EOSC_zarr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80a22474-81c0-4c65-a0fb-058c2a36ff4b",
   "metadata": {},
   "source": [
    "DaskGateway test with netcdf file on NFS and zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c99fbd2d-dae3-4d98-b86f-c1101465163e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f64b811-4358-4519-bbc8-7029ccec84b9",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 19, lon: 1343, lat: 1008)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-01-11 ... 2022-07-01\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0\n",
       "Data variables:\n",
       "    crs      |S1 b&#x27;&#x27;\n",
       "    NDVI     (time, lat, lon) uint8 ...\n",
       "Attributes:\n",
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       "    institution:  openEO platform</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-79563261-1bbf-4295-be0b-677b8422e2c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79563261-1bbf-4295-be0b-677b8422e2c5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 19</li><li><span class='xr-has-index'>lon</span>: 1343</li><li><span class='xr-has-index'>lat</span>: 1008</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-765dd79f-847f-422c-b4b0-99f8c93a53d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-765dd79f-847f-422c-b4b0-99f8c93a53d4' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-07-01</div><input id='attrs-6f4e492d-a6b5-4c6a-8a50-7d0f43cc0c4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f4e492d-a6b5-4c6a-8a50-7d0f43cc0c4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-611d9389-639c-43cb-a5c9-e1335b6f396f' class='xr-var-data-in' type='checkbox'><label for='data-611d9389-639c-43cb-a5c9-e1335b6f396f' 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>t</dd><dt><span>long_name :</span></dt><dd>t</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-21T00:00:00.000000000&#x27;, &#x27;2022-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-02-11T00:00:00.000000000&#x27;, &#x27;2022-02-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-03-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-21T00:00:00.000000000&#x27;, &#x27;2022-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-11T00:00:00.000000000&#x27;, &#x27;2022-04-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-01T00:00:00.000000000&#x27;, &#x27;2022-05-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-21T00:00:00.000000000&#x27;, &#x27;2022-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-11T00:00:00.000000000&#x27;, &#x27;2022-06-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.001 9.004 9.007 ... 12.99 13.0</div><input id='attrs-46d41c78-c192-4dbd-b568-8bf35a05ce06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46d41c78-c192-4dbd-b568-8bf35a05ce06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51579bbb-ab15-4a9b-95ba-e6760055a2dc' class='xr-var-data-in' type='checkbox'><label for='data-51579bbb-ab15-4a9b-95ba-e6760055a2dc' 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>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 9.001488,  9.004464,  9.00744 , ..., 12.989583, 12.99256 , 12.995536])</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'>45.0 45.0 44.99 ... 42.01 42.0 42.0</div><input id='attrs-2b355427-f037-465f-b67b-4fc50324abc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b355427-f037-465f-b67b-4fc50324abc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-136caf35-ce90-47c8-b1da-71fe879d57ea' class='xr-var-data-in' type='checkbox'><label for='data-136caf35-ce90-47c8-b1da-71fe879d57ea' 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>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44.998512, 44.995536, 44.99256 , ..., 42.00744 , 42.004464, 42.001488])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-88298471-2ebe-4cd3-a558-f30ab19a814b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88298471-2ebe-4cd3-a558-f30ab19a814b' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea9cc62a-96b1-4522-af02-ef27514dc71a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea9cc62a-96b1-4522-af02-ef27514dc71a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fa1c0fd-e857-42c1-843e-c37a6db50e9b' class='xr-var-data-in' type='checkbox'><label for='data-0fa1c0fd-e857-42c1-843e-c37a6db50e9b' 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>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;, DATUM[&quot;World Geodetic System 1984&quot;, SPHEROID[&quot;WGS 84&quot;, 6378137.0, 298.257223563, AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]], AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]], PRIMEM[&quot;Greenwich&quot;, 0.0, AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]], UNIT[&quot;degree&quot;, 0.017453292519943295], AXIS[&quot;Geodetic longitude&quot;, EAST], AXIS[&quot;Geodetic latitude&quot;, NORTH], AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;, DATUM[&quot;World Geodetic System 1984&quot;, SPHEROID[&quot;WGS 84&quot;, 6378137.0, 298.257223563, AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]], AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]], PRIMEM[&quot;Greenwich&quot;, 0.0, AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]], UNIT[&quot;degree&quot;, 0.017453292519943295], AXIS[&quot;Geodetic longitude&quot;, EAST], AXIS[&quot;Geodetic latitude&quot;, NORTH], AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NDVI</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fb2efff-7e99-4836-a435-d10b5a91da30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fb2efff-7e99-4836-a435-d10b5a91da30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af60694c-b476-4a0d-8c4b-7bd30356057c' class='xr-var-data-in' type='checkbox'><label for='data-af60694c-b476-4a0d-8c4b-7bd30356057c' 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>NDVI</dd><dt><span>units :</span></dt><dd></dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><pre>[25721136 values with dtype=uint8]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-534598fa-57aa-4b43-906c-7039054000c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-534598fa-57aa-4b43-906c-7039054000c5' class='xr-section-summary' >Attributes: <span>(2)</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-1.8</dd><dt><span>institution :</span></dt><dd>openEO platform</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 19, lon: 1343, lat: 1008)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-01-11 ... 2022-07-01\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0\n",
       "Data variables:\n",
       "    crs      |S1 ...\n",
       "    NDVI     (time, lat, lon) uint8 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.8\n",
       "    institution:  openEO platform"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cgls_ds= xr.open_dataset(\n",
    "    '../data/C_GLS_NDVI_20220101_20220701_CENTRALITALY_S3_2.nc').rename(\n",
    "    x='lon', y='lat', t='time')\n",
    "cgls_ds"
   ]
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  {
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   "id": "3d2a6173-b214-4ece-9bda-a18738cef2da",
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       "  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",
       "  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:  (time: 19, lon: 1343, lat: 1008)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-01-11 ... 2022-07-01\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0\n",
       "Data variables:\n",
       "    crs      |S1 b&#x27;&#x27;\n",
       "    NDVI     (time, lat, lon) uint8 dask.array&lt;chunksize=(19, 510, 500), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.8\n",
       "    institution:  openEO platform</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-55d2375d-3ab3-4ca6-8963-47534c95b186' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55d2375d-3ab3-4ca6-8963-47534c95b186' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 19</li><li><span class='xr-has-index'>lon</span>: 1343</li><li><span class='xr-has-index'>lat</span>: 1008</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-80dd79f6-0bb2-4b57-b332-a4c1cbf8aeaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80dd79f6-0bb2-4b57-b332-a4c1cbf8aeaf' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-07-01</div><input id='attrs-6a510306-2845-41ec-b61a-04d1c64da5ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a510306-2845-41ec-b61a-04d1c64da5ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3210cf70-e7ae-4244-a1db-98dc31fe77a0' class='xr-var-data-in' type='checkbox'><label for='data-3210cf70-e7ae-4244-a1db-98dc31fe77a0' 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>t</dd><dt><span>long_name :</span></dt><dd>t</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-21T00:00:00.000000000&#x27;, &#x27;2022-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-02-11T00:00:00.000000000&#x27;, &#x27;2022-02-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-03-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-21T00:00:00.000000000&#x27;, &#x27;2022-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-11T00:00:00.000000000&#x27;, &#x27;2022-04-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-01T00:00:00.000000000&#x27;, &#x27;2022-05-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-21T00:00:00.000000000&#x27;, &#x27;2022-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-11T00:00:00.000000000&#x27;, &#x27;2022-06-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.001 9.004 9.007 ... 12.99 13.0</div><input id='attrs-928c8382-62af-4de4-812e-f0751ea0d3b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-928c8382-62af-4de4-812e-f0751ea0d3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8dd59e4-79d7-4aee-a3d2-8555b4af9917' class='xr-var-data-in' type='checkbox'><label for='data-d8dd59e4-79d7-4aee-a3d2-8555b4af9917' 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>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 9.001488,  9.004464,  9.00744 , ..., 12.989583, 12.99256 , 12.995536])</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'>45.0 45.0 44.99 ... 42.01 42.0 42.0</div><input id='attrs-79d0c74f-824a-47ff-9d85-e606a114ed24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79d0c74f-824a-47ff-9d85-e606a114ed24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7f09932-510e-4b36-b663-ab5990c8725a' class='xr-var-data-in' type='checkbox'><label for='data-f7f09932-510e-4b36-b663-ab5990c8725a' 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>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44.998512, 44.995536, 44.99256 , ..., 42.00744 , 42.004464, 42.001488])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9304b6ce-388a-47e9-9dcc-3759f6b33bd6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9304b6ce-388a-47e9-9dcc-3759f6b33bd6' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>b&#x27;&#x27;</div><input id='attrs-fe2fe867-0e00-4359-85cb-97bc20f75014' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe2fe867-0e00-4359-85cb-97bc20f75014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bbd2fb1-c91f-41bc-a05a-0f4cbc371295' class='xr-var-data-in' type='checkbox'><label for='data-4bbd2fb1-c91f-41bc-a05a-0f4cbc371295' 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>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;, DATUM[&quot;World Geodetic System 1984&quot;, SPHEROID[&quot;WGS 84&quot;, 6378137.0, 298.257223563, AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]], AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]], PRIMEM[&quot;Greenwich&quot;, 0.0, AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]], UNIT[&quot;degree&quot;, 0.017453292519943295], AXIS[&quot;Geodetic longitude&quot;, EAST], AXIS[&quot;Geodetic latitude&quot;, NORTH], AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;, DATUM[&quot;World Geodetic System 1984&quot;, SPHEROID[&quot;WGS 84&quot;, 6378137.0, 298.257223563, AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]], AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]], PRIMEM[&quot;Greenwich&quot;, 0.0, AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]], UNIT[&quot;degree&quot;, 0.017453292519943295], AXIS[&quot;Geodetic longitude&quot;, EAST], AXIS[&quot;Geodetic latitude&quot;, NORTH], AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NDVI</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(19, 510, 500), meta=np.ndarray&gt;</div><input id='attrs-55679618-07d7-4674-91fe-5601c874561f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55679618-07d7-4674-91fe-5601c874561f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72d2a5c5-df3c-48e6-a866-e33301a29a3b' class='xr-var-data-in' type='checkbox'><label for='data-72d2a5c5-df3c-48e6-a866-e33301a29a3b' 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>NDVI</dd><dt><span>units :</span></dt><dd></dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 24.53 MiB </td>\n",
