From 558c2c6242ea5898ac7731022262de7a26ffa3d1 Mon Sep 17 00:00:00 2001
From: EddyCMWF <Edward.Comyn-Platt@ecmwf.int>
Date: Wed, 30 Aug 2023 15:13:00 +0100
Subject: [PATCH] varname and eg notebooks

---
 earthkit/climate/shapes.py |    1 +
 notebooks/aggregate.ipynb  |    4 +-
 notebooks/shapes.ipynb     | 3904 ++++++++++++++++++++++++++++++------
 3 files changed, 3291 insertions(+), 618 deletions(-)

diff --git a/earthkit/climate/shapes.py b/earthkit/climate/shapes.py
index 5f35b49..3e00e98 100644
--- a/earthkit/climate/shapes.py
+++ b/earthkit/climate/shapes.py
@@ -472,6 +472,7 @@ def _reduce_dataarray(
     mask_dim_index = get_mask_dim_index(mask_dim, geodataframe)
 
     out_xr = xr.concat(reduced_list, dim=mask_dim_index)
+    out_xr = out_xr.rename(new_short_name)
 
     # #  TODO: the following creates an xarray that cannot be saved to netCDF
     # out_xr = out_xr.assign_coords(
diff --git a/notebooks/aggregate.ipynb b/notebooks/aggregate.ipynb
index 08b7189..855833d 100644
--- a/notebooks/aggregate.ipynb
+++ b/notebooks/aggregate.ipynb
@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Notebook to demonstrate the climate tools available\n",
-    "#  Based on data retreived via the cdsapi, you will need a cdsapi key to download the data"
+    "Based on data retreived via the cdsapi, you will need a cdsapi key to download the data"
    ]
   },
   {
@@ -2647,7 +2647,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {
diff --git a/notebooks/shapes.ipynb b/notebooks/shapes.ipynb
index 7214024..1e7a557 100644
--- a/notebooks/shapes.ipynb
+++ b/notebooks/shapes.ipynb
@@ -31,54 +31,29 @@
     "from earthkit.data.testing import earthkit_remote_test_data_file"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "GRIBReader(/var/folders/l2/529q7bzs665bnrn7_wjx1nsr0000gn/T/earthkit-data-edwardcomyn-platt/url-3d7959e4f797b5d51104c5e8ac9a9e8b2f790a4196face12c09985a77c8a6e4a.grib)"
-      ],
-      "text/plain": [
-       "GRIBReader(/var/folders/l2/529q7bzs665bnrn7_wjx1nsr0000gn/T/earthkit-data-edwardcomyn-platt/url-3d7959e4f797b5d51104c5e8ac9a9e8b2f790a4196face12c09985a77c8a6e4a.grib)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get some demonstration ERA5 data, this could be any url or path to an ERA5 grib or netCDF file.\n",
-    "remote_era5_file = earthkit_remote_test_data_file(\"test-data\", \"era5_temperature_europe_2015.grib\")\n",
-    "era5_data = ek_data.from_source(\"url\", remote_era5_file)\n",
-    "era5_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use some demonstration polygons stored, this could be any url or path to geojson file\n",
-    "remote_nuts_url = earthkit_remote_test_data_file(\"test-data\", \"NUTS_RG_60M_2021_4326_LEVL_0.geojson\")\n",
-    "nuts_data = ek_data.from_source(\"url\", remote_nuts_url)\n",
-    "# nuts_data#.to_pandas()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Mask dataarray with geodataframe"
+    "# Load some test data\n",
+    "\n",
+    "All `earthkit-climate` can be called with `earthkit-data` objects (Readers and Wrappers) or with the \n",
+    "pre-loaded `xarray` or `geopandas` objects.\n",
+    "\n",
+    "In this example we will use hourly ERA5 2m temperature data on a 0.5x0.5 spatial grid for the year 2015 as\n",
+    "our physical data; and we will use the NUTS geometries which are stored in a geojson file.\n",
+    "\n",
+    "First we lazily load the ERA5 data  and NUTS geometries from our test-data repository.\n",
+    "\n",
+    "Note the data is only downloaded when\n",
+    "we use it, e.g. at the `.to_xarray` line, additionally, the download is cached so the next time you run this\n",
+    "cell you will not need to re-download the file (unless it has been a very long time since you have run the\n",
+    "code, please see tutorials in `earthkit-data` for more details in cache management)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -447,49 +422,9 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (FID: 37, number: 1, time: 1460, step: 1, surface: 1,\n",
-       "                         latitude: 201, longitude: 281)&gt;\n",
-       "array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "...\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (number: 1, time: 1460, step: 1, surface: 1, latitude: 201,\n",
+       "                 longitude: 281)\n",
        "Coordinates:\n",
        "  * number      (number) int64 0\n",
        "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
@@ -497,74 +432,20 @@
        "  * surface     (surface) float64 0.0\n",
        "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
        "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
-       "    valid_time  (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
-       "  * FID         (FID) object &#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; &#x27;EL&#x27; &#x27;ES&#x27; ... &#x27;CY&#x27; &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;\n",
-       "Attributes: (12/30)\n",
-       "    GRIB_paramId:                             167\n",
-       "    GRIB_dataType:                            an\n",
-       "    GRIB_numberOfPoints:                      56481\n",
-       "    GRIB_typeOfLevel:                         surface\n",
-       "    GRIB_stepUnits:                           1\n",
-       "    GRIB_stepType:                            instant\n",
-       "    ...                                       ...\n",
-       "    GRIB_shortName:                           2t\n",
-       "    GRIB_totalNumber:                         0\n",
-       "    GRIB_units:                               K\n",
-       "    long_name:                                2 metre temperature\n",
-       "    units:                                    K\n",
-       "    standard_name:                            unknown</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>FID</span>: 37</li><li><span class='xr-has-index'>number</span>: 1</li><li><span class='xr-has-index'>time</span>: 1460</li><li><span class='xr-has-index'>step</span>: 1</li><li><span class='xr-has-index'>surface</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 201</li><li><span class='xr-has-index'>longitude</span>: 281</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e9cb7ab9-9023-43d5-ad0b-a5fd82ce7191' class='xr-array-in' type='checkbox' checked><label for='section-e9cb7ab9-9023-43d5-ad0b-a5fd82ce7191' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "...\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ca2de61e-3706-4426-b47c-1783f4da7438' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca2de61e-3706-4426-b47c-1783f4da7438' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>number</span></div><div class='xr-var-dims'>(number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f83116f2-0484-49f9-b1ac-3be7ed1a2e32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f83116f2-0484-49f9-b1ac-3be7ed1a2e32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e4f6ecc-b88f-408d-84ca-68188a470c0d' class='xr-var-data-in' type='checkbox'><label for='data-6e4f6ecc-b88f-408d-84ca-68188a470c0d' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-3ff8c494-2cfc-4645-a971-61e1f6faaed5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ff8c494-2cfc-4645-a971-61e1f6faaed5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acec7ee3-afdc-4725-b77d-081033341793' class='xr-var-data-in' type='checkbox'><label for='data-acec7ee3-afdc-4725-b77d-081033341793' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
+       "    valid_time  (time, step) datetime64[ns] ...\n",
+       "Data variables:\n",
+       "    t2m         (number, time, step, surface, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.1...</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-50098839-378f-4ce0-9698-485472f534c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-50098839-378f-4ce0-9698-485472f534c7' 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'>number</span>: 1</li><li><span class='xr-has-index'>time</span>: 1460</li><li><span class='xr-has-index'>step</span>: 1</li><li><span class='xr-has-index'>surface</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 201</li><li><span class='xr-has-index'>longitude</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-45760852-dca8-4d7a-8f09-01493cbac919' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45760852-dca8-4d7a-8f09-01493cbac919' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>number</span></div><div class='xr-var-dims'>(number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-e94b63fe-ef3b-42c2-a35b-3384520ded0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e94b63fe-ef3b-42c2-a35b-3384520ded0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1fb9436-f94d-49ea-b02e-bf2e5d38de2b' class='xr-var-data-in' type='checkbox'><label for='data-a1fb9436-f94d-49ea-b02e-bf2e5d38de2b' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-1481efdb-d1fe-433c-9c54-2b32681044b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1481efdb-d1fe-433c-9c54-2b32681044b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13ca2887-1124-470b-a859-6199d099e10d' class='xr-var-data-in' type='checkbox'><label for='data-13ca2887-1124-470b-a859-6199d099e10d' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
        "       &#x27;2015-01-01T12:00:00.000000000&#x27;, ..., &#x27;2015-12-31T06:00:00.000000000&#x27;,\n",
        "       &#x27;2015-12-31T12:00:00.000000000&#x27;, &#x27;2015-12-31T18:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-3aa18207-bfc5-45ef-bbcf-096923639a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3aa18207-bfc5-45ef-bbcf-096923639a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f93762-1a07-4793-ad43-dcbdbc86623d' class='xr-var-data-in' type='checkbox'><label for='data-85f93762-1a07-4793-ad43-dcbdbc86623d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([0], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>surface</span></div><div class='xr-var-dims'>(surface)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-56694650-bbd7-4e01-a148-925bc6eca846' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56694650-bbd7-4e01-a148-925bc6eca846' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8ac286c-4a42-49e9-95c7-05915b458009' class='xr-var-data-in' type='checkbox'><label for='data-b8ac286c-4a42-49e9-95c7-05915b458009' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>80.0 79.75 79.5 ... 30.5 30.25 30.0</div><input id='attrs-cf3e05d5-9845-4705-b47a-968aab01d5f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf3e05d5-9845-4705-b47a-968aab01d5f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fef4bfe-6e0b-4cb0-ad66-73aff779d3f4' class='xr-var-data-in' type='checkbox'><label for='data-7fef4bfe-6e0b-4cb0-ad66-73aff779d3f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([80.  , 79.75, 79.5 , ..., 30.5 , 30.25, 30.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.75 -9.5 ... 59.75 60.0</div><input id='attrs-e3dd5231-7765-45b9-80bb-db072046717e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3dd5231-7765-45b9-80bb-db072046717e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fad61b2-0a14-40f1-baad-4d84aebb8dac' class='xr-var-data-in' type='checkbox'><label for='data-5fad61b2-0a14-40f1-baad-4d84aebb8dac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-10.  ,  -9.75,  -9.5 , ...,  59.5 ,  59.75,  60.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>(time, step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-75e5a602-9554-4a8e-96ef-5e455313b625' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75e5a602-9554-4a8e-96ef-5e455313b625' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f48093e-0e35-4881-a40c-9bca6e343e61' class='xr-var-data-in' type='checkbox'><label for='data-4f48093e-0e35-4881-a40c-9bca6e343e61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
-       "       [&#x27;2015-01-01T06:00:00.000000000&#x27;],\n",
-       "       [&#x27;2015-01-01T12:00:00.000000000&#x27;],\n",
-       "       ...,\n",
-       "       [&#x27;2015-12-31T06:00:00.000000000&#x27;],\n",
-       "       [&#x27;2015-12-31T12:00:00.000000000&#x27;],\n",
-       "       [&#x27;2015-12-31T18:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FID</span></div><div class='xr-var-dims'>(FID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; ... &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;</div><input id='attrs-e25a4d51-ec67-4f98-a145-5e6b4bb3be35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e25a4d51-ec67-4f98-a145-5e6b4bb3be35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f166887-ee79-4de3-aff1-7d94e7c5d275' class='xr-var-data-in' type='checkbox'><label for='data-7f166887-ee79-4de3-aff1-7d94e7c5d275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
-       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
-       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
-       "       &#x27;NO&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b555147f-1e07-4e1f-9358-8eb00b9f8315' class='xr-section-summary-in' type='checkbox'  ><label for='section-b555147f-1e07-4e1f-9358-8eb00b9f8315' class='xr-section-summary' >Indexes: <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-index-name'><div>number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-05798c5c-d533-4d1c-863b-f67a40f967b1' class='xr-index-data-in' type='checkbox'/><label for='index-05798c5c-d533-4d1c-863b-f67a40f967b1' 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(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;number&#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-b75f9aef-3540-41b6-8448-9d2a483e2f1b' class='xr-index-data-in' type='checkbox'/><label for='index-b75f9aef-3540-41b6-8448-9d2a483e2f1b' 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;2015-01-01 00:00:00&#x27;, &#x27;2015-01-01 06:00:00&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-c6f88dba-7123-48f0-9116-d765daa2a360' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6f88dba-7123-48f0-9116-d765daa2a360' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1ffbe26-211f-4422-9e08-5cc81dd3e17d' class='xr-var-data-in' type='checkbox'><label for='data-d1ffbe26-211f-4422-9e08-5cc81dd3e17d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([0], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>surface</span></div><div class='xr-var-dims'>(surface)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-f3199a76-ccba-4b88-8b4e-fb73139375b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3199a76-ccba-4b88-8b4e-fb73139375b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30b3fdb5-1bfa-4483-afc4-38fdf56571bc' class='xr-var-data-in' type='checkbox'><label for='data-30b3fdb5-1bfa-4483-afc4-38fdf56571bc' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>80.0 79.75 79.5 ... 30.5 30.25 30.0</div><input id='attrs-fb841774-33a7-466a-8c62-a8bc3a784070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb841774-33a7-466a-8c62-a8bc3a784070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ab7d7b3-86c3-40cd-8bf6-deca7fd6a673' class='xr-var-data-in' type='checkbox'><label for='data-3ab7d7b3-86c3-40cd-8bf6-deca7fd6a673' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([80.  , 79.75, 79.5 , ..., 30.5 , 30.25, 30.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.75 -9.5 ... 59.75 60.0</div><input id='attrs-243132a8-14fe-41b8-88db-c59e8ecee7e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-243132a8-14fe-41b8-88db-c59e8ecee7e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c8f352f-3552-459d-a3aa-232fa27e557b' class='xr-var-data-in' type='checkbox'><label for='data-1c8f352f-3552-459d-a3aa-232fa27e557b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-10.  ,  -9.75,  -9.5 , ...,  59.5 ,  59.75,  60.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>(time, step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f5d58d8-56ee-4213-96df-d68f07c0462f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f5d58d8-56ee-4213-96df-d68f07c0462f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-802f60e3-35e7-4d50-82e2-f07fe26fe4c0' class='xr-var-data-in' type='checkbox'><label for='data-802f60e3-35e7-4d50-82e2-f07fe26fe4c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>[1460 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4751c5b5-a309-49be-9144-5a93e5a1afb6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4751c5b5-a309-49be-9144-5a93e5a1afb6' 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>t2m</span></div><div class='xr-var-dims'>(number, time, step, surface, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41f935ea-d2fe-4e64-899d-52978bc5a20f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41f935ea-d2fe-4e64-899d-52978bc5a20f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e37ea0c3-3531-4dbb-9b8d-7dcdcf88da0d' class='xr-var-data-in' type='checkbox'><label for='data-e37ea0c3-3531-4dbb-9b8d-7dcdcf88da0d' 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>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>[82462260 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc6b0d95-dad6-400a-8e22-f78631c4f438' class='xr-section-summary-in' type='checkbox'  ><label for='section-dc6b0d95-dad6-400a-8e22-f78631c4f438' 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>number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60af588c-6bf3-4f7a-a24a-c19540181d0b' class='xr-index-data-in' type='checkbox'/><label for='index-60af588c-6bf3-4f7a-a24a-c19540181d0b' 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(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;number&#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-043979eb-74ca-48da-939e-ea03cef856b0' class='xr-index-data-in' type='checkbox'/><label for='index-043979eb-74ca-48da-939e-ea03cef856b0' 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;2015-01-01 00:00:00&#x27;, &#x27;2015-01-01 06:00:00&#x27;,\n",
