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 >= 1.0.2\n",
    "* JupyterLab version must be >= 3.4\n",
    "* The xcube JupyterLab extension [xcube-jl-ext](https://github.com/dcs4cop/xcube-jl-ext) \n",
    "  must be installed and enabled.\n",
    "* This example uses the xcube data store `ccizarr` which requires the\n",
    "  [xcube-cci](https://github.com/dcs4cop/xcube-cci) plugin to be installed too. \n",
    "  However, in priciple, you can use any other xcube data store, such as `cciodp`, `cmems`, \n",
    "  `sentinelhub`, `s3` or `file`.\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."
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   "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"
   ]
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  {
   "cell_type": "markdown",
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   "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\"]. "
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       " 'ESACCI-ICESHEETS_Greenland_GMB-2003-2016-v1.1.zarr',\n",
       " 'ESACCI-L3C_CLOUD-CLD_PRODUCTS-AVHRR_NOAA-1982-2016-fv3.0.zarr',\n",
       " 'ESACCI-L3C_SNOW-SWE-1979-2018-fv1.0.zarr',\n",
       " 'ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr',\n",
       " 'ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-1992-09-fv2.0.1.zarr',\n",
       " 'ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-2015-v2.0.7b.zarr',\n",
       " 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-2002-2018-fv3.00.zarr',\n",
       " 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_NIGHT-2002-2018-fv3.00.zarr',\n",
       " 'ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr',\n",
       " 'ESACCI-PERMAFROST-L4-ALT-MODISLST-AREA4_PP-1997-2018-fv02.0.zarr',\n",
       " 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-NH25KMEASE2-2002-2012-fv2.0.zarr',\n",
       " 'ESACCI-SEAICE-L3C-SITHICK-SIRAL_CRYOSAT2-NH25KMEASE2-2010-2017-fv2.0.zarr',\n",
       " 'ESACCI-SEAICE-L4-SICONC-AMSR_50.0kmEASE2-NH-2002-2017-fv2.1.zarr',\n",
       " 'ESACCI-SEALEVEL-IND-MSLTR-MERGED-1993-2016-fv02.zarr',\n",
       " 'ESACCI-SEALEVEL-L4-MSLA-MERGED-1993-2015-fv02.zarr',\n",
       " 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2020-fv05.3.zarr']"
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    "store = new_data_store(\"ccizarr\")\n",
    "store.list_data_ids()"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (time: 12784, lat: 720, lon: 1440, fields: 16,\n",
       "                         field_name_length: 50, fieldsp1: 17)\n",
       "Coordinates:\n",
       "  * field_name_length   (field_name_length) int32 1 2 3 4 5 6 ... 46 47 48 49 50\n",
       "  * fields              (fields) int32 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
       "  * fieldsp1            (fieldsp1) int32 1 2 3 4 5 6 7 ... 11 12 13 14 15 16 17\n",
       "  * lat                 (lat) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "  * lon                 (lon) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
       "  * time                (time) datetime64[ns] 1982-01-01T12:00:00 ... 2016-12...\n",
       "Data variables:\n",
       "    analysed_sst        (time, lat, lon) float32 dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    analysis_number     (time, lat, lon) float32 dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    anomaly_fields      (time, fields, lat, lon) float32 dask.array&lt;chunksize=(16, 16, 720, 720), meta=np.ndarray&gt;\n",
       "    field_name          (fields, field_name_length) |S1 dask.array&lt;chunksize=(16, 50), meta=np.ndarray&gt;\n",
       "    gradient_fields     (time, fieldsp1, lat, lon) float32 dask.array&lt;chunksize=(16, 17, 720, 720), meta=np.ndarray&gt;\n",
       "    median_type         (time, lat, lon) float32 dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    standard_deviation  (time, lat, lon) float32 dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.4\n",
       "    acknowledgment:             Funded by ESA\n",
       "    cdm_data_type:              grid\n",
       "    comment:                    \n",
       "    creator_email:              science.leader@esa-sst-cci.org\n",
       "    creator_name:               SST_cci\n",
       "    ...                         ...\n",
       "    summary:                    An ensemble product with input from a number ...\n",
       "    time_coverage_end:          20170101T000000Z\n",
       "    time_coverage_start:        20161231T000000Z\n",
       "    title:                      Global SST Ensemble, L4 GMPE\n",
       "    uuid:                       dc0c5b25-93bf-4943-aba1-7f0de9109620\n",
       "    westernmost_longitude:      -180.0</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-1cf698cc-8067-4899-acd6-76988e397e87' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cf698cc-8067-4899-acd6-76988e397e87' 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>: 12784</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>fields</span>: 16</li><li><span class='xr-has-index'>field_name_length</span>: 50</li><li><span class='xr-has-index'>fieldsp1</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b32f5959-ee6a-4e7d-885b-e4a55eb45efe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b32f5959-ee6a-4e7d-885b-e4a55eb45efe' 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'>field_name_length</span></div><div class='xr-var-dims'>(field_name_length)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 45 46 47 48 49 50</div><input id='attrs-00e0e22b-b204-48d8-87a0-d13da23eb3df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00e0e22b-b204-48d8-87a0-d13da23eb3df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a944f53-b2b9-4475-925e-ff491b541d52' class='xr-var-data-in' type='checkbox'><label for='data-4a944f53-b2b9-4475-925e-ff491b541d52' 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>Length of string array for names of inputs</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fields</span></div><div class='xr-var-dims'>(fields)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 