       "                        <td> 4.62 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (19, 1008, 1343) </td>\n",
       "                        <td> (19, 510, 500) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 209 Tasks </td>\n",
       "                        <td> 6 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> uint8 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-25c09ea5-8843-42eb-8364-3f5b8aa6c596' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25c09ea5-8843-42eb-8364-3f5b8aa6c596' class='xr-section-summary' >Attributes: <span>(2)</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-1.8</dd><dt><span>institution :</span></dt><dd>openEO platform</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 19, lon: 1343, lat: 1008)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-01-11 ... 2022-07-01\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0\n",
       "Data variables:\n",
       "    crs      |S1 b''\n",
       "    NDVI     (time, lat, lon) uint8 dask.array<chunksize=(19, 510, 500), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.8\n",
       "    institution:  openEO platform"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunk={'lon':500,'lat':510}\n",
    "cgls_ds=cgls_ds.chunk(chunk)\n",
    "cgls_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6680b0a9-0217-41a5-b70a-73cd51e6d405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-12558676-0e01-11ed-946f-fefa14bb1166</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Direct</td>\n",
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       "\n",
       "        \n",
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       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42847/status\" target=\"_blank\">http://127.0.0.1:42847/status</a>\n",
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       "                <td style=\"text-align: left;\"></td>\n",
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       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
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       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-62bf053a-323a-42e3-8668-7fb3a8d3d468</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:32965\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
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       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:42847/status\" target=\"_blank\">http://127.0.0.1:42847/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 8\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 31.36 GiB\n",
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       "    </div>\n",
       "\n",
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       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:42263\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:33375/status\" target=\"_blank\">http://127.0.0.1:33375/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.84 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39295\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/jovyan/dask-worker-space/worker-epo3i5mt\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks executing: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in memory: </strong> 0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks ready: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in flight: </strong>0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>CPU usage:</strong> 4.0%\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Last seen: </strong> Just now\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory usage: </strong> 153.38 MiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Spilled bytes: </strong> 0 B\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Read bytes: </strong> 41.99 kiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Write bytes: </strong> 41.99 kiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:38139\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:33189/status\" target=\"_blank\">http://127.0.0.1:33189/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.84 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43659\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/jovyan/dask-worker-space/worker-sau300nl\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks executing: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in memory: </strong> 0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks ready: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in flight: </strong>0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>CPU usage:</strong> 4.0%\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Last seen: </strong> Just now\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory usage: </strong> 153.77 MiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Spilled bytes: </strong> 0 B\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Read bytes: </strong> 42.08 kiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Write bytes: </strong> 42.08 kiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:41365\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44021/status\" target=\"_blank\">http://127.0.0.1:44021/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.84 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44025\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/jovyan/dask-worker-space/worker-tbfl2d9a\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks executing: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in memory: </strong> 0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks ready: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in flight: </strong>0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>CPU usage:</strong> 4.0%\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Last seen: </strong> Just now\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory usage: </strong> 153.62 MiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Spilled bytes: </strong> 0 B\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Read bytes: </strong> 25.87 kiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Write bytes: </strong> 25.87 kiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37451\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34033/status\" target=\"_blank\">http://127.0.0.1:34033/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.84 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42913\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/jovyan/dask-worker-space/worker-o04z2lxe\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks executing: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in memory: </strong> 0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks ready: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in flight: </strong>0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>CPU usage:</strong> 4.0%\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Last seen: </strong> Just now\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory usage: </strong> 153.41 MiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Spilled bytes: </strong> 0 B\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Read bytes: </strong> 42.03 kiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Write bytes: </strong> 42.03 kiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:32965' processes=4 threads=8, memory=31.36 GiB>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client(\"tcp://127.0.0.1:32965\")\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02f8ddf2-7c64-479e-a791-10e3d649b837",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc7b5ef4-876c-4830-ae84-2b6537800f98",
   "metadata": {},
   "outputs": [
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       "  content: '►';\n",
       "  font-size: 11px;\n",
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       "  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",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "  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",
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\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",
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       "\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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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;NDVI&#x27; (lat: 1008, lon: 1343)&gt;\n",
       "array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0</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'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1008</li><li><span class='xr-has-index'>lon</span>: 1343</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e5b47c5c-a6a2-49a6-b5d6-cc454e4b8a38' class='xr-array-in' type='checkbox' checked><label for='section-e5b47c5c-a6a2-49a6-b5d6-cc454e4b8a38' 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>117.4 106.7 96.79 107.2 108.3 108.4 ... 161.0 163.0 169.5 174.3 175.8</span></div><div class='xr-array-data'><pre>array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])</pre></div></div></li><li class='xr-section-item'><input id='section-748a2499-e240-4373-9209-16c5397bee73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-748a2499-e240-4373-9209-16c5397bee73' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.001 9.004 9.007 ... 12.99 13.0</div><input id='attrs-dee23a01-1081-4ebf-bb84-b28f29dcfd31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dee23a01-1081-4ebf-bb84-b28f29dcfd31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04daa5c4-1cee-4102-86f4-a5995a0fb30a' class='xr-var-data-in' type='checkbox'><label for='data-04daa5c4-1cee-4102-86f4-a5995a0fb30a' 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>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 9.001488,  9.004464,  9.00744 , ..., 12.989583, 12.99256 , 12.995536])</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'>45.0 45.0 44.99 ... 42.01 42.0 42.0</div><input id='attrs-b43fad78-6563-4ad7-a5b5-1145606742bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b43fad78-6563-4ad7-a5b5-1145606742bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4ca00d7-28b7-4c0d-9fef-d90666465e60' class='xr-var-data-in' type='checkbox'><label for='data-a4ca00d7-28b7-4c0d-9fef-d90666465e60' 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>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44.998512, 44.995536, 44.99256 , ..., 42.00744 , 42.004464, 42.001488])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0207b59-44ac-4554-b369-31e4574f5d11' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c0207b59-44ac-4554-b369-31e4574f5d11' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'NDVI' (lat: 1008, lon: 1343)>\n",
       "array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cgls_ds.NDVI.mean(dim='time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff7883ab-f823-4610-92cc-86400975693a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask_gateway import Gateway\n",
    "from dask_gateway.auth import BasicAuth\n",
    "auth = BasicAuth(password=\"pangeo_dask\")\n",
    "#auth=\"jupyterhub\"\n",
    "gateway= Gateway(\n",
    "    \"http://api-daskhub-dask-gateway.daskhub:8000/\",\n",
    "    auth=auth,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2942a56d-d555-46a0-a673-8e48b4432ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/dask_gateway/client.py:1014: RuntimeWarning: coroutine 'rpc.close_rpc' was never awaited\n",
      "  self.scheduler_comm.close_rpc()\n",
      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    }
   ],
   "source": [
    "clusters=gateway.list_clusters()\n",
    "for cluster in clusters:\n",
    "    cluster = gateway.connect(cluster.name)\n",
    "    cluster.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d4c8a9b-c3b3-47ec-964c-d238622242fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d510fddb26cb4dbc8c8dc45a14513953",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h2>GatewayCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n<style scoped>\\n    …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster = gateway.new_cluster()\n",
    "cluster.scale(4)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2638deca-93ff-44d4-976f-9ecd04c2fdfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/client.py:1274: VersionMismatchWarning: Mismatched versions found\n",
      "\n",
      "+---------+--------+-----------+---------+\n",
      "| Package | client | scheduler | workers |\n",
      "+---------+--------+-----------+---------+\n",
      "| lz4     | 4.0.0  | None      | None    |\n",
      "+---------+--------+-----------+---------+\n",
      "  warnings.warn(version_module.VersionMismatchWarning(msg[0][\"warning\"]))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-aa829fea-0e03-11ed-961e-fefa14bb1166</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/services/dask-gateway/clusters/daskhub.d0c798962f604531bc9622ae7b930983/status\" target=\"_blank\">/services/dask-gateway/clusters/daskhub.d0c798962f604531bc9622ae7b930983/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