        "               &#x27;2015-01-01 12:00:00&#x27;, &#x27;2015-01-01 18:00:00&#x27;,\n",
        "               &#x27;2015-01-02 00:00:00&#x27;, &#x27;2015-01-02 06:00:00&#x27;,\n",
        "               &#x27;2015-01-02 12:00:00&#x27;, &#x27;2015-01-02 18:00:00&#x27;,\n",
@@ -575,62 +456,18 @@
        "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-30 18:00:00&#x27;,\n",
        "               &#x27;2015-12-31 00:00:00&#x27;, &#x27;2015-12-31 06:00:00&#x27;,\n",
        "               &#x27;2015-12-31 12:00:00&#x27;, &#x27;2015-12-31 18:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1460, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>step</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e424121-1532-40de-96ed-3e42f7688e94' class='xr-index-data-in' type='checkbox'/><label for='index-1e424121-1532-40de-96ed-3e42f7688e94' 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(TimedeltaIndex([&#x27;0 days&#x27;], dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;step&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>surface</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e43412ae-014d-47f1-ac6b-4472c3a9cfe8' class='xr-index-data-in' type='checkbox'/><label for='index-e43412ae-014d-47f1-ac6b-4472c3a9cfe8' 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(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;surface&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fa1776a2-d668-4289-9a60-bf9244e4275c' class='xr-index-data-in' type='checkbox'/><label for='index-fa1776a2-d668-4289-9a60-bf9244e4275c' 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(Index([ 80.0, 79.75,  79.5, 79.25,  79.0, 78.75,  78.5, 78.25,  78.0, 77.75,\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1460, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>step</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-71ca672f-4a7f-4c94-8f62-0580d58f514f' class='xr-index-data-in' type='checkbox'/><label for='index-71ca672f-4a7f-4c94-8f62-0580d58f514f' 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(TimedeltaIndex([&#x27;0 days&#x27;], dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;step&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>surface</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e3ead31-8edc-41b2-a8e9-106cb78c0dbe' class='xr-index-data-in' type='checkbox'/><label for='index-9e3ead31-8edc-41b2-a8e9-106cb78c0dbe' 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(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;surface&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0995f0a4-0cbb-412a-b41d-6f3337823eff' class='xr-index-data-in' type='checkbox'/><label for='index-0995f0a4-0cbb-412a-b41d-6f3337823eff' 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(Index([ 80.0, 79.75,  79.5, 79.25,  79.0, 78.75,  78.5, 78.25,  78.0, 77.75,\n",
        "       ...\n",
        "       32.25,  32.0, 31.75,  31.5, 31.25,  31.0, 30.75,  30.5, 30.25,  30.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=201))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6a92723b-fdee-4e7a-b7c9-8362d8dc10e6' class='xr-index-data-in' type='checkbox'/><label for='index-6a92723b-fdee-4e7a-b7c9-8362d8dc10e6' 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(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=201))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e43a7b25-458f-4253-839c-ce5959ed2bfc' class='xr-index-data-in' type='checkbox'/><label for='index-e43a7b25-458f-4253-839c-ce5959ed2bfc' 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(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,\n",
        "       ...\n",
        "       57.75,  58.0, 58.25,  58.5, 58.75,  59.0, 59.25,  59.5, 59.75,  60.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=281))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>FID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0abbcabc-e049-4235-9e58-3110ede42110' class='xr-index-data-in' type='checkbox'/><label for='index-0abbcabc-e049-4235-9e58-3110ede42110' 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(Index([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
-       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
-       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
-       "       &#x27;NO&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;FID&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35d63ed1-3fa1-4c84-b30d-6b1da9a72268' class='xr-section-summary-in' type='checkbox'  ><label for='section-35d63ed1-3fa1-4c84-b30d-6b1da9a72268' class='xr-section-summary' >Attributes: <span>(30)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=281))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02c3230d-40df-4772-8975-dbc8c2b6bc86' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02c3230d-40df-4772-8975-dbc8c2b6bc86' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.31.0 with {&quot;source&quot;: &quot;N/A&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 't2m' (FID: 37, number: 1, time: 1460, step: 1, surface: 1,\n",
-       "                         latitude: 201, longitude: 281)>\n",
-       "array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "...\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
-       "\n",
-       "\n",
-       "\n",
-       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            ...,\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan],\n",
-       "            [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32)\n",
+       "<xarray.Dataset>\n",
+       "Dimensions:     (number: 1, time: 1460, step: 1, surface: 1, latitude: 201,\n",
+       "                 longitude: 281)\n",
        "Coordinates:\n",
        "  * number      (number) int64 0\n",
        "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
@@ -638,217 +475,34 @@
        "  * surface     (surface) float64 0.0\n",
        "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
        "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
-       "    valid_time  (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
-       "  * FID         (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'\n",
-       "Attributes: (12/30)\n",
-       "    GRIB_paramId:                             167\n",
-       "    GRIB_dataType:                            an\n",
-       "    GRIB_numberOfPoints:                      56481\n",
-       "    GRIB_typeOfLevel:                         surface\n",
-       "    GRIB_stepUnits:                           1\n",
-       "    GRIB_stepType:                            instant\n",
-       "    ...                                       ...\n",
-       "    GRIB_shortName:                           2t\n",
-       "    GRIB_totalNumber:                         0\n",
-       "    GRIB_units:                               K\n",
-       "    long_name:                                2 metre temperature\n",
-       "    units:                                    K\n",
-       "    standard_name:                            unknown"
+       "    valid_time  (time, step) datetime64[ns] ...\n",
+       "Data variables:\n",
+       "    t2m         (number, time, step, surface, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.1..."
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "masked_data = ek_climate.shapes.masks(era5_data, nuts_data)\n",
-    "masked_data"
+    "# Get some demonstration ERA5 data, this could be any url or path to an ERA5 grib or netCDF file.\n",
+    "remote_era5_file = earthkit_remote_test_data_file(\"test-data\", \"era5_temperature_europe_2015.grib\")\n",
+    "era5_data = ek_data.from_source(\"url\", remote_era5_file)\n",
+    "era5_data.to_xarray()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Masked Germany Zoom')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x300 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 3, figsize=(15,3))\n",
-    "era5_data.to_xarray().t2m.mean(dim='time').plot(ax=axes[0])\n",
-    "axes[0].set_title('Original data')\n",
-    "masked_data.sel(FID='DE').mean(dim='time').plot(ax=axes[1])\n",
-    "axes[1].set_title('Masked for Germany')\n",
-    "germany_data = masked_data.sel(FID='DE').dropna(dim='latitude', how='all').dropna(dim='longitude', how='all')\n",
-    "germany_data.mean(dim='time').plot(ax=axes[2])\n",
-    "axes[2].set_title('Masked Germany Zoom')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reduce data\n",
-    "### Default behaviour inserts reduced data into geodataframe\n",
-    "[An] additional column[s] is[/are] added to the geodataframe which contains an xarray.DataArray of the reduced data. The column header is constructed from the variable name and the how method applied"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
-      "MultiIndex: 54020 entries, ('DK', Timestamp('2015-01-01 00:00:00')) to ('NO', Timestamp('2015-12-31 18:00:00'))\n",
-      "Data columns (total 15 columns):\n",
-      " #   Column      Non-Null Count  Dtype          \n",
-      "---  ------      --------------  -----          \n",
-      " 0   id          54020 non-null  object         \n",
-      " 1   NUTS_ID     54020 non-null  object         \n",
-      " 2   LEVL_CODE   54020 non-null  int64          \n",
-      " 3   CNTR_CODE   54020 non-null  object         \n",
-      " 4   NAME_LATN   54020 non-null  object         \n",
-      " 5   NUTS_NAME   54020 non-null  object         \n",
-      " 6   MOUNT_TYPE  54020 non-null  int64          \n",
-      " 7   URBN_TYPE   54020 non-null  int64          \n",
-      " 8   COAST_TYPE  54020 non-null  int64          \n",
-      " 9   geometry    54020 non-null  geometry       \n",
-      " 10  number      54020 non-null  int64          \n",
-      " 11  step        54020 non-null  timedelta64[ns]\n",
-      " 12  surface     54020 non-null  float64        \n",
-      " 13  valid_time  54020 non-null  datetime64[ns] \n",
-      " 14  t2m         51100 non-null  float32        \n",
-      "dtypes: datetime64[ns](1), float32(1), float64(1), geometry(1), int64(5), object(5), timedelta64[ns](1)\n",
-      "memory usage: 6.2+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data)\n",
-    "reduced_data.info()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
-      "Index: 37 entries, 0 to 36\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column      Non-Null Count  Dtype   \n",
-      "---  ------      --------------  -----   \n",
-      " 0   id          37 non-null     object  \n",
-      " 1   NUTS_ID     37 non-null     object  \n",
-      " 2   LEVL_CODE   37 non-null     int64   \n",
-      " 3   CNTR_CODE   37 non-null     object  \n",
-      " 4   NAME_LATN   37 non-null     object  \n",
-      " 5   NUTS_NAME   37 non-null     object  \n",
-      " 6   MOUNT_TYPE  37 non-null     int64   \n",
-      " 7   URBN_TYPE   37 non-null     int64   \n",
-      " 8   COAST_TYPE  37 non-null     int64   \n",
-      " 9   FID         37 non-null     object  \n",
-      " 10  geometry    37 non-null     geometry\n",
-      " 11  t2m_mean    37 non-null     object  \n",
-      "dtypes: geometry(1), int64(4), object(7)\n",
-      "memory usage: 3.8+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data, return_as=\"pandas_compact\")\n",
-    "reduced_data.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DK FID\n",
-      "RS FID\n",
-      "EE FID\n",
-      "EL FID\n",
-      "ES FID\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1186ccb80>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feature_index = \"FID\"\n",
-    "plot_var = \"t2m_mean\"\n",
-    "# plot_x_vals = reduced_data.attrs[f\"{plot_var}_dims\"][\"time\"]\n",
-    "fig, ax = plt.subplots(1)\n",
-    "for feature in reduced_data.index.get_level_values(feature_index).unique()[:5]:\n",
-    "    print(feature, feature_index)\n",
-    "    temp = reduced_data.xs(feature, level=feature_index)\n",
-    "    temp[\"t2m\"].plot(ax=ax, label=feature['FID'])\n",
-    "    # feature['t2m_mean'].plot(ax=ax, label=feature['FID'])\n",
-    "fig.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Can provide additional dimensions to reduce along\n",
-    "\n",
-    "This is advisable with such analysis as it ensures correctly handled and weihted missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -883,7 +537,6 @@
        "      <th>COAST_TYPE</th>\n",
        "      <th>FID</th>\n",
        "      <th>geometry</th>\n",
-       "      <th>t2m_mean</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -900,7 +553,6 @@
        "      <td>0</td>\n",
        "      <td>DK</td>\n",
        "      <td>MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...</td>\n",
-       "      <td>282.48444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -915,7 +567,6 @@
        "      <td>0</td>\n",
        "      <td>RS</td>\n",
        "      <td>POLYGON ((21.47920 45.19300, 21.35850 44.82160...</td>\n",
-       "      <td>285.00317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -930,7 +581,6 @@
        "      <td>0</td>\n",
        "      <td>EE</td>\n",
        "      <td>MULTIPOLYGON (((27.35700 58.78710, 27.64490 57...</td>\n",
-       "      <td>280.56302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -945,7 +595,6 @@
        "      <td>0</td>\n",
        "      <td>EL</td>\n",
        "      <td>MULTIPOLYGON (((28.07770 36.11820, 27.86060 35...</td>\n",
-       "      <td>288.2147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -960,7 +609,6 @@
        "      <td>0</td>\n",
        "      <td>ES</td>\n",
        "      <td>MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7...</td>\n",
-       "      <td>287.7985</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -981,96 +629,43 @@
        "3          0           0  EL   \n",
        "4          0           0  ES   \n",
        "\n",
-       "                                            geometry   t2m_mean  \n",
-       "0  MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...  282.48444  \n",
-       "1  POLYGON ((21.47920 45.19300, 21.35850 44.82160...  285.00317  \n",
-       "2  MULTIPOLYGON (((27.35700 58.78710, 27.64490 57...  280.56302  \n",
-       "3  MULTIPOLYGON (((28.07770 36.11820, 27.86060 35...   288.2147  \n",
-       "4  MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7...   287.7985  "
+       "                                            geometry  \n",
+       "0  MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...  \n",
+       "1  POLYGON ((21.47920 45.19300, 21.35850 44.82160...  \n",
+       "2  MULTIPOLYGON (((27.35700 58.78710, 27.64490 57...  \n",
+       "3  MULTIPOLYGON (((28.07770 36.11820, 27.86060 35...  \n",
+       "4  MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7...  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mnotebook controller is DISPOSED. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
-    }
-   ],
-   "source": [
-    "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data, extra_reduce_dims='time')\n",
-    "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data, extra_reduce_dims='time', how='max')\n",
-    "reduced_data.iloc[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TODO: Use earthkit polygon plotting here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Can provide weights for reduction\n",
-    "\n",
-    "Or use predefined weights options, i.e. `latitude`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mnotebook controller is DISPOSED. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
     }
    ],
    "source": [
-    "import numpy as np\n",
+    "# Use some demonstration polygons stored, this could be any url or path to geojson file\n",
+    "remote_nuts_url = earthkit_remote_test_data_file(\"test-data\", \"NUTS_RG_60M_2021_4326_LEVL_0.geojson\")\n",
+    "nuts_data = ek_data.from_source(\"url\", remote_nuts_url)\n",
     "\n",
-    "reduced_data_xr = ek_climate.shapes.reduce(era5_data, nuts_data, weights='latitude')\n",
-    "reduced_data_xr"
+    "nuts_data.to_pandas()[:5]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Can return the object as an xarray\n",
+    "## Mask dataarray with geodataframe\n",
     "\n",
-    "TODO: how to attach to original geometry?"
