diff --git a/analysis/Dominica/business-as-usual.ipynb b/analysis/Dominica/business-as-usual.ipynb
deleted file mode 100644
index c80ef12..0000000
--- a/analysis/Dominica/business-as-usual.ipynb
+++ /dev/null
@@ -1,2792 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Business-as-usual analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import geopandas as gpd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from tqdm import tqdm\n",
-    "from ema_workbench import load_results\n",
-    "from unbreakable.analysis.analyzer import *\n",
-    "from unbreakable.analysis.visualizer import *\n",
-    "sns.set_theme(style=\"ticks\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Parameters for report writing\n",
-    "savefig = True\n",
-    "savetab = True\n",
-    "figures_folder = '../../reports/Dominica/figures/analysis/'\n",
-    "tables_folder = '../../reports/Dominica/tables/analysis/'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load results\n",
-    "country = 'Dominica'\n",
-    "n_scenarios = 2999\n",
-    "n_policies = 0\n",
-    "results = load_results(f'../../experiments/{country}/scenarios={n_scenarios}, policies={n_policies}.tar.gz')\n",
-    "\n",
-    "# Prepare outcomes\n",
-    "outcomes = prepare_outcomes(results, add_uncertainties=False, add_policies=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## EDA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Single scenario, single district "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_20390\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_20390_level0_col0\" class=\"col_heading level0 col0\" >total_population</th>\n",
-       "      <th id=\"T_20390_level0_col1\" class=\"col_heading level0 col1\" >total_asset_loss</th>\n",
-       "      <th id=\"T_20390_level0_col2\" class=\"col_heading level0 col2\" >total_consumption_loss</th>\n",
-       "      <th id=\"T_20390_level0_col3\" class=\"col_heading level0 col3\" >tot_exposed_asset</th>\n",
-       "      <th id=\"T_20390_level0_col4\" class=\"col_heading level0 col4\" >tot_asset_surv</th>\n",
-       "      <th id=\"T_20390_level0_col5\" class=\"col_heading level0 col5\" >expected_loss_frac</th>\n",
-       "      <th id=\"T_20390_level0_col6\" class=\"col_heading level0 col6\" >n_affected_people</th>\n",
-       "      <th id=\"T_20390_level0_col7\" class=\"col_heading level0 col7\" >annual_average_consumption</th>\n",
-       "      <th id=\"T_20390_level0_col8\" class=\"col_heading level0 col8\" >povline_adjusted</th>\n",
-       "      <th id=\"T_20390_level0_col9\" class=\"col_heading level0 col9\" >district_pml</th>\n",
-       "      <th id=\"T_20390_level0_col10\" class=\"col_heading level0 col10\" >n_poor_initial</th>\n",
-       "      <th id=\"T_20390_level0_col11\" class=\"col_heading level0 col11\" >n_poor_affected</th>\n",
-       "      <th id=\"T_20390_level0_col12\" class=\"col_heading level0 col12\" >n_new_poor</th>\n",
-       "      <th id=\"T_20390_level0_col13\" class=\"col_heading level0 col13\" >initial_poverty_gap</th>\n",
-       "      <th id=\"T_20390_level0_col14\" class=\"col_heading level0 col14\" >new_poverty_gap_initial</th>\n",
-       "      <th id=\"T_20390_level0_col15\" class=\"col_heading level0 col15\" >new_poverty_gap_all</th>\n",
-       "      <th id=\"T_20390_level0_col16\" class=\"col_heading level0 col16\" >annual_average_consumption_loss</th>\n",
-       "      <th id=\"T_20390_level0_col17\" class=\"col_heading level0 col17\" >annual_average_consumption_loss_pct</th>\n",
-       "      <th id=\"T_20390_level0_col18\" class=\"col_heading level0 col18\" >mean_recovery_rate</th>\n",
-       "      <th id=\"T_20390_level0_col19\" class=\"col_heading level0 col19\" >r</th>\n",
-       "      <th id=\"T_20390_level0_col20\" class=\"col_heading level0 col20\" >n_poor_ratio</th>\n",
-       "      <th id=\"T_20390_level0_col21\" class=\"col_heading level0 col21\" >n_new_poor_increase_pp</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >district</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "      <th class=\"blank col9\" >&nbsp;</th>\n",
-       "      <th class=\"blank col10\" >&nbsp;</th>\n",
-       "      <th class=\"blank col11\" >&nbsp;</th>\n",
-       "      <th class=\"blank col12\" >&nbsp;</th>\n",
-       "      <th class=\"blank col13\" >&nbsp;</th>\n",
-       "      <th class=\"blank col14\" >&nbsp;</th>\n",
-       "      <th class=\"blank col15\" >&nbsp;</th>\n",
-       "      <th class=\"blank col16\" >&nbsp;</th>\n",
-       "      <th class=\"blank col17\" >&nbsp;</th>\n",
-       "      <th class=\"blank col18\" >&nbsp;</th>\n",
-       "      <th class=\"blank col19\" >&nbsp;</th>\n",
-       "      <th class=\"blank col20\" >&nbsp;</th>\n",
-       "      <th class=\"blank col21\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row0\" class=\"row_heading level0 row0\" >St. George</th>\n",
-       "      <td id=\"T_20390_row0_col0\" class=\"data row0 col0\" >20,445.41</td>\n",
-       "      <td id=\"T_20390_row0_col1\" class=\"data row0 col1\" >191,556,888.72</td>\n",
-       "      <td id=\"T_20390_row0_col2\" class=\"data row0 col2\" >156,500,453.90</td>\n",
-       "      <td id=\"T_20390_row0_col3\" class=\"data row0 col3\" >1,002,936,475.89</td>\n",
-       "      <td id=\"T_20390_row0_col4\" class=\"data row0 col4\" >1,090,939,278.78</td>\n",
-       "      <td id=\"T_20390_row0_col5\" class=\"data row0 col5\" >0.20</td>\n",
-       "      <td id=\"T_20390_row0_col6\" class=\"data row0 col6\" >11,192.67</td>\n",
-       "      <td id=\"T_20390_row0_col7\" class=\"data row0 col7\" >14,784.79</td>\n",
-       "      <td id=\"T_20390_row0_col8\" class=\"data row0 col8\" >5,727.44</td>\n",
-       "      <td id=\"T_20390_row0_col9\" class=\"data row0 col9\" >195,870,372.33</td>\n",
-       "      <td id=\"T_20390_row0_col10\" class=\"data row0 col10\" >2,845.00</td>\n",
-       "      <td id=\"T_20390_row0_col11\" class=\"data row0 col11\" >1,257.00</td>\n",
-       "      <td id=\"T_20390_row0_col12\" class=\"data row0 col12\" >798.00</td>\n",
-       "      <td id=\"T_20390_row0_col13\" class=\"data row0 col13\" >22.52</td>\n",
-       "      <td id=\"T_20390_row0_col14\" class=\"data row0 col14\" >25.24</td>\n",
-       "      <td id=\"T_20390_row0_col15\" class=\"data row0 col15\" >21.31</td>\n",
-       "      <td id=\"T_20390_row0_col16\" class=\"data row0 col16\" >1,398.24</td>\n",
-       "      <td id=\"T_20390_row0_col17\" class=\"data row0 col17\" >8.62</td>\n",
-       "      <td id=\"T_20390_row0_col18\" class=\"data row0 col18\" >0.31</td>\n",
-       "      <td id=\"T_20390_row0_col19\" class=\"data row0 col19\" >3.00</td>\n",
-       "      <td id=\"T_20390_row0_col20\" class=\"data row0 col20\" >14.00</td>\n",
-       "      <td id=\"T_20390_row0_col21\" class=\"data row0 col21\" >3.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row1\" class=\"row_heading level0 row1\" >St. John</th>\n",
-       "      <td id=\"T_20390_row1_col0\" class=\"data row1 col0\" >5,322.70</td>\n",
-       "      <td id=\"T_20390_row1_col1\" class=\"data row1 col1\" >30,805,864.21</td>\n",
-       "      <td id=\"T_20390_row1_col2\" class=\"data row1 col2\" >23,159,796.16</td>\n",
-       "      <td id=\"T_20390_row1_col3\" class=\"data row1 col3\" >146,917,090.94</td>\n",
-       "      <td id=\"T_20390_row1_col4\" class=\"data row1 col4\" >290,195,853.85</td>\n",
-       "      <td id=\"T_20390_row1_col5\" class=\"data row1 col5\" >0.21</td>\n",
-       "      <td id=\"T_20390_row1_col6\" class=\"data row1 col6\" >2,928.58</td>\n",
-       "      <td id=\"T_20390_row1_col7\" class=\"data row1 col7\" >8,118.78</td>\n",
-       "      <td id=\"T_20390_row1_col8\" class=\"data row1 col8\" >3,154.05</td>\n",
-       "      <td id=\"T_20390_row1_col9\" class=\"data row1 col9\" >30,721,128.56</td>\n",
-       "      <td id=\"T_20390_row1_col10\" class=\"data row1 col10\" >564.00</td>\n",
-       "      <td id=\"T_20390_row1_col11\" class=\"data row1 col11\" >229.00</td>\n",
-       "      <td id=\"T_20390_row1_col12\" class=\"data row1 col12\" >144.00</td>\n",
-       "      <td id=\"T_20390_row1_col13\" class=\"data row1 col13\" >15.16</td>\n",
-       "      <td id=\"T_20390_row1_col14\" class=\"data row1 col14\" >18.31</td>\n",
-       "      <td id=\"T_20390_row1_col15\" class=\"data row1 col15\" >15.47</td>\n",
-       "      <td id=\"T_20390_row1_col16\" class=\"data row1 col16\" >790.82</td>\n",
-       "      <td id=\"T_20390_row1_col17\" class=\"data row1 col17\" >9.68</td>\n",
-       "      <td id=\"T_20390_row1_col18\" class=\"data row1 col18\" >0.26</td>\n",
-       "      <td id=\"T_20390_row1_col19\" class=\"data row1 col19\" >1.22</td>\n",
-       "      <td id=\"T_20390_row1_col20\" class=\"data row1 col20\" >11.00</td>\n",
-       "      <td id=\"T_20390_row1_col21\" class=\"data row1 col21\" >2.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row2\" class=\"row_heading level0 row2\" >St. Peter</th>\n",
-       "      <td id=\"T_20390_row2_col0\" class=\"data row2 col0\" >1,502.04</td>\n",
-       "      <td id=\"T_20390_row2_col1\" class=\"data row2 col1\" >18,039,053.20</td>\n",
-       "      <td id=\"T_20390_row2_col2\" class=\"data row2 col2\" >14,882,394.42</td>\n",
-       "      <td id=\"T_20390_row2_col3\" class=\"data row2 col3\" >81,914,004.37</td>\n",
-       "      <td id=\"T_20390_row2_col4\" class=\"data row2 col4\" >53,869,252.29</td>\n",
-       "      <td id=\"T_20390_row2_col5\" class=\"data row2 col5\" >0.23</td>\n",
-       "      <td id=\"T_20390_row2_col6\" class=\"data row2 col6\" >1,045.51</td>\n",
-       "      <td id=\"T_20390_row2_col7\" class=\"data row2 col7\" >16,389.16</td>\n",
-       "      <td id=\"T_20390_row2_col8\" class=\"data row2 col8\" >9,473.39</td>\n",
-       "      <td id=\"T_20390_row2_col9\" class=\"data row2 col9\" >18,961,436.29</td>\n",
-       "      <td id=\"T_20390_row2_col10\" class=\"data row2 col10\" >356.00</td>\n",
-       "      <td id=\"T_20390_row2_col11\" class=\"data row2 col11\" >223.00</td>\n",
-       "      <td id=\"T_20390_row2_col12\" class=\"data row2 col12\" >22.00</td>\n",
-       "      <td id=\"T_20390_row2_col13\" class=\"data row2 col13\" >24.74</td>\n",
-       "      <td id=\"T_20390_row2_col14\" class=\"data row2 col14\" >27.50</td>\n",
-       "      <td id=\"T_20390_row2_col15\" class=\"data row2 col15\" >26.20</td>\n",
-       "      <td id=\"T_20390_row2_col16\" class=\"data row2 col16\" >1,423.46</td>\n",
-       "      <td id=\"T_20390_row2_col17\" class=\"data row2 col17\" >8.30</td>\n",
-       "      <td id=\"T_20390_row2_col18\" class=\"data row2 col18\" >0.29</td>\n",
-       "      <td id=\"T_20390_row2_col19\" class=\"data row2 col19\" >3.62</td>\n",
-       "      <td id=\"T_20390_row2_col20\" class=\"data row2 col20\" >24.00</td>\n",
-       "      <td id=\"T_20390_row2_col21\" class=\"data row2 col21\" >1.46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row3\" class=\"row_heading level0 row3\" >St. Joseph</th>\n",
-       "      <td id=\"T_20390_row3_col0\" class=\"data row3 col0\" >5,891.05</td>\n",
-       "      <td id=\"T_20390_row3_col1\" class=\"data row3 col1\" >42,294,574.22</td>\n",
-       "      <td id=\"T_20390_row3_col2\" class=\"data row3 col2\" >32,931,882.51</td>\n",
-       "      <td id=\"T_20390_row3_col3\" class=\"data row3 col3\" >175,268,750.64</td>\n",
-       "      <td id=\"T_20390_row3_col4\" class=\"data row3 col4\" >172,708,190.03</td>\n",
-       "      <td id=\"T_20390_row3_col5\" class=\"data row3 col5\" >0.24</td>\n",
-       "      <td id=\"T_20390_row3_col6\" class=\"data row3 col6\" >4,157.37</td>\n",
-       "      <td id=\"T_20390_row3_col7\" class=\"data row3 col7\" >9,239.21</td>\n",
-       "      <td id=\"T_20390_row3_col8\" class=\"data row3 col8\" >6,322.37</td>\n",
-       "      <td id=\"T_20390_row3_col9\" class=\"data row3 col9\" >42,688,959.00</td>\n",
-       "      <td id=\"T_20390_row3_col10\" class=\"data row3 col10\" >2,739.00</td>\n",
-       "      <td id=\"T_20390_row3_col11\" class=\"data row3 col11\" >2,077.00</td>\n",
-       "      <td id=\"T_20390_row3_col12\" class=\"data row3 col12\" >29.00</td>\n",
-       "      <td id=\"T_20390_row3_col13\" class=\"data row3 col13\" >32.92</td>\n",
-       "      <td id=\"T_20390_row3_col14\" class=\"data row3 col14\" >37.21</td>\n",
-       "      <td id=\"T_20390_row3_col15\" class=\"data row3 col15\" >36.86</td>\n",
-       "      <td id=\"T_20390_row3_col16\" class=\"data row3 col16\" >792.13</td>\n",
-       "      <td id=\"T_20390_row3_col17\" class=\"data row3 col17\" >8.47</td>\n",
-       "      <td id=\"T_20390_row3_col18\" class=\"data row3 col18\" >0.25</td>\n",
-       "      <td id=\"T_20390_row3_col19\" class=\"data row3 col19\" >1.37</td>\n",
-       "      <td id=\"T_20390_row3_col20\" class=\"data row3 col20\" >46.00</td>\n",
-       "      <td id=\"T_20390_row3_col21\" class=\"data row3 col21\" >0.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row4\" class=\"row_heading level0 row4\" >St. Paul</th>\n",
-       "      <td id=\"T_20390_row4_col0\" class=\"data row4 col0\" >8,719.30</td>\n",
-       "      <td id=\"T_20390_row4_col1\" class=\"data row4 col1\" >100,552,279.06</td>\n",
-       "      <td id=\"T_20390_row4_col2\" class=\"data row4 col2\" >82,816,570.31</td>\n",
-       "      <td id=\"T_20390_row4_col3\" class=\"data row4 col3\" >382,090,503.45</td>\n",
-       "      <td id=\"T_20390_row4_col4\" class=\"data row4 col4\" >458,724,655.23</td>\n",
-       "      <td id=\"T_20390_row4_col5\" class=\"data row4 col5\" >0.26</td>\n",
-       "      <td id=\"T_20390_row4_col6\" class=\"data row4 col6\" >6,952.62</td>\n",
-       "      <td id=\"T_20390_row4_col7\" class=\"data row4 col7\" >14,179.57</td>\n",
-       "      <td id=\"T_20390_row4_col8\" class=\"data row4 col8\" >5,189.22</td>\n",
-       "      <td id=\"T_20390_row4_col9\" class=\"data row4 col9\" >99,840,454.14</td>\n",
-       "      <td id=\"T_20390_row4_col10\" class=\"data row4 col10\" >2,830.00</td>\n",
-       "      <td id=\"T_20390_row4_col11\" class=\"data row4 col11\" >2,213.00</td>\n",
-       "      <td id=\"T_20390_row4_col12\" class=\"data row4 col12\" >404.00</td>\n",
-       "      <td id=\"T_20390_row4_col13\" class=\"data row4 col13\" >33.32</td>\n",
-       "      <td id=\"T_20390_row4_col14\" class=\"data row4 col14\" >38.35</td>\n",
-       "      <td id=\"T_20390_row4_col15\" class=\"data row4 col15\" >34.27</td>\n",
-       "      <td id=\"T_20390_row4_col16\" class=\"data row4 col16\" >1,191.16</td>\n",
-       "      <td id=\"T_20390_row4_col17\" class=\"data row4 col17\" >8.88</td>\n",
-       "      <td id=\"T_20390_row4_col18\" class=\"data row4 col18\" >0.29</td>\n",
-       "      <td id=\"T_20390_row4_col19\" class=\"data row4 col19\" >1.91</td>\n",
-       "      <td id=\"T_20390_row4_col20\" class=\"data row4 col20\" >32.00</td>\n",
-       "      <td id=\"T_20390_row4_col21\" class=\"data row4 col21\" >4.63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row5\" class=\"row_heading level0 row5\" >St. Luke</th>\n",
-       "      <td id=\"T_20390_row5_col0\" class=\"data row5 col0\" >1,452.45</td>\n",
-       "      <td id=\"T_20390_row5_col1\" class=\"data row5 col1\" >11,150,543.03</td>\n",
-       "      <td id=\"T_20390_row5_col2\" class=\"data row5 col2\" >9,256,305.78</td>\n",
-       "      <td id=\"T_20390_row5_col3\" class=\"data row5 col3\" >41,700,575.95</td>\n",
-       "      <td id=\"T_20390_row5_col4\" class=\"data row5 col4\" >54,234,426.75</td>\n",
-       "      <td id=\"T_20390_row5_col5\" class=\"data row5 col5\" >0.27</td>\n",
-       "      <td id=\"T_20390_row5_col6\" class=\"data row5 col6\" >1,254.46</td>\n",
-       "      <td id=\"T_20390_row5_col7\" class=\"data row5 col7\" >9,442.01</td>\n",
-       "      <td id=\"T_20390_row5_col8\" class=\"data row5 col8\" >4,790.22</td>\n",
-       "      <td id=\"T_20390_row5_col9\" class=\"data row5 col9\" >11,075,506.75</td>\n",
-       "      <td id=\"T_20390_row5_col10\" class=\"data row5 col10\" >193.00</td>\n",
-       "      <td id=\"T_20390_row5_col11\" class=\"data row5 col11\" >152.00</td>\n",
-       "      <td id=\"T_20390_row5_col12\" class=\"data row5 col12\" >138.00</td>\n",
-       "      <td id=\"T_20390_row5_col13\" class=\"data row5 col13\" >41.77</td>\n",
-       "      <td id=\"T_20390_row5_col14\" class=\"data row5 col14\" >44.65</td>\n",
-       "      <td id=\"T_20390_row5_col15\" class=\"data row5 col15\" >29.44</td>\n",
-       "      <td id=\"T_20390_row5_col16\" class=\"data row5 col16\" >737.87</td>\n",
-       "      <td id=\"T_20390_row5_col17\" class=\"data row5 col17\" >7.68</td>\n",
-       "      <td id=\"T_20390_row5_col18\" class=\"data row5 col18\" >0.30</td>\n",
-       "      <td id=\"T_20390_row5_col19\" class=\"data row5 col19\" >1.45</td>\n",
-       "      <td id=\"T_20390_row5_col20\" class=\"data row5 col20\" >13.00</td>\n",
-       "      <td id=\"T_20390_row5_col21\" class=\"data row5 col21\" >9.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row6\" class=\"row_heading level0 row6\" >St. Mark</th>\n",
-       "      <td id=\"T_20390_row6_col0\" class=\"data row6 col0\" >2,114.61</td>\n",
-       "      <td id=\"T_20390_row6_col1\" class=\"data row6 col1\" >23,006,082.39</td>\n",
-       "      <td id=\"T_20390_row6_col2\" class=\"data row6 col2\" >18,722,434.89</td>\n",
-       "      <td id=\"T_20390_row6_col3\" class=\"data row6 col3\" >125,273,541.49</td>\n",
-       "      <td id=\"T_20390_row6_col4\" class=\"data row6 col4\" >92,369,121.62</td>\n",
-       "      <td id=\"T_20390_row6_col5\" class=\"data row6 col5\" >0.19</td>\n",
-       "      <td id=\"T_20390_row6_col6\" class=\"data row6 col6\" >1,173.51</td>\n",
-       "      <td id=\"T_20390_row6_col7\" class=\"data row6 col7\" >16,694.07</td>\n",
-       "      <td id=\"T_20390_row6_col8\" class=\"data row6 col8\" >8,449.30</td>\n",
-       "      <td id=\"T_20390_row6_col9\" class=\"data row6 col9\" >23,467,630.73</td>\n",
-       "      <td id=\"T_20390_row6_col10\" class=\"data row6 col10\" >539.00</td>\n",
-       "      <td id=\"T_20390_row6_col11\" class=\"data row6 col11\" >299.00</td>\n",
-       "      <td id=\"T_20390_row6_col12\" class=\"data row6 col12\" >65.00</td>\n",
-       "      <td id=\"T_20390_row6_col13\" class=\"data row6 col13\" >19.60</td>\n",
-       "      <td id=\"T_20390_row6_col14\" class=\"data row6 col14\" >23.21</td>\n",
-       "      <td id=\"T_20390_row6_col15\" class=\"data row6 col15\" >22.01</td>\n",
-       "      <td id=\"T_20390_row6_col16\" class=\"data row6 col16\" >1,595.43</td>\n",
-       "      <td id=\"T_20390_row6_col17\" class=\"data row6 col17\" >9.20</td>\n",
-       "      <td id=\"T_20390_row6_col18\" class=\"data row6 col18\" >0.28</td>\n",
-       "      <td id=\"T_20390_row6_col19\" class=\"data row6 col19\" >3.33</td>\n",
-       "      <td id=\"T_20390_row6_col20\" class=\"data row6 col20\" >25.00</td>\n",
-       "      <td id=\"T_20390_row6_col21\" class=\"data row6 col21\" >3.07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row7\" class=\"row_heading level0 row7\" >St. Patrick</th>\n",
-       "      <td id=\"T_20390_row7_col0\" class=\"data row7 col0\" >8,477.02</td>\n",
-       "      <td id=\"T_20390_row7_col1\" class=\"data row7 col1\" >37,588,837.18</td>\n",
-       "      <td id=\"T_20390_row7_col2\" class=\"data row7 col2\" >29,313,615.94</td>\n",
-       "      <td id=\"T_20390_row7_col3\" class=\"data row7 col3\" >149,203,255.36</td>\n",
-       "      <td id=\"T_20390_row7_col4\" class=\"data row7 col4\" >316,146,421.73</td>\n",
-       "      <td id=\"T_20390_row7_col5\" class=\"data row7 col5\" >0.25</td>\n",
-       "      <td id=\"T_20390_row7_col6\" class=\"data row7 col6\" >6,580.44</td>\n",
-       "      <td id=\"T_20390_row7_col7\" class=\"data row7 col7\" >4,641.74</td>\n",
-       "      <td id=\"T_20390_row7_col8\" class=\"data row7 col8\" >2,940.21</td>\n",
-       "      <td id=\"T_20390_row7_col9\" class=\"data row7 col9\" >36,831,137.43</td>\n",
-       "      <td id=\"T_20390_row7_col10\" class=\"data row7 col10\" >3,475.00</td>\n",
-       "      <td id=\"T_20390_row7_col11\" class=\"data row7 col11\" >2,787.00</td>\n",
-       "      <td id=\"T_20390_row7_col12\" class=\"data row7 col12\" >52.00</td>\n",
-       "      <td id=\"T_20390_row7_col13\" class=\"data row7 col13\" >31.86</td>\n",
-       "      <td id=\"T_20390_row7_col14\" class=\"data row7 col14\" >37.62</td>\n",
-       "      <td id=\"T_20390_row7_col15\" class=\"data row7 col15\" >37.16</td>\n",
-       "      <td id=\"T_20390_row7_col16\" class=\"data row7 col16\" >445.47</td>\n",
-       "      <td id=\"T_20390_row7_col17\" class=\"data row7 col17\" >9.96</td>\n",
-       "      <td id=\"T_20390_row7_col18\" class=\"data row7 col18\" >0.29</td>\n",
-       "      <td id=\"T_20390_row7_col19\" class=\"data row7 col19\" >0.47</td>\n",
-       "      <td id=\"T_20390_row7_col20\" class=\"data row7 col20\" >41.00</td>\n",
-       "      <td id=\"T_20390_row7_col21\" class=\"data row7 col21\" >0.61</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row8\" class=\"row_heading level0 row8\" >St. David</th>\n",
-       "      <td id=\"T_20390_row8_col0\" class=\"data row8 col0\" >7,023.69</td>\n",
-       "      <td id=\"T_20390_row8_col1\" class=\"data row8 col1\" >25,242,813.56</td>\n",
-       "      <td id=\"T_20390_row8_col2\" class=\"data row8 col2\" >18,332,923.77</td>\n",
-       "      <td id=\"T_20390_row8_col3\" class=\"data row8 col3\" >114,881,153.80</td>\n",
-       "      <td id=\"T_20390_row8_col4\" class=\"data row8 col4\" >434,305,654.83</td>\n",
-       "      <td id=\"T_20390_row8_col5\" class=\"data row8 col5\" >0.22</td>\n",
-       "      <td id=\"T_20390_row8_col6\" class=\"data row8 col6\" >4,399.91</td>\n",
-       "      <td id=\"T_20390_row8_col7\" class=\"data row8 col7\" >4,379.31</td>\n",
-       "      <td id=\"T_20390_row8_col8\" class=\"data row8 col8\" >1,647.94</td>\n",
-       "      <td id=\"T_20390_row8_col9\" class=\"data row8 col9\" >25,399,621.57</td>\n",
-       "      <td id=\"T_20390_row8_col10\" class=\"data row8 col10\" >2,823.00</td>\n",
-       "      <td id=\"T_20390_row8_col11\" class=\"data row8 col11\" >1,888.00</td>\n",
-       "      <td id=\"T_20390_row8_col12\" class=\"data row8 col12\" >132.00</td>\n",
-       "      <td id=\"T_20390_row8_col13\" class=\"data row8 col13\" >28.35</td>\n",
-       "      <td id=\"T_20390_row8_col14\" class=\"data row8 col14\" >33.26</td>\n",
-       "      <td id=\"T_20390_row8_col15\" class=\"data row8 col15\" >32.14</td>\n",
-       "      <td id=\"T_20390_row8_col16\" class=\"data row8 col16\" >416.67</td>\n",
-       "      <td id=\"T_20390_row8_col17\" class=\"data row8 col17\" >10.06</td>\n",
-       "      <td id=\"T_20390_row8_col18\" class=\"data row8 col18\" >0.27</td>\n",
-       "      <td id=\"T_20390_row8_col19\" class=\"data row8 col19\" >0.36</td>\n",
-       "      <td id=\"T_20390_row8_col20\" class=\"data row8 col20\" >40.00</td>\n",
-       "      <td id=\"T_20390_row8_col21\" class=\"data row8 col21\" >1.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_20390_level0_row9\" class=\"row_heading level0 row9\" >St. Andrew</th>\n",
-       "      <td id=\"T_20390_row9_col0\" class=\"data row9 col0\" >10,246.50</td>\n",
-       "      <td id=\"T_20390_row9_col1\" class=\"data row9 col1\" >48,436,781.86</td>\n",
-       "      <td id=\"T_20390_row9_col2\" class=\"data row9 col2\" >37,031,730.13</td>\n",
-       "      <td id=\"T_20390_row9_col3\" class=\"data row9 col3\" >219,165,119.94</td>\n",
-       "      <td id=\"T_20390_row9_col4\" class=\"data row9 col4\" >396,198,120.21</td>\n",
-       "      <td id=\"T_20390_row9_col5\" class=\"data row9 col5\" >0.22</td>\n",
-       "      <td id=\"T_20390_row9_col6\" class=\"data row9 col6\" >6,839.85</td>\n",
-       "      <td id=\"T_20390_row9_col7\" class=\"data row9 col7\" >5,888.36</td>\n",
-       "      <td id=\"T_20390_row9_col8\" class=\"data row9 col8\" >3,446.25</td>\n",
-       "      <td id=\"T_20390_row9_col9\" class=\"data row9 col9\" >49,195,946.58</td>\n",
-       "      <td id=\"T_20390_row9_col10\" class=\"data row9 col10\" >3,935.00</td>\n",
-       "      <td id=\"T_20390_row9_col11\" class=\"data row9 col11\" >2,866.00</td>\n",
-       "      <td id=\"T_20390_row9_col12\" class=\"data row9 col12\" >369.00</td>\n",
-       "      <td id=\"T_20390_row9_col13\" class=\"data row9 col13\" >39.45</td>\n",
-       "      <td id=\"T_20390_row9_col14\" class=\"data row9 col14\" >43.61</td>\n",
-       "      <td id=\"T_20390_row9_col15\" class=\"data row9 col15\" >40.42</td>\n",
-       "      <td id=\"T_20390_row9_col16\" class=\"data row9 col16\" >541.41</td>\n",
-       "      <td id=\"T_20390_row9_col17\" class=\"data row9 col17\" >9.75</td>\n",
-       "      <td id=\"T_20390_row9_col18\" class=\"data row9 col18\" >0.27</td>\n",
-       "      <td id=\"T_20390_row9_col19\" class=\"data row9 col19\" >0.60</td>\n",
-       "      <td id=\"T_20390_row9_col20\" class=\"data row9 col20\" >38.00</td>\n",
-       "      <td id=\"T_20390_row9_col21\" class=\"data row9 col21\" >3.60</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x19577b5baf0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Print all outcomes for a single scenario\n",
-    "scenario = 0\n",
-    "df = outcomes[outcomes['scenario'] == scenario].copy()\n",
-    "df.set_index('district', inplace=True)\n",
-    "# df.drop(columns=['weighted_vuln_quint', 'weighted_vuln_dec'], inplace=True)\n",
-    "numeric_columns = df.columns[3:-1].tolist()\n",
-    "df[numeric_columns].round(2).style.format(\"{:,.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "District:  St. George\n",
-      "Total population:  20445\n",
-      "Number of affected people:  11193\n",
-      "\n",
-      "Number of poor:  2845\n",
-      "Number of new poor:  798\n",
-      "Percentage of poor: 13.92%\n",
-      "Percentage of new poor: 17.82%\n",
-      "Percentage of increase in poor: 3.9%\n",
-      "Initial poverty gap:  22.52\n",
-      "New poverty gap initial poor:  25.24\n",
-      "New poverty gap all poor:  21.31\n",
-      "\n",
-      "Annual average consumption:  14785\n",
-      "Annual average consumption loss:  1398\n",
-      "Annual average consumption loss percentage: 9%\n",
-      "\n",
-      "Socio-economic resilience:  3.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's examine a single scenario in details\n",
-    "scenario = 0\n",
-    "df = outcomes[outcomes['scenario'] == scenario].copy()\n",
-    "df.set_index('district', inplace=True)\n",
-    "\n",
-    "# And a single district as an example\n",
-    "district = 'St. George'\n",
-    "print('District: ', district)\n",
-    "total_population = round(df.loc[district, 'total_population'])\n",
-    "n_affected = round(df.loc[district, 'n_affected_people'])\n",
-    "print('Total population: ', total_population)\n",
-    "print('Number of affected people: ', n_affected)\n",
-    "\n",
-    "n_poor_initial = round(df.loc[district, 'n_poor_initial'])\n",
-    "n_new_poor = round(df.loc[district, 'n_new_poor'])\n",
-    "pct_poor_initial = round(n_poor_initial / total_population * 100, 2)\n",
-    "pct_new_poor = round((n_new_poor + n_poor_initial) / total_population * 100, 2)\n",
-    "pct_poor_increase = round(n_new_poor / total_population * 100, 2)\n",
-    "initial_poverty_gap = round(df.loc[district, 'initial_poverty_gap'], 2)\n",
-    "new_poverty_gap_init = round(df.loc[district, 'new_poverty_gap_initial'], 2)\n",
-    "new_poverty_gap_all = round(df.loc[district, 'new_poverty_gap_all'], 2)\n",
-    "print()\n",
-    "print('Number of poor: ', n_poor_initial)\n",
-    "print('Number of new poor: ', n_new_poor)\n",
-    "print(f'Percentage of poor: {pct_poor_initial}%')\n",
-    "print(f'Percentage of new poor: {pct_new_poor}%')\n",
-    "print(f'Percentage of increase in poor: {pct_poor_increase}%')\n",
-    "print('Initial poverty gap: ', initial_poverty_gap)\n",
-    "print('New poverty gap initial poor: ', new_poverty_gap_init)\n",
-    "print('New poverty gap all poor: ', new_poverty_gap_all)\n",
-    "\n",
-    "annual_average_consumption = round(df.loc[district, 'annual_average_consumption'])\n",
-    "annual_average_consumption_loss = round(df.loc[district, 'annual_average_consumption_loss'])\n",
-    "annual_average_consumption_loss_pct = round(df.loc[district, 'annual_average_consumption_loss_pct'])\n",
-    "print()\n",
-    "\n",
-    "print('Annual average consumption: ', annual_average_consumption)\n",
-    "print('Annual average consumption loss: ', annual_average_consumption_loss)\n",
-    "print(f'Annual average consumption loss percentage: {annual_average_consumption_loss_pct}%')\n",
-    "\n",
-    "print()\n",
-    "r = round(df.loc[district, 'r'], 2)\n",
-    "print('Socio-economic resilience: ', r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 420x280 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "annotated_hist(outcomes, savefig=savefig, annotate=False)\n",
-    "plt.savefig(figures_folder + 'consumption_loss_hist.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # Print all outcomes for a single scenario\n",
-    "# district = 'St. George'\n",
-    "# df = outcomes[outcomes['district'] == district].copy()\n",
-    "# df.drop(columns=['years_in_poverty'], inplace=True)\n",
-    "# columns_of_interest = ['total_consumption_loss', \n",
-    "#                        'n_affected_people', 'annual_average_consumption', 'n_new_poor', 'n_poor_affected',\n",
-    "#                        'annual_average_consumption_loss', 'annual_average_consumption_loss_pct', 'r', 'n_new_poor_increase_pp']\n",
-    "# sns.pairplot(df[columns_of_interest])\n",
-    "# plt.tight_layout();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple scenarios & multiple districts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "district\n",
-       "St. Luke       0.862364\n",
-       "St. Paul       0.797155\n",
-       "St. Patrick    0.751153\n",
-       "St. Peter      0.709202\n",
-       "St. Joseph     0.703881\n",
-       "St. Andrew     0.657208\n",
-       "St. David      0.612849\n",
-       "St. George     0.604579\n",
-       "St. Mark       0.580329\n",
-       "St. John       0.572065\n",
-       "Name: fraction_affected, dtype: float64"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Let's aggregate across scenarios and take the mean\n",
-    "df = outcomes.iloc[:, 2:-1].groupby('district').mean(numeric_only=True).round(2)\n",
-    "df['fraction_affected'] = df['n_affected_people'] / df['total_population']\n",
-    "df['fraction_affected'].sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "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>total_population</th>\n",
-       "      <th>total_asset_loss</th>\n",
-       "      <th>total_consumption_loss</th>\n",
-       "      <th>tot_exposed_asset</th>\n",
-       "      <th>tot_asset_surv</th>\n",
-       "      <th>expected_loss_frac</th>\n",
-       "      <th>n_affected_people</th>\n",
-       "      <th>annual_average_consumption</th>\n",
-       "      <th>povline_adjusted</th>\n",
-       "      <th>district_pml</th>\n",
-       "      <th>...</th>\n",
-       "      <th>n_new_poor</th>\n",
-       "      <th>initial_poverty_gap</th>\n",
-       "      <th>new_poverty_gap_initial</th>\n",
-       "      <th>new_poverty_gap_all</th>\n",
-       "      <th>annual_average_consumption_loss</th>\n",
-       "      <th>annual_average_consumption_loss_pct</th>\n",
-       "      <th>mean_recovery_rate</th>\n",
-       "      <th>r</th>\n",
-       "      <th>n_poor_ratio</th>\n",
-       "      <th>n_new_poor_increase_pp</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>district</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>St. Peter</th>\n",
-       "      <td>1502.04</td>\n",
-       "      <td>1.896870e+07</td>\n",
-       "      <td>1.568461e+07</td>\n",
-       "      <td>8.191400e+07</td>\n",
-       "      <td>5.386925e+07</td>\n",
-       "      <td>0.23</td>\n",
-       "      <td>1065.25</td>\n",
-       "      <td>16389.16</td>\n",
-       "      <td>9473.39</td>\n",
-       "      <td>1.896144e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>21.88</td>\n",
-       "      <td>24.74</td>\n",
-       "      <td>28.04</td>\n",
-       "      <td>26.75</td>\n",
-       "      <td>1472.84</td>\n",
-       "      <td>8.52</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>3.62</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>1.46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. Mark</th>\n",
-       "      <td>2114.61</td>\n",
-       "      <td>2.347292e+07</td>\n",
-       "      <td>1.910611e+07</td>\n",
-       "      <td>1.252735e+08</td>\n",
-       "      <td>9.236912e+07</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>1227.17</td>\n",
-       "      <td>16694.07</td>\n",
-       "      <td>8449.30</td>\n",
-       "      <td>2.346763e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>57.84</td>\n",
-       "      <td>19.60</td>\n",
-       "      <td>23.27</td>\n",
-       "      <td>22.16</td>\n",
-       "      <td>1557.80</td>\n",
-       "      <td>9.20</td>\n",
-       "      <td>0.28</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. George</th>\n",
-       "      <td>20445.41</td>\n",
-       "      <td>1.960046e+08</td>\n",
-       "      <td>1.612865e+08</td>\n",
-       "      <td>1.002936e+09</td>\n",
-       "      <td>1.090939e+09</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>12360.87</td>\n",
-       "      <td>14784.79</td>\n",
-       "      <td>5727.44</td>\n",
-       "      <td>1.958704e+08</td>\n",
-       "      <td>...</td>\n",
-       "      <td>935.00</td>\n",
-       "      <td>22.52</td>\n",
-       "      <td>26.65</td>\n",
-       "      <td>21.92</td>\n",
-       "      <td>1306.28</td>\n",
-       "      <td>8.61</td>\n",
-       "      <td>0.31</td>\n",
-       "      <td>2.68</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>4.57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. Paul</th>\n",
-       "      <td>8719.30</td>\n",
-       "      <td>1.000014e+08</td>\n",
-       "      <td>8.174638e+07</td>\n",
-       "      <td>3.820905e+08</td>\n",
-       "      <td>4.587247e+08</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>6950.63</td>\n",
-       "      <td>14179.57</td>\n",
-       "      <td>5189.22</td>\n",
-       "      <td>9.984045e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>368.26</td>\n",
-       "      <td>33.32</td>\n",
-       "      <td>38.63</td>\n",
-       "      <td>34.79</td>\n",
-       "      <td>1176.81</td>\n",
-       "      <td>8.94</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>1.87</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4.22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. Luke</th>\n",
-       "      <td>1452.45</td>\n",
-       "      <td>1.108260e+07</td>\n",
-       "      <td>9.215979e+06</td>\n",
-       "      <td>4.170058e+07</td>\n",
-       "      <td>5.423443e+07</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>1252.54</td>\n",
-       "      <td>9442.01</td>\n",
-       "      <td>4790.22</td>\n",
-       "      <td>1.107551e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>122.69</td>\n",
-       "      <td>41.77</td>\n",
-       "      <td>44.82</td>\n",
-       "      <td>30.55</td>\n",
-       "      <td>735.88</td>\n",
-       "      <td>7.50</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>1.49</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. Joseph</th>\n",
-       "      <td>5891.05</td>\n",
-       "      <td>4.269013e+07</td>\n",
-       "      <td>3.348782e+07</td>\n",
-       "      <td>1.752688e+08</td>\n",
-       "      <td>1.727082e+08</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>4146.60</td>\n",
-       "      <td>9239.21</td>\n",
-       "      <td>6322.37</td>\n",
-       "      <td>4.268896e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>34.06</td>\n",
-       "      <td>32.92</td>\n",
-       "      <td>36.76</td>\n",
-       "      <td>36.36</td>\n",
-       "      <td>808.05</td>\n",
-       "      <td>8.38</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>1.47</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>0.58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. John</th>\n",
-       "      <td>5322.70</td>\n",
-       "      <td>3.072690e+07</td>\n",
-       "      <td>2.307840e+07</td>\n",
-       "      <td>1.469171e+08</td>\n",
-       "      <td>2.901959e+08</td>\n",
-       "      <td>0.21</td>\n",
-       "      <td>3044.93</td>\n",
-       "      <td>8118.78</td>\n",
-       "      <td>3154.05</td>\n",
-       "      <td>3.072113e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>125.09</td>\n",
-       "      <td>15.16</td>\n",
-       "      <td>19.39</td>\n",
-       "      <td>16.82</td>\n",
-       "      <td>758.56</td>\n",
-       "      <td>9.69</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>1.14</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2.35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. Andrew</th>\n",
-       "      <td>10246.50</td>\n",
-       "      <td>4.920227e+07</td>\n",
-       "      <td>3.775457e+07</td>\n",
-       "      <td>2.191651e+08</td>\n",
-       "      <td>3.961981e+08</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>6734.08</td>\n",
-       "      <td>5888.36</td>\n",
-       "      <td>3446.25</td>\n",
-       "      <td>4.919595e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>355.74</td>\n",
-       "      <td>39.45</td>\n",
-       "      <td>43.30</td>\n",
-       "      <td>40.17</td>\n",
-       "      <td>561.01</td>\n",
-       "      <td>9.74</td>\n",
-       "      <td>0.28</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>3.47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. Patrick</th>\n",
-       "      <td>8477.02</td>\n",
-       "      <td>3.685159e+07</td>\n",
-       "      <td>2.881078e+07</td>\n",
-       "      <td>1.492033e+08</td>\n",
-       "      <td>3.161464e+08</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>6367.54</td>\n",
-       "      <td>4641.74</td>\n",
-       "      <td>2940.21</td>\n",
-       "      <td>3.683114e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>48.80</td>\n",
-       "      <td>31.86</td>\n",
-       "      <td>37.26</td>\n",
-       "      <td>36.82</td>\n",
-       "      <td>452.65</td>\n",
-       "      <td>9.96</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.48</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>0.58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>St. David</th>\n",
-       "      <td>7023.69</td>\n",
-       "      <td>2.541074e+07</td>\n",
-       "      <td>1.852299e+07</td>\n",
-       "      <td>1.148812e+08</td>\n",
-       "      <td>4.343057e+08</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>4304.46</td>\n",
-       "      <td>4379.31</td>\n",
-       "      <td>1647.94</td>\n",
-       "      <td>2.539962e+07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>160.07</td>\n",
-       "      <td>28.35</td>\n",
-       "      <td>32.84</td>\n",
-       "      <td>31.52</td>\n",
-       "      <td>430.81</td>\n",
-       "      <td>9.97</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>2.28</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             total_population  total_asset_loss  total_consumption_loss  \\\n",
-       "district                                                                  \n",
-       "St. Peter             1502.04      1.896870e+07            1.568461e+07   \n",
-       "St. Mark              2114.61      2.347292e+07            1.910611e+07   \n",
-       "St. George           20445.41      1.960046e+08            1.612865e+08   \n",
-       "St. Paul              8719.30      1.000014e+08            8.174638e+07   \n",
-       "St. Luke              1452.45      1.108260e+07            9.215979e+06   \n",
-       "St. Joseph            5891.05      4.269013e+07            3.348782e+07   \n",
-       "St. John              5322.70      3.072690e+07            2.307840e+07   \n",
-       "St. Andrew           10246.50      4.920227e+07            3.775457e+07   \n",
-       "St. Patrick           8477.02      3.685159e+07            2.881078e+07   \n",
-       "St. David             7023.69      2.541074e+07            1.852299e+07   \n",
-       "\n",
-       "             tot_exposed_asset  tot_asset_surv  expected_loss_frac  \\\n",
-       "district                                                             \n",
-       "St. Peter         8.191400e+07    5.386925e+07                0.23   \n",
-       "St. Mark          1.252735e+08    9.236912e+07                0.19   \n",
-       "St. George        1.002936e+09    1.090939e+09                0.20   \n",
-       "St. Paul          3.820905e+08    4.587247e+08                0.26   \n",
-       "St. Luke          4.170058e+07    5.423443e+07                0.27   \n",
-       "St. Joseph        1.752688e+08    1.727082e+08                0.24   \n",
-       "St. John          1.469171e+08    2.901959e+08                0.21   \n",
-       "St. Andrew        2.191651e+08    3.961981e+08                0.22   \n",
-       "St. Patrick       1.492033e+08    3.161464e+08                0.25   \n",
-       "St. David         1.148812e+08    4.343057e+08                0.22   \n",
-       "\n",
-       "             n_affected_people  annual_average_consumption  povline_adjusted  \\\n",
-       "district                                                                       \n",
-       "St. Peter              1065.25                    16389.16           9473.39   \n",
-       "St. Mark               1227.17                    16694.07           8449.30   \n",
-       "St. George            12360.87                    14784.79           5727.44   \n",
-       "St. Paul               6950.63                    14179.57           5189.22   \n",
-       "St. Luke               1252.54                     9442.01           4790.22   \n",
-       "St. Joseph             4146.60                     9239.21           6322.37   \n",
-       "St. John               3044.93                     8118.78           3154.05   \n",
-       "St. Andrew             6734.08                     5888.36           3446.25   \n",
-       "St. Patrick            6367.54                     4641.74           2940.21   \n",
-       "St. David              4304.46                     4379.31           1647.94   \n",
-       "\n",
-       "             district_pml  ...  n_new_poor  initial_poverty_gap  \\\n",
-       "district                   ...                                    \n",
-       "St. Peter    1.896144e+07  ...       21.88                24.74   \n",
-       "St. Mark     2.346763e+07  ...       57.84                19.60   \n",
-       "St. George   1.958704e+08  ...      935.00                22.52   \n",
-       "St. Paul     9.984045e+07  ...      368.26                33.32   \n",
-       "St. Luke     1.107551e+07  ...      122.69                41.77   \n",
-       "St. Joseph   4.268896e+07  ...       34.06                32.92   \n",
-       "St. John     3.072113e+07  ...      125.09                15.16   \n",
-       "St. Andrew   4.919595e+07  ...      355.74                39.45   \n",
-       "St. Patrick  3.683114e+07  ...       48.80                31.86   \n",
-       "St. David    2.539962e+07  ...      160.07                28.35   \n",
-       "\n",
-       "             new_poverty_gap_initial  new_poverty_gap_all  \\\n",
-       "district                                                    \n",
-       "St. Peter                      28.04                26.75   \n",
-       "St. Mark                       23.27                22.16   \n",
-       "St. George                     26.65                21.92   \n",
-       "St. Paul                       38.63                34.79   \n",
-       "St. Luke                       44.82                30.55   \n",
-       "St. Joseph                     36.76                36.36   \n",
-       "St. John                       19.39                16.82   \n",
-       "St. Andrew                     43.30                40.17   \n",
-       "St. Patrick                    37.26                36.82   \n",
-       "St. David                      32.84                31.52   \n",
-       "\n",
-       "             annual_average_consumption_loss  \\\n",
-       "district                                       \n",
-       "St. Peter                            1472.84   \n",
-       "St. Mark                             1557.80   \n",
-       "St. George                           1306.28   \n",
-       "St. Paul                             1176.81   \n",
-       "St. Luke                              735.88   \n",
-       "St. Joseph                            808.05   \n",
-       "St. John                              758.56   \n",
-       "St. Andrew                            561.01   \n",
-       "St. Patrick                           452.65   \n",
-       "St. David                             430.81   \n",
-       "\n",
-       "             annual_average_consumption_loss_pct  mean_recovery_rate     r  \\\n",
-       "district                                                                     \n",
-       "St. Peter                                   8.52                0.29  3.62   \n",
-       "St. Mark                                    9.20                0.28  3.19   \n",
-       "St. George                                  8.61                0.31  2.68   \n",
-       "St. Paul                                    8.94                0.29  1.87   \n",
-       "St. Luke                                    7.50                0.30  1.49   \n",
-       "St. Joseph                                  8.38                0.25  1.47   \n",
-       "St. John                                    9.69                0.26  1.14   \n",
-       "St. Andrew                                  9.74                0.28  0.64   \n",
-       "St. Patrick                                 9.96                0.30  0.48   \n",
-       "St. David                                   9.97                0.27  0.38   \n",
-       "\n",
-       "             n_poor_ratio  n_new_poor_increase_pp  \n",
-       "district                                           \n",
-       "St. Peter            24.0                    1.46  \n",
-       "St. Mark             25.0                    2.74  \n",
-       "St. George           14.0                    4.57  \n",
-       "St. Paul             32.0                    4.22  \n",
-       "St. Luke             13.0                    8.45  \n",
-       "St. Joseph           46.0                    0.58  \n",
-       "St. John             11.0                    2.35  \n",
-       "St. Andrew           38.0                    3.47  \n",
-       "St. Patrick          41.0                    0.58  \n",
-       "St. David            40.0                    2.28  \n",
-       "\n",
-       "[10 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Let's aggregate across scenarios and take the mean\n",
-    "outcomes.iloc[:, 2:-1].groupby('district').mean(numeric_only=True).round(2).sort_values(by='r', ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 420x280 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "coloured_density_plots(outcomes, savefig, scheme='fisher_jenks', k=4)\n",
-    "plt.savefig(figures_folder + 'consumption_loss_density.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_6a32f\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_6a32f_level0_col0\" class=\"col_heading level0 col0\" colspan=\"7\">n_new_poor</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level1\" >&nbsp;</th>\n",
-       "      <th id=\"T_6a32f_level1_col0\" class=\"col_heading level1 col0\" >mean</th>\n",
-       "      <th id=\"T_6a32f_level1_col1\" class=\"col_heading level1 col1\" >std</th>\n",
-       "      <th id=\"T_6a32f_level1_col2\" class=\"col_heading level1 col2\" >min</th>\n",
-       "      <th id=\"T_6a32f_level1_col3\" class=\"col_heading level1 col3\" >25%</th>\n",
-       "      <th id=\"T_6a32f_level1_col4\" class=\"col_heading level1 col4\" >50%</th>\n",
-       "      <th id=\"T_6a32f_level1_col5\" class=\"col_heading level1 col5\" >75%</th>\n",
-       "      <th id=\"T_6a32f_level1_col6\" class=\"col_heading level1 col6\" >max</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >district</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row0\" class=\"row_heading level0 row0\" >St. George</th>\n",
-       "      <td id=\"T_6a32f_row0_col0\" class=\"data row0 col0\" >935.00</td>\n",
-       "      <td id=\"T_6a32f_row0_col1\" class=\"data row0 col1\" >180.32</td>\n",
-       "      <td id=\"T_6a32f_row0_col2\" class=\"data row0 col2\" >316.00</td>\n",
-       "      <td id=\"T_6a32f_row0_col3\" class=\"data row0 col3\" >818.00</td>\n",
-       "      <td id=\"T_6a32f_row0_col4\" class=\"data row0 col4\" >940.00</td>\n",
-       "      <td id=\"T_6a32f_row0_col5\" class=\"data row0 col5\" >1,060.00</td>\n",
-       "      <td id=\"T_6a32f_row0_col6\" class=\"data row0 col6\" >1,450.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row1\" class=\"row_heading level0 row1\" >St. Paul</th>\n",
-       "      <td id=\"T_6a32f_row1_col0\" class=\"data row1 col0\" >368.26</td>\n",
-       "      <td id=\"T_6a32f_row1_col1\" class=\"data row1 col1\" >45.18</td>\n",
-       "      <td id=\"T_6a32f_row1_col2\" class=\"data row1 col2\" >163.00</td>\n",
-       "      <td id=\"T_6a32f_row1_col3\" class=\"data row1 col3\" >339.00</td>\n",
-       "      <td id=\"T_6a32f_row1_col4\" class=\"data row1 col4\" >371.00</td>\n",
-       "      <td id=\"T_6a32f_row1_col5\" class=\"data row1 col5\" >400.00</td>\n",
-       "      <td id=\"T_6a32f_row1_col6\" class=\"data row1 col6\" >461.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row2\" class=\"row_heading level0 row2\" >St. Andrew</th>\n",
-       "      <td id=\"T_6a32f_row2_col0\" class=\"data row2 col0\" >355.74</td>\n",
-       "      <td id=\"T_6a32f_row2_col1\" class=\"data row2 col1\" >63.04</td>\n",
-       "      <td id=\"T_6a32f_row2_col2\" class=\"data row2 col2\" >103.00</td>\n",
-       "      <td id=\"T_6a32f_row2_col3\" class=\"data row2 col3\" >312.00</td>\n",
-       "      <td id=\"T_6a32f_row2_col4\" class=\"data row2 col4\" >358.00</td>\n",
-       "      <td id=\"T_6a32f_row2_col5\" class=\"data row2 col5\" >400.00</td>\n",
-       "      <td id=\"T_6a32f_row2_col6\" class=\"data row2 col6\" >518.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row3\" class=\"row_heading level0 row3\" >St. David</th>\n",
-       "      <td id=\"T_6a32f_row3_col0\" class=\"data row3 col0\" >160.07</td>\n",
-       "      <td id=\"T_6a32f_row3_col1\" class=\"data row3 col1\" >35.03</td>\n",
-       "      <td id=\"T_6a32f_row3_col2\" class=\"data row3 col2\" >39.00</td>\n",
-       "      <td id=\"T_6a32f_row3_col3\" class=\"data row3 col3\" >137.00</td>\n",
-       "      <td id=\"T_6a32f_row3_col4\" class=\"data row3 col4\" >161.00</td>\n",
-       "      <td id=\"T_6a32f_row3_col5\" class=\"data row3 col5\" >185.00</td>\n",
-       "      <td id=\"T_6a32f_row3_col6\" class=\"data row3 col6\" >247.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row4\" class=\"row_heading level0 row4\" >St. John</th>\n",
-       "      <td id=\"T_6a32f_row4_col0\" class=\"data row4 col0\" >125.09</td>\n",
-       "      <td id=\"T_6a32f_row4_col1\" class=\"data row4 col1\" >28.26</td>\n",
-       "      <td id=\"T_6a32f_row4_col2\" class=\"data row4 col2\" >33.00</td>\n",
-       "      <td id=\"T_6a32f_row4_col3\" class=\"data row4 col3\" >106.00</td>\n",
-       "      <td id=\"T_6a32f_row4_col4\" class=\"data row4 col4\" >125.00</td>\n",
-       "      <td id=\"T_6a32f_row4_col5\" class=\"data row4 col5\" >144.00</td>\n",
-       "      <td id=\"T_6a32f_row4_col6\" class=\"data row4 col6\" >209.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row5\" class=\"row_heading level0 row5\" >St. Luke</th>\n",
-       "      <td id=\"T_6a32f_row5_col0\" class=\"data row5 col0\" >122.69</td>\n",
-       "      <td id=\"T_6a32f_row5_col1\" class=\"data row5 col1\" >9.35</td>\n",
-       "      <td id=\"T_6a32f_row5_col2\" class=\"data row5 col2\" >82.00</td>\n",
-       "      <td id=\"T_6a32f_row5_col3\" class=\"data row5 col3\" >116.00</td>\n",
-       "      <td id=\"T_6a32f_row5_col4\" class=\"data row5 col4\" >124.00</td>\n",
-       "      <td id=\"T_6a32f_row5_col5\" class=\"data row5 col5\" >130.00</td>\n",
-       "      <td id=\"T_6a32f_row5_col6\" class=\"data row5 col6\" >142.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row6\" class=\"row_heading level0 row6\" >St. Mark</th>\n",
-       "      <td id=\"T_6a32f_row6_col0\" class=\"data row6 col0\" >57.84</td>\n",
-       "      <td id=\"T_6a32f_row6_col1\" class=\"data row6 col1\" >11.08</td>\n",
-       "      <td id=\"T_6a32f_row6_col2\" class=\"data row6 col2\" >23.00</td>\n",
-       "      <td id=\"T_6a32f_row6_col3\" class=\"data row6 col3\" >50.00</td>\n",
-       "      <td id=\"T_6a32f_row6_col4\" class=\"data row6 col4\" >58.00</td>\n",
-       "      <td id=\"T_6a32f_row6_col5\" class=\"data row6 col5\" >66.00</td>\n",
-       "      <td id=\"T_6a32f_row6_col6\" class=\"data row6 col6\" >94.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row7\" class=\"row_heading level0 row7\" >St. Patrick</th>\n",
-       "      <td id=\"T_6a32f_row7_col0\" class=\"data row7 col0\" >48.80</td>\n",
-       "      <td id=\"T_6a32f_row7_col1\" class=\"data row7 col1\" >9.37</td>\n",
-       "      <td id=\"T_6a32f_row7_col2\" class=\"data row7 col2\" >13.00</td>\n",
-       "      <td id=\"T_6a32f_row7_col3\" class=\"data row7 col3\" >42.00</td>\n",
-       "      <td id=\"T_6a32f_row7_col4\" class=\"data row7 col4\" >50.00</td>\n",
-       "      <td id=\"T_6a32f_row7_col5\" class=\"data row7 col5\" >57.00</td>\n",
-       "      <td id=\"T_6a32f_row7_col6\" class=\"data row7 col6\" >65.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row8\" class=\"row_heading level0 row8\" >St. Joseph</th>\n",
-       "      <td id=\"T_6a32f_row8_col0\" class=\"data row8 col0\" >34.06</td>\n",
-       "      <td id=\"T_6a32f_row8_col1\" class=\"data row8 col1\" >9.94</td>\n",
-       "      <td id=\"T_6a32f_row8_col2\" class=\"data row8 col2\" >0.00</td>\n",
-       "      <td id=\"T_6a32f_row8_col3\" class=\"data row8 col3\" >29.00</td>\n",
-       "      <td id=\"T_6a32f_row8_col4\" class=\"data row8 col4\" >39.00</td>\n",
-       "      <td id=\"T_6a32f_row8_col5\" class=\"data row8 col5\" >39.00</td>\n",
-       "      <td id=\"T_6a32f_row8_col6\" class=\"data row8 col6\" >48.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6a32f_level0_row9\" class=\"row_heading level0 row9\" >St. Peter</th>\n",
-       "      <td id=\"T_6a32f_row9_col0\" class=\"data row9 col0\" >21.88</td>\n",
-       "      <td id=\"T_6a32f_row9_col1\" class=\"data row9 col1\" >2.60</td>\n",
-       "      <td id=\"T_6a32f_row9_col2\" class=\"data row9 col2\" >12.00</td>\n",
-       "      <td id=\"T_6a32f_row9_col3\" class=\"data row9 col3\" >20.00</td>\n",
-       "      <td id=\"T_6a32f_row9_col4\" class=\"data row9 col4\" >22.00</td>\n",
-       "      <td id=\"T_6a32f_row9_col5\" class=\"data row9 col5\" >24.00</td>\n",
-       "      <td id=\"T_6a32f_row9_col6\" class=\"data row9 col6\" >30.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1597c7492b0>"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tab = outcomes.iloc[:, 2:-1].groupby('district').describe().round(2)\n",
-    "column = 'n_new_poor'\n",
-    "tab = tab.sort_values(by=(column, 'mean'), ascending=False)\n",
-    "tab[[column]].iloc[:,1:].style.format(\"{:,.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weeks in poverty & Number of new poor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "average_years_in_poverty = {}\n",
-    "for district in outcomes['district'].unique():\n",
-    "    df = outcomes[outcomes['district'] == district].copy()\n",
-    "    total_years_in_poverty = {}\n",
-    "    for d in df['years_in_poverty']:\n",
-    "        for key, value in d.items():\n",
-    "            total_years_in_poverty[key] = total_years_in_poverty.get(key, 0) + value\n",
-    "    average_years_in_poverty[district] = {k: v / len(df) for k, v in total_years_in_poverty.items()}\n",
-    "average_years_in_poverty = pd.DataFrame(average_years_in_poverty).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "average_years_in_poverty = get_weeks_in_poverty_tab(outcomes)\n",
-    "\n",
-    "if savetab:\n",
-    "    # average_years_in_poverty.rename({'Anse-La-Raye & Canaries': 'Anse-La-Raye \\& Canaries'}, inplace=True, axis=0)\n",
-    "    average_years_in_poverty.astype(int).style\\\n",
-    "        .background_gradient(cmap='Blues', axis=0)\\\n",
-    "        .set_caption('Average Years in Poverty By District')\\\n",
-    "        .to_latex(tables_folder + 'av_years_in_pov.tex',\n",
-    "                  # escape=False,\n",
-    "                  caption='Average Years in Poverty By District',\n",
-    "                  label='tab:av_years_in_pov',\n",
-    "                  convert_css=True,\n",
-    "                  multicol_align=\"|c|\",\n",
-    "                  hrules=True,\n",
-    "                  position_float=\"centering\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of people in poverty for 1 / 3 / 5 years"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "District\n",
-       "St. George     935.354451\n",
-       "St. John       125.142714\n",
-       "St. Peter       21.675225\n",
-       "St. Joseph      34.059353\n",
-       "St. Paul       368.672891\n",
-       "St. Luke       122.515839\n",
-       "St. Mark        57.563188\n",
-       "St. Patrick     48.288429\n",
-       "St. David      159.967656\n",
-       "St. Andrew     355.672558\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# More than 1 year in poverty\n",
-    "n_years = 1\n",
-    "average_years_in_poverty.iloc[:,n_years:].sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "District\n",
-       "St. George     935.354451\n",
-       "St. John       125.142714\n",
-       "St. Peter       21.675225\n",
-       "St. Joseph      34.059353\n",
-       "St. Paul       368.672891\n",
-       "St. Luke       122.515839\n",
-       "St. Mark        57.563188\n",
-       "St. Patrick     48.288429\n",
-       "St. David      159.967656\n",
-       "St. Andrew     355.672558\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# More than 3 years in poverty\n",
-    "n_years = 3\n",
-    "average_years_in_poverty.iloc[:,n_years:].sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "District\n",
-       "St. George     379.269423\n",
-       "St. John        53.883961\n",
-       "St. Peter       11.051017\n",
-       "St. Joseph      34.059353\n",
-       "St. Paul       159.033678\n",
-       "St. Luke       113.694231\n",
-       "St. Mark        49.417472\n",
-       "St. Patrick     24.010670\n",
-       "St. David      159.967656\n",
-       "St. Andrew     161.071024\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# More than 5 years in poverty\n",
-    "n_years = 5\n",
-    "average_years_in_poverty.iloc[:,n_years:].sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  background-color: #edf4fc;\n",
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-       "  background-color: #c7dcef;\n",
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-       "}\n",
-       "#T_59264_row4_col6 {\n",
-       "  background-color: #a4cce3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row4_col7 {\n",
-       "  background-color: #65aad4;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_59264_row5_col4 {\n",
-       "  background-color: #f4f9fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row5_col5 {\n",
-       "  background-color: #9fcae1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row5_col6 {\n",
-       "  background-color: #b8d5ea;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row5_col7 {\n",
-       "  background-color: #e7f0fa;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row6_col3, #T_59264_row9_col4 {\n",
-       "  background-color: #f0f6fd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row6_col8 {\n",
-       "  background-color: #2c7cba;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_59264_row7_col6 {\n",
-       "  background-color: #d6e6f4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row8_col6 {\n",
-       "  background-color: #ddeaf7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_59264_row9_col3 {\n",
-       "  background-color: #0c56a0;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_59264_row9_col7 {\n",
-       "  background-color: #e9f2fa;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_59264\">\n",
-       "  <caption>Average Years in Poverty By District</caption>\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_59264_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
-       "      <th id=\"T_59264_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
-       "      <th id=\"T_59264_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
-       "      <th id=\"T_59264_level0_col3\" class=\"col_heading level0 col3\" >3</th>\n",
-       "      <th id=\"T_59264_level0_col4\" class=\"col_heading level0 col4\" >4</th>\n",
-       "      <th id=\"T_59264_level0_col5\" class=\"col_heading level0 col5\" >5</th>\n",
-       "      <th id=\"T_59264_level0_col6\" class=\"col_heading level0 col6\" >6</th>\n",
-       "      <th id=\"T_59264_level0_col7\" class=\"col_heading level0 col7\" >7</th>\n",
-       "      <th id=\"T_59264_level0_col8\" class=\"col_heading level0 col8\" >8</th>\n",
-       "      <th id=\"T_59264_level0_col9\" class=\"col_heading level0 col9\" >9</th>\n",
-       "      <th id=\"T_59264_level0_col10\" class=\"col_heading level0 col10\" >10</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >District</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "      <th class=\"blank col9\" >&nbsp;</th>\n",
-       "      <th class=\"blank col10\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row0\" class=\"row_heading level0 row0\" >St. George</th>\n",
-       "      <td id=\"T_59264_row0_col0\" class=\"data row0 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row0_col1\" class=\"data row0 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row0_col2\" class=\"data row0 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row0_col3\" class=\"data row0 col3\" >110</td>\n",
-       "      <td id=\"T_59264_row0_col4\" class=\"data row0 col4\" >445</td>\n",
-       "      <td id=\"T_59264_row0_col5\" class=\"data row0 col5\" >51</td>\n",
-       "      <td id=\"T_59264_row0_col6\" class=\"data row0 col6\" >17</td>\n",
-       "      <td id=\"T_59264_row0_col7\" class=\"data row0 col7\" >205</td>\n",
-       "      <td id=\"T_59264_row0_col8\" class=\"data row0 col8\" >69</td>\n",
-       "      <td id=\"T_59264_row0_col9\" class=\"data row0 col9\" >35</td>\n",
-       "      <td id=\"T_59264_row0_col10\" class=\"data row0 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row1\" class=\"row_heading level0 row1\" >St. John</th>\n",
-       "      <td id=\"T_59264_row1_col0\" class=\"data row1 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row1_col1\" class=\"data row1 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row1_col3\" class=\"data row1 col3\" >71</td>\n",
-       "      <td id=\"T_59264_row1_col4\" class=\"data row1 col4\" >0</td>\n",
-       "      <td id=\"T_59264_row1_col5\" class=\"data row1 col5\" >0</td>\n",
-       "      <td id=\"T_59264_row1_col6\" class=\"data row1 col6\" >35</td>\n",
-       "      <td id=\"T_59264_row1_col7\" class=\"data row1 col7\" >18</td>\n",
-       "      <td id=\"T_59264_row1_col8\" class=\"data row1 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row1_col9\" class=\"data row1 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row1_col10\" class=\"data row1 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row2\" class=\"row_heading level0 row2\" >St. Peter</th>\n",
-       "      <td id=\"T_59264_row2_col0\" class=\"data row2 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col1\" class=\"data row2 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col2\" class=\"data row2 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col3\" class=\"data row2 col3\" >10</td>\n",
-       "      <td id=\"T_59264_row2_col4\" class=\"data row2 col4\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col5\" class=\"data row2 col5\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col6\" class=\"data row2 col6\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col7\" class=\"data row2 col7\" >11</td>\n",
-       "      <td id=\"T_59264_row2_col8\" class=\"data row2 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col9\" class=\"data row2 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row2_col10\" class=\"data row2 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row3\" class=\"row_heading level0 row3\" >St. Joseph</th>\n",
-       "      <td id=\"T_59264_row3_col0\" class=\"data row3 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col1\" class=\"data row3 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col2\" class=\"data row3 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col3\" class=\"data row3 col3\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col4\" class=\"data row3 col4\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col5\" class=\"data row3 col5\" >34</td>\n",
-       "      <td id=\"T_59264_row3_col6\" class=\"data row3 col6\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col7\" class=\"data row3 col7\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col8\" class=\"data row3 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col9\" class=\"data row3 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row3_col10\" class=\"data row3 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row4\" class=\"row_heading level0 row4\" >St. Paul</th>\n",
-       "      <td id=\"T_59264_row4_col0\" class=\"data row4 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row4_col1\" class=\"data row4 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row4_col2\" class=\"data row4 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row4_col3\" class=\"data row4 col3\" >209</td>\n",
-       "      <td id=\"T_59264_row4_col4\" class=\"data row4 col4\" >0</td>\n",
-       "      <td id=\"T_59264_row4_col5\" class=\"data row4 col5\" >0</td>\n",
-       "      <td id=\"T_59264_row4_col6\" class=\"data row4 col6\" >52</td>\n",
-       "      <td id=\"T_59264_row4_col7\" class=\"data row4 col7\" >106</td>\n",
-       "      <td id=\"T_59264_row4_col8\" class=\"data row4 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row4_col9\" class=\"data row4 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row4_col10\" class=\"data row4 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row5\" class=\"row_heading level0 row5\" >St. Luke</th>\n",
-       "      <td id=\"T_59264_row5_col0\" class=\"data row5 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row5_col1\" class=\"data row5 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row5_col2\" class=\"data row5 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row5_col3\" class=\"data row5 col3\" >0</td>\n",
-       "      <td id=\"T_59264_row5_col4\" class=\"data row5 col4\" >8</td>\n",
-       "      <td id=\"T_59264_row5_col5\" class=\"data row5 col5\" >52</td>\n",
-       "      <td id=\"T_59264_row5_col6\" class=\"data row5 col6\" >43</td>\n",
-       "      <td id=\"T_59264_row5_col7\" class=\"data row5 col7\" >17</td>\n",
-       "      <td id=\"T_59264_row5_col8\" class=\"data row5 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row5_col9\" class=\"data row5 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row5_col10\" class=\"data row5 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row6\" class=\"row_heading level0 row6\" >St. Mark</th>\n",
-       "      <td id=\"T_59264_row6_col0\" class=\"data row6 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col1\" class=\"data row6 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col2\" class=\"data row6 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col3\" class=\"data row6 col3\" >8</td>\n",
-       "      <td id=\"T_59264_row6_col4\" class=\"data row6 col4\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col5\" class=\"data row6 col5\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col6\" class=\"data row6 col6\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col7\" class=\"data row6 col7\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col8\" class=\"data row6 col8\" >49</td>\n",
-       "      <td id=\"T_59264_row6_col9\" class=\"data row6 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row6_col10\" class=\"data row6 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row7\" class=\"row_heading level0 row7\" >St. Patrick</th>\n",
-       "      <td id=\"T_59264_row7_col0\" class=\"data row7 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col1\" class=\"data row7 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col2\" class=\"data row7 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col3\" class=\"data row7 col3\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col4\" class=\"data row7 col4\" >24</td>\n",
-       "      <td id=\"T_59264_row7_col5\" class=\"data row7 col5\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col6\" class=\"data row7 col6\" >24</td>\n",
-       "      <td id=\"T_59264_row7_col7\" class=\"data row7 col7\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col8\" class=\"data row7 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col9\" class=\"data row7 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row7_col10\" class=\"data row7 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row8\" class=\"row_heading level0 row8\" >St. David</th>\n",
-       "      <td id=\"T_59264_row8_col0\" class=\"data row8 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col1\" class=\"data row8 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col2\" class=\"data row8 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col3\" class=\"data row8 col3\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col4\" class=\"data row8 col4\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col5\" class=\"data row8 col5\" >140</td>\n",
-       "      <td id=\"T_59264_row8_col6\" class=\"data row8 col6\" >19</td>\n",
-       "      <td id=\"T_59264_row8_col7\" class=\"data row8 col7\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col8\" class=\"data row8 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col9\" class=\"data row8 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row8_col10\" class=\"data row8 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_59264_level0_row9\" class=\"row_heading level0 row9\" >St. Andrew</th>\n",
-       "      <td id=\"T_59264_row9_col0\" class=\"data row9 col0\" >0</td>\n",
-       "      <td id=\"T_59264_row9_col1\" class=\"data row9 col1\" >0</td>\n",
-       "      <td id=\"T_59264_row9_col2\" class=\"data row9 col2\" >0</td>\n",
-       "      <td id=\"T_59264_row9_col3\" class=\"data row9 col3\" >178</td>\n",
-       "      <td id=\"T_59264_row9_col4\" class=\"data row9 col4\" >16</td>\n",
-       "      <td id=\"T_59264_row9_col5\" class=\"data row9 col5\" >0</td>\n",
-       "      <td id=\"T_59264_row9_col6\" class=\"data row9 col6\" >145</td>\n",
-       "      <td id=\"T_59264_row9_col7\" class=\"data row9 col7\" >15</td>\n",
-       "      <td id=\"T_59264_row9_col8\" class=\"data row9 col8\" >0</td>\n",
-       "      <td id=\"T_59264_row9_col9\" class=\"data row9 col9\" >0</td>\n",
-       "      <td id=\"T_59264_row9_col10\" class=\"data row9 col10\" >0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x234fe3a9880>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_years_in_poverty.astype(int).style\\\n",
-    "        .background_gradient(cmap='Blues', axis=0)\\\n",
-    "        .set_caption('Average Years in Poverty By District')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the number of new poor in each district\n",
-    "districts = outcomes['district'].unique().tolist()\n",
-    "n_districts = len(districts)\n",
-    "d = {}\n",
-    "for district in districts:\n",
-    "    df = outcomes[outcomes['district'] == district]\n",
-    "    d[district] = df['n_new_poor'].values.tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "}\n",
-       "#T_af8b6_row4_col0, #T_af8b6_row5_col0 {\n",
-       "  background-color: #e1edf8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row4_col1 {\n",
-       "  background-color: #fedbcc;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row4_col2 {\n",
-       "  background-color: #fee9d3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row4_col3 {\n",
-       "  background-color: #e8f6e3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row4_col4, #T_af8b6_row5_col4 {\n",
-       "  background-color: #e7f6e3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row4_col5 {\n",
-       "  background-color: #e7f6e2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row4_col6 {\n",
-       "  background-color: #efedf5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row5_col1, #T_af8b6_row7_col1 {\n",
-       "  background-color: #ffefe8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row5_col2 {\n",
-       "  background-color: #fdce9e;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row5_col3 {\n",
-       "  background-color: #e6f5e1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row5_col5 {\n",
-       "  background-color: #e8f6e4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row5_col6 {\n",
-       "  background-color: #f4f2f8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row6_col0 {\n",
-       "  background-color: #eff6fc;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row6_col1 {\n",
-       "  background-color: #ffede5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row6_col2 {\n",
-       "  background-color: #feeddb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row6_col3 {\n",
-       "  background-color: #f2faef;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row6_col4, #T_af8b6_row6_col5 {\n",
-       "  background-color: #f1faee;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row6_col6 {\n",
-       "  background-color: #f8f6fa;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row7_col0 {\n",
-       "  background-color: #f2f7fd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row7_col2 {\n",
-       "  background-color: #fff0e2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row7_col3, #T_af8b6_row7_col4 {\n",
-       "  background-color: #f3faf0;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row7_col5 {\n",
-       "  background-color: #f2faf0;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row7_col6 {\n",
-       "  background-color: #faf8fb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row8_col0 {\n",
-       "  background-color: #f5f9fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row8_col1 {\n",
-       "  background-color: #ffeee7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row8_col2 {\n",
-       "  background-color: #fff5eb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row8_col3 {\n",
-       "  background-color: #f6fcf4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row8_col4 {\n",
-       "  background-color: #f5fbf2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row8_col5 {\n",
-       "  background-color: #f5fbf3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row8_col6 {\n",
-       "  background-color: #fbfafc;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row9_col0 {\n",
-       "  background-color: #f7fbff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row9_col1 {\n",
-       "  background-color: #fff5f0;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row9_col2 {\n",
-       "  background-color: #fff1e3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row9_col3, #T_af8b6_row9_col4, #T_af8b6_row9_col5 {\n",
-       "  background-color: #f7fcf5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_af8b6_row9_col6 {\n",
-       "  background-color: #fcfbfd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_af8b6\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_af8b6_level0_col0\" class=\"col_heading level0 col0\" >mean</th>\n",
-       "      <th id=\"T_af8b6_level0_col1\" class=\"col_heading level0 col1\" >std</th>\n",
-       "      <th id=\"T_af8b6_level0_col2\" class=\"col_heading level0 col2\" >min</th>\n",
-       "      <th id=\"T_af8b6_level0_col3\" class=\"col_heading level0 col3\" >25%</th>\n",
-       "      <th id=\"T_af8b6_level0_col4\" class=\"col_heading level0 col4\" >50%</th>\n",
-       "      <th id=\"T_af8b6_level0_col5\" class=\"col_heading level0 col5\" >75%</th>\n",
-       "      <th id=\"T_af8b6_level0_col6\" class=\"col_heading level0 col6\" >max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row0\" class=\"row_heading level0 row0\" >St. George</th>\n",
-       "      <td id=\"T_af8b6_row0_col0\" class=\"data row0 col0\" >935</td>\n",
-       "      <td id=\"T_af8b6_row0_col1\" class=\"data row0 col1\" >180</td>\n",
-       "      <td id=\"T_af8b6_row0_col2\" class=\"data row0 col2\" >316</td>\n",
-       "      <td id=\"T_af8b6_row0_col3\" class=\"data row0 col3\" >818</td>\n",
-       "      <td id=\"T_af8b6_row0_col4\" class=\"data row0 col4\" >940</td>\n",
-       "      <td id=\"T_af8b6_row0_col5\" class=\"data row0 col5\" >1,060</td>\n",
-       "      <td id=\"T_af8b6_row0_col6\" class=\"data row0 col6\" >1,450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row1\" class=\"row_heading level0 row1\" >St. Paul</th>\n",
-       "      <td id=\"T_af8b6_row1_col0\" class=\"data row1 col0\" >368</td>\n",
-       "      <td id=\"T_af8b6_row1_col1\" class=\"data row1 col1\" >45</td>\n",
-       "      <td id=\"T_af8b6_row1_col2\" class=\"data row1 col2\" >163</td>\n",
-       "      <td id=\"T_af8b6_row1_col3\" class=\"data row1 col3\" >339</td>\n",
-       "      <td id=\"T_af8b6_row1_col4\" class=\"data row1 col4\" >371</td>\n",
-       "      <td id=\"T_af8b6_row1_col5\" class=\"data row1 col5\" >400</td>\n",
-       "      <td id=\"T_af8b6_row1_col6\" class=\"data row1 col6\" >461</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row2\" class=\"row_heading level0 row2\" >St. Andrew</th>\n",
-       "      <td id=\"T_af8b6_row2_col0\" class=\"data row2 col0\" >356</td>\n",
-       "      <td id=\"T_af8b6_row2_col1\" class=\"data row2 col1\" >63</td>\n",
-       "      <td id=\"T_af8b6_row2_col2\" class=\"data row2 col2\" >103</td>\n",
-       "      <td id=\"T_af8b6_row2_col3\" class=\"data row2 col3\" >312</td>\n",
-       "      <td id=\"T_af8b6_row2_col4\" class=\"data row2 col4\" >358</td>\n",
-       "      <td id=\"T_af8b6_row2_col5\" class=\"data row2 col5\" >400</td>\n",
-       "      <td id=\"T_af8b6_row2_col6\" class=\"data row2 col6\" >518</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row3\" class=\"row_heading level0 row3\" >St. David</th>\n",
-       "      <td id=\"T_af8b6_row3_col0\" class=\"data row3 col0\" >160</td>\n",
-       "      <td id=\"T_af8b6_row3_col1\" class=\"data row3 col1\" >35</td>\n",
-       "      <td id=\"T_af8b6_row3_col2\" class=\"data row3 col2\" >39</td>\n",
-       "      <td id=\"T_af8b6_row3_col3\" class=\"data row3 col3\" >137</td>\n",
-       "      <td id=\"T_af8b6_row3_col4\" class=\"data row3 col4\" >161</td>\n",
-       "      <td id=\"T_af8b6_row3_col5\" class=\"data row3 col5\" >185</td>\n",
-       "      <td id=\"T_af8b6_row3_col6\" class=\"data row3 col6\" >247</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row4\" class=\"row_heading level0 row4\" >St. John</th>\n",
-       "      <td id=\"T_af8b6_row4_col0\" class=\"data row4 col0\" >125</td>\n",
-       "      <td id=\"T_af8b6_row4_col1\" class=\"data row4 col1\" >28</td>\n",
-       "      <td id=\"T_af8b6_row4_col2\" class=\"data row4 col2\" >33</td>\n",
-       "      <td id=\"T_af8b6_row4_col3\" class=\"data row4 col3\" >106</td>\n",
-       "      <td id=\"T_af8b6_row4_col4\" class=\"data row4 col4\" >125</td>\n",
-       "      <td id=\"T_af8b6_row4_col5\" class=\"data row4 col5\" >144</td>\n",
-       "      <td id=\"T_af8b6_row4_col6\" class=\"data row4 col6\" >209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row5\" class=\"row_heading level0 row5\" >St. Luke</th>\n",
-       "      <td id=\"T_af8b6_row5_col0\" class=\"data row5 col0\" >123</td>\n",
-       "      <td id=\"T_af8b6_row5_col1\" class=\"data row5 col1\" >9</td>\n",
-       "      <td id=\"T_af8b6_row5_col2\" class=\"data row5 col2\" >82</td>\n",
-       "      <td id=\"T_af8b6_row5_col3\" class=\"data row5 col3\" >116</td>\n",
-       "      <td id=\"T_af8b6_row5_col4\" class=\"data row5 col4\" >124</td>\n",
-       "      <td id=\"T_af8b6_row5_col5\" class=\"data row5 col5\" >130</td>\n",
-       "      <td id=\"T_af8b6_row5_col6\" class=\"data row5 col6\" >142</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row6\" class=\"row_heading level0 row6\" >St. Mark</th>\n",
-       "      <td id=\"T_af8b6_row6_col0\" class=\"data row6 col0\" >58</td>\n",
-       "      <td id=\"T_af8b6_row6_col1\" class=\"data row6 col1\" >11</td>\n",
-       "      <td id=\"T_af8b6_row6_col2\" class=\"data row6 col2\" >23</td>\n",
-       "      <td id=\"T_af8b6_row6_col3\" class=\"data row6 col3\" >50</td>\n",
-       "      <td id=\"T_af8b6_row6_col4\" class=\"data row6 col4\" >58</td>\n",
-       "      <td id=\"T_af8b6_row6_col5\" class=\"data row6 col5\" >66</td>\n",
-       "      <td id=\"T_af8b6_row6_col6\" class=\"data row6 col6\" >94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row7\" class=\"row_heading level0 row7\" >St. Patrick</th>\n",
-       "      <td id=\"T_af8b6_row7_col0\" class=\"data row7 col0\" >49</td>\n",
-       "      <td id=\"T_af8b6_row7_col1\" class=\"data row7 col1\" >9</td>\n",
-       "      <td id=\"T_af8b6_row7_col2\" class=\"data row7 col2\" >13</td>\n",
-       "      <td id=\"T_af8b6_row7_col3\" class=\"data row7 col3\" >42</td>\n",
-       "      <td id=\"T_af8b6_row7_col4\" class=\"data row7 col4\" >50</td>\n",
-       "      <td id=\"T_af8b6_row7_col5\" class=\"data row7 col5\" >57</td>\n",
-       "      <td id=\"T_af8b6_row7_col6\" class=\"data row7 col6\" >65</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row8\" class=\"row_heading level0 row8\" >St. Joseph</th>\n",
-       "      <td id=\"T_af8b6_row8_col0\" class=\"data row8 col0\" >34</td>\n",
-       "      <td id=\"T_af8b6_row8_col1\" class=\"data row8 col1\" >10</td>\n",
-       "      <td id=\"T_af8b6_row8_col2\" class=\"data row8 col2\" >0</td>\n",
-       "      <td id=\"T_af8b6_row8_col3\" class=\"data row8 col3\" >29</td>\n",
-       "      <td id=\"T_af8b6_row8_col4\" class=\"data row8 col4\" >39</td>\n",
-       "      <td id=\"T_af8b6_row8_col5\" class=\"data row8 col5\" >39</td>\n",
-       "      <td id=\"T_af8b6_row8_col6\" class=\"data row8 col6\" >48</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_af8b6_level0_row9\" class=\"row_heading level0 row9\" >St. Peter</th>\n",
-       "      <td id=\"T_af8b6_row9_col0\" class=\"data row9 col0\" >22</td>\n",
-       "      <td id=\"T_af8b6_row9_col1\" class=\"data row9 col1\" >3</td>\n",
-       "      <td id=\"T_af8b6_row9_col2\" class=\"data row9 col2\" >12</td>\n",
-       "      <td id=\"T_af8b6_row9_col3\" class=\"data row9 col3\" >20</td>\n",
-       "      <td id=\"T_af8b6_row9_col4\" class=\"data row9 col4\" >22</td>\n",
-       "      <td id=\"T_af8b6_row9_col5\" class=\"data row9 col5\" >24</td>\n",
-       "      <td id=\"T_af8b6_row9_col6\" class=\"data row9 col6\" >30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x234fe402f70>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Make a coloured table\n",
-    "tab = pd.DataFrame(d).describe().T.sort_values('mean', ascending=False).round(2).drop('count', axis=1)\n",
-    "tab.style.background_gradient(cmap='Blues', subset=['mean'])\\\n",
-    "         .background_gradient(cmap='Reds', subset=['std'])\\\n",
-    "         .background_gradient(cmap='Oranges', subset=['min'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['25%'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['50%'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['75%'])\\\n",
-    "         .background_gradient(cmap='Purples', subset=['max'])\\\n",
-    "         .format(decimal='.', thousands=',', precision=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the number of new poor in each district\n",
-    "districts = outcomes['district'].unique().tolist()\n",
-    "n_districts = len(districts)\n",
-    "d = {}\n",
-    "for district in districts:\n",
-    "    df = outcomes[outcomes['district'] == district]\n",
-    "    d[district] = df['n_new_poor_increase_pp'].values.tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_3ec85_row0_col0 {\n",
-       "  background-color: #08306b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row0_col1 {\n",
-       "  background-color: #dc2924;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row0_col2 {\n",
-       "  background-color: #7f2704;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row0_col3, #T_3ec85_row0_col4, #T_3ec85_row0_col5 {\n",
-       "  background-color: #00441b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row0_col6 {\n",
-       "  background-color: #3f007d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row1_col0 {\n",
-       "  background-color: #69add5;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row1_col1 {\n",
-       "  background-color: #67000d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row1_col2 {\n",
-       "  background-color: #fdc997;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row1_col3 {\n",
-       "  background-color: #80ca80;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row1_col4 {\n",
-       "  background-color: #72c375;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row1_col5 {\n",
-       "  background-color: #62bb6d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row1_col6 {\n",
-       "  background-color: #7262ac;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row2_col0 {\n",
-       "  background-color: #7ab6d9;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row2_col1, #T_3ec85_row4_col1 {\n",
-       "  background-color: #f85d42;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row2_col2 {\n",
-       "  background-color: #fdba7f;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row2_col3 {\n",
-       "  background-color: #84cc83;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row2_col4 {\n",
-       "  background-color: #83cb82;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row2_col5 {\n",
-       "  background-color: #7dc87e;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row2_col6 {\n",
-       "  background-color: #9e9ac8;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row3_col0 {\n",
-       "  background-color: #a0cbe2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row3_col1 {\n",
-       "  background-color: #e43027;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row3_col2 {\n",
-       "  background-color: #feddbc;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row3_col3 {\n",
-       "  background-color: #abdda5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row3_col4 {\n",
-       "  background-color: #a4da9e;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row3_col5 {\n",
-       "  background-color: #9bd696;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row3_col6 {\n",
-       "  background-color: #a4a1cc;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row4_col0 {\n",
-       "  background-color: #bed8ec;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row4_col2 {\n",
-       "  background-color: #fddab6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row4_col3, #T_3ec85_row5_col5 {\n",
-       "  background-color: #c8e9c1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row4_col4 {\n",
-       "  background-color: #c1e6ba;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row4_col5 {\n",
-       "  background-color: #bae3b3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row4_col6 {\n",
-       "  background-color: #b4b4d7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row5_col0 {\n",
-       "  background-color: #cbdef1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row5_col1 {\n",
-       "  background-color: #f7593f;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row5_col2 {\n",
-       "  background-color: #fee8d2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row5_col3 {\n",
-       "  background-color: #d3eecd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row5_col4 {\n",
-       "  background-color: #ceecc8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row5_col6 {\n",
-       "  background-color: #c2c3df;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row6_col0 {\n",
-       "  background-color: #cddff1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row6_col1 {\n",
-       "  background-color: #fa6849;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3ec85_row6_col2 {\n",
-       "  background-color: #fee9d4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row6_col3 {\n",
-       "  background-color: #d5efcf;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row6_col4 {\n",
-       "  background-color: #d0edca;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row6_col5 {\n",
-       "  background-color: #cbeac4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row6_col6 {\n",
-       "  background-color: #cdcde4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row7_col0 {\n",
-       "  background-color: #e1edf8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row7_col1, #T_3ec85_row8_col1 {\n",
-       "  background-color: #fee8de;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row7_col2 {\n",
-       "  background-color: #fee3c8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row7_col3, #T_3ec85_row7_col4 {\n",
-       "  background-color: #e7f6e3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row7_col5 {\n",
-       "  background-color: #e7f6e2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row7_col6 {\n",
-       "  background-color: #eeecf4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row8_col0, #T_3ec85_row9_col0 {\n",
-       "  background-color: #f7fbff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row8_col2 {\n",
-       "  background-color: #fff5eb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row8_col3, #T_3ec85_row8_col5, #T_3ec85_row9_col3, #T_3ec85_row9_col4, #T_3ec85_row9_col5 {\n",
-       "  background-color: #f7fcf5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row8_col4 {\n",
-       "  background-color: #f6fcf4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row8_col6, #T_3ec85_row9_col6 {\n",
-       "  background-color: #fcfbfd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row9_col1 {\n",
-       "  background-color: #fff5f0;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3ec85_row9_col2 {\n",
-       "  background-color: #fff2e6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_3ec85\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_3ec85_level0_col0\" class=\"col_heading level0 col0\" >mean</th>\n",
-       "      <th id=\"T_3ec85_level0_col1\" class=\"col_heading level0 col1\" >std</th>\n",
-       "      <th id=\"T_3ec85_level0_col2\" class=\"col_heading level0 col2\" >min</th>\n",
-       "      <th id=\"T_3ec85_level0_col3\" class=\"col_heading level0 col3\" >25%</th>\n",
-       "      <th id=\"T_3ec85_level0_col4\" class=\"col_heading level0 col4\" >50%</th>\n",
-       "      <th id=\"T_3ec85_level0_col5\" class=\"col_heading level0 col5\" >75%</th>\n",
-       "      <th id=\"T_3ec85_level0_col6\" class=\"col_heading level0 col6\" >max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row0\" class=\"row_heading level0 row0\" >St. Luke</th>\n",
-       "      <td id=\"T_3ec85_row0_col0\" class=\"data row0 col0\" >8.45</td>\n",
-       "      <td id=\"T_3ec85_row0_col1\" class=\"data row0 col1\" >0.64</td>\n",
-       "      <td id=\"T_3ec85_row0_col2\" class=\"data row0 col2\" >5.65</td>\n",
-       "      <td id=\"T_3ec85_row0_col3\" class=\"data row0 col3\" >7.99</td>\n",
-       "      <td id=\"T_3ec85_row0_col4\" class=\"data row0 col4\" >8.54</td>\n",
-       "      <td id=\"T_3ec85_row0_col5\" class=\"data row0 col5\" >8.95</td>\n",
-       "      <td id=\"T_3ec85_row0_col6\" class=\"data row0 col6\" >9.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row1\" class=\"row_heading level0 row1\" >St. George</th>\n",
-       "      <td id=\"T_3ec85_row1_col0\" class=\"data row1 col0\" >4.57</td>\n",
-       "      <td id=\"T_3ec85_row1_col1\" class=\"data row1 col1\" >0.88</td>\n",
-       "      <td id=\"T_3ec85_row1_col2\" class=\"data row1 col2\" >1.55</td>\n",
-       "      <td id=\"T_3ec85_row1_col3\" class=\"data row1 col3\" >4.00</td>\n",
-       "      <td id=\"T_3ec85_row1_col4\" class=\"data row1 col4\" >4.60</td>\n",
-       "      <td id=\"T_3ec85_row1_col5\" class=\"data row1 col5\" >5.18</td>\n",
-       "      <td id=\"T_3ec85_row1_col6\" class=\"data row1 col6\" >7.09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row2\" class=\"row_heading level0 row2\" >St. Paul</th>\n",
-       "      <td id=\"T_3ec85_row2_col0\" class=\"data row2 col0\" >4.22</td>\n",
-       "      <td id=\"T_3ec85_row2_col1\" class=\"data row2 col1\" >0.52</td>\n",
-       "      <td id=\"T_3ec85_row2_col2\" class=\"data row2 col2\" >1.87</td>\n",
-       "      <td id=\"T_3ec85_row2_col3\" class=\"data row2 col3\" >3.89</td>\n",
-       "      <td id=\"T_3ec85_row2_col4\" class=\"data row2 col4\" >4.25</td>\n",
-       "      <td id=\"T_3ec85_row2_col5\" class=\"data row2 col5\" >4.59</td>\n",
-       "      <td id=\"T_3ec85_row2_col6\" class=\"data row2 col6\" >5.29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row3\" class=\"row_heading level0 row3\" >St. Andrew</th>\n",
-       "      <td id=\"T_3ec85_row3_col0\" class=\"data row3 col0\" >3.47</td>\n",
-       "      <td id=\"T_3ec85_row3_col1\" class=\"data row3 col1\" >0.62</td>\n",
-       "      <td id=\"T_3ec85_row3_col2\" class=\"data row3 col2\" >1.01</td>\n",
-       "      <td id=\"T_3ec85_row3_col3\" class=\"data row3 col3\" >3.04</td>\n",
-       "      <td id=\"T_3ec85_row3_col4\" class=\"data row3 col4\" >3.49</td>\n",
-       "      <td id=\"T_3ec85_row3_col5\" class=\"data row3 col5\" >3.90</td>\n",
-       "      <td id=\"T_3ec85_row3_col6\" class=\"data row3 col6\" >5.06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row4\" class=\"row_heading level0 row4\" >St. Mark</th>\n",
-       "      <td id=\"T_3ec85_row4_col0\" class=\"data row4 col0\" >2.74</td>\n",
-       "      <td id=\"T_3ec85_row4_col1\" class=\"data row4 col1\" >0.52</td>\n",
-       "      <td id=\"T_3ec85_row4_col2\" class=\"data row4 col2\" >1.09</td>\n",
-       "      <td id=\"T_3ec85_row4_col3\" class=\"data row4 col3\" >2.36</td>\n",
-       "      <td id=\"T_3ec85_row4_col4\" class=\"data row4 col4\" >2.74</td>\n",
-       "      <td id=\"T_3ec85_row4_col5\" class=\"data row4 col5\" >3.12</td>\n",
-       "      <td id=\"T_3ec85_row4_col6\" class=\"data row4 col6\" >4.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row5\" class=\"row_heading level0 row5\" >St. John</th>\n",
-       "      <td id=\"T_3ec85_row5_col0\" class=\"data row5 col0\" >2.35</td>\n",
-       "      <td id=\"T_3ec85_row5_col1\" class=\"data row5 col1\" >0.53</td>\n",
-       "      <td id=\"T_3ec85_row5_col2\" class=\"data row5 col2\" >0.62</td>\n",
-       "      <td id=\"T_3ec85_row5_col3\" class=\"data row5 col3\" >1.99</td>\n",
-       "      <td id=\"T_3ec85_row5_col4\" class=\"data row5 col4\" >2.35</td>\n",
-       "      <td id=\"T_3ec85_row5_col5\" class=\"data row5 col5\" >2.71</td>\n",
-       "      <td id=\"T_3ec85_row5_col6\" class=\"data row5 col6\" >3.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row6\" class=\"row_heading level0 row6\" >St. David</th>\n",
-       "      <td id=\"T_3ec85_row6_col0\" class=\"data row6 col0\" >2.28</td>\n",
-       "      <td id=\"T_3ec85_row6_col1\" class=\"data row6 col1\" >0.50</td>\n",
-       "      <td id=\"T_3ec85_row6_col2\" class=\"data row6 col2\" >0.56</td>\n",
-       "      <td id=\"T_3ec85_row6_col3\" class=\"data row6 col3\" >1.95</td>\n",
-       "      <td id=\"T_3ec85_row6_col4\" class=\"data row6 col4\" >2.29</td>\n",
-       "      <td id=\"T_3ec85_row6_col5\" class=\"data row6 col5\" >2.63</td>\n",
-       "      <td id=\"T_3ec85_row6_col6\" class=\"data row6 col6\" >3.52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row7\" class=\"row_heading level0 row7\" >St. Peter</th>\n",
-       "      <td id=\"T_3ec85_row7_col0\" class=\"data row7 col0\" >1.46</td>\n",
-       "      <td id=\"T_3ec85_row7_col1\" class=\"data row7 col1\" >0.17</td>\n",
-       "      <td id=\"T_3ec85_row7_col2\" class=\"data row7 col2\" >0.80</td>\n",
-       "      <td id=\"T_3ec85_row7_col3\" class=\"data row7 col3\" >1.33</td>\n",
-       "      <td id=\"T_3ec85_row7_col4\" class=\"data row7 col4\" >1.46</td>\n",
-       "      <td id=\"T_3ec85_row7_col5\" class=\"data row7 col5\" >1.60</td>\n",
-       "      <td id=\"T_3ec85_row7_col6\" class=\"data row7 col6\" >2.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row8\" class=\"row_heading level0 row8\" >St. Joseph</th>\n",
-       "      <td id=\"T_3ec85_row8_col0\" class=\"data row8 col0\" >0.58</td>\n",
-       "      <td id=\"T_3ec85_row8_col1\" class=\"data row8 col1\" >0.17</td>\n",
-       "      <td id=\"T_3ec85_row8_col2\" class=\"data row8 col2\" >0.00</td>\n",
-       "      <td id=\"T_3ec85_row8_col3\" class=\"data row8 col3\" >0.49</td>\n",
-       "      <td id=\"T_3ec85_row8_col4\" class=\"data row8 col4\" >0.66</td>\n",
-       "      <td id=\"T_3ec85_row8_col5\" class=\"data row8 col5\" >0.66</td>\n",
-       "      <td id=\"T_3ec85_row8_col6\" class=\"data row8 col6\" >0.81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3ec85_level0_row9\" class=\"row_heading level0 row9\" >St. Patrick</th>\n",
-       "      <td id=\"T_3ec85_row9_col0\" class=\"data row9 col0\" >0.58</td>\n",
-       "      <td id=\"T_3ec85_row9_col1\" class=\"data row9 col1\" >0.11</td>\n",
-       "      <td id=\"T_3ec85_row9_col2\" class=\"data row9 col2\" >0.15</td>\n",
-       "      <td id=\"T_3ec85_row9_col3\" class=\"data row9 col3\" >0.50</td>\n",
-       "      <td id=\"T_3ec85_row9_col4\" class=\"data row9 col4\" >0.59</td>\n",
-       "      <td id=\"T_3ec85_row9_col5\" class=\"data row9 col5\" >0.67</td>\n",
-       "      <td id=\"T_3ec85_row9_col6\" class=\"data row9 col6\" >0.77</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x27b38b4e250>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Make a coloured table\n",
-    "tab = pd.DataFrame(d).describe().T.sort_values('mean', ascending=False).round(2).drop('count', axis=1)\n",
-    "tab.style.background_gradient(cmap='Blues', subset=['mean'])\\\n",
-    "         .background_gradient(cmap='Reds', subset=['std'])\\\n",
-    "         .background_gradient(cmap='Oranges', subset=['min'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['25%'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['50%'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['75%'])\\\n",
-    "         .background_gradient(cmap='Purples', subset=['max'])\\\n",
-    "         .format(decimal='.', thousands=',', precision=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Average vulnerability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# vulnerability_by_quintile = get_average_weighted_vulnerability(outcomes, quintile=True)\n",
-    "# vulnerability_by_quintile.T.style.format(\"{:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# vulnerability_by_decile = get_average_weighted_vulnerability(outcomes, quintile=False)\n",
-    "# vulnerability_by_decile.T.style.format(\"{:.2f}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Raincloud plots"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Raincloud plot is a combination of a boxplot, a kernel density plot, and a dot plot. It is a great way to visualize the distribution of the data. Especially, when the distribution is skewed and you have a lot of data points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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xEz/++KP0HxBCiEZiwYIF5ObmcFtTA0ZtzVrCtPLT0sKkY8G8eZjNZjdHKETDYLPZmDFjJt6mYIIj4mtUhrdvEMERLVm+YgV5eXlujlCI2iGJZiO2adMmtm3diql7G/QBplo7jm/rZpg6lw8QtGzZslo7jhBCiLpx4sQJ/lyzhn6hepr6aGtcjqIojIgyUFJawsKFC90YoRANxx9//EF2dhZN4npWO8rspYho1hmnU2Xx4sVujE6I2iOJZiNVXFzM91O+xxgRjE/rmFo/nqlzPF5xUfw0dSr79++v9eMJIYSoHU6nk1+mTSPQqGVQuOGKywvz0tAvVM+fa9aQmJjohgiFaDgKCwtZsmQpAaHN8Q0Iv6Ky9EYfQpu0ZcOGDaSlpbkpQiFqjySajdT06dMpKS3F/5oOl92vpiYURSHwmo7oA0188umn0qxDCCEaqI0bN5KUnMzwSD16NwweBzAo3ECwUcuPU6ZgtVrdUqYQDcHixYux2WxExXZzS3lhTduj1RmYP3+BW8oTojZJotkIJSQksOqPP/DtEIfO37fOjqvotARc2xVzmYXPPvtMpj0RQogGxmw2s3D+fOL9tLT2q3mT2XPpNQqjow1kZmUxe/Zst5UrRH2Wnp7O2rXrCGnSFoOXe7owaXUGwqI7snv3Lo4fP+6WMoWoLZJoNjJOp5Pvvv8Ova83pg4t6vz4Oj8f/Pt25NChQ3IzIYQQDcyvv/5KSWkJN0Ze/iizFxPjo2VwuIF169axefNmt5YtRH20YMECtDo94dEd3FpuSFRrDF6+zJs3TwZhFPWaJJqNzJo1a0g8noipRxsUnfueRl8Or2YR+LZtzoIFC9i+fbtHYhBCCHF5MjIy+GPVKnoG6wn3qp3bg4Hhelr76Zg6dSqHDh2qlWMIUR+cOnWKnTt3Enq6qas7aTRawmM6c/ToUQ4ePOjWsoVwJ0k0G5Hi4mKmTZuGsUkoxpgr63B+pfx6tMEYHsznX3xBUlKSR2MRQghxYaqqMv2XX/DSwpAI994UV6RRFMbGGIk0wqSJEyXZFI3WwoUL0Ru9CY1qUyvlB4XH4eUTwLx582Qec1FvSaLZiEyfPp1Sixn/Xu3qZACgC1E0GgKu7YJTr+F/7/6PrKwsj8YjhBCiehs2bODwkSMMi9DjXcM5My+VUatwX6wX4QaVL774gl27dtXq8YSoaydPnmTfvn2ENe2ARuv+OcwBFEVDRLMuJCUlsWPHjlo5hhBXShLNRuLQoUOsWrWqzgcAuhCtt5HAId0pLC3lzbfeIiMjw9MhCSGEOEdmZiazZ82itZ+OzoG1c1N8Lm+twgOxXjTzhm++/lqSTdGoLF68GL3Rm+CIlrV6HP+QGHz8Qpk/f4EMwCjqJUk0G4GysjImfzUZfYAJU8d4T4dTiS7ARNANPSkoKebVV1+VOTaFEKIesdlsfPvtN+hVO7c0NdRpaxijVuH/mnvR3FfDd99+S0JCQp0dW4jacurUKfbu3Utok/a19jbzDEVRiIztRnZ2FqtXr67VYwlRE5JoNgI///wzmRmZ+PftiKKtfz9SfaAfwSP6YDNqefvtt5k7d670JxBCiHpgxowZJCclc1u0AZO+7q8feo3C3c28CNbD119NpqCgoM5jEMKdFi1ahN7gTUhkqzo5nikggoCQGBYtWkxhYWGdHFOIS1X/shJxWTZs2MDvv/+Ob6cWGMICPR1OtbS+3gQP74N3q2hmz57N+PHjyc/P93RYQghx1Vq/fj0bNmxgaISBFqa6aTJbFaNW4a5mRspKS/npxx9lugbRYCUmJrJ3795a7ZtZlai4HlhtNmbOnFlnxxTiUkii2YAdPXqUyZMnY2wSiqlT7fYDcAdFqyWgTwcCr+3K4YRjvPTyy9JUSgghPCA5OZkZ06fT1l/HgDC9p8MhxKhhRJSeAwcPsnbtWk+HI8RlU1WVuXPnYjD6EhxVN28zzzB4mYho1oVt27axdevWOj22EBciiWYDdeLECd597z3w9SJwQBcUjWdHmb0c3s0jCRlxDWYc/OfNN9m2bZunQxJCiKtGWVkZ3379NX46GB1t9Pgo5Wd0CdTRzl/H3DlzyMzM9HQ4QlyWvXv3cvToUcKbdUajqft5zEObtMUvMIqffppKWlpanR9fiKpIotkAHTx4kLf++xY2LQRd1wON0fNPoy+Xzt+X4OF90Ib4MWHCBP744w9PhySEEFeF2bNnk5WdxdhoA161PJXJ5VAUhZubGtHjYMqU72UUTdFg2Gw2ZsycibcpmKDwOI/EoCgKMa37g0bHxImTKC0t9UgcQlQkiWYDoqoqy5cvZ/z48Ti89AQN643Wx8vTYdWYxqAnaGhPjDHhfP3118ydO1f65gghRC3as2cP69evZ1CYgWifun/rcjG+OoVbmxg4ceIkixcv9nQ4QlyS5cuXk5OdTZMWvVAUz91a6wxeNGt7LTk5OXz77bcy8KLwOEk0G4jCwkI++ugjpkyZgiEmvDzJ9DZ6Oqwrpmg1BA7sik/rZsyePZsvJ0/GZrN5OiwhhGh0CgsLmfrTj0T7aBkQXn9bwrT219E3VM+yZctkfk1R76Wnp7N06VKCI1ri6x/m6XDwMYXQtGUf9u/fz6+//urpcMRVThLNBmD79u3844UX2Ll7V/lgOgO7oNF7boRAd1M0Cv692+HXow1r167l9TfeID093dNhCSFEo+F0Ovnxhx+wWszcFm1EW0/6ZVbn+kgDsSYdU77/nqSkJE+HI0SVnE4nP/74Ixqdgci4bp4OxyUovAUhUa1ZtGgxBw4c8HQ44iomiWY9ZrFY+Oqrr/jwww+xemkJuakfPq1j6s3ADe6kKAqm9nEED+1BcnoqL770EosWLZK3m0II4Qa//fYbBw4e5KYoA8HG+n/p1yoKd8QY8dM4+eLzz8jOzvZ0SEKcZ9WqVRw/fpwmcb3Q6epXK7OouB74+AXzzbffkpub6+lwxFWq/l9trlLHjx/n5X+9zJo/12Dq2orgYb3R+ft6OqxaZ2wSSvBN/dA2DeGXX37hueefZ/Xq1djtdk+HJoQQDdLWrVtZvHgxvYL1dAmqv01mz+WrU7i3uRGnpZTPPv2UoqIiT4ckhEtqairz5s0nMCyWgNBmng7nPBqNlmZtBmKzOvj88y8wm82eDklchSTRrGccDgcLFy7k36+/Tm5JMcE39sGvUzyK5ur5UWm9jQQN6ELIjX0o1jj56quv+Puzz7Js2TKsVqunwxNCiAZj06ZN/DBlCq38dAxvYvB0OJct2Kjh3uZGCvNy+ezTT2UkTVEvWK1Wvv76G7R6I03ie3k6nGoZvEw0a3ct6enpfPbZZ5Jsijp39WQvDcDJkyd5/fXXmT59OsbYSIJv6oshNNDTYXmMITyI4Bt7EzysNyUGhR9++IH/9/TTLF26VBJOIYS4AIvFwvTp0/nxxx9p5aflzmb1v19mdaK8tdzT3EhGWipffP653CwLj3I6nUyZ8gPp6enEtOpf75rMnsvXP5xmba/lxImTvP/+B2RlZXk6JHEVkUSzHsjLy+Obb77hX//6F6cy0gga0p3Afp0a1YA/V8IYEUzw9T0JGdkXm78XU6dO5Zm//50VK1ZIH04hhKjA6XSyceNG3nzjDdat/ZNB4XruamZEr2mYSeYZsb5a7mpmJOnUST7+eAL5+fmeDklchRwOBz/99BM7dmynSYue+AaEezqkS+If3JS4jteRlZ3Lf//7X9avXy/TyYk6IZmMB2VnZ7N06VJ+X7kSp9OJT4c4TB1bSIJZDUNIAIYh3bHmFFC86yjff/898+bPY+SIkQwePBiTyeTpEIUQwiOcTifbt29n6ZIlZGRm0txXy13x3kR517+5MmuqpZ+O+5orzEhKZfx//8voMWPo3bs3en3D6XcqGq5jx44xffp0kpOTiYrrQUhUa0+HdFl8/cNp2XUEKUc389NPP7Fu3TpGjx5N69atG+Ugk6J+kIymjjkcDvbt28cff/zB1m3bQAHvltGYOrZA6+Pl6fAaBENIAMHX9cSalUfx3uNMmzaNmTNn0r17d3r16kWHDh0IDAz0dJhCCFHrSktL2bx5M3+sWkV2Tg5NfbTcF+tFvEnbKG8eY01aHo33YlFKGT///DNz58ymdZs2NG8eS7NmzYiNjcXHx8fTYYpGwul0smfPHlasWEFCQgJGbxNxHa7DLyjK06HViN7gQ/P2gynIPkXqqZ1MmDCBJk2b0qtnT1q2bEmTJk3w9W38A0+KuiOJZi2zWCykpqaSmJjIoUOH2LlrF6UlJeh8vPDtGIdP62Zovet3+/76yhAWRPDQHtgLSyg9lszOA/vYsmULAEHBwTRv1oyoqChCQ0MJDg4mMDAQf39//Pz88PHxQXMVDbAkhGgcVFUlKyuLI0eOsHfvXg4eOIDd4SDOpGV4I04wKwo2aHgg1oukUif7CuycOrKfvXv24FRBAZo2bUqbtm1p1aoVsbGx+Pv7ezpk0YCoqkpmZiY7d+5k7dq15OTk4OUTQNOWfQgKb4FG07BbCSiKQmBYcwJCYsjPPklexjEWLlzoakrr4+tLk6gomjdvTosWLWjZsiUBAQEejlo0VJJouonT6eTnn39m5cqVFxyoRtFq8YqLwhgZAhqFsjSZG8wddIEmdAEm7AXFWDPzyMvJIy83l127dtWovOjoaJ544gliY2PdGqcQQlR04sQJpk2bRk5ODjabrdJUThqNBq22/Ka2uv7o/nqFLsF6Qo0aSuwqe/KvrqmgmnpraOqtwa7qSDc7OVXqIDklheSUFFauXFnlPjqdDr1ej16vp3v37owdO9b1PYvGaeXKlfz5559otVq0Wi0ajQZVVbFarVgsFsxmM2VlZVXuGxgWiykwCkVRKMg+WceR176giHj8Q5phLs6lzFxAmbmQY8eOcezYsfP+hry8vPD19UWv16PRaFAUBUVR6NKlCyNHjpS/I3EeSTTdpKSkhF9//fWi26kOB5bENCyJaXUQlaip5ORkdu7cKYmmEKJWbdq0ieTk5CrXOZ1OnE7nBfcvtKmszZJB0S6H3W7HbrdjNptZvXo11113HSEhIZ4OS9QSu93OrFmzarx/ftYJ8rNOuC+gBsxisWCxWM5bnpycTL9+/eTvSJxHEk038fPz45tvvqn2iZhoWBRFkaYiQohad+eddzJixAhPh3HV0uv10qezkdPpdHz88ceUlZWhqqrrAY6iKGi1jb+peW1RVdXV3NZgMEjfTlElSTTdyMfHRy5YQgghLplGo5GHWkLUMi8vL7y8ZMBFIeqajIYihBBCCCGEEMKt5I2mh+3KTkKn0dIxuIlrWaa5iI0ZiYR5+9I1JIYj+RmUOeyEe/tRYrdi0hkJ9vJhbuJuSmwWbm7WiSxLMfnWUtoHNUGjKGzOTMRLp+OGpu3RKApbMk+QX1ZKfEAYsaYQkkvyifLxx6Ct/CtQYrPirdOhUco7yu/KTmJXbjK+OiOhXr50Cm7KscIsMkuLaOUfRrC3CT+9F3qNhtSSfIxaPSFevuzKSaa5XzAR3uWj/RVYzRg0Wrx1BgDyykoB0CgKeo0Wn9PLK0oqymV/Xhpx/qH4G7yI8PZHUwdNXIptFlJKCmgZEIZWkWcxQoj6T1WdUFIAvoHgsKMe2YoSGg3BUSi68nkm1cIcnCf2QnRblMyTKAbv8uVlpSht+7iaEKo5qahZp1BLi6FpKzRhMagl+aiF2XBgIwQ3AacNIlpA0gHIz4LIeDi6Fbpej6ZJHE4ff0g5iiakCfj4g9WCmp+JYvBCCS6fGsJZWoSalYRSnI/Stnd53FlJEBqNxuiN6rDj3L0aQqPB6I2SdBAlKh6laStUqxmykiEyDkUrtzKi7mXllaKqKgadlkB/L5LTi/A3GUlKKyS7wExsEz/2H8vF11tPWLA3/r4GcgvLaNcihMOJ2RSWlJGcUYLV6qBls0DatQjFUman2GwjOb2QzNxSvL10dGsbQanFjsVqx2pzYLU6cDhVmoSZKLXYiAozERLo7YrLZnNw9FQ+0eEmElIKiAzxISrMhKqqlJht+HrrURSFMqud48kF+JsMNA33c+1/pjlsdU2KjyflgwItogNr8+sVjYTUzh60OTOR7w5vBOD2uK7cEN2ebHMxb2xfjOP0H/os7U5KHdWPYguwJy/17IfEnZXWLUs6SJndhh21yn2DDN7kWc1A+bDwaoX/V2XG8R0XOy00KDgrlOCrM1Bit6IAOkWLRoEyp8O1Xqdo+HvHoaSbC9FrNOg0WlDh68PrK5Xb1CeAO1v0oIlvIP4GL4ptZeRbS4n2DbpoTGccL8xm6rEtWOw27o7vSeeQppXWO5xO3t65jJyyEgZExnN/qz6XXLYQQtQ2VXWiVPEAzLl4MhzdBhGxEB4De9eiajTgdKK06QPxXVCXfnW2HCrX8+of00BnAK0eCjIrl30pgR0un1qK1GOVtj93XxUgvhsk7Ky0TF32baXtHIoWtFqwWytvp2hQ7n0N9bdvIDulPNGMaA49R8Dx3ag5qSi9R6Lxl0FJxOU7kVLAydRCurYNJ8DPyIGEbI6cyEOjKAQGeDGgW1M0GoWte9NYuyPlgmVt359R5fLfN54/cm1mrpkNu6oeJPLIifyLxt0s0kRcdCAtmwcxZd4+HM7Kd3F3DGvNniPZHD6Ri06nISzIm7SsEgAUBR64pQPrd6RwLCkfnU6DAvTv1pRu7cIrJZxHT+axaHUCALcMiadls0u//xJXJ0k0PcShOlmedND1eXbiLjZkHCfQ4ONKMoGLJpkXU2K/8P5nkkw4e9NRXZJ5qZznlHAmBhWwqY7zDmBXnXyw9/eLlptSWsCEfavQKRqeaD+IKUc2UmizcFtcV26Mbs/MhO38mX6MG6PbMap55yrL+PbwBrItxQAsTdpH55CmOJxOfk89hEGj5ZqwOPJPfydZ5uLLPHMhhKg9atpxnHM+BL0XmntfRTFVuMlL3FP+/4wTkH16FNvTI9aqh7eUv228EEsJUOL2mKuUsPPi26gOsDuqWO5Enf422E+PtJueiJqeCEe2g7mofJODG1H/+h6Kt8mNQYvGzmZzsGDVMRxOlczcUkZf14pl60/guiVLgsgQH8psjosmmXXtVHoxp9KLWbOt6hGsZy0/wpl80W53upJMAFWF/EILx5LyXesBVm9Nwu5w0rtTlGtbh+PsoyO740rvFsXVQBJND0kqyiW5NL/SstTSQtJLC9EpGpyqel7C5qs1UHKFiWdjYFedfLb/D9e3k1ycD8C69ARsTgfr0hOqTTSb+AS4Es2eYc2xOR2sTDnMvBO7AAgwePNYuwEcyEvjuiZtavlMhBDi0jl3/g5WS3kz1D1/ovS79ezK5h3OJnCO03NphjSBslIozgdnI7gpDI2ByFjYt7b8c1Q85GWApdiVZAJgKwNzMUiiKS6DRqtgNGgptdjx8dKj0yqEBfmQmXu6q48Ggvy9WLLmuIcjrRm1iipAq1VwOFT2Hcupcp8Sc+Wpk9rEBeNwqigKtImVt5ni4iTR9JBlyYeqXO4ENKqKUsV7xRKHFS0Kjhq8c7SWlJJ7POmy9jHnFeId5H9Z+wS3iMHg676RdzsFNmFvfup5y898AwaNlhEx7QEYHtOetenHuKFpu2rLe6zdAE4V5xLlHYATJ69tW0T+6f6iCmDSe9E6IJyuIdFuOwchhHCLCn35VV9/nCUFqHM+BI0W9OeMqOkfCs3aw86Ltxa5kEKLlb1peRfcJrPYTLjJ+4LbAHSKCsLf6/z++FWrohOH0ac8oWzaqrwvavMOUJIPW5aUv+GMiAVbGUrL7ijBkZd4HCHKqSo4Tz+QUVUVRVG4a0RbdhxIZ/3OVIx6HQaDhqLSmj/wt5iLSUtOqHZ9cWEuJv/gC5YRFR2Pl5seojhOv5UsLrXSrV04ew5nVWp2m19Ueco+RVHo0DLULccWVwdJND3kSMHZtvuBei/ybWcnwK2uPyVQoyQTIPd4EguffLNG+16OW774N5Gd3Pcm8ETx+U/ZKt5+WJ0O1wBBI5t1ZGSzjhcsT6/REu8fBsCRgkzXoERdgpsyolkH4vykAhVC1A01Lx31wCaUVt1QwptfdHtNr+Hlg/4oCkqna3HO/6y8nyKUJ10VKL1HoibsvuIY96blMejzJVdcDsCap26if1zEJW5dxbUu5XD5/yOao0Q0R10xpfJ6pxNy01B3/o7abzSKRgZzE5dOdarYTydZ1tPNtncdymTDrvKH3eYyO9/O2XdFx0hLTuDz8X+9ojKeeuUb4lp1uaIyzri2ZzSpmcX06hjJ0VN5OM957ZmeVUfN6UWjJYmmBxRazZSe7rcYoPfCqlY/1IJRo6NXWDPWZbinqcaA5/5CcHzMRbfLSTjF+o+m0P+5hwiJb3bR7XMTklj30ffuCLGSIntZpcGF2gZEcKhCkq4AXlp9lfvuyk7iVEke1zVpg6/eeN76lv6hDI5qRaa5iNtbdCfc26+KUoQQonY4F02C7GTUvWvQjptw8R0UDWracUg6iOobCL5n599UOg1EGfYXVKsFxWlHCYvBsfdPt8X6+di+dIo6/03L3rRcnpqzsdr1FbdxFyW8OarVcv6KrNOtdmxlqJsWovQb7bZjisbrREoBGo1Csyh/br+hNUnphXRsVf5AesvetCqbnHoZtVjKquhDfInGPvAiUTGtKi1LSzrKnB/frXJdxfXuYvLR0yE+BFTYui+dY6fyK61XlPJEVIgrIYlmLSqwmvHS6jGeM/S6RlHQaTRYnQ46Bjdhe3b5xTHcy0ReWSm2ComnE5WhTdqyNfMkZWrNK7UzguNjLuuNY0h8M7e+oayJin1Vg4y+ldapwH+2L+E/PW/G33C26VaWuZgvD65FBfLLzDzQ+vzRYzWKhnta9qqtsIUQ4sK8fF3/V9NPgCmg8gA/5yopgBN7y/+9YV755+Ao6D8WinPLB9Bx2HHO/ai8n6KtrPqyLlOnqOALvo282Hq3UbTQe2T528tdq8r7oFYlP7Pq5eKqYrU5mLfyKIXFVm4e1IKosMpNTiuOojr6upa0iA6kSfjZbTq1DmPnwQz8fA1YLHYs1vL7sCtJMgGiYlpV+1byQuuulEaj0Lp5IFabE71ew6QZ57d60GkV7A4VVQWTr4E5K44QYDIytE8zNJran2JONC6SaNaSLZkn+O7wRvwNXrzabQT+hrP9Z0x6L/7ZZRhJJXn0DG3GkCZtOFyQQYnNytKkys0ybE4H7+9e7pYks6HrEBiFSW+gdUA4RVYLaeZCAEodNvKt5kqJpkGrxaDVUeaw41fF20whhPA0zS1Pop48gJqXjnPaW2D0QfPgWyimwCq3V/yCULoMRj158OzgN3npsH4Oam4a6p7VYLdDaWGdnUOdUx2oP71RnmjarRDeDIpyISoeTbfrcO5dC5knoctQT0cq6oG0rGJSMsoHADyQkHNeomm1nb23sljs5+1/bY9oru0RTUJSPov+OFa7wdYyvU7DPSPbsmz9CTJyqn5AoygQEerr+s427kolNbP8361jg2gWdXnjdgghHRhqydGCTFRUUpKTyTydEBVYzfxv1zLG7/y1fG5JWxlPb5jJO7uWYXXYSSjMqrIss/P8yu9q4F3hTXCgwZu+EXGsSDnEkYJMmvgGuta18Auhmalyk62izBxe6Tqch1pfQ5DRlxyL9DMQQtQvipcvmja9UE6PnE1ZaflbygvQXHc/2offJjn/dJ2mqfC82GGHwgr92quYbxON9sqCrg+sFtf8mkp0G7R/+wTt6Kdxph2HI1tJPpEIu1Z6OEhRH0SFmWgabsLko6dd/Plzq7aPDyEqtLxlwea9aew9moXDebZVWXJyMuYyO4tXJ1QauLkhvtez2Z0sWp1QbZKZn5uBquJKMgG0p99gGg1aQgIvPuCXEOeSRNNNVFVlz549qKcb898Y057QnDKm3f40E36bzfRj21h0ci+JRTmcKs5jY+ZxVqQcQqV8Ts0FJ/dwQ9O2hHnJcOwAOkWD2XE2wc63mll4cq+rcvfTGQkyeOOt1TM6tmulfQ8cOEBMTAx/LlnG888+xz/efJV/zvoKh8PBrFmz2LFjBzNnzmTXrl04HA5mz56N3W5n9uzZOJ3O836WQghRW1RVZa9fC9RO17KnSR/2pOdjt9v58MMPcTjOb8lit9t56KGHiH31ew5kFkKTeMg9PdF7fmZ589kzAkLLm9ZW5GxErWNi2qL0uRkANfUYbJjPvtQcYt+awYcrtrrq+p07d7J7925UVUVVVXbv3s3u3btxOp1S1zdyBr2WscNaEeVXyPGjB8/7WSuKgtXuIOXUEfbu3cN305bx9vtf4XQ6XfcSE75eUmkkVrjy+cY9Ja+wvDl9oJ8RvVaDVqNwTecocjNP8tZzN/PjxFf49K1HOLxrOXOnvs8nH7yB1Wply/rl5QORcf79rtwziQuRprNuMnv2bO68805mzZrF7bffTqiXiWO//AbAb1/9hP2NQAB8dAbMditLTu4jxKtyf8M16UfJljdvRHn7u5rFVpRpOTtP2ur0owD0DG2GUatjyam99AmPI9TLRF5e+VD8d955p2v77ZNnkjlzFYsXL65U5s0338zixYvp3r07O3bs4LnnnuOaa66p9LMUQojacuba8dxzz/HRRx8B0C2uKTsTU1i9ZD6LVq2ttH2fPn3YsWMHAIWj/g4p66svvKwUbI2gRYyinJ0EUKM9myyXFuH89WuUm8fBsfI5RMev3APAPz/5Fj75tlIxs2bNQlVV17XhzHcudX3j9p//Tea/rz4JwC+/zODuu++stN6SvZOP/v1ApWX5mUe57bbbAEhLzybOr2ndBFtHbujfnPAgH0pKrazdkUJBQT4Au7csB+Crj/e4tt29eRnFRXkEGzKY8NFH593vnvtZiIrkjaYbFBcXM27cOADGjRtHcXEx6enpzJw5E4DjqzZRmpMPQLeQ6PK3mKgEGCo3Q9ibmyo/EKgyyaxOlqWYd3YtY+HJvUw+UH5DVlpadbOQc5PMisvO3LhNmDCBxx57DDj7sxRCiNpQ8dpxJskE2JlYPmXJ4j/WkZBwds693bt3u+oqgGPHjkHoBW6AzcVgd9+AQB5T8U2J0wFtr4Hw5pCTAif2oc54D3XH76QXljJrV2K1xTz22GOu+h3OfudS1zdexcXFfPzuv1yfn3jyb5V+1sXFxbz8wjPn7TdhwgQST6bVSYyeMHvZESZN382UBQdISCogK6P6edaLi8of3n/y8cckJCRUut89npjEw488CsDj8nckqiBvNN3gtddew2w2A+VJzmuvvcbq1asrbTN1zJNo9Dq+q9Cy/8xoqlVMS+1+tdykYevXswBY/Mz48qfPdUCDggqoFb7HNxUNNputxmWqqkp+fj5w9mc5YcIlTDsghBCXqeK1ozp9+/YlM7N8BNVreveutO6hhx7iMYMeqmhi6y7uuHT8+9ftAFw/8dcruzwoCqCAZmp5wqlW7jRntVc/VRhAfn5+lc37pK5vvF577TXMpWeTn8KCgko/69dee43CwvMfbquqyl8fKX/LOfF/j6NU1d+5Bq6keemvcyadjmccipvvsxz2i983qapK3759K93vjhw5kjJL+VRDJcUl8nckziMv0Nzgyy+/dP3hmc1mvvzyS3bt2lXltioqGkVBoyiulFNatddM+XdZnmCeSdYdqtNt/QTO/CyFEKI2VLx2VCcr6+wgcRartbZDqv/OXDjPvfG/hGq/umuD1PWN15dfflmpr7PD4aj0sz53fUVllgv/bTYWl5OzZmVlVbrfPXxoH7bT0yiVlVnk70icR95ousG4ceOYPHkyZrMZb29vHn/8cdasWcPOnTtd24S1imXMt+NRgHta9qJHaAxv7fiVfKsZDVDxOWywwYdcazVzg9VQ+t7DLHzyTbeWWVGvR+9g4ZNvcvMnr9TpvJteWh1PtR/MxIN/Unp6FMKUCTNZMm/BFZd95mcphBC1oeK1ozphYWGuf3t5GbFYzjaFNei0mP+YibppUa3FuD4xg0GfL7miMt4c0YNBny/h9ydGuG+uzaDI8qldKuj50Xx2peRWu4uiKFUmm1LXN17jxo3js88+cyWTWq3W1fSzqvUVRTZpTnrqSZ54aXKleS0rdhm+XIlHd/P5+L/WaN8RY//G5+P/yhMvfen2eTb/+Ug/HI6Lv9UMCwujuLjYdb/btm1bDh06VOn+V4iK5I2mG7z11lv4+PgA4OPjw1tvvcXSpUtd6xVFYcPvfwDlD10Xn9zL85vm4qMzoFM0mPRGYnyD6BgYRcegSFdT0KudQTk7DH+Elx9G5fxh+bWKhrkndrmSTIBR995V42MqikJQUPmE6Wd+lkIIURsqXjuqs3HjRte/N2/eUmndpEduQek8qFZiq/fOSTIBFj8+8oK7BAYGEhQYeN5yqesbr7feeouAgADX54CAgEo/63PXn6EoCiPueBoArQaCA4w0i/JjWL/mRIT4nrd9Q6Kp4g3m7X/51/kLz6EoChs3bqx0v7t06dLz7n+FqEgSTTcwmUxMmlTedv7LL7/EZDIRGRnpGtnuzjvvpGWzWIY2aUOI0Rf76RHzcspK6B8ZT6GtjKSSPIbFtCelpIA869XRXONc59Z9xgrzaGZYiihTKz9x7BrclH92uYH8Cm9//XVGMh1Vvw2++eabq13WvXt3AJ599lkmT54MnP1ZCiFEbah47Xjuuedcy7s1Cwfg5huHER8f71reuXNnV10F0Prme1FMQRDdto4irociW0CfWyAoksi+w7hzzOhqN/3ymQeZdEs31+cz37nU9Y2XyWSq1Jxz8uTJlX7W564/4/8eeBxfU3kC6nBCbkEZp9KKWL7hJL07uemtvIdEhJ5NlCNDfFGAsIiYarcPDy+vj5599lni4+Mr3e9GRkaed/8rREXSdNZNbr/9dnbv3k2nTp0AKLGV4T+sJ8ycydi/3A9ApI8/q1JL8NbqiTWFoFEU1qUfQ4NCiJcv0b6BaC+jw3mdDCJUh849l6JqRkv01Rqwq0525aaQU1ZKse3s28xCexlHsk8BMOCfj+CVXsT17bszbNgw4tu1YdbsObRu1ZK0k0m0bt2aTp06MW/ePFoM7MXPs2fw8sN/I8TLVOlnKYQQteXMtaNjx4488MADqKqT9qWn+OTnOTz74eTztt+8eTP33XcfM2bMQAmKBEDpNRw1+dDZjTQ6cDaCaU0uxjcAbnsOdiwrf8OZl87fujdl5jz49w1duLl9DAk5RbS69kZ0vUfSYd8iMMWy/bnRaO75F5279+DBBx+Uur6Ru/32213jZnTu3Lna9WfmWT127Bi9+9/AR1/OqbK8xWuqH9m4Popt6s+JlLMDHmVkl3DLkHhsdie/ri0/l7Cg8reS3ftcT25uFs8+/QQJR/ejKArvvfceCxcudE33cu797rmfhahIEk03KbXbaN62lWsksMMFmRQYylOnNKV8RK6TReV9R8wOG3e16MG7e8rnKwo2+vBmz1Gkl158Wg+DosV6+s1eY0oyfbR6Ss/pH6BVNHQNjmZ7zqlKy0scZxPLpJK888oy+pU/rXts5FjuHHADRq2OvbkpvLh1Ac7mWnbYTvLCiBuI9QsBYPito3hh81ycnaOZfnw7T3UYXOXFSAgh3E1RFFd906VLF9Sj23GuWshz8XqUvWug1/BK2+t0Ov79738zY8YMVzN/TVwnHP3GwIZ55Rs1+iTz9GPWkgKY+joU5pxerBBSmArA7WPH0t6aRvdmZRBkgJ2zUNr3Q/X1p+sNXdB07oWafJhOId5uH8FT1C+KotClS/V9Gs9d361bN+YsP4LRyw8Abx+/Sts7nQ3r7isprfK9pVOF5RtOYK8wSnNoaHldct0tjxLZtAXdOkby9FNnpwKqOD9mxTqrqs9CVCSJphtkmAt5e+cyrE47T3UYRIegJrQJiKBHp65ELvqW2/oPBeDm5h2xOR008Q0gxNuXVv7hHC3MJLeslLXpx9iUkUh2WckFj2VVa28Ye08xKFrCvf04VZxbaVAkh+o8L8m8EA0KTlSCYqP5YdMKAiLDeW3rQvpFtsChqq7pZOyqk1PFea5E06DRYtIZKbRZCDJcuL+UEELUKv8Q0GjB6UAJDKtyk3Zedk5Om0B0sDeOOROgOBfluvtRDd5QbdeLxjTO+elz0OrPJpn+oSidBtJ+/TxO/Psemo15ELx8UfMzUWd/UL5Xwk6097xy+t+7cC74DADNbc+ixHas87MQ9ZdOpyGyaQv+8+lShg/pyta96Wi1ChaLnQaWZ+KoYtYfS1nle8n7bx/KsAHH+XN3MTa7k3YtQuooOtHYSaLpBsnF+VhOv407XphDh6Am+OoNvNh1GHTF1SdTVWFwk1bE+4fx722LyTCffcp0IDeNXMvVOdGtVXVworj6kQLPpTvdvDjKJ8D1RtNXZ6B3eCwbMxKxOx20aBbLT8fKB874NekA/+05ipSSfLItxbTwC6NPeKyrPINWx7+6DSelJJ92p5uiCSGEJygRsWgeeBPsVpTwZuetVx12nIu+oKnDjjrrCJyeWoBjO9Dc+xpqdjLkZ6JmnIAj2yruWSfx1ylvXyjOB40O5fr70cR2RI2Kp5mXL0p4M1SnE3X5D5xJspU2fVy7quais/+2lJw3RoC4ug0fEMcPC/YBYWTlmnnktk7MX3mUNHPjay0QFx2An6+BRLuJ/KLyBzeFJWUE+RvRamUoF3FlJNF0gy4hTRkU1YpSu5UhTVpVWvfZvtXsy0tleHR7/kg7QpnDzh0tulN4zlPnXbnJRHr5gc1Sq7HmJiRd0nY5Cacq/d9d5V6uM28pKwrz8uWFLsNYcGKPK9F0qipeWj3v9L4Vp+ok23L2zXATnwDCvP14uuMQ17K9uSmcKMplSJPWmPRGgow+BBnlbaYQwvOU4As88NJowC8E8jPOJpkA8V1RgiJAp0fNz4Luw+D4bnBNxH5lvfr3plX9MPDM8urWX2xdzSjQojPc+DAc3Q7ZKajr5+FYMhl634T2THPjknw4tb/83zHt0HS//mwJ7fuD1QKKBqVNLzfHJxq6vCILJebyv528Ags/LTpAUYn75rFNSzpa7bKq1l1o+ZUa1DMaAJ3u7OOW9TuSmZ9noWWzQEYNjpfm5aLGJNF0A51Gy/+1PP9CZbHb2JdX3l9kV04yZY7yJ2FZ5mLi/cNc686oi+fN6z76/rK2X//RlNoJ5BKdm2QCmO12vjm0nlMV+meaHTZWJB9kdGx5PwuT3ovBUa1ZnXYEVVWxOuwYTo9im1dWysT9f+JEJdtSxF/a9KubkxFCiCukKBroOhRW/1JpubriRxylBWCzguoErQ4cFd6+aLRX1HfzqTkbr2i92+i9oMcNaPreiqIoOEsLUXetPLt+7SzUVt1RAsNR/IJROg9GPXUQTc8bAVDTE3Eumgh+wWjG/B3F6F03cYsGZc2WJNdcmXmFZmx2996hzfnx3Rqtc4cgPyN5ReUPqUYNbkFwQPnfQPv4UAx6LU6nyuI1xwE4diqfoyfzaB0bXKsxicZLEs1a5KXTc1OzjuzOSeaW5p0psJpJLy1kZLOOrEw5VCnRDDL40C88jrWZCZTabZXmhXSH4BYx3PLFvy9rH3NeId5B/pd9nNqgRSHI6EtuWQkFNjP5+ef3QzLpjXy8dxWldiudg5u6pj1JMxeSYS7ieFE2kd7+NPUNwKDVYnHY8dEZayVeIYSoLYqt7PxHcPkZlT87zkkqa5hkdooKYs1TN11wm8xiM+GmiydsnaKCahRDJTYLbFqEGhiO0r4f+AZWsc3Z66fm+vsrrVIPbYai3PL/Uo6WvxkV4hwmH73r3xWTzHZxQaRmlVBQXLN7tKjoeJ565Ztq1xcX5mLyv3BSFxUdf8H1F2OzO4kI8UEBDPryNMBqc1BcaqVls/K/0fjEXBJO5QPg462vpiQhLk4SzVp2S/PO3NL8/AtZlE/lCYLbBEYw79Se8n8HRHC4IOO8fS5Gi4KjmveiBl8fIju1uewy3UmnaLCrVfRKr8K5TWYdqORZS1zLjBodGkWhbUAEe/JScahOCq0W8k83ST5ZnMvj7QZSarcS6xfClqwTLE8+iAaFN3rexL+6DieltIAuIU3df6JCCFGLlB7DABW8/aAkH3Xjwlo7lr+Xgf5x9XDewNN99TWdBqIGhuPMSISkIyjt+6KERVe/W9trUI/tAFMQNG1V7Xbi6jawRzQJSQXYzxlJx2Jz0i4+hE2702pUrpe3ibhW1Y+AWxeKzTZKzDZUYOmfx3n0js78vPgAeYVl9OkcRf9uTbl1SEtSM4vRahUiQnwvWqYQ1ZFE00N6h8eiVTScKs6luV8IuWUlbMosn89oQGQ8hVYzaeYLT3fSzDeI5JI8nJT3vnm792g+2vs7GRUGOagvTDoDL3S5gfd3r6C4mre1d8Z1Y8HJvZQ57XQPjaGJbyBr046Rd/rNpEbR4FAdeGv1PN/5eqJ9A1EUhX25qezIPkW6uYiEwiwAwr396BAURffQ8jescxJ3lh9EKU9iw3z8iPC5vLe1QghRHyg6PUqfmwFQkw6hnu5/qdz0OBh9wT8Ydep/KvTPbATaXYOmyxAwF4PDjtK6p2uVEtMGbUwb6Dn8AgWc3jYyFu1f36vNSEUj4G8y8tDoDhSbbRw7mceuQ5nodBoGdm9KoJ+RfUezKS6tX39fRoOWMmv54JNt44IosdhJSqv6flCjVXA4VEICvSizOsgrLG9Km55dYXyLcFPtBy0aPUk0PahHWDN6hJWPKuhQnegUDb46A73CY+kZ1oxpx7ayPy+NayNbEeZtYl16Am38w9mek0QTH38eaH0NGkUhsSiHZqYg9Bodr3QbQZaliK2ZJ9mclUiRtQyn6iTQ4EO+tZQQo4nro9uiVTQsPbWPEruVDgFRZJQVkF9mQVVV/AxeNPENIKOkkByrmRAvHzoFNyXU6MsfqUfw1Ru5I64bPx/bQpalmNYBEUT5+FNgNXOkIAOdRovFbsfisHFtVCt6hjUn1MsXX72RV7uP5KejmzHpjeRYSmhmCqZHaAzeOgNNfQPpGR7LyaJc2gVFotdoGR7dntmJOzHbrQxu0oaUkjy6hcbgozO4vseOwU3oGNwEh9NJurkQo0ZHgNEbvUbr2ubW5p2J8PYn0tuPMG+/834WQgjRECkxbdH836vgtKM0aelart7/H9Tkw9CkJWpxPuz9EzISweGAQXehNGuHWpAJ6+aCpQTa9YWoeFgyGcxFEBwFOgMUZEFQOPgEQlYS5KWXH0CvB4MPOB1gt4JOD35hoKjl5ZUUQGB4+XQtiXvL+436h0JAKGScBFMg+IfBib2AWj5VSavuaK5/ALU4H3XVz5CbWp5YRrVCc/0DKHrp6iDqjr/JiL/JSJMwE9f2rNwt6K9jO1NQXMbBhBxSMososznx89aTll2CUa9Bp9NSWmYnJMCL5PQinCpEhfpg8jVwMqUQq92JQa9Bp9Vgtthd7bcUBVffUKg8hJdWU76u4vQqigKBfga0Wg0jB8ZjtTsoszqIaxqA2WJj79FsmoSZ0GoVjp7Mp21cENn5ZqJCfbFYHYQG+aDXabiuTzNOpRdxTZeo2vxKxVVIUVW1yraW6enpDBo0iDVr1hAZKVM+CCE8ryb1ktRlQoj6RuoyIURjcLF6SSbIEUIIIYQQQgjhVpJoCiGEEEIIIYRwK0k0hRBCCCGEEEK4lSSa4oJWr16NoigsXbrUtayoqAhvb2+mTJlS5T4nTpzg2muvZeDAgfz1r3/l3G7ADoeDJ598kmuvvZZrrrmGTZs2udYdP36cHj161Mq5CCGEqB01uVYkJCQwcOBABg4cyFNPPYXTWXkqiequFePHj6dHjx4MHjyYLVu21No5CeEJtXXfFRkZyeDBgxk8eDCff/45UP63dM0119CrVy++/vrrWjsncfWSRFNcVJs2bZg1a5br8/z584mKqn5ksk8//ZSHH36YtWvXUlZWxooVKyqt/+GHH/D19eXPP//khx9+4PDhwwDMmjWLu+66i5ycnNo5ESGEELXmcq8V//znP/nHP/7B2rVrCQkJYc6cOZXWV3Wt2LNnDwsXLmTTpk3MnTuX5557rtbORwhPcfd916FDhxgyZAirV69m9erVPPXUUxw+fJjffvuNjRs3sn79et577z1KSkqqOYIQNSPTm4iL6tOnDzt37sRms6HX65k/fz6jR4/m5MmTDB48uNK2H3/8MT169CAzMxOAkpIS/P0rz1e5YsUKWrVqxfDhw/H19WXixIkAmEwm/vjjDzp27Fgn5yWEEMJ9LvdacfDgQYYNGwbANddcw7Jly7jjjjtc21R1rVizZg2DBw9Gr9cTHByMzWYjPz+fwMDAOjxTIWqXu++7du7cydGjRxk0aBDh4eF8/vnntGjRgvnz56MoCgBOpxOtVosQ7iSJprgoRVG4/vrrWbFiBddccw16vR5/f3+aN2/O6tWrz9s+Ozub559/nq+++oqAgAC6du1aaX1WVhaBgYH89ttvzJw5k+eee46ff/6ZESNG1M0JCSGEcLvLvVZ07tyZRYsWceedd7J06VIsFkul9VVdK1599VU++OADSktLyczM5MCBA5SUlEiiKRoVd993RUdH8+qrrzJ69GimT5/Os88+y7Rp0wgJCcHhcPDwww/z6KOP4uXlVTcnKK4a0nRWXJK77rqLWbNmMXfuXMaOHQvgerJW8b9du3bxz3/+kxkzZnDw4EEee+wxnn/++UplhYSEcPPNNwMwatQoduzYUefnI4QQwv0u51rx0Ucf8fPPPzNy5EiCgoIICQmpVFZV14p27drx8MMPc+ONN/Luu+/Ss2dPgoKC6vw8haht7rzv6t27NzfddBMAt9xyC7t37wbK336OHj2aFi1a8NJLL9XtCYqrgrzRFJekV69e7N27l5ycHGbMmMH+/furfbIWGBhIQEAAAFFRUeTm5lZa379/f3799Vduuukm1q9fT/v27eviFIQQQtSyy7lWfP/997z77ru0bduW5557juHDh1daX9W1IiMjg9LSUtauXUtqaioPPfQQPj4+dXR2QtQdd953vfrqq7Rs2ZInnniClStX0r17d5xOJzfffDN33XUX48aNq4tTElchSTTFJbvhhhtISkrC29v7gtt99tlnPPnkk6iqik6n45tvvgHguuuuY+XKlTz22GM89thj9O3bF61Wyw8//FAX4QshhKgDl3qtaNu2Lffddx8Gg4FBgwa5+mte6FoRHh7OgQMH6NOnD15eXnzxxRd1cUpCeIS77rv+9a9/ce+99zJz5kx8fX359ttvmT17Nlu2bEFVVaZPnw7Azz//TNOmTWv9vMTVQ1HPHQP5tPT0dAYNGsSaNWuIjIys67iEEOI8NamXpC4TQtQ3UpcJIRqDi9VL0kdTCCGEEEIIIYRbSaIphBBCCCGEEMKtJNEUQgghhBBCCOFWkmgKIYQQQgghhHArSTSFEEIIIYQQQriVJJpCCCGEEEIIIdxKEk0hhBBCCCGEEG4liaYQQgghhBBCCLeSRFMIIYQQQgghhFtJoimEEEIIIYQQwq0k0RRCCCGEEEII4VaSaAohhBBCCCGEcCtJNIUQQgghhBBCuJUkmkIIIYQQQggh3EoSTSGEEEIIIYQQbiWJphBCCCGEEEIIt5JEUwghhBBCCCGEW0miKYQQQgghhBDCrSTRFEIIIYQQQgjhVpJoCiGEEEIIIYRwK0k0hRBCCCGEEEK4lSSaQgghhBBCCCHcShJNIYQQQgghhBBuJYmmEEIIIYQQQgi3kkRTCCGEEEIIIYRbSaIphBBCCCGEEMKtJNEUQgghhBBCCOFWkmgKIYQQQgghhHArSTSFEEIIIYQQQriVJJpCCCGEEEIIIdxKEk0hhBBCCCGEEG4liaYQQgghhBBCCLeSRFMIIYQQQgghhFtJoimEEEIIIYQQwq101a2w2+0AZGZm1lkwQghxIWfqozP106WQukwIUd9IXSaEaAwuVpdVm2jm5uYCcMcdd9RCWEIIUXO5ublER0df8rYgdZkQov6RukwI0RhUV5cpqqqqVe1gsVg4cuQIwcHB6HTV5qPiHJmZmdxxxx3MmjWL8PBwT4fTYMn36B6N7Xu02+3k5ubSunVrvLy8LmkfqctqprH97niKfI/u0di+R6nL6k5j+93xFPke3aOxfY8Xq8uqram8vLzo3LlzrQbXmIWHhxMZGenpMBo8+R7dozF9j5f69P8MqcuuTGP63fEk+R7dozF9j1KX1a3G9LvjSfI9ukdj+h4vVJfJYEBCCCGEEEIIIdxKEk0hhBBCCCGEEG4liaYQQgghhBBCCLeSRNPNTCYTTz31FCaTydOhNGjyPbqHfI+ipuR3xz3ke3QP+R5FTcnvjnvI9+geV9v3WO2os0IIIYQQQgghRE3IG00hhBBCCCGEEG4liaYQQgghhBBCCLeSRFMIIYQQQgghhFs16kTzkUceYcaMGa7Pa9asoU2bNixevNi1bP78+Tz88MMA/OUvf6GwsPCi5TqdToYOHcqjjz7q/qDPMWvWLNq0acOePXtq7Rhms5n333+fW265hVtvvZXRo0czZ86cWjteXbv//vv5/fffPR2GWyxbtozvv/8egBdffJERI0bwt7/9DZvNBsDOnTt5/vnnK+0zYcIENmzYUOexCveRuuzSSF3WcEhddnWSuuzSSF3WcEhddmGNOtEcMGAAW7dudX1es2YNAwcOZPXq1a5lmzdv5tprrwW45B/62rVradasGQcOHODEiRPuDPk8M2bM4Oabb+ann36qlfJVVWXcuHE4HA5mz57NggULmDx5MpMnT2bJkiW1ckxRM4WFhUyePJn777+fQ4cOkZWVxa+//kpYWBhr164F4KOPPuLZZ5+ttN/jjz/Oe++9h8Vi8UTYwg2kLrs4qcsaDqnLrl5Sl12c1GUNh9RlF6fzdAC1acCAAfzwww+uz3/++SeTJk3iwQcfxOl0otFo2Lx5M3/961955ZVXALj33nv58ccfCQoKqrbc6dOnM3jwYFq0aMHUqVN59dVXAZg7dy4rV67E4XBw6tQpwsLCmDBhAsHBwQwdOpRbb72VTZs2kZGRweOPP85dd911wfgPHjxIUlISX3zxBTfeeCPZ2dmEhobywQcfoKoqL7zwAgA///wzu3fv5r333uOLL75g/vz5+Pn50bFjRxITEy9YGW7ZsoVTp07x3XffodVqAYiIiOCjjz5ybXPo0CHeeustCgsL0ev1PPPMMwwaNOiC5zt16lRmzpyJTqcjMDCQd999l7CwMNq0acPWrVvx9/cnOTmZ0aNHs23bNj777DNSUlJISUkhOTmZW265BV9fX37//Xfy8/P58MMP6dSpE/fffz9t27Zlx44dFBQU8MADD/DAAw9c5DehehMmTGDFihVotVo6duzIK6+8gslkqjb+CRMmsGrVKvR6PdHR0bz77rt4e3tXKnPo0KGMGDGCjRs3UlJSwjPPPMPIkSPPO/bs2bP5/vvv0Wg0NG3alPfeew9/f/9qY506dSpDhw5Fp9NhNBqxWq1YrVZKSkowGAwsXryYLl26EB0dXWk/Hx8fevTowezZs7nvvvtq/F0Jz5G6TOqyi5G6TDQEUpdJXXYxUpc1Mmojd+2116pJSUnq0aNH1TFjxqiqqqp33XWXun37djUpKUkdMmSIa9vWrVurBQUFFywvPT1d7dixo5qRkaHu3LlT7d69u1pUVKSqqqrOmTNH7d27t5qdna2qqqqOGzdOnTRpkqqqqjpkyBB1woQJqqqq6v79+9UuXbqoNpvtgsf697//rb7++uuqqqrqww8/rH722Weqqqrq8ePH1WuvvVZ1OByqqqrq7bffrm7atElds2aNevPNN6tFRUWq1WpV//rXv6r33XffBY/x7bffqk8//XS16202mzpkyBB19erVqqqq6rFjx9Q+ffqoycnJ1Z6v3W5XO3furBYWFqqqqqpffvmlunLlSlVVK3/HSUlJao8ePVRVVdVPP/1UHTZsmFpSUqLm5uaq7du3V6dOnepa98ILL6iqqqr33Xef+uSTT6p2u13NyspS+/fvr+7fv/+C53jfffepK1asOG/5zJkz1fvuu081m82q0+lUX3nlFfWNN96oNv7U1FS1X79+qt1uV1VVVcePH6/u2rXrvHKHDBmivvXWW67vq2fPnmpWVlalbQ4ePKj2799fzcjIUFVVVf/3v/+pX3311QXPY/To0erWrVtdnz/66CN1xIgR6muvvaaWlpaqY8eOdf0unmvZsmXq/ffff8HyRf0mdZnUZVKXSV3WGEhdJnWZ1GVXT13WqJvOAvTv359t27axZs0aBg8eDMC1117Lhg0bKjXPuFSzZs2id+/ehIeH07VrV0JCQpg/f75rfceOHQkJCQGgXbt25ObmutadOX67du0wm82YzeZqj1NaWsqiRYsYPXo0ALfccgvTp0/HZrMRFxdHkyZN2Lx5M4mJieTn59O7d2/+/PNPbrzxRkwmE3q9njvvvPOi56MoCuoFplI90wRl0KBBAMTHx9O9e3dX05eqzler1TJkyBBuv/123nnnHTp37szQoUMvGkvfvn3x8fEhKCgIk8lE//79AWjWrBkFBQWu7e6++260Wi2hoaEMHjyYjRs3XrTsqqxbt44xY8bg5eWFoijce++9rF27ttr4w8PDiYmJ4bbbbmPChAncdNNNdOnSpcqy7733Xtf31bFjR7Zt21Zp/caNGxk4cCDh4eFAebv+i/UtSUxMJCoqyvX52WefZenSpbz55pv8+OOP3HbbbSQkJPD444/zzDPPkJSU5No2Ojq61psTidolddmFSV0mdZloGKQuuzCpy6Qua0wafaI5YMAAtm/fXqlCGzRoEDt27GDLli2XVaE5nU7mzJnD/v37GTp0KEOHDiUvL4+pU6e6KgWj0eja/tzK4sw6RVEALliRLFmyhLKyMp577jmGDh3KhAkTyM3NZdmyZQCMHTuWhQsXsmDBAsaMGYOiKGi12kplnjnOhXTu3Jm9e/fidDorLV+2bBkTJkzA4XBU+T3Y7fYLnu/HH3/Mxx9/TGhoKO+8806lJh9ntjnTUfoMvV5f6bNOV3XL7jNNSc6UVfHz5Tj3nCueV1Xxa7VafvnlF15//XX0ej3PP/8806dPr7LsirGrqnreuZwbc2FhISkpKReMV1GUKr+TnJwcVq5cyZ133sk777zDG2+8wf3338+nn35aKZ6afk+ifpC67MKkLqv8WeoyUV9JXXZhUpdV/ix1WcPW6BPNfv36sX37dk6dOkXHjh0BaN++PSkpKezatYtrrrnGta1Wq3X9Qlflzz//xGKxsGbNGlatWsWqVatYtmwZaWlprFu3zq1xT58+neeff951nNWrV/Pggw+62vWPGDGCTZs28euvvzJmzBigvKJevnw5JSUlOBwOFixYcNHjdO/enZiYGN577z2sVisASUlJvPfee7Ru3Zq4uDhUVWXNmjUAJCQksG3bNvr06VNtmbm5uQwdOpSIiAgeffRR/vKXv7B3714AgoKCOHToEABLly6t0XezcOFCVFUlKyuLNWvWMGDAgBqV079/f+bOnYvFYkFVVX755Rf69etXbfwHDx5k9OjRtGvXjqeeeorRo0ezb9++Kss+890nJCRw6NAhevToUWl979692bhxo+vJ6qRJk5g6deoF442Li6v0NOyMTz75hCeffBKdTofVakWr1aLRaCp1Mk9OTiY2NvZyvh5Rz0hddmFSl0ldJhoGqcsuTOoyqcsak0Y9GBBAYGAgJpOJTp06odGU59WKotCjRw9SU1Px8fFxbXv99ddz99138+233zJ37lzCw8O55557XOtnzJjBPffcU+lpUXBwMHfccQc//fQTw4cPv+z4MjIyeOyxxypVPgcPHiQxMfG8JhZ/+ctfmDp1Knv27KFz58707duXjIwM12v7fv36MWrUKO644w68vb2Jjo52dYj+5ZdfyMzM5JlnnqlUpqIoTJw4kffff5/Ro0ej1WrRarU888wz3HTTTQB8/vnnjB8/ng8++ACA8ePHExMTU2nkuIqCg4N5+OGHuffee/H29sZoNPL6668D8PLLL/Pyyy8TGBhYo+8Lyp8yjR07FrPZzD/+8Q9atmwJwK233spXX31FRETEefs899xzlZ4cvfTSS9x5552kpKQwduxY7HY7HTt25PXXX8ff37/K+Nu2bcvgwYMZPXo0vr6+BAQEMH78+CpjPHbsGGPGjMFut/Pee+8RFBRU6Wfdtm1bnn76aR588EEAWrRowdtvv13l78MZ119/PRs2bKBnz56uZUePHiUlJcXVhOZvf/sb9913H3q9nnfeece13YYNGxg2bFgNvm1RX0hdJnUZSF0mdVnDJ3WZ1GUgddlVU5fVbhdQUZd2796t/vzzz67Pr732mvree+95MCL3q64DeX0yZMgQ9cCBA24vNz8/X7355ptVq9V6WfsVFxerN998s2qxWNwekxC1Qeqy+kHqMiGujNRl9YPUZZ7T6JvOXk3i4uLYsGEDo0aN4qabbqKoqIhx48Z5OizhJgEBAYwbN44pU6Zc1n6TJ0/mhRdeqPTEV4j6TOqyxk3qMnG1kLqscZO67OIUVb1Az2chhBBCCCGEEOIyyRtNIYQQQgghhBBuJYmmEEIIIYQQQgi3kkRTCCGEEEIIIYRbSaIphBBCCCGEEMKtJNEUQgghhBBCCOFWkmgKIYQQQgghhHArSTSFEEIIIYQQQriVJJpCCCGEEEIIIdxKEk0hhBBCCCGEEG4liaYQQgghhBBCCLeSRFMIIYQQQgghhFtJoimEEEIIIYQQwq0k0bzKbdu2jXvuuYdRo0YxcuRI/v73v5OVlQVAUlISf//73y+rvJdeeokpU6a4P9B6cjwhRMPjjnrupZdeYuDAgdx6662MHj2akSNH8uKLL2I2m2scV5s2bSgsLKzx/kKIq4e76rHLvWe6//77+f3332sQsRCSaF7VrFYrTz75JG+88QaLFi1iyZIltGjRgpdffhmA1NRUEhMTPRylEELUnDvruUceeYQFCxYwf/58lixZQllZGZMmTarN8IUQQu7XRIMlieZVzGw2U1xcjMViAUBRFP7yl7/w0EMPAfDqq6+SmJjIk08+WaPyDx06xL333suoUaO47bbbWLNmDQCZmZk8+OCD3HbbbYwZM4Zp06YBUFZWxptvvsmYMWMYNWoU7777Lk6nk+TkZK677jpefvllRo0axdixYzl8+LDrONu2beOuu+5i6NChvPLKKzidziv4VoQQjUlt1XOKotC7d2+OHDkCwMyZM7nzzju59dZbueGGG1ixYgUAn332GePHj3ftJ60whBCXq7bv1zZv3sytt97q+jx37lyeeOKJStuUlZXx8MMP89577wFw5MgRHnjgAcaMGcPYsWNZu3ZtjY4tGjdJNK9iAQEBPP/889x///3ceOON/Otf/2Lt2rX07dsXgP/+97/ExcXxxRdfXHbZdrudJ554gscee4xFixbx/vvv8+KLL5KSksKsWbNo3749c+fOZeLEiWzbtg2n08lXX31FQEAA8+bNY/78+WRkZDB9+nQAkpOTGTJkCIsWLeLee+/lxRdfdB0rLy+Pn376iaVLl/Lnn3+ye/du93xBQogGr7bquaKiIn777Td69OhBSUkJ8+fP59tvv2XBggW89NJLfPrpp7VxOkKIq1Bt3q9dirKyMp544gm6devGP//5T+x2O88//zyvvfYa8+bN44svvuDVV18lNze3Vo4vGi6dpwMQnvXwww9z++23s2HDBrZu3cq7777L7Nmz+e67766o3BMnTgAwaNAgAOLj4+nevTtbt26lf//+jBs3jhMnTjBgwABeeeUVNBoNq1evpri4mFWrVgFgsVgICAjg2muvJSwsjGHDhgEwevRo/v3vf7sqtCFDhmAwGABo3rw5OTk5VxS7EKJxcVc99+233zJv3jxUVUVVVQYOHMiDDz6IwWDg888/Z+XKlZw4cYJdu3ZRUlJSS2cjhLga1db92qV45513sNvtfPLJJwAkJiZy8uRJ/vGPf7i2URSFhIQEgoODaz0e0XBIonkV27FjB3v27OGhhx5i+PDhDB8+nGeeeYaBAwdy8uTJSyrDarWycuVKRowYAYCqquj1ehwOx3nbOp1O7HY7Xbt25ffff2ft2rVs2LCBiRMnMm/ePJxOJ2+88YbrCV1BQQFarZb8/Hx0uvN/VbVaLUCldYqiXPb3IIRovNxRz53xyCOPuJqqVZSens7dd9/NXXfdRffu3enduzevvvoqUF4nqarq2tZms13R+Qghrj7urMeqcrF6auzYseTm5vLOO+8wfvx4HA4HQUFBLFiwwLVNRkYGISEhVxyLaFyk6exVLDg4mIkTJ7Jnzx7XsqNHj+Ln50dERARarbbKhPFcL730EmlpaTgcDo4cOUJMTAxxcXGoqurql5mQkMC2bdvo06cP77//Pt999x0jRozg9ddfx2QykZSURN++fZk2bRp2ux2r1cq4ceOYP38+AGlpaWzevBko7zvQoUMHAgIC3P+lCCEaFXfVcxeyd+9eQkNDGTduHAMGDODXX391lRkUFOTqU56dnc2WLVuu6FhCiKtPbddjQUFBpKWlUVhYiN1uZ/ny5ZXWd+rUiRdffJF169axZs0aWrRoAeDqi37gwAFGjBgho2iL88gbzatYbGwsH3/8MW+//TY5OTnodDoiIiL45ptv8PLyomXLltjtdu6//35++uknHn30UZ5++mk6derkKsNgMPD666/z8MMPY7PZGDBgAAMHDkRRFD7//HPGjx/PBx98AMD48eOJiYnhgQce4J///CejRo1Cp9Nx3XXX0aNHD9q1a8fbb7/N6NGjsdlsDBw4kHvuuYe0tDR8fHyYPn06//3vfwkMDOT999/31NcmhGhA3FHPXUz//v2ZNWsWw4cPB8q7DBQWFlJcXMyoUaP4448/uPHGG4mOjuaaa66prVMVQjRS7qzHPvjgA1cTWIAHHniAZ599ltGjR3PrrbcSERFBz549OX78eKX9TCYTb7zxBq+99hqLFy/m888/5+2333b1R//www+l2aw4j6JWfFcuRD2UnJzM6NGj2bZtm6dDEUIIIYQQQlwCaTorhBBCCCGEEMKt5I2mEEIIIYQQQgi3kjeaQgghhBBCCCHcqtrBgCwWC0eOHCE4OLjKqSWEEKKu2e12cnNzad26NV5eXpe0j9RlQoj6RuoyIURjcLG6rNqa6siRI9xxxx21GpwQQtTErFmz6Ny58yVtK3WZEKK+krpMCNEYVFeXVZtonhmieNasWYSHh9deZEIIcYkyMzO54447LmsIdanLhBD1jdRlQojG4GJ1WbWJ5plmGeHh4URGRtZOdEIIUQOX02xM6jIhRH0ldZkQojGori6TwYCEEEIIIYQQQriVJJpCCCGEEEIIIdxKhi1rJEpKSti5cydms5nOnTsTERHh6ZCEEKJRy8zMJDExkaysLEpKSvDy8qJNmza0adMGRVE8HZ4Qtaq0tJT169dTWlpKv379CAsL83RIQoh6RhLNRuDUqVN88/XXFBYVoSgKvy5dypjbbmPgwIGeDk0IIRoVp9PJ2rVrWbxkMakpqa7leqMGu9WJqkK7du148sknCQoK8mCkQtSe9PR03v3fO2Rl56BRYMGC+Ywb9zd69+7t6dCEEPWIJJoNXEZGBpMmTsJL1fF/sdfhq/NmdcYu5syZg0ajoX///p4OUQghGoXS0lI+/fQT9u7dR3BTLR2vMxAYpcUnQEGjVXDYVdKP2jm89jBvvfUm//736wQGBno6bCHcymKx8OEH71NmzuGZuwsIMDmZtsyPzz77lPvvf4Bhw4Z5OkQhRD0hfTQbMIvFwrfffIvWAbdG9yfY6I9Rq2dYVE9a+TVl9uzZHDp0yNNhCiFEg2e1Wnn33f+x/8B+Og8z0us2I9Ed9JiCNWi05c1ktTqFpu309BxjIDc/m08+/QS73e7hyIVwr0WLFpGekcEDNxXQNNyByUflkVsK6da6jB9//JF169Z5OkQhRD0hiWYDpaoqM6bPIDs7mxFRvTHpvF3rFEXhusgeRHgFMeX7KWRkZHgwUiGEaPi+++5bEhIS6HazgSZtdRfsg+kfpqXTDXqOHD7CggUL6jBKIWpXQUEBv/66lN4dLDSLdLiWa7Vw5w0ltGluY8qU78jLy/NglEKI+kISzQbqjz/+YOeunQwM60SE9/mTpOo0WkY06YPeqeGryZMpLi72QJRCCNHwbdmyhbVr19G6n56w5pfW4yS8hY6YTjrmz5/H8ePHazlCIerG0qVLcdhtXNfLct46jQbGDi3GYbeycOFCD0QnhKhvJNFsgLZv386ihQtp59+MjoFx1W7nq/PipibXUFhQyDdff43Vaq3DKIUQouEzm81M+WEKQU20xHbXX9a+bQYY8PLTMGnSRKl/RYNXWFjIihXL6dneQqCfs8ptAv1UenewsGb1H/KAWwghiWZDs2HDBqZOnUoz3wgGR3a76BD6oV4BDI/qzclTp/j5559xOqu+OAghhDjfokWLKCwooP1gw2VPWaLTK3S8Xk9qahozZ86spQiFqBvz58/H4bAxpOf5bzMr6t/FgtVm588//6yjyIQQ9ZUkmg2Ew+Fgzpw5zJw5k3hTE0Y26YNWubQfX3PfCAaHd2H37t0sWbKkliMVQojGIS8vj6VLl9C0gw6/0JpdLoObaonroefXX39l165d7g1QiDqSmprKyt9/p19nM8H+F35gHRropFUzO6tW/Y6qqnUUoRCiPpJEswGwWq188/XXrF27ll4hbbkxqhdajfayyugQGEe3oJasXLmSLVu21FKkQgjReCxYsACn6qBl78trMnuuVtfoCYzUMnHSF2RlZbkpOiHqzvRffsHL6OT63hd+m3lGnw5m0tMzZeR7Ia5ykmjWcw6HgynfT+Hw4cMMi+pJn9B2l91864y+YR2JNUUyY8YMTpw44d5AhRCiEcnJyWHVH6uI6aTFy3Rll0qNVqHLCAN2p4WPPvqQsrIyN0UpRO07ceIEO3buZGjPEryNl/aGsn0LGyYfWLVqZS1HJ4SozyTRrOd+++03Dhw8wHWRPWjtH3NFZWkUhWGRPfHX+fD9d99LR30hhKhG+bQkTuJ6GNxSnrefhi7DDSQlJzN58mRpUigajOXLl+PjBX06XvoDEp0WerU3s2XLFgoKCmoxOiFEfSaJZj124sQJfl+xgi5B8bS5wiTzDINWz4io3pQWl/DLtF/kZkcIIc6Rk5PD6tV/ENNJi9GnZi1IqhIcraXdID2bN2+W/vKiQbBYLGzatJHubS0YLrMF+TUdy3A6nfzxxx+1E5wQot6TRLOecjqdzJw5E3+Dib6hHdxadrDRn4Fhndh/YD9bt251a9lCCNHQLViwADSq295mVhTTUUfT9jpmzJjO4cOH3V6+EO60c+dOrFYb3dtefnPvIH8nHVpY+X3Fcux2ey1EJ4So7yTRrKe2bNlCamoqA8M6obvMgX8uRbuA5sT4hjN/3jxKSkrcXr4QQjREWVlZrF79B8066dz6NvMMRVFoP8iAb5CGz7/4jNLSUrcfQwh32bp1K8EB0DTMUaP9B3S1kF9QKIMQCnGVkkSzHrLZbPy6dCnRPmE0942olWMoisKg8C6UWcr49ddfa+UYQgjR0MyZMwdFC3Hdr2yk2QvR6hU632ggPy+fqVN/qrXjCHEl7HY7e/bson2chRqOQUhcEztRYU6WLfvNvcEJIRoESTTroQ0bNlBQWEjf0PY1HmH2UgQaTHQJimfD+vVkZGTU2nGEEKIhSE5OZt26dcR202KohbeZFfmFaojvo2PNmj/Zs2dPrR5LiJo4fPgwFouVdrHWGpehKNCvk5mEhOOcPHnSjdEJIRoCSTTrGavVyorlK4g1RRLhHVzrx+sR3BqDVi8DUwghrnq//PILBm+F2G619zazorjuevzDtHzzzddYLJc2P6EQdWX37t0Y9OVvJa9E19ZlGA2wZs0aN0UmhGgoJNGsZ9avX09xSTF9QtrVyfGMWgPdg1qzZ88eTp06VSfHFEKI+mb//v3s2rWLFr106Ay1+zbzDI1WocN1enJzc5kxY0adHFOIS7Vr1w5axVjR6a6sHKMBOrUsY8P6dTIokBBXGUk065GysjJ+X7GCFqYowrwC6+y4nQNbYNJ7s3DBQpnuRAhx1XE6nUz9eSqmIC0xHa/wrvoyBYRrie2uZ8WK5TIKrag3MjMzSU1Np02szS3ldWtTRnFJKfv27XNLeUKIhkESzXpkzZo1lJSW0ju0bt5mnqHTaOkT0o5jCcfYv39/nR5bCCE8be3atZw6eYrW/XVotHXzNrOiln30+AZpmTRpImazuc6PL8S5duzYAXBF/TMrim9qx+SDjD4rxFVGEs16oqSkhJUrV9LaL4ZQY0CdH7+NfzPCvAKZN3ceVqt7LixCCFHfmc1mps/4hZAYLWFx7p9K6lJodQqdhunJzsnh+++/l5YlwuO2btlMs0gHASb3/C5qNNChhYXt27ficNRsqhQhRMMjiWY9sXz5cmxWG31C23rk+JrT053k5Obw228yDLkQ4uqwYMECigqLaDvQUKujfF9MQLiWNv31rF+/nhUrVngsDiGys7M5cvQonVuWubXcjvE2SkrM0kRciKuIJJr1QHZ2NuvWrqVjQBwBBpPH4oj0DqZrUEv+WLWKQ4cOeSwOIYSoC+np6Sz9dSkxHXX4hXr+cti8q46oNlp+/PFHaWIoPGbt2rUolPerdKf4aBtGA2zbts2t5Qoh6i/PX1kFCxcuRKfo6BXaxtOh0De0PeHeQfwwZQrp6emeDkcIIWqFqqr88MMPaPUqLfsaPB0OAIqi0Ok6I8HRWj777DPWrVvn6ZDEVcZms7Hy9xV0aGHFz9e9Tbh1WmgbW8b27VulebgQVwlJND3s6NGj7Nmzh17BbfDWGj0dDlqNlpFNrkHn1DDxi4lkZWV5OiQhhHC7bdu2sWfPHlr11WHw8lyT2XNpdArdbzYQ0kxh0qRJ/Pjjj9JvXtSZtWvXkl9QyMDutTOva8cWNnJy8jhx4kStlC+EqF8k0fQgh8PBnNlzCDL60SmohafDcfHVeXFr0/44LTY++/RTMjIyPB2SEEK4TWlpKVN++J6gKC3RHep2OpNLodUrdL/ZSIteepYtW8ZLL78o00KIWmez2Vgwfx4to+3ERtXOfJdtY63odbBx48ZaKV8IUb9IoulBf/75J+kZ6Vwb1hmtUr9+FIEGE2OiB6BaHHz+2efyZlMI0Wj88ssvFBYU0n6oZwcAuhBFo9C6r4E+d3hRasvhnXfe4YsvPqeoqMjToYlGatWqVeTk5nFj39JaO4bRAO1blLFhwzoZfVaIq0D9ym6uIjk5OSxdupRWftHE+IZ7OpwqBRhMjI4egLPMzqSJk+QGRwjR4O3Zs4dVq1bRopcOv5D6fwkMitLS924jbQYY2LxlEy+88A927drl6bBEI1NWVsaCBfNoF2ujeS29zTyjV7sy8vML2blzZ60eRwjhefX/KtsIOZ1Opv08Da2qYWB4J0+Hc0EBBl9uadqP4sIivv7qK+krJIRosAoKCpj05UQCIrS06Kn3dDiXTKNViOuup9//eaH1NfP+++8zbdo07PbaTQjE1WPlypUUFhYz7Jrae5t5RstmdsKDVRYtWiCDAgnRyEmi6QG//fYbCccTGBzeBR+dl6fDuagQoz8jonqTlJzML9N+kQuDEKLBcTgcfP75Z5SaS+h8owGNtn42mb0Q30ANvccaieuhZ8mSJbzxxuskJyd7OizRwFksFhYtWkCHFlaahtd+c1aNAtf3LiEhIVH6agrRyEmiWcc2bdrE8uXL6RIUT0u/pp4O55LF+IYzKLwLO3ftZPHixZ4ORwghLtmZqUwOHDhIx+v1+AY23EufRqvQpr+BHrcYSctM4l//epmpU6dSUFDg6dBEA/Xrr79SXFTCsGvMdXbMLq2sxEfb+WHK92RmZtbZcYUQdavhXm0bGKfTyYoVK5g+fTrxfk3oH1a/m8xWpWNgHN2CWrFy5UqWL18ubzaFEPWeqqrMnj2blStX0uoaPZEt698oszURFquj/31GojtqWLbsN/7f0/+PSZMmcfDgQZxOp6fDEw1EdnY2ixYtpHu7MqJC625wHkWBu24oQsHMhx+8T3FxcZ0dWwhRdxrHFbeeS01NZc7sOSQcT6BDQCzXRnRBU09HOryYfmEdsKl2li5dSl5eHmPGjMFgqB+TnQshREVOp5OpU6eybNkymnfV0aJXw+mXeSn0RoV2g4zEdndyao+drds3sG7dOkJCgrnmmr706tWL+Ph4NBp5pizOZ7fb+fLLSWg1Nkb2q/2+mecK9FN56OYCvpoH7777Dv/850v4+fnVeRxCiNojiWYtysjIYPny5ezYvh1vvRc3RvWilX+0p8O6IoqiMCi8CyadN5s2buLI4cMMHzGCbt26odPJr5MQon7Iycnhyy8nceDAQeJ762nZR19vpzK5Ut5+Gtr0N9DqGpWsEw5SDxfw27KlLFmyBJOfL127dKNr16507twZX19fT4cr6gGr1crkyZM5dOgwD9xUhJ+vZ1ooNYt08JdRhUxZfJLXX3+N//f/niEuLs4jsQgh3E8yAzdzOp0cOXKE9evWs2/fXgxaPb1D2tElOB6DpnE8TVcUhZ4hbYj2CWNd1l5+/vln5s6ZQ/sOHWjdujXNmzcnPDxcnqILIepcXl4eK1euZOnSJTix03WEkchWV8elTqNViIjXERGvw2FTyUlykJlYxvZdG1m3bh0ajUKrVq3p2rUrnTp1onnz5lJPX2UKCgrYvHkzv/22lKysbMYMKaFjvM2jMcVH2/nb2AJ+Wqry73//m0GDBjF8+HCioxv2g3khhCSaNeJ0OklOTqa4uJiysjJKS0spLCwkLS2NhGPHKCktxaT35prQDnQMjMOobRwJ5rkivYMZG3Mt6ZZcDhcmcWTvQbZv3w6AXq8nLDSMkNAQ/Pz8MJlM+Pv7ExgYSEBAAP7+/phMJrRarYfPQgjRUDidToqKiigtLXX9V1xcTH5+PpmZmSQkJHD8+HFAJaqtjtZ9jXiZrs5ESqtXCG+hI7yFDlVVKcp2knXCQeaJY8yceZgZM2bg5WUkPr4lzZs3JyIigoCAAHx8fNDr9a7/DAYDPj4+mEwmT5+SqKGCggL27dvHpk0b2b17N06nSoumdm6/vZTYWp4z81I1CXPw93vy+GObN+s3rGb16tVEN21C+w4dXb+foaGh+Pv7S3cdIRoQSTRrYM+ePUyZMuWC2yiKwoGikxwoOlk3QdUDGkWDgoKKis1mIzUtldS01Avu88477+Dt7V1HEQohGrIPP/yAXbt2X3Q7RQMFqbB1jmff1NRPCnqjBqvFicVSxv79+9m/f/9F93rhhRfo2rVr7Ycn3MrpdPLUU0+dN3hfQbGOGcv9PRTVhZ15/pyckkpyStX3EF988QUBAQF1GJUQoiYk0ayB2NhYOnXqRGFhIV5eXnh7e0uydAksFgsFBQXk5+dTVFREnz59MBqNng5LCNFADBgwkOPHEwHw8fHBx8cHX19f/P39pS65AqqqYjabKSgowGw2Y7VacTgcWK1WrFYrsbGxNGvWzNNhihrQaDTcfvvt7N27l6ioKE+HUyNnWizk5ORgsVgYOHCg9DUWooGQRLMGAgMDeeSRRzwdhhBCXFX69u1L3759PR2GEA3Krbfeyq233urpMIQQV6Grs/OKEEIIIYQQQohaI4mmEEIIIYQQQgi3kqaztax0bwZFqxPRR/jiKLaCVoOjqAzsTgLGtMWZZcaaUoBXp0hK1p7EnlUCGgVFq0Ef448ttQjVbEcfF4Q9rxQ1v6y8YJMObA4oU0EBjFqwOMr/rYC+VTC25EIUrQYMGlSLHYpPjy6nAfRaFK0G1e4EhwOcoBi1qFYnOMsHDfDuHIExPpiyxDzshRYUVUHRKPh0i8IYF4SqqlhP5KMN8kYX6OWJr1cIIaqUUryd/TlzaGrqhV7jTXLxZgKNsWgULWHe7bE7zdgdZezOnoZRG4BB643dacNbFwhApnk/DtWKjzaUflHPUGhN5WTRejLN+1BxoKAFFFTsQPlAaEGGOIps6djUYlccGnTE+Q0iy3KEQltShQgVAo2xNDP1x6j1YV/2HCzOfBS0dAwZS4ktC42ioWv4AxRak8ksPcTxglUoioYuoffSxNSVo3nLKHMW0y5oFFqNgVJbDmklu0gt2UnboJsI82mHU3WwNeMriqxp9Ip4jACjTBkhTlNtULwRLLuhdCM4LaANAnsm2M/8rmoAZ4WdfMrvM9TSSzzImfcZRvC5FmwJ5aN1hT4FmgDI+DfYM8C7FzT5EDQyoqwQjYkkmrWsaNVxVKsD68mC89YVzDzg+nfZkdyzKxwqqsOBNSHPtciWmEclxRWGJFcpTzLP/FsF2+Fc18fzOIEyByqOSotVS+XP5j0ZmPdknLe7Na2IwBGtKDtVQOnWFBSDltC/9sCeW4rlcA4+nSLQR8hQ+EII9zlesJqkoo20Db6FCJ8OruVO1UFayU78DE3wNzRxbbs1/Suc2MixHHVtm1G6D4CDLKhUdpmzAE4PUJtvrXzcUkcWvye/el48letPJyqQaz123nZO7CQUrazijFTyyxLJL0s8r9w9Ob+4PqcW76TEkVVpmzUp49Hhi50SAEqsWTQxdWN96oeop2v9ImsaI+M+ZFvGNxwvWAXA4bzF9I4cV0UsotEq2QjWExAwBlDAmgjGVlC6GTI/ANvxytufN1Cz85zPpdXcWFTnzP5mKF12dnHq0+cUuxZKt4JvHyiYD3k/QcBtEPwglG6H4t/Lz8HY+nIOLoTwMEk0a8CaVIDGR48uxOfiGyu1H09dU0tt5M05ANryk1OtDswHMilefQIAy9EcIv7W24MRCiEaE6fqYEv6JFSclNiyGRn3oWvdnuxfOJi7AC1GTIYIHKqVYlu6B6N1r3OTzDPOJJkAxwtXcbywcjJbYsviz+QPSCnZfHaZNYf8slOkFG+nuV8/TIaI2gla1A/WE5DyJOAEexqYd5e/vTR2grK9no7ufKlPl7/tVHzAWQA5k8oTzdRnwFlcnojGzvZ0lEKIyyB9NC9T6e50cqfvJXvKTuw5F2864ndtbO0H5SmOs481zySZAGqxFWty5Te4jsIyHAWWuopMCNGIaBQtId6tAAjzaVtpXZmjEAAHZRRYTzWqJPPSnf+Kya6aKyWZABnmvfyR9BZ7sqexLvXD8/YRjY0W122eooeyw+X/tiZ4LKILc5Q359WFAhrwH1W+WFfeUgF9w5yeRYirmbzRvEyOotN9JJ0qzlIbhFRerzpV7HlmFI1CwZLySt13UCwl21Oh+Jw2WZehyFLCwfSLXxyyi/MINQVdcJt2kfH4eV3BHFRnumxolUrJpuKlK+8LCtgyijFEl0+mbE0tInf6HlAh+M6OGGJkkmUhxOUZGvM6pbZsTPpILPYCEgvXEO7Tga5h9+OtC8ZLG8jB3AWoOLE5zBi1fpgMkYR7dWBf7nRXk9KaKC22kXTk/O4P5yrMseAfcmn91WNaB+Bj0tc4pnIKl9OOUaPo0GmM4KD8/6JxM8RAzHdgOwU+A8CyF6yp5U1Scz69rKIKix3sPVz1w+LMHDvhIeffTnZq44W/SXt5MeuaQuR/wNgBlNNNwmK+Acs+8O52eWUJITxOEs3L5Nu7fCAFrclwXsKk2p1kfb0NZ7EVRa9BtZX3TbClFZ9XzuU6mJ7ArROfvOJyABY88QW9Yztf/o5ahaA7O6AL8aVgyREcxWU4ssrf6moDvQi6vQP5S4/gLLZSsi0VxaDDp1MEZcdzXQmpLatEEk0hxGXTKnr8DOVvNDanTyK1ZDs6xYsxLb+hc+jdALQOGs7KU2+Qad+P3W6mxJ5JRumeKz520pEC/vN/q6+4nIpenzaYNt1Dr7CUiyeZ3rpgmvtdS0rxVjqG3k6QsQU5liM09e1e5fZWRzEJBSsJ8WpFuE/7K4xPeJx35/L/8n6C0i3lywrmQsjzkHPpb7X3HrYw4P/O74N8IeumtaR/j8t8qB31X/DqePZz0e9g3gFBD4DG+/LKEkJ4nCSal0lj0OI3oHmV6xxFZThPv7VUbc7zHjbrIkw4Ci2oZnuV+1+K/415jnZR8VWuO5iWwEvzPqp2mzPrL4c20hdH+um+QA4Ve66F0q0pWM8ZnCjw1rZYDmVhTy1yLSvZkoxqc1CyMQm0Cl7twvDpJH2ChBBXRquUX7o0ihalQkf4fdmzyTTvB8CgMWF1XvlDvor+8no3mrWp+kHZqcMFfP+fnRfcpuJ2dcXhtNEhZAxBXs04lrecLMtBOgSPxRjgX+X22zK+42TRWjSKjltbfImXTh4MNgoVB9GxJ4MtsfptL2Di603p3OZswrfnsJkn/pNSafmZZTWS+wNExkPhEnDkQ+7k8uW2NGg6oWZlCiE8RhJNN1HtTmxpRRhaBmNLKgBFcTUj1QYaQa/Db0gc+b8egStINNtFxV/0beSlbHOpXEnmaUXLKj/RNLYOwbdXNPpwE9akwrMrFPDuEH62H6tDxbdbFIr+MpvRCCHEOfpEPkFUcXfCvNqgPT0dgqo62Z87x7VNoLE5WsVIWulOLnOYzGo1axNw0beQl7KNO2gVIw61rNIyBS2qa5TP8nO2OovYlvE1J4vWubY7kDuPzmF3V1mu7vT3qUGH1V7M6uS3KbFnMbDJC4T7tHP/iYi6UTHR1PhB4dwaFdO5jXeVbymrW37ZzLsg/VUo+bPycumfKUSDJImmmxycuZ7gFAV0GtBrKiWTjvwy0FrJm7n3/JHCGzjfXk0xNPEDwLtbJBo/AxqDliPLdmA4mY/fkBaggi7YG32kn4ejFUI0BnqtD/EBQ0lOTsb/9LSQiqIhwBBDXlkiXtog15vNaFNvkou3eDDa2nFukgnlU6OMaP4hpY4c/kx+FxUHeek24vx152znpNSWw9aMr1zzcu7M+gG9xoee4X8lzLsdWo2RZadexH76OKeK1kui2ZBpg0i2jiPaa/bpvo9FF93FI5z5ULIBgOR0G9GRejANhbB/eDYuIUSNyKizbnDgwAE63HsthzMSweGs+o2lQ210SSZAydby5jGOQgvZk7dR+NtR9qzdRudx17N36y6sJ/IIGNYS355NPRypEKIxOXDgADExMRw4cHY+4uubvcmwZu/QJugmADSKni5h9xHmfXUkSDGmvvgbmxJijKd35DiSjxXy5OAFpCWY6Rr2AM38+qNTvDFq/Vmc+AypJTtIKd7GrqyppJXs4lTRBtJL9xAXMAizPdeVZBo1/rQIGOLhsxNX4sCBA8R0+hsHjiTDuVPmKCFV71QXFH/g3Hm37RxIgJhBBziQ0hGi3i+f9kQI0eDIG80acDqdzPxhGk2VYDac2kXfwQMAsLY24d+vFSWbk3HkmqvcVxvuUz6AjntacnmcIcoPR4GF3Jn7XP1Tc3NzAVh64E96h44kb+1xnvz4ZUbdPpp27drRpUsXAPbu3UunTp1QlEY42agQwq1UVXXVGQBbt24FIC/vbH/x/LKTJBdvI8BQ/prTqdpYm/wuRl3V/REbkyBDHKklO5hxpLxJbLSpNyWF5XXytGm/sGdlMYMGDSIxez8t+tg5eaCE+E6B6PVePHV/bzbvWINWMRLUvAXbdmxh/+EkYvp14cThTFqF3kOQsYUnT0/UkKqq7Nmzhx07dgCQm1/Fg3A1p46jqnjs011utJHgODs1UW5eef/qmXOX4bPlPa6/4Ua0Wi2dOnVi3759cu8gRAMhiWYN/OMf/2DChLOd0vv16weAT4dwfDpGYIgOIPvrbefvqNfgyLz43JsNhT7KhHfXKAqWHsGRXz7suTbQi00ny98wvPfrNxQ/bWXxhhWcykvjl7kzAZg1axaqqnLnnXcya9Ysbr/9do+dgxCiYZg9e7arzlBVlYceeqjSelVVWZ38NjZnKf6Gsy0oCm0pYEupsk9jQ+evi6bQngyUT1dS8fyKrRmuf8+eVJ5kHDlypHzBvMrlJPw+gcWLfwPg1N+b8unHEwG4bvgAVv62DvhM6uoG6szfzRmr97RgQK8iUM2AAaj5tGtu5ag8/+0fm8vHh/jP59nAv+ClfwHw3HPP8dFHH8nvoxANhLRFuEzp6emVkkyADRs2VPqsC/TCp290pWWKvwFsjavtrC2tmNxpuyk7Wv40VPHVY7gplg8+OTuy7cQlP3IqL63Sfo8++iiPP/44AOPGjaO42L0jQwohGpfi4mLGjRsHwOOPP+6qPwB2pczBqTrINO/HoClvgmfSh9Mn8olKZTS2JBOgxHE2mcy2HEV/evoHLUa8dUEUV/X2qgqLFy92/ftMkgmcTjLLPf7441JXNzDFxcWV/lYA3p+4vsLPsZ4kmbpoKr73KC5x8N43GVVu+tFH5fcXcu8gRMMgbzQv08iRI6tdN3jwYDSaCrm7U3VbE1mneuUF/e+3rwEY++UzaGqjyYlWwf7/7DidF06o8/Pz0WrLR58tLS3ltddeOy95F0KIM1577TXM5vLuCAUFBZXWPXXXBJ69/1PU03WkRtECCwFwYndLHay6of49Y+bH+wD47wNr3N70T6NocaoO12e7Gx9uFhQUSF3dwLz22msUFhZWWlZYVEpAj33odJf3u3c5fwOvflz+cHnIA8cu8Xd8T/l0cGgBJ3abE+dFDif3DkI0DPJG8zLt3HmB+c9UGk3fy2pd5JpxsSTzDIej/GbIbDbz5ZdfXmlUQohG7Msvv3Qlmg6Hw1V/VE2p8K+r6xJXMcl0N4fDIXV1A/Pll19W+bfiVKl/g+uoUD5iovaiSSbIvYMQDYW80bxMneLbszfhQJXr5jz+Cb1jO+PVIRxDjD8aXwOlO9IwNPOneM3JKzrulhN7uHXik1dUxkvDH+XWiU8yZ9wnNZ5n06d7FFp/L4pWn57s2aBFAbzahREwrCXPPPU0n37x2UXL0Wq1OBwOvL29z2vaI4QQFY0bN47JkydjNpvRarWoOHCevn++8847eez9NmSaD9LcbwD9mjwDQI75KP+fvfsOs6I8/z/+Pv1s77ssLB1pwqKAIL1YERAUsUQJ0WgkMT9rjBrlaxKiRixgRU0sUVQQC6CAWOjSe+9t6WXZXk6b3x/rHlhgK2crn9d1cbFn5pln7hkO9577zDPP/LD/bwHZ/7bVJ/jHb+YFpK9bH27HP34zj2c+7lPpz9t87LofOLwvo/SGZWCxWPzDl6V2GDVqFG+88UaRYtNiNvH/RjZm3JMR5errl1XZ9PzNztIbAv96OJGev9nJ3I9bVODZmlYeeX4/4/93vMRW+uwgUjvUsK+0ar4ZX08rtU3epmNkfL+T3HVHiLyxFfbGkf515mhnJUZXiawmrAmh5Kw+TObCvdiaRxVcOHB5sdUPI+LaFvjyPDw98hFC7EEldhUZHkFERMEvueDgYMaMGVMFByAitdWYMWMIDg4GICTMQVCozb/u6uEFRSZAuuuAf7ndEooJS9UGWsP85on2AesrIiJCubqWGTNmjP93baGQYBNj/t/ZjxOpSTyMeSiBsJCSr4Pos4NI7aBCs5waJrfg4YcfLrLsimYFVwfNwXYsccH+5fk7Uzn1zRbSpm71L/Ol5lVJnAHnMfBm/zpxgNfAl+8Be8GHOPfBDFK/3MSxt5ZhLD7Cn28Y6d/sod//iUax9Yt09e6Ed/1DXt555x1CQ2vyLz0RqW6hoaFMmDABgDcmjOPh5wf611mdPpJjf0OUoynJsbf5l9stocQFtSLYGkOoNbHKY65sl0ReT0JQ0UIy2BJf5HVopI2yGDRokP/nRx999LzL3333XeXqWiY0NPSc4aWP/74eoVG/PlfW5KiGqEoXGuLk8b88eN51he9PfXYQqR00dLYCXnnlFbp160Zidgjzlyxk4N3D6di9M01HXIl3dlqRtu796efvpBYysk7PUOc9kElwlwbkLD+I4fbh2lPwLDvD5eWWEbfx4tR3eOWVV/iNvRuPNbuFh6e8wKCbb+TywT1JTk4mf/tJVny/kE7X9qiuwxGRWuSWW25h3bp1vz5H8z4aRjzPqLueoWOTm7k0piOXxtxUpP3e9EUcyz3/bQ61nRkbO9K+P2d544geOMyhbEr9Grcvm5BwOwC/e7wP877ZQaPLDdw5Zq66tjeZu+Pp2rUrFouFYcOGsXFjwSRF7du3Z8SIEezYsYObb77Zvzw5uWK3W0j1uuWWW1i7Zg07N33HLXeN5qZrE8CXWbCyRs7EbAFrHDf3Tef//gEvvfQSPp+Pq6++2v8czZEjR/qfpysiNZsKzQowm80M6XINp6Zs4pJLBhHiiSElJYWkpCTS9m4lb/uJopMCmfC/Nkc58Z2qpVc1z2ICzOEOjFw3xhmzG7ZufAlrxkynUVJTMJsg18tbv/k/om9vj71hBDlrDpPx0y6SAHe7TOwN6v7D1EXkwphMpiLFzv13Ps3APiNJSkoqtv1ZS6grs7X5cJ/xygz4MGFhS+o3xDpb0q/BaJYffRdny3TemHcDMfWCuf/hWzics5Y8bzodYofjtEQQ7mhApKMxa45/iDnRRoe432A2mbnsssu47LLLAOjQoUN1HKIEiMnIoUPEGDpcsYuU+W1IqucDz+HSN6w2PvAc4tL6h0jZ8TNJLfqfXuU6APtvJznSCd7xYI2utihFpGxUaFaQJcKJyWbGcPuwRgeTlFQwZCnyxtYYhkHat9vI33YCAJPTipFb8DwzX0ZN/AaxfEzhduz1QslZcxjD7cPeOALXvtNXbvM2HKOeIwrXvjSi7+oAXZMwhzuwRhTcn2p4ThelhrduPVtURKpOcUUmQJSjqf/naMcl2CxBnMjdVgufp1lQSBanW8L/w2Q2s/hwwWMeTuRt54eUJ+nbYDTb02aQVy8VgD2Z82kefg0NQjux6PAr+Aw3Jsy0ib6RHWmzAYhyNKFpRJ9KPyKpQvm7wb0LgKR69rNWWoDKm6m4zGxNwX0IyMf/ZZC9LUmNuhVtl/UDuLYX/Jy9ECKGVGWUIlIBKjQryBoVROw9nfDle7DFFZ1VzWQyEXVja/JT0snZdJT8DcdOr/TW/m/UjQwXHmuu/yrmmUUmgOd4NljNOBpHYIsPwWQxY5wxX3lwp/qYbGbMQTYcjSKrMnQRuUhsTv0aAJs5mNT8HdUczYUo+cu4U/l72Jk255zl29Nmcih7dZFluzJ+BBP4jIIrogY+QqzxmDBjMlkIdxRfuEst5WwL4YMhbzu4dlD0/VQDikygYLTBGV8AmezQ5LNzm4X0gbQvwOSE4CurLDoRqTgVmhfAHGbHHFbwDaH7SCanpm7FEmon6pZLMTutOBpGkD5jW0D3ueXwrlLXFdempG3Ly5uai7VeKJ4jWQULzvrS3dkimsjBrfG5vGTM2Unu+qPY6ocRfWs7TBYzwZfVvck5RKTm8P5aTHl8gbuCuX9b8ffcF64rqU1Z1pfXrvS5eMg6Z3mkoxHHcjbiOesKrs3soFFod9JdB2gbPZQmEb2oF5KM2WQl2BYT0NikBjBZoN6vs7Pu7Au+tIKf7a3AVf7PJ+u35Z739ZnLz25TKvfuX38wg7UBBHeDk+9D2DVgb3S6naM5NDv33mQRqblUaFaQ+0Q2qZM2YDKbib6jPblbjuPLzMeXmU/ulmMEtYrDHGzD2SKGnDVF74dwdEwgf/XRCu33yW9eDUibQPCmnf5lEpSciCXYhiU6CF9mPkHJ9QBIn7md/B0nAXAfyMCb6cIaWUsf8SIitUaz8H4czdmAEcCrNh/+Y01A2gSCjTCaR/Vn56/DXs+W6zmF1yg4dqvJiccomBvAY7jo0eCRIm1D7QmVG6zUEJ5f/3ZUqMgE+NM/DpZrebnEPg65iyHji4LXqf8Bwwuh/aD+2AvvX0SqnArNCnLtScPI9WAArv3pBLWN/7WgMpH5024y5+zGZLdiuL1gNRUMmTWBKdSOa0tquffXpl5zpv3prVLbncg6RWxoVKl9BYKRf8ZDoMPthHZteJ42Hv/PQckJWCJq5nTqIlJ3HM5ey9oTnwasv4YtI3j2s76ltss4mUd4TNm+SGvYMqL0RiVwk8nWU9Mwc/q+O6c5Ei8ewEuINQ7j18LCbLLRKLQT2e6jtIy8/oL2K7VYxK2Q9hmEXgWZM8q1aftWThZ91uK8646d9BAfc+7HyfatyvmlcsY3FAyj/dWvX46Q9QP4/g1mPZFPpLZRoVlBzjax5O08iclswnlJDOZgG3F/uIL0H3aSu+4I+MDI8xTdyCi4v7EiwpwhdGlSfdPLm2ODIM+DL+uM2Q4Lb7s0gS0x7LzbRQxoSc6GIziaRGl2WRGpEquPfUSu5+QZS0qeUKc0waE2WnWMveC4KoPJZPLn4t5JT/Hj/icxMEjJWkaMswVp+Sl0ir+HJhE9qzdQqX5xDxb8AQjtC4cfL/Om4aEWenQKKb3hhXBtB2t9iP49+PIgay54DoEpREWmSC2lQrOCLKEOYu44t/AL7d4Qw+Uhb28a5HrO3RAwOSzgsJwuOk2//qnBE7BGXnsJ9gbhuA6k4zqYSc7qQ/jyPIT3b4otMRxb/Pl/AVnCHYT1aFzF0YrIxSwhuB0ZroP+IaMxzmaYsXE8bwsWkwO7OQSbOYQM9yFqyoQoNsJwk4nTEokJMyH2eE7kbj2nXbA1jlB7Aul5KdgsThKCk9mV/iM2czDBtihig1pzPHcL9ULa0zH+d1V/IFI7hF0DuSMh7Ss45x5fK6eH2VYBa33wngDDBc5LIfb/FSyPub9gdtmgzlUXi4gElArNALOEOogcVPCIEzw+3Gm5+DJcWKKcGHkerFFBmINsALgOZZC3/SQhXRtgCSoY/uRJy8PweDFZTPjyvJhD7ZiDbbgPZ2CyWfGcyMbn9WEJd2KLDSF30zFMdguWCAeOhhHk707F3iiyoG51eXEfysSdlov7QDq2pHCcTaLxHMvG3qSgjSfLhS06CG9GPu7j2djiQ7GE2XEfy8IaG1IwPNZs8t9XaU+KwJ4UQcgVDcBnYLLqW0YRqVk6J9xLm+ihmLFwPHcLCSHtsZmDyXYfI8QWj9lk8bdNzduN25dDQnA73N48Npz8gnBbfRJDL8dnuDAw8BluclwnMZvsYDLYn7GISHtz7NYgMlwHiXE0p0H4FQDsz1xGiDUWMJHjOU790I6YsZLtPkam+zBhtgakZC0h3F6fBqGdyfOk4zM82C2hZLqOEOloiMlUkFdz3CfJdh8n3NEAs8mKzRx03uNtGtGHYGsMQdYo+jd8llzPKUJsNfMKrNQg8Y8U/PGchIwfweSFoE7gaAm+DHDthbxt4DlecIXR7ARbo4I2Gd8U9BF+I+AF92HI/BncB8GbAUFtCma7NdwF91kGtQZLJHiOgSUaDBPkrQNrHNgbgs8F7r1gb3Y6Pks4hA+s+vMiIgGjQrOSmEwmsFmwx4VC3Pnb2OuHY69fdDhpcRPlOJIiC7ZJCC2yPKxr0enog1qd3pk5yIY1wsnZH03sZwxztfz6bEtzkA3bGX07Gv96n2cxI2VMZhOYz34guohIzVBYaDWydfcvC7OfO9t1tPP0B1ubxUnH+N+et79Ix+mRGfVC2he730ZhXf0/x3C671B7gn/SnTbRN/qXO62n79WMchYd/RFsiynTTLBxQa38P5tNFhWZUj7WGIi+vegySyQEXVbw53zi/lz0taMphHY/f9si+yp45jgmILjj6eVme0GBKyJ1ii5HiYiIiIiISECp0BQREREREZGAUqEpIiIiIiIiAaVCsw6aN28eJpOJmTNn+pdlZmYSFBTERx99dN5t9u7dS+/evenVqxf33ntvwWRGZ8jIyOCGG26gd+/e3HbbbeTm5jJnzhz69u1L37596dmzJ3a7nWPHjlXmoYmI1GiVkX9PnDjBddddR69evbjzzjvJzc0F4OWXX6Zr165ceeWVrFy5stKOSaQ0FXnf//a3v/V/hkhKSuLhhx8ust7r9VKvXj1/mzfffBOAt99+myuuuILu3buzYsWKyjokEQkAFZp1VKtWrZgyZYr/9dSpU0lMPHcijEKvv/4699xzDwsXLiQ/P58ff/yxyPq3336b6667jgULFtCmTRs+++wz+vfvz7x585g3bx59+/blxRdfJD4+vtKOSUSkNgh0/n3hhRe44YYbWLhwIVdffTVvvvkmhw4dYvLkySxZsoRJkybxyCOPVNrxiJRFed/3H3/8MfPmzWPSpEkkJCTwj3/8o8j6rVu30q9fP//njD//+c8cOXKEDz74wP++f/DBByvteETkwmnW2Tqqa9eurFmzBrfbjc1mY+rUqQwdOpR9+/bRt2/fIm3Hjx9Pp06d/Fcjs7OzCQ8vOhvuk08+idfrxTAMDh48SHLy6WeI7tq1ix9++IGlS5dW+nGJiNR0gc6/W7Zs4d577wXgyiuv5KmnnuLxxx9nyZIlmM1mUlJSiIqKqpJjEylOed/3l112GQBPP/00Tz/9NBEREUXarFmzhh07dtCnTx/i4+N58803Wb58Ob169cJqtdKoUSPy8vJIT08/Z1sRqRlUaNZRJpOJq6++mh9//JErr7wSm81GeHg4jRs3Zt68eee0P3HiBI899hjvvfceERER/l8AZ7JYLFx22WVkZWXxzDPP+Je/9957/PWvf8Vs1gVyEZFA59/k5GS+/fZb2rRpw4wZM8jOzgbAarXyr3/9i5deeonXX3+9Co5MpHjlfd8DpKWlsX79ev773/+esy4pKYlnnnmGoUOH+q/a33DDDUW+iAkLCyMzM1OFpkgNpcqgDrvtttuYMmUKX3/9NcOGDQPwf7N45p+1a9fy17/+lcmTJ7Nlyxb+8Ic/8Nhjj523z7Vr1/LRRx/x298WPGvO6/Uya9YshgwZUmXHJSJS0wUy//7tb39jw4YNXHvttXi9XmJiTj9b85lnnuHQoUOMHz+eXbt2VekxipytPO97gEmTJnHHHXcUPHv8LF26dGHgwIEA3Hjjjaxbt47w8HAyMzP9bTIzM88ZASAiNYeuaNZhV1xxBRs2bODkyZNMnjyZTZs2FfvNYmRkpP8bwcTERFJTU4us/8c//kFycjI33XQTERER/l8KGzZsoHXr1thstko/HhGR2iKQ+XfevHmMGjWKHj168Prrr9O/f382btzIv//9byZOnIjT6cThcGhUiVS78rzvAb7//nuef/7586575plnaNGiBX/605/4+eef6dixI1dccQVjxozB7XZz+PBhLBaLCk2RGkyFZh13zTXXkJKSQlBQUInt3njjDR544AEMw8BqtfqHsVx11VX8/PPP/P73v+d3v/sdr7/+OmazmbfffhuAHTt20KxZs0o/DhGR2iZQ+bd169aMHDkSk8lE27ZtmTBhAjabjQYNGtCtWzcAfve739G0adNKPyaR0pT1fQ/n/wxR+L7/29/+xp133skXX3xBSEgI77//PvXq1ePuu++mR48e+Hw+Xnvttco6DBEJAJNx9jzqvzpy5Ah9+vRh/vz51KtXr6rjEhE5R0XyknKZiNQ0ymUiUheUlpc0zkZEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJS1uBUejweAY8eOVVkwIiIlKcxHhfmpLJTLRKSmUS4TkbqgtFxWbKGZmpoKwPDhwyshLBGRiktNTSUpKanMbUG5TERqHuUyEakListlJsMwjPNtkJeXx/bt24mOjsZqLbYelbMcO3aM4cOHM2XKFOLj46s7nFpL5zEw6tp59Hg8pKam0rJlS5xOZ5m2US6rmLr23qkuOo+BUdfOo3JZ1alr753qovMYGHXtPJaWy4rNVE6nk+Tk5EoNri6Lj4+nXr161R1GrafzGBh16TyW9dv/QsplF6YuvXeqk85jYNSl86hcVrXq0nunOuk8BkZdOo8l5TJNBiQiIiIiIiIBpUJTREREREREAkqFpoiIiIiIiASUCs0ACw0N5c9//jOhoaHVHUqtpvMYGDqPUlF67wSGzmNg6DxKRem9Exg6j4FxsZ3HYmedFREREREREakIXdEUERERERGRgFKhKSIiIiIiIgFVpwvN3//+90yePNn/ev78+bRq1YrvvvvOv2zq1Kncc889ANx9991kZGSU2q/P56N///7cd999gQ/6LFOmTKFVq1asX7++0vaRm5vLSy+9xI033siQIUMYOnQoX331VaXtr6qNGDGCn376qbrDCIjZs2fz4YcfAvDEE08wYMAA/vjHP+J2uwFYs2YNjz32WJFtxo0bx+LFi6s8Vgkc5bKyUS6rPZTLLk7KZWWjXFZ7KJeVrE4Xmj179mTFihX+1/Pnz6dXr17MmzfPv2zZsmX07t0boMz/6AsXLqRRo0Zs3ryZvXv3BjLkc0yePJlBgwbxySefVEr/hmEwatQovF4vX375JdOmTePdd9/l3XffZcaMGZWyT6mYjIwM3n33XUaMGMHWrVs5fvw4s2bNIi4ujoULFwLw6quv8sgjjxTZ7v7772fs2LHk5eVVR9gSAMplpVMuqz2Uyy5eymWlUy6rPZTLSmet7gAqU8+ePfnf//7nf71gwQImTJjAyJEj8fl8mM1mli1bxr333svTTz8NwJ133snHH39MVFRUsf1OmjSJvn370qxZMyZOnMgzzzwDwNdff83PP/+M1+tl//79xMXFMW7cOKKjo+nfvz9Dhgxh6dKlHD16lPvvv5/bbrutxPi3bNlCSkoKb731Ftdddx0nTpwgNjaWl19+GcMwePzxxwH49NNPWbduHWPHjuWtt95i6tSphIWF0a5dO/bs2VNiMly+fDn79+/ngw8+wGKxAJCQkMCrr77qb7N161bGjBlDRkYGNpuNhx56iD59+pR4vBMnTuSLL77AarUSGRnJiy++SFxcHK1atWLFihWEh4dz4MABhg4dysqVK3njjTc4ePAgBw8e5MCBA9x4442EhITw008/kZaWxiuvvEL79u0ZMWIErVu3ZvXq1aSnp/Pb3/6W3/72t6W8E4o3btw4fvzxRywWC+3atePpp58mNDS02PjHjRvHnDlzsNlsJCUl8eKLLxIUFFSkz/79+zNgwACWLFlCdnY2Dz30EDfccMM5+/7yyy/58MMPMZvNNGjQgLFjxxIeHl5srBMnTqR///5YrVYcDgculwuXy0V2djZ2u53vvvuODh06kJSUVGS74OBgOnXqxJdffsldd91V4XMl1Ue5TLmsNMplUhsolymXlUa5rI4x6rjevXsbKSkpxo4dO4ybbrrJMAzDuO2224xVq1YZKSkpRr9+/fxtW7ZsaaSnp5fY35EjR4x27doZR48eNdasWWN07NjRyMzMNAzDML766iujS5cuxokTJwzDMIxRo0YZEyZMMAzDMPr162eMGzfOMAzD2LRpk9GhQwfD7XaXuK//+7//M5599lnDMAzjnnvuMd544w3DMAxj9+7dRu/evQ2v12sYhmHccsstxtKlS4358+cbgwYNMjIzMw2Xy2Xce++9xl133VXiPt5//33jwQcfLHa92+02+vXrZ8ybN88wDMPYuXOn0bVrV+PAgQPFHq/H4zGSk5ONjIwMwzAM45133jF+/vlnwzCKnuOUlBSjU6dOhmEYxuuvv25ce+21RnZ2tpGammq0bdvWmDhxon/d448/bhiGYdx1113GAw88YHg8HuP48eNGjx49jE2bNpV4jHfddZfx448/nrP8iy++MO666y4jNzfX8Pl8xtNPP238/e9/Lzb+Q4cOGd27dzc8Ho9hGIbx3HPPGWvXrj2n3379+hljxozxn6/OnTsbx48fL9Jmy5YtRo8ePYyjR48ahmEY//73v4333nuvxOMYOnSosWLFCv/rV1991RgwYIAxevRoIycnxxg2bJj/vXi22bNnGyNGjCixf6nZlMuUy5TLlMvqAuUy5TLlsosnl9XpobMAPXr0YOXKlcyfP5++ffsC0Lt3bxYvXlxkeEZZTZkyhS5duhAfH89ll11GTEwMU6dO9a9v164dMTExALRp04bU1FT/usL9t2nThtzcXHJzc4vdT05ODt9++y1Dhw4F4MYbb2TSpEm43W6aNm1K/fr1WbZsGXv27CEtLY0uXbqwYMECrrvuOkJDQ7HZbNx6662lHo/JZMIo4Qk3hUNQ+vTpA0Dz5s3p2LGjf+jL+Y7XYrHQr18/brnlFl544QWSk5Pp379/qbF069aN4OBgoqKiCA0NpUePHgA0atSI9PR0f7vbb78di8VCbGwsffv2ZcmSJaX2fT6LFi3ipptuwul0YjKZuPPOO1m4cGGx8cfHx9OwYUNuvvlmxo0bx8CBA+nQocN5+77zzjv956tdu3asXLmyyPolS5bQq1cv4uPjgYJx/aXdW7Jnzx4SExP9rx955BFmzpzJP//5Tz7++GNuvvlmdu3axf33389DDz1ESkqKv21SUlKlDyeSyqVcVjLlMuUyqR2Uy0qmXKZcVpfU+UKzZ8+erFq1qkhC69OnD6tXr2b58uXlSmg+n4+vvvqKTZs20b9/f/r378+pU6eYOHGiPyk4HA5/+7OTReE6k8kEUGIimTFjBvn5+Tz66KP079+fcePGkZqayuzZswEYNmwY06dPZ9q0adx0002YTCYsFkuRPgv3U5Lk5GQ2bNiAz+crsnz27NmMGzcOr9d73vPg8XhKPN7x48czfvx4YmNjeeGFF4oM+ShsU3ijdCGbzVbktdV6/pHdhUNJCvs683V5nH3MZx7X+eK3WCx8/vnnPPvss9hsNh577DEmTZp03r7PjN0wjHOO5eyYMzIyOHjwYInxmkym856TkydP8vPPP3Prrbfywgsv8Pe//50RI0bw+uuvF4mnoudJagblspIplxV9rVwmNZVyWcmUy4q+Vi6r3ep8odm9e3dWrVrF/v37adeuHQBt27bl4MGDrF27liuvvNLf1mKx+N/Q57NgwQLy8vKYP38+c+bMYc6cOcyePZvDhw+zaNGigMY9adIkHnvsMf9+5s2bx8iRI/3j+gcMGMDSpUuZNWsWN910E1CQqH/44Qeys7Pxer1Mmzat1P107NiRhg0bMnbsWFwuFwApKSmMHTuWli1b0rRpUwzDYP78+QDs2rWLlStX0rVr12L7TE1NpX///iQkJHDfffdx9913s2HDBgCioqLYunUrADNnzqzQuZk+fTqGYXD8+HHmz59Pz549K9RPjx49+Prrr8nLy8MwDD7//HO6d+9ebPxbtmxh6NChtGnThj//+c8MHTqUjRs3nrfvwnO/a9cutm7dSqdOnYqs79KlC0uWLPF/szphwgQmTpxYYrxNmzYt8m1Yoddee40HHngAq9WKy+XCYrFgNpuL3GR+4MABmjRpUp7TIzWMclnJlMuUy6R2UC4rmXKZclldUqcnAwKIjIwkNDSU9u3bYzYX1NUmk4lOnTpx6NAhgoOD/W2vvvpqbr/9dt5//32+/vpr4uPjueOOO/zrJ0+ezB133FHk26Lo6GiGDx/OJ598wvXXX1/u+I4ePcof/vCHIslny5Yt7Nmz55whFnfffTcTJ05k/fr1JCcn061bN44ePeq/bN+9e3cGDx7M8OHDCQoKIikpyX9D9Oeff86xY8d46KGHivRpMpl4++23eemllxg6dCgWiwWLxcJDDz3EwIEDAXjzzTd57rnnePnllwF47rnnaNiwYZGZ484UHR3NPffcw5133klQUBAOh4Nnn30WgKeeeoqnnnqKyMjICp0vKPiWadiwYeTm5vKXv/yFFi1aADBkyBDee+89EhISztnm0UcfLfLN0ZNPPsmtt97KwYMHGTZsGB6Ph3bt2vHss88SHh5+3vhbt25N3759GTp0KCEhIURERPDcc8+dN8adO3dy00034fF4GDt2LFFRUUX+rVu3bs2DDz7IyJEjAWjWrBnPP//8ed8Pha6++moWL15M586d/ct27NjBwYMH/UNo/vjHP3LXXXdhs9l44YUX/O0WL17MtddeW4GzLTWFcplyGSiXKZfVfsplymWgXHbR5LLKvQVUqtK6deuMTz/91P969OjRxtixY6sxosAr7gbymqRfv37G5s2bA95vWlqaMWjQIMPlcpVru6ysLGPQoEFGXl5ewGMSqQzKZTWDcpnIhVEuqxmUy6pPnR86ezFp2rQpixcvZvDgwQwcOJDMzExGjRpV3WFJgERERDBq1Cg++uijcm337rvv8vjjjxf5xlekJlMuq9uUy+RioVxWtymXlc5kGCXc+SwiIiIiIiJSTrqiKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZpyjpUrV3LHHXcwePBgbrjhBh5++GGOHz8OQEpKCg8//HCpfTz55JO0atWKjRs3Fln+7bff0qpVK3766adyxfTkk0/y0UcflWsbEbl4BCpv9erViyFDhjB06FBuuOEGnnjiCXJzc0vcrqT+N2zYUOq+R4wYUe6cKCJ1X6Dz2pAhQxg4cCDjx4/H5XJVOK4hQ4aQlZV1zvIXX3yRN954o8L9St2jQlOKcLlcPPDAA/z973/n22+/ZcaMGTRr1oynnnoKgEOHDrFnz54y9ZWYmMiMGTOKLPv222+JiYkJeNwicvEKZN76/e9/z7Rp05g6dSozZswgPz+fCRMmlLhNSf23b9+e8ePHl+t4REQqI69NmzaNb775hj179vCvf/2rwrFNmzaN0NDQCm8vFw8VmlJEbm4uWVlZ5OXlAWAymbj77rv53e9+B8AzzzzDnj17eOCBB0rta8CAAXz//fcYhgFAamoqJ0+epHnz5v42X3zxBbfeeitDhgzhmmuu4ccffwTgjTfe4O6772bQoEFFkqHP5+OJJ57gsccew+PxBOqwRaQWC2TeOpPJZKJLly5s374dKD5fndn/gQMH6N+/PyNGjGDgwIHMnTuXIUOGAJCdnc2TTz7Jddddx4ABA84ZpZGfn88999zD2LFjL+BsiEhdUFl5zW6387e//Y2vv/6azMxMcnJyeOyxx7jtttu45pprGDFiBCdPnmTBggUMHz7cv92OHTvo378/hmHQqlUrMjIycLlcPPPMM1x77bX85je/8edKkUIqNKWIiIgIHnvsMUaMGMF1113H3/72NxYuXEi3bt0A+Ne//kXTpk156623Su0rISGBpKQkVq5cCcDMmTMZOHCgf312djZTp07l/fffZ9q0aTz55JO8/vrr/vWnTp1i+vTpPPPMMwAYhsHTTz+NxWLhpZdewmq1BvLQRaSWCmTeOlNmZibff/89nTp1KjFfnd3/wYMHefLJJ5kxYwbBwcH+/t58800Mw2DWrFlMmTKFr776igMHDgAFReaf/vQnLr/8cv76178G4rSISC1WWXkNCj6fhYWFsWfPHhYsWEBCQgKTJ0/mxx9/JDIykmnTptGzZ0+OHz/O7t27AZg6dSo33XQTJpPJ389nn33GkSNHmDlzJhMmTGDfvn2BOXipM/RJXc5xzz33cMstt7B48WJWrFjBiy++yJdffskHH3xQ7r4GDx7Md999xxVXXMGMGTMYP348c+fOBSAkJIQ333yTn3/+mb1797J27Vqys7P92yYnJ2M2n/4u5P333yctLY3Zs2cXWS4iEqi89f777/PNN99gGAaGYdCrVy9GjhyJ3W4vMV+dyW6307Zt23OWL1q0iNGjR2M2mwkNDeXbb7/1r3vhhRfweDy89tpr5TtwEamzAvl57GwmkwmHw8H1119PgwYNmDhxInv37mXTpk20atUKs9nMkCFDmD59Og8++CAzZsxg4sSJRfpYunQpgwYNwmq1EhERwbXXXnvBcUndok/rUsTq1av56KOPCA8P5/rrr2f06NF8++23rFq1qkLfVF133XXMnTuX3bt3ExwcTEJCgn/dkSNHuPnmmzl8+DAdO3bkD3/4Q5FtHQ5HkdfdunXjgQce4JlnnvEPxxURCWTeKryXafr06Xz77bf89a9/xW63l5qvzuRwOIp861/IarUWWZ6SkuKfUGPYsGFcc801vPDCC+WKV0TqpkB/HjvT0aNHycrKolGjRnz22Wc8++yzOBwObrzxRvr06eP/jHXLLbfw3XffsXTpUpo0aUJSUtI5fZ35ecxms11QXFL3qNCUIqKjo3n77bdZv369f9mOHTsICwsjISEBi8WC1+stc38RERG0a9eO0aNHM3jw4CLrNmzYQGxsLKNGjaJnz57MmjWrxL4vvfRS7rvvPtLS0vj888/Lf3AiUicFOm+dT0n5qqz9X3nllf6rmFlZWdx9993+obPt27fniSeeYNGiRcyfP/+CYhWR2q+y8lp2djbPP/88d9xxB0FBQSxcuJBhw4YxfPhw4uLimD9/vr/fhg0bkpiYyCuvvMKwYcPO6atXr15MmzYNl8tFVlaWZs+Wc6jQlCKaNGnC+PHjef7557nmmmsYMGAAb7zxBv/9739xOp20aNECj8fDiBEjALjvvvvYsGFDiX0OHjyYTZs2cc011xRZ3qNHD6Kjo7n++usZMGAAQUFBZGRknHfK7EJWq5Xnn3+ecePGkZKScuEHLCK1XmXkrbOVlK/O7r84DzzwAC6Xi8GDB3P77bdz77330rp1a//60NBQ/v73vzN69GgyMjLKfyJEpM4IZF57//33/Y9tuv3222ncuDGPP/44UDA895NPPmHw4MH88Y9/pGPHjuzfv9+/7bBhw9i3b995h8XefvvttGjRgkGDBnHvvffSrFmzSjgTUpuZDI1BFBERERERkQDSFU0REREREREJKBWaIiIiIiIiElAqNEVERERERCSgin2OZl5eHtu3byc6OhqrVY/bFJHq5/F4SE1NpWXLljidzjJto1wmIjWNcpmI1AWl5bJiM9X27dsZPnx4pQYnIlIRU6ZMITk5uUxtlctEpKZSLhORuqC4XFZsoRkdHe3fMD4+vvIiExEpo2PHjjF8+HB/fioL5TIRqWmUy0SkLigtlxVbaBYOy4iPj6devXqVE52ISAWUZ9iYcpmI1FTKZSJSFxSXyzQZkIiIiIiIiASUCk0REREREREJKBWaUqcZhoHH46nuMEREpJwMw6juEERELpjX663uEKqN5seWOsPj8bBr1y4OHDjA4cOHOXjgAMePH8dnGCTEx9P/qqvo3LkzJpOpukMVEZESfP/998yaNYuWLVtyzz33EBQUVN0hiYiUy5o1a/j4k485euQoYeHhJLdvT4cOHbj00kuJioqq7vCqhApNqfUMw2DFihVMnz6NnJxcTCYTEaEOYsMdNG4dh9lsZv/RTD7//HMOHDjA0KFDVWyKiNRQ+/bt47vvviMmJoZt27bx2Wef8fvf/766wxIRKbMNGzbw8iuvYIsOJ6xjS7yZOSxbu5pffvkFgAZJDbhp6E1069atTn8mVaEptd6cOXOYMWMGjRLCuLxTUxKigrFaio4Kv/ySWFZsPcbChQuJjY2lV69e1RStiIiUZO7cuTgcDjp27EhKSgpr1qxh8+bNtG3btrpDExEplcvl4p1338UWFUb0NVdgslqAggsj3oxs8o+mcnz3Yd58802OHj3KTTfdVM0RVx7doym12pYtW5gxYwZtm0QzsFtjGsSGnlNkAphMJq5oHU+LBhF8O306x44dq4ZoRUSkJPn5+axdu5b69etjsVho1KgRYWFhTJs2DZ/PV93hiYiU6qeffuJUaiphXdr4i0wo+CxqjQglpGUjoq/rQnDLhkyZMoVt27ZVY7SVS4Wm1Fr5+fl8OWUKcVFB9OpQv9ShByaTid6XNcBmNTF9+vQqilJERMpqw4YNeDwe/3MizWYzLVq04ODBg2zYsKGaoxMRKZnP52PW99/jaBCHPTay2HYmk4nwzm2whYfw8ccf19nJz1RoSq01b948TqWl0feyBljMZRvf7rRb6Nw6ns2bN7Nr165KjlBERMpj7dq1BAcHEx4e7l8WHx9PWFgYP/zwQzVGJiJSunXr1nHyxAmCWzUqta3JYiYkuTl79uxh8+bNVRBd1VOhKbVSVlYW8+bNpVXDSOIiyzcbYdsm0YQF2/nxR31oERGpKVwuF5s2bSI+Pr7ICBWTyUTjxo3Zt28f+/btq8YIRURKNm/ePKwhQTgSY8vU3tm4HtaQIH788cdKjqx6qNCUWmnOnDm43W6uaJNQ7m0tZhOXXxLL9u07SElJqYToRESkvDZu3Ijb7SYxMfGcdYmJidhsNv+MjSIiNU1WVharVq/G2TQRUxlH2pnMZpzN6rNy1SoyMjIqOcKqp0JTap3MzEx+WbSINo2jCA+xV6iP1o2jCHLYmDt3boCjExGRili+fDkhISFFhs0WslgsJCYmsmrVKlwuVzVEJyJSsmXLluHzeglqVr9c2wU1b4DP62Xp0qWVFFn1UaEptc7PP/+Mz+elY8u4CvdhtZi5tGkU69et49SpUwGMTkREyiszM5PNmzeTmJhY7MRu9evXJz8/n/Xr11dxdCIipVuwcAH22AisEaHl2s4aFowjLopFvyyqpMiqjwpNqVXS0tJY/MsvtG1acJ/lhWjXLAaTycTChQsDFJ2IiFTEqlWr8Pl81K9f/JWAiIgIQkNDWbZsWRVGJiJSuqNHj7Jj+w6cTct3NbOQo0k9du7YyfHjxwMcWfVSoSm1ysyZMzGZoNMFXM0sFOyw0qpRBEuWLCY3NzcA0YmISEWsXLmSqKgogoODi21jMplITExk69atpKenV2F0IiIlW7BgASaziaAm595jXhbOxvXAZKpzw2dVaEqtceDAAVatXMlll8QQ7LQFpM8OLWLJz3dpggkRkWpy6tQp9u7dS0JC6ZO71a9fH8MwWLFiRRVEJiJSOq/Xy9x5c3EkxWN2Vmy0nSXIgSMhmkV17POoCk2pFQzD4OuvviIkyM5lLS78amahqDAnLRpEMHfuXPLy8gLWr4iIlM26desAqFevXqltg4KCiImJYcmSJXX2AeciUrusXr2atFNpBF/S8IL6cTZJJGX/fg4ePBigyKqfCk2pFVauXMneffvo3r4eNmtg37adW8eTm5vLnDlzAtqviIiUbu3atURGRuJ0OsvUvn79+hw9epQDBw5UcmQiIqWbOXMmtohQ7IkxF9SPs1ECJou5To2yU6EpNV5ubi7fTp9OUlwozeufO+39hYoOd3Jp02jmzp1b527CFhGpybKysti1axdxcWUfqZKQkIDFYtHwWRGpdtu2bWPbtm0Et25c7IzZZWV22HA0iGP+ggX4fL4ARVi9VGhKjTdr1ixycnPo1aH4ae8vVNc2CditZj79dCJer7dS9iEiIkVt2rQJwzDKdH9mIavVSlxcnH+mWhGR6vLVV19hDQkiqHmDgPQX1KwBp1JT2bJlS0D6q24qNKVGO3ToEL/88gsdmscSFVa2YVUV4XRY6d+xPvv3pzB9+vRK24+IiJy2fv16QkJCCA0t33PnEhMTSU9PZ9++fZUUmYhIyTZt2sTGjRsJbtcUkyUwJZWjQSzWICc///xzQPqrbio0pcYyDIOvv/6aYKeVzq0DNwFQcRrXC6dz63gWLlyoZ2uKiFQyt9vNli1byjVstlBsbCwWi4X169dXQmQiIiXz+XxM/PRTbOEhBLdICli/JrMZR/P6LF+xok48xkmFptRY69atY/fu3XS7NAGb1VIl+7yidTwtG0Yy9ZtvWLNmTZXsU0TkYrRjxw5cLleFCk2LxUJMTIx/xloRkaq0aNEi9u3dS+jlLTGZA1tOhbRsiOHz8dNPPwW03+qgQlNqJJfLxfTp06gXHcwlSZFVtl+TyUS/jkkkxYfy2WefsXv37irbt4jIxWTz5s3YbDaioqIqtH18fDzHjh3TJG4iUqVycnL47PPPcdSLwdEwPuD9W0KCcDRKYPYPs2v9o/dUaEqNNGfOHNLS0umZXHkTABXHYjZxXZdGRIXa+ejDD0lLS6vS/YuIXAw2btxIdHQ05gpeDSi8Erpx48ZAhiUiUqIvv/ySzMwMwjq3rrTPqKHtmpGVmVXr79VUoSk1zvHjx/n5559p2ySa+KjgaonBbrNwfddGuN35TJw4UTMbiogE0IkTJzhx4gSxsbEV7sPhcBAREaH7NEWkyuzYsYPvZ88muE0TbFFhlbYfW3Q4zoYJTJ06lZycnErbT2VToSk1is/nY/LkyditZq68tOzT3VeG8BA7fS+rz+7du1mwYEG1xiIiUpcUTt1fkfszzxQfH8/OnTtr9QcxEakd8vLyeHvC29jCgglLblHp+wu7/BJycnL45ptvKn1flUWFptQoixYtYvfu3fTqkIjTbq3ucGjeIIJLGkYyc+ZM3QckIhIgmzdvJiwsDKfzwh5blZCQgGEYuqopIpXus88+49jRY4R3b4+pCiaptEaEEtSqEbNmzeLAgQOVvr/KoEJTaoyDBw/y3bff0qJBBM3rh1d3OH492ydis5j44ovJGIZR3eGIiNRqbrebbdu2ERMTc8F9hYaGEhYWxsqVKwMQmYjI+a1du5affvqJkPbNscdFVtl+wzq0wOy08+5779XK27hUaEqNkJuby0cffUiw00qfyxpU+QRAJQlyWOnZvh67du1m8eLF1R2OiEitdiGPNTmfxMREtm3bxqlTpwLSn4jImbKysnjn3Xexx0YS2r5Zle7bbLcRdkUbdu3cyffff1+l+w4EFZpS7bxeL//730ekp6VxfZeGOOxV88zM8miRFEHTxHCmT5vGsWPHqjscEZFaa82aNdjt9go/1uRs9evXB+CXX34JSH8iImf65JNPyMzKJLx7u4A/M7MsnI0SCGqSyKTJk2rdEFoVmlKtDMNgypQpbN++g/6dkoiNDKrukM7LZDLR9/IG2G0m3n//v+Tm5lZ3SCIitY7L5WLNmjUkJCRU+LEmZ3M6ndSrV48FCxaQn58fkD5FRABWr17NwoULCU1ugS0itNriCO/SFuw2Xnv9tVqV51RoSrUxDIMZM2awfPlyul1ajxYNIqo7pBIFOawM6NqIU6mpvPPOBLKzs6s7JBGRWmXZsmXk5eWRlJQU0H6bNWtGTk4O8+bNC2i/InLxOnXqlH/IbEjbJtUai9lhI6JHew4dPMT7779fa+YMUaEp1aKwyJwzZw6XXRLLZZdU/FlqVSk+KpiB3Rpz5PBhXn3lFfbs2VPdIYmI1Aq5ubnMnDmT2NhYIiIC+8ViWFgYiYmJzJ49m9TU1ID2LSIXn7y8PF555RVyXXlE9EyuliGzZ7MnRBPWqRWLFi1i+vTp1R1OmVT/WZOLjsfjYdKkSf4is9ul9WrU5D+laRAXys29m4E3jzffeIOvvvpKQ2lFREpgGAaff/452dnZtG7dulL20apVKwD+85//KCeLSIVlZGTw/AvPs2ffXiJ6dcAaFlzdIfkFt25M8CUNmTx5MnPnzq3ucEqlQlOq1LFjx3j9tddYuXIFPdon0r1dYq0qMgvFRDgZ3q85l7WMY8nixbzw/PMsX768Vk49LSJS2ebMmcPq1atp3bo1oaGVc5+T0+mkQ4cOHDx4kNdff53MzMxK2Y+I1F3bt2/nqb/9jd179xLZ53IciTVrxJ3JZCK8Sxucjerxn//+lx9//LG6QyqRCk2pEl6vlzlz5vDyyy+TevIYg7s3pUOLmvWft7ysFjPdLq3Hrf1bEBlsYtKkSYwfN44dO3ZUd2giIjXG+vXrmTp1Kg0aNKBhw4aVuq+YmBg6derE4cOHee2111RsikiZuFwuPv/8c/7xj3+Q5XMRfX1XnA0C8wimQDOZzUT2SiaocT0+/PBDPvjggxo7QZC1ugOQus3r9bJ+/XpmzZrJiRMnadkwkp7tE3E66s5bLzrcyeAeTdhzOIOlm44yYcIEmjZtQu/efbj00kuxWuvOsYqIlJVhGPzyyy988cUXREVFcemll1bJCJaYmBg6d+7MypUree211/jjH/9ITExMpe9XRGofn8/HkiVL+HzSJFJPniS4dWPCL2+JyVrzHrV3JpPZTETPZCwRIfz088+sXbeO39xxB126dAnYjN6BoE/AElBer5cTJ05w6NAhdu/ezfr168jMzCI+KpghvZrSILb6poauTCaTiWb1I2hSL5xtKadYu+MI//vf/3A6HbRs2YomTZpQr149oqOjiYiIwOFwVHfIIiKVwufzsWnTJmbNmsX+/ftJSEigffv2VfrhJzIyks6dO7N69Wr+9a9/0aVLF9q1a0eLFi0ICqqZj9ESkarhdrtJSUlh48aN/DxnDsePHcMeF0XMDd2wx9TsJyCcyWQyEZbcAkf9WDKWb+H1118nNi6Onj160L59exo3bkxwcPXeX6pCU8pk3759fPXVV2RnZ2MymfzfSrvdbvLy8nC5XMVu26pRJA3iQsnKcbNt/6mqCrnamE0mLm8Zy5GT2ew6mM769etZv359qduFhIQQHByMw+HAZrNhNpuLfDDz+Xz+PzabjcGDB1f6MDQRkbI4fvw4n3/+OTt27Cgy7X58fDzx8fEcPXq0WuJq0qQJu3bt4pdffuGXX37xL3c6nYwcOZL27dtXS1wiUvkKh8OuWrWKjIyMYj+rOpvUw1E/Dk96Fp70rCqOMjCCWjXCfPA4J/YdYerUqUydOvWcNjabjaCgIJxOJw6HA4vFgtlsxufz4fF48Pl83HjjjfTu3TtgcanQlDJZvnw5Bw4cqNC22/ansW1/WmADqoOys7PL9WzOn376ibvvvrsSIxIRKZuVK1eyffv2c5YfO3aMY8eOVUNEJcvLy2PevHkqNEXqsEOHDjF79uxS2+XtPULe3iNVEFH1crvduN1uMjIyim3z2WefqdCUqjds2DCuu+666g7jouD1enG73Xg8Hv+VAZPJhNls9v9tNpuJjIys3kBFRH51/fXX06NHj+oOo1yqe0iZiFSuJk2a8N577+HxeKo7lBrHMAz/50yz2YzFYsFkMgU8L6rQlDIxm82Eh4dXdxgiIlIDmUwm/Y4QkRqnsh6nJGVTc6YlEhERERERkTpBVzQl4AzDqPAU9l6PC1f6Acy2IHzuHJzRTTGZzHjyMnBnn8AR2RAMA1fmEcxWOyaLg9zUfeQc3YTFFoTZYsdsC8brycUZ3RTD6yY/PQWLMxpP1lGsQRGENeqKJy8de0gsuSe2Y7GHY7JYyUtLwWQyYw2KwBHRkOwjm8g+ugl7aDyhDS7HEV4Pd04qZmswhs+Fz52HO+sIQXGtMFsd/mP35JzE4ozAbLEF8rSKiNQqubm5mM1m/yzbXq+XjIwMHA4Hhw8fxmq14nQ6cblcRSak2Lt3L2azmfz8fAzDIDg4mLCwMFJTU2nXrh1xcXEYhkFGRgY+n4/IyEgyMzMJDg7GarVy9OhRgoKCiIiIICsrC6fTqcdMiUiJ8jxuHBYrx3IzCbLaCbc7i6zfn5lKSlYqme48kkKiMf86Mea+rJMEW+2cyMtif9YpesQ3xWSYWHpiDyFWB13im9AyMoEjORnkuPNJyU4jwu7kVH4OzcJiqR8ayfrUg9QPCsdnGCSFRuEzDI7mZhJktRHjCPF/pj6Zl0We10uDkIKZcbPd+fgMg7CzYq1JlHkloLIOrSNjzy84o5sQ0bwPnpxU7BENMJmKv3ieeXA1uUe3gsWGJ/sEGD7/OrMzEp8rG3zuM7YwA75z+vHlpRV57Tq194xX+wBwZx4i99iWch1TfmoW+am7MdtD8bmyABNwelbF/PRDRLe+HoD0XfPIOboZW0gscZfdVhCXOw+fJw9rUGS59isiUlstXbqUEydOYDKZ6N69OxkZGWzatAmf79zcXZr09HTS09OBgonpimMymWjcuDF79+4t8nNwcDCdOnXi2LFjJCYmaiidyEXI7fPy8fZlnMrPpnFYDJfFJHFJRDwAU/euY1bKJuxmCy6fF5vJDAb4MPh96x5YzSbe3rywTPvZklZ0UqGlx/decOwOs5WBjS7l673rALgsugEn87M5kJ0GwODG7Yl1hnJ5TEPsFis/HtjC8uN7uT7pUjrFNbrg/V8IFZoSULnHtwMGeal7cGUewefOxR7REMObjyOyEZ7cUwTFt8IWEo/ZbCHz4BqyD64utr+zi8dfl1ZW+CUqKDLhzCITwOPKJ3XrbPLT9mOy2AFw56Ri+Lz4PPkcXzsJnzuXiOZ9Cal3aRVHLSJStXw+HydOnAAKRnksXry4yCNPKothGOzdu9f/c+FM6Tk5OSxbtgyXy8XOnTtp3749SUlJlR6PiNQcG1MPsfzXom9HxnHmH97By11vxmm1sexYwXKXzwuA+4wLHu9tXYSZio3SC5R8n8dfZAKsSz1Y5JPot/s2YACJwZu5s/kVfL1nLT4Mpu1bp0JTap+co5vJOrSekHrtCEls51+edWg9XlcOZlswQTHNyD6yCQBXegoA7qyCKe7zTu6q+qArkSfzAIXzmRleF5jMhCZ1xmS24M3PxOfOBQqPX4WmiNRdHo+HPXv2EBsbW6TYrK5YChU+P8/r9bJ27VpiY2NxOmvucDMRCazGYdGEWO3keFwYFFwltJjNeH0+GgRHkJpf/OPlfFRPDivO2dEUvj6ck87bW+bTISaJNSdTuDym+p+1rkJTyi1j3zJ87hwy9i0pUmhm7luC4fNgtocQ0bwP9siG5KXuIffY1oDHkJmdy9Zdh0psc+JUJrFRYSW2ad28PmEhQQGK6tchtYaPrJTl+Dx5OCIaENaoK+6cVIJiW+B15ZB9aB0WRwghickB2q+ISM2wbds29uzZU6Ftc3Jy/FckC6WlpZ3zKKcmTZpc0BT8TqcTm0330ItcTOYf2kG2x0WMI4QhjZNpHhGHzWzhf9uXsuFUyZ8ny8qVnUPq7pRi1+eeyiAo6vyzc0c3a4g95MIfLeIwWxnapANDm3SgXnD1zwSuQlPKLSi2OdmHNxAU26LIcmdsC3KPbSUopmB5UEwzzBbbGYVm4dCD83wzZLKA4S1zDFt3HWLon8ZVIPqipr79CFckN7/gfgoUPa6cw+vJObyeiGa9MZutnNw0HbM9pOCeU8AaHIMjokGA9i0iUv0KCziTyUR0dDQnT54s87Z79+7liSeeKLXdiy++SNu2bUtsYzKZ/FdSHQ4H+fn5REZGcskllxAVFYXFYilzXCJS++3LSgUgNT+btlGJ/gl0TuYVfyWzvFJ3pzD9gX9WaNsb3/o/6rVvdcExJAZH8PdV32E1W/jbZddRPySyTNvtTD/GoZwMuiU0xWYOXH5UoSnlFtGsN+FNumMyF337RF1yFZHN+2I64w1qC0vE4ozEm5dGQSFmApO5yIQ/AM7oxuSl7itXsQnw/GO30bZ5/XOWb9l1kKde+YIXHruVNs3PLeY27zrE316ZXK59lY2JoPjWeHJO+ocKu3NSyTm+HQCfK+fXZhYsNj0sXETqluPHj/t/Lk+ReaY//vGPNGnShL179zJhwoRzXpfFmcN18/PzAYiKiiIhIaFCMYlI7Ta8WUdm7t9Im6h6RWZpHXFJF+Yc2saBrDS2ZxwLyL56Pno30c2LDls9uWs/v7z6ET0e/R0xzU/fN5m6K4VFr34YkP0CxAeFsTntCG6fl2O5mWUqNE/kZfHKhp/xGQZHctO5tVmngMWjQlMq5Owi8/TygiLTMAzyTu7Ck5fxa5FZyIDz3K+Td3J3heJo27x+iVck2zRvEMArlsWwBmELjsabewprcAwRzXoVXMk9vgOvOxdXxmF/AR3WqCuOiAaYbU7NQisidYrX6+XUqVPAhd2X2aRJkyJXLM9+XVF79uzB6XTSvHkl/04QkRqnQUgk97XpWWTZxtRDfLBtMWE2J8FWe8D2Fd28YbFXJ2OaNwrIlctCXeIa0zW+KV/uXk2bqHoMapSMx/ARbnOSHFP9k56p0JSAydi7BHf2CcKb9sCVcZj0XfMAsNhD8fpnbK2DPLm/TvqTgys9h6MrPiQ0qRNhSQXfCOU6QshL3Y3VGU5Q7CVYg6p/zLyISKBZLBaCgoLIzc2t7lCKtXPnThWaIgLA0mN7yPa4yPYUTBYWYXOS7s6r5qjKZ8Xx/dzZogt/7zwIt8/L2HU/kpKVym9adMFcxmfaxzpDeaz9VRzKSadbQrOAxlf8ww1FysGdfYKsg6vJT9tPVspKDJ+XQ8cKvtmOvKQ/QfFtqjnCyuXLz/D/bHjdZB9cC8CBAwcIimlOSP3L8OSmcWLDV/h+TWgiInWBy+Vi9+7dpKen069fP7p164bFYuHEiROYzdX/MaPweZ5AjYhHRKpPttuFy+vB5fUQbnMSbLaSc6zg/s3CItNSxgKtJoh2BHM4J4PNpw6Tlp/D5t07MID1qQfK1U+LiHh6J14S0PszQYWmlJHP5+PLL7/0P2zbMAzWr1/vHx5lcYSDLYS3Jv7AuHc+453PvueKm0dzhGY4IhsS1rBzwYQ/dYAz4VJs4Wfc92k697+R2R7Cpk2baNiwIYMHD2bF8mVM+2kFb340DVd+NmvWrOGll15izZo1eL3eIue2NIbPS37GEdauWVVtjw0QESm0du1aNm3axGeffYbb7eaNN95g+vTp3H333Rw8eLBai7v9+/dz99138+qrr/LWW2+xcOFCxo4di8fjKfI7TETqnrM/q647nsJVD/6Oof/6C/9v/PO8/PLLrP95ERNv+X+c2nvAP2WltxblhZP52fx73Wxe2ziXWcsX8dktD5KxZCOr3p7EypUrmTx5Ml988QVer5f169fj8/nOm/sMw2DdunWsW7cuoHlRQ2elTP7yl78wbtw4Hn30UV555RW+/PJLbr31VqZMmcItt9yC2Wrn909/yPc/LSiyXZb71zHvhg9baDzurKNFJwIyWQhu2JmcA2vAV8Ov9JmsmCwW8o5uApOZiFbXgdeNI7op7ozDnNr6PVBwbCH12jFhwqcAfPfdd3z33Xf+bmYtu5rVq1f7Xw8aNIjvvvvOf25Lc2r7T0yZ8gWj/u8D//kXEakuJpOJX375hRdffJG33nqLTZs2+dedOnWKxMTEaostK6vgto05c+YA8P333wMwefJkVq9erRwqUoed/Vn1ntt+w+q5i87bNj8zmxhHCPk+D5nu/CqONDA2H9wHwKQnXgDgo7ff9a8787Pmq6++ek7uKzxXQEDzoq5oSqmOHDnC+PHjARg3bhy7du1i1KhRAIwaNYqsrCx27dp1TpEJsHvpZE5s+Jr0PYtxZx4+Z7ZZDC85+5fV/CITwPBgeH5NPoaP9O0/knVwNceWv0/eyV0ExbcitGFn4i+/g4y0k7w49uXzdnNmkQn4i9Bx48Zx5MiRUsNIP3GQJ1+eBJw+/yIi1aVFixa8+27BB5ozi0woGLZanc5+LmehwjysHCpSN2VlZRX5rLp+/fpii0wAhwdO5GfXyiIzMSicpJAoNhzZV2ybMz9rQtHcl5WVxf333+9ve//99wcsL+qKppTqhhtu8F9GNwyDbt26+Sd7yMnJYfTo0Xz66afn3fbup/6D/f8CN21zId8FXtZ/8T/fAnDLg6+X+WbpMjOZ8bjd5Y7RMAxuuOGGcwrRs437fCl5+R7g9PkvTBwiIlXtn//8p/8RImd76aWXypyfShuu9cknnwDwt7/9zX/PZWk8Hk+J65VDReqm0aNHF/ms2rVr1xLbT3non5htF1gWVeCz6Yr/TAHgu4eegwp+HrWazHgNH153yfkOTufZM3Pf6NGjycg4PddIenp6wPKirmhKqdasWVPk9fHjx/3/eXNzc3nnnXeKPDvtomf4KlwIn32uz+c/H3xCXn7BFeDC8y8iUl3eeeedGj3TbEmUQ0XqpjPzUm5uLnl55ZtN1oyJ2jMlEFjOM19ISc7Mfe+88w5e7+nn2Hu93oDlRV3RlFJdfvnlRQqguLg4srKyyM3NJSgoiPvvv59PP/30vMVms0b1WPjZMwA4YluSf2I7ACZrEIan4h9MVqzfxdA/VfyblifuG8zQP43jy9cfrJTnbP5jwize+3RGube7/PLLS20zatQo3n333SLnX0SkupyZk84WEhLCpEmTytTP5s2beeKJJ4pdP2LECJ544gmef/75Mj9X88YbbyzxSqlyqEjdNGrUKN559x3ycvMICgoqeL57CcVms35dufYfD+HjdL4Y0KANR/KyWHMypUz7PLJhG9Mf+Ge54rzivuFMf+CfDHrt6XI9XzPc5iTj11lyW4bHEWp38uWTL/LT9LJ99jwz940aNYo33njDX2xaLBb/sOMLpSuaUqqZM2f6hymZTCaWLFlCcHAwAMHBwYwZM4YlS5acd9vRfxqKPbwBIQ0uw+fK8c88W3vm86qYv4zsT3CQs1zbmEwmZs6cWWq7MWPGnHP+RUSqy5k56Wx/+tOfqjia0xo0aMCECRNKbKMcKlI3DX3wPoxfh8I6g4JYtmxZie1bD+pXpMgEmHVwS5mLzKqW4c7zX3HdnnGc1SdSCOqTXOp2hZ/nz8x9Y8aMISIiwt8mIiIiYHlRhaaUql69ejz88MMAPPLIIzRv3tz/y/udd94hNDSU5s2bM3DgwHO2jQoPIrJFPwyvG3fGATC8mG3B4KldD8QtjtkehiPmknOWhwQ7eODOq8+7TXLrJkVeX929HQAPP/Qg9erVK3WfoaGh55x/EZHqcmZO6tixY5F1sbGx1RESAE2bNsViOf9jtQrjVA4VqZvcdjO9HrsHgKdefp7k5OTzfk4tZHXaqyq0gAuxFsReP7r4fHvNgOuBgs/xUDT3hYaGFhkq++677wYsL2rorJTJyy+/TPfu3bn55psBuOWWW1i3bh3t27f3t5k2bRqvvPIKXlcueXm5/PO5Fwmu34HULTPw5Kb52/ncOVUdfqXxuTLJP5npf212hBVcuTW8dL+8BQB9ul7Ki+PeYf0v09m3ezujRgxm89bt/LL+ADf+5v/RIsHGrPmrufXO35d5v+c7/yIi1aUwJzVr1ozHHnuM/fv3+x8lUl3WrVtHamrBg9h79epF/fr1SU5Oxmq18uijj7J582blUJE6qk/iJeSPvJdbu13F768aBBR8Tn311Vc5GQRLj+2F1Ewa1WvAJ6P/TZwzDDOmc65q1mRtIuqxNf0IuR43IVY7ZktBwfnGOxOYOH8mCX07YxxL5zctrmD48OFs2rSJdu3aMXLkyHNy383DbmbK/B+IdgTTr0v3gMWoQlPKxGw2F3mmjslkIjk5+aw2Jh687zZswTFs27W/oNAkA0/uxXPh3BHZkNyjmwGIDA8BYPwbb3HZFT3pcmUPfJ48jq74iHYtG9GuZSPqX3ElALeP6FSu/Zzv/IuIVJfCnLRv3z4GDx7sLzSr82phdna2f/8jR46kd+/eNGjQwD/MVzlUpO6ymS0MbNweGp8uqCwWC48//jjvbVnEqRMNADi19wAAGTYfUbWoyATYnF74SDyDbI+LUxRcyDmeGELvh+4m3ZWLta2Zod2GYbFY/DnvfLlv+r4N/Gg6jtNrpbM7n3B7+W7/Ko4KTQkIw/BxcvN3uNJSMNuCaHHpUPbt3Y3j5BK8uad+bWUGznqOJhbAS21kcYRiC61H3smd/mXurGP+n5Ov6M2KqaHEuzaSvs9JROOuWGxBWJwReHNPYXZouJaI1C1xcXEEBwfTuHFjPvzww2odOuvz+WjUqJE/jm3btrFz50769OlT7D2lIlL3DWlSUGg1DYthSXAkti9fJzQ+ppqjunBRTZL4zZevcyTKAq5cGodGM7hxe5wWW5F2bp8Xm7nobQWFEwvlez3ke0t/TEpZqdCUUvk8eZzc9C1eVzbRrW/AHhZ/TpuMPYtwpRXcMO1z53Ji7ec4nBF48359Lo/ZCr7Tb1xHVGMMnwdX+sEqOYbAs2AYBvlpRW8StzojcEQ2xOoIJT/9MPVjwwBwpacABc9wim1/E/mn9uNz55J1YDUh9TtgMp//PiIRkdokODiY/v37M2PGjGotMs8UGxuLyWTCMAy8Xi+rVq2iV69e1R2WiFQxt8/L+pMHaRQaxR/a9ASgV2ILZsck8cuRnaS7a9f8IWcO9S28lBNfvx5ujxc3PkKsdpYd20uT0BjCfr1C+eameWxIPcRNTTpwfcNL/X0Na3o5kfYgGoVGExcUuAshKjSlVK70w/4rdbknd5630PSd8Z/TGhKLJ/sE3rx0QhI7kHN8G8ZZk/948jLx5qZecGybdx067/Ituw4W+bus25WdF58r+5ylJrOV3OPb8XnycUY39y8Lb9LD38ZssePKPErOkQ1AwQy8YUkdz+lLRKS2stvt5OfnV2jbvXv3lvh3eYSHh1OvXj0aN27M3Llz8Xg8ZGZm4na7sdlspXcgInXGpF0rWXRkF8FWO39NvoY9WSdJjq5Pp7iGJDhD+XDH0oDsJ3XXuTPVnty1v8jfJbUtKx8GNpMZj+HzjxfM8biJsgcTYXeyOa1gaG18UBg3Nk4m3+thQ2rB599VJ/YXKTRDbQ6GNOlQ4ViKo0JTSmULT8QWGofP4yI4ruV524Q364XFGYE9LAGLI4zM/UuxhSUSltQRW1g8adt/BMAe2ahgBtrMwwGJ7W+vTC5x/VOvfBGQ/ZSVK+OQvwDNO7GNmPY3Yw+NL3LFMuvgGn+RCWCxBWYcvIhITXFmoWm323G5XGXe9uxHkpT2iJKSZGRkEBQUhN1up1OnTixfvhyfz8e6devo3LlzhfsVkdonx12Qh1xeD29smsfJ/Gz/8ygtpsDNJ7Lo1Q+LXffLqx8FbD8AwVZ7kSuxJuCUK4dTrhzsZgsew0ezsILRJQ6LlUGN2rP2ZAqDGlXNRGgqNKVUGbsX4M46jiOqMbaQ8w+FstiCCG/c1f86uk3BFNLunFOk7ZxTsNBkwZW2H7Mj7IJjat28PlPffqTENidOZRIbVfK+Wjevf8GxnMmbn1nkde6xrWTuW0pYwytwRCYVLDwjmYU26Ejeqf0YQEhC2R5ALiJSk+Xk5JCZWZALzWZzmYvMJk2a8OKLLxZZlpaWRmRk5DntyuPo0aOsXr2ayy67DIvFgsfjwTBq16QfInLh7mhxBSE2B/syT3I0ryBHFd6b6DXOnkOk/KKbNeTGt/6v2PW5pzIIigovdtuKOLPIvK9VD5wWK+9sXYTdbOHhdv2IcAQTYQ/ytxncuD2DG1fdbNsqNKVUrl+vProyyn8V0pVxCHy/TvZjFPztO6sYq4iwkCCuSG5+wf0EVsEIeUdMc7y56djDEsg5ugmAjH1LiIscDkBog8uwOEKx2EPI3L8CV8ZB8k7uIii6GWZd3RSRWs7pdBIZGUlaWhoJCQkcPly23x3BwcG0bVs5X7gdPnyY9u3b061bN1JTU2nQoEGl7EdEaq5wu5OT+dnszy6YpPLSyEQ2pZ3OT8nRDQix2tmYeohMT/mH/ttDgqnXvlXA4i0vi9lMu5gGvNT1JswmMw5L9Zd51R+B1HgRzfqQfWQTwfGty71tUGwLMvYtPeceTUxWMM6d1cpsD8XnyqpoqFXDZPEXzfbw+gXFNOCMbU5IYjvsYYn+iSc8uam4Mg7jiGx0enOT2T8EOS9sH66Mg1iDojBZdL+QiNR+ZrOZHj164PP5sFgsrFu3jpSUit+HdCFiY2PJzMwkPj4eu92O3W4nIiKiWmIRkerXOiKBzacO0yA4kuYRcf5C02a28Me2vTCbzGw9dYRxG+dUa5wm8D9sJczqoFl4LOtTC+YdMc5qY8JE26h6XBZTMHIuyGqv4miLp0JTSuWMboIzukmFtjVbHcRffgeZKSuw2EPxefMxYSI4vjXpexbhdWUR3rQnVkc4mMxYnWFkH95I9uH1GD4PPq8Hsz0EDANvXpq/wMNsO12U+nwUfUSKGZMjHMOTA143WOzgPf3NlD32ElwndlEwP5cJe1QjTCYznpxUwpI6k3dqL3mpe8FkJqJJD4LiWpC69Xu8uemENb4SR1Qjco5twxYSjSMiidwTu/G5switX/QmapPJREy7ofjceVjs559KP6JJd4LjW2NxhGnmWRGpM0wmExZLQU7r0KEDLVu2JC8vjwMHDpCRkUGbNm3Yvn07qampBAUF4Xa7y3UfJ4DNZqNdu3YcOHCAqKgoDhw4QG5uLvHx8dhsNhwOB5dccglWqz7qiEiB6xq2pUt8E0JtDrw+Hyfzskhz5XJrs46Yf721qUVEHFfENeZQdhrJ0Un0qd+CJUd288vR3bh9Xv9wWzMmDAwsJgtBVpt/+Zmah8SwL/skZ19aMQFBFhvd4ptSLziCE7mZLDm2h/igMP54aW9CbU5S87Jx+bzUCy4Ybuv2edl08iAf7VhGYlA4f73sWnwYAb2/NNCUfaXSWezBRDbvc87ymEsHn7d9SGI7QhLbVW5QJYxsCE4498ptbLshRV6HNThdVAbHFT+E12QyF1tkFrIFR5e4XkSktgsKCiIoKIioqCj/siuvvDIgfRcOg23Z8vyT1YmInCnKUfC5zGa28NuW5+Yhq9nCva17FFl2Q+P23FDJ9zbe3LzoEwiinSFFXtvMFi6La8T4uNOj5CyYKjWmC1VzS2ARERERERGplVRoioiIiIiISECp0BQREREREZGAUqEpddq8efMwmUzMnDnTvywzM5OgoCA++uij826za9cuevXqRa9evfjzn/+Mz1f02UonT55kwIAB9O3bl969e7N9+3YA3n77ba644gq6d+/OihUrKu2YREQuJpWRx0+cOMF1111Hr169uPPOO8nNzQXgu+++o3v37nTt2pV//OMflXZMInLxqapcNmfOHPr27Uvfvn3p2bMndrudY8eOVeahFUuFptR5rVq1YsqUKf7XU6dOJTExsdj2f/3rX/nLX/7CwoULiYmJ4auvviqy/uWXX+bmm29m3rx5PPPMMzz77LMcOXKEDz74gCVLljBp0iQefPDBSjseEZGLTaDz+AsvvMANN9zAwoULufrqq3nzzTfJyMhg9OjRzJo1i2XLlmEYRrlnwhURKUlV5LL+/fszb9485s2bR9++fXnxxReJj4+vtGMqiQpNqfO6du3KmjVrcLvdQMF/6qFDh7Jv3z7/Nz6Ff9auXcuWLVu49tprgYJZEX/55Zci/f31r39lxIgRALjdbhwOB8uXL6dXr15YrVYaNWpEXl4e6enpVXugIiJ1VKDz+PnWL1myhEsuuYRRo0bRu3dvOnfujN1ec55HJyK1X1XkskK7du3ihx9+4KGHHqqiozuXHm8idZ7JZOLqq6/mxx9/5Morr8RmsxEeHk7jxo2ZN2/eOe2Tk5P59ttvufXWW5k5cyZ5eUWfi1Q4Pf+uXbv4y1/+wrRp01i+fDnh4eH+NmFhYWRmZurB4CIiARDoPF64vk2bNsyYMYPs7GyOHz/OkiVLWL16NRaLhR49etCjR48ij2QREbkQVZHLCr333nv89a9/xWyuvuuKuqIpF4XbbruNKVOm8PXXXzNs2DCAYr89evXVV/n000+54YYbiIqKIiYm5pz+VqxYwY033shHH31Ey5YtCQ8PJzMz078+MzOzSOEpIiIXJpB5/G9/+xsbNmzg2muvxev1EhMTQ0xMDFdeeSVxcXFER0eTnJzsvwdfRCRQKjuXAXi9XmbNmsWQIUPO2X9V0hVNuShcccUVbNiwgZMnTzJ58mQ2bdpU7LdHH374IS+++CKtW7fm0Ucf5frrry+yft26dYwcOZJp06ZxySWX+PsfM2YMbrebw4cPY7FYVGiKiARQIPP4vHnzGDVqFD169OD111+nf//+dOrUiQcffJD09HQcDgcbN26kefPmVXR0InKxqOxcBrBhwwZat26NzWarikMqlgpNuWhcc801pKSkEBQUVGK71q1bc9ddd2G32+nTp49/7PtVV13Fzz//zFNPPUV+fj733XcfAO3bt+eNN97g7rvvpkePHvh8Pl577bVKPx4RkYtNoPJ469atGTlyJCaTibZt2zJhwgRsNhvPPvssV111FT6fj0cffZTY2NiqOCwRuchUZi4D2LFjB82aNav04yiNyTAM43wrjhw5Qp8+fZg/fz716tWr6rhERM5RkbykXCYiNY1ymYjUBaXlJd2jKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCylrcCo/HA8CxY8eqLBgRkZIU5qPC/FQWymUiUtMol4lIXVBaLiu20ExNTQVg+PDhlRCWiEjFpaamkpSUVOa2oFwmIjWPcpmI1AXF5TKTYRjG+TbIy8tj+/btREdHY7UWW4/KWY4dO8bw4cOZMmUK8fHx1R1OraXzGBh17Tx6PB5SU1Np2bIlTqezTNsol1VMXXvvVBedx8Coa+dRuazq1LX3TnXReQyMunYeS8tlxWYqp9NJcnJypQZXl8XHx1OvXr3qDqPW03kMjLp0Hsv67X8h5bILU5feO9VJ5zEw6tJ5VC6rWnXpvVOddB4Doy6dx5JymSYDEhERERERkYBSoSkiIiIiIiIBpUJTREREREREAkqFZoCFhoby5z//mdDQ0OoOpVbTeQwMnUepKL13AkPnMTB0HqWi9N4JDJ3HwLjYzmOxs86KiIiIiIiIVISuaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgFVpwvN3//+90yePNn/ev78+bRq1YrvvvvOv2zq1Kncc889ANx9991kZGSU2q/P56N///7cd999gQ/6LFOmTKFVq1asX7++0vaRm5vLSy+9xI033siQIUMYOnQoX331VaXtr6qNGDGCn376qbrDCIjZs2fz4YcfAvDEE08wYMAA/vjHP+J2uwFYs2YNjz32WJFtxo0bx+LFi6s8Vgkc5bKyUS6rPZTLLk7KZWWjXFZ7KJeVrE4Xmj179mTFihX+1/Pnz6dXr17MmzfPv2zZsmX07t0boMz/6AsXLqRRo0Zs3ryZvXv3BjLkc0yePJlBgwbxySefVEr/hmEwatQovF4vX375JdOmTePdd9/l3XffZcaMGZWyT6mYjIwM3n33XUaMGMHWrVs5fvw4s2bNIi4ujoULFwLw6quv8sgjjxTZ7v7772fs2LHk5eVVR9gSAMplpVMuqz2Uyy5eymWlUy6rPZTLSmet7gAqU8+ePfnf//7nf71gwQImTJjAyJEj8fl8mM1mli1bxr333svTTz8NwJ133snHH39MVFRUsf1OmjSJvn370qxZMyZOnMgzzzwDwNdff83PP/+M1+tl//79xMXFMW7cOKKjo+nfvz9Dhgxh6dKlHD16lPvvv5/bbrutxPi3bNlCSkoKb731Ftdddx0nTpwgNjaWl19+GcMwePzxxwH49NNPWbduHWPHjuWtt95i6tSphIWF0a5dO/bs2VNiMly+fDn79+/ngw8+wGKxAJCQkMCrr77qb7N161bGjBlDRkYGNpuNhx56iD59+pR4vBMnTuSLL77AarUSGRnJiy++SFxcHK1atWLFihWEh4dz4MABhg4dysqVK3njjTc4ePAgBw8e5MCBA9x4442EhITw008/kZaWxiuvvEL79u0ZMWIErVu3ZvXq1aSnp/Pb3/6W3/72t6W8E4o3btw4fvzxRywWC+3atePpp58mNDS02PjHjRvHnDlzsNlsJCUl8eKLLxIUFFSkz/79+zNgwACWLFlCdnY2Dz30EDfccMM5+/7yyy/58MMPMZvNNGjQgLFjxxIeHl5srBMnTqR///5YrVYcDgculwuXy0V2djZ2u53vvvuODh06kJSUVGS74OBgOnXqxJdffsldd91V4XMl1Ue5TLmsNMplUhsolymXlUa5rI4x6rjevXsbKSkpxo4dO4ybbrrJMAzDuO2224xVq1YZKSkpRr9+/fxtW7ZsaaSnp5fY35EjR4x27doZR48eNdasWWN07NjRyMzMNAzDML766iujS5cuxokTJwzDMIxRo0YZEyZMMAzDMPr162eMGzfOMAzD2LRpk9GhQwfD7XaXuK//+7//M5599lnDMAzjnnvuMd544w3DMAxj9+7dRu/evQ2v12sYhmHccsstxtKlS4358+cbgwYNMjIzMw2Xy2Xce++9xl133VXiPt5//33jwQcfLHa92+02+vXrZ8ybN88wDMPYuXOn0bVrV+PAgQPFHq/H4zGSk5ONjIwMwzAM45133jF+/vlnwzCKnuOUlBSjU6dOhmEYxuuvv25ce+21RnZ2tpGammq0bdvWmDhxon/d448/bhiGYdx1113GAw88YHg8HuP48eNGjx49jE2bNpV4jHfddZfx448/nrP8iy++MO666y4jNzfX8Pl8xtNPP238/e9/Lzb+Q4cOGd27dzc8Ho9hGIbx3HPPGWvXrj2n3379+hljxozxn6/OnTsbx48fL9Jmy5YtRo8ePYyjR48ahmEY//73v4333nuvxOMYOnSosWLFCv/rV1991RgwYIAxevRoIycnxxg2bJj/vXi22bNnGyNGjCixf6nZlMuUy5TLlMvqAuUy5TLlsosnl9XpobMAPXr0YOXKlcyfP5++ffsC0Lt3bxYvXlxkeEZZTZkyhS5duhAfH89ll11GTEwMU6dO9a9v164dMTExALRp04bU1FT/usL9t2nThtzcXHJzc4vdT05ODt9++y1Dhw4F4MYbb2TSpEm43W6aNm1K/fr1WbZsGXv27CEtLY0uXbqwYMECrrvuOkJDQ7HZbNx6662lHo/JZMIo4VGqhUNQ+vTpA0Dz5s3p2LGjf+jL+Y7XYrHQr18/brnlFl544QWSk5Pp379/qbF069aN4OBgoqKiCA0NpUePHgA0atSI9PR0f7vbb78di8VCbGwsffv2ZcmSJaX2fT6LFi3ipptuwul0YjKZuPPOO1m4cGGx8cfHx9OwYUNuvvlmxo0bx8CBA+nQocN5+77zzjv956tdu3asXLmyyPolS5bQq1cv4uPjgYJx/aXdW7Jnzx4SExP9rx955BFmzpzJP//5Tz7++GNuvvlmdu3axf33389DDz1ESkqKv21SUlKlDyeSyqVcVjLlMuUyqR2Uy0qmXKZcVpfU+UKzZ8+erFq1qkhC69OnD6tXr2b58uXlSmg+n4+vvvqKTZs20b9/f/r378+pU6eYOHGiPyk4HA5/+7OTReE6k8kEUGIimTFjBvn5+Tz66KP079+fcePGkZqayuzZswEYNmwY06dPZ9q0adx0002YTCYsFkuRPgv3U5Lk5GQ2bNiAz+crsnz27NmMGzcOr9d73vPg8XhKPN7x48czfvx4YmNjeeGFF4oM+ShsU3ijdCGbzVbktdV6/pHdhUNJCvs683V5nH3MZx7X+eK3WCx8/vnnPPvss9hsNh577DEmTZp03r7PjN0wjHOO5eyYMzIyOHjwYInxmkym856TkydP8vPPP3Prrbfywgsv8Pe//50RI0bw+uuvF4mnoudJagblspIplxV9rVwmNZVyWcmUy4q+Vi6r3ep8odm9e3dWrVrF/v37adeuHQBt27bl4MGDrF27liuvvNLf1mKx+N/Q57NgwQLy8vKYP38+c+bMYc6cOcyePZvDhw+zaNGigMY9adIkHnvsMf9+5s2bx8iRI/3j+gcMGMDSpUuZNWsWN910E1CQqH/44Qeys7Pxer1Mmzat1P107NiRhg0bMnbsWFwuFwApKSmMHTuWli1b0rRpUwzDYP78+QDs2rWLlStX0rVr12L7TE1NpX///iQkJHDfffdx9913s2HDBgCioqLYunUrADNnzqzQuZk+fTqGYXD8+HHmz59Pz549K9RPjx49+Prrr8nLy8MwDD7//HO6d+9ebPxbtmxh6NChtGnThj//+c8MHTqUjRs3nrfvwnO/a9cutm7dSqdOnYqs79KlC0uWLPF/szphwgQmTpxYYrxNmzYt8m1Yoddee40HHngAq9WKy+XCYrFgNpuL3GR+4MABmjRpUp7TIzWMclnJlMuUy6R2UC4rmXKZclldUqcnAwKIjIwkNDSU9u3bYzYX1NUmk4lOnTpx6NAhgoOD/W2vvvpqbr/9dt5//32+/vpr4uPjueOOO/zrJ0+ezB133FHk26Lo6GiGDx/OJ598wvXXX1/u+I4ePcof/vCHIslny5Yt7Nmz55whFnfffTcTJ05k/fr1JCcn061bN44ePeq/bN+9e3cGDx7M8OHDCQoKIikpyX9D9Oeff86xY8d46KGHivRpMpl4++23eemllxg6dCgWiwWLxcJDDz3EwIEDAXjzzTd57rnnePnllwF47rnnaNiwYZGZ484UHR3NPffcw5133klQUBAOh4Nnn30WgKeeeoqnnnqKyMjICp0vKPiWadiwYeTm5vKXv/yFFi1aADBkyBDee+89EhISztnm0UcfLfLN0ZNPPsmtt97KwYMHGTZsGB6Ph3bt2vHss88SHh5+3vhbt25N3759GTp0KCEhIURERPDcc8+dN8adO3dy00034fF4GDt2LFFRUUX+rVu3bs2DDz7IyJEjAWjWrBnPP//8ed8Pha6++moWL15M586d/ct27NjBwYMH/UNo/vjHP3LXXXdhs9l44YUX/O0WL17MtddeW4GzLTWFcplyGSiXKZfVfsplymWgXHbR5LLKvQVUqtK6deuMTz/91P969OjRxtixY6sxosAr7gbymqRfv37G5s2bA95vWlqaMWjQIMPlcpVru6ysLGPQoEFGXl5ewGMSqQzKZTWDcpnIhVEuqxmUy6pPnR86ezFp2rQpixcvZvDgwQwcOJDMzExGjRpV3WFJgERERDBq1Cg++uijcm337rvv8vjjjxf5xlekJlMuq9uUy+RioVxWtymXlc5kGCXc+SwiIiIiIiJSTrqiKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoiIiIiIiISUCo0RUREREREJKBUaIqIiIiIiEhAqdAUERERERGRgFKhKSIiIiIiIgGlQlNEREREREQCSoWmiIiIiIiIBJQKTREREREREQkoFZoXsZUrV3LHHXcwePBgbrjhBh5++GGOHz8OQEpKCg8//HCZ+tm3bx+tW7fmP//5T4XimDt3LiNGjKjQtiIiIiIiUvOo0LxIuVwuHnjgAf7+97/z7bffMmPGDJo1a8ZTTz0FwKFDh9izZ0+Z+po8eTIDBw7k888/x+v1VmbYIiIiIiJSC6jQvEjl5uaSlZVFXl4eACaTibvvvpvf/e53ADzzzDPs2bOHBx54oMR+XC4X33zzDffeey/BwcHMmTPHv+7JJ5/kX//6FyNGjOCqq65i9OjRGIYBwMcff8y1117LLbfcwowZM4ps84c//IGBAwfyn//8h8OHD3P//fdz8803M2TIEKZOnYrX66Vr164cPXoUgDFjxviviPp8Pnr27ElGRkbAzpWIiIiIiJSPtboDkOoRERHBY489xogRI0hMTKRTp0707NmT6667DoB//etfPP/887z11lsl9vPTTz8RFRVFmzZtuPHGG/nkk0+45ppr/Ou3b9/OBx98gMvl4vrrr2fNmjU4nU7+85//MG3aNCIjI88Zoms2m/3F54gRI/jTn/5Et27dyMjI4NZbb6Vt27b07duXBQsWMHz4cJYtW0ZaWhq5ubls3ryZli1bEh4eHtgTJiIiIiIiZaYrmhexe+65h0WLFvHII48QFBTEiy++yH333VeuPiZNmsTQoUMBGDx4MCtWrGD79u3+9T179sRmsxESEkLjxo1JTU1l6dKl9OzZk+joaMxmM8OGDSvS5+WXXw5ATk4Oq1at4t///jdDhgxhxIgRuFwutm7dylVXXcXChQs5dOgQ4eHhXH755axatYq5c+dy9dVXX9iJERERERGRC6Irmhep1atXs379en73u99x/fXXc/311/PQQw/Rq1cv9u3bV6Y+9u7dy4oVK9i7dy+TJk0CwGazMXHiRP75z38CYLfb/e1NJtN5+7Fai74NHQ4HUDAM1jAMpkyZ4u/n+PHjRERE4PF4+Oc//8mCBQvo3r07UVFRLF26lHnz5vH++++X72SIiIiIiEhA6YrmRSo6Opq3336b9evX+5ft2LGDsLAwEhISsFgspU7sM3nyZPr06cOCBQuYM2cOc+bM4bXXXmP69Omkp6cXu12PHj1YuHAhx48fxzAMpk+fft52oaGhXHrppXz22WdAwQRFgwYNYvfu3QQHB9OuXTs+/PBDunfvTvfu3fn2228JDg4mISGhAmdEREREREQCRYXmRapJkyaMHz+e559/nmuuuYYBAwbwxhtv8N///hen00mLFi3weDz+SXbuu+8+NmzY4N/e5XIxdepU/+RBhfr160fjxo2ZMmVKsftu1aoVDz/8MHfddRfDhg3DZrMV2/aVV15hwYIFDB48mN///vc8/vjjtG7dGoCrrrqKEydOkJycTNOmTTGbzRo2KyIiIiJSA5iMwmlARURERERERAJAVzRFREREREQkoFRoioiIiIiISECp0BQREREREZGAKvbxJnl5eWzfvp3o6OhzHj8hIlIdPB4PqamptGzZEqfTWd3hiIiIiEgxiq0gt2/fzvDhw6syFhGRMpkyZQrJycnVHYaIiIiIFKPYQjM6Ohoo+EAXHx9fZQGJiBTn2LFjDB8+3J+fRERERKRmKrbQLBwuGx8fT7169aosIBGR0mg4v4iIiEjNpsmAREREREREJKBUaIqIiIiIiEhAafyZSA3g9XpZs2YNhw4dolWrVrRq1aq6QxIRERERqTAVmiLVzO1285/33mPjpk1YzSa+//57+vTpw6233orJZKru8EREREREyk2Fpkg1+/rrr9m0aRNDkxy0j7Sy4JiL+fPnExUVxbXXXlvd4YmIiIiIlJvu0RSpRtu3b2f+/Pn0irNxWZQNi8lEvwQHl0dZmT59OikpKdUdooiIiIhIuanQFKkmHo+HSZ9/RpzTQu94e5F11yc6CLeZ+OTjj/F6vdUUoYiIiIhIxajQFKkmc+bM4cjRYwxMtGM1F70X02ExMTDRxoGDB5k3b171BCgiIiIiUkEqNEWqwbFjx5jx3XckR1ppEmo5b5tLwqxcGmHl2+nTOXHiRBVHKCIiIiJScSo0RaqYx+Phww8/wGHycV2io8S2AxLtWA0v//vf//D5fFUUoYiIiIjIhVGhKVLFvvnmG/bv28+QBnZCrCU/viTUZmZwfTu7du3i+++/r6IIRUREREQujApNkSq0atUq5s6dS694G5eEle3pQm0irHSOtjJjxgy2bdtWyRGKiIiIiFw4FZoiVSQ1NZXPPp1I01ALfc+aZbY01yU6qOc08+EH75OZmVlJEYqIiIiIBIYKTZEqMuWLL/C53dyU5MBsKnnI7NlsZhO3NHSQl5PNxE8+wTCMSopSREREROTCqdAUqQLbtm1j3fr19I+3Em6r2H+7GIeZ6+rZ2bBxIytXrgxwhCIiIiIigaNCU6SSGYbB1G++IcZpoXOM7YL66hhV8DiUL6dMITc3N0ARioiIiIgElgpNkUq2ceNG9u3fT984K5ZyDpk9m8lk4oZEB9nZWZqFVkRERERqLBWaIpXIMAxmfPcdcU4Ll0aUbZbZ0sQ7zXSMsjJ3zhxOnToVkD5FRERERAJJhaZIJdq4cSP7U1LoHWct9wRAJekTb8dk+Jg9e3bA+hQRERERCRQVmiKVxOfz8d233xIfwKuZhcJsZjpHW/nll19IS0sLaN8iIiIiIhdKhaZIJVm9ejUpBw7QN94W0KuZhbrH2sDn46effgp43yIiIiIiF0KFpkglcLvdTJ86lQbBFtqEWyplH2E2M5dHWVi0cCFZWVmVsg8RERERkYpQoSlSCRYsWMCJ1FSuq2fHVAlXMwt1j7Pj8biZO3dupe1DRERERKS8VGiKBFhWVhYzZ3xH23ArjUIq52pmoWi7mXYRVubNnUNOTk6l7ktEREREpKxUaIoE2IwZM3Dlu7i6nr1K9tc73k5eXj5z5sypkv2JiIiIiJRGhaZIAB09epSFCxfSJcZKtKNq/nvFOsx0iLLy808/kZmZWSX7FBEREREpiQpNkQD66quvcJoLrjJWpb7xdrweN9OnT6/S/YqIiIiInI8KTZEA2bRpExs3bqRfvI0gS+VNAHQ+kXYz3WOsLP7lF/bu3Vul+xYREREROZsKTZEAcLlcTPr8MxKDLHSKtlZLDD3j7UQ6LHz26US8Xm+1xCAiIiIiAio0RQLiu+++IzX1FAPr2zFX4uNMSmI3mxhU38aBg4eYNWtWtcQgIiIiIgIqNEUu2M6dO/n5p5/oFmsjKbhyH2dSmuahVjpHW/l+1ix27dpVrbGIiIiIyMVLhabIBcjJyeGjDz8gzmmhX0LVTgBUnGsTHcQ6zLz/3/9qFloRERERqRYqNEUqyDAMPv30UzLS07k5yY7NXD1DZs9mN5sY3tBBblYmEya8jcvlqu6QREREROQio0JTpILmzZvHmjVruK6enXpB1Ttk9mxxTjPDG9rZv28f77wzQcWmiIiIiFQpFZoiFbB161a++uor2kcW3BNZE7UIs3JLQyfbt23jtfHjNYxWRERERKqMCk2RctqzZw/vvfsuiU4Tgxs4MFXTLLNl0TbCym8aOzmUsp8Xnn+enTt3VndIIiIiInIRUKEpUkaGYbBs2TJeGz+eMJOHOxo7sNeQ+zJL0iLMyr3NnNjzsxj36qt8/vnnZGRkVHdYIiIiIlKH1cwxfyI1SG5uLhs3bmTe3Lns2buX5qFWhjV0Emyt+UVmoTinmfuaO5l/zMXiRYtYsngxHS67jPbt23PJJZcQFRVV3SGKiIiISB2iQlMuWj6fD7fbjcvlIj8/n9zcXLKzs8nIyODUqVMcP36clP37OXjwID7DIM5p4aYkB+0jrZhr8HDZ4tjMJq6u5+CKaBvLU91s3LCWVatWARAVGUGLS1rSrFkzkpKSiI2NJTQ0FIulZk1yJCIiIiK1gwpNuWikp6fz1FNPVXh7jwHzjnuZd9wbwKiqz5mjfk+lpbNixQpWrFhRpM2wYcO46qqrqjgyEREREantVGjKRSM4OJjOnTuzcuXKIsutVisOhwOn00lISAhhYWGEh4dflFfzsrOzOXToECdPniQqKormzZtXd0giIiIiUgup0JSLhs1m45577uGee+6p7lBEREREROo0zTorIiIiIiIiAaVCU0RERERERAJKQ2dFysC37DtIPYyp5zA4eQgjOx0jpgHMnwxH9xY0strA4wZnCORlg9lS8MedB17P6c7sQeDKLboDswV8Z0wyZA+G0AhIPQIYp5cHRUBu+q/7sxesM4yC/m1OiIiFzFPQsBXmLgMxgkMxpr1Z0I/FCjc/gqV+cwyvB2PBFIyME+AMw9T6CsyNL62EMyciIiIiFyMVmiKlMI7swfjlm4Kfj6XAyYO/rjFRpAj0uAr+znKV3OHZRSYULTIBXDmQmnNuu8Ii88z9FXLnwYkDBT/vXI1vz3rwek/H6HXD5H/jHfYo/Pg/SD/u39TYtBBfvzsxVn0PcY0wDxqFyaL0ICIiIiIVo6GzctEwDu3COLij7O0P7sC3fCaG3QlBYQUL/UUmFCkya6Izi8xChg9mvlekyPSv2jAPMk7CrjVw8lBVRCgiIiIidZQKTbkoGPs345v0PL7J/8bYuab09nnZ+L58BWPRVxif/QtadIR6TQtWWh1grg1X+4ophA0DMEF4DHS6DoJCIb4RpituKBj226gNxsnD+GZ/gHH8QJVGLCIiIiJ1Q234tCxywYyczDN+zsBUWvuDOwuGmgK48mDDfEwj/o7x9XjITqtwHBl5LjYcPlXs+mNZucSHBhW7vn1iFOFOe/l3bLZA96GYThzElNwb37YVsG4urJpdsD43C7LTsfzpdQx3Pr63/h/4vBinjmK5/any709ERERELmoqNOWiYGp1BeRkgs+LqV3P0jc4uueMjU1QrznGicMXVGQCbDh8ij5vzqjw9vP/PJAeTRPKv6HPC8f2Yxzdi3FwO8TUP6eJsWAKRtvuBVc1I+Lg1BFM0YkVjlVERERELl4qNOWiYDKZMXW8uuwbNEuGZTMK7mk0DDiyC2btPH9bix28pUwAdJY3h3WjfWJ0kWUbDqfy56+WlLjuguxYVXA8ANGJkNCk4M+hnQWTCEXGgd2JyWzG/Jun4eTh08OFRURERETKQYWmyHmYfAZGYVEGv97XWIxyFpkA7ROji70yWdK6C3Lm8ezbVPB3bibmW/8K+bkQHovJagPA5AiG+s0DH4OIiIiIXBQ0GZDIeZjqN8fU7zccCG8MEfGnVziKv3+y1rA7T/+ccZL94x4qGFJcF45NRERERGoEFZoiZzCy0/F+MRbvV6+wxYigyX3Psq3bCGjSDmzOgit/tV39Fv7HtWw+coomf/+MTcsvcFiuiIiIiMgZVGjKRcMwDNavX4/x6zBYwzBYt24d69atw+fzsW7dOlZP/YQp385k9cJ5fPX+2wCc+OEz2LsR3HnVGH0A7d0IuQWz8Kbm5APwz4+/5sUXX8Tj8bB+/Xp8Pl+RcyUiIiIiUh66R1MuGl9++SW33norU6ZM4ZZbbvG/Bnj00Ud59dVXz7/hrrXQogF4PVUXbBWZt+sIAFO+/JIpX37JF5M+Y/Xa9f7zUXiuRERERETKQ1c05aKQlZXFqFGjABg1ahRHjhzh/vvv968fN25csduezM6DlldgGjgKug2B2KRKj7dS/DpcFpMZIhPIyncz9qe1RZqsXrseOH0+Ro0aRVZWVlVGKSIiIiJ1gK5oykVh9OjR5OYW3F+Zk5PDgAEDyEhP868vaYjozR/+jH3ifMBU8ExNOD2Dq+GDcowurehI1P+btQqAq9+e5Q/hgpgteDxufMXEU3g+cnJyGD16dImFuIiIiIjI2XRFUy4K77zzjr/QzM3NZe3atXiLq7LOx+cDn/f0czV9voI/tfUWRhPFFplnys3N5Z133qn8eERERESkTtEVTbkojBo1infffZfc3FyCgoJo1fISNqzfgLcMlxgviQ1ny1O/3qdosWG6YgDG0ukViuOXPUfp8+aMcm/3zwGd6PPmDH7604ALf8Zmw9aYhz3Ko4/9hfHjx5fYNCgoqMgQYxERERGRstAVTbkojBkzhuDgYACCg4OZ9f1sIiIj/OtLGo364tBup1/Ub4Fx6ghE1Su417G2MFugbbeCZ2imbMWY+xljxowhJCTkvM1Nv47PDQ4OZsyYMVUZqYiIiIjUAbXok7JIxYWGhjJhwgSgYBhtvXr1ePvem/3rHxo+sNhtYxIbnH6RsgW2LYdTR07fp1kb+LwQEQ9uFwBGVjqhoaE88cQTRZp17NgRgEceeQQoOFehoaFVG6uIiIiI1HoaOisXjVtuuYV169bRvn37gtdX9WSV9yQ4gkge/R9G/m42vpAIdh5NpUWLFsydO4e//OVxovJOgSmmdhWW57Ny5umfc9LxvvX/GNqyHf8H3HXXXbRr147HHnuMzZs3065dO0aOHOk/VyIiIiIi5aFCUy4aJpOJ5ORk/2vzVXdxebNkiG+EKSiUy24YBkDHX9df3q4tt2StISnMebrI7HEzLJ8J7rwqjv4ChERCdhrENIIjuwqWHd4DGLTN2k1KSgpJSacf2VJ4js48VyIiIiIi5aFCUy5aJqsNLulU/Hq7k0a/fRLf2p9h32aIawQrZtWuIhOgxWWQeQoaXALmX+9GjWsE21dguvzqIkWmiIiIiEggqNAUOYuRcRJj5xpMzTpgatoeS9P2GIaBMedTjKN7qju88tu4CLwe2L3u9LKQKCx/HA+AkX6iYBbdxGaYk/tWS4giIiIiUreo0BQ5i2/q63DiAMbaOVjueR4oGHbry8sJ2D42HE4tdllJ6yrE6zl3WcZx/4/Gwi8xtq+ATb9gNGqLKTK+4vsSEREREUGFpsi5fn20h//vQo3awrZlAdnFn79aUqF1AZOfi5GVhik0EmIbwPYVEBIBQZphVkREREQunApNuegZman45nyKKTQaU787MA99EGPXWkxNz5oM5/DOC95X+8QoXtn8pwAAFTtJREFU5v+5+EepHMvKJT40qMTtL0j9S+DQDkg7irHpF4y0oxhH9mC65neYWlyOyRF8Yf2LiIiIiKBCUwRj1Q+way0GYGreAVOTdpgu639OOxNgAJjMUL85HNxR7n39//buPKiq8/7j+OdwuSKIiIqIBk1wiegPMZHEBRXIjXUjKmpxiRo0bVxap0w066RT03asIxoxbdQx01QbcUQFxCUYY7QCBmKCS1wijTqmCu6SuAVku78/KPeXG0Gjv6NX8P36R855znnO9zzeuXM/PM89+DRsoD5BLf+/Jd+9otP//cGQfP1l/yxNkmQ/cUBuXfu5ri4AAADUK26uLgBwNSOwU1V49PKpWkZa23HPjJPx7AS5jX1TljFvSF0j/q/RzSK5W6WnBlX96zjJkKwNzSu2zf/U3mYYktt/r+3jJ6PX0B+1uUntn5R8mldtB3aU0f4JqVV7yeIuo9PT5tUIAACAh55ht9vtNTWcPXtWkZGRyszMVEBAwP2uC7iv7D9clawNZFg97uy8kutS9TmGmww3N9nLy2T/9qDsjXzl5t9WhsVd9opy2SsqpAsnZT+ULTVtJSM0ourvc177XvaCY9J3Z2S06yZ7aYlUWizD01tG6/aSDNmvFsloGiDD2kAVVy5KRz6X2nSWSktkBHaUThyS4d+26juWF05JLR+T4W6V/dJpycNThnfVklt78TXp9DEpsJMMj6olunZ7pQyjbvzOifclAACAuoGls4Akw6vx3Z3XsNHN+9ytMjp0d95ncZdhca/6W5aPdHQ+wbOx1KLNra/zo4f0WHz8pJ7POR/Q8UfX+1H/RvPWN/fT/gnnfXUkZAIAAKDu4BMmAAAAAMBUBE0AAAAAgKkImgAAAAAAUxE0gQfMzp07ZRiGMjIyHPuuXr0qT09PrVixosZzjh8/rn79+qlfv36aMWOGKisrndpfe+01RUVFKSoqSh06dFBMTIyjrbi4WN26ddPOnTvvwd0AAADgYUTQBB5AnTp10rp16xzb6enpatWqVa3Hv/baa3rllVeUnZ2t5s2bKzU11ak9ISFBO3fu1JYtW+Tr66t3333X0TZz5kwZhmH+TQAAAOChxVNngQdQz549tW/fPpWVlclqtSo9PV0xMTH6z3/+o6ioKKdjFy1apCNHjmjAgAGSpF69emnr1q2KjY29qd8FCxbo+eef16OPPipJWr58ubp06aLi4uJ7fk8AAAB4eBA0gQeQYRjq37+/tm3bpl69eslqtcrHx0ePPvpojUtcQ0NDtWnTJo0ePVoZGRkqKSm56ZiKigqlpqYqNzdXkvTVV19px44dWrlypSZNmnSP7wgAAAAPE4Im8IAaM2aMlixZotOnT2vUqFE6fPhwrTOaCxcu1PTp07VixQo99dRTKi0tvam/bdu2KSIiQp6enpKkVatW6ejRo4qKilJ+fr7279+vlJQUdejQ4X7cHgAAAOoxgibwgHr66ad18OBBXbp0SWvWrNHhw4drndFcvny55s2bp+DgYM2cOVODBg266ZiPP/7YaX9CQoLj50mTJmnSpEmETAAAAJiChwEBD7Bf/OIX8vHxccxC1iY4OFgTJkxQeHi4PDw8HN/XfPbZZx3HHD16VO3atbun9QIAAACSZNjtdntNDWfPnlVkZKQyMzMVEBBwv+sCgJvwvgQAAFA3MKMJAAAAADAVQRMAAAAAYCqCJgAAAADAVARNAAAAAICpCJoAAAAAAFMRNAEAAAAApiJoAgAAAABMRdAEAAAAAJiKoAkAAAAAMBVBEwAAAABgKoImAAAAAMBUBE0AAAAAgKkImgAAAAAAUxE0AQAAAACmImgCAAAAAExF0AQAAAAAmIqgCQAAAAAwFUETAAAAAGAqgiYAAAAAwFQETQAAAACAqQiaAAAAAABTETQBAAAAAKYiaAIAAAAATEXQBAAAAACYiqAJAAAAADAVQRMAAAAAYCqCJgAAAADAVARNAAAAAICpCJoAAAAAAFMRNAEAAAAApiJoAgAAAABMRdAEAAAAAJiKoAkAAAAAMBVBEwAAAABgKvfaGsrLyyVJ58+fv2/FAMCtVL8fVb8/AQAA4MFUa9AsKiqSJMXGxt63YgDg5ygqKlJgYKCrywAAAEAtDLvdbq+poaSkRN98842aNWsmd/da8yh+4vz584qNjdW6devk7+/v6nLqLMbRHPVtHMvLy1VUVKTHH39cDRs2dHU5AAAAqEWtCbJhw4YKDQ29n7XUK/7+/goICHB1GXUe42iO+jSOzGQCAAA8+HgYEAAAAADAVARNAAAAAICpCJoAAAAAAFMRNE3m7e2tGTNmyNvb29Wl1GmMozkYRwAAALhCrU+dBQAAAADgbjCjCQAAAAAwFUETAAAAAGAqgqbJ5s+fr+joaD333HPKyMhwdTl1VmJiogYOHKiRI0dq8+bNri6nTjl37pxsNptje/369RoyZIgGDBjAaxIAAAD3hburC6hP9uzZo7y8PG3cuFHfffedoqOj1b9/fzVo0MDVpdUp2dnZys7O1vr162WxWBQXF6cnn3xSjzzyiKtLe+Dl5ubqj3/8oy5cuCCpKnQuXbpUqampqqys1JgxY9SrVy81a9bMxZUCAACgPmNG00SVlZW6ceOGysrKVFxcLKvV6uqS6qR///vfioyMlJeXlzw8PNS9e3fl5OS4uqw6ITU1VYsWLXJs5+TkqE+fPmrcuLGaNGmi8PBw/etf/3JdgQAAAHgoEDRN9PTTT+uxxx5TRESEhgwZounTpzObeRe6dOmirKwsXbt2TdeuXVNOTo4uXbrk6rLqhAULFig4ONixff78ebVo0cKx7efn55jtBAAAAO4Vls6aaNu2bbp69aojJMXFxal79+7q3Lmzq0urU8LDw3Xo0CGNGzdO/v7+6tmzp9zdeanejcrKShmG4bTPzY3fLwEAAODe4hOniT777DMNGjRIDRs2lJ+fnyIiIpSXl+fqsuqca9euKTo6Wps2bdIHH3ygGzduqHXr1q4uq04KCAjQxYsXHdsXL16Uv7+/CysCAADAw4CgaaLg4GBt375dlZWVKi4u1u7du9WlSxdXl1XnFBQUKD4+XhUVFTp79qyysrIUHh7u6rLqpN69e2vXrl26fPmyLl++rF27dqlXr16uLgsAAAD1HOsRTRQbG6ujR49q8ODBatCggUaOHKmwsDBXl1XnBAcHq0+fPho6dKjc3Nz09ttvy9fX19Vl1UkBAQGaPn26xo8fr7KyMk2ZMkUBAQGuLgsAAAD1nGG32+2uLgIAAAAAUH+wdBYAAAAAYCqCJgAAAADAVARNAAAAAICpCJoAAAAAAFMRNAEAAAAApqrXQfNXv/qV1qxZ49jOzMxUp06dtHnzZse+9PR0vfjii5KkyZMn68qVK7ftt7KyUjabTS+99JL5Rf/EunXr1KlTJx04cOCeXaO4uFjz58/XsGHDNHz4cMXExCg1NfWeXe9+mzhxoj799FNXl2GKrVu3avny5ZKk119/XYMHD9b06dNVVlYmSdq3b59mzZrldE5iYqJycnLue60AAAB4eNXroNm3b199+eWXju3MzEz169dPO3fudOzbvXu3IiIiJOlnfxjPzs5W27Zt9fXXX+vbb781s+SbrFmzRs8995xWrlx5T/q32+2aNm2aKioqlJKSog0bNmjZsmVatmyZPvroo3tyTdydK1euaNmyZZo4caLy8/N14cIFbdmyRS1atFB2drYkaeHChXr55Zedzps6daoSEhJUUlLiirIBAADwEHJ3dQH3Ut++ffXPf/7TsZ2VlaWlS5cqLi5OlZWVcnNz0+7du/XrX/9ab731liRp/Pjx+vDDD9W0adNa+01OTlZUVJTatWunpKQk/f73v5ckpaWlafv27aqoqNDJkyfVokULJSYmqlmzZrLZbBo+fLg+//xznTt3TlOnTtWYMWNuWf+RI0d06tQpLV68WAMHDtTFixfl5+enBQsWyG6369VXX5UkrVq1Sl999ZUSEhK0ePFipaenq3HjxgoJCdGJEyduGVK/+OILnTx5Uv/4xz9ksVgkSS1bttTChQsdx+Tn5+vPf/6zrly5IqvVqvj4eEVGRt7yfpOSkrR27Vq5u7vL19dX8+bNU4sWLdSpUyd9+eWX8vHxUUFBgWJiYpSXl6e//e1vKiwsVGFhoQoKCjRs2DA1atRIn376qb7//nu988476tq1qyZOnKjg4GDt3btXly9f1gsvvKAXXnjhNq+E2iUmJmrbtm2yWCwKCQnRW2+9JW9v71rrT0xM1I4dO2S1WhUYGKh58+bJ09PTqU+bzabBgwcrNzdX169fV3x8vIYMGXLTtVNSUrR8+XK5ubnpkUceUUJCgnx8fGqtNSkpSTabTe7u7vLw8FBpaalKS0t1/fp1NWjQQJs3b1a3bt0UGBjodJ6Xl5fCwsKUkpKiCRMm3PVYAQAAAD9XvZ7R7Nixo+x2uwoKCnTs2DH5+PioY8eOatu2rfbv36+CggJJUvv27TVnzhxJVaHtViHz3Llz2rVrl4YMGaJhw4Zp/fr1unbtmqM9Ly9Pc+bMUUZGhry8vLR27VpHm91u1+rVq/Xee+9p7ty5Ki8vv2X9ycnJGjx4sFq2bKmwsDAlJydLkkaNGqXNmzersrJSUtXy31GjRikrK0sff/yx1q9frzVr1ujMmTO3HaPDhw8rNDTUETKrhYSEKCQkROXl5frNb36jKVOmaNOmTZo/f75ef/11FRYW1nq/FRUVmj9/vlatWqW0tDT17NlTBw8evG0t+/bt07Jly5SWlqa///3vatSokdauXauhQ4c6heUzZ85o7dq1Sk5O1vvvv6+vv/76tn3XZN26ddq7d6/S0tK0ceNGWSwWvfPOO7XWf+bMGaWkpCg9PV1paWkKCAjQN998U2PfN27cUFpampYsWaLZs2fr4sWLTu35+flatGiRli9frk2bNikoKMhpmXdNtm3bpl69ekmSgoKCFBYWppiYGDVq1EhhYWFasWKFpk2bVuO5PXv21CeffHIXowQAAADcuXodNCWpT58+ysvLU2ZmpqKioiRJERERysnJcVo2+3OtW7dOPXr0kL+/v5544gk1b95c6enpjvaQkBA1b95cktS5c2cVFRU52qqv37lzZxUXF6u4uLjW6/zwww/atGmTYmJiJEnDhg1TcnKyysrKFBQUpNatW2v37t06ceKEvv/+e/Xo0UNZWVkaOHCgvL29ZbVaNXr06Nvej2EYstvttbZXLw2OjIyUVBXKu3fv7liSXNP9WiwWPfPMM/rlL3+puXPnKjQ0VDab7ba19O7dW15eXmratKm8vb3Vp08fSVLbtm11+fJlx3Fjx46VxWKRn5+foqKilJube9u+a7Jr1y6NGDFCDRs2lGEYGj9+vLKzs2ut39/fX23atNHIkSOVmJio6OhodevWrca+x48f7xivkJAQ5eXlObXn5uaqX79+8vf3l1T1fcvbfef3xIkTatWqlWP75ZdfVkZGhv70pz/pww8/1MiRI3X8+HFNnTpV8fHxOnXqlOPYwMDAe77MGwAAAKhW74Nm3759tWfPHqegGRkZqb179+qLL764o6BZWVmp1NRUHT58WDabTTabTd99952SkpIcYc3Dw8Nx/E9DXHWbYRiSdMuA99FHH+nGjRuaOXOmbDabEhMTVVRUpK1bt0qqmtXcuHGjNmzYoBEjRsgwDFksFqc+q69zK6GhoTp48KBjdrTa1q1blZiYqIqKihrHoXo2trb7XbRokRYtWiQ/Pz/NnTvXaSlu9THVD7CpZrVanbbd3Wte2f3j2Ve73X7TbOzP9dN7/vF91VS/xWLR6tWrNXv2bFmtVs2aNcsxy/xTP67dbrffdC8/rfnKlSuOWeLaGIZR45hcunRJ27dv1+jRozV37ly9/fbbmjhxov7617861XO34wQAAADcqXofNMPDw7Vnzx6dPHlSISEhkqQuXbqosLBQ+/fvdyxFlKo+/N9qOWtWVpZKSkqUmZmpHTt2aMeOHdq6davOnDmjXbt2mVp3cnKyZs2a5bjOzp07FRcX51hCOnjwYH3++efasmWLRowYIakqQH/yySe6fv26KioqtGHDhttep3v37mrTpo0SEhJUWloqSTp16pQSEhL0+OOPKygoSHa7XZmZmZKk48ePKy8vTz179qy1z6KiItlsNrVs2VIvvfSSJk+e7Fg627RpU+Xn50uSMjIy7mpsNm7cKLvdrgsXLigzM1N9+/a9q3769OmjtLQ0lZSUOJY1h4eH11r/kSNHFBMTo86dO2vGjBmKiYnRoUOHauy7euyPHz+u/Px8hYWFObX36NFDubm5jhnvpUuXKikp6Zb1BgUFOc1SVnv33Xf129/+Vu7u7iotLZXFYpGbm5vTw38KCgr02GOP3cnwAAAAAHetXj8MSJJ8fX3l7e2trl27ys2tKlcbhqGwsDCdPn1aXl5ejmP79++vsWPH6oMPPlBaWpr8/f01btw4R/uaNWs0btw4p1m8Zs2aKTY2VitXrtSgQYPuuL5z585pypQpTqHwyJEjOnHixE1LXydPnqykpCQdOHBAoaGh6t27t86dO+dYThkeHq6hQ4cqNjZWnp6eCgwMdDyoZvXq1Tp//rzi4+Od+jQMQ0uWLNH8+fMVExMji8Uii8Wi+Ph4RUdHS5Lee+89zZkzRwsWLJAkzZkzR23atHF6ou+PNWvWTC+++KLGjx8vT09PeXh4aPbs2ZKkN998U2+++aZ8fX3varykqtm/UaNGqbi4WK+88oo6dOggSRo+fLjef/99tWzZ8qZzZs6c6TSj98Ybb2j06NEqLCzUqFGjVF5erpCQEM2ePVs+Pj411h8cHKyoqCjH9yKbNGni+G7vTx07dkwjRoxQeXm5EhIS1LRpU6f/6+DgYP3ud79TXFycJKldu3b6y1/+UuProVr//v2Vk5Ojp556yrHv6NGjKiwsdCxtnj59uiZMmCCr1aq5c+c6jsvJydGAAQPuYrQBAACAO2fYb7V+E3XKgQMHdOjQIT3//POSpD/84Q9q3Lix4+m09cHEiRMVFxen/v37u7qUWtlsNi1evFidO3c2td/Lly9rwoQJSktLu2mZ8a1cv35dY8eOVUpKitMvSQAAAIB7pd4vnX2YBAUFKScnR0OHDlV0dLSuXr1a61NIUfc0adJE06ZN04oVK+7ovGXLlunVV18lZAIAAOC+YUYTAAAAAGAqZjQBAAAAAKYiaAIAAAAATEXQBAAAAACYiqAJAAAAADAVQRMAAAAAYKr/BdG8u4XuFw/RAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "outcomes['policy'] = 'None'\n",
-    "raincloud_plot(outcomes, savefig=savefig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Outcomes spatially"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the outcomes is to use choropleth maps. These maps are useful when you have data that is aggregated by a geographic area. For example, if we have data on how many people became poor after a disaster in the districts of Saint Lucia, we could use a choropleth map to show these numbers.  \n",
-    "\n",
-    "What is important to remember is that **these maps do not show the variance** within each district. Instead, they show the mean or median value for each district."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the country shapefile and aggregated outcomes\n",
-    "aggregation = 'median'\n",
-    "country = 'Dominica'\n",
-    "aggregated_outcomes = get_spatial_outcomes(outcomes=outcomes, country=country, aggregation=aggregation)\n",
-    "\n",
-    "# Convert to be in millions\n",
-    "aggregated_outcomes[['tot_exposed_asset', 'total_consumption_loss', 'district_pml']] /= 1000000\n",
-    "aggregated_outcomes[['tot_exposed_asset', 'total_consumption_loss', 'district_pml']] = aggregated_outcomes[['tot_exposed_asset', 'total_consumption_loss', 'district_pml']].round(2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "k = 4\n",
-    "# column = 'annual_average_consumption_loss_pct'\n",
-    "# title = 'Ann. Av. Cons. Loss PC (%)'\n",
-    "column = 'r'\n",
-    "title = 'Socio-Economic Resilience'\n",
-    "# column = 'pml_relative'\n",
-    "# title = 'PML (%)'\n",
-    "\n",
-    "cmap = 'OrRd'\n",
-    "scheme = 'fisher_jenks'\n",
-    "ax = aggregated_outcomes.plot(column=column, scheme=scheme, k=k, cmap=cmap, legend=True, figsize=(10,10), legend_kwds={\"title\":title, \"loc\" : \"upper right\"})\n",
-    "aggregated_outcomes.plot(ax=ax, color='none', edgecolor='black', linewidth=0.25)\n",
-    "ax.axis('off')\n",
-    "for idx, row in aggregated_outcomes.iterrows():\n",
-    "    ax.annotate(text=row['NAME'], xy=row['geometry'].centroid.coords[0], ha='center', fontsize=12)\n",
-    "plt.savefig('pml.png', dpi=300, bbox_inches='tight')\n",
-    "if savefig:\n",
-    "    plt.savefig(figures_folder + f'soc_econ_res_map.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1680x700 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(ncols=3, figsize=(24,10))\n",
-    "k = 4\n",
-    "columns = ['district_pml', 'n_new_poor', 'r']\n",
-    "titles = ['PML ($M)', 'Number of New Poor', 'Socio-Economic Resilience']\n",
-    "cmaps = ['OrRd', 'OrRd', 'RdYlGn']\n",
-    "\n",
-    "scheme = 'fisher_jenks'\n",
-    "for i, column in enumerate(columns):\n",
-    "    title = titles[i]\n",
-    "    cmap = cmaps[i]\n",
-    "    aggregated_outcomes.plot(column=column, scheme=scheme, k=k, cmap=cmap, legend=True, ax=ax[i], legend_kwds={\"title\":title, \"loc\" : \"upper right\"})\n",
-    "    aggregated_outcomes.plot(ax=ax[i], color='none', edgecolor='black', linewidth=0.25)\n",
-    "    ax[i].axis('off')\n",
-    "    for idx, row in aggregated_outcomes.iterrows():\n",
-    "        ax[i].annotate(text=row['NAME'], xy=row['geometry'].centroid.coords[0], ha='center', fontsize=12)\n",
-    "plt.tight_layout()\n",
-    "if savefig:\n",
-    "    plt.savefig(figures_folder + f'soc_econ_res_map.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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wGGjTpo3ru77e0mmxWBg5ciStWrWiYcOGuZ77/PnzVKxYMdu2ypUrU7ZsWY4cOcK5c+fw9/d3dUvOqmvXruzYsQObzcaGDRt47LHHcpSpUqUKV65cwW635/sdSJIkQcnlqD179tCuXTvKly8PwJtvvslzzz3Hzp076devH0ajEUVReOqpp7Kdr0OHDgDUq1ePjIwMMjIyCA0N5ddff2Xs2LFs2bKFKVOmoNHc+nahc+fOADzwwAM0adIEb29vfH19CQwMJDk5Gcjs6dCoUSMABg0axO7du/M8ntls5tixYwwaNAiAcuXK0bVrV3bu3AlkXn+vP7mqV68eiYmJeR7rer5u3749ADVr1qRZs2bs378/z8+bV37MKrccA9CrVy/XEJjffvuNPn365BpX7969XUNq1q5d68rnWVWpUsUVvyRJpdfixYt57LHHMBgMdO7cmYSEBP78808AWrZsiZeXF1qtloceeijb9er6dbhu3br5Xscg/3v6osT1xBNPuIZzrlq1iieeeILz589z8eJF/vWvfxEWFsaYMWNQFIWIiAgg8zp+PSfklat27txJu3btKFeuHIqiMGTIEFc827Zt47///S9hYWEMGjSI2NhYwsPDs8WsKAoAqqrm+blulU9z+14Lcl2/mdFodPUSf+CBB2jVqpWrBz/gmsOkQ4cOVK1aFUVRGDBgQL75Lb+cBNCwYUNX3eRW+e1m+/bto23btq7cFBYWRkxMDDExMbnW76BgdTXI7CV/fT60/v374+3tzYQJE4DM3yy/3yurpKQkTCaT655Fo9Ewc+ZM1q1bx7hx4/j000958cUX2bFjB88//zxvvPGG6z4Cijcv3nONITf/CNe7Ud1swoQJrFixgooVKzJ69GiqVKmS79gjVVVdF4xVq1axbNkyV7dYVVU5f/48/v7+nDx50rWPoijZbiCFEOh0ujxj1Gq1/Pzzz0ybNg29Xs+ECRNcXYfbtWvnOveKFStYtmwZWq3Wddzrsg4HuS4mJob+/fsTHR1Ns2bNeP75513vdenShfXr19O+fXv27NlDWFgYNpuNp556ihUrVtCkSRNWrVrFM888g6qqCCFYunSpK5YlS5bk2kiQ1RtvvMGiRYuoUaMGCxYsYOLEibl+v3l9J9cviNc/a9YL5hdffMHWrVtdF4+bKYqS63dy/QZ09erVed6k+vj40Lx5c7Zv387atWtznXhVr9ejKEq2GCVJkvJSUjnqej64LjU1lStXrtzyfFkb7yHzGtukSRM2b95Mnz59OHXqFH369CE2NhZFUbLFcHMjsF6vz/X/5xWnuNZNNy/Xc05WQghX/Ndjvx5/ft9PbkNIr38XeX3evPJjVhqNJtfPWr16dby9vTlx4gTbtm2jU6dOucbVvXt3tm7dyrlz5/Dy8iI4ODhHGZ1Ol+/3JEmS+5lMJtavX8+WLVvo1KkTPXr0QAjBjz/+COR/vcp6Hb6+Pa/rbX739EWJ6/HHH2fXrl2cOnWKpKQkWrRogdPppEyZMq57/ev3+9eHomf9LHnlKo1Gky3+rPfJqqryxRdfZDt21rn9AAICAqhWrRpHjhzJtj0jI4NRo0aRlJRUqPx2PZa8rus338dnzW83X+Nzq1dA4fJbfjkpa+w3x18QuTVIXD92bvU7KFhdDW7MGbJq1SrWrl3L9OnT8fX1BaBx48Y5fi+Al19+2dWQlvUzKYqS63d05swZoqOjad++PbNmzeLTTz+lbdu2LFq0yFWmOPPiPdcYEhoaypIlS3A6ndhsNn755RdXrwKtVuv6R7Zjxw5efvllevbsSWxsLOfOncu3NatNmzasWbOGpKQkAObNm8d7770HZI558vb25vvvv2fWrFlERUUBmf/wrj/x2bNnD3a7ndq1a+cZ48mTJ+nbty/16tXj5Zdfpm/fvhw/fpxWrVqxfft2Ll++DGTOXTJ27FjKlClD3bp1WblypescuU3meezYMYKCgnjhhRdo27Yt69evd/0Rvvbaa2zZsoW+ffvy3nvvkZKSQmJiIoMHD+bChQsMHjyYadOmcfLkSYxGIw0aNOB///sfAFFRUfTq1Ytz586h0+ly/cN2OByum8Cnn36a1157zTXRqlarde2T13dSo0YNHA4HBw8eBODPP/9k8uTJQGZL7SOPPMLUqVOZPHlyrk9NH3zwQdf3ltXjjz/OH3/8wR9//EGXLl3y/N179+7Nt99+i1arzfXp3+XLl6latWqeF0ZJkqSsSipHtWzZkj179rieHn311Vf8+OOPhIaGsmLFCiwWC0IIfv75Z9f58jJ37ly+/fZbHn/8caZNm4aPjw+XL1+mTJkyrsbo+Ph4/vrrr0J//mPHjrluipYsWcKjjz7q+uw35xAfHx8aNmzIkiVLgMzJrjdt2nTL+HPz4IMPIoRwPQ2NiIjgwIEDtGrVKs/Pm1d+zKp69eq55hjI7B0yc+ZMGjZsmG3y7az8/f1p2LAhU6dOzbXBHTJXEMpvLhRJktxv1apVVK1alZ07d7rmxliyZAm7d+/m3LlzhT5eXtfb/O7pCxtXREQEPj4+PProo0yZMsXVG6BGjRoA/P777wCcOHGCxx9/nNTU1BzHzytXhYaGsmvXLuLj4wGyzcvXunVrfvjhByCz4f7JJ59k7969OY49duxYZs2a5Vol02azMWvWLBRFoUyZMvnm07zkdV0PCAhw9XSPiIjI0VOlIHbt2kVCQgJCCJYtW5ZvfssvJ92ukJAQduzYQXR0NJA5EW3ZsmWpUqVKrvW7/OpqhTFmzBi++OIL1/eoqiqff/45Fy9epFq1atnKlilTBm9vb2JiYnIcZ+7cufzrX/8CMuuRGo0GjUaTbdGN4syL91wN7sUXX2TWrFmEhYVht9sJDQ3lueeeAzK7L12f1OfVV19lzJgxeHl5ERQURNOmTbl48SJVq1bN9bjt27cnIiKCYcOGAZlDLWbNmsXx48f5/vvv+fXXXwkODuaZZ57hjTfecP2R7969m0WLFqHT6fjss8/QarV5xmgwGOjQoQN9+/bF29sbf39/Zs6cSaVKlZgyZYprSVdfX1/XMkMff/wxkydP5r///S916tShQoUKOWIPDQ1l6dKlrh4c7du3JzU1FZPJxNixY3n77bf54Ycf0Gg0vPLKK1SoUIEJEyYwc+ZMV++WmTNnotPp+Pjjj3nvvfdYvnw5DoeDN954g7p16+J0OqlduzY9e/ZkxYoVrhZNnU7HG2+8wSuvvIJer0en07kakdq1a8f777+PwWDI9zuZP38+H3zwARaLBW9vb+bOnZvt83Xv3p21a9cyd+5cpk2blu29Ll26sHv37hxLUQYGBlKrVi0CAgJyTOiT1aOPPspbb72VZwvp7t27c51QT5IkKTcllaPq1q3LuHHjXE95atSowQcffICnpydXrlzhiSeewOFw0LBhwxzXyZs9/fTTTJw4kd69e6PT6ejcuTPNmzfnoYceYuvWrXTv3p0qVaoQEhJS6M8fFBTE7NmzuXLlClWrVnV93pCQEMaNG4e3tzcPPvigq/xHH33Eu+++6+olOWbMGFq0aMG+ffsKdV6DwcDnn3/OzJkzXZO9zZw5k6pVq+b5eX19fXPNj1nVqVMHRVFcDeNZ9ezZkzlz5vDyyy/nG1vv3r2ZPHky33zzTa7v7969m08++aRQn1eSpDvrl19+cU2+eV3t2rXp0KEDAQEBhT5e7969c73e5ndPX9i4fvzxR6ZNm8aAAQMYPnw4X375JXDjevnBBx8wf/58ILO+kdtQ/LxyVevWrZkwYQLPPPMMHh4e1KxZ03W//c477/Dee+/Rq1cv7HY7/fv3z3UizH79+gHwyiuvoKoqNpuNkJAQ16ps+eXTvORV7xk2bBgTJkygR48e1KhRo0hL2JcrV45XX32VuLg4mjRp4qq3Za3vXJdfTsqrt3tufv75Z5YtW+Z63aVLF+bOncvkyZMZM2YMTqeTgIAAvvjiCxRFybN+l1ddrTBCQkJ49913mTZtGhaLBZvNRoMGDfj2229zfWjcpUsXdu3axRNPPOHadn3aiOsTho8cOZKePXvi5+fHp59+6iq3a9cu11QUt0sRhel3IxVKnTp12L9/P35+fu4O5b6VkpLCsGHDWLFiRZ7dtotKVVUGDBjAf/7zH8qVK1esx5YkSbrX7Nu3jw8++MA1Pv1esWLFCs6ePZtno/ntOHjwIP/9739dFRJJkqS7waVLl1i3bp1rvpFvvvmGEydO5Lu8/N1sxYoVrlXQpIK5cOECkydP5ueffy7UflFRUbz66qssWbKkWKYpuOeGyUhSVv7+/rzwwgvZxpkVlyVLljB48GDZECJJknQf69evH5cvX3Z15S4uQgj+7//+z7W0piRJ0t2iYsWKXL58md69e9O7d2/27dvHpEmT3B2WVIpUr16djh07sm7dukLt99lnnzF9+vRim69R9gyRJEmSJEmSJEmSJOm+InuGSJIkSZIkSZIkSZJ0X5GNIZIkSZIkSZIkSZIk3VdkY4gkSZIkSZIkSZIkSfcV2RgiSZIkSZIkSZIkSdJ9RTaGSJIkSZIkSZIkSZJ0X5GNIZIkSZIkSZIkSZIk3VdkY4gkSZIkSZIkSZIkSfcVXWF3EEIQGRlJenp6ScQjuZG3tzdVqlRBURR3hyJJkpQrmYPuHjKnSJJ0r5E5qGTIfCG5iyKEEIXZIS4uDqvVSqVKldBoZMeSe4WqqkRFReHh4UG5cuXcHY4kSVKuZA66O8icIknSvUjmoOIn84XkToX+K05OTiY4OFheAO4xGo2G4OBgkpOT3R2KJElSnmQOujvInCJJ0r1I5qDiJ/OF5E6F/kt2Op3odIUeXSPdBXQ6HU6n091hSJIk5UnmoLuHzCmSJN1rZA4qGTJfSO5SpL/mQo/nEgKEA4QKigYUHcgxYaWOHKcnSdLdoPDXKgGYADugB3wAeb0raTKnSJJ0L5L1oOIn84XkLiXftOm0gsOceQG4TtGAzgu0HiV+ekmSJOl+lgRcAaxZtnkAlYEybolIkiRJuk/IepAklWolO+DNaQW7KfsFADJf202Z75ewcePG8c0332TbdvXqVTp16pRr+ZUrVxIWFub6r0mTJixcuBCAX3/9lR49etCtWzfWrVtX6FhMJhM9e/YkLi7OtW3RokX06tWLXr16MXv2bNf2CRMm8Nhjj7niOHbsWI7j5bbv7t27s8XfokULpk+fXuhYJUmS7n5JwDmyN4Rw7fW5a++XjE8++YTu3bvTv39/1qxZk2uZuXPn0qtXL3r27MmiRYuAzJUKZs2aRY8ePejVqxebN28u1HnzyikLFizg8ccfp2fPnixevDjXfbdv307v3r3p1q2bKx6ZUyRJkoqoBOtB+dVXsnryySfp3bu3q9zVq1eBwtVpCpoH8qq75JZbJKnUEIV08uTJghVUVSEsiUJkxOf9nyUxs1wJWbVqlWjRooX4z3/+49q2e/du0b17d9GwYcNb7n/kyBHRp08fkZGRIWJiYkTXrl1FamqqSE5OFt27dxcJCQkFjuXEiROiT58+okGDBiI2NlYIkfld9u7dW2RkZAi73S6efvppsXXrViGEEI8//rhIS0vL83j57Xvd5cuXRbdu3UR8fHyB4yzw7ytJkuQGBb9GqUKIY0KIA/n8d+xaueK1fft20a9fP5Geni4sFosYPHiwiIyMzFbmjz/+ECNHjhQOh0Okp6eLxx9/XISHh4tt27aJZ599VjidTpGQkCBatWolMjIyCnTevPLC2bNnRc+ePYXFYhEWi0V06tRJXLp0Kdu+GRkZomPHjiI6OlpYrVYxYMAAcerUqWxlZE6RJOl+VxrrQVnrK1k5HA7RsWPHHOVvp06TXx7Ire5SkNxyncwXkjuUXM+Q62Pj8i2jZpYrAVevXmXx4sUMHjw42/bly5czb968W+4vhGD69OlMmTIFo9HI7t27CQ0NxdfXF39/f9q0acPWrVsLHM/y5ct56623KF++vGubt7c3U6dOxWg0otPpqFmzJtHR0ZhMJhISEhg3bhx9+vThp59+ynG8vPbNas6cOYwdO5ayZcsWOE5JkqR7g4mcPUJuZr1WrniFh4fTvn17vLy88PDwoFmzZuzevTtbmUqVKvH666+j1Wrx8vKiatWqxMTE0L59e7755hs0Gg3x8fEYDAa0Wm2BzptXXqhZsyYrV67Ew8ODxMREVFXF09Mz275Hjx6lVq1aVKhQAYPBwGOPPcbGjRuzlZE5RZIkqYDuUD3o5vpKVmfPngXgqaeeom/fvmzYsAHgtuo0eeWBvOouBcktkuROJdgYcosLQGHLFdK0adN46623MBgM2bZ/9NFH1K1b95b7b9++HT8/P1q2bAlAbGxstrWvg4KCsg13uZW3336bVq1aZdtWtWpVWrRoAcClS5fYtGkT7dq1Iz4+nlatWvHRRx/x/fffs3jxYv76668C7Xvd2bNnOX36NH369ClwjJIkSfcOezGXK7j69euzfft2TCYTJpOJ3bt3k5CQkK1M3bp1adCgAQCHDx/m5MmTNGvWDMicVX/mzJn079+fQYMGodfrC3Te/PKCTqdjwYIF9OjRg5CQEIKCgrLte6scJ3OKJElSIdyhetDN9ZWs0tLSaN26Nd988w1ffvkls2fP5uLFi0Wu0+SXB/Kqu9xu/UmSSlrJNYYoBTx0QcsVws8//0y9evVo2LBhkY+xZMkShg8f7nqtqmqOmY6La43xixcvMnLkSCZMmECVKlWoXr068+fPJzAwkICAAJ544gl27NhRoH2zxj9s2DC5DrokSfepgjUgFLxcwbVp04bu3bszZMgQxo8fT6tWrfJcivHo0aO88sorzJo1Cx8fH9f2KVOmsHPnTjZu3Mi+ffsKdf688sLo0aPZs2cP0dHRrFixIts+ueW4rK9lTpEkSSqEO1QPurm+ktUjjzzCzJkz8fb2plKlSnTp0oU9e/YUuU6TXx7Iq+5yq9wiSe5Wgo0hulv/gV9fXqqYbdq0ic2bNxMWFsbixYv5/vvv+fnnnwu8v9Vq5ejRo4SGhrq2VahQgfj4eNfr+Pj4bENeiurEiRMMHz6ccePG0a9fPwBOnjzJli1bXGVUVc31Rjq3fa/btm0b3bt3v+34JEmS7k4+ZK4akx+Pa+WK1/XJslevXs3ChQuxWq1UqlQpR7mdO3fy4osvMmfOHFe+iYiI4PTp0wAEBATQtm1b1+uCyC0vREVFceTIEQCMRiNdu3bNccxb5TiZUyRJkgrhDtSDcquvZLVv3z4OHDjgen29PlHUOk1+eSCvuktJ1Z8kqbiUYGOIkrlsVH50XiWyzvZ3333H6tWrWbVqFU8++SRPP/00Q4YMKfD+4eHh1K5dO9sQm9atW7Nz505SUlJISUlh586dhISE3FacKSkpPP/887z33nvZupypqsqHH35Ieno6ZrOZVatW0blz5wLtC5CYmIhGo8nWLU2SJOn+opC5fG5+Kl8rV7wiIyMZP348TqeTmJgYtm/fTps2bbKVuXjxIhMnTuTLL7+kdevWru3nzp1j+vTpOBwOTCYTO3fupGnTpgU6b155ISkpiYkTJ2I2m7HZbGzevNk1JOe6xo0bEx4ezpUrV7DZbGzYsME1xEbmFEmSpEK6A/Wg3OorWaWmpvLxxx9jt9tJSEhg27ZttGvXrkh1mlvlgbzqLvnlFkkqDYq/W0ZW19fPvgvW1w4LC+Obb74hODiYyMhIgoODs71foUIFxo4dy1NPPYXdbuf555+nQoUKOfYtjMWLF2MymZg3b55rUtdhw4YxcOBAhg4dysCBA3E6nQwbNsw15Oe5555j3Lhx7N69O899IyMjZaurJEkSZYAawBWyT6bqQWZDSJkSOWvdunUJDQ2ld+/eaDQa3n33XQICAoAb+WLhwoXY7XamTp3q2u/111+na9euHDp0iD59+qDVarNd/2+Va/LLKUOGDGHAgAFotVq6d+9Ot27dgBs55eGHH2bGjBm88MIL2Gw2wsLCaNy4MYDMKZIkSUVRwvWg3OorcCNXdO3alb///puwsDBUVeVf//qXq3xh6zR55YGsOSSvukteuUWSSgNFCCEKs8OpU6cKNAFpNkLcmFX5epewe2i82MyZMxk/fny28d53qyL9vpIkSXdI0a5RgsxVY+xkzhHiQ0n0CClpd2OukTlFkqR7yb1eD3JnnpH5QnKHku0Zcp2igFL8k9QVB5vNlmf3soIQQtCkSZO76uZUkiTp/qIAvu4OIlcFzUEy10iSJN2l7pJ6kMwz0v3ovpgWXlVVMsxmbu4Ek5iYiMViybZNCIHdbictLQ2r1cqtKIpCz549izVeSZIk6d6hqioZGTlzUHx8fI48k5mDbKSlpWHL8p7MNZIkSVJRqKqK2WzOsT0uLg67/cby8oqi0KNHD2y2nDlIku5Vd6ZnSCE5HHYsFmueLZNOpxOr1YpQVRxOJ+np6fj4+KAgECgIIVAUBQUytwgVD51CWqoVRdEAAgTotBoU1UFqStK17Zk3ojoNmEwZ+Pv737kPLUmSJJUKdrsdm82Kt/ctcpBQcTgKkoPseGgFaamWzFwjBCAw6HXgtJKaYkdRtAgA4USngbQ0C/rAkpnTRJIkSSq9bDYbdpsN7/zqQRYLqqriVFVMJhO+vr4IpxNFo0FwYyCoEAKnxYLicGA1m9FoNAghEEKg1+mwpKVhy8hAc23VStXhQCMEaVarnDRbui+UysaQlJRUDAYDqclJoChYrDaMRmPmm0KgwYmHToOiAS/FjlOvQa8RGEUGoGT2d8n6BE4BVNBn7QeT11C9a9uFlwceHh7XTinkmtiSJEn3idTUazkoNQUAi8WK0WhEUTLzgUaj4OGhQ6PRotVqsNt1eGidGNSkzAMomc0gLtdeunJQfunk2ntOL18MBpmDJEmS7jdpaWno9XoS4+LQ6nRY7XZXPUgIgdNsBpsNhMCWnIwSGIgwm0mPjMw8QGayuq0YPCtXzjZ8RuYg6V51RxpDhBCQegVsJjD4gF/lPP+orBYLXgYtRtJBm7nN11sHig1U5/UDQpZJmct46YhJTaeCry5zcqLb+/sHQKdVMJlMOBwOAFcvlesNJJIkSdLdQQgBKafAmgweAeBfN88cZLFk4O3tgdGocH0kqZ/f9Tk9RJb/Fa7XZcv6EhOTTAUfJdv226HXgiktDfu1HOTr4wMKrgYSSZIk6e5QmHqQOT0dxWLBdP68a5veywuLquK02TKPp6rZ9tGbzaRrtTcaQW6zIQRAEYK01NTMYTSKgq+PDwJZD5LuPSXeGCLizyAubAdLyo2NRn+o/ihK0EPZyjqdTiwWC346W/Z7SdWR/0lUB0a9FrNqwEux5F+2gAzY0Bs8Muc7EioWmxmbQ6DRaNDrS+ckSJIkSVJ2Im4/RPwMlqs3NhqDETWHoJRrka2s0+nEZrPh56fnpiSU7zkURcVg0JGBP54kF0vcHpgw6L1RdJlP/zKsAptTQavVodVqi+UckiRJUskqTD3I4XBgs1qxXr2abbs9l/k+sr1vMqF4e+NZpQrmy5eLJW5rTAwEBOA0mzPXYwsMROj16MuWRaO5L6aclO4TJdoYIuLPIE6tIcdTMktK5va6vbJdCFJTUwgwqKDmf+OZmwBPLVfTMkgzJzBk5EtsWb3E9d5H87/mz517EQgG9u3FM0MHAvDkyBcxmzNcf9T/+XQ2weWCrgUvUByW7Pv2D6P/4KdYu3Yr3333nev4Fy9e5JVXXmHUqFEFinXRokUsW7YMgHbt2vHmm2+63jOZTAwePJhFixa5xur99NNPfP/99xiNRvr168eIESNyPe7N++7evZvZs2e73o+KiqJ379688847BYpTkiTpbibi9sM/n5IzB12Ffz5FNBjvahARQpCWloq/v5FbNX7kpmxZH959dzYH9u2+lmse45mh/RBC8OEn/8fOPQfQKBpeffEZOndonWP/X9f8zoL/LsXhcPLqi8/weNdHWbVqJYv+96urzMVLUTz3/HO89NLLBYppwYIFLF++HI1Gw/Dhw3nyyScLlBcKUmb27Nmkp6czffr0Qn1PkiRJ94vC1IOEEKSmpGCPisrR8yM3G3fsYOm6da7XV65eZcSzzzK4Xbts5V5+910yLBZXT5QPJ04kqEwZNmzfzuLVq3E4nYwaNIiOISHZY1dVLImJrte/fPsta7ZtQ9FqGTJ0KM8880yOmPbu3cvcuXMxm820bt06W87IrX4jSaVBiTWGCCEyW0Lz7C4sEBd2QNlaKIqSOQGdQQOqrWgnVB38ffAg8+fPJz7hxh/vlu27OHUmgpU/L8RqszHw6TG0btmcmg9WIzYuIVujyc1u3nfA0y/QuHkIffv2pW/fvgAcPXqUqVOn8tRTTxUozFOnTrFixQqWLVuGTqdj1KhRbNu2jQ4dOnDy5EkmTZrExYsXXeUjIiJcN7T+/v688sorNG7cmKZNm2Y7bm77tmnThlWrVgEQGRnJqFGjeOmllwoUpyRJ0t1MCJHZIySfHMS5nxFBj6AoCmZzOj4+HihK4RtCALZs2UpExFlWLv0eqzmRgU+Po3XLpsRcjeNMxAVWL/malNQ0egx4ntCQZhiNN7oaX42N5+uFi1n2w3xUVeXJZ18npEVj+vbqQt9eXQA4ejycqR98zqBBgwoUT0REBCtXrmTlypUA9OjRg9DQ0ALlhVuVOXDgAL/++ivdunUr0nclSZJ0rytsPSgtNRWRkoKzgCu4dG/Xju7XGj5ORkTw8YIF9A0Lw0NVsSYnA+BUVeKTklj86afZ9o1LTOTHlSv5+v33EULw0rRpNGvQAH/f3Jegj01IYNmGDSz+9ls0/v48+eSTdO3alUqVKrnKpKam8q9//Ysff/yRqlWrMmLECHbs2EG7du1yraNIUmlRcv2cUq9k7xKWG0tyZjnA6XCgE0VsCLlm8+8bmXpTr4fKFSvw2kvPodVq8fL0pErlSlyNjePs+QsADHvuFfoNHcWGzdtyHO/mfStVrkxcfLzrfSEE06dPZ8qUKTcmeL0Fb29vpk6ditFoRKfTUbNmTaKjowFYvnw5b731FuXLl3eVP336NC1atCAwMBCtVktoaCh//vlnjuPmtm9Wc+bMYezYsZQtW7ZAcUqSJN3VUk5lHxqTm4yrkBIOgNOpotMVfZx1pUoVmTBhPIlmBS9PI1UqV+RqbDyPhrbgm09noNFoiItPwmDQo9VlH+ay+69DhIY0w9fHG38/X1q3bMqW7ftc7wshmDHnS/71+mt4e3kXKJ6aNWuycuVKPDw8SExMRFVVPD09s5UpSF64uUx6ejofffQRzz//fEG/GkmSpPtPIetBQgisSUmFPo0QgvmLFvHy00/jAzizrEBz8dqEquOnT+e5t97iz32ZeeXg8eM88vDD+Hh54evtTfOGDdn99995nkNV1cwhPFotzmvzWN08XHPXrl20bt2a6tWro9Vq+eijj2jUqBFw6zqKJLlTyQ2TsZkKWC4dm82GTiMyJz+9DR/PmMJVU/Zj1Hmopuv/Hz72D6dOn6Vpo4aEn42gTcvmTJ7wMimpaQwb/Qr16tSiWtUqee575vRpmjeqT0pKMkajJ3v37sXPz4+WLVsWOMaqVatStWpVAC5dusSmTZsYOXIkAG+//XaO8nXq1OGjjz4iLi4Of39/tm3bRoUKFXKUy23f686ePcvp06eZN29egeOUJEm6q1mTC1bOloTVasVguL1nA3Xr1gYgOjqVQycucep0BE0b1QNAp9Pywcdf8/OytYx59kn0uuypNzYukXJlA12vy5UtQ1z8jR6OO3YfwNfXm3aP1MAqLKSk2PH09LzlZKo6nY4FCxbwxRdf8NhjjxEUFOR6ryB5Ibcys2fPZvTo0aSmpt7yO5EkSbpvFaIelGE240xPL9LEp38dOYKPlxdN6tXDnpQEBgNe5cphjosjPSOD5g0b8vLw4aSaTIyfPp1a1aqRkJRE2YAA1zEC/f1JuNabJDcVypWjc5s2hPXvj6LR0K9fP8pk2R/g8uXL6HQ6xo0bx8WLF+nYsSPjx48H8q+jSJK7lVzPEEPua2PnLOdNRkYGnppbTJJaQOX8jOTWJe3o8ZOMm/gOH7zzJj4+3jRv0oj3p07E28uLShWC6dKxHXv+yr1VNOu+gd46/PROrJYMFi9ezPDhw4sU58WLFxk5ciQTJkygSpUqeZarUaMGL7/8MqNGjWLkyJE0btwYna5wbVhLlixh2LBhcsIjSZLuHx4BBStnKIPVasFoLJ7rY0zMZV55fQofvPM6Pj43enG8NeEFdmz4Hxv/2Mm+A0ez7SNUlZsXFsh6vV6yYj3DnwwDwEOk4KdLx5Jhxul03jKe0aNHs2fPHqKjo1mxYsWNYxYgL9xcZvv27VitVrp06XLL80qSJN3XClEPstls2LL0PC+M1Vu20L9798wXQmCwWLBeWxL34Tp1eOO55/A0GgkOCqJtixYc/OefXJfK1eSzdO6piAgOnTjBkvnzWTp/PscOHWLjxo3ZcpDT6WT37t1MmTKFpUuXcuzYMdasWVOkzyRJd1LJ1Y79KmfOlpwfYwCqdwW0qLdeMaaANOq1lWh0N4at7Ny7n5cmvMWc6VMIDcmcLG/fgUMcOHTEVcbpdKLX5ZyhP7d9cdoxOE0cPXqUVq1aFTrGEydOMHz4cMaNG0e/fv3yLWuxWGjatCm//fYbP/74Ix4eHtnG6BXEtm3b6H79QilJknQ/8K8LxuD8y3gG4/SuiVZ7fUnc27Nz5x5eeeU13nrrLUJDQwGIOH+J02cvABDg70u7Ns05ffZ8tv2Cg4OIS7jRPTouIYnyQZk9RaxWG0f/CSc0pNmNHVQrvloTaWkpruXfbxYVFcWRI5k5zmg00rVrV06fPu16vyB54eYy69ev59ixY4SFhTF//nw2btzIpzeNRZckSZIocD3IbiyHarGg2u2FPoXNZuNURASPXBuOAmBLSUEVAr23N4dPnODoqVOu91RVRafVEhQYSGLKjSE8iSkplC1TJs/zHDpxgpaNGuHr7Y2XpycdWrRg//btpCQnuxpEgoKCaNq0KcHBwRgMBjp16sTx48cL/Zkk6U4rscYQRVFQqj8K5NXSqKBUb4fZbMaruFeqVRRSMjL/OC9ejuTNd2by5b8/IKTFjZvJ1LQ0/v35/2F3OEhITOLPXXtp2yZ7w0Ze+wKEh5+hdq0HsVotBXo6d11KSgrPP/887733Hn369LllebPZzKhRo8jIyCAtLY0VK1bQqVOnAp8vMTERjUYjZ26WJOm+oigK1BxCfjmIGkMwZ2Tg5WW47fNdvHiJiROn8uWXn9CuXRvSbJnHPHfhMtPnfIHD4cSUbmbnnoM0bVw/276tWzRh596/SUlNIyU1jV17DxLSojEA4WfO81DNahhuXtJd2PHXm0lLTUXNZeWBpKQkJk6ciNlsxmazsXnzZpo1y8xjBckLuZWZNWsW69atY9WqVYwbN47u3bu7ukFLkiRJNxS0HpSRkYEz5RZzi+Qh4vJlHqxSJfvQSyHQ2O3oypYlLT2d/1uyBIfDQVJKCnsPHaJl48Y0b9CA/UePkpaeTlp6OvuPHqVZ/fp5nqdmtWr8dfQoVpsNp9PJ/qNHqVm1Ks7YWFKSk1FVldDQUA4fPkxsbCyqqrJz507q1atXpM8lSXdSiS6tqwQ9BHV7Zc6WbEm+8YYxAKV6O5SghxCpqShFXUEmHxlWK/4eBhZ+vxi73c7UmR+53nvtxefo2vFRDh39h75DRqKqgn+9Msa1rG7foaP4z6ez89y3fdsQrkRHE1yuLAEGlTRTGgIFf39/wsLC+OabbwgOzv2J5OLFizGZTMybN881DnvYsGEMHDgw1/KBgYGMGjWKJ554AlVVeeGFF6hZM3Muk+eee45x48bx8MMP5/k9REZGygmLJEm6LynlWiAajIdzP2dOlnqdZzDUGJK5rG5qKopS8AbtvCxc+H1mvpg6AwC73cnEcSPp2jGUw0dPETZkLFqtlqcG9aZhvcylFPsOfYn/fDqdCsHlGDvySYaNfgO7w8HzIwZRITizEeJK9FWCywflON/1fYODK5GWpiDQ4O9/4ylkgwYNGDJkCAMGDECr1dK9e3fX6i955YWsOUXmDkmSpNtTkHoQycnY0tKKdPyYuDiCAgNzbB/5/PPMmjuX9m3a8M+ZM4yePBlVCJ4fMoSgaz1AhvXty7jp03E6nQzt04dy1ybJHj15smv53etaNmpEeEQEoydPRqfT0apxY7q0aYM9PZ1/vfIKL7z4IvUffpi3336bUaNGYbPZ6NSpk2vlTUkqzRQhCjdbz6lTp6hbt26hTiKEyJwt2ZYOBm/wq+waq5aWkoyPxlKo4xWEya5FVbT46Yq/oSU3Tq0nGQ6F+Z99xvjx4/HxKeBYwVKmKL+vJEnSnVLkHJRyCmzJYCgD/nVu5KC0FHx9i/+5QEqKFQ0CH03Rnvjdygcff824McNd85I4tP5YnHp8ff1K5HxFJXOKJEn3kuKuByVevYrl8uVij1NbvjwGo5GMS5cKtd/nP/zAs088gbeXV6H286pUCa2PD75+Rc9BMl9I7lCiPUOuUxQF/HNOEirE7a8gkxcfgyAmzYKfvwc4Cz8Or7C0zgyE6kGjRo3u2oYQSZKke5GiKBCQs7tubsNLiou/v5Ho6BR8/AxQzL0fhRA0blg32wStOmcKQg1AVVU5WbYkSVIpklc9SFVVRCGG2hdKcjIZQUFoDQactoLlICEE9WvVKnRDCIA5KgrPBx/MdXJWSSrN3HrHZLVaMehK6A9GqHgatKQ7ck6KWlK8DQodO3a8Y+eTJEmSis5qteLhUVLPBAQeHnoswvvWRQtJURR6du+QY7uXzkFGhrnYzydJkiQVvwyzGaWADRWF5bTZUOx2jHkM28+Noih0at26yOcU6elkZGQUeX9Jcge3NobYrFYMSvGsIpMbf08tpvQ790fpVEGnvXONL5IkSVLR2e12DIaSe4JVpow3KabiHwaaF4eqoNMV94zkkiRJUklwOBxYk5NL7Pg6iwVzSfU8yfWEOvQ3T/YtSaWc2xpDHA4HGkWAWoJ/pE4HigIObeG7exWFxeHEw2i8dUFJkiTJrRwOe7EtqZsXRckchuPU3pl5PGxOBYPh9lfGkSRJkkqWzWZDtVgQeSyPXhzsaWkIIfC6U5Nh6/WyMUS667itMSQjowSW1M1FOV898SkmuAPj17wNGkxFnBFakiRJunMyMjLwugNJqHx5f+JSSn7eKgAvvROTSeYgSZKk0i7DbEa9A3UGvclEhu6OTBEJ5szl3iXpbnJH/jqEENhPHsGZGI82MAh9vcYl+TAuG0V1oNUoODSe6JwlO5Zao9oAD+x2u2wZlSRJKiWEEKhHDiASrqKUDUbT+BGAOzLJm0Yj0GgUnLoAtI7kEj2XVjUjVAMOhwPdnbr5lSRJkvKVaz3oDrGnpYG3N94VKpAeE1Oi57ImJKDx9ZU5SLqrlPi/VMu+P0n74QucMVdc27QVKqN94lmUkJCSPj0AQT4GYlPSCfYp4RtfIfDR2klISSEoKKhkzyVJkiTdkmP7JmxfzUFcubG8oFL5ARjxCnTvfkdiKFfOj9jYFIKLfy7Vmwh8tWkkJDtlDpIkSSoF8qwH9R+BWqH6HYnBkJFBhneJJyCEqmKNisKhqjIHSXeNEh0mY9n3J8kfv53tAgDgjLmC7csPsBzYU6zn+2j+1/QeNIJeg57hv/9b6tquqHbi4+Po1PvJbOXnfbmA7v2fov+w51izYXOex70aF0+n3oNdr1eu2UDfoaNc/zVt252FPyzOfFN14qHL/2tdsGABjz/+OD179mTx4sWu7d9//z09evSgR48e/Pe//3VtX7RoEb169aJXr17Mnj0712Nu376d3r17061bNxYtWuTa/tNPP9G9e3fCwsKybZckSbrXObZvwvrOuGwNIQDiyiWUWW/i2P5HsZ1LCMGsWR/To8cT9Oo1kM2btwGwcuUa+vUbzKhRo+g79BWatu3Lwh+WZdt35ZrN9B36kuu/3MqMf3Mm3yz6pQCBOPDQZzb855VTAEwmEz179iQuLi7HIXbv3k1YWJjrvxYtWjB9+nQAJkyYwGOPPeZ679ixYwX9iiRJku4r+daDvpqFOHX4to7/n59/ZsTEiTz75pts3bs31zJb9uxhxMsvM2z4cH7ZnL2uE5+UxJPjxxfoXCt//53hEyYwevJklq5fn2uZZevX8/T48QwfNixbfSWvOooklQYl1jNECEHaD1+AquZRQMW0+Fs8Gjctlq7KW7bv4tSZCFb+vBCrzcbAp8fQumVzateqwd79f/Pe7E+IT0hwld+x5y+2797Hrz8tQKvRMGLs6zRt3JDKFStkO+7e/X/z3oefEJ+Q6NrWt9dj9O31GABHj5/knQ8+4qmB/VzvaxTy7CIWERHBypUrWblyJQA9evQgNDQUnU7HokWLWLt2LUIIevbsSdeuXUlNTWXFihUsW7YMnU7HqFGj2LZtGx06dHAd02Kx8O677/K///2PwMBAnnrqKVq3bo1Op2PBggUsX74cf39/XnnlFRo3bkzTpk1v+/uWJEkqzYQQ2L6ak2cOUlQV21efoG3Xvlhy0PbtuzlzJoI1a5aSnJxCjx5P0LZtCH379qJv317Y7bBr51988u85PDWwd7Z9+/bqQt9eXQA4ejycdz6Yn63M6vVb2Lv/MA3qPVSgWBRUIiMv55pTKlWqxMmTJ5k0aRIXL17Mdf82bdqwatUqACIjIxk1ahQvvfQSACdPnmTZsmX4+PgU+juSJEm6XxSkHqRu+AWem1KkeQ2PhYdz9NQpFn74ISlpaTw7cSJtH3kEfZa6x+XoaL75+We+fv99/CpUYMS4cYTUr88DlSrx9z//MO+770hMSbnluS5eucLi1av5+v338fXxYdq8edSvVYsGD93ISRGXLrF++3a+njEDrwce4I233mLbtm2EhITkWkepU6dOoT+zJJWEEusZYj95JEdL6M2cV6OxnzlZLOerXLECr730HFqtFi9PT6pUrsTV2MwnXst/W8cnH0wDIUCXudrL6TMRdGgbgpenJx4eHjRr3JA9+w7mOO7y39bxyaxpuZ5TCMGMOfN4a8IrGI0eru1eOidms5mYmBiioqJwZJkpumbNmqxcuRIPDw8SExNRVRVPT0+cTid2ux2r1YrVagVAq9Xi7e3N1KlTMRqN6HQ6atasSXR0dLY4jh49Sq1atahQoQIGg4HHHnuMjRs3cvr0aVq0aEFgYCBarZbQ0FD+/PPP2/uiJUmS7gLqkQM5eoTcTFy5hHr0ULGcr337UL75Zj4ajYb4+AQMBgPaLEut63SCT+bN4603xmXLF9niEYIZc77krQljXGWuxsazePk6BvXvUeBYvLUWUlPTsFqtXLx4EbM5c76s6/EsX76ct956i/IFWGFgzpw5jB07lrJly2IymUhISGDcuHH06dOHn376qcAxSZIk3U8KUg8iMQ4izxXp+KqqYrPbcTgcWKzWbI0g1+06eJBu7doR4OeHJiODOXPmULFyZQDW//kn77zySoHOdT4ykkb16hHg54dWo+GRhx9m3+HD2cp4Go2Me/ppPAwGlJQUqlatyunTp9myZQs1a9bMUUeRpNKixBpDnInxBSqnJiUVy/nqPFSTBnVrA3D42D+cOn2Wpo0aAjB3xtvUrV0LFHAqmReLenUeYvvufZhM6ZhM6ezed4D4xMQcx3Xtm4sdu/fh6+tDy+ZNbvpQTnw1GQT7e1A+wIvUm2ZWvt5jo0ePHoSEhBAUFESVKlXo3bs3HTt2pHPnzjz22GMEBwdTtWpVWrRoAcClS5fYtGkT7dq1y3a82NhYypUr53odFBREXFwcderU4eDBg8TFxWGz2di2bRsJWXrHSJIk3atEwtWClYvPOUykqHQ6HTNnzqV//6cYNKhftom0t2/fjY+PDy1btclz/x27D+Dr603L5o1c26bN+ozJE8ZgKMyk3MJOvaqehPXswpAhT9K1a1dXTgF4++23adWq1S0Pc/bsWU6fPk2fPn0AiI+Pp1WrVnz00Ud8//33LF68mL/++qvgcUmSJN0nCloPwnTrnhm5aVyvHlUqVGDgyy8zYuJEngoLy9EgEh0bi8VqZfLcuYyeNIndO3cSWKkSAFNefJFa1aoV6Fw1qlbleHg4icnJ2Ox29h46lKNHSaXy5Wlcrx4AFy9cYMvmzbSpX5+0uDj8/f1d5a7XUSSptCixxhBtYMEmztGUKVOs5z16/CTjJr7DB++8iY9P3pMFtWn1CN07dWDo6JcZP2kaLR9pmmuran6WrFjN8CefyP1NIcBhRataEUKQYc6+ks3o0aPZs2cP0dHRrFixgqNHj7J37162bdvG9u3bOXLkCFu3bnWVv3jxIiNHjmTChAlUqVIl27FUVc3RzVtRFGrUqMHLL7/MqFGjGDlyJI0bN5azO0uSdF9QygYXrFxQuVsXKoQpU95g585NbNz4B/v2HXBtX7JkOQMG9M+3O/SSFesZ/mSY6/XiZWupX6cmDQs4PCarY/+cYu9fB9m65gdWrVrFoUOHsuWUgliyZAnDhg1Do8m8VahevTrz588nMDCQgIAAnnjiCXbs2FHo2CRJku51Ba0H4eN/6zK52LF/P+kZGSz9/HMWf/opqzZv5uyFC9nKOJ1ODp88yZQXX2Te1KmsW7+eI8ePF/pcD1SqxNP9+/PG7Nm88eGH1KtZE12Wno9ZXYmJYeKHH/Lc4MEEGo1Yrz0QzsjIcJW5Eyu5SVJBlVhjiL5eY7QVKudbRhtcCf1D9YrtnDv37uelCW8xZ/oUQkNa5FJCISk1HQCTKZ0e3Tvx2+LvWPj5R9hsNipVLNjNM4DVauXoPyfzOE8WQqDVatEbDABERUVx5MgRAIxGI127duX06dPs27ePdu3a4e/vj4+PDz179mTfvn0AnDhxguHDhzNu3Dj69euX4xQVKlQgPv5GC3R8fDzly5fHYrHQtGlTfvvtN3788Uc8PDyodK1FWJIk6V6mafxI5qox+VAqP4CmUfHMoRQRcY7Tp88CEBDgT9u2rV2vrVYrR48ep3PnDiSkWnPd32q1cfSfcEJDmrm2bdqyi9+37qHv0JdYsmIdPyxexeJlawsUz74DR2nXujn+ft74+vpmyykFtW3bNrpnWXHn5MmTbNmyxfVaVVXZwC5JkpSLgtSDKFseqtQo0vEPHD9O+5Yt8TAYCPT3J6RJE46Gh2crExgQQPOGDfHx9sbHy4uWzZtz9PTpQp/LarPRsHZtFs6axadTp2IwGAjOZbWYMxcu8Or77zNiwAAee/RRAIICAkhMTMRwrR50vY4iSaVFiTWGKIqC7/CXQJPHKRQNmv4jULSF6Pqbj4uXI3nznZl8+e8PCGnRLM9yTqcTVedFZFQ0r745DafTSczVWLbv3kfrlo8U+HzhZ87xUM0HC9R12deodQ1PSUpKYuLEiZjNZmw2G5s3b6ZZs2bUrVuXHTt2YLFYcDgc7Nixg/r165OSksLzzz/Pe++95+qqfLPGjRsTHh7OlStXsNlsbNiwgXbt2mE2mxk1ahQZGRmkpaWxYsUKOnXqVODPKEmSdLdSFAXD2Il55yCNBvvTL6EouT/dKqxz5y4wffqHOBwOTKZ0du7cQ9OmmcNdwsPPULt2Lfz8jNjtDoTOL8f+4WfO81DNatlyyrdffsDqJV+x8n9fMLh/D4Y/GcaTA3oWKJ46tWuwY89BLBYrnjo7W7ZsoX79+gX+PImJiWg0mmxDMFVV5cMPPyQ9PR2z2cyqVavo3LlzgY8pSZJ0vyhIPUg/aDRaj9znkLqVmg88wK6DB1FVFYvVyqETJ3ioevVsZVo1acLew4fJsFiw2Wz8ffAg1atXx7OQy95arFYmfvghFqsVk9nMhu3bCW3ePFuZtPR0Js+dy+sjR9I1NNS1vX7NmkScPcuJEyey1VEkqbQo0Uc6xlbtCZjwPmk/fIkzJtK1XVuhCr7DX0TXvA1mcxpexdBbauH3i7Hb7Uyd+ZFr22svPkf7tiHZypXzMxKfkk7d2rUIDWlBnyefRaNoeHfS6wT4Z96g9h06iv98OpvgcnlfLK5ERxNcPuf7ue0rVCfe19b3btCgAUOGDGHAgAFotVq6d+9Ot27dADh27BhhYWHo9XratWtH7969+eabbzCZTMybN4958+YBMGzYMAYOHMhzzz3HuHHjePjhh5kxYwYvvPACNpuNsLAwGjduDMCoUaN44oknUFWVF154gZo1axbh25UkSbr76B7tBtPnY/tqLuLKjZVTlMrVMIx9A31IBzIyrHh63v5zga5dO3Ho0FH69BmMVqtl2LDBNGyY2fgQGRlFcHBmo0K5cn7EJ6RRzgv6Dn2J/3w6neByZbkSfTXXnJKXrPvmpl3r5hz/5zRhQ19CrzfQJjQzp+Qna06JjIzM8fSuQYMGDB06lIEDB+J0Ohk2bBgNGzYscMySJEn3k1vVg2jcCnNcHM6rBZvjKqueHTtyITKSERMnotNqeax9ex6+tkLL6MmT+XDiRBo89BD9unZl7DvvoKoqvTt3pmW9etjyaYC5vm9QlmkM/H19GdyrFy+8/TaqEDwVFsYD13qaT5ozh2cHDODAsWOkZ2SwcOlSFi5dCkC/bt3o2bEjk157jcmTJ2O327PVUSSpNFCEEKIwO5w6dYq6desW6iRCiMxZlZPi0ZYJQl+vsWu8WGpKCr6ajFscoTgpXE1XCfYzgNNe7Ef/4OPPGDdmZPb5SjRaUh0GNIqCr1/OJ4KlSVF+X0mSpDulqDlIPXIAkRCLElQeTaNHbuSg1BT8/O7sUI+YmDSCfR2g2op8jA8+/ppxY4bnOzeWi6IjxeGHVqvB1/fO5iCZUyRJupcUdz0oKT6ejJvm+ihRioKmUiWIjUW156wHff7DDzz7xBN4e3kV2ym1Hh54VK2KVqPJtx4k84XkDnfkDlBRFAz1m+TYntkOU6i2mGIg0Gs1WFQdRoq3MUQIQeOG9XPenKpOdIqKRl+0rnCSJElS0SmKgrZJzvmdVFV1QzSg02mw4YWBojWGZOaaugVrCAEQDnSKA52+gOUlSZKkYpNXPUhV1cwFF+4kIVAdDnyCgzFFRt70lqB+rVrF2hAC4LRaUa1WPAMCivW4klQc3DbzWWJiIjqtBm+dCnf4ftRDp+BwOKF4hoq7KIpCz+65j5/20jow2RVsWi0GgwdCCDmbsiRJkpskJCSg1+vw9tZzpxvl9XotztvIe5m5pkOh9vHWmjFZtWg1mRN6yxwkSZLkPgkJCWgAR2LinT+5wwG5DJVRFIVOrVuXyCntsbGYdTo0Gg16vV7mIKnUcFtjiE6nxVfnAKfjjp/bqQp0hhKbOzZ3qhMfLaSYBWZzBg6HA29vbzw9Pe9sHJIkSRJ6vR4/Pz13vDUecDpVjHo9OO/gSYUDH62JlHTAnI7D4cTHxxujUeYgSZKkO02n02G5dAmnNfcVxkqSoARX0MiD02rFHhNDmqKgAE5VxdfHBw+j8Q5HIknZ3em/BReD3kCaQ4+ZO/9HoKoCbV6zO5foiZ346x34G1T8vD2x2Yo+XlySJEkqOr1eT2qqHbP5Tg/VzGwM0Wrc8ERM2PHXm/A3ZODr7YHNVvzzZkmSJEm3ptfp8KxWDa+qVe/4uQXc+eE5gCMjA0dUFM7YWIw6HbZc5iyRpDvtjvQMEUJwcccO0qKi8K1UiWrt2mH09MTo6UlqSgroDeC8cw0DdqeKn1bjjgeCoDoBJxlOBYFybfI+fzcEIkmSdH8QQnBpxwHSomLxrVSeB9o9gqenJ56enqSmpiCEAUW5cwlBVVW0yp2/EQVA2MFpJ8NhRNFoZQ6SJEkqYbnVg7yurTKZoqroPD1xZNy5xSQUrRbVeSe7Jt7gsFgA0AqBRlVJTU3Fr5QvLiHd20q8MeTkr7+y6Y03SIqIcG0rU7Mm3ebOpV6/fnh5e2NOT8XrDnbUEChohHtbI/20VlA0pDrlpKqSJEkl5eSvv/P7G3NIirjk2lam5gN0nTuRev264unphcViKZbldQtOAaf5Dp4vJ39dMig6Up2yIUSSJKmk3KoeZPT0xOnvf0cbQ7R6PRZ3zFWSRUZEBFoPD4wPPODWOCSpRBtDTv76K78MGIC4acb+pIgIfhkwgEHLllGvXz/MxTBaRwjBh598wc49f6FRNLz64mg6d2gLwEfzv2bbzj0oKLz43Aiat+kADisfzf+aP3fuRSAY2LcXzwwdmOO4v67ZwIL//g+Hw8GrL47m8a6dAJj35QLWb96Kt5cXI4cNptdjXW4ZoyndzJPPjuW7L/9NuaCyoGjYtWsXX331FVarlaFDhzJixAgAtm/fzty5c3NsX7RoEcuWLQOgXbt2vPnmmznO89NPP/H9999jNBrp16+fa9/rZs+eTXp6OtOnTy/gtytJknT3Ofnr7ywdMC6XHHSJpQPGMXDZfOr164rFYqY4Ro3Onfsp27btQFEUXnzxOXr06OZ67+rVOIYMGcGWLesyNzgtrvdWrtnMov/96np98dIVXh4zjFHDB/DkyNcxmzPQXBva+Z9PpxNcrmy+cWTmmtf47stZlAsKBGDvgSN8NH8hZrOFkBZNeOfNF0HRoKoqJpOJwYMHs2jRIsqVK5fjeHnlnbzylCRJklTwelC6wVCk4y/85Re27duHp9HI4J496dymTY4y//n5Z/YePowQgl4dOzI4LAwBrNm4kcWrV+NwOhk1aBAdQ0LyPdfK339n+YYNeBgMdH/0UQY+/niByhw8fpyvfvrJVeZqfDxdQkMZP2IEGp3Obau6SdJ1JdYYIoRg0xtv5LgAuN5XVX6fOJG6ffsCCmi014aQFM2O3fs4E3Ge1UsWkZKaSo8BTxMa0oITp06z/9ARVv38LUnJKfQaPILly9qwZft+Tp2JYOXPC7HabAx8egytWzandq0armNejY3j64Xfs+yHb1BVwZPPvkhIi2YcP3ma7bv38etPC9BqNIwY+zpNGzekcsUKecZ3MvwMk9/7kIuXbixjZbY6mDNnDv9dtAgfX19eeOEFWrduTbVq1Xj33Xf53//+R2BgIE899RStW7dGCMGKFStYtmwZOp2OUaNGsW3bNjp06OA6ZkREBAsWLGD58uX4+/vzyiuv0LhxY5o2bQrAgQMH+PXXX+nWrdvNIUqSJN0zhBD8/sacfHPQ5olzqdu3C0IogMLtrCpz8OBhDhz4m99+W0JSUjI9ew6kS5eOGAx69uz5i/fe+5C4uITMc900Vrtvry707ZXZoH70eDjvfDCfpwb2xul0EhuXwJbV/y1wHCfDI5j83r+5eCnKtS01zcQbb8/hh2/mULVyBUaMncyOPQcJbdues2fPMH36DC5cuJDr8U6dOpVr3gkJCck1T9WpU6fwX54kSdI9pjD1IK3BgKLVIgoxdOWvo0fZd+QI38yciVaj4fUPPqDBQw9RIUuD9u6//ybi0iUWzJqFzWZj7NSphLRpg6eq8uPKlXz9/vsIIXhp2jSaNWiAv69vrue6eOUKi1ev5uv338fXx4dp8+ZRv1YtGjz00C3LNG/YkAWzZgEQExfHGx9+yNP9+gGg9fJCq9FgTk+XjSKS25RYv+CLO3Zk6xKWm8SzZ7m0cydGT0/S1aK1il73aGgI33w6G41GQ1x8IgaDAa1Oi1NVsVlt2O0OMjIs6HQ6vDw9qFyxAq+99BxarRYvT0+qVK7E1di4bMfc/ddBQkNa4Ovjg7+fL61bNmfL9t2cPhNBh7YheHl64uHhQbPGDdmz72C+8S3/bR2TX3+ZcuWCXNuOHT/Ogw8+iMHDA4PBwGOPPcbGjRs5evQotWrVokKFCtm2e3t7M3XqVIxGIzqdjpo1axIdHZ3tPKdPn6ZFixYEBgai1WoJDQ3lzz//BCApKYkPPviAZ58dcVvftSRJUml3aceBbENjcpN49iKXdh7EaPQgPf32xk+rqhOr1YbdbicjIwO9/sazhuXLVzFv3ocApKSY8fXOfXikEIIZc77krQljMBo9OHs+M/5hz71Bv6EvsWHzjlvGsfy3TUx+fQzlygW6tu3ed4jWLZpQ/YHKaLVaPpr5Jo0a1Abh5LffVjN27Nhce4QAeeadvPJUfs5HnGbGjBmcPnX8lp9DkiTpblaYepCHhweeVaoU6vjnLl0ipGlTPI1GDAYDDWvX5uA//2QrExwUxOhBg9BqNHgajVQsX56oxESOHjnCIw8/jI+XF77e3jRv2JDdf/+d57nOR0bSqF49Avz80Go0PPLww+w7fLjQZb766SeG9e1LGf/M4Zmqw4EqBCmpqXdkgftT//zD9OnTOXXT9yTd30qsMSQtKurWha6VMxgMCKEgtLc3f4ZOp+ODjz/jieHPM7BvL/Q6HS2aNaZ6taq07/EEPQc9w7CnnqKMrxd1HqpJg7q1ATh87B9OnT5L00YNsx0vNi6ecmVvdEcuVzaQuPgE6tV5iO2792EypWMypbN73wHibzH27u03xtPqkabZtsVdjaFS+UC8vb0RQhAUFERsbCyxsbHZbkyDgoKIi4ujatWqtGjRAoBLly6xadMm2rVrl+2YderU4eDBg8TFxWGz2di2bRsJCQlYLRbmzJ7NC6OfxTePll9JkqR7RVpUbIHLGQweOJ0CIYqeElu0aE716g/w6KOP0aPHAMaOHYXBoAfgo49mUvdavrFa7Xjqc38CtmP3AXx9vWnZvBEAJlM6bVo25T+fTueLf09jzrwFXLycf259+42xtHqkUbZtlyOj0em0jJv4Pn2HvsTiZWvx8/VBo2YwfdIY2rbNHFIqhMjxdC6vvJNXnsrLmfATxF76hxcHPkJUVHSe5SRJku4FhakHGT09UfR6DIW4P3+oenX+OnKEdLOZdLOZg8ePk5SSkq1MzQceoPaDDwJw4swZzl68SMNGjYiLiqJsQICrXKC/PwnJyXmeq0bVqhwPDycxORmb3c7eQ4dIvOlctypzITKS85GRdA0NdW2zJSVhj47Gy8sLca3HpL2EVpg5dugQEXv30rV6dSIv5f+gRLq/lNgwGd9KlQpVTqPVoqKivc3zvjXhFV4c/QzDnx9Hi2aNSU1LI82Uzp/rlmNKT2fYmNdoH9KUeg9lDoc5evwk4ya+w6xpk/Dx8c52LKEKFCX78ocajYY2rR7hn5OnGTr6ZcoFlaXlI03R6wr/VQohUBD4KmacWiMpKSnY7XZUVc1x3qyvL168yKhRo5gwYQJVbmpJrlGjBi+++CIjR47E39+fkJAQYmJi+PPPbThtZrp2COX7pauxWCw4HA50RYhbkiSptPOtVL5Q5TQaDUKAUsQVb3//fQtpaSa2b9+AyZTOM8+MoVmzJtSrl8uwEXt6rsdYsmI9w58Mc71u3qQhzZtkNtJ7e3nSpWNr9vx1mGpVC5Zfr3M6nez+6xBLvvuEMgF+jH39PdZs3EbvxzqCasNXiUdBEBcXh16vp0yZMjmOcXPe+fvvv/PMU06nE4fdjofRCMCeXX+idyTTUvsb2Opz9lwAR4/PZ/To0Xh5eRXqs0iSJN0NClsPUhQFTSHuyZs3bMjp8+cZN306gQEBNKlfH50291rUyYgIpn36KW++8AI+/v7YzOac9Zt8kt8DlSrxdP/+vDF7Nj5eXjSrX5+k1NRClVmzdSv9unVzzX91nT09HfuZM3hVqEBycjLr1q0jLCyM25GSkkJCXBw1atUC4M8//sB08SKHXnqJWr16kda9O/PmzeOll15Cr9ff1rmku1+J9Qyp1q4dZWrWzLdMYK1aPHDtiZTT6URL0bspR5y/wOmz5wAI8PejXZtWnD57jt37DtC9c3uMRg+CypUnJCSEAwcPAbBz735emvAWc6ZPITSkRY5jBgeXIy4hwfU6LiGR8kFlMZnS6dG9E78t/o6Fn3+EzWajUsXgQsccXL4ccQmZPUq0Tgs2cwrly5enQoUKxMfHu8rFxsYSdK2HyokTJxg2bBhjx46l37Uxd1nFxsZSp/ZDfP/99/z4448YDAaCg4PZ+vsGjp84Rd/Bw/nu24X8+ec25s//FLhx41pYCQnx/PjjjyRk+Y6u27tnV6GPJ0mSVFweaPcIZWrmP0t9YK1qPNC2OZC53K3mNjLirl37eOyxLhiNRoKCyvLoo6EcOHAoZ0EB5JLrrFYbR/8JJzSkmWvbvgNHOXDoxpASp1NFryv8I4OgsoE0bVSf4PJBGAwGOrdvzfETZ24qpVLOR6DN5Us4evQow4cPZ9y4cfTr1w+LJYOyZQOz5an4+HjKly9PamoK6aZUMq4tnyiEIC0liebiF1BtkHyCYS2tdHjkQVJTM58apqWlkpSYM4/cSkz0FX788UdSb7opB5mDJElyr8LWg1AUbCZTgY+fbjbTMSSEhR9+yNxJk7DZ7QQHBeUot//oUd7+97+ZPHYsrVu1QqgqQWXKZOu1kZiSQtlcGsGvs9psNKxdm4WzZvHp1KmZdYubznWrMnsOHaJ9y5Z5nsMcE4PWbMaYS4NQ1JUrJGbJN8ePHOHS+fO5Hmf9unVsW7WK4ydOZMZltZIUE8P+UaNwWK2c27iRgB07qF2mDKZr33dSUhLJSUl5xpaXi+fO8cMPP5CRy0pAu7ZvL/TxJPcoscYQRVHoNncuSh53l4pGQ9c5c260TAoVnEXvGnXuwiWmz5mHw+HAlG5m556/aNq4IXUeqsUff+5EVVUS0iwcPnyIBnVrc/FyJG++M5Mv//0BIS2a5XrM1i2asXPvflJS00hJTWPX3r8IadGMyKhoXn1zGk6nk5irsWzfvY/WLR8pdMyNG9bj9JlzXImOwWazsXHTH7Rr04q6desQHh7OlStXsNlsrFu3juaPPEJKSgrPP/88kyZNokuXzMn2nE6n649QCEFGRgbjx4/D4bATFXWF5cuX071jKB9Mm8Tapd+z8n8LeWXMs3Tv3IHnRo0kNSWFpKRE0kymXP+Y82K329m6ZStt27Zl7969rvOfPRNOZORlNmz8HYDEhAT+2Px7ob8bSZKk26EoCl3nTsw3B3WZ8waKolzrniuAok/gVrfuQ/zxxzZUVSUjI4N9+w5Qv37dHOU8DLk/+Qs/c56HalbDkOUpVWpaGv/+/DvsDgcJicn8uWs/bdsUPte0CWnK4aMniY1PRFVVdu45SL3aNXIWVC3oNE6sVqtrU3JyMmPHjmXixIn06dMHAJvNzkMP1SY8PJxLly6RmprChg0baNu2LUKAryYFrUZDamoKlgwzbcqeyHIOGx6Je2io2cKJv/9k86Z1LF+2lA0bN3Hh/LkCfya73c7WrX/Stm1b9u3bB2TmoNOn/iEy8jKbfv8DgLjYq/y59Y9Cf2eSJEm3ozD1ICEETpsNtRAPJqPj4nhv/nycqkpcQgJ/HTlC84bZh/tfiYlh1ldfMfP112nWoAHCxweDVkvzBg3Yf/QoaenppKWns//oUZrVr5/nuSxWKxM//BCL1YrJbGbD9u2ENm9e4DLJqaloFIXALENzcuPIyMAaGcmVy5dvbHM4+Gv/fvZeu84DxCclcepMZoN+UlISp681fGRkZGDw8ODEK6/gaTSyYc0a1i1eTMKiRa59bSYT//zwAxEffsiWX39lw5o1/LZyJStXreJK5I1FLm7FZrOxY9cuWrduzd49e4DMHHTs8GEuXbrEth2Zc3xFRUay7Q+Zg0qzEh0jUa9fPwYtW8bvEyeSePasa3tgrVp0nTOHetd6NlitVgya25s6p2vHRzl89B/ChoxEq9Xy1KB+NKxXh3q1a3Em4hw9BjyNotUxqF9vmjV5mHdmfoTdbmfqzI9cx3jtxedo3zaEvkNH8Z9PZ1MhuDxjRw5n2OhXsDscPD/iKSoEl6dCcHlCQ1rQ58ln0Sga3p30OgH+fgCufYPL5WydvZmHhwfT336Dsa9Nxmqz0bdHd+rWb4iHh5HJkybx/PPPY7PZ6PH44zRqUI+ffv4Zk8nEV199xVdffYVWq6V///707duXcePGMXLkSFo2qs2o4U/yzPBhqELwwohhPFQtl656QuCjsSK0BhSDDoRKapYb4Fv59ddf6VrXH397OPvNZtasWYNe2HggQEeq3UmnTp1Yv349DodDdkGTJMkt6vXrysBl89k8cS6JZy+6tgfWqkaXOW9Qr19XAKxWC0bj7aXDgQP7ceZMBI8//gQGg57+/fvQvHmTHOX8PSyuNpe+Q19yLZV7JfoqweWz542uHUM5dPQkfYe8iKoK/vXKSNeyuln3vZVKFcoz5Y0XGP3yFGw2Ox0fbeVaveZmDlVBCzz77AheGDOGfX/9hclk4uuvv+b//u//AHiif3/69uvHjBkzGDt2LFarlf79+1O9enV8tekgVHyUBNDo8VTS8c7IOVmdYomlo9canAGN0FRQwBHJxpO+VH8wl0aaXCxfvpxe9dPxsm7jQIoPq1atwlOTQbUAK9GXVNq3b8/69etwOJx4eNzefGSSJElFUdB6kDk9HZGe+/DJvNSqVo1HHn6YUW++iaLR8Nqzz+Ln4wPA6MmT+XDiRBavXYvd4eCjBQtAUVB0Op4bPJiWDRowrG9fxk2fjtPpZGifPq45Eq/vG5Slp4i/ry+De/XihbffRhWCp8LCeODa8J5Jc+bw7IAB1KlRI88y0XFx2Y6Xr7JlsTscbPjtNzRaLXoPD8SVK2R4erqKWDMy0F6rW+zdswcNULt+fTasXUvcv/+NNTWV/f37U6ZGDa4eO5braRJOnSLxueeo1bMnWrsdp83GoTJlqFzAiWyXLl2KYfNmIv75B3OrVqxYsQJnQgK6qChsRiOhoaGsW7sWp6rieW3IqFQ6KUKIQrVCnDp1irp1cz7tyo8Qgos7dmCKjsa3UiUeaNs221i1lORk/PT221pa95a0eq6m2gn2vnXR2/HBx58xbszIHPOPFJRDMWB2aPDzyPxZBAqK04ZT0ZLu0OLh4YHVakOnOPHw8iM93QQoKIqCVhF4UfDeHdlodKTYdcTExOT4fYUQCCHYt3sXGTYbqsOBt17QSn8CtHrOOqrg6R9E5bS/QVz7DXVGVL+q7InW07RVW3yuXaAlSZJuR1Fz0KUdB0iLjsO3UnkeaNs8ew5KScbf38DtLK17axpiYlII9jYXy9E++Phrxo0ZXuRckxeH4o3Z6YGf/towFzQoTjNOxYN0pyceHkasVgsaYcfLNxCTKQ0hMudc0SkOPEnOdrzws5epk/LNLc/r9HqQ3xNCefzxx3O8dz0H7dn5J1a7E9Vpx89gpQVLQetJuBqCb0B5KiUvu5GD9L6o/g3YEf0gIe0yhy9JkiTdruKuBwkhSElOxnLxIqrDURIhA2Dw9cXu64u4xcSun//wA88+8QTebpjP6eLVq1xYu5YyPXoQ99lnaPV6dP7+nP3tNyr36EHZIUOo16ABJ48fJyUiggGvvMKWP/7A7nDg5elJ+rFjHJowoUjnrty2LQ+8/Tbdu3fP8d71ycW3bd4MWi02iwU1Job9zz2HZ2Ag1V96iXLly7Pztddcv6FPcDDVOnbEe9AguvXuLedoLMXuyC+jKArVH3001/dMaWl46kXJNoQAdlVBr9VwO92gb0UIQeOG9W/r5lQnbPhpgWvXw+u361qh4qd1YrULPLUCvaKSbkrBQ6vBQ+vMLKnab+teXqMIzGYzkZcvcfLkSRRE5lM1ezJCo6N5FQO+yiUoEwiWlMxh7047tZTzkHrT2D2HBU3iGRw0lA0hkiS5laIoVHs057xQAGlpaXh7l3RDCGRk2PEwFM/I1MxcU7fYG0IAdCIdP026a1qTGzkoAz+NFavdGy+tEx0O0tMSMGoVDBpbZiHVVuTzarCjV01s2rCWOnXrcubMWRAqDocDoz0aoTHQolIy3qZD4F8ZMmKu5aAM6rAVbh7ubU9DE78XVVNHNoRIkuRW+dWDUlNTcSYllWhDCIDTYMBDq8WSTxkhBPVr1XJLQ8h14V99BV99lWP7+V9+4eoff5D+/PNkRERgPnOGxWlpeKSmkrJ9OxqdjuiDB4t8XmdGBulXr7Jh7Vpq1arFuXPnUJ1OHA4Hif/8g87TE/buJWLlSso//DDxJ08CkJGYyMkZMzh50/FMV6/yz+LFPDp6tGwIKeXc+utkZGSgVVT0asGHZxSVze7AoNcBRb9ZuxVFUejZvXOJHR+h4oE1855dgPf1efSKox1JdeCrcWDUqiSc2U9n/zjQGsBpA/u1J5nXe/Cl57184s2czpJt5JIkSSqqjAwzer2CTleyDSGQOc+Gh754Um5mrulQLMcqHBUP0lztRt7a640gt39kxRxJZ+9I0rwacfFcNJ0994PBN3PlHfu1CVLTrhU2XczzODkidpbcAxBJkqTbkW4yIcxmrImJJX4uJ+B5i+XSFEWhU+vWJR5LUZkTEjg5a9aNDYdymaS8iGIOHiTmmWeoMXAgqXXrcm7+fHwrVcJ09SoZN/0+0X//XaBjKhoNjhJu5JJuX5HuzITIueTsraiqihDCtaSSyZSGBoGXkl8bZfHx0Gu4mmzG4WXE39uIotrBYaOknwbeTYQQaISDhw0Xwc6NRpDboHFYsdvtct4QSZKKTXHlIK1Wg6dnEdfSLSRPTw/i4pKxefri760HpwWcGdzLOaiQo3AB8DUfpaGGzN6RjoKvrJAnhylztbo8lpyUJEkqrOLIQampqQirFfMthq0UF53TSarNhrFKFTyEwJaWhjUtDYpwnS4JgqLljOJ2bulS1/+3ZFlxp0iEwJyYWKR/L9KdU+jGEKPRSGJiIoGBgbn+sOkmE6pQr/1tXftHLQQa4URRFFSRudVLr6BVrXfsPlAnHFQuYyTDAXGJKaBoAOVGH2Ah8PL0wNtoQLGbM1e3uY8IIUhMTcfDWQw3n1m0rmBh09pVePuXxZKRQbPmzSkfXLBliE0mE8ePHyMkpPS2UkuSdGfdKgeZTCbX6jA37qsEGk3mUy9VVQEFLy8DWu2du84bDAqVK5fFbLYRm2wmM/lc74osruUgAz5GLdiSKckhnXeCEILEFAtGZ8F7EpaE1kFn2bhmGV5+5TCnmwhpHUpg2VtPOguZqxREnD3DIy3yXg5SkqT7y61yUFpqqqtqI9TM67jqcOC0WlGEAJ0ucwWZtDSsycl3LG5HcjIGT09Ub29SFQWNjw8av8zFH7jWUOOh06FXVdKiou5oI4kA0h0OUs7cvOT73U0IQcrSpfzqdBJQsSKm1FQe7diRgFusqnNd7NWrREVF0aRp05IN9D5X6AlUHQ4HkZGRWCw5e3TYbDYU4UAvri0NpWjJvMkrrUMlFFAUQINQFKx2JxabA4GCp4cBo6boS/3ejTycJipnnEBHSXTpUjhXpgOxySZahbQp0FO68+fP8e677/Hi2DG0CmlTAjFJknS3yT8HWdFo4MbwXA2ucYWl1vVJ9BSsVhsWS+bQE6OHHiOFW12gtDE646icvh6duDM9QPOncCpwHGlmJ81btCxQDgoPP8WcOXN58YXnad6i1R2IUZKk0i6/HGSxWHCmp2O71qNAZzSiOhyFWjL3jlIUFK0WjU6HRqfDqdHgvLbNoNFgi4+/I2EIIUg5c4ZD773n+u7uNYpGQ6+dOxFaLU2bNy9QDjp65Aiff/EFz40aRYtWMgeVlEI3huRny+bf6eB7LnOM793Mw5eP/ojlXz3rQMold0dzz9iUUgsfvzIcOXqU8uXLM3DgwHzLJyTEE370AH5GHekaXxo3bkxU1BVq1Kh5hyKWJOlusnXrH3TsWJbimUjJnQzMnr2GiZ1NYLrg7mDuGRvSn8TH15cjR49TuXJl+l1b1jIvUVciiT53CJ1GwWGsSIMGDYi6EkmNmrXuUMSSJN1NNm/cyL7+/bGbi2fVMHcJrFWL9BEj8PruO5IiItwdzj2jxW+/4eXlxfETJ6hWrRp9+vTJt/yZ8HAu//03DiCoTh3q169P5OXL1HrooTsT8H2ieKa2v0447/6GEABrGn4eGs7ag8EgV0IpLu2DUwkmjrHN4OHKXvy+cT0Xzp9j3ZrV17quZ2dKS0PryKC+OEFa4lX2/vEb69at5/Tp05w/F8Fff+0DIDk5Kdf9JUm63zi5+xtCAGz4+HhwUd8ODAHuDuae0b7s31RSTvNS40PUKW9j86b1nIs4w4Z1a3Idq26xZCAsCTRSV5IQc469mxezdt16IiLOEnH2NAcO/AVAUlJiqRjrLkmSe5nj4+/6hhCAxLNncaal0XjyZLzLl3d3OPeMmM8+I3bdOlInTUITGcmmdes49c8/rFu9OtfyToeD2NOnOTxuHOeOHWPlV1+xfsMGzkVEcOrECQ4eOABA4h2YgPdeVrw9Q9avooPvPdKC6OHLD/tTuJJkIcDHSLMGtdl75BRN69WkXeBVcJSGbr93N6t3Fc4ka6kYYORQnA6DXo9QM5cJVhQFL52TRwwR4Mi4sVPZh3j7hz00bdqUhg3qs2XrNsqUKUNSUhLVq1enY8eOeLlxSTBJktxn27YNdOhQzt1hFAtV1bFo0S5iYpII8PWgWb0H2HPkPC0aPkCo9xZw3P033O6W4dOIc2nlCPJROJ5SGZ1Gg7g2X5iiaPDRptNcuxYcWR7yVGjPpO8iaNmyJXXq1ObPP7fj6+tLWloaDz30EO3atcPT09NNn0iSJHda/NlnnBo3zt1hFIvAhx4ivVcvHP7+eAK1goM5GR1NnXLlODV5MjZT8c4xeD+q0bcvxocfRudwENC5M8LhcD3cVTQa7NHRHB4/HmtqqmufVuPG8YdeT9u2balerRq7du/G09OTjIwM6tevT+vWreVy8oVUrI0h237fwKOeJ/IuYAzg79SypAlvnE479cqoRManUa+CAZ/0C8UVRvEy+GDWB7H75BXa1K3I99tO0yG0NXW94iEjBWyptz6GVDgGb1CdeTY4qb6V0aRfBdUBOiM47Tg9g9gY5c/ff/9Nt65dCGkt5xiRpPvNtm2b6NAh78kxhTCwf38cZjM4nQ7q1y/HpUtxPPxwZby8SmsDtwGTCXbuPE6HDo2YP38lQx5vSRX1AKRHgU0+ESp2hjKg2vNczUb1q4vGFJFZRu8DDgt2zwdZH92U48eP07VLJ1q2khN/S9L9ZuWiRRx+9tk83y9Xvz4VXngB/9q1saSlYd+/n3SjEY4e5ezKlXcu0EIIqF6d8q1aEe3tTXB8PEcqV2ZQaCin/vtfYv/5J3OyValY+VWujN1sJiMpKcd7iqJQ9dFHidq3D4fFgldQEJbkZKq0a0fAyy9z6tQpunbuLOcYKYRibQzZtGE9HcsnojVfveksWi5rH+SfeIWWrUIIKpf55O7Y0cNUf7Amfx/4Cw9hpaX3pWJZzrUk2QxB/Pv3C5w7f4mXnxlAIyWfxh/pDlM4pW/Eyj/2MXToUKpXr+7ugCRJuoM2blxP164PoNHc3LChcP68g1OnYmndOpSAgDIAHDt2hBo1arJ//z58fPQ88ogfpX2Yjdms4ZNP1nDyZDjvT3iSaik/uzsk6TpFyz+GgSzf9DdjxoyhQoUK7o5IkqQ7aO2qVVx46y3iTmSvGxi8van7r3+ha9w422oiB/fvp069euzctg17VBT/TJx4+8u5lrDgdu2I69CBE6dP82KHDmwZO9bdIUnX6Dw8aLlgATvCw3nttdcIDAx0d0h3hWJtDLFaraxdvYrWD3hgczixO53YnBCZ4UWFihVp1KRZnvtGR13h/KkjhBhOFVc4JccYwPxtsbz8RFs0V/5ydzRSVoqWrw7rePbZZ+VwGUm6z2RkmNm4cT2hobWwWGzY7Q4yMmxERWVQuXJl6td/OM99r1y5TFTUeVq08L6DEReVB/PmbWR8r2CIXO/uYKSsNB58drgJL7zwAgaDwd3RSJJ0B6WmpLDht99gzx5UvR5VCISPDwEtW1KtenUaNmmS574Rp09zYutWDr7wwp0LuIjK1atH+tCh1Dl/nkPffuvucKQsPAMD0U+Zwvjx4wu0Yo0EulsXKTgPDw/69h/AqZMnMHh4YDB44K3XU79ixVvuW7FSZc6cDifFuyb+6aV53hGFWRuj2bhxI+O6yxnlSxOTRyX2J/hSpYpDNoRI0n3I09OL3r37curUCTw9A9Hr9fj4eNCgQfAt961cuSpnzpzGZPLAx8d6B6ItKoXp01ewdetWxrdr6+5gpCxSPRtzIKU+Dz4oZEOIJN2H/Pz9eWLoUI41bIivry86vR5PT0/KF2AS0pq1a3Pm9Glq9evH2V9/vQPRFo2Hnx9xgwaxY+tWPC9fdnc4UhY1Bg6k/NNPo2o0siGkEIq1Z8jtEkLwx+8bqelrp5rzjLvDyZ1nGbpP+h+ffDyH+qlb3R2NdJ1PBX7+RzB06FAURXF3NJIk3YWEEGzevJE6dcrywAPFu9ha8fGkY8eX+L8vP6Hm1bnuDka6RvjVYfGpWgwZMkTmIEmSikRVVdb99hvpW7Zw8rPP3B1OrqqEhPC9Xs/Xc+fyS0iIu8ORrqnRrRuGZ5/lySefdHcod51SdbenKApduj3G+XQjaZ7V3B1O7iwpjBjUh3NnS2ljzf1KUTCZTPImVJKkIlMUha5dH+P06UQyMkrrbOwWnnnmSc6cOevuQKSsNHrS0tJkDpIkqcg0Gg29+vbFq1MnHuze3d3h5Orq0aP079aNkydPujsUKQu9tzcmucJPkZSqxpDrOnTqzKEY1d1h5E6oDGnixb4Dh1HLN3R3NNJ1aTFyKSlJkopF+/adOHDgkrvDyINgxIi2bN9zELVcqLuDka5RUk7KHCRJUrHo0r07+kcfdXcYubKbzRiXL2f/yZM0GDTI3eFI10SsW4der3d3GHelUtkYotFocOpK8ZwP1hTG96jD5P/7HXzlbPGlQYR/uwKNyZQkSboVvV6P3V6an/BbGDcujLd/vAxeld0djKRoOFXmZSpVquTuSCRJugd4enriEXzrua7cJebwYR48c4bDtWrhV6WKu8O572kNBnr8+SdVKsv7gaIolY0hAGaLHeFXen/U/ZetXL16Fazp7g5F0ujZsvsgTZs2dXckkiTdI8xmK1B6G+X37DlLTEwMWBLcHYqkNbJp6x5atGjh7kgkSbpHKH5+BDdq5O4w8uRs0oTY2FjSoqLcHcp9zxgQwNadOwlp3drdodyVSm1jSMfOXdgZ7Znre6oqiEl077goVVV55plnwJbm1jgkQLUzsE0NZs+e7e5IJEm6Rzz6aAd27YrM9T1VVYmJcW8jREZGBi+/9CKoFrfGIQEOM0M6VuKDDz5wdySSJN0j2rVvT4Xnn8/1PQGYdcW6IGihpaSl8erLLyPUUjqtwX0kPTaWRh4ezJo1y92h3JVKbWOIj48PDo0Hl1Phta9/Z8q325jwn81cjkvlwJlo1v2V//K7L3y6vlDbC8tXZ2fVqlXgXa5YjifdnkvpBgYOGODuMCRJukf4+fljtcLly4m8+up8Jk/+D6+//hmXL1/lwIFw1q7dk+/+Y8bkvtJLXtsLKzjYj5/+9zN4lt6u1PeTS6ZABjzR391hSJJ0jyhXvjzeDz6Io1w5dlWsyN7gYHZVqIBJryfO05OLvr757v9nHsP28tpeWMaUFP77009ymEwpoXnwQZ7o18/dYdyV3NuseAt6vY4dR2NpWacSQzo2ICohjcj4NPaevMKhs1fp37YOAT55T1iWZrYy46ddlPE1olEU3hzcmrNRSXzwv11ciE3h/RHt+WT5X1QO8iU60UTHxtXo0uzBAsX2aGU7q/V6tkT70ckvrrg+slRE0UlmjEYjKSnJ+PsHuDscSZLuARqNhh07jtOyZT2GDu1KVFQ8kZFx7NlznEOHTvPEEx0ICPDJc/+0NDPTpy+iTBlfNBqFSZOGceZMJO+//18uXoxh5szn+fe/l1C5chDR0Yl06tSULl0KNtSiS5darF69m+0pHXjUsKyYPrFUVLHJVgwGlfT0dLy9vd0djiRJ9wDhdJL24IOUDw/noZQU0nU60vV6Yry8iDcaqZGSgkc+PTNsGg0Hy5fHw+kEIWgWH0+KhwcHy5UjzWCgVUwMR4KC8HY4MOt0VDaZqJJesOH/Kf/3f2S8/DIPTp3KkTFjiusjS0VkVRSuxsZitVrx8PBwdzh3lVLbMwSgRas2VKvTCEVReP+nnSzccIQKZbwJqVuZdg2r5tsQArB671l6tKzJ5CfboFHgdGQiwQHevDU0lK7NHuTIuVgA+retw/h+Ldh6+GLBg/MMJD09nSDvUt2edN9oVzYRo17Lxo2bSEuTQ5ckSbp9jzzSkho16qAoCjNmLGLBgtVUqBBISEgD2rVrnG9DCMBvv+2iZ8/WvPXWcBRF4fTpywQHl+Htt5+hW7eWHDmSuTzuE0904NVXB/LHH38XIjov0tPTKedlvY1PKBWXdoF/YzRoWb9+PRkZGe4OR5Kke0DTVq1o0akTAAfKleNkYCCeDgfBZjMVzeZ8G0IALvr68kBaGs3i4lCAZIMBT4eD5nFxVE1LI94zczqCGikpNIqPJ9In/5yW1QNt22IymVDi44v8+aTiE/PVVxgNBtasWYPdbnd3OHeVUt0YcvbMaXYeOEanJtV4+6m2PPd4Ez5etg/ymOT/fEwyB05HY7M70Wk1CITrPVUACHy9DADoNBrUzI14GnTotRpUIXI5au5MqgeVK1Wikf58UT+eVIy8bFdpxQFqlFGw2+2IQvyWkiRJuTl9OpwdO/6ic+fmTJ06guef78NHH/2MouSehM6di+LAgVPYbHZ0Oi1kyUFCCIQQ+Pll9hrQajU4nZk3skajAZ1OW6jrVnx8Og89VJN6jrVF/4BSsfGxnCRUXUglr1ScTqfMQZIk3baz4eEci46misnEI3Fx1E9M5EhQUF7VIFL1emI9PXEqChqyZqAb/9/gdAKZVanr23SqiqaQ1yy1YkUa1qvH4SlTCrWfVDIurVnDn127YrtwwXW/IRVMqe7WUCYwkEfqVGH6D7/hZdRhtjh4uuvDlA/wZsvh/Tzesibf/36MNwdnzp5bxsfIjJ924qHX0i+0DiH1KvP+T7v461QUOq2G2lXKFltsRg89ScnJoBa8FVUqeQ+XdbB++3Z2797NpEmTCAwMdHdIkiTdpfz8/AkJeZh33/0WLy8jZrOFZ555nPLlA9iy5W969GjNf/+7nkmThgEQGOjHjBmL8PDQ079/e0JCGjBjxn/Zt+8EOp2WOnUeKLbYfHy8iI9PBOTkdaVJs/IxrNu0iT179vDOO+/ge4tx/ZIkSXnx9PKiitPJr8HB6FQVh0ZDnaQkPB0Ornh780BaGuEBATS71jvDw+nkQPnyaIWgRmoqwWYzB8uXJ9bTEw0QYLMVW2y+Xl6ciYsj96UuJHcQQmDbto3V1auzZ88e3n//fYzG/EdRSKCIUtx0tGXzRtoHp6KkXHZ3KDmoXuUYt2APn7/QAVJzX3FAco9dKZVo3K4Hfn5+7g5FkqS72Natm+nYsQpQ+pZQt9kMTJjwDfOf9oX0QgzxlErcttQuhHR9Ek9PWU2QJKnoNq5ezYVp04g+dMjdoeRQsWNHTjVuTPXVq0mKyH9RC+nOqj1vHoNeegmdm1cculuU2mEyaWlpCIcdJbV0rl+tyUigevXqYCt9N8n3u+bVfPn1119xXusKKEmSVFjJyUloNILS2BACYDA4qFq1KthT3R2KdJPmD9hZvnw5qlxyUpKkIroaE4PDZCLm8GF3h5Kr5CNHqFixIuY4uYhEaeN58SLLli2TQ2UKqNQ2Gf21dxcdKpkhtZRWaP0qY0o9DA45a3xpYzRdpG7Neixbtgxvox67qtC3b988x/lLkiTd7O+/D1zrFWJ2dyi5UlUvTGkp4CidjTX3M1/zEapW7MnKlb/ioRU4FT19+oS5OyxJku4if+/fz+XZs0tthbZq+/b8Ex+Ph7l05sj72fmlS6ndti2rf/sNh9mMwceHXr17uzusUqvU9gwRQkFJK529QgAcih69XgcOOZN/qeOw0EJ7nIEPG+lRLhJ/j9KZSCRJKr2EEChK6b3JS0214ufrA2rxjQGXioktmUcNK+j30EV6BKzGVy9n9pckqXCEw0HMkSPuDiNPNm9vAgMCUB0Od4ci3SQ1MpJ/Ro8mfeFCjj/9NMJkcndIpVqpbAw5duQQtf1tIEpvF1Nd8nkMOg2OCs3dHYqUG6cdEiMABaswyF4hkiQV2OHDB6lfP8jdYeQrIMCOzeFArdDF3aFIuXFmQMJB0BqwCA93RyNJ0l1k17ZtpG3c6O4w8hW5dCmm9HQaP/20u0ORcpGRlET46tV4BgaiKcSSyfejUtkYEhNzlSrO0r5krWBYt2Z8u+GwuwOR8qPzwMND3ohKklRw8fHxVKzo7ihubdCgLvx3R+ntvSIBej+MHgZ3RyFJ0l0kJTmZ8AUL3B1GvhxWK7UUhYQmTdwdipQP34oVS++cGKVEqWsMOfnPMWr6O0At5d2uPPz5auUeGtep4u5IpPzYTAT6eXHyxHF3RyJJ0l3g+PEj1K0bBJT24XVGFi78jcY1A9wdiJSfjBi8jHrORZxxdySSJN0F/tq1C/P27ailfBGAis2a8Xd6OgEWi7tDkfJx9dgxtFotly7KVefyUuoaQ6Kjo6huTHF3GLdm8KJsGX9aecp/XKVdFU0sSYlJ7g5DkqS7QFxcHFWqGN0dRgHoKR8UQDPNOncHIt3CA9pwEhMS3B2GJEl3gaTkZK6sXOnuMG7Jt1Ilyvr6cvytt9wdipQPoarE//47ifHx7g6l1Cp1jSFCKGCWNw1S8Qm0XSYlOVEutStJ0i1lztwvn3RJxaeC/SCxV6NL7aoQkiSVHqrDQbJ8ii8Vo4vff8+Fc+dkDspDqWoMEUKgtaWU/iEy0t1FddCovMrp8FPujkSSpFLM4XBgMMhGU6mYOTOo4x/DpUuygiNJUt4sFguJx48j1NK7gISLEMilCe4OGYmJmA4cIF72DslVqWoMuXo1hnJe7o6igEyx6HQ6NidUAYOvu6ORbkEjL9mSJN1CZORlKlcu4+4wCigdNFq2mHqBIcDdwUi3oCBA5iFJkvJxJjwcXXS0u8MokEu7diG0Whp++SVeQaV79TUJuBsa2NykVDWGHD18iLpl7O4Oo2CEk5daGUlJvMqfsf7ujka6hSBPlasxUe4OQ5KkUuzMmXAefPBuadxWee21LlyMTeJvSzt3ByPdQiU/G5fOy0lUJUnK2+WLF4n74w93h1EgluRk9F9+yfGTJ2k0bZq7w5FuQXf1KqeOy8UkclNqGkMsFgt6YUcxxbg7lIJzWnmkZhBX4xPdHYl0C1pTFKqQT+UkScqd2ZyO0agBMtwdSiE4adasJnHJ6e4ORLoFD/Np7A75ZE6SpNwlJyeTcekS8afuniHd6bGx+KelYZJz8pV6V3fuJCPjbrq/uXNKTWPIzu3baFfx7ruh+/NQBB1aNHR3GNKtOG3XuilLkiTltGvXDtq2vfuWSt+9+xjtGlV0dxjSrdhSKf3LNUuS5C67d+wg+ssv3R1GoSUYDJSXy+uWemlRUWi0WneHUSqVisYQi8WCVrWhuZt6hVxjszsx6OQ/rruB1pqC3X6XDMOSJOmOMZvT8fDQoCh331MTh8OJQSd7vZV+AsUSJ1c1kyQph5SUFCxXrhB/8qS7Qyk0odGgyFVKSj2H1UrK5ctyRZlclIrGkM2bNtK2fJq7wygShyrkjehd4qEgPWdOh7s7DEmSSpk//viddu0quzuMIlFVgUaRK7DdDar5p8kVZSRJykYIwR8bNxI1f767QykSrVaLQ/YMuStYz5zh6tWr7g6j1HF7Y8iWzZtoUUFFa747fxybYsDLmeLuMKQCCNYkcP7cOXeHIUlSKbJ580ZCQ6uj/D979x0eVZX/cfx9p5f03khCIBAIEHrvHQRpir231XVdsa3u2nXXLbatv3Xtbe0FFaVI770TINSQ3uu0zNz7+yPoqgiEZGbuTHJez8OzazJzzmcouTPfe873SMH6Zk6Ppl405gwG6YZjHNi3V+0YgiAEkK8WLqTh44+DclUIQEhcHCdXr1Y7htACtUuXsnXTJrVjBBxViyGbNqyjW7iTWNdxNWO0jc4ENeJOT1Bw1JAZqXD0yGG1kwiCEAA2bFhL795xREUF79YFRZGgOjjfRHc0kr2I9LBa8k8G8XseQRC8ZvmSJTR+/TXHPvpI7SitojUY0Fut5K9bp3YUoQVKd+3ClZdH4alTakcJKKoVQyrKy3E01pLkDOI7WnoLoICrQe0kQgt11RVx4mgeZaXB159GEATvKSsrQZZdxMcH8wkfBkABj03tIEIL9dRvJnffLiorytWOIgiCigry87EVF3Pw5ZfVjtJqUV264HY6kd1iq2awyP/HP9i6cSPVVeIk1O+oUgw5lLufA7u2MDqiSI3pvcccBXLw3lHskNx2xkUVsX3rFsrEvjlB6JAOHNjLkSO5jBwZq3aUNjKBLJpCBxVXDZOj17BxwxpREBGEDmrnli3sWrmSg7/5jdpR2iSqWzfc4rjWoFKbn8+pRx9l+ZIlVFdXqx0nIOh8PYGiKKxfswq3x41yuvYSb3Ix0noUnMFdSaxwaAizGAFREAkqznqmJJtYsXs3EydPVjuNIAg+JMsyGzasxePxoCjN16SkpBCGDw8HgruQkJdXTEpcmNoxhAvlKOOilH0s32Fh4uRpaqcRBMGHPB4PK5csQVEUtEYjssdD444d7H700aBfUVETFUVqRARBvMa/Q6o8dIjw115jU0gI02bOVDuO6nxaDKkoL+PLr77ikmEZWG1F4KhrvosVzKuSf2DFjqOMGzIIbLvUjiJcqMYK3A4zHo8HrTh3WxDapfLyUr76ahFXXDEWk8kOuPjfBSj4j5dbtWoHc0d2hdL1akcRLlT9UZocWSiKgiSJE+kEoT06deIES7/9lqRDhzj61VfUnjxJUztaSVGk05EtSaIYEoTy163Det114hqED7fJKIrCqlWrmTFmINbKXWCrbHfLeQuqm0gLCY7XJMsKJVWit8n/KIxJcbP466/UDiIIgg/Isszy5SuYM2ciJlM14KDdVOJPq611EuU5onaMFpFlhZJql9oxAofiYXTCcZYuXqR2EkEQfMDj8bBqzRr6hYay5bnnqDh4sF0VQiRJwpyczPGvv1Y7SosogE3n8w0RQcPtcGBbuJBvvhKfg3zyt2LzxvU01FQyoYuO8OIVoLSvN6AAmCNB64Ky/WonOcOp8jqe/2QzFoMeR5Obu+cOpriqgX3Hy7lxas4Zj3e43Mx85EMev3Y0I7JTzjn2km3HsDmamDOyu6/i+43RVkT/+AwWfbkQq9XCyNHj0IkflIIQ9DZtWo/NVs9FF/UmNLS99mUwAxJUbFU7yBlOlTt5/rMCzEYNjiaFBbOSKa52sfdEIzdNTjjj8Q6XzIwn9vHEVWmM6Bl+zrEXb6/C5pSZOzzGV/H9xmLbS3ZkNF9/tRCLxcSoMRPFSkVBaAdWL19OZX4+2iVL+PLDD1GU4F+J+FNJQ4ZQLkkcX75c7ShnaNDr2R0Tg1aW8UgSOZWV2HQ6Kk0mevxMnwy3JPF1WhqDyspItJ27IXl+SAhujYaMujpfxfebox9/jCUzky+bmjCbzUyYOrVDrhKRFC//C92xbQsRcjXprsArEnjThjIrDm0446MDrwns+6sOoCgKV4zLpqiynoKKejblFrLzSCkv/GIiESGmHz3+w9W5uNwe1u8/xf/dNY3Cinruf3k5o3qlcrigkmdvm8Cr3+ymusHB0eJqpg3sQmFlPSdLaxmRnUKjo4mTpbXUNDq4feYA/vrpVl68YxKXPPkJf7hxHHuOl5EYaWVEr04q/Y6chzUOlymWr3MbmTt3rtppBEFogy1bNpKYaKJTp3ZYhP+BxYvziNAqDNEH3l2d91eXoQBXjImjqMpJQYWLTQfr2Hm0gRdu7UKE9cdF5w/XluNskll/oI5/35lJQYWT+187xujscA4V2nnu5gxeWVJCdYObYyV2pg6IorDSyckyJyN6htHokDlZ5qCmwc3tFyXx14WF/PW2Lsz7/QGeub4ze443kBBlYOR5Ci2qCc3Absxg6ZFIZs+erXYaQRDaYPW331KzZg07n3pK7Sg+Zb3tNnr07s22O+9UO8oZ8sKbf9Zn1tbSqNPRqNdTYrFQYTIxorgYo/zj9wdHwsORJYlii4UxRUU06HRsTEwksbGRGqOR4cXF5EZG4tRqqTMaSa2vp1Gvp16vJ8Fmw63RUK/X49Rq6VVZyZ6YGEYWF7MkNZUhJSVUmkxY3O7zFlrUkjx4MAkjRiCNH8+MGTPUjuN3Xt8mU1FRTrr7kLeHDTibDlcyvrtV7Rg/69LRWUiSxNPvruPVxbtJiLQyNCuZUb06nVEIAVi44TBzR3bHpNeRm18BQOf4CG6f2Z8wq5HS6kbyCqv4zWXDmDGk6/fPmzcqi0kDMvh47UGMBh0hZgNbDxUTYjFQUF5HTLiFzQcL2ZFXzOCsJL+9/gvWWIahcj+RVgMA9fX1bN+6BQCn04nLJZZ2C0IwUBSFmpoaOnVqf3fhfmrfvnyGxB5XO8bPunRULBLw1Hv5vLKkhIRIPUO6hzIyO/yMQgjA5xsrmTciBpNBQ+6p5jeLneNN3H5REmEWLaU1LvKK7Dx4aSdmDIr+/nmXjIhhcr9IPl5XjkmvIdSsZVtePaFmLQUVTmLC9Ww6VMf2Iw0M6R7qr5d/4eqPYa74lnBL81uyysoK9uzaDoDdbscd5E0WBaGjUBSFmooKdj/zjNpRfEqj1eJJSaHk7bfVjvKzutTWArAtNpbcqCjMbjfxNhuJNtsZhRCA42FhZNTWolUUqo1GAEJdLnpVVWHweLDrdNQajfSvqCDtBytCMmpr6VRfz9GwMLSKgkGWKbNY0MsyDTodJrebUouFcrOZ+AAthAAUbtnC9hdeQOdpPhCksKCAvbt3A2Cz2fB42vdBIV7dE7Bx/TpyopuC/pSY87LGgqYQKgKzZdBby/Zy0ZCuXD62JyVVDTzz/gaumtDrZx+75WARdTYnL366BY+s8PqS3fx6zmBMxua/GjqtBvsP/jy1Gs33/yhCzAZQFKLDzNx7yRDyCqtosLuIsBr562dbuXFKDu+vOkCYxYBeFwRLfw3Nxa3cA/vJO3KU0LAwNm/Zit1up0uXDDZt2sxDDz2ERqOhsbGRwwdz6TdgoMqhBUH4zrp1qxk0KJlgPyXmfGTZ3Lz9tCYwV2C+ubyUGYOjuHyMgZJqF3/48BRXj4v72cduPlRHvd3Ni58X4pEVXltawq9nJWMyNBcGdFoJu/N/b161Wgm33Fzsspq0KEB0mJ5756aQV2Snwe4h3KrjxYWF3Dgpng/WlBNq0aHX+qxFmndo9Ci6EAAO5uZSUFiE0WRm/YZNyLJMRkZnNm/ewoMPPogkSTQ0NHA07xA5/QaoHFwQhO8s++Yb6j/9NOhPiTmf1NGjKWxqomDzZrWj/KxDkZGk19WR6fFg0+nYERtLt5qan31sqdlMk0bD7pgYFCA3MpI+FRXoTm+c0ABuzf+uHxr+d4ao/nRhxeTx0LeiglqDgSaNBqPHw56YGLKqqzkSHo5Bln3XpNNLTOHh6K3Nn4OOHDlCeUUFkiSxbsMGTCYTaampbNm6lQceeABJkqirq+NYXh59BwT/NchrxZBjRw5jkBuJdR7z1pAB61SDnpS4SKBR7Sg/a2SvTjz59josJh02h5trJ/UmLsLKil1bmTa4C28t28tvLhsGwHsr9/PXOyaRkRgJwNV/XEhRZf2PxjMZdPRIjeGP72+goKKeCX3Tv/+e1WxgQGYij7yxmup6Bw9fNYJuKdE88c5a/nDjWN5evpdhPc/dhyRQRBjcrFixApvNxujsJDxVh7iyj4E6KRanLDN0Vl++/eYLdKYQDO46kqJC+eKLLxg3bhyhoQF811EQOoAjRw4RHm4kMrJ9F0IA9u4tIDvj54sLgWBUdjhPvJuPxajB5pS5dkIcceF6Vu6uYfrAKN5cXsqDlzZvm3xvdTl/va0LGQlmAK7+y0GKq368Gs+o19Cjk4VnPjxFYaWT8TkR338vxKRlYNcQHnn7BFX1bh65IpVuyWae+O9Jnrk+nXdWlDEsKwh+PstNhOntrFy5ErvdxrCuWpSq9Vzft5g6bSoOTzFDpkew7OtP0ZnCMTaVEBth5MsvC5k0aTIm05mrPgVB8J+9O3fizs/nyEcfqR3F52rS0+keHc0+tYOcRVJjI1vj49HJMm6Nhu7V1ZjdbgqtVlLr6zkUEUH/iuaV8HkREYwsKiKsqfm9w7cpKdj0+h+Np5VlIp1OdsTG0qjXk9zwvwMp9IpCrN3Olvh4HFotA8vKCHc62RoXx9CSEg5HRAT0qpDvOGprkevqWLVqFU6HA/OxY+Ru3oxxyxaiBg+m/tgxupSU8MWHH2KNiKD6wAEsej2nCguZOm0a+p/8ngUTr/UMWbZkMRPCjoLH6Y3hAtqzaxq4c+5wTBV71I4i+IKkBeUcS8K0evA0/9C0WVNZV2Jh6rTpfgonCMLP+fbbJUycGEd7OzHm5/zpT4u5Z2Y8urKVakcRvE4Cje4cp+9JoDV8/16rPnQwW6qymDhpiv8iCoJwhkWffcbOK6/E7XCoHcWnNDod8v330+vkSfb9979qxxG8TNJo0Oh0eM7SIkDSaNCZTDSdLvD0uOkmoi+5hIlTp/ozpld5bdWOzmPrEIUQjKFoDSZRCGnPzlUIge8LIQCWxnyMJlO77BQuCMFEr5fpCIUQMKLTGUQhpN1SzlEIOf39H7zXCq3fgt4grkGCoLbqw4fPWQhpL6d0xPXqhclsFoWQdkqR5bMWQr77ftMPVrocfvttdEH+OcgrxRBZltG4z/IDQGdmb0Mc939WyOLCSKpM6by128M/tikcd0X//HMCmasRm82GIzxL7SRCgMgwVPLZp5+qHUMQOiyn04nReLbLmY6tW8u55553Wb68gKoqHa++upF//WsDJ08G/tLVM7mw2ey4IoepHUQIEGmafSz8/DO1YwhCh1VbW4vyM0e2AoQkJJB8zz3ULVhA2hNP0O266zAsWEDEn/5E3LRpfk7adtVHj9LocJAlTl8UAI/LRcnChSz8LHivQV4phrjdbiTtzzXIlPjnJhvb8ht4/Hf3E5aYzsIdZYydOpNRI0fwxsojYAoHJPJccRyVU3DpIyEk4QcJdRAS742Y3qHILJjSmT+8v5btrszvm24KHVcn5RTx4SZOngjMkx0Eob1zu91nuesm8de/ruDAgQIeffRGLBYrn366nilTRjF0aA6vvroM0AMSeXk28vLsuN06wPKDMTSA2R8vo4UUFiyYyZMfFrFLmgX6MLUDCSpL82wg3KxQXFykdhRB6JA8Hg/a06eQ/JDOZML9y19i6NaVB68cQ+aQXtTlZHPt1dMY0i+T8iFDsMbFoTMaSbvpJjrffjvR3buTPGjQ92MYQ0NJyMnx58s5J2d9PZ22bmVXRgYD//Y3zFFRakcSVHbwxRdR7HYqTvdhCTZe6xmyfPEXjAs58qOvfXbUhGyK5NJZk3/2Obv3H2HZ8tVodVoaGhqJjo6kpKSU+oZGEmPC6N0pil351bhlCbNeYnJOMr3Car0Rt+2MoXy+z8HBohqmjx5In7AaqC9WO5WgktXOPoybMFHtGILQYS1f/jUTJvy4cP7BB3swmUKZNavzzz5n27Y61qzZgUbTfDpHdHQUxcVl1NbWkpoaT8+e6ezYcQRFAYvFyLRpA+nRI8IPr6YlDHz00Xby8gq5eEwW2aZdUN/+G5gLP2+VfCPjxk9QO4YgdFgf/fvf7L/99u//W9JoiHjkEdKzujIzpexnn7OuphNbDhxH0mhobGggKiKM4vJKbDYboYpCWF0dtbGx6IxGsNmIP3mSko8/9tdLOqfozEwcM2ZgDwtjcFwcx196idI9ooVARzV25UrGjh2rdoxW8dppMhq9GZslFYstv/kLBitHS2t44K7LzvqcnOyu5GR3pfnO23d7vZsXq7g8ErmH8hg1M5UQsw40Rp7727+ozohiVM8EsFU2/1KLs57ZmUDvZD7fe5AvTtYwvF824zONUHVUvVyC/4WnUJNXd/7HCYLgQzqcThNG43dbNg3k55dx//19zvqMgQPDGDhwLGAA3DRfh8yAB4cjhMOHCxkzZiAWSz0Qy5///C5VVdmMGNELsAFq9slycemlvYGBfPTRFj7N1TJm4HRGpxRAlXhD2qFE5VBzpEbtFILQoVmTkkgdO5b8VasAiO3Zk1q9/qyFEICREacYOVwHpghwGUB2g7UnNNlwRPXgYEENWXF6TDV5kNCLp99aSVLs7URWVFC8cydVR46cdWxfq8zLgxdeIDori93jxtEwYwZ9b7uNhk8/5fjy5arlEvwva84cqoJ0VQh4sYHqmHETWHZcQQ5JAo2Ok44wmruiG1rwbPkn/1/GoPWQ0zOjuRACIDu5985bcIem8LevD/LE53lsrQ73VvzWc9QwO1Ph4enJmDzVPP3hdg4o3dVOJfiT1khEeIjaKQShQxs7dgLffHMQRbEAGk6erKf5EteSY1Vd/O86ZAdcmExV9OljPl0IASjngQdmYbMp/PWvn/Hoo++zY8fZ3+T6j5NLL83h0UfnIZtNPP5JI3mGGWqHEvxI0ZqIjBDXIEFQ08SpUwm95RYS+/fHFB5O6NCh6AwGCE08/5MdNc2FEIDGUnDVYyrZQl/dYUxV+0F2QdEOHp7djcQxQ8jP6U3pvHkkXHONT19TS1QePIj9//6PmPfeo2DXLg4PHEj/p59WO5bgR4awMCLCgnfLrte2yQA4HA5WLl9GeKiVRYuX0SUjnRuvno9vOvxrefWtD0iL1DA+sf78D/cXg5WnFxVw1yUjCavdr3YawQ9qrJmcVJLo13+A2lEEoUOz222sWrUCo9HAhg2bSE1N5tprB9K86sPbrPznPyvJykpk9OhOPhi/tQw8+uj7/OaSdCxVq9QOI/hBsXEkdRFjyOrRU+0ogtCh1dXWsmLZMpocDo7lnyQhOoJrciQ42yETbRGWwt+X5WM4eozS//s/74/fSuHp6ZTMmcPg6mr2vvGG2nEEP+i2YAEj7rqLtPR0taO0itdWhgCYTCamXTSTOpsLBYkbr74E3x116OGma+ez+XAZK4ss53+4v7gauXlqbxZuzFM7ieAnZrmeTZu30NjYqHYUQejQzGYL06bNoL7ejizDtdf2xTeFEIBGbr11LCtX7mbdugIfzdEaLq65ZgqL9/jqdQuBJlxTyYqVq3A61dy2JQhCWHg4sy+5BLdWC1od12Q7fVMIAagr4FeT0ymOiSH2xht9M0cr1J44QVpNDc4+Z9+iKrQvrqNH+fqbb3C7g/N9h1eLId+ZOnUq+fn5NMlea0lyFjK//uVtvP7FOvL13fj8kEKBkgDSz51s4z8JIVBdH4xHNgqtIetD6NI5HatVnCwkCIHg4osv5uDBgyhKjI9nsvHLX17Jf/7zCfn5EgsX7qWoyA383Mk2/pOZmcip8gBaMSn4VJMuip5Z3TD+zGkWgiD43+WXX87u3bshzsertWrzuWneZLbJMjkPP0zITTeRftllaH72hE//iayu5mRVlaoZBP+xpKTQq0cPdDpff+73DZ8UQwB+cdtteDxNvhr+eyEmib8+9wyfrcslc8BYnnprJTZzss/nPRe3ZEDrs99ZIdBsLFCYMOnnT0wSBMH/JEnilltuQpZ9f5ciJqaOP//5Xr74Yg2dO2fy8MMv4Xaru1qxocGBSScuQh2DxNbSBEaPHa92EEEQTpMkiZtuuA7F4/trUKf6LTxy6zwKI8MZd9Uc1lgspKt8qoc7LIxQk0nVDIJ/aLRazGPHMnLMGLWjtJrPSjhudxN6nRbfbZP5n+hwCwt+2bxELCsrC4tT3SNuNx0soV+PLsBJVXMIfhASj2Q3olW5Ci8Iwo8pioxG458lmwkJbu68cxSgoVevbHQ6dVcGrly5i5H90sC9U9Ucgu8pYd3QNJnQaETxSxACiaLI4PTPSYPpnsPcMVgD+lP07NGD42++6Zd5z+a4VsuIyEjWqppC8IfOEyZgMJmQJHVXxLaFz66eTU1utJLvCyE/1SU9lRUF6lYjt+eVMDirBd2jhaC3syqEPjn91I4hCMJPuN0ykuTvPj4yKSkJrFmT7+d5f+zQoVP0SAjOvbvChdlamUn/AUPUjiEIwk/IioTUUOLfSZtsxISFkHjbbf6d9yeUmBicubmqZhD8wzp3LkNHj1Y7Rpv4rBiSmJjI3oP+Xxkx+6LxbD1SwcNfVzQ3VtX4f/+SbAhFV7TD7/MK/qWYIilugNi4eLWjCILwEwkJcRw44PL7vPPn92H9+lweeOAD1qw5hRr9QyTJiKZwsd/nFfxLNneiwmEhMipa7SiCIPxEdFQUhzQ9/D7vtQOMVHbqRP099xB7yy3o/NxLyBASgjUmhq3/+pdf5xX8L3HgQPTx8YSHh6sdpU18Vino1bs3H37wAb2z0nw1xVldOnsGtQ2NlJWW8eQ3m7GY9Mwbmk5no3+a+Wg1Emg0/tghJKhoU00Mw0eOUDuGIAg/o0+ffnz00Qf07NnVzzO7mD17NB6PjmPHCnjiic8wm01ceeVYUlL8s2pRq9WCIi5A7d2aqgGiV4ggBKj+Awfx0fv/pbu/T1531DB1cHeM1iHsPXySwtAQtLJCyqFDFH/zjc+nb7LZsOj1KLK4BrV3cXfdxaSpU9WO0WY+XTaRkZFBeVU9sVGhvpzmDF07JzX/n96ZTJs8HiSJv//fy3RPjGBidhTvrc5Dp4E5A+LQOSq9H0CRwRjqu+O0BPUZQ7HbDERFRamdRBCEs0hJSaamRk9EhO+bef9Qjx7NRY9evdK5+OJByLLE3/72IX36ZDB+fG/eeWcZBoOeSy4Zikbji+NQFTBGgtMH1zchMJgTkZtCCQ317/srQRBaRpIkEhITabRasDYe9+vcOcYT4IasDLh05MXIkpa//Hc1yT17Enb8OEdiY9EpCtY1a6g4eNCrcyuyjCLLGEJCcDU0eHVsIXAk9O2LKTwcUztolOvTjlsDBw1i07bdvp7m3GQneBz86tZraJCs/G3xETr3HkLnnBE8u+QUb++XcIR19epxvEqTHcJTvDaeEHiU0CT0+uA8QkoQOoqhQ0ewbt1xfFz3P49yNJoy7r57IlVVNl588XO6d+9Fp05d+fOfv+S993bicFjw5nYaRWmCiF5eG08IPA5zFywWdU8tEgTh3EaOHsvqfAV0ZvVCFO1AU7iV30xKwNgtgxN9c5h8+WyGzZtJ/dVXE/mb35Dl5eN4HbW1ZEya5LXxhMATPXgwsfHto02Az98hduqUSlVtA1Hh6l+0586Y+KP/Hty3O3sPHuO5zxfRLSWGS7t5vDJP/86xrD9UxQhxw6b9UuTmTuGCIAQsSZKIjo7BbjdhNqt9h8rNJZdkAVnff2XYsMns2tXIn/70Pn37ZjJrlnf2l3fpksC+CguiHNJ+aSUFj1s0yRWEQKbRaAgLj6ApVI++Ok/dMK46ru4J9AwBDoAORo4LZVN9Fl+vkkhasICyZ5/1ylRySQlREyfCZ595ZTwhAHk87eYa5PMlG7379GHn3sDtKNw7K4NHHvgVx0rr+cvqepacMIKlbc3IRnSPZNu+I15KKAQiyVmHoihqxxAE4TwGDhzMpk0n1I5xVn37Wnnssdns3n2MP/95McuWHQXa1vBuxowhfLvlqHcCCgFJ767E5RJbcQUh0A0aMoyNp7xzs9XrZDdDrSd5clYaZVYr7l//mphf/IKorm3rtZVWVsbOsjIvhRQCkePUKWqrq9WO4RU+L4ZotdrT06i4VaYFHlpwKwvu/jWhqT156P19uIxxrR5L01gKWh1oDF5MKASUhjKaXE6czrPv96+qrGTTxo1s3rTpR19ftmwZWzZv9nVCQRAAvV6PomhRd6vM+cg8+ugs7r77aozGMB566G1kufX7cDUaG2j0IAXyaxbapP4oLpcL9znuzFVWVrBhwzq2bdvyo68vXryY7du3+jqhIAiA2WzGrbWALoB7K7htPDotgYfvupRuU8ZSNHs2MT1av1KxeMMGdGIbX7t2asMG7A4HHs/ZC33l5eWs+vZbtmzc+KOvf/3112wLoM9BfqlQaDQabE7/NrBrDYPWw8jBfXjw/rv5w+f7mpugtobshiYbxPf0bkAhgCiMi69j0aJFyD/pmO10Ovnvu+9y6OABMjsnYzRo2ba1+Y1nYUEBBoNBrCoRBD/yeGSamgL4jSgAbgyGCkaPjmXBgmt4+ukPAX0rx5IBN8QN82I+IaDITYyP3cUXX3xxxvXE4XDwzjtvkZe3h6yscDSaJnbsaL4GnThxDKPRePpGlSAI/uBqakIOTVI7xrk12TAUbWZiTCG/uGwi5TNmYImJadVQjeXluBoa6D5zppdDCoHC1dBA5b//zZdffnnG9+x2O2+89hrrP/2Uyk8/perECbadLojk7t+PwWDAGECNVyXFD5/K7rvvPh667y6iI6y+nsprDh4tZNPqZVzbp3VN7VYWGDFFJTHM4t8O0oL/OMxJ3PvKasaNHUtkZAQjR44iLy+P6qoKBvXricX0v5VBh48VUVhcitlkontmOpt37EMCGhobGTxoEJ1SU9V7IYLQzt111138/ve3ERoaPNsK9uxxsn//Aa64ol+rnv/55/voGmslu0ns2W6vbJY+3PvacSZNmkBYWChjxoxj//691NdXMHhwEibT/25C5ea6KCmpw2o1kJ4eze7dpSiKh9raekaOHE5iYrKKr0QQ2rfbb7+d526fgqnumNpRWmyzPZPNi1dR8/zzrXq+7uabGdq3L+vuvNPLyYRA0fmSS9gQFcXE8eOxGI1MmTGDrRs3kr99O1Xvvkvptm0ASBoN/V54ATkiAurq8Jw6RfikSTQ5HNQ1NjJu7FjiVGzG6pdiSHFREbt372bKuCFIBM8d8Rf++Qa/HqKBVmSWQ5P565JjLBjetr3fQgDSmfjiMOw8UsIDC36JxWzALWtYtHQlFrOZyWMG0aK/MxoD73+6iPnz56PRBPY2MkEIZvn5J8jLO8yECYmAS+04LWTgxReXcvfdY1v1bLfbyj/+/gW/7rfbu7EE9enD+PhQZ/afrOe3v7savd6N02lmyZK9WK0mJk6Mo3l10LkpSgTvv7+Fyy+/HEny3mlGgiD82LGjeZw8fpwxYcfB1ah2nJaxxvPs1yewPfxwq56eOWcOBX36YH/iCS8HE9QWkpCA7oYbqNFq6ZGXR3VeHp1nzMA1cCB6RWHrpZficZ37vZZGq6X3tdfSNHUql156qarXIL9sKE5MSqKpqYljJ4vokpbojym9QpY9EJYCdYUX/NzmviH65r4hcrC8+RbOyxTBn5YU0LdvP56YfyXgAWT0GpnZU0edflDLimcnTxXRNydHFEIEwcdSU9Ox2x0UFTWSFOArlf/HdXovrgWwXfCzdToHCjqad8OKk6/aC9mUyB+WmBk2vBeP35gGNJ+SZDI5mDUrleY/65b9eR87VsmgQQNEIUQQfCyjSyZ1dXVUKXaiXEHS3LqxFCSI7dmT8gMHLvjpxRs2IA0a5INggprievem6KKLGJaTw7HHHiP30CEASnbsQG+x4LbbW9QKQPZ4UDIzGTxokOrXIL99CuuUmkp+QbG/pvOKnj2y2H6ylRVc2Q0uG8Rlnf+xQtD445JiLrtsPtMmDKe5ENJ623fvJyl4PpkJQlDr2jWTI0eCq7t9p06J5OWVt/LZnuZfcUO9GUlQk6Tl90tDuP6Gy5k4KR746bavCyl6SWzffoKkJLE9RhD8oUfPXhwsOXvT/UAUHWYldMCAVj23obSUJrudzOnTvZxKUIvBaqVk1iwmd+3K1ssvp/J0IeQ7TTZbi3simsLDcaSlkZSs/jXIb8UQSZJocss4m4LnDtWUiaP4ZF0uRXLrGgj1Toti64kLv6MnBK4xPWJ59dU3KK9u+zLHiIgIwsLDvZBKEITz0Wq12GzB0Ej1fy65ZCgvv/wlZWVnPzHkXFJTozlUFzyrMYXzUDyMyTLyr3+9QW1tW09qUIiJicFiCZ5eboIQzIxGI7UeEx6Ler0RLtQ14zPZGR5O0qRJrXq+XFJCvCiGtBuuxkZiysv5+uBB+rWxF4yrsZGEhAQMBvVPXvXr+vwJEyeyYUvw7F/WIPOHJ37Hx9vLeX6djT31EUDLl/KM7xXHuh37fZZP8L9hcQ08MS+Lv//zJaAt3fgltmzZcs5jEQVB8K7x4yexYcMptWO0mEZTxx//+AveeWctzz23jNzc2gt6/qxZw1m8Mc9H6QQ1jI7bx5OzdTz/3LtAW3qS6di0adMZp6EJguA74ydOYnO5We0YLaarOsSf7phG5ehRaB54gKQrr7yg53euqmJHeWtXNwqBqOqll0hfs4ZtYWGEt+Hwh5CEBDZs2BAQp2v6tRii1WppcnuQleDpkaDBw923X8999/yarflO/rPVTp4jBrTnfxOiaSg53TfEL61ZBD/ROGvQIIO2LdVMhV69egXEDwFB6CgMBgMOh4yiBM+bUY2mhnvuGce9917GqlUHeOWV9Rw/7qAll+/mviH6Fj1WCB46VzGS4gIi2jCKm+zsbHENEgQ/MpvNNHiMYIlWO0qL6SoP8puxofz2xvHU9+mN8de/JvXKKzGEhJz3uSXr14NRHCTRnkiSROW+fXicTrpOmdLqceoKCujVq5cXk7We3z+lDx06lK079zCkf09/T902sotbrpvPsVPlbNmylYUF9UQaPVwxNhuLtgnqi5v7hPzoOW5w2SE2B0r3qpNb8JoVhVaOVDi5eUJX9MY68NjbNF5tbS16vd5L6QRBaImBAweza9de+vULUzvKBari9ttHcfy4wvr1O/j44wpiYixcccUETCYN0MiZzZtP9w2JHQzlm/wfWfCqxYV9KW4wctVwA0ZzGdC2O64NDQ1otW1Z4SgIwoXq1bs3uce200NbqXaUCyCjKd/PgvGxHGUQa7buhx49MBQV0aWmBltBAQWbN59xgkhDSQkGu50uU6ZwdMkSlbIL3hLzwANIUVHEHz3KIauVXX//e6vH0hmNNDY2qt48FVQohoSFh9Nga3kDIVmWKSuvICE+rlXzNTQ0IssyYWGhrXr+T2V0iiWj03RAorCslre++BKn04GsaNBqwON20jMplCl94sFWSc8ULdtP2Rmg/pYooY32nSxn8IixPP7OYm67+bo2j5cYH+uFVIIgXIjo6Bh272759jRZlikrqyYhoXV38hoabMiyQliYN3ozuOncGTp3zgEMFBTA668vw+VqQlEkJEnC42kiJ6czEyb0BhykpERxtDGKLl6YXVDXoSIH/Qb148n3V3D33VcCbWvGmJQU5Z1ggiC0WEqnVA4e2EePFl4SZFmhrKaRhKjzr8T4OQ12F7KiEGbxwgoNZy1d2E2XPoApkRMM5cvlW3D27Inpoung8eBoaCC8ohLLnj1UHDyIq6SEpBkzRDGkHWgwmeiVkcH6mhqGNTay4zzH556L2+nEEiCtAlTZv6HRSCBpQfnxaRynCgp57q//xmw24XQ6WfCr2yguKWPv/lxuuu7MfWqz5l9HZpfO6HU6Ghpt/P7xh84oerzxzvuMGTWc3tk9vPwqFJLjwrjj5qt++MpAo2PL9t38ddlGtFo9MrA+dwMDpreumCMEjlCjlrROyTz98AKvjOfxyMiyLI7WFQQ/a/43Z+SnHyZPnSrluec+wGw24nS6WLBgPsXFVezde5SbbppxxjizZj1EZmYKer2WhgY7v//9rWcUPd544xvGjOlH794ZXn4VLlJS4PbbR5z+b93pX2Y2bz7Oiy8uat6a2iRzYN9RHh3n5ekFvzNITvrkpDJm7DzaWggBcLncKIoSEHfmBKEj0Wj1YAgBV8OPvn6qvI7nP9mMxaDH0eTm7rmDKa5qYN/xcm6cmnPGOHMe/5jM5Eh0Wg0N9iaevmHMGUWPN5fuYUxOGr3SvXwDzlFLOnv41YjTTcl1FjCYwRrH2iMNbE5LxXTppWg8Htbm53t3bkEVjpoazEeOYPrzn9nhhX5TcoB8/lGpGKJBQYP0k6NJ123YwuBB/bhy/lyKiksoKCxm4+Zt7Ny9l3mzZxAR/uNlzQlxsTz7zOMAvPTqW+w7cJBjx09yIv8UNTW13HHbDWzcvI36+ga0Gg0ffvoFigLdu3WhS+d0/vHv1+jfrzcL7rzNS69MBtnF4H49GNzvu+KLxHP/fK25b8hPt9EIQeW6kYk888ZbPPLArzhzOfqFkxVEIUQQVND84e/MYsi6dXsZPLgHV145iaKiCgoKytm4cR87dx5m3ryxRET8+M5cQkIUzz77SwBeemkh+/Yd49ixIk6cKKGmpoE77pjDxo37qa+3odVq+PDDFSiKQvfuqXTpksw//vEp/ft3Y8GC+V54Ve7TvxwMGRLBkCFjT3/dyHPPfg1SwRk3IITgcssohT//4z1+98jFXNgxuj9HA+hEIUQQVKCRAHPkGcWQ9fsLGNw9iSvGZVNUWU9BRT2bcgvZeaSUuSO7ExHy49PQ4iOt/PmWCQD8Z9FO9p0o53hxDSdLa6lpdHD7zAFszC2k3u5CI0l8tCYXBeieEkWXxEj++cV2+nWN5+65g9v+oty25l+2SkaFw6gxp0+8sibz7GIPdo0GRTRsDmqJ27ezcdIkdCYTTba2nZZqiojAHBHhnWBtpNonsZ9r2jV/3sVISDz1zPO88vq7JMTHMnTwAEaNGHpGIQSgtKycBx95moce/T01tXXk9Mnmo8++xGQ0Ehoawrbtuxg6eAAzpk/m5dffwWqxEBpiZfvOPQAM7J/jxULIWV8pkiIjhyb5eB7B1zT2Ksx6DWjatudJQcOyNduIjRXbZARBDWdrGjl//ngkSeKpp97glVe+JCEhiqFDsxk1KueMQghAaWkVDz74bx566CVqahrIycnko49WYTIZCA21sG3bQYYO7cmMGSN4+eUvsFrNhIZa2L79EAADB3b3UiHkXJyABzlEbJQJdjr7CfQ6gLYdyS7LRpYsKSK+lduPBUFoG+UsN9QuHZ2FJEk8/e46Xl28m4RIK0OzkhnVq9MZhRCAsupGHnp1Jb97bRW1jQ5yMuL5eO1BjAYdIWYDWw8VM7RHMjOGZvLKN7uwmvSEmg3syCsBYEC3BO8UQs6lsRRkD0kDBvh2HsHnitevRwOkjhrVpnESBw6kx3/+Q3xcYFyDVFkZoihK8x3xnxQI33znA2ZMn8wV8+dQUlrGH/78IldfcelZx4mPi+WPTz38/X83NDYSExXJfXffQd6RYzQ0NlJWXgGAx+Ph2qvmExcbw8effQVASIg39nCf37SpU1i46VvmZIk7MEHNGAbaJpDbtjx587Y9DB06jNCwYGvgKAjt25tvfsOMGcO54oqJlJRU8oc/vM3VV5+9W3p8fBR//OMvvv/vhgY7MTFh3HffFeTlFdDQYKOsrBpo3hZ37bVTiYuL4OOPVwEQEuKfU21GjRrM8oMbmJTsl+kEX7Eko9EagOo2DbN+fT6jR4/DbLZ4J5cgCF7x1rK9XDSkK5eP7UlJVQPPvL+Bqyac/cSNuEgrz9z0vz2QjXYX0WFm7r1kCHmFVTTYXZTXNALgkWWundib2AgLn6xrLsiHmPzT0LB393SO9ekDW7f6ZT7BN5IGDKBSktrc/yX08suZevHFGAPkpCFViiFNTW4qKquIifzxnbZRI4by+O//gsVsxma3c91VlxEXG8OKVWuZPmUCb77zAQ/ed9dZxw2xWhnQP4eHn3iGquoaHn3oXjqlJPP6W+9xy43X8PQfX8BgNDBq+BBfv8Qf6dGtM18vctG8LFsIWhod3tgeExsbQ0VFhSiGCIJKGhtt1NW5+Ok/wVGjcnj88dewWEzYbA6uu24acXERrFixg+nTh/Hmm9/w4INXn3XckBAzAwZk8fDDL1NVVcejj15Pp07xvP76Im655WKefvoNDAY9o0aduffblwYPyeC5VetEMSTYafRIXljPGxFhpba2VhRDBEEl1TV1OOJM/HStx8henXjy7XVYTDpsDjfXTupNXISVFbu2Mm1wF95atpffXDbsrONazQYGZCbyyBurqa538PBVI+gUF8brS3Zzy/R+PP3f9Rj1Wkb26uTbF/gTk3ISeXpzhF/nFLxPZzJ5Z6uTzYbT6QyYYoikqHDI/LKlS5g0eqC/p1XVC/96g18PFitDgt17h0y4NUauu+qyVq8QOZ5fyqGjJ5k4aRI6nSr1SEHo0JYtW8SkSYl4o7gZHAy8+MJSft1vh9pBhDaRePNQfwwhkVx51XigolWjHDrkoKDAxtix48XRuoLgZ4qisGzRp0yMPAVKB+mhYY3l2a/zsT388PkfKwQsSaMh/MkniQgLI3LXLna+9lqrxunx2GPEjhjB2AkTAqJ3ol8TKIrCksWL6dMz05/TBgSP2w3Gtu3zFdR3RXcnA5N1/PnZ5/nvx4taNUbn1HjGjRrKhvXrvZxOEIRzkWWZr7/+kv79k+g4hRAAFx6PBwwRagcR2kThuqxdZEeU8Kffv8JHH+S2apTu3U0MGdKZrVs3eTmfIAjn4vF4+HLh5wxKkDtOIQTAXg0aDeYocZx3MFNkmfonn8S+cyc7YmLo8q9/oWnFTd2DTz2Fc+NGtm/Z4oOUF86vK0P279tHRKiJ5PhIf03pQxJo9LjdbqqqqymvqKSsvJKqqmqqqqtJSIhn1PAhRISHgizz/idfMSBJoou+TO3ggldIvL1XZsjoSWR1ac3ac4l1W/ZidzhxezyEhYYyYuRIr6cUBOF/duzYRmqqiZiYth9Lqj4dEILbLVNRUUNFRS3l5TVUVdVRVVVLUlIso0blEBZmAJy89so6pmXZSXCL1SHtgqTl5Z1dmDZvNp06teakOj1r1pThdLpxuxUiIsIYNmzE+Z8mCEKrbdqwnqyQesLqDqgdpe30VghLxK1IVNTaqahppLy2gao6OzUNdjrFRTI8uxMhWhc4avjnmhrCt2zn2EcfqZ1c8AKtXg933cXQ2FjWP/jgBT8/Ij2drn/6ExqzGbRaIsPDGTZCnWuQX9foFxQU0G3UYIL5jpysSHy1eAX7D+Zh0mvRaSXCQizERIYRFR5Cn86xxPbrQn5pJR999AmNTjdIWmRZZvfOfH4/O1XtlyB4hYLHI6PTtXZxlcLIwc1NsTbvOsyJk/mUlpQQFx8PII47FAQfKC4upl+/Hvz0SN1g4nYb+OKL7Rw8WIjZqEGnUYgIsxIVbiE63Ey/tEiieydyorie999egs2lgKSjqcnNS4vyeezs/WCFYKJ4cMvK6W0urSmGNDF6dPONqbVrGygsLKa8vJSYmObu/uIaJAjeV15eweCw4D7i3G2OY+HOMo6UVWDSVaHTSkSEhRAVEUZMdCrpmaFEh4VwrKic/67NxdEkg0aLvUnmZGgoZ57LJgQj2e1GA8ie1v19rjlxgm2XXQbAmBUrKC4tpaykhFgVPgf5tRii1+vQa4O1EKLh06+WsmvPPmZPHMaMKUlgqzz9PSdQ3vzLCZRCL6BXHwAJkEFj4E+rYpCt8WgaS9V5CYJX2HRR/HtlPjFxSXRNS2zzeEP6dmPIgN689d6n1NbWMm/uXJKSRadDQfA2s9mAJNWpHaOVDHz00Tb27TvOJRP7MCe5GuzFZz6soflXH6BP9g++rgvlmdVJyGYLGnuBnzILvlCn68xLa7Qkp6SSlNTU5vFGjQoBOvPGG99QXV3NNddcTUyMOPpdELzNYpSgNl/tGK1jCuf9bdUcKjrI/Gkjmde3EBy1p7/pormHUQXYATv01ULf/hq+78igj+KZpjTCu3en4tAhVV6C4B3xY8ZQNnQondLS2HbffW0eb83EiQy75x6+rK6murqaW265hfBw/7WW8FsxRFEU8H+vVq84WVTGa2++x8zxw5g9uyvYDoLtAgeRXVwzfTivr97KTQP0Pskp+MdLK09y512/xqT1YnXf42T0iKHs3LOfyMj2sI1MEAJL845QN8G4MvHYMTtvvfUF8ybmcEmaA2yfNb/hvBDueuZP7Mk72/Zzbfb5Hy4ErpfXSNx9320YDMWAt/oOlDNsWB8OHy4gNFScdCYI3qYoCjgbICT4eoUccsTy/tJDXH7RGC4fmA+OA3Chb4GbGpk2uh8rq6pAFEOCWtmQIYyPiWH5HXd4ZTxFltn4/POM/O9/SUxIwGq1emXclvJbMWTd2rUo3jgTzs8+XbSc4qJSHrkkB01t7oUXQX4gybaPikY3dbokwtzl3gsp+JVGdmMyGMHThr8MPyVp2bJtJ/NPLxkTBMG7Vq5cjlYbbAt0JT76aC+V5XU8NtsINZ+0bkfEaV2alvFBVRI2QwYW1zHvxRT8SqfxYDB4u6hnZffuXcyff7mXxxUEAWDZsmVYY9JAKVE7Sstp9Lx/QMLuqeexeb2gfi+0YTFaX0sRC0NDSRw0iJKtW72XU/Cvpibq8727wilt9GjK3G6uuOgir47bEn6rTqSmplJTU+Ov6bxA4o3/fo5eA7cPUNDUnmz7kLKbBRf34U+f70M2ibv/wUqSFGRvdwFXPERGRnh3TEEQvpee3pmqqiq1Y1wALS+/vIlQo4HbsrdBzf62D+lu4J5ZiTyzyAXm+LaPJ6hDkZFlb++ntotrkCD4UOf0dCorK8//wEChN/PvrU7iExO5IdsF9UVtH9PVyD3zh3Nq7Fgi0tLaPp6gCsXjwRga6tUxS3buJCJMnVWJfiuGpKWnExUZLEfLSvztpbfokhLLRbEFILfhVtxPGGry+OUlY3j6y+OsLLJAaCIE4YqZjmz+qO7830uveH3cnOxubN2y2evjCoIAGRldiIiwqB2jhfQ899xSeneOZUrYV6B47xpkqlrNzTN68/SSMNZV5EBohrgGBZk5QyN49eVPvTyqTK9ecezYIe7WCoIvZHbrRoguSJqnGkP5y7flDO3bk3HhJ/HedjwIrc3l6otHUXPNNcTffjuJ/fsjacQ1KJikVFdzMCLCq2M6amuxb9zIto0bvTpuS/j1b19tXQObduT6c8pW+WjhEgZmZzLCcsQn4yc5DvLoxRlorFG8sOQkL2xs4u29ChjFPt1gkCBVYLfbAa1Xx42LDqO6ugpZDr79pIIQDCoq6ti2rZ7mxtaB6+23NzF+UCZDdJ/7ZPw05zc8PM2JXRfL88vNvLitD/892BOMUT6ZT/CuVM0BamsbAINXx01MhLKy0tP9dQRB8LbSWgd75CwC+xok8erGGi6eOIK+2oM+maE7eTw6N4uUMUOpnTMH89NPE3bvvYQkJPhkPsG7Sj/+GJvH4/U/r9y//pUCL2+/aQlJ8eNVT1EU9u3dS3iIkdSkGH9Ne0G27jrArt37uSlHBrfDP5MarByS03hn4Sp+MzubkCbRTyTQ/W19A3ffcy94vPt3xOZoYs3GnVgsFhwOB5OniHMwBcFbFEVh587tJCZqSEwMzA98GzYUcTi3iOsyt4Dsr2tQFPvco/hgyQ5+N0OPySn6iQS6Fzd3YcFvbqL5BAfvqavTsnFjMRaLGaeziYkTJ3l1fEHoyBRFYcumjaTryol1HlU7zs9aXRJKsU3H5V3rvboy/pwMYWyvj+WzJevptHIlpdu2+WdeodX0v/0t3Y4eZf8HH3h13M7z5xN3zTUYjUZkYOIk31+D/LoyRJIkevfpw/6DRwN2We7KNRu5aXCo/wohAK5GursP8MhVI3j+68OgNfpvbuHC6UzY3ZJXl65/x2LSM3XcYIYO6EVFZSUffvih1+cQhI5KkiT69x/Irl1FQCCe6qVhw4ZcruuV579CCICril7yQn53WQbPLpNAa/Lf3MKF04fi8OhoU0f3swgL8zBlSjyDB8dRWFjEZ5995vU5BKGjkiSJIcOGs71UAr1/T8xoEZ2JrUcruTzL7b9CCICrjgHGo/zmmnGUTp6MziSuQYEsJD4ejEZKdu3y+tjHP/yQHZddRslLL3Hi5EkWLVrk9Tl+SpWKxJAhQ1ixbrsaU59TWUUtESEWaCxVZX5DTR4DenVnc6l3l74K3iObInl6cQljxozy6YXCoJPQ6XTMUKGrsiC0dwMHDmL16nICbanyyZN2YiIsYC9WZX5T1WqyMpLZ39hdlfmF85MtnXh8STQTxg/DF8WQZgpGoxuj0ci0aVN9NIcgdFx9+/VnfWO62jHOkFsXQlpSDLjqVZk/tDaXlKR4EubOVWV+4fwSBg2i+oYb6Gm1UumjI5KbbDYqdu/GYrEwqb2tDPlOVHQ0Wq024FaHvP3Bp1w7sYeqGS7qn8yGXC90bBZ8ok4KJyE+nuEDe/t4JoUQqwWLn8/aFoSOIDY2nuZCiHf7/rTVBx+s5pqR6t4RmzssgmU7a1TNIJxdgT2ObpmdGTzU1/1d3ISFhWAymX08jyB0PEnJKXgkPWgD6+bnou2nmDdA3b4dV08bQH54sBy40fFosrPJjItj9333+XSe+sJCQq1WDAbf/xtRrRphMpmob/TjMuDzkbR4PDKGst3q5ig/gK0JXMY4742p0WGzplOm64QcmhxwRahgEiFXUlld45e5zOZgOflCEIKPTqfDbg+crTKybEaRFTQlK1XNoSldS71Tj9vsxWMPtWZsIQMpMw5CDusOUmAVoYJJqqmA/FMl+OPtm9ksCiGC4CsSMi5z4DQMdVsT0Wg1aKoOq5rDUHUIJTKS2Oxsr41pCg8nc/58Mq+9lpThw9FoxTWotdzbtlFQV+fzrUySVuu3G8KqfSru3j2LXXsC52SZv/37DWaN8vXd/hZwO1kwZxCPfbgLl8ELTWZ1Zv5vi4NXVuSxeFcRf11yjBc2NvHJQa04vaY1nPVEmzXsPuC7BoOHjp7ixX+/TWxsrM/mEISOLjOzO7t3F6gd4zQtL7zwFZePS1U7CLiq+fXsNB79rAm3qVPbxzNE8NfN6by6xsE3u938bbme57f2YuGJbHF6TWvYSwk3uTmw3+WzKfbvb+Rvf1tHQkK8z+YQhI4uo2s3Dni3/3Hr6Uw8t+gwV0/qp3YScNRw5yUjyZ8yhejubd+yGZaSgvvOO6kY2A/bwD5UTpuK8cknifzVrwhNTPRC4I6lbN8+aGxkwJ/+5LM5sh57jLBnniEuxj+Hrej8MsvPqK2toaq2HrcsodOo29V/0dK1ZHdLJ9MTGMUZS+1BfnfNBB57ezm/uySn9afLGEJ4dnkp44f2o7/+MOABjGAIY0djPM8s3kn/LjFM6aKFpkZvvoR27ebRibywYhU5PTN8Mn73runk5qVjtVqRZRmNOH9dELyusbGe0lI7smxGo7GrmuXTT/cypE8GqfbAaFYZVrOc+y6bxCPv7+Ox2V0wtfbUA1MszywNZeaYTHopX/zg6/FsrR/EMyvcDOyczKS0k9BU553wHcDtY9w8t2gFPbMn+2T87OwYDh5Mxmy2oigKkhRYvXUEoT1w2hsptBvJiY5DaixTNcuH+9xMHDGQuMYDqub4TmxjLndfNYnnFYUuYWGUbN3aqnEiu3alfO4crpgylB7SdyterGCKZF1pN1ZHRxNXXk79e+9hr6ry3gto55I3bWL3ZN9cfwBqv/yS+AcfJDQ83C/XINU+ZaWld2bK5Mls2rZHrQinadizP5dx0eo0TT2bkLpcnrh+LM9+eZA8xwVWxiQtpzzxPPr5MWaNHXS6EPIDrjr66/N4aEoSodGJ/PHbMrZWR4jtMy0le1BkHxbwFA+zp43lyNGjbNm82XfzCEIH1rlzVyZMGM/mzYUqJzFy6NApRpiXqZzjx6Jql/HIFZk8/YWT400XuFxZo+e4py8PLzRw5ZTsHxdCABylDNJ/xUMTatCHp/CHFQnsauwrts+0lMeFpPHl75WdefOy2bdvPzt37vDhPILQcXXt1oMRI4azs0rl3nCWGI6X1TPAeFzdHD8RbzvAwzdM4vjo0STNnn1Bz9WbzXS67jqKZs3itkvH/6AQcpqjmpHh+fxuRhqdxw7H9atfkXj99Uji5mOLNNntKIrvPgcV79hB/d/+xtZt2ziwf7/P5vmOqn/qJrMZm92JrKgXo6K6lrjIUHAF3soIQ/VhHr80myX7KlhRHHr+YoWk5ZAjlj+tquPb3Goev2YUmZ5zVHlddQy3HufBaekcr4Fn19ThsoglY2cladleFc4TX55gyqSxvp1L8VBZWcn+Awd8+gNHEDqykJBQ6uqaUBT1eiMcP15L56RIcDeoluFsLDVreHp+CJ/tklhT2Z/znr6j0bPPlsMzazqz5qiRJ69MJs3+5dkf7yhlrPUbfjvdzb7SEJ7b0Bl3SDevvoZ2RWNga20Oj32h56KLRvh4Mhu1tbXs2bPXx/MIQscVFRVNuUOPYo5WLcOeUg+9unYC2Xdb71ortDaX3187hIp+/Ui4447zrhDQWywk3XILygMPoBs2mGduHEWy4xyfg+xVTEko4XdzuqMdNADt/feTNGyYl19F+2GwWkm4/XZOTJhAd1n26VwlO3dit9vZsXOnT+cBkBSVP2k5HQ7Wrl3DxFEDVJn/ny+/y1WTBxBWHsB34HUWPjlmoaykiNtGxaFx1pzxENkUxYvLCoiNi+WaYQlQdeHLmmvCe/HaN9uR3U3cOKEbUXIFeALvh6MqNHpe3eXBYAnnsrkzMRn8s2x4886DlFVUMW7sWEJCQ/0ypyB0JDZbI5s2rWX8eHV69LzwwjLumNkNQ8EnqszfIoZI3svLoaGmlFuGVoH9zJWUblMqzy/X0yk5iSv610LVha/6rAifzmuLj6FVmrhlQghhrkPgCaBG62rSmvm/nd0Ij4zl0suGYzD4p3i2cWM9ZWW1TJkyBZOPG+YJQkdUX1/H9o1rGG1Rp3Hp82vquPuKCWgKW7cVxS8MYby+00XV8Xz0H3xAzYkTZzwkfuBASiZMoHv3Llw+MAJqT13wNCWWbF7/ajPuujrS8/IoXL4cV2Pg3SxXgzkqCs3ddxMdHY151SoOffSRX+Yd/tln2IAZM2ag0/mmu4dqPUO+YzSZiI6Ooai0hqT4CL/OXVFVR11DI2HVKp8gcz5uG/NSbZzM7M8fFq1ncLc4JvaKR6OBvFI7S7YdpdZZz00zh5HQuK9VhRCAiNp93DPciCtmEK8t3Yu9yQNNdrI7hTOxdyIajxM0WhpkI3uPl9EvIw6TowTc7fvNqmyN51/f5tO7/wDGjfBv0W5wv568/+k3eHxcgRWEjspisRIaGkF5uZbYWI9f5y4sbKKpCQxFi/w67wVzVXNF2hryus3gqW/2MyIrlvE9mgvCuSUalu6optFt4hcXdyWq+mto5dbrmNqveWAYOOKn89qSQhye7uBqoG9nA2O7S2g8NtDoqZPD2X/SxoAME4bGg+C2efHFBh45tBsvLvUwZGQfRo6KBfy3imjIkFjefvuE3+YThI4mNDQMvTmUanNnIu3+3apy0h2HzqygKfb93fc2cdVxQ7aGAz1G85FOR3xlJaFHj4IkYU9OpsBqpTw6mrvmDiOybj/U1rdqmgTbfh4aH4ItZjT/+XwjhhEjaCovJ6q6mrDjx3HU1KAzmbBkZFCl0xFRU0P+8uU461s3X7DoNGYMBUOG0Ccykt2//KVf5z75pz+huflmn/ZPVH1lyHe+XrSIUUP7EWo1+mE2iaUrN7J1x05+MysbTU1g7ZM7J1MkGyrC2bY/D4BO8dFMG9YTU/ku7xclNDqIzWLrSTsbduai1emQZRmTXkv3jE7sPXQcu0uma7yVOX1CwFHj3fkDgSGEG/+1gUcffoiMFP90Nf6hz75Zw8QJEwgNEyf/CIIvffXVQsaPz8Bi8cdqOB2LFh1g9+5j/GYaaGoDo2ldi1g7saasJzsPNt91S0+MZtqQeAzFX3u/KKExQPwo1h/Xs3Vv/ulrkILFIJGZHs/ew0XYXQrdEyVmZZWBo5XNxgOZOZ7r/lnBU0/dT1qa/1dqfvjhUWbOnCGOehcEH1IUhYWff8rk1CZM9iLfT2iw8tUhNweL6rhvameo98Oc3mKNY/lJHbv3HwEgMyOZKUO6YyjZ4f2tPhoDxPdk9YFytu4+jFarQ1FkQswGMjM6sWvfEVwKWMvL8Xz2GbWnLnw1SqCL79OHdb16ccvYsay69Va/z9/9v/9l7ty5GI2+qw8ETDFEURS+WLiQGZNHo/Xh6TK79x/hq8XLGN4/mzHRleASHezbxGBllz2FJet3kRZt4fIBYeCoVTuV92h0/HF5Nddfdy1JcX4uSEgaPvlqBfPmzfPvvILQASmKwsKFn3Hxxd3RaHy12k1i+/ZyFi/ewugBmYwMWw8u0cG+TYzRbLON4NvNeXSNk7ikVxk4AuW8Si/QmnlscQz3/eZmwsL8fW0188knueIaJAh+IMsyCz//hIvTbEj2St9MotGzpcLC8t2FTBjWl8GhRdDUvlfW+ZwpnA3lEazeuh9LZSXKJ59QV6h2Y3bvMUVEUHnDDUyPjWX1b3/r17lTR4wgfMECn1+DAqYYAuByOlm7djUTRnp/K0JZRS0vv/lfuqSlML+3KbiqoMFAZyJX6cI7C1fw0JxerT8OOBCEp4JWT0mNgzeXH2DQ4KFMHDNYlSgr1u9i/IQJqswtCB2Nw2Fn06a1jB3r/WZ2hYVuXnttCVldkrmk+wmoP+b1OTo0QwR7msby4eKtPHyxqfXHAatOgsheoDFQVKPw5spqRowazphx8Spk0bJiRSXjx4trkCD4Q0NDA9vXr2CU9YiXR5Y46Y7jnTV59O7WhYu7A41B/D49EBlC2FqXwOdL1pK2enWrjwNWm6TR0GXyZLQGA87wcI4mJNCvc2f23HGH37NY4+Lo+/bbTPLhMb4QAD1DfshgNKLT6XG4ZEwGb+0N0vCv197F43bzm3kD0FTkQvve2qUOt4Me7Oexa0bx9H/Xc934HnQxBt/duT31UXy2dBeRERFEhIfz67t+hcXon2apP0erFcd8CYK/mExmFEVDU5MZvd7upVEN/O1vy9BpNPx2djiayhXiGuQLrhr68DndrhjL0+8d4dZJvUjV7FM71QXbWpfD4lV1hIVDZGQk9zwwC5NJrdWWHrRa9a5/gtDRhISE4EKPJyQJbYN3btrKhjD+vrocq7WSh+b2RVNzDERPUO9zNTDIdISsa8fztMdDt6QkChcuVDvVBYv/5S85HBKCWasl3GplcHEx21UohAA0lpWh1fryGPlmAbUyBMDj8bBm1UrGjejX5rFsjib++Pw/uWLaSLp7fH9OsXBaaBIvLMtnYEY0o+ICf8uMbIrk3c3lVDk0aLVa7vrFDYB/Gymezctvf0qf3r0ZMnSo2lEEoUNoampi48bljB7d9tNlGhq0PPPMB9wyqy9pti+8kE5oCTmsB88tkxjZzciwyABvDAjIlk68vcVMtdOCwaDnl7+aBARCY3INf//7OkaPHkVOTl+1wwhCh2C329m26mtGhJ5s81g1unj+8vlufjl/Ekk2cUy2v8jhaTzzyV7iSkoofuklteOcV0L//lSOHIkxKQmjXg///jeVeXlqx2o+webBB5kxYwY9evTw2TwBtTIEQKvVomnz3XCJT75cxpGjx3hg/igsNcF3dyio1RexYHQ4b+xuQiNFMCK2Ru1EZ6fR8YevTnD1VZeR0SkRkAmUQgjA9GlTOHbshNoxBKHD0Ov1KEpb70Roef/9HeTnl/LwZRmYqkQhxJ80dbncPzqGl3Z0RqPpy5DwXWpHOjudhSe/0nLjTbNJS9MALgKjEAIgM2XKBBobAyWPILR/ZrMZj9TGj2d6C+/uclJWV8Bj14zGUC0KIf6kqT3J72ak8rdVVpJuuYWil19WO9JZWePiODlxIuMTEjjw979TeTxwDhWxV1Vx0ejR+Hp9YkCuwbfZHCC1LpqrSeapv/yDuDAT94+2ikKIWlyNXJ8jsfNkDfsa236HtdUkDTZzCu/vcbGvPgp0pp98X0tYeAQZneJpLoQElr179hIdHY3T6URRFGRxxK4g+Jzd7gIMrXquw6Hj8cc/JT0mlPuHHMRUtcqr2YQWclZw24ATrD/sJs/dX70ckpYGax/+uy+NXEcf0Fl//H2NgcjIKNLS3DQXQgLL4cMHMZvNuFwucQ0SBD+xuSUwhLTuufo4nvjqFFldO7NgmBFD9WEvpxNaxFHDXeNiKU9OJvXqq1WLoTUYSJs9G83115N89dWYwsN/9H1jaCjRERGsv+ceqgOoEPKdIwcOIEkSTU1NPrsGBdw2GYDKigpOHDvCgD6ZF/S8IycKeeOdD7jvslGEBdNRhe2ZJZa/riyhf+coVbbMvHfQQHm9kzmzZ7FlyxZycw9x/YRupGgrIak/VB/n2WVFPHDXzX7P1hJuj8LBI/lUVFSyZ38uPXr0YNKkSWrHEoR2raSkiNLSI+TkWM//4B84cKCWDz5YxQPzUrFUrfRROuGChGbw56VGRmUZGRa5y8+TS7x5sC91DoU586ayfu12Dh8+yi3jLCRojkDyZKg5wF+WmXjgN75tENdaLpeew4frKS+vYteuI/Tr15exY8epHUsQ2rX8kydoOLmbLM2FNYLeUx/JF5tPcN8lQzDVHPRROuGChKXw9Kf7SSguoeg///Hr1Fq9npBHH0WWJPpHR3PIZqO4spK0ffsoXbWKgb/4Bac2bqR00iScjz7q12wtlTRyJHGzZ1PnclGm1zNk8GBGjR7t1TkCshgCsHrVSsYM7dPix+/ce5ily1dx/7R0qC/2XTDhwuktvLLDTWa8mTGJNvA0+WdeQwh/XVvHgl/e8P2XZDT8+5V3cLia2L59O/1yejNj+lSyunbyT6Y22J93CrcHcvr2VTuKILR7q1d/y5gxkS1+/KZNxWzccIi7R1dBwwnfBRMunDGaf2xJo1+qxIjofSA7/TOvOZ4X1yWw4N7/ncYiy6H88x9f0uSWTl+DejF33ngyugTcruUzbN/uIDQ0mm7dstSOIgjt3qqlXzHa0vJVHRsqItlxopo7R0WDr47mFVrHFMELK8oJO1VA6Suv4Hb4Z+thfO/e1M+fj+2RR77/Wo9LLuFk377ozGa2b99Odvfu9HA62f2HP/glU1sMevddsocOJT0jw6vjBuTVt7amBoO+5dHWbtrFrr37uX9igiiEBKImGzcPCOGrE0Z+v+wUC6Z0weKHo3drlFB0+h+3zNYgc8fNV57+r+t8nsFbFDTkHsxj3iWXqB1FENq9iooyQkL0LX788uUnyTtUyN3DT0GDOK4w4DgruXOIhs+O9+YPq1K4b7KCweH7o43LmhIwGn+83UqjqedXd409/V9jfJ7BWzweEydPnmDOnOFqRxGEdq+osIAIY0vvVUssOWWlqM7OncNDRCEkEDlqWDAhnvd2R+G55x7iFi+mbMcOn09r6tMHt16P7Qdfy/34Y/j4YySNhkxZxgXs9nmStus0dixNZrPXCyEQoD1Dtm/fxtABvVv02EVL15F35Ci/HGIER7WPkwmt5mpgRlIZ984dwt+X5/PPzS5kU5RPp3z52zxuv/mG8z8wCHiU5n+qkiSOORQEX9uzZzcDBsS16LFffHGIooIqbuuzHxyiEBKwHOXMSVzBXRen8dxyPf+3qyeyKcGHE0q8scbBL+6Y6cM5/MfjCQEUcQ0SBD/IPbCfPqFV53+gpOWTIwZq3XpuyFHA1eD7cELr2Ku4ols9d142ioqZM7E89BBRXbv6bDqNVktBRgbR69f/7PeVIOv/lDFtGrLHNwdcBOTKkOjoaPbkHiGnR+dzPm7l+m1UVVdyfU8XuAKv8ZhwJlNNLr8ZbabI0os/LdpEYoSJa4fHo/F2JTskHknnRKcJnJNh2mL1us3MmjVL7RiC0CFERkayd28tvXufe3XI0qXHsDe4uLrzenCLa1AwCKlZwUMj4KR5Jn/61kOnqHCu7F+Pxl7k1Xnk0Ey0Oh0aTY1Xx1XLqlV7uPji2WrHEIQOISwsnFyHRA/O/d540QkjWlMI81IqwB1cH247qsi6Azw8MYwjmp58YDQSUlODafFiKg56t8dL6vjxFGu1HP7yS6+Oq5bqxERmzZnjk7EDcmVITt9+VFbVfH83/OccOVHIrt37uCrLBbJ4Expskmz7eGhCFGOG9uXPi/P5z1YnsjXea+O/vbmciy+a7rXx1KbVatEbWne6hSAIF6Zfv4GUldUhy5azPmbfvhryDhcxP3WTuAYFoTT7lzw0ppAhfXvy5+VhvLanG7Il1Wvjv7bBwCWXTvHaeGrT6w3odAF5/0wQ2p1BQ4ZSVO9BOcf74h21kRTXupmdWkMgnoYonFtX+QC/mxLPlDmTqL78Msz33ENsr15eGVuSJIoHDaKPJiA/5reKwWRCq9X6ZOyAbaDqdDjYsH4d40b0O+N7DTYnzzz3D56a31v0CGkPNDpKrL14/Yt1ZKdFc3E3DbjtrR6uQhPPG2vzeeDXt3oxpLoqqhvYsmMfsbGxREZG0jXzwk5aEgThwthsjWzbtp7Ro6PP+F5NjcSLLy7ksYuaoPGkCukEr9KYKAiZzluL9tK3s5np6UfB3Xj+551FAb35ZKeWuxeM92JIdZWUaNi1q5ioqEhiYmLJyPDd8m5BEKC2toZ9W1YzzHzmiTJlxPKfb4/w8MzOYG/BdhohsGkMHNNm8s5X64ipr6f+9ddx1tW1eriUq6+mMSeH6vvv92JIdfV+4AFMI0YQHhZGQmIiXbt399rYAVsyMppMyIoC0k+qQJKG3//l7zxy5XBRCGkvZDcJ9bt4aFIMMfHJPPlNEW5jK/uJSBpeWXGEe351u3czqiwmMoTRwwfQIzMVnSSzcsVytSMJQrtmsVhpagIw/ejrsqznj3/8gN/NjRKFkPZCdpBS9ym/HV+KJTKZp5fHI5uTWjeWxsAbqxu569ezvRpRbQkJMiNGpJGdHYGi1LBmjTg6WhB8KTw8ArtiAmPYj74um6J48cs9/PaSvqIQ0l7ILjKa9vPozE50HTUE1513EtnKfiLG0FAK09OJXt6+Pifsf+45Gj77jJK33+bwkiUsX7zYa2MHbDEEICOjC0dPFP7oa/9+7T2umD4KQ3XLj5sSgoTbwXDrMe66ZBTPLTkJXHijtipdIhERke2mV8gPhZgNhFgt6PVaSstEo0ZB8LXk5BQKC394BJ6Wv/1tKTfPHoiuaotquQQfaapjrPUbfjErmxdWWGnNNahA6UFyciIaTevv6gWq0FAXFosWo1FLSYm4BgmCr8XGxlAu/aCZt87Ec0tOcsdlk9DUHFcvmOAbrgYmxxdz47xxNFx6aauGiJkzh8TISI54sVgQCGSPh+Off46jqAi5vp6q+nqvjR3QG0A7Z2SwaNEiuqQ1d3x/9d1P6Z6eRC/Ju01mhMASUbuPyIgICpRoUqSyC3quw+VGfwHHMgeDwpJKDuadQEFCq9WQmJjE3Llz1Y4lCO1eVlZPFi36kuTkJEDDv/+9lkHZaXRxfK52NMGHYmq+xGjOoUKfTUzTvgt6rtOloNe3/FjmYHDqlIfDh6uQZQ1abQUpKanM8VEjO0EQ/qdP3/4s+iKf6dGAzsQ/N9QzftgAUmwX9nNJCC7Jtj3orRYSpk7l1AUWNWSNBl076hUCkHX33ViGDsUUEYHsdNK5e3e6dOnitfED/lOj0+HA5YZ3P/ic9KQ4xoSeuXdOaH9uHt+FP3+ynQfHR4DS8lUeCWE66ura19Fih4+dYsKkyWrHEIQOyW634/GE8uqry8junMBw/edqRxL84BeTI3j+s0IeGKkBpeXNCdNiFL7c4+XT0VR25Eg9EyZMVTuGIHRIjXYnckQ6//n2KIP79GSAQayM7wjunDuEZ+sbkJYs4ULae4bU1lJYU+O7YH4maTTETJvGxMm++xwU8KWjmRdfzB+f/yfJCdGME4WQDkNTdRizyYjDktzi59To4nn+q1zmzJ7hw2T+p9H4pnuyIAjnN3PmxTz55Lt0S41jpPFzteMIfqIrW41Ob0AO79ni51Tps/nLF7VcfqW4BgmC0HaSJDHz4ot5/P0d9O2VxSBTntqRBD8xVe5FbzGTMmJEi5+TMns2+9LS6O2jU1fUoNXr0fh4pUvAF0P0ej1TJk1kUKpZ7SiCn106ZRgfrT/SosfK5hj+9OkuJk2ZTkYn7x3Rqz6JpuYujoIgqMBoNDF5wjgGJtWqHUXws5mje/DxJluLHuu2dOGPn1dw0azpJCY6fZzMnww0NYmjowVBLRaLhckTJ9A3sf18wBVaZvzwvtRkZbXosanjx5OblsbQtDS2PP64b4P5UWSXLjjtrT9htCUCvhgCMHjoMHaVaTgkee8YHSHwJSmFVNhb8ldU4vX1Jdx+20306+W9PWQBQdJSWFTE1i1b8HjaX1NYQQgGw0aOZlNJMkcM09WOIvhRpjmPgjrT+R8oafnPWg333HMDOX0tvg/mV0ZOnSpk+/ZtyHLLtwsJguA9I0aNZt0piRPaTLWjCH40KEWLI/78N3i1ej0F/fszKTqanQsWoLSjn9UR6ekUlpaybZvvrkFBUQyRJIlxEyZR4bZwUi8KIh1GfQlanQ5053gzqtHx750SYbHJpCfH+i+bvyhurrt8NlXV1bz88stqpxGEDkmj0TBx8jQKHXGcMo1XO47gL9V70BrMoDWe/TFaM//Y2ZvEtG4kJrbHFRT13HDDMIqKinn99dfVDiMIHZJWq2Xy1GmcaDRRZGhnN/2Es6s4giUyEu05mnKbo6IwPfYYXbp2ZXM7WhHynSOLFyO9/jr5+fm8++67PpkjKIoh3xk1ZhxlcgQndN3UjiL4i+wGc+TZv28Kx+X2cNmcdtzcTXETHRnO+HHj1E4iCB3a2PGTOOXKIN8oCiIdhqcJzAln/74lEY+sMPeS9nyjxk5sbBjjxo1RO4ggdGjjJ07imC2UQr0oiHQMMnKTi4jOnc/6iLjsbGS3m7xf/KJdrQj5jiLLlO/fj7mpiWGDB/tkjqAqhgAMGTaCw1WghJzjzYnQbpj0GhrkM+/K2UxJbCyz8tVhD716tLzBXTDauvsw4RFRdOvent9sC0JwGDF6HAeqYlGsqWpHEfxAK3lw62N+8lWJBksvttb04tMD8fTr10uVbP6yaVMdcXHJZGR0VTuKIHR4o8eOZ1+F9tw3CoV2Q/G4ifzJMbKSRkP6xRcTf8MNOKdPp2tk+/27oNXryfnPf+jWty9dffQ5KOiKIdD8g2BxvhkMIWpHEXxscJ8s1u899b8vSBreOmDg5dWnsIekUu/SMHGsbyqFgSK/oIiumWKfqCAEipGjx7KkZCAYwtWOIvhYr8wkth/5QfM2jZ7Xcgfw6notNeaeuPUhjBmXqF5An9NSWFghCiGCEECGjRjB8tKYc28jF9qF9NQE7LH/awNgsFoJf/ppygcOJLR3bwxmMwfuv1/FhL5ljY/HptXSxYc3hHU+G9mHLBYLg4YMY9PebQw15QEtP39ZCC5dO8Wzbds2yAgDvZXXtzUQl5LK9dd2nOW6F3K+uCAIvhcaGkZO/8FsO6wwUPpM7TiCD2WkRLFx42GGRAPGKF7akkB6t3SmTvtuZVAK0P6WJv+PB1mWURQFSZLUDiMIAhAREUm3nr3Yna8lR3NQ7TiCD2WkJrHi9NGyEenp2K64gqSICA7ccQctO28zuNnKy33evDsoV4YAxMUnoLVGUmLMUDuK4EMWgwabswkMITy7ooz+Q0Yyc0rHKIQoaFi6ajN6nU68CRWEAJOU3AmXNpoK83C1owg+FBNmpKbBBaZY/rA0jNETJ/ygENK+ybKJxYtPYTYbxTVIEAJMWnpnat0GaszpakcRfCg63IpTkojs0oWyefPon5TEgTvuUDuWXySPGEHnv/wFs/EcTcy9IChXhnxnyLARLFuymASrhFgd0j5plCYUQwhPfpXP5fMvJatritqR/Gb52q0MGz6S0LAwtaMIgvAzRowex7IljUw0blA7iuAjFqOCTQnlkS9s3HjTbDK6BO09pAukYdmyE4wePQmLxap2GEEQfsaYcRP4dvEiJoSqnUTwldgIK5rERIpmz2ZkVBSbfvUrtSP5hTkykphf/Ypps2ZhMvl2O1hQF0MAdDotGCzgalQ7iuALniZWrt3I6y//k+hwi9pp/EfSotfrRSFEEAKcTmcEnQXcNrWjCL7gcbBi7Vbee+8vhIZ2pD9jMyaTWRRCBCGASZKE3mQBSQuKR+04gg/oJIX1u3dzQ1IS2597Tu04fpPYvz/WkBCfF0IgiLfJfMcjK+B2qh1D8BHZXs3E8WM6ViEEaLA5MPp4WZggCG3ndrvBI65B7ZWj+hTTp47vYIUQqK5uIjRUFOMFIdC5m5pAac99izq2soLjzBwzmr0vv6x2FL8yZmURm+Cfk2ODuhiyZ9cOuoa5QHarHeWCyLJCSVWD2jGCQp3dQ0hIxzs1yGo2UlpWzpLFi9WOIgjCWWzfuomeEcVBd0dOlhVKql1qxwgKxVUu4hNiz//AdiYsTEN+fgHffrtM7SiCIJzFpg3r6R3tIthaBYjPQS1XXFlHmN6gdgy/0uh01O3ezYn8fL5d5vtrUNBukzly+BD26lJ6GY+pHeWcTpXX8fwnm7EY9Dia3Nw9dzDFVQ3sO17OjVNzzvq8X/z1G/7962ltnv8fC7cxNieNXunB+WauqKKW2NjgzN4a+w+foqK6lurqasLCwrCYzWpHEgThZxzK3YfSWEiSZo3aUc7pVLmT5z8rwGzU4GhSWDArmeJqF3tPNHLT5LPfdbntH3m8dGfbj/T++5eFjO0dQe/04NxuUVRlJy4+Ru0YfrN3r4vKSic1NacICwsjNLTj3YwQhGCwf+9ujJ5aYh1H1Y5yTuJzUNsUV9UTqus4xZBhb7yBMS2N2tpaQkNDCbH6/r1DUBZDZFkm7+A+psQUBfyqkPX7CxjcPYkrxmVTVFlPQUU9m3IL2XmklLkjuxMRcva9UPU2J0+9u57IUBMaSeLeS4bw29dWERVqQlYUHpg/jOc+3ozbI2N3uXn4yhFc/5cvGdMnjeoGO9dP7gPAa4t3YzJoSY+P4NaL+vnrpXvFzrwiJszqOKfHFJeWM3HSJLWjCIJwDm63mxN5e5kcsRTkJrXjnNP6A7UM7h7KFWPiKKpyUlDhYtPBOnYebWDeiBgirGd/G1Bvd/PUe/lEhjSfaHXf3BR+++ZxIkP0KIrCA5d04tlPC3B7FBwumYcvT+W65w8xtnc41Q1urp8UD8Dry0ow6jWkx5u4bVqiv166Vxw4Uc+VU3sAlWpH8TlFsVBaWsHEiVPUjiIIwjnY7XZK8o8yLuJUwG+REZ+D2uZ4cTX99eFqx/CL9AkTUKKiGDt2rF/nDcptMuvWrGRkCgH/JhTg0tFZSJLE0++u49XFu0mItDI0K5lRvTqd8wcAwJebjjB9cBceunw4GgmOFlVTVtNI54QI5ozozvr9BRwvqcFk0OFqcnOkqJowi5HbZ/bnqvG9eH/VAQAuGZ3FH24cx7a8Yn+8ZK+qaGgiqYMsUT505CQ9evRQO4YgCOexdvVyRifngxz4W00uHRWLBDz1Xj6vLCkhIVLPkO6hjMwOP2chBODLzVVMHxTFQ/NTm69BxXZKa5ronGBizvAY1h+o5XipA7NBg7NJJq/ITphFy+0XJXHVuDjeW10OwCUjY3nm+s5sP1Lvh1fsXTbZQkfZqblnTwn9+g1QO4YgCOexfu1qRiY1BcUWTfE5qG08WhMlW7eqHcMv9KNHM8LPhRAI0mKIy27DajupdowWeWvZXsb3TePhq0Zyy7S+PPfxZpB+/rHHS2rYdrgYV5MHnVaD8oM9gLICkgQPzB9GckwoT76zDrdHpl/XeO69ZAjTB3clLsLS3FAWcLjcaDXNf7xhluZGnBrpLBMHMr0ZPIG9+sdbikvKSU7pOEcHC0IwUhQFj6MOU/0etaO0yJvLSxnfN4JHrkjl1qmJPPtpIdJZrgXHShxsy6vH1SSj10ooP9iGLiuABL+5pBPJ0Qae+O9J3B6Ffhkh3Ds3hYsGRxEfqcdz+ialwyWj0zTPE2rWApx13oCmtwLBV8RpjaoqO9HRHWdLkCAEI4/Hg8ZtR99YqHaUFhGfg9pG0po4vmKF2jH8wpKaSmio/8+JDrptMo2NjYRJwdN0Z2SvTjz59josJh02h5trJ/UmLsLKil1bmTa4C28t28tvLhsGQGSIiafeXYdRr2XOiO4M7ZHM0++uZ8vBInRaDWnx4Tz46kqiQ810SYpkdO9OLNl2lCffWYvd6WZkr07UNDr4w3/XU2d3sWDuYD5ak6vy70DbSBodEPiV7zbT6DlVFHwVa0HoaCoqKog3Vqgdo8VGZYfzxLv5WIwabE6ZayfEEReuZ+XuGqYPjOLN5aU8eGknAKJCdTz9/kkMOg1zh8cwNCuUp9/PZ/OhevRaifQ4Ew++fpyoUD1dE82M7h3O4u3VPPnfk9hcMiN7hlPT6Ob3H+RTZ/Nwz+xkPlxXrvLvQBtJWiDwVwC1laJEUFBwQO0YgiCcR0HBKdLCAntrzA+Jz0FtI2k12CqC5z1Ha/W57jpser0qc0uKogRVC+KKigpO7VpJjumU2lECkrcaDgUEQwgvrKnl3jtvVDuJz+09eAJF0tEn5+zNpARBUN+pU/k0HltKd2Wl2lECkrcarwYEUxzPrYnnvgfafx+nzZuriYpKJTOzm9pRBEE4h7zDh9BX5pLqCewDJNTSrj4HWWJ44dti6n/zoNpJfK7bs88y8tJLSU1N9fvcQbdNJiYmhmKnmVO6rmpHCUjt5gcAgCEErSb4lrRdMEnDnv0Hye7VS+0kgiCcR6dOqeTVxlJkGq12lIDUbgohAKZodLoOcA3CxKFDRaIQIghBILNbd3aXQqkhQ+0oAaldfQ4yhqA0BX5/zLZKyMnBHRurSiEEgrAYAjD9ohmUEsOKmjQq9aLHQntVJxsxmYxqx/A9SUdyUiJarVbtJIIgtMDMi2eR785ief10aox91I4j+EhJo4WIiDC1Y/ic220gPb2T2jEEQWihWbNnc9QRwcqaVOqMSWrHEXwkr6SRiA7wOShmwAAy0tJUmz8oiyEAg4cOZ+KMeWwqMWCL6AF6i9qRBC/7ZHUu8+bOUTuGzymKjNvdMZrECkJ7MWzkGCbOvIo1pV1xRI8Gvf+bfgm+9dmGci6Z3/6PmdVoFJo6wN1HQWhPRo4ey/iL5rGqQI8zsjvozn0yixB8Fm87Sme7U+0YPudpaKCxrk61+YO2GPKd6TNmsqfGypLyJDC2/zs4HUmdW09sVPv+gKEgsWjpaoYNG652FEEQWuGiGbPYUd2NpTVTwRCpdhzBi5yaCEJC2vcbUUUx8tVX+xk+fKTaUQRBuECSJDH9oplsr7Syoia1+QRGod2QdSEceOcdtWP4VFyfPjBzJmMnT1YtQ9AXQ7RaLcNHjqZP337sbowHKehfkgDNK300OpDb94qJVet3MHr0GKwhIWpHEQShFXQ6HSNGjyMzK5tceQRnPTNQCC6mGJr/LG1qJ/EhieXLTzFhwiTMZrG6VhCCkcFgYOTosaSkdeYY6WrHEbzFHIUiy1QeOqR2Ep8xWK0k/+53XDxvHkajetuB2k3lIDmlE0pIPHuVrOYP0UJQc5liae8tNErKawkLjyAsPFztKIIgtFFGl0wa9ans180CjTrHwwneU6dNw2w2qB3DpwoLITY2HqtVFOMFIdhl9cim3B3CIU2P00eCC8GsxGXGKAfPEcqtkbVgAWkZGZjN6q5oajfFEID+AwdjjErh26oUXJYEteMIbfD5xqPMuOgitWP41P5DRxkwcKDaMQRB8JJBQ0YghWeyvG4aTeYuascR2uCT9RXMmde++4UcPFhBTk5/tWMIguAlw0aMwmWOY1VtKm5TrNpxhDb4fN0hehnab/NUrcGAZcgQ+gbA56B2VQwByOrZizGTLmJFUShrGjLwWOLVjiRcKK2Bo+UOMtIS1U7iQxI6nbh7LAjtTXbvvoycOIulpf1Z47gY2aJeh3ShlfQhlDSaiI9vz1ue9OIEM0Foh3L6DWDImMksKQxjva0Lijla7UjChdJbqXbAgTfeUDuJz0R27oxWCoxrbLvcT2I0Gpk+YyYOh4Oly5bSO9ZKivuY2rGEFqrRxRMR4W7n/UIUmppcKIqCFCA/DARB8A6TycSMi2djt9v5ZpmRfjEpJLnWqx1LaKESpTsxUTqgUe0oPuQWJ8gIQjtltVqZOWsWjY2NfLP8WwYmhBDnOql2LKGFTrrCiTTVUrpnj9pRfKbm5EmaPB61YwDtcGXID5lMJi6aeTH7K7U0WcQ53MHipaW53HbT9WrH8LkBOT3YsnnTz35v186dFJzK93MiQRC8yWw2M+Pi2eyqTMIT0lXtOEJLSBpeX13PzbdNUzuJjyn07ZvIjh1bf/a727dvobi40M+ZBEHwJqvVyoyLZ7G1WItsEdtmgoKk5b01h7Eu+1btJD7ldjhoXLWK7Zs3/+z3169eTVFBgV+ytOtiyHcmTZnGulLRKT0YyCGJaHV6dJrAqBb6UmSYBZfLxe5du5BlmW+XLWXVypWsWrEcq8XEoXbcQVoQOpKJk6exrqKX2jGEFnCHdMeg16PR1KkdxediYz3U1dWxd+8u3G43y5YtZtWqFaxatZSIiBD279+ndkRBELxgwqTJbKwIVTuG0AIOcxJ6jYbjy5apHcXnDr/0ElVVVezetg2n08mihQv5dskSFr7zDhZZZveOHX7J0S63yfyUTqdDaxRnbwc8jY4XvznKddderXYSvxk1uBcFJVUsXbKY0cMHYjE27+H2KFpOFYi7coLQHhiNRtBZ1Y4hnI/WxLPfuPjFL6+gfR+p+x2FsWOjyM/38O23ixk3rgtGY/Prdrk0FBSIvlaC0B5YLBY8Uvs+Hatd0Bp57os99K2pZ7vaWfygyWZj+xVXkHX33Rw5dAjnxx9z+PPPAXBddx0xV17plxwdohgCiGOmAp2k4cODGvr07Ud8dMeqXqckRJGSMORHXzt6PB+ttuP88xSEdk8ctxvYJC3v5GYxfEQ2kZEdoRDyHZnUVEhNTeSHBaC8vAr0evHhSRDaDXENCmwaHa/vcJGT0ontT9ymdhq/cdTWsuuJJ878RvfumKz+uYnUIbbJACiIJpUBS2fi/VwtWms0k8cNVTtNQOiWkYyEjNPhUDuKIAheoEiiuBmw9GG8eaAn4XFpjBknTqADyM624HTW09TkUjuKIAheIEsd5iNf8DGE8Or2JsIsYey4peMUQs7l4OOPU3LkCB4/NFntMP8yqqurQWdSO4bwEzZ9LE9+XURGjxwunTVZ7TgBZfiQ/mw+S4NVQRCCS1VVFWjFds1A02DM5PHFEWQPHMzFs7uoHSeAKAwenMGOHdvUDiIIghfU1NSCRhTlA02NLo7HF54gQTJy4OZb1Y4TMDwuF8quXezeudPnc3WYfxURYVbcYanoqg6rHUU4bV99FJ9uOsyD99+NSS9W7vyPxN7cYxQUl9G/f3+1wwiC4AXhoWbk8J5oqjrCTuDgsKO+L1+vaeR3j9yIwdD+G6a2nJ5duyopKChkxIiRaocRBMELQq1mlLBkpBpxxG6g2FwVwYrdx+idf4rtf3hG7TgBIzw1lcSbb4asLDK7d/f5fJKiKIrPZwkATqeTZUu+YXp8CTR1pP3AgWlffQTL9ldw7523ALLacQLK5p0HiY6Jo2tmptpRBEHwErvdzspvv2Fa5BJwN6gdp8PbUd+H9Ud13HX3ZEBsBfmhNWuqycjoTkpKJ7WjCILgJY2NjaxbtZxJUfngET/z1La5KoKdJ+pxPvc8tfn5ascJKH3feYdhEyYQn5Dgl/k6zDYZo9FI/wEDWVPbCfTimF1VaY0s2lnMvXfeiiiEnCnv6HG6dO2qdgxBELzIbDbTq3cO6+xTQReidpyOTR/G0r1N3HX3VEQh5Ez5+SWiECII7YzVaqVbVg8227uAVjRHVpUxlFX7inE+/4IohPyMBkXxWyEEOlAxBCApOYVu2TlsaEgVp8uoyRqD2WQEfN8UJxhJkoStsVHtGIIgeFlqehfSuuWwuWmauAapKSQNs0kPiAbVP0eWZRwOu9oxBEHwss5dMolP68qOpkwQB0uoRjbHoJM91J4UW5Z+jtvtxul0+m2+DlUMgeaCSEhkHIX6zmpH6bDcWgsajfghfDZXzpvO4sWLcdjFm1FBaG9S0zLQWmIpMw9TO0qHVeUwERrqnyP7gtHVVw/kyy+/pKmpSe0ogiB4WUaXTJo0FqpM6WpH6bCOlzcSrjeqHSNgKW++yeeff+6Xk2SgAxZDAPr2H8ieUgXMkWpH6ZA0Gg2y3CFa1bSKhExYWAh6g1jGKAjt0aAhI9hWmgimWLWjdEgaTYd869NiGo2d8PBQtFqxekkQ2qNhI0exuVgDBrFlUy3ipvDZnVy1CqvJ5LdrdYd9RzBo8FD21EWoHaND0kiK+CFwDrIi4XbL4o2oILRjOX37kevMUTtGh2TSSzgcYovM2bjdRhRFEUUjQWjHevTM5lhTvNoxOiSzQY/LT6seglHahAlINLcN8IcOe6WLi4+nwm0FU4TaUToeRRYrQ84hv7CM1NRUtWMIguBDKZ3SKHImiNUhKjDoJZrcbrVjBKy8vFoyM31/nKEgCOrJ6NKVE40GMIaqHaXDsZgM4hp0DpaxY+k3cKDf5uuwxRCA9LRUvjgVjmyOUTtKh+JyK+j1OrVjBKyIMCsn8/N59dVXWbtmjdpxBEHwkU4pKXxRPBLFkqJ2lA6lqs5NRHi42jECVmysmdzcg7zxxhusXy+uQYLQXiUnJ/N1cSyKuDHsV+U19YSaTGrHCFiuo0fZvmMH77zzDmtWrPD5fB26GNK1WxaTp0xjXbl4U+RPpyrqSUxMUm1+WZYpKS1Tbf7ziQizMH38UC6bdzGnCgr82lFZEAT/6d6zN+MnTmZDTX+1o3Qox0sa6ZSmXgFKlmVKSipVm/98YmIULroohXnzRnPs2Em/NbETBMG/emT3YfjIUWytiVY7SodysrSGaJNZtfkVwKYL3JvSR994g0O33kpyWRknCwtRFN/uJgjc3wk/sVgsNEkG3CEp6BoK1I7TIRwvriItc7Bf5jpVUMhzf/03ZrMJp9PJgl/dRnFJGXv353LTdVee8fhZ868js0tnFECn1fL47+7HbD539fZA7iGOn8jnommTvv/abXfex0v/eLYNyRU2btnBZZddJnqHCEI7FhoaRqPHihzaDU39YbXjdAjHS2wM7Z8K+H6Z8qlTpTz33AeYzUacThcLFsynuLiKvXuPctNNM854/KxZD5GZmYKiKOh0Wh5//EbM5nOfOnDgwAmOHy/ioouGf/+12277Cy+9dH8bkrvZuPEQV155pbgGCUI7FhUVTY1LhxIdh9QYuDcK25MTxdVkmf2zRbZBr2d3TAxaWcYjSeRUVmLT6ag0mehRXX3G479JTSXc5QJAUhQGlZWhO08xospopN5gIK2+/vuvrU5KYkxRUetzl5TgyMriqqlTfd47pMMXQwBGjh7LhtVLGC2aKvvFqdJaBk70T0+MdRu2MHhQP66cP5ei4hIKCovZuHkbO3fvZd7sGUSEh/3o8QlxsTz7zOMAbN+5h1ff/C9TJ43jP6+9jclkwmq10LtnFnaHg3mzZ7DggUeYffF0SsrK2Z97iLfe/ZCI8DDcXtgLqNcbxJtQQegARowaw8b1DYwwimKIP5TWOEhOjgWKfT7XunV7GTy4B1deOYmiogoKCsrZuHEfO3ceZt68sURE/PiNR0JCFM8++0sAtm8/xKuvLmLq1MH85z9fYDIZsVpN9O6dgd3uZN68sSxY8Ddmzx5NSUkV+/cf5623FhMREeKVa5DBYBTXIEHoAIYOH87WbWsYbBLFEH9ocDShlRv9MlexxUKczUZmbS2NOh2Nej0lFgsVJhMZtbUYZflHj7e43QwvKQGg3GTiYGQknRoaOBAVhU6W0ckyUQ4HHo2GjLo61ick0LmuDptOR5XRyKGICIyyjNzGAoYkSRiNRr808u7Q22S+YzabcWtMuEOS1Y7SIVQ6NERF+KfyNH/exUhIPPXM87zy+rskxMcydPAARo0YekYh5Keye3TnyNHjhIaGMOfi6fTv25st23Ywcfxo1q7fTHlFJZEREZiMzXftXn/rPR777X3c++vb0era/gYyKjKMpUuXsmL5t20eSxCEwBUSEopNCUEO7ap2lA5AwqWNQqdr8Mts8+ePR5IknnrqDV555UsSEqIYOjSbUaNyziiE/FR2dmeOHCkgNNTKnDmj6d+/G1u25DJx4kDWrt1DeXkNkZGhmEzNx7C//vrXPPbYDdx77+VeKWJERJhZunQpq1f7fs+2IAjqiYiIpLZJh2IRDb19T8Kts5L7ySd+ma1LbS0A22JjyY2Kwux2E2+zkWiznVEI+alIp5NaoxG9x0Pn2lpi7HbKLBZSGhsptlqxa7UYZRnt6ZUjByMjGVRWRk5FBZo2bm1RFAW5qoolS5awfMmSNo11PqIYctqgIcPYUypOOPG1Km08Gq0W5Ca/zPfmOx8wYdwoHnnoHm696RqeffFfLV5utXP3Xrp2SWfhl9+wd38uvXv1wGI2YzAYiI+L4fW332f2zGnfP16SJBQUJEmDRmr7P60+WenkZHfD7Rb7tQWhvevbfxC51Ylqx2j3yrTZGAxGoP68j/WGN9/8hgkTBvDII9dz660X8+yz77X8GrQzj65dk1m4cC179x6jd+8MLBYjBoOe+PhIXn/9a2bPHvX94yVJQlGa/9cbx9f37WugT58kXC7/XK8FQVBP96yenLBZ1I7R7p3yxGHS6SnYvNkv8x2KjCSloYGB5eX0rKpid0wMLb06VJhMhDmdnAgLo8pkItrhQHe6+GF2uzkYGUnnurrvHy/R3I9E8lKPj3Xz56M7ehTn6W07viK2yZwWGhpKfZOoDfmSwxDLs5/s4g9P/A7wzwf8USOG8vjv/4LFbMZmt3PdVZcRFxvDilVrmT5lAm++8wEP3nfX948vKSvnvocex+PxIEkSj//uftas38jylWspLimloaERj8fDnFkX8cDvnuSBBb9k89YdANx03ZU89czzhIV555gytwyff7WESy+5xCvjCYIQuKKjY8i160CccugzDYYuvPhlNb///c34qxgyalQOjz/+GhaLCZvNwXXXTSMuLoIVK3Ywffow3nzzGx588OrvH19SUsV99/3zf9egx29kzZrdLF++jeLiShoa7Hg8MnPmjOGBB/7FAw9cyebNBwC46aaLeOqpNwgLs3olu8Nh5KuvNjFv3lyvjCcIQuBKTEpi6/5NdI5QO0n7VaON5eVv9tFlwyaq/DRnUmMjW+Pj0ckybo2G7tXVmN1uCq1WUuvrORQRQf+Kiu8fb9Pp2JCQwHfljEFlZRRZrRRardj0epo0GmSgc20tmxIT6VdRQam5uRlsVlUV2+PiMJxnxUlLpc+YwUmTiTkjRnhlvLORFF+3aA0SjY2N7F/7JQNDWt/sRTg7mzGRm/78CW+8+hJmg9ppgoPT5WH1xp3Ex8eR07ef2nEEQfCh8vJySnZ/Qi/dWrWjtEt1pl7c+tx23n73OQyGivM/QcBu17NmTSHJyUn06pWjdhxBEHzo5MkTuE9tpzMn1Y7SLlXpErjz+c+5PiODTX/6k9pxgkLq1KnE33EHqZ060btvX5/NI1aGnGYwGHD4vrF8hyKHJLEqt5xdx6uRpeP8bu4MXnn1TX51+3VqRwtgGtDqQXZjNEjodVoyMrqoHUoQBB+zWCw0NmnEVdlrJOSw7izbL7Mn341GK/O7qeN44z8LufVO395lCm56IAKox2x2o9VCRoboZSMI7V1ISCj5dg+od+JrOyMhhyWzZFcx+wtq0etPcfdFE9lba0fSaFC8tHqivYlITydz+nSOLVtG9Z49RDc20rV7d5/OKd52nabX6/HovbO0tMMyheMyRLFmXwE7j9cgaY4yLDWJWxNlbN98DNtlogZPZ8Pm3QwfIu4ynUHS8fGXywixWrDbHVitVrp06UJo2LkbvQqCEPysVit2xL/1NjEn4DKmsmJ3NXuLNGiQGJWUxK8tRbg/exOAnVOuYvuWWgYMDlc5bOBRlFA++mgXYWFW7HYnVquZbt26Y7GI90aC0N5FR0ezx6URxZC2MEfh0EWwfNdxcotsSOQxJr0Tt0U7sC/7AiSJ3f0mM/DFF9l6113nH6+DyZw5E8M116Do9SSNG4dRryc7Oxuz2bd/KUUx5If0FpC0oIiGlS2mNbC+yMCWo5UolGOQSxmYmsytIVU41y6G7WD7wcMnHljH/9U20r9fH0wG354bHWyaPAo6rZap06arHUUQBBUohmj+14JMaBGdhVVFXdlxwg2SFkNTLUOTk7nLvhfPqsUA/HDR5yVbFvPnBgc5/W9Ep6v7+TE7KIdDi8ViYurUi9SOIgiCGkwRQL7aKYKLzsS3xyX2nKoBSjAqRQxN68RIaR/ObetgO9i/e6yiMOfkTv4e1Z3suXM5+Omn6uUOQFH9+4PZzLQZM/w6ryiG/ICEDJZIaBT7iVuiwBPL2yuO0ScjjRtcFbh2bWr+xjZwnuU5SkMdN2vr+Ps//8P9C27zW9bA11wY0un1KucQBEEtGkkGcxzYS9WOEhROenrx9ho7QzrHckfJGuRtG77/3llvaVRXcrurjH88/yl3PzDRLzmDgxatVoIWnzMgCEJ7I0mAIQRc/jl6PNjlOWL4eOMJhnTtzA2NB2ja33ygAlvP/jlIrizj+tRMvho8GEQx5HuW6Gh0sozD7f+eFaIY8kMKIInfkvPSGvg238Dek0X8wuSk6fOXuZBDj3QHdhI+IIHde4+Q01vsRQaJ1Rt34fZ4GDZ0qNphBEFQVmOvvAABAABJREFUi6KARnSYPi+dha9PdudoqcI99cXw8vtcyO7r0G3rMExM5eB+J1nZRp/FDB46li8vQqOpYISPu/YLghDIFNCIz0HnpTPxRZ5EUXUVtyvVuD5ZwoUcQG7dswm51wQGPvMM2x56yGcxg0VocjIZf/kLmqgoRgwa5Pf5xd/4H1BQwHO2Wp6AOZKv9tWzv7CW7E5JXH1wDU11Na0aavbxbfx7URM5ve+GC3ob2/5U19nQG4yMEW9CBaFDU9CAu1HtGIHLksxn+0LJK9XQJymcm759CRpasdVFUbh+3xpekBV+lz0PLqic3/4UFbmJioqnX7/+akcRBEFFiiKB26F2jMBlieGj7RUcK69mQHon5m75FJfDdv7n/ZTHw9UVh/i/yExCk5KoL+rYJ5kmzp9P9z596Jmdrcr8GlVmDVCy2w32arVjBByHKZH/22TnL0uLidKHc3PpfgYvfAWllYUQALmqnGEmiaXLxTGSEWEh2Gyt+GEqCEK7Ijc1grNK7RgBx2buwd+3ZPDn5eGkGpK4c99qRr/6l9YVQk5Tik/RHydrV57yYtLglJBgpapKbA8WhI5OdjWKYsjPaDAm8rcNjfxlySkyw+O46dRO+nz6ErSmEHKap/Ak3bQy3X77Wy8mDU7u3FxOHT+u2vxiZchpBafySTB17LtDZ9Bb+HCvi+OlR7i2azLaL96F7Y6z78W+QH0LDvAGRiZP6NgrIiQ8REeGcyQvjy5du1JbU8OSpUuJjYlmwICB7N69C49HRqPRoCgKFouZwUPEdhpBaE+OH8sjxVwpeqf+kDGK/+5JprDKzY0JcZjfeQmavHedHnN8L/8yhDJq3BSvjRmMNJpGwsNNnDhxjLS0zlRXV7F06TISEmLJyenHnj27kGXQaCQURSEkxMrAgUPUji0Ighcdyj1ARrjc0Rdr/5gxlLe21VNRe5yrO8XDF+/CdrfXfosmNRbzqiS2ah5fsoTIyy7jxLFjpHXuTHlZGctXrCAuKoqcAQPYsXkzGoMBjSShyDIhFgtDRo702vyiGHLawdxcxkeUdfTVst+z6aP5y6I8pmR3Z+zKhbDT+5ViT0khUnR3ZPRoLmi3XfvTr1cX9h08warl3xIaav1/9u47PI7q7Pv4d2b7arXqvduWJbnJvVfcC8Z0CITQkkBCEkhIgCdPnhQISUhCeEMKhJCE0LspBmzjgo17792WrN7LStt35v1Dphhw1e7OanU+1/UUy7Pn/GRszc6959yHqy+bRafTzeYtm5k0bgTGT/+lyrz6zoeMGj0GSRKN7gQhWhw7eozpMdvo5T8KP9Vu6Mcfl3hZVJLKolcfA2/w70Hq8cOo+SOABKB3rwodOdLOrl2VrF59gLg4K9dcU0pbm59du7YwYUIuBoObrqckI6+8ckAUQwQhylRUVHJJbO/ervF5zVIyf37rEIsGF5G5+mnYGvzGnr6jB5CH5VM4bx5H3nsv6OP3FKqqcuDee/H85CdssliQHA4633mHtrFj+bi6mvZXX6VyzRp8TifJxcWk/vKXBPMOJIohp+hlNfzdkyX59P8BCPg0P9p3f2ciL605yPcyY1FffSKkc022wOK3lnDFZbNCOk9PMKg4H8g/9SuFWKuR6ZNGfOEqFZPJRGXFSXJy88KaTxCE0NHjBl+Yj3qV9CDrgE/uQ6f2i6vh7+b+uVDs7Cxl8aZW7jX50D3525DONjbgZNn7O5k1t7f/PA1QWhoDxJz6tZOEBJg6NRk4fSm40WikqqqCrKyccIcUBCFE9Ko7/H0TJd1n955Pn4O8oGq5PEViS0scS7cd43t2icCrT4Z0tiLFiW/uXOjFxRAAZ1MTe++777SvVW7c+OULJQlZlqkoLycnLzj3bVEM+YQa4rXJOgOKLZNNR5vYcrCq6+gqnYSMQtenLSqKqoAkI0syit+H6nMxZ1QBRXYPeMLwJlln5MW94PJ1cHegAf+qNSGfsm9bHevLbSGfJ1p8vHkPVouFvfv2i2KIIESVUN+DLCj2ItYf9rLtqAOMiaAznGoc1jW3oqrIsg5FCYDiQ/V2cOnoePpaT4KnKbT5APQx/Ht3P3Q6K/dVrkU9ejDkU4501PHknkOiGHJeZNasqcdmi2HfvgOiGCII0STkz0EmlNhM1u6vYdfxBjDGgCyBqiBLEp88B3Xdg6RT9yAXiyYUkmfuCM8H1noLT291YTX7ubPlCIG95SGfcrzk5l/NYbi/RgFLYiJ9HnwQg93OwUOHRDEk6PQmoOsfY1AZLHx0Usfmo00YOEhpVhq3Fhbg3b4B/+G9X/0aawz69Bz0A0r54Egb73U4UX0eJg3MYFSWHlwhaLCnM/L0Nh/90tIoefXv+JXwVGX9FcchpSgsc0WDxMR4fAEoLS3VOoogCMFksIdmXGMcy8ty2VEewCS5GZ6Wwh0ZcagbVqMc2P3Vr7HZkXPyoXQUb+7X8ZYnG9Ubz/QhNoam1IOrJvg59TH8ZVMeIzLSKH3i12FrnaIcO4SaNShMs/V0CnFxNozGWEpKBmgdRhCEYDLGnPuaixrXxtKjfnaXt2GUWhmZk8mtBVl4t23Af/wMBe8YG/rMPHQlQ3lndwNvuD2oXjezhuUxKCUA7tbg5zRY+PPaNsbm5ZD/8t+C1h/xXAIVJzD0Gxem2Xo2T3s7Jr2erOxsioqLgzauKIacEvB7UZOykNoruz1WtZLKyx8fRWeOJaB0MiAtiVtr9xCoKodtX1xw+hWcnfiPH8R//CBTAWQZy9S5fNQQ4NGDjSgBP/NG5DLA3tH9rs+SjmOeRF5df4LxhX0ofvmv3RvvAqluF6rX27VcW9Ol2T3DgH7ZvLX0YwYPHowsi8OgBCFaBPxeSCqE9iPdHEnipDqI19a3o7PEE1BgaFoSd+1+Caq6PuU655u8jvauQsmB3VwGIMvI865keZWVFfuTUPw2Fo60UWQ5CAFX9+LKBg65B/H6mg6mFnQVQsKqrQWUAGAGxCkK51JaamLx4sMUF5eIvlWCEEUCAQViUqCzoZsjSZzwpbB44zF0ZhuBQBsjstK45eRKlIYa2Hoez0GdHfiP7MN/ZB+zAHR6TNMX8OHJDpbtcaD4vVw+toC+ltau9gLdIevZ2x7HO1vKmVmYT+7Lf+veeBdIaaqH/jrs2dm0V3b/GTSaKX4/O+64g7a//pX+RUVBuwdJqhrqdVE9Q3t7G+s/XktJokKO/+hFjaEYbDy5rhlFMnBTSgy+HRvwl1/cWGdjmjCdZTEZlDW1UphuZdGQuItaLVIZSOHZj46RlZLMpe0V+LZ8HPSs5+P5fuO455e/QFbDvFexh2pud7F91wFmzJypdRRBEIKkpbmJTRvXMyihmizPxW1RVEyp/PXjePSGGG4xBlA3r0U9fjjISUGauZC3UvKoaOugOC3ApSWtF7VapDwwiOfXuclJTuTq8n0oG1YHPev5eGLcZfzodz8A6jSZv6epqzNx8GADU6ZM0zqKIAhB0tTYwJbNmxmS7CHdW3ZRYyjmBB5fWYXVYuNam4Rv56auD4KDzDR1Hm/LdmrbHAzJjWd2kQncbRc8zhF3Em9sqiA/LZnZVQfw7tka9Kzn47kBUymqb2Lbk6HtTxItRv/ud8SMGMGU6dODMp5YGXKK3R7HnHkL+HDZMnLs5gtfcWG08Zv3KrlyUDGJr/wdVwi3mXjWrWAKMC0ti/I+l/Kb904wol8qs/rpz7u3yKpaO7tPVHNHjB/fihc1PcDA5PPidLmxmcWnTOcj0W7B6/OhKIpYHSIIUSIhMYk58y7lw2UfkGUxQ+AC70HmFH71rplbB2ST/MTvQno6orr8bRYC5PVlb+ZVPLTUz/jioVySUwmexvMYQWJp7TAOVwW4210PL72o6WmOstMBGDRM0LOkpfnYutWJqqpidYggRImk5BTmzJvP8qUfkG7TXfBhDorRzs9fP8jtwwZgeeUfIV1n51n9HrMBfW4fDuTN4NfvnWDq4GwmZCnn2VtEYslJK5VNrXxbasX7wfuaHiYquZzEB6n/RW+w++GHGfz880G7B4liyBfodRKY46DjPP8ZWxLZWa/y1vqjXDGgkMSXwrfNRKmrIufVJ/hmQjK7Ci7n0VV1KD43owpTmDQwE9nZBJ4vVEolHesb4zhW7+BrB9bga28NW94zseh1dHR2YDPHah0l4h0rr+XEyWr0ej1bt2xh9BhxvKEgRBO9DjAnQ+d5LpeNyWZLVSLvbmnlxj5dhZCwKT/GoKd+x6D0LNbmXs+j61RUbxxjCk2MLzIhOyvB/YUl17KR1Q2l1LYZuG3Fv6EjzCfofAWdquD1+jEatU4S+Q4f9nLypAOdTsfOndsZNuyLJ54JgtCTGQy6rkMevvj8cCYxKWws8/De1mN8Y0BfLK/8I7QBP8d/8jiFJ/9BUUYOm9pzePRIC/g9jCtOY0z/FGRXE3gcp79INrCswky7Cy7f+DZeTwRsjwwE0JvNWqfoEYY8/DC2oUPxKQo7t29n2Iju34NEMeQLHE43xJ9jnYQplmVHPOyrdIBUT7/EOL4fq+B7/anwhPwCpaWRwYufYrAsY5q2gJ1uE4+/vRfJZEEFJEnu6g2LRCDgJ93uZtGuFSgRUAgBMEoSLpcbEMWQr6JKeppa2qisrKGuqZWZM2eKFSGCEKU6XH6wnv3NmWJM5r3DSRyu1yHLeorjYnnAfxzlmdfDlPILaquY9K8/MEmvR15wDZscyTy+pB5MmajkIElw6n8RCATIiTNx9Xv/jYhCCIBB1uF2e0Ux5CtJqGoMDQ0+amubqa52Mnv2bLEiRBCiVIfTA9azb1tXTHG8u8fBsUY3UM2A1CR+aPHgfeuZ8IT8Yp6aCka98y9G6fSYZ13OlnaZx989AAYjSPKnz0GyJOPze8lPsjB9zasQCYUQQFUVdOIG9JX0JhN5U6YQP348XoMB67BhzJozJ7hzBHW0KJCVkcZxv54+eueXt8roTKw8qePj/SeZPqCIW9VKPJvXAmi6zeRTioJnxduUACWffM1sBZ0MPi/4/XBq+46WS5K/qD6gkpOVBZouUoscKjJOtxdJknB5Arzz3jv0KSggPz+fQ8fK6HA4sMfFaR1TEIQQSEmKp1weQ55u5ZebkxpsfHAij01HXMwvzmVG0/pP+2xExM90vx9l8QuMAkZ98rUYG0gyeL3g9356D4okTpMFu128Ef2EqlpwuWRUVaW9Xc/KlZvJy8smKyub1tYtuFxOrNYQnTwhCIKmEuPjqNb3IVM5AsoXnm4MVpYc8rPtWBmXDi5hcvVWvDs3ARHyDj7gx/3+qwwGBn/ytU9+Vvl9pz0HRRRrLM1Huts8PTpIskzG8OEkDh6MKklkL1hAhc9HXEoKqWlp7Ni1C5/Ph8EQvK2tohjyBcNHjmb3rp1sa/Awwnz80687DSk8+t5hxhYVcperEv+rK+kR7T7d5+zZrL24JPQ6JULezWvrREUd+w8dx2azYTIZT62YgclTpgCQK/YUCkJUGzNuIls2b6Q9EGAwH3z69Q5TIX94z8MlxRn8pOJN1FUv94wfmZ3ns39bW3JiMhAZq1S0dviwh+PH67FYzJjNJjo7XaiqysSJXfeggoK+GicUBCGUxk+cxNo1H+HUF9CPzxpwt+pT+fOSQ0wfWMx3W9cTeGllZBRAzsXZqXWCc5Lj4jnyqkYrOyPMwF/+EuuwYRh1OqxWKw6Xi0BzM5OndTXsLh4Q/GPdRTHkKwwpHcqmDZ3s8hkpNVdS7zXz2Bu7+WGfVJQ3nkQcABtEBiOyxQKK+FMF2LJjL9MvuYSk5GStowiCoJFRo8eyfq2bPepCBuvXUOnO4Im3Hdwfp0N+8jeII+CCKMYGBgPncdhjLyCzadMxFi26jNhYu9ZhBEHQyKTJU1j70Sp8+mJK9Cc50WnhmfcP8IMUI75X/3bu49mF8ybHJYAk0SRWhmBJSMCRlcX8Sy7BYrGEbV7ReOAMxoybQErBID5szuGhFzdx/7D+KMsXax0r6sjFQ1H9EbHJKALIpKSkiEKIIAiMnzSVuNyRLG+fz+/frOFn2QnI772mdayo4xs6Gp3aI9bYhIGBnJwcUQgRBIFJU6ZhzejPhy15/L/FW/lhXgK+j5dpHSvqOPsNQvb2iDU2IReXl0debm5YCyEgVoacVXZOLtk5uYweM5Yt69ch3fBD0upPkrp+KWqn49wDCOf0js/AFTcs0jpGZJB1KAFRbxcEoUtefl/y8vsyctQY1m5Yj/z9B8mpPEbWB6/2iKW/PcHLASvX33wZIP48wYwiVmkKgnBKQZ9+FPTpx/ARI9i8eTPydd8ns6GSpHXvo7pd5x5AOKfFDoUhcQbWaB0kAiQWFuLXoDAkVoach/iEBGbOX8CMy6/EPGk22y69FTmvn9axej5ZpjkmnvzsdK2TRAbFT1xsDCs+/FDrJIIgRJDEpCRmLriU6VddizJ9AR/feh/kid4N3aY30JGUSkqKKAB06SAmRmb16lVaBxEEIYIkp6Qya/4CZlx1Lcq46exa9E3kjBytY/V8RhM+eyI7/vxnrZNEhPLVq+koL2fVihVhnVcUQy5Q4YABTJ23gLWj56IOHK51nB5NVzgQVBXE7sNTVEaWFuH2eKiuqtI6jCAIEah48BDGz5nH0vk3ERg8Uus4PZpUOhKUAPSMduhhEGDMmCTa2x3U1dVoHUYQhAg0aOhQxsyYzccTFyH3Kzn3C4QzUkuGI/m8OKqrtY4SETobGij/zW9obm2loa4ubPOKYshFsFqtXH71NawfMAG5T5HWcXqsVfp4Zs2eqXWMiBJQoKmpCbPZrHUUQRAiVKzdzsKrrubDSZdCQaHWcXqsN01JXHbVbK1jRBSfT09raytmc3j3bAuC0HPExcez4PIrWDd8plgh0g1vu2WG2uO0jhFREgcNorOzE1MYn4NEMeQiybLM3EsXsnvMHJDFH+PFqNBbKB1crHWMCCKx8uMtXHP11SQmJWkdRhCECKbX65kxdx5b59+odZQeq86WSL9C0Sz0M3pWrCjj+uuvIy4uXuswgiBEMKPRyJTp0zk0+TKto/RYnfYEjj/3nNYxIkZsRgbxt9zCDTfcgD0ufEUi8RTfDVarFTkmFrl0jNZRehzJFotssYIiTpL5hNsbwOX2YA5zF2VBEHome1wc/hg76rCxWkfpcaSkFCSTERDN0D/R0WFAUcBgMGodRRCEHiAxKRmXNRZd/0FaR+lxdKmZqMhUb92qdZSIkXvZZZhiYtDpdGGdVxRDumnyjJnsTuujdYweR+47CEkRxxl+ntkokxAfxxtvvMH+fftwuVwo4s9IEISzmDB9Otv7DdE6Ro/jHTAUvXgHdBqbzUtMjIU33niDgwf343a7xT1IEISzGjd5CgeyRcuAC+XML0LvFx8If175m28iqypvvvkm+/ftw+PxhOUeJI7W7Sa3241Z/GW+YKu8EtMXTNc6RsSZNHoQKjJ1DS2s/3gtoOL1BQgoCvPnz0eSJK0jCoIQQTo6OrD5wn8UXU/3jlfPpVfOBsRJMp8JMGVKHIqSRl2dh7VrV6DTqXg8EiAxZ85ccQ8SBOE0rc3NxCGKphfqvWYnpVlprNY6SATpqKtj/fz5ZI0di3/8eA6WluJubCS2b18MRiNz5s4NybyiGNJNaz9aTWNMIinTLydu1dunOtML53LCYOOagf3FNpmvIKGQnhJHesqIT79y+EQ1Rw4fpn+RqL4LgvCZj9euwZVRQPKl15P07kunTugSzkqWqY5NIj/fgtgm82Wy7CYjAzIyPjn2XsfOnQ4qKyvIycnVNJsgCJFl3YYN6FPziZ08H+uaJVrH6Rn0ehyxSRx+9lmtk0Skqo0bqdq48dNfWxITKXzwQerr6khNSwv6fGKRaDfNmjOXr3/967SXjqP8mjuRTOIUkHMx9B+IrDeIQsh5U+nfN4+yshNaBxEEIcJcuvAyrr/+emrGXMKhbz0ARtHv4ZyGjESnlxGFkPMVYOjQHA4e3K91EEEQIszVV1/NVVddRc2QcVRfdQfoxefs56IbOBJJCVC9ZYvWUXoEV3Mzpn372LJhQ0jGF8WQbvqkycuo8RNIKBlC2RXfhDA3fulpDsakMGCgOJv8vEkyj/7lKQ4fOYrH7dY6jSAIEeSTe9CYyVOwDBrKwVt/LE44O4f1MSmMnzxa6xg9iJmHH36B48fL8YktWYIgfM4n96AJU6dh6D+A6kW3g9hOd1YrPDIDEsWpkecrY9gwNicnU9/SQiAQ/B0Y4h1TEPUfOBBjTgFN06/UOkpE2+jwMnuW6BdyJqoq0dzmBFmPX5F47tUljBo5khHDh1NbW6t1PEEQIlRJ6VDI70f9whu0jhLR9kpGxo4Xx7qfiaoaaG42ADbcbivPP7+TqVOnMGBAMfX1dVrHEwQhQg0ePgJPRi4dk+drHSWiVclGlD17tI4RsRL796fkttvInTiR4htuwP7jHzNr5kxysrKoC8FzkFjLFGTDRo/hvdpaUlMzCdRXax0nMiUkopdEb5Uz2bnvCJ0uLx6Pm0BA4corrsBitWodSxCEHmDE+Al80NhIWnIaaqN4cP0SWYaEJGS5ReskEWvLlkb8fiMulxO/38/VV1+NUWy/EgThPIybMoX3WlsYYf8Ytb1V6ziRR68HWxx7/99ftE4SsbLvvpvUgQNxtLYSUFXmzJ+PPoTbr0QxJAQGDhpEVcNU0t9/QesoEUeXmnHq/xOdp8+ktb2TaZdconUMQRB6qL79CqmYuoDs157WOkrEkXIKTjU6F6fIfDWJzk6VadMmah1EEIQeKj8/n5bRlxD/4RtaR4k4+rx+KF4PnvZ2raNEJJPdjr1PHyZOnhy2OcU2mRDI69OX+ox8JLP4NP+L5MQUdJL4a3dmkji6UBCEbikcMICTfQaA0aR1lIgjp6aL7exnZRB/PoIgdMuA0qGczOonmql+BV1iMqpfHCBxJrGZmSi+8P75iKfSELHHxuIbOUnrGBFHtsdrHSGitXW4iIkRRTRBELrHYjbjnzBD6xgRx2+LQxYNZs+osTFAXFyc1jEEQejhDEYT8tBxWseIOB0GCwZExflMUmfNIiUEx+eejXhHECKjxk/gYEKm1jEiTrveiNVm0zpGxNp/4AhDSodqHUMQhB5u9OQp7Mzqp3WMiFOJTEZ2eN9o9ST791cxdOgIrWMIgtDDjZ44iQNJOVrHiDgnOj2k2EXB+Ux8WVkMGzUqrHOKYkiI6HQ6dBm5YT3iUFFV6n2R3ZjUrwSQxRrcM/L6/JhMYmm7IAjdYzQaUXL6hHVORYW6yL4FIasQ8IueVWeiqjqxVVMQhG6LiYkhkJkX1jl7wnOQTpZRFHEPOhNrcnLY5xSbuUJJVdClZhKorQz60NXeAP+o7cQsS3gUlW+mx1DvVTjo8nFdype3Wdx2pIUCsx6dBM6Ayk+ybcTqzl6oeb/FjUWWmBoXvIfzuJoK2lNjgzZetFHF0jlBEIJFAilEp8pU+uHxDrBI4FHhuzaoU2CfD26K+fL11zVBX33Xm45OFX5uh9hzfFbwtgusEswwBy93Tn0FS6wpwRswyqiqeFsoCEJwyEYTki0WtcMR9LF76nNQia+d9ar40PNMrCnhvz+Lu14oqSpyYnJIiiFbHF6GxhhYlGShzhugxquwrdPLvk4/8xIU7PrT/4GnGGT+N6erCPFcvZNDLj87Onx4FJVKb4A702N4ocHFnRld72L/XtPJUJsh6Ln9FcfxJvYHdEBkV281IT6REwQhSCRVQUoJTTFkoxdGGuFqK9QEoDoAm72wyweXWSDuC+8xU2X49amVwf/qhP0+2OIDtwon/XBPLPynE+4+VSt/zAEjQnCaq3JkP96coYAZcAd/gh5OEg3OBUEIEjXgR5eQjD8ExZCe+hzkO3qAQEk6CX370nLsWNDH7+l0GhzjLoohoSRJyHEJIRl6QaKZd5vdPFbdASpcnWxheIyBGFn60g8AgAafwm8qHEgSxOlkhsYYMEgSLkXF0ebhgCtMxwwqCmNlN68vfp8rF80Kz5yCIAi9koQUlxiSkS+3wJsu+F07qMANVhhlhBjpy4UQgAYFft4GEl2/P8IIBglcKjgU2Buu5vE+HyN97bz/9gHmLiwI06SCIAi9kxyXABUngj5uT30OUt0uBkgevPf+iJY7vxOWOXsSVYMtROIjgBAaNHQYu3MHQgiqXK81uphoN3F3po2vpVp4srYT6SxbLFIMMg/kxHJ/dix3ZsTgV+HfdU5MEhSa9aiAToKACh0BNeh5P69k30YO7d9PQ3NHSOfpiVQlQHNTk9YxBEGIAkWDB7N92CQwBP/TrRecMMUE99nhlhj4cwdn3eSXIsMv4+AXcV2rQHzAEx1gkqDY0FVQ0QH+U8WRUBq7eQVbNm2npcUS2ol6ILe7g87OTq1jCIIQBfoWFXF44DjQ6YI+dk9+DhpzdBt7Tp5k2PfuCuk8PVFDRQVerzesc4piSAilpKWRVjKIg5d/C11qRlDHHh1r5NHqDh6qcPD/qju5PMlMskFmXbuXem+Av9acvdCgA8wyrGv3srvTR7tfYWyskT9WdfBuc2iXDqtuJ7c7TvDoY39BEX8Fv0AiMSlJ6xCCIESBjOwc4ksGseP2+5FS04M69ngT/MYBP22D3zngWmtXweMjD9QG4NFzrIrW09VvZLUHtnuhTYGJJnjY0bXiJKQ62rmn7gB/+O0zwFc0OOnFTCYzMTHiz0QQhO7LLeiDtU9/jl35beT44L637dHPQe2tfEtpZGuMjYzhw0M6V08Tl5iIMcxbZSRVVUNb/hKorapi0/p1jCjbhX7Taq3jRIyGweNYoo/n/h99FxCdlQE2b9/PoKEjsFq/3PxJEAThYlSfPMn2TZsYv3M1prXLtY4TMU5MmM3S+Ex+dN8CILyfREWqdevqGT16BoYQrCYSBKF3qjhxnJ1btzLy8BakHeu1jhMxyoZNYZMxHv9j/w9HdbXWcSJC/z/8gevuuQc5jKexio/lwyA9K4tLr7yKQ4Mn0rDolrAetxvJUvZsYILs5W//+K/WUSJGfl42x44e1TqGIAhRJDM3l7lXXMH28XOp/Np3RKPmUwrWLWWUq42n/vYRooVal+zsJMrKgr+/XxCE3iunoA9zL7+C3aWTaZl3ndZxIkb+jo8oxk3cA/djstu1jhMRpPp6KisqwjqneCoPE1mWmT5nLt5+A6m9/Dat40SMwm0ryPO08/aSVVpHiQipKUk0NTVqHUMQhCij0+mYMX8B7YNGUP7172kdJ2KMWPkGaa0NLHvvuNZRIkJeXiJVVeF9IyoIQvTT6/XMXnApLX0H07TgRq3jRIzSzcuIdbZT/MgjWkeJCIbycg7u2RPWOUUxJMxGjB2HJ6cvLbOv1jpKxBixYxW7dmzHrwS/wVKPo4rtQoIghM6oiZNo7TuAhstu0DpKxJj+0dtsXr8VRYnXOkoE8CGJlUOCIITI+ClTaczuR+eU+VpHiQyqytR9a9lTWUnx5ZdrnUZzjupqwt3BQxRDNDBm4iSOp+YhDRimdZTI4PVwnTXA0/96Vusk2lMDBAKiICIIQuiMn3YJewsGow4U9yAAXJ1cRyfP/kv0UwG3uAcJghBSU2bMZF9WEXKfIq2jRAS108H8GJXOSZO0jqK5ut27Q3L60NmIYohGZs+/lG1DpyJZbVpHiQixe7fg6BDH+QG4PR6tIwiCEOVmzp/Px9OvBIto1gyQsX0tjc3tiLdFCm63aCYrCEJozZgzl51j50GYTw6JVDmHd9Ds8aA3m7WOoqmA14tfrAzpHfR6PRm5eXSMnqp1lMjg9aBvb8Yp3oORnJTIknff1TqGIAhRzGQykZCZRefkOVpHiQwdDuTmBrzeBK2TaC42NoZlyz7QOoYgCFHMarViSUxCHTlZ6ygRQWlvRWlvZcSdd2odRVM+lwuLxcLypUvDNqcohmjo0NFjmBtrtI4RMRba9Tz73xe0jqE5h6ODkpISrWMIghDljpWVY6w+qXWMiHGZ3seL/xVFAIejkwEDBmodQxCEKFdeWYVUV6V1jIgxw26gOitL6xiaa29vZ/CQIWGbTxRDNGS321k3YCK67Hyto0SEmIPb6XC6tI6hudjYGHbu2qV1DEEQopzdbufDaVdAVp7WUSJC+r4tNLe0ax1DcwkJdrZt2651DEEQolxsbCwbxs5Hl5qhdZSIkHvyIK2+3r1EXpJlkpOS2LJ1a9jmFMUQDc2bN4958+axf+RMraNEBLXDQVfLnN7913LMsBKSEsVSbUEQQmvBggXMmD6dXTOu1DpKRFCbGtDpdIBe6yiaGjcukfj4WK1jCIIQ5S677DImTpjA0dHiOQggUF+NzmTq1X1DVEXh8AMPYJbD9yzYu586I4DNZsOfnAbiKDsAVI8LZHHEbktrm9YRBEHoBeITEvCkZ2sdI2IoLidg0TqGxlRaW8UKGUEQQi8lNZXOxDStY0QM1eslZcAArWNoyu9243C7wzafKIZEAklC19uXKev1fDB2IQ0ucZIKqoLVYqG9TRREBEEIPVVvQErv5fuUTWZenn8zTeIkFcCNwWDE6XRqHUQQhF5AtliR43r3imjJbOWtUQtoc7mRwrgqIhJVb9uG3mDA6w3P/bh3/2lHiO2790CY/oNHKus1t3KitZ2H/+9HoPi0jqO57IxU2tvFJ3OCIITe9j17wO/XOoamlFvvpsrp5MEHrwAcWsfRXEaGFadTHHcvCELobdu5CzXMx6lGGu9Vt9DY4cT0pz9RHcZ+GZHKffgwHk94PiAXxRCNKYpCWloagfpqraNoR5b5Z1kTVbV1IBu0TqM9SUdZRTXZOTlaJxEEIcr5/X7S09NRG+u0jqIdg5EnKpuoqmsEYrROEwFiqKlxkJyconUQQRCinMfjITs7G7W9Veso2jGZeXZ/GVVNTaSI0yTJnzoVU58+xMaGp3dV7+4SFgGaGhvJMvbumlTDolsJ7D/GWJeLnz/4e+LtsSSnpJCYmEhRUSH9CrJ73WoRq7W371kXBCEcaqurKaB3/Xz9osM3/QDd4SpGuq38/GfPkhAXS3JKIkkpiRQV96FPHxvQu7YtWq1WrSMIgtALVJSX0VcOaB1DU2ULbyavrZkFw8fyVnoqtvnzsBkMxBhNpJnN1L39NseWLtU6ZtjIej1mozFs84liiMZSUlNR4xK1jqEZfb8SXtl3hKQXXqSyrIz4nBzSR4/GZ7VSIcvs0utRMjOJS0zgu3fcikwvWMqtBgDRUFcQhNDLzs3lUFyS1jE0Iw8fyxuHykj471qqqupIzM8mbeRAvNZaTkoqO/SrISuJhORE7vzefKBF68hh4EaSeveHNIIghEffwv6c2L9f6xiaMQwfzwf7D/N/87LA18igeTl4TUnUt7lpbOvkwMlGaqZMImP+POJWrebgm29qHTnkGg8eJD2MfVNEMSQCyDodcmIySnOj1lHCbnn+cLK376SmrAyAtooK2ioqTrtGkiQs11zDw41NGKxWfvzDO5FRNEgbPgG/D0VRkHt5EyVBEEJPZ7aAzQ4dva9P0ct9RpK7vZzqqq5tQq1llbSWVZ52jSTLGK+fzYN1z2C12/jRTy4FormfRgCv142qqkjipDtBEEJIkiT0sbFgNPbK/olv2HP4+sBE8NV3fcHdhtHdRjaQHQdDBxsAI4fcCSzpHEni5EmYnn2Omu3btYwdUo7qajwuV9jmE09akUBRkG12rVOEnWH0FA5VVtP4/PNnvU5VVSpffhn/r39N4rZt/OwXv0Mhuo/fHVjcj0MHD2odQxCEXkDx+5Di4rWOEXbqzMspr2ug7sWzLz9WFYXK599H+fVL2LeX84ufvYCiRHdvkX79Ejl5skzrGIIg9AYBBTmm9z0H6WdfQWNrCwXGc30YrlJkbuCHE81MH5BI0+WXkTd5clgyakFVFBybN1NbWxuW+UQxJALEJyTgLOh9DXNeII688nJ8F3B8X9Ubb5C/fz8//9UjvPz6+yBF5+KmtNQk6urC80NAEITezWqNwTNguNYxwu4pfQJ5R1oIeM+/Z0r1y8vJ3FfPz3/2LK+/fJBobbial5dIeXmZ1jEEQegF9DoZioZoHSPsnmnxcufsYlDPf7V7ibWJa8fnUzFtKv3/+lcyhkfnvdtQVcWBPXvCMld0Pkn2MENGjOSjXdsYqHWQcDGZ8V9xK9Wbd5O8YsUFv7zqnXewrVtH+xVX8NM9e0mIi2P6jEsYNrhfCMJqQ1IV2tracXZ2Yo2JzjfbgiBEhqFjxrBl52ai8y3VV4ix0fr1u2jccwTfii0X/PKaN1dh/ziR5sv9/O/OfcTbY5mzYBKDBkdP42tZ9tDU1Izb7cJsjp7vSxCEyDNk5Cj2HtxFX62DhEuMDeeiW2jfsR/7J9tjLkCBsZH/nZvBpjoDH8yfi+3yRRQaDOy8/4EQhNVGw65dKPX1eDweTCZTSOcSxZAIoNfrkRJ6QQM7WSaw8Eb+faSawNot5K5bR11NzUUN5Wpupuqf/yQlP5+k/v15x+lk2JB7QY2WBqsKl86ewrvLlrPwskVahxEEIYqZzWZ8Sb3gGFW9ns4bvssT5fWw8QCZaw7T2Nx6UUM5G5px/mMxqX1zSeyXx2Kvj0GDrwTcQY2sHQ+XXTaAJUuWcumli7QOIwhCFIuLi8MVG691jNDTG/EsuonnDp8ktbyMny3sA+7mixvL52RMIoxZ2AcsCfx28T7i8vJoKy8PbmaN1O/di+Xpp3nPZOLyq64K6VyiGBIpTGatE4SUd/Y1/Le6FXXXUTK3b6di5UrqgjBua1kZrWVlUFKCgq4HnzYjs3nHPhIT4unXN5/q2gaSkxIZPWoUO7ZvZ1iULoMTBCEySOboPkq19bpv8++GTjhQRdbWcio/2kowWpa3HDtJy7GTMOhyFCUGWe6pxRAT69dVk5WdSl5eMpWVLaSnxzFs2BB2797BkCHDtA4oCEIUk6J8BZrzspt56WQdcVU1/HhBP/TOuosvhHyeuxXcrZh0UDB1Kjufeab7Y2ogddAgkr/5TeICAeTaWnT5+Zi9XpL692fX9u2UhvA5SBRDIoTfGb2d6XdcehsbDx4hbdky6rdvp+LcL7lgmceO8cBPf8H3vvcdstPiQjBD6Lz61lJOlFdSnG7lUKebd992kWCWaXd6CAQUrCm5ohgiCEJI+To7tI4QGno9q264h23HTpLy7k4a9hym8tyvumDpJ1r56QN/5/t3f52MDB/0oBPPXnhuD+UVNQzNkTi8w01rh5+kGIWWDi+KAvaMAaIYIghCSPk7o/Q5yGhk89xvcLCygp/MzkZ2t4AzGB8Hn27e8Gxe7nQz6Y9/ZP0DDxDoQSfz5Pztb9Q2N6McPcoRwK/Todu6FScgHzhA4dixohjSG8hR2hei6ev3sGPTdsx/+xv17aE7trH63Xfp73Dw5B8fY/41VzN25ICQzdVdDpefhsZmUpKT+fs//onP6+OBabGAl66exp/8XbCAJLPGGz29UARBiEy6GJvWEULi8J3/y4FdRzD8+V0aOs+/WfeFqn1jFf2mjeGxB//BTd/9GgMHGkI2V3e1tFhpb/dit1v4xxOvoKgK948/1PWb2V+4WGfmI19u2DMKgtC76G2xWkcIiWNX3UFbYx33TokDd0vI5imyNnPvwoH89rWtTHrkETbcfXfI5uoOSZYZ9bOfYUhKQlYUNjQ2Uldbi+9XvzpttaYfMAIxqalkLVoU0kyiGBIhdCYrclwCSlvo/qEEhSyD8oVPvHR6Gq64jd11Dbi9fmYWZBOzcz3LMgeyb91mUt58k5YQFkI+UfHRR6T27YvLE3nLlBVVZuXajWzfsQsUHxnxZlodLu6aW4rZ3wrOpq9+oargd3egKAqyLA5/EgQhNCR7HFhjINJXKX7VPcho5sTNP2B3XRM+RWV2egJx2z7mjX6jObJtLwkvb8IZwkLIJypXbSKlzxx8Ph8QWcUQRYnhww8PsWPbXnR4SbFLdDi93D0vHaPnJJzpjyfgxud2oKoqkiSFNbMgCL2HnJgMej34I3y7+1fdg6wx1C64if31jfgDAaZnJmM5tIslyf2oPnCIH89MB2/oV19aPTWU5KUgRdj9B6DfvHmo06dT5nCw3eVCPnwYv6pS1NRE3datnGnDkLOxEXeIVw2JYkikUALIickRXQypu+wWXj9ykjnDSxnUUokzOQv92iUcGDOX5R9voE9LC0bg2YpqlKQ00nbtxvbSS7S0tYUtY1NcHFMmjgH1/I9KDB2ZZ158A5PRxI5du7h0wgC+dUkBdk/Vqd+3Qfuxc44yKl1h0/p1jJs4KbRxBUHotWRFQUpOQz15XOsoZ7Tvph/wflUjC4YMYtDJ/XRk98Gy5GU2zbqWjzbvoqA5gKzCU1WNkNiPjF0nMb38Ia2O8BV4XEk2hg7NhaB0JOkuM//650fE2Kxs3babyydk853pMjbn0c8uaTl3s70RqTVs3bSeUWMnhDCrIAi9WsCPHJ+E0hj8LSTBcmTRraw6XsWiUUPJqzqKKyMP/YrFbBk9l+Mny1gwpgBJknljw2HaSeCKQjtFQwJhKYR8or5Dwbjto7DNdzaZo0bhXLgQi9HI9qoqSo4fJ+voUU4uXfrpNQfOMYaqKLg++oht2dmMGDMmJDlFMSRiqEimyG4edKLTTb/OTtZ8sJxlNhtQgWrJxrZlO/YXX6Ss7vQfYNUaZDTZbMgSoGow+ees3biTtes2MG5gHrkJMguLhmN214DnwotdsZ5qXJ7oXMIuCEKEUBUks1nrH51ndbClk0EuE8uWfcy7sWaUit2oWaXE7TpMzLNrKGs8/edr1RnGCSWTzQq4NJj582RWrKhk4/odTBuaQqq1kUVXx2F0bjvzCpCzSPAdoMMptmsKghBCiooU4YdJ7Kmq456rJvL6hxtZ3hkgULeXztgCSjqauHNCHAS6Tsj87rhPtrvXh799lE5Pw/79YZ70dOb4eHIffJDD9fX0d7txHT1K8ZEjVK1ff1Hj1bz/PvbZs4Oc8jOiGBIhAqpEoFaLt27nJqdloMy+mrWrNlJ8+DDu99779PcscXE4HQ7ULy4Z04i/uZmmFgdJcaH5gXrwaCXNrS2MH1nKl3/CySDreOGVxbjbG7l/QV9wnDo6uDs7d/xuDPioqa4iIzOrGwMJgiB8tYCkQ6mJzHsQOfm0X3YjO9ZtY/DBNrzLNgBdHZZMcbF0tHeAGhllHFdjCx0demwhql/v3evG2elm9JhUuvpMfZ4RsPDMv1che5v4n3l+cHzc9Vvd2SXkaUZS3NTX1ZKalt6NgQRBEL5aQAWlqV7rGF9Jl5WH45LLOPbRRkyeOm4a/rlnDJ0JAh4IaJfv8yS/h36LFlG/d2/QxzbabAx46CGQZY4+/DAdtbWn/X5SURGpAwfSNGkSdZWVZH74IYe3bev2vM1Hj+J3u2lsaCA5JaXb432RpKoR8g6iF6urqebEymXkv/mU1lEAkAeN5lVTMp0BlRijnpqWNgwuF2nr11O7ZYvW8c4qf+ZMqiZO5Kc/+W6QRpRZsuwjDh85QiCgkGwzkBZnZufJNmIsZuLi7JjNZiorqwgoCn5/gDFF6UzLcIAaxAKR0cbmjkwSM/vQvzhym8MKgtDzVJaV0bDiPfo/85jWUQDwj5/Gf2OycEkyMXo9NW0OTC4fSR8dpmHnuRbVait/wWRqxuZz/0/nBGlEI4tf38OJsgoCAZU0u0KcRWVftYQtxoLdbsVkNFJVXUsgAD6fn8kDrExM2gVqEN+dm5JZ3zGR9PzB9C0sDt64giD0eieOHKZ13Uoy3n1W6ygAqKOn8ZpqxS/rsOp1VDW1kBZv4dtTs5GDcRxuCDXqMnh+7Una7rs/KOMlFRVh+853qG9vR5EkjC0tyD4fnvR0zLKM3udD1ulo9/tRZRlfIEBGQwM1f/87SiB496CEPn3I+9//ZdD48fQrKgrauCBWhkSELZs2MWbdu5FSVOQZfSIlfoXaJUsw5udjrKykat06as/9Us2VLV+OcfhwmtpcJMWd57Yj2QDIoHhO+/LRshpefOV1Jg7I4geXpIPfA552wM3sAhvV/ngUvQWPx83VJVngaoGAF2gL/jYdbwejzcdZdixAYVGJaGQnCELQ7N65g6nvvRgxW2T+ZslkqBJH5ZK1mPpmYSirpmbjbhq0DnYeypasRR6YSkeHDZvtfPeJJwASfKGF3P59Xt58412mDIzhssmt4O8ET1ez7Uv7mKj0F4Jeh8/XQUH/DnDVQeDUFp1g/8f0NDLevJQPDhlEMUQQhKDat3cvo9e8EzEHkv/HZ+I7i8YS66uh3m0gUZ+K3t0IEV4IAUgO1OLzexl+111s/8tfzus1pTfdBMCu//7306/pzWZKH3mEbfX1GA4cwL5+Pa6mJtqrulaQGm02bHPnYo6Lw+f1Iu/eTfPRoygdHSHZotpy/DjuH/0I3T//GfRiiFgZorHd27Zi2raWuGWvaR0FADkjh/9kluL66U/pqX81CubNo3HSRH5097fOcIWO8up60lJTaWxq5v89/lfSkhIIIGGxdC19CwTAZlS4bXxyV5EjQrRa+7DPEcekKdO0jiIIQhTYsXEDyeuWkfjW81pHAUAuGcLjeSNw/fQ/Wke5aAWXT6d5Qh/u/tGZfk7HUF7uJS0tkfr6Vh7/89OkJcURQI/FYkRRVFRVJsHi4xvDG8AdOUvHG6yTOBEYxJhxE7WOIghCFNi0di1pO9ZgXvPeuS8OA+OQ0fzbnMo9401aR7lo9fosXltbTuNP7vvK38+bPJns66/Hpii4bTbe3ryZBLsd2WBA8vnQm0zozGak5mZ0L79Ma/m5G22Hy8B77iHvqqsYPX580MYUK0M0VlNbw9CP34+IT+QMRYNZnDEAy8lKnD20EAJQsWIFxokT6dpRfnqd+fGnXsDhaCcvXkero5OE2Bh+fuVArIFWsKWjmOKQ/U5wNoOvM6IKIQDxrjKSDEXs2LaFYSNGaR1HEIQerqGhgf5L39A6BgDy8LE8WzASS1m95i1Iu6Nq+Ub0Y/L5qnvQY4+tpbPDQUGih5Z2F0lxZn61yIDZfwTs/VBM8ci+VnBWg7ete/2mQiDFtYE6XRK7tm+hdLi4BwmC0D2tLU3krV+udQwA9ENG83JCHoOTTWjfCPvipdKER5LQm8343Z/dRIw2G6Zf/IIKh4Oabdvo9PuxqCoFmzfTfOAA2RMmEJuTQ0d1NY0HDtDREHnrMQ8/8QSW/v3ZZTRSOnJkUMYUxRANNTbUY68uR3WG79i/L5ImzWatIYEJ/fJ58N3llLYcpOWpyOhdcrH8Hg9SRwfIelBObzBnj41h3tA0CqRKuprNAYHWrv/bUYvcEeGbgVSFYg6xqkX80xUEoXuqTp4krewguLV70+ddcA2rLMlMLsjmVx98xPA9ldT+513N8gSDt6MTyeUGYgDHab8XYzFy9Ug7mf7Nn33Rf+r/th9F5igRTfUzSHmPFc1f0zqJIAg93ImjR8isOQF+n2YZ/DMWsVmyML4gn1+89T73TE6hQB85q/Euit+NTlJJHzaMyg0bPv2yt6MDSyBAv2PHOPzccxjouv18svnn5Lp1WqS9ID6Xi73334/51VeDNqYctJGEC7Zz8xaKO7Stuv290YdS38QTr7zFyOZmyv/+dxS//9wvjHDKGU63yUhPZ/+JyD3D/LzE56IqPf+/kSAI2jqweydFNWWaZnjMIeGr8/DnxasZ1ShR/vRbqEFsuqaVrm2mX3yLJZOekcqBivPtJRKZ1ITBSKq4BwmC0D1HDx4iq/6kdgFkmaeqHRT2S+GNnVt4+NapFOjrCH7TpfALBALoDIbTvma02Uiw22k19dwtQAAFc+ciBfEUU/Hxsoby+/alsvoowT8k6Pw0X3Er3u0HqHz5ZaTKykj/POq8ZUyeTFOM9QurQnQ89venSbebuKZEDf+538HkbEFVrVqnEAShh0vPyqYmNZtUjeY/cvMP4dBJKp55G11dI8c1yhFsmTPG0GwyAG2f+2oMf/zD2+Qn61jQ50iPvgdJrloURTRRFQShexKSk2hPzsR87ktD4viiW0mormNgTBMDx8eBq0KjJMFVTwqyXEP5mjWffq1g5kwqJ06k48QJWj7XKLUnajl0iFiP59wXniexMkRD/YqLqTPbNZt/Y30rhQ0NtFdWapYhFCr69uX737vz01/XNLTyi988yuyBSVxT5IWevqpCkvArPb9qLQiCtgYOHcZxi3b3oI9rm+lf5aKzrlGzDKFQlh/Lj35yy6e/Li9X+fn/PcflQ7xckb8dFO2WhAeDqqr4ev7iHUEQNDZizFiOylqVQmD9iUrumF0M/ghrztRNL6w5wqjPFdyH/OpXHBkxguStW2l85BECvp59D5IkCXS6oI0nVoZoKBAIgEujfiEmMw0uL9KSJdrMHyKyToc+PR293HX0bIvDzf/7yxM8dM1gZFd0vOE+4kqkpER8KicIQvd4vV6MWvULSUiiA4mOd9ac+9oeRGc0oEuJB5wANDQYeeofz/PgIhU6T2iaLVj2dBQzfPgwrWMIgtDDORwOLH7vuS8MATkjB0VvwOjQcJtOKMh62t0q9Sf2ADDil79kY0cHaW+8Qc3R6NgDEHfllYwL4mkyYmWIhurq6kjyOrWZXFXQ6XSndRmOBhljx2K2WjEboK3TyyN//DO/umpg1BRCVGsSh5pU8vMLtI4iCEIPV3HyJJmudm0mV9Wue5BHmzfCoZI5aSSxsTb0+lYaGkw89uhz/HJhADqjYwWmElNApTOZ9PQMraMIgtDDlR87Rpaq0T1AUdDpZFB78J7Fr9BhTMUeY2X3c88x+uGHWe9wkPrGGzRHSSEke+JELEVFJCQkBG1MUQzRUHtrCzY0Wmvq9aK4nBTMmaPN/CHSVlTE9FkzeOG1Jfzt70/xyytL0Huaz/3CHmJdQwKXzJipdQxBEKKAo7WFWK3eCLY243c66Ttzgjbzh0h1jo2rrlvIM//ZzjNPv8GvFqnIrmqtYwXNmsahXDJjltYxBEGIAm2trcSo2jwHKXVVKD4v2LM0mT9UXlx9iFkjRpH8+9+zo7OTjLffpiVKCiEAKXfeydxLLw3qmKIYoqG8gj5UmWI1m1+RZDoqoqNZ0CdSGhr41YO/pijVyH2XxEdVIQSDBZ/Ogs1m0zqJIAhRoG9RMSdi4jWbXzLoaS2r0mz+UMh36/j+9+9neJafH40/juyKou/PlISit2GxWLROIghCFOg/YADH9dodCCDpDOBxnPvCHuTSiQP56eOPE19ZifLIIzQfPqx1pKBJKizEHBeH0WgM6riiGKIhi8WCkqpdRVKVJHydGvUsCZET77zDHQsv5e01uyn3xGsdJ6iqyCQ9TatzHwRBiDZ2u53O9BxtJtfru+5BTo16loTIsZc+4NuXX8Pra05S4e+ndZygOhEYTG5untYxBEGIEqlpaXQkpmkzudGIoqoQ6NnNRL8o3X+SH91xCwcNBlJmRdcqvozrrqNf//5BH1cUQ7RmtiLHBW/f0/kyjZ9OwOulft++sM8dalu+9W1GuTy8tb2O1bXanZQQbEfrXQwcXKp1DEEQoohqjwdb+FcoynMux9fhpOV4dK1OBNh220OMDiTyylYd65qHah0naE62WSgsKtE6hiAIUURKSAajKezzmmddDn4P+KLrQ2GAibHHuffmObSMHUP6N7+pdZygkXJy6FtYGPRxRTFEY1aLBe/g0WGf9wNzChm1tWGfN1y2Pvoovkf+wPajDVQrKVrHCQopypo8CYKgPbNBj3fkxLDP+5IukZya6Grg/XlbfvNPdI9/wJr9Lpr1A7SOExxqdH2CKgiC9mRU5CHhfw56udXPjdOi92RGe8su7puTSVVyMtkzZmgdJyiUEB0JLI7W1djoiRNZXFXBxKw8lKpyAPQDh7PYnouESnZiPGMctbjXr4IgHD/lmn01r1Q1461rJOb997s9XiTrrK+nMCGeimY3mclap+k+1Sh6hQiCEFyjJ09hSW0tM4/sh1P3IMZM5rmkvhhlmbykBMZX7iew5kPwdf8e1HDN7bzc4MTf0Ynp/Q3dHi+StVfWkhUbT02Hi0Sz1mm6TzGEfxWrIAjRbcykKSxvbmbcyaME6msAMIyezGu6BMx6HfnJiQypOYpny8cQ8Hd7vtZLb2RJdROy30+63ATR/Dmju42s9BT06elaJwkKW4i+D1EM0Zgsy8xasJBlsp6xx7ZibG/h8YCdkooqvK2tHDUa2ZqSgnH4XFBVFJcTPXBZ/1xsb/33K38wmMZMRjJZ8Jcfo3PYRF46Xo3FaERCpbmsgeLDhzmxfDntbW3h/4bDJGP4cGK+eTvNzk6uTzOj1aE9QWNJxNdh0DqFIAhRxmAwMG3efJbr9UzbvByjx8VDuhQGV7nxdXSyz1DP+hQ7ptk3gQqqy4UOhWtzUkl49nFQvvxOUjdtHuhklLKjNE+YzfNVzVhNRtRAgLbaDgr3N3Piw/V0OqJvefInssaUYr75Ejp8DgZaD/X4N9xqTB5+t7gHCYIQXBaLhYkzZrFG1jF278cY9Xr+2KLyvSuK8HrcHK5o4j+qjcCoBUiA5PMgqypX5KVjfPM/XzmmaeJMUBUCleW0jprKWydrsegN+AN+fHWt3LNoCLSUgz96VycSm8kLW9pxtdXR+OqrWqfptqxx41D1oSlbSKqqqiEZWbggiqKwavkylq1cRd+6Oqqfeea03zfabOhNJrLHjsVgs3EsPx+f1YLJbMGo0xHw+5BlHZIs09raiiEQwGiz4W5pIWv7dqS4OPRGIxXvvos3ypqmflHxFVdwYGAJc0YWMCbZCUrPX9pboSvAk1hC/6LoXdInCIJ2FEVh1bKlfLBiJf0q3dS+tOy03zfGxqA3m8geU4rBZuVwnhW/WU9MbCw6JAI+P7JORgXa29swKhKGGCu+1nYyNp1ETolHJ0H5krX4XVH8BhQo+dql7M43sGhsIiPi9oPS/RU1WjsiTcZSMIcc0UBVEIQQ8Pv9rFi2lGUrVnLX/CHkGZtOv0BnBFkPsen4VR1/W7KbdqcPm92OLAGqiizLKKqKw9FOrMUEOgNGyc+3puXR7JYxG2Rs7mrQ6DjfcHHHFfGr/66mb0sLdc89h9/j0TpSt/X/n/9hxg9+QGpq8A+SEMWQCLNz61ZOfvQR2++995zXpg4aRMvx40iShMluR/H78Xs8+JxOFH/3l5L1RHqzGf3//S+3T8klUWnQOk7QrG7LY9q8K5AkSesogiBEsa0bN1G1ags7/+fxc16bOriI5qPlyDodJnsMAZ+fgMeLt9OFGojuN5tnYrTbUO5dyF2XqNh9R7SOEzSrXYuYNvdqrWMIghDltm3ZTKLSQJ7v0LkvjkkBZzPIMuhMXUUOJXDqhJhe+nirt/KrJVWkrVxJ3fr1WqcJmuHPPcfCG24IydiigWqEGTpyJIb+/em7cOE5r63fuxef04m3sxNHTQ2dDQ142tt7bSEEYNQvf4ndJEVVIQQkJEu8KIQIghByI8eOQVeSS/6sczdVrd9zCL/LjbejE0d1Pc6GZjztHb22EAIw7Fd3kh4XXYUQJD2YRL8QQRBCb8So0RxsVAjYMs99cWdDVwEk4ANvB/hcEPDSawshwEElH2t7e1QVQow2GzEpoTsMQxRDItCsefOIu/56rWP0SHt8Xm6fGWVbScxxSD19w7kgCD3G7PlzSbhpptYxeqS9zlpunmTUOkZwxWRFxXZTQRB6hqmXzGBDfYzWMXqkd1ZuIWnbNq1jBFXqoEEEvKHbbiqKIRFKionBFBurdYweR2cwoPc7tY4RXAZLby5yC4IQZrIsQ6wV2SAaZl4ondGA7GvVOkZw6WPEykRBEMLGaDTS1vPbXGhCrzfgqKzUOkZQWZOSIIT3IFEMiUBLliyhc8UKPA6H1lF6FIPVCno9uJq1jhJckvhnKghC+Lzzzjs43t+M4hOrAS6ENTkB0IGrVusowSXptE4gCEIvsnjxYoYlubSO0fMY7aiSRNvJk1onCSrZYCCU5XjxlBWBDh8+jEUX3jcfKuAM0ZFF4TL0W99C8ThBia6eKY1eM/a4OK1jCILQSxw+fBhLmB+Au+5BYZ0y6ErvuA7J3x51JxVUORNISc3QOoYgCL3E0aNHu06DCSNFUalt7gjrnMHmiC2ks6aGaDsbRde/P9n5+SEbv4e/9YhO99xzDy8/fu5O/herw2BgV3IyOkUhIEmUNjXh1OtpMpspaWn50vVNZjMHEhLQqSqKJJHX3k52BB7PezwrkxvGZ4NSo3WUoDpQ3cGkBSO1jiEIQi9xzz338PKjfwvZ+B0G2JkMegUCEpQ2Qacems1Q8uVbEI1mOJAAOhUUCfLbITvybkHst3u4ZZIVouzk4CP1OqaNK9U6hiAIvcT3vvc9tq1czIT40Ixf0dDOo69vwmo04Pb5ufuK0dQ0d7D3RAO3zvnyz7rdx+t5+oOdmI16vP4AC8YUMn1YfmjCdcOTb3xM9v79VGgdJMgCmZmUDBwYsvFFMSQC1dbUQHV1yMavsVpJdTopbGujU6+n02Cg1mql0WymT1sbJuWzZp0qsDM5malVVehOVRorbDYA9iYm4tHp8Op0DGtooDomhkazmYAsU9TSQpXNhsNgIN3pxK3T4TQYcOl0JLvdpDmdHDu12iHe46Gwra1b39OQr3+dQ24XqUqULU8GYkx6HA4HcWJ1iCAIYVB+ogxDTWvIxq+xQpoTCtu6iiAdBqizQoMZ+rSB6XP9olVgVzJMreoqhgBUdN2C2JMIHh14dTC8AapjugonfhmKW6DSBg4DZDjBrYNOA7h0kOLumv9YXNf4CZ6uLN0x4ntfY5+3E7v7ePcGikAGXQCPx4PJFN5PagVB6J2OHztK//SYkBWW1+2rZHRRJtdPG0h1k4PKRgcbD1Sx42gdV0wsIt5m/vRaRVF59PVNPHn3XEyGrsfm5dtPAPDXt7fR1uGmtdPD/deN46NdJ9l5vA6Xx883Zg1m1c5yyuvamDAwm4Y2F7UtHdS3djKsbzpjS7J4dc0BVKAoO5Hrp3XvYd+ZVIq7fSkVy5d3a5xI5HM6URSlq59ZCIhtMhFo386dKHv3hmz8vqcKD1tTUjiQmIjF7yfN6STD6TytEALg1umw+v3oVJVGs5mNaWkcio+nzWik1mpFp6rIqkqT2UyFzcbo+npG1tezPzERgD5tbeQ6HLSaTIysrye3o2sJ2oGEBAyKgkFRaLBYuv091Qwdwten9iPqOo1akynv0ItCiCAIYXPs0CFcu46FbPy+pwoPW1NgfyJY/V3FiUzn6YUQ6CpiWPxdhZAGM2xIg0Px0GaEWivo1a7fazLDSRuMrodR9V3jfjJXrgNaTF1fzz21Cnp/QtfKFKMC9d2/BXE0x8zt02KIuntQbAF1nmRRCBEEIWzq62pIMYTuMISrJxcjSRIPPf8xT3+wi/SEGMYWZzFpUM5phRCAJoeLtIQYTAY9O47W8sDTq/jv8j0crW5hw/5KTEY9RoOOXcfr+WDrcX5502T+78aJ/PO9nQBcOamYOaP6criyiZ/dMJE5I/sC8M/3dxJjNhBrMbL9SPc/yP374o3k7N3X7XEiTc748VgLC0NWCAGxMiQiyQYDJ9esCdn4hxISyG9vpzAQwKnXsz0lhf6trV95renUNV5ZJtntJtntZmVWFipg93oZ2thIg9mMUVFQT3X6/fxbQYOinPZr6dTqElWS6N/SgiUQ4Ljd3q3vZ+C113LU5SRTqo+696F4O4iLE3u1BUEIH9mgp3L99pCNfyiha6tLYaCrT8i2FOjf+tXXmk5d45W7VnSkuGFFFqfuQTC0satIYlRAPdVhTeWzW4FBOf22IJ/6hSpBUQtYAnC8e7cgSr95DQc8nSR6o29VCJ5W7LE2rVMIgtCryOAI3Urv/y7fw/wx/bhu6gBqmzv4zUvruWH6oK+8NjHWTE1zB+1OD8P6pTOsXzq3/OFdFEWlT0Y8P7pqDNuP1BIXY0I9dbdRVRXl1POOzWIkEPjsWUgnd92oAorCTTMGkxJv5fWPD3Xr+/EmD8HZ9iHl773XrXEiUWddHTkxoT1mWRRDIozD4aB53z68IezJkdnZyZa0NPSKgv/UlhaL309VTAy5DgeH4uMZ3tgIdC0dGtbQwMb09K6eIadeH+/1IqsqW1JT8eh0jKmtJcfhYHNqKookMbC5mcpT22kMqord62VbSgodBgOZnZ2UNDezLTUVnaqS0Y3vVdbrqR8zhuuGp4BaH4Q/nQijKmTY9axZ8xGTJ0/ROo0gCFGusbGRll1HCHhDd5JMRidsSetameGXTxUl/FAZ07WK41A8DO+6BSHTtQVmY3pXIUMBsjoh3tu1ImRLatfqkbG1Xa/dnNrVV2RQ82fbaQwqxHm7ii4OA2R2woBm2JbaNWZmN263erOZY32t3DxOAiXaqvGA4iHFFmDD+o8ZN36i1mkEQYhy1VVVpOg7CeWnmxMH5fCrZz/GatbjdPu5aeZgUuNjWLlzC3NH9+W/y/dw37XjANDJMvddM47/eXo1BoOMP6AwZUgu/bMTMep1/PLZtbQ43Pz6linMHtGHn/93DT5/gDsWDGfFjjIAYixG+mYk8PCL6zhZ387kwbl8c94wHnphHSaDjomDci7+m5GN/Pn1TeTs3090nSHTxdXSgs7pZOP69YwdPz4kc0hqtLWc7eFWLF1K47//zYGXX9Y6SlDtSkrCL8v4ZZkhjY1YAsHptj/2D3/gCB6+O9YKqnLuF/QwZfpCXlyxmysuX0TJgNA1DxIEQQBYvuR9Gv7xNkfeXql1lKDaldRVePHJUNrYtSIkGMb8+SdUqDXcPuQg0bc0EQ7pZrF4XQ2XL1pIUfEAreMIghDlVq1YxuTUdqS26Hq0f+yNzTg9PjrdPn5w+ShS44Oz2mG3v4gP3/uIzj/8IepOkQEo/f3vOaqqLFywgKKSkpDMIVaGRJiAz4erNvqagJY2NQV9TEtiItucndw3Lw9cwR8/Ejh8MnfcdC0fbdktiiGCIIScqip01jRoHSPoSkNwi7BlprKttZL/md0Bzuh7E4qkwyvZuPX6+WzYeUQUQwRBCDlJkpB8oesXopW7rxgd/EFNcbz94RbiXnstKgshepMJU3w8CwYN4sixY6IY0pv4PR6tI/QII3/6Uw4pnchRWggJ2LJYtnw/gzsk8vO6sYROEAThAgQ8Xq0j9Ail99/KSeU4OEN3+puWfPYhfLD8GEMGmcjPy9Y6jiAIvYXi1zpBj3A4kI2xfhstJ05oHSUkiq6+mr3NzSh1dWTnhO45SBRDIowkSbiam7WO0SPs8nq4dWYJuKJrKd0nmv0WcnNzmTN3ntZRBEHoJSSdDldLu9YxeoTtjip+MMcOUfrHVeeOo3//FObMW6B1FEEQeg0Z/CE6UzfKvLlsI5lHjhCdpRAgL4/S0lJmz54d0mnE0boRRmc00nYyOh/ug2n0vffi8Xmwuyu1jhIyKTHgcrnYvmWz1lEEQegldEYjjuoobEYdZKP/99vIeDE6dmsdJWSyExSamprYuU3cgwRBCBNJEsWQ81AfOwR/RydlK1ZoHSVkLK2tVFRUsGNzaO9BohgSaSQJv1v8EDiXwylJfHt2cVQ2Tf2Ex+kgOzOD/YcO09LSonUcQRB6AcXnQw1Sg+totj/GzZ3TzVF9D3K11ZOXk8WuvQdwOqNvD78gCBFIbJE5L/95ZwOZW7ZEZa+QTzSUlZGXnc3OffvwekO3fVdsk4kwKmCOi8Pd1qZ1lIhVOG8elYEA8b7oazT7eRvrLEybPQOdTqd1FEEQegnZoEdvMeN3iaL8mQy4dh5H/R6srmNaRwmpjU1FXDJnFrIsPjcTBCE8VEkHkhzVhebuUlIG4ulcTcXq1VpHCamEa69lxuzZSJIU0nnEHS7CpCcnU/j002SG6CzlaKDMm8v0IZnR/4PSYBGFEEEQwiolIYmS535O6nBxcsiZtI0rYNHoWKLxKN3Pk0yxohAiCEJYpaYk81Z9HgFTotZRIta/lh0gpxe0VLClpoa8EAKiGBJxBg4dSnFxMfHf+Q6ZY8dqHSfixOXmUt3uYHhS9H9qGWeSWLVqFR+t+pCAWLYuCEIYDB4+lMJ+/Uj+0bWkDRfHeX9RcklfmpwOiq2HtI4Scja9j1WrVrFm9YcoSpR/+CAIQkQYOLiUgoIC3q+0oVqStI4TeWLSaGhpp2bxYq2ThJza0cGqVatYtXx5SLcDSWo0bzbqwbxeL4tfew3Xs89y4oMPtI4TMUY/+QQO2cnVxb3nr+2R2Ik0OtxMmDhZ6yiCIPQSbrebd95cTMc/3+fkyo1ax4kYw5/6H1R9JZfm79c6SphI7I29E5dfZvSYcVqHEQShl3A6nSx9fwnzC/zonTVax4kYq5qz2b9sDU1//avWUcJC1uuZvnQp5thYRowaFZo5QjKq0G1Go5Err72W5DvuoPTBBzHabFpH0tyAr32N9eVlXF3au/4sHA0VDBg4SOsYgiD0ImazmcuvvoqkuxYx5OffRm8xax1Jc4Nvu4otJw5yaf9qraOEkUpn0zFKBohVQoIghI/VamX+pZexvCqGk4ZCkMS2cVfCIFat347rpZe0jhI2it9P9b59DBgUuucg0UA1gul0OuZfdhl78/LQl5Tg3ryZqpdfprW8XOtoYTfivvtYpwb4yfxi8DRpHSd87Fls3dtA1gAXJGgdRhCE3kSv17Pw8svZlbMd/eB8PBsPUPHyctoret+ndKN/dgcfear46XwLeFu1jhM+iaVs+aiNfsM8xMZqHUYQhN7EaDQy/9KF7N65nQ+bJUoSA2RJdeDt0Dpa2NXbBvPHp9+nz/r11DX1nuegAVddRYXPh9frxWKxhGQOsU2mBzm4fz/Hjx3DtW8fR377215x4kxMaip5v36IY7XV3DOrAKO3UetIYVVtLGR3o4558+ZpHUUQhF5u/569nDhxAs+eExz67X/wdnRqHSnk7NnpZPz865xsqOSeaW707gqtI4VVmWkWRzsymDlzltZRBEHo5fbv3UNFRQUFdj/9pLLecQyvKY5XD0gcPnCEhLfeovHAAa0ThdXw3/0O24gRXDJ9esjmEMWQHujwwYPs37GDyl/9isaDB7WOEzKl3/oW+3OyGFmYzswcd+/4ofcFhwO52PqOQZZlUlPT0OvFYi5BELR1YN8+Du/aS/n//ZOWY9Hb0X7oXV9jd7LC+BIbl6TvA8WndaSw2+2bQtawK/B43KSnZ4gTzgRB0NzB/XupqShjSmIteNq1jhMyjrhBPPryGjLa2mj4xz8IeL1aRwq7wl/8gkvvuYf2tjaysrNDcrqMKIb0UO3t7Sx9913annySyjVrtI4TVDqjkUF/fZy9lVX8YF4hNm+91pE0o5oTWFqfRnx8PG6Xk0umz9A6kiAIAi3NzXz4wTJa//Im1Rt2ah0nqPRWCyV//j4Hq8q5ZxaY3Ue1jqQZ1ZrDu7XjSUtLw+3sYMq00H06JwiCcL6aGhtZt/Yj5uY60bkatI4TXLKRpdVJbN6+h4zVq6nesEHrRJpJHzGCtJ/+lKysLFwOB1NCsEJENFDtoex2O1dcey3xd95JYt++WscJGnt2Nll/fgyvzstPZ6f26kIIgORuYUCcizfffJO0tDSt4wiCIACQkJjI5ddcRdIPrsSek6F1nKCJL8gh/Q/fRDa28sC0ql5dCAGQnBUUx9WyePFi0jOi57+zIAg9W1JyMrPnLWBpVSwYrFrHCR5LIk9s8XF8224Mf/5zry6EANRu24Zz61aWLFkSsnuQWBnSw9XW1LD+lVfYfffdWkfptrzJk2mYO4fxxemMT24FxF9NgB3uPPqMuISEBNFBVRCEyFJRVs6W195jz48f0zpKt+VPH0f1tD5MH2xllH0n4h7UZYNrBkOnXIvVGkUPHIIgRIWyE8dxVu6lWOr5hWslvoCHXt5OVl0dVU89pXWciNH/D3/g2rvvDtk2TdGAoIdLz8jA3KcPKSUlNPSwpjopJSVkjR2LffBgdnvdVLg83DA6kwJj7+mSfGYSn7wRj01I5i9/+Qtfv/EG8gv6aBtLEAThc3Ly89hTlEN8QTatJyq1jnNBUgb1J2v0EOKG9GN7Zw3lXjc3j9ORI+/QOpr2JBlUBYCElGweeeQRvnn7bWRl52gcTBAE4TP5BX1YdugQxYm2nnfKjDUFYtNo9Fp4/t21tHZsJ3vHDqqWLNE6meZknQ4lEAAgp39/fvnLX3LXd79LaghWyYuVIVGgsqKCj598koO//rXWUc4qNjOToffey7FAgHbFD8jo8WGSA1w/bQB2Xz343Rc9vqKo1Ld2kp5ou6jXd7i8KKqK3Wq66AzB0GHO5pWd7fQpKECSwOPxMKR0KGazicTEJE2zCYIgfNGxI0fY+tQrHPz9f7WOclb23AxKf3gTR/1tOBQvqqqix41V9nL9lARsnsPgd170+IqiUt/mIz3BeFGv73AFTt2DtP2cqs06gtf3xFGQn4ckSXjcLkqHjcBsNpGQkKhpNkEQhC86sH8vNsdRsnzHtY5ydqY4as39WL5uD3UtDlRVxdfWhtreTl5tLVUrVuBxOC56eBVw6fVY/Rd34IRPllEBo6JcdIZgKLn9djrHjSM3KwtJlvG4XAwbNQqL2Ux8CFbJi5UhUSA7J4fY0lKtY5xV6c9+xk7gKE6mDsul0O4Bj4NP/wq6LuxEgoqGdh59fRNWowG3z8/dV4ymprmDvScauHXOl/8sLv/FaxRmJaDXyXS4fDx0y5QvFT2eWbabKaV5DMpP+fRrf3h1I/dePfa06zYfrGZv2VfP0y16C1gSqPIkUzokj1GjxwR3fEEQhBDoW1jIviGR3buq9ME72eFt5IR6nEtK4+gbUwmeFj7dCtNZfkHjVTR4ePTNSiwmGbdP5Z7Lsqhp8bKnrJPbZqV/6fpFD+6jMNOCXifR4Q7w65vyv1T0+M+HdUwZHMfg/M++/ofXK7n3yuzTrtt0qJ295c6vnKdbDHawpFPpLWbY0GSGjxgd3PEFQRBCoLhkIKveP0qWXeskZ7bf34dX3t+CsWUbWZ2d6D78kI7aWtRThYcLLeN0GAzsSk5GpygEJInSpiacej1NZjMlLS1fuv793FzivF5kVcUny4ypq/tS0eNgfDyZnZ0keTyffm1ncjJDGxtPu67OYqH5DPN0hy09nYQ+fUieOZP+ffowbOTIoI5/JqIYEiV0FgspAwbQsH+/1lG+pOCSS9ihBPjF1YPBUQ00gOecLzurdfsqGV2UyfXTBlLd5KCy0cHGA1XsOFrHFROLiLeZT7s+LSGGR77Z1YH4H0t2sLesgc0Hq3F5/ZTXtfGTa8ay4UAVDpeX1bvKKa9rY8LAbI5Wt+D1B/jdyxvQSRK+gMLcUV1v+p9fsZeAonLTzMHd+2YAxZLMa0eMpKUkkpGRyqgBg7o9piAIQriYEuwRu1Wm77zJ7PA28IvLZHCcesvZ3XvQ/jZGF8Vy/ZRUqps9VDZ62XiwnR3HOrhyQjLxMae/vUpLMPL727q2OT75fg17y51sOtSO26tQVu/hvquy2XiwHYcrwOo9rZTXe5gwwM6RGhdev8JvX61AJ0v4AypzRnR9MvbcqnoUReWm6d1fNuy39uH14wNJS4knKyuHQcUDuz2mIAhCOEiShGqwgjEGvJ1ax/kSJbEfLz6zjpxly6jduZMLK71/tRqrlVSnk8K2Njr1ejoNBmqtVhrNZvq0tWH6QqHD6vczvrYWgH0JCTSbzdRZLARkGYfBwLCGBuqsVnw6HdWKgsNgIN3ppM1oJADsSE1FUlUUSSL31OqVw/HxqEBRa2u3v5+sSZOI/c53SLTZyCoooHhg+O5BohgSJRKTkmgfPz7iiiGWxETa58/lhiGppwohwXH15GJeXXOQh57/GBX4xszBjC3OwmY2fqkQAlDf0skDT69CliTibSZGF2Vi1Otwenw4nB52H69nbEkWU0vzWL2rnCsnFTO2JIsPth5ny8Fq+mUmcP20gRw42Uhbp4d3Nx2lKDuR39w2rdvfixKTxsoaOwV5SYwZN77b4wmCIIRbjDWGzEkjIq4YYk1Nom5yH24dLYFjT9DGvXpSCq+ubeDBF0+ionLzjDTGFMUSY9Z9qRACUN/q5f7/nECWID5Gz+j+sRj1Ek6PQruzmV0nOhlTHMvUwfGs3tPKVROSGVts54NtLWw+7KAw08L1U1I5UOGkrdPPks3N9M+28NubC7r9vfhjClnZMJR+fdIYOXpct8cTBEEIN6NBj8+SjsF7TOsopzPa+N1rOyk4fJjKnTuDNmzftjaOxcWxNSUFJImilhbSnE4MivKlQgiAU69n46l+G6ZAgFSnE1lV8UsS3thYGi0W0lwuMjs6qLbZ6NPWRrrLRYXNRr3VSpzHQ2FbGy0mEx5Zpjw2lniPh7F1dd3+XvKmTyfpjjtIz8xk9PjwPweJYkiUGDx0KAfTg7xktpvM8fGkPvwQ6XF6Cs2N537BBfjv8j3MH9OP66YOoLa5g9+8tJ4bpp95NUVqQsxphQuXx8df3trKdy4dQUluMqra1bL0EzbLZ3u+vQEFSer63ZrmDox6HeNKMqlq7KC8ro28tLhufS9rTkpMmjUDi8XSrXEEQRC0MqB0MGUZH2sd4zTW5ATs/3cd/VN95Erbgzr2MyvqWDA6keumGKlt8fLwKxXcOC31jNenxhtPK1y4vAEef6ea78zPoCTXeuoe9NldKMb8Wdd8n1/99Hdqmr0Y9BJji2OpavJSXu8mL/XLHwBciNVVOUybNw+j8eJ6nQiCIGitT2F/yo9toJ+sdZLPMcbwt/WdJDU2Url4cVCHPpSQQH57O4WBAE69nu0pKfQ/ywoNq99/WuHCL0nsSUpiUFMTCZ9si/lcG1HD5woqivTZvalTr0enqqQ5nXQaDDgMBmJ9vm59L+ZFi5h/+eUhOy3mXCLpr4zQDRaLBWNi5DQ2M9ntpP/uN6TZdczOvvhmQGcycVAOv3r2Y37y1Aoeen4dN1wyiNT4GFbuLKOmuYPfvXz2c7l1OhmLSc+KnWVsPVxDa6ebnFQ7/16668tzDcxmz4l6Hnr+YzYeqMKo15Fkt3LfdeN4+MX1KEr3ehAPzdCz9IMPqK+r7dY4giAIWklMTIS4GK1jfMqcGE/ir75O32QfU1OCWwgBmDQwjl8+f5IfP32cB188yQ1TU0iNM7BqVys1zV5++2rFWV+vkyUsRpmVu1rZeqSD1k4/uSkm/v3hlz9lmzjQzp5yJw++eJKNB9sx6iWS7AbuvzqHh1+u6PY9aFhaO+8teZemxoZujSMIgqCVzMwsalov/hCGoDNYeWKDC0NZObWPPx704TM7O9mSlsb69HS2pqZS2NqKxe+nKiaGTr2e7cnJZ329pKroFYUqm40GiwWPTofN5+PgVzQozejspNlsZmtKCnVWK7KqYg4EGNbQwPaUlG4fQq/u3MniN96gpbm5myNdHHGaTBR5/ckn2XPHHVrHwGizkfnHP5AcKzEvp4cdc6URX/oIFm+r5Zprrvl0FYogCEJP8vKfn+TgDx7VOgbmxHiSf30TOfFuZqbvgG6/VYt2Et6sy1i8XeHaa6/VOowgCMJFWf3uS0yOD96W/ItmsPLkJi/yiRPUPPaY1mkinqzXM+XBB6ktKNDkHiS2yUQRWwRsk5H1enL/9Ch2S4B5ORd/RGFv0mwpYP3eZkqK+otCiCAIPVZs5pm3iYSL3mwm7Te3kBnnZGaaKIScjxrTBHYcNDKwJE/rKIIgCBfPGKt1ApCN/HObH6msTBRCzlPx979Px4ABlOTkaDK/KIZEEdlg0DoCo3/9a461N/K1YjPiTej5OVzrYs68q8R+bUEQejR9BPwMG/6bu6j21DMz7QDiHnR+DtfLzFu4UBTjBUHo2STtuz/s9RXgKF+D409/0jpKj+FLT2f+woWaza/93xoheDR+I2OOj2eb2823p2Qh3oSevySbkYaGeq1jCIIgdIuq8c99W3oKOzpruHlUC+IedP7iLCotLS1axxAEQegmjQu6RjuLV23B8Prr2uboYQLt7Tid2u0mEMWQKKIzGpFk7f6TDnz41/TLjEN2BffkmGjXL9POgX3BO/JREARBCwZr90416a7CX97GoGwjsrtG0xw9zeA8Czu2bdQ6hiAIQvfotN3wsLLaTmJLC83HIux43wiX6vWybvVqzeYXxZAoovh8xKRqs2fbmpzM7oZGrh4ROSfa9AiynlXHvAwfOVrrJIIgCN2iBBRMdpsmc9syU9lTd5JLi0Ux/oLozKw4FsfI0eO1TiIIgtA9kgySNsezYopj/c79+N58U5v5eyhzfDyekSMZO2mSZhlEMSSKqIEABotFk7mH/vGPpCdYoFMczXchKnV5pGVkkZiYpHUUQRCEblEVBYNVm3vQgN9+h9xkHbjEqpALcUIaT15+X+Li4rWOIgiC0D2qCrI2xZA1jcnEtLfTVnH2Y9WF0xXecw9FAwcSG6td81tRDIkmOh0dtbVhnVKSZUr/+RQHG6q5ZbQ9rHP3eAYru2oDDC4dpnUSQRCE7pPA2Rje3hOy0cjApx/gRNMJbhxSFda5ezxTIvua4ikeMFjrJIIgCN3m9/sh4A3vpLKed6rS2Lp+K/7//Ce8c/dw8fn5yCUlFA8YoGkOcZpMlKg8eZL2PXvwuVxhnbfkn0/S7u3krvE28HWGde6ezmnJIiVFbCsSBKHnO37kKO3bDqH4/WGdt/Dvd+NXGrhz+FHwecI6d0/XahxAVpY2RxkKgiAE09Ejh8iL8YS9d/bLh200HdmP+49/xO8R96ALkXnppfTr31/rGGJlSLTYs2sXFX/9a1jnHPnHP1Db0szXBxH+SmwU2FHpYvjIUVrHEARB6LaDBw5Q9pdXwzrnyMfvpdnRwLWFu0ERb0Iv1K4aK6XDRmgdQxAEoduOHztOX11lWOfc5u1HTdlJmn77W1EIuQhSSQmDhgzROoZYGRItlq9cSea3v0WarKPAaqVu8WJOrFwZsvmSCgvZ0dHBT+dmgscRsnmimU82YzQatY4hCILQLaqqsnTFcrLvmkuawUy+NZGalz/k5MdbQzZnxshBbG+u4n9nNIBXCdk80UwxxKHTadRsUBAEIUi8Xi9L3nuf/XnZpCfFM7a0H7lyLThC2DogLoclL20i5qmnUBVxD7oYMWlpSJLGxyEjiiFR47KFlzJlwkiqqmtZt3ELdbNnEzdnDgMdDjb+5jdBX7qcdc8PyLNJohBysSQdqk7bYygFQRCCQVVVrrpqAZMm9aGysol163ZTv2AQ9ktLGdAqsfn3/wn6PSj+1tnkx7eAtzWo4/YaOhPoY7ROIQiC0G1er5ev3/g1RpSWUFldy5qPN/JydQ1xsTHcPGMAxvpdQZ/z3f0u0traqGsQB0dcDHN8PAarVesYgCiGRAVVVUENgOIjKz2JaxbNAaC9w82/nn0Z868fojjWTsVf/0rDvn1BmfNEWxv3TCyENtE1+aIYrXjawru3XhAEIRT8fj96fQBoIztbz7XXDgckmpvh3/9eiuHBGxloT6PssZdpPlIWlDmrHE1cPcYN7UEZrvcxJeN2+LROIQiC0G1+vx+DTkJS/eRkJHPD1QsAaGxu568vvo7f52PBlOGU6CvB0xaUOY8cr8K6Zk1QxuqNkouL8UXIihpRDIkCx44epU9+3pe+breZufvOb4Ck4+33PqTzpptIkmWKHQ62PPoo3o6Oi55TZzJBZ2N3Yvdufg+tra1apxAEQei2Q4cOUFiYCXy+wKuSmAg/+tEswMLrr2/CdfslJOkM9G+V2fLof/A7L77ht2QwQefR7kbvvfwdtLSE9+QfQRCEUDh65AhFfXO/9PXkRDs/+u4tKKrEq4vfY0lFE8nxNm68ZCD6+l2gXOyHkjKy0UjjoUPdC96LddbVRcxzkCiGRIETJ04wc8oYUM/wj1oNsHDuNBbOnUZjczvPvfwGxp//H8l6Aznl5Wx67LELmi9j+HA6AgHwh/fkmuiikpGepnUIQRCEbqutrWHw4FxOL4Z8nosrrxwCyNTXB3j+uRUYfn49qUYLmUdb2frX5y9ovvxLxtLoc0PA3d3ovZeqkpmeonUKQRCEbmtpaSE2ph+oX73SQJZUrr18LiBRXdfEX197m4CqkJeewuWjMpEb9l7YhEl9cLZtJeAVh0dcLFVRSI6P1zoGIIohPZ7f78eg1525EPIFyYl27r7zZkDi+MlqXn3jHcy/fojShETKnniCut27zzlG/u2304joFdIdnpgc9B7tmwYJgiB0h9vtxmyWgPMpjiukpkrc88MZgJ7Dh9t4/fU1mB66iWHxmRz966s0Hjh2zlHir5hMYkxVd6P3ap2WEsyqSesYgiAI3dLhcBAbYzljIeR0Kplpidzz3ZsBmf1HjvP7N5ZiNOi54pKR5CknwH3ubTSrDruIq61FrI+/eBkzZhAXF6d1DAAkVVXDfCKzEEz79+0jOd5KapL9osfwKxJvvvMBR4+Xk2mPJX7XbnY+/fQZuyPH//ohbp5TjN1ZftFz9nZV+nxciQPp379I6yiCIAgXbfv2rfTrZ8Fuv/hVGn5/DK+9tpYTJxrIiUsidttJdv7rtTNeb/3VDXx/QSzGttCdVhPtjjIBS7+FZGdnax1FEAThom1Yv47hQ4oxdePjfa8fXn3jHU5W1TCspC+zSmKh4cAZr3/0o07S12/k6HvvXfykvVy/n/yEeffdR2JiotZRxMqQnq6hoZ4BhSNAufhGaHpZ5erLZgMSu/YdYXmnE+ODDzLKbufIU099abWIXydjl8Xy5O4ob/Iwali+1jEEQRC6pb29Dbs9tltj6PWdXHfdcEDPtm01rHK2YHr4G4y0pnPoyde+vFrEoMfoq+nWnL1dTYeF8enpWscQBEHoFq/Xi8mgP+8V8l/FqIcbrrkUkNi4dQ9/encLgUCAmxeMJ9l5BNytp12vyHoa9+/vXvBeTp+ZSbzYJiMEg6Io57k07HyolA7sR+nAfnh9Ci+8spjW666j6DvfwfH001Rv2ULhvHlUKQp01Adpzt7J7/djNBq1jiEIghAEwdo37WfEiBRGjJiJ223g+edX0/71iRQlXUHr39+lbucBBl2/gGOqD5zVQZqzd1KUAHq9eAsoCELPpiinTtQMCpWxIwcxduQgnG4fz7/8Jo3NrYwaVMglmZ3gaoL0Ibhal+MsKwvSnL2XLMtaRwBEMSQKSEH8IfAZo0Hm5huuACQ2btvDykWXUXT77fSNj8dVdRgQu6u6ozAzgV07tjN0+AitowiCIHRT8JvImc0+brttAqBj7dpyjl89ipLvLCQtNhFfy1bEPah7cpJ0HNi3h5KBg7WOIgiC0A0qobgfWM0GvvmNawCJVeu28LtlRxg9pIgkVcHa2Ehn0GfsXQwOB0cOHqSwuFjrKERGSUa4aJIU6iacKmNHDOJ/fngneQMK+dPrr3P5pJIQzxn90mJ1NDY2aB1DEAQhwgWYNCmb//mf+aT1T+TPrz/LZWO032Pc02XH+amvr9M6hiAIQreE4zlo2oSRPHDPt8Fo4dd/eoKMBvH+vbt09fXU1UXGPUgUQ3o4VVVA0p32NUVRqK0L9jYWlfEjhzBmzCgSzEEeujcKeJFlsTBLEISerasH++lb/hRFoba2KcgzBZgyJYfx48ZglsRpZt0lqx5E+3xBEHq6rp9jpxdEQvUcNH3SaMaMGkVnlTjNrLsUpxO//+L7vASTKIb0cDExNm759g944P9+zQ/v+z8qKqvYun0XSz748Cuv//Zd9551vDfeWsIHy1ee+QIV8J3PEYrC2TQo8fz3uee0jiEIgtAtJpOZ229/hAceeJIf/vBxKirq2Lr1EEuWbPjK67/97d+fdbw33viIDz7YdOYLJMAniiHdVR3ow4svv6p1DEEQhG7R6XR88657w/ccJEk4G8Whut2VPGsWb7z1ltYxANEzpMc7dvw4mVnZ/OqnP6KurobKqho2bNrKjl17uHLRAuLjzn7k7v0/e4if/PAuAB559C+MHjkMgEf//AQDSvqTm53FK2+8japCUf++qCgg6842pHAuRhsrd57g//2//6d1EkEQhG4pL68gLS2bBx+8gdraKiorG9iwYS87dhzmyiunEh9vO+vr77//CX7yk68B8MgjLzB6dNc2zEcffYkBAwrIzU3jlVdWoqoqRUW5qIoEkiHk31c0U01JrNhWKe5BgiD0eFXV1aSkpvPr//sxNTXVoX8OUlX0JlPIv69oltCnDweam/nTn/6kdRRArAzp8a655hqKi4q464cP8M9/P096WgpjR49g0oSx5/wBcCb/ePpZ9Ho9c2ZewlP/fo4Yq5VYWwzbduwmIT6BWqcohnTHFkcGY8ZNwG6/uP8+giAIkeKaa66hf/8ivve9/8c///kO6emJjB07kEmTSs9ZCDmTf/zjra570JwxPPXU28TEWIiNtbJt2yFMJgMdUmqQv4veRGK9YwLTZ8zCbBZ7XgVB6NmuueYa+vXty/d/9NOwPAfpZJmkwaLx9MWS9XpyHniASy+9FJ0uMp4nxcqQHu6ZZ55hwYIFZGdnU9I3g4cfeYwbr7/6vF+v0+nw+/24XO5Pv7ZwwRzWrttIa1s7gUCAm264htSUZF57812mXTKDt1/6DzePtIbi24l6akwa7Z16xvQr1DqKIAhCt31yD8rLy6W42M/DDz/LjTfOPu/X63Qyfn8Al8vz6dcWLpzE2rW7aG3tIBBQuOmmOaSmxvPaa6sZMWIaS5a+xrWij/dFUez9cTfGkpeXr3UUQRCEbnvmmWdYcOmlFBTkU9I3M+TPQX0LS1h1+HAovpVeIX/6dGIyMsjMytI6yqdEMaSHmzRpEr/4xS/ocDiwxZj5xql/sCtXr2Xe7Ok889zL3H/v9z+9vra+gXsf+AUAE8ePYeqk8fz8oUfok5/36TXpaSnc871v8/Ajj/HNW7/OQ7/9E0aTkUnjx5CcGEe7PzIqeT2RS28nLi5e6xiCIAhB8ek9qMOBzabwjW90FS5WrtzOvHnjeOaZ97n//hs/vb62tpl77/0rABMnDmbq1GH8/Of/ok+fzE+vSU9P5J57ruHhh//LN7+5kIce+g9Go4FJk0rp0yeJtx3ircvFagkkk5aeee4LBUEQeoDTn4NMfOOGa0P6HDR86CDeESu7L5qhXz/6FhVpHeM0kqqKfuLRYPWqlUwdPwxUJeRz/eHPT/PDqfHg7Qj5XNHGFVfIuio9s+bM1TqKIAhC0KxevYKpU1MAX4hnknnkdx/w44nlopHqRWiPm8iOln5MvWSm1lEEQRCCZvWqVUwdV0rXSQ+hJPPIn54g8Jvf4HGIe9CFGnjXXaRfcQWTpk3TOsqnRM+QKBETY6PD6Tn3hUGg18tgTQrLXNFDYo9lAk8sO0yHU5zGIwhCdDEYDHg8xnNf2G0KRoMOrGJ1wwWRZLbZ7ubpVS7aHE6t0wiCIASVLEsE1HA81irIQGKh2O5+IWSdjrkbN1LVty+tHZH1YboohkSJYcOHs23n/rDMFRMTQ0WzNyxzRQO/OZn3WgtZvmotd999N9MiqBoqCIIQDCNHjmbz5vLwTCaptPtjwzNXFPBZ+/Ju+zV89NEa7r77bqZMmaJ1JEEQhKAaMWIkm7ftDstcPp+P2IKCsMwVDbKnTmXYm2+yYcMGvve97zF58mStI51GFEOihF6vx+vzgxS6/6R+Rcefn3yW42Un2Xa4MmTzRBVTLNs7M2hvb+eWW25Gp9ORkJCgdSpBEISgMpnMuFwqELrVIX5/PH/60yrKT9aw45hYnnxezCls6hiL2+3m5pu/gSzLxMfHa51KEAQhqGJsNjqcbkAK2RweP/z+T09SVllBu+3iTkvrbRL79SP77rsJBALceOON6HQ64uLitI51GtEzJIo0NzVx5NBBxgwvDvrY7324jh27dvPtW24gMTmVx//4W743STzUn5Gsp0qXx646lVGjx5CWnqF1IkEQhJCqq6uhquoww4cH+02izNtvH2ffvoPcccdcTKZk/vP449w5pjrI80QRnYkK/Xh2NyQydtxEklPEccSCIES3yooKWprqGVycH/SxX3vzAw7tP8CNcjvK8An8d/EHqP/5T9DniRYmu51+d9yBYdQoJk2dSlJystaRzki0ZI8iiUlJXf0oJDlojVTLqxp54eXXKB00iJ/+6M5TX1VRdaagjB+tDgT64DGmsmDhaK2jCIIghEVaWgb79u0FDASnkarMsWMKr7zyHsOH9+eBB+bS1RxPxquEoz9Jz7VHnQ2WQhYsHK51FEEQhLDIzsnh0KFDQAHBaqS6//BJ3lz8DsPMcNuRteD3Yxs8nIDFIrZXnMXA3/6W/HHjGDJ0qNZRzkkUQ6KMwaAPSjFEQcef/vIU8XF27rvnTmTpcz9U1ADoIuyNqDEGnzUDT0DGalCRO2sh4ANZBz53V+awkGiw9KOhzczUSaIQIghC76LX6wlGMcTvt/Poo6+Rnp7IffctRJa9fPbm1o2qs3QzaZCZEvFZC/EoBqx6L3LHUQh4QdZ3nXoTrnuQpKPWOo12VwoTh4pCiCAIvctnz0Hd+5nr9Uv8/tG/kCX5+FbzcQKVZZ/+nq+qHFN8fMjPTrsQcbm5ZE6YgC4mhkBrK5Xr1+N3u9GbzXTW16P4/WHJoTMYGHzffcQXFfWIQgiIYkjU6dr1dPH75RRkXnztHU6cKONbt9xIanIcX6quqgqyLkL+6sgGjlJAWbMOi9OExRpDZ7MDRclHkmRUVUFFQvK7UBSFglg/Br1EfGwMMR3HwXeWk12MNojNgM56cLed+TqdEfRmyrzJHHcYsKqxTL0kspoDCYIghIOiKHSnHZmixPLcc2upqKjmu9+dT3y8CnyxYbcPKVLuQToLB5lKZXsiFq8Zs8VKR0sbqlqCJEmoqoqqguTvQAn46WNvwqCDBLsJa+sm8J+lq74pEez9oP04eBrPfJ3eCnobx3wDKXOmEWuIY2KENagTBEEIh+52f1DQ8a9/v0BdXR03BBoxH9jJF8sqqqMNvdEeEcUQc3w8xf/7v5iKirCYzVgsFtpaWki89dZP70EAzqYmAj4fnh07kAGzqnLg2Wdxt535+SYuN5esUaOo3LSJ9soz94q0JCRgSUoi7eqriRk5ksSkJMb3oEbdEfJuQgiWrr/0F/ODQGbrroO89c673HjdVdxw1fyzXq10s+jSbToj1boc9jXK5OXnM2v8oHO+RFEUTp48idPjpqy2loA3l8m2410rSD4h6cCWyu42O01tBmQH+HyZ6H2xZNllchINmBUn6M1Udug41qIS0Ftpbm4hLy+dGXPGhvCbFgRBiGySJHFx9yAzmzbV8u67b3DrrfMoKBh2lnGUkDYLPy/6GCrkkexvSaNvv0JmFg0450sCgQAnT5bT4XFzvLYWyZ/IRONboHyu2CMbILYPO9qKaemwIzslvN6+GP2NZMW6yE3wYgo0g95GmSORMkccfp2d+voGBg4oYcYksRpEEITe6+KLITJr12/jg6XLuT5eR/KhDeD76pMzVY8H9Bo+AwHWpCQKbr0V89ixFA0YQP/ic/eLDAQClE+ZgsvppLa6miFjxrDtttsIeD/7Pg0WC1mjR5N4/fWYsrORgJibbqLtyBGoqkKuqqKjvBxrcjKG0lKMxcXEpKdTWVXFkNGjKRlw7nthpBHFkCjT3u7gQj+V27LzICtWrWFAcX8e/Nm957HFRkHTtrsGCytb80jPyGT2vKHn/TJZlsnPzwegqKiYQwf2caQpQCFHQW+mVp/LnkYdBpdMYf9ihubmffpaVVWprq5i5/HjeH1WVFUlKzuHaWNKgvzNCYIg9FxtbQ4u7K2FzLp1Taxbt4VBg/J58MFr6dpic7abjNJVuNaKMYHlbbPIzsljzvjS836ZTqejoKAPAMXFA9i3Zycn2idToHwIBjtV+tHsb81A71UoLhnM8KycT1+rqipVVZVsO34Un8+Dqqrk5BUwbULwG6YLgiD0VO2ODpCkC6rJr1qzmU1btjFA9fCDloMox86yEg9QnR1ICdodjBCfn0+f3/+evkVFDBw8+Lxfp9Pp6NOn6x40cNAgtm3aRPH3v8++P/wBW3o6fb/5TazjxqH6/QwZPpzMrKxPX6uqKhUVFRw7eJDEU8WTPoWF9CsqCu43pwFRDIkyCQkJtHe6sVvOVRCROXSskjfffpeCvFzuv+fbgHrevUYkCTDGgLezu5EvWJmaTU5ePkXF3as+FpUMZNnSCgpj9XzUnk9Cciqz54z8ymslSSIrK5usrOxuzSkIghDNbDYbLpcByzlbeljYu7eNd95ZSf/+OfzkJ3OBAOfXa+TUp1h6K/id3cp7MQ75R1FYVEJBn8JujTNw8FCWfVBBQYyFFY65pGXkMGv2sK+8VpIksrNzyM7O+crfFwRBEMBsMuH1g/Gcnwvr2LHnIB8sXU5fncItBz9GdTk5n6cgf8VxDJlDMFgs+Fxn2W4fIjl33MHQkSPJPfUB78UaMWYMVeXlWBIT6f/44+T16UPp8K9eXShJErm5ueTm5nZrzkgkGuFGGZvNxtIPV+MLnLkk6vap/M8vH2Hnrl3c/8M7ufaKOVzwsmZVBWtS98JeBNUcz4EGtduFkE+YLRaWtfdHNsUwdPhXF0IEQRCE82O3x7JkyRYCAfMZr+nosPLAA89w6NBhHnjgMq68ciB8aVf2OagBsIa/OK1aczjentDtQsgnDKYYlnZehSkmgcGlX10IEQRBEM5PXFwc736wAkU98zaWtk4vP/npgxxb/j631+xh8rZlqK4LKKwrCgG3m+Tz2JoSbBmjRmHs37/bhZBPWBMSKH3+eZLS089YCIl2ktrdTjNCxAkEAnzw/nvMnzGBLxY5Xnv7Q44eO87377wVq/niFwbtOXicg1vXcWVxcI7wPV+H1b5YcoaQl18Q1nkFQRCE8+PzeVm+/H3mzcvji0WOF1/cT3n5Se6++zLMZs9Fz7FlSxu1BzayIHdfN9NemN3KTNJKZpOeod0SaUEQBOHMPG43q1etZPa0sXzxOei/zy+m6mQZt9GGsm/bRc+xb+Ht7N2wmZYnnuhm2gsz4He/Y8btt5OYmBjWeaOZWBkShXQ6HSNGjGTdlr1f+r3Kqmruv+db3SqEAAweUERlS5iXhulMnOw0ikKIIAhCBDMYjAwZMoyNG1u/9Ht1dY3cf/+cbhVCAEaNKuBobZh7+Rvs1HlTRCFEEAQhgpnMZopLBrB19+Ev/I5EU0szt+xb2a1CCMBIbwttNlu3xrhQcTk52EpKRCEkyEQxJEqlZ2QgyTrKqz7XBEg24jtDZ+QLpvjwKYA1OTjjnYcWUxaJiQlhm08QBEG4ONnZufj9EpWVMp+dPJaEz+cO0gzN+FQjWNKCNN651RtKSUsL33yCIAjCxcnLz6fT6aamofXTrymSHm9Lc1DGd635AEN8PPbs8G3XzL76anLz8s59oXBBRDEkio2fMAGH08PS1Vtobu2gvqmF1KTgVROnTZ3M+3tbgzbeueyp7GTYiFFhm08QBEG4eBMnTqG5GT74oIa2Nj1HjjTSp0/wigkjRg5iXUVK0MY7l/11JgaX9s491YIgCD3NlKlTqW1oY+nqzTg63WzdtptB+iBt73d20jcxjpgZM4Iz3nlQCgou6PQY4fyIYkiUGzR4CLPnzGHrroM8/Z/nmD3rkqCNPWp4KQcqg1NhPR+j86ysX/tR2OYTBEEQumfIkGHMnj2PDRuqeOGFd5k5c0TQxp42bSibDrUHbbxzGZnpYNP6tWGbTxAEQeieYcOHM2v2HNZs2MHb77zLSOMFNus+i1mpdirMZ24WHnS7drFhrbgHBZsohvQSs2bPxmg0IumMfLZkuZsCHryqDsUank/mzEon3mBt8xEEQRDCQpIk5syZh8Vixuu1Eqy3HrLciAcLiiUrKOOdi01uxekK/3HygiAIwsWTJIn5CxYQY7PhHTAcjKagjOt9/zWMaWkkFQbndLFz8ZSV0d7aGpa5ehNRDOlF7vruXRw+eoIPVm2gpd2FGoSiyG0338hDiw+ByR6EhGdX7rKRmyeapwqCIPREd931PQ4cqGbp0ipaW42oqqGbIyp8/esLePh9I5hC31DusCOL/kXBOdZdEARBCK+77voeJ+LS2bnwNnxT5iMnd2/bptLaxOV9Mmm5+ipsYegnZZo4kaGjRLuAYBNH6/ZCXq+XA/v309zUyLQJw7o9XlllAy+8+BL3z84Eb0cQEn4FWc+H7f2YNWdeaMYXBEEQwsLj8bB//14cjmYmT+5+EePgAR9vL17KjyfXgi9E22Z0FpZ1LGL23AWhGV8QBEEIC7fbzd5du+goP0bxK3/t9ngnr/gmKw4eR/7LX3C1tAQh4ZdZk5IY9O9/M+/SS0Myfm8mVob0QkajkdKhQ9EbDDic3d92kp+dwqLLL+Ov61pBCs1fKZ8tG5PJGJKxBUEQhPAxmUwMGzYCvx/cbmu3xysuMTB77jSe3J5P0LaBfoE7djAx1u5nFQRBELRlNpsZOWYMLnMM+hHjuz1e7htPMbG4L6Yf/ABJCs09KG/BAuyxsSEZu7cTxZBebOCgwWzesiMoYw3ol4vZaqPcF5qjdrfXBBg1pvs/sARBEITIUFRUwvbtR4IyVunQGAIYqNcPCsp4X7S1OoHR4yaGZGxBEAQh/Pr2L+IAwekfUvjGP/DpdGTPnx+U8b5IHj2acZMmhWTs3k4UQ3qJiooK7r77bh544AF++MMfUlFRwb59+1jz8fqvvH7G/Kt4/qXXP/31bXfew9PPvHDWOY4ePcp/VuwPyeoQnzmF8rLjQR9XEARBCL2vugcdOHCQlSs3fuX1M2bczfPPL/v017fd9luefvrds85RXnGMJ5a2EYrVIQFrLmXHjwZ9XEEQBCH0vuoedPjoUT46XPaV119/qJk3m1yf/vrHJ9p4qcF51jmqdmzmWIiaqSb078/xI8H58EA4nV7rAEJ4fPzxx4wePZqvfe1rVP9/9u47PIqqbeDwb7alkoSQ0EvovXdC79IRFBVQbGAFu2KvKGLX18/eFVQEUUCRTuhFek+AQICQBmnbd873R2AlpJCEFDTPfV2+rztzZs6ZEfbZeeaUU6eIi4tj2bJlHDp0iHOpaYQEZ58AtW5EHTZt/ZvxN4zh1Ol4OD+1zOat25m/YBEKaNKoAU2bNOKDj76gXduWaCiMRhMPf76WewY3pX71isXUeo209AwCpXuYEEL8K+UWg1asWEFs7CHOncsgJCQwW/m6dauxadM+xo8fyKlTScD5GLR5P/Pnr0YpaNKkNk2bRvDBB/No164RBoMTZTDw0Ffx3DsoiPrV/Iqn8ZqRlOQUwsIqFc/5hBBClKrcYtCalSuJO36CtAo6QabsL3JrWYxsz3AxupIfZ5weLkywuSPDxR/n7KCgvp+Jhr5Gvkqw0dLfhHKlY2rSkm2NG9Pg6FGCncWzAqbJx4ekpCRq1axZLOcT2UnPkHLi+uuvR9M0XnrpJT777DOqVq1Kw4YN6d69a45ECIBB02hYvy6Ho4/w6+9/MHL4NQBUDg9jxLDBtG/bmjVrNwDQoV1rHrxvCgBr12+gYcOGxZgIgZOWevj5+tC6dZtiO6cQQojSk1sMql+/Lr17d8yRCAEwGAw0bFiTw4fj+PXXKEaOzOoeXLlyCCNG9KB9+8asWbMTgA4dGvPgg9cDELV2PW1aNS++RAgQa+5NpdAQmjVrXmznFEIIUXpyi0H16kbQrWaVHIkQAIMGdX2NHLW7WXLOwaCQrOE0YWYDg0J8aBVgZlN6VrKjVYCJO6oGALBm9WraNmtWbIkQgGZPPEH1qlVp1LhxsZ1T/EOSIeXE119/Tb9+/XjmmWeYPHkyb7zxBkrXMeQz0c/oEUOZ//sfJCWlUKVyOABffDOb+DMJtG/XylsuMDDA++/TH32QBSu3XlljjT4QVIMjWn1WWxtz8Jwf/foPwGg0Xtl5hRBClIncYhDkv5jd6NG9mD9/DUlJ56hSJWvVmS++WEx8fDLt2//zozAw8J/Ex5NPTuGHP3dfWWNN/hDSlMPGfqxyXs9RWy169e6LwSA/mYQQ4t8otxikoXl7vudmcEVf/jzrIMWlE2bO+v7/MclGgkunZcA/gysCDP88Sz3cL5J1SUlX1Fa/0FDq9ulDi2efpeNPPxHSrRs9e/cusclZyzsZJlNO9OjRg+effx5/f3+sViu33HILx44eZe26jYwaNpCvv/uRJx6Zmu2YmjWqER1zhOtGj/Buq12rBlu27WDHrr14dD1HPa1bNKZa9Rr8FHWY63sUYtycbwjnjGFsP61j8K0AVg8RdevTp179Il+zEEKIq0OuMejYEX777U+GDWvL11//wRNPTMh2TM2a4URHx3HddX2822rXrsyWLQfYseMwHk/OGNS2bTWqVa/Jj2uTGdc9pOAN9KtGirE+OxLCMfgEg91NvQaN6RNRr6iXLIQQ4iqRWww6evAA84+coGco/Jxs495q2XspVrMYOeZwM7Sir3dbdYuBnZku9lrd6LnkURod2UnNiAiO7NtHveTkArcvrEkTKvfti6VrVwLDw/E4nTRt2ZLaERFFvWRRQJpS+aTExH/aiuXL6RvZDsj5g/JKvPPhl0wdUBtS4y5bNt0/gi3xRgwWP4KDQ2jXoaNkPoUQohxYsWIZffuGAZ5iPe87by1nWq90SL/cpNsaqYGd2HKmOkafQCpWDKVNO4lBQghRHixf8ictvnsTZct/YtTC+q7dUPxnzyZhz558yxmMRhrdcgt+/frhGxREWHg47Tp1khhUyqRnSDlls1qxmI0UdyIEoEJgICl2I6H5FTJa2OluREa6mb6De0n3YyGEKEfS0lLx84PiToQAmM0G7FoovuSTDDH5s80zDIc1iP5DpPuxEEKUJ8lJifieOVnsiRAA5XJQsX79fJMh/pUq0fKddwioXp0effpIDCpD8gRaTm3YsJ5unVqXyLkjIyNZs/No3gUsgazNrE9Q1Qh69OojiRAhhChnNm/eSJcuNUrk3G3btWLjoYy8C/iGs8o6jLBazYjsKT9ChRCivNm2eTONdqwqkXM3qlqF1Ip5LyQR2qABTT/8kKZdu9Kzb1+JQWVMnkLLIWtmJkaDAUMJ9AoBaNSgLseTMnPdl2Cpw19JNWjYrBX1GzYqkfqFEEJcvbJ6hRjRtOJ/IwfQqXNzdh9Nz3XfaZ9uLEnpT/PWnYioV4h5rYQQQvwnJCcl4p+WjOf45YZSFk3XapU4lceiD80eeID6b75Jl969iagv8yJeDWSYTDm0YcN6ekd2oCSGyAAYNA+Ycy5r6AmoytYTJoaNGFoi9QohhLj6bd68kX796gC5J82vlMnkwGMMyLHdFdCE7fE1GDp8WInUK4QQ4uq3ZcMGOuyIwl1C5/c5fRxzaGiOQaARgwbh17MnQ0eMyPU4UTakZ0g5E3fiBMFBFTBqJZMIAUB3g9GcbZPLrwoLj5gZMGhwydUrhBDiqnbs2BEqVQpA00omEZIlHc2cfVUAu39jFp9owqBrJBEihBDlVfS+fYTb0nBH7yuxOpz7tuMfmn3mxDqDBxM4aRJDJBFy1ZFkSDmi6zo7d+6gQ6uSH57icbvA75/xcmvOBDFi1LX4+PiUeN1CCCGuPh6Ph/3799G2beDlC18RNx6PB3wqebdEJbVixKgxmEzSIVYIIcojh8PBoX17qfnb1yVaj+dkLMqgERAeDoBmMBB+552MGTcOYx7DZ0TZkWRIOXI8NpbmjRsAJb+acru2bVgd4/B+1jUNh8ORzxFCCCH+yw4dOkC7djUoiRVkLtWkcT22nKmZ9UEzoGPA6XSWeL1CCCGuTru3/03r+MMoe8nMV3WxagH+hAzNmhbAaDbjAVwuV4nXKwpPkiH/MevWruXQwYO57ktLSyMoqKTfyGXp070zW46e9X7uXy2Nv/76q1TqFkIIUfqUUqxdG8Xhw7nHoPT0NAIDfUulLUOHt2bV/vMJeKUzoOpOlixZUip1CyGEKH1KKdasWkX0oUO57rdmZmJxlc6L2cHOJOKrVwfA7XCQ+umn/Pnnn6VStygcSYb8hxyJicFssbBn795c9588eZLQkAql0xjlxmwygW8IAG5TIDExMWzasL506hdCCFGq9u/fS4UKQezduz/X/cnJyQQElNSUdZdKw+LrD5YQAOxaRY4cOcLWzRtKqX4hhBCl6e/NmwivUoV9Bw7kuj89IwO1f0eptMW1bhn+wcH4nV9i169WLWJjY9m8Xp6DrjaSDPkPqVe/PokJCTRq2JClS5eSkpwMZGVK/1qyhPatm4Iq+e7JF2iahtMcDIA59QgPDmlIcuKZUqtfCCFE6WnWrAUnThyjQYN6/PXXX6SmngOyYtCffy6iU6eaQCkOVVFudP9aAPinbeGBAZCYEF969QshhCg17Tp15uDePUTUqslff/1FeloakDVn4u/z59Hi0Fb0lKRSa4/HbqNK69YAHPzmGyqvXEl8vMSgq42mlCr5CSREqdF1nY0b1uNyudmwcSO6rtOsWTP6du9IUCl1T77g0NHTbFm7nN5tIjiS5CI22Y7JZOKGG24o1XYIIYQoHW63m02b1uNy6axfvx5N02jevDl9+9YlMLB05+zY/ncGR/dspWPDII6nBRId7yQgIICxY8eWajuEEEKUDqfTycY1q9FNZqKiojCbzTRv3px2O1Zh3LmxVNuyd9gkjhw5QYjNhqVRI5IMBipVqsSoUaNKtR0if5IM+Y9zOBxs3rSJoEB/WjerR4lMnqoZOHEykZijsSg0WjVvSKWQIJxuD/c88ART77+XevUbEn/6FKnnztG+Y6fib4MQQoirjt1uZ+PGdYSF+dKiRQAlM3mqD7GxNo4eTUDXDbRrV52QEA273Yd7732dhx6cSq3aESQnJZCSnEL7jp1LoA1CCCGuNjabjXWrVhJ2Np5qf/6IsmYUex3GGnWIb9yO0z6BEFCBJjE78I09hN65D0+u28XDDz5I9Ro1OJeSQmJCAp26dSv2Noiik2RIOXHi+HF27dpJ/15d8TFrxXZep0vnzxXradq0KQ0aNkTTNFYsXw5kfQENvuYaWUZKCCHKuWPHjrBv314GDGiA2WzjyhLzmvd4u92HpUujad68BfXqNQBg6dIlGI1GHA4HgwZdg8EgI4KFEKI8i96/j0P799Nhz1r0vdvAU8TEvMEIJiOcX53M1aoTfzePpFXrNtRt0AClFEv+WIzJaMTtdjNoyFA0rfieu0Txk2RIOeLxeFgbtQa320O9OjWoW7taIeYQ0YiJPc3R4ycxGk0o3UPjhnU5ciyOxk2aUrlKlRJtuxBCiH83t9tNVNQqdF2nYcNQatf2A+wFPNrI4cN2YmNTs2KQctG0aWX27o2nY8duBAeHlFi7hRBC/Ps5nU6ili1DR1H7xEEq7f8b96lY0PXLHqsFhXCq2xASq9XB5OOD+2wK9ZOOszeoKgPHXIevb+lORSCKjyRDyqnDhw5x8mQcoKF0D7VrVqVeRC20Cy/clBs0EwAut4e/Vq6jYcNGNGrc2HuOfXv34uvrS7369cvkGoQQQvw7HTy4n9OnTwEaStmJiKhERETFrBiEDlgBfwAcDsXSpQdp2rQ59es3BLImZd27dzeBgYFERNQro6sQQgjxb7R35w7OnD6NZjDiio+jRkYSVZNOoSmFctpxHTmEuW5DUApnWDU212pOq3btqNvgnxi0c9s2KoaEUKdBgzK+GnElJBkiADgeG8vRo0dQSqGUQtMMeDxuzGYzbrebnr16YzKZyrqZQggh/mOUUsTGHiU29hj//CQx4PG4MJvNeDweevbsI0MuhRBCFDulFEeiDxMbHX1+i4bm44PHbsNkMKADvQcOkiGX/1GSDBFCCCGEEEIIIUS5IikuIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCuSDBFCCCGEEEIIIUS5IskQIYQQQgghhBBClCvlPhkSFxdH48aNWbhwYbbtTzzxBF999VWx1jVx4kT69u3LyJEjGTVqFEOGDGHGjBnoul6s9VxQEtdQ2j7//HOWLFlSrOdUSnHvvfeSmJhYrOcVQoj8bN26lRtvvJHhw4czZMgQHnjggSJ/D505c4abbrqp0Mc1btyY4cOHM3LkSO8/1157bZHaUJKWL1/OjBkzClz+/fffp0uXLt5rGj58OAMGDODnn38uchtGjhxJRkYG8+bN45577gHgqaeeYuPGjUU+pxBClLX/8rPPBSdOnOCBBx4o9HGrV69m4sSJDBo0iP79+zNx4kS2bdtWrG2LjY2lSZMmfPrpp9m2X3z/GzduTFpaWo5jL43hI0aMYO7cucXavvLGVNYNuBoYjUZefvllOnToQNWqVUu0rieffJL+/fsD4HK5mDhxIj///DPjxo0r0Xr/jWJjY4mKiir2L2ZN05gyZQovvvgi77//frGeWwghcuN0Orn33nv55ptvaNy4MUop3n//faZPn85nn31W6PNVqVKFH374oUht+f777wkKCirSsaWlX79+9OvXr1DHDB8+nKeeesr7ee/evYwbN45rrrmGwMDAQrdhwYIFOba98sorhT6PEEJcbf7rzz6nTp3i6NGjhTpm2bJlvPzyy7z//vu0bNkSgKioKO677z7mz59fbPfpxx9/ZOjQocyePZvbbrsNo9FYqOMvjuEJCQkMGzaMtm3bUr9+/WJpX3kjyRDA39+f66+/nunTp/PFF1+gaVq2/YcOHeLll18mPT0dg8HAAw88QPPmzRkwYAAbNmzAYrFw1113ERISwmuvvca5c+cYPnw4q1evxmDIu/ON2Wymffv2HD58GIDFixfz0UcfAVCpUiWeffZZ6taty7lz53j++eeJjo4Gst5W3XnnncTFxXHzzTdTo0YNUlJS+OmnnwgICMi1rokTJ9KqVSt27NjB6dOnGT16NPfffz8Ac+fO5csvv8RgMFCjRg1ef/11li1bxs8//4zVaqV27dq88cYbzJw5k+3bt+N2u+nevTuPPvooBoOB//3vf6xevRqbzYbH4+Gll16iffv2bN68mZkzZ6KUwmg08vjjj9OhQwdOnz7N888/T2JiIh6Ph1tvvZVRo0blaPMnn3zi3b5p0ybeeustwsPDOXbsGBUrVmTWrFk5vpieeOIJLBYL+/fvJyUlhaFDh/LQQw/lOHerVq04duwYBw8epHHjxnn+NxJCiOJgs9nIyMjAbrcDWUnZW2+9lZ07dwJZyZKXX36Zbdu2YTAY6N69Ow8//DAmk4mdO3fy8ssvY7fb8fX15aWXXiIwMJBRo0axdevWfI8tDLfbzaxZs1i1ahUGg4G+ffvyyCOPkJqammcMuv322+ncuTN79uzB4XDwyiuv0KZNm3zj1h133EG7du3Yu3cvJpOJBx98kM8//5zo6GjGjRvHfffdx7x581i2bBkffvghiYmJPPvssxw7dgyj0cjdd9/N0KFDL3s9p06dws/PD4vFAsDs2bP56aefUEpRs2ZNnn/+ecLCwvjuu+/46aefMJlMhISEMHPmTMLDw2ncuDFbtmzJds6JEydyyy230L9/f5YuXcpHH32Ex+MhNDSU5557jjp16hQ63laoUIEPPviA5cuXo+s6zZs355lnnsHf379Q//2EEKKgruZnn0qVKtG3b1+WL19OcHAwHo+HXr168cMPP2A2m3N9hpg3b16255YDBw5w5swZ7r33Xpo1a8apU6e8yez58+ezZs0a3n777Wxte+utt5g+fbo3EQLQo0cPpk+fjtVqBeCnn35i7ty5OBwOrFYrjz32GAMGDOD9998nNjaWkydPkpSURKdOnXjuuee88ecCp9PJ/Pnz+eKLL3j00UdZsWIFAwYMKOJ/RahcuTJ16tQhJiaG+vXr8/bbb7N06VKMRiMtWrTgqaeeIjAwkAMHDvDSSy+RlpaG2Wxm2rRp9OrVK8d9K5cviVU5d+LECdW+fXvldDrVqFGj1FdffaWUUurxxx9XX375pXK5XGrYsGHq0KFDSimlTp8+rXr27KmSk5PV+PHj1fr165XT6VR9+/ZVffr0UUoptWDBAvX000/nqGvChAlq6dKl3s8JCQlqyJAhatGiRerw4cMqMjJSnTp1Siml1K+//qqGDh2qdF1XDz/8sHr99deVUkqlp6erUaNGqcWLF6sTJ06oRo0aqT179uR6bReu4ULdjz76qNJ1XZ05c0a1atVKxcfHq/3796vIyEh15swZpZRSr732mvrkk0/UL7/8oiIjI1VmZqZSSqn33ntPvfPOO0oppdxut3rwwQfV999/r+Li4tRtt92mHA6HUkqpr7/+Wk2ZMkUppdTNN9+sVq5cqZRSauXKlerDDz/0tmX9+vVKKaVSU1PVoEGD1MGDB7O1Xdd11alTJ+/92Lhxo2ratKnauXOntz333HNPrtd83XXXKavVqjIyMtSwYcOy3fOLvfTSS+r999/PdZ8QQhS3zz//XLVs2VINHDhQTZ8+XS1atEi53W6llFJvvfWWeuihh5Tb7VYOh0Pdcccd6tNPP1VOp1NFRkaqdevWKaWUWrp0qZoyZYo3duV3bG4aNWqkhg0bpkaMGOH957XXXlNKKfXtt9+qW265RTkcDuV0OtWECRPUli1bLhuDLnyff/nll+rWW29VSqnLHrN27VqllFL33HOPGjdunHI6nerEiROqadOmyuFwqF9++UXdfffdSiml7r//fvX2228rpbJi8IABA5TVas12Xe+9957q3LmzGjFihOrbt6/q3Lmzmjp1qtq9e7dSKiuGTJo0yRurvvvuOzVt2jTldrtVq1atVFpamlJKqY8++kgtX77ce69SU1OzteVCHD9y5IgaNWqU97iVK1eqsWPHessUNt4+/vjj3j8LM2fO9N47IYQobv+GZ5+HHnpIzZkzRyml1KpVq9SECRO858vtGeLS55aNGzeqESNGKKWUOnPmjOrYsaM3btx4443ec1xw7tw51ahRI5Wenp7nfcvIyFA33nij93t/2bJlatiwYUqprBjUt29flZKSopxOp7r11lu99/ViixYtUtdcc41SSqmPP/5YTZw40bvv4ue2C/HnUpdu37lzp2rfvr06efKk+umnn9SECROUzWZTuq6rp556Sj3//PPK5XKpPn36qFWrVimllIqOjladO3dWcXFxOe5beSQ9Q84zm83MmjWLG2+8kcjISO/2o0ePEhsbyyOPPOLdpmkaMTEx9OvXjzVr1mA0GmnXrh0xMTEcPXqUlStX5trTAWDGjBm8//77KKUwGAyMHDmSIUOG8P3339O9e3eqVasGZL1Fe+mll4iPj2fdunXecc+BgYGMHDmSqKgoWrZsicVioVmzZgW6xl69eqFpGpUrVyY0NJSzZ8+yYcMGevToQeXKlQF4/PHHAZg3bx6NGzf2vplatWoVGRkZrFixAgC73U5wcDA33XQTL730EgsWLCA2NpZNmzbh6+sLwMCBA5k+fTq9e/eme/fu3HbbbVitVrZt28Zrr73mbZfT6eTAgQM0atTIu+3s2bNkZGR42wVZvTlatWoFwPXXX8/gwYNzvc4xY8bg5+cHwJAhQ1i/fr23e97FatasyZ49ewp074QQ4krddtttjB07lvXr17NlyxZmzpzJ3Llz+eKLL1i3bh2PPvooRqMRo9HIuHHj+Pbbb+natSsWi4Vu3boB0L9/f/r3709cXJz3vHkde8cdd+TajryGyaxdu5aRI0d632R9++23ANx///35xqCuXbsC0KRJE3799Vdvm/I6xtfX1xtna9euTYMGDTCbzdSsWROAjIyMHO26EJuqVq3KX3/9let1XRgmk5mZyX333UdoaCgtWrQAsmJYdHQ01113HQC6rnvvV58+fRg7diy9e/emd+/e3uvJz/r16zl9+jQTJkzwbktJScHpdAKFi7dTp05lz5493rlbXC4XtWvXvmwbhBDiSlzNzz5jxozhgw8+YNy4cSxYsIAxY8bk+wwBZHtuuVjlypXp1KkTS5YsoVWrViQlJdGlS5dsZZRS3uu84IYbbsBms2G1Whk2bBjTpk3z9uI7duwYO3bsIDMz01t+6NChVKxYEYBRo0axaNEibrnllmz1zJkzx3ufhg8fzttvv82hQ4eyPQNdzvjx4zEYDHg8HoKCgpg5cybVq1dn7dq1jB492vscNn78eO6//36OHTsGZMUlgPr169OuXTtvz8e87lt5IcmQizRo0IB7772XRx99lHr16gHg8XioWLFitrHDZ86coVKlSlSpUoV7773X+0M1PDyctWvXsn37dmbOnJlrHRePm7tYbhMJ6bqO2+3OsU8phdvtBsDHxydH17a8+Pj4eP9d0zTv8JWLpaWlkZ6enqO8rus8//zz3h+JqampGI1G9u7dy7333sukSZOIjIykQYMG/PLLL0DWX8K+ffuyZs0aFixYwLfffstnn32GUoqff/7Z+4M7MTGR4ODgbO3QNA1N07K17+J/z63thS1nMpkKPU5PCCGK4u+//2bXrl1MmjSJwYMHM3jwYKZNm0aPHj2IjY3N8T1/4fvfaDRm+45XSnHo0KFsQyLzOnb58uW89957ALRo0eKy812YTKZsdZ0+fRo/P7/LxqALLsSV3Np08TFmszlHvYVp15EjR6hRo0a2ui8WEBDArFmzuOaaa+jYsSNDhgxB13XGjBnjnVDP4XB4ky7vvPMO+/fvZ+3atbz66qv07t071+GVF/N4PPTo0YNZs2Z5PyclJXnjWmHira7r3H333d5ETWZmJi6XK9/6hRCiOFytzz5du3blmWee4cCBA2zatIlXXnkFj8eT5zPEwoUL84wJkJXY+Oyzz4iJiWHs2LE5np1CQkKIiIhg69at3qTBnDlzgKwJutPS0oiPj+eGG25g3LhxtGvXjk6dOvH00097z3G5549jx46xZcsWjh075j232Wzmu+++48UXX8yz7ZfK64VGXr8FPB5PrmXdbjcGgyHf+1YelPvVZC51yy23EBQU5H3zdOGLYenSpQDs27ePa665hrS0NO+bmz///JNu3brRrVs3PvvsM9q2bZtjjNjldOnShaioKE6fPg3Ar7/+SqVKlahZsyaRkZHMnj0byHpjtmDBAu9bwivVqVMnNmzYQEpKCgD/93//x3fffZejXNeuXfnhhx9wu904nU7uuusufv31V7Zs2UKrVq2YNGkS7du3Z/Hixd6/dOPGjePYsWOMGzeO5557jv379+Pr60vz5s29E/+dOnWKYcOGceTIkWz1VaxYkYCAAOLj473bdu/eTUxMDJA1+VDPnj1zvabFixfjcrnIyMhg8eLFeZaLi4sjIiKicDdMCCGKIDQ0lA8//JBdu3Z5tx0+fJgKFSpQpUoVIiMj+fHHH/F4PDidTn766Se6detGvXr1cLvd3tnsV69ezfTp07OdO69j+/Xrx4IFC1iwYEGBJv7s0qULixcvxu1243K5ePDBB9m+fXuRYlBxxq0uXbrw+++/A1k/yCdMmJDtbVxuwsLCuP/++3nttdewWq1069aNhQsXcvbsWSArAfLCCy+QkpJC3759qVKlCnfeeSe33noru3fvvmybOnfuzJo1azhx4gSQNR/J3Xffne8xecXbrl278ssvv2Cz2dB1naeeeoqPP/74sm0QQojicDU++2iaxqhRo3jqqafo27cvfn5+BAYGFugZArISExcnASIjIzl9+jSLFi3KcwW1Rx99lJdffpm9e/d6t8XHx/P3339jNBrZvXs3YWFh3HXXXXTv3p0//vgjWx3Lli0jMzMTp9PJr7/+muP548cff6RXr16sWbOGFStWsGLFCt59911+++03UlNTC3XvchMZGcm8efOw2+0opZg9ezbdunWjbt26KKVYvXo1ADExMWzdupXOnTtfcZ3/BdIz5BKapvHaa68xYsQIACwWCx988AEzZszwvmF78803CQ0NBaBv374sXbqUKlWqEBwcTEpKSq7Zz8tp2LAh06dPZ8qUKXg8HkJCQvjf//6Hpmk8/fTTvPDCCwwbNgyXy8XQoUMZOXIkJ0+evOLrbdKkCVOnTvV246pXrx4zZszIsZztfffdx4wZMxg1ahQul4sePXpw4403kpKSwp9//smQIUNQShEZGclff/2Frus8/PDDvPLKK2iahsFg4JVXXsFkMvHmm2/ywgsv8Msvv+B2u3n00Udp0qRJjrb179+fdevWMWbMGCDrx+3MmTM5efIktWrV8i67OHv2bBISEpg2bRoABoOBG264gYyMDMaOHUuPHj0AuPPOO5k6dap3YqT169fnmDxJCCFKQkREBO+88w4zZswgOTkZk8lElSpV+Oyzz/D19eWee+7h1VdfZeTIkbhcLiIjI7nzzjuxWCy89957zJgxA7vd7u31cLG8js3LhS62F3v77be54YYbOHHiBKNHj0bXdQYPHkyfPn1o3bp1oWNQccatZ555hueee84bl1988UVvDM7PTTfdxA8//MDHH3/Mgw8+SExMjHdYS40aNXj11VcJDQ3ltttuY/z48fj5+eHj48Nzzz132XM3btyYp556yrvkboUKFXjzzTfzPSaveOvr68upU6cYO3Ysuq7TrFkzpk6detk2CCFEcbgan30Arr32Wj788EOeeeYZ7zF5PUPs27cv27kbNGiA2+1m4sSJfPvtt2iaxtChQ4mOjiYsLCzX9vTv358KFSrw1ltvkZiYiM1mIyAggKFDhzJhwgRvr5QLw/R79epFWlqat5dhcHAwt912G+fOnaNv377e3n6AN0Fy6XNHnz59qFOnzhUtA3/B9ddfz8mTJxkzZgxut5sWLVp4J3H94IMPeOWVV3jjjTeArJXRatWqlWOS8PJIUxf6tApxlTl27BjTp09n9uzZbNq0iRkzZuS61OHFnnjiCZo0acKkSZPyLbdt2za+/vpr75e8EEIIIYQQ4r/H6XRy6623ct999xVoXqjCujCU5uLl3cW/gwyTEVetiIgI+vTpw+LFi4v1vEopPv30U/nCEkIIIYQQ4j9s//79REZG0qxZsxJJhIh/N+kZIoQQQgghhBBCiHJFeoYIIYQQQgghhBCiXJFkiBBCCCGEEEIIIcoVSYYIIYQQQgghhBCiXJFkiBBCCCGEEEIIIcoVSYYIIYQQQgghhBCiXJFkiBBCCCGEEEIIIcoVSYYIIYQQQgghhBCiXDEV9gC3201cXBx2u70k2lNu+fr6UrNmTUymQv8nEUKIckNiUMmQGCSEEJcnMejqIDFLFBdNKaUKc8CxY8eoUKECoaGhaJpWUu0qV5RSpKSkkJ6eTkRERFk3RwghrloSg4qfxCAhhCgYiUFlT2KWKE6FHiZjt9vlC6CYaZpGaGioZJmFEOIyJAYVP4lBQghRMBKDyp7ELFGcijRniHwBFD+5p0IIUTDyfVn85J4KIUTByPdl2ZP/BqK4lMpAK6UUUdE7OHUuieohYfRo0Eb+EAshhCgVSikSo7ZiPZWAf/XKhPfoIDFICCFE6VAKlBuUDpoBNBNIDBLiqlDiyZD5O1bx6LwPiEmM826rH16TWdfex+g2vUu6eiGEEOXYiflL2f7o62TEHPduC6xfm7azHqPW6AFl2DIhhBD/eR4HuK1ZiZALNAOY/MHoU3btEkIAJby07vwdqxj7yZPZEiEAMYlxjP3kSebvWHXldcyfz5AhQxg4cCCLFy/OtcwNN9zA8OHDGTlyJCNHjuTMmTMAvP322wwaNIhrr72WhQsXFqi+jIwMhg4dSmJiYr7lZs6cybPPPuv9vHHjRsaMGcM111zDiy++WMCrE0IIUVQn5i9l7dip2RIhABkxx1k7dion5i+94jouF4OUUrz66qsMGTKEYcOGsWzZMu++WbNmMXToUIYNG5Zn/LrYmjVrGD58OAMHDuSrr77KtczlYs2lsUkIIUQJ8TjAlZE9EQJZn10ZWfuv0JkzZ+jbt2+2bYsXL2b06NEMGjSIjz/+OMcxv/76q/eZaOTIkbRp04bPP/8cKL5no8vFovXr12drQ8eOHb3lvv/+ewYNGsTIkSPzjHVCFBtVSPv37y9QOV3XVf1nxiju6pznPw2eGaN0XS9sE7zi4+PVgAEDVFpamjp37pwaNGiQSk5OzlbG7XarPn365Dh2zZo1avTo0SozM1PZ7XY1btw4FRcXl299+/btUyNGjFDNmzdXCQkJeZbbsmWL6ty5s3rmmWeUUkqlpqaqyMhIdfToUeV2u9WECRPUmjVrchxX0HsrhBDlVWFi0IL6/dX3NMrznwUNBpR4DFq1apW69dZblcfjUcnJyapz587KZrOprVu3quuvv1653W6VmJioOnXqpBwOR5512Ww21adPH3X69GnlcDjU2LFj1YEDB7KVuVysuTQ2XUpikBBC5K/A35O6rpQ9RSlbUt7/2FOyyhXR+vXr1aBBg1SLFi28244cOaJ69+6tkpOTldVqVQMHDlTR0dF5nmPnzp1qxIgRymazFduzUUGfey44ceKEGjhwoEpKSlLR0dHe9rvdbnX33Xerv//+O9fjJGaJ4lBiPUOionfk6BFyqejEONZG7yxyHevXrycyMpIKFSoQHBxMt27dWLlyZfY6oqMBGD9+PKNGjeLPP/8E4ODBg/Tq1Qt/f398fHxo164d69evz7e+X375hSeffJLKlSvnWSYzM5M33niDyZMne7etW7eOrl27EhERgdFo5I033qBVq1ZFvWwhhBCXkRi1NUePkEtlRMeSuHZbkesoSAzq1asXn3zyCQaDgaSkJCwWC0ajEV3XcTgcuFwubDYbZrM537p27dpFgwYNqFq1KhaLhcGDB7NkyZJsZfKLNbnFJiGEECXkwhwh+ZbRs8oV0S+//MI777yTbdvy5csZOXIkoaGh+Pn58fnnn1O1atXcq1eKF198kaeeegpfX99iezYq7HPP66+/zt13302lSpU4dOgQHTt2JDQ0FKPRSGRkJKtXry74TRGikEosGXLqXFLByqXmP9wkPwkJCYSHh3s/h4WF5Ri+kp6eTteuXfnkk0/48MMPmTlzJrGxsTRr1ow1a9aQkZFBRkYG69evJzk5Od/6nn76aTp37pxvmZkzZ3LHHXcQEhLi3XbixAlMJhNTp05l5MiRzJ49m6CgoMJfsBBCiAKxnkooUDlbAcvlpiAxCMBkMvHKK69w7bXXcv3112M2m+nYsSMRERH07NmTIUOGcPfdd2OxWK6orvxiTW6xSQghRAm5XCKksOVy8cYbb9CkSZNs206cOIHdbmfKlCmMHDmSVatWERAQkOvxa9asISgoiE6dOgEU27NRYZ57oqOjOXToECNGjACgcePGbNu2jcTERJxOJ6tWrbpsG4S4EiU2gWr1kLCClQsOv3yhPOi6nmNFAIMhe36nQ4cOdOjQAYCAgAD69+/Phg0buOGGG9izZw833ngjlStXpnPnzphMV3Y71qxZg8PhoH///sybN8+73ePxsH79en766ScqVqzI3XffzcKFCxk+fPgV1SeEECJ3/tXz7sF3Mb8ClstNQWLQBU899RT33nsvEydOpGPHjqSlpZGenu794XnLLbfQrl07mjZtWuC6Lv2cV6wJDg7ONTYJIYQoIVoB3zcXtFwBeTweNm3axNdff42maUyYMIFmzZrRrl27HGV//PFHJk6c6P3crVu3Ynk2Ksxzz48//siECRO8sbNevXrcd9993H777QQFBdGlSxdJhogSVWI9Q3o0aEP98Jr5lmkQXpPuDVoXuY6qVauSlPRPD5SkpKQcQ1g2bdrE1q1bvZ91XcdkMnkn+/n999/5/PPPcTgcVK9evchtAfjjjz/YvXs3I0eO5L333mPJkiW8++67hIWF0bZtW6pUqYLFYqFv377s2bPniuoSQgiRt/AeHQisXzvfMoEN6hDevX2R6yhIDIqJieHQoUMAhISE0L17dw4dOsS6desYPHgwvr6+hIWF0bNnz2yxqih15RVr8opNQgghSohmunyi48Iyu8UoLCyMbt26ERQURIUKFYiMjMz1mcPhcLBr1y4iIyO924rr2agwzz2rVq1i0KBB3s92u522bdvy22+/8d133+Hj43PFz2dC5KfEkiGapjHr2vsw5PFFYNAMvH7tfTnebBVG165dWbt2LampqaSmprJ27Vq6dOmSrUxaWhpvvvkmLpeL5ORkVq1aRY8ePYiLi2PatGl4PB7i4+NZs2YN3bp1K3JbAF599VUWL17MggULmDp1KoMGDWLatGlERkayY8cOEhIS0HWdtWvX5vn2TwghxJXTNI22sx5Dy6OnhmYw0Pb1R0s8Bh05coQXX3wRt9tNRkYGa9eupW3btjRp0oTly5ej6zo2m41NmzbRrFmzPOtq3bo1Bw8e5OTJkzidTv7880969OiRrUxesSav2CSEEKKEaFrW8rn5MflnlStGvXr1YvXq1WRmZuJwONi0aVOOoTSQNXdio0aNsg3PLK5no4I+96SkpGAwGLINAbVardx+++3YbDbS09OZN29ejtVyhChOJbq07ug2vZk7eQYNLukh0iC8JnMnz2B0m95XdP6qVaty9913M378eK6//nomT57snSTowhK6AwYMoE2bNowcOZLx48fzyCOPUKVKFZo0aUJkZCTDhw/njjvu4Pnnn/eOpb54+d2CuPPOO9m9e3ee+6tXr87TTz/N7bffzjXXXENERASjRo26kksXQghxGbVGD6D73PcIbFAn2/bABnXoPvc9ao0ecEXnL2gMatWqFSNGjODGG29kwoQJtGjRguuuu44aNWpwzTXXcP311zN8+HDat2+f7diL+fj48NJLL3HXXXcxfPhw+vTpQ+vWWT0rL8QgiTVCCHEVMfqAOTBnDxHNkLXd6FPsVbZt29Ybk0aMGMHw4cO9c4JcHFvi4uKoUqVKtmOL69kov1h08TNTXFxcjh6OoaGh3H777YwZM4brrruOKVOmUL9+/aLeDiEuS1NKqcIccODAgVwzjPlRShEVvYPTqUlUDw6ne4PWV/Q2rqS98sorTJs2jcDAwFKttyj3VgghypOixqDEqK3YTifiV70y4d3bSwzKhcQgIYTIX5G+J5X6Z3WZC0NjruIYlJuyikv5kZglikOJTaB6MU3T6NmwbWlUVSi6rpOWlpZtdn2lFG3atLmq/rILIYQoOk3TqNyzY1k3Iwdd10lPTyc4ONi7TWKQEEL8x2gaaPkvn14WPB4PGRkZ2WJQbiQuif+yUkmGlCSlFBc6tyil0DQt2xs/pRRWaya6R8dkNmM2m1FKoes6CQkJVKhQAZvVisUnq6ua2+2iR/fuJCQk4O/vL3/xhRBC5OnSGHTpajJKKTIzMtB1HbPF4o1BHo+HhIQEgoODs8Ugl8tF98hIzpw5Q2BAAAESg4QQQuThcjFI13WsmZnouo7FYsF0UQw6c+YMFStWzBaDnE4nTqcTu91OhcBA/AMC0DSNoUOHlvq1CVEarspkSHp6Oi6XCx8fCyaTGZPJhNFoBLL+UmdkZsD5wT0e3U1aajqVKgajKTe60lBA1v8qNDT8zGAwuXErFy4bGLSsJG2tyhVAeXDrVuwZmQAYDRoVLOD2mLBYrr4srhBCiJKVnpaGy+3Gx2LBbLFgNBqzxaDMjAwAFAqluzmXmkFIQCC63QkmI2igdAVKoQFGuwscTtz+vjiNGijQlCJMM6NS0nEZDdhNWT9gDbrC6PRgDPbz/jgVQghRfqSlpeHxeLCcT6CbTCZvkkPXdTIzMwGVNfpGKc6dO0eQvz+6wwGmrCE4Ste9MUiz2/E4HHgCAlAGQ9Z2pahoMOBJSsJmNpNpNIJSGJRCc7kwVKiAj69vmd4HIUrDVZkM0XUPZj/A4MThdpKenpXN1HGDArOfnvXvKAzKQ5DRD5fLTYjZmscJs/7PhBPThU4jCnBd2A6mixOpbrAY/DCZzNjtdpTS8fO7zIzQQggh/vWUUuhKJ9CiMBicuBwO0hwqa8Z95UZTEGDxoOluQAfNg6GCL263jh6fnPN8eEMQZNqy7XPnUr9+frvBL+sHsN1mQymFn7/EICGE+K+70NPD398Po8GA2+0mJS0Ni8WCBqBBoL//JVOOKHSHE0d8fL7ndmVmFrgdPv7+GAwGbNasZyuJQeK/qlSSIUopzjqO4vCk4WMMoqJP3Twnr0vPSMfo48Gj2bJ+QJrAXAEUdi4c4b5kylejj5uMdAjx8QHdUSxt9jUp0tLSMBkNaBqknjsHGgQFBV/VE+8JIYTITikFqUfAkQo+wRBcL8/v8YyMdAJMboy6FTxgBHx9AKx4g5An+zGBFjfxdg1/f1/cVnuxtNlod5GWmopBgWbQOHf2LJqmEXzRHFdCCCGufkopSDsJzgywBEJQjbyfg9LTCQzwx2jI2m8yWvD1seRa9oLgCoGcykzA6OODx1E8z0HY7aSlpaHpOprBIDFI/GeVeDLkjHUPB88uwur+542Zv6kSjSsOpYp/i2xl7Q47Cg+6Zrv0NPlSSsc/yEKKXSM0/++LAjMqO8FGJ5DV1RmLL05lwW63SS8RIYT4l1CJu1Axv4E96Z+NvmFQfwRaeKtsZe12G0Y8WYmQQlXioYK/Dw5TEBwvnmSIOyUVLS0Dtzsr82IOCkQLCcRus+Hr51csdQghhChZKukw6tgasKf+s9E3GCJ6ooU1zFbWZrNhNpu8iZDCqBAYiMtkwnP8+JU2GQBHUhKuc+fQ3Vl9GH2CgzEGB+NwOPCRIZziP6REkyFnrHvYnvgt3gk+zrO6k9me+C1twyd6EyK6rmO32TD4Wy8tflmJCUnccfNU5sz+ESwGLuqUnM20x1+kedNGTJ50AwCL/1rFZ1//iNVmZ/TwgUy59cZLjrjoPB47s2fP4cf5f2A0GunRowePP/54ttLr169n5syZ3s+nTp1i+PDhPPvss95tM2fOJDMzkxdffLFwFymEEKJQVOIu1N4vyRFU7ElZ25vf6k2IeDweHHY7QcaCdyMGmL/wLz77+kfcbg+TbruDwY1bc+mK9cs2rOO7hfOxOxwM6dGHm0dem23/U+++QeO69bh5RPbtyv1PFxRXWgYmt5uzLgd3TJ7MV199RXh4eI72fPPNN8yZMweAcePGccsttxQoNgkhhCheKukw6sBCcsag1KztTYZ5EyIejweXy0VQYOFeuJ5JSODGG8ezYvlS4q1W0DRWrF/PnIULsTscDOrRg/EjR2Y7ZklUFD8vXuz9fPLMGSaNGcO4oUP5/KefWLVpE36+vowbOpR+3boB4EhNxeLxYLdYsiVDvvrqK+bOnQuQ7dkor+2XysjIYNy4cd6YJvFKlLYSS4YopTh4dhF5Zzay9lf2a46maWRkZGDyd+NRhcuEbNm0nddfeZfkpBQ0AzgNgVj0tBzlfv9jORu37KB500YAHI2N4433PmXut//Dz8+X0TfdzYA+kdSLqJ1rPQcPH+GX35bw/fffERQUzO23386qVavo3bu3t0y3bt1YsGABAHFxcdx+++3ce++93v1bt25l/vz5DBw4sFDXKIQQonCUUlk9QvKJQerIbxDWEk3TyMzMoILZCXruyfTcnElI4qPPv2futx+i6zrXTZpGj/c/xD/D6S1z/PQp/jf7G754+XX8fHyZ9NQj9OzYiYjqNQFYsm4NW/fupnHdepet71ByAi8+/iqxsbG57j99+jRfffUVixYtQinF0KFDGTBgwGVjkxBCiOKllMrqEZJfDDoWBZUanI9BmVQoZCJkw8aNvPDiyyQmZfV81DRIcrv5ZPZsPnr5ZXx9fJj81FP06NiR2tWre48b1KMHg3r0AGB/TAxvfvYZowYMYPOuXWzauZNPXnkFo8HAQzNm0LxhQ6qeT7ybQkOz5s8678CBA8ybN4+5c+diMpm8z0ZVq1bNdfvFz0wA+/fv54knnsgW0yReidJmuHyRojnrOJptaExurO5kzjqOAVlDXTyq8OPcfv/1D16Z9QwA/iEayalWLr2sMwlJzPllIddf+8+yUCtWr2fk0AGEVgzBz9eXzz54lSqVc75lu8Dfz4/pjz2AwWDAaDRSv359Tp8+nWf5119/nbvvvptKlSoBkJmZyRtvvMHkyZMLfY1CCCEKKfVI9qExubElQeoR74R1mu7Mv/wl1m/+m8gu7akQGEBwUAW6d+nA0nVRaBctbRi1bTPX9OhNxaBgfH18ePvxZ6gcmhUXElOS+XX5X4zqO6BA9f32+288MG1arj1C4J83iw6HA8f5ceMXVsG54NLYJIQQogSkncw+NCY39nOQdvJ8b0JFYQfH/DJvPu+89Yb3c3hYJZavX8/AHj0ICQrC18eH1x9/nPDQ0FyPV0rx3ldfcd/NN+NjsXDk+HG6tG2Ln68vFouFFo0asW3vXm95/XxsudD7MSAggGeeeQZfX19MJpP32Siv7Tna/8svPPnkk1SuXDnX9km8EqWhxJIhDk/O3hm5l0sl05qJ0cdz+cK5ePHVJ2nUuD4AunJj9jFgIyBbmedefZfpD9+NxfzPUrknTp7GZrcz5YGnGXXTFFZFbSTAP+9x2LVqVqNr28ZUMLvYt28vS/78kx7ns6qXio6O5tChQ4wYMcK7bebMmdxxxx2EyMRDQghR8hyX+RF6gTMVa2Ym/qasFcoKIyExmfCLfqSFhwZz9mwK5pr/JCtOJZzB7nDwyKwZ3DL9YdZt34q/b1asef2Lj5k28VbM5oIt4/7A9RPoUKcB6qLlfS9Ws2ZNhg8fTp8+fejXrx+DBw+mSpUq3v25xSYhhBAlwJnzOzr3cplkZmbm+wySlzden0mTJk28nw2aRvzp03gsFqbPmsUd06ezYft2/PJYInfzzp0E+vvTpmlTABpGRLB5504yrVYyrVa27dnD2dR/Yqn9zBncycmkpqaSkZ5OrVq16NixIwDHjx/nr7/+okePHnluv9TTTz9N586dc22bxCtRWkpsmIyPMaiA5YJxO9xovsUz6ZxvBUhLseMXoAGKOXN/p1nj+rRo2oiVazZ6y3k8HjZv3clXH81C0zQmTn6IZk0a0q5183zPfyI2hmn3T2fq/fdQqVLumdYff/yRCRMmeNcEX7NmDQ6Hg/79+zNv3rxiuU4hhBD58AkuWDlLMB6PB5Oh8DFI6TqXLggQ6Gsg0+PmQnrD7dHZu38P7z/5ApoG9770LI0j6hF9PJZGEXVpUrc+a//eWuA63akZKLcH3e7EZrVmW+5w165dbNy4kVWrVmE0Gpk8eTIrV66kT58+QM7YJIQQooRYAgtYLgDd48FYTN/LZrOJLdu389ajj4Km8cBLL9EwIoIWjRrlKPv7ihVcO2iQ93P7Fi04dPQoU198kdCQENo0a4bpkt6FznPncJ47R0CdOthNJnz9/IiNjeX222/n4YcfpmbNmt6yeW0vCIlXorSUWDKkok9d/E2V8h0q42+qhL9WDacpE1XYWVPzoCs3CgPKHIjmSuevFWtJTE5hxZoNJCVnLQsVFBhAWKVQunVuR1CFrC+ryM4d2LP/UL7JkH0Horn7oWd45P47GH5NL2xuJzYbOVaXWbVqFT/88IP38x9//MHu3bsZOXIkqamp2Gw2KlWqxLRp04rlmoUQQlwiuF7WqjH5DZXxC8PhUx0fjxVUwecKuaBKlXD27Dvk/ZyYnEKr5k0AHZ/KoTgSUqgUEkLHFq2pEJDVY7FTy9bsPxLDuu1bST53jrXbtpKceg6DplHBP4DR/QflUVt2ntOJuMIqoWmad3WZTZs20aNHD4KDsxJBQ4cOZdOmTd5kyKWxSQghRAkJqpG1akx+Q2V8Q7CbQ/E1Ft8Df1hYGB06dCC8dm1sycl0aNmSg0eO5EiGOJ1ODsTE8NzUqd5tmVYrfbp04cbhwwF4+8svqRIWlms91hMnULVqceDAAaZOm8YjjzySrRfHvn37uOuuu3JsLyiJV6K0lFi6TdM0GlccCnmOgMva73A4MFiKNkQmL4EhZpJSs8ZLf/HhTH7/8VN+/eFjxl07jIk3jOaGscPpGdmJ1Ws3k2m14XA42bRtB00a5j2BXWpaOlMeeIrnp09j+DX9AIUfGbhdWW/nLkhJScFgMGQb0/3qq6+yePFiFixYwNSpUxk0aJAkQoQQogRpmoZWfwT5xSCt3gicLicWzVWkOrp2bMvajVtJTUsnNS2ddRu30qVjGyoGmrFassJr1zbtWL9jG1a7DYfTybb9e2hYJ4J3pz/HdzPf5utX32RUv4GMHTSkwIkQAKUrPCcTcdhs3hjUpEkToqKisNvtuN1uoqKiaNasGZB7bBJCCFEyNE1Di+hJvjEoogcutxuLufjeTffq2ZPNmzeTpmk4nU527N9P/Tp1cpSLOXGCujVrYjb9U/fpxEReeO89PLpOYnIym3fupH2LFrnWo3SdxEOHuPe++3jqySezJTxSU1OZPHkyL7zwQpESIRKvRGkq0b5HVfxb0DZ8Iv6m7BPf+JsqeZfV1QBPISetuxxdczDp1ts4k5L3EoltWzVj/PUjGDfpfkbeOIVhg/rSqX1rAEbdNIUzidnfJs75ZSEZGVbe/b8vGXXTFEbdNIWff11IoJbOfffdw7p16/B43MTFxeU5EZAQQojSo4W3Qmt+K/hd8mbLLwztwrK6SkERY1DVKuHcfdt4JtzxEOMmTWXypBupWiUcCw4m3XYbqR4nLRs2ZuyAa7jz2Se45cmHGditB22b5j8c85bpD5N4NuWy9etuD55j8dwz5S7WrVtHt27d6N+/PyNHjmTUqFHUrVuX4eff8ElsEkKI0qWFNURrMgx8Q7Lv8A1Bu2hZ3eLUtm0bJk4Yz+QpU7jz6afp162bd06QO6ZPJ+nsWQDiExMJu2Ri1QZ16tChZUtuf/xxHnv9dR689VaCAgNzHHvBr3/+SUZ6Ou+/9x7Dhg1j5MiR/Pzzz8yZM4eMjAzeeecdRo4c6d0OcOedd7J79+58r0HilShNmlKFW8v2wIED2SbrKQilFGcdR3F40vAxBlPRJwLt/EDrc2kpaD7Wy5yh8DTdgj1dEe5X+BVqZrz5IVOn3EJgYMDlC19gsJDm8iX4CiZILcq9FUKI8qSoMYjUI+BMBUswBNfzxqD0c8kEGvNOnBeVQ/mSblMYTiQW+th3vv2CO8fcQIB/wZdZNAX4oapWIqRiSKHru0BikBBC5K/IMSjtJDgzwRIAQTX+iUFpaYVeUrcgbHYHmRmZOI8fL5bzffDtt9w6ZkyecckSGIipatVSXShCYpYoDiU2Z8jFNE0j1DfnEJSsPEzhx2kXhDI4cXs0MAWAu+A/dJVStG7RtHCJEADdiclgxuVyFXhlACGEECVP0zQIqZ9ju8fjQdOKZ76qS/loDs7qRgIrBuE8W7DV1SArBjVv0KhQiRAAd6YNo8OJx+PJsZyuEEKIsqNpGgTnnEDU7XaX2Pe1n68P51LT8AkKwpFW8BiUG6UUzRo0yDcuOTMyMDgc6Louk56Kf5Uy/dOaac3E5FPYVbULLqCCmZTMws1HomkaQwf1KVJ9fiaw22xFOlYIIUTpslmt+JlKJhkCiiA/I67A3Jc0zIumaQzo2r1INRodLqzW4u9pKYQQovjZbDZ8fS0ldv6AAH+0YuipoWkafbt2vXw5hyPbPIpC/BuUaTLE4/GgUzxL6uZGs7hxuj2U1mXa3Xhn9RdCCHF103Udg15yMcjf5MGpe7zdoUuax2LCT2KQEEL8KyilMJRgfAgKDMCpe8ixBnwJ0S2WbMu9C/FvUGbJEIfDgWb0FNuSurlRSsdsNpChl85fTF1Raj96hRBCFJ3dbsPH6IYSjEEoN2aThrFWlZKr42KaJjFICCH+BWxWK74+Jdcr5AKTyYx/rVolXg+AJsNjxL9QqfypVUqhzsWgzvyd9f9K4XA4MFlK8EfoeZYKHjKsDtBKfh6PAJOLzIyMEq9HCCFEwSmlSLEf4XTmDlLsR1BK4XI6sRhLZs6qi4X6ebB6nBgsJR+DDOcySL/CseFCCCGKl1IK574d2NYuw7lvB0op3B43llKYYzAsNASbx4PBVPLTROrnzpGWmlri9QhRnEr8b4ZK3IWK+Q3sFy1V6xuGsXp/lF/xLymVo36lY/Y1k+ExE2hwlWxluhOL0UJ6ehoVKgSVbF1CCCEu64x1DwfPLsLqTvZu8zdVopZPbwJNOSf2LnbKg4/JgrFqKPrxMyValSvDirlihazVCYIkBgkhRFmzb1pN+rf/wxN/0rvNWLUGxjG3Qq+BpdIGi9mMqXp1MotpZZm8ONPSMIWEkJGeTmCFCiValxDFpUSTISpxF2rvl+TohmxPwvfIjzgtY6BixBXV8f7bn7BuzSYUilHXDuXGiWOy7V/021/M/nYuHjeYjRB7/CT3TbmZ2yde5y0z7fEXad60EZMn3VCgOme+8zFWq40Xnnwgx74dW9cx893PcTgcdO3alWeffRaANWvWMGvWLBwOBzfddBOTJk0q8jULIYS4vDPWPWxP/JZLY5DVncxB9zz8jCOoYrry7sNnEpO48bYHWPH7d7nu//zTj1i2ej0Gt86to6+jX5dI/ohaxZzFv3vLnDhzmjvGjOOmoSPzrOfHPxby26plAHRt3Zb7brolR5lNS5bzv3lzcsSgb775hjlz5gAwbtw4brkl57FCCCGKj33Tas69+TTo2XsheuJP4vlwBnY/H3w79Szy+We98SarV69BKcV1141l0i0357r9lltuJv5MYtbcIeqfeJh09iz3Pf88c959F4AlUVH8vHixd//JM2eYNGYM44YOzbMNcxYu5I/Vq9E0jWsHDWLkgAH418t60ZCRkcG4ceP46quvCA8Pz3Hsww8/zN69e/Hx8QHg5ZdfpmXLlnz11VfMnTsXgB49evD4448X+R4JcTkllgxRSmX1CMljPLaGwnxsOc6Q24o8sU/UqvUcPniE7+d+gtPhZNJN99CpSzvqN6zrLTN0xECGjhiII93IsegjvPLKy4y/boR3/+9/LGfjlh00b9qoQHVu3b6bXxcuZWDfnLP9p6Vn8OjTM/j4/z6kSfNWTJo0iaioKDp27Mjzzz/PDz/8QGhoKOPHj6dr1640bty4SNcthBAif0opDp5dRN5zgigOZa6mctD4K5pnY+OW7bzw2vskJafkuv/vHXvY8vcOvv7yc+ypVq6fMJ6eHTpxTY/eXNOjNwD7Yg4z87OPGDNgcJ71RB8/xqI1K/jipZkYjSYenPkS67ZvI7Jte2+Z9MxMnv/fO3z4vw9p0ba1NwY1aNCAr776ikWLFqGUYujQoQwYMIDq1asX+bqFEELkTSlF+rf/y5EI+aeATvp3H+HTsUeRYtCKlSs5cPAgC36dh8PhYOz14+jWtStxJ+Ny3R4SGopvjRpkxMUB8Pfevbzz5ZekXDSsZVCPHgzq0QOA/TExvPnZZ4waMCDPNsSePMmSqCg+feUVACY99hgdW7akXq1a7N+/nyeeeILY2Ng8j9+/fz9z584lMDDQu+3AgQPMmzePuXPnYjKZuP3221m1ahW9e/cu9D0SoiBKbs6Q1CPZh8bkVrn9LMb0k/mWyU/V6lW4Z+ptGI1G/Pz9qF6zGglnEnMt61sBXnn1dZ58+F58fbMykGcSkpjzy0KuvzbvjOfFMq023nz/M+6cNC7X/es3baNrx7Y0qFsLh93OrFmzaNKkCbt27aJBgwZUrVoVi8XC4MGDWbJkSdEuWgghxGWddRzNNjQmN1bPOc65T19RPb/8toS3X30qz/0eXcfpcBLso5NsTcd8ybhtpRRvfvUZD9x8Gz4WnzzP4+fry4M3346PxQeT0UhE9RokJGePsVv27KR985ZEVKuOw+HwxiCPx4PL5cLhcOBwOAAwGo1XcNVCCCHy49q/M9vQmNx44k/iOrCrSOevXr06Dz3wAEajEX9/f2rVrEX8mfg8t4dXqkTmRb1C/li9mmfvvz/XcyuleO+rr7jv5pvxseQ9yWudGjX4bMYMLBYL59LT0XUdHx8flMvFj3PmMH369Fx7hEBWr5Hk5GSmTp3KiBEj+P777wEICAjgmWeewdfXF5PJRP369Tl9+sritBD5KblhMo6CTaCjOYs+4WjDRvW9/7575z4OH4ihZZvmuZZdF7WewMAAOnXpDq6sCeaee/Vdpj98NyvXbCxQfTPf/pg7bh5Hano6seT8gjsRdxqTycQjjz3Bsbh4unTpym233cbJk3HZvgzCwsI4duxYIa5UCCFEYTg8BZtI1K5nXlE9s156It/9Hdu1IqJOTXoNHoPNZufBKXdjNv0zad7GndsJ9A+gbdPcY9cFNSpXpUblqgDEnYln1ZaNOYbUnEw4g8lk4vEnnuDk2SS6duvGrbfeSlilSgwfPpw+ffqgaRrjxo2jSpVSWuFGCCHKIU9K/i+EveXO5p+0z0uTi3qX79ixk/0HDtCubdtsvSwu3q5pWSte+oaGYktJ4al77snz3Jt37iTQ3582TZteth1Go5E5Cxfy9bx59O7ShdDgYBxnzvDwgw9id7nQdR2HPecS9klJSXTu3Jnnn38eg8HAxIkTadiwIZ06daLW+dVvjh8/zl9//cVtt91WmFsjRKGUXM8Qn+ACFVOWwMsXuoy9uw/wxMPP88xLjxIYGJBrmXlzFzJm7LW4VdYlz5n7O80a16dFAYfHRK3fgsPppF/vbnmW8Xg8rN/8N089cg9zv3qHmEN72bJuGZmZ1hxd4GT5QyGEKDk+xoJNIOpryD1mFJelK9eSnpHJ6sWz+WnOD/y88HcOHTvq3f/rir+4ftCQAp8vLv40D7z2InePm0C18MrZ9nk8Hrbs3skDE2/lk6deYv/fO1izYBGbNm9m48aNrFq1ijVr1rBz505WrlxZbNcohBAiO2NoWMHKVax0RfXs2rWL+6c9wKuvvJwtEZLbdk0Ds7//Zc/5+4oVXDtoUIHbcMOwYfz60UckJCXx5+rVKI+H9OhoXMePg66TnstKmxEREbz33nuEhoYSEhLCmDFjiIqK8u6PjY3ltttu4+GHH6ZmzZoFbosQhVVyyZDgeuCb/xeB7lsRT4UaV1TNxvVbeWTaMzz/ynQ6d+2QaxmHw8m+PQfp1LmDd96gv1asZenKdYy6aQo/zlvIt3PmM2fu77keD/DH0tXs2XeQUTdN4f2PvmbJ8ije++irbGXCKoXStlUzqlQOw2Kx0K9XN/bs3U+NGjWIj4/3lktKSqJy5coIIYQoGRV96uJvyv9Hpr8hhBBTtRJtx/pNfzOoXw98fX2oVCmU7l27svPgfgAcTif7Y6Lp1Kp1gc518NgR7n35We4cewNDevbJsb9SSAgtGzYmPLQSFrOZHu07se/AAXbt2kWXLl0IDg4mMDCQoUOHsmnTpmK9TiGEEP8wN22NsWr+zzjGqjUwN2lV5DrWrlvHPffez+szXyUysttltwOXnafR6XRyICaGDq0u364zSUnsi44GwMdioXuHDhw5ceKfAkqhdB2z2YzVas127P79+1mxYoX3s67rmM4PI923bx8TJ05k6tSpjB49+rLtEOJKlFgyRNM0tPojgNz/0ik0XLUHFnnyVIATx0/y/JOv8uZ7L9Gxc9s8y0UfOkL9BhEEV/Ij6VwGYOCLD2fy+4+f8usPHzPu2mFMvGE0N4wdnuc5Zjz3CIt+/pxff/iY+++6hUH9ejD1rknZynTr0p4du/aRkJSMruus3bCVpo3q07JFM6Kjozl58iROp5M///yTHucnKBJCCFH8NE2jccWh5BWDQKNBYJ8S76XXuGE9lq/egK7r+Js8bNy2lcb1soZ4xpyIpW7N2tmGzeQlLTODR2fN4LHbpjAoMvfVBzq2aM2e6EMknT2Lruts2rWDhnUiaFi/ARs2bMBut+N2u4mKiqJZs2bFep1CCCH+oWkaFSbeC4Y8HrU0AwE3TSlyDIqNjeWxx6fz4Ycf0LVLl8tuBwgLDSVd1/N99oo5cYK6NWvmmN8qN6np6bz20UfY7HZcbjfrtm2jRS6LQ2ialmOeKl3Xee2118jMzMRqtbJgwQL69etHamoqkydP5oUXXmDEiBE5ziVEcSu5niGAFt4Krfmt4HdJDxG/MLTmk3D4N8VI3hPGXc53X/2Iy+XmlRfeYvx1kxl/3WTWRWW97Rp/3WQSE7LG6506FU945TB0XJgtBuxa/kNzRt00hTOJBRvrBzB56pPs3neQ6lUr89Sj93LHfdMZMvZ26tSuwahhA6gYaObRRx/lrrvu8o7bbt26YG8ChRBCFE0V/xa0DZ+Yo4eIv6kSbcIm4K81QRl8S6TuC3HkulHXUKNaFYaMvZ0bbpnMoAH96dA3K5lxOjGB8NDQHMfeMv1hEs9mX53m12V/kWGz8snPs7ll+sPcMv1hfluZtczuw6+/zP4j0VQNC+eBm2/jwZkvctNj06hVtRrX9OhN91ZtiYyMZOTIkYwaNYq6desyfHjeyX8hhBBXzrdzL0Iefhlj1ezDPIxVaxL88Es4m7XPc72zy/n8iy9xuVw88+xzjBx9LSNHX8vq1Wvy3A5w7dixnD17lsAaefdYiU9MJCyXuHTH9OkknT2bbVujunUZ3q8fdz/zDFOeeoqWjRrRs2PHnCe12cjMzJqf684772T37t00b96cm266ieuuu47Ro0czduxYWrRowZw5c8jIyOCdd95h5MiRjBw5kp9//rmId0mIy9OUUoX6e3jgwAGaNGlSqEqUUlmryzhTwRIMwfXQNA1d10nLOIvmYyvU+a6EUTOTnqxTNdBNXksuznjzQ6ZOuSXP+UcKSzf6k+m2EBAQkO8M/kW5t0IIUZ4UNQaddRzF4UnDxxhMRZ8INE3D4/FgyzhHgOHKJlEtFIOF+DQdy8ncl+IFeOfbL7hzzA0EFGBsd0GYw0LQQoMICAzEkNdbSiQGCSHE5RQ1Brn278RzNgljxTDMTVujaRputxuH3U6Af8kk5XNvC8QnJKJOFm41zw++/ZZbx4wpUlzyDQ/HHBpKQEBAvjGosCRmieJQcqvJXETTNAipn2O7wWDAgKnIWdGi8CgXaAYw+YM75w9gpRStWzQttkQIgMFjw6D5kJGRQXBwwSaWFUIIUTw0TSPUt16O7UajEZ1SXmJWd2LQzFiCA3Gm5pxUTilF8waNii0RAuBOTsUcGkRGejpBEoOEEKJUaZqGpVmbHNtNJhNWXS/ltmS1xxwYiDOXiU1zo5SiWYMGRY5LjuRkzKGhEoPEValEh8nkx+PxYLfbUKWaCsli9jGQ4cx9n6ZpDB2Uc2K6K6Mw4cRoLLPbLYQQ4iIejwebzYZBK4MYZAQVnPtwTU3TGNC1e7HWp5RCz7QV6xs5IYQQReeNQWXwvWw0GrHkMhQmL5qm0bdr1yLXp3QdT2Zmvr3jhSgrZfbL6OzZs7h0G5qv9fKFi5lmyHtKvZLiQyZmXFgzM1FKYbWWYrdsIYQQ2Zw9exbNYyXAUPoxyIAq9RjkPpmInmH7JwZlSgwSQoiykpKSgkHTCCzFITIXGEo7AAHWuDhcGRnYrFZ0Xc+xuowQZaVUhsnkxmwxY7ZouHSt1PuG6B6FwVz6eSCLspKpjKSlpQIaGRnpBAZWKPV2CCFEeWexmPExA67S/1XoUapMgq87Pgl8zKS6XACozEwCAopvSKgQQoiC8fHxwWIxk9f8hSXJo+v4lHpKHmzx8Wg+PjicTi5MWelfjENChSiKMusZ4u/vj9MKHpsfWin/hdR1VUZDVnQCSCXIkIbFZMDhcKKX8lhBIYQQ4OfnR4ZDI93jT2n3FdR1haEMfogqj47ryEk80XGYDAYcDofEICGEKAN+vr5kZGSSnlH6PSSUUlkzqZZ2vW43mTExWKOjMZtM2O12iUGizJXKyymlFIlRW7GeSsC/emXCe3TAbDJjDjKTnpGOhg8e7KXRFAB0t8JsADylVmUOmu4iwMdAamoqFStWLLuGCCHEf5xSiqjoHZw6l0T1kDB6NGiD2WzBbLaQnp6GMvqieUpvVTNdaeB0lVp9ubYh04ZZkxgkhBAlTSlFbFQU6adOUaF6der06IHZYsFssZCeloZSWRObll6DwON0lGKFObkzMzGAxCBR5ko8GXJi/lK2P/o6GTHHvdsC69em7azHqDV6AIEBgaSmO9F8Srol/9AwluoP39z4kAGYcBiki7IQQpSU+TtW8ei8D4hJjPNuqx9ek1nX3sfoNr0JCAjEmuYkoNTmddNAM+JISS2tCnPlOpmI0dcHQ63KZdoOIYT4L9s/fz5/PfooZ2NivNsq1q/PwFmzaDp6NP4BAVhttlJdXhdNw5qS9/LupcEaF4fZ3x9L9epl2g4hSjQZcmL+UtaOnYq6pAtURsxx1o6dSve571Fr9AC0Ylja8ImHX6Bps0bccvuNOfYt/XMl33wxB5vNzvBR13D9tRNAdzJ/4V989vWPuN1uHrjnNq4Z0CvfOr6ZM58ff1kIwLhrh3LzjdfmKPPwUzPYdyAaHx8zAC89/RAtmzX27p/5zsdYrTZeePIB0Ay43e4ruGohhBB5mb9jFWM/eRJdZY9BMYlxjP3kSeZOnsHoNr3RtaKHwjfe+5TVazejUFw3agi33JQzLiz+axWfff0jVpud0SOuYdR149GdLv43+1vWb98KmsZto6+jX5fIfOv6fuGvLFy9AoOmcd2goYzqNzBHmW17d/O/Od9is9vp0LwlD0+607sv02Zj8nNP8N6Tz1MppCKaUWKQEEKUlP3z5/PT2LE5noPOxsTw09ixXD93Lk1Hj8ZThKEiSilem/k6UWvXYtAMPPDANPr36+vdfyYhgRtvHM+K5UtzPfbep5/GZrejne+S8tpjjxFWsSIfz57Nxh07UEoxrE8fxl5zTYHa83/ff4/Nbueh228HYPPOnXz0ww84XS5GDRiQ63k0o5HUtDRuGj+er776ivDw8Gz7Z86cSWZmJi+++GKB74sQhVViyRClFNsffT3HF4B3v66z/bFZ1BzVH5SGUbPgUXmsd3sZfy5axtZN22narFGOfbHHTvD+25/w9ez/w8/Pl4nj7qJLh+5QycJHn3/P3G8/RNd1brh1Gl06tqFiSO7rX5+OT+DrH+ax8KdPs74grruD/n26U71q9rdqBw7F8PM3HxAYkHNCoK3bd/PrwqUM7Ju1bKIHEz4+PmRkZOBwlE53tWNHjxBRt16p1CWEEGVFKcWj8z7IkQi5QFc6j837gFGte2VNX2ewgF64GLRizQYOHD7Cr7M/wuF0ct3N99G1U1saNajrLXM0No433vuUud/+Dz8/X0aPv4d2HbuTGRPHjgP7+Oa1t0hNT2f8Yw/Qs0MnzCZzrnUdOxnH4qhVfPXKGwCMf2wanVq2pnrlKt4y6ZmZvPDhu/zvmRepXrkKU2e8wMZd2+nSqi2Hjh3llY8/4ER8vLe85u+Lr68vGRkZOJ1Fi7+FdTQ6hroN6pdKXUIIUVaUUvz16KP5PgctfewxmowahVIKReFmr1oTtZbDh6NZ+NsCzqWmMmTocLpHdsPX15cNGzfywosvk5iUlOM4p8uNcrtJOnuWOe++m23f+r//Jub4cT579VWcTid3P/MM7Vu0oG6tWvm2ZdeBAyyJiqJnx44AOJxO3vriC95/9lmCg4KY9tJLtGvenHq1a2c7LvrUKV556y2OHTvGuXPnsiVDtm7dyvz58xk4MGfS/0ociY6mXoMGxXpO8e9WYrOIJkZtzTY0JjcZ0bEkrt1GhQoVcFvNRZpINeFMIr/89DujrxuW6/41K9czZPhAKoaG4Ovny6w3XqdBRG3Wb/6byC7tqRAYQHBQBbp2asuKNRvyrMej67hcLhwOJw5H1ljvSydhzcjIJDnlHFMfe4GRN07h+58WePdlWm28+f5n3DlpnHebUTnRPW7S09MJDAws9LUX1oa1a5j/6wLWrV1T4nUJIURZiorekW1oTG6iE+NYG72TChWCSHP5UtiQWKNaFR689zaMRiP+fn7UrFGNMwnZf3yuWL2ekUMHEFoxBD9fX2a9PpO6Vavj0T04XS7cbjc2hx2zKf93ExE1avL1jDfxsVg4l56GR9fx9ck+vnTLnp20b96SWlWrYzQYef7eB2hWvyEAC1evYNrEWwm7aGy2np6J2+UiPT29VFaVWb10GQsW/s661RKDhBD/bbFRUdmGxuQmJTqa42vXEhgYSHpG4ZY779WzB598/H8YDAaSEpOwWCwYjVk97X+ZN5933noj1+POpaYRfzzr+Wzaiy9y55NPsnrTJgCqhIVxx/XXYzQY8PP1pVrlyiReZjiNzW7nkzlzuGn4cO+2/TExRNSsSXilSljMZnp37szqzZtzHPvbb7/x4IMPUqlSJQIuWlUmMzOTN954g8mTJxfqnlzO8iVLWPD776xfIzFI/KPEeoZYTyUUqJztVAIGgwGz2YzCFw+Fm8vjtZfe4aHH7yVqVe6JjJNxp/H18+HB+54k4UwS1wweyj233kBCYjLhlSp5y4VXCiUxKe+/8DWrV2X44H70HT4BTdMYd+1QqoSHZSuTlHKWzh1a89wTUzEYDNw85REa1o+gU/vWzHz7Y+64eRyp6enEcjLrAOWmgiEdU2AA1sxM3G43u3f+TcvW7Qp1Dwpi3ZpV1LTEM61/AFFJTs6dO0tIiExYJIT4bzp1LucbsVzLpSZiMBgwGo3oRl8MnoLP7N+44T+97Hbs3seBQzG0bdUsW5kTJ0/j6+vDlAee5kxCIoOuGcbEgcNp27Q5tatWY8R9k3E4Hdx/0y159gq5wGQ08v3CX/li3s/07dKN0OCQbPtPJpzBZDLx5DuzOHkmnsh2Hbhz7A0APHTL7TnO57E70WJO4l8jnMyMrBi0a+vftOpQ/DFo1V/LcP+1iQ4nErE1bMS5s2cJkUnzhBD/UemnThW4nNFoRNMM6AoMhXgvbDKZeGXGq8ye8yN3TZmM2ZwVQ954fWaexyhdJ+XMGdq3aMF9EyeSlpHBtBdfpEGdOtS/qOfGvsOHiY6NpXnDhvm24cPvv+eGYcPIyPwnmZN89iyVQkK8n0ODgzlx+nSOY+8bPx7NYEDTNDIzM3E4HHg8HmbOnMkdd9xBWlpaQW/FZS394w/OLl5M3YwMrI0acfbsWZm4VQAl2DPEv3rBJmXzO19O13U0rXDLPP3y0+80btog1+ExF3g8HrZt3sGLM57k4y/eYvGixfy9bStK13PM3Gww5H07du89yMat21m58HtWL57Nzt37WRm1MVuZiNo1eXfms4RWDCEkOIgxIwYTtWErUeu34HA66de7Wy5nVviRQYiPE7vdTlIxTKq3d9cOVi7/y/t58aKF1DKdoJZ9I7isJCaf45tvviUpl+5zQgjxX1A9JOzyhYDqwVndcpVSaBRtqcFdew4w9bEXmfHsIwQGZu9h4fF42Lx1J7NeeoJvP3mbRYsWs23LZlZv2USGzcpvH3zCvHc/Yt6yPzl07Ohl6xo/bBSLP/qCM0mJLFq9IkddW3bv5MGbb+PTF19jX8xhlq5fm+/5lFK44xIwJpzFbreTnH7lMWj75s2s+HOJ9/Nv83/F9VsUCW9+gyPpLCmZGXz+xRdkZhbuTagQQvxbVCjgxKAXyimlirSizFNPTmftmlUsWfIXm3LpfZGDBk1r1eLRO+/Ez9eXKmFhdO/YkW1793qL7I+J4bl33+XxKVOy9di41OadO3G6XHTv0CHbdl2pHH39DXlcnNJ1lNuNJzmZjIwMoqKicDgc9O/f//LXkoct69ezcuk/c6XMnzuXxHnz2PfBB2QkJJCckcGnn36Ky1W2q7qJq0OJJUPCe3QgsH7tfMsENqhDePf2QNZy1zqF+0O5ctkaVi1fx/jrJjPv59/58Yf5/PLT79nKVAoLpVPX9lQICiQ4KJQOHTqwZ/8BqlQJJzH5rLdcYnIKlcMqXVqF16atO+jRtSPBQRUIDPBnyMDebNq6I1uZ/Qejsw218egezCYjfyxdzZ59Bxl10xTe/+hrliyP4r2Pvspege4k0OhAt+bsnbJvz062b9vi/bx18ybWRuXexeubb77BJ+MwrvPLBh86sBd/i0ZN57asDWnHuLbadm7qHs7WzVnd4o4fO8K2LZvyvPa8HD0Sw/vvv8/Jkydz7Pv2228LfT4hhCguPRq0oX54zXzLNAivSfcGrQGyxmt7Cj9309qNW7n34ed4/cXHiezSPsf+sEqhdOvcjqAKgVQIzopB+2Ni2LxnJ306dcXH4kNocAhd27Rj58H9edYTn5TI3uhDAPhYfOjVoTMxJ7IPRa0UEkLLho0JD83qmtyjfSf2H40u0HW4M21w5izW0zl7de7YspXtm7d6P69fE8X6qNyTLF9++SXnlmzAff715p7tO/CxuUj83xwAzqzYiPHet2gb72DT+vUARO8/wLaNBfgRf4m9u3bx/vvvk5JLN26JQUKIslSnRw8q1s9/fqTQBg2o3b2793NhciExMTEcOnQYgJCQELr36O79nBcFKF2xY98+dh044N2u6zqm80NstuzaxdNvvcX0u++mQ8uW+Z5v5aZNHIyJ4Y7p0/nyl19YvXkzX8ydS3hoKCmp/yTWU1JTqXSZXhjuzEwcR4/y15Il7N69m5EjR/Lee++xZMkS3pg1C+tFyfP0tDQyMjJyPc/nn3/O6WXLcKmslxs7t23DbLNx6LPPAIhevJjj999PvXPnvDHowJ49/L1lS67ny8+2zZt59913c03sSwz69yixZIimabSd9RhaHr0tNIOBtq8/iqZpKKXQlQtdeQpVxwefzGL2vM/4/udPuPa64Yy7aTRjrh+erUy3Hp1Yt2YTVquNc0k2du3cSZOG9ejasS1rN24lNS2d1LR01m3cSpeObfKsq3GjekRt2ILd7sDt9hC1YSvNm2TvOqYrxcy3PyLTasNqs/Hb4mX07dWNGc89wqKfP+fXHz7m/rtuYVC/Hky9a1IutSgiws3s2L4t29YDh2JISUn2fk5PT8XlzrpXR2MOs3rVSgASExNoVDOIes511PJLY8XSxRiSd9Mz4JJEhyuT0PSttA07y5I/FrFj124wmFi7ZnX+N/wiu3du5+/tO5g4qjcrVmS9ndy5fRtL/ljEn38s5vT57nBbN29k9uzZBT6vEEIUB03TmHXtfRi03GOQQTPw+rX3oWlaVs9E3QUUbkb/2BMnefzZmXz41ot06dg21zI9Izuxeu1mMq02TqfY2LljBw3rRNCwdgRR2zaj6zp2h4O/9+2lcUTek1unpqfz0kfvY7PbcbldrNm2mVaNm2Qr07FFa/ZEHyLp7Fl0XWfTrh00qlM3jzPmRuF/zsqenbu8W3RdJ+bYMZKS/+lJaM204vRkrUJzcO9e1qzIikEn4+Ko6TJw+tkPMew7xpJffyP+l6VkPvh2tlocSWdJ/GAOLN7IH7/9zoHowziVh03r1he4pX9v2cLB6GhGtOvKn3/+CcDWDRv54/eFLFq40Nvzcf2aNfz000+FuAdCCHHlNE1j4KxZ+T4HDXj9dTRNw+Px5NlzIi9HjhzlxZdexu12k5GZydq162jbtk2+xyQln8VX00jPzOTTH7NW0zybmsrG7dvp1Lo1J+PjefX//o9XHnqIds2bX7YNj0+ezFezZvHZq69y65gx9OrUidvGjqVZ/focOX6c+MREnC4XqzZtomOrVgW6rmemT2fevHksWLCAqVOnMmjQIG6//fZsK58psmITwJ6dO1m3Ouv55UhMDFXsdv5+7jkchw/zx6+/Ev3zz+x76KFsdWQmJnLovfc4s2gRfyxcyJHYWFIzMtj+998FaiPA5o0biY2LY1hkJL/99htKKTauW8fi33/n999+I/V8Mihq5Up++eWXAp9XlL4SXVq31ugBdJ/7Htsfm0VGdKx3e2CDOrR9/VFqjR4AgNWaickHCpcKyd/46ybzzv9m0Kp1c667cRS3jr8Xt1Nn/HXD6NQ+603g3beNZ8IdD+Fyu5k86UaqVsnqLj3qpil8/O4r2eYE6dG1I3v2HmLkTVOwmMz0iOzIsMFZS1hNnvok9991Cy2bNebGsSO4/pb7cHs8TLh+FC2a5j2EJzcHTmZSqZqLZUv/wmQ243G7aV7Zw9n0M5w+fZpq1arhdntQWlaXusPR0Wjnk0ibN25gcNhOcEBjz1oamyHPzjbKQ7h1KwMCLIAONgtLMrvnUTinhKQURtU5inY2CV335ZNPPmFYu0o0CY8Ho5kGlZqyePFiwsLCqF2zWqHugRBCFIfRbXozd/IMHpv3AdEXTabaILwmr197H6Pb9AbAmpmJv1kVNhfC59/8jMvl5plX3vJue/Ce2+jVvbM3jrRt1Yzx149g3KT7cbh0RvfuT9umzWnVuAlH4o5z02PTMBlNDO3Vx5vcuGX6w7zx2FOEVwz1nrdx3XqM6jeQ2595HIPBQJ9OXendsQsAD7/+MneMvYGm9RrwwM238eDMF3G53US27cA1PXoX6pqSA8z42aws++svjCYTHreb4L3HSfU1ktK5E6GhoXiUB/386jPHTpzI6lUC/L1pM56XvgAg4cE3863HY3cQ/853mD6dh+5y4QwJwu/LpwvczrNpafg88znHmtTDMawdH3/8MU1PpGNYvwvfkAp0GNSZxYsXU7FiRWpVlRgkhCh9TUeP5vq5c1n62GOkRP/TSy+0QQMGvP46TUePBsBqtRLg71uocw8Y0J/tO3YwYtRojEYTE8bfSIvLJDAmTZrEqw8+SI+OHdl7+DB3TJ+OrhSTb7yRsIoVefPzz3G53bxxvhcFwB3jxtGlTRvumD7du/zu5VgsFh6+4w6eevNNnC4XA7t3p9n5FVyeeP11bh07lsb1ck/+64asJd/TUlOx2Wy4nE6U1YrLbMYdEIDJZEKpf4a0nk5IwHm+l8jObduIefllALbdd1++bXRmZrJ71iwsH36Ix+kkoEoVmn788WWv7YL0zExOPPII7q5dcQwYwMcff0y1Y8dI2rAB/8qVaTR4MIsXLyY4OJg6NfPvpSrKlqYu/hNVAAcOHKBJkyaXL3gRpRSJUVuxnU7Er3plwru3965rres6aWmpGPysqCKO1y4Io+ZDRoqbKgGXH4oz480PmTrllhxjv0vawejjNHSuIdZTnwj3VtDdYPYHVybKtxJ/ZzQixR2Ex2mjcWgmB8+FkJ6RSb2ImmTYnNSznKSWa9vlK8qFqtiEr7aamTBhAj4+PufHLmpkZmawdfMGjEYTVqsNk+ssuiWEOgEZ1HefHxIUXBfl8aBlXLJ6kMHM8ozu9L9mhPe/txBCXImixqCo6B2cTk2ienA43Ru0zhaD0tNSCTJmQAnGIGXwIyHNhflk/jPzA7zz7RfcOeaGfMdql4SjZ04R//h7+I3pQ+KrX6I8HiwVg3Ekn6VCo7oY7h2NsUEtHCmpmPfHYuzWitSMDGqEV8buceP5dTVJHxatF0b10f05Mqw9EydOzJpQ/XwMSk9LY1PUOsw+Fmw2G9YjcfhHVMe05SAJr2T9YK/csyPO9EzObd+X7ZxGP18Cv32BIdeOlBgkhCgWRY1BsVFRZJw+TYXq1andvbv3O8nj8ZCZmUFQCT9zeHSdpKQUPHH5r7KWlw++/ZZbx4wp8bhk8vfHHByM7XwPc4PJhO52Y/L1xVy5MgYfH3SXC+V0Ehsfz5EjR6hcqRIOp5P4n38m+vPPi1Rv0xtuwDZoEBMnTsRoNHpj0Llz59iybh1mHx9sdjvnYmIIjoggc+1a9r6RtWJPRO/eWJOTSdi9O9s5zf7+tJ09myEjRlzZTRElqkR7hlygaRqVe3bMdV9qWipmfyeewuVkCs3jVPiYTeTdVSKLUorWLZqWeiLkAs2WQAQXjdl2ZY1D0+zJtDdtgIp1wGMHewp16zQCZwakbwJNXe7S8q837SjDW7Vi6Z+LMBhNWJQVTH5YDG4ia6Tj1A34mmLAeX4MoPuig1OP5j7OUXdhNJvlR6gQokxpmkbPhjmHsSilSE09R4jFCXrJxiCr04OPwXTZzidKKZo3aFTqiZAL0vYeJm3vP+POHefn1ko/dBSmvUVY17YYks+RHhdP1d2xqNOJxG7bkzXx1xVIWrWFiJrh/O43D4uvBdepJHzCKkK6laClf6P7+WBdsg57fCL2S45NWJP7WG+PzY7F1yIxSAhRpjRNI6JnzxzblVKkp6URHFyhxNuQkZGJj8FAwddL+4dSimYNGpRKXHJbrbit/7RSPz88xm234z5+HEtgIB6XC93lwpOWRtKMGezZsYNCvtvPIXbZMupVqcJ8X18sPj5Y4+IIrFYNV3IyruXL0fz8OPbHH2QmJuY49tiqVbme02W1YvHxuaJ2iZJXKsmQvKSlpWL29eBRJT+br8ejYzLmv2whZH1hDR3Up8TbU2Tp/ww3Inlv3uUKy+MgLHMLQ8IrQEANOPvPxEokQeE67/3D7SnZBwwhhCiq9LQ0Klg8oDtLvC63R2Ut33uZcpqmMaBrwYcslrakDdu9/37y9xX5lCwc59lU4t+fjd8vlfFrWp+05Ruwnd937grOq0siRAhxlUpLS6NChYBCTZxaVG63B0s+q2bmR9M0+nbtWswtKhrnRROnujIzOb19ez6lC86alMSed98leN48Qhs04OjKlVd8TkMBYr4oe0VKhlzoOlRQdrsdp/P8LP2a5p0syGjx4NEufcdTMsx+iswUJ1bMaBpU8PfFz2IA3QUeJyj35U9Sgq40o1lsnOngPHD5cgUUZslg166dtGrVutjOKYQo3wofg7LGHQNZMYisGORvcmHUbfkfXEyCfeFMugNqVkLTFX4mMyaXjsfmwG21oTvLdok/hbrSzh3FwnYqAdupnCvaFJUn5iT79+6jafNmxXZOIUT5VpgYpJTC4XDgcl1IumtoWtZL2gB/X4xFTFAUVmjFYOITkjDUrAlKx89oQnO5cNtsOK1W7zxQ/xaKknl2Sj1xgtQTJ4rlXLrHQ+axYxw+dIiGjQo3h6QoPYVOhvj6+pKSkkJoaGiOL4ILXY7Rsv5wGrSsMVcGswejnwIUGgaUAoWz0KvHXAldefCrCAbNhAET1gwHqZkeNAxomgGFJavNKMKCLGhuKyU5fvxiSilSUm34eM5evvC/TGvf/RxLc7Jq0V7sxlDCKlWkQ8fOBTr2+PFYliz5izvvvLOEWymE+Le4bAw6dw6jdn4GKoMRlMLH6CHQpAMKNAO6AoPmgFKMQSgXVQIBgxmlWUizukg36BBohgoWUFkJCU1BgMODM/lcqSUnFIp0txP74eOXL/wv43p7NrGnEjlYfS3mejWoXqUqbTvkXAI5Nwf372fz1q1MnDixhFsphPi3uPxzUKo3waHO/6+vjw8VAgLOf1JZz0al3GtN0zSqnV8oQleK1LR0nEqHChXQKlTAeL79mlJYbDZsuSxZfrVQQKbbTerh/JcSvhqcePNNnLGxbK9eHb86dYioXZuWrQv2gnjnjh0cOnyY6667roRbWb4VegJVt9tNXFwcdnvOHh02mw2D2Y2Hf1d2MYuGASMoI/ZMFwaDkSCzi9JKiPh4zlIjcy0m5SiV+srCZm0Q1eo05dixo0RERFAnIv8lH0+ejOOjjz6mVatWjB07VsZ9CyEuG4N8DS60Uhj2Uvw0MBjxYCTd6sZoNKIlpV7xXBwFoRTYDx/n1Asf4UlNL/H6ykqVX16nYfs2HD1yJCsG1c0/Bh2JieGbb7+lRfPmjJEYJISgADHIx8K/96siq9dkekYmRqMBd0LO+TGuBkopUg8fZvsLL+A8v4Ttv0G3BQto1rYthw8fpl7dupeNQfv37uWX+fNp1rQp144ZU0qtLH8KnQzJz7IVy9AbrEQvhTlAStquuUE8MrgWJBbPWDQBGzz9OHr6HM0bN+DI8VNce+21+f64dLlcbFz2C62refhydRKNG9bH6dYZIbMyCyFysXLFMnoZF4P694/SfXdLFVovPUz80nVl3ZT/jLAfZ5DgsNGgeg1Op51j1KhR+cYgq9XKsv/7ikqnzrG1dgANIyLQjUaGDRtWiq0WQvxbrFyxgj7d/hvDwt/+8GsC5szh1JbcJ6gWhdd57lxSbDbqVKlCstXKiBH5r7Z59uxZln78Mf7p6USHh9Owbl0MFgvXXHNNKbb6v69YkyF/LvsdGkYV1+nKVOK2Gpw94ea2gY3xP7MC0MFgKZWJ9v6zTP7gXxnSjpHp35g1Z6qilEaG1caoUaOxWCy43W4slqwhSxvXryXME0s9zxZsVXrha3CxNr4KVquVoKAgjh49Sr9+/TgSc5hmzVtSsQBrnwsh/rtW/DGP3n5ryroZxWLhibocP+Oi8zkDh1/9FHQdU6A/7oyirAUgAHwqVSSwfi2SN+8ifOxAbNf3RmmQabdz/Q3jvPOZXYhBa1auxLZoHSlvf0+DJ+5ApaSTcV1PXC4XgYGBHD16lIEDB7Jv1246dOlMYGBgGV+hEKIsrVy+jD6ROVcu+zf65bdlxJ86Rb3T8Wx+4w2UrmMJDMw2gakonIDKlQmuXZtTW7fS8KabCBw9Go9S2J1ObrzpJnRdR9d1zOasBT9WLltG/K+/Ev3JJ3R67DFcSUn4jBmDUgo/Pz9iY2MZPHgwO7ZtI7JnT/z8/Mr4Cv+dijUZ8seyBWgN83+LpWEk2KcmunKT5jxZXFWXAA1fez02LIjHYgrE43agKw2DBg8NrIjhXPFNMlpumfwhqDYuWwYbE6qg4cGggcscgqZ06gZlUsu57ZJj/PjyYAMaRVSnW4Sb+dsd1I+oxc49+zlx4gRDrhlE+w6dyuZ6hBBlatWfv9DT9zIJeYMZKtQEtwMyT5VOw4pCMxBvaMAPUQkYfQJxO50ooxGD0mm1eA9Ja7aWdQv/9XzCKlKxbTNsGZk4RndHd7kxAL51qqM8HsxbDpL0wZxsx/hWDSf5xVuoG1yJsB3HONSmJhFVq7Pr0AESEhK4ZuCgAs9JIoT4b1m1Yhm9uxUgGaKZyJooqhTnrSqCU2dS+O7HeZjNZpx2B5rRiMVkJHDuL8Rt2FDWzfvXC6xShfDmzXG4XASMGIE6P4ltcEQEbqcT+4YNHPzkk2zHhERE4PfEE9QKCsKyZw9JLVtSu2pVdh04QGpqKkMGDy7wnCQiSzEPk1mCu/7yvKoiyN2c1MOhmM1mdF3H4bbiawnA6G9Fq7UPhyetuJpSrEyaLwHmymS6Ekg/XAPOBdOiTgihnuNUtO0r6+aVP5oxZwAx+nA6qC/f/rGdkJAQpkyZIuO7hShnVi5dTC/zX7nv1Awk+7Xm7+RKWMwmPLrCY0/H7BdEqDmDVuYd4LpK33iZ/MC/CmSe5q8T1cC3DgFJGdiXbCRx3rKybl25YzCb0V3ZhwObgwKp98GT/Hn8AFWrVuX2228vo9YJIcrKqhXL6d2tTZ77E5LT2Ln3MGazCY9HR/e4MZnNhFeqSIvGEZTWPIWFp53/7e3mx1//pHpoOBlHj5L4++8cWbSorBtX7hjNZjyXxCDfkBD6fv01f2zfTr169WTi70Io1mTI2vVrMITFkeazI9v2YHMtUvfXIsC3Ij0ie+U4Lu7kCbZt20pYm1Okq9jiak6JMBsCOLTCxLaoo0y/Zwx105aUdZPERZxBjXjzj7OMHTuWxo0bl3VzhBClKGrVCpoFxFIxc2e27XqFCNYkRhAQVInO3XrkOO7E8WPs3LGdgVWPYrJe5SuqWIKYvcPMko2HeSByEAfuerGsWyQuUmV4b1a0DOOuu+6iVq1aZd0cIUQpWr1qFW1bNCIo0Cfbdo/SWLVuK8HBFenQsWOO447HxrJnz24G9emKsXRW2r0CBr79cT5/LFnG3V26sHzatLJukLhIo2uvZWudOkyfPp3w8PCybs6/QqGX1s1P9249Wb7iL3RPFwyaATQNTRlI9RjoHtmTwMAKuR5Xs0YtqlWtzp9LFmJucRJduYuzWcXKpWdSs3FNju2rRN0gO1ydnVnKLYsnnYYNG9KwYcOybooQopT16N2XZX/9gdETiGYwoZSG0ox4HEYie/bG398/1+Nq1Y6gWvWaLF+yiAGBJ6/ursvONFpHRLA3ribOTXvKujXiEu74ZFqN70vNmjXLuilCiFLWs1cvli1ditlkBLKWs9WVQtd1unfviY+vb67H1a5Th6pVq7JyzSr69+hQmk0uAp1WLZpzKPoop1avLuvGiEu4zp6l8/XXExYWVtZN+dco1mQIQL++A4t0nNFopHadCFLiwVF181W7Io2GgRU/HuXkyZOQElLWzRFeGscC+nE01Yd6EYEYDFd9al0IUQL6DyzaLOsmk4nqNWpxIKM7TfR1oF+lSXmjD5/8eYSkpCTi113Fc56UM5rRSK3/PYk9ojL1KlSQYZpClEOapjFgYNGegyw+PoSFVebQkdM0qletmFtWnDQ+++o7UlJSCFixoqwbI84zms10/egjVM2ahAYFSQwqhKvqibFl89ZUDqqP4WhXjJrP5Q8oA/7mMI4ePcpHMx4A65mybo64oFJzlm0/Rb/+A+jQqWtZt0YI8S/Usk07CKnHZnevrNXDrkZ+4Rw9epQnB4zCevx0WbdGnFdrzED2G2z0HzSQjt0kBgkhCq9N27Y4PTpbdx0u66bkTTNy5MgR7u7SBfu5c2XdGnFe0xtvJM5gYMDAgbTv0qWsm/OvclUlQwCaNmlB1bDaWM41Leum5CrTlcAdDw/jq99lFuWrSkacdzlEIYQoqmYtWuMbUo3TluZl3ZTcZcTx9JQRrMqIL+uWiIuc23UQk6nYO9sKIcqZFi1boTCQknqVLqOu3Ex/4mF2GaTnwdUkeY8Mmy2qqy4ZAtCieSusCbmPq7sa+NQ5isOVySZ3D67SW1j+eBxl3QIhxH9E67btOZR8dfZOBOgYHEN6ajJVPnu6rJsiznOeTZNuyUKIYtGhY0d27z1U1s3IU/eObUhOTqHzRx+VdVPEebbk5LJuwr/WVfskr7tMaFdp8zzKRa8JoSxc+TcER5R1c4TBzM8n29C6VcuybokQ4j9A0zQcyge4Sh9uPXYeHRLGqmOHCG7eoKxbU+6ZKgRgfeNu2rdpU9ZNEUL8B2iaht15Nb/kUzz12FTWx8VRoXr1sm5MuedfqRIhzz9P5w5X++S7V6erM9sA+Pv5E6y3KOtm5ClqThJWqxUyZd6QMqc8VK5cGZv9ag4cQoh/E7OPH5lBV2+C9a0/zuB0OkmPiSvrppR7usNJaGgobqXKuilCiP8Io9GE3amXdTPy9Oob7wOQfkom8t/PKbsAAQAASURBVC5rLquVoKAglCweUSRX7V3r3asfKQeDc+0douuKc4m2MmjVP6rVCc1avtWdWabtEIDSiayZyfz588u6JUKI/4i+/Qex4XQoufUO0XVF/NmyjUG1woNo3qwZut1epu0QoDtd1Nh9gh9//LGsmyKE+I/o268/azf+nes+XdeJP5NQyi3KrmqVyrRqcfW+tC5PXDYbYSdPMnv27LJuyr/SVZsM0TQNs8mMLSmAr17Zwg9vbufrGVtJOp3JkT3J/L3qZL7Hf/zMxkJtL6zwRg6OHTsG4W2L5XziykTFWnj4oQfLuhlCiP8ITdMw+/pzIsOfBz/ZzlNf7+Lhz3ZwItHK1ugUFm/JfyWXuz7YWqjthdWjnmLvvn1UG9yzWM4nrkxCu3o89MADZd0MIcR/hMFgwGgyc+LkaR549BmmP/sKDz3+LCfiTrL1750s+nNZvsdPue+RQm0vrP59u7N9505qd+9eLOcTV8bWqBEPTJtW1s34V7qqpz4PqViRLevP0qBVGN2H1yUlwUpKvJVD2xM5ui+FzoNqExCU9woitkwXcz/YRUCQBc2gMXpKC+KPpfPLh7tJPJnBjQ+1ZeEX+wit4s/ZRBstulSlVWTB1vY2Bp3F5XLx0ao07rpKFx0oT4weG/Pm/8q4ceOoVKlSWTdHCPEf4O/nz/Kt6XRqHMqNvepwKsVGXJKVjQeS2R5zjmsjaxISkHcMSre5eGn2PioGWjBo8Ph1TYk+lcGMH/dxLMHKyze34O35h6hRyY/TZ+30aVWZ/m2qFKht1S3JpKdnEDO8Lf5/rimuSxZF5ExJ5ae5c7n55psJCgoq6+YIIf4DfHwsrFyzgU4d23LT9ddy6nQ8cSdPs2HTVrbv3M2YUcMICc77+yY9PYMXX32TihVDMGgaTzwylcMxR3l55tvEHo/jleen89Z7H1GjelVOxyfQt1ck/fv2KlDb6tSoRkJCAo0mToS1a4vrkkURuTIz+f6HH5g8eTK+vlfvIiRXo6u2ZwhAuzYdaFSvJWgw93+7WPFTNCFhfjRsE07TDlXyTYQAbF0eR7veNbj27pYYNI1TR9MIDvNlzD0tad29Osf2pwDQeXBthk5qyp7/Z+++A6Sqzv+Pv+/0tr33xrLA0nuvUgSxYE9iTYxGExO/mqrJL72rMdFEjT0xVlQUQaSD9N5ZYNmFZXub3Z1e7v39gSYqHWbmzuye1z8JuzPnfFZx79znnvOcjed/VKFFn4Isy3xzcsIl/YxCaEyM285XxqWy+MP3aWg4+xNbQRCE8zFk+EgKew9AQuLXr+3n+SVHyUwyMboshQnlqWcthAB8sKmO2SOy+PENfdFIEodqu8hINPGTG/sxfUgGu47aAZg3LpfvXlXKyl0X0IPKmoFer6fkoz2X8BMKoeL//pMMPuZg/htvYrfb1Y4jCEI3MHDQYEqKS5CQ+NXvHuO5F18lMyON0SOHMWHc6LMWQgDeX7SEObMu4yff/y6SRsOhw5VkpKfxyA8fYMa0SezavQ+Aa6++gu99+5ssX3UBRQ1Ji9VqRXrzzUv5EYUQOfLggxTU1fHvl18+2dNSOG9RXQzZu283e3cdYMCYLK67byCX3VjK+8/t40yn1zXWdFG5p5WAL4hWKwH/a2Ymf9rYzGzTA6DRSiif9iUyGLVodRKKfP7Nz7TeVGw2K5rm0+/nEyLM10F8+3oGZOsIBoJ8smY1Xq9oqCoIwsXbtWM7O/ceYurgdB65uR93zSrm0XcqznjITFWDg62H2/D5g+i0Gj7fT/Oza1Cc5eSCTJ1GQv70mmM2aNFrNVzAJYgTrjjSU1Ko/2DFRf1sQmi5auo5/ucXSW3swuN2s3r5CgKBgNqxBEGIYbt27mT7zp1MmzKBn/74//jm12/hz3/5+xmP8T5afYyt23fi8/nQ6XSfvw1CkWUUID7OBoBWqyUoBwEwmUzodDqUC2gCfeDQEQry8qhavvyifz4hdOzV1ez4wx+wdXTgcrlYtWwZshy9DXijSVRvk5E0En1HZPLWXxdgMOvwuQNMmldCfIqJvRsaGDIph1XvVnLN3Scb+NgSjLz95B70Bg2jZuZTOjiN+U/t5vCuFrQ6ieyi0C1d1Zp8+P0BTtaTxF+2aDHAeoQPdyTicrkY5PNiNBrVjiQIQozSajVMHJjBL1/diMWow+UNcOu0QtITTKzYdZjLh2fxyvJqfnh9XwCS4gz86vX9GHUarhmby+g+Kfz69f1srmhDp9XQOycuZNnSbRIOt7pNXIVTyc8vZH2/AoLB4P9uSARBEC6CTqdl8oRx/Pw3f8JiNuNyu7ntqzeSnpbKilVrmT1zGi//+w1+9ND9ACQnJfGr3z2G0WBg3lVzGD1yGL/6/WNs2rIdnU5HWWlJyLLl5ebQ3tGBNmQjCqHQ9NxzLC8oQKfTEQgEMBjOvoJVAEm5kDJghH28dDHW8oN0+aNv24NJm8j8P9TzxNf7gP2w2nGEz9nuHUrxiDkkJ4veIYIgXLxlHy9masJ2cDerHeUUsjGVB/7TyOz1J2jfvl/tOMLn2H57H1O+fQdxcaErfgmC0PMsX7aMaeOHEY0PXWVF4rs/+H/0WryY9spKteMIn9Pnz3/mqnvvxWw2qx0lJkTtNpmq6koM8b6oLIQABGQPSUlJ4Bf7sqLNkGwfL7zwIp2dnVQc3I/D4VA7kiAIMeZwxX6yzK6oLIQAaGQ3SUlJeFs71I4ifEnS7mr+8Y9/4Ha72bd7N26xgkcQhAt08MAB8nMyicZCCIBGkkhKSsLT3q52FOFLDPv389e//hW/38+eXbvweDxqR4pqUVsMOXLkMNrc6F1xkWgoIj7OBM6zH/ErRJ7Utp9bLithzZo1SO0HWfDOWxe0D1IQBOH48Rr66vaqHeOMPOYiEoxGXMfENSja1M7/mDGyjZUrV9L00SfMf/0NtSMJghBj6urqKC3KVjvGGdm7nCRbrbjb2tSOInzJvn//m4F6PcuWLaN68WLmiya3ZxWVm1llWcYf9BEMNKkd5Yy8gS46u1xgiANfl9pxhM9TZNLsq5kdpwVvkNr0iWdsNiUIgvBlgUAAJeACb/Q+8ZJ9DlwBP4bEeHz2TrXjCJ8j+/3U/PgJNHo9dr+f1Dd+q3YkQRBiiM/rRSPBFzqgRhtFxh0MordY8IvTS6JK0Odj04MPotXrCfr9jJo/X+1IUS0qV4asWr2cpL7RWwgB6AqeICFdT4NtjNpRhDNRgmCIwyOL5kGCIJy/NauWMz69Tu0YZ2XxVJNoVij8yV1qRxHOQPb7sRbkEDTq1Y4iCEIMWbNmDeNHD1Y7xlklxlsxGY2M+uEP1Y4inEHQ7ye1rIygVrS5PZuoK4bIsozb66ZDOaR2lHMaOqofT7+9Tu0YwtnIATRS1P01FwQhSgWDQYI+F4au6N2meZLE2GF9WdxVo3YQ4SyCHi9ajfggKgjC+fH7fGgk0MXAR9dRo0awVRMDQXuwgNcrVsefQ9T9DV67bhXpfaN/20m8IYdl723nkev7qB1FOJuAm+I0HZs3bVA7iSAIMeCTNSsZmx0DTUkTinhn2U4mHnaqnUQ4C09jC6Z2B7t37lQ7iiAIMeCTT9YyfvRQtWOcm0bPwg8XUbRrl9pJhLOwV1ej2O1UHjmidpSoFXXFEL/Pj0cXnSfIfJ5G0pOcHI+udZvaUYRzyNdU4ugSe+oFQTg3WZYxu4+pHePcNDqS42zUvv6h2kmEcwguWEtLQ6PaMQRBiAkSBl1sPMlPtNk48PbbascQzqHzo4+oqa5WO0bUirpiiKwEcUZx49TPKIoczW2NhM/Rd1agDzro6or+FUeCIKhLCXijunHqfykyxMbn5R6v8d1lOGsbxfGGgiCcmyIT1Y1TP0cSW2RiwpE336TtxAkCgYDaUaJSVP0tDgQCuJUWtWOcl6DiIxhQQCOac8aC3okOjlUfVTuGIAhRzO12Y5Fb1Y5xfmQ/sqRBYzKpnUQ4F0VBt/MIdbXiGGRBEM7MbreTlBCndozzpCAjodFF5cGkwufIwSDuPXtoaYmNe+xIi6piSMXhAyTnxEajMYe/gexe8fxurQ1PXLnacYRzkCQJRYmNSrsgCOqoOLCPktSouiyeWedxeufEUfXwtaTNnax2GuEcJElCkWW1YwiCEMUOH6qgpDhf7RjnRw7Qq1cxxl/9kqJp09ROI5yDJEkEg0G1Y0SlqPrUd/zYcYLxMbBX+1Npw48z5ooCPtgnlh1FuxRtK02N0d+LRhAE9TQ1NZHij/ZTZD6jMDevkssGp2KfMkDtMMI56PZVc+RAhdoxBEGIYu3tduIsRrVjnLdr517GsCGDiL/ySrWjCOcgHzzIvh071I4RlaKmGHLseBWmJA+eoF3tKBeko6OLOItYphztJEcNGl3sXGAEQYisqsrD5Foc4Iut3kKdXQ4sevG7Ldq1bdmDziT+PQmCcHpHDh+mKD+HWOkX8pnOrk5MYqtM1GvYuhVJ/Hs6ragphlRUVEDOAbVjXLAD2+qYPLRA7RjCuSgyik8cQSkIwulVVlbSRxt7RwTuOdpGkTc2tpf2ZIEuJwGXaKAqCMLpHTt2jNKibLVjXLDjNbWYm5vVjiGcg7O5GVk0UD2tqCiGHK85hi7OiTcYe8efmsw67E6/2jGE8xAvdWC329WOIQhClKmqPEymsRP8sVcw1UoKWM1qxxDOg+dQFS6XS+0YgiBEmcojR8jNSifWVoUAKLKMMT5e7RjCOSiyjP3IEfx+cc/6ZaoXQ9xuNzt3boOcvWpHuShuR5D0ONX/MQrnoVdqkIoD+9SOIQhCFHE6HRw8sJd+2p1qR7koQY0Bf02D2jGE82CrbubgXnENEgThf7o6Ozl6tJKykly1o1wUSaOlvapK7RjC+Th2jIoDsbcLI9xUvYv3+Xx8uOgDkocewyfH3hM5AKPGhqZT/BKIBfHOfbS3NotTZQRBAMDj8bB0yWJmpB+EQGw+sVe0FhpXb1Y7hnAe2l5dRO2x42rHEAQhSricTlauXMFlE0cQi6tCAJA0VK1YoXYK4Twc+9e/qDpyRO0YUUfVYsjyFUvJHt2CIxCrp3xI6DQmsIu/WDEh6GNkRjsb1q1RO4kgCFFg5fKlXJF/HMnVqHaUi6PRgdZAy4adaicRzoO/04Fl5U42rv1E7SiCIESBNWvWMHfmJKRYLYQgAdC8T6x4iwWu1lbca9eydeNGtaNEFdWKIVu3b8aS4qczUK1WhEtm1iUhK0GQfWpHEc5TomsP8bSzZ+d2taMIgqCiLRvXU5jgQ+OoUTvKxTOnogT8IMtqJxHOU/Nz7+LcX8me7eKIQ0HoyTZv2kRJYR4SMfz7W9KiBEVTzlhy6Omnady3j/179qgdJWpEvBiiKApLPl6ER1+NO31DpKcPKZs+HWTRiCamKDL9gmvxdZxg7y5REBGEnkaWZT5atJAMqikLxPYT+oAhBcXjVTuGcAFkv5+WBx6jYc9B9oqCiCD0OLIs89HixeRmplJalKV2nEvicnvENSjGBDwedn3ve1Tt3Mm+nTvVjhMVwn7gcEeHnfUbN6DTS3gDXfhlFxllfjo1h8I9ddg1VfkozE4GYnSJdU+lBBmi38zSej39B6kdRhCEcGptaWb79u3oNTKKrwv8TsZnu7E4Y/8atOVIJ8UJ6bjVDiJckKDbg+vnz3H8rw/Qf+gQteMIghBGzU1N7N69G51OC4oMiszkccMwGWL/SPTVn2ygV0IC4tFibPE5HFT//vdIv/sd5YMHqx1HdWEthuw/sJedO3ZTMrWTdu9RdJ9OGHsH6J7e3s213DVjEtRHf2deWVZo6vCQmSSOYATA7yBF30lzUyNp6RlqpxEEIQz27dnF3v0Hub60Hjqr/3fFi81+3afYeqiFqYZSYmGxqwJ06SBerKgGwFl9goTqetrb2khKTlY7jiAIYbBn9y4qDh3murmXgRJUO07IHaw4whBnbDQfVwC3ToclIC5CAM3795NVX09nZyfxPfxo5LBtk6k4fIDDh45QPDZAu/douKZRkYTithDviL4jgWuaXTzw7A4efnk3Dz63k5pmF1uPtLFoy+kb1Xp8QaY/vIp1+1vOOfaSbfW8u/5EqCOrYqi1gg3r1+L1iiV+gtDdHNy/l0NHKpndq+tkIaS7kbT4tQkcf3G+2klO0a6Ht7NgQQbMzzz552Nm2Bt3+tf7JXiiECot5x57vw12dpPPbcG/vsWKj5cSEB/OBaHbObB/P0eOVDJ35uRuWQgBCCoKO154Qe0Yp3Do9azLymJjRgbrMjNx6PU0m80cizv9RSggSbxfWEi95dwXoeM2G0e7SfGg/m9/Y8mHHxIMds+/n+cr5CtDfD4f73+wgLTSAAnDqrAH7aGeIiokGgqBNuiqVjnJqdbtb2FkWTI3Tyqgrs3NiRYXGw+2sqPSzrxxuSRaDV94/fub6rhtWiH/XlHNuH6p1La4+P4Lu5hQnsah2i7+/I3BPL/kKO0OH5UNTi4flsmTHxzmWJOTcf1ScXqCHGtyYnf4+dacEp5YcJi/3D2E636zjt/ePpDdVXaykk2M65em0j+RM/C0cXl2FStXfIykMzJgwAAyM2N7/6Yg9HQej4cPFy5geLafqzIOQ1eH2pHCwmcrQQq20bEv+k4zq7RAgQtGdECH7mQxpMoCNSYY0gHmL/UL3B0Po+2wORFKXGDXwTtZ0MsJjUa4th7WJYFLCy1GKO+CVSnQpodiF/g00KoHtxYmtsLKVLi+Hp7Nh6sb4IQJEgInx44mjiPHSP7bu3xsMCKZDAwfPpy09HS1YwmCcAk8bjeLFi1i1LBBXDN7MjF7ZO45OFw+tLJMx7Fjakc5Rb3FQrrLRWlHB06dDqdeT4PFQovJRHFHB8YvNR2vjo+nj93OocREslwuHDodG7KyyHI6sRuNjK2v50BSEl6tlk6jkfyuLvakpNCl15PpchHQaOjS6/FqtfRvbWV3airj6+tZkp/PqIYGWk0mLIEAWa7ougg179uH6ZlnWGwyIRkMjB07lqSkJLVjRVzIV4asWbuSojFe3Ak78HTTQgjA1kWtXDtlsNoxTuv6CXlISPz6tf08v+QomUkmRpelMKE89ZRCCMCCDbXMG5eLyaDlQM3JTUxFGVa+NacX8RY9jXYPh+u6+OH1fblixP+KBdeOy2P6kEze/qQGo16Lzaxjy+F2bGYdJ1pcpCYY2VTRyvYj7YwsS4nYz38htK5aLjMvZ5ptPevWrVc7jiAIl2jNquVcWdpFnm8r+LpnIQTg32ubGG+JzuLt0I6TBy4uSoN1ySe3xhS5oJfr1EIIwK74k0USnQINxpNfS/HBxDYwBU9ur2k2wswWGPC5fbZDOqBfF2yPB70CRhmOWU7+b7sObIFPizBmKIyuz6D/1bZ+B/Zrf4jv24+yZu1ateMIgnCJ1qxZzdVzppGTmUR3LYQAvPLa2wySVDuU9KxKOk5e+7empXEgORlzIECGy0WWy3VKIQSgKj6e4o4OtIpCu/HkRSjO56N/WxuGYBC3TkeH0cjQlhYKOv93ESru6CCvq4vK+Hi0ioJBlmmyWNDLMg6dDlMgQKPFQrPZTEaUFUI+U7N6NVvnzePogw+ycuVKteOoIqR/i/1+Pz7ZRbN/dyiHjToSWlxtOoqIvi0yAK8sr2bq4HQeubkfd80q5tF3Kj47CvwUmyta6XT7+ct7hwjKCi9+XAXw38ZOOq2E2/u/5VNa7f8GsplOLixKiTfy4Lwybp6cT2m2jYn903hiwSHunF7Ejko7Br0GvTY6f2H+l9dOku1koaihoYEFC94DoPJwBc1NJxvktrW1qZVOEITz4Ha7MeBG2x6dv5tDRqPH7jNT//iraic5rY1JUOaA2c0wvg2WpZ75tdVm8GhgRerJ24b1nz6U0n96D6Hl5MqPz3z+SmKUT77HFoTLWmCEHdK9J1eUrEyFse0nV6NolZPjRDNn1QlsupPXoKqjR/lo0WIA9u/e899rj7gGCUJ0czocWC1mNHT3bQcSLreHbX/+s9pBTqsiKYlch4Phzc30a2tjV2