From e524f1d90aa9a9513345fba8b474bda6faf0315a Mon Sep 17 00:00:00 2001
From: claudia <claudia.vitolo@gmail.com>
Date: Fri, 30 Apr 2021 12:13:17 +0000
Subject: [PATCH] Complete re-write of the data pre-processing step to avoid
 resampling. This addresses issue #13.

---
 notebooks/data_preparation.ipynb | 9736 +++++++++++++++++++++++++++---
 1 file changed, 8830 insertions(+), 906 deletions(-)

diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb
index 5df91d1..15eac7d 100644
--- a/notebooks/data_preparation.ipynb
+++ b/notebooks/data_preparation.ipynb
@@ -14,7 +14,8 @@
     "import pandas as pd\n",
     "from datetime import datetime\n",
     "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split"
+    "from sklearn.model_selection import train_test_split\n",
+    "from fastai.tabular.all import *"
    ]
   },
   {
@@ -416,7 +417,7 @@
        "    abg_baccini_vod95th    (time, latitude, longitude) float64 ...\n",
        "    abg_saatchi_vod5th     (time, latitude, longitude) float64 ...\n",
        "    abg_saatchi_vodmean    (time, latitude, longitude) float64 ...\n",
-       "    abg_saatchi_vod95th    (time, latitude, longitude) float64 ...</pre><div class='xr-wrap' hidden><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-a2459983-20e9-42bd-b888-c7ef0120d615' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a2459983-20e9-42bd-b888-c7ef0120d615' 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'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 84</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a7f00806-fc8f-4e39-8003-731b1829e70f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7f00806-fc8f-4e39-8003-731b1829e70f' 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'>2010-01-01 ... 2016-12-01</div><input id='attrs-021a16b3-905f-4c2a-b7af-b98620e3d988' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-021a16b3-905f-4c2a-b7af-b98620e3d988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37d3b924-097e-4285-9af3-c89473e66765' class='xr-var-data-in' type='checkbox'><label for='data-37d3b924-097e-4285-9af3-c89473e66765' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
+       "    abg_saatchi_vod95th    (time, latitude, longitude) float64 ...</pre><div class='xr-wrap' hidden><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-00666cca-8d2f-4005-a5b3-f63788f854d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-00666cca-8d2f-4005-a5b3-f63788f854d6' 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'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 84</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-329580cd-5db2-4484-9309-d07129ced8f2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-329580cd-5db2-4484-9309-d07129ced8f2' 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'>2010-01-01 ... 2016-12-01</div><input id='attrs-404f289b-47bf-45bb-9074-e9a0b0f56e17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-404f289b-47bf-45bb-9074-e9a0b0f56e17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f908810-44fc-4faf-9f0d-b4cfa484e563' class='xr-var-data-in' type='checkbox'><label for='data-2f908810-44fc-4faf-9f0d-b4cfa484e563' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2010-03-01T00:00:00.000000000&#x27;, &#x27;2010-04-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2010-05-01T00:00:00.000000000&#x27;, &#x27;2010-06-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2010-07-01T00:00:00.000000000&#x27;, &#x27;2010-08-01T00:00:00.000000000&#x27;,\n",
@@ -458,9 +459,9 @@
        "       &#x27;2016-07-01T00:00:00.000000000&#x27;, &#x27;2016-08-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2016-09-01T00:00:00.000000000&#x27;, &#x27;2016-10-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2016-11-01T00:00:00.000000000&#x27;, &#x27;2016-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-13ef88bf-ac6c-416e-a1ae-e9c334027cce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13ef88bf-ac6c-416e-a1ae-e9c334027cce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7c6bd3-4a68-4cf1-a19b-90cc00138886' class='xr-var-data-in' type='checkbox'><label for='data-1a7c6bd3-4a68-4cf1-a19b-90cc00138886' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</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([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-2c4c3d05-9edf-4776-8dcc-5c14c93a6de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c4c3d05-9edf-4776-8dcc-5c14c93a6de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18e051b7-08c6-45b9-9c50-d1d343946066' class='xr-var-data-in' type='checkbox'><label for='data-18e051b7-08c6-45b9-9c50-d1d343946066' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63634e6f-e50d-4d4b-92fd-f2c63c5a32ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-63634e6f-e50d-4d4b-92fd-f2c63c5a32ac' class='xr-section-summary' >Data variables: <span>(9)</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>abg_avitabile_vod5th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b092fc5a-2efc-4e20-9afa-dac84e656b9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b092fc5a-2efc-4e20-9afa-dac84e656b9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b85ada3f-c0e6-448f-8e30-9811c95df963' class='xr-var-data-in' type='checkbox'><label for='data-b85ada3f-c0e6-448f-8e30-9811c95df963' 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>AGB estimation using a relation Avitabile et al. as a fct of 5thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_avitabile_vodmean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a103f4a9-e332-472e-9203-a7b491ec1b1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a103f4a9-e332-472e-9203-a7b491ec1b1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11dad773-edde-4df6-9b9b-037de91ad29a' class='xr-var-data-in' type='checkbox'><label for='data-11dad773-edde-4df6-9b9b-037de91ad29a' 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>AGB estimation using a relation Avitabile et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_avitabile_vod95th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64f5113e-57c9-44d6-be42-7666fd2e1878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64f5113e-57c9-44d6-be42-7666fd2e1878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f11fc03-6104-4579-b2aa-4baf3ebf766e' class='xr-var-data-in' type='checkbox'><label for='data-5f11fc03-6104-4579-b2aa-4baf3ebf766e' 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>AGB estimation using a relation Avitabile et al. as a fct of 95thpercentile</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_baccini_vod5th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-afb5f1a5-1419-43df-84f5-b92b46c04281' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afb5f1a5-1419-43df-84f5-b92b46c04281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79b24252-a70b-42ba-a6a7-ea303edc9553' class='xr-var-data-in' type='checkbox'><label for='data-79b24252-a70b-42ba-a6a7-ea303edc9553' 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>AGB estimation using a relation Baccini et al. as a fct of 5thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_baccini_vodmean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3170a40c-3815-4630-9cbb-e358e0f2e690' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3170a40c-3815-4630-9cbb-e358e0f2e690' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-360bbca6-57f4-46bc-9ea8-73afdd0945f3' class='xr-var-data-in' type='checkbox'><label for='data-360bbca6-57f4-46bc-9ea8-73afdd0945f3' 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>AGB estimation using a relation Baccini et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_baccini_vod95th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc7db628-252e-46f3-904f-c616b098fc9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc7db628-252e-46f3-904f-c616b098fc9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3b12ef3-eb15-4b45-8d52-fc88105d802a' class='xr-var-data-in' type='checkbox'><label for='data-e3b12ef3-eb15-4b45-8d52-fc88105d802a' 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>AGB estimation using a relation Baccini et al. as a fct of 95thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_saatchi_vod5th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-db314955-7bc5-40e8-9965-f31dd5832cae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db314955-7bc5-40e8-9965-f31dd5832cae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afd29644-66b9-486d-9d38-65a83b4f1366' class='xr-var-data-in' type='checkbox'><label for='data-afd29644-66b9-486d-9d38-65a83b4f1366' 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>AGB estimation using a relation Saatchi et al. as a fct of 5thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_saatchi_vodmean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4cdf89bd-f117-4f27-834f-4252673eaa50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cdf89bd-f117-4f27-834f-4252673eaa50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edcddce8-13d5-4f66-94d2-2a2fa76b8670' class='xr-var-data-in' type='checkbox'><label for='data-edcddce8-13d5-4f66-94d2-2a2fa76b8670' 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>AGB estimation using a relation Saatchi et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_saatchi_vod95th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5df33397-670a-41a3-b28d-a3803218fc5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5df33397-670a-41a3-b28d-a3803218fc5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3503db88-88ca-4315-9b97-78820f940174' class='xr-var-data-in' type='checkbox'><label for='data-3503db88-88ca-4315-9b97-78820f940174' 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>AGB estimation using a relation Saatchi et al. as a fct of 95thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b53577f4-1119-4489-8c3b-c476554f98c1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b53577f4-1119-4489-8c3b-c476554f98c1' 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>"
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-83ac9d34-b1d7-4ecf-a825-c1e7edb29586' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83ac9d34-b1d7-4ecf-a825-c1e7edb29586' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b94a2495-cf1a-498e-a8da-4564e721ab28' class='xr-var-data-in' type='checkbox'><label for='data-b94a2495-cf1a-498e-a8da-4564e721ab28' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</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([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-6724cbd9-8eef-462c-abe8-3fc13cd5b98d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6724cbd9-8eef-462c-abe8-3fc13cd5b98d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15dc7d60-bd1b-466a-afcb-9510205c356f' class='xr-var-data-in' type='checkbox'><label for='data-15dc7d60-bd1b-466a-afcb-9510205c356f' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bcbaf105-bf0c-4600-a6e8-beadbb4caef1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bcbaf105-bf0c-4600-a6e8-beadbb4caef1' class='xr-section-summary' >Data variables: <span>(9)</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>abg_avitabile_vod5th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ee5f52fb-59e7-46aa-a531-5e8446734551' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee5f52fb-59e7-46aa-a531-5e8446734551' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-784d6f14-f501-4f43-9f6a-7dfb8e346196' class='xr-var-data-in' type='checkbox'><label for='data-784d6f14-f501-4f43-9f6a-7dfb8e346196' 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>AGB estimation using a relation Avitabile et al. as a fct of 5thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_avitabile_vodmean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f6300629-5e50-4c51-9e86-da186e608ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6300629-5e50-4c51-9e86-da186e608ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1450b832-2ef7-4ff9-b9aa-99fe36485696' class='xr-var-data-in' type='checkbox'><label for='data-1450b832-2ef7-4ff9-b9aa-99fe36485696' 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>AGB estimation using a relation Avitabile et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_avitabile_vod95th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70e29bff-5455-4179-b2b9-00292392bfb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70e29bff-5455-4179-b2b9-00292392bfb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74de4bf1-3ef0-4571-a741-80e781447c2f' class='xr-var-data-in' type='checkbox'><label for='data-74de4bf1-3ef0-4571-a741-80e781447c2f' 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>AGB estimation using a relation Avitabile et al. as a fct of 95thpercentile</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_baccini_vod5th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-187a4c76-97be-4136-a715-995994e82d40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-187a4c76-97be-4136-a715-995994e82d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-589512b3-0ef5-4be1-90ab-9e0681b64c11' class='xr-var-data-in' type='checkbox'><label for='data-589512b3-0ef5-4be1-90ab-9e0681b64c11' 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>AGB estimation using a relation Baccini et al. as a fct of 5thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_baccini_vodmean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4be95d74-f7bd-4484-bc6a-3a25a0e54e9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4be95d74-f7bd-4484-bc6a-3a25a0e54e9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-952c0f3b-2bf3-4523-a86c-7f6ca213fabd' class='xr-var-data-in' type='checkbox'><label for='data-952c0f3b-2bf3-4523-a86c-7f6ca213fabd' 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>AGB estimation using a relation Baccini et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_baccini_vod95th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dac74669-7e1f-41ba-baad-a1ea44a6b9c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dac74669-7e1f-41ba-baad-a1ea44a6b9c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc4490ed-f629-459c-a99a-262701e00a47' class='xr-var-data-in' type='checkbox'><label for='data-fc4490ed-f629-459c-a99a-262701e00a47' 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>AGB estimation using a relation Baccini et al. as a fct of 95thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_saatchi_vod5th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-60de11de-3d31-4630-aa4f-504f9ac25426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60de11de-3d31-4630-aa4f-504f9ac25426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8c8e5ac-1e4c-49c4-8dad-28284edc55c1' class='xr-var-data-in' type='checkbox'><label for='data-e8c8e5ac-1e4c-49c4-8dad-28284edc55c1' 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>AGB estimation using a relation Saatchi et al. as a fct of 5thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_saatchi_vodmean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3140d331-e0cf-46d1-846d-561a129eae12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3140d331-e0cf-46d1-846d-561a129eae12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba914b37-a4ed-4b20-b1d3-5349aef677ed' class='xr-var-data-in' type='checkbox'><label for='data-ba914b37-a4ed-4b20-b1d3-5349aef677ed' 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>AGB estimation using a relation Saatchi et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abg_saatchi_vod95th</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84b3cbd9-0dec-4114-9ff9-69e724a9e0ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84b3cbd9-0dec-4114-9ff9-69e724a9e0ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-303512a3-3ee8-4feb-aa70-7c13d1feaee9' class='xr-var-data-in' type='checkbox'><label for='data-303512a3-3ee8-4feb-aa70-7c13d1feaee9' 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>AGB estimation using a relation Saatchi et al. as a fct of 95thpercentile VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div><div class='xr-var-data'><pre>[87091200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02cf0fec-9dc0-4c10-8a27-f0c5ca6b077b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02cf0fec-9dc0-4c10-8a27-f0c5ca6b077b' 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.Dataset>\n",
@@ -897,7 +898,7 @@
        "  * latitude   (latitude) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
        "Attributes:\n",
        "    long_name:  AGB estimation using a relation Avitabile et al. as a fct of ...\n",
-       "    units:      Mg/h</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'abg_avitabile_vodmean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 84</li><li><span class='xr-has-index'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-24c4a2c8-4370-4ecd-951b-68424556b145' class='xr-array-in' type='checkbox' checked><label for='section-24c4a2c8-4370-4ecd-951b-68424556b145' 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>...</span></div><div class='xr-array-data'><pre>[87091200 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-dba1bb0e-01b1-48df-9321-81dfb28df56d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dba1bb0e-01b1-48df-9321-81dfb28df56d' 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'>2010-01-01 ... 2016-12-01</div><input id='attrs-fb610962-1f10-4ca0-9a7e-505668fb510c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb610962-1f10-4ca0-9a7e-505668fb510c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a1d5478-25d1-423c-b9ec-e00f48eefa44' class='xr-var-data-in' type='checkbox'><label for='data-0a1d5478-25d1-423c-b9ec-e00f48eefa44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
+       "    units:      Mg/h</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'abg_avitabile_vodmean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 84</li><li><span class='xr-has-index'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-810c3771-8a27-479e-9e2a-f204f7cfc040' class='xr-array-in' type='checkbox' checked><label for='section-810c3771-8a27-479e-9e2a-f204f7cfc040' 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>...</span></div><div class='xr-array-data'><pre>[87091200 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-bb3f80a6-872c-417b-8712-ee60c06ac0c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb3f80a6-872c-417b-8712-ee60c06ac0c2' 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'>2010-01-01 ... 2016-12-01</div><input id='attrs-b1556c50-8309-42b7-a7d0-a4147d1cb039' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1556c50-8309-42b7-a7d0-a4147d1cb039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddea036b-fe37-46a0-8ba6-1abc94bde1cb' class='xr-var-data-in' type='checkbox'><label for='data-ddea036b-fe37-46a0-8ba6-1abc94bde1cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2010-03-01T00:00:00.000000000&#x27;, &#x27;2010-04-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2010-05-01T00:00:00.000000000&#x27;, &#x27;2010-06-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2010-07-01T00:00:00.000000000&#x27;, &#x27;2010-08-01T00:00:00.000000000&#x27;,\n",
@@ -939,9 +940,9 @@
        "       &#x27;2016-07-01T00:00:00.000000000&#x27;, &#x27;2016-08-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2016-09-01T00:00:00.000000000&#x27;, &#x27;2016-10-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2016-11-01T00:00:00.000000000&#x27;, &#x27;2016-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-38fb37e5-efd5-4b68-98dc-65b7c2439e60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38fb37e5-efd5-4b68-98dc-65b7c2439e60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13172b2d-abe9-4419-99cc-8d7f500935f9' class='xr-var-data-in' type='checkbox'><label for='data-13172b2d-abe9-4419-99cc-8d7f500935f9' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</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([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-075c1db1-19dd-48c2-8dc4-d3855dd83c8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-075c1db1-19dd-48c2-8dc4-d3855dd83c8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbaf44ee-df1e-461e-b2ba-cf1f1f56de76' class='xr-var-data-in' type='checkbox'><label for='data-bbaf44ee-df1e-461e-b2ba-cf1f1f56de76' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b72927cb-a860-40f7-b47c-c902f8351cdc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b72927cb-a860-40f7-b47c-c902f8351cdc' 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>long_name :</span></dt><dd>AGB estimation using a relation Avitabile et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-05b87a1d-e64d-40eb-877f-db652664a1b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05b87a1d-e64d-40eb-877f-db652664a1b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-468382ed-1472-494d-b2a7-b3ffcb66b47f' class='xr-var-data-in' type='checkbox'><label for='data-468382ed-1472-494d-b2a7-b3ffcb66b47f' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</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([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-002eb942-bb86-4853-9d5e-23f0fadf530d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-002eb942-bb86-4853-9d5e-23f0fadf530d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aed679a-a117-44f5-a02f-efd82b120898' class='xr-var-data-in' type='checkbox'><label for='data-0aed679a-a117-44f5-a02f-efd82b120898' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d675f53-d8ed-4d45-9207-3e25c017fc13' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d675f53-d8ed-4d45-9207-3e25c017fc13' 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>long_name :</span></dt><dd>AGB estimation using a relation Avitabile et al. as a fct of mean VOD</dd><dt><span>units :</span></dt><dd>Mg/h</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'abg_avitabile_vodmean' (time: 84, latitude: 720, longitude: 1440)>\n",
@@ -981,7 +982,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d7bfa1be0>"
+       "<matplotlib.collections.QuadMesh at 0x7fb775a8be80>"
       ]
      },
      "execution_count": 5,
@@ -1026,7 +1027,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d7be04f70>"
+       "<matplotlib.collections.QuadMesh at 0x7fb775900310>"
       ]
      },
      "execution_count": 7,
@@ -1105,7 +1106,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d7bce70d0>"
+       "<matplotlib.collections.QuadMesh at 0x7fb77583fe20>"
       ]
      },
      "execution_count": 9,
@@ -1143,7 +1144,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d7bc10550>"
+       "<matplotlib.collections.QuadMesh at 0x7fb7757baca0>"
       ]
      },
      "execution_count": 10,
@@ -1190,7 +1191,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d7bd166d0>"
+       "<matplotlib.collections.QuadMesh at 0x7fb77586d3a0>"
       ]
      },
      "execution_count": 12,
@@ -1237,7 +1238,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d7bb11f70>"
+       "<matplotlib.collections.QuadMesh at 0x7fb77560d7c0>"
       ]
      },
      "execution_count": 13,
@@ -1275,492 +1276,4385 @@
     "load_data.to_netcdf(folder_path + \"load_2010-2016.nc\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "3ec0c9ac",
-   "metadata": {},
-   "source": [
-    "# Static predictors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fe89ecd",
-   "metadata": {},
-   "source": [
-    "## Climatic regions"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "745d141b",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d7ba06a90>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"Load climatic regions data into an xarray dataset\"\"\"\n",
-    "cr_data = xr.open_dataset(\"/data1/raw_data/Beck_KG_V1_present_0p0083.gridName0320.nc\")\n",
-    "# Rotate longitude coordinates\n",
-    "cr_data = cr_data.assign_coords(\n",
-    "    longitude=(((cr_data.longitude + 180) % 360) - 180)\n",
-    ").sortby(\"longitude\")\n",
-    "# Interpolate to match load resolution\n",
-    "climate_region = cr_data.climatic_region.interp(\n",
-    "    coords={\n",
-    "        \"latitude\": load_data.latitude.values,\n",
-    "        \"longitude\": load_data.longitude.values,\n",
-    "    },\n",
-    "    method=\"nearest\",\n",
-    ")  # Wikilimo used default method ('linear')\n",
-    "climate_region.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "da835da3",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Replicate the same layer 81 times to match load's temporal resolution\n",
-    "cr_data = [climate_region for i in range(0, 81)]\n",
-    "cr_data = xr.concat(cr_data, \"time\")\n",
-    "cr_data[\"time\"] = load_data[\"time\"]\n",
-    "# Mask using the load\n",
-    "cr_data = cr_data.where(load_data >= 0)\n",
-    "cr_data[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "cr_data.to_netcdf(folder_path + \"climatic_region_2010-2016.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21253f10",
-   "metadata": {},
-   "source": [
-    "## Slope"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "d8795f88",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d73032730>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"Load slope data into an xarray dataset\"\"\"\n",
-    "slope_data = xr.open_mfdataset(\"/data1/raw_data/slope_O320.nc\")\n",
-    "# Rotate longitude coordinates\n",
-    "slope_data = slope_data.slor.assign_coords(\n",
-    "    longitude=(((slope_data.longitude + 180) % 360) - 180)\n",
-    ").sortby(\"longitude\")\n",
-    "# Interpolate to match load resolution\n",
-    "slope_data = slope_data.interp(\n",
-    "    coords={\n",
-    "        \"latitude\": load_data.latitude.values,\n",
-    "        \"longitude\": load_data.longitude.values,\n",
-    "    },\n",
-    "    method=\"linear\",\n",
-    ")  # Wikilimo used default method ('linear')\n",
-    "slope_data.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "462f678b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Replicate the same layer 81 times to match load's temporal resolution\n",
-    "sl_data = [slope_data for i in range(0, 81)]\n",
-    "sl_data = xr.concat(sl_data, \"time\")\n",
-    "sl_data[\"time\"] = load_data[\"time\"]\n",
-    "# Mask using the load\n",
-    "sl_data = sl_data.where(load_data >= 0)\n",
-    "sl_data[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "sl_data.to_netcdf(folder_path + \"slope_2010-2016.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed56e546",
-   "metadata": {},
-   "source": [
-    "## Biomes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "f4eac353",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0d73041730>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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U2fyzZqFm7608s4SLS89bBo2yB1Y/ficDNtoWQUu/0OuattsBNwF7mjcQSSuBl5jZ/ZJ2Aa4ys4bevJD0yMNuzmnF9FKWfjnUFHPyqjtyhWc/pTa+/L6bayGVfmilb6LJpk3wv1O2XV66hYvWHsjRU25g/Jg5NXu+v+76bHSeZmgkCAdd6/dp16yTvQ+rSI88feaT7YMXhnVx6n5XRZMOsAfwR+Abkm6UdHbqvNjJzO5P2/we2KnbA+t2vpRhYMm6sdrSz7jY+Xbyw48fM6f2PbPf1/kI8o7Jth8/Zk5dX26/++xr8+5/X4t3QtBF+PjLMNHo+/RK+zfCip/0UwGUXgv8LYEDgUVm9hxgHRnzTar5576GSJonaZmkZet5vPRkvkDK/kiPPOI4lqwbq/3vfpxHHnEcr9j1AF6x6wEsWTdWtz5o5I05z4zjfw59K1o0Y+/a4gRYN/0Bk2buW1v8bJc+/t81a4Ypwr9ftl31UO17Odu++44XrT2Q8WPmcMN3ZzJv8SWbRRTlPQTcNr+t39+RRxzHAyftspnQd2SFftbsM4jmnbIx570xZh8Gro37rXbqLdOgE6kVOkqvR3IvcK+Z/Sr9/H0Sgf+ApF08k84f8g5OJy+MQ2LSafbkeT/22ozLxZc0JdT9tiGv51mTwLDhzBhVpxCuAn+W7PgxcxinOORz7ti6miln/uwpkLZ1wnrN7Cm19Z3Ovp95Uzbvx/U9f+EJhdo/UEvEBokfgMXUnL8PnLQL8xZfspkzOI9sRM8gMVjj7a9c9yH0VMM3s98D96TpQSFJHHQbcDHw5nTbm4GLOjWGuWPrNvsRdlJ7z4bsuTeKSDXkRdX4OIEaaoZxHHnEcSw45dzN+pm6dG1No7/huzOZv/CEFkY98YB043CmHp88Ye/PAoZE2N/56Wdw56efsVmah24xaG8VrWI0NdO2bSR9KWD5ZMM+eh2HL+nZwNnAE4DfAieSPIguAHYDVgPHmdmfGvXTSk1bP877sRnb1wopN6uZOhNCqGbvt+20pu+/zrbrtO4nB2yWSTP35TcnPYVdr07uZ/e3zMbtu7+VL0z9OHtX2MTF7Gfxtfmjp9xQt23N7ClMXbq21nbN7Cm1h4T/IFhwyrmbCXigzsnr+irS6rMa/52ffgbXnDXO3he8jV2vNq45a7xmHspL/dxPb5hVFCxq9t6swmk7beZ2duoFLwxq+2/7X9b2+SStBj5a0uxDZvaMop29NulgZjcBeReiKxXJnUboBEQ7ZJ1xZZOcaj/4gsRdkebICvsiPveB47nmivE6H0OeFuwEL9QLabdvnAlhvcYz98xfeAJTl66tE/b++vyFJ8DsZH0qa+vO594W5i88gTWzp9T6WjN7CtvdtZGHd59U++weIlOXrmWvs2/nxLtfxIuefxs3rJ5ZZx763AeOr78maf/j6cNpUENxez3ZzEyVae+BfN7MzmnUoGzOUs81/KoI0fCzMzd9Yd9uBsaslu80qOysTF+wuB9zXqbGTszWrHKmcb9o+0W5d5wA3vnzP889JlsI3j/G4YS82+60d6e5F2n2O519Pw+ctEtte7ZfH/84J6Dfd8kbARhbvUVtv3sDLeor+2bhxj9/4Qmsm76Jfc7+c+0c7kFx8/sXFY6rFzS6p05edUdd5lEn7N3vpFlfURUa/tT9t7e3ffewoLbzn/XffRGW2XMNv5dUodVn8QX1knVjDUP+FpxyLuNLNw/Va9R3fBPYHP/B7ZvmGnHYqfPYluJ2U5eurf1t3Gcf9yBwQtUXxDtxf209VNgDNeHsWDd9U93xD+9e39e66ZsYW51omL85aUKxG1s90d+CU85l7tg6Dlh9cm1/NwV9p7TwfnDuJgVQJvV6GABI+qiZfbysXa/DMjuOH64HiU03b2kXF5Lo02iCjfvfj+CACWedH7PtlvFj5rTl5K1Kuz/yiOOCslp2GjeGbVc9xMO7T6qt+/z+vS+oa+9s82UPhTWzp9QWZ47xPztBPH7MnM0E9w3fnVknqKcuXbtZG9dflrHVW9QtjfD3+8f4zmV3Hzkh38ip3Ikw2iozfTqfiX983u+uWyRO276Jwz8ppNHQC/xekhXMjWZXZoWKH+7n7L6+862Xk5wGweabJ2AhGbsrUeh/j0amEl+Dd5/deiMN3u87axbK2xeKe4AUfUdfqLu+D/jMyblts4S2C6Vs7kBemxD6QcOHJD1yyFIFkh4pWB4Fdg3pIwr8DpE17fjb3TbfRFNz5nn4Qt+18cm+BXSLfsox5AR3niAtEohuEpUv3PLaOv+KL2D9fkNs9FlC2zYS6EXt83CKxM3vX1Rbirj0igtYcMq5lU6aywrmPEHfjPAumt3cC3ow0/YhYIaZTc4sTwbPjtiAoRf4VZtuysi+XubdoFlbvL/uC5c8YZJ9CGTP1ckfwmGnzqv134nc9K2w7aqH6iJSHL4AdKacx2Zsz5FHHJd7/Yo0cKfd+47QrNBvBr//vAdJVtBn74fsOYvG4YR7sz6fuWPragnmOi1UnaBv9uHixtUPgQOb2CJoqYhzgekF+74d0sHQC/x+oCj97tyxdXU3e56WmocfkudTNnkI2tPO/cLr/UzRdXN2e+fkzQrWrDDP69ePzGkWJ9jXTd/UlPYe0m/2u7RLuxp+M8d34+HSCcyorABK2PnsI2Z2XcG+D4b0EQV+B/Dzyjic5pQV/vPSFA6+o81R9sMtEjpFPx4XNeQcv9l2fjpfl0M/m3/ocx84vuGYuon/5uZf67xr6Vgze0pQFI/rI/Qh3AhfIG9318bafIFsm2b7atRHVuC2IsCLFIhG9RUO+MzJHHnEcYXX6cS7X1SndLi+xo+ZUxgIcPl9N9d+N3m/rV5hiA2bJgUtnULSx5ppHwV+F/CFa3ayT95MS2hNsLjjGxX0WLJurBb3n23nJxN7bMb2DYt/DFp4aJlz1J8wlX2DqlIb33bVQx0JB3Y0clZDWDGcvJTZ2X3Zdf+z83v453NJ0dyMYGjODt+vbwAb03w6ZUsHOaqZxg0FvqSLA5ZvtjXcEcIJW58iW7j7wZQJmzwtzNfInTblv2E0EtZzx9YFm2z6ub5A0cMz9K3JmXeKbPtFVPlwaJciwV4UZeW3LxP2Dl+RcdfKPSx9Ie3y+wwTfRKW2VTnZRr+M4D/aLB8Dnhe82McHbKvn9nkWFlbfN4DoOg13k3S8YV+NpSzHbZd9dBmD5ROp5ytkrK3pHmLL6kTbP41zoY2hjqps+fs1QNgzewptdnEeanAix4GzgGeDTIoal/2prdm9pS6SWF+nw53bYuCKl6x6wF9Y8apR5UlT8sr9VrQbmv/f+C5zYy4bCSnm9nVDZargAXNnHDUyWqeeQIhVLiMrd6iTqty8eXvu+SNtSiNPG3dZVh0P7qsI/ewU+fVZq06+r2aVRY/Vt7h1v189VlBlrX/u2PafXhCa2a6Vpm6dG3tu/kT9rKae1ar9xO6+RP+isg+UPyoIf+aPbz7JLZd9RCnnXle05lK+5kKa9rmlXrN48uStgEWApjZpoJ2uTQU+GZWOsMmpE1kAn8WbVa45KXEDcEJkr0veFvddt9u72t115w1XquxumTdGPe9uP6G9B8S/RJ+GUKeAxcmInCg/vvkXesiwb7glHPr/l55D+pGzuKiY6qk7IHiBL9vz3f/tzKD259tDMUT0VweH588jT07K77fMYP1myYFLY1IS70eBnwt6df+bmYP5bQ7DFgG/Ay4Pv3cFEG5dCTtA7yfJAa0doyZvazZE4462ZS4efvz2maLbuQxtnoLtl21ll3ZvpY7ZW460cY/3mn0LvMiJ1FLqwubh18OQtnCLI2Eq3/93VuWy1yZTZA2n+QBsFl646XFDws/x02jMM9WyZv85YT4AZxcer4i80yeabARWQHvn9c9/PyHwSApDyG4iVeB7Chpmfd5PC3gBPWlXg8ArgfebWbZ12qRuA5adgqERul8D7gB+AiJ4HdLpEn8FLjZ7f7/2XVHiABxUSB+Ii537JFHHMdpZ57HaWeex3Z3bazZV/3Uwv6r/6AJeoezz2cfjtmoKN85HurcbWTeWXDKuXx2zrdaHXYQeWaqI484jgM+czI3v39RV1Nf5GXp9HH3+bAJe0cTJp0Hzewgb/FtraWlXgHM7GrgYOBFwEFmdk2z4w3NlrnBzPorl+qA4udTd2QFe6P6pyH4P7wDPjOh8fm51993yRvhxcnr9m9OekpdwQyYEPT95yirJ1u/1i8sMg4we/PZydm/gR+J4471t+UJso0rVrLzimT99+99wUQa5lOS/2pZKj9zMr856Sm5Zo0QQh7wfhuXLuLmKxbVzDedqi1cdI2mLl3LfE6o85n4uAIzeXRjNnxVuCidCigq9ZrH283sb5JOaeVEZWGZO0jaAfhvSadI2sVtS7dXgqRJkm6U9MP08x6SfiXpDknflfSEqs416DQzGSubbXHN7Ck8NmN75o6tY5+z/1zTRD8751tdLTheNX6GUff9XUhlUUoCH/+a+tWm2nEm+rOoWxX2/tiy8wiy0UUun5BbfPt8J8he27xxNxPdNKhUEaXToNRrHotSp+1ZrYy3TMO/nnqbkW/GMWDPVk6aw7tJvNKT08+fJqnucr6kLwNvBYbmDaPdqIRmoz2yGmxZlES/a/VljB8zh6msrRP6kBRD2cjEW8DGFSs30zSztvuinEa/f+8L6rZPmrlv+iaR9Dee6dP1VRXZyVHdfmBnfQj+NsfN719UGMmVd00GSbsHoNoY+3cC56XKrSv1WkfGafsNSYc1a9Ypi9LZw8z2BJ6RrtcW4JnNnKgISdOAOSR1bZEk4GUkrzUA5wBzqzhXv9CO1hOipZbtzxbsGHT865mXeCyP7EMumwmzSLPOO4drV5aGod1rnn1T6aS5poyiZHXOPzWo+XGawYANtkXQUtqX2U2pbX+Wmc01s7zXwq45bTevE5e/rRW+AHwAcB7GKcBDZrYh/XwvMDXvQEnzJC2TtGw9j1c0nM7gtOZm4rnznLtFFNl6fSGR5yjuRDGYbtKJGG7frAP5GSsh32zkk7d9zewpwde4yInsHLOdNts0Ik9pyNZraEQ/zUhulW7PtK3CaVtmw99Z0nOBJ0p6jqQD0+UlwJNaGHO2/1cDfzCz61s53szGndd7K7YuP6APKLMN+0J+/Jg5m014KtJg88wOflx43oSiYdHyixK6tZK6uMz84j84i8w9VZHt21/vpc+l6Ps2O8t7GO7BHqRWeLuZ/ZVaeEBzlNnwXwH8MzCNJJWCG/kjwL+1csIMLwSOknQksA2JDf+LwPaStky1/GnAmgrO1XfkxeRnPxcV2s6aEvyHRE07TW8Jfx1S4X9KquFX+YV6hJ+MrJFNuRF+8jSfvD6cjyB7Pn8M/vFloYuNyHM4VynsfedvWb95pq3csNfZ5ZPQhoEm4/CrOWcSoXMm8ElJG4EfAbOA95pZaTxwmQ3/HBKP8dvN7GVm9tJ0OdrMflDB4D9sZtPMbHfg9cBPzOx44KfAa9NmbwYuavdc/YIfl1xmjsiLYc4KAPc5G2LohyIWzS4dpgiKsmyYMDGT07X11+cvPCFx6paYtYrelPzF35d3vDuvG3cI2QdH1bH2rdjb3cMu+92z9yRQ5xz316cuXTuQpkRHhakVmuEIM3sEeDVwF7A3gfOiSm34aa6G97Yzuhb4IHCapDtIbPpf6/L5O07IK2/eZCzIFyRFwnuYhHqrFDldm8mi6Sh6SOcdnyfMXYRQUb79buPn22mFMvPOUGM9y5bpLDNzgO+Z2cOhB8ps82IMmzWSPgU8CHyXZBYYAGb2p+bG2Tkmawc7RIf3ehhBFE06KdP6y5KtzV94Qm1av9/ON/c408V2d23ktDPPG/gQTIcLk2yWMs0y+7fy2/v7Qh8e7aRaqCJNg+/sDTXnZFM55FGbeEYyGa4Zgd8L7f5K+/71ZnZQO31M3ncne96XwwoC/eRln2/7fI5UHs8F/krixN0e+KGZHVJ2bOhM29el/5/qbasyDj9C/SxRh/85dLp/3gxHlytm/sITaoWshz1srpuECuJ2BLbL4ePmCpQJ1SKzT6g5qNWw0rKghGHR/nthwwcwsw+ldvyHzWyjpL8AR4ccGxSWmY3B9+LzIxXjhL7/OQRnInCza2vpBQoycw6jsO/HiI+iKJtWCHnwO9q18fv3oCsG0y7DaF40U9BS/XntT2a2MV1fl87WLSU0W+ZWwMkkKTwBrgK+YmbrWxhrpIQ8TT+kvTPn5Dl7XfROo9mPg4gzq7SqOftmmSJzTd5+/3OjdL55k7SqwN0bRSaaUJasG2Pu2LraRCnf7AdMvEnMzj/ezwzaCP9eHFQHbR4dcMh2lFCTziJgK9Kk+8Cb0m0ndWJQkeacXnmaU/Z4ZwYYRs0eOpOGuB/Jatqthmg64Z73kBg/Zg4LFqeC/hTqHgA+2Wyso4ZZZcnTukaowH+emflq4U8k9W9B0z6nTJNshqyw9wt9+ELfxeIPi5O2E5T9LfqxMEfIg8495LOT+LIpsP3QXrd++vK5nDFrSZJfKCP0d73auO/FYt30TbWspYtm7J375jOc953YuCk0WUGFZ5W2ylpXJO1oZg+WHRs62o2S9vI63xOGYs7OQJNN+7vP2X8uDOXsZo70XtBt7b6f/AVltvG88pRu25FHHFc4N2PN7ClseW2Sz9BFgG1318batX5498aVnEaBbtrwJb1U0r3A/ZKukLS7t/uKkD5CBf77gZ9KukrS1cBPgH9tarSRXIrsmc04uPIqYbnJLH7t1kGOjvDL3/VDKbx+Mh/NX3gC8xZfUlic3Gnwfu6dItNeXo2A05fPZerStYyt3oKHd59U91axz9l/Zmz1FrnlOQc1P1Mo3c6lA5wJvMLMdiRJyLpU0vPTfUEnCY3S+TEwA3gXSRrPfc3sp82PN1IVeTM1i6azD3vVoV7QSw0/72Fz+vK5hYXZYUKQ+4K+zJ+z4dBHWL1gq822wYRPaM3sKbVtI4cldvyQpSKeYGa3ApjZ90li8c+RNDcZTTnNGKCeC8wEng28TlJ42sdIR8hOty9Ke1xk5om0Ti80/LwMno4tr53M6cvn1gT0/r84vib4syadvKpr/nbX75bXTq6ZdLKC32n+Gw59hOnzRzdYr8upFdZL2tl9SIX/4cDHSBTyUkLDMv8L2Au4iQnbvQHDnyGpBzih3Sg00/0o3evyohl7s3OasXrR5yccZEk5uYl+p5Ifhhjpbxo5Z10+Gyec18wGeIR5iy9h/18cz/T56+ve7k5fPjdZWVDfz+nL58KCRNBvOPSR2v/+eVYfulWi+R9a/wAYxXvJoCMx9g34ELATUIu5N7N7Jb0YeEdIB6Ea/kHAC83sFDN7Z7q8q+nhRoJot7xeZPholJEz72HghP+W105mzewpnL58Lqcvn1u7r86YtaSufe0hwITZxhf20+ev30zLd4yuqVBs3BS2VIGZXUlqupH0LG/7w2Z2RkgfoQJ/BbBzaatIS+Q5tkJsxEU/wKL+/TTK0J9hhkUMs/MvhLwZu41m8U5dujY33bO7Z3wBn8U9LPKOy2s7yspJD2bavkXSDJKyr00TKvB3BG6TdLmki93SygkjYbgfbCe0p9HVyAabZhzF2bbN2NmbccJuOPSRkb2fEodsNQJf0l2SbpF0k6RlBW3mk8jsXwJbSPpos2MOnXj1sWY7jrRPo6ibRhpaGaOskQ0qzVaHyqZ08O+ZM2Ytaev+yZLcT6P59lXxTNuXNpo8ZWYLJB1FIreXmtl/N3uCIIGf1lIsRNIvzOwfmj15pJ6NK1bmmlnayTDoim04ATB/4QlsWJBocNPnr6+dr9/NJYNkfmqHvL9DNl9QkfCv08yX1mv58xeeAOn+VoW978Td8trJtZTIqy+cybRjW+py4Kkw5DKUg83sFEmfAJoW+FXNC96mlYMkPV3STyXdJulWSe9Ot+8gaamkVen/T6lonENBK8J/3uJLRjtmekhoRsvfcOgjbDj0EaYuXcuCU86tM+vk2elD+ss6e6E5c9EwYYhNm7YIWoAdJS3zlnmbdQdXSLo+Z5+PmzL/vVbGHGrSKaPV59wG4F/N7AZJTwaul7SUpI7uj83sU5I+RBKO9MFqhtrf+Bqeyz/i8pSUce+FMzfbNu3YFWxYMDPR6tJQO6chrl4wEUM9aea+fa/ljyobV6zk9+99AZAf/++2rT50Ky9ufkITh4n6CM1q99Pnr69TFBbN2JtprGjlawwlTQi+B0sKoBxqZmskPY1kBu2vzeyanHZvkXQW8BbgPc2MFarT8FvCzO43sxvS9UeB24GpJMn8z0mbnUMyoyzikadpheyDCS2xrF2k/3B/OyeAffOO+3v6E6dafaPztfapS9c21OL7Ka9QV6nQaWtma9L//wAsJqlkVUcVTtuqBH7bnos0EdBzgF8BO5nZ/emu35NMNsg7Zp57RVrP4+0OYWAoi4oI1eKqdNxFusvEJKsJYTt+zBw2HPrIZsXEW2FUI2+axgKXBkgaSy0cSBoDjoDNX6PMbAFwJUmp2aVm9vFmhxs603YM+KuZbZK0D7AfcJmXovNNzZ440/+2wIXAe8zsEWni+WFmJin3kpnZOEkSISZrh+67T7pEXkGU8WPmMJ31rM4x42SZduzEveObfdzr+tSla9M465k1Tc7FXfvHRjpLI5OaXzOWmfvWZsI6/Dh5p9WvXrAV0+evZ+OKlTXz4BmrltRSIjTCVwbK7oUNhz7CvYfObNhmWKkoxn4nYHEq97YEvm1mPypoe0g3nLbXANtImkqShvNNwDfdTjNr+a+cVtO6EDjPzH6Qbn5A0i7p/l2AP7Ta/zCQ1bac4A+deFXGmtlTmD5/fe0BELW7/sb93f18Ok74t+JAHVWnaxVUkTzNzH5rZgeky/6NZs2a2enp///eynhDnbYys79Ieiuw0MzOlHRTKyes6zR5pH0NuN3MPuftuhh4M/Cp9P+L2j3XIJPkw9k8LDEknvreC2fWaV3Tjl3BpJn7snrBVmxcsZKd3a7UabvzChhfOodpK1bUjveP7TRFJQeHmSq/Z1YbzxatWDRj75qW72z87pjs251bL/q75wUJjBJmYL0pgPJq4BPAdBIZLhJjSGnoVehoJekfgOMBp/5VUf3ghSRvCy9LZ5jdJOlIEkE/W9Iq4OXp50gOzlHXjJZW1ZtBpLesmT2lzl7vsluWhVw6Ye/wj1kze0p05jdBl9MjO75AoghPMbPJZvbkEGEP4QL/PcCHgcVmdmta8artfPhmdq2Zycxmmdmz0+VSM1trZoeb2Qwze7mZ/andcw06fjEJt/iJsBoJ8XsvnLmZpp7V2nwt01/vtk221aIZg2qGakW7d+Y3fyKWT529Pwcn3LNJ0tzDY/7CE2pCv9HDI+8BM3JafwVO2xa4B1hh1vyjpJmZtldLelL6+bckxVAifUI7IXjDwCini2i2gHvZfTJ16VpOP3Rum6MaBSpPjBbKB4BL0+qDtfDEjFk8lyANX9I/SLoN+HX6+QBJC1scbKQDuEIUbhnG13LfhNFv9GJc/ltdVedvJZSzn8o9dp3eaPhnAH8hyXDwZG8pJdRp+wXgFSTOVMzsZkmHNT3MSKX44XbTWAGew3P+whOYXgu3LCbPGdyvztJGgqVZLbdKQs/tX1d33VuZ4TzRvnnzycmr7mBRWhvJN7/UQnOXTrRNcuA/Ap/P78sl96vqbSAbYND3WNcLoDh2NbOWbGfBLmYzuyezKRsAEOkjnAAaRk0/D1/gdlPbzp6rX99AynDjXr1gq1r8fqhzv5W8PEODKWyplkslHdHKgaEa/j2SXgBYGjf/bpI0CJE+Ik+D9LMkZnHhmVAf5eNv7yecICqjW5p+dkarX5gkbwydCDn1J2CVOWuz+EJ958//vDZ5ys+/04hauuVTzuX05XObekjkMVDavaM30z1PBt4n6XFgPR0Iy3w7cCpJnps1JIXMT21pqJGu0owjt+jH2i8Tc/plHI5sIXH3OU/YD1oUkV8jN4QzZi0ZzZDOHtjw0zDMLczsiR0JyzSzB83seDPbycyeZmZvNLMR9tQMJ1kNLW8iTqQ1RiGKaORqMRtdNelIKi0zW9YmNJfOPsAikqRmMyXNAo4ys08GjTTSdfIce2Ux0r7JxP/fCfxeOtWy5qo8U9SoMe3YFXV/U7fe6G/knPxQ/yD3/7b3Xjizpt03mmXrQoEXzdg7+TzbhQaPTl6dLhdAuRQ4sJ02oSadr5JMvFoPYGbLgdcHHhsZAELt4/1AdqyN3kCyaQIa7R9UnNmuKnNKqCknay4cyXkgmxS2VMMBkh5psDxKQWZhR6jT9klmdp2fxZKkeEmkz/G1rEbO2FBhH6JFdpo8Yd/qA8u9wbT7sHN9hIRoVhn26mvl2ZQJIce6v+eW106uOZWbdf6O3Oxaj/w8vp3BzNpOZxMq8B+UtBep+0HSa4H7Gx8S6TfKNOFQrblfyQsnDPFDVPFm0y9vRxsOLY6bLz1uaXm7MkZqxndnJlV1lFCBfypJ3vn9JK0BfkeSSC0yQPjaoCNPww0R9lmtrpsaf6OJR3lj37hiJdOOrQ+LzD4cGgnsZt4AqhKczZK15zeiqN1Eps36tu5ve/KqO7hobWIevnfN5ufPth9+OhJj31FKbfiSJgGnmNnLgacC+5nZoWa2uuOji3ScUE2+XzTYdnETi9x60fdq1VQ0LNepEdet2W30wi+L6E1qhZYpFfhmthE4NF1fl9aejQwJLveOo+gBMIgmniyNTD3ZB0HRvkjC6cvncvDUu3s9jN7TI4EvaZKkXSXt5pag40IybEpaRDLp6nvAOrfdq1DVcyZrBztEh/d6GH1P3ut8yAzJsjaD9Brf7kziVrX4fstR1MgElGf+y+IE/n3Pn7DZ99LU1wxX2vevN7OD2ulj692ebrt88D1BbVe/431tn88h6Z3AfOABYFO62cxsVtmxoWGZ25DMtX4Z8Jp0eXXzQw1H0islrZR0h6QPdfJco06zZp3sW8Gg0a62HrX9iEMWtgT1lWjtN0r6YUnTdwP7puUQn5UupcIewvPhnxjSripSv8FZwGzgXuB/JV1sZrd16pytZC3sN5xTsuh7tBM+Vybk+iFcs5s0a9sfhHvr4Kl3c92aIMtAjevW7JZUpB5VqjXXuBxlZbG19wAPt3KC0Jm2X8rZ/DCwzMw6UW/2YOCOtNAKks4HjgY6JvAj4UQNN2H1gq1qzsthSClw3ZrdWhL6kfaRNA2YQ5Lr/rSS5r8FrpJ0CZ0ogEJi0nk2sCpdZgHTgLdK+kJgH80wleQp5rg33VaHpHmSlklatn7ie0dKGGRzTL8xf+EJvR5CT4gPhoQmTDo7OlmVLvMyXX2BpJLVJsq5myT49wl0qADKLOCFacSOc+L+jCR655bAPirHzMZJ5gcwWTu09XI1CK/c7ZI3KalqbT1bO3cQaDZJXP0Dcy3jS+cUmnj6+b5qFLvvthcJ9lBhX2ZGbJSrZyDun/A4/AeLnLaSXg38wcyul/SS0lOaLUiP2zb9/FjoIEI1/KcA23qfx4Ad0gdAJ1TrNcDTvc/T0m2RNsgKtOznUdX8QyKUytpPn79+4FIg55EV5M7M49NsDL67fkMXu28k+njI0pgXAkdJugs4H3iZpG8VNZY0U9KNwK3ArZKul7R/yJBDBf6ZwE2SviHpm8CNwGckjQFXBvbRDP8LzJC0h6QnkCRqu7gD5xkqNq5Y2VCjdD+8IsFUpbZ/xqwlTJq5b93SL2SjjLIZQovalZG14/ezdt+IRvH1B0+9u+kY/Eb31b0Xzqxbivb1K1VE6ZjZh81smpntTiLrfmJmb2xwyDhwmplNN7PpwL+SJLgsJTQf/teAFwBLgMXAoWZ2djoR6/0hfTSDmW0A3gFcTuK1vsDMbq36PKPI9Pnra5WK/G1Vc/ryuQMxYako945P6Hdw7Qa1zKFPVqAfPPXuum2t2O+z991Q0JuJV2Nm9tPaEMyuIrG6lBIk8JWkyTwcOCCNytlS0sEtDDQYM7vUzPYxs73M7IxOnmuU6HSK4DNmLRl401CRpl/2vdz+XhVTr5IigR4dtRkqFvhmdpWZlc1x+q2kf5e0e7p8hCRyp5RQp+1CEkvUy4CPA4+SRN8+L/D4yIhw+vK5sCAR/FmNrhM1XVvBnXv6/GIzk7PJu1qt/vZGrF6w1WA4G1P8sTrTyXVrdqt9z40rVnKdN8fi4F/eXWvj478p5fUZSpkTuZ+ubTOTqirmLcACwGU6+Fm6rZRQgX+ImR2YOgowsz+ntvXIgNGJyJxs/wDjzIEFg+EILhrj+DFzmE5z428mH/0w4NIhN3tPdfo+7BrVFTcJxsz+DLyrlWNDBf76dPary4f/VMLiRSMjRl6ZxCqpckb0xhUrGzqTm51Nm7Rfy8YqBtcDnPZcX0Jy4vpMmrkvD5yUtl3hafGH1pdHzNPGsyGgzQh7/9h+C/vtpoYv6Qtm9h5J/02OocjMjirrI1Tgf4nEWfs0SWcArwU+0sxgI8PNIGjyzdIodXLevmG8Br5gDqnmNXJ016TzX+n/n221g9AonfNIZoH9X5JKV3PN7HutnjTSGyZs19ULpm5F5PRDqGM7kTyDQFIwZkVtcfHzznSzesFW3HvhzFq47RmzltRp9kX3l+vP0UxZzeyxbntPCQzJrOotwMyuT1efbWZX+wtJJoRSGgp8STu4BfgD8B3g28AD6bZIJMJwaveO8WPmbGam823wvnO+WRPYwNObsMw352z755ADyzT864Fl6f9/BH5Dkkvnj+m2yIDSKwE1qDHqwzCLtgqylcDyfDZVCfLQMNie0kWBL+kNqf1+D0kXe8tPgT+F9NHQhm9me6Qn+iqw2MwuTT+/Cpjb1ugjA8EZs5YwfsycptMPFDF16dq+MMs0Imujn7f4kqayYTpHcDZEsyx9db/ixuvXugXYmG5bfeHMulnci2bsXWtTu451juD1tT6zobr+tfPvudywTG8Gd6+uaZfDMn9OYlLfEfgPb/ujwPKQDkJTKzzfCXsAM7uMZOZtZMhxM2ZDGIpX9ByGIfVxLxnW+wLoqoZvZqvTWbXHA7/y7Pe3k+QbKyU0Sue+dDaXS+hzPHBfk+ON9AG+FtUoFrqV1/OQSUmwuabYb2S/R7Mavo/vWHT9DkwmyECmHbuiFoq6aMbem4W6NhOmW/Q20aito+uTs3o38eoC6hXujSTlZ0snwoZq+G8AnkoSmvmDdP0NzY0x0o+U/RjznHVQn/kw5Ic8yFpeVRr+IOQW6hUDe11647Td0sz+XhtCsh40ETa0xOGfSMpvRYaMsh9a1knnaGUW6qDQ7ISrUPLSTUQSps9fP5gT1nqj4f9R0lFmdjGApKOBB0MOLAvL/FhZByFtIv2LnxTMF3KNcuW3KgzzYql7SdEs26qE/bRjV9QlXhsFX0BZGuxG0U5VpdDuVny+6G4cvsfbgX+TdLeke4APAm8LObBMwz9J0iMN9oskf/PHQk4W6X98u777/4xZS5LcOC0ysK/rkcpp9NBLopq6OJh2MVAPEsyY2Z3A81upeFUm8L9Kea3EoMT7kf7Bd3aFaFUhmmmnzCCDzKSZ+9Y5fPOySQ4LzWrnRe075XjdbBZwVYVZe2DSkbQ1cCywO0mq+mQoZh8vO7YsDn9BBePLRdJngNcAfwfuBE40s4fSfR8G3krifX6XmV3eqXFENseP0AkV4kmWyM3zrIy6dj8KZpyqGMgMmr2x4V8EPEwy+bWpErOhYZmdYCnwYTPbIOnTwIeBD0p6JomZaH9gV+BKSfu4AuqRasmbAOPIzqTMw82c3XDoI8lfdMTJhgr6mSeHjU6VrZw+fz20kBXVz9iZxTnMa3+L/6/tYQI9C8ucZmavbOXA0LDMyjGzK9JShgC/ZGLiwNHA+Wb2uJn9DrgD6Gh1rUg+8xZfUirsXfbELa+dPLBpE7pBNHeF0cnr1JEi6r0Jy/y5pGe1cmDPBH6GtwCXpetTgXu8ffem2zZD0jxJyyQtW9/cm00kAJdSwUVWFEVYTF26NtHwM8T8MwkDaaoYYDoi2PNInbYhSyMkbSPpOkk3S7pVUpkp/VDgekkrJS2XdIukoNQKQSYdSfsAi4CdzGympFnAUWb2yZLjrgR2ztl1elobF0mnAxuA80LG4mNm4yQV3JmsHXrzcjXEuBJ/bj1rj3ba/ZrZU9jy2nqzzuoFW9XFnPejo7KsAEqzfWXplMmjV/Tz96k349yx2X6X32cayX3466pOXI3UeRx4mZk9Jmkr4FpJl5nZLwvav6rVE4Xa8L8KvB/4CoCZLZf0baChwDezlzfaL+mfgVcDh5uZu3RrgKd7zaal2yJdZvyYpExhs47HqM1GRoUqbPip7HOhlVulS6OeWz5rqMB/kpld58J/UjYUNQ5B0itJiqq82Mz+4u26GPi2pM+ROG1nANe1c65IazjBXZRLZkKwJ+ac7OSsQRD8jZzWVeJfmxh9EEYzmTD9Moh+ts6OEy56d5S0zPs8nlooAEhLyF4P7A2cZWa/atDXJemZBWwD7AGsJAl0aUioDf9BSXsxUdP2tSRpOtvhP0li/JdKuknSlwHM7FaS5EC3AT8CTo0ROr3DpUf2aSTIs5O2IgnxegwhoQ7b5KHwoJkd5C3jdV2ZbTSzZ5NYNA6WVDhd2MyeZWaz0v9nkAS1/CJkyKEC/1QSc85+ktYA7wFODjw2FzPb28yebmbPTpe3e/vOMLO9zGzfNBVzpIdkna9nzFqyWbhmjNKJjBpqYgklnYv0UyA47NLMbgAOCWkbmjztt8DLJY0BW5jZo6GDGWUuv+9mXrHrAb0eRsv42r1v1nG2fV9r9aN0+tFB2w8MWuGTfuHkVXcEmWl6cd9VkVpB0lOB9Wb2kKQnArOBTzdof5r3cQvgQALT1TcU+JmO/e0AmNnnQk4SGUxOXz6X6ayP4ZUVMLDZICONqSZKZxfgnNSOvwVwgZn9sEF7P93NBhKb/oUhJyrT8F3H+5Ik1784/fwaoiO1lEHW7iERUk7Yh0bqjKJ2X6S5R41+BKgmSmc58JyydpL+y8zeBDxkZl9s5VxBuXQkXQMc6Ew5aUrkqPYNOWtmT2kqJDNqsZGRovsVr54raVfgLZLOJeMeSOuWNCTUabsTSZIzx9/TbZEhxk2symP6/PVsee3khjn0R4FRMnfFN5Ycupta4cvAj4H9SEI4/WVZg+NqhAr8c4HrJH0s1e5/BZzT7Ggjg8OoFy4PYZSEvSNGYtVTRWqFUMzsS2b2DODrZranme3hLXuG9BEapXOGpMuAF6WbTjSzG1scd2QACEliNXXp2rpUCpHRwE+aN+r0IlummbUcEh+k4UvajaRm4uJ0WZtui4wATqsr0mjnLb6k78oXNksr5opFM/bu7qzOPmDq0rVMXbq2K5p+379BNTfxqi8ITa3gpvICPJEmpvJGBhunzRU5cCfi86N9d9TotKY/fsycwRD6A0SQhu9N5Z3V7FTeyHCw4JRzc7ePakWnUXdghgr6jStWsnHFyqbeCAbFT9DDIubJ+aXJknZwS8gxLeXDb2Yqb2Sw8V/fB+WH2C6j8j3boZV7wjcNhhzX99o99MSkI+ltkn4PLKfJKJ3QfPgtT+WNDDbuhzl+zBymsrb2Gt/shKxBIjokGzNv8SXMX3gC0Nq1cscWMTDX30CbemLTeR8w08webPbAUBt+y1N5I/1NSFpg/wfo1p19dd7iS4bGcVmWKnnUzTgwoXUvOOXc2sM+5Lrs/Pmf8/v3vqBOs8/zAeRp/q3eXyevmiiE0ql7tEc1be8E/lLaKodQgX+bmX3P3yDpH4HvFbSPDAkDo21FBopGpsLaPXdKt0fVAr0R+B8mqWv7K5io7Wpm7yo7MFTgf5jNhXvetsiAUWaaKfoxblyxcmg0+yxRky+m/m/e3HXa+fM/BxLNe3zpnM3CO93nooI77eBr+wBXVnTr9kjD/wrwE+AWoKlpXQ2dtpJeJen/AVMlfclbvkmbFa+8c/yrJJO0Y/pZ6TnuSAv0HljFeSL5hPywopYf6RRTl66tRYDNW3xJ3XpVdNTP1Js4/K3M7DQz+4aZneOWkAPLNPz7SLy/R5F4gh2PAu9tbawTSHo6cARwt7f5VSRlDWeQRAItIkYEdYyyH1a3JtlERhPfAdzJc3SE7idPc1wmaR7w39SbdEqTp5Vly7wZuFnSeWZWiUaf4fMkdW0v8rYdDZybFvb9paTtJe1iZu2WVIwU4BywRZpQ1PAjVZKYhSbMQUVzPPrdZCiqy5PTJG9I//+wt82A0nw6ZSadC9LVG1PzSt3S2lhrfR8NrEkfKj5TgXu8z/em2/L6mCdpmaRl6ycedJEmCbWZRk0/Mqh0zKxjFrZUesq6pGmVJk97d/r/q1sZmKQrgZ1zdp0O/BuJOadl0kLA4wCTtcOATXLuPb4jq5HQd07MnVcQ891HKmXRjL03c6hWQRJaW9/v+DFzPIf8TZWcp0cmHdIi588EtnHbzCz/VcmjzKTjzCinmNkHMyf8NPDBzY+qO/7lBYN9Fkk+npvTconTgBskHQysAZ7uNZ+Wbot0kGGcQBWJdJQeJUaTNB94CYnAv5TE73ktSRr7hoSGZc5mc+H+qpxtQZjZLcDT3GdJdwEHmdmDki4G3iHpfBJn7cPRfh+JDC/OVj9p5r6VOFjdxLn6iWGdCbWtqIj500mE9U4kj5DxkhKGrwUOAG40sxMl7QR8K+RcZUXMTyaZ/rBnxmb/ZOB/Qk7QApcCR5K8j/0FOLFD54lEIpG2qMhpuwH4VzO7QdKTgeslLTWz2wra/9XMNknaIGky8AfqrSKFlGn43wYuA/4v8CFv+6MhIUChmNnu3roBp1bVdyQSGU06XqjFqMQhm1ow7k/XH5V0O0mgSpHAXyZpe+CrJOHyjxGYvbjMhv8w8DBpGJCkp5E4CbaVtK2Z3d3o+MjgE2edRrpFu7O3szmQdv78zzseZFC101bS7sBzSMrI5mJmLunElyX9CJhsZkFRk6EVr14jaRXwO+Bq4C4SzT8yBESHbSTSIuEzbXd0IeTpMi/blaRtSZJSvsfMHik6ZZqN4I2SPmpmdwEPpQEvpYQ6bT8JPB+40syeI+mlwBsDj430KYtm7B2ULTNSPe66xzeowcUVQAnkQTM7qLAvaSsSYX+emf2gpK+FJDl0XgZ8nCTzwYXA88oGEVoAZb2ZrQW2kLSFmf0UKBx8JBKJDD2hk65K7PxKYtO/BtxuZp8LOPMhZnYq8LdkGPZn4AkhQw7V8B9KXzeuAc6T9AdgXeCxkUgkMpRUFKXzQuBNwC2Sbkq3/ZuZXVrQfr2kSaTGIklPJTBrZqjAP5rkafJe4HhgO5JXicgA48wKnUhHG2lMs6acaAIqpxfXpgqnrZldS2IhCuVLwGLgaZLOIInL/0jIgUEC38x8bT4oDWckEokMNQZ0ucShpC1Igmc+ABxO8qCYa2a3hxxfNvHqUfInD4skZH5yc8ON9BMbV6wMKufncp34bwFR0+wu8Xr3KV1OrZBOuDrLzJ4D/LrZ48vi8J/caH9kOIjmnEikNXqUPO3Hko4FfpBOVA0m1IYfGVLKShxm90VNMxLxqDj1cSBvA04DNkj6G01YXKLAj0QikVaw3hRAacfyEhqHHxkBOlYKLhIZQpKJVxa0VHpe6cch2/KIGn4EmBD27n8/p0lVZhzfQRxNQ5GhoIsavqRtgCeRpGl4ChOhnJMpqAqYJQr8SCQSaZGqtfcS3ga8B9iVJEumE/iPAP8Z0kEU+CNONwtFR60+MlR0ueJVWhTli5LeaWb/r5U+emrDl/ROSb+WdKukM73tH5Z0h6SVkl7RyzFGIpFIPoY2hS1VIOl5knZ2wl7SCZIukvQlSTuE9NEzgZ9m3DwaOMDM9gc+m25/JvB6YH/glcDCNG9EJBKJ9BcVJE9rgq8AfweQdBjwKZLSiA8D4yEd9FLDPxn4lJk9DmBmf0i3Hw2cb2aPm9nvSEodBuV6jkQika6RhmWGLBUxyas0+DqS2rcXmtm/A0G22V4K/H2AF0n6laSrJblczlOBe7x29xLogY5EIpGu0l0Nf5Ik53c9HPiJty/IH9tRp62kK4Gdc3adnp57B5LCKs8DLpC0Z5P9zwPmAWzDk9obbKSUbN6dIidszOwYGRm6O9H2O8DVkh4E/gr8DEDS3iRmnVI6KvDN7OVF+ySdzEQuiOskbQJ2BNZQX4F9Wrotr/9xUtvVZO3Qm6wWkUhkZOlmWKaZnZFOsNoFuMLLo7MF8M6QPnoZlrkEeCnwU0n7kFRseRC4GPi2pM+RxJvOAK7r1SAjkUgkFwM2dlfPNLNf5mz7TejxvRT4Xwe+LmkFief5zekT61ZJFwC3ARuAU82s08XnIwW0UvM2mnIio4CoPm1Cp+mZwDezv1NQCN3MzgDO6O6IIpFIpEmiwI8MOiFafdTi26MTeYVC+gx9Y/OPL+t30sx9C7f7xw2lM78igS/p68CrgT+Y2cxKOs0hZsuMRCKRVjCS5GkhSznfJJlo2lGihh+pUab9DZVm1iVCNOpm/CShmnsrvpeQ45vdnre/lbeMkH57U8S8Gg3fzK6RtHslnTUgCvxIJBJpCYNNPaiA0gZR4EcikUgrGM3Y8HeUtMz7PJ7OI+oqUeCPIK2+7hc55/qNou/nxt6qCaVdM0kV9MMYusVABA+EK/gPmtlBHRxJEFHgRyKRSIvEOPxIRyjTWpuhFU231XNVTTsabivHjpJGPYg0+vt05X6tLizzO8BLSEw/9wLzzexrlXTuEQV+JBKJtIIZbKzGaWtmb6ikoxKiwI9EIpFWiSadiE+rZpPQ47ppcui20zaaUyJ9TxT4kUgkMgIYUFG92m4RBX4A3dai+5lO5kPp9+8eGQy69xZqYHHiVSQSiYwG0aQzOESNsnXitYv0K10L1TQqi9LpFiMt8CORSKQtBkzD71l6ZEnPlvRLSTdJWibp4HS7JH1J0h2Slks6sFdjjEQikWIsEfghS5/QSw3/TGCBmV0m6cj080uAV5HUsZ0BHAIsSv9viJ64NZP2jmaGSCRSTM3cc0sFnRkDly2zlwVQDJicrm8H3JeuHw2cawm/BLaXtEsvBhiJRCINiRp+MO8BLpf0WZIHzwvS7VOBe7x296bb7s92IGkeMA9gm60mZ3dHIpFIZ+kjYR5CRwW+pCuBnXN2nQ4cDrzXzC6UdBzwNeDlzfSf5pMeB9juSbsM1pWPRCKDjRm2cWOvR9EUHRX4ZlYowCWdC7w7/fg94Ox0fQ3wdK/ptHRbJBKJ9BcDNtO2lzb8+4AXp+svA1al6xcDJ6TROs8HHjazzcw5kUgk0nOiDT+YfwG+KGlL4G+ktnjgUuBI4A7gL8CJvRleJBKJNMBiTdtgzOxa4Lk52w04tfsjikQikSbpI+09hDjTNhKJRFoiOm0jkUhkNIjpkSORSGSEGLD0yL2M0olEIpGBxQDbZEFLGZJeKWllmkPsQ50acxT4kUgk0gqWFkAJWRogaRJwFkkesWcCb5D0zE4MOQr8SCQSaZGKNPyDgTvM7Ldm9nfgfJKcYpUjG7CwoiIk/RFY3YGudwQe7EC/7dKP44pjCiOOKZxOjWu6mT21nQ4k/YhkfCFsQzLfyDGepoZB0muBV5rZSennNwGHmNk72hlfHkPjtG33j1eEpGVmdlAn+m6HfhxXHFMYcUzh9Ou4AMzslb0eQ7NEk04kEon0lq7lD4sCPxKJRHrL/wIzJO0h6QnA60lyilXO0Jh0Osh4rwdQQD+OK44pjDimcPp1XJVhZhskvQO4HJgEfN3Mbu3EuYbGaRuJRCKRxkSTTiQSiYwIUeBHIpHIiBAFvoekf5R0q6RNkg7ytu8u6a+SbkqXL3v7nivplnRK9JckqRtjSvd9OD3vSkmv8LZ3ZZp2eq6PSVrjXZsjy8bXLbp5HUrGcVd6j9wkaVm6bQdJSyWtSv9/SofH8HVJf5C0wtuWO4a0+NCX0uu2XNKBXRxT395PQ4GZxSVdgGcA+wJXAQd523cHVhQccx3wfEDAZcCrujSmZwI3A1sDewB3kjh8JqXrewJPSNs8s4PX7GPA+3K2546vi3/Lrl6HkrHcBeyY2XYm8KF0/UPApzs8hsOAA/37uGgMJAWILkvv6ecDv+rimPryfhqWJWr4HmZ2u5mtDG0vaRdgspn90pK78lxgbpfGdDRwvpk9bma/I6kQdjBdnKZdQtH4ukW/XIcijgbOSdfPoeL7JouZXQP8KXAMRwPnWsIvge3Te70bYyqi1/fTUBAFfjh7SLpR0tWSXpRumwrc67W5N93WDaYC9+Scu2h7J3lH+ur/dc800Ytx+PT6/D4GXCHpekmulOdONlGr+ffATj0YV9EYen3t+vF+GgpGLg5f0pXAzjm7TjeziwoOux/YzczWSnousETS/j0eU9doND5gEfAJEqH2CeA/gLd0b3QDwaFmtkbS04Clkn7t7zQzk9TT+Oh+GENKvJ86yMgJfDN7eQvHPA48nq5fL+lOYB+S6c/TvKYtTYluZUw0no5d6TTt0PFJ+irww4DxdYNen7+Gma1J//+DpMUkpogHJO1iZven5pI/9GBoRWPo2bUzswfcep/dT0NBNOkEIOmpac5qJO0JzAB+m74OPyLp+Wl0zglAtzTyi4HXS9pa0h7pmK6ji9O0oebHcBwDuIiLovF1i65ehyIkjUl6slsHjiC5RhcDb06bvZnu3Tc+RWO4GDghjdZ5PvCwZ/rpKH18Pw0HvfYa99NCcoPdS6LNPwBcnm4/FrgVuAm4AXiNd8xBJDflncB/ks5e7vSY0n2np+ddiRcdRBJl8Zt03+kdvmb/BdwCLCf5Ue5SNr4u/j27dh0ajGFPkuiSm9N76PR0+xTgx8Aq4Epghw6P4zskpsn16f301qIxkETnnJVet1vwosO6MKa+vZ+GYYmpFSKRSGREiCadSCQSGRGiwI9EIpERIQr8SCQSGRGiwI9EIpERIQr8SCQSGRGiwI9EIpERIQr8EULSYx3o8yiXeljSXEnPbKGPq7KpnwPar5R0VM6+3f10u8OOpH/z1p+YphT+u6QdezmuSH8SBX6kLczsYjP7VPpxLkka225wvJl1dOasm13d59QEvpn91cyeDdzXu+FE+pko8EeQdMr8ZyStSAtzvC7d/pJUe/6+pF9LOi9NGYGkI9Nt16fFMX6Ybv9nSf8p6QXAUcBnUi1zL19zl7SjpLvS9SdKOl/S7WlumSd6YztC0i8k3SDpe5K2Dfg+z5V0s6SbgVO97ZPS7/m/afbFt6Xbt5C0MP0+SyVdKum16b67JH1a0g3APxaNJz3n1en1uNylBJD0Lkm3pec7v8GYx5Rkg7xOSRbWo9Ptu0v6WXq+G9LriqRdJF2TXtsVkl4k6VOA0+rPC/rjR0abXk/1jUv3FuCx9P9jgaUkRUJ2Au4GdgFeAjxMkphqC+AXwKHANiSpafdIj/8O8MN0/Z+B/0zXvwm81jvfVaTT8oEdgbvS9dOAr6frs4ANJCkqdgSuAcbSfR8EPprzPWr9pp+XA4el658hLagBzAM+kq5vDSwjKZ7xWuDS9DvuDPzZjZukWMkHvDFvNh5gK+DnwFPT7a/zvs99wNbp+vYN/hb/B3ija0eSAmIMeBKwTbp9BrAsXf9XJtIyTAKe7P9NM33fRabgSlziYmajly0zAiRC/DtmtpEkY+LVwPOAR4DrzOxeAEk3kVT7eowkWdzv0uO/QyJMW+Uw4EsAZrZc0vJ0+/NJTEL/k75YPIHkoVOIpO1JBOs16ab/Al6Vrh8BzHLaO7AdiRA9FPiemW0Cfi/pp5luv1synn2BmSSpjiERwC652HLgPElLgCUNhn4EcJSk96WftwF2I3lg/KekZwMbSbKyQpIM7uuStgKWmNlNDfqORHKJAj+S5XFvfSPt3SMbmDAbbhPQXsBSM3tDG+fM9vdOM7u8bqNXJ7WAdY3GI+lZwK1m9g85x84heaC9Bjhd0rPMbEPB2I61TDUzSR8jSZJ3AMm1+xsk1aEkHZb2/01JnzOzc0u+RyRSR7ThjyY/A16X2rifSiKgGqWaXQnsKWn39PPrCto9CjzZ+3wX8Nx0/bXe9muAfwKQNJPErAPwS+CFkvZO941J2ocGmNlDwEOSDk03He/tvhw4OdWKkbSPkhTF/wMcm9rydyIxZeVRNJ6VwFMl/UO6fStJ+0vaAni6mf2UxPyzHVDkg7gceKfnI3lOun074P707eNNJG8PSJoOPGBmXwXOJqkFC7Defb9IpIwo8EeTxSSmh5uBn5DYrH9f1NjM/gqcAvxI0vUkgv3hnKbnA+9PnZB7AZ8lEbg3ktjDHYuAbSXdDnwcuD49zx9JfALfSc08vwD2C/g+JwJnpSYoedvPBm4DblASqvkVkjeWC0nS8d4GfIsk5fVm36doPJbUyH0t8OnUUXwT8AIS4fwtSbcANwJfSh9IeXyCxBewXNKt6WeAhcCb0373Y+Jt4yXAzem1fB3wxXT7eNpHdNpGSonpkSNBSNrWzB5LNdKzgFVm9vkejeUq4H1mtqyNPtz3mULydvPCRg+9QSKNhjrIzB7s9Vgi/UXU8COh/EuqQd9KYnb4Sg/H8icSO/ZmE6+a4Ifp9/kZ8IlhEPZKJ16RvDls6vFwIn1I1PAjkQ4i6UTg3ZnN/2Nmp+a1j0Q6SRT4kUgkMiJEk04kEomMCFHgRyKRyIgQBX4kEomMCFHgRyKRyIjw/wM9/Me1ReH8CAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"Load slope data into an xarray dataset\"\"\"\n",
-    "biomes = xr.open_dataset(\"/data1/downloaded/landcover_25.nc\")\n",
-    "# Convert to data array and select the only time step available\n",
-    "biomes = biomes.STRF[0]\n",
-    "biomes.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "8365b359",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Replicate the same layer 81 times to match load's temporal resolution\n",
-    "biomes_data = [biomes for i in range(0, 81)]\n",
-    "biomes_data = xr.concat(biomes_data, \"time\")\n",
-    "biomes_data[\"time\"] = load_data[\"time\"]\n",
-    "# Mask using the load\n",
-    "biomes_data = biomes_data.where(load_data >= 0)\n",
-    "biomes_data[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "biomes_data.to_netcdf(folder_path + \"biomes_2010-2016.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6aa13e2f",
-   "metadata": {},
-   "source": [
-    "# Dynamic predictors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa0f55fc",
-   "metadata": {},
-   "source": [
-    "## Leaf Area Index\n",
-    "\n",
-    "Remote sensing LAI methods generate a map of dimensionless LAI values assigned to each pixel. Values can range from 0 (bare ground) to 6 or more, but since rangeland vegetation is generally sparse, values commonly range from 0-1. A LAI value of 1 means that there is the equivalent of 1 layer of leaves that entirely cover a unit of ground surface area, and less than one means that there is some bare ground between vegetated patches. LAI values over 1 indicate a layered canopy with multiple layers of leaves per unit ground surface area. LAI and fPAR data are commonly packaged together (e.g., MODIS products)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "d4eb31fd",
+   "id": "60626ba7",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  x = np.divide(x1, x2, out)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e40f593a0>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"Load LAI data into an xarray dataset\"\"\"\n",
-    "lai_data = xr.open_mfdataset(\n",
-    "    \"/data1/raw_data/LAI_interpolated_2010_2017/LAI_201[0-6]*.nc\"\n",
-    ")\n",
-    "# Rename lat/lon dimensions\n",
-    "lai_data = lai_data.LAI.rename({\"lon\": \"longitude\", \"lat\": \"latitude\"})\n",
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+       ".xr-section-item input + label {\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       "}\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 (time: 81, latitude: 720, longitude: 1440)&gt;\n",
+       "dask.array&lt;multiply, shape=(81, 720, 1440), dtype=float64, chunksize=(1, 720, 1440), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude  (longitude) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 81</li><li><span class='xr-has-index'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a4714561-8441-468e-b3d8-a0e932a46f0f' class='xr-array-in' type='checkbox' checked><label for='section-a4714561-8441-468e-b3d8-a0e932a46f0f' 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>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 671.85 MB </td> <td> 8.29 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 720, 1440) </td> <td> (1, 720, 1440) </td></tr>\n",
+       "    <tr><th> Count </th><td> 904 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"200\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"30\" y2=\"20\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"90.717946\" y=\"100.717946\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"170.717946\" y=\"50.717946\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,170.717946,50.717946)\">720</text>\n",
+       "  <text x=\"10.358973\" y=\"90.358973\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.358973,90.358973)\">81</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-a2a70229-61b3-4867-bed4-e9a4e40bf536' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2a70229-61b3-4867-bed4-e9a4e40bf536' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-4093d051-955e-4242-872b-c38ce1cbca01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4093d051-955e-4242-872b-c38ce1cbca01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15a6167f-4fd1-4ad3-8f24-30b391387c69' class='xr-var-data-in' type='checkbox'><label for='data-15a6167f-4fd1-4ad3-8f24-30b391387c69' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-04-01 ... 2016-12-01</div><input id='attrs-49cc2b05-ce2d-4788-a4d0-9c123549b208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49cc2b05-ce2d-4788-a4d0-9c123549b208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31b47098-f28c-4cf7-975c-fff7842e9048' class='xr-var-data-in' type='checkbox'><label for='data-31b47098-f28c-4cf7-975c-fff7842e9048' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-04-01T00:00:00.000000000&#x27;, &#x27;2010-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-06-01T00:00:00.000000000&#x27;, &#x27;2010-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-08-01T00:00:00.000000000&#x27;, &#x27;2010-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-10-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-04-01T00:00:00.000000000&#x27;, &#x27;2011-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2011-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-10-01T00:00:00.000000000&#x27;, &#x27;2011-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-12-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-01T00:00:00.000000000&#x27;, &#x27;2012-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-04-01T00:00:00.000000000&#x27;, &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-08-01T00:00:00.000000000&#x27;, &#x27;2012-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-10-01T00:00:00.000000000&#x27;, &#x27;2012-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-04-01T00:00:00.000000000&#x27;, &#x27;2013-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2013-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-10-01T00:00:00.000000000&#x27;, &#x27;2013-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-12-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-02-01T00:00:00.000000000&#x27;, &#x27;2014-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-04-01T00:00:00.000000000&#x27;, &#x27;2014-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-06-01T00:00:00.000000000&#x27;, &#x27;2014-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-08-01T00:00:00.000000000&#x27;, &#x27;2014-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-10-01T00:00:00.000000000&#x27;, &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-028221f5-dcd4-4d8d-aab1-2498d4907909' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-028221f5-dcd4-4d8d-aab1-2498d4907909' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f88fbdd-fea9-4ae8-880c-6e20448e7032' class='xr-var-data-in' type='checkbox'><label for='data-2f88fbdd-fea9-4ae8-880c-6e20448e7032' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</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([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3544c959-d5e2-4d1d-9dfd-f1491fd54980' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3544c959-d5e2-4d1d-9dfd-f1491fd54980' 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 (time: 81, latitude: 720, longitude: 1440)>\n",
+       "dask.array<multiply, shape=(81, 720, 1440), dtype=float64, chunksize=(1, 720, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude  (longitude) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "load_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "69d55d83",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude     fuel_load\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485\n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730\n",
+       "2      2010-04-01   -39.375    175.125  11915.029964\n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199\n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716\n",
+       "...           ...       ...        ...           ...\n",
+       "720482 2016-12-01    45.125      2.875   2764.222562\n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499\n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494\n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571\n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059\n",
+       "\n",
+       "[720487 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert cells with non missing values to dataframe\n",
+    "df = load_data.to_dataframe(name=\"fuel_load\").dropna().reset_index()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "97d99c55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example: retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "# y = x[\"index\"].sel(longitude=target_lon, latitude=target_lat, time=target_t, method=\"nearest\")\n",
+    "# Where\n",
+    "target_lon = xr.DataArray(df['longitude'], dims=\"points\")\n",
+    "target_lat = xr.DataArray(df['latitude'], dims=\"points\")\n",
+    "target_t = xr.DataArray(df['time'], dims=\"points\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ec0c9ac",
+   "metadata": {},
+   "source": [
+    "# Static predictors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fe89ecd",
+   "metadata": {},
+   "source": [
+    "## Climatic regions (categorical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d06651fd",
+   "metadata": {},
+   "outputs": [
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+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
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+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
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+       "\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",
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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",
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+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
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+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
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+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
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+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:          (latitude: 601, longitude: 1200)\n",
+       "Coordinates:\n",
+       "  * latitude         (latitude) float64 90.0 89.7 89.4 ... -89.4 -89.7 -90.0\n",
+       "  * longitude        (longitude) float64 -180.0 -179.7 -179.4 ... 179.4 179.7\n",
+       "Data variables:\n",
+       "    climatic_region  (latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2020-12-20T06:06:38 GRIB to CDM+CF via cfgrib-0....</pre><div class='xr-wrap' hidden><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-506d9bfb-b7d8-4d04-bc20-3f898d6a2c3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-506d9bfb-b7d8-4d04-bc20-3f898d6a2c3a' 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'>latitude</span>: 601</li><li><span class='xr-has-index'>longitude</span>: 1200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3335b46f-8288-4490-9229-5c9ec44d006b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3335b46f-8288-4490-9229-5c9ec44d006b' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.7 89.4 ... -89.7 -90.0</div><input id='attrs-198dd656-46bc-4968-8bf8-ef82c614e5f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-198dd656-46bc-4968-8bf8-ef82c614e5f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15a23e58-78d5-4fda-9686-7d0e3952075d' class='xr-var-data-in' type='checkbox'><label for='data-15a23e58-78d5-4fda-9686-7d0e3952075d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  89.7,  89.4, ..., -89.4, -89.7, -90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.7 ... 179.4 179.7</div><input id='attrs-2986728a-990a-498c-981b-649f0c1e5c88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2986728a-990a-498c-981b-649f0c1e5c88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9b5ae1a-7cd4-4fb8-b950-b5a44112785e' class='xr-var-data-in' type='checkbox'><label for='data-a9b5ae1a-7cd4-4fb8-b950-b5a44112785e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180. , -179.7, -179.4, ...,  179.1,  179.4,  179.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5be7924f-b6c2-426f-abb8-e99608d246fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5be7924f-b6c2-426f-abb8-e99608d246fc' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>climatic_region</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd1b23bf-5941-4d52-aa8e-5644dae1e0d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd1b23bf-5941-4d52-aa8e-5644dae1e0d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382d378b-d702-4343-a977-dbf288b6706f' class='xr-var-data-in' type='checkbox'><label for='data-382d378b-d702-4343-a977-dbf288b6706f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>212028</dd><dt><span>GRIB_shortName :</span></dt><dd>~</dd><dt><span>GRIB_units :</span></dt><dd>~</dd><dt><span>GRIB_name :</span></dt><dd>Experimental product</dd><dt><span>GRIB_cfVarName :</span></dt><dd>p28.212</dd><dt><span>GRIB_dataType :</span></dt><dd>af</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>721200</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/longitude </dd><dt><span>GRIB_Nx :</span></dt><dd>1200</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.7</dd><dt><span>GRIB_Ny :</span></dt><dd>601</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>long_name :</span></dt><dd>Experimental product</dd><dt><span>units :</span></dt><dd>~</dd><dt><span>coordinates :</span></dt><dd>time step heightAboveGround latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><pre>[721200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9bf4745b-0ccd-4b69-aa82-a9cd7b9bd8c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9bf4745b-0ccd-4b69-aa82-a9cd7b9bd8c1' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2020-12-20T06:06:38 GRIB to CDM+CF via cfgrib-0.9.8.5/ecCodes-2.19.1 with {&quot;source&quot;: &quot;/tmp/mv.21440.smose_roshni/marsPqqkYt&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (latitude: 601, longitude: 1200)\n",
+       "Coordinates:\n",
+       "  * latitude         (latitude) float64 90.0 89.7 89.4 ... -89.4 -89.7 -90.0\n",
+       "  * longitude        (longitude) float64 -180.0 -179.7 -179.4 ... 179.4 179.7\n",
+       "Data variables:\n",
+       "    climatic_region  (latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2020-12-20T06:06:38 GRIB to CDM+CF via cfgrib-0...."
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load climatic regions data into an xarray dataset\"\"\"\n",
+    "cr_data = xr.open_dataset(\"/data1/raw_data/Beck_KG_V1_present_0p0083.gridName0320.nc\")\n",
+    "\n",
+    "# Rotate longitude coordinates\n",
+    "cr_data = cr_data.assign_coords(\n",
+    "    longitude=(((cr_data.longitude + 180) % 360) - 180)\n",
+    ").sortby(\"longitude\")\n",
+    "\n",
+    "cr_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "61843b63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.406028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.018566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.044484</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude     fuel_load  climatic_region\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485         1.000000\n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730              NaN\n",
+       "2      2010-04-01   -39.375    175.125  11915.029964         3.000000\n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199         3.000000\n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716         3.000000\n",
+       "...           ...       ...        ...           ...              ...\n",
+       "720482 2016-12-01    45.125      2.875   2764.222562         2.406028\n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499         1.018566\n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494              NaN\n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571              NaN\n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059         2.044484\n",
+       "\n",
+       "[720487 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"climatic_region\"] = cr_data[\"climatic_region\"].sel(longitude=target_lon,\n",
+    "                                                       latitude=target_lat,\n",
+    "                                                       method=\"nearest\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b93a8dc",
+   "metadata": {},
+   "source": [
+    "The climatic regions are 5 categories (1 to 5), the floating number make me think they might have been previously pre-processed in the wrong way. Let's fix this by rounding to the nearest integer and then converting to a categorical type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "499992c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude     fuel_load climatic_region\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0\n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN\n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0\n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0\n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0\n",
+       "...           ...       ...        ...           ...             ...\n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0\n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0\n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN\n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN\n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0\n",
+       "\n",
+       "[720487 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"climatic_region\"] = df[\"climatic_region\"].round().astype('category')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed56e546",
+   "metadata": {},
+   "source": [
+    "## Biomes (categorical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f4eac353",
+   "metadata": {},
+   "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;STRF&#x27; (latitude: 720, longitude: 1440)&gt;\n",
+       "[1036800 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "  * latitude   (latitude) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "    time       datetime64[ns] 2000-01-01\n",
+       "Attributes:\n",
+       "    long_name:  Stream function\n",
+       "    units:      m**2 s**-1\n",
+       "    code:       1\n",
+       "    table:      255</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'STRF'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9b1201d3-f2fe-4f21-9800-842d398dde4c' class='xr-array-in' type='checkbox' checked><label for='section-9b1201d3-f2fe-4f21-9800-842d398dde4c' 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>...</span></div><div class='xr-array-data'><pre>[1036800 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-b5462697-0540-4573-9c87-174928d4bcd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b5462697-0540-4573-9c87-174928d4bcd7' 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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-cab6d821-f7f0-4dd8-81bf-a6748db4e6f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cab6d821-f7f0-4dd8-81bf-a6748db4e6f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd0d6fb3-c9c4-4d3f-8303-bb7da8aa9355' class='xr-var-data-in' type='checkbox'><label for='data-dd0d6fb3-c9c4-4d3f-8303-bb7da8aa9355' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</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([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-2e29aed6-67dd-4277-8412-201a9f74dc49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e29aed6-67dd-4277-8412-201a9f74dc49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8545b540-2122-450a-82e9-afc3f768ae5d' class='xr-var-data-in' type='checkbox'><label for='data-8545b540-2122-450a-82e9-afc3f768ae5d' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01</div><input id='attrs-45c4d318-96ee-47df-98de-b4ce91c6ae8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45c4d318-96ee-47df-98de-b4ce91c6ae8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3d89425-4c81-449a-854a-8d45176399b3' class='xr-var-data-in' type='checkbox'><label for='data-e3d89425-4c81-449a-854a-8d45176399b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2000-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd445630-02f3-4ea6-a7b2-18683b437d4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd445630-02f3-4ea6-a7b2-18683b437d4c' class='xr-section-summary' >Attributes: <span>(4)</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>Stream function</dd><dt><span>units :</span></dt><dd>m**2 s**-1</dd><dt><span>code :</span></dt><dd>1</dd><dt><span>table :</span></dt><dd>255</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'STRF' (latitude: 720, longitude: 1440)>\n",
+       "[1036800 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "  * latitude   (latitude) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "    time       datetime64[ns] 2000-01-01\n",
+       "Attributes:\n",
+       "    long_name:  Stream function\n",
+       "    units:      m**2 s**-1\n",
+       "    code:       1\n",
+       "    table:      255"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load slope data into an xarray dataset\"\"\"\n",
+    "biomes = xr.open_dataset(\"/data1/downloaded/landcover_25.nc\")\n",
+    "# Convert to data array and select the only time step available\n",
+    "biomes = biomes.STRF[0]\n",
+    "biomes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d2633072",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude     fuel_load climatic_region  biome\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0    1.0\n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN    7.0\n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0    7.0\n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0    1.0\n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0    1.0\n",
+       "...           ...       ...        ...           ...             ...    ...\n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0    1.0\n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0    7.0\n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN    1.0\n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN    1.0\n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0    7.0\n",
+       "\n",
+       "[720487 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"biome\"] = biomes.sel(longitude=target_lon,\n",
+    "                         latitude=target_lat,\n",
+    "                         method=\"nearest\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98b5c497",
+   "metadata": {},
+   "source": [
+    "Biomes are also categorical and there datatype must be fixed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d32e9dd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude     fuel_load climatic_region biome\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0\n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN   7.0\n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0\n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0\n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0   1.0\n",
+       "...           ...       ...        ...           ...             ...   ...\n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0   1.0\n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0   7.0\n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN   1.0\n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN   1.0\n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0   7.0\n",
+       "\n",
+       "[720487 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"biome\"] = df[\"biome\"].astype('category')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21253f10",
+   "metadata": {},
+   "source": [
+    "## Slope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d8795f88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
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+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
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+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+       "  display: none;\n",
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+       ".xr-section-item input + label {\n",
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+       ".xr-section-summary > span {\n",
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+       "\n",
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+       ".xr-section-summary-in + label:before {\n",
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+       "  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",
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+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
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+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
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+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
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+       "\n",
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+       "\n",
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+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
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+       "}\n",
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+       "  display: none;\n",
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+       "\n",
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+       "  padding: 0 !important;\n",
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+       ".xr-dim-list li {\n",
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+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
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+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
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+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
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+       "  background-color: var(--xr-background-color-row-even);\n",
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+       "  padding-right: 5px;\n",
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+       "\n",
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+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
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+       "\n",
+       ".xr-attrs,\n",
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+       "\n",
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+       "}\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;slor&#x27; (latitude: 601, longitude: 1200)&gt;\n",
+       "dask.array&lt;getitem, shape=(601, 1200), dtype=float32, chunksize=(601, 1200), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.7 89.4 89.1 ... -89.4 -89.7 -90.0\n",
+       "  * longitude  (longitude) float64 -180.0 -179.7 -179.4 ... 179.1 179.4 179.7\n",
+       "Attributes: (12/28)\n",
+       "    GRIB_paramId:                             163\n",
+       "    GRIB_shortName:                           slor\n",
+       "    GRIB_units:                               ~\n",
+       "    GRIB_name:                                Slope of sub-gridscale orography\n",
+       "    GRIB_cfVarName:                           slor\n",
+       "    GRIB_dataType:                            an\n",
+       "    ...                                       ...\n",
+       "    GRIB_jScansPositively:                    0\n",
+       "    GRIB_latitudeOfFirstGridPointInDegrees:   90.0\n",
+       "    GRIB_latitudeOfLastGridPointInDegrees:    -90.0\n",
+       "    long_name:                                Slope of sub-gridscale orography\n",
+       "    units:                                    ~\n",
+       "    coordinates:                              time step surface latitude long...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'slor'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 601</li><li><span class='xr-has-index'>longitude</span>: 1200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4c60a375-7ba4-4d27-bf77-dd02be6deffe' class='xr-array-in' type='checkbox' checked><label for='section-4c60a375-7ba4-4d27-bf77-dd02be6deffe' 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>dask.array&lt;chunksize=(601, 1200), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 2.88 MB </td> <td> 2.88 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (601, 1200) </td> <td> (601, 1200) </td></tr>\n",
+       "    <tr><th> Count </th><td> 4 Tasks </td><td> 1 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.099999999999994 0.0,60.099999999999994\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.100000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1200</text>\n",
+       "  <text x=\"140.000000\" y=\"30.050000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.050000)\">601</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-761350df-72f7-4bf9-bc94-9f9cb517db52' class='xr-section-summary-in' type='checkbox'  checked><label for='section-761350df-72f7-4bf9-bc94-9f9cb517db52' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.7 89.4 ... -89.7 -90.0</div><input id='attrs-197b0d4f-8625-462b-b322-4e7810bf3f07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-197b0d4f-8625-462b-b322-4e7810bf3f07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-740de5d9-ee58-4f75-8eba-5a138996fccc' class='xr-var-data-in' type='checkbox'><label for='data-740de5d9-ee58-4f75-8eba-5a138996fccc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  89.7,  89.4, ..., -89.4, -89.7, -90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.7 ... 179.4 179.7</div><input id='attrs-cc127b9a-e6e4-47d1-a01e-38b3e5fda3a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc127b9a-e6e4-47d1-a01e-38b3e5fda3a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80825b42-b76c-4549-8609-33af333056bc' class='xr-var-data-in' type='checkbox'><label for='data-80825b42-b76c-4549-8609-33af333056bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180. , -179.7, -179.4, ...,  179.1,  179.4,  179.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5616a38-5eaa-4851-ae7b-0b5000ea857a' class='xr-section-summary-in' type='checkbox'  ><label for='section-d5616a38-5eaa-4851-ae7b-0b5000ea857a' class='xr-section-summary' >Attributes: <span>(28)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>163</dd><dt><span>GRIB_shortName :</span></dt><dd>slor</dd><dt><span>GRIB_units :</span></dt><dd>~</dd><dt><span>GRIB_name :</span></dt><dd>Slope of sub-gridscale orography</dd><dt><span>GRIB_cfVarName :</span></dt><dd>slor</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>721200</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/longitude </dd><dt><span>GRIB_Nx :</span></dt><dd>1200</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.7</dd><dt><span>GRIB_Ny :</span></dt><dd>601</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>long_name :</span></dt><dd>Slope of sub-gridscale orography</dd><dt><span>units :</span></dt><dd>~</dd><dt><span>coordinates :</span></dt><dd>time step surface latitude longitude valid_time</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'slor' (latitude: 601, longitude: 1200)>\n",
+       "dask.array<getitem, shape=(601, 1200), dtype=float32, chunksize=(601, 1200), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.7 89.4 89.1 ... -89.4 -89.7 -90.0\n",
+       "  * longitude  (longitude) float64 -180.0 -179.7 -179.4 ... 179.1 179.4 179.7\n",
+       "Attributes: (12/28)\n",
+       "    GRIB_paramId:                             163\n",
+       "    GRIB_shortName:                           slor\n",
+       "    GRIB_units:                               ~\n",
+       "    GRIB_name:                                Slope of sub-gridscale orography\n",
+       "    GRIB_cfVarName:                           slor\n",
+       "    GRIB_dataType:                            an\n",
+       "    ...                                       ...\n",
+       "    GRIB_jScansPositively:                    0\n",
+       "    GRIB_latitudeOfFirstGridPointInDegrees:   90.0\n",
+       "    GRIB_latitudeOfLastGridPointInDegrees:    -90.0\n",
+       "    long_name:                                Slope of sub-gridscale orography\n",
+       "    units:                                    ~\n",
+       "    coordinates:                              time step surface latitude long..."
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load slope data into an xarray dataset\"\"\"\n",
+    "slope_data = xr.open_mfdataset(\"/data1/raw_data/slope_O320.nc\")\n",
+    "\n",
+    "# Rotate longitude coordinates\n",
+    "slope_data = slope_data.slor.assign_coords(\n",
+    "    longitude=(((slope_data.longitude + 180) % 360) - 180)\n",
+    ").sortby(\"longitude\")\n",
+    "\n",
+    "slope_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8f2e24cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>slope</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.013915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude     fuel_load climatic_region biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0   \n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN   7.0   \n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0   \n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0   \n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0   1.0   \n",
+       "...           ...       ...        ...           ...             ...   ...   \n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0   1.0   \n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0   7.0   \n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN   1.0   \n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN   1.0   \n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0   7.0   \n",
+       "\n",
+       "           slope  \n",
+       "0       0.003584  \n",
+       "1       0.013915  \n",
+       "2       0.014664  \n",
+       "3       0.016293  \n",
+       "4       0.033126  \n",
+       "...          ...  \n",
+       "720482  0.020528  \n",
+       "720483  0.054576  \n",
+       "720484  0.005878  \n",
+       "720485  0.005559  \n",
+       "720486  0.037897  \n",
+       "\n",
+       "[720487 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"slope\"] = slope_data.sel(longitude=target_lon,\n",
+    "                             latitude=target_lat,\n",
+    "                             method=\"nearest\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6aa13e2f",
+   "metadata": {},
+   "source": [
+    "# Dynamic predictors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52c4177f",
+   "metadata": {},
+   "source": [
+    "## Vegetation Optical Depth (VDO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a62d2fbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  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.Dataset&gt;\n",
+       "Dimensions:    (latitude: 450, longitude: 900, time: 81)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude  (longitude) float32 -180.0 -179.6 -179.2 ... 178.8 179.2 179.6\n",
+       "  * latitude   (latitude) float32 -90.0 -89.6 -89.2 -88.8 ... 88.8 89.2 89.6\n",
+       "Data variables:\n",
+       "    SM_IDW     (time, latitude, longitude) float64 dask.array&lt;chunksize=(1, 450, 900), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><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-8d7114a0-784a-4c29-8400-a6a58bdcb09d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d7114a0-784a-4c29-8400-a6a58bdcb09d' 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'>latitude</span>: 450</li><li><span class='xr-has-index'>longitude</span>: 900</li><li><span class='xr-has-index'>time</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-279092f3-8c24-4b13-b7ec-5e99447df23c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-279092f3-8c24-4b13-b7ec-5e99447df23c' 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'>2010-04-01 ... 2016-12-01</div><input id='attrs-3b992e95-9e2b-4c32-a546-4ae9f0f195a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b992e95-9e2b-4c32-a546-4ae9f0f195a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3fa0c91-f2f3-4f98-9787-5c687088063b' class='xr-var-data-in' type='checkbox'><label for='data-a3fa0c91-f2f3-4f98-9787-5c687088063b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-04-01T00:00:00.000000000&#x27;, &#x27;2010-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-06-01T00:00:00.000000000&#x27;, &#x27;2010-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-08-01T00:00:00.000000000&#x27;, &#x27;2010-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-10-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-04-01T00:00:00.000000000&#x27;, &#x27;2011-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2011-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-10-01T00:00:00.000000000&#x27;, &#x27;2011-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-12-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-01T00:00:00.000000000&#x27;, &#x27;2012-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-04-01T00:00:00.000000000&#x27;, &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-08-01T00:00:00.000000000&#x27;, &#x27;2012-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-10-01T00:00:00.000000000&#x27;, &#x27;2012-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-04-01T00:00:00.000000000&#x27;, &#x27;2013-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2013-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-10-01T00:00:00.000000000&#x27;, &#x27;2013-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-12-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-02-01T00:00:00.000000000&#x27;, &#x27;2014-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-04-01T00:00:00.000000000&#x27;, &#x27;2014-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-06-01T00:00:00.000000000&#x27;, &#x27;2014-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-08-01T00:00:00.000000000&#x27;, &#x27;2014-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-10-01T00:00:00.000000000&#x27;, &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.6 ... 179.2 179.6</div><input id='attrs-62aad165-da09-4c1a-9ccb-4c63f14f51a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62aad165-da09-4c1a-9ccb-4c63f14f51a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71f1d6e1-4690-42fd-b2ef-6272ee45349f' class='xr-var-data-in' type='checkbox'><label for='data-71f1d6e1-4690-42fd-b2ef-6272ee45349f' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</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([-180. , -179.6, -179.2, ...,  178.8,  179.2,  179.6], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -89.6 -89.2 ... 89.2 89.6</div><input id='attrs-8c2b293d-b1c4-4369-8085-b9c537b3abd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c2b293d-b1c4-4369-8085-b9c537b3abd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14ae2dd0-bed4-4a0d-9b51-3c39de225348' class='xr-var-data-in' type='checkbox'><label for='data-14ae2dd0-bed4-4a0d-9b51-3c39de225348' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([-90. , -89.6, -89.2, ...,  88.8,  89.2,  89.6], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d0fb577-4104-42e6-b376-7f810451c32f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d0fb577-4104-42e6-b376-7f810451c32f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>SM_IDW</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 450, 900), meta=np.ndarray&gt;</div><input id='attrs-9d4c4033-2ddd-4fe3-9a20-e56351a1a8a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d4c4033-2ddd-4fe3-9a20-e56351a1a8a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20b65d2f-d6b0-4751-b037-c26b9090e689' class='xr-var-data-in' type='checkbox'><label for='data-20b65d2f-d6b0-4751-b037-c26b9090e689' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 262.44 MB </td> <td> 3.24 MB </td></tr>\n",
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+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (latitude: 450, longitude: 900, time: 81)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude  (longitude) float32 -180.0 -179.6 -179.2 ... 178.8 179.2 179.6\n",
+       "  * latitude   (latitude) float32 -90.0 -89.6 -89.2 -88.8 ... 88.8 89.2 89.6\n",
+       "Data variables:\n",
+       "    SM_IDW     (time, latitude, longitude) float64 dask.array<chunksize=(1, 450, 900), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load VOD data into an xarray dataset\"\"\"\n",
+    "vodfiles = [\n",
+    "    os.path.join(d, x)\n",
+    "    for year in range(2010, 2017)\n",
+    "    for d, dirs, files in os.walk(\"/data1/downloaded/ESA_VOD/\" + str(year))\n",
+    "    for x in files\n",
+    "    if x.endswith(\".nc\")\n",
+    "]\n",
+    "vod_data = xr.open_mfdataset(vodfiles)\n",
     "# Calculate monthly means\n",
-    "lai_data = lai_data.resample(time=\"1MS\").mean(dim=\"time\")\n",
-    "# Interpolate to match load resolution\n",
-    "lai_data = lai_data.interp(\n",
-    "    coords={\n",
-    "        \"latitude\": load_data.latitude.values,\n",
-    "        \"longitude\": load_data.longitude.values,\n",
-    "    },\n",
-    "    method=\"linear\",\n",
-    ")  # Wikilimo used default method ('linear')\n",
-    "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
-    "# Therefore here we remove Jan-Feb-Mar 2016.\n",
-    "lai_data = lai_data.loc[\"2010-04-01\":\"2016-12-31\"]\n",
-    "lai_data[0].plot()"
+    "vod_data = vod_data.resample(time=\"1MS\").mean(dim=\"time\")\n",
+    "\n",
+    "vod_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd57dbcb",
+   "metadata": {},
+   "source": [
+    "Please note this data is limited between 2010-04-01 and 2016-12-31!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "138e61d4",
+   "execution_count": 27,
+   "id": "b3f9c71c",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  x = np.divide(x1, x2, out)\n",
       "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
       "  x = np.divide(x1, x2, out)\n"
      ]
     },
     {
      "data": {
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\n",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.013915</td>\n",
+       "      <td>0.638954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 8 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "             time  latitude  longitude     fuel_load climatic_region biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0   \n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN   7.0   \n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0   \n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0   \n",
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+       "...           ...       ...        ...           ...             ...   ...   \n",
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+       "\n",
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-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Mask using the load\n",
-    "lai_data = lai_data.where(load_data >= 0)\n",
-    "lai_data[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "lai_data.to_netcdf(folder_path + \"lai_2010-2016.nc\")"
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"vod\"] = vod_data[\"SM_IDW\"].sel(longitude=target_lon,\n",
+    "                                   latitude=target_lat,\n",
+    "                                   time=target_t,\n",
+    "                                   method=\"nearest\")\n",
+    "df"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "52c4177f",
+   "id": "aa0f55fc",
    "metadata": {},
    "source": [
-    "## Vegetation Optical Depth (VDO)"
+    "## Leaf Area Index\n",
+    "\n",
+    "Remote sensing LAI methods generate a map of dimensionless LAI values assigned to each pixel. Values can range from 0 (bare ground) to 6 or more, but since rangeland vegetation is generally sparse, values commonly range from 0-1. A LAI value of 1 means that there is the equivalent of 1 layer of leaves that entirely cover a unit of ground surface area, and less than one means that there is some bare ground between vegetated patches. LAI values over 1 indicate a layered canopy with multiple layers of leaves per unit ground surface area. LAI and fPAR data are commonly packaged together (e.g., MODIS products)."
    ]
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   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "a62d2fbd",
+   "execution_count": 28,
+   "id": "d4eb31fd",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
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-     "text": [
-      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;LAI&#x27; (time: 81, latitude: 560, longitude: 1440)&gt;\n",
+       "dask.array&lt;getitem, shape=(81, 560, 1440), dtype=float32, chunksize=(1, 560, 1440), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
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+       "  * longitude  (longitude) float64 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'LAI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 81</li><li><span class='xr-has-index'>latitude</span>: 560</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-059dd337-8979-42e4-ae36-aea27cd055f8' class='xr-array-in' type='checkbox' checked><label for='section-059dd337-8979-42e4-ae36-aea27cd055f8' 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>dask.array&lt;chunksize=(1, 560, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-b696ef48-2afd-4c64-95e6-adbc0551d240' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b696ef48-2afd-4c64-95e6-adbc0551d240' 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'>2010-04-01 ... 2016-12-01</div><input id='attrs-dd0add9c-14a2-481e-9165-b8d0ccd9895a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd0add9c-14a2-481e-9165-b8d0ccd9895a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84d47e98-de23-4451-802b-ad77ba986928' class='xr-var-data-in' type='checkbox'><label for='data-84d47e98-de23-4451-802b-ad77ba986928' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-04-01T00:00:00.000000000&#x27;, &#x27;2010-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-06-01T00:00:00.000000000&#x27;, &#x27;2010-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-08-01T00:00:00.000000000&#x27;, &#x27;2010-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-10-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-04-01T00:00:00.000000000&#x27;, &#x27;2011-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2011-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-10-01T00:00:00.000000000&#x27;, &#x27;2011-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-12-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-01T00:00:00.000000000&#x27;, &#x27;2012-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-04-01T00:00:00.000000000&#x27;, &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-08-01T00:00:00.000000000&#x27;, &#x27;2012-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-10-01T00:00:00.000000000&#x27;, &#x27;2012-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-04-01T00:00:00.000000000&#x27;, &#x27;2013-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
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+       "       &#x27;2013-10-01T00:00:00.000000000&#x27;, &#x27;2013-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-12-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-02-01T00:00:00.000000000&#x27;, &#x27;2014-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-04-01T00:00:00.000000000&#x27;, &#x27;2014-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-06-01T00:00:00.000000000&#x27;, &#x27;2014-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-08-01T00:00:00.000000000&#x27;, &#x27;2014-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-10-01T00:00:00.000000000&#x27;, &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
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+       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-59.88 -59.62 ... 79.62 79.88</div><input id='attrs-7c0fad42-a6de-439a-8d03-c177c7521023' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c0fad42-a6de-439a-8d03-c177c7521023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac01af1d-91bb-49fb-85ec-10107851818c' class='xr-var-data-in' type='checkbox'><label for='data-ac01af1d-91bb-49fb-85ec-10107851818c' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>DIMENSION_LABELS :</span></dt><dd>lat</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-59.875, -59.625, -59.375, ...,  79.375,  79.625,  79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-cd0c3f09-faf6-4fb8-a7a3-647908c78617' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd0c3f09-faf6-4fb8-a7a3-647908c78617' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffe360ff-e68e-4fe7-9680-bf392bc41161' class='xr-var-data-in' type='checkbox'><label for='data-ffe360ff-e68e-4fe7-9680-bf392bc41161' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>DIMENSION_LABELS :</span></dt><dd>lon</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c2ae624-2979-4d4b-86ef-c83658feaf48' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2c2ae624-2979-4d4b-86ef-c83658feaf48' 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": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e40695940>"
+       "<xarray.DataArray 'LAI' (time: 81, latitude: 560, longitude: 1440)>\n",
+       "dask.array<getitem, shape=(81, 560, 1440), dtype=float32, chunksize=(1, 560, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * latitude   (latitude) float64 -59.88 -59.62 -59.38 ... 79.38 79.62 79.88\n",
+       "  * longitude  (longitude) float64 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "\"\"\"Load VOD data into an xarray dataset\"\"\"\n",
-    "vodfiles = [\n",
-    "    os.path.join(d, x)\n",
-    "    for year in range(2010, 2017)\n",
-    "    for d, dirs, files in os.walk(\"/data1/downloaded/ESA_VOD/\" + str(year))\n",
-    "    for x in files\n",
-    "    if x.endswith(\".nc\")\n",
-    "]\n",
-    "vod_data = xr.open_mfdataset(vodfiles)\n",
-    "# Select variable of interest: SM_IDW\n",
-    "vod_data = vod_data.SM_IDW\n",
-    "# Calculate monthly means\n",
-    "vod_data = vod_data.resample(time=\"1MS\").mean(dim=\"time\")\n",
-    "# Interpolate to match load's resolution\n",
-    "vod_data = vod_data.interp(\n",
-    "    coords={\n",
-    "        \"latitude\": load_data.latitude.values,\n",
-    "        \"longitude\": load_data.longitude.values,\n",
-    "    },\n",
-    "    method=\"linear\",\n",
+    "\"\"\"Load LAI data into an xarray dataset\"\"\"\n",
+    "lai_data = xr.open_mfdataset(\n",
+    "    \"/data1/raw_data/LAI_interpolated_2010_2017/LAI_201[0-6]*.nc\"\n",
     ")\n",
-    "\n",
-    "vod_data[0].plot()"
+    "# Rename lat/lon dimensions\n",
+    "lai_data = lai_data.LAI.rename({\"lon\": \"longitude\", \"lat\": \"latitude\"})\n",
+    "# Calculate monthly means\n",
+    "lai_data = lai_data.resample(time=\"1MS\").mean(dim=\"time\")\n",
+    "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
+    "# Therefore here we remove Jan-Feb-Mar 2016.\n",
+    "lai_data = lai_data.loc[\"2010-04-01\":\"2016-12-31\"]\n",
+    "lai_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "5c0186b3",
+   "execution_count": 29,
+   "id": "d07fe7d2",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  x = np.divide(x1, x2, out)\n",
       "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
       "  x = np.divide(x1, x2, out)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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+       "<style scoped>\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "      <td>0.655549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.013915</td>\n",
+       "      <td>0.638954</td>\n",
+       "      <td>2.144423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "      <td>0.599994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "      <td>1.111100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 9 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "             time  latitude  longitude     fuel_load climatic_region biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0   \n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN   7.0   \n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0   \n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0   \n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0   1.0   \n",
+       "...           ...       ...        ...           ...             ...   ...   \n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0   1.0   \n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0   7.0   \n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN   1.0   \n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN   1.0   \n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0   7.0   \n",
+       "\n",
+       "           slope       vod       lai  \n",
+       "0       0.003584  0.177971  0.655549  \n",
+       "1       0.013915  0.638954  2.144423  \n",
+       "2       0.014664  0.832961  3.433299  \n",
+       "3       0.016293  0.551958  1.811093  \n",
+       "4       0.033126  0.676935  4.477733  \n",
+       "...          ...       ...       ...  \n",
+       "720482  0.020528  0.325193  0.777770  \n",
+       "720483  0.054576  0.310260  0.655549  \n",
+       "720484  0.005878  0.160282  0.599994  \n",
+       "720485  0.005559  0.262777  1.111100  \n",
+       "720486  0.037897  0.306111  0.055555  \n",
+       "\n",
+       "[720487 rows x 9 columns]"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Mask using the load\n",
-    "vod_data = vod_data.where(load_data >= 0)\n",
-    "vod_data[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "vod_data.to_netcdf(folder_path + \"vod_2010_2016.nc\")"
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"lai\"] = lai_data.sel(longitude=target_lon,\n",
+    "                         latitude=target_lat,\n",
+    "                         time=target_t,\n",
+    "                         method=\"nearest\")\n",
+    "df"
    ]
   },
   {
@@ -1773,82 +5667,1012 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "id": "eaad4f88",
    "metadata": {},
    "outputs": [
     {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (latitude: 601, longitude: 1200, time: 81)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.7 89.4 89.1 ... -89.4 -89.7 -90.0\n",
+       "  * longitude  (longitude) float64 -180.0 -179.7 -179.4 ... 179.1 179.4 179.7\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "Data variables:\n",
+       "    spi03      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 601, 1200), meta=np.ndarray&gt;\n",
+       "    spi06      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 601, 1200), meta=np.ndarray&gt;\n",
+       "    spi12      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 601, 1200), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2021-03-24T08:59:39 GRIB to CDM+CF via cfgrib-0....</pre><div class='xr-wrap' hidden><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-64e69783-394e-4009-b57e-38844a0990a7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-64e69783-394e-4009-b57e-38844a0990a7' 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'>latitude</span>: 601</li><li><span class='xr-has-index'>longitude</span>: 1200</li><li><span class='xr-has-index'>time</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e74eebf1-75db-4e9a-8e8f-ecb019c569d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e74eebf1-75db-4e9a-8e8f-ecb019c569d6' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.7 89.4 ... -89.7 -90.0</div><input id='attrs-939a68e6-fab1-4279-8b93-cfb039f39c7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-939a68e6-fab1-4279-8b93-cfb039f39c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5be3c58-1645-4955-9777-245011bcab9d' class='xr-var-data-in' type='checkbox'><label for='data-c5be3c58-1645-4955-9777-245011bcab9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  89.7,  89.4, ..., -89.4, -89.7, -90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.7 ... 179.4 179.7</div><input id='attrs-2a36265f-1ec6-43cf-aa27-e4445c6eb91c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a36265f-1ec6-43cf-aa27-e4445c6eb91c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0de38692-5149-4f42-95ba-8a2148c52ba5' class='xr-var-data-in' type='checkbox'><label for='data-0de38692-5149-4f42-95ba-8a2148c52ba5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180. , -179.7, -179.4, ...,  179.1,  179.4,  179.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-04-01 ... 2016-12-01</div><input id='attrs-46d51d9b-13b3-43f0-8ae7-5fd04649ead6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46d51d9b-13b3-43f0-8ae7-5fd04649ead6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b749151-af63-4424-8120-36732676e9e5' class='xr-var-data-in' type='checkbox'><label for='data-9b749151-af63-4424-8120-36732676e9e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-04-01T00:00:00.000000000&#x27;, &#x27;2010-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-06-01T00:00:00.000000000&#x27;, &#x27;2010-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-08-01T00:00:00.000000000&#x27;, &#x27;2010-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-10-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-04-01T00:00:00.000000000&#x27;, &#x27;2011-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2011-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-10-01T00:00:00.000000000&#x27;, &#x27;2011-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-12-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-01T00:00:00.000000000&#x27;, &#x27;2012-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-04-01T00:00:00.000000000&#x27;, &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-08-01T00:00:00.000000000&#x27;, &#x27;2012-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-10-01T00:00:00.000000000&#x27;, &#x27;2012-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-04-01T00:00:00.000000000&#x27;, &#x27;2013-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2013-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-10-01T00:00:00.000000000&#x27;, &#x27;2013-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-12-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-02-01T00:00:00.000000000&#x27;, &#x27;2014-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-04-01T00:00:00.000000000&#x27;, &#x27;2014-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-06-01T00:00:00.000000000&#x27;, &#x27;2014-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-08-01T00:00:00.000000000&#x27;, &#x27;2014-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-10-01T00:00:00.000000000&#x27;, &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3cf18a09-fb60-400e-98c4-3614391d5cf6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3cf18a09-fb60-400e-98c4-3614391d5cf6' class='xr-section-summary' >Data variables: <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>spi03</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 601, 1200), meta=np.ndarray&gt;</div><input id='attrs-ce9aa722-5193-48fb-bfaf-4603faa7cc8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce9aa722-5193-48fb-bfaf-4603faa7cc8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e70d0e66-ae73-4eb8-afac-195b4f6ae469' class='xr-var-data-in' type='checkbox'><label for='data-e70d0e66-ae73-4eb8-afac-195b4f6ae469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>80</dd><dt><span>GRIB_shortName :</span></dt><dd>~</dd><dt><span>GRIB_units :</span></dt><dd>~</dd><dt><span>GRIB_name :</span></dt><dd>Experimental product</dd><dt><span>GRIB_cfVarName :</span></dt><dd>p80.128</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>721200</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>avgad</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>1200</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.7</dd><dt><span>GRIB_Ny :</span></dt><dd>601</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>long_name :</span></dt><dd>Experimental product</dd><dt><span>units :</span></dt><dd>~</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 233.67 MB </td> <td> 2.88 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 601, 1200) </td> <td> (1, 601, 1200) </td></tr>\n",
+       "    <tr><th> Count </th><td> 417 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spi12</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 601, 1200), meta=np.ndarray&gt;</div><input id='attrs-c84491c6-dddc-4435-8cec-591a9c79abbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c84491c6-dddc-4435-8cec-591a9c79abbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fee4803f-b445-45ec-bdfd-1dba7b4322c2' class='xr-var-data-in' type='checkbox'><label for='data-fee4803f-b445-45ec-bdfd-1dba7b4322c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>83</dd><dt><span>GRIB_shortName :</span></dt><dd>~</dd><dt><span>GRIB_units :</span></dt><dd>~</dd><dt><span>GRIB_name :</span></dt><dd>Experimental product</dd><dt><span>GRIB_cfVarName :</span></dt><dd>p83.128</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>721200</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>avgad</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>1200</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.7</dd><dt><span>GRIB_Ny :</span></dt><dd>601</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.3</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>long_name :</span></dt><dd>Experimental product</dd><dt><span>units :</span></dt><dd>~</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 233.67 MB </td> <td> 2.88 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 601, 1200) </td> <td> (1, 601, 1200) </td></tr>\n",
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+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-13a7ed9e-f3e5-4d79-a138-fb90375f80a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13a7ed9e-f3e5-4d79-a138-fb90375f80a6' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2021-03-24T08:59:39 GRIB to CDM+CF via cfgrib-0.9.8.5/ecCodes-2.16.0 with {&quot;source&quot;: &quot;/tmp/mv.2888.wikilimo/marspQ8I3N&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e404fab80>"
+       "<xarray.Dataset>\n",
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+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.7 89.4 89.1 ... -89.4 -89.7 -90.0\n",
+       "  * longitude  (longitude) float64 -180.0 -179.7 -179.4 ... 179.1 179.4 179.7\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "Data variables:\n",
+       "    spi03      (time, latitude, longitude) float32 dask.array<chunksize=(1, 601, 1200), meta=np.ndarray>\n",
+       "    spi06      (time, latitude, longitude) float32 dask.array<chunksize=(1, 601, 1200), meta=np.ndarray>\n",
+       "    spi12      (time, latitude, longitude) float32 dask.array<chunksize=(1, 601, 1200), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2021-03-24T08:59:39 GRIB to CDM+CF via cfgrib-0...."
