diff --git a/README.md b/README.md
index 89d1d2b..634b40e 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,14 @@ pip install -U pip
 pip install -r requirements.txt
 ```
 
-This includes all the packages required for running the code in the repository.
+This includes all the packages required for running the code in the repository, with the exclusion of the notebooks in the folder `notebooks/ecmwf` (see `notebooks/ecmwf/README.md` for the additional dependencies to install).
+
+The content of this repository is split into 2 types of experiments:
+
+1. target is the fuel load = burned areas * above ground biomass
+2. target is dry matter = burned areas * above ground biomass * combustion coefficients / grid cell areas
+
+## Experiment 1
 
 ### Data Description
 7 years of global historical data, from 2010 - 2016 will be used for developing the machine learning models. All data used in this project is propietary and NOT meant for public release. Xarray, NumPy and netCDF libraries are used for working with the multi-dimensional geospatial data.
@@ -82,7 +89,7 @@ Args description:
       Where root_path is the root save path provided for pre-processing.py
      ```
 
-## Training
+### Training
 
 Entry-point for training is [src/train.py](src/train.py)
 ```
@@ -92,7 +99,7 @@ Args description:
       * `--exp_name`:  Name of the  training experiment used for logging.
 ```
 
-## Inference
+### Inference
 
 Entry-point for inference is [src/test.py](src/test.py)
 ```
@@ -103,20 +110,20 @@ Args description:
       * `--results_path`:  Directory where the result inference .csv files and .html visualizations are going to be stored.
 ```
 
-### Pre-trained models
+#### Pre-trained models
 Pre-trained models are available at:
 - [LightGBM.joblib](src/pre-trained_models/LightGBM.joblib)
 - [CatBoost.joblib](src/pre-trained_models/CatBoost.joblib)
 
 
-### Demo Notebooks
+#### Demo Notebooks
 Notebooks for training and inference:
 - [LightGBM_training.ipynb](notebooks/LightGBM_training.ipynb)
 - [LightGBM_inference.ipynb](notebooks/LightGBM_inference.ipynb)
 - [CatBoost_training.ipynb](notebooks/CatBoost_training.ipynb)
 - [CatBoost_inference.ipynb](notebooks/CatBoost_inference.ipynb)
 
-## Fuel Load Prediction Visualizations:
+### Fuel Load Prediction Visualizations:
 - CatBoost for Mid-Latitudes
 <img width="1025" alt="midlats-prediction-july16" src="https://user-images.githubusercontent.com/7680686/113362982-4d263500-936d-11eb-922e-5a0609e7a67e.png">
 
@@ -124,16 +131,20 @@ Notebooks for training and inference:
 <img width="1025" alt="tropics-prediction-july16" src="https://user-images.githubusercontent.com/7680686/113362967-45ff2700-936d-11eb-93a3-5ad380393f03.png">
 
 
-## Adding New Features:
+### Adding New Features:
 - Make sure the new dataset to be added is a single file in `.nc` format, containing data from 2010-16 and in 0.25x0.25 grid cell resolution.
 - Match the features of the new dataset with the existing features. This can be done by going through `notebooks/EDA_pre-processed_data.ipynb`.
 - Add the feature path as a variable to `src/utils/data_paths.py`. Further the path variable is needed to be added to either the time dependant or independant list (depending on which category it belongs to) present inside `export_feature_paths()`.
 - The model will now also be trained on the added feature while running src/train.py!
 
 
-## Documentation
+### Documentation
 Documentation is available at: [https://ml-fuel.readthedocs.io/en/latest/index.html](https://ml-fuel.readthedocs.io/en/latest/index.html).
 
 
+## Experiment 2
+Please refer to `notebooks/ecmwf/README.md` for a description of this experiment, instructions to install additional dependencies and the notebooks with the steps to perform the experiment.
+
+
 ## Info
 This repository was developed by Anurag Saha Roy (@lazyoracle) and Roshni Biswas (@roshni-b) for the ESA-SMOS-2020 project. Contact email: `info@wikilimo.co`. The repository is now maintained by the Wildfire Danger Forecasting team at the European Centre for Medium-range Weather Forecast.
diff --git a/notebooks/ecmwf/README.md b/notebooks/ecmwf/README.md
new file mode 100644
index 0000000..b6c8e12
--- /dev/null
+++ b/notebooks/ecmwf/README.md
@@ -0,0 +1,33 @@
+# Notebooks for pre-processing and modelling
+
+Notebooks in this folder contain all the steps for data exploration, pre-processing, modelling and explainability using the H2O.ai framework. 
+
+#### Install dependencies
+
+```bash
+conda install cartopy
+conda install -c h2oai h2o
+```
+
+## Notebooks
+
+### 1. Data preparation
+
+This notebook takes the raw/downloaded information and pre-processes it into a data frame. The data is then split into a train and test set using a stratified sampling strategy to make sure both datasets have the same proportion of biomes.
+
+### 2. Exploratory data analysis
+
+This notebook explores the data assembled in notebook `data_preparation.ipynb`. It looks at the probability distributions of outcome and predictors and identifies possible data transformations as well as correlations and redundandies amongst variables.
+
+### 3. Model benchmark tests
+
+This notebook uses the H2O.ai AutoML framework to benchmark various possible data transformations for outcome and predictors. It also compares model results in case all versus non-redundant features are used. The final result is a pre-processed dataset that will be used for the final modelling step in `model_definition.ipynb`.
+
+### 4. Model definition and evaluation
+
+This notebook uses the H2O.ai AutoML framework to model transformed outcome and predictors. It visualises averaged results over a map and uses the H2O.ai explainability module to identify model limitations and possible future avenues for improvements.
+
+
+## Info
+
+These notebooks were developed by the Wildfire Danger Forecasting team at the European Centre for Medium-range Weather Forecast for the ESA-SMOS-2020 project. For any queries, please contact ECMWF support portal: https://confluence.ecmwf.int/site/support.
diff --git a/notebooks/ecmwf/data_preparation.ipynb b/notebooks/ecmwf/data_preparation.ipynb
new file mode 100644
index 0000000..12deb5f
--- /dev/null
+++ b/notebooks/ecmwf/data_preparation.ipynb
@@ -0,0 +1,11918 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3e50aaae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Bad key \"text.kerning_factor\" on line 4 in\n",
+      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
+      "You probably need to get an updated matplotlibrc file from\n",
+      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
+      "or from the matplotlib source distribution\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import seaborn as sb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "516565d1",
+   "metadata": {},
+   "source": [
+    "This notebook takes the raw/downloaded information and pre-processes it into a data frame. This pre-processing procedure DOES NOT assume all gridded data is in the same spatio-temporal resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b32b7b8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Decide whether to interpolate all the grids to the same spatial resolution (True) or\n",
+    "# point-inspect grids at the original resolution (False).\n",
+    "use_interpolation = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7826db32",
+   "metadata": {},
+   "source": [
+    "# Outcome variable (or predictand)\n",
+    "\n",
+    "The outcome variable is the dry matter per unit area, defined as CC * AGB * BA / CellArea"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d9a40f6",
+   "metadata": {},
+   "source": [
+    "## Area (Km^2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ca8ab6ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3fd41cafd0>"
+      ]
+     },
+     "execution_count": 3,
+     "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 area data into an xarray dataset\"\"\"\n",
+    "area_data = xr.open_mfdataset(\"/data1/downloaded/area.nc\")\n",
+    "area_data = area_data.layer\n",
+    "area_data.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de5629dc",
+   "metadata": {},
+   "source": [
+    "## Combustion completeness (CC)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1c1702f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3fd40d66a0>"
+      ]
+     },
+     "execution_count": 4,
+     "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 CC data into an xarray dataset\"\"\"\n",
+    "cc_data = xr.open_dataset(\"/data1/downloaded/GFED4_basis_regions_cc_all.nc\")\n",
+    "cc_data = cc_data.layer\n",
+    "cc_data.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e08681ba",
+   "metadata": {},
+   "source": [
+    "## Above Ground Biomass (AGB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b2d9b8b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
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+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                (latitude: 720, longitude: 1440, time: 84)\n",
+       "Coordinates:\n",
+       "  * time                   (time) datetime64[ns] 2010-01-01 ... 2016-12-01\n",
+       "  * longitude              (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n",
+       "  * latitude               (latitude) float32 -89.88 -89.62 ... 89.62 89.88\n",
+       "Data variables:\n",
+       "    abg_avitabile_vod5th   (time, latitude, longitude) float64 ...\n",
+       "    abg_avitabile_vodmean  (time, latitude, longitude) float64 ...\n",
+       "    abg_avitabile_vod95th  (time, latitude, longitude) float64 ...\n",
+       "    abg_baccini_vod5th     (time, latitude, longitude) float64 ...\n",
+       "    abg_baccini_vodmean    (time, latitude, longitude) float64 ...\n",
+       "    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-d855067d-f52e-4e17-b0cd-306adbb65a72' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d855067d-f52e-4e17-b0cd-306adbb65a72' 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-656788de-12b8-4ddc-820b-519bda330ccc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-656788de-12b8-4ddc-820b-519bda330ccc' 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-345fb7de-10a5-4ac6-8ac8-0e0b634cde1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-345fb7de-10a5-4ac6-8ac8-0e0b634cde1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0b00960-b828-4c4d-b166-2b217f0cde33' class='xr-var-data-in' type='checkbox'><label for='data-e0b00960-b828-4c4d-b166-2b217f0cde33' 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",
+       "       &#x27;2010-09-01T00:00:00.000000000&#x27;, &#x27;2010-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-11-01T00:00:00.000000000&#x27;, &#x27;2010-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2011-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-03-01T00:00:00.000000000&#x27;, &#x27;2011-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-05-01T00:00:00.000000000&#x27;, &#x27;2011-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-07-01T00:00:00.000000000&#x27;, &#x27;2011-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-09-01T00:00:00.000000000&#x27;, &#x27;2011-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-11-01T00:00:00.000000000&#x27;, &#x27;2011-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-03-01T00:00:00.000000000&#x27;, &#x27;2012-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-05-01T00:00:00.000000000&#x27;, &#x27;2012-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-07-01T00:00:00.000000000&#x27;, &#x27;2012-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-09-01T00:00:00.000000000&#x27;, &#x27;2012-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-11-01T00:00:00.000000000&#x27;, &#x27;2012-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-03-01T00:00:00.000000000&#x27;, &#x27;2013-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-05-01T00:00:00.000000000&#x27;, &#x27;2013-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-07-01T00:00:00.000000000&#x27;, &#x27;2013-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-09-01T00:00:00.000000000&#x27;, &#x27;2013-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-11-01T00:00:00.000000000&#x27;, &#x27;2013-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2014-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-03-01T00:00:00.000000000&#x27;, &#x27;2014-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-05-01T00:00:00.000000000&#x27;, &#x27;2014-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-07-01T00:00:00.000000000&#x27;, &#x27;2014-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-09-01T00:00:00.000000000&#x27;, &#x27;2014-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-11-01T00:00:00.000000000&#x27;, &#x27;2014-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-03-01T00:00:00.000000000&#x27;, &#x27;2015-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-05-01T00:00:00.000000000&#x27;, &#x27;2015-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-07-01T00:00:00.000000000&#x27;, &#x27;2015-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-09-01T00:00:00.000000000&#x27;, &#x27;2015-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-11-01T00:00:00.000000000&#x27;, &#x27;2015-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-03-01T00:00:00.000000000&#x27;, &#x27;2016-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-05-01T00:00:00.000000000&#x27;, &#x27;2016-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#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-dab2ac39-8531-4ea9-9af6-5da2f968e6c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dab2ac39-8531-4ea9-9af6-5da2f968e6c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce8c7cef-d824-4dae-ac32-80affb36a8ab' class='xr-var-data-in' type='checkbox'><label for='data-ce8c7cef-d824-4dae-ac32-80affb36a8ab' 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-28285cbd-ab65-49f0-b775-b6e30818ef6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28285cbd-ab65-49f0-b775-b6e30818ef6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73c54698-7267-40bb-b9cc-f76d81332d3c' class='xr-var-data-in' type='checkbox'><label for='data-73c54698-7267-40bb-b9cc-f76d81332d3c' 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-1dbe84a0-7759-40cd-bb64-4df7f51dedd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1dbe84a0-7759-40cd-bb64-4df7f51dedd8' 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-8767f8e6-8876-428c-bab9-e1fcb7d9f38e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8767f8e6-8876-428c-bab9-e1fcb7d9f38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7a98203-8ea7-4613-8ed7-3469a1884348' class='xr-var-data-in' type='checkbox'><label for='data-b7a98203-8ea7-4613-8ed7-3469a1884348' 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-93b21535-ee6d-4953-807c-d1ee6114823d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93b21535-ee6d-4953-807c-d1ee6114823d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-479ec771-db56-4edb-af80-75b8aa84e60e' class='xr-var-data-in' type='checkbox'><label for='data-479ec771-db56-4edb-af80-75b8aa84e60e' 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-f56894dd-471a-47c6-b31c-d40c313800ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f56894dd-471a-47c6-b31c-d40c313800ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-439f8a8c-3392-411f-8f13-32de8e47cd06' class='xr-var-data-in' type='checkbox'><label for='data-439f8a8c-3392-411f-8f13-32de8e47cd06' 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-3e8a57ae-17af-46e0-aa2c-8116747c274c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e8a57ae-17af-46e0-aa2c-8116747c274c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f503fefe-4d28-403a-87f8-f2f30fe24fe2' class='xr-var-data-in' type='checkbox'><label for='data-f503fefe-4d28-403a-87f8-f2f30fe24fe2' 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-c6fa5aff-4a15-4332-bb5b-0be88a32448b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6fa5aff-4a15-4332-bb5b-0be88a32448b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dc56822-92bf-4014-a8ff-15b69170d67f' class='xr-var-data-in' type='checkbox'><label for='data-2dc56822-92bf-4014-a8ff-15b69170d67f' 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-5b8ef864-9bb6-499a-9888-c43e09e5be6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b8ef864-9bb6-499a-9888-c43e09e5be6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e30717a7-9237-4a5d-b74e-e347093183b7' class='xr-var-data-in' type='checkbox'><label for='data-e30717a7-9237-4a5d-b74e-e347093183b7' 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-da09126e-d42d-4cb1-85a2-2e8f45e500d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da09126e-d42d-4cb1-85a2-2e8f45e500d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3036dc9-0b48-45ec-8a55-3b49036961cc' class='xr-var-data-in' type='checkbox'><label for='data-d3036dc9-0b48-45ec-8a55-3b49036961cc' 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-a49ffba8-9135-46bf-a3a8-2c7f33f35e83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a49ffba8-9135-46bf-a3a8-2c7f33f35e83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382612ca-2c84-4cf0-a1b4-084e42e99908' class='xr-var-data-in' type='checkbox'><label for='data-382612ca-2c84-4cf0-a1b4-084e42e99908' 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-6c8887f9-b21b-4cbf-9caf-1eb142023b16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c8887f9-b21b-4cbf-9caf-1eb142023b16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-385f9000-1cee-4254-a98c-ab5c8abaecde' class='xr-var-data-in' type='checkbox'><label for='data-385f9000-1cee-4254-a98c-ab5c8abaecde' 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-e438272b-0ee0-44ea-9e99-3e8e6ca82acc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e438272b-0ee0-44ea-9e99-3e8e6ca82acc' 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",
+       "Dimensions:                (latitude: 720, longitude: 1440, time: 84)\n",
+       "Coordinates:\n",
+       "  * time                   (time) datetime64[ns] 2010-01-01 ... 2016-12-01\n",
+       "  * longitude              (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n",
+       "  * latitude               (latitude) float32 -89.88 -89.62 ... 89.62 89.88\n",
+       "Data variables:\n",
+       "    abg_avitabile_vod5th   (time, latitude, longitude) float64 ...\n",
+       "    abg_avitabile_vodmean  (time, latitude, longitude) float64 ...\n",
+       "    abg_avitabile_vod95th  (time, latitude, longitude) float64 ...\n",
+       "    abg_baccini_vod5th     (time, latitude, longitude) float64 ...\n",
+       "    abg_baccini_vodmean    (time, latitude, longitude) float64 ...\n",
+       "    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 ..."
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load AGB data into an xarray dataset\"\"\"\n",
+    "agb_data = xr.open_dataset(\"/data1/raw_data/veg_2010_2016/all_veg_data.nc\")\n",
+    "agb_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b349a134",
+   "metadata": {},
+   "source": [
+    "Now select below which data variable you would like to use as outcome, this will be used to mask all the other features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c3d865a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Mg/h'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We suggest to select one of the maps by Avitabile et al.\n",
+    "# This is the outcome variable and also works as mask for all the features.\n",
+    "# All the features geerated from here onwards, will depend on this choice!\n",
+    "agb_vartype = \"mean\"  # it can be: 'mean' or '5th' or '95th'\n",
+    "author = \"avitabile\" # it can be: \"avitabile\", \"baccini\", \"saatchi\"\n",
+    "agb_varname = \"abg_\" + author + \"_vod\" + agb_vartype\n",
+    "\n",
+    "agb_data = eval(\"agb_data.\" + agb_varname)\n",
+    "# Units\n",
+    "agb_data.units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bfec3ce3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
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+       "\n",
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+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
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+       "  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;abg_avitabile_vodmean&#x27; (time: 84, latitude: 720, longitude: 1440)&gt;\n",
+       "[87091200 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-01-01 2010-02-01 ... 2016-12-01\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",
+       "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-5fe118e3-8c4e-4714-9084-e285e872c3fe' class='xr-array-in' type='checkbox' checked><label for='section-5fe118e3-8c4e-4714-9084-e285e872c3fe' 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-c3515bd8-d017-4e9a-85ca-8664d2577408' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3515bd8-d017-4e9a-85ca-8664d2577408' 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-97c0f2f0-7ec8-4427-a76b-ec344e9d93b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97c0f2f0-7ec8-4427-a76b-ec344e9d93b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ff19513-b813-4f0b-9cec-9b790a2e2135' class='xr-var-data-in' type='checkbox'><label for='data-9ff19513-b813-4f0b-9cec-9b790a2e2135' 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",
+       "       &#x27;2010-09-01T00:00:00.000000000&#x27;, &#x27;2010-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-11-01T00:00:00.000000000&#x27;, &#x27;2010-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2011-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-03-01T00:00:00.000000000&#x27;, &#x27;2011-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-05-01T00:00:00.000000000&#x27;, &#x27;2011-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-07-01T00:00:00.000000000&#x27;, &#x27;2011-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-09-01T00:00:00.000000000&#x27;, &#x27;2011-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-11-01T00:00:00.000000000&#x27;, &#x27;2011-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-03-01T00:00:00.000000000&#x27;, &#x27;2012-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-05-01T00:00:00.000000000&#x27;, &#x27;2012-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-07-01T00:00:00.000000000&#x27;, &#x27;2012-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-09-01T00:00:00.000000000&#x27;, &#x27;2012-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-11-01T00:00:00.000000000&#x27;, &#x27;2012-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-03-01T00:00:00.000000000&#x27;, &#x27;2013-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-05-01T00:00:00.000000000&#x27;, &#x27;2013-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-07-01T00:00:00.000000000&#x27;, &#x27;2013-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-09-01T00:00:00.000000000&#x27;, &#x27;2013-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-11-01T00:00:00.000000000&#x27;, &#x27;2013-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2014-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-03-01T00:00:00.000000000&#x27;, &#x27;2014-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-05-01T00:00:00.000000000&#x27;, &#x27;2014-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-07-01T00:00:00.000000000&#x27;, &#x27;2014-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-09-01T00:00:00.000000000&#x27;, &#x27;2014-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-11-01T00:00:00.000000000&#x27;, &#x27;2014-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-03-01T00:00:00.000000000&#x27;, &#x27;2015-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-05-01T00:00:00.000000000&#x27;, &#x27;2015-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-07-01T00:00:00.000000000&#x27;, &#x27;2015-08-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-09-01T00:00:00.000000000&#x27;, &#x27;2015-10-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-11-01T00:00:00.000000000&#x27;, &#x27;2015-12-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-03-01T00:00:00.000000000&#x27;, &#x27;2016-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-05-01T00:00:00.000000000&#x27;, &#x27;2016-06-01T00:00:00.000000000&#x27;,\n",
+       "       &#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-3f7e3b66-073c-4c4f-b196-7b332e231bd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f7e3b66-073c-4c4f-b196-7b332e231bd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a05f505a-0050-4fa7-994d-9e2e278ac540' class='xr-var-data-in' type='checkbox'><label for='data-a05f505a-0050-4fa7-994d-9e2e278ac540' 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-5d99e901-645f-4926-ac8e-e4ad2b297875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d99e901-645f-4926-ac8e-e4ad2b297875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d32cd8e8-0a56-4b06-b725-d588de96f8f1' class='xr-var-data-in' type='checkbox'><label for='data-d32cd8e8-0a56-4b06-b725-d588de96f8f1' 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-76d6f47f-47f5-4244-b7cf-09317fe168a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76d6f47f-47f5-4244-b7cf-09317fe168a2' 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",
+       "[87091200 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-01-01 2010-02-01 ... 2016-12-01\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",
+       "Attributes:\n",
+       "    long_name:  AGB estimation using a relation Avitabile et al. as a fct of ...\n",
+       "    units:      Mg/h"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agb_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "373b197b",
+   "metadata": {},
+   "source": [
+    "Please note the units of AGB are Mg/h!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9b657ed3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3f99074220>"
+      ]
+     },
+     "execution_count": 8,
+     "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": [
+    "agb_data[0].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8b4db7cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
+    "# Therefore here we remove Jan-Feb-Mar 2016.\n",
+    "agb_data = agb_data.loc[\"2010-04-01\":\"2016-12-31\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc726c71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3f9005fa00>"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "# Mask out where AGB is zero\n",
+    "AGB_THRESH = 0\n",
+    "\n",
+    "agb_data = agb_data.where(agb_data > AGB_THRESH)\n",
+    "agb_data[0].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d211b25d",
+   "metadata": {},
+   "source": [
+    "## Burned Area (BA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2547c49f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Load BA data into an xarray dataset\"\"\"\n",
+    "ba_source = \"ESA-CCI\" # it can be 'ESA-CCI' or 'GFED4'\n",
+    "\n",
+    "if ba_source == \"ESA-CCI\":\n",
+    "    ba_data = xr.open_mfdataset(\n",
+    "        \"/data1/raw_data/burned_area_2010_2018/201[0-6]*-ESACCI-L4_FIRE-BA-MODIS-fv5.1.nc\"\n",
+    "    )\n",
+    "    # Extract only burned areas\n",
+    "    ba_data = ba_data.burned_area\n",
+    "    # Please note the units of BA are m2! Units: ba_data.units\n",
+    "    # Convert m2 to hectares\n",
+    "    ba_data = ba_data * 0.0001\n",
+    "    # Rename lat/lon dimensions\n",
+    "    ba_data = ba_data.rename({\"lon\": \"longitude\", \"lat\": \"latitude\"})\n",
+    "elif ba_source == \"GFED4\":\n",
+    "    ba_data = xr.open_mfdataset(\n",
+    "        \"/data1/downloaded/GFED_nc_2010_2016/gfed_burned_area_20*.nc\"\n",
+    "    )\n",
+    "    # Rename lat/lon dimensions\n",
+    "    ba_data = ba_data.rename({\"lon\": \"longitude\", \"lat\": \"latitude\"})\n",
+    "    # Convert area from Km2 to hectares\n",
+    "    area_data_h = area_data * 100\n",
+    "    ba_data = ba_data.burned_fraction * area_data_h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f512ec0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# One of the predictors (VOD) is available from April 2010 to December 2016.\n",
+    "# Therefore here we remove Jan-Feb-Mar 2016.\n",
+    "ba_data = ba_data.loc[\"2010-04-01\":\"2016-12-31\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5f8d0132",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3f8d028400>"
+      ]
+     },
+     "execution_count": 13,
+     "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": [
+    "# Mask out low values (small fires)\n",
+    "BA_THRESH = 50  # hectares\n",
+    "ba_data = ba_data.where(ba_data > BA_THRESH)\n",
+    "\n",
+    "ba_data[0].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbb7b5bc",
+   "metadata": {},
+   "source": [
+    "## Dry Matter (DM)\n",
+    "\n",
+    "The fuel load or dry matter (Mg/Km2) is the Above Ground Biomass (Mg/h) * Burned Area (h) * Combustion Completeness (this is unitless rate) / the area of each cell (Km2). This operation is straightforward because the grids have the same spatial and temporal resolution! Please note the result shows values where BA, CC and AGB are not equal to NA. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2a99d5e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3f8ce83af0>"
+      ]
+     },
+     "execution_count": 14,
+     "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": [
+    "dm_data = agb_data * ba_data * cc_data / area_data\n",
+    "dm_data[0].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "39615147",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3f8cdc1580>"
+      ]
+     },
+     "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": [
+    "dm_data[80].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "43d1b7f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store data, if needed.\n",
+    "# folder_path = \"~/ai-vegetation-fuel/data/inputs/drymatter/\"\n",
+    "# Path(folder_path).mkdir(parents=True, exist_ok=True)\n",
+    "# dm_data.to_netcdf(folder_path + \"drymatter_Mg_over_Km2_2010-2016_\" + ba_source + \"_\" + agb_varname + \".nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "60626ba7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
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+       "  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;truediv, 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-d6862a93-f400-4767-afb1-c3a227600a47' class='xr-array-in' type='checkbox' checked><label for='section-d6862a93-f400-4767-afb1-c3a227600a47' 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> 1071 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",
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+       "  <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-45e229f9-7f61-45eb-804c-7f15ba81f95d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45e229f9-7f61-45eb-804c-7f15ba81f95d' 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-87bf9870-12e9-40b3-b821-aca170537e7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87bf9870-12e9-40b3-b821-aca170537e7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e521e30-9691-4e20-9d4f-0e2d54c5f646' class='xr-var-data-in' type='checkbox'><label for='data-4e521e30-9691-4e20-9d4f-0e2d54c5f646' 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-ad53e92a-e425-4c44-b6c6-fdd3c4bb7751' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad53e92a-e425-4c44-b6c6-fdd3c4bb7751' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5050335f-9d39-4de7-9028-0417562c42c8' class='xr-var-data-in' type='checkbox'><label for='data-5050335f-9d39-4de7-9028-0417562c42c8' 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-cd4d3521-b1cd-4831-8281-99e1f1a8dfb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd4d3521-b1cd-4831-8281-99e1f1a8dfb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-092297e8-511d-46e0-b520-3737d7a41b39' class='xr-var-data-in' type='checkbox'><label for='data-092297e8-511d-46e0-b520-3737d7a41b39' 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-49f9b3ba-e939-4597-bb33-3092e573831d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-49f9b3ba-e939-4597-bb33-3092e573831d' 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<truediv, 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": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dm_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "69d55d83",
+   "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>dry_matter</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>0.235708</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>5.152665</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>15.806868</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>7.532907</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>21.445339</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>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708\n",
+       "1      2010-04-01   -39.625    -73.125    5.152665\n",
+       "2      2010-04-01   -39.375    175.125   15.806868\n",
+       "3      2010-04-01   -39.125    -72.375    7.532907\n",
+       "4      2010-04-01   -39.125    -72.125   21.445339\n",
+       "...           ...       ...        ...         ...\n",
+       "720462 2016-12-01    45.125      2.875    3.711282\n",
+       "720463 2016-12-01    48.125   -120.125    9.861333\n",
+       "720464 2016-12-01    49.125     -0.375    2.790013\n",
+       "720465 2016-12-01    49.125     -0.125    1.610908\n",
+       "720466 2016-12-01    49.625   -114.625    2.074960\n",
+       "\n",
+       "[720467 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert cells with non missing values to dataframe\n",
+    "df = dm_data.to_dataframe(name=\"dry_matter\").dropna().reset_index()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "099c827f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    720467.000000\n",
+       "mean         81.575953\n",
+       "std         228.129134\n",
+       "min           0.000003\n",
+       "25%           2.829180\n",
+       "50%          12.308750\n",
+       "75%          56.434086\n",
+       "max       12042.022608\n",
+       "Name: dry_matter, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['dry_matter'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "97d99c55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example: retrieve data at the grid cells nearest to the target latitudes, longitudes and time\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": 21,
+   "id": "d06651fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;climatic_region&#x27; (latitude: 601, longitude: 1200)&gt;\n",
+       "[721200 values with dtype=float32]\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:                             212028\n",
+       "    GRIB_shortName:                           ~\n",
+       "    GRIB_units:                               ~\n",
+       "    GRIB_name:                                Experimental product\n",
+       "    GRIB_cfVarName:                           p28.212\n",
+       "    GRIB_dataType:                            af\n",
+       "    ...                                       ...\n",
+       "    GRIB_jScansPositively:                    0\n",
+       "    GRIB_latitudeOfFirstGridPointInDegrees:   90.0\n",
+       "    GRIB_latitudeOfLastGridPointInDegrees:    -90.0\n",
+       "    long_name:                                Experimental product\n",
+       "    units:                                    ~\n",
+       "    coordinates:                              time step heightAboveGround lat...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'climatic_region'</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-f524a65d-3578-4cce-8e11-f9083aeb22c2' class='xr-array-in' type='checkbox' checked><label for='section-f524a65d-3578-4cce-8e11-f9083aeb22c2' 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>[721200 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-198682eb-e453-430d-b075-0bd69fc6e1fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-198682eb-e453-430d-b075-0bd69fc6e1fb' 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-e6a58efb-3f06-4bac-95c7-cc01c0df1985' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6a58efb-3f06-4bac-95c7-cc01c0df1985' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f87ae98d-01c0-4286-9f67-72d1b1fa79b9' class='xr-var-data-in' type='checkbox'><label for='data-f87ae98d-01c0-4286-9f67-72d1b1fa79b9' 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-92cfc554-3b85-4fd7-a028-d00d477c3ccf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92cfc554-3b85-4fd7-a028-d00d477c3ccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82e22b44-5cea-4747-a873-af4dc5cfbea2' class='xr-var-data-in' type='checkbox'><label for='data-82e22b44-5cea-4747-a873-af4dc5cfbea2' 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-76baf82b-05db-4855-92a1-8b86f17010d2' class='xr-section-summary-in' type='checkbox'  ><label for='section-76baf82b-05db-4855-92a1-8b86f17010d2' 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>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></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'climatic_region' (latitude: 601, longitude: 1200)>\n",
+       "[721200 values with dtype=float32]\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:                             212028\n",
+       "    GRIB_shortName:                           ~\n",
+       "    GRIB_units:                               ~\n",
+       "    GRIB_name:                                Experimental product\n",
+       "    GRIB_cfVarName:                           p28.212\n",
+       "    GRIB_dataType:                            af\n",
+       "    ...                                       ...\n",
+       "    GRIB_jScansPositively:                    0\n",
+       "    GRIB_latitudeOfFirstGridPointInDegrees:   90.0\n",
+       "    GRIB_latitudeOfLastGridPointInDegrees:    -90.0\n",
+       "    long_name:                                Experimental product\n",
+       "    units:                                    ~\n",
+       "    coordinates:                              time step heightAboveGround lat..."
+      ]
+     },
+     "execution_count": 21,
+     "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 = cr_data.climatic_region\n",
+    "cr_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7ff48233",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    cr_data = cr_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"climatic_region\"] = cr_data.sel(longitude=target_lon,\n",
+    "                                    latitude=target_lat,\n",
+    "                                    method=\"nearest\")"
+   ]
+  },
+  {
+   "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. Conversion to categorical type should be done just before modelling (as it is lost when saving data to csv)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "499992c2",
+   "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>dry_matter</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>0.235708</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>5.152665</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>15.806868</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>7.532907</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>21.445339</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>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0\n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN\n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0\n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0\n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0\n",
+       "...           ...       ...        ...         ...              ...\n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0\n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0\n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN\n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN\n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0\n",
+       "\n",
+       "[720467 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"climatic_region\"] = df[\"climatic_region\"].round()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed56e546",
+   "metadata": {},
+   "source": [
+    "## Biomes (categorical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f4eac353",
+   "metadata": {},
+   "outputs": [
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+       "  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;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-5212352e-6357-4d27-b5c7-a1bc8f08f4c4' class='xr-array-in' type='checkbox' checked><label for='section-5212352e-6357-4d27-b5c7-a1bc8f08f4c4' 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-23c7bfa2-1462-4df2-974e-0ceaa3f42747' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23c7bfa2-1462-4df2-974e-0ceaa3f42747' 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-40bc4848-50ad-4e79-8341-8516b0d7317a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40bc4848-50ad-4e79-8341-8516b0d7317a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90102819-225c-4f33-a5fd-ea1ab5e323ba' class='xr-var-data-in' type='checkbox'><label for='data-90102819-225c-4f33-a5fd-ea1ab5e323ba' 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-13ec586e-894b-4828-8c43-4f198c245bf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13ec586e-894b-4828-8c43-4f198c245bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66f82c9e-c7fa-4ce7-b0cb-21eca72cad2f' class='xr-var-data-in' type='checkbox'><label for='data-66f82c9e-c7fa-4ce7-b0cb-21eca72cad2f' 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-b930874c-819e-44d6-8200-2c0c1763fa6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b930874c-819e-44d6-8200-2c0c1763fa6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5b1456-0db6-4dbf-b46f-00715c8ac7c7' class='xr-var-data-in' type='checkbox'><label for='data-7b5b1456-0db6-4dbf-b46f-00715c8ac7c7' 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-aa5c2322-be61-49c2-8131-0dd53d4d4afc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa5c2322-be61-49c2-8131-0dd53d4d4afc' 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": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load slope data into an xarray dataset\"\"\"\n",
+    "biomes_data = xr.open_dataset(\"/data1/downloaded/landcover_25.nc\")\n",
+    "# Convert to data array and select the only time step available\n",
+    "biomes_data = biomes_data.STRF[0]\n",
+    "biomes_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ac23dd62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "        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>dry_matter</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>0.235708</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>5.152665</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>15.806868</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>7.532907</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>21.445339</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>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0\n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0\n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0\n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0\n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0\n",
+       "...           ...       ...        ...         ...              ...    ...\n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0\n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0\n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0\n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0\n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0\n",
+       "\n",
+       "[720467 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# No need to interpolate, spatial resolution already matching the load\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"biome\"] = biomes_data.sel(longitude=target_lon,\n",
+    "                              latitude=target_lat,\n",
+    "                              method=\"nearest\")\n",
+    "    \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9182bfb3",
+   "metadata": {},
+   "source": [
+    "## GFED basis region (categorical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "11150784",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
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+       "\n",
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+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
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+       "  display: inline;\n",
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+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
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+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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+       "\n",
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+       "  display: contents;\n",
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+       "\n",
+       ".xr-section-item input {\n",
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+       "\n",
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+       "\n",
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+       "  padding-bottom: 4px;\n",
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+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
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+       "  display: contents;\n",
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+       ".xr-array-wrap {\n",
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+       "}\n",
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+       ".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",
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+       "  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",
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+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
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+       "  margin: 0;\n",
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+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
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+       "\n",
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+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
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+       "\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",
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+       "  grid-column: 1;\n",
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+       "  grid-column: 3;\n",
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+       ".xr-var-name:hover,\n",
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+       ".xr-var-attrs,\n",
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+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
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+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
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+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
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+       "  padding-right: 10px;\n",
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+       "\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;layer&#x27; (latitude: 720, longitude: 1440)&gt;\n",
+       "dask.array&lt;open_dataset-25361df0b18e7609ff770980d8a035bblayer, shape=(720, 1440), dtype=float32, chunksize=(720, 1440), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float64 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "  * latitude   (latitude) float64 89.88 89.62 89.38 ... -89.38 -89.62 -89.88\n",
+       "Attributes:\n",
+       "    grid_mapping:  crs\n",
+       "    proj4:         +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=...\n",
+       "    min:           1.0\n",
+       "    max:           14.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'layer'</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-f9ff6202-caaa-4149-bc06-452573263025' class='xr-array-in' type='checkbox' checked><label for='section-f9ff6202-caaa-4149-bc06-452573263025' 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=(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> 4.15 MB </td> <td> 4.15 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (720, 1440) </td> <td> (720, 1440) </td></tr>\n",
+       "    <tr><th> Count </th><td> 2 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=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">720</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-2185663a-108f-41e5-8bd5-b1fa66d9d7b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2185663a-108f-41e5-8bd5-b1fa66d9d7b9' 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'>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-eb8fee9d-bf83-49ef-89ac-5e092703e6af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb8fee9d-bf83-49ef-89ac-5e092703e6af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b646776-43fc-49ca-bf89-87fffa72e0e5' class='xr-var-data-in' type='checkbox'><label for='data-8b646776-43fc-49ca-bf89-87fffa72e0e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</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><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 89.38 ... -89.62 -89.88</div><input id='attrs-a66c744d-ad3f-4232-9951-102133edf3a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a66c744d-ad3f-4232-9951-102133edf3a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fadc2e9-acd1-48bc-b5d0-232172f67a97' class='xr-var-data-in' type='checkbox'><label for='data-5fadc2e9-acd1-48bc-b5d0-232172f67a97' 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([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7fbadbaf-5b07-4f80-a24f-b24928ebe1a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fbadbaf-5b07-4f80-a24f-b24928ebe1a7' 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>grid_mapping :</span></dt><dd>crs</dd><dt><span>proj4 :</span></dt><dd>+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0</dd><dt><span>min :</span></dt><dd>1.0</dd><dt><span>max :</span></dt><dd>14.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'layer' (latitude: 720, longitude: 1440)>\n",
+       "dask.array<open_dataset-25361df0b18e7609ff770980d8a035bblayer, shape=(720, 1440), dtype=float32, chunksize=(720, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float64 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "  * latitude   (latitude) float64 89.88 89.62 89.38 ... -89.38 -89.62 -89.88\n",
+       "Attributes:\n",
+       "    grid_mapping:  crs\n",
+       "    proj4:         +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=...\n",
+       "    min:           1.0\n",
+       "    max:           14.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load GFED basis region into an xarray dataset\"\"\"\n",
+    "gfedregions_data = xr.open_mfdataset(\"/data1/downloaded/GFED4_basis_regions_id.nc\")\n",
+    "\n",
+    "gfedregions_data = gfedregions_data.layer\n",
+    "gfedregions_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dcadbd08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
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+       "      <td>0.235708</td>\n",
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+       "      <td>2010-04-01</td>\n",
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+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions  \n",
+       "0               5.0  \n",
+       "1               5.0  \n",
+       "2              14.0  \n",
+       "3               5.0  \n",
+       "4               5.0  \n",
+       "...             ...  \n",
+       "720462          6.0  \n",
+       "720463          2.0  \n",
+       "720464          6.0  \n",
+       "720465          6.0  \n",
+       "720466          1.0  \n",
+       "\n",
+       "[720467 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# No need to interpolate, spatial resolution already matching the load\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes and longitudes\n",
+    "df[\"GFEDregions\"] = gfedregions_data.sel(longitude=target_lon,\n",
+    "                                         latitude=target_lat,\n",
+    "                                         method=\"nearest\")\n",
+    "    \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21253f10",
+   "metadata": {},
+   "source": [
+    "## Slope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d8795f88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</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-4331dd97-6889-434d-bc87-13e549575a9f' class='xr-array-in' type='checkbox' checked><label for='section-4331dd97-6889-434d-bc87-13e549575a9f' 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",
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+       "    <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",
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+       "\n",
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+       "\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-dce5eb97-fb58-413a-8cd3-f46c350f8855' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dce5eb97-fb58-413a-8cd3-f46c350f8855' 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-bb2f6203-57df-45b6-8f26-a42b59e1da5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb2f6203-57df-45b6-8f26-a42b59e1da5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d865c2a-7557-426c-80fb-9221b3ac8f50' class='xr-var-data-in' type='checkbox'><label for='data-6d865c2a-7557-426c-80fb-9221b3ac8f50' 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-b84afca3-14c1-46c4-86fd-79258894f91e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b84afca3-14c1-46c4-86fd-79258894f91e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04206eff-4ec4-4d83-9999-011a33bca712' class='xr-var-data-in' type='checkbox'><label for='data-04206eff-4ec4-4d83-9999-011a33bca712' 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-14f837f8-a115-4810-a9ac-456be2f3b3ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-14f837f8-a115-4810-a9ac-456be2f3b3ee' 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": 28,
+     "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",
+    "slope_data = slope_data.slor\n",
+    "\n",
+    "# Rotate longitude coordinates\n",
+    "slope_data = slope_data.assign_coords(\n",
+    "    longitude=(((slope_data.longitude + 180) % 360) - 180)\n",
+    ").sortby(\"longitude\")\n",
+    "\n",
+    "slope_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "bfde6ecd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3f8cf59e50>"
+      ]
+     },
+     "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": [
+    "slope_data.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "88ba2831",
+   "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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</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>0.235708</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>5.152665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope  \n",
+       "0               5.0  0.003584  \n",
+       "1               5.0  0.013915  \n",
+       "2              14.0  0.014664  \n",
+       "3               5.0  0.016293  \n",
+       "4               5.0  0.033126  \n",
+       "...             ...       ...  \n",
+       "720462          6.0  0.020528  \n",
+       "720463          2.0  0.054576  \n",
+       "720464          6.0  0.005878  \n",
+       "720465          6.0  0.005559  \n",
+       "720466          1.0  0.037897  \n",
+       "\n",
+       "[720467 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    slope_data = slope_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# 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",
+    "\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": 31,
+   "id": "a62d2fbd",
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;SM_IDW&#x27; (time: 81, latitude: 450, longitude: 900)&gt;\n",
+       "dask.array&lt;stack, shape=(81, 450, 900), dtype=float64, chunksize=(1, 450, 900), chunktype=numpy.ndarray&gt;\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</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'SM_IDW'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 81</li><li><span class='xr-has-index'>latitude</span>: 450</li><li><span class='xr-has-index'>longitude</span>: 900</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fad83593-815c-495a-a15f-430cc77de073' class='xr-array-in' type='checkbox' checked><label for='section-fad83593-815c-495a-a15f-430cc77de073' 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, 450, 900), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
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+       "    <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",
+       "    <tr><th> Shape </th><td> (81, 450, 900) </td> <td> (1, 450, 900) </td></tr>\n",
+       "    <tr><th> Count </th><td> 13186 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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+       "</table></div></div></li><li class='xr-section-item'><input id='section-9b97827f-7ed6-424a-a3ec-0e79360b7c96' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b97827f-7ed6-424a-a3ec-0e79360b7c96' 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-8c4037c5-61db-46b4-95c3-b3a0b95c1e77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c4037c5-61db-46b4-95c3-b3a0b95c1e77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9944fad6-1164-4b52-9a44-fbe338080eea' class='xr-var-data-in' type='checkbox'><label for='data-9944fad6-1164-4b52-9a44-fbe338080eea' 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",
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+       "       &#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",
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+       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#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'>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-bab563f0-ac94-4bde-82c3-142c6ef15117' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bab563f0-ac94-4bde-82c3-142c6ef15117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43a9bd8f-26c7-4f1b-9d3a-b31eb5f0062d' class='xr-var-data-in' type='checkbox'><label for='data-43a9bd8f-26c7-4f1b-9d3a-b31eb5f0062d' 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-cd923d01-c09b-43bb-b004-758faca8a43e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd923d01-c09b-43bb-b004-758faca8a43e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f957079c-9c71-4b70-aed9-0e68c51d31c8' class='xr-var-data-in' type='checkbox'><label for='data-f957079c-9c71-4b70-aed9-0e68c51d31c8' 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-f8f0a1f8-c548-482c-866e-246beda8e66e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8f0a1f8-c548-482c-866e-246beda8e66e' 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 'SM_IDW' (time: 81, latitude: 450, longitude: 900)>\n",
+       "dask.array<stack, shape=(81, 450, 900), dtype=float64, chunksize=(1, 450, 900), chunktype=numpy.ndarray>\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"
+      ]
+     },
+     "execution_count": 31,
+     "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",
+    "vod_data = vod_data.SM_IDW\n",
+    "\n",
+    "# Calculate monthly means\n",
+    "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": 32,
+   "id": "744e7357",
+   "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/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",
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+       "\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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</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>0.235708</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>5.152665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope       vod  \n",
+       "0               5.0  0.003584  0.177971  \n",
+       "1               5.0  0.013915  0.638954  \n",
+       "2              14.0  0.014664  0.832961  \n",
+       "3               5.0  0.016293  0.551958  \n",
+       "4               5.0  0.033126  0.676935  \n",
+       "...             ...       ...       ...  \n",
+       "720462          6.0  0.020528  0.325193  \n",
+       "720463          2.0  0.054576  0.310260  \n",
+       "720464          6.0  0.005878  0.160282  \n",
+       "720465          6.0  0.005559  0.262777  \n",
+       "720466          1.0  0.037897  0.306111  \n",
+       "\n",
+       "[720467 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    vod_data = vod_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes, longitudes and time\n",
+    "df[\"vod\"] = vod_data.sel(longitude=target_lon,\n",
+    "                         latitude=target_lat,\n",
+    "                         time=target_t,\n",
+    "                         method=\"nearest\")\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa0f55fc",
+   "metadata": {},
+   "source": [
+    "## Leaf Area Index (LAI)\n",
+    "\n",
+    "According to [this wiki](https://wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:leaf-area_index), 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": 33,
+   "id": "d4eb31fd",
+   "metadata": {},
+   "outputs": [
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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",
+       "  * 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</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-49701830-802a-4f7c-9995-ccbe26153b42' class='xr-array-in' type='checkbox' checked><label for='section-49701830-802a-4f7c-9995-ccbe26153b42' 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",
+       "<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> 261.27 MB </td> <td> 3.23 MB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (81, 560, 1440) </td> <td> (1, 560, 1440) </td></tr>\n",
+       "    <tr><th> Count </th><td> 1509 Tasks </td><td> 81 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
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+       "</table></div></div></li><li class='xr-section-item'><input id='section-81949ae5-4e57-4822-a6ba-00e2035d03b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81949ae5-4e57-4822-a6ba-00e2035d03b6' 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-2931bee2-42b5-4633-8583-dc99ffa1ddec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2931bee2-42b5-4633-8583-dc99ffa1ddec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62dc60e0-3b74-4d09-bdb6-217caddf2253' class='xr-var-data-in' type='checkbox'><label for='data-62dc60e0-3b74-4d09-bdb6-217caddf2253' 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",
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+       "       &#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",
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+       "       &#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",
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+       "       &#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-d3d0af14-7529-4c9f-b439-69677c6b468a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3d0af14-7529-4c9f-b439-69677c6b468a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eb48460-f7d4-4979-ab0c-2407cf5dee7d' class='xr-var-data-in' type='checkbox'><label for='data-6eb48460-f7d4-4979-ab0c-2407cf5dee7d' 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-f4847fb4-62b7-4dcf-a4c8-a8d1caf31916' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4847fb4-62b7-4dcf-a4c8-a8d1caf31916' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d84caabf-8e6c-4e10-8d63-975dd9986816' class='xr-var-data-in' type='checkbox'><label for='data-d84caabf-8e6c-4e10-8d63-975dd9986816' 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-d27fb65c-7a67-4df0-81da-a6fa8600286f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d27fb65c-7a67-4df0-81da-a6fa8600286f' 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 '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": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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",
+    "lai_data = lai_data.LAI\n",
+    "# Rename lat/lon dimensions\n",
+    "lai_data = lai_data.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": 34,
+   "id": "dd6f939b",
+   "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/html": [
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+       "<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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</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>0.235708</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>5.152665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "      <td>0.599994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "      <td>1.111100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope       vod       lai  \n",
+       "0               5.0  0.003584  0.177971  0.655549  \n",
+       "1               5.0  0.013915  0.638954  2.144423  \n",
+       "2              14.0  0.014664  0.832961  3.433299  \n",
+       "3               5.0  0.016293  0.551958  1.811093  \n",
+       "4               5.0  0.033126  0.676935  4.477733  \n",
+       "...             ...       ...       ...       ...  \n",
+       "720462          6.0  0.020528  0.325193  0.777770  \n",
+       "720463          2.0  0.054576  0.310260  0.655549  \n",
+       "720464          6.0  0.005878  0.160282  0.599994  \n",
+       "720465          6.0  0.005559  0.262777  1.111100  \n",
+       "720466          1.0  0.037897  0.306111  0.055555  \n",
+       "\n",
+       "[720467 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    lai_data = lai_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes, longitudes and time\n",
+    "df[\"lai\"] = lai_data.sel(longitude=target_lon,\n",
+    "                         latitude=target_lat,\n",
+    "                         time=target_t,\n",
+    "                         method=\"nearest\")\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c3378ef",
+   "metadata": {},
+   "source": [
+    "## Standardised Precipitation Index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "eaad4f88",
+   "metadata": {},
+   "outputs": [
+    {
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+       ".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: 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-1c402fdd-5d06-4f54-8a25-473eeaa9dc5e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c402fdd-5d06-4f54-8a25-473eeaa9dc5e' 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-75da422c-fada-498d-9594-c2127fd0bb1c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75da422c-fada-498d-9594-c2127fd0bb1c' 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-edd33b24-7644-43ef-a644-ca0faf0ca511' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edd33b24-7644-43ef-a644-ca0faf0ca511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97ebbf15-c00b-412a-856e-5c8c50aa89cb' class='xr-var-data-in' type='checkbox'><label for='data-97ebbf15-c00b-412a-856e-5c8c50aa89cb' 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-704048cf-b4b5-4d47-9862-a48f97b47840' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-704048cf-b4b5-4d47-9862-a48f97b47840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4308caf8-a779-4379-9310-74aa33c8eab5' class='xr-var-data-in' type='checkbox'><label for='data-4308caf8-a779-4379-9310-74aa33c8eab5' 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-b9238c05-2446-43ac-bc1a-1c4461fb45fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9238c05-2446-43ac-bc1a-1c4461fb45fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9378e23-46bc-46c3-abf9-9ed3c3f21b69' class='xr-var-data-in' type='checkbox'><label for='data-d9378e23-46bc-46c3-abf9-9ed3c3f21b69' 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-dd646674-dbd3-4a8c-b280-5514c6126987' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd646674-dbd3-4a8c-b280-5514c6126987' 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-69ce4a57-26d1-4ce6-bef0-a9d8e14f0bb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69ce4a57-26d1-4ce6-bef0-a9d8e14f0bb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123a7ef3-9f25-4f9f-a8d0-a9c43e524a1e' class='xr-var-data-in' type='checkbox'><label for='data-123a7ef3-9f25-4f9f-a8d0-a9c43e524a1e' 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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spi06</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-860134e7-5c86-40c6-8d06-69c71cfb8975' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-860134e7-5c86-40c6-8d06-69c71cfb8975' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c011f8e9-9721-43e1-8f47-e1607b54e387' class='xr-var-data-in' type='checkbox'><label for='data-c011f8e9-9721-43e1-8f47-e1607b54e387' 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>81</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>p81.128</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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+       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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+       "</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-58a13bb0-cea1-4df2-8a27-37f8bf9bd8a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58a13bb0-cea1-4df2-8a27-37f8bf9bd8a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0c8654e-bc09-4733-b57e-fd00e98476ff' class='xr-var-data-in' type='checkbox'><label for='data-c0c8654e-bc09-4733-b57e-fd00e98476ff' 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",
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+       "  <tbody>\n",
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+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-62a8e3f6-6fdf-4c86-8ffd-80330cdc4e1f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-62a8e3f6-6fdf-4c86-8ffd-80330cdc4e1f' 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": [
+       "<xarray.Dataset>\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<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": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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",
+    "\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",
+    "\n",
+    "# Fix time stamps\n",
+    "spi_data[\"time\"] = dm_data[\"time\"]\n",
+    "\n",
+    "spi_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0f52e2d6",
+   "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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</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>0.235708</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>5.152665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</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>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</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>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</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>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</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>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</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>720467 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope       vod       lai     spi03     spi06  \\\n",
+       "0               5.0  0.003584  0.177971  0.655549  0.776954  0.525000   \n",
+       "1               5.0  0.013915  0.638954  2.144423 -0.711328  0.038671   \n",
+       "2              14.0  0.014664  0.832961  3.433299 -1.662499 -1.019922   \n",
+       "3               5.0  0.016293  0.551958  1.811093 -0.465234  0.118750   \n",
+       "4               5.0  0.033126  0.676935  4.477733 -0.428124  0.118750   \n",
+       "...             ...       ...       ...       ...       ...       ...   \n",
+       "720462          6.0  0.020528  0.325193  0.777770 -0.266016 -0.875389   \n",
+       "720463          2.0  0.054576  0.310260  0.655549  0.026953 -0.254295   \n",
+       "720464          6.0  0.005878  0.160282  0.599994 -1.494532 -2.109764   \n",
+       "720465          6.0  0.005559  0.262777  1.111100 -1.486719 -2.152733   \n",
+       "720466          1.0  0.037897  0.306111  0.055555  0.056250  0.458595   \n",
+       "\n",
+       "           spi12  \n",
+       "0      -0.447657  \n",
+       "1       0.396093  \n",
+       "2      -0.342188  \n",
+       "3       0.388281  \n",
+       "4       0.384375  \n",
+       "...          ...  \n",
+       "720462  0.050391  \n",
+       "720463 -0.213281  \n",
+       "720464 -0.639062  \n",
+       "720465 -0.605859  \n",
+       "720466 -0.818749  \n",
+       "\n",
+       "[720467 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    spi_data = spi_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes, longitudes and time\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac66773",
+   "metadata": {},
+   "source": [
+    "## Weather Anomalies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c62bf2e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.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-26c75665-7f00-4021-a52c-8f8ba61d1f0d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26c75665-7f00-4021-a52c-8f8ba61d1f0d' 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-6721415d-d9f8-436b-ad6c-992913d91af6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6721415d-d9f8-436b-ad6c-992913d91af6' 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-698f9b4e-9dfe-4d02-950e-6805e89aade3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-698f9b4e-9dfe-4d02-950e-6805e89aade3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1f3dc89-a4f8-4905-803e-fb44f266c937' class='xr-var-data-in' type='checkbox'><label for='data-a1f3dc89-a4f8-4905-803e-fb44f266c937' 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-033f66fa-7380-4d4f-b006-d2579fc7a190' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-033f66fa-7380-4d4f-b006-d2579fc7a190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0e0141-3045-4101-b697-ce9b40e809e3' class='xr-var-data-in' type='checkbox'><label for='data-6c0e0141-3045-4101-b697-ce9b40e809e3' 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-3fea970b-314a-4927-a151-a8a6fdfea8e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fea970b-314a-4927-a151-a8a6fdfea8e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95694828-352d-4fac-a8e3-d5fcaa137778' class='xr-var-data-in' type='checkbox'><label for='data-95694828-352d-4fac-a8e3-d5fcaa137778' 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",
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+       "</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-ac4e8c03-dee8-4b53-b444-d11d2e676bb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac4e8c03-dee8-4b53-b444-d11d2e676bb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e42b6747-8b66-479b-952e-07923eafe31e' class='xr-var-data-in' type='checkbox'><label for='data-e42b6747-8b66-479b-952e-07923eafe31e' 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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+       "</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-63235b8d-26d9-4a7d-bcae-47dbbcd086a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63235b8d-26d9-4a7d-bcae-47dbbcd086a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73d621c4-22d0-446e-9825-cbdd4b8b2157' class='xr-var-data-in' type='checkbox'><label for='data-73d621c4-22d0-446e-9825-cbdd4b8b2157' 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",
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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-72ff8a15-3f3c-4cbd-bb14-6cbdc1e1b50a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72ff8a15-3f3c-4cbd-bb14-6cbdc1e1b50a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65ea882f-e48d-4f6e-9306-f338bcae31bb' class='xr-var-data-in' type='checkbox'><label for='data-65ea882f-e48d-4f6e-9306-f338bcae31bb' 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>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-64f39aff-5993-48d8-acba-d2b0a4ee379e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64f39aff-5993-48d8-acba-d2b0a4ee379e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e68db8e5-fc85-42db-b631-311aa1ac0f98' class='xr-var-data-in' type='checkbox'><label for='data-e68db8e5-fc85-42db-b631-311aa1ac0f98' 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",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd40ba6f-f499-478e-8b20-0f1b4781e339' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd40ba6f-f499-478e-8b20-0f1b4781e339' 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": [
+       "<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": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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",
+    "\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f0f3c26e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</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>0.235708</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>5.152665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</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>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</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>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</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>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</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>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</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>720467 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope       vod       lai     spi03     spi06  \\\n",
+       "0               5.0  0.003584  0.177971  0.655549  0.776954  0.525000   \n",
+       "1               5.0  0.013915  0.638954  2.144423 -0.711328  0.038671   \n",
+       "2              14.0  0.014664  0.832961  3.433299 -1.662499 -1.019922   \n",
+       "3               5.0  0.016293  0.551958  1.811093 -0.465234  0.118750   \n",
+       "4               5.0  0.033126  0.676935  4.477733 -0.428124  0.118750   \n",
+       "...             ...       ...       ...       ...       ...       ...   \n",
+       "720462          6.0  0.020528  0.325193  0.777770 -0.266016 -0.875389   \n",
+       "720463          2.0  0.054576  0.310260  0.655549  0.026953 -0.254295   \n",
+       "720464          6.0  0.005878  0.160282  0.599994 -1.494532 -2.109764   \n",
+       "720465          6.0  0.005559  0.262777  1.111100 -1.486719 -2.152733   \n",
+       "720466          1.0  0.037897  0.306111  0.055555  0.056250  0.458595   \n",
+       "\n",
+       "           spi12       d2m     erate        fg10        si10     swvl1  \\\n",
+       "0      -0.447657  0.406953  0.827660 -196.723999 -154.286743  0.003297   \n",
+       "1       0.396093  0.287460  3.016632  431.780701   28.249649  0.005519   \n",
+       "2      -0.342188  0.013466 -3.028435  755.989075  154.893127  0.050565   \n",
+       "3       0.388281  0.387394  5.030487  413.472107   42.046005  0.016681   \n",
+       "4       0.384375  0.381312  6.291336  393.559875  -27.938068  0.003139   \n",
+       "...          ...       ...       ...         ...         ...       ...   \n",
+       "720462  0.050391 -1.367281  1.625626  292.504852   59.417725  0.006094   \n",
+       "720463 -0.213281  0.455697  0.501305 -115.465073  -19.559784 -0.018831   \n",
+       "720464 -0.639062 -0.879230 -2.990351  710.314941  152.261505  0.015199   \n",
+       "720465 -0.605859 -0.919799 -2.017698  706.356201  156.933868  0.015889   \n",
+       "720466 -0.818749  0.713625  0.389204 -201.990448    1.390381 -0.014896   \n",
+       "\n",
+       "             t2m     tprate  \n",
+       "0       0.254569   1.853317  \n",
+       "1       0.299962  55.425812  \n",
+       "2      -0.337244  32.763611  \n",
+       "3       0.273752  71.841888  \n",
+       "4       0.076797  92.562195  \n",
+       "...          ...        ...  \n",
+       "720462 -1.379390   9.860245  \n",
+       "720463  0.631343  -2.685104  \n",
+       "720464 -0.608153   6.517075  \n",
+       "720465 -0.627131   4.443192  \n",
+       "720466  0.647231  -5.461624  \n",
+       "\n",
+       "[720467 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    wa_data = wa_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes, longitudes and time\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "490bdd01",
+   "metadata": {},
+   "source": [
+    "## Fire anomalies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "d531a3ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
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+       "  --xr-border-color: #1F1F1F;\n",
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+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
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+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
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+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
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+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
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+       "\n",
+       ".xr-section-item input:enabled + label {\n",
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+       "  color: var(--xr-font-color2);\n",
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+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
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+       ".xr-section-summary {\n",
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+       ".xr-section-summary > span {\n",
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+       "  padding-left: 0.5em;\n",
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+       "\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "\n",
+       ".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",
+       "\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",
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+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
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+       "\n",
+       ".xr-var-dims {\n",
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+       "  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",
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+       "  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",
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+       "\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",
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+       "\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",
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+       "  display: grid;\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.Dataset&gt;\n",
+       "Dimensions:      (latitude: 640, longitude: 1280, time: 81)\n",
+       "Coordinates:\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, 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-ed70fdc7-be07-439e-b9fe-56c63680d5bc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ed70fdc7-be07-439e-b9fe-56c63680d5bc' 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-b69d023e-8859-4d62-8952-b0f360a1f882' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b69d023e-8859-4d62-8952-b0f360a1f882' 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-a74c214f-5d9f-4a00-b5c6-7d9fd618931a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a74c214f-5d9f-4a00-b5c6-7d9fd618931a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-212538e6-afa1-4f84-bbc5-3cd2ff796897' class='xr-var-data-in' type='checkbox'><label for='data-212538e6-afa1-4f84-bbc5-3cd2ff796897' 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-7c498739-c9f9-4b07-bf24-692e3ff91626' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c498739-c9f9-4b07-bf24-692e3ff91626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3994fae1-9b98-43c5-8443-700556b2c355' class='xr-var-data-in' type='checkbox'><label for='data-3994fae1-9b98-43c5-8443-700556b2c355' 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-41e27b0e-58de-4991-b4cd-d0e675d17303' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41e27b0e-58de-4991-b4cd-d0e675d17303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-899bf46f-afc6-41fc-92a8-6e9ba1d9040c' class='xr-var-data-in' type='checkbox'><label for='data-899bf46f-afc6-41fc-92a8-6e9ba1d9040c' 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-5abf3a37-da21-4e6c-81ae-152b909a9687' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5abf3a37-da21-4e6c-81ae-152b909a9687' 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-706239d1-56f0-4dc8-b3b7-eca824fa6456' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-706239d1-56f0-4dc8-b3b7-eca824fa6456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f024230e-2f56-46f6-b62a-1baf34ece86a' class='xr-var-data-in' type='checkbox'><label for='data-f024230e-2f56-46f6-b62a-1baf34ece86a' 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",
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+       "</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-8fd18eb3-64ad-4925-a4fd-173be5dd3ecc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fd18eb3-64ad-4925-a4fd-173be5dd3ecc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d162eca8-c543-4b0b-98a0-426893dd9ca8' class='xr-var-data-in' type='checkbox'><label for='data-d162eca8-c543-4b0b-98a0-426893dd9ca8' 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",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c1dfb18-436a-4c13-b8c0-01118bcad7bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c1dfb18-436a-4c13-b8c0-01118bcad7bf' 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: 640, longitude: 1280, time: 81)\n",
+       "Coordinates:\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, 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",
+       "    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..."