+    "`shapes.mask` applies all the features in the geometry object (`nuts_data`) to the data object (`era5_data`).\n",
+    "It returns an xarray object the same shape and type as the input xarray object with all points outside of\n",
+    "the geometry masked"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/edwardcomyn-platt/miniconda3/envs/earthkit/lib/python3.10/site-packages/xarray/core/variable.py:2002: RuntimeWarning: All-NaN slice encountered\n",
-      "  data = func(self.data, axis=axis, **kwargs)\n",
-      "/Users/edwardcomyn-platt/miniconda3/envs/earthkit/lib/python3.10/site-packages/xarray/core/variable.py:2002: RuntimeWarning: All-NaN slice encountered\n",
-      "  data = func(self.data, axis=axis, **kwargs)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1437,62 +1032,70 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (FID: 37, time: 1460)&gt;\n",
-       "array([[279.88135, 280.61102, 281.33185, ..., 277.7212 , 280.78748,\n",
-       "        281.04398],\n",
-       "       [264.86768, 266.3454 , 269.08966, ..., 268.93408, 273.9906 ,\n",
-       "        269.51077],\n",
-       "       [277.11377, 277.34735, 277.97247, ..., 271.02783, 271.52966,\n",
-       "        270.31937],\n",
-       "       ...,\n",
-       "       [274.09814, 275.03876, 276.59552, ..., 269.32666, 273.7621 ,\n",
-       "        271.876  ],\n",
-       "       [280.16846, 279.87665, 278.94513, ..., 281.21338, 283.03357,\n",
-       "        282.04788],\n",
-       "       [281.73486, 282.1579 , 281.6502 , ..., 281.2251 , 280.1527 ,\n",
-       "        280.6592 ]], dtype=float32)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (number: 1, time: 1460, step: 1, surface: 1, latitude: 201,\n",
+       "                 longitude: 281)\n",
        "Coordinates:\n",
-       "    number      int64 0\n",
+       "  * number      (number) int64 0\n",
        "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
-       "    step        timedelta64[ns] 00:00:00\n",
-       "    surface     float64 0.0\n",
-       "    valid_time  (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
-       "  * FID         (FID) object &#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; &#x27;EL&#x27; &#x27;ES&#x27; ... &#x27;CY&#x27; &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;\n",
-       "Attributes: (12/30)\n",
-       "    GRIB_paramId:                             167\n",
-       "    GRIB_dataType:                            an\n",
-       "    GRIB_numberOfPoints:                      56481\n",
-       "    GRIB_typeOfLevel:                         surface\n",
-       "    GRIB_stepUnits:                           1\n",
-       "    GRIB_stepType:                            instant\n",
-       "    ...                                       ...\n",
-       "    GRIB_shortName:                           2t\n",
-       "    GRIB_totalNumber:                         0\n",
-       "    GRIB_units:                               K\n",
-       "    long_name:                                2 metre temperature\n",
-       "    units:                                    K\n",
-       "    standard_name:                            unknown</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>FID</span>: 37</li><li><span class='xr-has-index'>time</span>: 1460</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b46d7e14-8ef8-4523-9cd7-902b36a61eb8' class='xr-array-in' type='checkbox' checked><label for='section-b46d7e14-8ef8-4523-9cd7-902b36a61eb8' 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>279.9 280.6 281.3 281.1 281.8 283.0 ... 285.6 283.5 281.2 280.2 280.7</span></div><div class='xr-array-data'><pre>array([[279.88135, 280.61102, 281.33185, ..., 277.7212 , 280.78748,\n",
-       "        281.04398],\n",
-       "       [264.86768, 266.3454 , 269.08966, ..., 268.93408, 273.9906 ,\n",
-       "        269.51077],\n",
-       "       [277.11377, 277.34735, 277.97247, ..., 271.02783, 271.52966,\n",
-       "        270.31937],\n",
-       "       ...,\n",
-       "       [274.09814, 275.03876, 276.59552, ..., 269.32666, 273.7621 ,\n",
-       "        271.876  ],\n",
-       "       [280.16846, 279.87665, 278.94513, ..., 281.21338, 283.03357,\n",
-       "        282.04788],\n",
-       "       [281.73486, 282.1579 , 281.6502 , ..., 281.2251 , 280.1527 ,\n",
-       "        280.6592 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ac7c5ff1-812e-4ad6-9662-1293d825ffd4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac7c5ff1-812e-4ad6-9662-1293d825ffd4' 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>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-935be735-94d3-4b01-8e66-a7ebf84fc815' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-935be735-94d3-4b01-8e66-a7ebf84fc815' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84d109ae-7eb2-44b7-960f-3cb9455c4734' class='xr-var-data-in' type='checkbox'><label for='data-84d109ae-7eb2-44b7-960f-3cb9455c4734' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-36d238d9-4dd0-4a33-af62-3f94ea5581dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36d238d9-4dd0-4a33-af62-3f94ea5581dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-132a44c3-9243-4fc9-aad0-7261c807c000' class='xr-var-data-in' type='checkbox'><label for='data-132a44c3-9243-4fc9-aad0-7261c807c000' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
+       "  * step        (step) timedelta64[ns] 00:00:00\n",
+       "  * surface     (surface) float64 0.0\n",
+       "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+       "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+       "    valid_time  (time, step) datetime64[ns] ...\n",
+       "Data variables:\n",
+       "    t2m         (number, time, step, surface, latitude, longitude) float32 na...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.1...</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-039229fb-11a7-4fac-8c85-62a4a86c58ce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-039229fb-11a7-4fac-8c85-62a4a86c58ce' 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'>number</span>: 1</li><li><span class='xr-has-index'>time</span>: 1460</li><li><span class='xr-has-index'>step</span>: 1</li><li><span class='xr-has-index'>surface</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 201</li><li><span class='xr-has-index'>longitude</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ee0dabd0-cd87-44f0-bbd9-052cbefd3991' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee0dabd0-cd87-44f0-bbd9-052cbefd3991' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>number</span></div><div class='xr-var-dims'>(number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c26d33fb-784c-4c0d-bf8d-6c8862b0c8cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c26d33fb-784c-4c0d-bf8d-6c8862b0c8cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9e1b762-c77a-4d5f-b035-51bc62aa8990' class='xr-var-data-in' type='checkbox'><label for='data-c9e1b762-c77a-4d5f-b035-51bc62aa8990' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-1f7cb876-7317-44db-ae7f-c264019e10dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f7cb876-7317-44db-ae7f-c264019e10dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dbc1f77-3fb1-4959-8a03-efe4a47362af' class='xr-var-data-in' type='checkbox'><label for='data-8dbc1f77-3fb1-4959-8a03-efe4a47362af' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
        "       &#x27;2015-01-01T12:00:00.000000000&#x27;, ..., &#x27;2015-12-31T06:00:00.000000000&#x27;,\n",
        "       &#x27;2015-12-31T12:00:00.000000000&#x27;, &#x27;2015-12-31T18:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-a444e271-b689-4a2e-949f-ff6ef620348d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a444e271-b689-4a2e-949f-ff6ef620348d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68126da-5980-4db6-a452-224c9f846e84' class='xr-var-data-in' type='checkbox'><label for='data-b68126da-5980-4db6-a452-224c9f846e84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-a3790dd9-d8b5-4bb1-b8b7-cda5e0f63084' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3790dd9-d8b5-4bb1-b8b7-cda5e0f63084' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cf4265b-e350-4437-87c4-16e3d2e88a1e' class='xr-var-data-in' type='checkbox'><label for='data-0cf4265b-e350-4437-87c4-16e3d2e88a1e' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_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'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-52d17305-170f-456e-8198-b772379dc536' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d17305-170f-456e-8198-b772379dc536' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de63906d-3752-4678-86bc-3512533573f7' class='xr-var-data-in' type='checkbox'><label for='data-de63906d-3752-4678-86bc-3512533573f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-01-01T12:00:00.000000000&#x27;, ...,\n",
-       "       &#x27;2015-12-31T06:00:00.000000000&#x27;, &#x27;2015-12-31T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-12-31T18:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FID</span></div><div class='xr-var-dims'>(FID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; ... &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;</div><input id='attrs-b9644c4c-91ee-4ac6-b6ed-1b12166247df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9644c4c-91ee-4ac6-b6ed-1b12166247df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-879426b1-5987-429f-9869-c5ec1ac33782' class='xr-var-data-in' type='checkbox'><label for='data-879426b1-5987-429f-9869-c5ec1ac33782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
-       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
-       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
-       "       &#x27;NO&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0973de5-bd60-439c-b089-6fd095494ee9' class='xr-section-summary-in' type='checkbox'  ><label for='section-e0973de5-bd60-439c-b089-6fd095494ee9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00c693b4-8e8f-43ab-ae55-10220b964ff6' class='xr-index-data-in' type='checkbox'/><label for='index-00c693b4-8e8f-43ab-ae55-10220b964ff6' 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;2015-01-01 00:00:00&#x27;, &#x27;2015-01-01 06:00:00&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-5d4f4896-34d9-4398-b78f-c5541a964935' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d4f4896-34d9-4398-b78f-c5541a964935' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02d623a7-6f6b-4c80-a9ab-999ed2d8f7f2' class='xr-var-data-in' type='checkbox'><label for='data-02d623a7-6f6b-4c80-a9ab-999ed2d8f7f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([0], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>surface</span></div><div class='xr-var-dims'>(surface)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-2c77b5c0-a818-45e9-ba22-740f016fbc93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c77b5c0-a818-45e9-ba22-740f016fbc93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d58abbcc-952e-45e4-b792-8892b5738aef' class='xr-var-data-in' type='checkbox'><label for='data-d58abbcc-952e-45e4-b792-8892b5738aef' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>80.0 79.75 79.5 ... 30.5 30.25 30.0</div><input id='attrs-f6e18020-d8de-43dc-9825-ee1ae364e170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6e18020-d8de-43dc-9825-ee1ae364e170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d21d8b-fd1f-4200-9347-c752c5bbd824' class='xr-var-data-in' type='checkbox'><label for='data-99d21d8b-fd1f-4200-9347-c752c5bbd824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([80.  , 79.75, 79.5 , ..., 30.5 , 30.25, 30.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.75 -9.5 ... 59.75 60.0</div><input id='attrs-c3f98d32-ff51-44ce-a541-2edcadf1c824' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3f98d32-ff51-44ce-a541-2edcadf1c824' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e5c92b-e4c2-45c3-8ce9-a7ad44bbddcb' class='xr-var-data-in' type='checkbox'><label for='data-16e5c92b-e4c2-45c3-8ce9-a7ad44bbddcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-10.  ,  -9.75,  -9.5 , ...,  59.5 ,  59.75,  60.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>(time, step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-edc8da33-68b1-4d46-9dac-af3f16cacff2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edc8da33-68b1-4d46-9dac-af3f16cacff2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0939279b-6dbf-424f-af56-6f344d51e87b' class='xr-var-data-in' type='checkbox'><label for='data-0939279b-6dbf-424f-af56-6f344d51e87b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>[1460 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06ec1f79-8d69-4c31-b29e-75b9e7ed7f7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06ec1f79-8d69-4c31-b29e-75b9e7ed7f7d' 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>t2m</span></div><div class='xr-var-dims'>(number, time, step, surface, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-fcc2c15d-0a33-41cf-8523-37d5cf9f96cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcc2c15d-0a33-41cf-8523-37d5cf9f96cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c11d8a2-e48f-4d2e-b753-a87d21c8123a' class='xr-var-data-in' type='checkbox'><label for='data-8c11d8a2-e48f-4d2e-b753-a87d21c8123a' 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>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           ...,\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "        [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           ...,\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "...\n",
+       "\n",
+       "\n",
+       "\n",
+       "        [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           ...,\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "        [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           ...,\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan],\n",
+       "           [nan, nan, nan, ..., nan, nan, nan]]]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c384b0e6-087b-4208-8d82-e4edfd08e32b' class='xr-section-summary-in' type='checkbox'  ><label for='section-c384b0e6-087b-4208-8d82-e4edfd08e32b' 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>number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b0622ceb-de03-424b-9b53-d9eb37b50037' class='xr-index-data-in' type='checkbox'/><label for='index-b0622ceb-de03-424b-9b53-d9eb37b50037' 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(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;number&#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-947b2e98-5799-4235-85b1-26bda175586d' class='xr-index-data-in' type='checkbox'/><label for='index-947b2e98-5799-4235-85b1-26bda175586d' 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;2015-01-01 00:00:00&#x27;, &#x27;2015-01-01 06:00:00&#x27;,\n",
        "               &#x27;2015-01-01 12:00:00&#x27;, &#x27;2015-01-01 18:00:00&#x27;,\n",
        "               &#x27;2015-01-02 00:00:00&#x27;, &#x27;2015-01-02 06:00:00&#x27;,\n",
        "               &#x27;2015-01-02 12:00:00&#x27;, &#x27;2015-01-02 18:00:00&#x27;,\n",
@@ -1503,131 +1106,3200 @@
        "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-30 18:00:00&#x27;,\n",
        "               &#x27;2015-12-31 00:00:00&#x27;, &#x27;2015-12-31 06:00:00&#x27;,\n",
        "               &#x27;2015-12-31 12:00:00&#x27;, &#x27;2015-12-31 18:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1460, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>FID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d41f1f5c-783d-4531-9f0d-2f563272f956' class='xr-index-data-in' type='checkbox'/><label for='index-d41f1f5c-783d-4531-9f0d-2f563272f956' 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(Index([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