       ]
      },
-     "execution_count": 25,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
     "\"\"\"Load SPI data into an xarray dataset\"\"\"\n",
     "spi_data = xr.open_mfdataset(\"/data1/raw_data/SPI_GPCC/output_201[0-6]*.nc\")\n",
+    "\n",
     "# Rotate longitude coordinates\n",
     "spi_data = spi_data.assign_coords(\n",
     "    longitude=(((spi_data.longitude + 180) % 360) - 180)\n",
     ").sortby(\"longitude\")\n",
-    "# Interpolate to match load resolution\n",
-    "spi_data = spi_data.interp(\n",
-    "    coords={\n",
-    "        \"latitude\": load_data.latitude.values,\n",
-    "        \"longitude\": load_data.longitude.values,\n",
-    "    },\n",
-    "    method=\"linear\",\n",
-    ")  # Wikilimo used default method ('linear')\n",
+    "\n",
     "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
     "# Therefore here we remove Jan-Feb-Mar 2016.\n",
     "spi_data = spi_data.loc[dict(time=slice(\"2010-04-01\", \"2016-12-31\"))]\n",
-    "spi_data.spi03[0].plot()"
+    "\n",
+    "# Fix time stamps\n",
+    "spi_data[\"time\"] = load_data[\"time\"]\n",
+    "\n",
+    "spi_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "fc464e8b",
+   "execution_count": 31,
+   "id": "a481b2c3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>spi06</th>\n",
+       "      <th>spi12</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.776954</td>\n",
+       "      <td>0.525000</td>\n",
+       "      <td>-0.447657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.013915</td>\n",
+       "      <td>0.638954</td>\n",
+       "      <td>2.144423</td>\n",
+       "      <td>-0.711328</td>\n",
+       "      <td>0.038671</td>\n",
+       "      <td>0.396093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</td>\n",
+       "      <td>-1.662499</td>\n",
+       "      <td>-1.019922</td>\n",
+       "      <td>-0.342188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</td>\n",
+       "      <td>-0.465234</td>\n",
+       "      <td>0.118750</td>\n",
+       "      <td>0.388281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</td>\n",
+       "      <td>-0.428124</td>\n",
+       "      <td>0.118750</td>\n",
+       "      <td>0.384375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "      <td>-0.266016</td>\n",
+       "      <td>-0.875389</td>\n",
+       "      <td>0.050391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.026953</td>\n",
+       "      <td>-0.254295</td>\n",
+       "      <td>-0.213281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "      <td>0.599994</td>\n",
+       "      <td>-1.494532</td>\n",
+       "      <td>-2.109764</td>\n",
+       "      <td>-0.639062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>-1.486719</td>\n",
+       "      <td>-2.152733</td>\n",
+       "      <td>-0.605859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "      <td>0.056250</td>\n",
+       "      <td>0.458595</td>\n",
+       "      <td>-0.818749</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 12 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "             time  latitude  longitude     fuel_load climatic_region biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0   \n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN   7.0   \n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0   \n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0   \n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0   1.0   \n",
+       "...           ...       ...        ...           ...             ...   ...   \n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0   1.0   \n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0   7.0   \n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN   1.0   \n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN   1.0   \n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0   7.0   \n",
+       "\n",
+       "           slope       vod       lai     spi03     spi06     spi12  \n",
+       "0       0.003584  0.177971  0.655549  0.776954  0.525000 -0.447657  \n",
+       "1       0.013915  0.638954  2.144423 -0.711328  0.038671  0.396093  \n",
+       "2       0.014664  0.832961  3.433299 -1.662499 -1.019922 -0.342188  \n",
+       "3       0.016293  0.551958  1.811093 -0.465234  0.118750  0.388281  \n",
+       "4       0.033126  0.676935  4.477733 -0.428124  0.118750  0.384375  \n",
+       "...          ...       ...       ...       ...       ...       ...  \n",
+       "720482  0.020528  0.325193  0.777770 -0.266016 -0.875389  0.050391  \n",
+       "720483  0.054576  0.310260  0.655549  0.026953 -0.254295 -0.213281  \n",
+       "720484  0.005878  0.160282  0.599994 -1.494532 -2.109764 -0.639062  \n",
+       "720485  0.005559  0.262777  1.111100 -1.486719 -2.152733 -0.605859  \n",
+       "720486  0.037897  0.306111  0.055555  0.056250  0.458595 -0.818749  \n",
+       "\n",
+       "[720487 rows x 12 columns]"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Fix time stamps\n",
-    "spi_data[\"time\"] = load_data[\"time\"]\n",
-    "# Mask using the load\n",
-    "spi_data = spi_data.where(load_data >= 0)\n",
-    "spi_data.spi03[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "spi_data.to_netcdf(folder_path + \"spi_2010_2016.nc\")"
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"spi03\"] = spi_data[\"spi03\"].sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    time=target_t,\n",
+    "                                    method=\"nearest\")\n",
+    "df[\"spi06\"] = spi_data[\"spi06\"].sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    time=target_t,\n",
+    "                                    method=\"nearest\")\n",
+    "df[\"spi12\"] = spi_data[\"spi12\"].sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    time=target_t,\n",
+    "                                    method=\"nearest\")\n",
+    "df"
    ]
   },
   {
@@ -1861,80 +6685,1496 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "id": "c62bf2e3",
    "metadata": {},
    "outputs": [
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+       "}\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.Dataset&gt;\n",
+       "Dimensions:    (latitude: 451, longitude: 900, time: 81)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 -180.0 -179.6 -179.2 ... 178.8 179.2 179.6\n",
+       "  * latitude   (latitude) float32 90.0 89.6 89.2 88.8 ... -89.2 -89.6 -90.0\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "Data variables:\n",
+       "    d2m        (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;\n",
+       "    erate      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;\n",
+       "    fg10       (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;\n",
+       "    si10       (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;\n",
+       "    swvl1      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;\n",
+       "    t2m        (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;\n",
+       "    tprate     (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2020-11-19 14:28:47 GMT by grib_to_netcdf-2.17.1: grib_to_n...</pre><div class='xr-wrap' hidden><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-e060679d-7183-478a-a46f-ccc6ef8c3e20' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e060679d-7183-478a-a46f-ccc6ef8c3e20' 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'>latitude</span>: 451</li><li><span class='xr-has-index'>longitude</span>: 900</li><li><span class='xr-has-index'>time</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cf17ff5a-56d9-4e45-91d0-d81577190569' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf17ff5a-56d9-4e45-91d0-d81577190569' 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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.6 ... 179.2 179.6</div><input id='attrs-e0faab78-84cf-41ed-b130-9deef6c0368b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0faab78-84cf-41ed-b130-9deef6c0368b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50401e0b-cd75-4b57-bf9a-4bd02d04c74c' class='xr-var-data-in' type='checkbox'><label for='data-50401e0b-cd75-4b57-bf9a-4bd02d04c74c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180.     , -179.6    , -179.20001, ...,  178.79999,  179.20001,\n",
+       "        179.6    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.6 89.2 ... -89.6 -90.0</div><input id='attrs-0dcea38c-e8d1-47ce-9789-0ed0e6ec8377' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dcea38c-e8d1-47ce-9789-0ed0e6ec8377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-244f0a25-c1da-42b1-822b-4fc7307ba01c' class='xr-var-data-in' type='checkbox'><label for='data-244f0a25-c1da-42b1-822b-4fc7307ba01c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  89.6,  89.2, ..., -89.2, -89.6, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-04-01 ... 2016-12-01</div><input id='attrs-ee2c4761-3473-4188-852e-dfbaff572737' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee2c4761-3473-4188-852e-dfbaff572737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb43b8df-e2e3-493a-9b78-94c57ec21a89' class='xr-var-data-in' type='checkbox'><label for='data-bb43b8df-e2e3-493a-9b78-94c57ec21a89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-04-01T00:00:00.000000000&#x27;, &#x27;2010-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-06-01T00:00:00.000000000&#x27;, &#x27;2010-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-08-01T00:00:00.000000000&#x27;, &#x27;2010-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-10-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-04-01T00:00:00.000000000&#x27;, &#x27;2011-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2011-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-10-01T00:00:00.000000000&#x27;, &#x27;2011-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-12-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-01T00:00:00.000000000&#x27;, &#x27;2012-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-04-01T00:00:00.000000000&#x27;, &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-08-01T00:00:00.000000000&#x27;, &#x27;2012-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-10-01T00:00:00.000000000&#x27;, &#x27;2012-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-04-01T00:00:00.000000000&#x27;, &#x27;2013-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2013-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-10-01T00:00:00.000000000&#x27;, &#x27;2013-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-12-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-02-01T00:00:00.000000000&#x27;, &#x27;2014-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-04-01T00:00:00.000000000&#x27;, &#x27;2014-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-06-01T00:00:00.000000000&#x27;, &#x27;2014-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-08-01T00:00:00.000000000&#x27;, &#x27;2014-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-10-01T00:00:00.000000000&#x27;, &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14db116e-1535-4c3d-be16-2cecf6c03077' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14db116e-1535-4c3d-be16-2cecf6c03077' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;</div><input id='attrs-0db333ae-cd3f-43af-a74e-778da65de0fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0db333ae-cd3f-43af-a74e-778da65de0fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95658389-67a0-48d1-a9c1-7bb6d292449f' class='xr-var-data-in' type='checkbox'><label for='data-95658389-67a0-48d1-a9c1-7bb6d292449f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 131.51 MB </td> <td> 1.62 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 451, 900) </td> <td> (1, 451, 900) </td></tr>\n",
+       "    <tr><th> Count </th><td> 417 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
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+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>erate</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;</div><input id='attrs-711fba5e-7769-431a-ac80-5c03ed1b3be6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-711fba5e-7769-431a-ac80-5c03ed1b3be6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0ee2cb0-f92a-45d8-9a7d-40c8f019da98' class='xr-var-data-in' type='checkbox'><label for='data-a0ee2cb0-f92a-45d8-9a7d-40c8f019da98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm month**-1</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fg10</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;</div><input id='attrs-50d3a42c-f1cb-42b5-910b-f67d78ec0b85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50d3a42c-f1cb-42b5-910b-f67d78ec0b85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f3ced2-6358-4db4-bd66-d7f6eb73d8a5' class='xr-var-data-in' type='checkbox'><label for='data-30f3ced2-6358-4db4-bd66-d7f6eb73d8a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>km h**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind gust since previous post-processing</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
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+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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+       "    <tr><th> Shape </th><td> (81, 451, 900) </td> <td> (1, 451, 900) </td></tr>\n",
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+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>si10</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;</div><input id='attrs-1d6b28f3-f93d-4d2d-a07b-0c336e6e1d23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d6b28f3-f93d-4d2d-a07b-0c336e6e1d23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2db8c6b7-23f6-4c85-8a2a-c6a5c805f288' class='xr-var-data-in' type='checkbox'><label for='data-2db8c6b7-23f6-4c85-8a2a-c6a5c805f288' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>km h**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind speed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;</div><input id='attrs-cd2353b0-b745-4ec8-a6d5-e4b7a3a576af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd2353b0-b745-4ec8-a6d5-e4b7a3a576af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32f36332-77f4-43d8-82a3-b0996e25e787' class='xr-var-data-in' type='checkbox'><label for='data-32f36332-77f4-43d8-82a3-b0996e25e787' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;</div><input id='attrs-ff86f553-c1a6-4b5a-a60a-f85ab3f316e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff86f553-c1a6-4b5a-a60a-f85ab3f316e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-240a5e51-7edc-46c4-8bc3-9c3d327a41f2' class='xr-var-data-in' type='checkbox'><label for='data-240a5e51-7edc-46c4-8bc3-9c3d327a41f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tprate</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 451, 900), meta=np.ndarray&gt;</div><input id='attrs-7776c482-16d3-49f0-a100-badd727aa64c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7776c482-16d3-49f0-a100-badd727aa64c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6393f394-515b-4b59-8a09-47efcdfbc9b5' class='xr-var-data-in' type='checkbox'><label for='data-6393f394-515b-4b59-8a09-47efcdfbc9b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm month**-1</dd><dt><span>long_name :</span></dt><dd>Mean total precipitation rate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "    <tr><th> Shape </th><td> (81, 451, 900) </td> <td> (1, 451, 900) </td></tr>\n",
+       "    <tr><th> Count </th><td> 417 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
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+       "</tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa998856-52ba-4b02-b875-500ff7406079' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa998856-52ba-4b02-b875-500ff7406079' 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-1.6</dd><dt><span>history :</span></dt><dd>2020-11-19 14:28:47 GMT by grib_to_netcdf-2.17.1: grib_to_netcdf -o /scratch/ma/mamv/FIRE/S5_data/processed_data/final_anomalies_nc/S5_anomaly_20100101_10fg.nc /scratch/ma/mamv/FIRE/S5_data/processed_data/final_anomalies/S5_anomaly_20100101_10fg.grib</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e40b7a5e0>"
+       "<xarray.Dataset>\n",
+       "Dimensions:    (latitude: 451, longitude: 900, time: 81)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 -180.0 -179.6 -179.2 ... 178.8 179.2 179.6\n",
+       "  * latitude   (latitude) float32 90.0 89.6 89.2 88.8 ... -89.2 -89.6 -90.0\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "Data variables:\n",
+       "    d2m        (time, latitude, longitude) float32 dask.array<chunksize=(1, 451, 900), meta=np.ndarray>\n",
+       "    erate      (time, latitude, longitude) float32 dask.array<chunksize=(1, 451, 900), meta=np.ndarray>\n",
+       "    fg10       (time, latitude, longitude) float32 dask.array<chunksize=(1, 451, 900), meta=np.ndarray>\n",
+       "    si10       (time, latitude, longitude) float32 dask.array<chunksize=(1, 451, 900), meta=np.ndarray>\n",
+       "    swvl1      (time, latitude, longitude) float32 dask.array<chunksize=(1, 451, 900), meta=np.ndarray>\n",
+       "    t2m        (time, latitude, longitude) float32 dask.array<chunksize=(1, 451, 900), meta=np.ndarray>\n",
+       "    tprate     (time, latitude, longitude) float32 dask.array<chunksize=(1, 451, 900), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      2020-11-19 14:28:47 GMT by grib_to_netcdf-2.17.1: grib_to_n..."
       ]
      },
-     "execution_count": 27,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
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ydLBLMh8O8KWVLW8leRwJIETQ/UaQKAav03/xbuAxPOR87HoVrtP33l3qcUgEk11P441ylUEMA5cqfkmfSxGvz28KNw8+e7sm8mQmIf802zsdtirQ9pwtMAFeAX4x8Jvi538W+A94HPDY9Qbepu3mNnJ+IyXFB/kNHXz7HEbn+xl244J6GjvhqVWrwLHuPOvOsWr0Lw3Yy8bROAWLs3XHqnFsNh3txtFsOtpNh4gwmlYcXhtzMCnZdJ7CSAaO2Ugv7/Gq5XjVcm/ZUBrJ8fo6eh1plr5ounwM1vShk2uziqowzKsi5xxAB6KUqxgVhrpQMKgL9TSMQGGEaSVMSQ+0Dk4ppAE63HcuUFhhXBpWrefusuX1ZZvzH6UVppWlssK4MHHMMlsJ4D7E4wG7lQca2m44qYhTbytcyCW1cXY79GTeKJc09ODStUyhxBwCNMK4tLz3+oSDcYmN59HE0F1pTZ7974YIh2ys3ZDVMBQ1BI3W6QRhWtkMFAkktkJNg7xFDxqCIXoOrdMQ8RAQ4HL2U/DZ2xDfgbsEOPLFc/ErfgsMLoWmSEYRY5AYOh6Gj7fMK4DkfOUTDG9dha3eIQshvCwi/ynwBbQr2v8XDVMdhxASaeUl4LnLvi8i3wV8F8ALzz27zXh6s7YLOo/Kb0TQcD7O+FPSOw82Q+BQoFi26nGk/ylUtWpdntmnMFECjGVctmnclocBYK2h2hvxcz50nX/2g0dcn1ScbTo+92DJ/fOGO2cbfvSLx9mDGVeWo1nNzXnNjT1t7DerixwGSmGyocezmwyflJZxafNsuLZmK7ltDdTW5pltYbkwo92NlQO9ZyEKIIlOPC4Ns8ryhZM1nfO8/9qE6+MihwNTTsjHBPBloZrh8RoR2kHFYRqcFdh99Lr096dQ3hBIx1VfETQ8R0MbehKzUUFpDJNSQex43W5te1pajiYVk9LG8F1gVGiO5/pEO+OmfNeQSXeBnDAAjgTm6VSkHIYRDQmOrGSgqKxQxWuVwq3p/hLZ8TLigC6uAd8hLoafkgcyCDftehAZKFyrwNA1eOe2Et0XLIGHuaQKK0YCxFpCUenUITkVRLB5WLThCYavrhLm72zY6hDt0/w+tO3nXwQeWzwshPDdwHcDfNPP/lkh32iXeR/D0FM+gEvqNC6sIxcT4iHgYmLW+ZBnzQk4ktdx3nQRODybLoar4iCVwCJ7GJ3nfNNxvGxYNY4uhqBCHDDECEWVmDGCjWGOxnm+eLzifNMxKS0fPJqy3BvxYNXyhXtLztYdLgQqq17BC9cnebA43+hnVoTzTcf5uuPeecPJquF42fL8tQnPH465PquZlDYmsHsPY1QYSqOJ7QQiGSTk0YAxXC5CrmfJM2ARpoWhmmrOxBr1YIbnt3GaOE8e3bL1nDddP+DGsGBp+6T7kMWV1tUEvF7PeVUwnup99NLJipNli/OBj93e4+a04t6yYdkqoFSF2fIioJ+lJw+iNP25ujmtLtByS6Ne2v6o2KJfL1t3oXhSz59OWvQkJTdIwISLjLYIHImqayX5Y3r+V63Hed1/COl3GL3l08DfdQoQvlPQ6Jr+/TDcNPQovH4neRDBO0ICi/S+i8zI3XATXAAMMVY9h8FyKcvt9by7HEDMwBsZTgSfgCl4fHWjxzsZtvqlwOdiL2hE5K8A3wociEgRvY/ngZcfa2sDPuGlIaf8ehsQMrU2PqA58JE87xDwMdnrgicMQGK3DiMxgFLeYNMpcKxd712sGsfZuuN803HvfMNy3dFsdOZWlDbH40UEWxqKCBTDxDXApvO8crzileMVH729x/tuTDFGeLBqOd90eQbsfOBgUuaZ86S0tM7TGD2m++dN9nisCLf3x3ztc/scxlnx4bhkvy6Y13YrgZ1mrcMwR0pqD9k2RrYr2vPlGpzj0oCIoTQ9zTMAJ2vXJ49RwkHKgwy9uWGuJjHTRoXNQFcaybmE0uo5Ka3hvOn49J0Fd043nKwarDHc3h/x3LUxB+OSZ/dGOVz18tk6g032ynYH7PjexR/X4vGtsueKnTCU8UIrPeU3AS70eR09N3Ih9LYVdjOD/fpE8+0puSEI1ug9bIMm163Xa7VoFTRrK1jABIdpVuCa3rsYehBxubj2gjcRnIJC8L1XERI45P9xudtdPiyGsfm/WEswTpcZjxSlPtfGxtf28klgWua9vkwhKzHIE8x5PLY8yVNq7yR4fAH4eSIyQcNWvwT4QeDvAr8eZVz9FuCvvZmNZuCIYawhaIS8LIWdYizb98CxBRqQK7kz/XSQ00j5i81OeGrTORZt712crzUMdb7pOFu3+n7R0G5c9jDKuqAe6yC/m1xNCes0w01MKdCZ6U++csqnXztjUlmqwubQSGUN81HR5y/iIFNaw7QqWHeO/UnJbFTkWfT1Wc3+qGC/LvKseVYVVHY7eVqabc/CSp+MTZZnsVvXJwJ8BOww0ONKYUBrhFXrWXVa6Wxt/x3nUYpzPN8nmy6f9/NNx7iyzOuCSZnAbjv2n85raYT9uuBrn5kzriyvHCuovHh9yqgwLFvH8arVcx1DRGmCsfs+WcqPDK9fum6lSd6azvY1bDbM16TkPnkfQPaa0uvLalXSuvmzWHnuCFHzJnp4kXQgsVjQGgUvG8NYmpdoMOuzGKLapsvq562CRNv03kQCje7yWqsLYBFfZ+AZ1mpEcAAIQ5AoFTjE27hPkIJ4A+4AQiLPDIsOzUPyJ2/DrsJW75CFEH5ARP4S8MNoYe7H0TDU/wf4XhH5w3HZ97zhxkQItgRbDBLbehNu02ovJrgDffgpAUUCjv7zix7Gsk2U254xdRYTzufrmOSOAHK8bDhfd6wXLZu1PmBFaTUcFR8UY4SudayG58hr4jaEQPBB1y9tDptAHwOvB9TbRJ2tCsONyHpqOs/7DyecN53ONguD3ehvvHu64bN3zxlXBZ+/t+Qjt+dcf2ZPw1K7NOXBeaqMbHkLhYD4NsfDgT5RmS7VYFsWtr1Bq7ej+A5blbRevRJQryONtT5ei0RA2HSek5WeVw0bmS3gSDmP9N0hmExKy/uvTfRcTSrGpWXjvIaT6iLvZxg6Son1NJgnzyDlQob1LYlJZSNRYFYXzCqbw36QivnUk7WGrfqWPidxUVVgVytrV+tKwV1BQu9rPW4RIHojrQ+sOl0+qyrKcaVeR0ouxxyHeN8Dx2ZN6BQ88n/ntnIVEinXW8tTUrxre9AZgBAoYGCMft/YHkC6ktC2SFkiRYUUpX5WlhDfbzEiRb4CYasnsql/au0dZVuFEP4Q8Id2Fn8W+JY3tSERKKqHyoSkWW5Ocg8S3MmrCIO6jCFQtJktFem0MeG9iiGqdadgcb7uOF61nK/bzI5qYrLbu57iaa3BOY+1JtdfFKUO9JvGEXygiOEm0M9d53NMV5PnnoNrY6oi1laUlqbzfOTZOdcnVY7tDwsO64nOpvdHJUY0GZtYPdOq4EO3ZpTG4ENgVllmleVorInbe6uWV8465rXO6isrjMXgAlRiKAXENRrOGMTGM0cf+tlhmhUmT9AWYAqCKTRcZQqcKWl94IdeOeMbn5kxLg24/holL++8cdxbNpkUMC0tdaH5mb4uQmsijO1ZYv3gq4Ps/qhkf1Tm7aa/5DkOvYih1wdc8DBmoyLnPVKorIj/5xE4ppW9VHxQKcq6rVHMJTkfWHWBtfNZgeCNKuVForMQoA2e1uv1diEwKS3z0rBxgcoq8UG9ETjd6H3e+APE6vGMC8OsMoybE4r7XyCcHeObNXRtDwzJi2DgORijg7wx22GsmIDv7ryUbmiakzPCgMxgqgIxBlMVmF2QMAapRvraWihKpIiAUo8URIpa7y3I95YC0ZMb7uSqSPAdp+o+GQvkuGycHwJeH89gCEj2MFLYaVcaJOUyEmCkYj5Nyvb02nXnWTRdBoxl4zhZad7gfN2x2XS4zmfA8HHWGnzIywHaTcdoUmGMEOIsdRLDR7uehY2hp6bznC0aQgjcOd1QFYajWYUVYdU6Pvmls1y5ndZPlebXZhXTqmDVOvZGZQ4/Jbqungdd97xxfObeks8K7I1Kbs9qnt8bse4895ct+yOl7JZG1CuSOBQm4HBtpnNq7Dz0FMk4sARbgXF9clMEb0vNaTgNWX3dzSlrFzRmn45xcD3OG83tHE6qHBJ6nBoOiHTcoDePa3tqcuf0mg89h62QYarDgAuyISbRWqPXkKrl60JZV/OqoC76nBD0npg1OlBPS4Mh0IVUVNqHo0TA0rOpHsdGMewmFZnmnLy5IekjkxbYBrQUSgTo7nzxoaGp/iSo1yFFhVSjftAva4KtENfgz4+pJnPc2TF0DaPDm/izB7jFOb7t1cHcuiGUHrEG4x20LZQlwXukaHuvpKygiveXd4ixOm0Q0VxJ8IRgNOz2BBlXV57HU2Gh540PEmhhkN9oXQ8cKeHdxqR3NwCMRKUdhqTONx1r5zlZtgPAaDlbt5oEjwV7XevV08hsqTQuSizkiyDi0kwMNquWorL4/RHVgA56mc6UNcLBvN4a9AFOVtsF+M4HZqMiFwger1rGpWXRdAqKzjOyCTT67yX21DDk40Pg5bM1s8ry3F7NqKh4fdlQWYOVQGGEwodeYTXGxqXdZAAJXdsLV8aBhMJDOSJQ51CjVp1rUeW6C5xGFlnKr/hABvXzpqM0wqQs82B+mVjjZXUSlwlGtl0Y5DQUJBq2CyJLY2JuR39tnc7TYL850W0NtVUm1bSyjIttqqweR39MlRVmlW534/q82jBEZYwWSzq/Lb++K7kvQvYYtggN6f4YfNcaTZbrcQt1fG2NUnlNs8Se3YW7n6c7udeHoJJXMai50ImB3a74NlY/i/dFsBVmPCV0LdZY3IM7hOUZdC22rrHjCaFrMogE5/OfKQvdf/J4jM2ez5aZtXonooARiOFTU8CgN83bsUQO+Wq2pwQ86HMdtiSYgi7Gd11klLgQ6FwCjsulQlJoakirPV93nCzbDBjDGoyudXSNj8Ch4aSu9Rk8gBieEmYHI2wc8EVkK4rjnGd13rCMg1nwgaK0FKWlrC3txrFZtxgRxvOKui64eXOG84GX7i9pOs9sVHA0U/qu7YSq89w93fD5ZonzgUllmY2KnMCvCsO4jPmWOOi1sTq7LiRTXNMAvOk8n72/wgWtTJ+UNrOuGgdFUfWy+IPkql+cEbpGZ4P1CIlehx/vE8pRvm7iWiZFzaSwtAHurxxGiswwS5pZKaSUEuKXWQKTlOtIdNxhuKfXh5LohQx0xOJqW5pfD2FWDfeZQCW9Tp7HyBr2a8uoEPyAcJEosuOir6h3GTQUXBJolBDzcoIzMf8RpfglRKn1mCOelBLzKkmgMHlKl+uu6e/pB8TSClXokLVm4IKtMAe3KMuK0DaE9RJ37xUdlEdTZDxVL6Ma6frrJf7sARQKFBpKKvs8yIBm65dntPdep13ovoy1yCUtGMUbght4IV0LRbkVNhPnEO8w3hHaBqnHiK/BVoSizGHRJ2HCFdvqqQKPFNdsg8Z4h6Eq57eBYxjbTmyddQSOVDA3ZEkNK72HuYwkC5JCUmEwmw0+0HmHKUxmVcnghpMYZrHWIGUPJABFaajqgnrgjbSbjq7xWNuHo+ajgqow7I8rAFaty1TdYSHb2brLGliJ9nttVmUasBfRAUq2Z+DlVphGB9jbs5r7q5ZJaWmceh8qyT1AROjj4EWls9GiIpRjsImOGXMdriGYguL4Jdz8JuOTL3Hj2vu4u+zygKZpH02Gzyt7QdV2dzBvo+xKG48lhZh2pT2SZ6dAGcCoR9U6T4lszdJTqMvFbZa2P29OBOOHarj6eWUN40IBwhqhcSGCAhTRO0iWjkVEsIRc/U2qL/IQCDnpbYkdB0PyNhQsKyuZIp3EFDMxZAAcqT5J95mAKtA6wBZINacQoJriJ4fI/u0cmixe+FqkW+t1TgnpGKY07YrgHe7eq4RmnRlTOUcx8E6k0DBUt1zjncdE4BBjEBv/jMmgMgQR6VpMKmAksqm8w3un91z6rIpRieLJVZdfJcyfFvAIQYuTZByVbXtvIyXAXQSO1uvsMoHGWeM4b7rsZexWeyc9qU3SnGoc3vkMHMGHXMwHERC84E3/vqoLusapoupg3aKyWKsJc4CytowKTVK7zmcasTFCURnEFHStZ71s+eyrZ9y+NmY/MoSU2aWuflUoI2seQ1fWSPaenA+aJzHCJ1854+Zc9a/2JyUlA3roIOGbJS8cbILnR1875Xzd8YFr48g8UmC2tgRTIKbQhzUlzkFnh4AxsdeJGEL0EsV3hHLMl+rbbJaByeQFps5vifIZgcLqgKxeUaTcDujBOWfle6n2Yb3EZWaN6JSeANZgYl2PEa0M1/fbIotZsiRIrMMQdM498DyMYVpZ9irDvLY5p+J8osvGQd61efDtpUU02Z+KKxOtPEgfckr3tjV9uOqs8ezXNv/mEGufTGRsiajMSAA6T84jpVvSSKDzCqKV1wmGM4IVgy1HmBQmMgXBd1COtzSusmwJaEJ9syKsFpo8L2MOJCbAQVlXoVlTTEbUzuOaFrduaBfrPPEwZYFYg43/h2BiqkLDWV2BRCZWKCqkazWJ3jX6t1mr11uNwD651g1XYaunwCTOeIZz0SRMuJvjWLd+i1WTEqS7MiEqHdKr1g5ZN3m/KTwSBw1jNJ9hjO2FEn2ga3QANYM8hTGC75R1pYlKyZRcUFaVMl49YkRrQhJYGaFZd3z+S2dUo4Jre3UOe8xHZc6JzEZFLg6cRQ8leVRd3M+nX9Mk+/OHE545GDGFqH9lKO1OAVoInGwUYPfHJT9xd8EHr00YFSrZXdU1tmwJrtWEeFERVotMzZSiIpQVUrRIs9DZoa3yOellXRylsZQxLFYYDY2FoHmH/XrAdoohp3USnIzhKSOpGVK/7tA7GYawNO4vsT9I9CIiW8v7AJZeDsUHhg3hWx8wAbx31AEoDBPRSvxxYZQpht6Hgv6e5AV0PmCLEgmBEPcpZpvF40MgxN/hfcg1NT7SbctIl25c4GztmJSGMRoek7jf4W8NqOeRPI0gCoZdSmXYwMgaDZFtPU8a8slgkRRxB5TsDBxiMJM5Zn6o+Y6iUg/EGAWFZp1puqFZ49ZNfFYiYFQF3WJNt+5zeWINJjGwygIb/yuwlHm5KQsFqXqsINK2ChxdA4tTzHjKkzCRi97uV5s9FeARTIGvZwQx2w9KiMKEzrNutQ4j5TfOG5c587v9slXErtc1mlSWujB0PvTihTZ6ID7gXUA6yWGp5DWEHbBJyXQfvQnQeo/RtMx1HwmQehCJANS6vC/vA7aQmBcxnK+7rUHy5t4oA8eoUPCYlpZFqw/6ndNNDm2tIrDdOVP2VrU/UqkV5znzgXWuzNa8RwLFj3/hmE+WlvMXD3nxcMyNiXoz02qK8R2hXWLKGjGWQAvGYqZzpJ5E6mQRZ4ULwniftQucN45ZFD90IUq0iwcRzqLuVVUIBrPVenfdaXW8Mot2EsI7z3fyRnZnjS5AWfQ5Ehfgwarl7mKzpUnlfGBW9wKRw/st5U4SeIWgVdzDgsrd5P1543FBCQfz2mK6Tc4DBenZW20EHyda+1KTlBH0PrciTOZV3FfPBlt1gZON43TTbdWSTEqtNSkiay5NazyxH0wEE4Esh2K7FWZzrsDfbpRuXVSZpJLED4MpkOkehbH45Slho+EtvzwjrBa054ucBHetehuu6QjO49sO13Zb1N3gVOLEA2G5iiEtS3BOmV0RWMQaTFlQjCvsqKKaTykPDzGzA8zRbcz+dUJZ82RMkK/yuNVTAR6EKNpmS3WtSdTOBAS9nEVKjNvE3okChgk8qsJwYJSlNOyfYUVB5Hgnab5ad3Q4tK5KkKBhKlv4zKzarDoNTXUeCoMqJvT5kS72pDCFIbUt3QUZiUFsF3Mdqh6hK3edx8ZeGpPoaeScSKUCfBvnebBqcy3C/XPVsLp9MAZUKHEVSQGzqmDTeYwRNs7nMFbSXbp/3vDS/SUHk5IvHK+Y1QW1tdFLsIxsRShGhHaD1CP9vZ0CSChqQjVRem+zIBQjfDlGgvDCXsUmFgOWRqgNmGZFU05Zdx7FwQiuQXKytzQGb3XQK63Z6jC4CyQPawGbtK9ONh13zjd86WR9QUo95YeaQokFk9Jus6F2kvCL1tG4RJJQryOFz/LMP9Nwtap+VNQKNK7V8FAs9qus4CIwajRKBjVL5J7iw859yVxQYD7bdFkxoI0U6MoKlRFsoftp3KA6XlKBZSQfrE8xqxOk2xDaDXiHKUqwZV8YaiyhHOPuvoyZzAmthqayp+EdpiwIxisLADJQbB6c4wZU3fx4e49velBxsR6qW3W4xiHWUM1KJjf3GF3bZ/PgHLEGt25ozhYUo7uUd1+mfPGj2P2jC9t/KyYCdpCP/Gq0pwI8xLXIZgGm0AKj/GDFh8BoRfVZ01eDpxCEFZ2VD1Vkh5LaZ+uOo1HFtZgnGJ9bjpctVdExHwVWIxUUBHINRxgM8sEr4ypXiBuTcx+aAHe0my4yq4qe9mlFE/BBmVfB96q6aVujSRnpwbqvm3sjmhh/WDYOG4X9lrG2Y915pZuWsRK9MIyDpbJ9aOXmvM4Fduu2p0AO8wpn644P3ZozHyngnG86XrcNVWQN1aMxyIlSJauRDiDdog9xrM/y4CbNimBLxsZwUhzgfGBcGqY2YFbHYCs6r+KOJibzNewDGi+SWKquD3KbddMBtnMVlyXNk1dlRHXBXjnb8Pr5JtfarGKfklQpnthqAHd9r3N1OKnYHxWx8FI4i/mn0pisPzVKNSMiGuozfcW3pZ/saAK8gKCAYCIVvRD9fgip1kNDlVjZUsXV5lH6m2eVAH39UCIajEqtKZmVSUofytCBcX2VYWJFeY80m1gE6iD+D22TvYpkZv86vp5RPPt+3P1XVX5kvSC0Lb5Z0y3WiDV0qw3dco1rOnzb0a0b2tNFDlWZUocm33YEF/DO49pE2w2IFYpxgakswXma85bm/B6T0yXV3gRTlpoPqQrK6Yh2saI4/sfUN69fGD/ekglXnsc7fQBPwkKzQV75DPLMBwAoq1kOTYj0DJjzpuNLpzqjHDbfuRfVbFMIJwkKAhwNGEmJvTSJ+lOTytJ5BZV7RtjEHhthUAXsO4/J2kxD6k5P47Vx8A4+0HU+hr40zAVgbT+I+6CFh6URmk2Hc56yLnDO89L9pSbIRZRWHCXGAdbO8+rxmqbzPHMwwvkQtar09yQm1mfvLhhXlltzde9T4RtEBlPQRPy9RUPjPLf3Ryxbh1m3jApDZYVpKUyN7Ws8jMGMNNaces2Hsgbv8ZND9USKmrkIezaAWyPrNdiKppxSANMSWi8xhk+mqzqjDl0SbFyLh87HWgq5VM48RY6yKrAI91ctdxY6cF2b9nkYN+n7c6SQJWzLsidCgjXCjb2aD9gprfOcblRZ+fqkzIN2abSifVKaHDqqrca0XEBpz3G2r0Co3kERaxYEZTdJLNarUvJZDMFuz4STOm49MsyqmmdnJatOQ7mTUq8VQC0e064yk0riayKtVVyjYaqU07ClxlO9x68XOZ8l1Qh8h1mfETbLeE8Pw086+JfzCWIM3XJNc7bErTe0izWr15e06w5bWozVsFACCtf4zLKqZmXeZr1XY8uCzemKdtGyfrBm8doCW1n233ed6eFMw1d7Ez2GZhvs3rKJ5Of6q9WeCvBAhNCsMeszKMdYW1IXFT4684aAqYQPXZvywv6In7hzPlAzldw/+9q02tIpSvHtVO8wKiyzuqBxnv2B6GGSCbFGaEp9WKpRkYUPQYGh3XQ0G3chF+JiaGj3fc57eJfzIYmdVZTqwYiRmJT3iCjA3T1bM650tnm27nLRYFLzTaG342XL0azmYFJSmCjNvum4Oa+5vT9iFivShw2gEmvraFrx0oMld0S9GGOEdaeJ19ajOlVikHqCjGaqPRYZV9KucAfPalimqPuYebfRsJYtCeUk5w5EVDerspIZVZ0HF6mvRqCVgI9UXm8DJooD+hCyMu0wtAS9jtR503ESJepVhLAHTBfUU02WPJHnD8eZoZfulVXjePV4zQv746z8WxeG15ctD5YKTNenVcwpWQ0dRbA3ot6USVKRZnic0ivZgvbYSMwmUyiYiNnWEUvUWd8h3YZx8IyqKfO63FJYAK27CLaMyrkt0qyQZqEfGjtQy3U5z6Aquk0GDgD34E5kVq3xi1PCeklYL/CrhYae2o52scKOKkxVaM6waelWDb7pJ0jdukPstlqArSxYi6msgksViw9RUkm9N8ZWls3pJi4LuHWDGEMxHWHnB70n9YQs7f+r1Z4O8AhBb+TNAhlvwI0YFQZnLBSpYlYFS3wwfPNz+5ysdcBQ0TtNiHsfMre/wTOL6qx10Rd8uaD9sl853+SZ5yb+T0n2pGzbdJ575xvOly3NuqPd6ExeCtHnPoamtLjQ5fCUEcmgA2wDR2UuhLi0x4c+bOmY3ntUM64sH31mTusC91cqzni+gVWjA97tg7GCRJQzee7aOIdrvA985u45t+Y147KEmDP55CunOB/Yn1SR+rthUlkOJ1VmJAHKsbeWYMc6Sz17XQeessLdeD9uco2TjaNrArPKMJKgABO8cvcBm7vU6QzaisEUNd6Y3HTLB3LAX/tWhJzcd4kxFQJ1HFSHifLSSiwMDFG40GxVjEOc/cenJCXOx5U2cLo+qfjYjRkvn25Yto7XzzfsT0pWqbGU87z8YJX7z9fRo9XQacHhOHZzrPTerILSfq2HUaFS6YXQA8cus8l14NQbSRphqUg23ztOq/vFtepVFBVSz6PmlLK3nA+IKTDtCaZdRlCK6rXrhWpYdS1+eZY1qxKY4H1+7ZdnmRLrHtzNBXzdUmf75WxKdeMmeIdbnFHN1Ruo1g2b4zP1MF5bYEcFrnG41uMHz0ExLihHBWIFWxY5P2JjiEuswVYWW0XR0emYbt3QLdbYeoHUoycGICJceR7v9AE8CZNqRHH7/fhqjHRrghsr+8p7OqfJ81UbuL9sOYsznDLmNnwIjGP4IC1fd552XDKvrCaPnd/SKUoz0ZNNx7jsJdBTTPxk2XPJb+9rBcDZuuMzr50pZ98Hmk2Hd0rj7VqXBRCHdF5vejVdI9oEyloTvQ+d7RelpYgxea0i7116IPb17kM341K9p/NNpxIjEXCSve/aJFee35zVrAe/9aX7WgX83qPpliDjtVmVE9FJ5TaM5jir9R7n5QH2pnBv1dH6QCXCXusZF4aq1lqHLP89bDYEPS00DqChqDHlCGsKTFHHUKAggahgFg+k6Jl3w+56yZIw4bJttSDQGvZtH64cFYbDcamgsGxR4Wf1OlNe7MGqpbaG5/ZqLZ6c13zhZMWn7pxvhbfSTH8Z770h48qHQL02uZf8tXHJc3s1lbXbEuLpvKRz4jr1KlyrzCxjtN9GrL8Ituqr92383Ov3TLOgtBWFLZFujdmcK2sqeAX6xQNcpFhLUfYquJtVJj6kYwIt9Ev/15/+MYrZDHv0DGHdgLFUR0dQVDlPImWFqUbAGcYa2pgfUgquZX26oZpW0Gp+Y3J9HCm5BrGiOZN1k/Mi6bUtC+yB0nc1ROawVXwfZU1MrIJ/EnaV83gazFj8eB9fT1U+OuoopT4Fqy5QWJhVRR78SyssY0jmcFxmeWxl3WgsvC60o10Rb5KzjccTqAvLh4op503H2cb1M9c4+Jw3Ha8vW07XbQaW2bpjPipy34l75xtOli3rhQKNyq2bTMcNRjBef8SwKh10wEkhKzF969OhmOLxquVsox5GKhTcRHHH9x5NmMf+5inckgQAXz5dM64s+3WZ9bNO1y0vP1jx6smK/UmVW9mOK8tnXjvXXMCkp62uXeDEFczHB9xbdrjG5zBh1niK18ekJOywZWnUxkqJ29RnQgXuNBhJPSOVXJjEQornRwvnekly5eT3eeAEJE2k/9aFoSb2+Ta6leuxGdZ5nmyY+JnJ3pURiS1sPXcWm7zelqxJrL9JlopNt6VP4P5aK/avjUtC0GOrYgW7ScKTw77hwfcqxkkOBjQMmDyP3Fa5iOspKAdv8nekWSr1tlnEbTb4to1V4F6ZUpv1low69J0AFVzITCtTFXTn55j9tu8m2LZ9TgQImzW+WWOrkvW9U5ozTZT7xlGMCzjd0CwagguZ0WRsH8a1ZYGMdloFeJ816V0EIwt0qwZTrijnE8x0D3t4kydhInLFtnqnD+CJWAg6uLiaUI4IRa0FWEao0AGmspa9mOgGYgc1S+t97h2dEpWgNQaJl580bOa1ycVyI6sD4bVxolrqQKVy7Z7bc52xvh5j3ZXVcMfJsmUy6Ax4Dygq9TraTfQSnI8eyXYrWjFK563qAmPV8xgK9+2PK27u1Xz+3pKmc1SF5c7ZhuNVy0GUVx96SsfRQxr2Amk6z/3zhtENQ+u1gPK10zWfvXtO5wNHU61oTyCcEsmpg58LfatYiTPTBCojKxQDccDAsPBscD29jzNijbsHMfjxPvhOB8d4jfHKRmpDUkruRQIrK7mWbwggoL5JYl7Na0vr9Ppr+9cQm2FZlm3InlsKZ5XQ82KB803HgyjVcmfA0hqqIydPJVlOwBuXczBN57k5rSmtRODo9a8qa7FGqd6pMG/Y2U9/ZNR2CwParIReMNT1FFjpmlyWLu0y9x2XbqM5iuVpFDSMoodl9BripCxsVn2/ja5Fijh4e4+pRqzuvMLm+Mcop2Mt3KsKbFniNyv8eqm027X2MnfrhiKud/K512niZKpbxe6a44Jm0VK6EJlVgXaxwQwGbh9VH7rY08VYQzmts5ZW8lByJ8InYcKFSd1Xm72j4CEiB8B/BXwdOnz8q8CngD8PvAj8DPAbQggPHrkh77TCPPWQCIGSpD9kosyFXuhVVG1tW8/h2CKR4pkCHqrUapgUwqJTIHBGpSTGJiU2e0mHNCQIZD0hLc7y7NfawvW1RcPIeialZa8uWDufmzZVhek1s0rH8nRDUapsiS1crjQ3VigqSx3DUrNJmUEI4EO35hwvW156sMp1KNZoQvdgUuJ84GTZ6CAb+q6CQ48F4Pqs5vpMPYvjVcvd0w2ffu2Mk2XLh56Zc21WZfkNWwo/9/3XuL9osNILDIooIH1p2WsOVQk00iULOrZ5EYytEOkIrtWBzBhCNcUVo3SjgBh8NdYEsS21Ilt0dq5UWJO1y/pHuq+7GBbpLVofK9n1HhmVOtgfTUqem5dUVvjUvTUvnW5yxfqQ8pvqO0pjmJSaED9rugy+ic0HbNWJpPBgWqb5KacTi1JlRdadZ7+O9Uku9AWCRY11LbSbGK5qVF02WjAFoZrmyVPKe0i30ZxHkiMfhr+S+nEME4Zmnb2E0GlNRtIlC85BZCpJWeXnDiA06l0AuNWScjrOKrjN2QLfdNjRGZvjc7rFmsOPvhfvNBci1rC5d0LwHlNZ3IM1XaRCexcI8cSLFbpVx+Z0k9lY5VSfhc1pg60M9V5NtTfBjmqMMXjvceuGzfG5amNV9/r+6W/bJOtwPZGtiVi0k+rLIYTveGIb/jLaO+15/Engfwwh/HoRqYAJ8AeB/yWE8MdE5N8B/h3g337URkLQWZO0y7jAI15v8CCGEk0yhqKmskVutpMAoPF95W3nAo0PhE69j9R6tUDpjK6eqYfiWhrRBka7InuBlHwVoMrMmxQmK63BTbZnotou1uCuTzhbd5wtW7rW0Wxcpuxq8aHk5lHqLVj2xyXHyzar/qbQSNd5isKwbBxfuvsAWxhEJFekjyvLwaSiLgzPHIxoOs+Xjleaz4j5m6SHtT8p1euo+j7rqZVrXRjuLZqYI/BMWs+ZcSxiXsPEcJGK+qnoH4MiN1JoJgnsRQaRnpyOYT/6rWQwUTgwhCjZkWTLty0Vz7kQWMWK7kXjspZUkj0ZFVqYKK6lspozUaVczXmtWqUG14Whi8CzP1Ia7oNVi+Nih8fUCTCpFexK7OdjjCABSbeqV88d9q7X2Jvvc0RpWX4YohjgsAUz6P/YZyV7HgNqbx5Ujeln59731NauURl2GISv6LXLYk5FrMGOqkGNxRimxMrvms2DM8Qayuk4y5IU44pu1eRkeHPeYitDMVJGlq2sFgSmYtqR/t7NacPhB2/g2geUo4LxzUPGR3t0qyaq7OrxzJ67QXX7eZ6oPfk6j98N/CSw93Y2IiLvBT4UQvifRWQMFCGEsydxgLv2joGHiOwD3wb8VoAQQgM0IvJrgG+Pq/1Z4Pt4A/DAOfzpfcy+IQRPcE2mgOZYcQhQe8pyTGEKjaUHj7clpvN4o55Dg3oOi9ZTW2FEB0FBiOCxm3OdpZmCypLlxIMYCpT2mGoRjMCsMtyaVdxdNOqxmEARiOqnJgNH6l2+arR39jw2hjpetiwWDV2rVFxjDXVlszeVZripP3ruftd5HZydZ7nQkFjqNzKalJqgT7UecVBL4ZaUA0nV9XVhGFdFbKe6/cAYI0yMZROT8KmC+v6qZV6pjIcOftG3iycmtVVIiW6TVJGHGw9eRRbT22E726CDcfA9cJRGwSlte1g0p8WSPvcF6UUIdewtLMxKk8M818ZFrhSvo8RLaQ2bzvHiwYTXlw13Fg33Vy2Ho5LzTZfFFGFQiCgog8/0x3Gxv3nvIfmguZfKxjCcjd8RMEnWxRRgum0ASaGq1BTNtQquPoJvs8n1GuI76Daa+I7dN8WY3FJWD8YqeLRNfp/CWKEDMS4rHOTPvMOW2i5WZdId5cGBbtMYrFdvJFWUV3tTXNvRnCmVt1k0GGuwlaFbd3TrjrKtsKWhmlXc+sYXqOYTfNvx6j/+LAfv76vFUxirXawJzlPtTagP5oyefw9mfgiAX5zypEyeIHiIyPPArwT+CPB73sZ2/g/AdwHXgA8AzwN/GvglT+AwL9g76Xm8D7gL/BkR+dnAD6HoeyuE8Epc51Xg1htuKXjtL9CsMdM5phoRqun2zCpJf5drQjXR+LmtCEaTyRJj4KlX9iwK2q2CxQWd/VWjPezqWJkskVYqnSZ2E0iJ7yiKOlc6i8DIGuaVnurh4LvuPB86muK8dsc7WXfcXzXMRgUnS02uz0YlJ6OCO8frqObby7FbY5jXRUyMuxwKSfkTbUDlsxx88AFTGJpVR1kXQMe9CFD3RgUHk2orXj+rtaXqftStqmJuSH9Hf/qNCDenVW689MrZJvfpbpxRaXAtlQYGysKmBxMAa0v1PqKlBPlWJ0Lok+hGj4+oROsjcKQe9DlshYJLZVXSxKFV7MMJfWmUWDGrNJ+0XHdMShNlPIRbs4pF47izUC/y2rjkZKNe3p2uyRIwSVKmdVswqF5MkKzQmwgWqeug99oitrZake4jAaBzGmIMoLUYRUWI0iXit5uAKbHA9p0dxdD3II9EkkhI0INy2aPLjZxIBA5LME7prdADyRAwCoAyJ8aJ9R4JSIjU7CTJHpo11juakzO69YbmdMn63gmbB5rbSglo13hc4+lWHd2qY3pryvWP3aKaT7DxOTp4/1EEihBDWCMOP/weqqMj2gcPGH/sG5HRRIUQE+140Cv9SdibCFtdF5EfHLz/7hDCdw/e/wng9wPzt3lI/ye0hfcPAIQQPiMiT4YhcIm9k+BRAN8I/OshhB8QkT+JhqiyhRCCyOWtv0Tku1CU5YVnbkJR4c8eqHpnNUJGC1XWTDRCMYSiVgBpV9pXArDG4k2l8tjdhlml9FQTgcRITLD6wCYEpNrPA2e1ukeoJpq0jINesCXGtVFa3We9oNIK+3WJNeSmQ0YG/bY3yvQaFZZzOq7NqgwSs1pDbXdON2xWGs5KtM9NV17QYAohbAGHj1XruaDQ+6ipZShKjy20On4Ym980jq97z4FqYRV6m1RRxiIBoEkUYiEDRwKSMp4kHzQPAZrvEBMwxNoCHeF6APEBIzFJK0AwAxaWbD/4sYeDMYX+NqKOlVeF2q02fSh4Wa/9L6wh5xMqO/gugdcWLTcmBfPK8uFrY04bz91Fw0sna+rCsO48XzxZUVgNXQ1/Z21NUkmJXhD5M1BA8RKoBwBcFyaz9EZF38kxhKhZZSIYBunDd9FjCNbnhHkGWtfGdfqBLcRQYPZEkkUVAIbhLci1G6n4D9D3oLmP9WIANlq0qN5IfAZiYj0n1WMyPcmxizX4KEuiiXPP5rRhfDjCNY4bX3ud0y+ql7C8t6KO7D63blTbat1gRzViDde//oMUkxFmPMXMDpB6TPHcBzJoBK/0YylK7WD5hExEsOVjg8frIYRvfsh2vgO4E0L4IRH59rd5WJsQQpOEMUWkYJuK8kTtnQSPl4CXQgg/EN//JRQ8XhOR2yGEV0TkNnDnsi9H5P5ugG/+WR8Lxc3ncPdexa8WsFpgmrU2ninKnPTTZjSVJtMmB0hbENo1VTnS2HpRYUNgXI7ofNg6OcMr4OLslskR1fqByoq7hlCM4gDnKLoNUzFMrWU80TqIZRsi6ydAp//TQJwG3lFheOFwwnnT0TitYFd59TIDhhtIZYyrNtNwk9eRe4FE4PBeVVtVT0ur1U3rEaMSKabQwsPXG09RGTarDt95ftyc8JHbezkxb41kwDAx3DJs85qAZBjLTxpULoAJaNe71K8ifmZioRqxEtyIhrNMzIXk6o0heyglf+NnObxlEjD1xxBC3ya3MNB5bc7U+cBBbTlvPS7O8A9G8bdK4Lz1fOlsw+vLJnsUyWtKdOkU2kr/k5XW5Da16Z5JHQ3TeytwOC4zzk1Kk3Nlu5Pa1Cs+xDCThpqK3GIV70E69dx8R6CIyR4TmQKldm4kFg4OG3elEFYq/DNmQDoYWFFqkn630G4A6sF71TNLs/yu947CFhhFQsvelHJvytR53HrDtQ9ZbFkwOZpy+tIJh+8/oN4ba1jNeYLVXIpYg5lPNLcy3YvAMeo7GkZwk5TDST1knlQPc+HSjodvwb4V+NUi8iuAEbAnIv9dCOFffgvb+nsi8geBsYj8c8DvAv6HJ3GQl9k7Bh4hhFdF5Isi8jUhhE+hcbmfiH+/Bfhj8f9fe8NticD8CDveo3AN7sEd/Mk92Kx0plNW+QYSYxVQvM8zkVDUhKIi+DHUBtNtKG3Vb3vruDVG72Il+mYU46nFlMnybn5AiTpEiGFkHdcnYx6s+4ds0xlWXWTydEFn67FAMSXXExtrXFlumjpXjydhPoA7p5rQTDkT7QlCpvj6uMzFabAtDM2my58XpcUWpvdO1pJfL0433N/TCvLxgOas1dgSQy79A7QlOij9jNySXpOrviEoQIR+sA8+RO2mHoCCCNrCCEQiR3bYO8KTZ9BF8v7iJYuOSAaSyiqYqeS7/pa7q45pobUbt8ax6Mwr3fiZacGNScHf/3xHK4HO+Qwgu70chgCSWtAm3SyzAwapqj2dk+G6pe0bYFnTJ/tFRLWqoPdAhmaiPIlr8sAfKNTlEqNyMb4fvHPVuhiCb7V5U9vmRHjy2LM0CShYWwvVqE+kp0LB1Kuja1TupB3kT9LxGV23Xax6L8V5fNtm3auh5Mfs9lwp6VUqBtwgsaq8GFXMX7hFsbenrXBTs6lqFJ/3BBw7QPekwIMnU2EeQvgDwB8AiJ7H73uLwAGaG/4dwD8BfifwN1E265fF3mm21b8O/LnItPos8NvQ5+UviMhvBz4P/IY33IqIzvqLkQ4yk0OKGyvCyZ3sjaSmNFhL6BpMUcYkodJDQzWBEDDLByoRXo40T7JbmiwGI4bCmCh3ErRVJ57l5AaTxWuDh9WjwhNQieH6eKSDqZFYCJYoo8rwWrYmh7G0QK3LIauX7q/40K0ZrxyvOakaxlWRq8PvnK45X3dZyfey5yOPtXHQSgwu13mqusD7QD0utnqSiHG8drzmmf1x7g1ipfc+SmsuqNQauShPnl6HkFhXQGRgOR9iIVzIdNrCJAJ1PPYEILHr4HaXIg9egUVcS2lLgqmywnFyghLg+RBBvwuMrLDqtOeFQTKt2Ac42XiO1x2jUsNKPjh88rqsbPUNSeciJb4TeKQ+7ykpnphXgygdLmhfkt4D1fqh3S6KyjlIRZS7fDLSxY06PLGQ0GgCPd2Loax7ZpYxGs5xTaweX2u4KVWixxxFGHjwoNXmphrhvSM0qmGVBmvQsFZYxfoc73Ny3JQFoVlnqZLmbEm7WOPbVkNYzmdvpGs7xBqKUUUxHVNOdEK2vn+S