+      ]
+     },
+     "execution_count": 39,
+     "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\"] = dm_data[\"time\"]\n",
+    "\n",
+    "fa_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f29e69ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
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+       "  <tbody>\n",
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+       "      <th>0</th>\n",
+       "      <td>2010-04-01</td>\n",
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+       "      <td>-0.604823</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>2010-04-01</td>\n",
+       "      <td>-39.625</td>\n",
+       "      <td>-73.125</td>\n",
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+       "      <td>0.638954</td>\n",
+       "      <td>2.144423</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
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+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</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",
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+       "      <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>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</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>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
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+       "      <td>1.0</td>\n",
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+       "      <td>2.0</td>\n",
+       "      <td>0.054576</td>\n",
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+       "      <td>-0.304651</td>\n",
+       "      <td>-0.000867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "      <td>0.599994</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>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "      <td>1.111100</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>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</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>720467 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope       vod       lai  ...       t2m     tprate  \\\n",
+       "0               5.0  0.003584  0.177971  0.655549  ...  0.254569   1.853317   \n",
+       "1               5.0  0.013915  0.638954  2.144423  ...  0.299962  55.425812   \n",
+       "2              14.0  0.014664  0.832961  3.433299  ... -0.337244  32.763611   \n",
+       "3               5.0  0.016293  0.551958  1.811093  ...  0.273752  71.841888   \n",
+       "4               5.0  0.033126  0.676935  4.477733  ...  0.076797  92.562195   \n",
+       "...             ...       ...       ...       ...  ...       ...        ...   \n",
+       "720462          6.0  0.020528  0.325193  0.777770  ... -1.379390   9.860245   \n",
+       "720463          2.0  0.054576  0.310260  0.655549  ...  0.631343  -2.685104   \n",
+       "720464          6.0  0.005878  0.160282  0.599994  ... -0.608153   6.517075   \n",
+       "720465          6.0  0.005559  0.262777  1.111100  ... -0.627131   4.443192   \n",
+       "720466          1.0  0.037897  0.306111  0.055555  ...  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",
+       "720462    -0.001600  0.213408  3.380115   0.573486    2.652710  0.321904   \n",
+       "720463     0.000000 -0.034499 -4.597795  -0.155521  -92.605637 -0.084250   \n",
+       "720464    -0.002031  0.067574  0.882902   0.393799  131.938141  0.012117   \n",
+       "720465    -0.002258  0.045247  0.583693   0.309570  123.781250 -0.005449   \n",
+       "720466    -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",
+       "720462   0.713867  0.011017  \n",
+       "720463  -0.304651 -0.000867  \n",
+       "720464   0.793365  0.001631  \n",
+       "720465   0.623535  0.000401  \n",
+       "720466  -0.306207 -0.001705  \n",
+       "\n",
+       "[720467 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    fa_data = fa_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes, longitudes and time\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5e589b0",
+   "metadata": {},
+   "source": [
+    "## Fire Radiative Power (FRP)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "16fee468",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
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+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
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+       "}\n",
+       "\n",
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+       "  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",
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+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;frpfire&#x27; (time: 81, latitude: 1800, longitude: 3600)&gt;\n",
+       "[524880000 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude  (longitude) float32 -180.0 -179.9 -179.8 ... 179.8 179.9 180.0\n",
+       "  * latitude   (latitude) float32 89.95 89.85 89.75 ... -89.75 -89.85 -89.95\n",
+       "Attributes:\n",
+       "    long_name:     Wildfire radiative power\n",
+       "    units:         W m**-2\n",
+       "    cell_methods:  time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'frpfire'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 81</li><li><span class='xr-has-index'>latitude</span>: 1800</li><li><span class='xr-has-index'>longitude</span>: 3600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-12bcd573-b2aa-4ddc-8edb-8a3e2899b694' class='xr-array-in' type='checkbox' checked><label for='section-12bcd573-b2aa-4ddc-8edb-8a3e2899b694' 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>[524880000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-9ad29909-79aa-474f-a78e-c289110e6c1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ad29909-79aa-474f-a78e-c289110e6c1a' 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-9b2aed86-6d3b-4dbd-897b-7dd4ed7a5c3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b2aed86-6d3b-4dbd-897b-7dd4ed7a5c3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b21c85b1-7170-42c5-a68a-b1e427ac926e' class='xr-var-data-in' type='checkbox'><label for='data-b21c85b1-7170-42c5-a68a-b1e427ac926e' 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 -179.9 ... 179.9 180.0</div><input id='attrs-2684e3bf-2bf4-4fa3-a7c3-3df25eef901a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2684e3bf-2bf4-4fa3-a7c3-3df25eef901a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb646dfb-1821-46ee-a6b1-3695b7b4666c' class='xr-var-data-in' type='checkbox'><label for='data-cb646dfb-1821-46ee-a6b1-3695b7b4666c' 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([-179.95001, -179.85   , -179.75   , ...,  179.75   ,  179.85   ,\n",
+       "        179.95001], 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'>89.95 89.85 89.75 ... -89.85 -89.95</div><input id='attrs-2a6a8f2b-f238-45f4-919a-feaa8a71fe9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a6a8f2b-f238-45f4-919a-feaa8a71fe9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf258819-f069-4d08-b941-a67f783b9fa2' class='xr-var-data-in' type='checkbox'><label for='data-cf258819-f069-4d08-b941-a67f783b9fa2' 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([ 89.95,  89.85,  89.75, ..., -89.75, -89.85, -89.95], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eab0574a-7b64-48ea-b4f5-0148c765231e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eab0574a-7b64-48ea-b4f5-0148c765231e' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wildfire radiative power</dd><dt><span>units :</span></dt><dd>W m**-2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'frpfire' (time: 81, latitude: 1800, longitude: 3600)>\n",
+       "[524880000 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2010-04-01 2010-05-01 ... 2016-12-01\n",
+       "  * longitude  (longitude) float32 -180.0 -179.9 -179.8 ... 179.8 179.9 180.0\n",
+       "  * latitude   (latitude) float32 89.95 89.85 89.75 ... -89.75 -89.85 -89.95\n",
+       "Attributes:\n",
+       "    long_name:     Wildfire radiative power\n",
+       "    units:         W m**-2\n",
+       "    cell_methods:  time: mean"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Load FRP data into an xarray dataset\"\"\"\n",
+    "frp_data = xr.open_dataset(\"/data1/downloaded/CAMS_frpfire/CAMS_daily_2010-04-01_2016-12-31_monavg.nc\")\n",
+    "frp_data = frp_data.frpfire\n",
+    "\n",
+    "# Fix time stamps\n",
+    "frp_data[\"time\"] = dm_data[\"time\"]\n",
+    "\n",
+    "# Rotate longitude coordinates\n",
+    "frp_data = frp_data.assign_coords(\n",
+    "    longitude=(((frp_data.longitude + 180) % 360) - 180)\n",
+    ").sortby(\"longitude\")\n",
+    "\n",
+    "frp_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "5f45f48e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "<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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</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>frp</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>0.235708</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</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>5.152665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.013915</td>\n",
+       "      <td>0.638954</td>\n",
+       "      <td>2.144423</td>\n",
+       "      <td>...</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",
+       "      <td>0.019714</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</td>\n",
+       "      <td>...</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",
+       "      <td>0.007889</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <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>720462</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720463</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720464</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>2.790013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005878</td>\n",
+       "      <td>0.160282</td>\n",
+       "      <td>0.599994</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720465</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.125</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>1.610908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.005559</td>\n",
+       "      <td>0.262777</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720466</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>720467 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.625    -73.125    5.152665              NaN    7.0   \n",
+       "2      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "3      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "4      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "720462 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "720463 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "720464 2016-12-01    49.125     -0.375    2.790013              NaN    1.0   \n",
+       "720465 2016-12-01    49.125     -0.125    1.610908              NaN    1.0   \n",
+       "720466 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope       vod       lai  ...     tprate  \\\n",
+       "0               5.0  0.003584  0.177971  0.655549  ...   1.853317   \n",
+       "1               5.0  0.013915  0.638954  2.144423  ...  55.425812   \n",
+       "2              14.0  0.014664  0.832961  3.433299  ...  32.763611   \n",
+       "3               5.0  0.016293  0.551958  1.811093  ...  71.841888   \n",
+       "4               5.0  0.033126  0.676935  4.477733  ...  92.562195   \n",
+       "...             ...       ...       ...       ...  ...        ...   \n",
+       "720462          6.0  0.020528  0.325193  0.777770  ...   9.860245   \n",
+       "720463          2.0  0.054576  0.310260  0.655549  ...  -2.685104   \n",
+       "720464          6.0  0.005878  0.160282  0.599994  ...   6.517075   \n",
+       "720465          6.0  0.005559  0.262777  1.111100  ...   4.443192   \n",
+       "720466          1.0  0.037897  0.306111  0.055555  ...  -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",
+       "720462    -0.001600  0.213408  3.380115   0.573486    2.652710  0.321904   \n",
+       "720463     0.000000 -0.034499 -4.597795  -0.155521  -92.605637 -0.084250   \n",
+       "720464    -0.002031  0.067574  0.882902   0.393799  131.938141  0.012117   \n",
+       "720465    -0.002258  0.045247  0.583693   0.309570  123.781250 -0.005449   \n",
+       "720466    -0.000151 -0.041893 -3.639202  -0.159950  -15.920909 -0.092828   \n",
+       "\n",
+       "          fbupinx    fdsrte       frp  \n",
+       "0      -48.407936 -5.157768  0.000000  \n",
+       "1       -0.873047 -0.086460  0.019714  \n",
+       "2        5.850041  0.220082  0.000000  \n",
+       "3       -3.456243 -0.198016  0.007889  \n",
+       "4       -3.067599 -0.133346  0.000000  \n",
+       "...           ...       ...       ...  \n",
+       "720462   0.713867  0.011017  0.000000  \n",
+       "720463  -0.304651 -0.000867  0.000000  \n",
+       "720464   0.793365  0.001631  0.000000  \n",
+       "720465   0.623535  0.000401  0.000000  \n",
+       "720466  -0.306207 -0.001705  0.000000  \n",
+       "\n",
+       "[720467 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if use_interpolation == True:\n",
+    "    # Interpolate to match load resolution\n",
+    "    frp_data = frp_data.interp(\n",
+    "        coords={\n",
+    "            \"latitude\": dm_data.latitude.values,\n",
+    "            \"longitude\": dm_data.longitude.values,\n",
+    "        },\n",
+    "        method=\"nearest\",\n",
+    "    )\n",
+    "\n",
+    "# Retrieve data at the grid cells nearest to the target latitudes, longitudes and time\n",
+    "df[\"frp\"] = frp_data.sel(longitude=target_lon,\n",
+    "                         latitude=target_lat,\n",
+    "                         time=target_t,\n",
+    "                         method=\"nearest\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98e68a53",
+   "metadata": {},
+   "source": [
+    "## Feature engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9703f324",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "time               datetime64[ns]\n",
+       "latitude                  float64\n",
+       "longitude                 float64\n",
+       "dry_matter                float64\n",
+       "climatic_region           float32\n",
+       "biome                     float64\n",
+       "GFEDregions               float32\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",
+       "frp                       float32\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check data types by column\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "3b2727b5",
+   "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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</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>frp</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>0.235708</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.003584</td>\n",
+       "      <td>0.177971</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</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>15.806868</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.014664</td>\n",
+       "      <td>0.832961</td>\n",
+       "      <td>3.433299</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</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>7.532907</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.016293</td>\n",
+       "      <td>0.551958</td>\n",
+       "      <td>1.811093</td>\n",
+       "      <td>...</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",
+       "      <td>0.007889</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>21.445339</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.033126</td>\n",
+       "      <td>0.676935</td>\n",
+       "      <td>4.477733</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</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>24.395179</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.007279</td>\n",
+       "      <td>0.527810</td>\n",
+       "      <td>4.866618</td>\n",
+       "      <td>...</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",
+       "      <td>0.003937</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>662536</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.375</td>\n",
+       "      <td>19.511560</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.012388</td>\n",
+       "      <td>0.337806</td>\n",
+       "      <td>1.122211</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662537</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.625</td>\n",
+       "      <td>120.068342</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.017362</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.599994</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662538</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>45.125</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>3.711282</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.325193</td>\n",
+       "      <td>0.777770</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662539</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>48.125</td>\n",
+       "      <td>-120.125</td>\n",
+       "      <td>9.861333</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.054576</td>\n",
+       "      <td>0.310260</td>\n",
+       "      <td>0.655549</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662540</th>\n",
+       "      <td>2016-12-01</td>\n",
+       "      <td>49.625</td>\n",
+       "      <td>-114.625</td>\n",
+       "      <td>2.074960</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.037897</td>\n",
+       "      <td>0.306111</td>\n",
+       "      <td>0.055555</td>\n",
+       "      <td>...</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",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>662541 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             time  latitude  longitude  dry_matter  climatic_region  biome  \\\n",
+       "0      2010-04-01   -39.875    -65.375    0.235708              1.0    1.0   \n",
+       "1      2010-04-01   -39.375    175.125   15.806868              3.0    7.0   \n",
+       "2      2010-04-01   -39.125    -72.375    7.532907              3.0    1.0   \n",
+       "3      2010-04-01   -39.125    -72.125   21.445339              3.0    1.0   \n",
+       "4      2010-04-01   -38.875    -73.125   24.395179              3.0    1.0   \n",
+       "...           ...       ...        ...         ...              ...    ...   \n",
+       "662536 2016-12-01    45.125      2.375   19.511560              3.0    1.0   \n",
+       "662537 2016-12-01    45.125      2.625  120.068342              2.0    1.0   \n",
+       "662538 2016-12-01    45.125      2.875    3.711282              2.0    1.0   \n",
+       "662539 2016-12-01    48.125   -120.125    9.861333              1.0    7.0   \n",
+       "662540 2016-12-01    49.625   -114.625    2.074960              2.0    7.0   \n",
+       "\n",
+       "        GFEDregions     slope       vod       lai  ...     tprate  \\\n",
+       "0               5.0  0.003584  0.177971  0.655549  ...   1.853317   \n",
+       "1              14.0  0.014664  0.832961  3.433299  ...  32.763611   \n",
+       "2               5.0  0.016293  0.551958  1.811093  ...  71.841888   \n",
+       "3               5.0  0.033126  0.676935  4.477733  ...  92.562195   \n",
+       "4               5.0  0.007279  0.527810  4.866618  ...  36.827591   \n",
+       "...             ...       ...       ...       ...  ...        ...   \n",
+       "662536          6.0  0.012388  0.337806  1.122211  ...  11.752808   \n",
+       "662537          6.0  0.017362  0.325193  0.599994  ...   9.860245   \n",
+       "662538          6.0  0.020528  0.325193  0.777770  ...   9.860245   \n",
+       "662539          2.0  0.054576  0.310260  0.655549  ...  -2.685104   \n",
+       "662540          1.0  0.037897  0.306111  0.055555  ...  -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",
+       "662536     0.001543  0.293289  3.657032   0.905957    2.282520  0.338811   \n",
+       "662537    -0.000420  0.246845  3.500041   0.730444    2.304420  0.331265   \n",
+       "662538    -0.001600  0.213408  3.380115   0.573486    2.652710  0.321904   \n",
+       "662539     0.000000 -0.034499 -4.597795  -0.155521  -92.605637 -0.084250   \n",
+       "662540    -0.000151 -0.041893 -3.639202  -0.159950  -15.920909 -0.092828   \n",
+       "\n",
+       "          fbupinx    fdsrte       frp  \n",
+       "0      -48.407936 -5.157768  0.000000  \n",
+       "1        5.850041  0.220082  0.000000  \n",
+       "2       -3.456243 -0.198016  0.007889  \n",
+       "3       -3.067599 -0.133346  0.000000  \n",
+       "4       -1.998169 -0.181037  0.003937  \n",
+       "...           ...       ...       ...  \n",
+       "662536   1.021387  0.018449  0.000000  \n",
+       "662537   0.832579  0.014133  0.000000  \n",
+       "662538   0.713867  0.011017  0.000000  \n",
+       "662539  -0.304651 -0.000867  0.000000  \n",
+       "662540  -0.306207 -0.001705  0.000000  \n",
+       "\n",
+       "[662541 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop NAN and reset the index\n",
+    "df = df.dropna().reset_index(drop=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bdcac05",
+   "metadata": {},
+   "source": [
+    "Calculate time elapsed (in days) since 2010-01-01 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "8ce1c1be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def elapsed_days(last_date):\n",
+    "    return (last_date - datetime(2010, 1, 1)).dt.days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "693d7102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract month and year from time, as well as other dateparts\n",
+    "df['daysElapsed'] = elapsed_days(df['time'])\n",
+    "df['timeYear'] = df['time'].dt.year\n",
+    "df['timeMonth'] = df['time'].dt.month\n",
+    "df = df.drop('time', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "d4e67aa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "latitude           float64\n",
+       "longitude          float64\n",
+       "dry_matter         float64\n",
+       "climatic_region    float32\n",
+       "biome              float64\n",
+       "GFEDregions        float32\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",
+       "frp                float32\n",
+       "daysElapsed          int64\n",
+       "timeYear             int64\n",
+       "timeMonth            int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check data types by column\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fd56481",
+   "metadata": {},
+   "source": [
+    "## Save data frame with raw features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "82ac91be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"~/ai-vegetation-fuel/data/inputs/full_dataset_interp\" + str(use_interpolation) + \".csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a09affb5",
+   "metadata": {},
+   "source": [
+    "### Splitting dataset into train/test sets and targets\n",
+    "\n",
+    "I want to make sure in train and test data we have approximately the same proportion of biomes. In order to achieve that we use stratified sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "38c443ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train, test = train_test_split(df,\n",
+    "                               test_size=0.2,\n",
+    "                               stratify=df[[\"biome\"]],\n",
+    "                               random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "01f00266",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.to_csv(\"~/ai-vegetation-fuel/data/inputs/train_interp\" + str(use_interpolation) + \"_raw.csv\")\n",
+    "test.to_csv(\"~/ai-vegetation-fuel/data/inputs/test_interp\" + str(use_interpolation) + \"_raw.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/ecmwf/exploratory_data_analysis.ipynb b/notebooks/ecmwf/exploratory_data_analysis.ipynb
new file mode 100644
index 0000000..1769157
--- /dev/null
+++ b/notebooks/ecmwf/exploratory_data_analysis.ipynb
@@ -0,0 +1,1328 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e55e06e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Bad key \"text.kerning_factor\" on line 4 in\n",
+      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
+      "You probably need to get an updated matplotlibrc file from\n",
+      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
+      "or from the matplotlib source distribution\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "471afef3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define plot size\n",
+    "plt.rcParams[\"figure.figsize\"] = (15,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "293df17a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define working directory\n",
+    "os.chdir('/home/moc0/ai-vegetation-fuel')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1425c1d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load raw train data\n",
+    "train = pd.read_csv(os.getcwd() + \"/data/inputs/train_interpTrue_raw.csv\",\n",
+    "                    dtype={'climatic_region':'category',\n",
+    "                           'biome':'category',\n",
+    "                           'GFEDregions':'category'})\n",
+    "train = train.drop('Unnamed: 0', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e4ad63",
+   "metadata": {},
+   "source": [
+    "# Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37084306",
+   "metadata": {},
+   "source": [
+    "This notebook explores the data assembled in notebook `data_preparation.ipynb`. It looks at the probability distributions of outcome and predictors, possible data transformations as well as correlations and redundandies amongst variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "795abaf0",
+   "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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>...</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>frp</th>\n",
+       "      <th>daysElapsed</th>\n",
+       "      <th>timeYear</th>\n",
+       "      <th>timeMonth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-32.375</td>\n",
+       "      <td>28.125</td>\n",
+       "      <td>1.001038</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.014507</td>\n",
+       "      <td>0.228241</td>\n",
+       "      <td>0.222220</td>\n",
+       "      <td>-0.398828</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.302874</td>\n",
+       "      <td>13.643188</td>\n",
+       "      <td>109.829220</td>\n",
+       "      <td>1.191657</td>\n",
+       "      <td>22.684143</td>\n",
+       "      <td>2.530905</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>212</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17.875</td>\n",
+       "      <td>82.375</td>\n",
+       "      <td>27.360060</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.024084</td>\n",
+       "      <td>0.385490</td>\n",
+       "      <td>0.555550</td>\n",
+       "      <td>-0.349998</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-3.897657</td>\n",
+       "      <td>-43.599540</td>\n",
+       "      <td>-57.730576</td>\n",
+       "      <td>-3.418025</td>\n",
+       "      <td>-41.623190</td>\n",
+       "      <td>-7.454767</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1520</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-31.625</td>\n",
+       "      <td>28.375</td>\n",
+       "      <td>3.641749</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.020789</td>\n",
+       "      <td>0.275492</td>\n",
+       "      <td>0.288886</td>\n",
+       "      <td>-0.404686</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.759307</td>\n",
+       "      <td>-13.695341</td>\n",
+       "      <td>-106.551190</td>\n",
+       "      <td>-0.762760</td>\n",
+       "      <td>-19.835646</td>\n",
+       "      <td>-1.497616</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1277</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13.375</td>\n",
+       "      <td>37.125</td>\n",
+       "      <td>17.883150</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.042149</td>\n",
+       "      <td>0.256732</td>\n",
+       "      <td>0.433329</td>\n",
+       "      <td>-0.295313</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.007990</td>\n",
+       "      <td>-88.729330</td>\n",
+       "      <td>26.709599</td>\n",
+       "      <td>1.302401</td>\n",
+       "      <td>-74.875960</td>\n",
+       "      <td>3.221945</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>455</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.125</td>\n",
+       "      <td>-1.625</td>\n",
+       "      <td>1.747495</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.006682</td>\n",
+       "      <td>0.199548</td>\n",
+       "      <td>0.722215</td>\n",
+       "      <td>1.403907</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.280416</td>\n",
+       "      <td>-128.986300</td>\n",
+       "      <td>-210.755860</td>\n",
+       "      <td>-1.751252</td>\n",
+       "      <td>-125.311600</td>\n",
+       "      <td>-7.706358</td>\n",
+       "      <td>0.007889</td>\n",
+       "      <td>1492</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   latitude  longitude  dry_matter climatic_region biome GFEDregions  \\\n",
+       "0   -32.375     28.125    1.001038             3.0   1.0         9.0   \n",
+       "1    17.875     82.375   27.360060             4.0   5.0        12.0   \n",
+       "2   -31.625     28.375    3.641749             3.0   1.0         9.0   \n",
+       "3    13.375     37.125   17.883150             4.0   1.0         8.0   \n",
+       "4     8.125     -1.625    1.747495             4.0   1.0         8.0   \n",
+       "\n",
+       "      slope       vod       lai     spi03  ...   ffmcode    dufmcode  \\\n",
+       "0  0.014507  0.228241  0.222220 -0.398828  ...  3.302874   13.643188   \n",
+       "1  0.024084  0.385490  0.555550 -0.349998  ... -3.897657  -43.599540   \n",
+       "2  0.020789  0.275492  0.288886 -0.404686  ... -5.759307  -13.695341   \n",
+       "3  0.042149  0.256732  0.433329 -0.295313  ...  1.007990  -88.729330   \n",
+       "4  0.006682  0.199548  0.722215  1.403907  ... -1.280416 -128.986300   \n",
+       "\n",
+       "      drtcode   infsinx     fbupinx    fdsrte       frp  daysElapsed  \\\n",
+       "0  109.829220  1.191657   22.684143  2.530905  0.000000          212   \n",
+       "1  -57.730576 -3.418025  -41.623190 -7.454767  0.000000         1520   \n",
+       "2 -106.551190 -0.762760  -19.835646 -1.497616  0.000000         1277   \n",
+       "3   26.709599  1.302401  -74.875960  3.221945  0.000000          455   \n",
+       "4 -210.755860 -1.751252 -125.311600 -7.706358  0.007889         1492   \n",
+       "\n",
+       "   timeYear  timeMonth  \n",
+       "0      2010          8  \n",
+       "1      2014          3  \n",
+       "2      2013          7  \n",
+       "3      2011          4  \n",
+       "4      2014          2  \n",
+       "\n",
+       "[5 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b41e8bee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>dry_matter</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>...</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>frp</th>\n",
+       "      <th>daysElapsed</th>\n",
+       "      <th>timeYear</th>\n",
+       "      <th>timeMonth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "      <td>530032.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.655381</td>\n",
+       "      <td>18.944044</td>\n",
+       "      <td>81.688910</td>\n",
+       "      <td>0.008974</td>\n",
+       "      <td>0.372455</td>\n",
+       "      <td>1.198224</td>\n",
+       "      <td>-0.150948</td>\n",
+       "      <td>-0.150079</td>\n",
+       "      <td>-0.104575</td>\n",
+       "      <td>0.207567</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.992243</td>\n",
+       "      <td>9.640836</td>\n",
+       "      <td>24.398638</td>\n",
+       "      <td>0.486638</td>\n",
+       "      <td>10.198012</td>\n",
+       "      <td>1.021441</td>\n",
+       "      <td>0.016800</td>\n",
+       "      <td>1299.705472</td>\n",
+       "      <td>2013.057719</td>\n",
+       "      <td>7.037356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>25.075860</td>\n",
+       "      <td>62.861161</td>\n",
+       "      <td>231.033890</td>\n",
+       "      <td>0.010678</td>\n",
+       "      <td>0.174444</td>\n",
+       "      <td>1.240191</td>\n",
+       "      <td>0.966769</td>\n",
+       "      <td>0.990232</td>\n",
+       "      <td>1.020436</td>\n",
+       "      <td>1.449496</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.104970</td>\n",
+       "      <td>50.228942</td>\n",
+       "      <td>112.639016</td>\n",
+       "      <td>1.591400</td>\n",
+       "      <td>48.270810</td>\n",
+       "      <td>3.765627</td>\n",
+       "      <td>0.065342</td>\n",
+       "      <td>720.506871</td>\n",
+       "      <td>1.975931</td>\n",
+       "      <td>3.128706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-53.375000</td>\n",
+       "      <td>-178.375000</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.013333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.090235</td>\n",
+       "      <td>-3.090235</td>\n",
+       "      <td>-3.090235</td>\n",
+       "      <td>-10.326317</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-29.217953</td>\n",
+       "      <td>-487.233900</td>\n",
+       "      <td>-995.420800</td>\n",
+       "      <td>-14.604648</td>\n",
+       "      <td>-434.393680</td>\n",
+       "      <td>-42.597305</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>2010.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-14.375000</td>\n",
+       "      <td>-13.875000</td>\n",
+       "      <td>2.865247</td>\n",
+       "      <td>0.002679</td>\n",
+       "      <td>0.238871</td>\n",
+       "      <td>0.444440</td>\n",
+       "      <td>-0.787499</td>\n",
+       "      <td>-0.814844</td>\n",
+       "      <td>-0.787499</td>\n",
+       "      <td>-0.582896</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.309050</td>\n",
+       "      <td>-9.317945</td>\n",
+       "      <td>-33.849669</td>\n",
+       "      <td>-0.313922</td>\n",
+       "      <td>-10.052906</td>\n",
+       "      <td>-0.584911</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>638.000000</td>\n",
+       "      <td>2011.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-4.875000</td>\n",
+       "      <td>24.875000</td>\n",
+       "      <td>12.255775</td>\n",
+       "      <td>0.005189</td>\n",
+       "      <td>0.339245</td>\n",
+       "      <td>0.811103</td>\n",
+       "      <td>-0.156640</td>\n",
+       "      <td>-0.141014</td>\n",
+       "      <td>-0.076073</td>\n",
+       "      <td>0.090870</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.649816</td>\n",
+       "      <td>2.638136</td>\n",
+       "      <td>14.453959</td>\n",
+       "      <td>0.306134</td>\n",
+       "      <td>3.965799</td>\n",
+       "      <td>0.381645</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1308.000000</td>\n",
+       "      <td>2013.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>11.875000</td>\n",
+       "      <td>39.125000</td>\n",
+       "      <td>56.225450</td>\n",
+       "      <td>0.010857</td>\n",
+       "      <td>0.477850</td>\n",
+       "      <td>1.399986</td>\n",
+       "      <td>0.472265</td>\n",
+       "      <td>0.513283</td>\n",
+       "      <td>0.591407</td>\n",
+       "      <td>0.900308</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.224207</td>\n",
+       "      <td>25.823007</td>\n",
+       "      <td>78.100291</td>\n",
+       "      <td>1.179688</td>\n",
+       "      <td>28.332432</td>\n",
+       "      <td>2.539695</td>\n",
+       "      <td>0.011826</td>\n",
+       "      <td>1946.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>72.125000</td>\n",
+       "      <td>178.125000</td>\n",
+       "      <td>12042.022608</td>\n",
+       "      <td>0.148362</td>\n",
+       "      <td>1.246869</td>\n",
+       "      <td>6.933264</td>\n",
+       "      <td>3.089850</td>\n",
+       "      <td>3.089455</td>\n",
+       "      <td>3.089487</td>\n",
+       "      <td>9.319929</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.290535</td>\n",
+       "      <td>859.559200</td>\n",
+       "      <td>1768.237300</td>\n",
+       "      <td>17.965261</td>\n",
+       "      <td>808.088000</td>\n",
+       "      <td>45.783276</td>\n",
+       "      <td>6.347862</td>\n",
+       "      <td>2526.000000</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            latitude      longitude     dry_matter          slope  \\\n",
+       "count  530032.000000  530032.000000  530032.000000  530032.000000   \n",
+       "mean        3.655381      18.944044      81.688910       0.008974   \n",
+       "std        25.075860      62.861161     231.033890       0.010678   \n",
+       "min       -53.375000    -178.375000       0.000003       0.000100   \n",
+       "25%       -14.375000     -13.875000       2.865247       0.002679   \n",
+       "50%        -4.875000      24.875000      12.255775       0.005189   \n",
+       "75%        11.875000      39.125000      56.225450       0.010857   \n",
+       "max        72.125000     178.125000   12042.022608       0.148362   \n",
+       "\n",
+       "                 vod            lai          spi03          spi06  \\\n",
+       "count  530032.000000  530032.000000  530032.000000  530032.000000   \n",
+       "mean        0.372455       1.198224      -0.150948      -0.150079   \n",
+       "std         0.174444       1.240191       0.966769       0.990232   \n",
+       "min         0.013333       0.000000      -3.090235      -3.090235   \n",
+       "25%         0.238871       0.444440      -0.787499      -0.814844   \n",
+       "50%         0.339245       0.811103      -0.156640      -0.141014   \n",
+       "75%         0.477850       1.399986       0.472265       0.513283   \n",
+       "max         1.246869       6.933264       3.089850       3.089455   \n",
+       "\n",
+       "               spi12            d2m  ...        ffmcode       dufmcode  \\\n",
+       "count  530032.000000  530032.000000  ...  530032.000000  530032.000000   \n",
+       "mean       -0.104575       0.207567  ...       0.992243       9.640836   \n",
+       "std         1.020436       1.449496  ...       3.104970      50.228942   \n",
+       "min        -3.090235     -10.326317  ...     -29.217953    -487.233900   \n",
+       "25%        -0.787499      -0.582896  ...      -0.309050      -9.317945   \n",
+       "50%        -0.076073       0.090870  ...       0.649816       2.638136   \n",
+       "75%         0.591407       0.900308  ...       2.224207      25.823007   \n",
+       "max         3.089487       9.319929  ...      34.290535     859.559200   \n",
+       "\n",
+       "             drtcode        infsinx        fbupinx         fdsrte  \\\n",
+       "count  530032.000000  530032.000000  530032.000000  530032.000000   \n",
+       "mean       24.398638       0.486638      10.198012       1.021441   \n",
+       "std       112.639016       1.591400      48.270810       3.765627   \n",
+       "min      -995.420800     -14.604648    -434.393680     -42.597305   \n",
+       "25%       -33.849669      -0.313922     -10.052906      -0.584911   \n",
+       "50%        14.453959       0.306134       3.965799       0.381645   \n",
+       "75%        78.100291       1.179688      28.332432       2.539695   \n",
+       "max      1768.237300      17.965261     808.088000      45.783276   \n",
+       "\n",
+       "                 frp    daysElapsed       timeYear      timeMonth  \n",
+       "count  530032.000000  530032.000000  530032.000000  530032.000000  \n",
+       "mean        0.016800    1299.705472    2013.057719       7.037356  \n",
+       "std         0.065342     720.506871       1.975931       3.128706  \n",
+       "min         0.000000      90.000000    2010.000000       1.000000  \n",
+       "25%         0.000000     638.000000    2011.000000       5.000000  \n",
+       "50%         0.000000    1308.000000    2013.000000       7.000000  \n",
+       "75%         0.011826    1946.000000    2015.000000      10.000000  \n",
+       "max         6.347862    2526.000000    2016.000000      12.000000  \n",
+       "\n",
+       "[8 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fe6eb768",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['latitude', 'longitude', 'slope', 'vod', 'lai', 'spi03', 'spi06', 'spi12', 'd2m', 'erate', 'fg10', 'si10', 'swvl1', 't2m', 'tprate', 'danger_risk', 'fwinx', 'ffmcode', 'dufmcode', 'drtcode', 'infsinx', 'fbupinx', 'fdsrte', 'frp', 'daysElapsed', 'timeYear', 'timeMonth']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define target variable (y) as well as categorical and numerical features\n",
+    "y = 'dry_matter'\n",
+    "cat = ['climatic_region', 'biome', 'GFEDregions']\n",
+    "cont = train.columns.to_list()\n",
+    "for i in cat + [y]:\n",
+    "    cont.remove(i)\n",
+    "print(cont)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "148dd8e2",
+   "metadata": {},
+   "source": [
+    "## Check distributions of outcome variable and features\n",
+    "\n",
+    "Are the target and all the continuous features normally distributed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "94eefde8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b696354f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train[y].hist(bins = 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffa99281",
+   "metadata": {},
+   "source": [
+    "It looks like the target is highly right skewed. This could be corrected by log-transforming the target variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f7cfa60d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b2b20ef70>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.log(train[y]).hist(bins = 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e12e2be6",
+   "metadata": {},
+   "source": [
+    "Let's now look at the features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "636d7f68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2b0e3040>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2b081f10>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2b0a8a30>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2b060280>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2b007af0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2b03f3a0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2afcc2b0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2aff5b50>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2af49ca0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2af80550>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2af2bd90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2aee1610>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ae8ce50>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ae416d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ae6bf10>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ae20790>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2adccfd0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ad818b0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2adbaee0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ad61970>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ad1cfa0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2acc3a30>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2acfc2b0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2aca5af0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ac5a3a0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ac05be0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2ac3c460>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2abf4a90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2abf4e20>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7b2aba96d0>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1296 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train[cont].hist(figsize = (15,18))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e75dcad",
+   "metadata": {},
+   "source": [
+    "The following variables are also right skewed: slope, vod, lai, frp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6d34e324",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'whiskers': [<matplotlib.lines.Line2D at 0x7f7b2a5e02b0>,\n",
+       "  <matplotlib.lines.Line2D at 0x7f7b2a5e05e0>],\n",
+       " 'caps': [<matplotlib.lines.Line2D at 0x7f7b2a5e0910>,\n",
+       "  <matplotlib.lines.Line2D at 0x7f7b2a5e0c40>],\n",
+       " 'boxes': [<matplotlib.lines.Line2D at 0x7f7b2a620be0>],\n",
+       " 'medians': [<matplotlib.lines.Line2D at 0x7f7b2a5e0f70>],\n",
+       " 'fliers': [<matplotlib.lines.Line2D at 0x7f7b2a620cd0>],\n",
+       " 'means': []}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Are there outliers in the dry matter?\n",
+    "plt.boxplot(train.dry_matter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "53df7b94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'whiskers': [<matplotlib.lines.Line2D at 0x7f7b28d56220>,\n",
+       "  <matplotlib.lines.Line2D at 0x7f7b28d56580>],\n",
+       " 'caps': [<matplotlib.lines.Line2D at 0x7f7b28d568b0>,\n",
+       "  <matplotlib.lines.Line2D at 0x7f7b28d56be0>],\n",
+       " 'boxes': [<matplotlib.lines.Line2D at 0x7f7b28d561f0>],\n",
+       " 'medians': [<matplotlib.lines.Line2D at 0x7f7b28d63280>],\n",
+       " 'fliers': [<matplotlib.lines.Line2D at 0x7f7b28d56190>],\n",
+       " 'means': []}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AxDtx4kSOHTuWxcXFXLlyJYuLizl27FhOnDgx9mgAsMENSwCYeG/flOTxxx/Pyy+/nNnZ2Tz55JNuVgLAjuI7bwAAADuE77wBAAB0TrwBAAB0QLwBAAB0QLwBAAB0QLwBAAB0QLwBAAB0QLwBAAB0QLwBAAB0YEc9pLuqLib5xdhzADDR7ktyaewhAJhYv95aO7DZiR0VbwAwtqpaaq3Njz0HALybbZMAAAAdEG8AAAAdEG8AcL3nxh4AADbjO28AAAAdcOUNAACgA+INAJJU1fNV9VpVnRt7FgDYjHgDgLd8Nclnxx4CALYi3gAgSWvtB0leH3sOANiKeAMAAOiAeAMAAOiAeAMAAOiAeAMAAOiAeAOAJFW1kOSHST5eVctVdWzsmQDgWtVaG3sGAAAAbsKVNwAAgA6INwAAgA6INwAAgA6INwAAgA6INwAAgA6INwAAgA6INwAAgA6INwAAgA78L798x9eaI5x/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot(np.log(train.dry_matter))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2324945f",
+   "metadata": {},
+   "source": [
+    "It is convenient to transform variables that show pronounced skeweness. A comparison of various approaches is presented in model_definition_benchmark.ipynb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8732ad24",
+   "metadata": {},
+   "source": [
+    "The variables in the above dataset vary in different ranges. On the one hand, this is not a problem for Decision Trees, Random Forest because these algorithm care about the order of the data, not the scale. AutoML with H2O, implements automatic normalisation for some of the algorithms.\n",
+    "For Deep Learning models this is a problem and in the next section I show how to remove skeweness (e.g. using transformations) and normalise the data.\n",
+    "\n",
+    "At least 4 variables look left skewed: dry_matter, slope, vod and lai. Let's apply a log-transformation.\n",
+    "Note that the log-transformation can be applied only to values that are > 0 (log of 0 and negative number is undefined). The minimum of 2 variables (lai and frp) is zero, therefore we will have to filter out these zeros before transforming the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6cd03d2",
+   "metadata": {},
+   "source": [
+    "## Identify correlations amongst variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2769dad8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Correlation matrix\n",
+    "C_mat = train[[y] + cont].corr()\n",
+    "fig = plt.figure(figsize = (15, 15))\n",
+    "\n",
+    "sb.heatmap(C_mat, center=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b140364a",
+   "metadata": {},
+   "source": [
+    "From the plot above, the vod and frp seem to be the most correlated features to the dry matter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d42c10b3",
+   "metadata": {},
+   "source": [
+    "# Identify possible redundant features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d475ff98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Source: https://github.com/fastai/book_nbs/blob/master/utils.py\n",
+    "# from fastbook import *\n",
+    "# from fastai.tabular.all import *\n",
+    "# or load the following function\n",
+    "import scipy\n",
+    "from scipy.cluster import hierarchy as hc\n",
+    "def cluster_columns(df, figsize=(10,6), font_size=12):\n",
+    "    corr = np.round(scipy.stats.spearmanr(df).correlation, 4) # 30x30 matrix\n",
+    "    corr_condensed = hc.distance.squareform(1-corr)\n",
+    "    z = hc.linkage(corr_condensed, method='average')\n",
+    "    fig = plt.figure(figsize=figsize)\n",
+    "    hc.dendrogram(z, labels=df.columns, orientation='left', leaf_font_size=font_size)\n",
+    "    plt.show()\n",
+    "    \n",
+    "# Identify important vs redundand features\n",
+    "cluster_columns(train.drop(y, axis = 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601a9745",
+   "metadata": {},
+   "source": [
+    "From the plot above, it seems the following features could be redundant:\n",
+    "- timeYear -> daysElapsed\n",
+    "- fbupinx -> dufmcode\n",
+    "- fdsrte -> fwinx\n",
+    "\n",
+    "The effect of removing potential redundant information is explored in model_definition_benchmark.ipynb\n",
+    "\n",
+    "However, redundancy/strong correlation between predictors could have an impact on the regression analysis and the subsequent conclusions. When predictor variables are correlated, the estimated regression coefficient of any one variable depends on which other predictor variables are included in the model. Also, the precision of the estimated regression coefficients decreases as more predictor variables are added to the model. These effects are commonly known as 'multicollinearity'.\n",
+    "\n",
+    "The effect of multicollinearity can be quantify using a 'variance inflation factor'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "69dbef96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate variance inflation factors to estimate multicollinearity\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "def sklearn_vif(exogs, data):\n",
+    "\n",
+    "    # initialize dictionaries\n",
+    "    vif_dict, tolerance_dict = {}, {}\n",
+    "\n",
+    "    # form input data for each exogenous variable\n",
+    "    for exog in exogs:\n",
+    "        not_exog = [i for i in exogs if i != exog]\n",
+    "        X, y = data[not_exog], data[exog]\n",
+    "\n",
+    "        # extract r-squared from the fit\n",
+    "        r_squared = LinearRegression().fit(X, y).score(X, y)\n",
+    "\n",
+    "        # calculate VIF\n",
+    "        vif = 1/(1 - r_squared)\n",
+    "        vif_dict[exog] = vif\n",
+    "\n",
+    "        # calculate tolerance\n",
+    "        tolerance = 1 - r_squared\n",
+    "        tolerance_dict[exog] = tolerance\n",
+    "\n",
+    "    # return VIF DataFrame\n",
+    "    df_vif = pd.DataFrame({'VIF': vif_dict, 'Tolerance': tolerance_dict})\n",
+    "\n",
+    "    return df_vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bb2bb8fb",
+   "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>VIF</th>\n",
+       "      <th>Tolerance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>latitude</th>\n",
+       "      <td>1.165061e+00</td>\n",
+       "      <td>8.583242e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>longitude</th>\n",
+       "      <td>1.076477e+00</td>\n",
+       "      <td>9.289560e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slope</th>\n",
+       "      <td>1.056147e+00</td>\n",
+       "      <td>9.468378e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vod</th>\n",
+       "      <td>1.720640e+00</td>\n",
+       "      <td>5.811790e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lai</th>\n",
+       "      <td>1.652676e+00</td>\n",
+       "      <td>6.050794e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>spi03</th>\n",
+       "      <td>1.744887e+00</td>\n",
+       "      <td>5.731029e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>spi06</th>\n",
+       "      <td>2.498931e+00</td>\n",
+       "      <td>4.001711e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>spi12</th>\n",
+       "      <td>1.923490e+00</td>\n",
+       "      <td>5.198884e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d2m</th>\n",
+       "      <td>8.605499e+00</td>\n",
+       "      <td>1.162048e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>erate</th>\n",
+       "      <td>3.365486e+00</td>\n",
+       "      <td>2.971339e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fg10</th>\n",
+       "      <td>4.876421e+00</td>\n",
+       "      <td>2.050684e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>si10</th>\n",
+       "      <td>4.938596e+00</td>\n",
+       "      <td>2.024867e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>swvl1</th>\n",
+       "      <td>7.249975e+00</td>\n",
+       "      <td>1.379315e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t2m</th>\n",
+       "      <td>5.459977e+00</td>\n",
+       "      <td>1.831510e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tprate</th>\n",
+       "      <td>4.182489e+00</td>\n",
+       "      <td>2.390921e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>danger_risk</th>\n",
+       "      <td>1.096355e+02</td>\n",
+       "      <td>9.121136e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fwinx</th>\n",
+       "      <td>3.368237e+02</td>\n",
+       "      <td>2.968913e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ffmcode</th>\n",
+       "      <td>9.775058e+00</td>\n",
+       "      <td>1.023012e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dufmcode</th>\n",
+       "      <td>1.077591e+02</td>\n",
+       "      <td>9.279959e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>drtcode</th>\n",
+       "      <td>5.670577e+00</td>\n",
+       "      <td>1.763489e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>infsinx</th>\n",
+       "      <td>3.117142e+01</td>\n",
+       "      <td>3.208066e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fbupinx</th>\n",
+       "      <td>1.405852e+02</td>\n",
+       "      <td>7.113124e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fdsrte</th>\n",
+       "      <td>1.176580e+02</td>\n",
+       "      <td>8.499210e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>frp</th>\n",
+       "      <td>1.014273e+00</td>\n",
+       "      <td>9.859277e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysElapsed</th>\n",
+       "      <td>1.440085e+06</td>\n",
+       "      <td>6.944035e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timeYear</th>\n",
+       "      <td>1.445220e+06</td>\n",
+       "      <td>6.919364e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timeMonth</th>\n",
+       "      <td>2.519448e+04</td>\n",
+       "      <td>3.969124e-05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      VIF     Tolerance\n",
+       "latitude     1.165061e+00  8.583242e-01\n",
+       "longitude    1.076477e+00  9.289560e-01\n",
+       "slope        1.056147e+00  9.468378e-01\n",
+       "vod          1.720640e+00  5.811790e-01\n",
+       "lai          1.652676e+00  6.050794e-01\n",
+       "spi03        1.744887e+00  5.731029e-01\n",
+       "spi06        2.498931e+00  4.001711e-01\n",
+       "spi12        1.923490e+00  5.198884e-01\n",
+       "d2m          8.605499e+00  1.162048e-01\n",
+       "erate        3.365486e+00  2.971339e-01\n",
+       "fg10         4.876421e+00  2.050684e-01\n",
+       "si10         4.938596e+00  2.024867e-01\n",
+       "swvl1        7.249975e+00  1.379315e-01\n",
+       "t2m          5.459977e+00  1.831510e-01\n",
+       "tprate       4.182489e+00  2.390921e-01\n",
+       "danger_risk  1.096355e+02  9.121136e-03\n",
+       "fwinx        3.368237e+02  2.968913e-03\n",
+       "ffmcode      9.775058e+00  1.023012e-01\n",
+       "dufmcode     1.077591e+02  9.279959e-03\n",
+       "drtcode      5.670577e+00  1.763489e-01\n",
+       "infsinx      3.117142e+01  3.208066e-02\n",
+       "fbupinx      1.405852e+02  7.113124e-03\n",
+       "fdsrte       1.176580e+02  8.499210e-03\n",
+       "frp          1.014273e+00  9.859277e-01\n",
+       "daysElapsed  1.440085e+06  6.944035e-07\n",
+       "timeYear     1.445220e+06  6.919364e-07\n",
+       "timeMonth    2.519448e+04  3.969124e-05"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = sklearn_vif(exogs=cont, data=train.drop(y, axis = 1))\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "07b9a92b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrr}\n",
+      "\\toprule\n",
+      "{} &           VIF &     Tolerance \\\\\n",
+      "\\midrule\n",
+      "latitude    &  1.165061e+00 &  8.583242e-01 \\\\\n",
+      "longitude   &  1.076477e+00 &  9.289560e-01 \\\\\n",
+      "slope       &  1.056147e+00 &  9.468378e-01 \\\\\n",
+      "vod         &  1.720640e+00 &  5.811790e-01 \\\\\n",
+      "lai         &  1.652676e+00 &  6.050794e-01 \\\\\n",
+      "spi03       &  1.744887e+00 &  5.731029e-01 \\\\\n",
+      "spi06       &  2.498931e+00 &  4.001711e-01 \\\\\n",
+      "spi12       &  1.923490e+00 &  5.198884e-01 \\\\\n",
+      "d2m         &  8.605499e+00 &  1.162048e-01 \\\\\n",
+      "erate       &  3.365486e+00 &  2.971339e-01 \\\\\n",
+      "fg10        &  4.876421e+00 &  2.050684e-01 \\\\\n",
+      "si10        &  4.938596e+00 &  2.024867e-01 \\\\\n",
+      "swvl1       &  7.249975e+00 &  1.379315e-01 \\\\\n",
+      "t2m         &  5.459977e+00 &  1.831510e-01 \\\\\n",
+      "tprate      &  4.182489e+00 &  2.390921e-01 \\\\\n",
+      "danger\\_risk &  1.096355e+02 &  9.121136e-03 \\\\\n",
+      "fwinx       &  3.368237e+02 &  2.968913e-03 \\\\\n",
+      "ffmcode     &  9.775058e+00 &  1.023012e-01 \\\\\n",
+      "dufmcode    &  1.077591e+02 &  9.279959e-03 \\\\\n",
+      "drtcode     &  5.670577e+00 &  1.763489e-01 \\\\\n",
+      "infsinx     &  3.117142e+01 &  3.208066e-02 \\\\\n",
+      "fbupinx     &  1.405852e+02 &  7.113124e-03 \\\\\n",
+      "fdsrte      &  1.176580e+02 &  8.499210e-03 \\\\\n",
+      "frp         &  1.014273e+00 &  9.859277e-01 \\\\\n",
+      "daysElapsed &  1.440085e+06 &  6.944035e-07 \\\\\n",
+      "timeYear    &  1.445220e+06 &  6.919364e-07 \\\\\n",
+      "timeMonth   &  2.519448e+04 &  3.969124e-05 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.to_latex(index=True))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/ecmwf/model_benchmark_tests.ipynb b/notebooks/ecmwf/model_benchmark_tests.ipynb
new file mode 100644
index 0000000..f7830cb
--- /dev/null
+++ b/notebooks/ecmwf/model_benchmark_tests.ipynb
@@ -0,0 +1,1236 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ad5202fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Bad key \"text.kerning_factor\" on line 4 in\n",
+      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
+      "You probably need to get an updated matplotlibrc file from\n",
+      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
+      "or from the matplotlib source distribution\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sb\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import PowerTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8486a667",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
+       "<td>2 days 19 hours 29 mins</td></tr>\n",
+       "<tr><td>H2O_cluster_timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O_data_parsing_timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O_cluster_version:</td>\n",
+       "<td>3.32.1.1</td></tr>\n",
+       "<tr><td>H2O_cluster_version_age:</td>\n",
+       "<td>2 months and 12 days </td></tr>\n",
+       "<tr><td>H2O_cluster_name:</td>\n",
+       "<td>H2O_from_python_moc0_j96c9b</td></tr>\n",
+       "<tr><td>H2O_cluster_total_nodes:</td>\n",
+       "<td>1</td></tr>\n",
+       "<tr><td>H2O_cluster_free_memory:</td>\n",
+       "<td>23.28 Gb</td></tr>\n",
+       "<tr><td>H2O_cluster_total_cores:</td>\n",
+       "<td>16</td></tr>\n",
+       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
+       "<td>16</td></tr>\n",
+       "<tr><td>H2O_cluster_status:</td>\n",
+       "<td>locked, healthy</td></tr>\n",
+       "<tr><td>H2O_connection_url:</td>\n",
+       "<td>http://localhost:54321</td></tr>\n",
+       "<tr><td>H2O_connection_proxy:</td>\n",
+       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
+       "<tr><td>H2O_internal_security:</td>\n",
+       "<td>False</td></tr>\n",
+       "<tr><td>H2O_API_Extensions:</td>\n",
+       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
+       "<tr><td>Python_version:</td>\n",
+       "<td>3.8.8 final</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "--------------------------  ------------------------------------------------------------------\n",
+       "H2O_cluster_uptime:         2 days 19 hours 29 mins\n",
+       "H2O_cluster_timezone:       UTC\n",
+       "H2O_data_parsing_timezone:  UTC\n",
+       "H2O_cluster_version:        3.32.1.1\n",
+       "H2O_cluster_version_age:    2 months and 12 days\n",
+       "H2O_cluster_name:           H2O_from_python_moc0_j96c9b\n",
+       "H2O_cluster_total_nodes:    1\n",
+       "H2O_cluster_free_memory:    23.28 Gb\n",
+       "H2O_cluster_total_cores:    16\n",
+       "H2O_cluster_allowed_cores:  16\n",
+       "H2O_cluster_status:         locked, healthy\n",
+       "H2O_connection_url:         http://localhost:54321\n",
+       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
+       "H2O_internal_security:      False\n",
+       "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
+       "Python_version:             3.8.8 final\n",
+       "--------------------------  ------------------------------------------------------------------"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# H2O AutoML framework for benchmarking\n",
+    "import h2o\n",
+    "from h2o.automl import H2OAutoML\n",
+    "h2o.init(max_mem_size = 32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8494c80",
+   "metadata": {},
+   "source": [
+    "This notebook uses the H2O.ai AutoML framework to benchmark various possible data transformations for outcome and predictors. The final result is a pre-processed dataset that will be used for the final modelling step in `model_definition.ipynb`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d54a49d8",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b45ac98d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define working directory\n",
+    "os.chdir('/home/moc0/ai-vegetation-fuel')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a881b39b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load raw train data\n",
+    "train = pd.read_csv(os.getcwd() + \"/data/inputs/train_interpTrue_raw.csv\",\n",
+    "                    dtype={'climatic_region':'category',\n",
+    "                           'biome':'category',\n",
+    "                           'GFEDregions':'category'})\n",
+    "train = train.drop('Unnamed: 0', axis = 1)\n",
+    "\n",
+    "# Load raw test data\n",
+    "test = pd.read_csv(os.getcwd() + \"/data/inputs/test_interpTrue_raw.csv\",\n",
+    "                    dtype={'climatic_region':'category',\n",
+    "                           'biome':'category',\n",
+    "                           'GFEDregions':'category'})\n",
+    "test = test.drop('Unnamed: 0', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bf10193c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define outcome variable\n",
+    "y = 'dry_matter'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7081a4e8",
+   "metadata": {},
+   "source": [
+    "Define possible transformations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "997a30cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform(train, test, features, transformation, output_transformer = False):\n",
+    "    # copy of datasets\n",
+    "    train_transformed = train.copy()\n",
+    "    test_transformed = test.copy()\n",
+    "    if transformation == \"log\":\n",
+    "        for i in features:\n",
+    "            # transform the training data column\n",
+    "            train_transformed[i] = np.log(train_transformed[[i]])\n",
+    "            # transform the testing data column\n",
+    "            test_transformed[i] = np.log(test_transformed[[i]])\n",
+    "            return train_transformed, test_transformed\n",
+    "    else:\n",
+    "        if transformation == \"normalization\":\n",
+    "            # data normalization with sklearn\n",
+    "            for i in features:\n",
+    "                # fit on training data column\n",
+    "                transformer = MinMaxScaler()\n",
+    "                norm = transformer.fit(train_transformed[[i]])\n",
+    "                # transform the training data column\n",
+    "                train_transformed[i] = norm.transform(train_transformed[[i]])\n",
+    "                # transform the testing data column\n",
+    "                test_transformed[i] = norm.transform(test_transformed[[i]])\n",
+    "        if transformation == \"standardization\":\n",
+    "            # data standardization with sklearn\n",
+    "            for i in features:   \n",
+    "                # fit on training data column\n",
+    "                transformer = StandardScaler()\n",
+    "                scale = transformer.fit(train_transformed[[i]])\n",
+    "                # transform the training data column\n",
+    "                train_transformed[i] = scale.transform(train_transformed[[i]])\n",
+    "                # transform the testing data column\n",
+    "                test_transformed[i] = scale.transform(test_transformed[[i]])\n",
+    "        if transformation == \"power\":\n",
+    "            # PowerTransform with sklearn\n",
+    "            for i in features:\n",
+    "                # fit on training data column\n",
+    "                transformer = PowerTransformer()\n",
+    "                yj = transformer.fit(train_transformed[[i]])\n",
+    "                # transform the training data column\n",
+    "                train_transformed[i] = yj.transform(train_transformed[[i]])\n",
+    "                # transform the testing data column\n",
+    "                test_transformed[i] = yj.transform(test_transformed[[i]])\n",
+    "        if output_transformer == False:\n",
+    "            return train_transformed, test_transformed\n",
+    "        else:\n",
+    "            return train_transformed, test_transformed, transformer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c060f01d",
+   "metadata": {},
+   "source": [
+    "# Run benchmark tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50f64328",
+   "metadata": {},
+   "source": [
+    "## STAGE A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7fd28bd",
+   "metadata": {},
+   "source": [
+    "### 0. The basic benchmark model\n",
+    "\n",
+    "This is given by the regressor model fitted on raw outcome (no target transformation), raw features (no features transformation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4c0fc64e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define Regressor model\n",
+    "aml = H2OAutoML(max_runtime_secs = 10*60, seed = 1, stopping_metric = \"mae\",\n",
+    "                preprocessing = [\"target_encoding\"])\n",
+    "# Fit\n",
+    "aml.train(y = y, training_frame = h2o.H2OFrame(train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7e8db8c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "54.08793142495884"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# MAE\n",
+    "obs = test[y]\n",
+    "pre = aml.predict(h2o.H2OFrame(test))\n",
+    "mean_absolute_error(obs, pre.as_data_frame())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d1c5be6",
+   "metadata": {},
+   "source": [
+    "### 1. Log-transform target variable\n",
+    "\n",
+    "Assess whether there is an improvement by transforming the target variable. This means comparing the results of a basic model using raw outcome/features versus transformed outcome and raw features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c445412d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_log_target, test_log_target = transform(train = train, test = test,\n",
+    "                                              features = [y], transformation = \"log\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "389cdc46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define Regressor model\n",
+    "aml = H2OAutoML(max_runtime_secs = 10*60, seed = 1, stopping_metric = \"mae\",\n",
+    "                preprocessing = [\"target_encoding\"])\n",
+    "# Fit\n",
+    "aml.train(y = y, training_frame = h2o.H2OFrame(train_log_target))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c06e0656",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "47.763069430447985"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# MAE\n",
+    "pre = aml.predict(h2o.H2OFrame(test_log_target))\n",
+    "pre_inverse_transformed = np.exp(pre.as_data_frame())\n",
+    "mean_absolute_error(obs, pre_inverse_transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7ef6991",
+   "metadata": {},
+   "source": [
+    "### 2. Power-transform target variable\n",
+    "\n",
+    "Assess whether there is an improvement by transforming the target variable. This means comparing the results of a basic model using raw outcome/features versus transformed outcome and raw features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7f9508c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_power_target, test_power_target, transformer = transform(train = train, test = test,\n",
+    "                                                               features = [y], transformation = \"power\",\n",
+    "                                                               output_transformer = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "df85a315",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define Regressor model\n",
+    "aml = H2OAutoML(max_runtime_secs = 10*60, seed = 1, stopping_metric = \"mae\",\n",
+    "                preprocessing = [\"target_encoding\"])\n",
+    "# Fit\n",
+    "aml.train(y = y, training_frame = h2o.H2OFrame(train_power_target))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3ae79ecb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "48.40078069096282"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# MAE\n",
+    "pre = aml.predict(h2o.H2OFrame(test_power_target))\n",
+    "pre_inverse_transformed = transformer.inverse_transform(pre.as_data_frame())\n",
+    "mean_absolute_error(obs, pre_inverse_transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89c2742d",
+   "metadata": {},
+   "source": [
+    "From the 3 experiments above it seems the best way to transform the target variable is to use the logarithmic function. For this reason, we add to the train and test sets an additional column with the lo-transformed target variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "71dea312",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train['log_dry_matter'] = np.log(train[y])\n",
+    "test['log_dry_matter'] = np.log(test[y])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b0f6489f",
+   "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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>...</th>\n",
+       "      <th>dufmcode</th>\n",
+       "      <th>drtcode</th>\n",
+       "      <th>infsinx</th>\n",
+       "      <th>fbupinx</th>\n",
+       "      <th>fdsrte</th>\n",
+       "      <th>frp</th>\n",
+       "      <th>daysElapsed</th>\n",
+       "      <th>timeYear</th>\n",
+       "      <th>timeMonth</th>\n",
+       "      <th>log_dry_matter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-32.375</td>\n",
+       "      <td>28.125</td>\n",
+       "      <td>1.001038</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.014507</td>\n",
+       "      <td>0.228241</td>\n",
+       "      <td>0.222220</td>\n",
+       "      <td>-0.398828</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.643188</td>\n",
+       "      <td>109.829220</td>\n",
+       "      <td>1.191657</td>\n",
+       "      <td>22.684143</td>\n",
+       "      <td>2.530905</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>212</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.001037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17.875</td>\n",
+       "      <td>82.375</td>\n",
+       "      <td>27.360060</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.024084</td>\n",
+       "      <td>0.385490</td>\n",
+       "      <td>0.555550</td>\n",
+       "      <td>-0.349998</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-43.599540</td>\n",
+       "      <td>-57.730576</td>\n",
+       "      <td>-3.418025</td>\n",
+       "      <td>-41.623190</td>\n",
+       "      <td>-7.454767</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1520</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.309084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-31.625</td>\n",
+       "      <td>28.375</td>\n",
+       "      <td>3.641749</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.020789</td>\n",
+       "      <td>0.275492</td>\n",
+       "      <td>0.288886</td>\n",
+       "      <td>-0.404686</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-13.695341</td>\n",
+       "      <td>-106.551190</td>\n",
+       "      <td>-0.762760</td>\n",
+       "      <td>-19.835646</td>\n",
+       "      <td>-1.497616</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1277</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.292464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13.375</td>\n",
+       "      <td>37.125</td>\n",
+       "      <td>17.883150</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.042149</td>\n",
+       "      <td>0.256732</td>\n",
+       "      <td>0.433329</td>\n",
+       "      <td>-0.295313</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-88.729330</td>\n",
+       "      <td>26.709599</td>\n",
+       "      <td>1.302401</td>\n",
+       "      <td>-74.875960</td>\n",
+       "      <td>3.221945</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>455</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.883859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.125</td>\n",
+       "      <td>-1.625</td>\n",
+       "      <td>1.747495</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.006682</td>\n",
+       "      <td>0.199548</td>\n",
+       "      <td>0.722215</td>\n",
+       "      <td>1.403907</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-128.986300</td>\n",
+       "      <td>-210.755860</td>\n",
+       "      <td>-1.751252</td>\n",
+       "      <td>-125.311600</td>\n",
+       "      <td>-7.706358</td>\n",
+       "      <td>0.007889</td>\n",
+       "      <td>1492</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.558184</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>530027</th>\n",
+       "      <td>10.625</td>\n",
+       "      <td>-13.875</td>\n",
+       "      <td>36.482942</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.008239</td>\n",
+       "      <td>0.415102</td>\n",
+       "      <td>1.566651</td>\n",
+       "      <td>0.874609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>126.336510</td>\n",
+       "      <td>63.899292</td>\n",
+       "      <td>1.229383</td>\n",
+       "      <td>116.538080</td>\n",
+       "      <td>3.192863</td>\n",
+       "      <td>0.015762</td>\n",
+       "      <td>1216</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.596845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530028</th>\n",
+       "      <td>-15.375</td>\n",
+       "      <td>34.625</td>\n",
+       "      <td>1.640657</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.016233</td>\n",
+       "      <td>0.205404</td>\n",
+       "      <td>1.022212</td>\n",
+       "      <td>0.112892</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-13.123533</td>\n",
+       "      <td>22.029633</td>\n",
+       "      <td>-1.279937</td>\n",
+       "      <td>-11.235716</td>\n",
+       "      <td>-2.089784</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>577</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.495097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530029</th>\n",
+       "      <td>11.875</td>\n",
+       "      <td>36.625</td>\n",
+       "      <td>13.193764</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.022857</td>\n",
+       "      <td>0.218347</td>\n",
+       "      <td>0.377774</td>\n",
+       "      <td>1.757421</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.771729</td>\n",
+       "      <td>19.831726</td>\n",
+       "      <td>0.491823</td>\n",
+       "      <td>4.540405</td>\n",
+       "      <td>1.184292</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1096</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.579744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530030</th>\n",
+       "      <td>2.875</td>\n",
+       "      <td>19.375</td>\n",
+       "      <td>850.061983</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.002305</td>\n",
+       "      <td>0.789469</td>\n",
+       "      <td>1.566651</td>\n",
+       "      <td>-0.131250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>62.941635</td>\n",
+       "      <td>53.213240</td>\n",
+       "      <td>3.535935</td>\n",
+       "      <td>56.135567</td>\n",
+       "      <td>7.314124</td>\n",
+       "      <td>0.003937</td>\n",
+       "      <td>2191</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.745309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530031</th>\n",
+       "      <td>57.125</td>\n",
+       "      <td>66.375</td>\n",
+       "      <td>39.315957</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.000804</td>\n",
+       "      <td>0.350035</td>\n",
+       "      <td>0.166665</td>\n",
+       "      <td>2.015236</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.222433</td>\n",
+       "      <td>46.653778</td>\n",
+       "      <td>-0.674004</td>\n",
+       "      <td>-3.326023</td>\n",
+       "      <td>-0.165738</td>\n",
+       "      <td>0.003937</td>\n",
+       "      <td>1551</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.671630</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>530032 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        latitude  longitude  dry_matter climatic_region biome GFEDregions  \\\n",
+       "0        -32.375     28.125    1.001038             3.0   1.0         9.0   \n",
+       "1         17.875     82.375   27.360060             4.0   5.0        12.0   \n",
+       "2        -31.625     28.375    3.641749             3.0   1.0         9.0   \n",
+       "3         13.375     37.125   17.883150             4.0   1.0         8.0   \n",
+       "4          8.125     -1.625    1.747495             4.0   1.0         8.0   \n",
+       "...          ...        ...         ...             ...   ...         ...   \n",
+       "530027    10.625    -13.875   36.482942             4.0   1.0         8.0   \n",
+       "530028   -15.375     34.625    1.640657             3.0   1.0         9.0   \n",
+       "530029    11.875     36.625   13.193764             4.0   1.0         8.0   \n",
+       "530030     2.875     19.375  850.061983             4.0   5.0         8.0   \n",
+       "530031    57.125     66.375   39.315957             2.0   1.0        10.0   \n",
+       "\n",
+       "           slope       vod       lai     spi03  ...    dufmcode     drtcode  \\\n",
+       "0       0.014507  0.228241  0.222220 -0.398828  ...   13.643188  109.829220   \n",
+       "1       0.024084  0.385490  0.555550 -0.349998  ...  -43.599540  -57.730576   \n",
+       "2       0.020789  0.275492  0.288886 -0.404686  ...  -13.695341 -106.551190   \n",
+       "3       0.042149  0.256732  0.433329 -0.295313  ...  -88.729330   26.709599   \n",
+       "4       0.006682  0.199548  0.722215  1.403907  ... -128.986300 -210.755860   \n",
+       "...          ...       ...       ...       ...  ...         ...         ...   \n",
+       "530027  0.008239  0.415102  1.566651  0.874609  ...  126.336510   63.899292   \n",
+       "530028  0.016233  0.205404  1.022212  0.112892  ...  -13.123533   22.029633   \n",
+       "530029  0.022857  0.218347  0.377774  1.757421  ...    6.771729   19.831726   \n",
+       "530030  0.002305  0.789469  1.566651 -0.131250  ...   62.941635   53.213240   \n",
+       "530031  0.000804  0.350035  0.166665  2.015236  ...   -2.222433   46.653778   \n",
+       "\n",
+       "         infsinx     fbupinx    fdsrte       frp  daysElapsed  timeYear  \\\n",
+       "0       1.191657   22.684143  2.530905  0.000000          212      2010   \n",
+       "1      -3.418025  -41.623190 -7.454767  0.000000         1520      2014   \n",
+       "2      -0.762760  -19.835646 -1.497616  0.000000         1277      2013   \n",
+       "3       1.302401  -74.875960  3.221945  0.000000          455      2011   \n",
+       "4      -1.751252 -125.311600 -7.706358  0.007889         1492      2014   \n",
+       "...          ...         ...       ...       ...          ...       ...   \n",
+       "530027  1.229383  116.538080  3.192863  0.015762         1216      2013   \n",
+       "530028 -1.279937  -11.235716 -2.089784  0.000000          577      2011   \n",
+       "530029  0.491823    4.540405  1.184292  0.000000         1096      2013   \n",
+       "530030  3.535935   56.135567  7.314124  0.003937         2191      2016   \n",
+       "530031 -0.674004   -3.326023 -0.165738  0.003937         1551      2014   \n",
+       "\n",
+       "        timeMonth  log_dry_matter  \n",
+       "0               8        0.001037  \n",
+       "1               3        3.309084  \n",
+       "2               7        1.292464  \n",
+       "3               4        2.883859  \n",
+       "4               2        0.558184  \n",
+       "...           ...             ...  \n",
+       "530027          5        3.596845  \n",
+       "530028          8        0.495097  \n",
+       "530029          1        2.579744  \n",
+       "530030          1        6.745309  \n",
+       "530031          4        3.671630  \n",
+       "\n",
+       "[530032 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac0f9da0",
+   "metadata": {},
+   "source": [
+    "## STAGE B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39e301df",
+   "metadata": {},
+   "source": [
+    "Assuming we will always log-transform the target variable (now `log_dry_matter`), in this stage we assess which features should be transformed and how to transform them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b99ee052",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Re-define outcome variable\n",
+    "y = 'log_dry_matter'\n",
+    "\n",
+    "# Skewed features\n",
+    "skewed_features = ['slope', 'vod', 'lai', 'frp']\n",
+    "all_features = ['latitude', 'longitude', 'climatic_region', 'biome', 'GFEDregions', 'slope', 'vod', 'lai',\n",
+    "                'spi03', 'spi06', 'spi12', 'd2m', 'erate', 'fg10', 'si10', 'swvl1', 't2m', 'tprate',\n",
+    "                'danger_risk', 'fwinx', 'ffmcode', 'dufmcode', 'drtcode', 'infsinx', 'fbupinx', 'fdsrte',\n",
+    "                'frp', 'daysElapsed', 'timeYear', 'timeMonth']\n",
+    "\n",
+    "trainB = train.drop('dry_matter', axis = 1)\n",
+    "testB = test.drop('dry_matter', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9e04e7e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_norm_skew, test_norm_skew = transform(train = trainB, test = testB,\n",
+    "                                            features = skewed_features, transformation = \"normalization\")\n",
+    "train_norm_all, test_norm_all = transform(train = trainB, test = testB,\n",
+    "                                          features = all_features, transformation = \"normalization\")\n",
+    "train_stand_skew, test_stand_skew = transform(train = trainB, test = testB,\n",
+    "                                              features = skewed_features, transformation = \"standardization\")\n",
+    "train_stand_all, test_stand_all = transform(train = trainB, test = testB,\n",
+    "                                            features = all_features, transformation = \"standardization\")\n",
+    "train_power_skew, test_power_skew = transform(train = trainB, test = testB,\n",
+    "                                              features = skewed_features, transformation = \"power\")\n",
+    "train_power_all, test_power_all = transform(train = trainB, test = testB,\n",
+    "                                            features = all_features, transformation = \"power\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a44dc74c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define a list of datasets to work with\n",
+    "trainX = [h2o.H2OFrame(train_norm_skew),\n",
+    "          h2o.H2OFrame(train_norm_all),\n",
+    "          h2o.H2OFrame(train_stand_skew),\n",
+    "          h2o.H2OFrame(train_stand_all),\n",
+    "          h2o.H2OFrame(train_power_skew),\n",
+    "          h2o.H2OFrame(train_power_all)]\n",
+    "testX = [h2o.H2OFrame(test_norm_skew),\n",
+    "         h2o.H2OFrame(test_norm_all),\n",
+    "         h2o.H2OFrame(test_stand_skew),\n",
+    "         h2o.H2OFrame(test_stand_all),\n",
+    "         h2o.H2OFrame(test_power_skew),\n",
+    "         h2o.H2OFrame(test_power_all)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "de2247c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# model fitting and measuring the model metric\n",
+    "mae = []\n",
+    "for i in range(len(trainX)):\n",
+    "    # Define Regressor model\n",
+    "    aml = H2OAutoML(max_runtime_secs = 30*60, seed = 1, stopping_metric = \"mae\",\n",
+    "                    preprocessing = [\"target_encoding\"])\n",
+    "    # Fit\n",
+    "    aml.train(y = y, training_frame = trainX[i])\n",
+    "    # Get performance\n",
+    "    preds = aml.predict(testX[i])\n",
+    "    mae.append(mean_absolute_error(obs, np.exp(preds.as_data_frame())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c62d1423",
+   "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>MAE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Normalization - skewed features only</th>\n",
+       "      <td>46.512712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Normalization - all features</th>\n",
+       "      <td>46.477336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Standardization - skewed features only</th>\n",
+       "      <td>46.441018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Standardization - all features</th>\n",
+       "      <td>46.350385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Power transformation - skewed features only</th>\n",
+       "      <td>46.567469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Power transformation - all features</th>\n",
+       "      <td>46.508879</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   MAE\n",
+       "Normalization - skewed features only         46.512712\n",
+       "Normalization - all features                 46.477336\n",
+       "Standardization - skewed features only       46.441018\n",
+       "Standardization - all features               46.350385\n",
+       "Power transformation - skewed features only  46.567469\n",
+       "Power transformation - all features          46.508879"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# visualizing the result\n",
+    "df = pd.DataFrame({'MAE':mae},\n",
+    "                  index=['Normalization - skewed features only',\n",
+    "                         'Normalization - all features',\n",
+    "                         'Standardization - skewed features only',\n",
+    "                         'Standardization - all features',\n",
+    "                         'Power transformation - skewed features only',\n",
+    "                         'Power transformation - all features'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a07eeebf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lr}\n",
+      "\\toprule\n",
+      "{} &        MAE \\\\\n",
+      "\\midrule\n",
+      "Normalization - skewed features only        &  46.512712 \\\\\n",
+      "Normalization - all features                &  46.477336 \\\\\n",
+      "Standardization - skewed features only      &  46.441018 \\\\\n",
+      "Standardization - all features              &  46.350385 \\\\\n",
+      "Power transformation - skewed features only &  46.567469 \\\\\n",
+      "Power transformation - all features         &  46.508879 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.to_latex(index=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "78424411",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f20a61a1760>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5a18eb0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a59b4970>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a59dcfa0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5982a30>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f20a59352b0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a59421c0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a58e9a90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a58b6be0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a58e8460>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5890cd0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5846550>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a57f0d90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5824610>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a57d0e50>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5785700>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a572ff70>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a57637f0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a571ae20>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a56c38b0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f20a567bee0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a56a39d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a565a250>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5601a90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a55b9370>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f20a55e3bb0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5599430>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5542c70>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a55062e0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f20a5506670>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1296 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_stand_all.drop(['climatic_region', 'biome', 'GFEDregions'],\n",
+    "                     axis = 1).hist(figsize = (15,18))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "593caa2b",
+   "metadata": {},
+   "source": [
+    "The above test shows that the best result is achieved by standardising the features and that there is no clear improvement in removing redundant features. This is an interesting interim result: ensuring that predictors vary in comparable ranges (standardisation) yields better results than correcting the variable distribution using a statistical transformation. Therefore, the final model makes use of a log-transformed target and all the features (standardised). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "fd70d3ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_stand_all.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/train_interpTrue_preprocessed.csv\")\n",
+    "test_stand_all.to_csv(\"/home/moc0/ai-vegetation-fuel/data/inputs/test_interpTrue_preprocessed.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/ecmwf/model_definition.ipynb b/notebooks/ecmwf/model_definition.ipynb
new file mode 100644
index 0000000..5ce02c0
--- /dev/null
+++ b/notebooks/ecmwf/model_definition.ipynb
@@ -0,0 +1,2428 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "771c680f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Bad key \"text.kerning_factor\" on line 4 in\n",
+      "/home/moc0/miniconda3/envs/ml-fuel/lib/python3.8/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
+      "You probably need to get an updated matplotlibrc file from\n",
+      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
+      "or from the matplotlib source distribution\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sb\n",
+    "import geopandas as gpd\n",
+    "from cartopy import crs as ccrs\n",
+    "from sklearn.metrics import mean_absolute_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dfd95cf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define plot size\n",
+    "plt.rcParams[\"figure.figsize\"] = (15,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fd91fdf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
+       "<td>3 days 4 hours 0 mins</td></tr>\n",
+       "<tr><td>H2O_cluster_timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O_data_parsing_timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O_cluster_version:</td>\n",
+       "<td>3.32.1.1</td></tr>\n",
+       "<tr><td>H2O_cluster_version_age:</td>\n",
+       "<td>2 months and 12 days </td></tr>\n",
+       "<tr><td>H2O_cluster_name:</td>\n",
+       "<td>H2O_from_python_moc0_j96c9b</td></tr>\n",
+       "<tr><td>H2O_cluster_total_nodes:</td>\n",
+       "<td>1</td></tr>\n",
+       "<tr><td>H2O_cluster_free_memory:</td>\n",
+       "<td>16.30 Gb</td></tr>\n",
+       "<tr><td>H2O_cluster_total_cores:</td>\n",
+       "<td>16</td></tr>\n",
+       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
+       "<td>16</td></tr>\n",
+       "<tr><td>H2O_cluster_status:</td>\n",
+       "<td>locked, healthy</td></tr>\n",
+       "<tr><td>H2O_connection_url:</td>\n",
+       "<td>http://localhost:54321</td></tr>\n",
+       "<tr><td>H2O_connection_proxy:</td>\n",
+       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
+       "<tr><td>H2O_internal_security:</td>\n",
+       "<td>False</td></tr>\n",
+       "<tr><td>H2O_API_Extensions:</td>\n",
+       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
+       "<tr><td>Python_version:</td>\n",
+       "<td>3.8.8 final</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "--------------------------  ------------------------------------------------------------------\n",
+       "H2O_cluster_uptime:         3 days 4 hours 0 mins\n",
+       "H2O_cluster_timezone:       UTC\n",
+       "H2O_data_parsing_timezone:  UTC\n",
+       "H2O_cluster_version:        3.32.1.1\n",
+       "H2O_cluster_version_age:    2 months and 12 days\n",
+       "H2O_cluster_name:           H2O_from_python_moc0_j96c9b\n",
+       "H2O_cluster_total_nodes:    1\n",
+       "H2O_cluster_free_memory:    16.30 Gb\n",
+       "H2O_cluster_total_cores:    16\n",
+       "H2O_cluster_allowed_cores:  16\n",
+       "H2O_cluster_status:         locked, healthy\n",
+       "H2O_connection_url:         http://localhost:54321\n",
+       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
+       "H2O_internal_security:      False\n",
+       "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
+       "Python_version:             3.8.8 final\n",
+       "--------------------------  ------------------------------------------------------------------"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# H2O AutoML framework for benchmarking\n",
+    "import h2o\n",
+    "from h2o.automl import H2OAutoML\n",
+    "h2o.init(max_mem_size = 32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3a2cbcb",
+   "metadata": {},
+   "source": [
+    "This notebook uses the H2O.ai AutoML framework to model transformed outcome and predictors. It visualises averaged results over a map and uses the H2O.ai explainability module to identify model limitations and possible future avenues for improvements."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a21347c0",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5a8dd936",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define working directory\n",
+    "os.chdir('/home/moc0/ai-vegetation-fuel')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0cc921ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load train data\n",
+    "train = pd.read_csv(os.getcwd() + \"/data/inputs/train_interpTrue_preprocessed.csv\",\n",
+    "                    dtype={'climatic_region':'category',\n",
+    "                           'biome':'category',\n",
+    "                           'GFEDregions':'category'})\n",
+    "train = train.drop('Unnamed: 0', axis = 1)\n",
+    "train_data = h2o.H2OFrame(train)\n",
+    "\n",
+    "# Load test data\n",
+    "test = pd.read_csv(os.getcwd() + \"/data/inputs/test_interpTrue_preprocessed.csv\",\n",
+    "                    dtype={'climatic_region':'category',\n",
+    "                           'biome':'category',\n",
+    "                           'GFEDregions':'category'})\n",
+    "test = test.drop('Unnamed: 0', axis = 1)\n",
+    "test_data = h2o.H2OFrame(test)\n",
+    "\n",
+    "# Load raw test data\n",
+    "test_raw = pd.read_csv(os.getcwd() + \"/data/inputs/test_interpTrue_raw.csv\",\n",
+    "                    dtype={'climatic_region':'category',\n",
+    "                           'biome':'category',\n",
+    "                           'GFEDregions':'category'})\n",
+    "test_raw = test_raw.drop('Unnamed: 0', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7f8fe517",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data = h2o.H2OFrame(train)\n",
+    "test_data = h2o.H2OFrame(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c0d981f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define outcome variable\n",
+    "y = 'log_dry_matter'\n",
+    "\n",
+    "# Define Regressor model\n",
+    "aml = H2OAutoML(max_runtime_secs = 10*60*60, seed = 1, stopping_metric = \"mae\",\n",
+    "                preprocessing = [\"target_encoding\"])\n",
+    "# Fit\n",
+    "aml.train(y = y, training_frame = train_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "78574cff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "43.86178687465157"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# MAE\n",
+    "pre = aml.predict(test_data)\n",
+    "pre = np.exp(pre.as_data_frame())\n",
+    "obs = test_raw['dry_matter']\n",
+    "mean_absolute_error(obs, pre)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "595cfcf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">   rmse</th><th style=\"text-align: right;\">    mse</th><th style=\"text-align: right;\">     mae</th><th style=\"text-align: right;\">  rmsle</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>StackedEnsemble_AllModels_AutoML_20210607_163002   </td><td style=\"text-align: right;\">                 1.26954</td><td style=\"text-align: right;\">1.12674</td><td style=\"text-align: right;\">1.26954</td><td style=\"text-align: right;\">0.879706</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20210607_163002</td><td style=\"text-align: right;\">                 1.30452</td><td style=\"text-align: right;\">1.14216</td><td style=\"text-align: right;\">1.30452</td><td style=\"text-align: right;\">0.89436 </td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_56        </td><td style=\"text-align: right;\">                 1.327  </td><td style=\"text-align: right;\">1.15195</td><td style=\"text-align: right;\">1.327  </td><td style=\"text-align: right;\">0.903173</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_44        </td><td style=\"text-align: right;\">                 1.36617</td><td style=\"text-align: right;\">1.16883</td><td style=\"text-align: right;\">1.36617</td><td style=\"text-align: right;\">0.91748 </td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_25        </td><td style=\"text-align: right;\">                 1.3715 </td><td style=\"text-align: right;\">1.17111</td><td style=\"text-align: right;\">1.3715 </td><td style=\"text-align: right;\">0.920402</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_34        </td><td style=\"text-align: right;\">                 1.37222</td><td style=\"text-align: right;\">1.17142</td><td style=\"text-align: right;\">1.37222</td><td style=\"text-align: right;\">0.918598</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_16        </td><td style=\"text-align: right;\">                 1.37375</td><td style=\"text-align: right;\">1.17207</td><td style=\"text-align: right;\">1.37375</td><td style=\"text-align: right;\">0.921342</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>XGBoost_grid__1_AutoML_20210607_163002_model_51    </td><td style=\"text-align: right;\">                 1.38061</td><td style=\"text-align: right;\">1.17499</td><td style=\"text-align: right;\">1.38061</td><td style=\"text-align: right;\">0.921333</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_78        </td><td style=\"text-align: right;\">                 1.38208</td><td style=\"text-align: right;\">1.17562</td><td style=\"text-align: right;\">1.38208</td><td style=\"text-align: right;\">0.923067</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_99        </td><td style=\"text-align: right;\">                 1.38243</td><td style=\"text-align: right;\">1.17577</td><td style=\"text-align: right;\">1.38243</td><td style=\"text-align: right;\">0.920229</td><td style=\"text-align: right;\">    nan</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View the AutoML Leaderboard - uses validation metrics\n",
+    "lb = aml.leaderboard\n",
+    "lb.head()\n",
+    "# lb.head(rows=lb.nrows)  # Print all rows instead of default (10 rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7dcb88fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "45.310096410231814"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Predict using test data and second model\n",
+    "aml2 = h2o.get_model(aml.leaderboard[1, \"model_id\"])\n",
+    "pre = np.exp(aml2.predict(test_data).as_data_frame())\n",
+    "\n",
+    "# Get performance - transformed \n",
+    "mean_absolute_error(obs, pre)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e51db3f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "46.23623950571551"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Predict using test data and third model\n",
+    "aml3 = h2o.get_model(aml.leaderboard[2, \"model_id\"])\n",
+    "pre = np.exp(aml3.predict(test_data).as_data_frame())\n",
+    "\n",
+    "# Get performance - transformed \n",
+    "mean_absolute_error(obs, pre)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "08b9c031",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
+      "\n",
+      "ModelMetricsRegressionGLM: stackedensemble\n",
+      "** Reported on test data. **\n",
+      "\n",
+      "MSE: 1.2343500368031128\n",
+      "RMSE: 1.1110130677913346\n",
+      "MAE: 0.867289494712053\n",
+      "RMSLE: NaN\n",
+      "R^2: 0.7178156241812261\n",
+      "Mean Residual Deviance: 1.2343500368031128\n",
+      "Null degrees of freedom: 132508\n",
+      "Residual degrees of freedom: 132486\n",
+      "Null deviance: 579630.3950803857\n",
+      "Residual deviance: 163562.4890267437\n",
+      "AIC: 403991.29937958176\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Performance on test data\n",
+    "aml.leader.model_performance(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4db3b5ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/moc0/ai-vegetation-fuel/data/outputs/StackedEnsemble_AllModels_AutoML_20210607_163002\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The leader model is stored here\n",
+    "# aml.leader\n",
+    "\n",
+    "# Save the leader model\n",
+    "leadermodel_path = h2o.save_model(aml.leader, path = \"/home/moc0/ai-vegetation-fuel/data/outputs\", force = True)\n",
+    "print(leadermodel_path)\n",
+    "\n",
+    "# Load the model\n",
+    "# saved_model = h2o.load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fba6cbf5",
+   "metadata": {},
+   "source": [
+    "# Visualise results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbd06d69",
+   "metadata": {},
+   "source": [
+    "Explainability\n",
+    "See https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html#explainability and https://docs.h2o.ai/h2o/latest-stable/h2o-docs/explain.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cf535321",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>Leaderboard</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# Leaderboard"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# Leaderboard"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
+      ],
+      "text/plain": [
+       "\n",
+       "> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">   rmse</th><th style=\"text-align: right;\">    mse</th><th style=\"text-align: right;\">     mae</th><th style=\"text-align: right;\">  rmsle</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo           </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>StackedEnsemble_AllModels_AutoML_20210607_163002   </td><td style=\"text-align: right;\">                 1.26954</td><td style=\"text-align: right;\">1.12674</td><td style=\"text-align: right;\">1.26954</td><td style=\"text-align: right;\">0.879706</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">              8851</td><td style=\"text-align: right;\">                 0.045374</td><td>StackedEnsemble</td></tr>\n",
+       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20210607_163002</td><td style=\"text-align: right;\">                 1.30452</td><td style=\"text-align: right;\">1.14216</td><td style=\"text-align: right;\">1.30452</td><td style=\"text-align: right;\">0.89436 </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">               765</td><td style=\"text-align: right;\">                 0.004911</td><td>StackedEnsemble</td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_56        </td><td style=\"text-align: right;\">                 1.327  </td><td style=\"text-align: right;\">1.15195</td><td style=\"text-align: right;\">1.327  </td><td style=\"text-align: right;\">0.903173</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             25170</td><td style=\"text-align: right;\">                 0.003743</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_44        </td><td style=\"text-align: right;\">                 1.36617</td><td style=\"text-align: right;\">1.16883</td><td style=\"text-align: right;\">1.36617</td><td style=\"text-align: right;\">0.91748 </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             17868</td><td style=\"text-align: right;\">                 0.00544 </td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_25        </td><td style=\"text-align: right;\">                 1.3715 </td><td style=\"text-align: right;\">1.17111</td><td style=\"text-align: right;\">1.3715 </td><td style=\"text-align: right;\">0.920402</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             18995</td><td style=\"text-align: right;\">                 0.002782</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_34        </td><td style=\"text-align: right;\">                 1.37222</td><td style=\"text-align: right;\">1.17142</td><td style=\"text-align: right;\">1.37222</td><td style=\"text-align: right;\">0.918598</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             22237</td><td style=\"text-align: right;\">                 0.003631</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_16        </td><td style=\"text-align: right;\">                 1.37375</td><td style=\"text-align: right;\">1.17207</td><td style=\"text-align: right;\">1.37375</td><td style=\"text-align: right;\">0.921342</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             14428</td><td style=\"text-align: right;\">                 0.003112</td><td>GBM            </td></tr>\n",
+       "<tr><td>XGBoost_grid__1_AutoML_20210607_163002_model_51    </td><td style=\"text-align: right;\">                 1.38061</td><td style=\"text-align: right;\">1.17499</td><td style=\"text-align: right;\">1.38061</td><td style=\"text-align: right;\">0.921333</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             28355</td><td style=\"text-align: right;\">                 0.001478</td><td>XGBoost        </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_78        </td><td style=\"text-align: right;\">                 1.38208</td><td style=\"text-align: right;\">1.17562</td><td style=\"text-align: right;\">1.38208</td><td style=\"text-align: right;\">0.923067</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             16272</td><td style=\"text-align: right;\">                 0.004717</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_99        </td><td style=\"text-align: right;\">                 1.38243</td><td style=\"text-align: right;\">1.17577</td><td style=\"text-align: right;\">1.38243</td><td style=\"text-align: right;\">0.920229</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             39663</td><td style=\"text-align: right;\">                 0.004755</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_69        </td><td style=\"text-align: right;\">                 1.38396</td><td style=\"text-align: right;\">1.17642</td><td style=\"text-align: right;\">1.38396</td><td style=\"text-align: right;\">0.924127</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             15838</td><td style=\"text-align: right;\">                 0.005228</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_61        </td><td style=\"text-align: right;\">                 1.38551</td><td style=\"text-align: right;\">1.17708</td><td style=\"text-align: right;\">1.38551</td><td style=\"text-align: right;\">0.925907</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             12322</td><td style=\"text-align: right;\">                 0.004353</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_5         </td><td style=\"text-align: right;\">                 1.38861</td><td style=\"text-align: right;\">1.17839</td><td style=\"text-align: right;\">1.38861</td><td style=\"text-align: right;\">0.924053</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             18153</td><td style=\"text-align: right;\">                 0.003283</td><td>GBM            </td></tr>\n",
+       "<tr><td>XGBoost_grid__1_AutoML_20210607_163002_model_29    </td><td style=\"text-align: right;\">                 1.39094</td><td style=\"text-align: right;\">1.17938</td><td style=\"text-align: right;\">1.39094</td><td style=\"text-align: right;\">0.920863</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">            122067</td><td style=\"text-align: right;\">                 0.002432</td><td>XGBoost        </td></tr>\n",
+       "<tr><td>XGBoost_grid__1_AutoML_20210607_163002_model_16    </td><td style=\"text-align: right;\">                 1.39295</td><td style=\"text-align: right;\">1.18023</td><td style=\"text-align: right;\">1.39295</td><td style=\"text-align: right;\">0.921588</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">            138964</td><td style=\"text-align: right;\">                 0.003957</td><td>XGBoost        </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_89        </td><td style=\"text-align: right;\">                 1.39513</td><td style=\"text-align: right;\">1.18115</td><td style=\"text-align: right;\">1.39513</td><td style=\"text-align: right;\">0.928252</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             14894</td><td style=\"text-align: right;\">                 0.002325</td><td>GBM            </td></tr>\n",
+       "<tr><td>XGBoost_grid__1_AutoML_20210607_163002_model_44    </td><td style=\"text-align: right;\">                 1.39629</td><td style=\"text-align: right;\">1.18165</td><td style=\"text-align: right;\">1.39629</td><td style=\"text-align: right;\">0.925907</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             18488</td><td style=\"text-align: right;\">                 0.001136</td><td>XGBoost        </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_15        </td><td style=\"text-align: right;\">                 1.39838</td><td style=\"text-align: right;\">1.18253</td><td style=\"text-align: right;\">1.39838</td><td style=\"text-align: right;\">0.929277</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             22856</td><td style=\"text-align: right;\">                 0.003174</td><td>GBM            </td></tr>\n",
+       "<tr><td>GBM_grid__1_AutoML_20210607_163002_model_57        </td><td style=\"text-align: right;\">                 1.39844</td><td style=\"text-align: right;\">1.18256</td><td style=\"text-align: right;\">1.39844</td><td style=\"text-align: right;\">0.928957</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             16400</td><td style=\"text-align: right;\">                 0.003175</td><td>GBM            </td></tr>\n",
+       "<tr><td>XGBoost_grid__1_AutoML_20210607_163002_model_48    </td><td style=\"text-align: right;\">                 1.39974</td><td style=\"text-align: right;\">1.18311</td><td style=\"text-align: right;\">1.39974</td><td style=\"text-align: right;\">0.927135</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">             22447</td><td style=\"text-align: right;\">                 0.001397</td><td>XGBoost        </td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>Residual Analysis</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# Residual Analysis"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# Residual Analysis"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see \"striped\" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see \"striped\" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable."
+      ],
+      "text/plain": [
+       "\n",
+       "> Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see \"striped\" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>Variable Importance</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# Variable Importance"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# Variable Importance"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>The variable importance plot shows the relative importance of the most important variables in the model.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> The variable importance plot shows the relative importance of the most important variables in the model."
+      ],
+      "text/plain": [
+       "\n",
+       "> The variable importance plot shows the relative importance of the most important variables in the model."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>Variable Importance Heatmap</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# Variable Importance Heatmap"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# Variable Importance Heatmap"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
+      ],
+      "text/plain": [
+       "\n",
+       "> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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zPn/Ps4XzAozY7DWl8g927TRU0Sa3aWZmNjhJOiCtVPyIpBNrnD9I0j1pYv8dkvasN28t7nEwMzNrgIHocZDUCfwUeCfwJDBD0qURcV8u2TXApRERknYALgDeWGfe1bjHwczMrHXtBjwSEY9GxDLgd1S95h8RCyOisijhSF5doLDPvLW4x8HMzKys/nurYlxab6hiagpLULExkJ/Q9iSw+2rVkw4Bvgu8Fnj3muSttlZ7HPLRJCV9S9J+DShzrKTjcvsbSbqobLkF6jEg1zUzs0FtbkRMyn2mVp2vFSBjtZAHEXFJRLwROBj49prkrTZgPQ4R8Y0GFTUWOI4syiQR8RRwaJkCJXVGxMo1ydOI65qZWWsawLcqngQ2ze1vAjzVU+KIuEHSVikq9BrlrejX25R0ZJrJebekc6rOTZN0aNqeI+l/JN2SZnzuLOlKSX+XdExKM0rSNZLulDRLUmUc5hRgqzRb9DRJ4yXdm/J0Sjo9pb9H0md6qescSd+QdBPwgfRgr5D0N0k3SnpjSreVpFslzUi9JgvT8fx1h0v6VbruXSkWBpImS/p9KvdhSd9r6AM3M7OBMXCxKmYA20jaQtJQ4EPApatUTdpaKXRniqs0FHi+nry19FuPg6Ttga8Cb42IuZLWA07oJcsTEbGHpDOAacBbyaJNzgamAEuAQyJifmop3ZrCVZ8ITKhEnJQ0Plfm0WSBp3aKiBWpDr1ZEhF7pnKuAY6JiIcl7U7Wo/F24IfADyPivEqjpoZPA0TEm1OD4ypJb0jnJgI7AUuBByX9OCJWWXBB0tGp7oxYt1GRvs3MbLBJv9uOB64EOoGzImJ25fdTREwB/g9wpKTlwGLgsDRZsmbevq7Zn0MVbwcuSjEhiIgX1Hus8korZxYwKiIWAAskLZE0FngZ+B9JewPdZJM6+vqtuh8wJSJWVOrQR/rzIevdAN4CXJir87D0dQ+yMSKA3wKn1yhnT+DH6ZoPSHocqDQcromIeek695FF5Vyl4ZDGsKYCrLfptn2ON5mZ2cAS0NHrr7j+ExGXA5dXHZuS2z4VOLXevH3pz4aDqGOSRc7S9LU7t13ZH0IWVXIDYJcUCnsOWY9EI+vwcvraAbxU6cUooLcfn/y9rcRvtpiZWQvpzzkO1wAflLQ+QB3DBH0ZAzybGg37kv2lDrAA6GkN2KuAYyQNWZM6RMR84DFJH0j5JGnHdPpWsm4fyMaDarmBrKFDGqLYDHiwnmubmVlrGqA5Dmtdv1UrjZOcDFwv6W7g+yWLPBeYlN5nPRx4IF3neWC6pHslnVaV50zgH8A9qQ7V4bJ7czhwVMo3m1cXxfgs8HlJtwOvB2oFGfgZ0ClpFtnwx+SIWFojnZmZWUvp127yiPg18Osezk3ObY/PbU8jmxy52jmy+QW1yqpuEExIx1cAn0+fvuo6vmr/MeCAGkn/CfxbWrrzQ8AdKf2c3HWXAJNrXGMaq97be/qql5mZtYA2Cqvt8fU1twvwk/Rqy0vAxwe4PmZmZmtN2zUcJF1C9opm3pcj4sp68kfEjcCOfSZskOWLVvDPu58plHdEiRDRANdf9Uip/EV1rbtO4bzD1in3I/38vxYWzrvs+UWF85YJ8wywaEHxkbBlc4vXu3t5d+G8ZanEFPZt996870Q9eLnEswaYN6T4n6XrjiseVvuFznJT/pfOXVwq/2DXTmG1267hEBGHDHQdzMxssBEdA/U+5lrWJu0jMzMza4S263EwMzNrOIFKDge1Cvc4mJmZWd3ccMjJB6qqOn6mpO0Gok5mZtb8RDZht9GfZuShijpExCcGug5mZmbNwD0Oqxsi6dcpDPdFkkZIuk7SJABJH07hsu+V9ErQEEkLJZ2awnD/RdJuKd+jkt6X0nSm0N8zUvmfGqibNDOzBlL79Di44bC6bYGpEbEDMB84rnJC0kZkEcbeThYee1dJlUiZI4HrImIXsvgZ3wHeCRwCfCulOQqYFxG7ArsCn5RUvaYEko6WdIekO5Yvnd8f92hmZlaIhypW90RETE/bvwFOyJ3blaxx8ByApHOBvYE/AMuAK1K6WcDSFJBrFjA+Hd8f2EHSoWl/DLAN8Fi+Avmw2qPX38phtc3MWkC7vFXhhsPqqn9R5/d7+6lYHhGVtK+EBo+I7kp0zpT/M/WuUmlmZi1CzTu00GgeqljdZpIqwbQ+DNyUO3cb8DZJ4yR1pvPXr0HZVwLHSuqCLOS2pOJryJqZma1l7nFY3f3ARyX9AngY+DnwXoCIeFrSfwF/Jes9uDwi/ncNyj6TbNjizhQk6zng4F5zmJlZS/BQRRtKobFrrdewTy7Nb4Hf1sg7Krd9Uq1zEdENfCV9zMzMWo4bDmZmZiVJtE2QKzccBrGlz75cKv+4N44rnHfB8pWF865ctLxw3pGvLTdlZJ2RQwvnffqfxV+djZXlXp7ZdqfXF847+7rH+k7Ug9dMeG3hvAArFi4rnHfsJq8pnPeRGU8Vzjt0veGF8wKMKRE2/p8Pzi2cd9Q26xXOCzB8eFep/DZ4uOFgZmbWAH6rwszMzKyKexzMzMzKaqOw2m44mJmZlSbU0R6d+C13l5LGSjoubW8k6aIGlXuSpJC0de7Y59KxSQXLPDgfjjsfLMvMzKwVtVzDARhLCjwVEU9FxKF9pF8Ts4AP5fYPBe4rUd7B1F4XwszMBhGloYpGf5pRKzYcTgG2kjRT0oWS7gWQNFnSHyT9UdJjko6X9HlJd0m6VdJ6Kd1Wkq5I4a9vlPTGXNl/AA5K6bYE5pGt7kg61ltI7ZMl3Z2u9TpJbwHeB5yW6rpVSv4BSbdLekjSXv35oMzMzBqtFRsOJwJ/j4iJwBerzk0APgLsBpwMLIqInYBbgCNTmqlkgaZ2Ab4A/CyXfz7whKQJZHEozq+cqCOk9q0RsSNwA/DJiLgZuBT4YkRMjIi/p7RDImI34LPAf9e6QYfVNjNrPepQwz/NqBUbDr35a0QsSGGv5wF/TMdnAeMljQLeAlwoaSbwC6B65ZzfkQ1XHAxckjv+SkjtiFgBVEJqQxZS+7K0/TdeDaNdy+/7ShcRUyNiUkRM6hpWfJEbMzOzRhtsb1UszW135/a7ye61A3gp9Vb05I/AacAdETE/i0UF1B9SeyW9P9eldaYzM7NWIS8A1cwWAKOLZIyI+cBjkj4AoMyOVWkWA18mG+rIKxJSu3BdzcyslTR+YqQnRzZIRDwPTE+TIk8rUMThwFGS7gZmkyZDVl3jdxFxZ9Wxp4FKSO27gTvrCKn9O+CLaYLmVn2kNTMza3ot2VUeER+pcWwaMC23P77WuYh4DDigRv6TerjWPrntekJqXwRclLans+rrmPmy5tL7XAgzM2sR7RQds+V6HMzMzGzgtGSPQzuJ7u7CoYeHrlsu/O9zs54pnLdMaOyh40YUzvvErU8WzgvQMWxg/kmUeV4Ad1/2YOG8w0qEIn/50RcL5wXo6Cr+t8szJcLGl/k+L39xceG8AAuWPtd3oh6Uqffify4onBdgQXd3qfztwJMjzczMzKq4x8HMzKysNoqO6R4HMzMzq5t7HMzMzBrAcxzaUD5kd4PKm5xiXJiZ2WAmLwDVrl4J2V2vtIpkTyYDbjiYmdmg4aGKVb0SshtYDiwGnge2JYt6eVxEdEtaCHwf+HfgPyW9HXgvsA5wM/Ap4P8Ak4BzJS0G9iBbDOr7wChgLjA5rUhpZmYtTIA62uNv8fa4y/pVh+zeDfhP4M3AVsD7U7qRwL0RsXtE3AT8JCJ2jYgJZI2H96QVJO8ADk/lrQB+DByaQnqfxerxMIDqsNrl3r02MzNrJPc49O72iHgUQNJ5wJ5ky0mvBC7OpdtX0peAEcB6ZDEw/lhV1rbABODqFHGzE6jZ2xARU4GpAKPW3SJqpTEzsybSRtEx3XDoXfUv7cr+kohYCSBpOPAzYFJEPCHpJKDWko0CZkfEHv1VWTMzs/7moYpVVYfB3k3SFpI6gMOAm2rkqTQS5koaBRzaQ3kPAhtI2gNAUpek7RtaezMzGzAdnWr4pxm5xyEnIp6XVAnZvRi4hWzC5JvJJkdeUiPPS5J+CcwC5gAzcqenAVNykyMPBX4kaQzZs/8B2bCGmZlZS3DDoUolZLekfYAvRMRhNdKMqtr/GvC1GukuZtW5EDOBvRtZXzMzG3iSPMfBzMzM6tesCzY1mhsOPYiI64DrBrgaZmZmTcUNhyYXK4MVC5YVyrt83tJS1y7T7Vam5b30mZcL512xsNizqlBXbwuB9q57yYrCeTu6Bm6e8pKnFxbO2710ZQNrsmY6hpX4Xi3vLn7h7nJvSJeq99JFhfN2rlPuv/tSz6wdtNHrmH6rwszMzOrmHgczM7NGaJM5Du5xMDMzs7q5x8HMzKwsz3FobZLGSjoubW8k6aIGlLm/pFuUAk1I6pQ0U9JbypZtZmbWKgZlwwEYCxwHEBFPRcShfaTvU0RcBTwOHJUOfQaYERE3lylXknt9zMxanqCzo/GfJjRYf2mdAmwlaSbwMPCmiJggaTJwMFlkygnA/wOGAkcAS4F3RcQLkrYCfgpsACwCPhkRDwCfA26SdAtwPFnYbSS9Dvg5sBnQDZwQEbdK+jfgDLJ4FouAyRHxsKRPAPsBo4BhwDv7+4GYmVk/EtAmQxWDteFwIjAhIiZKGg9cljs3AdiJ7Jf5I8CXI2InSWcAR5LFj5gKHJN+ye9OFv3y7RHxtKQfkMWwOCEiXkhl/gj4XmosVK43Abgf2DMiVko6APgOWbAsyGJXTIyIF6srL+lo4GiAocPXa8TzMDMza4jB2nDozV8jYgGwQNI84I/p+CxghxTh8i3AhWk6A2S9AhU/BU6JiGm5Y/sB2+bSrytpHbIhk7NTD0a1q2o1GgAiYipZ44WRrxlfbrUZMzNbK7zk9OCVX06xO7ffTfY8OoCXImJircwR0S2p+pe5gN0iYpVlCyWdDFwZET+TtDVwRe508eURzczMktSj/UOyYfgzI+KUqvOHA19OuwuBYyPi7nRuDrAAWAmsiIhJfV2vOWdelLcAGF0kY0TMBx6T9AEAZXbsI9tfgE9XdiRVGh1jgH+m7clF6mNmZi2gMseh0Z++Lit1kvWEHwhsB3xY0nZVyR4D3hYROwDfJvVo5+wbERPraTTAIG04RMTzwHRJ9wKnFSjicOAoSXcDs4GD+kj/aeCtku6RdB/wyXT8VOA0SdML1MHMzKwvuwGPRMSjqdf7d1T9zoqIm3ND47cCm5S54KAdqoiIj9Q4Ng2YltsfX+tcRDwGHNBL2aOq9p8DVnvlMyJuAt6QO/S1dPzMOm7BzMxahvpryelxku7I7U9N8+AqNgaeyO0/CezeS3lHAX/O7QdwVRqC/0VV2TUN2oaDmZnZ2qL+Wzlybh9DCLUuWnNSvaR9yRoOe+YOvzUinpL0WuBqSQ9ExA29VcgNhyYnFQ+53Dmiq9S1y+YvKkqELV5ZMsxzmfDWw147pnDezuHFQy1DudDYw18/qu9E/XBdKPcfbZnvddcA/WyXNVD/JrNrt8cbAy3oSWDT3P4mwFPViSTtAJwJHJiG84FskcT09VlJl5ANffTacBiUcxzMzMzWuoFZOXIGsI2kLSQNBT4EXJpPIGkz4PfAERHxUO74SEmjK9vA/sC9fV3QPQ5mZmYtKiJWSDoeuJLsdcyzImK2pGPS+SnAN4D1gZ+l9YYqr12+DrgkHRsC/DYirqhxmVW44WBmZlbWAEbHjIjLgcurjk3JbX8C+ESNfI8CfS03sBoPVZiZmVndmrrhIOkkSV9oUFmTJT2XQmFXPttJGp/We1jrJJWbWWZmZk0ivY7Z6E8TarehivMj4vj8gRSUyszMzOrQdD0Okr4q6UFJfwG2Tcc+KWmGpLslXSxphKTRkh6T1JXSvEbSHEldkk6QdF9ayfF3a3Dt8ZJulHRn+rwlHd9H0g2SLknlTpHUIalT0jRJ90qaJelzKf1Wkq6Q9LdU3hvT8S0k3ZLu5dsNf3hmZjYwhHscBoKkXcheJdmJrG53An8Dfh8Rv0xpvgMcFRE/lnQd8G7gDynfxRGxXNKJwBYRsVTS2NwlDpOUX/hij6oqPAu8MyKWSNoGOA+oLLyxG9k64I+TBat6P9n63xtHxIRUt8q1atEdorUAACAASURBVIblJgtC8vOIOFvSK7EtajyHXFjt9ft8bmZmNvAGanLk2tZsPQ57AZdExKIUbKryLuqE9Jf7LLI4Etun42cCH0vbHwN+lbbvAc6V9H+BFbnyz0+BPCqfxVXX7wJ+ma5zIVlDoeL2tBb4SrIGxZ7Ao8CWkn6copPNrwrLPRP4BfD6VMZbU16Ac3p6CBExNSImRcSkrqHFF+cxMzNrtKbqcUhqLRs4DTg4Iu6WNBnYByAipqfhhbcBnRFRmeT4bmBv4H3A1yVtv3qRNX0OeIbs9ZQOYEkv9YqIeDFFzvx3skBXHwQ+Sy9huXu4PzMza2WVoYo20Gw9DjcAh0haJ61m9d50fDTwdJrPcHhVnrPJ/or/FYCkDmDTiPgr8CVgLFDvn+1jgKcjohs4gmwxjYrd0hyFDuAw4CZJ44COiLgY+Dqwcx9huaeTDalQ4z7MzMyaXlM1HCLiTuB8YCZwMXBjOvV14DbgauCBqmznAuvy6hBAJ/CbNNxwF3BGRLyUzh1W9TrmW6rK+hnwUUm3kkW1fDl37hbgFLLlOB8DLiGLSnZdGpKYBvxXSttTWO7/AD4taQZZI8XMzAYFQUdH4z9NqOmGKiLiZODkGqd+3kOWPYGLKo2DiFjOqpG/KuVOIxdSu8qElOZhYIfc8f/KbS+KiMOq8t0N7FzjWjXDcqfj+QmZp/RQHzMzs6bUdA2HNSHpx8CBwLsGui5mZtbGBGqTOQ4t3XCIiM+spetcB1y3Nq61GqnwD2PH8HLf3uXzlhbOWyZM9PANi79JsmLekr4T9UJdxesdy4uHeV62sPizBhgyamjxa79Q7pmVUeZntExY7TLPq8y/Cyj3yyVWFp9bPWR08XuGcj/fNri0dMPBzMysabTJOg5uOJiZmZXl1zHNzMzMVuceBzMzs9LkJafbQU9huyUdLGm7WnkKXGOapEMbUZaZmdlAc49DFUlDgIOBy4D7Brg6ZmbWCgR0tsff4u1xlzk9hO2+TtL/SLoe+DJZjIvT0uqSW0naWtJfUljvO9MxSTotF1L7sFSWJP0khd/+E/Da3LV3kXR9Crd9paTX16iimZlZ02qrHodewnYDjI2It6V02wCXRcRFaf824JSIuETScLIG1/uBiWQBscYBMyTdQLYy5LbAm4HXkfVanJXibPwYOCginksNjZOBj9eo56thtddxWG0zs6bXRm9VtFXDgVzYbgBJl+bOnV8rQwq2tXFEXAIQEUvS8T2B81KY7WdSb8WuZFE5K8efknRtKmpbsqWtr5YEWUyNp2tdMyKmAlMBRo3dwtE0zcysabRbwwF6Dmv9cg/He2pC9ta0rHUNAbMjYo8a58zMrIUJ/FbFINVT2O5qC8hCeZPCZD8p6WAAScMkjUhlHSapU9IGZD0Nt6fjH0rHXw/sm8p8ENhA0h6pnC5J2/fPbZqZ2VolZUMVjf40obZqOPQStrva74AvSrpL0lbAEcAJku4BbgY2JAurfQ9ZhMxrgS9FxL/S8YeBWWQRPa9P114GHAqcmsJtzwSqw3qbmZk1tbYbqughbPfpVWmmA9XrOLy9RnFfTJ983gCO7+HaM8l6JszMbLDxUIWZmZnZqtqux8HMzKzhVC5keitxw6HZdYjOEV2FssbKcm9ylvlHoK7OwnlffvTFwnlXLllZOC/AkBL1ju7iz7t7eXfhvAArFiwqnLfM96rMPQNoyYrimVcWf2Zln3cZZa5dZtb+ykXLC+eF8v+f2ODhhoOZmVkjdLTH6H973KWZmZk1hHsczMzMSpN7HAYbSSdIul/SeSlg1cxKYKp+vu54Sff293XMzGwAiazh0OhPE2qnHofjgAPJAk+dGhETB7g+ZmZmLac5mzMNJmkKsCVwOTAdmJgLmT0nhdS+RdIdknZOIa//LumYXBlfSuGz75Z0Sjo2UdKtku6RdImkddPxXVK6W4BP58roTKG4Z6Q8n1qrD8LMzPpPhxr/aUJt0XCIiGOAp8iiY74DuDEiJkbE31OSJ1LwqRuBaWRLQ/8b8C0ASQcCBwO7R8SOwPdSvrOBL0fEDmRLTP93Ov4r4IQaAa2OAuZFxK5kkTQ/KWmLRt+vmZlZf2mnoYreVMJrzwJGRcQCYIGkJZLGAvsBv6qE446IFySNAcZGxPUp76+BC2scP4dsiARgf2AHSYem/THANsBj+cpIOho4GmDYiPUbfKtmZtZwap/JkW44ZJamr9257cr+ELJpL/WuftJbWgGfiYgreysgIqYCUwFGrbelV10xM7Om0R7No/KuAj6ewmkjab2ImAe8KGmvlOYI4PqIeAmYJ2nPdPzwXDlXAsdK6krlvEHSyLVzC2Zm1q/8VoVVRMQVkiYCd0haRjbJ8ivAR4EpqUHxKPCxlOVjwFmSFpE1FirOBMYDd0oS8BzZ3AkzM2tpHqoYdCJifNq8Ln2qjxMR08gmR9Y6dwpwSlWZM8kmUVZf62/AjrlDJ6Xj3WQNjq8UuAUzM7MB1zYNBzMzs35TWQCqDbTHXZqZmVlDuMehycXKbpbPW9p3whqKhuNuhBXzlhTPu6h4qOXOYcVDREO5sMXdS4vXu6OrXBu+o2to4bxlfk6WPvNy4bwA0V38vkuFA19eLvx6GWV+xsqEMVdnucWEyuZvC+5xMDMzM1uVexzMzMzKaqMFoNrjLs3MzKwh3ONgZmbWCE0alKrR3ONQg6QTJN0v6dyq43MkjVuDciZL2qjxNTQzs6bjlSPb2nHAgRHxWJ8peyCpE5gM3EsWmdPMzKzlueFQRdIUYEvgUknTgH8HNgBuJ1vigxRf4gJgE6AT+HZEnC9pDnAWWRTMKcAk4FxJi4E9gO2A7wOjgLnA5Ih4eq3dnJmZ9Q9PjmxfEXEMWQ/BvsDmwE0RsRNZ6O3NUrIDgKciYseImABckStiSUTsGRG/Ae4ADo+IicAK4MfAoRGxC1kD4+RadZB0tKQ7JN2xYtnCfrhLMzOzYtzj0Lu9gfcDRMSfJL2Yjs8CTpd0KnBZRNyYy3N+D2VtC0wArs7iW9EJ1OxtyIfVHjlmvMNqm5m1AKk9/hZ3w6Fvq/3ijoiHJO0CvAv4rqSrIuJb6XRPS+kJmB0Re/RTPc3MzPpdezSPirsBOBxA0oHAuml7I2BRGo44Hdi5h/wLgNFp+0FgA0l7pDK6JG3fj3U3M7O1Rn6rwgD4JnCepDuB64F/pONvBk6T1A0sB47tIf80YEpucuShwI8kjSF79j8AZvdf9c3MzBrLDYcaImJ8bnf/3Pbn0tcr06e3fETExcDFuUMzyeZNmJnZYNJGYbXdcDAzM2uENlk50g2HJiepcMjlrjHDSl17ydPFXwUtEya6TGjs7uXdhfMCqGT+wkr+pbJy0fIGVaR1lAljrs7iocRLh+TuHJi/SmOlX9CyxnDDwczMrCwvAGVmZma2Ovc4mJmZNYJ7HMzMzMxW1ZYNh3zYbElfaFCZx0g6shFlmZlZCxqgBaAkHSDpQUmPSDqxxvnDJd2TPjdL2rHevLW061DFccCBwEcbVWBETGlUWWZmZvWQ1An8FHgn8CQwQ9KlEXFfLtljwNsi4sW0CvJUYPc6866m7Xoc8mGzyRZ02lHStZIelvTJlGYfSZfl8vxE0uS0PUfSqZJuT5+t0/GTKr0Xkq7LpXlI0l7p+OclnZW23yzpXkkj1uLtm5lZf9CALTm9G/BIRDwaEcuA3wEH5RNExM0RUQnSeCuwSb15a2m7hkNV2OwzgB2Ad5MtCf2NFIeiL/MjYjfgJ2TLRtcyJKX5LPDf6dgPgK0lHQL8CvhURCyqzpgPq7182YI1uDszMxsw/dNwGFf5fZA+R1dddWPgidz+k+lYT44C/lwwL9C+QxV5/xsRi4HFkv5K1gJ7qY885+W+ntFDmt+nr38DxgNERHfqubgH+EVETK+VMR9We9TYLbxqi5lZ+5obEZN6OV9rucqavzck7UvWcNhzTfPmueGw+kMKYAWr9sYM7yVPTw95afq6klWf8zbAQqCeng0zM2sFYqCWnH4S2DS3vwlZr/oqJO0AnAkcGBHPr0neam03VFHDQZKGS1of2AeYATwObCdpWIpk+Y6qPIflvt5S74VSWT8kC3S1vqRDy1bezMza2gxgG0lbSBoKfIhsDt8rJG1G1gt+REQ8tCZ5a3GPA9wO/AnYDPh2RDwFIOkCsiGFh4G7qvIMk3QbWcPrw2twrTOAn0XEQ5KOAv4q6YaIeLbsTZiZ2UAamCWnI2KFpOPJIjZ3AmdFxGxJx6TzU4BvAOsDP5MEsCIiJvWUt69rKsJD6GtC0hxgUkTMXRvXGzV2i3jzXv/dd8Iaho4r98LGQAW5KhOMp2yQq84RxYMf0V3i2gMY5KpzePGgYisWLCucF6BjePG/XUrdc4nvc/eS4sG1sosPTEevBjBy44y/fOJvfYzTt7xJb940Zvzhsw0vt2PrLzTds3OPg5mZWSO0yZLTbjisoYgYP9B1MDMzGyhuOAxiy+ct7TtRL0p125e6bvFWe9l7LtP9PWT00MJ5y3Z/q7N4N3THsBL/DSwqWe8S3edl8pb52S4zlAYQ3QMzPFxm+BDKDwMOem0UVtsNBzMzs0ZQezQc2uMuzczMrCHc42BmZtYI7nFoTflgU72kOS+FF/3cWqpT8fcazczMmkjb9ThI2hB4S0RsPtB1MTOzQaKNJkcOiruU9FVJD0r6C7BtOnadpElpe1xauAngKuC1kmZK2iulO0PSDZLul7SrpN+nMNvfyV3jyNRLcbekc9KxzSVdk45fk5b1JC3feYukGZK+XVXXL6bj90j6Zv8/HTMzs8Zp+YaDpF3I1tfeCXg/sGsfWd4H/D0iJkbEjenYsojYG5gC/C/waWACMFnS+pK2B74KvD0idgT+I+X7CXB2ROwAnAv8KB3/IfDziNgV+FeurvuTBbnaDZgI7CJp7xr35LDaZmatRmr8pwm1fMMB2Au4JCIWRcR86gjQUUMlzyxgdkQ8HRFLgUfJIoe9Hbiossx0RLyQ0u8B/DZtn8OroUrfyquht8/JXWf/9LkLuBN4I1lDYhURMTWtIz6pa+joArdjZmZrnToa/2lCg2WOQ60VVfKhsavDYlerrBrUnduu7A8hC5haz6otfYXbFvDdiPhFHWWZmZk1neZszqyZG4BDJK0jaTTw3nR8DrBL2i4bvvoa4IMp9DaS1kvHbyYbJgE4HLgpbU+vOl5xJfBxSaNSORtLem3JupmZ2YBT2/Q4NGet1kBE3AmcD8wELgYq8xZOB46VdDMwruQ1ZgMnA9dLuhv4fjp1AvAxSfcAR/Dq3If/AD4taQYwJlfOVWRDG7dImgVcBHgswszMWobDaje5MmG11VU8XPJAKrOmftlYFWXiEAxkrIoy8Q+GjCpe72UvLimcF6BzWPGf0TJxRbrWW6dw3rKhxAcqVkWZZw3lYlW0RVjtieNjxlXfaHi5Ha87qumeXcv3OJiZmdnaM1gmR5qZmQ0gNe2chEZzw6HJRUThLsKuUcPKXby7RBjdMiuolbhu2dDBDCuev2y45TLKhJhuxeuWVeZ7VXaoYcC+VyVCrwNQfGTIBhk3HMzMzBrBPQ5mZmZWtyZd6bHR2qN5ZGZmZg3hHgczM7PS2mdyZHvc5QCqitJ5sqQnJC0c6HqZmZkV4YbD2vVHssiYZmY2mIjsbbJGf5pQc9ZqgEkaKelPku6WdK+kL0v6fTp3kKTFkoZKGi7pUUlvknR7Lv/4tAz1KiLi1oh4em3ei5mZWSN5jkNtBwBPRcS7ASSNAY5J5/YC7gV2JXt+t0XE/akhsWVEPAocBlxQ9OKSjgaOBhg6fP3id2FmZmuJkOc4tLVZwH6STpW0V0TMAx6R9CayoYbvA3uTNSIqQbUuAD6Ytg8jC7xVSERMjYhJETFpyNBRhW/CzMys0dzjUENEPCRpF+BdwHclXUXWQDiQbP20vwDTgE7gCynb+cCFaUgjIuLhtV5xMzMbOG3S4+CGQw2SNgJeiIjfpDcgJgM/AM4Gzo6I5yStD2wIzAaIiL9LWgl8nRK9DWZm1oKEGw5t7s3AaZK6yXoYjiVrILwOuCGluQd4NlaNS34+cBqwRa1CJX0P+AgwQtKTwJkRcVK/3IGZmVk/cMOhhoi4EriyxqlhuTRH18h3OnB61bF9cttfAr7UsIqamVmTkJecNjMzM6tWV4+DpDcAPwdeFxETJO0AvC8ivtOvtbNShowo16G0dO7iwnlj+bLCedXVWThv0RDkFV3rFg9FvvzF4s+rdDjwAVI2VHPH8OI/oyuXriyct3N48Z+xlYtK3nOJ73WZkN4dw9zB3O+adMGmRqv3Ln8J/BcpIntE3AN8qL8qZWZmZs2p3iboiIi4XauO36zoh/qYmZm1pjZ5q6Leu5wraSsgACQdCnjpZDMzszZTb4/Dp4GpwBsl/RN4DDi832plZmbWStQ+YbX7bDgoW3x7UkTsJ2kk0BERC/q/aq1F0jHAoog4W9J6ZGs6jAfmAB+MiBcl7UbWAINsuZCTIuKSgaivmZk1WJs0HPq8y4joBo5P2y+70VBbREyJiLPT7onANRGxDXBN2ocsONakiJhIFkjrF5I81dnMzFpGvc2jqyV9QdKmktarfPq1Zk2gRnjtwyTNScGvbk+frVPakyRV4lYcBPw6bf8aOBggIhZFRGVS6XDSnBEzM2t1yl7HbPSnCdX71+7H09dP544FsGVjq9N0aoXXPhWYHxG7STqSLIbFe6ryvS4ingaIiKclvbZyQtLuwFnA5sARuYYEuTQOq21mZk2proZDRNSMvdAGZgGnSzoVuCwibkyvpJ6Xzp8HnLEmBUbEbcD2KUT3ryX9OSKWVKWZSpoLMXLMePdKmJm1gjaZ41DvypFH1jqeG9MflHoIrw2rDjHU+sX+jKTXp96G1wPP1ij7fkkvAxOAOxpddzMzs/5Qb/No19xnL+Ak4H39VKemkcJrL4qI35AFr9o5nTos9/WWGlkvBT6atj8K/G8qb4vKZEhJmwPbkr11YWZmrUykVzIb/GlC9Q5VfCa/n8b6z+mXGjWXWuG1LwKGSbqNrOH14Rr5TgEukHQU8A/gA+n4nsCJkpYD3cBxETG3n+/BzMysYYq+CrgI2KaRFWlGtcJrpzkOP42Ib1alPSm3/TzwjhrlnUN7NLjMzNqMF4BahaQ/8upYfgewHXBhf1XKzMys5bjhsIrTc9srgMcj4sl+qE/Ti4jxA12Hei17cUnfiXpRJmRy99ISL4MsLx4uuayVC5cWzts1pnhI7hULiochh3LhxGNl8e9VmbwAUeZ7vbL4PZd6XiVCWw/ktVcuWl44b9lr2+BSb8PhXRHx5fwBSadWHzMzM2tX0SY9DvXe5TtrHDuwkRUxMzOz5tdrj4OkY4HjgC0l3ZM7NRqY3p8VMzMzax0ioj16HPoaqvgt8Gfgu7waqAlgQUS80G+1MjMzs6bUa/MoIuZFxJyI+HBEPA4sJnu7YpSkzdZKDZuMpBMk3S/p3F7SnCXpWUn3Vh1fT9LVkh5OX9ft/xqbmdnaEHQ2/NOM6upXkfReSQ8DjwHXk612+Od+rFczO45ssujhvaSZRhYgq1pP4bbNzKyFRRqqaPSnGdVbq+8A/wY8lAJevYM2nOMgaQpZRNBLJX0x9RrcKekXkh6XNA4gIm4Aag3l1Ay3bWZm1irqbTgsT6shdkjqiIi/AhP7sV5NKSKOAZ4C9iULi31tROwMXALUM3SzSrht4LW1Ekk6WtIdku5YsWxhYypvZmb9Kuho+KcZ1buOw0uSRgE3AudKepZsIah2tidwCEBEXCHpxUYV7LDaZmbWrOptOBxENjHys8DhwBjgW/1VqRZRZFnFPsNtm5lZKxLdTTonodHqusuIeBnYFNgnIn4NnAmUWyO39d0EfBBA0v5APW9I1Ay3bWZm1irqfavik2ThpH+RDm0M/KG/KtUivgnsL+lOslU0nwYWAEg6D7gF2FbSkym8NmThtt+Z3lB5Z9o3M7NBoF1ex6x3qOLTwG7AbQAR8bCkmhP7BrtKkCtJw4B/j4gVkvYA9o2IpSnNh3vIWzPctpmZWauot+GwNCKWSdmwvqQhvBpmu11tBlwgqYNs2OaTA1wfMzMbIBHts+R0vXd5vaSvAOtIeidwIfDH/qtW84uIhyNip4jYMSJ2jYgZA10nMzMbOAP1OqakAyQ9KOkRSastLCjpjZJukbRU0heqzs2RNEvSTEl31HO9enscTgSOAmYBnwIuJ5sgaf1MEh1dxVqxQ0YNLXXtlYuWF87bOaKr1LWLiuUryxXQUfwvhpVLil97yJjhhfMCLJu7qHDe6B7AzsMSz5vO1vzrTp1FXshqwHW7So6Xl/23Zf1CUifwU7J5c08CMyRdGhH35ZK9AJxAz4sO7hsRc+u9Zl/RMTeLiH9ERDfwy/QxMzOzKt0Ds2DTbsAjEfEogKTfkS2h8ErDISKeBZ6V9O5GXLCvu3zlzQlJFzfigmZmZla3cZWVhNPn6KrzGwNP5PafTMfqFcBVkv5Wo+ya+hqqyPepbbkGFTEzM2sj/TY5cm5ETOr1wqtbk/HHt0bEU+lNyaslPZDiLfWor7uMHrbbnqSxko5L2xPTxJPZku6RdNhA18/MzNrCk2QLNFZsQhZTqS4R8VT6+ixZ3KXd+srTV8NhR0nzJS0Adkjb8yUtkDS/3ooNUmPJQmwDLAKOjIjtycJp/0DS2AGrmZmZrVXBgC0ANQPYRtIWkoYCHyJbpbhPkkZKGl3ZBvYH7u0rX69DFRHRnMtWNYdTgK0kzQSujogvQtZ6S0HANiALDjYH+C1ZRM0u4Gjgu8DWwGkRMWUgKm9mZq0vLUJ4PHAl0AmcFRGzJR2Tzk+RtCFwB/AaoFvSZ4HtgHHAJWmNpiHAbyPiir6uWe/rmLa6E4EJEbFKeHFJuwFDgb/nDj8REXtIOgOYBrwVGA7MBtxwMDNreQO3AFREXE62TEL+2JTc9r/IhjCqzQd2XNPrueHQQCni5TnAR9MrrBWVbqNZwKiIWAAskLRE0tiIeKmqnKPJeiYYus76a6HmZmZWVr0LNrW69rjLtUDSa4A/AV+LiFurTi9NX7tz25X91RpvETE1IiZFxKSuoaP7pb5mZmZFuMehuAVAZVLJULLZqGdHxIUDWiszM1vrAtHdJrEq3HAoKCKelzRd0r3ASLLxo/UlTU5JJkfEzAGroJmZWT9ww6GEiPhIHWnG57ankU2OXO2cmZm1tjpfn2x57dGvYmZmZg3hHgczM7PSBu51zLXNDYcmF91ROFxz59IVpa69YuGywnk7hhX/0SoTdjhWllwZfWXx0MEdw4vfc+lw4CWUfmZldHf3naYHZZ5Zqz7vUiHQSzzr0te2QcUNBzMzswZol3Uc3HAwMzMrK2iboYr2uEszMzNrCPc4mJmZlRSIbr+OaWtC0pmStkvbJ0t6QtLCqjTDJJ0v6RFJt0kaPxB1NTMzK8oNhwaJiE9ExH1p94/AbjWSHQW8GBFbA2cAp66t+pmZWf+K6Gj4pxk1Z62anKSRkv4k6W5J90o6TNJ1kiYBRMStEfF0jawHAb9O2xcB71AKhG5mZtYKPMehmAOApyLi3QCSxgDH1pFvY+AJgIhYIWkesD4wN59olbDaw9drYLXNzKx/qG1ex2yPu2y8WcB+kk6VtFdEzKszX63ehdVWVcmH1R7S5bDaZmatwEMV1qOIeAjYhawB8V1J36gz65PApgCShgBjgBf6pZJmZmb9wEMVBUjaCHghIn6T3pyYXGfWS4GPArcAhwLXRoTXcTUza3F+HdP68mbgdkkzga8C38mflPQ9SU8CIyQ9KemkdOr/A9aX9AjweeDEtVhnMzOz0tzjUEBEXAlcWXV4n9z5LwFfqpFvCfCBfq2cmZkNiGadk9Bo7XGXZmZm1hDucWhy6hCdI7qK5e0qN95WJjR2mbDFHV3F7rcRyjyzIaOGFs67/MXFhfNCuVDk6igRxrxsqOWO4n+7lKl3qbDvBcPcv3LtruL33L28RGjsEs8aoGPg/lm2CL+OaWZmZrYa9ziYmZmVFEB3m7wk54aDmZlZWQFlR+5ahYcqzMzMrG5uODSIpGMkHZm215N0taSH09d1c+l2kHSLpNmSZkkaPnC1NjOzxgi6o/GfZuSGQ4NExJSIODvtnghcExHbANek/coy078BjomI7cnWflg+ANU1+//ZO+94O4ry/78/uemVEgIhlFASCIRQEsBAaNKbdBFQxEaRpn6xiyKIgiAi3aBIL9ID8qNKlRp6E6QKggJSQyhJ7vP7Y+Z4N5tz7z1n99y7Z+993q/XvM7Z2Xl2n2fOnt3ZmWfmcRzHyYQ3HDqgnfDZL8XgVvfHtGIse4Skw6JoMnz2OcCO8fsWwGNm9iiAmf3XzPLN7XIcx3EKJzhHNj41I95w6JhK+OzVzWwicH3Mf9/M1gFOAU6sIre4mb0OED9HxfzxgEm6QdJDkhZYXRJCWG1JMyXNnPPpBw01yHEcx3Hy4A2HjmkvfPZFic+pdRyvLzAN2Ct+7iRp03ShZFjtfv09rLbjOE4ZcB8Hp6Pw2clfs9ov+x9JowHi5xsx/1XgdjN7y8xmA9cBa3WJ8o7jOI7TBXjDoQNi+OzZZnY+cDxtD/ndE5/3VBGthM8mfl4dv98ATJI0ODpKbgQ81RW6O47jON2HWe/pcfAFoDpmNeA4Sa2E2Q8HAJcBAyTdR2h47VFF7hjgz5K+BvyTGBHTzN6RdALwAKGn4joz+0vXm+E4juN0NfOa8znfcLzh0AHVwmdLAjjVzH6eKntE4vt/gQV8F+K+8wlTMh3HcRyndHjDwXEcx3Fy4rEqnHYxs7FF6+A4juM4ReENh2bHDJuTbY2o1o/n5jp1y8CWzLKfzPo0s2zfEXlW4S5uIc4573yUXbhPPj9l9cn+pqMWZZad90m+9cv69GvNJZ+V1jnFnDfvuS3HikBZ7yONOHdvobdUN6VukQAAIABJREFUkc+qcBzHcRynZrzHwXEcx3EaQG/xcfAeB8dxHMdxasYbDl2IpH3iIlKO4zhOD8a6IMBVs/pM+FBFTiS1dBDhch/gCeC17tPIcRzH6X6ad6XHRuM9Dp0g6YsxfPYjkn4vqUXSLElHxtUjp0r6qaQHYujt6QrsCkwBLoiygyRNlnS7pAdjhMzRBZvnOI7jOHXhDYcOkDSBEI9ifTNbA5hHiGw5BHjCzNY1s7uAU8xs7Rh6exCwnZldBswE9oqyc4GTgV3NbDJwFnB0O+dNhNWe1dVmOo7jODmpLADlsSqcTQnRMR+IS00PIkS6nAdcnii3iaTvAYOBRYAngWtSx1oJmAjcFI/VArxe7aRmNh2YDjB0xNjmvHIcx3GcXok3HDpGwDlm9sP5MqXDKn4NkgYCpwFTzOwVSUcA1VYwEvCkmU3tYp0dx3GcAmhWZ8ZG40MVHXMLsKukUQCSFpG0bKpMpZHwlqShwK6JfR8Aw+L3Z4DFJE2Nx+onadWuU91xHMdxGo/3OHSAmT0l6SfAjZL6ENYzPjBV5l1JZwKPAy8RQmZXOBs4Q9JHwFRCo+IkSSMIdX8iYVjDcRzHKTnN6pPQaLzh0AlmdglwSSp7aKrMT4CfVJG9nPl9IR4BNmy0jo7jOI7TXXjDwXEcx3FyYk28YFOj8YaD4ziO4zQAH6pwmoM+fWgZ3C+TqPplD4udl76Dc1xarcWFPM5FztDYRZHnOmkZkO9GmefcuWRzhBLPIwugPtnl84S2zqu341TwhoPjOI7j5MR8yWnHcRzHcZwF8R4Hx3Ecx2kAvcU50nscHMdxHMepGe9xyEFcXnoWsDiwPfAp8DzwFTN7t0DVHMdxnG7EgHnu4+DUwU3ARDObBDwL/LCT8o7jOE5PIq7j0OjUjHiPQ51I+jGwN/AK8CbwoJndmChyLzFehaR9gB0JkTAnAr8B+gNfAj4BtjGzt7tNecdxHMfJifc41IGkycAXgDWBnYG1qxT7KvD/EtsTgT2BdYCjgdlmtiZwD6EBUu08+0qaKWnmnE/eb6AFjuM4TlfRatbwVAuStpL0jKTnJP2gyv6VJd0j6RNJh9UjWw1vONTHBsCVZjbbzN4HZiR3xt6IucAFiexbzewDM3sTeA+4JuY/DoytdhIzm25mU8xsSr8Bwxttg+M4jtNDkNQCnApsDawC7CFplVSxt4FDgOMzyC6ANxzqp2oTUNKXge2AvczmayZ+kvjemthuxYeKHMdxegRGYT0O6wDPmdkLZvYpcDGww3y6mb1hZg8QIjzXJVsNbzjUxx3ATpIGSRpGmEmBpK2A7wOfM7PZRSroOI7j9ChGVoauY9o3tX8MweeuwqsxrxYyyfobbx2Y2UOSLiGEx34ZuDPuOgUYANwkCeBeM9u/GC0dx3GcIuiiWRBvmdmUDvZXC0JSqyaZZL3hUCdmdjTByTHJ8e2UPRs4O7E9tr19juM4jpOBV4GlE9tLAa91paw3HBzHcRwnJxUfhwJ4ABgnaTngX4SZf3t2paw3HJodM1o/mZtJNK8DS+uc7OGt88jy8bzMokWGEs9D3pDHlnZ56ibyhHkukjyhrXsrHpa7Ewxac9z2Mp/WbK6kg4AbCGsGnWVmT0raP+4/Q9ISwExgONAq6VvAKmb2fjXZzs7pDQfHcRzHKTFmdh1wXSrvjMT3fxOGIWqS7QxvODiO4zhObozWkvbA1YtPx3Qcx3Ecp2a8x8FxHMdxclKgc2S34z0ODULS/pL2jt93k/SkpFZJUxJlNpf0oKTH4+dni9PYcRzHcerHexwaRNIRBXiCEATr96libwHbm9lrkiYSPFlrXeHLcRzHaVYM5rmPgyNpiKS/SHpU0hOSdpf0kqRjJd0f04qx7BGVqGNm9rSZPZM+npk9bGaVxTWeBAZKGtB9FjmO4zhOPrzh0DFbAa+Z2epmNhG4Pua/b2brEJaaPjHjsXcBHjazT9I75gur/ekHGQ/vOI7jdBcGtLZaw1Mz4g2Hjnkc2Cz2MGxgZu/F/IsSn1PrPaikVYFjgf2q7Z8vrHb/YVn0dhzHcbqZgqJjdjvu49ABZvaspMnANsCvJN1Y2ZUsVs8xJS0FXAnsbWbPN0ZTx3Ecx+kevOHQAZKWBN42s/MlzQL2ibt2B46Jn/fUcbyFgL8APzSzvzVYXcdxHKcgzJp3aKHR+FBFx6wG3C/pEeDHwC9i/gBJ9wGHAt9OC0naSdKrhGGMv0i6Ie46CFgROFzSIzGN6nIrHMdxHKdBeI9DB5jZDYQpk/9DEsCpZvbzVNkjEt+vJAxHpI/3C9oaH47jOE4PwnscHMdxHMdxUniPQ52Y2diidXAcx3GaC7Pe0+PgDYcmx1qNubPnZpIdMLhfrnO3tLRklm39JJvOeZk769Nc8i0Dstusftllbc68zLIArXNaM8u2DFZm2Xmz52SWhXx656mzubOKs7mo68QG5LvdWy95KDqd4w0Hx3Ecx2kAzbruQqPxhoPjOI7j5MTwWBWO4ziO4zgL4D0OjuM4jpMXXwCqOUlGoGxGJH1O0g862L+PpFO6UyfHcRzHaSTe41ADkvqaWYfTBGKZGcCMblLLcRzHaSK8x6FJkPRjSc9IuhlYKeZ9Q9IDkh6VdLmkwTH/bEknSbpb0guSdo35fSSdJulJSddKui6xb7Kk2yU9KOkGSaNj/m2SfinpdsLS0tV0O1vSCZJuBY5N9ihI2k3SE1HHO6rIbivpHkkjq+z7X1jtuR5W23Ecx2kimrrHIUam/AKwJkHXh4AHgSvM7MxY5hfA14CTo9hoYBqwMuHt/zJgZ2AsIfbEKOBp4CxJ/aLcDmb2pqTdgaOBr8ZjLWRmG3Wi5nhgMzObJ2mfRP5PgS3N7F8xuFXSrp2A7wDbmNk76QOa2XRgOsCQ4WN7RxPWcRynxBg+HbNZ2AC40sxmA0iqDANMjA2GhYChzB9P4iozawWekrR4zJsGXBrz/x17CCD0YEwEbooxKFqA1xPHuqQGHS81s2qrsvwNOFvSn4ErEvmbAFOALczs/RqO7ziO4zhNQ7M3HCA05NKcDexoZo/Gt/yNE/s+SXxX6jONgCfNbGo7+z+sQb+qZcxsf0nrAtsCj0haI+56AVie0FMxs4bjO47jOM1OL1pyutl9HO4AdpI0SNIwYPuYPwx4PQ417FXDce4Cdom+DovT1tB4BlhM0lQASf0krdoIxSWtYGb3mdlPgbeApeOulwlDJ+c26lyO4zhOsRih4dDo1Iw0dcPBzB4iDBc8AlwO3Bl3HQ7cB9wE/L2GQ10OvAo8Afw+yr5nZp8CuxIcGx+N51mvQeofJ+lxSU8QGkCPVnaY2TOEBs+lklZo0Pkcx3Ecp8tp+qEKMzua4LCY5vQqZfdJbQ+Nn62SDjOzWZIWBe4HHo/7HgE2rHKsjWvQLX2+swnDKJjZzlVEkvsfBlbp7ByO4zhOGTDmuXNkj+PaOLuhP3CUmf27aIUcx3Ecp2z0moZDLT0I7SHpx8BuqexLY29I1yLo0y/biFKekMUAcz/IHqK6ZWD20MFFkid0sOUIJa4+2cM8Q74w5p++U9xbUh67W+cVo3fe34p52f+Xec6tlpx6Ox1iBq35brmlodc0HPLQwXCJ4ziO4/QqvOHgOI7jOA2gWWdBNBpvODiO4zhOA+gtDYemno7ZXUi6u4YyG8RYF49IGtROmevSy0s7juM4Tk/CexwAM6tl7Ya9gOPN7E8dHGebxmnlOI7jlAWz3hOrwnscAEmz4ufGMSrmZZL+LukCBb4OfB74acwbLemO2PvwhKQNovxLkkZKGivpaUlnxl6KG+Pql31jVM+NY/lfSXKnS8dxHKc0eI/DgqwJrAq8RghUtb6Z/UHSNOBaM7tM0v8BN5jZ0ZJagMFVjjMO2MPMvhEDXe1iZufH2BqXSToE2ApYtzuMchzHcbqW3uLj4A2HBbnfzF4FkPQIIRz3XakyD9AWlvuquPpkmhcT+Q/G42BmT0o6D7gGmBqXvZ4PSfsC+wL0H7hoboMcx3Ecp1H4UMWCJKNrzqNK48rM7iAsU/0v4DxJe9d5nNWAd4HFqYKZTTezKWY2pW//oXWq7ziO43Q3hjGvtfGpGfGGQwYkLQu8YWZnAn8E1qpDdmdgUULD4ySfheE4juOUCR+qyMbGwHclzQFmAdV6HBZA0kjgGGBTM3tF0inA74Avd5WijuM4Tjdg7uPQq0hE0bwNuC2Rf1Di+z6J7+cA51Q5ztj49S1gYiL/+ESx8Yn8k3Kq7jiO4zQBhk/HdBzHcRzHWQDvcXAcx3GcBuBDFU5zYNnDY+cNbF1UGN6Wwf0yy8597+Nc584TFbffiAGZZS1niOhcoZ5zhHnOq7fNmZdLPvN5c+idJ/Q65AwlnvFeAEBLvpjPRf1WTvPhDQfHcRzHyUsvco50HwfHcRzHcWrGexwcx3EcJyeGeY+D4ziO4zhOGm84dIKkQ2Kkyws6KTdFkq/L4DiO0xsxaJ1nDU/NiA9VdM43ga3N7MWOCpnZTGBm96jkOI7jNBvWmm/mSlnwHocOkHQGsDwwQ9IHkhZS4L+VwFaSzpO0maSNJV0b846QdJak2yS9EENoI2ltSY9JGihpiKQnJU1sXwPHcRzHaS684dABZrY/8BqwCXABsD6wKvACsEEs9hng3iriKwNbAusAP5PUz8weAGYAvwB+DZxvZk+kBSXtK2mmpJlzP/2gwVY5juM4jcbidMxGp2bEGw61cychouWGwOnAapLGAG+b2awq5f9iZp+Y2VvAG7SF0D4S2ByYQmg8LMD8YbWHNdoOx3Ecx8mMNxxq5w5CL8MGhEBYbwK7EhoU1fgk8X0ebf4kiwBDgWHAwK5Q1HEcx+luGt/b4D0OJcfMXgFGAuPM7AXgLuAw2m84tMd04HDC0MexDVXScRzHcboYn1VRH/fRFgLiTuBXhAZETUSHyrlmdqGkFuBuSZ81s782XlXHcRynu7BetOS0Nxw6wczGJr5/KfH9bhI9NmZ2G2EIAzM7InWMysyJl4BzY948YN2u0NlxHMdxugpvODiO4zhOA2jWBZsajTccmpw+/VoYNCbbzIrWT+bmOvfA5RfOLNtnYDGX1ujVF++8UAe883r26a8Dh2cPq/3pp+UMWfzJGx/mkp/3cXa7x6y5RGbZfz/9ZmbZebPnZJYFGLT0iFzyWZn90ru55POEja/bE6yM9KKhCneOdBzHcRynZrzHwXEcx3FyYoB5j4PjOI7jOM2OpK0kPSPpOUk/qLJfkk6K+x+TtFZi30uSHpf0iKSa4i15j4PjOI7j5MZoLSDIVZzafyphReJXgQckzTCzpxLFtgbGxbQuYfXj5Ky+TeIqxzXRND0OkhaXdGEMCvWgpHsk7RSDR70XW0OPSLo5lj9C0r8S+Y/EIFSV8g/HFtgdkrZrsK7XSVqokcd0HMdxnAysAzxnZi+Y2afAxcAOqTI7AOda4F5gIUmjs56wKXocJAm4CjjHzPaMecsCnwPeAe40s2oP/9+a2fGpY5EsL2kN4CpJH5nZLamyfc2s7qkHZrZNvTKO4zhOD6brZlWMTA0hTDez6YntMcArie1XWXCNoGplxgCvE9wzbpRkwO9Tx65Ks/Q4fBb41MzOqGSY2ctmdnLeA5vZI4TAUgcBSDpb0gmSbgWOjeGtz5L0QOyl2CGWGyzpz3E86BJJ90maEve9JGlk/P4dSU/E9K2YN1bS05LOjKGzb5Q0KO47RNJT8bgX57XPcRzH6dG8VQl6GFP6wa4qMukWTEdl1jeztQjDGQdK2rAzhZqix4EQqvqhDvZvIOmR+P1SMzs6fv+2pC/G7++Y2SbtyD8EfDexPR7YzMzmSfol8Fcz+2ocfrg/DoccEI85SdJE4JH0QSVNBr5CaN0JuE/S7YReknHAHmb2DUl/BnYBzgd+ACxnZp+0N9whaV9gX4ABQ0Z2UC2O4zhOM2AUtgDUq8DSie2lgNdqLWNmlc83JF1JGPq4o6MTNkuPw3xIOlXSo5IeiFl3mtkaMR2dKPrbRH57jQZYsLV1aVzyGWAL4AexYXIbIWLlMsA0wlgRZvYE8FiV404DrjSzD2No7SsI0TMBXoy9HQAPAmPj98eAC2KDp+owSTKsdr8Bwzswy3Ecx2kK4lBFAdExHwDGSVpOUn/gC8CMVJkZwN5xdsVngPfM7PXY4z4MQNIQwvPwic5O2Cw9Dk8S3sgBMLMD41BATVNDamBN4OnEdnK5OwG7mNkzSYHod9EZHZVJh9UeFL9vC2xI8N84XNKqWfwsHMdxHMfM5ko6CLiBEITxLDN7UtL+cf8ZwHXANsBzwGxCTznA4sCV8XHXF7jQzK7v7JzN0nD4K/BLSQeY2ekxb3AjDixpEiGM9dfbKXIDcLCkg83MJK1pZg8Tol5+HrhV0irAalVk7wDOlnQMoRGxE/ClKuUquvQBljazWyXdBewJDAXyrQXrOI7jFIpRcw9B489tdh2hcZDMS/oMGnBgFbkXgNXrPV9TNBziA3tH4LeSvge8SegV+H4nokkfB4Ad4+cGkh4mND7eAA5Jz6hIcBRwIvBY7GV4CdgOOA04R9JjwMOEIYb3Uno/JOls4P6Y9Qcze1jS2HbO1QKcL2kEoaHxWzPzRoPjOI5TGpqi4QBgZq8TxmaqcVuV8kcAR1Qp+xLQbhQZM9sntf0RsF+Voh8DXzSzjyWtANwCvBxlxibkTwBOSB3zJWBiYjs5ZXRae7o5juM4JcV6z5LTTdNwaEIGE4Yp+hF6Bw6Ii2s4juM4Tq/FGw7tYGYfAFOK1sNxHMcpB70lrLY3HJqcvgNbWHT8oplk+/SpZWJI++T5E7z9/NuZZQeMGpJZ9oP3Ps4sCzBw+IDMsnPnZl+nfsiw7OcFeO+1D3LJZ2XuB/k64dSvJbPsvx5MT1WvnX4LD+q8UDvkuT4B5rz3SeeF2qHv0P6ZZfPYDNB/kYG55Hs6ZoWt49DtNOU6Do7jOI7jNCfe4+A4juM4uSkmOmYReI+D4ziO4zg14z0OXYSkWWY2tJMyd5vZet2lk+M4jtM1GL3HOdJ7HArEGw2O4zhO2fAehy5G0lDgamBhoB/wEzO7Ou7rtFfCcRzHKQHWe3ocvOHQ9XwM7GRm78fAXfdKmhHXDq9KMqz2oBGjuklNx3Ecx+kcbzh0PSIE8NoQaAXGECKS/bs9ATObDkwHWHjM+N7RhHUcxyk53uPgNIq9gMWAyWY2R9JLgK+k4jiO04MwA/MFoJwGMQJ4IzYaNgGWLVohx3Ecx8mK9zh0PRcA10iaCTwC/L1gfRzHcZyGYz5U4eSjMlvCzN4CpnZUxnEcx3HKgjccHMdxHCcvvWg6pvs4OI7jOI5TM97j0OTMm9PK+/+ZlUn2k7c+ynXukRNGZpbN413cOid7oJhPc4Z5zoNasocxzxsC3XK86fQbkT2k97yP5mSWBWgZ1C+z7NxZ2X/rPOGpP31rdmZZyHd925x5mWX7DMh3u583O99v3dMxwDzIleM4juM4zvx4j4PjOI7j5KUXrePgDQfHcRzHyY3lGjIsEz5U4TiO4zhOzXjDoU4k3SZpStF6OI7jOE1EHKpodGpGvOHgOI7jOE7NeMOhAyQNkfQXSY9KekLS7qn9e0h6PO47NpE/S9JvJD0k6RZJi8X8FSRdL+lBSXdKWrm7bXIcx3G6Bmu1hqdmxBsOHbMV8JqZrW5mE4HrKzskLQkcC3wWWANYW9KOcfcQ4CEzWwu4HfhZzJ8OHGxmk4HDgNOqnVTSvpJmSpr56Ufvd4VdjuM4jpMJn1XRMY8Dx8fehGvN7E7pfwv1rA3cZmZvAki6ANgQuApoBS6J5c4HrpA0FFgPuDRxjKor75jZdEIjg+GjVmzOJqfjOI7ThgHzescCUN5w6AAze1bSZGAb4FeSbkzsrmepPyP07rxrZms0UkfHcRzH6U58qKID4nDEbDM7HzgeWCux+z5gI0kjJbUAexCGJSDU667x+57AXWb2PvCipN3isSVp9e6ww3Ecx+lajMb7NzSrj4P3OHTMasBxklqBOcABhAYEZva6pB8CtxJ6H64zs6uj3IfAqpIeBN4DKk6VewGnS/oJ0A+4GHi0u4xxHMdxughfOdIBMLMbgBtS2Rsn9l8IXNiO7OHA4am8FwkOl47jOI5TSrzh4DiO4zgNoFmHFhqNNxy6ADMb2qhjjV/ofW7a7uZswkOH5Dr3K6sfmVl26Yd/kllW41bKLDvv+lszywJoWPZwy1p5xewnfvb57LKAPvOZzLK2xITMspcP+E5mWYA1Jme/Bc39aG5m2cGLDc4sO/vNfGG1+w7KbvP7/81u8xtvZhYFYKkx2WUn5Tu102R4w8FxHMdxGkFr75iO6bMqHMdxHMepGe9xcBzHcZy8WPMGpWo03nBwHMdxnAbQW5wjfaiiC5B0iKSn4zLUjuM4jtNj8B6HruGbwNZx3QYAJPU1s+wu0Y7jOE7TYr4AlJMVSWcAywMzJC1DCHY1FngrxrrYiRDcajngQjP7eVG6Oo7jOE69eMOhwZjZ/pK2AjYBDgK2B6aZ2UeS9gHWASYCs4EHJP3FzGYmjyFpX2BfgGVGNWxJCMdxHKcLcR8Hp1HMMLOPEts3mdl/Y94VwLS0gJlNN7MpZjZlsRGDuk1Rx3Ecx+kM73Hoej5MbaebpL2jieo4jtOTMYN5vgCU0zVsLmkRSYOAHYG/Fa2Q4ziO49SK9zh0P3cB5wErEpwjZ3ZS3nEcx2l2rPf4OHjDoQsws7Hx6xFVdr9hZgd1nzaO4zhOd9BbpmP6UIXjOI7jODXjPQ7diJmdDZxdsBqO4zhOo/GhCqdZsOGLMnezvTPJXvHsMrnOvePAJzPL/ueE2zPLLnzpHpllB2ySnsRSJ0MXySxq9+bwcx08OLssMHfGXzPLtizzSGbZVVbOLApA3wHZb0EP3Jt9IdY9Pjoqs2zrVZdmlgWY+/qszLL9xi+WWdZmf5xZFkDL57ifTMle307z4Q0Hx3Ecx8mJ0XuiY7qPg+M4juM4NeM9Do7jOI6TFwNafQEopwuRdLakXYvWw3Ecx3HqwXscHMdxHKcB9BYfB2845EDSscDLZnZa3D4C+ABYAtia0Hn1CzO7RJKAk4HPAi8CKkRpx3Ecp/H0oumYPlSRj4uB3RPbnwfeAtYAVgc2A46TNBrYCVgJWA34BrBeeweVtK+kmZJmvvXmu12lu+M4juPUjfc45MDMHpY0StKSwGLAO4RGw0VmNg/4j6TbgbWBDRP5r0lqd+K9mU0HpgNMnjKhdzRhHcdxSk3vmY7pDYf8XAbsShieuBhYoYOyveOqchzHcXosPlSRn4uBLxAaD5cBdwC7S2qRtBihp+H+mP+FmD8a2KQohR3HcZwGU5mO2ejUhHiPQ07M7ElJw4B/mdnrkq4EpgKPEi6l75nZv2P+Z4HHgWeB7GsyO47jOE5BeMOhAZjZaonvBnw3JlL5Hk7bcRynh1KUj4OkrYDfAS3AH8zsmNR+xf3bALOBfczsoVpkq+FDFY7jOI5TUiS1AKcSlgBYBdhD0iqpYlsD42LaFzi9DtkF8B4Hx3Ecx8mJFbeOwzrAc2b2AoCki4EdgKcSZXYAzo093/dKWij62o2tQXYBvOHQ5Dz04N/fGtB33Zc7KDKSsHZEFvLIdt25+00u5rxdL+96d59sh/J7Ddq+K8/tv9WCLJvxuKXho9n/vOHh+w8c2QWHHihpZmJ7epyyX2EM8Epi+1Vg3dQxqpUZU6PsAnjDockxs8U62i9ppplNyXLsPLJFntttLs+53ebynLtIm3sCZrZVQaeutgpxuuujvTK1yC6ANxwcx3Ecp7y8Ciyd2F4KeK3GMv1rkF0Ad450HMdxnPLyADBO0nKS+hPWFZqRKjMD2FuBzwDvmdnrNcougPc4lJ/pnRfpEtkiz+02l+fcbnN5zl2kzU5GzGyupIOAGwhTKs+K6wvtH/efAVxHmIr5HGE65lc6ku3snApOlo7jOI7jOJ3jQxWO4ziO49SMNxwcx3Ecx6kZbzg4juM4jlMz3nDo5cQ1zHsVeWwua32VVe8sSCrU6TtrXUsaLKlfo/VxnEbjDYeSIqlP/Kz7JhVvUEPgf8G36pEdLWmles+ZkF9V0rcyyo6UNLFie52ygyUNhWBzPfWWp76i/HKStq5XLsrmqa88v3NmnSvnTm3XU999U9v1yE4grsOfBUljJK2eUXYY1H99RdkJwCnAqCznjsdoySg3TNLIxHY99Z3rfuCUE284lBBJ44HDJS2T4SG4KnAjcKak30gaW6t8fPA+CPxA0tQMeq8MXEBYdKRe2VUJU4ZOAS6tU3YC8BfgIkl/hNofpHnqK8qvBFwMDO6sbBXZvPWV9XfOrHNC/kpJv5a0H9RV3+OBX0vaV9LGklTrNR5lzwW+JunbGfReFbgNWE7SwDpllwHukrQP1Nd4iL/zOcBdZvav1L5O79Hxt13CzObV23iI/43rgfMU4hTU81vluh845cUbDiVD0rLAXcB6wCH1NB4kLQacBvwW+CKwGPBHYP1abjhmNgu4B3gH2ELSBnWq/1XgT2b2a0l9JC0tqX9nvSeSRhHeIn9pZhsDi0vqNKBFlF2R8BA8F9gHmCzpyBplc9WXpHGEh/cvzezyaHM9/7ms9ZVZ77w6SxoLXE1oqL1MiLiX3N/udRqv7euBF4GFgN2Ao2ppPMRGw2XAr4G1CYvajKhD7+HAb4BfmdlVZvZxan9ndbAsMAj4jqQDoe0B3IneI4CTgSfN7KyYt4WkaZKWNLPWjs4d7X4GeFbScvU0HqLsJYTYB1sDE2PjqSYacD9wSoovAFU+FiHETr8R2BH4lqQTzeyflRtsB7ItwHvAffGG9C3gVuBzhJv1v2o4xlOE66YvsFG84fYBroVO31aGAG/E75cR3qTfB66XdLGZfdqOXH9CAJ2H4/aIaHcr8AfCm9oC54033PUIi5r8KebtB3ypAx0qy9XhAAAgAElEQVTT581TX8sTHoD/jNvTgwqaCxyZfrusQtb66ptD77w6rwZca2YnSVocuEphgZk5wJlRn/bOPRG40sxOljQI+D5h0Zq+kn7YXj0r+AXsBPzazC6Nb9FTY7q+hmsa4ANCsJ/L4/FOAt4k9Lp8rxO9IVybFxNW3TtN0rtmdkEN554D3EwIZLQbYWGej4CPCY2fPczs+Q7s3hM4hHCt3C1pPTN7UVJfM5vb3knjf2MScIKZnROzWwm9NXOA35lZp0sPk+9+4JQU73EoCZJGSVrYzB4GjjezBwhvdrMJD9Hl4ltZR93aswjrkG8Z3za2BB4nrFX+f9D+H11t4873Ac+a2eGE8diLgNUt0okZVwHbSjoNeJTwILsd2BgYXeWcy8YelVeBfwAnS7oHeAg4INryDaBqt7KZtRLeYK9MZLcCGyqO/bdj6yhJiwDvEh4mdddX3HcDsBdwrqTn4/FOIDR8ftnOuZMOcnXVV5QXoZH1eha9o857E7qua9I5xbuEt+6vEhor/wD6EXoBjuno3ISH5Y6SJpvZR8B/gf9HeHiv0Y69g81sDnCamZ0fj/80ocflJ5JGd3Zdxmt7aLRxfeC7wFzgXkLUx4s6agDEfbMIvStDCA//H0l6HZgWyyxwr41yswm9ae8Rfpu/m9kuhMbtbcAuHZxzDnAWcI2ZnQCcQWg8LN9RowH+99+4wszOjsc7Bvg7oZdqDKHnphby3A+csmJmnpo8AasCNwFbVdm3LuGG/lPCzeYEoH9i/wrAfsCmhDfJzxBuVDPiMUcAw4ELgT6pY4+L+f0SeSsT/AyWI9xobgSOAKZW0W1hYGBiewzwY+AOYPdE/tXAZinZ8cALhDF+EeLGrwGcD6yVKHc7sG5Kdjng68DWwGKJfBEeuHfF7Q2AH7VT11vG7R0JXcmd1lc1m2PeLsApie2+wN+AZVLlJhAeBGPi9pJ11NfKhJv9UdHGjeLvVMvvXKmvbYElYt5utegc9y0fZQfE7c8DhwKXJcpsTHi4p2VHErr5h8TtbxN6O04hNJaWJzQCvllFdgJwNrBkIq9P/FwC+D2wftxuqSKf/p12JAQDujKRtwShV2tQFZu3Yf7/xqHAOvE6e4HQu/PlTv7bldV7hwFbVbZj3g+BH1SRGUfojembtDl+/ynwb2AAsEzyukn91tskfuu+wLKJMv2Au4GxKdkV4++zF23XaM33A089JxWugKdOfqBwY30U2KeDMksTuklnM/8DZmWC89JxwOGEgCbLxH2LASPi960Ja5kPSt24vkp4Qz8pcZMaSXj4PAccBCwKHA2snNJpVeBtwtvbsET+lHhDv4jwMBkPPEJ4S6mUWQm4n/AG9Qdg4cS+/WJaPN7IHgPGJ/avAswkjOlPj2lEYn8f4E/A5vG8O9VS13FfZ/VV1ea4r2/i+7qEN7UlUr/V/cBXU3KToi0X11BfPwKOB06N+QM707uj+upM58S+ewiNm72JD1hCI/FOYErc3jKWWTRx7pVjfV9M6NkZHfPXj2nJuP0V4Fupc1atr1SZ44Bb29m3CuE6/lwqf0/CNb9D3N4wnmfxdmz+Im0Npi2AW4CXgO/E6+FRQqNXKXmlv6fyJsff+bMpuZXi77B3Kj/ZgDmQMATyJrBpO/+N35P6byTKrRPPMTqRNyHWw28I/8tjCP+lEfH3e54O7geeelYqXAFPnfxA4Q97dWL7QMKDabVE3lRCN++2cbtyIzqcxBsLcE28Qa+SyNsy5m1b5dzjCb0T1zH/W9hXga8nttMPycHx5nIh4Y33IGB4Yv9KhLfSG+L+XRP7loo3rT3j9v8D/pjYvy1wXrQl/eBflDBeXJFdLZatNJYqN7oPCG+WW6TqK13XB8e6Xr2z+mrH5qFV6nRDwrjw9om8EYRegT+lzrNetGkE4S1vgfqKZY8Afhy/TyY8EDYCxiXsXkDvdurrfBbsCVlA59T+owmzAo4EvpLI/wphqOInhIbmNol9SxOGT74ct48lDG30Tx17c8Kb++ad1NcWhGGBZO/DQMK1u1LqmKMID7oZhN6kao2HJwi9N0+nf+v2bI56nQrsn6zjVH2vWcN/fhqhkb99Kn8pgiPiXnG7H6EXaXCq3IoEP4nOfuv//TdiXku8bp4Etkvkj4z1uE/c3pnQKFs21vHOwH7t3Q889bxUuAKeOvmBws3o7HgzuIzwcDoWeBZYJ5ZZpXKTIXSTVh6EPyV4iVeOdQjh7eABYhd+vDFvU5FNlO1DeFP6XbxBXUJ4cN0OLJIsV0XnfsB68fvawF8JDZ4RVWwbnjw3wfkz+Za0UrR7SiJvBcLb3KSU7FBgX+YfHrkS+GLqvOcRGw011vU/EnW9RTv11Z7Nyd6W4cD30vKERsf3gZ8RhgiuAy4n9MrcCyyXsG94lXN/P/5OaxFm3NwInEhwrFylPb1rqa+o83er2Zwosy3hDXRfwnDJoYTx+uHRnkOAjVIyU5n/AbsIcGmqzKKE665aI61afV1AeCteIZZrIfHgTsgPJTQOxhN6r85kwcbDuHgdrF7N7io2Hwx8C1ix2n+D4Nj6Y4IPwZS0TqljL0xsYKR+51Hx9z0qbl9CaOg9CWyS+L2Oq9hT52+9OOGeUe06mZYodydhKOMkQq/L0h3dDzz1vFS4Ap46+YFCi/4swlvkMYn8Qwhv48kbwf8aDXF7OYKT3MnxJve3eGM5qbObV+IYp8QbxxjC287TNcq1JG486xAepAfH7UnEMdKUTHrsXfEmeh5wUDK/imzlXP3iZ2Vo5YTKzTE+DIZQ5eFbY133zWHzRMKb2wI2xs/hhAfPvQRPdwiNt+MIXv0dnXdpQiNjOnBTIv/HhIditcZdLfW1IqEx1ZndawKnx+/fIwyZ/bETmcHM/7Y7gOD0WukdGhw/R6T07ay+ft1ZfaXsXoK2xsOOlWPXIJ+2+SPgD51co6sTejF+AazdznE7fPASHu7XAPOA42LeQYQhu8pQT8VfRMz/ItHZbz2cxJBHO/+R9YBDEtu/IfoJVbPZU89MPquiybEwn/xwwtvRJnGePYSb7OuEscxKWTOL/2CpxcxeJDhDvkDomv+imb1PGOMe09F5E17grxCctC4n3PBelnR5Z3PFzWyemZmkPmZWGX/fIs4QuJbQqEnLtKa2zczeITzMvyNpzUp+FVmLn5X6qBzrbWCWpEmEBsi4WAcLHKeGul7gvHXYfD1h3HcBG6OH/PuEsecjaJv50Br172gef4uZvUIYyjiJ0Dis8DzwafqcSdtrqK/lrHMP/YeBjyStC3yZ8GB7V9Le7V0nZjbbzP5ZsSFm9wP+K2l94GKFBYbm+61qqK93qGG2WMVuM/s34Xp8CNhA0i+A65RYSbFGm2ckbO6TvLbiTBfM7FFCA68F2EnS2lUOrSgzVNLm6dkYZvYfQs/B98zsuzHvFELDYbm4/WGlrpL3hE5+6/OB5RNlKudL/0fuNrOTEln/aa+s04MpuuXiacFEasggfi5J8Kb/FaH34DHiG1IHstU8yacSFoyZ3JFsIm8S4aGZ9JVo722p6htHwoZDCQ2dz2WQ/S3hzaqaTe2+6RDeSu8jOIkuMEafp64baTOpN+r4vaqDXAe/1ShCI/EYYHfC2gLVxucbUl+Va4zglX8mYXrsQTF/P2DVWs8b908nPITvBXbu6vqqUuYkwrTIzzfa5qjbJNrWyfgVoSGeHIJriZ8LERqAa3ZwfbUkvk8l+KCsUqVcw37rKvvXivW9aUflPPW8VLgCnuIP0YHjVOKGsgjB2/zLxDFHwhtKrU5XEwlvV7vUeN7KTW9s/KzW5V3ruRchjMnuVq/esfw+JBo7dZz36wTH0U1qOW9ndd1dNlPFQa4TvSsNlRUJq2SeTnRwq/O8C9RX/Ew6t1ZrtExIXVfJB1uHsol99wEfknBazVNfdZ57BeBTEr4BeWxO/SZbExxBDyasTbFKlD2KMAtmnYTMQiSmA3ekN6EBsz7B8TTtCNnw/0ZKdmvCjJGqDrOeenYqXAFPBjU4TnVw86jH6WoQcb52vEnULBtl0uPz9Zy7L20OmXWfO8d5RxPfQGs9bycPmG6xmZSDXI16Vxo9fUi8keepr8R1cxtwQLU6qnJdJJ0CO5RNlicMOWySsa4XcCis5dypY6ybqLM8Ng9JfB9DcCgeS1jP4yHi1E5CI+9Y4nRiwloONwOb1aJ3rJ/tSDie5vmt65RdlNiQ76hOPfXMVPmDOQWjEJFvV8If+GoLK0NWK9diZvPqlW1v5bs6ztvHqoyV1yrfzjG72ub/ySXtz3Pe7rA5b11Xk89TX3F7S8IUxFOtbfnuBa6p5Lkr3+uQHWRhxUgqSybnqa9a9U7/zolzZ7F5EcIiSY9ZWP66P2Gmwj8IQxn7mNmzknYiNA4+shgXQyHWw2wze7COc/ez6JdQZ51l/m+k7K1lOW+nh+HOkQVTj+NU5c+edpyqRZb4W2eRjedtTcrWq3fOc2e1uXKMocBmec/bXTZDdQe5rL9V3vqK8jcAPwAOVVhOGrP5A08lzj248r1O2Y+ibF/CrIFc9VXruRM6VPSem9VmggPth8AUSdtbiCcyibCQ2Wax0bBOPO6SlgimZWZ3mtmDdZ57TsY6qxyj7v9Gsr5JXSdO78B/8AKptNYlTVbwbP6Y0HUpYAdJUxJlKw+yhQjrKbwVb1jdLkt02C6R3jf2Qptz1xdxFomkRSWNMLObCQ51B0v6CrQ9zFLy1xCmvmaVXTFPXReo90oWZgE9TBiaOEDSZwgzXu4G/ijpe4RVG39pZk+SoNIo6OY6y/3fsHZ6e5wejDXBeElvTORznNqwINkNSqq321ynzYm8HQkLLF1FmNUyDPgswRFxv1im4lsxgrDk8gY5ZMuqd0V2M4LT4JcID/RTomx/whDGN4CNY9lqfgvdXWcNqW9PvSsVrkBvS+RznPpr4o/enbI3k1hpsUR6u83127xhwua1CG/KlaWUb6NtcaEtCMsyLxW3FyKsajgto+zdCdky6X0XYUaHCN37R9O26NcowhooV5JaObOde0N31lnDrhNPvS8VrkBvSgTP76Nom57XnzCX+8vxJlD5c+7EgpEltybMG+9u2Q0IwZXKprfbXL/NWwPHJvK2IqyauVuUXz7mj42fi8bPPoTx+Asyyn6HsNR32fTen9RbN2Fxphm0RY9cjPDAP4lUgDBC7Imseueps7zXyQJrwHjqXalwBXpTin/CHxBa8JXYEjMICwRVltitRKZLLyRTiGxZ9XabM9k8hrDw0IlxezXCipcP0PYQ24Gw/PZCKdnlc8guUVK9B8bP1Qlv6isRor/+lDAssSQhENQMqi/OlEfvzHWW9zrx5KlwBXpLom1cckvCokDXEZaDHkboJryIsOb9w8SQvkXLllVvtzmTzZWp2VMJY/Q/j9vHEpz5dgS2Ibw9b9dA2VLqnTjGtoSplucQnAX/j9BrcBQhhPVDdLBqaHfXWd769uTJzNdx6FYkbUYICnM8Iaz0y8AVhHHSA4FZwD/M7LbKjIuiZcuqt9ucyebtCd3VbxK6pK8mhMQ+iPA2PBC42Myuq3LuPLJl1XsIIYLq6WZ2t6RNCSHIn7CwhsOKwBwze7kL9C7sOnGcwlsuvSGRw3GqKNmy6u02Z3bMG0J446wsP7wcwWP/J8kyjZQtq96pY1xGIhonwe/hOjqPKNrtddaI+vbkycyjY3YLFphHaNlvLmmMmb1BWBRmHLCLpCWaSbaservN9dscaSW8ab4ft/9JcL47WNLvYt5HjZQtm96JdRZWlLRGzD4XWETSNnH7vnjMwV2hdxNcJ47jDYeuInGTWV3SLpJWIox3zgQ+L2lJws3lBeAMC+F9C5Utq95ucy6bl5U0zMJSzzOAcyUtGR8wrxG64i+BtrDnDZItld7xu0najrDGwoWSfga8RBhqOEDSJQQfgQsthm5vsN7dfp04TjXcx6ELkbQtcCJhitMShFXa5hCmaG1NaLgdYWYzmkW2rHq7zZls3hr4OcG5bwhhfH0aweP+T4SIpF80s781WLZUekv/W+FVhGmL5wH/Bi4grLXwZ8JqlROBf5nZoxWZZqizvPXtOAtgTTBe0hMT4cZwHrBe3N6UcNOozJ1ekUSkymaQLavebnMmm5cFngTWI6wn8DXiwj/A5sD2tLPIT07Zsuq9HeHh/teE7mMJvQbHkgjB3Ux1lre+PXmqlnyoooswsw8JoXmnxe1bgNeBryhEsXvOzF6O+6wZZMuqt9tcu2yl65oQEOkxM7vbzF41sz8SlhHewcxuMrNrzOyO5DnzyJZZb0kTCFMs7weeBb4vaZKZvQQcSljHoapvQJF1llfWcdrDGw4NIjGWWLfjVFGyZdXbbc5uMzA8fv4HGCfpF4li7xIWLWq4bNn0ThxjEmHq4h1mdjrwM8KiTT+TtKaZvQjsaGbPNlrvIq4Tx6mJWrsmPHWeCN2ZTwBPEW4wkwgLwlxDcHj6O+Em0zSyZdXbbc5k87bAg4TpeJsAowkPkemE+fyPAZ/tAtlS6h3lhwGXEqYrjo55owirQ14T97c0U53lrW9PnjpL7hyZEym741RRsmXV223O5Zi3BGHBn6sJ4+qrEZzkbiY46X0C3G1m15Eii2xZ9U7JV0JIDwP+CPyLEFvi35IWJ6yz8EI7st1aZ42qb8epiaJbLj0hkcNxqijZsurtNmeyeW3g68Bv4vZIYE/Cm+8XUmXTznV5ZEupd2pfJaT6cOBCwnLQS3Sh3oVdJ5481ZoKV6DsiRCz/kbgAMIc7KuBSXHfcoSgNeObSbaservNmWyeBjwPnE5YUGjdmL8wYbnjPwJLd4Fs6fSm/SGHSuNhBHA5VQJWFV1neevbk6d6UuEKlDkRxg6vIy4TS+iS/F68uawZ8wY2k2xZ9XabM9k8njCuvWHc/hZhfHuduL0IsGQXyJZGb2Bl4JeJ7fYaD5XgUP26SO/CrhNPnupNPqsiHy8CHwKTJY02s/8Qlo19FDgyjo3OaTLZsurtNtchK6kPYVx9CWA3ADM7ETgNuFLSZ8zsbTN7rZGyZdJb0ljgL8Buki6M5edJakkfO+b3NbM5kvpLSs9mKKzOcso6Tv0U3XIpa6LtDWQYwfHot8SxT0KLf/lmky2r3m5z/TbHcgMJCwudDhyayP8OnQf7yiRbJr2BjQmOioMIsw0uSttRxa6FCH4Eo5uhzhpR35481Zt8VkUOJPUxs1ZJwwnjih8AP7Ma1nsvSraservN9dscjzEY2IowHfBVMzs2sa9Dr/qssmXSW9IiZva2pEUID3wzsy/EfcPN7P2EPSMI4aePMrPbGql30deJ49SDD1XUSDvdl63xT/s+wSlpJGEcsylky6q321y/ze1hZrMJjnF3AcvH7vnKvg7fGmqRLaveie23E58HAH0kTZc0DfiRpBGJRsPVhJgOt+U5dzNeJ45TF43otuipiRyOU0XJllVvtzmTzUsR4wzUcC0PJjGNMKdsKfWuNQH3EMJe7xS3+xJ8FXYuos7y1rcnT41OhSvQrIkw//l5QiS7CxP57f1p+8bP/vGPXoTs4JLq7TbXb/N4QvyDa4EV67y288jmqevC9I7ynQZxAtYE/glsn5QBphRUZ7muk3rryJOnWpIPVbTPWIIT1CSgRdJFUN3rWmGFubmSFiIsELNKQbIjSqq321yf7KIEJ7yfEYIuHS5pRdqhcjxJC0n6FbBDRtmjgRXKqLfCjAhrr3yCJYCvmtk1khQOoX7ARlnPTb46y/vfcJzGU3TLpZkTsEjlk+B1fXFi3/D4mVwc5hZg4yJly6q321y37IrAgPj998A5wLhK+cRxKm+gC0X5DfPIllnvDP9/Jb4XWWe5/huePDU6Fa5AWVL80/6ZsGzsNOAYYETcN4KwHvwGzSRbVr3d5tpkSTy0CN705wJDgPVIBDEiPMRuTMrnke0JerdjS+Wh3+EKkkXUWaP+G548NSIVrkDZEu07TnX6Zy1Ktqx6u82dy9LmEKf4ELkNeB3YJuYPAG6gyhtoHtkS6H1rLXpXOebChEWhFmq03s1yjXnylDcVrkCZEu07Ti3crLJl1dttrl028TDbkODAt11i32J0MBMgj2yT630LcEkir6NAVsnFnW6kgzDbRddZXllPnhqRfAGoOpC0NTDHzG6uOE6ZWWszy5ZVb7e5btkhhNgIT5vZFVLtIZPzyDab3jDfegl/A+4xs8M6OEYldPZChNgOR5jZnV2pd5Qv7L/hOHnxhkNG6r1RNINsked2m7teVtIQM/swY0Mrs2zqOIXqTWg3mKQ1gAOBPYBrzGyPtH6pRsNVwE/N7I7u0Dt1nML+G46TBZ+OWSOS+sbPFuh89bpmkC3y3G5zftnKG3SN8n0qD7F43nrOPZ9snQ/uptCbMHzQGs+/FsGB8AzgM8A4SedW9Kvom2g0XAH8FOi0p6E9vfPWWXfIOk6j6JUNB0mL11m+Mj96YeD6eLPpbtkZksaVUG+3uT7ZGZLGQO0P4ShfWRb5YEkDM8r+n6SBJdT728AxkgbE3X2Be83sQTN7AlgX2EhxDYT40J8XZS8HjjGzOwqos7zXSc2yjtNIel3DId4Y/ijpklRee+WTbyaXAEMJ87i7W/Y3wG9LqLfbXJ/s74FvStoLOn8Ip+RvANYCdskgex2wE2F9grLp/QwhQNUSkpYHXgOWlDQ+Hm8eIWrkNEkrWVtAqFuA4wmNiix6562zXP8NM3u3vfKO06VYE3hodleC+RZ0+RtwfCflk97WtxCnOhUlW1a93ebaZQnhkf8P+DWwS7VrN5FXWXegsujPFjlky673YoSeh79EXQ4E7gZ2B/YiDEesljjOBMIwRh69C/9vePJURCpcgW41ts0ZdA3gTGAWcFF6f/ye/LPeRtsKcN0uW1a93ea6ba7kbU4Yo7+e+af6VZNfFJiZOHcW2Wkl13sz4ELCssxHxu+DgV0JDYIZqWP2SR2v2+ssr6wnT0WmwhXoFiPjm0L8vhZhvfnJwMR4Ezg3VT75Z7018UfvTtm/ApuUUG+3uX6bkw2lTYHHCW/KlwMnkXoTTsiPILyxbtUA2bLqPQ64DFg1bi8LHAWcR4xOSdtS0ckHciP07vbrpIj7pydP6VS4Al1uYIhNf2zi5rFO8g8KtAAvE1v8zL/m+x3ABQXI3gJsV0K93eb6bf48sEzME3AEcFjcXhg4DLiSxJLGcd9ChIfYzhll7wDOL6Het9D2tj8IOAF4Ctg0UW4ZwnV0RSzTkti3fA6989RZ3uvEhyc8NU0qXIEuNxAWJ0SnWzbeNJYCbgbGJ8p8C3gFWCluDye8AWxfkOy0kurtNtdv876Eh0j/uO/LBKe7ZeP2UOBh4GRgdMyrxCT4dQ7Zz5VU74pvwJqEBsJSsdwvmN+HYVlgXJX7QR6989RZruuk6PuoJ0/JVLgC3WJkRsepImXLqrfbnMnmUYS35gnxWMcR3nyXJ4QMvwZYJZbtQ3hIrt8A2bLqPRG4iOCTsFQsfzxhLYY1argfFFJneevbk6dmSYUr0OUGZnScKlK2rHq7zZls3pzwIDwGuJ/w4JoKHE1425wJ7JCSGdQA2bLqvT1wL2GI4XLCio9jYzqF4N8wtIP7QSF1lre+PXlqplS4Al1qXEbHqSJly6q325zJ5jUJb5nrxO0jgAeBFRLHW7oiz/zOfXlky6q3COsebBa3xwI/JPgkjI52rdLB/aCQOstb3548NVvqsQtASRoEHACsAiwBYGYvE6Y/vQacFsvMjfusaNmy6u02Z7J5JMHR7kMzuz+WOYLwBn2TpAlm9rKZvVKRrxwjp2wp9U6wCOHtHTN7CbiPMARwDDDLzJ6iCkXVWd76dpympOiWS1ckMjpOFSlbVr3d5roc8ypTAIfFzy8B77Bg1/gRpLzo88iWXe9UuZWBa4Fvxu3JhFUjzyIVDrvIOmvEf8OTp2ZNhSvQMEPabhJ1O04VJVtWvd3mXDavAzxJ2+yAzxPWEfhcBzKNkC2N3iSmT1bZ3wJsHOX/TJjCOIEwLXO/Buvd7deJJ09lSD1mqMLMTNL2wB8Ia9d/SHCWIn6OAnaRNLRZZMuqt9uc2eYtga8R3nqvkrS+mf0Z+BnwO0k7pmUaJFsavYGVgCMlLdeOPvPM7DbClNDjCctdLwpsSVjvoFF6F3KdOE4pKLrl0qhEDsepomTLqrfbnMnmpYEnaFu86CDCGHdle3faWRkwp2xp9I66/gL4lLCmwYR2jpucrbAKYYbC6s1QZ3nr25OnMqQe0+MQyeQ4VbBsWfV2m+uTfYsw/e8VhbDOpwCXAldHx7xLzOyOdiIk5pEtjd5mZsDthKWYVwM2rUS4TGIhumVL3HwR+JaZPdpEdZb3v+E4TU2PaTjEm87hwERJ34zZ7xHGM+cRxkGbSraservN9dtMGJsfDnzBzFpj3uXAo8AV0eu/cp6GyZZBb2BYnFmAmd1EcH4cDEwBdpY0LnnQRIjpRQnDFXMaqXfB14njND9Fd3nUm8jhOFWUbFn1dpvrt7kduYrD3PKEt+ATCfP4HyTMEjgFWCqPbIn1XhJ4gzBVcp8osw5hBsRawJ+A79O2DHM6hsMmRdRZV9S3J09lSYUrUJey4U9/NLBcJ+VGxJvPMgQnqieBLQqSXbGkervN9dvc7uI9tD3wlgK+DvwYWBXYJD5glsghO62kei8Rr5PLgRsJzoTfITQU7gPWi7peDPwIGBKPVwl21e70zS6us1zXSUcynjyVIRWuQM2K5nScKlC2rHq7zfXJDqTN8W4CMbZBe7Jx+zPAPwhv1lllJ5ZU77UTeRMIsSJOBg4BvkCYCfHbuH8SMCl+70dYhXGzAuusIQ6cnjyVNfWlJJiZSbod2JHgODVX0jwzezZVrrUyBkqb49QbkkYVIQtQRr3d5rpllwQmSDqM8KDYkhQWx9oT8i8A6xIe3ttmkTWzt3PWdRF6fxE4XtIjwHNmdrKkRYAhhFkIZ2NdAX4AAAp4SURBVAMbAX2iY+NjiePNkbQ/0L/AOsv133CcstP0zpGShudwnPodYbyxu2WPBwaWUG+3OaNjnpm9BnxMCL38gJm9GMv3aUd+OLAn8G5G2a9EmbLp/W3gN4QZDg8RHr6Y2d8I/gCthGGFt83sEWtzbET63+yLtwqos4ZcJ47TI+isS6LIRD7HqTsJ3Z3dLXsLIeJd2fR2m+u3eRPaHPEmAH1jPfyIEPFwTNy3CImgSYQx+tuAqRll7wbeLqHedwGvAycmyjwP/Bz4FTAAWIHgzHgioUcjHTAqq9556iz3dVL0vdSTp0amwhXoULl8jlN7FSS7QUn1dpszOuYB2wDPAWvF7fWB44BfEh5spwKLJ+RvpG0J5CyyZdT7FsKiSwcTokJuEG04G9iO8PC9mtALug4wvoP7QnfXWUPq25OnnpIKV6CqUjA08b1ex6kriSvCdbPsZcCWJdTbba7f5g1TNj+TKDOKsH7AOoS4BH8nxkUgdG3fAGyUUfbmlGxZ9L6T0NvwQ0KD4WBCz8NlCX2WAi4Ahndyb+jOOmvYdeLJU09KTeccKWll4PdZHKcI3ZyVteBX7y5ZCw5bxwPHStqmLHq7zZls3t/M3pIkMzNgGOGhOELSkcBWhC70vczsSElnWJtT3JKEpY+fi9v1yE4jPCB3ljSpRHpvSOiyPxn4s5m9J+k+QhjpzSWta2b3EZZinhjLvk+KjHpnrjMadJ2k7XCcnkBTOUdKmkAIDpPFcaoie1E3yyrKH18yvd3mDI55wH/j58Ix7wHC/+gwwpvwhsDfgM/Gcm/SxvNm9o96ZaPePwXOLZnew4DvAr8zszPN7L24f1fCrIjzgW9LOpQwxfFHZvZK4rxJ/buzznJfJ95ocHo03dW10VkijAm+RAbHqaJky6q321y/zalrdSvCw+pc4NTUvkmEBYYWWFcgi2xZ9Y75fYFzgBGJvK/Esv8g+CXsTehhqAwvLKB7d9ZZI+vbk6eemgpXwCz4NMQ/Y92OU0XJllVvtzm3Y95qhKiLmxKc4q4Gbo37phLeUD/XCNmy6h3zRXgIPw5sk8g7kPAwXpzg+zCJuAIj1RsN3VZnjaxvT556cipeAVgp/hnrdpwqSraservN+RzzYtlVgDNSedcSnOX6ABNjXrWHYM2yZdW7iux+wFm0zYBoiZ9TgWuAkYmyhdVZo+vbk6eenIo9ebgpPAB8g9idSRj7PAC4grDKG4R5548CSxctW1a93eb6bY770usIjCWEfZ6cyPsZsHejZMuqdzv/8cUIcR3OIPgh9CU4LT4IbN0MddaI+vbkqTel4k4cvKNvAb6Wyt8TOBTYmTA3+lDC+Oa2RcuWVW+3uX6bY9nKuPcWwHSCM+iahDH3t+KxdgMeAzZuhGxZ9e7kv7444Q3+KYLD4b3Ajo3Su+jrxJOn3paKO3EOx6miZMuqt9tcv80JmQ0JD6ovE95A/0tY9GdDQnjm84hj+I2QLaveNf7nFwdG0rbCY0P0bobrxJOn3pSKOWkOx6miZMuqt9tct2z/1LX6deAnie2NgaeB0XG7T+L4mWXLrHfOe0GRddYQB05PnnpbKmQdBwu8S3iD2FXSWmZmBCeot4HlgXeB1ywGrylatqx6u821y0paCbhA0o8kbRqz+wKTE8e+DbiD4IGPtc3hH59VNupWWr2zkkfvvHWWV9ZxejNFLwB1BcFz+f+3d7chVlRxHMe/PytyTTHXyrIwocCC8IHoTWlJEj1jUpBiYSD1JiQKIXqAVOhFEEEvetAXRVZoFFRiVpKiFpFGKisFQeADGqaBlK0RZv9enNm6LpuNO3vn3rnz+8Dg7pn73/OfC9f535kz5zwk6UbS/CnTSR/kl6NhEpUB/pNqVWxV8/YxnyKWNKp+JWmQ3EWk++pExKtAt6RVksZKmkFaG2F4X6DSbKeDiv0PVc07t1a+Z034bJjVS7T4kgc5B061U2xV8/YxDxwLXAjsJrtETpr++BvSPfY7sra3SSe6rX1tRWM7Me+cx9ay96xZnw1v3uq09d3razlJ44ATwNkRcUD6Z276to2tat4+5pNjsymGHyPNEPgeaUbAXtKjd1OA7RHxnKQRpAXYDvXFF4nt5Lz/55ha9p41M9asLtqmcDArm6TzgOERsV/SVNKjfvOBDRGxUNIw0iX0ORHx4FDFOu/y+zazodN2q2OalUFpYN4K4FNJmyLiy+zENRo4Kuni7BvnMGCSpO5IA+YKxTrv8vs2s6HlKw5WO9ml8tXAC8BbEXGi374FwB+k+92PAssiYl3RWOddft9m1gTNHEDhzVu7baSrbKuBRf3aHyCd2M4CLgdeAo7w72A9FYl13uX37c2bt+ZsrX4c06w0kkZExJ+kk9mGhvYFpCmFzyeNqN9NWlvhhohYK0lA12BjI6LofAe1y7to32bWPC4crOMpGQNsygbo/QLc1fCSPcDsiLgfOApcERG7IqInO/meO5hYKPbcfx3zLtq3mTWfCwfraH3fXiPiCOnxv0tIyydPkHQzQERsjoh9SpP+XEa6314o1nmX37eZlcOFg3W6kQ0//wbcBnxCWm1xtqRHJE2QdAvwCvBsRPwwBLHOu/y+zawEfqrCOpakkaRFiraR1hw4BEyMiEWSziYtmTyLNDivF1gRER8WjXXe5fdtZuVx4WAdTdIU4BxgHmm9g4Wk6YwPAhuBncBxoDcifm8cmFck1nmX37eZlcOFg9WG0nTC7wKLgbuBMaSVGOdExL5mxTrv8vs2s+bxzJHW8RoG7P0k6TAwPiIez/aNj4gfmxHrvMvv28yaz4MjreNFRGSPJ0K6VD6pYffBZsUWVce8zaz9uXCwWmi4j74NuFbSqOyb8V/NjC2qjnmbWXvzGAerlWxConER8W2ZsUXVMW8za08uHMzMzCw336owMzOz3Fw4mJmZWW4uHMzMzCw3Fw5mZmaWmwsHs4qRFJLebPj9TEmHJa09zb+zJ3vq4bRfc4r2JZIWn04eZlYtLhzMqqcXuEpSV/b7TcCBFuZjZjXiwsGsmj4Gbs9+nges6tshqVvSB5J6JH0laXLWPlbSekk7JC0H1BBzn6RtknZKWi7pjLyJSHpK0veSPuPkWSLNrAO5cDCrptXAXEnDgcnA1oZ9S4EdETEZeBJYmbU/A3wREdOANcAEAElXAvcC10XEVOAEMD9PEpKuBuYC00jLZl9T8LjMrM15kSuzCoqIHkkTSVcb1vXbPZ20GiURsTG70jAauJ50ciciPpJ0JHv9LNKqlV9nS0x0AYdypjIDeD8ijgFIWjPYYzKzanDhYFZda4DngZnA2IZ2DfDa6PdvIwFvRMQTg8zD08+a1YhvVZhV12vAsojY1a99C9mtBkkzgZ8j4td+7bcCY7LXbwDukXRBtq9b0qU5c9gCzJHUJWkUcGeB4zGzCvAVB7OKioj9wIsD7FoCvC6pBzgGLMjalwKrJG0HNgP7sr/znaSngfWShgHHgYeBvTly2C7pHdLy2XuBzwsdlJm1PS9yZWZmZrn5VoWZmZnl5sLBzMzMcnPhYGZmZrm5cDAzM7PcXDiYmZlZbi4czMzMLDcXDmZmZpbb343+Yes0WYZ8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>Model Correlation</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# Model Correlation"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# Model Correlation"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
+      ],
+      "text/plain": [
+       "\n",
+       "> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>SHAP Summary</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# SHAP Summary"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# SHAP Summary"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
+      ],
+      "text/plain": [
+       "\n",
+       "> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>Partial Dependence Plots</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# Partial Dependence Plots"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# Partial Dependence Plots"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
+      ],
+      "text/plain": [
+       "\n",
+       "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h1>Individual Conditional Expectation</h1>"
+      ],
+      "text/markdown": [
+       "\n",
+       "\n",
+       "# Individual Conditional Expectation"
+      ],
+      "text/plain": [
+       "\n",
+       "\n",
+       "# Individual Conditional Expectation"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<blockquote>An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.</blockquote>"
+      ],
+      "text/markdown": [
+       "\n",
+       "> An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction."
+      ],
+      "text/plain": [
+       "\n",
+       "> An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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iXW8JMLBQcyE0chPjPwrUmr8Twck00ySoZL4jNjHPOqC65PX2hSfm+w+5CH8ldqBzbgD+qpWxef/EV8evaWH4YmBE+OO5tGwftDB+qbJ1Cqt9j2h59E1qdzk6uH02N+8o/qTrduDL1vTW2aXr3wcfBizGn9Q9UXFy3cc592V8lfBGfJOSSs39IP5x+P5e4VXP0yhft039iF4MDLKN/RRA6/fv5rRm3h36gR/WRHkMmIE/aWgyz7Dmxs34k77B4f5/nY3baBHNb+uCLwEjzezqkvcWAfMq9l9f51zh5G1zn+d2c8494pw7HB+Kvo1ft0KZzq0oU5Vz7pl2Lqq577nFzYy3CF9Lp3S51WHtiWaFTRNOAg4xs6VmthTfuefeVnFHm00JaxjNw580P1QxeAX+ZLa05k7p8bepfbQIX/umtmSd+jnnmms2gnPul865ffDNM8YB32puvBJn4IOsheG6P4APcwpBfIvf94VFVrxeTPl6Ftan7HMcBnV/B75MJ4Ur4ff6Lfhg8fiwJk7BG5TUdAuPp50pafpjvn+x7wDTnHNtud125bx3wtdInBO+9Sotf7+8AewVlr1gL5o2STodH6y+14ZyteTVNo4fY+P30hvAThXfpXuzsbyb2xaY2WX42lgfd86ta2NZRER6JYUrIl0s/DH/Ar7ddCKs2l5Z9bp0/By+yvfP8CfW/2hh1PuBM81s97DK+Pcrhr+M76CuOjyBP7tkWF/8VdDlQMzMvkfTq1gtuQN/IvGg+Q47I2Y22HxHlp/A929Rh+8gNG7+lp1H4/vg2Jy/AnuY2afDmg4X0PQkodSH+L4RmtORcnRk+2zOJeHfz+P7Sbm9onnKJ8zsIPOdVV4OPBc2E/kLMM7MPheuT9zM9jOz3cLaOb8Ffm6+09So+Y5rk+E6BJRvp774K5drzGxHmp7Ytbhdw7I8A/zYzFLmOyI9m2b6P2mrrpq3mR1rvrPogebtj7+6/O9wlMr1rcGfZC0Ppz+LjUEM+KZK3zSzfcL57WLlTWnW4/sq+ZiZ/SR873lgnZldZL7z2qiZfcTMCh3X3g9cHJZxOL6mQIeZ2XZmdkx4kprG7/fCLbVvCJe5Rzhuf/P9yXRE4XvuYHxTtubumHIz8CUzOyDcfjVm9smKE8FKx4Xl3h3ffG8Cvr+gJynvpLY1zgYOq6hlQ9ik6n7gCjPrG+7Tb7CxX5b7gQvMbLiZDcSf4BemXYIPxq8yf6v6iPnOf5s0tws/tweYbxZThw9GW7zNefgZnYbfnoV13xv4PzbW3HkZf7yNNLP+wMUVs6k8xv+G/z45xXynt5/Bb9u/0NQlwCGu5b41IuHntfDYXN85v8bvu6Nd06Z9f8A3YTvefB9X38P3dfI2gPlOkn8EHN5cgBF+L6bwv29jYXkK3693AUeb2cHh5+EH+KahhdoavwPOMrOdwv+pF5Vsj5n4fXSB+Y63C7VtZlQU4XR8Z8utEm77FBAFomF5C7X8/oXvk+ricLwp+I51Hwmn/YL5vrsKdzq6GN/cFOfcHPwx8f1wnp/Ch0EPtmZbmNnF+BpehzdTu1JERFqgcEVkyzgF3zndKnwIsrkqw3fjO459IAxbmnD+jgi/wP+4e5emP/Kuxle5/hDfB0PpCeoj+KuRc/BVwRtpZXOLsAr2f+GvgP8DWIc/cazFBwEZfAefR+KvBP8KOL2kTf2m5r0C36/AT/DNj8YCT29ikun4NuhrrOTOOeG82l0OOrB9QsPM35Wj9HG8me2DP1k7PTyR+z/8Sfx3Sqa9G3+MrMJ3JHpquD7r8dXBP4u/6rw0nL5wIvNN/N0g/hNO+39AJLzyfAXwdLidJuE7aJ2Ir43zV5pewf8xcGk4/jebWb+TgdFhOf6AvyNFSyFgW3XFvFfj+/l4B3+83gn8zDlX+EzcAuweru8fnXNv4vvaeBb/+dmTkuPQOfcAfpvejQ9S/ogPQikZZw2+X48jzezycH8fjT8xnoc/Jn+Db24Gfp8UalU8SvO1BP5ccUz9oRXrHgEuxG/PVfhQ6bywjH/AHyf3mm8e9jr+89JeS/HbejH+++ZLzX3enHMv4PfHdeH477L5Oz+dgW+ysdA5t7TwCOdxqrXyttzh8ueGZWjO+fjA4z3gKfw+Ltwm92b8d8MrwCyafm5OxzcrejNcr9/TfBPKfuG8VuP3+Up80NqSz+E7vn60Yt1/ia9N8ZHwM3IfvrbDizQNSa7B98+02sx+GZ4wH4U/NlbiOys+qqTJVZHzfQ89Vfl+iZPxTVgKjxabzIWB1bn4z8HSkmO58D23HN/nyRXh9jmA8n5rfojv9+s/JdPeUDL85rAMJ+P7rGoItx/O9xv0JfyxuQwfMp9Xsp6/xf9vfg6/X9KEtzYO/58ch9/Ha/Dh+HHh+4V1m4zvJ6U1t2AuuDQs43fwNQgbwvcIa/Qci69ltTZct9L/YVOA18ysDh+W/Y2N4T347bYvfjv+BDgh3L6b3Rb4AGsk8E7Jdi6dt4iINMPUhFJEZOtgZrcC7zvnLu3usoiIiIiISOup5oqIiIiIiIiISAcoXBEREZFuF/YXUtmcrvDotA5+t1Zm9kYL635qd5etLXr7fiylbSEi0ruoWZCIiIiIiIiISAeo5oqIiIiIiIiISAcoXBEREekGZnammW3qLiw9hpk587eEFxEREemRFK6IiIj0YmY22szmh8/nm9noLl7erWGwdGZ4hywRERGRbZ7CFRERERERERGRDlC4IiIivZKZ7WZmM81sTXinlmNKht1qZteb2V/NbL2ZPWdmO29iXgeZ2TPhvBaZ2Znh+/3N7HYzW25mC8zsUjNr8r83rD3izCxW8t5MM/tC+PxMM3vazK4Ol/GemR0Yvr/IzJaZ2RntLX8z5ZlkZkvNLFry3qfM7NXwedLMfmFmi8PHL8wsWTLut8xsSTjs861droiIiMi2SuGKiIj0OmYWB/4MPAoMBc4H7jKz8SWjnQxcBgwE3gWuaGFeI4G/A9cCQ4AJwMvh4GuB/sBOwCHA6cBZ7Sz2AcCrwGDgbuBeYD9gF+A04Doz69PW8jvn5jvnRofPR4ev/w3UAYeVjHpKuFyA7wKTwnXdG9gfuBTAzP4b+CZwODAW+K+K5Z3pnLs1fJzZ5q0gIiIishVSuCIiIr3RJKAP8BPnXMY5NwP4Cz6QKHjIOfe8cy4H3IUPEppzKvBP59w9zrmsc26lc+7lsNbHZ4CLnXPrnXPzgauAz7WzzPOcc79zzuWB+4ARwA+cc2nn3KNABh+0tLX8LbmHcHuYWV/gE+F74Nf5B865Zc655fgQp7BeJwG/c8697pyrA6a3dUVFREREtjUKV0REpDcaBixyzgUl7y0Adix5vbTkeT0+jGnOCGBuM+/XAolwvi0toy0+LHneAOCcq3yvtIytLX9L7gY+HTb3+TQwyzlXWJdhNF2vYSXDFlUMExEREenRFK6IiEhvtBgYUdH/yUjgg3bMaxHQXH8mK4AsMKoVy6gL/1aXvLd9O8rSaZxzb+KDkSMpbxIEfvtVrtfi8PkSfOBUOkxERESkR1O4IiIivdFz+EDj22YWN7OpwNH4fkza6i7gv8zsJDOLmdlgM5sQNt+5H7jCzPqa2SjgG8CdlTMIm9Z8AJxmZtGwE9hWd0Dbhe4GLgA+BjxQ8v49wKVmNsTMaoHvsXG97gfONLPdzawa+P6WLLCIiIhId1C4IiIivY5zLgMcg6+VsQL4FXC6c+7tdsxrIb4/kguBVfjObPcOB5+PD3HeA57ChxW/bWFW5wDfAlYCewDPtLUsXeAeYCowwzm3ouT9HwIv4DvYfQ2YFb6Hc+7vwC+AGfiOdGdswfKKiIiIdAtzznV3GUREREREREREtlmquSIiIiIiIiIi0gEKV0REREREREREOkDhioiIiIiIiIhIByhcERERERERERHpgFh3LLS2ttaNHj26y5dTV1dHTU1Nly9Hth7a572L9nfvo33eu2h/9z7a572L9nfvs7Xu8xdffHGFc25Id5dDtm3dEq6MHj2aF154ocuXM3PmTKZOndrly5Gth/Z576L93fton/cu2t+9j/Z576L93ftsrfvczBZ0dxlk26dmQSIiIiIiIiIiHaBwRURERERERESkAxSuiIiIiIiIiIh0gMIVEREREREREZEOULgiIiIiIiIiItIBCldERERERERERDpA4YqIiIiIiIiISAcoXBERERERERER6QCFKyIiIiIiIiIiHaBwRURERERERESkAxSuiIiIiIiIiIh0gMIVEREREREREZEOULgiIiIiIiIiItIBCldERERERERERDpA4YqIiIiIiIiISAcoXBERERERERER6QCFKyIiIiIiIiIiHaBwRURERERERESkAxSuiIiIiIiIiIh0gMIVEREREREREZEOULgiIiIiIiIiItIBCldERERERERERDpA4YqIiIiIiIiISAcoXBEREelCzrnuLoKIiIiIdLFYdxdARESkJwqcY3Umx4ZsQCJipKIRqmIRkhHDzLq7eCIiIiLSiRSuiIiIdCLnHPX5gNXpHHkHNbEIucCxLptnXTZPBEjFIlRF/SMaUdAiIiIisq1TuCIiItJJcoFjVTpHQz4gHjGGpGIko74FbuAcDfmAhlxAYz6gPhcAqFaLiIiISA+gcEVERKSDnHOsz+ZZk8kDMCARpV88WhaURMyoiUWpiUVxzpENNoYtqtUiIiIism1TuCIiItIBmXzAynSOTOBIRSMMTsaIbSYUMTMSUSMRjdA/oVotIiIiIts6hSsiIiLtEDjH2kxY48SgNhmjOhZpVwDS1lotqWhkswGOiIh0v8Id41zh4QrP/ZPy9x0uHHnj++F726jKWpwiPZnCFRERkTZqyOVZGXZY2ycWYUAyRrSTfjyqVsu2qXACFIQnSEF4chQ4h3MQhOP4v+GJFUbUfLgWIfwbvo4aGGi/Vihs57zz2zZwkHdu4/Nw2wfOkXetvRV627extfii/UpnYwYRLPzrj4NIxfubGqe3HzuF46QszGjynis+h+bH2fT7riQoKbzvype5JVZ2K9cvHu3uIohsMQpXREREWikfOFZlctTnAuJm1FbFSIUd1nYV1WrpfO0JQgJHOKylcdomQutOvspDl43Po1jZ6+LfMLDZFk6sC9uvEIQEztFn8BDWZXJNw5OS8TaldHvFzIhs5rPQ4ZPfTcygvfMunLTncLig/BhtCx+wNBe8+O1j+G1VOo5/r/lxjPaFNpsKOpJ9+tKQC5oJKko/oy0EI82M39mhRmGdKa5/+bagsO0wLBL+LY5vJdusEJ5VbstNvR8OKV2eiGzVFK6IiIhshnOODTl/e2UH9E9E6d8NVZ1bW6slHjHfKW4PrdVSOMku1FgoPO/KIMTCAKNw8hnDn7g3d4JaeRJbOFktHafANVPjouwvrixoyAVhLY3NlLlQzspaMdHSMAY6rbaMayEIKa9dUvF+M/MZMmYXVocdQxfLiw9KohFrdj1Ka//0tGO9VGnAELjNh4TF503Gcbig/Z+H5gIYaF/QMWzXj7CsMbvZ5ZUGDhtr5oQ1dwCLlH/uSoOLQmi0MaRoPsgoD1F69rEkIl1D4YqIiMgmZIKAVY050oEjGTUGJ2PEI11bW6W1elKtFlcSkuTD5/2335FV6Zx/HZSEKJuYT1cGIV3BwnAg2sbr0s01jwlKgo3KQCPnHEHQuhoQlaGLDzP8dvHLdGXLzG9mnqVBT9R8k7aIWVntm0JY8p/nnuPAyZN0ctuMQkgAfn90Vl2GypBmYyBZElBWBDilYWUQ7v0I4ecpYk1Ci5aCjldfeYUJEyaUBTblNTp0HIjItkPhioiISDNc2GHt2jCgGJyMUdPODmu3hK2xVkvhBKw0GAmaCVHyQfO1GAYNH0ldNu9PviP+pDxqER9GhCfkhecR610nYaVNONp6kt1SGFNZqyQI91cuKNSi8Usq1hQxiEci5bVJqKhNEv5trVwm3abxpeM6cix1VOP6dV3etFJEZEtRuCIiIlKhIRewKp0j5xw1sQgDO7HD2i2lK2u1NAlIgpaDk+YUTuSiZsTMSMZ8k4/SsCRqxtNP/otDDjmk07aJeIXQo60n0s65XhVgiYiItIXCFRERkVDeOVanc9TlApRcHVYAACAASURBVGIGQ1NxqmLb/lXVttZqSUatvKZJUB6YtNQEZGMtEiNeGZZENj5vbZOP1t3pRbYUBSsiIiItU7giIiK9nnOOurDD2gB/68j+iWiPbZ6wuVotlPQvGSEMTCKQiESa1C7prc1yREREREopXBERkV4tGzhWpbM05h3JiDEoGSPRi/oAaK5WSzZwxeBEgYmIiIjI5ilcERGRXsk5x7psnrXhbV8HJWP02Yo7rN1SImYko717G4iIiIi0lcIVERHpdRrzvsPabOCojvoOa7fWWxSLiIiIyNZP4YqIiPQaQdhh7YZcQNRgSCpGdSza3cUSERERkW2cwhUREenxnHPU532HtXkHfeNRBvTgDmtFREREZMtSuCIiIj1aLnCsSudoyAckIsaQVIxkL+qwVkRERES6nsIVERHpkZxzrM/mWRN2WDswEaVvPNrrO6wVERERkc6ncEVERHqcdNhhbSZwVEUjDFKHtSIiIiLShRSuiIhIjxE4x5pMnvXZPFGD2lSM6qhurywi0hs553DZLC6dxmUz/m8mTZDJEEkksKpqItU1RKqqsKg6NxeRjlG4IiIiPUJ9Ls+qsMPaPjF/e2V1WCsi0n1cLufDjHQGl0mXhRxBJkNizttsiEJQfH/jeEEm7V+XhiLpNC6TKc6n7HVheCFMCefTWpZKEamqJlLtH5aq8sFL+LpsWPF5Tdn7peNZdQ0WjyvcF+lFFK6IiMg2LRf42yvX5wPiEaM2FSOlDmtFRLqEy2ZIvzeXxtlvkZ79Nuk5b5NbtarZUIR8fpPzGgS8v5nlWTKJJZJYMulrmxReJxJYIkG0b19scG04TsKPE45viQSRkueFcSwWx+WyuPp6gsKjofJ5Ha6+nuyqFSXvN+AaG1q/sWIxIqmqsuDFis9rWghlCs/Lgx1LpbDItve/LTq4VgGT9BoKV0REZJvknGNDzt9e2QEDElH6qcPaXifIZFh12y1kP1hE9QGTqTlgCrFBg7q7WCI9Qn7dWhrnvE169tsbw5T35kIuC4ClqkiOHUdyzE7+5D8MPSIVYUYkkSwLPArjvPzGm0ycNCkMQUpDkSSRZAJiW1/ND5fPEzQ2bAxmmgllgvp6P7zJMD88t2I5rn4BQUND8T2CoLtXrUuMf+ENUJMr6SUUroiIyDYnE3ZYmw4cqagxKBkjvg1e0ZOOaXjjNZZ872Iy771LpE9f1v7pITAjtevu1Bx4EDWTD6JqrwlYPN7dRRXZqjnnyC7+gPTst4ohSuPst8ktXVwcJ1Y7hOS4XamZcjCp8buRHL8riRGjOtRXSbYxS9Uee3bGKmwxFo0SrekDNX06bZ7OOVwmE4Ywdc3XqGmoB9dpi9xytrJwTKQrKVwREZFthlmE1ekc67J5IsDgZIyamDqs7W2CdJoVN17HqttuIVY7hOG/vJGaAw+i8a03qHvmKeqefYqVt/6GlbfcSKSmhur9J1Mz+SBqDjyIxI7Du7v4It0qyGTIvPduSYjyFuk5swk2rPcjRCIkRo2hesJHSY4/hdT4XUmO25XY4NruLXgPZma+2VMyCQMHdndxRKSdFK6IiMg2IRs4hu2xF+uyeWrCDmujClV6nYbXX2XJ9y8h89679D/2eIZeeBHRvv0AqPrIXlR9ZC9qv3ge+fXrqH/+39Q9+xQbnnmKDY//E4D4yFHUTD6IPgceRPW++xOprunO1RHpUvk1q2mcM9vXSAmb96TnzYVcDgCrqiY1dhz9jjzKhyjjdyO581giVVXdXHIRkW2PwhUREdnqZfIByxqzRGMxhqbiVMXUBKi3CdJpVtxwHatuv4XYkKEMv+5m+kw5uMXxo3370Xfax+k77eM458gsmOdrtTzzFGv/9CBr7rsLYnGqJ+7ja7VMPojkuPGqBSXbJBcEJc163i6GKbmlS4rjxIYMJTl+N/p8bCrJ8buRGjee+PCRugWxiEgnUbgiIiJbtXQ+YFlDFjNY8vYb7DTpgO4ukmxhDa+94murzJtL/+NOYOg3LiLat2+rpzczkqN3Ijl6JwadcjpBOk3DSy9S96xvQrT8mitZfs2VRGuHUDPpQN9fizrGla1UkE6TmfvOxhops98m/c7bBBs2+BEiERKjd6L6o/v4EKXQrGfQ4O4tuIhID6dwRUREtlqNYbASNRhaleDdttwCU7Z92SzLrrmSVbf/1tdWuf5m+hzYcm2V1ookkz5EmXQgfP3bZJd9SN2/n6bu6afY8ORM1v3lT75j3N322Ngx7p57q2Nc2eJyq1eTnlPoF8XXSEnPe694i+NIdTXJsePp94ljypv1pFLdXHIRkd5H4YqIiGyV6nN5VjTmiEWMoak4sYiaa/QmDa++TO2VV7Bq2VL6f+pEhn79222qrdIW8aHbMeCYTzPgmE/j8vnyjnF/dzMrf3MDkT59qN5vkjrGlS6RX7+ezML5ZBcuIP3eXBrn+M5mcx8uLY4TG7odyfG70mfqNJLjdiU1fjfiw0dgulOaiMhWodPCFTOLAi8AHzjnjuqs+YqISO9Tl82zIp0jETGGVsXVcW0vEjQ2suKGa1l1x++wfv0Z8avfUDP5oC22fItGW+4Y9+mNHeMmRo0uBi3V++5PpKp6i5VRtk1BQz2ZhQvJLJxfDFIy4SO/auXGEaNR36xnn/1Ijt+V1LjdwmY9aqYmIrI168yaK18D3gL6deI8RUSkl1mfzbMqnSMZBisRBSu9RsMrL7Fk+iVk5s9jwPEnMXvfyeyxBYOV5jTpGHf+PN9XyzNPseaPv2f1vXdi8ThVH1XHuOL7Q8m+vygMTUpClAXzyS1fVjZurHYI8ZGj6HPIYSRGjio+4iNG+VvyiojINqVTwhUzGw58ErgC+EZnzFNERHqftZkcazJ5qqIRalMxBSu9RNDYyIpfXcOqO28ltt0OjPj1LdRMmsLbM2d2d9HKmBnJMTuRHLPpjnFjtUOonjyFPpMPonrSFGIDB3Z30aUTuWyW7OIPwvBkwcYgZcF8f3ce54rjRgcMJDFyFNWTDiQxcrQPUEaNJjFipG4DLiLSw5gr+QfQ7pmY/R74MdAX+GZzzYLM7IvAFwG22267fe69994OL3dzNmzYQJ8+fbp8ObL10D7vXbS/e5aBO45gwA7D2bBqBcvnvVt2glKgfd7zxOfNpf89txNb/iH1kw9m/TGfxqWqgG1vf0fWrCY5+y0Ss98kOfstIvV1ODNyw0eS3nV30rvuQXbUGNCtb1u01ezzICC6ZhXRZcuIrVhGdLl/xJZ/SHTVSiwINo6aqiI3dCj52qHkhwwlN2QouSHbkR8yFKfmYpu01exv2WK21n1+6KGHvuic27e7yyHbtg6HK2Z2FPAJ59x5ZjaVFsKVUvvuu6974YUXOrTc1pg5cyZTp07t8uXI1kP7vHfR/u4ZnHOszuRYnw3oE4swKBlrsUmF9nnPUVlbZYfv/9DfvafEtry/KzvGbXjtFcjnfce4+0+mZvIU4sN2xCJRiEbCv1F/7EejvpPSaBQikabjhMMsEoFIFIs2/5dIZJtrnrQl97lzjtyyD8v7P1nga6Nk31+Iy2aL41pVdVnTncTI0cTDv9GBA7e57by12JY/49I+W+s+NzOFK9JhndEsaApwjJl9AkgB/czsTufcaZ0wbxER6cGcc6xM56jLBfSLRxmQiOokpReof3kWS6dfQmbBfAac8FmG/M83idZsfVcyO6KljnE3PPMkdc88zYYZ/9gyBSkEMBFrPogpDXHK/laEOKV/Y3EsFsNiMYjF/Ot4+LpieNk4xeGx8nnEN75OvvkmGxLNzSN8HY2Wjd9keMWdc5xz5FetLDbdyZYEKJlFC3Elt3e3RIL4iFEkRo+hzyGHkhgR9oEychSxIUP13SQiIpvU4XDFOXcxcDFASc0VBSsiIrJJzjmWN+ZoyAcMSETpF1ew0tMFDQ0sv/4XrL77dmLb78CIG35HzQGTu7tYW0Rlx7jZhQvIr1mNCwLI5/3fII/LV/7NQxDggjzkN/4tGycImh2nbNzS5VT+LV1O5d+K5bl8DpduJKjL4XL+QS7rn+fz5a+Lw3Ot3k4Dgfc7sqHNysIW8jmC+vqNw2MxEjsOJz5qNNX7TyrrByW23fa6rbGIiLRbZ94tSEREpFUC51jemKUx7xiUiNE3oT4oerr6l15kyfRLyC5cwIATT2bI1y7scbVVWsvMSIwaDaNGd3dRtgjnHGwifHG5bHH4C889x8S99vKvSwOafDPjVwQ4zc2TSIT4jsN9iDJqNPEdhvnQRUREpJN16n8X59xMYGZnzlNERHqWvHMsa8iSCRyDkzH6xBWs9GSltVXiOwxjxI23UrP/pO4ulmxBZgbxOBaPb3bc3JIPqZ4wcQuUSkREpHMpuhcRkS0mHzg+bMySDRxDUjGqYwpWerL6WS+wZPp3yS5awICTTmHo1y7U7WdFRESkR1K4IiIiW0QucHzYkCXvHENTcapi6tugpwoaGlh+3dWsvucO4sN2ZMRNt1Kzn2qriIiISM+lcEVERLpcNgj4sCGLc7BdVZxkVMFKT+Vrq1xCdtFCBnzmVIZe8A3VVhEREZEeT+GKiIh0qUw+4MPGLOCDlYSClR4paKhn+bVXs/reO4nvOJwRN99Gzb4HdHexRERERLYIhSsiItJlGvMByxqyRMwHK3Hd5rRHqn/xPyy57LtkFy1k4GdPY8gF3yBSVd3dxRIRERHZYhSuiIhIl2jIBSxvzBI1Y7uqOLGIdXeRpJMFDfUs/+XPfW2V4SMYefPtVO+7f3cXS0RERGSLU7giIiKdri6XZ0VjjnjE2C4VJ6pgpcepe+E5ll52Kdn3FzHw5M8x5Pyvq7aKiIiI9FoKV0REpFNtyOZZmc6RjBhDq+JETMFKTxLU17Hsl1ex5r67iY8Yycjf3EH1Pvt1d7FEREREupXCFRER6TTrMjlWZ/KkosaQlIKVnqbuhedYOv27ZBd/wMBTTmfIV79OpKqqu4slIiIi0u0UroiISIc551ibzbM2k6c6GqE2FcMUrPQY5bVVRvnaKhP37e5iiYiIiGw1FK6IiEiHOOdYncmzPpunJhZhcFLBSk9S959/+9oqSxYz8NQzGPKV/1FtFREREZEKCldERKTdnHOsTOeoywX0jUcZmIgqWOkhgvo6lv3iStY8cA/xkaMYecudVH90n+4uloiIiMhWSeGKiIi0i3OOFY056vMB/eNR+itY6THqnv83Sy8La6ucdiZDzvuaaquIiIiIbILCFRERabPAOZY3ZmnMOwYmovRL6N/Jtsg5R27Zh6TfmUP6ndmk3w3/vjOHxKjRjPztXVRPmNjdxRQRERHZ6unXsIiItEngHMsasqQDx+BkjD7xaHcXSVohqK8jPfdd0u/MpnFOIUiZQ7BubXGc2PY7kBw7jn5HfJKBp55BJJXqxhKLiHRMPp+nsbGx+KitrSWZTLJixQoWLVpELBYjHo8X/+6www4kEgnS6TSZTKY4LBpVzUwR2TyFKyIi0mr5wLGsMUsmcNSmYtTEFKxsbVw+T/b9hTQWa6O8Q/qd2WQXLSyOE6muJrnLOPodfgTJseP9Y5exRPv178aSi3QN5xzZbJZsNksmk6F///5EIhFWrVrFihUryGQyZDKZ4vApU6YQiUR49dVXeeedd8qmDYKAc845B4A///nPvPTSS2XTJpNJfvKTnwBw9dVX8+yzz5bNv7a2lnvuuQeAL37xizz55JPFabPZLOPGjeOJJ54A4Pjjj+fVV18lmUySSCRIJpNMmDCBX//61wBceOGFLFmypDgskUjwkY98hHPPPReAX//61zQ0NJRNv9NOO3HQQQcB8MQTT2BmxWHJZJJBgwax/fbbA7BmzRoSiQSJRIJYrH2nDEEQkE6ni+FGTU0N/fr1o76+npdffplZs2ZRX19fHL7ffvsxduxYFi9ezG233UZDQ0NZOPKFL3yB/fffn5deeomLLrqobFhjYyM33XQTU6dO5eGHH+aEE04gm82WlefJJ5/koIMO4m9/+xtnnHFGk/K+9NJLTJgwgVtuuYWvfOUrZcNisRhvv/02O++8M9deey1XXHFFk3DmX//6F7W1tdx0003cfvvtTYY/8MADJJNJ7rzzTv75z3+WDYvH41x11VUAPPzww7z66qtlw2tqajj77LMBePzxx1mwYAGRSIRoNEokEqFPnz4cffTRADz77LMsX768OCwSidCvXz8mT54MwCuvvMKGDRvKhvft25fx48cDMHfuXDKZTNnwmpoatttuOwCWL1+Oc644LBqNkkgkqAqbj2az2eIwhVLSmyhcERGRVskFjg8bsuSdY2gqTlUs0t1F6vVyq1eTftc340kXaqPMfQfX2OhHiERIjBxFatfd6X/UcWGQMo74sB2xiPafbN1yuRzr1q1j7dq1ZX8PPvhg+vfvz/PPP88f/vCHJuPcd999bL/99lx55ZVccsklTU6wly9fTm1tLVdeeSU//vGPmyy3oaGBVCrFzTffzHXXXVc2LBqNFsOVhx56iFtvvZVoNFo8OR4yZEgxXJk3bx6vv/468Xi8GFLk8/nivIYPH86ee+5JIpEojjNixIji8L322otkMlmsRZFOp4nH48Xhc+bMYfbs2WXDp06dWgxXrrjiCj744IOy8p900knFcOXYY49l7dq1ZcPPOussfvvb3wIwePBggiAAIBKJkEgkOP/88/npT39KOp1mjz32KAYzQRDQ2NjIeeedx9e+9jUWL17MmDFjyGQyZfO/8sorufDCC1m0aBFTpkxpsu1vuOEGxo4dy9KlS7nkkksAqKqqIpVKkUqlOPLIIwEfmK1fv55UKkVtbW1xeL9+/QDYZZdd+OY3v1l8v/DYeeedAZg2bRp//OMfi8FZLpcjm80ycuRIAA488EB+9rOfFd8v/B04cCAA48aN47jjjisblsvlSCQSgA9iUqkU2WyW+vr64vBC0DB37lwef/zxsmnNrBiuPPTQQ9x2221l22bQoEHFcOX666/nwQcfLBs+cuTIYrgyffp0Hn300bLhe+yxB6+//joAX/7yl3n22WfLhk+aNKn43nHHHVcct+Dwww8vznP//fdn/vz5ZcM//elPF8u0ww47sHLlSv75z38ybdq0JvtZpKcy59wWX+i+++7rXnjhhS5fzsyZM5k6dWqXL0e2HtrnvYv295aTDQI+bMgSOBhaFScV7Z4T8966z4NMhsy8uRv7Rgn7RcmtWF4cJzpwUDE8SY4dR2rseBI77bJNN+3prfu7p2hsbGTevHnF0KMQgBx22GGMGTOG1157jauvvrps+NKlS7n//vuZNGkSd911F6eddlqT+T7//PPst99+3HzzzZx33nn079+/+OjXrx+33347I0eOZObMmTzyyCPFYKMQYJxzzjnU1NTw8ssv8+abb5aFH/F4nEMPPZRoNMqiRYtYvXp1cXghQNlhhx0AH/4Urs5vjRobG8uCl0wmQyqVYtiwYQA888wzTcYZNWoUBxxwAM45rrnmmuKwwvApU6Zw7LHH0tDQwDnnnEM6nSadThONRkmlUhx//PGccMIJbNiwgSuuuKJJuDF58mT22msv6uvrefLJJ3nrrbeYPHlycfj2229P//79CYKAXC5HPB7vlTUfnHPkcrmy8CWfzzNkyBDAB4R1dXXk83mCICAIAiKRCGPHjgXg3XffZe3atcVh+XyeVCrFxIm+D63//Oc/rF69umx4//79+djHPgbAI4880mT49ttvzxFHHAHAnXfeybp164rDgiBg55135phjjgHgqquuor6+ntNOO40xY8aUrdvW+r1uZi865/bt7nLItk3hivQo2ue9i/b3lpHJB3zYmIUwWEl2U7ACPX+fO+fILV1SDE8aw2Y9mfnvQXjF2+JxEjuP9SHKLuNIjfPNemKDa7u59J2vp+/vrVHhpC4ej5NOp3n++edZu3Yta9asKQYghx12GJMmTWLhwoWcd955TWqWXHPNNXzuc5/j2Wef5cADD2yyjPvuu4+TTjqJJ554glNPPbUYivTv35/GxkauueYa9t57b+bMmcPf//734rDCeLvvvjs1NTUEQYCZ9cqT755Cn/HeZ2vd5wpXpDOoWZCIiLQonQ9Y1pDFDLarjhPfSq/QbovyGzb4Zjxhx7KFQCXYsL44TmyHYaTGjqfvodNI7jKO5NjxJEaOwtrZ/4H0fIW+O2pqanDO8dhjj5WFI2vWrGGfffYp1j448sgjm4Qnl156KZdddhlr1qwpXsku9ZOf/IRJkyYRiURYsmQJ/fr1Y8yYMcUQpND0Yvz48dx9991l4Um/fv2K/TYccsghvP/++2XznjlzJnvvvTfgm16MGzeuxXXdWmuMiIhI76RfZyIi0qyGXMDyxixRg+2qEsQiujrcHi6XI7NoYTE8KdzyOPvBxpPKSE0NybHj6XfkUT5EGTee5M5jifbt240lly2t0I9EJpOhttbXRJoxYwZLliwpC0d23nnnYr8aRx55JPPnzy8Ob2ho4LTTTuOOO+7AzDjmmGNoaGgoLsPM+OpXv8qxxx5LMpnEOceIESPYc8896d+/PwMGDOCQQw4BfJ8bjz76aPH9Qu2RVNjUbPjw4bz44ostrs+gQYM4+eSTu2pziYiIbFUUroiISBP1uTzLG3PEI8bQVFzBSjutf+xRlnz/YoK6Ov9GNEpi5GhSe+xF/+NOKPaNEtthmJo2bKWy2WyTO5bkcjl23313AF5++WUWLVpUdkeURCLB6aefDsCtt97KG2+8URyWTqcZOnQoV155JQCnnXYaTz/9NGvWrCn2YTBlyhSeeuopAC644ALeeOONYnlSqRRHHXVUMVwZOnQoffv2LYYfAwYMYMKECcXxZ8yYQXV1dXF43759izU+IpFI8c40zYnFYhx++OGduDVFRER6LoUrIiJSZkM2z8p0jkTEGFoVJ6qT/nZZ9/e/sPh/LyK12x4MPPFkkuPGkxizM5FksruL1qOsW7eOl156iffff78swDj33HOpqanhkUce4W9/+1uTW7bed999pFIprr76am677bbidI2NjWQyGVasWIGZ8eUvf5lbbrmlbJl9+/Zl3bp1gG8ic99995UN32GHHYrhyh/+8Af++c9/lnXqWbjdKcCYMWMws7JwpLQDyAceeIBoNFocnqw4firvKFJp0qRJbd+oIiIi0mYKV0REpGh9Ns+qdI5U1BiSihNRsNIuax5+iKXTv0vVxH0Zfs2vidb06e4i9QgrV65k1qxZzJo1i7POOouhQ4dy44038u1vf7vJuCeeeGLxjjC33357k7uWFO6c0r9/f0aPHk0qlSKZTBaHO+cwM0488UR23333smmrq6uLy7n88sub3PK1qqqqOPxPf/rTJtfp8ssv3+Tw3XbbrY1bSURERLqDwhUREcE5x7psnjWZPFXRCENSMTVTaafVv7+XD6+YTvWkAxn+8+uJlJxoS+sFQQDA66+/zve+9z1mzZrFggULisMnTJjAEUccwQknnMCee+7JmDFjqKqqKgYcffr4QOuiiy7ioosuanE5n//85/n85z/f4vAjjjiiePvR5hRufSoiIiK9m8IVEZFezjnHmkyeddk8NbEIg5MKVtpr1d23s+xnP6LmY1PZ8afXqAlQKzjn+OCDD5g1axYvvvhisWbK9OnTGTt2LNFolNdff51Jkybxla98hYkTJ/LRj36UQYMGAb5ZTWkzGhEREZHuoHBFRKQXc86xKp1jQy6gTyzCIAUr7bbytzex/Nqf0+eww9nxJ1dh8UR3F2mr45xj/vz5zJo1i0GDBnHooYeyevVqRowYAfgOVnfddVcOO+yw4u18d9ttN+bMmdOdxRYRERHZLIUrIiK9lHOOFekc9bmAfvEoAxJRBSvt4JxjxY3XsfLG6+n3359kh8v/D4vp32up73//+zz99NPMmjWL1atXA/CpT32KQw89lEGDBnHLLbew6667svfee1NTU1OcbubMmd1UYhEREZG20a8/EZFeKHCOFY05GvIBAxJR+if076A9nHMs/+XPWXXrzfQ/5lNs/70fYtFodxdri8vn88yePbusWU8qleKRRx4B4IknnmDDhg2ceOKJTJw4kYkTJ7LnnnsWp99UnyciIiIi2wL9mhYR6WVygWNZY5Zs4BiUjNE33vvCgM7gnGPZlT9m9d23M+CEz7Ldxd/DIpHuLlaXy2azvPnmm7z11lt89rOfBeDkk0/mgQceAKCqqoq9996b/fffvzjN448/rlpRIiIi0qMpXBER6UXS+YBljVmcg6GpOFWxnh8GdAUXBHz4o8tY8+B9DDzldIZ+8+IeHR489dRT3Hnnnbz44ou89tprpNNpAA4//HAGDx7M2WefzVFHHcU+++zD+PHjiVU0i+rJ20ZEREQEFK6IiPQadbk8KxtzRA2GVMdJ9IJaFl3B5fMs/cGlrH34Dww66xyGnP+NHhMeLFiwgBkzZvDUU08xa9Ys7r77bnbbbTfefvtt7rvvPiZOnMj555/PxIkT2WeffRg4cCDAJm9VLCIiItIbKFwREenhnHOsy+ZZk8mTjBhDquJEe0gYsKW5bJYl3/sO6/7fX6n90lcZ/MWvbNPBinMOM2PWrFmcdNJJzJ07F4DBgwezzz77FGuonHHGGZx99tnb9LqKiIiIdCWFKyIiPZhzjpXpHHW5gJpYhMG61XK7uWyGD75zIRtm/IMhF1zI4LPO6e4itdnatWt54oknmDFjBo899hhnnnkmF154ISNHjmT33Xfn/PPPZ9q0aeyxxx5lx0k8Hu/GUouIiIhs/RSuiIj0UHnnWN6QJR04+iei9I/rVsvtFaTTfPCtC6h78gmGfusSBp1yencXqVUKNVOCIOCQQw7hmWeeIQgCUqkUBx10ECNHjgSgtraWhx9+uJtLKyIiIrLtUrgiItIDZYOAZQ1Zcg5qkzFqdEegdgsaGnj/G1+h/t/PsN0l0xl44me7u0gtymazvPDCCzz22GPMapXl9AAAIABJREFUmDGDaDTKP/7xDyKRCHvttReHHnoohx12GJMnTyaZTHZ3cUVERER6DIUrIiI9TEMuYEVjFgy2r4qTjKrj2vbK123g/a99mYaXXmT7y37EgGM+3d1FKlOomQIwffp0rrrqKjZs2ICZMWHChLKOZq+//vruKqaIiIhIj6dwRUSkB1mfzbMqnSMeMYam4sQiagbUXvn163n/q+fQ8MZrDPvhT+l35FHdXSScc7zzzjvFmilPPPEEb731FoMHD2b06NF87nOfY9q0aUydOpXBgwd3d3FFREREeg2FKyIiPYBzjtWZPOuzeVJRY0gqTkT9q7Rbfu0aFp33BRrnzGbH/7uavtM+3m1lKdROKXRA+/777wMwfPhwPvGJT1BXV8fgwYM588wzOfPMM7utnCIiIiK9mcIVEZFtXOAcKxpzNOQD+sYjDEzojkAdkVu1ikVfOovMgnkM//m19Dl46hZd/vLly5k5c2axdsp3v/tdzjjjDEaNGsXkyZM57LDDmDZtGrvssov2s4iIiMhWQuGKiMg2LBc4ljVmyQaOgYkY/RLquLYjcsuXsfDcs8gu+YDh1/yamklTunyZhZopdXV1TJkyhVdeeQWAvn37csghh7D99tsDsMsuu3D//fd3eXlEREREpO0UroiIbKPS+YDljVkCB0NTMapiClY6Irt0CQu/eCa5FcsZce1NVO+7f5csp7GxkWeeeYYZM2YwY8YMRo4cyb333ktNTQ177703J554ItOmTWOfffYhHo93SRlEREREpHMpXBER2QbV5/KsaMwRCe8IlNAdgTok88H7LPriGeTXrWXEr35D9YSJnTbv0jv6fOUrX+GWW24hnU4TjUbZb7/9mDhx47Juu+22TluuiIiIiGw5CldERLYhzjnWZfOsyeRJhHcEiuqOQB2SWTCPheeeRdDQwIgbfkfVHnt2ynzfeecdrr32Wh588EHmzZv3/9l77zi5ynrx//2cMn221yS76SQhQCiBQGgBURDwUlSs4EUUQ9XLLfD7Wrj3Wi4KXi9IUVAUBSsCKopoRKSrdEJCIAnJbpLtdWannfL8/jhnZmc2m76b3SXP+8XhPOc5ZZ6Zye7Oec/n+XwIBAIsXLiQyy+/nFNPPZWTTjqJsrKyMXkshUKhUCgUCsXEouSKQqFQTBGklPRkbYZsl4ihUR00VEWgfSS7YT0tKy8Gx6H5znsILVi4T9eTUrJq1Spuvvlmfv/732MYBhdccAGJRILq6mquuuqqMRq5QqFQKBQKhWIyoeSKQqFQTAEcKelKW2RdSbmpUx7QVaWYfSSz7g1aV14MhkHzXT8iOHfePl/z+eef5z3veQ91dXV86UtfYuXKlYWEtAqFQqFQKBSKdy5KrigUCsUkx3JdOtM2tpRUBw1ipkpcu6+kX3+N1ss/hRYO0/zdHxKYOWuvrtPS0sJtt92GEIIbbriBpUuX8tBDD3HGGWcQDAbHdtAKhUKhUCgUikmLyoCoUCgUk5iM7dKesnCR1IdNJVbGgNTLL9K68mL0eJzm79+7x2JFSslTTz3FBz/4QebMmcNNN91EW1tbIXHtOeeco8SKQqFQKBQKxQGGkisKhUIxSUlaDh0ZC10TNIQDhFRFoH0m9fzfab38U+hV1TR/78cEps/Y42t89atf5cQTT2TVqlVcc801bNy4kXvuuUdN01IoFAqFQqE4gFHTghQKhWKSIaWkP+cwaDmEdEFtyFSJa8eAoWefYss1V2JOm0Hzd+7GqK3brfPa29v5zne+w3vf+16WLVvGBz/4QWpqarjwwguJRqPjPGqFQqFQKBQKxVRAyRWFQqGYRLhS0p2xSTsuMUOjKmioiIgxIPnEX9j6b1cTmD2Xpjvuxqiq2uU5L7zwAjfffDM/+9nPsCyLcDjMsmXLWLBgAQsWLNgPo1YoFApFMVJKsC2wckg7B1bWa1s5sLxtaecglwM7h9zRfisHuo4wAmCYYAa8thkAI4Dw1xim1zaL2v5xwjALbXT1t1qhUCi5olAoFJMG25V0ZSxyrqQyoBM3VUWgsSDx5z+y9bp/JXTQAppu/x56ecUuzznvvPN46KGHiMVirFy5kquuuor58+fvh9EqFIoDiazjMpi1SeQcBnM2gzmbrO2CEOR/+xfWIr8t8P/bbn/+LOH/r+QY/5rbX8+/asl2/nqFRxzlet4ekU1hpBIIJ4dm5xC2hWZlvbWTQ1hen7CzaLaFsHPeYuWY3ttN14an/f3D/cL25IiwLYSVLezDthBS7vXrLXUDjADSMBHS9SSLbXntfUGIIulSLGzMUmGTlzQlIsccVdiUihz/mlPwM4E2a5H6LKM4YFByRaFQKCYBWcelK2PhSqgNGUQMlbh2LBh85GG2ffFawosPZcatd6HH46Me19PTw09/+lMuv/xyNE3jlFNO4eSTT+biiy+mvLx8P49aoVC8k5BSknFcT6BkbQZ9kZLI2WSdYVGgC0FZQCfqJy7P75H+/yQg/d68X5D5ReaPcQvHl1xDFp1bdL3hY2TpNYvawrUJJHoID3QSGuggPNBJeLCzsA5kknv8mriagWOYBIwAjm7iGAHc4rUZww17+1xjeO0aJo7ur0ecU3zcqMfrAdBGz10mXAfNl0G6Y/ltb627FoZjoTsWpmOju17bW2x0/xzdsdGcnHeOf65w/OvkLLRUCuHkJZLlt4e336mEv/UHhK5uORUHBupfukKhUEwwKduhO2OjCWgImwRU4toxof83D9D+n58nfORSZtx8B3o0tt0xr7/+OjfffDP33nsv6XSaI444guOPP56rr756AkasUCimMlJKUrZLIucLlKKIFMsdliimJigLGDRGg5QFDcoCOvGAQcTQJuQbfiklpAZxu9uQPe24PW3InjZ/uw3Z1wluUWSHbiCq6tGqGxEHLUZUNyBilV4Ehhn0ojECQaReNKXGDIIZQOajRjQdieTJJ5/ihBNO8MZRGI+/9g1QqQTK75ejyqcdnjdiv/S3HSlxpTcl1/XbpX3eemRfTkqcUc6ThfOLzx0+b8dvgovm2AWho/nypiB4HAvNzu3pWzspOEmozzSKAwclVxQKhWKCkFIyaDn05xwCmqAuZKJrKnR2LOi7/2d0fPU/iRy7nBn/extaOFyyv6Ojg49//OOsWrWKUCjExz72Ma6++moOO+ywCRqxQqGYKkgpSVrO8FSerFMQKk7RlJWgLogHDJriIU+g+CIlpO9/iSKtHLKvE7d7G7Kn3ZMnPW3Ibm9NJlV6QrwCrboRbdbBaEvfhahu9GRKdQOiogahjUV0pUCT7gHzhYKUnhAaKVzcUURNqegZ7puKqClBigMJJVcUCoViApBS0pu1SdouEUOjOmioikBjRO9PfkTnjV8jetIKpn/jZrRgEIDBwUHWrl3LsmXLqKmpIZ1O89WvfpVLL72UmpqaCR61QqGYbLhSksgNi5PBnE0i65Cw7JIohLChEQ/ozC4PURYwiAd0yoIGwf0oDaSUkOgfFia9w+JE9rQh+7uHQ0LAy+NR1YBW04gx99CCONFqGhHVjYhgeMcPNkWQUgIuSKd0Ib8G0EBoeAlkRqzR/BwnRWvEXssC4ee80YRQN2AKxTsU9bOtUCgU+xlHSrozFhlHUmbqVARU4tqxoucHd9F1yzeJnfpupt/wTYQZYMOGDXz729/m7rvvJhwO09raSiAQ4Kmnnpro4SoUikmA7UoSueKksp5QSeYcimMFoqZGPGBQH40UpvKUBXTM/SRRZC6D7OnYbtqOt90OuUzJ8aK8GlHdiD7/cE+YVDd4EqWmERGvQuwg/8hej0/KInlhby81isWGv9TFepGJ1dv17+yc0ftHeTyckrFZgzmyvWkyvWlyfVmEoWFGTQx/ybf10M4r/8gdiZedSZndETdFAmd04TNFI3wqT1KfcRQHDEquKBQKxX7EciWdaQtbSqqDBjFTJa4dC6SU9Nx5G93fuZWyM86i8ctf5+XXXuP666/n4YcfxjAMLrjgAj772c8SCAQmeriKAxzHlWQdL/GohvdNthDeWvMrsqibkV0jXReGBpGJXuRgH+5gb6EtE33IwV5IJQABuo4UGo7QsNGwEdgILL8tNW9/UNNo0A2aDAPD1AkYBqZhEDBNhK57QkLTC4vUNHLayH6tZC103bs5Ll5vd07RebnMsDjpbkP2tiG725GDPaUvQCCEqGlEq5mGWHBkQZxo1Y2IqgZEILhnr6d0wM36S2YHbX/bGbFP2sCeV9xZVAv0to6yRwOhly4Mt6UU5AYtsr1psj1pMt0psr0pMj0psj1DZHuGyPQkyXYnyPYkyHYP4lrOKI+zPUITGLEQRjSEEQtjRoP+dhDTXxuxoN8fxIgGvHY0gBELYEYDGJEARszwhU0ATRcgXbzkL/m140cTudvvw93BvilI5YkM15lSKN7ZKLmiUCgU+4mM49KV9ioC1IdNQgfIPPPxRkpJ1y3/S+8P7yJw5vsIfu4/EIbBwMAAzz33HF/4whe47LLLaGxsnOihKt6hSCnJOp4wyTouWdslk2/72/n9GcfF3mlmS4/89AFN4C958SJKt4UoCJqdH+e3RxwrBGgjjtWKpi+UHCfyz9dfl7TldhVs8CvUlFa78U4qPWa4Yk0mVMamjh60ZB8i0YeW9JdCewAt2Yee7ENL9o9aQtc1AjjxSpxoJXa4AsdxsR0H6TgI10VIG+E6GEgi0kVHoksHTbpo0vWSt0oHHBfpOuA6OK7j9fvbJQlexwshEBW1XvTJoqXD4qS6Ea2mEWIVJRLOixyxfemRQGa6dy5IRm5LexfjMUALDi9GDLRqry3MHcgQY/v+IlHyt78/z7Jjl+O6kOtLku0eINs1QLa7j2xXL5muXrJdnWQ6e/xtrz/b3Yd0RpclRjxKqK6aYG0V0dlzqTqmilBtFUF/CdVWEqypRDouVmIIO5ny10PYiSGsZAq7pN9bpztTDG7ow87vH0qN+vijoQUDmPEoRizir6MY8ShmLIJR0h/x2/GSY814FD0aHvNoo/1BTIkVxQGEkisKhUKxH0haDj1ZG0MI6sImpkpcOyZIKem86X9Y+8Pv86uaRn7ynbv5WCLLLbfcwsknn0xLSwuhUGiih6mYYkgpsV05LEjsInHiCxJPmOSXHcuSoK4R0gVBQ6PS9PJw5BdN4CWqxEtYKUdWKfGFxeh9RccCtutu159Pnll8/kSlxNTsHIHUAMHUAMFUf9G6n0BqsNBenhpAH6Uqiqvp5MLlZCPlZCMVZJuavHWknFy+z187ZqggHXQhiAV0yvw8KPmpPFFT36c8V57IcIeFi+N4246D9NeUCBlvLd2R/Y7f7+93HIQuEFVViPIyhOaUShAnA24HZFoglUGWCJIsxVNhRkUEigRJCIyK0m09CCLorbVQiUwRYvdvG1zbJtvTT7azh0xXN9muPl+U9BZJkz56N7WydShDrnfAew1Gwawo8+VIJfG5TdQcu8SXJ5W+LKkqWevB/ROdKF0XO5XeqZCxk0OF7e2OG0iQ3toxvD85hJt755Vk/rC9xoveUigOAJRcUSgUinFESkl/zmHQcgjpgpqQia7C/ccE6br8/rJPc+svfsmfBoeQbOKcc87hggsuALxv9JVYUeTJT8XZXo74ESUlssTdYdlUUxOeHDG8/Bs1ukbQlyehInESNDQC2t4nvxwvhiuWFAkXX9BsL2JGbLsWXkJPP7GndBGpAS+qJNGP8KNMRMJfkv3+ug+RTo4+nmg5xCu9ZVozbck00xYshngloqwSEa+CeBV6NE5Q04iTnzaFN4ZCe3jiwXi85lK6IC0/MsT2c3z42/nFtQELgQ2aDcIGYXlt/xgx6jUcb+3aFORIDugabSQCtCCuNJGuieuaSDeKdMtxHd3bdnRcV0c6Gq5r4Dqa13YEOC6uZePaDtKyvLVt4VoppO3gWjbStrc7prh/5HHSdsgNJIrESR+53v7RX0ghCFaVF6JIjOYGZhx8UKkk8cVJqLaKYE0lmmmO+fs5FghNw4xFMWNRxir9r5PLjS5i8pEyU7Bi0GT7HahQjCdKrigUCsU44UpJT8Ym5bjEDI2q4M6T5Cl2D8uyMDSN9v/+Anfffz/PZm0+9y//wpVXXsmsWbMmeniK/YjtStK2Q9oefSpOpkie7GgqjiYoSJGQoVHhV3kJGnlRIvzoE69vqlf1Gq5YghdFIVNIJwWZfmSyB4b6kKl+ZGoAUglkKolMD0EmhcxkkUNZZMpbk86OHgoTMBDRsLdUhRHNTV47FvWWaARiUUQ0itANLw+Jn7Az3tlFpL4SL4nnIJAEdwskipJ6FtZi1G1ZchzbC5CRUiQvNuTIpah/L/KJeM/BGF40s6gdABH1217f4FtdrL31j3Q9+yau7SJt1xMbtossyA4H17Yn5CZbaBrCNNAMw1/raKaBMAzMeJRgbRUVhy0omn5TNSxJ8kt1BZoxfPvx+OOPc8yKFfv9uUxW9EAAvSpAsKpiooeiUCj2AiVXFAqFYhywXUlXxiLnSioDOnFTVQTaV9rb27nnnnv4yEc+wvdPP5U5r7zAl6+7jh+uvJJ4PD7Rw1OMIVJKcq7ENoK0JbOkbbcgUYrb1ijCREBR9IggauqF7ZChlUzLCRkCXUy+6JK9QTq2J0US3cihXhjqLZIkg8hUEtJDyPQQMpOGdAaZtZCZHGR342Y9GESEw4hYDK26BpqjRbIk3454EsU0GE7A6Y5YF/Xjerk+8vukSzyYgszW4f3FST0L5+2rWNCKpIY5QoCERu8XZkGCFLaLxEhJX76f3fu93/OPV1lzw120Pvgn9FCQae89CT0aRjN0hGH4AkNHM02vzxccxf2iSHQM948UITs6zu/f2TGGMSXzfSgUCsX+RMkVhUKhGGNyjktnxsKVUBsyiBhqrvHeIqXkmWee4dZbb+VXv/oVlmWxYlYz2Wefpva6z1N98acneoiKPcSVkoztkrJdMr4kSdkOmRHixJVA3Vye3jZQODeka4QNjZipUxsOEDa97ZChE57EU3F2BymlFx2STiLTSUglcVMDXiTJUN+wJEknfFGSQmZSkMkiMxnYVSUUXYNQEBEKIUIhiNeg1UURkThE4ohIOSJSAdEqvx1HRGKIcAzCUYQ2tr/HCvllXOlNO3K95Yknn+K45cv9Pm+/4+933PxxLq7rIl0XR3o5TqT0tqXr4Ba2HU+2hYJEgkHC4TDRUIhwKDDh/0aklLSveoY1N9xJx2PPYVaUsfjzK1lw9UWEaqsmdGwKhUKh2DuUXFEoFIoxJGU7dGdsNAENYZOAqgi0V0gpEUKQTqc5++yzkVKy8qKLOK1tMwdta6Xu3/8fVR+9aKKHqRiB5bqkLXeHkSZpP6/JSDQBYUMnbGhUhcxCe+O6tSxdcpgvUKbelJxC9RZpI7NJZPtm3La3cdtbcTu2QKIPmU4h0ynIZHYjesREBA0IBRChIFplHML1iHDUkyB5SRKtgGglIlKFiNVApBItuGf5h1xX0jWQpmVLks0d3bR2JtnaPUTWckuFiCySHnkhUixDiveXnLfjx/7WqlV7NNY9RRMQDZlEwwaxkEk0bBIrbvv7oqER/X47HDTQ9jIpues4bHnwT6y54U56X3id8LQ6jrjpWuZdegFmPDbGz1ShUCgU+xMlVxQKhWIMkFKSsBz6cg4BTVAbMjFURaA9ZsOGDdx+++0899xzPPXUU0QiEX5zzz1M+/vTWI/+Dgk0fOG/qHj/hyZ6qAcUXqlhd1RZUtweLa9JQBOEDY2woVMZNAgZGhFTJ+T3hXcSbbI1k6A6PH7JLAtJSgt5OPLJRvM5OXZje8Q+mcsge/pwu3txu/qRPQncngSyv6hsq64hqmKIWAitLIQIlkE4jAhFEZEohMsQkTJPkkQqEbEqCFcizCjoERBjF3nhOC5tvSlaOpNs7kjSml93JcnkhqNhyqMBZtRGiYdNNM0v06x5i17U9vq9/XpRX6FdvJ0/ttAWbNywgQUHzS+5lr7DxxnZ511LL9ovJaSyNsm0xVDaYihjk8x47WRmuH9rd6rQTud2HgUkBERDnnzJC5i8lIkVpIzXjoRMYiGDiCZJ/PZRttz6I4bWbyY+fxbLvvcVZn38nP1W3UahUCgU44uSKwqFQrEPeAk1XYZsh6wriega1SFjyn3DPpG4rsujjz7KrbfeyiOPPIKu65x//vn0vPUm9i9/Su1Dv8IWUPH+D/HmwkNYdO55Ez3kdwz53CYZ26uWk3GKp+d44iS/PVKbCPAFiUZZwKA+ohUkStgYbuvjKBmlkwarF6w+cFJFiUq3lx6jbu9NklI/r4Z0dWT/EG5PErd7ANndj9vVi+ztH45A0TREdQ3ajPloR01DNMxAa2hCVE9DmPlSt2G/zO34/s7I2Q5bu4ZKJUpnkq1dQ1hF0UQ15SGa62KccXQTzfUxmuu8pSIWHNfx5XncbWHF8bP2y2PtCMdxGcraDKU9+TKajElm7GFZk7bo6E2xIe2Jm1TGLlzLyGVY8PKzLH7+r0SGBumun8Fr//QJug89gmhHkOhtz/lixiAW9iJkyqMBGirDNFRFaKiKUBUP7nWkjEKhUCj2H0quKBQKxR5iuZJUvkKJ/029KQQVAZ0ylbh2j/nNb37DeeedR0NDA1/60pe4+PzzCP7+1/R8/ANICRXnfYDqT16K2dDIG48/PtHDnRI4riTjlxculialfd56tNkZuhBE/Kk4NUW5TYrFSUjX9su/dSldsAc8gWL1gd03LFTczIijtRFVWczhthEu2meOSFJanIi0aL9mIqVA9vTgdmxFtrfgtm/GbduE7NwCjn8TLTRE7TS06YvRls5Ca5yJ1jALUTcDYezfMrLprE1rZ9KTKP66pSNJW89QYSqOENBQGaG5PsbRC2qZWR+jqS5Gc22M6DhGCk0VdF2jLBKgLLJ3ESWOK+lr7WTdt3/Elh/+HGcgQWjZUQQuvIDgoYdRkbEZylgk08NRNJ39Gd5uTxT6izENjfqCbAnTUBkpiJeGqjDxsKn+7igUCsUkQMkVhUKh2AVSSiwpSflJOPMVSgKaJ1QihoapqijsNi+//DK33XYbixYt4pprruGss87iF7/4BWctP47EfffQ/+kLSbsu5f90HjWXrMScNn2ihzwp2FGUSYk08dejVdEBr4pO2K+YU+ZP0QkV9eUjUYwJ+Pcs3eywQLH6wO5l6fQt0PIaJREmWgTMSojMw9Yq6BoKs7nHoC8dIBYJEg0ZxMP+tAw/V8buRM9I10X2tuO2bcZt3+QJlLZNuB0tYFuF40R1A1rjbLTFx6I1zkY0zkSrb0aY+3dqx2Aq50mUjlKJ0tmfLhyja4LpNVFmN8Y5eUkjM+tiNNfHmFEbI2iqRNvjwdDmray96W42fP9+nEyWpvPezaJrP03NMYft9jWylkNHX5r23pS/pGnv89rrWvpJpK2S4yMhwxcuowuYUEC91wqFQrE/UHJFoVAoRiF/I5sXKrYf5h/UBJUBnbChY6ow7d0ml8vxwAMPcOutt/L0008TDof513/9VwDE4AAntWyg9ZtfQdo25WefS/WnLyMwfcYEj3r/sO9RJhAyvBwm+ek5eVFSLE8C+sQnhJVSgpMokii9w9EozlDRkRoY5aStINGaQ7Aopy0RYmO3zsb2HJs7ErR0Jmnvbd9pYtQ8kZDh58MwiYd0GowUTU43DVYXNZlOKpJtxBLb0O3c8FjLa9EaZ2IuOBLROAutcZYnUYLhsX9hdoCUkr5ktiQXSl6i9CWzheMChkZTXYzFsyp5b32TJ1HqYkyriWKopNr7hf7Vb7LmG99j808eRmgasy78Jxb9+yWUL5y7x9cKmnphOtZoDKUt2vtStPV6AqbDFzBbu4d44c0uslbpdLeKWMATLUUCptEXL7UVIfVvRKFQKMYIJVcUCoXCR0pJtiBUHBz/pi2kC8oMg7CuqSS1e8kll1zCvffey9y5c/nmN7/JxRdfTFxKOv/vRvp+fh8yl6PsrH+i5lOXEWieOdHDHTNcKelOW4XcJWMRZVK8nqgok10hXQvs/lKBkpcosmjKgxYEoxJCzWBWkXbL2DpgsqFDsLkzxUtvtJC0cnT2txROMXQvGmPe9HJOPWI6zXUxZtZ701kKeTFSFsl0jlxfN3pnK6G+TUQGtlHR2UZ1uoOQOywmeonytlZNq1hMi1FNq1ZNq6gmlQ3CJjC3aMTDOrFwO7Fwt5+otGgJmcQi+bZBPDLcHwkZuzVdI1+Zp1ietHQmaelMlEwRiYQMmutiHLOwlib/eTfXxamrDI9rbhvFjul65kXW3HAnW3/7F4xohIOuvpCF//LPRJsax+0xo2GTueFy5k4r326flJL+ZG446sWPgGnrTfFGax9PvNaGW/Q7RxNQWxEu5Hip9yNfGn0ZU6nyvSgUCsVuo+SKQqE4oJFSknH8HCqOWxAqYV0j4t+86mou+x4hpeTJJ5/ktttu44YbbmD27NlcffXVfPSjH+X0009HDg7Q86O72fCz+5CZNGVnvo+aT19GYObsiR76mOFKSWsiy9qeIZLWcOWRkVEmdZEiYVIkTYKTIMpkV0gpwU1tL1CsPnAGSw82ynyJMgPMSpJ2jNZekw2d9vC0lo5+ehMdhVMChkZlBBbPqeTM+iaa6+PMrIvRWB0p+aZdSgnJftz2jVS25Usde3lRSCWGxxAt96JPGo9Aa5iJNm02WsNMAsEYsYzFzLRFIm15CUzziUxHWXoTWVo7hwqJTndWPVkTFFWR8SVMPnFpyKQvmS2IlKxVWpmnuS7GiiXTfIkSp7kuRnXZ+Ce+VewaKSVtf3iC1//nTrqefJ5gdQWH/tdVHHTFxwhWV07o2IQQVMaDVMaDLJq5/Vgcx6VrIFMiXvJTj/6xroveRLbk+InM9yLzJb3zJb6LSnk7cmR5b0r7i/YP91HSJxiueiXEKG2/WpUQXjUzXXjtnZ0jxHD1q5Ft3b+TCy3QAAAgAElEQVSO8K+jUCjeeSi5olAoDjiklKQdb7pP2nZx8SqfFAuVyX5jOxkZGhrivvvu49Zbb+W1116joqKCCy+8kNmzZ3P00UfjDA7Qc8e36fvJPbjpNPH3vJeaS68gOGfPw+YnK1JKWoqkSnnAYFljGRV+5MlUzM0jpeMnlB0hUKxekMPTaBAGmFUQbATzYDAq6M/F2NSjs6k1XSRR2hhMDeeMCAd0muvjLF1QW5gKMbPei8Z48om/smLFEd44HAfZ04a75lVyHa3IjhbcjlbcztZSiRKOoTXOxDjiZE+i+FN6RHz0m14TqIwFqdyLajiuK0ln7SIp4y2JtMVQ2usfylgkUl7/UMaieyBNMuUlMs1LlDOXNZVIlPKoKs07GXFtm5Zf/oE1N9xJ/6vriDQ1ctTNn2fuJR/AiEYmeni7ha5rBTkyGnub7yUSMraTGq6UOCPkR6HPb2ezOW75y6O4LkXHeuudicupTrHA2ZmQGSlvpiI/vPYUFVmnOGBQckWhUBwQuNIrmZxyhsvKCiBieEIlNAUiBSYz6XSa2bNn09XVxeGHH873vvc9PvKRjxCJRHASg/Tedw99992Dm0wSf/cZ1HzmCoJz50/0sMcM6UeqrCmSKsc1ljEtNnUiDaSUnkTJdUCua1ig2ANQnO1Fj3kJZaMLwaxEGpV0p8Js6pIliVU3d24qKUkbD5s018c4/pAGTyL4ZX5ry0Mlr5FMJ3E7W3A3tDB99VNk1j+O29GC7N42XJ0HEGVViLomT6LUNyPqmz2JUl69315zTRNE/fK5incuTibLxh8+wNobv09yYytli+Zy7A9vYOZHzkIPvLNE2N7me8nkbExTK4gBTfMWPd/2o0BG9rW1baOpaQaaEOgapef7a734/O36Sq+vb9cnRvRROB88oSMlBakzWlvK4agXKb2oFym935lOyXGUiKGStvTbblF7xHVHtouvn3/cqcjU+AuoUIwNSq4oFIp3LE5eqNguacdL8KcJiBoaEUMnpIspc+M72XAch0ceeYQnnniCb3zjG4TDYa6//noOP/xwli9fjhACJ5mk+87b6b33h7iJQWKnvpuaz1xJ6KAFEz38MSMvVdb2DJGwHMoD+pSRKtJJQbYDcu3+uqOotLEOZgUEaiAyH8wqXKOCjsEgm7qyI3KDtJdMaamMBWmuj/GuI6b7OUG8iIyKWKDwmkjXRfZ34batxn65FbejBbdzC7KjFTnYU7hWg9Bw62ag1TehHbrcEyj1TWh1TYjI6Dd/