, 12.875   , 12.958333,\n",
       "       13.041667, 13.125   , 13.208333, 13.291667, 13.375   , 13.458333,\n",
       "       13.541667, 13.625   , 13.708333, 13.791667, 13.875   , 13.958333])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;</div><input id='attrs-d3238fbe-87e4-4a64-a009-aac9722405cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3238fbe-87e4-4a64-a009-aac9722405cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4fccee-8d49-4520-b99f-a7c7cfd1e145' class='xr-var-data-in' type='checkbox'><label for='data-8e4fccee-8d49-4520-b99f-a7c7cfd1e145' 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></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> 1.50 kiB </td>\n",
       "                        <td> 1.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (96, 2) </td>\n",
       "                        <td> (96, 2) </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",
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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 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'>2016-01-05 ... 2018-12-31</div><input id='attrs-8781455a-1c92-4454-9df2-e792facd9551' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8781455a-1c92-4454-9df2-e792facd9551' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5868c0fb-6a62-40da-8bae-8f3c95b0605f' class='xr-var-data-in' type='checkbox'><label for='data-5868c0fb-6a62-40da-8bae-8f3c95b0605f' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-01-05T00:00:00.000000000&#x27;, &#x27;2016-01-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-21T00:00:00.000000000&#x27;, &#x27;2016-01-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-06T00:00:00.000000000&#x27;, &#x27;2016-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-22T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-03-09T00:00:00.000000000&#x27;, &#x27;2016-03-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-03-25T00:00:00.000000000&#x27;, &#x27;2016-04-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-04-10T00:00:00.000000000&#x27;, &#x27;2016-04-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-04-26T00:00:00.000000000&#x27;, &#x27;2016-05-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-12T00:00:00.000000000&#x27;, &#x27;2016-05-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-28T00:00:00.000000000&#x27;, &#x27;2016-06-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-13T00:00:00.000000000&#x27;, &#x27;2016-06-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-29T00:00:00.000000000&#x27;, &#x27;2016-07-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-15T00:00:00.000000000&#x27;, &#x27;2016-07-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-31T00:00:00.000000000&#x27;, &#x27;2016-08-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-08-16T00:00:00.000000000&#x27;, &#x27;2016-08-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-09-01T00:00:00.000000000&#x27;, &#x27;2016-09-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-09-17T00:00:00.000000000&#x27;, &#x27;2016-09-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-10-03T00:00:00.000000000&#x27;, &#x27;2016-10-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-10-19T00:00:00.000000000&#x27;, &#x27;2016-10-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-11-04T00:00:00.000000000&#x27;, &#x27;2016-11-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-11-20T00:00:00.000000000&#x27;, &#x27;2016-11-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-06T00:00:00.000000000&#x27;, &#x27;2016-12-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-22T00:00:00.000000000&#x27;, &#x27;2016-12-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-05T00:00:00.000000000&#x27;, &#x27;2017-01-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-21T00:00:00.000000000&#x27;, &#x27;2017-01-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-02-06T00:00:00.000000000&#x27;, &#x27;2017-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-02-22T00:00:00.000000000&#x27;, &#x27;2017-03-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-10T00:00:00.000000000&#x27;, &#x27;2017-03-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-26T00:00:00.000000000&#x27;, &#x27;2017-04-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