       "  <h3>GatewayCluster</h3>\n",
       "  <ul>\n",
       "    <li><b>Name: </b>daskhub.d0c798962f604531bc9622ae7b930983\n",
       "    <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/daskhub.d0c798962f604531bc9622ae7b930983/status' target='_blank'>/services/dask-gateway/clusters/daskhub.d0c798962f604531bc9622ae7b930983/status</a>\n",
       "  </ul>\n",
       "</div>\n",
       "\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tls://10.244.5.9:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from distributed import Client\n",
    "client=Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "797b97c9-0197-4ae2-b557-e75ed09bb091",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk={'lon':500,'lat':510}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "431fcc78-48c8-481f-be61-9f34cec9e3f1",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KilledWorker",
     "evalue": "('original-xarray-NDVI-24ed78e0e1a465f9a2bf3a1867bcea8c', <WorkerState 'tls://10.244.8.9:32975', name: dask-worker-d0c798962f604531bc9622ae7b930983-8znv8, status: closed, memory: 0, processing: 1>)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKilledWorker\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcgls_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNDVI\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/dataarray.py:947\u001b[0m, in \u001b[0;36mDataArray.compute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    928\u001b[0m \u001b[38;5;124;03m\"\"\"Manually trigger loading of this array's data from disk or a\u001b[39;00m\n\u001b[1;32m    929\u001b[0m \u001b[38;5;124;03mremote source into memory and return a new array. The original is\u001b[39;00m\n\u001b[1;32m    930\u001b[0m \u001b[38;5;124;03mleft unaltered.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    944\u001b[0m \u001b[38;5;124;03mdask.compute\u001b[39;00m\n\u001b[1;32m    945\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    946\u001b[0m new \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnew\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/dataarray.py:921\u001b[0m, in \u001b[0;36mDataArray.load\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    903\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataArray:\n\u001b[1;32m    904\u001b[0m     \u001b[38;5;124;03m\"\"\"Manually trigger loading of this array's data from disk or a\u001b[39;00m\n\u001b[1;32m    905\u001b[0m \u001b[38;5;124;03m    remote source into memory and return this array.\u001b[39;00m\n\u001b[1;32m    906\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[38;5;124;03m    dask.compute\u001b[39;00m\n\u001b[1;32m    920\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 921\u001b[0m     ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    922\u001b[0m     new \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n\u001b[1;32m    923\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable \u001b[38;5;241m=\u001b[39m new\u001b[38;5;241m.\u001b[39m_variable\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/dataset.py:861\u001b[0m, in \u001b[0;36mDataset.load\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mda\u001b[39;00m\n\u001b[1;32m    860\u001b[0m \u001b[38;5;66;03m# evaluate all the dask arrays simultaneously\u001b[39;00m\n\u001b[0;32m--> 861\u001b[0m evaluated_data \u001b[38;5;241m=\u001b[39m \u001b[43mda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mlazy_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    863\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, data \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(lazy_data, evaluated_data):\n\u001b[1;32m    864\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables[k]\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m data\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/dask/base.py:600\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m    597\u001b[0m     keys\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_keys__())\n\u001b[1;32m    598\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m--> 600\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    601\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/client.py:3000\u001b[0m, in \u001b[0;36mClient.get\u001b[0;34m(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m   2998\u001b[0m         should_rejoin \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   2999\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3000\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgather\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpacked\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3001\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m   3002\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m futures\u001b[38;5;241m.\u001b[39mvalues():\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/client.py:2174\u001b[0m, in \u001b[0;36mClient.gather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m   2172\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2173\u001b[0m     local_worker \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2175\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gather\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2176\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2177\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2178\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2179\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocal_worker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_worker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2180\u001b[0m \u001b[43m    \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2181\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/utils.py:320\u001b[0m, in \u001b[0;36mSyncMethodMixin.sync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m future\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback_timeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/utils.py:387\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error:\n\u001b[1;32m    386\u001b[0m     typ, exc, tb \u001b[38;5;241m=\u001b[39m error\n\u001b[0;32m--> 387\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/utils.py:360\u001b[0m, in \u001b[0;36msync.<locals>.f\u001b[0;34m()\u001b[0m\n\u001b[1;32m    358\u001b[0m         future \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mwait_for(future, callback_timeout)\n\u001b[1;32m    359\u001b[0m     future \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mensure_future(future)\n\u001b[0;32m--> 360\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m future\n\u001b[1;32m    361\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    362\u001b[0m     error \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/tornado/gen.py:762\u001b[0m, in \u001b[0;36mRunner.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    759\u001b[0m exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    761\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 762\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    763\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    764\u001b[0m     exc_info \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/client.py:2037\u001b[0m, in \u001b[0;36mClient._gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m   2035\u001b[0m         exc \u001b[38;5;241m=\u001b[39m CancelledError(key)\n\u001b[1;32m   2036\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2037\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exception\u001b[38;5;241m.\u001b[39mwith_traceback(traceback)\n\u001b[1;32m   2038\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m   2039\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[0;31mKilledWorker\u001b[0m: ('original-xarray-NDVI-24ed78e0e1a465f9a2bf3a1867bcea8c', <WorkerState 'tls://10.244.8.9:32975', name: dask-worker-d0c798962f604531bc9622ae7b930983-8znv8, status: closed, memory: 0, processing: 1>)"
     ]
    }
   ],
   "source": [
    "cgls_ds.chunk(chunk).NDVI.mean(dim='time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1f549e47-6078-4899-933c-5e1278032a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n8LtE7ZnVLwO7f/9+jRo1Sps3b9azzz6rMWPGmB4JqJMnn3xSd911l0aOHKkXX3xRjRo1Mj0S4oSewTb0LHHRM9iGniUuegbb0LPERc9gG3qWuOgZbJNAPdtr7Wbljh07dMUVV+jkyZNatGiRunTpYnokICZWr16tUaNGqV27dnrttdfUsmVL0yPBZfQMtqJniYeewVb0LPHQM9iKniUeegZb0bPEQ89gqwTpmZ3vWbl+/Xr1799fjRo10urVqwkTrDJgwAC9++67Ki4uVp8+fbR582bTI8FF9Aw2o2eJhZ7BZvQssdAz2IyeJRZ6BpvRs8RCz2CzROmZdZuVCxYs0JAhQ9S/f3+tXbtWrVu3Nj0SEHPt2rXT2rVr1b59ew0cOFD5+fmmR4IL6BkSAT1LDPQMiYCeJQZ6hkRAzxIDPUMioGeJgZ4hESRCz6zarHz00Uc1duxY3XLLLcrLy7P59XsBtWjRQkuXLtXll1+ua665Rs8//7zpkRBD9AyJhJ7ZjZ4hkdAzu9EzJBJ6Zjd6hkRCz+xGz5BIbO9Z0rRp06aZHiIW7rvvPj344IOaPn26fvWrX6lePav2YYGQkpOTNXr0aB09elQ/+9nPlJGRoQsuuMD0WKgjeoZERM/sRM+QiOiZnegZEhE9sxM9QyKiZ3aiZ0hEFvesxPeblY7j6O6779Zjjz2mmTNn6rbbbjM9EhBXgUBAw4YNU8OGDXX33XcrEAho8ODBpsdCFOgZEh09swc9Q6KjZ/agZ0h09Mwe9AyJjp7Zg54h0VnaM39vVpaXl+uWW27RM888o3nz5mnChAmmRwKM6d+/v1q2bKl77rlHx48f17Bhw0yPhAjQM+Bb9Mzf6BnwLXrmb/QM+BY98zd6BnyLnvkbPQO+ZVnPSpJNTxCt8vJy3XzzzZo/f77mz5+vUaNGmR4JMO62225Tenq6brrpJh05ckQzZszgJRB8gJ4BweiZP9EzIBg98yd6BgSjZ/5Ez4Bg9Myf6JF9dcAAACAASURBVBkQzKae+XKz8uTJkxo/frzeeOMNLV68WEOHDjU9EuAZEyZMUHJysm688UZVVFToySefVCAQMD0WqkHPgOrRM3+hZ0D16Jm/0DOgevTMX+gZUD165i/0DKieLT3z3WZleXm5Jk2apDfeeEP5+fkaMGCA6ZEAzxk/frxSU1M1btw4NWzYUH/84x9Nj4QQ6BlQO3rmD/QMqB098wd6BtSOnvkDPQNqR8/8gZ4BtbOhZ756z0rHcfTjH/9Y8+bN0yuvvKIhQ4aYHgnwrE6dOqlz5876+c9/rvLycl1yySWmR8J30DMgfPTM2+gZED565m30DAgfPfM2egaEj555Gz0DwufznpX4arPyZz/7mZ566iktWLBAV1xxhelxAM/r3Lmz2rRpo6lTpyo1NZVnHnkIPQMiQ8+8i54BkaFn3kXPgMjQM++iZ0Bk6Jl30TMgMj7umX82K++77z794Q9/0AsvvKDRo0ebHgfwjZ49eyojI0NTp05VkyZNdNFFF5keKeHRMyA69Mx76BkQHXrmPfQMiA498x56BkSHnnkPPQOi49Oe+WOz8tFHH9WDDz6oWbNmacKECabHAXznggsuUGpqqn7xi1+oQ4cO6t69u+mREhY9A+qGnnkHPQPqhp55Bz0D6oaeeQc9A+qGnnkHPQPqxoc98/5m5fz58/XjH/9Y06dP1y233GJ6HMC3Bg4cqJKSEt1///0aOHCgsrOzTY+UcOgZEBv0zDx6BsQGPTOPngGxQc/Mo2dAbNAz8+gZEBs+61lJwHEcx/QU1Xn33Xd1ySWX6JZbbtFjjz1mehzA9xzH0Q033KDFixdr9erV6tq1q+mREgY9A2KLnplDz4DYomfm0DMgtuiZOfQMiC16Zg49A2LLRz3b69nNyp07d+qiiy5S37599be//U1JSUmmRwKs8M0332jo0KHas2eP1q1bpzPOOMP0SNajZ4A76Fn80TPAHfQs/ugZ4A56Fn/0DHAHPYs/ega4wyc98+Zm5f79+9WvXz+lp6dr5cqVSktLMz0SYJX9+/froosuUtOmTbVixQr+jbmIngHuomfxQ88Ad9Gz+KFngLvoWfzQM8Bd9Cx+6BngLh/0bG890xOcrrS0VKNGjVJpaakWLVrkxW8a4HsZGRlauHChdu7cqUmTJsmDz1mwAj0D3EfP4oOeAe6jZ/FBzwD30bP4oGeA++hZfNAzwH1+6FnStGnTppke4rvuuOMOLVu2TG+99ZbatWtnehzAWpmZmcrJydEvf/lLJScna+DAgaZHsg49A+KDnrmPngHxQc/cR8+A+KBn7qNnQHzQM/fRMyA+PN6zEk9tVs6ZM0f333+/XnjhBQ0ePNj0OID1srOz1aRJE91777264IILdM4555geyRr0DIgveuYeegbEFz1zDz0D4oueuYeeAfFFz9xDz4D48nDPSjzznpWbN29Wv379dMcdd+iRRx4xPQ6QUG666SYtXLhQ7733Hs9gigF6BphDz2KLngHm0LPYomeAOfQstugZYA49iy16BpjjwZ7t9cRm5YEDB9S3b19lZ2dr6dKlSk5ONj0SkFCOHz+uAQMGqLy8XGvXrlWjRo1Mj+Rb9Awwi57FDj0DzKJnsUPPALPoWezQM8AsehY79Awwy4M921vP9ATl5eX693//d5WVlWn+/PmECTCgYcOGmj9/vnbv3q2f/OQnpsfxLXoGmEfPYoOeAebRs9igZ4B59Cw26BlgHj2LDXoGmOfFnhnfrPzd736nlStXasGCBcrMzDQ9DpCwOnTooBdeeEHPPfecXnjhBdPj+BI9A7yBntUdPQO8gZ7VHT0DvIGe1R09A7yBntUdPQO8wWs9M/oysH//+9/Vr18//fa3v9Xdd99tagwA33HXXXdp5syZev/99730BrueR88A76Fn0aFngPfQs+jQM8B76Fl06BngPfQsOvQM8B6P9Mzce1YePXpUvXv31tlnn638/HzVq2f8Ik8Akk6cOKELL7xQKSkpWrNmjerXr296JM+jZ4A30bPI0TPAm+hZ5OgZ4E30LHL0DPAmehY5egZ4k0d6Zu49K++880599dVXeuaZZwgT4CGpqal68cUXtXXrVv3mN78xPY4v0DPAm+hZ5OgZ4E30LHL0DPAmehY5egZ4Ez2LHD0DvMkrPUuaNm3atHgv+uqrr+qee+7R7Nmz1a9fv3gvD6AWWVlZatKkie6//34NHz5cZ511lumRPIueAd5Gz8JHzwBvo2fho2eAt9Gz8NEzwNvoWfjoGeBtHuhZSdxfBnbPnj3q1q2brr32Wj399NPxXBpABBzH0ZVXXqnt27dr8+bNaty4semRPIeeAf5Az2pHzwB/oGe1o2eAP9Cz2tEzwB/oWe3oGeAPhnsW//esHDNmjN5//31t2bKFeAMet2/fPnXu3Fk33nijpk+fbnocz6FngH/Qs5rRM8A/6FnN6BngH/SsZvQM8A96VjN6BviHwZ7F9z0rFy1apAULFujPf/4zYQJ84IwzztDvf/97zZgxQ2vXrjU9jqfQM8Bf6Fn16BngL/SsevQM8Bd6Vj16BvgLPasePQP8xWTP4nZlZXFxsTp37qzhw4dr1qxZ8VgSQIxceuml2rt3rzZu3KiUlBTT4xhHzwD/omdV0TPAv+hZVfQM8C96VhU9A/yLnlVFzwD/MtCz+F1Z+dOf/lQnTpzQo48+Gq8lAcTIX/7yF+3cuZN/v/+PngH+Rc+qomeAf9GzqugZ4F/0rCp6BvgXPauKngH+ZaJnSdOmTZvm9iIrV67UnXfeqWeffVYXXHCB28sBiLFmzZqpfv36evDBB3XdddcpKyvL9EjG0DPA3+jZt+gZ4G/07Fv0DPA3evYtegb4Gz37Fj0D/M1Az0pcfxnY0tJSde/eXeecc44WLlzo5lIAXFRWVqYLL7xQ6enpWrFihelxjKBngB3oGT0DbEHP6BlgC3pGzwBb0DN6Btgizj1z/2Vg/+d//ke7d+/WjBkz3F4KgIuSk5P1l7/8RatWrdJLL71kehwj6BlgB3pGzwBb0DN6BtiCntEzwBb0jJ4Btoh3z1y9svLrr79Wx44ddccdd+jXv/61W8sAiKNJkybp7bff1vbt29WoUSPT48QNPQPsQ8/oGWALekbPAFvQM3oG2IKe0TPAFnHqmbtXVv7yl79UWlqa7rnnHjeXARBHjz76qA4fPqzHHnvM9ChxRc8A+9AzegbYgp7RM8AW9IyeAbagZ/QMsEW8eubaZuW2bdv07LPP6pFHHlFaWppbywCIszPOOEM///nP9Zvf/EafffaZ6XHigp4BdqJn9AywBT2jZ4At6Bk9A2xBz+gZYIt49cy1l4G99NJLdeDAAb377ruqV8/1t8YEEEfffPONOnfurIEDB2r27Nmmx3EdPQPsRc8A2IKeAbAFPQNgC3oGwBZx6Jk7LwO7cOFCLV++XDNmzCBMgIUaNGig3/3ud5ozZ47ef/990+O4ip4BdqNnAGxBzwDYgp4BsAU9A2CLePQs5ldWOo6jXr16qX379srLy4vlqQF4TE5OjjIzM7Vo0SLTo7iCngGJg54BsAU9A2ALegbAFvQMgC1c7Fnsr6zMy8vTli1b9J//+Z+xPjUAj3nwwQe1ePFirV+/3vQorqBnQOKgZwBsQc8A2IKeAbAFPQNgCzd7FtMrKysqKtSzZ0917dpVL774YqxOC8DDBg0apNTUVL3xxhumR4kpegYkHnoGwBb0DIAt6BkAW9AzALZwqWexvbJy3rx5+uCDD3gWBZBAHn74YS1btkwrV640PUpM0TMg8dAzALagZwBsQc8A2IKeAbCFWz2L2ZWV5eXl6tKli3JycvTcc8/F4pQAfGLIkCE6efKkVq1aZXqUmKBnQOKiZwBsQc8A2IKeAbAFPQNgCxd6FrsrK+fMmaOdO3fqgQceiNUpAfjEQw89pNWrV+utt94yPUpM0DMgcdEzALagZwBsQc8A2IKeAbCFGz2LyZWVjuOoe/fu6tWrl2bPnh2LuQD4zJAhQ5SamqolS5aYHqVO6BkAegbAFvQMgC3oGQBb0DMAtohxz2JzZWV+fr62bt2qu+66KxanA+BDU6dOVX5+vjZv3mx6lDqhZwDoGQBb0DMAtqBnAGxBzwDYItY9i8mVlcOGDVNSUpKWLl0ai5kA+JDjOOrWrZv69u2rWbNmmR4navQMAD0DYAt6BsAW9AyALegZAFvEuGd1v7Jyy5YteuuttzR16tS6ngqAjwUCAd11112aO3euPv/8c9PjRIWeAZDoGQB70DMAtqBnAGxBzwDYItY9q/OVlTfeeKM2bdqkLVu2KBAI1HkgAP514sQJtW3bVpMmTdJvf/tb0+NEjJ4BOIWeAbAFPQNgC3oGwBb0DIAtYtizul1Z+cUXXyg3N1dTp04lTACUmpqqn/zkJ3rqqadUUlJiepyI0DMA30XPANiCngGwBT0DYAt6BsAWsexZnTYrZ86cqebNm+v666+v0xAA7HHbbbfp+PHjmjdvnulRIkLPAJyOngGwBT0DYAt6BsAW9AyALWLVs6g3KysqKjRr1ixNmjRJKSkpdRoCgD1atGih0aNHa+bMmaZHCRs9AxAKPQNgC3oGwBb0DIAt6BkAW8SqZ1FvVubn5+vTTz/VD3/4wzoNAMA+kydP1rvvvqtNmzaZHiUs9AxAdegZAFvQMwC2oGcAbEHPANgiFj0LOI7jRPOJ1157rQ4fPqy33nor6sUB2Ou8887TsGHDNGPGDNOj1IqeAagJPQNgC3oGwBb0DIAt6BkAW9SxZ3ujurJy7969Wrx4sSZPnhzNpwNIAD/4wQ/0wgsv6NixY6ZHqRE9A1AbegbAFvQMgC3oGQBb0DMAtqhrz6LarJw1a5aaNGmia6+9NqpFAdhv0qRJOnbsmBYsWGB6lBrRMwC1oWcAbEHPANiCngGwBT0DYIu69iyql4Ht2LGjLr/8ck2fPj2qRQEkhjFjxujAgQOefnkIegYgHPQMgC3oGQBb0DMAtqBnAGxRh55F/jKwf//737Vjxw5NmDAh0k8FkGCuv/56rVy5Ul9++aXpUUKiZwDCRc8A2IKeAbAFPQNgC3oGwBZ16VnEm5W5ublq27at+vTpE/FiABLL5ZdfrsaNG+vll182PUpI9AxAuOgZAFvQMwC2oGcAbEHPANiiLj2LeLNywYIFGj9+vAKBQMSLAUgsDRo00FVXXaX58+ebHiUkegYgXPQMgC3oGQBb0DMAtqBnAGxRl55FtFm5YcMGffzxxxo3blzECwFITOPGjdOaNWv06aefmh6lCnoGIFL0DIAt6BkAW9AzALagZwBsEW3PItqszM3NVfv27XX++edHtAiAxDVixAg1a9bMcy9lQc8ARIqeAbAFPQNgC3oGwBb0DIAtou1Z2JuVjuMoLy9P48ePj3g4AIkrJSVFV111lXJzc02PUomeAYgGPQNgC3oGwBb0DIAt6BkAW0Tbs7A3KwsKCvTpp59q9OjREQ8HILGNGTNG69ev1759+0yPIomeAYgePQNgC3oGwBb0DIAt6BkAW0TTs7A3K5csWaKWLVtyyTeAiA0ZMkQpKSlaunSp6VEk0TMA0aNnAGxBzwDYgp4BsAU9A2CLaHoW9mZlfn6+Ro4cqUAgENVwABJXWlqaBg4cqPz8fNOjSKJnAKJHzwDYgp4BsAU9A2ALegbAFtH0LKzNykOHDmn9+vUaMWJE1MMBSGwjRozQ0qVLVV5ebnQOegagrugZAFvQMwC2oGcAbEHPANgi0p6FtVm5bNkyOY6jYcOG1Wk4AIlr5MiROnDggN577z2jc9AzAHVFzwDYgp4BsAU9A2ALegbAFpH2LKzNyiVLlignJ0ctWrSo03AAElfnzp3Vpk0b4y9lQc8A1BU9A2ALegbAFvQMgC3oGQBbRNqzsDYrly5dyiXfAOpsxIgRxv/YomcAYoGeAbAFPQNgC3oGwBb0DIAtIulZrZuVH330kb788ksNHTq0zoMBSGxDhgzR3//+d5WUlBhZn54BiBV6BsAW9AyALegZAFvQMwC2iKRntW5Wrl69Wg0aNFDv3r1jMhyAxDVgwACVlZVpw4YNRtanZwBihZ4BsAU9A2ALegbAFvQMgC0i6Vmtm5Vr165V3759lZKSEpPhACSuM888U9nZ2VqzZo2R9ekZgFihZwBsQc8A2IKeAbAFPQNgi0h6Vutm5Zo1a9S/f/+YDAYA/fv3N/bHFj0DEEv0DIAt6BkAW9AzALagZwBsEW7PatysPHjwoLZv365+/frFbDAAia1///5av369ysvL47ouPQMQa/QMgC3oGQBb0DMAtqBnAGwRbs9q3Kxcu3atHMdRTk5OTIcDkLgGDBig4uJiFRYWxnVdegYg1ugZAFvQMwC2oGcAbEHPANgi3J7VuFm5bt06dezYUVlZWTEdDkDi6tKli5o2baq1a9fGdV16BiDW6BkAW9AzALagZwBsQc8A2CLcntW4WVlQUKC+ffvGdDAAia1evXrq1auXNm3aFNd16RmAWKNnAGxBzwDYgp4BsAU9A2CLcHtW42blli1b1K1bt5gOBgDdunXTli1b4romPQPgBnoGwBb0DIAt6BkAW9AzALYIp2fVblYeOHBAn3/+OXECEHPdunXT1q1bVVFREZf16BkAt9AzALagZwBsQc8A2IKeAbBFOD2rdrPy1C5n9+7dYz8ZgITWvXt3HT16VDt37ozLevQMgFvoGQBb0DMAtqBnAGxBzwDYIpye1bhZ2aJFC7Vu3dqV4QAkri5duqhevXpxeykLegbALfQMgC3oGQBb0DMAtqBnAGwRTs+q3awsLCxUjx49XBkMQGJLS0tT+/bt4/bHFj0D4BZ6BsAW9AyALegZAFvQMwC2CKdn1W5Wbt26VV27dnVlMADo1q2bCgsL47IWPQPgJnoGwBb0DIAt6BkAW9AzALaorWfVblZ+9NFH6tixoytDAcA555wTt9fcp2cA3ETPANiCngGwBT0DYAt6BsAWtfUs5GblkSNHVFRUpOzsbLfmApDg2rZtq08++cT1degZALfRMwC2oGcAbEHPANiCngGwRW09C7lZuWvXrspPBgA3ZGdn6/Dhwzp48KCr69AzAG6jZwBsQc8A2IKeAbAFPQNgi9p6VuNmZZs2bVwbDEBiO/XHj9vPDqNnANxGzwDYgp4BsAU9A2ALegbAFrX1LORm5SeffKKsrCw1btzYvckAJLQ2bdqoXr16lX8MuYWeAXAbPQNgC3oGwBb0DIAt6BkAW9TWs5Cblbt37+aSbwCuSk1NVcuWLV1/Zhg9A+A2egbAFvQMgC3oGQBb0DMAtqitZ9W+DCyXfANwW7t27Vx/Zhg9AxAP9AyALegZAFvQMwC2oGcAbFFTz0JuVu7Zs0etW7d2cyYAUKtWrbR3715X16BnAOKBngGwBT0DYAt6BsAW9AyALWrqWcjNyqKiImVkZLg6FABkZmaqqKjI1TXoGYB4oGcAbEHPANiCngGwBT0DYIuaehZys3L//v3KzMx0dSgAyMjI0P79+11dg54BiAd6BsAW9AyALegZAFvQMwC2qKlnQZuV5eXlOnToEM+kAOA6t//YomcA4oWeAbAFPQNgC3oGwBb0DIAtItqsPHjwoCoqKngmBQDXuf0yFvQMQLzQMwC2oGcAbEHPANiCngGwRUQvA3vqQOIEwG2ZmZkqLS3VkSNHXDk/PQMQL/QMgC3oGQBb0DMAtqBnAGxRU8+CNitPXYLJZd+AGatXr1YgENDDDz9sehTXneqMWy9lQc8As+hZ7NAzwCx6Fjv0DDCLnsUOPQPMomexQ88As+jZvwRtVp7a0WzSpInLYwFIdE2bNpUkFRcXu3J+egYgXugZAFvQMwC2oGcAbEHPANiipp4FbVaeOHFCkpSamuryWAASXUpKiqRvuxNr9AxAvNAzALagZwBsQc8A2IKeAbBFTT0LuVkZCARUv3599yeLofz8fAUCAU2fPl1vv/22+vXrp7S0NJ111ll65JFHKo+bMWOGzj33XDVo0ECdOnXSSy+9FHQux3H07LPPql+/fkpPT1fDhg3Vo0cP/elPf5LjOFWOfeeddzRhwgR16NBBqampysrK0lVXXaU1a9ZUO9/69es1ePBgpaWlKSMjQ5MmTdKBAwei+rrDXV/615slT5kyRS1btlTDhg3Vp08fLVq0SM8995wCgYDy8vLqdLwf5vzuz2HlypUaNGiQ0tPT1adPH0mR/ewjnTOc78HDDz+sgQMHSpIeeOABBQKByo9TIpnR6079EVRaWurK+ekZPYv2eD/MSc+8hZ6FRs/oGT2jZ6ejZ/Qs2uP9MCc98xZ6Fho9o2f0jJ6djp7Rs2iP98Oc9MxbauyZc5oXXnjBSU1NPf1mz1uyZIkjyRk/fryTnJzsSKry8cc//tH5+c9/HnR7vXr1nIKCgsrzVFRUOBMmTAg67tTH5MmTK4/ds2dPtcclJyc7K1euDJrv+uuvd1JTU4OOv/jiiyP+miNZ//jx407Pnj2DjgsEAs748eMdSc5LL70U9fF+mfPUz2Hs2LFVfk/OP//8iH72ka4b7vfgoYceqvY4x4ns99MPioqKHEnOm2++6cr56Rk9i+Z4v8xJz7yFnoVGz+gZPaNnp6Nn9Cya4/0yJz3zFnoWGj2jZ/SMnp2OntGzaI73y5z0zFtq6NmeoM3KZ555xklPT4/PZDF06pdOkvMf//Efzq5du5ySkhInLy/PqV+/vtO0aVMnPT3dmTlzpvPVV185+/fvd6ZOnepIciZOnFh5nueff96R5HTr1s15/fXXnf379zslJSXOypUrnR49ejiSnLVr1zqO4zh79+51hg8f7ixcuND57LPPnNLSUmffvn1Obm6uk5aW5owcOTLkfLfddpuzY8cO59ixY87q1auds88+25HkbNq0KaKvOZL1H330UUeSc+655zpvvvmmc+TIEeeTTz5x7rjjjsq5vvuPKdLj/TLnd38ON998s/OPf/zDKSsri/hnH+m6kXwPVq1a5UhyHnrooaDvZSQz+sGRI0ccSc7rr7/uyvnpGT2L5ni/zEnPvIWehUbP6Bk9o2eno2f0LJrj/TInPfMWehYaPaNn9IyenY6e0bNojvfLnPTMW2roWfBm5ZNPPulkZGTEZ7IYOvVLN2LEiKD7Ro8e7Uhy/vCHP1S5vayszGnatKnTp0+fytsuueQSJykpyfnyyy+DzrNt2zZHknPPPfdU3rZx40Zn7Nixzplnnhn0DI7vfe97QfNdeumlQed94oknHEnO7NmzI/66w13/ggsucAKBgLN169agcwwfPjzoH1Okx/tlzlM/h5ycHKeioqLK8ZH87KP5/oT7PagpTpH+fnrdiRMnHEnOq6++6sr56Rk9i+Z4v8xJz7yFnoVGz+hZOMfTM2+hZ6HRM3oWzvH0zFvoWWj0jJ6Fczw98xZ6Fho9o2fhHE/PvKWGnu1J1mlKS0t9/Wa6gwYNCrqtTZs2kqSLL764yu1JSUlq3bq19u3bV3nbtm3bVF5errPOOkuS5DhO5ev+nvrfTz/9VJK0du1aXXLJJdW+Xvjx48eDbhs8eHDQbe3atZMkHTlypMav7XSRrP/xxx+rdevW6tKlS9Bxl112mZYtW1bltkiOLyoqUlZWVpVjMjIyVFRU5Kk5v2vYsGFVXvdZiuxnH+m60fyuhBLJjH6QkpKiQCDg2huE0zN6Funx9Kz2delZaPSsZvQs9Pr0rCp65g30rGb0LPT69KwqeuYN9Kxm9Cz0+vSsKnrmDfSsZvQs9Pr0rCp65g019aze6TeUlZUpKSkpLoO5oUGDBkG3nfolrO6+ioqKyv8+9f/Ly8tVXl6uioqKKr8A0rdv/vnII4+otLRUv/rVr/TRRx/p+PHjlcefe+65Iedr2LBhtfN9d41wRLr+6f8YT6lu3UiP99OcGRkZQbdF8rOPdN1ofldCiXRGP0hOTtbJkyddOTc9o2fRHu+nOemZd9Cz6tEzehbOnPTMO+hZ9egZPQtnTnrmHfSsevSMnoUzJz3zDnpWPXpGz8KZk555R3U9C9qsTE5OVllZWVyG8qJOnTqpUaNGOnToUOUP/fSPvLw8SdLOnTt1xhlnaNq0aWrfvr0aNGigQCCgjz/+WDt27HB91kjWb9++vT7//HN98MEHQecJ9WyDSI7PzMwM+h6dehaFl+asTSQ/+0jXjeR7UK/ev/5Zhvp3GMmMfuA4jsrKypSSkuLK+ekZPYv0eHpW+7r0LDR65i56Fvnx9Kz2delZaPTMXfQs8uPpWe3r0rPQ6Jm76Fnkx9Oz2telZ6HRM3fRs8iPp2e1r0vPQqupZ0Gblampqb7biY2lH/7whzp27JiGDRumRYsW6euvv1Zpaal2796txYsXa/To0XrzzTclSWeffba++uorPfHEEzp8+LAOHz6s119/XZdffnmVZ2e4JZL1R48eLcdxNGbMGK1YsUJHjx7V7t27ddddd+mNN94IOnekx9swZyQ/+0jXjeR70KJFC0nSqlWrtH///qhn9IOTJ0/KcRzX/tiiZ/QsmuNtmJOexR89cxc9i+54G+akZ/FHz9xFz6I73oY56Vn80TN30bPojrdhTnoWf/TMXfQsuuNtmJOexV+NPXNO88wzzzjp6emn3+x5p94o9bHHHgu6b+rUqY4kp7CwMOi+Ll26OK1bt67874qKCuemm26q8manp38sWbLEcRzHefXVV0Pef/755ztdu3at8sbENc136r4ZM2ZE9DVHsv6xY8ec7t27Bx0bCAScsWPHBr2paaTH+2XOmn4OkfzsI103ku9BWVmZ07p166BjI53RD4qLi12dmZ7Rs2iO98uc9Mxb6Flo9Iye0TN6djp6Rs+iOd4vc9Izb6FnodEzekbP6Nnp6Bk9i+Z4v8xJz7ylhp7tCXllpVtv1usHgUBAs2bN0vz58zVs2DA1b95cKSkpateunUaNGqVXXnlF41PiugAAIABJREFUw4YNkyRdc801mjt3rrp3766GDRuqVatWuvXWW/Xmm2/G5U2JI1m/YcOGevvtt3Xrrbfq3/7t39SgQQP17t1br732mjp37ixJat68edTH2zBnJD/7SNeN5HuQlJSkvLw8DRgwQGlpaVHP6AennrXl1r8XekbPojnehjnpWfzRM3fRs+iOt2FOehZ/9Mxd9Cy6422Yk57FHz1zFz2L7ngb5qRn8UfP3EXPojvehjnpWfzV2LPTty9zc3OdQCDgVFRUxGcrFUaVl5c7559/vhMIBJyioqKYHx8rts6Z6L744gtHkrN69WpXzk/PEoutnfDLnImOniGWbO2EX+ZMdPQMsWRrJ/wyZ6KjZ4glWzvhlzkTHT1DLNnaCb/Mmehq6FnwlZUpKSlyHEcnT550cf8UJkydOlVz5szR7t27dezYMW3atEnjxo1TQUGBBg8erIyMjDodz5zxWdcmbj8zjJ7Zy9ZO+GVOBKNniJatnfDLnAhGzxAtWzvhlzkRjJ4hWrZ2wi9zIhg9Q7Rs7YRf5kSwiK6szM/PdyQ5xcXFbm+iIoSCgoIaX3/41Mc111wT8bmvuOKKkOdq3LixU1BQUOfjY8XWORFs+/btjiRn8+bNrpyfnplFz+ydE8Homd3omb1zIhg9sxs9s3dOBKNndqNn9s6JYPTMbvTM3jkRrIae7QnarFy3bp0jyfn000/jMx2qcDNO//znP52bbrrJ6dChg5OamupkZWU5Y8aMCflGw9EcHyu2zolgbveGnplFz+ydE8Homd3omb1zIhg9sxs9s3dOBKNndqNn9s6JYPTMbvTM3jkRrIbe7Ak4juPoO3bs2KGOHTuqoKBAPXv2FAC4ZfHixbryyit19OhRNWrUKObnp2cA4oWeAbAFPQNgC3oGwBb0DIAtaujZ3qD3rMzMzJQkFRUVxWk8AImqqKhIDRs2dOUPLYmeAYgfegbAFvQMgC3oGQBb0DMAtqipZ0Gblc2aNVNycjJxAuC6oqKiyj+I3EDPAMQLPQNgC3oGwBb0DIAt6BkAW9TUs6DNykAgoObNm2v//v2uDwYgse3fv18ZGRmunZ+eAYgXegbAFvQM+D/27ju+qvr+4/j7ZrJXwghWCQpRka2UKSJKDeAEoVaoe7fUOnBVfkLVVuvWiooWR6EiRJQZhigqG5URHEFKABHCECQkBMg4vz80VEhCkpt77/ec73k9Hw8efZhxz0cety9P8rnnXNiCngGwBT0DYIvj9azUslKSEhISeCUFgLAL98mWRM8ARAY9A2ALegbAFvQMgC3oGQBbVHlZmZiYyCspAIRdJE626BmASKBnAGxBzwDYgp4BsAU9A2CLKi8rk5KS9P3334d1KAD4/vvvlZSUFNZj0DMAkUDPANiCngGwBT0DYAt6BsAWx+tZmcvK5ORkbdq0KZwzAYCysrLUsmXLsB6DngGIBHoGwBb0DIAt6BkAW9AzALY4Xs9YVgIwIj8/Xzt37lRycnJYj0PPAIQbPQNgC3oGwBb0DIAt6BkAW1TUs3KXlXv27NG+ffvCORsAH8vKypLjOBF5ZRg9AxBO9AyALegZAFvQMwC2oGcAbFFRz8pcVpZ8Ma+mABAuJX1p0aJFWI9DzwCEGz0DYAt6BsAW9AyALegZAFtU1LNyr6wMBALECUDYZGVlKSEhQfXr1w/rcegZgHCjZwBsQc8A2IKeAbAFPQNgi4p6VuaysmbNmmrSpImysrLCOhwA/9q0aVPY77cv0TMA4UfPANiCngGwBT0DYAt6BsAWFfWszGWlJKWkpCgzMzMcMwGAMjMz1apVq4gci54BCCd6BsAW9AyALegZAFvQMwC2qKhn5S4r27Vrp7Vr14ZlKABYu3at2rdvH5Fj0TMA4UTPANiCngGwBT0DYAt6BsAWFfXsuMvKjIwMOY4TlsEA+Ne+ffu0ZcuWiJ5s0TMA4UDPANiCngGwBT0DYAt6BsAWlelZucvK9u3ba//+/bypLoCQKznxidTJFj0DEC70DIAt6BkAW9AzALagZwBsUZmeHffKykAgoIyMjLAMB8C/1q5dq/r16+vEE0+MyPHoGYBwoWcAbEHPANiCngGwBT0DYIvK9KzcZWXdunXVsmVL7lMNIOQyMjLUvn17BQKBiByPngEIF3oGwBb0DIAt6BkAW9AzALaoTM/KXVZKP136zSspAIRaSZwiiZ4BCAd6BsAW9AyALegZAFvQMwC2qEzPjrus7NSpk1auXBnSoQD4W2FhoVavXq2OHTtG9Lj0DECo0TMAtqBnAGxBzwDYgp4BsEVle3bcZWWPHj2UlZWl77//PqTDAfCvVatWKS8vT7169YrocekZgFCjZwBsQc8A2IKeAbAFPQNgi8r27LjLym7duikmJkZLly4N6XAA/Gvx4sVKSEjQqaeeGtHj0jMAoUbPANiCngGwBT0DYAt6BsAWle3ZcZeVderUUbt27bR48eKQDgfAvxYvXqyePXtG7M3BS9AzAKFGzwDYgp4BsAU9A2ALegbAFpXt2XGXlZLUs2dPLVmyJGSDAfC3JUuWqGfPnkaOTc8AhBI9A2ALegbAFvQMgC3oGQBbVLZnlVpWrlq1SgcOHAjJYAD8a+PGjdq2bZvRky16BiAU6BkAW9AzALagZwBsQc8A2KIqPatwWdmjRw8VFBRoxYoVIRkOgH8tWbJE8fHxOvPMM40cn54BCBV6BsAW9AyALegZAFvQMwC2qErPKlxWnnTSSTr55JO1YMGCkAwHwL8++OADdevWTTVq1DByfHoGIFToGQBb0DMAtqBnAGxBzwDYoio9q3BZKUmpqalKT0+v9mAA/MtxHM2bN0+pqalG56BnAKqLngGwBT0DYAt6BsAW9AyALaras0ovK7/44gvt2LGjWsMB8K9Vq1Zp+/bt6t+/v9E56BmA6qJnAGxBzwDYgp4BsAU9A2CLqvasUsvKvn37Ki4uTvPmzavWcAD8Kz09Xc2aNVP79u2NzkHPAFQXPQNgC3oGwBb0DIAt6BkAW1S1Z5VaVtauXVtnn302l34DCNqcOXPUv39/BQIBo3PQMwDVRc8A2IKeAbAFPQNgC3oGwBZV7VmllpXST5d+z507V0VFRUEPB8CffvzxRy1btsz4/fZL0DMAwaJnAGxBzwDYgp4BsAU9A2CLYHpW6WVl//79tWfPHi1fvjyo4QD417x58+Q4jvr162d6FEn0DEDw6BkAW9AzALagZwBsQc8A2CKYnlV6WdmmTRudfvrpSktLC2o4AP41ZcoU9enTRw0bNjQ9iiR6BiB49AyALegZAFvQMwC2oGcAbBFMzyq9rJSkyy+/XFOmTFFxcXGVhwPgT3l5eUpPT9fQoUNNj3IUegagqugZAFvQMwC2oGcAbEHPANgi2J5VaVk5dOhQbd26VUuWLKnSQQD41/Tp03Xo0CFdeumlpkc5Cj0DUFX0DIAt6BkAW9AzALagZwBsEWzPqrSsbNu2rc444wxNnjy5SgcB4F+TJ0/WeeedpyZNmpge5Sj0DEBV0TMAtqBnAGxBzwDYgp4BsEWwPavSslKShgwZoilTpqioqKiq3wrAZ/bv36+5c+e67hYWJegZgMqiZwBsQc8A2IKeAbAFPQNgi+r0rMrLyiuuuELZ2dlatGhRlQ8GwF+mTZumwsJC193CogQ9A1BZ9AyALegZAFvQMwC2oGcAbFGdngUcx3Gq+k0dO3ZUly5d9Oqrr1b5gAD8o3///oqOjtbMmTNNj1IuegagMugZAFvQMwC2oGcAbEHPANiiGj3LrvKVlZJ03XXXadKkScrJyQnm2wH4wHfffaf58+fr+uuvNz3KcdEzABWhZwBsQc8A2IKeAbAFPQNgi+r2LKhl5VVXXaXi4mK98847QR0UgP1ee+01JSYm6sILLzQ9ynHRMwAVoWcAbEHPANiCngGwBT0DYIvq9iyoZWWDBg102WWXcdk3gDIVFxfrjTfe0DXXXKPY2FjT4xwXPQNwPPQMgC3oGQBb0DMAtqBnAGwRip4FtayUpBtvvFErV67UqlWrgn0IAJZKT0/Xd9995/pbWJSgZwDKQ88A2IKeAbAFPQNgC3oGwBah6FnAcRwn2G9u06aN+vbtq3/+859BDwDAPpdddpn27dunDz/80PQolUbPAJSFngGwBT0DYAt6BsAW9AyALULQs+ygr6yUpGuvvVYTJ05UXl5edR4GgEW2bdumWbNmeeZVYSXoGYBj0TMAtqBnAGxBzwDYgp4BsEWoelbtZeXhw4f1+uuvV2sIAPZ44YUXlJCQoMsvv9z0KFVCzwAci54BsAU9A2ALegbAFvQMgC1C1bNqLSsTExN1zTXX6Omnn1ZRUVG1BgHgfXl5eXr11Vc1YsQIxcfHmx6nSugZgF+iZwBsQc8A2IKeAbAFPQNgi1D2rFrLSkm66667tGXLFr333nvVfSgAHvfqq68qPz9fN998s+lRgkLPAJSgZwBsQc8A2IKeAbAFPQNgi1D2LOA4jlPdBxk0aJC2bt2qFStWVHsgAN5UVFSklJQUXXjhhXruuedMjxM0egaAngGwBT0DYAt6BsAW9AyALULcs+xqX1kp/fRqipUrV2rx4sWheDgAHjR58mRt3rxZf/rTn0yPUi30DAA9A2ALegbAFvQMgC3oGQBbhLpnIbmyUpK6d++uZs2acfk34FNdunRRcnKypkyZYnqUaqNngL/RMwC2oGcAbEHPANiCngGwRYh7FporKyXpnnvu0fTp07V27dpQPSQAj5g9e7Y+//xz3XPPPaZHCQl6BvgXPQNgC3oGwBb0DIAt6BkAW4SjZyG7stJxHHXt2lUnnHACr6YAfKZr165q1qyZpk2bZnqUkKBngH/RMwC2oGcAbEHPANiCngGwRRh6lh0TqkcKBAL6v//7P1100UVasWKFfv3rX4fqoQG42Pvvv6+VK1fqiy++MD1KyNAzwJ/oGQBb0DMAtqBnAGxBzwDYIlw9C9mVlSW6d++uRo0aadasWaF8WAAu5DiOOnXqpNatW1txr/1j0TPAP+gZAFvQMwC2oGcAbEHPANgijD0L3XtWlhgzZoxmz56tTz/9NNQPDcBlpkyZooyMDI0aNcr0KGFBzwD/oGcAbEHPANiCngGwBT0DYItw9izkV1ZKUu/evRUXF6cPPvgg1A8NwCWKiorUrl07derUSRMnTjQ9TtjQM8B+9AyALegZAFvQMwC2oGcAbBHmnoX+ykpJeuSRR7RgwQLNmTMnHA8PwAXGjx+vDRs26KGHHjI9SljRM8B+9AyALegZAFvQMwC2oGcAbBHunoXlykpJGjx4sL766iutXbtWsbGx4TgEAENycnJ06qmn6oorrtAzzzxjepywo2eAvegZAFvQMwC2oGcAbEHPANgiAj0Lz5WVkvTkk09q06ZNGjt2bLgOAcCQMWPG6PDhw3rwwQdNjxIR9AywFz0DYAt6BsAW9AyALegZAFtEomdhW1a2bNlSf/7znzVmzBjt3r07XIcBEGEbNmzQiy++qEceeUQJCQmmx4kIegbYiZ7RM8AW9IyeAbagZ/QMsAU9o2eALSLVs7DdBlaScnNzlZKSossuu0wvvvhiuA4DIIIuvPBCZWVlac2aNYqJiTE9TsTQM8A+9IyeAbagZ/QMsAU9o2eALegZPQNsEaGehe82sJJUp04dPfrooxo3bpzWrVsXzkMBiIC5c+dq1qxZev755311oiXRM8A29IyeAbagZ/QMsAU9o2eALegZPQNsEcmehfXKSkkqLi5Wt27dFBcXp08++URRUWHdjwIIk/z8fLVv317t2rXT1KlTTY9jBD0D7EDP6BlgC3pGzwBb0DN6BtiCntEzwBYR7ll4r6yUpKioKL322mtasWKFXnrppXAfDkCYjBkzRjt37tTzzz9vehRj6BlgB3pGzwBb0DN6BtiCntEzwBb0jJ4Btoh0z6JHjx49OtwHadq0qfLz8/X3v/9dw4cPV/369cN9SAAhtHbtWl177bV66qmndN5555kexyh6BngbPfsfegZ4Gz37H3oGeBs9+x96BngbPfsfegZ4m4Ge5Yb9NrAlDh06pE6dOqlly5aaNWtWJA4JIASKiorUrVs3xcTEaPHixdy6QfQM8Cp6Vho9A7yJnpVGzwBvomel0TPAm+hZafQM8CZDPQv/bWBLxMfH6+WXX1Z6erreeeedSB0WQDU988wzysjI0L/+9S9OtH5GzwBvomel0TPAm+hZafQM8CZ6Vho9A7yJnpVGzwBvMtWziF1ZWeLmm2/WtGnTtG7dOiUmJkby0ACq6L///a/at2+v++67T6NGjTI9juvQM8A76Nnx0TPAO+jZ8dEzwDvo2fHRM8A76Nnx0TPAOwz2LDviy8p9+/apffv26ty5s957771IHhpAFRQWFurss8/WwYMHtXz5csXFxZkeyXXoGeAN9Kxi9AzwBnpWMXoGeAM9qxg9A7yBnlWMngHeYLhnkbsNbIn69etrwoQJmjFjhsaNGxfpwwOopL/+9a9avXq13nrrLU60ykHPAG+gZxWjZ4A30LOK0TPAG+hZxegZ4A30rGL0DPAG0z2LHj169OhIH7RFixY6ePCgxowZo8GDB3P5N+Ayixcv1vXXX69nn31WF154oelxXI2eAe5GzyqPngHuRs8qj54B7kbPKo+eAe5GzyqPngHu5oKe5Ub8NrAlCgsL1atXLxUUFGjp0qW88gRwiX379qljx446/fTTNWvWLAUCAdMjuR49A9yJnlUdPQPciZ5VHT0D3ImeVR09A9yJnlUdPQPcySU9i/xtYEvExMRo4sSJ+vbbb/XQQw+ZGgPAMW677TYdOHBAr7/+OidalUTPAHeiZ1VHzwB3omdVR88Ad6JnVUfPAHeiZ1VHzwB3ckvPjNwGtkSjRo2UmJio+++/X926dVOrVq1MjQJA0vjx4/W3v/1NaWlp6tixo+lxPIWeAe5Cz4JHzwB3oWfBo2eAu9Cz4NEzwF3oWfDoGeAuLuqZudvA/tK1116radOm6bPPPtPJJ59sehzAl1avXq0ePXrojjvu0KOPPmp6HM+iZ4B59Cw06BlgHj0LDXoGmEfPQoOeAebRs9CgZ4B5LutZtiuWlfn5+erVq5eKioq0ZMkS1apVy/RIgK/s2bNHZ511lk4++WTNnTtX0dHRpkfyLHoGmEXPQoeeAWbRs9ChZ4BZ9Cx06BlgFj0LHXoGmOXCnpl7z8pfqlmzpt59911t3bpVN910k+lxAF8pLi7WsGHDVFRUpEmTJrkhTJ5GzwBz6Flo0TPAHHoWWvQMMIeehRY9A8yhZ6FFzwBz3NozVywrJSk5OVlvv/22Jk2apLFjx5oeB/CNv/zlL1q4cKHS0tKUmJhoehwr0DPADHoWevQMMIOehR49A8ygZ6FHzwAz6Fno0TPADLf2LHr06NGjTQ9R4pRTTpHjOHrwwQd17rnn6qSTTjI9EmC1d999V7fffrtefvllXXjhhabHsQo9AyKLnoUPPQMii56FDz0DIouehQ89AyKLnoUPPQMiy8U9y3XFe1b+UnFxsQYNGqTFixdryZIlat26temRACstX75c5557rq699lq9+OKLpsexEj0DIoOehR89AyKDnoUfPQMig56FHz0DIoOehR89AyLD5T3Ldt2yUvrpDXb79u2rnTt3aunSpWrSpInpkQCrZGVlqXv37urcubOmT5+umJgY0yNZi54B4UXPIoeeAeFFzyKHngHhRc8ih54B4UXPIoeeAeHlgZ65c1kpSbt27VL37t2VlJSk+fPnq0aNGqZHAqywb98+9erVS5K0aNEi1a9f3/BE9qNnQHjQs8ijZ0B40LPIo2dAeNCzyKNnQHjQs8ijZ0B4eKRn2VGmJyhP48aNNWPGDH355Ze66qqrVFxcbHokwPMKCgo0ePBg7dmzR7Nnz3ZrmKxDz4DQo2dm0DMg9OiZGfQMCD16ZgY9A0KPnplBz4DQ81LPokePHj3a9BDlady4sbp06aJRo0bpwIEDOv/8802PBHiW4zi6+uqr9dFHH2nBggVKSUkxPZKv0DMgdOiZWfQMCB16ZhY9A0KHnplFz4DQoWdm0TMgdDzWs1xXLyslqWXLlkpOTtbIkSNVo0aNI5erAqiaESNG6N///rfee+899ejRw/Q4vkTPgNCgZ+bRMyA06Jl59AwIDXpmHj0DQoOemUfPgNDwWM/cv6yUpA4dOigxMVF33nmn6tat64W/WMBV7r//fj3//POaMGGCLrnkEtPj+Bo9A6qHnrkHPQOqh565Bz0DqoeeuQc9A6qHnrkHPQOqx4M988ayUpJ+/etfq0GDBrr77rvVvHlznXnmmaZHAjzhkUce0SOPPKJx48bp97//velxIHoGBIueuQ89A4JDz9yHngHBoWfuQ8+A4NAz96FnQHA82jPvLCslqVu3biosLNR9992n1q1bq127dqZHAlzthRde0L333qsXX3xRN910k+lx8Av0DKgaeuZe9AyoGnrmXvQMqBp65l70DKgaeuZe9AyoGg/3LDfG9ARV9fDDDys3N1dXX321atasqUsvvdT0SIArjRs3Trfffrsef/xx3XrrrabHQRnoGVA59Mz96BlQOfTM/egZUDn0zP3oGVA59Mz96BlQOV7vmaeurCxxwQUXaPv27XrggQfUunVrtW3b1vRIgKs899xzGjFihEaPHq0HHnjA9Dg4DnoGHB898w56BhwfPfMOegYcHz3zDnoGHB898w56BhyfBT3z1m1gSwQCAQ0cOFA5OTm65557dMIJJ6hz586mxwJc4fHHH9fIkSP1j3/8Q/fff7/pcVABegaUj555Cz0DykfPvIWeAeWjZ95Cz4Dy0TNvoWdA+SzpmTeXldJPgfrNb36j+Ph43X777apXr566d+9ueizAqIceekj/93//p2effVZ33HGH6XFQSfQMKI2eeRM9A0qjZ95Ez4DS6Jk30TOgNHrmTfQMKM2innl3WVmiV69eqlWrlkaOHClJ6tOnj9mBAAMcx9Gdd96pJ598Uq+99ppuueUW0yMhCPQMoGe2oGcAPbMFPQPomS3oGUDPbEHPACt75v1lpST17NlTjRs31r333qvc3Fydf/75CgQCpscCIuLw4cO69tpr9cYbb2jSpEm68sorTY+EaqBn8DN6Zhd6Bj+jZ3ahZ/AzemYXegY/o2d2oWfwM0t7lhtjeoJQue2221S/fn1df/312rRpk9566y3VrFnT9FhAWO3du1eDBg3SF198oZkzZ6pfv36mR0II0DP4ET2zEz2DH9EzO9Ez+BE9sxM9gx/RMzvRM/iRzT0LOI7jmB4ilJYsWaJLLrlEp5xyiqZPn64mTZqYHgkIi6ysLA0cOFD79u3TzJkz1alTJ9MjIcToGfyCntmPnsEv6Jn96Bn8gp7Zj57BL+iZ/egZ/MLynmVHmZ4g1Hr06KGlS5dqz5496tatm7755hvTIwEht2LFCnXv3l0xMTFatmyZbWHCz+gZ/ICe+QM9gx/QM3+gZ/ADeuYP9Ax+QM/8gZ7BD/zQM+uWlZLUqlUrLVq0SE2aNFHPnj318ccfmx4JCJl3331X5557rs4880wtWbJEJ554oumREEb0DDajZ/5Cz2AzeuYv9Aw2o2f+Qs9gM3rmL/QMNvNLz6xcVkpSkyZN9OGHH+rcc89Vv3799Nxzz5keCaiWoqIiPfDAAxoyZIiuueYaTZs2TXXq1DE9FiKAnsE29My/6BlsQ8/8i57BNvTMv+gZbEPP/IuewTZ+61n06NGjR5seIlxiY2M1ZMgQxcfH695779XXX3+t/v37Ky4uzvRoQJX88MMPGjRokCZNmqSxY8dq1KhRioqy9rUGKAM9gy3oGegZbEHPQM9gC3oGegZb0DOU17OYmBgVFRXxfIBn+LBnuQHHcRzTU0TCwoUL9dvf/laNGjXS1KlTdfrpp5seCaiUVatWafDgwSooKFBaWpq6du1qeiQYRs/gVfQMx6Jn8Cp6hmPRM3gVPcOx6Bm8ip7hWCU9q1Onjtq0aaPp06crEAiYHguokE97lm31KvaX+vTpo+XLl6tWrVrq0aOHZs6caXokoELjx49Xjx491KpVK61atcovYUIF6Bm8iJ6hLPQMXkTPUBZ6Bi+iZygLPYMX0TOUpUePHho2bJiysrI0f/58zZo1y/RIQIX83DPfLCslKTk5WYsXL9Zll12miy++WHfccYcOHjxoeiyglH379mnYsGG64YYbdMcddyg9PV2JiYmmx4KL0DN4BT1DRegZvIKeoSL0DF5Bz1ARegavoGcoz4oVK9SxY0c988wzchxH559/Pj2Dq9EzyTe3gT3WlClTdPPNNyspKUn/+c9/1KFDB9MjAZKkZcuWafjw4dq/f7/Gjx+vgQMHmh4JLkfP4Fb0DFVFz+BW9AxVRc/gVvQMVUXP4Fb0DGXJz8/XmDFj9MQTTygqKkqFhYVq1qyZtm3bprS0NHoGV6Jnkvx0G9hjDRkyRKtWrVJCQoK6deumxx9/XMXFxabHgo8VFhZq9OjR6tWrl0455RStXr3ar2FCFdEzuA09Q7DoGdyGniFY9AxuQ88QLHoGt6FnKM+nn36qM844Q0899ZSKi4tVWFiouLg4XXnllQoEAvQMrkPPjuH4XEFBgTN69GgnJibGGTBggJOdnW16JPjQhg0bnG7dujk1a9Z0XnzxRae4uNj0SPAgegY3oGcIBXoGN6BnCAV6BjegZwgFegY3oGcoy48//ujccMMNTiAQcKKjox1JR/1ZunTpUV9Pz+AG9KyU7b69srJETEyMHnroIX3yySf65ptv1KZNG73xxhty/Hl3XERYYWGhnnrqKbVv3175+fn67LPPdNtttykQCJgeDR5Ez2ASPUMo0TOYRM8QSvQMJtEzhBI9g0n0DOWZOXOmTj31VL355ptyHEdFRUVHfb5Zs2bq2rXrUR+jZzCJnpXP98vKEt27d1dGRoZuvPFi31jnAAAgAElEQVRG3XDDDerTp48yMzNNjwWLrV27Vj179tT999+vESNGaPny5WrTpo3psWABeoZIo2cIF3qGSKNnCBd6hkijZwgXeoZIo2coy44dOzRs2DBddNFF2rVrlwoKCkp9TVxcnIYPH17uEoieIdLo2fGxrPyFWrVq6bHHHtPKlSuVm5urTp06afTo0WXGDghWfn6+Ro8erS5duigmJkarV6/WY489pvj4eNOjwSL0DJFAzxAJ9AyRQM8QCfQMkUDPEAn0DJFAz1CeN998UykpKZoyZYoklfu+k4cPH9bll19+3MeiZ4gEelZJBu9B62qHDx92/vrXvzrx8fFO586dneXLl5seCRaYM2eO06pVK6devXrO2LFjnaKiItMjwQfoGcKBnsEEeoZwoGcwgZ4hHOgZTKBnCAd6hvIUFRU5o0aNcgKBgBMVFVXq/Sl/+ScpKalK7wNIzxAO9KzSeM/K8sTGxmrUqFFavXq16tatq27duunqq6/Wtm3bTI8GD1q/fr0uuugipaamql27dvrqq6906623KiqK/wsi/OgZQomewSR6hlCiZzCJniGU6BlMomcIJXqGikRFRemvf/2rZs6cqTp16igmJqbMr6voFrBloWcIJXpWdfzNVOC0007TwoULNW3aNC1atEitW7fW6NGjlZ+fb3o0eEBubq5Gjx6t9u3ba8OGDZo9e7amTp2qE044wfRo8CF6huqgZ3ATeobqoGdwE3qG6qBncBN6huqgZ6iqAQMGaN26dWrbtq2io6NLff7w4cMaMmRIUI9Nz1Ad9KwaTF/b6SX5+fnOI4884tSpU8dJTk52Jk+ebHokuFRhYaEzbtw4p0mTJk5iYqIzduxYp6CgwPRYwBH0DJVFz+B29AyVRc/gdvQMlUXP4Hb0DJVFz1Bdf/zjH534+HhHkhMIBI7cArZ58+ZVugVseegZKoueVdt2lpVB2LZtm3PTTTc5UVFRTvv27YkUjiguLnamT5/udOjQwYmJiXFuuukmZ9euXabHAspFz1AeegavoWcoDz2D19AzlIeewWvoGcpDzxAKkyZNciQ5b775pvPaa685cXFxTkxMjBMbG+uMHDkypMeiZygPPQsZ3rMyGElJSXrllVe0cuVKnXjiiRo6dKh69eqljz76yPRoMMRxHL3//vvq2LGjLr30Up1xxhn68ssv9corrygxMdH0eEC56BmORc/gVfQMx6Jn8Cp6hmPRM3gVPcOx6BlCJTMzUzfddJNGjBihq666Stdff72WLl2qZs2aqaCgIOhbwJaHnuFY9Cz0WFZWQ+fOnTVz5kwtW7ZMDRs2VN++fYmUD33wwQfq2rWrBg0apJNOOkmff/65Jk6cqJSUFNOjAZVGzyDRM9iBnkGiZ7ADPYNEz2AHegaJniF0cnNzNWjQIJ1++ul68sknj3y8c+fOWrNmjW699VadddZZYTk2PYNEz8KFZWUIdO3aVTNmzNDHH3+s6Oho9e3bV3379tXs2bPlOI7p8RAGhYWFeuedd9SlSxf169dPzZo10xdffKEZM2aoY8eOpscDgkbP/IeewVb0zH/oGWxFz/yHnsFW9Mx/6BnC4frrr9fu3buVlpamuLi4oz7XqFEjjR07VoFAIKwz0DP/oWfhx7IyhHr37q2PP/5YCxYsUFxcnC688EK1bdtW//rXv3To0CHT4yEEcnNz9dxzz6l169YaNmyYWrZsqRUrVmj69OlECVahZ/ajZ/ALemY/ega/oGf2o2fwC3pmP3qGcHn66aeVlpamf//73/rVr35lehx65gP0LHICDqv+sMnIyNDTTz+t//znP2rUqJFGjBihW265RY0aNTI9Gqpo+/bteuGFF/Tyyy/r8OHDuvbaa3XHHXfo5JNPNj0aEBH0zB70DH5Hz+xBz+B39Mwe9Ax+R8/sQc8QTkuXLlWfPn00ZswY3XfffabHKRM9swc9i7hslpURsGPHDr300kt64YUXlJeXp4svvlg33XSTzjvvvLBfko7gFRcX68MPP9S4ceP0/vvvq2HDhrr11ls1YsQIJSQkmB4PMIKeeRM9A0qjZ95Ez4DS6Jk30TOgNHrmTfQMkbBjxw6deeaZ6ty5s6ZNm+b6JtAzb6JnRrGsjKTc3Fy9/fbbeu2117RixQqlpKTohhtu0FVXXaWmTZuaHg8/27Jli8aPH6/x48dr69at6tOnj2644QYNHjxY8fHxpscDXIGeeQM9AypGz7yBngEVo2feQM+AitEzb6BniJTCwkKdf/75+v7777Vy5Uo1aNDA9EiVRs+8gZ65AstKU9asWaPXXntNEyZM0IEDB3TRRRdp2LBh6t+/v2rUqGF6PN/Zv3+/pk+frgkTJmjevHlq0qSJrr76al1//fVq3bq16fEAV6Nn7kLPgODRM3ehZ0Dw6Jm70DMgePTMXegZTLj77rv14osvavHixercubPpcYJGz9yFnrkOy0rT8vPzlZaWptdff10ff/yxateurYsvvlhDhw7VBRdcwOY+jPLy8jRjxgxNnjxZ6enpKioq0m9+8xtdd911uuiiixQbG2t6RMBT6Jk59AwILXpmDj0DQouemUPPgNCiZ+bQM5g0bdo0XXbZZRo/fryuueYa0+OEBD0zh565GstKN9m+fbveffddvfPOO1qyZInq1q2rSy65RIMHD9Z5552n2rVrmx7Rs5YuXaru3bvrxx9/1Lx585SWlqZZs2bp8OHD6tu3r4YOHarLLruMNzsGQoSehR89AyKDnoUfPQMig56FHz0DIoOehR89gxt8++236tKli373u9/ppZdeMj1OWNCz8KNnnsGy0q22bt2qtLQ0TZ48WcuWLVNcXJzOPvtspaamqn///mrTpo3pET3BcRytWrVKF1xwgZKTk7V69Wo5jqNzzjlHQ4cO1eDBg5WYmGh6TMBq9Cw0Sno2Z84cpaena9myZfQMiDB6Fhr0DDCPnoUGPQPMo2ehQc/gNnl5eeratatq1aqlTz/91BdXG9Kz0KBnnsWy0gt27NihuXPnKj09XfPnz9cPP/ygFi1aKDU1VX379lWvXr3UvHlz02O6RlZWlhYtWqQFCxZozpw52rFjhwKBgBo3bqznnntOF1xwgRo2bGh6TMCX6FnVlNWzpKQkpaamKjU1Vf369aNngCH0rGroGeBe9Kxq6BngXvSsaugZ3Ozqq6/WzJkz9dlnn6lly5amx4k4elY19MwKLCu9pqioSCtXrtScOXM0Z84cff755yosLFRycrJ69eqlHj16qFevXjrjjDMUFRVletywKyws1OrVq7V48eIjf7Zt26b4+Hh169ZNqampOv/889WlSxdJ0sMPP6wHH3zQ8NQAJHp2rMr0LDU1VR06dFAgEDA9LoBfoGdHo2eAd9Gzo9EzwLvo2dHoGbzkhRde0J///GfNmjVLqamppscxjp4djZ5Zi2Wl1+Xm5mrFihVatGiRlixZoqVLlyonJ0f169dX586d1a5dO7Vr107t27fXGWec4en7XO/bt0/r1q1TRkaG1qxZo3Xr1mnVqlXKy8tTQkKCevTooZ49e6pnz54666yzVKNGDUnSnj17lJCQIEmKiorSkiVL1LVrV5P/KgDKQM8q7hkAb6Bn9AywBT2jZ4Atgu3Z5s2b1aJFC8PTVw09g5ctX75cvXv31oMPPqhRo0aZHseVOD+jZ5ZiWWmboqIirVu3TosXL9bq1au1Zs0affnll8rLy1NUVJROPvlkdejQQa1bt1ZycrKSk5PVsmVLtWjRwhX3/s7Pz1dWVpY2bdp05H8zMzOVkZGhTZs2SZLq169/JLqdO3dWjx49dPrpp5f7SolNmzYduV1AdHS0mjdvrnXr1qlevXqR+tcCEAQv9Cw7O1u1atUqsyfh6BkAb3JTz4qKihQdHV2l76FnAEqEqmfr169XSkpKxOenZwBKVKZnbdu21Zo1a3Tvvff64vdngGk7d+7UmWeeqdNPP13p6elV/rnFr9z082Yw6Bl+xrLSD4qLi7Vx40atXbtWGRkZysjI0IYNG7Rp0ybt27dPkhQIBNS8eXMlJyerefPmSkxMVGJiohISEo76U/Lms7Vq1ToSswYNGhwVhuLi4iOPm5+fr4MHD6q4uFg//PCDdu/erR9++OHIn927d2vXrl3atm2bNm3apOzs7COP06hRI7Vs2VKtWrVS+/btjwQpOTm5Sv/+a9euVYcOHY78c2xsrK688kq98cYbwfx1AjDIbT2bNm2adu7cqbZt20akZwDsYapnb7/9trp06aKEhASj52cA7FHVntWuXVubN2/W0KFDrfh5E4A9ju1ZWlqavvzyS9WtW1c5OTmS7P79GWBScXGx+vfvr8zMTH3++edH7pKH4Ljt92f0DJXAstLv9uzZo02bNh155cLmzZu1bdu2owLyww8/6NChQyE7Zo0aNY6EruR/k5KS1LJlyyOv7EhOTg7ZlY+LFi3S2WefXerjb7/9tq644oqQHAOAeSZ6VvIqvzPOOOPISV04ewbAH/xwfgbAH8rq2fvvv6+dO3cqJSWFngFwrR9//FEtWrRQTk6OVqxYoVNOOYXzMyCM7r//fj377LNatGiRzjzzTNPjWI2fN+FSLCtRObm5uUde+SD97xUSkrR3796jvjYqKkr169eXJNWsWVM1atRQVFTUkVdjRPo+2bNnz9bAgQOP+lggEFDt2rW1bt06z733AIDqCVXPSl7ttXv3bk2dOlWXXXZZZP9FAPheVXsWHx+vQYMGqUGDBpo1a5ZiYmKMnZ8B8KetW7cqOTlZRUVFysrKOvIqeS//vAnATvfff7+eeuopFRcX66WXXtKNN95Yqe+jZ0DVzZw5UxdffLFeffVVXX/99abHwc/oGSIsO8b0BPCGOnXqqE6dOp5c7OXk5CgQCOiXe3nHcXTo0CH97ne/06effso90AEfCVXP1q1bp927dysQCOjJJ59kWQkg4qras6lTp6qwsFC7du1STk6OzjvvvDBPCABHe/rppxUVFSVJWrhwoa655hpJ3v55E4B9tm/frmeeeUYFBQWKjY3V6tWrK/299Ayomk2bNunqq6/W8OHDWVS6DD1DpEWZHgAIt5ycHMXElN7LFxQUaMWKFXriiScMTAXA6+bPn6+YmBg5jqMlS5bos88+Mz0SABzXu+++q5iYGMXGxuq1114zPQ4An9m7d69efvllFRQUKBAIaOHChaZHAoAyjRkzRsXFxZJ++t3RypUrDU8E2OngwYMaPHiwTjzxRL3yyiumxwFgGMtKWC8nJ+fIq3ePVVRUpAcffFDLly+P8FQAvG7evHlHfoCNjY3V888/b3giAChfQUGBZsyYoYKCAhUUFOjdd98tdeseAAinsWPHqqCgQJJUWFioDz74wPBEAFBaVlaW/vWvfx3plSRlZGQc+dkPQOjcdtttysrK0tSpU1WzZk3T4wAwjGUlrLd///4Kv2bo0KGV+joAkH76pf8nn3xy1Ktt3377bWVnZxueDADKtnDhwqPOdRzH0dtvv21wIgB+cujQIT377LMqLCw88rHvv/9eW7ZsMTgVAJR2//33KxAIHPWxgwcP6ttvvzU0EWCnl19+WW+88YbGjx+vk08+2fQ4AFyAZSWst3///qPer/JYRUVF2rJli+64444ITgXAy5YuXaoDBw4c9bFAIKCXX37Z0EQAcHzvvfee4uLijvxzcXExzQIQMW+++ab27Nlz1MeioqL08ccfG5oIAErLyMjQ5MmTj7qqUvrpZ72qvG8lgONbvXq17rzzTj344IO69NJLTY8DwCVYVsJ6OTk5pW7XER0draioKAUCAXXs2FFjxozRLbfcYmhCAF6zYMGCo37pL/10deXzzz+vgwcPGpoKAMrmOI7effddHT58+MjHiouLlZGRoVWrVhmcDIAfFBcX64knnij1AtLo6GiWlQBcZeTIkYqJiSn18bi4OK1Zs8bARIB99uzZo0GDBqlHjx566KGHTI8DwEVK/xcYsMy+fftUWFio2NhYFRQUqG7duqpXr56GDx+uu+66S40bNzY9IgCPmT179lG/9C+xb98+TZo0Sddcc03khwKAcqxYsUI7d+4s9fHY2Fi9/vrr6tSpk4GpAPjF+++/rw0bNpT6eEFBgebPn29gIgAobdGiRZo7d26Znzt8+LA+++yzCE8E2Ke4uFjDhw9XYWGhJk2apOjoaNMjAXARrqyE9fbv36/TTz9dd999tz799FPt3btXCQkJys/PZ1EJoMr2799/3CuRHn/88ePeehoAIu29995TbGxsqY8XFBTozTff5IpwAGH12GOPlfvLyC1btuj777+P8EQAUNp9991X5lWV0k93qWBZCVTfmDFj9MEHH2jSpElKTEw0PQ4Al2FZCetNnjxZX331lf72t7+pV69eio6O1oABAzRz5kzTowHwoA8//LDUraVLFBcX65tvvuGWZgBcpaz3XiqRm5ur999/P8ITAfCLTz/9VCtXrlRRUVGZn4+KitInn3wS4akA4GgzZszQ4sWLVVhYWO7X7N27V9nZ2RGcCrDL/Pnz9eijj+r5559Xjx49TI8DwIVYVsJ69erVK/WxAQMGaOPGjcrMzDQwEQAv++CDD8q8QqlETEyMnn766QhOBADl+/rrr5WVlVXu5wOBgMaNGxfBiQD4yWOPPXbc8ybetxKAacXFxbrvvvsUCAQq/NrVq1dHYCLAPps3b9aVV16p3/72t7rllltMjwPApVhWwpd69OihRo0aafbs2aZHAeAx5b1fZYnCwkLNmjVLGzdujOBUAFC28m4BW6KoqEgLFy6kWQBC7uuvv1Z6enq5V3ZLP92Oet68eRGcCgCOtmPHDvXv31/9+/dXcnLyUbetjo+PV1xcnKSfXuDFshKouoMHD2rw4MFKSkrSq6++anocAC7GshK+FB0drX79+ik9Pd30KAA8ZOvWrdq4caOio6OP/OAaFVX6P6WBQEBvvvmmgQkB4GhpaWnHXRRIP70P01tvvRWhiQD4xcyZM9WkSZNS7wEXFRWluLg4xcXFKRAIKCsri1srAjAmKSlJTz75pGbNmqWsrCzl5+fr9ttvV9OmTfXwww/rqquuUrdu3dSgQQOWlUAQ/vjHP2r9+vWaPHmyatWqZXocAC5W9jt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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "execution_count": 10,
   "id": "1935cc1d-dc01-497d-99e6-9a6d8c837373",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;NDVI&#x27; (lat: 1008, lon: 1343)&gt;\n",