CsVYkRtI8NYdP2Xd/91DpqOb6mVLOPJ/r2P6+05FTMEotLFgZ/le9obHH+9hxYrFY3IthUKhmGiUXFEoFO8oHFeScryEtBk/gYouIG56JZODmhIq+0JPTw933303d9xxB2+//TbTpk3juuuuo6qqiiuuuAIAZyhJ38/upffHP8AdGCC24l3UrLyS0IJFEzz6sWM0qXJsYxnTJ6lUkW7Oi0bJtnsSJdvuVe0BQIBZDZG5EKiHQD0tfUE2d6b8CJQELR2dbOl+G8serkJSUx5iZn2MM5c1FyRKc12MsqIpLTKXxe3agnzrNayOVtz8dJ7OLWAV5XYIx9Dqm9EXLR0WKPXNPLl6HSef+q799CopFB7p9i7W/d89vHXHT7EGkzSefgIHX3cpdScfMyl/vhUKhUIxOVByRaFQTHls10tIm7Jdsn4VBEMIynyhElBCZUxYtWoV73vf+8hkMpx00kl8/etf59xzz8U0vSkRbjpF389/Qu8938Pp7yd64snUrryK0MGHTPDIx46RUqVsEkoVKR2werxolLxMsXopTO3RyyDYAMHDfZlSh9C893Bj2yC3PPAaazb3A15egPrKMDPrvJwo+eo0TXVRoiHTfzwJiX4v+uTlVrIdLci8SOnroJA4QQhEVT1afTPG/MMLAkWrb4JYxaivn9TWj/vrpVDkSWxoYe1N32fjDx5AWjZNHzyDg6/9NFVHHDzRQ1MoFArFFEDJFYVCMSWxXJfyhmm0pXLkfKFiaoLygE5E1zCVUNlnstks999/P+Xl5Zx99tkcffTRfPKTn2TlypUceuihhePcdJq+X/6U3h9+D6evl+jyE6m57CrChxw2gaMfWwpSpXeIRG7ySJWSPCl5kZLrBOlP09FCnkCJzIOgF5Ui9O0TWaazNj/+05s88NQm4mGTK85ZzOJZlcyojREK6N5jOTayextux+u461rJdvrTeTpaIZ0cvlgghFY3A232IrRlpxckiqidjgjsedJYhWK86Xt5LWu+fhctv3gEYejM+efzWfTvlxCf984pC69QKBSK8Wef5YoQIgQ8AQT9690vpbx+X6+rUCgUxUgpsVxJyk9Ka7mSqhneB9+KgBehMhUrsUxGWltb+e53v8tdd91FZ2cn5513HmeffTbl5eXcdttthePcTIb+X/2cnh/chdPTTeTY5dSuvIrwkiMmcPRjy2STKtIZGs6PMjJPijAgUAexw3yR0gBG2U7HKaXkmdc7uO3Xr9M9kOG9xzRxybtnE+1txd2yDvlCC5l8FEr3NnCHc6uI8mpEfTPGUad48qSuCa2+CVFRe8Dmo1BMHaSUdD35PK/fcCdtjzyBEY+y8N8+ycLPfYJwY91ED0+hUCgUU5CxiFzJAqdKKZNCCBN4SgjxiJTyuTG4tkKhOICRUpLLCxXbwfZnFwQ1QWXA4JV//I0Tj18+sYOcgkg3B0NrIfGa1xGe4+fbqOOLX/wiN9xwA67rcvbZZ3PllVdy2mmnlZzvZrP0P/ALeu++E7u7i8gxx1Jz481EjjhqAp7N+CClZEsiy5oJlCpenpTO0qSzo+ZJafBkilmNELsvNTr6Utz20Ou8uGYrKyr6+ciSFFVbV+H+5xtkHL/cqm4gaqejNc5EO/zEgkDR6poQ4ejYP2mFYpyRrsvWh//CmhvuovvZlwjWVrHkq//C/Ms/SqCibKKHp1AoFIopzD7LFSmlBPLxwKa/TM1aYQqFYlJguS4Jy8uh4uekJaQLygxvyk+hhKKVm8BRTj2kPQiJVyCxGmQOAvUkUzY/vvv/+ND7Dqequp4lcw2u+eynWHnFvzFnzryS891cjoGHfkXP97+D3dlB+MilNP7PTUSXLpugZzT2TJRUkdKBXE/p9J7iPClGGQQbR82TsqdYqSGe+O1jtP3jOc5xtvDvbjtahwOdGjTNxzz5XLQ5h6A1zERUNyJ0feyeqEIxQbiWxaaf/o61X7+LgTXric6aztLbvsSci9+PEQ5N9PAUCoVC8Q5AyDGomS6E0IEXgHnAbVLKa0c55lLgUoD6+vqjfvazn+3z4+6KZDJJLKZKNR5IqPd8aqMZBpXTmojX1CGB9EAfQ329pAf6cB1nu+PV+707SMpDQ0wv66YmMgBAZ7KcR//Wy4O/fYJVq1YxNDTEV754BZd++Cgqwwl0TWI7Gj3pMrqHyulLhAn87e/EVj2C3tdLbvZckme8j9z8BV7G0/3IeL3nEsiFykjFa3HMILqVIZLoIpBJMPbPUBIycpQFU8T9JRZIo2ve3+Oco5PIRkoWy93770I0K0usZwtl3ZsJtG+ifLAdAxcHQbK8kVT9TBI1M0lUN+GakysnivoZP/AY6/fczeRI/f4phn7xJ5yOXow504l95AzCpxylxOEkQP2MH3hM1vf8lFNOeUFKuXSix6GY2oyJXClcTIgK4EHgKinl6h0dt3TpUvn888+P2ePuiMcff5wVK1aM++MoJg/qPZ+auFIyaDkM5hwkEDM0KgJGIUJlR6j3e8dIacPQmzD4MlhdXmLT2CGk9HkcfewprF27lmAwyAc+8AGuvPJKli1bhhAC6VqQaYXUBmTiLQYfe5WeX7yA1ZkgdPB8ai7/LNHl75qwJK5j/Z5LKdmS9Kr/DPqRKouqo8wYw0iV0jwpflSK65chzudJCdR7FXwC9bvMk7LLx0sP4Wxcjbv+VZz1r+C2vgmuiyM03qKetyOzWXDyiRyy4nhEaPvktpMJ9TN+4LGz91xKiZ0YItc/SK5vsLC2RmwX9w+s2UCut5/aE47i4OsuZdqZJ6tk55OI8f4Zl1Yahrq8JdWFLLR7ABc0E3QTNMNfirdN0Ev7RUm/OeoxhbZmjLiW1xbagS31JuvvdSGEkiuKfWZMqwVJKfuFEI8DZwA7lCsKhUIB3gflpO0ykLNxJIR1jcqgrhLT7gPSHoLkq14+FTfNYCbCrx4bZOPWDr7ylc8QBU4//XSuueYaPvCBD1BRUVFyvtBMZKCZwVWv0H3Xg1hbWgktmEnd5acTXVKDEK9DRy/Sz9MizMqJeaL7yGhSZVlj2T5LFSldyHVDrg0ybd7aHvT35vOkzPdlyp7nSRn1MVNJX6a84suU9SBd0A205gW0HHwmP9sc4RW7nvedvICPnjaPcEAVC1RMDE4uh9WfINc3QK5obfnrgVdW87ef/Jlc38CI4zxZIl13xxcXArM8TqAiTqCynEBFnGlnnsS8z3yIuhPUPdM7ESldSPcWBIr0JUpBqOSSpSeYUYjWQNk00HRwLHBtb3EssFLg2OBaw33u8PZYfCUtEb5oGSlgjNH7iwXNFEQs+dg+/51TKKYKY1EtqBawfLESBk4Dvr7PI1MoFO9YpJSkHZf+nIPlSgKaoCZkENLVH9+9RWY7IPEyDL2JbVus+scg9z70Mg/+5o+k02kWLVrEl770nwQCAb71rW+Nfg3HYfAPv6P7ztuwWjYTXHgwM26+g+iJK7wDrB5IbYD0Ruh/GvqfRppVRQlx6yf9N8IjpUp8H6WKdNJeNEq2zVtyHSDzyWCjXp6U+BIv6Wygdq/zpJQ8ZiqBs+E1nLdewV3/Ku7WDb5MMdFmLcJ8z0fR5y+hJdzEt3/7JqvX93HIrEq+df6hzG6I7/PjKw5sCtEjI+TI9jKkVJrkt51UeucPYBpsra4sCJJgXTXxBbMJVJQRqCwrrM38ti9RApXlGPEomprq845DWqlhWTLUhSyWJ6me4dLzAEKHSDVEa2HaUkS01mtHayFSgwjsfSJuKeWwiHGtIgmTb+f7rSI5M/IYC7mD/hKZ42Q9MVQsflxrDF7NCWDJxyZ6BArFfmMsvrpqBO7x865owC+klA+PwXUVCsU7kKzj0pezyToSQ3hSJaJrk/6mfDIipevJjsRLkG1DYiDih/GtO//Ktf/ff1FRUcEnPvEJLrroIo499tgdvsbScUj88RG677qd3NsbCR60kOn/exuxFaeWnhOo8ZaKZV5y3NRGSG+AwRdg8HnQo4WIFkIz8P4sTA7GQqpIKb0ks3mRkm0Du8/fKyBQC7GD/cSzjaDHx+TftRwaLJIpr+Bu2whSgmGizToY8/SPoc9fgjZzESIQJJ2z+fGq9dz/xN+JhAyu+eBhvOeoGWi7mGanUIxGcmMr7aueoX3VM3Q++QLZzp49jh4pWzAbs2g7UFm+3XZ+/eRzz07KKQOK8UO6DqT7vIiTZGepPBnqAmuo9AQz6smSipkwfSkiUiRQwlXjNu1GCD/iRDeB8N5fZ+yGpFAoJhljUS3oVeCIMRiLQqF4B2O5kv6cTcp20QRUBQxippIqe4N0MpBcDYlXaG/bxk8eXsO9D77E//vC9XzwgpP56MfnMu+gQzjrrLMIBnecoFS6Lok//5Hu79xKbuN6gvPmM+3Gm4mf+m7ELqZmCaMMyg6HssO98aTf9iJahtZC8jUQAWR4lidawjMR2sQkSpVSsjWZZU2xVGkoY0Z811JFulk/KqUoMkX6Faq0sJcjJbbIEymB+jGJSgGQyQGcDa8OR6Zs2+jtMANosxdjnnGRL1MWIsxAybnPrung9l+/TkdfmtOPnsGnzlxEeTQwyqMoFKOT6e6l47HnaF/1LO2rnmHo7S0AhKfX0/Du5USbG3cqScyy2C5/fygOPGQuVTJdJz995+iBFuRv7ttx9EnlrFJ5EqlFBCZ3riiFQnHgoiZdKxSKccWRkoGcQ8JyEEC5qVMW0NGUVNljZK4HEi/jDK7hlw8/z48feo0//vVVHMfhmGOOIRorB2DGjBnMmDFjp9dyEgm2fPYy0i89T2D2XKZ9/VvETzt9r26KhB7yJENskRfunGnxREtqI6TeBDRkqMkXLXMQxt6HZe8ueypVpJRg95dGpVg9wweY1RA9CILTPKliVIxdwttEH876V/3lFWTbJv8xg2hzFmOe9c/o85agNS/YTqbk6exLc/tvXueZ1zuYWR/jm5cdx6Gzq8ZkfIp3NnYqTddTL/jRKc/S99IaAMyyGPWnLGPRv36ShtOOI37QbCXDFTtEuvZOcp90bx99EohBtJaEXk149kml03dClQd80leFQjE1UXJFoVCMC66UJCyHgaIKQOUBA0NNTdgjpJSQ2YTb/yIb33yJebMa0OILuf6W/yOTtbj22mu58MILWbhw4W5f00kkaL3iU2TWvk7Dl75C+T+dN2YlSYVmQGQOROYgq1xPUqQ3etOXeh8DHkMGGvxj5iLMsRUAo0mVYxrKaBohVaRr+dV78jKlHVw/F4QIeNEokfmeSAk2jGnkjTvY6yef9aJTZEeLtyMQQp+zGO2oU32ZchDC2Hk0jO24PPTUJn70pzeRUnLJexdy/omzMQ0VOaAYHddx6HvxdS8y5U9P0/X0i7g5C800qVl+BId9+bM0nLacqqWHoBnqY+KBgieYM2ClwU5BLgV22tu2UmClvdwn+f1F/VhpyA54uZ/yCB0iNX70yRxPnhQiUGoQphd98sbjj9OweMXEPGmFQqEYY9RfTYVCMaZIKRmyXfqLKgBVBHQCKlntHiHdHCTX8uYrf+DHv3yMex98gYFEjm1bNxGOVvHnxx5n+vTpaHsYaVIsVqZ/42bip7xrnJ4BXnWA0HQITUdWnODlK0lv8ERL/zPQ/wzSqCyIFgINe/3N+M6kCgBOAlkclZLrBvwbAaMSwrOHc6WYVWP6Db070FMqUzpbvR3BMPqcQ9COeQ/6vMM8maLv/p/l1zf1cssDq3m7PcGyRXVccc5iGqpUuLyiFCklifWb6fAjU9ofew6r36tgVbFkIQdddSENpx1H3YlLMaLq389UReYr3RSWtCdHfElSECPF6xHyhF3VwhE6mBEww/4SgVAFGGEIlSOidUW5TypVhRiFQnHAoeSKQqEYM9K2l6y2UAEoaBBS36DvEdIagMQrPPHnh7j2aw/wt5c3o2kap532Li666BPoZgyApqamPb72sFhZM+5iZSRCCAhUe0v5MUg7MRzRMviSlxRXj4xIiLvrP1F5qbK2Z4iBvFSpj9IUHIDcZuj2ZYrjh6QLw6vcU3aUL1MaEPreJybcFfbzfyZ779fBdSEU8WTKcWd4kSkz5u9VxNBgKsf3f/8Gj/y9lZryENdfdBTLF0/+Sk3jSe9La0hubCU+byaxuU2YsfGfejaZyXT20P7nZwtTfVIt2wCINE+j+f3vof605TSceiyhuuoJHqliJDKbhMEt20kSmUuNHjGSlySuvYsri1IpYoQhXA1l4YIwEfl+M1wkUSLDbc08oH/PKBQKxa5QckWhUOwzOb8CUMaRGAJqggYRQyWr3V2klOQSb/P7B77P/Okuiw+aRiDWxJAV5sYbb+SjH/0o06ZN26fHcBKDtF7+KTJvrGX6jTcTX3HqGI1+7xBG3CtRHF/iJY5Nv+3laBla5yXrFWZRQtxZ203LKZYq2VyS6WYvx5T1UyY7ob8L8JMjGmUQnAGhfFRKzX77NtV56xWy992ENucQAud+Bm36vH2afiWlZNWLW7nz4bUk0hYfOGk2F777IMLBA/dPedezL7H6v2+j7Q9PlvSHGmqJz59JfF4z8Xkzic+fSWyet23GYxM02vHDHkrR+cQ/Cklo+19dB4BZUUbDqcey+LpP0/Du44nNbVa/lychMt0HbS8it70I3etKp9fk0YOlUSPBOMTqwCgSI8VSxAhDIFLYjxFUkSQKhUIxzhy4n8gUCsU+Y/sVgIZsFw2oDOjETV19eN9NXNfiH0/8ih/dczc///Uz9PQN8bnLPsT/3vL/OLY5xqtnfG5MXsvJJlZGIrQgRBdCdCFS2pBp9ZPhboDUW3gJcWdAZA4BI0dnXys9A61E3E6O17oJB/2olKwOwTooW1KY4iP0iYlgcNs3k/ne9YiaRkKf+i9EJL5P12vpSHDLg6t5dWMvB8+s4OrzD2VOY9kYjXbq0fnEP3jtv2+j48/PEqypZMn//CsNpx3H0NtbSLy1mcT6zSTWt7DtkSfJtD9Qcm6ovsaTLvNnESuSL/F5MzHLpoZ4cW2b3udXFyJTup95Cdey0AImtSccxZKvXUPDacdReeRitDHKp6QYW2SyA7a9iNz2AvT5FcHijXDQmYiaBV7CVzM8LE9UgleFQqGY9Ci5olAo9hhHSgZzDoN+BaAyU6dcVQDabaQ9hEy8wonvvohnXthAKGRy7lmnceHFl/Oe089AjGESSScxSOtll5BZ9wbTb7qZ+MmTS6yMRAjDy38Sno2sOsVLNFtIiPs4xzUBg2upFWAbEYzQtOGolEDtbk0lGm/kYB+Z73weDIPQyq/tk1jJ5Bx++thb/PKvGwkHDD73/kM54+gmtAMwMbSUko7HnmP1l2+n869/J1RfwxE3Xcv8lR8u5AqpXnrodudZySGS61sKwiXx1iaS61to++NTpH/YWXJssLaqIFqKo13i82cRKN83QbYvSCkZXLeR9lXP0rHqGTr+8jeswSQIQeURB7PgXz5Bw2nLqT3+SIzI+E1z299IKbFdb3EcieW6O247EseV2K6LVdR2XDB0gaEJTF3D0ASGrmFqwu/XMHSB6a8Lx/n79DH6WZNSwuAWT6Zse9Gb+gNQMRNx8PnQeCSibN8iFBUKhUIxsUz8p1CFQjFlkEUVgFwgamhUqApAu8Xg4CC//On3+cuq3/KjG89FCMkF553CJz/1Gd7/4U9TUVEx5o851cTKSLyEuNNIavW8mjqERK6LBrmFaVW11JQ3YxjxSRclJXMZMnd9EZnsJ3TVTWjVDXt9rb+v7eTWX6+mvTfNaUdN59NnLaIyNnZVi6YKUkraHn2S1V++ne5nXiI8rY6jbv48cz99AUY4tMvzzViUysMXUXn4ou322UMpEhtaPPny1iZPvqzfTPufn+XtHz1UcmywptKTLfOHhUt+2lGgsnzMnm+edFunnzfFm+qT3toBQGxOEzM/fJaXhPaUZYRqxrfktislGcshbTmkcw6WLzRGSowdtW1nWJCUbo/eTmcqWPWb1Z44cXeRYHU/oAnQRxUz2ghh48kYs1jYCEmVtZWG5BpqEq8TzvUgEQzFZ5OceS7p6iUQqfbOdTXMRKZwjaChY+pi0v2OUygUCsWOUXJFoVDsEiklKT9ZrSMhpAsqA4aqALQLbNvmT3/6Iz/+wR08+NtHyWQs5s+uoz11JY3zT+Wznx97oZLHSQzSuvKTZN5cNyXFCoDlurzRk+Kt/hQCwcKqabS/to0lBx0y0UMbFek6ZH/0P7gt6wh+6r/QZ+5+eexiuvrT3PHbNTz1WjtNdVFu/MyxLJl74CUelVKSeeYVHv2PW+n9x2tEmho5+vbrmXPx+9FDYyOZjGiEysMWUnnY9u+VnUqT3NBSEC6JtzaTXL+Zzsf/zqYf/7rk2GB1RUmUS/F0o2DV7v2cW4kknU88T/ufnqZ91bMMvP5W4dr17zqOhtOW0/Cu44jN2fNk1iMFSSq/zvl9/nbaKurz1xnL2VUNmVERMBwZMiJKxNC8dsDQiGiiRF50tCeY2VSHUdSva8MRJXva1gTDcsdxsfy17cjhdpEUym9bzrD0sYpFUGG/W9jOWMP7XMdmmtPCQc5bLJLrKWMIW2psoInVHM4a5pAcjMIgsGkAGNjpaxgwNAK6RtDwXi9vrXvrov7CPl0jaOhFx27fP1bROJMBKSWupOT9GPkeW467/fvqSgK6RtjUiQR0wgGdiKkTCRhKaikUir1GyRWFQrFT0n5Z5ZxfAag6aBBWFYB2im3b6MLitz+/lfM//h9Ulkf45wtO4KKLPsGyFR9C03f9bfu+4AwO0HrZJWTfWseMb95C7KRTxvXxxhopJZsHM6zuHiLjuMwsC3FITZSwodOxV7d5+4fcQ9/FefVpAu+/AuPQ5Xt8vuO4/PqZzdzz6DocV3LxGQv4wElzMA+wnzfpumx5aBWrv3IHfS+tITp7Bsfc9RVmX3QOeiCw38ZhRMJUHLqAikMXbLfPTmdIbmwtCJe8fOl88gU2/eRhkMP/TgOV5f4Uo9L8LtHZM0iu31yITOl+7hWkbaOHgtSeuJTZF51Dw2nLqTx8EULTCoKkO5kdXYbsgyDRNTF8k2nqxIMGdfEgEdO76QwXrUdGauyovbc38I8/voEVh43t9BhjnNOVSMeCzte9KT9tL4M9BHoA6g+FxiOh9lBm6iGmOZJTi0RNiRAokjk5x/UW2yVrF68dso5LKmfTn/L7HG+9J1E+hiZKpIvX1keImN2TOUHdO08IX3AUPbfh51csqLznmt/3VjpE5vX2IpFVKrBGlVxFbctxx/yvgq4JX7QMS5dwwCj8fEQC+Z8VY8Qxanq0QnGgo+SKQqEYFa8CkEPGcdEFVAcNoqoC0A7p6Ojgvvvu40f3/IDzz1zKFy9bynuP1rj/+//GWed9kmDFgv1SqaFYrEy/aeqJle60xSudCfqyNlUhg+XTyqkKmxM9rF1i/fVB7McfwFhxPubJ5+3x+Wtb+rjlgdVs2DbI0QtqufLcQ2isjozDSCcvruPQev+jrP7KHQysfpPYvJlUXPsJzvjyv6OZk+vfgBEOUbF4PhWL52+3z8lkPfEyIsdL9zMvsfmnvysRLwAIQeDQhUQ++WHk0UeSW7yILULnLcsh3WeT+uM60pZD1tr5TeSeCJJIwCjpV9/U7znSSkPHa55Q6XgV7KyXgLbhcMS0I6HuEIThRVgF/GU8sV1PwpQIGae4zymImCcXq70AACAASURBVPwx2RH7hlJ2YZ/lHzv+RHhrXac3jWqUqVf5aVZhszg/jj8tqyhXjrndNK2ivDp+uziXjqEJco7ricicQ8qyi9rDkV0py6Y/bbFtIOP9HO7iNQmZGpFRpMtogkZFyygU7zyUXFEoFCWoCkC7j5SSp598jNtv+zb3P/g7LMvmmCXNzJ8ORBcQbDic9x9Us9/G4wwO0Lryk2TXv8n0b36b2Ikr9ttj7yspy+G17iStiSxhQ+OYhjKa4sEp8e/OfvVpcg/cjn7YCQTO/cwenZtIWfzgD2/wu7+1UBUP8sULj+SEQxqmxPMeK1zbZvPPf8/rX7mDwTc2UrZwDsfdeyMzP3QmTzz11KQTK1JKsrY7HCFSuBmzCzdlaStEqn4e6arZpA8/oRBJkk1lMDs6Cba1EezoIFddTfKQxThxL1muoQnCvZmC9CgLm9SXhYqkiC9J8jdqSpDsV2Q2AW0ve0Klaw24NgTLYMZxnlCpXYjQJuajtaFpGAGNyBhaHFd6USJZ29lO2BQiafx+oCgPzQjJMUri4Pz6maee5JQVJ0/Iv90oULmHDttx5U5kjD1CzDj0pyxSlk0657Cz4KLdiZYJmTpTcUbXkhkVKqJHccCg5IpCoQC8D1EDOYeEHz5eZuqUBXR09QfRq/LgDIHVA1YvmUQ7IT0JVi83fvlW/vq39Vz28eV85sLTWHTk6RA7FKHv34odpWLlVmInnrxfH39vsV3Jm30p1vUOIYFFVREWVEWnTJJkZ/MbZO/5GlrzAoIXXbfb5VKllDz20ja++/AaBodynHv8bD7xnoOIhA6cP8uuZbHpvt+y+qvfIbl+M+WHHMTxP/8WTe8/fdzLB0vp5V4YzkFiFwRI/oZp5PSa/A1S2tr5TZImIBIwCpEisaBBbVEESeSo5oIgiQSMEnFiqjxWkw6Z7oVtL3lCpXsdICFSDbNP9YRK9bz9EpU4EWhCEDC8KUTjVStLE0wpKahrgljIILaHv6tHl7J2iYjZ22iZyc6SGeOXX06hmGwcOJ/iFArFqHgVgFwGcvYBXwFISgn2INi9kOsFq2iROVava+OOe5/mJ79+gecf/Tpz5x/Mt2/+BlV1s4hWzAA9NiEfEp3BAVpWXkxu/VtTRqxIKdmSyPJqd5K07TIjFuTQ2hhRc5yTI4whbk8bmTu/gCirJHTplxGB3cul09qZ5NsPrebl9T0saKrga5ccw7zpY19tZrLi5HK8fc9DvP617zC0aSuVhy/ixAduZcY570Joe3eTmrYcOgYzpd8ijyJFisWJvRNDImB4Ko0vPiqj4WEhUhRFMjLMP6Cr6ZNTHZnsgG0vekKlb6PXGZ8GC85CTDsKypvVe6zYI4QQhEwv+mRvo2XStuN9TpliqJ8UxYGEkisKxQGKlJKU49KftbH9CkAVAYPgAfDNqZQu2AO+OOkBq69IotjDB+oRcjLOg39q444f/J4nnn6BYDDIhz70IbS6sxDVc2ie4CIuzkA//z975x1nRXX+//fcfu/2XlmWXhcEFkFAihUVjSVoohF7NCbGRKNfY9AYTWwRkxhroolG8zMmGhsasQFKsVCk111gl+399jIz5/fH3F12KbKwfTnv12tec2fmzLln7t6dO/OZ5/M8JT+6tk8JKw3BCN9Ue6kLRki0Wzg5M560zoxl7waE30Pw2V+BpuG46UGUuKSj7hOKaPzr0938e1kxNquJn140lnOm5PWryh3fhhYMUfS319n68F/xl1aQPLmAwj/fQ/Z5s4/7RrXSHWRlUS1rSxqIaIfedDgspjZ5RjLirEb0yEHCSevcB06rGbvVJMPYTyCEEOAuRZSvg/J14N5vbEjMRxl9CWRPRInL6tlBSk5YWqJl5G2bRNLrkf+lEskJSFDTaQgZFYCsJoV0uwVHP/TrC6FCpDEqntS1ikRpBLQDDc2xYE2B2AKwJoM1mQhx2BxxuGtquOrmq8jJyeHRRx/lmmuuITW1+/KofBtaUyMlN11LuGgXOY8/ReyMmT09pG8lqGpsrvWx1x3EblaYlBFHfryjz33vRCRM8Pn7ELUVOH78CKaMvKPus2ZHDU++tZnyOj+nTcjmh/NGkRzXtVWjeguqP8Duv/6bbY8+T6C8mtRpEzj5rw+QddaM4/rb60KwrcLNiqI6dtd4sZgUJg5IZGx2AjF2S4tQ4rCaTxjhSnLsCKFDfbEhqFSsBV8NoEDKMJSC70P2BBRX7zjXSyQSiaRvIMUVieQEIqLrNIQ0Av2sApDQI6A2HGrlURuhdX0NS4IhnjjzW0QUrMkoJiNqQtd1Pv74Y55+eiENDQ0sX76ctLQ0vvrqKwoKCjAdp2WhK2gRVop393phRdMFuxr9bK/zownB8CQXo5JdfTK/hBCC0KuPo+/egH3BLzEPHfet7ctqfLy4ZAfLN1aQmxrDIzdMYcKwE+OGLeL1sfvZf7Ht9y8QrK4jfdbJnPLyo2TMmXpc55xAWOOrffWsKqqj3h8m0Wnl3DGZnJyfTIxdXs5Ijo7QVajdGS2ZvA6CTaCYIX00yrBzIeskFMeJY9GTSCQSSecir0YkkhMAVRc0hVW8qo4CJEYrAPW1sHehh9pYeMZmFCPK9hh5UlpQwJIItmSIGQbWJCMqxZJ0xCoO9fX1/P3vf+eZZ56hqKiItLQ0rr/+ejRNw2w2M378+O45wHZyiLAy/dSeHtJhEUJQ7guzscaDL6KTFWNjXFoscba++9MTef8ltDUfY513LZbC04/YbmdpI68tK2LF5kosZhMLzhrOpbMHY7P0nZwyx0vE7WXnU/9k++N/J1TbQOYZ0xh7z82kz5x8XP1VuYOsLKpjTUk9EU0wODWG8wqyGJMVLyNTJEdFaGGo3hKNUPkGIj4w2yCjwMifkjkOxXpilT2XSCQSSdfQd69wJRLJERFCoApBQNUJaDrBaC6COKuZhD5QAUgIHUKVB1l56kHztmplxm62gi0PYka3ikRJRFHadwOr6zomk4l//etf/OIXv2D69Oncf//9XHLJJdjt9q45uA6iNTYYwsqeol4trDSFVL6p9lATiBBvM3NqTiIZMX0rr8rBRL74gMiSV7BMPQfrmd8/ZLsQgvW763ht6W7W767D5bBw6ewhXDQj/4SwAIUb3ex44h/s+OM/CDc0kXXOTMbeczNpp0w45r50Idhe6eFLdxzvfbwTi0lhwoBEpg9JJSexeytxSfomIuxH7HwP9nwKagisLsgcbwgq6WNQLL3zHC+RSCSSvosUVySSfoIuBEEtKqaoOmrUDWNRFOKsRqSKtQ885RWhSqhfCuFqY4ViMUQTR24bKw+WBNYu/4zZw2cfU/9+v5/XXnuNp59+muuvv54bb7yRK6+8khkzZjBu3LdbPHqaNsLKH54idlrvE1ZCms6WWh/FTQGsJoWT0mIZnOjsc1FSB6PtWEv4X3/APHIStstubWNr0XTBys2VvLa0iF1lTSTH2bn+3JGcNyWPGKe1B0fdPYTqGtj+x5fY+cTLRNxecs6fw9h7biZl8rH/PwXCGl/vq2dVcR11vjAOxcw5YzKZIq0/knYitAgUf4rYsRgifsidgpI3DdJGHjF6USKRSCSSzkD+ykgkfRQhBBEhCB4UnaIADrOJeIsJh9nUJwQVAKEFoHEleLeAOQZSzjQEFXNcp+SE2blzJ88++ywvvvgiDQ0NjB49muTkZADi4uKksNJBdCEoagywtc6HqguGJDoZnRLTL6pP6eXFBF/4DUpmHvZr7kUxGz+dYVXj47Vl/Gd5MWW1PrJTXPzskgLOmJiDrQ+VlD5egjX1bF/0N3Y+9U9Ur58BF5/FmIU/InnC6GPuq9oTZEVRHWv3NRDWdAaluDhnTCZ1u77htBEndcHoJf0NIXTY/yVi65vgrzWiU8bMR0k8esJpiUQikUg6AymuSCR9iJbolKig0lx51GpSiLeacZhNfa7qjxA6eDdD4yrQIxA/ERKmtCSZ7VjfouWzuPrqq/n666+5+OKLufnmm5k5c2af+ZzUhgZKb7qG8N7iXimsVPpCbKjx4glrpLusjE+LI6GfRBnoTbUEn/sVit2J48bfoThj8AUjvPdFCf/9fA/1nhDDchJY+IOJTB+beULkAAlU1rDtsb+x65lX0QJB8i49h7G/uonEghHH1I8uBDsqPawoqmVntRezSWFCbiLTh6SQm2TkwFi2uyuOQNLfENVbEZv/DU0lkJCHMv12lPQxPT0siUQikZxg9I+rX4mknyKEIKILAlG7T+ig6BSnxYTTbMLSR2/o2liA7LmQPBvFltLhfisrK3nhhRd46aWXWL16NSkpKTzzzDOkp6eTlZXVCSPvPlqElX17yP3j08ScMqOnh9SCJ6yyocZLpS9MrNXMtOwEsmJsfUa0Ohoi6Cf07K8Qfi+OW/9AkyWBN/+3nXdX78MXVJkwNIU7v3cSE4am9Jtj/jb8ZVVsffSvFP3l3+jhCAMvn8eYu28iYdSQY+onENFYs6+BVUW11PrCxDsszB2dwZT8FGId8rJE0n5EUwli8+tQvRlcKSiFNxg2IKXvR8xJJBKJpO8hr2Ikkl6GLqJiiqoTPEx0itNiwm7qW9EpByM0vxGp0mwBSj0HXMM6dExCCD7//HOefvpp3njjDVRV5cwzz6Suro6UlJReV/GnPagNDZTeeDXhkr3k/uGpXiOshDWdbXU+djcGMJsUClJjGZro7FdRG0LTCL34W/SKPfi+t5AXvgqwZM2nqJrOjLGZXDZ7CMMHJPb0MLsF374ytj7yV4peeB2h6Qxa8B1G//JG4oflH1M/1Z4gq4rqWFPSQEjVGZjs4uwxmRRkJ/Sr746k6xH+OsP+U7oarC6UsZfC4NNRzP0/x5FEIpFIei9SXJFIepg20SmqTkg/EJ3SHJni6MPRKa0xLECboHF11AI0CRJO7hQL0J49e5g1axaJiYnccsst3HTTTQwfPrwTRt0ztBFW/vg0MVOn9/SQEEKwpynI5jovYU0wKN7BmNRYHJb+9ZRYCEH49T+jbf2KjwddzDNvejCbfJwxKYf5swaTmxbb00PsFrzFpWx56DmKX3wTRVEYfM3FjL7rBmIHDWh3H7oQ7KzysKKojh1VHswmhZOi1p8BSbL8reTYEGEfYuf7UPSRsWLY2SjDz0OxxfTswCQSiUQiQYorEkmPoLVKRBvQdKJ6CrZ+FJ1yMCJUAfXLDAuQIxeS56BYk4+7v02bNvGHP/yBv//977z00ksMHjyY9957j9mzZ+Ny9e2bNkNYuYpwyb5eI6xU+8NsqPbSFFZJdVoZnxZLkqP/PSUWQrDv3y+StnIx/zUX8kb9EC6ZmcfFMwaRktA/yykLIYi4vQQqqglW1BCoqKHiw5XsfeUdFLOJoTdexug7rycmL7vdfQYjGmtKGlhZVEut17D+nDUqg6mDkonrh98bSddySAWgAaegjL4IxdVxG6lEIpFIJJ2FFFckkm5ACEE4Gp0SbBWdYgIc0egUp9nUL0PjheaHhpXg2wrm2A5ZgMLhMG+88QZPP/00K1aswGq1smDBgpbEteeee24XHEH3otbXU3rT1VFh5Rlipk7r0fF4wxqbar2UeUO4LCamZsWTE2vvV8IfgK4LVm2tYus773BF5b/50jYSyznX8sq0QcT20XLKQtcJ1TUSqKgmUFHTIpwYU3WbZS0QbLOv2WFn+E+uYNQd1+PKyWj3e9Z4Q6wqquXrfa2sP5MzGZsTj8XUvyKcJF3PoRWAxqKM/S5KgqwAJJFIJJLehxRXJJIuQhOiJW9KQNXRo+ttJoWEaHSKrZ9Fp7SmKyxAv//971m4cCFDhgzhscceY+jQoXznO9/pxFH3LL1JWInoOtvr/exq8KMAY1JiGJ7k6ncCYETV+WR9Gf9eVkRs1U7uD79BY+pQpv3i9zhczp4e3mHRVZVgVd1hRJPqFrEkWFFDoLIWoaqH7G+Nj8WZlYYjK42UKeNxZqW1LBuv04nJy8IS074IMF0IdlV7WbG7lu1VHsyKwvgBCcwYnMqA5L4dRSbpOUT1FsTm/8gKQBKJRCLpM0hxRSLpJFqiU6J2n3BzdIrSKneKxYS5n4oprRHBcsMCFKkBxwCjCtAxWoB0XefDDz/kmWee4brrruOCCy7g2muvpbCwkDPPPBOTycSyZcu6ZPw9gVpfb1iBSkvI/dOzxEw5pUfGIYRgnzvI5lofQU0nL85OQWosTqu5R8bTVfiDKu9/ZZRTrm0KMiUtwu3K+9jSMkm47RGUHhBWtGCojUByQDhpK5oEa+pBiEP2t6cmRUWSdBJGD20RTQ4IJ+k4s9KwdNKxBSMaa0saWFlUR403RJzdsP5MGZRMvLT+SI6TGLUefeXjsgKQRCKRSPocUlyRSDqA1qpMcrBVdIrdpJBgM+M09+/olIMxLEArwLctagE6F1xDj+n4dV3n5Zdf5oEHHqCoqIj09HQuueQSALKysvpcKeX2oNbXGclr95f2qLBSF4jwTbWHhpBKksPCKdkJpPRRS8yRaPSGeGvlXt5ZtRdvQGX8kBTuOC+fEe/8BmEx47jpQZSYhE59z4jXR6Cs6iDRpK2IEqioIdLoPmRfxWzGkZGCMysN14AsUk4ed5BoYggmjowUzLaOJ4ZuD7XeECuL6lizr56gqpOX5OLyyQMoyEmQ1h/JcdNcAWiiZxUEY1DGXgaDT5MVgCQSiUTSZ5DiikTSToQQaAIiuiCoGXaf5ugU8wkYndIaIXTwbDQsQEKF+EJImHxcFqCLLrqId955h0mTJvHqq69y8cUXY+umm8aeQK2vo+SHVxMpKzWsQD0grPgjRl6VUk8Ih9nE5Mw48uIc/UoUrKz38/pnxSz5upSwqjNtTAaXzh7CyEwXwafuRG+swXHLIkxpOZ32nkIIdvzxJdbf+ftD7Dkmu63FgpMwaggZp01tI5o4s9JxZKVhT03CZO75qCEhBDurvawsqmV7pQeTojAuN4EZQ1LJk9YfSQcwKgC9B0UfA7DfPoa8M26SFYAkEolE0ueQ4opEchB6tDRyRBeouiAiDrxuHYhvNykkRqNTrCdQdMrBGBagpRCpBUde1AKUdEx97N27l+zsbGw2G1deeSXz58/n8ssvx9TPn4K3EVb+9CwxJ0/t3vfXBTsb/Oyo9yGAkckuRia7+lX0QXGFm38vK2LZhgpMCpw+MYf5s4aQlx5rJHx98bfoe7div/oezINGd9r76qrKmlseYPez/yLngtPImz+3xZbjzErDmhjfJ84ZIVVj7b4GVhbXUe0JEWu3cMbIdKYOSiG+n0U1SboXowLQJ4gd7xkVgPKmoYy6kD1fbWKgFFYkEolE0geR4orkhEQIQzRR9UOFFP2gVAYWBSwmEw6rIaJYFAW7WcHUB26MuhKh+aJVgI7fAuR2u3nwwQf5wx/+wCOPPMLPfvYzvvvd73bhqHsPPSWsCCGoD6qUuIOUeoKEdUFOrJ1xabHE9JO8KkIINu9t4LWlu/lqew1Om5mLZ+Rz0YxBpCUeyDcSefcFtG8+w3bhjVgmzOy094+4vay49FYqlqxg1J3Xc9JDt6P0McGq1htiVXEdX+81rD+5iU6+VziA8TkJWMx961gkvYsDFYD+C/46WQFIIpFIJP0GKa5I+i2tbTwHIlB0VF2gHiSgmBSwKgouswmLScEanSzKiRuRciQObwE6GcXU/qfYmqbxt7/9jYULF1JdXc2CBQuYP39+F466d6HW11Fyw1VEyveT+8SzxEzuemHFEzYElRJPEF9Ex6RAdqydIQlO0lz9w3al64Ivt1Xz2rIitu5rICHGxlVnD+f8UwYSf9AxRla8S+ST17DMuADLnM4T9Hwl5SyfdyNNW4s4+S8PMPSGSzut7+6g3hfmg62VfFPaiKLAuJxEZgxNIS/JJc+Fkg5zaAWgq2UFIIlEIpH0G6S4IunzaK0iUJJyBlATiLREpbTWUBTAalKwmU3EKFHxJCqinOhRKO1FBMuiVYCO3wIEcPXVV/PKK68wffp0Fi9ezOTJkzt/sL0Uta7WiFjpBmElqOqUeoKUuIM0hIycH+kuK6OSY8iJtWPtJxEIqqbz6fpy/rO8iH1VXjKSnPzkwjGcVTgAh+3QaBx1y5eE//NnzGOmYLvkx50mGtSt2cTy83+E5g8w54PnyTyj50ppHyv+sMon26tZWVyHSYHZw9OYPiSVBGn9kXQCoqkEsfl1WQFIIpFIJP0aKa5I+gS6OBB9crCVR2/VLiEjm7AusJoUnNZoFIpiiChmBfnk9TgxLEArwLcdzHGQdh44hxzT57lr1y5SUlJITk7mRz/6Eeeffz7z588/of4mbYSVPz9HTOGUzn8PXVDuDbHPHaTaH0YAiXYL41JjGRBn71cllQNhlQ++KuX1z4qpaQwyKDOOu75/ErPGZWE+gnCkle4i9PcHMOUMwX71QpROShZb+uZHrLriFzgyUjj9kxdJGD20U/rtaiKazsqiOj7dUU0wolE4MImzRmWQ2E+imSQ9S3MFIEpXg9UlKwBJJBKJpF8jxRVJr0EIw65jiCd6GyFFO8jGY1bAYlJwWUxtIlBWfv45s2fN6pkD6IcYFqAN0PgFCA3iJ0erALX/wrihoYEHHniAJ598kltuuYVFixYxbVrfeaLfWah1tYYVqKK804UVXQiq/WFK3EHKvCE0AS6LieHJLvLiHCTY+9epvskX5u2Ve3l71V48/ggFg5K59aICJo9M+1axTq+vIvTcQpSYOOw3/hbF7jxi2/YihGD7or+x/s7fk3LyOGa+/TTOjNQO99vV6EKwvrSRD7ZU0hiIMDIjjnPHZpKV0PHPRCI5uAIQw+aiDD9XVgCSSCQSSb+mf11xS/ocAVXDE9FbolJa02zjcZgPRKA0CylHtPEc1Ifk+DEsQEshUgeOgZA865gsQKqq8txzz/HrX/+a+vp6rrvuOu64444uHHHvRa2tMSJWKsoZ8OfncBWe3OE+hRA0hA4kpg1pRsRWXryDvDgHqU5rv4oKEkKwvbSRJVtCLProU0IRjVNGZ3Dp7MGMyU8++v4BL8HnfoUIB3D+7E+YElI6PCY9EmHNTx5g919eI2/+XKa+9AgWp6PD/XY1u6o9vLepgrKmIDmJTi6dlMuw9LieHpakH3CkCkCKq+P/bxKJRCKR9HakuCLpMSK6oCaoYlLAZjLhNLWKQlEUTNLG0yMI1QeNn4Nvx3FbgABuv/12nnjiCebMmcPjjz/OSSed1EUj7t0YwspVRCoqOkVY8YY1SqJ5VLwRDZMCWTF28uIcZMbYMJv6z/+MEIJd+5tYtqGCzzZWUN0YwGyCORNyuGz2EAZmtE8QEJpK8G/3I6pKcfzoIUzZgzo8tnCThxXzb6Xyo5WM/uWNjP/tz3p9RaDypgDvb65kR5WHJJeV7xcO4KQBiTLnlKTDCKFD6ZeIbdEKQBljUcbICkASiUQiObGQ4oqkRxBCUBeMAJDptGHpRzeEfRUhtKgF6MvjtgBt27YNu93O4MGD+elPf8ppp53GBRdccMKKZJ0lrIQ0nf2eIPvcQeqDRmLaNKeVEckucmLt2PpJYlowzg1F5W6Wb6hg+cZyKusDWMwKk4alcdXZw9HqdzH3zPYLdUIIwv/6A/qOddiuuAPziIkdHqN3736Wz7sJ9449THnhdwy5tneXD2/0h1mytYq1JQ04rGbmFWQxbXBKv0loLOlZZAUgiUQikUgMpLgi6RG8qk5IF6TYLVJY6QWI4P5oFaDjswDV1tZy33338eyzzzJ//nxeffVVhgwZwpAhQ7pu0L0ctaaakhuvNoSVJ/+Ca9KxVURSdUGFL0SJO0ilz0hMG28zMzY1hrw4B65+lJhWCMGeSg/LN5SzfEMF5XV+zCaFCcNSueL0YUwbk0mcyxD5li3bfUx9Rz78J+qXS7DOvRLrlLM7PNbarzby2fk3oYXCRkWg00/pcJ9dRSCisWxHNZ/trkUAM4elctqIdFw2+dMv6TiisQSx5T9QvQVcqSiFP4Tck2UFIIlEIpGcsMgrLEm3o+pGrgiHWSHGIi/CehKheo0qQP5mC9A8cA5ud6RJOBzmqaee4v7778fj8XDjjTfym9/8potH3ftRa6qNiJWqqmMSVoQQVPsjlHiMxLSqLnBaTAxLcpEX7yCxnyWm3dssqGysYH+ND5NJ4aQhKVw2ewjTx2YSH9OxijXq1x8Tee9FLJPPwHrOgg6Pt+SNJaz+wR04stI4fdnLJIzqneKhqut8saeej7dV4QtrTBiQyNzRmSR38POUSACEvzZaAegLowJQwfdg0BxZAUgikUgkJzz960pd0usRQlAXMuxAKfb+lXCzL3HAAvQFCB0STob4wmOyAAE88sgj3HvvvZx99tk8/vjjjB49uotG3Hc4RFiZWPit7YUQNIZUSjxBSt0hgpqOxaSQG2snL95BWj9LTFtS7eWzqOVnX5UXkwIFg1O4+NRBzBibSWKsvVPeR9v1DaH/9ximYSdh+/7tHfoMhRBs+/3zfPN/j5F6ygRmvvUUjvTel6BTCMHGsib+t6WSOl+YoWmxnDc2k9wkV08PTdKHEUJAsBGaShDVW2HPUmPDsLkow89Dscnvl0QikUgkIMUVSTfjVXWCmiBZ2oF6DMMCtBQi9eDMh6RZKNbEdu+/ceNGwuEwhYWF/OQnP6GwsJBzzjmn6wbchzgWYcUX0SiNJqZ1hzUUICvGRl68g6wYe79KTFtW42P5RsPys6fSg6LA2PxkfnLhGGYUZJIc17kVdvTKfQSfvw8lNRvHdb9GsRz/E3U9EuHrm39D0fP/Ie+yc5n694d6ZUWg4lof722qoKTBT2a8g+um5TMiI65fCXOSrkcIHTwV0FSKaCqBplJo3Adhb7SFIisASSSSE4a1a9emWyyW54GxgAy3l+jAZlVVr580aVL14RpIcUXSbbS2A8VKO1C3Y1iAPgf/TjDHQ9r54BzU7puvqqoq7rnnHl544QVmS3SrswAAIABJREFUzZrFp59+SlJSkhRWooTL9rP/x9cTqa4+orAS1nT2e0KUeILUBqIRXA4rE9Jd5MbZsfejBKMVdX6Wbyznsw0V7C53AzAmP4kfXTCamQVZpCR0jUAh3A0En/0VisWK46YHUVzHX2I43Ojm8+/+lKpPVjPmVzcx7v5be11FoGpPkPc3V7Klwk28w8KlE3OZNDBJVgCSHBWhhsC934hIaSwxEtK6y0ALGw1MFojPgawJKIl5kJAH8bkoVmfPDlwi6TACUKNzBeOeWWk1SSQGFovl+czMzFFpaWkNJpNJ9PR4JD2LrutKTU3N6MrKyueBCw7XRoorkm6htR0oWdqBuhXjSeQ3B1mAJqOY2vfvHwwG+dOf/sTvfvc7AoEAP/3pT7nnnnu6eNR9Az3gx7P0E5refRP/l6tRHE4GPPVXXBMmtbTRmhPTekJU+kLoAuJsZsakxJAX7yCmHyWmraz389lGo2zyzv1NAIzKS+TGeaOYOS6LtMSuvSkToQDBvyxEeBtx/HQRppTM4+7Lu6eUZefdiHd3CVP//hCDr764E0facdzBCB9tq+KrvfVYzSbmjs7k1KGp2KRwLTkMIthkRKE0lRgRKY0l4K3CuLkErDGQmAeDZhvlkxPzIDaz3b8TEknPI4AwEInOw0dZ/jYOFluOdfl49mnvcl8knr47dsZKYUXSjMlkEmlpaU2VlZVjj9RG/mpKugVf1A6UZLNg7Ud2h96O0AJQ+wEES47LAgTwyiuvcNddd3H++efz2GOPMXz48K4ZbB9B6Dr+dV/jXvw2no8+QPf7sWRlk3L9j0j4zsXYcnIRQlATiFDqDrLfGyKiCxxmE0MSnC2JafuLwFjdGGgRVLaXNAIwYkACN5w3kpnjssjopnwfQtcI/eMh9NJd2K+/D3PeiOPuq/aLb1h+wY/QIypzPnyBjNlTOm+gHSSkaizfVcvynTWous4pg1I4Y1QGsf0s2bHk+BBCB18NNO5DRMUUmkog2HSgkSsVEgagDJgKCQOMiBRncr85J0n6EzptxZFvE04iR+hDAWzRyYFxo2+NLoMhyjRP+nEua8e4z4nGrJ4eQEcwSWFF0pro9+GIT7Lk1Ziky1F1QX1IxW5SiLPKp6rdhQhVQs17oAUg+XSIHdPui+e1a9dSVVXFueeey1VXXcWwYcOYNatP/zh2mHDJPpoWv437vbeJlJdhcrmIO3MuCfMuxDmxEMVkoimksqPGS4knSEDVMSsKuXF2BsTZSXfZ+o1Vo7YpyOebKli+oYKt+xoAGJoTz3XnGIJKVkr3J7gMv/ks2qZV2L77EywF0467n5L//I/VC/4PZ3Y6s9//C/EjBnfiKI8fTRd8va+eD7dW4QmpjMtJYO6YTNI6KQGwpO8htEjU1lOKaNwXjUwpBS1kNFDMEJ8N6WNRmkWUhAEotpieHXiX0nwDq2HcxDZPzU/+TYd5LeledFoLIpmZAPs4vHiiHqEPE4cKJs3L1lavbYCZ3vd37oigI5FIejNSXJF0KUIYwgoYuSXkk7GuRwgB3k1Q/xmYXZA5H8We0a59y8vLufvuu3nppZcYN24c55xzDlar9YQVVjSPG8+H/6Pp3bcIbFgPioJryimk/vhnxM05g4DFRk0gTE21l5pAGF9ERwEyYmwUpDrIjrX3m8TN9Z4gn2+sZPnGCrbsrUcIGJwVxzVzRzCzIIuctJ67YYss+y/q8jexzL4E68wLj6sPIQRbH/4LG+5+nNRpE5j51tM40pI7eaTHOa4KN+9vqaTaEyI/xcWCqQPJT+nPN8iSgxEhb0sUimgqjdp6KgyrJ4DFYYgn+aceEFLisntReeTWQod20LztupwcgJKjtjvyumOhteXicMLL4V53drvD7dPe340j3Wwf7Sa8K7artM+S01YwGTkSYA+GCNIsjriARA4vlljp+7cvrf/G/ccaLOkaXn/99fhf/OIXebqu84Mf/KD2wQcfrAR44oknUi644AJ3fn5+BCAnJ6dgzZo127Kyso6kSrJ48eK473//+0Nyc3PDoVBIueiii+oXLVpU0V3H0kxtba35+eefT77rrrtqAPbu3Wu96aabBnzwwQfFixcvjlu0aFHG0qVLd3f3uDqDvn52kvRyfKpOQNNJspmlHagbEHrEqATk2waOgZB6Nor56Hku/H4/ixYt4uGHH0ZVVe68807uvvvuE1IME6qKb/UKmha/jXfZJ4hwGNugIaT+9DasZ82jITaJnYEINRU+/KoHAKtJIc1pZViSjdxYB45+kveiwRtixaZKPttQzsY9hqAyMCOWK88czsxxWeSlx/b0EFE3riT85jOYx83AduEPj6sPLRzm6x/dR/Hf3mDg9+cx9W8PYnb0fERISb2f9zZXUFzrIy3WxlVTBzImK/6E/L88URBCgL8WGksOVOtpKoFA/YFGziRDPMmecCA/iisVRenK844KeAB39HV7xI7Wr9v/xH3YMIDi6FJr0cF80NxymHWHe63QNgJAP8zrb9vWOhLmSPvIiILDY+GAOBLL4cSSL75Yx9SppyJFBonkUFRV5ec//3nekiVLdg4ePDgyfvz4UZdccknjpEmTgq+88krqSSedFGgWV9pLYWGhd+nSpbvdbrepoKBg9IUXXth06qmn+tszFoulc6SDuro68wsvvJDeLK7k5+dHPvjgg+Kj7dcXkOKKpMtorg5k2IHkj2ZXIyKNhg0oUgsJUyBhSrtvwpYvX869997LJZdcwqOPPsrgwb3DCtGdBHdsp+ndN3F/8B5aXS3mxERiLvwuodPOpXbAMLYFIwR8Ovg82EwKaS4bw5xW0lxWEmz9J4dKky9sCCoby9lQVIcuIDcthitOH8bMcVnkZx5/9Z3ORtu3ndBLD2LKG4F9wV0opmM/z4QbmoyKQJ9+wdh7bqbgNz/t8b9lrTfEB1sq2VDWRKzdwsUn5XByfnK/Ks/duQigAePJeBzGU+/e/1kJXQV3edsks+5SiASiLRSIy4KU4dFqPQMMW489vhtGF8QQUpqik7fVNoUjixlWwH6E7e1bt3LlF0yffip9y7bT2sLRERGn9etmjvYZ9JbtZtoKJ1baI5gEg837SiSSg1m2bFnMwIEDQ6NHjw4DXHzxxfWvv/564saNG0ObN292LViwYLDD4dDXrFmzDeDRRx9NX7JkSYKqqsprr71WPGHChOCR+o6Pj9cLCgr8O3bssJ9yyin+H//4x7krV66MC4fDyg033FB9xx131C5evDjugQceyEpPT49s3brVVVRUtOXJJ59MeeKJJzIURWHUqFGBt956a095ebnlmmuuGVhWVmYDePzxx0vOOuss32233ZZdWlpq27dvn728vNx20003VS1cuLD69ttvzy0tLbWPHDly9KxZs9y33XZb9bx584bt2rVrS+sxut1u03XXXZe3bds2p6Zpyq9+9avyH/zgB41d+Zl3FCmuSLqEZjuQAFIc/efGs7ci/MVQuwQUE6R/B8WZf9R9vvjiC7Zs2cJ1113H3LlzWbduHRMmTOj6wfYi1Lpa3O+/S9Pitwnt3A4WC8opM/HNmkvl2MkEFOOCzx4Ik+q0kea0kuayEW8z96vvtNsfZtWWKpZvKGf97jp0XZCd4uKyOUOZNT6LQZlxve549dpygs8tRIlPwvHDB1Bsx17a2VtcyrLzfoi3qJSpLz3C4AXHZynqLHwhlY+3V7O6uA6TCc4Ymc6sYWk4pDh9BMJARXRqff1oxhBZmqd4jBv+nvkOCy0CvipwlyM85Ua5Y085eKtBaNEh2wzxJHeqYetJHAjxOShm27d33jkjBHwcEFKagGjeFkwYn9/A6DyBrr50jESg791st7YXSSSS/sh9D3w6oKi4vlOTyg0ZnOy/757TSo+0vbS01JaTk9NS3io3Nzf85Zdfxj700EOVzzzzTPpjjz1WOnPmzJaok9TUVHXr1q3bHn744bSHH34447XXXtt3pL4rKyvN69evj7nvvvvK//jHP6YmJCRomzdv3hYIBJTJkyePPP/8890AGzdujFm/fv2WkSNHhtesWeN47LHHslavXr09KytLraqqMgPceOONA2677baqs88+27tr1y7b2WefPay4uHgLwO7dux2rVq3a0djYaB41atTYO+64o2bRokX7582b59y+fftWgB07dhz2x+7uu+/OmjNnjvs///nP3traWnNhYeGoCy64wB0fH99rM0NLcUXSJfijdqBEmxmrSV5sdBVC6NC4GtxrwJYOaeehWL79qWZJSQl33XUXr776KoMHD2bBggVYrdYTRljRQyG8yz+l6d038a1eCZqGOnw0Ddf9nNqpc9DiE7GbTVEhxUqa00ZcPxNTwEiQumJTBR+u2c+6XbVouiAr2cX8WYOZNS6LIdm9134ifG6Cz/4KdA3HTQ+hxCUdcx81q9bx2XduRmg6cz76GxmzTu6CkbaPiKbz+e5alu6oJqTqnJyfzFmjMoh39pZ8Gb0JgXHzXw7URJcTgcEYESueVtN+DkQAWDkgtDSLLp0rXAgtYpQ39pQh3OWGgOIuN4SV5twoKBCTbiSazZoYzY8yAGIzutjW05pmi0+zkOLGsL2A8ZkkRKd4DCuH/A2XSCSSnkCIQy2HiqIc0Yd4+eWXNwCcfPLJ/nfeeeewF0dr1qyJHTVq1GiTySRuvfXWysLCwuDChQuzt2/f7mrex+PxmLdu3eqw2Wxi3LhxvpEjR4YBlixZEn/++ec3NOd1ycjI0ABWrlwZv2vXrpY8BF6v19zQ0GACOOussxqdTqdwOp1qcnJyZP/+/e3WH5YtWxa/ZMmSxCeeeCITIBQKKbt377ZNnDjxiBE5PY0UVySdjhatDmQzKcTLJ65dhtD8UPs/CO6H2LGQPAtFOfK/tNfr5ZFHHuGxxx4DYOHChfzf//0fVmv/v4ETQhDYsJ7at9/E/9H/wOclkpxG47zv0TjzbJSBg0lzWRnvtJHqshJn7X9iSjO6LlixuZKXP9rJviovGUlOLj51ELPGZTEsN6HXH7eiqQRfuA9RV4njx49gyhhwzH3s/dd7fHH1XbgGZDH7veeIHz6oC0Z6dHQhWFvSwJKtVTQFIozOjOPcsVlkxB97FE7/RwUqMUQVP8blS3Z0ap3cNxbIir7WMewszWKLG2iVuwQ7bcWWONpzWWSIKJXgKUe4ywwBxVMOvuq2IkpsOsTlQM4kIvYM6rUkavyx1DREqC8LUFfvR9cENnsZNmslNrsFm9WMzWZMdpsZa/PcasZut7Rss9nMRlu7MTebv00ACdE2KqW1xScGyOBAVIqDvmPFkUgkku7j2yJMuoq8vLxws9UGYP/+/bbs7Owj5lhxOBwCwGKxCFVVD3syb8650nqdEEJZtGhRySWXXOJuvX7x4sVxLpdLb9XusOKOEII1a9Zsi42NPWSb3W5vWWc2mznSuA6HEILXX3999/jx40NHb907kOKKpNOpD6voSDtQVyJCFVDzPugBSDkDJXbMUfcpLi7moYce4tJLL+Xhhx8mLy+vG0Z67Agh0PduQ9v2NYrDBTEJKDHxB6bYBHDEoBwlIkoIQf3eEurefYvIkncxl+9HtztwnzwT/5xziJ08hZxYJye5rMT2YzGlGSEEK7dU8fKHO9lT6SEvPZZfXTGBUwuyMPWRXB56TRmDv34LvXw79gW/xDx03DHtL4Rgy4PPsnHhH0mbMYlT33wSR2rPVATaUeXhvc0VVDQFGZDk5PuFAxiS1vMJgnsbsbEAO4AqDLEkDhgBpHN060izpaV1NJ/KAcHFHZ3XtNruolloEZoTvB5wV0btPOXgKTPsPNGIGIGJsDUZDynU6vmUe2PZW+9kd6WNqroQdfV+6upqCQQqDxld8ynnMA8mjxmL2YTVZsLpsDBsmINxBU7GjHYwaoSNtDTjcwqHBSUlOvtKBWVlCpVVZiCI1RrBbmtoEWq+TeSx2SwkJTlISXbhlJFVEolE0qXMmjXLt3fvXsf27dtt+fn5kf/+97/J//znP4sBYmNjtaampk55in3mmWc2PfPMM2nz5s3z2O12sXHjRvvhEuXOnTvX/d3vfnfo3XffXZWZmalVVVWZMzIytBkzZrgfeeSR9AceeKAKYNWqVc5p06YFDn0ng4SEBM3n8x01LHLOnDnuRYsWZbz44oslJpOJlStXOqdPn37EfnsDUlyRdCo+VcOvGnYgm7QDdTpCCPBshIbPwBILmZei2NKP2L64uJh3332XW2+9lXHjxrF7927y8/O7b8DHgIiEUdcvQ13+Fnrpzm9vrJggJp6xipnAN28ZgosrjrAzDo9mwb29GHX9N1iLiwCIjB5P+NKrSThrLnkpicScAGJKM0IIvthazcsf7WR3uZvc1Bju+v5JzBqf3ScSpOoNNWjrl6Ou+xS9ZCfJgPWCG7AUnn5M/WjhMF/98F72vPQm+Vecz5QXHsRs7458Fm0pawzw3uYKdlV7SXbZuOLkPMbn9P6Ioe5FA6qBcgoLwRBWMjCiVDqaUNmCYSNKbFkjNB94isCzD9G0DeGpQvE0gM/LARFFwSPi2O+OYUvpQLbtt7GlzEJJvYOI1vxbpwKNJCYESU52kpLiYuzoDFJSXMYUXZeSbCwnJjgwmxVUTScc0ghHtAPzcPOkEgprRMJam3nzNl1oJMaHSU1TyUzXyMwSNBe6crsFRXs0ln0WZPuOMMV7wvj9GpGIdpg+1WMWeVwua8uxpKa4SE52ktp8rAcdr1VGsUp6AUIIAgEVrzeE1xfGZFKw2SwHRMSoqNhXHjhI+j9Wq5VFixaVzJ07d7imaVx++eW1hYWFQYAFCxbU3nLLLQPvuOOOloS2x8vPf/7z2r1799oLCgpGCSGU5OTkyPvvv190cLvCwsLg7bffXnHqqaeONJlMYuzYsf433nhj71/+8pfS66+/Pm/48OGjNU1TpkyZ4pk2bVrJkd4vMzNTmzRpknfYsGFjTjvttKbbbrut+nDtHn744fIf/vCHeSNHjhwthFByc3NDvb1Es3I4L1dXU1hYKNasWdPl77Ns2TJmz57d5e8jMdCEoNwfxqIoZDqtPXKz0J//5kKPQN0n4N8BznxIORvFfHj7gKZpPPnkk9x9991YLBZ27NhBZmZm9w64neiNtagr3iGy6n3wNqJk5GGdeSGWk88EXUf4mhA+N8LnBp8b4W1C+I151b49xFpNaJ5G1NIKwiU1BGv9CF1gcVpwpbuISXdhcViMx8SuuGgETELbSBiXMW8TIROTADFxx1WBpjcghODr7TX846Od7NzfRHaKiyvOGMZpJ2UfxULQ8whPI+qGz1HXLkUv3gRCYBowHMukOXwdtDHt3O8cU3+h+kY+v+QWqpd9RcF9tzD23h93+/mpwR/mg62VrC9pxGkzc8bIDE4ZlIyll/8tuhcfhu2nCkOocLFrl59hw6Zj5Ew5PiKqRkN9gPpaN4HqErSmMiz+ClxaLYnmepLtXprvpVQdSuodlHtcBKzxWJMTSc1PYVBBGnHxhhgXDguqqxUam6wEgk4U4nG54khNiSEpydnFQsLRLD7N9p5js/gIIVBVvZWoExVeIhqhkNoiyISCKg2NQerq/NTW+amv9xuv6/3U1QXweA4fuZ0Qbye5lRDTRnxJcZGa7CI5xUVSooPPP/+s3/6OSw6lvddtQgj8/ghebxivL2wIJN4wXm8Yz2HWGcut1vnC+HxhNO3o9z0Wi6mtLc9mxm6zRMUX0yGCjN0enbduazNF523btY4Ka+6njdWv1XJv/60+XnrrtbqiKGuFEIWt123YsGHv+PHja3tqTJLeyYYNG1LHjx+ff7htMnJF0mnUh1R0Ie1AXYGINETLLNdB4ikQP/mIn/H27du59tprWb16Neeeey7PPfdcrxNWhBDoe7YQWf4m2oYVIHTMY6ZinXURpuET2hyb4oyB1GyEEDSGVGr8EWoCYWoDEUxpe0j87AOSPl+Dpb4GYuOIOf8ikk8/HXteLvijgkzz5G06INY0VKPv343wNUEkfOTBtggyB0QZYhNQXHGtBJnoPDHNGG8PIoRg7a5a/vHhTraXNJKR5OS2+eM4Y2JOr76RFwEf6saVaOuWou1YC7puCG3nXIVl4mxM6bkAhJctO6Z+PUUlLDv3Bnx7yzjl5UcZ9INjE2Y6SjCi8cmOalbsNq7NZg9PY87wdJy2vinadT46UIshqjRiiAFpGFEqCZSVLWfYsKMLKxs2VrB+QyV1dX48DW7MoWriRR0ptkZy4rwMTg0wNDF0QESxKpT6HOz0xlEbzsZDCkF7Oua4TBLT4kgZ6SInevOfnOTEbjcDAcCDzeYmN9dDbq4Xw1rkxrical2dKA4jp0tHOLiKj5sDVZGaLU95HEg+e/wClKIoWK3GDWBMB05hoZBKfUOAujo/dfXR+UFCzOatVdTV+QkE1EP2N5kUYlwmsp6vIjn524WYhHi7vNboI+i6wOcPHxA+mkUPX5h16+rZs29dy3KzWOLztRVJfL4wuv7twojZrBAbYyM21k5srI3YWBtZmXHR19F1MbaWuS5EG0ExHG6OIFMJR/SWCLHDRZU1NUWOsI9KOKx1it3PbFZa5Vcy7Hp98Sv/5n8u77dCkURyMFJckXQK/qgdKMFmxiZPoJ2K8O+G2o+iZZYvRHEOPGLbQCDAzJkz0TSNl19+mSuuuKJXXXyKSBh17aeon72Fvn83OGOxzL4Y64zzMaVmt2mrC0FjUKUmEKYmEKE2EEHVBWZ3I+lffMrQz5Zg3rUNzGZipp9KwrwLiZ05B5P92G9oRDgYFV5aizFNraJkouuaatHLi4124cMkKldMmAaOwDxiEuaRkzDlj0Ixd99pdv1uQ1TZsreBtEQHt15cwFmFuVgtvfN/UoRDaFu+QF23FG3Ll6BGUJIzsZ52KZZJp6FkD+rQ97dm5VqjIpCA0z5+kfRTC4++Uyeys8rD6+v30+iPMDEvibNHZ5Dk6n4rUu8kiCGoVAARjCiLQRjJaNv3GQkh+OrLvSx961Piw3sYnenjtLQg2XkHRBRNKLi1RHzmgZQ4M1ASc3GlDSQ+ewBDXE6GHNOYXdEpI7qsY4gfrSsUtY6CtnFohaJvE0A0DAHlSFV84oEcDDGld1bxsdstZGXGkZV5dPuW3x+J5qTxtxFjNm3ejd0eR129n737Gqir8xOJHFp102IxtYgubYSYZjEmui4hwYGuG5E5EVVHVXVUVYvOdTRNtLxuWa9Ft6kCVdNabT9o0o6wvqXvb29/yPbopOsCk0lBMSmYFKJzBZM5OjcpKM1zE4euUxRM0fWKqe02kwIms6ltv+3t76B1ZvOB5VBIPSRSpFlI8fnDRxEbKrGYTS2CSLP4kZMd32a5jUjSal1cdJ2jlzzca28k2JHsfuGIHhVrDuxv7Ksd/c17Ib3hbyKRdBdSXJF0GE0Y1YGsJoUE6avuNIwyy6vAvRZsGZB27hHLLO/atYuhQ4fidDp55ZVXGDduXK+KVtEbqlFXvGtYf3xNKFn52C77GZbC01HszjZthRAUNQXYXOtDjT6likMnf/OXxCz9H9rqFaCp2EeMoubC+Uz8ya1YUlI7ND7F5kBJdkByxtEbN48zHDIEF78bosKMXrEXbcdaIh/+PyJLXgG7C/Ow8ZhHTMQ8chJK+oAuucjYWFzHPz7cycbielITHNxy0VjOnpyLzdL7/h+FGkHbsRZ17TK0TSshFECJT8YyfR6WiXMMQaoTPqO9/+9dvrjml8QMzGHWe88RPyy/44NvJ4GwxuJN5Xy1r4G0WDs3zxpCfkrPRjT1DgRGtZ4yDlTtScGIUkmmPRYWIXRE4z6Kv1qFd+8GxibWM3mKjhAgYrMwJQ5HicuG+ByIy8ISm0GKyUJKlxyPiQOiSTMahybMrWu13UFbsSXMASHF06qdCyNp77FbfPoKLpcVlyuBAbkJbdYvW+ZvYxkQQuD1hqmtay3ENEfEGFWXamt9bN9RQ0NDoF22j87EbFawWExYzCZj3jKZsVhMB7a3Wm+3W4iJMR263WzsYzIZiY6FEOi6QNdB13V0IRC6EQnSsq15XXRZtKwT6OJAW03TUSOipV1zv839HNLfIeuM/oTedp0WXbbZzFHBwxA6BuQmtBJA7IcIJ83rNm1cw5lnzcZh7x3CSGfQWZFgEomk7yHFFUmHaQipaALSe8kTg/6A0HxQ8z8IlUFsASTPPGyZ5XA4zO9+9zsefPBBXnjhBRYsWMBZZ53VAyM+FCEEevFmw/qzcQUIMBdMwzrzQkzDxh/2u6LpgnXVHva5g2Q4rQyoKML68Xv4PnwfvakJUlJJvvxK4uddiGP4CPYtW9ZhYeV4UWx2FFsaJKW13XDe1Qi/F23XN2g71qJtX4u2ebWxT1KaEdUyYhLm4RNQ4hIP03P72bK3nn98uJP1u+tIjrNz83dGc+7Jedh6mcgpdA199ybUdZ+ifvM5+D3gisMycTaWSadhGjqu03LbCCHY/MBTbPr1n0mfOZlT//tn7ClJndJ3e9ha4ea/68twByPMGZ7GmaMysJ7w0XxhjAiVcoycITZgIEaUylHKTgthlDuu2YZevQ21chtWggwGSmwxlNvGkT/pFCyZo1BsvaHakpkDgkgzEQzBpVlAacJI2NtMs0jTORaf/oaiKMTF2YmLszMo/9v/l3Vd0NQUbBFiauv8uN1BTOa24ofV0lYIMR8ijLRzu9kkr3s6yL69VpwO+X2XSCT9AymuSDqEX9XwqToJVmkH6ixEsBxq3wc9BClnocSOOmy7r7/+mmuvvZbNmzdz5ZVXct5553XzSA+PCIcOWH/KisAVh3XOfCynXoDpMJEheiiEWlmOp3Q/O7ftJlJRzmh3LY6iHQT3FhOy2YidfQYJ519IzNRpKJbef9pSXLFYxs/AMn4GAHpdhSGy7FiHunEl6hcfAGDKHXrAQjR4LIq1fXaIbSUNvPzhLtbsrCEx1saN80Yx75SB2Hs7VkZ5AAAgAElEQVSRqCKEQN+3HXXtUrT1yxHuOrA5MBdMwzLpNCOSx9K5F9RaKMyXNyxk78tvk3/ld5jy1992W0UgX0jlnY3lrCttJDPewVVTBzIg2dUt7907ERgiQhlGThWBUaFnCJDKkSwtQgjw10DNdkTNNqY2bUB8Yljwqr1OVhfFssc3lILZszjtyglYeqnlrS1WICk6NRPCEFys9FaLT1/EZFJISnKSlORk2NCuiVeSSCQSieRI9P67FEmvRW9tB5LJGTuMUWb5G2hYAZY4yPyOERlxGB599FF++ctfkpWVxeLFi3uFsKLXVx2o+uP3YMoejO17t2EaewpqbT3+rTuIVCwlUl5GpKKMSHkZakU5am1NSx9JAGYz1oxMLHkDSb7yauLOnIs57vB2qL6CKSUL0/R5WKfPM6I4SndFxZa1RJa9QeST18BqwzykAPOISZhGTsKUPfiQJ6I79zfx8oc7+XJ7NfEuK9efO5Lzpw3Eaes9p3K9vBh17VLUdUsRdZVgtmIeMwXLxNmYx05FsR0lUuE4CdU18PnFt1D92dcU3P9Txi68udueKG8qa+K/35ThD6ucMTKd00emYzlhS9FHMKr9lAN+jMuMHAzrz+HFJhFogNrtiOptULsN/IaNRtgT2NmUwqeb4vh4o5XY9Gyuv6aQn80e1A+SI9rpeMJbiUQikUgkvYnec0Uu6XM024HSpB2owwg9HC2zvBOcgyH1LBTToRfeQggURWHMmDFcd911/P73vychIeEwPXYPQgjUTV8Q+N9rhDetRQ2q6PEZ6PYc1M0NRD78LVpDfdudLFasWVlYs3KImX4q7uQMSp1JWLJzGF8wnMTsrD4RnXK8KCYz5oEjMQ8cCWdfgQgF0HZvQNu+zrARvf0XeBuUuCRMIyZiHjGJ/YnDePGLOlZvqSLOaeWauSP4zrR8XI7e8TnpNWWo65airluGqNgLJhPm4RMxz70Sy7jpKM6utWu4d+1l+Xk34ttXxrT/t4j878/r0vdrxhtUeXNDGRvLmshJcHDD9EFkJzqPvmO/xI0hqFRjJHqNA0Zg5A1pK76LkNcQU2q2Qc128FYYG6wxkDaSyMAzWfKNlade2EdNjZ+xYzK4895JnDp9oPytkUgkEskJxfz58/M/+eSThJSUFHXXrl1bmtdXVVWZL7roosFlZWX2nJyc0Ntvv12clpamrVq1yllaWmq77LLLmgBuu+227NjYWO3++++v+rb3ycnJKYiJidEURSE1NTXy6quv7snLyzu0rFsX88QTT6RccMEF7vz8/AjAZZddNvDOO++smjRpUjAnJ6dgzZo127Kysrp9XO2ld1yZS/ocAVXHq+rEW83Y+/wTxJ5FROqjZZYbIHE6xE865AbC6/Vy9913k5aWxj333MN5553XLdEqQgh0j5tIRXk04iQ6319CuHgnalUVerjt+U2x+7FmZWPNysYxchSWrGys2TlYs3KwZudgSU1DMZlQdcHaKjelnhDZMTYmZ8afkLkpFLsTy5ipWMZMBUBvrEXbsQ5t+xoiW9egrfmEdOByUwqX5I9jyKzZuEbnoNh79vStN9SgrV+Ouu5T9JKdAJiGFGCdfwuWk2aixHVPnpPqz9fw2YU/RlHg9E9fIm36pC5/TyEE3+xv5K0N5YRUnbmjM5k9PA2z6US78dcwxJQyDIuLCaOSTjatk7yKSADqdkXFlG3QVAoIsNghZQRK/qmQNgqvOZ1/v7GVf766gcbGIIWTcrjkwlRuuO48KapIJBKJ5ITk2muvrb311lurr7nmmkGt1//617/Omj17tufBBx/cdffdd2fee++9mc8880zZmjVrXGvWrIlpFleOheXLl+/MyspSf/KTn+Tce++9WS+++GJpe/ZTVRVLJz0YfeWVV1JPOumkQLO48tprr+3rlI67CSmuSI4ZXQjqQhGsikKitAN1COHbBXUfgWKB9ItQnAMOafPxxx9zw/9n78zDo6jSvn1Xb+l09p0kkA5JCCEsAQIoASECsogbOIiMGh39BF9fnXFAHBUUBQedeVFncEFmdHQiLogiiAuIyiKLS0ACARJICNnXzp7eu873R0NYJBigWVP3deVK0nXqnFNdvdT51fP8nvvvp6ioiJkzZ3p2fCFwNTTgKC89QTxxVpS7U3cqypFbWk7YR9JqUeskNDoVhphwdH0G4jXoGnQxsWijolEHh/zmQqjV4WJ7eSMNNie9Q3xICjYoi6cjqAJDKY0dyrv5oXwv9yPRr55pXZvo6zyMdHgrvL0Bs1qDqnvyMb+Wbj08Zgh7OkRzA87s73Hu2IB8aA8IgapbIrpbZqAeMBJVUPh5n8PxFC5bzY/3zcEnNpr0L/6FX0L7Zco9RaPFwcpdZeyraCImyMBtqV2J8D8/qU6XLq24o1QqcQssBqAHbmFFg3A5oG7/MTGlvhCEDCoNBCcg9boFwnpBUCySSkN9g4UPPtzNhyvW09JiZ9jQGO77Qyr9UyLZuHGj8tmgoKCgoNBpmTBhQkteXt6vDOTWrl0buGnTpjyAGTNmmEaOHNnTarWWP//881FWq1WVlJTkO2vWrAqA/fv3ew8ZMqRneXm57oEHHqiaO3du9cn9HU96enrzK6+8Eg6wcuVK//nz50fZ7XbJaDTaPvzww8MBAQFydHR032nTptVu2LDBf8aMGdVpaWnm6dOnG00mk0atVosVK1Yc6t27t+2pp56K+PTTT4Ptdrs0ceLEhpdffrk8Ly9PN2HChB5DhgxpycrK8o2IiLCvW7cuf8WKFYE5OTmGjIyMOL1eL2dlZe0fNWpU4qJFi0pGjBhhPn6Or7/+evCSJUsiHA6HNHDgwNbMzMwiTwk858LFn4HCZUdbOpD3lZ0OVF5efv46FzJ+8m585YPYpRDqVUOR69VQf2zMxsZGFixYwAcffEBcXBwrV65kyJAhp5xXVFTUaYaSseXtx15cdGL0yZHfwmo5ob3K19cdZRIZjSF1CJrIKNSSHalkL1LpPlRaFZqUa9COnOQ2YT3D10BVq50fKxoRwLCoACJ9Fd+Bo5RUt7Dsm4NszC5Hr1UzdVQPbh3RHX+D+ztVOOzIh/a0pRA5vngbxxdvg8EPdeKAtpLPqpBIj81JWFpx7t6Ka+cGXHk7QJaRImLQTrgbzcB0VOFdPTZWh+ckBHuefZWcZ18lPH0I13zyCl7B51Z5qSNjZhXV89mecpwuwQ19I7kmIRTVFfwZeCIyUINbVGnEXRY4DIhCyD5QXwS1XyFqcsF0EGQnSCoI7A49JiCF9YKQBCT1sevDWpOZd9/7iY9X5mCxOBmVHse99wwkudeFFekUFBQUFBR+ixc/yu52uLLZo071sV38zLNuS+lQdMjJmEwmjdFodAAYjUZHXV2dRq/XiyeeeKI8KyvLJzMzsxhg5syZ3vn5+fpt27blNTQ0qHv16tVn9uzZNV5eXu3Wrf/ss88Ck5OTLRUVFZqFCxdGbt68+YC/v788Z86cLgsWLIhYtGhRBYBer5d37NiRB9CvX7+kRx99tDIjI6PBbDZLLpdLWrlypX9+fr5+9+7d+4UQjBkzJuGrr77yjYuLsxcXF+uXLVt2KC0trej666+Py8zMDHrwwQfrlixZEn4qMeV4du7cqf/444+Ds7Kycr28vMSdd94Z88Ybb4Q89NBDprN5Lj2JIq4onBFKOtC5oxIWglw/oBO1tKoSaFKluBchJ1FSUsLKlSt58MEHmTlzJt7eHfdyEEJgy91H09ovaPr6K5yVFcfGDwhAGxmNLrY7PkOHHUnZOZK6ExXdZh4rbBacWd/i2LzK7aPhE4D2+jvQDLsR1cnlhzs4pwP1ZvbUtuKvU5MWFYDvJWTEejEpN7Wy7JuDfLezDK1GzW0j4/ndyDgCfE68USFpdW2lnOF+RHM9rgO/tFUicu3a7G4XGoU66UjJ5x79kQxn5nki7DZce3/AuXMDrr0/gtOBFNwF7ajb0KSOQorqftGEVWF3sP2u2Rx+bw3d757EkH/NR607vxWB6s12Pt5ZyoHqFrqH+DAltSthnUYUtOAuo1yB26xWjxCx0OiCmnxEzddgygOnzd08oBvEjToipiQiaX/9uVVR2cx/3/2FVZ/tx+mUGTc2gXvvTiU+LvjCHZaCgoKCgkInYezYsQ3e3t7C29vbGRwc7CgtLdXEx8c7Tm43cuTIRJVKRa9evcwvvfRS2fr1630LCgr0Q4YMSQJwOBxSampqW0h7RkZGPUB9fb2qqqpKl5GR0QBgMBgEINauXeu/efNm/+Tk5GQAs9msys3N1cfFxdmjo6NtaWlpFoABAwaYDx8+3OELq7Vr1/rl5OQYUlJSegFYrVZVeHj4JeHDoqxsFDrM0XQgjaRUBzpbdHINga4fkHBQr74KqyrmhO11dXWsW7eOadOm0adPH7Zv305ExK/LF7eH7VABTeu+oHndl9iLDoNGg8/QYYQ9+Ce8knqhjYxG7Xv6hbZsqsDx/Wc4t38FlhZUXXugvWM2moHXdrhU8Mk4ZUFWZROlLTa6+noxqItfJ66mcoyKOjPvf3uQ9TvK0KgkJl/TnSnp8QR1cOEu+QWhSR2FJnUUQghEdYlbaMndgfPnb3BuWQOSCpWx57EUotheSOpff/QLpwNX3g6cOzbi2rMVbBYk/2A0w25AM/Ba934XQFCRXS5stfVYq2qxVpmwVpvcv6tqsVXXUbP5R5yHykj5659JfmLGeZ2TLAQ/FtbxRU4FQsAtKVEMjQvpBNEqAjDhjlKpc1cya1FBTTOiZq/bhNbR6m7q2wW6pbnFlNCeSF5+7fZaXNzA25k7+eLLAyDBDdf35J6MgcR0u3im3AoKCgoKCh3hbCNMzhchISHOoqIirdFodBQVFWmDg4PbFReOj1JRq9U4nc5TXsgc9Vw5+r8QguHDhzetWbOm8FTt/fz85KPtToUQgkceeaRi9uzZtcc/npeXp9PpdMfPSVgslg4vDIQQ0pQpU0yvvfZaWUf3uVAo4opCh2mwu9OBIrw1nWBx4WGEwEc+gJ+8Bxc+1GlG4JROXFCsWbOGOXPm0NTUxLBhw4iJiemQsCJXVWJa+zlN677EdiAXJAnD4KsIzrgXv1HXoQ78bWNRIQTygZ04Nq3CtfcHkFSo+1+DdsQkVN2Tz2kB22J3sq28kSa7i76hPiQGKf4q1fUW3v8un3U/l6BSSdycZuS29HhCzsG7Q5IkpIgYVBExaEdOQjgdyEW5R8SWLBxfv49j3TLwMqDukdKWQiQa63Du/A7nru/B3AwGPzQD09GkjkKV0M8jXi4uq+2ISFKLtbrulMLJ0cdttfVwii9plU6LPjwEyVfPsOUvY7zt+nOe1+mobbHx8S+lFNS00iPcl98N6Eqwz/mNkLk0qAfyEeYqqKlF1NRDTTFYj/jieQdDZH+3mBLWC8n7tz9fCg7V8Z93drBufT5arYpbJ/cm487+RHZpX4hRUFBQUFBQaJ9x48Y1LF26NGThwoWVS5cuDRk/fnwDgL+/v6ulpcUjdzDT09NbZ82aFZOTk+PVp08fW3Nzs6qwsFDbr18/2/HtgoOD5S5dutjffffdwLvuuqvBYrFITqdTmjBhQtMzzzwTNX369LqAgAC5sLBQe7yocip8fX1djY2Np734HD9+fNPkyZMTnnzyyaro6GhnVVWVurGxUZ2YmGj3xHGfC4q4otAhrC6ZZoeMn1aNXkkHOiMk4SDA9TPeogyLFE2jejBC0rZtr66uZs6cOXz55Zf069ePDz/8kJiYmNP0CHKdCceWTTg3fYcrdx8tgL5vCuGzn8T/uvFowjrmWSBsFpw/rcfx/WpEZRH4BqId+3t36k9g6LkcNgAVrTZ+qmhCAq6JDiSiUyxO26e20coH3+Xz1U/FSEjccHUMU69NIDTA84aokkaLOr4v6vi+MPEehLkZ18Fdx1KIcrYfa6zTo+6bhiZ1FOqkVCSNtv2OOVKCu7n1mGByslBy0v+OppZT9qPxNaCPCEUfHoJfQgxhwwa0/a+PCDnydzD6iFC0AX5IksTGjRsxpqd78Jk6EVkIthaY+GpvBWpJ4ncDohkSG9wJBEEzwr4XSncgig5Bw5GbTF7+EJaEFOoWU/AJ6/BzsW9/NW+9s4MNGwvx9tZw5+9TuPP3/QkN8WjKuoKCgoKCwhXLjTfe2P2HH37wq6+v10RERPR7/PHHy//85z/XPvvssxWTJk2KNxqNoVFRUfZVq1YVAEyYMKF50aJFkUlJSclHDW3PlqioKOfSpUsP33777XF2u10CmDdvXtnJ4grAsmXLCu+//37jggULorRarVixYkXB5MmTm/bu3asfPHhwEoDBYJDfe++9Qo1G067AkpGRUfvwww8bZ8+eLWdlZe0/VZvU1FTr3Llzy0aPHp0oyzJarVYsXry4+FIQV6T2wnjOJ4MGDRJZWVnnfZyNGzeSfh4vwjsLshBUmN2v1UiD7pKOWvHkOfeEoa1GNBLk3I6aFppVfWlVJcJxz5/D4eCaa66hurqaWbNmMWPGjHZLmYnmJhzbvsex6Ttce7JBllF1j0c74lqip9yOLrrjxqJyTTmO71fj/HEtWFpRxSSiGTEJzYCRZ536c8JchSC3zsxeUysBXhrSogLw0Xo+lexyeY+bmqws31DAFz8WI4Rg/OBu3D4qgfDAjvvoeBq5thzXgV1I3j6oe18FGh22uoZfRZJYq0zYqk1YjkvNsVbV4rL+6nsVAK+QQLxOEEd+LZToI0LQh4egMZz58Z/Pc17dbOWjHaUU1ZlJivDj1gHRBBqubEFQCBtUbUAU/wgVJSDLENANqVsaRPQGv+gzFpZ2ZVfw5ts72La9GD8/L26/rS/TpvYj8CxExMvlPa7gOZRz3rlQznfn41I955Ik7RBCDDr+sezs7MMpKSm17e2j0DnJzs4OTUlJiT3VNiVyReE3abC7cAqI0GsvaWHlUkMvFxPgykKgoU49ErvqmAlsTU0NoaGhaLVa5s+fT1xcHAkJCb/qQ1gsOH/YimPzdzh3ZoHTiSoqGt1td6AdeS3qmFgAdKepFtTWlxBuL47Nq3Dt+9Gd+jNgJNoRt3jUT8Mhy/xc2Ux5i41ufl6kRvijUXXO1019s42PNhawZnsRTlkwblBXpo1KoEvwxblzL2SZlkMl1Gfn0pCdS312Lq1F5W4BpaYO4XL9ah9JrW4TRbzCQ/BPisM7IuSIgOIWSrwjQvEKD0YfFoxKe/qol0sRlyzYnF/D1/uq0KpV3D6oGwO7BV7R0SqiuRxRtBZKdoLVDDpv6J6OFDMCKfD0kXOn7E8Ifvq5lDff3sGOneUEBup5+MGrmXJrH3x9r2yBSkFBQUFBQUEBFHFF4TdwpwO58NWq0GuUdKAOIWT85Wx85HzsUij16quRJfcdeiEE7733Hs899xzz5s1j2rRpjB079sTd7XacWT/i2PQdzp9/AJsNKTQM3U2T0Y4YhSqhxxkt+oTswrn1CxybPkVUlyD5BaEddyeaYTegCgjx6KE3H/FXabG76BfmS49A7yt6gdoeja12VmwqYPXWIhxOF2NSu/L70QlEhfhcsDk4W8007DlwTEjZlUvDnjycLe7KdpJKhV/P7vjGxxAyqM+xiJKTIkx0QQFIV7D5cGWjleU7SihtsNAnyp9J/aPx119+AlFHEA4zlP6EKN4EdUXuKLqIWCTjKOhyFZLqzC8JhBBs3lLEW29nkbO3mrAwH2Y9MozJtyTj7X1lPo8KCgoKCgoKCqdCEVcU2kUWApPViVqCIKVkbodwl1nejk6YaFH1oFnVr63MclFREbNnz2br1q2kpaWRlpbWtp9wOnHt2olj8wYc27eAuRUpIBDtmPFoR45C3av3WS1whcOOLfN5XNnfozImobvrCdQDRvymp8bZUN5i46fKJlQSXNM1kPArPJ3iVDSZ7Xyy+RCrthzG5nBx7YBo7hzdg+iw8yeqCCEwl1ScEI3SkJ1Hc35RmzGsNsCPoJQk4v5wK0EpPQlMSSKgdw803p73erlccMmC7/Kq+Ta3Gr1WxZ1DYugXHXDFiYFCyFCTiyjeAuU7wOUAvwCkPldDt+uQ9LHAmR+zyyXz7YZD/OedHRw4aCIq0o8n/zKSm25IQqdUk1NQUFBQUFDohCgrZoV2abS7cApBuJIO1CF0cvWRMssu6tVXY1V1a9u2YsUKnnjiCdRqNX/729+44447QAicOdnuCJUtmxFNjeDjg3bYNWhHXIs6ZSCS+uwXKaK1Ceu/n0Y+lINu0gNor/2dJw7z1+MIwf46M/tMrQR5aRgaFYDhPPirXKrUt9jYlW9iV34tm7IrsNidpKdEcceYHsSEn77s9Znistpo3Jf/KyHFXt/Y1sY3PoaglCRi77yRoJQkAlOS8DGeuW/GlUxpvZmPdpZS0Wilf9dAbk6Jwtfryvo6FK3ViKKtULwNLCbQekFMdyRjTwgcjCRFAWcu2DqcLtZ9fZD/vLOTw0UNxBoDmT9vFOPG9kCr6TzvewUFBQUFBQWFk7myriYVPIbNJdPkcOGrUeGtpAOdHiHwkfPwk/fgxI8GTRpOyf+EJsHBwQwdOpQXXniBLq3N2N56A8fmDQhTLXh5obkqDe2IUWgGDfaIoaxcV4V1yROI2gq87pmLZmD6Ofd5KhwumZ8qm6hotWP01zMw3A/1Fe6v0mpxsPtQHbsKavkl38ThymYAfPQaruoVzrRRCcR6oMSspbLmVyJKU+6hNl8UtcGbwH49ibltfJuIEtg3Ea2fZwWdKwmnS+ab3Go2HKjGR6fh7quN9IkK+O0dLxOE0wplWYiiLWA6AEgQHofUpw9EdkNSxwBG4Mwj1+x2F599nss7mTspr2imR0IIL/x1LKOvjUOtVJBTUFBQUFBQUFDEFYVfI4TAZDuSDnSF3c31NJJwEOj6Cb0oxyJ1pVE9CCFpcTqdLF26FFmWefjhh0lPiCNtzEic8/5Ca0U5aDRoUoegvXcGmqvSkLw9VzXGVZqP7Y05CIcN/YMvoO6R4rG+j6fJ5vZXaXW46B/mS/wV6q9itbvYV1TPL/m17Mo3cbC0AVmAl1ZF79hgRg2IYkBCKAlR/me1yJQdDpryCo8TUvJoyM7FWnXMnN7QLZLAlCS63jK6TUjxjY9BdQ6RTZ2N4jozy3eUUN1sY1BMEDf2i8RwBaQ7CiHAdMAtqJRlgcsGPhFIyeMgJhjJWwuEAnHAmRspW6wOVn66j8z3dlFT00qf3hHMnnUNI4Ybr8j3u4KCgoKCgoLC2XL5X1kqeJwGuwuHrKQD/RYa0XCkzHIrjaoUzKoeIEns37+fmTNnsnv3bm4YkELz/l2IokJQqVD3G4D2tjvQDh2O5Hfu0Q0n48rdgfWtZ5EMPnj/7z9QRcZ6fAyAsmYrP1c2o1bBiK6BhF1B/ipOl0xeSQO/HEn12V/UgMMlo1ZJJMUE8vvRPegfH0KSMRDdGaZB2OoaTohEqc/OpXHvQWS7AwCVTktA7x5EXT+CwJQkt5DSrydewYHn41A7BXanzLr9lXx/sBZ/by33pXUnyQORRRcbYTZB8VZE8VZorQGNHrpehWRMgWAXktQC+AIJwJm/flpa7Kz4JIdlH2RTX29h0MAo5j89iiGDuyqiioKCgoKCwgUgPz9fe8cdd3SvqanRqlQq7r777pqnnnqqGqCqqko9adKkuLKyMq/o6Gjb6tWrD4WFhbm2bdvmXVJSops6dWojwMyZM6N8fX1d8+fPrzrdWNHR0X19fHxckiQRGhrq+OCDDwpjYmKcF+I4j2fx4sUhN910U1NsbKwDYOrUqcbHHnusKjU11RodHd03Kytrf2Rk5AWfV0c5Z3FFkqRuQCbQBZCBfwkh/nmu/SpcHI6mA/ko6UCnxVsuIsC1AxktJnU6DlUodrudxX/7G6/++9/4azT8IzaKccKKyscH7QMPoxk+ElVQ8Hmbk+On9djfX4TUJQb9A8+jCgz1+BhCCPaaWsmtMxOk1zA08vL3V5FlwaGKJnblm/glv5Y9hXVY7S4kCeKj/Ll5WCwDeoTQJzYY7w5GcglZpjm/qE1IORqNYi6paGujjwglMCWJnn/KaBNS/Ht2vyxLGV+qHKptZcWOEmpb7VzdPZiJfSLRX8avV+GyQ/lOd5RKzX5AQFgvpKSbISoZSVMG1AA6IAmI4EzNauvrLXywfDfLP86hudlG2tAY7rsnlQH9Iz1+PAoKCgoKCgrto9VqefHFF0uHDx9urq+vVw0YMCD5+uuvb0pNTbXOmzcvMj09vXnhwoUHn3zyyS5PP/10lyVLlpRlZWUZsrKyfI6KK2fCpk2bDkRGRjofeuih6KeffjrynXfeKenIfk6nE43GMzEby5YtC+3fv7/lqLiyfPnyIo90fIHwxLPgBGYJIXZKkuQH7JAkab0QYp8H+la4gByfDhSspAOdmuPKLNukMBrUV+NssuHctoacVZ/yyufrmBDkx5PDriZi7Hi016SjCu9ynucksH/9Po7P/4MqcQD6++YheXved8Pukvmxookqs53u/nr6X6b+KkIISmta3Wk+BSayC0w0m92RI93CfRg7qCv940PoFxeCv0/HInKEEFSu30rJJ1+7o1L2HMBltgAgqdX494onfMSgY9EoKUl4R3he/FJwY3O6+Cqnkq2HTAQbdMwYHkeCh82FLxRCCKg/5BZUSn8CpwUMoUhJN0HMMCSfQKAIyMYtpBiBGODMRKTyimaWvb+LVav3Y7U5uTa9O/fdk0pyr3CPH5OCgoKCgoLCb2M0Gh1Go9EBEBQUJMfHx1uKi4t1qamp1rVr1wZu2rQpD2DGjBmmkSNH9rRareXPP/98lNVqVSUlJfnOmjWrAmD//v3eQ4YM6VleXq574IEHqubOnVt9unHT09ObX3nllXCAlStX+s+fPz/KbrdLRqPR9uGHHx4OCAiQo6Oj+06bNq12w4YN/jNmzKhOS0szT58+3WgymTRqtVqsWLHiUO/evS7bSVUAACAASURBVG1PPfVUxKeffhpst9uliRMnNrz88svleXl5ugkTJvQYMmRIS1ZWlm9ERIR93bp1+StWrAjMyckxZGRkxOn1ejkrK2v/qFGjEhctWlQyYsQI8/FzfP3114OXLFkS4XA4pIEDB7ZmZmYWeUrgORfOeQZCiAqg4sjfzZIk7QeiAUVcucxoPJIOFKbXKOlAp8Dtr/IDelFJkzWG+p8aaPluLt9s/p7x/j4kdu3Gusdm0uPWKai7xlyYScky+vWZOH75DnXqKLzumH1eyiw3HvFXMTtcDAz3Iy7Qcx4xF4Lqegu7CmrbolNMTTYAwgO9SUuOICUhhAHxoYQEnHlp4urvs8ie8zI132ehDfQnqH8SCfdPaRNSApITUOu9PH1ICu1woLqZj3eW0mB2MDw+hPG9u+B1GVaxEZZ6KNnurvjTUgFqHUQNQjIOh9BE3B/RlcCPgAN3lEp34MxewwWH6ngncydrvz6IhMT1ExLJuLM/cd3PX5SdgoKCgoLC5cZHO0q6VTZZz9y87DR08debb0vt1qHokLy8PN2+ffsMI0eObAEwmUyao8KL0Wh01NXVafR6vXjiiSfKs7KyfDIzM4sBZs6c6Z2fn6/ftm1bXkNDg7pXr159Zs+eXePl5SXaG+uzzz4LTE5OtlRUVGgWLlwYuXnz5gP+/v7ynDlzuixYsCBi0aJFFQB6vV7esWNHHkC/fv2SHn300cqMjIwGs9ksuVwuaeXKlf75+fn63bt37xdCMGbMmISvvvrKNy4uzl5cXKxftmzZobS0tKLrr78+LjMzM+jBBx+sW7JkSfipxJTj2blzp/7jjz8OzsrKyvXy8hJ33nlnzBtvvBHy0EMPmTr+7J8fPCrvSJIUCwzAfbV38rbpwHSAiIgINm7c6MmhT0lLS8sFGedKQOdtIKpXX1rrTRQW5l/s6Zw1njznDoej7W+d2sGA6BLUrib2v7oDfn6TXQ2NzCmv4bDZSsBfF9B10BCQJHIbm6Fxr0fmcDpUTgc9dq7Bq+ogFYlDKY0ZClu2enwcm96f5sAoVMKFf10pxeUWij0+ytnR3vlutQmK6lwcrnVRZHJRb3Z/fxh0YAxRMyRGR2yImkCDhCTVQ1M9e345s9e9Pa+I5v+sxvbTXlTB/gT88XYME4cj6bQ0A81ASVMN/FBz7geq0EZ759whS+y3eFNi0+OjcnG1XysB9XVs33Lwwk/yLJGEixBHCV1sBQQ5y5EQNGrCqTQMpVYXi6tFC3srCQqqJD4efH2hoQEKCqC5uQo4bTr1CRwuMvPtxlr27mtBp5UYNjSIkdeEEBSoorhoN8WXSBCu8j3e+VDOeedCOd+dD+WcnzmNjY2qyZMnx7/wwgslwcHB8pnuP3bs2AZvb2/h7e3tDA4OdpSWlmri4+MdJ7cbOXJkokqlolevXuaXXnqpbP369b4FBQX6IUOGJAE4HA4pNTW15Wj7jIyMeoD6+npVVVWVLiMjowHAYDAIQKxdu9Z/8+bN/snJyckAZrNZlZubq4+Li7NHR0fb0tLSLAADBgwwHz58uMN3IteuXeuXk5NjSElJ6QVgtVpV4eHhl4QPi8fEFUmSfIFPgEeEEE0nbxdC/Av4F8CgQYNEenq6p4Zul40bN3IhxrncEUJQYXHgEoKkmGh6G7te7CmdNZ485+Xl5QBoRCPBzu9xlNVQ9Px3WEvKWazzJbOglK5du/LBW39nxIgRHhmzo0jmZgwfv4S66hCW6+4i4ca7SfDwGLIQ5NS2cqDeTIhey9VR/nhroj08yrlx9Hy3Wo+UR843kV1Qy6EKd3lkg15DSlw4/RNC6B8fQmwXv3M242zcl8/up/5J7cqv0QUH0v/vs0n83zvQGC6vaJ7LlVO9x/dXNvHJL2U02RykJ4YxtlcE2sukPLAQAhqKEMVboORHcLSCdxDEXY9kHE6QbwRBba1bgQKgDneESjyBgaGkpnbsNS2EYNsPJbyTuZMdO8sJ8Pdixv2DmTqlL4FnEbV1IVC+xzsfyjnvXCjnu/NxuZ7zjkaYeBqbzSZNnDgxfsqUKXV33313w9HHQ0JCnEVFRVqj0egoKirSBgcHtysuHB+lolarcTqdp7xwOOq5cvR/IQTDhw9vWrNmTeGp2vv5+clH250KIQSPPPJIxezZs2uPfzwvL0+n0+mOn5OwWCwdvnATQkhTpkwxvfbaa2Ud3edC4RFxRZIkLW5h5T0hxEpP9Klw4Wh0HEsHUivpQCegk6sIcm2j5ediKl9ej1Ot4feNVvIO53Hvvffy+OOP4+Pjc0HnJDVU47P8/1A112Ge9BDOnoM9PobNJfNjRSPVZgdxAd70D/e9pFLFbA4X+w7XszHPzsqcrRwobUSWBTqNit7dg/nDeHd55B7RZ1ce+VS0HCph9zOvcHjZZ2h8DfR95mGS/nwPWv/L08vjSsBsd/LZ7gp2FNcT4e9FxlUJxAR7NGL3vCFsTVDyg9tLpakUVBqIGogUMxzCk5Gk41+3duAwUI7bSyUO6Ap07LXtdMp8+10Bb2fu5MBBExHhPsx6ZBiTbk7GYFAMlBUUFBQUFC5FZFnm9ttvNyYmJlqfeeaZE8JTx40b17B06dKQhQsXVi5dujRk/PjxDQD+/v6ulpYWj1z8pqent86aNSsmJyfHq0+fPrbm5mZVYWGhtl+/frbj2wUHB8tdunSxv/vuu4F33XVXg8VikZxOpzRhwoSmZ555Jmr69Ol1AQEBcmFhofZ4UeVU+Pr6uhobG0+bzz1+/PimyZMnJzz55JNV0dHRzqqqKnVjY6M6MTHR7onjPhc8US1IAt4C9gshXjr3KSlcSOwumUa7C4NGheEy9CU4n3jLh/F3/EztxznUf7AFVVw8AXPn8+TuHPR6PcOHD7/gc1JVFuLz0Ysgu2i9/S+4uiZ6fIwGq4Nt5Y1YXTKpEX50D7j4ERlOl8yB0ka3CW2+iX1F9TicMioJkmIkpl0bT/+EUHqdRXnk38JcVkXOc69T8ObHqDRqej16L8l/uR+vkKDf3lnhvLGnrJGVu8ow252M7hnOmKRwNJd4tIqQnVC5xx2lUrkbhAuCuiP1vwuir0LSnSwMyUAZbmHFBUQBsbirAf02NpuTNV/kkfneL5SWNhEbG8gzT41iwrgeaC/jqkkKCgoKCgqdgfXr1/uuWrUqpEePHpakpKRkgGeffbZs6tSpjc8++2zFpEmT4o1GY2hUVJR91apVBQATJkxoXrRoUWRSUlLyUUPbsyUqKsq5dOnSw7fffnuc3W6XAObNm1d2srgCsGzZssL777/fuGDBgiitVitWrFhRMHny5Ka9e/fqBw8enARgMBjk9957r1Cj0bQrsGRkZNQ+/PDDxtmzZ8tZWVn7T9UmNTXVOnfu3LLRo0cnyrKMVqsVixcvLr4UxBWpvTCeDncgScOB74E9uK8EAZ4UQnzZ3j6DBg0SWVlZ5zRuR7hcw84uFEIIKi0OnEIQZdBdEVErnjjnQgho/BG58nvKFm/DvH0/tmEjyBk8jGuvu84zEz0LNAXZGFa9ivD2o3Xqo8ghUW3boqKiTrNnxylusrKjqgmdWsXQyACCvS/eXe1DFU3sPFhLdr6J3YdMWI6WR470d6f5JIRSX7qXcWOuPS/jW2vq2PfCvzj4+vsIl0z8/VPoPecBDFER52U8hY6x7rtN1PjEkl3WSFSAnttSuxF9CRssC9kFTWWI4m1Qsh3szeAVADFDkWKGIfmfKtVOALW4U4CsQDAQD3QsSq6lxc7HK3N474NsTHUWeieH84e7B5I+ojuqy6zCl/I93vlQznnnQjnfnY9L9ZxLkrRDCDHo+Meys7MPp6Sk1La3j0LnJDs7OzQlJSX2VNs8US1oC+76jwqXGU0OF3ZZEKqkA7UhhAtM32LP/4GShd/gLKuFu/8fD674lKx//5etW7d6TMg4E7TZm/Be+zZyeDdap8xC+AZ6tH9ZCHbXtJDfYCHUW8vVkQHoNRcnCkAIwRtr9vHplsMAdA3zYUxqV/onhJByUnnkjZWeL0pmb2wm98X/kPvyO7jMVrpn3EKfef+Lb+zl60V0pbCvoonNjQG4GpsYnxxBemL4JVEOXLgc0FoNrTXQWo1orXb/31ID5lp3hIqkhsj+SDHDIKIvkqq9yJFmIB9oBAxAP9ziym9jMpl5f/luVnySQ0uLnauHdOWvdw9kcGr0OXsNKSgoKCgoKCgonJ6LXwxa4aLgkGUa7C4MahU+SjoQAEK2Qc0XtG7bSvlLGxCSGu3Tf+XB199g27ZtLF68+MILK0LgtXU1+i0rcXTvg/mWh8HLs3fprU63v0qNxUFCoDf9wi6ev4oQgtdW7+WzbUXcnGZk6rUJhF4go01nq5m8V5ax/+9vYq9vJGbKePrO/yMBSfEXZHyF9nG4ZNbsqWD7IRP+apn705PocoENWIXdfERAqT5OQHGLKVjqT2ys8QafMAjoBtGpSL4R0KU/kpffaUawAYdwV/zRAj2ASDriq1JW3kTmsl189vl+7HYXo6+N556MAST3Cj/bw1VQUFBQUFBQUDhDFHGlEyKEoNbqRAUEeykvAQDhbEJUraZu+bfULvsRrx49Uc+ew5+ff4Gvv/6av/71r9x6660XdlKyC+91/0WXvRF7n+FYJtwLas+erzqrg+3ljdhcMoO7+GH0v3jpFbIseHVVDp//UMxt6XHcNyHpgtxtd9ns5P9rOXv/+gbWqlqiJqbTb8GfCB6QfN7HVvhtyhstvP9TMVXNNkYkhGIwHTgvwooQAmyN0OIWTURr1THxpLUG7C0n7uDlDz7hENoLyTfcLab4hLt/dL5n8Np1ASVAMe50oG6AkY58PR/MN/FO5k6+/iYfSZK4cWJPMu4cgDHGs5FtCgoKCgoKCgoKv42ysu6ENB9NB/LSXBIh9RcbYa9GLv6Yyn98RfPWg/iNvZ7IZ57jnQ8+ZOXKlTz++OPcc889F3ZSdhuG1a+iLcjGmnYTtmtuBQ8LDYcbLeysbkavVnFttyCC9BfPX0WWBf9cuYevfiph2qh47hnX87wLK7LTSeF/P2XP/NcxF5cTnj6Ea1a+QljawPM6rkLHkIVga0EtX+RUYtCq+X/DutMzwo+NGw+cdZ9CdoHFdCx9p6X6hHQeXMf7oElgCHGLJVGDkI6KJ0eEFElzrgKPwB2lcgh3NaAw3FWAflvg/GVXBW9n7mTL1iIMBi2/n9qPO6alEB6uVK5SUFBQUFBQULhYKOJKJ+NoOpC3WoXhInlqXEoIy2Hsez6g7PkvsBfXEfanRwm++z4kSWLcuHEsX76cYcOGXdA5Sa1NGFa8iLrqMJZx92AfMMqj/ctCkF3TQkGDhbAj/ipeF/G14JIF//h4N+uySrljdAIZYxPPq7AiZJmij75iz7zFNB84TMiQflz91l+JGD1U8aW4RGiyOlieVcKB6haSu/gxJbUbvh2MshMu+wkRJ6LluAgUs8ntf3IUleZIxEkEhPVCahNPwsEQgqQ6X1+RDbjNapsBPyAZOH20iRCCLVuLePu/O9m1u5LAQD0PzhjClFv7EHCBU6QUFBQUFBQUFBR+jSKudCKEEJisTiTc6UCdfSEpmvfQ+l0m5YvWg6Sl6ytL8U27hldffZURI0YQGhp6wcstq+qrMCz/P1QtDZgnP4KzxwCP9m91uvihoolai4MeQd70Db14/irgFlZeXJHNNzvKuOu6Htx1nedLSx9FCEHZ5xvYPfcfNOzOI7BvIiNWv070jaM6/XvhUmJ/RRPLd5Zgc8hM6h/N0O7Bvzo/wt56ooHs8REo1pP8T7TebrEk0AjRg90RKL4RblFFH4gkXUhh0YJbVKkFvIAkIILTecI7nTLrv83n7cyd5OfX0aWLL4/NGs7NN/XC+yJGmykoKCgoKCgoKJyIIq50IpodLmyyIMRLg6YTpwMJIRD1W6l/9y1qMn9A1z2eri+/jq5bDG+88QYPP/wwDzzwAE899dQFnZe6vADDipcAaP39E7iiPGukarK4/VUcssxVXfzp5n9x73a7XDJ/X57Nhl3l3D0ukTtG9zhvY1V+u53sOS9j+jEb3wQjae+/iHHq9UgqJXrrUsHhkvl8TwXbDpmI9PfizqtDCJMaoOQAcmvVkeo7VQxtKEN8kXnizl4B7oiT8ORj6Ttt/ic+l4B45gSKgFLcQkosbm+V9s3ErVYnn32eS+ayXyivaCauexDz541m3NgEtIoJuYKCgoKCwhWP2WyWrrrqqiS73S65XC7pxhtvrH/55ZfLAaqqqtSTJk2KKysr84qOjratXr36UFhYmGvbtm3eJSUluqlTpzYCzJw5M8rX19c1f/78qtONFR0d3dfHx8clSRKhoaGODz74oDAmJsZ5IY7zeBYvXhxy0003NcXGxjoApk6danzssceqUlNTrdHR0X2zsrL2R0ZGXvB5dRRFXOkkOGTRlg7k04nTgYRwIpd+QeX//Zvm7/PxHT2WqPnPozL48P777/Pggw9yww03sHjxYmpqai7YvDQHf8Gw+jVk30DMtz2KHNzlnPt0yQK7S8YmC2rNdnbXtuCtUTG8axCBXhf3jrfLJfO3D7PZmF3OvRN6cvu1CedlnJrtv7B7zstUbfgRQ7dIhvz7OeLuvgWVVrnjf7ERQoC9GVqqaagpZX9BPjE2EyP1LQRY65E2mxFtrY/5n9RoY4lO7H+cgBKGpPG6iEdyOqxAOVABOIAuQHfcUSunprnZxopPcnj/w93U1Vvo2yeC2TOHc83wWFSdWBRXUFBQUFDobOj1erFly5a8gIAA2WazSYMHD+757bffNo4ePbp13rx5kenp6c0LFy48+OSTT3Z5+umnuyxZsqQsKyvLkJWV5XNUXDkTNm3adCAyMtL50EMPRT/99NOR77zzTklH9nM6nWg0npEVli1bFtq/f3/LUXFl+fLlRR7p+AKhiCudAHc6kAPo3OlAwmXFkfMuZc/+F9thE6H/+wgh981AkiQ+++wzMjIySE9P56OPPkJ7ARfful++Q//1f3FFxGKeMhPhE3DivAXIgEOAU4BTSG1/O4SEEyipcFf8sbuOCCougUuIE/qJMOi4KtIfnfriimtOl8wLH+xi8+4K7p+YxJSRni91XJ+dS/bcf1D++Qb04SGk/nMOCdOnotZfqovwKxN3BZ6mtqgT0Vp9pBpPlfu30wJAADAECac+GF1AF/BJdJcv9gl3p/AYQpHU7vdk/saNdO2RfvEO6jcRQB1uUcV05LEQ3NEq7Zdirqlt5f0Pd/PxJzm0mh2kDY3hDxkDGDggqtN+ZisoKCgoKHRmVCoVAQEBMoDdbpecTqd09Jpg7dq1gZs2bcoDmDFjhmnkyJE9rVZr+fPPPx9ltVpVSUlJvrNmzaoA2L9/v/eQIUN6lpeX6x544IGquXPnVp9u3PT09OZXXnklHGDlypX+8+fPj7Lb7ZLRaLR9+OGHhwMCAuTo6Oi+06ZNq92wYYP/jBkzqtPS0szTp083mkwmjVqtFitWrDjUu3dv21NPPRXx6aefBtvtdmnixIkNL7/8cnleXp5uwoQJPYYMGdKSlZXlGxERYV+3bl3+ihUrAnNycgwZGRlxer1ezsrK2j9q1KjERYsWlYwYMcJ8/Bxff/314CVLlkQ4HA5p4MCBrZmZmUWeEnjOhYs/A4XzTovTHb0Q3InTgYSjEfM3/6T8hZUIWU3Xf76B7zUj3duE4K233mLgwIGsXr0ab+/zV45YCHfhVadwRxP5blmJ9w+raemewuGJD2NX63Fa3Pe4nUI6IqCAOI0ngxqBHideagm9RoW/ToOXWkKnVqFTq/BSS1BVQ5iP10UXVhxOmYXv72RrThUzbujFrSPiPNp/U94hds97heLlX6IN9Cdl4Ux6/vEuND4Gj46jcIwTShgfFVCOiietVeC0HWssqcAQCr7hOAK6s9OkY2+zN4Gh0Ywf1Adfw+VszGrHHaFSDtgALRADRAHtH1dJaSOZy35hzRd5OJ0y142O556MgfRMDL0Qk1ZQUFBQUFDoAFmVTd0abU6PXlAGeGnMg7r4nzY6xOl00qdPn+Ti4mKvu+++u3rUqFGtACaTSWM0Gh0ARqPRUVdXp9Hr9eKJJ54oz8rK8snMzCwGmDlzpnd+fr5+27ZteQ0NDepevXr1mT17do2Xl5dob8zPPvssMDk52VJRUaFZuHBh5ObNmw/4+/vLc+bM6bJgwYKIRYsWVQDo9Xp5x44deQD9+vVLevTRRyszMjIazGaz5HK5pJUrV/rn5+frd+/evV8IwZgxYxK++uor37i4OHtxcbF+2bJlh9LS0oquv/76uMzMzKAHH3ywbsmSJeGnElOOZ+fOnfqPP/44OCsrK9fLy0vceeedMW+88UbIQw89ZGpvnwuFIq5c4ThlQb3NiV4t4dtJ04FkawX1bz5Lzdub0MV0o+s//oXOGNu2XZIkPvroI8xmM35+7d9ZPhkh3E4KRwWQ4yNKTvn3kbYCCcnlpM+GtwjM3UxJ8rXsTb8XgQqNE7QSaCTwkgS+KvffGklue1wrCfdjuNtKEkRFRZx6jrLM7nmL2fvcEgD0XcLwT+qOf1LcsZ+e3fGJiTrv/iMOp8xz7+1k+94qHrw5mVuGdfdY361FZex59lUK/7sKtbee3nP/h16z7kUX6O+xMTozQshgbTwWgdJyzAPlVyWMJfURASUCQhPdFXjaIlDcFXj2Vzbx0Y5SrA4XN6REkhYXcplGZwjclX/KcZvUCtxVf+KBUKD991TegVreydzJ+m8LUKslbrohibvuGEBMt4B291FQUFBQUFDoXGg0GnJzc/fV1taqJ06cGP/zzz/rBw8ebD2TPsaOHdvg7e0tvL29ncHBwY7S0lJNfHy84+R2I0eOTFSpVPTq1cv80ksvla1fv963oKBAP2TIkCQAh8Mhpaamthxtn5GRUQ9QX1+vqqqq0mVkZDQAGAwGAYi1a9f6b9682T85OTkZwGw2q3Jzc/VxcXH26OhoW1pamgVgwIAB5sOHD3c4vHzt2rV+OTk5hpSUlF4AVqtVFR4efkn4sCjiyhWMEAKTzf2+CfHSXqaLl3PDVb+PqgVP0rQhF9+RI4h87iXUvr4A7Nmzh8cee4xly5YREhKCl9dvv6dlAQesKppc7nSc9qp8SIgjQoj7TeatEsdEE0crXb98BZ+iPTSmTUI77BYGqQQaXJztKSovL//VY84WM/v/uBDTuq1E/G4sPkndMecXY84vpu7DL3A2NLe1Vem98I7riiEh5thPfAyG+K6ovc89msDhlHn1i0KyC5t46Jbe3JQWe859ArjqGsl6eAH5S5eDSiLxTxn0fnw6+vAQj/TfmRBCBkvDkaiTk1J4Wmt+LaAcrboT1gvJN9xdztg3HLxDkFSnNlx1uGS+zC5jS4GJLv56ZlwTR5eLbKx8djiAKtyiihn3uzwad5RK+ze1hBDs/KWctzN/Ydv2YnwMWjLu7M+0qf0IC/W5EBNXUFBQUFBQOAt+K8LkfBMaGuoaPnx485o1awIGDx5sDQkJcRYVFWmNRqOjqKhIGxwc3K64cHyUilqtxul0nnLFcdRz5ej/QgiGDx/etGbNmsJTtffz85OPtjsVQggeeeSRitmzZ9ce/3heXp5Op9MdPydhsVg6fJdXCCFNmTLF9Nprr5V1dJ8LhSKuXMG0OGWsrs6bDmTP/4ayx5/FVlBDyIwZhE7/U1t0Rn5+Ptdddx0ajYbm5mZCQjq2GC9zSNS5VIRpZLwk0ErykciSYxElWsl9v/pUQonU0oDPihdRVZdgnnAfpIzkfCQhWYoryPnDHFoPFpGw4GGi/zDpBHFNCIGjrtEtthSUtIkuzbvzqPl8E8hyW1uvrhHHxJY24aUbuvBfl8g9FXanzKufF7L7cBN/nNyHG642nvPx2Uz17Pv7m1T/879UuwTx991Kn7kPYuh67kbAnQVhN8PhTYi6/GMRKPJx38sqjVtA8YmA8N5IR8UTnwgwBJ9xCePKRivv/VxMZZOV4fEhXN8nEu1FTlM7MwTQjFtQqcbthOSPu5xyGKer/CPLgu+3HObtzJ3s3lNFcJA3D/3PVUy5tQ9+fooPkIKCgoKCgsKvKS8v1+h0OhEaGupqaWmRNm7c6P/oo49WAowbN65h6dKlIQsXLqxcunRpyPjx4xsA/P39XS0tLR65wEpPT2+dNWtWTE5OjlefPn1szc3NqsLCQm2/fv1sx7cLDg6Wu3TpYn/33XcD77rrrgaLxSI5nU5pwoQJTc8880zU9OnT6wICAuTCwkLt8aLKqfD19XU1Njaetizi+PHjmyZPnpzw5JNPVkVHRzurqqrUjY2N6sTERPvp9rsQKOLKFUpnTgcSQmDe9Bblz7yKcAiiX1qM37Vj27aXlJQwZswYXC4XGzZsIDY2tkP9ml1QalcRopHpoZd/e4eTUJnK8Vm+CMnSjPl3f8YZn3LGfXSE+m272Dt9HghBv2V/J3hE6q/aSJKELiQQXUgggVf1O2Gby2rHcrisTXAxF7h/V/z0BbL5WBSi2s8HQ0K3E0WXhBi8jVGodG7zUbtTZvFnh9hb3MwfxnQ7Z2HF0dRC7j/eIffFt3E0t+I9ZghjljyHX3zMOfXbmRDWRkT+11C40W0o6xcJvl0got+RCJQjKTzeQWcsoJxyPCHYdsjE53sq0GvV3JcWS1KXyyldy4lbTCkHWnBLpxG4o1R+O40w70Atc+d9Q8GhOqKj/HnisRHcODEJvV75+lVQUFBQUFBon5KSEu0999zT3eVyIYSQbr755rpp06Y1Ajz77LMVkyZNijcajaFRUVH2VatWFQBMmDChedGiRZFJSUnJRw1tz5aoqCjn0qVLD99+++1xdrtdApg3b17ZyeIKwLJlywrvv/9+44IFC6K0Wq1YsWJFweTJk5v27t2rHzx4cBKAwWCQ33vvvUKNRtOuwJKRkVH78MMPG2fPni1nZWXtP1Wb1NRU69y5oWwlqQAAIABJREFUc8tGjx6dKMsyWq1WLF68uPhSEFek9sJ4zieDBg0SWVlZ532cjRs3kp6eft7HudQQQlBtdWBzCSINOrSdKGpl88Zv6VPwDdVLVqONDCP6n2+hjztW5re6upoRI0ZQUVHBhg0bGDhwYLt9HZ9qIwTssaixyjDAx4X2DJ9SdekBDB+/DCo15ikzcUV61sj1KGX/XU3+06/g3b0rff7zHIa4rh7rWwiBraL2BMHFnF+MpaAEW8VxZavVKryN0ejjurEPPw5pgrhmfF+GjumDsXfSWY3ttFg5+Pr77Ht+KTZTA90mj6Xv/D+yq6asU77HzwbRWo04uBaKtoDsgujBSInXIwWeP2Gqxerko50l7K9sJinCj9tSu+KnP7dKXBfuc70Ft6BShduG2ge3oBJBR+9LbN1WxF/mfI2fr44/PjSU60YnoOlkYve50lm/xzszyjnvXCjnu/NxqZ5zSZJ2CCEGHf9Ydnb24ZSUlNr29lHonGRnZ4empKTEnmqbcuvsCqT1SDpQkE7TqYQVl7ke4+olVG/MwWfoQKL+thT1SQa1ZrMZrVbL559/flph5WTKHRItskSi15kLK5q8LAxrliD7BdM6dTYiMPzMOugAst1B/tOvUv7uZwSPvprk1+ai8fOsh4MkSeijwtBHhf0qGsbZYsZy6Gh6UQnNB4so/SWf8OoqIl0u5LUfshXICgs+0Uw3KY6ApDgMxihU6l9HALrsdg699TE5zy3BUl5N5Ljh9HvuEUIG9XU32HjJpVpecojGUsSBL6HsJ3e1nphhSD3Gu0sdn0fyqpr5MKsEq8PFzSlRDLssTGtloAa3qNKI21MpHLeo4k97Hkun4pNP9/LC/20mIT6ExS9NJCxM8VRRUFBQUFBQULiSUcSVKwynLKizOfFSSfhpO88dUntZHuUzH8B6oIKQe24n9OGnT6h+Y7Va0el0xMbGsmvXLtSnWMi3h0WGEruKYLVMSPtRbKdEt2M9+vXLcEXFYf7dTISh49WIOord1MDeGc/QuD2bmId+T/fH7kU6g+PzBBpfA379euLXrydWu4sPVx/iQJ8W7h/TlQE+9jbRhYpamnIPUfrpemy19W37q7x0+CfGniC6OM1W9i58g9bCUsKGpzLsg5cIHzH4gh7X5YwwHXSLKpXZoPGC+LFICdcheQed13EdLpmv9lbyfX4tEf5eTB/enciA81fe3DNYcAsqlbjNar1xV/yJAHRn1JMsC15b8gNvZ/7CsKExvPDXsfj4nFkfCgoKCgoKCgoKlx+KuHIFIYRbWAEI0Xee6kCtP35N+eOPI9scWO+/m7AHnzhhu91u55ZbbqFr1668+eabZySsCAEFVjUSEOcld7yaj5DRb1yB149f4OgxEPNN/wNazxtXtuwrIOfeudhr6uj16hwiJo3x+BhngsXu4uVVBeRXtPLA+Fiu6uleyHvHRhMyZihRUVFtba21dTTnFdKUV0hT7iGacg9RvyuXkk++Rhwx1A1O7c3g1+cROe6aTvN6PheEEFC1xy2qmA6Azhep1y0QNwpJ53vex69qcpvWVjRaGRYXwsS+l7JprQyYcIsqR4W+UNxRKkGcSZTKUex2F/Pmf8u69fncOimZvzw6QkkDUlBQUFBQUFDoJCjiyhWCUxY02p1YXDJBOnWnSQeqf28xVf94A21YAN2WvMkPlU0nbHc6ndxxxx2sW7eON99884z7r3RINMkSCV4udB1dI7mceH/5Jrq927ANGIX1ugxQeX6BVfPV9+z/40I0/r70/+Sf+Pc/Oz8TT2GxuXhxVQGHKlt5YEIsQxJPHyGhDw1GHxpM2LATU4xcNjstBcXYG5sJvbq/Iqp0ACFkKMtyiyqNxeAdjNRvGhhHIGnOfzUaIQTbD5lYs6cCL62K/8/encdVWaf/H3/dZ1/gsMumoIgLuKBRVrbI2OYyU2k1tvL9tmlTOpNbi6ZWNrY51dii1m+mxnSqr2aZLZQzmVY2TWhpmJoosovKduDs574/vz8OpBkoIrjk5/l4nIcB9wYHTtxvrs913XZ+dzIST9WmtT6gklCo4gfMQHcgsem/26e+3suU+z7i2+8q+eM95/E/twyW37uSJEmSJElnEBmunOb8qoYzoOIKhv7SH2bQEW48sUtCTgbV52PfnydTv/pT7Genk/jU3zBExcPez37aRtM0xo8fz4oVK3jmmWe4/fbbj+kcXg2K/Toi9RpxbV0O5HVjf2cBhuIf8A67Dt95v215JvNxEEJQ/Nzr7Jn/KuGDM+j/t7mY49s2SrqzuH0q898ppHifm7tH9+Ds9Mh2H0tvNhGRmX70DSWEGoCSL0ONal37ICwR5azboNt5KLoT8/Le6AuyfGMpP+xtoE9T01rHcTat7XiCUHVKBdDcly6aUJVKNKEJQO1XVl7PHyd/QHmFk8fnXsYVl/c6ruNJkiRJkiRJpx8ZrpyGhBD4NIHTr+JRNRQg3KjHYdRjOAMqVgL7qiifciverbuJHjec2GnPomvhr/MPPPAAr776KrNnz2by5MnHdA4hBLt8oRuunm1cDqQ01GD/v7+gq67A/dsJBPpfcEznbAvV7WH75CfZ//464q+5jN5PTUNvObn9HFzeIPPf2UXJfg/3jO7BWT3bH6xIbSMCHtizDlH4MXjrIbI7ypB7IGlwh4xPbqsfm5rWugMqVw5M5IKesehOqWoNP6E+KhWAFzAC3QiFKh3TB6ZgaxX3Tv2QoKqx8PkrOWtw0tF3kiRJkiRJkn51ZLhyGhFC4FE1nH4VnybQAREmPeFGPfpT6oam87g351Mx9W7URjdJs+8i/Oo/tVp6f/nll6PX63n44YeP+Tx7nF7qVR1pZhVzG+5VdfvLsC+fj+J1475uKsEe/Y/5nEfjLdtLwW2zaNy2m56z/kDXCded9GUHjd4gT68spLzay6Tf9mBQWsRJvZ5fO+FrQOz6F+z+FAIuiMtAyb4D4jJP6PdCsKlp7frCA8SHm7njgh4kRZ4qTWsFoUk/FYQm/wggAugBxHG8VSqH+mxdETNmrSEmxsbzz42me2rnNguWJEmSJOnMEwwGGTBgQGZCQoJ/7dq1hQBVVVX6MWPGpJWXl5uTk5N9q1at2h0XF6du2LDBWlpaaho3blw9wJQpU5LCwsLURx99tOpI50hOTh5gt9tVRVGIjY0NvPHGG0UpKSnBE/H5HWrBggUxV155pbN79+4BgHHjxqXed999VdnZ2d7k5OQB+fn52xITE0/4dbWVDFdOA0IIXEGNer9KUAj0CkSbDNiNulPsr8RtU1FR0a79Ah+vwvPSCxhjbEQ/NZPG9GE0Vlb+fJtAgPXr15Oenk5mZiaZmZlUHrbN0fg0+M6tx6GD+DYsB9KXbMf+9nMIg5HGm2aixace0/naou6/37P1jtlogQADljxOzG+GdPg5jlWjJ8hTKwupqAkFK1k9ZLDSWYS7BlGYB3vWg+qHxLNQeo9CiU474ddS5fTyz29KqKj3cn5aDL87ZZrWBoEqQqGKC9ATqlBJAjp+DPIbb21h/rNf0C+zC88+PYqYGFuHn0OSJEmSJOmxxx6LT09P9zQ2Nv7U+2HOnDmJOTk5DfPmzds5Y8aMhNmzZycsXLiwPD8/35afn29vDleOxbp1635MTEwMTpw4MXn27NmJr732Wmlb9gsGgxgMHRMrLF26NHbQoEGe5nDlrbfeKu6QA58gp8JvxFIrNBFqUlvu9lPtC6IoEGs2kGwzEW7Sn5bBSnuIQADfS0/jWfBXbP2ScDz7NIH0YS1um5eXR05ODp9++mn7ziVgt0+HAHpa1KMuBzJu+xr7W0+hhUXQmDunU4KVin9+wObfT8EQEU726pdOiWDF6Q7w5Ns7qazx8qffpclgpZOIhkq0jX9DfHI/7F4LyeegXPIYuvMmnvBgpblp7V/X7qTeE+DW87szdlDyKRCsNAA7gA3ATkJTfvoAQ4FedHSwoqoa85/9gqef+YJhF/Vg8UtXyWBFkiRJkqROsWvXLuPHH38cceeddx449P15eXmREyZMqAaYMGFC9UcffRTl9XqVxx9/PGn16tVRffv2zXzllVeiALZt22YdMmRIn65duw547LHHuhztnDk5OQ1FRUVmgJUrVzoGDRrUNzMzM2PkyJFp9fX1OghVukybNi0xOzu7z9///veogoIC89ChQ3v36dMnMzMzM2Pr1q1mgFmzZsX3798/o3fv3pmTJ09OAtixY4cpLS2t3/XXX5+anp7e74ILLujV2NiovPrqq1EFBQW23NzctL59+2Y2NjYqQ4YM6bN+/fpf/KL10ksvRQ8YMCCjb9++mTfeeGNqMHhqFLPIypVTUFATNARUGgIqArDoFWKMBix65aQvAznRtNoavI8/RHDrdiKvzka59X4ChtgWt129ejXPPfccw4YN44IL2tfv5EBQoVbV0d2kYj3KPaPpv3lYP/0nwa69cV9zL8LasaNutaDKrkdepPzv7xCVcw6ZL87CGBneoedoD6c7wFNvF1JV5+Peq9Lol3KqToU5fYnaotDkn4pNoDdC2m9Q0q9AsZ2cxsUuX5Dlm8rYWumkV5cwrs/uhsN6MpvWqsA+QlUqDYT+TtCFUJVK530/erwBHprzL9Z+VsQNvx/AlHsvQH/SwyVJkiRJkjrbAW+gW0ATHfrXFKNOccdajEesDrnnnnu6PfXUU2X19fU/m1hSXV1tSE1NDQCkpqYGampqDBaLRTz44IMV+fn59iVLlpQATJkyxVpYWGjZsGHDjrq6On1GRkb/6dOn7zebza2W57/33nuRmZmZnsrKSsO8efMS169f/6PD4dBmzpyZMHfu3Pj58+dXAlgsFm3jxo07AAYOHNh32rRpe3Nzc+vcbreiqqqycuVKR2FhoWXLli3bhBBceuml6R999FFYWlqav6SkxLJ06dLdQ4cOLR41alTakiVLou6+++6ahQsXdpk/f37pxRdf7G7t+jZt2mRZsWJFdH5+/naz2SxuvvnmlEWLFsVMnDixuu1f/c4hw5VTSEAL9VNpbJr8YzPocBj1mM/QX97VH7fj+fNMhLOBLlN+R2D4eFSl5b9Cr127lkmTJpGRkcErr7yC2XzsI1X9GhT5dITpBInGIywHEhqWT9/A/M3HBPqcg/t3E8DQsU1lA7VOtt71CHVfbKLr+OtImzkBneHkT4Gqc4WClQNOP5Ov7klmt5Mf9vxaCCFg/zbEjx/A/m1gtEGf0Sg9L0Uxn7wA68d9DbyVX4rLr/LbAYlclH6ymtaqQD3p6QBfEVoGZAPSgXhCzWo7T02Nm3unfcTWH6qYNvkCbrw+q1PPJ0mSJEnSme2NN96IiI2NDV500UXu999/v92/dF9++eV1VqtVWK3WYHR0dKCsrMzQs2fPwOHbDRs2rLdOpyMjI8P9zDPPlK9ZsyZs165dliFDhvQFCAQCSnZ2dmPz9rm5ubUAtbW1uqqqKlNubm4dgM1mE4DIy8tzrF+/3pGZmZkJ4Ha7ddu3b7ekpaX5k5OTfUOHDvUADB482L1nz54237zl5eWFFxQU2LKysjIAvF6vrkuXLqdE6YoMV04BvqYmtW714Dhlh8mA8QyY/NMa/78+xvvCXzBEWol78jbcvcYilJYDjPLycu644w769OnD3LlzsdnaFyoX+XWoQPqRlgNpKtbVizFt+w++7MvxXnIj6Do2/HL9uIfvb52Jr2I/fZ65n8RxIzr0+O1V2xhaClTbGGDK1Wn07SqDlY4ghAaV34YqVWqLwByB0u866JGDYjx5TWKDmkbe1r2s23mALuFmbhvag+QT3rTWA9QA1UAdoJGUBAfHKEcQWgbUuYpL6ph07/vsP+Di6SdGMDznxPe6kSRJkiTp5DlahUln+OKLL8LWrFkTmZycHOHz+XQul0t31VVX9Vi1alVRTExMsLi42JiamhooLi42RkdHtxouHFqlotfrCQaDLf7y1NxzpfltIQQXXnihc/Xq1UUtbR8eHq41b9cSIQT33ntv5fTp03+2pGnHjh0mk8l06DUJj8fT5hsqIYRy3XXXVb/44ovlbd3nRDkzSyJOAUIIPEGVvR4/ez0BvKqGw6inq81EjMV4xgYrIhjEu/gFvM8+ia1vF7o88ydcva5rNVgBSE5O5rHHHmPZsmXY7e3rr1AdVKgO6uhm0rC19lMhBNaPX8O07T94h12H99KbOjxYOfDJBjb97h40t5dBy589hYIVP0+s2EldY4ApV/fsgGBFEBqT6wJqCS3xKAOKCPXPKAC2AMWAs2n7XxehBRHFXyL+PQvx9Yvgd6EMykW54imU3iNParCyr8HLC2sLWbfzAOf1iOZPv+l1goIVlVCQshP4uumxE3ADicAAvvgCIBOI5EQEK99+V8n/3v42LneAl1+6WgYrkiRJkiSdEC+++GJ5VVXVlvLy8u9fe+213eedd17DqlWrigCuuOKKusWLF8cALF68OGbEiBF1AA6HQ21sbOyQG5ScnBxXfn5+WEFBgRmgoaFBt2XLll9UmERHR2sJCQn+119/PRLA4/EoDQ0NupEjRzpff/312OY+LUVFRcby8vIjFneEhYWphy+BOtyIESOc77//flTzsaqqqvQ//vhjxy4jaCdZuXKCNU/+cQZUAlpo8k+USU+Y8cxpUNsarb4Oz+MPo36/hajfDcRy2600GgfSWhlJYWEhbrebgQMHcsMNNwBQVXXEKWMtCjQ1sbXrBElHWA5k/vxtTJvX4R16Jb7zf3fM5zkSIQQlL75B0RP/j7ABvej/t8ewJMV16Dnaq7rBz5MrduL0BJk6pie9klrqLSNQFA2dTkWnU9HrVXS64E9vh94XBMoJhSq/qEQ8hJGDSzxqCAUuBiCKUMVCFGDpsM/vRBNBHxSvR+z8GDw1ENEN5ZwJkHQ2iu7kLv0SQvD1nhre21KBUa/jf85LpX9SZzcrdhN6nmtork4J5f6RQDKh5/xgNZqmdfLlHOLjNTuZ8+inJCaEs+DZ0XTrKhs3S5IkSZJ08j3yyCOVY8aM6ZmamhqblJTkf/fdd3cBjBw5smH+/PmJffv2zZw6deqxjUw9TFJSUnDx4sV7rr/++jS/368AzJkzp3zgwIG+w7ddunRp0Z133pk6d+7cJKPRKJYvX75r7Nixzq1bt1rOOeecvgA2m01btmxZkcHQ+jjW3NzcA5MmTUqdPn26lp+fv62lbbKzs70PPfRQ+SWXXNJb0zSMRqNYsGBBSe/evf3H8/l2BKW1Mp7OdPbZZ4v8/PxOP89nn31GTk5Op5+nLTQhaAyoOAMqqgCjTsFh1GM36M64JrUtjWJWd+3EPfchqKsh/u4cxKU34tH1aPUYpaWlXH311dhsNtauXfvT+K+tW7fSr1+/Y7qenV4dB4IKA60q9lbubU3ffIz138vwZ+XgGXFrq4FPe6geHzumPcW+dz+ly9XD6TP/PvTWY+8Z0/E06t1elq3bg8moMu7ieBKiDE3BycHwpPltRWn5tUTTdGiaHk3TYzKFEQpOTIf9e+h/H/q19ROqbKlp+rf5NdMKRPP99+UMGHAhp0NOLPwu2P0pYtca8DdCTG+U3qMgfsAp8Rrg8gVZ8W0ZBRVO0uPCuP7sbkR0StNalVCI0rzcx9v0/tBzGnpEEhql/Esn4nVdCME/Xv+WBS/+h0FZiTzz9EgiI07fQO90dir9f1w6MeRzfmaRz/eZ51R9zhVF2SiEOPvQ923evHlPVlbWgdb2kc5Mmzdvjs3Kyure0sdO/TuS05wqBA3+0OQfDTDrFKLNeqz6My9UaU3gs3/jWfA0hjATiY9fg6fP1fh1Ca1uX1VVxfXXX4/X62Xp0qXHNVe9JqiwP6ijq1FrNVgxbt2A9d/LCPQ+G88V/9uhwYq3Yj8Ftz9E4/c76fHgnaTcc0Mnfl+IX1SSHHz750FJ6KGRBDyWbiD0UlEXOopQUFV9U2BiIBg0o2n6pvcZfgpSmrc5dPVhUqhhxjEwEWpWGk9oaVDzMqJaoJIBAwC+JDQhprmyJZwTsVykrYS3DlH4CRSthaAP4gei9BmNEtPrZF/aT3bua+DN/FJcPpXR/RO5uFdHNq0VhHqnVHOwOkVwsDqlG6Hn7eQtgzpUMKjx1F8+Z8XKrVx+aTqPzB6O2Sz/VylJkiRJkiQdmfyNsZMENEFDIEhjQEMAVr0Oh0mP5Qyd/NMSoWn4Xvt/+N9+E2u/JOKnX4kz9nKCSmSr+9TW1nLjjTeyb98+3nzzTTIyMtp9/mDTciCrTtDV1PJaA8PuLVg/eIVgSgbuK+/q0B4r9Rt/YOsds1BdHvr//TFiLx/aYcdupigadnsddnsdOl2wxVxICH4KRDTNgN9vxOWFf39XS7VTMHxAArEO208fF0Lh5IQXChDW9OgGaHz33XoGDepG6KZ9T9PDQOimvXkJ0cm5aReN+xA7P4KSL0FToesQlN6jUCK6nZTraUlQ0/j4hyrW/bif2DAzt57fna5RHTFlUOVgxVENP69OaV7qE0Fr1Skni9sd4IGHPuGLL4v5n1sGM+nu89Cdof2vJEmSJEmSpGMjw5UO5lc16gMq7qZxynaDjgiTHmMHNz493Qm/H88zTxD8/DMiRvQj+o5R1FqGoSlHvrFbtGgRRUVFLFmyhOzs7OO6hmKfDr+AARaVlu6f9OWF2N5ZgBbXFdc193bouOW9/5fHjvufwZwYR9ab87H3aX0JVHsoiobNVkdYWC16vYrXayMQcKCqhkOCFD2qakAIHYeGJVV1Pp5csRNfUOO+selEWG0EjtQi5aTRUVcHkNb0aF5C1PxoruK0cDBoiaKzX/ZEfUlo8k/ZN6DTQ+qFKL1GoNi7dOp5j0bVBPsbfVTWe6is91JZ76W8zkODL8i53aO5cmASJkN7X6cEv+yd0lydEsWpVp3Skv0HXPxpyof8uPMAM+6/mGvH9j/ZlyRJkiRJkiSdRmS40gGEEHhVgTMQxKsKFMBh1BNu1GOQf/X8BdHgxDP3AYJbtxP3P+dhHTOCGsP5COXo/R2mTZvGiBEjGDx48HFdQ11QoSqoI8moEd7CH891B8qxLf8Lmj0S1++ngbljbgq1oMruPy+m7OXlRF4wmH6L5mCM7sgmmRp2ez1hYTU/hSo1NTEEAm27/r21Xp5YUYiqCe6/Jp2UuI6oYjhRDl9C5OZg0LIXaO71c/gSouMPPoW7BmoKESUboGoLGCzQawRK+mUoltYrsTpLgzcQClCc3qYgxUNVgw9VC/XF0SsKXRxmenUJI6trJJmJjnacJcjPe6c09zazcbA6JZLTYSjdrt01TJr8PvX1Xp6bP4oLL0g92ZckSZIkSZIknWZkuHIchBC4VQ2nX8WvCXQKRJpCocqZPvmnNf6SbXinjUfdW02XqaMhZxx1SvIR+5gEAgGeeOIJ/vCHPxAbG3vcwYoqYJdPh0URdGthOZDirMb+1tOgN+C6/j6EvWPCj0B9Iz/c/Si1n31D8m1j6Dn7bnTGjvoR/Hmo4vNZqa2Nxe9veyhUUePlqbd3ompw/zXpdI09dasMjk4B7E2ProQm0Dg52Bi3uOmh52BFS/MSoiP/7AotCHUloTClphBqdoGnNvRBUzhK5hjocQmKqfODqYCqsa/hkGqUpjCl0Rf8aRuHxUBihJXe8eEkOiwkRliICzdjOOZquubAqrl3Sn3T+/SEQpRUQoHK6dX49b/5ZUy7Pw+L2cD/WzSGjL6nxpQuSZIkSZIk6fQiw5V2EELQGAyFKkEhMCgK0WYDYWfg5J+2Epof7zfLKXvwLwi/StSjf8Q7cDQoR+65oGkakydP5p133iEjI4Nrr732uK+lxK/DJ6C/VUV/2NOleBqwv/UUit9D440zEZEds5TDXVjC97c+hLe0kt5PTSXppt92yHFBw2ZzEh5eg14fbApVYvD7j+3Gvrzaw5NvFwLwwLXpJMeczsFKS5qbpzZXkQQ4WNVSw8+XEB0athgR3nqo2XUwSKndA1rTOilbDMT0QolOh+ieobHKuo5/WRVCUO85tBolFKbsb/TRVIyCQaeQ4LCQkRBOYoSVxAgLiQ4L9uNqxhrk571TmqtTmkOr5t4pp351Skve/3A7j/75M1JTIlnw7GgSE8JP9iVJkiRJkiRJpykZrhwD9ZBxypoAk04h1mzAJif/tEoIAa4fcP1rGeVPvofeEY7tz/MIpvZp074zZszgnXfe4cEHH+yQYMWpQmVAIcEocBye6/i92JY/g67uAK5x09HiU477fADVa//LD3c/is5oJOutvxB57sAOOKrAZgtVqhgMQXw+C7W1CU2VKsf2vVh2IBSs6BS4/9peJEWfXpUH7WMEujQ9mqfZ1CK0A+DcBjV7ETX7oaYaXM7QLjoDRKZC2m9+ClMUa1SHX5k/qLH3kACluRrFE1B/2ibKZiQxwkr/pIhQiBJhITbM3AEVc80TmZrDlEOrU6I4XatTDieE4JW/5bPolW845+xk5j8xgvDwU2H8uSRJkiRJ0kHJyckD7Ha7qtPpMBgMoqCgYBtAVVWVfsyYMWnl5eXm5ORk36pVq3bHxcWpGzZssJaWlprGjRtXDzBlypSksLAw9dFHH61qy3kURSE2NjbwxhtvFKWkpASPtE9nWLBgQcyVV17p7N69ewBg3Lhxqffdd19Vdna2Nzk5eUB+fv62xMTEE35dbSXDlTYIagJnQKUxoCIAi14hwmjArFdkqHIEwlsBteuo+2AdVQvXY05Po+vzf2N/UD36zsDjjz/O66+/zj333MPEiROP+3pUAYVePWYFUg9fDqQGsb3zPPrK3bjH/BE1pe9xn08IQdni5ez682LsfXsw4NXHsHRtfcR0G4+KzeYkLKwagyGI32+hujoen89Geyb4lO738NTKQvQ6hfuvSSfxjAhWDhL+RqjZHapKqS6E2iJQm6ozzGEQ0wWlRy+I7gKRsSj6WA5WtRzfsh9NCGpc/p9Vo+yt91Lt8tNUjILZoCM2vXnHAAAgAElEQVTBYSGra8RP1SgJDgtWY0dO2WmuTmle7uNven9zdUoMoT41p2d1yuECAZXHHl/H6g+289tRfZg1Iwdjh349JUmSJEmSOs66det+PDxQmDNnTmJOTk7DvHnzds6YMSNh9uzZCQsXLizPz8+35efn25vDlfacZ+LEicmzZ89OfO2110rbsl8wGMRg6JhYYenSpbGDBg3yNIcrb731VnGHHPgEkeHKEfi10NIfV9PkH5tBR4RRj0mOUz4iEWyA2i8Qrh1Uv7WZ6je/wnb+hSQ//Rx6exhUVBz1GA0NDeTl5ZGbm8uDDz7YIddV6tfhFQqZlsOWAwkN6wcvYyz6HvfI2wn2Pr4pRACq18+PDzxD1fKPiR11MRl/fQC97XiW2gis1tDyH4MhgN9vPq5QBaB4n5unVhZiNui4/9pexEf+uv9yL4QGDZWhJT7VTUt8GitDH1R0ENEtNNknuidEp4Mtpik8DRBq3Nq8PKa66YhmDjbGjSTUULdlHr/6s+U8lU4ve+u9+NXQa4uCoEu4iZRoC+f2CCfBYSLBYcZh1aFTBKGeMYLQshxP09vN79MOe7TlfYe+7eHn1SkxTZ/Tr+/7oaHRx30PfszX/y1j/B1nM+GOc2RALkmSJEnSaScvLy9y3bp1OwAmTJhQPWzYsD5er7fi8ccfT/J6vbq+ffuGTZ06tRJg27Zt1iFDhvSpqKgw3XXXXVUPPfTQviMdOycnp+H555/vArBy5UrHo48+muT3+5XU1FTfm2++uSciIkJLTk4ecMMNNxxYu3atY8KECfuGDh3qHj9+fGp1dbVBr9eL5cuX7+7Xr59v1qxZ8e+880603+9XRo8eXffss89W7NixwzRy5MheQ4YMaczPzw+Lj4/3f/zxx4XLly+PLCgosOXm5qZZLBYtPz9/2/Dhw3vPnz+/9OKLL3Yfeo0vvfRS9MKFC+MDgYBy1llnuZYsWVLcUQHP8Tj5V3CKcgdV9nuDKEC4UY9DTv45KqEFwLkRnBsRwSB7XynAmfcVEVddQ8LMh1GMR58GBKGKj/DwcN577z0cDkeH3Pw0qFARUOhi0Ig0iIMfEALLv5Zh+uE/eHJ+TyBr2HGfy1dVTcHts2j4dhvdp/0vqX+6BaXdo7gFVmsD4eHVh4QqSfh8dtobqgDs2efm6bcLMZt0PHBNL7r8CoMVEfBA7e6DYUrtLgh4Qh80hUF0OkrKUIhJh8juKIbWvgZGIK7pAc1LiA72atnb9P4wNBGGLxDEGwziDwYJqEGCmgoITHpBz1joGw8mAxj1YNAL9IpAUcQvztp+CqEqk+Z/dS28rW/6vBQgllCY8uupTmnJ3qoG/jj5A/bsqePhWcO58rfHX50mSZIkSdKZYns3cHXwtAK7G/oetTrkkksu6aUoCrfeeuv+adOmHQCorq42pKamBgBSU1MDNTU1BovFIh588MGK/Px8+5IlS0oApkyZYi0sLLRs2LBhR11dnT4jI6P/9OnT95vN5lZ/+XzvvfciMzMzPZWVlYZ58+Ylrl+//keHw6HNnDkzYe7cufHz58+vBLBYLNrGjRt3AAwcOLDvtGnT9ubm5ta53W5FVVVl5cqVjsLCQsuWLVu2CSG49NJL0z/66KOwtLQ0f0lJiWXp0qW7hw4dWjxq1Ki0JUuWRN199901Cxcu7NJSmHKoTZs2WVasWBGdn5+/3Ww2i5tvvjll0aJFMRMnTqxubZ8TRYYrrbDodUSa9IQZ9ejlXzaPSAgB7h+h9gtQG1FFNyqeWoX766+JvWsiMePvaXNA8vbbb7N27VqeffZZIiM7ZoStJqDQp8ekQHfzz5cDmb9ajXnjGnznjMB/7ujjPpfzu+0U3D6LoLORfv/vUeJGXtTOI4VClbCwaozGAIGAmZqaJLze4wtVAHbvdTH/nV1YTXoeuDaduIjTP1gJ9fbZF5rgU70LagrBWU6oIkMBRzIkDwn1SolJB3uX4wjtrE2PJEAQCNaz37UXHbXYTFUENYWACqqmoNfpMOn1GPR6jHo9ZoMBo96AorQUeLT0dnu2ka9Xh9u+Yz9/nPIBXk+Q558bzblDup3sS5IkSZIkSTqqL7/8cnv37t0D5eXlhuHDh/fu16+fd+TIkY3HcozLL7+8zmq1CqvVGoyOjg6UlZUZevbsGTh8u2HDhvXW6XRkZGS4n3nmmfI1a9aE7dq1yzJkyJC+AIFAQMnOzv7p3Lm5ubUAtbW1uqqqKlNubm4dgM1mE4DIy8tzrF+/3pGZmZkJ4Ha7ddu3b7ekpaX5k5OTfUOHDvUADB482L1nz54235Dk5eWFFxQU2LKysjIAvF6vrkuXLqdEHxYZrrRCpyhEmOSX52iErwpq14GvEkxxBLRsyu97GN/uQhIe/jORV13T5mN98sknTJ48mXPPPRdVVTG2sdLlaMr8OjyaQl+LiuGQ+07jd2uxrF+Bv99QvMOvP+I46Laoeudf7Jj2NMbYKM5a9QJhmT3bcRSBxdJIeHg1RqOfQMBETU0iXm8YHXHTvKvSxfx3CgmzGLj/2l7EOlpfynIqE0Ef1O2hm/d7tK+2hMIUf9NrvcEaajablB0KUqJ6oBg79g8NmhAUHXCxsaSWLeX1+IIakdZIBiRHkNTUGyU+3IxBLiE86b7cUMz9Mz8hPMzE314eQ6/0mJN9SZIkSZIknXaOXmHSGZp7jyQnJwdHjx5d99VXX9lHjhzZGBMTEywuLjampqYGiouLjdHR0a2GC4dWqej1eoLBYIs3FYf3dhFCcOGFFzpXr15d1NL24eHhWvN2LRFCcO+991ZOnz79wKHv37Fjh8lkMh16TcLj8bT5l2YhhHLddddVv/jii+Vt3edEkemB1C5CdUHtBnD9ADorRF+Cr8pI2aS70Jz1dFuwCPv5F7b5eJ9//jl33XUXAwcO5NVXX8Vi6ZjGqi4VygMKcQaN6EOWAxl2fIP149cIpA3EM+qOUM+NdhKqStFTf6fkhX8Scd5A+r38CKaYY6266dxQBWBnRSN/eXcXDquB+6/pRcxpEqwIIcBTDdWFiJqmqpT6MhAqPQD0CZCQ1VSV0hPCk5oqQzre/gYfG0tq2VRaS607gNmgY2ByBNkpUfSItXfAtB6pI739zlaeeHo96T1jWPDMaOLi7Cf7kiRJkiRJktrE6XTqVFUlKipKczqdurVr1zpmzpxZAXDFFVfULV68OGbevHl7Fy9eHDNixIg6AIfDoTY2NnbIL8I5OTmuqVOnphQUFJj79+/va2ho0BUVFRkHDhzoO3S76OhoLSEhwf/6669H3nLLLXUej0cJBoPKyJEjnQ8//HDS+PHjayIiIrSioiLjoaFKS8LCwtT6+vojThoYMWKEc+zYsekzZsyoSk5ODlZVVenr6+v1vXv39h9pvxNBhivSMREiCM7voP4bEEFwnAURQ3Bv/JbyKZPQWSyk/H0Zlj4ZbT7mpk2buO222+jRowevv/46YWFhHXStoeVAhsOWA+mLf8D23kLUpJ64r54E+vb/GAQbXGyb+Geq//UViTf/jl5zJ6EzHUvFzeGhipGamgS83nA6cnnHj+WNPPPuLiLsRu6/Jp3o8FM7WBHeOij9OjTFp6YQvE0Nz/UmiEqDXiNQYtL58odKLhw+olOvxe0P8l1ZPRtLaimpcaMAvbqEMbJfAv0SIzAZZHXKqUbTBC8u+ppX/7GJoeen8OSfL8duP7W/5yVJkiRJkg5VVlZmGDNmTDqAqqrKNddcU33ttdc6AR555JHKMWPG9ExNTY1NSkryv/vuu7sARo4c2TB//vzEvn37ZjY3tG2vpKSk4OLFi/dcf/31aX6/XwGYM2dO+eHhCsDSpUuL7rzzztS5c+cmGY1GsXz58l1jx451bt261XLOOef0BbDZbNqyZcuKDAZDqwFLbm7ugUmTJqVOnz5dy8/P39bSNtnZ2d6HHnqo/JJLLumtaRpGo1EsWLCg5FQIV5TWyng609lnny3y8/M7/TyfffYZOTk5nX6eM0GoemA31H4OwXqw9oCoi1CMUdR/tJrK2TMwpaTS7YWXMSYmHfFYFYdNC/r666+ZM2cO//jHP4iPjz+u69y6dSv9+vUDoMyvUOLX08eiEtP0M6zbu4ewf85Dc8Tgumkmwtr+IMddVE7BrTNx7y6l19w/kvw/Vx3D3gKz2UV4eDUmk49g0EhDQwweT8eGKgDfFdWz8MM9RIUZuf+aXkSFdcxyq/ZISjry94Zw7UfszIPiz0ELgi0WYtIPTvBxdEXRHQyzO+tnPKhpbN/bwMaSWrZVNqAKQYLDQnZKFIO7RRJhPXlfwzPd0Z5zv1/l4bmfkvfJTsZenckD0y/GIAOw05b8//iZRz7nZxb5fJ95TtXnXFGUjUKIsw993+bNm/dkZWUdaG0f6cy0efPm2KysrO4tfUxWrkhHJfwHoHY9eEvBGA1drkaxpiKEoPrVV9i/4C9Ys8+h6zMvoHdEtPm4fr8fk8nEueeey4cffoiu3RN1fsmthUYvx+i1g8FKbRX2/5uPsNhx/X76cQUrNes38sMfHgFFIeuN+URdMLiNe/4yVKmtTeiUUEUIwSff7ufNz8tJibMy+aqeRNpPzVBAOMsRP34IZV+HlmilXIDSawRK2PGFbcd0DUJQWuthY0kt35XV4farhJkNDO0ZQ3ZKFEkRFjm29xRXX+9l6v15bPq2gkl3n8f/5g6Wz5kkSZIkSZJ0QshwRWqVUD1Q9x9o/B50JogaBuEDUBQ9QlWpevIx6pa/gWPEaBIeeRydqe1l9263m2uuuYarr76aCRMmdGiwIgTs8urRAz2algMpjXXY33wKhMA17j6EI7rdxy979R0K57yAvVcq/f/+GNbUI1djNF0VZrO7KVTxEgwaqK2Nx+Nx0BnTXYKqYOlnpXz2fTXZ6RGMvyIVs/GIyxdPClGzG/HjB1D5bWjJT89LUdKvQLFGnbBrqHX72VRSx8aSWvY3+jDoFPolOchOiaJ3l3D0cgT7aaGsvJ4/Tv6A8gon8x69jBFX9DrZlyRJkiRJkiSdQWS4Iv2CECo0fA/1/wHND+EDIeJcFL0VAM3joeLBqTSu+5To/72TuEmTUY4hHNE0jUmTJlFQUMDUqVM7/PorAwoNmkIvs4pJB3hd2N96GsXtxHXjg2gxie06rtA0ds1dRNnLy4m5fCgZz8/EEHa0KTQCk8mNw3EwVKmri8ft7pxQBcDlDfLiB0X8UNrI6LPjueaCxFOq0aoQAg5sR+x4H/ZvA6Md+l6JknYpirlj+u0cjTeg8n1FqI/K7v0uBJAWayendxwDkiOwnoJBlNS6gq1V3Dv1Q4KqxkvPX0n24LYEnpIkSZIkSZLUcWS4Iv2M8BSHlgAFasDSDaIuRjHF/vTxYE01ZX/6A94fCoh/YBZR42465nM8/vjj5OXl8cgjj3DppZd25OWjGkyU+HVE6TViDQICfuxvP4euugL3dVNRE9PadVzN52fbvU+w/721JN82hvSH70HRH/kG3GQKVaqYzR5U1UBdXRfc7gg6K1QBqKrz8eyqXeyv93P7ZSlc1O8UGjsrNETlt4gdH0DtbjBHoPT/PXQfhmK0dvrpNSHYua+RjSW1FFTUE1AFsXYTl2XEk50SRbRseHpa+mxdETNmrSE62srzz/2WHt1PXNWTJEmSJEmSJDWT4YoEgAjUhprVeorAEAFxvwNrj5/1K/AX76F04p0ED+wnef7zhP/mkmM+z9/+9jdeeuklcnNzuf322zvyU0AI8MR0QwHSzBqKULG99xL60h/xXPUHgj36t+u4gfpGtt4+i7qvviPtoQl0u2vcEfs4/DxU0TeFKg6gc5tq7ihrZMH7uwG4b2w6fbqemCqQoxIqluoC7BXrEZ4qsMWiDLoFUi5E0Xd+D5i99V7yS2r5trQWpzeI1agnOyWK7JQoUqNtsifHaeyNt7Yw/9kvyMzownPzRxETc7RKMkmSJEmSJEnqHDJcOcMJzQd1X0PDZlAMEHkhOLJQlJ9/a3g2f0vZvX8AFFJe/gfWAVntOl8wGGT48OHMnTu3w29qq4IKqiWMnmYVsyKwfvQqxp2b8Fx2C4GM89p1TG/Ffr6/5X7cu0rJeGEm8WNar7QxmTxNoYobVdVTXx+HyxVBZ4cqAJ9vrea1f5cSF2Fi8lU9iY80d/o5j0oLYj3wLfaK9Rh8tQSsXVDOvhOSh/xs4k9naPAG+K6sjo3FtZTXe9Ep0Dch1EclIyEco15OjzmdqarGsws28M83t5BzcQ/+/OilWOUEJ0mSJEmSJOkkkuHKGUoIDRq3Qt1XoHkgrB9Eno+it/9i24ZP11AxYxqGLgl0e+FlTCmpx3w+TdPQ6XRMmDCB3/72tx0erPg02OPTofc00MVuxbxuOaYt6/FecDX+7MvadczG7UV8f/P9BBvdDHz9CaIuym5xO6MxFKpYLCc+VNGE4O0vK/kgv4rMbmHcM7oHdsvJ/bFWVB/Wfd9gr/wSfaABvz2Z2pRR+KL6kJTctdPOG1A1tlY62Vhcy4/7GtAEdI20clVWEoO6RhJmli93vwZ+v8Z9Mz5m7WdF3PD7AUy59wL0MiyTJEmSJOlX6MCBA/qbb745dceOHVZFUXj55Zf3XHrppa6qqir9mDFj0srLy83Jycm+VatW7Y6Li1M3bNhgLS0tNY0bN64eYMqUKUlhYWHqo48+WnWk8yQnJw+w2+2qoijExsYG3njjjaKUlJTgifksD1qwYEHMlVde6ezevXsAYNy4can33XdfVXZ2tjc5OXlAfn7+tsTExBN+XW0l7zbOQMJbBjXrIHAAzEkQdTWKuUuL29a88Tr7np6Hpf9Auj63EEP0sU/Zqa6u5rLLLuOxxx5j1KhRHR6sCAG7fKGbK1tNGeaiQiz/eR/f4OH4LhzTrmPWffUd39/2EHqblcEr/0pYZs9fbGM0egkPP3BIqBKL2x2JECfmRs8XUHn542I2FtaTMyCGm3O6YdCfvCUuStCNbe9/sFd9hS7owedIo77ntfgdadBJS2+EgN0HXGwsqWVLWR3eoEaE1ciwXnFkp0QR77B0ynmlE0/TBBu+KuGFRXsor/Ay9d4LuOmG9lXQSZIkSZIknQ7Gjx/f7fLLL3fm5eXt9nq9SmNjow5gzpw5iTk5OQ3z5s3bOWPGjITZs2cnLFy4sDw/P9+Wn59vbw5XjsW6det+TExMDE6cODF59uzZia+99lppW/YLBoMYDB0TKyxdujR20KBBnuZw5a233irukAOfIDJcOYOIoDPUV8VdCPpwiB0Jtl4thh1C09j/3NPUvP4qYTmXkDRvPjrrsTcd9fv9XHPNNfzwww9ERkZ2xKfxC/uDCnWqjh4mFX3RJqzffUCgzzl4L8tt1039vvfWsu1Pj2NNTWLgsiexJMf/7OOhUKUai8WFpulwOmNxuU5cqAJQ2xjgr+/tonifhxsuTubywXEnrXeIzu/EXvkl1n3foNP8eCP74koaRiC8W6ed80Cjj40ltXxZH4Fn/S5Meh0DkiPITomkZ1zYKTUdSTo+Hm+ADz7cwT/f2sKePXU4wg08/cQIhue0rzm1JEmSJEnS6aCmpkb39ddfh69YsWIPgMViERaLRQXIy8uLXLdu3Q6ACRMmVA8bNqyP1+utePzxx5O8Xq+ub9++YVOnTq0E2LZtm3XIkCF9KioqTHfddVfVQw89tO9I583JyWl4/vnnuwCsXLnS8eijjyb5/X4lNTXV9+abb+6JiIjQkpOTB9xwww0H1q5d65gwYcK+oUOHusePH59aXV1t0Ov1Yvny5bv79evnmzVrVvw777wT7ff7ldGjR9c9++yzFTt27DCNHDmy15AhQxrz8/PD4uPj/R9//HHh8uXLIwsKCmy5ublpFotFy8/P3zZ8+PDe8+fPL7344ovdh17jSy+9FL1w4cL4QCCgnHXWWa4lS5YUd1TAczxO/hVInU5ofqjPB+emUNgQcR44slF0LT/9ms9H5ewHaPjkIyLH3UT89BlHnYzT4nmFYMKECaxbt45ly5YxdOjQ4/1UfsGvQZFPR7hO0LXkO+ybPySYmon7d3fBMYyHblb68nJ2PfISEecOpP/f5mKMcjR9RGCxuLDbazGbPU2hSgwuV9QJDVUA9uxz89f3duPxqfzpyjQGpUWc0PM303trsFd+jnX/JhAa3piBuJIuJmiLP/rO7eD2B9lcFhqfXFzjRgFiDBpXndWdAUkRmAxyacivyb59jby1ooCV72yl3ukjo28cjz1yKSZDuQxWJEmSJEk6ocSBNd0IVHds53xjjFuJvazV6pDt27ebo6Ojg9ddd133H374wTZw4EDXK6+8UupwOLTq6mpDampqACA1NTVQU1NjsFgs4sEHH6zIz8+3L1mypARgypQp1sLCQsuGDRt21NXV6TMyMvpPnz59v9lsFq2d97333ovMzMz0VFZWGubNm5e4fv36Hx0OhzZz5syEuXPnxs+fP78SwGKxaBs3btwBMHDgwL7Tpk3bm5ubW+d2uxVVVZWVK1c6CgsLLVu2bNkmhODSSy9N/+ijj8LS0tL8JSUllqVLl+4eOnRo8ahRo9KWLFkSdffdd9csXLiwS0thyqE2bdpkWbFiRXR+fv52s9ksbr755pRFixbFTJw4sbq9T0VHkeHKr5gQAlzboe5LUF1g6wNRF6AYwlvdR62vo2zyRDzf5hM3eTrRt9zW7oqIJ598ktdee405c+Zw4403tvfTaJUQsNunQwAZ1duxv/s8Lkc86tg/geHYmlsKTWPX3EWUvbycuNHD6LtgBnqLCUVRsdmc2O11GAwBgkED9fVxuN0OhOjcpqwt2VhYx+K8YsKsemb+vjfd4jp/hPHhDO4q7BXrsVRvAUWHJ+4sXIkXolo6fuyzqgl2VDWwsaSWrZVOVE0QH25mVP8EBneN5Lv/biA7RY7e/TX5Yds+lr25mTVrdqFqGjnDenDT9VkMHpSIoih89lnFyb5ESZIkSZKkThcMBpVt27bZ/vrXv5YMHz7cdeutt3abNWtWwl//+tdj+mXo8ssvr7NarcJqtQajo6MDZWVlhp49ewYO327YsGG9dTodGRkZ7meeeaZ8zZo1Ybt27bIMGTKkL0AgEFCys7Mbm7fPzc2tBaitrdVVVVWZcnNz6wBsNpsARF5enmP9+vWOzMzMTAC3263bvn27JS0tzZ+cnOwbOnSoB2Dw4MHuPXv2tHkaR15eXnhBQYEtKysrA8Dr9eq6dOlySvRhkeHKr5Tw7Q31VfHvBVMXiB2FYkk64j7+ijLKJo4nUFZK0hPP4LhiVPvPLwTFxcXccMMNzJkzp93HOZLqoEKNqqO3s5iot59BC49i2znX0tt8bIGD5vOz7d4n2P/eWpJvH0v6nLsxmDTs9n3YbE50Og2fz4LTGYvXGwac+CUnQgg+2riP5V9U0CPexh+vTCPSfmKnoxgbS7GXr8NStx1NZ8KdMBRX4gVoJsfRd24jVRPsb/RR5fRSVO3iu9I6XH4Vu1nP+T1iyE6JJDnSKscn/8qoqsa6z/ew7I3NfPtdJTabkeuu7c8N4wbQNfnkVGZJkiRJkiQ1O1KFSWfp3r27Pz4+3j98+HAXwLhx42qfeOKJBICYmJhgcXGxMTU1NVBcXGyMjo5uNVw4tEpFr9cTDAZb/EW6uedK89tCCC688ELn6tWri1raPjw8XGveriVCCO69997K6dOnHzj0/Tt27DCZTKZDr0l4PJ42l6ALIZTrrruu+sUXXyxv6z4nigxXfmVEsDFUqeLaDnobxFwG9oyj3ox6t22l9I93IXw+ui38O7bsc9p/DUKgKAovvfQSqqp2yo1woKlqJaZxHz1WPgUGI65x9xEoP+ISwl8ep76RrbfPou6r70ibNYGek35HWFgVFksolPV4wnG5oggETl5j1KCq8Y9/l/L5DzUM6R3JHZennrglMEJgcu7GXrEOs3M3mt5KY/JvcMWfjzC2vzJSE4LqRj97nV72Or1UNYT+PdDgR216gTboFDITQ+OT+8SHo9fJQOXXxuXys2r1Nt78v+8pK3eSmBjOlD8N5aorMwgPOwXGiUuSJEmSJJ0kKSkpwYSEBP/mzZvNWVlZvk8++cTRp08fL8AVV1xRt3jx4ph58+btXbx4ccyIESPqABwOh9rc9PZ45eTkuKZOnZpSUFBg7t+/v6+hoUFXVFRkHDhwoO/Q7aKjo7WEhAT/66+/HnnLLbfUeTweJRgMKiNHjnQ+/PDDSePHj6+JiIjQioqKjIeGKi0JCwtT6+vrj7g8YMSIEc6xY8emz5gxoyo5OTlYVVWlr6+v1/fu3dvfEZ/38ZDhyq+A0HzgKQHPHnD/GFov4zgbIs5B0ZmOun/jF+spv+9e9BGRpCx6FXPP9HZfS1lZGePGjeOVV14hMzOzwzpHH67Ip0PncXLWqidQAj4ab5qJiIyDYwhXvBX7+f6W+3HvKiXr5Sn0+p+BmEzlaJqOxsZoXK4INO3EVoccrtET5Pn3i9hR3shV5yZw1