-       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
-       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
-       "       &#x27;NO&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;FID&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32ccd7ef-f626-436a-8987-fbbe1aa8c87f' class='xr-section-summary-in' type='checkbox'  ><label for='section-32ccd7ef-f626-436a-8987-fbbe1aa8c87f' class='xr-section-summary' >Attributes: <span>(30)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1460, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>step</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd281736-4a24-4d6b-8dcc-fcf2773e5627' class='xr-index-data-in' type='checkbox'/><label for='index-cd281736-4a24-4d6b-8dcc-fcf2773e5627' 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(TimedeltaIndex([&#x27;0 days&#x27;], dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;step&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>surface</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50c44e0c-ecd6-49c9-85c1-c914555376c5' class='xr-index-data-in' type='checkbox'/><label for='index-50c44e0c-ecd6-49c9-85c1-c914555376c5' 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(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;surface&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c60918ad-6146-40e1-9a01-696dfe3df46a' class='xr-index-data-in' type='checkbox'/><label for='index-c60918ad-6146-40e1-9a01-696dfe3df46a' 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(Index([ 80.0, 79.75,  79.5, 79.25,  79.0, 78.75,  78.5, 78.25,  78.0, 77.75,\n",
+       "       ...\n",
+       "       32.25,  32.0, 31.75,  31.5, 31.25,  31.0, 30.75,  30.5, 30.25,  30.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=201))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4a3e33b2-0377-471c-b93b-9579bb695eae' class='xr-index-data-in' type='checkbox'/><label for='index-4a3e33b2-0377-471c-b93b-9579bb695eae' 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(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,\n",
+       "       ...\n",
+       "       57.75,  58.0, 58.25,  58.5, 58.75,  59.0, 59.25,  59.5, 59.75,  60.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=281))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de400418-473e-4ba0-a0c7-1e9c0b09df57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de400418-473e-4ba0-a0c7-1e9c0b09df57' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.31.0 with {&quot;source&quot;: &quot;N/A&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 't2m' (FID: 37, time: 1460)>\n",
-       "array([[279.88135, 280.61102, 281.33185, ..., 277.7212 , 280.78748,\n",
-       "        281.04398],\n",
-       "       [264.86768, 266.3454 , 269.08966, ..., 268.93408, 273.9906 ,\n",
-       "        269.51077],\n",
-       "       [277.11377, 277.34735, 277.97247, ..., 271.02783, 271.52966,\n",
-       "        270.31937],\n",
-       "       ...,\n",
-       "       [274.09814, 275.03876, 276.59552, ..., 269.32666, 273.7621 ,\n",
-       "        271.876  ],\n",
-       "       [280.16846, 279.87665, 278.94513, ..., 281.21338, 283.03357,\n",
-       "        282.04788],\n",
-       "       [281.73486, 282.1579 , 281.6502 , ..., 281.2251 , 280.1527 ,\n",
-       "        280.6592 ]], dtype=float32)\n",
+       "<xarray.Dataset>\n",
+       "Dimensions:     (number: 1, time: 1460, step: 1, surface: 1, latitude: 201,\n",
+       "                 longitude: 281)\n",
        "Coordinates:\n",
-       "    number      int64 0\n",
+       "  * number      (number) int64 0\n",
        "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
-       "    step        timedelta64[ns] 00:00:00\n",
-       "    surface     float64 0.0\n",
-       "    valid_time  (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
-       "  * FID         (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'\n",
-       "Attributes: (12/30)\n",
-       "    GRIB_paramId:                             167\n",
-       "    GRIB_dataType:                            an\n",
-       "    GRIB_numberOfPoints:                      56481\n",
-       "    GRIB_typeOfLevel:                         surface\n",
-       "    GRIB_stepUnits:                           1\n",
-       "    GRIB_stepType:                            instant\n",
-       "    ...                                       ...\n",
-       "    GRIB_shortName:                           2t\n",
-       "    GRIB_totalNumber:                         0\n",
-       "    GRIB_units:                               K\n",
-       "    long_name:                                2 metre temperature\n",
-       "    units:                                    K\n",
-       "    standard_name:                            unknown"
+       "  * step        (step) timedelta64[ns] 00:00:00\n",
+       "  * surface     (surface) float64 0.0\n",
+       "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+       "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+       "    valid_time  (time, step) datetime64[ns] ...\n",
+       "Data variables:\n",
+       "    t2m         (number, time, step, surface, latitude, longitude) float32 na...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.1..."
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mnotebook controller is DISPOSED. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "reduced_data_xr = ek_climate.shapes.reduce(era5_data, nuts_data, how=np.nanmax, weights='latitude', return_as='xarray')\n",
-    "reduced_data_xr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mnotebook controller is DISPOSED. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
     }
    ],
    "source": [
-    "reduced_data_xr.to_netcdf('test_data/test_reduced.nc')"
+    "single_masked_data = ek_climate.shapes.mask(era5_data, nuts_data)\n",
+    "single_masked_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x157ee5bd0>"
+       "Text(0.5, 1.0, 'Masked data')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x400 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mnotebook controller is DISPOSED. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1)\n",
-    "for feature in nuts_data[:4]:\n",
-    "    reduced_data_xr.sel(FID=feature[\"FID\"]).plot(ax=ax, label=feature[\"NUTS_NAME\"])\n",
-    "\n",
-    "fig.legend()\n"
+    "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n",
+    "era5_data.to_xarray().t2m.mean(dim='time').plot(ax=axes[0])\n",
+    "axes[0].set_title('Original data')\n",
+    "# Single masked data\n",
+    "single_masked_data.t2m.mean(dim='time').plot(ax=axes[1])\n",
+    "axes[1].set_title('Masked data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`shapes.masks` applies the features in the geometry object (`nuts_data`) to the data object (`era5_data`).\n",
+    "It returns an xarray object with an additional dimension, and coordinate variable, corresponding to the \n",
+    "features in the geometry object.\n",
+    "By default this is the index of the input geodataframe, in this example the index is just an integer\n",
+    "count so it takes the default name `index`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (index: 37, number: 1, time: 1460, step: 1, surface: 1,\n",
+       "                 latitude: 201, longitude: 281)\n",
+       "Coordinates:\n",
+       "  * number      (number) int64 0\n",
+       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * step        (step) timedelta64[ns] 00:00:00\n",
+       "  * surface     (surface) float64 0.0\n",
+       "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+       "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+       "    valid_time  (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * index       (index) int64 0 1 2 3 4 5 6 7 8 9 ... 28 29 30 31 32 33 34 35 36\n",
+       "Data variables:\n",
+       "    t2m         (index, number, time, step, surface, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.1...</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-487bdae6-72b8-49f0-98bf-c55f02604c80' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-487bdae6-72b8-49f0-98bf-c55f02604c80' 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'>index</span>: 37</li><li><span class='xr-has-index'>number</span>: 1</li><li><span class='xr-has-index'>time</span>: 1460</li><li><span class='xr-has-index'>step</span>: 1</li><li><span class='xr-has-index'>surface</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 201</li><li><span class='xr-has-index'>longitude</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e80f7e86-2ab0-4f5c-881f-bd11a104c694' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e80f7e86-2ab0-4f5c-881f-bd11a104c694' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>number</span></div><div class='xr-var-dims'>(number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-0669df6e-027c-4806-9490-daa0447bd7ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0669df6e-027c-4806-9490-daa0447bd7ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6127c787-4917-4cf6-ae75-9b55df8aaf30' class='xr-var-data-in' type='checkbox'><label for='data-6127c787-4917-4cf6-ae75-9b55df8aaf30' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-c30bb58e-2f9a-47a0-940c-b678c67d8919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c30bb58e-2f9a-47a0-940c-b678c67d8919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71080be4-c6ef-4cf9-9220-84197760d2ae' class='xr-var-data-in' type='checkbox'><label for='data-71080be4-c6ef-4cf9-9220-84197760d2ae' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-01T12:00:00.000000000&#x27;, ..., &#x27;2015-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-31T12:00:00.000000000&#x27;, &#x27;2015-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-f5ddd9bb-1d80-41ef-8118-d4c0c8deaa04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5ddd9bb-1d80-41ef-8118-d4c0c8deaa04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac59aead-b274-4d4c-bd37-15a7eb2520e1' class='xr-var-data-in' type='checkbox'><label for='data-ac59aead-b274-4d4c-bd37-15a7eb2520e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([0], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>surface</span></div><div class='xr-var-dims'>(surface)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-0cd2b243-7cc7-4af7-9b34-9dce5612c483' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cd2b243-7cc7-4af7-9b34-9dce5612c483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dff5054-f25b-4649-83be-3b79f839691e' class='xr-var-data-in' type='checkbox'><label for='data-2dff5054-f25b-4649-83be-3b79f839691e' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>80.0 79.75 79.5 ... 30.5 30.25 30.0</div><input id='attrs-89c02523-7b6a-475c-ab34-a9b735934a57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89c02523-7b6a-475c-ab34-a9b735934a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b061b7e4-671c-43c4-9510-347a076cbc19' class='xr-var-data-in' type='checkbox'><label for='data-b061b7e4-671c-43c4-9510-347a076cbc19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([80.  , 79.75, 79.5 , ..., 30.5 , 30.25, 30.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.75 -9.5 ... 59.75 60.0</div><input id='attrs-1fad65e5-0d2c-4e96-aa61-4d1064cc3f04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fad65e5-0d2c-4e96-aa61-4d1064cc3f04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08eef4bc-9212-45d4-a165-a277f2f64521' class='xr-var-data-in' type='checkbox'><label for='data-08eef4bc-9212-45d4-a165-a277f2f64521' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-10.  ,  -9.75,  -9.5 , ...,  59.5 ,  59.75,  60.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>(time, step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-2929c758-a49f-451b-9585-ecba601d8600' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2929c758-a49f-451b-9585-ecba601d8600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ce327d2-162d-47a0-aba9-80609b86d5bd' class='xr-var-data-in' type='checkbox'><label for='data-8ce327d2-162d-47a0-aba9-80609b86d5bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-01-01T06:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-01-01T12:00:00.000000000&#x27;],\n",
+       "       ...,\n",
+       "       [&#x27;2015-12-31T06:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-12-31T12:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-12-31T18:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 31 32 33 34 35 36</div><input id='attrs-f08ab045-4fbd-4760-ab54-1b444d7e805e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f08ab045-4fbd-4760-ab54-1b444d7e805e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1612fc3-8514-4957-84fd-840d4731589e' class='xr-var-data-in' type='checkbox'><label for='data-b1612fc3-8514-4957-84fd-840d4731589e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3990897-d038-47c6-8de2-02dec2711a19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3990897-d038-47c6-8de2-02dec2711a19' 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>t2m</span></div><div class='xr-var-dims'>(index, number, time, step, surface, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-d7ddbfe8-020a-4e35-a62a-580c37c34a13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7ddbfe8-020a-4e35-a62a-580c37c34a13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3d4306c-76af-4303-8406-429ba4030d49' class='xr-var-data-in' type='checkbox'><label for='data-d3d4306c-76af-4303-8406-429ba4030d49' 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>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "...\n",
+       "\n",
+       "\n",
+       "\n",
+       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29a5a936-0ade-4b67-842a-fbead2126112' class='xr-section-summary-in' type='checkbox'  ><label for='section-29a5a936-0ade-4b67-842a-fbead2126112' class='xr-section-summary' >Indexes: <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-index-name'><div>number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-70518edd-a950-4d83-9bcd-320cb2035bfb' class='xr-index-data-in' type='checkbox'/><label for='index-70518edd-a950-4d83-9bcd-320cb2035bfb' 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(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;number&#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-aa11331f-270f-4147-b438-2bf44509cef0' class='xr-index-data-in' type='checkbox'/><label for='index-aa11331f-270f-4147-b438-2bf44509cef0' 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;2015-01-01 00:00:00&#x27;, &#x27;2015-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2015-01-01 12:00:00&#x27;, &#x27;2015-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2015-01-02 00:00:00&#x27;, &#x27;2015-01-02 06:00:00&#x27;,\n",
+       "               &#x27;2015-01-02 12:00:00&#x27;, &#x27;2015-01-02 18:00:00&#x27;,\n",
+       "               &#x27;2015-01-03 00:00:00&#x27;, &#x27;2015-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2015-12-29 12:00:00&#x27;, &#x27;2015-12-29 18:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 00:00:00&#x27;, &#x27;2015-12-30 06:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-30 18:00:00&#x27;,\n",
+       "               &#x27;2015-12-31 00:00:00&#x27;, &#x27;2015-12-31 06:00:00&#x27;,\n",
+       "               &#x27;2015-12-31 12:00:00&#x27;, &#x27;2015-12-31 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1460, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>step</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1d2d9864-9374-4cdf-9325-7f4fa0003742' class='xr-index-data-in' type='checkbox'/><label for='index-1d2d9864-9374-4cdf-9325-7f4fa0003742' 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(TimedeltaIndex([&#x27;0 days&#x27;], dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;step&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>surface</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b69c24d-0fab-46cc-b78f-3803938912bf' class='xr-index-data-in' type='checkbox'/><label for='index-5b69c24d-0fab-46cc-b78f-3803938912bf' 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(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;surface&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d0a9c148-3271-4b37-abf5-ae8e3aa25000' class='xr-index-data-in' type='checkbox'/><label for='index-d0a9c148-3271-4b37-abf5-ae8e3aa25000' 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(Index([ 80.0, 79.75,  79.5, 79.25,  79.0, 78.75,  78.5, 78.25,  78.0, 77.75,\n",
+       "       ...\n",
+       "       32.25,  32.0, 31.75,  31.5, 31.25,  31.0, 30.75,  30.5, 30.25,  30.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=201))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c2345f21-c987-488a-bf5c-b2bd941224b5' class='xr-index-data-in' type='checkbox'/><label for='index-c2345f21-c987-488a-bf5c-b2bd941224b5' 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(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,\n",
+       "       ...\n",
+       "       57.75,  58.0, 58.25,  58.5, 58.75,  59.0, 59.25,  59.5, 59.75,  60.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=281))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0af6fffa-7b89-483d-beba-f848669379af' class='xr-index-data-in' type='checkbox'/><label for='index-0af6fffa-7b89-483d-beba-f848669379af' 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(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;index&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7100d6bb-4631-4d5a-8326-bc510de34528' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7100d6bb-4631-4d5a-8326-bc510de34528' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.31.0 with {&quot;source&quot;: &quot;N/A&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:     (index: 37, number: 1, time: 1460, step: 1, surface: 1,\n",
+       "                 latitude: 201, longitude: 281)\n",
+       "Coordinates:\n",
+       "  * number      (number) int64 0\n",
+       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * step        (step) timedelta64[ns] 00:00:00\n",
+       "  * surface     (surface) float64 0.0\n",
+       "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+       "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+       "    valid_time  (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * index       (index) int64 0 1 2 3 4 5 6 7 8 9 ... 28 29 30 31 32 33 34 35 36\n",
+       "Data variables:\n",
+       "    t2m         (index, number, time, step, surface, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:10 GRIB to CDM+CF via cfgrib-0.9.1..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "masked_data = ek_climate.shapes.masks(era5_data, nuts_data)\n",
+    "masked_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is possible to specify a column in the geodataframe to use for the new dimension, for example in NUTS the\n",
+    "`FID` (= feature id) which contains the two letter identier code for each feature:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "  min-width: 300px;\n",
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+       "\n",
+       ".xr-text-repr-fallback {\n",
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+       ".xr-header > div,\n",
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+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (FID: 37, number: 1, time: 1460, step: 1, surface: 1,\n",
+       "                 latitude: 201, longitude: 281)\n",
+       "Coordinates:\n",
+       "  * number      (number) int64 0\n",
+       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * step        (step) timedelta64[ns] 00:00:00\n",
+       "  * surface     (surface) float64 0.0\n",
+       "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+       "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+       "    valid_time  (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * FID         (FID) object &#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; &#x27;EL&#x27; &#x27;ES&#x27; ... &#x27;CY&#x27; &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;\n",
+       "Data variables:\n",
+       "    t2m         (FID, number, time, step, surface, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:11 GRIB to CDM+CF via cfgrib-0.9.1...</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-c08b3f46-e290-4e20-90ec-60bbe9a2d2b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c08b3f46-e290-4e20-90ec-60bbe9a2d2b9' 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'>FID</span>: 37</li><li><span class='xr-has-index'>number</span>: 1</li><li><span class='xr-has-index'>time</span>: 1460</li><li><span class='xr-has-index'>step</span>: 1</li><li><span class='xr-has-index'>surface</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 201</li><li><span class='xr-has-index'>longitude</span>: 281</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3c557e4f-821e-40c9-b370-434adedef8b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c557e4f-821e-40c9-b370-434adedef8b5' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>number</span></div><div class='xr-var-dims'>(number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-62caeb09-5335-4bf4-ad91-8e4c78bb4c0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62caeb09-5335-4bf4-ad91-8e4c78bb4c0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6abc5851-7b3d-4d88-bfe0-4e4f687c67fa' class='xr-var-data-in' type='checkbox'><label for='data-6abc5851-7b3d-4d88-bfe0-4e4f687c67fa' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-d995dbc5-f8ba-4e6b-ae70-610bba06a5f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d995dbc5-f8ba-4e6b-ae70-610bba06a5f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa724e3-3886-4a3e-928e-ff31723fc902' class='xr-var-data-in' type='checkbox'><label for='data-faa724e3-3886-4a3e-928e-ff31723fc902' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-01T12:00:00.000000000&#x27;, ..., &#x27;2015-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-31T12:00:00.000000000&#x27;, &#x27;2015-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-a78b5e21-2ddf-40e4-bd95-1e18aee5d8dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a78b5e21-2ddf-40e4-bd95-1e18aee5d8dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3b74c05-780f-490f-8425-11c44df66739' class='xr-var-data-in' type='checkbox'><label for='data-c3b74c05-780f-490f-8425-11c44df66739' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([0], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>surface</span></div><div class='xr-var-dims'>(surface)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-6eacff44-9214-498d-a876-df276181316d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6eacff44-9214-498d-a876-df276181316d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1f47f54-52ae-4851-ba1f-4929036cf458' class='xr-var-data-in' type='checkbox'><label for='data-a1f47f54-52ae-4851-ba1f-4929036cf458' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>80.0 79.75 79.5 ... 30.5 30.25 30.0</div><input id='attrs-2ac04871-dd14-4bd0-bb0e-e6c67fa88dd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ac04871-dd14-4bd0-bb0e-e6c67fa88dd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5026508b-db26-4119-b4d8-b154d0c818a3' class='xr-var-data-in' type='checkbox'><label for='data-5026508b-db26-4119-b4d8-b154d0c818a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([80.  , 79.75, 79.5 , ..., 30.5 , 30.25, 30.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.75 -9.5 ... 59.75 60.0</div><input id='attrs-8b285796-dd63-4947-9faf-09f97847922f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b285796-dd63-4947-9faf-09f97847922f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-349ffd14-688a-4d1f-b903-38f71d433380' class='xr-var-data-in' type='checkbox'><label for='data-349ffd14-688a-4d1f-b903-38f71d433380' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-10.  ,  -9.75,  -9.5 , ...,  59.5 ,  59.75,  60.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>(time, step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-12b83e8c-5d42-4006-aa83-876d6eea8e2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12b83e8c-5d42-4006-aa83-876d6eea8e2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31ef4858-5762-4250-8730-93d0845084fe' class='xr-var-data-in' type='checkbox'><label for='data-31ef4858-5762-4250-8730-93d0845084fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-01-01T06:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-01-01T12:00:00.000000000&#x27;],\n",
+       "       ...,\n",
+       "       [&#x27;2015-12-31T06:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-12-31T12:00:00.000000000&#x27;],\n",
+       "       [&#x27;2015-12-31T18:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FID</span></div><div class='xr-var-dims'>(FID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; ... &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;</div><input id='attrs-4d25229e-883c-4f58-8d09-d595802f980d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d25229e-883c-4f58-8d09-d595802f980d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08b24158-a460-410b-8083-4f6108bca455' class='xr-var-data-in' type='checkbox'><label for='data-08b24158-a460-410b-8083-4f6108bca455' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
+       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
+       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
+       "       &#x27;NO&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-904817c4-8432-440e-885f-990b735691d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-904817c4-8432-440e-885f-990b735691d9' 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>t2m</span></div><div class='xr-var-dims'>(FID, number, time, step, surface, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-bbaad73e-e8b7-4dd0-902a-1e7c810b9470' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbaad73e-e8b7-4dd0-902a-1e7c810b9470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e9fc675-67de-43c0-8a61-8486a82de0a9' class='xr-var-data-in' type='checkbox'><label for='data-7e9fc675-67de-43c0-8a61-8486a82de0a9' 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>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "...\n",
+       "\n",
+       "\n",
+       "\n",
+       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "         [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            ...,\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan],\n",
+       "            [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-395d839b-bde2-4608-8535-e1f4b232f246' class='xr-section-summary-in' type='checkbox'  ><label for='section-395d839b-bde2-4608-8535-e1f4b232f246' class='xr-section-summary' >Indexes: <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-index-name'><div>number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b550cb14-3e2d-4200-b8a6-d7089983d78e' class='xr-index-data-in' type='checkbox'/><label for='index-b550cb14-3e2d-4200-b8a6-d7089983d78e' 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(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;number&#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-84ec05fc-dae9-4e4d-8d90-dd554af8680a' class='xr-index-data-in' type='checkbox'/><label for='index-84ec05fc-dae9-4e4d-8d90-dd554af8680a' 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;2015-01-01 00:00:00&#x27;, &#x27;2015-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2015-01-01 12:00:00&#x27;, &#x27;2015-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2015-01-02 00:00:00&#x27;, &#x27;2015-01-02 06:00:00&#x27;,\n",
+       "               &#x27;2015-01-02 12:00:00&#x27;, &#x27;2015-01-02 18:00:00&#x27;,\n",
+       "               &#x27;2015-01-03 00:00:00&#x27;, &#x27;2015-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2015-12-29 12:00:00&#x27;, &#x27;2015-12-29 18:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 00:00:00&#x27;, &#x27;2015-12-30 06:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-30 18:00:00&#x27;,\n",
+       "               &#x27;2015-12-31 00:00:00&#x27;, &#x27;2015-12-31 06:00:00&#x27;,\n",
+       "               &#x27;2015-12-31 12:00:00&#x27;, &#x27;2015-12-31 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1460, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>step</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61998d80-3e9e-47b1-b6a4-a2cab33f3710' class='xr-index-data-in' type='checkbox'/><label for='index-61998d80-3e9e-47b1-b6a4-a2cab33f3710' 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(TimedeltaIndex([&#x27;0 days&#x27;], dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;step&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>surface</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-645b33e9-2502-4ea1-8dec-4a89e3793b58' class='xr-index-data-in' type='checkbox'/><label for='index-645b33e9-2502-4ea1-8dec-4a89e3793b58' 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(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;surface&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f365d4b2-715f-4761-b1f6-a3a916c064fe' class='xr-index-data-in' type='checkbox'/><label for='index-f365d4b2-715f-4761-b1f6-a3a916c064fe' 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(Index([ 80.0, 79.75,  79.5, 79.25,  79.0, 78.75,  78.5, 78.25,  78.0, 77.75,\n",
+       "       ...\n",
+       "       32.25,  32.0, 31.75,  31.5, 31.25,  31.0, 30.75,  30.5, 30.25,  30.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=201))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-92f8b5ae-a23a-47c0-93ed-445ec9ac4a4e' class='xr-index-data-in' type='checkbox'/><label for='index-92f8b5ae-a23a-47c0-93ed-445ec9ac4a4e' 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(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,\n",
+       "       ...\n",
+       "       57.75,  58.0, 58.25,  58.5, 58.75,  59.0, 59.25,  59.5, 59.75,  60.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=281))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>FID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4dadd565-3ddd-437e-ba47-1b5b84efc02a' class='xr-index-data-in' type='checkbox'/><label for='index-4dadd565-3ddd-437e-ba47-1b5b84efc02a' 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(Index([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
+       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
+       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
+       "       &#x27;NO&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;FID&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e3c24a4-cf68-4f8b-8d47-cfe0f0beb39d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e3c24a4-cf68-4f8b-8d47-cfe0f0beb39d' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2023-08-30T15:11 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.31.0 with {&quot;source&quot;: &quot;N/A&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:     (FID: 37, number: 1, time: 1460, step: 1, surface: 1,\n",
+       "                 latitude: 201, longitude: 281)\n",
+       "Coordinates:\n",
+       "  * number      (number) int64 0\n",
+       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * step        (step) timedelta64[ns] 00:00:00\n",
+       "  * surface     (surface) float64 0.0\n",
+       "  * latitude    (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+       "  * longitude   (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+       "    valid_time  (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * FID         (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'\n",
+       "Data variables:\n",
+       "    t2m         (FID, number, time, step, surface, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2023-08-30T15:11 GRIB to CDM+CF via cfgrib-0.9.1..."
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "masked_data = ek_climate.shapes.masks(era5_data, nuts_data, mask_dim=\"FID\")\n",
+    "masked_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we demonstrate what we have done by plotting the masked objects we have produced"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Masked Germany Zoom')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15,3))\n",
+    "era5_data.to_xarray().t2m.mean(dim='time').plot(ax=axes[0])\n",
+    "axes[0].set_title('Original data')\n",
+    "masked_data.t2m.sel(FID='DE').mean(dim='time').plot(ax=axes[1])\n",
+    "axes[1].set_title('Masked for Germany')\n",
+    "germany_data = masked_data.sel(FID='DE').dropna(dim='latitude', how='all').dropna(dim='longitude', how='all')\n",
+    "germany_data.t2m.mean(dim='time').plot(ax=axes[2])\n",
+    "axes[2].set_title('Masked Germany Zoom')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reduce data\n",
+    "### Default behaviour\n",
+    "\n",
+    "The default behaviour is to reduce the data along the spatial dimensions, only, and return the reduced data\n",
+    "in the Xarray format it was provided, i.e. `xr.DataArray` or `xr.Dataset`.\n",
+    "\n",
+    "The returned object has a new dimension `FID` (feature id) which has a coordinate variable with the values\n",
+    "of the `FID` column in the input `geodataframe`.\n",
+    "\n",
+    "The new variable name is made up of the original variable name and the method used to reduce, e.g. `t2m_mean`"
+   ]
+  },
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+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m_mean&#x27; (index: 37, time: 1460)&gt;\n",
+       "array([[278.70923, 279.765  , 279.77222, ..., 275.24063, 276.06476,\n",
+       "        278.1447 ],\n",
+       "       [257.4673 , 256.88065, 264.83875, ..., 266.2654 , 271.9407 ,\n",
+       "        267.0766 ],\n",
+       "       [275.7629 , 275.43472, 275.91312, ..., 264.8501 , 266.2538 ,\n",
+       "        264.78198],\n",
+       "       ...,\n",
+       "       [270.75067, 271.9417 , 274.1975 , ..., 267.43457, 271.5376 ,\n",
+       "        269.48703],\n",
+       "       [274.46927, 273.63748, 275.65186, ..., 273.50412, 276.47733,\n",
+       "        275.96637],\n",
+       "       [272.67392, 272.93872, 273.56546, ..., 274.4205 , 274.17746,\n",
+       "        273.82974]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    number      int64 0\n",
+       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "    step        timedelta64[ns] 00:00:00\n",
+       "    surface     float64 0.0\n",
+       "    valid_time  (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * index       (index) int64 0 1 2 3 4 5 6 7 8 9 ... 28 29 30 31 32 33 34 35 36\n",
+       "Attributes: (12/31)\n",
+       "    GRIB_paramId:                             167\n",
+       "    GRIB_dataType:                            an\n",
+       "    GRIB_numberOfPoints:                      56481\n",
+       "    GRIB_typeOfLevel:                         surface\n",
+       "    GRIB_stepUnits:                           1\n",
+       "    GRIB_stepType:                            instant\n",
+       "    ...                                       ...\n",
+       "    GRIB_totalNumber:                         0\n",
+       "    GRIB_units:                               K\n",
+       "    long_name:                                2 metre temperature\n",
+       "    units:                                    K\n",
+       "    standard_name:                            unknown\n",
+       "    reduce_dims:                              [&#x27;latitude&#x27;, &#x27;longitude&#x27;]</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'>'t2m_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 37</li><li><span class='xr-has-index'>time</span>: 1460</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c495a252-2c4b-4e40-a938-68a79bbc54cb' class='xr-array-in' type='checkbox' checked><label for='section-c495a252-2c4b-4e40-a938-68a79bbc54cb' 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>278.7 279.8 279.8 279.6 279.8 281.8 ... 273.2 274.3 274.4 274.2 273.8</span></div><div class='xr-array-data'><pre>array([[278.70923, 279.765  , 279.77222, ..., 275.24063, 276.06476,\n",
+       "        278.1447 ],\n",
+       "       [257.4673 , 256.88065, 264.83875, ..., 266.2654 , 271.9407 ,\n",
+       "        267.0766 ],\n",
+       "       [275.7629 , 275.43472, 275.91312, ..., 264.8501 , 266.2538 ,\n",
+       "        264.78198],\n",
+       "       ...,\n",
+       "       [270.75067, 271.9417 , 274.1975 , ..., 267.43457, 271.5376 ,\n",
+       "        269.48703],\n",
+       "       [274.46927, 273.63748, 275.65186, ..., 273.50412, 276.47733,\n",
+       "        275.96637],\n",
+       "       [272.67392, 272.93872, 273.56546, ..., 274.4205 , 274.17746,\n",
+       "        273.82974]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-68bc17c7-034b-4ee5-b292-8d2b3721f76f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68bc17c7-034b-4ee5-b292-8d2b3721f76f' 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>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-3e96e7fc-509d-4292-a629-f74f65bf2bc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e96e7fc-509d-4292-a629-f74f65bf2bc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c362b669-545d-41d5-ab1b-910e38301ff7' class='xr-var-data-in' type='checkbox'><label for='data-c362b669-545d-41d5-ab1b-910e38301ff7' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-ddbb25fa-3f48-4523-98bd-ff529d3191c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddbb25fa-3f48-4523-98bd-ff529d3191c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed41c611-e6ce-43ea-9622-f669c11ea385' class='xr-var-data-in' type='checkbox'><label for='data-ed41c611-e6ce-43ea-9622-f669c11ea385' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-01T12:00:00.000000000&#x27;, ..., &#x27;2015-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-31T12:00:00.000000000&#x27;, &#x27;2015-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-5875ae46-ea8f-48d6-b03a-2be24ded0f3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5875ae46-ea8f-48d6-b03a-2be24ded0f3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d3e117d-6d29-47bb-9ea8-0ffb9f3de697' class='xr-var-data-in' type='checkbox'><label for='data-3d3e117d-6d29-47bb-9ea8-0ffb9f3de697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-4d4590a7-ebe1-4d54-925f-f3363d86642b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d4590a7-ebe1-4d54-925f-f3363d86642b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6e8f9d3-015b-4d35-bf29-9ebd8d6ab479' class='xr-var-data-in' type='checkbox'><label for='data-e6e8f9d3-015b-4d35-bf29-9ebd8d6ab479' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_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'>2015-01-01 ... 2015-12-31T18:00:00</div><input id='attrs-505f80bf-d6ac-4a7e-a427-a1e5a31acf46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-505f80bf-d6ac-4a7e-a427-a1e5a31acf46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bd54e2b-e7a7-450e-88dd-acee448ec3b3' class='xr-var-data-in' type='checkbox'><label for='data-0bd54e2b-e7a7-450e-88dd-acee448ec3b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-01T12:00:00.000000000&#x27;, ...,\n",
+       "       &#x27;2015-12-31T06:00:00.000000000&#x27;, &#x27;2015-12-31T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-31T18:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 31 32 33 34 35 36</div><input id='attrs-fac7431b-9701-4059-940d-47e73bebfa48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fac7431b-9701-4059-940d-47e73bebfa48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51ced76c-6615-45e1-bb50-122a26fd69eb' class='xr-var-data-in' type='checkbox'><label for='data-51ced76c-6615-45e1-bb50-122a26fd69eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6209949-3bd7-48f8-87c3-33b2b347f421' class='xr-section-summary-in' type='checkbox'  ><label for='section-b6209949-3bd7-48f8-87c3-33b2b347f421' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2a0039f7-1381-48c5-ad67-575ebe342870' class='xr-index-data-in' type='checkbox'/><label for='index-2a0039f7-1381-48c5-ad67-575ebe342870' 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;2015-01-01 00:00:00&#x27;, &#x27;2015-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2015-01-01 12:00:00&#x27;, &#x27;2015-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2015-01-02 00:00:00&#x27;, &#x27;2015-01-02 06:00:00&#x27;,\n",
+       "               &#x27;2015-01-02 12:00:00&#x27;, &#x27;2015-01-02 18:00:00&#x27;,\n",
+       "               &#x27;2015-01-03 00:00:00&#x27;, &#x27;2015-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2015-12-29 12:00:00&#x27;, &#x27;2015-12-29 18:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 00:00:00&#x27;, &#x27;2015-12-30 06:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-30 18:00:00&#x27;,\n",
+       "               &#x27;2015-12-31 00:00:00&#x27;, &#x27;2015-12-31 06:00:00&#x27;,\n",
+       "               &#x27;2015-12-31 12:00:00&#x27;, &#x27;2015-12-31 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1460, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ce421f15-0756-44ee-b4d0-b4e723d70ca9' class='xr-index-data-in' type='checkbox'/><label for='index-ce421f15-0756-44ee-b4d0-b4e723d70ca9' 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(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;index&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b660dea2-c3ee-4e59-989b-fbeb5b728b85' class='xr-section-summary-in' type='checkbox'  ><label for='section-b660dea2-c3ee-4e59-989b-fbeb5b728b85' class='xr-section-summary' >Attributes: <span>(31)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>reduce_dims :</span></dt><dd>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 't2m_mean' (index: 37, time: 1460)>\n",
+       "array([[278.70923, 279.765  , 279.77222, ..., 275.24063, 276.06476,\n",
+       "        278.1447 ],\n",
+       "       [257.4673 , 256.88065, 264.83875, ..., 266.2654 , 271.9407 ,\n",
+       "        267.0766 ],\n",
+       "       [275.7629 , 275.43472, 275.91312, ..., 264.8501 , 266.2538 ,\n",
+       "        264.78198],\n",
+       "       ...,\n",
+       "       [270.75067, 271.9417 , 274.1975 , ..., 267.43457, 271.5376 ,\n",
+       "        269.48703],\n",
+       "       [274.46927, 273.63748, 275.65186, ..., 273.50412, 276.47733,\n",
+       "        275.96637],\n",
+       "       [272.67392, 272.93872, 273.56546, ..., 274.4205 , 274.17746,\n",
+       "        273.82974]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    number      int64 0\n",
+       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "    step        timedelta64[ns] 00:00:00\n",
+       "    surface     float64 0.0\n",
+       "    valid_time  (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+       "  * index       (index) int64 0 1 2 3 4 5 6 7 8 9 ... 28 29 30 31 32 33 34 35 36\n",
+       "Attributes: (12/31)\n",
+       "    GRIB_paramId:                             167\n",
+       "    GRIB_dataType:                            an\n",
+       "    GRIB_numberOfPoints:                      56481\n",
+       "    GRIB_typeOfLevel:                         surface\n",
+       "    GRIB_stepUnits:                           1\n",
+       "    GRIB_stepType:                            instant\n",
+       "    ...                                       ...\n",
+       "    GRIB_totalNumber:                         0\n",
+       "    GRIB_units:                               K\n",
+       "    long_name:                                2 metre temperature\n",
+       "    units:                                    K\n",
+       "    standard_name:                            unknown\n",
+       "    reduce_dims:                              ['latitude', 'longitude']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data)\n",
+    "reduced_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reduce along additional dimension\n",
+    "\n",
+    "For example, any time dimension, this is advisable as it ensures correct handling missing values and weights.\n",
+    "\n",
+    "The extra_reduce_dims argument takes a single string or a list of strings corresponding to dimensions to\n",
+    "include in the reduction.\n",
+    "\n",
+    "It is also possible to select a column in the geodataframe to use to populate the dimension and coordinate \n",
+    "variable created by the reduction using the `mask_dim` kwarg, here we choose the `\"FID\"` column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       ".xr-text-repr-fallback {\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m_mean&#x27; (FID: 37)&gt;\n",
+       "array([282.48444, 285.00317, 280.56302, 288.2147 , 287.7985 , 277.20306,\n",
+       "       285.11716, 285.96515, 285.27744, 282.67667,       nan, 286.60617,\n",
+       "             nan, 281.4668 , 283.13324, 280.9107 , 283.51895, 284.88483,\n",
+       "       292.72778, 277.5139 , 284.11755, 282.87704, 285.1915 , 282.5734 ,\n",
+       "       283.9551 , 283.00146, 288.69153, 284.04517, 285.8487 , 281.1512 ,\n",
+       "       283.72302, 285.0767 , 279.59122, 292.9115 , 282.85074, 283.27975,\n",
+       "       273.8031 ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    number   int64 0\n",
+       "    step     timedelta64[ns] 00:00:00\n",
+       "    surface  float64 0.0\n",
+       "  * FID      (FID) object &#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; &#x27;EL&#x27; &#x27;ES&#x27; ... &#x27;CH&#x27; &#x27;CY&#x27; &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;\n",
+       "Attributes: (12/31)\n",
+       "    GRIB_paramId:                             167\n",
+       "    GRIB_dataType:                            an\n",
+       "    GRIB_numberOfPoints:                      56481\n",
+       "    GRIB_typeOfLevel:                         surface\n",
+       "    GRIB_stepUnits:                           1\n",
+       "    GRIB_stepType:                            instant\n",
+       "    ...                                       ...\n",
+       "    GRIB_totalNumber:                         0\n",
+       "    GRIB_units:                               K\n",
+       "    long_name:                                2 metre temperature\n",
+       "    units:                                    K\n",
+       "    standard_name:                            unknown\n",
+       "    reduce_dims:                              [&#x27;latitude&#x27;, &#x27;longitude&#x27;, &#x27;time&#x27;]</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'>'t2m_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>FID</span>: 37</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7cf0d14a-8d95-405f-b4a3-1b5ee047f5ca' class='xr-array-in' type='checkbox' checked><label for='section-7cf0d14a-8d95-405f-b4a3-1b5ee047f5ca' 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>282.5 285.0 280.6 288.2 287.8 277.2 ... 279.6 292.9 282.9 283.3 273.8</span></div><div class='xr-array-data'><pre>array([282.48444, 285.00317, 280.56302, 288.2147 , 287.7985 , 277.20306,\n",
+       "       285.11716, 285.96515, 285.27744, 282.67667,       nan, 286.60617,\n",
+       "             nan, 281.4668 , 283.13324, 280.9107 , 283.51895, 284.88483,\n",
+       "       292.72778, 277.5139 , 284.11755, 282.87704, 285.1915 , 282.5734 ,\n",
+       "       283.9551 , 283.00146, 288.69153, 284.04517, 285.8487 , 281.1512 ,\n",
+       "       283.72302, 285.0767 , 279.59122, 292.9115 , 282.85074, 283.27975,\n",
+       "       273.8031 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-95b29763-b4e1-4943-9db1-c689c9fca7e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-95b29763-b4e1-4943-9db1-c689c9fca7e4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ec6589b8-1364-4337-b91d-680d739045b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec6589b8-1364-4337-b91d-680d739045b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce5e112d-6b33-42f3-a2c1-92ea55c7008d' class='xr-var-data-in' type='checkbox'><label for='data-ce5e112d-6b33-42f3-a2c1-92ea55c7008d' 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>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-c274c132-1d2d-4e38-a770-9d4be4b61ebe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c274c132-1d2d-4e38-a770-9d4be4b61ebe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d16a3c62-7806-4e1b-b207-c4b751f59fad' class='xr-var-data-in' type='checkbox'><label for='data-d16a3c62-7806-4e1b-b207-c4b751f59fad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-9123228c-e9fd-4a58-8a9f-6a738e0af5ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9123228c-e9fd-4a58-8a9f-6a738e0af5ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8252e3c6-89a7-4733-9931-0ea3e0c34df0' class='xr-var-data-in' type='checkbox'><label for='data-8252e3c6-89a7-4733-9931-0ea3e0c34df0' 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>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FID</span></div><div class='xr-var-dims'>(FID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; ... &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;</div><input id='attrs-3d0707f1-8041-4e56-a993-641522fd7fc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d0707f1-8041-4e56-a993-641522fd7fc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5227f0b-8c2f-4248-a5ab-1ef87d574353' class='xr-var-data-in' type='checkbox'><label for='data-a5227f0b-8c2f-4248-a5ab-1ef87d574353' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
+       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
+       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
+       "       &#x27;NO&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe0edede-8571-4ab1-9542-a4ee1b9b0cd1' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe0edede-8571-4ab1-9542-a4ee1b9b0cd1' 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>FID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-132a0d6e-5062-4555-ac13-7977120ae757' class='xr-index-data-in' type='checkbox'/><label for='index-132a0d6e-5062-4555-ac13-7977120ae757' 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(Index([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
+       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
+       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
+       "       &#x27;NO&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;FID&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3c5c4b6-039c-4a4c-b5f4-b7bbbfdfb8d8' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3c5c4b6-039c-4a4c-b5f4-b7bbbfdfb8d8' class='xr-section-summary' >Attributes: <span>(31)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>56481</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>281</dd><dt><span>GRIB_Ny :</span></dt><dd>201</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>80.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>30.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-10.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>60.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>reduce_dims :</span></dt><dd>[&#x27;latitude&#x27;, &#x27;longitude&#x27;, &#x27;time&#x27;]</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 't2m_mean' (FID: 37)>\n",
+       "array([282.48444, 285.00317, 280.56302, 288.2147 , 287.7985 , 277.20306,\n",
+       "       285.11716, 285.96515, 285.27744, 282.67667,       nan, 286.60617,\n",
+       "             nan, 281.4668 , 283.13324, 280.9107 , 283.51895, 284.88483,\n",
+       "       292.72778, 277.5139 , 284.11755, 282.87704, 285.1915 , 282.5734 ,\n",
+       "       283.9551 , 283.00146, 288.69153, 284.04517, 285.8487 , 281.1512 ,\n",
+       "       283.72302, 285.0767 , 279.59122, 292.9115 , 282.85074, 283.27975,\n",
+       "       273.8031 ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    number   int64 0\n",
+       "    step     timedelta64[ns] 00:00:00\n",
+       "    surface  float64 0.0\n",
+       "  * FID      (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CH' 'CY' 'CZ' 'DE' 'NO'\n",
+       "Attributes: (12/31)\n",
+       "    GRIB_paramId:                             167\n",
+       "    GRIB_dataType:                            an\n",
+       "    GRIB_numberOfPoints:                      56481\n",
+       "    GRIB_typeOfLevel:                         surface\n",
+       "    GRIB_stepUnits:                           1\n",
+       "    GRIB_stepType:                            instant\n",
+       "    ...                                       ...\n",
+       "    GRIB_totalNumber:                         0\n",
+       "    GRIB_units:                               K\n",
+       "    long_name:                                2 metre temperature\n",
+       "    units:                                    K\n",
+       "    standard_name:                            unknown\n",
+       "    reduce_dims:                              ['latitude', 'longitude', 'time']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data, mask_dim=\"FID\", extra_reduce_dims='time', all_touched=True)\n",
+    "reduced_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Weighted reduction\n",
+    "\n",
+    "Provide numpy/xarray arrays of weights, or use predefined weights options, i.e. `latitude`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m_mean&#x27; (FID: 37)&gt;\n",
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+       "    step     timedelta64[ns] 00:00:00\n",
+       "    surface  float64 0.0\n",
+       "  * FID      (FID) object &#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; &#x27;EL&#x27; &#x27;ES&#x27; ... &#x27;CH&#x27; &#x27;CY&#x27; &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;\n",
+       "Attributes:\n",
+       "    reduce_dims:  [&#x27;latitude&#x27;, &#x27;longitude&#x27;, &#x27;time&#x27;]</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'>'t2m_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>FID</span>: 37</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b3781812-3704-4df7-a000-ba4f0008254a' class='xr-array-in' type='checkbox' checked><label for='section-b3781812-3704-4df7-a000-ba4f0008254a' 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>282.5 285.0 280.6 288.2 287.9 277.4 ... 279.6 292.9 282.9 283.3 275.2</span></div><div class='xr-array-data'><pre>array([282.48775633, 284.98695114, 280.56394728, 288.24561204,\n",
+       "       287.85414585, 277.40288238, 285.13271905, 285.98062449,\n",
+       "       285.27876617, 282.68034112,          nan, 286.70484213,\n",
+       "                nan, 281.46997469, 283.13532151, 280.91171209,\n",
+       "       283.53361561, 284.88482875, 292.72787062, 277.82223848,\n",
+       "       284.12009341, 282.88711896, 285.22108381, 282.63366093,\n",
+       "       283.95918518, 283.00942749, 288.71911579, 284.06446913,\n",
+       "       285.86108182, 281.13270045, 283.71696092, 285.07195837,\n",
+       "       279.57436098, 292.91067502, 282.85261266, 283.27636956,\n",
+       "       275.2053521 ])</pre></div></div></li><li class='xr-section-item'><input id='section-70dc878e-5da3-493b-b71e-1b5619962936' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70dc878e-5da3-493b-b71e-1b5619962936' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-7ca80226-b119-4e35-9885-8a3e4a0678e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ca80226-b119-4e35-9885-8a3e4a0678e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6f94c23-bbd5-4e79-b42d-c7800c1f0bc9' class='xr-var-data-in' type='checkbox'><label for='data-e6f94c23-bbd5-4e79-b42d-c7800c1f0bc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-307d6a1b-7b47-4fe0-aae1-3f308ba76e01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-307d6a1b-7b47-4fe0-aae1-3f308ba76e01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14532d40-6c03-4063-a8a9-d8cb64470dd1' class='xr-var-data-in' type='checkbox'><label for='data-14532d40-6c03-4063-a8a9-d8cb64470dd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-01e67c99-3dc7-437d-b5fe-1287a3ee1c85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01e67c99-3dc7-437d-b5fe-1287a3ee1c85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3580d4a-cec8-46a4-b746-261b56f7aef2' class='xr-var-data-in' type='checkbox'><label for='data-d3580d4a-cec8-46a4-b746-261b56f7aef2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FID</span></div><div class='xr-var-dims'>(FID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DK&#x27; &#x27;RS&#x27; &#x27;EE&#x27; ... &#x27;CZ&#x27; &#x27;DE&#x27; &#x27;NO&#x27;</div><input id='attrs-5a9a4535-c6dd-41e3-948b-4560cc5bc69e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a9a4535-c6dd-41e3-948b-4560cc5bc69e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-923cc63e-5446-4045-a63b-66656af2eeee' class='xr-var-data-in' type='checkbox'><label for='data-923cc63e-5446-4045-a63b-66656af2eeee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
+       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
+       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
+       "       &#x27;NO&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b687c72-9808-42c5-9c2a-29b63f9f22b0' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b687c72-9808-42c5-9c2a-29b63f9f22b0' 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>FID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a155008e-b28c-4647-b1e8-669791acd23e' class='xr-index-data-in' type='checkbox'/><label for='index-a155008e-b28c-4647-b1e8-669791acd23e' 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(Index([&#x27;DK&#x27;, &#x27;RS&#x27;, &#x27;EE&#x27;, &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
+       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UK&#x27;,\n",
+       "       &#x27;NL&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n",
+       "       &#x27;NO&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;FID&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cad21d19-e3d3-48b1-8d1b-a9251dc1a7b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cad21d19-e3d3-48b1-8d1b-a9251dc1a7b0' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>reduce_dims :</span></dt><dd>[&#x27;latitude&#x27;, &#x27;longitude&#x27;, &#x27;time&#x27;]</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 't2m_mean' (FID: 37)>\n",
+       "array([282.48775633, 284.98695114, 280.56394728, 288.24561204,\n",
+       "       287.85414585, 277.40288238, 285.13271905, 285.98062449,\n",
+       "       285.27876617, 282.68034112,          nan, 286.70484213,\n",
+       "                nan, 281.46997469, 283.13532151, 280.91171209,\n",
+       "       283.53361561, 284.88482875, 292.72787062, 277.82223848,\n",
+       "       284.12009341, 282.88711896, 285.22108381, 282.63366093,\n",
+       "       283.95918518, 283.00942749, 288.71911579, 284.06446913,\n",
+       "       285.86108182, 281.13270045, 283.71696092, 285.07195837,\n",
+       "       279.57436098, 292.91067502, 282.85261266, 283.27636956,\n",
+       "       275.2053521 ])\n",
+       "Coordinates:\n",
+       "    number   int64 0\n",
+       "    step     timedelta64[ns] 00:00:00\n",
+       "    surface  float64 0.0\n",
+       "  * FID      (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CH' 'CY' 'CZ' 'DE' 'NO'\n",
+       "Attributes:\n",
+       "    reduce_dims:  ['latitude', 'longitude', 'time']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_data_xr = ek_climate.shapes.reduce(era5_data, nuts_data, weights='latitude', mask_dim=\"FID\", extra_reduce_dims='time', all_touched=True)\n",
+    "reduced_data_xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Return as a pandas dataframe\n",
+    "\n",
+    "**WARNING: Returning reduced data in pandas format is considered experimental and may change in futureversions of earthkit-climate** \n",
+    "\n",
+    "It is possible to return the reduced data in a fully expanded geopandas dataframe which contains the geometry \n",
+    "and aggregated data.\n",
+    "Additional columns for the data values and rows and indexes added to fully describe the reduced data.\n",
+    "\n",
+    "The returned object fully supports pandas indexing and in-built convenience methods (e.g. plotting),\n",
+    "but it comes with memory usage cost, hence in this example we reduce along all dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Returning reduced data in pandas format is considered experimental and may change in futureversions of earthkit-climate\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>NUTS_ID</th>\n",
+       "      <th>LEVL_CODE</th>\n",
+       "      <th>CNTR_CODE</th>\n",
+       "      <th>NAME_LATN</th>\n",
+       "      <th>NUTS_NAME</th>\n",
+       "      <th>MOUNT_TYPE</th>\n",
+       "      <th>URBN_TYPE</th>\n",
+       "      <th>COAST_TYPE</th>\n",
+       "      <th>FID</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>number</th>\n",
+       "      <th>step</th>\n",
+       "      <th>surface</th>\n",
+       "      <th>t2m_mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DK</th>\n",
+       "      <td>DK</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>282.484436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RS</th>\n",
+       "      <td>RS</td>\n",
+       "      <td>RS</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RS</td>\n",
+       "      <td>Serbia</td>\n",
+       "      <td>Srbija/Сpбија</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RS</td>\n",
+       "      <td>POLYGON ((21.47920 45.19300, 21.35850 44.82160...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>285.003174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EE</th>\n",
+       "      <td>EE</td>\n",
+       "      <td>EE</td>\n",
+       "      <td>0</td>\n",
+       "      <td>EE</td>\n",
+       "      <td>Eesti</td>\n",
+       "      <td>Eesti</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>EE</td>\n",
+       "      <td>MULTIPOLYGON (((27.35700 58.78710, 27.64490 57...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>280.563019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EL</th>\n",
+       "      <td>EL</td>\n",
+       "      <td>EL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>EL</td>\n",
+       "      <td>Elláda</td>\n",
+       "      <td>Ελλάδα</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>EL</td>\n",
+       "      <td>MULTIPOLYGON (((28.07770 36.11820, 27.86060 35...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>288.214691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ES</th>\n",
+       "      <td>ES</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>España</td>\n",
+       "      <td>España</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>287.798492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FI</th>\n",
+       "      <td>FI</td>\n",
+       "      <td>FI</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FI</td>\n",
+       "      <td>Suomi/Finland</td>\n",
+       "      <td>Suomi/Finland</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FI</td>\n",
+       "      <td>MULTIPOLYGON (((28.89670 69.04260, 28.47820 68...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>277.203064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FR</th>\n",
+       "      <td>FR</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>France</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>MULTIPOLYGON (((55.84980 -21.18580, 55.78580 -...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>285.117157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HR</th>\n",
+       "      <td>HR</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>0</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Hrvatska</td>\n",
+       "      <td>Hrvatska</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>MULTIPOLYGON (((17.65150 45.84780, 17.91210 45...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>285.965149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HU</th>\n",
+       "      <td>HU</td>\n",
+       "      <td>HU</td>\n",
+       "      <td>0</td>\n",
+       "      <td>HU</td>\n",
+       "      <td>Magyarország</td>\n",
+       "      <td>Magyarország</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>HU</td>\n",
+       "      <td>POLYGON ((22.12110 48.37830, 22.15530 48.40340...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>285.277435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IE</th>\n",
+       "      <td>IE</td>\n",
+       "      <td>IE</td>\n",
+       "      <td>0</td>\n",
+       "      <td>IE</td>\n",
+       "      <td>Éire/Ireland</td>\n",
+       "      <td>Éire/Ireland</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>IE</td>\n",
+       "      <td>POLYGON ((-7.18850 54.33770, -6.86420 54.33020...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>282.676666</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     id NUTS_ID  LEVL_CODE CNTR_CODE      NAME_LATN      NUTS_NAME  \\\n",
+       "FID                                                                  \n",
+       "DK   DK      DK          0        DK        Danmark        Danmark   \n",
+       "RS   RS      RS          0        RS         Serbia  Srbija/Сpбија   \n",
+       "EE   EE      EE          0        EE          Eesti          Eesti   \n",
+       "EL   EL      EL          0        EL         Elláda         Ελλάδα   \n",
+       "ES   ES      ES          0        ES         España         España   \n",
+       "FI   FI      FI          0        FI  Suomi/Finland  Suomi/Finland   \n",
+       "FR   FR      FR          0        FR         France         France   \n",
+       "HR   HR      HR          0        HR       Hrvatska       Hrvatska   \n",
+       "HU   HU      HU          0        HU   Magyarország   Magyarország   \n",
+       "IE   IE      IE          0        IE   Éire/Ireland   Éire/Ireland   \n",
+       "\n",
+       "     MOUNT_TYPE  URBN_TYPE  COAST_TYPE FID  \\\n",
+       "FID                                          \n",
+       "DK            0          0           0  DK   \n",
+       "RS            0          0           0  RS   \n",
+       "EE            0          0           0  EE   \n",
+       "EL            0          0           0  EL   \n",
+       "ES            0          0           0  ES   \n",
+       "FI            0          0           0  FI   \n",
+       "FR            0          0           0  FR   \n",
+       "HR            0          0           0  HR   \n",
+       "HU            0          0           0  HU   \n",
+       "IE            0          0           0  IE   \n",
+       "\n",
+       "                                              geometry  number   step  \\\n",
+       "FID                                                                     \n",
+       "DK   MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...       0 0 days   \n",
+       "RS   POLYGON ((21.47920 45.19300, 21.35850 44.82160...       0 0 days   \n",
+       "EE   MULTIPOLYGON (((27.35700 58.78710, 27.64490 57...       0 0 days   \n",
+       "EL   MULTIPOLYGON (((28.07770 36.11820, 27.86060 35...       0 0 days   \n",
+       "ES   MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7...       0 0 days   \n",
+       "FI   MULTIPOLYGON (((28.89670 69.04260, 28.47820 68...       0 0 days   \n",
+       "FR   MULTIPOLYGON (((55.84980 -21.18580, 55.78580 -...       0 0 days   \n",
+       "HR   MULTIPOLYGON (((17.65150 45.84780, 17.91210 45...       0 0 days   \n",
+       "HU   POLYGON ((22.12110 48.37830, 22.15530 48.40340...       0 0 days   \n",
+       "IE   POLYGON ((-7.18850 54.33770, -6.86420 54.33020...       0 0 days   \n",
+       "\n",
+       "     surface    t2m_mean  \n",
+       "FID                       \n",
+       "DK       0.0  282.484436  \n",
+       "RS       0.0  285.003174  \n",
+       "EE       0.0  280.563019  \n",
+       "EL       0.0  288.214691  \n",
+       "ES       0.0  287.798492  \n",
+       "FI       0.0  277.203064  \n",
+       "FR       0.0  285.117157  \n",
+       "HR       0.0  285.965149  \n",
+       "HU       0.0  285.277435  \n",
+       "IE       0.0  282.676666  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_data_pd = ek_climate.shapes.reduce(era5_data, nuts_data, return_as=\"pandas\", mask_dim=\"FID\", extra_reduce_dims=\"time\")\n",
+    "reduced_data_pd[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reduced_data_pd.plot(\"t2m_mean\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix\n",
+    "\n",
+    "### Unadvised: return_as = 'pandas' for time-series\n",
+    "\n",
+    "This results in very heavy memory usage but may be useful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Returning reduced data in pandas format is considered experimental and may change in futureversions of earthkit-climate\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>NUTS_ID</th>\n",
+       "      <th>LEVL_CODE</th>\n",
+       "      <th>CNTR_CODE</th>\n",
+       "      <th>NAME_LATN</th>\n",
+       "      <th>NUTS_NAME</th>\n",
+       "      <th>MOUNT_TYPE</th>\n",
+       "      <th>URBN_TYPE</th>\n",
+       "      <th>COAST_TYPE</th>\n",
+       "      <th>FID</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>number</th>\n",
+       "      <th>step</th>\n",
+       "      <th>surface</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>t2m_mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">DK</th>\n",
+       "      <th>2015-01-01 00:00:00</th>\n",
+       "      <td>DK</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2015-01-01 00:00:00</td>\n",
+       "      <td>278.709229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-01-01 06:00:00</th>\n",
+       "      <td>DK</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2015-01-01 06:00:00</td>\n",
+       "      <td>279.765015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-01-01 12:00:00</th>\n",
+       "      <td>DK</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2015-01-01 12:00:00</td>\n",
+       "      <td>279.772217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-01-01 18:00:00</th>\n",
+       "      <td>DK</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2015-01-01 18:00:00</td>\n",
+       "      <td>279.575684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-01-02 00:00:00</th>\n",
+       "      <td>DK</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>Danmark</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DK</td>\n",
+       "      <td>MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2015-01-02 00:00:00</td>\n",
+       "      <td>279.826721</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         id NUTS_ID  LEVL_CODE CNTR_CODE NAME_LATN NUTS_NAME  \\\n",
+       "FID time                                                                       \n",
+       "DK  2015-01-01 00:00:00  DK      DK          0        DK   Danmark   Danmark   \n",
+       "    2015-01-01 06:00:00  DK      DK          0        DK   Danmark   Danmark   \n",
+       "    2015-01-01 12:00:00  DK      DK          0        DK   Danmark   Danmark   \n",
+       "    2015-01-01 18:00:00  DK      DK          0        DK   Danmark   Danmark   \n",
+       "    2015-01-02 00:00:00  DK      DK          0        DK   Danmark   Danmark   \n",
+       "\n",
+       "                         MOUNT_TYPE  URBN_TYPE  COAST_TYPE FID  \\\n",
+       "FID time                                                         \n",
+       "DK  2015-01-01 00:00:00           0          0           0  DK   \n",
+       "    2015-01-01 06:00:00           0          0           0  DK   \n",
+       "    2015-01-01 12:00:00           0          0           0  DK   \n",
+       "    2015-01-01 18:00:00           0          0           0  DK   \n",
+       "    2015-01-02 00:00:00           0          0           0  DK   \n",
+       "\n",
+       "                                                                  geometry  \\\n",
+       "FID time                                                                     \n",
+       "DK  2015-01-01 00:00:00  MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...   \n",
+       "    2015-01-01 06:00:00  MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...   \n",
+       "    2015-01-01 12:00:00  MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...   \n",
+       "    2015-01-01 18:00:00  MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...   \n",
+       "    2015-01-02 00:00:00  MULTIPOLYGON (((15.16290 55.09370, 15.09400 54...   \n",
+       "\n",
+       "                         number   step  surface          valid_time  \\\n",
+       "FID time                                                              \n",
+       "DK  2015-01-01 00:00:00       0 0 days      0.0 2015-01-01 00:00:00   \n",
+       "    2015-01-01 06:00:00       0 0 days      0.0 2015-01-01 06:00:00   \n",
+       "    2015-01-01 12:00:00       0 0 days      0.0 2015-01-01 12:00:00   \n",
+       "    2015-01-01 18:00:00       0 0 days      0.0 2015-01-01 18:00:00   \n",
+       "    2015-01-02 00:00:00       0 0 days      0.0 2015-01-02 00:00:00   \n",
+       "\n",
+       "                           t2m_mean  \n",
+       "FID time                             \n",
+       "DK  2015-01-01 00:00:00  278.709229  \n",
+       "    2015-01-01 06:00:00  279.765015  \n",
+       "    2015-01-01 12:00:00  279.772217  \n",
+       "    2015-01-01 18:00:00  279.575684  \n",
+       "    2015-01-02 00:00:00  279.826721  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_data_pd = ek_climate.shapes.reduce(era5_data, nuts_data, return_as=\"pandas\", mask_dim=\"FID\")\n",
+    "reduced_data_pd[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x169055690>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feature_index = \"FID\"\n",
+    "plot_var = \"t2m_mean\"\n",
+    "# plot_x_vals = reduced_data.attrs[f\"{plot_var}_dims\"][\"time\"]\n",
+    "fig, ax = plt.subplots(1)\n",
+    "for feature in reduced_data_pd.index.get_level_values(feature_index).unique()[:5]:\n",
+    "    temp = reduced_data_pd.xs(feature, level=feature_index)\n",
+    "    temp[plot_var].plot(ax=ax, label=feature)\n",
+    "fig.legend()"
    ]
   }
  ],