{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "62e16498-69a3-4822-aa8f-e3bffc4100d7",
   "metadata": {
    "id": "62e16498-69a3-4822-aa8f-e3bffc4100d7"
   },
   "source": [
    "# Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR) - 2026\n",
    "\n",
    "Let's see a quick example of how to use Suppor Vector Regression for tree height estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "997e577a",
   "metadata": {
    "id": "997e577a"
   },
   "source": [
    "### Here are some general installation instructions:\n",
    "\n",
    "1) Create a conda enviroment if you don't have one and activate it:\n",
    "\n",
    "``conda create --name geo_comp_2026 python=3.9``\n",
    "\n",
    "``conda activate geo_comp_2026``\n",
    "\n",
    "2) Install the needed packages for this tutorial\n",
    "\n",
    "``conda install -c conda-forge pandas numpy scikit-learn scipy rasterio matplotlib``\n",
    "\n",
    "\n",
    "### Notes:\n",
    "\n",
    "1) If you are using jupyterlab of VScode, you might need to add this new environment to the list of kernels. In that case, do the following\n",
    "\n",
    "- Install ipykernel:\n",
    "```conda install ipykernel```\n",
    "\n",
    "- Add the environment to the list of kernels\n",
    "``python -m ipykernel install --user --name geo_comp_2026 --display-name \"Python geo_comp_2026\" ``\n",
    "\n",
    "2) If you haven't loaded the Tree Height dataset, follow the steps below\n",
    "\n",
    "- Install gdown on your conda env:\n",
    "```pip install gdown```\n",
    "\n",
    "- Download the dataset and unpack it\n",
    "\n",
    "```gdown 1Y60EuLsfmTICTX-U_FxcE1odNAf04bd```\n",
    "\n",
    "```tar -xvzf tree_height.tar.gz```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e40151e-7639-42ae-b65a-a6d3f102a03f",
   "metadata": {
    "id": "5e40151e-7639-42ae-b65a-a6d3f102a03f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.svm import SVR\n",
    "import scipy\n",
    "\n",
    "# For visualization\n",
    "import rasterio\n",
    "from rasterio import *\n",
    "from rasterio.plot import show\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "# from sklearn.pipeline import Pipeline\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt\n",
    "from pyspatialml import Raster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b19fa16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NumPy 2.x compatibility patch for pyspatialml\n",
    "if not hasattr(np, \"find_common_type\"):\n",
    "    np.find_common_type = lambda arr, sc: np.result_type(*(list(arr) + list(sc))) if (list(arr) + list(sc)) else np.dtype(\"float64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9249b3e1-eff7-4f68-bf95-3d5fd82b8f1c",
   "metadata": {
    "id": "9249b3e1-eff7-4f68-bf95-3d5fd82b8f1c"
   },
   "source": [
    "We will load the data using Pandas and display few samples of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beb6a6bf-f7ba-41ae-b83f-826a019264de",
   "metadata": {
    "id": "beb6a6bf-f7ba-41ae-b83f-826a019264de",
    "outputId": "ea3899fc-1f3b-4869-b7b9-325bcbf76c7e",
    "tags": []
   },
   "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>ID</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>h</th>\n",
       "      <th>BLDFIE_WeigAver</th>\n",
       "      <th>CECSOL_WeigAver</th>\n",
       "      <th>CHELSA_bio18</th>\n",
       "      <th>CHELSA_bio4</th>\n",
       "      <th>convergence</th>\n",
       "      <th>cti</th>\n",
       "      <th>devmagnitude</th>\n",
       "      <th>eastness</th>\n",
       "      <th>elev</th>\n",
       "      <th>forestheight</th>\n",
       "      <th>glad_ard_SVVI_max</th>\n",
       "      <th>glad_ard_SVVI_med</th>\n",
       "      <th>glad_ard_SVVI_min</th>\n",
       "      <th>northness</th>\n",
       "      <th>ORCDRC_WeigAver</th>\n",
       "      <th>outlet_dist_dw_basin</th>\n",
       "      <th>SBIO3_Isothermality_5_15cm</th>\n",
       "      <th>SBIO4_Temperature_Seasonality_5_15cm</th>\n",
       "      <th>treecover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.050001</td>\n",
       "      <td>49.727499</td>\n",
       "      <td>3139.00</td>\n",
       "      <td>1540</td>\n",
       "      <td>13</td>\n",
       "      <td>2113</td>\n",
       "      <td>5893</td>\n",
       "      <td>-10.486560</td>\n",
       "      <td>-238043120</td>\n",
       "      <td>1.158417</td>\n",
       "      <td>0.069094</td>\n",
       "      <td>353.983124</td>\n",
       "      <td>23</td>\n",
       "      <td>276.871094</td>\n",
       "      <td>46.444092</td>\n",
       "      <td>347.665405</td>\n",
       "      <td>0.042500</td>\n",
       "      <td>9</td>\n",
       "      <td>780403</td>\n",
       "      <td>19.798992</td>\n",
       "      <td>440.672211</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6.050002</td>\n",
       "      <td>49.922155</td>\n",
       "      <td>1454.75</td>\n",
       "      <td>1491</td>\n",
       "      <td>12</td>\n",
       "      <td>1993</td>\n",
       "      <td>5912</td>\n",
       "      <td>33.274361</td>\n",
       "      <td>-208915344</td>\n",
       "      <td>-1.755341</td>\n",
       "      <td>0.269112</td>\n",
       "      <td>267.511688</td>\n",
       "      <td>19</td>\n",
       "      <td>-49.526367</td>\n",
       "      <td>19.552734</td>\n",
       "      <td>-130.541748</td>\n",
       "      <td>0.182780</td>\n",
       "      <td>16</td>\n",
       "      <td>772777</td>\n",
       "      <td>20.889412</td>\n",
       "      <td>457.756195</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.050002</td>\n",
       "      <td>48.602377</td>\n",
       "      <td>853.50</td>\n",
       "      <td>1521</td>\n",
       "      <td>17</td>\n",
       "      <td>2124</td>\n",
       "      <td>5983</td>\n",
       "      <td>0.045293</td>\n",
       "      <td>-137479792</td>\n",
       "      <td>1.908780</td>\n",
       "      <td>-0.016055</td>\n",
       "      <td>389.751160</td>\n",
       "      <td>21</td>\n",
       "      <td>93.257324</td>\n",
       "      <td>50.743652</td>\n",
       "      <td>384.522461</td>\n",
       "      <td>0.036253</td>\n",
       "      <td>14</td>\n",
       "      <td>898820</td>\n",
       "      <td>20.695877</td>\n",
       "      <td>481.879700</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6.050009</td>\n",
       "      <td>48.151979</td>\n",
       "      <td>3141.00</td>\n",
       "      <td>1526</td>\n",
       "      <td>16</td>\n",
       "      <td>2569</td>\n",
       "      <td>6130</td>\n",
       "      <td>-33.654274</td>\n",
       "      <td>-267223072</td>\n",
       "      <td>0.965787</td>\n",
       "      <td>0.067767</td>\n",
       "      <td>380.207703</td>\n",
       "      <td>27</td>\n",
       "      <td>542.401367</td>\n",
       "      <td>202.264160</td>\n",
       "      <td>386.156738</td>\n",
       "      <td>0.005139</td>\n",
       "      <td>15</td>\n",
       "      <td>831824</td>\n",
       "      <td>19.375000</td>\n",
       "      <td>479.410278</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6.050010</td>\n",
       "      <td>49.588410</td>\n",
       "      <td>2065.25</td>\n",
       "      <td>1547</td>\n",
       "      <td>14</td>\n",
       "      <td>2108</td>\n",
       "      <td>5923</td>\n",
       "      <td>27.493824</td>\n",
       "      <td>-107809368</td>\n",
       "      <td>-0.162624</td>\n",
       "      <td>0.014065</td>\n",
       "      <td>308.042786</td>\n",
       "      <td>25</td>\n",
       "      <td>136.048340</td>\n",
       "      <td>146.835205</td>\n",
       "      <td>198.127441</td>\n",
       "      <td>0.028847</td>\n",
       "      <td>17</td>\n",
       "      <td>796962</td>\n",
       "      <td>18.777500</td>\n",
       "      <td>457.880066</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6.050014</td>\n",
       "      <td>48.608456</td>\n",
       "      <td>1246.50</td>\n",
       "      <td>1515</td>\n",
       "      <td>19</td>\n",
       "      <td>2124</td>\n",
       "      <td>6010</td>\n",
       "      <td>-1.602039</td>\n",
       "      <td>17384282</td>\n",
       "      <td>1.447979</td>\n",
       "      <td>-0.018912</td>\n",
       "      <td>364.527100</td>\n",
       "      <td>18</td>\n",
       "      <td>221.339844</td>\n",
       "      <td>247.387207</td>\n",
       "      <td>480.387939</td>\n",
       "      <td>0.042747</td>\n",
       "      <td>14</td>\n",
       "      <td>897945</td>\n",
       "      <td>19.398880</td>\n",
       "      <td>474.331329</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>6.050016</td>\n",
       "      <td>48.571401</td>\n",
       "      <td>2938.75</td>\n",
       "      <td>1520</td>\n",
       "      <td>19</td>\n",
       "      <td>2169</td>\n",
       "      <td>6147</td>\n",
       "      <td>27.856503</td>\n",
       "      <td>-66516432</td>\n",
       "      <td>-1.073956</td>\n",
       "      <td>0.002280</td>\n",
       "      <td>254.679596</td>\n",
       "      <td>19</td>\n",
       "      <td>125.250488</td>\n",
       "      <td>87.865234</td>\n",
       "      <td>160.696777</td>\n",
       "      <td>0.037254</td>\n",
       "      <td>11</td>\n",
       "      <td>908426</td>\n",
       "      <td>20.170450</td>\n",
       "      <td>476.414520</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6.050019</td>\n",
       "      <td>49.921613</td>\n",
       "      <td>3294.75</td>\n",
       "      <td>1490</td>\n",
       "      <td>12</td>\n",
       "      <td>1995</td>\n",
       "      <td>5912</td>\n",
       "      <td>22.102139</td>\n",
       "      <td>-297770784</td>\n",
       "      <td>-1.402633</td>\n",
       "      <td>0.309765</td>\n",
       "      <td>294.927765</td>\n",
       "      <td>26</td>\n",
       "      <td>-86.729492</td>\n",
       "      <td>-145.584229</td>\n",
       "      <td>-190.062988</td>\n",
       "      <td>0.222435</td>\n",
       "      <td>15</td>\n",
       "      <td>772784</td>\n",
       "      <td>20.855963</td>\n",
       "      <td>457.195404</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>6.050020</td>\n",
       "      <td>48.822645</td>\n",
       "      <td>1623.50</td>\n",
       "      <td>1554</td>\n",
       "      <td>18</td>\n",
       "      <td>1973</td>\n",
       "      <td>6138</td>\n",
       "      <td>18.496584</td>\n",
       "      <td>-25336536</td>\n",
       "      <td>-0.800016</td>\n",
       "      <td>0.010370</td>\n",
       "      <td>240.493759</td>\n",
       "      <td>22</td>\n",
       "      <td>-51.470703</td>\n",
       "      <td>-245.886719</td>\n",
       "      <td>172.074707</td>\n",
       "      <td>0.004428</td>\n",
       "      <td>8</td>\n",
       "      <td>839132</td>\n",
       "      <td>21.812290</td>\n",
       "      <td>496.231110</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>6.050024</td>\n",
       "      <td>49.847522</td>\n",
       "      <td>1400.00</td>\n",
       "      <td>1521</td>\n",
       "      <td>15</td>\n",
       "      <td>2187</td>\n",
       "      <td>5886</td>\n",
       "      <td>-5.660453</td>\n",
       "      <td>-278652608</td>\n",
       "      <td>1.477951</td>\n",
       "      <td>-0.068720</td>\n",
       "      <td>376.671143</td>\n",
       "      <td>12</td>\n",
       "      <td>277.297363</td>\n",
       "      <td>273.141846</td>\n",
       "      <td>-138.895996</td>\n",
       "      <td>0.098817</td>\n",
       "      <td>13</td>\n",
       "      <td>768873</td>\n",
       "      <td>21.137711</td>\n",
       "      <td>466.976685</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID         X          Y        h  BLDFIE_WeigAver  CECSOL_WeigAver  \\\n",
       "0   1  6.050001  49.727499  3139.00             1540               13   \n",
       "1   2  6.050002  49.922155  1454.75             1491               12   \n",
       "2   3  6.050002  48.602377   853.50             1521               17   \n",
       "3   4  6.050009  48.151979  3141.00             1526               16   \n",
       "4   5  6.050010  49.588410  2065.25             1547               14   \n",
       "5   6  6.050014  48.608456  1246.50             1515               19   \n",
       "6   7  6.050016  48.571401  2938.75             1520               19   \n",
       "7   8  6.050019  49.921613  3294.75             1490               12   \n",
       "8   9  6.050020  48.822645  1623.50             1554               18   \n",
       "9  10  6.050024  49.847522  1400.00             1521               15   \n",
       "\n",
       "   CHELSA_bio18  CHELSA_bio4  convergence        cti  devmagnitude  eastness  \\\n",
       "0          2113         5893   -10.486560 -238043120      1.158417  0.069094   \n",
       "1          1993         5912    33.274361 -208915344     -1.755341  0.269112   \n",
       "2          2124         5983     0.045293 -137479792      1.908780 -0.016055   \n",
       "3          2569         6130   -33.654274 -267223072      0.965787  0.067767   \n",
       "4          2108         5923    27.493824 -107809368     -0.162624  0.014065   \n",
       "5          2124         6010    -1.602039   17384282      1.447979 -0.018912   \n",
       "6          2169         6147    27.856503  -66516432     -1.073956  0.002280   \n",
       "7          1995         5912    22.102139 -297770784     -1.402633  0.309765   \n",
       "8          1973         6138    18.496584  -25336536     -0.800016  0.010370   \n",
       "9          2187         5886    -5.660453 -278652608      1.477951 -0.068720   \n",
       "\n",
       "         elev  forestheight  glad_ard_SVVI_max  glad_ard_SVVI_med  \\\n",
       "0  353.983124            23         276.871094          46.444092   \n",
       "1  267.511688            19         -49.526367          19.552734   \n",
       "2  389.751160            21          93.257324          50.743652   \n",
       "3  380.207703            27         542.401367         202.264160   \n",
       "4  308.042786            25         136.048340         146.835205   \n",
       "5  364.527100            18         221.339844         247.387207   \n",
       "6  254.679596            19         125.250488          87.865234   \n",
       "7  294.927765            26         -86.729492        -145.584229   \n",
       "8  240.493759            22         -51.470703        -245.886719   \n",
       "9  376.671143            12         277.297363         273.141846   \n",
       "\n",
       "   glad_ard_SVVI_min  northness  ORCDRC_WeigAver  outlet_dist_dw_basin  \\\n",
       "0         347.665405   0.042500                9                780403   \n",
       "1        -130.541748   0.182780               16                772777   \n",
       "2         384.522461   0.036253               14                898820   \n",
       "3         386.156738   0.005139               15                831824   \n",
       "4         198.127441   0.028847               17                796962   \n",
       "5         480.387939   0.042747               14                897945   \n",
       "6         160.696777   0.037254               11                908426   \n",
       "7        -190.062988   0.222435               15                772784   \n",
       "8         172.074707   0.004428                8                839132   \n",
       "9        -138.895996   0.098817               13                768873   \n",
       "\n",
       "   SBIO3_Isothermality_5_15cm  SBIO4_Temperature_Seasonality_5_15cm  treecover  \n",
       "0                   19.798992                            440.672211         85  \n",
       "1                   20.889412                            457.756195         85  \n",
       "2                   20.695877                            481.879700         62  \n",
       "3                   19.375000                            479.410278         85  \n",
       "4                   18.777500                            457.880066         85  \n",
       "5                   19.398880                            474.331329         62  \n",
       "6                   20.170450                            476.414520         96  \n",
       "7                   20.855963                            457.195404         86  \n",
       "8                   21.812290                            496.231110         64  \n",
       "9                   21.137711                            466.976685         70  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = pd.read_csv(\"tree_height/txt/eu_x_y_height_predictors_select.txt\", sep=\" \",  index_col=False)\n",
    "pd.set_option('display.max_columns',None)\n",
    "# change column name\n",
    "predictors = predictors.rename({'dev-magnitude':'devmagnitude'} , axis='columns')\n",
    "predictors.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4bca739-c98f-4396-b034-a30658291cbc",
   "metadata": {
    "id": "a4bca739-c98f-4396-b034-a30658291cbc",
    "outputId": "8c468396-73e8-467b-a2dc-f6eed92b8f5b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1267239"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictors) # original: 1267239"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b005fe-c5bf-4399-b692-2c3b5c3dc2ef",
   "metadata": {
    "id": "d3b005fe-c5bf-4399-b692-2c3b5c3dc2ef"
   },
   "source": [
    "As explained in the previous lecture, 'h' is the estimated tree heigth. So let's use it as our target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aacb84f1-34d7-4012-9905-c1ad2ac05d29",
   "metadata": {
    "id": "aacb84f1-34d7-4012-9905-c1ad2ac05d29",
    "outputId": "debd2640-0b92-4bd2-b88b-465af6341358",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.2886e+04, 4.7517e+04, 3.0371e+04, 2.9201e+04, 3.0742e+04,\n",
       "        3.2932e+04, 3.6990e+04, 4.1454e+04, 4.7479e+04, 5.3498e+04,\n",
       "        5.9376e+04, 6.3533e+04, 6.7949e+04, 7.0729e+04, 7.4511e+04,\n",