XkJJ6ZZq9Aw127HXrEek6sM1RiOM2UEni7nIPRtv+kVQuD0aVR7VA64g3jKS9jr9LKvwUdQO/jaGm0zEe8wk5ngIN5hIcFhoUu4GaMcsfurVFHh5M3l3/Puqm00uvxkDUzgj/ecT86wHhhk7xxJkiRJkiQAnn/++ZKbbropze/3KykpKb433nhjD8AjjzxSOWbMmJ6pqamxSUlJ/nfffXcXwMiRIxvmz5+f2Ldv38zmhrbtlZSUFFy8ePGe66+/Ps3v9ysAc+bMKT88XAFYunRp0Z133pk6d+7cJKPRKJYvX75r7Nixzq1bt1rOOeecvgA2m01btmxZkcFgaDVgyc3NPTBp0qTU6dOna/n5+dta2iY7O9v70EMPlV9yySW9NU3DaDSKBQsWlJwK4YrSWhlPZzr77LNFfn5+p5/ns88+Iycnp9PPc6IJIUJjlD17Qg9fJSBAZwZrD4g4D48USoUAACAASURBVMXYtlL6upXL2TvvYcy9+tBtwSIMcS2PZG6LxsZGLrroInbt2sWGDRvo379/i9tVVBxfz4TqoEKh08+Fqx7DWl2Ga9x9qN36ALB161b69et39GvdXsT3t9yP2ujiwhV/IOmy3gQCJlyuSDwexwlvUtuSyhovz723m+oGP7ddlsLQvsc+BvuYCRVL9ffYK9Zj9OwjaI7ClXgRnrjBoGs9aBJC0OgPhSjV7mBTmKJS4wkS0A5uF2E1Eh9uJiHCQkJ4U4jiMGM2tK9/za/1Z/zXSAjBlu/3suyNLXz62W4UBS4d3pObbsiif7+2N0GWz/mZRT7fZx75nJ9Z5PN95jlVn3NFUTYKIc4+9H2bN2/ek5WVdaC1faQz0+bNm2OzsrK6t/QxWblymvhZdYq3ONSgFsAUF6pSsXYHcwKK0rZQQAjBgYXPU/3KS9iHXkTSU8+it4e1+/pUVeXGG29ky5YtfPDBB60GK8crKKDIrXLOR89i3VeMe+yffgpW2sr59X/ZcusjGO0mhn8+FWvv3lRXR+Hz2TgZ/VRa8kNpAy+8X4Rep3D/Nen0Smr/c9MmWgDr/m+xV36OwVdLwNqFup7X4Y3pD8rPgw93QOOAO0i1W/2pIqXGo+JTDwa1NqNCjNVAvy4WYqwGYm16oq160lK6du7nIZ1yAkGVTz/dzbI3N1OwdR/h4WZuuWkQ467rT0J86821JUmSJEmSJOl0IsOVU9TPq1OKwVcBCFBMYE0NPSzdUQz2Yz92IEDl3Fk4V79LxNXXkjBjDorx+Ja/3HfffaxevZoXXniBESNGHNexjqTICxlrFhFVWoB79J0Eew1u454Cs9lF7Yd5fHfHYsJ6xnHuypkEotPx1JxavR3WFRxgyaelxEdamHxVGnERnXd9iurDWvVf7Hu/RB9oxG/vSm3qKHyRffCqcKBBpdrt/1lFiid4MESxGBRirHr6xJqJseqJtRmIseqxGk9+5Y90cjmdXlau2sZb/7eFqn0uunWN4P5pF/G70X2x2U7ucjtJkiRJkiRJ6mgyXDmF/FSd4i0OBSpq06QrYxw4spuqUxLbXJ3SErWxkfLpf8T9nw3E3jWJmPF3H3fDWZ/PxzfffMOkSZO45557jutYR1IbgNhPl5K08z94fnM9gQEXHXUfRdGwWp3Y7bUUPv8h305dQfTQDDJf+TMBRxScEkO7QjRN8H9fVJC3aR8DUsP5w6ge2MydM+5ZCbixV32Fbe9/0KkeGuw92BFzFYVaV6r3alTvrsN1yHoekz4UovSMNhFjNRBj0xNjNWA3KnJyj/QzJSV1/POtLaz+YDseT5Czs5N54L5hXHRBKjrZlFiSJEmSJEn6lZLhSis0twt/STGm1O7orO2finIkB6tTig/pnaI1VaekhMIUSyqKoWOWhAT2VVE2aQK+3YUkPDKPyCvHdshxzWYza9asQa/vnCAAQsuB1C/eo+f3n+AdMhL/uUceE63TBbDb67Db61EIkj/5XXYu+Ji40RfTd8FM9JajN/o9kbx+lUV5e/hut5NLsmK5cVjXDp+OE1AFTmctYVVfkuTchFEE2KH0ZI04m9LGRGgEg85HjNVAaoTxpwAl1qYnzKTrkBDlePvtHC4QCHT4MZslJR15dLl0kBCC/I0VLHtzM59/sQe9XsfIK3px4/VZ9Okde7IvT5IkSZIkSZI6nQxXWuHZspnSP9wGgCE+AVNqD0zde2BK7R76t3sPjPGJKMcYKAjNB97Sg8t9fqpOiQXHWYf0TunYoMK3ayelE8ejNTjptmAR9vMvPO5jbtu2jQcffJC///3vREd3brNV1zdr6fn1Clz9LiT4m3GtbudwKERGVmK1NoT2qzfz9f8uoWrV5yTfPpb0OXcf83PW2aob/Dy3ajfl1R5u/k1XLs2K65DjeoMaJfUBSur8uOqrGOTP///s3XlclNX+B/DPM/sMwwDDzgADyI6CiJKhJqmlqJlLpt6K2455NUuzm+aSeq/duv6sS6lh1+q6m2apecUWd9FqXCCQRbZhR9ZhhtnnOb8/EHMBUxyULuf9evkCZ57nnAPPgD7fOedzEIdcMGCRxYQhU/gAiIMX/CVcxIq5cJXw4CS0TxGF6h3MZhsOfX8J23ZkIb+gHs7OIrz43EBMfaIv3Fy7pyhNURRFURRFUT0RLa50QhgaDp/3P4RZXQpzaQnM6hK0/Hc/WJ326jGMQACBv/Kawkvg1cIL11EGoH12SsMN2SnXzE4RKQFxgN1mp3Sk9ZczqJw3BxyRCP4bt0AUFnHXbdbX12P8+PHQ6XTQarXdWlwx5/wCv8NfQBMYC4x9HrhpWRSBSKSDVNoEHx8eWLYVra0uaK7iIfPZ5dCczkTQkpnwS3myxxUOimta8a99xTBbWbz+eB/0C5Dd/smsDRyLFlyLDhyzFjBrodc1w2RoATFpIbDqEINWDGH04IKFjeHisqw/dN5D4OnkjjE97HtB/XE0NRmwe08Odn2VjfoGPYICXbBkUSKSRodCJKL/rFAURVEURVG9D/1fcCd4cjlkj1wfzEoIga2x4Wqxpb3wYiosgPboj4DNdvVYroszBL6uEHg7QODjAIGPMwQBAeAHxIBxDL6SndL9Myg0B/ejeukiCJQB8PsoDXzvu1/qYDKZMGnSJFRWVuLo0aNQKpV2GGnHmJIcuB5YD413CNhJs8Dl/vaSZRgbJBINHByaweNZYbXykZdng5NTMAwV9ch6Zh4MxRWI+PhteE4a1W1j7KqfC5rw6SE1nBz4eHNKMBSuYgAAYzODY9GCY9aCa9Fe+Vx3zedXHrfqb2rTBYAOYhg4UrAiR1hF3tBLZCACGYzyKDACR9D9WaiuKipuxLYdmfhvegFMJhsSHvTH8unRGPyAX48rXFIURVEURf2RZWZmCqdNm9an/e8VFRXCN998s3Lp0qWXa2truZMmTQqqrKwUKhQK0969e4vd3d1tGRkZ4vLycsG0adM0ADBv3jwfqVRqW7FiRe2t+lIoFP0cHBxsDMPAzc3Nsn379hJ/f/97nk6ZmprqOmHChJaAgAALAEybNk355ptv1sbFxRkVCkU/lUqV6+3t3YNSM69Hiyt3gGEY8FzdwHN1gyRu0NXHCSEghsuwFP0Mc1EmzKVFMFc2wVzVAt1PNbBpWn9rhMeDwNfvt1ku/r8tM+K6yO12g0IIQePnn6LuozWQDIyHYs3HV2fT3G27L774Ik6ePIkdO3Zg8ODBdhhtxzg1JZDs+Rdanb2hnTIPMkHbrjlcrhlSaRPE4hZwOAQmkxgajQdMJgeUl18Ep7IUWU//FbZWA6K3vAeXoQO6bYy3jRAwNgO4Zi0YsxZZ+RW4rK7B62E2DPTnQlR7GpyKthkoHNZ08+kMFyxfCgtPimbGCQ1cb1TbRGhkHaCFAxihI5xlzvBwcYbCSQgBt212j+XKH4rqKkIIMs6UY+v2TJz5qRxCIRfjksLwp2nRCArq3uWAFEVRFEVRvVVMTIwpLy/vIgBYrVZ4eXnFTJ8+vRkAli1b5p2YmKhdtWrVpUWLFnktXbrUa/369ZUqlUqiUqkc2osrd+LYsWMF3t7e1tmzZyuWLl3q/cUXX5TfznlWqxU8nn3KClu2bHHr37+/ob24snPnTrVdGr5HaHGliwhrviY7pRSMTQeBDBDEBwDDE6/Z2YcLW4umbbbLlRkvptK2WS+tGSdALL/d+nIcZdfnuigDIQwIBN9PCY7w9rfjJVYrat//O5p3bYcsaTy83lkFjsA+Aa6XL1/GiRMnsGLFCkyb1nn2yd3iNNZA8uVqmERSlE5+E/5SMQQCPaTSJohErSCEgcHgCJ3OGVar6Op55swCnF+5EVyJGLF7/gVpZJ9b9GIHhAXH0tq2PMd8ZWbJtZ+bryzbsejAkN+KrA8DeFgJsBwBWKMjWIEUFokXWOcQsHwpbHxHsHxHGLlSlBlFKNbyUNZiRVNr2+woCZ+Bv1wApRMfMU58SAU9K0eG+uMzGq04cDAf23ZkoaS0CW5uEsyaGY8pk6Lg4iy+38OjKIqiKIrqNfbt2yfz9/c3hYaGmgEgPT3d+dixY/kAkJKS0jB8+PAwo9FY9e677/oYjUZOeHi4dP78+dUAkJubK46Pjw+rqqoSzJw5s3bx4sWXb9VXYmKi9qOPPvIAgD179shWrFjhYzabGaVSadqxY0epk5MTq1Ao+s2YMaP+yJEjspSUlMsJCQn6l19+WdnQ0MDjcrlk165dxVFRUaYlS5Z4fv3113Kz2cyMGzeu+YMPPqjKz88XJCUlhcTHx+tUKpXU09PTfOjQocJdu3Y5Z2dnS5KTk4NEIhGrUqlyR4wYEbp69eryhx566Lop++vWrZOvX7/e02KxMAMGDGjdtGmT2l4Fnrtx/0fwB9GWndJ4ZZvkEsDYnp3CB0T+gDj+SnbKzYsuuDIniKP7Qxzd//o2bTZYqqvaii5lv2W76H8+g5Zv9/52IMOA76PoMNuF5+5x3WwX1qBH1VvzoTt+BPLnXoL77NfBcLq+dfONPD09cf78eTg7O9utzRsx2iZIdr4PG2GQNfFNxPoBjlI1+HwzbDYutFo5WludwbLXv3wv7z0MzaJ1kAQqEL3lPYgUnnYfG9fYAGnlUfD0NVcKKa1gQG46juWJrxZIzGI32PiO0EOCA1l6XLzMYGCUH4bHBQI80XXn2ViCap0VZRozymotqNFZQWADn2ODr4yPaE8R/J34cBVz6TIMqlvU1bfiy93Z+GpPDpo1RoSHuWHFspEY/Ugw+HxaxKMoiqIoqvdhz37mB22lfdP6HRV6TtzztzU7ZPv27fInnniiof3vDQ0NPKVSaQEApVJpaWxs5IlEIrJw4cIqlUrlsGnTpjIAmDdvnriwsFCUkZGR39zczI2IiOi7YMGCOqFQePMNzBX79u1zjoyMNFRXV/NWrVrlffz48QKZTMa+/fbbXitXrvRcvXp1NQCIRCL27Nmz+QAQHR0d/sYbb9QkJyc36/V6xmazMXv27JEVFhaKsrKycgkhGDVqVPDBgwelQUFB5rKyMtGWLVuKExIS1GPHjg3atGmTy6xZsxrXr1/v0VEx5Vrnzp0T7d69W65SqfKEQiF5+umn/T/55BPX2bNnN3R2zr1Ciyu30DY7peLq7BTYroTZ8l0BWf8rs1N8upydwnC5bUuEfP0APHTdc6y+9bdMF3X7rJdS6M+fBTH89lpjxJK2mS5XZru0njoB48VseC5cBpcnZ3RpXB356aefsG3bNqxevRouLi52a/cmxlY4fPlPQK9Dw7N/wfDIVvC4LbBYBGhq8oTB4Ajg5mJRedqXKFqxHvy+fRC78wPwne2bLMJY9ZBWHoWk9icQhgOzLAgWBx+wAserRZTfPpcCnOt/tCobDPhwbzGaWx3x0mgl4kPbvoeEEDQYbG27+mjMqGixwsISMAA8pTzEK8TwdxLAW8qz+9bMFHWtvPw6bN2eiUPfF8JmYzF8WCCemhGNAbE+tJBHURRFURR1nxiNRuaHH35wWrNmTUVXzn/00UebxWIxEYvFVrlcbqmoqOD16dPnpuSA4cOHh3I4HEREROjXrFlT+f3330uLiopE8fHx4QBgsViYuLg4XfvxycnJTQDQ1NTEqa2tFSQnJzcDgEQiIQBIenq67Pjx47LIyMhIANDr9Zy8vDxRUFCQWaFQmBISEgwAEBsbqy8tLb3tZRrp6emO2dnZkpiYmIgr3x+Oh4dHj8hhocWVThCDGri8H4DtyuwUP0A8CBArwfDuPrvk93AkDhBFREEUEXX9uAiB9XLtb6G6Vz4af82E9ruDYIQiKP7vYzgmjrDbWNRqNSZMmACpVIqlS5fC1dXVbm1fx2KCdPdqcBqrIUyZjj5hYphMIjQ3ucBsFgO4+QaPsCyKVqxHxae74T5uOMgrE+1bWGGtkNT+BGnlUTA2IwzuA6DzHQVWcPt9/FragnX/LQGfx8HCqSHwkItxsc54paBiQauFBQA4iziIdBfC34kPXxkfIp79ZhxRVEdsNhYnTqqxdUcmzp6rgljMwxOTozD9yWj4+znd7+FRFEVRFEX1CLc7w6Q77N692ykyMlLv5+d3tYDg6upqVavVfKVSaVGr1Xy5XN5pceHaWSpcLhdWq7XDd83aM1fa/04IwdChQ1v2799f0tHxjo6ObPtxHSGE4LXXXqtesGBB/bWP5+fnCwQCwbVjIgaD4bZvfAghzNSpUxvWrl1bebvn3Cu0uNIZgRsgi2nbKlnkA4bpGd8qhmHA9/QC39MLDg88eN1zrNHYlgEitt+MtZaWFowfPx4mkwlHjx7tpsIKgYjfAtHej0Eqi8D78xSUew2AqNYJYDvPimFNZuTOfRd1+49C8cIUBL8zCxdzc+00JAJhYw4cyw+BZ2qCySkEWv/RsEq87qiZHzPrsP14JfwVjojv646TtWY0lBoAAGIeAz8nPpROAvg78SET0iUXVPfT6y34+ZcKnMxQ4+QpNS7XtcLLS4rX5jyISY9HwtHx9vOdKIqiKIqiqO61Y8cO+ZNPPtl47WOjR49uTktLc121alVNWlqa65gxY5oBQCaT2XQ6nV3eoU1MTGydP3++f3Z2trBv374mrVbLKSkp4UdHR1+3+4ZcLme9vLzMmzdvdn7mmWeaDQYDY7VamaSkpJZ33nnH5+WXX250cnJiS0pK+NcWVToilUptGo3mljdFY8aMaZk8eXLwokWLahUKhbW2tpar0Wi47Xk091PPqBj0QAzXAXAZdr+HcUc4ItHvH3QHrFYrZsyYgdzcXKSnpyMiIsKu7QOASKSFo2Md2J17YM3Nh27cVJyRTUCfFkDE6/xnz6LRIfuFxdCczkSfJa/AN2Wq3ZYu8LVlcCw7CIGuHBaxJxrD/gyzc8htn88SgiqtBQcyG3DZYEPUQAXAMChoMkMh41+ZnSKAu4TmplD3RnmFBidPqXHilBpnz1XCYmHhIOHjgXg/PPpIMEYkBoFHZ0pRFEVRFEX1KFqtlnPy5EnZf/7zn+t2zVm+fHn1pEmT+iiVSjcfHx/zN998UwQASUlJ2tWrV3uHh4dHtgfadpWPj481LS2tdPr06UFms5kBgGXLllXeWFwBgC1btpS89NJLypUrV/rw+Xyya9euosmTJ7fk5OSIBg0aFA4AEomE3bp1awmP1/lNXnJycv2cOXOUCxYsYFUqVYfvmsfFxRkXL15cOXLkyFCWZcHn80lqampZTyiuMJ1N4+lOAwcOJCqVqtv7OXr0KBITE7u9n/9VmZmZSEhIwJo1a5CSkmK3dquqqgAAfL4Bbm7lMH59FOzRk2gZMgkZsU/AlUcQKmI7Pd9YeRlZz/wVhuIKhH/4Fjwnjrz6XE5ODqKiojo991a4xkZIy7+DuDEbNr4UOt9RMLgPAJhb33QSQtBsZKHWmFGmsaC8xQKzjYAQAj4hiFFIEOAsgI8jHzyam2JXd3O9f4+Pj0+3tHsvWCw2nLtQjVNXZqeUqpsBAAFKZwwdosTQIUrExnj/IQNq6e/13oVe796HXvPehV7v3qenXnOGYc4SQgZe+1hmZmZpTExMfWfnUL1TZmamW0xMTEBHz9GZK1SnYmJiUFBQAIVCYfe2ORwL5PIqmL47A/boSZgGjELmgCngEiBQ2HlhRZdbjKyn/wpbqwHRW96Dy9ABdz0Wxmq4ElZ7BoThQKd4GK3eQ0G4nS+P0FtYlGssVwsqWnPbmB34DFoa9bh8uRXjYtwwKtr9rsdHUbejrr4VpzLKcPKUGj/9XI5WvQV8PgdxAxSYOqUvhg5Rws+X5qhQFEVRFEVRVHegxRXqJocOHUJpaSlSUlK6pbACsHBxqYL1vz/C9v1JmCMHo+ihZ6CzchAqtIHfyeSOpowLyH5hMbgSMWL3/AvSyD53OQwrJLU/Q1p55EpYbeyVsNqOA4utLEFBgwmZtUbU6NqynoTcttyUQU58wGjFv9NLYWMJZo8LRKS/fXcsoqhrsSxBzsXLV5f75OXXAQA83B0w+tEQDBuiRPwgX4jF/Ps8UoqiKIqiKIr630eLK/9DtGYr6g0WcBmm7Q8H4HHaPm//yOUw4DEMOAw6zPvIycnBk08+iYCAADz77LMQCu0dbkngJKsB9u2F9YQK5phEND7yLMqMPMi5LFw7WYJXu/cw8l77B8QBPoje8h5ECs+7GAKBsOkiHMsOgWdqhEnWB1plUqdhta1mFlm1BmRdNkJvIZCLuUjwk0DpxIeHAw8chsHpvEZs/L4Mcikfrz3eBz5y++bfUBQAaLUmZJwpw6mMMpw6XYamJgM4HAbRfT0x+5UHMDRBiZAQV5rlQ1EURVEURVH3GC2u/MERQlBnsOBSkx7VrXeW4dNWdMHV4ktLQz3mTBoLnkiM5f/ehqwmE7iMua1Ic01h5tqPVws317TT/pHTwQ0esanB3bMFVlU2TA+MhWH4NBQaeeAACBKy6OiesDztSxStWA+nB6LR97O/3dVWy3xdORzVByHQlcEi9kBjWDLMTiHoqOManQXna4woaDCBJUCgMx+xXmL4O/Gv3rwSQvD16Wrs/akGYQop5owPhFRMf6wo+yCEoLikCSdOtWWnZGZVw2YjcJIJkfCgP4YOUeLBwf5wdqLFPIqiKIqiKIq6n+xyF8gwzGcAxgO4TAjpa482qVuzsQTlWiMuNRugMVkh5DKIkEvgL2u7ybIRAivbdlzb520fbSyB9bqPbce26g1YnvIMmurqsObL/ZC4e6FOb/6tnS4EHzPAdYUYD34TIv+7Bmx2AcoSpqJm0ATYTAy0LINgoQ2CG3JjCcuiaPl6VPx7N9zHDUd46iJwRZ1vzXwrbWG130Pc+CtsfCk0gY9fCau9PtDTxhIUNppxvsaAap0VfA6DaE8R+nuK4SK+/lizlcXG79T4qaAZQyPleHakH3hcuuMKdXcMRgtUqsq2rZIzylBdrQUAhIa44s/PxGLYECX6RnmCS19rFEVRFEVRFNVj2Ost9i8AfAxgk53aozphsrIo0hhQ1GyAycZCJuAiztMR/o4icO9iJ5pvvjmMi+fPYteuXZj82MibnieEgCW4oTBzbdHmt+euLea0f+SZmhGy5x9gC0tRnJiMiujRYFnABsCDx8L9huVANqMZea+9i7r9R6F4YQqC35kFhnPnN5OM1QBp1TFIak4DDAc6RSJavYfdFFZrsLD49bIRmbVG6MwsnIQcDFc6IMpdCGEHW9Q2t1qQur8YJTV6PDnUB0lxHnQpBtVlVdVanDylxslTpfjlbCVMJhtEIh4eGOSL5/88AEMTlPD0lN7vYVIURVEURVEU1Qm7FFcIIccZhgmwR1tUxzQmKwqb9FBrjWAJ4CURIMRFAg8J3y439RMnTsTFixcRFhbW4fMMw4DLAFwwwB3u3kpam2DcvBRsRSWEz8yDq6I/XGHr9HhLsxbZLyyG5kwW+ix5Bb4pU+/8a2StkFz+BdLKw2CsRhjc+kPn98hNYbV1rVZcqDEgt94EGwH8nfgYGShFgDO/w2VNAFBeZ8CH+4qgNdgwe3wg4oKd72xsVK9nsdqQlVWDE6fUOJVRhqLiRgCAr68Mkx+PxNAhSgyI9YFQSJeYURRFURRF9VbLly/32Lx5szvDMAgPD9fv3LmzVCKRkNraWu6kSZOCKisrhQqFwrR3795id3d3W0ZGhri8vFwwbdo0DQDMmzfPRyqV2lasWFF7q34UCkU/BwcHG8MwcHNzs2zfvr3E39/fem++yt+kpqa6TpgwoSUgIMACANOmTVO++eabtXFxcUaFQtFPpVLlent73/Nx3S6GdGG5R4cNtRVXvu1sWRDDMC8DeBkAPD0943bs2GGXfm9Fp9NBKv3jvttLAFiEDjA4uMIikgKEhUjfDFFrI3jWO8tX6czRo0fh4uKCmJgYu7R3I75Bi36qbeA2NaL64SmocAyFxWLp9Hjb5SZo3l4HW+VlOC54BqKHB3Z6bEeMBgP8+PUINmVCQrRo5HrikiAWOq7L1WMIAeqsQqgtEjTZBOCAwIdvgD9fDym386IPABTVsdj/qxVCHjC5Pw+eMro0434yGo0Qibonb4TPt+8uO1qdFXn5OuTm6ZBXoIPRyILLBYICHRARJkVkhBTubgI6A+p3/NF/r1N3hl7v3ode896FXu/ep6de84cffvgsIeS6G4/MzMzSmJiY+vs1ppKSEv7QoUPD8/Pzs6VSKRk7dmzQmDFjNK+++mrDzJkzfeVyuXXVqlU1ixYt8mpqauKuX7++MjU11VWlUjls2rSpDLiz4kp74WL27NkKnU7H+eKLL8pvZ5xWqxU8nn3eEIyPjw9bvXp1+UMPPaS/1Rjt0lkXZWZmusXExAR09Nw9e1uUELIBwAYAGDhwIElMTOz2Po8ePYp70Y+92VgCdYsRl5r10JptEHE5CHUWI8hZDCG34x1tuiIjIwP/+Mc/MGzYMLz66qt2v6lj66tgXDsfRKeB6JV5CA4ZjWAAVVVVHR6vyy1G1hvvAK0GxGz7J1yGxN5Rf3xdBTi5P8DFWNcWVus/EWanUCivfF1GK4ucy0ZcqDWixcTCUcDBUIUI/TxEEHWw9OdahBAcPHsZX1+ogtJDjLkTguAi7Vr+C2U/OTk5iIqK6pa2fXx87up8liXIy69rW+6ToUbOxcsgBHBzlWD0I6EYOkSJBwb5QUpfR3fkj/p7neoaer17H3rNexd6vXsfes3vjM1mY1pbWzlCodBmMBg4vr6+FgBIT093PnbsWD4ApKSkNAwfPjzMaDRWvfvuuz5Go5ETHh4unT9/fjUA5ObmiuPj48OqqqoEM2fOrF28ePHlW/WZmJio/eijjzwAYM+ePbIVK1b4mM1mRqlUmnbs2FHq5OTEKhSKfjNmzKg/cuSILCUl5XJCQoL+5ZdfVjY0NPC4XC7ZtWtXcVRUlGnJkiWeX3/9tdxsNjPjxo1r/uCDD6ry8/MFSUlJIfHx8TqVSiX19PQ0Hzp0IrQN0AAAIABJREFUqHDXrl3O2dnZkuTk5CCRSMSqVKrcESNGhHZUbFm3bp18/fr1nhaLhRkwYEDrpk2b1PYq8NyN+z8C6iqD1YaiZgOKmw0wswTOQh4GeTnCz1HU6RKVriopKcHEiRPh5+eHHTt22L+wUlUC47o3QaxGiGbPBlf56C2Pbzp1HtkvLAHXQYzYr1MhjQi67b64pqa2sNqGLJgZITQBE2DwiLsaVttosOJCjREX64ywsIDCkYeH/B3QRy64re+rxcriP4fLcfJiIwaFOOPFR5UQ8umMFepmOp0ZP/1cjhOn1Mg4XYb6Bj0YBoiK9ETKi4MwdIgS4WHu4NxFPhJFURRFURR1b5m2/tOPrS6V2LNNjneAXvjUgk5nhwQGBlr+8pe/1AQGBkYLhUJ22LBhLZMnT24BgIaGBp5SqbQAgFKptDQ2NvJEIhFZuHBh1Q0zV8SFhYWijIyM/ObmZm5ERETfBQsW1AmFwk6Xr+zbt885MjLSUF1dzVu1apX38ePHC2QyGfv22297rVy50nP16tXVACASidizZ8/mA0B0dHT4G2+8UZOcnNys1+sZm83G7NmzR1ZYWCjKysrKJYRg1KhRwQcPHpQGBQWZy8rKRFu2bClOSEhQjx07NmjTpk0us2bNaly/fr1HZzNX2p07d060e/duuUqlyhMKheTpp5/2/+STT1xnz57d0LUrYT+0uNIDNBstuNRsQFmLEQSAj0Nbnoqb2D55KjfSaDQYP348LBYLvv32W7i6utq1fZs6D8b1C8HwAfHcFHC8H0Xb3kEdq917GHmv/QPiQAWiN78HkcLjtvphrAY4VB2HQ81pAIDOZzhUGneEe/YHIQSlTW27/qg1FnAZIMxNiFgvMTwcbv9l36K34ONvS1BQ1YqJg73w+ANedNkGdROdzowPUjOw/0AerFYWUqkADw72w7AhSiQM9odcbtd/iymKoiiKoqj/cXV1ddwDBw44FxYW/urq6mobN25c0Lp16+SzZs1qvJN2Hn300WaxWEzEYrFVLpdbKioqeH369Lkpp2H48OGhHA4HERER+jVr1lR+//330qKiIlF8fHw4AFgsFiYuLk7XfnxycnITADQ1NXFqa2sFycnJzQAgkUgIAJKeni47fvy4LDIyMhIA9Ho9Jy8vTxQUFGRWKBSmhIQEAwDExsbqS0tLhTeOpzPp6emO2dnZkpiYmAgAMBqNHA8Pjx6Rw2KvrZi3A0gE4MYwTAWAZYSQjfZo+38VIQTVrWZcatKjzmABl2HQx1mMYGcxpILurXmlpaWhoKAAhw4d6jTAtqtsBedh/HQpGKkYotnPgOM6Erd6mVVtO4CCBavhNDgafTf+DXxnx9/vhLVdE1ZrgNEtBlrfR8AKnWDSXMSFGgMu1BjRZLRBwmfwoK8E0Z4iSO5wtkl5vQEf7i1Gi96CWWMDEB/q8vsnUb2O6mwllq08jNpaHaZMisKjo/ogOtoLfN4dJj9TFEVRFEVRPdKtZph0l/3798v8/f1NPj4+VgCYOHFic0ZGhnTWrFmNrq6uVrVazVcqlRa1Ws2Xy+WdFheunaXC5XJhtVo7fKf42LFjBdfmmRBCMHTo0Jb9+/eXdHS8o6Mj235cRwgheO2116oXLFhwXW5Nfn6+QCAQXDsmYjAYbvtGjRDCTJ06tWHt2rWVt3vOvWKXtQ2EkBmEEG9CCJ8Q4ksLK52zsiwKm/Q4VNqIjCoNdBYb+rk5YFyQK/p7OHZ7YQUA3njjDWRkZGDEiBF2bdeadQrGTxaBkTtD9Noz4LgOASDu9Hib0YziVRvg/GB/RG/95+8XVgiBsDEXbr9+BJn6W1gknmjo+wo0fZ5AI5HiWKkOx3VuOFLaCiGXQVKwFC/GyjHYV3LHhZXzxRr8fWcBbCyLhVNDaGGFuonRaMXqD07i5Vl7weNysDFtIha++RDiBihoYYWiKIqiKIq6KwEBAeZz585JtVoth2VZHD582DEiIsIIAKNHj25OS0tzBYC0tDTXMWPGNAOATCaz6XQ6u9zjJyYmtqpUKml2drYQALRaLScrK+umGSZyuZz18vIyb9682RkADAYDo9VqOUlJSS2bN29202g0HKAtoLeysvKWN7tSqdSm0Whu+R/pMWPGtHz77bcu7W3V1tZyCwoKekSIIV0WdI/oLTYUNhtQojHAwhLIRTw84CaDQiq0e55KZ7Zs2YKhQ4ciICAAgwYNsmvb1l9+gGnr++D4KSGaORmMQ18A8lueU7f/CKxNLVDOfRpc0a1/Hni6SsjKDkKgLYVV5I6m0GdgdApBudaK8/ktKG4yg8MAHjwTEsO84O3YtZ1e2oNrd52kwbVU5y7mXsbid35AaWkznnyiL+bOfhBisX13F6IoiqIoiqJ6rxEjRrQ+9thjTdHR0RE8Hg9RUVH6efPm1QHA8uXLqydNmtRHqVS6+fj4mL/55psiAEhKStKuXr3aOzw8PLI90LarfHx8rGlpaaXTp08PMpvNDAAsW7asMjo62nTjsVu2bCl56aWXlCtXrvTh8/lk165dRZMnT27JyckRDRo0KBwAJBIJu3Xr1hIej9dp3ktycnL9nDlzlAsWLGBVKlVuR8fExcUZFy9eXDly5MhQlmXB5/NJampqWWhoqH22070LdtuK+U4MHDiQqFSqbu+nJ6RRNxosKGjWo1JrAgHgKxUixEUC13t8I3bgwAFMmDABzz77LDZutO/EIsvxb2De/TE4oX0hejEJjMgPQAQ6y1lp3y3o7PhXYNPqMejoF53mmHBMTXAs/wHihkzYeA7Q+Y5Ei+sA5DVYcb7GgAaDDWIeg36eIsR4iqC+lNfl3WOuDa6ND3HGCzS4tse717sFWaw2/Puzs/jsi7NwlUvwzpIRGPyAX7f0T3WsJ/xep+4der17H3rNexd6vXufnnrNGYbpcVsxUz1Tj9iKuTdhCUGVzoRLTQY0GC3gcRgEu4gR7CyBA//eLxfIysrC9OnTERMTg9TUVLu1SwiB5bttsBz4HNx+D0D47CgwfEcAYbhVgC0AtGTmQ3s+D8Er53RYWGGsxithtRkA2sJqa10TcL4e+PWCBkYrgbuEi0eDpAhzE4J3l7uv0OBa6vcUFTdiyTs/Ii+/DuOSQvHm/GFwdLzt7C2KoiiKoiiKov6H0eKKHVlsLEpajChs0kNvZeHA5yDGXYoAJxH4nPszA6Kmpgbjx4+HTCbD/v374eDgYJd2CSEw790A6+Fd4A0aCcGfhoPhEgB9Afx+Aanqi2/AkYjg9cQNWzSzNojrVHCs+BEcqx561xgUyh/Gz/UCFGYZAAB95ALEeomhcOTZpQBCg2upW7HZWGzdnol1aT/DwUGAf/5jNEY+3Od+D4uiKIqiKIqiqB6EFlfsQGe2obBZj9IWI6wsgZuYjxh3R/hIBfd99sPixYvR0NCAEydOQKFQ2KVNwtpg3vEhrGcOgvfQ4xBMfggMpwlAfwCi3z3f0qjB5X2H4TV1DHgy6ZVGCYTNeXAsOwSesR5Gx0D8KhuB400uuFxghZBrwQBvMWI8RXAS2W/2z/liDdIOlkIk4GLRk6EI9KRb5lK/qajUYNmKwzh/oRoPDw/E228Np9sqUxRFURRFURR1E1pc6SJCCOoNFlxqNqBKZwIDwM+xLU/FRdRzgi0//PBDPPfccxgwYIBd2iNWC0yb3oXtwnHwRz8N/tiHwDBlAIIBON9WG9U7D4I1muHz7EQAAK+1Eo7qdAi1JTAL3XBa/gS+0/hC3wLIxQQjAh0Q6SYCn2u/QtW1wbUBnhK8+lggDa6lriKE4Kuvc7DmX6fA5XCwYtkIjEsKu+/FUoqiKIqiKIqieiZaXLlDLCGo0JpwqUmPJpMVAg6DMLkEwU5iiO9Dnkpntm/fjsceewxSqRRDhgyxS5vEZIBp43LY8lQQTJwJ/ojhAHIAeAG4vVkxrM2Gqk374DQ4GtLwQEgrfoS08gisXAlOOzyCb1vDYTVyEejMR6yXGP5OfLvf0NLgWupWGhoNSF17AWfP1SJ+kC+WLX4Y3l6/s004RVEURVEURVG9Gi2u3CazjUWxxoDCJgOMNhaOfC5iPRyhlInuOkzV3jZv3ozk5GSsWLECS5YssUubRK+DccPbYEtyIZgxH/wHHwJwDoAjgBD8XoBtu+r0EzCWVSNo4UsQaIohrTyCPG4EtlsTwRpE6OspRH9PMVzE3VOoosG1VGcIITh+shLrN2TCYmbx5hvD8OSUvuD0sJ9viqIoiqIoiqJ6Hvp2/e/Qmq04V6vFgeJ6ZNe3QibkYoiPEx4NkKOPs7jHFVZOnjyJF198EQ8//DD++te/2qVNom2C8aP5YNX5ED67GPwHRwH4FW3BtbcXYNvu0rptEHi6wv3ReEiL9qAeztjLjMIDSjlejHXBwwHSbiuslNcbsHx7AUpq9Zg1NgATB3vTwgoFANC0mPDe6l/wzzUqKHykSF3zMKZP7UcLKxRFURRFUdR9s3LlSo+QkJCo4ODgqBUrVni0P15bW8tNSEgIUSqVfRMSEkLq6uq4AJCRkSHeuXOnU/tx8+bN81m6dKnn7/WjUCj6hYaGRoaFhUUOGTIkpKys7L5MwkhNTXUtLS29mrExbdo05dmzZ0XtY6yuru7Rk0NocaUTGpMVJyubcai0EaUtBvg5ijBKKcdDvi7wlgp75E35Tz/9hIkTJyIgIABfffUVBIK7zxBhG2th+NfrYC9XQPjySvBihwG4CMAEIArA7W9FqysuR9XB4/B+ajwcqn4E39KM/dxHMKWfGwZ4iyHkdd/L8XyxBn/fWQAbS7DoyVC6IxB11c+/1OAvcw/jzM/VSH46Eu+vGgaFQnq/h0VRFEVRFEX1Yr/88oto06ZN7ufOncvNzc3NSU9Pd/7111+FALBs2TLvxMRErVqtzk5MTNQuXbrUCwBUKpXkwIEDTrduuWPHjh0ryM/PvxgbG6tfunSp9+2eZ7Vau9Jdh7Zs2eJWVlZ2tbiyc+dOdVxcnNFuHXQzWly5hSajBRFyCcYGumGglwzOwp5XKCOEXP04a9YscDgcfPvtt3BxufviAVtbDuO/XgPRNkH0l/fAixgEoBhAE9qWAt3Zz+2l9dvAcLnwn9APsrqfcYr0R9+QCEgF3ZdVQwjBf1W1SN1XDG+5CMtm0B2BqDZ6vQWpa89jxaozcHYSYs37iXhySii4XPprkaIoiqIoirq/fv31V/GAAQN0jo6OLJ/Px5AhQ7Q7d+50BoD09HTnlJSUBgBISUlpOHjwoIvRaGTeffddn/3797uEh4dHfvrppy4AkJubK46Pjw/z9fXt97e//c3jVn0CQGJiorakpEQIAHv27JH1798/PDIyMiIpKSlIo9FwgLZZJG+88YZ3XFxc2GeffeaSnZ0tTEhICA0LC4uMjIyMyMnJEQLAkiVLPPv27RsRGhoa+frrr/sAQH5+viAoKChq+vTpyuDg4KghQ4aE6HQ65vPPP3fJzs6WJCcnB4WHh0fqdDomPj4+7Pjx4zfdvK1bt07er1+/iPDw8Mg//elPSnsWeO5Gz6sW9BBOQh7GBbmB0wNnqABAXV0dNmzYgM2bN+PMmTNwdnbG5s2b4evrC5lMdtft28ovwbh+IcAAojn/B65vMIBaAOUAfK78uX1WgxFFn+2B78QRcNYcQT1xhtb3EQQ7dd8OPTS4lupMVnYdPkw9h/oGA56YHIKnpoeD34MCqSmKoiiKoqieo/qdRX6mwkt2fYdWGByi935nVXlnz/fv39+wYsUKRU1NDdfBwYF8//33TjExMa0A0NDQwFMqlRYAUCqVlsbGRp5IJCILFy6sUqlUDps2bSoDgHnz5okLCwtFGRkZ+c3NzdyIiIi+CxYsqBMKhaSzfvft2+ccGRlpqK6u5q1atcr7+PHjBTKZjH377be9Vq5c6bl69epqABCJROzZs2fzASA6Ojr8jTfeqElOTm7W6/WMzWZj9uzZIyssLBRlZWXlEkIwatSo4IMHD0qDgoLMZWVloi1bthQnJCSox44dG7Rp0yaXWbNmNa5fv95j9erV5Q899JC+s/GdO3dOtHv3brlKpcoTCoXk6aef9v/kk09cZ8+e3dC1K2E/tLhyCz2xsHLhwgWkpqZi27ZtMJlMeOSRR9DQ0ABnZ2dERkbapQ9b0a8wpr0NRuwA0V/+CY6HLwAtgHy0zVYJvuM21TsOwNzYDO+H3SC2qnHYcQYGKu6+CNQZGlxLdcRksmHT1ovYu78I3t4OeO/vwxAR7nq/h0VRFEVRFEVR1xkwYIBx7ty5NSNGjAiVSCRsZGSknse789v3Rx99tFksFhOxWGyVy+WWiooKXp8+fSw3Hjd8+PBQDoeDiIgI/Zo1ayq///57aVFRkSg+Pj4cACwWCxMXF6drPz45ObkJAJqamji1tbWC5OTkZgCQSCQEAElPT5cdP35cFnnlJlWv13Py8vJEQUFBZoVCYUpISDAAQGxsrL60tPS2sybS09Mds7OzJTExMREAYDQaOR4eHj1i6gotrvyBFBcXIzY2FhKJBM899xzmzJljt4JKO+vFn2HauByMiwdEf3kPHBcPAGYA2Wh7uUThTleTEUJwae1WOIb6IdCrFGd5cYgJj+i2Ykd5vQEf7i1Gi96CWWMDaL4KBQAouNSENf86i4pKHcYlBeK55CiIRPRXIEVRFEVRFHVrt5ph0p1ef/31+tdff70eAGbPnq3w9fU1A4Crq6tVrVbzlUqlRa1W8+VyeafFhWtnqXC5XFit1g5vwo4dO1bg7e19tR1CCIYOHdqyf//+ko6Od3R0ZNuP6wghBK+99lr1ggUL6q99PD8/XyAQCK4dEzEYDLd9g0kIYaZOndqwdu3ayts9516hayR6sMbGRrz//vuYP38+ACAoKAjbtm1DRUUF1q9fb//CyvljMH26FBxPP4jnrrlSWGHRFmBrRtvOQHe+jKfh5yw0ns2B38NOaGScIYkYA0E35VoU1rE0uJa6jtXKYsu2XLzx1nEYjTb87Z0EvPJyDC2sUBRFURRFUT1aZWUlDwAuXbokOHDggPMLL7zQCACjR49uTktLcwWAtLQ01zFjxjQDgEwms+l0OrvcaCUmJraqVCppdna2EAC0Wi0nKyvrphkmcrmc9fLyMm/evNkZAAwGA6PVajlJSUktmzdvdmvPaSkpKeG3fz2dkUqlNo1Gc8u1+mPGjGn59ttvXdrbqq2t5RYUFHRf1sQdoHcXPVBOTg5SU1OxefNmGAwGPPLII7DZbOByuZgxY0a39GnJ+C/MOz8EJzASopS/gRG375ZSDKAZQDiAri3jKVi7FRyJAGHDHFAZ8QzkDvYPlCWE4ODZy/j6ghWBnhK8+lggXKQ94meMuo/UZS1Y86+zKCrWYESiH15+sR+kDvR1QVEURVEURfV8EyZM6NPc3Mzj8Xjkww8/LHN3d7cBwPLly6snTZrUR6lUuvn4+Ji/+eabIgBISkrSrl692js8PDxy/vz51XfTt4+PjzUtLa10+vTpQWazmQGAZcuWVUZHR5tuPHbLli0lL730knLlypU+fD6f7Nq1q2jy5MktOTk5okGDBoUDgEQiYbdu3VrC4/E6zXtJTk6unzNnjnLBggWsSqXK7eiYuLg44+LFiytHjhwZyrIs+Hw+SU1NLQsNDTXfzddrD0xn03i608CBA4lKper2fo4ePYrExMRu78eeNmzYgJSUFIhEIjz11FN49dVXER0d3a19Wn78Eua9G8CNGAThC8vACERXnqkBkAdAgbbdge6csa4RXyuGIXiUG+RvPI0+I15CVVWVnUbe5trg2jBPDuY90Y8G1/YSOTk5iIqKuulxm41g7/5CbN6WC4mYh7+80h8Jg+8shNnH586Op+6NP+Lvdarr6PXufeg1713o9e59euo1ZxjmLCFk4LWPZWZmlsbExNR3dg7VO2VmZrrFxMQEdPQcnblyn2k0Gnz22WcYMGAAhg8fjjFjxmDVqlV46aWX4Obm1q19E0JgOfA5LN9tAzd2OITPvAWG176teAvaAmydAfTpch8XUjeDWKzwHBsGxdBn7DDq67XoLfjo2xJcuhJcGyxtoIWVXq6mphVrUs/hYm4DBj/gjdkz+8PZ+bYzsiiKoiiKoiiKou4YLa7cJ/n5+fjoo4/wxRdfoLW1FfPnz8fw4cPh7++PhQsXdnv/hGVh/motrCf2gpcwFoIn54LhtC9vaw+wFQCIRFejeVoNJpRt2AyPvk5wnfwauFdnxNhHe3Ct1vBbcG1OTqNd+6D+OAghSP+uFBu/yAaHw+D1VwdgRKIf3SWKoiiKoiiKoqhuR4sr98GLL76IjRs3QiAQYMaMGZgzZw7i4uLuWf/EZoVp62rYVD+AP/JJ8Ce8dM0NKAsgB4AVQCy6EmALACwh+GHNelgva6F4fQocfOwbvnu+WIO0g6UQCbhYODUUgZ72z3Gh/jjqGwz4aO15nD1/Gf1j3DF3dizc3ehrgqIoiqIoiqKoe4MWV+4BrVaLbdu24bnnnoNAIMCgQYPg7++PlJQUeHp63tOxEIsZps9XwpZ9Gvzxz4P/yIwb3tkvBKABEAHAscv9HL1YDtGuXbC5ihD62tt3OerftAfX7jpZhQAaXNvrEUJw9Hg5Pvk0CxYzi5kvRWPsmEBwOHS2CkVRFEVRFEVR9w4trnSjoqIirF27Fhs3bkRLSwsUCgXGjx+PlJSU+zIeYtTD+OlSsJcuQDB1DvjDHr/hiKorf/wAdL3ok1ejBXPkczRk1qPvX/8ErsjhLkb9m2uDa+NDnPHCo0qar9KLaVpM2LqjBr/mFCIs1AXzXo2DQiH9/RMpiqIoiqIoiqLsjBZXuoFGo8EzzzyDb7/9FlwuF1OnTsXcuXPxwAMP3LcxkVYNjOsXga24BOEzC8EbNPKGIzQALgFwARDU5X4aW83I+OkUog8dQS2Pg5DXZt3FqH9zY3Dt4w940SyNXuznX2qQuu48tFoTkp+OxJSJweByaaGNoiiKoiiKoqj7g96N2Iler8fp06cBADKZDK2trXj77behVquxbdu2+1pYYTX1MKTOB1tVDOELyzsorJjQlrMiRFuAbdeKFhYbi+1nLmGs/gBKjtTBd8qjEHu5393g0RZcu3x7AdSX9Zg1NgATB3vTwkovpddb8K+Pz2HFqjNwdhZi9kx/PDkllBZWKIqiKIqiqP85U6dODZDL5TEhISFR1z5eW1vLTUhICFEqlX0TEhJC6urq2ncmwcKFC738/f37BgQE9P3qq69k7Y+/9dZbXu2f5+fnC25ssyPz5s3z8fDwiA4PD48MCQmJ2rp1q5O9vrY7kZGRId65c+fVvrdu3eq0aNEir/YxLl269N5mbXSCzly5S2q1GmvXrsW///1v2Gw2VFZWQiqV4scff7zfQwMAsPVVMK59E0SngeiVd8EN6X/DETa07QxkBRAHgH9TG7eDEIKvL1QiWvMDWk4Uw9JqRejsu996mQbXUu2ysuvwYeo51DcYMHVKKP40LQwFBXnd1l9VVVW3tW1PPj4+93sIFEVRFEVRVDd4/vnn6+fOnXv5ueeeC7z28WXLlnknJiZqV61adWnRokVeS5cu9Vq/fn3l2bNnRXv27JHn5+fnqNVq/iOPPBL6+OOPZ/N4PKSmpnr/4x//qLnTMcycObN2xYoVtefOnRONHDkybPr06ZlcLvd3z7NYLODzu3ZveSOVSiVRqVQO06ZN0wDAU089pUHb0osehb7d20U5OTmYMmUKgoKCsGbNGowaNQoHDhyAg4N98kXsga0qgfHD10EMrRDN/mcHhRWCtqVAWrQF2HZ97D+XNqJBnYUEXEDhYR2co8PgPqTrOyARQvBfVS1S9xXDWy7Cshm0sNJbmUw2bNiYhUVLToHL4+C9vz+EPz8dCT7/93+pUxRFURRFUdQfVVJSks7d3d164+Pp6enOKSkpDQCQkpLScPDgQRcA2L17t/PkyZMbxWIxCQ8PNyuVStPRo0cdZs2apTCZTJzw8PDICRMmBAKAzWbD9OnTlcHBwVFDhgwJ0el0t1waMGDAACOXy0VNTQ2vqqqKN3r06D59+/aN6Nu3b8R3333nALTNIpkxY4ZyyJAhIZMnTw60Wq14+eWXfUNDQyNDQ0Mj//73v3sAwIkTJySDBg0Ki4qKihg6dGiIWq3mA0B8fHzYK6+8oujXr19EQEBA3/T0dKnRaGTeffddn/3797uEh4dHfvrppy6pqamuycnJ/jeOMScnRzhs2LCQqKioiLi4uLDz58+L7vYa3Ak6c+UOGI1GtLS0wMPDAyaTCUePHsWbb76JV155Bf7+N13b+8qmzoNx/UIwfAHEcz8Axzugg6MqAdQAUALo+vKd8kY9DlwoxTzuj2go5qE5vwbxabO6vHSHBtdS7fILmvBB6llUVOowLikQzyVHQSSiv7YoiqIoiqKoe+fM84v8mrML7PpOr3PfUP3gz1aVd+XchoYGnlKptACAUqm0NDY28gCgsrJSMHjwYF37cT4+Puby8nLBunXrKr/44guPvLy8i0DbsqCysjLRli1bihMSEtRjx44N2rRpk8usWbMaO+vz8OHDDhwOh3h7e1sff/zxwHnz5tWOHj1ad+nSJcHo0aNDiouLcwAgKytL8tNPP+VJpVLy3nvvuavVamFOTs5FPp+P2tparslkYl599VX/AwcOFPr4+Fg//fRTlzfeeEOxa9euUgCwWq3Mr7/+mrtz506nFStW+IwZM6Zg4cKFVSqVymHTpk1lAJCamura0RhffPFF5YYNG9T9+vUzHT582OGVV17xP3PmTEFXvsddQe9SbkNlZSXWr1+PtLQ0jB07Fv/5z38wYMAAVFVVQSgU3u/h3cRWcB7GT5eCkTpDNPt9cFy9OziqCW3bLrsCCOhyX60mKzb9pMZj3Aw42jQ4c4oLvpMjAp56rEvt0eBaCgAsFhY7d+Xjy68KIHcR4W/vJKB/jMf9HhZFURRFURRF9VhlymYEAAAgAElEQVSEkJseYxjm5gcBKBQKU0JCggEAYmNj9aWlpR3e2H7yySeeX375pauDg4Nt06ZNxRwOB6dOnZJdunRJ3H6MTqfjNjU1cQBgzJgxzVKplADA4cOHZTNnzqxrXx7k6elp++WXX0SXLl0SjxgxIhQAWJaFu7u7pb2tqVOnNgFAQkJC64IFCwS3+7VrNBrO+fPnpVOnTu3T/pjZbL6nN5K0uHILP//8Mz744APs3r0bNpsNjz/+OJ5//vmrz/fEwoo16xRMX/wNjLsColnvgePUUVHPCOAiAAnalgN17TXHEoKtv5TB3ViCOJyH0XkwyvelImTWDPAc7rywW15vwId7i6E1WDBrbADiQ126NC7qj4sQgtNnqvH5phxU17RiRKIfXn6xH6QOt/17laIoiqIoiqLsqqszTLqLq6urVa1W85VKpUWtVvPlcrkVAHx9fc3l5eVX/+NcVVUl8PX1tXTUhkAguFp04XK5xGAwdLhUoD1z5drHCCFQqVS57UWUazk4OLDXHndjcYcQwgQHBxsuXLjQYXiiSCQiAMDj8WCz2W77RtVms8HR0dHaPjvnfqBrLW5h+/btOHjwIObOnYuioiJ8/fXXGD58+P0eVoeI2QjzoS0wfbYcHEUfiF9d00lhpT3AlgXQF3dTXzuUU4PSy014RnAYcPBA8UkTWIsFIbP+dMdtnS/W4O87C2BjCRZODaWFlV7oUmET3lp8Eqve/xl8PgfLlz6IeXPjaGGFoiiKoiiKoq4xevTo5rS0NFcASEtLcx0zZkwzAEyZMqV5z549coPBwOTl5QlKS0tFiYmJrQDA4/GIyWSyy0yOoUOHtrz33ntXp5VnZGSIOzpu1KhRLZ988om7xdJW36mtreVGR0cbGxsbeT/88IMDAJhMJkalUt0yG0Umk9l0Ot0taxdyuZz19fU1f/bZZy5A24yY06dPdziu7kKLK7ewZMkSVFRUYPXq1QgMDPz9E+4DYrPBkvFfGFb8GZYDX4AbPRSiv7wPxkHW0dEA8gHo0LblcteXDWZXaXC4oA7PyVQQmhtBopNRuGE3vB4ZAlno7X+vbg6uDaPBtb1MXb0e//ehCq8vOIbKSh1mv9IfH33wMOJie8SOahRFURRFURR1Xzz22GOBQ4cODS8pKRF6enpGf/DBB24AsHz58uojR47IlEpl3yNHjsiWL19eDQADBw40Tpw4sTE0NDRqzJgxoWvWrFHzeG1vpj/11FN1ERERVwNt78aGDRvKz5075xAaGhrZp0+fqI8//rjDAM/XX3+9ztfX1xweHh4VFhYWuXHjRrlIJCI7duwoeuutt3zDwsIio6KiIo8dOya9VX9JSUnagoICcXugbWfHbd++vfjzzz93CwsLiwwJCYn66quvnO/2a70TTEfrsrrbwIEDiUql6vZ+jh49isTExG7v534ghMCWfRrmff8GqS0DJyASgokvgxvU9xZnlQMoAhCIthDbrqnTmpB65BL6i2owqXUb0GcUKovccWLybDz0zVr4Pj7qludXVVWBJQQXy7T47nwdskpb7BZcm5OTg6io392yneoB9AYLvtpzCV/vKwQhwMQJwZg6OQQSye1v2Uavd+/bivl/+fc6dTN6vXsfes17F3q9e5+ees0ZhjlLCBl47WOZmZmlMTEx9fdrTFTPlJmZ6RYTExPQ0XM0c+UPyFZyEea9G8AWZ4Px8IPwhXfAjR7yO8GvjWgrrLgB6PrORmYri//8VAohY8Xj7HeAgweYyCkomJcCiZ83fMYl3vL8llYzDp6txZGselzWmCGT8PDkUB8kxXnQ4NpewmYj+P5HNbZsz0VzswnDH/LFn5+KhIcHnbFEURRFURRFUdQfEy2u/IGwlytg3r8RtswTYBxdIJj2GniDk8Bwub9zpgFtAbYOAMLR1QBbQgh2navA5RYT3vI5D051PZhhf0VLYQVqfzyNmL+/Dg7v5pcUIQR55c3Yf1qNY5nVsFhZhCocMDnBBwODncDj0tVpvcX5/2fvvsOjqhI3jr8zk95JIY0aIEACSO8qCiJ2RV0VXduqa1vLrq6FEggIoqyuuro/yyoWFBsW1EVFBaUTOqEEEAKkkEJ6Mklm5v7+iKC4BNJvyvfzPD7C5N5z3+EiZF7vOWdzlv4zf7sOpBaqd69gTX1smHrGBpsdCwAAAADqhXKlBTAK81Sx5G05Vn0huXvK/YKb5H7uVbJ41mR9HoeqFrCV6ruA7cp9udp8OF/XdilVUOpPUrdxsoT21J7EWbJ6uKvbbVefcHxZhUPLNqVr8epU7U0vlI+nmyYM6ahh3bzVIbRJ1xaCyQ4eKtTrbyYracMRhYf76NGHh2jUiCieVgIAAADQKlCuNGNGeZkqv/9Qld9/KFVWyG3UJfI4/wZZAmq6k44haZekEkn9JNW90NifU6LF29J1RriXBmQvOD4dqLK4RPvf/ESdrp4gr/ZVuxMdPFKkL9Yc1LcbDqvE7lBMpL/uu6KPzh0QLR8vN6Wnp9c5B1qW/Pxyvfv+Li355oC8vWy69aZ4XXJRjNzdT/e0FQAAAAC0HJQrzZDhdMix6itVLnlbRlGebP3PksfFt8ravkMtRzooKUdSjKS6T70oLKvU22tTFezjoWt81kpZ2bKc+Ygsbp468M4iVRYWK+bO6/Tj1gwtXp2qLfty5W6z6sx+EbpkRGfFdW7HEwptTEWFU59/sU8ffJwiu92pCyd00XXX9FJggKfZ0QAAAACgwVGuNCOGYci5dYUqFv9HRtZhWbv1k+ftibJ16V2H0XIl7ZfUXlLHOmdyugy9s+6g7A6n7o53yLbph+PTgQzD0M7n35HRLUYPfH9UR4szFd7OW3+6oJfOH9JBQX58kG5rDMPQTyvTNP+tZGVll2nI4HDdelMfdezgb3Y0AAAAAGg0lCvNhHPfNlV89qpcB3bIEtFZnrfPlK3P8Do+8VGqqgVs/ST1VF0XsJWkL7dnaH9uiW4Y2F7BKc9Kvu2lXhOVlJKt7+YvUcede7Vqwh/Uo2OQLhneWYN6hslm5SmVtmjnrqN67Y1t2p2Sp65dAjTr3oHq3++kW94DAAAAOA2bzTaoR48eZU6n09K9e/eyDz744IC/v7+rpucnJia2f/DBB3OOnXP22Wd3//jjj/eHhoY6qzsnOjq6b1JS0s7IyEjH71/39fV1SpLT6bRcdNFFeXPnzs3w9vY26vr+6mPo0KE9582bd+iss84qNeP6J0O5YjJXZmrVDkDbVskSGCKP6/4mt6Hja7ADUHUckrZJsqpqAdu6r22x+VC+ftqbo9HdQtS34DupJFvL/f+oN59drfTcUo376isZfn56/NUHFN0hpM7XQct2JKtE89/eoZ9WpKldO0/dd88AjT2nk2w2SjYAAACgrjw9PV27du3aIUmXXnpp13/84x9h06dPP1KTcx0Oh15++eXw22+//eixcmX58uV765Nn+fLlKZGRkY6CggLrDTfc0Pn666/vvGjRogP1GbM1YQ9ck7gKclS+8BmVzbldzpTNcr/4VnlPfVPuI2qytXJ1DEk7VbX1cpwkrzrnyyy064ONh9Ql2Ef9rBnSz99p8ZHumvNNqYL9PfXwuI7quHuret9xNcVKG1VSUqk33krWnfd+p3XrMnXdH3rqlRfP0/hxnSlWAAAAgAY0evTo4r1793pK0rhx47rFx8f37t69e/y8efNCjx3j4+Mz4IEHHojq169fr0cffTQyKyvL/eyzz44dNmxYrFT19ElGRobbqcaoicDAQNebb76Z+u233wYdOXLEJklTp04N79OnT+/Y2Ni4Bx98MEqSdu/e7dG1a9f4iRMndomNjY2bMGFCTFFRkVWSfvrpJ58hQ4b0jI+P7z169Ogeqamp7lLVEyl33XVXdN++fXt36dKlz5IlS/wkqbi42HLxxRfHxMbGxl100UUxdrv9+AeORYsWBfTv379XXFxc7wsuuCCmoKDAeuz9Pvjgg1FxcXG9Y2Nj4zZt2uQlSQUFBdarrrqqS2xsbFxsbGzc/Pnzg041Tk3x5EoTM8pKVPn9B6r84WPJ6ZDbWZfJY/z1svgH1WU0VU0BKvjNP3ZJ3SXVdEeh/1VW6dT81QdkMaS92w/JPWSRMuSntPAJevmy7uoaGaBtM/6lbIdDPe66rs7XQcvkdLq05JsDWrBwlwoLK3TumI668fo4hbK9NgAAAFqpoUOH9vz9axMnTjz66KOPZhcVFVnHjh3b4/dfv+GGG3Luu+++3IyMDLfLLrus22+/tm7dut01vXZlZaW+/vrrgPHjxxdK0oIFCw6Eh4c7i4uLLQMGDIi74YYb8iIiIpxlZWXWPn36lP3zn/9Ml6T33nsv9NjTJr8fs7oxapopODjYFR0dXZGcnOy1cuVK2969e722bt260zAMjRs3rvt///tfv5iYmIoDBw54vfzyywfGjx9fcvXVV3d5+umnwyZPnpx13333dfryyy/3RkVFOV599dV2Dz30UPSHH354QJIcDodl27ZtO99///3AxMTEqAkTJqTMmzevvbe3tyslJWXH2rVrvUeNGhUnSRkZGW6zZ8+O/PHHH1MCAgJckydPjpg5c2b4vHnzMiQpNDTUsWPHjp1PPvlk2JNPPhn+/vvvpz766KORAQEBzpSUlB2SlJ2dbTvdODVBudJEDEelHCu/UMXXC6TifNkGniOPi2+RNTSqFqM4JRX+8k/BL/8+9t+Ju6QASZ0kRdY5Z+qRIr2+cr9KDEP7dmTr3o6bFOVVovJhD+nuqDhJkquyUntfeV+RE86Uf/fOdb4WWhbDMJS08Yhen5+sQ4eL1Cc+RLfd0lfdu9WlGAQAAABwKuXl5dZevXrFSdKwYcOK7r///hxJmjt3bviXX34ZJEmZmZnuycnJXhERESU2m00333xzXk3Grm6M2uQzjKrlVpYsWRLw448/BsTFxcVJUmlpqXXXrl1eMTExFRERERXjx48vkaQ//vGPuc8//3z7rVu3FuzZs8f73HPPjZUkl8ulsLCwymPjXn311XmSNHLkyJKHH37YQ5JWrFjhd99992X98mtRFhsbWypJy5Yt8923b5/X0KFDe0lSZWWlZdCgQcXHxpo0aVKeJA0dOrT0888/bydJP/74Y8DChQt/PnZMWFiY87333gs81Tg1QbnSyAzDkHPTclV88bqMnHRZe/SXx2W3y9bpf4rPkyjXr0+kFEoqVtXTKpLkIylMVYVKoCRv1XXhWofTpVXJR7R4darSyyoU1SlIvg5D/7g8UF327pRixsr7l2JFkg5/9p3K0rM09OXEOl0PLc/+AwV6ff52bdqSrchIX01+dKiGD41ki20AAAC0Cad60sTf3991qq9HRkY6avOkyjG/XXPlmC+++MJ/+fLl/klJSbv8/f1dQ4cO7VlWVmaVJA8PD5eb2+k/4p9qjJrKy8uzpqene/Tt29f+3nvv6YEHHsh4+OGHc357zO7duz1+/3nBYrHIMAxL9+7dyzZv3rzrZGN7eXkZkuTm5ian02n57bm/ZxiGRo8eXbh48eL9pxnLcDgclmPn/H6s041TE6y50oicezbL/o97VT5/luThKc87Z8vr3qerKVYMSUWS0lS108/qX/7ZISlDVbeqo6S+kkZJGqqqnYAiVVW01P5DbnZ+md76JkU3zP5es97ZqKP2SkV1ClLvCH8lTOylLhkfSb5hssRfdcJ5KS8ukG+XaEVecFatr4mWJS/Prudf3KT7//aD9uzL1+239tVLz43ViGFRFCsAAABAE8vPz7cFBgY6/f39XZs2bfLasmWLb3XH+vr6Ok+2bkhtxjiZgoIC6y233NL5vPPOyw8LC3NecMEFhW+//XbosWvt37/fPS0tzU2SMjIyPJYuXeorSe+++27wyJEji/v162c/evSo27HXy8vLLUlJSadcMHT06NHF77zzTrAkrV+/3islJcVHksaMGVOSlJTkt337dk9JKioqsm7dutXzVGONGTOm8Jlnnml/7OfZ2dm2uozzezy50ghc6ftVsfg1OZPXyhIUJo/r/y63IWNlsf52oVqHTpzeU6iqaT+S5KGqp1GOPZXip4bqwVwuQ5v25uiLNalavSNLhmFoSM8wjR3cQcsP5snP003XD+0kJS+USrJlOfMRWdx+/T2Vn7xHWcvWqf/ch2St88K7aO7s5Q59+vk+fbQoRQ6HS5dc1E3XXt1T/v4eZkcDAAAA2qwrr7yy4JVXXgmLjY2N69atm/2MM86odirPTTfdlHPBBRf0aN++feXatWtT6jLGb5199tmxhmFYXC6XLrzwwvy5c+emS9LEiRMLk5OTvYYMGdJLknx8fFwLFizY7+bmZsTExNhff/31kLvvvrtz165dyx966KFsLy8vY+HChfvuu+++TkVFRTan02m56667jgwePNhe3bUfeuihrGuvvbZrbGxsXHx8fGnfvn1LJCkqKsrx8ssvH7j22mtjKioqLJKUkJCQ1q9fv/LqxpozZ07GLbfc0qlHjx7xVqvVePzxx9Nvuumm/NqO83uWY/OkmtLgwYONpKSkRr/OsmXLNGbMmEa/zjGuvGxVfjVfjnXfSl7ecj9vktzPulwWDw9VLTR7rEwpkPTb37++qipRjhUqXqrrFJ/qFJZW6Jukw/pyzUGl5ZQo0NdDE4Z21IXDOiks0Esv/bhPWUXluu+c7gorT5Xx01wpZqysZ1x/wjjr75mhff/5SJcfXi6v0OA6ZUlPT2+It3RSycnJio+Pb7TxWzuXy9DyHw/rzXd2KCe3TCOGRermG+MVHeVndrST4n5LUVG1Wbep5WvqP9dhLu5328M9b1u4321Pc73nFotlg2EYg3/72pYtWw6cccYZOdWdg9PbvXu3x8UXX9xjz549yWZnaShbtmwJPeOMM7qc7Gs8udIAjNJiVS5dqMrliySXIbdzJsrjvEtk8XVJ2quqMqXil6NtqipQQvVrmdJ4t2H3oXwtXp2qZZvTVeFwKb5LO/3xvB4a3TdCHm5VT558vOmwDuWV6cZhnRXmbZGx+vWTTgeqLCzW/rc+VedrLqxzsYLma/uOHP3nje3aszdf3bsF6aEHB6lPfK12ZQMAAACANolypR6Mygo5VixWxdfvSGXFsg0aKo+LzpU1xF3Snl+O8pQUpF+LFF819lI39gqnfticpi9WH9SetAJ5e9g0fnAHXTyis2IiA044dn3qUa3Zf1TnxIapb3SgXFsWnHQ6kCTtf/szOYpL1eOeE59mQcuWnlGs+W8la9WaDIWEeOmv9w/UmLM6ymplTRUAAAAAddOzZ8+K1vTUyulQrtSaIcNVIufGr1XxxQcyjubK1itG7pdeI1uHCEn++nWtlEBVlStNI7fQrg+W7dO3Gw6ruMyhLhH+uvfyeI0dGC1fL/f/OT4tv0yLNqWpe5ifzo+LkJGzW/r5OylmrCyhJy66axiGUl5coODBfRQ6tF9TvSU0ouLiCr33wW59+d+f5eZm1Q2TeuvyS7vJy5M/FgAAANDmuVwul8VqtTb9Ohpollwul0WSq7qv8ynqtJyq2gK5aq0U5+5NqvjsG7kOZ8oaHSHPu/8sW6+hqipUAlQ17afp7UzN04y3NqiwtEJn9o3UxSM6q0+XdtXu6FJa4dCbaw7I19NN1w/pJKurQsbGk08HkqSsZWtVuHOfhr8xp7HfChpZZaVLXy3Zr4Uf7FJxSaXOG9tZN1zXW8HBp1ygGwAAAGhLtmdnZ8eFhYUVULDA5XJZsrOzAyVtr+4YypVqFapqvZQiSYacaUdU+dkyOXftkSU4VJ5/fFC2QRN+twOQOb5ef0jPL9qu0EAvvXjfaHX93dSf33MZht5df0iFZQ7dfXY3+Xm5ybXl/WqnA0lSyovvyiM4SJ2uubCx3gYamWEYWrsuU6+/uV3pGSU6o1+Y/nRzH8V0DTQ7GgAAANCsOByO2zIzM1/LzMzso8Ze1wEtgUvSdofDcVt1B1CuVKuqNHEd9Vbll/+VI2mF5O0nj8vvlNuZl8ribv6WtE6nS698uVOfrDigAT1CNfn6AQrwOX2upTuPaPeRIk3sH61OwT6nnA4kSaWHM3X406Xq9deb5ebN0w0t0d59+frP/O3atj1HHaL9lDB5uAYPCq/2ySYAAACgLRs0aFCWpEvNzoGWg3KlGkapS5XfrFflj59KFovcx/5B7uOuk8WneWxJW1hSoVkLNmrz3lxdMbqr7riol2y20xeqOzMK9e2uLA3q1E7DuwbLcJSfcjqQJO195X0ZLpd63HVdQ78NNIEVK9P01DPr5efnobvu6Kfzz+siNzfKdwAAAABoKJQr1XAdTFHlDx/Jbeh4uV94k6zt2psd6bj9GYVKeDNJuQXleugP/TR+cMcanZdbUq73kg4pKtBLVw6IlsVikWvHx6ecDuSsqNDeVz5Q1IVny69rza6D5iNpwxHN+2eSesUGa9qU4fLzNf+JKwAAAABobShXqmHrNUjeU96UNSzK7CgnWLEtQ0+9v0U+nm6ad9dw9e7UrkbnVThcenNNqiTpxuGd5W6zVk0H2re02ulAknRo0beyH8lR7D2TGuw9oGlsT87R7KfWqnOnACVMGSFf3//dMQoAAAAAUH+UK6fQnIoVl8vQO0v36J2le9SrU5AS/jhIIYE1W//EMAwt2pymzAK7bh3ZRSG+njWaDiRJe15cIL9unRR5/pkN9VbQBPbszdOMJ9aofZiPEqeNpFgBAAAAgEZEudIClNodeur9zVqVfETnDeqg+yf2kYd7zXcpWrP/qDYczNN5vcPVK6JqJyHjNNOBJClv6y5lr9igAfMekcXKGh0tRerBQk2bsUoB/h6aNWOUAgNPfn8BAAAAAA2DcqWZS88tUcL8JB3KLtFdl8bp8lFdarXDy8GjpfpsS7p6hftrXK+qdWNqMh1Ikva8+K5sXp6KuWVivd8HmkZGRommTl8ld3ebZk0fpdAQb7MjAQAAAECrR7nSjG1IydbsBZskizTnT0M1oEdorc4vtjv01tpUBXq767ohHWW1WH6ZDvTGaacDVRQUaf87n6vzpIvlGRxU37eCJpCTU6Yp01fK4XBpzqzRioz0NTsSAAAAALQJlCvNkGEYWvTTfr365U51CvfXjJsGKzLEp1ZjOF2G3lmfqpJyh+4d010+HlW3umo6UJYso/9e7XQgSdr/5idylpYp9p7r6/Ve0DQKCso1ZfpKFRZVaHbiKHXuFGB2JAAAAABoMyhXmpmKSqf+uWiblm5I06g+4fr7Nf3l7Vn727RkR6b2ZZfomkEdFB1UNTXkhOlAYb2qPdcwDO156V2FDO+v4IHxdX4vaBolJZWalrhKWdmlSpw2Uj2612wHKQAAAABAw6BcaUZyCuya8dYG7T6UrxvPi9Wksd1ltdZ8fZVjtqYVaFlKtoZ3DdbgzsGSVOPpQJJ05LvVKty9XyPefqpO7wNNx253aMas1Uo9WKipjw1Xn/jaTR0DAAAAANQf5UozsTM1TzPe2qCycocSbhykUX0i6jROVpFdH2w4pI7tvHVZv1+3kq7pdCBJSnlxgTxD26nTVRPqlAFNo7LSqSfmrtOulKP6+9+GaNDAcLMjAQAAAECbRLnSDHy9/pCeX7RdoYFemnP7MHWN8K/TOOUOp95ckyo3q0U3DussN1vV9sk1nQ4kSSUH05X2+ffq/ffbZPNiC9/myul06al/JGnT5izdf+8AjR4ZbXYkmCQ9Pd3sCDUWFRV1+oMAAACAFohyxUQOp0uvfLFTn648oAE9QjX5+gEK8PGo01g/5xTro41pyiku1+2juyrol3GOTwfyCZUl/srTjrP35fclST3uvLZOOdD4XC5Dz/1rk1avzdDtt/bVeWM7mx0JAAAAANo0yhWTFJZUaNaCjdq8N1cTz+yq2y/sJdsvT5rURmmFQ19tz9TaA0fVzsddfxrVVT3a//rky4nTgbxOOZazvEJ7X/1AURePkW9nnoRojgzD0MuvbdX3yw7p+ut66bJLupkdCQAAAADaPMoVE+zPKFTCm0nKLSzXQ384Q+MHd6j1GIZhaGtagT7dkq6ScofO7hGq8b0j5OH2a0FTm+lAknTwoyUqzz7K9svN2NsLdurL/+7XxMu669qre5odBwAAAAAgypUm99O2DD39/hb5eLpp3p3D1btT7bfNzSut0Ceb07Qzs0jRQd7608gu6tDO54RjajsdSJL2vPiu/Ht0UcS4kbXOhMb30aIUffBxiiaM76JbboqXxVL7naQAAAAAAA2PcqWJuFyG3lm6R+8s3aNenYKU8MdBCgk89TSd/xnDMLRqX66W7MiUyzB0cd9Ije4WKttJtmuuzXQgSTq6aYdyVm/SwGcfk8Va++lJaFxfLdmv+W/v0NlndtBdd5xBsQIAAAAAzQjlShMotTs09/3NWp18ROMHd9B9V/SRh7utVmOk55fpo02HdSivTD3D/TWxf7SCfU+++K2RkyLt+06KObdG04Ekac+LC2Tz8VbMzRNrlQuN74dlh/TvV7Zo6JAIPXjfQNlsFCsAAAAA0JxQrjSy9NwSJcxP0qHsEt11aZwuH9WlVk8dVDpd+nbnES3fky1vD5smDemo/h2Cqh2jajrQ65JPiCzxV9XoGhV5BTrw7hfqcsOl8ggKqHE2NL7Va9P17Asb1Tc+VI8+NERubjxVBAAAAADNDeVKI9qQkq3ZCzZJFmnOn4ZqQI/QWp2fklWkRZvSlFtSoSGd2+nivpHy8Tj1LavtdCBJ2vfGIjnL7Cxk28xs3pKlufOS1KN7kKY8PkweHrV72gkAAAAA0DQoVxqBYRha9NN+vfrlTnUO99f0mwYrMsTn9Cf+oqTcocXbMrThYJ5CfT3059Ex6t7e7+TXcjmkokyp8LCMgoO1ng5kuFza89K7Chs1UO3OqNk5aHw7d+Vq5py16hDtp+lTRsjH293sSAAAAACAalCuNLCKSqf+uWiblm5I0+g+EXr4mjPk7VmzX+W92CcAACAASURBVGbDMLTxUL4Wb01XWaVT5/Zsr3G92svdZpVhuKSSbKnwsFSYJqMwTSpMk4qPSIazagCLVQrtWePpQJKU8c0KFe87qH4z76/L20Uj+Hl/vqbPXK2QYC/NTBgpf/+Tr60DAAAAAGgeKFcaUE6BXTPeStLuQwW6cXysJp3bXdaT7ORzMrkl5Vq0KU0pWUWKC3To0t4uBTs3ydiUJldRmlSYLrkqfz3BN0zyj5YiB8gSEC0FREt+EbLYaveEQ8qL78orPFQdrxxfq/PQOA6nFWnq9FXy8XHXrBmj1K5d7XaUAgAAAAA0PcqVBrIjNU+Jb21QWblDCTcO0qg+Eac9xygvlCv/sPb9vFt5mft1nnJ1ky1PboV2aatkSJJXUFVxEnPOLyVKB8k/ssbrqZxK8YHDSv9ymeIn3ymbB09HmC0rq1RTElbJYrFo1vRRah9W86lkAAAAAADzUK40gK/XH9Lzi7YrNNBLc24fpq4R/id83agolYrSTpzOU5gmVRTJIqm7JLvFW25BHeTWrrcs/r88iRIQJYvHyddaaQh7/2+hLFarevz52ka7Bmrm6FG7JiesVJndoSdnjVZ0dOPddwAAAABAw6JcqQeH06VXvtipT1ce0IAeoZp8bbz8HTkyUrdWlShFVeujqCzv15PcPOXyi9Jhz1htKfdVoUe4BsX3Ua/O0bJam26bXae9XPte+1AdLhsrnw6nf8oGjaeoqEJTZ6xSXr5ds6aPUtcugWZHAgAAAADUQoOUKxaLZYKk5yTZJL1mGMaTDTFuc2W4HCrJOqRPv16pgMJ0/XukQ118CmVZ+oqMqsk8ktVN8o+SQntVTefxj5ICOmh3oZsWbclQXmmlhncN1lXxkfI2YYvd1A/+q/LcfPW4e1KTXxu/Ki2rVMLM1UrPKFbClBHq1TPY7EgAAAAAgFqqd7lisVhskl6UdJ6kw5LWWyyWzw3D2FHfsc326w49J07pMYoz5GO4NClAcgVYZfUNlwI6ydJxxC/TeaIl3/ayWH8tTYrslfp8a7o2Hy5Qe39P3X1WN3UN9TXtve15cYECesUo/NzhpmVo68rLnZo5e6327svX5EeGqn+/MLMjAQAAAADqoCGeXBkqaa9hGD9LksViWSjpMkktulwxspJlrHlBclb8+qJPqI4qVN+n++uIs50uOm+kuvaIPeUOPYZhaH1qnr7YlqEKp0vje4frnNgwudmabgrQ7+UmbVPuuq0a9PwUWSw1280IDauy0qUnn16n7ck5+tsDgzRsaKTZkQAAAAAAdWQxDKN+A1gsV0maYBjGbb/8/I+ShhmGce/vjrtD0h2SFB4ePmjhwoX1um5NFBcXy8+vbguDejqLFV2+UyW2IJXa2qnYGqBle6WVeysVHWTVlQM95ed16oKk2GnVthJfHXW4K9itUn18S+Rvc9UpT0PKmztf9mUbFf7hXFn9vJv8+pWVlac/qI7sdru8vJr39sUul6H3PszU1m3FmnhZew0bwhorddUS7jd+5e5eu63iT6Y+f66j5eF+tz3c87aF+932NNd7fs4552wwDGOw2TnQsjXEkysne/ThfxobwzBekfSKJA0ePNgYM2ZMA1z61JYtW6b6XediSVKJvVJPLdyi1XuP6PzBHfSXiX3k4Vb9OikOl0vLU7K1cleW3GwWXTUgUkO6BMvaDJ4SKc/N06fLNqrbzVdo6MUXmJIhPT290cZOTk5WfHx8o41fX4Zh6IWXNmvrtmLdcmO8rryih9mRWrTmfr9xoqioqHqPUf8/19GScL/bHu5528L9bnu452jNGqJcOSyp429+3kFS4316bmJpOSWa/maSDmWX6O7L4nTZyC6nnEpzILdEH29KU2ahXf2iA3VZvygFeNf//9Y2lH2vfyynvVyx91xvdpQ2xzAMvfbGdn2zNFXXXB1LsQIAAAAArURDlCvrJfWwWCxdJaVJulZSq9iCJiklW7MXbJTFYtGc24ZqQPfQao+1Vzr13+RMrf45VwHe7rplRBfFRQY0YdrTczmd2vPv99T+rCEK6hNrdpw2570Pduuzxft0yUUxuuG63mbHAQAAAAA0kHqXK4ZhOCwWy72SvlbVVsyvG4aRXO9kJlu784gS5iepc7i/pt80WJEhPtUeuz29QJ9uTlehvVKjuoXo/LgIebk3/fbKp5Ox5CeV7D+s/k/+zewobc5ni/fq3YW7NO7cTrr91r4sJAwAAAAArUhDPLkiwzC+kvRVQ4zVXPTrFqKrz+6mSWO7y9vz5L9MBWWV+nRLmranFyoiwEs3Du+sTsHVlzBmS3lxgbwjw9TxivPMjtKmfLP0gF59fbtGjojSX+7uL6uVYgUAAAAAWpMGKVdaI28PN/3pwl4n/ZrLMLR2/1F9tT1DDpehC+IjdHaPMNma8Yfmon0HlbHkJ/WZdo+sDbBjB2rmp5VpeuGlzRo0oL0efnCQbCZuwQ0AAAAAaByUK7V0pNCujzYd1oHcUnUP89OVA6IV6udpdqzT2vPv92Sx2dT9jj+YHaXNWL8hU/OeTVJc7xA99shQuTfDqWIAAAAAgPqjXKmhSqdL3+/O0g+7s+XpbtU1gzpoUKd2LWLtDEdpmX5+/WN1vGKcfKLCzY7TJmzbnqM5T61Tl84Bmvb4cHlVM7UMAAAAANDy8YmvBn7OKdZHG9OUXVyuAR2DdGm/KPm1oA/LqQu/VEVegXqw/XKT2J2Sp8Qn1igi3FeJCSPl68s0LAAAAABozVpOQ2CC0gqHvtyeqXUHjqqdj7tuG9VVPcP9zY51Uunp6Sd93TAMJf9zvnx6dpGje3S1x6FhHEgtVMLMVQoI9NDM6SMVGND8p4wBAAAAAOqHcqUa+3NK9NbaVJVWOHR2jzCN7x0uD7eWtxhp0aadKt62Rz1mP9AipjC1ZOkZxZo6Y6U8PGx6YvoohQR7mx0JAAAAANAEKFeqEezroYgAL13cN1LRQS33Q3La/E9l8/NR+JVsv9yYcnLKNCVhpZwOQ08+MUoREb5mRwIAAAAANBHKlWoEervrz2fGmB2jXipy85W1eJkiJ10kNz8fs+O0Wvn55ZoyfaWKSyo1O3G0OnUMMDsSAAAAAKAJtbx5LqixjPe+klFRqeibLjM7SqtVXFKhaYmrlJ1dpoTJI9S9W5DZkQAAAAAATYxypZUynE6lv/25gkYOkG9sF7PjtEp2u0MzZq3RwUOFevzRoYqPCzE7EgAAAADABJQrrVTud2tUfviIom/mqZXGUFHh1Kwn12p3ylE9/NfBGjQg3OxIAAAAAACTUK60UmnzP5VHRKhCzh9tdpRWx+l06alnkrR5S7buv2egRo2INjsSAAAAAMBELGjbCpXuO6S85Unq8tAtsrrZzI7Tqjidhv75wkatWZuhP9/WV2PP7WR2JKDFSE9Pr/cYlZWVDTLOqURFRTXq+AAAAGh9KFdaofS3PpfF3U2R119sdpRWxeFw6R/PbdBPK9J04/W9dclF3cyOBAAAAABoBihXWhlnaZkyPvivwi48S57tg82O02pUVjo1d956rVmXqVtujNeVV/QwOxIAAAAAoJmgXGlljnzynZyFJYq6+XKzo7Qa9nKHZs9dp42bsvTn2/vpkgtjzI4EAAAAAGhGKFdaEcMwlDb/U/n2jlHgkD5mx2kVysocSpy9RtuTc3TfPf01flwXsyMBAAAAAJoZdgtqRY7+sE4lO/Yp+ubLZbFYzI7T4pWUVGrqjFVK3pGrvz0wiGIFAAAAAHBSPLnSChTs3Kfkh+cq+8vl8owMU/jEcWZHavEKCys0LXGVDqQW6JGHBrPdMgAAAACgWpQrLVjxz4e0bca/dOCdz2X19lTnB/6oDnf8QTYfb7OjtWh5+XZNnb5KaenFmvzIMA0ZHGF2JAAAAABAM0a50gKVHs7U9lkvad9/PpbVzaZef71Z7W66VB7BgWZHa/Fycss0JWGlsnPKlDBlhPr3CzM7EgAAAACgmaNcaUHsWblKnvOy9vz7PcllqPufr1GfyXfKO7K90tPTzY7X4h3JKtHkaStVUFihGdNGqE9cqNmRAAAAAAAtAOVKC1CRV6Cd817X7ufekrPMrq43X6E+U++WX5cOZkdrNdLSizUlYaVKyxyaNX2Uesa2MzsSAAAAAKCFoFxpxiqLirX7ube0c97rqiwoUudrL1LfGX9RQGxXs6O1KgcPFWpywko5nYbmzBylmK5BZkcCAAAAALQglCvNkKPMrj0vvasdT76i8pw8dbhsrPom3qd2/XqZHa3V+Xl/vqZMXyWbzaInZ41Wp44BZkcCAAAAALQwlCvNiLOiQvte+1DJs/6tsoxsRYwfrX4z71fo0H5mR2uVdqfkaVriKvl4u2nWjFGKjvIzOxIAAAAAoAWiXGkGXA6H9r/9mbbPeFElqWkKGz1IoxY+q/ZnDTE7Wqu1fUeOZsxao8AADz2ROErh7X3NjgQAAAAAaKEoV0xkuFw6+OESbUt4XoW79yt4ULyGvDxDkeNHy2KxmB2v1dq8JUsz56xVWKi3Zs0YpdAQb7MjAQAAAABaMMoVExiGobTF32vr1OeUv3W3AuN76MxF/1KHy8dRqjSy9UmZmv3UOkVH+2lWwigFBXmaHQkAAAAA0MJRrjQhwzCUuXSVtk75p3LXbZVf984auWCeOl1zoaw2m9nxWr2Vq9P09DNJ6tolUInTRsrf38PsSAAAAACAVoBypYlkrUjS1inPKWv5Ovl0itKw12ap601XyOrGLWgKPyw/pGef36iese00fcoI+fq6mx0JAAAAANBK8Mm+keUmbdPWqc8pY8lP8ooI06AXpqr77X+QzZOnJprKN0sP6IWXNqtvfKimPj5c3t78tgcAAAAANBw+ZTaS/O0p2jrteR3+5Ft5BAep/1MPK/ae6+Xmw+KpTWnxVz/r5Ve3atCA9nr8kWHy9GT6FQAAAACgYVGuNLCivanamvC8Ut/7Um5+Puo7/S/q9eDNcg/wMztam/PxJ3v0xlvJGj4sUo/8bbDc3SlWAAAAAAANj3KlgZQcTNf2mS/p5zcWyerhrri/36beD/9JniHtzI7W5hiGofc+2K13F+7SWaOj9df7B8nNzWp2LAAAAABAK0W5Uk9lmdlKnv2y9r68UJLU455Jin/sz/KOCDM5WdtkGIbefHuHPvpkj8ad20l/uXuAbDa2twYAAAAANB7KlToqz83TjqdeU8oL78hVUamYWyaqz9S75dspyuxobZbLZejzL7O1ak2BLpzQVXfe3k9WK8UKAAAAAKBxUa7UUmVhsXY9O1+7nnlDlUUl6jLpYvWd/hf5d+9sdrQ2zek09OL/bdaqNQW6/NJu+tPNfWSxUKwAAAAAABof5UoNOUpKlfLiAu2Y+5oqjuar48Tx6pt4n4Lie5gdrc1zOl365wsb9cPywzp3TDDFCgAAAACgSVGunIazvEJ7X3lfyU/8n+xHchR5wVk6Y+b9Ch7Ux+xokFRZ6dLTzyZp1ep03Xh9b8X3dlCsAAAAAACaFOVKNVyVlfr5zU+1PfFFlR7KUPuzh2r0R8+p/ejBZkfDLyoqnJrz9DqtTzqi22/to8su6a7k5GSzYwEAAAAA2hjKlWqkfbFM626fopCh/TT89dkKHzuCJyKaEbvdoVlPrtXmLdm6584zdMH5Xc2OBAAAAABooyhXqtHhsrE655vXFTFuJKVKM1NaWqkZT6zRzl25evAvAzX23E5mRwIAAAAAtGGUK9WwWK2KPG+U2THwO8XFFZqWuFr7fs7Xw38dojNHRZsdCQAAAADQxlGuoMUoKCjX1BmrdPBQkR57eKiGD4s0OxIAAAAAAJQraBmOHrVryvSVyjxSoqmPD9OgAeFmRwIAAAAAQBLlClqArOxSTU5Yqbw8u6ZPHaF+fcLMjgQAAAAAwHGUK2jWMjNL9Pi0FSouqdTMhFHq3SvY7EgAAAAAAJyAcgXN1qHDRZqcsFKVFU7NThyt7t2CzI4EAAAAAMD/oFxBs3QgtUBTElZJkubMGq0unQNNTgQAAAAAwMlZzQ4A/N7effl6bMoK2dwsevIJihUAAAAAQPNGuYJmZeeuXD0+bYV8fNw1d9aZ6hDtb3YkAAAAAABOiWlBaDa2bs9W4hNr1K6dl2YnjlJYqI/ZkQAAAAAAOC3KFTQLGzYd0RNPrlVEuK9mTR+l4GAvsyMBAAAAAFAjlCsw3Zq1GXpy3np16uivmQkjFRjoaXYkAAAAAABqjHIFplqxMk1PP5ukbjFBSpw2Qn5+HmZHAgAAAACgVihXYJqVq9P01DNJ6tUzWNOnDJePj7vZkQAAAAAAqDXKFZhi7boMPfWPJPWMbafpU4fLx5tiBQAAAADQMrEVM5rc+g2ZmvP0OnWLCdL0KSMoVgAAAAAALRrlCprUps1Zmj13nTp3ClDitBHy9aVYAQAAAAC0bJQraDJbt2Vr5pw16hDtp5kJo1i8FgAAAADQKlCuoEls35GjGU+sUUS4r2ZNH6WAAIoVAAAAAEDrwIK2aHQ7dx3VjJlrFBbqrScSRykw0NPsSABQrfT0dLMj1FhUVJTZEQAAACCeXEEjS9mTp4SZqxTUzlNPJI5SuyAvsyMBAAAAANCgKFfQaPb9nK+pM1bJ399DsxNHKSTY2+xIAAAAAAA0OMoVNIr9Bwo0JWGlfHzcNDtxlMJCfcyOBAAAAABAo6BcQYM7eKhQUxJWysPTptkzRiu8va/ZkQAAAAAAaDSUK2hQh9OK9Pi0lbLZrJqdOFqRkRQrAAAAAIDWjd2C0GDSM4r1+LSVkiE9kThK0VF+ZkcCAAAAAKDRUa6gQWQeKdHkaSvlqHRp9szR6tjR3+xIAAAAAAA0CcoV1FtWdqkmT1upMrtDsxNHqUvnALMjAQAAAADQZChXUC85uWWaPG2liosr9ETiaMV0DTI7EgAAAAAATYpyBXWWl2fX5GkrlV9QrlnTR6p7N4oVAAAAAEDbw25BqJP8/HI9Pm2lco+WacbUEeoZG2x2JAAAAAAATEG5glorKCzXlOkrlZVVqoTJIxTXO8TsSAAAAAAAmIZyBbVSVFShqdNXKT2jWFMnD1ffPqFmRwIAAAAAwFSUK6ixkpJKTUtcpYOHijT5kWHq3y/M7EgAAAAAAJiOcgU1UlpWVazsP1Cgx/8+VIMGhpsdCQAAAACAZqFe5YrFYrnaYrEkWywWl8ViGdxQodC82O0OTZ+5Rnv35euRh4Zo6JAIsyMBAAAAANBs1PfJle2SJkr6sQGyoBmylzs044k12rU7Vw8/OFgjhkWZHQkAAAAAgGbFrT4nG4axU5IsFkvDpEGzUlHh1Kw5a7U9OUd/e2CQRo+KNjsSAAAAAADNjsUwjPoPYrEsk/SQYRhJpzjmDkl3SFJ4ePighQsX1vu6p1NcXCw/P79Gv05zUFlZ2aDjORwuvfVuhlL2lOrqK8I1aGBAg47fWOx2u7y8vMyOgSbC/W57uOcncnd3NztCo2pLf4+jCve8beF+tz3N9Z6fc845GwzDYJkL1Mtpn1yxWCxLJZ1skY3JhmF8VtMLGYbxiqRXJGnw4MHGmDFjanpqnS1btkxNcZ3mID09vcHGqqx0ac7T67Q7pVT33tVfE8Z3abCxG1tycrLi4+PNjoEmwv1ue7jnJ4qKat1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+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Explain an AutoML object\n",