60KC86zvnTC/cSs+2zE8bW3OsWR7goDRb/Pd1c8jUn1/PITwEeC//Ers8x0FjxDCjwCXxQLfEjtAXfo4KlUgB7co6rHKKjRrTQImBdB6DFb7KfvJYdyAz0VrwQ248dC7vBIQYjVtjLfrXNpQC/jxAWb5IMbpIyUVENNhfYsRg1kvQAyF7xhXU1bWct54bk5LHqwcpfXaCKmo6KKS7c29msNJhfP6elgzMKsL7p6t+dI9pSrbwmTvwxjJXkfXuFxk2DUu50a61iFOcx8pjAU6KD57bZw1mSaxFmF3sByq1Q5n2ruz8/TWE5AgCLE1alBRQ01s9wP4pc+mmDhAuh4RgwfR/vW+qGPupE86p82kLnybtu/tXlkYVoz7eE486kncH0jNpLBceq370PWSfE1pRXubFJIH/uRFFEbl1JNYoydWuce6mxA0rFYXCWx6r6kwotTTIXEg0ZeHb2NBYLBFpNV2SBdn+ZGBmGtAvIdQIEWpQoapirxttQdLBA69aF5DwlHc0p/e0/yH73WwktZVMt90GRB89Bh80yloxCrwDC6xeyFoBbkdVUxvH2VPZH3vFFPqsXSLFfZgTrU3JWxWMDvQiIKx+ZgkhqnyiSHmfnYng2/R5MmFrbKFEL4PZZe+le86EfmUiLwQQvjCEz2wh9g77Xk8WQsxrCESmVQFEplVJiYK863jtRY5NSbSDxO/PVbwJsCIYYIUhgqm0Nlu8JqITw9ju9LPR3OkWYJvQayCke/AFwo8XYNpFlrYZSvqSuPehe+75Wl3PFi2fUVy61VH6mTZsj8pqQoTVX215mPZOO49WEXAUJ0sUXoRAOXIaoi7GcaoAw5dZ7PSGpLaqI4WwBfv6ixyNtqj9Z79SbWV2xgylt6op3MYjuSDZV7IAJKadKUaEMu2LlWI6rHx6c0bSbkACSHXfxj6WX6qak+JegOZRTdPVeAh1VmAQagKoXSah/IhYAYU2xTCSzTe1Pp3UmpTrAQYNp6rwspWJ8GUpLeidOgEJtNBbVHyUEXQDoNDGZehRRp5lmoX0f82DtzlJNZ36PkLRa0FK6DhJN9pnqJrsxRJiMq5uRCxLJXNGNu4SjXKIWCKSsNVKZSVvBprtrwP1aTa4JsOU6mwoR3VW0q9Yi3FqKLam1DNp1mnqls3sFjj1hvsqFJp9qNrfVvZYeW4d8o0SCG73ef7SZjwZhLmXyk7BH5cRP5/wCItDCH86i/Hzp4O8EhUzvgQJQDBlMpAiYlFQAsJbV9AyHA2srWdAMFpnN2zBSQykMLGDBgssn0DKwPGqChd7MdA8KqAGt+b5QPM5pxpPaMqx1gRlq1wfaKifK0PTEvLdD/SGAPsT8oBXVfYn5TMRrFYb2/EK8cr7rcrNqtW6zuix+Ebn8NVGrvXByAlg5NsfFXb7H10jed42eY+H7Oq4NpYK+dbn7wfGNY3JK9jmPcwCF7U4xh6FI/CGx/zS7uglK6bRE9x+7PI/gpxcN7dnmjP73QMyRsJaEtYEf09Ej2GOtK9s2BmphmTlytLrgeO2moNTxm9DTsAAz1f/bkyMY9jBOalyQWpSruNdG/XkHttxJoijO3b+brIbuqa/FrvV0G6DbZZKb2cjU6iEk19NI8kD4+ZRk8jSZG4mOyOieewWmDG05iMNtpBEDRX4iJNN4ommtFU7/9xi1+c4mMYyjcd5XSsLKumo5yOVDI9to21ZYnYvp2sKYs+WmANm+Mz7KjCtx2z527o8Uz29L+xGl4bMLzE+Avex5MyecIV5k/I/r2v5M6eDvB4lIkBYggJCOUkLh9OZ+P0ToQ8Kgw/A92GpHgwOgOkxypdtxf3C2WNbDqdBQav8eSiUo8kqZ1GsT82Z9hmgdRziukRRgyH4yK2fVXF12Xrck1A68lFfeOBxk9VGA7GZfZUitLQbBzrWGSYbDxX8OwapfLKDq21az3julDF3sowHxVUheHu6YajSYWLPU6SNljq6DdkGz3KCzH0HktWcBcd6A1seTO7oa+tS5MnCwMTmy+npT+mdGRG+ortECu/g5Dl3TsXuxXGLygV16Lum8FmSrTkz7WhkxZuVvF9el1ZTXRrYV4M0cSBzJsySuKQ19deHW0/0Uj1GUM9K4h5tSqHroiaV4iBbhMHz9jbYrXQXMBkX8/ZeoE7exkxFnv92UgYUbprMJYQE+J+rbpVUlbI+FB70I/n+NP76nlYmwsCfbMmVZ77s+MobxInOHVNc3KGa1qKcU19MKdbb3K/jXI6pjqY6f6jpyODGi1BO9aKNTSnC4pRzfi970VG0wxoUpax9mSQ79jyOmTbO3m7Jryrch4AIYS/95Xc39MBHonXnJrhJKE9CYMb6ZJ4ScqP7ArzDWmMg/dbzXaSsmmKOaeHeDCYpQZI4DC+w5tILw1t7/UkwGk3ikHGMB0f0lSWxgWtDQglRoSTTUtptQPiCqcJWPoud0Au5juaVRwbQUyrdF3XYir1KMZRu6prXWyF63OiXEzfIc0WwnxaURU215HcOd9wc1pFVpEmoBUofGZVAVn7aWhD0EiWcgZ67OoJ5LBTxPMksLgbrtk9hzl8KPThq8Fsv0/ub2uT5aZIW96TFhqGILnNrRFPGUIuAExgnlSYUzV9Ao0SlcjQA2i3aiyCKTBAaQuMaL2HDOXonbKVpNv0vz0lwpNEPQtCOdCxcrHl62ats/VqlMM6qk210vBSGvAXp7h7r2hLg2ffp15FUcFqgXtwJzOv7I3nkL3req5dg9m/rud+s4DNGh/bzIKGs8JmFdvHeuyoT6YX4zrnMMpprLMqS4rpCDONrCm4tJDPGKsKDs5TP/NM73GMJtvsKlvu5DhMBo4tuZa3a1+GnMfbNRE5ox/JKhRzFyGEtyV58jB7OsADiW452yDxRjfKYKacB6bgL34vgYYEDWOFQS4keSAx5p4ZQaZAxU7iQ20rpFnpg+RUNTYUIw2/NCv9TrPSWakY5vU+jdNZ9HD27UNg6bST37J1W8WBq8Zp7cK45DzKwS8h1nHob0q1H0VlMSKMZzXr2H61HpcMm05NxmWm6Y5Lyyx6IHWkthovuUjORX7s0OPYuKQfZbJjN6xhGKZA0hnPzKL4WaKnZrn2oQ1DgfE69bInA+AXs5U/KGJyOhUV+kBO1qbDL2LYbY1HvOZdrI/n0PagNwzRaWfCIXAMAGPYi4MY6vQddcpBtE2ezEhUy5Vuk6Xopd1s3Y9ZjqRZ6XkqSvzJPdzJPQ0/GUtx4zncg7ukZk2yXuLPHihZxBj8eklYLWhfeZnywV2KZ1/UCvaYuzAj9dIzm8oYfDlXiZNurQNyKhb06nmIMQoEtmdf+fUyA4kdT3CrJaYsKGcxfzLdw0zmSFnlvEe2yP4KRYlUI+y+wxzexIymWteRPI0hsSURZ3YB44mGroR3WyfBEEKuUBdlWvwa4Od9ufb3lIAH/VM/mHVsDTjDGyclXIcDU1o+/H8JiGidgcsgAv0guBV/F0MoKq0ATuGpEAXDI0BJjD1rfDsqyrYWsRVlOWZWlaydqvH6EFh3nvOmY2QNx1F+ejeO7nzg3qJh2aiibrtx+Bgu8V7b1c4PtQthWVud4cfahevXJ5wtGq39iB7MtVnNvFZdK2361Osvtc4xKW2efacQUfI6kueRqKoyAIXdSydxAH5oICBfm5C/JGkATdtx7YVwpEicDMQKa9Upk+id6LF5F3I9hUn3hGgxY2WEDo3MGQQfK9SHv8KgwCGQ+8KL63KuYsuT9YO+JUH7gosttc7IVgoUsY+4tOu+CdYqatsVUe4jhohC22j4yDu6uy8T1gtdp7LqPawXuJN72P0jDe+Mp5jRVL2FzYr29BRTFrSnp0j5ss7gU9+NZo1M9vBnx9h6ohMjEwfyCIZmOkfqES56KaFtkKqvPmd5ihlNMOsGW9dgDHY8QeoR9ugZrccCzHQvJu99Di9lLS3vkNGEsF5G9YiJqkeUdR+a2glP5WcxMdKedM5DeDfmPLIFdaP/3yLyh9hR7nhS9nSAh8jWQL91o1waFzdbAJKBBi5fl52Q2CChDhE4vEfsRa8lVBP8eB971hfK+2qqKsBRWlvZWytlr3QbzOoEjGU8u6khKuNz9fmmM3gJPLs34mfuL3Oth4uFbau2762uPdO7TMvtGo8tDKvzhtGkoqwN3vkMIpPKMqnGzEYl49JmryO1qS2N4bXTNe/ZH+ffoh0YYy5mqB4Sw1pGJPfTSNTVALlHil6+RGntGVxCBMYU64ctT3F4/vM1M7YPR6b4fwgQE9DDQUai5+AGYGFiH3SIDK0BBdkFcBL7s4fcdyjrdqnHYZRp5TZR5Xkd760u35ep+x+DENbWa9ch7QqzOiFslpkqmymooMtsiRQ1Uvuc52jvfIni4Brt3dcoDw8JRaWV12fHWp8xPySsFoRqpICwf0Rz7z7NaoGtSrrTk8yMKiZjgncY7zWf4FrYLAl7N2K+JR5DuwJapBppbqUeKThF78UDLE/7ax0lg0LXUtx6gTbWhCRACBvtVpkS30Cv5BsLAKUaacJfjNZtDUPGO57GhVDVE6LqIpKLF98tJiL/wuCtQcsg1l+u/b27fv0TsAwcxhCICe6dAV0yAEQASRXMeSMDIBmEsdLnIW1j+F6iZ2F7+YgcI26W+NFcw1YQxRQ14SmuIRSlsldifkQAaVfYbs2krFi0BmtcbpvbOs9Z02X12+NlS2GEk1XLh27N+PufukvTqJx7s+q2qLlJoqQeB6rKYk1B03mevzbBec+4Krg5r7k2q7h7qj25b0xrfAh85s4548ryxZMVR5OKeezlnUACooYUykwqibNw0dl5ql3IfP6BN2LoPRTSFUnAsSWKuAPuw2vhBtcr+JhQvjhY5FCY9GUJCcsyndcMqLVZ7kTpw+kIUvMwKxLpuamYjwwImpsgTxSka2F9pr+tnmiyulRZG2nX6nE0Kx2siZOSotSQTttoWMc7QlkR6ml/b24W+LZj8TOfpz6Yc/65L1CMasS+zPiDH8EePYN7cFcHb2N11r84o755g+bePe25MZ7gmzXLV+5RpUI9HiBFyebeq1Qf/HqdRKGTomBLDTdG7Si7f0ToGmS6hx1PNfeyOIVqRHXrdqT3GpUIKkswBWZ+GEFnDPW0fw5S7mJwPXuiQKnPmSm2PYyH2XByecn989bs3Re2An7V4HWH9kP6NV+unT014JFzELt2ybIEKhrGSB4I2+Gqy26yFK/efe+7eBOrUJ3OiHakxNN+0oMBOkCkehJbIX6FX5xpKKBZYdanVJMbVFZieEh/S+uDVpRH+XTnA+ebjuNlw9/+0Vf40LN7fOLTr9O1LosjupTnAExszLRetZSxgv142cT/LXdO19zcG/H179mnNNpRcNheVaukdSBtfeBs43I3xtQXJRULisT3pg9PlYP8xzC/MUyQb3mCQ49jcG3kEaAyDCHu0nnz8lgDglGdq2HVeKo/SfReExPwNoJIKupLuZxU1LhFXEukisya0opuv1rEWL6DaWwRm0CgiyFOWyJRuRkxSD3JM/ZQjpXNl/TU4r6KyYh7P/ZZxN6hnI5ozJLmbMn5y3dpF2tu/+JfgLnxHObwFu0XPoW7+3JWzXVtS/PgGNe0VHtT6ps3tDFUHYFtpGBgRhso66x2LO1KKbL7R1uS51LF70331KNwmncxU83dmukcX08x+0eYWy+qArZTbzGUiUXWh5YJvg+FDfq75PUeBh67OcwnmjB/17Wh/a9CCP9wuEBEvpWH6AO+XXtKwEP6G+pRtnUj7QDF0L19SNL80veXrbe7TWuQrtEwwmaBt6nC1/XbKErCZpFj2ZQaJrOyrRmVktWgelYJRBLj6ts+epOm87z3+T0+/Zl72tK2dVk7CpRV1G46xBQKMCIczSpeO14jRnuEVNYog6uC0lo2neN42XL7YKR5j+gFGUHpup2GsFrvNZGe8x2pUK5PKuf8gmzXW2z5CEPQeJS3ccm1eGSSdDiTNRaLkBopDhs1qbcEQRQgDQMWltcq+QSQ+pn+BueDCgAWrgeC4PtufZtlVp2lGmFiXUZi7OmMOsXEhr8xaN7GWPVqYuG7OAfWaiuA2+/j9i+csn7lNV7/xE8xOtqnW6xoThfc+uaP4E7uIaMp9tozmPGUZrWke/0+AM3pEluWlNOxFuGNplBENdpRZGw1a/zJ65qoNhZTjRUwvMevF/i4fTOe4jcrpKyw+0eDupEWme5F0DxU4JtdIxQjzfWwJtjxTt8WzRNhy9hmt89hDYv/hhGHbJdEHZ6UCfKuY1sB/w9UqfyNlj0RezrAQ4jsJt540E9mTD/mAODzzXkh0DEEhN1tbi3TWZBWlENWNU3f9TGsYuJs03cKJuN9/Sy6/H1xVv9bUjI6aSQlldvcsc4I3/jeQ156sIpNogr2r09Y/cxxToqL0f82dskLXms9isrkUE3XOs5ax/mszvRfgOuTil/xsVtbCfrWh+iRRPVc9P9QVsiQZDYik4pt4EjyGyYCZPY6EoWaHaB4g2u7BRwpZj4IP2Y2nGvzzN4XdR78k2RKOqcFfZ8XHwYU3yh9Qvw9SafKimilPCiIxBqPlGyWakQ4uadFd/MDQlkhlR5rKCqwFT55pqnQNOXkfAedVemRHKpzeZAUazF7R5h793j2276B+sM/h8WP/K+8/Pd+lOZ0weKVe+x7j5kfgDFUz7+f8Pmfol2sVJTQGorJOHsjUo1ych7Anz3IRYN4hz28qXpVr3wOikp7m3ctXH9Gv1+U2Usym4V62rYk2ErvedCCXavFvFu5x3h9Q/C5UvzC9Y0ex1bUYficfjnDSgLmXRK2EpGfD/wC4IaI/J7BR3ts18k+UXs6wCNRdXfDTmmGGQaDNgxmL+QbVPsieBCV8LigIXQZE2uwTp9QD2wVFMbZSTAFplnk75jlg7w9OXlVH9JqpA8S6AzUVlu9s0eFyZLeQ5VbgKZz/PDnH7CItFsxGpo6vDVjdb7BdSEfNkAblXVd56nHJXdEclK9qAyvHCsIHU0rzjYdP+9917Save477A3VYrUHSoThCHRZ3M/0uY9U0T2UDMmfDXMcg3DPBbvMM8x3QhzUk97S1vccdnEvezR+vA9Gz2ProiJxIEq4Sw6l1YUBPI1XEcvBBmm9Aos1MDOGzofYfnesIo1xVo1r8qTB1lPcz/y4ztQH7CnpGkIZmULlKFeSa+6sRbq1srjaFbI60Xt+fQajOcGW2CMdtCc/69t5UB2xAKqv+UV8+J//HO0P/y8AuLNjVj/2gxSzGd35OXd/5NPYUU01n+AaVdAdyo7482NNYsfq8USd9c0ad/dl2vMFzemC6bM3MftHhK6le/UL2MObei/HvvPIilDPIARdlvJSRd2HeUU7geKjpzZoPHWBMTWMNDwqnzFgb21FF56AvYs8jwqYoeP5sKHUKfDrv1w7fUrAA3J1eEqQDnIYfbFQDyoXZji2UAB5mJcxHNDoZ51bNkyu77KzBje7SpIsCO0mF3MBhHKsidWyVhe/WVLVs8ji0WZD6yhIuF+XnGzarGtlM71R9aluH4z43MtnPPvMjPP1COcDD147xxSG4AMW/V9UluAD62Wb6bvzacWkspyvW5wPfO1ze+zVBbXVGg89xSmxLT011wrtRl2ustr235LXkXMc0rOqUlFffx7DFohk25L4fuidQM+GG1T82wp7fhc/muPrORI8rRQKzqnKHfWoXKwBKYg5kBCyd5HyHKpHpR5cZSN9Oc4VTAisOuiMpa5KLVh0GnYxywe48SHFrfdofmt+EO+PxBQzuT7BlZOswltVpdYAGaXxtre+Rn/T2WsEU2Dv/BRhs0YObuJmNxgHuLvsuFkWdNdexH7bb8T89D8m/NQnWL5yD+6dcP7yXVavn+HaE2a396n2tMWrHU21MLAe03zyDn69pFusMaUOF+1ihVs3uLbDNx314SzXkgD49VKnu3HCdkFCJk70gim0KDF7CkXf8bMDGQJGTJCn5yxR3MPgebtwv8A2cDxBE5F8Pt5pi5Xlf09E/usQwue/Uvt9d/z6t2spYZbpUG+Q+IaLdSAP+zxtbwAcsO2ZJJCQ/DpoQi34mJzvYrXwWltzdiplbWYHSsdMFdTS9c1+rPaoMM2SUTFi2UquYh6XltYHJt6yqmzsMW45mJS5PsMagy2E4/OGF2/OuHe+4bwusIXBWKFrfa7/0J8Y2L825mBS8v4bsxz++vr37HNjUkVZ8pCZRT3FFSRKk8wrg0hF04XsdQwhJOU1tpPkfZ+KXLcxPO/eXwhbhejVbVX8m/5ay25tRTSzOqHbf5ZgLBtnstowbIegJFagDyvP0+sEHNqeVnuVNE5VchvnsSGyytB+6pvYg0WMCkpKNVEQmxwg5bifhKxOoZ7i6ymhmuJsjQuBVetpPVFWpaIqa+zBnFUXGBkw9Yzi3s/gN9qCuZvfxJ69Rju7pdLuAuVrn0GaBe3ZMb5rcW3H6s4DbePqA75xdKuG9b1T/Y1W60WkGuEWZ7h1o3Iix2e4pqNbrOnWKoFiypLR0R5irBIBuhZ79IyCiSmQzQKqntodht38ikqfjaHWXLpettjyNFTHa0d2JL0PniAl0rFdVzPUnDPb23vbJu/KnMdSRP4T4GvRxlIAhBB+8ZdjZ08HeIQQef3Sv4dtT+GSMFN+7weD1fB7uyGU4bpDk8vDZUBuE0rwyOqUEN1o2b+Jr6eIKQibBVLWhHYDo0qrz0OF2A1mfcp0PmZRSO6DDnDedJTGMKsLmomPLWI115Ek2kfjks1GVXdPzhv2r42ZjQpWjePsbEPT+djLHJ67NeUXfOg609LyYNUyrizPH4zZqws2ztM6T+tNbFJVcDQucghn47wOlLEbX2mTpyGDUBaxG55kZlJiY2UvcZjjiAnmCwC/6zUmS+GJfJ26PjRSTnDFCG58kGBsHuzTgJ+6+okROpRmFUJg2ervS/RiG8k/RoRRqfUrIeY6uhhCXLYqG3M4LtivVcK+deqdGAJmc64V5T6KGrZrQjkiTA77HM36jKLYYMsx50EJEi7EVsiddhycl6LKzEXNSwcf4frtgvL8Dnb1APeFn6TarLn14se0HkMECh20TTXSENW6wa2buMyyPl4R/F2a0wXre6cEpxPYNEC2i75cwK03rB8s6NYds9v7GXSKcU0xGSntFvAnr2P2ryPrM6XXFpV6D8GD0dyHiEGaJb6eaZvo4PWYIdfF6Em3eVkwFgkBJzYTsgzqKkqH9uMJHmkWMRQ46pPtT8rkXRW2SvbngD8PfAfwr6HN9O5+uXb2dIAHbLmwwIVB50ISfChrkdffDnUMtYSyDcMr0AMW9N4H0MdV4oDYrrJsRKinhGqskuzTa0g1xhcjzIOX9BtR7VTrArSYsLKGcSlAgRWh9YFNzDPMRgXOhyxgeDApdVD0gU1leen+kioW+zWdAk3qICgi7M8qvvl915iWNlOCk+x66wPL1rGJXsq688xriwuBvdqwaD1F7AaYQjpJ22n39HgAn5phRbl0NJsuxLCe7/tnXGpDrxJyDF53kDyP5mIiFWht3+sjJbeLWGGeBmcbCzXWbpsJZkKgiZc+scZSkrwnMxgORgWfP17nAsS9SpV2GxcIVmB2A3t+F0wfTvXlWGX8Y1U5YqBZEkzBuBhhnZ670qhir6ruOhXZbNc8YwtYdJjFfWVd3XpBT8fJ68i120i3wd1/FX9+jERGVfAqc66hJ0e77lg/WNEuGpZ3TvI5s5XFNQ7XemxpKKc1SfDQNZ52scaOlpTTUVbM9YtTTSeyRMZTDcPmos34jASPWZ8SbJFro9KkIBEGQojR3vhsDycNqRFbdPKxQXMkQQxtvJdqU0BYb4XNHkq+eAv2LqwwPwohfI+I/O5BKOsff7l29o6DR+yA9YPAyyGE7xCR9wHfCxwBPwR8ZwihedQ28rb8IGcB2zONhyVZU3IuKZ8OvQ3XZUDJ+xh6MgAYre0Y1HX0ABJfiOpXBReTka4hhHFk/ehAZ5YPCJsVpohJRvR7acZUGYuzBh88HsOkNLHzoMcHkwGkKrR17bwuIluqi7LtFSfLJku5V5Wl6zyTUcHNvRFVYbRDoBHG1tDFxPwrZ2sORiV1YWh9wAfHa+cNIhWVEaalwXkNzSSZjsJIDkmls6TJaMELDFvppaJCElX3YaHGgSxJXjSgQqdkNMnT8y42jdJraX2LiYPIsgt9j47o+iTuwVBxfdi3w6PtaTUQ2TPEQAjSkwSsCB8+GnPeapfI11cdVoRb00JVewETpUgwVtUEbBEJFSuVNBEthBPfMSpVRTl4DQUG0AmFa8C1rObPMjn7EqZd4mbX1RuxlVao3/6Q5ljuv6oyIF2DjKZUz+1h7r5Mc7qkW6xo3Rpb6jXvVh3Nos2Ksd2qw1YWsUI1LXGtz8KZxmrcvxippHq7WFEfzgnrBT4m3f3BsxquFbPlDSKGMJr0OUjfacvnENQbF/XUsuSMGFzs3+KiHlmIuahK3bus1lBEHTPtBDokHTw54BB5VxYJppnXKyLyK4EvAde+XDt7x8ED+N3AT6K0MoD/GPjjIYTvFZE/Dfx24E+90UYyzTarknbbMXG4wAHvQ1MhC9VJzE9c8EJ2BjQZJOB1VhS2pBJS0RcSq9ddg4ynykApavz0SEHj7DX8eJ9is1BJh/EUsz7r1VJjDqWyWsi2kVStbeIAOGDiTchCiePKcnNes+nKnl47KvOAmJZ94MaM2aiIpyTw06+rXMT5uuMDN6bcno+YR9n31F71IPb6PtloVzkrwijOwkTIOlYGMu01eRoEFTf3aPtZH4L27REgViznCv6tcNUF3zGfX4ihim6jA09KkpdjZTH5rt+mGCpb5Kp3Pzi3rU/ekhB7PFEXRr2GDCqJNabv+6ZNEgUWA/dX2pt+Gs9b4zyL1rNXKaEjVFOKuz/N6ubXsBldZ+6XvXxN+t2FXv9Fpyyw2gojOhoKQlGzMiNG9Zxxc4bbewbO7lCcfAmCx81v4Q6f19fGYoNXeRKIkukPaE+Xuc+GKQu6dYdrPJvTDbaybE42ClijAlsa6v0aMYJrPa5xVNMKMcL09hHjGwekpk++6TDFmtC21N/8S2Fz3k+stsK5jaryJooyKOAbS4gFmAHBxHCbRhBDPJ9hUKgbEBO9SltTRPKFC2CKGj85HIiVWkJ4QgO+vPvkSYA/LCL7wO9F6zv2gH/ry7Wzd/TXi8jzwK8E/gjwe6IS5C8GflNc5c8C/wGPAR6XsqLy/xhPiWq4Q9AYUkLVE3C9EmoOX7n41utsw9j+MzG9p+FQmZIdNU8BMAXdtRcoHryE27ulAxvgR/uazCtrpIjqpVG+hKiEK66hiA9RYiclPaXS6MDtfYCyH6xS4rzp+park8pyvDQ0zjOOAPLctXFsXKS9vZvO03TasbDMrVX180mpyfnUKS+ZDyEX2uWq8kH1eKrS3rpcAQIhDhQBh2DSbDEM2gHH87ub90ghqb6DoIKFF8G4VqmtzSInY4e1N46e/B7i8RUxXOVCqhaPYB1zIq2PNRwS8rlPvy2By7J1LFvH68uGSWmBimllORwVWsGevmNLzm98DecbTwiBajSlirF/0KRyqKacB+3Nkn55K+pdtkEYl0YbPT14iSI1gPIuh5RSqMa097LUiSaoPVKPKfcm2GMVW2wXG7pVR7fW+z6FqYwVxocjZrf3EGtoF2vWD9YcfuhG7jt+9M98A1KUdPdepVusaRcqx14dXUOCx1eROZrlWjqdxEUPMZTj7WfWGyQmx0OALtbTtC61AEj3mbLbANYx92aNqPeC3sfBCtaUCvb5pnuC3se7yPOIEZwPhRD+BnAC/KIv9z7faej8E8Dvp+cmHwHHIYRE7n8JeO6yL4rIdwHfBfDC88/3A370OC6AAzEBNwhpDcNUErzSY4eeRnoYB/UCgZ38SXIwGOQ6nEPsIHQlBl9PtQCsnmqP6ZTAG++Da3HTI4rVKaEYEaqxyrQHr2Gt1mLEMC6ntF6lziF6AnEnhTVsOp/7Y1fW0BQ+h12GAoqb+P9oVnF7PmK/LjhvOj5/vGJcWZ49GDOvLCYBRgSR1CmvMINkN72Hkfph7GpWDVUmhvIdHrKEuo0DtxjRRHKirQ7OYb4GKZeRGDhicKZk3Xka7xlZy9iCdswrc7FdstIMpEVisjyBhuR1tHdT6zW0lRLjvYaV5O/FCBzrTgkCCWRTIl3zP5pfaVzAucDx2rF2GtpadYF5XbM/m2CaJaEcs3aBddefrOPWURjh5thimgXn7YT5+iTO3nVCkyYkKuneaJiqVVZUEWtAMBY3u0G1OsGM/o62eF01BB9oFm3OcYwPR0yuj9l/8RaT29dY3zvFNR2zZ+ecfv4+e++9xsGLt5VyfHKP8rkPUDRrNl/8HMF5qg9+veb0hhOoEEkNLjLqkmc9vMZxEheMxQRHG4yGPOlp1Km4NITIZJO+U6N4Rxu16loXELs9yD2pnIeIYN5F8iSxh/m/BPzxr9Q+3zHwEJHvAO6EEH5IRL79zX4/hPDdwHcDfNPP+QZ9yoY3hh94EDHWuhv4GIaoUthq6GUQueupu1m2YfOZKGaIsTmUpVRSUS89Hlf2NKZH2JNXCeN9gik4aWFcjqkAt/8sfnZdlXX9a3octlL9IO+oxDAuRqy6vj1qbQ2+UuruOoJH67wCRNiuTF/HJPr5WvMgX/vMnP1Rye1ZzVkTZ50BTZobpQZPYhK9MKnZUaxyN31CvAePreubX5sQ8JmpdOE6ZiVbfNSPMravyrY+F471G4xeXfxbU+A6z6oLmd7qa8s09XjZCVcYAl1K1tODfJoYJDAYFwJo2KouhOG0wcaEeGGEAo2771WGtSs42RQYtHnXOIa9Kiu5IVXnAwcjy6fvtTmXdLbx2r/ejjCdFhp2Mc8B6hnNSsOyCwQzYVoIPxMOeeH5ffAOsz7N8u3png+jOffKQ2aVpWjOc6HiiZlxYCz28CaHX2fZ/8Bz3P2Rz1CMHtCtO8ppRX0wZ/bcda798n+B5tMfZ3zjlPGNA+7/+OeYP3/Ae379r9Vztl5qP44oYSLG4E7uqYaV7zCb861K+WyF1r1It1E5+lILB6VdRU8yKIB4vT8K6Rt6WSPZm3XxngkpjGgsbeu3KeHpuX6SbCvelWyrfygi/xnKuBr2MP/hL8fO3knP41uBXy0ivwLlJO8BfxI4EJEieh/PAy+/4ZZElOaXHp5hYix6FZp36Pq8R2bo9N3bQtdmwAByd7StSmVj+x4KUaohg8juzeR9DyBpm7FASpoF62o/VicHynKMNxZvS8QUWNfhJ4f6MCUvCX0gRlbYxDiRLfse4qXRnuet6SmyQ3Ohr0zfn5R5+VnTMSoM1ydVrhovjcmd8obAkWVR8sM60KQSubR40iNbABJXvdSG3pp2goyJcDtIpEvfgtXbkq71rFqtAE89xic2QLAP5fan89PF5OtQZ8sF8sBdDJLmadAKIVAaKOlzJKUBawx1oWB6svH5XDZOQW2/jh314j3hQmBWKbh88XTN9UnFpDQ5ByP0DKvGh3wt7q9UDPNT95as3Yi9quLm3jPY5X29r23BWbHHy6ctt2dCdfxFfDnWsJUYZpVBFi12/4jTH/04ez/75/DMLzzi5b/99zBlw+TmIfMXbjF64UXc3ZfxC6WYr+4es7y3YP7cIcuf/ASTj369SpIYo2q6xiL1GLt/hD87VoFP2Jq8ZYZZNVOSRLcGc9h7IIO8yDAxXsVnKwF7CNt3mo/XLNXjJGl/a0RdyJSkf1K1HvKuBI9viP//w8GygKYCnri9Y+ARQvgDwB8AiJ7H7wsh/GYR+YtoSf33ojzlv/bGW5MYBgoxtq2idwHyoLtVp7GbDHfttqcxAA2tnB3Oel2/bbPjtm4xvcLWCKm9ys8J0yPcwbPIZkEo95iUWpmtcdsScZrwnk8OlZ+e3H7XYjbnjGZj2sJQdQHodaVa7yljZ7/WCHVhtuLDy9ZjxTEqLHYi3J7VMcSi9SHOw/6oyINqSignYcOhx1GYixIjyYYP9nD/CUAQDVcNk+mp0nzrVIpBJOY8hoJ4kD2OYMs8yKdB49Wzhg8f6SzWG7t1GdJ4o6GoPum6cTqIp2O3Aj4CYeNCDslZ6Zs9uZC8JhinFrTNOUEMXTli7QIiRc6TFDGUl46hxHNr2pMZRoXh46+c8uLBmOuTinltmMWcV+cDmy4wK/vzmaRi/uqPvcoHr0/52M0Zz80PGBd6bK7z3J6VHJ5/Ee5+HnP9PbjRsxACdn2qrQLmB0xf0KiwObzJ7LkbuKbDVgV2VBHWC7qzY5rjc1b3Tjj+qS8BUIxqlnceMPkouAd3sWkSVVns4Q2VYu+aOBmLCfJEALFWw1nVWGtcbJXDW7nGQxJQaB1Our9Mvo49cEi8TpLut/jZloebJyIPn0y8eXv3sa1CCF/2PMfQ3umcx2X2bwPfKyJ/GPg48D1v9IUAsXDIE0IMdZioZAuD/MZ2DiSFqZLHkXo7J8DIkgtDfRxjwfT1HKkgaljtmmdYg//6Rm/sZbnHbHXCWBy+KBHvKA2xSZDHVxNccUObAnVn+mC2S8R1+NEepalyPYXkGbNhUibmj3oiySNJEhdtTKi7yNC6NauYlHarjWqy9N4O4sl9/4rUyyJ15ItRQ0n7jrmASIHNoSAk6kYN9xNPXwSiLRCJD/12cy/pZ5BiKIMHgboQDkrhuVFJsIY2wOuLNrPB0ldTuGlamhwSqq3EivFBqE3IXkwarM5bn3uUO0/u/dGZwMittLjv/C6zZz6KjCzL1ve9S0Q4bzScOK8tKyfDW4JnZhVnG8fhuOTaWIsLz1vPplMywueO17igzLfWB146bXhhv+b3fOsLHK8dn7y3onWBo4lSVceFYb+5j7gG99zHCON99dJ8wBYjiljZbY9u44/v0Hzu05y/fJejr30/xXSkCfHVgub4nAef/gK+7dicNtjKINZgq4KzH/0hZh/7WSrw2MS+6UWFFFUvGphosmIIRYmfHmVpEXv+Ot3Ri8o+RLBBRSo7r8rFRtSTCOi1SNch5adSYWa6rsmSXExiHhamoCiKXBvyJEzMu49tJSK3gP8r8GwI4ZeLyMeAnx9CeMMx9K3Yu+LXhxC+D/i++PqzwLe8qe+jN1IZGTqEoueUSxrYDMKw1d1F4Ahdo6CR/g/7KScA8S7OdMoLx5GPZ1jANmBdETzSrqhHe5rfiA9z1W0yQIRSk6b27DXVugoeWWncOIzmNLamaT0xBxvDHyliZnLxmnpjunzdqZxGG+PrqdXqrOopq4Ul96jYBY30cJamD1MVAgSnbUOTJUKCaAtX9xjP6S4B4UK4LTHbhmAsBh8psy5octTgCcbiY9XxF08ajXTlBKuGiaaFYWSFdReYlgOgk4seUzKXkuvofZZCSgk8CyPIakkYzXEiEDxGNGm+6TxlBN3KCvPSctamRLqJfUSEuhC+9sYkF1ieNgocrQ/cXbbcmFZ86vUl1ycVP3H3nG9+dp/P3F9R3ZhwMLJ863vmSAiUr31Ki1Abgz19FT+73v8OrwKQthBCPceLoXjmBcLhDaoPV4ze+4F4m3rC8hSpRpTeM755yPLVe1SzkmquqhfFZMT4xjUVQPROJ2+pQr4o+3BjSpoXKhKJKWiLMVW7oL35IZyNVFwfwJS00fNW0O+PO/VK6XzI3mCy5AU733uJaV2JdPBgU1HhkwEPeHexraL918CfAf4v8f2n0fzH0wseb9dC0PACVihTkjRVsyZWh+/I4omJfRVDVcE5dbObdQaNnKOA2BKzv1EkuenW9tTdnZDKVntMeuqkNEsY7eUq2jxQeacPV+LBd42KJy5PVPDu2m38aN4zTuIs2YrQiaq7ioQ8AwtBaw4ApeJaH9VhFUye3xttVYErOPTvU9e/BBbWbDNaLhRQitFTLiazmCQWeu0OxgluTEyW2wFe+BC2bsotUUuG3o3SY1Nc/LTxfOremmvjktZr8nlUKJi2sarcB1h1gfPWMykNLtZmDCnF+XKEPsGfK8Tp02nWqLez6QKrzlNPDlk4YTbRRPC61Wsxry3nTbzfgm5U1ZH1WNRLiIVupYLycaOA44wOfrdnFQF4/7UxVoQXD8YsW8eHro1ZdzqR2P+hv4yZ7iGHN1TeJjWJ8h1hvI90G6qworQVsmk0XFRNcc98jZ7bdo259h7s+V3cgzuwf6S9PCZ77I8mVPMJdqS1KLPnblDuTShf/GiWbzfGImWpwBH3m0KLoZro76/G4DtKtyEUNa0UWugXz82qC4NzH3LFm6H3NoaqxkZ6wkaaBAiB4HpdMkLAST9BeGImcjFs/c7b9RDCXxCRPwAQQuhExL3Rl96qPR3ggQ4QAogVClshRAls6XqKbpSyUM2cnkkVmrX+bdZbXPk0swiD11mWOoHGDnDkuO5g0NvtqV6cvgpicLMbeRlAY+ssTx6qCWwWenzewWhOU+/TtD7XIaSHIYWvQhjmK8CFJGAYYt2BPmSzquBwXOQBNYFC2mYu9KMHDSOCCQ7pum0mWwRnDQ9KDNv1yc30EOvDncJAcZkogCRuvpHkefTeop6/ASCmUBKpp4jmc75wsmbTeTbOM6ss86rI+aB15zlvOpaxyXrrA9/4zCw3efL0vdXTwOUvvB5UoROQICxjocGhdSxcyT/84imlEb7hmRnrzlNZwx5rGjvKydwODZet4neTp2GNYJol0q3Zn1yLcjCG08YxLS33V47CwrQ0XJuU/NS9JQcj/Y3j0lDc1NyF23uGxew2Y79WHa0yKjYXNbI5Q4BNNVc2VxsYlzMAxtMZxeaUsD6L9UYGKSuK8RS7f0Rx83nK6U9QTMaY/SPs4U3MdJ7DtQlEtu55Mfh6lvePKRS0ipouAkFOgDMME257f0H66+5h6z4NIRadpi8E6ELIFOtkhbnIZ3nb9u4Dj4WIHBEfIBH5eWjNx5fFng7wCDpIOIlxUCMUpgDjoKj6GozgETokJshDq95GWC8Jm5W2+OwG2kpFieQzVPYPVVEhZdXPsgYPS0oIXgocQKqixbVIu2KUhOvQsMhri1aZT7aEeqo5Fa+JxFWnA9mokOyip4cvH7KJyd1YU5D6TYyjcOK0ssxKrduACB6DMBX0CVkbPYdEadacTNfX0qTzb32UlDCZkpm0nYjnPgw9BQYJ8xhZ7HWk9AshJrthGzDygB5nmi+fNXz63pLTdcusLtivCzadYOOEKxXunWw6XjtXMchxZblzvuEXvHDIqBAqIxRW8gw3HWOuLR14JNBTfNP5xjsmZUXrPD/y8hmfvrfgl77/OpVVosTh2efw81vcc2Mqq7mPdaTvjgqhdBtkvdTOg+2K4sFLmJsfZuKX7M+vUb/2Sa5df59KmLQrbi1+ig+/9yN86swzrwwP1o7RrQ/RzW8qM8wHTkKNr2r2a4t4x8IJ4/E1Vp1nsXaZAQYwr616crFgT2ptApV6i8t4iq1HjEcTpBph5td6JQXfaVFrUfYsuBjq9dWs7+fhO0JR42yNDyHuHxofBnmy7clFuh+GrYFbHxjZmD/zfQFlGHzf+QEpIk2wgmD8E3U9tqIRb3krIu8B/hvgFnqbfXcI4U++xc39HuCvAx8QkX8I3OCqn8ejTeP+McbudfSxhc2d4oL1fctX53QAjGEq9ThWMeHXM660mZAByt7DQENWUpZbXsdQHvoCcFzG7ojhNLM64bXyBjfNBl9NKQT2a0tphcAEWdxDjMF3DXjHvBQ8qsnU0A/ySiXtxQiJoSAfH7ba9sntcWEZFdIn/NlmTaVYviHEnMbA0/AdEo8lm7GaD5JUXHn5A+rZHpS1yrvPePigIOLijHEIGMK2PHoCji7WttxbNrna3oiwbF1PEnCeB6uWk03H/fOGKoKmKwKfeO2M65OK2hree1DHnI/eR/u14bVlR3VJrGOYlpmVBvEeh/DRGzNeOl1zY6KhU2vAV1P1bn7gr3Hwbb85037HhYl0X68hUNcNQpcnFPc/j7QrbPfTtJ/9MYpvGGNWJ/jju7zyN/46pip43+/+T3l92fGJ1xaUt49olx2j2J/eGu1mqGw0i/Ueg9aPOB9Ytp792jKvtfDTbs713l08UA98rO1kh8+YmR8iUdBTtd+a/v6OYdmQchug8uvpOSjHBFvhguZdkie2dU+EkMOF6RynWzqFDF0IOKOy+ek76f5weYKi3mEIUSZHlK0rqSL1SZjwpHqYd8DvDSH8sIjMgR8Skf8phPATb3ZDcRu/EPgaPUI+FUJ4A6XRt25PBXhA731gAiaqvEr0PsQ1mXGljWbaSCcc/B8CB/TgkHMbKVy1c8NE4NjS70nhK+inrjsHm+SmrRHs8Zdgdp3V+IhVp3z+nEswltC1OqivHiDlBIpRlsgwwWGtxZq+oCwEZTpp3w3RJC4q5T0utOAqMU+GleKS2GjpXAylWzo9h8O6GEyhuWwxlzfHQkEhDQgJQPp1BRvYkikJ0WO5kLQeLEiThfPGMyktHzqa8vqyoS5sZtNsOk9pDWeNeh33F00/cFc2F0LeOd+wdp6jScm40EE35ThKI3HQYSsvM/xtLoCrZwgwqwz//Aev4zzMa8O40MS+n92gfP4DhJ/8PsyHv5WSllDUubNkNzqAepZDgY5bmPWp1lc0a2Syh//8j8Foil8vqPYmvPz9P4H/T/5NXvutf5QvnqywojmRm+OSdfQ40yRh03nGtKycyp2susDJumO/tqw6jxGDBcziHt2dlzV34h2Y0fZEoWvUGwfotHJdk+M9qSEpJOhzUWjOI06o2qBV340PdK4HDR9CHvyHJVpKeIiTCwKd0/82dm5M91X63Pk+1xcCeNGxwBOB4wlihzIlqre9mRDCK8Ar8fWZiPwkqqrxpsFDREbA7wL+WfTX/gMR+dMhhPWjv/nW7KkBj8ssgYV0Ktcg7Ua796UQVQSOCzPpqAiaH4zUpS+GrXJhYJxVJUnpLeAYTk+Hr3fowkcsCEWJdBtAB6DWBWo0aR7W2mBHfKcAZ5RNVBsQt4EQsNKpYqyHXEkfPLUxOKM9KurCMPIbpGlicrvomxvvij5GwMjKwkPAGBQsai4oChHuWMoRpIE+Pegp9ATEpkuJ7qtTJX9Jg6bt7faMmy+erhkVhlmlOlKgIJNCckm2/nyjx7s/KZnVBUcTpShrjxKdCf/IK6ccTSpePBxR1VpRX1vDKvjsKan+V6AaFEp2A1Dbry3TUkEjWesDMtpDjt6LfOlTlC9/om/HWtbaDbAc48pJBnNTz/HjfWw5wpze0V7iMUwqZcn8az7Mi9Mxi1fu8Q2f+qt89Ft/Ez/y6oJXzxqcV22zW9OS+ytNpk9Kw+tdAZEB97+9dMzPujln7TxWDK0JVKagOH2V9sEd7P5RLigEl1sJ5Na+g7xgFjNMelRFjR8fqsdvLJvOUyB0LuQwqnodIV/P5DGka6vAL5TBAKHPbYQQve6ATaAxzI0MgGPX0rKHFae+WZMvQ52HiLwI/BzgB97iJv4b4AwVRQTVCPxvgX/xbR/cJfZUgIdAdk9TjwZrBFoNUYlrY7glyhR07aCGYxs4Ensq9XBWBkkCDU0iYqyWdu+GpS7LcQwqZoeWi/+aJX56hFmfURtVD727dkxHI+3DsFrobK9dI7YieKfCf2KUVWMrCNFriEVQQ2n6yhZYIwo2q/NebHDYnnNYgAdboHGpBtiw7uXCxdhenkFj8ETnXAIxTq2Zc+CNn+wQQkyUBw05FVpzUZqUw4lx7xAwLjApDXsjzU2NCsNeXXB9UuFDYN1JDvtNSsu1cUnn+oEmeXfDwSuRCER6xd0kBWOl/wUmuJi3CXQe7GjO6Qe/jf3Pfj+cHavK7f4RzK/rebZE8UQQI/hyglRTTOofvn9Ta4KqluLWC4yrEdXRNe783e/n+oM7/IJv/438g7MZdxcNN6YVD9aOL56s+cj1ST4fIcAXTtZ84+09SmOYFgYPnDWemW1wD+5i5od6v8WiPmUrxmmGsb2Hnp6VKNiZQlQUFRuvHoN3WvVvopfROb1une+ZgMnTSMe36TzrLmXFfLwOPcNueF9dZgYtykyvh2BhRZ4YeGgo4LHDVtdF5AcH7787yiv1mxOZAX8Z+DdDCKdv8ai+LoTwscH7vysib9qDeVx7KsADetAoTeR9uxbp1ki7UU2fQU0H0Oc1BrkMYPBQVIPcRp8kz8AyYFZdyHFseRr+oQASd6yVtkWtMe5yoiyeWivfxVj84p56JiJ51NV+Dl1PRRYT6ZHb4o5lMcIUI8RtVLI8HU9miMl2kBl672gXNCADx1up1L2sQCuksFUQHNsFhDCg9dLTNVOIo42dC0XI3kZi1KT4eN+zQzW75rVlXqs6bd15zhunki+FyYrByWNSumzMswgU0vcn0ZBW2JLEaNEB0gWYVTZLn1QiuHrG6XnL7P3fgvnk38fde0X7l5sCbKlg5gLWgxUlPNh6huxdh6LElxOV8y8d8kxNuX+Eufcq+8fH3PsnP4X55B/j2/+lf4VP7v0sQogdDSPInTU+3zo3phUhqIdbWKUaNy6wGc+ZTOda6BdJH5Ikewb1G4lQIsbo8UTKObYi2BI32mOzcdqDJLKehnkrF/rwU7r+6d5oneawWu+hI7bf1fYD6bqaHQDYUjeI/y298GVP5+0FPZ+MyZsBj9dDCN/80C2JlChw/LkQwl95Gwf1wyLy80II/1vc7s9FeyV9WeypAA8jKmKXtH9MFwfKromhq9jbYyhyaCxiPVSj7SR5zHFQlDnPIdkDKbPHMZRa6ENV5iGD8UXgyEJxwROqCb6eYxb3KJol+/UBSMhVu2Z+GL9kSBIsfV+EvgFSsLH5jYvelu8IZgWTQ6RbY1qV/BZ6z2fXc9o65iFo5JOtdOdU0/E4dtnzmnIgQiz48jpgtmF7QBDpB+SkYnve+JjjMEyNyYwxEahMr+ul3oGwPypUdLDoSQUheqeJwlzZXnbFml65tbIx2RqyQn6uhD/eqJbYuDCxH4gOjJNYV1IYvScNAekaXDB8aWN5z+0PUO0f4afXOJ8/x6oN1FbzAMbCykFloapGdNffD0FVByTmhIxr8eN9zHifaVFSTD7OyU+/zBe+57u5/eJt9n7Rr+TkuW/ipx/AT95dsnGeF/ZHjMteTn/Vacjq5rTg5bNWAfPa85jNWidOA2XpfN9HaXcZzbTbpSm090w1xo/m+NE+JxvHqlVoWHdhK6wHmvNofeoBvy1Dt6WG4NX7GI7PNpIiEhgM760Q7w8/CHYmz2MYkXiStR5PImwV21B8D/CTIYT/+9vc3DcB/0hEvhDfvwB8SkT+CRBCCF//Nre/ZU8FeIiwDRyu1eSej8yqOJPe+o6xhCLeAD55HX3IKhUCboWqErvKlprnSMCx63Fs5TsGXscwdDMceLsGsRvdv62YG4O0y+gFWai0B3OwxUVvIPi83ZTrwCtJQJIg3aaMlMqeeCHD4031LI/rTQw8j4cyytKqccBPZ/+ycEOKXKUPdyvTU4V3mrm+cq75of2Bt1Eapdzuam05I5jS9rNQdPaZmkA5r99PGl8pUZ6E+DTcphu00udsUpV55+DMec6ajjYeuEhJZQOjIkq5FJo8HllNVj/Yex97By2+qHn9rOXl0w3vOxwRArlQTsM4AVD9q/OFThBUHsUyH+0j7QZz9BzVBx0H1YiTT3+Osy+8xuov/fdc+5ZP840f/iZeuv7BGCoKUbVXf4t6ZmxRdn01xV5/nqyi0G16OnoCjrLG11N8NdX7pl3ix/t0owMWUU6li15f41IIipzHSDpsPoBJBZc7s4vEmiuteh2l7eVzEgDk8GCqIE/srJ3QZwozDuV2nojFEN0TsG8FvhP4JyLyI3HZHwwh/M23sK1f9iQO6HHt6QAPYuGQazNgSGruNASN6FWk4UAoNfeRtjMEDDsIacWZV04KXgYciXHSb+zyg70s/1FN1EsoNT5dWoEWwuFzsPlpzHhKGEo+gIaPhsV60H/mGqRThWERg9mcaU/0BDbJ40nHnooi8ZcCSc7P7B77ZcCxs44PZBZMYJsdY5E4GOuH4ZIHO4e64vcfrFwekKAXbaysyeyiBD5JAqRKsmOx/kXHz15MsbKqBeWCegy1FaxvCWIojCZth78nxDoFVfH1lMbwytmGjfPUtu97EkLydDyFMdyoGl72hmXr6WzBet1SGomMMMc6Fg4aEUaF4ThoPmVUGu4utMGUFfWWqr2ScTUmuAZ57muox1P2u5bFl+5QjGr8yT26z36C29/0PHf9mFfPW3wIjCcFqy4wKXXQPW88ldEuhZQj/GgP05zH8KTVey6SOUI1BRH8eF+bbLkGP9rHj/ZZtJ5V52NSPGQa9aZLpARP6U1UBIihxOhJlAMqW4lkRWdr+oF/qOqc7rgkRTJsBzAkN6Zi2aR6vDuxeFsmPBGqbgjh+3mcZN/jbevzInIIvIfB2P40SrI/QUt1CX4rpJMry0VVd6VEi5m6VntyABJDWRksBn5yjuumpkOpc13S60mz98tCQMkeketI/yV4mnJKGTqk21CUI1b1IU1xwOGNqE7atZjzu4Ryol0HU05iCB5xfxLZWb3H4zX3Mzg3etbYBhHYTobvEADC7m9M5zaBaa57kZzY7nyItRlhwL/Xr7sdLr4beGawTcUEPdUvn61Zd579Wm/dBBwp7GQEyvjd0pBj70nSpbLb3okAZdC6H1PUlIVEandAQktR1Bhre/pwzE2cbTR0VlphXmsI7PUz7RHvQ+DmtGZ/VESPRqvArZTUVmfkjUs9VgIfvDbmcw9WsQthyyL2VqkKw93TDddmeq+OChspuapnVY/28/U0156n+ohD6h+nuXeP9vQUKUvKz/4g+1/zC/mHX1xye1bzynnDtbEWnDbOs1er1yWrB5mdqEWpBeIKfDXW9rVi8JMDEIObXEO8wzbqdawjaSBpcSWPo41eTRtjU4kqndoZq8dnt/IQItsV5Ak4hjprw6fMBVH5uuHwu5Xr6EEjhSKfjMnDSSPvkInIfwT8VuCn6Wc8AfjFX479PRXgIbkK+pKGL5FKK0RqafBICl6nEM8wsDoY1BPldkjF3cpt7IDFZWGffKsONKBiccTWd4wILQVlLGg82ahm0t7hC5Sv/DhhvSDceH+k7JZbHtZwOw/tuR5rXfKx7LKsdhlXF37IRS+j97qKrdxPUjPdTnDH70RPJFeSS9I2Ejwhx7KHCdUU415FJs7ubNVInyexMYxhBVy86lnYMBXPyUANOHhtSCTa1jX3dknta32HsSbWJKi3selCrloHcrfFu6cbqsLw+eMVJ5uO27Oam9M6Fn1qYeBebXh9qdeh8SF7PR+5PskSHSkPUBrh46+e8fc/83rWMjuYlLxnf8x5q0rM+/Vcj9d12NkBxe33QVGx+OxnOfmpT3BtcUb5gW/heuzf8tq5tqydVYXKmhhhZIXi3quxCnykzCnIyXA/mhNiT/UwmqtH5ztt4mQrmqbvpdJF6jPEIskg0YuQfK0mpWVUqHehzMjIkorX35rtsFRaJ4Wchlpklr6KfGiXgcYw3PW2TXgzCfOvlP0G4AMhhOYrsbOnAjyAC14HkAezDBzGKNXwQtHeYGBPg/pu7caFvMYjBtrdhlNp/eDiKGYvfN8QWLnAKpTMTWCvMsqEkYJSTE70B1uCLXKTqOQp7HZqyzRL6L2QRArYpeUyAL5dJtUjQcPqAJJA1qjsdar2dzkBGmPqUSTRxxiUDhh6cRwaMnKXCCkmVdzPH68Hy6IeVLweWXcqhqZc9HiSx1BK1AAjXoIY8kuSK8P2xNvXLOXLTM4RrJ3nZNPx+rLB+8Bz8xG35zU39mo+8cUTrBFuzmtGMW9yONY+2oVR6fcQf9OqC6zawNHEMjZ6vd3g9hGBb7w956WTFZ985UzVZY1wb9loZbgRCmOpbEFZzzDVGHnuQ9iuoZq/RnO6pHzuA3GQczFxPmavtowL7YM+LgzF4nW6Vz6Lff5r8ON9QlEhzUpPRzUl2EKrxqMXXsZ7yY0Pcs1NYre1g4RV0ikbFZq3SNL/o9JEb6IPRQHZCx3qtm15Itu3RZb53x3C+9xW72kkfbYn5njwrhRG/DHgALjzldjZUwIe4SIgiIGkopRmx9Bn6S6zodcxHDQf0kTm0n7ICXSCZyDgmzc/1H/SbUePwXeAzfUQ1ggHI6uhm6LGTPcIzTluepS3P6TPhuF+028wVgfHfDyXeFhbP2gAjEluYhc8oscVYsIwRIpmMImaGjLzaDfX4fx2UZ3fqfq9TMI9zTZT+GNU6MBTR62uBCxD72N4K7gYthIxmakjISgbLfb5Bvrkp6Quhf3v9GLpcr9xlfe4c77hbNPx6vGar7s1p7SWn//8IZ/44gnn65bKGu6vW4xJbC89L40PzEoT2/0azjaeL5403F+12sSK3usycTD9VV9zk1eP1xyvWm7u1bQ+8Pqy1YLAWcleZdmvJ1irg74ZTSmPrnP9w18HH/s27rYFk9LxhZM1Hzkac8vdx97Xxk6hntL99Cega3D7tzmzsxhqO4bG4Ed7+CSumLxApzpVq1avR/I6QmS9VcaoDpULlJXJnSlFIrHBXuwRA2SF4zTwp/d6222vl2Rqhnps/S26HZbc6jvjH/H8v1l7l4WtgD8KfFxEfgzYpIUhhF/95djZO9nD/FJBMBG5hmrQvwj8DPAbQggP3nCDO2Eh7d8B4LMWz8MPZmdw3Fn+OMARxPTAMVw/tU+Nar4Ys30oMX8SxMRQC9qTIrKBzhvPtKiUAWMrQjnJLCr9jUOvKXpaEj0c7whr7QVCUeUczgVwzMfSh+WyXtdlXkdkhSnrrKIL4FwfqlIuv75uYzVxAo6ULE1J8t5z2J2xbl+K843+zhQiSnLrwxh2Gkx8CJkVZaKrkXIqReZ2eqRd670yvD8GObKUv1nHfuJaEBh47byh9YGTZcvZuuPjr5zyLc8dMCoMB+OSyposf19bDc8YUaZR5wKFTbNhHUhbr3pcmwF6ivRFiQbh5754yCfvnPP+o2lU3NVajvONVdHLdqneQqSk2xvPYV74GA/sHquN43Bc8MqZqHKtd/jFKe0XP4M9egZ37xXKFz6ML8e5cjt5lq74/7P35+G2JdldGPhbEXvvc84d3n1TZlZWDjVIVZrQVBJCYMwkYQMtEJ9x0wZjm6EpmtlgG5DF17axZYtubBDdNk21Ubf4WjIWDTRiNqIR0EJCkktoKEloqDkr53zDHc6w947Vf6xYESvi7HPvfZn3ZT6SWt/33r13j7Fjx17Db01zrHqJlNL5HV2XAgYwqJNfxtySQxN1FC2XYwtvJuFh/RiAqbNm3+f2mogvb6uUvxUWenzShYzQmFT4Xg+RhPA/YvRtAP4kgB9DDnB8aPRWWh6TBcEgDp9/wMzfTER/DMAfg3QX3E07rA72jeQkcADgtwWIzce4hP8CQAmNWb9B3fipvmZRD1pjFEthI70ypLzErHHgwFg0JNnxjRR5XFKH2WwOWt5DKjMf72WLMtIYNerFtfzBDGtoqDGAAl5LlosGA/g2C4oJGIubGUbXJpxby0yk4nVGkKjg0Nh+dZ4DAlNtkYtlOgxQ8dzxCtKnXSKROu9SkyWHrK1qe1giKiwZNXiY5R3qnLAygHHYeh8cQ3dVcGi5E63Ue7Ia8NrpGs/dWSK88wgBjGdv7+FnXjiR90mENr73wCz1l1jqeQmjlTyUkR2evjbHZuSkidslHcC4sWjxxOEM77m+wL31gHsrCZ1dtA6PLzzc/XviD/Mt/NEtcLvAywfP4mQ9ghmpVwtDrA3nPXi9RLjzkoSiA5KI6vdAYZSCjsMmMfDNmPuJa5TTGICTUcJuKcKCtjrzGK3BJkZIqaXROqQqENqFMsPO8U9ycAWUmN+JB8U4PbNkKoWD1OQtrn2F/JTwKFoeZ8z8Z9+sm72VPcx3FQT7egC/LB72bZAOg+cLjylK/TeMpm+dygBAxtEchlzMUA+/KFIq/p4Y79QxE6U/inO1Dha5VJrcRhltAmM8ehK0OoZbn2BvOMHaHaJxHuAmf0KRoSuGLwIgWkN6z3aWcj1SIUcAaCJE5bvsy5jy+aTnbTHAYTOEojqqOMhz0yQVHNqcSUnb46a/mY0ztASlLSN1JHDVtbmPoZtZe2WgwNst91VYS/0dTFKDSUu5sO/KCrFpnLkeUx9/P+tHnGxG3Fv2OF4PUqF2I+HD89bhc27s4WQ14PnKP6PCkBRm0+eDFqx0OO1DSp7UZ1d6x8EMrXO4tddgNQTMGpmDRUMpaGK48TRoswT5BsPNd2Ozyo5rAvCeGwvJxdg7xN7+DYynIuSax5+G27+GcdwAfg/sPNywTk2cgCw8vENs4oUUUefUn4AylFZ7bTY+94ppYniug0CHRQ6UUcoI4xYKwPEbIXKgiZjbJDBsRKENEqnWxRshAl1VVd2rpH9CRP8NpCy7ha3evqG6VUGwJ6JgAYAXILDW1DkfBPBBAHjmmaernZW27FAsqKlCflOAu/ondlGhmVtn9Q7rI2k/tq5UxSxtH+3AUmyPjlcAOYzXnxLnLoDTvcexf/pi0s3Vka7Vbu29gx1PixR5xd1+zJRvBMJJcJUrIqgUxlG/xiZmCde1inKpiWx99NW8hhGFE7149ipk0/5+e6/Fa8sBh12TepNYY872CZFzSk1Uo770c99QA2qaJKy8Fv0zDGxIVodEEy37gOPNiJPNgHtnPZabETcPZri+J90LmYF3XV/gXdcX+J9/5DN47XSDZ44WKQRZhcaQkgklbLhzwN3VWDTPUsqCE3hsXyraSvIc4WaEyMADhqMncdYHzGZHaHnA82chWmGIDZSA24sGq1GgMCzvo3v2feDVKdy1m5JTFNfJEBjkOviG4BhYjyE5610ct4sWSOuSZzFBUQGV0zuOWaPcnAqNMJbMvXYQWniVRxCJIsYAdlVX3/pe7d9XJDjSQz16DvMvjz+/2mxjvF1DdeuCYBbDZmamHUX4Y2GxDwHAV3z5lxk18wFNyWgZcDvbcqZN+jWUdsBU0/WtePeHodAZS99rLViuzPVkE3BtdpAsgp720SDgbAT2tJEUOYFfXANgI13bqvlIQiGE2NVN/BbggGF2DW42wq3u5bFpQmQUGmNgDDEJbGOEhq2Ym0pha04H5+xlS/1Y1pza1eEtuSdYugX2Y5DkwugrSF3+gmkohJxFLcyLUhDCGEF7jlFTAnVxyia3VmKA+AdWgwiOzchYjyPWw4hNNKN8zETvGpfqbHkH7DcOR3stPvHKGdaPj+jHBqGJvp/Y3dETxX4mwHooy3Qoqc8mOZKjhn/YNQjMOJx5XJs59Ohwth5xbx1wfe5xb3QYOUjSI6xAplSSH87DH17HCIBmCwTXYFxcB8X3CwCL6JvSxmM25DWA0LoIw8V3pcLfAykpT99DaixmC27WYeU1HGzWb6IYHXgpmlL8rgy6okdOeDDzL38z7/eWCo8dBcFeJKInmfl5InoSDxB2dl55jckFZ/Md7LHnLTBrNeg1ipNdYuhb+6sPgjjG1SRT3MODCobrCdj4hSRcjcA1P4D6JUbsI+zdgFsfI0UIAWJ9bI3HZV9HGJMgCr5NIa3N6h7gOwzdQeoeqKG3fWQoiv9bJ3gup82TcJSlqWNq8q52msZpJ+BkMybl0VbptbWvbD6B+kGsIFd4TVvQSiFEFotErSxyWA0xYzrWqzrdjDjrJWNatfCDWYPrey0WnY+FCMW5PTLwtZ9zGx966RNwRFiPAb6XiKMlx9IcxNFZLk54XyhNpcWkGrwKAu+A6/MWezFPYwiSuBgg72c9hq25d4aR07iR4InZHLg3IhzfgZsfws8PwX4vzc299RjvlyHC9J5I8mhutIyzkUxbLyEVNno+Me8orVMpVfb3Xd/zRcEvk0LjCq0OACDKpVseESKiJwD81wDeycy/moi+EMAvZOa/8DDu91ZGWxGmC4J9F4D/AMA3x59//SrutwVB6cI11sYWPgqUmntxQXOutUKMVWGvt8thR6P0WmcOUvgOOfoE8edpHxIswOTAs0OMyxHHg8O1bh/3Rw8H4AAbKUOi/hsjyFLb0NjTPZBP3foaR9KDAZJvEkAI1KTeEKnonwnJ1GzxMUwLhF2CA9j2ayjt2q6XurfusR4CNmOAj+GgagUxxzyOeKw6nm1YZ67lhFRPa2SBQJqYDwRIn/FNECtrGKUHujrJtc/2wbzBwbzB7f0O1+YtjuZNzOWQZ7+1kFL4J5sxWjAe91ZDKj3SehHKB10jkUworQQrQNJaiX8fdhLSvde6mGORIcOTTSiKA2oZGHk/sby8azBef0qqOK9XoP1rGA9ug12Dzjuc9mPG/+JceiqT8+R9Ad3zHwE9/n4sqSssDYIJkVV4isN0cEttZez85riciPo6U2Tvd5UO8/Pu+dbR/xPA/wPAN8a/fxoSufr2Eh7YURAMIjS+k4h+B4BPQLImz6dLZP4UzNsIji2BUpw0sTjqOHFXCgzVXPM1uSwjYj+QYkw1s42RLcj4sSdhbA2HVD58hSZCFAAaDzClMFsmyfXQy0sBuUYY6RhSEhUHTgJL7huSE1yZrbU2LERlBcdU2K0lraJqy6arpWF9GDXi14eAj99dYrkZUwKiwlQb1bijL2XROOy3ufqqXEOY5yYm6Gl0WJ0joPM1DAHLPiTfzlk/So2mMaRSG13sDXJrr8PtvQ7zRpL85jN5iPUY8PTNBU42A9YD4WQz4GjWoPUO6zHgxryVWlUa17EDjLHJcnZbE0+UxEokiGoqSz/PZSwHEwasD55AOyzhY1g7L47Su+u8ZPOnCrZGcNgxEBHGVz4DPPF5ha+uyOCvvzEg+y3qh50SGsXvpQP9wqoIk1GYV0X0KAqP28z8nUT0DQDAzAMRjRed9HrprYy2Oq8g2Ndc1X22HHK10DgvfNcsTvsBbDnKjWavGn/d+7uoGWXhshCQQOOKVMNrzRrdjIxNxPE7L5pn5wj31iP22wYtSQe3u6tRCvyxVmdF6lOh1xWoJ2Y1U3YqA0hWhuLfU4KjFhpyDwu5lQxlZE4wsWSCZ8GheRC27IjSWT/ipWMp/XHWjxhCE91EpeBYbaRf+2ZkdB5w/SqXG2mka98YOCcWArFAJqQeF3xsNIXkz7EWRx9yjaz91uPGQiyOo3n5Gc0awlnPpme6k8mdiUB+9kiSAfX57auvLRBAx5od0Fojyml5efNONaKLzd8aDaW0RItVH3DQLuBmh5JBHnNdGhILbj1k4apnaoiui/BgiwB3eB0r11WQVfxlStM3g0uRjsC2lbFLaNh9VZDKtpCibQFyhfR6eto8ZDololuIwAURfTWAew/rZm+5w/yqaTLaohYalw3bqxbHVi6EHqMZ6NbPwbFwYb/KOQUalmJNdZ/7PDNRwqtUo1TysbSFkvbZVgx/CNI/oSfC8/c2IAKuxYY/Djn808pGByBWAC/2W7/AeUJDxlXNWZCL10KgCNW1znLS2kdbsy9jYcZZH3CyGvDex/YRGDhej0m73YyMzSDO7FuxftPIgA893Oq+5C7Emk3d/k1p+xrLl4QIdTUIADswKDFh7wCMcq/1qFaHCJ7DWYMb8xa39zpcXzSYe4eXzzb4+J0l/s3PvSl5DwDefXsffQgSZusdnjyYbc2L7W7HvG2BWEe0um8CBGpb9gGN89OMG7utmdNeHnI5MDrfgGeHWI2MBUThWQwrLFyD0B3idGCTuc/xHhTH5DA+/rn5fjrO6rtKjD1ZC3Ku3b/10MXf1Xe4g2lv3Uev9TAECG2P6xGgPwyB/T+HiL4XwGN4SC1ogbeZ8NiO6d5haSSv64TZqwu3Dr3109V0k8WhH47iu2GU0ugqOAphk0NpOTq7A6zgoC2fgWqbdquW+hC4RsImVyPjeDNgv/OC5weAqcSp9dJWOCXfALLwsILD0nnd2KYCUNTKQNCihYqNl0JjV+2hMTCeurHAXutjtdZNqo8kloLM8TACnYu1sIY1aHMKtzqWXhTNDCBC2LuBjhw4JXgx3OYU7BoJ1/Wz2HTKYTWIY1wnShi/x17rcHuvw+MHbYr8Wg0BP/TJu/i82wd49kiCF959fYE+MN5zfVEIXn1OZe52ftXySHARsh/BznvrgOUgPo6JlIctnqnzHsy2ITDgGkn27AMWJI3G/OmroPUp+sffD+/m2xeP1yNHcJtTdPNDhGaWPyuKyX+GiU8LEPPwl/BjTAqNAiIO0wLkoRBdCi5/k+kjAH4pgM+DiLd/gcJ7dbV0KeFBRO8H8OcgORg/j4i+BMCvY+b/6mEN7IGIedqnAey2NnaZ1DXuOpHzUAuNMUanOHASHKRQFYCdVXkhzsuBkUJ0dTmO5itXgyMUzCAze2ZJxBpZVsoT+y2ONyHBDjYzOOxQwoqoKc5/67Sc943XVGPu+rcUP4wWR7WkpwSHFFIUBn5jIT6CvVb6YQBIzmttQasZ3EQEpgbUrxHuvoywOgVCgH9sCTCD54cyolECDNRKZACeengA11sHZo+RO3zi7gqzxkMDoI9mDZ487HAj1h5zJGN44d4SP/7SMbrmGvZbh6N5gxsL+cQ2Yw4aGMHSGClWhLXzZQUHKbxG2/hugARSaJ+L+t1wtVZctFicsR7GwBgW13BvPaZSIkwOCAHh9L6sYVdeR94nS8XiwPDdPth3cP0KvZ+l91sIEGA3Y6/9GDtopx/kMvQQrA8GykTbR4O+j5k/ABEiAAAi+jCADzyMm1326f/vAP4TAH8eAJj5R4noOwA8GsIDqJzPORJqJw5K5ouzAqP2ZZgMcAtNBeQY+PShj6aHBjkpHGiuoTkTAEpGHb/KXF2UC4Fhw1Lt02iorEPGowMLI5sqMpimyggduUe2amx2u/oF9L51l7aarAYcGKmXdIghUF2zrWkX50eHb71/5h2eOpzjaN5gPQTstVKmJLBYVRnOk5+tA2h1Igxr6DG++gKGu6+hefV5tM/ch3v82cTIQruXfFRSln0lEWvDCre7ffSz29hrpZbW0azFXkvYb51Agnc+idDtY9i/jR954RjHqwHf+3Ov4vH9DjcWLW4smvSOgJh9TbFUSRQkKkSAsuR4SqiLgqMWrundM4CQLbhdykFSSiJsqUl+J5sxwZ/3mw5Hw10gjAgnd0HrU1B7mNbVFPH8ED1Ln/Z2XKP3M2jNMoZxnEeBYf0cl6VLCw79joEspIBs6TBfnTVi+MZbTUT0Dkh1jgURfTny9F4DsLfzxDdIlxUee8z8A1UjlWHXwW8pTST2FQsJKFZuEhjAttCofBkKTY3JF6CCg7ZxXit0TKnyMHL6GBlZcGjUE1BaHfZpNMJIfs8CZgSgkfYiGHLTo/TIRIUQiihSdLQLRNP53JEtacOkjG2a6e+yRPR4K3SSsNqRHqyWSuHwJQlt3e98LLDnU2l3FdqL1iXBtWhyUULuFvA3HkNYnsL3G/R37oD7j6BZnUr9p76Hv3YTYX4YO0+GlPUszY9uwG2Adx7OsGgcjmYOC/RwZ6+CTpbAS5+Av/44lotbeHxfnPEnqx7HmxHvur4QH0aEmzTrXnxVOm9lsuR585neWfydmbHfuiRYVEClcFo9pxA4xhKM21WYAOI/4tkhqP8YmseewuDbcwXHyMCAFgseMPoZ/LhOUVQpmCLCO1Mw1i7agnqBnYy67j6p90kCJB14hYLjgjG9BfRvQmoCPg3gv0XmcPchEawPhS4rPF4hos9B9uL/24h1qR4ZqheGKVrGcGWIbZ3oV0VMqZWgsFRimpW1wEDMuCUAPFG+3OX+FiGk5ki15q+aYM9ZQGj0E5Atj9LqKL9oqQYkWjvJE2NIkUW5XSeQr70ZAjwRPnVvjbN+xHtvLsop5arn8zmMbRIZIEALBHDkKoEBx/lCdTipvZ6e10ahJo2CGB1Jee8hcKrSumikxWmKTHPRl3RwG817DtC+893YfPynML76PIbPfBzDZz4Ot3+Itm1B2gUxJlCylx4lvetAFPDkQStF/05egVvdg1ufgpfH4jchh+P1iB/9zH2sNyMQnfabMeCgk85/Z70IDcfI0WtWQThHgNiQYqC0BLVO1HIos64Z26G1QBYyIa4Fj5wsSdH/xK5B2L8Jbhc4md2cVBhGRnrZBMAfvwAcPAb2bVlG3Y6phrGm6ByhMdWiePIS5j5bAmTi79dPj47lwczfBuDbiOg3MPNfebPue1nh8XshpUA+n4ieA/AxAL/loY3q9dBE+F5Brtw3WUbECBCFpcBcsDeN0lENC4haFhFAPjHRhG8HrTG0G4oiIgyVQKnJQkt6jfRoEQpRvFzDYj0Bp0PAfuOKY4kkK3s5BKz6gGeOZvj+T93DnWWLe6sBTxx0eP54jS9+fD+Fp04xozw2fY5yTABAUVAwFNMvhaAVJJPXppy/0AfF7hmOJetZS33YDOj1EDCfH4Fmh+Isdx6jn6F57HPgfup7AQDDJ38aPPSgbo4w25eSLSo8OhGi7bjGzcUcbliDlku4zan0hh970HwfRA5hto9764DHrs3w7scPcGu/wyfunAEAvuixA8wa6cvywkmPNrbK3WXBKUxl53LXvCchwrncufqAds1oYE6WCiBr0EJlDsA6AHT0TtwZG7hdJgckSk3Xxem1pzFzcq6OSa9pS+/zRCRZuWE72qJW3Oqw8MngDYsEaIAMT+SbvEF61EJ130zBAVxSeDDzRwF8LRHtA3DMfPxwh/U6qHZ0V9vld0rbiugn46dQSyMYZg8Y7Z9RCBNmFqc5azvVLBxyG1YutgMlAwVvh8AWu83v2Rox24AidwFA0iYFG4/lJFgb9ohgvNZ5MItP4b0391I29L3VgKePJMrmoPM43kznGRF2a7m6qbSgym3n+TCtRVIE5hBSHodHLraXYBtI3/LABB+LPurE+W4f/tkvBF75FGZf/AsRTu+DQ8B48Bi4XUhYLweEbg+uX4Fj+RY0M/ixl+q7tELYuy5CxHuE+SEWI2HuHd772D68I/y8Jw4xa1xKaLy3HgtoTaE8+4yaj2Ed6MV8Wh3HQJD63BowMWXFWN9WuY9SyLeOawyM+9wC4J0NIbSi8Rgr7Tow1kEc8K2nZFWpPeTM+wGMcDPQpNLUN1Aob8biYeSeJ5ZKazmqK2wg7KuiR0x4vNl0rvAgoj+8YzsAoCor8tYRUe5RAWxbGHGbNX3tGiXrxA5aUrzEehkljJQKxMUdZk5Ssp2Fp3YJjikGuounnqMI5mPiRV0cy0En0UkzL0X8RmYQIyYYykheXQ44iWXFPUk5Dq1SC4hwWY/TBen0I6632TGL74NLIbA1bmX+2WleK5X2fIdsaTiIJeUdYYT0tdZooCE+R+cIG9+gO3onaP8WaH0KnP4YqGlBmzOEbg+hmUsF3fWIa90sOdHXQ8C824df3ouRdCO4nUlV4mYOGoF3HM7hHOFLn7iGeePQB8kL6Udpw6qWhVqFY9gOQFDBUVggE3NurVats6WVeqei1pIVM3G1eSO1sVTIbDQkfMdao7jmz/qQvoHNyDicOVC0PmxUFoCkRGWrqhwHT6x4GyFon7k+V8nOlygZBLKtPJ0XJe2qGD7RdFz6W0hENGPm9UXbroousjwO48/PA/DzIQkoAPBrAfzAwxjQ6yNKhQGnBESIGveuL0K7oSZ/hLEudjF9Fx3TWkabmMtrFPefHvV5mvcUWS1+CiKyPhTNR3QADlppFarPYSG0s16sitYRzqJqf9B5HK9HcVRHjflyiHMcR/WMVsNWQVrnsaiwmIq2ArJmnvtey3bvKDExpc3IUqmFtXxJtFYCwXczUDODHzZojm5huPksgFyKRYVNz0DrGiBaatQv4fqz3MXRNeCmw5I6bMKAZ4/mOOw89jspFy/WD0u7XJcr/cqzEuCmn1Pfq2W20gtE95fMVJs8qfWZHOUT7yVUL5GZU4iu3ruwaKvxaY7QiKycjAzMGodlH8SvQzmSpo74S9djLtayVjio78nAViCKDtC+bxuCbiFcYfAXdBF9A/SowVYAvg/bYblT266EzhUezPxfAAAR/WMAH1C4ioj+cwB/62EM6PUQQz52AKnYn4WZLnsNe3wtACwpc1a4SclmaRcfwS6hVTF9WxvKnqHfSQ0RWV5Qd1JT34Iy5cZR0e9CoB/Cfuex6qVW1g1qoaW+W+dSAyBNGgRK5+15ZC0O+4x1QUVLW7kfyMl0qZWpy+1NLXY/cinchzEnPCqNMTS1cYT9xRFWz3xFsr76Ida+YtHkNyNjJIHA5o1omaE7gBtHYHUMcg3INeib65h7h84xjmZzdJ5w2ocUhiu+GEIX558ZgGMQ173wyufPsJ9pp2qFJJU5O5q/Yaew1tLdxDo8Xo+4Pvc42Ugl3uyzQ5pbJVWWNDlTtuXgkXXMwJ86tx5PLVhGc84u6yI9B+XjSU4ykGBcExANMK6Uc6/3+ojwqHQSfNRDdZ8AsDF/b7CjSdNbQRfBTMC0ZlNco/hItjUwy7QtpDIFQV1WYClNwTm7ICJLUwIjjbkal/WHhHjOyBKttCFKobDKODov9ZlONqNh0oD1b+/6xC8jOOomUVrbqtjmswBJ/8xz67VHlFi5I0Jv/mYonBH7aYwMRx6rzRgz6LVtbYRGWEq9aKOjZc/YuBaH80NgdV8uujqGA7B/9BQ2Y26UBGQBuxoCOu9M8UPR/lMbVcKkkqDPYN+ZCo7C4jTzrccAJQMurdXt/WOEoDyVDN2eZ8+V59Kwbi7X3AUK0y4l6rLH6Rjs96jv1lo1QLlOHwoRHiWfhw3Vta6EYzwCobp/EcAPENFfi3//ekiL2EeCGNNZ2LXWtGtR1kbtLiFgTWMrQHYdP6VF7TKgLcSWtpn9U4JjipFM0ZQilyrLOsmT6GJ3OtunY9YQXjztcWvRpmsog1dcnszYVGDXgkPnpbY4NFJqKmLGVtjVPhm1Y95Gtsk5Jk/FWHF62sjAOEqOTh8ovUPN19FramCBVu8FgM4D3M4QFkegpgMdvwIKA5rTV3B7cYQVmmQBjXFeuya3y5VrSwe+Edh6ZuvMVqFRlDg/hxlKBJqFn/J5aX8lCHRbiILy2sylygLlHJfXqq0KIioUNXsfa+nuovp7sNF49bn2eyq+xanr7lISLxjP5YkeGeHxSIfqMvM3EdHfAfCvx02/jZl/+OEN6wHpEjCRwlCTp+8QEru2ZYdg/rtmbNa/cJGDXK6x7Tbc9YGcp1RZ6Gxqn13u6kNom3xNTwRyjHVMZNvvPP7Fq2f43JuLVEKdSEJsFWffRSX8UY7JUVkQURLpOP2uEJU6ma3fRRmtXk+FRp37svX8Zo61Lhhx3gdEh308Tn1hBBGap8Fjce0dcP0KOHxCCi+GARtq0Dmx1DSB1BHhoHXpfqkCALLQswIhhcyihKdUkNh3F5D36dxaATGd42HnQeZq1gg06cgmEIp1xGZN2/swMxqTgFqEoKNaXyiFzxScNcV+d7HkLRjuvLW3Y/uDogLnEbtHrjzJ3ySi3wzg3TC8nZn/xMO42WVrWz0L4BUAf81uY+ZPPoxBvV66rFksx5Z/XwRnTUV9TAkUe0zNyGurwmrs542jht8Y2Slo4TP7TDakUaNsaktpDKJR11CQMnJPwCaW3d2MgoeHIJh3IMm1YNotzBLj4ww52d7WdkzQfSbaqC7RUc5JFjQXCQ2lWgCrxVoy1vIdqWIwslSh9Y7B1GHBK/DsAADQUBZG4jdRy4zScyklxkjTmnkNT7nqvPMUkXpedX/tCHeEmJDKCarcVDDiuZYOttf4FBVCELHqADIKUFc9qP8+j84THMC0QneVgkMW9KNheRj665AS7P8rgAsjrIjoz17imveZ+Y9P7bis6PxbyN/eAsB7IBUbv+iS5z8wEdGvAvAtkG4X/yMzf/N5x1+06HZ9dJdxbNt9+sGfd+wuobFLSFzkjymuPWnllPepj7WaeiBgNQZxoleCwzIZ76RKrXdSCLAfOZUTD1xlPRurQa+jyX+B2JQ2MZxkgkqmWYas7p6PiyfNCuqpe9bHbt9D9hAjOdWpWWAe1qAwwJ2+inFxC6uBsRwElnOxskDnqNC6rfZsixRaqn0d05b07ue1l9slBFpHKWhiRCnI6kROYLvHuvURWdLDamvRrnELs20/e1bSLgrLTefgfEujhu2ujC5w7F/+Mg/G686hp5n5Vz3A8V8P4P94wTF/DMDrFx7M/MX2byL6AIDfc5lzXw8RkQfw3wP4lQA+DeAHiei7mPknLnuNXX4MYHc0yNR5U9cYUa6bGoIo71UKjl2WhaPtD6wer9XirBNbqU628lRus4xZo5emnk+tms4DPkaUWB8LM0qrx1o5lC0NwNS1goaS7v6KawupCFLQZ6iedZc1N/W3ZTJcbaOJbTWJH4Sx7AOWA6NxLW7M52g3Z6k9rmrwGhVW9FR39XxnYWyft4auan/dLuXnPN9IbX3IGCll7VuaUrRqBuzM9ayA2/JhUD5Gni13cTyP4RdClqaVpjTGiW12vA9FcFyRz+MqeJ2hf0pEX8zMP3bJ4/909JecN74bu/a9LtCOmT9MRL/g9Zx7SfoqAD8bM9tBRH8JIiV3TujFjmi77WKT2557mX27jlWtvNbAarKbLdNXIWCvB2wzCYuLy4dXMyoZ4HLg5Eco7m8EAyC9uBkAsRQmtL6LqXLqOlYVIDUprLarQVE9jqlr1IJDf7fCq6ZaMJx773icPba0ChnrId9zPTJOe8LB4ePAekwlWLTeVrvrZVe0C8bL970Y0qlzfS5LXGk+9TdRCx0iSs2o7DXs+OoIrDy23YLebr/IZ/N66EEg7Utf82pgqwfmdefQLwbwW4noYxDYigAwM3/JjuN3OteJ6OuY+W8y85/ZdcxlfR4209xBkk4+c5lzXyc9BeBT5u9PAyiEFRF9EMAHAeCpZ55J2y8DSZ1XQ8rSFCSyqxHSeRbsLg1/6tx6Ofr4sU7tS+OEfNQeWfu0GDkzUoHGPnDB1Opxq2ARZ3e0XjwVUVRFz4mJ8Uwxa4Eqds/BRWTvU5fZqGGzmuo9ZLbzjuPqZ+DI7TSkNQkllsRC7yj5lxoneR2W8YZ4s53KQ7VdqyOfR5fRqK3fY5f1oZbAecpUDf9oKwC5Nm19X5p7zTrOSnDYn7uswzdCr0dZfGC6vPC4TUQ/ZP7+EDN/KP5+Ia97APrVD3j83yeiX8XMH7cbiei3A/hGAH/zvJMva3kcmt8HiA/kTS3CVVOc/A8BwJd++Qe4+EjzMen3qSiky+DlwLbAqJ2ZU9js69WU6msrnWdZkWFKKjgULtGkydYB4yhY91TL1ymcO/cXicwlctAyH2Cbidezeh6Tqy0euU51TD3WCevLCrzL0hQceNE47f08SXb1auDkMG+85Mh0Pgt9Ebyy385dAc2YeyRlgbcjBKfW+WXWWg2tlvsYzNtlTfQcq4zY8SrVvo86/8QKGn/OYBPkacew8+htQTPl37hstOODEoMuFO6GXmHmr7yaO28TEV1j5vuQvI4HoT8M4H8hov8NM/9MvNY3APjNkI6E59JlhcdPMPNfrgb8vwXwl3cc/0bpOQDPmL+fjtt20nmO78toHFMWxXlCotbwy/OmF9WUH2PqGAtx1YmPlkJUf+uIFb2GPd4yLdU0VRO1zzmVXFg7sREziqeecpcAuYiompsphj7tvzHnUYbFdgkRe+1djtRdY5e8GEky9CS/exJhMQSxhloXo9F2P2q+j3GYB95eS9PlKHcLjilBXN5veru9f70m6vOKNRz3ab5Qea98koOsv8bRlsDe9Vy71ldxj4lz3wzBoXd/EEXlHHpgXjdB3wHg6yBRVvXUMYD3Tp3EzH+biNYA/g4R/XoA/3sIjPZLmPnORTe9rPD4BmwLiqltV0U/COB9RPQeyET+OxBp+LqozqwGIv5eMaO6tEMd576VuKXXv2CV74pP3+W4twLAkh61LShpkqGIkKBUi2gVGAFcFOSr8zRUgEwxouyPOP+BawGiTHrqW5u6vo5dw5IJF78rIGrqE9CMXq++djEO5PlWwVsLFj1Hk/46TykQgCKkpRDleZp+DUNaq6ROypuiLcts4u/dgmIKupp+D/ZnfY4+wxgk72NqX3GPeMhonlXh0wQBVj8fhC6KqHoILo+rSjh8w7yOmb8u/nzPg96cmf8BEf02AN8D4J8C+BXMvLrMuRdV1f3VAH4NgKeqmOBreIidBJl5IKLfB+DvQeDTb2Xmj1xwWqI63G/KkVevzqnErK3tUIuj3nb5pT51aI66ieMk074TQF0yXs5BPEcWscfEx8NcRMVsAqdqtMoQwg7radeYlSluMROraSqsFH9POSeX1Izt/ex0XQYunKJdTOWyWch15I4jESCNkxyT1WhL3W/f47LO2ss+z9RhdWmaKQsCKOc4LS+rtU9YEfb4+h12XiK2ps6xpLXgNqP43Igk7FktOCs4lC7qI1Nc31g0Dye6qqQp5eJ1XecN8rqaiOjXAfgl8c/vYeadfgsiOkaW1TMAXwPgJZKFyMx87bx7XWR5fAbADwH4dRCTSOkYwB+64Nw3RMz8twH87TdyDf0YL/JJ1JZFbVXYbZOMnfHgfQImjvfaZyR+xMQBCCNsIxtpm+uhPUkcAG1epaX2dE0z5bDHzhOwHtG43RFbQKk1arZxGrJhNjUTsU5rKwRfr2m/S7u21l99XIAEDSioptbPFENJyX8hP7Nqx8rEaqVBLQ5PyB0LkYswqj+pZspbz5aeKQ8+KTg7mOVUUMUuYeP1esXcbF+nPl0TOmsoM5+Qr6X7y543u8ftIEUmHXLCqewghFA2pQLiJ8CcrLHie0U+Bti2KreeCedbY6+XriqC6yp4HQAQ0TdDqp9/e9z0B4noFzHzZH0rZj6c2n5Zuqiq7o8A+BEi+nZmfjR7lkd6PQ7qKU02C4ltQeJd7FDGkNLcKgBqC+Gy95/qtw6giOLgkO/FAUQOCE6OMX3SHTmQ88lpzPF51PrwlP0BSpepO2QtjV2Ksf0w6/wLhnS4GwMuDF3VSC6J6tp+pzVsWJPNi6B4rV0CRKOnCqtKn0fHEwWMi87xRSvlPBonvVECMzbxFWo2fE0PymD0nhcZIbugzXRfKq0gFSi1clBeVJWNHft30HmvVR9/jPf3jtDE9dIHTm1w2WWfiCIGfcil7M/L87hwfCgF3lXQVVkeV0y/BsCXMQtjIaJvA/DD2FEckYg+zMwfOO+C5x1zEWz1ncz8GwH8MBFtTdU58cOPNO3S4nYJDmttZEaeBQZFxq5/5xu5vG0qrK/ur2zPq4/nABrl3hw75JH+TgyieC3nU+QRwMnpOqoQgODOHVGC9HbG6OPyHxxNCCdAPrAxIJY+d6b/xPSXp9utX6aGBS9iIgrVqQM9W0I2b0LGdd7z6X20D/wuZpET4LZLjU/lZ+yqUnBeVnUaE6bXak3MZYc9jmOzQRE2L0jHopYTUd5fP0vtQ2Te7WeyULDOoVp4Wtr9dAhonfSh15BhNpN9mXmpnz2N4dJnPSDx9rt+ROg6gNfi70cXHPsFRPSj5+yn865xEWz1B+PPr7vguLecLgpdLJOVpplGLTi8ERoIagFwKTD0X5Bleh58dVFSkT1X4KkpIcLSLjUMYNeAvAgmdg0oXoOjReJBGEl6fw+BsWgcTvoxYs4KXZU/8222+41PjnliHkfWbopSJHAIjOP1iL0WQCsCRDsGxkfaIo73rh36mv9Qa+ca9QPIO/RRUgSCEaYAITvye+atbn52nSRoi6U7YSqTTkh9QPR+gbfnarKaQPUTuLyGb0nXquaXyDiM1UdltVuWCBF5pq0opO06U2PMPFflQ0rMbD+Q3XSRdVD7jpT2G4c+MO5vRuw3UuGZXB6Pjk3fux1FPce76so9DHoYiYdvkP4biKL/DyFL5JdAyovsos+/xDV3Bf1dCFs9H3/9Pcz8R+0+IvqTAP7o9llvPdUMwG4HsmZ6njJT7ErCYkJwjIP5+3z4ilJLXFXHdlgjzCCMojqrH8TlcykExEYU8iE56c3M6AAXIbUoQEQQyrj2W8JJvxvqKMdaaZmXOEn7vgvsxBhG6VSo//ZaSR07r0RJuhZy4UXAWhKK55cDEhdfeQ1vuJWO31oirXFwjxHH4kqjjJ4mAIRlH5JTfBNxeuursAJM6UEZTGKS9bOl321gR2mdJet4Ij9nhECuKgxVMaitIPGhZYtCi06O0K6NdqxmnoxQ8G67FhZX354+qz5D6+KcprGX85aE2wNZINvbrgpqUuXmUSJm/p+I6Hsgfg8G8EeZ+YVzjv/EG7nfZZWeXzmx7UGzGR8accRJC3O1+pDtft2n2HrQ/TCaGsoIovKGRnikvwXSOvffuIk/e3GE679kvYxZQOlxYx9/H5LlIk71fAyNgwixcVNeDxo+mh3nQO6vDiimX/5TqpPYphLFrHNY+0FI3w75zvsQ8MrZBmf9eGnnueUP8q6yhVIm3lXjKcaeoRIiYcaa2OedlIMnlJaNWkrpGZiTQOhjVFUfGGexU6AjcQLbsSrpetP1pf+maDsRkLfWL1DOfx3tlyxkDiJAmNMcaD6KQl21lTk1NhfnpjPl19V3NVbPPMWQ67WklMeUBaEjoPGSla+1wWzEZD6Xzr2ffZ6adp33Rkjhuov+vcn0CwH8svjvFz7MG13k8/jdkAKI762wsUMA3/swB/Z66SJNr8RDxQZO5nmI5nswIYQcLYbal8E7YKpKsBAHcYTbaCnEayZLxOXthUCKOawhwlLkkoNcx0DjBswNyHkjzJp0XQ2D7OOXc63zuL8ZMbI4LmtFjrn+EMns40mfiINAQLaRlHYL7EfG4/sz9CGgjT2uawXyImVSyoFwZPaZqVgNvRY4Po5LD9AMamYCsfbhjtuCWEzDKBaPaOyqZTMakiTAxlGuIcaM0z5WpQ3bTG5XLapdUGAw+/X5auENZAEwSXbtkCv6f6feKYG3IqT0NiWUZcYsEgce+Vjd37qoNETnd+fdFsOs3296lkobWbROBDNrwIPeqxQoAIoyO5cJz30YIbyPmsOciP4HAJ8L4H+Km34XEX0tM//eh3G/i3we3wHg70CwNIudHTPza9OnPLpU48z6gdpFQJF5BBJ4I8SMYRd9CmCSb9NYARFpNr8jw1uAWAbp+urYzgIDO6KsrMCicQO4Rs6p4C4KQ4SvAOi9yIFI4CvVsMfAOOwcloP0q25QMqcpB3kZqllGw9Q9sVVwbAbpE6Fa/F7rsUf+gfJhdCZrQWPhK2DbKYtkhQqXTeNXBhktMQRJntRrbMbc5bAPAfPGoXUOAbnr4siMVSzxgqiVOwDDDoVF58iuO4WJzrNCdsEBlnlOOpCtkrMrSAPZjyPX2d6vjzPJHKv51ucgkuz62idlneu7niWNi8UvtxkZi6ZsOFUf7ycCGN5MHwTzdlmWR4B+BYAv4DgRMdrqdeeMXEQX+TzuQZqL/KY4mMcBzAEcENEBPyLNoBSesHRZra9emIk5MYMpNxoS07oFhTFnwao1oZFQZMp2kCuOKW4AAOMofg07bmdgKXuu3geVxWLngIPwC85j0J/et+ggTLAhCDwQhLF7o71PsRrr65DIHNHeLQym8+MdYxiA9Thi7GW7WgpaotxOw7ag2mYoheCAQFgOu9VvwdtLJmv9X1qwD05eNEdo6ngtQld7qwdmzHxOfvOOwCMBnsHaZdFR9C9Ya9bcw8zbFCxkz9l+7m0GrM9R/3wQIqIIaW0LNuByMEvyHaZrbkea6Tab/V/eJ1sTNsS6oRJKVKtnKtvf+lmmih+q3+Zh0aMnO/CzAJ4F8In49zNx20Ohy1bV/bWQxurvBPASgHcB+Ek8xGZQb4QcZbilFiJWE7KLe4rSgg2cEu4cOXg/g/MMhAEIjUBHiPwsGCZvBYcdRyFQDBsZhOMWjnULh4SQLZaJFpgUhqx9xn+sd/AtRgbWAXg83MWncRQzfAnO5ezomiFp5noKW8X0x6qM46wfsRoC7q0GPHk4w7zx6EeBM+ooJitA6l4e9ZSlbVAfCMFjB7TjDAxE6hcw86T/O7FQ+hDQj4zVEHDWj8kn4mnAXuvRekLrHGaNQxh1FGJ5iEWCNPY4tHw/FZ7Y3SY3QWHplKl+7WnUxXPU/o4pYqJUIFPnhCHfiKveoz7D1FC3Q45p0iqoxwyYQp2s1km8ZmDMfJWzYpU5bL8/+xxFQMzkGHTsk0N83ZTX4SNFhwB+koh+ADLErwLwQ0T0XQDAzL/uKm922dpW/xWArwbw3cz85UT0ywH8lqscyFWQXWC6sM7DnqcSzra0YcvwoGWyhesplMXoQNFvQcMm4j8uWx8OQAg7uOHEkk/bagEEgGNOh4bkQm+A7AexfhYdl2tSOXG0cyzY4bSXBkbeORDnrOk6Ws0bZkjRGhsjNIFgGAHLB9WPuWhc6xw6r8UCeQsSydFcvGV5EG0zjSk2NRWNREbAafi1h8HOg4TeatvYPjDWY8B6DHCBMIwBzlGqwdT6gD649EyACI/9xokFR7YdbhaEKiBbRxhJHOy71uNlyIbn2gZd6fkr31o+T2BYVazI/F7nbuh9lHJuTN2USn638I3TMZKGuwOfORnQecnjsNWGrZCSoIbdgkiDCNLfZpxba8lcn9K6Rcqgvyp65ETHxV0Br5QuKzx6Zn6ViBwROWb+h0T0Zx7mwB6UagGgjt8aXtl1XoEeYfc5iuluC5AIFTUQv0N0bFMYwGEoBUhtleyinYLFQfJONmDfyDbXZCESI7HYNSLM4vbGt2ByGLoDNL0kZvVBOuPttw6A4Mw2Msd+IOlvJxqr+os4cPGhekdovcN6CDjsgP1WGKz1MyQhYuZc+6JfVmgoRMS8nQ/iCakOWBxyGiABqUfJsg8IYJz1I+6t1DfFWA8S3h4Co/EO88ahHyWU9KAjrIeAeevg1G9CvDO6CJB1KHWguNo/bSHrubWjPCs8+Wa0SwO2jnOiFKbsCMmS1nlSi0DeQ76EQpUyRiQIyVqeWSGQj06hp3nj8OJpj0/dW+GJgw6LxiV/iwqOkSUUQrPP9f3YZwwRQlY40N5fj+dqXHbubbh5vU7eCD1qDnOIs/wfcyyv/rDpssLjLhEdAPjHAL6diF4CcPrwhvXgZBGeOlJkCi+15wElZKDHkDleQ/0EbskCJEB8CqQ1p0IDciIwMA5AkG0IA+BCdqTvKv9q6TwBo5jxOAiUpZCVicSS8F+A+lW6VutbrEbC9fUr4O4WZiCsB8ZpHzCyw15LxQemUUdWOOQPX5mPzRp2GMOIg87joGswaxRWifIUSBJJcznk91KAhK13kLV5+77stjrzOu/PiXTeESiMYpE5wr11wE+/coqTzYi7qz7nNQRxoB/MG+y1HsPocOYIe63HWUy03Ot9itjyMUFiSoAolKWF/qb6QJyn4GzVwEpWRBQ80Y9RnBOtz/y3nKcJk1pCLQVA2Ik0ZL8fG+Jdt8bVMjAaats6wslmxE+/usSdVY+91uOJ/XbLTyF91Bk+LqAiMZdFgJdBGw6gbFmptcuI7yxG0z1IpNvrpUcPtcKzAP48Eb0bUovwHwP4J8z8zx/GzS4rPL4ewApSDPHfhaSs/4mHMaA3QrscZPoB1FZGDRXUgqMwiXcIkJT05LwwaoqQETnAN8n/wRZS4gCQaLnJKR7UOW4eYFdG+i5GY6rv6rVZQ3jHAeTEIvKuw/H8NvY84dXliM4LU1nFiKO2cYXAIGUuIUbwRC0WMfRG+EVkGl7a1upPLVPOBDDEx8CUw0U1YVAFhvY5t39PCY4actxVsiM7l+M1mCV/JjLcj91d4yMvHOPuWY9ZI6HNY2B0jcNmkKTAvdZLaCiQBEfrvPhKgtS50mrF9fKLMgWtK3NCpug8+FT3bweG7F4OckBeEypgdI6YKCkCtYW5dRkALjLnlHgZfSYjZH77wLi3GuEd4KnFST/izqrHK2cbHM2aFDDBnP0eZ3FSXOew58w7qssAVROTYGJyoPjOpPYcEqy69QzMl4YHLyIp2vhoSQ9m/s8AgIgWAH4ngP8EwJ/BdgzHldClhAczWyvj2x7GQN4IEba1itr5awVHDUttO1PLiJZcu2h6sYSIeSetP2p1CX8eI2zFBOYoRKIwYbUQvNsWJFNkg+OnjqsESHqusQfHcietBzZx+zvDa7jT3ELno/CIzMj1K3Azi36WkJzxokS7rMUGTnM09w7DCOy1HvPWofHCVBT7BgiBeJI5pYzyysIRXpW17DrySN+nFRw1/JHmgMRSozCkuTrZDPjBj72GMTBuHXToGo+uyXN3tNdKBJYHWlDKXbmxkO3tzMVIsuxXsOPyjrDvGWsmicqjjL3XSo2eN0WXRVtsTlG5Y3ubC6LouBR2TtOO91i1GQ4IY/l8ul6GwLizHPDkQYs76xFnfcDJWgIRfvRT9zAGxlPX5nh8vymCDO4se8yaLrX2LcfM2VK3z2AEB5xPwSOeYmXpOLF6vV0+mzdKjxpsRUR/HMC/BuAAUhDxPwbwTx7W/S5KEtR671u7ADBfUO/9rSJrGgPTgiNtQ/nxAtvanQ0lnLqXVy0dMJVxOVkdAispAwgAefkO1EKIzIz141DeVSUibtXGaprimGI/G0vGeblvUMhsQOManPUBm/lt3Dh7ETzbR98cYDkEtA5YEIH6JbhdFNeM9gZAPkE8BMQY/xiBBJesGe0dQvHDViHCMVRWnegULQ6ECJMRp1h+hcRqaJEohwLXgsObY5QSY4xzMCxu4sOf/Dm8/NIpfLQ0bh106Rpd47HcjDhaMPbaBrMYt3t/PeCVsx5P7HcIYLTOpYRSlxiU+I/2PSOQxzAGaHjsiFI5SeOzyg4uRwniAXCRL61MQNV3NyaTPd2+Toh1Ho4cAlye4yB1w4gI4xgQGNjvPLwj3F0OOJo3+PjdM/RRwflnP/cqNkPA597ax5e94wDa5+XeWiLa9psLntgm3+o35prka2xcAyYki9h5sUbq4I+rIsYjCVv9W8htwv8RgO9j5vXDutlFeR5vqN77LiKi/zOAXwtRgH8OwG9j5rtx3zcA+B2QpO8/wMx/72ruWQoOKzTq0EjZF1m9WSGpImzcpgtXiYlA0T6RKrfGB6HftWqGmkBgmX6tZalGmG4wYVWoBeOa4pz6OHaxCm8YgNGh61qMQfI97i6ewMH/98/j5ld8LZa3PhdnfYBvO3RhCRrWYN/KRcIYJ1EgEIV4fISuGg90DPSBpLcFqQOe4KKwULWbiaC5FiHW3hKfuqqgBKJcuDBEB32dWwDkaDAbzppxc5QU3wf1a3xm7PG9H3kRx68tMd9vEQKjXw/SX6Jx2Fu0OJg1GANj1khBx+dP1vBEmEVm5yD+lJwPZKAZMNj5IkxWheguxlP6cvLqYkQtPzqlJx2/Rskgu5Z0dwzgQBgL66v4CWSLVgtzugbwLRrfyb0B9HE8cCzRaYFxNPN4ZTngpdM1PnrnDC+drPH83RVePVnjlTtLfPL5E1w7muHnv+cmfuOXvBOvnG3wyXtLtI5wY9Gk8q2yNkxSLrtyvNHPhzBEGFjWtkJZzlhKiAKEUQZNXAVdpkbbm0nM/AEiugaxPn4lgA8R0UvM/Isfxv0u6/O4avr7AL6BpYvWn4S0tP2jRPSFkDaMXwTJKfluIno/M++s7PggZIXElOBQoVF3xavXm201uvtmLjn88sWiMAj592QNcLRIIqyVhAyyxbGzYu8OgZFILQZj6bixR+sbDIGxHAJufukvRXjhY+gWN+APbuNkE9B0e3BDVFzqh43jdSR1atX6EvgLqZSHd/mjVctCOCSDONox0QLJPg0zv7QtpKfhSPtuzQGVwNUcGXIeT+w3GDYjVmcbBGaMY8Bm6TCOAU3rMQ4B9w46nKwHeEfoGodP31ni8588TJozkAMH7FpReaGZ61pfTJ4vRzTV01rnH0074BXT14TJCDdZZQHYUkQ0dFuiFxwwblKZ/zQYq4BoYc4GKfG1cQ0CCCEyZlvPTMu5n/UBH33lFM/fW+HnXjjG6qzH/deWeO1Tz8HPFvjpH30B//gnX8LBXovHr82x13q86/oCmzEmZhKJAKmqN+S/S8GXKiwQJ0vKxXdMyOVTrpoeNcuDiH4egH8dwC8F8JUAPoW3CrZ6WMTM/4v58/sB/Nvx968H8JeiqfUxIvpZSKLL9517QdU6mS8Vz63f45TgADL0ARjtdULrz3kUu7WZhM3CXKMq6lOUNYmwFjhjtoV1kU7KgiRBVrXgqGCsBDlxAA1rtK3DMu5++ebn4doPfTdaAM27vgSYPx5L0Y+SVV/fP84NwZTtjnCGjxaCWh36bD5aGxphpRZIjrDafmlcSw45DW2CwoTBZmty+11QTJZMuTe+BTcdFvc+jb1rM4yfHDBsYjhz6zH0I/qVPNWrJxssOo+PvnwCADiYt7i56DCPlgeR3r+0BjSgYggZDrLwZ0rOu4QirIdw/Kc5RyOi4I4CRJ61grAUorKKjF3LHLYtEZM3pGuTWyfHASDfSs4QEMu5MPYbh/XAONkMeG25wc+8eIznXj7F2ckG/XrA8mSN05c/ifXxHQDAyYvvx+E7nsUrT13D44czvPv6AjcXXpzq0ZrNPjaIwItrkOwzxN9Tgmx+AWKNuCb53epQ3jdC8h4eMekBfDNEWPxZAD/IzP3DvNlbZXlY+u0A/uf4+1MQYaL06bhti4jogwA+CABPP/NMinqaatCSHbDyQZ8X663H2jazRbOndKCBmoDMvAu8mADEoob244yZ4gCi+Z2hqy0hkq5VBUxEAQO9t17bjq1mpPY5wgAa1mjcHD5IK1V3eB2nP/z92Acw/6JfCeqXcKv7cmq3BxAlAaJzwuTQOBmb5kHsx2xDFeAqjB1HIaFOVrkyANHiiXPfDRUaaq2oVqulY5QBq+C2vpHCD6DTVQuQZo7QDvj3fvG78Sd/7jWc3b2L608+Bg6MMAT0Y8DQjxiHgL3DGZwnfM5TR/j8Jw/x1LUZDrsmZqNXryVZpdLHZDUAB13Mbxh5y3mbodDM2EKcAAkCyTCrfZs2aVVzN1R4pteu1oZdwxXTlbUyZgYcKgUnwqGqBDEA+BaDcUjfXghDv7secbwece+sx9lmxGY94uzeGpv1gM3ZKdr9I4ybFfrlCYbNEqevvICD63O8dLzGz7x6BgB45+EMTx600CROZxNto9JRWFX6HNWzJTgrjPDOg3yLMfC5it6DEHMuZfOoEDN/XYy0evZhCw7g8n65ByYi+m4i+vGJf19vjvlGCA/59t1XmiZm/hAzfyUzf+Xt27dTAlW9NhKUgRJDLsYafwausOhaYKQS6oyipHrU3GjcbGPNSRpZ/4cIjKloKVsscQuOsv8mH8TsU41z6jzFsZG19pEBHnr0p0v0n/xp0ez1GmGMDaZcCYVU86OlvztPaJwUyut8LrutCYgavuud/GtczkgmIwBqsu+HWZishk8DD6gNOg+0c/yGL3gMN544AHkRgEM/YhwDxiEgBEYYGb/8S5+EbxyevrHAe27s4eaixbx12Gv9FnSRrYuc88DMk7CJLcqpjmjr1wnxXMXs63+ad6HXl23xdwbYeXlvzud1peX7h02shmAskpDfN0b5JwJlSD9BBNevsOANDvu7eIxOccArLFqX3mn9rEM/gscRvukwO7qN/ceeQbd3hGZxgNmihXeE+6seLxyv8dqyT88SopKQqk6n70eei8kUF63L9ViBGUa4sY/Ji1fl88jl+y/692ZRLCP1zwH83fj3l2lpkodBD83yYOavPW8/Ef1WSIfCr+FsSz4HKeal9HTcdi4RbOMfiuW2p2kKQ7akhfeS8nbJsMekhhHH6CZkS8Sa32kgmBQgWxrVefcssH2uOK4RIvYj03FFahzBkyzy2fu/DHd/7Ccw3L+PWYyzZ9/GvAiXnfL2nsjwlQxJ8PfWAYFLK08T5ByEOfiIOTkADJap4AwFWbKWRUCc5niefe6pxDmuhZ4+iw9YbO7ha7/iKfytZY+hD2hn+ZNwjrBe9vhHP/4C3vnYPm4edDjoPOYxD8aZ8PkUro1sQRBJA646+k+PUetDo/hUyKpVFeKzOtYQ6rLIY3qd+tMIolRAMq7FBPU4J36O2PdlyyKJ29LlR8h6CWNsfHYKf/8l4ORV0Hwf3C7ATQc6eAyNW+Cg87h50GGvy8JYLjuCnEe3dwQOIxY3Hkc7azDfb7FoPTZDkJ7mo/RLOSAX663J+5WWA5U/kKl0PGytTwPVBrXMrkZffkRhq/8cAvN/DwAw8z8novc8rJu9JbAVEf0qAH8EwC9l5jOz67sAfAcR/XcQh/n7APzAhddDLJtOgAsCjdTaT211lFFX2xLFRk7pPWId9+yo2yFYuI56Air8Vj8CCyPtEhpc/r0Lkjrv76nxFIcKg+uf+hI89m/cRf/cz4H6ZQkDDJL3EcjDGfxcy6B45zGOXJSZ6Fw5twqviINXhEEfEMN0I0N0uyOR0jUi1h9idvcYHQFScsT06YjvEMiy3c6H9kj5nV/1DD59Z4lPv3aGk7srHL8W0K8HrFZncE2H4ztLDLf2AAAnmxGnm4D9zhXlRnJ0Xr7N3BNONgGL1mEM23WbVMjYrG3NiWGi5JC2HRT1GCW9riau6rvcEiDW6sQqMl+UgsMJNlhPvwoTf/IKwsufxLhZgZen4BDQPPEM3OFNcLePo/19LPdaPH1tjpcfO8Dzry3RLVoM/QjXdvCzBThaOuQIs0WLvYXAScuNbG894fmTHk9f67BoIrwHFmWBKMG35HwOvXY+W1C7gko4QJN4r4R4u1viI0A9M9+r1tlDk3Bvlc/j/wpgBuDvxwf9fmb+PzDzR4joOwH8BATO+r2XjbQigjgOo7ecXNnD+SKLY/eFM/MR7W1M27OxcY51Umn8hTCyFC2R5DyvBUYdwpscozserPKDJKd6iEIrQgDC9MTyeK33uPl5vxj+7HhbQMbkukCN4NCuEYuEOTkrBb8n+NCbrocltKOjVQ3bU26+5ElmmTEdBqnvIIXtMkv5dccxSi4zzWQFGAukuJYKFpL+2UeLFot3HOIn1gPuMWO+12EFxKgrxqv3VnjtZIOTgxmepzWePJjhYFb6qdRKELgJ2OscNjFUNODiNF+OFkZKcqSqmKOhZGgDhfWilYbVUitO00gr14Bd1spTNvc5lHrTNB3GOy8Dg6SZhpO7oNkCCCNaHrBoHG7vdXjm5h4evz7Hyb0VOMh8jpHbDv2IpvXoFsJ+Xjtdo2sczvoRL55scHuvw731iM438MhWl0JALg5cFD8vgiT5FW1TtOq9X2GY7iNqeXyEiH4zAE9E7wPwBwD804d1s7cq2upzz9n3TQC+6fVe20JYNot34j5Ji7MZzIXCbv5I+RvixYwbS62WakadTia9aRIEdeOo7Qepne9uG1IAkOCp84SIOk0B1ImGFDXdYRSM/sS3uP7M5yP0K3A7zwc5B4w90Eiopk/CMc5bGCQ8ctyIQFEBFSAYtUUX9BxIqXvvJNKqg0uY/Wic5zb8VZUAdX76WPJk1KgmN92tcMryYiIwiQXx9M0FTlYDfso7zBYN1ssB7axB03r4hsQiWQ14+WyDRaxvpfCVJRUcgRl7nrHvAu4Nrogam4r4KVaAsSZg/CJm9xaN5gZaMVfLj6TlR06WAzpZC0GqQOuZHIacn5TmjbJvoZ0J7NXNEVZSdIL7Hhg2oP4M1C+xaA5wY9Hi9l6Lz3/yGs42I16bLbFe9ujX2erwXhSX9WbEWeNwOG+wGQPurHrcXLTYjIzVwJg3EUI2c6bapPrIXPRlUMxHIrVurMIlN96euNdJDGwVuXwE6PcD+EYAa0g3wb8H4L98WDd7FKKt3jDlGj3Z5NbQXY1isZhxskhYmBdF7dtCLIW2rDACCe6aLIMEP/l4jisXqnWWA1lFjVgtGwEhWlOMPwr28EqL2nLIqz+jEiK1QGLR5xMGnKJwBGZ6+loLR0DHA4Zb7wb1Z2Dfgdan4G4Bnh0i+BaOdT5cCQGMveSEWB+LU2tN5qIuuZ5CoqNUcDHsdIjbpkxOhQq0UyEg2gF5SvCVqqYe2wEU5bUYznkczTweP5jhYNbg5rUZTu6tcHJ3hWHT4/DmAfavzdG0Dp++cwbvCO+5sZdCdecNFevGwqXHA2G/bTHymCrI1uxLLQqpDlz6OaYqGugxVoeR7dJal1mhHkQLHECEfNRvILPTpegpGocckhv/TqSJggDC7BDN/jXwZgU+uw8eegmmWK/gNktgeQ/7Bx1uLVqsjxboY8jtp68v8dL9FV586RRDP2LYjFgvJRio6Tz25g2OVwPedWsPN+YiADpPWA7SLsAn31BJEgSjwkPmXcrVu/S8NmpSJvyKBAg/nNyRN0LRBfCN8d9Dp7eF8ACXJqQNe9RIovqDLPgWqqSr+vJGAy6Tl5AYpTD0KEiYk6NSo0SKukEalssRzIjWRBJK6kwvBrGdMZy2AzkMdWrw1vJBkIgb35hDpFlTd+cT4GYGnh3kfc0M3MzRUwOOORyU8OU851r2pMTW7RiNME3nZEjGC4eTcH6X+5bLu8znpJ4gsYwJRZhmlBcMaA4FUQFf7SL2HWYccG3WSKXczqObybMC4jRvWocbR3Nc3+tw66CL/T0Ie215bWUm+i7P+oDlECIsBzBT2UIXIjQSpBr9HAExm3wi8EPhOv0ZYLB3Rix9wikYQa2YtLaJoNnbjOzPyAmErshDSlFOzkuhz2YuTHnvGrBZyUFNCwxr0PoU1C5wY/82NmOD1TBHPzJuHnR4+f4cHw6M115bYukE8lJBcv94jZ8B8PTNBVrncDhr0AfpGZ/faxSQIa8Z+6wh+Zy04kEVvuwuAg0fjGRtPhrCg4j+Bs7xbVx1Eyilt4fwwHYRNA1vBIwVAhR9KIAMa2lF1JCEzgVMp2LUKQOWJazDCg1mAAwwxHpJvBzZ6aeQUsafB5EzMdRwZ5w+IPdN99OQRjLWSim0yoEHAIRmc4IwPwR+8v8H/uKvhRad4/khQjMrsJIi7yU5/LO2mi0q0Y8TozKskMIQS0n47Pw2UUpESNFDFqjLTkqN8CLVAKSwIhghVn7VelMpA3uCBga6folnjxZ46XSNRdfAeYLzDsNmRDvzaFqPWwczPH44w6LzmDUOY0CqRmCnOo8OMXclPi9JWQ/7BqZ04CQUVLmpjptqm6xaueoJzACcdA8M6kty2fHsnREgHH82cgELuaYkWNfEkN8G3LSg2Rxu2Ae3bY4sbGYyz2MPP6xw0HV4x0EH74BXzhrMvQQNfHTe4CeP11gve3BgnJ1sQI7wWh/wQx97DV/yzHV4Ambeod1v0zPKuzfPiSxQAhEcle9BlUaZ0zfg8zyHHqE0jz8Vf/5bAN4B4P8V//5NAF58WDd9WwgPRmlC1takLBwq/rZCxlM839lQSN4tQKw/gnKGOUUoqoDRpphWdW9HHtT42GeCoJm8CJt43cisYy0fOdlGyJgPPuZwTGajR4auZeGT1kkE9i388Yvg93+1OV4sKAqjLBRySAEDmpVswpFVgNgKvIUAUeGoY6HoMzAfvDBd/Ts7Sa0cDNHPQQRoK1lVHwJlqIqS9k7ZN6MCKmqoQ2Bs2n3cXAwAZnjXrT38zGfuwznCwfVDzPc6zDqfyrQvWi/dERtKY5R5R2q2pGQVFFkP6sTerqVmlkWatYtII9SsnwWM1HZWynwwRgLaeB/vCOMo2rr3bQx3jj4PH/J4dE0ZhSN0+6DZIdziFDSOIOyBvAd1c1n3vhEBEgbM2xk6T5h5j6MZR4iQ8N7H9vHqyQZhZAwxwso3Dpv1gI99+j66xuP2foezfsTpxuOw88m3MATe+ra1rpmn6DsjtVrzvDrwlTN6q2g+TDqvDmAaC/M/isf+t8z8lWbX3yCiH3pYY3tbCA9g2olo+NJE4cN8Xvp4jG8EEHvGio+pENBiDLpfBQdyKOWusggDC37vd8Tkg13JdAFJ4LK/E4EQsiDR56k+/kSV1cAsPS7c2V0MB48VVgTCmHwbSSDZ/IA0qWVeyaSDvrBYopYLH6GlGJYaYSoHgWAKyKqCrxDhKzjJThcGpU2XZM6ZyvDdmnSN3Jh7rEfGe27u4WCvxeHNBcgR2plUij1atHjs2gzvOJhJyXmvwrq8nlojU2/bJkpelqZwfr3WEGInPrPegGzxaJMu2x3SFrIUn4iBsNwAsIcWGkzvUqEsDgizA7huAafIJgfJ9YiQllopBNMgzEtnydY5PHVthqdv7uH0WMqWuMahncm6Xxx0AIDVGCL8lJ8zMGMM2QrNaKyMXvKLytwf/Q7surkyC4TfNJ/HZB3AHcfuE9F7mfmjABBzPPYf1sDeNsIDmAb9rPZn141+MLqY2ByrHEud6dYamQoBJc5lD5KGbK5ns4QtlUJMGVxMYAsjyMIH6YHqkF0DbATVHIdoUWwLDwbELxPEtyLVSBvQ5gzj0TtjCO4GWq6CfZseioZ1eT2NNtNzQhZg+YYGvuJQQmgcQGEE16VXDHkHEQwR/5/SINVvrlaLj0JIrLecdwKU7w0QZq+O3UXj8N6be3jvYwe4f7zGZj2i6zwO5w2u77V46nCOZ48WKceDqHRc1/CV3paQoTi7z5YyOY8PFQKUsu+OzNqpT8+aMaVwVxcZasovIZKIw1hxGb7LfpCArbVDYQC3C4T5Ech3yRIOs8OYdCnQFpxPFm3XEAJLG99Z43CyGdE1DrdvLbDcjLi+1+Jg3uLTr52haxze98QBbu91uL3Xxaz1KDg4FmA0FqgGAWqpNDbzYhMm0/gxrWS+HmK8OdFW59QBnKI/BOB7iOijkMd9F2IJp4dBbwvhwaiYf72fy+QqXU+EkhkpnFU701MMPVmNB+fWyan3TI1P76cLXTUmB5QfboqMCiUnqf0ORogQhlRBtobZJrPwwgie7SfBIdnvq+wsNX4XVgGnzxr9NInpOGwxHhUaqVKwGa+Froo5pGiRaTguYuHLWETRBjeoAAFywIR9P7X1Ufc3Dyw1qG4sGnzxU9fwiVdPcbIasOg8Fl2Dg3kjnREdsGhc6ooHIBc5RAlfMTR/RSAVF+9lBYc8Zyl1rKWqcFd+UET/TikAi0hD2G8iChDOaztwjnQLLDlR8F0+N4QyPNYEXAwM0PwaqOlSmG+YHUD9bOwaDHDYxITR1hFGY2p97M5ZKmkv89vg8cMZusZhuRlxMG/wvpv7uBGLJGrk2BgtD1vuI6KWEjwRAy60p32CMSu4+qrozYKtKvrtyHUAt4iZ/27M7/j8uOmn3rJ+Hv8ykY2wqljpZKTVFL+qy0gk30iM3CiqmGIbsrBBTVqqXO+psfe7ck60Qqz4WkxkkoEOGGI0WNjKWiWGnUhdIg8Uvc2VwVeMfY4BYf8mqF8KQ2g6UNgkhs8cBchohVgQ34r6Omx0WAjRZDBQlo7V5sKoxhvDR8k4PouGQxHakwirHG2V3mWlFFyGrLWoFuBe63CTG3zlU0f4zL0VXj3ZYAyMJ6/PcW3e4saixbWZxyL6O5KvAcLs03qjXEPJAWi8lqenAkbSd6bMfAxsamPp2KLFZRbOGChp23FaCkEamLcEyBgijGSsHYV25L5igeh8sJZrTxMma3IIjLPQYm82g1+IFafFGQHxS4wsvclVM2+dw0Hn0Y8B91uP63st3nV7D9//c6/CO8LZZsThrMGijQU2Q0Bgn7PqWXKQNgNHKyRf1zUZttTACRglwQpZC0m/YWIu+rRcQLcr38OHmPlD+gcRfTfE0V3TNzLzX4/HXKoOYBQWP3LZgb0RensID1brIv6pQsCskxB3KPRRRqxEWKbyTdiKpursBLIz3VohU2R3afTHRdqPZnzbYoaTZd1hBIcy5PRAsXy6MnF7XuWrYHJwm1O401cRZocCTcW5ojBk4ePaHJI8ETI8FUZshdSu83RM3vnC7NMABoqatviOxBJhIIVvqqafBEhivrszuqcsRt1y0Dlcm3l82VNHuLPs8dLJGrf2OxzNGtxatFjEPufyTMh11EwEn72vJrAl6yOuJ40ESyVb4sFpLSIyf0bq2JfGXy28who2/KwQIPGfloWxjbNUs0/hrMkfZSdILNYmllwhYrROHdGcBLD2zlCBJMUN5Uq39zo8d3+FRefxiVfOcH2vw9GixcvHK7zr1j4e22sxb8q3psiAzpVAV1FJ1L9DXAfxm2QTUDEVrXYVxJiGUHfQK5Uju7zW66sD+JbT20N4VFR/SJZX6GKaegf1tu2PNGtr5wmQOmRQ7jsdLuoIBSYPIOWSKOSkfhAaN+kYtSAms2gTo66c2HaMhjmwa6TKql+DNmcZkgoDqF/FMWVhVjvF0/70UJWvphZcpkKv1gHT6CcrQIgjTGE7EAIAiR/EkoWGkJ9chL4+M5UWh25TmInCCBp73Jx3+NybexgC4/njNfZaj6N5g0VDWDSUlAxhmFlo7PqutdJwEjphhNRZcgIZ6RyyCeDg7NxWjdpCWBZeTdOObIVMQSrWN2TXdhIggdFoiHYM4c0TJZaHPsNm5OjIjvc2FqA4toUWDWETgI1zeOl0jYNYeNI7wslqwGYIWHQNvCMczAQabJ36lMrs+pG5KIPuSP7uGkpzo3k/6jy/KkNjit6kaKtddQDfcnpbCg+l2oGplGB5KjNXXTova4D6keoarD9auziVMVH8MO1HLOUiymurM77ut526wpEDmg5a1M32bE6MHxDmW7cVTWMq29wWTDuW2nZndzB87Mfh3/eBZHHk/I1RBIhCVPF+ifQ4LTNvMpK3fDJjbBsaQnau6v3i81oLhJPgkJ/OYcu61HnbLulOScOX+c9JmjZSjomSAAEcaBDN+B37DTaBcdCJf6PzhIPWoUnKgEsO3Jx7USkBcZCNI7Q8AH0u3peEvylaqC1UNdQ7+cSQmTuRwFZA3EeUch6AbQGShLsRcAEyN2qFJOiMkFvATlke8ToCR+VeJBpw0DpKmfRqKQBA5wjXZh53VlL3yhFhr/X4Fy+dwDvC9UWbbnN3JZnnnSd0jrAc8rX6MVsdAl05qQgd1PrMjcjsWtD1cJXEDPRvTmXEyTqAUweSHPDvAngvM/8JInoWwDuY+cLisq+H3lbCw0a+KBURLtU+m1wFoKheeh5ZCAtAYVEUDIqqkhzqz55A51UrzoMLGZ8mrUmEnC+BGN2kXQfVfxDPtYUQ9dx8XROdxQx/8jL6Fz+J5t1fBBpW0DCW3PccQL8CNV3u62HDeS9DVqi5SriEMTk2iDlBdyPEBxL5BJgpWRFW4wWy5aFk8ykozq+2/LVvNwkSIjAI7DsgjFi0LbrAmEeLoSWAhnVRRdi5BhyzoB3ZGsyIAsAoBf2YLUcNgCAHRget1kTq64qhro5cTPIzEElgWKMrwfzYhl3TdEcBmWE1juPLVliKOmQUY7DmO7sG9zcjhrg023hP7/IYtqx3fR9OEv9u7bXox4CzHjjaa3HvrE99XbQv/J1Vj6eutclCmuL7QQVKCHDk4VO+j3lu5DVw1fSAsNXrv885dQAn6H+AsMBfAeBPADgG8FcA/PyHMLS3ifAwGK6WbFAqQykrCApZU1OqrZB4+YupxvM5SJJa0ioJHrnrm17Xrj9bwqSIrHIuxRZJqe6owbqQmXigoibWVgHE2lHODA2r3Xz0I+AwgpsW/OpzoP1rwOJaMQni/4hj0L4JtYVhhUIdbRUd6qmyr+ZJaN+QWBWVXWMCBoRRBiqzrhWOsMyhTr5TSMlac0VXvfRgrpgzbhcSOMCMBgGNAyj0UkG4X8l4m06ik9yYKrqCx1hlOPqfFP4hBwohW3MckiO6bD+MDOn5DCl5cnCxlSobc6RYp7RdPDFdEjnSrAzgwMX5JpS/Hsnf8DhoGcfrkAIM5B7i7O9Vd4mMXS0apb3WwzvgycMZnj9e43osx941Dp2XGlZHsyY1s5oi9aNY0ogyFZABkMZPChPCBL9cIT0q5UkM/QJm/gAR/TAAMPMdIuoe1s3eFsKDkM1vXUG1ALFUa0f1fnUu1tv13FR4rhhEHQ5bO7OzJaL+ksBSv4dY0hFz3ohxaMeeEwBy2Y8QgNGVTJoGkLa05amRm2cw0VxudR/L5z6B2btEwRmP74JWp/DPXoeWpoD6YIAEdYGc+Ee08u65NyyjvIgDeBwKiEstHIrzR76VxeliGCqJ1aECt0HpL/KV5i/3HcuIIds1L72jXGI/FXp0PlsJYQQNq9h5LwcTpPpgsWwHgEJA5OS6HCpNg4ZBG0tDhT+5uH0N+E2GK2PTKucadN4L7o+YbY1sWafoLaYiCijDrbkelp4UDHffSl6MgRrFmoYw7ycPmgSjJQd5hO9GVkd2niq19a7NZPzzxuFoLm18z/oRc+9wMGsSnPXM0RwzT4V270jqiQVm9ANPWhOqDznIPxsY4EgE5tU5zN/cLoGXpJ6IPBIrpMewDcZcGb0thAeg0SuccGAo7mo0VEtWc6vNfdW3NMR3BJWai2qAyE1qBPI2VTwnMrutL0Rx+mkcXmtkCQ6vHzGpI9vFLPJYHwqITJUDEBlgwTSLQRi/BTm45T2Mqw3C2TH8ix8Hho1YITbBkLT0hMsMLQzgpiutj6pkyvQcGO2fXeFHSZCSy/Cfdx4ttrXGDFdFn4XR+tPzqfU2ESrMluHbeakpDKBxAI09qF9JGPO4gWZhswuSmW0ZbYirx8UoOb2v7d6ncxapCH4w+9jnnAvXOFmTKjQnqheoeKwTBwu5oIIGpv4TM7yn9LudR1ufzVZOmKzqEKW7ZLOXeRkh+iS8Aw5nDV5bDtiL5V4Ougb9GHB7r8WiycoZmffcOoc+rjG5N6PV8Psi4o7S9955U9ofWXC+YeJHr6ougD8L4K8BeJyIvgmSUPjHH9bN3lLhQUT/EaSo12PM/Ep0+HwLgF8D4AzAb2XmD194HUSzFLnFKTO2SnrXpusUTls7ZG1Gr15Dz7H9QBCFCCOXKCkc4EDOK6jXL1nBESvLKqRBiIzYJ4uCgmi9HC0NYbpd4eie0hrt/ZLQWZ9g7/O+ELw6FatjthCm168Rmvn2YGP5CdevRKDYEFxTjbVgpphgjnpYhKpSXSySwux6DQoj2oj9F+fZuVWhFbIVAa4YtaUpBl7DcBpcoNZCFEQ63mQpkUCKNRwl4wkAReE0mrpfE/OTggb0/glrdYBTuGuDxnfQEGadEY6wlYVitSxLzd5sj3TL/LyzUYSYXDtTUFS+blbatCYZkSo128fPvWSPH8Xoq6NZI0Kla9D5XB5eLQjvINnqvQgRCxlpfTFtH+2sEIkfInM+7ipIBeijQiTwxMcgkVlfA5n6X8/MP/mw7vmWCQ8iegbAvwHgk2bzr4a0nn0fgF8A4M/FnxdfD1mA2Oqsan14bC/6XfYcs2nviegTYU4l/nL05LQmkyNb0sPuPsYkHKp2h9iHIJgy0rbBDTsPhNg5zcXIqXEwKiVPM0gVGtEhDgBh7zrad30+hhc+Dn/rSVDTSp+GpgU3s3TPPBCxVsLiSJzrde+HrQfNFklRrdWQMOTOHD+CsMlMGtFCtNBXCNsCSZ/NWBu7LKAtKwWAJjuSFRbDKh/vTQ5CGERwJHgqWzpEoagDhhDEctHs/XgeAYAJv7ZjsVYlwpCYKWGDxjXw3mOMUG1ghbEoharqAptilyOL07z272losKzxbDEx/JbFwebaWWjIHTnuJeQOj1aAaJDSu64v8OpZjz4E3NprwQzstRLZpjkjKeiBYnRX62JJGal/1TqHNpaKIcoogIXg3NijpwZDVarmjRAzsBkeGiL0wMTMgYj+e2b+cgA/9Wbc8620PP40REr+dbPt6wH8xZgI8/1EdJ2InmTm5y9zQRUgqSYV54ZCtXVRWOZU/l3UpIqAkuX/Gm2V8VSavI7SLmhUP0iBJZCuxRzhhEK7klLuqYx7xSiJHDhkjTeF425FcLkUMQVyCPMjcDOHv7UGH96WYXAAzw7Bs4MI0RgMP2rAieG6vF0mwZU/ART9SXwMN66PN3WxCv+HPYZDrjxsr6HHqG+GxzSmorpwmuvyPHXWp2uqALOCxzUyD/WzxXNYFxWLtyyVYdFTwpD9G3H+GRAo0kauWf+WQnkBAIwAic5/ck0SGDkCSyrp+mS2lmTrP9VUBJRQWfyzhqp0LEqqZKkDn+IaZkYxPlubqnWExw9aDGMsDkqUwn/1HhaeHCGwV9cAPkhZfD3P1oiLAFX85gnBtxiGgDpv5I0Qgx8pyyPSPyCi3wDgr74ZyYRvifAgoq8H8Bwz/0jli3gKwKfM35+O27aEBxF9ELHo1zPPPFPs89HJOkwYBvqNT8FXdltZe6h0nmt9Is3RSDAVlzecen1WYFgIQK2ZVJNnarv5cG0Zd9VSUxa6WiNG6xemSFlLVqbZRsa/uAZu5vK78wizA2z8DDPVltMDmBwQN7F8jK+kqAZcKf9qNdRRYbIzVuSNHQjTcQmOYsD5clxA9mMkPNJYYnb8ZqysUVFpjiorDSgtOiDfO46jsIBcbAamQi423Urvx1p/5DHZQKu+L4DUez7OD6Jwda5JiYZhzBZ3nf+RLgOk+k/28mR+anSWwj0h+hJtcc/tfKdYVwpsgk0Et1IhIoVFkTLedTo1qdxCwqOJR/YEjCoMHcMxiTBxMs5agGgplsDAPF57ZKQcnSshfrRgq0i/C8AfBjAQ0QqRzTDztYdxs4cmPM6r1wLgP4VAVq+bYm2YDwHAl3/gAwWfTtqRRpfodmwLDuu7yNqL/NS/lRfZvIGaNNKn9m/I9eK2+Hcqix2yoNBvZTQfKSH7bbR0T7ofcayV5OG8Oqi3mWUKhS18E01m8FqSu9sTxgefGPVmZMyAzMw04igyaY4VVFNvD3NskVBonOz5AVzm/ZpgqFTAbU54UA1DVVYAV0LLPruOMR1PJoqMVT1W6yZi5L7dhuFiyG0OsQ3i93ENWHutRL8IAIR2ka2MmQOtQhZWCsmZ/JyikVcc61bIte2VEuQYgpRMaQHwKIEirJBtuRzSpWOen6xnl9d/Fg6yCFXBGcO2v2Mqy902s9JCotH2iM79eBwEFdA6cUUiLpD6kRCJMthAoDkVSMy5coAKjlTxmiX73ROwGkWAdJ6ulNmrJfYoETMfvpn3e2jCY1e9FiL6YgDvAaBWx9MAPkxEXwXgOQDWjHg6bruQ6hIRaraeh0qmftBGgAC1RpXGXdQlApCSz1J7WaCANHI2bB5XyF9ninQJnOteWcenLdltSUOFw8gYSXFcB+9n5ZiZgeBLGAYlk2LXSI8GQKKnXCOMM4w4wMrASjHiKAzScdB5MLVg5+HGPuu4FbNjwFhERoC4RjTptA9bc0hhkDa4WpolMt48EdnnoBFBFjok3+ZoNu1J4ttCyATyIvg5gPozea52npm79dOQQE9pe9OBu33JDF8fSyTWID6MMNuXcfku31Mz7OM8B3Jwq/tlaHWE3tg3pUCtSt1nCGvaerN6hEY8EUzBxrjWrDBRv2AKR+fs37DrUq6fP5I6EMU7StcSqAlgjoEgyGvbJSVPGPFyYKyJceRyt01P0YcSS8+UAS0Uj8n3Doj1tUbGXuvkOaLj/Ly+Og9K6hd9lIiI/gEzf81F266K3nTYipl/DMDj+jcRfRzAV8Zoq+8C8PuI6C9BHOX3LuvvKHwalXWhpLisakQBVPSQtma8MiIHm0uQ92tG8672puk6cWzW5AeyhWHHrr3WE6TA28lSdrsVIgCgLg+rxXnfCrRFLmP2QNbeyQG+LRimXCTCLwb3l/4eXWr6A0wIzjoqKhgHsUYw8YDgJY9hq+lQMXnZ2tAkNTf2eXclOFRbVpLEOHH+WmHAGpAQc24oRi/BdxJhFoUnA/L72BeObY7QHzdzhDi/csMG3Ma2re1etnp0jN1eehaFuwoozFxb5jZaNvad7ZgnrWWQrOa4a7rGVaksAVmTzrW15DzNJ7Gk195lqQPI/se4ptWJraVVFOICoiBywLyZ/m59fD+etqMh628WgdEzm/wQtVj0+KvDrh4V4UFEcwB7kOq9N5A//2sQ2P+h0KOW5/G3IWG6PwsJ1f1tD3qBmkmLszsvmNb4EOoMW7UurO9BayTZ/cDlBIeGLNbF7LZhsywwUrmc6pj0DPEao+yIH2W0UEIsxW2sJQ4MTx7k/Dajt5aI78QSgZee5ZAoFW1XmyKm5ofCUOWG8sMkMtbE6dg4o7FvRNo+lZ8Rr00AtCUvO8k8hrEm2HlhTiEWqazuHSyHIQ/tHy9+1BjWGaPzNASXfZtzbFjmxunzOydWQxSIqfPj2Iu/CBFWSomVPo2fAMC3CCpExx7O9n1XIRFCMQeFoKghOqICxvPOw1HZcjUCPSkpNfqQYQsjjnGjWjO2+2VdgmMX750SSGqlk4G3HKwwMtYLiwLQGEMUkPOamMNF5DDoGjdXSJYRJIGSIc54har0u73KLPPAjPWjE231uwD8hwDeCeDDZvt9SG2sh0JvufBg5neb3xnA733Qa+giVU1AzHOklS6hfqW5rr4Pqz3ZOkmBqThPz1Uc9kEohfZS+XddW0sFx2TmqtmWtCrI+EZwKgTnLRNl+dBGyh8QwZkSDy46Rj0o9lJglhBErTMkkyNZ5aFdgJsZQmT8BAMXKSPVcdYQlDrsmcHNHGt2YGbM2rlYRlaARF8Hq/VjSP1FBJfKfjNylzkVtDV+r0zUvj8KowgGM78KzSWmPKwFalP/RwhiQQCAnq8WBnP2pSiphVG8yzw3Ye9GjvTanIlQBCQqremk1lbt47GYlC2sqNp5ZPzeaRMou3akjIgqTt5pwIckw8oxwBgXY73+Ldk1bcvE2PO2znFZKFkhMAbGrJE6YerwTzAxAS7Ov0Zj2SoNDKAfGRsG1oNc8R2HDfrA6LxLxwKXKMnyAPSoWB7M/C0AvoWIfj8z/1/erPu+5cLjaihWCaVcomKqzpGSRmLU2otSYKB1WS+y2lvhTwBMQ6ESk5brPNji2irUaBb91PU4qtEp2osIcBnqSj4TuXgWsvEATzANe2KEWtjWNhN5qayrDJyJkiNVHZ+aJAnTWlYUW1/6hzgLcYl2KnNakmPYOK2t9jyaOVLtspzHkunpswNIkBWTl3wGIIfwqjM6jcUIEvt+K99L2jYRBgxzbQAiEJwp0Oi8WGDqiNeyJ4Dk2pBLpVHYt6YKsVghKTgjrgFPWt05+9MAsbL03TPk3QdoGCzQunIehcHnJle6+NVPWJT+iD8l0jEy/Op7UUWjD2Xkky09z1RGTrl4nfRW4vy6GHnnyAn86IBNL7XIFrHmVucJ2oSt/MLfOD2KPg8A30pEfxzAs8z8QZKugp/HzH/zYdzs7SE8SIVD1p6mFq/V3iQfhApGDcgSs8zG8uvE/CoSqJ0SI7LXnRhqukdAzkUJbKPBSk2xFm52nzwbYlw+S4qDalmu7PudYax4PqQzXOMowQlEhJmiJsrMYla54u0OmotiS1TkEVI1WIUVAIUhPHwVqqMMXebYwDVmTnN0mnSqy/MgjE9/1+up5ai3Kj/2OHdJw8j+BAKgpUQkObDLa8chJi56cx624cAd0FPRQ51iKLMKy5gDk0KmAYQodBNUGJ3w9np2zWiekz6zJts5CNRZK94c4Z4AaVDZurwWNWTXtFPJa4e3Q1/FIqz8gWwEcIQ3bcmQwDJQR8AGSHW5VBjZdr+5CGgMg45zrR0Q554QvPSidzxufzQ7oNXXS49gbatvBfC/AvhF8e/nAPxlAJ8VHruIovatRQ0YsnjVubrVAS85cJGwenL1Rxi1KrsCgz1/2+ehGqBeJ1k2UXPalWClcIsyt3pfTVNOdM0D0Y+OCEmQpO+POcEajuRDHaKTtHXSn3scTUkLDStlB2rK4pwWJgJEm7QQhtwuP7N1YPajhhmr5aBQnmQRh+jXoHEDJhFw4xhSkIEVBBT3M2etN80TrIWY51M1ZJ1LIiT/ieMxlcBHECtBAwVsBWBhYPEg45BP86ZTyMbCiI7tlIAXHfiEMQvnVIl3SEKC4zrV9aW4PxCFgpnn9GzRgtSgD10nMFaBjQZUWkUcnxHzJRxvKSd57uQGuubk/tFKMD3vEySYEjE5WW5aMViVvCR4kJU/tgJIScv4R9/YHOJw76lBA61inO9T5BxdAT2iSYKfw8z/OyL6TQDAzGd01Y1MDL0thAcgUAecF8xf32lcbJM1lTTEMUUAyYewqwvbzvsax2NqBjVhKpy3zlQ7vAwxA6G6uGPKjIKitRI/eA9hIvrBK+QAymGLIzNO+3KOFk08R5liyAUaA1whOBQCTM9QWCTZDwFkiEJ7SWhFVmVAklkf//YzKfU9yjFD4MIBWt9LtVb1BamgIGz7t9SJm7TrCiLU/Bebpc7NLFskxUupIsp0s/H3lC15jZWm0WPWGQ4RWhyd9iHBmVPFCDnlYdTCM3COFgws752pXN9q2SZlKe63YbzaatbFHIsp0vkmTFe8laHaRRGS5eViy+Qmfr+AKm0MhBw4EW+U5jQFbBhLpA25GjKK7ztsv7c3QOobfMRoQ0QLRO5DRJ8DYP2wbva2ER7ZMRu1Mhu1AkwunMmPmih39lOoa0doZBJOcOV2GHwemclOkY5Km+rU2oyNELGCI5v9iOUoclkTFSLBMG0o04WEK+tFN6NGbAkj0WfvomPIqV/AyQdKHNA4l6wAm8NSWBxAEZfvIczfwh7jWDLDkTMzk2fndB+1kEJ6eGV223NqazclXxCAfFaeO6sxEwFMpS+DkE/K1kW0DGzPb/VDTEWO1TSl1Ez5UNI8lFF7ZRXdLIA12q4mVWa2Qm6pFJpTSlOGv7LyYaOnIJfOuSFbz6rmNuXfuVwdalVMFo0kh6KUfrIk6xbM4gPJDz3x7V8hbCWW2SMnPP4zAH8XwDNE9O0A/jUAv/Vh3extIzwCCE6x42iupgUWnZEAkMIbjck9qRVyKKyTLetloh6RnqftREFaXbTsNaF5KDVm6gDAZSe0HgvsgKqslqmaoTlP4R1HlIpDWupjZIuWyR5HOWfmM7MdoyBomlnR6rbzHhsIwxKBi9TbARD82Y5jOXCKoumDccICyWmb+kEgWw8qNGqBlOAyVugmC2Ai4z+iyFQjA1QBIvOC6FiPwi9aUKk3SO0875fyi28lZ8YyNR2ZtSpsKDQqCMu8yARZJQvGvkdj3cW15B3lXt5xzkYjUNOIqPSrAWVNNiCHecsTkA4pLTwVqjXbrTs3ipVjrfAdjFW/KwDJH1JZXclpxkFubMvBpAGad1N821wpijY6bUIRfL3Ejx5sxcx/n4g+DOCrIYvhDzLzKw/rfm8b4UGElJCGiFsDAMY+maw58kUXmH7wE1aJ4rN2YxFOahdOqW2m/tSkUUg5+qfuGwJES8Bczlot9vmY8wcewJMfdTG8ZHRwwSR0xMzCxLWAnIM4KCW5K0ddKbNpTOQQMWPmxFcyxg+piVh1awTH3IsA1chf5hgthYzHMwMD53ajut9+oFq5VWfawnypwyAzxmCL5OV5VYevUpGVrMdYbbryaZHNLDd4fhFNVbyw0nG+VcQx+TumyfrfGFkQ2EiyWlxYeLAICwfBxz7wOk91trVYDflvNkIjxH85TDaeY+5TlO5RiMgy6jocu56T6LuwUDLISQ5NHf68y5I/D26+Yme5WB6PlvCI9BRET2wA/JL4nv/qw7jR20d4qBaokIC1QKzOnbSXxM0Ed9bFObXI6g8hCp+kWSoDKRa4yfpFCT8BJTPQv/sHWItJiBBvfTPGDVMO2WiGngjscqjuemAsmjLJKzFVE8XmiIqcDscjPJtMaCclvUfmBJ1ZeGg0YyusDzbC1MAzyl4cbz+nMleF7LT3ilohNVl4jVFGDynpn0VXR33PMXkStlmWZXYTSX28pVVXUWTpOCryZqzyUMyHWiBAsvZ0LpM/C5icA12DKRm2CmfWc5i5aOuba0iZ/Y62ypurxUY1ZKzPa78XImDIlqz+1Cvqsydh4lokJa3+hs+j6AtJdEV+D+Yyc/1RICL6VgBfAuAjyBotA/is8DiPbCRF6oWw5auwZi9kv1omeojVcnThMiOGLmXOzEjMIt2PEfEW0xWPclJe+mANF3yQpVxDVQBiKCUXx9S0qwmOp1x4cYxj8dD4/kxaHn4E0HrFvHPm/a7GU7rfJo9pP3Jg+9lrJ3v+HUWHvPp5rLAMZqNCXxp9ZslqzVP5QGp51B0VgaioRFhSbm4FBmFSepvzU25QPFf/lrWxPSd1YAKgkGK5P51TzYEw+WxZTFlagIGhdHyEcg7TOVQoBDpvWiNM7q3fzYTvUa00cgn+Zd9N1unS71q/pYh9lgPfwnTd9O9Tf79OekQtj69m5i98s272thEeaVGEsVyA+iHHsMpck8kwO7toOSAluFWRNdqASHDUKjwzXlOK3OUID/lGfJGgpRpgHWVlHcyFwGGrNW7LQMdU/D1FNUadtscMYx+vqfeqPwsJ57VjiTWh4ApmqBFCeqcCXtJzkweasyw28BQb4VL4dazQrRhGza9rwWFZhuY/qGaehVwllKjsabEdZWVehAoROy4LxUyQ9W1wmn/rwN4+h4AibwbxOUcrLGthYgRMgqJ4W3jofsCG99aCQ6gOp6U6NHfCkleYLtUY861E89lKwVsDqvxFZPJrdlkRdl53RLy9UWLmRzHa6vuI6AuZ+SfejJu9bYTHl7wFJgAAP2ZJREFUZJvTCkbaWshTkTEsXd+2FqbFrhWvNj+3Ij80kgQAUl+KbYGRHMXIH36IvEijbOqPfEuLnrQ2UHzcNSk+njKIKQuuKcailoNi7gn/BqUQ2XiA/KjuZbXmNEYgPautK7ZtkWxbG+naE/t3CY6yGrL8XsJ0KtxNuHft++BwvpQ+b1+1BpOwRZY/xXMka4qKHKAa1ovqjAjbkCsO5Mi2bbZ8GcGxvT/PmbXStDnVTp9gFb6cvxURMuyaEm4GknKGmulnbcMgA2Ze7fwbi/Fh2AiPoOXxFyEC5AVIiC4BYGb+kodxs7eN8CgWr6n3UycGblHaT0iZvrsiqYy2Q/anWcBFw58UJRIKDVadv0BmAERa7TeXrg5AigACpj94YLcW6YgK7FofcxeNAXBOtf7t3iXWB6LOaGVm2uSnHhdQwitTZebt+NWJX8TLFAx1OydDn2tLWzawSzoOu+dxF00lhG5BMRPNqbBD653q+aLPYG9DyKHQF+UC6Z31GjZPA9i2VqfWTGEwEE3sL8uXpOtNhaxO+H9sPkxBzkPS4scMZdWWRS1AgKgFOMRs363jJqHrKyLmR1J4/AUA/x6AH8ODIeKvi942wiNFrhAm6wnVi3ErmUvxZ9+BrOCpIkImt2uoZRiAUYroUbynVltlijkWFXRFNcfQfdE3YpnlZHltYIvJ27a4uzB/da4qcz1LSYLqJN12PFundxhz+Y9UDA+lZqyhwnqFPpT1kxLEEqfbAaBYuiJFa9qyJ6r5Yjp8tNCMjeCw82MZYj1vl6GaIU2VUpk8J/1hI7DYHLM9kmz8UHKS67ithZY8o1z7jeL+4n3k/buhq7IunJ5bQljGoo2aPaXnI6DIl9HfjXVQw46uST4mqqwJ6z+cRBO24EZV4CasxiskfvSEx8vM/F1v1s3eNsKjME0r5y2FoYxqiZEs5YdvrIEa0tLFrOb1FvZt7uenGYnNYGeU/QZ0fWskEqePuuzAZrXtrGFmqEWFzBRjPY+YpaIpkBluaqplIpSmHK1bkJrZluCVuK91pTCaSp7U5j96GEd/jmZIp5ugvJYdi1hxNcSSz5kSGlOwWFJmq3FeFj/nSsgUfrW0IwuSLUsEVYQUpjLM4yUrwaFkLb4i7LmiKchqSrDabZNxAbvmxcJ9VNuWiH6PbesBqIRvpQROw9XbwvmqiRklXPto0A8T0XcA+BswmeWfDdU9h3jHojvv+KJ4HxE0P8RiqckhvsviSBesIo2sdsomczY2ASLOfQ5sWQnNirYfsiZ5qW/AkggJMsdnwTEFVU3NTgEPoRQcxSNGBpR8H3afjsf8nhL7DGRVjl1zFraJKPbDrp57V+ip/j5lbUwJjV2CooZ2CuZTW6oPQBbinCzPbiKu7LsMzIUAqWmbBcdLpoCDUgnYVeboIl9HHZVWn5fuW1tkMMydtsu3bGWSR2d70QPGWhb2Ghaesq18Lepgr6vK5ZXBWDz5nbzFtIAIDdvim/F2C9Ulot8P6d0xAvhbzPxH4vZvAPA74vY/wMx/78Jr7XiJhVDZpY0o1+Axh9wC5c9ocUiNJ1OgLcRsWZ8rzqZoLpNkyMlhHoAANOQwWCiBM1RVh7FaBlu329Thp2ZQMH07CgE0MWek8fylJTCaCKiaHDOGKPhsprIyZFsssbY67D0uIusoVsFZaMTW8sFuoTHl4CXasV7YwC7W71ApC5Oa7g5sfldNtS0/XOUT04dMuTyVzyNYYVpFUQFZaNRlY3QuqttUczttmdnoqvTYainTdlQaV8y9yPFQ/6ARAlkoREVO61VZitFr9Ryz+e4mlbsLYMXXRQyMj1i0FTP/tjfzfm+J8CCiXw7g6wF8KTOviejxuP0LAfw7AL4I0hXru4no/cy2aM0lSDU9DSOd0vbsz/j7TliBCED0aVjoShUnFU61eQ3kCqnRsUeQAo5NrCaq/gOLbwNIJSWsFl47m2tncIomqjTJXb6BbeenMCObPOeQC+tpchhNnBsmBEeuoDEtMOpPzzIuOx9TAiSfNCUkKssLleDgMGkFXAYTp8m1Y0yWLcEyIYlNxFBioEZTlzFnjTtZIRG6stadVQ7SbYyvaisHxPg57LDqOdTf7b4HovMYtX4bKK2yRFU0lv3JbkJ4P8j9r8jvwVd3qUsREf1HAP4UgMceZsmRB6G3yvL43QC+mZnXAMDML8XtXw/gL8XtHyOinwXwVQC+76ILFolXQGlpWOebJh2pk9zSRIRHakuqpbfVMa/kK4tjAt6YgiooDCDfJghLHaKJMUAq4mrfZsX+dV/djAfIWmTAtjY5VbzQhsfqNr2er/ZpZ0YbFWX9IFPhuCkceAcUZknHa+fWWiDaa76Gb2pmt0toAEhVCCYZVhr01LYdAqA+ftcj1susEhQ2WsjmH5EeE/OEOJa58chZ+1PWau1zOo/sOtkVhVaHM++yPmT8PM24zxMmtSAu3oHL29V6n/Bppm+wiLYie6oc+0DewPPpzYKtiOgZCBT1yTflhpekt0p4vB/Av05E3wRgBeA/ZuYfhNRl+X5z3KdxiQbujByXzyjLZyilfgDKRFGWM9kybY3FogKDfSvaTzyuYAIT2qOMYcKiYQBOmJl3Hi76BTQsM4W+RubcVgxePoTtL91aGltQRW0poIR4chiuXFp7NSRhYe5ohYHPG1PZ9KKQoREgxVgJqJtGJWuHcjY1QftPxNvYZzI05dNIlkaEFy+lKlbMq1YGztV6Kytip3/MRBHtZLSqqGi1XedTnTREn11SOHRuTdGppEAQTQqRi4wJa3XUiaOpCRlQ5MQkCGsXU63n1VoY1TG2SnEWshPh0ECew13wYUb3MF13+HUQv6kO8z8N4I8A+Otv1g0vQw9NeBDRdwN4x8Sub4z3vQmp/vjzAXwnEb33Aa//QQAfBIBnnnkGQNaKtJIsiMrMV2ACklJHnQn7qyyGUtuJEBaHkplw2NJomdyWIEnXZU79stk1cOQSjOWBLY3eMuldS7aunZXuG38qM2ZMhfci1aQCMJmFzMiZ5t65WMdoSJn4TTOD89KmNjAVAsf+rsLBOt8t3MSGOdVUO8DtM14oNC7SFO37s//0Hhdh5nYtARK2DWwLnHo8NScnB/YNyDUJ9iQN4HAxlBkxOiu+9MAlvOiAXDDSXLpYIzDWilHP9T07Ki3MuvCiHJcvWBffrJ95C/Kzglp9P1sO9OjncE2GiHXOqnlNlprezz7564HeziV+kFDd20T0Q+bvDzHzhy5zIhF9PYDnmPlHzoMPiejzIYr2P2PmE7P9VzHz373sQB+EHprwYOav3bWPiH43gL/KwiV+gIgCgNuQtonPmEOfjtumrv8hAB8CgA984Cs4l67OP30UBKmL2HmangwsHX8R7awQapmNjnUKW+dc6oTGXqAJU1eriaia1cD1o1aBcpkw2Xq//hyr8zM8VVkovO1LKBg/xa57Y4Drl2jbBbz3qS9Hk44rW/NOFf6rSYVMISQmnOCyPe7fZWmc4+uQ88z7U6Y1VoX7gMn1I9CSsTjs/acsGftvyk+mdZ5cLG/iGoi/TI4hciBHRe92V0FZAIqINZ0CG0xR6/D2XVsqkuGM876GCAt07zwrY8J6sOX+C0uCoq8xDNAE3ByhFo9LfVVCYc2lUVso64qMBWZgHLefYwe9wsxfuWvnBYr2f4oyemrq/D8ACT76SQB/gYj+IDOrlfJfQ3p8XDm9VbDV/wfALwfwD4no/QA6AK8A+C4A30FE/x3EYf4+AD9w4dUoR/uYTbJQohnNqYYOthevtTSmnOsG7rIMyP5Mx04Nz1gcRS0si9VGP4jXccRYG9U083OV+LsVKFZzt0KktgD0+JFzCXYrnOy5lplsRskH0fax5AguBNCwBvqVjH9zBkcOjY+BBTb72DeiQfoWzndS2qQeq3G41/WbgBK+snWVwJhm2FYT1aZD1fY8mSEz/mhNFQ2KJohdA6JQZuqZ61lhIVbatkApru8FmmHfyTsZAPJB5o2DMNBYQcE7DwRORRJrUrhSIS6Fs+wwa2XWvn+tDCDHm4ANnX+F0Kp3UQri8rvZmnMzV1vvDECq9jBKj3db/cEKk7JqsSmVX9FVGiBXlSS4S9Emoi8G8B4AanU8DeDDRPRVzPyCOfR3AvgKZj4honcD+H8T0buZ+VswbcBfCb1VwuNbAXwrEf04pO/9fxCtkI8Q0XcC+AkAA4Dfe6lIqxhiCpRx/9pidMsC2GVZUNmIZwu7nYCmLsTA03WrkMSJZ0if9o7xiUNcxmezztWZDaBgJHUVVmUMtfMbKJmyHgtzrtxfmjPBSW0rqpggjb1oiJXWya4BN3Nw08nvrhF/Tiitjymn+3krP8FT8T4wZflrX1P2eVUMLIQtZlccO8XkE8TpivDuNI40cUZwcJBuWzxhFVl/mXlmpnl5rUojZ/Oed84RsjWicBaAFL1WJ20GlGXea6qtUILmLPHkXNYC1CoUW8day99VFtlU8i2VxUm3LByitNbyM+6eqwelh51hzsw/BuBx/ZuIPg7gKyeirZxCVcz8cSL6ZRAB8i683YQHM28A/JYd+74JwDe9nuvW7zIxTnXiTURi2DpDzPmkDIMIU5rUmuxC3sVkKrowzLASHIUiWz1fXa6krmBb01Syl42+UsZtHd56i2VMTDnsHDoffUljDxo3oH4FGtagzVkSIGnMvgPmc7BvRRt0Huz81rPU968hNa0foI7+nWSsPAClIBiHksHUDYpqZr+rzagz2m6EkXaNw1obWwLMHlto0VJtNt2LpGyOwjPBt9J6NvDWvKUhKlRlbqNh1vYY+/77KhM1IIdqW4FhIUMpw26EROrmWQqLwpqrhQmQn58c4Fu5efT92P3W4k/TpwpZochs8rdEZZ+UqyBmfpQyzF8koi9j5n8OANEC+TqIkv7FD+umb4sMcyAv7prU+kg5B1BIK2qs8TgbdkeosP/aERq32Sir+tYWApuK2CmaCQFJS6oXuurm5/JLZIZfb7fnbjHkhIVvC47NyOgDYzkwWkd4+loLf/IK0DtgbBIzdv1ShEa/RDi9j7BZJabr9g+Bw4U8l2/MfEQtMw5Qe3BbbL2uq6XvzTrZNTDCJWtAwBQ5wCR1WqFRlA43ggLIkJI9377P9IfCNXG/U235nES1dG5lXSpsWeEpmllN45B8dsG3SfByfG9WYZBABtqCIAlAA/FJ1dYwDfF9NV0eCyCRheSicrAU2KjpCiFm50sLilK/EqViWAFDP92XfOzlZ7xHATERZXjTNUC3l8dJLgURpMPHzbZSYCx9CoN0I/StQIHnah4PRm92hjkzv3vHrn8fgtTYYwcA/z4R/fmHNZ63hfAgcOGY1R7gOVkta2iWptaRalYRIpaFS6ZlqEIGBePPdbUKrWiH446NwLDXSmM3Yz2vrIRSKkcxsZbtmVP76+q2IzOGUayOs57x1GGDg/ufAr28EejJNQBLL28aN6DNElifIixPEc7ug3thDNS24HHM0Fb6R6JdwycBoIJji+VyKUw8EThwoShImo3MYwT0prEJhaR2+SDMcYVw0Xm077sWKswxinS7GrNYW0hrRK0vfT4aVsXxyWJh6Q3DTZPWiG2F68ihaRyIOTF46lfyLPH6OVCkfD5uZuBuD7Q5gz99Fdx0sYB3BYuZ3uoUBmB9LLsbsSThDfsYB7jVPVC/zr68QvN3oGENDgZeDAHknPh0Cj/HCFAv/ju1IFj8QND7hpgrNaxFaI1m7l0cu2vAoQU3EoBAY//ApWXOo4tAhDeLmPnT5+z73od137eF8AAH0LiJiz2a9pEhWW069+SezoWwTsCi2ZFrQOTAtjlUvQgnHXd6cV9sK0s3ZAgnTGjgclDZz/wioupnTbXQVMfoEKGQkaU97dPXWhw+92HwZgWa74P6ddaSwyhzvhLBwasz8HoJjgwB3ovmOW4ygwQA38nYGg9wFmgBZcVXeWxO+wB5N7b8igPQsIb9OnjvhZmGAbG5hZkUB+1tn62QHZAkB/DQm1Od3MzAXTIgn4QKA+I4t4wrQjDsG8CLv4fGDWh1DH92B3x2D3zjnVJ52fo0nM+4v7VQmUHjOisxYRBmPayy78U1YHQpuZU2p/GaTcHQaRALgVWzj1ZDnHj5psiBu0VR+oNGuSd8FFBeLBbanAHL+wjHd+AW+8D+jRxV2K9BTQs0M6ifCGMvc6NQZrRMk0DXuR822boZNjK/uobWp8D6FBg24DG7Rsl7UNMBs32gnZV90Hc40h+UmB+98iRvNr19hEe/BLp9BI6LOjLcGhJRHFcEh2xTZuQ1hyJIGQ4tWgiSPuikEVvKPFT4pNAV68yjtK3UwErnXdgh4JTq7O9dNCkIafuY2krRxMQ+AJsxxFwBxrtwB+EH/wkG74WJ3XsV7uA6yHv5UENA2KzAmxW434A3qzQv3HRAv0FYnQL3Pdx+D24X4HEDbhdAO48RWW0S8g5ilNT+Fq2XlYnT83lHaBnw8T01LLBW45oITWqOgIuoVoj4lxMmOKyBsU8Cr9SKx6zBY0IQO58FRGL6wti4XSDMDgoLA0SgzSmaO59OjBMAwsd/DP7ZL0DoDrKvYIxQp0I3zgusZGpEgQNoiIKZJSghLI5kV7uXhAs4wJ3dRdg/TNaX65fAuBFt3TkR6GqZjRvw+kyef/8G6P5LcO0s3RNhBM8O8jyEAXT3BVA3x/Dq84DzCKfHcADG02NwGEFNK8zcOVk/zovgRQ90C7FkoqBy69M8tjinAKIiEqPlwn1wCKKsxDWIIIIbzoOcrFk3bOD2jsBt9ildmKtzWWI8dIf5o05vD+ERRoFPXAPXOoxwSWBYSEQFyIgpxiqMp3VICQYqQJQYgHctnAqRdDLSx6cXq/0XQPRhVJCUjS7Sce6KopnKe9Cxg3nLmsKOv3VeVGhoMcTOO9xuetCP/wP0n/mYfOh9j+HkBO2NGwjHd/NcDL0wmDAmrY+8MoX8TAEADxtQ04EW+3DdAuxbhNkBaLaPebePsWmxGQM2Y6zCC7GC+sAY4/jG+DsgUJUjQhOjvpqI9beO0HkCe0LrmtxrPMIuKRoswh28PgP3G2DoZbzOy/OEMTK7EdR28aZRS27kp2rc3C1KIRLfvT99FTSs4Y5fQji+C3d4HWhmco31Gfj4LjiMcPN98CvPyVidB83moqVHjB5NK8x97OVeSgq3RWsjzA9FMFvnNUSQh/2bQExIpVGel/oleOgjY58BYQBvVghxPuA8cFeqBvHp/WIN8b1Xy79DAE6P5ZyhR+hPEeI51HYZ0m26rbBxnN6Fa2cy/jBDWByBNmcC10ZYNH1rYy9jXJ6K0FivCqUFTQdqxC9E3mMceri+B83mcN08v6crIb4y5/u/rPS2EB7EAW51H6OPH0K1QLS4HlzJyJUYkFIZDtIPPGVbcZG1C0iIoyfAubYIFS0+2BgeOMqJpXAwjmkga9ljyL/L9u3IEI2lr60TFRpM4v9hztnGdQNO8WsIcxZmLePrA2MOqeXVvOM98IfXMbzwSfQvfgp+3oH7HjxGJjL04DCK9jcGUAyjdE2bGLBq/OXNR2AQ2MX1Z2AOCBzg2z3M2jn6EACIoFDBMQTGeghYDQHrMSRhPm8c9lqPeeswh0uCVf8lDhUGuPWpOPWHddLceXUKXq+ydQAII7JjdS7BLdzOELqDVC6chjXc2R1wGDAePIaNn4EAtOMa7cs/Cxy/Al6vMEQmF86OxXKbzRFO7wvjjhq4aMwR0uw3cn3zCOAgAqRfZ4tWrTzfJYsjQXCazxI1dYUYxVJZg/s1wnoJhADuNyC/StYkb1YiPHVcQAEjsbXMgGL+AABNKzCfnmPWALVtPj6uE30+6tdw5MCx4gKaDswLULuQ8Q8roFuAZgN8N0c4PUZQy3CzkvkcNvItN3Gc6yXG5Smo7UDdHNTNt9fk6yTGZy2Pt4Xw0A/EbU4RnEM3OwT5BgNJYhuAXECOFLrKnflyxFW0TpwkVhGQKt6myBWWarccixbmMMZoLiPDUardA5lpbwuIrFln+ApbeSsA4Cj3p7b5LJ4IRHk8PkJyNFHKRK2O5RCwipFURzOH0z7g43dXWI0d3vWOL4C/9xm0i0OMd14Wy6FtwX2PsFlhXG0SzMPGUeHaAa5rQMMGThlj34sWDwgjVuYxjoCL+DlEAM6bmTy38hQG1kPA8WbAyWbEWT8iBEbjHYAGe61H6wiLhrDXSghxA/F/0foMtFnC9Wfiq1mdJK1aGSBZxte0cLO5CJT4N7XioOV2JpE64wZucwJan4KHHnx6Hxw+jWb/OeCZL8PGz9C88lFgeT/NFZ/eF+YGEQw0M7kbNmoJSLBOzZDVitVSNnAeFNYCBXpjBWsggAqN5P8ZRHCMPbhfg5enycJCCGAjJHRu1FpMgs3sS0y/oiRwggoIVwrIphPBgmitOC9/q3U4buLcAxwcuN1DmB0mqFh9KO7sDqjbRzNuwMtjCdY4PU5WCG/0GfoEP1I3F19M006O/YHps7DV20N4cGDxeZCDCwN4s0TXLdC2e5jN5sYRnCEbZdJKpS8k+h+qnIJQMf8Qs9rjGVtx8yowFBaq8fwQfRybMffR6KM10IeAflQNXAWJ/LTbVAOfNQ77nc/QDRMaD1i0XoSUaPONI1zrCKdDwJ21fGytJ4wBuL8JONq7AX92J85vAHVz8DjCNS24DRhX8qHzGBAibMVjAIcglkrU6imMkRG1cp0I/3DTJphHoBdhOA5A6whDAEYWwfHC8RqvrXp4Itza6/DkwQxPXetwfeax1xDc5hToN8BaImrc8h74zgsY77yE/tUXMC7PEPoBw3KdBB6PAa5r0MxncG2D/nRZ7JvdOER7/TpothCtVZmfOor7jTCnYYPh+Y8DH/0IuvkextkCPI5i2Qw9eL2Uc5pO4CBA5sV78LApfQKR5F6zPE8aPusa8GxfftfQZw7AsNkORbbWR8y9SDCdFRxhBIZNXuQK3fU9OEI+IvRNNWnnpwWwc1ngIMOYSUjo9e15NlQ3BBDiWJglTLnbQ2gX4GaWgiHGayLM3foY/vhFuIMR/sYKvDrFeHwX4d6r2Yq0lmW/udKMuUcoz+MtobeF8AAigwsDMBKIzyTEb30K5xp43+YY9caLczFaHuoTucwyqBslqYObiFKMfZ1vIcKHEg/niOlvRk4WhkIzZ/2I1RDQhyhAxpAgqtZR6tcBZMGDIWDWuHRtMs5kT2XnvyFCY+IrkHEsIBr7QevQOcJryxH9zAOtzJO/9Q7wi58UWGC2kKKKYcS42oDHgLHPUUkagVNQraVSjMTyXQoZ5WaGDTVYDQKlrUbGcgh45WyD5+6v8dpyA+8IT12b46nDOQ5mMlanvqZxA7c6lmihMErOybABnIe/9Q64qJHOnE8Mn3vRSmn/GtzeoezbCAMa7r6G1av3wGNAd+tmXF9jaRFUWjj3G4zL0/JZ1ZKIWHy9D86L4IiaOc0WImSbOeBl/rldpNI6YXFdpnB9glRLyyTgJYe7ZvmPY7I8so8qJMEhlkG2FLJlYaKIwggEX1qNAMjN8/4ausKE4KjnzgYc6LoQkx2gIQY8NKD1Cdy4Abd7KdGUBgkHXraHWF7bhydRmObjEs295wD8NMaXn4uW3iIK52hJXpHlwcwIl69t9bakt4fwSLiTaJ4YAcK67L2hzsyosWmykyawsTKzGDqrUT5WsDh7L4jQ8ASBSwgmIUrGMhUzy65BII/NGIRJ9gEbEsujj1h/P4YkHPZaj9t7La7NGsybXA4CANaDHD9vCDPviiQxFyGsJpaOoDAgtK1YN1FjmjkUJbT3W4dNYMkg75fA6hjjy89hOFuhW+yD5vuJAbl2g4ABjZ/l+fAOrm3gmlY0VmUahomk8iRe4u+5mWF0LfohiIUY5349BKwHmYd543HYeey1Hi+drrEeGxwczeXZYpIbayhsGMDtDN63cI89C8X5NJSVOADDGmF5Kow3atY89FErD/C37qO5+TLC8R30d++iu+VB8/3snLW4vfp4LIZvyWjdysCoi9q8/q0O8maGEKOqUkZ+rBHGXpg19WdRSJicDOZScIxD3qa5FUZgZgGSfRvkPAhtXKNerJEILSG9R5csr/x88yxAo98EIRSKBAeNevPRysS24JAPSn6MQ3asq2Ds1ym3hKKlNNu/Bd86tGEDwKFvFhhvvw/uJ38gBULAl+O/Svqs5fF2oBi2V3zY5+CyialpjH6MARdmJglQvtuLH3SEDKqaVxqrTsMGWOfkLQDZbDfYcz6Z4MmhaTosmjmu7c9x0Ads5k2ybDYj4+5qwGvLHnutx+P7LZ52x/oAKbMb4wY0SDZvykuYH4KbFmF+BLTzgqE4iKO3ieGy4+wAY2C0LNnLi9ZhL36MtBKznxb78EMP2rsGty/X5uYUftiAvJN/Ea6gtk2OSZotEoxBUZigmYlG7bs018G36MdsAfbRkX/Wj7iz7PHRV7I2f7wZcXuvxe29DiMzTvqARbNAd7CXa1xFAT5yQGhmcMM6MVJan4jW2regbj+9c4QBGHq4TpiS2z+EP7oFHkfMECOG+g3GOy8jnNzNOHpiSsaPARRCUywP0XwFc4/CQsNwvUcgly0xkxMi4bg5idCt7qfsbOI+Of+Tr6POXQlDITg4CgQVHIU10LRJMNBsngWBFRhGWBZUb3NOEkTt/ilL47wS4/rtAKBoCbGpUsC+g9ucYrY5g1sfI3QHaPdugNYnGI7vgKNvrtF7r06BMM/Rc1dAvIPH/KtCbw/hwSxJahHntA105KfBYMMomhUM/qwQwrAG+7XgzP1yu8quequBrOkB2YmJuMAVwx0H+JNXwL5F/9jnwh+/JNfT0GIOaMKAa80M3DZYhxTkhUVDmLcOp5sRq4FxvH8d185ejEJrU2LZMXKIhx5856U03Oaxp+SDjXH6/MpzoLZFM9/HePAYms0ZfMwS9kHGQptTuNUxhhc/ifWnfw5nL76C2fXDgiHy0BeRVikMd74nMMF8TwRI2+X59W20NubCJKOQZlb4LwuO4/WA482If/rR1/AzLx7jYN7gS5+5nqyPWSM9Q0YinGxCCi4Qf49HYKDzDXxgsJ9JxNlIUuqi6UC+Ew0+RvnoOmB0Mr/egRZtWWzRefijW6C2RTg9LrbX/pACkorzQN08WbtBmaDJ4+C69Ac5gAnczGUcCsnVpVVS2XgTohsdzElwjGMSHOn7MN8GJSHX5W8hrfkd2rqZm+z83hYqSVBSqf1f2AJYoTMrDDVfZuwRZjHiqZ2D+yX8vc+Af/qfIRyLn46aDs2BKDRynbFI/HzDxPxZ4fFWD+BKiLkIs+QaT9btKaplzMluQDRto6Y49qDB5w9btaN60ScrQ5yY1K+SZtS8+nH0j78fzasfRzi5C/Ie7fJ+EWmSktKGTXIud9duIswPEWaHmHd7WLRzvLZ0mMXaK9Sfge6/jHB6XxKwNis5f+gRTu9jOFshbGyJmx8EAIRxFIfxagPfNth/6jHM3v/lcIfXc2JczNcIJ3fR33k5ZouP8G0rvo07L4POhGmG5WmKuHLdXGpYNZ0wn/keXIS4MjTocmXd2T64XaCnJuZySH7Hegw47QM+/Jn7+Ogrp/iZF4/x6skGi87j6Rt7eOfRHLf2OtxciKXy/MkGM++xaGVONxF/7rzDoiEsWhdb6XJMHOzg5zO4sReGPNsXZrtZSpRPGOSdjFLPaYs5Qqww55wIglV2PicnMzLW77q5aPOzBShGa6kfo8g+b+dlyZK8WA30tElFKItSHLq/OCds+XW2yITSUswtQdOB2lm2xL3fKsFT3ydVCo7bkuDS78JWtNXM+/p6qoAB+VyFtgBJ6lErKiZ3Aojh10vJV9mscPYj3xtPddHynU9bSVfE8BmfFR5vD+FBlPFMxVyrKJAt0kxU6/hUq8QFUBsXLcTBy688JxnTYZR4cQDh+G4yg8M4JogidHM06zOgm8MdXJfkpqGX5CYLHQy9mPfeg+Z7CPdfA7/yGRnLeoWDYYP9oU9Z3JuhRxh6jKtNJSSQIp6cQg8RUgqbAc57+K5Fu7/AsFrj3s89h/EjH4Ofd9h7/EbK0wBQhN4Oq3VyivenS/hOtDjXNvDzDm6+B3d4A27/EO7gOtz+tZ3aNbcz8OwQPTVYj4zTfsRyCFgPnMJxP353iY++fIqXjtdYdA3e+3iH9z1xgHddX+Bo3mLmHVbRD7IaAu7xEGteAY4IrSfstR5D8FiPEhhgw5flMR08zdE2Imjc7FD8ZBEi0pBWrRDMm2US8IXmqvkMuuZ0nam2PSU4fBf/xQCOWmjYnCENsU1Z1UN2hPNEhV61NMKYfDd2rSXfhkZ1RTiKunlKgOSmLYVbtIgKhm8sHi3Xk62dAcCsKPpZPOOUFZOgN84W+0SlBo7WB6vlPm6AzQqrj/wAwmYlVm/0H9HetdI3Uzvsr4KYJfT7X2F6ewgP5+HmMYSxiCTxGcu1ES/mQwNQFPMrHJzjBsOLn8Qr//AfYnHrCLPbN8XBmhKOfCrNYWPKQ9TG3d41uWZT4qyKOafyCs6D+g1osZ8sJWrbZBFpmCvCCBdGNMAkowi9aZTjXXJcw4nDl5pWoonuvCzHmFyNlOynU9SLJh42YrGosJLw1g6Lx2+AZgv4G4/B33wHwmwfYzPPQQjOJcZn/RubPloag/y7s8wM+fZeh6OnG8wanwQCIMxf+7MAOQcGyFFojoDWOYTobA9MKeucAqfIMxdzYjZEWI1jhLpm0hTLt+BGfEikdajaGWizjD6AMc11dj4b+CrBdBNWRCBpDxmrxop235bH1KRrtBASYfrYuK7tGM8rKZ8c9c0sVpw1vhaXa03VWdlkrQAS5YoBpGq3Wo4n+hStn6K2NvLchHxtO21ah4sD4ABmCYDhbgGEFlidwh1eB8Vvz+0dSvScfpvDpsgzKdCGN0qfha3eHsKDmhb0+LMSPQNkjHSCiiJv0Uzm9csY770KXp2K1tK2OP3RD+OFH/gJ8Mg4fFb6sVDbpoijECEcZe5u7zCbynEM4ey+JCftGyETrQiazUVY7F/LyYUp/j4ygFFDGv1WfHr62yzgJMQUCovk9g7hjm6J0PMezZPvQVidIhzfzRFEMbZ/XJ5hXG0wbvoE16g1ohZK6Aes7xxjBojG9+TnYDx6CtyYGkhAmdzlfMqx2YyMAHGKP3+yxkHXYN44HHQegT1WMWwZQGFZzBuXwpJbn3Ow69a5IzNcMLXLQBhJ83A4naMJlo0Tq6RzHm3ToG0XoGYpQqRfg3wHzA/homMd61MJ0BjEokwa/jiKQB76okQLtZER6ppUrd7CQmbOKBhYSNepqWCwRaoM2fXhfISCQhFRJePJPhhuZyI0bHSXOqUtzKRj1H/OgV2XGH8S51Uxx53dOXWcHAAX4bZqH+L4U98UA4sxOdDhbQyvfq/Ap4c3ZPt6Bcz35VuNARy2ACNNoRCvkz4rPN4CIqIvA/B/AzCH1KH/Pcz8AyQZdt8C4NcAOAPwW5n5w5e5pmDIUVOJ0R72w4s7cshmrAjbf+bjOP3pn8JwtsL6zgn6syV4DPDzGQ6ffQKz6wforu2nxDfu+wyDaQ2gVkI9aVOWuwAAWksoqVgPYxYIWsgtYuWJkibrtmERS+kjyNqraL4ZOuHNCrw8FetidQYeehEk+4dw124Cjz0liVX3XpXEqr6Ha2N02RgwREum3S+jiVzboLu2D9fNhWm9+HF4cljdeHfsje5if+0ObSsNg5gIwyBWx8fvrnDWj2i9w5MHs9QRsQ8CR62H/FG2JrelD4x+M8IZK6KNiSwuwlKyL28DtoN6HCiGYwNwjDAyPOdtowO6dk/yg8gBG0jU06iC0AGxZhM1LQgCmajfiDcr6WuyOgWtzuD2D0F7R/KeqWaucXDktzpXSn94H8vnROZKZRFH7dlN6EthNTpQKKEbtTa4XQhE5bOTnjVE2FgNRdKhFXy+m64RZUNuVQDtsDpSKR8bjWhL/KiwIBOYEgBoteBYqmX2/i/D8NzPofvSXyKh2nc+I/XYXvikRMY5D3dwHc073yMQ4lVV1f2sz+Mtszz+TwD+C2b+O0T0a+LfvwzAr4b0LX8fgF8A4M/Fn+fSeP81nPztb8e4EX/A2A8ImwHjaoNhtca42sB1DeY3j9DuC8O7/7HncfrSMTanPZwjNIsG8xsLdId78PMZ2r05moXATZv7p6DTJXzbFv4Bxf4BAL18vApT2aQqjnHprpsD0V8ymXCGDLtxkfXrsiVSh1lqpEwUmBwT1ZonnhFNM4bYpozldobAWlp7mYQZzffhYiQVrZdwbYM27Em9KoX+TAQRmg40m6N54l0Yrr0DYe9GsirWQ8D9TcCyD1iPI8YgloL6KlrncGPRIjDjeD3iZDMkRg8ArXdoHSWhMjIkYTLWGvMEzBqH1jm0vhQWKkCkZHv8SSj+TtNq82+0NA0AsWgCOt/Ct4sIv7QSpTWsQJS1ePGFbLIlat+Tk9LgHJP1UtuAfiWM0XciHFSYREHAXiKUyA3gqPxwyJYIm8iqpK1X4bo5TNYVcBJHP0Thd6nyLAqYyefqaFPtBriSzHXtuCQOWWc2XyP1Pa/qzFknepE5HwMG2DXgxZFEXj39BehuPw1+6ZMYoyXN0Tcp4cmSdb759EdTBYQrIf6s5fFWCQ8GcC3+fgTgM/H3rwfwF2M/8+8noutE9CQzP3/uxQJjWK3Rn0q0kUYWjas1htUGw7IHB8bylWM47xDGgLEP4Ji+3a8GrO9vcPriGVx3B+28wfzGHPMb++gOxepwXYMeAgWV5S06tPsL+HkHv9jLpnH8KdqeESZVolXCYE12L8eFj2GTI7JsuXDnU1jsZDCAlsxwHm6xL9hvNxeHdnRcMweQb+CCaMpQvD6M4G6emJ9b7JdJbeoEjjDAOL8GtHME8hjHkJpXzRuChsycBcnZUGe3kjB7wtG8TRbGyJxKtNhjW++k6rF3khAZhUbrKTGhdN0JoQEgbQMy7Getk7K0jJSecb4TB7UstGw99LG8t/OgQep+aVFBZ+dLI/liopsLAcxBhILbiBBWeAcG97cQEQAtK5/gGyYwjAKimr6ea7altUau7NJXCQvblGynEKj+2JUot6tBWZp/IumSme4D00rAp3eCgAR7MkllXMRqu+7sXuwhcjdFHkqUWa5hFnrhB3WAyRsnRvis8HhL6D8E8PeI6E9BOMwvitufAvApc9yn47Yt4UFEHwTwQQB46ugA/ekKw+kK/ekS/ekK46pHGAM4MMbNiHGTi/iFwOCRMSyHJEicJ5Aj0EgY+4D1/Q3GPmB15xTNQiJrfNfELOpWLJgxFP6AVDXUCotYF2in4KiiYhAznZPvIgqPRMk/EpKVkfJbNCAgxuvTbAF37SbcwXVxaHf7KXqG+lXC3qnpIoAYAwcgVhIt9kHzWEk2hlqGmMwGE2IafFswEWUcjsTiaANh1rjCklBq3XZPcu8I6uVRvwSAJCha5wqIKkWDVtfZsj50Cm3Cp55bLzAIQwskjvQ46VmItHIykZN33PSpeyKAInM8O7ul7AZiDw64UTD8Kb+AfY4JX0eCX7zx4cXcnyxwkP0YVmBUvghOQhOpbYA+/3l9ZKxgmDpqSq6MUUhMdXvUygj6TjwBTYK7JMeDmxm4XUiSKgeE9Uq+r6aV+maIvr8Q4JoWLr66/nQpUYpXVFKEmaUEzr/C9NCEBxF9N4B3TOz6RgBfA+APMfNfIaLfCOAvAPjaB7k+M38IwIcA4Eve+Riv755gXK0RNkNhmoaRk4WhQkOEyQgO0i2w22/RLBrs3Vqg3Z+Jj6NtpEKsy07isR+SsOAxAC1SSQ71s1CDaCFEh51G3+wQHLtLWsSwYQ07Vn+JCpzT+whDn8bjZzNgsQ937Rb80S24azfB3QKh3cOgzF4x/AiPgByCa4C9GwLFAEj9LjZLMx6H0O5JjkZM8hsiRLUZGJv1kJzh60Ec4ffWA/oxJEd36xzmcbUp81YhAEhJ+j6EWNOLCwhqHgWPN1ZC1mCzZaFkhYW1LHaxZ7U4LONiWWMi6MiLzCYSJtx0Uv67mZe5F2GEs9YCB8kI15Bw5+XY9QCiZcnEtXQOgKLrn3Wu28gllmZQiMmmKYzVebCfZYFhI59i3kUq96+K/w4roRAi1f66y2PePj3Htgq0vg9LDrk2nCfkitXDWkrpD5sUtehieX0mJ0EqYRSn+2IfcB4eQDg9xrg8S1BVM59J9GB/RRYI89VFbv1LSg9NeDDzTmFARH8RwB+Mf/5lAP9j/P05AM+YQ5+O284lcoTZ9QOEjfgzJLx0neCrYdlj7CPTd1U44MjwnUe332J2bYHF4zcwu36A2fVD+Llg0mHok1UzrDZw3sEbuMp18xzZ0XQpSYlszH9VE4i8zw5OYBuLdV4EEQDejNIb4myJmpq9hZQTj/kW/ugWsH9Dmus0M0lCS7kXbe5X1cxAs8Miqkcz1qlfAZ10i+NWssGTExWIQQcNlgPjeDOmviBjQLQsGHutA9rsl1CqFT89B0AMpyXMfOm7AEroCcgwBzMQiLcsDD3XG8FwEdUMLTBiaX6Wd8kh+wiiEOaxlfnQbP/K95Ayv52BocIIDtHKS1Zpk7XrNJ4YKsvSErYQSvG68SI53NY65OO+XWQFQtGkzDx/0ePenmvgJj2/PgbYFtjJG1MLKhLlzpO0TlAhxwobsljgGsGnfg/qFqDFErS8D16eyv3DKKV42jYltCqczVdkeQCf9Xm8VbDVZwD8UgDfA+BXAPiZuP27APw+IvpLEEf5vYv8HQDguxb777gl1sGmzzjnGAofyBCZ/7gZo88j+z0AYH1/ibEfsL57jHbvbvJ1uFi/yXUNFreO0OxLZzItqmcdyCmr3eaX2CQlm2NiNZdY8pobFGGecNHycF4ivmJ2ut7DH90C/f/bO9tY246yjv/+a59zbm/vvbzUW7GC2hZBWwwqLeZGsfqBIJBI61uKfgDUYIL6yfhSUz8Q+aKiMTYQfElIwJjbBBL0pgG0aKSKrYULfU2pbWmNraUFjUDr9d6etR8/zMzas9aetffa52XvfU+fX7Jz1p41a+bZc2bNM/PMMzNHXsDo2Ivg2PGgLDYPt1Yu22iLMZocRtXqHgoILrJVtQkVaPNY09iOpOZAqXTAVW1Q18aRTXF4Y4NzY2O7Ns7F7UXO1WH0sDkKbrBpy5XaLCwYnuqdamoupGtygunGvYmfmbjUen6aGdspNVvyV7Hnmza+rLLNI0MC8Vz7NMFtG1Bvw3jUKGCzMWjSUDUKIVvX0IRXHZfWvoZ/XGd2thGmaBItucYm81Q22qjDXjDZFv9t2bqKoMk2uw6jlkm8kkLJy3iqqTYr/l/Sa1jHDsGGwbgKZk1tXoCil9W4s2CxOvcs2ng2mKgOXYjOfIPxxiZ2OLjUj8ZjNtJ6qu2eFfc7wdd5rEx5vBP4E0kbwP8R5y6AjxPcdB8muOr+/JDEqguPcuS1PzIxBUHbgymdfJeOr4wurPXZs805DwDVaNSYoUYXbLX2JQojiLj5X5rT6OyKWvRrzyc+c9v1rIoXN9IDWgaZdO5DdeQYdujoRFFsbFHn+ySpau0OTGbLLtmpU9j2ODUGcc6CsGPw1qgK7rYx3kgwGil6V4VV4mlvKgkObYjDG5MyGJMajNjId5x7mPql0yON7pxFUg4qjCzak+fzaZmt4t/J7sRx9GNkuzRni+NSIqON5rtFl/DWSAFC/QBoD0Jb9WWq8c/rUpU1nKV1FJ35jMZEle0MPTaas+yTUp+U66QAS4qkq/SbEzpjiSg2/N3J9kb8WM51zz0JKrPQ0bHgCFOP0/qcikqTWjRSOLDN4lkf4yMX0d1dmPGY6rkzzW4Bac+vvcGVx0qUh5n9M3BVIdyAX1k4vY0LqL/1ivAlneuRuTMqm+icWqCUSOtCui98E7nwoqb883hd76dxxyumleTEy0XpxZgxeZpcLevMjz7Y1YMdtxmxjKpGcaTRRpfc6wWCm+o5g+263Zs8s20U+o/N8bXhOvztMxF1lzh2O//d9Rh9SqGVRmGiOz+vfZaZKr+VH+BVmidRVL5Ti/TSCIROPVIFVk8UTXc1dYqTx4fp3Wa7dSAfYcR4LUXTCeu60Ka6kRTGLPpGIHmZpzjp1zXzKD0Y7Z0BcrlCnqGca8J6nrSWZ1QFpbJZ0ZxNI4m6tqBMVIHSvIzBaIutUcVoHEYZ9daF6MIXTyvyXRKqxN6ZwM5HDsQK822N+J/RC9mIp+htiObAmDAhWPgnJ/tptDMXySt7mmROlF7w1rM9FavbWOS9yU6cqes87bwhStf1ORpXzujumB9/C+2XPdzvNhBqVF23AW5PmNJKr4+hjXtptFAcVfQ8P4S+/LsKA9pKI3zvzDfE62JnI1MszTxRtvitLdR0nSrWjY4pa3rH53a6ivMGXYYeQVGaw8jnOUqKpbvSv6+OQXsUmd+fdEjiOC6u7anHFjzfROu97J7OKcG5umZUBVMsTIpxnvfYQri31cFQHpWCeWVs0QMIgC2otqZ6xym+RpNeZ9dWrjRq6ZydDBR7fOGAo7D9xvZ4cvxsIvWYSvlB1sOFKaXTOiekCayaicfk+ppMUkb01tyumxeqeaxj2qlo9+by8mnidcraOnd2eh7OlPLohPeNPIbojKqvM5DSyH9fU+7RxXZcGCk039udidb26MVMOuaoqcwLHYmeeY++g5NayiZzvTVjalI6meTS88mzKTdn5g1sqj/zJsWb39hTF9L/o6REuqORrmkyyZQyyOey1Lw/YlNMjSjTvFWSO9T1HfQ6Spiv8zgQysMMnstqdKnxGxXqzNjCwxPnvXwiUWEiNBVRwbzRvJw1UI+bfCuVzQMpP2tksUIj2XE8TQ1CloaNrTnpMO911T0teZNH8z2mRdlElDcyQ01B89jJo10lMEcnhDilVjPRjBI6pqb8XvN9ThpNhhPPq7B5X4/yGWriSCPczt+mE9GENzWimbhvbcMT/1r0YEtmpWS+auqOdepWNgleUhx9VDD3nzxZhNl3f/aoMz1b51/ic7W1n521nmQvMHBX3VULsFeklc3TK4jj90LLM+VV0tju4wvV9JgK+dHOp0SpsR1bJ704cVkPrOPpWSU5o5lpbLBRpZczG9YnWXrSKymOEn0rs9tp7e5d7SbZ3etpcAPcl35fww7TJsq+51KU7mT2LBPmnLSK+Q5JrzDXEWTL13JMz03NWv09JtQnM4sdpvTM5J/Tt7l5SblXLD5S6UtnFuM8n5jw0HdqR7i31cFQHlUljh2artJTjc9Ur7HzMi/QPS5uDNekM6whaSfY36gsKuNMu3orXkEBzGv9s957+7ldTh4u8vw+9SYbOptbmjY734eVb4i78+Fa6WeW5gdCeB5gvSOFUkcol7AZoe9C7nkj1FJHrvRMKZl5Jsnl4crjQCgPmbUbvQEN0Y4auz5PrZkZ7UCRLMhMRQa95aFSQzLwd3XznCvDPBbc7XRoozxLz8zyDmqeH5RJnuaOUthzSg00BK+lpeS/QNwhCmFRnTG3E7RbfMIc7Zn3wQqR9BXg3/cp+ePAV/cp7Z3iMg3DZRrOOsq1XzJ9h5ldvJsEJH2SIN8Qvmpmb9xNfuvIgVAe+4mkz5nZ1auWI8dlGobLNJx1lGsdZXImLGkQ6ziO4xwkXHk4juM4C+PKYz5/vmoBCrhMw3CZhrOOcq2jTE7E5zwcx3GchfGRh+M4jrMwrjwcx3GchXHlEZH0M5LulzSWdHUWfqmkM5Luip8/ze5dJeleSQ9Lukl9K7P2WKZ477djvg9K+rEs/I0x7GFJN+ylPD0yvlvSE1n5vHmejMtg2eUwQ47HYh25S9LnYthFkm6V9FD8++J9luGDkp6WdF8WVpRBgZtiud0j6TVLlGkt65LTg8VDgp7vH+AK4LsIpxtenYVfCtzX88ydwAnCgtpPAG9akkxXAncDh4DLgEcIWw6N4vXlwFaMc+U+l9u7gV8vhBdlXNL/cunlMEOWx4DjnbA/AG6I1zcAv7/PMlwDvCavx30yEA5j+0Ss0yeAf12iTGtXl/zT//GRR8TMHjCzB4fGl3QJ8AIzu8NCDf8wcN2SZLoWuNnMzprZo4STF38gfh42sy+Z2Tng5hh3FfTJuAzWqRxKXAt8KF5/iD2uN13M7DbgvwfKcC3wYQvcAbwo1vVlyNTHKuuS04Mrj2FcJukLkj4t6Ydj2EuBx7M4j8ewZfBS4D8KefeF7ze/Gk0cH8xMMKuSZdV5dzHg7ySdlpSOW36JmT0Zr78MvGQFcvXJsOqyW7e65PRwIDZGHIqkTwHfUrh1o5n9Tc9jTwLfbmb/Jekq4K8lvWrFMi2VWTICHwDeQ2gk3wP8EfALy5Nu7XmdmT0h6ZuBWyV9Mb9pZiaVtqhcHusgQ8Tr0nnE80p5mNnrd/DMWeBsvD4t6RHglcATwMuyqC+LYfsuU8zn23ry7gvfMUNllPQXwC0DZNxvVpl3CzN7Iv59WtLHCOaWpyRdYmZPRpPQ0ysQrU+GlZWdmT2VrteoLjk9uNlqDpIulsKB4JIuB14BfCkO+b8u6UT0snobsKyRwingrZIOSbosynQn8FngFZIuk7QFvDXG3Tc69vCfAJL3TJ+My2Dp5VBC0hFJx9I18AZC+ZwC3h6jvZ3l1ZucPhlOAW+LXlcngK9l5q19ZU3rktPHqmfs1+VDqKyPE0YZTwF/G8N/CrgfuAv4PPDj2TNXEyr4I8D7iCv291umeO/GmO+DZF5eBG+Zf4v3blxCuf0lcC9wD+Elv2SejEv6fy61HHpkuJzgJXR3rEM3xvBvAv4eeAj4FHDRPstxkmB+fS7Wp1/sk4HgZfX+WG73knn5LUGmtaxL/il/fHsSx3EcZ2HcbOU4juMsjCsPx3EcZ2FceTiO4zgL48rDcRzHWRhXHo7jOM7CuPJwVoqkZ/YhzbeknXQlXSfpyh2k8Y/dnYwdx5ngysM5cJjZKTP7vfj1OsKurI7j7CGuPJy1IK5ofq+k++L5F9fH8B+No4CPSvqipL+KK/qR9OYYdjqeQXFLDH+HpPdJ+kHgLcB74/kQL89HFJKOS3osXh+WdLOkB+I2Iocz2d4g6XZJn5f0EUlHl1s6jrN+PK/2tnLWmp8Evg/4XuA48FlJt8V73w+8CvhP4DPADykcrPRnwDVm9qikk90EzexfJJ0CbjGzjwKo/7yudwH/a2ZXSHo1YTcBJB0Hfgd4vZk9K+m3gF8DfncPfrPjnLe48nDWhdcBJ82sJmza92ngtcDXgTvN7HEASXcRDuh6hrDH2KPx+ZPAL3UTXYBrgJsAzOweSffE8BMEs9dnouLZAm7fRT6OcyBw5eGcD5zNrmt2V2+3mZhrLxgQX8CtZvazu8jTcQ4cPufhrAv/BFwvaSTpYsJIYNbOqQ8Cl0u6NH6/vifeN4Bj2ffHgKvi9U9n4bcBPwcg6XuAV8fwOwhmsu+M945IeuWQH+Q4BxlXHs668DHCbqp3A/8A/KaZfbkvspmdAX4Z+KSk0wQl8bVC1JuB34gnQb4c+EPgXZK+QJhbSXwAOCrpAcJ8xumYz1eAdwAnoynrduC7d/NDHecg4LvqOuctko6a2TPR++r9wENm9serlstxng/4yMM5n3lnnEC/H3ghwfvKcZwl4CMPx3EcZ2F85OE4juMsjCsPx3EcZ2FceTiO4zgL48rDcRzHWRhXHo7jOM7C/D8ZMTvsi0DGWAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
     "\"\"\"Load Weather data into an xarray dataset\"\"\"\n",
     "wa_data = xr.open_mfdataset(\"/data1/raw_data/SEAS5_anomalies/S5_anomaly_201[0-6]*.nc\")\n",
+    "\n",
     "# Rotate longitude coordinates\n",
     "wa_data = wa_data.assign_coords(\n",
     "    longitude=(((wa_data.longitude + 180) % 360) - 180)\n",
     ").sortby(\"longitude\")\n",
-    "# Interpolate to match load resolution\n",
-    "wa_data = wa_data.interp(\n",
-    "    coords={\n",
-    "        \"latitude\": load_data.latitude.values,\n",
-    "        \"longitude\": load_data.longitude.values,\n",
-    "    },\n",
-    "    method=\"linear\",\n",
-    ")  # Wikilimo used default method ('linear')\n",
+    "\n",
     "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
     "# Therefore here we remove Jan-Feb-Mar 2016.\n",
     "wa_data = wa_data.loc[dict(time=slice(\"2010-04-01\", \"2016-12-31\"))]\n",
-    "wa_data.d2m[0].plot()"
+    "wa_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "5a1d8d43",
+   "execution_count": 33,
+   "id": "61da9abf",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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07MIHoE1Zlhqstiq1h7YdIGk22C712uPm8dC2A6x9/mBYTRV9E32xo5vZLY00BUks6g1f39hE/8Ew2fQf2IUU0H+gi9qZYa+sWS2N9HV1hAnAUhCVIDTQFyaK6LUqEe2PqrAsVjvY5qFkP9bbhfX1YqkkpFLUvPTy8KnJJPSO3u1XQUDyiTtIdhyk/5avDiaf+ovfM+y4xI6nEGCxWpIb7yF2/PnUnnMFAL2/vA7r64UghiVTU3fBpCpr8wCQ1MHQuI5aoAboMrOSfAP15FFl0omjOXGIExeEHyz3b93PzIYakgaXnLCYbzywlYFUivefs7ySoVaNzMRRykTykqWHz9GZLlmcvqhtcF9M0NPbS0NTC/0HdkE6HkuBpaiddQT9+8MG94E9W6iZt2zocYn08KugtwNSCYL+nvCxtMxG875uBCheg6VS9D39KEHTDIjaOmqip8RPe82Yry849jz677sZEgM0XPI+en953bDHU888SKoufC2x5WeS2nw/jEgO9a95F323f5tUdwfxqZo4AFDVJUYzGyzKShLhVE+rcj9jYjx5VKn0N1IIe2Ddv3U/e7r6+e6j26mPBwz0T2ganCljZKIoRwkkU2bSSIsFoqH+8K7YmX/TbB0mlBygtnUOAIkXdgOEiQMIjj4bCKuWMqujEut+GT45lQxLF4D1do17cF66BAFhIhgpvvikwR5d2fTfcyMEMWIz55HqOoTiNVNyipJwJcHqSh6Zotk5fiLpU5RoctnqffVumFVHziKZMuriAQPJFI0103shmmoWD0R/+z5geMKAaGxGhuTz6wdvpxMHhCXQ+BHHYf09WP9Q43rs+PPDUkUQI/ncI8RPew3x016D6ptIHthLxzNbSRwqzhQ47dd9ctj9dAJDIrF9AxarJTj2vMHH+27/dvhwXT1BcxtB62yC1tmofiihpxeRmvQkgtp4Xlv5QtIfZGxvlHQt0Fuq63nJowpktnNkGtgbdtdOjyT+/ZMX8q2Ht5E0SCaNf7p7M3/z8mPKGqsbW0N9PWQpecBQtWRi3S+Jn/YaYktOGfVcsePPP2xfcMyqwa64w/at+w3J/sRhx49XrobubIMRE+t+SWzuomHVY4l1v8R6uweTR99dN6DGqdIBpPqqrYA3ZNxOAFsIq65KwpNHhQzs3TY0vURGffbAQ2tQECN+1uuzPi8mEYuLq85awmdue5pP/WIjf/fa48sRsiuifNogRpOtS23jFR+mUkuWZXs98dNew8ADPxm+b/aCw46blASKVV3p/5tmdm/mDknnkWN+wIny5FEh6cSRfH49RCOjU5vvJ2hoGqy7Hjl3EcBVZy0B4F/vCfv2J1PG+29+nK9ccWo5wnZlkNjxFPFFJ1Q6jKJI9/YCiJVo2phKEKq63lbAvxNOLjvWvqLw5FFBiZ2bEBBfdAKp34VfGFRbTyyjHjmXWCBq4wF/++rjeP/Nj5c4UldOUyVxjJSrND0pCYIqqbaSdA5wLjBX0ocyHpoBlKx4VB2vfpqKL1wxWOcdZEkYA7ufy/nc2lhAbTzgk7c+xVeuOJW3/c/Dwx6/+Ou/LW6wriwSO54aHDHuqlsxpmQvklqgmbAw0JKxHQLeWKqLesmjymRLItm8Z9UyvnTvs8ydUccHf7ye/37r8Cknfvbn5wJw3rV3cu/HLih6nK5EFKBUgr5D+6mbEQ5GHDl5IgxNyxL0hj2rqn2utORzj4x76pNqJImgpjo+PqOR5XdL+i8zK3z943GqjlfvsqqZn30Q4K0bd5M0aKuvoaM/wcK2et5/8+N09oU9bf7rLUP/pLkSx+994Vf85qOvLH7QbkLSvbFG+8fc097FvNYmDnZ201blSSNtKiUOIJqSveoqbrol/RNwEjDY3c/MSvLtsepe/XSWb6kDIHNaneb6OLOba1k8s2FY4hiNJ47JY2SpY15reL+tuVJ9q1yxVhIsshuAjcBy4O8Iu+o+VKqLefKYhF53/Hxa6+PMqIsPTlHS2lgzxrOmrv79LwxO9+FcuQSxIK+tjGab2XXAgJndbWbvBEpWZ13x5CEpJulRSbdE95dLekDSZknfj1YZdJHn9nUAcO6yoaVqa4KAD51/NJ993Qlc+6tNlQqtIvoP7AoXRVLF38puGpHCQYL5bGU0EP3cKen1ks4ASra6XDX8x30QeCrj/heAL5rZMcAB4PAJdqapXe1hI+mGnYfYsPMQLXVhzfi7X3okX7t/CxB24b32V5umVRKpnbngsGlAnCupKpyeBPispFbgr4EPE65fXrJJxSqaPCQtBl5PtEh7NBPkBcAPo0OuBy6vSHBVaEFrEw01ARJ09CdIZszZ/p5VywCY11zHx165go+9cvT1qa++6bESRlo+njRcpVRTySNaw3yFmbWb2RNm9kozO8vM1pTqmpXubfVvwEcI+yQDzAYOmll6gp7twKIsz0PS1cDVAEuXHj4Seyrq37ed5sbZJFNwsHeAmpi4fdNeBpLh9CavO37+4LHffDDssZdr7Y/Vbzq95PE6N1VJIqii6UmiNczfDJRt5smKJQ9JlwB7zGytpFcU+nwzWw2sBli5cqWNcfiUUDtnMT1dPfQlU9RlvHHPXTGXe559ERiaviSdNG5ctwOAK09bxHcf3c7+3gHauwe45lXHljl656aWKuyqe6+krwDfZ/ga5o/kfsr4VbLkcR5wqaSLCfskzwC+BLRJikelj8XAjgrGWBHtXeEU3K1Nhy+vWp/soTaoHVw7OxbAYzsO0lIX4/6t+wdXuQNY8+QurjxtEWue3MWaJ3cB8P5zlvO5O35Xhlfh3BRWhSsJAqdHPz+Tsc8oUY+riiUPM/s48HGAqOTxYTP7Y0k/IBxSfyNwFfDTSsVYKdmSRlrdjFk0d3bTkzAgIBYwuM75YzsOAuFyqQCXnriAe559cXDtj0tPDNsHvNTh3ERV35TsZlbWwVuVbvPI5qPAjZI+CzwKXDfG8dNOLBCxIPzjHTdvBpv3dmA2tKpd5nKpNTHRWDN9x4A4VwoKVO6eVGOSNB/4B+AIM3udpBOBc6KxH0VXFa/ezO4C7opuPwucXcl4ql1LYwMtjfDsvg62vtiJZWnx2bj7EABt9XG6oiVrb9+0F4ALV8wtW6zOTVXVVvIA/gv4NnBNdP93hO0fJUkeVffqXah/3/Yxj6kNRExQFxcr5rUMe6yxJhiszjprSRtnLWmjLhZ44nCuGCQUxPLaymiOmd0EpACiduNkqS5WFSUPd7jaOYvHPGbxrOZRH9/T3kVjzdD3g7lNXn3lXNGUNzHko0vSbMJGciStAoqzoH0WnjwmmZ7e3nCN7DE0pZNGMqzTSk9r4pwrBkERqq0kLQG+A8wn/NBfbWZfGufpPgSsAY6WdC8wF1/Pw+Wrp7eXZMqoi4mehFEfTb9bE4jlc1rGeLZzLi/FW8M8Afy1mT0iqQVYK+k2M3uy0BNF53g5cFwYIU+b2cAYTxs3Tx6TzMhSR19nO3XNrfR1dVDXFCaHWCCUStISh96U2NXeRW3mHO7OuYmRID7xOVvNbCewM7rdIekpwlk1Ck4ekuqB9wK/R1iKuUfS182sd8KBZuHJY5Kra24Nf0aJQ6kk9Y1Ng8lkoLuHWoNEaloMwneuLFTYOI85kjLXiV4dzZAx/JzSMuAM4IFxhvUdoAP49+j+W4D/Bv5wnOcblSePKaa+MVwoKJ1MWhpzDzh0zo2TKKTBfJ+ZrRztAEnNwM3AX5rZoXFGdbKZnZhx/1eSCi7B5Mu76k4iyS2Pjet5vT099Pb0FDcY56Y1hckjn22sM0k1hInjBjP70QSCeiTqYZU+70uBh0c5fkK85DGZWGpcTwt6wulKBjp2UzNvWREDcm76KsYgwWgZiuuAp8zsXyd4urOA30raFt1fCjwtaT1gZnbqBM8/jCePSSS2PL/1yQ+TSkEQeOJwrlgUFKXBnHCC2LcB6yU9Fu37hJn9bBznuqgYAeXLk8c0kM+AQ+dcAYrUVdfMfhOebeLMbKukmcASMj7bp+KU7M45N0kVZ5BgMUn6e+BPgGeIRpkzFadkd865Sauw3lbl8ibgaDPrL8fFPHk451zBVO5JD/PxBNAG7CnHxTx5TAOpzfdDKpxcMzj2vApH49wUUWXVVsDngUclPQH0pXea2aWluFgl1zDPOiGYpFmEc9AvA7YAbzKzA5WKcyoIjllFcuM9KAhIPfNguO9oXzLFuXFTgIrT26qYrge+AKwnmpa9lCqZOtMTgp0IrALeF6189THgDjNbAdwR3Xd5Gti7Lev+2PHnD5U6FJDcuq6MUTk3xYiw5JHPVj7dZvZlM/uVmd2d3kp1sYolDzPbme5CZmYdQHpCsMsIMyjRz8srEuAklF5AamDXMzmPCY4+G4v5uh5TUU9vL909JZkDz40ghGKxvLYyukfS5yWdI+nM9Faqi1VFm8eICcHmRzNNAuwirNbK9pyrgasBli5dWoYoK6v/4B400Euqrpm6GbOyHxSLY5ZCqdEXD4sdedqw++kPnMaGsdcJcZW3bX8nWw/2cf5Rsw97zP+GZVKdva3OiH6uytg3dbvqjpwQLBytHzIzk5R1OthoVsrVACtXrpzyU8b217ZAbQs1iZ5h068PoyDchtrKCrvGwT3Uts2bWKCu5PqTh7/d93d0096XJNkxwDFzfd2W0lPVJQ8ze2U5r1fR7gI5JgTbLWlh9PhCytTtrNo1NzbQkzAOUY8Geujs7mHb/k762/cNHmPxOrAUVlvYTLoDKSPesx9SiWKH7UrADBY0H95Y296X33LVHd0+SeaESShek9dWvpA0X9J1km6N7p8o6V2lul7FkscoE4KtAa6Kbl8F/LTcsVWruTMamTujkVRdC/t7ww+K/pom2rt62HGgC8ywmgZqZy4o6LytTQ3UzjqC2llHlCJsV2QtdQEr5g0vXXT0p2iujeVV6kimjL6u3MsS72rvYld714TjnPLSJf2xtvL5L+AXQPof+XfAX5bqYpUseaQnBLtA0mPRdjFwLfBqSZuAC6P7LkMK0VwT0BAPiAWiJ5EiFhAWo8v7ZnUVYAbb93cO3n+xo5vm2iDv6qq25sbs1Z6Eje6tdTH6ElO+JniCVI3JY46Z3UTUTdfMEkB+xdFxqFibxxgTgr2qnLFMNo0N9TRm3G+oj74t9orWuoB9B7roHkixYl4Lu9q7WNDaVLFYS+m5fR3D1mV/aNsBZtTHOG7ejApGVXoL25rYeXCoZCBgVktj7ifkcLCzGwiTCUBfVwdSQH1jE0d6u/uYrPq+qHVJmk00r1W0tkd7qS5Wda/ejU9/0pCgN2mYGSvmtfDcvo6sjauT1dfu3wLALU/tZsPOwxdbm1FfXQ2YpbSwbfgXgvFUMw1Ew8j6Og4C4eqTSgx1tkh3/XZZiGoseXyIsNr/aEn3Eg7C/kCpLubJY4pYOquZGbXhnzMeEw9tO8DyOS3EVJTZnqvG7Zv2ApA0oz9lPP5CO2ufPwhAZ1+qYqWO9Tvb+eaDW8t2vV3tXTy7r4On9xyiL2nYOL4jzJ3RSF08CDtapGV82CnRR/9B76+SnUB5buWzAXg5cC7wbuAkYGOpLpZX8pB0rKQ7ojlTkHSqpE+WKig3Pm3NjSxobRqsptq8N3ej6GRz47odLJoR1qWkzOgeSNIzMDQDw9rnD3LWkrZhz0ns3MTAQ2smdN2n9xzioW3DZ8fZ+mLnsPsd3T0c7ElwoGdgQtcqVGNNwHHzZtDZn6I3Mb7ZKPb3JKlvCHvnpZcqHhxkqgCrGbvn3sDu58Z17cnMAIvF89rK6D4zS5jZBjN7wswGgPtKdbF8Sx7/CXwcGAAws8eBK0sVlJu4lyydSUywaObUaO+48rRFLGtrYGZ9DfOaaqkJAmpjASkzUmZhh4ER0nXS+SSQxI6nhm5v30By6zoS2zcwsy7GS5bOHHysvSv8gO1v30fqmQdJPr8eAQOpFD+6t3wlj/SXhPU721kxr2VY28941Tc0UNfSRs2Cowd/Z0HnvjGeBTXzl0/42pOOqqfBXNICSWcBDZLOyBhd/gqg8MawPOWbFhvN7EENL4L5oIAqFwumVpVVXVzEJGpMxANhBk01MfoD46SF2aurYrMWkDq4t6DrxBefFM79Fatl3ojOBo3de2lQQLDlSVKpFGqbRyJlxCTu+8Tk6+dhWeq7EjueQkesgEQv8UUnVCCqSaJ6GsxfS7gI1GLgXxjqiHQI+ESpLppv8tgn6WiGWvHfCOwc/Smu0pKpiTWWP7svrPY6qgjfaovhuHkz2Lj7EHGFSSSRMgZSlrVaubunl8YFRwNHH1a87rvrBqy3C+sNG5nrTj2f+FErSW5dN1TNUNtIqqaBnQe7aKkNqB3oQn2d1MxbRnLLY+E/QhB+szzUn2JWYw1XfOsBbn7nS0v4GzjcrPo4Ow50jbuEqVHq5D1xjEZVkzzM7HrgeklXmNnN5bpuvsnjfYRTgRwvaQfwHPDWkkXliiLfqoz1O9s5ZWErAGue3AXApScuoDYQSWNCH07Fdvz8oRLGpj0dxAOyjm8YbY4n6++l/qKrAUg+eRdY2F6QqmsOH69twOL1KJVgZl1AfUMD/Qc7UHKA5JbHUKIXNc0ABaTqmujoS/HQjnYWthY2sr8YzIzFs5rH/fxsf9dU81xqW+dMJKxpodq66pYzcUCeycPMngUulNQEBNEsuG6KSCeOOzfvJZCoCcSdm/eyrK2eltpY1qqNajBylHW+6l8TztiQePTn4Y7GNgBqFhxNb08P9Q0NdPf0EkuG3VYPdnbTqAAL4giwuiZIJkEiVdfE7FiM+ljAV644daIvqWATSRy5eOLIU5Ulj3IbNXlI+lCO/QCMmFbETWJ3P7OPC46Zyy1P7SZlRmNNnGTJl5OpLEv0E2udjQF9h/ZTN2PWYM+jxoZ6+jr7SFh6EF0jAz3tYCk0kMBq6kjVtWA1jcyIxzhuTvE/xAtVTSXEKU/VNzGipDoz6xtrX7GMlTpbom0l8B7C9TYWAX8OlGyeeFceI7uc3vLUbgDS4woHUkZ/0gYbjSd7198dB7oGR2b3dbajhUeTamwj2TyHVG3j4Ijr9IC7uuZW+pPG3kPd9HUcpGbB0SiVCCeQTAxg8Vo00M2B3iRzGis+QbUnjjIzBXltZZStW27JuuqO+o43s78DkPRr4Mx0dZWkTwP/V6qgXHkcOXvo23JNTMxqiHPusnCNiCd3HaI/mWJ/r5Hc38nSqHpk056OcVcXVZO65lZobh22r729i60vdg77vaQTaV1LWzjiuvUI4vu3Qv8hapefycDu5+iPVUcRbfv+znFXY7V39dDalF+bTTq5TtVpb/KjqlnDXNICwi/1DZLOYKi31QyqoKvufKA/434/ORZpcpNTOmmknbggbJjOLG0cM7eFTXsmb+ljrG/mC1qb2PpiJ9sykmV6epedB7sgPpOFbU0k9w0fFFeMMRbFMJH2j3wTB0AyRdZxNdNKenqS6pDZVTezKaGDKuiq+x3gQUk/ju5fztBSsW6KGDnRIIQJ4/EX2kmv1TgVSh1pz+0bSoTp133k7Ga2ZcxY259MEUjUxsIvc32d7cSaZ6GBsBp5qgyQ64zW+GhuHD2JbN/fiaQp3x42Nu+qm29vq89FC4ycH+16h5k9WrqwXCXk+gZ96hGtWfdPZs/u68g5fiWZssHHl89p4dl9HfQmjKaagIGgFtoWk4rXEdu3ndo5i8sceWk0Nzaw91A3o5VddhzoIhaozNM1VS8LKt/ONcItkt4CLCPjs93MPlOKi+X16iUtBfYBP87cZ2bbShGUc6U22sDHkR2T08fuONBF50CKGbV19A6kmDVFEkc+dh4Me3LtONDForbp3NYRUfWUPDL8lHAK9rXksRa1pC/ncc5DZpZ1HsN8U+f/MfQ/1QAsB54mnLWxJCRdBHwJiAHfNDNfFKqC7t+6n1VHzqp0GGWRK7EsmtnE9v2d9CRSzB6xfkb//hcm/UqMc2eM3bbqPboyFKkIVsTPusVmdlEBx18G/O0Yx3wMGH/yMLNTMu9LOhN4bz7PHQ9JMeCrwKuB7cBDktaY2ZOluqYb3XRJHGMppFE63XaytAQD+YolvSJhrte182AXC9uaDls/xBWn5FHkz7rfSjrFzNbnefwXo/aS0eKbmeuxcb16M3sEKOUkPmcDm83sWTPrB24kzJLOlcSz+zoG5/Iaj5Gljq0vhj22qjlxQJg0RkuI2ZJG5iqG01mRxnkU87Pu94C1kp6W9Lik9ZIeH+X4nI3rki4BMLN/y3VMvm0emSPNA8IBgi/k89xxWgQ8n3F/OyOSlaSrgasBli5dinPjlW/S2Ly3I+91wjPHikxF6RLJtJZ/yWOOpIcz7q82s9XR7TE/6wrwugKPv03SRWa2JXOnpHcC1wC3jPbkfNs8Mv9jEoRtIGWdhGuk6Je/GmDlypXVOfmSm1TGqsHOlTj2d3QPriE+VQZRjmbaJw3AEKkx3zGD9pnZylLFImmGmR0iHNdRiA8Bv5T0ejPbFJ3r48BbCFckHFW+yeNJM/vBiID/EPhBjuMnagewJOP+4mifc0WTrbvuyLEuIwdJjmWqJw6XFi5CVgTF+Kz7LnAJYS8rY/j3IAOOyvYkM/uZpD7gVkmXA39KWI32MjM7kO05mfItd308z33F8hCwQtJySbWEqxZObD1R50ZIL3eSbdmTXG0gmQMIIZzWI13q2HGgix0HvD1gurA8tzFM+LPOzNLtE8vN7KjoZ3rLmjgynnsH8A7gLsIkc0E+iQPGnlX3dcDFwKIRfYJnUMKVBM0sIen9wC8Iu699y8w2lOp6bvrKLFmk/9Gfy9EGsnlvB/Wx4VUVmdN6TNdurOmEOZ1ev5H9S0fB5ynyZ52kS4GXRXfvMrOc7RaSOhgqqdQBrwL2KJw23cws+/KckbGqrV4AHgYuJSwSpXUAfzXGcyfEzH4G/KyU13DTW2Y11Oa9HQTK3u6RXs03Ht3Ye6g7rzER08V0ShqZirXOTbE+6yRdC7wEuCHa9UFJ55pZ1vmtzGxCdaxjzaq7Dlgn6QYz8zXL3ZSX+XGQ/mYZk4gJ6uIBZjYscRzs7I7W+/AeSNNJsUoeRXYxcLpZuDSmpOuBR8kxOaKkR8xs1KU1RjtmrGqrm8zsTcCjkg77VZlZ+ZdOc1Urc32QI2c3F9S1tVLSMR4zt2WwCium8MMhXeKIaWgBtOSI/4J04oCiDTietjbuPkRMmhydDuzw90KVaAP2R7fHmpTuhDHGgWi0c4xVbfXB6OclYxznHMBgD5RNezpI5dNcWAXS08ynP/zTUacsTCBJA2H0JYyGmuF9THa1dxEA81qbpvn6FtNPFS7P/HnCL/q/Ivzgfxnh9CK5HJ/HOZO5Hhir2mpndPO9ZvbRzMckfQH46OHPctNVMvpnSqaGble79BolKYxY1OKRnm48NdiWaMjCHiLxlLF9fye1MQ2usOiK4/j5o7bPVhUDqm1WejP7nqS7CNs9DPiome0a5fitE7levl11X51lX6GjGd0UsG1/52HdVbe+2MnWFztJpsKla9OJY7J8GKSrSZJmwzazaF8KEtEnxYLWpsEqLGCw1OGmH7P8tjI7B3hFtJ1TyguN1ebxHsIJEI8aUTfWAtxbysDc5BAuDhT+k6QIP2jN4KSFkyNxpBNhTKIvEf6nD6TCBaBiCteuSK82mi6RJFLGopnh9COeOKavamswl/QfwDHA96Jd75Z0oZm9rxTXG6vN47vArYR1aZl1Zx1mtj/7U9xUlm2ivwBAQx+uvYlqK9DntnRW82BDeWbCe2jbAeprAjCwZFgCSXfVnerzVrmxpUulVeYC4ASLGmOi3lYlGx83VptHO+HiIm+OgpkH1APNkpp9MaipaX9HN0nLvb7DnvYuUhxehVMTiJ5JlDjSRvYIu33TXmoC0T2QpC4eUB8POH1RGwDrd7YDcMrCqbe6oitM9eUONgNLgXRbxpJoX0nk1eYh6Q2SNgHPAXcDWwhLJG6KCnJ0Oz3Y2U1tTASESSQWQCwYai+QoHsgyS1P7S5rvMV04Yq57O8Z4OVHz2HVkbMGE4dzaeE4D8trK6MW4ClJd0U9rp4EZkhaI6no0zvlOzHiZ4FVwO1mdoakVwJvLXYwrvIOdnYD4diG9q4eWpsaaO/qIZkyamJCEmaGpHBEdlR8D4C6mNjbNQDAJSfMr+CrmLjfP3khAP/9yHbedubQcrMTKXF0dPfQ0tgw9oFTSOaMw1NN9RU8xlwVsKjyTR4DZvaipEBSYGa/kvRvpQzMVYai0dTpkkd3Ty81gQan7ghENCAi/NdJ2VDxNZGCmpjo7M/ZNXzSKeY3x+k6hvDm9S9wxSmTe4nebKqtwZywsfzX6enVSy3frroHJTUDvwZukPQlwKcPnYJamxpobWpAhB92jQ31NDbUh1N0BCIehL2Q4kF4P6ZwzqfFs5qpi4ePNdbk+7aqXp/6xUY+/n9PctVZS4bt/+6j28d9zv4qHZJcSg+90ElfIsU3HpjQkIKqVIVddZcC35D0rKQfSPoLSaeX6mL5/pdfBvQQTob4c+AZ4A2lCspVXpwUsaj40dvdRWBJlEoiszCZRImkJiZmtTQOLk0aSNTFYtz9zL5Khj9u77rxUQD+7rX5DL7NX3tXT852pKnkzs172ba/k2f3dbB+ZzuxQOw41Fvuuv+SsxFjgkbbyhjTp8zsAuAk4B7gbxg+oW1R5VVtZWaZpYxRF0x3k9u2/eHa2/WNh49fUHKAupa28HYqOXjMix1hO0kAzGmMT+qurNddecbg7c+//sTDHn/LGYsP2zeW7p5eAAYmX0e0gh0zq4GeRIqegRQ1QUAgaKiN8Z5VyyodWtFVW7WVpE8C5wHNhBMifpgwiZTEWIME0/O9H/YQecz37iafkeM4+g7th3hd1vWau3t6SZoNVl1JkEoaG3cfGhxdPnJlvummt7sLFCOZsilf8nh2Xwd1sfB98sjOQ0A4+HJ/Zz8Xf/23g8f97M/PrUh8xWRUZVfdP2BomfC7gfvMrK9UFxu12srMWsxsRpatZSKJQ9I/Sdoo6XFJP5bUlvHYxyVtlvS0pNeO9xqueOobm6hvaABLhckkIjHYFlIbEwtamwbbPDbuDj88pnPi2Hmwi+6kBqsuZk/RXkdpdbGA/b0JdnX0D+7beaiXv331cXT3DLDz+faciWPt8wfLFGXxpLC8tnKJpk6/EHiQcEqp9ZJ+U6rrVapl8zbg5GhK998RLWkr6UTCZRhPAi4C/kNSrEIxOoBYzagPNzbUD97e39EdjvlIGbWxKf41uwDVVr1RKkkzTlnYSk1MbH2xm3euXMrHXrkCgI79PTz6uXA6vNYLPkHjOR+oZKhFUW0N5pJOBv4YuAr4I8K10O8s1fUqkjzM7JcZi0vdT7joO4QN8zeaWZ+ZPUc4OvLsSsToQnVNGSUHBYPVV/WNTQSJsEQcD0Qy+oSsCURNII6axiWOtIVtTfSnDLOpnUzTnSXSVZ5P7uk8rMPB2r+/aPB2b/vew85x1pK20gVYAlU6SPBawiXCv0w4TckrzaxkYz+qoU/lOxkarb4IeD7jse3RvsNIulrSw5Ie3rv38DejK766ljbqWtrCevwR0utcxANRFw8GG9Gnu/6kTZuSR9qfnn3kYfsWvXk1i968GoC+tf9Z7pCKzgwGkpbXVr6Y7BLCxPGimQ2U+nr5DhIsmKTbgQVZHrrGzH4aHXMNYQPPDVmOG5WZrQZWA6xcuXKa/XtWRl9XOIEgCujt7qK+uZXe7i4EpBQjUDhOBPDkQbhKYUM8TKqJKZhBklvXQRCwcMkpYx6743tXD7vffd+Xcx67fmf7JJg7rLzdcPMh6Q3APwO1wPJojMdnzOzSUlyvZMnDzC4c7XFJf0K4QuGr0rNAEtbRZY7KWhztc1XAFIRJwpIolaCvsx2C8C0UYIPjQmDqNw7nK2mGoSlZ+ogdeRqpZx8u+nkP9iTGPqjC0tVWVebThNX8dwGY2WOSlpfqYhWptpJ0EfAR4FIzy/yKuga4UlJd9KJXEPYccFVAFg5UqG9swmK1YIZSCZRKgKWm3bxNY6mPhSPuwwWyKh1NaQRHrax0CJVh0YqZeWxlNBDNhD4i0tIoWcljDF8B6oDboim97zezPzezDZJuIpwNMgG8z8ymzkRJU0BgSXp6e4fmaYq+fWUbVDjd1UYTScYE/VOx6FEie7pKNjShaKq05LFB0luAmKQVwAeA347xnHGrSPIws2NGeexzwOfKGI7LQ3/7PlBAIt5AYEOlECCaKNGNlF5lcE+7TwOXr6/dv2VSjEY3wiWXq8xfANcAfYSrCf4C+PtSXaxSJQ83yVgQJ1VTT4xwahJgcKoSNzpfqjZ/kyFxAFG1VXUlj6gJ4JpoKzlPHi4vqZp6EikL1/WO9vV1tlPXXO29Ylwl3b91P6uOnFXpMIrOKPsYjpwk/S+jtG1Mut5WbvLqP7gHgNq2eYP7Gurr6ejuCe8oQMlwCor+9nD23NrWOeUNsoLu3ByOK5rVUOOrDE5jVdQJ4p+jn39AODzif6L7bwZKtqSnJw+Xt3Rvqr6ujmld4khZuFTtdFToaohTsdQB1dVgbmZ3A0j6FzPL7P72v5KK35c6Ug0jzF2VqW2bBxkN4n0dB8Ofh/aHYzsy9k1H0zVxjJTc8hjJLY8V/byfue1pbt1Ysi/MxRG1eeSzTcRok8hm0STpqIznLgdK1uDmycNlVTszY3KAzJ5VZpDy3tPTWfoLRGzZ6cSWnV7088+fEU62+fgLI4csVI90b6t8tgnKOolsDn8F3CXpLkl3A78CPjjRAHLxais3tmgyxLoZs8L2EAXUts6hv33ftGrrSJvOa5SkjHCAaAmtPCKsEj31iFZ2HOhi0czq661WrmorM/tlxt37gTeOcuzPo/Ed6VkpN5ZyPQ9PHm5M6S65fR0HqctoRJ9uiWNDtMDRSQun7xporU0N7GrvYkEJJxM4a0kbj+04WN1J2oxU/qWKOSPaHlZHc/MV6p3A90cPy/qAdeM4d8E8eTiXp7q4pvTU6vnqT1rJSwT18equUTcK6m21b0RD9jClnkS2VDx5uLxNh0GB/Qd2QRDPWqqqj4nFsybv+uzFZGZsfbGTmMj7d3L7pr15dTa4c/NeLjhmLruqfGR+saqtxjmJbMVVd3p3FZPYviHr/v72fWGvq4zlaKecVDir64sd3cOmlvfEEUpkaQh+bl9H3s+/ef0L3Lz+hZyPb3ox/J0vqOKR+eF6Hqm8tokYZRLZbMdK0lsl/W10f6mkki2m58nDZRVffFLOx2Qp6mZMzf77tTMXDHYQmN3S6FPLZ5FMQSIVfvNOJ9R82ibqYgFrnz/IFaccQU3s8I+ebz28jevXPk9rfZyHth0oetzFlK62ymeboK8ALYSTyD4m6eujHPsfwDmEgwMBOoCvTjiCHLzaytH9/c/T395B29X/MOax06GRPHNkvTvcinnja8Q+/6jZg7frYgF3P7OPlx89/P1UFw+oiwV09Ff/mh5l6m2VcxLZLF5qZmdKejR67gFJJesa58nDUXfKOTSe+IrB+z23fJWGS943eH9gzxZq5i0rf2BuymqpizGQNL754FaCaBGxxpoYjTUx6mIBy9rqKxzh6KwKVxIEBiTFiOa5kjQXKNmKIl5t5eh64M5h9zMTB+CJwxXductm09GfpLEmBsCcxlrqYgGNNTHmNNbSUhurcIRjKNMI8wJ9GfgxME/S54DfAGNXJ4xTRZOHpL+WZJLmRPcl6cuSNkfD8c+sZHzTQWL7BuqPP43+e28CIPnEHRWOyE0n8ShhJFPG/OZaZtbXML+p+itEjOpKHpIC4DnCxvXPAzuBy83sB6W6ZsX+SpKWAK8BtmXsfh3h0rMrgJcCX4t+uhKKLTwaO7ArvH3yqwb39x/c4/X/rmQuOWE+D207QPdAON3Nuctms3H3oUnRq80M+hPlXWN2NGaWkvRVMzsD2FiOa1YyxX+RMEv+NGPfZcB3or7M90tqk7TQzHZWJMJpIFUfTgVRW4I5ipwby0uWzhx2//j5xR+939vTQ31DcYfEG2WvksrHHZKuAH5UjvEgFUkeki4DdpjZOg1fwnQR8HzG/e3RvsOSh6SrgasBli5dWrpgp7qa+qw9qPr3v0DtrCMqEJBzxVXsxAFU5UqCwLuBDwEJSb2AADOzksynU7LkMdqQe+AThFVW4xbNDbMaYOXKlVX3V5wscnW99cThXG7pNo9qYmZlnQisZMkj15B7SacAy4F0qWMx8Eg0EnIHsCTj8MXRPuecqxpWhSUPSXeY2avG2lcsZa+2MrP1wGArrKQtwEoz2ydpDfB+STcSNpS3e3uHc64aVUvykFQPNBLO3juTsLoKYAZhtX9JVFufuJ8BFwObgW7gHZUNxznnDpcyo696elu9G/hL4AjgkYz9hwinNymJiicPM1uWcduA9+U+2jnnqkO1lDzM7EvAlyT9hZn9e7mu6yPMnXOuQFadI8y/JemTklYDSFoh6ZJSXcyTh3POjUPSLK+tjL4F9APnRvd3AJ8t1cU8eTjnXIHSgwSrrORxtJn9IzAAEK3/UbKlLyve5uGcc5NNtU1PEumX1MDQrLpHA32lupgnD+ecK1A4SLDqksengJ8DSyTdAJwH/EmpLubJwznnCmXVN7eVmd0m6RFgFWF11QfNbF+prufJwznnClSN05NEFgExws/2l0nCzH5Uigt58nDOuQKZQaLKkoekbwGnAhsYWkHQAE8ezjlXDaq05LHKzE4s18U8eTjnXIHMrBp7W90n6UQze7IcF/Pk4Zxz41CFJY/vECaQXYRddNPreZxaiot58nDOuQJV45TswHXA24D1DLV5lIwnD+ecGwervuSx18zWlOtinjycc65AZpCqvuTxqKTvAv9Lxshy76rrnHNVw7DyTnqYjwbCpJG5xPfU66or6S8I1+5IAv9nZh+J9n8ceFe0/wNm9otKxeicc1kZJKust5WZlXXxvIokD0mvBC4DTjOzPknzov0nAlcCJxGuinW7pGPNLFmJOJ1zLhsDrLpyR9lVakr29wDXmlkfgJntifZfBtxoZn1m9hzhcrRnVyhG55zLyczy2opB0l9LMklzinLCIqhU8jgWOF/SA5LulvSSaP8i4PmM47ZTwgXcnXNuXKIG83y2iZK0hLAdY9uET1ZEJau2knQ7sCDLQ9dE151FOPvjS4CbJB1V4PmvBq4GWLp06cSCdc65glg5u+p+EfgI8NNcB0g6nvCL9gNm1pmx/yIz+3kpgipZ8jCzC3M9Juk9wI8sLNM9KCkFzCFcNnFJxqGLo33Zzr8aWA2wcuXKquv24Jybuswgmcy70WOOpIcz7q+OPr/GJOkyYIeZrZOyLwoo6QOEnY+eAq6T9EEzSyeafyBc46PoKtXb6ifAK4FfSToWqAX2AWuA70r6V8IG8xXAgxWK0Tnnciqg5LHPzFbmenCMWppPMLzrbTZ/BpxlZp2SlgE/lLTMzL7EFFyG9lvAtyQ9Qbhg+1VRKWSDpJuAJ4EE8D7vaeWcq0bFqrbKVUsj6RRgOZAudSwGHpF0tpntyjg0SFdVmdkWSa8gTCBHMtWSh5n1A2/N8djngM+VNyLnnMufWXEaw8e4xnpgXvq+pC3AyiyrA+6WdLqZPRY9r1PSJYRf0k8pVXw+wtw558ahikaYv52wpmaQmSWAt0v6Rqku6snDOefGodyDBM1sWY7920d5zr2liseTh3POFciqcHqScvPk4ZxzhbKqnJK9rDx5OOdcwYxU9bR5VIQnD+ecK1A4MaInD+ecc4XwaitPHs45Nx5VuJJgWXnycM65ApkZqfzntpqSPHk459w4eMnDOedcwSw1vafd8+ThnHOFMvPkUekAnHNusjE8eXjycM65QpmRGuivdBQV5cnDOecK5dVWnjycc248pnvyCCpxUUmnS7pf0mOSHpZ0drRfkr4sabOkxyWdWYn4nHNuNOk2j3y2qapSJY9/BP7OzG6VdHF0/xXA6wjXLV8BvBT4WvTTOeeqh3nJo1LJw4AZ0e1W4IXo9mXAd6L1zO+X1CZpoZntrESQzjmXnZHy5FERfwn8QtI/E1adnRvtXwQ8n3Hc9mjfYclD0tXA1QBLly4tZazOOTeMmZFKeG+rkpB0O7Agy0PXAK8C/srMbpb0JuA64MJCzm9mq4HVACtXrpze8wQ458rLDEt6yaMkzCxnMpD0HeCD0d0fAN+Mbu8AlmQcujja55xzVWW6t3lUpLcVYRvHy6PbFwCbottrgLdHva5WAe3e3uGcqzrmva0q1ebxZ8CXJMWBXqK2C+BnwMXAZqAbeEdlwnPOudH4IMGKJA8z+w1wVpb9Bryv/BE551z+wmVofT0P55xzhfDeVp48nHOuYObjPDx5OOdcgQy8q26lA3DOuUnHZ9X15OGcc4Xz5OHJwznnCuUN5ijsHTu5SdoLbC3R6ecA+0p07vHymPLjMeWvGuMqVUxHmtnciZxA0s8J48vHPjO7aCLXq0ZTInmUkqSHzWxlpePI5DHlx2PKXzXGVY0xuSGVmp7EOefcJObJwznnXME8eYxtdaUDyMJjyo/HlL9qjKsaY3IRb/NwzjlXMC95OOecK5gnD+eccwXz5BGR9IeSNkhKSVqZsX+ZpB5Jj0Xb1zMeO0vSekmbJX1ZksoRU/TYx6PrPi3ptRn7L4r2bZb0sWLGkyPGT0vakfH7uXisGMuh3L+HUeLYEr1HHpP0cLRvlqTbJG2Kfs4scQzfkrRH0hMZ+7LGEC3E9uXo9/a4pDPLGFNVvpdcDmbmW9jucwJwHHAXsDJj/zLgiRzPeRBYBQi4FXhdmWI6EVgH1AHLgWeAWLQ9AxwF1EbHnFji39ungQ9n2Z81xjL9Lcv+exglli3AnBH7/hH4WHT7Y8AXShzDy4AzM9/HuWIgXIzt1ug9vQp4oIwxVd17ybfcm5c8Imb2lJk9ne/xkhYCM8zsfgvf4d8BLi9TTJcBN5pZn5k9R7jy4tnRttnMnjWzfuDG6NhKyBVjOVTT7yGby4Dro9vXU+T3zUhm9mtgf54xXAZ8x0L3A23Re70cMeVSyfeSy8GTR36WS3pU0t2Szo/2LQK2ZxyzPdpXDouA57NcO9f+Unt/VMXxrYwqmErFUulrj2TALyWtlZRebnm+me2Mbu8C5lcgrlwxVPp3V23vJZfDtJoYUdLtwIIsD11jZj/N8bSdwFIze1HSWcBPJJ1U4ZjKarQYga8Bf0/4Ifn3wL8A7yxfdFXv98xsh6R5wG2SNmY+aGYmqaL95ashhoi/lyaRaZU8zOzCcTynD+iLbq+V9AxwLLADWJxx6OJoX8ljiq6zJMe1c+0ft3xjlPSfwC15xFhqlbz2MGa2I/q5R9KPCatbdktaaGY7oyqhPRUILVcMFfvdmdnu9O0qei+5HLzaagyS5kqKRbePAlYAz0ZF/kOSVkW9rN4OlKuksAa4UlKdpOVRTA8CDwErJC2XVAtcGR1bMiPqw38fSPeeyRVjOZT995CNpCZJLenbwGsIfz9rgKuiw66ifO+bTLliWAO8Pep1tQpoz6jeKqkqfS+5XCrdYl8tG+GbdTthKWM38Ito/xXABuAx4BHgDRnPWUn4Bn8G+ArRiP1SxxQ9dk103afJ6OVF2Fvmd9Fj15Th9/bfwHrgccJ/8oVjxVimv2dZfw85YjiKsJfQuug9dE20fzZwB7AJuB2YVeI4vkdY/ToQvZ/elSsGwl5WX41+b+vJ6OVXhpiq8r3kW/bNpydxzjlXMK+2cs45VzBPHs455wrmycM551zBPHk455wrmCcP55xzBfPk4SpKUmcJznlpeiZdSZdLOnEc57hr5EzGzrkhnjzclGNma8zs2uju5YSzsjrnisiTh6sK0Yjmf5L0RLT+xR9F+18RlQJ+KGmjpBuiEf1IujjatzZag+KWaP+fSPqKpHOBS4F/itaHODqzRCFpjqQt0e0GSTdKeiqaRqQhI7bXSLpP0iOSfiCpuby/Heeqz7Sa28pVtT8ATgdOA+YAD0n6dfTYGcBJwAvAvcB5ChdW+gbwMjN7TtL3Rp7QzH4raQ1wi5n9EEC51+t6D9BtZidIOpVwNgEkzQE+CVxoZl2SPgp8CPhMEV6zc5OWJw9XLX4P+J6ZJQkn7bsbeAlwCHjQzLYDSHqMcIGuTsI5xp6Lnv894OqRJy3Ay4AvA5jZ45Iej/avIqz2ujdKPLXAfRO4jnNTgicPNxn0ZdxOMrH3bYKh6tr6PI4XcJuZvXkC13RuyvE2D1ct7gH+SFJM0lzCksBoM6c+DRwlaVl0/49yHNcBtGTc3wKcFd1+Y8b+XwNvAZB0MnBqtP9+wmqyY6LHmiQdm88Lcm4q8+ThqsWPCWdTXQfcCXzEzHblOtjMeoD3Aj+XtJYwSbRnOfRG4G+ilSCPBv4ZeI+kRwnbVtK+BjRLeoqwPWNtdJ29wJ8A34uqsu4Djp/IC3VuKvBZdd2kJanZzDqj3ldfBTaZ2RcrHZdz04GXPNxk9mdRA/oGoJWw95Vzrgy85OGcc65gXvJwzjlXME8ezjnnCubJwznnXME8eTjnnCuYJw/nnHMF+//si/Yq3DeSfQAAAABJRU5ErkJggg==\n",
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+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>spi06</th>\n",
+       "      <th>spi12</th>\n",
+       "      <th>d2m</th>\n",
+       "      <th>erate</th>\n",
+       "      <th>fg10</th>\n",
+       "      <th>si10</th>\n",
+       "      <th>swvl1</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>tprate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.776954</td>\n",
+       "      <td>0.525000</td>\n",
+       "      <td>-0.447657</td>\n",
+       "      <td>0.406953</td>\n",
+       "      <td>0.827660</td>\n",
+       "      <td>-196.723999</td>\n",
+       "      <td>-154.286743</td>\n",
+       "      <td>0.003297</td>\n",
+       "      <td>0.254569</td>\n",
+       "      <td>1.853317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.013915</td>\n",
+       "      <td>0.638954</td>\n",
+       "      <td>2.144423</td>\n",
+       "      <td>-0.711328</td>\n",
+       "      <td>0.038671</td>\n",
+       "      <td>0.396093</td>\n",
+       "      <td>0.287460</td>\n",
+       "      <td>3.016632</td>\n",
+       "      <td>431.780701</td>\n",
+       "      <td>28.249649</td>\n",
+       "      <td>0.005519</td>\n",
+       "      <td>0.299962</td>\n",
+       "      <td>55.425812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</td>\n",
+       "      <td>-1.662499</td>\n",
+       "      <td>-1.019922</td>\n",
+       "      <td>-0.342188</td>\n",
+       "      <td>0.013466</td>\n",
+       "      <td>-3.028435</td>\n",