       "array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0</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'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1008</li><li><span class='xr-has-index'>lon</span>: 1343</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-81925db3-c740-402c-9205-664f486a4743' class='xr-array-in' type='checkbox' checked><label for='section-81925db3-c740-402c-9205-664f486a4743' 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>117.4 106.7 96.79 107.2 108.3 108.4 ... 161.0 163.0 169.5 174.3 175.8</span></div><div class='xr-array-data'><pre>array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])</pre></div></div></li><li class='xr-section-item'><input id='section-2c623364-9e19-4433-ae16-44fdf8cea31f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c623364-9e19-4433-ae16-44fdf8cea31f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.001 9.004 9.007 ... 12.99 13.0</div><input id='attrs-f4bcda62-e6ab-47ad-9a70-c7091041cd96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4bcda62-e6ab-47ad-9a70-c7091041cd96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9a0b9b8-d36d-4e71-83a8-867ec3faf30b' class='xr-var-data-in' type='checkbox'><label for='data-f9a0b9b8-d36d-4e71-83a8-867ec3faf30b' 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>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 9.001488,  9.004464,  9.00744 , ..., 12.989583, 12.99256 , 12.995536])</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'>45.0 45.0 44.99 ... 42.01 42.0 42.0</div><input id='attrs-874b5654-231f-459f-b36a-8e419cad8f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-874b5654-231f-459f-b36a-8e419cad8f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1681dda5-f006-43dc-bfa8-9fd9e3a22675' class='xr-var-data-in' type='checkbox'><label for='data-1681dda5-f006-43dc-bfa8-9fd9e3a22675' 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>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44.998512, 44.995536, 44.99256 , ..., 42.00744 , 42.004464, 42.001488])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8c00aabf-77ac-4f96-9280-c0864a6b9f59' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8c00aabf-77ac-4f96-9280-c0864a6b9f59' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'NDVI' (lat: 1008, lon: 1343)>\n",
       "array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0"
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       "</table></div></div></li><li class='xr-section-item'><input id='section-a4b0a1c9-7c7e-43cc-b776-a3c9d44cb515' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4b0a1c9-7c7e-43cc-b776-a3c9d44cb515' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-07-01</div><input id='attrs-a92f5f0e-d3d9-496b-95ab-bdec3208dbf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a92f5f0e-d3d9-496b-95ab-bdec3208dbf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a753d775-370a-48da-b84e-6821683b54fe' class='xr-var-data-in' type='checkbox'><label for='data-a753d775-370a-48da-b84e-6821683b54fe' 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>t</dd><dt><span>long_name :</span></dt><dd>t</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-21T00:00:00.000000000&#x27;, &#x27;2022-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-02-11T00:00:00.000000000&#x27;, &#x27;2022-02-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-01T00:00:00.000000000&#x27;, &#x27;2022-03-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-03-21T00:00:00.000000000&#x27;, &#x27;2022-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-11T00:00:00.000000000&#x27;, &#x27;2022-04-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-01T00:00:00.000000000&#x27;, &#x27;2022-05-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-05-21T00:00:00.000000000&#x27;, &#x27;2022-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-11T00:00:00.000000000&#x27;, &#x27;2022-06-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.001 9.004 9.007 ... 12.99 13.0</div><input id='attrs-6cab6224-4683-49b8-a261-637d8603d219' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cab6224-4683-49b8-a261-637d8603d219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea3b20e2-e5d5-4d5d-a15e-104410d16243' class='xr-var-data-in' type='checkbox'><label for='data-ea3b20e2-e5d5-4d5d-a15e-104410d16243' 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>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 9.001488,  9.004464,  9.00744 , ..., 12.989583, 12.99256 , 12.995536])</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'>45.0 45.0 44.99 ... 42.01 42.0 42.0</div><input id='attrs-c5a7e595-0e09-4c14-8bd9-f5764a9a2f03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5a7e595-0e09-4c14-8bd9-f5764a9a2f03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-879239ca-46c0-49c4-832b-226d68db3c5e' class='xr-var-data-in' type='checkbox'><label for='data-879239ca-46c0-49c4-832b-226d68db3c5e' 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>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44.998512, 44.995536, 44.99256 , ..., 42.00744 , 42.004464, 42.001488])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb2737c9-7524-455c-bbca-6992bcf34868' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb2737c9-7524-455c-bbca-6992bcf34868' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>NDVI</dd><dt><span>units :</span></dt><dd></dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'NDVI' (time: 19, lat: 1008, lon: 1343)>\n",
       "dask.array<xarray-<this-array>, shape=(19, 1008, 1343), dtype=uint8, chunksize=(19, 510, 500), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-01-11 ... 2022-07-01\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0\n",
       "Attributes:\n",
       "    long_name:     NDVI\n",
       "    units:         \n",
       "    grid_mapping:  crs"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=cgls_ds.NDVI.compute().chunk(chunk)\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d43819d-364b-4897-b8d0-d5314ca6fb47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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iiy80bNgw9e/fX5mZmfr444/18MMPE6Y4qVKlih5++GF99NFHysjI0NVXX61hw4Zp//79pqcBgUPPzKJnQPTQM7PoGRA99MwsegZEDz0zi54B0UPPzKJn/sVhpUGvvvqqWrdurffff18LFy7UrFmzVK9ePdOznHTeeefpjTfe0LRp07Ro0SJlZGTo7bffNj0LCAx65h/0DIgMPfMPegZEhp75Bz0DIkPP/IOeAZGhZ/5Bz/yHw0oDDh06pGHDhmno0KEaMmSItm7dqt69e5ueBUlDhgzRhx9+qG7duqlPnz564IEHeO9qoBz0zL/oGRAaeuZf9AwIDT3zL3oGhIae+Rc9A0JDz/yLnvkHn1kZZytXrtStt96qo0ePatKkSerXr5/pSSjDlClTdN999yk9PV3Tp09XmzZtTE8CfIWeBQc9A8pHz4KDngHlo2fBQc+A8tGz4KBnQPnoWXDQM6P4zMp4Gjt2rHr06KHWrVtr8+bNhMnnhg0bpo0bN6p69erq3Lmzpk+fbnoS4Bv0LFjoGVA2ehYs9AwoGz0LFnoGlI2eBQs9A8pGz4KFnpmVNHr06NGmR9ju2LFjGjFihJ555hn96le/0vjx41W9enXTs1ABderU0W233abDhw/rwQcf1Oeff67evXsrKSnJ9DTACHoWXPQMKI6eBRc9A4qjZ8FFz4Di6Flw0TOgOHoWXPTMmMO8DWyM7dixQwMHDtTnn3+uGTNmqFevXqYnIUyvvvqq7rzzTl1yySV6/fXX+fBjOIee2YOewXX0zB70DK6jZ/agZ3AdPbMHPYPr6Jk96Flc8TawsbRmzRp16dJFSUlJ+te//kWYAu7HP/6xVq9erc8//1xdunTRxx9/bHoSEDf0zC70DC6jZ3ahZ3AZPbMLPYPL6Jld6BlcRs/sQs/ii8PKGJkzZ46uuuoqdejQQcuWLVOTJk1MT0IUtG3bVmvWrNF5552nLl26aOnSpaYnATFHz+xEz+AiemYnegYX0TM70TO4iJ7ZiZ7BRfTMTvQsfjisjIHnnntON954o26//XYtWLBANWvWND0JUZSWlqZFixapZ8+e6tOnj2bOnGl6EhAz9Mxu9AwuoWd2o2dwCT2zGz2DS+iZ3egZXELP7EbP4iNp9OjRo02PsMlTTz2lhx56SH/84x/11FNPKTGR82AbpaSk6MYbb9T+/fv18MMP64ILLtDFF19sehYQVfTMDfQMLqBnbqBncAE9cwM9gwvomRvoGVxAz9xAz2LuMIeVUTR69Gj99re/1dixY/XAAw+YnoMYS0hIUJ8+fZSamqqRI0eqZs2a6tKli+lZQFTQM7fQM9iMnrmFnsFm9Mwt9Aw2o2duoWewGT1zCz2LKQ4ro+WBBx7Q008/rcmTJ2vEiBGm5yCOsrKyVKVKFT300EOqXLmysrKyTE8CIkLP3EXPYBt65i56BtvQM3fRM9iGnrmLnsE29Mxd9CwmOKyMhkcffVRjx47V9OnTNXToUNNzYEDXrl11zjnnaNSoUapTp446d+5sehIQFnoGegZb0DPQM9iCnoGewRb0DPQMtqBnoGdRx2FlpJ544gk98cQTmjBhgm699VbTc2BQ586dVatWLd1///1q2LChLr30UtOTgJDQMxShZwg6eoYi9AxBR89QhJ4h6OgZitAzBB09QxF6FlUcVkZi7NixeuSRR/S3v/1Nd911l+k58IHMzEwVFhbqkUceUatWrdS2bVvTk4AKoWf4PnqGoKJn+D56hqCiZ/g+eoagomf4PnqGoKJn+D56FjUcVobrtdde04gRI/T000/rvvvuMz0HPnLllVfqwIED+uUvf6nu3burSZMmpicB5aJnKAs9Q9DQM5SFniFo6BnKQs8QNPQMZaFnCBp6hrLQs6g4nOB5nmd6RdCsW7dOPXr00K233qrx48ebngMf8jxPN998sxYtWqRVq1apVatWpicBpaJnOBt6hqCgZzgbeoagoGc4G3qGoKBnOBt6hqCgZzgbehaxfRxWhignJ0eZmZnq1KmT5s2bp6SkJNOT4FPHjh3T1Vdfrb179+qDDz5QgwYNTE8CiqFnqCh6Br+jZ6goega/o2eoKHoGv6NnqCh6Br+jZ6goehYRDitD8e2336pz586qVq2ali5dqmrVqpmeBJ/78ssv1aVLF5133nlasmSJUlJSTE8CJNEzhI6ewa/oGUJFz+BX9AyhomfwK3qGUNEz+BU9Q6joWdj2JZpeEBSe5+knP/mJ9u/frzlz5hAmVEj9+vU1f/58bdq0SaNGjTI9B5BEzxAeegY/omcIBz2DH9EzhIOewY/oGcJBz+BH9AzhoGfhSxo9evRo0yOCYMyYMXrhhRf05ptvKiMjw/QcBEjdunXVtGlTPfTQQ2ratKk6dOhgehIcR88QLnoGv6FnCBc9g9/QM4SLnsFv6BnCRc/gN/QM4aJnYTnMYWUFvPfee7r99ts1ZswYDRkyxPQcBFDbtm116NAh/f73v9d1112n+vXrm54ER9EzRIqewS/oGSJFz+AX9AyRomfwC3qGSNEz+AU9Q6ToWcgO85mVZ7F//361b99el112mV5//XXTcxBg+fn56tGjhw4dOqS1a9eqcuXKpifBMfQM0ULPYBo9Q7TQM5hGzxAt9Aym0TNECz2DafQM0ULPQsJnVp7Nz372M3mepwkTJpiegoBLTk7WK6+8os8++0yPPvqo6TlwED1DtNAzmEbPEC30DKbRM0QLPYNp9AzRQs9gGj1DtNCz0HBYWY7Jkyfrtdde08svv6y0tDTTc2CBpk2baty4cfrLX/6i+fPnm54Dh9AzRBs9gyn0DNFGz2AKPUO00TOYQs8QbfQMptAzRBs9qzjeBrYMu3btUps2bXT33Xfr6aefNj0Hlhk6dKiWLVumbdu2qXbt2qbnwHL0DLFEzxBP9AyxRM8QT/QMsUTPEE/0DLFEzxBP9AyxRM/Oah+HlWW47rrr9NFHH2nTpk28lzCibv/+/WrdurUGDhyo8ePHm54Dy9EzxBI9QzzRM8QSPUM80TPEEj1DPNEzxBI9QzzRM8QSPTsrPrOyNDNmzNCbb76pv/3tb4QJMXHOOedo7NixmjBhgt59913Tc2AxeoZYo2eIF3qGWKNniBd6hlijZ4gXeoZYo2eIF3qGWKNnZ8crK78nLy9PrVu31vXXX88JN2JuwIAB+u9//8szdhAT9AzxRM8QS/QM8UTPEEv0DPFEzxBL9AzxRM8QS/QM8UTPysQrK7/vV7/6lZKSkvR///d/pqfAAS+88II+//xzPfvss6anwEL0DPFEzxBL9AzxRM8QS/QM8UTPEEv0DPFEzxBL9AzxRM/KxmHlGbZt26aXXnpJf/jDH1SrVi3Tc+CARo0a6ZFHHtFTTz2lnTt3mp4Di9AzxBs9Q6zQM8QbPUOs0DPEGz1DrNAzxBs9Q6zQM8QbPSsbbwN7hmuuuUZ5eXlat26dEhM5x0V8HD9+XK1bt1ZWVpamTJlieg4sQc9gAj1DLNAzmEDPEAv0DCbQM8QCPYMJ9AyxQM9gAj0rFW8DW2TevHlavHixnnvuOcKEuKpcubLGjBmjqVOnau3atabnwAL0DKbQM0QbPYMp9AzRRs9gCj1DtNEzmELPEG30DKbQs9LxykpJhYWFysjIUOvWrTVr1izTc+Cobt26KTU1VYsXLzY9BQFGz+AH9AzRQM/gB/QM0UDP4Af0DNFAz+AH9AzRQM/gB/SsGF5ZKUmvvvqqPvroI/3ud78zPQUOe+qpp/Tuu+9q6dKlpqcgwOgZ/ICeIRroGfyAniEa6Bn8gJ4hGugZ/ICeIRroGfyAnhXn/CsrCwoK1LZtW3Xq1In3B4ZxPXv21IkTJ7RixQrTUxBA9Ax+Qs8QCXoGP6FniAQ9g5/QM0SCnsFP6BkiQc/gJ/TsNF5ZOXXqVH366af6zW9+Y3oKoMcff1wrV67kpd8ICz2Dn9AzRIKewU/oGSJBz+An9AyRoGfwE3qGSNAz+Ak9+x+nX1npeZ7atWunjh076h//+IfpOYAkqVevXpKkd955x/ASBAk9gx/RM4SDnsGP6BnCQc/gR/QM4aBn8CN6hnDQM/gRPZPk+isrs7OztW3bNt1///2mpwCnjRo1SosXL9aGDRtMT0GA0DP4ET1DOOgZ/IieIRz0DH5EzxAOegY/omcIBz2DH9Gz7zj9ysqePXuqUqVKys7ONj0FKObiiy9Wu3bteN90VBg9g1/RM4SKnsGv6BlCRc/gV/QMoaJn8Ct6hlDRM/gVPXP4lZUbN27UkiVLeBYFfOkXv/iFXn31Ve3Zs8f0FAQAPYOf0TOEgp7Bz+gZQkHP4Gf0DKGgZ/AzeoZQ0DP4GT2TnD2sHD9+vFq3bn36/YABPxkyZIjOOeccvfTSS6anIADoGfyMniEU9Ax+Rs8QCnoGP6NnCAU9g5/RM4SCnsHP6Jmjh5VHjhzRjBkzNGLECCUkJJieA5RQqVIlDRs2TH//+99VUFBgeg58jJ7B7+gZKoqewe/oGSqKnsHv6Bkqip7B7+gZKoqewe/omaOHlTNmzNCJEyd08803m54ClOnOO+/U7t279c4775ieAh+jZwgCeoaKoGcIAnqGiqBnCAJ6hoqgZwgCeoaKoGcIAtd7luB5nmd6RLxlZmaqefPmmjZtmukpQLl69OihtLQ0vf7666anwKfoGYKCnuFs6BmCgp7hbOgZgoKe4WzoGYKCnuFs6BmCwuGe7XPulZX//e9/tWbNGt1xxx2mpwBnNXz4cL311lv65ptvTE+BD9EzBAk9Q3noGYKEnqE89AxBQs9QHnqGIKFnKA89Q5C43DPnDitnzpypc889Vz169DA9BTir66+/XomJiZozZ47pKfAheoYgoWcoDz1DkNAzlIeeIUjoGcpDzxAk9AzloWcIEpd75txh5axZs3TjjTcqKSnJ9BTgrGrWrKnevXtr1qxZpqfAh+gZgoSeoTz0DEFCz1AeeoYgoWcoDz1DkNAzlIeeIUhc7plTh5UfffSRtm7dqkGDBpmeAlTY4MGDtXjxYn311Vemp8BH6BmCiJ6hNPQMQUTPUBp6hiCiZygNPUMQ0TOUhp4hiFztmVOHlbNnz1bDhg3VtWtX01OAChswYICSk5P11ltvmZ4CH6FnCCJ6htLQMwQRPUNp6BmCiJ6hNPQMQUTPUBp6hiBytWdOHVZmZ2erb9++Skx06tdGwFWvXl3du3fXwoULTU+Bj9AzBBE9Q2noGYKInqE09AxBRM9QGnqGIKJnKA09QxC52jNn/lt64MABrV27Vn379jU9BQhZ37599c477yg/P9/0FPgAPUOQ0TOciZ4hyOgZzkTPEGT0DGeiZwgyeoYz0TMEmYs9c+aw8p133pHnebrqqqtMTwFC1rdv39P/DxagZwgyeoYz0TMEGT3DmegZgoye4Uz0DEFGz3AmeoYgc7FnzhxWLlq0SF26dNE555xjegoQslatWqlZs2bOvfQbpaNnCDJ6hjPRMwQZPcOZ6BmCjJ7hTPQMQUbPcCZ6hiBzsWfOHFYuX76cZ1Eg0Hr06KH333/f9Az4AD1D0NEzFKFnCDp6hiL0DEFHz1CEniHo6BmK0DMEnWs9c+Kw8ssvv9Snn36qyy+/3PQUIGxdu3bVmjVrdPLkSdNTYBA9gw3oGSR6BjvQM0j0DHagZ5DoGexAzyDRM9jBtZ45cVi5cuVKJSYmKjMz0/QUIGxdu3bVsWPHtHHjRtNTYBA9gw3oGSR6BjvQM0j0DHagZ5DoGexAzyDRM9jBtZ45cVi5atUqtW3bVrVq1TI9BQhby5YtVb9+fa1cudL0FBhEz2ADegaJnsEO9AwSPYMd6BkkegY70DNI9Ax2cK1nThxWrl+/Xp07dzY9A4hIQkKCLrvsMq1bt870FBhEz2ADegaJnsEO9AwSPYMd6BkkegY70DNI9Ax2cK1nThxWbtmyRRkZGaZnABHLyMjQli1bTM+AQfQMtqBnoGewBT0DPYMt6BnoGWxBz0DPYAuXemb9YeWuXbuUl5dHnGCFdu3a6aOPPtKJEydMT4EB9Aw2oWduo2ewCT1zGz2DTeiZ2+gZbELP3EbPYBOXemb9YeXmzZslffcfKhB07dq1U35+vv7zn/+YngID6BlsQs/cRs9gE3rmNnoGm9Azt9Ez2ISeuY2ewSYu9cz6w8qtW7eqUaNGqlOnjukpQMRatmypSpUqaevWraanwAB6BpvQM7fRM9iEnrmNnsEm9Mxt9Aw2oWduo2ewiUs9s/6wcseOHWrRooXpGUBUpKSkqEmTJtqxY4fpKTCAnsEm9Mxt9Aw2oWduo2ewCT1zGz2DTeiZ2+gZbOJSz6w/rMzJyVHTpk1NzwCipmnTpsrJyTE9AwbQM9iGnrmLnsE29Mxd9Ay2oWfuomewDT1zFz2DbVzpmfWHlbm5uUpPTzc9A4iapk2bKjc31/QMGEDPYBt65i56BtvQM3fRM9iGnrmLnsE29Mxd9Ay2caVnVh9WFhYWaufOncQJVnHlmRQojp7BRvTMTfQMNqJnbqJnsBE9cxM9g43omZvoGWzkSs+sPqz8+uuvdeLECTVq1Mj0FCBqGjdurD179sjzPNNTEEf0DDaiZ26iZ7ARPXMTPYON6Jmb6BlsRM/cRM9gI1d6Zv1hpSTVq1fP8BKUZeHChUpISNDYsWNNT5Hkvz2lqVu3rvLz83Xw4EHTUxBH9Mz//NYPv+0pDT1zEz3zP7/1w297SkPP3ETP/M9v/fDbntLQMzfRM//zWz/8tqc09MxN9Mz//NYPv+0pjSs9c+KwMi0tzfASIHqK/j4X/f2GG+gZbETP3ETPYCN65iZ6BhvRMzfRM9iInrmJnsFGrvTM6sPKvLw8SdI555xjeAkQPXXr1pX0v7/fcAM9g43omZvoGWxEz9xEz2AjeuYmegYb0TM30TPYyJWeWX1YuX//ftWsWVOVKlUyPQWImqJnUtgeJxRHz2AjeuYmegYb0TM30TPYiJ65iZ7BRvTMTfQMNnKlZ1YfVh4+fFjVq1c3PaPY+x6/9957uvzyy1WtWjU1atRIf/zjH0/f7vnnn1erVq1UuXJlXXTRRXrttddKXMvzPE2aNEmXX365atSooSpVqqh9+/b661//WuIDVpcvX66bb75ZLVq0UGpqqurVq6cBAwZo5cqVZe5bvXq1evTooWrVqiktLU233Xab9u/fH9bv7Xme/vGPf6h79+6qXbu2atSooU6dOmnixInKz88vcfuK3PdLL72khIQEzZ49u8TPF31v7ty5Uf/dtmzZoiZNmujcc8/VmjVrQvyTiK5q1aopISFBR44cMboD8UXP6Bk9gy3oGT2jZ7AFPaNn9Ay2oGf0jJ7BFvSMntGzAPMsNmbMGC89Pd30DC87O9uT5A0ePNhLTk72JBX7509/+pP30EMPlfh6YmKit2HDhtPXKSws9G6++eYStyv656677jp9271795Z5u+TkZG/ZsmUl9g0dOtRLTU0tcfvu3buH/DsXFhZ6gwcPLnPDO++8E9Z9T5w40ZPkvfbaayXus+h7c+bMCft3K7r9n//859Nfmz9/vlejRg2vbdu2Xm5ubsh/FrGQmprqTZ061fQMxBE9o2f0DLagZ/SMnsEW9Iye0TPYgp7RM3oGW9AzekbPAmuv1a+sPHnypK9e8j1z5kz9/Oc/V25urg4fPqzZs2crJSVFv/vd7/S3v/1NL730kr788kvl5eXpwQcfVGFhof785z+f/vmpU6dq2rRpateunRYsWKC8vDwdPnxYy5YtU/v27TVx4kR98MEHkqSEhAT16tVLb775pnbt2qWTJ0/qiy++0KxZs5SamlrsGRxFpk+fruHDh+uTTz7R0aNHtWLFCjVu3FjLly/Xpk2bQvpdJ02apJkzZyotLU3jx4/Xzp07dfjwYa1bt0533nmnUlJSYnbfpQn3+s8995yuvfZaZWVladWqVWrSpEnEW6KhUqVKOnHihOkZiCN6Rs8ivT49g1/QM3oW6fXpGfyCntGzSK9Pz+AX9IyeRXp9ega/oGf0LNLr0zODTB+XxtJvfvMbr23btqZnnD6d79OnT4nv/ehHP/Ikec8++2yxr+fn53u1atXyOnbsePprV155pZeUlOR9/vnnJa6zbds2T5L38MMPn/7a+vXrvZtuusk777zzSjyD44ILLiix75prrilx3XHjxnmSvJdffjmk37lbt27FnjFRllDvO9xnUlT0+kW3f/rpp7177rnHk+T9/Oc/9/Lz8yv0e8dLWlqa98ILL5iegTiiZ/SMnsEW9Iye0TPYgp7RM3oGW9AzekbPYAt6Rs/oWWDtTS7vIDPoTp06peRk//yKV1xxRYmvFZ3Md+/evdjXk5KSdP755+uLL744/bVt27apoKBAjRo1kiR5nnf6famL/ufOnTslSatWrdKVV16pkydPlrrl2LFjJb7Wo0ePEl9r1qyZJOnbb78t93f7vo8++kh16tTR1VdfXaHbR/O+o3H9xx9/XIcOHdKjjz6qp556KuL7j7ZKlSqV+Z8t7ETP6Fm416dn8Bt6Rs/CvT49g9/QM3oW7vXpGfyGntGzcK9Pz+A39IyehXt9emae1W8Dm5ycrIKCAtMzTqtcuXKJryUkJJT7vcLCwtP/d9H/XlBQoIKCAhUWFhYLlKTTf2H/+Mc/6uTJk/rtb3+rTz/9VMeOHTt9+1atWpW6r0qVKmXu8773Yb3RVtH7Tkz87q/smX8uRUoLbqjXL9K/f3+dc845mjRpUlRedh5tp06dKvHSediNntGzUK9fhJ7Bb+gZPQv1+kXoGfyGntGzUK9fhJ7Bb+gZPQv1+kXoGfyGntGzUK9fhJ6ZZ/VhpW2nzRdddJGqVq2qb7755nSUvv/P7NmzJUk7duxQgwYNNHr0aDVv3lyVK1dWQkKCtm/frk8++SQuWw8cOKB33303qtetX7++JCknJ6fE95YsWRK1++nUqZOWL1+upKQkXXHFFVqxYkXUrh0NJ0+eVGpqqukZiCN6Rs/CRc/gN/SMnoWLnsFv6Bk9Cxc9g9/QM3oWLnoGv6Fn9Cxc9Mw8DisD5Cc/+YmOHj2qq6++Wm+99Za++uornTx5Up999pnmz5+vH/3oR6dj0LhxY3355ZcaN26cDh48qIMHD2rBggXq169fqc9CiLbbbrtNkjRkyBBNnDhRu3fv1pEjR/Svf/1LI0aM0LJly8K6buvWrSVJY8eO1dKlS3Xs2DHl5ubqwQcf1Ny5c6O2X5LatGmjlStXql69errmmmu0YMGCqF4/EidOnLA+TiiOntGzSNAz+Ak9o2eRoGfwE3pGzyJBz+An9IyeRYKewU/oGT2LBD0zy+rDytTUVKvidNttt+n222/X+vXrNWDAANWvX1+pqalKT09X//799cYbb+jUqVOSpLvvvlue5+m+++5T7dq1Vbt2bf3whz9UtWrV1LZt25hvHT58uG688UZ99dVXGjFihBo1aqTq1aurY8eOmjhx4umdoWrWrJluuOEG7du3T1deeaWqVq2qpk2b6rnnntOtt94a5d9CSk9P14oVK9SqVSsNHDhQ06dPj/p9hOPUqVPWxwnF0TN6Fil6Br+gZ/QsUvQMfkHP6Fmk6Bn8gp7Rs0jRM/gFPaNnkaJn5lh9WFm1alUdOXLE9IyoSUhI0OTJkzVz5kxdffXVqlOnjipVqqRmzZpp4MCBmjNnzukPsL3uuus0bdo0ZWRkqEqVKmrYsKHuvvtuvfvuu3H5S52YmKhZs2ZpwoQJyszMVLVq1VSzZk1ddtlleumll0r9lnL3GQAAIABJREFUgNuK+vvf/6477rhDaWlpqly5srp06aLFixeX+FDiaGnQoIGWLl2qzMxM3XLLLfrrX/8ak/upqKL3Gy/tfbdhL3pGz6KBnsEP6Bk9iwZ6Bj+gZ/QsGugZ/ICe0bNooGfwA3pGz6KBnpmR4MX6k1INeuONN3TjjTfqxIkT1n/4KNyxa9cuNW7cWB988IEyMzNNz0Gc0DPYiJ65iZ7BRvTMTfQMNqJnbqJnsBE9cxM9g40c6dk+q19ZmZaWJs/zdODAAdNTgKjJy8uT9N3fb7iDnsFG9MxN9Aw2omduomewET1zEz2DjeiZm+gZbORKz6w/rJSkr7/+2vASO2zcuFEJCQln/WfgwIGmp1qt6O+z7XFCcfQsuuiZP9AzN9Gz6KJn/kDP3ETPooue+QM9cxM9iy565g/0zE30LLromT+40jMnDiuLTp4BG+zfv1+JiYmqXbu26SmII3oGG9EzN9Ez2IieuYmewUb0zE30DDaiZ26iZ7CRKz1LNj0glurXr6+UlBTt3r3b9BQrdOjQQRZ/xGlg7Nq1Sw0bNlRiotXPNcD30LPoomf+QM/cRM+ii575Az1zEz2LLnrmD/TMTfQsuuiZP9AzN9Gz6KJn/uBKz6z+7ZKSknTBBRcoNzfX9BQganJyctS0aVPTMxBn9Aw2omduomewET1zEz2DjeiZm+gZbETP3ETPYCNXemb1YaUkpaenEydYJScnR+np6aZnwAB6BtvQM3fRM9iGnrmLnsE29Mxd9Ay2oWfuomewjSs9s/6wsmnTpsrJyTE9A4gaV55JgZLoGWxDz9xFz2AbeuYuegbb0DN30TPYhp65i57BNq70zPrDymbNmmn79u2mZwBRUVhYqNzcXCfihJLoGWxCz9xGz2ATeuY2egab0DO30TPYhJ65jZ7BJi71zPrDyjZt2ignJ0eHDh0yPQWI2Keffqpjx46pXbt2pqfAAHoGm9Azt9Ez2ISeuY2ewSb0zG30DDahZ26jZ7CJSz2z/rAyIyNDnudp27ZtpqcAEdu8ebOSkpLUunVr01NgAD2DTeiZ2+gZbELP3EbPYBN65jZ6BpvQM7fRM9jEpZ5Zf1jZtGlT1axZU5s3bzY9BYjYli1b1KJFC1WtWtX0FBhAz2ATeuY2egab0DO30TPYhJ65jZ7BJvTMbfQMNnGpZ9YfViYkJKht27basmWL6SlAxLZs2aK2bduangFD6BlsQs/cRs9gE3rmNnoGm9Azt9Ez2ISeuY2ewSYu9cz6w0pJuvTSS7VmzRrTM4CIrV27Vh07djQ9AwbRM9iCnoGewRb0DPQMtqBnoGewBT0DPYMtXOqZE4eVXbt21caNG3X48GHTU4Cw5eTkaM+ePcrKyjI9BQbRM9iAnkGiZ7ADPYNEz2AHegaJnsEO9AwSPYMdXOuZE4eVWVlZys/P17p160xPAcK2cuVKpaamOvNMCpSOnsEG9AwSPYMd6BkkegY70DNI9Ax2oGeQ6Bns4FrPnDisPP/889W4cWOtWrXK9BQgbCtXrtSll16qypUrm54Cg+gZbEDPINEz2IGeQaJnsAM9g0TPYAd6BomewQ6u9cyJw0pJ6tatm5YtW2Z6BhC25cuXq2vXrqZnwAfoGYKOnqEIPUPQ0TMUoWcIOnqGIvQMQUfPUISeIehc65kzh5W9evXS8uXLeZ9qBNKuXbv04Ycfqnfv3qanwAfoGYKMnuFM9AxBRs9wJnqGIKNnOBM9Q5DRM5yJniHIXOyZM4eVvXv31smTJ3k2BQJpwYIFqlatmjMfpovy0TMEGT3DmegZgoye4Uz0DEFGz3AmeoYgo2c4Ez1DkLnYM2cOK88991xdfPHFys7ONj0FCFl2drZ69uyp1NRU01PgA/QMQUbPcCZ6hiCjZzgTPUOQ0TOciZ4hyOgZzkTPEGQu9syZw0pJ6tu3rxYsWGB6BhCSEydOaMmSJU695BtnR88QRPQMpaFnCCJ6htLQMwQRPUNp6BmCiJ6hNPQMQeRqz5w6rBw4cKBycnK0fv1601OACsvOztaRI0d03XXXmZ4CH6FnCCJ6htLQMwQRPUNp6BmCiJ6hNPQMQUTPUBp6hiBytWdOHVZ27NhRLVq00KxZs0xPASps1qxZysrK0vnnn296CnyEniGI6BlKQ88QRPQMpaFnCCJ6htLQMwQRPUNp6BmCyNWeOXVYKUk33XSTXn31VXmeZ3oKcFZHjx7Vm2++qcGDB5ueAh+iZwgSeoby0DMECT1DeegZgoSeoTz0DEFCz1AeeoYgcblnzh1WDho0SLt27dKaNWtMTwHOasGCBTp27JhuuOEG01PgQ/QMQULPUB56hiChZygPPUOQ0DOUh54hSOgZykPPECQu9yzBc/ApBW3btlXXrl314osvmp4ClKt///46deqUFi1aZHoKfIqeISjoGc6GniEo6BnOhp4hKOgZzoaeISjoGc6GniEoHO7ZPudeWSlJd955p6ZPn65vv/3W9BSgTLt379bChQt11113mZ4CH6NnCAJ6hoqgZwgCeoaKoGcIAnqGiqBnCAJ6hoqgZwgC13vm5GHlrbfeqvz8fD5YF742adIk1alTRwMGDDA9BT5GzxAE9AwVQc8QBPQMFUHPEAT0DBVBzxAE9AwVQc8QBK73zMnDyrS0NN1www2aOHGi6SlAqQoLCzV58mTddtttSk1NNT0HPkbP4Hf0DBVFz+B39AwVRc/gd/QMFUXP4Hf0DBVFz+B39MzRw0pJ+ulPf6o1a9bwwbrwpXnz5mnnzp0aMWKE6SkIAHoGP6NnCAU9g5/RM4SCnsHP6BlCQc/gZ/QMoaBn8DN6JiV4nueZHmFKZmamGjduzMu/4Ttdu3ZVvXr1NHfuXNNTEBD0DH5FzxAqega/omcIFT2DX9EzhIqewa/oGUJFz+BX9Ez7kk0vMOn+++/XzTffrO3bt6t58+am5wCSpLVr12rVqlV6//33TU9BgNAz+BE9QzjoGfyIniEc9Ax+RM8QDnoGP6JnCAc9gx/Rs+84/crK/Px8XXjhhRowYICee+4503MASdKPfvQj7d69m7ckQEjoGfyIniEc9Ax+RM8QDnoGP6JnCAc9gx/RM4SDnsGP6JkkaZ+zn1kpScnJyXrggQf00ksv6fPPPzc9B9CWLVs0d+5cPfTQQ6anIGDoGfyGniFc9Ax+Q88QLnoGv6FnCBc9g9/QM4SLnsFv6Nn/OP3KSkk6fvy4LrzwQg0cOFDPP/+86Tlw3PXXX6/t27dr48aNSkx0+rkECAM9g5/QM0SCnsFP6BkiQc/gJ/QMkaBn8BN6hkjQM/gJPTvN7VdWSlLlypX12GOPacKECcrNzTU9Bw7797//rXnz5unJJ590PUwIEz2DX9AzRIqewS/oGSJFz+AX9AyRomfwC3qGSNEz+AU9K875V1ZK0smTJ9WqVSv16tVLEyZMMD0Hjurfv7++/PJLrVmzRgkJCabnIKDoGfyAniEa6Bn8gJ4hGugZ/ICeIRroGfyAniEa6Bn8gJ4VwysrJalSpUoaPXq0Jk2apC1btpieAwctXrxY8+fP15NPPkmYEBF6BtPoGaKFnsE0eoZooWcwjZ4hWugZTKNniBZ6BtPoWUm8svL/V1hYqC5duqh69ep69913Tc+BQwoKCnTxxReradOmmjdvnuk5sAA9gyn0DNFGz2AKPUO00TOYQs8QbfQMptAzRBs9gyn0rFS8srJIYmKixo4dq/fee4+/IIir8ePH6+OPP9bTTz9tegosQc9gCj1DtNEzmELPEG30DKbQM0QbPYMp9AzRRs9gCj0rHa+s/J4hQ4Zo/fr12rJliypXrmx6DiyXl5enVq1aafjw4cQJUUfPEE/0DLFEzxBP9AyxRM8QT/QMsUTPEE/0DLFEzxBP9KxM+5JGjx492vQKP+ncubPGjBmj48ePq2fPnqbnwHI/+9nPtHPnTs2aNUupqamm58Ay9AzxRM8QS/QM8UTPEEv0DPFEzxBL9AzxRM8QS/QM8UTPynSYt4H9nkaNGunJJ5/UmDFjtGHDBtNzYLH33ntPU6ZM0fPPP6+aNWuangML0TPECz1DrNEzxAs9Q6zRM8QLPUOs0TPECz1DrNEzxAs9Kx9vA1uKwsJCXXHFFTpx4oQ++OADJSUlmZ4Eyxw9elQZGRnq0KGDZs+ebXoOLEbPEGv0DPFCzxBr9AzxQs8Qa/QM8ULPEGv0DPFCzxBr9Oys9vHKylIkJibqxRdf1ObNm/Xss8+angMLPfbYY8rLy9Pzzz9vegosR88Qa/QM8ULPEGv0DPFCzxBr9AzxQs8Qa/QM8ULPEGv07Oz4zMoy1KtXTykpKfr1r3+tPn366Pzzzzc9CZZ4++23dd9992nixInq2rWr6TlwAD1DrNAzxBs9Q6zQM8QbPUOs0DPEGz1DrNAzxBs9Q6zQswo5zNvAlqOwsFC9evXSzp07tWHDBlWvXt30JATcV199pfbt2ysrK0uzZs0yPQcOoWeINnoGU+gZoo2ewRR6hmijZzCFniHa6BlMoWeINnpWYbwNbHkSExP18ssvKy8vT6NGjTI9BwHneZ7uuOMOpaamauLEiabnwDH0DNFEz2ASPUM00TOYRM8QTfQMJtEzRBM9g0n0DNFEz0LD28CeRc2aNdW8eXM98sgjatGihTIyMkxPQkCNGTNGEyZM0Lx589SyZUvTc+AgeoZooWcwjZ4hWugZTKNniBZ6BtPoGaKFnsE0eoZooWchOcxhZQW0bt1ahw4d0q9//Wv169dPDRs2ND0JAbNkyRINHz5czzzzjAYNGmR6DhxGzxApega/oGeIFD2DX9AzRIqewS/oGSJFz+AX9AyRomch4zMrKyo/P1+9evVSbm6u1q9fr7S0NNOTEBC7du3SpZdeqp49e2rGjBmm5wD0DGGjZ/AbeoZw0TP4DT1DuOgZ/IaeIVz0DH5DzxAuehaWfRxWhuCLL75Qx44d1aZNG7311ltKTk42PQk+d+TIEXXv3l0nT57U6tWrVa1aNdOTAEn0DKGjZ/AreoZQ0TP4FT1DqOgZ/IqeIVT0DH5FzxAqeha2fYmmFwRJgwYNNGfOHK1YsUL33nuv6TnwuYKCAg0ZMkS7d+/WvHnzCBN8hZ4hFPQMfkbPEAp6Bj+jZwgFPYOf0TOEgp7Bz+gZQkHPIsNnVobovPPOU/v27fXQQw8pNTVVWVlZpifBp37xi1/otdde04IFC/ggZvgSPUNF0TP4HT1DRdEz+B09Q0XRM/gdPUN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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.mean(dim='time').data.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7eef24f1-f639-497a-9450-abafbdca4dd6",
   "metadata": {},
   "outputs": [
    {
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       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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;NDVI&#x27; (lat: 1008, lon: 1343)&gt;\n",
       "array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0</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'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1008</li><li><span class='xr-has-index'>lon</span>: 1343</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-796fab3c-905b-4bfc-8bd6-bb535dc4055e' class='xr-array-in' type='checkbox' checked><label for='section-796fab3c-905b-4bfc-8bd6-bb535dc4055e' 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>117.4 106.7 96.79 107.2 108.3 108.4 ... 161.0 163.0 169.5 174.3 175.8</span></div><div class='xr-array-data'><pre>array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])</pre></div></div></li><li class='xr-section-item'><input id='section-4f95d646-76fb-484a-8004-71d7cd913ff4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f95d646-76fb-484a-8004-71d7cd913ff4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.001 9.004 9.007 ... 12.99 13.0</div><input id='attrs-175fc73b-4b79-4deb-bdd4-128f07de8a90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-175fc73b-4b79-4deb-bdd4-128f07de8a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36a9f65c-c4f9-487f-9e47-025eef4ade75' class='xr-var-data-in' type='checkbox'><label for='data-36a9f65c-c4f9-487f-9e47-025eef4ade75' 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>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 9.001488,  9.004464,  9.00744 , ..., 12.989583, 12.99256 , 12.995536])</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'>45.0 45.0 44.99 ... 42.01 42.0 42.0</div><input id='attrs-7756bdd0-25a4-4774-84ec-515071faa517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7756bdd0-25a4-4774-84ec-515071faa517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d58d111-87b5-41c2-aa6a-fa39eedd35db' class='xr-var-data-in' type='checkbox'><label for='data-5d58d111-87b5-41c2-aa6a-fa39eedd35db' 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>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44.998512, 44.995536, 44.99256 , ..., 42.00744 , 42.004464, 42.001488])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a17fa8c-19c7-4f6d-b2c0-d85a51dcfa99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a17fa8c-19c7-4f6d-b2c0-d85a51dcfa99' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'NDVI' (lat: 1008, lon: 1343)>\n",
       "array([[117.36842105, 106.68421053,  96.78947368, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 99.10526316,  85.89473684,  82.21052632, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       [ 88.21052632,  81.84210526,  82.42105263, ..., 129.        ,\n",
       "        129.        , 129.        ],\n",
       "       ...,\n",
       "       [177.21052632, 180.47368421, 177.42105263, ..., 172.36842105,\n",
       "        170.31578947, 167.47368421],\n",
       "       [179.73684211, 182.        , 180.57894737, ..., 171.84210526,\n",
       "        170.78947368, 173.47368421],\n",
       "       [184.89473684, 189.15789474, 187.52631579, ..., 169.47368421,\n",
       "        174.31578947, 175.78947368]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 9.001 9.004 9.007 9.01 ... 12.99 12.99 12.99 13.0\n",
       "  * lat      (lat) float64 45.0 45.0 44.99 44.99 44.99 ... 42.01 42.01 42.0 42.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.mean(dim='time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6aa81908-43d9-4512-8726-c0cdf24ab598",
   "metadata": {},
   "outputs": [
    {
     "ename": "KilledWorker",