       "        7.8514e+04, 7.9429e+04, 7.6759e+04, 6.9654e+04, 6.0192e+04,\n",
       "        4.9049e+04, 3.7490e+04, 2.7254e+04, 1.8778e+04, 1.2967e+04,\n",
       "        8.6820e+03, 5.6980e+03, 3.6230e+03, 2.4370e+03, 1.5850e+03,\n",
       "        1.0550e+03, 6.8900e+02, 4.7600e+02, 3.4300e+02, 2.6100e+02,\n",
       "        2.3600e+02, 1.6900e+02, 1.7000e+02, 1.4700e+02, 1.3700e+02,\n",
       "        1.4500e+02, 1.2300e+02, 9.8000e+01, 1.0800e+02, 9.3000e+01,\n",
       "        1.0500e+02, 8.7000e+01, 1.1100e+02, 8.0000e+01, 6.7000e+01,\n",
       "        8.5000e+01, 8.1000e+01, 7.5000e+01, 6.1000e+01, 5.8000e+01,\n",
       "        6.4000e+01, 6.0000e+01, 4.5000e+01, 4.8000e+01, 5.2000e+01,\n",
       "        5.0000e+01, 4.9000e+01, 5.6000e+01, 3.3000e+01, 3.6000e+01,\n",
       "        3.6000e+01, 3.5000e+01, 2.9000e+01, 2.2000e+01, 2.3000e+01,\n",
       "        1.9000e+01, 1.5000e+01, 1.4000e+01, 2.2000e+01, 1.7000e+01,\n",
       "        1.7000e+01, 1.0000e+01, 8.0000e+00, 1.2000e+01, 1.2000e+01,\n",
       "        1.3000e+01, 5.0000e+00, 1.5000e+01, 9.0000e+00, 5.0000e+00,\n",
       "        1.0000e+01, 7.0000e+00, 6.0000e+00, 4.0000e+00, 4.0000e+00,\n",
       "        5.0000e+00, 8.0000e+00, 6.0000e+00, 2.0000e+00, 2.0000e+00,\n",
       "        4.0000e+00, 3.0000e+00, 3.0000e+00, 5.0000e+00]),\n",
       " array([   84.75      ,   230.16666667,   375.58333333,   521.        ,\n",
       "          666.41666667,   811.83333333,   957.25      ,  1102.66666667,\n",
       "         1248.08333333,  1393.5       ,  1538.91666667,  1684.33333333,\n",
       "         1829.75      ,  1975.16666667,  2120.58333333,  2266.        ,\n",
       "         2411.41666667,  2556.83333333,  2702.25      ,  2847.66666667,\n",
       "         2993.08333333,  3138.5       ,  3283.91666667,  3429.33333333,\n",
       "         3574.75      ,  3720.16666667,  3865.58333333,  4011.        ,\n",
       "         4156.41666667,  4301.83333333,  4447.25      ,  4592.66666667,\n",
       "         4738.08333333,  4883.5       ,  5028.91666667,  5174.33333333,\n",
       "         5319.75      ,  5465.16666667,  5610.58333333,  5756.        ,\n",
       "         5901.41666667,  6046.83333333,  6192.25      ,  6337.66666667,\n",
       "         6483.08333333,  6628.5       ,  6773.91666667,  6919.33333333,\n",
       "         7064.75      ,  7210.16666667,  7355.58333333,  7501.        ,\n",
       "         7646.41666667,  7791.83333333,  7937.25      ,  8082.66666667,\n",
       "         8228.08333333,  8373.5       ,  8518.91666667,  8664.33333333,\n",
       "         8809.75      ,  8955.16666667,  9100.58333333,  9246.        ,\n",
       "         9391.41666667,  9536.83333333,  9682.25      ,  9827.66666667,\n",
       "         9973.08333333, 10118.5       , 10263.91666667, 10409.33333333,\n",
       "        10554.75      , 10700.16666667, 10845.58333333, 10991.        ,\n",
       "        11136.41666667, 11281.83333333, 11427.25      , 11572.66666667,\n",
       "        11718.08333333, 11863.5       , 12008.91666667, 12154.33333333,\n",
       "        12299.75      , 12445.16666667, 12590.58333333, 12736.        ,\n",
       "        12881.41666667, 13026.83333333, 13172.25      , 13317.66666667,\n",
       "        13463.08333333, 13608.5       , 13753.91666667, 13899.33333333,\n",
       "        14044.75      , 14190.16666667, 14335.58333333, 14481.        ]),\n",
       " <BarContainer object of 99 artists>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGdCAYAAADwjmIIAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAOUlJREFUeJzt3Xt0FPX9//FXLuwmgLvhYrKkBEiLFSJRJIGwin6/fk1ZbaSlYgs0xRRQCk2oEMtNMaKtYrEqV0mtrXhOoQLnKFUiwTQIFIhcIigBifQnNShugl9MFigkkP38/ujJfFkISriFDM/HOXOOmc97Zj7vOcC+nMzMhhljjAAAAGwmvLknAAAAcCkQcgAAgC0RcgAAgC0RcgAAgC0RcgAAgC0RcgAAgC0RcgAAgC0RcgAAgC1FNvcEmlMwGNSBAwd0zTXXKCwsrLmnAwAAzoExRocPH1Z8fLzCw89+veaqDjkHDhxQQkJCc08DAACch/3796tz585nHb+qQ84111wj6T8nyeVyNfNsAADAuQgEAkpISLA+x8/mqg45Db+icrlchBwAAFqYb7rVhBuPAQCALRFyAACALRFyAACALRFyAACALRFyAACALRFyAACALRFyAACALRFyAACALTUp5NTX1+uxxx5TYmKioqOj9Z3vfEe/+c1vZIyxaowxysvLU6dOnRQdHa309HTt3bs3ZD+HDh1SZmamXC6XYmJiNHr0aB05ciSk5sMPP9Rtt92mqKgoJSQkaNasWWfMZ/ny5erRo4eioqKUnJyst99+uyntAAAAG2tSyPnd736nhQsXav78+froo4/0u9/9TrNmzdK8efOsmlmzZmnu3LnKz8/X5s2b1aZNG/l8Ph0/ftyqyczM1K5du1RUVKSVK1dq/fr1GjNmjDUeCAQ0cOBAde3aVaWlpXr22Wc1Y8YMvfTSS1bNpk2bNHz4cI0ePVrbt2/X4MGDNXjwYJWVlV3I+QAAAHZhmiAjI8OMGjUqZN29995rMjMzjTHGBINB4/F4zLPPPmuNV1dXG6fTaf76178aY4zZvXu3kWS2bt1q1axatcqEhYWZzz//3BhjzIsvvmjatWtnamtrrZopU6aY66+/3vr5Jz/5icnIyAiZS1pamvnFL35xzv3U1NQYSaampuactwEAAM3rXD+/m3Ql55ZbblFxcbE+/vhjSdIHH3ygDRs26O6775Yk7du3T36/X+np6dY2brdbaWlpKikpkSSVlJQoJiZGqampVk16errCw8O1efNmq+b222+Xw+Gwanw+n8rLy/XVV19ZNacep6Gm4TiNqa2tVSAQCFkAAIA9NekLOqdOnapAIKAePXooIiJC9fX1euqpp5SZmSlJ8vv9kqS4uLiQ7eLi4qwxv9+v2NjY0ElERqp9+/YhNYmJiWfso2GsXbt28vv9X3ucxsycOVNPPPFEU1oGAAAtVJOu5CxbtkyLFy/WkiVL9P777+vVV1/V73//e7366quXan4X1bRp01RTU2Mt+/fvb+4pAQCAS6RJV3ImTZqkqVOnatiwYZKk5ORkffrpp5o5c6aysrLk8XgkSZWVlerUqZO1XWVlpXr37i1J8ng8qqqqCtnvyZMndejQIWt7j8ejysrKkJqGn7+ppmG8MU6nU06nsykt4xIaNG/DGeveGj+gGWYCALCjJl3J+fe//63w8NBNIiIiFAwGJUmJiYnyeDwqLi62xgOBgDZv3iyv1ytJ8nq9qq6uVmlpqVWzZs0aBYNBpaWlWTXr16/XiRMnrJqioiJdf/31ateunVVz6nEaahqOAwAArm5NCjmDBg3SU089pYKCAv3rX//SG2+8oeeff14/+tGPJElhYWGaMGGCfvvb3+rNN9/Uzp07df/99ys+Pl6DBw+WJPXs2VN33XWXHnzwQW3ZskUbN25UTk6Ohg0bpvj4eEnST3/6UzkcDo0ePVq7du3S0qVLNWfOHOXm5lpzeeihh1RYWKjnnntOe/bs0YwZM7Rt2zbl5ORcpFMDAABasib9umrevHl67LHH9Mtf/lJVVVWKj4/XL37xC+Xl5Vk1kydP1tGjRzVmzBhVV1drwIABKiwsVFRUlFWzePFi5eTk6M4771R4eLiGDBmiuXPnWuNut1vvvPOOsrOzlZKSoo4dOyovLy/kXTq33HKLlixZounTp+uRRx7RddddpxUrVqhXr14Xcj4AAIBNhBlzyuuKrzKBQEBut1s1NTVyuVzNPR1ba+z+m8ZwTw4A4Juc6+c3310FAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsqUkvAwQutdPfp8N7cwAA54srOQAAwJYIOQAAwJYIOQAAwJYIOQAAwJYIOQAAwJYIOQAAwJYIOQAAwJYIOQAAwJYIOQAAwJZ44zEuidPfXAwAwOXGlRwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLTQo53bp1U1hY2BlLdna2JOn48ePKzs5Whw4d1LZtWw0ZMkSVlZUh+6ioqFBGRoZat26t2NhYTZo0SSdPngypWbt2rfr06SOn06nu3btr0aJFZ8xlwYIF6tatm6KiopSWlqYtW7Y0sXUAAGBnkU0p3rp1q+rr662fy8rK9L3vfU8//vGPJUkTJ05UQUGBli9fLrfbrZycHN17773auHGjJKm+vl4ZGRnyeDzatGmTvvjiC91///1q1aqVnn76aUnSvn37lJGRobFjx2rx4sUqLi7WAw88oE6dOsnn80mSli5dqtzcXOXn5ystLU2zZ8+Wz+dTeXm5YmNjL8qJwbkbNG9Dc08BAIAzhBljzPluPGHCBK1cuVJ79+5VIBDQtddeqyVLlui+++6TJO3Zs0c9e/ZUSUmJ+vfvr1WrVumee+7RgQMHFBcXJ0nKz8/XlClTdPDgQTkcDk2ZMkUFBQUqKyuzjjNs2DBVV1ersLBQkpSWlqa+fftq/vz5kqRgMKiEhASNHz9eU6dOPef5BwIBud1u1dTUyOVyne9puOpdypDz1vgBl2zfAICW6Vw/v8/7npy6ujr95S9/0ahRoxQWFqbS0lKdOHFC6enpVk2PHj3UpUsXlZSUSJJKSkqUnJxsBRxJ8vl8CgQC2rVrl1Vz6j4aahr2UVdXp9LS0pCa8PBwpaenWzVnU1tbq0AgELIAAAB7Ou+Qs2LFClVXV+vnP/+5JMnv98vhcCgmJiakLi4uTn6/36o5NeA0jDeMfV1NIBDQsWPH9OWXX6q+vr7RmoZ9nM3MmTPldrutJSEhoUk9AwCAluO8Q86f/vQn3X333YqPj7+Y87mkpk2bppqaGmvZv39/c08JAABcIk268bjBp59+qr///e96/fXXrXUej0d1dXWqrq4OuZpTWVkpj8dj1Zz+FFTD01en1pz+RFZlZaVcLpeio6MVERGhiIiIRmsa9nE2TqdTTqezac2iWTV2vw/36QAAzsV5Xcl55ZVXFBsbq4yMDGtdSkqKWrVqpeLiYmtdeXm5Kioq5PV6JUler1c7d+5UVVWVVVNUVCSXy6WkpCSr5tR9NNQ07MPhcCglJSWkJhgMqri42KoBAABo8pWcYDCoV155RVlZWYqM/L/N3W63Ro8erdzcXLVv314ul0vjx4+X1+tV//79JUkDBw5UUlKSRowYoVmzZsnv92v69OnKzs62rrCMHTtW8+fP1+TJkzVq1CitWbNGy5YtU0FBgXWs3NxcZWVlKTU1Vf369dPs2bN19OhRjRw58kLPBwAAsIkmh5y///3vqqio0KhRo84Ye+GFFxQeHq4hQ4aotrZWPp9PL774ojUeERGhlStXaty4cfJ6vWrTpo2ysrL05JNPWjWJiYkqKCjQxIkTNWfOHHXu3Fkvv/yy9Y4cSRo6dKgOHjyovLw8+f1+9e7dW4WFhWfcjAwAAK5eF/SenJaO9+RcHJf7ZYDckwMAV7dL/p4cAACAKxkhBwAA2NJ5PUKOqxvfVQUAaAm4kgMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGyJkAMAAGwpsrkngCvboHkbmnsKAACcF67kAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAW2pyyPn888/1s5/9TB06dFB0dLSSk5O1bds2a9wYo7y8PHXq1EnR0dFKT0/X3r17Q/Zx6NAhZWZmyuVyKSYmRqNHj9aRI0dCaj788EPddtttioqKUkJCgmbNmnXGXJYvX64ePXooKipKycnJevvtt5vaDgAAsKkmhZyvvvpKt956q1q1aqVVq1Zp9+7deu6559SuXTurZtasWZo7d67y8/O1efNmtWnTRj6fT8ePH7dqMjMztWvXLhUVFWnlypVav369xowZY40HAgENHDhQXbt2VWlpqZ599lnNmDFDL730klWzadMmDR8+XKNHj9b27ds1ePBgDR48WGVlZRdyPgAAgE2EGWPMuRZPnTpVGzdu1D/+8Y9Gx40xio+P18MPP6xf//rXkqSamhrFxcVp0aJFGjZsmD766CMlJSVp69atSk1NlSQVFhbq+9//vj777DPFx8dr4cKFevTRR+X3++VwOKxjr1ixQnv27JEkDR06VEePHtXKlSut4/fv31+9e/dWfn7+OfUTCATkdrtVU1Mjl8t1rqfhqnIlfkHnW+MHNPcUAADN6Fw/v5t0JefNN99UamqqfvzjHys2NlY333yz/vjHP1rj+/btk9/vV3p6urXO7XYrLS1NJSUlkqSSkhLFxMRYAUeS0tPTFR4ers2bN1s1t99+uxVwJMnn86m8vFxfffWVVXPqcRpqGo7TmNraWgUCgZAFAADYU2RTij/55BMtXLhQubm5euSRR7R161b96le/ksPhUFZWlvx+vyQpLi4uZLu4uDhrzO/3KzY2NnQSkZFq3759SE1iYuIZ+2gYa9eunfx+/9cepzEzZ87UE0880ZSWcQU6/eoSV3YAAI1p0pWcYDCoPn366Omnn9bNN9+sMWPG6MEHHzznXw81t2nTpqmmpsZa9u/f39xTAgAAl0iTQk6nTp2UlJQUsq5nz56qqKiQJHk8HklSZWVlSE1lZaU15vF4VFVVFTJ+8uRJHTp0KKSmsX2ceoyz1TSMN8bpdMrlcoUsAADAnpoUcm699VaVl5eHrPv444/VtWtXSVJiYqI8Ho+Ki4ut8UAgoM2bN8vr9UqSvF6vqqurVVpaatWsWbNGwWBQaWlpVs369et14sQJq6aoqEjXX3+99SSX1+sNOU5DTcNxcH4GzdsQsgAA0FI1KeRMnDhR7733np5++mn985//1JIlS/TSSy8pOztbkhQWFqYJEybot7/9rd58803t3LlT999/v+Lj4zV48GBJ/7nyc9ddd+nBBx/Uli1btHHjRuXk5GjYsGGKj4+XJP30pz+Vw+HQ6NGjtWvXLi1dulRz5sxRbm6uNZeHHnpIhYWFeu6557Rnzx7NmDFD27ZtU05OzkU6NQAAoCVr0o3Hffv21RtvvKFp06bpySefVGJiombPnq3MzEyrZvLkyTp69KjGjBmj6upqDRgwQIWFhYqKirJqFi9erJycHN15550KDw/XkCFDNHfuXGvc7XbrnXfeUXZ2tlJSUtSxY0fl5eWFvEvnlltu0ZIlSzR9+nQ98sgjuu6667RixQr16tXrQs4HAACwiSa9J8dueE/OmVrir6h4ugoAri6X5D05AAAALQUhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2FJkc08AzWfQvA3NPQUAAC4ZruQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbalLImTFjhsLCwkKWHj16WOPHjx9Xdna2OnTooLZt22rIkCGqrKwM2UdFRYUyMjLUunVrxcbGatKkSTp58mRIzdq1a9WnTx85nU51795dixYtOmMuCxYsULdu3RQVFaW0tDRt2bKlKa0AAACba/KVnBtuuEFffPGFtWzY8H9f8jhx4kS99dZbWr58udatW6cDBw7o3nvvtcbr6+uVkZGhuro6bdq0Sa+++qoWLVqkvLw8q2bfvn3KyMjQHXfcoR07dmjChAl64IEHtHr1aqtm6dKlys3N1eOPP673339fN910k3w+n6qqqs73PAAAAJsJM8aYcy2eMWOGVqxYoR07dpwxVlNTo2uvvVZLlizRfffdJ0nas2ePevbsqZKSEvXv31+rVq3SPffcowMHDiguLk6SlJ+frylTpujgwYNyOByaMmWKCgoKVFZWZu172LBhqq6uVmFhoSQpLS1Nffv21fz58yVJwWBQCQkJGj9+vKZOnXrOzQcCAbndbtXU1Mjlcp3zdnZhl28hf2v8gOaeAgDgMjrXz+8mX8nZu3ev4uPj9e1vf1uZmZmqqKiQJJWWlurEiRNKT0+3anv06KEuXbqopKREklRSUqLk5GQr4EiSz+dTIBDQrl27rJpT99FQ07CPuro6lZaWhtSEh4crPT3dqjmb2tpaBQKBkAUAANhTk0JOWlqaFi1apMLCQi1cuFD79u3TbbfdpsOHD8vv98vhcCgmJiZkm7i4OPn9fkmS3+8PCTgN4w1jX1cTCAR07Ngxffnll6qvr2+0pmEfZzNz5ky53W5rSUhIaEr7AACgBYlsSvHdd99t/feNN96otLQ0de3aVcuWLVN0dPRFn9zFNm3aNOXm5lo/BwIBgg4AADZ1QY+Qx8TE6Lvf/a7++c9/yuPxqK6uTtXV1SE1lZWV8ng8kiSPx3PG01YNP39TjcvlUnR0tDp27KiIiIhGaxr2cTZOp1MulytkAQAA9nRBIefIkSP6f//v/6lTp05KSUlRq1atVFxcbI2Xl5eroqJCXq9XkuT1erVz586Qp6CKiorkcrmUlJRk1Zy6j4aahn04HA6lpKSE1ASDQRUXF1s1AAAATfp11a9//WsNGjRIXbt21YEDB/T4448rIiJCw4cPl9vt1ujRo5Wbm6v27dvL5XJp/Pjx8nq96t+/vyRp4MCBSkpK0ogRIzRr1iz5/X5Nnz5d2dnZcjqdkqSxY8dq/vz5mjx5skaNGqU1a9Zo2bJlKigosOaRm5urrKwspaamql+/fpo9e7aOHj2qkSNHXsRTg5aisafEeOIKANCkkPPZZ59p+PDh+t///V9de+21GjBggN577z1de+21kqQXXnhB4eHhGjJkiGpra+Xz+fTiiy9a20dERGjlypUaN26cvF6v2rRpo6ysLD355JNWTWJiogoKCjRx4kTNmTNHnTt31ssvvyyfz2fVDB06VAcPHlReXp78fr969+6twsLCM25GBgAAV68mvSfHbnhPjj3ek9MYruQAgH1dsvfkAAAAtASEHAAAYEuEHAAAYEuEHAAAYEtNeroKF+b0G30v982xdr7RGACA03ElBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2BIhBwAA2FJkc0/ArgbN29DcUwAA4KrGlRwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLPEJuUzzCDgC42nElBwAA2NIFhZxnnnlGYWFhmjBhgrXu+PHjys7OVocOHdS2bVsNGTJElZWVIdtVVFQoIyNDrVu3VmxsrCZNmqSTJ0+G1Kxdu1Z9+vSR0+lU9+7dtWjRojOOv2DBAnXr1k1RUVFKS0vTli1bLqQdAABgI+cdcrZu3ao//OEPuvHGG0PWT5w4UW+99ZaWL1+udevW6cCBA7r33nut8fr6emVkZKiurk6bNm3Sq6++qkWLFikvL8+q2bdvnzIyMnTHHXdox44dmjBhgh544AGtXr3aqlm6dKlyc3P1+OOP6/3339dNN90kn8+nqqqq820JAADYyHmFnCNHjigzM1N//OMf1a5dO2t9TU2N/vSnP+n555/X//zP/yglJUWvvPKKNm3apPfee0+S9M4772j37t36y1/+ot69e+vuu+/Wb37zGy1YsEB1dXWSpPz8fCUmJuq5555Tz549lZOTo/vuu08vvPCCdaznn39eDz74oEaOHKmkpCTl5+erdevW+vOf/3wh5wMAANjEeYWc7OxsZWRkKD09PWR9aWmpTpw4EbK+R48e6tKli0pKSiRJJSUlSk5OVlxcnFXj8/kUCAS0a9cuq+b0fft8PmsfdXV1Ki0tDakJDw9Xenq6VdOY2tpaBQKBkAUAANhTk5+ueu211/T+++9r69atZ4z5/X45HA7FxMSErI+Li5Pf77dqTg04DeMNY19XEwgEdOzYMX311Veqr69vtGbPnj1nnfvMmTP1xBNPnFujAACgRWvSlZz9+/froYce0uLFixUVFXWp5nTJTJs2TTU1Ndayf//+5p4SAAC4RJoUckpLS1VVVaU+ffooMjJSkZGRWrdunebOnavIyEjFxcWprq5O1dXVIdtVVlbK4/FIkjwezxlPWzX8/E01LpdL0dHR6tixoyIiIhqtadhHY5xOp1wuV8gCAADsqUkh584779TOnTu1Y8cOa0lNTVVmZqb1361atVJxcbG1TXl5uSoqKuT1eiVJXq9XO3fuDHkKqqioSC6XS0lJSVbNqftoqGnYh8PhUEpKSkhNMBhUcXGxVQMAAK5uTbon55prrlGvXr1C1rVp00YdOnSw1o8ePVq5ublq3769XC6Xxo8fL6/Xq/79+0uSBg4cqKSkJI0YMUKzZs2S3+/X9OnTlZ2dLafTKUkaO3as5s+fr8mTJ2vUqFFas2aNli1bpoKCAuu4ubm5ysrKUmpqqvr166fZs2fr6NGjGjly5AWdEAAAYA8X/WsdXnjhBYWHh2vIkCGqra2Vz+fTiy++aI1HRERo5cqVGjdunLxer9q0aaOsrCw9+eSTVk1iYqIKCgo0ceJEzZkzR507d9bLL78sn89n1QwdOlQHDx5UXl6e/H6/evfurcLCwjNuRgYAAFenMGOMae5JNJdAICC3262ampqLfn/OuXx31FvjB1zUYzb1+HZ2Kc8tAKB5nevnN99dBQAAbImQAwAAbImQAwAAbImQAwAAbOmiP10FXAlOv/GaG5EB4OrDlRwAAGBLXMmxgav9cXEAABrDlRwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLkc09ATTdoHkbmnsKAABc8biSAwAAbImQAwAAbImQAwAAbImQAwAAbImQAwAAbImQAwAAbImQAwAAbImQAwAAbKlJIWfhwoW68cYb5XK55HK55PV6tWrVKmv8+PHjys7OVocOHdS2bVsNGTJElZWVIfuoqKhQRkaGWrdurdjYWE2aNEknT54MqVm7dq369Okjp9Op7t27a9GiRWfMZcGCBerWrZuioqKUlpamLVu2NKUVAABgc01643Hnzp31zDPP6LrrrpMxRq+++qp++MMfavv27brhhhs0ceJEFRQUaPny5XK73crJydG9996rjRs3SpLq6+uVkZEhj8ejTZs26YsvvtD999+vVq1a6emnn5Yk7du3TxkZGRo7dqwWL16s4uJiPfDAA+rUqZN8Pp8kaenSpcrNzVV+fr7S0tI0e/Zs+Xw+lZeXKzY29iKfoubF240BADg/YcYYcyE7aN++vZ599lndd999uvbaa7VkyRLdd999kqQ9e/aoZ8+eKikpUf/+/bVq1Srdc889OnDggOLi4iRJ+fn5mjJlig4ePCiHw6EpU6aooKBAZWVl1jGGDRum6upqFRYWSpLS0tLUt29fzZ8/X5IUDAaVkJCg8ePHa+rUqec890AgILfbrZqaGrlcrgs5DWc4l3Dy1vgBF2U/+Gbncq4BAC3DuX5+n/c9OfX19Xrttdd09OhReb1elZaW6sSJE0pPT7dqevTooS5duqikpESSVFJSouTkZCvgSJLP51MgENCuXbusmlP30VDTsI+6ujqVlpaG1ISHhys9Pd2qOZva2loFAoGQpTkNmrfhjAUAAFwcTQ45O3fuVNu2beV0OjV27Fi98cYbSkpKkt/vl8PhUExMTEh9XFyc/H6/JMnv94cEnIbxhrGvqwkEAjp27Ji+/PJL1dfXN1rTsI+zmTlzptxut7UkJCQ0tX0AANBCNDnkXH/99dqxY4c2b96scePGKSsrS7t3774Uc7vopk2bppqaGmvZv39/c08JAABcIk268ViSHA6HunfvLklKSUnR1q1bNWfOHA0dOlR1dXWqrq4OuZpTWVkpj8cjSfJ4PGc8BdXw9NWpNac/kVVZWSmXy6Xo6GhFREQoIiKi0ZqGfZyN0+mU0+lsassAAKAFuuD35ASDQdXW1iolJUWtWrVScXGxNVZeXq6Kigp5vV5Jktfr1c6dO1VVVWXVFBUVyeVyKSkpyao5dR8NNQ37cDgcSklJCakJBoMqLi62agAAAJp0JWfatGm6++671aVLFx0+fFhLlizR2rVrtXr1arndbo0ePVq5ublq3769XC6Xxo8fL6/Xq/79+0uSBg4cqKSkJI0YMUKzZs2S3+/X9OnTlZ2dbV1hGTt2rObPn6/Jkydr1KhRWrNmjZYtW6aCggJrHrm5ucrKylJqaqr69eun2bNn6+jRoxo5cuRFPDXNg5uPAQC4OJoUcqqqqnT//ffriy++kNvt1o033qjVq1fre9/7niTphRdeUHh4uIYMGaLa2lr5fD69+OKL1vYRERFauXKlxo0bJ6/XqzZt2igrK0tPPvmkVZOYmKiCggJNnDhRc+bMUefOnfXyyy9b78iRpKFDh+rgwYPKy8uT3+9X7969VVhYeMbNyAAA4Op1we/Jacma+z05uHx4Tw4A2Mclf08OAADAlYyQAwAAbImQAwAAbImQAwAAbImQAwAAbKnJbzwGWqLGnnbjiSsAsDeu5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFtqUsiZOXOm+vbtq2uuuUaxsbEaPHiwysvLQ2qOHz+u7OxsdejQQW3bttWQIUNUWVkZUlNRUaGMjAy1bt1asbGxmjRpkk6ePBlSs3btWvXp00dOp1Pdu3fXokWLzpjPggUL1K1bN0VFRSktLU1btmxpSjsAAMDGmhRy1q1bp+zsbL333nsqKirSiRMnNHDgQB09etSqmThxot566y0tX75c69at04EDB3Tvvfda4/X19crIyFBdXZ02bdqkV199VYsWLVJeXp5Vs2/fPmVkZOiOO+7Qjh07NGHCBD3wwANavXq1VbN06VLl5ubq8ccf1/vvv6+bbrpJPp9PVVVVF3I+AACATYQZY8z5bnzw4EHFxsZq3bp1uv3221VTU6Nrr71WS5Ys0X333SdJ2rNnj3r27KmSkhL1799fq1at0j333KMDBw4oLi5OkpSfn68pU6bo4MGDcjgcmjJligoKClRWVmYda9iwYaqurlZhYaEkKS0tTX379tX8+fMlScFgUAkJCRo/frymTp16TvMPBAJyu92qqamRy+U639PQqEHzNlzU/eHie2v8gOaeAgDgPJzr5/cF3ZNTU1MjSWrfvr0kqbS0VCdOnFB6erpV06NHD3Xp0kUlJSWSpJKSEiUnJ1sBR5J8Pp8CgYB27dpl1Zy6j4aahn3U1dWptLQ0pCY8PFzp6elWDQAAuLpFnu+GwWBQEyZM0K233qpevXpJkvx+vxwOh2JiYkJq4+Li5Pf7rZpTA07DeMPY19UEAgEdO3ZMX331lerr6xut2bNnz1nnXFtbq9raWuvnQCDQhI4BAEBLct5XcrKzs1VWVqbXXnvtYs7nkpo5c6bcbre1JCQkNPeUAADAJXJeIScnJ0crV67Uu+++q86dO1vrPR6P6urqVF1dHVJfWVkpj8dj1Zz+tFXDz99U43K5FB0drY4dOyoiIqLRmoZ9NGbatGmqqamxlv379zetcQAA0GI0KeQYY5STk6M33nhDa9asUWJiYsh4SkqKWrVqpeLiYmtdeXm5Kioq5PV6JUler1c7d+4MeQqqqKhILpdLSUlJVs2p+2ioadiHw+FQSkpKSE0wGFRxcbFV0xin0ymXyxWyAAAAe2rSPTnZ2dlasmSJ/va3v+maa66x7qFxu92Kjo6W2+3W6NGjlZubq/bt28vlcmn8+PHyer3q37+/JGngwIFKSkrSiBEjNGvWLPn9fk2fPl3Z2dlyOp2SpLFjx2r+/PmaPHmyRo0apTVr1mjZsmUqKCiw5pKbm6usrCylpqaqX79+mj17to4ePaqRI0derHMDAABasCaFnIULF0qS/vu//ztk/SuvvKKf//znkqQXXnhB4eHhGjJkiGpra+Xz+fTiiy9atREREVq5cqXGjRsnr9erNm3aKCsrS08++aRVk5iYqIKCAk2cOFFz5sxR586d9fLLL8vn81k1Q4cO1cGDB5WXlye/36/evXursLDwjJuRAQDA1emC3pPT0vGeHJyK9+YAQMtwWd6TAwAAcKUi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFuKbO4JAFeKQfM2nLHurfEDmmEmAICLgSs5AADAlgg5AADAlgg5AADAlgg5AADAlgg5AADAlgg5AADAlgg5AADAlgg5AADAlpocctavX69BgwYpPj5eYWFhWrFiRci4MUZ5eXnq1KmToqOjlZ6err1794bUHDp0SJmZmXK5XIqJidHo0aN15MiRkJoPP/xQt912m6KiopSQkKBZs2adMZfly5erR48eioqKUnJyst5+++2mtgMAAGyqySHn6NGjuummm7RgwYJGx2fNmqW5c+cqPz9fmzdvVps2beTz+XT8+HGrJjMzU7t27VJRUZFWrlyp9evXa8yYMdZ4IBDQwIED1bVrV5WWlurZZ5/VjBkz9NJLL1k1mzZt0vDhwzV69Ght375dgwcP1uDBg1VWVtbUlgAAgA2FGWPMeW8cFqY33nhDgwcPlvSfqzjx8fF6+OGH9etf/1qSVFNTo7i4OC1atEjDhg3TRx99pKSkJG3dulWpqamSpMLCQn3/+9/XZ599pvj4eC1cuFCPPvqo/H6/HA6HJGnq1KlasWKF9uzZI0kaOnSojh49qpUrV1rz6d+/v3r37q38/Pxzmn8gEJDb7VZNTY1cLtf5noZGNfYVAWh5+FoHALjynOvn90W9J2ffvn3y+/1KT0+31rndbqWlpamkpESSVFJSopiYGCvgSFJ6errCw8O1efNmq+b222+3Ao4k+Xw+lZeX66uvvrJqTj1OQ03DcRpTW1urQCAQsgAAAHu6qCHH7/dLkuLi4kLWx8XFWWN+v1+xsbEh45GRkWrfvn1ITWP7OPUYZ6tpGG/MzJkz5Xa7rSUhIaGpLQIAgBbiqnq6atq0aaqpqbGW/fv3N/eUAADAJXJRQ47H45EkVVZWhqyvrKy0xjwej6qqqkLGT548qUOHDoXUNLaPU49xtpqG8cY4nU65XK6QBQAA2NNFDTmJiYnyeDwqLi621gUCAW3evFler1eS5PV6VV1drdLSUqtmzZo1CgaDSktLs2rWr1+vEydOWDVFRUW6/vrr1a5dO6vm1OM01DQcBwAAXN2aHHKOHDmiHTt2aMeOHZL+c7Pxjh07VFFRobCwME2YMEG//e1v9eabb2rnzp26//77FR8fbz2B1bNnT91111168MEHtWXLFm3cuFE5OTkaNmyY4uPjJUk//elP5XA4NHr0aO3atUtLly7VnDlzlJuba83joYceUmFhoZ577jnt2bNHM2bM0LZt25STk3PhZwUAALR4kU3dYNu2bbrjjjusnxuCR1ZWlhYtWqTJkyfr6NGjGjNmjKqrqzVgwAAVFhYqKirK2mbx4sXKycnRnXfeqfDwcA0ZMkRz5861xt1ut9555x1lZ2crJSVFHTt2VF5eXsi7dG655RYtWbJE06dP1yOPPKLrrrtOK1asUK9evc7rRAAAAHu5oPfktHS8Jwfng3fnAEDzapb35AAAAFwpCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWmvwt5MDV7vQvX+ULOwHgysSVHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEu8DBC4QKe/HFDiBYEAcCXgSg4AALAlQg4AALAlQg4AALAlQg4AALAlbjwGLgG+qRwAmh9XcgAAgC0RcgAAgC0RcgAAgC1xTw5wGfDCQAC4/LiSAwAAbImQAwAAbKnFh5wFCxaoW7duioqKUlpamrZs2dLcUwLOyaB5G0IWAMDF1aLvyVm6dKlyc3OVn5+vtLQ0zZ49Wz6fT+Xl5YqNjW3u6QFNwn07AHBxhRljTHNP4nylpaWpb9++mj9/viQpGAwqISFB48eP19SpU79x+0AgILfbrZqaGrlcros6N/7PHJcCoQcAzv3zu8Veyamrq1NpaammTZtmrQsPD1d6erpKSkoa3aa2tla1tbXWzzU1NZL+c7IuthPHjl70fQJ3zVp9XtstG+u9yDMBgObT8Ln9TddpWmzI+fLLL1VfX6+4uLiQ9XFxcdqzZ0+j28ycOVNPPPHEGesTEhIuyRyBK4V7SnPPAAAuvsOHD8vtdp91vMWGnPMxbdo05ebmWj8Hg0EdOnRIHTp0UFhY2AXtOxAIKCEhQfv377/ov/q60tH71df71dq3RO9XY+9Xa9/Sldu7MUaHDx9WfHz819a12JDTsWNHRUREqLKyMmR9ZWWlPB5Po9s4nU45nc6QdTExMRd1Xi6X64r6g3A50fvV1/vV2rdE71dj71dr39KV2fvXXcFp0GIfIXc4HEpJSVFxcbG1LhgMqri4WF4v9x8AAHC1a7FXciQpNzdXWVlZSk1NVb9+/TR79mwdPXpUI0eObO6pAQCAZtaiQ87QoUN18OBB5eXlye/3q3fv3iosLDzjZuTLwel06vHHHz/j12FXA3q/+nq/WvuW6P1q7P1q7Vtq+b236PfkAAAAnE2LvScHAADg6xByAACALRFyAACALRFyAACALRFyLpIFCxaoW7duioqKUlpamrZs2dLcU2qSmTNnqm/fvrrmmmsUGxurwYMHq7y8PKTm+PHjys7OVocOHdS2bVsNGTLkjJcxVlRUKCMjQ61bt1ZsbKwmTZqkkydPhtSsXbtWffr0kdPpVPfu3bVo0aJL3d45e+aZZxQWFqYJEyZY6+zc9+eff66f/exn6tChg6Kjo5WcnKxt27ZZ48YY5eXlqVOnToqOjlZ6err27t0bso9Dhw4pMzNTLpdLMTExGj16tI4cORJS8+GHH+q2225TVFSUEhISNGvWrMvSX2Pq6+v12GOPKTExUdHR0frOd76j3/zmNyHfgWOXvtevX69BgwYpPj5eYWFhWrFiRcj45exz+fLl6tGjh6KiopScnKy33377ovd7qq/r/cSJE5oyZYqSk5PVpk0bxcfH6/7779eBAwdC9mHH3k83duxYhYWFafbs2SHrW2rvZzC4YK+99ppxOBzmz3/+s9m1a5d58MEHTUxMjKmsrGzuqZ0zn89nXnnlFVNWVmZ27Nhhvv/975suXbqYI0eOWDVjx441CQkJpri42Gzbts3079/f3HLLLdb4yZMnTa9evUx6errZvn27efvtt03Hjh3NtGnTrJpPPvnEtG7d2uTm5prdu3ebefPmmYiICFNYWHhZ+23Mli1bTLdu3cyNN95oHnroIWu9Xfs+dOiQ6dq1q/n5z39uNm/ebD755BOzevVq889//tOqeeaZZ4zb7TYrVqwwH3zwgfnBD35gEhMTzbFjx6yau+66y9x0003mvffeM//4xz9M9+7dzfDhw63xmpoaExcXZzIzM01ZWZn561//aqKjo80f/vCHy9pvg6eeesp06NDBrFy50uzbt88sX77ctG3b1syZM8eqsUvfb7/9tnn00UfN66+/biSZN954I2T8cvW5ceNGExERYWbNmmV2795tpk+fblq1amV27tzZLL1XV1eb9PR0s3TpUrNnzx5TUlJi+vXrZ1JSUkL2YcfeT/X666+bm266ycTHx5sXXnghZKyl9n46Qs5F0K9fP5OdnW39XF9fb+Lj483MmTObcVYXpqqqykgy69atM8b85x+FVq1ameXLl1s1H330kZFkSkpKjDH/+YsVHh5u/H6/VbNw4ULjcrlMbW2tMcaYyZMnmxtuuCHkWEOHDjU+n+9St/S1Dh8+bK677jpTVFRk/uu//ssKOXbue8qUKWbAgAFnHQ8Gg8bj8Zhnn33WWlddXW2cTqf561//aowxZvfu3UaS2bp1q1WzatUqExYWZj7//HNjjDEvvviiadeunXUuGo59/fXXX+yWzklGRoYZNWpUyLp7773XZGZmGmPs2/fpH3aXs8+f/OQnJiMjI2Q+aWlp5he/+MVF7fFsvu6DvsGWLVuMJPPpp58aY+zf+2effWa+9a1vmbKyMtO1a9eQkGOX3o0xhl9XXaC6ujqVlpYqPT3dWhceHq709HSVlJQ048wuTE1NjSSpffv2kqTS0lKdOHEipM8ePXqoS5cuVp8lJSVKTk4OeRmjz+dTIBDQrl27rJpT99FQ09znKjs7WxkZGWfMzc59v/nmm0pNTdWPf/xjxcbG6uabb9Yf//hHa3zfvn3y+/0h83a73UpLSwvpPSYmRqmpqVZNenq6wsPDtXnzZqvm9ttvl8PhsGp8Pp/Ky8v11VdfXeo2z3DLLbeouLhYH3/8sSTpgw8+0IYNG3T33XdLsm/fp7ucfV6Jf/5PV1NTo7CwMOv7DO3cezAY1IgRIzRp0iTdcMMNZ4zbqXdCzgX68ssvVV9ff8ZbluPi4uT3+5tpVhcmGAxqwoQJuvXWW9WrVy9Jkt/vl8PhOOMLTU/t0+/3N3oeGsa+riYQCOjYsWOXop1v9Nprr+n999/XzJkzzxizc9+ffPKJFi5cqOuuu06rV6/WuHHj9Ktf/UqvvvqqpP+b+9f92fb7/YqNjQ0Zj4yMVPv27Zt0fi6nqVOnatiwYerRo4datWqlm2++WRMmTFBmZmbInOzW9+kuZ59nq7kSzoP0n/vupkyZouHDh1tfQmnn3n/3u98pMjJSv/rVrxodt1PvLfprHXBpZGdnq6ysTBs2bGjuqVxy+/fv10MPPaSioiJFRUU193Quq2AwqNTUVD399NOSpJtvvlllZWXKz89XVlZWM8/u0lm2bJkWL16sJUuW6IYbbtCOHTs0YcIExcfH27pvNO7EiRP6yU9+ImOMFi5c2NzTueRKS0s1Z84cvf/++woLC2vu6VxyXMm5QB07dlRERMQZT9tUVlbK4/E006zOX05OjlauXKl3331XnTt3ttZ7PB7V1dWpuro6pP7UPj0eT6PnoWHs62pcLpeio6MvdjvfqLS0VFVVVerTp48iIyMVGRmpdevWae7cuYqMjFRcXJwt+5akTp06KSkpKWRdz549VVFRIen/5v51f7Y9Ho+qqqpCxk+ePKlDhw416fxcTpMmTbKu5iQnJ2vEiBGaOHGidSXPrn2f7nL2ebaa5j4PDQHn008/VVFRkXUVR7Jv7//4xz9UVVWlLl26WP/mffrpp3r44YfVrVs3SfbqnZBzgRwOh1JSUlRcXGytCwaDKi4ultfrbcaZNY0xRjk5OXrjjTe0Zs0aJSYmhoynpKSoVatWIX2Wl5eroqLC6tPr9Wrnzp0hfzka/uFo+DD1er0h+2ioaa5zdeedd2rnzp3asWOHtaSmpiozM9P6bzv2LUm33nrrGa8J+Pjjj9W1a1dJUmJiojweT8i8A4GANm/eHNJ7dXW1SktLrZo1a9YoGAwqLS3Nqlm/fr1OnDhh1RQVFen6669Xu3btLll/Z/Pvf/9b4eGh//RFREQoGAxKsm/fp7ucfV6Jf/4bAs7evXv197//XR06dAgZt2vvI0aM0Icffhjyb158fLwmTZqk1atXS7JZ75ftFmcbe+2114zT6TSLFi0yu3fvNmPGjDExMTEhT9tc6caNG2fcbrdZu3at+eKLL6zl3//+t1UzduxY06VLF7NmzRqzbds24/V6jdfrtcYbHqUeOHCg2bFjhyksLDTXXntto49ST5o0yXz00UdmwYIFzf4o9elOfbrKGPv2vWXLFhMZGWmeeuops3fvXrN48WLTunVr85e//MWqeeaZZ0xMTIz529/+Zj788EPzwx/+sNFHjG+++WazefNms2HDBnPdddeFPGpaXV1t4uLizIgRI0xZWZl57bXXTOvWrZvtEfKsrCzzrW99y3qE/PXXXzcdO3Y0kydPtmrs0vfhw4fN9u3bzfbt240k8/zzz5vt27dbTxBdrj43btxoIiMjze9//3vz0Ucfmccff/ySP0r8db3X1dWZH/zgB6Zz585mx44dIf/mnfq0kB17b8zpT1cZ03J7Px0h5yKZN2+e6dKli3E4HKZfv37mvffea+4pNYmkRpdXXnnFqjl27Jj55S9/adq1a2dat25tfvSjH5kvvvgiZD//+te/zN13322io6NNx44dzcMPP2xOnDgRUvPuu++a3r17G4fDYb797W+HHONKcHrIsXPfb731lunVq5dxOp2mR48e5qWXXgoZDwaD5rHHHjNxcXHG6XSaO++805SXl4fU/O///q8ZPny4adu2rXG5XGbkyJHm8OHDITUffPCBGTBggHE6neZb3/qWeeaZZy55b2cTCATMQw89ZLp06WKioqLMt7/9bfPoo4+GfLjZpe9333230b/XWVlZxpjL2+eyZcvMd7/7XeNwOMwNN9xgCgoKLlnfxnx97/v27Tvrv3nvvvuurXtvTGMhp6X2frowY055zScAAIBNcE8OAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwJUIOAACwpf8PBKnCqS9vNWoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(min(predictors['h']),max(predictors['h']),100)\n",
    "plt.hist((predictors['h']),bins,alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02eb033f-bdca-40ee-86af-5f409b504927",
   "metadata": {
    "id": "02eb033f-bdca-40ee-86af-5f409b504927",
    "outputId": "74d972e4-602c-4c12-bed2-8369424aed78",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>h</th>\n",
       "      <th>hm</th>\n",
       "      <th>BLDFIE_WeigAver</th>\n",
       "      <th>CECSOL_WeigAver</th>\n",
       "      <th>CHELSA_bio18</th>\n",
       "      <th>CHELSA_bio4</th>\n",
       "      <th>convergence</th>\n",
       "      <th>cti</th>\n",
       "      <th>devmagnitude</th>\n",
       "      <th>eastness</th>\n",
       "      <th>elev</th>\n",
       "      <th>forestheight</th>\n",
       "      <th>glad_ard_SVVI_max</th>\n",
       "      <th>glad_ard_SVVI_med</th>\n",
       "      <th>glad_ard_SVVI_min</th>\n",
       "      <th>northness</th>\n",
       "      <th>ORCDRC_WeigAver</th>\n",
       "      <th>outlet_dist_dw_basin</th>\n",
       "      <th>SBIO3_Isothermality_5_15cm</th>\n",
       "      <th>SBIO4_Temperature_Seasonality_5_15cm</th>\n",
       "      <th>treecover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>913378</th>\n",
       "      <td>913379</td>\n",
       "      <td>8.882207</td>\n",
       "      <td>49.672347</td>\n",
       "      <td>3185.25</td>\n",
       "      <td>31.8525</td>\n",
       "      <td>1518</td>\n",
       "      <td>13</td>\n",
       "      <td>2643</td>\n",
       "      <td>6330</td>\n",
       "      <td>26.204020</td>\n",
       "      <td>139056832</td>\n",
       "      <td>0.951690</td>\n",
       "      <td>-0.053181</td>\n",
       "      <td>341.708923</td>\n",
       "      <td>27</td>\n",
       "      <td>-54.174805</td>\n",
       "      <td>-95.832520</td>\n",
       "      <td>-97.327881</td>\n",
       "      <td>0.056747</td>\n",
       "      <td>15</td>\n",
       "      <td>718380</td>\n",
       "      <td>20.596432</td>\n",
       "      <td>486.906921</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399194</th>\n",
       "      <td>399195</td>\n",
       "      <td>7.218343</td>\n",
       "      <td>49.815899</td>\n",
       "      <td>1471.00</td>\n",
       "      <td>14.7100</td>\n",
       "      <td>1392</td>\n",
       "      <td>12</td>\n",
       "      <td>2388</td>\n",
       "      <td>5732</td>\n",
       "      <td>14.451588</td>\n",
       "      <td>-119020368</td>\n",
       "      <td>2.273924</td>\n",
       "      <td>0.106378</td>\n",
       "      <td>584.937500</td>\n",
       "      <td>23</td>\n",
       "      <td>125.125488</td>\n",
       "      <td>-236.454834</td>\n",
       "      <td>-412.245117</td>\n",
       "      <td>-0.019090</td>\n",
       "      <td>33</td>\n",
       "      <td>629871</td>\n",
       "      <td>21.379816</td>\n",
       "      <td>433.546661</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051554</th>\n",
       "      <td>1051555</td>\n",
       "      <td>9.219725</td>\n",
       "      <td>49.608233</td>\n",
       "      <td>2272.25</td>\n",
       "      <td>22.7225</td>\n",
       "      <td>1532</td>\n",
       "      <td>12</td>\n",
       "      <td>2091</td>\n",
       "      <td>6350</td>\n",
       "      <td>-41.441132</td>\n",
       "      <td>-150667936</td>\n",
       "      <td>1.742874</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>470.262482</td>\n",
       "      <td>26</td>\n",
       "      <td>23.418945</td>\n",
       "      <td>-161.524658</td>\n",
       "      <td>-430.280762</td>\n",
       "      <td>0.010492</td>\n",
       "      <td>13</td>\n",
       "      <td>722706</td>\n",
       "      <td>18.597925</td>\n",
       "      <td>473.179382</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82847</th>\n",
       "      <td>82848</td>\n",
       "      <td>6.352083</td>\n",
       "      <td>49.747792</td>\n",
       "      <td>551.50</td>\n",
       "      <td>5.5150</td>\n",
       "      <td>1510</td>\n",
       "      <td>16</td>\n",
       "      <td>2235</td>\n",
       "      <td>5948</td>\n",
       "      <td>16.482677</td>\n",
       "      <td>-123181768</td>\n",
       "      <td>-0.630119</td>\n",
       "      <td>-0.100164</td>\n",
       "      <td>313.726349</td>\n",
       "      <td>9</td>\n",
       "      <td>327.617676</td>\n",
       "      <td>50.381592</td>\n",
       "      <td>-119.103271</td>\n",
       "      <td>-0.073008</td>\n",
       "      <td>12</td>\n",
       "      <td>710051</td>\n",
       "      <td>20.216003</td>\n",
       "      <td>473.799133</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988551</th>\n",
       "      <td>988552</td>\n",
       "      <td>9.062895</td>\n",
       "      <td>49.725683</td>\n",
       "      <td>1859.00</td>\n",
       "      <td>18.5900</td>\n",
       "      <td>1521</td>\n",
       "      <td>14</td>\n",
       "      <td>2478</td>\n",
       "      <td>6404</td>\n",
       "      <td>23.876968</td>\n",
       "      <td>-283314592</td>\n",
       "      <td>-0.580429</td>\n",
       "      <td>0.139336</td>\n",
       "      <td>330.208832</td>\n",
       "      <td>8</td>\n",
       "      <td>381.459717</td>\n",
       "      <td>406.993652</td>\n",
       "      <td>146.392822</td>\n",
       "      <td>0.079916</td>\n",
       "      <td>11</td>\n",
       "      <td>718421</td>\n",
       "      <td>20.972237</td>\n",
       "      <td>499.222168</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323579</th>\n",
       "      <td>323580</td>\n",
       "      <td>7.042325</td>\n",
       "      <td>49.899161</td>\n",
       "      <td>1133.25</td>\n",
       "      <td>11.3325</td>\n",
       "      <td>1528</td>\n",
       "      <td>13</td>\n",
       "      <td>1871</td>\n",
       "      <td>6224</td>\n",
       "      <td>-8.266154</td>\n",
       "      <td>-363724384</td>\n",
       "      <td>-0.128035</td>\n",
       "      <td>0.152906</td>\n",
       "      <td>307.110260</td>\n",
       "      <td>24</td>\n",
       "      <td>-26.110840</td>\n",
       "      <td>-37.309082</td>\n",
       "      <td>31.492554</td>\n",
       "      <td>0.327583</td>\n",
       "      <td>12</td>\n",
       "      <td>612429</td>\n",
       "      <td>19.715183</td>\n",
       "      <td>459.910187</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101032</th>\n",
       "      <td>1101033</td>\n",
       "      <td>9.342970</td>\n",
       "      <td>48.969061</td>\n",
       "      <td>3206.00</td>\n",
       "      <td>32.0600</td>\n",
       "      <td>1528</td>\n",
       "      <td>15</td>\n",
       "      <td>2542</td>\n",
       "      <td>6560</td>\n",
       "      <td>20.984264</td>\n",
       "      <td>15857734</td>\n",
       "      <td>-1.031843</td>\n",
       "      <td>-0.000128</td>\n",
       "      <td>308.007874</td>\n",
       "      <td>26</td>\n",
       "      <td>121.762451</td>\n",
       "      <td>168.121338</td>\n",
       "      <td>323.751709</td>\n",
       "      <td>-0.019902</td>\n",
       "      <td>9</td>\n",
       "      <td>829244</td>\n",
       "      <td>17.987387</td>\n",
       "      <td>484.482605</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288095</th>\n",
       "      <td>288096</td>\n",
       "      <td>6.958537</td>\n",
       "      <td>48.805559</td>\n",
       "      <td>2310.50</td>\n",
       "      <td>23.1050</td>\n",
       "      <td>1538</td>\n",
       "      <td>11</td>\n",
       "      <td>2206</td>\n",
       "      <td>6320</td>\n",
       "      <td>38.485756</td>\n",
       "      <td>208938096</td>\n",
       "      <td>-1.168826</td>\n",
       "      <td>-0.004806</td>\n",
       "      <td>245.023285</td>\n",
       "      <td>23</td>\n",
       "      <td>309.372803</td>\n",
       "      <td>381.092285</td>\n",
       "      <td>210.433716</td>\n",
       "      <td>0.010540</td>\n",
       "      <td>8</td>\n",
       "      <td>867325</td>\n",
       "      <td>19.326263</td>\n",
       "      <td>485.738098</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116470</th>\n",
       "      <td>116471</td>\n",
       "      <td>6.465766</td>\n",
       "      <td>49.493455</td>\n",
       "      <td>2551.75</td>\n",
       "      <td>25.5175</td>\n",
       "      <td>1527</td>\n",
       "      <td>12</td>\n",
       "      <td>2707</td>\n",
       "      <td>5931</td>\n",
       "      <td>-25.785652</td>\n",
       "      <td>-231972688</td>\n",
       "      <td>1.294931</td>\n",
       "      <td>-0.038748</td>\n",
       "      <td>374.514069</td>\n",
       "      <td>23</td>\n",
       "      <td>306.397949</td>\n",
       "      <td>115.095459</td>\n",
       "      <td>240.195557</td>\n",
       "      <td>0.030025</td>\n",
       "      <td>13</td>\n",
       "      <td>721560</td>\n",
       "      <td>19.611473</td>\n",
       "      <td>446.943024</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012491</th>\n",
       "      <td>1012492</td>\n",
       "      <td>9.119549</td>\n",
       "      <td>49.763407</td>\n",
       "      <td>2600.50</td>\n",
       "      <td>26.0050</td>\n",
       "      <td>1511</td>\n",
       "      <td>13</td>\n",
       "      <td>2406</td>\n",
       "      <td>6390</td>\n",
       "      <td>-18.716288</td>\n",
       "      <td>-199299120</td>\n",
       "      <td>0.868141</td>\n",
       "      <td>-0.041656</td>\n",
       "      <td>354.596039</td>\n",
       "      <td>23</td>\n",
       "      <td>238.734863</td>\n",
       "      <td>-53.580078</td>\n",
       "      <td>-90.442383</td>\n",
       "      <td>0.027391</td>\n",
       "      <td>17</td>\n",
       "      <td>699834</td>\n",
       "      <td>20.477091</td>\n",
       "      <td>456.734619</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID         X          Y        h       hm  BLDFIE_WeigAver  \\\n",
       "913378    913379  8.882207  49.672347  3185.25  31.8525             1518   \n",
       "399194    399195  7.218343  49.815899  1471.00  14.7100             1392   \n",
       "1051554  1051555  9.219725  49.608233  2272.25  22.7225             1532   \n",
       "82847      82848  6.352083  49.747792   551.50   5.5150             1510   \n",
       "988551    988552  9.062895  49.725683  1859.00  18.5900             1521   \n",
       "323579    323580  7.042325  49.899161  1133.25  11.3325             1528   \n",
       "1101032  1101033  9.342970  48.969061  3206.00  32.0600             1528   \n",
       "288095    288096  6.958537  48.805559  2310.50  23.1050             1538   \n",
       "116470    116471  6.465766  49.493455  2551.75  25.5175             1527   \n",
       "1012491  1012492  9.119549  49.763407  2600.50  26.0050             1511   \n",
       "\n",
       "         CECSOL_WeigAver  CHELSA_bio18  CHELSA_bio4  convergence        cti  \\\n",
       "913378                13          2643         6330    26.204020  139056832   \n",
       "399194                12          2388         5732    14.451588 -119020368   \n",
       "1051554               12          2091         6350   -41.441132 -150667936   \n",
       "82847                 16          2235         5948    16.482677 -123181768   \n",
       "988551                14          2478         6404    23.876968 -283314592   \n",
       "323579                13          1871         6224    -8.266154 -363724384   \n",
       "1101032               15          2542         6560    20.984264   15857734   \n",
       "288095                11          2206         6320    38.485756  208938096   \n",
       "116470                12          2707         5931   -25.785652 -231972688   \n",
       "1012491               13          2406         6390   -18.716288 -199299120   \n",
       "\n",
       "         devmagnitude  eastness        elev  forestheight  glad_ard_SVVI_max  \\\n",
       "913378       0.951690 -0.053181  341.708923            27         -54.174805   \n",
       "399194       2.273924  0.106378  584.937500            23         125.125488   \n",
       "1051554      1.742874  0.002864  470.262482            26          23.418945   \n",
       "82847       -0.630119 -0.100164  313.726349             9         327.617676   \n",
       "988551      -0.580429  0.139336  330.208832             8         381.459717   \n",
       "323579      -0.128035  0.152906  307.110260            24         -26.110840   \n",
       "1101032     -1.031843 -0.000128  308.007874            26         121.762451   \n",
       "288095      -1.168826 -0.004806  245.023285            23         309.372803   \n",
       "116470       1.294931 -0.038748  374.514069            23         306.397949   \n",
       "1012491      0.868141 -0.041656  354.596039            23         238.734863   \n",
       "\n",
       "         glad_ard_SVVI_med  glad_ard_SVVI_min  northness  ORCDRC_WeigAver  \\\n",
       "913378          -95.832520         -97.327881   0.056747               15   \n",
       "399194         -236.454834        -412.245117  -0.019090               33   \n",
       "1051554        -161.524658        -430.280762   0.010492               13   \n",
       "82847            50.381592        -119.103271  -0.073008               12   \n",
       "988551          406.993652         146.392822   0.079916               11   \n",
       "323579          -37.309082          31.492554   0.327583               12   \n",
       "1101032         168.121338         323.751709  -0.019902                9   \n",
       "288095          381.092285         210.433716   0.010540                8   \n",
       "116470          115.095459         240.195557   0.030025               13   \n",
       "1012491         -53.580078         -90.442383   0.027391               17   \n",
       "\n",
       "         outlet_dist_dw_basin  SBIO3_Isothermality_5_15cm  \\\n",
       "913378                 718380                   20.596432   \n",
       "399194                 629871                   21.379816   \n",
       "1051554                722706                   18.597925   \n",
       "82847                  710051                   20.216003   \n",
       "988551                 718421                   20.972237   \n",
       "323579                 612429                   19.715183   \n",
       "1101032                829244                   17.987387   \n",
       "288095                 867325                   19.326263   \n",
       "116470                 721560                   19.611473   \n",
       "1012491                699834                   20.477091   \n",
       "\n",
       "         SBIO4_Temperature_Seasonality_5_15cm  treecover  \n",
       "913378                             486.906921         85  \n",
       "399194                             433.546661         85  \n",
       "1051554                            473.179382         85  \n",
       "82847                              473.799133         27  \n",
       "988551                             499.222168         60  \n",
       "323579                             459.910187         85  \n",
       "1101032                            484.482605        100  \n",
       "288095                             485.738098         63  \n",
       "116470                             446.943024         85  \n",
       "1012491                            456.734619         85  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors_sel = predictors.loc[(predictors['h'] < 7000)  ].sample(100000)\n",
    "predictors_sel.insert ( 4, 'hm' ,  predictors_sel['h']/100 ) # add a col of heigh in meters\n",
    "print(len(predictors_sel))\n",
    "predictors_sel.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5cb1d17-2a29-4e9d-90db-e8681eaae0d3",
   "metadata": {
    "id": "c5cb1d17-2a29-4e9d-90db-e8681eaae0d3",
    "outputId": "8b7295ea-796e-4669-9c0b-18a73a343e18",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 24)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors_sel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f438fe3e-f402-49e8-bb92-0b15d09e73ad",
   "metadata": {
    "id": "f438fe3e-f402-49e8-bb92-0b15d09e73ad",
    "outputId": "cd3c37ee-d1a6-4787-ae5d-8667db9702ca",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(min(predictors_sel['hm']),max(predictors_sel['hm']),100)\n",
    "plt.hist((predictors_sel['hm']),bins,alpha=0.8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "365f5775-e998-4494-b0ca-9c6b94934fda",
   "metadata": {
    "id": "365f5775-e998-4494-b0ca-9c6b94934fda",
    "outputId": "b366f788-3103-4c38-bac1-3a44853f4a99",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What we are trying to beat\n",
    "y_true = predictors_sel['hm']\n",
    "y_pred = predictors_sel['forestheight']\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_true)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(5,5))\n",
    "ax.scatter(y_pred, y_true)\n",
    "ax.set_xlabel('Prediction')\n",
    "ax.set_ylabel('True')\n",
    "ax.set_title('slope: {:.4f}, r_value: {:.4f}'.format(slope, r_value))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60e9a38c-fe60-4674-bd52-08ec33b43570",
   "metadata": {
    "id": "60e9a38c-fe60-4674-bd52-08ec33b43570",
    "outputId": "94df79a0-dabe-4f2b-e8a9-faad652225a8",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAPdCAYAAACXzguGAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XlcVNX/P/DXAM4MijMIxJaImKbiAooKuGKSqFiZ5BYpKupHAxMpTctcK8rcMFFaVKwklW9pJYYSbqm4oaSikpaFpQOawQgqIJzfH/7m5sgiIOOwvJ6Px308nHvec+d9r5w798w99xyZEEKAiIiIiIiIiKqdibETICIiIiIiIqqr2OgmIiIiIiIiMhA2uomIiIiIiIgMhI1uIiIiIiIiIgNho5uIiIiIiIjIQNjoJiIiIiIiIjIQNrqJiIiIiIiIDISNbiIiIiIiIiIDYaObiIiIiIiIyEDY6CYiIiIiIqqj5s+fD5lMhuvXrxs7lXqLjW6qtJiYGMhkMhw/fhzAfxVZtzRs2BDNmjXDc889h/Xr1yM/P9/IGRNRVb3yyitQKpX49ddfS5R98MEHkMlk2L59uxEyI6LqMHDgQDRp0gSZmZklynJycuDg4ABPT08UFxcbITsiorqBjW6qNmvWrMGXX36Jjz/+GBMmTMCNGzcwfvx4dOvWDZcvXzZ2ekRUBcuWLUPDhg0xefJkvfWXLl3CwoULERAQgMGDBxspOyJ6VKtXr0ZBQQGmT59eouytt97C9evX8emnn8LEhJeMRERVxTMoVZuXXnoJr7zyCoKDgzF37lwcPHgQX331Fc6cOYNhw4YZOz0iqgJbW1t8+OGH2LNnDzZs2CCtf/XVV9GgQQNERkYaMTsielQuLi6YN28evv76a+zatUtaf+zYMURHRyM8PBxubm5GzJCIqPZjo5sMKjAwEBMmTMCRI0eQmJho7HSIqAomTJiAHj164I033sA///yDTZs2ISEhAe+++y6efPJJY6dHRI8oPDwcHTt2xKuvvoo7d+6gqKgIkydPhrOzM+bNm2fs9IiommRnZ2Ps2LGwtLSEWq3GuHHjcOvWLWOnVS+w0U0GN3r0aADQ+wWdiGoPmUyGTz75BDk5OZgyZQqmT5+OLl26ICQkxNipEVE1MDMzw6effopLly5h0aJFWLVqFU6cOIE1a9agYcOGxk6PiKrJ8OHDcfPmTURERGD48OGIiYnBggULjJ1WvWBm7ASo7mvfvj0A4LfffjNyJkRUVe3atcMbb7yBiIgImJqaIj4+ns94EtUhnp6eePXVV/HRRx9BoVBg1KhR8PPzM3ZaRFSNOnXqhLVr10qv//nnH6xduxYffvihEbOqH3jFRAZnYWEBALh586aRMyGiR2FjYwMAcHR0lH5MI6K647333oO1tTVMTEywfPlyY6dDRNXswUFRe/XqhX/++QdardZIGdUfbHSTweXm5gIAGjdubORMiKiqLl++jHnz5qF9+/a4fPkyFi9ebOyUiKiaqVQqtG7dGk5OTrCzszN2OkRUzZo1a6b3ukmTJgCAf//91xjp1CtsdJPBnTlzBgDQsmVLI2dCRFUVGhoKAPjxxx8xbNgwvPfee/j999+NnBURERFVlKmpaanrhRCPOZP6h41uMrgvv/wSAPhsGFEttXXrVnz//fdYtGgRmjZtihUrVkAul3MgNSIiIqIKYKObDCo2Nhaff/45vL290a9fP2OnQ0SVdPPmTbz22mvo1KkTpk6dCuDeM92LFi1CQkIC4uLijJwhERERUc3G0cup2vzf//0fLCwsUFBQgL///hs7d+7EwYMH4ebmxgtzolpqzpw5uHLlCr799lu9bmkhISHYsGEDwsLCMGDAAI7ZQERERFQGNrqp2kyZMgUAoFQqYWNjA3d3d6xbtw4vv/wyFAqFkbMjospKSUlBVFQUXn31VXTt2lWvzNTUFNHR0fDy8sKcOXMQGRlppCyJiIiIajaZ4JPzRERERERERAbBZ7qJiIiIiIiIDISNbiIiIiIiIiIDYaObiIiIiIiIyEDY6CYiIiIiIiIyEDa6iYiIiIiIiAykXk8ZVlxcjCtXrqBx48aQyWTGToeoBCEEbt68CUdHR5iY8DeyymIdp5qOdfzRsI5TTcc6/mhYx6mmq2gdr9eN7itXrsDJycnYaRA91OXLl9G0aVNjp1HrsI5TbcE6XjWs41RbsI5XDes41RYPq+P1utHduHFjAPcOkkqlMnI2RCVptVo4OTlJf6tUOazjVNOxjj8a1nGq6VjHHw3rONV0Fa3j9brRreumolKpWJGpRmOXqqphHafagnW8aljHqbZgHa8a1nGqLR5Wx/lwCREREREREZGBVLrRvX//fjz33HNwdHSETCbDtm3b9MqFEJg7dy4cHBxgbm4OX19fXLhwQS/mxo0bCAwMhEqlgqWlJYKDg5Gbm6sXc+rUKfTq1QtKpRJOTk5YvHhxiVzi4uLQpk0bKJVKdOjQATt27Kjs7hAREREREREZTKUb3Xl5eXBzc0NUVFSp5YsXL8bKlSsRHR2NI0eOoFGjRvDz88OdO3ekmMDAQKSlpSExMRHbt2/H/v37MWnSJKlcq9Wif//+cHZ2RkpKCj766CPMnz8fn376qRRz6NAhjBo1CsHBwTh58iSGDBmCIUOG4MyZM5XdJSIiIiIiIiKDkAkhRJXfLJNh69atGDJkCIB7d7kdHR3x+uuv44033gAA5OTkwM7ODjExMRg5ciTOnTsHV1dXHDt2DF26dAEAJCQkYNCgQfjrr7/g6OiINWvW4O2334ZGo4FcLgcAzJo1C9u2bcP58+cBACNGjEBeXh62b98u5ePl5QV3d3dER0eXmm9+fj7y8/Ol17oH33NycmrccyLNZ8VX+b1/fOBfjZnUDzX1eGu1WqjV6hr5N1ob1OTj9yh/cwDreV1Rk/9GawMeP6ouhroO4N/oo6nLx6+mXntS5VT0b7RaB1K7dOkSNBoNfH19pXVqtRqenp5ITk7GyJEjkZycDEtLS6nBDQC+vr4wMTHBkSNH8OKLLyI5ORm9e/eWGtwA4Ofnhw8//BD//vsvmjRpguTkZISHh+t9vp+fX4nu7veLiIjAggULqm+Ha6hHvZivrXgCIiIiqp/q67UPEdUO1dro1mg0AAA7Ozu99XZ2dlKZRqOBra2tfhJmZrCystKLcXFxKbENXVmTJk2g0WjK/ZzSzJ49W6+hrrvTXRaewKkieMeSiIiIiIjKUq+mDFMoFFAoFMZOgwyEP5IQEREREVFNU61Thtnb2wMAMjMz9dZnZmZKZfb29sjKytIrv3v3Lm7cuKEXU9o27v+MsmJ05URERERERETGVq13ul1cXGBvb4+kpCS4u7sDuNeF+8iRI5gyZQoAwNvbG9nZ2UhJSYGHhwcAYPfu3SguLoanp6cU8/bbb6OwsBANGjQAACQmJqJ169Zo0qSJFJOUlISwsDDp8xMTE+Ht7V2du0RERDUEB50hIiKi2qjSd7pzc3ORmpqK1NRUAPcGT0tNTUVGRgZkMhnCwsLw7rvv4vvvv8fp06cxZswYODo6SiOct23bFgMGDMDEiRNx9OhRHDx4EKGhoRg5ciQcHR0BAC+//DLkcjmCg4ORlpaGzZs3IzIyUu957GnTpiEhIQFLly7F+fPnMX/+fBw/fhyhoaGPflSIiIiIiIiIqkGl73QfP34cffv2lV7rGsJBQUGIiYnBzJkzkZeXh0mTJiE7Oxs9e/ZEQkIClEql9J6NGzciNDQU/fr1g4mJCQICArBy5UqpXK1WY9euXQgJCYGHhwdsbGwwd+5cvbm8u3fvjtjYWMyZMwdvvfUWWrVqhW3btqF9+/ZVOhBERERERERE1a3SjW4fHx+UN7W3TCbDwoULsXDhwjJjrKysEBsbW+7ndOzYET///HO5McOGDcOwYcPKT5iIqA5iV2siIiKi2qFaB1IjIiIiIiIiov+w0U1ERFRPREREoGvXrmjcuDFsbW0xZMgQpKen68XcuXMHISEhsLa2hoWFBQICAkrMFpKRkQF/f380bNgQtra2mDFjBu7evasXs3fvXnTu3BkKhQItW7ZETExMiXyioqLQvHlzKJVKeHp64ujRo9W+z0RERMbGRjcREVE9sW/fPoSEhODw4cNITExEYWEh+vfvj7y8PClm+vTp+OGHHxAXF4d9+/bhypUrGDp0qFReVFQEf39/FBQU4NChQ9iwYQNiYmIwd+5cKebSpUvw9/dH3759kZqairCwMEyYMAE7d+6UYjZv3ozw8HDMmzcPJ06cgJubG/z8/EpMK0pERFTbVeuUYURERDXRozwDD9Sd5+ATEhL0XsfExMDW1hYpKSno3bs3cnJysHbtWsTGxuKZZ54BAKxfvx5t27bF4cOH4eXlhV27duHs2bP46aefYGdnB3d3dyxatAhvvvkm5s+fD7lcjujoaLi4uGDp0qUA7s1ccuDAASxfvhx+fn4AgGXLlmHixIkYN24cACA6Ohrx8fFYt24dZs2a9RiPChERkWHxTjcREVE9lZOTA+DeAKcAkJKSgsLCQvj6+koxbdq0QbNmzZCcnAwASE5ORocOHWBnZyfF+Pn5QavVIi0tTYq5fxu6GN02CgoKkJKSohdjYmICX19fKaY0+fn50Gq1egsREVFNx0Y3ERFRPVRcXIywsDD06NFDmm5To9FALpfD0tJSL9bOzg4ajUaKub/BrSvXlZUXo9Vqcfv2bVy/fh1FRUWlxui2UZqIiAio1WppcXJyqvyOExERPWZsdBMREdVDISEhOHPmDDZt2mTsVCps9uzZyMnJkZbLly8bOyUiIqKH4jPdRERE9UxoaCi2b9+O/fv3o2nTptJ6e3t7FBQUIDs7W+9ud2ZmJuzt7aWYB0cZ141ufn/MgyOeZ2ZmQqVSwdzcHKampjA1NS01RreN0igUCigUisrvMBFRNXvUsUKofuGdbiIionpCCIHQ0FBs3boVu3fvhouLi165h4cHGjRogKSkJGldeno6MjIy4O3tDQDw9vbG6dOn9UYZT0xMhEqlgqurqxRz/zZ0MbptyOVyeHh46MUUFxcjKSlJiiEiIqoreKebiIionggJCUFsbCy+++47NG7cWHp+Wq1Ww9zcHGq1GsHBwQgPD4eVlRVUKhWmTp0Kb29veHl5AQD69+8PV1dXjB49GosXL4ZGo8GcOXMQEhIi3YWePHkyVq1ahZkzZ2L8+PHYvXs3tmzZgvj4/+4MhYeHIygoCF26dEG3bt2wYsUK5OXlSaOZExER1RVsdBMREdUTa9asAQD4+PjorV+/fj3Gjh0LAFi+fDlMTEwQEBCA/Px8+Pn5YfXq1VKsqakptm/fjilTpsDb2xuNGjVCUFAQFi5cKMW4uLggPj4e06dPR2RkJJo2bYrPP/9cmi4MAEaMGIFr165h7ty50Gg0cHd3R0JCQonB1YiIiGo7NrqJiIjqCSHEQ2OUSiWioqIQFRVVZoyzszN27NhR7nZ8fHxw8uTJcmNCQ0MRGhr60JyIiIhqMza6iYjqmUcZ/OWPD/yrMRMiIiKiuo8DqRGRnv379+O5556Do6MjZDIZtm3bplcuhMDcuXPh4OAAc3Nz+Pr64sKFC3oxN27cQGBgIFQqFSwtLREcHIzc3Fy9mFOnTqFXr15QKpVwcnLC4sWLS+QSFxeHNm3aQKlUokOHDg+9s0ZEREREVNPwTjcR6cnLy4ObmxvGjx+PoUOHlihfvHgxVq5ciQ0bNsDFxQXvvPMO/Pz8cPbsWSiVSgBAYGAgrl69isTERBQWFmLcuHGYNGkSYmNjAQBarRb9+/eHr68voqOjcfr0aYwfPx6WlpaYNGkSAODQoUMYNWoUIiIiMHjwYMTGxmLIkCE4ceIE2rdv//gOCFUrTrFCRERE9Q0b3USkZ+DAgRg4cGCpZUIIrFixAnPmzMELL7wAAPjiiy9gZ2eHbdu2YeTIkTh37hwSEhJw7NgxdOnSBQDw8ccfY9CgQViyZAkcHR2xceNGFBQUYN26dZDL5WjXrh1SU1OxbNkyqdEdGRmJAQMGYMaMGQCARYsWITExEatWrUJ0dPRjOBJERERERI+OjW4iqrBLly5Bo9HA19dXWqdWq+Hp6Ynk5GSMHDkSycnJsLS0lBrcAODr6wsTExMcOXIEL774IpKTk9G7d2/I5XIpxs/PDx9++CH+/fdfNGnSBMnJyQgPD9f7fD8/vxLd3e+Xn5+P/Px86bVWq62GvS4b79oSERER0cPwmW4iqjDdnL4PTuljZ2cnlWk0Gtja2uqVm5mZwcrKSi+mtG3c/xllxejKSxMREQG1Wi0tTk5Old1FIiIiIqJqxUY3EdUZs2fPRk5OjrRcvnzZ2CkRERERUT3HRjcRVZi9vT0AIDMzU299ZmamVGZvb4+srCy98rt37+LGjRt6MaVt4/7PKCtGV14ahUIBlUqltxARERERGRMb3URUYS4uLrC3t0dSUpK0TqvV4siRI/D29gYAeHt7Izs7GykpKVLM7t27UVxcDE9PTylm//79KCwslGISExPRunVrNGnSRIq5/3N0MbrPISIiIiKqDdjoJiI9ubm5SE1NRWpqKoB7g6elpqYiIyMDMpkMYWFhePfdd/H999/j9OnTGDNmDBwdHTFkyBAAQNu2bTFgwABMnDgRR48excGDBxEaGoqRI0fC0dERAPDyyy9DLpcjODgYaWlp2Lx5MyIjI/UGTps2bRoSEhKwdOlSnD9/HvPnz8fx48cRGhr6uA8JEREREVGVcfRyItJz/Phx9O3bV3qtawgHBQUhJiYGM2fORF5eHiZNmoTs7Gz07NkTCQkJ0hzdALBx40aEhoaiX79+MDExQUBAAFauXCmVq9Vq7Nq1CyEhIfDw8ICNjQ3mzp0rTRcGAN27d0dsbCzmzJmDt956C61atcK2bds4RzcRUR3FGSGqR0REBL799lucP38e5ubm6N69Oz788EO0bt1airlz5w5ef/11bNq0Cfn5+fDz88Pq1av1BjDNyMjAlClTsGfPHlhYWCAoKAgREREwM/uv+bB3716Eh4cjLS0NTk5OmDNnDsaOHauXT1RUFD766CNoNBq4ubnh448/Rrdu3Qx+HIhqEja6iUiPj48PhBBllstkMixcuBALFy4sM8bKygqxsbHlfk7Hjh3x888/lxszbNgwDBs2rPyEiYiISLJv3z6EhISga9euuHv3Lt566y30798fZ8+eRaNGjQAA06dPR3x8POLi4qBWqxEaGoqhQ4fi4MGDAICioiL4+/vD3t4ehw4dwtWrVzFmzBg0aNAA77//PoB7PeH8/f0xefJkbNy4EUlJSZgwYQIcHBzg5+cHANi8eTPCw8MRHR0NT09PrFixAn5+fkhPTy8x0wlRXcZGNxERERFRHZGQkKD3OiYmBra2tkhJSUHv3r2Rk5ODtWvXIjY2Fs888wwAYP369Wjbti0OHz4MLy8v7Nq1C2fPnsVPP/0EOzs7uLu7Y9GiRXjzzTcxf/58yOVyREdHw8XFBUuXLgVw7/GyAwcOYPny5VKje9myZZg4cSLGjRsHAIiOjkZ8fDzWrVuHWbNmlcg9Pz8f+fn50mutVmuQY0T0uPGZbiIiIiKiOionJwfAvV5oAJCSkoLCwkL4+vpKMW3atEGzZs2QnJwMAEhOTkaHDh30upv7+flBq9UiLS1Nirl/G7oY3TYKCgqQkpKiF2NiYgJfX18p5kERERFQq9XS4uTk9Ki7T1QjsNFNRERERFQHFRcXIywsDD169JDGRNFoNJDL5bC0tNSLtbOzg0ajkWLub3DrynVl5cVotVrcvn0b169fR1FRUakxum08aPbs2cjJyZGWy5cvV23HiWoYdi8nIiIiIqqDQkJCcObMGRw4cMDYqVSIQqGAQqEwdhpE1Y53uomIiIiI6pjQ0FBs374de/bsQdOmTaX19vb2KCgoQHZ2tl58ZmYm7O3tpZjMzMwS5bqy8mJUKhXMzc1hY2MDU1PTUmN02yCqL9joJiIiIiKqI4QQCA0NxdatW7F79264uLjolXt4eKBBgwZISkqS1qWnpyMjIwPe3t4AAG9vb5w+fRpZWVlSTGJiIlQqFVxdXaWY+7ehi9FtQy6Xw8PDQy+muLgYSUlJUgxRfcHu5URUb3FOWCIiqmtCQkIQGxuL7777Do0bN5aen1ar1TA3N4darUZwcDDCw8NhZWUFlUqFqVOnwtvbG15eXgCA/v37w9XVFaNHj8bixYuh0WgwZ84chISESN2/J0+ejFWrVmHmzJkYP348du/ejS1btiA+/r/v1vDwcAQFBaFLly7o1q0bVqxYgby8PGk0c6L6go1uIiIiIqI6Ys2aNQAAHx8fvfXr16/H2LFjAQDLly+HiYkJAgICkJ+fDz8/P6xevVqKNTU1xfbt2zFlyhR4e3ujUaNGCAoKwsKFC6UYFxcXxMfHY/r06YiMjETTpk3x+eefS9OFAcCIESNw7do1zJ07FxqNBu7u7khISCgxuBpRXcdGNxERERFRHSGEeGiMUqlEVFQUoqKiyoxxdnbGjh07yt2Oj48PTp48WW5MaGgoQkNDH5oTUV3GZ7qJiIiIiIiIDISNbiIiIiIiIiIDYaObiIiIiIiIyEDY6CYiIiIiIiIyEDa6iYiIiIiIiAyEjW4iIiIiIiIiA2Gjm4iIiIiIiMhA2OgmIiIiIiIiMhA2uomIiIiIiIgMhI1uIiIiIiIiIgNho5uIiIiIiIjIQMyMnQAREVFN13xWfJXf+8cH/tWYCRERVZdHObcTVQbvdBMREREREREZSLU3uufPnw+ZTKa3tGnTRiq/c+cOQkJCYG1tDQsLCwQEBCAzM1NvGxkZGfD390fDhg1ha2uLGTNm4O7du3oxe/fuRefOnaFQKNCyZUvExMRU964QERERERERPRKDdC9v164dfvrpp/8+xOy/j5k+fTri4+MRFxcHtVqN0NBQDB06FAcPHgQAFBUVwd/fH/b29jh06BCuXr2KMWPGoEGDBnj//fcBAJcuXYK/vz8mT56MjRs3IikpCRMmTICDgwP8/PwMsUtERERERERG96jd4vnY0+NnkEa3mZkZ7O3tS6zPycnB2rVrERsbi2eeeQYAsH79erRt2xaHDx+Gl5cXdu3ahbNnz+Knn36CnZ0d3N3dsWjRIrz55puYP38+5HI5oqOj4eLigqVLlwIA2rZtiwMHDmD58uXlNrrz8/ORn58vvdZqtdW850RERERERET/MUij+8KFC3B0dIRSqYS3tzciIiLQrFkzpKSkoLCwEL6+vlJsmzZt0KxZMyQnJ8PLywvJycno0KED7OzspBg/Pz9MmTIFaWlp6NSpE5KTk/W2oYsJCwsrN6+IiAgsWLCgWveViIiIiIiMg4OhUW1Q7c90e3p6IiYmBgkJCVizZg0uXbqEXr164ebNm9BoNJDL5bC0tNR7j52dHTQaDQBAo9HoNbh15bqy8mK0Wi1u375dZm6zZ89GTk6OtFy+fPlRd5eIiIiIiIioTNV+p3vgwIHSvzt27AhPT084Oztjy5YtMDc3r+6PqxSFQgGFQmHUHIiIiIiIiKj+MPiUYZaWlnj66adx8eJF2Nvbo6CgANnZ2XoxmZmZ0jPg9vb2JUYz171+WIxKpTJ6w56IiKgm279/P5577jk4OjpCJpNh27ZteuVCCMydOxcODg4wNzeHr68vLly4oBdz48YNBAYGQqVSwdLSEsHBwcjNzdWLOXXqFHr16gWlUgknJycsXry4RC5xcXFo06YNlEolOnTogB07dlT7/hIRERmbwRvdubm5+O233+Dg4AAPDw80aNAASUlJUnl6ejoyMjLg7e0NAPD29sbp06eRlZUlxSQmJkKlUsHV1VWKuX8buhjdNoiIiKh0eXl5cHNzQ1RUVKnlixcvxsqVKxEdHY0jR46gUaNG8PPzw507d6SYwMBApKWlITExEdu3b8f+/fsxadIkqVyr1aJ///5wdnZGSkoKPvroI8yfPx+ffvqpFHPo0CGMGjUKwcHBOHnyJIYMGYIhQ4bgzJkzhtt5IiIiI6j27uVvvPEGnnvuOTg7O+PKlSuYN28eTE1NMWrUKKjVagQHByM8PBxWVlZQqVSYOnUqvL294eXlBQDo378/XF1dMXr0aCxevBgajQZz5sxBSEiI1DV88uTJWLVqFWbOnInx48dj9+7d2LJlC+LjOZACERFReQYOHKj3KNj9hBBYsWIF5syZgxdeeAEA8MUXX8DOzg7btm3DyJEjce7cOSQkJODYsWPo0qULAODjjz/GoEGDsGTJEjg6OmLjxo0oKCjAunXrIJfL0a5dO6SmpmLZsmVS4zwyMhIDBgzAjBkzAACLFi1CYmIiVq1ahejo6MdwJIiIiB6Par/T/ddff2HUqFFo3bo1hg8fDmtraxw+fBhPPPEEAGD58uUYPHgwAgIC0Lt3b9jb2+Pbb7+V3m9qaort27fD1NQU3t7eeOWVVzBmzBgsXLhQinFxcUF8fDwSExPh5uaGpUuX4vPPP+cc3URERI/g0qVL0Gg0ejOEqNVqeHp6Ijk5GQCQnJwMS0tLqcENAL6+vjAxMcGRI0ekmN69e0Mul0sxfn5+SE9Px7///ivFlDYTie5zSpOfnw+tVqu3EBER1XTVfqd706ZN5ZYrlUpERUWV2a0NAJydnR/6XJePjw9OnjxZpRyJiIioJN0sIaXNEHL/DCK2trZ65WZmZrCystKLcXFxKbENXVmTJk3KnIlEt43ScOpPIiKqjQz+TDcRERFRdeDUn0REVBux0U1ElTJ//nzIZDK9pU2bNlL5nTt3EBISAmtra1hYWCAgIKDEbAMZGRnw9/dHw4YNYWtrixkzZuDu3bt6MXv37kXnzp2hUCjQsmVLxMTEPI7dI6rXdLOElDZDyP0ziNw/2CkA3L17Fzdu3KiWmUh05aVRKBRQqVR6CxERUU3HRjcRVVq7du1w9epVaTlw4IBUNn36dPzwww+Ii4vDvn37cOXKFQwdOlQqLyoqgr+/PwoKCnDo0CFs2LABMTExmDt3rhRz6dIl+Pv7o2/fvkhNTUVYWBgmTJiAnTt3Ptb9JKpvXFxcYG9vrzdDiFarxZEjR/RmGcnOzkZKSooUs3v3bhQXF8PT01OK2b9/PwoLC6WYxMREtG7dGk2aNJFiOBMJERHVB9X+TDcR1X1mZmal3o3KycnB2rVrERsbi2eeeQYAsH79erRt2xaHDx+Gl5cXdu3ahbNnz+Knn36CnZ0d3N3dsWjRIrz55puYP38+5HI5oqOj4eLigqVLlwIA2rZtiwMHDmD58uUcMJHoEeXm5uLixYvS60uXLiE1NRVWVlZo1qwZwsLC8O6776JVq1ZwcXHBO++8A0dHRwwZMgTAvfo4YMAATJw4EdHR0SgsLERoaChGjhwJR0dHAMDLL7+MBQsWIDg4GG+++SbOnDmDyMhILF++XPrcadOmoU+fPli6dCn8/f2xadMmHD9+XG9aMSIiorqAd7qJqNIuXLgAR0dHtGjRAoGBgcjIyAAApKSkoLCwUG9E4jZt2qBZs2Z6Ix936NBBbwAlPz8/aLVapKWlSTGVHdUY4MjGRBVx/PhxdOrUCZ06dQIAhIeHo1OnTlJvk5kzZ2Lq1KmYNGkSunbtitzcXCQkJECpVErb2LhxI9q0aYN+/fph0KBB6Nmzp15jWa1WY9euXbh06RI8PDzw+uuvY+7cuXpzeXfv3h2xsbH49NNP4ebmhv/7v//Dtm3b0L59+8d0JIiIiB4P3ukmokrx9PRETEwMWrdujatXr2LBggXo1asXzpw5A41GA7lcDktLS733PDjycWkjFuvKyovRarW4ffs2zM3NS82NIxsTPZyPjw+EEGWWy2QyLFy4UG+qzgdZWVkhNja23M/p2LEjfv7553Jjhg0bhmHDhpWfMD1WzWfFP9L7//jAv5oyoarav38/PvroI6SkpODq1avYunWr1FMFAIQQmDdvHj777DNkZ2ejR48eWLNmDVq1aiXF3LhxA1OnTsUPP/wAExMTBAQEIDIyEhYWFlLMqVOnEBISgmPHjuGJJ57A1KlTMXPmTL1c4uLi8M477+CPP/5Aq1at8OGHH2LQoEEGPwZENQ0b3URUKQMHDpT+3bFjR3h6esLZ2RlbtmwpszH8uMyePRvh4eHSa61WCycnJyNmRERUvzxqo50eXV5eHtzc3DB+/Hi9MVV0Fi9ejJUrV2LDhg3SIyR+fn44e/as1KMlMDAQV69eRWJiIgoLCzFu3DhMmjRJ+rFNq9Wif//+8PX1RXR0NE6fPo3x48fD0tJS6tFy6NAhjBo1ChERERg8eDBiY2MxZMgQnDhxgj1aqN5ho5uIHomlpSWefvppXLx4Ec8++ywKCgqQnZ2td7f7wZGPjx49qreNio5qrFKpym3YKxQKKBSK6tgtKgMvqImIaraBAwfq/UB+PyEEVqxYgTlz5uCFF14AAHzxxRews7PDtm3bMHLkSJw7dw4JCQk4duwYunTpAgD4+OOPMWjQICxZsgSOjo7YuHEjCgoKsG7dOsjlcrRr1w6pqalYtmyZ1OiOjIzEgAEDMGPGDADAokWLkJiYiFWrViE6OvoxHAmimoPPdBPRI8nNzcVvv/0GBwcHeHh4oEGDBnojEqenpyMjI0Nv5OPTp0/rTTmUmJgIlUoFV1dXKYajGhMREVWvS5cuQaPR6I2bolar4enpqTf2iqWlpdTgBgBfX1+YmJjgyJEjUkzv3r0hl8ulGD8/P6Snp+Pff/+VYio7PgvHZqG6io1uIqqUN954A/v27cMff/yBQ4cO4cUXX4SpqSlGjRoFtVqN4OBghIeHY8+ePUhJScG4cePg7e0NLy8vAED//v3h6uqK0aNH45dffsHOnTsxZ84chISESHepJ0+ejN9//x0zZ87E+fPnsXr1amzZsgXTp0835q4TERHVarqxU0obN+X+cVVsbW31ys3MzGBlZVUt47PoyksTEREBtVotLXxEjOoKNrqJqFL++usvjBo1Cq1bt8bw4cNhbW2Nw4cP44knngAALF++HIMHD0ZAQAB69+4Ne3t7fPvtt9L7TU1NsX37dpiamsLb2xuvvPIKxowZozdok4uLC+Lj45GYmAg3NzcsXboUn3/+OacLIyIiqsNmz56NnJwcabl8+bKxUyKqFnymm4gqZdOmTeWWK5VKREVFISoqqswYZ2dn7Nixo9zt+Pj44OTJk1XKkYiIiErSjZ2SmZkJBwcHaX1mZibc3d2lmPsfAQOAu3fv4saNGw8de+X+zygrRldeGo7NQnUV73QTEREREdUDLi4usLe31xs3RavV4siRI3pjr2RnZyMlJUWK2b17N4qLi+Hp6SnF7N+/H4WFhVJMYmIiWrdujSZNmkgxHJ+F6B42uomIiIiI6ojc3FykpqYiNTUVwL3B01JTU5GRkQGZTIawsDC8++67+P7773H69GmMGTMGjo6O0lzebdu2xYABAzBx4kQcPXoUBw8eRGhoKEaOHAlHR0cAwMsvvwy5XI7g4GCkpaVh8+bNiIyM1Ju2c9q0aUhISMDSpUtx/vx5zJ8/H8ePH0doaOjjPiRERsfu5URERER1CKf2q9+OHz+Ovn37Sq91DeGgoCDExMRg5syZyMvLw6RJk5CdnY2ePXsiISFBmqMbADZu3IjQ0FD069cPJiYmCAgIwMqVK6VytVqNXbt2ISQkBB4eHrCxscHcuXOl6cIAoHv37oiNjcWcOXPw1ltvoVWrVti2bRvn6KZ6iY1uIiIiIqI6wsfHB0KIMstlMhkWLlyoN4Dpg6ysrBAbG1vu53Ts2BE///xzuTHDhg3DsGHDyk+YqB5g93IiIiIiIiIiA2Gjm4iIiIiIiMhA2OgmIiIiIiIiMhA2uomIiIiIiIgMhI1uIiIiIiIiIgNho5uIiIiIiIjIQNjoJiIiIiIiIjIQztNNREREVM2az4p/pPf/8YF/NWVCRETGxjvdRERERERERAbCRjcRERERERGRgbDRTURERERERGQgfKabiIiIqIZ51GfCiYio5uCdbiIiIiIiIiIDYaObiIiIiIiIyEDY6CYiIiIiIiIyEDa6iYiIiIiIiAyEjW4iIiIiIiIiA+Ho5URERESl4AjiRERUHXinm4iIiIiIiMhA2OgmIiIiIiIiMhA2uomIiIiIiIgMhI1uIiIiIiIiIgPhQGpERERERET1xKMMEvnHB/7VmEn9wTvdRERERERERAbCRjcRERERERGRgbDRTURERERERGQgbHQTERERERERGQgb3UREREREREQGwkY3ERERERERkYFwyjAiIiKqsx5lahwiIqLqUOvvdEdFRaF58+ZQKpXw9PTE0aNHjZ0SEVUj1nGiuo11nKhuYx0nquWN7s2bNyM8PBzz5s3DiRMn4ObmBj8/P2RlZRk7NSKqBqzjRHUb6zhR3cY6TnSPTAghjJ1EVXl6eqJr165YtWoVAKC4uBhOTk6YOnUqZs2a9dD3a7VaqNVq5OTkQKVSlShnlzR6HP74wL/Msof9jdZ1rONUF7COl83