+       "      <td>755.989075</td>\n",
+       "      <td>154.893127</td>\n",
+       "      <td>0.050565</td>\n",
+       "      <td>-0.337244</td>\n",
+       "      <td>32.763611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</td>\n",
+       "      <td>-0.465234</td>\n",
+       "      <td>0.118750</td>\n",
+       "      <td>0.388281</td>\n",
+       "      <td>0.387394</td>\n",
+       "      <td>5.030487</td>\n",
+       "      <td>413.472107</td>\n",
+       "      <td>42.046005</td>\n",
+       "      <td>0.016681</td>\n",
+       "      <td>0.273752</td>\n",
+       "      <td>71.841888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</td>\n",
+       "      <td>-0.428124</td>\n",
+       "      <td>0.118750</td>\n",
+       "      <td>0.384375</td>\n",
+       "      <td>0.381312</td>\n",
+       "      <td>6.291336</td>\n",
+       "      <td>393.559875</td>\n",
+       "      <td>-27.938068</td>\n",
+       "      <td>0.003139</td>\n",
+       "      <td>0.076797</td>\n",
+       "      <td>92.562195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "      <td>-0.266016</td>\n",
+       "      <td>-0.875389</td>\n",
+       "      <td>0.050391</td>\n",
+       "      <td>-1.367281</td>\n",
+       "      <td>1.625626</td>\n",
+       "      <td>292.504852</td>\n",
+       "      <td>59.417725</td>\n",
+       "      <td>0.006094</td>\n",
+       "      <td>-1.379390</td>\n",
+       "      <td>9.860245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.026953</td>\n",
+       "      <td>-0.254295</td>\n",
+       "      <td>-0.213281</td>\n",
+       "      <td>0.455697</td>\n",
+       "      <td>0.501305</td>\n",
+       "      <td>-115.465073</td>\n",
+       "      <td>-19.559784</td>\n",
+       "      <td>-0.018831</td>\n",
+       "      <td>0.631343</td>\n",
+       "      <td>-2.685104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "      <td>0.599994</td>\n",
+       "      <td>-1.494532</td>\n",
+       "      <td>-2.109764</td>\n",
+       "      <td>-0.639062</td>\n",
+       "      <td>-0.879230</td>\n",
+       "      <td>-2.990351</td>\n",
+       "      <td>710.314941</td>\n",
+       "      <td>152.261505</td>\n",
+       "      <td>0.015199</td>\n",
+       "      <td>-0.608153</td>\n",
+       "      <td>6.517075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>-1.486719</td>\n",
+       "      <td>-2.152733</td>\n",
+       "      <td>-0.605859</td>\n",
+       "      <td>-0.919799</td>\n",
+       "      <td>-2.017698</td>\n",
+       "      <td>706.356201</td>\n",
+       "      <td>156.933868</td>\n",
+       "      <td>0.015889</td>\n",
+       "      <td>-0.627131</td>\n",
+       "      <td>4.443192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "      <td>0.056250</td>\n",
+       "      <td>0.458595</td>\n",
+       "      <td>-0.818749</td>\n",
+       "      <td>0.713625</td>\n",
+       "      <td>0.389204</td>\n",
+       "      <td>-201.990448</td>\n",
+       "      <td>1.390381</td>\n",
+       "      <td>-0.014896</td>\n",
+       "      <td>0.647231</td>\n",
+       "      <td>-5.461624</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 19 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "             time  latitude  longitude     fuel_load climatic_region biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0   \n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN   7.0   \n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0   \n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0   \n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0   1.0   \n",
+       "...           ...       ...        ...           ...             ...   ...   \n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0   1.0   \n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0   7.0   \n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN   1.0   \n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN   1.0   \n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0   7.0   \n",
+       "\n",
+       "           slope       vod       lai     spi03     spi06     spi12       d2m  \\\n",
+       "0       0.003584  0.177971  0.655549  0.776954  0.525000 -0.447657  0.406953   \n",
+       "1       0.013915  0.638954  2.144423 -0.711328  0.038671  0.396093  0.287460   \n",
+       "2       0.014664  0.832961  3.433299 -1.662499 -1.019922 -0.342188  0.013466   \n",
+       "3       0.016293  0.551958  1.811093 -0.465234  0.118750  0.388281  0.387394   \n",
+       "4       0.033126  0.676935  4.477733 -0.428124  0.118750  0.384375  0.381312   \n",
+       "...          ...       ...       ...       ...       ...       ...       ...   \n",
+       "720482  0.020528  0.325193  0.777770 -0.266016 -0.875389  0.050391 -1.367281   \n",
+       "720483  0.054576  0.310260  0.655549  0.026953 -0.254295 -0.213281  0.455697   \n",
+       "720484  0.005878  0.160282  0.599994 -1.494532 -2.109764 -0.639062 -0.879230   \n",
+       "720485  0.005559  0.262777  1.111100 -1.486719 -2.152733 -0.605859 -0.919799   \n",
+       "720486  0.037897  0.306111  0.055555  0.056250  0.458595 -0.818749  0.713625   \n",
+       "\n",
+       "           erate        fg10        si10     swvl1       t2m     tprate  \n",
+       "0       0.827660 -196.723999 -154.286743  0.003297  0.254569   1.853317  \n",
+       "1       3.016632  431.780701   28.249649  0.005519  0.299962  55.425812  \n",
+       "2      -3.028435  755.989075  154.893127  0.050565 -0.337244  32.763611  \n",
+       "3       5.030487  413.472107   42.046005  0.016681  0.273752  71.841888  \n",
+       "4       6.291336  393.559875  -27.938068  0.003139  0.076797  92.562195  \n",
+       "...          ...         ...         ...       ...       ...        ...  \n",
+       "720482  1.625626  292.504852   59.417725  0.006094 -1.379390   9.860245  \n",
+       "720483  0.501305 -115.465073  -19.559784 -0.018831  0.631343  -2.685104  \n",
+       "720484 -2.990351  710.314941  152.261505  0.015199 -0.608153   6.517075  \n",
+       "720485 -2.017698  706.356201  156.933868  0.015889 -0.627131   4.443192  \n",
+       "720486  0.389204 -201.990448    1.390381 -0.014896  0.647231  -5.461624  \n",
+       "\n",
+       "[720487 rows x 19 columns]"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Mask using the load\n",
-    "wa_data = wa_data.where(load_data >= 0)\n",
-    "wa_data.d2m[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "wa_data.to_netcdf(folder_path + \"weather_anomalies_2010_2016.nc\")"
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"d2m\"] = wa_data[\"d2m\"].sel(longitude=target_lon,\n",
+    "                                latitude=target_lat,\n",
+    "                                time=target_t,\n",
+    "                                method=\"nearest\")\n",
+    "df[\"erate\"] = wa_data[\"erate\"].sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    time=target_t,\n",
+    "                                    method=\"nearest\")\n",
+    "df[\"fg10\"] = wa_data[\"fg10\"].sel(longitude=target_lon,\n",
+    "                                  latitude=target_lat,\n",
+    "                                  time=target_t,\n",
+    "                                  method=\"nearest\")\n",
+    "df[\"si10\"] = wa_data[\"si10\"].sel(longitude=target_lon,\n",
+    "                                  latitude=target_lat,\n",
+    "                                  time=target_t,\n",
+    "                                  method=\"nearest\")\n",
+    "df[\"swvl1\"] = wa_data[\"swvl1\"].sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    time=target_t,\n",
+    "                                    method=\"nearest\")\n",
+    "df[\"t2m\"] = wa_data[\"t2m\"].sel(longitude=target_lon,\n",
+    "                                latitude=target_lat,\n",
+    "                                time=target_t,\n",
+    "                                method=\"nearest\")\n",
+    "df[\"tprate\"] = wa_data[\"tprate\"].sel(longitude=target_lon,\n",
+    "                                      latitude=target_lat,\n",
+    "                                      time=target_t,\n",
+    "                                      method=\"nearest\")\n",
+    "df"
    ]
   },
   {
@@ -1947,63 +8187,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 34,
    "id": "d531a3ab",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e4058fc70>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"Load fire data into an xarray dataset\"\"\"\n",
-    "fa_data = xr.open_mfdataset(\n",
-    "    \"/data1/raw_data/SEAS_FIRE_ANOMALIES_2010_2018/ECMWF_FWI_201[0-6]*_anomaly_m1.nc\"\n",
-    ")\n",
-    "# Rename lat/lon dimensions\n",
-    "fa_data = fa_data.rename({\"lon\": \"longitude\", \"lat\": \"latitude\"})\n",
-    "# Rotate longitude coordinates\n",
-    "fa_data = fa_data.assign_coords(\n",
-    "    longitude=(((fa_data.longitude + 180) % 360) - 180)\n",
-    ").sortby(\"longitude\")\n",
-    "# Interpolate to match load resolution\n",
-    "fa_data = fa_data.interp(\n",
-    "    coords={\n",
-    "        \"latitude\": load_data.latitude.values,\n",
-    "        \"longitude\": load_data.longitude.values,\n",
-    "    },\n",
-    "    method=\"linear\", # danger_risk is an integer but for the purpose of this work it does not matter!\n",
-    ")  # Wikilimo used default method ('linear')\n",
-    "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
-    "# Therefore here we remove Jan-Feb-Mar 2016.\n",
-    "fa_data = fa_data.loc[dict(time=slice(\"2010-04-01\", \"2016-12-31\"))]\n",
-    "fa_data.fwinx[0].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "11d727b0",
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2360,67 +8546,68 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (latitude: 720, longitude: 1440, time: 81)\n",
+       "Dimensions:      (latitude: 640, longitude: 1280, time: 81)\n",
        "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 2010-04-01T12:00:00 ... 2016-12-01T12:...\n",
-       "  * latitude     (latitude) float64 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
-       "  * longitude    (longitude) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "  * time         (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude    (longitude) float64 -180.0 -179.7 -179.4 ... 179.2 179.4 179.7\n",
+       "  * latitude     (latitude) float64 89.78 89.51 89.23 ... -89.23 -89.51 -89.78\n",
        "Data variables:\n",
-       "    danger_risk  (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
-       "    fwinx        (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
-       "    ffmcode      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
-       "    dufmcode     (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
-       "    drtcode      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
-       "    infsinx      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
-       "    fbupinx      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
-       "    fdsrte       (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
+       "    danger_risk  (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
+       "    fwinx        (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
+       "    ffmcode      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
+       "    dufmcode     (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
+       "    drtcode      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
+       "    infsinx      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
+       "    fbupinx      (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
+       "    fdsrte       (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;\n",
        "Attributes:\n",
        "    CDI:          Climate Data Interface version 1.9.8 (https://mpimet.mpg.de...\n",
        "    Conventions:  CF-1.6\n",
        "    history:      Sun Apr 25 20:57:44 2021: cdo -R -f nc -remapbil,n320 -setg...\n",
        "    institution:  European Centre for Medium-Range Weather Forecasts\n",
-       "    CDO:          Climate Data Operators version 1.9.8 (https://mpimet.mpg.de...</pre><div class='xr-wrap' hidden><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-03e6da93-9612-4abd-9f49-24898195e2f3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03e6da93-9612-4abd-9f49-24898195e2f3' 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'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>time</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7bdc92fd-3051-4c41-91ff-77cb459186ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7bdc92fd-3051-4c41-91ff-77cb459186ba' 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'>2010-04-01T12:00:00 ... 2016-12-...</div><input id='attrs-12a9a7f5-98c6-40ae-967e-a9f1ea80f738' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12a9a7f5-98c6-40ae-967e-a9f1ea80f738' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac16b646-09a9-489f-a502-2d8d70d75463' class='xr-var-data-in' type='checkbox'><label for='data-ac16b646-09a9-489f-a502-2d8d70d75463' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-04-01T12:00:00.000000000&#x27;, &#x27;2010-05-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-06-01T12:00:00.000000000&#x27;, &#x27;2010-07-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-08-01T12:00:00.000000000&#x27;, &#x27;2010-09-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-10-01T12:00:00.000000000&#x27;, &#x27;2010-11-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-12-01T12:00:00.000000000&#x27;, &#x27;2011-01-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2011-02-01T12:00:00.000000000&#x27;, &#x27;2011-03-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2011-04-01T12:00:00.000000000&#x27;, &#x27;2011-05-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2011-06-01T12:00:00.000000000&#x27;, &#x27;2011-07-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2011-08-01T12:00:00.000000000&#x27;, &#x27;2011-09-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2011-10-01T12:00:00.000000000&#x27;, &#x27;2011-11-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2011-12-01T12:00:00.000000000&#x27;, &#x27;2012-01-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-02-01T12:00:00.000000000&#x27;, &#x27;2012-03-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-04-01T12:00:00.000000000&#x27;, &#x27;2012-05-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-06-01T12:00:00.000000000&#x27;, &#x27;2012-07-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-08-01T12:00:00.000000000&#x27;, &#x27;2012-09-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-10-01T12:00:00.000000000&#x27;, &#x27;2012-11-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-12-01T12:00:00.000000000&#x27;, &#x27;2013-01-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-02-01T12:00:00.000000000&#x27;, &#x27;2013-03-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-04-01T12:00:00.000000000&#x27;, &#x27;2013-05-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-06-01T12:00:00.000000000&#x27;, &#x27;2013-07-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-08-01T12:00:00.000000000&#x27;, &#x27;2013-09-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-10-01T12:00:00.000000000&#x27;, &#x27;2013-11-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-12-01T12:00:00.000000000&#x27;, &#x27;2014-01-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-02-01T12:00:00.000000000&#x27;, &#x27;2014-03-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-04-01T12:00:00.000000000&#x27;, &#x27;2014-05-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-06-01T12:00:00.000000000&#x27;, &#x27;2014-07-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-08-01T12:00:00.000000000&#x27;, &#x27;2014-09-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-10-01T12:00:00.000000000&#x27;, &#x27;2014-11-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-12-01T12:00:00.000000000&#x27;, &#x27;2015-01-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-02-01T12:00:00.000000000&#x27;, &#x27;2015-03-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-04-01T12:00:00.000000000&#x27;, &#x27;2015-05-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-06-01T12:00:00.000000000&#x27;, &#x27;2015-07-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-08-01T12:00:00.000000000&#x27;, &#x27;2015-09-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-10-01T12:00:00.000000000&#x27;, &#x27;2015-11-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-12-01T12:00:00.000000000&#x27;, &#x27;2016-01-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-02-01T12:00:00.000000000&#x27;, &#x27;2016-03-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-04-01T12:00:00.000000000&#x27;, &#x27;2016-05-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-06-01T12:00:00.000000000&#x27;, &#x27;2016-07-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-08-01T12:00:00.000000000&#x27;, &#x27;2016-09-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-10-01T12:00:00.000000000&#x27;, &#x27;2016-11-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-12-01T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-586bc86d-ba49-45f8-bf86-0804336c0992' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-586bc86d-ba49-45f8-bf86-0804336c0992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3de060df-1f5d-469f-bb3b-eeda6206b184' class='xr-var-data-in' type='checkbox'><label for='data-3de060df-1f5d-469f-bb3b-eeda6206b184' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-b0f3fcaf-8ca0-4f4d-8f71-758aceddc77d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0f3fcaf-8ca0-4f4d-8f71-758aceddc77d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99b79f02-4954-403a-b6f1-e91640341efe' class='xr-var-data-in' type='checkbox'><label for='data-99b79f02-4954-403a-b6f1-e91640341efe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efca21ad-0de5-4791-a4d4-17cecb05a368' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efca21ad-0de5-4791-a4d4-17cecb05a368' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>danger_risk</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-debcf967-8a4e-4192-b942-16b168981f69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-debcf967-8a4e-4192-b942-16b168981f69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e491f18e-56c7-4961-8564-beacc5c3f657' class='xr-var-data-in' type='checkbox'><label for='data-e491f18e-56c7-4961-8564-beacc5c3f657' 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>param :</span></dt><dd>27.212.192</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    CDO:          Climate Data Operators version 1.9.8 (https://mpimet.mpg.de...</pre><div class='xr-wrap' hidden><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-a99c8836-6244-449c-8ff1-8a0e65b650a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a99c8836-6244-449c-8ff1-8a0e65b650a8' 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'>latitude</span>: 640</li><li><span class='xr-has-index'>longitude</span>: 1280</li><li><span class='xr-has-index'>time</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-93f0b79d-3f17-4af0-9648-147d7898e420' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93f0b79d-3f17-4af0-9648-147d7898e420' 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'>2010-04-01 ... 2016-12-01</div><input id='attrs-82ac0a90-b5c0-49fd-975c-77b0cd6d2f06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82ac0a90-b5c0-49fd-975c-77b0cd6d2f06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d5cf2ee-8c1a-4dc5-bcc3-a68d632909bc' class='xr-var-data-in' type='checkbox'><label for='data-9d5cf2ee-8c1a-4dc5-bcc3-a68d632909bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-04-01T00:00:00.000000000&#x27;, &#x27;2010-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-06-01T00:00:00.000000000&#x27;, &#x27;2010-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-08-01T00:00:00.000000000&#x27;, &#x27;2010-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-10-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-04-01T00:00:00.000000000&#x27;, &#x27;2011-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2011-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-10-01T00:00:00.000000000&#x27;, &#x27;2011-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-12-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-01T00:00:00.000000000&#x27;, &#x27;2012-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-04-01T00:00:00.000000000&#x27;, &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-08-01T00:00:00.000000000&#x27;, &#x27;2012-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-10-01T00:00:00.000000000&#x27;, &#x27;2012-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-04-01T00:00:00.000000000&#x27;, &#x27;2013-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2013-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-10-01T00:00:00.000000000&#x27;, &#x27;2013-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-12-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-02-01T00:00:00.000000000&#x27;, &#x27;2014-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-04-01T00:00:00.000000000&#x27;, &#x27;2014-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-06-01T00:00:00.000000000&#x27;, &#x27;2014-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-08-01T00:00:00.000000000&#x27;, &#x27;2014-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-10-01T00:00:00.000000000&#x27;, &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.7 ... 179.4 179.7</div><input id='attrs-5130237a-3d6c-4e51-b4ef-851597f30a7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5130237a-3d6c-4e51-b4ef-851597f30a7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb2a8e6a-5004-4a02-bae5-559331a86ae1' class='xr-var-data-in' type='checkbox'><label for='data-fb2a8e6a-5004-4a02-bae5-559331a86ae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180.     , -179.71875, -179.4375 , ...,  179.15625,  179.4375 ,\n",
+       "        179.71875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.78 89.51 89.23 ... -89.51 -89.78</div><input id='attrs-495d56f6-2061-436d-b603-9e8842c866cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-495d56f6-2061-436d-b603-9e8842c866cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56177a8c-9436-456f-a403-cfc64a34e2d1' class='xr-var-data-in' type='checkbox'><label for='data-56177a8c-9436-456f-a403-cfc64a34e2d1' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</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([ 89.784877,  89.506203,  89.225883, ..., -89.225883, -89.506203,\n",
+       "       -89.784877])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61c31039-b2ac-4920-bbd8-a5b0e9383b1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61c31039-b2ac-4920-bbd8-a5b0e9383b1a' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>danger_risk</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-73ba7b2d-52e0-4258-b3f0-df19d11fcfa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73ba7b2d-52e0-4258-b3f0-df19d11fcfa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec8de0b-9a08-45c4-ad89-1f1cd8e3495d' class='xr-var-data-in' type='checkbox'><label for='data-4ec8de0b-9a08-45c4-ad89-1f1cd8e3495d' 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>param :</span></dt><dd>27.212.192</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -2428,19 +8615,19 @@
        "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr><th> Bytes </th><td> 335.92 MB </td> <td> 4.15 MB </td></tr>\n",
-       "    <tr><th> Shape </th><td> (81, 720, 1440) </td> <td> (1, 720, 1440) </td></tr>\n",
-       "    <tr><th> Count </th><td> 841 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Bytes </th><td> 265.42 MB </td> <td> 3.28 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 640, 1280) </td> <td> (1, 640, 1280) </td></tr>\n",
+       "    <tr><th> Count </th><td> 417 Tasks </td><td> 81 Chunks </td></tr>\n",
        "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</td>\n",
        "<td>\n",
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+       "  <text x=\"10.568970\" y=\"90.568970\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.568970,90.568970)\">81</text>\n",
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fwinx</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-1da086aa-81e5-4566-bf9e-2470d1c8125e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1da086aa-81e5-4566-bf9e-2470d1c8125e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c268f9a3-e860-44a2-a6f2-a1e377398a2a' class='xr-var-data-in' type='checkbox'><label for='data-c268f9a3-e860-44a2-a6f2-a1e377398a2a' 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>Forest fire weather index</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>5.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fwinx</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-aa3adf6f-66bc-4835-97cc-531620ebdc35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa3adf6f-66bc-4835-97cc-531620ebdc35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93562130-2c20-4790-bbb6-3707fe20a58d' class='xr-var-data-in' type='checkbox'><label for='data-93562130-2c20-4790-bbb6-3707fe20a58d' 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>Forest fire weather index</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>5.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -2522,19 +8709,19 @@
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        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr><th> Bytes </th><td> 335.92 MB </td> <td> 4.15 MB </td></tr>\n",
-       "    <tr><th> Shape </th><td> (81, 720, 1440) </td> <td> (1, 720, 1440) </td></tr>\n",
-       "    <tr><th> Count </th><td> 841 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Bytes </th><td> 265.42 MB </td> <td> 3.28 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 640, 1280) </td> <td> (1, 640, 1280) </td></tr>\n",
+       "    <tr><th> Count </th><td> 417 Tasks </td><td> 81 Chunks </td></tr>\n",
        "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</td>\n",
        "<td>\n",
-       "<svg width=\"200\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "<svg width=\"201\" height=\"131\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
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-       "  <line x1=\"10\" y1=\"0\" x2=\"30\" y2=\"20\" style=\"stroke-width:2\" />\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ffmcode</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-4b1f9c39-8e11-4ced-8c16-ab450c23fb77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b1f9c39-8e11-4ced-8c16-ab450c23fb77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ad1c700-63da-4382-bf88-4021898b6db8' class='xr-var-data-in' type='checkbox'><label for='data-8ad1c700-63da-4382-bf88-4021898b6db8' 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>Fine fuel moisture code</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>6.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dufmcode</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-0bd516df-9c48-4260-90a7-7a4b2b6fec3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bd516df-9c48-4260-90a7-7a4b2b6fec3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d41cf6c-fcee-4d2e-9fad-759ef677e7f3' class='xr-var-data-in' type='checkbox'><label for='data-0d41cf6c-fcee-4d2e-9fad-759ef677e7f3' 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>Duff moisture code</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>7.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dufmcode</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-50c435ba-6a17-4b2f-b829-ab6cf4b3bc5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50c435ba-6a17-4b2f-b829-ab6cf4b3bc5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9eb4b42-2564-402c-831b-d1e65350942b' class='xr-var-data-in' type='checkbox'><label for='data-d9eb4b42-2564-402c-831b-d1e65350942b' 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>Duff moisture code</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>7.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drtcode</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-daa51864-62f6-4696-82d0-706fbbcffb94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-daa51864-62f6-4696-82d0-706fbbcffb94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65104432-e2f8-40be-860c-4d9e1607a400' class='xr-var-data-in' type='checkbox'><label for='data-65104432-e2f8-40be-860c-4d9e1607a400' 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>Drought code</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>8.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>infsinx</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-76ca57c1-fff1-4852-a3b1-6d0b4dd13c81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76ca57c1-fff1-4852-a3b1-6d0b4dd13c81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-076975e7-8882-4d30-a0bf-5a7e5806034d' class='xr-var-data-in' type='checkbox'><label for='data-076975e7-8882-4d30-a0bf-5a7e5806034d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Initial fire spread index</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>9.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fbupinx</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-d548b078-d7a3-4652-8684-34a144593f5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d548b078-d7a3-4652-8684-34a144593f5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d70251ad-64f5-4a5a-be55-1e966136b11d' class='xr-var-data-in' type='checkbox'><label for='data-d70251ad-64f5-4a5a-be55-1e966136b11d' 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>Fire buildup index</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>10.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
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-       "    <tr><th> Count </th><td> 841 Tasks </td><td> 81 Chunks </td></tr>\n",
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+       "    <tr><th> Shape </th><td> (81, 640, 1280) </td> <td> (1, 640, 1280) </td></tr>\n",
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        "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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        "<td>\n",
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        "</tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fdsrte</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 640, 1280), meta=np.ndarray&gt;</div><input id='attrs-9a02ee03-792a-40e5-b850-75b90193a840' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a02ee03-792a-40e5-b850-75b90193a840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee0b8e8d-c3e0-428d-b9ee-53b73568abeb' class='xr-var-data-in' type='checkbox'><label for='data-ee0b8e8d-c3e0-428d-b9ee-53b73568abeb' 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>Fire daily severity rating</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>param :</span></dt><dd>11.4.2</dd><dt><span>institution :</span></dt><dd>ECMWF</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -3086,19 +9273,19 @@
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        "  <tbody>\n",
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-       "    <tr><th> Count </th><td> 841 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Bytes </th><td> 265.42 MB </td> <td> 3.28 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 640, 1280) </td> <td> (1, 640, 1280) </td></tr>\n",
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        "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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        "</table>\n",
        "</td>\n",
        "<td>\n",
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-       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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@@ -3145,51 +9332,51 @@
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        "\n",
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        "</td>\n",
        "</tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-677374b8-631f-4e5f-8db4-c7616734c5af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-677374b8-631f-4e5f-8db4-c7616734c5af' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Sun Apr 25 20:57:44 2021: cdo -R -f nc -remapbil,n320 -setgridtype,regular -setmissval,-9999.0 -setdate,20100101 -chname,param5.4.2,fwi -chname,param6.4.2,ffmc -chname,param7.4.2,dmc -chname,param8.4.2,dc -chname,param9.4.2,isi -chname,param10.4.2,bui -chname,param11.4.2,dsr -chname,param27.212.192,danger_risk 20100101/ECMWF_FWI_20100101_1200_anomaly_m1.grib ECMWF_FWI_20100101_1200_anomaly_m1.nc</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b85ab30-a984-4834-b1c3-bed785a0db2c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b85ab30-a984-4834-b1c3-bed785a0db2c' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Sun Apr 25 20:57:44 2021: cdo -R -f nc -remapbil,n320 -setgridtype,regular -setmissval,-9999.0 -setdate,20100101 -chname,param5.4.2,fwi -chname,param6.4.2,ffmc -chname,param7.4.2,dmc -chname,param8.4.2,dc -chname,param9.4.2,isi -chname,param10.4.2,bui -chname,param11.4.2,dsr -chname,param27.212.192,danger_risk 20100101/ECMWF_FWI_20100101_1200_anomaly_m1.grib ECMWF_FWI_20100101_1200_anomaly_m1.nc</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:      (latitude: 720, longitude: 1440, time: 81)\n",
+       "Dimensions:      (latitude: 640, longitude: 1280, time: 81)\n",
        "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 2010-04-01T12:00:00 ... 2016-12-01T12:...\n",
-       "  * latitude     (latitude) float64 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
-       "  * longitude    (longitude) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "  * time         (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude    (longitude) float64 -180.0 -179.7 -179.4 ... 179.2 179.4 179.7\n",
+       "  * latitude     (latitude) float64 89.78 89.51 89.23 ... -89.23 -89.51 -89.78\n",
        "Data variables:\n",
-       "    danger_risk  (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
-       "    fwinx        (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
-       "    ffmcode      (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
-       "    dufmcode     (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
-       "    drtcode      (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
-       "    infsinx      (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
-       "    fbupinx      (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
-       "    fdsrte       (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
+       "    danger_risk  (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
+       "    fwinx        (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
+       "    ffmcode      (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
+       "    dufmcode     (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
+       "    drtcode      (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
+       "    infsinx      (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
+       "    fbupinx      (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
+       "    fdsrte       (time, latitude, longitude) float32 dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>\n",
        "Attributes:\n",
        "    CDI:          Climate Data Interface version 1.9.8 (https://mpimet.mpg.de...\n",
        "    Conventions:  CF-1.6\n",
@@ -3198,43 +9385,449 @@
        "    CDO:          Climate Data Operators version 1.9.8 (https://mpimet.mpg.de..."
       ]
      },
-     "execution_count": 30,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "\"\"\"Load fire data into an xarray dataset\"\"\"\n",
+    "fa_data = xr.open_mfdataset(\n",
+    "    \"/data1/raw_data/SEAS_FIRE_ANOMALIES_2010_2018/ECMWF_FWI_201[0-6]*_anomaly_m1.nc\"\n",
+    ")\n",
+    "# Rename lat/lon dimensions\n",
+    "fa_data = fa_data.rename({\"lon\": \"longitude\", \"lat\": \"latitude\"})\n",
+    "# Rotate longitude coordinates\n",
+    "fa_data = fa_data.assign_coords(\n",
+    "    longitude=(((fa_data.longitude + 180) % 360) - 180)\n",
+    ").sortby(\"longitude\")\n",
+    "\n",
+    "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
+    "# Therefore here we remove Jan-Feb-Mar 2016.\n",
+    "fa_data = fa_data.loc[dict(time=slice(\"2010-04-01\", \"2016-12-31\"))]\n",
+    "\n",
+    "# Fix time stamps\n",
+    "fa_data[\"time\"] = load_data[\"time\"]\n",
+    "\n",
     "fa_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "729a2a5f",
+   "execution_count": 35,
+   "id": "f29e69ba",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>...</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>tprate</th>\n",
+       "      <th>danger_risk</th>\n",
+       "      <th>fwinx</th>\n",
+       "      <th>ffmcode</th>\n",
+       "      <th>dufmcode</th>\n",
+       "      <th>drtcode</th>\n",
+       "      <th>infsinx</th>\n",
+       "      <th>fbupinx</th>\n",
+       "      <th>fdsrte</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.776954</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.254569</td>\n",
+       "      <td>1.853317</td>\n",
+       "      <td>-0.408514</td>\n",
+       "      <td>-6.540662</td>\n",
+       "      <td>0.409787</td>\n",
+       "      <td>-28.476147</td>\n",
+       "      <td>-435.756500</td>\n",
+       "      <td>-0.604823</td>\n",
+       "      <td>-48.407936</td>\n",
+       "      <td>-5.157768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>4853.018730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.013915</td>\n",
+       "      <td>0.638954</td>\n",
+       "      <td>2.144423</td>\n",
+       "      <td>-0.711328</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.299962</td>\n",
+       "      <td>55.425812</td>\n",
+       "      <td>-0.067000</td>\n",
+       "      <td>-0.084618</td>\n",
+       "      <td>6.273533</td>\n",
+       "      <td>-0.538086</td>\n",
+       "      <td>-20.080078</td>\n",
+       "      <td>0.267007</td>\n",
+       "      <td>-0.873047</td>\n",
+       "      <td>-0.086460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</td>\n",
+       "      <td>-1.662499</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.337244</td>\n",
+       "      <td>32.763611</td>\n",
+       "      <td>0.218694</td>\n",
+       "      <td>1.595769</td>\n",
+       "      <td>6.249068</td>\n",
+       "      <td>3.641309</td>\n",
+       "      <td>6.909269</td>\n",
+       "      <td>0.687314</td>\n",
+       "      <td>5.850041</td>\n",
+       "      <td>0.220082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</td>\n",
+       "      <td>-0.465234</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.273752</td>\n",
+       "      <td>71.841888</td>\n",
+       "      <td>-0.128096</td>\n",
+       "      <td>-0.487267</td>\n",
+       "      <td>4.348554</td>\n",
+       "      <td>-1.812235</td>\n",
+       "      <td>-52.787262</td>\n",
+       "      <td>0.159193</td>\n",
+       "      <td>-3.456243</td>\n",
+       "      <td>-0.198016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</td>\n",
+       "      <td>-0.428124</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.076797</td>\n",
+       "      <td>92.562195</td>\n",
+       "      <td>-0.099173</td>\n",
+       "      <td>-0.372817</td>\n",
+       "      <td>4.207914</td>\n",
+       "      <td>-1.145141</td>\n",
+       "      <td>-49.045151</td>\n",
+       "      <td>0.194680</td>\n",
+       "      <td>-3.067599</td>\n",
+       "      <td>-0.133346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720482</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "      <td>-0.266016</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.379390</td>\n",
+       "      <td>9.860245</td>\n",
+       "      <td>-0.001600</td>\n",
+       "      <td>0.213408</td>\n",
+       "      <td>3.380115</td>\n",
+       "      <td>0.573486</td>\n",
+       "      <td>2.652710</td>\n",
+       "      <td>0.321904</td>\n",
+       "      <td>0.713867</td>\n",
+       "      <td>0.011017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720483</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.026953</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.631343</td>\n",
+       "      <td>-2.685104</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.034499</td>\n",
+       "      <td>-4.597795</td>\n",
+       "      <td>-0.155521</td>\n",
+       "      <td>-92.605637</td>\n",
+       "      <td>-0.084250</td>\n",
+       "      <td>-0.304651</td>\n",
+       "      <td>-0.000867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720484</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>1927.843494</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "      <td>0.599994</td>\n",
+       "      <td>-1.494532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.608153</td>\n",
+       "      <td>6.517075</td>\n",
+       "      <td>-0.002031</td>\n",
+       "      <td>0.067574</td>\n",
+       "      <td>0.882902</td>\n",
+       "      <td>0.393799</td>\n",
+       "      <td>131.938141</td>\n",
+       "      <td>0.012117</td>\n",
+       "      <td>0.793365</td>\n",
+       "      <td>0.001631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720485</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1113.105571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>-1.486719</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.627131</td>\n",
+       "      <td>4.443192</td>\n",
+       "      <td>-0.002258</td>\n",
+       "      <td>0.045247</td>\n",
+       "      <td>0.583693</td>\n",
+       "      <td>0.309570</td>\n",
+       "      <td>123.781250</td>\n",
+       "      <td>-0.005449</td>\n",
+       "      <td>0.623535</td>\n",
+       "      <td>0.000401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720486</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "      <td>0.056250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.647231</td>\n",
+       "      <td>-5.461624</td>\n",
+       "      <td>-0.000151</td>\n",
+       "      <td>-0.041893</td>\n",
+       "      <td>-3.639202</td>\n",
+       "      <td>-0.159950</td>\n",
+       "      <td>-15.920909</td>\n",
+       "      <td>-0.092828</td>\n",
+       "      <td>-0.306207</td>\n",
+       "      <td>-0.001705</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720487 rows × 27 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "             time  latitude  longitude     fuel_load climatic_region biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0   \n",
+       "1      2010-04-01   -39.625    -73.125   4853.018730             NaN   7.0   \n",
+       "2      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0   \n",
+       "3      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0   \n",
+       "4      2010-04-01   -39.125    -72.125  20342.802716             3.0   1.0   \n",
+       "...           ...       ...        ...           ...             ...   ...   \n",
+       "720482 2016-12-01    45.125      2.875   2764.222562             2.0   1.0   \n",
+       "720483 2016-12-01    48.125   -120.125  10569.716499             1.0   7.0   \n",
+       "720484 2016-12-01    49.125     -0.375   1927.843494             NaN   1.0   \n",
+       "720485 2016-12-01    49.125     -0.125   1113.105571             NaN   1.0   \n",
+       "720486 2016-12-01    49.625   -114.625   1883.780059             2.0   7.0   \n",
+       "\n",
+       "           slope       vod       lai     spi03  ...       t2m     tprate  \\\n",
+       "0       0.003584  0.177971  0.655549  0.776954  ...  0.254569   1.853317   \n",
+       "1       0.013915  0.638954  2.144423 -0.711328  ...  0.299962  55.425812   \n",
+       "2       0.014664  0.832961  3.433299 -1.662499  ... -0.337244  32.763611   \n",
+       "3       0.016293  0.551958  1.811093 -0.465234  ...  0.273752  71.841888   \n",
+       "4       0.033126  0.676935  4.477733 -0.428124  ...  0.076797  92.562195   \n",
+       "...          ...       ...       ...       ...  ...       ...        ...   \n",
+       "720482  0.020528  0.325193  0.777770 -0.266016  ... -1.379390   9.860245   \n",
+       "720483  0.054576  0.310260  0.655549  0.026953  ...  0.631343  -2.685104   \n",
+       "720484  0.005878  0.160282  0.599994 -1.494532  ... -0.608153   6.517075   \n",
+       "720485  0.005559  0.262777  1.111100 -1.486719  ... -0.627131   4.443192   \n",
+       "720486  0.037897  0.306111  0.055555  0.056250  ...  0.647231  -5.461624   \n",
+       "\n",
+       "        danger_risk     fwinx   ffmcode   dufmcode     drtcode   infsinx  \\\n",
+       "0         -0.408514 -6.540662  0.409787 -28.476147 -435.756500 -0.604823   \n",
+       "1         -0.067000 -0.084618  6.273533  -0.538086  -20.080078  0.267007   \n",
+       "2          0.218694  1.595769  6.249068   3.641309    6.909269  0.687314   \n",
+       "3         -0.128096 -0.487267  4.348554  -1.812235  -52.787262  0.159193   \n",
+       "4         -0.099173 -0.372817  4.207914  -1.145141  -49.045151  0.194680   \n",
+       "...             ...       ...       ...        ...         ...       ...   \n",
+       "720482    -0.001600  0.213408  3.380115   0.573486    2.652710  0.321904   \n",
+       "720483     0.000000 -0.034499 -4.597795  -0.155521  -92.605637 -0.084250   \n",
+       "720484    -0.002031  0.067574  0.882902   0.393799  131.938141  0.012117   \n",
+       "720485    -0.002258  0.045247  0.583693   0.309570  123.781250 -0.005449   \n",
+       "720486    -0.000151 -0.041893 -3.639202  -0.159950  -15.920909 -0.092828   \n",
+       "\n",
+       "          fbupinx    fdsrte  \n",
+       "0      -48.407936 -5.157768  \n",
+       "1       -0.873047 -0.086460  \n",
+       "2        5.850041  0.220082  \n",
+       "3       -3.456243 -0.198016  \n",
+       "4       -3.067599 -0.133346  \n",
+       "...           ...       ...  \n",
+       "720482   0.713867  0.011017  \n",
+       "720483  -0.304651 -0.000867  \n",
+       "720484   0.793365  0.001631  \n",
+       "720485   0.623535  0.000401  \n",
+       "720486  -0.306207 -0.001705  \n",
+       "\n",
+       "[720487 rows x 27 columns]"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Fix time stamps\n",
-    "fa_data[\"time\"] = load_data[\"time\"]\n",
-    "# Mask using load\n",
-    "fa_data = fa_data.where(load_data >= 0)\n",
-    "fa_data.fwinx[0].plot()\n",
-    "\n",
-    "# Store data, if needed.\n",
-    "fa_data.to_netcdf(folder_path + \"fire_anomalies_2010-2016.nc\")"
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"danger_risk\"] = fa_data[\"danger_risk\"].sel(longitude=target_lon,\n",
+    "                                latitude=target_lat,\n",
+    "                                time=target_t,\n",
+    "                                method=\"nearest\")\n",
+    "df[\"fwinx\"] = fa_data[\"fwinx\"].sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    time=target_t,\n",
+    "                                    method=\"nearest\")\n",
+    "df[\"ffmcode\"] = fa_data[\"ffmcode\"].sel(longitude=target_lon,\n",
+    "                                  latitude=target_lat,\n",
+    "                                  time=target_t,\n",
+    "                                  method=\"nearest\")\n",
+    "df[\"dufmcode\"] = fa_data[\"dufmcode\"].sel(longitude=target_lon,\n",
+    "                                  latitude=target_lat,\n",
+    "                                  time=target_t,\n",
+    "                                  method=\"nearest\")\n",
+    "df[\"drtcode\"] = fa_data[\"drtcode\"].sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    time=target_t,\n",
+    "                                    method=\"nearest\")\n",
+    "df[\"infsinx\"] = fa_data[\"infsinx\"].sel(longitude=target_lon,\n",
+    "                                latitude=target_lat,\n",
+    "                                time=target_t,\n",
+    "                                method=\"nearest\")\n",
+    "df[\"fbupinx\"] = fa_data[\"fbupinx\"].sel(longitude=target_lon,\n",
+    "                                      latitude=target_lat,\n",
+    "                                      time=target_t,\n",
+    "                                      method=\"nearest\")\n",
+    "df[\"fdsrte\"] = fa_data[\"fdsrte\"].sel(longitude=target_lon,\n",
+    "                                      latitude=target_lat,\n",
+    "                                      time=target_t,\n",
+    "                                      method=\"nearest\")\n",
+    "df"
    ]
   },
   {
@@ -3242,96 +9835,524 @@
    "id": "1fd56481",
    "metadata": {},
    "source": [
-    "# Convert gridded information to table"
+    "# Save data frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b416e69c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/full_dataset.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98e68a53",
+   "metadata": {},
+   "source": [
+    "## Feature engineering"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9a8e54bf",
+   "execution_count": 37,
+   "id": "3b2727b5",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  x = np.divide(x1, x2, out)\n",
-      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  x = np.divide(x1, x2, out)\n"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>fuel_load</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>...</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>tprate</th>\n",
+       "      <th>danger_risk</th>\n",
+       "      <th>fwinx</th>\n",
+       "      <th>ffmcode</th>\n",
+       "      <th>dufmcode</th>\n",
+       "      <th>drtcode</th>\n",
+       "      <th>infsinx</th>\n",
+       "      <th>fbupinx</th>\n",
+       "      <th>fdsrte</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.875</td>\n",
+       "      <td>-65.375</td>\n",
+       "      <td>221.199485</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.776954</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.254569</td>\n",
+       "      <td>1.853317</td>\n",
+       "      <td>-0.408514</td>\n",
+       "      <td>-6.540662</td>\n",
+       "      <td>0.409787</td>\n",
+       "      <td>-28.476147</td>\n",
+       "      <td>-435.756500</td>\n",
+       "      <td>-0.604823</td>\n",
+       "      <td>-48.407936</td>\n",
+       "      <td>-5.157768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.375</td>\n",
+       "      <td>175.125</td>\n",
+       "      <td>11915.029964</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</td>\n",
+       "      <td>-1.662499</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.337244</td>\n",
+       "      <td>32.763611</td>\n",
+       "      <td>0.218694</td>\n",
+       "      <td>1.595769</td>\n",
+       "      <td>6.249068</td>\n",
+       "      <td>3.641309</td>\n",
+       "      <td>6.909269</td>\n",
+       "      <td>0.687314</td>\n",
+       "      <td>5.850041</td>\n",
+       "      <td>0.220082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.375</td>\n",
+       "      <td>7145.629199</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</td>\n",
+       "      <td>-0.465234</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.273752</td>\n",
+       "      <td>71.841888</td>\n",
+       "      <td>-0.128096</td>\n",
+       "      <td>-0.487267</td>\n",
+       "      <td>4.348554</td>\n",
+       "      <td>-1.812235</td>\n",
+       "      <td>-52.787262</td>\n",
+       "      <td>0.159193</td>\n",
+       "      <td>-3.456243</td>\n",
+       "      <td>-0.198016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.125</td>\n",
+       "      <td>-72.125</td>\n",
+       "      <td>20342.802716</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</td>\n",
+       "      <td>-0.428124</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.076797</td>\n",
+       "      <td>92.562195</td>\n",
+       "      <td>-0.099173</td>\n",
+       "      <td>-0.372817</td>\n",
+       "      <td>4.207914</td>\n",
+       "      <td>-1.145141</td>\n",
+       "      <td>-49.045151</td>\n",
+       "      <td>0.194680</td>\n",
+       "      <td>-3.067599</td>\n",
+       "      <td>-0.133346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-38.875</td>\n",
+       "      <td>-73.125</td>\n",
+       "      <td>23222.566453</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.007279</td>\n",
+       "      <td>0.527810</td>\n",
+       "      <td>4.866618</td>\n",
+       "      <td>-0.637109</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.346304</td>\n",
+       "      <td>36.827591</td>\n",
+       "      <td>0.001888</td>\n",
+       "      <td>0.155949</td>\n",
+       "      <td>4.679501</td>\n",
+       "      <td>-1.645196</td>\n",
+       "      <td>4.946845</td>\n",
+       "      <td>0.350790</td>\n",
+       "      <td>-1.998169</td>\n",
+       "      <td>-0.181037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662550</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.375</td>\n",
+       "      <td>14532.521819</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.012388</td>\n",
+       "      <td>0.337806</td>\n",
+       "      <td>1.122211</td>\n",
+       "      <td>-0.680079</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.386452</td>\n",
+       "      <td>11.752808</td>\n",
+       "      <td>0.001543</td>\n",
+       "      <td>0.293289</td>\n",
+       "      <td>3.657032</td>\n",
+       "      <td>0.905957</td>\n",
+       "      <td>2.282520</td>\n",
+       "      <td>0.338811</td>\n",
+       "      <td>1.021387</td>\n",
+       "      <td>0.018449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662551</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.625</td>\n",
+       "      <td>89428.823217</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.017362</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.599994</td>\n",
+       "      <td>-0.484766</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.379390</td>\n",
+       "      <td>9.860245</td>\n",
+       "      <td>-0.000420</td>\n",
+       "      <td>0.246845</td>\n",
+       "      <td>3.500041</td>\n",
+       "      <td>0.730444</td>\n",
+       "      <td>2.304420</td>\n",
+       "      <td>0.331265</td>\n",
+       "      <td>0.832579</td>\n",
+       "      <td>0.014133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662552</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>2764.222562</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "      <td>-0.266016</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.379390</td>\n",
+       "      <td>9.860245</td>\n",
+       "      <td>-0.001600</td>\n",
+       "      <td>0.213408</td>\n",
+       "      <td>3.380115</td>\n",
+       "      <td>0.573486</td>\n",
+       "      <td>2.652710</td>\n",
+       "      <td>0.321904</td>\n",
+       "      <td>0.713867</td>\n",
+       "      <td>0.011017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662553</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>10569.716499</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>0.026953</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.631343</td>\n",
+       "      <td>-2.685104</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.034499</td>\n",
+       "      <td>-4.597795</td>\n",
+       "      <td>-0.155521</td>\n",
+       "      <td>-92.605637</td>\n",
+       "      <td>-0.084250</td>\n",
+       "      <td>-0.304651</td>\n",
+       "      <td>-0.000867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662554</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>1883.780059</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "      <td>0.056250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.647231</td>\n",
+       "      <td>-5.461624</td>\n",
+       "      <td>-0.000151</td>\n",
+       "      <td>-0.041893</td>\n",
+       "      <td>-3.639202</td>\n",
+       "      <td>-0.159950</td>\n",
+       "      <td>-15.920909</td>\n",
+       "      <td>-0.092828</td>\n",
+       "      <td>-0.306207</td>\n",
+       "      <td>-0.001705</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>662555 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude     fuel_load climatic_region biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    221.199485             1.0   1.0   \n",
+       "1      2010-04-01   -39.375    175.125  11915.029964             3.0   7.0   \n",
+       "2      2010-04-01   -39.125    -72.375   7145.629199             3.0   1.0   \n",
+       "3      2010-04-01   -39.125    -72.125  20342.802716             3.0   1.0   \n",
+       "4      2010-04-01   -38.875    -73.125  23222.566453             3.0   1.0   \n",
+       "...           ...       ...        ...           ...             ...   ...   \n",
+       "662550 2016-12-01    45.125      2.375  14532.521819             3.0   1.0   \n",
+       "662551 2016-12-01    45.125      2.625  89428.823217             2.0   1.0   \n",
+       "662552 2016-12-01    45.125      2.875   2764.222562             2.0   1.0   \n",
+       "662553 2016-12-01    48.125   -120.125  10569.716499             1.0   7.0   \n",
+       "662554 2016-12-01    49.625   -114.625   1883.780059             2.0   7.0   \n",
+       "\n",
+       "           slope       vod       lai     spi03  ...       t2m     tprate  \\\n",
+       "0       0.003584  0.177971  0.655549  0.776954  ...  0.254569   1.853317   \n",
+       "1       0.014664  0.832961  3.433299 -1.662499  ... -0.337244  32.763611   \n",
+       "2       0.016293  0.551958  1.811093 -0.465234  ...  0.273752  71.841888   \n",
+       "3       0.033126  0.676935  4.477733 -0.428124  ...  0.076797  92.562195   \n",
+       "4       0.007279  0.527810  4.866618 -0.637109  ...  0.346304  36.827591   \n",
+       "...          ...       ...       ...       ...  ...       ...        ...   \n",
+       "662550  0.012388  0.337806  1.122211 -0.680079  ... -1.386452  11.752808   \n",
+       "662551  0.017362  0.325193  0.599994 -0.484766  ... -1.379390   9.860245   \n",
+       "662552  0.020528  0.325193  0.777770 -0.266016  ... -1.379390   9.860245   \n",
+       "662553  0.054576  0.310260  0.655549  0.026953  ...  0.631343  -2.685104   \n",
+       "662554  0.037897  0.306111  0.055555  0.056250  ...  0.647231  -5.461624   \n",
+       "\n",
+       "        danger_risk     fwinx   ffmcode   dufmcode     drtcode   infsinx  \\\n",
+       "0         -0.408514 -6.540662  0.409787 -28.476147 -435.756500 -0.604823   \n",
+       "1          0.218694  1.595769  6.249068   3.641309    6.909269  0.687314   \n",
+       "2         -0.128096 -0.487267  4.348554  -1.812235  -52.787262  0.159193   \n",
+       "3         -0.099173 -0.372817  4.207914  -1.145141  -49.045151  0.194680   \n",
+       "4          0.001888  0.155949  4.679501  -1.645196    4.946845  0.350790   \n",
+       "...             ...       ...       ...        ...         ...       ...   \n",
+       "662550     0.001543  0.293289  3.657032   0.905957    2.282520  0.338811   \n",
+       "662551    -0.000420  0.246845  3.500041   0.730444    2.304420  0.331265   \n",
+       "662552    -0.001600  0.213408  3.380115   0.573486    2.652710  0.321904   \n",
+       "662553     0.000000 -0.034499 -4.597795  -0.155521  -92.605637 -0.084250   \n",
+       "662554    -0.000151 -0.041893 -3.639202  -0.159950  -15.920909 -0.092828   \n",
+       "\n",
+       "          fbupinx    fdsrte  \n",
+       "0      -48.407936 -5.157768  \n",
+       "1        5.850041  0.220082  \n",
+       "2       -3.456243 -0.198016  \n",
+       "3       -3.067599 -0.133346  \n",
+       "4       -1.998169 -0.181037  \n",
+       "...           ...       ...  \n",
+       "662550   1.021387  0.018449  \n",
+       "662551   0.832579  0.014133  \n",
+       "662552   0.713867  0.011017  \n",
+       "662553  -0.304651 -0.000867  \n",
+       "662554  -0.306207 -0.001705  \n",
+       "\n",
+       "[662555 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Each dataset to be converted to dataframe, removing NAs.\n",
-    "# Note the last three frames have difference labels/row indices compared to the others\n",
-    "# In order to join all the frames, the indices should be resetted (which converts indices to columns)\n",
-    "frames = [\n",
-    "    load_data.to_dataframe(name=\"fuel_load\").dropna().reset_index(),  # outcome (unnamed) feature\n",
-    "    cr_data.to_dataframe(name=\"climatic_region\").dropna().reset_index(),  # static (unnamed) feature\n",
-    "    sl_data.to_dataframe(name=\"slope\").dropna().reset_index(),  # static (unnamed) feature\n",
-    "    biomes_data.to_dataframe(name=\"biome\").dropna().reset_index(),  # static (unnamed) feature\n",
-    "    lai_data.to_dataframe(name=\"lai\").dropna().reset_index(),  # dynamic (unnamed) feature\n",
-    "    vod_data.to_dataframe(name=\"vod\").dropna().reset_index(),  # dynamic (unnamed) feature\n",
-    "    spi_data.to_dataframe().dropna().reset_index(),  # dynamic (named) features\n",
-    "    wa_data.to_dataframe().dropna().reset_index(),  # dynamic (named) features\n",
-    "    fa_data.to_dataframe().dropna().reset_index(),  # dynamic (named) features\n",
-    "]"
+    "# Drop NAN and reset the index\n",
+    "df_engineered = df.dropna().reset_index(drop=True)\n",
+    "df_engineered"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7fd67d20",
+   "execution_count": 38,
+   "id": "f0302902",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.concat(frames, axis=1, join=\"inner\")\n",
-    "df.shape"
+    "# Extract month and year from time, as well as other dateparts\n",
+    "df_engineered = add_datepart(df_engineered, 'time')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bdcac05",
+   "metadata": {},
+   "source": [
+    "As this is monthly data, we can drop some columns."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8ddf5e9d",
+   "execution_count": 39,
+   "id": "8ce1c1be",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Remove duplicated columns\n",
-    "df = df.loc[:,~df.columns.duplicated()]\n",
-    "df"
+    "df_engineered = df_engineered.drop([\"timeDay\", \"timeIs_month_end\", \"timeIs_month_start\",\n",
+    "                                    \"timeIs_quarter_end\", \"timeIs_quarter_start\",\n",
+    "                                    \"timeIs_year_end\", \"timeIs_year_start\",\n",
+    "                                    \"timeWeek\", \"timeDayofweek\", \"timeDayofyear\"], axis = 1)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "98e68a53",
+   "id": "8a3d3fd6",
    "metadata": {},
    "source": [
-    "## Feature engineering"
+    "Make sure categorical variables are defined as such."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "f0302902",
+   "execution_count": 40,
+   "id": "f4ee559a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Extract month and year from time\n",
-    "df[\"year\"] = pd.DatetimeIndex(df[\"time\"]).year\n",
-    "df[\"month\"] = pd.DatetimeIndex(df[\"time\"]).month\n",
-    "df"
+    "df_engineered[\"timeYear\"] = df_engineered[\"timeYear\"].astype('category')\n",
+    "df_engineered[\"timeMonth\"] = df_engineered[\"timeMonth\"].astype('category')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d4e67aa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "latitude            float64\n",
+       "longitude           float64\n",
+       "fuel_load           float64\n",
+       "climatic_region    category\n",
+       "biome              category\n",
+       "slope               float32\n",
+       "vod                 float64\n",
+       "lai                 float32\n",
+       "spi03               float32\n",
+       "spi06               float32\n",
+       "spi12               float32\n",
+       "d2m                 float32\n",
+       "erate               float32\n",
+       "fg10                float32\n",
+       "si10                float32\n",
+       "swvl1               float32\n",
+       "t2m                 float32\n",
+       "tprate              float32\n",
+       "danger_risk         float32\n",
+       "fwinx               float32\n",
+       "ffmcode             float32\n",
+       "dufmcode            float32\n",
+       "drtcode             float32\n",
+       "infsinx             float32\n",
+       "fbupinx             float32\n",
+       "fdsrte              float32\n",
+       "timeYear           category\n",
+       "timeMonth          category\n",
+       "timeElapsed         float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check data types by column\n",
+    "df_engineered.dtypes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "82ac91be",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Drop column time as no longer needed\n",
-    "df = df.drop([\"time\"], axis=1)"
+    "df_engineered.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/full_dataset_engineered.csv\", index=False)"
    ]
   },
   {
@@ -3346,49 +10367,630 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "fcc3cde5",
+   "execution_count": 44,
+   "id": "82393fe6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_data, test_data = train_test_split(df, test_size=0.2, stratify=df[\"biome\"], random_state=1)\n",
-    "\n",
-    "train_data"
+    "# Define dependent variable\n",
+    "dep_var = \"fuel_load\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "4807254b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split columns in continuous (cont) and categorical (cat)\n",
+    "cont,cat = cont_cat_split(df_engineered, 1, dep_var=dep_var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "c8c3895b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['latitude',\n",
+       " 'longitude',\n",
+       " 'slope',\n",
+       " 'vod',\n",
+       " 'lai',\n",
+       " 'spi03',\n",
+       " 'spi06',\n",
+       " 'spi12',\n",
+       " 'd2m',\n",
+       " 'erate',\n",
+       " 'fg10',\n",
+       " 'si10',\n",
+       " 'swvl1',\n",
+       " 't2m',\n",
+       " 'tprate',\n",
+       " 'danger_risk',\n",
+       " 'fwinx',\n",
+       " 'ffmcode',\n",
+       " 'dufmcode',\n",
+       " 'drtcode',\n",
+       " 'infsinx',\n",
+       " 'fbupinx',\n",
+       " 'fdsrte',\n",
+       " 'timeElapsed']"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cont"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "77bb4a4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['climatic_region', 'biome', 'timeYear', 'timeMonth']"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "4eb1c5c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(662555, 28)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = df_engineered.drop([dep_var], axis = 1)\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "9b10c916",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(662555,)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y = df_engineered[dep_var]\n",
+    "Y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "38c443ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,\n",
+    "                                                    test_size=0.2,\n",
+    "                                                    stratify=X[[\"biome\"]],\n",
+    "                                                    random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "01f00266",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/X_train.csv\", index=False)\n",
+    "Y_train.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/Y_train.csv\", index=False)\n",
+    "X_test.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/X_test.csv\", index=False)\n",
+    "Y_test.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/Y_test.csv\", index=False)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3241af0e",
+   "id": "d1483ecf",
    "metadata": {},
    "source": [
-    "### Normalizing the data\n",
-    "\n",
-    "It would be problematic to feed into a neural network values that all take wildly different ranges. The model might be able to automatically adapt to such heterogeneous data, but it would definitely make learning more difficult. A widespread best practice to deal with such data is to do feature-wise normalization: for each feature in the input data (a column in the input data matrix), you subtract the mean of the feature and divide by the standard deviation, so that the feature is centered around 0 and has a unit standard deviation. This is easily done in NumPy."
+    "### Normalise the continuous variables\n",
+    "Make sure continuous variables are normalised."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b501cd1a",
+   "execution_count": 52,
+   "id": "dce2c1f9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Normalizing the data\n",
-    "mean = train_data.mean(axis=0)\n",
-    "std = train_data.std(axis=0)\n",
+    "train_data = pd.concat([X_train, Y_train], axis = 1)\n",
+    "test_data = pd.concat([X_test, Y_test], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "6aa444b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "latitude       float64\n",
+       "longitude      float64\n",
+       "slope          float32\n",
+       "vod            float64\n",
+       "lai            float32\n",
+       "spi03          float32\n",
+       "spi06          float32\n",
+       "spi12          float32\n",
+       "d2m            float32\n",
+       "erate          float32\n",
+       "fg10           float32\n",
+       "si10           float32\n",
+       "swvl1          float32\n",
+       "t2m            float32\n",
+       "tprate         float32\n",
+       "danger_risk    float32\n",
+       "fwinx          float32\n",
+       "ffmcode        float32\n",
+       "dufmcode       float32\n",
+       "drtcode        float32\n",
+       "infsinx        float32\n",
+       "fbupinx        float32\n",
+       "fdsrte         float32\n",
+       "timeElapsed    float64\n",
+       "fuel_load      float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.drop(cat, axis = 1).dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b501cd1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>spi06</th>\n",
+       "      <th>spi12</th>\n",
+       "      <th>d2m</th>\n",
+       "      <th>erate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>danger_risk</th>\n",
+       "      <th>fwinx</th>\n",
+       "      <th>ffmcode</th>\n",
+       "      <th>dufmcode</th>\n",
+       "      <th>drtcode</th>\n",
+       "      <th>infsinx</th>\n",
+       "      <th>fbupinx</th>\n",
+       "      <th>fdsrte</th>\n",
+       "      <th>timeElapsed</th>\n",
+       "      <th>fuel_load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>391276</th>\n",
+       "      <td>0.248079</td>\n",
+       "      <td>0.241404</td>\n",
+       "      <td>0.082894</td>\n",
+       "      <td>-1.052306</td>\n",
+       "      <td>-0.509130</td>\n",
+       "      <td>2.120620</td>\n",
+       "      <td>1.754121</td>\n",
+       "      <td>0.352443</td>\n",
+       "      <td>-1.270124</td>\n",
+       "      <td>1.146253</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.451149</td>\n",
+       "      <td>-1.440420</td>\n",
+       "      <td>-1.243176</td>\n",
+       "      <td>-2.354001</td>\n",
+       "      <td>-1.284542</td>\n",
+       "      <td>-1.510830</td>\n",
+       "      <td>-2.369869</td>\n",
+       "      <td>-1.360720</td>\n",
+       "      <td>0.348794</td>\n",
+       "      <td>-0.350429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456779</th>\n",
+       "      <td>-0.599357</td>\n",
+       "      <td>-1.003416</td>\n",
+       "      <td>-0.709846</td>\n",
+       "      <td>0.052044</td>\n",
+       "      <td>-0.195765</td>\n",
+       "      <td>0.239214</td>\n",
+       "      <td>0.200181</td>\n",
+       "      <td>0.023425</td>\n",
+       "      <td>0.459796</td>\n",
+       "      <td>-2.133155</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.104777</td>\n",
+       "      <td>1.098836</td>\n",
+       "      <td>0.872980</td>\n",
+       "      <td>0.094951</td>\n",
+       "      <td>2.062537</td>\n",
+       "      <td>0.634209</td>\n",
+       "      <td>0.444461</td>\n",
+       "      <td>0.852007</td>\n",
+       "      <td>0.645809</td>\n",
+       "      <td>-0.341992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192416</th>\n",
+       "      <td>0.457445</td>\n",
+       "      <td>1.390774</td>\n",
+       "      <td>1.615924</td>\n",
+       "      <td>1.129737</td>\n",
+       "      <td>0.645842</td>\n",
+       "      <td>0.764069</td>\n",
+       "      <td>1.998650</td>\n",
+       "      <td>0.951655</td>\n",
+       "      <td>-1.727460</td>\n",
+       "      <td>1.439608</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.231193</td>\n",
+       "      <td>-1.929887</td>\n",
+       "      <td>-1.322743</td>\n",
+       "      <td>-1.287559</td>\n",
+       "      <td>-0.705770</td>\n",
+       "      <td>-1.284176</td>\n",
+       "      <td>-1.274695</td>\n",
+       "      <td>-1.339229</td>\n",
+       "      <td>-0.747661</td>\n",
+       "      <td>1.081251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620523</th>\n",
+       "      <td>-0.699055</td>\n",
+       "      <td>-1.114774</td>\n",
+       "      <td>-0.741302</td>\n",
+       "      <td>-0.311069</td>\n",
+       "      <td>-0.544943</td>\n",
+       "      <td>0.366390</td>\n",
+       "      <td>-0.939639</td>\n",
+       "      <td>-1.824422</td>\n",
+       "      <td>0.230440</td>\n",
+       "      <td>-0.461082</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.029780</td>\n",
+       "      <td>0.062817</td>\n",
+       "      <td>-0.163294</td>\n",
+       "      <td>2.451509</td>\n",
+       "      <td>1.562433</td>\n",
+       "      <td>0.198345</td>\n",
+       "      <td>2.485958</td>\n",
+       "      <td>0.250718</td>\n",
+       "      <td>1.532689</td>\n",
+       "      <td>-0.287408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594936</th>\n",
+       "      <td>-0.389990</td>\n",
+       "      <td>-0.044945</td>\n",
+       "      <td>-0.250480</td>\n",
+       "      <td>0.714099</td>\n",
+       "      <td>1.729189</td>\n",
+       "      <td>0.410802</td>\n",
+       "      <td>0.740511</td>\n",
+       "      <td>0.851705</td>\n",
+       "      <td>0.329106</td>\n",
+       "      <td>0.363723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.633098</td>\n",
+       "      <td>0.255349</td>\n",
+       "      <td>0.669322</td>\n",
+       "      <td>-0.075568</td>\n",
+       "      <td>-0.139482</td>\n",
+       "      <td>0.399108</td>\n",
+       "      <td>-0.075274</td>\n",
+       "      <td>-0.050362</td>\n",
+       "      <td>1.405000</td>\n",
+       "      <td>0.150752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195436</th>\n",
+       "      <td>0.168320</td>\n",
+       "      <td>0.221518</td>\n",
+       "      <td>-0.829167</td>\n",
+       "      <td>-1.126471</td>\n",
+       "      <td>-0.849354</td>\n",
+       "      <td>0.178654</td>\n",
+       "      <td>-0.474245</td>\n",
+       "      <td>0.404551</td>\n",
+       "      <td>4.153734</td>\n",
+       "      <td>-0.869612</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.547699</td>\n",
+       "      <td>2.507792</td>\n",
+       "      <td>0.883973</td>\n",
+       "      <td>3.942361</td>\n",
+       "      <td>0.132384</td>\n",
+       "      <td>3.813997</td>\n",
+       "      <td>3.612565</td>\n",
+       "      <td>3.630717</td>\n",
+       "      <td>-0.707412</td>\n",
+       "      <td>-0.333744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64898</th>\n",
+       "      <td>0.228139</td>\n",
+       "      <td>0.098229</td>\n",
+       "      <td>-0.503223</td>\n",
+       "      <td>-0.595003</td>\n",
+       "      <td>0.287712</td>\n",
+       "      <td>0.204896</td>\n",
+       "      <td>-0.659614</td>\n",
+       "      <td>-0.487315</td>\n",
+       "      <td>-0.462450</td>\n",
+       "      <td>-0.018828</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.427589</td>\n",
+       "      <td>-0.391638</td>\n",
+       "      <td>-0.588338</td>\n",
+       "      <td>-0.201240</td>\n",
+       "      <td>-0.292399</td>\n",
+       "      <td>-0.460274</td>\n",
+       "      <td>-0.224211</td>\n",
+       "      <td>-0.291882</td>\n",
+       "      <td>-1.424965</td>\n",
+       "      <td>-0.339423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346080</th>\n",
+       "      <td>-0.679116</td>\n",
+       "      <td>0.046528</td>\n",
+       "      <td>-0.779950</td>\n",
+       "      <td>-0.291714</td>\n",
+       "      <td>-0.553896</td>\n",
+       "      <td>-0.178651</td>\n",
+       "      <td>-0.091646</td>\n",
+       "      <td>-0.414624</td>\n",
+       "      <td>0.202465</td>\n",
+       "      <td>0.005184</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.097607</td>\n",
+       "      <td>0.395338</td>\n",
+       "      <td>-0.142431</td>\n",
+       "      <td>0.344547</td>\n",
+       "      <td>0.700294</td>\n",
+       "      <td>0.934988</td>\n",
+       "      <td>0.442341</td>\n",
+       "      <td>0.865775</td>\n",
+       "      <td>0.054556</td>\n",
+       "      <td>-0.208775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423465</th>\n",
+       "      <td>-0.978211</td>\n",
+       "      <td>0.436280</td>\n",
+       "      <td>1.058543</td>\n",
+       "      <td>-0.496301</td>\n",
+       "      <td>-0.741915</td>\n",
+       "      <td>1.737071</td>\n",
+       "      <td>-0.647781</td>\n",
+       "      <td>0.096117</td>\n",
+       "      <td>-0.112240</td>\n",
+       "      <td>-0.844817</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.183039</td>\n",
+       "      <td>-0.214448</td>\n",
+       "      <td>-0.059789</td>\n",
+       "      <td>-1.364817</td>\n",
+       "      <td>0.508883</td>\n",
+       "      <td>-0.038167</td>\n",
+       "      <td>-0.947092</td>\n",
+       "      <td>-0.232491</td>\n",
+       "      <td>0.518120</td>\n",
+       "      <td>-0.349042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371976</th>\n",
+       "      <td>0.896118</td>\n",
+       "      <td>-1.989727</td>\n",
+       "      <td>1.210805</td>\n",
+       "      <td>0.153173</td>\n",
+       "      <td>-0.428550</td>\n",
+       "      <td>0.514845</td>\n",
+       "      <td>1.367610</td>\n",
+       "      <td>0.710153</td>\n",
+       "      <td>-2.907113</td>\n",
+       "      <td>1.814046</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.520615</td>\n",
+       "      <td>-2.616993</td>\n",
+       "      <td>-1.668386</td>\n",
+       "      <td>-1.081096</td>\n",
+       "      <td>-1.427753</td>\n",
+       "      <td>-2.199400</td>\n",
+       "      <td>-1.217548</td>\n",
+       "      <td>-2.266329</td>\n",
+       "      <td>0.180856</td>\n",
+       "      <td>-0.247145</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>530044 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        latitude  longitude     slope       vod       lai     spi03     spi06  \\\n",
+       "391276  0.248079   0.241404  0.082894 -1.052306 -0.509130  2.120620  1.754121   \n",
+       "456779 -0.599357  -1.003416 -0.709846  0.052044 -0.195765  0.239214  0.200181   \n",
+       "192416  0.457445   1.390774  1.615924  1.129737  0.645842  0.764069  1.998650   \n",
+       "620523 -0.699055  -1.114774 -0.741302 -0.311069 -0.544943  0.366390 -0.939639   \n",
+       "594936 -0.389990  -0.044945 -0.250480  0.714099  1.729189  0.410802  0.740511   \n",
+       "...          ...        ...       ...       ...       ...       ...       ...   \n",
+       "195436  0.168320   0.221518 -0.829167 -1.126471 -0.849354  0.178654 -0.474245   \n",
+       "64898   0.228139   0.098229 -0.503223 -0.595003  0.287712  0.204896 -0.659614   \n",
+       "346080 -0.679116   0.046528 -0.779950 -0.291714 -0.553896 -0.178651 -0.091646   \n",
+       "423465 -0.978211   0.436280  1.058543 -0.496301 -0.741915  1.737071 -0.647781   \n",
+       "371976  0.896118  -1.989727  1.210805  0.153173 -0.428550  0.514845  1.367610   \n",
+       "\n",
+       "           spi12       d2m     erate  ...  danger_risk     fwinx   ffmcode  \\\n",
+       "391276  0.352443 -1.270124  1.146253  ...    -1.451149 -1.440420 -1.243176   \n",
+       "456779  0.023425  0.459796 -2.133155  ...     1.104777  1.098836  0.872980   \n",
+       "192416  0.951655 -1.727460  1.439608  ...    -2.231193 -1.929887 -1.322743   \n",
+       "620523 -1.824422  0.230440 -0.461082  ...     0.029780  0.062817 -0.163294   \n",
+       "594936  0.851705  0.329106  0.363723  ...     0.633098  0.255349  0.669322   \n",
+       "...          ...       ...       ...  ...          ...       ...       ...   \n",
+       "195436  0.404551  4.153734 -0.869612  ...     1.547699  2.507792  0.883973   \n",
+       "64898  -0.487315 -0.462450 -0.018828  ...    -0.427589 -0.391638 -0.588338   \n",
+       "346080 -0.414624  0.202465  0.005184  ...    -0.097607  0.395338 -0.142431   \n",
+       "423465  0.096117 -0.112240 -0.844817  ...    -0.183039 -0.214448 -0.059789   \n",
+       "371976  0.710153 -2.907113  1.814046  ...    -2.520615 -2.616993 -1.668386   \n",
+       "\n",
+       "        dufmcode   drtcode   infsinx   fbupinx    fdsrte  timeElapsed  \\\n",
+       "391276 -2.354001 -1.284542 -1.510830 -2.369869 -1.360720     0.348794   \n",
+       "456779  0.094951  2.062537  0.634209  0.444461  0.852007     0.645809   \n",
+       "192416 -1.287559 -0.705770 -1.284176 -1.274695 -1.339229    -0.747661   \n",
+       "620523  2.451509  1.562433  0.198345  2.485958  0.250718     1.532689   \n",
+       "594936 -0.075568 -0.139482  0.399108 -0.075274 -0.050362     1.405000   \n",
+       "...          ...       ...       ...       ...       ...          ...   \n",
+       "195436  3.942361  0.132384  3.813997  3.612565  3.630717    -0.707412   \n",
+       "64898  -0.201240 -0.292399 -0.460274 -0.224211 -0.291882    -1.424965   \n",
+       "346080  0.344547  0.700294  0.934988  0.442341  0.865775     0.054556   \n",
+       "423465 -1.364817  0.508883 -0.038167 -0.947092 -0.232491     0.518120   \n",
+       "371976 -1.081096 -1.427753 -2.199400 -1.217548 -2.266329     0.180856   \n",
+       "\n",
+       "        fuel_load  \n",
+       "391276  -0.350429  \n",
+       "456779  -0.341992  \n",
+       "192416   1.081251  \n",
+       "620523  -0.287408  \n",
+       "594936   0.150752  \n",
+       "...           ...  \n",
+       "195436  -0.333744  \n",
+       "64898   -0.339423  \n",
+       "346080  -0.208775  \n",
+       "423465  -0.349042  \n",
+       "371976  -0.247145  \n",
+       "\n",
+       "[530044 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Normalizing the data (continuous variables only)\n",
+    "train_data_cont = train_data.drop(cat, axis = 1)\n",
+    "test_data_cont = test_data.drop(cat, axis = 1)\n",
     "\n",
-    "train_data -= mean\n",
-    "train_data /= std\n",
+    "mean = train_data_cont.mean(axis=0)\n",
+    "std = train_data_cont.std(axis=0)\n",
     "\n",
-    "test_data -= mean\n",
-    "test_data /= std\n",
+    "train_data_cont -= mean\n",
+    "train_data_cont /= std\n",
     "\n",
-    "train_data"
+    "test_data_cont -= mean\n",
+    "test_data_cont /= std\n",
+    "\n",
+    "train_data_cont"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "f795e48e",
    "metadata": {},
    "outputs": [],
@@ -3402,49 +11004,371 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "id": "b088a8b0",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Separate features and outcome\n",
-    "train_targets = train_data[\"fuel_load\"]\n",
-    "test_targets = test_data[\"fuel_load\"]\n",
-    "train_data = train_data.drop([\"fuel_load\"], axis=1)\n",
-    "test_data = test_data.drop([\"fuel_load\"], axis=1)"
+    "train_targets = train_data_cont[\"fuel_load\"]\n",
+    "test_targets = test_data_cont[\"fuel_load\"]\n",
+    "train_data = pd.concat([train_data_cont.drop([\"fuel_load\"], axis=1), train_data[cat]], axis = 1)\n",
+    "test_data = pd.concat([test_data_cont.drop([\"fuel_load\"], axis=1), test_data[cat]], axis = 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "fb13ba48",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>spi06</th>\n",
+       "      <th>spi12</th>\n",
+       "      <th>d2m</th>\n",
+       "      <th>erate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tprate</th>\n",
+       "      <th>danger_risk</th>\n",
+       "      <th>fwinx</th>\n",
+       "      <th>ffmcode</th>\n",
+       "      <th>dufmcode</th>\n",
+       "      <th>drtcode</th>\n",
+       "      <th>infsinx</th>\n",
+       "      <th>fbupinx</th>\n",
+       "      <th>fdsrte</th>\n",
+       "      <th>timeElapsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "      <td>5.300440e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.502898e-18</td>\n",
+       "      <td>2.450499e-17</td>\n",
+       "      <td>-2.228009e-07</td>\n",
+       "      <td>1.010362e-16</td>\n",
+       "      <td>-2.306030e-08</td>\n",
+       "      <td>-7.483692e-09</td>\n",
+       "      <td>2.763252e-08</td>\n",
+       "      <td>1.125160e-08</td>\n",
+       "      <td>1.563754e-08</td>\n",
+       "      <td>4.898506e-08</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-6.582181e-10</td>\n",
+       "      <td>1.068764e-07</td>\n",
+       "      <td>-3.381863e-08</td>\n",
+       "      <td>-4.552078e-08</td>\n",
+       "      <td>2.937938e-08</td>\n",
+       "      <td>-9.525656e-09</td>\n",
+       "      <td>3.439821e-08</td>\n",
+       "      <td>2.553362e-08</td>\n",
+       "      <td>5.190894e-09</td>\n",
+       "      <td>1.842472e-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-2.274289e+00</td>\n",
+       "      <td>-3.139097e+00</td>\n",
+       "      <td>-8.313354e-01</td>\n",
+       "      <td>-2.059142e+00</td>\n",
+       "      <td>-9.657467e-01</td>\n",
+       "      <td>-3.039112e+00</td>\n",
+       "      <td>-2.968833e+00</td>\n",
+       "      <td>-2.924330e+00</td>\n",
+       "      <td>-7.264777e+00</td>\n",
+       "      <td>-7.391815e+00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.805733e+01</td>\n",
+       "      <td>-6.170027e+00</td>\n",
+       "      <td>-8.511577e+00</td>\n",
+       "      <td>-9.725815e+00</td>\n",
+       "      <td>-9.896300e+00</td>\n",
+       "      <td>-8.916382e+00</td>\n",
+       "      <td>-9.485180e+00</td>\n",
+       "      <td>-9.214123e+00</td>\n",
+       "      <td>-1.158768e+01</td>\n",
+       "      <td>-1.678955e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-7.189950e-01</td>\n",
+       "      <td>-5.221918e-01</td>\n",
+       "      <td>-5.897838e-01</td>\n",
+       "      <td>-7.657307e-01</td>\n",
+       "      <td>-6.076158e-01</td>\n",
+       "      <td>-6.590949e-01</td>\n",
+       "      <td>-6.714460e-01</td>\n",
+       "      <td>-6.690390e-01</td>\n",
+       "      <td>-5.455540e-01</td>\n",
+       "      <td>-4.487264e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.687768e-01</td>\n",
+       "      <td>-4.968460e-01</td>\n",
+       "      <td>-4.960430e-01</td>\n",
+       "      <td>-4.189093e-01</td>\n",
+       "      <td>-3.773644e-01</td>\n",
+       "      <td>-5.169602e-01</td>\n",
+       "      <td>-5.032740e-01</td>\n",
+       "      <td>-4.196351e-01</td>\n",
+       "      <td>-4.270167e-01</td>\n",
+       "      <td>-9.183753e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-3.401413e-01</td>\n",
+       "      <td>9.425241e-02</td>\n",
+       "      <td>-3.544459e-01</td>\n",
+       "      <td>-1.905224e-01</td>\n",
+       "      <td>-3.121577e-01</td>\n",
+       "      <td>-7.063309e-03</td>\n",
+       "      <td>1.086797e-02</td>\n",
+       "      <td>2.916400e-02</td>\n",
+       "      <td>-8.066957e-02</td>\n",
+       "      <td>8.457552e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.161521e-01</td>\n",
+       "      <td>-1.062461e-01</td>\n",
+       "      <td>-1.081524e-01</td>\n",
+       "      <td>-1.101226e-01</td>\n",
+       "      <td>-1.390857e-01</td>\n",
+       "      <td>-8.776981e-02</td>\n",
+       "      <td>-1.130059e-01</td>\n",
+       "      <td>-1.288251e-01</td>\n",
+       "      <td>-1.697267e-01</td>\n",
+       "      <td>1.153004e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.278376e-01</td>\n",
+       "      <td>3.209448e-01</td>\n",
+       "      <td>1.761723e-01</td>\n",
+       "      <td>6.043474e-01</td>\n",
+       "      <td>1.623656e-01</td>\n",
+       "      <td>6.449673e-01</td>\n",
+       "      <td>6.714899e-01</td>\n",
+       "      <td>6.814597e-01</td>\n",
+       "      <td>4.769868e-01</td>\n",
+       "      <td>5.039009e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.654886e-01</td>\n",
+       "      <td>4.810157e-01</td>\n",
+       "      <td>4.793848e-01</td>\n",
+       "      <td>3.968795e-01</td>\n",
+       "      <td>3.223577e-01</td>\n",
+       "      <td>4.774291e-01</td>\n",
+       "      <td>4.357939e-01</td>\n",
+       "      <td>3.760719e-01</td>\n",
+       "      <td>4.027734e-01</td>\n",
+       "      <td>8.970220e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2.730568e+00</td>\n",
+       "      <td>2.552075e+00</td>\n",
+       "      <td>1.305250e+01</td>\n",
+       "      <td>5.013151e+00</td>\n",
+       "      <td>4.621096e+00</td>\n",
+       "      <td>3.348382e+00</td>\n",
+       "      <td>3.270595e+00</td>\n",
+       "      <td>3.128071e+00</td>\n",
+       "      <td>6.284714e+00</td>\n",
+       "      <td>7.951678e+00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.765109e+01</td>\n",
+       "      <td>6.442316e+00</td>\n",
+       "      <td>7.588995e+00</td>\n",
+       "      <td>1.071997e+01</td>\n",
+       "      <td>1.692898e+01</td>\n",
+       "      <td>1.586427e+01</td>\n",
+       "      <td>1.113215e+01</td>\n",
+       "      <td>1.653699e+01</td>\n",
+       "      <td>1.260159e+01</td>\n",
+       "      <td>1.702015e+00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           latitude     longitude         slope           vod           lai  \\\n",
+       "count  5.300440e+05  5.300440e+05  5.300440e+05  5.300440e+05  5.300440e+05   \n",
+       "mean   5.502898e-18  2.450499e-17 -2.228009e-07  1.010362e-16 -2.306030e-08   \n",
+       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
+       "min   -2.274289e+00 -3.139097e+00 -8.313354e-01 -2.059142e+00 -9.657467e-01   \n",
+       "25%   -7.189950e-01 -5.221918e-01 -5.897838e-01 -7.657307e-01 -6.076158e-01   \n",
+       "50%   -3.401413e-01  9.425241e-02 -3.544459e-01 -1.905224e-01 -3.121577e-01   \n",
+       "75%    3.278376e-01  3.209448e-01  1.761723e-01  6.043474e-01  1.623656e-01   \n",
+       "max    2.730568e+00  2.552075e+00  1.305250e+01  5.013151e+00  4.621096e+00   \n",
+       "\n",
+       "              spi03         spi06         spi12           d2m         erate  \\\n",
+       "count  5.300440e+05  5.300440e+05  5.300440e+05  5.300440e+05  5.300440e+05   \n",
+       "mean  -7.483692e-09  2.763252e-08  1.125160e-08  1.563754e-08  4.898506e-08   \n",
+       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
+       "min   -3.039112e+00 -2.968833e+00 -2.924330e+00 -7.264777e+00 -7.391815e+00   \n",
+       "25%   -6.590949e-01 -6.714460e-01 -6.690390e-01 -5.455540e-01 -4.487264e-01   \n",
+       "50%   -7.063309e-03  1.086797e-02  2.916400e-02 -8.066957e-02  8.457552e-02   \n",
+       "75%    6.449673e-01  6.714899e-01  6.814597e-01  4.769868e-01  5.039009e-01   \n",
+       "max    3.348382e+00  3.270595e+00  3.128071e+00  6.284714e+00  7.951678e+00   \n",
+       "\n",
+       "       ...        tprate   danger_risk         fwinx       ffmcode  \\\n",
+       "count  ...  5.300440e+05  5.300440e+05  5.300440e+05  5.300440e+05   \n",
+       "mean   ... -6.582181e-10  1.068764e-07 -3.381863e-08 -4.552078e-08   \n",
+       "std    ...  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
+       "min    ... -1.805733e+01 -6.170027e+00 -8.511577e+00 -9.725815e+00   \n",
+       "25%    ... -2.687768e-01 -4.968460e-01 -4.960430e-01 -4.189093e-01   \n",
+       "50%    ... -1.161521e-01 -1.062461e-01 -1.081524e-01 -1.101226e-01   \n",
+       "75%    ...  2.654886e-01  4.810157e-01  4.793848e-01  3.968795e-01   \n",
+       "max    ...  1.765109e+01  6.442316e+00  7.588995e+00  1.071997e+01   \n",
+       "\n",
+       "           dufmcode       drtcode       infsinx       fbupinx        fdsrte  \\\n",
+       "count  5.300440e+05  5.300440e+05  5.300440e+05  5.300440e+05  5.300440e+05   \n",
+       "mean   2.937938e-08 -9.525656e-09  3.439821e-08  2.553362e-08  5.190894e-09   \n",
+       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
+       "min   -9.896300e+00 -8.916382e+00 -9.485180e+00 -9.214123e+00 -1.158768e+01   \n",
+       "25%   -3.773644e-01 -5.169602e-01 -5.032740e-01 -4.196351e-01 -4.270167e-01   \n",
+       "50%   -1.390857e-01 -8.776981e-02 -1.130059e-01 -1.288251e-01 -1.697267e-01   \n",
+       "75%    3.223577e-01  4.774291e-01  4.357939e-01  3.760719e-01  4.027734e-01   \n",
+       "max    1.692898e+01  1.586427e+01  1.113215e+01  1.653699e+01  1.260159e+01   \n",
+       "\n",
+       "        timeElapsed  \n",
+       "count  5.300440e+05  \n",
+       "mean   1.842472e-15  \n",
+       "std    1.000000e+00  \n",
+       "min   -1.678955e+00  \n",
+       "25%   -9.183753e-01  \n",
+       "50%    1.153004e-02  \n",
+       "75%    8.970220e-01  \n",
+       "max    1.702015e+00  \n",
+       "\n",
+       "[8 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "train_data.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "162f8f8d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    5.300440e+05\n",
+       "mean     3.820526e-18\n",
+       "std      1.000000e+00\n",
+       "min     -3.513680e-01\n",
+       "25%     -3.400035e-01\n",
+       "50%     -3.018261e-01\n",
+       "75%     -1.168117e-01\n",
+       "max      5.684978e+01\n",
+       "Name: fuel_load, dtype: float64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "train_targets.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "id": "3f577d47",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_data.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/train_data.csv\", index=False)\n",
-    "train_targets.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/train_targets.csv\", index=False)\n",
-    "test_data.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/test_data.csv\", index=False)\n",
-    "test_targets.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/test_targets.csv\", index=False)"
+    "train_data.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/X_train_normalised.csv\", index=False)\n",
+    "train_targets.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/Y_train_normalised.csv\", index=False)\n",
+    "test_data.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/X_test_normalised.csv\", index=False)\n",
+    "test_targets.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/Y_test_normalised.csv\", index=False)"
    ]
   }
  ],