11 12 13 14 15 16</div><input id='attrs-ff73912c-25bf-4407-bc76-fa65421b0208' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff73912c-25bf-4407-bc76-fa65421b0208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-890aa603-1b89-4af3-a5ad-f03e41a136b6' class='xr-var-data-in' type='checkbox'><label for='data-890aa603-1b89-4af3-a5ad-f03e41a136b6' 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>Fields number</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fieldsp1</span></div><div class='xr-var-dims'>(fieldsp1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 12 13 14 15 16 17</div><input id='attrs-a5a8b4db-4d22-4f45-b40b-d04bc9691152' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5a8b4db-4d22-4f45-b40b-d04bc9691152' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb4e64d-38ff-4bfd-83d4-2a7b16777de7' class='xr-var-data-in' type='checkbox'><label for='data-3eb4e64d-38ff-4bfd-83d4-2a7b16777de7' 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>Fields number plus one</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17])</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'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-12222377-a970-4741-b37a-2001344f42ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12222377-a970-4741-b37a-2001344f42ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae6f67b7-070b-4dd4-b7b1-5e7a5b808bd4' class='xr-var-data-in' type='checkbox'><label for='data-ae6f67b7-070b-4dd4-b7b1-5e7a5b808bd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-a5d72195-6cea-46fa-a37c-a15bf8ca3902' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5d72195-6cea-46fa-a37c-a15bf8ca3902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17d982cc-c9c9-45dc-bb8f-c065e08bf930' class='xr-var-data-in' type='checkbox'><label for='data-17d982cc-c9c9-45dc-bb8f-c065e08bf930' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1982-01-01T12:00:00 ... 2016-12-...</div><input id='attrs-cf447c36-391b-4c2f-8a0c-cc6b8f5b51d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf447c36-391b-4c2f-8a0c-cc6b8f5b51d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-388a71a3-61f7-43d6-b459-be1fa3f28c17' class='xr-var-data-in' type='checkbox'><label for='data-388a71a3-61f7-43d6-b459-be1fa3f28c17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-01T12:00:00.000000000&#x27;, &#x27;1982-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-03T12:00:00.000000000&#x27;, ..., &#x27;2016-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-30T12:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f38949ca-055e-499c-a565-215114c0e570' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f38949ca-055e-499c-a565-215114c0e570' class='xr-section-summary' >Data variables: <span>(7)</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>analysed_sst</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=(16, 720, 720), meta=np.ndarray&gt;</div><input id='attrs-070366b1-2328-458e-baea-a6a2ce74458a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-070366b1-2328-458e-baea-a6a2ce74458a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbccf7aa-6721-4e4f-9765-9a45f1efabed' class='xr-var-data-in' type='checkbox'><label for='data-dbccf7aa-6721-4e4f-9765-9a45f1efabed' 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>median SST from GMPE</dd><dt><span>source :</span></dt><dd>SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>valid_min :</span></dt><dd>-300</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> 49.38 GiB </td>\n",
       "                        <td> 31.64 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12784, 720, 1440) </td>\n",
       "                        <td> (16, 720, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1598 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analysis_number</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=(16, 720, 720), meta=np.ndarray&gt;</div><input id='attrs-0a4e3325-cf60-4002-9ed6-8d25a217633b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a4e3325-cf60-4002-9ed6-8d25a217633b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3650af6a-694f-47ad-9f5f-f1b386ef2637' class='xr-var-data-in' type='checkbox'><label for='data-3650af6a-694f-47ad-9f5f-f1b386ef2637' 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>comment :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Number of contributing analyses</dd><dt><span>source :</span></dt><dd>SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST</dd><dt><span>units :</span></dt><dd></dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>valid_min :</span></dt><dd>-127</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gradient_fields</span></div><div class='xr-var-dims'>(time, fieldsp1, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(16, 17, 720, 720), meta=np.ndarray&gt;</div><input id='attrs-346dc718-4217-48e3-89c4-ef1664338c25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-346dc718-4217-48e3-89c4-ef1664338c25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ecf3e68-dd53-460a-9386-bdd85fae118e' class='xr-var-data-in' type='checkbox'><label for='data-1ecf3e68-dd53-460a-9386-bdd85fae118e' 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>comment :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Horizontal SST gradients of input analyses</dd><dt><span>source :</span></dt><dd>SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST, GMPE median</dd><dt><span>units :</span></dt><dd>kelvin m-1</dd><dt><span>valid_max :</span></dt><dd>3000</dd><dt><span>valid_min :</span></dt><dd>-3000</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 839.40 GiB </td>\n",
       "                        <td> 537.89 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> 49.38 GiB </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 49.38 GiB </td>\n",