+    "exa = aml.explain(test_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a11f7d0",
+   "metadata": {},
+   "source": [
+    "## Time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ec101125",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot predictions\n",
+    "plt.plot(obs)\n",
+    "plt.plot(pre)\n",
+    "plt.title('Model predictions')\n",
+    "plt.ylabel('Dry Matter')\n",
+    "plt.xlabel('')\n",
+    "plt.legend(['observed', 'predicted'], loc='upper right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "16d26c0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select a biome\n",
+    "idx = test['biome'] == 1\n",
+    "\n",
+    "# Plot predictions\n",
+    "plt.plot(obs[0:100])\n",
+    "plt.plot(pre[0:100])\n",
+    "plt.title('Model predictions')\n",
+    "plt.ylabel('Dry Matter')\n",
+    "plt.xlabel('')\n",
+    "plt.legend(['observed', 'predicted'], loc='upper right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8b786b1",
+   "metadata": {},
+   "source": [
+    "# Map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "212b3f04",
+   "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>dry_matter</th>\n",
+       "      <th>climatic_region</th>\n",
+       "      <th>biome</th>\n",
+       "      <th>GFEDregions</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>vod</th>\n",
+       "      <th>lai</th>\n",
+       "      <th>spi03</th>\n",
+       "      <th>...</th>\n",
+       "      <th>infsinx</th>\n",
+       "      <th>fbupinx</th>\n",
+       "      <th>fdsrte</th>\n",
+       "      <th>frp</th>\n",
+       "      <th>daysElapsed</th>\n",
+       "      <th>timeYear</th>\n",
+       "      <th>timeMonth</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>residual</th>\n",
+       "      <th>residual_sign</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-14.125</td>\n",
+       "      <td>-43.625</td>\n",
+       "      <td>0.637926</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.001862</td>\n",
+       "      <td>0.280403</td>\n",
+       "      <td>0.899991</td>\n",
+       "      <td>0.124609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.630047</td>\n",
+       "      <td>18.051723</td>\n",
+       "      <td>1.645894</td>\n",
+       "      <td>0.003937</td>\n",
+       "      <td>1765</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2.064887</td>\n",
+       "      <td>1.426961</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-15.875</td>\n",
+       "      <td>18.375</td>\n",
+       "      <td>2.027269</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.003352</td>\n",
+       "      <td>0.235407</td>\n",
+       "      <td>0.588883</td>\n",
+       "      <td>0.878517</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.969392</td>\n",
+       "      <td>63.219906</td>\n",
+       "      <td>4.600344</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1277</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5.639390</td>\n",
+       "      <td>3.612121</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.375</td>\n",
+       "      <td>28.875</td>\n",
+       "      <td>791.847741</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.003062</td>\n",
+       "      <td>0.531788</td>\n",
+       "      <td>0.922213</td>\n",
+       "      <td>-1.109352</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.449267</td>\n",
+       "      <td>5.097896</td>\n",
+       "      <td>0.836676</td>\n",
+       "      <td>0.417923</td>\n",
+       "      <td>1795</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>12</td>\n",
+       "      <td>763.954092</td>\n",
+       "      <td>-27.893649</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-18.875</td>\n",
+       "      <td>33.375</td>\n",
+       "      <td>182.594167</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.014721</td>\n",
+       "      <td>0.364745</td>\n",
+       "      <td>0.311108</td>\n",
+       "      <td>-0.205468</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.335389</td>\n",
+       "      <td>-28.620920</td>\n",
+       "      <td>-1.740059</td>\n",
+       "      <td>0.055191</td>\n",
+       "      <td>243</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9</td>\n",
+       "      <td>220.696843</td>\n",
+       "      <td>38.102675</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.625</td>\n",
+       "      <td>-2.625</td>\n",
+       "      <td>137.816458</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.003877</td>\n",
+       "      <td>0.616257</td>\n",
+       "      <td>0.999990</td>\n",
+       "      <td>1.105078</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.576173</td>\n",
+       "      <td>-5.271719</td>\n",
+       "      <td>6.633287</td>\n",
+       "      <td>0.019714</td>\n",
+       "      <td>1096</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>1</td>\n",
+       "      <td>287.054485</td>\n",
+       "      <td>149.238027</td>\n",
+       "      <td>positive</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>132504</th>\n",
+       "      <td>-13.875</td>\n",
+       "      <td>22.125</td>\n",
+       "      <td>11.398151</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.000576</td>\n",
+       "      <td>0.312607</td>\n",
+       "      <td>0.288886</td>\n",
+       "      <td>-0.283592</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.838159</td>\n",
+       "      <td>22.556030</td>\n",
+       "      <td>1.998066</td>\n",
+       "      <td>0.003937</td>\n",
+       "      <td>608</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9</td>\n",
+       "      <td>12.224819</td>\n",
+       "      <td>0.826668</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132505</th>\n",
+       "      <td>4.375</td>\n",
+       "      <td>17.625</td>\n",
+       "      <td>781.737678</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.008820</td>\n",
+       "      <td>0.589686</td>\n",
+       "      <td>0.877769</td>\n",
+       "      <td>1.925390</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.149598</td>\n",
+       "      <td>-9.457446</td>\n",
+       "      <td>-0.957595</td>\n",
+       "      <td>0.023651</td>\n",
+       "      <td>334</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>12</td>\n",
+       "      <td>469.220669</td>\n",
+       "      <td>-312.517009</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132506</th>\n",
+       "      <td>-11.875</td>\n",
+       "      <td>21.375</td>\n",
+       "      <td>202.957233</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.000306</td>\n",
+       "      <td>0.290317</td>\n",
+       "      <td>0.266664</td>\n",
+       "      <td>0.187110</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.089159</td>\n",
+       "      <td>-16.323260</td>\n",
+       "      <td>0.064647</td>\n",
+       "      <td>0.051254</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>7</td>\n",
+       "      <td>169.885501</td>\n",
+       "      <td>-33.071732</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132507</th>\n",
+       "      <td>18.625</td>\n",
+       "      <td>-102.625</td>\n",
+       "      <td>81.223411</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.043172</td>\n",
+       "      <td>0.505241</td>\n",
+       "      <td>0.399996</td>\n",
+       "      <td>0.075781</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.053280</td>\n",
+       "      <td>50.202015</td>\n",
+       "      <td>7.200427</td>\n",
+       "      <td>0.011826</td>\n",
+       "      <td>1216</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>5</td>\n",
+       "      <td>36.788492</td>\n",
+       "      <td>-44.434919</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132508</th>\n",
+       "      <td>-6.625</td>\n",
+       "      <td>-46.625</td>\n",
+       "      <td>1.211840</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.008414</td>\n",
+       "      <td>0.364359</td>\n",
+       "      <td>1.155544</td>\n",
+       "      <td>-0.814844</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.866540</td>\n",
+       "      <td>13.814033</td>\n",
+       "      <td>4.414381</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>181</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7.059729</td>\n",
+       "      <td>5.847889</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>132509 rows × 34 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        latitude  longitude  dry_matter climatic_region biome GFEDregions  \\\n",
+       "0        -14.125    -43.625    0.637926             4.0   1.0         5.0   \n",
+       "1        -15.875     18.375    2.027269             3.0   1.0         9.0   \n",
+       "2          5.375     28.875  791.847741             4.0   1.0         8.0   \n",
+       "3        -18.875     33.375  182.594167             3.0   1.0         9.0   \n",
+       "4          7.625     -2.625  137.816458             4.0   1.0         8.0   \n",
+       "...          ...        ...         ...             ...   ...         ...   \n",
+       "132504   -13.875     22.125   11.398151             3.0   1.0         9.0   \n",
+       "132505     4.375     17.625  781.737678             4.0   1.0         8.0   \n",
+       "132506   -11.875     21.375  202.957233             3.0   1.0         9.0   \n",
+       "132507    18.625   -102.625   81.223411             4.0   1.0         3.0   \n",
+       "132508    -6.625    -46.625    1.211840             4.0   1.0         5.0   \n",
+       "\n",
+       "           slope       vod       lai     spi03  ...   infsinx    fbupinx  \\\n",
+       "0       0.001862  0.280403  0.899991  0.124609  ...  0.630047  18.051723   \n",
+       "1       0.003352  0.235407  0.588883  0.878517  ...  1.969392  63.219906   \n",
+       "2       0.003062  0.531788  0.922213 -1.109352  ...  0.449267   5.097896   \n",
+       "3       0.014721  0.364745  0.311108 -0.205468  ... -0.335389 -28.620920   \n",
+       "4       0.003877  0.616257  0.999990  1.105078  ...  3.576173  -5.271719   \n",
+       "...          ...       ...       ...       ...  ...       ...        ...   \n",
+       "132504  0.000576  0.312607  0.288886 -0.283592  ...  0.838159  22.556030   \n",
+       "132505  0.008820  0.589686  0.877769  1.925390  ...  0.149598  -9.457446   \n",
+       "132506  0.000306  0.290317  0.266664  0.187110  ...  0.089159 -16.323260   \n",
+       "132507  0.043172  0.505241  0.399996  0.075781  ...  3.053280  50.202015   \n",
+       "132508  0.008414  0.364359  1.155544 -0.814844  ...  1.866540  13.814033   \n",
+       "\n",
+       "          fdsrte       frp  daysElapsed  timeYear  timeMonth  prediction  \\\n",
+       "0       1.645894  0.003937         1765      2014         11    2.064887   \n",
+       "1       4.600344  0.000000         1277      2013          7    5.639390   \n",
+       "2       0.836676  0.417923         1795      2014         12  763.954092   \n",
+       "3      -1.740059  0.055191          243      2010          9  220.696843   \n",
+       "4       6.633287  0.019714         1096      2013          1  287.054485   \n",
+       "...          ...       ...          ...       ...        ...         ...   \n",
+       "132504  1.998066  0.003937          608      2011          9   12.224819   \n",
+       "132505 -0.957595  0.023651          334      2010         12  469.220669   \n",
+       "132506  0.064647  0.051254         2007      2015          7  169.885501   \n",
+       "132507  7.200427  0.011826         1216      2013          5   36.788492   \n",
+       "132508  4.414381  0.000000          181      2010          7    7.059729   \n",
+       "\n",
+       "          residual  residual_sign  \n",
+       "0         1.426961       positive  \n",
+       "1         3.612121       positive  \n",
+       "2       -27.893649       negative  \n",
+       "3        38.102675       positive  \n",
+       "4       149.238027       positive  \n",
+       "...            ...            ...  \n",
+       "132504    0.826668       positive  \n",
+       "132505 -312.517009       negative  \n",
+       "132506  -33.071732       negative  \n",
+       "132507  -44.434919       negative  \n",
+       "132508    5.847889       positive  \n",
+       "\n",
+       "[132509 rows x 34 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add transformed back observations, prediction and residuals to the test data frame\n",
+    "test_raw['prediction'] = pre\n",
+    "test_raw['residual'] = test_raw['prediction'] - test_raw['dry_matter']\n",
+    "test_raw['residual_sign'] = np.where(test_raw['residual'] == 0, 'zero',\n",
+    "                                     np.where(test_raw['residual'] > 0, 'positive', 'negative'))\n",
+    "test_raw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6eed849d",
+   "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>dry_matter</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>residual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>44997.000000</td>\n",
+       "      <td>44997.000000</td>\n",
+       "      <td>44997.000000</td>\n",
+       "      <td>44997.000000</td>\n",
+       "      <td>44997.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>12.484752</td>\n",
+       "      <td>20.060212</td>\n",
+       "      <td>71.796812</td>\n",
+       "      <td>43.594660</td>\n",
+       "      <td>-28.202153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>29.955204</td>\n",
+       "      <td>74.327638</td>\n",
+       "      <td>199.257848</td>\n",
+       "      <td>98.934522</td>\n",
+       "      <td>143.103344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-48.375000</td>\n",
+       "      <td>-162.625000</td>\n",
+       "      <td>0.000054</td>\n",
+       "      <td>0.051472</td>\n",
+       "      <td>-7701.320189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-13.375000</td>\n",
+       "      <td>-48.375000</td>\n",
+       "      <td>3.510049</td>\n",
+       "      <td>3.642212</td>\n",
+       "      <td>-16.691602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.375000</td>\n",
+       "      <td>26.625000</td>\n",
+       "      <td>14.104767</td>\n",
+       "      <td>11.829675</td>\n",
+       "      <td>-1.209786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>46.125000</td>\n",
+       "      <td>77.125000</td>\n",
+       "      <td>56.598447</td>\n",
+       "      <td>39.972425</td>\n",
+       "      <td>2.255198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>72.125000</td>\n",
+       "      <td>178.125000</td>\n",
+       "      <td>8409.015059</td>\n",
+       "      <td>3139.159278</td>\n",
+       "      <td>1285.507564</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           latitude     longitude    dry_matter    prediction      residual\n",
+       "count  44997.000000  44997.000000  44997.000000  44997.000000  44997.000000\n",
+       "mean      12.484752     20.060212     71.796812     43.594660    -28.202153\n",
+       "std       29.955204     74.327638    199.257848     98.934522    143.103344\n",
+       "min      -48.375000   -162.625000      0.000054      0.051472  -7701.320189\n",
+       "25%      -13.375000    -48.375000      3.510049      3.642212    -16.691602\n",
+       "50%        7.375000     26.625000     14.104767     11.829675     -1.209786\n",
+       "75%       46.125000     77.125000     56.598447     39.972425      2.255198\n",
+       "max       72.125000    178.125000   8409.015059   3139.159278   1285.507564"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group results averaging over all days for each point (lat,lon)\n",
+    "grouped_test = test_raw.groupby(['latitude', 'longitude'],\n",
+    "                            as_index=False).agg({'dry_matter': ['mean'],\n",
+    "                                                 'prediction': ['mean'],\n",
+    "                                                 'residual': ['mean']})\n",
+    "grouped_test.columns = ['latitude', 'longitude', 'dry_matter', 'prediction', 'residual']\n",
+    "grouped_test.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6dbcfa4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crs = {'init': 'epsg:4326'}\n",
+    "# Get coastline\n",
+    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
+    "\n",
+    "gdf = gpd.GeoDataFrame(grouped_test,\n",
+    "                       geometry=gpd.points_from_xy(x = grouped_test.longitude,\n",
+    "                                                   y = grouped_test.latitude))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7a960108",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.set_aspect('equal')\n",
+    "\n",
+    "world.plot(ax=ax, color='white', edgecolor='gray')\n",
+    "\n",
+    "gdf.plot(column='dry_matter', #cmap='RdBu', \n",
+    "         scheme=\"User_Defined\",\n",
+    "         classification_kwds=dict(bins=[50, 100, 500, 1000]),\n",
+    "         markersize = 0.05,\n",
+    "         legend=True, \n",
+    "         ax=ax)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7665b2d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.set_aspect('equal')\n",
+    "\n",
+    "world.plot(ax=ax, color='white', edgecolor='gray')\n",
+    "\n",
+    "gdf.plot(column='residual', cmap='RdBu', \n",
+    "         scheme=\"User_Defined\",\n",
+    "         classification_kwds=dict(bins=[-1000, -500, -100, -50, \n",
+    "                                        0, \n",
+    "                                        50, 100, 500, 1000]),\n",
+    "         markersize = 0.05,\n",
+    "         legend=True, \n",
+    "         ax=ax)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e99b8a0",
+   "metadata": {},
+   "source": [
+    "## Group statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a11bf0fd",
+   "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>biome</th>\n",
+       "      <th>residual_sign</th>\n",
+       "      <th>dry_matter</th>\n",
+       "      <th>residual</th>\n",
+       "      <th>biome_names</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>123.023856</td>\n",
+       "      <td>-61.630003</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>30.876956</td>\n",
+       "      <td>18.391105</td>\n",
+       "      <td>SA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>82.059481</td>\n",
+       "      <td>-54.852678</td>\n",
+       "      <td>SAOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>11.243260</td>\n",
+       "      <td>11.706599</td>\n",
+       "      <td>SAOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>19.417161</td>\n",
+       "      <td>-13.452322</td>\n",
+       "      <td>AG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>2.370359</td>\n",
+       "      <td>2.617083</td>\n",
+       "      <td>AG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>26.982751</td>\n",
+       "      <td>-18.750605</td>\n",
+       "      <td>AGOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>2.945825</td>\n",
+       "      <td>3.572270</td>\n",
+       "      <td>AGOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>187.354151</td>\n",
+       "      <td>-118.088479</td>\n",
+       "      <td>TF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>27.311927</td>\n",
+       "      <td>23.247063</td>\n",
+       "      <td>TF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>175.206229</td>\n",
+       "      <td>-124.126791</td>\n",
+       "      <td>PEAT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>20.334309</td>\n",
+       "      <td>23.729519</td>\n",
+       "      <td>PEAT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>149.293533</td>\n",
+       "      <td>-89.435365</td>\n",
+       "      <td>EF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>22.526547</td>\n",
+       "      <td>17.954406</td>\n",
+       "      <td>EF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>294.288471</td>\n",
+       "      <td>-189.917244</td>\n",
+       "      <td>EFOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>39.944575</td>\n",
+       "      <td>40.218178</td>\n",
+       "      <td>EFOS</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   biome residual_sign  dry_matter    residual biome_names\n",
+       "0    1.0      negative  123.023856  -61.630003          SA\n",
+       "1    1.0      positive   30.876956   18.391105          SA\n",
+       "2    2.0      negative   82.059481  -54.852678        SAOS\n",
+       "3    2.0      positive   11.243260   11.706599        SAOS\n",
+       "4    3.0      negative   19.417161  -13.452322          AG\n",
+       "5    3.0      positive    2.370359    2.617083          AG\n",
+       "6    4.0      negative   26.982751  -18.750605        AGOS\n",
+       "7    4.0      positive    2.945825    3.572270        AGOS\n",
+       "8    5.0      negative  187.354151 -118.088479          TF\n",
+       "9    5.0      positive   27.311927   23.247063          TF\n",
+       "10   6.0      negative  175.206229 -124.126791        PEAT\n",
+       "11   6.0      positive   20.334309   23.729519        PEAT\n",
+       "12   7.0      negative  149.293533  -89.435365          EF\n",
+       "13   7.0      positive   22.526547   17.954406          EF\n",
+       "14   8.0      negative  294.288471 -189.917244        EFOS\n",
+       "15   8.0      positive   39.944575   40.218178        EFOS"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group results averaging over all days for each point (lat,lon)\n",
+    "grouped_test2 = test_raw.groupby(['biome', 'residual_sign'], as_index=False).agg({'dry_matter': ['mean'],\n",
+    "                                                                                  'residual': ['mean']})\n",
+    "grouped_test2.columns = ['biome', 'residual_sign', 'dry_matter', 'residual']\n",
+    "grouped_test2['biome_names'] = np.repeat(['SA', 'SAOS', 'AG', 'AGOS', 'TF', 'PEAT', 'EF', 'EFOS'], 2)\n",
+    "grouped_test2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "40a14725",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y = grouped_test2.residual\n",
+    "x = grouped_test2.biome_names\n",
+    "labels = test_raw.groupby(['biome', 'residual_sign'], as_index=False).size()\n",
+    "labels = labels['size']\n",
+    "ind = np.arange(len(x))\n",
+    "\n",
+    "fig, ax = plt.subplots()    \n",
+    "width = 0.75 # the width of the bars \n",
+    "ax.barh(ind, y, width, color=\"lightblue\")\n",
+    "ax.set_yticks(ind)\n",
+    "ax.set_yticklabels(x, minor=False)\n",
+    "for i, v in enumerate(y):\n",
+    "    ax.text(v + 1, i - .25, str(labels[i]), color='black', fontweight='bold')\n",
+    "plt.title('Residuals by biome')\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('')      \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "723b2f65",
+   "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>GFEDregions</th>\n",
+       "      <th>residual_sign</th>\n",
+       "      <th>dry_matter</th>\n",
+       "      <th>residual</th>\n",
+       "      <th>GFEDnames</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>212.749244</td>\n",
+       "      <td>-141.140860</td>\n",
+       "      <td>BONA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>31.856082</td>\n",
+       "      <td>30.385274</td>\n",
+       "      <td>BONA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>191.209817</td>\n",
+       "      <td>-123.149332</td>\n",
+       "      <td>TENA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>25.142089</td>\n",
+       "      <td>25.820831</td>\n",
+       "      <td>TENA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>31.021731</td>\n",
+       "      <td>-23.172906</td>\n",
+       "      <td>CEAM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>3.311345</td>\n",
+       "      <td>3.570343</td>\n",
+       "      <td>CEAM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>196.146812</td>\n",
+       "      <td>-139.027464</td>\n",
+       "      <td>NHSA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>19.727559</td>\n",
+       "      <td>20.101270</td>\n",
+       "      <td>NHSA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>143.500935</td>\n",
+       "      <td>-101.388431</td>\n",
+       "      <td>SHSA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>19.134057</td>\n",
+       "      <td>21.230677</td>\n",
+       "      <td>SHSA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>48.799376</td>\n",
+       "      <td>-31.264139</td>\n",
+       "      <td>EURO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>7.139696</td>\n",
+       "      <td>6.918161</td>\n",
+       "      <td>EURO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>30.331274</td>\n",
+       "      <td>-20.690219</td>\n",
+       "      <td>MIDE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>3.984022</td>\n",
+       "      <td>4.176093</td>\n",
+       "      <td>MIDE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>75.278686</td>\n",
+       "      <td>-51.624740</td>\n",
+       "      <td>NHAF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>9.806569</td>\n",
+       "      <td>10.256690</td>\n",
+       "      <td>NHAF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>42.820724</td>\n",
+       "      <td>-28.861454</td>\n",
+       "      <td>SHAF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>6.127276</td>\n",
+       "      <td>5.942588</td>\n",
+       "      <td>SHAF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>78.299959</td>\n",
+       "      <td>-51.607495</td>\n",
+       "      <td>BOAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>10.579508</td>\n",
+       "      <td>10.092437</td>\n",
+       "      <td>BOAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>31.247574</td>\n",
+       "      <td>-22.375565</td>\n",
+       "      <td>CEAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>3.784314</td>\n",
+       "      <td>4.102770</td>\n",
+       "      <td>CEAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>16.173458</td>\n",
+       "      <td>-11.030221</td>\n",
+       "      <td>SEAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>2.279186</td>\n",
+       "      <td>2.249713</td>\n",
+       "      <td>SEAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>199.929401</td>\n",
+       "      <td>-95.157618</td>\n",
+       "      <td>EQAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>52.552433</td>\n",
+       "      <td>30.424613</td>\n",
+       "      <td>EQAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>147.701216</td>\n",
+       "      <td>-73.691057</td>\n",
+       "      <td>AUST</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>38.833077</td>\n",
+       "      <td>22.636514</td>\n",
+       "      <td>AUST</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   GFEDregions residual_sign  dry_matter    residual GFEDnames\n",
+       "0          1.0      negative  212.749244 -141.140860      BONA\n",
+       "1          1.0      positive   31.856082   30.385274      BONA\n",
+       "2         10.0      negative  191.209817 -123.149332      TENA\n",
+       "3         10.0      positive   25.142089   25.820831      TENA\n",
+       "4         11.0      negative   31.021731  -23.172906      CEAM\n",
+       "5         11.0      positive    3.311345    3.570343      CEAM\n",
+       "6         12.0      negative  196.146812 -139.027464      NHSA\n",
+       "7         12.0      positive   19.727559   20.101270      NHSA\n",
+       "8         13.0      negative  143.500935 -101.388431      SHSA\n",
+       "9         13.0      positive   19.134057   21.230677      SHSA\n",
+       "10        14.0      negative   48.799376  -31.264139      EURO\n",
+       "11        14.0      positive    7.139696    6.918161      EURO\n",
+       "12         2.0      negative   30.331274  -20.690219      MIDE\n",
+       "13         2.0      positive    3.984022    4.176093      MIDE\n",
+       "14         3.0      negative   75.278686  -51.624740      NHAF\n",
+       "15         3.0      positive    9.806569   10.256690      NHAF\n",
+       "16         4.0      negative   42.820724  -28.861454      SHAF\n",
+       "17         4.0      positive    6.127276    5.942588      SHAF\n",
+       "18         5.0      negative   78.299959  -51.607495      BOAS\n",
+       "19         5.0      positive   10.579508   10.092437      BOAS\n",
+       "20         6.0      negative   31.247574  -22.375565      CEAS\n",
+       "21         6.0      positive    3.784314    4.102770      CEAS\n",
+       "22         7.0      negative   16.173458  -11.030221      SEAS\n",
+       "23         7.0      positive    2.279186    2.249713      SEAS\n",
+       "24         8.0      negative  199.929401  -95.157618      EQAS\n",
+       "25         8.0      positive   52.552433   30.424613      EQAS\n",
+       "26         9.0      negative  147.701216  -73.691057      AUST\n",
+       "27         9.0      positive   38.833077   22.636514      AUST"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group results averaging over all days for each point (lat,lon)\n",
+    "grouped_test3 = test_raw.groupby(['GFEDregions', 'residual_sign'], as_index=False).agg({'dry_matter': ['mean'],\n",
+    "                                                                  'residual': ['mean']})\n",
+    "grouped_test3.columns = ['GFEDregions', 'residual_sign', 'dry_matter', 'residual']\n",
+    "grouped_test3['GFEDnames'] = np.repeat(['BONA', 'TENA', 'CEAM', 'NHSA', 'SHSA', 'EURO', 'MIDE',\n",
+    "                              'NHAF', 'SHAF', 'BOAS', 'CEAS', 'SEAS', 'EQAS', 'AUST'], 2)\n",
+    "grouped_test3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0ef431e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define plot size\n",
+    "plt.rcParams[\"figure.figsize\"] = (15,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3a2bb4c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y = grouped_test3.residual\n",
+    "x = grouped_test3.GFEDnames\n",
+    "labels = test_raw.groupby(['GFEDregions', 'residual_sign'], as_index=False).size()\n",
+    "labels = labels['size']\n",
+    "ind = np.arange(len(x))\n",
+    "\n",
+    "fig, ax = plt.subplots()    \n",
+    "width = 0.75 # the width of the bars \n",
+    "ax.barh(ind, y, width, color=\"lightblue\")\n",
+    "ax.set_yticks(ind)\n",
+    "ax.set_yticklabels(x, minor=False)\n",
+    "for i, v in enumerate(y):\n",
+    "    ax.text(v + 1, i - .25, str(labels[i]), color='black', fontweight='bold')\n",
+    "plt.title('Residuals by GFED region')\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('')      \n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/preprocess/README.md b/notebooks/preprocess/README.md
index a20a432..97dc4aa 100644
--- a/notebooks/preprocess/README.md
+++ b/notebooks/preprocess/README.md
@@ -12,47 +12,57 @@ directory that is also located at the same level as the notebooks. In our typica
 flow, we use symbolic links to achieve this, because often times such large datasets of 
 raw files are present on data disks mounted elsewhere. 
 
-For example, if the raw data is in `/data1/raw_data/`, and we wish to store the processed
-data in `/data1/preprocessed_data/`, we would create symbolic links:
+For example, if the working directory is `~/ai-vegetation-fuel/`, the raw data are in `/data1/raw_data/`, and we wish to store the processed
+data in `/data1/processed_data/`, we would create symbolic links:
 
 ```bash
-ln -s /data1/raw_data ./raw
-ln -s /data1/preprocess ./preprocess
+ln -s /data1/raw_data ./notebooks/preprocess/raw
+ln -s /data1/processed_data ./notebooks/preprocess/preprocess
 ```
 
-This should give you a directory structure like this:
+This should give you a directory structure like this (`ls -l ./notebooks/preprocess/`):
 
 ```bash
 .
+├── preprocess -> /data1/processed_data
+├── preprocess_agb.ipynb
 ├── preprocess_burned_area.ipynb
 ├── preprocess_climate_regions.ipynb
-├── preprocess_leaf_area_index.ipynb
-├── preprocess -> /data1/preprocessed_data
-├── preprocess_agb.ipynb
 ├── preprocess_fire_anomalies.ipynb
+├── preprocess_leaf_area_index.ipynb
 ├── preprocess_slopes.ipynb
-├── preprocessing_spi_gpcc.ipynb
+├── preprocess_spi_gpcc.ipynb
+├── preprocess_weather_anomalies.ipynb
 ├── raw -> /data1/raw_data
-└── preprocess_weather_anomalies.ipynb
+└── README.md
 ```
 
 You can then create a symlink in the root of the repository with the name `data/` to 
-where you store the preprocessed data to get the following tree:
+where you store the preprocessed data.
+
+```bash
+ln -s /data1/processed_data ./data
+```
+
+to get the following tree:
 
 ```bash
 .
 ├── data -> /data1/preprocessed_data
+├── dev-requirements.txt
 ├── docker
 ├── docs
 │   └── _static
+├── LICENSE
 ├── notebooks
 │   └── preprocess
 │       ├── preprocess -> /data1/preprocessed_data
 │       └── raw -> /data1/raw_data
+├── README.md
+├── requirements.txt
 └── src
     ├── models
     ├── pre-trained_models
-    ├── results
     └── utils
 ```
 
diff --git a/src/utils/fuelload.py b/src/utils/fuelload.py
index b8b2125..153d94b 100644
--- a/src/utils/fuelload.py
+++ b/src/utils/fuelload.py
@@ -29,6 +29,11 @@ def fuelload(path_agb: str, path_ba: str, path_fl: str = None) -> xarray.Dataset
     agb_data.values[agb_data.values == AGB_THRESH] = np.nan
     ba_data.values[ba_data.values < BA_THRESH] = np.nan
 
+    # AGB units are Mg/h
+    # BA units are m2, therefore we convert BA to hectares.
+    ba_data = ba_data * 0.0001
+
+    # Now that units are consistent we calculate LOAD = AGB * BA
     fuel_load_dataset = agb_data * ba_data
 
     if path_fl is not None: