diff --git a/workshop/3_use-cases-and-preparations.ipynb b/workshop/3_use-cases-and-preparations.ipynb
index d31676a3cc33ef05e32fa26c8f2ebc5dc6fa4251..a947d573fc20027139d0f5301722f4d38f970a34 100644
--- a/workshop/3_use-cases-and-preparations.ipynb
+++ b/workshop/3_use-cases-and-preparations.ipynb
@@ -34,10 +34,933 @@
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+ " grid-column: 1;\n",
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+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
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+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
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+ "}\n",
+ "\n",
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+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 314MB\n",
+ "Dimensions: (lat: 192, time: 1032, bnds: 2, lon: 384)\n",
+ "Coordinates:\n",
+ " height float64 8B ...\n",
+ " * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
+ " * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
+ " * time (time) datetime64[ns] 8kB 2015-01-16T12:00:00 ... 2100-12-16T1...\n",
+ "Dimensions without coordinates: bnds\n",
+ "Data variables:\n",
+ " lat_bnds (time, lat, bnds) float64 3MB dask.array<chunksize=(60, 192, 2), meta=np.ndarray>\n",
+ " lon_bnds (time, lon, bnds) float64 6MB dask.array<chunksize=(60, 384, 2), meta=np.ndarray>\n",
+ " tas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
+ " time_bnds (time, bnds) datetime64[ns] 17kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+ "Attributes: (12/47)\n",
+ " Conventions: CF-1.7 CMIP-6.2\n",
+ " activity_id: ScenarioMIP AerChemMIP\n",
+ " branch_method: standard\n",
+ " branch_time_in_child: 60265.0\n",
+ " branch_time_in_parent: 60265.0\n",
+ " contact: cmip6-mpi-esm@dkrz.de\n",
+ " ... ...\n",
+ " title: MPI-ESM1-2-HR output prepared for CMIP6\n",
+ " variable_id: tas\n",
+ " variant_label: r1i1p1f1\n",
+ " license: CMIP6 model data produced by DKRZ is licensed und...\n",
+ " cmor_version: 3.4.0\n",
+ " tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</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-68bf8c03-8018-40c8-9f64-1626451b733f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68bf8c03-8018-40c8-9f64-1626451b733f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>time</span>: 1032</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 384</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d75405c3-6362-4216-b11c-4f9c0020c681' class='xr-section-summary-in' type='checkbox' checked><label for='section-d75405c3-6362-4216-b11c-4f9c0020c681' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c6d923a-e34e-4312-9999-47fc36ed71d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c6d923a-e34e-4312-9999-47fc36ed71d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f61a68-5ecc-4e18-828c-9b7042b6b4fb' class='xr-var-data-in' type='checkbox'><label for='data-85f61a68-5ecc-4e18-828c-9b7042b6b4fb' 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>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.28 -88.36 ... 88.36 89.28</div><input id='attrs-66aa3074-9ec2-4923-9132-2ffffe5fecd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66aa3074-9ec2-4923-9132-2ffffe5fecd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d59115f3-0c24-47d3-ac7a-3dfb92002d17' class='xr-var-data-in' type='checkbox'><label for='data-d59115f3-0c24-47d3-ac7a-3dfb92002d17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,\n",
+ " -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,\n",
+ " -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,\n",
+ " -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,\n",
+ " -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,\n",
+ " -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,\n",
+ " -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,\n",
+ " -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,\n",
+ " -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,\n",
+ " -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,\n",
+ " -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,\n",
+ " -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,\n",
+ " -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,\n",
+ " -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,\n",
+ " -10.75321 , -9.818148, -8.883087, -7.948025, -7.012963, -6.077901,\n",
+ " -5.14284 , -4.207778, -3.272716, -2.337654, -1.402593, -0.467531,\n",
+ " 0.467531, 1.402593, 2.337654, 3.272716, 4.207778, 5.14284 ,\n",
+ " 6.077901, 7.012963, 7.948025, 8.883087, 9.818148, 10.75321 ,\n",
+ " 11.688272, 12.623333, 13.558395, 14.493457, 15.428518, 16.36358 ,\n",
+ " 17.298641, 18.233703, 19.168764, 20.103825, 21.038887, 21.973948,\n",
+ " 22.909009, 23.84407 , 24.779132, 25.714193, 26.649254, 27.584315,\n",
+ " 28.519375, 29.454436, 30.389497, 31.324558, 32.259618, 33.194679,\n",
+ " 34.129739, 35.064799, 35.99986 , 36.93492 , 37.86998 , 38.805039,\n",
+ " 39.740099, 40.675159, 41.610218, 42.545277, 43.480336, 44.415395,\n",
+ " 45.350454, 46.285512, 47.22057 , 48.155628, 49.090686, 50.025743,\n",
+ " 50.960801, 51.895857, 52.830914, 53.76597 , 54.701026, 55.636081,\n",
+ " 56.571136, 57.50619 , 58.441244, 59.376297, 60.311349, 61.246401,\n",
+ " 62.181452, 63.116502, 64.051551, 64.986599, 65.921645, 66.856691,\n",
+ " 67.791734, 68.726776, 69.661816, 70.596854, 71.531889, 72.466921,\n",
+ " 73.401949, 74.336973, 75.271992, 76.207005, 77.142011, 78.077007,\n",
+ " 79.011992, 79.946962, 80.881913, 81.816839, 82.751728, 83.686567,\n",
+ " 84.621327, 85.55596 , 86.490367, 87.424304, 88.357004, 89.284228])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-8d91d640-17cd-45b5-a088-0d51d252f0ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d91d640-17cd-45b5-a088-0d51d252f0ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a7ba789-60f4-4be9-9d74-bb9a137415b7' class='xr-var-data-in' type='checkbox'><label for='data-5a7ba789-60f4-4be9-9d74-bb9a137415b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 0.9375, 1.875 , ..., 357.1875, 358.125 , 359.0625])</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'>2015-01-16T12:00:00 ... 2100-12-...</div><input id='attrs-87945860-5e78-4617-ad2e-59b04f3d45ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87945860-5e78-4617-ad2e-59b04f3d45ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a61b1641-69a3-4960-ba44-355ca501c880' class='xr-var-data-in' type='checkbox'><label for='data-a61b1641-69a3-4960-ba44-355ca501c880' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n",
+ " '2015-03-16T12:00:00.000000000', ..., '2100-10-16T12:00:00.000000000',\n",
+ " '2100-11-16T00:00:00.000000000', '2100-12-16T12:00:00.000000000'],\n",
+ " dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8515a4be-ad4a-4f60-9059-5bf954d45a0a' class='xr-section-summary-in' type='checkbox' checked><label for='section-8515a4be-ad4a-4f60-9059-5bf954d45a0a' class='xr-section-summary' >Data variables: <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>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 192, 2), meta=np.ndarray></div><input id='attrs-beafa294-a872-4df7-9882-949bf40e9289' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-beafa294-a872-4df7-9882-949bf40e9289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7864127-1ae7-4655-b156-209260ca09c3' class='xr-var-data-in' type='checkbox'><label for='data-c7864127-1ae7-4655-b156-209260ca09c3' 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'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 3.02 MiB </td>\n",
+ " <td> 180.00 kiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (1032, 192, 2) </td>\n",
+ " <td> (60, 192, 2) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 384, 2), meta=np.ndarray></div><input id='attrs-cce19196-fe53-4834-94cc-55ba0ce280a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cce19196-fe53-4834-94cc-55ba0ce280a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca9c4df-b8a8-4dad-af41-cb7b1541dafa' class='xr-var-data-in' type='checkbox'><label for='data-eca9c4df-b8a8-4dad-af41-cb7b1541dafa' 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'><table>\n",
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+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
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+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 6.05 MiB </td>\n",
+ " <td> 360.00 kiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (1032, 384, 2) </td>\n",
+ " <td> (60, 384, 2) </td>\n",
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+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 192, 384), meta=np.ndarray></div><input id='attrs-7f9aa570-4715-451d-bb5a-1546f2c03c82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f9aa570-4715-451d-bb5a-1546f2c03c82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de4bf0d7-79af-43db-9112-0198fe0fd2f5' class='xr-var-data-in' type='checkbox'><label for='data-de4bf0d7-79af-43db-9112-0198fe0fd2f5' 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>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z altered by CMOR: Treated scalar dimension: 'height'. 2019-07-20T13:41:51Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:51Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:52Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 290.25 MiB </td>\n",
+ " <td> 288.00 kiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (1032, 192, 384) </td>\n",
+ " <td> (1, 192, 384) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 1032 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ " </table>\n",
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+ "</svg>\n",
+ " </td>\n",
+ " </tr>\n",
+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2), meta=np.ndarray></div><input id='attrs-5eb54471-4b22-475a-aa47-fccaf000a4a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5eb54471-4b22-475a-aa47-fccaf000a4a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d191cfdb-a29f-4edc-ad3c-d348a979abfb' class='xr-var-data-in' type='checkbox'><label for='data-d191cfdb-a29f-4edc-ad3c-d348a979abfb' 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'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 16.12 kiB </td>\n",
+ " <td> 16 B </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (1032, 2) </td>\n",
+ " <td> (1, 2) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 1032 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ " </table>\n",
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+ " <td>\n",
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+ "\n",
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+ "</svg>\n",
+ " </td>\n",
+ " </tr>\n",
+ "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8443f37d-9571-4e36-a7bd-6916df55d000' class='xr-section-summary-in' type='checkbox' ><label for='section-8443f37d-9571-4e36-a7bd-6916df55d000' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e03616f-60d4-4bd8-9e89-0e91a2c14a37' class='xr-index-data-in' type='checkbox'/><label for='index-0e03616f-60d4-4bd8-9e89-0e91a2c14a37' 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([-89.28422753251364, -88.35700351866494, -87.42430374606988,\n",
+ " -86.49036676628116, -85.55596048489265, -84.6213271076488,\n",
+ " -83.68656681656385, -82.75172847343066, -81.81683872860319,\n",
+ " -80.8819133467975,\n",
+ " ...\n",
+ " 80.8819133467975, 81.81683872860319, 82.75172847343066,\n",
+ " 83.68656681656385, 84.6213271076488, 85.55596048489265,\n",
+ " 86.49036676628116, 87.42430374606988, 88.35700351866494,\n",
+ " 89.28422753251364],\n",
+ " dtype='float64', name='lat', length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ce805fa-1988-44cd-8c66-ebf15937a3c6' class='xr-index-data-in' type='checkbox'/><label for='index-8ce805fa-1988-44cd-8c66-ebf15937a3c6' 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, 0.9375, 1.875, 2.8125, 3.75, 4.6875, 5.625,\n",
+ " 6.5625, 7.5, 8.4375,\n",
+ " ...\n",
+ " 350.625, 351.5625, 352.5, 353.4375, 354.375, 355.3125, 356.25,\n",
+ " 357.1875, 358.125, 359.0625],\n",
+ " dtype='float64', name='lon', length=384))</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-bc514063-c4b1-47f8-88e8-a99cb54825d9' class='xr-index-data-in' type='checkbox'/><label for='index-bc514063-c4b1-47f8-88e8-a99cb54825d9' 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(['2015-01-16 12:00:00', '2015-02-15 00:00:00',\n",
+ " '2015-03-16 12:00:00', '2015-04-16 00:00:00',\n",
+ " '2015-05-16 12:00:00', '2015-06-16 00:00:00',\n",
+ " '2015-07-16 12:00:00', '2015-08-16 12:00:00',\n",
+ " '2015-09-16 00:00:00', '2015-10-16 12:00:00',\n",
+ " ...\n",
+ " '2100-03-16 12:00:00', '2100-04-16 00:00:00',\n",
+ " '2100-05-16 12:00:00', '2100-06-16 00:00:00',\n",
+ " '2100-07-16 12:00:00', '2100-08-16 12:00:00',\n",
+ " '2100-09-16 00:00:00', '2100-10-16 12:00:00',\n",
+ " '2100-11-16 00:00:00', '2100-12-16 12:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1032, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-521f5d99-3cba-4f95-b4f7-17b823220587' class='xr-section-summary-in' type='checkbox' ><label for='section-521f5d99-3cba-4f95-b4f7-17b823220587' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP AerChemMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>contact :</span></dt><dd>cmip6-mpi-esm@dkrz.de</dd><dt><span>creation_date :</span></dt><dd>2019-07-20T13:41:51Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>gap-filling scenario reaching 7.0 based on SSP3</dd><dt><span>experiment_id :</span></dt><dd>ssp370</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.DKRZ.MPI-ESM1-2-HR.ssp370.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>gn</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Deutsches Klimarechenzentrum, Hamburg 20146, Germany</dd><dt><span>institution_id :</span></dt><dd>DKRZ</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>project_id :</span></dt><dd>CMIP6</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPIâ€M Earth System Model version 1.2 (MPIâ€ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400,\n",
+ "Mueller, W.A. et al. (2018): A highâ€resolution version of the Max Planck Institute Earth System Model MPIâ€ESM1.2â€HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217</dd><dt><span>source :</span></dt><dd>MPI-ESM1.2-HR (2017): \n",
+ "aerosol: none, prescribed MACv2-SP\n",
+ "atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa)\n",
+ "atmosChem: none\n",
+ "land: JSBACH3.20\n",
+ "landIce: none/prescribed\n",
+ "ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m)\n",
+ "ocnBgchem: HAMOCC6\n",
+ "seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)</dd><dt><span>source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc</dd><dt><span>title :</span></dt><dd>MPI-ESM1-2-HR output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by DKRZ is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset> Size: 314MB\n",
+ "Dimensions: (lat: 192, time: 1032, bnds: 2, lon: 384)\n",
+ "Coordinates:\n",
+ " height float64 8B ...\n",
+ " * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
+ " * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
+ " * time (time) datetime64[ns] 8kB 2015-01-16T12:00:00 ... 2100-12-16T1...\n",
+ "Dimensions without coordinates: bnds\n",
+ "Data variables:\n",
+ " lat_bnds (time, lat, bnds) float64 3MB dask.array<chunksize=(60, 192, 2), meta=np.ndarray>\n",
+ " lon_bnds (time, lon, bnds) float64 6MB dask.array<chunksize=(60, 384, 2), meta=np.ndarray>\n",
+ " tas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
+ " time_bnds (time, bnds) datetime64[ns] 17kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+ "Attributes: (12/47)\n",
+ " Conventions: CF-1.7 CMIP-6.2\n",
+ " activity_id: ScenarioMIP AerChemMIP\n",
+ " branch_method: standard\n",
+ " branch_time_in_child: 60265.0\n",
+ " branch_time_in_parent: 60265.0\n",
+ " contact: cmip6-mpi-esm@dkrz.de\n",
+ " ... ...\n",
+ " title: MPI-ESM1-2-HR output prepared for CMIP6\n",
+ " variable_id: tas\n",
+ " variant_label: r1i1p1f1\n",
+ " license: CMIP6 model data produced by DKRZ is licensed und...\n",
+ " cmor_version: 3.4.0\n",
+ " tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds"
+ ]
+ },
{
"cell_type": "markdown",
"id": "18913b42-b75f-41dd-841e-b3a255525591",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
"### Server-side processing: Animated interactive geoplots\n",
"\n",
diff --git a/workshop/4_kerchunk-input-kerchunk-api.ipynb b/workshop/4_kerchunk-input-kerchunk-api.ipynb
index fa7a3a8ed99688624bd81ba0272a7b7f8b41054d..04355c0c1ef799f7a129c5161f1cbe1176d9ae90 100644
--- a/workshop/4_kerchunk-input-kerchunk-api.ipynb
+++ b/workshop/4_kerchunk-input-kerchunk-api.ipynb
@@ -147,7 +147,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 16,
"id": "9a43c4ce-be08-4493-8dd5-a3789f8c0647",
"metadata": {
"tags": []
@@ -160,14 +160,14 @@
"k204210 52510 4121939 0 10:12 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-c6562e74-6d50-4cf3-92d0-27e4f4cae6f8.json\n",
"k204210 56270 4121939 0 10:18 ? 00:00:07 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-e22a5303-3b28-41cb-b092-ed92a8ff6221.json\n",
"k204210 67839 4121939 0 10:42 ? 00:00:13 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-33489dce-534b-42ed-bbe7-6d125e3f6167.json\n",
- "k204210 198403 4121939 3 11:34 ? 00:00:00 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-16ae079b-b9fb-4460-bc7c-808797637e88.json\n",
+ "k204210 198403 4121939 13 11:34 ? 00:00:12 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-16ae079b-b9fb-4460-bc7c-808797637e88.json\n",
"k204210 198463 198403 0 11:35 ? 00:00:00 bash\n",
- "k204210 198487 198463 52 11:35 ? 00:00:05 python xpublish_references.py era5 /work/bm1344/DKRZ/kerchunks_single/testera/E5_sf_an_1D.parquet\n",
- "k204210 199212 198403 11 11:35 pts/2 00:00:00 /bin/bash -c ps -ef | grep k204210\n",
- "k204210 199216 199212 0 11:35 pts/2 00:00:00 ps -ef\n",
- "k204210 199217 199212 0 11:35 pts/2 00:00:00 grep k204210\n",
+ "k204210 198487 198463 9 11:35 ? 00:00:06 python xpublish_references.py era5 /work/bm1344/DKRZ/kerchunks_single/testera/E5_sf_an_1D.parquet\n",
+ "k204210 200138 198403 0 11:36 pts/2 00:00:00 /bin/bash -c ps -ef | grep k204210\n",
+ "k204210 200139 200138 0 11:36 pts/2 00:00:00 ps -ef\n",
+ "k204210 200140 200138 0 11:36 pts/2 00:00:00 grep k204210\n",
"k204210 4121487 4121482 0 08:57 ? 00:00:00 /bin/bash /var/spool/slurmd/job14671095/slurm_script\n",
- "k204210 4121939 4121487 8 08:57 ? 00:13:38 /sw/spack-levante/jupyterhub/jupyterhub/bin/python /sw/spack-levante/jupyterhub/jupyterhub/bin/batchspawner-singleuser jupyterhub-singleuser --SingleUserNotebookApp.default_url=/lab/tree//home/k/k204210 --ServerApp.root_dir=/ --KernelSpecManager.ensure_native_kernel=False --ServerApp.disable_user_config=True --ServerApp.ContentsManager.allow_hidden=True\n",
+ "k204210 4121939 4121487 8 08:57 ? 00:13:45 /sw/spack-levante/jupyterhub/jupyterhub/bin/python /sw/spack-levante/jupyterhub/jupyterhub/bin/batchspawner-singleuser jupyterhub-singleuser --SingleUserNotebookApp.default_url=/lab/tree//home/k/k204210 --ServerApp.root_dir=/ --KernelSpecManager.ensure_native_kernel=False --ServerApp.disable_user_config=True --ServerApp.ContentsManager.allow_hidden=True\n",
"k204210 4124097 4121939 0 08:57 pts/13 00:00:00 /bin/bash -l\n",
"k204210 4124109 4121939 0 08:57 pts/14 00:00:00 /bin/bash -l\n",
"k204210 4124210 4121939 0 08:57 pts/15 00:00:00 /bin/bash -l\n",
@@ -190,14 +190,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "af33c134-f4ba-42f7-9687-7bb9948d5dfe",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
- "!kill 1539362"
+ "!kill 198487"
]
},
{