       "                        <td> 31.64 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c8a5ebb-9320-4cb6-9ce8-10b343f39d01' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c8a5ebb-9320-4cb6-9ce8-10b343f39d01' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>field_name_length</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be045c82-4d32-4425-85ee-221eb78702b0' class='xr-index-data-in' type='checkbox'/><label for='index-be045c82-4d32-4425-85ee-221eb78702b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "            18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;field_name_length&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fields</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-697d8e2e-c5e2-4b5f-aeae-df0fc55685a2' class='xr-index-data-in' type='checkbox'/><label for='index-697d8e2e-c5e2-4b5f-aeae-df0fc55685a2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], dtype=&#x27;int64&#x27;, name=&#x27;fields&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fieldsp1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2bab892f-1503-4c8f-b07f-4e907a497738' class='xr-index-data-in' type='checkbox'/><label for='index-2bab892f-1503-4c8f-b07f-4e907a497738' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], dtype=&#x27;int64&#x27;, name=&#x27;fieldsp1&#x27;))</pre></div></li><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-0c05dadf-f28b-4dc3-8e2d-84ab161273af' class='xr-index-data-in' type='checkbox'/><label for='index-0c05dadf-f28b-4dc3-8e2d-84ab161273af' 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([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375,\n",
       "              -88.125, -87.875, -87.625,\n",
       "              ...\n",
       "               87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,\n",
       "               89.375,  89.625,  89.875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=720))</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-60a5ba3c-3c88-4b5b-b8c9-83e95a7e0e76' class='xr-index-data-in' type='checkbox'/><label for='index-60a5ba3c-3c88-4b5b-b8c9-83e95a7e0e76' 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([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625,\n",
       "              -178.375, -178.125, -177.875, -177.625,\n",
       "              ...\n",
       "               177.625,  177.875,  178.125,  178.375,  178.625,  178.875,\n",
       "               179.125,  179.375,  179.625,  179.875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</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-86990216-2acb-440d-a95d-ffd170045d24' class='xr-index-data-in' type='checkbox'/><label for='index-86990216-2acb-440d-a95d-ffd170045d24' 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;1982-01-01 12:00:00&#x27;, &#x27;1982-01-02 12:00:00&#x27;,\n",
       "               &#x27;1982-01-03 12:00:00&#x27;, &#x27;1982-01-04 12:00:00&#x27;,\n",
       "               &#x27;1982-01-05 12:00:00&#x27;, &#x27;1982-01-06 12:00:00&#x27;,\n",
       "               &#x27;1982-01-07 12:00:00&#x27;, &#x27;1982-01-08 12:00:00&#x27;,\n",
       "               &#x27;1982-01-09 12:00:00&#x27;, &#x27;1982-01-10 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2016-12-22 12:00:00&#x27;, &#x27;2016-12-23 12:00:00&#x27;,\n",
       "               &#x27;2016-12-24 12:00:00&#x27;, &#x27;2016-12-25 12:00:00&#x27;,\n",
       "               &#x27;2016-12-26 12:00:00&#x27;, &#x27;2016-12-27 12:00:00&#x27;,\n",
       "               &#x27;2016-12-28 12:00:00&#x27;, &#x27;2016-12-29 12:00:00&#x27;,\n",
       "               &#x27;2016-12-30 12:00:00&#x27;, &#x27;2016-12-31 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=12784, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-251ec8ed-aa47-49cb-ac20-b6d0d5c34e7e' class='xr-section-summary-in' type='checkbox'  ><label for='section-251ec8ed-aa47-49cb-ac20-b6d0d5c34e7e' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>acknowledgment :</span></dt><dd>Funded by ESA</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd></dd><dt><span>creator_email :</span></dt><dd>science.leader@esa-sst-cci.org</dd><dt><span>creator_name :</span></dt><dd>SST_cci</dd><dt><span>creator_url :</span></dt><dd>http://www.esa-sst-cci.org</dd><dt><span>date_created :</span></dt><dd>20180711T172223Z</dd><dt><span>easternmost_longitude :</span></dt><dd>180.0</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>history :</span></dt><dd>NULL</dd><dt><span>id :</span></dt><dd>UKMO-L4LRens-GLOB-GMPE</dd><dt><span>institution :</span></dt><dd>UKMO</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Creative Commons Licence by attribution (https://creativecommons.org/licenses/by/4.0/</dd><dt><span>metadata_conventions :</span></dt><dd>Unidata Observation Dataset v1.0</dd><dt><span>metadata_link :</span></dt><dd>not available</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>3.6</dd><dt><span>northernmost_latitude :</span></dt><dd>90.0</dd><dt><span>platform :</span></dt><dd></dd><dt><span>processing_level :</span></dt><dd>GMPE</dd><dt><span>product_version :</span></dt><dd>3.0</dd><dt><span>project :</span></dt><dd>Climate Change Initiative - European Space Agency</dd><dt><span>publisher_email :</span></dt><dd>science.leader@esa-sst-cci.org</dd><dt><span>publisher_name :</span></dt><dd>ESACCI</dd><dt><span>publisher_url :</span></dt><dd>http://www.esa-sst-cci.org</dd><dt><span>references :</span></dt><dd>Martin et al., Deep Sea Research II, 2011</dd><dt><span>sensor :</span></dt><dd></dd><dt><span>source :</span></dt><dd>SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.0</dd><dt><span>spatial_resolution :</span></dt><dd>0.25 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20161231T000000Z</dd><dt><span>stop_time :</span></dt><dd>20170101T000000Z</dd><dt><span>summary :</span></dt><dd>An ensemble product with input from a number of L4 SST analyses</dd><dt><span>time_coverage_end :</span></dt><dd>20170101T000000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20161231T000000Z</dd><dt><span>title :</span></dt><dd>Global SST Ensemble, L4 GMPE</dd><dt><span>uuid :</span></dt><dd>dc0c5b25-93bf-4943-aba1-7f0de9109620</dd><dt><span>westernmost_longitude :</span></dt><dd>-180.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (time: 12784, lat: 720, lon: 1440, fields: 16,\n",
       "                         field_name_length: 50, fieldsp1: 17)\n",