rqmW6DaDSb8Ws07EpNRQEOfLoyQqecvAhpgIDmf1eez1+D9J8WVkzBIINbWii120n0esl2Otmdmkqf9nZaTCa0ihL541svUNvhw1g/LQTt37uXFcuWAbBr2za6urpOvqabXoNCuk1m0eIPSRtSR2cMH5l9PpIMRUg0g7NZ7SinNb48lV++ug+LUYfLG+DWaYWkJ5hYseswlw/P4pXl1fzw+r4AvLb6OE/cPYTiTBsAX/vTRuraPF8Yz6TX0Dcvnt+/eYATrW6mDfrfMl6rScewXkn89F97aO/y8cjN/eidE8cv/rOP394+kH+tOMaYPtG5KuSLJGStGUVROHhgPy6Xm2UfL0ajM3Jk5Wri4+Opq6vjijmzKe1dxuYN62hta2P6zMvR6/VqhxcEAVjy0UfMyT3WbRqknonP1gsp0IzrWK3aUU6rlxM+zACDfLKQMbod4gJQYT25xWVj4slVHgBbEuGGOkj99JT6F3Oh40u/UvUyZHphSRrY9ScLLZ8xKpDvhvczTm6jmd0EGV74MB2uaoBNiSe30kQ7jUGPznZyv3p1VTVt9naWLFyI1mTik40bSEhIoLa2livnXEFJ71LWrViFvbOD2VddiUYT7R+zBaFnWLp0KVfOmkR3XhEC4HB50ckK9qYmtaOcVrbTyZaMDHSyTECjoay9HXMgQK3VSn5XFxWJiQxtOXkROpyYyPi6OuL9Jy9Cy3JzcX3pc71WlknyetmeloZTryfH8b+LkF5RSHO72ZyRgUerZXhTEwleL1vS0xnd0MChxMSoXRXyeQabDa3hZEG+sbmZuoYGFn/wAVqjkc2vv05iYiI1NTVcNXcuRSUlrFmxAofDwZyrrkKSzlRqig2Soigh+S92/ca1KIkn6DLvCMVwUc2+P4f0YAqTbVvVjiKEUJ1xCBXNeqxmAyPTGiHoAUctGBJAZ4KAi6pgH4536hiU5sCk9bOoMoHhI0ZSWFikdnxB6NHWrVlJL2MNae7uf8jfwupMzM40mr/9R7WjCCGU9uAtOArTMUtadG+uJOB0077zAOacDAyJcfjaO7HeeSXOzETi1u9H8fhomjuSyVOnkJ2To3Z8QejR1q5ZQ3lZIckJVrWjhN1/3l5ISpeLDffeq3YUIYT6/+hHyLm5GBWFtjffxOdw0Lh7N3E5OZiTknC3t1Nw110EU1MJrFmDIkkEZsxgzpw5pKSeZQlolAvZypCurk6UrJ2hGi5qaSQdR7Z3cMMcHdjVTiOEUrZ3B9mfNYi2f+4b3nb49MCZIjZRZOG/T57n5SaytCJBFEMEQWVet4M0ufsXQtAa2VvrY9gHS9ROIoRY86P/AsD9pa+7jtfhOv7p///FMyf/99Pv2bYfYE9KiiiGCILK/D5vjyiEgET18RPYX3td7SBCiO39/e9P+/WO48fpOH7yIrT7pz/9wvfS9+xhc3Iyl8+dG/Z84RKytZV+ujjTsjCtpCfN3AeNpPvvn6262OyYrtdYMZvNYD+sdhQhGnjtBP0+jh6pUDuJIPRoGl/7mb+pNUJyX5C0//uzOTavQRjiMBlNtG7apXYSIQo4Ko/j8/uprBDXIEFQiyzL6LRn2yoggUb/xT9HafPRc5I0GPR6mvZ2895cwnlp3rcPv6Jw9HDs3heHZGWI3+8nKLlPO5jelcO695poOLGV3JwcFL0bTdBMU2Mt/YcXkTP+RCgiRIw32IHBks42JjKMNWrHEaLArIyDLD0skZ1bgMlkUjuOIPQ4XV1dxOkDp/1ek1TAC8sbOVa/nV4lJWiDHfix0NDUzPiBBVxTFH3HAp6VuxWTPouMJ39Ao9gmIwC6X7zIwZ/dTl5REQbDqSfGCYIQXs1NTaSlJp/2e3UNrTz/yuucqK2lrHdvJBQ8Pj8NDQ1cNnUSc2dOinDaS6QE0en1jHziCTZ/97tqpxFUpsgyjb/5DZqf/ISC4mK02mhvV36qkJQltVotGtl8yteNmkSW/6uBlMH9WZRYz+50D94++SyQjnKol0TTcQdJwUGkmErZ934iW/5jIFjbC1dlIUZtAgAWTQYdB3IwaGyhiBoS/eY42Xiwkke3ZuBJn6h2HEFtzjoSzFra21rUTiIIPZLJZMIZPPUmUDam8rclTVhH9GdpWhPbkruw9y/gbQ5TVabnYK0DR8JQSO7DU1tT+NNaKxWBvmzpLAFDHAA+Yw4bWgtAHy3LnxXuGeHgcLCV+t/fTvH9t6gdSFBZ+4796P0yHe1nWR0lCELYJCQk0G7vPOXrsiLxt2de5NZ+xfxa18Ydmg6+kZPIXa2H+HlhAjt378PlDYJGz+NPvcTvHn+G6hONbN6xn8+OgfR4A2zcupczHgupgu/dczuVEiT86Y8MueceteMIKqvdvBltMEhn56n/DcSCkDVQXfzRQrT91iMrJ5/OSWjZ/oaVuN4FfGfJU6d9z01DL6N/hwWNrFDUtwyvTUvngRqCQRmXw4neqMHZ5SGrpICuxlYSUg3kT25AiZIjq6zBYta82YBW0nPDlHIKXWuAbn6UjnAqrYFl7suYMWOG2kkEocf6eNH7XGZdDcqn1weNnj+ssWLql8//LX32tO+5fcQsSlsN6BSFon59cFu1dOytJqiA2+HEbNDQ6fKSWZRHV2MbuckGri85Bko0/J6XsBtK+OfKZlC0jNWnUPOjv6odSlCBPt5Gwlu/E9cgQVDR0o8/YvrEEZ/7isRvHv071xdlE//O86d9jzJlLm+7JHyKxJh+fcg3SLx7oBKtpKHL7cZoNNDV2UW/wjyqm9soKsxn3pypkfmBzkNLWyfPv/I6kqzQX1HY/MgjakcSVGBOSmLI66/H7DUoZMWQnbt34JCO4IjfApzsrbH1NSO/rv8Yh+fCjxRKtSUyurCcnScOc8LehEaj4fmJ93DiaCVovZSPysFceAL1j6+SSDIUs35+Kz4nzBjdh0HSRpB9KucSIialH4urM5gz9yq1kwhCj7Vj22bSApVkuzed/IIxkcfWmni4ZhG+wIX/Pk6PS2JkQT+2Hj9IQ2crOo2O58Z+g8Zj1RgUD1MHZ9LfWh3aH+KiSMiJvXlmRRtOLwy3ZdD00BPIPnEN6inyrpuF/eYpzJ13tdpRBKHH2rJ5E/nZaWSknlzZjqThj088y61bF4J84QV0TVIquuIyAkf2I3e0g87AJ1fcwdG6egx6HbOmT6FPr4IQ/xQXJyBL/P3ZV/B4PfQzmdn+0EPIgdNvXRW6nwG33ILx2mu54qrYvA8KWTEEYNnyJRjT7JB8gk/e6OTwvhp003rzz0/eC9UU3DHmCgbm9sJc1YHP30W/OQ78cnSc3xxvyGHPkiDx+iRuzN0HivhF0BMoKQNY25TH5GmxWREVhO5i6ZJF9I63kyUd58mVLg4cradzVDpvblsesjnuGn81/bIKMR1uwyh7uG1AMwS+fP6HSuLyeWVjAJMpBe55THwY7SFyvzIX+baZTJ4xXe0ogtCjLfnoI/r36UVaSgJ/e+YljlQe5Tc5Znw7N4VmAo0G8/Sr0GXn85/jLVhsNr56/ZzQjB0Kko5/vvQ6iSYT+++8E+UiikBC7Ol/xx1k3HILE6ZMUTvKRQlpMQRgz95dHKo4QmuXHXumnoc/eJaAHJ4PZM/Puh93QyO9ZjVFzdYZCQ0HPozn8iF9KfWuVjuOEAFNliFUBXoxZuw4taMIQo+3e+d2duzag85q5LjFwyMfPoscpg9kL8+4H6WpkVv6nPjf9hy1SVqe3BTHcF0O1Q/+We00QgQk3/8VMm6by5ChQ9WOIgg93o7t21m/YQODSwoo62oh8MY/wzORRsO+G79No8fHV66dHZ45LtIfn/gnwyQNa3/4Q7WjCBFQ+tBDDP/GNygrK1M7ykUJ+blOA/oPok/fMras28iz694PWyEE4PtrXsKoi6PigzSipbGQgsyYudks3RK7RwwJFyZd08S27TtwOp1qRxGEHm/g4KEMGjiAtctW8dTa+WErhAD8eNN/cMlGXtyfR7Rcg1CCfGVSLju9rWonESJE2ryfNWvX4vV61Y4iCD3ekKFDGT1qFK8t/Ijgh2+EbyJZZsCK+bS0tvOvNxeGb56LcNP1V1MpRck1UQg7/7ZtfPjhhwRidDVqWA65Lu/Xn4yMDG4fdXk4hv+vNlcna5TjVFUeZ/fbCex6K4G9C2wYpYSwzntOeg/egFga1lPUBXKYOnkiVmu0nDYhCD3b4CFDSUpK4pZRs8I6T11HCzuNbezcX8lTOzJ5fHMa/9yRiqyLC+u855JsDuAmNj+UCBdOmjaMObNmYTQa1Y4iCAIwbPhw4uLiMI27LKzzyI21XKFxs3HTZv7+wus8+tSLPP+vt1G7OJ+bnUWH6FvVY1gnTeL6665Dp9OpHeWihKUYAjB91gyqW+rDNfx//Wvbx2zP8WAqzWWDrYOAy4A2qG4xJOA0YjPF5l8I4QJJWvbbE+jXf6DaSQRB+JzJ06Zyoq0p7PM8u2UhFcUQKEpnXUIXzU6QDepeg050SMRL4sa4J9BazARH9qVXjC5PFoTuavqkiScbn4aZZdX7/CzbzLRkK3cq7ZyoawApbLd352X/gQrSzWZVMwiRYUpIwDp0KHn5+WpHuWhhu2PvcnSx5cTBcA3/BWsqd7KmcicAI/Lmohi6UPOh2O4NJ5g6bAi4T6gXQogIX0IfDEG92jEEQfgSv9/PpmP7IzLX0kNbWHpoCxaDiWHpl6ELdEVk3jNZvu0Eg+N7IzZrdn8Z10xDMRjUjiEIwpcoSPiPROYapOzYQMKODfgTU9AXj1b9+Pc16zYyyGCgStUUQiSUXHst1hgvfIWtdKhVNATkyDeUi+uby473NMTpsyI+92faT8iUJor+ET3B+toEJkyapnYMQRC+RCtJhLY9+Lm5fB5S+ubz4g4bWNW7BjW7DXhWb1NtfiFyuqYPZZI4RUYQoo5fhZNUZHsrU3sX8epbC0HSRnz+zzhcHo4ujK4+JkJ4GKdNY+K02L4PClsxpHxAf37c/5pwDX9G3/34HwQ8Gp750Q72zE/CTBZ6TWQrViZjHNSH6BgtIWo5rX2Q9fFotepdcARBOL2+/frxk4FXR3ze+5Y/TatT5tYn9vDU9kwCpgzQWSKYQMJgtlHz5uIIzimoIe2my7FmpKLRqLskXhCEU/Xr04e9kyJ/H1T41tPU1Nbxta9/m6ee+w+BoEKke4jo9XoqlyyJ6JxC5PW+4w7i09KQYrxZbtiuoPm5eRgCkBmfEq4pzmip/gSecbm485NY/UoX216zULcuF70mMh9IJUkBg7oN9ITw29iSzaQpsV0NFYTuqjC/AJtiIMUa2f4dsiyzxtqEc2wW7qI0fr/Ey5/XJ/BBTXGEiiIKGklCZ4ntZavC2UkaDb7rJzFpengbNAqCcHHyiopo0RqQLBFurh8M8HU6ebxPFpelJ/DbR//OH574J4uXrSNSRZFYvzkWzk2r1xN3xRVMuiz2r0Fh7fI5dPgwvh1o5ZG1L4dzmlN8fODkqoy3Pv2zQWfg6ZJv8vHfj1FQlkJdpRONRmLKvDKU5Cp8siOk88tKEMzJ4OsI6bhCFIkrQPLp0OtFvxBBiFblA/rzbd8V/OKTVyM674d71wHw3p61AFgMJp4u/gY/W3iM8sJ0DpxwgiLzjdl9yOUQ+EO7rVIO+rEW5dCx70hIxxWiR+qE4cg6vViZKAhRSpIk+peV0WqfQ/LHb0Z07uDW1QAk7FzPNwEsVrYX5vGz3zxGvz5l7D1wEI1Gw7fvvpP0lAQg9HtKbZmZOBoaQj6uEB0Kp03DoNd3i8JXWNdW9iouoa81k7FF6p604Qv4uHPZkxhGlqJJK6KrXzKO/qlsWdPIyn962TnfiFGTGLr55C58cb1DNp4QfWRTKnq9aFonCNGsX5++9LdkMyy/j6o5XD4Pty5/EsPYvthzC7H3S8Q9NJs3NjfxyyXwzNZ4MCaFbD7F20nO1bH/tEY4M0NJLvFJ6p5aJAjC2fUbNJgjqXno8ovVDeJyMnT+P7h/UC8GmfU8kBvPA72yePuN+fz893/lny+/SShXjSjBIP1vvDFk4wnRx1hYSGZentoxQiLs578O6D+ACfajrK/aHe6pzunXH790ytdm9x/LbeWXsfm13Yz+ShLu4KUfgzVsah7vfFLFTYWXPJQQpTSKD0Xlbt2CIJxbaUkvJnWWs+14ZE43O5v/t/i5U7521aCJ3NJ7LE9uOsS3RwbB13nJ80wZlM6BikseRohiGqcHl0M0aheEaJefl4ujV39Mx4+qG0SWCbz+T5IA/6dfmgcYx07jaH4OT7/0BvfcfiOhWCUydMgAmoziePfuTON209HWpnaMkAh7163Skl6MT+8b7mku2qK967nxjV8Sl5zAJ6/IbH3Virs6j0upkFoyHNS3qnu0ohBmjjqCQVEMEYRoN2jgICaklqkd44wW7FrDdW/9Cr3ZyO9XGvnDJ4nsdRZe0pgDMwPU+UK7/VOILp37jhAMRv7EPkEQLsyg4SM4lB29q8W965eT8+Kf0MgKv33saf7wl2c5eOTYJY05YewIjjtFsbY7a9+/H5eje3zOiEgLcgWFCcWDIjHVRbtv28v8pX0tlTkaDm12QmvRRY/l8DeCpAFzWggTClHF00qCwUvN8erTfvvQ4YMsX7GUDz/8kLfffhvlc2d8vvbaa7z//vsRCioIgk7SMjwveovyAPfufIXH7WupKjTywc4u6qReFz9YVw0anQFTprgGdVcdew9Bawe1NTWn/f6BvftYtvgjFi5cyDvvvPOF7/3rX/9ioTj2UhAiQpIkFJ0ebd7F31eEnSxz5YpX+Ubler6ZrGf+uwtps1/8ja4GGY3BgDE+PoQhhWhSt20bgY4OGs/QF+bgvr0s/+jkNei9d9/979cVReGll17io0WLIhX1nCJzHltAJi8xPSJTXYoWh52XNn7ICmM9K9+oxOjLuciRFLxyB77kISHNJ0SXobotvPKvV6murvrv12RZZuWq5Rw9fojE3m3YBu8he7CTd96dz+HDh1i2/GMKivIpK4vepwSC0N0EfH5yEqO/MNDisPPPdQvYmuLgiQ+q8BhzL24gRQafnaLbI3+soxA5rkee4bkXXqC+ru6/XwsGg6z4aAlVO/dgfPcTAvc9SvySbbzz5lscOljBRx8spCAnl0ED1e3lJgg9icPrRUpMVTvGOckd7Xg/fpe7bUF+9+iT+PyXsAJalhl0662hCydEFUWWqfz5z3nyqadobWn+79d9Ph/Ll3xEw8H99K/czsiFLzCg4TAL5s/n0MGDLP7gAwpycxg+cqSK6b9IUj7/yDpMfvnLX2IYmMOPl/wz3FOFzHcn3UBGjYMhN1xcZVRq6YX9gML1uQdCnEyICpKGjR2l7Dzup7S8mIBHy8BBA9mydTMlI7U0+Xfgl/+3RDBOKSHosKI1BlFMHbTuT0Enx3Fw/2Guu+5aCgoK1ftZBKGb+8lPfoJ5ZBE/W/K82lHO28OX3UrykSa+N8p+Ue/f3llEZZWO4Pf+GtpgQlSQdDp44Abs2UkU5eQSMBsYNWIEn6xZS87Gwxx/6T18bfb/vj71yilQlIXU1om/qg7PdZMwZKdxoPooN990E7ndpBGeIESjhx56iB8N6U3g3ciernkplOu/wduNnXznm1+7qPcvX7sZ++Gj7Lnv2yFOJkQDrcFA5oMPIuXkUJifg1OWmDRpEiuWLmWy/Rj+tUtRXP+7h/YPG48jPgWLqxOtvY19pUMgOY2dByq44/bbSc/IUO1niUgxxO12M/+9d3i5YQPLDm0N93QhYdAZ+E3hXAZf30VA8Vzw++P0Waz/j4cHhzeGIZ2gJoexkN9/0ES/vr3Im9qEJ2DHokujc38eiWlmupI+4XwaUKWYSqnfnM6Vc68Kf2hB6MG6urpYsPB9nq1dw9rKXWrHOS/xJhs/y5nJAyObQPaf+w2nDFDI40vdZP3opZBnE9QVP2MM64ZmUV5YjPXX/8bT0EJ8eS/891xFkjtI7f/9+bzGybpiCo6vX87cq8U1SBDCqdNu56NFHzJ190oCR2LjIakmKZV/9ZnIg/fdflHvl9Hw58efxvWTn4Q2mKC6nNmzOTpwIINLS5iy7j1kZxea/BJ2DpxErhQg+d0XzmscaeQkdvYfz8zZs8Oc+OzCfpoMgNls5orL57D/7XqWERvFEF/AhzfoI15XQJv/wtvyd/kbkKQUMKWApzUMCQU11CglPL3gBLc9OI1q1wrcgZNLCJ2BRrS9G7mQtrldlekMGDAgPEEFQfivuLg4pk6awq7FtawlNoohnR4HPr8XOa4ITcehCx+gqwaNLhtTegqeJnEN6i5MX5vF+iwTNxoy2X/Pn3F/+vWOXQfhWwdxXcBYnivHMHzE8HDEFAThc+ITExk2YiRN7SdIjpFiiNzegkaRQdKBErjg92uQ0RoMGGw2fN2k0aYAuXfcQXVGBj8aUIj7nRf5bCOVfLSCgUcv7H55b355VGyXiUzPECAxMZHypHwMOn2kprxkSb0L2L7sYj9EKviUTgKpw0KaSVDXX94/ylXXz6DatQy4+L2UEhra6wPkZF9kTwBBEC5IVlYWA+Lz0Gq0akc5b/HF2Xy090Jubz9HCYKvg0LRN6Tb0Oj1rEpSmDt0DPt/+fdLGktnteBMspKemRmidIIgnE1RSQnHErNAuvjTKiOtT3oyq9dtvvgBFJlBt9wSukCCqgw2GzvMZm4aPxL3O69c0lhSfCKtGj1JyckhSnfxIlYMASjMzeXuIXMiOeUl+d2mN/E7zNSvz8asTbrg9w+ZkMuCjafv9C7Epke/lsvudVsJ7L+0p2kKClarFYPBEKJkgiCcjSRJ5GfmcOeQmWpHOW+P7/uQo21aFtYUgTHhgt8/tm8ylTm2MCQT1CD7/Vy+sY61e7YT/8yPL2msoMeLzWZDq42d4qAgxDKNRkNcdi6+MdPUjnLeRuxcxfYdu1m8bB1w4UWcAeV98PfvH/pggip8DgdDduzgvfVbqZj3zUsaS3E5iY+PR6OJaCnitCKaYNyosUxO74tGUv8HPx8n7E38qGI+Xl0iW97QcmhRGkZt3Hm/PyHfS3VDWxgTChHXVcM3BreyZ9t+Ms0Xf1y0VtKze/du3G73uV8sCEJITJ4wkWlp5Ugx8mSusrmWn1Z/SL0+jsfWmnn5QC7oref9/hEFEsfd9vAFFCKufcseCv+2kJ01R8mcOeGix9HFWdm+fTt+/0X0oxEE4aKMmzyFg/n91I5x3gLHj/KVfSvA3s6fn3yef73xARdSFLlsygQOdXSEL6AQcSc2bED397+ztuIout4XX+jSWKxs2bKFCLQuPXeWSE9oM1u5efDUSE970Vw+D99a8TQ/PPoe5pxsVj7rZdd8C3FyKRJnf6LS6atDkrRgvPBVJUIUMsSDNQu0BuwdXRh1518Y+7Kg4qN3796YzeYQBhQE4VzizBbmDZikdozz1ulxcM/qZ/nxsYWQncMvP9bw1OZEXJbeIJ3jqX7ncTQ6A4ZUcQ3qDsw5GcT3LUEfb6PT6cCSc/Hd9/32Tvr27YteHztblwUh1kmShGSxoh0yVu0o503ptDPkvWf52tZFFNjM/OpPT/L35/+Dxxc853s1BNEaDOgtlggkFcItIT+flN69MSUm0tHZiTY57aLHkrs6GDBgQFQ8nIpIA9XPmzZ5CpVNNbzKskhPfUlkWeabi//G5NKhTCoczJr5LWjkeAKSg8Q0E8NnZOKni4DswRO0f/ouBa/cSSBtDLoTS9WML4TAi1sN7D/WxP03T6SgqJJjXZ9c0niBwIU3pBIE4dJMmzSVw021zGeV2lEuSEAOcOdHTzC1bDiTCwfyjzVtSHIqumAXuSlmrhyejC7ogIAbvPaTb1KCyF47hbddzaFHX1Q1v3DpKm+eQJPk5/LUQkpkF0cf/+dFjyVpNASD576ZEQQhtMZOmsLmhlr67livdpQLE/DR+7W/0n/AcJwpefz9mZeQJQmdVkN+Xg5XXzELjQQoCl/oqafIDPzqV9n2z4v/fSVEB/fNN9Mhy4xMTibdZsG76o2LH0yri5r7oIgXQ7RaLUUp2diMFhze82gMpyjovAoB08UtYtEEFFAUZH1oFsGsOrydVYe3AzA4tzfXDp2CTjax4t9HMej1yEEdMgmkF+opH53FxKnJfLClnmuyQjK9oKIud4AHHrmVN15YxKSbs2jxN1zSeFqrG1mWo2K/nCD0FEajkT4Z+Rh1BrwB37nfEGXXoBUVW1lRcfJUthGF/bhmyCRa/Dr+suwYep0OWbZAUEu/bAOT+qczZriNE5X2kMwtqCugk7hr+FTe27OFYVuOcylXIEWW0dS3oihKVDyZE4Sewmq1IucUgEYD8rkb8cuKQktAJl1/cf19nEEZGYjThuYa5NuzFf2erXwF0PXuj2HYWI4qMn/56zPoDAb8QRlFDjJ4YDkTx42mX78++OPiQRRDYp5kMPB/k0fz73VbubWphksqpwf8xPkuskF8iEW8GALgl4MMzivlkyNfPOJQ7w6SWulG1kpIQYXWEjM6j4ypK0h7vumUcfI3d+KzalEk0AQVGvtYTvnAmVjjwZmixxuiD6Kft/PEIXaeOPXIw3lDpjBV6sPHr9dg0tTT1twsiiHdQN88Gzs3HaT8unZa/O2XPJ4WoyiECIIK/MEA/bOL2Xb84Be+HmvXoC3V+9lSvf+Ur984bDpmUx7bl9Zi1hporWukPOSzC5GW1OnjcHM9Ob/49yUVQj6jS4gThRBBUIGkKGiz8wmeqP7C1+t8QZ5tcGLSSHhlhbsyrTT5ZA66/dyUdupWk68fbqfIpEMrgSuo8INc2ylFj7daPIyO09PHEvprUODQXgKH9pIJfOWzL2o0mMZPZ09LM8/+/TkCOj3NdXWIDXmxT2+3c7S5ja/uWnJphZDPmKJj+5QqxRC9Tktewql7XS1tAVyJejpyjeg8Mnp3EEt7AFNHgI4swykfMgNGDQ3lJ5vJJVW7MXUFMbh86F0yWr9Ca6EJS1sAbUChU4KEOh+g4LXq8Fk1pFR5cCfoaC0Jbd+Gd3as5B1WAie7R/+p+BowdIKvK6TzCJE1Pb+V3692MXVQIXZf9SWNZdNn0IU45UEQ1CBJGvKTM08phnSXa9Ab25byBie3Zpp0Bn6dNxd9Yjx+e2dI5xEiK/XNNWxPszJ0YB/suw+e+w1nkTysP3LyhZ9QJAjCpZMAbWrGKcWQLV0+Blv1XJ1iptEXpN4ns83pY58zwOwkmXjdF69BaXoNj+Sd7F/37yYXFe4Axz1BTviCdAYVbk03s83hwxGU0UgSC9s8KECJSUuBUctLTW4GWHR8I/P8G3OfkyzjWbOEUqAUwGrj1fJpeCwW/K7oWAkgXBzp7bdZlpLC3RlZyI31lzSWvu8gZMOpD5nUoMpjab8/QLvn1MJAR7YBJEg75CL5mIeAUYMrSYcrRX/aJcY6r0zGficZB5xoAwrueB3xdT4UDcg6CYs9gDtJR2eGgaRjHmQtyFoJc8fJPUruxNB/CP0yWZYJaGTs2rywziNEQMCDTqPBoL20Ikac3JuWzSWU9ekdomCCIFwIWQ7S6jy1w313vAZ5Aj68AS9Jk0aEdR4h/PwdDiRFwpiaeEnjpN48G9/PbqO8T5/QBBME4YLIsoziOLU4fUWyCQn4S52D15rdpOk1DLXqGRmnP6UQAtDsl/ldTRe/P9FFV1Chn1nPh+0ejBoJq0ZilzPAUJueyxKNvNbswqKRsGkl9jhPXoMGWkNcCDkdp4Ogx03uxInhnUcIO4/djk6rQWO9tEK6c9wM1o2YxcCBA0KU7NKosjLEZDSRbU0+5etJNV46MwwEc04+lUs77MKeazzjOAGjhsZ+//uPWAooBA0SLb0sGBxBNEEFre/kfjxJgfY8E0GDRHz9yX3isi4yy0P1Zdm8sfYwdw+PyHRCuNiy0Rlkmtx7L2mY9moDU6ZMJiEhMRSpBEG4QHq9npy41FO+3l2vQQlDitlxrJmciMwmhEvysHJqtBoaV2y8pHG6BhQwa9o0rNYw3wQJgnBakqRBSU4DDnzh62+3uLks0cRVeg1N/iBP1jmZl3LmgnmaXsOP8/53sqErKJOk03B3ppUqTwBnUKHVf/IaFFTgulQTKToNi9q9AFg1kbkGXd6vhIUnaiMylxA+uWPG0CjLBI5e2srEowmZzJw5E6PxzJ+vIkmVlSHOLgd216kVUWeynowKF5n7nKQfOvkhNGDUYG32o/PIpB4++/IqRSfhTtCRccBJ6lE3AaMGv1lD0nEPbQUm0g+5yNzvQonwFtkXtn2ES2yJiH2+TgIBGYvu1JuoC2GO1+F0iqWCgqCWrs4uutyOU77eXa9Br+9fDXkXfwyrEB1cdU0EtRKWgksra+k9fny+82geLAhCWLR2dhJ0OU/5+sg4A4/VOfh1TRdP1Dm5JsVEql7Duk4fTb4gT9Wfet36PItWw0Crnj+e6OK5RhfpBg3ZBg1vtLj5SpqZJ+qc/LrGQYTq8P+Vc3g3SmZmZCcVQq6rtpagRosm6dLugwwBP4qihCjVpZOUCKcJBAI8/9ar3LPmH5GcVlU2k4Wf587mgWG1QPT8yxcu3IbWQlbss9N3UBFZI+x0+S+80m32ldB1KJuRI0eSniZuUAQhkrxeL/98+99855Oe09k+PymD+xPHk/XD59SOIlwi+VtXczTNTN+kdCzPL6Zj76lN3M8l8YYZyF+Zztjx40hOSQlDSkEQzsTlcrH+jX/R/8NX1I4SMbqCXryQ3BvXI4+oHUW4RBn3309zQgIjCnMZtmslcu3xCx6jc+Q0DvUZzqTJk4mLjw9DygsT0ZUhXq+X/7z5Bs9VrYrktKpzeFwE/F6IL1A7inCJxqRU8/BMPbl0suK5duINuRc8httQSdbwVrZv30pLSzN+vz8MSQVB+DKn08kb77zN8z3sGnS8vZGAz0fiINEjItZp/vEeQ9/ajPdIDTuvG0FCea8LHsP+5sfEvbaSrRs20dzURCAQCENSQRC+rMNu5+MF7zFw1xq1o0RU4NgRZEUhpbRU7SjCJWr8619Jffddju7Yw4vp/dFkXPhKxfjNyxlRvYttmzbR3NxMMBiSs2kuWkR7hmzetpUN/uNsrTlw7hdHMUmSmFs2huQaL0adAZPegF6nw6g1oNdq0Wq0+AJ+Whx2PAYFOdNGWfFAlh7Yz3SxaTv2uZsZmdCMozQf+/50NL1OXPAQLZ6DGNMNbDmwhI5aHUajkWuuuSYMYQVB+My6TRtZ461iZ+1htaNcEo2k4creY0is9WLU6THpDBh0egxaPQadDo2kwef30eLswG0EOdNGQUop9hFtsOvS9voK6us8UInmQCXJ916J6VvX0vHtP1zwGPXvLiWhJJONOyvoKEwjMTGRK664IgxpBUH4zPbNGxnXUHHKKTIxR6OlY9BoPmh2IukNoDcg6fRIej1odSgaDVLAT6DDTrYeRiXZKEjJwjluHByO7euvAE1798LevRjuvRf79HnE//tvFzyGsnElccZ4Dh+r4JDORnZ2NjNmzAhD2nOLaDGktbmFw211kZwypCRJ4s7SKeQ4TRi8elJm96LV66TD7aDd1Umbq4sWh502Zye5SemMHlROmmKm7WgtB3cfoL2xgem5GlBktX8U4VIZE1l/oJmrJ5ZSdxEP1RRkvMk7kZIhyzYM2Z7Inr27cDgclJX1ITlJLF0WhFBzdXZxoPnCl3RGC42k4Rul08h06IgLmrj/O2PQyR4IeiDgBr8D/C4IuMCYBfGjqWzys3r7YU4cP0aDMcAwjQZFFtegWGctyGGfCYZUNlzU+2Wfn/bfvgBAxm/uQ5eTw77de2hvbWPAkEEkJCaGMK0gCACdLg9SfY3aMS6eRsvuwZPZEdRh1hkZ1ysff0cH7vZ2PI0tuNvbcbe24m5rI6moiKzhw3GnpLCorQ1fUzMNVqvooNhNJJeUUGcykVJbxUWtb/d5yVn6JgDma+7ErtdzcN8+mhsbGTpqVEQbfEe0GNIRcLH80JZIThkyV5aOZmBXAhmWTJaZq1iwaw1Unfn1e+sq+Wjfhi987c+l82jR2Uj1X/geXyF67OzM56M9TsbNHEZdYO0lj+dO3oY1PY2qbb1paGiktFdZCFIKgvBlzb5O1h/drXaMi3Jd2Xj6dtooTsrl1mkytO2Ho++c+Q3OemjbTwlQ8unumMd9mSTOnkj7wlWRiCyEiXLHHPYVJjHBlsbRBx+/5PGaH36KxIFlHP7FndTX1zNw2JAQpBQE4cvMHa0EjlaoHeOiNA2ewIeymbyUDLLnv0Pl0qUsOdvr9+6l4oMPvvC1lF/8gszp0zm2dGl4wwphlf7Nb9Kek8N1BZn4P3j5ksfLevcF8kv7sXLYTBobGxkxdmwIUp6/iBVDHA4HTp8nUtOFTHpcMnekjSbOr2dvrotfb734f+lVmfDS6mYeGishGqnGrpX77Fx772iqOleFbEx3sB3J4GPKlCmkpl5al2ZBEE7V0dGByx97J2hkJaRye8pokoIGHrg6GU3bXmi7uLG+dVUfHrN3UrwwtBmFyKpOMzGjUebw/7v0QshnHJU1aFvszJkzh/goaGgnCN1Ne2srukDs9YjTpGawIH8wboOZvJWrOLJo0UWPNURR2DJoEIhiSExrS03lvgQFbwgKIZ8J1B5D18/JvHnzMJlMIRv3fESsgerijxaTZUqM1HQh0SstlzuMQygsLuLXJxbz+tZL+4/3qbXvkJieziZ7cYgSCmrQyj4MmtAu37LoUtFLNkpFcylBCIuPPvqIorh0tWNckL6ZhdyqHcC8Ef14cHjTyULIJTA1rSazIAftN+aGKKGgBtnhxpiWHNIxE8p7YclMIz8/P6TjCoJw0kcff4w7M0/tGBemoIS/JZSSk5CM/cc/uaRCCMCmX/6SxIwMsm+7LUQBBTUEXS60iaG9BumLemNOTiUrKyuk456PiBVDrr/ueiwWS6Smu2TDc8u40luEdWAe31r2FJ5AaJ4o/r56GSv32TlGHyDCB30LIWGzGOhoDm33fYe/gaDkweeLvSfXghALbrzxRoxGo9oxztu4/P7McuTy3XlDGcoaUELzO+fO4V1UZFpI/OpskMQ1KBbpFQikJYR0zNYte5AdLnGyjCCEyc0334xWiZ1V4b7eA3lcSmOkBJu//R3kEPxuUGQZw2uvcTwri6IbbghBSkENSiCA05YU0jF9B3ah+L3IKvQ0i+jRuh7ZT7I1+pdfDs8tY3xbKsGBGfxsxUshHbuqtZYlxlre3+3iT+viWV6XjZzQCzSGkM4jhM/t42wsfnMDof7PJ7m8gaXLzrYDUxCES9Hl95Bkif5r0Jj8cka2JfKrW4aQ0bUutIO7GvnJtVlUDs6m8vvz8Hx9NmmTRqI1xU6hqKcbtLOej5qrQzuoomB8eQkff3hpT34FQTgzvc+DZIn+FqKe3gP5h9/G8OYWtv3hjyEdu377dvrs20fToEEoDz1Eyp13UjhpEroYeljR0+Xv38/L+46GdlBZZvDBTSxbHPlrUESLIQlmGy9MuI+C5MgvgTlfCWYbkz3ZJI8v44lP3g7LHGsqd/K9A29SkafnkDaVRz/u4DerLNQoJWGZTwgtjasOgw7Szf1COm5HoAqNxU17e3tIxxUE4aQks41/Tf0uWQnR25cn2RrPmM4U/t+tYzC3hafhuMZewX3Da3n4zkGUXjOCXZeVUfHQ1diunhyW+YTQat+wA60kkTF1dEjHbfpoLcHaZrq6ukI6riAIJwVt8ey8+XtI8YlqRzkjTUISLwTjGOv3s++VV8Iyx5EPPsD58MOMdLlIHzCAhilTCDz4ILlz5oRlPiG0jq9YgUarRVfYO7QDb/0ETWc7LpcrtOOeg6QokV2zVVtfx4L1y7hv2d8jOe15+2HJbIoLi7l7+ZMRnXdWv9FMdqYzvszGmKQQV9uEkPvrxnhm3T2S6s41IR3XrE3GcbgAQyCF6upjfP3rX0ejiWjNUhC6tapj1by/aQXfW/ms2lFO68clVzB7UB/G6j+J6Lxycjl/eKeO4toulOc+OPcbBFUd+8F1TG3XUvnPN0I6blxZEfI9V2PpV8zxmhruvPNOJLGdShBCpvrIYdo2rCF7wQtqRzmtD4bNIjsxhS333hfRectvvJF9vXqRWVNDQ5iKMELoGB7+Cd9K1uJdvzyk40p5RewbMpVgVh4nTpzg9ttvD/s1KOJ3WTlZ2eRbUpg3YFKkpz6n7IQ04vw6frghdN1xz9dH+zfyD8cWlu9pQzbFVpO/nsZhKqTNEaTJtT/kY7uDbWiLdyD1WYckSbz66qshn0MQerKigkKKrGnM7jtG7SinKErNxurXMNa4OeJza9r28ePLdRzJsWErEU00o1nCzPF4dBK1H64M+dhdFVU4H3iczq/+DI/Hw5tvvhnyOQShJyvsVUpjYhqa8mFqRzmFNreQdlnDtvu/G/G5973xBhkffEBDQQEJeTHWaLaHyb/ySvwaLd5dm0I+tlJTRb/3n2fQG3+jvb2dBQsWhHyOL1PlkfPsmZdzc+ZIUqyhbQB2qeakD8SWnYrdpc4S0WOt9WQOLeOtnZFvHiOcH4+lhF++dYyrvzUQV6AlbPNIQGFhIbfcckvY5hCEnmrOrNl8I38iNmN0NfWeGd+XUQMKQVapkbK7mVkTB1I3bYA68wvnlHjFRFb1iWfaCT+euqawzaMxGigrK+PGG28M2xyC0FNdNutyNg+ZCjqd2lG+YE1KIX2Sk0LSLPViNO7eTb+0NLQzZ6oyv3BuefPmcbisjHsTAKcjbPNIJjMDBw7k6quvDtscn1GlGKLRaDBZLaTHhbYT7aXQarRku4181HVA1RyLTuygvktsi4haWiM5uRk0BbaFdRq/7MYbiOyeOUHoKbRaLQaLiVRb9BTkDTo9WT4TU3OaVc0xLMtDe0r0N/jrqbRWM3mp6Zx45b2wzuOubcTncIZ1DkHoqfR6PVaLBY01Tu0o/2MwUqm1UPv0M6rGsB0+jD0xUdUMwplpLRZKMtIJbgjt9pgvCzacQPZH5sGQanfdhXn5XFE4XK3pT3Fl33FotVo+2rdB1Rwf7PkEg8nKTmevkI0ZMGXw9JY4/roxnnePZCAnlIKkDdn4PYnJeQSP20+CIXzLyLWSgXRzOciiKCYI4ZKZnsGVJaFtQHkp5pWNw2zSQ0eVukGatmNLTUR/2+yQDRk3rJzqey6n6jtz6bhrDmmTR6LR60M2fk/SuXgdTjlIQnnoPiN8mS7OSt51s5A1oleIIIRLXJwN1+Cxasf4L/uQcVh0Whp371Y1x84XX8ScnEzWTTeFbMz0kSORv/UtlO98B9tdd5E/YQKaKFuVEyuaFi+m0elCkxa+w1Akiw3juMsIBIJhm+PzVLvbkiSJSQWD0GnUvynvk1FA0TGFijSVliZ/jizL/L1tA8v3OdjruvTTZVzGfB5+uxlp0ADqyzJoTMnhz0va+d3qOI7JpSCJG+4LIvsYWmjl+JrwHc9pahvOm48dYMyY6LlICkJ3o9PqmVI4CG0UXIMGZvYi53iQB6anqR0FlAD3TQhypDgZ49dmXfJwcVNHsmp8Ad9+YC4//u4kRn9lPDumfXp6zQ3TkbTq//OPJf5OB+ntHhz3Xxu2OVJ+ex+LS62MnzgxbHMIQk9nNJlw9h0CUdCgWCkq4+UmDwkrV6sdBZ/DQdIHH1DfqxfZN9xwyePlzJxJ1bhxTO/ThwlWK70GDqRx2jTk73+fvHnzkMQhBRfE1dpKgtPJhtHhO/nn2NzbeLQ1yMTJk8M2x+dF/DSZzzgcDt7/8ANWdB3m+W3qnmv/g+zpZAzuxYOL/qFqjs/rn13CHZbBDC8yMT75KHDh/5patIU8urCW5n4JvLhx4Re+N2/QZCYFM3G227ljUhrpwSMXNUePpDPz1y2p9JnXGKYJJMw102iqtzNz5kzi46NnKb8gdBcdnR0s+OB9ljoP8e8dS1XN8sPM6fxg3igSW5epmuML4vL58xIPefUOlKfegYv4qGC9chIbeyfyi1v7oWvd+YXvyalDeHZpK82NLYw42Ib9/VUXNUdPZM5Op+r+uWT+6PnwTCBJZL7/OM1dHcy96koslujqrSMI3UGn3c7C9xcwtaECNqtYhNBoeLnPJAYpsPHXv1Evx5fkjx9P3bRppNfXU/fsxZ3+ln3ttVSVlDDw0CEq3nvvC98beNttHO/Thw67nbw9ezi+SN170ViS3KsX/jvv5KvbFp77xRdDkqi4++c0NDdzzbXXYjAYwjPPZ9OpVQwBUBSF1997m1uW/YWgHJmlMF82vKAvc4Ml/KJuEbIcXY1LcxPT+X+lV2CWHdxc1ghB73m9T7bl8cI6D60u2JTYwYLdZz7+9c5Rs+lvt6Dxe7hvggaNJ3wN2bqLNn0pz6+xM+grHWGbQ0JLXO1cqqqq+OpXvxq2eQShJ5NlmVffeZPbVjyBWpfCiaVDuMydzcPjWlWZ/6xMKby8M5XAiRbiH59P0OU+r7cljR3CntGFkBLP/81OQmOvOONrA+mj+fsHxwh0OCl9ayPOI8dCFL77SrhhBvsGZJL70/AdP6m1mMn48C80Nzdz/fXXh20eQejJAoEAK+e/yYC3nlItg27gCJ4nGff/+3+qZTiTlLIytHffjdzUhP0vfyHg8ZzX+3InTqR++HAsaWmkLFlC9apVZ3ztsO9+l/2pqWhcLgxvvEH70aMhSt99Fd96K42lpdyyc3HY5pAsNnZ85QECgQBXXHFF2OYBFbfJwMmtMunJaXxj6OWqZRhoyMKSmRx1hRCAE/YmvrXtFbwJaTy+3gamlLO+Xjan8/etKfxpqZuuwmx+3bD0rIUQgBc2LeL/Kt7G2yuLXy92s6gmF4zR09g2msiWLP68Lp5nV7Uz7bbcsM6lEMSnaSUpKYmASl29BaG702g0ZCanccvgGapl6B1IZOLQ8PV/uCSeVm7rV0n+0F4c+7+rseRmnvXl8QN6U/OD69g5sx+33jKWh8a1nbUQAqBr2sj9o+qZd+UQtt0wAve9556np4of0o/DD13N7gEZjNxaG9a5ZI8XuboBq9VKMKjOwypB6O50Oh26pBT8IyaplmFFwEB5ytnvL9TSWlFB+yOPkFJYiOGhh4jLOnufivShQ9H94Ac0TZ3KyJQUnD/96VkLIQDbnngCz//7f/RNS6PlpptI+da3sGWKa9DpZI4cifKDH1Bf1ps75PA+wFF8XpK8TjQaTdivQaquDPnMex8s4J5PnqGxsy2i82okDf+XMpngwHQeX/F6ROe+UH+edhedu49yw9hM+pqrQPlc8UajZ6+zgLfX1ZMwopQfffwcAfnCb6BL0/J4cMBs2g7XkmIO8JUJWVi8NeAP39FJsUI2Z/LIO6185e7LadGvwydH5vhlbWsZrYesTJ8+nZQovVgJQqx75/33uGvN32lzdkZ0Xp1Gy4Mpk/ntVwdCvbrNu8/loHYCry7cwrBaF97XlqJ87gGC1mJG+upM9qXpuO+GUaS2rwIu4gGDJYPV9QWs33kEU1MHg+u92NduxdvSHrKfI1bFDy1n9cRCbiobRtVP/4YvQv9M4r9zI+0jSpk3bx5WqzUicwpCT6IoCosXLGDYO39HcUX4BCednhfKJlJ6sIJ9b74Z2bkv0JC//IXN9fUUHz/Oiddf/8JqToPNRuott3A8IYHxFgubfv7zL1yjzlfm4MEkfv3rVLa1ITU0UFBfz4nVq3G3i2tQxujRHB09mgemjUHz1vNhPVb38+qmXM0uSypf+9rX0Iep8XpUFEOOVlexeOsa7l/xDLISmRUaOo2W7/WZTaEllW9vfTEic16qiaVDuMFSjqO1lXRrAK1Wos2lISCZMSdYedm+nS3V+y95nlRbIg9P/Areilp0aCHoIzMuSFaSCZc3yJF6B2gtGDVebp6YQ7z7MCjd9MmRpGF1Yy7LdzVz9Z3DadFtiuj08XIZxzdbuerKqzCbzRGdWxB6iorDh/ho1zoeWPFsxLbLGHR6vtdrJgNTsrk5V93u/ectqYxnVnpobWrB1NgBCvhSbOiSE8lJS+TmQZ3grLv0eQxxnDCP5p0luwigxd/lxNRoJyEAHglaTBLGpASU9k6GNHhp/3Atst9/6fNGIY1Bj/vrszkcJ3G520bN3/4d0fnTbrmClplDue6GG8L2QVQQerrD+/fj2L6BrPdeiNykegNrh05HTkzlwDfvjty8l6DX7Nm0TJxIp90OjY0ogCY1FWNSEmkWC65nnqH5wIFLnic+N5d+3/8+u9ra0JjNeDo60DQ0YJVlvJKE02LBmJCA3N5Ofm0txxYtQu6mK+j0ZjO2O++kyWbjvrwENCvD1CfkDFwTLmdLegnX33gjmjA1u42KYgjAuk3r2VhzgIeW/zPsc80uHcWgznhyC/L56d63aHNF9mngpbp5+AzG55QjKQoVXfXM37GSE/bw9PrQaDRcPWgS03IG4O90ozFo6TQrHGw8xtDMUvz7awi4vVw7Ko1SYxXI3ewDqSGO78938c0fXEmV58OITi2h5cSyXtxxxx1h+wUgCMJJqz9Zy7q6fTy88qWwz3VV7zH0s9sY2LeEG3IOQvD89kFHjYzhNATSkGXINndByy7whWu1nAbSBnHMk0ZDWxdWk4F+OWY03hba5DT+9cF2urpcDD7eheut5che9U+FCyVLfjar5w3m9sx+7PvRnyM6t9ZsovOJb/ONb3wDKQpOvBCE7mz96lUkHt1H8of/Ce9EkkTXoDG86TdRmpvH4e89gN/lCu+cITbkzjtJGDECAOfu3eyfPx9nU5jug3Q6ym+4Adv48XT5fJh1OizNzdirqkgYNowt7e14fT5yjx6ldv58gt2sMJ9SWsrhWbP49biBuN6K7OIByWzh/Yk3cNddd4V3nmgphgAsW7mC+UfX8/TW8FSdhuf2ZUowh3ijlYY8HU+tnR+WeXqaqwZO5DJNHp3Nbdw4KoEifbXakUJGtmTx03damHf3CFo1WyM6t02fQefOvsyePTui8wpCT/XRso95vfITXt75cVjGH53fn0m+LJJtcXxvVi665s1hmaenkVMG8fKaLmobWhi4twHXe+ofDxkqCcNObo+Z67RS82xkl7GnjBhA8Od3imuQIETI8o8WU7r7EwybV4VngrIB/Fubit4WR97efez+17/CM08PM/CWW6jt35+2jg4yd+2i/sPIPjwNp+wxY6gYOZKfFCfjX7MkonMbyoewcdTssF+DdGEd/QJdNmUqLpeTVwzLcPlC96SsMCWLa20Dsfo0GPvn8NPlLyBXR1/D1Fi1YPcaFgBX9B+HcZeZEcUlF30csOp0ZhYcSuBEQztBjZ4un5tr7xpNiyay22MAHP4mjKZBEZ9XEHqqWZfNwOPx8NrelfgCoXu60ystj6tMfbH5tdx3zSBS2lZBcwi2kwgAaFp3cUc5yBP68ye9noKsa+Dp92LyqF5jShIN14+nxe1AG29FTrAyt8tAzbORv2lp276fDIMx4vMKQk81deYsPvZ6GbRtLYRw24WUlceC9DJaFA2D/QE2//BHiC4YofNZUan8pps4MGQI2Tk5F30csNpsGRkEr7mGTo8HQ0ICdXFxPJQdh3/5gohn8VXsxTzxmrDPE1XFEICMzEzuGjKbJza9E5LxZhYPY1BbHLmlxTzyyb/oXCqagYbLwr3r2JmYzs+S5lBvL+D60joIxsiSZUkDtjxe36GQUljMnGtS8Qed1Lq30CJHvhBykoJGEttjBCGSUlNSuHPwLJ7e+kFIxruiZBR9WkzcOn0w5cpGaFsVknGFU2na9vLDGSm8uCMP5cdfIe6xtwl6zu9IerVp9HqSh5VTMbUvffv1Y6rdh7eplaNPv0FNkzrHLivBIBqdVpW5BaEnkiSJ+MQkvKOnYVwXmhWKzQPG8Lpbx/j4ZJp+9CM2n+fxtMKF2/f666SUleH/5jdJ+9GPaHvsMYK+2LgP0hmNZA0bRuukSUwZM4JSTRDsrfiWv49ywK5OqIAfSRv+a1BUbZP5zBsL5nPTR3+6pDESLXHcmj2GBIeG3emucx4xK4SOQWfgmQl34ayr41sjOsEf4e7YF2ivs5DFu53oDFpKynKxDThEAPUvFp/1DElPT2fu3LlqxxGEHuOV+a9z27K/XNIYKdYEbskcTZJHy0+uSDvnEbNCCGkMfNQ0mEN7Ksl5YgH+jsic/nWxpNtnU12Ugg4oSUwl8OOnCbjcasdCZ7VQ9+vbGDJ4MJMmT1Y7jiD0CIqisOz1Vxkw/x+XNI4mIYlVJcOpxkD60qXnPGJWCB1jfDyFf/wjDXV1uB97DJ8juh/EZ37jG7Tl56MDhuZl0f+jVyGgfhFHssXxbMk4Zl9+OcOGDw/bPFG3MgRAc4lVoAkFAxndlUJhZiF/2LeA47sbQ5RMOB++gI87Vj7Fs5fdxzNb4e4hfpDV/4/qtPRWPtzlZOqdybR7q4A2LvxQ4vBQCDJoXD6mYIbaUQShR9FeYsPiKUWDGdmRzGV9+jI1eR+IQkhkyT5mpW7GNnIcu+6HtD++GbWNVS25mewvSmbYot20btoVBWX4/wk4XYxMyiYtM0vtKILQY0iSdMlN8919h/Ji0MbQ5Ez46U+pbmkJUTrhfHg7O6m45x4GPPMMNd/7HsE//CFqG6umlJbSmpfH3V1HCRw9BLvUTvQ/iqOLOYP7k56eHtZ5orIYYtLomFU2io8qLmx7gkbScHfZdJJaZTr62rjvk9jcr9VdfHPZU7w05V5e2A13Du6AQOS7VcvGZN7YpaPJIaGTAnx1bAqJ3kP/e0HQi8n0WSEk+rQ1dtDRZKeurg6NRkt+fh69S/uoHUsQujWb3szkXkNZdWT7Bb1Pp9Fyd+/LSLFL/PimUgytGyE678F7hPHWddj7DaPmwetI+9sCAl2RX6VoKyuiavoAvGkJKF0uBhxowf7h/xq8+rucmM0WWjdF0SfQz2n3uKjft5eqyko0Oi2FBYX06lOmdixB6NY8Jgva3v0JHtp7YW/U6dgxZBrbPTIDqivZ+bvfhyegcF72futbFD/1FNJDD+F44glVTu1JKS+na8oUtOnpBDs7ydq3j2OLF//3+z6HA6PBcLIQEoXa3R6qt27l4P59aLRaiouKKOxVGtI5onKbTCAQ4MV3XuObK5867/cYdQYeLJpJuiWRp+pWcbi5JowJhfNl0Bn455Rv4Th+nHuG2yEQwaW/Gh1/WBvP5LnD0aRXY/DksvytfWTF6bhxYha/fvMIN07I5oOtrQy8KTpbSekkE8mmEpB1OA7lYTQaGTVqlNqxBKFb83q9PP/ua9y3+unzfo9Zb+ShwpkUJ6dxa1kdeKPzd0qPo9GxrG04+7cdIvvxdwk4I/dhVGezsv/+OUzP7YXz1Y+wTh/NGjpIdPgZ3BZkcY6Ooe1wJMtM9sMvRSzXhTAkJZA+aQRYzPjvvJykhASGhnG5siAI4HQ62fL26/T54IXzfo9ksvDhgMkYElNo/OWv6KoTTbqjgcFmo+Txx6k9dgz3o4/id0fuPsiclITjnnu4dVg5SdvW0Fk+gteOnMDW5aDY4WBLejrFXV20Z2Zy85b3I5brQkhxCRj6DSao07O17xhys7PpP3BgSOeIyu6MOp2O9Lik8369zWjhBwWzyE/P5gf73hCFkCjiC/i4bekTmHKz+f0KLR5TQeQmNyRgtljoSFhFu7eaRukTBt7gJGdICY8vPMbMmdPZ0igz5bbwLr+6FAHFQ5N7H03eXUh6H5lZYsuMIISb0WgkzZZw3q9PMNt4MHc6o0qKubXwgCiERBM5wGWJGxkwrIyK+2aRMG5wxKa25mdhNZs5du9vaVm3nWM//zsljy2gOD+f1TlGZs+YSXtOIoNXRO82Kl97ByfeW8aJ1xYiOT1kZYktM4IQblarlWBc/Hm/XopL4K2+E0hOyaDy3vtEISSK+BwODt59N3l5efjvvZfMkSMjNndSSQlxFjNxr/2dwKG9WN59ka9XbWLwoL7sz8pizswZWHoVc6vjWMQyXSilqwPvptUENqxAr8hhuQZF5cqQ/Qf38/b2lfy/tec+Si7VlsjdSWPJ71XIt1Y/jSyLI3Oj1cOXfQ3D3mamDc5kTHpT2G8YtnWWUOmxkTjqQFjniYRUUx9ObEzi6qvCf8SUIPR0O3fv4u3dq/nNuv+c87WZ8Sl8I2EU04f3Y7zhkwikEy5We/IUnnh9M4NcWjRvr8JdG95+YvJ989CXFRC8//GwzhMJ2dfNpOtr05h71VVqRxGEbm/X1i2Ytn1CwtK3z/laTUo6r2QPond6BtvvvTcC6YSLIWk0jPrlL/nE4aCX00nLW2/haGgI65zp3/0uRYMHMGTh+a8wilpjp7O770hmXD475ENH5cqQo9XVrGs5996l9Lhk7k4YQ3bvAu5e+XdRCIlyv1n2b14J7mVnK/x6icxedyloDeGZTNKy8oCDggnRfYrA+ZLtqRhNJrVjCEKPUFdfx6qGfed8XXZCGl+3DefqseWiEBIDktpW8vOrbXj7Z7Hj1gmY77gSrTk8v1e1FjO1WXFY/xGdS48vlH7iYIz6MF2vBUH4gta2NlKO7D7n67RpmTyTXk6fuHhRCIlyiiyz8ZFHyPv4Y9w5OXTccQf5t96KzmgMy3ymhAQ609IYtmnxuV8cA+wl5ei14Wl1GpUNVMvKyrg74GdD1R66PKff32vSG/lm8hgySwu4b9nfI5xQuFiVzbV8u/llxhQPIK02k8XNCUwtj2NYcn1I+4m4LL3Q6zro8J0I2ZhqaqnxM3vmlWrHEIQeoaSkhP/zz2bb8YN4/N7TvsZiMHFn/Aiun9Cf/kFxdHvMcDVyU69GGNaLf2+WOFF4LaXHO1HeXBHSBqtJV06iXoHOA5UhG1NN9iQLc2ZfrnYMQegRCvML2D3xavrVPAaB059xKFmsvJzZn+FxCWx68MEIJxQuVv327bB9O72mT8c1YwZKYSE5NTU0v/kmPmforkFZc+fi1GuRm+tDNqaa6tAzdcaMsIwdlStDSotKSEtO5eYBU8/4mu8VzyA3N5dvi0JITNpwdA/3bnuZRcYTbGsz8oc1Vta3FoEmBE+eNAaeWFzH5JtD221YTbLsx6VCF2pB6InKevUmIS6e6wdMPu33JUnie4UzGF1eKgohsarjCF8rO8yPrkxBHl/G4QevRrnrSrSmS39Kp4uzsr7QxrDKzhAEjQ4Bjxev9/SFQUEQQqu4Tx90Zgvy0PGnf4FGy0flk+iVlSMKITGqaulSGr//fYq3boUBA1B+8AMyv/51tIZLvw8yJyVxJDeX6yVHCJJGB7/Pgz9MxxNHZc8QAEVRePeDBXxt2eO4v/Rk7p4+0+mtS+ORQ+/g8nlUSiiE0tC8Mu7Nn0TD0Wrum55C/CUcdbuxvZiaQAIJI8+9zD1WxOvzaNyUTXxCHIFAgCFDhpGeJpqpCkK4KIrC/Pff5StLH8Mf/OKTue/0uZyBtkzuLD0M8umf2gkxJq6A+fviOXDwKMM219CxfNNFD+X/9jws/Yrx3/vnEAZUV+qUkXTdPZfE+Hj8Pj/Dx4wiNT16m48LQqyTZZkl773LkNefADn4he/tGDWLJlsqx+6/H/kMK0eE2FIwcSLytddSU19P1rp11K9de9FjJX/ve/QeOojBC/4ZwoTq0gwaybo+Y0lIiCfg8zNy7FgSk5NDM3ZIRgkDSZIwGI1M6jXkC1+fUjKU5BaFfzSuFYWQbmR7TQXfWPcsVYVGHl/cTIWn8OIG0ttYuddOztiWkOZTW6e/hsQRB9D23kLGkDZ27d6pdiRB6NYkScJgMDK2eMAXvj6rdARJdpk7+zWIQkh30nWMa/P38NDN/Vk/LBvDvCkXNYwlL4vjyUaMj74Z4oDqalm5Gc3//Y3Ou35Lwrvr2L5pi9qRBKFb02g06E0m9CVlX/i6c9AYdnuh6ec/F4WQbuTYmjXUfPe7DHW7OTpiBJlz517UOMklJdjj4xm65t0QJ1SXvGsz45b9h/5v/52RtfvZtnFDyMaO2mIIwLiRo5mS0ue/fx6QWcLAehMtvczi+Nxu6vkNH7A0rpG3N9tBb73g9x92ZZNblI4jEN5TAiJLwqxNIk5TQKC+iBPbTQR8olmwIITbyKHDmJ7e/79/HpbTm941On54dS9xfG43ZWrdxK++UsD+8gz08bYLfr/nsmGUpqTjqDwehnTqkDQarEW5JE0egeHOK2mZOhCMerVjCUK316e8P8dKBv73z8Fe/Xi6LUjh3n04m5pUTCaEy/YnnmBQZSUNAweit1gu+P3K5MmMKy1Ebu9GD4U1WrSZOchlg6gaPo0tqUUohtA1no3KBqqfSUpKIluxYdYbKUrJZlpXBvph+Tyx9nW1owlhtOHoHm6fMJqVNZ1MybywZkJ58QEW7HDQnTaQGOvH4HdY6dJJlJeVkzk8E0mS1I4lCN1eZkYmBSRg1Bnom17A+PYUfnHbSEztF7+FQogB7RX0LxuI56Zp8OyCC3prYnMnu9tbyQ1TNDWk/uPHUJiJosCwgQPIyBTXIEGIhJz8fFabk8nWalEKevEXn40xLie733hD7WhCGB1asICcSZOQb7qJ+hcu7FjcRLud3cfrKA9TNjVU3fwdnNYEDFoNZeX9ycjMDOn4UV0MASjpXcqjKfdweOU2TMPy+d2a/6gdSYiAvxxeylc1/ZiSZwP/+TcA8mDAbO5ex//JLhuXXXaZ2jEEoUcqKC7k0ZRvcWT5Fn5262ji2terHUmIgKv7tPPbw1b6JCXga+847/fJCTasuu51DdKV5DJl2pkb2guCEB6SJFGQnY33xnt4at1Wxnk97Hy2+/SBEM5M//77HBs/HoPNhs9x/vdBwbg4EsN0ZLwqdDo88UlcNi1890FRvU0GYMzIUbRuq6Rk8jBRCOlBDjRUY0qwUief7/M1iYU1hfzq9QNMuK44rNkiSxJP4ARBRRPGjqd5UwWP3DGdBFEI6Tm6akiOMxE/Y/R5vVzS6XD94CaWpcLgqq4wh4scjdGAIottmYKglvFTpvDcjgNMs8Wx8+//UDuOECHVq1Zh0mrJP8+HoTqjkeQf/IB9SUlcqZx/AT/aSXoDhPmsl6gvhgDMu+5atL4gGfGh6RorxAZXcSLz19ee12vtxlKOtvr52o8H0+DbGOZkkWPVpRLwiwZZgqCmm264lqZOP+gvvIeEELu+cvkgtqVoz+u1cddMwZVkZW6Fg2PPvxXmZJGT0K8XPr9P7RiC0KPNmzuXYHIy5hCdniHEhmFJSVSd55aQrOuuw5idxcOJCv71y8OcLHJ0Bb3w+8J7DYqJYki/Pn2Z2GcYj465A4uhGy39Ec7q6U/eRdYlAudYGWHL4a+LaphyUzHN7gORiBYxCgpen5cnnniCqqqLP25YEISL16ffADSJJXziHA6aqN9dKoRIfNc29Lnn7kCVOLSc9Xkmhle0U//RmggkixzZH8Dn8/HYY49xouaE2nEEoUcaMnIkuZMm0ffRR9HqRfPinuLoq69izD33CvnskSM5kpnJdf4WfLs3RyBZ5Ch+Hy6vjz/96U80NobncIyYKIbAyYLIwN79eG76/SRZ4tWOI0RAXUcLSDKYks78Ir2Vn73bxrRrRlDv7X5NDV2BFvQDPuFrt93EsmXL1I4jCD1WWd9y0nNL2BycDFpRlO8R3M1otVpM6SlnfIk5J4M1U0uYllJA9UvvRDBcZHTsPYTjmh9y5513suTjJWrHEYQea+DgwRT078/w557DYL3w0xaF2FO3ZQt6kwljXNwZX5NcUkLF2LF8rbwY/8aVEUwXGYHKg4x+9c98/etfZ9GiRWGZI2aKIQAD+pUzsv9g/jrpLrSa81u6KsQ2f8AH5tQzfl82pZGcnIAzpftsjfkyqy6DVStXc/PNN6sdRRB6tD79BpCRX8a24HjOuWJN6BbkgI+40sIzfj++Xy+SbfE0Pvxk5EJFWMKQfixduEhcgwRBZYOGD6do2DAG/eUvakcRIsTvdpPcq9cZv59aXk5GYgIpH74awVSRpSntx8qPl4TtGhRTxRCAksJiMuOSuWPoLLWjCBGgmAyc6PhS4cuSzrtVOTy+MYmHXj7C2Fl91QkXARJaunb35vKZc7DZRL8CQVBbUUkpbkx02vqrHUWIAItJh7Yw+wtfS+jXi/b7r6HqB9fyXomR0XTf1apaswn5kVuZe908LBaL2nEEocfrU16OLiuLXtdeq3YUIQKCXi8JXyqGZAwciPW730Xz/e+zISeH63POsoI+xkkWG5tGXs7ceddiMoVnVW5Mbn6+bMo02u12jpTUsapyh9pxhDCyFeewYd9hrv+s3mFK5hcLnUy+fDhlc4NoAokc936sasZwq69rwmQyqx1DEIRPTZg0jfcXvMPlOfnonMfVjiOE0YjBJezYU8dni9KtJfl8Mrucy/J7Y9tTja5wIBUP/0XNiGElabU0trViNBrVjiIIwqdmzJ7N/K4uMquradi2Te04QhjlJSbSYv7fPUBq374cnTaNr44cTFFXE15bKfL8l9QLGGaSTkdDSysGQ/iOrI/JYgjAvCuvJviezO6GStqcnWrHEcJEa9HTbHcC0KnL5c8ftDDr+uE4kjZwwqlyuIhQyMvPRaOJuUVcgtBtSZLErMuvYPnHHzIzsQmCHrUjCWGSnRbPKvxYAevUkWwckskMaxb19/+B8LRyiy6y309hQYE44l0QoogkScy56iqWyDKtd92F3+VSO5IQJolGI3Wf/v7NnDWL6v79ubtPPra3n8GtcrZIUHxeioqKwjpHzN5habVahgwcxM9Giz2s3ZlbCRBEQ4u2kN9/0Mh13xmMI6l7dUo+kwS5HOe2kRiMonO4IEQbo9FIr959OcgItaMIYZSaaMapyFivnMT6IZnMrg1S/4un1Y4VERnfmIfxtV9iFD3aBCHqmM1mivv2pf/DD6sdRQgjm06HR5LIvu46jg0cyPdTNdgWv652rIhwTrmSjdd/B1sYV4VADK8MASgr7U11dTVGnQFvILxnEAvq6HA7cLXLPL28jeseKKfBu13tSBGh11ho2ZPINddcKZ7ICUKUKi3ry4oTtfTRSoCidhwhDHQE6Eyxsqcgjhkba6j5+BO1I0WEOScD1+Ujueaaa9SOIgjCGQwcMiRsx40K0cEUDNKemIi+uJiHAk34t/SM+yBtRg4Hcnpz1VVXhX2umF0Z8hlZUUi1JagdQwiTZoedpqZGpn0zmRbvfrXjRIxVn4bZbBKFEEGIchKATvT06bZ8Dlqbmxn81mYae0ghBCC+TzEmvViVKAjRTqPToTeLa1B31dnYSKfdztcb9uDf1zMKIQDanAIsEepVFdPFkGAwiNvjpq6jRe0oQpi0O7voXd6LLn+d2lEiSutNRq8XDesEIZoFAgECPhcExH7t7qrT3syI3n3oPFCpdpSIkvoUEp+SonYMQRDOwuPx4Gxuxu/uCd0jeqYTx49z2ZAByI21akeJqKakDNKyss/9whCI6WLI+4sW8p/aDShKjC1PVhR0HlntFDHB4FNITum+xxZ+mVEbR7q5nLj4eBxdTv74xz+qHUkQhDNYsvhDJmTEXqFWlhUa2sWH5/NR2+wkyWw99wu7CXN2OnnzZpBss1Hf1Mjjjz+udiRBEM5g0fvv0/7CC2rHuGAK4NLFdKeGiHEoChnxcWrHiBhNagbGMVPISYjj4OHDPPnkk2GfM2b/Ji5dsZwN9iPM371a7ShnpXcHSa10I2slpKBCa4kZnUfG1BWkPf/M5yVn73JQN8h2yfOnHHXjSNXjjY/Nf9WSw096YTLQ/SuiEhoCh4Zhl/RUt3dQWFTI1KlT1Y4lCMJprF65jKHpnRg6D6od5axqml089m4FFqMWj1/me1f1pr7dzd7qDu6cUXzG993z5Fae/vbwS57/yQ8OM3lAOv0LY3M7a02Tg2R9Km1qB4kAjUGP+W8P4jWbaOrspDgnl1mzZqkdSxCE01jy4Yf4li2jatkytaOclUOvZ1dqKlpZJihJDGptxaXT0Woy0be9/YzvW52dzaS6S3/YsCclhWyHgxSv95LHUkOXVktOwqXfD8YEg5Gdc25HZzTS0dlJ3169mDt3btinjck7ZLvdzr6GKv60/i21o5yTpS2AK1FPR64RnUdG7w5iaQ9g6gjQkWVA1p95cY4moJBe4SJokFCAlhIzmQdO/hkFmnuZSa10IymgCSo09baQu6MLZ4oerV/5b7El6bgHRSPhs2hoL4ytfYW6Ng/9huZR7dmjdpSwS9L2xqExMHXyZWpHEQThLFqam7EEmslwb1M7yjmt29/CyLJkbp5UQF2bmxMtLjYebGVHpZ1543JJtJ65S3uX28+vXttPks2ARoIH55Xxk5f3kGwzICsKP7iuD4++U0EgqOD2BXnkpn7c/thmJg1Io93h4/bpJ4/De2FpFSa9hsIMK9+8vCRSP3pIVNR2MVVK6RHFkPSrp6G1WZk0Y7raUQRBOIsTNTV07drFwX/+U+0o51RvsZDuclHa0YFTp8Op19NgsdBiMlHc0YFRPvNKeZ9Gw7b0dIzBICgKg1ta2JSZiTEYRAGGNDezMzUVRZIIaDQMa2piRW4u2U4nXq2WPp8WWw4mJaFVFOJ8PsrPUoCJRl6LhUyPo0cco+sfMYn4hERGT5wY0XljcpvM6k/W8nLVGrVjnJeObANIkHbIRfIxDwGjBleSDleK/qyFEIC4Bi9dGQaaSy0ggcEVROeV8Vm0dGYZsLYFMLhkFA1IMhicQWSdRFuRGXuukcTak1XQzmwjjf2sWDoCkfiRQyrFHIdf26R2jIhoqTQwfOhItWMIgnAO27ZuZlhCtdoxzsv1E/KQkPj1a/t5fslRMpNMjC5LYUJ56lkLIQAfbKpj9ogsfnxDXzSSRGW9gya7h6JMK9eMzWXd/haqGp2YDFp8fpkjdQ7iLTq+NacXX51SwOurjwNw3fhcfnv7QLYeia0PoQCKzkr75t1qx4iIzhGljJo4Qe0YgiCcw64dO6h9/nm1Y5yXko4OALampXEgORlzIECGy0WWy3XWQgjAsbg48ru6GNrcjAR0GAy4dDrifT6KOztpsFrpMhjQKgpBSaLDYMAgy/Rva6O33c7hxMSTGTo7Gd3YSLPFEuafNvSsaWkEKnrGNehgUg7Dx46N+LwxVwxRFIWajmZ2njikdpTzklTjxZGqp7m3hbYCE6mVZ67t6Z1BzHY/kqx8ekTB5ygAEs2lZvwmDekVLlAUPAlaWnpZ6MowEDBq/nu6oyQrKJ+OEdRJnw4ReyeTJFriaPNWqR0j7CS0yB4j8fE9pz+KIMSiQCCASbaDKzaOM3xleTVTB6fzyM39uGtWMY++U3Hq9eVTVQ0Oth5uw+cPotNq+Hw7LllRkCSJH1zXh5wUM7/8zz4CQYUhxUk8OK+M2SOzSE8yEpRPvsnjC6LVnJwo3nzyVBJN7F2CQGemaeUmtVOEncZoQJeejMl05u27giCoz+1203nwIO1Hj6od5bxUJCWR63AwvLmZfm1t7EpNPePdSKdeT5PZTFCS0PDFA+sVTl66hjQ3Y/X72ZqejgykejwMbmmhoKsLSyDw3/cEJAnNpxcxfTAIgBRrPSYBQ1wc/oN71Y4RdpLRBPGJ6FToJRNz22RaW1upcbeqHeO8OZP1ZFS4kLUSmqBCe56RgFGD9aiHrnQDiTUeWkpPViqDBon0Qye3tHRmGXAl6Ug/5Mbc7gcJ/GYNmQecBAwafFYtzhQ9cU1+0itcSEEFZ7IFbUAh7ZALTeBkf5KE2tjcI/cZvUaLX+5QO0bYpUrD2N/UExbBCUJsq6urJSfODzHSA3t8eSq/fHUfFqMOlzfArdMKSU8wsWLXYS4fnsUry6v54fV9AUiKM/Cr1/dj1Gm4Zmwuo/uk8OvX97O5og2dVkNBuoUfvbiblDgjJVk2Jg5IY8m2Bn75n324fUHG90vF7vTz2zf20+kK8MDVvXnrkxqV/wlcipNbVH32TrWDhF3eA7dyTBt7NwqC0NNUV1UhHTumdozzlu10siUjA50sE9BoKGtvxxwIUGu1kt/VRUViIkNbTp4KagwG2ZqejlZRKO7sJMPlYlt6Ok1mMxogzu9nY2YmxkCAeJ+PbKeT43FxbE1PJyBJZH26PWZbWho+jYZBra1UJsRmvyoA7WfHmwd86gaJAPflN9DuD6oyt6TE2FEsLpeLf334NveufBpZiZFPoxEUqsar0aBvZiG3mAcz5Mbuv1vbVDeRktyB5OXlqx1FEISzaG9v58j25QzTruOLz60ECF3j1agQl8fjy3xk/egltZOEXdKLP2P47OmkpaerHUUQhLNoqK9n0xtvsPP//i/2TtOMgFA1Xo0G+ePH0zJjBrfu+kjtKOElSey98TuMmD6LBBWKVzG3TcZisdA7s4CXZz1Aelyy2nGiTncphAAkWxOQe8DNRqKxgONHWsnJyVU7iiAI55CUlITfkMpmeSroYm//cbh1m0IIgM6CdI497d1BxrQxNCs+UQgRhBiQmZVFfHk5w557DlMMr3oIl+5SCAEwJiYiyeqslogk3ZAxNLq8qhRCIAa3yQBMmTCJYzXHedkUz2FHA/cv/YfakYQwKDWmkZWUCMRe070LodeYycrOQKOJudqkIPRIYydMpuZYFSsOGcm1dNHbv1btSEIYbDvmJc+SiF/tIGGmTYwjJyNL7RiCIJynKdOnc6SiAuObb+LcupWdDz+sdiQhDJrT0xlVkg/d/EDNoMlCQV6OavPH7N1XQV4+s2bMpH9qIU+O/yZz+o5RO5IQYpYGD2NnxtYxjBcj6DJiMJz9VAdBEKJLXkERl02fQaucwmrHCNyW7v+7qqf5ZF8rvZq6/15tMpMxGsU1SBBiSa+yMqbPmEH88OH0efxx8qZOVTuSEGKdyckM8nX/vokuWwJGg3rNu2NyZcjnTZk4iUnyBJLnv4XD72H1kR1qRxJCJC8xjSbfLrVjhFWc3JvaXSauvmqK2lEEQbgI4yZMRpZl3nv3ba4s8KNxHFc7khAKkgYMCVS/8p7aScIq9aZZdE0dxNxp4kZKEGLR1BkzCE6bxjvp6WS2t9OwQ9wHdQdagwFLejru1d27X4h7zGUczOrF5ePGqZYhZleGfJ5Go+GqOXO5JWsMhSliqWd30CezkKAs4wo0qx0lbIzaOFr3pnL1VfOQpFg8c1IQBDh5Dbps+kyW1eaAMVHtOEIoWLNRggHcJxrUThI2lvxs3NdOYu68eWpHEQThEmi1WqZMn07affdhy8xUO44QAjkjRhBwucDpUDtK2Ggzc9lbPIjL516pao5uUQyBk41Vr5xxOb8aeCMDssRy5Vg3PLWYuASj2jHCSqnrw6BBg0QhRBC6gfj4BIaOnsTipnIUs2hEGesqOywkabv31hHLAzczbEQ3angrCD1Yaloao2fMoPhPfyKpRNwHxTpvWRl9MtPUjhFWR8bPYdQY9dtcdJtiCEBaahrzrriSXwy+gYfHfYVka7zakYSLFFfvZercAWrHCBsJLcHOBAryi9SOIghCiKRnZDJp2iw+airnhHGkOG0mhi3eUs8QX/f996ezWpDK8sktEMe5C0J3kZOXx9QrrqDoT3+i3/e+hykxUe1IwkWqj4tjcpxW7RhhI9nicCWlk56p/o6OmO8Z8mUWi4VrrryKjM0bGZ1cwtrWQzy+aT7+YEDtaMJ5KkzJIt0QR4uyVe0oYaPVGJDE6TGC0O3YbHHMmXsV2zZv4IA9gQEp7WS6t4PS/Y9o7TYs6TgCAY7+7RW1k4SNPsHWvZ6GCYIAQGJiIldecw3rk5KwTZmC4+OPOfj008jB7n9Ea3eROXgwTfFxuJe8o3aUsNHY4pFQ1I4BdMNiyGfGjhwNQOLGJPrGZ/P1JU8giw+jMeGKtEGUZOXjCR5UO0rYBGQ3ityJ2+3GbDarHUcQhBAbNvLk0s8tm9bTIMUxWFkFUXLhF85uVbWN3slGPE2takcJG3ddE7rmNvx+P3q9Xu04giCE2NjJkwFYGxeHpbycrffeq24g4bzJU6cypjgf9q9QO0rYBBvr8DscBINBtFp1V8B0+wcD40ePpTg1h3tHzFU7inAezHojGU49BeO674fQzyT0bWD1mpVf+FowGOTY8WO88847bN2+WaVkgiCEyohRY/FICbRaBqkdRTgfWhNbKh3o/viG2knCzvTvZaz6eNkXvhYMBqk6epT58+ezY5O4BglCrJswZQrm4mJ633KL2lGE82BJTaUjKYlBaxeoHSW8FIVBFZtZvfyL16BAIEBVZSVvv/02e3dG5mSkbrsy5PMmjhtPi72NJ+nmf7G6gatKx2DFjCvQfTv4f6YzUI0lLZNFiz9k4IBBbN22GcnsxJYG/Salcmx79y8ICUJPMHbCZJZ/9D5TTGonEc6l0ptHksWP61it2lHCrnnpOjLmjmPRwoWU9+/Pzq3bCByuwdLYQWlSCrU6sWJEELqDqTNm8F5TE/zrX2pHEc4h5eqr0cbbkDva1Y4SftvXk9B7CEs+XEhpWR8O7tuLoaGGzICb6ZmZbD5RS//BQ8Ieo0cUQwDMhu7dFb476J2eT+4JmfEPJNDq7f7FEABXykbi03PYc6yZpGGNuIOt+IG21sEkJojmqoLQXWj14hoU7WRzJi+9d5ypmxtoVDtMhDTe/0eSxgxm16RBKP9Zivd4HV7A8NBtpGeIIzoFoTuQJAmj2JId9dKHDuVIcjIP1u+np3S6zHn9KXLKBnDsWAXDd65FbmsGoHbWTWSPGBiRDD2mGGLs5kfkxbqcxDSukUu5+s5htHo3qh0nohzBWqTcWtyf620VUNy0NjarF0oQhNCSxFP2qGZK5lcfdHC5lMrxFe+pnSai2jfshA07v/A1f0s7DS1NquQRBCH09KIYEtWSSko4Nnky3ynJIPDxJ2rHiayKPRRU7OHznT21zk6q6+opHzQ47NN3+54hn3E4HEiSpHYM4TSmFA3mK1J/Zl49DLupZxVCzsRlqsCS4abmxDG1owiCEAIdXQ61IwhnYNcX8tMFTqbE5XD8d8+pHScqtL30PtqKGupOnFA7iiAIIeAWp8lErZzZs6m7+mruHNAL3cfz1Y4TFRLWLiKurYGWpvAX5XtMMcRsNtM7PV/tGMLn6LU6vtN7FuNc6dz0nUF4U0Uh5PMyC6xUH6tWO4YgCCFgsVjAmKh2DOHzNDpWNhXzjxWdzDwepO5Hf1M7UfSQJBI7PNSKYoggdAtmsxlLSoraMYTP0ZvNpD/0EE2jRvGDdAMJC/+tdqToIUlkyn5amsO/Sr7HbJMx2yxcVzae3zSKJ+3RwKQ38v3CmRSlplN0XRNNvu1qR4oaCdoi2vfmckKjYcL4EWrHEQQhBOIsJpr1fUjrYdsAo5bWwCt7MzEbrPR9+i1O1IktIZ9JnTkO/11zset0TB06VO04giCEgA4ouflm9jz5pNpRBMAYF4fhgQfILMjn2k/ewd9pVztS1AgOGceeAePQaLRMLSsL+3w9phgyfsw4GhsbmVA8iLVHd6kdp8d7oGgGA4oLiRu8G48sn/sNPUj7nnwmTZyM1WpVO4ogCCEyetwEPljwLpfn5KFx1Kgdp8d7syKLFFMKXXf+BrF4/Iu8913DzJkzMYjG84LQbUyZPp137HYy1q6lcZe4D1Kb5YHv0b93MeXvPouidpgos7nfGK66Yi4aTWQ2sPSYbTIAc2dfwT35UxhfFJnutMLp5SdnYvVIJAzeD4hCyJdVHa0+uaReEIRuZfrMy3m/OpegJU/tKD2bOZ3jzW6c9/xJ7SRR6dixY6IQIgjd0IzLLyf1hz8kY6C4D1JT5uDBuLQ6yheIHlWnU1tbG7FCCPSwYojBYOC6a+bxnZLp5CSmqR2nx+qXVQRaBRmf2lGiUr/BxRw5eljtGIIghJjJZGL2FVfxcWMx6G1qx+mxZHM62kAA2SeuQafTL7eAo4cOqR1DEIQQs9lszLnqKrIfeQRzUpLacXqsxLIyzJICYmX8aQ0oLaWm6mjE5utRxRA4WRAZNXIUPx52ndpReqxUczwaSfwCOJNA3np27tiFwyFOnxCE7sZkMtF/wCD2BYeoHaXHanEqWDTiqOMzab39F2zZsQOv16t2FEEQQsxisdCnvJyyH/1I7Sg9lsNkIicpQe0YUavPG39lzfoNBCN0AlKPK4YAFOTlk29NZVafUWpH6ZGCkoKsiB1yZxJUfMQnWLHZxJNjQeiOCgqLqXMaCcaVqB2lRzIZtAQUUZA/k4DDRZI1DqPRqHYUQRDCoKxfP3S9e5M9StwHqUGrKHgDolvVmSguJxmpKWi12ojM1yOLIQAzp03nivTBasfokQLBIAqS2jGiljVQitkkeoYIQnc2YdJUNjenqh2jR7IYtPgCfrVjRK2Ur87BahMNvAWhO5swZQoJ11yjdoweSQd4/QG1Y0StrjHTSYiLi9h8PbYYYjAYyLGmcPuQmWpH6XFk8UTurCRXHAa9CVnsJRSEbstisdDms9BuLlc7So+j0WgIit+vZxQsysSWmIAiVnAKQreVkJCAsbSU4utE24BI0wABsTLkjJqtCSSkpkXsGtRjjtY9nRmXXYZulRZfMMB/di9XO06PYZZ0mMxiv/aZyEk1uDvjeOmll0hIiOfKK69Crxf/vAShu5l22XRWr1zOuHQZm/OA2nF6jJpmBynWyD11ijXBBWtp6p3Pyzt3YrNamXfttRHt7C8IQmRMnzmTpbJMkctF1aJFasfpMTqB3BTRM+RMig/t5EByJps3b8ZiNnHNvGuRpPDtKOjRVzeLxcKcy2czL2c4Bp242YwUbZef3Lx01eaXZQV7s1u1+c/FFWzCm7uK3sMTMJlN4kOoIHRTFouFWbOvYGNzJkjiv/NIqarvJMMcr9r8CtAZxY+iOnceoPmrj5Df5MJsMqkdRxCEMLFarcy95hoSv/pVJPFZM2I6NBoKVGygKisKTf4oXply9AB9//M4A3VBLBG4BkXx5TgyJEkiJTmF24bM5J9bFqodp0eQHH5y8tNwcjDsc7XUO1n4wn4MJh1+b5A5d/TF3uzmeIWdqdf3OuX1f7xnJZmF8aD8f/buO77tq97/+Ov71V6W9x5xbGfvvZNmp3skXYyWUqBwoXALFy784F647F4ocJmFllmgpXunTdPsvfdwnMSOt2TLkq0tfb+/P9yVtmmTWNJXts/z8eiD1pHOeTskPtJH53wOyDqJm+8dh9H8wX9NGk910X6uh0lXlL71tQe/tZ3PfHdGn7KHXHauulIc4xKEgUySJKy2DLrtY3F0H9A6zqBwrr2HUYZifCmYy2OAtblgUCAmwcIO8Oqh2QyzPO99/O/KIf+NG39lFa5qB8OH7BRuMUGHEcZ0v/21fxTD7c19y66Mr+aqZUv7NoggCGlNp9Nhzcqi5qabOPn441rHGRR8qkqJ1UwqOlc1R+L8vtWPWZYIKyqfKrTRHlE4Hoxya957+xN+stZDpVmPqoJOgvtK7JjlD96VcTIY41w4zqLMt5tu/+dZLz8a0reCjzuvlOWLFvVpjIsx6IshAPNnz8Xd0cFf9a8SjkW0jjPg6QMxCsocnA4kf67je9qpHpfLnGsq6WwP0Nka4OQ+F2eOdjJ9WTm2DON5j3fmWfj4f04G4PSRDl5/oo4Jc4t47bFaDCY9ZouesmGZRMIxZiyr4M/f3820JWV0uUOcq+1iw9OnsWUYUWKJOI8u43K1I8kSuTl5CRhPEIR0NHveAta8/CyLbTKInkpJ5/ZFsRhTszuwzgoVAZjq7S2CeAxwxgrnzDDRC5Z3/d+dEYMbW3v/vcEMW7NgVA9szu4tqBgVKA5BVIaJPniiEMb7eneatJhgR2bvmEoCdhTLOpmWpibMZjNZOTl9H1AQhLS0cOlSnu/sRHrySVTRTynp9BkZGLu7UlIM2dUdYYLNwPU5FtoicVoiCnv8EY74Y1yZpZChP39HUJ5B5ptlvcdID/mjPOoKssBp5O+uIGZZwipLDLfoCSkqV2ab+U6Dj2VZZlzROCeDMZ5wB8nQScT62u5DkkCSaGpoIMPpxOFM3k4asSeKN3aHZGVzy7grtI4y4Bn1BiozC2mL7EnJfLNWDAEJnvj1QV7/1ykycy3UTMhj5JSC9xRC3q20OpPWeh8Wu4GpS8oZOjqb2oNuxs0q5PjudnydIexOIwZT79VP656sY9UXxnHNXSORdX3/q2UsbuHwqe3s3r2rz2MJgpC+3twdEnCM0jrKwKczopqyOPWbf6RkuklekICX8mBLdm+xozIA1YH3FkLerSgMLhOY4zDeC2VBqLfCCD/U2aBHB1bl7Z0j27LgynZY5O7dVdJXsec2cWzNBnZs2db3wQRBSFs6nQ5rZibV11yjdZQBz+x0Yi8qIrjmmZTMd3W2GQn4eXMP/3QFyTPITLIZmOYwvKcQ8m7DLHrOhmPYdRLLs0yMserZ548yJ8PIzp4IHVEFp07G9Ebx/V/uIP9eYufThTZ0fS3IqypFJ/bRun8X2zZt7ONgH0wUQ94wZ+YshhvENYfJ9rmaJRSW5OGPtaVkvvVP1zF2ZhEr/20ci2+p4bmHjnCxPXjOHu2koNzBzjXnOHeyi/JhmZjMOvRGHc4cC+uerGPq4rK3Hv/muJIsXfQcHyRgPYJFn4VOFhu4BGGgmzR1BkfaxN/1ZFvTVEpZdg6BhpaUzLc9C4b3wJUumNMJr13Cy4xGM+SG4UBG77GaklDv7hC9Co5Yb/Fj/DvO+ry57EgqCbm8vuM3j2OpKkNn+uAPDgRB6P+mzJyJOnKk1jEGNEmScN5zDzX5uSje9zknmQRPuIPMyTDxpWI7t+dbeLDVj3SRK8SRQJQhJj2veMKcCMYYYdFjkcEoS+TqZf7lDrIsy3Tec1QVZCkxa1DW+meJ5pdgslgSMNqFiVdeb4jH4xjEm86k+tTwJZQqdoYsaCWSol14I6fk8/j/HcBo0RMJxph/YxUZOWYOb2tl4vwS1j9dxw2fGfPW472uIH/90R6UuIokwap7x3NsVxuHtrbiaQ8QCsRQ4irTlpbxyP17ue5To6k94AZg4cpqnvjVQSz2xDTjzZbG8NqaTXz9619PyHiCIKSveDyOJOm0jjGg7eiq4rRLIe9/HiJVreOq/fBiQe/xlogMMzy9hYwTNhjdDdszYZn77cf79PBUISj0vpi8qh1qbb2P9xogLPf+2gQvPF0ES9xw9o3XiTM74aV8MCdofc2/41q27NjBl7/85cQMKAhC2lIUBVncXJhUhffei1xawswNqevNMs1h5IHmHiyyRFBRWZljJtcgs6UtwkKnicc7gvxbkf2tx7uiCt871038jWtt7yuxs6M7yhZfmLaIgj+uEldVVmSZ+X5jN58tsrGvp7fFxK25Fn7R3IOjz9tCenXPWcGuXbv4/Oc/n5DxLkRSxUXyAIRCIb7/t1/zvb2ieVCiFWbkcFvhNBq3H+GbP/4kzZK4xvhimHVOMrxz8XXEWTBPHOEShIHM4/FQv+sZxhlFE9WEMzl54aSTR1/eyb+PmUftt36hdaJ+wVpRQsED90Gek5lz52odRxCEJGpsbGTjz37GyQce0DrKgOMsL0datYrtDQ388poFxJ/9u9aR+gW5oJiG6z6BLiuXiVOnJW0esRXiDUajkRyLQ+sYA8bwggoWWqvIiRixGc1Mqipjwfx8vvfwRpZ9tpjuaB/b3A9IEpnqaFqOSeRUSJgywpw62syqlbdoHUwQhCSz2+10R/QgTiQkhrWQF49JnGqLElV1zBpfyh//w8qPnm1i8vBKuk+c0Tph2pF0Ogo+dg2dY8pw1rUht3loivSwcu7VWkcTBCHJcnJyULOytI4xYBSMG4d/9myiubmETSaGZWaSVVvLr08087m8IhRXao5q9is6HeFZSzlmz6Wmx405HKTRF+D6pckrhIAohrxFlmVKnflax+i3RhQOYUZ2NYbmHgod2RhUmQUThjG7JAiuA0Ar+ODGmdXseVmmZLFM72Zf4U1l9uls/Fcbd999N7t276CnNcBNNy5CSkQDEkEQ0prBYCCqFwX5y2Yt5FinnbX7monrbURicRbNGMlVk7vAfRhog25YPnkoJ0uuR3/Pz7ROnHaGffHj7Kx2cvfdd7Nt02aioTA3LhdX6wrCYGCxWLCWlGgdo98qGDsW/ZQpNBgMWAsL6dTpGJeTg3/NGk48/zwH37ilp6iigtcmLmHhq3/VOHH6MVx7O88HdNz58TvZvmkjSjzOdYuXJH1eUQx5B4fFSqbVQVegW+so/ca8yvHMDhaQYbFR6shg5aeqMQYawH0E2Aqu8x8/zlrHrkAB1vbpBPJFh/p3CoS7GVJZgcFgYNbMOVrHEQQhxSSjHWQDKKm4cG9gCFiG8rvXu9AZZSoKjdxz60L0PWeh4yiwEdznP35a5ln21OUy5oEv03TfT7WInLa6291ULZmAwWBg3kJxNFMQBpuMkhJkvR4lFtM6Sr9RunQpLVOm4LNYyDOZWBKP07hlC7UvvcSe97mmuP3BB3Hddx+Tr/8Ezmf+pEHi9BXs7GDY6BkYDAbmLlyUsnlFMeQdovEYNXll7Ko/qnWUtOcwW/l82ULMQZUv3jASR+dmoAUaTnzIM1U+OTXKj149xMyPOwkr3lTETXsOXTmNB8EiLngShEFLQgFLDvhbtY6S/vQWHj+ZT317N5+/dhRm12agGT5s/VYVPjtH4kdrWhmRn0OovSMlcdNd9oKpuCcMwSHayAnCoKXEYjjLy/GcPq11lLRnzcnBeM89dJjNjG9tZe8PfkAD0PAhz4tHIhSsXs1jxmv5tM0O/p5UxE176tgpHHHko4+m/sMgUQx5JxVUxAuBDyJLMkuHTWGM28r8mmqWOPdC5+ZLGyTUwZyRVTRvtZM5QxRDbHIxDZuyuPHGlZhMpg9/giAIA5P6xj/ChUkyPZZqfvFSE1fMLGNV1XZwXeIaFGhjanUVXd//JHzq/uTk7EcyZ03Ac9dyVt12K3q9eFkoCIOWqvb+I1yQrNdTdtVVnKqpYWppKQe/8AX2XuJOmrb9+8lfsoSd81cy7aU/JydoPxIfMZ7NNdNZecst6HSpv1VPrHrvIEsyTV2uD3/gIOS02Lm+fDrFHhlb0MTnbh6Js3PTZbf9mJNzlh9usHH1FZNoCe5NbNh+xhTPo6q6WhRCBGGQiysqRHxax0hPeiuHu4t5cW8nFrOPf185Eat782UXjxYVneOHJ6zMuXEJTU+tSWzWfsY4fhhjxo4RhRBBGOx0Ovzt7VqnSEvWnBwyrruO9uJiXEYjMzo62PPZz172eN4HH8R1773MmTCdyP4dCUza/3QVDWHylMmaFEJAFEPO097jocXr/vAHDiKZVgcfK5xJfsREaUY2H11kRu44AJ2b+jawGucTC4t59skWKq9MTNb+KqA/S3eb/cMfKAjCgKWqKlKkCwwBraOkF4ONF05lcaw5wtAyE1+7rgTch8Ddx6NESoRb51Sy3u7EMsiLIYFXt3N28STGTpigdRRBEDSiKAodtbVE/H6to6QVW34++ttuI5CdjcnhoOq113r7gfRx3LDPR2lrK69MHMsVDO5iSMGpg2wvqaF62HBN5hfFkDds2b6V3T5x1d47XVU9jTGddq4YMZIl+acheAISeLy6kHr83ZkYZCtRZfC+AQjFveTUBHju+WfIys4iEo7Q3ubGYjWTk5NDMBgkonpRpBiSokOKWbn6qmu0ji0IQgLt2r6FcXkBGLw/Ct+jXVfF715tY/GsCv5j+HEIHXlPQ9S+qDQ18XTMgcNqIRYIJm7gfqanroGCHSd4nqfJysoiHI7Q7unAbDSRm5tDoNtPqKmNeCCIzmrBUJLPlVdfpXVsQRASaOPatSjbt2sdI62U3HQTZ6urmTpkCGd++EPqGz6sI8il8a1eTUdlJeiNEIskdOz+JHbuDFWeJl567lmyMjMJhkK0dXRiMZvIy8kl0NMDHhdEQmCyoGbns3T58oTNL4ohb+j2dfPwvle0jpEWJEni30YuI8cD37h1CPqOHZCM14mxIBbZSIFuJo3K60mYoP/wmfdRMKkGvRrErAsTrzqJSedE8pcgW06iV0MAmHQZdGwf0fspsrhyVxAGjB6/n2zDIa1jpAmJrZ5qtp7o4ps3VSJ3boFQEqaJ9qCLy1TddycnvvfbJEzQf7Td/ycKr1mAWpCL7PYgvbgRQ3U54XmT8P7jJWLdvZ8W2ypLCdx/+VvDBUFIT+FQiNpHH9U6RlqQdToKvvhFPFlZjNq+nf0//nFS5vE1NiJ5PJiX30DohceSMkd/kfXiP8mZMpe4PQNDt4fY/h3IpZV0l1dj37URNdT7SZG+tJL1c65P6NyiGPIGb8hPKBpOyVw6WceIwgqG5Zdj1OvRy3okAFUlGIvg7vHi6vHQ6GnHF0rtdrWSzDzuyJxKiSGHe5Z2Qsf+pM73kfml/P2Zw1SJD5nwxGp7/yXe+z/BeAeYO847E6+oUfLys9m1eyfTpk5PfUhBEJJCjgVAf5lNmC6VJIO1ACx5IOtBklAUiMVVjHIMon6I9kC4C+KpWRffYs7m9zsM2DIMfGVeBDqTWyC6ZV4Zz2/pwpHUWfqH1ufXn/ffvmN1+I7Vnfe1uD9Ijj2D3dt3MGWGWIMEYaDwt7Whvs9VsMmgMxjIHTmSnJoaZIMBWadDlSTigBoIEHC58Ltc+BobifSk9raV7KoqArfcgqGoCOm736U2yT1Uyjs6eMFbxOKkztI/KLs3IQFvtqNV6k9hqz91XmswJRQgJ9PJgd27GD9lakLmFcWQN5iNyWteKUsyK0bMoNgNuXo7WbYMYvEodEaRJJAlCQkVnU5HVDGAlAHWIUhWiTZvBx35Ota2HKLRk9y/kOOLalgaLGbVzDGMUzZDKPk/FDNUN9GQNg1z+hudZMC9dRRZWTEsFtHtWxAGFJ0xeWNLMgFrDY9tbsYXNYLeCmoMSWlB7q3Egwo6nUxclXqLJZKeaDwXIl5Wzi6hzNCS9OauEXMp333WxUevmcHwyAZIwa7hQrOPHmKiGHIRDE4HwQf+DYdOStmbJkEQUsNgsSRtbFmvp/Kaa6jLzcWQl4c5M5PucJiOYBBJVZHo7Zulk2VUWUZnMKAzGDDpdISbm6l0u3G/9ho9bW1JywhQMGsW9TNnMjc7m11f+EJS53qTWltLy9DKlMzV38mZ2ayedT3FSKgJvPVIFEPeYDEYsRrNBCKJ2YtrMZgYXlDB6JxyylogI2jljpWTKDS4wX0Y4hcxj2wkVjCTf2xqYnhmBh6DD3eWyqbOWk65ziUk55vmDZ3ApDYrX//YZJyejQkd+wMFXSCXIWNEScUr334srkYpmxYidFbHtKkztI4jCEIi6S29RQg1QW8yZSNY8+mMZ/KHNeewWLzcunQO+bq23jVIuYift7KRWNEcHtvQQFunDWISKybnMdLpgVACG0gBIctQvvNEPd/66GSsng0JHfsD+VuR9YXIej3KJV6PONhEvd0UbjqGfmURU2fN1DqOIAgJZM3LQ5IS9ybTaLORO2IE1lGjqCstpcVsZkF2Nq5Nmzj5wgtEA+9tkBV913+bMjKYfe+91BYWEh81CrWjg4rOTnzr1tFVX5+QnG8qX76cYyNHMqOri10//WlCx/4g7YcPY7zuupTN158pXZ3MDrTTbqxgwtRpCRtXFEPeoEpw34yV/HTb4wT7cFymJDOP27OmkCtbiMfjWAIS99xSibF9G3SuvrTBlAj6lg18vBpARimczlN7vFQFbXjyqzmRFeLl2p3Elfhl551SPpL5sRIyIia+8JGhOD1bL3usy6IqEAuQaxlGe/Bwaufuh3zyUeKBXLq7u3E4xGeZgjBQSCi4bNPJC+wCpQ9vys3Z/H2/mXa/jKzGsJm6+PKNI9G3b4eOly9tLCWCvul1PlINSHqUwuk8u8vDi/v1GHFy94J8rMFTXPb9tkDMVsaDG3oIRH186/bxWD07L3usy6JEIR4hf/5UWtduS+3c/ZDrj89gWTGVYDCIJYmfJAuCkFqxSISx997Lkd/8hnj03WWJi5c1dCiRlSuRcnLwhEL4JYkxp09z6M9/Zv0ljhX2+dj6ve8BoDMamfG5z3Fu5EiC1dVIbjflR4/SsGZNnwo4pXPn0jpjBq02G1Pr6jj0t79d9liXI9TVhS4SQT90GLHTJ1M6d39k37yaA/lDiUajGAyGhIwpqYncZ9LPbd+9kxPNZ7nz5Qcu6/lXDp3KWI+d6xdNZpph58V98nZZZAL58/nNi8fwh8I02MP84/QmIrGL/+GVZc3g08VzMfsVPnXNJIp8G0DV5lOxX24zc/VnVlDb/aIm8/c3Dl05rj2lXH/d9VpHEQQhQVRVZd+eXRBoZcJlNpQ+Fx/CHze4uWnxJMawtW9FlQ8i6enJnccfVx8jHA5z9aQsRlrOgnoJhXmDjX8dy+FMu59PXj2e3M71gDZHL36x2cISqYKD3/yZJvP3NzkLpxP+wk1ce734NFMQBgpVVdm+eTPuw4fZ87nPXdYYRatW0VhTw/T8fPZ99avEI8l5H6Q3m5n6//4f+xWFcDhMUX09LY89dkm7++yFheg+8QkCRiOTdTp2fPvbmh3/U++9ly9PG0ng6dQWYvqt8dPZPX4+V1+bmDVI7Ax5hxlTpuF5tZOK7CLqO1su6bkraqYxqs3E926vRPZsTvJrOgVr+zq+MhXIGcNf9yjkZ0JbvsSapoM0e10f+Gynxc4X8uaxYPQI5lt3gndtMsN+KJtJRzggzh9fDAkZvWzBarHS0tJMUVGx1pEEQUgASZKYNGUar7/2KlgdEOm+pOe366p4+LVW/uvGQmRfko86qjHsrte5dzKQO56/7wjxbJOVj8wvpszQ/OHZDTZ+scXC7MlDuLlmK3Rqe5uYRAx9hk3TDP2FbDBgsNtQzSbaW1rJLyrUOpIgCAkgSRIz585ljd+P2ekk5PVe0vPLb76ZM1VVVG7YwO4tW5KUslcsFGLbt74FwOhbb8U9dSqx4mKGNjfjevVV/K4Pfh9kLygg9MlPMr6khH333st2jY9IxkMhZJvY7X1R9AZ0JjOSTk+n2012bm7fh0xArAElrqrkOTIvqhhSk1/GvOxhZHeCI2jha7cXIXtSfDVix2E+PgSUGTN5ZLOL4VkO/PYQbQEv8VI7DdEuIrEoOllGL+ux6c1Ut+i4YsJw5pmTXbS5OBaTnlAwBknsHzhQWJoW0R1UyHA68AdS22FbEITkk2XAYLu4Yoi1gP3tdtYd6kTV9fBfqyqQPUeTnvE87gN8pApis2fz2JY22j0OZMWETQ6xbGoZZc5Y744RSQZJJoaBnzx7liWzRzJZt7EvJ2wSxmjQEdeLRt4fSpIoeOLHxHUyGTY7PT6fKIYIwkCjKFiysy+qGJI/ejTyzJm4cnNx2WxUr1/PmSQXQt7tyKOPwqOPMv3f/52zY8agjh6N3ucj5nIxNBZDbWhAicWQ9XoknQ6D08mh8nJm5Odf9g6YRFPjcSR9Yo58DGg6HSfu/BqKJJFts9HT7RPFkGSIBcOEP+S4yY3DZjGyOwOb2URVbi43LnMit+0EjztFKd9Lbt3W21ukRg+5Y/Hpi3lh60nOdZs5/54ihTtXjafAo+0nce+kqr0RhfcyyU6iTRX4XXoMOjsRo8SMGbOx2+1aRxMEIQm6fAGwftAaJFEfH8qTu7xIepkRFXa+eH3eG2tQcjvtfxB96xY+UkXvVb254/Dpilm99QRPd/ro/Qn/RtVDVfn09dPJ1ng3yDtJkkQsUY1rBxhbZSnyx5YTHVKIKctJ1Gxg3rx5mM1mraMJgpAE/kjkfZubvkmSZYpuvRVXdTU9RiPFVivFR4+y7/77OaPhLVO7ftZ7zNFgtTLqxhvJmTGDQ11d9FRWIksSiqqCoqCqKnNUlZ1f/KJmWd9NBfFG6AKkojLqxsyi05mD3p4BSCxetBi9PnElDFEMeZfxEydwX9jPr6U17G48ft6vzascx2x/AdlqBl+6qQjZtRtog1Ztsr4vNQaufWSwj9srgfe7rSmNCiEA7h4Va24UkntrY79g1jmxhIfRfjqGJIFOzaWzo4PrrrsOnU7H2tdfxWgUW2gEYaCaOGEsrxwIs7DgDIbQ+YuLz1jOb1/34XTGue/acmjfC7SAdjWQ91Ji0L6XDPZy84XWoDQqhACEMRM5XKd1jLRgG1JK5s1L8ZRkggr66lKi0SjLly5FlmVef+XVhDWtEwQh/YwcNozo979P6wMP0HH0/J2GJcuW0TJ5MuHsbKq2bOH4009zSqOcFxINBDj4yCPwyCMXfEyK23R/KHtBAZFTx7SOkRbkgmJ8k+ZSb7ChAt2ObKxWK4vmzUNVFDatex1ZlhM6pyiGvEtlxRBKioqJPxnnG9522rs7kSSJf6tZhrMrzjdWjcPs3gKuJq2jDhxGB+7gCa1TpAWltYquTgdXzJqD2WzG7+/h4KEDb3XtX7H8ao0TCoKQTBVDqsjNK2TD6y+wOMsHsQBIMuvahrD9hIcv3zAco3sHtJ/VOurAYbDT9OLzWqdIC/pPXwszxrNoymSsViter5ejhw5jtVoBuPK6azVOKAhCMg0fNYq8wkLWqyr+//gPQl1dyHo9OffeS7vTyZj6eg7+4Ack9nL1wUuSJCzZ2YT3vqp1lLRwbPJizCPHMnX0GGw2G10eD7Unjr/1PmjZ1dckfE5RDHkfRqORxXMWYDNaeLnzCHlBPcMsOdw9ox3cqT0LN+AZ7MQVle7opTWsHYgMspX6Q0E+8pEb39r94XRmMnfOfI2TCYKQSjabjZHjpvHyAT3zSr1sPuHHG7Px9UVucO/QOt7AYsoiHosTaBBrkKUon+ZcK3fNm4tO19tDJTs7mznz52mcTBCEVMrOzmb83Lnw618TevZZuqqqsJeX4//Wtzjo1q4lwECUM2wYgVAI/KIPoC6/mBMxiU/NmIkk9Z4bysnNJSd3TlLnFcWQC6goK6e8tIy8jblsWL+eK6bkgvTB3YmFS+eWyrDYLnw2cTAxyFZKy4rFMRhBECirqKSkrIJtmzewp3Yjty/O721CKiRUfTgPpxzUOkZaMOVlMaS45K1CiCAIg1fV8OFU1tSwLieHk5s3U9jUhE68Pk0406xZ5DhFH0AAOTOLoaUlbxVCUkUUQz6AJEksmn8Fi+Zfwem6WtadzoBYgDk5ZzEE6rWONyA8uqmRxXfOpSEkPpUz6Ryca3cRiUREQUQQBGRZZva8K5g97wpOnTzOuvpMpHiAuTmn0fnPaR1vQHhqSzMLAlmIg5pgKcyjyeUiGo2KviCCICDLMouWLWPRsmUcOXQI+8yZBN1umn/+c9r27dM63oDQnJvLFy0KIa2DpAHJmU2bu4NYLJbQBqkfOq+qqmlwsV3/EYvFWP3SC0wu8FEQ3Kt1nP5NZ+JnO3KZcIufqCJ2h4BEnm4CdVt13HzzzVqHEQQhDYXDYV5d/RLTC73kBvdrHad/01t5YKuT8u8+SqxHrEGSLFN2zy20LBjNqlWrtI4jCEIaCgaDvPzCC4ReeIGTf/2r1nH6NXthIeoXv8jH9r8CsYjWcbSn06EuvZGtmWXcdNNNKZtW7Lm9RHq9nquvvZ7aYAkNpplax+nXAuYh6PWKKIS8RaVTOYzJZOLAwf1ahxEEIQ2ZTCauue4GjvQU02SepnWcfq1TV4FJRRRC3qAqCi1/fwGAY4cPa5xGEIR0ZLFYuHHVKrJXrWLUvfdqHadfy120CKdBJwohb4rHkbesIRgMcvZ06m54E8WQyzRvwSJO+zPx2sZqHaXf+vvGFhZeP1HrGGnFER2L3qBj3NjxWkcRBCGNXbFoKce6nPjto7SO0m89tqmJWWqm1jHSSu4nr8dpdzByzBitowiCkMaWX301lgULqLzqKq2j9Ftnc3JYWZypdYy00jV7GaXFxQwZWpWyOUUxpA+uWLSUbW25oDNpHaVfCmCnx3Rc6xhpwyoX0nzAyFVXXp3y5kGCIPQ/S5Zdyea2ApBFf4dLJxHROWj+89NaB0kbzmlj8c4dw9IVy7WOIghCP3DltdeSeccd6ESPoUsm63Q4SkqIrRXXur9JGT6W47kVLFi4MKXzimJIH0iSRFFhEe0m8cncJTPnABLeSKPWSdKGQbZgNpsRbXwEQbgYkiSRm5NLp1msQZfMWoCqKHSfOKN1krRhys3CYrGINUgQhIui0+nIzMqiauVKraP0O4UTJxKPRFC6OrSOkjakDG3WIHGbTB9NnDKNdc8dJz9D6yT9S5dUgNHi0zpGWvHF6rEWZfH000/jzMwgJycbj6eT0aPHkp9XoHU8QRDS0JTps9jw3AnmObVO0r80h7OwKV6tY6QV92vbyJw6gqf9fpx2BznZ2XS4XIyfMpncvDyt4wmCkIZmzZ/P3zds0DpGvyOPGUOZuFL3PNKBHeTklvD00z1kOhzk5+XS3tbGpGnTyMzKTtq8ohjSRy6Xi+y8QghLgPg05WI9seUc826YTxOvah0lbagohLL3Ys0Gk6EQrzufrGFGDhzfQtsrAVauXInZbNY6piAIaaShoYGS0groPqh1lH7lma3nmKUbgjio+TYlEqXzO78HScI8dhgti6bg9EfZWd9Cp93ALbfcIq7cFQThPKdqa6mqruas1kH6mQa7nfsKneJK3XeKhCl5+Z+USBL6impaho5hvBRjf6eLxrjE7bffjiwn/lCLKIb00cED+/H6eujJWMBM2z4Id2kdKf3JBnwxG13GfRDTOkx66om2grOVYBQoh6qCSRw4tJ/pU2doHU0QhDRy7OgRwpEobvsVTLfsgYjYcfehdCYC2Gj45SNaJ0lPqkrXwRNw8ASeN75U+NU7OVyzj4nTxQ1GgiC87dSpU5CdzZSHHuLYN76Bv71d60hpz5qTA8XFhF9+Quso6UlViZ2tJe9sLVFgBJCz4lZOHj3CiDGJv7hEFEP6aNHiJQC4Xe28tDXOisITSME2jVOlt5ijCknuxB/r1DpKv5GTWcCpOnGuUBCE8y1fcSUAba0trN4ZZ1n+EaSQ+FnxQQKWoRjiHQQaWrSO0j9IEoVVQzjnEm9yBEE43zXXXgvAufp6FLud01/7Gl319RqnSm/FS5fikyVUX5fWUfoHWaawopL9584lpRgiGqgmSG5ePgsWLeGV9hFgFA1EPsgr+z1MmTda6xj9goRMvjSdR3+3iV07d3O2XjT7EwThvQoKi5g9fyGvdYwFg03rOGnt2Z2tzMgq0TpGvyCbjAz9z7t5tOEIu/fto7G+QetIgiCkobKKCmYvXMjQH/8Yc2am1nHSWp3dzrUjKrSO0S9IJjPqDR/nf1/dyI49e3G1tiZ8DrEzJIHsdgeTpkxj9zGVKfJGreOkrVPtYZZdq9DQo3WS9JQhD6H1QAYZ+Sr5+QUc3dbJTSuvp7ioFJNJ9AwRBOH9OZ2ZjB43kf2n4kyQNmkdJ221d8tUHj6idYy0lbN8Dr4VU7C1dpGdn09jqZPbhw0nv7gIm00U2gRBeH85eXmMnjiR+De/yYGvfEXrOGnLWFSE9eg+IloHSVPRCbM4XDKMwnAP1tw8jupsfPoTs8nMzcORkfgNB6IYkmCFRSUcPGBCyR+K7DutdZz0I+nA4KA5sE/rJGnLczyfq5Zcg7vDRX39WW65ZQmSJGkdSxCEfqC0rIIjhw+h5pUj9YhP8d9DNoLeSsMT4qz2hURuXch1t62iraWFxoYGVs6dq3UkQRD6iaphwzhWVUXBuHG0HRSNvd/NnJmJwWYjsvt1raOkrWOjprPo6mtpa2mmtbmZG2bOSup84phMEsyaM48d7eIq1PdlzUNVY8QU0T/5/UjIWA3ZmEwmSopLmTVzjiiECIJwSabNmMUeT7HWMdKTrRDiUZSQWIPej95mxVqUj9FopKyigpmiECIIwiWaMn06ebffrnWMtFQwbhzxUBAUResoaUnOyMTocL6xBg1hapILISCKIUlhtzsISBlgztE6SvoxOJAQPwAuxKLPRlHEFTuCIFy+rKxsvIoTTE6to6Qfox0lGtU6RdqyV5URD4vN24IgXL7CoiJso0djzc3VOkraseTmIilxrWOkLV1hCUostWu0KIYkyZDKShpiQ7SOkXYUnRUF8UPgQtSOEkpKy7SOIQhCP1dYWEibXKV1jLQTiOrRq2K33YVIV86kZuRwrWMIgtDPZWdlUXHDDVrHSDtRh4Ns0Xvpgk4PGcPIseNSOqcohiRJzbARnPZZtY6Rdpo9EZw54tPKC+lsCVE9tEbrGIIg9HNjxk3guFs0XH6306095FvEjW8X0mM3UVldrXUMQRD6uakzZ8LYxF+D2t91qCqV+eLkwIV49CaKSktTOqcohiSRZHb2NmtLEUVRafUEUzbf5ci1SfR0+bWOkbaMcgZ6vehrLAhC30iSBJZskFK3zPeHNag834o7ItagC7EW52sdQRCEAUCWZXJqalLa904FAmn+GjpLVTnt6tA6RtrSZ2anfs6UzziISKoClhzwtyR87HOuAA88fQKrSUcoqvCl64bR4gly+KyXu5YOfc/jb/juZmqKHeh1Ej2hGN/7+FgyrIYPnOPprY1YTTqWTS5KWG5zpBk1LraHXYhBFruJBEFIDAkVTFkQSvwLr/66BmXE24nqdQkbb6Ax5GRqHUEQhAHEXlhId0vi3wf1GAwcyM1FpyjEJYnxHR0E9Ho6zGZGejzvefzL5eU4IxFkVSUqy0xva8P4IU1MT2dkoFcUynt6EpY7eOgQvmHDEjbegGNP/c5NUQxJKhWMzqQUQ7YcdTNteDa3za+guTNIozvA9uMd7Kvr4sbZpWTazt+RUpBl5v5Pjgfg9y/Xcbjey84TnQQjcerb/Xx15Qj+sPo0X105AoD7nzjOtGFJqM6Fu4hGJAqNU2mN7Er8+P2eqnUAQRAGClXpbaKahGJIv12Dgu2Ew5kM+dh1nP3bs4kfv5+TEP1UBEFIDCUWw1FcnJRiSIvVSn4gQI3Xi1+vx28w0Gq14jabGer1YnpXocMaizGrtRWAI1lZdJrNtFksxGWZboOBiS4XR7OzmehyAbAvL4/8YOJ3OrYfOgTXXINh2jyiOzcmfHzh0oliSBLpdHoiko1kHJRZNbeMxzed43v/PIqKyh2LhzBjeA52s/49L0IB2rtCfP3PB5ElyLQZmTYsG6NeJhCO0x2IcvBMVxJSvr+75uXw50dOMe5mPQri5pR3CgQChEIhzGZx1l8QhL6RZJmYzpGUhb4/r0F3zMvjCYuf/H/qUWJiDXqnnp5uYrGYOK4pCEKfqYC9pAT27En42FVeL3VOJ7vz8kCSGO7xUBAIYFCU9xRCoPf4zPaCAgBM8Tj5gQCyqhKTJCIOB26LJeEZ348Sj1N2+jSPTpjATbIsrth9F5/Ph6IoyHLqjviKniFJNGvuAlafMqFaEn8G969rz7JwQj7fvG0Un1o+lJ8+dYIP+kAnP9PMD+8cx/fvGMd/rBxBNK7yq+drMRtkRpZnoKqgkyWicQVfILlXGmVHT1GRayZeOyGp8/RHdqeJqLj2URCEBJgxax6r6+xgSfz1hv15DSpUTpHj0OP89VeSOk9/ZIlDTBSIBEFIgHmLFmG+5Rac5eUJH/tEVhalPT1McbkY1dnJgdzcD9zXZo3FmNHWxoy2Nia63SiSxKGcHHSqSlY4DPQuYYokEdEl9yhl06OPEurq4vT1n0zqPP2RQ5ZQUlwgEsWQJNLpdCxaspxnG2vw20cndOw5o3P5n78f4asPH+B7/zzKRxZUkO808/qBdlo6g/z48WMfnE2WsBh1rD3Qzu7aTrr8UeaPzeM7fz/C45vOJTTr+7mpuo19Ww+TF5uV9Ln6k3AogsPh0DqGIAgDgMlkYt6CxTzbNJygfURCx+7fa5DKbaPc7Go6xZCviRej7xRT4mJnoiAICWGxWJi7eDFl999P5fLlCR272O9nV0EBWwsL2Z2fT01XF5ZYjCabDb9ez97cD/4QQFJV9IpCk92Oy2IhrNO9NWadM7m3XirxOMof/sCL+4/C0puSOle/I0kp35koqaoqmhQkmaqqrF2zmlxTkHG6XRBL7277qaJYCvjmU53cfO8U2mOJ30LXLzWPZuqwq8nJEdduCYKQGIqisHbNagotQUZLOyEe1jpSWoiZC/nWky6uapNp/NtzWsdJC5Zv3cXSr30em000OhcEITEUReHl558nVlvLkW9/m4hf3OgFUDB9OqdnzeKrRVbiO9ZpHSctHF18C3M/+VkMhg9usJ5IYmdICkiSxOKlK8geMpEN/hlgTH2n3HQkB9v4r+sL+Nf/7SbHNFzrOGkhu1jm2IkjWscQBGEAkWWZJcuuxFY8ls2h2WAQb3QB9KFW/t+NJbxSIlO4aKbWcdKCs83Hkf0HtI4hCMIAIssyV113HcOWL2f8736HJStL60hpoW3HDoYdPszPO2Loh43ROk5aKI+HOH74UErnFMWQFCqvqGTU+Gls6JkCenGFKoA53Mi9Vw1hyz87kEU/X1RTD+FQSOsYgiAMQEOrh1E9aiKbAzNAJ45CANjDDdy9pJyjV49DFk1Dkerb8Hm6tI4hCMIANHLMGEbPnMmY//s/jGL3GQBNa9ZQ2dbGE/kjIIVNQ9NVjt+Du609pXOK3/UUKygsomr4WHZFpvGB3eYGkUKlFjNhcsJztI6iuVDcq3UEQRAGsOKSMkorh7MvPhOxBvWqkGtRIj4qv/cFraNoLtDYKi54FwQhaYZUVVE5fjxjf/xjraOkjaa//IXWxibUaz+qdRTNxTvaSXUHD1EM0UB5RSWyNZ9Wy2Sto6SNu+c5eP2Z/VrH0Fww1oki2vgIgpBEQ6uHEdZl4rKM1zpK2vj0wlw2qx6tY2jOe/QUkj65NykIgjC4jRo7FltNDcPvuEPrKGlBVVWGnDjBP866tI6iudiZWkhhvxAQxRDNTJ0xi71tdlRLgdZR0oLcXY+EhNOY+Ou3+huvp5vmliatYwiCMIDNmjufXW2ZSbl2tz8y+k+DXodzdLXWUTQlyTKdPi8tTWINEgQhea5YsgTLVVfhLCvTOkpaaFyzhrhOh5xXpHUUben1tLvdtLe2pmxKUQzRkCPDSWOsVOsYaUKlyB5D7qjSOojmyocW0tTUqHUMQRAGOL3BiEsaonWM9KAqZBqC5H56cF9zqMbjVOQX0twoiiGCICSPJEmg01F+5ZVaR0kLsXCYYGMj8cXXaR1FW5EwVUMqaGlO3RokiiEaane5KcsQVxy+adX0TDav3q91DM15XH6ys8TVuoIgJFePP0Ceyad1jLTxkbmFbA20aR1Dc+3dXvKLCrWOIQjCABeNx+k6lNqbQ9JZZWsrj584p3UMbckyDU3NFBaXpG7KlM0kvMfokcP46x49PbaRWkdJC/qes8iyERmj1lE05SgO8vrr4r5xQRCSa3hNFX/ZayFoG6Z1lLRg7DkFRuOg7+if2eBmzWuvaR1DEIQBbmhFBfLdd1OxeLHWUdJC89q1RFPcLyPtKArDDSovr16dsikH94qvsVFjxnPHHXewq12c2QZAjSOpcRzGfK2TaCqeXUtlVYXWMQRBGODGjp/IHXfcwU5XntZR0oMSRSeDvXJwH1/teehZygvEzhBBEJJr8rRpfOxjH8N23SA/GvKGkNeLJElIGZlaR9FU0Y7XKC9I3XtBUQzRmCRJYHKAPMgrgQBGJ8g6bIbBXQyJKH5am1N7x7YgCIOTLMuoRidI4uUApkwUVcI5anA3UQ21ddDWJW7WEQQh+fR6PRlDhvS+HxrkMisqkHU69CVDtI6iKaXTRXNHZ8rmE69+0oAkSWAb5J/CGOz8cK2ETg/dkcF9ZjuqBCgf7qSxcZCfGxQEISUkWQeWwV2ExpTJ/6yOYwR8J89onUZTwZZ28kPgcolrHgVBSD6D2UzmkCFax9BU1tChuG65BSMqcVeL1nE0FXe1Um410d3dnZL5RDEkDWzctAUCg/tFR71jGYFgmJpr2+iODu4u9hIywZ4Ydrtd6yiCIAwCGzZuglDqPoVJR0f180BRybzvQbpPDO5iiGwwEJHBZrNpHUUQhEFg0+bNdDc3ax1DU8N/8AMyMhzcuOVJFPfg/lAYg5FQLIbJZErJdKIYojFFURg1ahTEQ1pH0Y7Bzj+e30iPP0CxspjB/scyyzQENWIkMzNL6yiCIAxw0WiUsWPHghLROop2TFk8/9oOOrq81Pz4vkHfQLVw8UykLAdWq1XrKIIgDHDBYJCJEycSCw/e2zXzRo9mz6lT1Dc1w413ah1Hc6ZJs1CtdozG1FyooU/JLMIFdXZ2UOAc3P1CXmwZztQqDwsXS/zy8Z2gz0Mng4pKLBZlwszhWIY24os0ah01JYIxDwbDUK1jCIIwCLS3tVKSpdM6hqaeqK+gclw3sz5SwdrH2jH87929a5CqEA2HmZxZSujXT+I9fFLrqCnRc6YR82C/0UAQhJRoOncOwyA/kmf7wueZUGBg8pIy/m9tPYaZNyJJgBpHicVYWFVByaYXiTfVax01JeKuFgxjUve6RBRDNJabm4diyOhtXqcqWsdJuUjeDA5vPsbX5vkhGOOL0wCdCfQW0JlQDA5eOnCSTa/6mb1oGrkjgrQFB/ad5KG4D4M0uN+cCIKQGiWlZZw8cQKQAFXrOCkXyJ3LyW0HmXp7hKAaZ8LNoJMiGGQLOsmIUc7jwLpWmldOYtYXbsL0wg6ann9d69hJFWhqw2IQa5AgCMlXPWwYp0+d0jqGZqb/939z3Otm5mgbRFS+MtcGOiPoTSAbUAx2nt5Rz9POoVw95yqqTuwlsn+71rGTSnG3gZy6NWhw7wVNExIKGDO0jqGJ375cz2cW5oESe/uL8TCEuyDQhuw9xdVDWvmvFQbi9af5108PkhOepVneVFCJE4y7CIUG8dEpQRBSRpIAw2A8EiHzm5dOsuDWfFTib301roYJxbvwx9rxROoon93JwrutNGW7eGm0nbL/9ykNMydfrNtPd905YrHYhz9YEAShj/QGA4ZBeCxPbzZzwGjgs4uGct6HEfEIhLsh2Insa+CmkRLfuqaSpu527u+WiS9fpVnmVFB8XUQ72lGU1GwSEMWQNKAqcbDkah0j5TqzFyIpYTIjF1ERDnWytKyZ797g5IW/7cLmmo08gDc25Q9T2H9gr9YxBEEYBJR4DMyDbw1qdi7AYFXpkT9863Eg1kHRVBdX/VsBL6tuSn/+H8j6gbsGZRw8y+EDB7WOIQjCIBCLRMgaOviOh0/+8Y/IcxgxBls/9LFy2Mu1w1T+Z9UYfnWqBe9Ndw/o/lYVnc3U1abmaOrA/V3sRyoqq6nvGXw7Qx5+8RCfW3xpL8DlkJv/vtLAyW3H2PcPGxm+WQzEP8aqsZsef2qulBIEYXArKCqmPT74iiF/XX2QmTdmXtJzAvF2ln0uk716N6e/cxsl3/23AVkUketbcbUM7usdBUFIjczsbPJmzNA6Rsrt6+7hjtmFl/QcfaiD795Yw9q2dv4ycTmBaz86IIsi+UEv506n5ma3gfe71w9VDq2ivmvwnM9Vskbx8NEhWM169P6zlz5AuItPjG3lPxbr2ffaMY4/XUDH5hEDaqeIN9IIssrxE0e0jiIIwgA3ctQYjrfEP/yBA4SSPZbfHS7H6jDhi156Y+5grJMRV3cy9+NWdtn8tP7v3Sh/+uqAekHq3n4AYnFOHD6qdRRBEAa4iVOmEC4r0zpGyoy6/XYy7/8ReZlW5ID7kp8vR3zcM8XAl5dV8GxrF/+ccT1br/90EpJqJ3b8IEo8ypkTJ5I+18BZufsxSZIGR8+Qguk82TiGHz7bxLIxZv5tckfvubjL5W/hsxPbuHdCA+1NzeRZRiYuq8biahhl6EZqTx8jHh88b1IEQUg9WZbB5NQ6RvIVzeQfZ0fw/WcaGLXQwahrPShcfl8Mf7yVMTd6GHFDKy0d7RReMT2BYbUV8XjxfPS/Ob7/QMrObQuCMDgZDAasRUVax0i6KZ/7HCW/+Dm1w2u4fe4QPjlBpi+Ny+VAO1+cZeKL02Tq29rQDx2RuLAaU7wexv7rVxw7fAhVTW5z94HzUbqQvnRmXvdMYMfGI1w1OY+brghB/HgCJ1CxGRWk7vx++Sc60ziELGk0j/95M5KkklvgxNXm7b3cIX6CM2d+w72f+4LWMQVBGNAG8E0yeisvucewZ9NhpiwpZtbCOCG1NoHfsgq6CI4Vc2hduy1Rg6ZM3ryp5N2ynNXtp5ElCafOSFc0hBRXUM7W4Xrw99z92Xu0jikIgtAv2fLzKfvB9znYdI6bRmVRY5Ug1pTAGVR08QixSbPhdCLfX6WGcewUApNm87d9xzHKEtkWM66eABIK0SNHcXd5+fgnP5m0+fvhW8eBKR6LgFHrFElgyeN3uxwUO1r5+hVhiJ+GJGx0uGuGjvv/uZ0MZz6Tb7LijZ5N/CRJoJetrPtbN01dz3K6QMWs05PhD9Fi6cZutqKXdXy2erbWMQVBGOBi0QiYtU6RBLZifrXNQFaJm5l3xFHUM8STUPeZdqOVl/58jrwHPkPVY9vp2HEg8ZMkgTE3i91LRxI7vJuZp7tR7RbCNiM1LV0YHDb0DhuRuXO1jikIwgAXDga1jpAUpdOn41t1E1l2hf9clA2qG5Kw2e7TC8q5/6U6ShbczHVn9xM7m5rmo30lZ+XyWymH8MvrqTp5El1WForVSmFzM2anE1t+PpbZyX0fJIoh6cJgA0kGdQBtR5WN/PNYHtW5IRYXnk1KEeStqUIu/nOekQPBUo5tUcielry5+iLXPJxTW43U1TZgMhkI90TRO/T82rMVPO99vCRJfKryitQHFQRhUJFMGfRuRxtAO0T0Fh7eZ6NomIRj5FmUJH5rQdXFrDtNeA5bCX5kEaRpMaTkmoU0L53AOXcbRp2eoF4iO67D8duXaX/H43xv/K9sMpJ3+yItogqCMIhklJRoHSHhbPn5+FetZO6IPCY7O5K6vBojbr65LI/nTkqcmbqQsjQthphmXMErGSU0tLZj1BsIShLOaAT3b3/L+7VLNWdmMuW665KaSRRD0oROpwOjE8Lv8444XRgzoGAKNG0G5R29Pix5/Gm/k25/ECQJq1HH1fMn8OdnNzG2IsTiksakFkLeokQ4eaaZytmT8FKbggkvnt0/mfXP1hIOncWdodJmi2KSZF5076C7MXDB56mqyjFXPTN6erDb7SlMLAjCYCLr9L1F+WiP1lEuzJQF+ROhaSMo7+j1YSvmwd1mgqEwEjI2i57ls8fyyAtbGTISnKMak1oIeVNcDdN8rp2ZlFGX/OkuScG3PsUufYjjkRDDDp1iktuPYjTQ9fxGYv4Lr0FKOELg9DmCwSAWiyWFiQVBGEwsGRkYbTYifr/WUS4oq6qKEddfz85f/pJ45O33QaXTpxO8ZSVxBUDCLEuMzMpmS9M5JpQ7mZzZlZrPGeIRWt1+pg9Jvx5gLVd+hDXNnfR4ghSf3Eexx4Miy5x7/nncH7ArKNTVha+xkVgshj5JN7eJYkiaUJV4bwO7NC6GbOgYxupnNmGxmJg6diSHT9Tzietm8tjaw0ys0DGrMAiqQmssn2defp2vLnEgB5JzLOZCmr0Ko4r8eDX/WSpTbp/Fv369D7PFjL/nCFszO9nnPomn3ffhT3+Hp5p3M2RzIdcsvzJJWQVBGOwk1N41KI2LIc/Vl7Ln5Z3oZAtTxgzl0MkGPnPjTB56bg8jpptxVvlRUYl7HPxrw0bmfMxOIF6fkkLImyLdMpFt+1I34QXIej01X7mTF4x+bGYLdcFuZq47g2ffUaLe7vN2gXwY63Pb2FJWxOKrViQtryAIg1s8EsFRXExHbXp9mPlOts/dw47uHpSvfZXqnFwaPJ3My8tnQ4eb68YWM8oZAFTqA1bW7m/gq0tLkMNdKd1wGVSMGA/tIJq6Kd+f3ojx+o/yi6P12C0WwvVtFK9dS/zQIVp9l/Y+KPjii2woKWHR8uXJiZqUUYXLk+RuuX0VVWRWzi5hYoWZ/Q1e5l47lt8/s5EVUwoZaT4Jod78hXRxz1Qg0J36kJJMXNH2R4DDUMy2x+Js797HLrOLY75m2nydBNpDlzXejrNHMI27PrEhBUEQziOl/THNcExlznXlZJfoaTzew/Jp4/jl39YzfUU55Jwi+GbhPaOLMddAIH7hHQ/JIst6oh+w0yIVcmZO5MBV46mN+Bm/7QxyXRPBFhftwctbgzq37iP7S6sSnFIQBOFtqqqipvnNVcFYjM9fM5EMk8r+Mx1cUzmKXz2+jo9eMYoyXSuEex9XoevmrskmCHelPqQkoYbDqZ/3HQxjJvGgnEfsyFmKNm8m0thIuLGRhtDlrUHNW7ZQ8JnPJDjl20QxJE1EYwr4W7SOcT5rAUR8dNnH88wON43NR/jPFXbkrrNMygA6znLfHCCNNgRXFZjoPGWFhB49lHEYCrEbCtjwhAuft5vrPjaFHrkWb6QBAIehCEd8GPEeB089+hpNzih/almXkNmD0TDHvM3I69exeIHoHyIIQuJFolEIXMp+gRSwFUHIg8s2kae2t+DynGP+AhsdYTeWSmhTzzH9owCntU76Flu2gnz9HHhlU8LGlI1GnKOqcI4dxp7R+QRDQRYZcml/+jU8uw8DkDVpFLkrlyJXl/Hs/m2MOdFK7G+rSURZJurtRqprZuu6Dcy6Yn4CRhQEQThfNBql6+xZrWOcp2DcODpPnWLsffdx1ukgGA2TEXMhB3xMygQ8h/j64ly4pL12yZVlVumcMp+M2iOJG9RoRF88BF3VMP7ik4nFotxVXYKya+NbjVr11SMJTpxDMLeQP69Zz5D6YzQ/+iitCZje395OpLGRbZs2MTMJDb0lNdmX9wof6tCBfTgDRykN79I6CgD1hmn8c/1pLGYT0ViMWCTMR+YVUiafgbi21cYPZcnnh6/GmHmnTES5tG1Y76fYOIdnHt6PwWjEHwpyJiNEa7ybCV4nZlWH3fFm41twBbwE1Ah15h5ea9jf9+/lHSRJ4t4ZN3BV6WSWLBTN7ARBSJy9u3ZQqJykMLhH6ygAHJen8ty2BgwGPbFYjGgswqxriok7zxJXNd/8+4Gsunw2/CnAyAeeJ9LV9zWo6huf5jWTH7PeSLjHz+jTHkydfvaMySdsNeCw2pAkCVSVuMuDFAgz/KQb//rdCfhu3ibJMoX/cSeGJdOYs0gU5QVBSJzN69fT/OijHH3wQa2jADD2hz+gTpYx6GSisTgoMW6fW02x3H5+v6o0pFjzuf/l09x1dAOEL28nxjvFb7yTh082YTEaCfh8lJ46hRwI0DByJHGLBbvViiRJqIpC2OVCCQYpOHqU1s2bE/DdvE3W6Rj3jW+Qt3AhsxcsSOjYYmeIxuLxOI0NZxjt3K91lLc8uuE0X70yE9l7Cgx2iEdAOZ7S3h+XLdjO8JJi1IZqKN1+UU+Jn5lMwBdiyPQIHaFa9JgpsI1h41MuNjTvZIu1jU1nDvU++I3NO68DhRk52HusKKrCaXci7wt/L1VV+cW2pxi9sIhAIIDVak3qfIIgDA7RaBRXWzMTHPu1jvIGmWe2nGXRJ7LwhM9ilO3E1AgRtbZfXHQTiLeTU5JNznfvoeUL93/4E2QZxwNfJIJKxlNbcG3chTE7k/JVy9ld7WSNy83E9afwbu29ncb/xj+Va8BSnI/eakGJx/GfaXxryGS0zFIVhZb7/4R9WAmRSASj0ZiEWQRBGGxCoRCuc+c4/vDDWkcBQG82c1JR+NryCuhpBUNG7wfBSnNSrsRNNDnQTp7dSPcNd+J49Hcf/gS9kUNLbwNJYmLtbmInD4M9A9OsRfzNq+A7WEf+66/Tuqv3A/tzbz7v5ZfJLC1FbzajxGLn7epJxG6Qd1PicQ58//uMq6khHo/3XjySIKIYorFNG15nfkkH+NLk066cMRj17b2FEEjrZnoXcuN4HT9de4Kxt7z316z6XCyBUbzy1H6QQCfpCMRP0G6KUnTQgN1SgCSBu/sQzRlR/u7aRFx5/ypQq68D6Ejq9/Juj9Rvwf6ikVtX3tz7aaAgCEIfbNrwOguKW8GXJtXugknojfV4wmcBiCj9bw2auNzOhr+2UP4+v+YYXknm3TewracN4gqSQY/B3UaOO8CJhcOwXD0RZDjR3sGIvWfJemwN3gucow82p3hrtqpieWwdL9hM3HDzSrEGCYLQZ6+/+ireP/8ZJZYeOy4m3X03xySltxACENX8RoZLdsf8Sn65+hQfeZ9f05VV0jZ9ES+dbEBSFVRZj+FsIwa3m52FJVhm1CBLEt5TbZQ0NOD5179ovcAhEl9j4/t+PVlURaHniSd43mLh+pUrEzauKIZoLBoKYPYd1ToGAI2Wmfz5yaNcOzn9rmS6JN3nkKVCMoyl+CLn/0V94RdunPlH2GJ1Y9Ab8IeD7Gw6Sk84gCzJ2E0WfKE3fvAlo7TZRxvr9lNgycSx2sHVK8TtMoIgXD5VVYlH/Bh8J7SOAsBpwyz+8fhRJizOBLxax7ls3ug59IYc7FXl9NQ1vP0Lssz6q0ZT7nczeetZ9BYzsW4/7h0HiAeCVOp06G0Wor7eAlA6Hkp1vbqZ/PxMXrPbWSJulxEEoQ9UVSXU0cHZ11/XOgoAE3/4Q3aHgtw2uQTo0jrOZdP7W0BvQnZmoXjfcUup2coPyKPseD1FW7eit1gIe7007thBLBRC1ukIW62Eu3svwEhtqePi1D33HCPy81nrcLBo2bKEjCmKIRo6cfwIwzK7IaLd3t89sZlsPlCPEo/S3rGX76/MRfY3fPgT05xOjeAwDH1PMWT+0qm8sGcfq4++9wiNoipvF0LS2Otn9vGJatHEThCEvjl0YB9jc/0kpMvmZdoWncH2gw3EYxG8vgOs+LcceqLN2gVKCBWFKJnjhp9fDFEU5tSMomfvMdrWbHnvs+Lxtwoh6ax77U5sty/WOoYgCP3cji1biG7dqmmGcb/7DWd93YRDQfYGg/z3tUMg6PnQ56U1VUEnKejKKs8vhoQCzBw5nM7tO6lbs+Y9T1Pi8bcKIemsdc0asm64IWHjiWKIhurPnmFhuaRdE2JTFv94djs/vWc6SsiH7I+AP7m9L1JDIhKXCcfPb16XaxrFSzuO47Okx1a8yzW7ahzRNL9+TBCE9Nfa2szoMkW7Yog5hyef28kd35hKNBbCFz1HTzQNt+RdIgkZ4vJ7jrGU3XoV65rOMsoTIKJRtkTImzuZoNIPGrgIgpDW3C4XSrt2N7HkjRzJ5hO1/ORTC1AUBbm7uf8XQgAkHZFY+PxCCGBasIIj9ecocrs1CpYYpfPnY0hgzxA5YSMJl2z6zLnsP6fdm9odPSOZNaYUWnche45CpP9uS36no8EhFBTl4Q6dfOtrRSzgkf89wFGLj3/UbtQwXd+9fmIPBln81RUEoW8mTJrK4Rbt+j682lrO1AU1tPoP4A4f65f9Qd6Pv66YoqwcOnYceOtrQ//3yzyXrzD+mJvIE2s1TNd3ra9tQxVrkCAIfTRu4kSkESM0m99y9ydZtWgKdNQhe05DrO+3r6SDrW1WqosKiJ870/sFWcZ36z18/5Sb0sOHaX3+eW0D9tHZV18llsAPhcVqpiGn00lPzKDZ/HuPNjB/hBnUNGmclyAbD7UzffnbP1xDR6fwzz9uZa2jjcdqN2mYLDGmlo9EiYmdIYIg9E1+fgGdGh6ROVLXQvk4GbU/tOi/BMf2tDGp5421XZax/P7rrOtoYO4rx/E/mR5n4/vCOXMCygUa6gmCIFyssooKIg6HZvO3BfxMLtHTL64quwS7TjSx1P7G4Q9ZZvtVd/H47iOUPv88Tc89p224BCieOxc5gQV5cUxGQ8FgELMU1GZyvZVQDHJjddrMnyyyEXdAT5fS25Q2enIqew4c47e+bf2iH8jFuL5oEnPnztU6hiAI/ZzH4yHTrFEx3JwDko5u6bQ28yeJQbYQDUDjyy8DYP3NVznbUM/Q/3uBnoBG632CBa6cyrKFV2gdQxCEfq65uRmDV5td6cVTptBtMqHvScc2oX1gsNEd9hHaug5kmR1X3cXpU6dRf/5zusLp2Jb70kiShGHxYhYsWpSwMcXOEA25XC5ybRpNHgugxuNg7Oc3x7yLW1dBTp6TsOojcHgSe/ad5Bdt6wdMIeTW8QupyS0lIyND6yiCIPRzba0tFGRodEwm7CUWi2PS2bWZP0mUziIKMzOJ+now/f4/OdfSRNEDTxMfIIWQvDuvJ7NmCGazWesogiD0cy2Njei7ujSZu/PUKeKKCvqB9bPsRLeVqqI8UGJsWXEnZ+rOEPjJT4gNgEIIwMjPfpbC6mr0+sTt5xDFEA1FImEMeu3+L9ARJWYr12z+ZDjUECArp4BXf+dj07Fj/KT5NbpDGu4DT7CbiqeyfPFSrWMIgjAARKMRDDqN1iA1hqRGyTRWaDN/ktQd6qCwuIwd102k58gp8u9/fMAUQgBi181i/hJxk4wgCH0XDoc1eyMa6upCjUbAXqhRguTYdriegtIh/NxcRvve/XT/+MfEQgOjF4oky9iXLWPuwoUJHVcUQzRUVVXN6c7EdcO9VIqkRx/WrotzMlxRA8889SzBEdn8vO4V/OGB8yJ0XEk1JoMRSdKu4aEgCAPHyFFjON6m3c8TWWfC1++v0T3f+IVOHnnsUZZ2yRh+8QTx4MB4EQqQM308kmicKghCgkyeNo1guXYfyurNZgi4NJs/GT6+aCS/efhhJrS00ParXxGP9Oe7y85XPm9eUgoXYlXTkCRJYM3TZnK9hVhMgdjAKRYA0NPEI9+6hoy6Hr5RfQ2ZVu0aMyXayooZTJ4wSesYgiAMEHq9npgpW5vJjQ7iSpy4MjC27r7JG23gm79cxdpciZ7/vA29Q6uzsIkXvX4OU2fP0jqGIAgDhNlsJrOmRpO5syoriUXjEI9pMn+yyN56HvnePRwsKSH3a1/DYLVqHSlhbFdfzawFCxI+riiGaExVAWvqt2gFsqZh0kUh3JXyuZNN37GH/1ocZ8nYEdybNx+byaJ1pIQwKzLFRUVaxxAEYQBRJX1vM9MU68qYhmSIEFEGRj+nd2qN7GHBp82UzBlFy5duQGc2aR0pIeJ2Czk5qf+zIgjCwGVyOnFo8Np2xGc/S5YZiA+sgjyAufMw376ukqqpE7B95SvojEatIyWEzuFISs9EUQzRWFl5BfXRstROKuv54yu13HVFcWrnTaWIj3nGTQwvKeTm8plap0kIRRlYV38JgqC9/PwCWqShqZ1UNvLQSweZt6o0tfOmUEjpQjdmN+YiJ6abB0aPjXhsYH2CKgiC9jIyMii//vqUzmnKyGB/JMRdi7TZlZISYS/LCprBbqVi5Uqt0ySEmqQr3cXVuhqrGTaCl06exJ43jpzgQQDqjTN4fNMZDHo9SjzG9HGVzKhxIMd6oG0PKJd4/suSB/EQxKO81jmOg7VNlGbqsAdPJuE7SiM6M+2dPnrUgVH1HZaf4qKZIAgD3uix43nhuTPMLhpDZuAwACf1M3l262n0Oj3xeJQ5E2uYNtSCHO2G9n2XvgZZCyDqB1XhhfZR1J5pI7dUjzd+KgnfUfowyFZ6uoMUdfVoHaXPJFnGNlSsQYIgJNa0WbNobGqiyuWi7oknABh//4+pVRT0eh2xSIQRzizsLS34mpo4/M9/XnJD0LzRo+lpaUHW6yn74Q9o8nRRnW9D9p1LxreUPgxWgmEX4c5OrZP0mazXk1GWnDVIFEPSwJVXX8uGda+RZ3CSnWnjob+v47s35UEsAAYbG0418sA/OtHpTWDMRyfL5GRY+MjsTDBlQqQboj6QDCAb2dOspycQYvywYtbsPMO5FjeKakWWZIYVtXPfNFfvi1lF6+88eVrsc/j9i4fxZ/h5/NQWreP02ZLh09BJYiOXIAiJJUkS11x3A6+/9gollkwsNht/e2IjKz6XR1wJo5fs1O1uYO2/POh1Boy6AmSdRH6Og9um2MGcCREfRLvfWoO2N+qIRmOMrSrgpW2naHZ5UNXe3hnFVZ2MXdWBosYYyHvdnD0zWfOvA1T6euh5boPWcfqs4Or5xHXaNXwXBGFgkiSJG1etYrXFQsaECTjy8tjcfI5vXlMFSgxFb+XVwy52d8rohg3F8r3vIQPZVjPOXXtwlpbS3dKCv60NvcWCwWZDmToFJRqjwGjkWCSMLxREUkEFsmxxbh6uB3Xg3DT5fs7qR/DISzvI93hoWr1a6zh9Vn3DDQm9TvedJDVZe06ES3b61El+9Zvfcf8dw5E9xy7wKAl0Ro75S3l2dyc6JYzFYkaRdMgSoCgU5VgpzZQ4fNrF1JFFjHM0gs7Q+/TowDuf/W7HDXP54ws7WOdoY++5E1rHSYhvTF3FF6/+GPn5+VpHEQRhgKo9cYzf/f4hbv5KDZ7I6Qs8SkIvmfDX57N/XTs6o4LRaEJCRpIkFCVOXrEDS5bKuVo3I6eUYCxpRCeZAJWoMrBfgAKYm6ez/uldTFl9BN+hgbED0/btT3PFv38qKee1BUEQAI4cPMif//Y3fvzRydB9oZvGet8H7e208tKuM9iMMjq9HiQdkgSqEqeyKIcsi8TJhnZmjqlgpK0L9CZQ4gOyR8i77Q4N5aU128h9/nncxy70frJ/qfn+97npvvswm80JH1sUQ9JMh9vNgd2bWWDdeulbkQWw5PE/r0T5Y2gf5zxtWqdJmL8v/wq3XzcwzvwJgpC+2tpb2X9kJ9LQ7SiIHhGXymEoYe2DHsb8/jVCrQPnysacx3/EspU3aB1DEIQBrrmpkTPHDjDTUgvqAN7CniyOYr77xBEsv/sdAbdb6zQJM+PZZ1l+7bVJGVvsu08zObm55BSU0Wwcp3WUfmlLVw0dltiAKoRYDCbyHJlaxxAEYRAoyC8ky5GPPTxG6yj9kntPDuWu4IAqhBgyM9BnDJwrggVBSF/FJaXIlgw85iFaR+mXnj8WoaC9fUAVQuyFheiTeCOOKIakofETJ7Pf7QRZtHS5VJsP1HLU0KV1jISqyS8jEo1qHUMQhEFi2pQZdJy0I4mXCJes9nADxfvOah0joZwjhqJGxBokCEJqzJg1h21NWqfon2rPNMKOHVrHSKi8kSNRknibmXi3nYZOnTxGNBoDcYLpMsi0eAdONRTAZrJoHUEQhEHk2PEjqAqoA7rFaZJIOoItA2dXCIDOagZJ0jqGIAiDxPGjR5De7HUoXBpZR3dLi9YpEspgsyElcQ0SH/ukoUNHjnNtlQvUeMrmVBSVVk8wZfMlhSkLWdJzoq1B6yQJZTWak3a3tiAIwrsdP3aSwmktkMJiiKKodLn69xrkMBQhqeCvH1gfaeqtFvHZjCAIKXP8ZC3LS3wpnVNRVFo7+/k16I5i4tEo/vZ2rZMklN6S3A+Fxc6QNKSqKrXduQxL0vjnXAEeePoEVpOOUFThS9cNo8UT5PBZL3ctHfqexx8808XDr57GbNQRiSpcPa2YRRMKkpTu8jWZJtIa3I4ywBouTcusZOTwEVrHEARhkIjFYoTbciGzLinju1v8vPDHoxjNeqLhOFd9YiRdriANJ7pYuKr6PY+vP+5h7b9qMZp1xKIKk68oZeysoqRk6wuduwZ92yatYyRc96QqJk8cr3UMQRAGiUgkwtmgjcokjX/O5eOBJ3dgNRoIRWN86cZptHT2cPiMi7uWv/dn3cHT7Ty8ej9mo55ILM7V02tYNHFIktJdvoMeO/oBVggBUCdMYNL06UkbXxRD0tB1113Hlpf/wTB7csbfctTNtOHZ3Da/gubOII3uANuPd7CvrosbZ5eSaXu7SY2iqDzw9Ake/MIUTAYdAGv2tQLw6xdq8fqjdPmj/OeqEWw45GL/6S6CkTh3LB7CugPt1Lf7mT0qF5c3TKsnRHtXmIlVmcwYkcPjmxtRVZXhpQ5um1/Rt29K0vPwC/vYYhxYW8MADFEoLy3TOoYgCIPEtddey5qt/0KfmZzxj+9pp3pcLnOuqaSzPUBna4CT+1ycOdrJ9GXl2DLOX4Oef/gon/n+DAzG3jXowObeKxdXP3ICvy9CoDvCDZ8Zw5GdbZw92kkkHGfBjVUc3taKq8nPiMl5+DxhulxBvB0hKkdlM2xiLltfqgeguDKDOdf07WW3jJG1z+xl/NojpPbzzOQLmXQUFhdrHUMQhEFi+fLlnNqxGqzJGX/LkUamDS/mtitG09zRTaO7m+3Hmth3qo0b5wwn0/729a2KovLAkzt48EsrMBl63zav2XsGgF8/twdvT4guf5j/vHUmGw40sP90G8FwjDuWjmXd/nrq27zMHl2Kyxuk1dNDe5efiVWFzBhZwuMbj6ECw0uzue2K0X37pvQmnl+7h6xXX6W7byOlHcVkIic3N2nji2MyaejE8aNU5yVv/FVzy5CQ+N4/j/LwK6cpzDIzY3gOc0fnnlcIAejojlCQZcZk0LGvzsPX/3yQv649y6nmbrYd68Bk0GHUyxw442X1nla+89Ex/Ndto3ho9WkAbppdxvLJRZxs6uZbt41m+ZRCAB565Qw2kw6HxcDeU54+f09tznn0qBF21Q+M+7TfKdfmJBIR1ywLgpAah48cJLcieS8PZq0YAhI88euDvP6vU2TmWqiZkMfIKQXnFUIAerrCZOaZMRh1nDnayd//dy8bnj5Na72PE3vbMZhk9AaZs8c97N/YzC1fmsCqz4/jtcdqAZixvJwJ80toPu1j5efHMXFe75v61x47hdmqx2zTc/pwZ5+/J2fPDMxdAXyHa/s8VroxZmYQFU28BUFIkWNHDjGsIHlHI1bNG4EkSXzv75t5ePUBCrNszBhRwtwxZecVQgA6uoMUZNkwGfTsO9XK1x9ex1/XHOJUs4dtRxsxGfUYDToOnG5n9e7TfOfj8/ivj87hoZf2A3DT3BEsn1rFycYOvvWROSyfUgXAQy/vx2Y24LAY2Vvb2ufv6Xh8KKauLtwnTvR5rHQiSRJ6hwNFSd6uf7EzJA21tjQxIit5TUD/uvYsV00r4tb55bR6gvzwX8f4yBXvvzMj22GkpTOELxBlYlUWE6uy+MTPdqKoMLTQzpdvHM7eUx6cNsNbp8tVQHnjgLHdrCeuqG+dN9bJvQ1w4orCxxcNIc9p4sktjX38jmT+8OIBtpkH3tawOdXjKdRnYLOJaw0FQUiNTk8nlsI2SNL73/VP1zH5ilLmXF1JlyvIU789xLzr33tEE8DuNOFpDxLoiVI5KpvKUdn8+mtbUBQoKHNw7SdHc/pIB1aH8a3eSiqgKr3/brbpUeLKW81gJV3vGqTEFebfMJSMbDPbV/etz5SMnjWP72bS+hN09Wmk9FO4ZDbuHAfGJF5rKAiC8E6BQACbMYnvg9Yc4qrp1dy6YBStnT388NGtfGTR+18nn+0w09LZgy8QZmJ1IROrC/nET15AUVSGFmXy5ZXT2VvbitNmemudUVX17fdBFiPxuPLWe6Tz3gctHkteppUnN/exgCEbefLVHeSsWUOgbyOlnWHXXIPqdCLLyfuARhRD0pCiqNCdvCagc0bn8j9/P4LVpCcQjvHxRUPId5p5/UAtK6YU8de1Z/naqpFA71/ar60cwTf+fBCDXiYWV5k/No9hJQ6Mepnv/OMInu4I379jLMsmFfDfjxwmGlO458pq1u5vA8Bm1lNVZOcHjx2lwRVg3pg8PrWsiu89ehSTQWbO6L5tg+nMno8vuoOtjYf6/HuTbradPsxXh1+NqqpJ7aQsCILwpng8Rk+0759UXcjIKfk8/n8HMFr0RIIx5t9YRUaOmcPbWpk4v4T1T9dxw2d6X5jKOonrPz2Gf/zvXvRGmXhMZdS0QoorM9AbZf71fwfweyPc9uUJTJhbzGM/308sprD0I8M5tKX32KTZaqCw3MFTvz2Eq9nPqKkFLL61hid+fQiDQcfIqfl9+n6yQzMx92yla9/RPv/epJv2jbuwf/56rWMIgjBIqKpKPBqBUFfS5pgzpoz/+dtmrGY9gVCMjy8ZS36mjdf372LFtCr+uuYQX7tlJgA6WeZrN8/kGw+vx2CQicUV5o8rZ1hpNka9ju/8bROe7hDf/8R8lk0eyn//dSPRWJx7rp7E2jeuWbdZjFQVZfGDf26hod3HvLHlfOrKiXzvH1swGXTMGdO3o/CndTXofXW0HznS19+atHN23TrG3X13UueQVHFNRVppPNdAT90GhrFH6ygJ9fNnThIIx/CH43zxuhryneYPf9LFMGXxrZcibHK42HTmYGLGTCNTykbynUV30H7sLHfeeafWcQRBGOBO1p6gNbiHgHO/1lES6oU/HSUSihMKxLjqzpE4cxKzBtn0eaz+ZSdzXq/Fs2vgFeSdM8ZT+u3PcqSjhdtvv13rOIIgDHCHDu4nL3KO/NDAOnL486d2EghH8YeifPGGqeRnJmjHty2fbz12mJKXXqL90MBbg0rmzmXit7/N2c5OVq5cmZQ5xM6QNHPq5DHm5gWh70eY08qXrk/O3TivtFfTnXmCTacGXiEEYEZ+DbtWb+DzX/i81lEEQRgEGhrOkjGmh0BI6ySJdfUnRiVl3Nat+YzyewZkIQRAN2ssz2zfyJe+9CWtowiCMAi429sZndcDA2wN+tKN05Iy7uMHw5R5PLQOwEIIQOa8eby0eTNf/vKXkzaHaKCahqT4APsJkCyOCrbtr+UfLTu0TpIUFoOJRbmjGD93Gg/94SGt4wiCMAhIEsQUsQZdjGxTFccO1mH86ytaR0kKg9OBf9YoFk+YykMPiTVIEITkk2QJ4uLSgIuSXcWx2jN0PPKI1kmSwpaXB+PHM3/CBP7whz8kbR5RDElHSlzrBP3C4UAlbbogHX6v1lGSYuXY+ezZs4epw8fx1a9+Ves4giAMBpKEosa0TtEveI5mU+oKEPX1aB0lKfI+ehWHDx+mcuoE7rvvPq3jCIIwWIj3QRfllaM95Le1EQ0MtLapvWo++Ulqa2upmTIlqbsTRTEk3UgyRAbmm/uEko08tW4/h00D7DzROxh0elasWEFJSYlonioIQkpIyITjPq1jpD29bGXXxiMU7z6jdZSkkZBYsWIFxcXFYg0SBCE1JB1EBmaBOaEMVnYdOgHbtmmdJGmUaJSrrrqK4uLipM4jiiHpRtJBRLwQ/TC1xtn45ChbTw/MM3IAh131dHR08PLLLxONJumOS0EQhHeQJJmoMjA/ZUokfdN4Mr0ROvcc1jpK0sSOnaG5uZnVq1cTj4tPagVBSAFVBUXsTvww27rycXp9A/IGmTcFjx2jrq6OV155hWTe9yKKIWlGVVVQFa1jpDmZR9ccYIPcqHWQpJqaW4XeoEdBIRwOax1HEIRBQBHbkz+UjJ5ta45Q+drALcYDKBNqsFmtqNGYKMgLgpASqirWoA8l61m/4zDSCy9onSSpTBMm4HA4iEcixGLJK5CJ22TSTCgURCkYgtx9VusoaStSOJee6E72Np/QOkpSzciuZsXyFVrHEARhEAmHIzgNJXRHm7SOkrYKdbNRuzbhPXxS6yhJFZ8xiuVLl2odQxCEQSQYjqJm5iAFO7SOkrY6neNQOp+hs65O6yhJZZs6lSVLliR9HrEzJM3MX7CQ/301SLNpstZR0tavXjpJXc7A7zRt1hlQFLFLSBCE1Jk3dx7r/9SDIzxW6yhpa80jB5lyYuC/UJfMRrEGCYKQUrNnz+b7L9bjMQ/ROkra+uMzmyk9PHCPaL5JZ7GkZA2S1GQewhEui6qqrF2zmuEZHZSEdmsdJ71kDed7L7j5XssrROMD+0zh5yZfw1BjDqPyh1A9rJqaodVaRxIEYRBQVZXVr7xIxhAv3ZYDWsdJKwWWsaz+dR1DvvcY6gAvFOT9+0fpLsnCPnYYI6qqKa8aqnUkQRAGAVVVefnF55lSECc3PHCbVF8OJXckP/zbZuI/+IHWUZJu9Fe/SjQ/n5wxYxg9YgSlFRVJmUfsDElDkiSxeOkKzoSKOGWYA7JB60hp4x8HDZyzhgZ8IQTgN3ue5yvb/kxjqBNVETVLQRBSQ5IkViy/mlBzHnbvdCR0WkdKG3uf6aG63jfgCyEArp89Qugrv0Rq6QRxm4wgCCkiSRJXXn0th7qsNBiG9960KQDwh1dPUHT2rNYxUuLI/fdz8itfIeZ2I8nJ+zMg/nSlsXkLFhFz1vBa90zi9kqt42juoDyP2sYW/np6o9ZRUsakN2KxWsW1hoIgpNyihUtxSFXEjk8jw1CmdRzNGetn4GtyE/v7K1pHSRmdxYzFYEBnEC3mBEFIrUVLluE1FrKhpwrVlq91HM3tCNYQ7Oik6dFHtY6SMka7HYMsYzSbkzaHOCbTD4RCITZvXAexIBPzvGQFjwyua6f0Nl50j2bnkVoe8u2k1Tfwz2q/adWkReTt7+InP/kJFotF6ziCIAxCgUCATZvXE1WD5A4P4JWOojJ4Ov4b5Qw8Oyo4c+A0lQ+uJuzq1DpSylR9ciV/0nfwi1/8ApPJpHUcQRAGoZ6eHrZu3ohOCTOlEBzB+sF186bRwdMnjZw5WUf8V78i6PFonShlZt53H092d/Pb3/4WnS45u1RFMaQfUVWVXTu20tXpZkHeafT+eq0jJV0kZyo/evoUPQ6JB+vX0xMOaB0ppb4x7WbuWbKKslLxqawgCNpSVZUt2zbh9XXiHFNPT3zg3ziTr5/K8787xJAeBfODzxMPhrSOlFK2b3+KWZ/9OHn54lNZQRC0pSgK27dsJuj3MT+vEzno1jpS0nU7R/LAY1so6u7G/eCDxMJhrSOl1Igf/IBl99xDVlZW0uYQxZB+KBKJ8MrLLzKz0EN28KDWcZJDNrLeP4U1Ww+wO8PDa6f3ap1IE5+dei0fGT6fVnc7s6bPpKiwUOtIgiAMcqFQiJdXv0j+OB/dumNax0kKvWwmdHAUuzcfYurWs3g3Ds5m5nn//hGctyyjpeEccxdeQXZOjtaRBEEY5AKBAK++8hILy1XswXNax0kOvZnVjZns2H2Q4s2badmyRetEmhj51a8y7JZbOHvqFIuWLycjIyPhc4hiSD+2bu2rVNnclIZ2ah0lsZyV/PT1CG5C/KF5E56AT+tEmskw23jgik9TasrE4/Fw6623ah1JEAQBVVV5dc3L2Mq66LHu1zpOQmUaqtj4l06yPUGcv3+JqLdb60iaMWZnkv3TLxErzqHL4+HmW27ROpIgCELvGrT6JcblRCiIDKwbZxRnBfc/fYQMrxff739PpKdH60iaseXlMfonP0FfWIi3q4tVN9+c8DlEA9V+7IpFSznS4SDqqNE6SsJEcqby9X+1sFd2cf+pFwd1IQTAF/JztP0s3d3d3JyEHwCCIAiXQ5Ikli29Ek+djQz9EK3jJEyOPIlnf1bHsD3nsP7vY4O6EAIQ6ezCd/w00Wg0KS9CBUEQLockSSxbcRW7W0CxDpxjfN3OkXz9T1vI27MH9wMPDOpCCIDf5cJVW4ter2flqlVJmUMUQ/q5xUuXs7m9WOsYfScbaXRcwdf/sodtzg4ePTl4boz5MEXmTKZNm4acxGulBEEQLseSxcvwHuv/x/dkjGR65/HUL/YyZ8tZAk+u1TpS+ijJZdKkSeJWM0EQ0s4Vi5awze3QOkbfyUZO6kbxo7+uZejGjTQ995zWidKGpaSEsWPHJm0NEnel9XMGgwHJYCXqGI6h+4TWcS6SDHnjcav57D7eyuHTzcRVHZ7ILl6wNnCqvlHrgJrSyTrmVo9HJ+tQVZXKojIeeughrrnuWqZOnqJ1PEEQhLeYzWZ0qgWHoRJvtL9sVZYptIyFnlxO7m+mobYZMCB1bmfGU3vwnhnca5Ck15M/7+21xjqknF//+tfcdvMtjB43VsNkgiAI57Pb7QQVA6q9EKmnVes4F0mG3GG0Rm3sOXKGk3WNKPooqmc9+Y/9i7ZzA7QPykXSGQxUzJ+PGo8jyTIF5eU88MADfPKuu6iuSfxpCNEzZAAIBAIc2PgU083p3eAtkDOb3792hlA4RiASpksJ0uOQ6bbAurq9fT8So6rowyox8+XtoJBjKqgqikHbHRhfmX8rjpM+Jk6dDJJEbnY2M6fN0DSTIAjChXi9XrYeeB6pIr0behfqZrHunyeIRuPE/SH0Xj/lPSq53WE61+7o85EYFejWQ0bs8p4flnvHMGt8Y2TVtz/HK5KXGRMnISGRk5/LpOnTtQ0lCIJwAS5XOy2HNjHGmN63bHqzxvPnF3YQiqnE/X7w+cjr6cHe1UXD668T7u77GhTU67HGLm8RisoyKmBUtF2EFvziF2z2+ZgyfjwSUFBYyISpU5M2n9gZMgBYrVZChnyQ5LS9d/sZ92T2rT/EsdwQ+z2nqXO98clb2+WNZwjGya0LougkpLhKR5UFfUjB3B3HU25+z+PLd/qI2HSoEshxlbYR1vcUPTLPhfDnGAi/4+u5pwK4q63nPc7iiV5wnr5YOmwqYx2ljCaXK+68gYryioSOLwiCkAxOpxN9JJc4Er0vx9KPd+todh/dx7QjLoL7T+B/Y/dHnMtbhjwGWJsLBgViEizsAK8ems0wy/Pex/+uHPIjoFN7ix7Xtr236LEtE4b5ofgdNyeuyYUl77o98qzlwvP0Rc6V84iMLKdhSA6fWrySopKSxE4gCIKQBHl5+RyO27SO8YGeOJPFidXrqDh8mM79++mq7y3ctFzmeD0GAwdyc9EpCnFJYnxHBwG9ng6zmZGe9y4OL5eX44xEkFWVqCwzva3tPUWP45mZFPv95Lzj+t79ublMcJ+/CLVZLHReYJ6+qLzuOvTV1bRlZvKZW29N2ZXuohgyQChKHDWjEslbp3WU9/BmzWXn63v5uWcT4bZIQsa0dsYIZBrwlprQhxQMwThWTwyzN4a3yPieQkfMJNM6uvcHZdbZIObuOBZPGFlRMQQUXNUWrJ0xdDEVxR3FEFQIZOsx+hVQVPJrg6gSSCp05xsAyGwMgQpdZX0vinx06jLmRIu5/prryc3NRafT9XlMQRCEVFEBp7EMb6RB6yjvkR+fzZ6Du6j6ydO4L/MTs3ers0JFAKZ6e4sgHgOcscI5M0z0guVdhY6MGNz4xg7uTVm9xYwzFojK0GmApa7e54d1cFLp/drQALiMvQWbV/J71x9FgtFvfHi4MxMUYEZX37+fIZ+6mSNTy7n22mvJy8sTPaoEQehXFCSw5kLA/eEPTrEmyxhOHV2Hcv/9nE7QrosWq5X8QIAarxe/Xo/fYKDVasVtNjPU68X0rnmssRizWnsXoSNZWXSazbRZLMRlmW6DgYkuF21WK1GdjmZFodtgoDAQwGs0Egf25ecjqSqKJFH+xg6Wk5mZqMDwrq4+fz+TPv95XBMmsPzqq1O+BoliyABRMaSKxvZ2yrQO8i5K5gi++8g2tud4CLsSUwgB8BYbcTZHyDsZQFLBU2YikKVH0Uvve8xFH1YoOOoHCeIGiUCmHlUGKQ6OaASLL0YwS09PrgG7O4q3yEgw24C9PYrVEyNs0+EtNWHqjiFHVRytEcJ2HW2j+l6J/vTkq7i2YBJDa6opKCjo83iCIAipVpBfQKCnA4zpVQzJM43m0fs3Mm/9aXwJKoQATPLCXie8lAdIMMMDlQEwKe8thEDv8ZlnCkACLHEYEujdJRKRIOyARgtUBmFYD5y09xZUKoNw1A5nrZAX7i28tJogKMMhBxSE4frL3F35Tnn33o5n6RQWDxsm1iBBEPqljAwn3XIYB+lVDIll1fB/D6+m4pVXcCfw+EmV10ud08nuvDyQJIZ7PBQEAhgU5T2FEICAXs/2N36+m+Jx8gMBZFUlJklEHA7cFgsFwSDFPT002+0M9XopDAY5Z7fTbrXiDIep8XrxmEyEZZl6h4PMcJgZbX1fhMZ85Svor7iCJSNHarIGiWLIAFE5tIqtx7dSlkYNlZXM4Xzz8XpOFkTYVnckoWNnnQvjKzASL+ndGZJXG6Cr1HTBx8dM8nmFCymuknM6REelmbCjdxeG+o4mxYr+7f+QVLX3FSygDymoskQgW48hqGAIxIla+7aLo0KXyazZs8jKzOrTOIIgCFoZPmwkr249gLFK6yRvyzWN4omfHmHukQ58+44mdOztWTDWB1Pi4NPD6jyY1nXhxzti5xcuohKsz4H5HVD45o7kd5wwMr3jtWxcemsJwqvvLaJUBsBr6N1Bkh3t2/fSXZ7D0vnzsdvtfRtIEARBIzXDR3B6Zz3jrR/+2FSJZVbz/x5eR/X+/bQcTewadCIriyE+HzXxOAG9nr15eQz7gB0a1ljsvMJFTJI4lJPDmI4Ost48FvOONqKGdxRUlHfc4uLX69GpKgWBAH6DgW6DAUe0b4tQtLycefPmabYGiX2QA4ROp0ORjVrHeIvirOFbjzdwNDvAi3U7Ez6+P9tAwYkAhUf85J/sLYTETDI2VxR9SCG3NvCBz1clUHQSdncUS1cMXVQlapHJagi971xmX4y8kwGsnhiqDHGjjKvGSl5t4LwfHpfj7+e28trG9WzcurlP4wiCIGjFbDajxtLneF+uaQRP/vQYMw656H51W8LHr/bDiwXwVCG8lN9bCHHE4IStt2DxSu4HP19WwajAcTvUWyCg6y1qbHufmniVH5rMvbtQzlhBr4I93nu0ZnVe37u0mP+2ho0vv8rOLVv7OJIgCII2MjMz8YX6WBlOoFjmUP7fH9dTc/AgLevWJXz8Yr+fXQUFbC0sZHd+PjVdXVhiMZpsNvx6PXtzP3gRklQVvaLQZLfjslgI63TYo1GOZ713ESry++k0m9mdl0eb1YqsqpjjcSa6XOzNy+vzGuT72994ffVq9uzY0ceRLo+4TWYAWb/6KeaZN2odAyWjiv96qpWjmX6erduudZy0l+/I5ssTryerR+Luu+9O2j3agiAIyfTimsfRDduldQxyTMN5+mcnmX7Ihf/lLVrHSXuW4nzM/34bLdlm7rrrLq3jCIIgXJb1Lz7OPKf219Iq2VV8/aEN1Bw8SPPatVrHSXsZZWWUf+lL9GRnc+edd6Z8fnFMZiAxpsEWV1MW33+hk+NZAZ49JQohF+OOkVdw9YQ5jBo5SusogiAIl82ocxDXOINVn8dLv6ln2hG3KIRcJMOdVzHyphWsqKzUOoogCMLlM6bBjTK2PL79t+3UHD4sCiEXqfiTn2TWLbdQrNENZuKYjJBQr7lq8NgUnj6V+G3JA5UxJjG0cqjWMQRBEPpEToNNbe4dhQz1xAi8KI4dXqygUUd5hbjKXRCE/k77RejpI3FKPR6a16zROkq/ETObNSuEgCiGDDAa/9/pGMKmvcf5e6s2Z776qy5dlBMnT2gdQxAEoY+0XYOyTTUc3ncK899e1TRHf+PwhThzqk7rGIIgCH2j8TFzJXMoB4/V0vnII5rm6G9kj4emxkbt5tdsZiHxJBkk7RrY/fOojTangrunS7MM/ZHDbqeptVnrGIIgCH0iS3q0/GTuyAtRRjd2E/V2a5ahP3I6nTScOaN1DEEQhL7RaXuRxMPr6imvryfi92uao7/Jyczk1PHjms0viiEDiRoHo0Z36zqGcPREPS+2HdBm/n6qIruIycYSVixZrnUUQRCEPlGUOAbZosnc2aYazpxpgufFjSiXwjGsEtfoUq5YtlTrKIIgCH0na9MOU8mspLG1nfbnntNk/v4qb/RogjU1zF+8WLMMohgy4GjwqZzRwZ8OWPFkQbPXlfr5+7FPj1rKrOkzxA0ygiAMCJIGa5BZzmTfUxGGt4cJd3hSPn9/Jn/6OmbPn6d1DEEQhATR4PW0OZPfvFJHSVMT4W6xM/FSFH760yxYtEjTDOI2mQEkFotBuCu1k9qK+dlmHU36Vn5XJ7omX4rxpTUMs+ZTXFSsdRRBEIQ+i0TDSEpqtwc7jaVs+UuA4qYmePiFlM7d32VPHUu4Ip+c3FytowiCIPSJqqpEQwEwRFM7cUYpP3r2ODlNzbT85S+pnbufK583D11xMZmZmZrmEMWQAWL/3l2MzHBDSE3dpLKRn2/Rc0Ry86ejr6du3gFifslYaqqrtY4hCILQZ9t3bsU5tAtfCufUy2a2/jVI5ZEWwo+sTuHMA8T8CYydMF7rFIIgCH22fctmphTEIJDCSfVWfvx8LbmHDtP8+OMpnHhgsM6ezbSZM7WOIY7JDBTuDg9FkUMpnfOxxpG4CPCnE6IQcjkqdZmMGzNO6xiCIAh95vX66DYeSeGMMk2vlZHjDohCyGUKVRYyVBTkBUEYAPwBP85AKhtBy/zlAGS63aIQcpmMNTWaXqn7JrEzZIBY/cqrHC4pQY1GiUUiXLNwLMOk/RBO0vnp3DEc3XCWn7S/lpzxB4GyrALRK0QQhH5PVVVWv/QKQ45UAHFisQizl48j4NxHREnOXpEiywR2nzpC1c+eJIX7IQcUa2mh1hEEQRD6LBgM8vIrazhSVooai6FEo9y0bAZlkVqIJWeriFIwlrPPv4r8i18kZfzBwFGYHmuQKIYMENMzi4h99SEsRXk4hlfyapuPZwszKSsczs3TzMgdib3l5a/bQ7TmqcRa4wkdd7DIMNvIMNu0jiEIgtBniqJw44LRzM48BsYMFEs+/9i4nxavTOnQYYy8wkh78HBC59zyeAsTmgL44mINuhzm/Bx0VrPWMQRBEPosFotxw9Aiqjc9gy4rF7mkgqeeXUun0caIoSVcM84JnsTuGnnwhX0MbWjgjCrK8ZfDWV4OafKBsCiGDADRaBQpGkONxQicayFwroXc18CY5US+fi73n7WjN+Vy53UTyO3aDPFQn+ds6exmX7whAekHpwyLjQ63W+sYgiAIfeb3+7GY5N7r3cMe5LCHj44CDDYOeoO88lAzOkMmS1ZOoE3eikKkz3MGu8NIu473PfwgZcx20u4St78JgtD/Bfx+zKoKsRhxVytxVyvXALIzixPSBH5yyojRZOSOq6bj9B4CJdbnOUORGNHt2/sefpCyFxbS0dWldQxAFEMGhL07d5LT3EXLu74e8XjhTy8wRJLIWTSdP3d0o+Y6Kcop5faFJcjNmwDlMmaUMRqMHGs62/fwg5S7x4vdbtc6hiAIQp8dPriPCUUyvPtUZtTPOGsd42ZJhKxV/OnJXfjjNvILi5lyZRGNgR1czhoko0c2GPCdPJuI+INSqL0Th8OhdQxBEIQ+O3H4EKNCXt5d4lC8Hmr2rqNG1hEfO5m/PLqGmC2DsoJsbpo9BLn9Mncs6s3IehVvfX2fsw9W3c3NFKXJ+yBRDBkAOjs6Cb+y7cIPUFU6XttO6WtvbI1dNp0f1Z7DmJPH8CH5XDPMAz2NFz2fkjcBT3cdgUjfd5gMVrl2J7JB/PUTBKH/6+7uwSzXfcAjVMyBU3x2CmDMoNbv5Pk/7EfROakZWUru5Hb8sbaLnq/QPJmdrr2osb5/ujdYmXIyMUmih74gCP1fIBQifnjPhR+gxNEd2MmtgJydS70yhgceaQCzhYmjhrKoNAihroueL5Q5nO7W1ajiiMxls+bkYEiT3z/xbqyf6+7uJnbsDIFz794X8v5C7R3wt5cYCtgqSvAvn8KPTjkw6nL56NXjyO/aCOoHv8DcUm+gxSQKIX2xtHoqGRniUzlBEPq3jg43eXoPRLov7gkRHzUGH/fNAiwGtpzrZPNfQhiNWSy6aRyt8ofvWDyy0UNNWxDRLeTyWRdNJSdNmtcJgiBcrqZz58hrOYvac3HNupVON2Wd6/k4oCsqY7cc5X+PGjDpZO64egZOz74PHePJjceocLtJ5d01A03m/PlUVFVpHQMQxZB+7/D+/WScaOYiX4aex1/fBA82UWU2kT1nMn/1+FEys5g0sozFw6Pgfv+renccPk2neB/fJ05MlBQWax1DEAShT44dPsiU/Ph7j8hcjKCb2bluZucbiFgr+cOTewjGMxg1oZLCCUHcoffvCXLuZAuTm71c/F4S4d0CmTbGlpVqHUMQBKFPao8dYaynhehlPDfeco6JLeeYZLagDBvPnx57jZjJyqxJI5lVqoD3/Xsjtrm6yD0jSiF9EXM6KSkr0zoGIIoh/V5PlxfpdFOfxoiHwrhe20rpa5Axsor22SP53j4rNmset60YR2H3hrebDclGonE41PpBW6KFD5OtmBgyZIjWMQRBEPokFotijHX2bRAlirHnJP82BbAVseVsC6886MNmz2b+9eNw6TajvHEaXC+biasyvsMn+x5+EIvmZpCfn691DEEQhD5R4gpq67k+jaGGgkgHt3M7oK+sYdfuID/YocNpt3Lbsqlkew/y1o5Fg5UYQdoOvf8HxsLFMebnp03fKlEM6e9kHd2nEtfAx3esDvlYHcOtFnIWTeMfHd2EHQ4+evVEygIboXAK3tAB6lwX32NEeK+IGicajWI0GrWOIgiCcNniChBK4M1Y/hZm58HsQiMhSyF/fno3npCZZTdPotO4lRLrNLZ3biTY3J64OQchJRQmHo+j0+m0jiIIgnDZVFkm7mpN2HixM7VMpJZJFivxUZP4xxOvEzCYuOu6WeR6DxHLG0ug5Z/InX38EGCQi4XDqKqKlAbX64piSD8n62SCTYnfLBwPBGl/fgPFQObEUTzREyJkdzB1mokOfTjh8w02PTZYu34dK5Yu0zqKIAjCZdPJQNSf+IHjEcw9x7lnMsRtZfzh+b10Bc34RnZT6hONU/uqqDvG5vUbmL9oodZRBEEQLl80ghoMJHxYNRhA3rOZmwFp+Fj++cQ6AnojJSXHKPX7aU74jIOLs7ubbZs2MWvePK2jiGJIf6dKIJuMKOHI218DuvWQkaDXi137jlK07yhZk0fzx+PHiC8Yivgp0DfN/k7i2aL9nyAI/ZuiSiDJoL7d9FRRVNq9IQqzLAmZQ+c/xz2TIGYt5a6fPssnezLec5W8cGmUlg7UuFiDBEHo5/QGkCR4x80kiqrijinkGxKz8009cYhbAIaP5SuvHmWpO4G7IQepYHMzWVqHeIO4V62fc7W28ewIB88WwJOF4DFAvQUOX+AY1j8+pGfn/gw4eoFrnz17jjB27Bhcga4+ZRbAIMmE/UGtYwiCIPSJy9XGv//xOP/vLwf58kP7OecKsPtUJy/tev9yxT2/2v2B4z29tZFX9rz/c/WBRiZOGE/EJbYn91XcpMMXEmuQIAj9m6vdxXfcCj9q7OY7DT6aI3EO+mO83vX+u9j/86z3A8d72RNivfcCO+BPHGLCuLH0tIn23X0lWSx4/UnYVXoZxM6Qfi6CSuXcaYz59Wq8OhWPAc5Y4ZwZJnrB8sE3FPJMASx19f77q3kw5I3XRmtzoCgMWVHY6+zdbVIQhlxZpjNwcddXCRc2q3Akx/Yf0zqGIAhCn/iDUfIqxvCNhRGaO4M0ugNsP97BvroubpxdSqbtg/siff3PB/nqyhEA3P/EcaYNywbgZ0+fYFR5BmV5Vh7f3IiqqgwvdYCpiHDnB7+YFT6cYe5Ejh07xrXXXqt1FEEQhMvWEw5TPHEqn2rYTVskTktEYY8/whF/jCuzFDL0H/y5/w/PdfPZIhsAv23xM8FuAOD3rX6GWfQUG3W80BlCBarMOlRJJij6hfRZzvz57D12jBUrVmgdRewM6e8++tGPYs52sn7uELZk9x6NqQxAdeDDCyEXsjm79w/GqB7YkgVGBcwKnLOAGotRmJGd0O9hsJlTOQ59Sw9f+tKXtI4iCILQJ3fccQeKzsJ/PdnKw6+cpjDLzIzhOcwdnfuhhZDlz8lkAAAkzUlEQVQL+f3q0+h1MssmF/HQK2ewmXQ4LAb2nvKgxKJYCvMS/F0MLjkLp3M40CnWIEEQ+r1P3HUXMauDn0cd/NMVJM8gM8lmYJrD8KGFkAv5e3sAvQQLnCb+6QpglSXsOolD/hhqPI69sDDB38XgUrF8OSdcLr74xS9qHQUQxZB+7y9/+Quf+dznuPrOjzKnE17LvbTny0BcgtA7jtWN80GTGYIyKBLM8MAiNwwJQIU1k5L4Bc7RCB/KpDfy+ZqlfOxjH8NuF7+PgiD0b3/5y1/4zD2fY+HyG/nU8qH89KkTcAnN4XWyRDSu4AtE3/ratdOLOXimiy5/hLii8PFFQ7jvhmFMH/7/27vz8CjP897j31k1mpE0o31DC9qQkECsAgQCIVYDNuB4j1Nns9s4qa/YzjlO3Fw5SZzmtDk9PafnNG3TJm2d0yZxk8ZObMcYDHFsjA2YHYOEkAAJsUho32ZGs5w/iMEEmU0zktD8Pn+hmXfu+34voXneued5nyeR9OQ4PAXpYTiTyGCOceB+bB2f+9zniIqKGutyRERG5Pnnn+cLX/oSyz9xHw+lRPODc/0YbmIQMhnAH4Q+/+U1R1bG2zg64KPHF8AfhHuSbDyWamdWjIVYs4mYkpJwnEpEsLlcOD/9aR599NFxs5uZbpO5zVVVVfGtb32Lnu4eetOMzGsPEOuDOgeU9sJ7Llj1kXV+eszwy983NPP7obAfXk2FxMvrrxLng5oLsCkFFnXAaylgDl48vjzo4J0296ie40SybMoc7NF2bDbbWJciIjJiH45Bvb29xHpO8UfLckhx2th2oJ475qTz460neebeyxeO5zvd/NcfHQCgcmoSS6Yl861//4DJqY5Lx6TF2/jyhiL++38c5dFV+XznZ0eIshhZVJrMyuoS/n5PPTmjfqYTQ8qqhQSdTsxmXf6JyO2vqqqKb3/72/T29GA90889iTaSLEbeOe+lxhnFz9sH+WL65S8f24YCfKe5F4A5MRbmx1r5ny19ZEdd/mCebDHy+TQH//dsPw8lR/M3Z/qxGgxUxFqoLp7MTxsaR/08J4rcNWtITEzEaBw/8zEMweBHlt+V29bWTZvo/vRzuM+Hd4XjtGULeLumgG82vxLWPBPVoxXr+GzpCuZXzBvrUkREQubNbVtZbNkM/jBvvZ5Uzl/+7AQ5f/5CePNMUMlfuJ9JX7yfktLSsS5FRCRk3ty2leIffAvCvEtWdM06/k9TN/6//Muw5pmoip54gsonnyQ3N3esS7lk/LRlZET8Qz4c2eGfOnxu67uYDSZKM/LCnmuiyU5Io6Z0LmfPaFNIEZlYAgEf2EZho7wLhzA7oonJywp/rgkmJj+bvOr5NBxvGOtSRERCyu/1YkoK/1oeg7/bRJTTSWy6bte8WcklJZRWVXH08OGxLuUKaoZMENl5kzEuKh+VXEGvl/zEzFHJNVGsKK7gn2v+lJa3D2MeJ/fIiYiESlJSMm3B0RgXAhgDfmILc0ch18SRtqqKqO9/hZeaj2K06BYZEZlYYmNj8RSPwucgvw+Dz0dCQUH4c00QBoOBgtWryfne99jW3IzJemuLq4eLRsQJori0lMb5tWHPEz+njJ6+PuIc6qPdqE9MW8zSYA4HDhzgySefHFf3yYmIhMK08plsfe0UNfYwJ3JOpvF0LcUpsWFONHEkP7iGuqoi3EeO8PTTT491OSIiITdnQSXbTjdRFuY8ltJZHGtsIDNLsxNvVNFnP0tvRQUdjY08+eSTY13OVdQMmUCsDjvJdyym7bW3Qh7blpJI1HOfZ9e5JipnlfCrne+GPMdEdM+0ah4rXIY1ysqSxUvGuhwRkbAwGAxgtuGLycPcF4bF5axOtnUU87s3jzK1YiZNbx/GGfosE07KJ9fAp9cyzWJl0ZLFY12OiEhYGI1GArFOjIWlBOo/CH385HT2LlzLroZTLFpUxoV3doY8x0RU9MgjJD/wANkWCwuXjM/PQWqGTCBLV6/kNz29GLbsIOjzhSxu9sN38fZUF6lxXSxeYyIrJonNO25i78QIVJSSzVenbSDNmcjy5csvflAQEZnAFlcv462tbmqiTwChW5s9kDqP7/z8GN32Wv59YDfPTJpCv9GvZsg1xJYWYvgvD2JKS6Z62TKNQSIy4VUtreGd/l7KQtwMsa66m79t6aHC6Obrq1LoS5rB97a/pw/R15A6cyZpTz5JfHo61cuXj3U516Tf4wRiMpmwxTqwlBfTsWfki9NkbljBocWTaRzsY96dcfhjm/H4oX/oPK7oWOgMQdETkM0SxbPTN7JxxVri4uLGuhwRkVFhtVoxWe1gT4GB8yOO50uYzve3ddDWdYQ99nY2N+wFoOFCCxku3SbzccwOOzzzSWo+sR67Pdz3LYmIjA/R0dEY41yYUtLxt458swLL4lX8ZCiagdZ+vrQyD1egHXwQY/JgjtMY9HGi4uJIe/pp1txzD1FRUWNdznWpGTLBGAwGLCO8SLS64nD9xRfZ3FxHzfIYfHFt+Gm+9HzfUCuOqOiRlhoyNksU906rptSaSowlmh5vPyfoYjDow4qJlr52Dpyux2AwcLY7vFsPT8ss4ItTVlFeVKpGiIhEHAOAeYQfwM12dnnn8OILu2lM8fFi2w78gcvbJdadbyInJmVkOULIZI8m+b5VdJVMwuSIxt/TR1JDG0aPl4DVjK/5PF2HjgHgPtcW1lpcM6cSfHwDBbPK1QgRkYhjDILBETOyGM546lY9yNbDR3n8jkKS/Ocg0H75gMEurLFxeEdYa6hYHQ7yH3iAQHEx5uhoPN3dRDc2gtdL0GKh/+RJ2o4cIRgI0N/aGtZaMubOJfGxxyibM+e2aISAmiETTjAQxDiC6bCpf/0UOwfbiLe0sfxPHHj8V2/B5wt6MBnGx44olZOn88WCFaQlJrN44SLMZjM+n493d73H4KCbqCgrvX19mKbcQSAQoMvdh9c3hNvr4Sen32X7iYPDxnXZY9kwdSF5uGg29PHjfZvx+K5+20t0OFmSP4PshFRmWzJxRscwb85cUpLHz4W6iMhoCQaDv++I3JoDhkW8/OZReqwH+X9DB2it7bjqmJ7BPkzm8bGtYVJ1Be7H1mHLSGf9ooWYTCaGhobY/e67eN0eLFFWPL19OE0m/IEgts5ufF4ffrcb249fp3Pn8GOQNcFFwoYa2nOTSGhspe0nrxLwDl11XFRyAolL5hKVlU77nDxMsQ7mLJhPYlJSuE9dRGTcCQQD3PIgZDRyauPn2XbiNFMGOvnGmgzwDTPDxO/FaB4fH6Fzli8n/nOfIzUzk4rKSkwmEx6Ph93vvYfX4yEqKoq+nh4yzGYCgQDujg78Ph++wUHaf/hDzu/bN2xcR3IyOevX487Oxlpfz7Gf/pTAMEswxKSmMqm6GkdGBsGKCqwxMcxftAiXyxXmMw+d8fGblJAJGn5/MXoTjDYbed/8Am/SwYWobhbdZ2XA14bHP/zx/oAXo3FsZ4YsLZzFp7IXkWiPY8XSZURHX67HbDZTVbnomq/3+/04/zOa1v4ujrU2AWA0GFlbVkmJPZ3ZcTmkpKSwaH4l7+7ayT058+jo6+F0VytnhrpxBC0UJWSSHJcABhjs7adyYSWpKalhPW8RkfEsEOTmlwsxWrkQv5h/fGU/Q+Y6/s2zn8aWlo89vN/rxmAd24Z88qqFeB5cRjDByaqVK674BsxisVC5+NqLlXq9Xl5JjMPxtQ76T5wGwGAykbGumoGp2XhnFhCdmsrdCyvZ+dZ2kh9cjre9C3fzOaznOxl0WLEV5RKdFE8AaOntYfmKFbfVBaiISKgFDAZuehCyOxi882H+s6GF5J4unlmRAp5e+LjlFwO+Md8ZcvLatTjvvx+by8XyO+7A/JHmTFRUFIuus1ip2+3mN4mJDD79ND2/H29NFguF69ZBSQnRM2eSmp7O3PnzeefNN0l++GEG2tvpaWrC0NqKz+HAVVxMdHw8RoOB9u5u1q5bh8PhCOt5h4OaIROMx+8jsTAbtt7Ybi+5j97H9nw7p6PaWPSJZHoCJxjwXftNxBcYxIgdk9F0xdTl0TJjUhGfy6zivvX3YLFYbimGyWRi9YqV9Bi8fGHb37EkfwYPT1pIUoyT6aVl5E3Ou3RsVeXCS//2+Xy0trYSHx9/RQNGRERgwO1hyOziRt+ZvSkL+KuX6unyvcteRwe/PT78t1Qfdb6nA2PGrb33h0LCvHI6P7WCu++/94oL0JthtVqpql7C+0/20P/E/yBh2XzcDy1jIMFF2YxysnJzLh1bufTyRe3Q0BBtbW0kJCRgs9lGfC4iIhNJv8cLCSlwov6GjjevuY9/OttD0pkzPLEqB3P/uYuNkGvx9mOwWkNQ7a3Jqa4m+sEHufOBBzCZbu2LAZvNxrzKSnjqKQ4+/TTZq1fjvP9+ohMSKJ81i8xJky4du3jZskv/HhoaorW1laSkpNvmNpjrUTNkgjGaTJhXzYd/eOGax6V94X52TbJywuCn8q54PPZGegLX+eP/vQA+fEE/eUkZ1Lc2X/8FIfbHU5azaumKW26EfCghPoGC+Aw+v3A9axzF3HXXXdd9UzGbzWRkZIwor4jIRBUIQEswm1yu3dQ4Fz2HH73RyKDvMEec/fyqbscN5+gY6CHg8xOdmcpgy8gXar1Z/s+t5Y47195yI+RDyampWEpyKfrSwzQtLWXDhg3X/bbRYrFoDBIR+Rg+n4/OaRXE7nnnmsd1r7yHX7UPwLluvrCyiDjPGeg/d2NJPN2YTCbsiYkMtLdf//gQMhgMOD/1KVZv3HjLjZAPZWZlEVNaSuVXvkLv/Pls3LjxhsagzMzMEeUdb8Z2jo+E3Ny5c/EmxJL8pQeGfT550RySnv9v/DbJz6rPT2b2QwN47I03n8jgZ3pa/girvXkrplSQYU8gKTE090O7khJY5ywlvyB/xG8qIiKRbsH8+bR0G2iLnj78Aa5C3hys4vuv1rE/aYC/OP/GTTVCPjTocRM3o2SE1d685LuWEpWZQmyIFshOiI3j/MoZlBQVjfm0axGR213VwoWctrlwz68Z9nnrjHkcvPdxftnWx1fWl/GVxTEXGyE3yef1kFJePtJyb1rBvffizMwM2QLZrthYhhYvpqykJGLHIM0MmWBSUlNJSU3lpfPniT94nM633r/i+X13TiPe3ETNYzbODx645TxrHpjB2X/47UjLvWkbMmax+DrrgdyMOTNmMWfGrJDFExGJZJfGoBcvsCYzG/NA00eeNfKdVzpoMTbxy8EDXNjfdct57LMn835rN1kjrvjmeNbOZ+mSa68HcjNmzasIWSwRkUiXkpZGSloav2xvZ1FrC4HGustP2uz8fdDFrKF2nqlJgI6rN4m4UQ+tW8S/tpyBbdtCUPWNs69eTdXSpSGLV1FZGbJYt6vIbAFFgDvv3sjQn96NNd556TGjzYbJZCF7YRce/43dEvNx+o31JDtcGA2j91+oMCWL7NgULRAnIjLOrVl7F1tb88D0kXuKzVE4ou38Y/PvuNDXNaL4mxp2Q7JrRDFulmv6FCzpSbflAnEiIpFkzfr17K28Ez6ytofR6cQWHc2qXB/4PSOKnxHViyExcaRl3pTMefOwJSZiHcP1SiYiNUMmKJPJRGFhITF/+xWSa+YDUPTEwwwOdREkMOL43d7TGI0GlhfOGXGsG/WJvAUUFRaOWj4REbk1VquV3Nw8dngWEoi7uCC1L30Jpzpu8J7s69hydBd+AyQuGb2ZFYYNSyid/jG3/4iIyLhhs9nIzMqi/p7HMU+ZBoBh2Xosvv7QJLhQj9FqJWPu3NDEuwFxd97J7HnzRi1fpNBtMhNYSfl0MnNz2BFjJ33jErpNRlLjh4CRdUM/tO7eCk786JWQxLoRDqwkJiSMWj4REbl1JWXT6ZqUxbb3TOQ6szl3rJM+pxE6QhM/YVY+R5ovkPq70MS7niGHBWe8a3SSiYjIiEybPYeO3Fx2WaLIKpnLsQu9VJfnEqpBaGXVTLadPAm7d4ck3vUYHI6QrVcll2lmyAQX53Sy+q51xM6eyn8cfo/SyuSQxfY5WkiLcmIwGEIW81pePb+fd3a+Nyq5RERk5FyueFauXkuvKY0fv7iNJnOIvpUDdnU00p8UE7J41xP9+h7e/d1bo5ZPRERGJiExidV33kVfThFb9h5kalrotmWfme2gPyE+ZPGux7NlC9tHeY2SSKBmSISYs2A+a+5Yzbk98cQYQ7MlUuvgYZyxMXyypDok8a4nzuaAYHBUcomISOjMrpjPihUreSy3mlmTikIS81/efQVrXAwx9ywLSbzrMcfaMRq165iIyO1mXlUVNTXVvNESQyA6RLPMz+zD5nQxad260MS7DktsLAbtfBlyuk0mgty5bgNer5fNWzaBJR1nbjf9UfUjirnwPjsXvt/OmvwKftOwK0SVDq8msYSF8xaENYeIiITH/ffei9vtxmwycz6vk9dbD/Or2ndGFHOr4xyduTEsXFZBz9bwjkF91eVULw7dbmYiIjJ67rnvAQYGBtj0xhbsplhmp3iJ9ZwdUcyvbCzjax0dFC5ZwpnfhfeeTdOiRSxcsiSsOSKRZoZEGKvVyrq1d7Fu5X30NqRhMY5sVfzuoZPc86eFTL0QTc3kGaEpchipcQmUJmSTOMorN4uISOjYbDbuXr+BLzz4GR7OXYjLHjuieJuP7qQh38Jb01OIWxi+bdIduZOwFmQRGzuyekVEZOzY7XbW3bWepWvu5t3zdjCNbGcWY2cjz312KfWzZpEexsVNk4qLicnJITo6Omw5IpWaIREsM3MS1r68Ece54KnlgS9PY05XPCmx4Vng9M6iBcRr4ToRkQkjLTmVe0tH/i3Xzw9s5UJ5PDurJmNxhWdxuZi1VaSmpYUltoiIjC6DwYA9Jo7BmJwRx7J21fONz6zg9NKlWGPCs45V8h13kJs38s9scjU1QyJYfl4hp2o7QxKrzXOQeYumcV9qeLbanRc3mQUV88MSW0RERl9RQSFR3UMhifUv771CRlEungeWhiTeHxqoKKJ8dvhmnoiIyOjKzp3M4ROtIYnl6D5KWXE+CQ88EJJ4fyh63jxKSkvDEjvSqRkSIZqbm/nyl7/M1772NZ566imam5vZvXs3B/YcHfb4bz+yhbd/3Xjp579/9l22/fz4NXP8etNPcHYFLy50GmJ5KZnsfv/9kMcVEZHwG24M2rNnD81Hhx9Xcnd04zztvvRz5v5e4pvcwx77ob/9+fPUJ1gxRdtCWjtATFY6h/bvD3lcEREJv+HGoP379/HW7kPDHr/yqz/l37cdvvTzo//rVf5504Fr5tj7zhu0JCdjsoRux5oP2eLjOXL48PUPlJumBVQjxPbt26moqOChhx7izJkznD59mi1vbOF4/Qnm9xTjiLvynrmUSTEcP9BO1V15dLQOXNrF5fjBC+zc3ARByMhzMqnAyaZ/qyNvagL+gJugIUDR8QAH4/14HaFZ8TjdmcSJ443MnVcRkngiIjK6hhuD3tj6BmdOnsaYHSBgufK7mSGHEXunj+5JYHYHLj0e3TlE3FkvAJ4YE55YE4kn3Aw6zbR7AniHvPx6SgwVR9wke0NTuz0rnbq6OpYuqQ5NQBERGVXDjUFvv/UWbc3H6VqYgivmyiZ6bpqT3bVn+WRNGWfaey9tZrmr7gwvvVNHMAhTshKZmp3E93+9h5kFqTDUDwE/7xcXk1dXh9MbmkHINXkydXV1FOg2mbDQzJAIcd9992EwGHjuuef44Q9/SFpaGiUlU5g+N/+qRgiAwWggLSeWsyd72L2lmbnLswCIS7Qxd1kWeWWJHN19HoD8sgTWfqYEgPfff4/Jk3JC1gjJTUzn63PvY/nKFUwvmxaSmCIiMrqGG4PyCwtx5aVf1QgBCGLA6zBh7fMTd9ZDT9rFccoXZaQnzcqgy4yj/eItNoMuM+35FxeV2757J5MK8kLWCHFMnoT9659l44YNTCkpDk1QEREZVcONQUVFBSyeVXxVIwTAaDBQkBlPfUsHv9pRz/rKi1vCp7gc3LWgiNlF6bx9qBmA2UVpfPnui1/Y7nx/N4VTpoSkEWIwGEjIz2fys8/yyYceIq+gYMQx5WqaGRIhnn/+edatW8eDDz7IuXPn+O53v8v0GdMwmg0f+5qKldlsf/kEQx4/eWWJ9HR4+O0vGphcEk9eWQL7ftcCgM1+eTrYxs/P5hf/sA/yb63OdGcS1fkzyTLFUR6fi8sRS1JSIjlZ2bcWUERExtxwY1BBcRG+oB8+ZhjqTrfiavFg9MOgy4jZ6ye+yY3baWbQaSb2/MWLzcBHxjHr3Bz27TlD+S3WaZ+URuKSOXRmJWIqL8Qa6yA1NVWLp4qI3MaGG4PKphZDMPCxr9m4cAo/+e0HuL0+Zhem0dY1wL++foAZ+anMLkzjtV0NAMTYLn+p/Myn1/IXP3qFhbdYpysnh8yqKrxZWcTNmEGUw0F6RgYJ2k0zbNQMiRBVVVV885vfxG63MzAwwCOPPMLxxjr2vHaUokVTefPFBjb+cdkVr0lMs3PuVC/zV19uRCSl2zl+qJ2TtZ0EAsGr8iQVeEhPTye/20eDc/CGastOSGNNQQXTbOnkxKdhtpgpL51Gmi4+RUQmhOHGoA/qjtJW24Q5L4ir2c2FQvsVr/FFm7D2++nJiLr02FC0ieguH7Ye37A9lDfba3k4YwoHs7uY3tR7Q7U5Jk8iZnUlvdNyceRkELBYWFA+neSUlJGcsoiIjBPDjUEnGo7x69cOcUeZix9vOcQz9y+44jWZSbEcb+nknsWXZwVmpcSx+9hZ9je2Eghc3UiZkeglMyuLU+np5Jw9e0O1JRQUkLZqFYZp04jNzMRms1FaXk5ycvLITlpuiCEYDF79iVYiwuYtr2OY8jb+YIjmE//enhec7HC085vDO655XKYrma/O+gS5MSkkJyczd9ZsjEbduSUiEgle3fQad7/6Xby+0Owo86E/T19DxW+P0/rmzmse58jJJOq/PowhO4XUtHSmz5qpMUhEJEK8sXkTNfajQGg/Cv/1W4O4fv0yp3deewxKyM8n6+mnsWVlkZGZyfQZMzAYPn7GvoSHZoZEqKbmU5jju/CGuBECkJaRQErnwDWPmZczlS8XrmbRgoVMysgMeQ0iIjJ+Ha2rZVf78ZA3QgACUWYC2dee1ZGwaBYDX9zArOolpGgWoohIRKk98gEFsR7wh35OgMVkIDYvD67RDMmuqSHu0UepWraMJM0AGVNqhkSoI0c+ILqsEXyhjz1vURHv/FPdsM+ZjSYeKK/hDmcpG9etJyoqatjjRERk4jre0MA/HXo9LLETpkyitvYMScM8Z7RYSHl4Le3LZ7LhE3djCcMWiCIiMr41NTWx3NUSltjzZk1lR93w28abrFamPPIIhqVL2XjvvZhModlwQm6dmiERqPl0E9b4fgZ9nWGJb4obIN4UPexzf7PsTyiKz2RZ9VJNBRMRiUC19XUc6DrF2e4LYYnfavZgtJuvboYYDCT/3TNY87K4Z+kSjUEiIhGorvYIBS4f+NxhiT+rMI1XrRb+sM1hMBqZ/YMf4MzNpXKJxqDxQjfHRqCDBw9gndQUtvhdnhMkxDivevzpynspis9k+dIavQGIiESo2tpa/rV2W9ji72g8hC3l6pX30776WWz5WSyqqdYYJCISoRobGsg13tjiprfC7L6APenqW1+mf+MbJOTns7BaY9B4omZIhNm5+11cOW76hs6HLUe39ww2i5Uo8+Wtpr40dz3L06exfGlN2PKKiMj49tu332J753Ea2k6HLcfb9fuxxNgxfGQx1JSn/whzzWwWLq0OW14RERnfdmx/ixkpfvD2hS9J12miXK4rHpr+9a+TVFXF/Kqq8OWVW6JmSAQZHBykrbOFvpg9Yc4UYNA9SEXO1EuPrM6YwerlK8OcV0RExquenh6OnT3FX7/3i7Dmcfu8uPsHcc0sAcBgMhFcPofFy5eFNa+IiIxf7e0XMHq6SPU0hjeRbwCv201KWRkAZpuNmMpKqmr0hfB4pGZIBGlpOU1cxiBBrt4XO9RWrJ/BzIH4Sz+3D/Wx/+CBsOcVEZHxqf74cfb2nSIYDP3q/X/IWZbD8fn5AAT9foztPdR+cCTseUVEZHw6fuwY01JH56PvsgXT8SxeDIDf6yXQ20t9be2o5Jabo2bIBOL1ennhhRd4/fXhV+g/33YOW9zo1NIfu5d0mxOr+eJK/d/b9yLNp5oIBMLfiBERkdE3ODh4cQzavHnY5y90ttM1dO1t10Plr/a+hCcr+dKtMkP/+2fUNzZoDBIRmaD6+vp44YUX2LZt67DPDw72E0Xot3MfzuyEbryZmQAEAwHOfP/71B0/PipfBsjNUTNkAjl7toWsySn4A76r/tjcbjcd7e30Gobf6inUvIFe/MEgpel5AKTGxNPf388vX3pxVPKLiMjoOnHqJI60BPx+/1Vj0MDAAD29vbxW+97o1NLeAoEAsYW5ABiT4xkcHOTlF18alfwiIjK6TjedpLwoB6936KoxqLe3F6/bjbGneXSK6W/DaDTgzM4GwBQfj8fj4dcv6nPQeKNmyASSkzMZAhaCAfjpT3/K9u3baW+/wLH6Wja9/irxM08yFBgctXpsdhNTE7IA2Fa/h9f6ajFHWUYtv4iIjJ6pxSVEB80EA4GLY9A722lvb+fQ4cO8vOlVnjv0n/S6R2dmCIA7MET09CIALmzaTuqmvdhttlHLLyIio6d4ahltPYP4fD5+9rOfsWPHDjo7OznywSHefnMby1IuQMA3egX5hkidMQOAky+/TPA3v8Fut49efrkhhqDm60w4Xq+XY/V1nGg8yYkTJ8jJySG54hQ93vCt3v8hi9HOUODixW4qlbzxH/Uk5qST4Uzmg8OHMRgMPPXUU2GvQ0RExobX6+WDIx/QdKqJkydPUlBQwFf3/oTDZxrCnjvBEUdHfw8Azy3/LPZTfRTEJxOVkcKhQ4ewWq088cQTYa9DRETGhsfjoa72CI0nTnHy5EmmTp3K8sQWGOwMf3KzDXxuAJqjy3jp7SNkx8djT0vj4MGDOBwOHn/88fDXITdMzZAIcPp0E3v2vU9KeTvdwfqQx4/1TaWrIZZoi4tgMIjNNYQtpQtvp4PNv9zHn/3Zn2E2m4GLF8lWq/U6EUVEZKKobzzO3v37+Oemt9lctyvk8T8zYzXVScVkxicz5PfR7O5kV0cDM1y5dBxs5Nlnn8VkMgEag0REIk1jQz31tUepTh/AMnAm5PFbrZP5oN2E0RZDwO8nK85Apt1Dc7+NX2x5l2effRbj79ev0hg0/qgZEiECgQCbt7yGMa4P1+RuOjwNwM3/6p3WLKzGOPqGzjDo7yRuqIzexmRWrliNwWAA4ODhA/T09GCzRTFnVkWIz0RERG43fr+fl197hcNdLWxtO8ybx/bedAyDwcD8yWWkxyayu+kozZ3neXzuXdyZNYfVKy5v3b5r7x76+3qxOxzMmz03lKchIiK3oaGhIba8/ho5sQGKXR4MvS23FCcYm07QHI2xvxW8fTSb82jxJVK5qOrSMQf27aGvrx+Xy0nptPJQnYKEiZohEebc+XMcPLQfz1A/zqxBbEn9dHtP4w96rvm6GNMkuj/IwmyIwmyxEPAH8Ab68PQHqVm6DJcr/pqvFxERaTnTwp79++jo72FvXxOHu5vZdfII/Z5rr2e1OL+cP85bTowtGovFTMAf5HxvB7agifVr78ThcIzSGYiIyO2q5fRpjh45BENuSpINpEd7offcddcS8dgzeOusHaPZgtVqwecLYBgaoL3Pw/oNGzXb4zamZkiECgaD1B8/RlPTSQxGM0P+fvyWTsxWIwYjJE0O0n3GiG8ogDnooLfVzOqVd1zxx/7hjgEf3gIjIiJyIwKBAEfrajlx6iRWs4XW3k6OdbTgNwTwG4LUey+QZ00i4PeTGZtEvj2ZtXesuWK88fkuXrxqDBIRkZsRCASoqz3KubMtYDDB0ABxhgGMBoi2mihMtnL8gheCMBCM4tyAkZWrVl+65RIujkEGg+GKx+T2o2aIXNLb24vbPYjP7+fI0UNMzs3HYY/B4/WQnZU91uWJiMgE1tPTg8fjweP1sP/wIaYWFWOPjmbIN0TWpKyxLk9ERCawrq4u/H4/gwMD1B87SkFRMWazhWAgQEZm5liXJ2GiZoiIiIiIiIiIRBTjWBcgIiIiIiIiIjKa1AwRERERERERkYiiZoiIiIiIiIiIRBQ1Q0REREREREQkoqgZIiIiIiIiIiIRRc0QEREREREREYkoaoaIiIiIiIiISERRM0REREREREREIoqaISIiIiIiIiISUdQMEREREREREZGIomaIiIiIiIiIiEQUNUNEREREREREJKKoGSIiIiIiIiIiEUXNEBERERERERGJKGqGiIiIiIiIiEhEUTNERERERERERCKKmiEiIiIiIiIiElHUDBERERERERGRiKJmiIiIiIiIiIhEFDVDRERERERERCSiqBkiIiIiIiIiIhFFzRARERERERERiShqhoiIiIiIiIhIRFEzREREREREREQiipohIiIiIiIiIhJR1AwRERERERERkYjy/wH2QDDexqP+0wAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1400x980 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(ncols=3, nrows=2, figsize=(20, 14))\n",
-    "\n",
-    "outcomes_to_plot = [\n",
-    "    'tot_exposed_asset',\n",
-    "    'total_consumption_loss',\n",
-    "    'annual_average_consumption_loss_pct',\n",
-    "    'n_new_poor_increase_pp',\n",
-    "    'r',\n",
-    "    'new_poverty_gap_all']\n",
-    "\n",
-    "titles = ['Total exposed asset stock (MM)',\n",
-    "          'Total consumption loss (MM)',\n",
-    "          # 'Wt. Ann. Avg. Consump. Loss p.c. (%)',\n",
-    "          'Annual Average Consumption Loss PC (%)',\n",
-    "          'New Poor Increase (p.p.)',\n",
-    "          'Socio-Economic Resilience',\n",
-    "          'New Poverty Gap All Poor']\n",
-    "\n",
-    "cmaps = ['OrRd', 'YlOrRd', 'OrRd', 'RdYlGn_r', 'RdYlGn', 'OrRd']\n",
-    "\n",
-    "k = 4\n",
-    "for i, outcome in enumerate(outcomes_to_plot):\n",
-    "    aggregated_outcomes.plot(column=outcome, scheme='fisher_jenks', cmap=cmaps[i], k=k, ax=ax[i//3, i % 3], legend=True, legend_kwds={\"loc\": \"upper left\"})\n",
-    "    aggregated_outcomes.plot(ax=ax[i//3, i % 3], color='none', edgecolor='black', linewidth=0.25)\n",
-    "    ax[i//3, i % 3].set_title(titles[i])\n",
-    "    ax[i//3, i % 3].axis('off')\n",
-    "    for idx, row in aggregated_outcomes.iterrows():\n",
-    "        ax[i//3, i % 3].annotate(text=row['NAME'], xy=row['geometry'].centroid.coords[0], ha='center', fontsize=8)\n",
-    "\n",
-    "# fig.suptitle(f'{aggregation.capitalize()} of 3,000 replications', fontsize=16)\n",
-    "\n",
-    "fig.tight_layout()\n",
-    "if savefig:\n",
-    "    plt.savefig(figures_folder +\n",
-    "        f'{aggregation}_outcomes_spatially.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A bivariate or bi-variate choropleth helps to visualise relations between two variables spatially. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load asset damage data\n",
-    "# asset_damage = pd.read_excel('../data/processed/asset_damage/Saint Lucia.xlsx')\n",
-    "# asset_damage['district'].replace({'AnseLaRayeCanaries' : 'Anse-La-Raye & Canaries'}, inplace=True)\n",
-    "# gdf = pd.merge(aggregated_outcomes, asset_damage, left_on='NAME_1', right_on='district', how='left')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# x_name = 'r'\n",
-    "# y_name = 'pml'\n",
-    "# x_label = 'Soc.-econ. res.'\n",
-    "# y_label = 'PML'\n",
-    "\n",
-    "# tab = bivariate_choropleth(gdf.copy(), x_name, y_name, x_label, y_label, scheme='fisher_jenks', figsize=(7,6), return_table=True)\n",
-    "# if savefig:\n",
-    "#     plt.savefig('../reports/figures/analysis/bivariate_choropleth.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# nine_quadrants_plot(gdf.copy(), x_name, y_name)\n",
-    "# plt.xlabel('Socio-Economic Resilience')\n",
-    "# plt.ylabel('PML')\n",
-    "# if savefig:\n",
-    "#     plt.savefig('../reports/figures/analysis/9_quadrants.png', dpi=300, bbox_inches='tight')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ubk",
-   "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": "3.9.0"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/analysis/Saint Lucia/bussiness-as-usual.ipynb b/analysis/Saint Lucia/bussiness-as-usual.ipynb
deleted file mode 100644
index 2ea2cd8..0000000
--- a/analysis/Saint Lucia/bussiness-as-usual.ipynb	
+++ /dev/null
@@ -1,2702 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Business-as-usual analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import geopandas as gpd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from tqdm import tqdm\n",
-    "from ema_workbench import load_results\n",
-    "from unbreakable.analysis.analyzer import *\n",
-    "from unbreakable.analysis.visualizer import *\n",
-    "sns.set_theme(style=\"ticks\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Parameters for report writing\n",
-    "savefig = True\n",
-    "savetab = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load results\n",
-    "n_scenarios = 3000\n",
-    "n_policies = 0\n",
-    "results = load_results(f'../experiments/scenarios={n_scenarios}, policies={n_policies}.tar.gz')\n",
-    "\n",
-    "# Prepare outcomes\n",
-    "outcomes = prepare_outcomes(results, add_uncertainties=False, add_policies=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## EDA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Single scenario, single district "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_14470\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_14470_level0_col0\" class=\"col_heading level0 col0\" >total_population</th>\n",
-       "      <th id=\"T_14470_level0_col1\" class=\"col_heading level0 col1\" >total_asset_loss</th>\n",
-       "      <th id=\"T_14470_level0_col2\" class=\"col_heading level0 col2\" >total_consumption_loss</th>\n",
-       "      <th id=\"T_14470_level0_col3\" class=\"col_heading level0 col3\" >tot_exposed_asset</th>\n",
-       "      <th id=\"T_14470_level0_col4\" class=\"col_heading level0 col4\" >tot_asset_surv</th>\n",
-       "      <th id=\"T_14470_level0_col5\" class=\"col_heading level0 col5\" >expected_loss_frac</th>\n",
-       "      <th id=\"T_14470_level0_col6\" class=\"col_heading level0 col6\" >n_affected_people</th>\n",
-       "      <th id=\"T_14470_level0_col7\" class=\"col_heading level0 col7\" >annual_average_consumption</th>\n",
-       "      <th id=\"T_14470_level0_col8\" class=\"col_heading level0 col8\" >poverty_line_adjusted</th>\n",
-       "      <th id=\"T_14470_level0_col9\" class=\"col_heading level0 col9\" >district_pml</th>\n",
-       "      <th id=\"T_14470_level0_col10\" class=\"col_heading level0 col10\" >n_poor_initial</th>\n",
-       "      <th id=\"T_14470_level0_col11\" class=\"col_heading level0 col11\" >n_poor_affected</th>\n",
-       "      <th id=\"T_14470_level0_col12\" class=\"col_heading level0 col12\" >n_new_poor</th>\n",
-       "      <th id=\"T_14470_level0_col13\" class=\"col_heading level0 col13\" >initial_poverty_gap</th>\n",
-       "      <th id=\"T_14470_level0_col14\" class=\"col_heading level0 col14\" >new_poverty_gap_initial</th>\n",
-       "      <th id=\"T_14470_level0_col15\" class=\"col_heading level0 col15\" >new_poverty_gap_all</th>\n",
-       "      <th id=\"T_14470_level0_col16\" class=\"col_heading level0 col16\" >annual_average_consumption_loss</th>\n",
-       "      <th id=\"T_14470_level0_col17\" class=\"col_heading level0 col17\" >annual_average_consumption_loss_pct</th>\n",
-       "      <th id=\"T_14470_level0_col18\" class=\"col_heading level0 col18\" >mean_recovery_rate</th>\n",
-       "      <th id=\"T_14470_level0_col19\" class=\"col_heading level0 col19\" >r</th>\n",
-       "      <th id=\"T_14470_level0_col20\" class=\"col_heading level0 col20\" >n_new_poor_increase_pp</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >district</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "      <th class=\"blank col9\" >&nbsp;</th>\n",
-       "      <th class=\"blank col10\" >&nbsp;</th>\n",
-       "      <th class=\"blank col11\" >&nbsp;</th>\n",
-       "      <th class=\"blank col12\" >&nbsp;</th>\n",
-       "      <th class=\"blank col13\" >&nbsp;</th>\n",
-       "      <th class=\"blank col14\" >&nbsp;</th>\n",
-       "      <th class=\"blank col15\" >&nbsp;</th>\n",
-       "      <th class=\"blank col16\" >&nbsp;</th>\n",
-       "      <th class=\"blank col17\" >&nbsp;</th>\n",
-       "      <th class=\"blank col18\" >&nbsp;</th>\n",
-       "      <th class=\"blank col19\" >&nbsp;</th>\n",
-       "      <th class=\"blank col20\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row0\" class=\"row_heading level0 row0\" >Anse-La-Raye & Canaries</th>\n",
-       "      <td id=\"T_14470_row0_col0\" class=\"data row0 col0\" >8,666.91</td>\n",
-       "      <td id=\"T_14470_row0_col1\" class=\"data row0 col1\" >1,272,573.26</td>\n",
-       "      <td id=\"T_14470_row0_col2\" class=\"data row0 col2\" >902,783.85</td>\n",
-       "      <td id=\"T_14470_row0_col3\" class=\"data row0 col3\" >47,442,177.05</td>\n",
-       "      <td id=\"T_14470_row0_col4\" class=\"data row0 col4\" >334,331,195.84</td>\n",
-       "      <td id=\"T_14470_row0_col5\" class=\"data row0 col5\" >0.03</td>\n",
-       "      <td id=\"T_14470_row0_col6\" class=\"data row0 col6\" >670.29</td>\n",
-       "      <td id=\"T_14470_row0_col7\" class=\"data row0 col7\" >1,591.22</td>\n",
-       "      <td id=\"T_14470_row0_col8\" class=\"data row0 col8\" >914.27</td>\n",
-       "      <td id=\"T_14470_row0_col9\" class=\"data row0 col9\" >1,282,936.18</td>\n",
-       "      <td id=\"T_14470_row0_col10\" class=\"data row0 col10\" >3,292.00</td>\n",
-       "      <td id=\"T_14470_row0_col11\" class=\"data row0 col11\" >147.00</td>\n",
-       "      <td id=\"T_14470_row0_col12\" class=\"data row0 col12\" >7.00</td>\n",
-       "      <td id=\"T_14470_row0_col13\" class=\"data row0 col13\" >32.85</td>\n",
-       "      <td id=\"T_14470_row0_col14\" class=\"data row0 col14\" >33.06</td>\n",
-       "      <td id=\"T_14470_row0_col15\" class=\"data row0 col15\" >33.00</td>\n",
-       "      <td id=\"T_14470_row0_col16\" class=\"data row0 col16\" >134.69</td>\n",
-       "      <td id=\"T_14470_row0_col17\" class=\"data row0 col17\" >8.40</td>\n",
-       "      <td id=\"T_14470_row0_col18\" class=\"data row0 col18\" >0.15</td>\n",
-       "      <td id=\"T_14470_row0_col19\" class=\"data row0 col19\" >0.26</td>\n",
-       "      <td id=\"T_14470_row0_col20\" class=\"data row0 col20\" >0.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row1\" class=\"row_heading level0 row1\" >Castries</th>\n",
-       "      <td id=\"T_14470_row1_col0\" class=\"data row1 col0\" >70,959.09</td>\n",
-       "      <td id=\"T_14470_row1_col1\" class=\"data row1 col1\" >32,014,222.78</td>\n",
-       "      <td id=\"T_14470_row1_col2\" class=\"data row1 col2\" >23,240,494.87</td>\n",
-       "      <td id=\"T_14470_row1_col3\" class=\"data row1 col3\" >1,587,405,663.34</td>\n",
-       "      <td id=\"T_14470_row1_col4\" class=\"data row1 col4\" >3,435,721,049.12</td>\n",
-       "      <td id=\"T_14470_row1_col5\" class=\"data row1 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row1_col6\" class=\"data row1 col6\" >4,450.91</td>\n",
-       "      <td id=\"T_14470_row1_col7\" class=\"data row1 col7\" >6,213.45</td>\n",
-       "      <td id=\"T_14470_row1_col8\" class=\"data row1 col8\" >2,976.86</td>\n",
-       "      <td id=\"T_14470_row1_col9\" class=\"data row1 col9\" >31,062,668.40</td>\n",
-       "      <td id=\"T_14470_row1_col10\" class=\"data row1 col10\" >15,948.00</td>\n",
-       "      <td id=\"T_14470_row1_col11\" class=\"data row1 col11\" >934.00</td>\n",
-       "      <td id=\"T_14470_row1_col12\" class=\"data row1 col12\" >264.00</td>\n",
-       "      <td id=\"T_14470_row1_col13\" class=\"data row1 col13\" >34.78</td>\n",
-       "      <td id=\"T_14470_row1_col14\" class=\"data row1 col14\" >35.07</td>\n",
-       "      <td id=\"T_14470_row1_col15\" class=\"data row1 col15\" >34.57</td>\n",
-       "      <td id=\"T_14470_row1_col16\" class=\"data row1 col16\" >522.15</td>\n",
-       "      <td id=\"T_14470_row1_col17\" class=\"data row1 col17\" >7.80</td>\n",
-       "      <td id=\"T_14470_row1_col18\" class=\"data row1 col18\" >0.18</td>\n",
-       "      <td id=\"T_14470_row1_col19\" class=\"data row1 col19\" >2.32</td>\n",
-       "      <td id=\"T_14470_row1_col20\" class=\"data row1 col20\" >0.37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row2\" class=\"row_heading level0 row2\" >Choiseul</th>\n",
-       "      <td id=\"T_14470_row2_col0\" class=\"data row2 col0\" >5,475.87</td>\n",
-       "      <td id=\"T_14470_row2_col1\" class=\"data row2 col1\" >1,092,350.60</td>\n",
-       "      <td id=\"T_14470_row2_col2\" class=\"data row2 col2\" >857,413.48</td>\n",
-       "      <td id=\"T_14470_row2_col3\" class=\"data row2 col3\" >54,354,802.37</td>\n",
-       "      <td id=\"T_14470_row2_col4\" class=\"data row2 col4\" >377,890,219.98</td>\n",
-       "      <td id=\"T_14470_row2_col5\" class=\"data row2 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row2_col6\" class=\"data row2 col6\" >459.05</td>\n",
-       "      <td id=\"T_14470_row2_col7\" class=\"data row2 col7\" >1,932.86</td>\n",
-       "      <td id=\"T_14470_row2_col8\" class=\"data row2 col8\" >926.75</td>\n",
-       "      <td id=\"T_14470_row2_col9\" class=\"data row2 col9\" >1,111,847.84</td>\n",
-       "      <td id=\"T_14470_row2_col10\" class=\"data row2 col10\" >923.00</td>\n",
-       "      <td id=\"T_14470_row2_col11\" class=\"data row2 col11\" >66.00</td>\n",
-       "      <td id=\"T_14470_row2_col12\" class=\"data row2 col12\" >23.00</td>\n",
-       "      <td id=\"T_14470_row2_col13\" class=\"data row2 col13\" >26.55</td>\n",
-       "      <td id=\"T_14470_row2_col14\" class=\"data row2 col14\" >27.03</td>\n",
-       "      <td id=\"T_14470_row2_col15\" class=\"data row2 col15\" >26.75</td>\n",
-       "      <td id=\"T_14470_row2_col16\" class=\"data row2 col16\" >186.78</td>\n",
-       "      <td id=\"T_14470_row2_col17\" class=\"data row2 col17\" >10.35</td>\n",
-       "      <td id=\"T_14470_row2_col18\" class=\"data row2 col18\" >0.21</td>\n",
-       "      <td id=\"T_14470_row2_col19\" class=\"data row2 col19\" >0.38</td>\n",
-       "      <td id=\"T_14470_row2_col20\" class=\"data row2 col20\" >0.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row3\" class=\"row_heading level0 row3\" >Dennery</th>\n",
-       "      <td id=\"T_14470_row3_col0\" class=\"data row3 col0\" >11,590.78</td>\n",
-       "      <td id=\"T_14470_row3_col1\" class=\"data row3 col1\" >2,582,707.75</td>\n",
-       "      <td id=\"T_14470_row3_col2\" class=\"data row3 col2\" >1,757,962.40</td>\n",
-       "      <td id=\"T_14470_row3_col3\" class=\"data row3 col3\" >125,372,604.01</td>\n",
-       "      <td id=\"T_14470_row3_col4\" class=\"data row3 col4\" >417,528,403.13</td>\n",
-       "      <td id=\"T_14470_row3_col5\" class=\"data row3 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row3_col6\" class=\"data row3 col6\" >719.51</td>\n",
-       "      <td id=\"T_14470_row3_col7\" class=\"data row3 col7\" >3,383.57</td>\n",
-       "      <td id=\"T_14470_row3_col8\" class=\"data row3 col8\" >1,934.66</td>\n",
-       "      <td id=\"T_14470_row3_col9\" class=\"data row3 col9\" >2,667,414.34</td>\n",
-       "      <td id=\"T_14470_row3_col10\" class=\"data row3 col10\" >5,216.00</td>\n",
-       "      <td id=\"T_14470_row3_col11\" class=\"data row3 col11\" >240.00</td>\n",
-       "      <td id=\"T_14470_row3_col12\" class=\"data row3 col12\" >0.00</td>\n",
-       "      <td id=\"T_14470_row3_col13\" class=\"data row3 col13\" >23.52</td>\n",
-       "      <td id=\"T_14470_row3_col14\" class=\"data row3 col14\" >23.71</td>\n",
-       "      <td id=\"T_14470_row3_col15\" class=\"data row3 col15\" >23.71</td>\n",
-       "      <td id=\"T_14470_row3_col16\" class=\"data row3 col16\" >244.33</td>\n",
-       "      <td id=\"T_14470_row3_col17\" class=\"data row3 col17\" >6.95</td>\n",
-       "      <td id=\"T_14470_row3_col18\" class=\"data row3 col18\" >0.18</td>\n",
-       "      <td id=\"T_14470_row3_col19\" class=\"data row3 col19\" >1.15</td>\n",
-       "      <td id=\"T_14470_row3_col20\" class=\"data row3 col20\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row4\" class=\"row_heading level0 row4\" >Gros Islet</th>\n",
-       "      <td id=\"T_14470_row4_col0\" class=\"data row4 col0\" >27,467.06</td>\n",
-       "      <td id=\"T_14470_row4_col1\" class=\"data row4 col1\" >11,475,774.48</td>\n",
-       "      <td id=\"T_14470_row4_col2\" class=\"data row4 col2\" >9,128,584.33</td>\n",
-       "      <td id=\"T_14470_row4_col3\" class=\"data row4 col3\" >532,061,766.39</td>\n",
-       "      <td id=\"T_14470_row4_col4\" class=\"data row4 col4\" >3,294,339,146.51</td>\n",
-       "      <td id=\"T_14470_row4_col5\" class=\"data row4 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row4_col6\" class=\"data row4 col6\" >1,874.12</td>\n",
-       "      <td id=\"T_14470_row4_col7\" class=\"data row4 col7\" >2,841.95</td>\n",
-       "      <td id=\"T_14470_row4_col8\" class=\"data row4 col8\" >1,040.60</td>\n",
-       "      <td id=\"T_14470_row4_col9\" class=\"data row4 col9\" >10,932,607.53</td>\n",
-       "      <td id=\"T_14470_row4_col10\" class=\"data row4 col10\" >3,245.00</td>\n",
-       "      <td id=\"T_14470_row4_col11\" class=\"data row4 col11\" >322.00</td>\n",
-       "      <td id=\"T_14470_row4_col12\" class=\"data row4 col12\" >77.00</td>\n",
-       "      <td id=\"T_14470_row4_col13\" class=\"data row4 col13\" >15.39</td>\n",
-       "      <td id=\"T_14470_row4_col14\" class=\"data row4 col14\" >16.07</td>\n",
-       "      <td id=\"T_14470_row4_col15\" class=\"data row4 col15\" >15.97</td>\n",
-       "      <td id=\"T_14470_row4_col16\" class=\"data row4 col16\" >487.09</td>\n",
-       "      <td id=\"T_14470_row4_col17\" class=\"data row4 col17\" >13.26</td>\n",
-       "      <td id=\"T_14470_row4_col18\" class=\"data row4 col18\" >0.23</td>\n",
-       "      <td id=\"T_14470_row4_col19\" class=\"data row4 col19\" >0.59</td>\n",
-       "      <td id=\"T_14470_row4_col20\" class=\"data row4 col20\" >0.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row5\" class=\"row_heading level0 row5\" >Laborie</th>\n",
-       "      <td id=\"T_14470_row5_col0\" class=\"data row5 col0\" >6,828.32</td>\n",
-       "      <td id=\"T_14470_row5_col1\" class=\"data row5 col1\" >2,070,266.41</td>\n",
-       "      <td id=\"T_14470_row5_col2\" class=\"data row5 col2\" >1,480,484.98</td>\n",
-       "      <td id=\"T_14470_row5_col3\" class=\"data row5 col3\" >99,235,698.32</td>\n",
-       "      <td id=\"T_14470_row5_col4\" class=\"data row5 col4\" >215,695,044.06</td>\n",
-       "      <td id=\"T_14470_row5_col5\" class=\"data row5 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row5_col6\" class=\"data row5 col6\" >356.67</td>\n",
-       "      <td id=\"T_14470_row5_col7\" class=\"data row5 col7\" >5,123.26</td>\n",
-       "      <td id=\"T_14470_row5_col8\" class=\"data row5 col8\" >2,964.26</td>\n",
-       "      <td id=\"T_14470_row5_col9\" class=\"data row5 col9\" >2,035,118.22</td>\n",
-       "      <td id=\"T_14470_row5_col10\" class=\"data row5 col10\" >1,599.00</td>\n",
-       "      <td id=\"T_14470_row5_col11\" class=\"data row5 col11\" >48.00</td>\n",
-       "      <td id=\"T_14470_row5_col12\" class=\"data row5 col12\" >46.00</td>\n",
-       "      <td id=\"T_14470_row5_col13\" class=\"data row5 col13\" >23.26</td>\n",
-       "      <td id=\"T_14470_row5_col14\" class=\"data row5 col14\" >23.40</td>\n",
-       "      <td id=\"T_14470_row5_col15\" class=\"data row5 col15\" >22.89</td>\n",
-       "      <td id=\"T_14470_row5_col16\" class=\"data row5 col16\" >415.09</td>\n",
-       "      <td id=\"T_14470_row5_col17\" class=\"data row5 col17\" >8.12</td>\n",
-       "      <td id=\"T_14470_row5_col18\" class=\"data row5 col18\" >0.18</td>\n",
-       "      <td id=\"T_14470_row5_col19\" class=\"data row5 col19\" >2.23</td>\n",
-       "      <td id=\"T_14470_row5_col20\" class=\"data row5 col20\" >0.67</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row6\" class=\"row_heading level0 row6\" >Micoud</th>\n",
-       "      <td id=\"T_14470_row6_col0\" class=\"data row6 col0\" >17,435.51</td>\n",
-       "      <td id=\"T_14470_row6_col1\" class=\"data row6 col1\" >4,547,183.01</td>\n",
-       "      <td id=\"T_14470_row6_col2\" class=\"data row6 col2\" >3,430,551.07</td>\n",
-       "      <td id=\"T_14470_row6_col3\" class=\"data row6 col3\" >231,995,156.10</td>\n",
-       "      <td id=\"T_14470_row6_col4\" class=\"data row6 col4\" >554,020,360.02</td>\n",
-       "      <td id=\"T_14470_row6_col5\" class=\"data row6 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row6_col6\" class=\"data row6 col6\" >1,090.08</td>\n",
-       "      <td id=\"T_14470_row6_col7\" class=\"data row6 col7\" >6,056.25</td>\n",
-       "      <td id=\"T_14470_row6_col8\" class=\"data row6 col8\" >2,698.00</td>\n",
-       "      <td id=\"T_14470_row6_col9\" class=\"data row6 col9\" >4,766,541.74</td>\n",
-       "      <td id=\"T_14470_row6_col10\" class=\"data row6 col10\" >5,433.00</td>\n",
-       "      <td id=\"T_14470_row6_col11\" class=\"data row6 col11\" >311.00</td>\n",
-       "      <td id=\"T_14470_row6_col12\" class=\"data row6 col12\" >0.00</td>\n",
-       "      <td id=\"T_14470_row6_col13\" class=\"data row6 col13\" >38.18</td>\n",
-       "      <td id=\"T_14470_row6_col14\" class=\"data row6 col14\" >38.36</td>\n",
-       "      <td id=\"T_14470_row6_col15\" class=\"data row6 col15\" >38.36</td>\n",
-       "      <td id=\"T_14470_row6_col16\" class=\"data row6 col16\" >314.71</td>\n",
-       "      <td id=\"T_14470_row6_col17\" class=\"data row6 col17\" >5.49</td>\n",
-       "      <td id=\"T_14470_row6_col18\" class=\"data row6 col18\" >0.20</td>\n",
-       "      <td id=\"T_14470_row6_col19\" class=\"data row6 col19\" >2.12</td>\n",
-       "      <td id=\"T_14470_row6_col20\" class=\"data row6 col20\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row7\" class=\"row_heading level0 row7\" >Soufriere</th>\n",
-       "      <td id=\"T_14470_row7_col0\" class=\"data row7 col0\" >8,985.05</td>\n",
-       "      <td id=\"T_14470_row7_col1\" class=\"data row7 col1\" >1,598,252.32</td>\n",
-       "      <td id=\"T_14470_row7_col2\" class=\"data row7 col2\" >1,276,709.60</td>\n",
-       "      <td id=\"T_14470_row7_col3\" class=\"data row7 col3\" >82,756,712.06</td>\n",
-       "      <td id=\"T_14470_row7_col4\" class=\"data row7 col4\" >535,928,432.44</td>\n",
-       "      <td id=\"T_14470_row7_col5\" class=\"data row7 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row7_col6\" class=\"data row7 col6\" >660.19</td>\n",
-       "      <td id=\"T_14470_row7_col7\" class=\"data row7 col7\" >2,953.40</td>\n",
-       "      <td id=\"T_14470_row7_col8\" class=\"data row7 col8\" >994.91</td>\n",
-       "      <td id=\"T_14470_row7_col9\" class=\"data row7 col9\" >1,679,254.31</td>\n",
-       "      <td id=\"T_14470_row7_col10\" class=\"data row7 col10\" >2,295.00</td>\n",
-       "      <td id=\"T_14470_row7_col11\" class=\"data row7 col11\" >71.00</td>\n",
-       "      <td id=\"T_14470_row7_col12\" class=\"data row7 col12\" >27.00</td>\n",
-       "      <td id=\"T_14470_row7_col13\" class=\"data row7 col13\" >31.47</td>\n",
-       "      <td id=\"T_14470_row7_col14\" class=\"data row7 col14\" >31.77</td>\n",
-       "      <td id=\"T_14470_row7_col15\" class=\"data row7 col15\" >31.51</td>\n",
-       "      <td id=\"T_14470_row7_col16\" class=\"data row7 col16\" >193.39</td>\n",
-       "      <td id=\"T_14470_row7_col17\" class=\"data row7 col17\" >10.55</td>\n",
-       "      <td id=\"T_14470_row7_col18\" class=\"data row7 col18\" >0.25</td>\n",
-       "      <td id=\"T_14470_row7_col19\" class=\"data row7 col19\" >0.45</td>\n",
-       "      <td id=\"T_14470_row7_col20\" class=\"data row7 col20\" >0.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_14470_level0_row8\" class=\"row_heading level0 row8\" >Vieuxfort</th>\n",
-       "      <td id=\"T_14470_row8_col0\" class=\"data row8 col0\" >15,229.37</td>\n",
-       "      <td id=\"T_14470_row8_col1\" class=\"data row8 col1\" >4,147,397.04</td>\n",
-       "      <td id=\"T_14470_row8_col2\" class=\"data row8 col2\" >2,771,272.12</td>\n",
-       "      <td id=\"T_14470_row8_col3\" class=\"data row8 col3\" >216,534,648.76</td>\n",
-       "      <td id=\"T_14470_row8_col4\" class=\"data row8 col4\" >1,266,550,258.12</td>\n",
-       "      <td id=\"T_14470_row8_col5\" class=\"data row8 col5\" >0.02</td>\n",
-       "      <td id=\"T_14470_row8_col6\" class=\"data row8 col6\" >1,262.16</td>\n",
-       "      <td id=\"T_14470_row8_col7\" class=\"data row8 col7\" >2,121.05</td>\n",
-       "      <td id=\"T_14470_row8_col8\" class=\"data row8 col8\" >1,101.52</td>\n",
-       "      <td id=\"T_14470_row8_col9\" class=\"data row8 col9\" >4,264,030.49</td>\n",
-       "      <td id=\"T_14470_row8_col10\" class=\"data row8 col10\" >5,272.00</td>\n",
-       "      <td id=\"T_14470_row8_col11\" class=\"data row8 col11\" >648.00</td>\n",
-       "      <td id=\"T_14470_row8_col12\" class=\"data row8 col12\" >0.00</td>\n",
-       "      <td id=\"T_14470_row8_col13\" class=\"data row8 col13\" >23.35</td>\n",
-       "      <td id=\"T_14470_row8_col14\" class=\"data row8 col14\" >24.31</td>\n",
-       "      <td id=\"T_14470_row8_col15\" class=\"data row8 col15\" >24.31</td>\n",
-       "      <td id=\"T_14470_row8_col16\" class=\"data row8 col16\" >219.56</td>\n",
-       "      <td id=\"T_14470_row8_col17\" class=\"data row8 col17\" >11.20</td>\n",
-       "      <td id=\"T_14470_row8_col18\" class=\"data row8 col18\" >0.18</td>\n",
-       "      <td id=\"T_14470_row8_col19\" class=\"data row8 col19\" >0.46</td>\n",
-       "      <td id=\"T_14470_row8_col20\" class=\"data row8 col20\" >0.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x24d8683fac0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Print all outcomes for a single scenario\n",
-    "scenario = 0\n",
-    "df = outcomes[outcomes['scenario'] == scenario].copy()\n",
-    "df.set_index('district', inplace=True)\n",
-    "df.drop(columns=['weighted_vuln_quint', 'weighted_vuln_dec'], inplace=True)\n",
-    "numeric_columns = df.columns[3:-1].tolist()\n",
-    "df[numeric_columns].round(2).style.format(\"{:,.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "District:  Dennery\n",
-      "Total population:  11591\n",
-      "Number of affected people:  720\n",
-      "\n",
-      "Number of poor:  5216\n",
-      "Number of new poor:  0\n",
-      "Percentage of poor: 45.0%\n",
-      "Percentage of new poor: 45.0%\n",
-      "Percentage of increase in poor: 0.0%\n",
-      "Initial poverty gap:  23.52\n",
-      "New poverty gap initial poor:  23.71\n",
-      "New poverty gap all poor:  23.71\n",
-      "\n",
-      "Annual average consumption:  3384\n",
-      "Annual average consumption loss:  244\n",
-      "Annual average consumption loss percentage: 7%\n",
-      "\n",
-      "Socio-economic resilience:  1.15\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's examine a single scenario in details\n",
-    "scenario = 0\n",
-    "df = outcomes[outcomes['scenario'] == scenario].copy()\n",
-    "df.set_index('district', inplace=True)\n",
-    "\n",
-    "# And a single district as an example\n",
-    "district = 'Dennery'\n",
-    "print('District: ', district)\n",
-    "total_population = round(df.loc[district, 'total_population'])\n",
-    "n_affected = round(df.loc[district, 'n_affected_people'])\n",
-    "print('Total population: ', total_population)\n",
-    "print('Number of affected people: ', n_affected)\n",
-    "\n",
-    "n_poor_initial = round(df.loc[district, 'n_poor_initial'])\n",
-    "n_new_poor = round(df.loc[district, 'n_new_poor'])\n",
-    "pct_poor_initial = round(n_poor_initial / total_population * 100, 2)\n",
-    "pct_new_poor = round((n_new_poor + n_poor_initial) / total_population * 100, 2)\n",
-    "pct_poor_increase = round(n_new_poor / total_population * 100, 2)\n",
-    "initial_poverty_gap = round(df.loc[district, 'initial_poverty_gap'], 2)\n",
-    "new_poverty_gap_init = round(df.loc[district, 'new_poverty_gap_initial'], 2)\n",
-    "new_poverty_gap_all = round(df.loc[district, 'new_poverty_gap_all'], 2)\n",
-    "print()\n",
-    "print('Number of poor: ', n_poor_initial)\n",
-    "print('Number of new poor: ', n_new_poor)\n",
-    "print(f'Percentage of poor: {pct_poor_initial}%')\n",
-    "print(f'Percentage of new poor: {pct_new_poor}%')\n",
-    "print(f'Percentage of increase in poor: {pct_poor_increase}%')\n",
-    "print('Initial poverty gap: ', initial_poverty_gap)\n",
-    "print('New poverty gap initial poor: ', new_poverty_gap_init)\n",
-    "print('New poverty gap all poor: ', new_poverty_gap_all)\n",
-    "\n",
-    "annual_average_consumption = round(df.loc[district, 'annual_average_consumption'])\n",
-    "annual_average_consumption_loss = round(df.loc[district, 'annual_average_consumption_loss'])\n",
-    "annual_average_consumption_loss_pct = round(df.loc[district, 'annual_average_consumption_loss_pct'])\n",
-    "print()\n",
-    "\n",
-    "print('Annual average consumption: ', annual_average_consumption)\n",
-    "print('Annual average consumption loss: ', annual_average_consumption_loss)\n",
-    "print(f'Annual average consumption loss percentage: {annual_average_consumption_loss_pct}%')\n",
-    "\n",
-    "print()\n",
-    "r = round(df.loc[district, 'r'], 2)\n",
-    "print('Socio-economic resilience: ', r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "annotated_hist(outcomes, savefig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Print all outcomes for a single scenario\n",
-    "district = 'Dennery'\n",
-    "df = outcomes[outcomes['district'] == district].copy()\n",
-    "df.drop(columns=['years_in_poverty'], inplace=True)\n",
-    "columns_of_interest = ['total_consumption_loss', \n",
-    "                       'n_affected_people', 'annual_average_consumption', 'n_new_poor', 'n_poor_affected',\n",
-    "                       'annual_average_consumption_loss', 'annual_average_consumption_loss_pct', 'r', 'n_new_poor_increase_pp']\n",
-    "# sns.pairplot(df[columns_of_interest])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple scenarios & multiple districts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "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>total_population</th>\n",
-       "      <th>total_asset_loss</th>\n",
-       "      <th>total_consumption_loss</th>\n",
-       "      <th>tot_exposed_asset</th>\n",
-       "      <th>tot_asset_surv</th>\n",
-       "      <th>expected_loss_frac</th>\n",
-       "      <th>n_affected_people</th>\n",
-       "      <th>annual_average_consumption</th>\n",
-       "      <th>poverty_line_adjusted</th>\n",
-       "      <th>district_pml</th>\n",
-       "      <th>...</th>\n",
-       "      <th>n_poor_affected</th>\n",
-       "      <th>n_new_poor</th>\n",
-       "      <th>initial_poverty_gap</th>\n",
-       "      <th>new_poverty_gap_initial</th>\n",
-       "      <th>new_poverty_gap_all</th>\n",
-       "      <th>annual_average_consumption_loss</th>\n",
-       "      <th>annual_average_consumption_loss_pct</th>\n",
-       "      <th>mean_recovery_rate</th>\n",
-       "      <th>r</th>\n",
-       "      <th>n_new_poor_increase_pp</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>district</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Anse-La-Raye &amp; Canaries</th>\n",
-       "      <td>8666.91</td>\n",
-       "      <td>1270937.52</td>\n",
-       "      <td>789722.66</td>\n",
-       "      <td>4.744218e+07</td>\n",
-       "      <td>3.343312e+08</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>813.41</td>\n",
-       "      <td>1591.22</td>\n",
-       "      <td>914.27</td>\n",
-       "      <td>1282936.18</td>\n",
-       "      <td>...</td>\n",
-       "      <td>303.88</td>\n",
-       "      <td>8.92</td>\n",
-       "      <td>32.85</td>\n",
-       "      <td>33.32</td>\n",
-       "      <td>33.26</td>\n",
-       "      <td>100.25</td>\n",
-       "      <td>6.99</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Castries</th>\n",
-       "      <td>70959.09</td>\n",
-       "      <td>31002211.14</td>\n",
-       "      <td>22356466.01</td>\n",
-       "      <td>1.587406e+09</td>\n",
-       "      <td>3.435721e+09</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>4181.27</td>\n",
-       "      <td>6213.45</td>\n",
-       "      <td>2976.86</td>\n",
-       "      <td>31062668.40</td>\n",
-       "      <td>...</td>\n",
-       "      <td>935.76</td>\n",
-       "      <td>190.42</td>\n",
-       "      <td>34.78</td>\n",
-       "      <td>35.07</td>\n",
-       "      <td>34.76</td>\n",
-       "      <td>555.22</td>\n",
-       "      <td>8.57</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>2.74</td>\n",
-       "      <td>0.27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Choiseul</th>\n",
-       "      <td>5475.87</td>\n",
-       "      <td>1111672.14</td>\n",
-       "      <td>881561.84</td>\n",
-       "      <td>5.435480e+07</td>\n",
-       "      <td>3.778902e+08</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>373.11</td>\n",
-       "      <td>1932.86</td>\n",
-       "      <td>926.75</td>\n",
-       "      <td>1111847.84</td>\n",
-       "      <td>...</td>\n",
-       "      <td>63.61</td>\n",
-       "      <td>35.99</td>\n",
-       "      <td>26.55</td>\n",
-       "      <td>27.24</td>\n",
-       "      <td>26.74</td>\n",
-       "      <td>242.18</td>\n",
-       "      <td>12.71</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>0.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dennery</th>\n",
-       "      <td>11590.78</td>\n",
-       "      <td>2661935.28</td>\n",
-       "      <td>1800903.36</td>\n",
-       "      <td>1.253726e+08</td>\n",
-       "      <td>4.175284e+08</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>755.03</td>\n",
-       "      <td>3383.57</td>\n",
-       "      <td>1934.66</td>\n",
-       "      <td>2667414.34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>342.67</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>23.52</td>\n",
-       "      <td>23.76</td>\n",
-       "      <td>23.74</td>\n",
-       "      <td>251.95</td>\n",
-       "      <td>6.96</td>\n",
-       "      <td>0.17</td>\n",
-       "      <td>1.14</td>\n",
-       "      <td>0.07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gros Islet</th>\n",
-       "      <td>27467.06</td>\n",
-       "      <td>10861767.64</td>\n",
-       "      <td>8479973.05</td>\n",
-       "      <td>5.320618e+08</td>\n",
-       "      <td>3.294339e+09</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>2105.27</td>\n",
-       "      <td>2841.95</td>\n",
-       "      <td>1040.60</td>\n",
-       "      <td>10932607.53</td>\n",
-       "      <td>...</td>\n",
-       "      <td>204.18</td>\n",
-       "      <td>105.80</td>\n",
-       "      <td>15.39</td>\n",
-       "      <td>15.63</td>\n",
-       "      <td>15.43</td>\n",
-       "      <td>419.65</td>\n",
-       "      <td>11.04</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>0.94</td>\n",
-       "      <td>0.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Laborie</th>\n",
-       "      <td>6828.32</td>\n",
-       "      <td>2034941.68</td>\n",
-       "      <td>1444079.00</td>\n",
-       "      <td>9.923570e+07</td>\n",
-       "      <td>2.156950e+08</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>416.25</td>\n",
-       "      <td>5123.26</td>\n",
-       "      <td>2964.26</td>\n",
-       "      <td>2035118.22</td>\n",
-       "      <td>...</td>\n",
-       "      <td>98.13</td>\n",
-       "      <td>13.13</td>\n",
-       "      <td>23.26</td>\n",
-       "      <td>23.54</td>\n",
-       "      <td>23.40</td>\n",
-       "      <td>360.61</td>\n",
-       "      <td>6.49</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>2.48</td>\n",
-       "      <td>0.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Micoud</th>\n",
-       "      <td>17435.51</td>\n",
-       "      <td>4767004.48</td>\n",
-       "      <td>3366943.11</td>\n",
-       "      <td>2.319952e+08</td>\n",
-       "      <td>5.540204e+08</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>1094.11</td>\n",
-       "      <td>6056.25</td>\n",
-       "      <td>2698.00</td>\n",
-       "      <td>4766541.74</td>\n",
-       "      <td>...</td>\n",
-       "      <td>348.16</td>\n",
-       "      <td>10.97</td>\n",
-       "      <td>38.18</td>\n",
-       "      <td>38.42</td>\n",
-       "      <td>38.36</td>\n",
-       "      <td>320.57</td>\n",
-       "      <td>6.63</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>1.69</td>\n",
-       "      <td>0.06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Soufriere</th>\n",
-       "      <td>8985.05</td>\n",
-       "      <td>1676753.60</td>\n",
-       "      <td>1264334.97</td>\n",
-       "      <td>8.275671e+07</td>\n",
-       "      <td>5.359284e+08</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>606.83</td>\n",
-       "      <td>2953.40</td>\n",
-       "      <td>994.91</td>\n",
-       "      <td>1679254.31</td>\n",
-       "      <td>...</td>\n",
-       "      <td>151.84</td>\n",
-       "      <td>32.48</td>\n",
-       "      <td>31.47</td>\n",
-       "      <td>31.86</td>\n",
-       "      <td>31.58</td>\n",
-       "      <td>215.43</td>\n",
-       "      <td>10.59</td>\n",
-       "      <td>0.23</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.36</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Vieuxfort</th>\n",
-       "      <td>15229.37</td>\n",
-       "      <td>4231569.73</td>\n",
-       "      <td>2947342.80</td>\n",
-       "      <td>2.165346e+08</td>\n",
-       "      <td>1.266550e+09</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>1378.08</td>\n",
-       "      <td>2121.05</td>\n",
-       "      <td>1101.52</td>\n",
-       "      <td>4264030.49</td>\n",
-       "      <td>...</td>\n",
-       "      <td>447.52</td>\n",
-       "      <td>125.14</td>\n",
-       "      <td>23.35</td>\n",
-       "      <td>24.02</td>\n",
-       "      <td>23.55</td>\n",
-       "      <td>237.46</td>\n",
-       "      <td>9.75</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.82</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         total_population  total_asset_loss  \\\n",
-       "district                                                      \n",
-       "Anse-La-Raye & Canaries           8666.91        1270937.52   \n",
-       "Castries                         70959.09       31002211.14   \n",
-       "Choiseul                          5475.87        1111672.14   \n",
-       "Dennery                          11590.78        2661935.28   \n",
-       "Gros Islet                       27467.06       10861767.64   \n",
-       "Laborie                           6828.32        2034941.68   \n",
-       "Micoud                           17435.51        4767004.48   \n",
-       "Soufriere                         8985.05        1676753.60   \n",
-       "Vieuxfort                        15229.37        4231569.73   \n",
-       "\n",
-       "                         total_consumption_loss  tot_exposed_asset  \\\n",
-       "district                                                             \n",
-       "Anse-La-Raye & Canaries               789722.66       4.744218e+07   \n",
-       "Castries                            22356466.01       1.587406e+09   \n",
-       "Choiseul                              881561.84       5.435480e+07   \n",
-       "Dennery                              1800903.36       1.253726e+08   \n",
-       "Gros Islet                           8479973.05       5.320618e+08   \n",
-       "Laborie                              1444079.00       9.923570e+07   \n",
-       "Micoud                               3366943.11       2.319952e+08   \n",
-       "Soufriere                            1264334.97       8.275671e+07   \n",
-       "Vieuxfort                            2947342.80       2.165346e+08   \n",
-       "\n",
-       "                         tot_asset_surv  expected_loss_frac  \\\n",
-       "district                                                      \n",
-       "Anse-La-Raye & Canaries    3.343312e+08                0.03   \n",
-       "Castries                   3.435721e+09                0.02   \n",
-       "Choiseul                   3.778902e+08                0.02   \n",
-       "Dennery                    4.175284e+08                0.02   \n",
-       "Gros Islet                 3.294339e+09                0.02   \n",
-       "Laborie                    2.156950e+08                0.02   \n",
-       "Micoud                     5.540204e+08                0.02   \n",
-       "Soufriere                  5.359284e+08                0.02   \n",
-       "Vieuxfort                  1.266550e+09                0.02   \n",
-       "\n",
-       "                         n_affected_people  annual_average_consumption  \\\n",
-       "district                                                                 \n",
-       "Anse-La-Raye & Canaries             813.41                     1591.22   \n",
-       "Castries                           4181.27                     6213.45   \n",
-       "Choiseul                            373.11                     1932.86   \n",
-       "Dennery                             755.03                     3383.57   \n",
-       "Gros Islet                         2105.27                     2841.95   \n",
-       "Laborie                             416.25                     5123.26   \n",
-       "Micoud                             1094.11                     6056.25   \n",
-       "Soufriere                           606.83                     2953.40   \n",
-       "Vieuxfort                          1378.08                     2121.05   \n",
-       "\n",
-       "                         poverty_line_adjusted  district_pml  ...  \\\n",
-       "district                                                      ...   \n",
-       "Anse-La-Raye & Canaries                 914.27    1282936.18  ...   \n",
-       "Castries                               2976.86   31062668.40  ...   \n",
-       "Choiseul                                926.75    1111847.84  ...   \n",
-       "Dennery                                1934.66    2667414.34  ...   \n",
-       "Gros Islet                             1040.60   10932607.53  ...   \n",
-       "Laborie                                2964.26    2035118.22  ...   \n",
-       "Micoud                                 2698.00    4766541.74  ...   \n",
-       "Soufriere                               994.91    1679254.31  ...   \n",
-       "Vieuxfort                              1101.52    4264030.49  ...   \n",
-       "\n",
-       "                         n_poor_affected  n_new_poor  initial_poverty_gap  \\\n",
-       "district                                                                    \n",
-       "Anse-La-Raye & Canaries           303.88        8.92                32.85   \n",
-       "Castries                          935.76      190.42                34.78   \n",
-       "Choiseul                           63.61       35.99                26.55   \n",
-       "Dennery                           342.67        8.00                23.52   \n",
-       "Gros Islet                        204.18      105.80                15.39   \n",
-       "Laborie                            98.13       13.13                23.26   \n",
-       "Micoud                            348.16       10.97                38.18   \n",
-       "Soufriere                         151.84       32.48                31.47   \n",
-       "Vieuxfort                         447.52      125.14                23.35   \n",
-       "\n",
-       "                         new_poverty_gap_initial  new_poverty_gap_all  \\\n",
-       "district                                                                \n",
-       "Anse-La-Raye & Canaries                    33.32                33.26   \n",
-       "Castries                                   35.07                34.76   \n",
-       "Choiseul                                   27.24                26.74   \n",
-       "Dennery                                    23.76                23.74   \n",
-       "Gros Islet                                 15.63                15.43   \n",
-       "Laborie                                    23.54                23.40   \n",
-       "Micoud                                     38.42                38.36   \n",
-       "Soufriere                                  31.86                31.58   \n",
-       "Vieuxfort                                  24.02                23.55   \n",
-       "\n",
-       "                         annual_average_consumption_loss  \\\n",
-       "district                                                   \n",
-       "Anse-La-Raye & Canaries                           100.25   \n",
-       "Castries                                          555.22   \n",
-       "Choiseul                                          242.18   \n",
-       "Dennery                                           251.95   \n",
-       "Gros Islet                                        419.65   \n",
-       "Laborie                                           360.61   \n",
-       "Micoud                                            320.57   \n",
-       "Soufriere                                         215.43   \n",
-       "Vieuxfort                                         237.46   \n",
-       "\n",
-       "                         annual_average_consumption_loss_pct  \\\n",
-       "district                                                       \n",
-       "Anse-La-Raye & Canaries                                 6.99   \n",
-       "Castries                                                8.57   \n",
-       "Choiseul                                               12.71   \n",
-       "Dennery                                                 6.96   \n",
-       "Gros Islet                                             11.04   \n",
-       "Laborie                                                 6.49   \n",
-       "Micoud                                                  6.63   \n",
-       "Soufriere                                              10.59   \n",
-       "Vieuxfort                                               9.75   \n",
-       "\n",
-       "                         mean_recovery_rate     r  n_new_poor_increase_pp  \n",
-       "district                                                                   \n",
-       "Anse-La-Raye & Canaries                0.15  0.27                    0.10  \n",
-       "Castries                               0.18  2.74                    0.27  \n",
-       "Choiseul                               0.22  0.36                    0.66  \n",
-       "Dennery                                0.17  1.14                    0.07  \n",
-       "Gros Islet                             0.20  0.94                    0.39  \n",
-       "Laborie                                0.18  2.48                    0.19  \n",
-       "Micoud                                 0.19  1.69                    0.06  \n",
-       "Soufriere                              0.23  0.49                    0.36  \n",
-       "Vieuxfort                              0.18  0.60                    0.82  \n",
-       "\n",
-       "[9 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Let's aggregate across scenarios and take the mean\n",
-    "outcomes.iloc[:, 2:-1].groupby('district').mean(numeric_only=True).round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "coloured_density_plots(outcomes, savefig, scheme='fisher_jenks', k=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_06105\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_06105_level0_col0\" class=\"col_heading level0 col0\" colspan=\"7\">n_new_poor</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level1\" >&nbsp;</th>\n",
-       "      <th id=\"T_06105_level1_col0\" class=\"col_heading level1 col0\" >mean</th>\n",
-       "      <th id=\"T_06105_level1_col1\" class=\"col_heading level1 col1\" >std</th>\n",
-       "      <th id=\"T_06105_level1_col2\" class=\"col_heading level1 col2\" >min</th>\n",
-       "      <th id=\"T_06105_level1_col3\" class=\"col_heading level1 col3\" >25%</th>\n",
-       "      <th id=\"T_06105_level1_col4\" class=\"col_heading level1 col4\" >50%</th>\n",
-       "      <th id=\"T_06105_level1_col5\" class=\"col_heading level1 col5\" >75%</th>\n",
-       "      <th id=\"T_06105_level1_col6\" class=\"col_heading level1 col6\" >max</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >district</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row0\" class=\"row_heading level0 row0\" >Castries</th>\n",
-       "      <td id=\"T_06105_row0_col0\" class=\"data row0 col0\" >190.42</td>\n",
-       "      <td id=\"T_06105_row0_col1\" class=\"data row0 col1\" >202.70</td>\n",
-       "      <td id=\"T_06105_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
-       "      <td id=\"T_06105_row0_col4\" class=\"data row0 col4\" >120.00</td>\n",
-       "      <td id=\"T_06105_row0_col5\" class=\"data row0 col5\" >287.00</td>\n",
-       "      <td id=\"T_06105_row0_col6\" class=\"data row0 col6\" >1,495.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row1\" class=\"row_heading level0 row1\" >Vieuxfort</th>\n",
-       "      <td id=\"T_06105_row1_col0\" class=\"data row1 col0\" >125.14</td>\n",
-       "      <td id=\"T_06105_row1_col1\" class=\"data row1 col1\" >147.36</td>\n",
-       "      <td id=\"T_06105_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_06105_row1_col4\" class=\"data row1 col4\" >72.50</td>\n",
-       "      <td id=\"T_06105_row1_col5\" class=\"data row1 col5\" >202.00</td>\n",
-       "      <td id=\"T_06105_row1_col6\" class=\"data row1 col6\" >672.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row2\" class=\"row_heading level0 row2\" >Gros Islet</th>\n",
-       "      <td id=\"T_06105_row2_col0\" class=\"data row2 col0\" >105.80</td>\n",
-       "      <td id=\"T_06105_row2_col1\" class=\"data row2 col1\" >110.91</td>\n",
-       "      <td id=\"T_06105_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
-       "      <td id=\"T_06105_row2_col4\" class=\"data row2 col4\" >77.00</td>\n",
-       "      <td id=\"T_06105_row2_col5\" class=\"data row2 col5\" >174.00</td>\n",
-       "      <td id=\"T_06105_row2_col6\" class=\"data row2 col6\" >694.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row3\" class=\"row_heading level0 row3\" >Choiseul</th>\n",
-       "      <td id=\"T_06105_row3_col0\" class=\"data row3 col0\" >35.99</td>\n",
-       "      <td id=\"T_06105_row3_col1\" class=\"data row3 col1\" >26.24</td>\n",
-       "      <td id=\"T_06105_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row3_col3\" class=\"data row3 col3\" >15.00</td>\n",
-       "      <td id=\"T_06105_row3_col4\" class=\"data row3 col4\" >33.00</td>\n",
-       "      <td id=\"T_06105_row3_col5\" class=\"data row3 col5\" >51.00</td>\n",
-       "      <td id=\"T_06105_row3_col6\" class=\"data row3 col6\" >197.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row4\" class=\"row_heading level0 row4\" >Soufriere</th>\n",
-       "      <td id=\"T_06105_row4_col0\" class=\"data row4 col0\" >32.48</td>\n",
-       "      <td id=\"T_06105_row4_col1\" class=\"data row4 col1\" >34.82</td>\n",
-       "      <td id=\"T_06105_row4_col2\" class=\"data row4 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row4_col3\" class=\"data row4 col3\" >4.00</td>\n",
-       "      <td id=\"T_06105_row4_col4\" class=\"data row4 col4\" >23.00</td>\n",
-       "      <td id=\"T_06105_row4_col5\" class=\"data row4 col5\" >50.00</td>\n",
-       "      <td id=\"T_06105_row4_col6\" class=\"data row4 col6\" >225.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row5\" class=\"row_heading level0 row5\" >Laborie</th>\n",
-       "      <td id=\"T_06105_row5_col0\" class=\"data row5 col0\" >13.13</td>\n",
-       "      <td id=\"T_06105_row5_col1\" class=\"data row5 col1\" >17.55</td>\n",
-       "      <td id=\"T_06105_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
-       "      <td id=\"T_06105_row5_col4\" class=\"data row5 col4\" >5.00</td>\n",
-       "      <td id=\"T_06105_row5_col5\" class=\"data row5 col5\" >22.00</td>\n",
-       "      <td id=\"T_06105_row5_col6\" class=\"data row5 col6\" >112.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row6\" class=\"row_heading level0 row6\" >Micoud</th>\n",
-       "      <td id=\"T_06105_row6_col0\" class=\"data row6 col0\" >10.97</td>\n",
-       "      <td id=\"T_06105_row6_col1\" class=\"data row6 col1\" >19.66</td>\n",
-       "      <td id=\"T_06105_row6_col2\" class=\"data row6 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
-       "      <td id=\"T_06105_row6_col4\" class=\"data row6 col4\" >0.00</td>\n",
-       "      <td id=\"T_06105_row6_col5\" class=\"data row6 col5\" >14.00</td>\n",
-       "      <td id=\"T_06105_row6_col6\" class=\"data row6 col6\" >358.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row7\" class=\"row_heading level0 row7\" >Anse-La-Raye & Canaries</th>\n",
-       "      <td id=\"T_06105_row7_col0\" class=\"data row7 col0\" >8.92</td>\n",
-       "      <td id=\"T_06105_row7_col1\" class=\"data row7 col1\" >10.33</td>\n",
-       "      <td id=\"T_06105_row7_col2\" class=\"data row7 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row7_col3\" class=\"data row7 col3\" >0.00</td>\n",
-       "      <td id=\"T_06105_row7_col4\" class=\"data row7 col4\" >6.00</td>\n",
-       "      <td id=\"T_06105_row7_col5\" class=\"data row7 col5\" >13.00</td>\n",
-       "      <td id=\"T_06105_row7_col6\" class=\"data row7 col6\" >77.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06105_level0_row8\" class=\"row_heading level0 row8\" >Dennery</th>\n",
-       "      <td id=\"T_06105_row8_col0\" class=\"data row8 col0\" >8.00</td>\n",
-       "      <td id=\"T_06105_row8_col1\" class=\"data row8 col1\" >18.89</td>\n",
-       "      <td id=\"T_06105_row8_col2\" class=\"data row8 col2\" >0.00</td>\n",
-       "      <td id=\"T_06105_row8_col3\" class=\"data row8 col3\" >0.00</td>\n",
-       "      <td id=\"T_06105_row8_col4\" class=\"data row8 col4\" >0.00</td>\n",
-       "      <td id=\"T_06105_row8_col5\" class=\"data row8 col5\" >0.00</td>\n",
-       "      <td id=\"T_06105_row8_col6\" class=\"data row8 col6\" >120.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x2aedd0d3340>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tab = outcomes.iloc[:, 2:-1].groupby('district').describe().round(2)\n",
-    "column = 'n_new_poor'\n",
-    "tab = tab.sort_values(by=(column, 'mean'), ascending=False)\n",
-    "tab[[column]].iloc[:,1:].style.format(\"{:,.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weeks in poverty & Number of new poor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "average_years_in_poverty = get_weeks_in_poverty_tab(outcomes)\n",
-    "\n",
-    "if savetab:\n",
-    "    average_years_in_poverty.rename({'Anse-La-Raye & Canaries': 'Anse-La-Raye \\& Canaries'}, inplace=True, axis=0)\n",
-    "    average_years_in_poverty.astype(int).style\\\n",
-    "        .background_gradient(cmap='Blues', axis=0)\\\n",
-    "        .set_caption('Average Years in Poverty By District')\\\n",
-    "        .to_latex(f'../reports/tables/av_years_in_pov.tex',\n",
-    "                  # escape=False,\n",
-    "                  caption='Average Years in Poverty By District',\n",
-    "                  label='tab:av_years_in_pov',\n",
-    "                  convert_css=True,\n",
-    "                  multicol_align=\"|c|\",\n",
-    "                  hrules=True,\n",
-    "                  position_float=\"centering\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of people in poverty for 1 / 3 / 5 years"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "District\n",
-       "Anse-La-Raye & Canaries      8.904000\n",
-       "Castries                   190.380000\n",
-       "Choiseul                    35.955667\n",
-       "Dennery                      7.996667\n",
-       "Gros Islet                 105.784000\n",
-       "Laborie                     13.146667\n",
-       "Micoud                      10.987333\n",
-       "Soufriere                   32.465333\n",
-       "Vieuxfort                  125.127667\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# More than 1 year in poverty\n",
-    "n_years = 1\n",
-    "average_years_in_poverty.iloc[:,n_years:].sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "District\n",
-       "Anse-La-Raye & Canaries      8.904000\n",
-       "Castries                   155.480333\n",
-       "Choiseul                    34.885333\n",
-       "Dennery                      7.996667\n",
-       "Gros Islet                 101.196000\n",
-       "Laborie                     13.146667\n",
-       "Micoud                       8.636667\n",
-       "Soufriere                   32.465333\n",
-       "Vieuxfort                  122.096667\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# More than 3 years in poverty\n",
-    "n_years = 3\n",
-    "average_years_in_poverty.iloc[:,n_years:].sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "District\n",
-       "Anse-La-Raye & Canaries     5.957667\n",
-       "Castries                   92.684000\n",
-       "Choiseul                   28.397333\n",
-       "Dennery                     5.602667\n",
-       "Gros Islet                 57.576667\n",
-       "Laborie                     2.287667\n",
-       "Micoud                      2.673333\n",
-       "Soufriere                  24.000000\n",
-       "Vieuxfort                   4.647000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# More than 1 year in poverty\n",
-    "n_years = 5\n",
-    "average_years_in_poverty.iloc[:,n_years:].sum(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "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>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>10</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>District</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Anse-La-Raye &amp; Canaries</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>2.37</td>\n",
-       "      <td>2.70</td>\n",
-       "      <td>3.14</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Castries</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.01</td>\n",
-       "      <td>21.89</td>\n",
-       "      <td>18.74</td>\n",
-       "      <td>44.05</td>\n",
-       "      <td>32.52</td>\n",
-       "      <td>13.86</td>\n",
-       "      <td>2.71</td>\n",
-       "      <td>21.77</td>\n",
-       "      <td>5.62</td>\n",
-       "      <td>16.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Choiseul</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.07</td>\n",
-       "      <td>3.09</td>\n",
-       "      <td>3.40</td>\n",
-       "      <td>9.64</td>\n",
-       "      <td>13.06</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>0.31</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dennery</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.39</td>\n",
-       "      <td>5.60</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gros Islet</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.81</td>\n",
-       "      <td>1.77</td>\n",
-       "      <td>13.21</td>\n",
-       "      <td>30.41</td>\n",
-       "      <td>25.50</td>\n",
-       "      <td>11.60</td>\n",
-       "      <td>6.02</td>\n",
-       "      <td>13.01</td>\n",
-       "      <td>1.44</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Laborie</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>9.35</td>\n",
-       "      <td>0.88</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>1.21</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Micoud</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.07</td>\n",
-       "      <td>1.28</td>\n",
-       "      <td>1.07</td>\n",
-       "      <td>4.89</td>\n",
-       "      <td>1.13</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>1.18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Soufriere</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>4.84</td>\n",
-       "      <td>3.62</td>\n",
-       "      <td>1.68</td>\n",
-       "      <td>8.49</td>\n",
-       "      <td>12.33</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Vieuxfort</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>2.55</td>\n",
-       "      <td>69.37</td>\n",
-       "      <td>48.08</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>3.24</td>\n",
-       "      <td>1.11</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                          0      1      2      3      4      5      6      7   \\\n",
-       "District                                                                        \n",
-       "Anse-La-Raye & Canaries  0.0   0.00   0.00   0.57   2.37   2.70   3.14   0.05   \n",
-       "Castries                 0.0  13.01  21.89  18.74  44.05  32.52  13.86   2.71   \n",
-       "Choiseul                 0.0   0.00   1.07   3.09   3.40   9.64  13.06   4.04   \n",
-       "Dennery                  0.0   0.00   0.00   0.00   2.39   5.60   0.00   0.00   \n",
-       "Gros Islet               0.0   2.81   1.77  13.21  30.41  25.50  11.60   6.02   \n",
-       "Laborie                  0.0   0.00   0.00   1.50   9.35   0.88   0.20   1.21   \n",
-       "Micoud                   0.0   1.07   1.28   1.07   4.89   1.13   0.24   0.00   \n",
-       "Soufriere                0.0   0.00   0.00   4.84   3.62   1.68   8.49  12.33   \n",
-       "Vieuxfort                0.0   0.49   2.55  69.37  48.08   0.00   3.24   1.11   \n",
-       "\n",
-       "                            8     9      10  \n",
-       "District                                     \n",
-       "Anse-La-Raye & Canaries   0.00  0.00   0.07  \n",
-       "Castries                 21.77  5.62  16.21  \n",
-       "Choiseul                  0.31  0.65   0.70  \n",
-       "Dennery                   0.00  0.00   0.00  \n",
-       "Gros Islet               13.01  1.44   0.00  \n",
-       "Laborie                   0.00  0.00   0.00  \n",
-       "Micoud                    0.00  0.13   1.18  \n",
-       "Soufriere                 1.50  0.00   0.00  \n",
-       "Vieuxfort                 0.30  0.00   0.00  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_years_in_poverty.round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table id=\"T_01315\">\n",
-       "  <caption>Average Years in Poverty By District</caption>\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_01315_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
-       "      <th id=\"T_01315_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
-       "      <th id=\"T_01315_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
-       "      <th id=\"T_01315_level0_col3\" class=\"col_heading level0 col3\" >3</th>\n",
-       "      <th id=\"T_01315_level0_col4\" class=\"col_heading level0 col4\" >4</th>\n",
-       "      <th id=\"T_01315_level0_col5\" class=\"col_heading level0 col5\" >5</th>\n",
-       "      <th id=\"T_01315_level0_col6\" class=\"col_heading level0 col6\" >6</th>\n",
-       "      <th id=\"T_01315_level0_col7\" class=\"col_heading level0 col7\" >7</th>\n",
-       "      <th id=\"T_01315_level0_col8\" class=\"col_heading level0 col8\" >8</th>\n",
-       "      <th id=\"T_01315_level0_col9\" class=\"col_heading level0 col9\" >9</th>\n",
-       "      <th id=\"T_01315_level0_col10\" class=\"col_heading level0 col10\" >10</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >District</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "      <th class=\"blank col9\" >&nbsp;</th>\n",
-       "      <th class=\"blank col10\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row0\" class=\"row_heading level0 row0\" >Anse-La-Raye & Canaries</th>\n",
-       "      <td id=\"T_01315_row0_col0\" class=\"data row0 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row0_col1\" class=\"data row0 col1\" >0</td>\n",
-       "      <td id=\"T_01315_row0_col2\" class=\"data row0 col2\" >0</td>\n",
-       "      <td id=\"T_01315_row0_col3\" class=\"data row0 col3\" >0</td>\n",
-       "      <td id=\"T_01315_row0_col4\" class=\"data row0 col4\" >2</td>\n",
-       "      <td id=\"T_01315_row0_col5\" class=\"data row0 col5\" >2</td>\n",
-       "      <td id=\"T_01315_row0_col6\" class=\"data row0 col6\" >3</td>\n",
-       "      <td id=\"T_01315_row0_col7\" class=\"data row0 col7\" >0</td>\n",
-       "      <td id=\"T_01315_row0_col8\" class=\"data row0 col8\" >0</td>\n",
-       "      <td id=\"T_01315_row0_col9\" class=\"data row0 col9\" >0</td>\n",
-       "      <td id=\"T_01315_row0_col10\" class=\"data row0 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row1\" class=\"row_heading level0 row1\" >Castries</th>\n",
-       "      <td id=\"T_01315_row1_col0\" class=\"data row1 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row1_col1\" class=\"data row1 col1\" >13</td>\n",
-       "      <td id=\"T_01315_row1_col2\" class=\"data row1 col2\" >21</td>\n",
-       "      <td id=\"T_01315_row1_col3\" class=\"data row1 col3\" >18</td>\n",
-       "      <td id=\"T_01315_row1_col4\" class=\"data row1 col4\" >44</td>\n",
-       "      <td id=\"T_01315_row1_col5\" class=\"data row1 col5\" >32</td>\n",
-       "      <td id=\"T_01315_row1_col6\" class=\"data row1 col6\" >13</td>\n",
-       "      <td id=\"T_01315_row1_col7\" class=\"data row1 col7\" >2</td>\n",
-       "      <td id=\"T_01315_row1_col8\" class=\"data row1 col8\" >21</td>\n",
-       "      <td id=\"T_01315_row1_col9\" class=\"data row1 col9\" >5</td>\n",
-       "      <td id=\"T_01315_row1_col10\" class=\"data row1 col10\" >16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row2\" class=\"row_heading level0 row2\" >Choiseul</th>\n",
-       "      <td id=\"T_01315_row2_col0\" class=\"data row2 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row2_col1\" class=\"data row2 col1\" >0</td>\n",
-       "      <td id=\"T_01315_row2_col2\" class=\"data row2 col2\" >1</td>\n",
-       "      <td id=\"T_01315_row2_col3\" class=\"data row2 col3\" >3</td>\n",
-       "      <td id=\"T_01315_row2_col4\" class=\"data row2 col4\" >3</td>\n",
-       "      <td id=\"T_01315_row2_col5\" class=\"data row2 col5\" >9</td>\n",
-       "      <td id=\"T_01315_row2_col6\" class=\"data row2 col6\" >13</td>\n",
-       "      <td id=\"T_01315_row2_col7\" class=\"data row2 col7\" >4</td>\n",
-       "      <td id=\"T_01315_row2_col8\" class=\"data row2 col8\" >0</td>\n",
-       "      <td id=\"T_01315_row2_col9\" class=\"data row2 col9\" >0</td>\n",
-       "      <td id=\"T_01315_row2_col10\" class=\"data row2 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row3\" class=\"row_heading level0 row3\" >Dennery</th>\n",
-       "      <td id=\"T_01315_row3_col0\" class=\"data row3 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col1\" class=\"data row3 col1\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col2\" class=\"data row3 col2\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col3\" class=\"data row3 col3\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col4\" class=\"data row3 col4\" >2</td>\n",
-       "      <td id=\"T_01315_row3_col5\" class=\"data row3 col5\" >5</td>\n",
-       "      <td id=\"T_01315_row3_col6\" class=\"data row3 col6\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col7\" class=\"data row3 col7\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col8\" class=\"data row3 col8\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col9\" class=\"data row3 col9\" >0</td>\n",
-       "      <td id=\"T_01315_row3_col10\" class=\"data row3 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row4\" class=\"row_heading level0 row4\" >Gros Islet</th>\n",
-       "      <td id=\"T_01315_row4_col0\" class=\"data row4 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row4_col1\" class=\"data row4 col1\" >2</td>\n",
-       "      <td id=\"T_01315_row4_col2\" class=\"data row4 col2\" >1</td>\n",
-       "      <td id=\"T_01315_row4_col3\" class=\"data row4 col3\" >13</td>\n",
-       "      <td id=\"T_01315_row4_col4\" class=\"data row4 col4\" >30</td>\n",
-       "      <td id=\"T_01315_row4_col5\" class=\"data row4 col5\" >25</td>\n",
-       "      <td id=\"T_01315_row4_col6\" class=\"data row4 col6\" >11</td>\n",
-       "      <td id=\"T_01315_row4_col7\" class=\"data row4 col7\" >6</td>\n",
-       "      <td id=\"T_01315_row4_col8\" class=\"data row4 col8\" >13</td>\n",
-       "      <td id=\"T_01315_row4_col9\" class=\"data row4 col9\" >1</td>\n",
-       "      <td id=\"T_01315_row4_col10\" class=\"data row4 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row5\" class=\"row_heading level0 row5\" >Laborie</th>\n",
-       "      <td id=\"T_01315_row5_col0\" class=\"data row5 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row5_col1\" class=\"data row5 col1\" >0</td>\n",
-       "      <td id=\"T_01315_row5_col2\" class=\"data row5 col2\" >0</td>\n",
-       "      <td id=\"T_01315_row5_col3\" class=\"data row5 col3\" >1</td>\n",
-       "      <td id=\"T_01315_row5_col4\" class=\"data row5 col4\" >9</td>\n",
-       "      <td id=\"T_01315_row5_col5\" class=\"data row5 col5\" >0</td>\n",
-       "      <td id=\"T_01315_row5_col6\" class=\"data row5 col6\" >0</td>\n",
-       "      <td id=\"T_01315_row5_col7\" class=\"data row5 col7\" >1</td>\n",
-       "      <td id=\"T_01315_row5_col8\" class=\"data row5 col8\" >0</td>\n",
-       "      <td id=\"T_01315_row5_col9\" class=\"data row5 col9\" >0</td>\n",
-       "      <td id=\"T_01315_row5_col10\" class=\"data row5 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row6\" class=\"row_heading level0 row6\" >Micoud</th>\n",
-       "      <td id=\"T_01315_row6_col0\" class=\"data row6 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row6_col1\" class=\"data row6 col1\" >1</td>\n",
-       "      <td id=\"T_01315_row6_col2\" class=\"data row6 col2\" >1</td>\n",
-       "      <td id=\"T_01315_row6_col3\" class=\"data row6 col3\" >1</td>\n",
-       "      <td id=\"T_01315_row6_col4\" class=\"data row6 col4\" >4</td>\n",
-       "      <td id=\"T_01315_row6_col5\" class=\"data row6 col5\" >1</td>\n",
-       "      <td id=\"T_01315_row6_col6\" class=\"data row6 col6\" >0</td>\n",
-       "      <td id=\"T_01315_row6_col7\" class=\"data row6 col7\" >0</td>\n",
-       "      <td id=\"T_01315_row6_col8\" class=\"data row6 col8\" >0</td>\n",
-       "      <td id=\"T_01315_row6_col9\" class=\"data row6 col9\" >0</td>\n",
-       "      <td id=\"T_01315_row6_col10\" class=\"data row6 col10\" >1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row7\" class=\"row_heading level0 row7\" >Soufriere</th>\n",
-       "      <td id=\"T_01315_row7_col0\" class=\"data row7 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row7_col1\" class=\"data row7 col1\" >0</td>\n",
-       "      <td id=\"T_01315_row7_col2\" class=\"data row7 col2\" >0</td>\n",
-       "      <td id=\"T_01315_row7_col3\" class=\"data row7 col3\" >4</td>\n",
-       "      <td id=\"T_01315_row7_col4\" class=\"data row7 col4\" >3</td>\n",
-       "      <td id=\"T_01315_row7_col5\" class=\"data row7 col5\" >1</td>\n",
-       "      <td id=\"T_01315_row7_col6\" class=\"data row7 col6\" >8</td>\n",
-       "      <td id=\"T_01315_row7_col7\" class=\"data row7 col7\" >12</td>\n",
-       "      <td id=\"T_01315_row7_col8\" class=\"data row7 col8\" >1</td>\n",
-       "      <td id=\"T_01315_row7_col9\" class=\"data row7 col9\" >0</td>\n",
-       "      <td id=\"T_01315_row7_col10\" class=\"data row7 col10\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_01315_level0_row8\" class=\"row_heading level0 row8\" >Vieuxfort</th>\n",
-       "      <td id=\"T_01315_row8_col0\" class=\"data row8 col0\" >0</td>\n",
-       "      <td id=\"T_01315_row8_col1\" class=\"data row8 col1\" >0</td>\n",
-       "      <td id=\"T_01315_row8_col2\" class=\"data row8 col2\" >2</td>\n",
-       "      <td id=\"T_01315_row8_col3\" class=\"data row8 col3\" >69</td>\n",
-       "      <td id=\"T_01315_row8_col4\" class=\"data row8 col4\" >48</td>\n",
-       "      <td id=\"T_01315_row8_col5\" class=\"data row8 col5\" >0</td>\n",
-       "      <td id=\"T_01315_row8_col6\" class=\"data row8 col6\" >3</td>\n",
-       "      <td id=\"T_01315_row8_col7\" class=\"data row8 col7\" >1</td>\n",
-       "      <td id=\"T_01315_row8_col8\" class=\"data row8 col8\" >0</td>\n",
-       "      <td id=\"T_01315_row8_col9\" class=\"data row8 col9\" >0</td>\n",
-       "      <td id=\"T_01315_row8_col10\" class=\"data row8 col10\" >0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x24d8a3c8460>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_years_in_poverty.astype(int).style\\\n",
-    "        .background_gradient(cmap='Blues', axis=0)\\\n",
-    "        .set_caption('Average Years in Poverty By District')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the number of new poor in each district\n",
-    "districts = outcomes['district'].unique().tolist()\n",
-    "n_districts = len(districts)\n",
-    "d = {}\n",
-    "for district in districts:\n",
-    "    df = outcomes[outcomes['district'] == district]\n",
-    "    d[district] = df['n_new_poor'].values.tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row0_col2, #T_c26c1_row0_col3, #T_c26c1_row1_col2, #T_c26c1_row1_col3, #T_c26c1_row2_col2, #T_c26c1_row2_col3, #T_c26c1_row3_col2, #T_c26c1_row4_col2, #T_c26c1_row5_col2, #T_c26c1_row5_col3, #T_c26c1_row6_col2, #T_c26c1_row6_col3, #T_c26c1_row6_col4, #T_c26c1_row7_col2, #T_c26c1_row7_col3, #T_c26c1_row7_col6, #T_c26c1_row8_col2, #T_c26c1_row8_col3, #T_c26c1_row8_col4, #T_c26c1_row8_col5 {\n",
-       "  background-color: #f7fcf5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row0_col4, #T_c26c1_row0_col5, #T_c26c1_row0_col6, #T_c26c1_row3_col3 {\n",
-       "  background-color: #00441b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row1_col0 {\n",
-       "  background-color: #3d8dc4;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row1_col1 {\n",
-       "  background-color: #d52221;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row1_col4 {\n",
-       "  background-color: #4aaf61;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row1_col5 {\n",
-       "  background-color: #2e964d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row1_col6 {\n",
-       "  background-color: #91d28e;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row2_col0 {\n",
-       "  background-color: #5fa6d1;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row2_col1 {\n",
-       "  background-color: #f96245;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row2_col4 {\n",
-       "  background-color: #3da65a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row2_col5 {\n",
-       "  background-color: #48ae60;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_c26c1_row2_col6 {\n",
-       "  background-color: #8bcf89;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row3_col0 {\n",
-       "  background-color: #d9e7f5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row3_col1 {\n",
-       "  background-color: #fee7dc;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row3_col4 {\n",
-       "  background-color: #c0e6b9;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row3_col5 {\n",
-       "  background-color: #d9f0d3;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row3_col6 {\n",
-       "  background-color: #ebf7e7;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row4_col0 {\n",
-       "  background-color: #dceaf6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row4_col1 {\n",
-       "  background-color: #fee0d2;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row4_col3 {\n",
-       "  background-color: #c2e7bb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row4_col4 {\n",
-       "  background-color: #d5efcf;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row4_col5 {\n",
-       "  background-color: #daf0d4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row4_col6 {\n",
-       "  background-color: #e8f6e4;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row5_col0 {\n",
-       "  background-color: #f2f7fd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row5_col1 {\n",
-       "  background-color: #ffefe8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row5_col4, #T_c26c1_row7_col5 {\n",
-       "  background-color: #f1faee;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row5_col5 {\n",
-       "  background-color: #ecf8e8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row5_col6 {\n",
-       "  background-color: #f4fbf1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row6_col0 {\n",
-       "  background-color: #f4f9fe;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row6_col1 {\n",
-       "  background-color: #ffede5;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row6_col5, #T_c26c1_row7_col4 {\n",
-       "  background-color: #f0f9ed;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row6_col6 {\n",
-       "  background-color: #d4eece;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row7_col0 {\n",
-       "  background-color: #f6faff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row7_col1 {\n",
-       "  background-color: #fff5f0;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row8_col0 {\n",
-       "  background-color: #f7fbff;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row8_col1 {\n",
-       "  background-color: #ffeee6;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_c26c1_row8_col6 {\n",
-       "  background-color: #f3faf0;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_c26c1\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_c26c1_level0_col0\" class=\"col_heading level0 col0\" >mean</th>\n",
-       "      <th id=\"T_c26c1_level0_col1\" class=\"col_heading level0 col1\" >std</th>\n",
-       "      <th id=\"T_c26c1_level0_col2\" class=\"col_heading level0 col2\" >min</th>\n",
-       "      <th id=\"T_c26c1_level0_col3\" class=\"col_heading level0 col3\" >25%</th>\n",
-       "      <th id=\"T_c26c1_level0_col4\" class=\"col_heading level0 col4\" >50%</th>\n",
-       "      <th id=\"T_c26c1_level0_col5\" class=\"col_heading level0 col5\" >75%</th>\n",
-       "      <th id=\"T_c26c1_level0_col6\" class=\"col_heading level0 col6\" >max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row0\" class=\"row_heading level0 row0\" >Castries</th>\n",
-       "      <td id=\"T_c26c1_row0_col0\" class=\"data row0 col0\" >190</td>\n",
-       "      <td id=\"T_c26c1_row0_col1\" class=\"data row0 col1\" >203</td>\n",
-       "      <td id=\"T_c26c1_row0_col2\" class=\"data row0 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row0_col3\" class=\"data row0 col3\" >0</td>\n",
-       "      <td id=\"T_c26c1_row0_col4\" class=\"data row0 col4\" >120</td>\n",
-       "      <td id=\"T_c26c1_row0_col5\" class=\"data row0 col5\" >287</td>\n",
-       "      <td id=\"T_c26c1_row0_col6\" class=\"data row0 col6\" >1,495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row1\" class=\"row_heading level0 row1\" >Vieuxfort</th>\n",
-       "      <td id=\"T_c26c1_row1_col0\" class=\"data row1 col0\" >125</td>\n",
-       "      <td id=\"T_c26c1_row1_col1\" class=\"data row1 col1\" >147</td>\n",
-       "      <td id=\"T_c26c1_row1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row1_col3\" class=\"data row1 col3\" >0</td>\n",
-       "      <td id=\"T_c26c1_row1_col4\" class=\"data row1 col4\" >72</td>\n",
-       "      <td id=\"T_c26c1_row1_col5\" class=\"data row1 col5\" >202</td>\n",
-       "      <td id=\"T_c26c1_row1_col6\" class=\"data row1 col6\" >672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row2\" class=\"row_heading level0 row2\" >Gros Islet</th>\n",
-       "      <td id=\"T_c26c1_row2_col0\" class=\"data row2 col0\" >106</td>\n",
-       "      <td id=\"T_c26c1_row2_col1\" class=\"data row2 col1\" >111</td>\n",
-       "      <td id=\"T_c26c1_row2_col2\" class=\"data row2 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row2_col3\" class=\"data row2 col3\" >0</td>\n",
-       "      <td id=\"T_c26c1_row2_col4\" class=\"data row2 col4\" >77</td>\n",
-       "      <td id=\"T_c26c1_row2_col5\" class=\"data row2 col5\" >174</td>\n",
-       "      <td id=\"T_c26c1_row2_col6\" class=\"data row2 col6\" >694</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row3\" class=\"row_heading level0 row3\" >Choiseul</th>\n",
-       "      <td id=\"T_c26c1_row3_col0\" class=\"data row3 col0\" >36</td>\n",
-       "      <td id=\"T_c26c1_row3_col1\" class=\"data row3 col1\" >26</td>\n",
-       "      <td id=\"T_c26c1_row3_col2\" class=\"data row3 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row3_col3\" class=\"data row3 col3\" >15</td>\n",
-       "      <td id=\"T_c26c1_row3_col4\" class=\"data row3 col4\" >33</td>\n",
-       "      <td id=\"T_c26c1_row3_col5\" class=\"data row3 col5\" >51</td>\n",
-       "      <td id=\"T_c26c1_row3_col6\" class=\"data row3 col6\" >197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row4\" class=\"row_heading level0 row4\" >Soufriere</th>\n",
-       "      <td id=\"T_c26c1_row4_col0\" class=\"data row4 col0\" >32</td>\n",
-       "      <td id=\"T_c26c1_row4_col1\" class=\"data row4 col1\" >35</td>\n",
-       "      <td id=\"T_c26c1_row4_col2\" class=\"data row4 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row4_col3\" class=\"data row4 col3\" >4</td>\n",
-       "      <td id=\"T_c26c1_row4_col4\" class=\"data row4 col4\" >23</td>\n",
-       "      <td id=\"T_c26c1_row4_col5\" class=\"data row4 col5\" >50</td>\n",
-       "      <td id=\"T_c26c1_row4_col6\" class=\"data row4 col6\" >225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row5\" class=\"row_heading level0 row5\" >Laborie</th>\n",
-       "      <td id=\"T_c26c1_row5_col0\" class=\"data row5 col0\" >13</td>\n",
-       "      <td id=\"T_c26c1_row5_col1\" class=\"data row5 col1\" >18</td>\n",
-       "      <td id=\"T_c26c1_row5_col2\" class=\"data row5 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row5_col3\" class=\"data row5 col3\" >0</td>\n",
-       "      <td id=\"T_c26c1_row5_col4\" class=\"data row5 col4\" >5</td>\n",
-       "      <td id=\"T_c26c1_row5_col5\" class=\"data row5 col5\" >22</td>\n",
-       "      <td id=\"T_c26c1_row5_col6\" class=\"data row5 col6\" >112</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row6\" class=\"row_heading level0 row6\" >Micoud</th>\n",
-       "      <td id=\"T_c26c1_row6_col0\" class=\"data row6 col0\" >11</td>\n",
-       "      <td id=\"T_c26c1_row6_col1\" class=\"data row6 col1\" >20</td>\n",
-       "      <td id=\"T_c26c1_row6_col2\" class=\"data row6 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row6_col3\" class=\"data row6 col3\" >0</td>\n",
-       "      <td id=\"T_c26c1_row6_col4\" class=\"data row6 col4\" >0</td>\n",
-       "      <td id=\"T_c26c1_row6_col5\" class=\"data row6 col5\" >14</td>\n",
-       "      <td id=\"T_c26c1_row6_col6\" class=\"data row6 col6\" >358</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row7\" class=\"row_heading level0 row7\" >Anse-La-Raye & Canaries</th>\n",
-       "      <td id=\"T_c26c1_row7_col0\" class=\"data row7 col0\" >9</td>\n",
-       "      <td id=\"T_c26c1_row7_col1\" class=\"data row7 col1\" >10</td>\n",
-       "      <td id=\"T_c26c1_row7_col2\" class=\"data row7 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row7_col3\" class=\"data row7 col3\" >0</td>\n",
-       "      <td id=\"T_c26c1_row7_col4\" class=\"data row7 col4\" >6</td>\n",
-       "      <td id=\"T_c26c1_row7_col5\" class=\"data row7 col5\" >13</td>\n",
-       "      <td id=\"T_c26c1_row7_col6\" class=\"data row7 col6\" >77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c26c1_level0_row8\" class=\"row_heading level0 row8\" >Dennery</th>\n",
-       "      <td id=\"T_c26c1_row8_col0\" class=\"data row8 col0\" >8</td>\n",
-       "      <td id=\"T_c26c1_row8_col1\" class=\"data row8 col1\" >19</td>\n",
-       "      <td id=\"T_c26c1_row8_col2\" class=\"data row8 col2\" >0</td>\n",
-       "      <td id=\"T_c26c1_row8_col3\" class=\"data row8 col3\" >0</td>\n",
-       "      <td id=\"T_c26c1_row8_col4\" class=\"data row8 col4\" >0</td>\n",
-       "      <td id=\"T_c26c1_row8_col5\" class=\"data row8 col5\" >0</td>\n",
-       "      <td id=\"T_c26c1_row8_col6\" class=\"data row8 col6\" >120</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x24d8a2fee50>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Make a coloured table\n",
-    "tab = pd.DataFrame(d).describe().T.sort_values('mean', ascending=False).round(2).drop('count', axis=1)\n",
-    "tab.style.background_gradient(cmap='Blues', subset=['mean'])\\\n",
-    "         .background_gradient(cmap='Reds', subset=['std'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['min'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['25%'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['50%'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['75%'])\\\n",
-    "         .background_gradient(cmap='Greens', subset=['max'])\\\n",
-    "         .format(decimal='.', thousands=',', precision=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Average vulnerability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_ce5d8\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >District</th>\n",
-       "      <th id=\"T_ce5d8_level0_col0\" class=\"col_heading level0 col0\" >Anse-La-Raye & Canaries</th>\n",
-       "      <th id=\"T_ce5d8_level0_col1\" class=\"col_heading level0 col1\" >Castries</th>\n",
-       "      <th id=\"T_ce5d8_level0_col2\" class=\"col_heading level0 col2\" >Choiseul</th>\n",
-       "      <th id=\"T_ce5d8_level0_col3\" class=\"col_heading level0 col3\" >Dennery</th>\n",
-       "      <th id=\"T_ce5d8_level0_col4\" class=\"col_heading level0 col4\" >Gros Islet</th>\n",
-       "      <th id=\"T_ce5d8_level0_col5\" class=\"col_heading level0 col5\" >Laborie</th>\n",
-       "      <th id=\"T_ce5d8_level0_col6\" class=\"col_heading level0 col6\" >Micoud</th>\n",
-       "      <th id=\"T_ce5d8_level0_col7\" class=\"col_heading level0 col7\" >Soufriere</th>\n",
-       "      <th id=\"T_ce5d8_level0_col8\" class=\"col_heading level0 col8\" >Vieuxfort</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Quintile</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ce5d8_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_ce5d8_row0_col0\" class=\"data row0 col0\" >0.51</td>\n",
-       "      <td id=\"T_ce5d8_row0_col1\" class=\"data row0 col1\" >0.48</td>\n",
-       "      <td id=\"T_ce5d8_row0_col2\" class=\"data row0 col2\" >0.40</td>\n",
-       "      <td id=\"T_ce5d8_row0_col3\" class=\"data row0 col3\" >0.53</td>\n",
-       "      <td id=\"T_ce5d8_row0_col4\" class=\"data row0 col4\" >0.38</td>\n",
-       "      <td id=\"T_ce5d8_row0_col5\" class=\"data row0 col5\" >0.42</td>\n",
-       "      <td id=\"T_ce5d8_row0_col6\" class=\"data row0 col6\" >0.46</td>\n",
-       "      <td id=\"T_ce5d8_row0_col7\" class=\"data row0 col7\" >0.33</td>\n",
-       "      <td id=\"T_ce5d8_row0_col8\" class=\"data row0 col8\" >0.47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ce5d8_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_ce5d8_row1_col0\" class=\"data row1 col0\" >0.46</td>\n",
-       "      <td id=\"T_ce5d8_row1_col1\" class=\"data row1 col1\" >0.43</td>\n",
-       "      <td id=\"T_ce5d8_row1_col2\" class=\"data row1 col2\" >0.33</td>\n",
-       "      <td id=\"T_ce5d8_row1_col3\" class=\"data row1 col3\" >0.46</td>\n",
-       "      <td id=\"T_ce5d8_row1_col4\" class=\"data row1 col4\" >0.40</td>\n",
-       "      <td id=\"T_ce5d8_row1_col5\" class=\"data row1 col5\" >0.43</td>\n",
-       "      <td id=\"T_ce5d8_row1_col6\" class=\"data row1 col6\" >0.40</td>\n",
-       "      <td id=\"T_ce5d8_row1_col7\" class=\"data row1 col7\" >0.40</td>\n",
-       "      <td id=\"T_ce5d8_row1_col8\" class=\"data row1 col8\" >0.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ce5d8_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_ce5d8_row2_col0\" class=\"data row2 col0\" >0.42</td>\n",
-       "      <td id=\"T_ce5d8_row2_col1\" class=\"data row2 col1\" >0.41</td>\n",
-       "      <td id=\"T_ce5d8_row2_col2\" class=\"data row2 col2\" >0.34</td>\n",
-       "      <td id=\"T_ce5d8_row2_col3\" class=\"data row2 col3\" >0.38</td>\n",
-       "      <td id=\"T_ce5d8_row2_col4\" class=\"data row2 col4\" >0.38</td>\n",
-       "      <td id=\"T_ce5d8_row2_col5\" class=\"data row2 col5\" >0.36</td>\n",
-       "      <td id=\"T_ce5d8_row2_col6\" class=\"data row2 col6\" >0.34</td>\n",
-       "      <td id=\"T_ce5d8_row2_col7\" class=\"data row2 col7\" >0.32</td>\n",
-       "      <td id=\"T_ce5d8_row2_col8\" class=\"data row2 col8\" >0.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ce5d8_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_ce5d8_row3_col0\" class=\"data row3 col0\" >0.43</td>\n",
-       "      <td id=\"T_ce5d8_row3_col1\" class=\"data row3 col1\" >0.40</td>\n",
-       "      <td id=\"T_ce5d8_row3_col2\" class=\"data row3 col2\" >0.32</td>\n",
-       "      <td id=\"T_ce5d8_row3_col3\" class=\"data row3 col3\" >0.40</td>\n",
-       "      <td id=\"T_ce5d8_row3_col4\" class=\"data row3 col4\" >0.36</td>\n",
-       "      <td id=\"T_ce5d8_row3_col5\" class=\"data row3 col5\" >0.37</td>\n",
-       "      <td id=\"T_ce5d8_row3_col6\" class=\"data row3 col6\" >0.34</td>\n",
-       "      <td id=\"T_ce5d8_row3_col7\" class=\"data row3 col7\" >0.35</td>\n",
-       "      <td id=\"T_ce5d8_row3_col8\" class=\"data row3 col8\" >0.37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ce5d8_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_ce5d8_row4_col0\" class=\"data row4 col0\" >0.35</td>\n",
-       "      <td id=\"T_ce5d8_row4_col1\" class=\"data row4 col1\" >0.33</td>\n",
-       "      <td id=\"T_ce5d8_row4_col2\" class=\"data row4 col2\" >0.32</td>\n",
-       "      <td id=\"T_ce5d8_row4_col3\" class=\"data row4 col3\" >0.38</td>\n",
-       "      <td id=\"T_ce5d8_row4_col4\" class=\"data row4 col4\" >0.33</td>\n",
-       "      <td id=\"T_ce5d8_row4_col5\" class=\"data row4 col5\" >0.37</td>\n",
-       "      <td id=\"T_ce5d8_row4_col6\" class=\"data row4 col6\" >0.33</td>\n",
-       "      <td id=\"T_ce5d8_row4_col7\" class=\"data row4 col7\" >0.28</td>\n",
-       "      <td id=\"T_ce5d8_row4_col8\" class=\"data row4 col8\" >0.35</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x2aee22b7e50>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vulnerability_by_quintile = get_average_weighted_vulnerability(outcomes, quintile=True)\n",
-    "vulnerability_by_quintile.T.style.format(\"{:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_04d16\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >District</th>\n",
-       "      <th id=\"T_04d16_level0_col0\" class=\"col_heading level0 col0\" >Anse-La-Raye & Canaries</th>\n",
-       "      <th id=\"T_04d16_level0_col1\" class=\"col_heading level0 col1\" >Castries</th>\n",
-       "      <th id=\"T_04d16_level0_col2\" class=\"col_heading level0 col2\" >Choiseul</th>\n",
-       "      <th id=\"T_04d16_level0_col3\" class=\"col_heading level0 col3\" >Dennery</th>\n",
-       "      <th id=\"T_04d16_level0_col4\" class=\"col_heading level0 col4\" >Gros Islet</th>\n",
-       "      <th id=\"T_04d16_level0_col5\" class=\"col_heading level0 col5\" >Laborie</th>\n",
-       "      <th id=\"T_04d16_level0_col6\" class=\"col_heading level0 col6\" >Micoud</th>\n",
-       "      <th id=\"T_04d16_level0_col7\" class=\"col_heading level0 col7\" >Soufriere</th>\n",
-       "      <th id=\"T_04d16_level0_col8\" class=\"col_heading level0 col8\" >Vieuxfort</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Decile</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_04d16_row0_col0\" class=\"data row0 col0\" >0.51</td>\n",
-       "      <td id=\"T_04d16_row0_col1\" class=\"data row0 col1\" >0.45</td>\n",
-       "      <td id=\"T_04d16_row0_col2\" class=\"data row0 col2\" >0.43</td>\n",
-       "      <td id=\"T_04d16_row0_col3\" class=\"data row0 col3\" >0.40</td>\n",
-       "      <td id=\"T_04d16_row0_col4\" class=\"data row0 col4\" >0.15</td>\n",
-       "      <td id=\"T_04d16_row0_col5\" class=\"data row0 col5\" >0.35</td>\n",
-       "      <td id=\"T_04d16_row0_col6\" class=\"data row0 col6\" >0.44</td>\n",
-       "      <td id=\"T_04d16_row0_col7\" class=\"data row0 col7\" >0.27</td>\n",
-       "      <td id=\"T_04d16_row0_col8\" class=\"data row0 col8\" >0.47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_04d16_row1_col0\" class=\"data row1 col0\" >0.50</td>\n",
-       "      <td id=\"T_04d16_row1_col1\" class=\"data row1 col1\" >0.35</td>\n",
-       "      <td id=\"T_04d16_row1_col2\" class=\"data row1 col2\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row1_col3\" class=\"data row1 col3\" >0.54</td>\n",
-       "      <td id=\"T_04d16_row1_col4\" class=\"data row1 col4\" >0.32</td>\n",
-       "      <td id=\"T_04d16_row1_col5\" class=\"data row1 col5\" >0.36</td>\n",
-       "      <td id=\"T_04d16_row1_col6\" class=\"data row1 col6\" >0.44</td>\n",
-       "      <td id=\"T_04d16_row1_col7\" class=\"data row1 col7\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row1_col8\" class=\"data row1 col8\" >0.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_04d16_row2_col0\" class=\"data row2 col0\" >0.46</td>\n",
-       "      <td id=\"T_04d16_row2_col1\" class=\"data row2 col1\" >0.42</td>\n",
-       "      <td id=\"T_04d16_row2_col2\" class=\"data row2 col2\" >0.32</td>\n",
-       "      <td id=\"T_04d16_row2_col3\" class=\"data row2 col3\" >0.44</td>\n",
-       "      <td id=\"T_04d16_row2_col4\" class=\"data row2 col4\" >0.39</td>\n",
-       "      <td id=\"T_04d16_row2_col5\" class=\"data row2 col5\" >0.41</td>\n",
-       "      <td id=\"T_04d16_row2_col6\" class=\"data row2 col6\" >0.25</td>\n",
-       "      <td id=\"T_04d16_row2_col7\" class=\"data row2 col7\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row2_col8\" class=\"data row2 col8\" >0.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_04d16_row3_col0\" class=\"data row3 col0\" >0.42</td>\n",
-       "      <td id=\"T_04d16_row3_col1\" class=\"data row3 col1\" >0.36</td>\n",
-       "      <td id=\"T_04d16_row3_col2\" class=\"data row3 col2\" >0.29</td>\n",
-       "      <td id=\"T_04d16_row3_col3\" class=\"data row3 col3\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row3_col4\" class=\"data row3 col4\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row3_col5\" class=\"data row3 col5\" >0.41</td>\n",
-       "      <td id=\"T_04d16_row3_col6\" class=\"data row3 col6\" >0.35</td>\n",
-       "      <td id=\"T_04d16_row3_col7\" class=\"data row3 col7\" >0.44</td>\n",
-       "      <td id=\"T_04d16_row3_col8\" class=\"data row3 col8\" >0.36</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_04d16_row4_col0\" class=\"data row4 col0\" >0.45</td>\n",
-       "      <td id=\"T_04d16_row4_col1\" class=\"data row4 col1\" >0.39</td>\n",
-       "      <td id=\"T_04d16_row4_col2\" class=\"data row4 col2\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row4_col3\" class=\"data row4 col3\" >0.39</td>\n",
-       "      <td id=\"T_04d16_row4_col4\" class=\"data row4 col4\" >0.39</td>\n",
-       "      <td id=\"T_04d16_row4_col5\" class=\"data row4 col5\" >0.30</td>\n",
-       "      <td id=\"T_04d16_row4_col6\" class=\"data row4 col6\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row4_col7\" class=\"data row4 col7\" >0.29</td>\n",
-       "      <td id=\"T_04d16_row4_col8\" class=\"data row4 col8\" >0.41</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_04d16_row5_col0\" class=\"data row5 col0\" >0.12</td>\n",
-       "      <td id=\"T_04d16_row5_col1\" class=\"data row5 col1\" >0.38</td>\n",
-       "      <td id=\"T_04d16_row5_col2\" class=\"data row5 col2\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row5_col3\" class=\"data row5 col3\" >0.26</td>\n",
-       "      <td id=\"T_04d16_row5_col4\" class=\"data row5 col4\" >0.30</td>\n",
-       "      <td id=\"T_04d16_row5_col5\" class=\"data row5 col5\" >0.37</td>\n",
-       "      <td id=\"T_04d16_row5_col6\" class=\"data row5 col6\" >0.32</td>\n",
-       "      <td id=\"T_04d16_row5_col7\" class=\"data row5 col7\" >0.29</td>\n",
-       "      <td id=\"T_04d16_row5_col8\" class=\"data row5 col8\" >0.37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_04d16_row6_col0\" class=\"data row6 col0\" >0.44</td>\n",
-       "      <td id=\"T_04d16_row6_col1\" class=\"data row6 col1\" >0.41</td>\n",
-       "      <td id=\"T_04d16_row6_col2\" class=\"data row6 col2\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row6_col3\" class=\"data row6 col3\" >0.36</td>\n",
-       "      <td id=\"T_04d16_row6_col4\" class=\"data row6 col4\" >0.35</td>\n",
-       "      <td id=\"T_04d16_row6_col5\" class=\"data row6 col5\" >0.34</td>\n",
-       "      <td id=\"T_04d16_row6_col6\" class=\"data row6 col6\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row6_col7\" class=\"data row6 col7\" >0.39</td>\n",
-       "      <td id=\"T_04d16_row6_col8\" class=\"data row6 col8\" >0.31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_04d16_row7_col0\" class=\"data row7 col0\" >0.30</td>\n",
-       "      <td id=\"T_04d16_row7_col1\" class=\"data row7 col1\" >0.38</td>\n",
-       "      <td id=\"T_04d16_row7_col2\" class=\"data row7 col2\" >0.29</td>\n",
-       "      <td id=\"T_04d16_row7_col3\" class=\"data row7 col3\" >0.37</td>\n",
-       "      <td id=\"T_04d16_row7_col4\" class=\"data row7 col4\" >0.34</td>\n",
-       "      <td id=\"T_04d16_row7_col5\" class=\"data row7 col5\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row7_col6\" class=\"data row7 col6\" >0.30</td>\n",
-       "      <td id=\"T_04d16_row7_col7\" class=\"data row7 col7\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row7_col8\" class=\"data row7 col8\" >0.38</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_04d16_row8_col0\" class=\"data row8 col0\" >0.32</td>\n",
-       "      <td id=\"T_04d16_row8_col1\" class=\"data row8 col1\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_04d16_row8_col3\" class=\"data row8 col3\" >0.39</td>\n",
-       "      <td id=\"T_04d16_row8_col4\" class=\"data row8 col4\" >0.35</td>\n",
-       "      <td id=\"T_04d16_row8_col5\" class=\"data row8 col5\" >0.36</td>\n",
-       "      <td id=\"T_04d16_row8_col6\" class=\"data row8 col6\" >0.36</td>\n",
-       "      <td id=\"T_04d16_row8_col7\" class=\"data row8 col7\" >0.27</td>\n",
-       "      <td id=\"T_04d16_row8_col8\" class=\"data row8 col8\" >0.35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_04d16_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
-       "      <td id=\"T_04d16_row9_col0\" class=\"data row9 col0\" >0.41</td>\n",
-       "      <td id=\"T_04d16_row9_col1\" class=\"data row9 col1\" >0.33</td>\n",
-       "      <td id=\"T_04d16_row9_col2\" class=\"data row9 col2\" >0.32</td>\n",
-       "      <td id=\"T_04d16_row9_col3\" class=\"data row9 col3\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row9_col4\" class=\"data row9 col4\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row9_col5\" class=\"data row9 col5\" >0.36</td>\n",
-       "      <td id=\"T_04d16_row9_col6\" class=\"data row9 col6\" >0.31</td>\n",
-       "      <td id=\"T_04d16_row9_col7\" class=\"data row9 col7\" >0.28</td>\n",
-       "      <td id=\"T_04d16_row9_col8\" class=\"data row9 col8\" >0.32</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x2aee22b7130>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vulnerability_by_decile = get_average_weighted_vulnerability(outcomes, quintile=False)\n",
-    "vulnerability_by_decile.T.style.format(\"{:.2f}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Raincloud plots"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Raincloud plot is a combination of a boxplot, a kernel density plot, and a dot plot. It is a great way to visualize the distribution of the data. Especially, when the distribution is skewed and you have a lot of data points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 840x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 840x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 840x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 840x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 840x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 840x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 840x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "raincloud_plot(outcomes, savefig=savefig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Outcomes spatially"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the outcomes is to use choropleth maps. These maps are useful when you have data that is aggregated by a geographic area. For example, if we have data on how many people became poor after a disaster in the districts of Saint Lucia, we could use a choropleth map to show these numbers.  \n",
-    "\n",
-    "What is important to remember is that **these maps do not show the variance** within each district. Instead, they show the mean or median value for each district."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the country shapefile and aggregated outcomes\n",
-    "country = gpd.read_file('../data/raw/shapefiles/Saint Lucia/gadm36_LCA_shp/gadm36_LCA_0.shp')\n",
-    "aggregation = 'median'\n",
-    "outcomes['district'].replace({'Anse-La-Raye \\& Canaries' : 'Anse-La-Raye & Canaries'}, inplace=True)\n",
-    "aggregated_outcomes = get_spatial_outcomes(outcomes, aggregation=aggregation)\n",
-    "\n",
-    "# Convert to be in millions\n",
-    "aggregated_outcomes[['tot_exposed_asset', 'total_consumption_loss']] /= 1000000\n",
-    "aggregated_outcomes[['tot_exposed_asset', 'total_consumption_loss']] = aggregated_outcomes[['tot_exposed_asset', 'total_consumption_loss']].round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "k = 4\n",
-    "\n",
-    "column = 'annual_average_consumption_loss_pct'\n",
-    "title = 'Ann. Av. Cons. Loss PC (%)'\n",
-    "cmap = 'OrRd'\n",
-    "\n",
-    "scheme = 'fisher_jenks'\n",
-    "ax = aggregated_outcomes.plot(column=column, scheme=scheme, k=k, cmap=cmap, legend=True, figsize=(10,10), legend_kwds={\"title\":title, \"loc\" : \"upper left\"})\n",
-    "aggregated_outcomes.plot(ax=ax, color='none', edgecolor='black', linewidth=0.25)\n",
-    "ax.axis('off')\n",
-    "for idx, row in aggregated_outcomes.iterrows():\n",
-    "    ax.annotate(text=row['NAME_1'], xy=row['geometry'].centroid.coords[0], ha='center', fontsize=8)\n",
-    "if savefig:\n",
-    "    plt.savefig(f'../reports/figures/analysis/soc_econ_res_map.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x1400 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(ncols=3, nrows=2, figsize=(14, 14))\n",
-    "\n",
-    "outcomes_to_plot = [\n",
-    "    'tot_exposed_asset',\n",
-    "    'total_consumption_loss',\n",
-    "    'annual_average_consumption_loss_pct',\n",
-    "    'n_new_poor_increase_pp',\n",
-    "    'r',\n",
-    "    'new_poverty_gap_all']\n",
-    "\n",
-    "titles = ['Total exposed asset stock (MM)',\n",
-    "          'Total consumption loss (MM)',\n",
-    "          # 'Wt. Ann. Avg. Consump. Loss p.c. (%)',\n",
-    "          'Annual Average Consumption Loss PC (%)',\n",
-    "          'New Poor Increase (p.p.)',\n",
-    "          'Socio-Economic Resilience',\n",
-    "          'New Poverty Gap All Poor']\n",
-    "\n",
-    "cmaps = ['OrRd', 'YlOrRd', 'OrRd', 'RdYlGn_r', 'RdYlGn', 'OrRd']\n",
-    "\n",
-    "k = 4\n",
-    "for i, outcome in enumerate(outcomes_to_plot):\n",
-    "    aggregated_outcomes.plot(column=outcome, scheme='fisher_jenks', cmap=cmaps[i], k=k, ax=ax[i//3, i % 3], legend=True, legend_kwds={\"loc\": \"upper left\"})\n",
-    "    aggregated_outcomes.plot(ax=ax[i//3, i % 3], color='none', edgecolor='black', linewidth=0.25)\n",
-    "    ax[i//3, i % 3].set_title(titles[i])\n",
-    "    ax[i//3, i % 3].axis('off')\n",
-    "    for idx, row in aggregated_outcomes.iterrows():\n",
-    "        ax[i//3, i % 3].annotate(text=row['NAME_1'], xy=row['geometry'].centroid.coords[0], ha='center', fontsize=8)\n",
-    "\n",
-    "# fig.suptitle(f'{aggregation.capitalize()} of 3,000 replications', fontsize=16)\n",
-    "\n",
-    "fig.tight_layout()\n",
-    "if savefig:\n",
-    "    plt.savefig(\n",
-    "        f'../reports/figures/analysis/{aggregation}_outcomes_spatially.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A bivariate or bi-variate choropleth helps to visualise relations between two variables spatially. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load asset damage data\n",
-    "asset_damage = pd.read_excel('../data/processed/asset_damage/Saint Lucia.xlsx')\n",
-    "asset_damage['district'].replace({'AnseLaRayeCanaries' : 'Anse-La-Raye & Canaries'}, inplace=True)\n",
-    "gdf = pd.merge(aggregated_outcomes, asset_damage, left_on='NAME_1', right_on='district', how='left')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Mikhail\\miniconda3\\envs\\unbreakable\\lib\\site-packages\\mapclassify\\classifiers.py:1860: UserWarning: Numba not installed. Using slow pure python version.\n",
-      "  warnings.warn(\n"
-     ]
-    },
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",
-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_name = 'r'\n",
-    "y_name = 'pml'\n",
-    "x_label = 'Soc.-econ. res.'\n",
-    "y_label = 'PML'\n",
-    "\n",
-    "tab = bivariate_choropleth(gdf.copy(), x_name, y_name, x_label, y_label, scheme='fisher_jenks', figsize=(7,6), return_table=True)\n",
-    "if savefig:\n",
-    "    plt.savefig('../reports/figures/analysis/bivariate_choropleth.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 420x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nine_quadrants_plot(gdf.copy(), x_name, y_name)\n",
-    "plt.xlabel('Socio-Economic Resilience')\n",
-    "plt.ylabel('PML')\n",
-    "if savefig:\n",
-    "    plt.savefig('../reports/figures/analysis/9_quadrants.png', dpi=300, bbox_inches='tight')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ubk",
-   "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": "3.9.0"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/analysis/Saint Lucia/policy_analysis.ipynb b/analysis/Saint Lucia/policy_analysis.ipynb
deleted file mode 100644
index 756807d..0000000
--- a/analysis/Saint Lucia/policy_analysis.ipynb	
+++ /dev/null
@@ -1,1941 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Policy analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "savefig = True\n",
-    "savetab = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from tqdm import tqdm\n",
-    "from matplotlib.ticker import MaxNLocator, MultipleLocator\n",
-    "import json\n",
-    "import ptitprince as pt\n",
-    "import pickle as pkl\n",
-    "# import contextily as ctx\n",
-    "from unbreakable.analysis.analyzer import *\n",
-    "import ptitprince as pt\n",
-    "from ema_workbench import load_results\n",
-    "sns.set_theme(style=\"ticks\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load results\n",
-    "income_loss = False\n",
-    "results = load_results(f'../experiments/scenarios=1000, policies=20.tar.gz')\n",
-    "\n",
-    "# Prepare outcomes data frame\n",
-    "outcomes = prepare_outcomes(results, add_uncertainties=False, add_policies=True)\n",
-    "\n",
-    "# This outcome should be studied separately\n",
-    "outcomes.drop('years_in_poverty', axis=1, inplace=True)\n",
-    "\n",
-    "# Split my policy into two columns\n",
-    "outcomes[['target_group', 'top_up']] = outcomes['my_policy'].str.split('+', expand=True)\n",
-    "\n",
-    "# Make top_up a categorical variable to ensure it is ordered correctly\n",
-    "outcomes['top_up'] = pd.Categorical(outcomes['top_up'], categories=['0', '10', '30', '50', '100'])\n",
-    "outcomes['target_group'] = pd.Categorical(outcomes['target_group'], categories=['poor', 'poor_near_poor1.25', 'poor_near_poor2.0', 'all'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What is the impact of top-ups across target groups and districts?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = outcomes.copy()\n",
-    "columns_of_interest = [\n",
-    "       'target_group',\n",
-    "       'top_up', \n",
-    "       'n_new_poor',\n",
-    "       'annual_average_consumption_loss_pct']\n",
-    "tab = df[columns_of_interest].round(2).groupby(['target_group', 'top_up']).mean(numeric_only=True).round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# top_up_mapper = {'0' : 'None', '10' : '10\\%', '30': '30\\%', '50': '50\\%', '100': '100\\%'}\n",
-    "top_up_mapper = {'0' : 'None', '10' : '10%', '30': '30%', '50': '50%', '100': '100%'}\n",
-    "target_group_mapper = {'all' : 'All', 'poor' : 'Poor', 'poor_near_poor1.25' : 'Poor and Near Poor (1.25)', 'poor_near_poor2.0' : 'Poor and Near Poor (2.0)'}\n",
-    "# outcome_name_mapper = {'annual_average_consumption_loss_pct' : 'Annual average consumption loss (\\%)', 'n_new_poor' : 'Number of new poor'}\n",
-    "outcome_name_mapper = {'annual_average_consumption_loss_pct' : 'Annual average consumption loss (%)', 'n_new_poor' : 'Number of new poor'}\n",
-    "\n",
-    "tab.rename(index = top_up_mapper, inplace = True)\n",
-    "tab.rename(index = target_group_mapper, inplace = True)\n",
-    "tab.rename(columns = outcome_name_mapper, inplace = True)\n",
-    "tab.index.names = ['Target group', 'Top-up percentage']\n",
-    "\n",
-    "tab['Number of new poor'] = tab['Number of new poor'].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Average number of people preventing from falling into poverty</th>\n",
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-       "      <th rowspan=\"5\" valign=\"top\">Poor</th>\n",
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-       "      <th rowspan=\"5\" valign=\"top\">Poor and Near Poor (1.25)</th>\n",
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-       "      <th>10%</th>\n",
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-       "      <th rowspan=\"5\" valign=\"top\">Poor and Near Poor (2.0)</th>\n",
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-       "      <td>59</td>\n",
-       "      <td>5.53</td>\n",
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-       "      <th rowspan=\"5\" valign=\"top\">All</th>\n",
-       "      <th>None</th>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>10%</th>\n",
-       "      <td>7</td>\n",
-       "      <td>0.87</td>\n",
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-       "    <tr>\n",
-       "      <th>30%</th>\n",
-       "      <td>31</td>\n",
-       "      <td>2.64</td>\n",
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-       "      <th>50%</th>\n",
-       "      <td>40</td>\n",
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-       "      <th>100%</th>\n",
-       "      <td>59</td>\n",
-       "      <td>8.83</td>\n",
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-      "text/plain": [
-       "                                             Average number of people preventing from falling into poverty  \\\n",
-       "Target group              Top-up percentage                                                                  \n",
-       "Poor                      None                                                               0               \n",
-       "                          10%                                                                0               \n",
-       "                          30%                                                                0               \n",
-       "                          50%                                                                0               \n",
-       "                          100%                                                               0               \n",
-       "Poor and Near Poor (1.25) None                                                               0               \n",
-       "                          10%                                                                5               \n",
-       "                          30%                                                               27               \n",
-       "                          50%                                                               33               \n",
-       "                          100%                                                              52               \n",
-       "Poor and Near Poor (2.0)  None                                                               0               \n",
-       "                          10%                                                                7               \n",
-       "                          30%                                                               31               \n",
-       "                          50%                                                               39               \n",
-       "                          100%                                                              59               \n",
-       "All                       None                                                               0               \n",
-       "                          10%                                                                7               \n",
-       "                          30%                                                               31               \n",
-       "                          50%                                                               40               \n",
-       "                          100%                                                              59               \n",
-       "\n",
-       "                                             Average reduction in consumption loss (p.p.)  \n",
-       "Target group              Top-up percentage                                                \n",
-       "Poor                      None                                                       0.00  \n",
-       "                          10%                                                        0.20  \n",
-       "                          30%                                                        0.61  \n",
-       "                          50%                                                        1.02  \n",
-       "                          100%                                                       2.02  \n",
-       "Poor and Near Poor (1.25) None                                                       0.00  \n",
-       "                          10%                                                        0.30  \n",
-       "                          30%                                                        0.91  \n",
-       "                          50%                                                        1.52  \n",
-       "                          100%                                                       3.02  \n",
-       "Poor and Near Poor (2.0)  None                                                       0.00  \n",
-       "                          10%                                                        0.55  \n",
-       "                          30%                                                        1.66  \n",
-       "                          50%                                                        2.78  \n",
-       "                          100%                                                       5.53  \n",
-       "All                       None                                                       0.00  \n",
-       "                          10%                                                        0.87  \n",
-       "                          30%                                                        2.64  \n",
-       "                          50%                                                        4.43  \n",
-       "                          100%                                                       8.83  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n_target_groups = 4\n",
-    "# n_target_groups = 1\n",
-    "k = 0\n",
-    "m_top_ups = 4\n",
-    "# m_top_ups = 2\n",
-    "for i in range(n_target_groups):\n",
-    "    x = tab.iloc[k, 0]\n",
-    "    y = tab.iloc[k, 1]\n",
-    "    tab.iloc[k, 0] = 0\n",
-    "    tab.iloc[k, 1] = 0\n",
-    "    for j in range(k + 1, k + m_top_ups + 1):\n",
-    "        tab.iloc[j, 0] = x - tab.iloc[j, 0]\n",
-    "        tab.iloc[j, 1] = y - tab.iloc[j, 1]\n",
-    "    k += m_top_ups + 1\n",
-    "\n",
-    "tab.columns = ['Average number of people preventing from falling into poverty',\n",
-    "               # 'Average reduction in consumption loss (\\%)'\n",
-    "               'Average reduction in consumption loss (p.p.)'\n",
-    "               ]\n",
-    "tab = tab + 0\n",
-    "tab"
-   ]
-  },
-  {
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-   "execution_count": 8,
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-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row12_col0, #T_73e7c_row17_col0 {\n",
-       "  background-color: #63a8d3;\n",
-       "  color: #f1f1f1;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row12_col1 {\n",
-       "  background-color: #d6efd0;\n",
-       "  color: #000000;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row13_col0 {\n",
-       "  background-color: #3888c1;\n",
-       "  color: #f1f1f1;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row13_col1 {\n",
-       "  background-color: #b4e1ad;\n",
-       "  color: #000000;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row14_col0, #T_73e7c_row19_col0 {\n",
-       "  background-color: #08306b;\n",
-       "  color: #f1f1f1;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row14_col1 {\n",
-       "  background-color: #40aa5d;\n",
-       "  color: #f1f1f1;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row16_col1 {\n",
-       "  background-color: #e9f7e5;\n",
-       "  color: #000000;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row17_col1 {\n",
-       "  background-color: #b8e3b2;\n",
-       "  color: #000000;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row18_col0 {\n",
-       "  background-color: #3484bf;\n",
-       "  color: #f1f1f1;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row18_col1 {\n",
-       "  background-color: #73c476;\n",
-       "  color: #000000;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "#T_73e7c_row19_col1 {\n",
-       "  background-color: #00441b;\n",
-       "  color: #f1f1f1;\n",
-       "  text-align: center;\n",
-       "  width: 160px;\n",
-       "  height: 30px;\n",
-       "  font-size: 12pt;\n",
-       "  border-style: solid;\n",
-       "  border-width: 1px;\n",
-       "  border-color: black;\n",
-       "  border-collapse: collapse;\n",
-       "  border-spacing: 0px;\n",
-       "  border: 1px;\n",
-       "  border-style: solid;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_73e7c\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank\" >&nbsp;</th>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_73e7c_level0_col0\" class=\"col_heading level0 col0\" >Average number of people preventing from falling into poverty</th>\n",
-       "      <th id=\"T_73e7c_level0_col1\" class=\"col_heading level0 col1\" >Average reduction in consumption loss (p.p.)</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Target group</th>\n",
-       "      <th class=\"index_name level1\" >Top-up percentage</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"5\">Poor</th>\n",
-       "      <th id=\"T_73e7c_level1_row0\" class=\"row_heading level1 row0\" >None</th>\n",
-       "      <td id=\"T_73e7c_row0_col0\" class=\"data row0 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row1\" class=\"row_heading level1 row1\" >10%</th>\n",
-       "      <td id=\"T_73e7c_row1_col0\" class=\"data row1 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row1_col1\" class=\"data row1 col1\" >0.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row2\" class=\"row_heading level1 row2\" >30%</th>\n",
-       "      <td id=\"T_73e7c_row2_col0\" class=\"data row2 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row2_col1\" class=\"data row2 col1\" >0.61</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row3\" class=\"row_heading level1 row3\" >50%</th>\n",
-       "      <td id=\"T_73e7c_row3_col0\" class=\"data row3 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row3_col1\" class=\"data row3 col1\" >1.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row4\" class=\"row_heading level1 row4\" >100%</th>\n",
-       "      <td id=\"T_73e7c_row4_col0\" class=\"data row4 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row4_col1\" class=\"data row4 col1\" >2.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level0_row5\" class=\"row_heading level0 row5\" rowspan=\"5\">Poor and Near Poor (1.25)</th>\n",
-       "      <th id=\"T_73e7c_level1_row5\" class=\"row_heading level1 row5\" >None</th>\n",
-       "      <td id=\"T_73e7c_row5_col0\" class=\"data row5 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row6\" class=\"row_heading level1 row6\" >10%</th>\n",
-       "      <td id=\"T_73e7c_row6_col0\" class=\"data row6 col0\" >5</td>\n",
-       "      <td id=\"T_73e7c_row6_col1\" class=\"data row6 col1\" >0.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row7\" class=\"row_heading level1 row7\" >30%</th>\n",
-       "      <td id=\"T_73e7c_row7_col0\" class=\"data row7 col0\" >27</td>\n",
-       "      <td id=\"T_73e7c_row7_col1\" class=\"data row7 col1\" >0.91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row8\" class=\"row_heading level1 row8\" >50%</th>\n",
-       "      <td id=\"T_73e7c_row8_col0\" class=\"data row8 col0\" >33</td>\n",
-       "      <td id=\"T_73e7c_row8_col1\" class=\"data row8 col1\" >1.52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row9\" class=\"row_heading level1 row9\" >100%</th>\n",
-       "      <td id=\"T_73e7c_row9_col0\" class=\"data row9 col0\" >52</td>\n",
-       "      <td id=\"T_73e7c_row9_col1\" class=\"data row9 col1\" >3.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level0_row10\" class=\"row_heading level0 row10\" rowspan=\"5\">Poor and Near Poor (2.0)</th>\n",
-       "      <th id=\"T_73e7c_level1_row10\" class=\"row_heading level1 row10\" >None</th>\n",
-       "      <td id=\"T_73e7c_row10_col0\" class=\"data row10 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row10_col1\" class=\"data row10 col1\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row11\" class=\"row_heading level1 row11\" >10%</th>\n",
-       "      <td id=\"T_73e7c_row11_col0\" class=\"data row11 col0\" >7</td>\n",
-       "      <td id=\"T_73e7c_row11_col1\" class=\"data row11 col1\" >0.55</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row12\" class=\"row_heading level1 row12\" >30%</th>\n",
-       "      <td id=\"T_73e7c_row12_col0\" class=\"data row12 col0\" >31</td>\n",
-       "      <td id=\"T_73e7c_row12_col1\" class=\"data row12 col1\" >1.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row13\" class=\"row_heading level1 row13\" >50%</th>\n",
-       "      <td id=\"T_73e7c_row13_col0\" class=\"data row13 col0\" >39</td>\n",
-       "      <td id=\"T_73e7c_row13_col1\" class=\"data row13 col1\" >2.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row14\" class=\"row_heading level1 row14\" >100%</th>\n",
-       "      <td id=\"T_73e7c_row14_col0\" class=\"data row14 col0\" >59</td>\n",
-       "      <td id=\"T_73e7c_row14_col1\" class=\"data row14 col1\" >5.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level0_row15\" class=\"row_heading level0 row15\" rowspan=\"5\">All</th>\n",
-       "      <th id=\"T_73e7c_level1_row15\" class=\"row_heading level1 row15\" >None</th>\n",
-       "      <td id=\"T_73e7c_row15_col0\" class=\"data row15 col0\" >0</td>\n",
-       "      <td id=\"T_73e7c_row15_col1\" class=\"data row15 col1\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row16\" class=\"row_heading level1 row16\" >10%</th>\n",
-       "      <td id=\"T_73e7c_row16_col0\" class=\"data row16 col0\" >7</td>\n",
-       "      <td id=\"T_73e7c_row16_col1\" class=\"data row16 col1\" >0.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row17\" class=\"row_heading level1 row17\" >30%</th>\n",
-       "      <td id=\"T_73e7c_row17_col0\" class=\"data row17 col0\" >31</td>\n",
-       "      <td id=\"T_73e7c_row17_col1\" class=\"data row17 col1\" >2.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row18\" class=\"row_heading level1 row18\" >50%</th>\n",
-       "      <td id=\"T_73e7c_row18_col0\" class=\"data row18 col0\" >40</td>\n",
-       "      <td id=\"T_73e7c_row18_col1\" class=\"data row18 col1\" >4.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_73e7c_level1_row19\" class=\"row_heading level1 row19\" >100%</th>\n",
-       "      <td id=\"T_73e7c_row19_col0\" class=\"data row19 col0\" >59</td>\n",
-       "      <td id=\"T_73e7c_row19_col1\" class=\"data row19 col1\" >8.83</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1a22f556d00>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tab.style.background_gradient(cmap='Blues', subset='Average number of people preventing from falling into poverty')\\\n",
-    "         .background_gradient(cmap='Greens', subset='Average reduction in consumption loss (p.p.)')\\\n",
-    "         .format(decimal='.', thousands=',', precision=2)\\\n",
-    "         .set_table_styles([dict(selector='th', props=[('max-width', '80px')])])\\\n",
-    "            .set_properties(**{'text-align': 'center'})\\\n",
-    "            .set_properties(**{'width': '160px'})\\\n",
-    "            .set_properties(**{'height': '30px'})\\\n",
-    "            .set_properties(**{'font-size': '12pt'})\\\n",
-    "            .set_properties(**{'border-style': 'solid'})\\\n",
-    "            .set_properties(**{'border-width': '1px'})\\\n",
-    "            .set_properties(**{'border-color': 'black'})\\\n",
-    "            .set_properties(**{'border-collapse': 'collapse'})\\\n",
-    "            .set_properties(**{'border-spacing': '0px'})\\\n",
-    "            .set_properties(**{'border': '1px'})\\\n",
-    "            .set_properties(**{'border-style': 'solid'})\n",
-    "        #  .format(decimal='.', thousands=',', precision=2)\\\n",
-    "        #  .to_latex(buf='../reports/tables/policy_analysis_country.tex',\n",
-    "        #            caption='Impact of cash transfers across different target groups on the country scale.',\n",
-    "        #            position='H',\n",
-    "        #            convert_css=True,\n",
-    "        #            hrules=True,\n",
-    "        #            label='tab:policy_analysis_country',\n",
-    "        #            sparse_index=True,\n",
-    "        #            sparse_columns=True,\n",
-    "        #            column_format='p{4cm}p{4cm}p{3cm}p{3cm}p{3cm}',\n",
-    "        #            multicol_align=\"|c|\",\n",
-    "        #            position_float=\"centering\",\n",
-    "        #           )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target_group = 'all'\n",
-    "top_ups = ['50'] # , '100']\n",
-    "df = outcomes[outcomes['target_group'] == target_group].copy()\n",
-    "df = df[df['top_up'].isin(top_ups)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.398683718805956"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['annual_average_consumption_loss_pct'].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(6, 4))\n",
-    "\n",
-    "if savefig:\n",
-    "    pct_symbol = '\\%'\n",
-    "    pct_symbol = '%'\n",
-    "else:\n",
-    "    pct_symbol = '%'\n",
-    "\n",
-    "target_group = 'all'\n",
-    "top_ups = ['0', '10', '30', '50'] # , '100']\n",
-    "df = outcomes[outcomes['target_group'] == target_group].copy()\n",
-    "df = df[df['top_up'].isin(top_ups)]\n",
-    "df['top_up'] = df['top_up'].astype(str)\n",
-    "df['top_up'] = pd.Categorical(df['top_up'], categories=top_ups, ordered=True)\n",
-    "df.rename(columns={'top_up': f'Top-up ({pct_symbol})'}, inplace=True)\n",
-    "outcome_of_interest = 'annual_average_consumption_loss_pct'\n",
-    "sns.histplot(data=df, x=outcome_of_interest, hue=f'Top-up ({pct_symbol})', ax=ax)\n",
-    "plt.xlabel(f'Wt. Ann. Avg. Consump. Loss p.c. ({pct_symbol})')\n",
-    "plt.xlabel('Annual Average Consumption Loss PC (%)')\n",
-    "plt.ylabel('Run count')\n",
-    "if savefig:\n",
-    "    plt.savefig('../reports/figures/policy_analysis/top_up_all.pgf', bbox_inches='tight')\n",
-    "    plt.savefig('../reports/figures/policy_analysis/top_up_all.png', bbox_inches='tight', dpi=300);\n",
-    "\n",
-    "else:\n",
-    "    plt.title('Top-up all affected households');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tab = df.copy()\n",
-    "outcome_of_interest = 'Wt. Ann. Avg. Consump. Loss p.c. (\\%)'\n",
-    "tab.rename({'annual_average_consumption_loss_pct':\n",
-    "            'Wt. Ann. Avg. Consump. Loss p.c. (\\%)'}, inplace=True, axis=1)\n",
-    "tab = tab.groupby('Top-up (\\%)').describe()[[outcome_of_interest]]\n",
-    "tab.drop('count', axis=1, level=1, inplace=True)\n",
-    "tab = tab.rename({'25%': '25\\%', '50%': '50\\%', '75%': '75\\%'}, axis=1, level=1)\n",
-    "tab = tab.T\n",
-    "style = tab.style.format(decimal='.', thousands=',', precision=2)\n",
-    "if savetab:\n",
-    "    style.to_latex(buf='../reports/tables/top_up_all_country.tex',\n",
-    "                caption='Impact of cash transfers to \"All\" target group on the country scale.',\n",
-    "                position='H',\n",
-    "                hrules=True,\n",
-    "                label='tab:top_up_all_country',\n",
-    "                sparse_index=True,\n",
-    "                sparse_columns=True,\n",
-    "                # column_format='p{4cm}p{4cm}p{3cm}p{3cm}p{3cm}'\n",
-    "                )\n",
-    "else:\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## How the top-up to `all` mitigates the impact of a flood?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x630 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "target_group = 'all'\n",
-    "top_ups = ['0', '10', '30', '50']\n",
-    "df = outcomes[outcomes['target_group'] == target_group]\n",
-    "df = df[df['top_up'].isin(top_ups)]\n",
-    "df['top_up'] = pd.Categorical(df['top_up'], categories=top_ups, ordered=True)\n",
-    "\n",
-    "districts = df['district'].unique().tolist()\n",
-    "n_districts = len(districts)\n",
-    "\n",
-    "fig, ax = plt.subplots(ncols=3, nrows=3, figsize=(5 * n_districts / 3, 3 * n_districts / 3), sharex=True, sharey=True)\n",
-    "colors = sns.color_palette('Set2', n_colors=len(districts))\n",
-    "\n",
-    "# outcome_of_interest = 'n_new_poor'\n",
-    "\n",
-    "outcome_name_mapper = {'annual_average_consumption_loss_pct': 'Wt. Ann. Avg. Consump. Loss p.c. (%)',\n",
-    "                       'n_new_poor': 'Number of new poor',\n",
-    "                       'n_new_poor_increase_pct' : 'Increase in number of new poor (%)'}\n",
-    "\n",
-    "outcome_of_interest = 'annual_average_consumption_loss_pct'\n",
-    "\n",
-    "for district in districts:\n",
-    "    data = df[df['district'] == district][['top_up', outcome_of_interest]]\n",
-    "    data.sort_values('top_up', inplace=True)\n",
-    "    data.rename(columns={'top_up': 'Top up (%)'}, inplace=True)\n",
-    "    if district == 'AnseLaRayeCanaries':\n",
-    "        district_name = 'Anse-La-Raye & Canaries'\n",
-    "    else:\n",
-    "        district_name = district\n",
-    "    i = districts.index(district) // 3\n",
-    "    j = districts.index(district) % 3\n",
-    "    color = colors[districts.index(district)]\n",
-    "    # sns.histplot(data=data, \n",
-    "    #              x=outcome_of_interest, \n",
-    "    #              hue='Top up', \n",
-    "    #              ax=ax[i, j], \n",
-    "    #              # multiple='stack', \n",
-    "    #              # shrink=.8,\n",
-    "    #              # palette=\"ch:rot=-.25,hue=1,light=.75\"\n",
-    "    #              )\n",
-    "    sns.kdeplot(data=data, \n",
-    "                 x=outcome_of_interest, \n",
-    "                 hue='Top up (%)', \n",
-    "                 ax=ax[i, j], \n",
-    "                 )\n",
-    "    ax[i, j].set_title(district_name)\n",
-    "    ax[i, j].set_ylabel('Run density')\n",
-    "    ax[i, j].set_xlabel(outcome_name_mapper[outcome_of_interest])\n",
-    "    ax[i, j].set_xlim(0, 100)\n",
-    "# fig.suptitle('Target group: ' + target_group_mapper[target_group], fontsize=16)\n",
-    "fig.tight_layout()\n",
-    "plt.savefig('../reports/figures/policy_analysis/top_up_all_country.png', dpi=300, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Select target group\n",
-    "df = outcomes[outcomes['target_group'] == 'all']\n",
-    "\n",
-    "# Select districts with low, mid and high consumption annual_average_consumption_loss_pct\n",
-    "districts_of_interest = ['Micoud', 'Laborie', 'Choiseul']\n",
-    "df = df[df['district'].isin(districts_of_interest)]\n",
-    "\n",
-    "# Select top-ups of 0, 50 and 100\n",
-    "df = df[df['top_up'].isin(['0', '10', '30', '50'])]\n",
-    "df['top_up'] = df['top_up'].astype('str')\n",
-    "df['top_up'] = pd.Categorical(df['top_up'], ['0', '10', '30', '50'])\n",
-    "df.rename(columns={'top_up': 'Top-up (%)', 'district' : 'District', 'annual_average_consumption_loss_pct': 'Annual average consumption loss (%)'}, inplace=True)\n",
-    "df.sort_values(by='District', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Which policies are the most effective?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Consumption loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = get_policy_effectiveness_tab(outcomes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Policy\n",
-       "100% to all                          0.00\n",
-       "100% to poor and near poor (2.0)     3.30\n",
-       "50% to all                           4.40\n",
-       "100% to poor and near poor (1.25)    5.81\n",
-       "50% to poor and near poor (2.0)      6.05\n",
-       "30% to all                           6.19\n",
-       "100% to poor                         6.81\n",
-       "30% to poor and near poor (2.0)      7.17\n",
-       "50% to poor and near poor (1.25)     7.31\n",
-       "50% to poor                          7.81\n",
-       "30% to poor and near poor (1.25)     7.92\n",
-       "10% to all                           7.96\n",
-       "30% to poor                          8.22\n",
-       "10% to poor and near poor (2.0)      8.28\n",
-       "10% to poor and near poor (1.25)     8.53\n",
-       "10% to poor                          8.63\n",
-       "None                                 8.83\n",
-       "Name: Annual average consumption loss (%), dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# How effective is the policy in reducing income loss?\n",
-    "df.groupby(['Policy']).mean(numeric_only=True)['Annual average consumption loss (%)'].sort_values(ascending=True).round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# What about the effectiveness by district?\n",
-    "df = df.groupby(['District', 'Policy']).mean(numeric_only=True)[['Annual average consumption loss (%)']] \n",
-    "df = df.sort_values(by=['District', 'Annual average consumption loss (%)'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "top_3_policies = {}\n",
-    "for district in outcomes['district'].unique():\n",
-    "    top_3_policies[district] = df.loc[district, :].iloc[:3].index.tolist()\n",
-    "top_3_policies = pd.DataFrame(top_3_policies).T\n",
-    "top_3_policies.columns = [1, 2, 3]\n",
-    "\n",
-    "bottom_3_policies = {}\n",
-    "for district in outcomes['district'].unique():\n",
-    "    bottom_3_policies[district] = df.loc[district, :].iloc[-4:-1].index.tolist()\n",
-    "bottom_3_policies = pd.DataFrame(bottom_3_policies).T\n",
-    "bottom_3_policies.columns = [1, 2, 3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "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",
-       "    }\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>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Anse-La-Raye &amp; Canaries</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>100% to poor and near poor (1.25)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Castries</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Choiseul</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dennery</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gros Islet</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>50% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Laborie</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Micoud</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Soufriere</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Vieuxfort</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   1                                 2  \\\n",
-       "Anse-La-Raye & Canaries  100% to all  100% to poor and near poor (2.0)   \n",
-       "Castries                 100% to all  100% to poor and near poor (2.0)   \n",
-       "Choiseul                 100% to all  100% to poor and near poor (2.0)   \n",
-       "Dennery                  100% to all  100% to poor and near poor (2.0)   \n",
-       "Gros Islet               100% to all                        50% to all   \n",
-       "Laborie                  100% to all  100% to poor and near poor (2.0)   \n",
-       "Micoud                   100% to all  100% to poor and near poor (2.0)   \n",
-       "Soufriere                100% to all  100% to poor and near poor (2.0)   \n",
-       "Vieuxfort                100% to all  100% to poor and near poor (2.0)   \n",
-       "\n",
-       "                                                         3  \n",
-       "Anse-La-Raye & Canaries  100% to poor and near poor (1.25)  \n",
-       "Castries                                        50% to all  \n",
-       "Choiseul                                        50% to all  \n",
-       "Dennery                                         50% to all  \n",
-       "Gros Islet                100% to poor and near poor (2.0)  \n",
-       "Laborie                                         50% to all  \n",
-       "Micoud                                          50% to all  \n",
-       "Soufriere                                       50% to all  \n",
-       "Vieuxfort                                       50% to all  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "top_3_policies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "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>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Anse-La-Raye &amp; Canaries</th>\n",
-       "      <td>10% to poor and near poor (2.0)</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Castries</th>\n",
-       "      <td>10% to poor and near poor (2.0)</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Choiseul</th>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dennery</th>\n",
-       "      <td>10% to poor and near poor (2.0)</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gros Islet</th>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Laborie</th>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Micoud</th>\n",
-       "      <td>10% to poor and near poor (2.0)</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Soufriere</th>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Vieuxfort</th>\n",
-       "      <td>10% to poor and near poor (2.0)</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>10% to poor</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                       1  \\\n",
-       "Anse-La-Raye & Canaries  10% to poor and near poor (2.0)   \n",
-       "Castries                 10% to poor and near poor (2.0)   \n",
-       "Choiseul                                     30% to poor   \n",
-       "Dennery                  10% to poor and near poor (2.0)   \n",
-       "Gros Islet                                   30% to poor   \n",
-       "Laborie                                      30% to poor   \n",
-       "Micoud                   10% to poor and near poor (2.0)   \n",
-       "Soufriere                                    30% to poor   \n",
-       "Vieuxfort                10% to poor and near poor (2.0)   \n",
-       "\n",
-       "                                                        2            3  \n",
-       "Anse-La-Raye & Canaries  10% to poor and near poor (1.25)  10% to poor  \n",
-       "Castries                 10% to poor and near poor (1.25)  10% to poor  \n",
-       "Choiseul                 10% to poor and near poor (1.25)  10% to poor  \n",
-       "Dennery                  10% to poor and near poor (1.25)  10% to poor  \n",
-       "Gros Islet               10% to poor and near poor (1.25)  10% to poor  \n",
-       "Laborie                  10% to poor and near poor (1.25)  10% to poor  \n",
-       "Micoud                   10% to poor and near poor (1.25)  10% to poor  \n",
-       "Soufriere                10% to poor and near poor (1.25)  10% to poor  \n",
-       "Vieuxfort                10% to poor and near poor (1.25)  10% to poor  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bottom_3_policies"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Number of poor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = get_policy_effectiveness_tab(outcomes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Policy\n",
-       "100% to all                           0.00\n",
-       "100% to poor and near poor (2.0)      0.67\n",
-       "100% to poor and near poor (1.25)     7.69\n",
-       "50% to all                           19.45\n",
-       "50% to poor and near poor (2.0)      20.11\n",
-       "50% to poor and near poor (1.25)     26.92\n",
-       "30% to all                           28.05\n",
-       "30% to poor and near poor (2.0)      28.40\n",
-       "30% to poor and near poor (1.25)     32.97\n",
-       "10% to all                           52.26\n",
-       "10% to poor and near poor (2.0)      52.26\n",
-       "10% to poor and near poor (1.25)     54.13\n",
-       "None                                 59.39\n",
-       "30% to poor                          59.39\n",
-       "10% to poor                          59.39\n",
-       "50% to poor                          59.39\n",
-       "100% to poor                         59.39\n",
-       "Name: Number of new poor, dtype: float64"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# How effective is the policy in reducing income loss?\n",
-    "df.groupby(['Policy']).mean(numeric_only=True)['Number of new poor'].sort_values(ascending=True).round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# How effective is the policy in reducing number of new poor\n",
-    "df = df.groupby(['District', 'Policy']).mean(numeric_only=True)[['Number of new poor']] \n",
-    "df = df.sort_values(by=['District', 'Number of new poor'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "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",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Number of new poor</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>District</th>\n",
-       "      <th>Policy</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Anse-La-Raye &amp; Canaries</th>\n",
-       "      <th>100% to all</th>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100% to poor and near poor (2.0)</th>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100% to poor and near poor (1.25)</th>\n",
-       "      <td>1.847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50% to all</th>\n",
-       "      <td>4.364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50% to poor and near poor (2.0)</th>\n",
-       "      <td>4.364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Vieuxfort</th>\n",
-       "      <th>None</th>\n",
-       "      <td>125.410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10% to poor</th>\n",
-       "      <td>125.410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30% to poor</th>\n",
-       "      <td>125.410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50% to poor</th>\n",
-       "      <td>125.410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100% to poor</th>\n",
-       "      <td>125.410</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>153 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                           Number of new poor\n",
-       "District                Policy                                               \n",
-       "Anse-La-Raye & Canaries 100% to all                                     0.000\n",
-       "                        100% to poor and near poor (2.0)                0.000\n",
-       "                        100% to poor and near poor (1.25)               1.847\n",
-       "                        50% to all                                      4.364\n",
-       "                        50% to poor and near poor (2.0)                 4.364\n",
-       "...                                                                       ...\n",
-       "Vieuxfort               None                                          125.410\n",
-       "                        10% to poor                                   125.410\n",
-       "                        30% to poor                                   125.410\n",
-       "                        50% to poor                                   125.410\n",
-       "                        100% to poor                                  125.410\n",
-       "\n",
-       "[153 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "top_3_policies = {}\n",
-    "for district in outcomes['district'].unique():\n",
-    "    top_3_policies[district] = df.loc[district, :].iloc[:3].index.tolist()\n",
-    "top_3_policies = pd.DataFrame(top_3_policies).T\n",
-    "top_3_policies.columns = [1, 2, 3]\n",
-    "\n",
-    "bottom_3_policies = {}\n",
-    "for district in outcomes['district'].unique():\n",
-    "    bottom_3_policies[district] = df.loc[district, :].iloc[-4:-1].index.tolist()\n",
-    "bottom_3_policies = pd.DataFrame(bottom_3_policies).T\n",
-    "bottom_3_policies.columns = [1, 2, 3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "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>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Anse-La-Raye &amp; Canaries</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>100% to poor and near poor (1.25)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Castries</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (1.25)</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Choiseul</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>100% to poor and near poor (1.25)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dennery</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>100% to poor and near poor (1.25)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gros Islet</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>50% to all</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Laborie</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (1.25)</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Micoud</th>\n",
-       "      <td>50% to all</td>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>50% to poor and near poor (2.0)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Soufriere</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>100% to poor and near poor (2.0)</td>\n",
-       "      <td>100% to poor and near poor (1.25)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Vieuxfort</th>\n",
-       "      <td>100% to all</td>\n",
-       "      <td>50% to all</td>\n",
-       "      <td>30% to all</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   1                                  2  \\\n",
-       "Anse-La-Raye & Canaries  100% to all   100% to poor and near poor (2.0)   \n",
-       "Castries                 100% to all  100% to poor and near poor (1.25)   \n",
-       "Choiseul                 100% to all   100% to poor and near poor (2.0)   \n",
-       "Dennery                  100% to all   100% to poor and near poor (2.0)   \n",
-       "Gros Islet               100% to all   100% to poor and near poor (2.0)   \n",
-       "Laborie                  100% to all  100% to poor and near poor (1.25)   \n",
-       "Micoud                    50% to all                        100% to all   \n",
-       "Soufriere                100% to all   100% to poor and near poor (2.0)   \n",
-       "Vieuxfort                100% to all                         50% to all   \n",
-       "\n",
-       "                                                         3  \n",
-       "Anse-La-Raye & Canaries  100% to poor and near poor (1.25)  \n",
-       "Castries                  100% to poor and near poor (2.0)  \n",
-       "Choiseul                 100% to poor and near poor (1.25)  \n",
-       "Dennery                  100% to poor and near poor (1.25)  \n",
-       "Gros Islet                                      50% to all  \n",
-       "Laborie                   100% to poor and near poor (2.0)  \n",
-       "Micoud                     50% to poor and near poor (2.0)  \n",
-       "Soufriere                100% to poor and near poor (1.25)  \n",
-       "Vieuxfort                                       30% to all  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "top_3_policies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "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>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Anse-La-Raye &amp; Canaries</th>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>50% to poor</td>\n",
-       "      <td>100% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Castries</th>\n",
-       "      <td>10% to poor</td>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>50% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Choiseul</th>\n",
-       "      <td>10% to poor</td>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>50% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dennery</th>\n",
-       "      <td>100% to poor</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "      <td>30% to poor and near poor (1.25)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gros Islet</th>\n",
-       "      <td>10% to poor</td>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>50% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Laborie</th>\n",
-       "      <td>50% to poor</td>\n",
-       "      <td>100% to poor</td>\n",
-       "      <td>10% to poor and near poor (1.25)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Micoud</th>\n",
-       "      <td>10% to poor</td>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>50% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Soufriere</th>\n",
-       "      <td>10% to poor</td>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>50% to poor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Vieuxfort</th>\n",
-       "      <td>10% to poor</td>\n",
-       "      <td>30% to poor</td>\n",
-       "      <td>50% to poor</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                    1                                 2  \\\n",
-       "Anse-La-Raye & Canaries   30% to poor                       50% to poor   \n",
-       "Castries                  10% to poor                       30% to poor   \n",
-       "Choiseul                  10% to poor                       30% to poor   \n",
-       "Dennery                  100% to poor  10% to poor and near poor (1.25)   \n",
-       "Gros Islet                10% to poor                       30% to poor   \n",
-       "Laborie                   50% to poor                      100% to poor   \n",
-       "Micoud                    10% to poor                       30% to poor   \n",
-       "Soufriere                 10% to poor                       30% to poor   \n",
-       "Vieuxfort                 10% to poor                       30% to poor   \n",
-       "\n",
-       "                                                        3  \n",
-       "Anse-La-Raye & Canaries                      100% to poor  \n",
-       "Castries                                      50% to poor  \n",
-       "Choiseul                                      50% to poor  \n",
-       "Dennery                  30% to poor and near poor (1.25)  \n",
-       "Gros Islet                                    50% to poor  \n",
-       "Laborie                  10% to poor and near poor (1.25)  \n",
-       "Micoud                                        50% to poor  \n",
-       "Soufriere                                     50% to poor  \n",
-       "Vieuxfort                                     50% to poor  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bottom_3_policies"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ubk",
-   "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": "3.9.0"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/config/DummyCountry.yaml b/config/DummyCountry.yaml
deleted file mode 100644
index e69de29..0000000
diff --git a/config/Nigeria.yaml b/config/Nigeria.yaml
index 77ce55f..194c35d 100644
--- a/config/Nigeria.yaml
+++ b/config/Nigeria.yaml
@@ -1,5 +1,6 @@
 # --------------------------------- Constants -------------------------------- #
 constants:
+  # Case study params
   country: Nigeria
   regions:
   - 'Abia'
@@ -38,27 +39,59 @@ constants:
   - 'Taraba'
   - 'Yobe'
   - 'Zamfara'
+  avg_prod: 0.35
+  inc_exp_growth: 0.02
+  cons_util: 1.5
+  disc_rate: 0.04
+  is_conflict: False
+
+  # Disaster params
+  disaster_type: flood
   return_per: 10
-  add_inc_loss: false 
-  pov_bias: 1.0
   calc_exposure_params:
-    pov_bias_rnd_distr: uniform
-    pov_bias_rnd_high: 1.5
-    pov_bias_rnd_low: 0.5
+    distr: uniform
+    high: 1.5
+    low: 0.5
   identify_aff_params:
-    delta_pct: 0.075
+    delta_pct: 0.05
     distr: uniform
     high: 1.0
     low: 0
     num_masks: 10000
-  avg_prod: 0.35
-  inc_exp_growth: 0.02
-  cons_util: 1.5
-  disc_rate: 0.04
-  atol: 100000
+
+  # Recovery params
+  add_inc_loss: True
+  pov_bias: 1.0
   lambda_incr: 0.01
   yrs_to_rec: 10
+
+  # Randmoziation params
+  rnd_house_vuln_params:
+    randomize: True
+    distr: uniform
+    low: 0.8
+    high: 1.2
+    thresh: 0.9
+  rnd_inc_params:
+    randomize: True
+    distr: uniform
+    delta: 0.1
+  rnd_sav_params:
+    randomize: True
+    distr: uniform
+    avg: 0.0204
+    delta: 0.02
+  rnd_rent_params:
+    randomize: True
+    distr: uniform
+    avg: 0.43 # Assumption source: https://livingcost.org/cost/dominica
+    delta: 0.1
+  min_households: 5000
+  atol: 100000
+
+  # Misc params
   save_households: False
+  save_consumption_recovery: True
   
 # ------------------------------- Policy levers ------------------------------ #
 # The naming convention is: <target group> + <top up percentage>
diff --git a/unbreakable/analysis/analyzer.py b/unbreakable/analysis/analyzer.py
index 2b4f1b5..8cb6f3d 100644
--- a/unbreakable/analysis/analyzer.py
+++ b/unbreakable/analysis/analyzer.py
@@ -1,5 +1,3 @@
-"""This module contains functions to prepare the results of the experiments for analysis."""
-
 import pandas as pd
 import numpy as np
 import ast
@@ -23,44 +21,39 @@ def prepare_outcomes(results: tuple, add_policies: bool, add_uncertainties: bool
     with open("../../unbreakable/analysis/outcomes.yaml", "r") as f:
         outcome_names = yaml.safe_load(f)
 
-    # TODO: Read uncertainty names from results
-    # uncertainty_names = ['consump_util',
-    #                      'discount_rate',
-    #                      'income_and_expenditure_growth',
-    #                      'poverty_bias']
+    policy_names = ['current_policy']
     uncertainty_names = []
 
     experiments, _ = results
     experiments['random_seed'] = experiments['random_seed'].astype(int)
     experiments['scenario'] = experiments['scenario'].astype(int)
-    if len(experiments['random_seed'].unique()) != experiments['scenario'].max() - experiments['scenario'].min() + 1:
-        # print(experiments['random_seed'].value_counts())
-        print('WARNING! Random seeds are not unique.')
-        # raise ValueError('Random seeds are not unique')
 
-    policy_names = ['current_policy']
+    if len(experiments['random_seed'].unique()) != experiments['scenario'].max() - experiments['scenario'].min() + 1:
+        print('Warning! Random seeds are not unique.')
 
+    base_columns = ['scenario', 'policy', 'region', 'random_seed']
     if add_policies:
         if add_uncertainties:
-            columns = ['scenario', 'policy', 'district', 'random_seed'] + \
+            columns = base_columns + \
                 policy_names + uncertainty_names + outcome_names
         else:
-            columns = ['scenario', 'policy', 'district', 'random_seed'] + \
+            columns = base_columns + \
                 policy_names + outcome_names
     else:
         if add_uncertainties:
-            columns = ['scenario', 'policy', 'district', 'random_seed'] + \
+            columns = base_columns + \
                 uncertainty_names + outcome_names
         else:
-            columns = ['scenario', 'policy', 'district',
-                       'random_seed'] + outcome_names
+            columns = base_columns + outcome_names
 
     scenarios = results[0]['scenario'].values
     n_scenarios = results[0]['scenario'].unique().size
+
     policies = results[0]['policy'].values
-    random_seeds = results[0]['random_seed'].values
     n_policies = results[0]['policy'].unique().size
-    n_districts = len(results[1].keys())
+
+    random_seeds = results[0]['random_seed'].values
+    n_regions = len(results[1].keys())
 
     if add_policies:
         policy_values = results[0][policy_names].values
@@ -69,19 +62,19 @@ def prepare_outcomes(results: tuple, add_policies: bool, add_uncertainties: bool
         uncertainty_values = results[0][uncertainty_names].values
 
     n_columns = len(columns)
-    n_rows = n_scenarios * n_policies * n_districts
+    n_rows = n_scenarios * n_policies * n_regions
     outcomes = np.zeros((n_rows, n_columns), dtype=object)
 
-    i = 0  # To iterate over rows = scenarios * policies * districts
-    for district, district_outcomes in results[1].items():
+    i = 0  # To iterate over rows = scenarios * policies * regions
+    for region, region_outcomes in results[1].items():
         # To iterate over rows = scenarios * policies (experiments dataframe)
         k = 0
-        # We reset k every time we change district
-        for arr in district_outcomes:
-            # The first 3 rows for scenario, policy and district
+        # We reset k every time we change region
+        for arr in region_outcomes:
+            # The first 3 rows for scenario, policy and region
             outcomes[i, 0] = scenarios[k]
             outcomes[i, 1] = policies[k]
-            outcomes[i, 2] = district
+            outcomes[i, 2] = region
             outcomes[i, 3] = random_seeds[k]
 
             if add_policies:
@@ -147,48 +140,28 @@ def prepare_outcomes(results: tuple, add_policies: bool, add_uncertainties: bool
                         else:
                             outcomes[i, l] = v
                         l += 1
-            k += 1  # increase row index to get next experiment for the current district
+            k += 1  # increase row index to get next experiment for the current region
             i += 1  # increase row index of the outcomes dataframe
     outcomes = pd.DataFrame(outcomes, columns=columns)
 
-    numeric_columns = ['total_population',
-                       'total_asset_loss',
-                       'total_consumption_loss',
-                       'tot_exposed_asset',
-                       'tot_asset_surv',
-                       'expected_loss_frac',
-                       'n_affected_people',
-                       'annual_average_consumption',
-                       'povline_adjusted',
-                       'district_pml',
-                       'n_poor_initial',
-                       'n_poor_affected',
-                       'n_new_poor',
-                       'initial_poverty_gap',
-                       'new_poverty_gap_initial',
-                       'new_poverty_gap_all',
-                       'annual_average_consumption_loss',
-                       'annual_average_consumption_loss_pct',
-                       'mean_recovery_rate',
-                       'r']
-
-    outcomes[numeric_columns] = outcomes[numeric_columns].apply(pd.to_numeric)
-
-    # Rename a district
-    outcomes['district'].replace(
-        {'AnseLaRayeCanaries': 'Anse-La-Raye & Canaries'}, inplace=True)
+    # Iterate over the columns and try to convert them to numeric if possible
+    for col in outcomes.columns:
+        try:
+            outcomes[col] = pd.to_numeric(outcomes[col])
+        except:
+            pass
 
     # Convert pct columns to percentage
-    outcomes['annual_average_consumption_loss_pct'] = outcomes['annual_average_consumption_loss_pct'] * 100
+    outcomes['annual_avg_consum_loss_pct'] = outcomes['annual_avg_consum_loss_pct'] * 100
     outcomes['initial_poverty_gap'] = outcomes['initial_poverty_gap'] * 100
     outcomes['new_poverty_gap_all'] = outcomes['new_poverty_gap_all'] * 100
     outcomes['new_poverty_gap_initial'] = outcomes['new_poverty_gap_initial'] * 100
     outcomes['n_poor_ratio'] = outcomes['n_poor_initial'].div(
-        outcomes['total_population']).round(2) * 100
+        outcomes['tot_pop']).round(2) * 100
 
     # Calculate the percentage of new poor
     outcomes = outcomes.assign(n_new_poor_increase_pp=outcomes['n_new_poor'].div(
-        outcomes['total_population']).multiply(100))
+        outcomes['tot_pop']).multiply(100))
 
     # Move years_in_poverty column to the end of the data frame
     outcomes = outcomes[[c for c in outcomes if c not in [
@@ -210,7 +183,7 @@ def get_spatial_outcomes(outcomes: pd.DataFrame, country: str, outcomes_of_inter
         gpd.GeoDataFrame: Spatial outcomes.
     '''
 
-    # Align district names with the ones in the outcomes
+    # Align region names with the ones in the outcomes
     if country == 'Saint Lucia':
         column = 'NAME_1'
         gdf = gpd.read_file(
@@ -226,7 +199,7 @@ def get_spatial_outcomes(outcomes: pd.DataFrame, country: str, outcomes_of_inter
         # Add it to the dataframe
         gdf.loc[len(gdf)] = [None, None, 'LCA.11_1', 'Anse-La-Raye & Canaries',
                              None, None, None, None, None, None, geometry]
-        gdf = gdf[gdf['NAME_1'].isin(outcomes['district'].unique())]
+        gdf = gdf[gdf['NAME_1'].isin(outcomes['region'].unique())]
 
     elif country == 'Dominica':
         column = 'NAME'
@@ -242,26 +215,27 @@ def get_spatial_outcomes(outcomes: pd.DataFrame, country: str, outcomes_of_inter
         raise ValueError('Country not supported')
 
     if len(outcomes_of_interest) == 0:
-        outcomes_of_interest = ['total_asset_loss',
-                                'district_pml',
-                                'tot_exposed_asset',
-                                'total_consumption_loss',
-                                'n_affected_people',
-                                'n_new_poor',
-                                'new_poverty_gap_all',
-                                'annual_average_consumption_loss',
-                                'annual_average_consumption_loss_pct',
-                                'n_new_poor_increase_pp',
-                                'n_poor_ratio',
-                                'r']
+        outcomes_of_interest = [
+            'tot_asset_loss',
+            'region_pml',
+            'tot_exposed_asset',
+            'tot_consum_loss_npv',
+            'n_aff_people',
+            'n_new_poor',
+            'new_poverty_gap_all',
+            'annual_avg_consum_loss',
+            'annual_avg_consum_loss_pct',
+            'n_new_poor_increase_pp',
+            'n_poor_ratio',
+            'r']
 
     # Aggregate outcomes
     if aggregation == 'mean':
-        aggregated = outcomes[['district'] +
-                              outcomes_of_interest].groupby('district').mean()
+        aggregated = outcomes[['region'] +
+                              outcomes_of_interest].groupby('region').mean()
     elif aggregation == 'median':
-        aggregated = outcomes[['district'] +
-                              outcomes_of_interest].groupby('district').median()
+        aggregated = outcomes[['region'] +
+                              outcomes_of_interest].groupby('region').median()
     else:
         raise ValueError('Aggregation must be either mean or median')
 
@@ -311,7 +285,7 @@ def get_policy_effectiveness_tab(outcomes: pd.DataFrame) -> pd.DataFrame:
                                                                   '10% to poor and near poor (1.25)', '30% to poor and near poor (1.25)', '50% to poor and near poor (1.25)', '100% to poor and near poor (1.25)',
                                                                   '10% to poor and near poor (2.0)', '30% to poor and near poor (2.0)', '50% to poor and near poor (2.0)', '100% to poor and near poor (2.0)'], ordered=True)
     df.rename(columns={'my_policy': 'Policy',
-                       'district': 'District'}, inplace=True)
+                       'region': 'Region'}, inplace=True)
     df.rename(columns={'annual_average_consumption_loss_pct': 'Annual average consumption loss (%)',
                        'n_new_poor': 'Number of new poor'},
               inplace=True)
@@ -320,21 +294,21 @@ def get_policy_effectiveness_tab(outcomes: pd.DataFrame) -> pd.DataFrame:
 
 
 def get_weeks_in_poverty_tab(outcomes: pd.DataFrame) -> pd.DataFrame:
-    '''Get the average across scenarios number of weeks in poverty for each district.
+    '''Get the average across scenarios number of weeks in poverty for each region.
 
     Args:
         outcomes (pd.DataFrame): Outcomes.
 
     Returns:
-        pd.DataFrame: Average number of weeks in poverty for each district.
+        pd.DataFrame: Average number of weeks in poverty for each region.
     '''
-    # Specify the districts
-    districts = ['Anse-La-Raye & Canaries', 'Castries', 'Choiseul',
-                 'Dennery', 'Gros Islet', 'Laborie', 'Micoud', 'Soufriere', 'Vieuxfort']
-    districts = outcomes['district'].unique()
+    # Specify the regions
+    regions = ['Anse-La-Raye & Canaries', 'Castries', 'Choiseul',
+               'Dennery', 'Gros Islet', 'Laborie', 'Micoud', 'Soufriere', 'Vieuxfort']
+    regions = outcomes['region'].unique()
 
     # Keep track of the averages
-    district_average = {}
+    region_average = {}
 
     # Get the number of scenarios
     n_scenarios = outcomes['scenario'].unique().size
@@ -343,10 +317,10 @@ def get_weeks_in_poverty_tab(outcomes: pd.DataFrame) -> pd.DataFrame:
     column_name = 'years_in_poverty'
     all_keys = outcomes[column_name][0].keys()
 
-    # Iterate through the districts
-    for district in districts:
-        # Subset the outcomes for a specific district
-        df = outcomes[outcomes['district'] == district]
+    # Iterate through the regions
+    for region in regions:
+        # Subset the outcomes for a specific region
+        df = outcomes[outcomes['region'] == region]
 
         # Get the dictionaries from the column
         dicts = df[column_name].tolist()
@@ -363,12 +337,12 @@ def get_weeks_in_poverty_tab(outcomes: pd.DataFrame) -> pd.DataFrame:
                     sums[key] += 0
 
         # Calculate the average
-        district_average[district] = {
+        region_average[region] = {
             key: sums[key] / n_scenarios for key in all_keys}
 
     # Convert the dictionary to a dataframe
-    result = pd.DataFrame(district_average).T
-    result.index.name = 'District'
+    result = pd.DataFrame(region_average).T
+    result.index.name = 'Region'
     result.columns = [i for i in range(0, len(all_keys))]
     # result.columns = [int(x) if int(x) < len(
     #     all_keys) else f'>{len(all_keys)}' for x in range(1, len(all_keys) + 1)]
@@ -376,21 +350,21 @@ def get_weeks_in_poverty_tab(outcomes: pd.DataFrame) -> pd.DataFrame:
 
 
 def get_average_weighted_vulnerability(outcomes: pd.DataFrame, quintile: bool) -> pd.DataFrame:
-    '''Get the average weighted vulnerability for each district.
+    '''Get the average weighted vulnerability for each region.
 
     Args:
         outcomes (pd.DataFrame): Outcomes.
         quintile (bool): Whether to calculate the average weighted vulnerability by quintile or decile.
 
     Returns:
-        pd.DataFrame: Average weighted vulnerability for each district.
+        pd.DataFrame: Average weighted vulnerability for each region.
     '''
-    # Specify the districts
-    districts = ['Anse-La-Raye & Canaries', 'Castries', 'Choiseul',
-                 'Dennery', 'Gros Islet', 'Laborie', 'Micoud', 'Soufriere', 'Vieuxfort']
+    # Specify the regions
+    regions = ['Anse-La-Raye & Canaries', 'Castries', 'Choiseul',
+               'Dennery', 'Gros Islet', 'Laborie', 'Micoud', 'Soufriere', 'Vieuxfort']
 
     # Keep track of the averages
-    district_average = {}
+    region_average = {}
 
     # Get the number of scenarios
     n_scenarios = outcomes['scenario'].unique().size
@@ -402,10 +376,10 @@ def get_average_weighted_vulnerability(outcomes: pd.DataFrame, quintile: bool) -
         column_name = 'weighted_vuln_dec'
         all_keys = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
 
-    # Iterate through the districts
-    for district in districts:
-        # Subset the outcomes for a specific district
-        df = outcomes[outcomes['district'] == district]
+    # Iterate through the regions
+    for region in regions:
+        # Subset the outcomes for a specific region
+        df = outcomes[outcomes['region'] == region]
 
         # Get the dictionaries from the column
         dicts = df[column_name].tolist()
@@ -422,14 +396,14 @@ def get_average_weighted_vulnerability(outcomes: pd.DataFrame, quintile: bool) -
                     sums[key] += 0
 
         # Calculate the average
-        district_average[district] = {
+        region_average[region] = {
             key: sums[key] / n_scenarios for key in all_keys}
 
     # Convert the dictionary to a dataframe
-    result = pd.DataFrame(district_average).T
+    result = pd.DataFrame(region_average).T
 
     # Rename the index and columns
-    result.index.name = 'District'
+    result.index.name = 'region'
     if quintile:
         result.columns.name = 'Quintile'
     else:
@@ -463,3 +437,34 @@ def calculate_resilience(affected_households: pd.DataFrame, tot_wellbeing_loss:
         # r = total_asset_damage / tot_wellbeing_loss
 
     return r
+
+
+def split_policies(outcomes: pd.DataFrame) -> tuple:
+    # TODO: Make the function more flexible and allow for different top ups and target groups
+    # Split ASP policies
+    asp = outcomes[outcomes['current_policy'].str.contains('asp')].copy()
+    asp[['target_group', 'top_up']] = asp['current_policy'].str.split(
+        '+', expand=True)
+    asp['target_group'] = asp['target_group'].str.split(':', expand=True)[1]
+
+    # Make top_up a categorical variable to ensure it is ordered correctly
+    asp['top_up'] = pd.Categorical(asp['top_up'], categories=['0', '10', '50'])
+    asp['target_group'] = pd.Categorical(asp['target_group'], categories=[
+                                         'poor_near_poor2.0', 'all'])
+
+    # Append None policy to ASP
+    none = outcomes[outcomes['current_policy'] == 'none'].copy()
+    none['target_group'] = 'none'
+    none['top_up'] = 'none'
+    none['current_policy'] = 'none'
+    asp = pd.concat([asp, none])
+
+    # Split retrofit policies
+    retrofit = outcomes[outcomes['current_policy'].str.contains(
+        'retrofit')].copy()
+    retrofit[['target_group', 'houses_pct']
+             ] = retrofit['current_policy'].str.split('+', expand=True)
+    retrofit['target_group'] = retrofit['target_group'].str.split(':', expand=True)[
+        1]
+    retrofit = pd.concat([retrofit, none])
+    return asp, retrofit
diff --git a/unbreakable/analysis/calculator.py b/unbreakable/analysis/calculator.py
index f759370..6525aba 100644
--- a/unbreakable/analysis/calculator.py
+++ b/unbreakable/analysis/calculator.py
@@ -1,91 +1,73 @@
-"""This module contains functions to calculate outcomes of interest.
-It has the main function `get_outcomes` which uses other functions to calculate individual outcomes."""
-
 import pandas as pd
 import numpy as np
 import yaml
 
 
-def calculate_outcomes(households: pd.DataFrame, tot_exposed_asset: float, expected_loss_frac: float, district_pml: float, years_to_recover: int, welfare: float) -> dict:
-    '''Calculate outcomes of interest from the simulation model.
-
-    Args:
-        households (pd.DataFrame): Households.
-        tot_exposed_asset (float): Total exposed asset stock.
-        expected_loss_fraction (float): Expected loss fraction.
-        district_pml (float): Probable maximum loss for a district.
-        years_to_recover (float): Number of years cut-off parameter when calculating consumption loss. Default is 10.
-        welfare (float): Welfare parameter.
-
-    Returns:
-        dict: Outcomes of interest, where the key is the name of the outcome and value is the outcome.
-    '''
-    # Save some values for verification. There are not going to be used for the analysis.
-    total_population = households['wgt'].sum()
-    tot_asset_surv = households['tot_asset_surv'].iloc[0]
-    annual_average_consumption = (
-        households['exp'] * households['wgt']).sum() / households['wgt'].sum()
-
-    affected_households = households[households['is_affected'] == True]
-    n_affected_people = affected_households['wgt'].sum()
-
-    # Recovery rate must be calculated only for the affected households only
-    mean_recovery_rate = affected_households['recovery_rate'].mean()
-    total_asset_loss = affected_households[[
+def calculate_outcomes(households: pd.DataFrame, tot_exposed_asset: float, expected_loss_frac: float, region_pml: float, years_to_recover: int, welfare: float) -> dict:
+    outcomes = {}
+
+    tot_pop = households['wgt'].sum()
+    pov_line_adjust = households['poverty_line_adjusted'].values[0]
+    aff_households = households[households['is_affected'] == True]
+
+    outcomes['return_period'] = households['return_period'].iloc[0]
+    outcomes['tot_pop'] = tot_pop
+    outcomes['tot_households'] = households.shape[0]
+    outcomes['n_aff_people'] = aff_households['wgt'].sum()
+    outcomes['n_aff_households'] = aff_households.shape[0]
+    try:
+        outcomes['n_retrofitted'] = households[households['retrofitted']
+                                               == True]['wgt'].sum()
+        outcomes['n_retrofitted_hh'] = households[households['retrofitted']
+                                                  == True].shape[0]
+        outcomes['n_aff_retrofitted'] = aff_households[aff_households['retrofitted']
+                                                       == True]['wgt'].sum()
+        outcomes['n_aff_retrofitted_hh'] = aff_households[aff_households['retrofitted'] == True].shape[0]
+    except:
+        outcomes['n_retrofitted_ppl'] = 0
+        outcomes['n_retrofitted_hh'] = 0
+        outcomes['n_aff_retrofitted_ppl'] = 0
+        outcomes['n_aff_retrofitted_hh'] = 0
+
+    outcomes['tot_asset_surv'] = households['tot_asset_surv'].iloc[0]
+    outcomes['tot_exposed_asset'] = tot_exposed_asset
+    outcomes['tot_asset_loss'] = aff_households[[
         'keff', 'v', 'wgt']].prod(axis=1).sum()
-    total_consumption_loss = affected_households[[
-        'consumption_loss_NPV', 'wgt']].prod(axis=1).sum()
-
-    # Poverty line adjusted (see `match_assets_and_expenditures` function in `households.py`)
-    # Poverty line ois the same for all households
-    povline_adjusted = households['povline_adjusted'].values[0]
+    outcomes['expected_loss_frac'] = expected_loss_frac
+    outcomes['region_pml'] = region_pml
+    outcomes['annual_avg_consum'] = weighted_mean(households, 'exp', 'wgt')
+    outcomes['pov_line_adjust'] = households['poverty_line_adjusted'].iloc[0]
+    outcomes['mean_recovery_rate'] = aff_households['recovery_rate'].mean()
+    outcomes['tot_consum_loss_npv'] = weighted_sum(
+        aff_households, 'consumption_loss_npv', 'wgt')
 
     # Get the model outcomes
     n_poor_initial, n_new_poor, n_poor_affected, poor_initial, new_poor = find_poor(
-        households, povline_adjusted, years_to_recover)
+        households, pov_line_adjust, years_to_recover)
 
     years_in_poverty = get_people_by_years_in_poverty(new_poor)
 
     initial_poverty_gap, new_poverty_gap_initial, new_poverty_gap_all = calculate_poverty_gap(
-        poor_initial, new_poor, povline_adjusted, years_to_recover)
+        poor_initial, new_poor, pov_line_adjust, years_to_recover)
 
     annual_average_consumption_loss, annual_average_consumption_loss_pct = calculate_average_annual_consumption_loss(
-        affected_households, years_to_recover)
-
-    tot_consum_equiv_loss = - affected_households['wellbeing'].div(welfare).multiply(
-        affected_households['wgt']).sum()
-
-    r = calculate_resilience(affected_households, tot_consum_equiv_loss)
-
-    # Get the weighted average vulnerability by consumption quintile and decile
-    # weighted_vuln_quint = get_weighted_vuln(affected_households, quintile=True)
-    # weighted_vuln_dec = get_weighted_vuln(affected_households, quintile=False)
-
-    outcomes = {
-        'total_population': total_population,
-        'total_asset_loss': total_asset_loss,
-        'total_consumption_loss': total_consumption_loss,
-        'tot_exposed_asset': tot_exposed_asset,
-        'tot_asset_surv': tot_asset_surv,
-        'expected_loss_frac': expected_loss_frac,
-        'n_affected_people': n_affected_people,
-        'annual_average_consumption': annual_average_consumption,
-        'povline_adjusted': povline_adjusted,
-        'district_pml': district_pml,
-        'n_poor_initial': n_poor_initial,
-        'n_poor_affected': n_poor_affected,
-        'n_new_poor': n_new_poor,
-        'initial_poverty_gap': initial_poverty_gap,
-        'new_poverty_gap_initial': new_poverty_gap_initial,
-        'new_poverty_gap_all': new_poverty_gap_all,
-        'annual_average_consumption_loss': annual_average_consumption_loss,
-        'annual_average_consumption_loss_pct': annual_average_consumption_loss_pct,
-        'mean_recovery_rate': mean_recovery_rate,
-        'r': r,
-        # 'weighted_vuln_quint': weighted_vuln_quint,
-        # 'weighted_vuln_dec': weighted_vuln_dec,
-        'years_in_poverty': years_in_poverty
-    }
+        aff_households, years_to_recover)
+
+    tot_consum_equiv_loss = - aff_households['wellbeing'].div(welfare).multiply(
+        aff_households['wgt']).sum()
+
+    r = calculate_resilience(aff_households, tot_consum_equiv_loss)
+
+    outcomes['n_poor_initial'] = n_poor_initial
+    outcomes['n_poor_affected'] = n_poor_affected
+    outcomes['n_new_poor'] = n_new_poor
+    outcomes['initial_poverty_gap'] = initial_poverty_gap
+    outcomes['new_poverty_gap_initial'] = new_poverty_gap_initial
+    outcomes['new_poverty_gap_all'] = new_poverty_gap_all
+    outcomes['annual_avg_consum_loss'] = annual_average_consumption_loss
+    outcomes['annual_avg_consum_loss_pct'] = annual_average_consumption_loss_pct
+    outcomes['r'] = r
+    outcomes['years_in_poverty'] = years_in_poverty
 
     # Save outcome names in a yaml file to pick up in preprocessing
     with open('analysis/outcomes.yaml', 'w') as f:
@@ -94,12 +76,12 @@ def calculate_outcomes(households: pd.DataFrame, tot_exposed_asset: float, expec
     return outcomes
 
 
-def find_poor(households: pd.DataFrame, poverty_line: float, years_to_recover: int) -> tuple:
+def find_poor(households: pd.DataFrame, pov_line: float, years_to_recover: int) -> tuple:
     '''Get the poor at the beginning of the simulation and the poor at the end of the simulation
 
     Args:
         households (pd.DataFrame): Households.
-        poverty_line (float): Poverty line.
+        pov_line (float): Poverty line.
         years_to_recover (int): Number of years cut-off parameter when calculating consumption loss.
 
     Returns:
@@ -115,8 +97,8 @@ def find_poor(households: pd.DataFrame, poverty_line: float, years_to_recover: i
     not_poor = households[households['is_poor'] == False]
     not_poor_affected = not_poor[not_poor['is_affected'] == True]
     x = not_poor_affected['exp'] - \
-        not_poor_affected['consumption_loss_NPV'] / years_to_recover
-    new_poor = not_poor_affected.loc[x < poverty_line, :]
+        not_poor_affected['consumption_loss_npv'] / years_to_recover
+    new_poor = not_poor_affected.loc[x < pov_line, :]
     new_poor = new_poor.assign(is_poor=True)
     n_new_poor = round(new_poor['wgt'].sum())
 
@@ -133,7 +115,7 @@ def get_people_by_years_in_poverty(affected_households: pd.DataFrame) -> dict:
         dict: Number of people in poverty for each year in poverty
     '''
     affected_households = affected_households.assign(
-        years_in_poverty=affected_households['weeks_pov'] // 52)
+        years_in_poverty=affected_households['weeks_in_poverty'] // 52)
     d = {}
     longest_years_in_poverty = 10
     for i in range(longest_years_in_poverty + 1):
@@ -158,15 +140,11 @@ def calculate_poverty_gap(poor_initial: pd.DataFrame, new_poor: pd.DataFrame, po
         Exception: If the index is duplicated
         Exception: If the poverty gap is greater than 1
     '''
-    # !: Current implementation doesn't work
-    # !: We got to negatives
-
-    # First we need to calculate the poverty gap at the beginning of the simulation
-    # Get the weighted average expenditure
     average_expenditure_poor_initial = (
         poor_initial['exp'] * poor_initial['wgt']).sum() / poor_initial['wgt'].sum()
 
-    # Calculate the poverty gap at the beginning of the simulation
+    # assert poverty_line > average_expenditure_poor_initial, 'Poverty line cannot be less than average expenditure of the poor'
+
     initial_poverty_gap = (
         poverty_line - average_expenditure_poor_initial) / poverty_line
 
@@ -175,7 +153,7 @@ def calculate_poverty_gap(poor_initial: pd.DataFrame, new_poor: pd.DataFrame, po
 
     # Expenditure of both were affected by the disaster
     all_poor = all_poor.assign(
-        exp=all_poor['exp'] - all_poor['consumption_loss_NPV'] / years_to_recover)
+        exp=all_poor['exp'] - all_poor['consumption_loss_npv'] / years_to_recover)
 
     # Now, get the average expenditure of the poor at the end of the simulation
     average_expenditure_poor_all = (
@@ -187,7 +165,7 @@ def calculate_poverty_gap(poor_initial: pd.DataFrame, new_poor: pd.DataFrame, po
 
     # However, we also want to know the poverty gap for old poor
     poor_initial = poor_initial.assign(
-        exp=poor_initial['exp'] - poor_initial['consumption_loss_NPV'] / years_to_recover)
+        exp=poor_initial['exp'] - poor_initial['consumption_loss_npv'] / years_to_recover)
 
     average_expenditure_poor_initial = (
         poor_initial['exp'] * poor_initial['wgt']).sum() / poor_initial['wgt'].sum()
@@ -221,13 +199,13 @@ def calculate_average_annual_consumption_loss(affected_households: pd.DataFrame,
 
     # Annual consumption loss
     annual_consumption_loss = (
-        affected_households['consumption_loss_NPV'].div(years_to_recover).multiply(affected_households['wgt'])).sum()
+        affected_households['consumption_loss_npv'].div(years_to_recover).multiply(affected_households['wgt'])).sum()
 
     # Weighted average
     annual_average_consumption_loss = annual_consumption_loss / \
         affected_households['wgt'].sum()
 
-    annual_average_consumption_loss_pct = (affected_households['consumption_loss_NPV']
+    annual_average_consumption_loss_pct = (affected_households['consumption_loss_npv']
                                            .div(years_to_recover)
                                            .div(affected_households['exp'])
                                            .multiply(affected_households['wgt']).sum())\
@@ -252,6 +230,7 @@ def calculate_resilience(affected_households: pd.DataFrame, tot_consum_equiv_los
     Returns:
         float: Socio-economic resilience
     '''
+    # TODO: Test resilience values, it should not go above, e.g., 10
     # total_consumption_loss = (
     #     affected_households[['consumption_loss_NPV', 'wgt']].prod(axis=1)).sum()
 
@@ -268,28 +247,29 @@ def calculate_resilience(affected_households: pd.DataFrame, tot_consum_equiv_los
     return r
 
 
-def get_weighted_vuln(affected_households: pd.DataFrame, quintile: bool) -> dict:
-    '''Calculate weighted average vulnerability of affected households by consumption quintile or decile.
+def weighted_sum(df: pd.DataFrame, col: str, wgt: str) -> float:
+    '''Calculate the weighted sum of a column in a dataframe.
 
     Args:
-        affected_households (pd.DataFrame): Affected households.
-        quintile (bool): Whether to calculate by quintile.
+        df (pd.DataFrame): Dataframe.
+        col (str): Column name.
+        wgt (str): Weight column name.
+
+    Returns:
+        float: Weighted sum of a column in a dataframe.
+    '''
+    return (df[col] * df[wgt]).sum()
+
+
+def weighted_mean(df: pd.DataFrame, col: str, wgt: str) -> float:
+    '''Calculate the weighted mean of a column in a dataframe.
+
+    Args:
+        df (pd.DataFrame): Dataframe.
+        col (str): Column name.
+        wgt (str): Weight column name.
 
     Returns:
-        dict: Weighted average vulnerability by consumption quintile or decile.
+        float: Weighted mean of a column in a dataframe.
     '''
-    df = affected_households.copy()
-    if quintile:
-        df['v_weighted'] = df['v'].multiply(df['wgt'])
-        v_weighted_by_q = df.groupby('quintile').sum(
-            numeric_only=True)[['v_weighted']]
-        pop_by_q = df.groupby('quintile').sum(numeric_only=True)[['wgt']]
-        average_v_by_q = v_weighted_by_q['v_weighted'].div(pop_by_q['wgt'])
-        return average_v_by_q.to_dict()
-    else:
-        df['v_weighted'] = df['v'].multiply(df['wgt'])
-        v_weighted_by_d = df.groupby('decile').sum(
-            numeric_only=True)[['v_weighted']]
-        pop_by_d = df.groupby('decile').sum(numeric_only=True)[['wgt']]
-        average_v_by_d = v_weighted_by_d['v_weighted'].div(pop_by_d['wgt'])
-        return average_v_by_d.to_dict()
+    return weighted_sum(df, col, wgt) / df[wgt].sum()
diff --git a/unbreakable/analysis/verifier.py b/unbreakable/analysis/verifier.py
new file mode 100644
index 0000000..a6b7ebd
--- /dev/null
+++ b/unbreakable/analysis/verifier.py
@@ -0,0 +1,139 @@
+import os
+import pandas as pd
+from tqdm import tqdm
+
+
+def filter_and_save_consumption_data(country: str = 'Nigeria', return_period: int = 100, run_subset: bool = False, n_runs: int = 1, include_regions_of_interest: bool = False, regions_of_interest: list = None, save_results: bool = False):
+    '''
+    Filter and save the consumption recovery data for the specified country.
+
+    Args:
+        country (str): The country for which to load the data.
+        return_period (int): The return period of the disaster.
+        run_subset (bool): If True, only the first n_runs runs are included. If False, all runs are included.
+        n_runs (int): The number of runs to include if run_subset is True.
+        include_regions_of_interest (bool): If True, only data from regions are included. If False, data from all regions are included.
+        regions_of_interest (list, optional): A list of regions of interest. Only used if include_regions_of_interest is True.
+        save_results (bool): If True, the results are saved to a CSV file.
+
+    Returns:
+        dict: A dictionary with keys 'Conflict' and 'No Conflict', each containing a dictionary with 'region' and 'data'.
+
+    Examples:
+        # Load only 1 run
+        run_subset = True
+        n_runs = 1
+
+        # Load only data from Oyo and Taraba
+        include_regions_of_interest = True
+        regions_of_interest = ['Oyo', 'Taraba']
+
+        # Run the function
+        results = filter_and_save_consumption_data(run_subset=run_subset, n_runs=n_runs, include_regions_of_interest=include_regions_of_interest, regions_of_interest=regions_of_interest, save_results=True)
+    '''
+    # Initialize the results dictionary
+    results = {}
+
+    # False - No Conflict, True - Conflict
+    run_types = [False, True]
+
+    # Loop through the two run types
+    for c in run_types:
+        # Set the path to the experiment data
+        path = os.path.abspath(
+            f'../../experiments/{country}/consumption_recovery/return_period={return_period}/conflict={c}/')
+
+        # Get the list of folders in the path
+        folders = [f for f in os.listdir(
+            path) if os.path.isdir(os.path.join(path, f))]
+
+        # Limit the number of runs if run_subset is True
+        if run_subset:
+            folders = folders[:n_runs]
+        else:
+            n_runs = len(folders)
+
+        # Initialize the dictionary to store the data for all regions
+        all_regions_data = {}
+
+        # Loop through the folders
+        for folder in tqdm(folders, desc=f"Processing {'Conflict' if c else 'No Conflict'} Runs"):
+            # Get the list of files in the folder
+            folder_path = os.path.join(path, folder)
+
+            # Limit the files to the regions of interest if include_regions_of_interest is True
+            if include_regions_of_interest:
+                files = [f for f in os.listdir(folder_path) if f.split('.')[
+                    0] in regions_of_interest]
+            else:
+                files = os.listdir(folder_path)
+
+            # Check if region should be included based on the include_conflict_regions_only flag and conflict_regions list
+            for file in files:
+                # Get the region name from the file name
+                region = file.split('.')[0]
+
+                # Load the data from the file
+                file_path = os.path.join(folder_path, file)
+                data = pd.read_pickle(file_path)
+
+                # t is the time index
+                t = list(data.keys())
+
+                # Get the consumption recovery data for each region
+                all_region_hh = [data[i]['c_t'].rename(
+                    i) for i in t if not data[i].empty]
+
+                # Concatenate the data for all households in the region
+                all_regions_data[region] = pd.concat(all_region_hh, axis=1)
+
+        # Store the results in the dictionary
+        results['Conflict' if c else 'No Conflict'] = all_regions_data
+
+    # Save the results to a CSV file
+    if save_results:
+        results_path = os.path.abspath(
+            f'../../experiments/{country}/consumption_recovery/')
+        if not os.path.exists(results_path):
+            os.makedirs(results_path)
+        for run_type, regions_data in results.items():
+            for region, data in regions_data.items():
+                data.to_csv(f'{results_path}/{run_type}_{region}_{n_runs}.csv')
+
+    return results
+
+
+def load_consumption_data(country: str = 'Nigeria') -> list:
+    '''Load the prepared consumption recovery data.'''
+    # List the files in the folder
+    folder = f'../../experiments/{country}/consumption_recovery/'
+    files = os.listdir(folder)
+
+    # Get the regional and mean files
+    regional_files = [f for f in files if 'mean' not in f]
+
+    # Ignore files if these are folders
+    regional_files = [f for f in regional_files if '.' in f]
+
+    # Load the data from the regional files
+    # Differentiate between conflict and no conflict
+    conflict_regional_data = {}
+    no_conflict_regional_data = {}
+
+    for file in regional_files:
+        # Get the region name from the file name
+        region = file.split('_')[1]
+        # Get the conflict status from the file name
+        conflict_status = 'No Conflict' if 'No Conflict' in file else 'Conflict'
+        # Load the data from the file
+        data = pd.read_csv(folder + file, index_col=0)
+        # Store the data in the appropriate dictionary
+        if conflict_status == 'Conflict':
+            conflict_regional_data[region] = data
+        else:
+            no_conflict_regional_data[region] = data
+
+    return conflict_regional_data, no_conflict_regional_data
+
+
+filter_and_save_consumption_data(save_results=True)
diff --git a/unbreakable/analysis/visualizer.py b/unbreakable/analysis/visualizer.py
index b75c985..873ef24 100644
--- a/unbreakable/analysis/visualizer.py
+++ b/unbreakable/analysis/visualizer.py
@@ -1,5 +1,3 @@
-"""This module contains functions to visualize outcomes of the simulation model."""
-
 import geopandas as gpd
 import matplotlib
 import matplotlib.pyplot as plt
@@ -28,15 +26,15 @@ def raincloud_plot(outcomes: pd.DataFrame, savefig: bool, color_palette: str = '
         None
     '''
 
-    districts = outcomes['district'].unique().tolist()
-    n_districts = len(districts)
-    colors = sns.color_palette(color_palette, n_colors=len(districts))
+    regions = outcomes['region'].unique().tolist()
+    n_regions = len(regions)
+    colors = sns.color_palette(color_palette, n_colors=len(regions))
 
     x_columns = [
-        'n_affected_people',
+        'n_aff_people',
         'n_new_poor_increase_pp',
         'n_new_poor',
-        'annual_average_consumption_loss_pct',
+        'annual_avg_consum_loss_pct',
         'r',
         'new_poverty_gap_initial',
         'new_poverty_gap_all',
@@ -53,11 +51,11 @@ def raincloud_plot(outcomes: pd.DataFrame, savefig: bool, color_palette: str = '
 
     for x_column, x_title in zip(x_columns, x_titles):
         fig, ax = plt.subplots(ncols=3, nrows=4, figsize=(
-            4 * n_districts / 3, 3 * n_districts / 3), sharex=sharex)
+            4 * n_regions / 3, 3 * n_regions / 3), sharex=sharex)
 
-        for district in districts:
-            # Select the district
-            df = outcomes[outcomes['district'] == district].copy()
+        for region in regions:
+            # Select the region
+            df = outcomes[outcomes['region'] == region].copy()
 
             # Convert to float
             df[x_column] = df[x_column].astype(float)
@@ -66,25 +64,25 @@ def raincloud_plot(outcomes: pd.DataFrame, savefig: bool, color_palette: str = '
             pt.half_violinplot(x=x_column,
                                y='policy',
                                data=df,
-                               color=colors[districts.index(district)],
+                               color=colors[regions.index(region)],
                                bw=.2,
                                cut=0.,
                                scale="area",
                                width=.6,
                                inner=None,
-                               ax=ax[districts.index(district) // 3, districts.index(district) % 3])
+                               ax=ax[regions.index(region) // 3, regions.index(region) % 3])
 
             # Add stripplot
             sns.stripplot(x=x_column,
                           y='policy',
                           data=df,
-                          color=colors[districts.index(district)],
+                          color=colors[regions.index(region)],
                           edgecolor='white',
                           size=3,
                           jitter=1,
                           zorder=0,
                           orient='h',
-                          ax=ax[districts.index(district) // 3, districts.index(district) % 3])
+                          ax=ax[regions.index(region) // 3, regions.index(region) % 3])
 
             # Add boxplot
             sns.boxplot(x=x_column,
@@ -99,31 +97,31 @@ def raincloud_plot(outcomes: pd.DataFrame, savefig: bool, color_palette: str = '
                         whiskerprops={'linewidth': 2, "zorder": 10},
                         saturation=1,
                         orient='h',
-                        ax=ax[districts.index(district) // 3, districts.index(district) % 3])
+                        ax=ax[regions.index(region) // 3, regions.index(region) % 3])
 
             # Set title
-            title = district
-            ax[districts.index(district) // 3,
-               districts.index(district) % 3].set_title(title)
-            ax[districts.index(district) // 3,
-               districts.index(district) % 3].set_ylabel('')
-            ax[districts.index(district) // 3,
-               districts.index(district) % 3].set_xlabel(x_title)
+            title = region
+            ax[regions.index(region) // 3,
+               regions.index(region) % 3].set_title(title)
+            ax[regions.index(region) // 3,
+               regions.index(region) % 3].set_ylabel('')
+            ax[regions.index(region) // 3,
+               regions.index(region) % 3].set_xlabel(x_title)
 
             # Remove y ticks and labels
-            ax[districts.index(district) // 3,
-               districts.index(district) % 3].set_yticklabels([])
-            ax[districts.index(district) // 3,
-               districts.index(district) % 3].set_yticks([])
+            ax[regions.index(region) // 3,
+               regions.index(region) % 3].set_yticklabels([])
+            ax[regions.index(region) // 3,
+               regions.index(region) % 3].set_yticks([])
 
             # Do not display floats in the x-axis
-            ax[districts.index(district) // 3, districts.index(district) %
+            ax[regions.index(region) // 3, regions.index(region) %
                3].xaxis.set_major_locator(MaxNLocator(integer=True))
 
             # Add text close to the boxplot's median
-            ax[districts.index(district) // 3, districts.index(district) % 3].text(df[x_column].median(), 0.2,
-                                                                                   f'M={df[x_column].median():.2f}',
-                                                                                   horizontalalignment='left', size='small', color='black')
+            ax[regions.index(region) // 3, regions.index(region) % 3].text(df[x_column].median(), 0.2,
+                                                                           f'M={df[x_column].median():.2f}',
+                                                                           horizontalalignment='left', size='small', color='black')
         # Remove 2 last subplots
         ax[3, 1].set_visible(False)
         ax[3, 2].set_visible(False)
@@ -438,34 +436,34 @@ def annotated_hist(outcomes: pd.DataFrame, savefig: bool, annotate: bool) -> Non
     Returns:
         None
     '''
-    sns.histplot(outcomes['annual_average_consumption_loss_pct'])
+    sns.histplot(outcomes['annual_avg_consum_loss_pct'])
     # plt.xlabel('Wt. Ann. Avg. Consump. Loss p.c. (%)')
     plt.xlabel('Annual Average Consumption Loss PC (%)')
     plt.ylabel('Run count')
     # plt.xlim(0, 50)
-    plt.axvline(outcomes['annual_average_consumption_loss_pct'].min(
+    plt.axvline(outcomes['annual_avg_consum_loss_pct'].min(
     ), color='red', linestyle='dashed', linewidth=1)
-    plt.axvline(outcomes['annual_average_consumption_loss_pct'].max(
+    plt.axvline(outcomes['annual_avg_consum_loss_pct'].max(
     ), color='red', linestyle='dashed', linewidth=1)
-    plt.axvline(outcomes['annual_average_consumption_loss_pct'].median(
+    plt.axvline(outcomes['annual_avg_consum_loss_pct'].median(
     ), color='black', linestyle='dashed', linewidth=1)
     if annotate:
-        plt.annotate(f"{outcomes['annual_average_consumption_loss_pct'].min():.2f}%",
-                     xy=(outcomes['annual_average_consumption_loss_pct'].min(), 0),
+        plt.annotate(f"{outcomes['annual_avg_consum_loss_pct'].min():.2f}%",
+                     xy=(outcomes['annual_avg_consum_loss_pct'].min(), 0),
                      xytext=(
-                         outcomes['annual_average_consumption_loss_pct'].min() - 5, 100),
+                         outcomes['annual_avg_consum_loss_pct'].min() - 5, 100),
                      arrowprops=dict(facecolor='black', shrink=0.05),
                      horizontalalignment='right', verticalalignment='top')
-        plt.annotate(f"{outcomes['annual_average_consumption_loss_pct'].max():.2f}%",
-                     xy=(outcomes['annual_average_consumption_loss_pct'].max(), 0),
+        plt.annotate(f"{outcomes['annual_avg_consum_loss_pct'].max():.2f}%",
+                     xy=(outcomes['annual_avg_consum_loss_pct'].max(), 0),
                      xytext=(
-                         outcomes['annual_average_consumption_loss_pct'].max() + 5, 100),
+                         outcomes['annual_avg_consum_loss_pct'].max() + 5, 100),
                      arrowprops=dict(facecolor='black', shrink=0.05),
                      horizontalalignment='left', verticalalignment='top')
-        plt.annotate(f"{outcomes['annual_average_consumption_loss_pct'].median():.2f}%",
-                     xy=(outcomes['annual_average_consumption_loss_pct'].median(), 0),
+        plt.annotate(f"{outcomes['annual_avg_consum_loss_pct'].median():.2f}%",
+                     xy=(outcomes['annual_avg_consum_loss_pct'].median(), 0),
                      xytext=(
-                         outcomes['annual_average_consumption_loss_pct'].median() + 5, 100),
+                         outcomes['annual_avg_consum_loss_pct'].median() + 5, 100),
                      arrowprops=dict(facecolor='black', shrink=0.05),
                      horizontalalignment='left', verticalalignment='top')
     plt.tight_layout()
@@ -479,7 +477,7 @@ def annotated_hist(outcomes: pd.DataFrame, savefig: bool, annotate: bool) -> Non
 
 
 def coloured_density_plots(outcomes: pd.DataFrame, savefig: bool, scheme: str = 'equal_intervals', k: int = 4, cmap: str = "OrRd", legend: bool = True) -> None:
-    '''Make colored density plots for each district. Color here matches the color of the choropleth map.
+    '''Make colored density plots for each region. Color here matches the color of the choropleth map.
 
     Args:
         outcomes (pd.DataFrame): The outcomes dataframe.
@@ -494,12 +492,12 @@ def coloured_density_plots(outcomes: pd.DataFrame, savefig: bool, scheme: str =
     # Let's make colors of density plots match the colors of the choropleth map
 
     if savefig:
-        outcomes['district'].replace(
+        outcomes['region'].replace(
             {'Anse-La-Raye & Canaries': 'Anse-La-Raye \& Canaries'}, inplace=True)
 
-    # Choropleth map uses median values to classify the districts, we're going to do the same
-    median_outcomes = outcomes.groupby('district').median(numeric_only=True)[
-        ['annual_average_consumption_loss_pct']]
+    # Choropleth map uses median values to classify the regions, we're going to do the same
+    median_outcomes = outcomes.groupby('region').median(numeric_only=True)[
+        ['annual_avg_consum_loss_pct']]
 
     # The map used equalinterval scheme, but it would be beneficial to allow for other schemes
     if scheme == 'equal_intervals':
@@ -514,35 +512,35 @@ def coloured_density_plots(outcomes: pd.DataFrame, savefig: bool, scheme: str =
     bin_edges = classifier.bins
     bin_labels = classifier.yb
 
-    # Map the district to the bin label
-    district_to_label_mapper = dict(zip(median_outcomes.index, bin_labels))
+    # Map the region to the bin label
+    region_to_label_mapper = dict(zip(median_outcomes.index, bin_labels))
 
     # Map the bin label to a color
     colors = mcp.gen_color(cmap=cmap, n=k)
     label_color_mapper = dict(zip(np.arange(0, k), colors))
 
-    districts = outcomes['district'].unique().tolist()
+    regions = outcomes['region'].unique().tolist()
     fig, ax = plt.subplots()
 
-    # Get districts with min and max values
-    descr = outcomes.iloc[:, 2:-1].groupby('district').describe()[
-        ['annual_average_consumption_loss_pct']]
-    min_distr = descr['annual_average_consumption_loss_pct']['50%'].idxmin()
-    max_distr = descr['annual_average_consumption_loss_pct']['50%'].idxmax()
+    # Get regions with min and max values
+    descr = outcomes.iloc[:, 2:-1].groupby('region').describe()[
+        ['annual_avg_consum_loss_pct']]
+    min_distr = descr['annual_avg_consum_loss_pct']['50%'].idxmin()
+    max_distr = descr['annual_avg_consum_loss_pct']['50%'].idxmax()
 
     # Now make the density plots
-    for district in districts:
-        df = outcomes[outcomes['district'] == district]
-        district_label = district_to_label_mapper[district]
-        label_color = label_color_mapper[district_label]
+    for region in regions:
+        df = outcomes[outcomes['region'] == region]
+        region_label = region_to_label_mapper[region]
+        label_color = label_color_mapper[region_label]
         color = label_color
 
-        # Make the line thicker for districts with min and max values
-        if district in [min_distr, max_distr]:
+        # Make the line thicker for regions with min and max values
+        if region in [min_distr, max_distr]:
             linewidth = 5
         else:
             linewidth = 1
-        sns.kdeplot(data=df, x='annual_average_consumption_loss_pct',
+        sns.kdeplot(data=df, x='annual_avg_consum_loss_pct',
                     ax=ax, color=color, linewidth=linewidth, alpha=1)
         if savefig:
             # ax.set_xlabel('Wt. Ann. Avg. Consump. Loss p.c. (\%)')
@@ -555,9 +553,9 @@ def coloured_density_plots(outcomes: pd.DataFrame, savefig: bool, scheme: str =
         ax.spines['left'].set_visible(False)
         ax.spines['bottom'].set_visible(False)
         ax.set_facecolor('lightgray')
-    # ax.set_xlim(5, 12.5)
+
     if legend:
-        ax.legend(districts, title='District', frameon=False)
+        ax.legend(regions, title='Parish', frameon=False)
     fig.tight_layout()
 
     if savefig:
diff --git a/unbreakable/data/generator.py b/unbreakable/data/generator.py
index 2164c93..fb24e24 100644
--- a/unbreakable/data/generator.py
+++ b/unbreakable/data/generator.py
@@ -1,8 +1,30 @@
-# Create dummy or synthetic data for testing purposes
+import numpy as np
+import pandas as pd
 
-def generate_households():
+
+def generate_households(num_households=1):
+    '''Generate dummy households.'''
+    np.random.seed(0)  # Ensure reproducibility
+    data = {
+        'exp': np.random.uniform(1000, 5000, num_households),
+        'sav': np.random.uniform(100, 1000, num_households),
+        'v': np.random.uniform(0.2, 0.8, num_households),
+        'keff': np.random.uniform(500, 15000, num_households),
+        'exp_house': 0,
+        'consumption_loss': 0,
+        'consumption_loss_npv': 0,
+        'net_consumption_loss': 0,
+        'net_consumption_loss_npv': 0,
+        'c_t': 0,
+        'c_t_unaffected': 0,
+        'wellbeing': 0
+    }
+    return pd.DataFrame(data)
+
+
+def generate_risk_and_damage():
     pass
 
 
-def generate_asset_damage():
+def generate_conflict():
     pass
diff --git a/unbreakable/data/randomizer.py b/unbreakable/data/randomizer.py
index 91337b0..318b38e 100644
--- a/unbreakable/data/randomizer.py
+++ b/unbreakable/data/randomizer.py
@@ -19,6 +19,11 @@ def randomize(households: pd.DataFrame, risk_and_damage: pd.DataFrame,
     # Ensure reproducibility
     if random_seed is not None:
         np.random.seed(random_seed)
+        households['random_seed'] = random_seed
+
+    # Save country and return period
+    households['country'] = params['country']
+    households['return_period'] = params['return_period']
 
     # Extra necessary parameters
     rnd_inc_params = params['rnd_inc_params']
@@ -29,19 +34,19 @@ def randomize(households: pd.DataFrame, risk_and_damage: pd.DataFrame,
     min_households = params['min_households']
 
     # Step 1: Randomize income
-    households = rnd_income(households, rnd_inc_params, random_seed)
+    households = rnd_income(households, rnd_inc_params)
 
     # Step 2: Randomize savings
-    households = rnd_savings(households, rnd_sav_params, random_seed)
+    households = rnd_savings(households, rnd_sav_params)
 
     # Step 3: Randomize rent
-    households = rnd_rent(households, rnd_rent_params, random_seed)
+    households = rnd_rent(households, rnd_rent_params)
 
     # Step 4: Calculate dwelling value
     households = calc_dwelling_value(households, avg_prod)
 
     # Step 5: Randomize house vulnerability
-    households = rnd_house_vuln(households, rnd_house_vuln_params, random_seed)
+    households = rnd_house_vuln(households, rnd_house_vuln_params)
 
     # Step 6: Resample households to ensure representativeness
     households = resample_households(households, min_households, random_seed)
@@ -78,9 +83,8 @@ def rnd_inc_mult(size: int, **params) -> np.ndarray:
             f"Distribution '{distr}' is not supported yet.")
 
 
-def rnd_income(households: pd.DataFrame, rnd_income_params: dict, random_seed: int) -> pd.DataFrame:
+def rnd_income(households: pd.DataFrame, rnd_income_params: dict) -> pd.DataFrame:
     '''Randomize household income.'''
-    np.random.seed(random_seed)
     households = households.copy()
     size = households.shape[0]
 
@@ -103,6 +107,10 @@ def rnd_income(households: pd.DataFrame, rnd_income_params: dict, random_seed: i
     else:
         raise ValueError("No income multiplier found.")
 
+    assert not households['inc'].isna().any(), "Income cannot be NaN"
+    assert not (households['inc'] < 0).any(), "Income cannot be negative"
+    assert not (households['inc'] == 0).any(), "Income cannot be 0"
+
     return households
 
 
@@ -123,11 +131,8 @@ def rnd_sav_mult(size: int, **params):
         raise ValueError(f"Distribution '{distr}' is not supported yet.")
 
 
-def rnd_savings(households: pd.DataFrame, rnd_savings_params: dict, random_seed: int) -> pd.DataFrame:
+def rnd_savings(households: pd.DataFrame, rnd_savings_params: dict) -> pd.DataFrame:
     '''Randomize household savings.'''
-    # Set random seed for reproducibility
-    np.random.seed(random_seed)
-
     # Get function parameters
     distr = rnd_savings_params['distr']
     size = households.shape[0]
@@ -148,6 +153,11 @@ def rnd_savings(households: pd.DataFrame, rnd_savings_params: dict, random_seed:
 
     # Estimate savings as a product of income and savings multiplier
     households['sav'] = households['inc'] * sav_mult_rnd
+
+    assert not households['sav'].isna().any(), "Savings cannot be NaN"
+    assert not (households['sav'] < 0).any(), "Savings cannot be negative"
+    assert not (households['sav'] == 0).any(), "Savings cannot be 0"
+
     return households
 
 
@@ -168,11 +178,8 @@ def rnd_rent_mult(size: int, **params):
         raise ValueError(f"Distribution '{distr}' is not supported yet.")
 
 
-def rnd_rent(households: pd.DataFrame, rnd_rent_params: dict, random_seed: int) -> pd.DataFrame:
+def rnd_rent(households: pd.DataFrame, rnd_rent_params: dict) -> pd.DataFrame:
     '''Randomize household rent.'''
-    # Set random seed for reproducibility
-    np.random.seed(random_seed)
-
     # Get function parameters
     distr = rnd_rent_params['distr']
     size = households.shape[0]
@@ -191,20 +198,38 @@ def rnd_rent(households: pd.DataFrame, rnd_rent_params: dict, random_seed: int)
     # Generate savings using the distribution-specific function
     rent_mult_rnd = rnd_rent_mult(size, **rnd_rent_params)
 
-    # Estimate rent as a product of income and rent multiplier
-    households['rent'] = households['inc'] * rent_mult_rnd
+    # Assign rent as a product of exp and rent multiplier to each of the rows
+    households['exp_house'] = households['exp'].mul(rent_mult_rnd)
+
+    assert not households['exp_house'].isna().any(), "Rent cannot be NaN"
+    assert not (households['exp_house'] < 0).any(), "Rent cannot be negative"
+    assert not (households['exp_house'] == 0).any(), "Rent cannot be 0"
+
+    # Remove rent for the households that own
+    households.loc[households['own_rent'] == 'own', 'exp_house'] = 0
 
     return households
 
 
 def calc_dwelling_value(households: pd.DataFrame, avg_prod: float) -> pd.DataFrame:
     '''Calculate dwelling value based on the average productivity.'''
+    # Get the dwelling value for the households that own
     households.loc[households['own_rent'] == 'own',
                    'k_house'] = households['inc'] / avg_prod
+
+    # Set the dwelling value to 0 for the households that rent
+    households.loc[households['own_rent'] == 'rent',
+                   'k_house'] = 0
+
+    assert not households['k_house'].isna(
+    ).any(), "Dwelling value cannot be NaN"
+    assert not (households['k_house'] < 0).any(
+    ), "Dwelling value cannot be negative"
+
     return households
 
 
-def rnd_house_vuln(households: pd.DataFrame, rnd_house_vuln_params: dict, random_seed: int) -> pd.DataFrame:
+def rnd_house_vuln(households: pd.DataFrame, rnd_house_vuln_params: dict) -> pd.DataFrame:
     '''
     Randomize house vulnerability.
 
@@ -212,7 +237,6 @@ def rnd_house_vuln(households: pd.DataFrame, rnd_house_vuln_params: dict, random
         households: Households
             Required columns: 'v_init' (initial vulnerability)
         rnd_house_vuln_params: Dictionary with parameters for randomization.
-        random_seed: Random seed.
 
     Returns:
         households: Households with randomized vulnerability.
@@ -220,14 +244,12 @@ def rnd_house_vuln(households: pd.DataFrame, rnd_house_vuln_params: dict, random
     Raises:
         ValueError: If the distribution is not supported.
     '''
-
-    np.random.seed(random_seed)
     distr = rnd_house_vuln_params['distr']
     if distr == 'uniform':
         # Unpack parameters
-        low = rnd_house_vuln_params['low']  # default 0.7
+        low = rnd_house_vuln_params['low']  # default 0.8
         high = rnd_house_vuln_params['high']  # default 1.0
-        thresh = rnd_house_vuln_params['thresh']  # default 0.99
+        thresh = rnd_house_vuln_params['thresh']  # default 0.9
 
         # Multiply initial vulnerability by a random value
         households['v'] = households['v_init'] * \
@@ -250,17 +272,19 @@ def resample_households(households: pd.DataFrame, min_households: int, random_se
     Args:
         households (pd.DataFrame): Households.
         min_households (int): Minimum number of households for the country sample to be representative.
-        random_seed (int): Random seed.
+        random_seed (int): Random seed for reproducibility.
 
     Returns:
-        pd.DataFrame: Resampled households.
+        pd.DataFrame: Resampled households.f
     '''
     sample = pd.DataFrame()
     households['id_orig'] = households['id']
     for rgn in households['region'].unique():
         rgn_households = households[households['region'] == rgn]
-        rgn_households = resample_region(
-            rgn_households, min_households, random_seed)
+        # TODO: Remove this condition when the data is fixed
+        if rgn != 'Borno':
+            rgn_households = resample_region(
+                rgn_households, min_households, random_seed)
         sample = pd.concat(
             [sample, rgn_households])
     sample['id'] = range(1, len(sample) + 1)
@@ -268,12 +292,13 @@ def resample_households(households: pd.DataFrame, min_households: int, random_se
     return sample
 
 
-def resample_region(households: pd.DataFrame, min_households: int, random_state: int) -> pd.DataFrame:
+def resample_region(households: pd.DataFrame, min_households: int, random_seed: int) -> pd.DataFrame:
     '''Weighted resampling with adjustment for household representation within a given region.
 
     Args:
         households (pd.DataFrame): Households of a specific region.
         min_households (int): Minimum number of households for the region sample to be representative.
+        random_seed (int): Random seed for reproducibility.
 
     Returns:
         pd.DataFrame: Resampled households of a specific region.
@@ -297,8 +322,13 @@ def resample_region(households: pd.DataFrame, min_households: int, random_state:
         if delta == 1:
             delta = 2
 
-        sample = households.sample(
-            n=delta, replace=True, random_state=random_state)
+        # ?: Check if fixing np random seed is affecting pandas random sampling
+        if random_seed is not None:
+            sample = households.sample(
+                n=delta, replace=True, random_state=random_seed)
+        else:
+            sample = households.sample(
+                n=delta, replace=True)
 
         # Keep how many duplicates by index
         duplicates = sample.index.value_counts()
@@ -454,8 +484,8 @@ def adjust_poverty_line(households: pd.DataFrame, scaling_factor: float) -> pd.D
     Returns:
         pd.DataFrame: Households data with adjusted poverty line.
     '''
-    poverty_line = households['povline'].iloc[0]
-    households['povline_adjusted'] = poverty_line * scaling_factor
+    poverty_line = households['poverty_line'].iloc[0]
+    households['poverty_line_adjusted'] = poverty_line * scaling_factor
 
     return households
 
diff --git a/unbreakable/data/reader.py b/unbreakable/data/reader.py
index 5a8565f..b43107b 100644
--- a/unbreakable/data/reader.py
+++ b/unbreakable/data/reader.py
@@ -1,24 +1,3 @@
-"""This module provides functions for reading and processing data related to disaster impact analysis and recovery planning.
-
-It includes two primary functions:
-1. `read_asset_damage`: Reads asset damage data from a specified file location. This data is essential for assessing the physical and monetary losses incurred by assets in different districts due to disasters.
-2. `read_household_survey`: Reads household survey data from a specified file location. This data provides insights into the demographics, economic conditions, and vulnerabilities of households, which are crucial for effective disaster response and recovery planning.
-
-Both functions are designed to handle common file formats such as CSV and Excel, and include error handling for robust data processing.
-
-Example usage:
-    import pandas as pd
-    from your_module_name import read_asset_damage, read_household_survey
-
-    # Load asset damage data
-    asset_damage = read_asset_damage('CountryName')
-
-    # Load household survey data
-    household_survey = read_household_survey('CountryName')
-
-    # Now asset_damage and household_survey are pandas DataFrames containing the respective data.
-"""
-
 import pandas as pd
 import logging
 from pathlib import Path
@@ -89,3 +68,10 @@ def read_conflict_data(country: str, base_path: str = "../data/processed/conflic
         logging.error(
             f"Error reading conflict data from {file_path}: {e}")
         raise
+
+
+def load_data(country):
+    '''Load all data for a given country.'''
+    all_households = read_household_survey(country)
+    risk_and_damage = read_risk_and_damage(country)
+    return all_households, risk_and_damage
diff --git a/unbreakable/model.py b/unbreakable/model.py
index f5d3105..d3c62d6 100644
--- a/unbreakable/model.py
+++ b/unbreakable/model.py
@@ -3,6 +3,7 @@
 import pandas as pd
 import yaml
 from pathlib import Path
+from typing import NamedTuple
 from ema_workbench import *
 from unbreakable.data.reader import *
 from unbreakable.data.randomizer import *
@@ -18,202 +19,180 @@ def model(**params) -> dict:
     '''Simulation model.
 
     Args:
-        params (dict): A dictionary of parameters. For a complete list of parameters, see `config/SaintLucia.yaml`.
+        params (dict): A dictionary of parameters. For a complete list of parameters, see, e.g., `config/Nigeria.yaml`.
 
     Returns:
         dict: A dictionary of outcomes. The key is a district and value is a dictionary of outcomes.
     '''
-    # Make sure that we have all required global parameters
-    validate_params(params)
-
-    # If a policy is not provided, use the default policy
-    current_policy = params.get('current_policy', 'none')
-    country = params['country']
-
-    # Fix random seed for reproducibility
-    random_seed = params['random_seed']
-    np.random.seed(random_seed)
+    replicator = params.get('replicator', False)
+    if not replicator:
+        random_seed = params['random_seed']
+        np.random.seed(random_seed)
+    else:
+        random_seed = None
 
-    # Read household survey and damage files
-    all_households = read_household_survey(params['country'])
-    risk_and_damage = read_risk_and_damage(params['country'])
+    check_params(params)
+    households, risk_and_damage = load_data(params['country'])
+    households = randomize(
+        households, risk_and_damage, params, random_seed=random_seed)
 
-    # Randomize households survey data
-    all_households = randomize(
-        all_households, risk_and_damage, params, random_seed=random_seed)
+    if params['is_conflict']:
+        conflict = read_conflict_data(params['country'])
+        households = get_conflict_intensity(
+            households, conflict)
 
-    # Save random seed for reproducibility
-    all_households['random_seed'] = random_seed
+    welfare = calculate_welfare(households, params['cons_util'])
 
-    # Store outcomes in a dictionary, where the key is a district and value is a dictionary of outcomes
     outcomes = {}
 
-    # Whether country has a conflict
-    is_conflict = params['is_conflict']
-
-    welfare = calculate_welfare(all_households, params['cons_util'])
-
-    # If there is a conflict, read conflict data and calculate its effect on the economy
-    if is_conflict:
-        conflict_impact = read_conflict_data(params['country'])
-        affected_economy = affect_economy(
-            conflict_impact, params['avg_prod'], params['inc_exp_growth'])
-
-    # If there is no conflict, use the base values specified in the config file
-    else:
-        avg_prod = params['avg_prod']
-        inc_exp_growth = params['inc_exp_growth']
-
     for region in params['regions']:
-        # Select households in a specific region
-        region_households = all_households[all_households['region'] == region].copy(
+        region_households = households[households['region'] == region].copy(
         )
 
-        # Get disaster data for the region and return period
-        exposed_stock, loss_fraction, region_pml = get_region_damage(
-            risk_and_damage, region, params['return_per'])
-
-        # Get affected average productivity and income and expenditure growth for the region
-        if is_conflict:
-            avg_prod = affected_economy[affected_economy['region']
-                                        == region]['avg_prod'].values[0]
-            inc_exp_growth = affected_economy[affected_economy['region']
-                                              == region]['inc_exp_growth'].values[0]
-
-        # For the dynamic policy
-        # cash_transfer = {52: 1000, 208: 5000}
-        cash_transfer = {}
+        total_exposed_stock, expected_loss_fraction, region_pml = get_region_damage(
+            risk_and_damage, region, params['return_period'])
 
         region_households = (region_households
-                             .pipe(estimate_impact, region_pml, params['pov_bias'], params['calc_exposure_params'])
+                             .pipe(calculate_exposure, region_pml, params['pov_bias'], params['calc_exposure_params'])
                              .pipe(identify_affected, region_pml, params['identify_aff_params'], random_seed=random_seed)
-                             .pipe(apply_policy, country, current_policy, random_seed)
-                             .pipe(calc_rec_rate, avg_prod, params['cons_util'], params['disc_rate'], params['lambda_incr'], params['yrs_to_rec'])
-                             .pipe(calc_wellbeing, avg_prod, params['cons_util'], params['disc_rate'], inc_exp_growth, params['yrs_to_rec'], params['add_inc_loss'], cash_transfer, is_conflict))
+                             .pipe(apply_policy, params['country'], params.get('current_policy', 'none'), params['disaster_type'], random_seed=random_seed)
+                             .pipe(calculate_recovery_rate, params['avg_prod'], params['cons_util'], params['disc_rate'], params['lambda_incr'], params['yrs_to_rec'], params['is_conflict'])
+                             .pipe(calculate_wellbeing, params['avg_prod'], params['cons_util'], params['disc_rate'], params['inc_exp_growth'], params['yrs_to_rec'], params['add_inc_loss'], params['save_consumption_recovery'], params['is_conflict']))
 
         if params['save_households']:
             save_households(
-                region_households, params['country'], region, random_seed)
+                params['country'], region, region_households, random_seed=random_seed)
 
         outcomes[region] = np.array(list(calculate_outcomes(
-            region_households, exposed_stock, loss_fraction, region_pml, params['yrs_to_rec'], welfare).values()))
+            region_households, total_exposed_stock, expected_loss_fraction, region_pml, params['yrs_to_rec'], welfare).values()))
 
     return outcomes
 
 
-def validate_params(params: dict) -> None:
-    '''
-    Validates that all required parameters are present.
-
-    Args:
-        params (dict): Dictionary containing parameters.
-
-    Raises:
-        ValueError: If any required parameter is missing.
-    '''
-
-    required_keys = [
-        'country', 'regions', 'return_per', 'yrs_to_rec', 'avg_prod',
-        'cons_util', 'disc_rate', 'lambda_incr', 'inc_exp_growth',
-        'add_inc_loss', 'random_seed', 'atol', 'pov_bias', 'calc_exposure_params',
-        'identify_aff_params', 'is_conflict', 'save_households'
-    ]
-
-    missing_keys = [key for key in required_keys if key not in params]
-
-    if missing_keys:
+def check_params(params: dict) -> None:
+    '''Check that all config parameters for a case study are present.'''
+    required_params = ['country', 'regions', 'avg_prod',
+                       'cons_util', 'disc_rate',
+                       'calc_exposure_params', 'identify_aff_params',
+                       'add_inc_loss', 'pov_bias', 'lambda_incr', 'yrs_to_rec',
+                       'rnd_house_vuln_params', 'rnd_inc_params', 'rnd_sav_params',
+                       'rnd_rent_params', 'min_households', 'atol', 'save_households']
+    missing_params = [
+        param for param in required_params if param not in params]
+    if missing_params:
         raise ValueError(
-            f"Missing essential parameters: {', '.join(missing_keys)}")
+            f"Missing essential parameters: {', '.join(missing_params)}")
 
 
-def save_households(households: pd.DataFrame, country: str, district: str, random_seed: int):
-    '''
-    Saves the households data to a CSV file.
-    '''
+def save_households(households: pd.DataFrame, params: dict, random_seed: int):
+    '''Save region households data to a CSV file.'''
+    country = params['country']
+    region = params['region']
     output_dir = Path(f'../experiments/{country}/households/')
     output_dir.mkdir(parents=True, exist_ok=True)
-    file_path = output_dir / f'{district}_{random_seed}.csv'
+    file_path = output_dir / f'{region}_{random_seed}.csv'
     households.to_csv(file_path)
 
 
-def load_config(country: str) -> dict:
-    """
-    Load configuration for the specified country.
-
-    Args:
-        country (str): The name of the country for which to load the configuration.
-
-    Returns:
-        dict: Configuration dictionary loaded from the YAML file.
-
-    Raises:
-        FileNotFoundError: If the YAML configuration file for the specified country is not found.
-    """
+def load_config(country: str, return_period: int, conflict: bool = False) -> dict:
+    '''Load configuration for the specified case country.'''
     config_path = Path(f"../config/{country}.yaml")
     if not config_path.exists():
         raise FileNotFoundError(
             f"Config file for {country} not found at {config_path}")
-
     with open(config_path, "r") as file:
-        return yaml.safe_load(file)
+        config = yaml.safe_load(file)
+    return_periods = [10, 50, 100, 250, 500, 1000]
+    if return_period not in return_periods:
+        raise ValueError(
+            f"Return period {return_period} not in available return periods: {return_periods}")
+    config['constants']['return_period'] = return_period
+    if conflict:
+        config['constants']['is_conflict'] = True
+    else:
+        config['constants']['is_conflict'] = False
+    return config
 
 
-def setup_model(config: dict, n_replications: int = None) -> Model:
+def setup_model(config: dict, replicator: bool) -> Model:
     """
     Set up the EMA Workbench model based on the provided configuration.
 
     Args:
         config (dict): Configuration dictionary loaded from the YAML file.
-        n_replications (int): Number of replications for the model. Defaults to None.
 
     Returns:
         Model: Configured EMA Workbench model.
     """
     # Initialize the EMA Workbench model
-    my_model = Model(name="model", function=model)
-    # my_model = ReplicatorModel(name="model", function=model)
+    if replicator:
+        # Set up the ReplicatorModel to iterate over multiple seeds
+        my_model = ReplicatorModel(name="model", function=model)
 
-    # Extract and set up uncertainties, constants, and levers from the config
-    # uncertainties = config.get("uncertainties", {})
-    constants = config.get("constants", {})
-    levers = config.get("levers", {})
+        # Extract and set up uncertainties, constants, and levers from the config
+        # uncertainties = config.get("uncertainties", {})
+        constants = config.get("constants", {})
+        levers = config.get("levers", {})
 
-    # Define seed as an uncertainty for multiple runs
-    seed_start = 0
-    seed_end = 1000000000
+        # Uncertainties
+        my_model.uncertainties = [
+            CategoricalParameter('is_conflict', [False, True])]
 
-    my_model.uncertainties = [IntegerParameter("random_seed", seed_start, seed_end)]\
-        #   + [RealParameter(key, values[0], values[1]) for key, values in uncertainties.items()]
+        # Constants
+        my_model.constants = [Constant(key, value)
+                              for key, value in constants.items()] + [Constant('replicator', True)]
 
-    # Constants
-    my_model.constants = [Constant(key, value)
-                          for key, value in constants.items()]
+        # Levers
+        my_model.levers = [CategoricalParameter(
+            'current_policy', [values for _, values in levers.items()])]
 
-    # Levers
-    my_model.levers = [CategoricalParameter(
-        'current_policy', [values for _, values in levers.items()])]
+        # Outcomes
+        my_model.outcomes = [ArrayOutcome(region)
+                             for region in constants.get('regions', [])]
 
-    # Outcomes
-    my_model.outcomes = [ArrayOutcome(region)
-                         for region in constants.get('regions', [])]
+        my_model.replications = 2
 
-    # my_model.replications = n_replications
+        return my_model
+    else:
+        my_model = Model(name="model", function=model)
 
-    return my_model
+        # Extract and set up uncertainties, constants, and levers from the config
+        # uncertainties = config.get("uncertainties", {})
+        constants = config.get("constants", {})
+        levers = config.get("levers", {})
 
+        # Define seed as an uncertainty for multiple runs
+        seed_start = 0
+        seed_end = 1000000000
 
-def run_experiments(model: Model, n_scenarios: int, n_policies: int, country: str, multiprocessing: bool, n_processes: int = 12):
-    """
-    Run experiments on the model using EMA Workbench and save results.
+        my_model.uncertainties = [IntegerParameter(
+            "random_seed", seed_start, seed_end)]
+
+        # Constants
+        my_model.constants = [Constant(key, value)
+                              for key, value in constants.items()]
+
+        # Levers
+        my_model.levers = [CategoricalParameter(
+            'current_policy', [values for _, values in levers.items()])]
+
+        # Outcomes
+        my_model.outcomes = [ArrayOutcome(region)
+                             for region in constants.get('regions', [])]
+
+        return my_model
+
+
+def run_experiments(experimental_setup: dict) -> None:
+    """Run experiments on the model using EMA Workbench and save results."""
+    country = experimental_setup['country']
+    return_period = experimental_setup['return_period']
+    model = experimental_setup['model']
+    n_scenarios = experimental_setup['n_scenarios']
+    n_policies = experimental_setup['n_policies']
+    multiprocessing = experimental_setup['multiprocessing']
+    n_processes = experimental_setup['n_processes']
 
-    Args:
-        model (Model): The EMA Workbench model to run.
-        n_scenarios (int): Number of scenarios to run.
-        n_policies (int): Number of policies to run.
-        country (str): Name of the country for which the experiments are run.
-        multiprocessing (bool): Whether to use multiprocessing.
-        n_processes (int): Number of processes to use for multiprocessing. Defaults to 12.
-    """
     if multiprocessing:
         with MultiprocessingEvaluator(model, n_processes=n_processes) as evaluator:
             results = evaluator.perform_experiments(
@@ -222,17 +201,18 @@ def run_experiments(model: Model, n_scenarios: int, n_policies: int, country: st
         results = perform_experiments(
             models=model, scenarios=n_scenarios, policies=n_policies)
 
+    save_experiment_results(country, return_period, model,
+                            results, n_scenarios, n_policies)
+
+
+def save_experiment_results(country: str, return_period: int, model: Model, results, n_scenarios, n_policies):
+    """Saves experiment results to a file, taking into account if there was a conflict."""
     results_path = Path(f'../experiments/{country}')
     results_path.mkdir(parents=True, exist_ok=True)
 
-    try:
-        is_conflict = model.constants._data['is_conflict'].value
-    except KeyError:
-        is_conflict = False
+    is_conflict = getattr(model.constants._data.get(
+        'is_conflict'), 'value', False)
 
-    if is_conflict:
-        save_results(results, results_path /
-                     f"scenarios={n_scenarios}, policies={n_policies}, conflict=True.tar.gz")
-    else:
-        save_results(results, results_path /
-                     f"scenarios={n_scenarios}, policies={n_policies}.tar.gz")
+    conflict_str = ", conflict=True" if is_conflict else ""
+    filename = f"return_period={return_period}, scenarios={n_scenarios}, policies={n_policies}{conflict_str}.tar.gz"
+    save_results(results, results_path / filename)
diff --git a/unbreakable/modules/conflict.py b/unbreakable/modules/conflict.py
index 3776e2f..2d67411 100644
--- a/unbreakable/modules/conflict.py
+++ b/unbreakable/modules/conflict.py
@@ -1,27 +1,38 @@
 import pandas as pd
 
 
-def affect_economy(conflict_impact: pd.DataFrame, avg_prod: float, inc_exp_growth: float) -> pd.DataFrame:
-    '''Calculate the effect of conflict on average productivity and income and expenditure growth.
+# def calculate_conflict_impact(conflict: pd.DataFrame, avg_prod: float, inc_exp_growth: float) -> tuple:
+#     '''Calculate the effect of a conflict on average productivity and income and expenditure growth by region.
+
+#     Args:
+#         conflict (pd.DataFrame): Conflict data.
+#         avg_prod (float): Base average productivity.
+#         inc_exp_growth (float): Base income and expenditure growth.
+
+#     Returns:
+#         pd.DataFrame: Affected average productivity and income and expenditure growth.
+#     '''
+#     # Decrease the average productivity by Avg. prod. decrease %
+#     conflict['avg_prod'] = avg_prod - \
+#         conflict['Avg. prod. decrease'] * avg_prod
+
+#     # Decrease the income and expenditure growth by Inc. exp. growth decrease %
+#     conflict['inc_exp_growth'] = inc_exp_growth - \
+#         conflict['Inc. and exp. growth decrease'] * inc_exp_growth
+
+#     return conflict.set_index('region')['avg_prod'], conflict.set_index('region')['inc_exp_growth']
+
+def get_conflict_intensity(households: pd.DataFrame, conflict: pd.DataFrame) -> pd.DataFrame:
+    '''Assign conflict intensity to each region.
 
     Args:
-        conflict_impact (pd.DataFrame): Conflict impact data.
-        avg_prod (float): Average productivity.
-        inc_exp_growth (float): Income and expenditure growth.
+        households (pd.DataFrame): Households data.
+        conflict (pd.DataFrame): Conflict data.
 
     Returns:
-        pd.DataFrame: Affected economy.
+        pd.DataFrame: Households data with conflict intensity.
     '''
-    # Make a copy of the conflict impact data
-    df = conflict_impact.copy()
-
-    # Decrease the average productivity by Avg. prod. decrease %
-    df['avg_prod'] = avg_prod - \
-        df['Avg. prod. decrease'] * avg_prod
-
-    # Decrease the income and expenditure growth by Inc. exp. growth decrease %
-    df['inc_exp_growth'] = inc_exp_growth - \
-        df['Inc. and exp. growth decrease'] * inc_exp_growth
+    households['conflict_intensity'] = households['region'].map(
+        conflict.set_index('region')['Bin'])
 
-    # Return affected economy
-    return df
+    return households
diff --git a/unbreakable/modules/disaster.py b/unbreakable/modules/disaster.py
index c9b39b9..9e82915 100644
--- a/unbreakable/modules/disaster.py
+++ b/unbreakable/modules/disaster.py
@@ -1,14 +1,14 @@
 import pandas as pd
 
 
-def get_region_damage(all_damage: pd.DataFrame, region: str, return_per: int) -> tuple[float, float, float]:
+def get_region_damage(all_damage: pd.DataFrame, region: str, return_period: int) -> tuple[float, float, float]:
     '''
     Extracts total exposed stock, loss fraction and PML for a given region and return period.
 
     Args:
         all_damage (pd.DataFrame): DataFrame containing damage data.
         region (str): The region to filter the data.
-        return_per (int): The return period to filter the data.
+        return_period (int): The return period to filter the data.
 
     Returns:
         tuple[float, float]: A tuple containing the total exposed stock, loss fraction and PML.
@@ -18,7 +18,7 @@ def get_region_damage(all_damage: pd.DataFrame, region: str, return_per: int) ->
     if 'rp' in all_damage.columns:
         filtered_data = all_damage.loc[
             (all_damage['region'] == region) & (
-                all_damage['rp'] == return_per),
+                all_damage['rp'] == return_period),
             ['total_exposed_stock', 'loss_fraction', 'pml']
         ]
     # If the doesn't have `rp` column use only the `region` column to filter the data
diff --git a/unbreakable/modules/households.py b/unbreakable/modules/households.py
index 27d5ef3..53e32a9 100644
--- a/unbreakable/modules/households.py
+++ b/unbreakable/modules/households.py
@@ -2,7 +2,24 @@
 import numpy as np
 
 
-def estimate_impact(households: pd.DataFrame, region_pml: float, pov_bias: float, calc_exposure_params: dict) -> pd.DataFrame:
+def calculate_exposure(households: pd.DataFrame, region_pml: float, pov_bias: float, calc_exposure_params: dict) -> pd.DataFrame:
+    '''Calculate the exposure of each household to the disaster.
+
+    Households have either have an equal change of being affected by the disaster or a change based on their poverty bias. `fa` stands for `fraction affected` and represents the impact of the disaster on each household. 
+
+    Args:
+        households (pd.DataFrame): Households.
+        region_pml (float): Region PML.
+        pov_bias (float): Poverty bias.
+        calc_exposure_params (dict): Parameters for calculating exposure.
+
+    Returns:
+        pd.DataFrame: Households with a new `fa` column representing the impact of the disaster.
+
+    Raises:
+        ValueError: If only uniform distribution is supported yet.
+    '''
+
     # Random value for poverty bias
     if pov_bias == 'random':
         if calc_exposure_params['distr'] == 'uniform':
@@ -10,37 +27,39 @@ def estimate_impact(households: pd.DataFrame, region_pml: float, pov_bias: float
             low = calc_exposure_params['low']
             # default 1.5
             high = calc_exposure_params['high']
-            povbias = np.random.uniform(low, high)
+            poverty_bias = np.random.uniform(low, high)
         else:
             raise ValueError("Only uniform distribution is supported yet.")
     else:
-        povbias = pov_bias
+        poverty_bias = pov_bias
 
     # Set poverty bias to 1 for all households
     households['pov_bias'] = 1
 
     # Set poverty bias to povbias for poor households
-    households.loc[households['is_poor'] == True, 'pov_bias'] = povbias
+    households.loc[households['is_poor'] == True, 'pov_bias'] = poverty_bias
 
-    # !: If keff is just k_house, why not just use k_house?
+    # Keff is the effective capital stock of the household
+    # Physical assets such as land, housing, and durable goods
+    # Currently, only the dwelling value k_house is considered
     households['keff'] = households['k_house'].copy()
 
-    # Calculate the exposure of each household
+    # How much each household is affected by the disaster
     normalization_factor = households[['keff', 'v', 'pov_bias', 'wgt']].prod(
         axis=1).sum()
 
-    # Distribute the impact of the disaster across households
+    # Distribute the impact of the disaster given region's PML across households
     households['fa'] = (region_pml / normalization_factor) * \
-        households[['pov_bias']]
+        households['pov_bias']  # `Poverty bias` allows to re-distribute impact between poor and non-poor households
 
     return households
 
 
 def identify_affected(households: pd.DataFrame, region_pml: float, ident_affected_params: dict, random_seed: int) -> pd.DataFrame:
-    '''Determines affected households.
+    '''Determine affected households.
 
     We assume that all households have the same chance of being affected, 
-    but based on `fa` value calculated in `estimate_impact`.
+    but based on `fa` value calculated in `calculate_exposure`.
 
     Args:
         households (pd.DataFrame): Households.
@@ -54,9 +73,10 @@ def identify_affected(households: pd.DataFrame, region_pml: float, ident_affecte
         ValueError: If no mask was found.
     '''
     # BUG: For whatever reason fixing random seed in the model.py doesn't work here
-    np.random.seed(random_seed)
+    if random_seed is not None:
+        np.random.seed(random_seed)
 
-    # Allow for a relatively small error
+    # Allow for a relatively small error between the total asset loss and the PML
     delta = region_pml * ident_affected_params['delta_pct']  # default 0.025
 
     # Check if total asset is less than PML
@@ -69,7 +89,7 @@ def identify_affected(households: pd.DataFrame, region_pml: float, ident_affecte
     high = ident_affected_params['high']  # default 1
 
     # Generate multiple boolean masks at once
-    num_masks = ident_affected_params['num_masks']  # default 2000
+    num_masks = ident_affected_params['num_masks']  # default 10,000
     masks = np.random.uniform(
         low, high, (num_masks, households.shape[0])) <= households['fa'].values
 
@@ -113,12 +133,16 @@ def identify_affected(households: pd.DataFrame, region_pml: float, ident_affecte
         raise ValueError(
             f'Total asset loss ({tot_asset_loss}) is not within the desired range.')
 
+    # num_affected = households['is_affected'].sum()
+    # print(f'Number of affected households: {num_affected}')
+    # print(households[households['is_affected']].index)
+
     return households
 
 
-def calculate_welfare(country_households: pd.DataFrame, cons_util: float) -> float:
-    # TODO: Make sure that the function name and what is returns is correct
-    # TODO: Add a docstring
-    welfare = (np.sum(country_households['exp'] * country_households['wgt']) / np.sum(
-        country_households['wgt'])) ** (-cons_util)
+def calculate_welfare(households: pd.DataFrame, cons_util: float) -> float:
+    '''Calculate the welfare of all households in a country based on their expenditures and a given consumption utility.'''
+    weighted_average_expenditure = np.sum(households['exp'] * households['wgt']) / np.sum(
+        households['wgt'])
+    welfare = weighted_average_expenditure ** (-cons_util)
     return welfare
diff --git a/unbreakable/modules/policy.py b/unbreakable/modules/policy.py
index 8e41808..ed4254f 100644
--- a/unbreakable/modules/policy.py
+++ b/unbreakable/modules/policy.py
@@ -1,30 +1,7 @@
-"""This module provides a function to apply a specific monetary policy to a set of households, based on their economic status and the impact of external factors.
-
-The main function in this module is `apply_policy`, which takes a DataFrame representing households and a policy string as input. The policy is defined by a target group and a top-up percentage, indicating the increase in savings or assets for the selected households. The target group can be 'all', 'poor', 'poor_near_poor1.25', or 'poor_near_poor2.0'. The function modifies the 'aesav' column of the DataFrame, representing the adjusted savings or assets after applying the policy.
-
-Example usage:
-    import pandas as pd
-    from your_module_name import apply_policy
-
-    # Load household data
-    households_data = pd.read_csv('households.csv')
-
-    # Define a policy to apply - for instance, increasing assets by 100% for poor households
-    policy = 'poor+100'
-
-    # Apply the policy to the household data
-    updated_households = apply_policy(households_data, policy)
-
-    # The updated_households DataFrame now contains the adjusted 'aesav' values for the targeted households
-"""
-# TODO: Update module description
-
-
 import pandas as pd
+import numpy as np
 
 
-# TODO: Allow to specify dynamic policies
-
 def cash_transfer(households: pd.DataFrame, current_policy: str) -> pd.DataFrame:
     '''
     Apply cash transfer to a specific target group.
@@ -33,7 +10,7 @@ def cash_transfer(households: pd.DataFrame, current_policy: str) -> pd.DataFrame
 
     Args:
         households (pd.DataFrame): Households.
-            Required columns: 'is_affected', 'is_poor', 'exp', 'povline_adjusted', 'keff', 'v', 'sav'
+            Required columns: 'is_affected', 'is_poor', 'exp', 'poverty_line_adjusted', 'keff', 'v', 'sav'
         policy (str): Policy to apply. Format: "<target_group>+<top_up>". Example: "poor+100".
 
     Returns:
@@ -47,7 +24,7 @@ def cash_transfer(households: pd.DataFrame, current_policy: str) -> pd.DataFrame
             "policy should be in the format '<target_group>+<top_up>'")
 
     # Get the adjusted poverty line
-    povline_adjusted = households['povline_adjusted'].iloc[0]
+    poverty_line_adjusted = households['poverty_line_adjusted'].iloc[0]
 
     # Filter affected households
     affected_households = households.query('is_affected')
@@ -63,13 +40,13 @@ def cash_transfer(households: pd.DataFrame, current_policy: str) -> pd.DataFrame
         if len(beneficiaries) == 0:
             return households
 
-    # FIXME: These must be different policies, poor and near poor
+    # TODO: Split into separate policies poor and near_poor
     elif target_group in ['poor_near_poor1.25', 'poor_near_poor2.0']:
         multiplier = 1.25 if target_group == 'poor_near_poor1.25' else 2.0
         # Define conditions for poor and near poor households
         poor_condition = affected_households['is_poor'] == True
         near_poor_condition = (~affected_households['is_poor']) & (
-            affected_households['exp'] < multiplier * povline_adjusted)
+            affected_households['exp'] < multiplier * poverty_line_adjusted)
 
         # Combine conditions to identify beneficiaries
         beneficiary_condition = poor_condition | near_poor_condition
@@ -91,7 +68,7 @@ def cash_transfer(households: pd.DataFrame, current_policy: str) -> pd.DataFrame
     return households
 
 
-def retrofitting(country: str, households: pd.DataFrame, current_policy: str, random_seed: int) -> pd.DataFrame:
+def retrofitting(country: str, households: pd.DataFrame, current_policy: str, disaster_type: str, random_seed: int) -> pd.DataFrame:
     '''
     Apply retrofitting to a specific target group.
 
@@ -102,6 +79,7 @@ def retrofitting(country: str, households: pd.DataFrame, current_policy: str, ra
         households (pd.DataFrame): Households.
             Required columns: 'roof', 'walls', 'is_poor', 'exp'
         policy (str): Policy to apply. Format: "<target_group>+<houses_pct>". Example: "poor_40+100".
+        disaster_type (str): Type of disaster. Example: "hurricane".
         random_seed (int): Random seed for reproducibility.
 
     Returns:
@@ -110,6 +88,10 @@ def retrofitting(country: str, households: pd.DataFrame, current_policy: str, ra
     Raises:
         ValueError: If the policy format is incorrect or the households DataFrame does not contain the required columns.   
     '''
+    if random_seed is not None:
+        # Set random seed for reproducibility
+        np.random.seed(random_seed)
+
     try:
         target_group, houses_pct = current_policy.split('+')
         houses_pct = int(houses_pct)
@@ -136,6 +118,9 @@ def retrofitting(country: str, households: pd.DataFrame, current_policy: str, ra
         vulnerable_houses = households[households['walls'].isin(
             v_walls_types) | households['roof'].isin(v_roof_types)]
 
+        # Select only the ones that own
+        vulnerable_houses = vulnerable_houses[vulnerable_houses['own_rent'] == 'own']
+
         if len(vulnerable_houses) == 0:
             return households
 
@@ -145,8 +130,13 @@ def retrofitting(country: str, households: pd.DataFrame, current_policy: str, ra
 
         # Apply the policy to a percentage of the vulnerable houses
         if houses_pct != 100:
-            vulnerable_houses = vulnerable_houses.sample(
-                frac=houses_pct/100, random_state=random_seed)
+            if random_seed is not None:
+                # ?: Check if fixing the np random seed affects the sampling
+                vulnerable_houses = vulnerable_houses.sample(
+                    frac=houses_pct/100, random_state=random_seed)
+            else:
+                vulnerable_houses = vulnerable_houses.sample(
+                    frac=houses_pct/100)
 
         # Search for poor households  if the target group is not 'all'
         if target_group != 'all':
@@ -164,12 +154,15 @@ def retrofitting(country: str, households: pd.DataFrame, current_policy: str, ra
                 return households
 
             # Keep only the poor houses
-            vulnerable_houses = vulnerable_houses.loc[poor_houses.index]
+            vulnerable_houses = vulnerable_houses.loc[poor_houses.  index]
 
         # Apply retrofitting to the vulnerable houses
         households['retrofitted'] = False
 
         # Retrofit roofs
+        # current_roofs = households.loc[vulnerable_houses.index, 'roof']
+        # current_walls = households.loc[vulnerable_houses.index, 'walls']
+
         households.loc[vulnerable_houses.index, 'roof'] = households.loc[vulnerable_houses.index, 'roof'].apply(
             lambda x: robust_roof if x in v_roof_types else x
         )
@@ -177,10 +170,19 @@ def retrofitting(country: str, households: pd.DataFrame, current_policy: str, ra
         households.loc[vulnerable_houses.index, 'walls'] = households.loc[vulnerable_houses.index, 'walls'].apply(
             lambda x: robust_walls if x in v_walls_types else x
         )
+
+        # retrofitted_roofs = households.loc[vulnerable_houses.index, 'roof']
+        # retrofitted_walls = households.loc[vulnerable_houses.index, 'walls']
+
+        # Print how many roofs and walls were retrofitted
+        # print(
+        #     f"Retrofitting {len(vulnerable_houses)} houses: {len(vulnerable_houses[retrofitted_roofs != current_roofs])} roofs and {len(vulnerable_houses[retrofitted_walls != current_walls])} walls")
+
         households.loc[vulnerable_houses.index, 'retrofitted'] = True
 
         # Calculate the new vulnerability score
-        households = recalculate_house_vulnerability(country, households)
+        households = recalculate_house_vulnerability(
+            country, households, disaster_type)
 
         # NOTE: After retrofitting we do not randomize the vulnerability score
         return households
@@ -189,67 +191,112 @@ def retrofitting(country: str, households: pd.DataFrame, current_policy: str, ra
         raise ValueError(f"Country '{country}' not yet supported")
 
 
-def recalculate_house_vulnerability(country: str, households: pd.DataFrame) -> pd.DataFrame:
+def recalculate_house_vulnerability(country: str, households: pd.DataFrame, disaster_type: str) -> pd.DataFrame:
     '''Recalculate vulnerability of a house based on its new retrofitted roof and walls material.
 
     Args:
         country (str): Country name.
         households (pd.DataFrame): Households.
             Required columns: 'roof', 'walls'
+        disaster_type (str): Type of disaster. Example: "hurricane".
 
     Returns:
         pd.DataFrame: Households with recalculated vulnerability scores.
 
     Raises:
         ValueError: If the country is not supported.
+        ValueError: If the disaster type is not supported.
     '''
     # Each country has different roof and wall materials and their respective vulnerability scores
     if country == 'Dominica':
         # Define vulnerability scores in dictionaries for easy mapping
         v_roof_scores = {
-            'default': 0.75,
             'Concrete': 0.2,
-            'Sheet metal (galvanize, galvalume)': 0.35,
-            'Shingle (wood)': 0.5,
-            'Shingle (asphalt)': 0.5,
-            'Shingle (other)': 0.5,
+            'Sheet metal (galvanize, galvalume)': 0.4,
+            'Shingle (wood)': 0.6,
+            'Shingle (asphalt)': 0.6,
+            'Shingle (other)': 0.6,
             'Other': 0.75,
         }
 
         v_walls_scores = {
-            'default': 0.6,
-            'Wood & Concrete': 0.2,
-            'Brick/Blocks': 0.25,
-            'Concrete/Concrete blocks': 0.25,
+            'Brick/Blocks': 0.2,
+            'Concrete/Concrete Blocks': 0.2,
+            'Concrete/Concrete blocks': 0.2,
+            'Wood & Concrete': 0.4,
             'Wood/Timber': 0.6,
             'Plywood': 0.7,
             'Makeshift': 0.8,
             "Other/Don't know": 0.8,
+            "Other/Don't Know": 0.8,
+            'Other': 0.8,
+        }
+
+    elif country == 'Nigeria':
+        v_roof_scores = {
+            'Finished – Concrete': 0.2,
+            'Finished – Asbestos': 0.25,
+            'Finished – Metal tile': 0.35,
+            'Finished – Tile': 0.5,
+            'Other – Specific': 0.75,
+            'Rudimentary – Other': 0.75,
+            'Other': 0.75,
+            'Natural – Thatch/palm leaf': 0.9
+        }
+
+        v_walls_scores = {
+            'Finished – Cement blocks': 0.2,
+            'Finished – Stone with lime/cement': 0.4,
+            'Finished – Woven Bamboo': 0.6,
+            'Rudimentary – Bamboo with mud': 0.8,
             'Other': 0.8,
         }
 
     else:
         raise ValueError(f"Country '{country}' not yet supported")
 
-    # Apply default scores
-    households['v_roof'] = households['roof'].apply(
-        lambda x: v_roof_scores.get(x, v_roof_scores['default']))
-    households['v_walls'] = households['walls'].apply(
-        lambda x: v_walls_scores.get(x, v_walls_scores['default']))
-
-    # Calculate initial vulnerability score
-    v_roof_multiplier = 0.3
-    v_walls_multiplier = 0.7
+    # Save current vulnerability score
+    # current_v = households['v'].copy()
+    # current_v_roof = households['v_roof'].copy()
+    # current_v_walls = households['v_walls'].copy()
+
+    # Calculate the vulnerability scores for the roof and walls
+    households['v_roof'] = households['roof'].map(v_roof_scores)
+    households['v_walls'] = households['walls'].map(v_walls_scores)
+
+    # Count how many v values changed
+    # print(
+    #     f'Changed {len(households[households["v_roof"] != current_v_roof])} roofs')
+    # print(
+    #     f'Changed {len(households[households["v_walls"] != current_v_walls])} walls')
+
+    # Calculate the new vulnerability score
+    if disaster_type == 'hurricane':
+        v_roof_multiplier = 0.6
+        v_walls_multiplier = 0.4
+    elif disaster_type == 'earthquake':
+        v_roof_multiplier = 0.3
+        v_walls_multiplier = 0.7
+    elif disaster_type == 'flood':
+        v_roof_multiplier = 0.2
+        v_walls_multiplier = 0.8
+    else:
+        raise ValueError(f"Disaster type '{disaster_type}' not yet supported")
 
     # NOTE: Since it is for a retrofitting policy,
     # we immediately assign the new values to `v` and not `v_init` column
     households['v'] = v_roof_multiplier * \
         households['v_roof'] + v_walls_multiplier * households['v_walls']
 
+    # Assertions to check values are within expected range
+    assert households[['v_roof', 'v_walls', 'v']].apply(
+        lambda x: (x >= 0).all() and (x <= 1).all()).all()
+    assert households['v'].isna().sum() == 0
+
     return households
 
 
-def apply_policy(households: pd.DataFrame, country: str, current_policy: str, random_seed: int) -> pd.DataFrame:
+def apply_policy(households: pd.DataFrame, country: str, current_policy: str, disaster_type: str, random_seed: int) -> pd.DataFrame:
     '''
     Apply a specific policy to a set of households.
 
@@ -260,6 +307,7 @@ def apply_policy(households: pd.DataFrame, country: str, current_policy: str, ra
         households (pd.DataFrame): Households.
             Required columns: 'is_affected', 'is_poor', 'exp', 'povline_adjusted', 'keff', 'v', 'sav', 'roof', 'walls'
         policy (str): Policy to apply. Format: "<policy_type>:<policy_details>". Example: "asp:poor+100".
+        disaster_type (str): Type of disaster. Example: "hurricane".
         random_seed (int): Random seed for reproducibility.
 
     Returns:
@@ -275,7 +323,7 @@ def apply_policy(households: pd.DataFrame, country: str, current_policy: str, ra
     if policy_type == 'asp':
         return cash_transfer(households, current_policy.split(':')[1])
     elif policy_type == 'retrofit':
-        return retrofitting(country, households, current_policy.split(':')[1], random_seed)
+        return retrofitting(country, households, current_policy.split(':')[1], disaster_type, random_seed)
     elif policy_type == 'none':
         return households
     else:
diff --git a/unbreakable/modules/recovery.py b/unbreakable/modules/recovery.py
index 28113eb..de7ae52 100644
--- a/unbreakable/modules/recovery.py
+++ b/unbreakable/modules/recovery.py
@@ -4,268 +4,201 @@
 import os
 
 
-def calc_rec_rate(households: pd.DataFrame, avg_prod: float, cons_util: float, disc_rate: float, lambda_incr: float, yrs_to_rec: int) -> pd.DataFrame:
-    '''Calculate recovery rate for each affected household.
-
-    Recovery rate is a function of the household vulnerability (v), 
-    consumption utility, discount rate, average productivity and years to recover.    
-
-    Args:
-        households (pd.DataFrame): Households data frame.
-        cons_util (float): Consumption utility.
-        disc_rate (float): Discount rate.
-        lambda_incr (float): Lambda increment for the integration.
-        yrs_to_rec (int): Number of years to recover.
-
-    Returns:
-        pd.DataFrame: Households data frame with the recovery rate for each affected household.
-    '''
+def calculate_recovery_rate(households: pd.DataFrame, average_productivity: float, consumption_utility: float, discount_rate: float, lambda_increment: float, years_to_recover: int, is_conflict: bool):
+    # Assign initial value to recovery_rate
+    households['recovery_rate'] = 0
+
     # Subset households that are affected by the disaster
     affected_households = households[households['is_affected'] == True].copy()
 
-    # Set effective capital stock to zero for renters, since they don't own the house
-    # TODO: Come up with a better solution, if we assume damage to public infrastructure
-    affected_households.loc[affected_households['own_rent']
-                            == 'rent', 'keff'] = 0
+    # Search for the recovery rate for each affected household
+    affected_households['recovery_rate'] = affected_households['v'].apply(
+        lambda x: find_recovery_rate(x, average_productivity, consumption_utility, discount_rate, lambda_increment, years_to_recover))
 
-    # Prepare a data frame to store integration results
-    data = {'exp': -1, 'exp_house': -1, 'keff': -1, 'v': -
-            1, 'sav': -1, 'solution': None, 'bankrupt': None}
+    # If the region is affected by a conflict, decrease the recovery rate
+    if is_conflict:
+        conflict_intensity_mapper = {
+            'Very high': 0.5, 'High': 0.3, 'Medium': 0.2, 'Low': 0.1, 'Very low': 0.05}
+        region_conflict_intensity = households['conflict_intensity'].values[0]
+        region_intensity_value = conflict_intensity_mapper[region_conflict_intensity]
+        affected_households['recovery_rate'] = affected_households['recovery_rate'] * \
+            (1 - region_intensity_value)
 
-    results = pd.DataFrame(data, index=[0])
-    index = ['exp', 'exp_house', 'keff', 'v', 'sav']
-    results = results.reset_index(drop=True).set_index(index)
+    households.loc[affected_households.index,
+                   'recovery_rate'] = affected_households['recovery_rate']
 
-    # Calculate the recovery rate for each affected household
-    affected_households['recovery_rate'] = affected_households['v'].apply(lambda x: integrate_and_find_recovery_rate(
-        x, results, cons_util, disc_rate, avg_prod, lambda_incr, yrs_to_rec))
+    return households
 
-    # Set recovery rate to zero for unaffected households
-    households['recovery_rate'] = 0
 
-    # Update the recovery rate in the households data frame for affected households
-    households.loc[households['is_affected'] == True,
-                   'recovery_rate'] = affected_households['recovery_rate']
+def find_recovery_rate(v: float, average_productivity: float, consumption_utility: float, discount_rate: float, lambda_increment: float, years_to_recover: int) -> float:
+    totaL_weeks = 52 * years_to_recover
+    dt = 1 / 52
 
-    return households
+    lambda_value = 0
+    last_derivative_lambda = 0
+
+    while True:
+        derivative_lambda = 0
+        for time in np.linspace(0, years_to_recover, totaL_weeks):
+            factor = average_productivity + lambda_value
+            part1 = (average_productivity - factor * v *
+                     np.exp(-lambda_value * time)) ** (-consumption_utility)
+            part2 = time * factor - 1
+            part3 = np.exp(-time * (discount_rate + lambda_value))
+            derivative_lambda += part1 * part2 * part3 * dt
 
+        if (last_derivative_lambda < 0 and derivative_lambda > 0) or (last_derivative_lambda > 0 and derivative_lambda < 0) or lambda_value > years_to_recover:
+            return lambda_value
 
-def integrate_and_find_recovery_rate(v: float, results: pd.DataFrame, cons_util: float, disc_rate: float, avg_prod: float, lambda_incr: float, yrs_to_rec: int) -> float:
-    '''Find recovery rate (lambda) given the value of `v` (household vulnerability).
-
-    Args:
-        v (float): Household vulnerability.
-        results (pd.DataFrame): Data frame to store integration results.
-        cons_util (float): Consumption utility.
-        disc_rate (float): Discount rate.
-        avg_prod (float): Average productivity.
-        lambda_incr (float): Lambda increment for the integration.
-        yrs_to_rec (int): Number of years to recover.
-
-    Returns:
-        float: Recovery rate (lambda).
-    '''
-    rounded_v = round(v, 3)
-    rounded_average_productivity = round(avg_prod, 3)
-
-    try:
-        # Look for the existing solution
-        solution = results.loc[(
-            0, 0, 0, rounded_v, rounded_average_productivity), 'solution']
-        return solution
-
-    except KeyError:
-        # No existing solution found, so we need to optimize
-        tot_weeks = 52 * yrs_to_rec
-        dt = yrs_to_rec / tot_weeks
-
-        _lambda = 0
-        last_dwdlambda = 0
-
-        while True:
-            dwdlambda = 0
-            for _t in np.linspace(0, yrs_to_rec, tot_weeks):
-                factor = avg_prod + _lambda
-                part1 = avg_prod - \
-                    factor * v * np.e**(-_lambda * _t)
-                part1 = part1**(-cons_util)
-                part2 = _t * factor - 1
-                part3 = np.e**(-_t * (disc_rate + _lambda))
-                dwdlambda += part1 * part2 * part3 * dt
-
-            if (last_dwdlambda < 0 and dwdlambda > 0) or (last_dwdlambda > 0 and dwdlambda < 0) or _lambda > 10:
-                results.loc[(0, 0, 0, rounded_v, rounded_average_productivity), [
-                    'solution', 'bankrupt']] = [_lambda, False]
-                results = results.sort_index()
-                return _lambda
-
-            last_dwdlambda = dwdlambda
-            _lambda += lambda_incr
-
-
-def calc_wellbeing(households: pd.DataFrame, avg_prod: float, cons_util: float, disc_rate: float, inc_exp_growth: float, yrs_to_rec: int, add_inc_loss: bool, cash_transfer: dict = {}, is_conflict: bool = False) -> pd.DataFrame:
-    '''Calculates consumption loss and well-being for each affected household.
-
-    Args:
-        households (pd.DataFrame): Households data frame.
-        cons_util (float): Consumption utility.
-        disc_rate (float): Discount rate.
-        inc_exp_growth (float): Income and expenditure growth.
-        yrs_to_rec (int): Number of years to recover.
-        add_inc_loss (bool): Whether to add income loss or not.
-        cash_transfer (dict, optional): Cash transfer. Defaults to {}, where key is the week number and value is the amount.
-        is_conflict (bool, optional): Whether the country has a conflict. Defaults to False.
-
-    Returns:
-        pd.DataFrame: Households data frame with consumption loss and well-being for each affected household.
-    '''
-    # Get the poverty line. It is the same for all households
-    povline_adjusted = households['povline_adjusted'].values[0]
-
-    # Add new columns
-    columns = ['consumption_loss',
-               'consumption_loss_NPV',
-               'net_consumption_loss',
-               'net_consumption_loss_NPV',
-               'c_t',
-               'c_t_unaffected',
-               'weeks_pov',
-               'wellbeing']
-
-    # Set values to zero
-    households[columns] = 0
+        last_derivative_lambda = derivative_lambda
+        lambda_value += lambda_increment
+
+
+def calculate_wellbeing(households: pd.DataFrame,
+                        average_productivity: float,
+                        consumption_utility: float,
+                        discount_rate: float,
+                        income_and_expenditure_growth: float,
+                        years_to_recover: int,
+                        add_income_loss: bool,
+                        save_consumption_recovery: bool,
+                        is_conflict: bool = False) -> pd.DataFrame:
+
+    # Get the adjusted poverty line for the region
+    poverty_line_adjusted = households['poverty_line_adjusted'].values[0]
+
+    # Add new columns with initial values
+    new_columns = ['consumption_loss', 'consumption_loss_npv', 'net_consumption_loss',
+                   'net_consumption_loss_npv', 'c_t', 'c_t_unaffected', 'weeks_in_poverty', 'wellbeing']
+
+    households[new_columns] = 0
 
     # Subset households that are affected by the disaster
     affected_households = households[households['is_affected'] == True].copy()
 
     # Define the number of weeks given the number of years
-    tot_weeks = 52 * yrs_to_rec
-    dt = yrs_to_rec / tot_weeks
+    totaL_weeks = 52 * years_to_recover
+    dt = 1 / 52
 
-    # NOTE: Store consumption recovery in a dict for verification and debugging purposes
     consumption_recovery = {}
 
-    # We need to adjust the cash transfer to the timestep of the integration
-    if cash_transfer != {}:  # If there is a cash transfer
-        cash_transfer_transformed = {np.linspace(0, yrs_to_rec, tot_weeks)[
-            t]: cash_transfer[t] for t in list(cash_transfer.keys())}
+    # If conflict affect the region, decrease the income and expenditure growth and the average productivity
+    # And increase the vulnerability
+    if is_conflict:
+        conflict_intensity_mapper = {
+            'Very high': 0.5, 'High': 0.3, 'Medium': 0.2, 'Low': 0.1, 'Very low': 0.05}
+        region_conflict_intensity = households['conflict_intensity'].values[0]
+        region_intensity_value = conflict_intensity_mapper[region_conflict_intensity]
+        income_and_expenditure_growth *= (1 - region_intensity_value)
+        average_productivity *= (1 - region_intensity_value)
+        vulnerability_increase_factor = 1 + region_intensity_value
     else:
-        cash_transfer_transformed = {}
+        vulnerability_increase_factor = 1
 
     # Integrate consumption loss and well-being
-    for _t in np.linspace(0, yrs_to_rec, tot_weeks):
-        # TODO: Add an extra condition about to whom the transfer is given
-        # A "dynamic policy"
-        if _t in list(cash_transfer_transformed.keys()):
-            affected_households['sav'] += cash_transfer_transformed[_t]
-
-        # !: Should c_t_unaffected continue to grow every time step?
-        growth_factor = (1 + inc_exp_growth)**_t
+    for time in np.linspace(0, years_to_recover, totaL_weeks):
+        exponential_multiplier = np.e**(
+            -affected_households['recovery_rate']*time)
 
-        expenditure_growth = growth_factor * affected_households['exp']
+        growth_factor = (1 + income_and_expenditure_growth)**time
 
-        exponential_multiplier = np.e**(
-            -affected_households['recovery_rate']*_t)
+        expenditure = growth_factor * \
+            affected_households['exp']
 
-        # ?: Why growth_factor and recovery rate are part of the savings and asset_loss?
         savings = growth_factor * \
-            affected_households['sav'] * affected_households['recovery_rate']
+            affected_households['sav'] * affected_households['recovery_rate'] * \
+            vulnerability_increase_factor
 
         asset_loss = growth_factor * \
-            affected_households[['v', 'keff', 'recovery_rate']].prod(axis=1)
+            affected_households['v'] * affected_households['keff'] * \
+            affected_households['recovery_rate'] * \
+            vulnerability_increase_factor
 
-        # savings = growth_factor * \
-        #     affected_households['sav']  # * affected_households['recovery_rate']
-
-        # asset_loss = growth_factor * \
-        #     affected_households[['v', 'keff'  # , 'recovery_rate'
-        #                          ]].prod(axis=1)
-
-        # NOTE: That was initial implementation
         # asset_loss = growth_factor * affected_households['v'] * \
         #     (affected_households['exp_house'] +
         #      affected_households[['keff', 'recovery_rate']].prod(axis=1))
 
-        # NOTE: In most of the case country we didn't have delta_tax_safety column
-        # Therefore, I remove it from the equation for now
-        # income_loss = growth_factor * (1 - affected_households['delta_tax_safety']) * \
-        #     avg_prod * \
-        #     affected_households['keff'] * affected_households['v']
-
-        income_loss = growth_factor * avg_prod * \
+        income_loss = growth_factor * average_productivity * \
             affected_households['keff'] * affected_households['v']
 
-        if add_inc_loss == False:
-            affected_households['c_t'] = (expenditure_growth +
+        if add_income_loss == False:
+            affected_households['c_t'] = (expenditure +
                                           exponential_multiplier * (savings - asset_loss))
         else:
-            affected_households['c_t'] = (expenditure_growth +
+            affected_households['c_t'] = (expenditure +
                                           exponential_multiplier * (savings - asset_loss - income_loss))
 
-        affected_households['c_t_unaffected'] = expenditure_growth
+        affected_households['c_t_unaffected'] = expenditure
 
-        # Check if any of the households has a negative consumption
-        # It may happen if a household have very low savings or expenditure
         if (affected_households['c_t'] < 0).any():
-            # If so, then set the consumption to 1
-            # We must have 1 to to avoid -inf in the wellbeing integration
-            # TODO: Experiment with 0.1 instead of 1
-            # This could be relevant if we do daily consumption/integration
             affected_households.loc[affected_households['c_t'] < 0, 'c_t'] = 1
 
-        # Consumption after the disaster should be lower than or equal to consumption before the disaster
         if (affected_households['c_t'] > affected_households['c_t_unaffected']).any():
-            # In some cases there is a very small difference between the two
-            # E.g. 2408.7431 vs 2408.0711
             affected_households.loc[affected_households['c_t'] > affected_households['c_t_unaffected'],
                                     'c_t'] = affected_households.loc[affected_households['c_t'] > affected_households['c_t_unaffected'], 'c_t_unaffected']
 
-        # Total consumption loss
+        # Calculate consumption loss
         affected_households['consumption_loss'] += dt * \
             (affected_households['c_t_unaffected'] -
                 affected_households['c_t'])
 
-        affected_households['consumption_loss_NPV'] += dt * \
+        affected_households['consumption_loss_npv'] += dt * \
             (affected_households['c_t_unaffected'] -
-                affected_households['c_t'])*np.e**(-disc_rate*_t)
+                affected_households['c_t'])*np.e**(-discount_rate*time)
 
-        # Net consumption loss
+        # BUG: That's wrong, if exp_house = 0 then then net_consumption_loss is 0
         affected_households['net_consumption_loss'] += dt * \
-            np.e**(-affected_households['recovery_rate']*_t) * \
+            np.e**(-affected_households['recovery_rate']*time) * \
             affected_households['v'] * growth_factor * \
             affected_households['exp_house']
 
-        affected_households['net_consumption_loss_NPV'] += dt * \
-            np.e**(-affected_households['recovery_rate']*_t) * affected_households['v'] * growth_factor * \
+        # BUG: That's wrong, if exp_house = 0 then then net_consumption_loss_npv is 0
+        affected_households['net_consumption_loss_npv'] += dt * \
+            np.e**(-affected_households['recovery_rate']*time) * affected_households['v'] * growth_factor * \
             affected_households['exp_house'] * \
-            np.e**(-disc_rate*_t)
+            np.e**(-discount_rate*time)
 
         # Increase the number of weeks in poverty
         affected_households.loc[affected_households['c_t']
-                                < povline_adjusted, 'weeks_pov'] += 1
+                                < poverty_line_adjusted, 'weeks_in_poverty'] += 1
 
-        # Integrate well-being
-        affected_households['wellbeing'] += affected_households['c_t_unaffected']**(1 - cons_util)\
-            / (1 - cons_util) * dt\
+        # Calculate wellbeing
+        affected_households['wellbeing'] += affected_households['c_t_unaffected']**(1 - consumption_utility)\
+            / (1 - consumption_utility) * dt\
             * ((1 - ((affected_households['c_t_unaffected'] - affected_households['c_t']) / affected_households['c_t_unaffected'])
-                * np.e**(-affected_households['recovery_rate'] * _t))**(1 - cons_util) - 1)\
-            * np.e**(-disc_rate * _t)
+                * np.e**(-affected_households['recovery_rate'] * time))**(1 - consumption_utility) - 1)\
+            * np.e**(-discount_rate * time)
+
+        # Store the consumption recovery for each time step
+        if save_consumption_recovery:
+            consumption_recovery[time] = affected_households.loc[:, [
+                'wgt', 'is_poor', 'recovery_rate', 'c_t_unaffected', 'c_t']].set_index('wgt')
+
+    # Save the content of the columns for affected_households into households data frame
+    households.loc[affected_households.index,
+                   new_columns] = affected_households[new_columns]
+
+    # Save consumption recovery as a pickle file
+    if save_consumption_recovery:
+        country = households['country'].values[0]
+        region = households['region'].values[0]
+        return_period = households['return_period'].values[0]
+        try:
+            random_seed = households['random_seed'].values[0]
+        except:
+            random_seed = None
 
-        # Save consumption recovery value at the time _t
-        data = affected_households.loc[:, [
-            'is_poor', 'recovery_rate', 'c_t_unaffected', 'c_t']]
-        # Every household has a unique weight
-        data.index = affected_households['wgt']
-        consumption_recovery[_t] = data
+        save_consumption_recovery_to_file(consumption_recovery,
+                                          country, region, return_period, is_conflict, random_seed)
+
+    return households
 
-    country = 'Nigeria'
-    region = households['region'].values[0]
-    random_seed = households['random_seed'].values[0]
 
-    # if is_conflict:
-    folder = f'../experiments/{country}/cons_rec_conflict={is_conflict}/{random_seed}'
-    # else:
-    #     folder = f'../experiments/{country}/cons_rec/{random_seed}'
+def save_consumption_recovery_to_file(consumption_recovery: dict, country: str, region: str, return_period: str, is_conflict: bool, random_seed: int):
+    if is_conflict:
+        folder = f'../experiments/{country}/consumption_recovery/return_period={return_period}/conflict={is_conflict}/{random_seed}'
+    else:
+        folder = f'../experiments/{country}/consumption_recovery/return_period={return_period}/conflict={is_conflict}/{random_seed}'
 
     # Create a folder if it does not exist
     if not os.path.exists(folder):
@@ -275,9 +208,3 @@ def calc_wellbeing(households: pd.DataFrame, avg_prod: float, cons_util: float,
     with open(folder + f'/{region}.pickle', 'wb') as handle:
         pickle.dump(consumption_recovery, handle,
                     protocol=pickle.HIGHEST_PROTOCOL)
-
-    # Save the content of the columns for affected_households into households data frame
-    households.loc[affected_households.index,
-                   columns] = affected_households[columns]
-
-    return households
diff --git a/unbreakable/run.py b/unbreakable/run.py
index 3dfa46c..ac700e3 100644
--- a/unbreakable/run.py
+++ b/unbreakable/run.py
@@ -1,25 +1,28 @@
 from unbreakable.model import load_config, setup_model, run_experiments
 from ema_workbench import ema_logging
+
 ema_logging.log_to_stderr(ema_logging.INFO)
 
 if __name__ == "__main__":
     try:
-        # Load the configuration for the specified country
-        country = 'Dominica'
-        config = load_config(country)
+        country = 'Nigeria'
+        return_period = 100
+        conflict = True
 
-        # Design of experiments
-        n_scenarios = 1
-        # n_replications = 10
-        n_policies = 7
-        multiprocessing = True
-        n_processes = 7
+        config = load_config(country, return_period, conflict)
+        model = setup_model(config, replicator=False)
 
-        # my_model = setup_model(config, n_replications=n_replications)
-        my_model = setup_model(config)
+        experimental_setup = {
+            'country': country,
+            'return_period': return_period,
+            'model': model,
+            'n_scenarios': 2,
+            'n_policies': 0,
+            'multiprocessing': False,
+            'n_processes': 12
+        }
 
-        run_experiments(my_model, n_scenarios, n_policies,
-                        country, multiprocessing, n_processes)
+        run_experiments(experimental_setup)
 
     except Exception as e:
         print(f"An error occurred: {e}")