     "evalue": "('original-xarray-NDVI-10007198de850e37ae762ba790fb5e14', <WorkerState 'tls://10.244.8.9:38043', name: dask-worker-d0c798962f604531bc9622ae7b930983-8znv8, status: closed, memory: 0, processing: 1>)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKilledWorker\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[0;32mIn [31]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcgls_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_zarr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtest.zarr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/dataset.py:2036\u001b[0m, in \u001b[0;36mDataset.to_zarr\u001b[0;34m(self, store, chunk_store, mode, synchronizer, group, encoding, compute, consolidated, append_dim, region, safe_chunks, storage_options)\u001b[0m\n\u001b[1;32m   2033\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2034\u001b[0m     encoding \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m-> 2036\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_zarr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2037\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2038\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2039\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunk_store\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunk_store\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2040\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2041\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2042\u001b[0m \u001b[43m    \u001b[49m\u001b[43msynchronizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynchronizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2043\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2044\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2045\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2046\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconsolidated\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconsolidated\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2047\u001b[0m \u001b[43m    \u001b[49m\u001b[43mappend_dim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mappend_dim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2048\u001b[0m \u001b[43m    \u001b[49m\u001b[43mregion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mregion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2049\u001b[0m \u001b[43m    \u001b[49m\u001b[43msafe_chunks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msafe_chunks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2050\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/backends/api.py:1432\u001b[0m, in \u001b[0;36mto_zarr\u001b[0;34m(dataset, store, chunk_store, mode, synchronizer, group, encoding, compute, consolidated, append_dim, region, safe_chunks, storage_options)\u001b[0m\n\u001b[1;32m   1430\u001b[0m \u001b[38;5;66;03m# TODO: figure out how to properly handle unlimited_dims\u001b[39;00m\n\u001b[1;32m   1431\u001b[0m dump_to_store(dataset, zstore, writer, encoding\u001b[38;5;241m=\u001b[39mencoding)\n\u001b[0;32m-> 1432\u001b[0m writes \u001b[38;5;241m=\u001b[39m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compute:\n\u001b[1;32m   1435\u001b[0m     _finalize_store(writes, zstore)\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/backends/common.py:166\u001b[0m, in \u001b[0;36mArrayWriter.sync\u001b[0;34m(self, compute)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mda\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[38;5;66;03m# TODO: consider wrapping targets with dask.delayed, if this makes\u001b[39;00m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;66;03m# for any discernible difference in perforance, e.g.,\u001b[39;00m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;66;03m# targets = [dask.delayed(t) for t in self.targets]\u001b[39;00m\n\u001b[0;32m--> 166\u001b[0m delayed_store \u001b[38;5;241m=\u001b[39m \u001b[43mda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    167\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    168\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    169\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    170\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflush\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m    \u001b[49m\u001b[43mregions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msources \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtargets \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/dask/array/core.py:1223\u001b[0m, in \u001b[0;36mstore\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   1221\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m compute:\n\u001b[1;32m   1222\u001b[0m     store_dsk \u001b[38;5;241m=\u001b[39m HighLevelGraph(layers, dependencies)\n\u001b[0;32m-> 1223\u001b[0m     \u001b[43mcompute_as_if_collection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mArray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore_dsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1224\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/dask/base.py:344\u001b[0m, in \u001b[0;36mcompute_as_if_collection\u001b[0;34m(cls, dsk, keys, scheduler, get, **kwargs)\u001b[0m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# see https://github.com/dask/dask/issues/8991.\u001b[39;00m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;66;03m# This merge should be removed once the underlying issue is fixed.\u001b[39;00m\n\u001b[1;32m    343\u001b[0m dsk2 \u001b[38;5;241m=\u001b[39m HighLevelGraph\u001b[38;5;241m.\u001b[39mmerge(dsk2)\n\u001b[0;32m--> 344\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/client.py:3000\u001b[0m, in \u001b[0;36mClient.get\u001b[0;34m(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m   2998\u001b[0m         should_rejoin \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   2999\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3000\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgather\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpacked\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3001\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m   3002\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m futures\u001b[38;5;241m.\u001b[39mvalues():\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/client.py:2174\u001b[0m, in \u001b[0;36mClient.gather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m   2172\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2173\u001b[0m     local_worker \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2175\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gather\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2176\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2177\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2178\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2179\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocal_worker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_worker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2180\u001b[0m \u001b[43m    \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2181\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/utils.py:320\u001b[0m, in \u001b[0;36mSyncMethodMixin.sync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m future\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback_timeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/utils.py:387\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error:\n\u001b[1;32m    386\u001b[0m     typ, exc, tb \u001b[38;5;241m=\u001b[39m error\n\u001b[0;32m--> 387\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/utils.py:360\u001b[0m, in \u001b[0;36msync.<locals>.f\u001b[0;34m()\u001b[0m\n\u001b[1;32m    358\u001b[0m         future \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mwait_for(future, callback_timeout)\n\u001b[1;32m    359\u001b[0m     future \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mensure_future(future)\n\u001b[0;32m--> 360\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m future\n\u001b[1;32m    361\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    362\u001b[0m     error \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/tornado/gen.py:762\u001b[0m, in \u001b[0;36mRunner.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    759\u001b[0m exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    761\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 762\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    763\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    764\u001b[0m     exc_info \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/distributed/client.py:2037\u001b[0m, in \u001b[0;36mClient._gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m   2035\u001b[0m         exc \u001b[38;5;241m=\u001b[39m CancelledError(key)\n\u001b[1;32m   2036\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2037\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exception\u001b[38;5;241m.\u001b[39mwith_traceback(traceback)\n\u001b[1;32m   2038\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m   2039\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[0;31mKilledWorker\u001b[0m: ('original-xarray-NDVI-10007198de850e37ae762ba790fb5e14', <WorkerState 'tls://10.244.8.9:38043', name: dask-worker-d0c798962f604531bc9622ae7b930983-8znv8, status: closed, memory: 0, processing: 1>)"
     ]
    }
   ],
   "source": [
    "cgls_ds.chunk(chunk).to_zarr('test.zarr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e53b2e3b-8955-4841-b83d-7481e804292a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    PID TTY          TIME CMD\n",
      "   5741 pts/2    00:00:00 ps\n"
     ]
    }
   ],
   "source": [
    "cgls_ds.to_zarr('test.zarr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ad33ea5e-a15e-4d12-a0d0-3bc88bb5f7a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 16\n",
      "-rw-r--r-- 1 jovyan jovyan  368 Jul 27 23:41 .zarray\n",
      "-rw-r--r-- 1 jovyan jovyan  201 Jul 27 23:41 .zattrs\n",
      "drwxr-xr-x 2 jovyan jovyan 4096 Jul 27 23:41 .\n",
      "drwxr-xr-x 7 jovyan jovyan 4096 Jul 27 23:41 ..\n"
     ]
    }
   ],
   "source": [
    "!ls -lart test.zarr/NDVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f3b71e59-9d57-4ab8-8ad6-d5599fbfd22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -rf test.zarr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0ab928-7b61-4869-abb7-38e633ab8e9f",
   "metadata": {},
   "source": [
    "# How does dask workers configured?\n",
    "no common disk space between dask workers and jupyter hub nodes in case of https://pangeo-xxlarge.vm.fedcloud.eu/jupyterhub/\n",
    "Thus example for creating temporaly zarr file would work only if;\n",
    "1.  we use local dask and create zarr file in jupyterhub side.\n",
    "2.  if we use daskgateway, create 'common' disk space from all dask workers which they can write file.  for example s3?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e2e10400-26d5-4be6-8695-592b2a75ecd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filesystem                                                                               1K-blocks     Used Available Use% Mounted on\n",
      "overlay                                                                                   81120644 12230576  68873684  16% /\n",
      "tmpfs                                                                                        65536        0     65536   0% /dev\n",
      "tmpfs                                                                                     16441476        0  16441476   0% /sys/fs/cgroup\n",
      "kubeserver.localdomain:/pv/daskhub-claim-todaka-pvc-dcb10ce2-a6e1-401f-9aa6-2a4ed454bf95 959852544   399360 910625792   1% /home/jovyan\n",
      "/dev/sda1                                                                                 81120644 12230576  68873684  16% /etc/hosts\n",
      "shm                                                                                          65536      316     65220   1% /dev/shm\n",
      "tmpfs                                                                                     16441476        0  16441476   0% /proc/acpi\n",
      "tmpfs                                                                                     16441476        0  16441476   0% /proc/scsi\n",
      "tmpfs                                                                                     16441476        0  16441476   0% /sys/firmware\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def linuxcmd(x) :\n",
    "    import subprocess\n",
    "    cmd=['ls', '/home']\n",
    "    cmd=['df']\n",
    "    proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "    o, e = proc.communicate()\n",
    "    #print(cmd)\n",
    "    return o.decode('ascii')\n",
    "test=linuxcmd(1)\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6f3412d4-1bed-4f17-ba38-fd9338c777dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Filesystem     1K-blocks     Used Available Use% Mounted on\\noverlay         81120644 12294008  68810252  16% /\\ntmpfs              65536        0     65536   0% /dev\\ntmpfs           16441476        0  16441476   0% /sys/fs/cgroup\\ntmpfs            2097152       12   2097140   1% /etc/dask-credentials\\n/dev/sda1       81120644 12294008  68810252  16% /etc/hosts\\nshm                65536       24     65512   1% /dev/shm\\ntmpfs           16441476        0  16441476   0% /proc/acpi\\ntmpfs           16441476        0  16441476   0% /proc/scsi\\ntmpfs           16441476        0  16441476   0% /sys/firmware\\n',)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from dask import delayed\n",
    "#import numpy as np\n",
    "f=delayed(lambda x: linuxcmd(x))\n",
    "\n",
    "#client.submit(f(2))\n",
    "client.compute(f(2))\n",
    "import dask\n",
    "dask.compute(f(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8ee0f19-5240-4d45-a3d9-89b109b87e3e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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