       "Coordinates:\n",
       "  * field_name_length   (field_name_length) int32 1 2 3 4 5 6 ... 46 47 48 49 50\n",
       "  * fields              (fields) int32 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
       "  * fieldsp1            (fieldsp1) int32 1 2 3 4 5 6 7 ... 11 12 13 14 15 16 17\n",
       "  * lat                 (lat) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "  * lon                 (lon) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
       "  * time                (time) datetime64[ns] 1982-01-01T12:00:00 ... 2016-12...\n",
       "Data variables:\n",
       "    analysed_sst        (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    analysis_number     (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    anomaly_fields      (time, fields, lat, lon) float32 dask.array<chunksize=(16, 16, 720, 720), meta=np.ndarray>\n",
       "    field_name          (fields, field_name_length) |S1 dask.array<chunksize=(16, 50), meta=np.ndarray>\n",
       "    gradient_fields     (time, fieldsp1, lat, lon) float32 dask.array<chunksize=(16, 17, 720, 720), meta=np.ndarray>\n",
       "    median_type         (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    standard_deviation  (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.4\n",
       "    acknowledgment:             Funded by ESA\n",
       "    cdm_data_type:              grid\n",
       "    comment:                    \n",
       "    creator_email:              science.leader@esa-sst-cci.org\n",
       "    creator_name:               SST_cci\n",
       "    ...                         ...\n",
       "    summary:                    An ensemble product with input from a number ...\n",
       "    time_coverage_end:          20170101T000000Z\n",
       "    time_coverage_start:        20161231T000000Z\n",
       "    title:                      Global SST Ensemble, L4 GMPE\n",
       "    uuid:                       dc0c5b25-93bf-4943-aba1-7f0de9109620\n",
       "    westernmost_longitude:      -180.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = store.open_data(\n",
    "    \"ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr\"\n",
    ")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a2698fd-3a0f-45b7-9847-473d62d9db8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 12784)&gt;\n",
       "array([&#x27;1982-01-01T12:00:00.000000000&#x27;, &#x27;1982-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-03T12:00:00.000000000&#x27;, ..., &#x27;2016-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-30T12:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1982-01-01T12:00:00 ... 2016-12-31T12:00:00\n",
       "Attributes:\n",
       "    axis:           T\n",
       "    long_name:      reference time of sst field\n",
       "    standard_name:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12784</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a22d35aa-9097-40ee-97cf-1d4c68af5c10' class='xr-array-in' type='checkbox' checked><label for='section-a22d35aa-9097-40ee-97cf-1d4c68af5c10' 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>1982-01-01T12:00:00 1982-01-02T12:00:00 ... 2016-12-31T12:00:00</span></div><div class='xr-array-data'><pre>array([&#x27;1982-01-01T12:00:00.000000000&#x27;, &#x27;1982-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-03T12:00:00.000000000&#x27;, ..., &#x27;2016-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-30T12:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-27232314-217d-46c7-9186-390412aad907' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27232314-217d-46c7-9186-390412aad907' class='xr-section-summary' >Coordinates: <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 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'>1982-01-01T12:00:00 ... 2016-12-...</div><input id='attrs-fccd7e1e-a128-428d-a098-e32d818d99cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fccd7e1e-a128-428d-a098-e32d818d99cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e93d9d1c-7d20-4120-9bb6-e102833fa888' class='xr-var-data-in' type='checkbox'><label for='data-e93d9d1c-7d20-4120-9bb6-e102833fa888' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-01T12:00:00.000000000&#x27;, &#x27;1982-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-03T12:00:00.000000000&#x27;, ..., &#x27;2016-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-30T12:00:00.000000000&#x27;, &#x27;2016-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85c615eb-890d-4393-8c33-220bd346163d' class='xr-section-summary-in' type='checkbox'  ><label for='section-85c615eb-890d-4393-8c33-220bd346163d' 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-ccea047e-3494-499a-843c-f9fd2212a3cb' class='xr-index-data-in' type='checkbox'/><label for='index-ccea047e-3494-499a-843c-f9fd2212a3cb' 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;1982-01-01 12:00:00&#x27;, &#x27;1982-01-02 12:00:00&#x27;,\n",
       "               &#x27;1982-01-03 12:00:00&#x27;, &#x27;1982-01-04 12:00:00&#x27;,\n",
       "               &#x27;1982-01-05 12:00:00&#x27;, &#x27;1982-01-06 12:00:00&#x27;,\n",
       "               &#x27;1982-01-07 12:00:00&#x27;, &#x27;1982-01-08 12:00:00&#x27;,\n",
       "               &#x27;1982-01-09 12:00:00&#x27;, &#x27;1982-01-10 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2016-12-22 12:00:00&#x27;, &#x27;2016-12-23 12:00:00&#x27;,\n",
       "               &#x27;2016-12-24 12:00:00&#x27;, &#x27;2016-12-25 12:00:00&#x27;,\n",
       "               &#x27;2016-12-26 12:00:00&#x27;, &#x27;2016-12-27 12:00:00&#x27;,\n",
       "               &#x27;2016-12-28 12:00:00&#x27;, &#x27;2016-12-29 12:00:00&#x27;,\n",
       "               &#x27;2016-12-30 12:00:00&#x27;, &#x27;2016-12-31 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=12784, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcbb294a-465d-4021-bc87-2e13a76dfde1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcbb294a-465d-4021-bc87-2e13a76dfde1' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'time' (time: 12784)>\n",
       "array(['1982-01-01T12:00:00.000000000', '1982-01-02T12:00:00.000000000',\n",
       "       '1982-01-03T12:00:00.000000000', ..., '2016-12-29T12:00:00.000000000',\n",
       "       '2016-12-30T12:00:00.000000000', '2016-12-31T12:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1982-01-01T12:00:00 ... 2016-12-31T12:00:00\n",
       "Attributes:\n",
       "    axis:           T\n",
       "    long_name:      reference time of sst field\n",
       "    standard_name:  time"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.time"
   ]
  },
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   "id": "243f088e-9bd8-4bef-8364-e08864d84554",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (time: 61, lat: 45, lon: 60, field_name_length: 50,\n",
       "                        fields: 16, fieldsp1: 17)\n",
       "Coordinates:\n",
       "  * field_name_length  (field_name_length) int32 1 2 3 4 5 6 ... 46 47 48 49 50\n",
       "  * fields             (fields) int32 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
       "  * fieldsp1           (fieldsp1) int32 1 2 3 4 5 6 7 8 ... 11 12 13 14 15 16 17\n",
       "  * lat                (lat) float32 52.62 52.88 53.12 ... 63.12 63.38 63.62\n",
       "  * lon                (lon) float32 10.12 10.38 10.62 ... 24.38 24.62 24.88\n",
       "  * time               (time) datetime64[ns] 2016-05-01T12:00:00 ... 2016-06-...\n",
       "Data variables:\n",
       "    analysed_sst       (time, lat, lon) float32 dask.array&lt;chunksize=(5, 45, 60), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.4\n",
       "    acknowledgment:             Funded by ESA\n",
       "    cdm_data_type:              grid\n",
       "    comment:                    \n",
       "    creator_email:              science.leader@esa-sst-cci.org\n",
       "    creator_name:               SST_cci\n",
       "    ...                         ...\n",
       "    summary:                    An ensemble product with input from a number ...\n",
       "    time_coverage_end:          20170101T000000Z\n",
       "    time_coverage_start:        20161231T000000Z\n",
       "    title:                      SST Baltic May/June 2016\n",
       "    uuid:                       dc0c5b25-93bf-4943-aba1-7f0de9109620\n",
       "    westernmost_longitude:      -180.0</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-7d4e6edd-4910-42a8-b6af-aa725f1d73c8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7d4e6edd-4910-42a8-b6af-aa725f1d73c8' 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>: 61</li><li><span class='xr-has-index'>lat</span>: 45</li><li><span class='xr-has-index'>lon</span>: 60</li><li><span class='xr-has-index'>field_name_length</span>: 50</li><li><span class='xr-has-index'>fields</span>: 16</li><li><span class='xr-has-index'>fieldsp1</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-658365e9-96ef-4701-8c33-60420ec7b864' class='xr-section-summary-in' type='checkbox'  checked><label for='section-658365e9-96ef-4701-8c33-60420ec7b864' 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'>field_name_length</span></div><div class='xr-var-dims'>(field_name_length)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 45 46 47 48 49 50</div><input id='attrs-f1ec471b-dc8c-4296-8433-275334e15a5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1ec471b-dc8c-4296-8433-275334e15a5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8b843cf-03f3-48a8-9ef0-8890af461359' class='xr-var-data-in' type='checkbox'><label for='data-a8b843cf-03f3-48a8-9ef0-8890af461359' 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>Length of string array for names of inputs</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fields</span></div><div class='xr-var-dims'>(fields)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 11 12 13 14 15 16</div><input id='attrs-4a4e6bb9-853f-4655-a58d-8de136728b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a4e6bb9-853f-4655-a58d-8de136728b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db0bebda-a445-4199-977e-688af1ef7e4e' class='xr-var-data-in' type='checkbox'><label for='data-db0bebda-a445-4199-977e-688af1ef7e4e' 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>Fields number</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fieldsp1</span></div><div class='xr-var-dims'>(fieldsp1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 12 13 14 15 16 17</div><input id='attrs-4f62048a-1a87-474f-a392-eee22dee5fab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f62048a-1a87-474f-a392-eee22dee5fab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f03ac38f-568d-4d58-a5f0-0631d938bff4' class='xr-var-data-in' type='checkbox'><label for='data-f03ac38f-568d-4d58-a5f0-0631d938bff4' 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>Fields number plus one</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17])</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'>float32</div><div class='xr-var-preview xr-preview'>52.62 52.88 53.12 ... 63.38 63.62</div><input id='attrs-180fcb75-6294-4f04-b2b3-b327e4539f76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-180fcb75-6294-4f04-b2b3-b327e4539f76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-902af924-6877-4ff9-8e49-46bd97f96fc0' class='xr-var-data-in' type='checkbox'><label for='data-902af924-6877-4ff9-8e49-46bd97f96fc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([52.625, 52.875, 53.125, 53.375, 53.625, 53.875, 54.125, 54.375, 54.625,\n",
       "       54.875, 55.125, 55.375, 55.625, 55.875, 56.125, 56.375, 56.625, 56.875,\n",
       "       57.125, 57.375, 57.625, 57.875, 58.125, 58.375, 58.625, 58.875, 59.125,\n",
       "       59.375, 59.625, 59.875, 60.125, 60.375, 60.625, 60.875, 61.125, 61.375,\n",
       "       61.625, 61.875, 62.125, 62.375, 62.625, 62.875, 63.125, 63.375, 63.625],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.12 10.38 10.62 ... 24.62 24.88</div><input id='attrs-66cfc41e-d287-42ae-ba2e-4ee3e199f619' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66cfc41e-d287-42ae-ba2e-4ee3e199f619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d25e56ef-0231-4523-b8f3-e946b762d5cb' class='xr-var-data-in' type='checkbox'><label for='data-d25e56ef-0231-4523-b8f3-e946b762d5cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([10.125, 10.375, 10.625, 10.875, 11.125, 11.375, 11.625, 11.875, 12.125,\n",
       "       12.375, 12.625, 12.875, 13.125, 13.375, 13.625, 13.875, 14.125, 14.375,\n",
       "       14.625, 14.875, 15.125, 15.375, 15.625, 15.875, 16.125, 16.375, 16.625,\n",
       "       16.875, 17.125, 17.375, 17.625, 17.875, 18.125, 18.375, 18.625, 18.875,\n",
       "       19.125, 19.375, 19.625, 19.875, 20.125, 20.375, 20.625, 20.875, 21.125,\n",
       "       21.375, 21.625, 21.875, 22.125, 22.375, 22.625, 22.875, 23.125, 23.375,\n",
       "       23.625, 23.875, 24.125, 24.375, 24.625, 24.875], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-05-01T12:00:00 ... 2016-06-...</div><input id='attrs-a50db9ff-70e9-424a-ad77-f1c83142c9c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a50db9ff-70e9-424a-ad77-f1c83142c9c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1db010f5-28fe-444b-a54e-dd054915e6fa' class='xr-var-data-in' type='checkbox'><label for='data-1db010f5-28fe-444b-a54e-dd054915e6fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-05-01T12:00:00.000000000&#x27;, &#x27;2016-05-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-03T12:00:00.000000000&#x27;, &#x27;2016-05-04T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-05T12:00:00.000000000&#x27;, &#x27;2016-05-06T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-07T12:00:00.000000000&#x27;, &#x27;2016-05-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-09T12:00:00.000000000&#x27;, &#x27;2016-05-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-11T12:00:00.000000000&#x27;, &#x27;2016-05-12T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-13T12:00:00.000000000&#x27;, &#x27;2016-05-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-15T12:00:00.000000000&#x27;, &#x27;2016-05-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-17T12:00:00.000000000&#x27;, &#x27;2016-05-18T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-19T12:00:00.000000000&#x27;, &#x27;2016-05-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-21T12:00:00.000000000&#x27;, &#x27;2016-05-22T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-23T12:00:00.000000000&#x27;, &#x27;2016-05-24T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-25T12:00:00.000000000&#x27;, &#x27;2016-05-26T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-27T12:00:00.000000000&#x27;, &#x27;2016-05-28T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-29T12:00:00.000000000&#x27;, &#x27;2016-05-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-31T12:00:00.000000000&#x27;, &#x27;2016-06-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-02T12:00:00.000000000&#x27;, &#x27;2016-06-03T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-04T12:00:00.000000000&#x27;, &#x27;2016-06-05T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-06T12:00:00.000000000&#x27;, &#x27;2016-06-07T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-08T12:00:00.000000000&#x27;, &#x27;2016-06-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-10T12:00:00.000000000&#x27;, &#x27;2016-06-11T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-12T12:00:00.000000000&#x27;, &#x27;2016-06-13T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-14T12:00:00.000000000&#x27;, &#x27;2016-06-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-16T12:00:00.000000000&#x27;, &#x27;2016-06-17T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-18T12:00:00.000000000&#x27;, &#x27;2016-06-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-20T12:00:00.000000000&#x27;, &#x27;2016-06-21T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-22T12:00:00.000000000&#x27;, &#x27;2016-06-23T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-24T12:00:00.000000000&#x27;, &#x27;2016-06-25T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-26T12:00:00.000000000&#x27;, &#x27;2016-06-27T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-28T12:00:00.000000000&#x27;, &#x27;2016-06-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-30T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e5366743-ad23-4725-bb40-c81509d8299f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e5366743-ad23-4725-bb40-c81509d8299f' 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>analysed_sst</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=(5, 45, 60), meta=np.ndarray&gt;</div><input id='attrs-16676315-ee5b-472e-ac95-802ccc3da022' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16676315-ee5b-472e-ac95-802ccc3da022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ca6f137-4600-4386-96e7-17c5cf520a52' class='xr-var-data-in' type='checkbox'><label for='data-4ca6f137-4600-4386-96e7-17c5cf520a52' 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>median SST from GMPE</dd><dt><span>source :</span></dt><dd>SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>valid_min :</span></dt><dd>-300</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> 643.36 kiB </td>\n",
       "                        <td> 168.75 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (61, 45, 60) </td>\n",
       "                        <td> (16, 45, 60) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbeaf21d-67d5-4665-8c97-778c84b6e641' class='xr-section-summary-in' type='checkbox'  ><label for='section-fbeaf21d-67d5-4665-8c97-778c84b6e641' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>field_name_length</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-03ae7686-fca5-4600-80bd-0ac526ff2635' class='xr-index-data-in' type='checkbox'/><label for='index-03ae7686-fca5-4600-80bd-0ac526ff2635' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "            18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;field_name_length&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fields</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-651e6b7b-90c0-4401-ae08-77021088c4cb' class='xr-index-data-in' type='checkbox'/><label for='index-651e6b7b-90c0-4401-ae08-77021088c4cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], dtype=&#x27;int64&#x27;, name=&#x27;fields&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fieldsp1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2f4c78ca-3a94-4bf9-b62c-e2916a096548' class='xr-index-data-in' type='checkbox'/><label for='index-2f4c78ca-3a94-4bf9-b62c-e2916a096548' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], dtype=&#x27;int64&#x27;, name=&#x27;fieldsp1&#x27;))</pre></div></li><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-7bd57deb-8110-4ac2-99f4-a157f3d7dbbe' class='xr-index-data-in' type='checkbox'/><label for='index-7bd57deb-8110-4ac2-99f4-a157f3d7dbbe' 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([52.625, 52.875, 53.125, 53.375, 53.625, 53.875, 54.125, 54.375,\n",
       "              54.625, 54.875, 55.125, 55.375, 55.625, 55.875, 56.125, 56.375,\n",
       "              56.625, 56.875, 57.125, 57.375, 57.625, 57.875, 58.125, 58.375,\n",
       "              58.625, 58.875, 59.125, 59.375, 59.625, 59.875, 60.125, 60.375,\n",
       "              60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125, 62.375,\n",
       "              62.625, 62.875, 63.125, 63.375, 63.625],\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-a3324102-2dda-403e-8b9a-0bf772340382' class='xr-index-data-in' type='checkbox'/><label for='index-a3324102-2dda-403e-8b9a-0bf772340382' 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([10.125, 10.375, 10.625, 10.875, 11.125, 11.375, 11.625, 11.875,\n",
       "              12.125, 12.375, 12.625, 12.875, 13.125, 13.375, 13.625, 13.875,\n",
       "              14.125, 14.375, 14.625, 14.875, 15.125, 15.375, 15.625, 15.875,\n",
       "              16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625, 17.875,\n",
       "              18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "              20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875,\n",
       "              22.125, 22.375, 22.625, 22.875, 23.125, 23.375, 23.625, 23.875,\n",
       "              24.125, 24.375, 24.625, 24.875],\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-bfd4c2ec-a468-43f8-a92e-f55ed2da2300' class='xr-index-data-in' type='checkbox'/><label for='index-bfd4c2ec-a468-43f8-a92e-f55ed2da2300' 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;2016-05-01 12:00:00&#x27;, &#x27;2016-05-02 12:00:00&#x27;,\n",
       "               &#x27;2016-05-03 12:00:00&#x27;, &#x27;2016-05-04 12:00:00&#x27;,\n",
       "               &#x27;2016-05-05 12:00:00&#x27;, &#x27;2016-05-06 12:00:00&#x27;,\n",
       "               &#x27;2016-05-07 12:00:00&#x27;, &#x27;2016-05-08 12:00:00&#x27;,\n",
       "               &#x27;2016-05-09 12:00:00&#x27;, &#x27;2016-05-10 12:00:00&#x27;,\n",
       "               &#x27;2016-05-11 12:00:00&#x27;, &#x27;2016-05-12 12:00:00&#x27;,\n",
       "               &#x27;2016-05-13 12:00:00&#x27;, &#x27;2016-05-14 12:00:00&#x27;,\n",
       "               &#x27;2016-05-15 12:00:00&#x27;, &#x27;2016-05-16 12:00:00&#x27;,\n",
       "               &#x27;2016-05-17 12:00:00&#x27;, &#x27;2016-05-18 12:00:00&#x27;,\n",
       "               &#x27;2016-05-19 12:00:00&#x27;, &#x27;2016-05-20 12:00:00&#x27;,\n",
       "               &#x27;2016-05-21 12:00:00&#x27;, &#x27;2016-05-22 12:00:00&#x27;,\n",
       "               &#x27;2016-05-23 12:00:00&#x27;, &#x27;2016-05-24 12:00:00&#x27;,\n",
       "               &#x27;2016-05-25 12:00:00&#x27;, &#x27;2016-05-26 12:00:00&#x27;,\n",
       "               &#x27;2016-05-27 12:00:00&#x27;, &#x27;2016-05-28 12:00:00&#x27;,\n",
       "               &#x27;2016-05-29 12:00:00&#x27;, &#x27;2016-05-30 12:00:00&#x27;,\n",
       "               &#x27;2016-05-31 12:00:00&#x27;, &#x27;2016-06-01 12:00:00&#x27;,\n",
       "               &#x27;2016-06-02 12:00:00&#x27;, &#x27;2016-06-03 12:00:00&#x27;,\n",
       "               &#x27;2016-06-04 12:00:00&#x27;, &#x27;2016-06-05 12:00:00&#x27;,\n",
       "               &#x27;2016-06-06 12:00:00&#x27;, &#x27;2016-06-07 12:00:00&#x27;,\n",
       "               &#x27;2016-06-08 12:00:00&#x27;, &#x27;2016-06-09 12:00:00&#x27;,\n",
       "               &#x27;2016-06-10 12:00:00&#x27;, &#x27;2016-06-11 12:00:00&#x27;,\n",
       "               &#x27;2016-06-12 12:00:00&#x27;, &#x27;2016-06-13 12:00:00&#x27;,\n",
       "               &#x27;2016-06-14 12:00:00&#x27;, &#x27;2016-06-15 12:00:00&#x27;,\n",
       "               &#x27;2016-06-16 12:00:00&#x27;, &#x27;2016-06-17 12:00:00&#x27;,\n",
       "               &#x27;2016-06-18 12:00:00&#x27;, &#x27;2016-06-19 12:00:00&#x27;,\n",
       "               &#x27;2016-06-20 12:00:00&#x27;, &#x27;2016-06-21 12:00:00&#x27;,\n",
       "               &#x27;2016-06-22 12:00:00&#x27;, &#x27;2016-06-23 12:00:00&#x27;,\n",
       "               &#x27;2016-06-24 12:00:00&#x27;, &#x27;2016-06-25 12:00:00&#x27;,\n",
       "               &#x27;2016-06-26 12:00:00&#x27;, &#x27;2016-06-27 12:00:00&#x27;,\n",
       "               &#x27;2016-06-28 12:00:00&#x27;, &#x27;2016-06-29 12:00:00&#x27;,\n",
       "               &#x27;2016-06-30 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c5303ff-4922-4f0b-b04b-b69412508801' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c5303ff-4922-4f0b-b04b-b69412508801' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>acknowledgment :</span></dt><dd>Funded by ESA</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd></dd><dt><span>creator_email :</span></dt><dd>science.leader@esa-sst-cci.org</dd><dt><span>creator_name :</span></dt><dd>SST_cci</dd><dt><span>creator_url :</span></dt><dd>http://www.esa-sst-cci.org</dd><dt><span>date_created :</span></dt><dd>20180711T172223Z</dd><dt><span>easternmost_longitude :</span></dt><dd>180.0</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>history :</span></dt><dd>NULL</dd><dt><span>id :</span></dt><dd>UKMO-L4LRens-GLOB-GMPE</dd><dt><span>institution :</span></dt><dd>UKMO</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Creative Commons Licence by attribution (https://creativecommons.org/licenses/by/4.0/</dd><dt><span>metadata_conventions :</span></dt><dd>Unidata Observation Dataset v1.0</dd><dt><span>metadata_link :</span></dt><dd>not available</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>3.6</dd><dt><span>northernmost_latitude :</span></dt><dd>90.0</dd><dt><span>platform :</span></dt><dd></dd><dt><span>processing_level :</span></dt><dd>GMPE</dd><dt><span>product_version :</span></dt><dd>3.0</dd><dt><span>project :</span></dt><dd>Climate Change Initiative - European Space Agency</dd><dt><span>publisher_email :</span></dt><dd>science.leader@esa-sst-cci.org</dd><dt><span>publisher_name :</span></dt><dd>ESACCI</dd><dt><span>publisher_url :</span></dt><dd>http://www.esa-sst-cci.org</dd><dt><span>references :</span></dt><dd>Martin et al., Deep Sea Research II, 2011</dd><dt><span>sensor :</span></dt><dd></dd><dt><span>source :</span></dt><dd>SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.0</dd><dt><span>spatial_resolution :</span></dt><dd>0.25 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20161231T000000Z</dd><dt><span>stop_time :</span></dt><dd>20170101T000000Z</dd><dt><span>summary :</span></dt><dd>An ensemble product with input from a number of L4 SST analyses</dd><dt><span>time_coverage_end :</span></dt><dd>20170101T000000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20161231T000000Z</dd><dt><span>title :</span></dt><dd>SST Baltic May/June 2016</dd><dt><span>uuid :</span></dt><dd>dc0c5b25-93bf-4943-aba1-7f0de9109620</dd><dt><span>westernmost_longitude :</span></dt><dd>-180.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 61, lat: 45, lon: 60, field_name_length: 50,\n",
       "                        fields: 16, fieldsp1: 17)\n",
       "Coordinates:\n",
       "  * field_name_length  (field_name_length) int32 1 2 3 4 5 6 ... 46 47 48 49 50\n",
       "  * fields             (fields) int32 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
       "  * fieldsp1           (fieldsp1) int32 1 2 3 4 5 6 7 8 ... 11 12 13 14 15 16 17\n",
       "  * lat                (lat) float32 52.62 52.88 53.12 ... 63.12 63.38 63.62\n",
       "  * lon                (lon) float32 10.12 10.38 10.62 ... 24.38 24.62 24.88\n",
       "  * time               (time) datetime64[ns] 2016-05-01T12:00:00 ... 2016-06-...\n",
       "Data variables:\n",
       "    analysed_sst       (time, lat, lon) float32 dask.array<chunksize=(5, 45, 60), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.4\n",
       "    acknowledgment:             Funded by ESA\n",
       "    cdm_data_type:              grid\n",
       "    comment:                    \n",
       "    creator_email:              science.leader@esa-sst-cci.org\n",
       "    creator_name:               SST_cci\n",
       "    ...                         ...\n",
       "    summary:                    An ensemble product with input from a number ...\n",
       "    time_coverage_end:          20170101T000000Z\n",
       "    time_coverage_start:        20161231T000000Z\n",
       "    title:                      SST Baltic May/June 2016\n",
       "    uuid:                       dc0c5b25-93bf-4943-aba1-7f0de9109620\n",
       "    westernmost_longitude:      -180.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_baltic = select_subset(\n",
    "    dataset,\n",
    "    bbox=[10, 52.5, 25, 63.8],\n",
    "    time_range=[\"2016-05-01\", \"2016-06-30\"],\n",
    "    var_names=[\"analysed_sst\"],\n",
    ")\n",
    "dataset_baltic.attrs[\"title\"] = \"SST Baltic May/June 2016\"\n",
    "dataset_baltic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4c9a0e6-dbf2-43d9-bdab-d594d2986a19",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26047332100>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_baltic.analysed_sst.isel(time=0).plot.imshow(vmin=270, vmax=300, cmap=\"inferno\")"
   ]
  },
  {
   "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).\n",
    "\n",
    "We create the viewer with a configuration that defines the color mapping style `SST`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0958f695-4332-486a-b546-04c0938c1fd3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "viewer = Viewer(\n",
    "    server_config={\n",
    "        \"Styles\": [\n",
    "            {\n",
    "                \"Identifier\": \"SST\",\n",
    "                \"ColorMappings\": {\n",
    "                    \"analysed_sst\": {\"ValueRange\": [270, 300], \"ColorBar\": \"inferno\"}\n",
    "                },\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98ed2dd-db96-4252-90bf-3a5979210a53",
   "metadata": {},
   "source": [
    "Then we add our datasets using the style `SST`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73136638-861e-491b-b079-de584a9c867b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'81c0a600-c197-42f0-810e-4b3440b9ee86'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer.add_dataset(dataset, style=\"SST\")\n",
    "viewer.add_dataset(dataset_baltic, style=\"SST\")"
   ]
  },
  {
   "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": 9,
   "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": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "404 GET /viewer/config/config.json (127.0.0.1): xcube viewer has not been been configured\n",
      "404 GET /viewer/config/config.json (127.0.0.1) 3.50ms\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": 11,
   "id": "508394d2-3a08-4da0-a73d-8056143d904d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x26048269580>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "404 GET /viewer/config/config.json (127.0.0.1): xcube viewer has not been been configured\n",
      "404 GET /viewer/config/config.json (127.0.0.1) 1.52ms\n"
     ]
    }
   ],
   "source": [
    "dataset_baltic.to_zarr(\"baltic.zarr\", mode=\"w\", safe_chunks=False)"
   ]
  },
  {
   "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 \"SST Baltic May/June 2016\". "
   ]
  },
  {
   "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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