-11T00:00:00.000000000&#x27;, &#x27;2017-04-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-27T00:00:00.000000000&#x27;, &#x27;2017-05-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-13T00:00:00.000000000&#x27;, &#x27;2017-05-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-29T00:00:00.000000000&#x27;, &#x27;2017-06-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-14T00:00:00.000000000&#x27;, &#x27;2017-06-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-30T00:00:00.000000000&#x27;, &#x27;2017-07-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-16T00:00:00.000000000&#x27;, &#x27;2017-07-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-08-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-17T00:00:00.000000000&#x27;, &#x27;2017-08-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-02T00:00:00.000000000&#x27;, &#x27;2017-09-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-18T00:00:00.000000000&#x27;, &#x27;2017-09-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-04T00:00:00.000000000&#x27;, &#x27;2017-10-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-20T00:00:00.000000000&#x27;, &#x27;2017-10-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-05T00:00:00.000000000&#x27;, &#x27;2017-11-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-21T00:00:00.000000000&#x27;, &#x27;2017-11-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-12-07T00:00:00.000000000&#x27;, &#x27;2017-12-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-12-23T00:00:00.000000000&#x27;, &#x27;2017-12-31T00:00:00.000000000&#x27;,\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>evaporation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 72, 96), meta=np.ndarray&gt;</div><input id='attrs-fe85f824-5511-458d-857e-3f083c2bec2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe85f824-5511-458d-857e-3f083c2bec2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f012d408-620e-473a-9bb1-40f8b0719744' class='xr-var-data-in' type='checkbox'><label for='data-f012d408-620e-473a-9bb1-40f8b0719744' 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>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>easting :</span></dt><dd>-180 degrees</dd><dt><span>history :</span></dt><dd>Thu Apr  9 02:11:08 2020 - ESDL data cube generation</dd><dt><span>institution :</span></dt><dd>Brockmann Consult GmbH, Germany</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd><dt><span>northing :</span></dt><dd>90 degrees</dd><dt><span>project_name :</span></dt><dd>GLEAM</dd><dt><span>source :</span></dt><dd>ESDL data cube generation, version 0.3.0.dev1</dd><dt><span>source_attributes :</span></dt><dd>{&#x27;long_name&#x27;: &#x27;Evaporation&#x27;, &#x27;project_name&#x27;: &#x27;GLEAM&#x27;, &#x27;references&#x27;: &#x27;Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., and Verhoest, N.E.C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geoscientific Model Development, 10, 1903–1925, 2017.&#x27;, &#x27;source_name&#x27;: &#x27;E&#x27;, &#x27;standard_name&#x27;: &#x27;water_evaporation_flux&#x27;, &#x27;units&#x27;: &#x27;mm/day&#x27;, &#x27;url&#x27;: &#x27;http://www.gleam.eu&#x27;}</dd><dt><span>standard_name :</span></dt><dd>water_evaporation_flux</dd><dt><span>time_coverage_end :</span></dt><dd>2018-12-23</dd><dt><span>time_coverage_start :</span></dt><dd>2003-01-05</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>url :</span></dt><dd>http://www.gleam.eu</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ozone</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 72, 96), meta=np.ndarray&gt;</div><input id='attrs-f67bca2c-947e-432f-9b9f-c4b9f81339cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f67bca2c-947e-432f-9b9f-c4b9f81339cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6646f3c-f3cd-450a-9499-2075b97fdc13' class='xr-var-data-in' type='checkbox'><label for='data-d6646f3c-f3cd-450a-9499-2075b97fdc13' 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>ID :</span></dt><dd>72</dd><dt><span>esa_cci_path :</span></dt><dd>/neodc/esacci/ozone/data/total_columns/l3/merged/v0100/</dd><dt><span>long_name :</span></dt><dd>Mean Total Ozone Column in dobson units</dd><dt><span>orig_version :</span></dt><dd>v0100</dd><dt><span>project_name :</span></dt><dd>Ozone CCI</dd><dt><span>time_coverage_end :</span></dt><dd>2011-06-30</dd><dt><span>time_coverage_resolution :</span></dt><dd>P8D</dd><dt><span>time_coverage_start :</span></dt><dd>1996-03-09</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>url :</span></dt><dd>http://www.esa-ozone-cci.org/</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.64 MiB </td>\n",
       "                        <td> 27.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (138, 72, 96) </td>\n",
       "                        <td> (1, 72, 96) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 138 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></ul></div></li><li class='xr-section-item'><input id='section-5f6c144b-15b4-474e-81e8-8799c1d84e57' class='xr-section-summary-in' type='checkbox'  ><label for='section-5f6c144b-15b4-474e-81e8-8799c1d84e57' 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-9f3d1065-209e-44fe-8002-3f9f2101ebef' class='xr-index-data-in' type='checkbox'/><label for='index-9f3d1065-209e-44fe-8002-3f9f2101ebef' 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([53.958333333333336,             53.875,  53.79166666666667,\n",
       "              53.708333333333336,             53.625,  53.54166666666667,\n",
       "              53.458333333333336,             53.375,  53.29166666666667,\n",
       "              53.208333333333336,             53.125,  53.04166666666667,\n",
       "              52.958333333333336,             52.875,  52.79166666666667,\n",
       "              52.708333333333336,             52.625,  52.54166666666667,\n",
       "              52.458333333333336,             52.375,  52.29166666666667,\n",
       "              52.208333333333336,             52.125,  52.04166666666667,\n",
       "              51.958333333333336,             51.875,  51.79166666666667,\n",
       "              51.708333333333336,             51.625,  51.54166666666667,\n",
       "              51.458333333333336,             51.375,  51.29166666666667,\n",
       "              51.208333333333336,             51.125,  51.04166666666667,\n",
       "              50.958333333333336,             50.875,  50.79166666666667,\n",
       "              50.708333333333336,             50.625,  50.54166666666667,\n",
       "              50.458333333333336,             50.375,  50.29166666666667,\n",
       "              50.208333333333336,             50.125,  50.04166666666667,\n",
       "              49.958333333333336,             49.875,  49.79166666666667,\n",
       "              49.708333333333336,             49.625,  49.54166666666667,\n",
       "              49.458333333333336,             49.375,  49.29166666666667,\n",
       "              49.208333333333336,             49.125,  49.04166666666667,\n",
       "              48.958333333333336,             48.875,  48.79166666666667,\n",
       "              48.708333333333336,             48.625,  48.54166666666667,\n",
       "              48.458333333333336,             48.375,  48.29166666666667,\n",
       "              48.208333333333336,             48.125,  48.04166666666667],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</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-2ac886a2-d919-4508-b72b-9de3a5207fe3' class='xr-index-data-in' type='checkbox'/><label for='index-2ac886a2-d919-4508-b72b-9de3a5207fe3' 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([ 6.041666666666657,  6.124999999999986,  6.208333333333329,\n",
       "               6.291666666666657,  6.374999999999986,  6.458333333333329,\n",
       "               6.541666666666657,  6.624999999999986,  6.708333333333329,\n",
       "               6.791666666666657,  6.874999999999986,  6.958333333333329,\n",
       "               7.041666666666657,  7.124999999999986,  7.208333333333329,\n",
       "               7.291666666666657,  7.374999999999986,  7.458333333333329,\n",
       "               7.541666666666657,  7.624999999999986,  7.708333333333329,\n",
       "               7.791666666666657,  7.874999999999986,  7.958333333333329,\n",
       "               8.041666666666657,  8.124999999999986,  8.208333333333329,\n",
       "               8.291666666666657,  8.374999999999986,  8.458333333333329,\n",
       "               8.541666666666657,  8.624999999999986,  8.708333333333329,\n",
       "               8.791666666666657,  8.874999999999986,  8.958333333333329,\n",
       "               9.041666666666657,  9.124999999999986,  9.208333333333329,\n",
       "               9.291666666666657,  9.374999999999986,  9.458333333333329,\n",
       "               9.541666666666657,  9.624999999999986,  9.708333333333329,\n",
       "               9.791666666666657,  9.874999999999986,  9.958333333333329,\n",
       "              10.041666666666657, 10.124999999999986, 10.208333333333329,\n",
       "              10.291666666666657, 10.374999999999986, 10.458333333333329,\n",
       "              10.541666666666657, 10.624999999999986, 10.708333333333329,\n",
       "              10.791666666666657, 10.874999999999986, 10.958333333333329,\n",
       "              11.041666666666657, 11.124999999999986, 11.208333333333329,\n",
       "              11.291666666666657, 11.374999999999986, 11.458333333333329,\n",
       "              11.541666666666657, 11.624999999999986, 11.708333333333329,\n",
       "              11.791666666666657, 11.874999999999986, 11.958333333333329,\n",
       "              12.041666666666657, 12.124999999999986, 12.208333333333329,\n",
       "              12.291666666666657, 12.374999999999986, 12.458333333333329,\n",
       "              12.541666666666657, 12.624999999999986, 12.708333333333329,\n",
       "              12.791666666666657, 12.874999999999986, 12.958333333333329,\n",
       "              13.041666666666657, 13.124999999999986, 13.208333333333329,\n",
       "              13.291666666666657, 13.374999999999986, 13.458333333333329,\n",
       "              13.541666666666657, 13.624999999999986, 13.708333333333329,\n",
       "              13.791666666666657, 13.874999999999986, 13.958333333333329],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bbb8fecb-84d6-4d78-958e-29ec9c074815' class='xr-index-data-in' type='checkbox'/><label for='index-bbb8fecb-84d6-4d78-958e-29ec9c074815' 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;2016-01-05&#x27;, &#x27;2016-01-13&#x27;, &#x27;2016-01-21&#x27;, &#x27;2016-01-29&#x27;,\n",
       "               &#x27;2016-02-06&#x27;, &#x27;2016-02-14&#x27;, &#x27;2016-02-22&#x27;, &#x27;2016-03-01&#x27;,\n",
       "               &#x27;2016-03-09&#x27;, &#x27;2016-03-17&#x27;,\n",
       "               ...\n",
       "               &#x27;2018-10-20&#x27;, &#x27;2018-10-28&#x27;, &#x27;2018-11-05&#x27;, &#x27;2018-11-13&#x27;,\n",
       "               &#x27;2018-11-21&#x27;, &#x27;2018-11-29&#x27;, &#x27;2018-12-07&#x27;, &#x27;2018-12-15&#x27;,\n",
       "               &#x27;2018-12-23&#x27;, &#x27;2018-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=138, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-280c873f-fdb4-4be4-a7a6-21e7a322508c' class='xr-section-summary-in' type='checkbox'  ><label for='section-280c873f-fdb4-4be4-a7a6-21e7a322508c' class='xr-section-summary' >Attributes: <span>(35)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Metadata_conventions  :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>acknowledgment :</span></dt><dd>The ESDL team acknowledges all data providers! </dd><dt><span>chunking :</span></dt><dd>1x2160x4320</dd><dt><span>comment :</span></dt><dd>none. </dd><dt><span>contributor_name :</span></dt><dd>Max Planck Institute for Biogeochemistry</dd><dt><span>contributor_role :</span></dt><dd>ESDL Science Lead </dd><dt><span>creator_email :</span></dt><dd>info@earthsystemdatalab.net</dd><dt><span>creator_name :</span></dt><dd>Brockmann Consult GmbH </dd><dt><span>creator_url :</span></dt><dd>www.earthsystemdatalab.net</dd><dt><span>date_created :</span></dt><dd>22.10.2020</dd><dt><span>date_issued :</span></dt><dd>23.10.2020</dd><dt><span>date_modified :</span></dt><dd>22.10.2020</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.958336</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.95761</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.9569</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.95833</dd><dt><span>geospatial_resolution :</span></dt><dd>1/12 deg</dd><dt><span>history :</span></dt><dd>- processing with esdl cube v0.1  (https://github.com/esa-esdl/esdl-core/)</dd><dt><span>id :</span></dt><dd>v2.1.1</dd><dt><span>institution :</span></dt><dd>Brockmann Consult GmbH </dd><dt><span>keywords :</span></dt><dd>Earth Science, Geophysical Variables</dd><dt><span>license :</span></dt><dd>Please refer to individual variables</dd><dt><span>naming_authority :</span></dt><dd>Earth System Data Lab team</dd><dt><span>processing_level :</span></dt><dd>Level 4</dd><dt><span>project :</span></dt><dd>ESA Earth System Data Lab </dd><dt><span>publisher_email :</span></dt><dd>info@earthsystemdatalab.net</dd><dt><span>publisher_name :</span></dt><dd>Brockmann Consult GmbH &amp; Max Planck Institute for Biogechemistry </dd><dt><span>publisher_url :</span></dt><dd>www.brockmann-consult.de</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF-1.7</dd><dt><span>summary :</span></dt><dd>This data set contains a data cube of Earth System variables created by the ESA project Earth System Data Lab. </dd><dt><span>time_coverage_duration :</span></dt><dd>P40Y</dd><dt><span>time_coverage_end :</span></dt><dd>31.12.2018</dd><dt><span>time_coverage_resolution :</span></dt><dd>P8D</dd><dt><span>time_coverage_start :</span></dt><dd>05.01.1979</dd><dt><span>title :</span></dt><dd>My ESDC Subset</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (time: 138, lat: 72, lon: 96, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                 (lat) float64 53.96 53.88 53.79 ... 48.21 48.12 48.04\n",
       "    lat_bnds            (lat, bnds) float64 dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
       "  * lon                 (lon) float64 6.042 6.125 6.208 ... 13.79 13.87 13.96\n",
       "    lon_bnds            (lon, bnds) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>\n",
       "  * time                (time) datetime64[ns] 2016-01-05 ... 2018-12-31\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array<chunksize=(138, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    air_temperature_2m  (time, lat, lon) float32 dask.array<chunksize=(1, 72, 96), meta=np.ndarray>\n",
       "    cfc                 (time, lat, lon) float32 dask.array<chunksize=(1, 72, 96), meta=np.ndarray>\n",
       "    evaporation         (time, lat, lon) float32 dask.array<chunksize=(1, 72, 96), meta=np.ndarray>\n",
       "    ozone               (time, lat, lon) float32 dask.array<chunksize=(1, 72, 96), meta=np.ndarray>\n",
       "Attributes: (12/35)\n",
       "    Metadata_conventions :     Unidata Dataset Discovery v1.0\n",
       "    acknowledgment:            The ESDL team acknowledges all data providers! \n",
       "    chunking:                  1x2160x4320\n",
       "    comment:                   none. \n",
       "    contributor_name:          Max Planck Institute for Biogeochemistry\n",
       "    contributor_role:          ESDL Science Lead \n",
       "    ...                        ...\n",
       "    summary:                   This data set contains a data cube of Earth Sy...\n",
       "    time_coverage_duration:    P40Y\n",
       "    time_coverage_end:         31.12.2018\n",
       "    time_coverage_resolution:  P8D\n",
       "    time_coverage_start:       05.01.1979\n",
       "    title:                     My ESDC Subset"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_subset = select_subset(\n",
    "    dataset,\n",
    "    bbox=[6, 48, 14, 54],\n",
    "    time_range=[\"2016-01-01 00:00:00\", \n",
    "                \"2018-12-31 23:59:59\"],\n",
    "    var_names=['evaporation', 'ozone', 'cfc', 'air_temperature_2m']\n",
    ")\n",
    "dataset_subset.attrs[\"title\"] = \"My ESDC Subset\"\n",
    "dataset_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c705bbb-4fbb-4078-88c9-45d5605d9005",
   "metadata": {},
   "source": [
    "---\n",
    "**Scenario 1**: Open xcube Viewer for a dataset instances opened or otherwise created in this Notebook (in-memory datasets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0958f695-4332-486a-b546-04c0938c1fd3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "viewer = Viewer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73136638-861e-491b-b079-de584a9c867b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1c48f3f9-5e74-4005-8361-25797740d806'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer.add_dataset(dataset)\n",
    "viewer.add_dataset(dataset_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5252fa-8748-439f-ba1d-6c5cb7671d48",
   "metadata": {},
   "source": [
    "You can click on the viewer link to open xcube Viewer in a new browser tab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "903fce9a-5cd5-4452-873d-e4fa56b38367",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Server: http://localhost:8888/proxy/8000\n",
      "Viewer: http://localhost:8888/proxy/8000/viewer/?serverUrl=http://localhost:8888/proxy/8000\n"
     ]
    }
   ],
   "source": [
    "viewer.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45907b06-e3bd-49af-915d-a4cce82b14e3",
   "metadata": {},
   "source": [
    "You can also open xcube Viewer inlined here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c03a6912-9879-4b7e-a2c9-25c141a548fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"http://localhost:8888/proxy/8000/viewer/?serverUrl=http://localhost:8888/proxy/8000&compact=1\" width=\"100%\" height=\"800\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "404 GET /viewer/config/config.json (127.0.0.1) 0.50ms\n"
     ]
    }
   ],
   "source": [
    "viewer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a665ce9-f537-49a5-927b-58045ecddec8",
   "metadata": {},
   "source": [
    "---\n",
    "**Scenario 2**: Open xcube Viewer for a dataset instances persisted in this Notebooks's workspace or from other sources (saved datasets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "508394d2-3a08-4da0-a73d-8056143d904d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-07 14:10:04,168 - tornado.access - WARNING - 404 GET /viewer/config/config.json (127.0.0.1) 0.50ms\n",
      "c:\\users\\norma\\projects\\xcube\\xcube\\util\\timeindex.py:158: UserWarning: We can't determine whether the time indexer has a time zone. We will therefore omit the check on whether the time indexer and the variable are incompatible in terms of timezone awareness. This may result in an error when an indexing operation is carried out. If such an error occurs after this warning, make sure that the time indexer has timezone information.\n",
      "  warnings.warn(\n",
      "C:\\Users\\norma\\mamba\\envs\\xcube\\lib\\site-packages\\xarray\\core\\indexes.py:211: FutureWarning: Indexing a timezone-naive DatetimeIndex with a timezone-aware datetime is deprecated and will raise KeyError in a future version.  Use a timezone-naive object instead.\n",
      "  indexer = index.slice_indexer(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x1430582edd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\norma\\mamba\\envs\\xcube\\lib\\site-packages\\rasterio\\features.py:362: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  _rasterize(valid_shapes, out, transform, all_touched, merge_alg)\n",
      "C:\\Users\\norma\\mamba\\envs\\xcube\\lib\\site-packages\\rasterio\\features.py:362: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  _rasterize(valid_shapes, out, transform, all_touched, merge_alg)\n"
     ]
    }
   ],
   "source": [
    "dataset_subset.to_zarr(\"esdc-subset.zarr\", mode=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c6cce1a-befa-4721-ae49-c7b6dd2268e0",
   "metadata": {},
   "source": [
    "Now, open the JupyterLab Launcher, and select _xcube Viewer_. You should see the dataset titled \"My ESDC Subset\". "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38b8ea5-f286-460d-a663-e8dc53aafca2",
   "metadata": {},
   "source": [
    "You should now find two new files in your workspace, [xcube-server.yaml](./xcube-server.yaml) and [xcube-server.log](./xcube-server.log). \n",
    "The first one is the configuration file used by xcube Server and therefore determines what datasets are shown in xcube Viewer. \n",
    "The second is the server's log file which is useful for debugging, for example, you may find details here why a certain dataset is not displayed.  \n",
    "\n",
    "Note, currently [xcube-server.yaml](./xcube-server.yaml) only applies to the server/viewer launched by the JupyterLab Launcher. It is independent from the server/viewer created from within Notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec537d6-aec3-41fb-aa21-30e2edd95f12",
   "metadata": {},
   "source": []
  }
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