QdRxgPSfDYx0vG+s43a+8ulJbVbSO19pnugsKCpCSkoLZs2dL60xMTODr64vk5ORS35Ofn4/8/HzpdU5ODoB7B6s0xfm3qjFjotKV9fd3f1kt/m2syljHqa5gHS/d46jjAOs5GR7reOlYx+lBzabHPdL7zyzwq6ZMqk9F63itbXRfv34dRUVFsLOz01tvZ2eH8+fPl/qeiIgILFiwoMR6Jycng+RIVBHqFQ+PuXnzJtRqtcFzqUlYx6muYB0vHes41RWs46VjHafqVpG6ZiwPq+O1ttFdFbNnz0Z4eLj0uri4GDdu3IC1tTVkMplerFarhZOTEy5fvlxvugPVt32uDfsrhMDNmzfh6Oho7FRqhYrU8drw/15T8dhVXVnHjnW8cirzPa7Dv9uq4XGrmgePG+t45VS0jte2v8/ali9Q+3I2Vr4VreO1ttFtY2MDU1NTZGZm6q3PzMyEvb19qe9RKBRQKBR66ywtLcv9HJVKVSv+0KpTfdvnmr6/9e2XcR1D1/Ga/v9ek/HYVV1px4513LDf4zr8u60aHrequf+4sY4bro7Xtr/P2pYvUPtyNka+FanjtXb0crlcDg8PDyQlJUnriouLkZSUBG9vbyNmRkTVgXWcqG5jHSeq21jHif5Ta+90A0B4eDiCgoLQpUsXdOvWDStWrEBeXh7GjRtn7NSIqBqwjhPVbazjRHUb6zjRPbW60T1ixAhcu3YNc+fOhUajgbu7OxISEkoM2FAVCoUC8+bNK9HFpS6rb/tc3/a3NjJEHef/e9Xx2FUdj13pDPk9rsNjXzU8blXD46bPUHW8th3n2pYvUPtyrun51up5uomIiIiIiIhqslr7TDcRERERERFRTcdGNxEREREREZGBsNFNREREREREZCBsdBMREREREREZSL1udEdFRaF58+ZQKpXw9PTE0aNHy42Pi4tDmzZtoFQq0aFDB+zYseMxZVp9KrPPMTExkMlkeotSqXyM2T6a/fv347nnnoOjoyNkMhm2bdv20Pfs3bsXnTt3hkKhQMuWLRETE2PwPOnx+fvvv/HKK6/A2toa5ubm6NChA44fP27stGq8oqIivPPOO3BxcYG5uTmeeuopLFq0CByHs6SHnXeEEJg7dy4cHBxgbm4OX19fXLhwwTjJ1mEffPABZDIZwsLCpHUajQajR4+Gvb09GjVqhM6dO+Obb74xXpI1UGnH7bfffsOLL76IJ554AiqVCsOHD0dmZqbxkqwB5s+fX+L6qE2bNlL5nTt3EBISAmtra1hYWCAgIKDeH7PqVNnr9+oSERGBrl27onHjxrC1tcWQIUOQnp6uF1OR//uMjAz4+/ujYcOGsLW1xYwZM3D37l29GENcj5ZWv2tivg+7VqvI9+iNGzcQGBgIlUoFS0tLBAcHIzc3Vy/m1KlT6NWrF5RKJZycnLB48eIq51whop7atGmTkMvlYt26dSItLU1MnDhRWFpaiszMzFLjDx48KExNTcXixYvF2bNnxZw5c0SDBg3E6dOnH3PmVVfZfV6/fr1QqVTi6tWr0qLRaB5z1lW3Y8cO8fbbb4tvv/1WABBbt24tN/73338XDRs2FOHh4eLs2bPi448/FqampiIhIeHxJEwGdePGDeHs7CzGjh0rjhw5In7//Xexc+dOcfHiRWOnVuO99957wtraWmzfvl1cunRJxMXFCQsLCxEZGWns1Gqch513PvjgA6FWq8W2bdvEL7/8Ip5//nnh4uIibt++bZyE66CjR4+K5s2bi44dO4pp06ZJ65999lnRtWtXceTIEfHbb7+JRYsWCRMTE3HixAnjJVuDlHbccnNzRYsWLcSLL74oTp06JU6dOiVeeOEF0bVrV1FUVGTchI1o3rx5ol27dnrXR9euXZPKJ0+eLJycnERSUpI4fvy48PLyEt27dzdixnVHZa9lq5Ofn59Yv369OHPmjEhNTRWDBg0SzZo1E7m5uVLMw/7v7969K9q3by98fX3FyZMnxY4dO4SNjY2YPXu2FGOI69Gyzos1Ld+KXKtV5Ht0wIABws3NTRw+fFj8/PPPomXLlmLUqFFSeU5OjrCzsxOBgYHizJkz4uuvvxbm5ubik08+qXTOFVVvG93dunUTISEh0uuioiLh6OgoIiIiSo0fPny48Pf311vn6ekp/ve//xk0z+pU2X1ev369UKvVjyk7w6pIo3vmzJmiXbt2eutGjBgh/Pz8DJgZPS5vvvmm6Nmzp7HTqJX8/f3F+PHj9dYNHTpUBAYGGimj2uHB805xcbGwt7cXH330kbQuOztbKBQK8fXXXxshw7rn5s2bolWrViIxMVH06dNH7+KyUaNG4osvvtCLt7KyEp999tljzrLmKeu47dy5U5iYmIicnBwpNjs7W8hkMpGYmGikbI1v3rx5ws3NrdSy7Oxs0aBBAxEXFyetO3funAAgkpOTH1OGdVdlr2UNKSsrSwAQ+/btE0JU7P9+x44dwsTERO8m1po1a4RKpRL5+flCiOq/Hi2rftfEfB92rVaR79GzZ88KAOLYsWNSzI8//ihkMpn4+++/hRBCrF69WjRp0kTaB91nt27dutI5V1S97F5eUFCAlJQU+Pr6SutMTEzg6+uL5OTkUt+TnJysFw8Afn5+ZcbXNFXZZwDIzc2Fs7MznJyc8MILLyAtLe1xpGsUtf3/mMr3/fffo0uXLhg2bBhsbW3RqVMnfPbZZ8ZOq1bo3r07kpKS8OuvvwIAfvnlFxw4cAADBw40cma1y6VLl6DRaPTOM2q1Gp6enjzPVJOQkBD4+/uXOJcD9/6ON2/ejBs3bqC4uBibNm3CnTt34OPj8/gTrWHKOm75+fmQyWRQKBTSOqVSCRMTExw4cOBxp1mjXLhwAY6OjmjRogUCAwORkZEBAEhJSUFhYaHesWzTpg2aNWvGev6Iqnotayg5OTkAACsrKwAV+79PTk5Ghw4dYGdnJ8X4+flBq9VK19jVfT1aVv2uifk+7FqtIt+jycnJsLS0RJcuXaQYX19fmJiY4MiRI1JM7969IZfL9XJOT0/Hv//+W+m8K6JeNrqvX7+OoqIivT8gALCzs4NGoyn1PRqNplLxNU1V9rl169ZYt24dvvvuO3z11VcoLi5G9+7d8ddffz2OlB+7sv6PtVotbt++baSsqLr8/vvvWLNmDVq1aoWdO3diypQpeO2117BhwwZjp1bjzZo1CyNHjkSbNm3QoEEDdOrUCWFhYQgMDDR2arWK7lxbm79LarJNmzbhxIkTiIiIKLV8y5YtKCwshLW1NRQKBf73v/9h69ataNmy5WPOtGYp77h5eXmhUaNGePPNN3Hr1i3k5eXhjTfeQFFREa5evWqEbGsGT09PxMTEICEhAWvWrMGlS5fQq1cv3Lx5ExqNBnK5HJaWlnrvYT1/dFW5ljWU4uJihIWFoUePHmjfvj0AVOj/vqxrTV1ZeTFVuR4tr37XxHwfdq1Wke9RjUYDW1tbvXIzMzNYWVlVar+qm5lBtkp1gre3N7y9vaXX3bt3R9u2bfHJJ59g0aJFRsyMqPKKi4vRpUsXvP/++wCATp064cyZM4iOjkZQUJCRs6vZtmzZgo0bNyI2Nhbt2rVDamoqwsLC4OjoyGNHNcLly5cxbdo0JCYmljng5zvvvIPs7Gz89NNPsLGxwbZt2zB8+HD8/PPP6NChw2POuGZ42HF74oknEBcXhylTpmDlypUwMTHBqFGj0LlzZ5iY1Mv7NgCg18unY8eO8PT0hLOzM7Zs2QJzc3MjZkaPS0hICM6cOVOje3xU5LxY09Tla7V6eca0sbGBqalpidH5MjMzYW9vX+p77O3tKxVf01Rlnx+ku8N18eJFQ6RodGX9H6tUKn6J1gEODg5wdXXVW9e2bVupSyCVbcaMGdLd7g4dOmD06NGYPn16mXcUqXS6c21t/i6pqVJSUpCVlYXOnTvDzMwMZmZm2LdvH1auXAkzMzP89ttvWLVqFdatW4d+/frBzc0N8+bNQ5cuXRAVFWXs9I3mYcetqKgI/fv3x2+//YasrCxcv34dX375Jf7++2+0aNHC2OnXGJaWlnj66adx8eJF2Nvbo6CgANnZ2XoxrOePrjquZatDaGgotm/fjj179qBp06bS+or835d1rakrKy+mstejD6vfdnZ2NSpf4OHXahX5HrW3t0dWVpZe+d27d3Hjxo1K7Vd1q5eNbrlcDg8PDyQlJUnriouLkZSUpHdn937e3t568QCQmJhYZnxNU5V9flBRURFOnz4NBwcHQ6VpVLX9/5jK16NHjxJTe/z6669wdnY2Uka1x61bt0rc1TI1NUVxcbGRMqqdXFxcYG9vr3ee0Wq1OHLkCM8zj6hfv344ffo0UlNTpaVLly4IDAxEamoqbt26BQD8O37Aw46bqampFGtjYwNLS0vs3r0bWVlZeP75542Yec2Sm5uL3377DQ4ODvDw8ECDBg306nl6ejoyMjJYzx9RdVzLPgohBEJDQ7F161bs3r0bLi4ueuUV+b/39vbG6dOn9RqFiYmJUKlUUmOzuq5HH1a/u3TpUqPyBR5+rVaR71Fvb29kZ2cjJSVFitm9ezeKi4vh6ekpxezfvx+FhYV6Obdu3RpNmjSpdN4VYrAh2mq4TZs2CYVCIWJiYsTZs2fFpEmThKWlpTQ63+jRo8WsWbOk+IMHDwozMzOxZMkSce7cOTFv3rxaOWVYZfZ5wYIFYufOneK3334TKSkpYuTIkUKpVIq0tDRj7UKl3Lx5U5w8eVKcPHlSABDLli0TJ0+eFH/++acQQohZs2aJ0aNHS/G6KQ9mzJghzp07J6KiojhlWB1y9OhRYWZmJt577z1x4cIFsXHjRtGwYUPx1VdfGTu1Gi8oKEg8+eST0pRh3377rbCxsREzZ840dmo1zsPOOx988IGwtLQU3333nTT9EqcMM4z7R+ktKCgQLVu2FL169RJHjhwRFy9eFEuWLBEymUzEx8cbN9Ea5sFR39etWyeSk5PFxYsXxZdffimsrKxEeHi48RKsAV5//XWxd+9ecenSJXHw4EHh6+srbGxsRFZWlhDi3jRMzZo1E7t37xbHjx8X3t7ewtvb28hZ1w0Pu5Y1pClTpgi1Wi327t2rN13crVu3pJiH/d/rpuDq37+/SE1NFQkJCeKJJ54odQouQ1yPPli/a1q+FblWq8j36IABA0SnTp3EkSNHxIEDB0SrVq30pgzLzs4WdnZ2YvTo0eLMmTNi06ZNomHDhpwyzFA+/vhj0axZMyGXy0W3bt3E4cOHpbI+ffqIoKAgvfgtW7aIp59+WsjlctGuXbta+UVdmX0OCwuTYu3s7MSgQYNq1Xyme/bsEQBKLLp9DAoKEn369CnxHnd3dyGXy0WLFi3E+vXrH3veZDg//PCDaN++vVAoFKJNmzbi008/NXZKtYJWqxXTpk0TzZo1E0qlUrRo0UK8/fbbelNt0D0PO+8UFxeLd955R9jZ2QmFQiH69esn0tPTjZt0HfXgxeWvv/4qhg4dKmxtbUXDhg1Fx44dS0whRiWP25tvvins7OxEgwYNRKtWrcTSpUtFcXGx8RKsAUaMGCEcHByEXC4XTz75pBgxYoTePMK3b98Wr776qmjSpIlo2LChePHFF8XVq1eNmHHdUt61rCGVdm4HoHetWJH/+z/++EMMHDhQmJubCxsbG/H666+LwsJCvRhDXY8+WL9rYr4Pu1aryPfoP//8I0aNGiUsLCyESqUS48aNEzdv3tSL+eWXX0TPnj2FQqEQTz75pPjggw+qnHNFyIQQwjD30ImIiIiIiIjqt3r5TDcRERERERHR48BGNxEREREREZGBsNFNREREREREZCBsdBMREREREREZCBvdRERERERERAbCRjcRERERERGRgbDRTURERERERGQgbHQTERERERERGQgb3USPYP/+/Xjuuefg6OgImUyGbdu2VXobQggsWbIETz/9NBQKBZ588km899571Z8sERERERE9dmbGToCoNsvLy4ObmxvGjx+PoUOHVmkb06ZNw65du7BkyRJ06NABN27cwI0bN6o5UyIiIiIiMgaZEEIYOwmiukAmk2Hr1q0YMmSItC4/Px9vv/02vv76a2RnZ6N9+/b48MMP4ePjAwA4d+4cOnbsiDNnzqB169bGSZyIiIiIiAyG3cuJDCg0NBTJycnYtGkTTp06hWHDhmHAgAG4cOECAOCHH35AixYtsH37dri4uKB58+aYMGEC73QTEREREdURbHQTGUhGRgbWr1+PuLg49OrVC0899RTeeOMN9OzZE+vXrwcA/P777/jzzz8RFxeHL774AjExMUhJScFLL71k5OyJiIiIiKg68JluIgM5ffo0ioqK8PTTT+utz8/Ph7W1NQCguLgY+fn5+OKLL6S4tWvXwsPDA+np6exyTkRERERUy7HRTWQgubm5MDU1RUpKCkxNTfXKLCwsAAAODg4wMzPTa5i3bdsWwL075Wx0ExERERHVbmx0ExlIp06dUFRUhKysLPTq1avUmB49euDu3bv47bff8NRTTwEAfv31VwCAs7PzY8uViIiIiIgMg6OXEz2C3NxcXLx4EcC9RvayZcvQt29fWFlZoVmzZnjllVdw8OBBLF26FJ06dcK1a9eQlJSEjh07wt/fH8XFxejatSssLCywYsUKFBcXIyQkBCqVCrt27TLy3hERERER0aNio5voEezduxd9+/YtsT4oKAgxMTEoLCzEu+++iy+++AJ///03bGxs4OXlhQULFqBDhw4AgCtXrmDq1KnYtWsXGjVqhIEDB2Lp0qWwsrJ63LtDRERERETVjI1uIiIiIiIiIgPhlGFEREREREREBsJGNxEREREREZGBsNFNREREREREZCBsdBMREREREREZCBvdRERERERERAbCRjcRERERERGRgbDRTURERERERGQgbHQTERERERERGQgb3UREREREREQGwkY3ERERERERkYGw0U1ERERERERkIGx0ExERERERERkIG91EREREREREBsJGNxEREREREZGBsNFNREREREREZCBsdBMREREREREZCBvdRERERERERAbCRjeVa/78+ZDJZLh+/bqxUyGiOkomk2H+/PnGToOIHpM//vgDMpkMMTExxk6FiCohJiYGMpkMx48ff2isj48PfHx8DJ9ULcFGNxHRA3RfKvcvtra26Nu3L3788Ue9WJlMhtDQ0HK35+PjI23HxMQEKpUKrVu3xujRo5GYmFjqe5o3b14iB91y584dvTzv//LT/VBW1qLRaCp0DAYNGoQmTZpACKG3/uTJk5DJZHB2di7xnt27d0Mmk+HTTz+t0GdUp6KiIjg6OkImk5X4PyK632+//Yb//e9/aNGiBZRKJVQqFXr06IHIyEjcvn0bQPn1b8CAASW2mZqaildeeQVOTk5QKBSwsrKCr68v1q9fj6KiIikuNzcX8+bNQ/v27dGoUSNYW1vD3d0d06ZNw5UrV0ps9+DBg3jxxRdhZ2cHhUKB5s2b43//+x8yMjJKxFbHj+Surq5wc3MrsX7r1q2QyWTo06dPibJ169ZBJpNh165dVf7cqsrOzoZSqYRMJsO5c+ce++dT3VTRc8TgwYNLff/evXshk8nwf//3f9K60q4r7l8OHz4sxVbkuqK4uBhffPEFPD09YWVlhcaNG+Ppp5/GmDFj9LZ1v9pUXzZv3oxXXnkFrVq1gkwmK7fxfuHCBYwcORJNmzZFw4YN0aZNGyxcuBC3bt16fAlXgJmxEyAiqqkWLlwIFxcXCCGQmZmJmJgYDBo0CD/88EOZX7Zladq0KSIiIgAAeXl5uHjxIr799lt89dVXGD58OL766is0aNBA7z3u7u54/fXXS2xLLpc/9PPWrFkDCwuLEustLS0rlG/Pnj3x448/4syZM+jQoYO0/uDBgzAzM0NGRgb++usvNG3aVK9M997KuH37NszMHu3raPfu3bh69SqaN2+OjRs3YuDAgY+0Paqb4uPjMWzYMCgUCowZMwbt27dHQUEBDhw4gBkzZiAtLU360ais+ufo6Kj3+vPPP8fkyZNhZ2eH0aNHo1WrVrh58yaSkpIQHByMq1ev4q233kJhYSF69+6N8+fPIygoCFOnTkVubi7S0tIQGxuLF198UW/bH3/8MaZNm4YWLVpg6tSpcHBwwLlz5/D5559j8+bN2LFjB7p3716tx6dnz55Yu3YtcnJyoFarpfW6en/s2DEUFhbqnasOHjwIU1NTeHt7V/hznJ2dcfv27RLnvMqKi4uDTCaDvb09Nm7ciHffffeRtkdUmXNEVeiuKx7UsmXLSm3ntddeQ1RUFF544QUEBgbCzMwM6enp+PHHH9GiRQt4eXmVeM/jri+P8kPcmjVrkJKSgq5du+Kff/4pM+7y5cvo1q0b1Go1QkNDYWVlheTkZMybNw8pKSn47rvvqpxDtRNE5Zg3b54AIK5du2bsVIgem/Xr1wsA4tixY3rrb9y4IRo0aCBefvllaR0AERISUu72+vTpI9q1a1di/d27d8Wrr74qAIiZM2fqlTk7Owt/f/9K51lddXbfvn0CgFi9erXe+pEjR4rnn39eWFhYiK+//lqvrH///sLa2loUFxc/0mdXxZgxY0Tnzp1FZGSkaNSokcjNzX3sORjjM6nifv/9d2FhYSHatGkjrly5UqL8woULYsWKFUKIitU/IYRITk4WpqamomfPnkKr1ZYoP3bsmFi/fr0QQogtW7YIAGLjxo0l4m7fvi1ycnKk1wcOHBAmJiaiV69eIi8vTy/24sWLws7OTjg4OIgbN25I66uj7m/YsEEAEDt27NBb7+XlJV5++WUBQCQnJ+uVPf3006JTp05V/sxH0bt3bzF06FAxffp04eLiYpQcWO/rjuo6R+zZs0cAEHFxcdK6sq4rSvOw6wqNRiNkMpmYOHFiibLi4mKRmZlZ6vuqo75UZj8eRUZGhigqKhJCCNGuXTvRp0+fUuPee+89AUCcOXNGb/2YMWMEAL1zpLGxezlVSHZ2NsaOHQtLS0uo1WqMGzdOr9uGritMXFwcXF1dYW5uDm9vb5w+fRoA8Mknn6Bly5ZQKpXw8fHBH3/8YaQ9Iao6S0tLmJubP/JdWR1TU1OsXLkSrq6uWLVqFXJycqplu9WhW7dukMvl0t1rnYMHD6J3797o1q2bXllxcTEOHz6M7t27QyaTAbh33ggLC5O63LZs2RIffvghiouL9bZZ2jPde/fuRZcuXaBUKvHUU0/hk08+kbrPPuj27dvYunUrRo4cieHDh+P27dt6v24vWbIEMpkMf/75Z4n3zp49G3K5HP/++6+07siRIxgwYADUajUaNmyIPn36lDgOulzOnj2Ll19+GU2aNKn0HX56vBYvXozc3FysXbsWDg4OJcpbtmyJadOmVWqbCxYsgEwmw8aNG9G4ceMS5V26dMHYsWMB3OuyCgA9evQoEafrwqqzaNEiyGQybNiwAQ0bNtSLfeqpp7B48WJcvXoVn3zySaXyfRjd3/D9f+937tzBiRMnMHToULRo0UKv7Nq1a/j111/1/vb//vtvjB8/XuoS365dO6xbt07vc8p6plt3DaFUKtG+fXts3boVY8eORfPmzUvkmpGRgZ9//hkjR47EyJEjcenSJRw6dEgqDw0NhYWFRaldTEeNGgV7e3u9rv8//vgjevXqhUaNGqFx48bw9/dHWlqa3vvGjh0LCwsL/Pbbbxg0aBAaN26MwMDAco4o1SaGOEcYwqVLlyCEKPVconsc7kEPqy+VdevWLfzvf/+DtbU1VCoVxowZo/c9CpT+THdWVhaCg4NhZ2cHpVIJNzc3bNiwocT2nZycYGLy8GaqVqsFANjZ2emtd3BwgImJSYV6Bj4ubHRThQwfPhw3b95EREQEhg8fjpiYGCxYsEAv5ueff8brr7+OoKAgzJ8/H+fOncPgwYMRFRWFlStX4tVXX8WMGTOQnJyM8ePHG2lPiCouJycH169fx7Vr15CWloYpU6YgNzcXr7zySrV9hqmpKUaNGoVbt27hwIEDemWFhYW4fv263lLRZ5Ru3LhR4r3Z2dkVzkupVMLDw0Mvp8uXL+Py5cvo3r07unfvrnfxffr0aWi1Wuni+9atW+jTpw+++uorjBkzBitXrkSPHj0we/ZshIeHl/vZJ0+exIABA/DPP/9gwYIFCA4OxsKFC7Ft27ZS47///nvk5uZi5MiRsLe3h4+PDzZu3CiVDx8+HDKZDFu2bCnx3i1btqB///5o0qQJgHvd1Hv37g2tVot58+bh/fffR3Z2Np555hkcPXq0xPuHDRuGW7du4f3338fEiRPL3S8yrh9++AEtWrSocJfs0urf9evXpWc6b926haSkJPTu3RvNmjV76PZ04yB88cUXJcZKuJ9uu7169Sq1GyoAjBgxAgqFAtu3b6/QvlRUixYt4OjoqFfvjx07hoKCglLrve6iXVfvMzMz4eXlhZ9++gmhoaGIjIxEy5YtERwcjBUrVpT72fHx8RgxYgQaNGiAiIgIDB06FMHBwUhJSSk1/uuvv0ajRo0wePBgdOvWDU899ZRevR8xYgTy8vIQHx+v975bt27hhx9+wEsvvQRTU1MAwJdffgl/f39YWFjgww8/xDvvvIOzZ8+iZ8+eJW4S3L17F35+frC1tcWSJUsQEBBQ/kGlWqO6zhHl/YCuu664fymv+3RpdOeSuLi4Cl8TPKy+VFZoaCjOnTuH+fPnY8yYMdi4cSOGDBlS7rnt9u3b8PHxwZdffonAwEB89NFHUKvVGDt2LCIjI6uUh65RHxwcjNTUVFy+fBmbN2/GmjVr8Nprr6FRo0ZV2q5BGPlOO9Vwuu5q48eP11v/4osvCmtra+k1AKFQKMSlS5ekdZ988okAIOzt7fW63c2ePVsA0Islqkl03aceXBQKhYiJidGLxSN0L9fZunWrACAiIyOldc7OzqXmMG/evBJ5lta9vLSldevWlToOM2bMEADEX3/9JYQQ4uuvvxZKpVLk5+eLHTt2CFNTU6lur1q1SgAQBw8eFEIIsWjRItGoUSPx66+/6m1z1qxZwtTUVGRkZEjrHtyv5557TjRs2FD8/fff0roLFy4IMzMzUdrX1uDBg0WPHj2k159++qkwMzMTWVlZ0jpvb2/h4eGh976jR48KAOKLL74QQtzrlteqVSvh5+en10X+1q1bwsXFRTz77LPSOt1xHjVq1EOOItUEOTk5AoB44YUXKhRfVv0DICIiIoQQQvzyyy8CgJg2bVqFtnnr1i3RunVrAUA4OzuLsWPHirVr15boCpqamlqh7Xbs2FFYWVlJr6vr0ZJhw4YJc3NzUVBQIIQQIiIiQuqKunr1amFrayvFvvHGGwKAVFeDg4OFg4ODuH79ut42R44cKdRqtbh165YQQohLly4JAFLXeyGE6NChg2jatKm4efOmtG7v3r3S8XpQhw4dRGBgoPT6rbfeEjY2NqKwsFAIca8+P/nkkyIgIEDvfbpu/vv37xdCCHHz5k1haWlZoquuRqMRarVab31QUJAAIGbNmlXOEaTaqDrPEbqltO7lZV1b3K8i1xW67tNNmjQRL774oliyZIk4d+5cmfEPqy8VpdsPDw8P6RwhhBCLFy8WAMR3330nrevTp49et/AVK1YIAOKrr76S1hUUFAhvb29hYWFR6iM6QpTfvVyIe9cb5ubmesf07bffrtR+PQ68000VMnnyZL3XvXr1wj///CN16wCAfv366XUB8/T0BAAEBATodbvTrf/9998NmDHRo4uKikJiYiISExPx1VdfoW/fvpgwYQK+/fbbav0c3YBnN2/e1Fvv6ekpfb5uGTNmTIW2+c0335R47/r16yuVl+7u1c8//wzgXpdTDw8PyOVyeHt7S13KdWVKpRJdunQBcO8X+F69eqFJkyZ6v+j7+vqiqKgI+/fvL/Uzi4qK8NNPP2HIkCF6g0q1bNmy1MHR/vnnH+zcuROjRo2S1gUEBJS4sz1ixAikpKRIXXyBe6OjKhQKvPDCCwDujUB94cIFvPzyy/jnn3+knPPy8tCvXz/s37+/RNf4B8+NVDPpvqtK6wJeltLqX2JiovS3Vtltmpub48iRI5gxYwaAe6MZBwcHw8HBAVOnTkV+fj6A/84DD9tu48aN9b6Dq0vPnj1x+/Zt6Q7zwYMHpTt/PXr0QFZWFi5cuCCVubi4wNHREUIIfPPNN3juuecghNCr935+fsjJycGJEydK/cwrV67g9OnTGDNmjN4AkH369NEbyFHn1KlTOH36tF69HzVqFK5fv46dO3cCuNfNdtiwYdixYwdyc3OluM2bN+PJJ5+Uzm+JiYnIzs6W3q9bTE1N4enpiT179pT4/ClTplTqmFLNV53niCVLlpT5nvuvK3RLVWbcWL9+PVatWgUXFxds3boVb7zxBtq2bYt+/frh77//1outSH2prEmTJukNhDhlyhSYmZlhx44dZb5nx44dsLe318ujQYMGeO2115Cbm4t9+/ZVKZfmzZujd+/e+PTTT/HNN99g/PjxeP/997Fq1aoqbc9QOHo5VciDXed0XTH//fdf6Tm0B2N0I586OTmVuv7BZz+Iappu3bpJjUjg3pdUp06dEBoaisGDB1fbs0K6C8IHv+xtbGzg6+tbpW327t0bNjY2j5RXjx49IJPJcPDgQYwcORIHDx7Es88+C+De8+2urq7SuoMHD6Jr167SMblw4QJOnTqFJ554otRtZ2Vllbn+9u3bpY7kWtq6zZs3o7CwEJ06dcLFixel9Z6enti4cSNCQkIA3OsGHh4ejs2bN+Ott96CEAJxcXEYOHCgdA7TNSSCgoLKPCY5OTnS+Q9Amd1/qWbR/R8/+MNWeR5W/6qyTbVajcWLF2Px4sX4888/kZSUhCVLlmDVqlVQq9V49913pfPAw7Z78+bNSjUQKur+57o9PT1x6NAhaZTj9u3bQ6VS4eDBg3ByckJKSgpGjBgB4N7z3dnZ2fj000/LHN25rHqvG2+hrHr/YGP9q6++QqNGjdCiRQup3iuVSmn2An9/fwD3fmxbsWIFvv/+e7z88svIzc3Fjh078L///U8aH0JX75955plSc7v/WXsAMDMz05u1geqG6jxHlDfuy4PXFVVlYmKCkJAQhISE4J9//sHBgwcRHR2NH3/8ESNHjpR+LAcqXl8qo1WrVnqvLSws4ODgUO6YTX/++SdatWpV4lnttm3bSuWVtWnTJkyaNAm//vqrVC+HDh2K4uJivPnmmxg1ahSsra0rvV1DYKObKkT33NODxH3PbpQVU5H3EtUGJiYm6Nu3LyIjI3HhwgW0a9euWrZ75swZAJWfMsTQrK2t0aZNGxw4cAC5ubk4deoU5s2bJ5V3794dBw4cwF9//YWMjAy9AYWKi4vx7LPPYubMmaVu++mnn66WHHXPpJU2oAxwr0eN7jnVXr16YcuWLXjrrbdw+PBhZGRk4MMPP9TLGQA++ugjuLu7l7q9B6dhMzc3r4a9IENTqVRwdHSU6lp1aNmyJczMzKQBQyvL2dkZ48ePx4svvogWLVpIU/jotnvq1Kky35ufn4/09PRquXh/kJubGxo3bowDBw5g0KBBuHHjhnSn28TEBJ6enjhw4ACeeuopFBQUSI10Xf155ZVXyvzhqmPHjo+cnxACX3/9NfLy8uDq6lqiPCsrC7m5ubCwsICXlxeaN2+OLVu24OWXX8YPP/yA27dvSz8U3J/3l19+CXt7+xLbe7ABpVAoKjTAE9UuhjhHPC7W1tZ4/vnn8fzzz8PHxwf79u3Dn3/+CWdn50rVl9po9erV6NSpU4kfwp5//nnExMTg5MmTVb55Ud3Y6CYiqoS7d+8CgF53xUdRVFSE2NhYNGzYsEaOft2zZ0+sW7cOu3btQlFRkd4AM927d8fXX3+NvXv3SrE6Tz31FHJzcyv9ZWdrawulUql311rnwXW60VdDQ0PRp08fvbLi4mKMHj0asbGxmDNnDoB7d71effVVpKenY/PmzWjYsCGee+45vZyBexdfNeVLmqrP4MGD8emnnyI5OblSc0qXpWHDhnjmmWewe/duXL58uUSvropq0qQJnnrqKeliv1GjRujbty92794tXTg/aMuWLcjPz8fgwYMfaR9KY2pqCi8vLxw8eBAHDhyASqXS6+LdvXt3bN68WfqRUFfvn3jiCTRu3BhFRUWVrj+6faxIvd+3bx/++usvLFy4ULpDpvPvv/9i0qRJ2LZtmzTg5fDhwxEZGQmtVovNmzejefPmenMY6+q9ra0t6309V93nCGPo0qUL9u3bh6tXr8LZ2bnS9aWiLly4gL59+0qvc3NzcfXqVQwaNKjM9zg7O+PUqVMoLi7W++Hq/PnzUnllZWZm6vU+0yksLATw3zVbTcCf6oiIKqiwsBC7du2CXC4v8eVVFUVFRXjttddw7tw5vPbaayW6MdYEPXv2RFFREZYsWYJWrVrpdRfv3r07cnNzsXr1apiYmOg1yIcPH47k5ORSnxfLzs4u84vQ1NQUvr6+2LZtG65cuSKtv3jxYonn3nR3uWfOnImXXnpJbxk+fDj69OmjNzprQEAATE1N8fXXXyMuLg6DBw/WG9nUw8MDTz31FJYsWVLqjyrXrl172OGiGmzmzJlo1KgRJkyYgMzMzBLlv/32W6VH0J03bx6EEBg9enSpfzMpKSnSdDi//PILrl+/XiLmzz//xNmzZ9G6dWtp3Zw5cyCEwNixY6XR0nUuXbqEmTNnwsHBAf/73/8qlW9F9ezZE9euXcP69evh6empd4HcvXt3pKen47vvvoO1tbV0LjQ1NUVAQAC++eabUu8Wlld/HB0d0b59e3zxxRd6x3Hfvn0lehLousrOmDGjRL2fOHEiWrVqVWIU8/z8fGzYsAEJCQkYPny43vb8/PygUqnw/vvvSxfqFc2b6hZDnCMMQaPR4OzZsyXWFxQUICkpCSYmJtKPYpWtLxX16aef6tWXNWvW4O7du6WOvaIzaNAgaDQabN68WVp39+5dfPzxx7CwsCjx43lFPP300zh58iR+/fVXvfVff/01TExMqqV3TXXhnW4iojL8+OOP0i+wWVlZiI2NxYULFzBr1iy9BvLx48elZx7v5+PjI90FysnJwVdffQXg3pQ1Fy9exLfffovffvsNI0eOxKJFi6o19//7v/8rtbvYs88+W2I+y/Lo8k9OTpbmG9Z5+umnYWNjg+TkZHTo0AGWlpZS2YwZM/D9999j8ODBGDt2LDw8PJCXl4fTp0/j//7v//DHH3+U+cz5/PnzsWvXLvTo0QNTpkxBUVERVq1ahfbt2yM1NVWK27hxI9zd3cu8w/j8889j6tSpOHHiBDp37gxbW1v07dsXy5Ytw82bN/W6mAL3us5+/vnnGDhwINq1a4dx48bhySefxN9//409e/ZApVLhhx9+qPCxo5rlqaeeQmxsLEaMGIG2bdtizJgxaN++PQoKCnDo0CHExcXp/Y3//fffUp29n4WFBYYMGQLgXgM0KioKr776Ktq0aYPRo0ejVatWuHnzJvbu3Yvvv/9eOjckJiZi3rx5eP755+Hl5QULCwv8/vvvWLduHfLz8/Xmqu/duzeWLFmC8PBwdOzYEWPHjoWDgwPOnz+Pzz77DMXFxdixY0epd3iWLVtWYm5vExMTvPXWWxU+VvfX+/vzAgAvLy/IZDIcPnwYzz33nPRsNAB88MEH2LNnDzw9PTFx4kS4urrixo0bOHHiBH766SfcuHGjzM98//338cILL6BHjx4YN24c/v33X6ne6xri+fn5+Oabb/Dss89CqVSWup3nn38ekZGRyMrKgq2tLTp37oyWLVvi7bffRn5+fol6r1KpsGbNGowePRqdO3fGyJEj8cQTTyAjIwPx8fHo0aNHjRuQiQyjsueIqrj/uuJ+3bt3R4sWLaTX5V1XKJVKdOvWDc888wz69esHe3t7ZGVl4euvv8Yvv/yCsLAw2NjYVKm+VFRBQQH69euH4cOHIz09HatXr0bPnj3x/PPPl/meSZMm4ZNPPsHYsWORkpKC5s2b4//+7/9w8OBBrFixQm+Miv3790sDrl67dg15eXnS8ejduzd69+4N4N61xo8//ohevXohNDQU1tbW2L59O3788UdMmDBBb0BWozPauOlUK5Q1BYluygDdtF8oZXoD3ZQgH330kd76PXv2lJhKgagmKW1qD6VSKdzd3cWaNWv0ppN6MO7+ZdGiRUKIe9Nm3L/ewsJCtGrVSrzyyiti165dpebg7Ows/P39K5RnRacMAyD27NlT6ePh6OgoAIhPP/20RNnzzz8vAIgpU6aUKLt586aYPXu2aNmypZDL5cLGxkZ0795dLFmyRG+qETwwZZgQQiQlJYlOnToJuVwunnrqKfH555+L119/XSiVSiGEECkpKQKAeOedd8rM+48//hAAxPTp06V1n332mQAgGjduLG7fvl3q+06ePCmGDh0qrK2thUKhEM7OzmL48OEiKSlJiqmu6Zno8fv111/FxIkTRfPmzYVcLheNGzcWPXr0EB9//LG4c+eOEKL86YBKm74qJSVFvPzyy8LR0VE0aNBANGnSRPTr109s2LBBFBUVCSGE+P3338XcuXOFl5eXsLW1FWZmZuKJJ54Q/v7+Yvfu3aXmun//fvHCCy8IGxsb0aBBA9GsWTMxceJE8ccff5SILa/um5qaVuoY5eXlSVP0lXaO6tixowAgPvzwwxJlmZmZIiQkRDg5OYkGDRoIe3t70a9fP73zR2lThgkhxKZNm0SbNm2EQqEQ7du3F99//70ICAgQbdq0EUII8c033wgAYu3atWXmrptm7P4pGN9++20BQLRs2bLM9+3Zs0f4+fkJtVotlEqleOqpp8TYsWPF8ePHpZigoCDRqFGjMrdBdUNFzxFlfUeXdp1b3pRhD9aFh11XaLVaERkZKfz8/ETTpk1FgwYNROPGjYW3t7f47LPPpGuUqtaX8uj2Y9++fWLSpEmiSZMmwsLCQgQGBop//vlHL/bBKcOEuHd+GDdunLCxsRFyuVx06NChxHlAiPLPZw9eLxw5ckQMHDhQ2NvbiwYNGoinn35avPfee5WeDs3QZEJwNCsiIqr5hgwZgrS0NGm0YSKq+9zd3fHEE08gMTHR2KkQEVUZn+kmIqIa58HnWC9cuIAdO3bAx8fHOAkRkUEVFhaWGOth7969+OWXX1jviajW451uIqJ65tq1aygqKiqzXC6Xw8rK6jFmVJKDgwPGjh2LFi1a4M8//8SaNWuQn5+PkydPlpgflIgeTqPRlFtubm4OtVr9mLIp6Y8//oCvry9eeeUVODo64vz584iOjoZarcaZM2dqzFy7RHXR7du3kZOTU26MlZUV5HL5Y8qo7mGjm4ionmnevDn+/PPPMsv79OkjTQNmLOPGjcOePXug0WigUCjg7e2N999/H507dzZqXkS11f0DnpUmKCgIMTExjyeZUuTk5GDSpEk4ePAgrl27hkaNGqFfv3744IMPpGm9iMgwYmJiMG7cuHJj9uzZw14nj4CNbiKieubgwYMlum/fr0mTJvDw8HiMGRGRof3000/lljs6OsLV1fUxZUNENcnVq1eRlpZWboyHh0epMyZQxbDRTURERERERGQg9Xqe7uLiYly5cgWNGzd+aLcrImMQQuDmzZtwdHSEiQnHPaws1nGq6VjHHw3rONV0rOOPhnWcarqK1vF63ei+cuUKnJycjJ0G0UNdvnwZTZs2NXYatQ7rONUWrONVwzpOtQXreNWwjlNt8bA6Xq8b3Y0bNwZw7yCpVCojZ0NUklarhZOTk/S3SpXDOk41Hev4o2Edp5qOdfzRsI5TTVfROl6vG926bioqlYoVmWo0dqmqGtZxqi1Yx6uGdZxqC9bxqmEdp9riYXWcD5cQERERERERGQgb3UREREREREQGwkY3ERERERERkYHU62e666rms+If6f1/fOBfTZkQEVWfRzm38bxGjxu/i4nqNtZxqgze6SYiIiIiIiIyEDa6iahMH3zwAWQyGcLCwqR1d+7cQUhICKytrWFhYYGAgABkZmbqvS8jIwP+/v5o2LAhbG1tMWPGDNy9e1cvZu/evejcuTMUCgVatmyJmJiYEp8fFRWF5s2bQ6lUwtPTE0ePHjXEbhIRERERGQwb3URUqmPHjuGTTz5Bx44d9dZPnz4dP/zwA+Li4rBv3z5cuXIFQ4cOlcqLiorg7++PgoICHDp0CBs2bEBMTAzmzp0rxVy6dAn+/v7o27cvUlNTERYWhgkTJmDnzp1SzObNmxEeHo558+bhxIkTcHNzg5+fH7Kysgy/80RERERE1YSNbiIqITc3F4GBgfjss8/QpEkTaX1OTg7Wrl2LZcuW4ZlnnoGHhwfWr1+PQ4cO4fDhwwCAXbt24ezZs/jqq6/g7u6OgQMHYtGiRYiKikJBQQEAIDo6Gi4uLli6dCnatm2L0NBQvPTSS1i+fLn0WcuWLcPEiRMxbtw4uLq6Ijo6Gg0bNsS6dese78EgIiIiInoEbHQTUQkhISHw9/eHr6+v3vqUlBQUFhbqrW/Tpg2aNWuG5ORkAEBycjI6dOgAOzs7KcbPzw9arRZpaWlSzIPb9vPzk7ZRUFCAlJQUvRgTExP4+vpKMaXJz8+HVqvVW4iIiIiIjImjlxORnk2bNuHEiRM4duxYiTKNRgO5XA5LS0u99XZ2dtBoNFLM/Q1uXbmurLwYrVaL27dv499//0VRUVGpMefPny8z94iICCxYsKBiO0pERERE9BjwTjcRSS5fvoxp06Zh48aNUCqVxk6n0mbPno2cnBxpuXz5srFTIiIiIqJ6jo1uIpKkpKQgKysLnTt3hpmZGczMzLBv3z6sXLkSZmZmsLOzQ0FBAbKzs/Xel5mZCXt7ewCAvb19idHMda8fFqNSqWBubg4bGxuYmpqWGqPbRmkUCgVUKpXeQkRERERkTGx0E5GkX79+OH36NFJTU6WlS5cuCAwMlP7doEEDJCUlSe9JT09HRkYGvL29AQDe3t44ffq03ijjiYmJUKlUcHV1lWLu34YuRrcNuVwODw8PvZji4mIkJSVJMUREREREtQGf6SYiSePGjdG+fXu9dY0aNYK1tbW0Pjg4GOHh4bCysoJKpcLUqVPh7e0NLy8vAED//v3h6uqK0aNHY/HixdBoNJgzZw5CQkKgUCgAAJMnT8aqVaswc+ZMjB8/Hrt378aWLVsQHx8vfW54eDiCgoLQpUsXdOvWDStWrEBeXh7GjRv3mI4GEREREdGjY6ObiCpl+fLlMDExQUBAAPLz8+Hn54fVq1dL5aampti+fTumTJkCb29vNGrUCEFBQVi4cKEU4+Ligvj4eEyfPh2RkZFo2rQpPv/8c/j5+UkxI0aMwLVr1zB37lxoNBq4u7sjISGhxOBqREREREQ1GRvdRFSuvXv36r1WKpWIiopCVFRUme9xdnbGjh07yt2uj48PTp48WW5MaGgoQkNDK5wrEREREVFNw2e6iYiIiIiIiAyEjW4iIiIiIiIiA2H38hqq+az4hwcRERERERFRjcZGNxERERER1Tu8yUWPC7uXExERERERERkIG91EREREREREBsJGNxEREREREZGBsNFNRERERFRHrFmzBh07doRKpYJKpYK3tzd+/PFHqfzOnTsICQmBtbU1LCwsEBAQgMzMTL1tZGRkwN/fHw0bNoStrS1mzJiBu3fv6sXs3bsXnTt3hkKhQMuWLRETE1Mil6ioKDRv3hxKpRKenp44evSoQfaZqKZjo5uIiIiIqI5o2rQpPvjgA6SkpOD48eN45pln8MILLyAtLQ0AMH36dPzwww+Ii4vDvn37cOXKFQwdOlR6f1FREfz9/VFQUIBDhw5hw4YNiImJwdy5c6WYS5cuwd/fH3379kVqairCwsIwYcIE7Ny5U4rZvHkzwsPDMW/ePJw4cQJubm7w8/NDVlbW4zsYRDUEG91ERERERHXEc889h0GDBqFVq1Z4+umn8d5778HCwgKHDx9GTk4O1q5di2XLluGZZ56Bh4cH1q9fj0OHDuHw4cMAgF27duHs2bP46quv4O7ujoEDB2LRokWIiopCQUEBACA6OhouLi5YunQp2rZti9DQULz00ktYvny5lMeyZcswceJEjBs3Dq6uroiOjkbDhg2xbt06oxwXImNio5uIiIiIqA4qKirCpk2bkJeXB29vb6SkpKCwsBC+vr5STJs2bdCsWTMkJycDAJKTk9GhQwfY2dlJMX5+ftBqtdLd8uTkZL1t6GJ02ygoKEBKSopejImJCXx9faWY0uTn50Or1eotRHUBG91ERERERHXI6dOnYWFhAYVCgcmTJ2Pr1q1wdXWFRqOBXC6HpaWlXrydnR00Gg0AQKPR6DW4deW6svJitFotbt++jevXr6OoqKjUGN02ShMREQG1Wi0tTk5OVdp/opqGjW4iIiIiojqkdevWSE1NxZEjRzBlyhQEBQXh7Nmzxk7roWbPno2cnBxpuXz5srFTIqoWZsZOgIiIiIiIqo9cLkfLli0BAB4eHjh27BgiIyMxYsQIFBQUIDs7W+9ud2ZmJuzt7QEA9vb2JUYZ141ufn/MgyOeZ2ZmQqVSwdzcHKampjA1NS01RreN0igUCigUiqrtNFENxjvdRERE9dQHH3wAmUyGsLAwaR2nEyKqe4qLi5Gfnw8PDw80aNAASUlJUll6ejoyMjLg7e0NAPD29sbp06f1RhlPTEyESqWCq6urFHP/NnQxum3I5XJ4eHjoxRQXFyMpKUmKIapP2OgmIiKqh44dO4ZPPvkEHTt21FvP6YSIarfZs2dj//79+OOPP3D69GnMnj0be/fuRWBgINRqNYKDgxEeHo49e/YgJSUF48aNg7e3N7y8vAAA/fv3h6urK0aPHo1ffvkFO3fuxJw5cxASEiLdhZ48eTJ+//13zJw5E+fPn8fq1auxZcsWTJ8+XcojPDwcn332GTZs2IBz585hypQpyMvLw7hx44xyXIiMid3LiYiI6pnc3FwEBgbis88+w7vvviut100nFBsbi2eeeQYAsH79erRt2xaHDx+Gl5eXNJ3QTz/9BDs7O7i7u2PRokV48803MX/+fMjlcr3phACgbdu2OHDgAJYvXw4/Pz8A+tMJAfemIIqPj8e6deswa9asUvPOz89Hfn6+9JojGxOVlJWVhTFjxuDq1atQq9Xo2LEjdu7ciWeffRYAsHz5cpiYmCAgIAD5+fnw8/PD6tWrpfebmppi+/btmDJlCry9vdGoUSMEBQVh4cKFUoyLiwvi4+Mxffp0REZGomnTpvj888+l+g0AI0aMwLVr1zB37lxoNBq4u7sjISGhxOBqRPVBpRrdERER+Pbbb3H+/HmYm5uje/fu+PDDD9G6dWsp5s6dO3j99dexadMmvYp8fwXLyMjAlClTsGfPHlhYWCAoKAgREREwM/svnb179yI8PBxpaWlwcnLCnDlzMHbsWL18oqKi8NFHH0Gj0cDNzQ0ff/wxunXrVsVDQUREdVXzWfGP9P4/PvCvpkxqhpCQEPj7+8PX11ev0f2w6YS8vLzKnE5oypQpSEtLQ6dOncqcTkjXjV03ndDs2bOl8opMJxQREYEFCxY86u4T1Wlr164tt1ypVCIqKgpRUVFlxjg7O2PHjh3lbsfHxwcnT54sNyY0NBShoaHlxhDVB5XqXr5v3z6EhITg8OHDSExMRGFhIfr374+8vDwpht3SiIiIaq5NmzbhxIkTiIiIKFFW06cT4sjGRERUG1XqTndCQoLe65iYGNja2iIlJQW9e/eu8d3SiIiI6rPLly9j2rRpSExMhFKpNHY6lcaRjYmIqDZ6pIHUcnJyAABWVlYAHt4tDUCZ3dK0Wi3S0tKkmNK6pem2oeuWdn9MRbql5efnQ6vV6i1ERET1RUpKCrKystC5c2eYmZnBzMwM+/btw8qVK2FmZgY7OztpOqH7PTidUGnTAOnKyovRTSdkY2NTpemEiIiIaqMqN7qLi4sRFhaGHj16oH379gBqfre0iIgIqNVqaXFycqr8jhMREdVS/fr1w+nTp5GamiotXbp0QWBgoPRvTidERERUvao8enlISAjOnDmDAwcOVGc+BjV79myEh4dLr7VaLRveRERUbzRu3Fj6oVynUaNGsLa2ltbrphOysrKCSqXC1KlTy5xOaPHixdBoNKVOJ7Rq1SrMnDkT48ePx+7du7FlyxbEx/83oF14eDiCgoLQpUsXdOvWDStWrOB0QkREVCdVqdEdGhqK7du3Y//+/WjatKm03t7eXuqWdv/d7ge7pR09elRve5XtlmZqalqlbml8FoyIiKh8nE6IiIioelWq0S2EwNSpU7F161bs3bsXLi4ueuUeHh5St7SAgAAApXdLe++995CVlQVbW1sApXdLe3CagrK6pQ0ZMgTAf93SOC0BERFRxe3du1fvNacTIiIiql6VanSHhIQgNjYW3333HRo3biw9P61Wq2Fubg61Ws1uaURERERERET/X6Ua3WvWrAFw79fr+61fvx5jx44FwG5pRERERERERDqV7l7+MOyWRkRERERERHTPI83TTURERERERERlY6ObiIiIiIiIyEDY6CYiIiIiIiIyEDa6iYiIiIiIiAyEjW4iIiIiIiIiA2Gjm4iIiIiIiMhA2OgmIiIiIiIiMhA2uomIiIiIiIgMhI1uIiIiIiIiIgNho5uIiIiIiIjIQNjoJiIiIiKqIyIiItC1a1c0btwYtra2GDJkCNLT0/Vi7ty5g5CQEFhbW8PCwgIBAQHIzMzUi8nIyIC/vz8aNmwIW1tbzJgxA3fv3tWL2bt3Lzp37gyFQoGWLVsiJiamRD5RUVFo3rw5lEolPD09cfTo0WrfZ6Kajo1uIiIiIqI6Yt++fQgJCcHhw4eRmJiIwsJC9O/fH3l5eVLM9OnT8cMPPyAuLg779u3DlStXMHToUKm8qKgI/v7+KCgowKFDh7BhwwbExMRg7ty5UsylS5fg7++Pvn37IjU1FWFhYZgwYQJ27twpxWzevBnh4eGYN28eTpw4ATc3N/j5+SErK+vxHAyiGsLM2AnUZc1nxRs7BSIiIiKqRxISEvRex8TEwNbWFikpKejduzdycnKwdu1axMbG4plnngEArF+/Hm3btsXhw4fh5eWFXbt24ezZs/jpp59gZ2cHd3d3LFq0CG+++Sbmz58PuVyO6OhouLi4YOnSpQCAtm3b4sCBA1i+fDn8/PwAAMuWLcPEiRMxbtw4AEB0dDTi4+Oxbt06zJo1q0Tu+fn5yM/Pl15rtVqDHCOix413uomIiIiI6qicnBwAgJWVFQAgJSUFhYWF8PX1lWLatGmDZs2aITk5GQCQnJyMDh06wM7OTorx8/ODVqtFWlqaFHP/NnQxum0UFBQgJSVFL8bExAS+vr5SzIMiIiKgVqulxcnJ6VF3n6hGYKObiIiIiKgOKi4uRlhYGHr06IH27dsDADQaDeRyOSwtLfVi7ezsoNFopJj7G9y6cl1ZeTFarRa3b9/G9evXUVRUVGqMbhsPmj17NnJycqTl8uXLVdtxohqG3cuphEfpFv/HB/7VmAkRERERVVVISAjOnDmDAwcOGDuVClEoFFAoFMZOg6ja8U43EUk44ikREVHdEBoaiu3bt2PPnj1o2rSptN7e3h4FBQXIzs7Wi8/MzIS9vb0U8+B3u+71w2JUKhXMzc1hY2MDU1PTUmN02yCqL3inuxwcCI3qG92Ip127dsXdu3fx1ltvoX///jh79iwaNWoE4N6Ip/Hx8YiLi4NarUZoaCiGDh2KgwcPAvhvxFN7e3scOnQIV69exZgxY9CgQQO8//77AP4b8XTy5MnYuHEjkpKSMGHCBDg4OEiDr+hGPI2OjoanpydWrFgBPz8/pKenw9bW1jgHiIiIqIYTQmDq1KnYunUr9u7dCxcXF71yDw8PNGjQAElJSQgICAAApKenIyMjA97e3gAAb29vvPfee8jKypK+cxMTE6FSqeDq6irF7NixQ2/biYmJ0jbkcjk8PDyQlJSEIUOGALjX3T0pKQmhoaEG23+imoiNbiKS1OYRT4mI6hI+6kVVFRISgtjYWHz33Xdo3Lix9Py0Wq2Gubk51Go1goODER4eDisrK6hUKkydOhXe3t7w8vICAPTv3x+urq4YPXo0Fi9eDI1Ggzlz5iAkJETq/j158mSsWrUKM2fOxPjx47F7925s2bIF8fH//e2Gh4cjKCgIXbp0Qbdu3bBixQrk5eVJ3+1E9QW7lxNRmWrTiKfAvalGtFqt3kJERFSfrFmzBjk5OfDx8YGDg4O0bN68WYpZvnw5Bg8ejICAAPTu3Rv29vb49ttvpXJTU1Ns374dpqam8Pb2xiuvvIIxY8Zg4cKFUoyLiwvi4+ORmJgINzc3LF26FJ9//rn04zkAjBgxAkuWLMHcuXPh7u6O1NRUJCQklBhcjaiu451uIiqVMUc8/ffff8sc8fT8+fNl5hwREYEFCxZUfmeJiIjqCCHEQ2OUSiWioqIQFRVVZoyzs3OJ7uMP8vHxwcmTJ8uNCQ0NZXdyqvd4p5uISqUb8XTTpk3GTqXCONUIEREREdU0vNNNRCXoRjzdv39/mSOe3n+3+8ERTx8cZbyyI56amppWacRTTjVCRERERDUN73QTkUQIgdDQUGzduhW7d+8ud8RTndJGPD19+jSysrKkmNJGPL1/G7qY0kY81dGNeKqLISIiIiKqDXinm4gkHPGUiIiIiKh6sdFNRJI1a9YAuDcwyv3Wr1+PsWPHArg34qmJiQkCAgKQn58PPz8/rF69WorVjXg6ZcoUeHt7o1GjRggKCip1xNPp06cjMjISTZs2LXXE02vXrmHu3LnQaDRwd3fniKdEREREVOuw0U1EEo54SkRERERUvfhMNxEREREREZGBsNFNRERUT0RERKBr165o3LgxbG1tMWTIEKSnp+vF3LlzByEhIbC2toaFhQUCAgJKzCSQkZEBf39/NGzYELa2tpgxYwbu3r2rF7N371507twZCoUCLVu2RExMTIl8oqKi0Lx5cyiVSnh6epaY+YCIiKguYKObiIionti3bx9CQkJw+PBhJCYmorCwEP3790deXp4UM336dPzwww+Ii4vDvn37cOXKFQwdOlQqLyoqgr+/PwoKCnDo0CFs2LABMTExmDt3rhRz6dIl+Pv7o2/fvkhNTUVYWBgmTJiAnTt3SjGbN29GeHg45s2bhxMnTsDNzQ1+fn56Mx8QERHVBZVudO/fvx/PPfccHB0dIZPJsG3bNr1yIQTmzp0LBwcHmJubw9fXFxcuXNCLuXHjBgIDA6FSqWBpaYng4GDk5ubqxZw6dQq9evWCUqmEk5MTFi9eXCKXuLg4tGnTBkqlEh06dHjoM6RERET1WUJCAsaOHYt27drBzc0NMTExyMjIQEpKCgAgJycHa9euxbJly/DMM8/Aw8MD69evx6FDh3D48GEAwK5du3D27Fl89dVXcHd3x8CBA7Fo0SJERUWhoKAAABAdHQ0XFxcsXboUbdu2RWhoKF566SUsX75cymXZsmWYOHEixo0bB1dXV0RHR6Nhw4ZYt27d4z8wREREBlTpRndeXh7c3NzKHERp8eLFWLlyJaKjo3HkyBE0atQIfn5+uHPnjhQTGBiItLQ0JCYmYvv27di/fz8mTZoklWu1WvTv3x/Ozs5ISUnBRx99hPnz5+PTTz+VYg4dOoRRo0YhODgYJ0+exJAhQzBkyBCcOXOmsrtERERUL+Xk5AAArKysAAApKSkoLCyEr6+vFNOmTRs0a9YMycnJAIDk5GR06NBBbyYBPz8/aLVapKWlSTH3b0MXo9tGQUEBUlJS9GJMTEzg6+srxZQmPz8fWq1WbyEiIqrpKt3oHjhwIN599128+OKLJcqEEFixYgXmzJmDF154AR07dsQXX3yBK1euSHfEz507h4SEBHz++efw9PREz5498fHHH2PTpk24cuUKAGDjxo0oKCjAunXr0K5dO4wcORKvvfYali1bJn1WZGQkBgwYgBkzZqBt27ZYtGgROnfujFWrVlXxUBAREdUfxcXFCAsLQ48ePdC+fXsAgEajgVwuh6WlpV6snZ0dNBqNFPPg1H261w+L0Wq1uH37Nq5fv46ioqJSY3TbKE1ERATUarW0ODk5VX7HiYiIHrNqfab70qVL0Gg0er9cq9VqeHp66v1CbmlpiS5dukgxvr6+MDExwZEjR6SY3r17Qy6XSzF+fn5IT0/Hv//+K8WU9yt6afgLORER0T0hISE4c+YMNm3aZOxUKmz27NnIycmRlsuXLxs7JSIiooeq1nm6db9Ol/fLtUajga2trX4SZmawsrLSi3FxcSmxDV1ZkyZNyvwV/WG/kC9YsKAKe0ZERFR3hIaGSo93NW3aVFpvb2+PgoICZGdn693tzszMhL29vRTz4CjjutHN7495cMTzzMxMqFQqmJubw9TUFKampqXG6LZRGoVCAYVCUfkdJiKqYZrPiq/ye//4wL8aM6HHoV6NXs5fyImIqD4TQiA0NBRbt27F7t27S/zA7eHhgQYNGiApKUlal56ejoyMDHh7ewMAvL29cfr0ab1RxhMTE6FSqeDq6irF3L8NXYxuG3K5HB4eHnoxxcXFSEpKkmKIiIjqimq90637dTozMxMODg7S+szMTLi7u0sxD04HcvfuXdy4ceOhv5Df/xllxfAXciIiotKFhIQgNjYW3333HRo3biz1DlOr1TA3N4darUZwcDDCw8NhZWUFlUqFqVOnwtvbG15eXgCA/v37w9XVFaNHj8bixYuh0WgwZ84chISESN+xkydPxqpVqzBz5kyMHz8eu3fvxpYtWxAf/9+dnfDwcAQFBaFLly7o1q0bVqxYgby8PIwbN+7xHxgiIiIDqtY73S4uLrC3t9f75Vqr1eLIkSN6v5BnZ2dL05MAwO7du1FcXAxPT08pZv/+/SgsLJRiEhMT0bp1azRp0kSKKe9XdCIiItK3Zs0a5OTkwMfHBw4ODtKyefNmKWb58uUYPHgwAgIC0Lt3b9jb2+Pbb7+Vyk1NTbF9+3aYmprC29sbr7zyCsaMGYOFCxdKMS4uLoiPj0diYiLc3NywdOlSfP755/Dz85NiRowYgSVLlmDu3Llwd3dHamoqEhISSjw6RkREVNtV+k53bm4uLl68KL2+dOkSUlNTYWVlhWbNmiEsLAzvvvsuWrVqBRcXF7zzzjtwdHTEkCFDAABt27bFgAEDMHHiRERHR6OwsBChoaEYOXIkHB0dAQAvv/wyFixYgODgYLz55ps4c+YMIiMj9eb3nDZtGvr06YOlS5fC398fmzZtwvHjx/WmFSMiIqL/CCEeGqNUKhEVFVXm1KAA4OzsjB07dpS7HR8fH5w8ebLcmNDQUISGhj40JyIiotqs0o3u48ePo2/fvtLr8PBwAEBQUBBiYmIwc+ZM5OXlYdKkScjOzkbPnj2RkJAApVIpvWfjxo0IDQ1Fv379YGJigoCAAKxcuVIqV6vV2LVrF0JCQuDh4QEbGxvMnTtXby7v7t27IzY2FnPmzMFbb72FVq1aYdu2bdK0J0RERERERETGVunu5T4+PhBClFhiYmIAADKZDAsXLoRGo8GdO3fw008/4emnn9bbhpWVFWJjY3Hz5k3k5ORg3bp1sLCw0Ivp2LEjfv75Z9y5cwd//fUX3nzzzRK5DBs2DOnp6cjPz8eZM2cwaNCgyu4OEREREVGdsX//fjz33HNwdHSETCbDtm3b9MqFEJg7dy4cHBxgbm4OX19fXLhwQS/mxo0bCAwMhEqlgqWlJYKDg5Gbm6sXc+rUKfTq1QtKpRJOTk5YvHhxiVzi4uLQpk0bKJVKdOjQ4aE9ZIjqqno1ejkRERERUV2Wl5cHNze3Mh8RWbx4MVauXIno6GgcOXIEjRo1gp+fH+7cuSPFBAYGIi0tDYmJidL0gvf3ONVqtejfvz+cnZ2RkpKCjz76CPPnz9d7zPPQoUMYNWoUgoODcfLkSQwZMgRDhgzBmTNnDLfzRDVUtY5eTkRERERExjNw4EAMHDiw1DIhBFasWIE5c+bghRdeAAB88cUXsLOzw7Zt2zBy5EicO3cOCQkJOHbsGLp06QIA+PjjjzFo0CAsWbIEjo6O2LhxIwoKCrBu3TrI5XK0a9cOqampWLZsmdQ4j4yMxIABAzBjxgwAwKJFi5CYmIhVq1YhOjr6MRwJopqDd7qJiIiIiOqBS5cuQaPRwNfXV1qnVqvh6emJ5ORkAEBycjIsLS2lBjcA+Pr6wsTEBEeOHJFievfuDblcLsX4+fkhPT0d//77rxRz/+foYnSfU5r8/HxotVq9haguYKObiIiIiKge0Gg0AFBiaj47OzupTKPRwNbWVq/czMwMVlZWejGlbeP+zygrRldemoiICKjVamlxcnKq7C4S1UjsXk5ERI9N81nxxk6BqML490r0eM2ePVuaGQm49+w4G95UF/BONxERERFRPWBvbw8AyMzM1FufmZkpldnb2yMrK0uv/O7du7hx44ZeTGnbuP8zyorRlZdGoVBApVLpLUR1ARvdRERERET1gIuLC+zt7ZGUlCSt02q1OHLkCLy9vQEA3t7eyM7ORkpKihSze/duFBcXw9PTU4rZv38/CgsLpZjExES0bt0aTZo0kWLu/xxdjO5ziOoTNrqJiIiIiOqI3NxcpKamIjU1FcC9wdNSU1ORkZEBmUyGsLAwvPvuu/j+++9x+vRpjBkzBo6OjhgyZAgAoG3bthgwYAAmTpyIo0eP4uDBgwgNDcXIkSPh6OgIAHj55Zchl8sRHByMtLQ0bN68GZGRkXpdw6dNm4aEhAQsXboU58+fx/z583H8+HGEhoY+7kNCZHR8ppuIiIiIqI44fvw4+vbtK73WNYSDgoIQExODmTNnIi8vD5MmTUJ2djZ69uyJhIQEKJVK6T0bN25EaGgo+vXrBxMTEwQEBGDlypVSuVqtxq5duxASEgIPDw/Y2Nhg7ty5enN5d+/eHbGxsZgzZw7eeusttGrVCtu2bUP79u0fw1EgqlnY6CYiIiIiqiN8fHwghCizXCaTYeHChVi4cGGZMVZWVoiNjS33czp27Iiff/653Jhhw4Zh2LBh5SdMVA+wezkRERERERGRgbDRTURERERERGQgbHQTERERERERGQif6SYiogprPive2CkQERER1Sq8001ERERERERkIGx0ExERERERERkIu5cTEREREVGtVB8fe3rUff7jA/9qyoQqine6iYiIiIiIiAyEjW4iIiIiIiIiA2Gjm4iIiIiIiMhA2OgmIiIiIiIiMhAOpEZEVM/Ux0FniIiIiIyFjW6qVo9yMc+RFImIiIiIqK5h93IiIiIiIiIiA+GdbiIiIiIionqCPVMfPza6iYiIiOoQXlATEdUs7F5OREREREREZCC8001EVAtxBHIiIiKi2oF3uomIiIiIiIgMhI1uIiIiIiIiIgOp9d3Lo6Ki8NFHH0Gj0cDNzQ0ff/wxunXrZuy0iKiasI4T1W2s40R1G+t43cKBGqumVje6N2/ejPDwcERHR8PT0xMrVqyAn58f0tPTYWtra+z0iOgR1eU6zmeyiep2Ha+tHvXcVJ8vqqkk1nGie2RCCGHsJKrK09MTXbt2xapVqwAAxcXFcHJywtSpUzFr1qwS8fn5+cjPz5de5+TkoFmzZrh8+TJUKlWJ+Pbzdhoueap2Zxb4GTuFaqfVauHk5ITs7Gyo1Wpjp/PYGbqOGxPPL7WLoc4vrOM1u46znlZeXfwufhSs44av46yntUddPD9UuI6LWio/P1+YmpqKrVu36q0fM2aMeP7550t9z7x58wQALlxq3XL58uXHUKtqFtZxLvVpYR3/D+s4l7q4sI7/h3WcS11cHlbHa2338uvXr6OoqAh2dnZ66+3s7HD+/PlS3zN79myEh4dLr4uLi3Hjxg1YW1tDJpMB+O/Xipp4Z6wstS3n2pYvYLychRC4efMmHB0dH9tn1hSGquOGxr9vw6tt+QJl58w6XvvqOFA7/wYroq7uF8DvcWNgHTc+7ofhVbSO19pGd1UoFAooFAq9dZaWlqXGqlSqGvef+jC1Lefali9gnJzrY3e0qqpMHTc0/n0bXm3LFyg9Z9bxiqtJdRyonX+DFVFX9wvg93hNxzpuGNwPw6pIHa+1U4bZ2NjA1NQUmZmZeuszMzNhb29vpKyIqLqwjhPVbazjRHUb6zjRf2pto1sul8PDwwNJSUnSuuLiYiQlJcHb29uImRFRdWAdJ6rbWMeJ6jbWcaL/1Oru5eHh4QgKCkKXLl3QrVs3rFixAnl5eRg3blyVt6lQKDBv3rwSXVtqstqWc23LF6idOdcFhqjjhlYb/1ZqW861LV+gdub8ONTGOg7U3f/PurpfQN3et5qMddy4uB81R62eMgwAVq1ahY8++ggajQbu7u5YuXIlPD09jZ0WEVUT1nGiuo11nKhuYx0nqgONbiIiIiIiIqKaqtY+001ERERERERU07HRTURERERERGQgbHQTERERERERGQgb3URERET0/9i787ic0v9/4K+2+24vabe0yBYRGWnsNG40RjPGNozKNkwhDYb5zNgJM3bRMMjMyDoYaySVMcKIxm4wkUFlqwiVun5/+HW+bu3p1vZ6Ph73g/uc65zzvu7u65zzvs8510VERCrCpPsNQUFBsLW1hba2NlxdXXHq1KnyDgkAEBgYiPfeew8GBgYwNzeHp6cnrl69qlTmxYsX8PX1Rc2aNaGvr48+ffogKSmpnCJWNm/ePKipqcHf31+aVhHjvXPnDgYPHoyaNWtCR0cHTk5OOH36tDRfCIGpU6fCysoKOjo6cHd3x7Vr18oxYlKFo0ePolevXrC2toaamhp27dqlNH/69Olo1KgR9PT0UKNGDbi7u+PkyZNKZR49eoRBgwbB0NAQxsbGGDZsGJ4+fapU5ty5c2jfvj20tbVRp04dLFiwQGUxv27UqFFQU1PDkiVLyi3mouL19vaGmpqa0qt79+7lFm9xYgaAy5cv46OPPoKRkRH09PTw3nvvISEhQZpfnP1eQkICPDw8oKurC3Nzc0ycOBEvX74sddxUOkX9vSvz8aCyn1MUZtWqVWjWrBkMDQ1haGgINzc3HDhwQJpfWetFJVdW3/Pi7JOjoqLQsmVLyOVyODg4ICQkpMzqURbf6fKuQ35KmxtUxLoUiyDJ5s2bhUwmE+vWrRMXL14UI0aMEMbGxiIpKam8QxMKhUKsX79eXLhwQcTFxYmePXuKunXriqdPn0plRo0aJerUqSMiIiLE6dOnRZs2bcT7779fjlG/curUKWFrayuaNWsmxo0bJ02vaPE+evRI2NjYCG9vb3Hy5Enx77//ioMHD4rr169LZebNmyeMjIzErl27xN9//y0++ugjYWdnJ54/f15ucVPZ279/v/jf//4nduzYIQCInTt3Ks3fuHGjCA8PFzdu3BAXLlwQw4YNE4aGhiI5OVkq0717d9G8eXNx4sQJ8ccffwgHBwcxcOBAaX5qaqqwsLAQgwYNEhcuXBCbNm0SOjo64scff1RJzLl27NghmjdvLqytrcXixYuV5r3LmIuK18vLS3Tv3l3cu3dPej169Kjc4i1OzNevXxcmJiZi4sSJ4syZM+L69evi999/VzqGFLXfe/nypWjatKlwd3cXZ8+eFfv37xempqZiypQppYqZSq+ov3dlPh5U5nOKouzevVvs27dP/PPPP+Lq1avim2++EVpaWuLChQtCiMpbLyq5svieF2ef/O+//wpdXV0REBAgLl26JJYvXy40NDREWFhYmdTjbb/TFaEObyptblAR61JcTLpf07p1a+Hr6yu9z87OFtbW1iIwMLAco8pfcnKyACCio6OFEEKkpKQILS0tsW3bNqnM5cuXBQARExNTXmGKJ0+eiPr164vw8HDRsWNHqWFVxHi//vpr0a5duwLn5+TkCEtLS/H9999L01JSUoRcLhebNm16FyFSOSgsgc2VmpoqAIjDhw8LIYS4dOmSACD++usvqcyBAweEmpqauHPnjhBCiJUrV4oaNWqIjIwMqczXX38tGjZsqLKY//vvP1GrVi1x4cIFYWNjo5R0l2fMBSXdvXv3LnCZivgZ9+/fXwwePLjAZYqz39u/f79QV1cXiYmJUplVq1YJQ0NDpXrQu/Xm37uqHQ8qyzlFadWoUUP89NNPVa5eVDKl+Z4XZ588adIk0aRJE6Vt9e/fXygUCpXVpSTf6YpWh7fJDSpaXUqCt5f/f5mZmYiNjYW7u7s0TV1dHe7u7oiJiSnHyPKXmpoKADAxMQEAxMbGIisrSyn+Ro0aoW7duuUav6+vLzw8PJTiAipmvLt370arVq3Qt29fmJubo0WLFlizZo00Pz4+HomJiUoxGxkZwdXVtUJ+R+jdyMzMxOrVq2FkZITmzZsDAGJiYmBsbIxWrVpJ5dzd3aGuri7dhh4TE4MOHTpAJpNJZRQKBa5evYrHjx+XeZw5OTn4/PPPMXHiRDRp0iTP/IoYc1RUFMzNzdGwYUOMHj0aDx8+rLDx5uTkYN++fWjQoAEUCgXMzc3h6uqqdEtycfZ7MTExcHJygoWFhVLMaWlpuHjxYpnGTKVX1Y4HleWcoqSys7OxefNmpKenw83NrcrUi0qnNN/z4uyTY2Ji8pznKhQKlXynSvOdrmh1eJvcoKLVpSSYdP9/Dx48QHZ2ttIfEQAsLCyQmJhYTlHlLycnB/7+/mjbti2aNm0KAEhMTIRMJoOxsbFS2fKMf/PmzThz5gwCAwPzzKuI8f77779YtWoV6tevj4MHD2L06NEYO3YsNmzYAABSXJXhO0Kqt3fvXujr60NbWxuLFy9GeHg4TE1NAbz6rpibmyuV19TUhImJifRdSUxMzPe7lDuvrM2fPx+ampoYO3ZsvvMrWszdu3fHzz//jIiICMyfPx/R0dHo0aMHsrOzK2S8ycnJePr0KebNm4fu3bvj0KFD+Pjjj/HJJ58gOjpa2mZR+713/b2g0qlKx4PKck5REufPn4e+vj7kcjlGjRqFnTt3wtHRsdLXi0qvtN/z4uyTCyqTlpaG58+fl0n8b/Odrih1AN4+N6hIdSkpzXLbMpWar68vLly4gGPHjpV3KAW6ffs2xo0bh/DwcGhra5d3OMWSk5ODVq1aYe7cuQCAFi1a4MKFCwgODoaXl1c5R0cVTefOnREXF4cHDx5gzZo16NevH06ePJknEawIYmNjsXTpUpw5cwZqamrlHU6xDBgwQPq/k5MTmjVrhnr16iEqKgpdu3Ytx8jyl5OTAwDo3bs3xo8fDwBwdnbG8ePHERwcjI4dO5ZneEQFqgznFCXVsGFDxMXFITU1Fdu3b4eXl5f04xdVT5X9e14VvtOVMTcoS7zS/f+ZmppCQ0MjTw95SUlJsLS0LKeo8vLz88PevXsRGRmJ2rVrS9MtLS2RmZmJlJQUpfLlFX9sbCySk5PRsmVLaGpqQlNTE9HR0Vi2bBk0NTVhYWFRoeIFACsrKzg6OipNa9y4sdTzcG5cFf07Qu+Gnp4eHBwc0KZNG6xduxaamppYu3YtgFffleTkZKXyL1++xKNHj6TviqWlZb7fpdx5ZemPP/5AcnIy6tatK7XHW7du4auvvoKtrW2FjPlN9vb2MDU1xfXr1ytkvKamptDU1CxyH1LUfq88P2MqvqpyPKgs5xQlJZPJ4ODgABcXFwQGBqJ58+ZYunRppa8Xlc7bfM+Ls08uqIyhoSF0dHTKpA5v852uKHUoi9ygotSlNJh0/38ymQwuLi6IiIiQpuXk5CAiIgJubm7lGNkrQgj4+flh586dOHLkCOzs7JTmu7i4QEtLSyn+q1evIiEhoVzi79q1K86fP4+4uDjp1apVKwwaNEj6f0WKFwDatm2bZyiJf/75BzY2NgAAOzs7WFpaKsWclpaGkydPVojvCJWvnJwcZGRkAADc3NyQkpKC2NhYaf6RI0eQk5MDV1dXqczRo0eRlZUllQkPD0fDhg1Ro0aNMo3t888/x7lz55Tao7W1NSZOnIiDBw9WyJjf9N9//+Hhw4ewsrKqkPHKZDK89957he5DirOfdnNzw/nz55V+UAgPD4ehoWGehJ7KT2U/HlS2c4q3lbt/rmr1osKVxfe8OPtkNzc3pXXkllHld6ok3+mKUoeyyA0qSl1KpVy7catgNm/eLORyuQgJCRGXLl0SI0eOFMbGxko95JWX0aNHCyMjIxEVFaU0hM6zZ8+kMqNGjRJ169YVR44cEadPnxZubm7Czc2tHKNW9noPhUJUvHhPnTolNDU1xZw5c8S1a9fExo0bha6urvj111+lMvPmzRPGxsbi999/F+fOnRO9e/euNEPEUPE9efJEnD17Vpw9e1YAEIsWLRJnz54Vt27dEk+fPhVTpkwRMTEx4ubNm+L06dPCx8dHyOVyafgOIV4NZ9WiRQtx8uRJcezYMVG/fn2l4axSUlKEhYWF+Pzzz8WFCxfE5s2bha6ubqmHsyos5vy82Xv5u465sHifPHkiJkyYIGJiYkR8fLw4fPiwaNmypahfv7548eJFucRbVMxCvBqOTUtLS6xevVpcu3ZNGqbkjz/+kNZR1H4vdziUbt26ibi4OBEWFibMzMw4ZFg5KOrvXZmPB1XhnKIgkydPFtHR0SI+Pl6cO3dOTJ48WaipqYlDhw4JISpvvajkyuJ7Xpx9cu4QVRMnThSXL18WQUFBZTpE1dt+pytCHQpS0tygItelKEy637B8+XJRt25dIZPJROvWrcWJEyfKOyQhxKvhSvJ7rV+/Xirz/Plz8eWXX4oaNWoIXV1d8fHHH4t79+6VX9BveLNhVcR49+zZI5o2bSrkcrlo1KiRWL16tdL8nJwc8d133wkLCwshl8tF165dxdWrV8spWlKVyMjIfNubl5eXeP78ufj444+FtbW1kMlkwsrKSnz00Ufi1KlTSut4+PChGDhwoNDX1xeGhobCx8dHPHnyRKnM33//Ldq1ayfkcrmoVauWmDdvnkpizk9+Sfe7jLmweJ89eya6desmzMzMhJaWlrCxsREjRozI8wNoRfyM165dKxwcHIS2trZo3ry52LVrl9I6irPfu3nzpujRo4fQ0dERpqam4quvvhJZWVmljptKp6i/d2U+HlSFc4qCDB06VNjY2AiZTCbMzMxE165dpeREiMpbLyq5svqeF2efHBkZKZydnYVMJhP29vZK23hbZfGdLu86FKQ0uUFFrUtR1IQQouyvnxMRERERERERn+kmIiIiIiIiUhEm3UREREREREQqwqSbiIiIiIiISEWYdBMRERERERGpCJNuIiIiIiIiIhVh0k1ERERERESkIky6iYiIiIiIiFSESTcRERERERGRijDpJiIiIiIiIlIRJt1EREREREREKsKkm4iIiIiIiEhFmHQTERERERERqQiTbiIiIiIiIiIVYdJNREREREREpCJMuomIiIiIiIhUhEk3ERERERERkYow6SYiIiIiIiJSESbdRERERERERCrCpJuIiIiIiIhIRZh0ExEREREREakIk24iIiIiIiIiFWHSTURERERERKQiTLqJiIiIiIiIVIRJNxEREREREZGKMOkmIiIiIiIiUhEm3UREREREREQqwqSbiIiIiIiISEWYdBMRERERERGpCJNuIiIiIiIiIhVh0k1ERERERESkIky6iYiIiIiIiFSESTcRERERERGRijDpJiIiIiIiIlIRJt1EREREREREKsKkm4iIiIiIiEhFmHQTERERUZE6deqETp06lXcYRO/U9OnToaamVt5hVEghISFQU1PDzZs338n2bG1t4e3t/U62VdaYdFOFltuYT58+XWRZngwQERG9nUuXLmH69Onv7CSaiKqWlStXIiQkpLzDqHCYdFcAN27cwBdffAF7e3toa2vD0NAQbdu2xdKlS/H8+XMAr37Z+fDDD/NdPioqCmpqati+fbs0LTdZLeh14sQJqayamhr8/PwKjTEnJwc///wzXF1dYWJiAgMDAzRo0ABDhgxRWtfrUlJSoK2tDTU1NVy+fLmkH0u5uXHjhhR3cZJ9IiKiquLSpUuYMWNGvkn3oUOHcOjQoXcfFBFVSJ9//jmeP38OGxsbaRqT7vxplncA1d2+ffvQt29fyOVyDBkyBE2bNkVmZiaOHTuGiRMn4uLFi1i9enWp1z9z5kzY2dnlme7g4FCi9YwdOxZBQUHo3bs3Bg0aBE1NTVy9ehUHDhyAvb092rRpk2eZbdu2QU1NDZaWlti4cSNmz55d6noUR1mdCIwfPx6amprIyMgok/URVVXp6enQ09Mr7zCI6B2RyWTlHQIRVSAaGhrQ0NAo7zAqBV7pLkfx8fEYMGAAbGxscOnSJSxduhQjRoyAr68vNm3ahEuXLqFJkyZvtY0ePXpg8ODBeV6mpqbFXkdSUhJWrlyJESNGYNeuXRg7diy+/PJLLF26FFevXsWXX36Z73K//vorevbsiYEDByI0NPSt6lEcMpnsrU8IDh48iIMHD2L8+PFlFBXRK3fu3MGwYcNgbW0NuVwOOzs7jB49GpmZmQCAf//9F3379oWJiQl0dXXRpk0b7Nu3T2kduXe1bN26FXPmzEHt2rWhra2Nrl274vr161I5Pz8/6Ovr49mzZ3niGDhwICwtLZGdnS1NO3DgANq3bw89PT0YGBjAw8MDFy9eVFrO29sb+vr6uHHjBnr27AkDAwMMGjQIAPD8+XOMHTsWpqamMDAwwEcffYQ7d+5ATU0N06dPz/M5DB06FBYWFpDL5WjSpAnWrVtXqnrmOnnyJHr27IkaNWpAT08PzZo1w9KlS5XKXLlyBZ9++ilMTEygra2NVq1aYffu3QX9uYiqtIL2R6tXr0bfvn0BAJ07d5bujouKigLAx7io6jt27Bjee+89aGtro169evjxxx/zLffrr7/CxcUFOjo6MDExwYABA3D79m1pfkmOw7l3s0ZFRaFVq1bQ0dGBk5OT1O527NgBJycnaGtrw8XFBWfPnlVa37lz5+Dt7S3dMWtpaYmhQ4fi4cOHebadu43X65ffM+u5d8Hu2rULTZs2lY7XYWFhSuXefKbb1tYWFy9eRHR0tLT/yN1nFPRsfH7PhQshMHv2bNSuXRu6urro3LlznvOSXCkpKfD390edOnUgl8vh4OCA+fPnIycnJ9/y5YVXusvRggUL8PTpU6xduxZWVlZ55js4OGDcuHHlEJmy+Ph4CCHQtm3bPPPU1NRgbm6eZ3pCQgL++OMPbNmyBTY2Nli8eDGOHz+O999/v1QxPHv2DF988QW2b9+OrKwseHp6YunSpahRo4ZUJrdR5+6kACA5ORlTpkzB3r17kZqaioYNGyIgIABeXl55tpGVlYVx48Zh3LhxqFevXqniJMrP3bt30bp1a6SkpGDkyJFo1KgR7ty5g+3bt+PZs2d4/Pgx3n//fTx79gxjx45FzZo1sWHDBnz00UfYvn07Pv74Y6X1zZs3D+rq6pgwYQJSU1OxYMECDBo0CCdPngQA9O/fH0FBQdKdNLmePXuGPXv2wNvbW/pl+pdffoGXlxcUCgXmz5+PZ8+eYdWqVWjXrh3Onj0LW1tbafmXL19CoVCgXbt2+OGHH6CrqwvgVUK+detWfP7552jTpg2io6Ph4eGR53NISkpCmzZtpIO5mZkZDhw4gGHDhiEtLQ3+/v4lqicAhIeH48MPP4SVlRXGjRsHS0tLXL58GXv37pX2nxcvXkTbtm1Rq1YtTJ48GXp6eti6dSs8PT3x22+/5fl8iaqywvZHI0aMwNixY7Fs2TJ88803aNy4MQBI/xJVZefPn0e3bt1gZmaG6dOn4+XLl5g2bRosLCyUys2ZMwffffcd+vXrh+HDh+P+/ftYvnw5OnTogLNnz8LY2LhEx2EAuH79Oj777DN88cUXGDx4MH744Qf06tULwcHB+Oabb6QLXIGBgejXrx+uXr0KdfVX107Dw8Px77//wsfHB5aWltJdshcvXsSJEyekRPfs2bPo3r07rKysMGPGDGRnZ2PmzJkwMzPL9/M4duwYduzYgS+//BIGBgZYtmwZ+vTpg4SEBNSsWTPfZZYsWYIxY8ZAX18f//vf/wAgz+dXHFOnTsXs2bPRs2dP9OzZE2fOnEG3bt2kCxWvf54dO3bEnTt38MUXX6Bu3bo4fvw4pkyZgnv37mHJkiUl3rbKCCo3tWrVEvb29sUqa2NjI7p16ybu37+f57Vr1y4BQGzbtk0qv379egFAHD58OE/5Bw8eKK0bgPD19S1w23fv3hUAhIeHh0hPTy9WvPPmzRP6+vri2bNnQggh6tWrJ7788stiLfu63Ho4OTmJ9u3bi2XLlglfX1+hrq4uOnToIHJycqSyHTt2FB07dpTeP3v2TDRu3FhoaWmJ8ePHi2XLlon27dsLAGLJkiV5trVgwQJhbm4uUlNTpe3+9ddfJY6Z6E1DhgwR6urq+X6fcnJyhL+/vwAg/vjjD2n6kydPhJ2dnbC1tRXZ2dlCCCEiIyMFANG4cWORkZEhlV26dKkAIM6fPy+ts1atWqJPnz5K29q6dasAII4ePSptw9jYWIwYMUKpXGJiojAyMlKa7uXlJQCIyZMnK5WNjY0VAIS/v7/SdG9vbwFATJs2TZo2bNgwYWVllWcfNGDAAGFkZCTtL4pbz5cvXwo7OzthY2MjHj9+nOdzzdW1a1fh5OQkXrx4oTT//fffF/Xr1xdE1UlR+6Nt27YJACIyMjLP/DePs0RViaenp9DW1ha3bt2Spl26dEloaGiI3JTp5s2bQkNDQ8yZM0dp2fPnzwtNTU1penGPw0K8OscHII4fPy5NO3jwoAAgdHR0lOL58ccf87TP3GPn6zZt2pRnO7169RK6urrizp070rRr164JTU1N8WZKCEDIZDJx/fp1adrff/8tAIjly5dL03LPl+Pj46VpTZo0yXc/MW3atDzbyW8dycnJQiaTCQ8PD6Vj+TfffCMACC8vL2narFmzhJ6envjnn3+U1jl58mShoaEhEhIS8myvvPD28nKSlpaGO3fuwMnJqdjLHDp0CGZmZnlenp6eBS7j7u6ep3ytWrVKFKuVlRWGDBmCffv2oXbt2vjkk0+wcOFCXLlypcBlNm7ciN69e0NHRwfAqytvW7duxcuXL0u07VwymQwREREYM2YMVqxYgXnz5uHo0aPYs2dPgcusXr0aly9fxvr167Fo0SKMGTMGERERcHNzw7fffosnT55IZRMTEzFr1izMmjULhoaGpYqRKD85OTnYtWsXevXqhVatWuWZr6amhv3796N169Zo166dNF1fXx8jR47EzZs3cenSJaVlfHx8lB6laN++PYBXt6jnrrNv377Yv38/nj59KpXbsmULatWqJW0nPDwcKSkpGDhwIB48eCC9NDQ04OrqisjIyDzxjh49Wul97q1mbz5mMmbMGKX3Qgj89ttv6NWrF4QQSttTKBRITU3FmTNnSlTPs2fPIj4+Hv7+/jA2Ns7zuQLAo0ePcOTIEfTr1w9PnjyRtvnw4UMoFApcu3YNd+7cyVNPoqqoOPsjouooOzsbBw8ehKenJ+rWrStNb9y4MRQKhfR+x44dyMnJQb9+/ZSOY5aWlqhfv7503CzucTiXo6Mj3NzcpPeurq4AgC5duijFkzs99zgIQDrXBoAXL17gwYMHUl9LucfV7OxsHD58GJ6enrC2tpbKOzg4oEePHvl+Ju7u7kp3fjZr1gyGhoZK21aFw4cPIzMzE2PGjFHaJ715Nxzwqv+o9u3bo0aNGkp/D3d3d2RnZ+Po0aMqjbUkmHSXk7S0NACAgYFBsZdxdXVFeHh4ntcPP/xQ4DJBQUF5yh84cKDE8a5fvx4rVqyAnZ0ddu7ciQkTJqBx48bo2rVrnhPWc+fO4fz58xg4cKA0Lfek/uDBgyXeNgCMHDkSWlpa0vvRo0dDU1MT+/fvL3CZ/fv3w9LSUikOLS0tjB07Fk+fPkV0dLQ0/euvv4a9vT2GDx9eqviICnL//n2kpaWhadOmBZa5desWGjZsmGd67i2dt27dUpr++gEYgPSYxePHj6Vp/fv3x/Pnz6Xnlp8+fYr9+/ejb9++0kHs2rVrAF4d1N/8ce7QoUNITk5W2o6mpiZq166dJ3Z1dfU8HTa+2Vnj/fv3kZKSgtWrV+fZlo+PDwDk2V5R9bxx4wYAFPrZXr9+HUIIfPfdd3m2O23atHy3S1RVFWd/RFQd3b9/H8+fP0f9+vXzzHv9+Hzt2jUIIVC/fv08x5TLly8rHU+KcxzO9ebxzsjICABQp06dfKe/frx/9OgRxo0bBwsLC+jo6MDMzEw6JqempgJ4dZx7/vx5vh0pF9S58psxAa+Ow69vWxVyz3ne/FuYmZkpPVYKvPp7hIWF5flbuLu7A6hYx3c+011Ocq+mvn61tSimpqbSl+h1mpoF/xlbt26d76/ZJaWurg5fX1/4+vri4cOH+PPPPxEcHIwDBw5gwIAB+OOPP6Syv/76K/T09GBvby91eqStrQ1bW1ts3Lgx32c9i/Jmw9PX14eVlVWh44jeunUL9evXl555yfVmInPixAn88ssviIiIyFOWqCIqqKdQIYT0/zZt2sDW1hZbt27FZ599hj179uD58+fo37+/VCa3k5FffvkFlpaWedb35r5FLpeXuo3kbmvw4MH59qkAvPoV/XXFqWdxtzthwgSlqxWvK+loDkREVD3l5ORATU0NBw4cyPcYpa+vL/2/OMfhXAUd74pzHOzXrx+OHz+OiRMnwtnZGfr6+sjJyUH37t3fqjOxsjgGv66gO2le79i1pHJycvDBBx9g0qRJ+c5v0KBBqddd1ph0lxNDQ0NYW1vjwoUL5R1KidWsWRMfffQRPvroI3Tq1AnR0dG4desWbGxsIITApk2bkJ6eDkdHxzzLJicn4+nTp0o7pfI2adIktG/fHnZ2dlIS/+DBAwDAvXv3kJCQkO+vfUTFYWZmBkNDw0Lbuo2NDa5evZpneu4jHK+Pf1kS/fr1w9KlS5GWloYtW7bA1tZWaXi/3NvGzM3N8/1BrzhsbGyQk5OD+Ph4pR/H3uxl3MzMDAYGBsjOzi71tt6UG/+FCxcKXKe9vT2AV3e5lNV2iSqr4uyPeIs5VUdmZmbQ0dGR7gB73evH53r16kEIATs7u2IldEUdh9/W48ePERERgRkzZmDq1KnS9DfrYW5uDm1t7XxHAMlv2tsoaB+Se5U6JSVF6ZGwN+/myz3nuXbtmnQMB17djfDmVfZ69erh6dOnleL4zst65ejDDz/EjRs3EBMTU96hlFruVfR79+4BAKKjo/Hff/9h5syZ2LZtm9Jr9erVePbsGXbt2lXi7by583j69Cnu3bun1LPym2xsbHDt2rU8v/K9mcgkJCTg6NGjsLOzk14TJ04EAHz00Ud5rr4RlYS6ujo8PT2xZ88enD59Os98IQR69uyJU6dOKe0L0tPTsXr1atja2ub7A1Zx9O/fHxkZGdiwYQPCwsLQr18/pfkKhQKGhoaYO3cusrKy8ix///79IreRe/V45cqVStOXL1+u9F5DQwN9+vTBb7/9lu8Jf3G29aaWLVvCzs4OS5YsQUpKitK83F/izc3N0alTJ/z444/Sfuptt0tUWRVnf6SnpwcAedoUUVWmoaEBhUKBXbt2ISEhQZp++fJlpUcjP/nkE2hoaGDGjBl5rvgKIfIM01XUcbgs4s7d9uve7LVbQ0MD7u7u2LVrF+7evStNv379eqkeOy2Mnp5evvuP3B/KX3/OOj09HRs2bFAq5+7uDi0tLSxfvlypXvn1RN6vXz/ExMTk+/hqSkpKqfuSUgVe6S5HkyZNwsaNGzF8+HAcOXIkT5f6N27cUBr2prwkJibi0aNHeU78MzMzpVuyc2/PzL21fOLEidDW1s6zru+//x4bN27E4MGDSxTD6tWr4ePjIz3XvWrVKrx8+bLAzh8AoGfPnjh06BC2bNkiPdf98uVLLF++HPr6+ujYsaO07jfHUTxy5AiWL1+OH374AY0aNSpRrERvmjt3Lg4dOoSOHTti5MiRaNy4Me7du4dt27bh2LFjmDx5MjZt2oQePXpg7NixMDExwYYNGxAfH4/ffvut1Ld0t2zZEg4ODvjf//6HjIyMPLe0GRoaYtWqVfj888/RsmVLDBgwAGZmZkhISMC+ffvQtm1brFixotBtuLi4oE+fPliyZAkePnwoDRn2zz//AFD+xXvevHmIjIyEq6srRowYAUdHRzx69AhnzpzB4cOH8ejRoxLVT11dHatWrUKvXr3g7OwMHx8fWFlZ4cqVK7h48aJ0EA4KCkK7du3g5OSEESNGwN7eHklJSYiJicF///2Hv//+u0TbJarMitofOTs7Q0NDA/Pnz0dqairkcjm6dOmS7/CgRFXJjBkzEBYWhvbt2+PLL7+UzhmbNGmCc+fOAXiVOM6ePRtTpkzBzZs34enpCQMDA8THx2Pnzp0YOXIkJkyYIK2zqOPw2zI0NESHDh2wYMECZGVloVatWjh06BDi4+PzlJ0+fToOHTqEtm3bYvTo0cjOzsaKFSvQtGlTxMXFlVlMLi4uWLVqFWbPng0HBweYm5ujS5cu6NatG+rWrYthw4Zh4sSJ0NDQwLp166TzjlxmZmaYMGECAgMD8eGHH6Jnz544e/YsDhw4AFNTU6VtTZw4Ebt378aHH34Ib29vuLi4ID09HefPn8f27dtx8+bNPMuUFybd5ahevXoIDQ1F//790bhxYwwZMgRNmzZFZmYmjh8/jm3btsHb2/uttnHgwIF8exl///33lW7ZOH36NGbPnp2nXKdOnaCtrY3WrVujS5cu6Nq1KywtLZGcnIxNmzbh77//hr+/P0xNTZGRkYHffvsNH3zwQb4JN/DqyvHSpUuRnJxcogN4ZmYmunbtKo1NuHLlSrRr1w4fffRRgcuMHDkSP/74I7y9vREbGwtbW1ts374df/75J5YsWSJ1YtetW7c8y+b+QtexY8cyeSaeqrdatWrh5MmT+O6777Bx40akpaWhVq1a6NGjB3R1dWFsbIzjx4/j66+/xvLly/HixQs0a9YMe/bsKVUfCK/r378/5syZAwcHB7Rs2TLP/M8++wzW1taYN28evv/+e2RkZKBWrVpo37691MFZUX7++WdYWlpi06ZN2LlzJ9zd3bFlyxY0bNhQaV9gYWGBU6dOYebMmdixYwdWrlyJmjVrokmTJpg/f36p6qdQKBAZGYkZM2Zg4cKFyMnJQb169TBixAipjKOjI06fPo0ZM2YgJCQEDx8+hLm5OVq0aKF0Ox5RdVCc/VFwcDACAwMxbNgwZGdnIzIykkk3VXnNmjXDwYMHERAQgKlTp6J27dqYMWMG7t27JyXdADB58mQ0aNAAixcvxowZMwC86vCsW7du+Z6XFnUcfluhoaEYM2YMgoKCIIRAt27dcODAAaVeyoFXyfCBAwcwYcIEfPfdd6hTpw5mzpyJy5cvFzoiUUlNnToVt27dwoIFC/DkyRN07NgRXbp0gZaWFnbu3Ikvv/wS3333HSwtLeHv748aNWrkOd+YPXs2tLW1ERwcLP1Yf+jQoTznRLq6uoiOjsbcuXOxbds2/PzzzzA0NESDBg0wY8YMqeO5CuFdj1FGef3zzz9ixIgRwtbWVshkMmFgYCDatm0rli9fLo0ra2NjIzw8PPJdPndM2/zG6S7otX79eqlsYeVmzZol0tLSxNKlS4VCoRC1a9cWWlpawsDAQLi5uYk1a9ZIY+j99ttvAoBYu3ZtgXWNiooSAMTSpUuL9dnk1iM6OlqMHDlS1KhRQ+jr64tBgwaJhw8fKpXNb/zQpKQk4ePjI0xNTYVMJhNOTk5KdS9quxynm6h0zp49KwCIX3/9tbxDISIiogL07t1bODg4lHcYVZ6aEKXsgo6IiAjA8+fPlcYJBQBvb2/88ssvuHnzZp4hT4iIiOjde/N4fe3aNTRp0gReXl5Ys2ZNOUZW9fH2ciIieisLFixAbGwsOnfuDE1NTRw4cAAHDhzAyJEjmXATERFVEPb29vD29oa9vT1u3bqFVatWQSaTFTjkFpUdXummcvH8+XOkpqYWWsbExAQymewdRUREpRUeHo4ZM2bg0qVLePr0KerWrYvPP/8c//vf//KM9U1ERETlw8fHB5GRkUhMTIRcLoebmxvmzp2rkmfNSRmTbioXISEhRXbSFBkZiU6dOr2bgIiIiIiIiFSASTeVi3v37uHixYuFlnFxcUGNGjXeUURERERERERlj0k3ERERERERkYpU64ftcnJycPfuXRgYGEBNTa28wyHKQwiBJ0+ewNraGurq6uUdTqXDNk4VHdv422Ebp4qObfztsI1TRVfcNl6tk+67d++yZ12qFG7fvo3atWuXdxiVDts4VRZs46XDNk6VBdt46bCNU2VRVBuv1km3gYEBgFcfkqGhYTlHQ5RXWloa6tSpI31XqWTYxqmiYxt/O2zjVNGxjb8dtnGq6Irbxqt10p17m4qhoSEbMlVovKWqdNjGqbJgGy8dtnGqLNjGS4dtnCqLoto4Hy4hIiIiIiIiUhEm3UREREREREQqUq1vL6eyZzt5X6mXvTnPowwjIaKCsJ0SFQ/bChFVRG+zbwK4fyoPvNJNREREREREpCJMuomIiIiIiIhUpERJd2BgIN577z0YGBjA3Nwcnp6euHr1qlKZFy9ewNfXFzVr1oS+vj769OmDpKQkpTIJCQnw8PCArq4uzM3NMXHiRLx8+VKpTFRUFFq2bAm5XA4HBweEhITkiScoKAi2trbQ1taGq6srTp06VZLqEBEREREREalUiZLu6Oho+Pr64sSJEwgPD0dWVha6deuG9PR0qcz48eOxZ88ebNu2DdHR0bh79y4++eQTaX52djY8PDyQmZmJ48ePY8OGDQgJCcHUqVOlMvHx8fDw8EDnzp0RFxcHf39/DB8+HAcPHpTKbNmyBQEBAZg2bRrOnDmD5s2bQ6FQIDk5+W0+DyIiIiIiIqIyU6KO1MLCwpTeh4SEwNzcHLGxsejQoQNSU1Oxdu1ahIaGokuXLgCA9evXo3Hjxjhx4gTatGmDQ4cO4dKlSzh8+DAsLCzg7OyMWbNm4euvv8b06dMhk8kQHBwMOzs7LFy4EADQuHFjHDt2DIsXL4ZCoQAALFq0CCNGjICPjw8AIDg4GPv27cO6deswefLkfOPPyMhARkaG9D4tLa0k1SciIiIiIiIqkbd6pjs1NRUAYGJiAgCIjY1FVlYW3N3dpTKNGjVC3bp1ERMTAwCIiYmBk5MTLCwspDIKhQJpaWm4ePGiVOb1deSWyV1HZmYmYmNjlcqoq6vD3d1dKpOfwMBAGBkZSa86deq8TfWJiIiIiIiIClXqpDsnJwf+/v5o27YtmjZtCgBITEyETCaDsbGxUlkLCwskJiZKZV5PuHPn584rrExaWhqeP3+OBw8eIDs7O98yuevIz5QpU5Camiq9bt++XfKKExERERERERVTqcfp9vX1xYULF3Ds2LGyjEel5HI55HJ5eYdBRERERERE1USprnT7+flh7969iIyMRO3ataXplpaWyMzMREpKilL5pKQkWFpaSmXe7M08931RZQwNDaGjowNTU1NoaGjkWyZ3HURERERERETlrURXuoUQGDNmDHbu3ImoqCjY2dkpzXdxcYGWlhYiIiLQp08fAMDVq1eRkJAANzc3AICbmxvmzJmD5ORkmJubAwDCw8NhaGgIR0dHqcz+/fuV1h0eHi6tQyaTwcXFBREREfD09ATw6nb3iIgI+Pn5lfAjoKrCdvK+Ui97c55HGUZCRERERET0SomSbl9fX4SGhuL333+HgYGB9Py0kZERdHR0YGRkhGHDhiEgIAAmJiYwNDTEmDFj4ObmhjZt2gAAunXrBkdHR3z++edYsGABEhMT8e2338LX11e69XvUqFFYsWIFJk2ahKFDh+LIkSPYunUr9u37v6QqICAAXl5eaNWqFVq3bo0lS5YgPT1d6s2ciIiIiIiIqLyVKOletWoVAKBTp05K09evXw9vb28AwOLFi6Guro4+ffogIyMDCoUCK1eulMpqaGhg7969GD16NNzc3KCnpwcvLy/MnDlTKmNnZ4d9+/Zh/PjxWLp0KWrXro2ffvpJGi4MAPr374/79+9j6tSpSExMhLOzM8LCwvJ0rkaVx9tcqSYiIiIiIqqISnx7eVG0tbURFBSEoKCgAsvY2NjkuX38TZ06dcLZs2cLLePn58fbyYmIiIiIiKjCeqtxuomIiIiIiIioYKUeMoyIiKqft30MhJ0WEhERUXXDK91EREREREREKsKkm4iIiIiIiEhFmHQTERERERERqQiTbiIiIiIiIiIVYUdqRERERERVxKpVq7Bq1SrcvHkTANCkSRNMnToVPXr0AAC8ePECX331FTZv3oyMjAwoFAqsXLkSFhYW0joSEhIwevRoREZGQl9fH15eXggMDISm5v+lDlFRUQgICMDFixdRp04dfPvtt/D29laKJSgoCN9//z0SExPRvHlzLF++HK1bt1b5Z/AusGNRKgle6SYiIqqm5s2bBzU1Nfj7+0vTXrx4AV9fX9SsWRP6+vro06cPkpKSlJZLSEiAh4cHdHV1YW5ujokTJ+Lly5dKZaKiotCyZUvI5XI4ODggJCQkz/aDgoJga2sLbW1tuLq64tSpU6qoJlG1Urt2bcybNw+xsbE4ffo0unTpgt69e+PixYsAgPHjx2PPnj3Ytm0boqOjcffuXXzyySfS8tnZ2fDw8EBmZiaOHz+ODRs2ICQkBFOnTpXKxMfHw8PDA507d0ZcXBz8/f0xfPhwHDx4UCqzZcsWBAQEYNq0aThz5gyaN28OhUKB5OTkd/dhEFUQTLqJiIiqob/++gs//vgjmjVrpjSdJ+RElVuvXr3Qs2dP1K9fHw0aNMCcOXOgr6+PEydOIDU1FWvXrsWiRYvQpUsXuLi4YP369Th+/DhOnDgBADh06BAuXbqEX3/9Fc7OzujRowdmzZqFoKAgZGZmAgCCg4NhZ2eHhQsXonHjxvDz88Onn36KxYsXS3EsWrQII0aMgI+PDxwdHREcHAxdXV2sW7euwNgzMjKQlpam9CKqCph0ExERVTNPnz7FoEGDsGbNGtSoUUOaXtFPyImoZLKzs7F582akp6fDzc0NsbGxyMrKgru7u1SmUaNGqFu3LmJiYgAAMTExcHJyUrrdXKFQIC0tTbpaHhMTo7SO3DK568jMzERsbKxSGXV1dbi7u0tl8hMYGAgjIyPpVadOnbf/EIgqACbdRERE1Yyvry88PDzynDRX9BNyXgUjKp7z589DX18fcrkco0aNws6dO+Ho6IjExETIZDIYGxsrlbewsEBiYiIAIDExUal9587PnVdYmbS0NDx//hwPHjxAdnZ2vmVy15GfKVOmIDU1VXrdvn27VPUnqmjYkRoREVE1snnzZpw5cwZ//fVXnnnv6oT88ePHBZ6QX7lypcDYAwMDMWPGjOJVlKgaa9iwIeLi4pCamort27fDy8sL0dHR5R1WkeRyOeRyeXmHQVTmeKWbiIiomrh9+zbGjRuHjRs3Qltbu7zDKTFeBSMqHplMBgcHB7i4uCAwMBDNmzfH0qVLYWlpiczMTKSkpCiVT0pKgqWlJQDA0tIyT+eJue+LKmNoaAgdHR2YmppCQ0Mj3zK56yCqTph0E5EkMDAQ7733HgwMDGBubg5PT09cvXpVqQx7NiaqvGJjY5GcnIyWLVtCU1MTmpqaiI6OxrJly6CpqQkLC4sKfUIul8thaGio9CKiouXk5CAjIwMuLi7Q0tJCRESENO/q1atISEiAm5sbAMDNzQ3nz59X6tQwPDwchoaGcHR0lMq8vo7cMrnrkMlkcHFxUSqTk5ODiIgIqQxRdcKkm4gk0dHR8PX1xYkTJxAeHo6srCx069YN6enpUhn2bExUeXXt2hXnz59HXFyc9GrVqhUGDRok/Z8n5ESV25QpU3D06FHcvHkT58+fx5QpUxAVFYVBgwbByMgIw4YNQ0BAACIjIxEbGwsfHx+4ubmhTZs2AIBu3brB0dERn3/+Of7++28cPHgQ3377LXx9faVbv0eNGoV///0XkyZNwpUrV7By5Ups3boV48ePl+IICAjAmjVrsGHDBly+fBmjR49Geno6fHx8yuVzISpPfKabiCRhYWFK70NCQmBubo7Y2Fh06NBB6tk4NDQUXbp0AQCsX78ejRs3xokTJ9CmTRupZ+PDhw/DwsICzs7OmDVrFr7++mtMnz4dMplMqWdjAGjcuDGOHTuGxYsXQ6FQAFDu2Rh41Rvyvn37sG7dOkyePDnf+DMyMpCRkSG9ZydLRMoMDAzQtGlTpWl6enqoWbOmND33hNzExASGhoYYM2ZMgSfkCxYsQGJiYr4n5CtWrMCkSZMwdOhQHDlyBFu3bsW+ffuk7QYEBMDLywutWrVC69atsWTJEp6QE5WB5ORkDBkyBPfu3YORkRGaNWuGgwcP4oMPPgAALF68GOrq6ujTpw8yMjKgUCiwcuVKaXkNDQ3s3bsXo0ePhpubG/T09ODl5YWZM2dKZezs7LBv3z6MHz8eS5cuRe3atfHTTz9Jx3AA6N+/P+7fv4+pU6ciMTERzs7OCAsLy9OXA1F1wKSbiAqUmpoKADAxMQFQdM/Gbdq0KbBn49GjR+PixYto0aJFgT0b+/v7A/i/no2nTJkizS/uUCPsZIno7fCEnKhyW7t2baHztbW1ERQUhKCgoALL2NjYYP/+/YWup1OnTjh79myhZfz8/ODn51doGaLqgEk3EeUrJycH/v7+aNu2rXQFrKL3bDxlyhQEBARI79PS0jjGJ1ERoqKilN7zhJyIiKhsMekmonz5+vriwoULOHbsWHmHUmwcaoSIiIiIKhp2pEZEefj5+WHv3r2IjIxE7dq1pekcaoSIiIiIqGSYdBORRAgBPz8/7Ny5E0eOHIGdnZ3SfA41QkRERERUMry9nIgkvr6+CA0Nxe+//w4DAwPpGWwjIyPo6OgoDTXCno2JiIiIiIrGpJuIJKtWrQLwqgOk161fvx7e3t4A2LMxEREREVFJMOkmIokQosgy7NmYiIiIiKj4+Ew3ERERERERkYow6SYiIiIiIiJSESbdRERERERERCrCpJuIiIiIiIhIRZh0ExEREREREakIk24iIiIiIiIiFeGQYZSH7eR95R3CO/c2db45z6MMIyEiIiIioqqEV7qJiIiIiIiIVIRJNxEREREREZGKMOkmIiIiIiIiUhEm3UREREREREQqwqSbiIiIiIiISEXYezkRUSVUHUcZICIiIqqMeKWbiIiIiKiKCAwMxHvvvQcDAwOYm5vD09MTV69eVSrz4sUL+Pr6ombNmtDX10efPn2QlJSkVCYhIQEeHh7Q1dWFubk5Jk6ciJcvXyqViYqKQsuWLSGXy+Hg4ICQkJA88QQFBcHW1hba2tpwdXXFqVOnyrzORBUdk24iIiIioioiOjoavr6+OHHiBMLDw5GVlYVu3bohPT1dKjN+/Hjs2bMH27ZtQ3R0NO7evYtPPvlEmp+dnQ0PDw9kZmbi+PHj2LBhA0JCQjB16lSpTHx8PDw8PNC5c2fExcXB398fw4cPx8GDB6UyW7ZsQUBAAKZNm4YzZ86gefPmUCgUSE5OfjcfBlEFwdvLiYjonXmb2+JvzvMow0iIiKqmsLAwpfchISEwNzdHbGwsOnTogNTUVKxduxahoaHo0qULAGD9+vVo3LgxTpw4gTZt2uDQoUO4dOkSDh8+DAsLCzg7O2PWrFn4+uuvMX36dMhkMgQHB8POzg4LFy4EADRu3BjHjh3D4sWLoVAoAACLFi3CiBEj4OPjAwAIDg7Gvn37sG7dOkyePDlP7BkZGcjIyJDep6WlqeQzInrXeKWbiIiIiKiKSk1NBQCYmJgAAGJjY5GVlQV3d3epTKNGjVC3bl3ExMQAAGJiYuDk5AQLCwupjEKhQFpaGi5evCiVeX0duWVy15GZmYnY2FilMurq6nB3d5fKvCkwMBBGRkbSq06dOm9bfaIKgVe6iYiIqMpip4NUneXk5MDf3x9t27ZF06ZNAQCJiYmQyWQwNjZWKmthYYHExESpzOsJd+783HmFlUlLS8Pz58/x+PFjZGdn51vmypUr+cY7ZcoUBAQESO/T0tKYeFOVwKSbiIiIiKgK8vX1xYULF3Ds2LHyDqVY5HI55HJ5eYdBVOZ4ezkRERERURXj5+eHvXv3IjIyErVr15amW1paIjMzEykpKUrlk5KSYGlpKZV5szfz3PdFlTE0NISOjg5MTU2hoaGRb5ncdRBVFyVOuo8ePYpevXrB2toaampq2LVrl9J8IQSmTp0KKysr6OjowN3dHdeuXVMq8+jRIwwaNAiGhoYwNjbGsGHD8PTpU6Uy586dQ/v27aGtrY06depgwYIFeWLZtm0bGjVqBG1tbTg5OWH//v0lrQ4RERERUZUhhICfnx927tyJI0eOwM7OTmm+i4sLtLS0EBERIU27evUqEhIS4ObmBgBwc3PD+fPnlXoZDw8Ph6GhIRwdHaUyr68jt0zuOmQyGVxcXJTK5OTkICIiQipDVF2U+Pby9PR0NG/eHEOHDlUaWiDXggULsGzZMmzYsAF2dnb47rvvoFAocOnSJWhrawMABg0ahHv37knDGPj4+GDkyJEIDQ0F8Or5jW7dusHd3R3BwcE4f/48hg4dCmNjY4wcORIAcPz4cQwcOBCBgYH48MMPERoaCk9PT5w5c0Z6ZoWIiIiIqDrx9fVFaGgofv/9dxgYGEjPYBsZGUFHRwdGRkYYNmwYAgICYGJiAkNDQ4wZMwZubm5o06YNAKBbt25wdHTE559/jgULFiAxMRHffvstfH19pdu/R40ahRUrVmDSpEkYOnQojhw5gq1bt2Lfvv/rRyEgIABeXl5o1aoVWrdujSVLliA9PV3qzbw6Y38T1UuJk+4ePXqgR48e+c4TQmDJkiX49ttv0bt3bwDAzz//DAsLC+zatQsDBgzA5cuXERYWhr/++gutWrUCACxfvhw9e/bEDz/8AGtra2zcuBGZmZlYt24dZDIZmjRpgri4OCxatEhKupcuXYru3btj4sSJAIBZs2YhPDwcK1asQHBwcL7xcRgCIiIiIqrKVq1aBQDo1KmT0vT169fD29sbALB48WKoq6ujT58+yMjIgEKhwMqVK6WyGhoa2Lt3L0aPHg03Nzfo6enBy8sLM2fOlMrY2dlh3759GD9+PJYuXYratWvjp59+koYLA4D+/fvj/v37mDp1KhITE+Hs7IywsLA8nasRVXVl2pFafHw8EhMTlYYGMDIygqurK2JiYjBgwADExMTA2NhYSrgBwN3dHerq6jh58iQ+/vhjxMTEoEOHDpDJZFIZhUKB+fPn4/Hjx6hRowZiYmKUejfMLfPm7e6vCwwMxIwZM8quwkREREREFYgQosgy2traCAoKQlBQUIFlbGxsinx0s1OnTjh79myhZfz8/ODn51dkTERVWZl2pJZ7+0p+QwO8PryAubm50nxNTU2YmJiUyTAFufPzM2XKFKSmpkqv27dvl7SKRERERERERMVWrYYM4zAERERERERE9C6V6ZXu3O7/CxsawNLSUqknRAB4+fIlHj16VCbDFHAIAiIiIiIiIqooyjTptrOzg6WlpdLQAGlpaTh58qTSEAQpKSmIjY2Vyhw5cgQ5OTlwdXWVyhw9ehRZWVlSmfDwcDRs2BA1atSQyhQ2TAERERERERFReStx0v306VPExcUhLi4OwKvO0+Li4pCQkAA1NTX4+/tj9uzZ2L17N86fP48hQ4bA2toanp6eAIDGjRuje/fuGDFiBE6dOoU///wTfn5+GDBgAKytrQEAn332GWQyGYYNG4aLFy9iy5YtWLp0qVLHaePGjUNYWBgWLlyIK1euYPr06Th9+jQ7aiAiIiIiIqIKo8RJ9+nTp9GiRQu0aNECwKvx91q0aIGpU6cCACZNmoQxY8Zg5MiReO+99/D06VOEhYVJY3QDwMaNG9GoUSN07doVPXv2RLt27bB69WppvpGREQ4dOoT4+Hi4uLjgq6++wtSpU6XhwgDg/fffR2hoKFavXo3mzZtj+/bt2LVrF8foJiIiKkBgYCDee+89GBgYwNzcHJ6enrh69apSmRcvXsDX1xc1a9aEvr4++vTpk+dxroSEBHh4eEBXVxfm5uaYOHEiXr58qVQmKioKLVu2hFwuh4ODA0JCQvLEExQUBFtbW2hra8PV1RWnTp0q8zoTERGVtxJ3pNapU6dChyJQU1PDzJkzlcbxe5OJiQlCQ0ML3U6zZs3wxx9/FFqmb9++6Nu3b+EBExEREQAgOjoavr6+eO+99/Dy5Ut888036NatGy5dugQ9PT0AwPjx47Fv3z5s27YNRkZG8PPzwyeffII///wTAJCdnQ0PDw9YWlri+PHjuHfvHoYMGQItLS3MnTsXwKu74Dw8PDBq1Chs3LgRERERGD58OKysrKQxfLds2YKAgAAEBwfD1dUVS5YsgUKhwNWrV/OMckJERFSZVavey4mIiKqzsLAwpfchISEwNzdHbGwsOnTogNTUVKxduxahoaHo0qULAGD9+vVo3LgxTpw4gTZt2uDQoUO4dOkSDh8+DAsLCzg7O2PWrFn4+uuvMX36dMhkMgQHB8POzg4LFy4E8OrRsmPHjmHx4sVS0r1o0SKMGDECPj4+AIDg4GDs27cP69atw+TJk/ONPyMjAxkZGdL7tLS0Mv+MiIiIylqZdqRGRJXf0aNH0atXL1hbW0NNTQ27du1Smi+EwNSpU2FlZQUdHR24u7vj2rVrSmUePXqEQYMGwdDQEMbGxhg2bBiePn2qVObcuXNo3749tLW1UadOHSxYsCBPLNu2bUOjRo2gra0NJycn7N+/v8zrS1SdpaamAnh1BxoAxMbGIisrC+7u7lKZRo0aoW7duoiJiQEAxMTEwMnJCRYWFlIZhUKBtLQ0XLx4USrz+jpyy+SuIzMzE7GxsUpl1NXV4e7uLpXJT2BgIIyMjKRXnTp13qb6RERE7wSTbiJSkp6ejubNmyMoKCjf+QsWLMCyZcsQHByMkydPQk9PDwqFAi9evJDKDBo0CBcvXkR4eDj27t2Lo0ePKvXJkJaWhm7dusHGxgaxsbH4/vvvMX36dKW+HY4fP46BAwdi2LBhOHv2LDw9PeHp6YkLFy6orvJE1UhOTg78/f3Rtm1bqT+UxMREyGQyGBsbK5W1sLBAYmKiVOb1hDt3fu68wsqkpaXh+fPnePDgAbKzs/Mtk7uO/EyZMgWpqanS6/bt2yWvOBER0TvG28uJSEmPHj3Qo0ePfOcJIbBkyRJ8++236N27NwDg559/hoWFBXbt2oUBAwbg8uXLCAsLw19//YVWrVoBAJYvX46ePXvihx9+gLW1NTZu3IjMzEysW7cOMpkMTZo0QVxcHBYtWiQl50uXLkX37t0xceJEAMCsWbMQHh6OFStWIDg4ON/4eOspUfH5+vriwoULOHbsWHmHUmxyuRxyuby8wyAiIioRXukmomKLj49HYmKi0i2hRkZGcHV1Vbr11NjYWEq4AcDd3R3q6uo4efKkVKZDhw6QyWRSmdwOlB4/fiyVKez21Pzw1lOi4vHz88PevXsRGRmJ2rVrS9MtLS2RmZmJlJQUpfJJSUmwtLSUyrzZm3nu+6LKGBoaQkdHB6amptDQ0Mi3TO46iIiIqgpe6a6CbCfvK+8QqIrKve2zsFtCExMT8/Q8rKmpCRMTE6UydnZ2edaRO69GjRoF3p5a1K2nAQEB0vu0tDQm3kSvEUJgzJgx2LlzJ6KiovK0QxcXF2hpaSEiIgJ9+vQBAFy9ehUJCQlwc3MDALi5uWHOnDlITk6W2np4eDgMDQ3h6OgolXmzD4bw8HBpHTKZDC4uLoiIiICnpyeAV7e7R0REwM/PT2X1JyIiKg9MuomoyuCtp0SF8/X1RWhoKH7//XcYGBhIP2IZGRlBR0cHRkZGGDZsGAICAmBiYgJDQ0OMGTMGbm5uaNOmDQCgW7ducHR0xOeff44FCxYgMTER3377LXx9faX2N2rUKKxYsQKTJk3C0KFDceTIEWzduhX79v3fj8IBAQHw8vJCq1at0Lp1ayxZsgTp6elSb+ZERERVBZNuIiq23Ns+k5KSYGVlJU1PSkqCs7OzVCY5OVlpuZcvX+LRo0dlcnsqbz0lKr1Vq1YBADp16qQ0ff369fD29gYALF68GOrq6ujTpw8yMjKgUCiwcuVKqayGhgb27t2L0aNHw83NDXp6evDy8sLMmTOlMnZ2dti3bx/Gjx+PpUuXonbt2vjpp5+k4cIAoH///rh//z6mTp2KxMREODs7IywsLM8dLkRERJUdk24iKjY7OztYWloiIiJCSrLT0tJw8uRJjB49GsCr20pTUlIQGxsLFxcXAMCRI0eQk5MDV1dXqcz//vc/ZGVlQUtLC8CrW08bNmyIGjVqSGUiIiLg7+8vbf/121OJqOSEEEWW0dbWRlBQUIEjGACAjY1NkUP4derUCWfPni20jJ+fH28nJyKiKo8dqRGRkqdPnyIuLg5xcXEAXnWeFhcXh4SEBKipqcHf3x+zZ8/G7t27cf78eQwZMgTW1tbSc5mNGzdG9+7dMWLECJw6dQp//vkn/Pz8MGDAAFhbWwMAPvvsM8hkMgwbNgwXL17Eli1bsHTpUqXnsceNG4ewsDAsXLgQV65cwfTp03H69GmeoBMRERFRpcIr3USk5PTp0+jcubP0PjcR9vLyQkhICCZNmoT09HSMHDkSKSkpaNeuHcLCwqCtrS0ts3HjRvj5+aFr167SbarLli2T5hsZGeHQoUPw9fWFi4sLTE1NMXXqVKWxvN9//32Ehobi22+/xTfffIP69etj165d0njCRERERESVAZNuIlLSqVOnQm9BVVNTw8yZM5We33yTiYkJQkNDC91Os2bN8McffxRapm/fvujbt2/hARMRERERVWBMuomIqFJ4m+EQb87zKMNIiIr2tsN38jtLRFR1MOkmIiIiIqoijh49iu+//x6xsbG4d+8edu7cKfW7ArzqUHHatGlYs2YNUlJS0LZtW6xatQr169eXyjx69AhjxozBnj17pMfEli5dCn19fanMuXPn4Ovri7/++gtmZmYYM2YMJk2apBTLtm3b8N133+HmzZuoX78+5s+fj549e6r8M6DC8Ufsd48dqRERERERVRHp6elo3rx5gSMQLFiwAMuWLUNwcDBOnjwJPT09KBQKvHjxQiozaNAgXLx4EeHh4di7dy+OHj2q1O9KWloaunXrBhsbG8TGxuL777/H9OnTsXr1aqnM8ePHMXDgQAwbNgxnz56Fp6cnPD09ceHCBdVVnqiC4pVuIiIiIqIqokePHujRo0e+84QQWLJkCb799lv07t0bAPDzzz/DwsICu3btwoABA3D58mWEhYXhr7/+QqtWrQAAy5cvR8+ePfHDDz/A2toaGzduRGZmJtatWweZTIYmTZogLi4OixYtkpLzpUuXonv37pg4cSIAYNasWQgPD8eKFSsQHBycb3wZGRnIyMiQ3qelpZXZ50JUnnilm4iIiIioGoiPj0diYiLc3d2laUZGRnB1dUVMTAwAICYmBsbGxlLCDQDu7u5QV1fHyZMnpTIdOnSATCaTyigUCly9ehWPHz+Wyry+ndwyudvJT2BgIIyMjKRXnTp13r7SRBUAk24iIiIiomogMTERAGBhYaE03cLCQpqXmJgIc3NzpfmampowMTFRKpPfOl7fRkFlcufnZ8qUKUhNTZVet2/fLmkViSok3l5ORFQO3rZnYyIioqpGLpdDLpeXdxhEZY5XuomIiIiIqgFLS0sAQFJSktL0pKQkaZ6lpSWSk5OV5r98+RKPHj1SKpPfOl7fRkFlcucTVSdMuomIiIiIqgE7OztYWloiIiJCmpaWloaTJ0/Czc0NAODm5oaUlBTExsZKZY4cOYKcnBy4urpKZY4ePYqsrCypTHh4OBo2bIgaNWpIZV7fTm6Z3O0QVSdMuomIiIiIqoinT58iLi4OcXFxAF51nhYXF4eEhASoqanB398fs2fPxu7du3H+/HkMGTIE1tbW0ljejRs3Rvfu3TFixAicOnUKf/75J/z8/DBgwABYW1sDAD777DPIZDIMGzYMFy9exJYtW7B06VIEBARIcYwbNw5hYWFYuHAhrly5gunTp+P06dPw8/N71x8JUbnjM91ERERERFXE6dOn0blzZ+l9biLs5eWFkJAQTJo0Cenp6Rg5ciRSUlLQrl07hIWFQVtbW1pm48aN8PPzQ9euXaGuro4+ffpg2bJl0nwjIyMcOnQIvr6+cHFxgampKaZOnao0lvf777+P0NBQfPvtt/jmm29Qv3597Nq1C02bNn0HnwJRxcKkm4iIiIioiujUqROEEAXOV1NTw8yZMzFz5swCy5iYmCA0NLTQ7TRr1gx//PFHoWX69u2Lvn37Fh4wUTXA28uJiIiIiIiIVIRJNxEREREREZGK8PbyCopj+BIREREREVV+vNJNREREREREpCJMuomIiIiIiIhUhEk3ERERERERkYow6SYiIiIiIiJSESbdRERERERERCrCpJuIiIiIiIhIRZh0ExEREREREakIk24iIiIiIiIiFWHSTURERERERKQimuUdABEREREps528r9TL3pznUYaREBHR2+KVbiIiIiIiIiIV4ZVuIqJSepsrUURERERUPfBKNxEREREREZGK8Eq3CvEqGBERERERUfXGpJuIiKq8t/0RlB1TERERUWkx6SZ6SzyZJyIiIiKigjDpJiIiIqpCONwYUfHwUVB6Vyp9R2pBQUGwtbWFtrY2XF1dcerUqfIOiYjKENs4UdXGNk5UtbGNE1XyK91btmxBQEAAgoOD4erqiiVLlkChUODq1aswNzd/6/Xz1y+i8qXqNk5E5YttnKhqYxuvengnTemoCSFEeQdRWq6urnjvvfewYsUKAEBOTg7q1KmDMWPGYPLkyXnKZ2RkICMjQ3qfmpqKunXr4vbt2zA0NMxTvum0g6oLnuj/uzBDUeC8tLQ01KlTBykpKTAyMnqHUVUMbONUURTWTt8G27hq2zjAdv6uqaqtVFZs46pv42+D+4d3qyruH4rdxkUllZGRITQ0NMTOnTuVpg8ZMkR89NFH+S4zbdo0AYAvvird6/bt2++gVVUsbON8VacX2/j/YRvnqyq+2Mb/D9s4X1XxVVQbr7S3lz948ADZ2dmwsLBQmm5hYYErV67ku8yUKVMQEBAgvc/JycGjR49Qs2ZNqKmpAfi/XytU9YtaRVOd6lsZ6yqEwJMnT2BtbV3eobxzqmrj5aUyfv/exDqUPbbxqtPGi6Oiff8qu8rwebKNv30bv3XrFpydnSv031kVKsP3u6xVxjoXt41X2qS7NORyOeRyudI0Y2PjfMsaGhpWmj92WahO9a1sda2Ot6OVVknaeHmpbN+//LAOZYttvPgqQxsvjor0/asKKvrnyTZefPm1cXX1V/0+V/S/s6pUx3pXtjoXp41X2t7LTU1NoaGhgaSkJKXpSUlJsLS0LKeoiKissI0TVW1s40RVG9s40f+ptEm3TCaDi4sLIiIipGk5OTmIiIiAm5tbOUZGRGWBbZyoamMbJ6ra2MaJ/k+lvr08ICAAXl5eaNWqFVq3bo0lS5YgPT0dPj4+pV6nXC7HtGnT8tzaUlVVp/pWp7pWFapo4+WlKnz/WAcqa1WpjRcHv39li59nxVcWbby6/p2rY72rcp0r9ZBhALBixQp8//33SExMhLOzM5YtWwZXV9fyDouIygjbOFHVxjZOVLWxjRNVgaSbiIiIiIiIqKKqtM90ExEREREREVV0TLqJiIiIiIiIVIRJNxEREREREZGKMOkmIiIiIiIiUpEqmXRPnz4dampqSq9GjRpJ8zt16pRn/qhRo5TWkZCQAA8PD+jq6sLc3BwTJ07Ey5cvlcpERUWhZcuWkMvlcHBwQEhIyLuoXh537tzB4MGDUbNmTejo6MDJyQmnT5+W5gshMHXqVFhZWUFHRwfu7u64du2a0joePXqEQYMGwdDQEMbGxhg2bBiePn2qVObcuXNo3749tLW1UadOHSxYsOCd1O9NRdXX29s7z9+3e/fuSuuoTPWlys/W1jbPd3LevHlKZSrD9y0oKAi2trbQ1taGq6srTp06Vd4h5auoY8CLFy/g6+uLmjVrQl9fH3369EFSUlI5RkzVRWVpQxXd0aNH0atXL1hbW0NNTQ27du0q75DoHSnO8bQqqG77iqKO21VBlUy6AaBJkya4d++e9Dp27JjS/BEjRijNf/0ENzs7Gx4eHsjMzMTx48exYcMGhISEYOrUqVKZ+Ph4eHh4oHPnzoiLi4O/vz+GDx+OgwcPvrM6AsDjx4/Rtm1baGlp4cCBA7h06RIWLlyIGjVqSGUWLFiAZcuWITg4GCdPnoSenh4UCgVevHghlRk0aBAuXryI8PBw7N27F0ePHsXIkSOl+WlpaejWrRtsbGwQGxuL77//HtOnT8fq1asrXH0BoHv37kp/302bNinNryz1papj5syZSt/JMWPGSPMqw/dty5YtCAgIwLRp03DmzBk0b94cCoUCycnJ5R1avgo7BowfPx579uzBtm3bEB0djbt37+KTTz4px2ipOqhsbagiS09PR/PmzREUFFTeoVA5KOx4WhVU131FUblbpSeqoGnTponmzZsXOL9jx45i3LhxBc7fv3+/UFdXF4mJidK0VatWCUNDQ5GRkSGEEGLSpEmiSZMmSsv1799fKBSKt4q9pL7++mvRrl27Aufn5OQIS0tL8f3330vTUlJShFwuF5s2bRJCCHHp0iUBQPz1119SmQMHDgg1NTVx584dIYQQK1euFDVq1JDqn7vthg0blnWVClVUfYUQwsvLS/Tu3bvA+ZWpvlQ12NjYiMWLFxc4vzJ831q3bi18fX2l99nZ2cLa2loEBgaWY1T5K+wYkJKSIrS0tMS2bdukaZcvXxYARExMzDuKkKqjytSGKhMAYufOneUdBr0jRR1Pq4LquK8oKnerCqrsle5r167B2toa9vb2GDRoEBISEpTmb9y4EaampmjatCmmTJmCZ8+eSfNiYmLg5OQECwsLaZpCoUBaWhouXrwolXF3d1dap0KhQExMjAprldfu3bvRqlUr9O3bF+bm5mjRogXWrFkjzY+Pj0diYqJSrEZGRnB1dZVijYmJgbGxMVq1aiWVcXd3h7q6Ok6ePCmV6dChA2QymVRGoVDg6tWrePz4saqrKSmqvrmioqJgbm6Ohg0bYvTo0Xj48KE0rzLVl6qOefPmoWbNmmjRogW+//57pcdVKvr3LTMzE7GxsUr7EXV1dbi7u7/zfV5xFXQMiI2NRVZWllJdGjVqhLp161bYulDlVxnbEFFFVdjxtLKrzvuKonK3yq5KJt2urq4ICQlBWFgYVq1ahfj4eLRv3x5PnjwBAHz22Wf49ddfERkZiSlTpuCXX37B4MGDpeUTExOVEm4A0vvExMRCy6SlpeH58+eqrJ6Sf//9F6tWrUL9+vVx8OBBjB49GmPHjsWGDRuU4s0v1tfrYm5urjRfU1MTJiYmRdb39W28C0XVF3h1a/nPP/+MiIgIzJ8/H9HR0ejRoweys7OleCtLfalqGDt2LDZv3ozIyEh88cUXmDt3LiZNmiTNr+jftwcPHiA7O7vQ/UhFUtgxIDExETKZDMbGxkrLVNS6UNVQ2doQUUVV1PG0squu+4qicreqQLO8A1CFHj16SP9v1qwZXF1dYWNjg61bt2LYsGFKz+46OTnBysoKXbt2xY0bN1CvXr3yCLnUcnJy0KpVK8ydOxcA0KJFC1y4cAHBwcHw8vIq5+jKXnHqO2DAAKm8k5MTmjVrhnr16iEqKgpdu3Ytl7ip6pk8eTLmz59faJnLly+jUaNGCAgIkKY1a9YMMpkMX3zxBQIDAyGXy1UdarVT2DFAR0enHCMjIqI38XhKReVuVUGVTLrfZGxsjAYNGuD69ev5znd1dQUAXL9+HfXq1YOlpWWeXgJze7a1tLSU/n2zt9ukpCQYGhq+05M6KysrODo6Kk1r3LgxfvvtNynO3NisrKyUYnV2dpbKvNk5w8uXL/Ho0aMi6/v6Nt6FouqbH3t7e5iamuL69evo2rVrpaovVVxfffUVvL29Cy1jb2+f73RXV1e8fPkSN2/eRMOGDSv8983U1BQaGhr5xlgR4ivK68eADz74AJmZmUhJSVG62l1Z6kKVU2VvQ0SqVJbH08qO+4pXisrdKqMqeXv5m54+fYobN24oJZ2vi4uLAwBpvpubG86fP6+UmIWHh8PQ0FBK+Nzc3BAREaG0nvDwcLi5uamgBgVr27Ytrl69qjTtn3/+gY2NDQDAzs4OlpaWSrGmpaXh5MmTUqxubm5ISUlBbGysVObIkSPIycmRfpBwc3PD0aNHkZWVJZUJDw9Hw4YN8/QcrkpF1Tc///33Hx4+fKj0960s9aWKy8zMDI0aNSr09foz2q+Li4uDurq69JhDRf++yWQyuLi4KO1HcnJyEBER8c73eaXx+jHAxcUFWlpaSnW5evUqEhISKkVdqHKq7G2ISJXK8nha2XFf8UpRuVulVN49uanCV199JaKiokR8fLz4888/hbu7uzA1NRXJycni+vXrYubMmeL06dMiPj5e/P7778Le3l506NBBWv7ly5eiadOmolu3biIuLk6EhYUJMzMzMWXKFKnMv//+K3R1dcXEiRPF5cuXRVBQkNDQ0BBhYWHvtK6nTp0SmpqaYs6cOeLatWti48aNQldXV/z6669SmXnz5gljY2Px+++/i3PnzonevXsLOzs78fz5c6lM9+7dRYsWLcTJkyfFsWPHRP369cXAgQOl+SkpKcLCwkJ8/vnn4sKFC2Lz5s1CV1dX/PjjjxWqvk+ePBETJkwQMTExIj4+Xhw+fFi0bNlS1K9fX7x48aLS1Zcqv+PHj4vFixeLuLg4cePGDfHrr78KMzMzMWTIEKlMZfi+bd68WcjlchESEiIuXbokRo4cKYyNjZVGeagoCjsGCCHEqFGjRN26dcWRI0fE6dOnhZubm3BzcyvnqKmqq0xtqKJ78uSJOHv2rDh79qwAIBYtWiTOnj0rbt26Vd6hkQoV53haFVTHfUVRx+2qoEom3f379xdWVlZCJpOJWrVqif79+4vr168LIYRISEgQHTp0ECYmJkIulwsHBwcxceJEkZqaqrSOmzdvih49eggdHR1hamoqvvrqK5GVlaVUJjIyUjg7OwuZTCbs7e3F+vXr31UVlezZs0c0bdpUyOVy0ahRI7F69Wql+Tk5OeK7774TFhYWQi6Xi65du4qrV68qlXn48KEYOHCg0NfXF4aGhsLHx0c8efJEqczff/8t2rVrJ+RyuahVq5aYN2+eyuuWn8Lq++zZM9GtWzdhZmYmtLS0hI2NjRgxYkSeHVVlqi9VbrGxscLV1VUYGRkJbW1t0bhxYzF37lylH4GEqBzft+XLl4u6desKmUwmWrduLU6cOFHeIeWrsGOAEEI8f/5cfPnll6JGjRpCV1dXfPzxx+LevXvlGDFVF5WlDVV0kZGRAkCel5eXV3mHRipU3ONpVVDd9hVFHberAjUhhCjPK+1EREREREREVVW1eKabiIiIiIiIqDww6SYiIiIiIiJSESbdRERERERERCrCpJuIiIiIiIhIRZh0ExEREREREakIk24iIiIiIiIiFWHSTURERERERKQiTLqJiIiIqFo6evQoevXqBWtra6ipqWHXrl0lXsfWrVvh7OwMXV1d2NjY4Pvvvy/7QImoUmPSTURERETVUnp6Opo3b46goKBSLX/gwAEMGjQIo0aNwoULF7By5UosXrwYK1asKONIiagyUxNCiPIOgoiIiIioPKmpqWHnzp3w9PSUpmVkZOB///sfNm3ahJSUFDRt2hTz589Hp06dAACfffYZsrKysG3bNmmZ5cuXY8GCBUhISICamto7rgURVUS80k1ERERElA8/Pz/ExMRg8+bNOHfuHPr27Yvu3bvj2rVrAF4l5dra2krL6Ojo4L///sOtW7fKI2QiqoCYdBMRERERvSEhIQHr16/Htm3b0L59e9SrVw8TJkxAu3btsH79egCAQqHAjh07EBERgZycHPzzzz9YuHAhAODevXvlGT4RVSCa5R0AEREREVFFc/78eWRnZ6NBgwZK0zMyMlCzZk0AwIgRI3Djxg18+OGHyMrKgqGhIcaNG4fp06dDXZ3XtojoFSbdRERERERvePr0KTQ0NBAbGwsNDQ2lefr6+gBePQc+f/58zJ07F4mJiTAzM0NERAQAwN7e/p3HTEQVE5NuIiIiIqI3tGjRAtnZ2UhOTkb79u0LLauhoYFatWoBADZt2gQ3NzeYmZm9izCJqBJg0k1ERERE1dLTp09x/fp16X18fDzi4uJgYmKCBg0aYNCgQRgyZAgWLlyIFi1a4P79+4iIiECzZs3g4eGBBw8eYPv27ejUqRNevHghPQMeHR1djrUiooqGQ4YRERERUbUUFRWFzp0755nu5eWFkJAQZGVlYfbs2fj5559x584dmJqaok2bNpgxYwacnJzw4MED9OrVC+fPn4cQAm5ubpgzZw5cXV3LoTZEVFEx6SYiIiIiIiJSEXarSERERERERKQiTLqJiIiIiIiIVIRJNxEREREREZGKMOkmIiIiIiIiUhEm3UREREREREQqwqSbiIiIiIiISEWYdBMRERERERGpCJNuIiIiIiIiIhVh0k1ERERERESkIky6iYiIiIiIiFSESTcRERERERGRijDpJiIiIiIiIlIRJt1EREREREREKsKkm4iIiIiIiEhFmHQTERERERERqQiTbiIiIiIiIiIVYdJNREREREREpCJMuomIKI+bN29CTU0NISEh5R0KUaXy119/4f3334eenh7U1NQQFxdX3iEVy/Tp06GmpoYHDx6U2Tq9vb1ha2tb6mX19fXLLBaqnmxtbeHt7V1m64uKioKamhqioqLKbJ1v423aGL1bTLqpxC5duoTp06fj5s2b5R0KERFRhZGVlYW+ffvi0aNHWLx4MX755RfY2NiUd1hK5s6di127dpV3GGXm2bNnmD59eoVJgogqgz179qBjx44wNzeHrq4u7O3t0a9fP4SFhQEAFi1aBDU1NRw+fLjAdaxZswZqamr4+eefoampicGDBxdY9smTJ9DR0cEnn3wCAAgJCYGamhpOnz5dthWrwJh0U4ldunQJM2bMYNJNRET0mhs3buDWrVuYMGECRo4cicGDB6NGjRrlHZaSd5l0r1mzBlevXlXpNp49e4YZM2Yw6SYqph9++AEfffQR1NTUMGXKFCxevBh9+vTBtWvXsHnzZgDAgAEDoK6ujtDQ0ALXExoaipo1a2LgwIH44IMP8Pvvv+PZs2f5lt2xYwdevHhRaGJe1WmWdwBEREREVUFycjIAwNjYuEzWl56eDj09vTJZV3nQ0tIq7xCIKrUXL15AJpNBXb1srpO+fPkSs2bNwgcffIBDhw7lmZ+7D7O2tkbnzp2xY8cOrFq1CnK5XKncnTt3cPToUYwcORJaWloYNGgQwsLCsHv3bgwYMCDPekNDQ2FkZAQPD48yqUdlxCvdVcSdO3cwdOhQWFhYQC6Xo0mTJli3bp00PzMzE1OnToWLiwuMjIygp6eH9u3bIzIyMs+6Nm/eDBcXFxgYGMDQ0BBOTk5YunQpgFe3g/Tt2xcA0LlzZ6ipqSk922Jra4sPP/wQx44dQ+vWraGtrQ17e3v8/PPPebaTkpICf39/1KlTB3K5HA4ODpg/fz5ycnKKHQ/w6na+GTNmoH79+tDW1kbNmjXRrl07hIeHv/XnSlRVFbXPKMiVK1fw6aefwsTEBNra2mjVqhV2794tzT99+jTU1NSwYcOGPMsePHgQampq2Lt3b5nWhagi8Pb2RseOHQEAffv2hZqaGjp16gQAOHLkCNq3bw89PT0YGxujd+/euHz5stLyuc9UX7p0CZ999hlq1KiBdu3aSfN//fVXuLi4QEdHByYmJhgwYABu376ttI5r166hT58+sLS0hLa2NmrXro0BAwYgNTUVAKCmpob09HRs2LBBOn6/+bxrSkoKvL29YWxsDCMjI/j4+OR79ao48eT3vOnDhw/x+eefw9DQEMbGxvDy8sLff/9dYB8Sd+7cgaenJ/T19WFmZoYJEyYgOzsbwKu+J8zMzAAAM2bMkOo0ffr0vH8gqnKioqLQqlUraGtro169evjxxx+ldlSQR48eYcKECXBycoK+vj4MDQ3Ro0cP/P3333nK/vfff/D09ISenh7Mzc0xfvx4ZGRklDjO4m4z93nxzZs349tvv0WtWrWgq6uLtLQ0AMCuXbvQtGlTaGtro2nTpti5c2eJY3nw4AHS0tLQtm3bfOebm5tL/x88eDBSU1Oxb9++POU2b96MnJwcDBo0CADw8ccfQ09PL98r48nJyYiIiMCnn36aJ3kvidzPZ+vWrZgxYwZq1aoFAwMDfPrpp0hNTUVGRgb8/f1hbm4OfX19+Pj45Pl7rV+/Hl26dIG5uTnkcjkcHR2xatUqpTJHjhyBuro6pk6dqjQ9NDQUampqecoXF690VwFJSUlo06YN1NTU4OfnBzMzMxw4cADDhg1DWloa/P39kZaWhp9++gkDBw7EiBEj8OTJE6xduxYKhQKnTp2Cs7MzACA8PBwDBw5E165dMX/+fADA5cuX8eeff2LcuHHo0KEDxo4di2XLluGbb75B48aNAUD6FwCuX7+OTz/9FMOGDYOXlxfWrVsHb29vuLi4oEmTJgBe3Q7WsWNH3LlzB1988QXq1q2L48ePY8qUKbh37x6WLFlSrHiAVycqgYGBGD58OFq3bo20tDScPn0aZ86cwQcffPAu/gRElUpx9hn5uXjxItq2bYtatWph8uTJ0NPTw9atW+Hp6YnffvsNH3/8MVq1agV7e3ts3boVXl5eSstv2bIFNWrUgEKheAe1JHq3vvjiC9SqVQtz587F2LFj8d5778HCwgKHDx9Gjx49YG9vj+nTp+P58+dYvnw52rZtizNnzuRJSvv27Yv69etj7ty5EEIAAObMmYPvvvsO/fr1w/Dhw3H//n0sX74cHTp0wNmzZ2FsbIzMzEwoFApkZGRgzJgxsLS0xJ07d7B3716kpKTAyMgIv/zyi3SsHDlyJACgXr16Stvv168f7OzsEBgYiDNnzuCnn36Cubm5dAwubjz5ycnJQa9evXDq1CmMHj0ajRo1wu+//55nX5ErOzsbCoUCrq6u+OGHH3D48GEsXLgQ9erVw+jRo2FmZoZVq1Zh9OjR+Pjjj6XnRZs1a1aaPyFVImfPnkX37t1hZWWFGTNmIDs7GzNnzpR+hCnIv//+i127dqFv376ws7NDUlISfvzxR3Ts2BGXLl2CtbU1AOD58+fo2rUrEhISMHbsWFhbW+OXX37BkSNHShxrcbeZa9asWZDJZJgwYQIyMjIgk8lw6NAh9OnTB46OjggMDMTDhw/h4+OD2rVrlygWc3Nz6OjoYM+ePRgzZgxMTEwKLPvJJ59g9OjRCA0NldpWrtDQUNjY2EjJu56eHnr37o3t27fj0aNHSuvdsmULsrOzpQT9bQUGBkJHRweTJ0/G9evXsXz5cmhpaUFdXR2PHz/G9OnTceLECYSEhMDOzk4peV61ahWaNGmCjz76CJqamtizZw++/PJL5OTkwNfXFwDQpUsXfPnllwgMDISnpydatmyJe/fuYcyYMXB3d8eoUaNKF7igSm/YsGHCyspKPHjwQGn6gAEDhJGRkXj27Jl4+fKlyMjIUJr/+PFjYWFhIYYOHSpNGzdunDA0NBQvX74scHvbtm0TAERkZGSeeTY2NgKAOHr0qDQtOTlZyOVy8dVXX0nTZs2aJfT09MQ///yjtPzkyZOFhoaGSEhIKHY8zZs3Fx4eHgXOJyJlxdlnxMfHCwBi/fr10vyuXbsKJycn8eLFC2laTk6OeP/990X9+vWlaVOmTBFaWlri0aNH0rSMjAxhbGystL8hqmoiIyMFALFt2zZpmrOzszA3NxcPHz6Upv39999CXV1dDBkyRJo2bdo0AUAMHDhQaZ03b94UGhoaYs6cOUrTz58/LzQ1NaXpZ8+ezbPt/Ojp6QkvL68803O3/2Yb/fjjj0XNmjVLHI8QQnh5eQkbGxvp/W+//SYAiCVLlkjTsrOzRZcuXfLsb7y8vAQAMXPmTKXttGjRQri4uEjv79+/LwCIadOmFVpvqlp69eoldHV1xZ07d6Rp165dE5qamuL19MbGxkbp+/7ixQuRnZ2ttK74+Hghl8uVvmtLliwRAMTWrVulaenp6cLBwaHAc+CCFHebufsPe3t78ezZM6Xyzs7OwsrKSqSkpEjTDh06JAAotbHimDp1qgAg9PT0RI8ePcScOXNEbGxsvmX79u0rtLW1RWpqqjTtypUrAoCYMmWKUtl9+/YJAOLHH39Umt6mTRtRq1Ytpc9g/fr1AoD466+/ih137ufTtGlTkZmZKU0fOHCgUFNTEz169FAq7+bmluezefNzFUIIhUIh7O3tlabl/q2bNGkiXrx4ITw8PIShoaG4detWseN9E28vr+SEEPjtt9/Qq1cvCCHw4MED6aVQKJCamoozZ85AQ0MDMpkMwKtfmh89eoSXL1+iVatWOHPmjLQ+Y2NjpKenv9Wt2Y6Ojmjfvr303szMDA0bNsS///4rTdu2bRvat2+PGjVqKMXs7u6O7OxsHD16tNjxGBsb4+LFi7h27VqpYyaqLoq7z3jTo0ePcOTIEfTr1w9PnjyRlnn48CEUCgWuXbuGO3fuAAD69++PrKws7NixQ1r+0KFDSElJQf/+/d9ZXYnK27179xAXFwdvb2+lKz/NmjXDBx98gP379+dZ5s2rKDt27EBOTg769eun1F4tLS1Rv3596TExIyMjAK8e4yioM6PieHP77du3x8OHD6VbXIsbT37CwsKgpaWFESNGSNPU1dWlK0zFjef18wmqfrKzs3H48GF4enoqXSV2cHBAjx49Cl1WLpdLz0dnZ2fj4cOH0NfXR8OGDZWOffv374eVlRU+/fRTaZqurq50h0hJFHebuby8vKCjoyO9z92PeHl5Se0cAD744AM4OjqWOJ4ZM2YgNDQULVq0wMGDB/G///0PLi4uaNmyZZ7HXgYPHowXL14oHc9zbyF/88p1t27dYGZmpnSLeXx8PE6cOIGBAweW2XPpQ4YMUeovwtXVFUIIDB06VKmcq6srbt++jZcvX0rTXv9cU1NT8eDBA3Ts2BH//vuv9BgO8OpvHRISgsuXL6NDhw7Yt28fFi9ejLp165Y6bibdldz9+/eRkpKC1atXw8zMTOnl4+MD4P86RdiwYQOaNWsmPfdsZmaGffv2KX3JvvzySzRo0AA9evRA7dq1MXToUGn4gOLK7wtZo0YNPH78WHp/7do1hIWF5YnZ3d1dKebixDNz5kykpKSgQYMGcHJywsSJE3Hu3LkSxUxUXZRkn/G669evQwiB7777Ls9y06ZNU1quefPmaNSoEbZs2SItv2XLFpiamqJLly7voJZEFcOtW7cAAA0bNswzr3Hjxnjw4AHS09OVptvZ2Sm9v3btGoQQqF+/fp62d/nyZand2dnZISAgAD/99BNMTU2hUCgQFBSkdIwvjjeP4bm9r+cew4sbT0Gfh5WVFXR1dZWmOzg45FteW1s7z+3Cb55PUPWTnJyM58+f5/u9Kei7lCsnJweLFy9G/fr1IZfLYWpqCjMzM5w7d06prdy6dQsODg55ng/Pry0XpbjbzPXmPiB3P1K/fv08ZUsTDwAMHDgQf/zxBx4/foxDhw7hs88+w9mzZ9GrVy+8ePFCKtejRw+YmJgoJdKbNm1C8+bNpUdGc2lqaqJ///74448/pB/hC0rQ38ab+6jcHyLq1KmTZ3pOTo7SZ/znn3/C3d1d6l/DzMwM33zzDQDk+Vu0bdsWo0ePxqlTp6BQKPIk9SXFZ7orudxOxwYPHlzgM1HNmjXDr7/+Cm9vb3h6emLixIkwNzeHhoYGAgMDcePGDamsubk54uLicPDgQRw4cAAHDhzA+vXrMWTIkHw7RsqPhoZGvtPF/382LTfuDz74AJMmTcq3bIMGDYodT4cOHXDjxg38/vvvOHToEH766ScsXrwYwcHBGD58eLFiJqouirvPePNKWe5yEyZMKPCZ7NdPdvr37485c+bgwYMHMDAwwO7duzFw4EBoavKwQ1SY16/EAK/anpqaGg4cOJDv8VVfX1/6/8KFC+Ht7S0dD8eOHYvAwECcOHGi2M9+FnUML0k8b6ugWIhKa+7cufjuu+8wdOhQzJo1CyYmJlBXV4e/v3+ejnzLa5tv7gNUydDQEB988AE++OADaGlpYcOGDTh58qTUKaSWlhb69euHNWvWICkpCQkJCbh27RoWLFiQ7/oGDx6MFStWYNOmTZgwYQI2bdoER0dHqe+oslDQfqGofdeNGzfQtWtXNGrUCIsWLUKdOnUgk8mwf/9+LF68OM/fIiMjQ+oo+saNG3j27FmeHwxLgmc/lZyZmRkMDAyQnZ0tXSXOz/bt22Fvb48dO3Yo/WqXe4XqdTKZDL169UKvXr2Qk5ODL7/8Ej/++CO+++67fH/1K4169erh6dOnhcZc3HgAwMTEBD4+PvDx8cHTp0/RoUMHTJ8+nUk30RuKu8+4efOm0nt7e3sArw7AxWm3/fv3x4wZM/Dbb7/BwsICaWlp+Q4jQlSV2djYAEC+Y1VfuXIFpqamRQ4JVq9ePQghYGdnJ/0gXRgnJyc4OTnh22+/xfHjx9G2bVsEBwdj9uzZAPDWx/CSxvM6GxsbREZG5jl5vX79eqnjKYtzEqpczM3Noa2tne/3pqjv0vbt29G5c2esXbtWaXpKSgpMTU2l9zY2Nrhw4QKEEErfsdKMO1/cbRYkdz+S32OUpYmnIK1atcKGDRtw7949pemDBg1CcHAwtmzZgvj4eKipqWHgwIH5rsPV1RX16tVDaGgoPvjgA1y8eBFz5swpsxjfxp49e5CRkYHdu3crXS0v6JGYadOm4fLly/jhhx/w9ddfY/LkyVi2bFmpt8/byys5DQ0N9OnTB7/99hsuXLiQZ/79+/elcoDy1eaTJ08iJiZGqfzDhw+V3qurq0u9gOZ2u597gpCSklLquPv164eYmBgcPHgwz7yUlBTp+YvixPNmGX19fTg4OJRqWAeiqq64+4w3mZubo1OnTvjxxx/zHJDzW65x48ZwcnLCli1bsGXLFlhZWaFDhw5lUwmiSsLKygrOzs7YsGGD0jHzwoULOHToEHr27FnkOj755BNoaGhgxowZSsdw4NUxPfcYmJaWpvTsIvAqAVdXV1c6Hurp6b3V8bu48eRHoVAgKysLa9askabl5OQgKCio1PHkJu9vUyeqXDQ0NODu7o5du3bh7t270vTr16/jwIEDRS775vd227Zt0u3QuXr27Im7d+9i+/bt0rRnz55h9erVpYq3ONssyOv7kddvgQ4PD8elS5dKFMuzZ8/ynPvnyv3s3rxlvW3btrC1tcWvv/6KLVu2oGPHjoXeOTNo0CCcPXsW06ZNg5qaGj777LMSxagq+eVCqampWL9+fZ6yJ0+exA8//AB/f3989dVXmDhxIlasWIHo6OhSb59XuquAefPmITIyEq6urhgxYgQcHR3x6NEjnDlzBocPH8ajR4/w4YcfYseOHfj444/h4eGB+Ph4BAcHw9HREU+fPpXWNXz4cDx69AhdunRB7dq1cevWLSxfvhzOzs7SsGDOzs7Q0NDA/PnzkZqaCrlcLo15V1wTJ07E7t278eGHH0rDiaWnp+P8+fPYvn07bt68CVNT02LF4+joiE6dOsHFxQUmJiY4ffo0tm/fDj8/v7L9oImqiOLsM/ITFBSEdu3awcnJCSNGjIC9vT2SkpIQExOD//77L8+Yo/3798fUqVOhra2NYcOGlVknKkSVyffff48ePXrAzc0Nw4YNk4YMMzIyKtZ40vXq1cPs2bMxZcoU3Lx5E56enjAwMEB8fDx27tyJkSNHYsKECThy5Aj8/PzQt29fNGjQAC9fvsQvv/wi/dCWy8XFBYcPH8aiRYtgbW0NOzs7uLq6Frs+xY0nP56enmjdujW++uorXL9+HY0aNcLu3bulfU5prlrr6OjA0dERW7ZsQYMGDWBiYoKmTZuiadOmJV4XVR7Tp0/HoUOHpOdus7OzsWLFCjRt2hRxcXEFLvfhhx9i5syZ8PHxwfvvv4/z589j48aN0t1cuUaMGIEVK1ZgyJAhiI2NhZWVFX755ZdS3V5c3G0WJjAwEB4eHmjXrh2GDh2KR48eYfny5WjSpInSeXxRnj17hvfffx9t2rRB9+7dUadOHaSkpGDXrl34448/4OnpiRYtWigtk5s4z507F8CrvpQKM3jwYMycORO///67lLBXBN26dZPunv3iiy/w9OlTrFmzBubm5koXE168eAEvLy/Ur19fuko/Y8YM7NmzBz4+Pjh//nyRdyjlq9T9nlOFkpSUJHx9fUWdOnWElpaWsLS0FF27dhWrV68WQrwa1mfu3LnCxsZGyOVy0aJFC7F37948w3ls375ddOvWTZibmwuZTCbq1q0rvvjiC3Hv3j2l7a1Zs0bY29sLDQ0NpaETbGxs8h2+q2PHjqJjx45K0548eSKmTJkiHBwchEwmE6ampuL9998XP/zwgzQUQHHimT17tmjdurUwNjYWOjo6olGjRmLOnDlKwwkQkbKi9hn5DRkmhBA3btwQQ4YMEZaWlkJLS0vUqlVLfPjhh2L79u15tnHt2jUBQAAQx44dexfVIipX+Q0ZJoQQhw8fFm3bthU6OjrC0NBQ9OrVS1y6dEmpTO6QXffv38933b/99pto166d0NPTE3p6eqJRo0bC19dXXL16VQghxL///iuGDh0q6tWrJ7S1tYWJiYno3LmzOHz4sNJ6rly5Ijp06CB0dHQEAGk4pYK2nzu0T3x8fIniESLvkGFCvBri67PPPhMGBgbCyMhIeHt7iz///FMAEJs3b1ZaVk9PL8/nkBvn644fPy5cXFyETCbj8GHVSEREhGjRooWQyWSiXr164qeffhJfffWV0NbWlsrkN2TYV199JaysrISOjo5o27atiImJyfc89datW+Kjjz4Surq6wtTUVIwbN06EhYWVasiw4myzoP1Hrt9++000btxYyOVy4ejoKHbs2JFvGytMVlaWWLNmjfD09JRyAl1dXdGiRQvx/fff5xleONfFixcFACGXy8Xjx4+L3M57770nAIiVK1fmO/9thgx78/MpaF357dN2794tmjVrJrS1tYWtra2YP3++WLdundI+bvz48UJDQ0OcPHlSaX2nT58WmpqaYvTo0cWO+XVqQrxxvwMRERER0Tuya9cufPzxxzh27Bjatm1b3uFQJebp6clhZKlC4r1+RERERPROPH/+XOl9dnY2li9fDkNDQ7Rs2bKcoqLK6M3v0rVr17B//3506tSpfAIiKgSf6SYiIiKid2LMmDF4/vw53NzckJGRgR07duD48eOYO3fuOx0qiSo/e3t7eHt7w97eHrdu3cKqVasgk8kKHI62rD1//jzfcbZfZ2JiAplM9k7iefToETIzMwucr6GhkWfc+4ogMzOzwL5kchkZGVX6/QNvLyciIiKidyI0NBQLFy7E9evX8eLFCzg4OGD06NHs/JRKzMfHB5GRkUhMTIRcLoebmxvmzp37zu6YCAkJgY+PT6FlIiMj39mV906dOhXau7aNjU2e4UArgqioKHTu3LnQMuvXr4e3t/e7CUhFmHQTERERERGVwL1793Dx4sVCy7i4uKBGjRrvJJ7Y2Fg8fvy4wPk6OjoVss+Ex48fIzY2ttAyTZo0gZWV1TuKSDWYdBMRERERERGpSLV+pjsnJwd3796FgYFBqcaGJFI1IQSePHkCa2trjnFcCmzjVNGxjb8dtnGq6NjG3w7bOFV0xW3j1Trpvnv3LurUqVPeYRAV6fbt26hdu3Z5h1HpsI1TZcE2Xjps41RZsI2XDts4VRZFtfFqnXQbGBgAePUhGRoalnM0RHmlpaWhTp060neVSoZtnCo6tvG3wzZOFR3b+NthG6eKrrhtvFon3bm3qRgaGrIhU4XGW6pKh22cKgu28dJhG6fKgm28dNjGqbIoqo3z4RIiIiIiIiIiFWHSTURERERERKQiTLqJiIiIiIiIVKRaP9NN+bOdvK/Uy96c51GGkRBVXW/TzgC2NSKqenj+QZUJj+NUErzSTURERERERKQiTLqJiIiIiIiIVIRJNxEREREREZGKMOkmIiIiIiIiUhEm3UREREREREQqwqSbiIiIiIiISEWYdBMRERERERGpyFsl3fPmzYOamhr8/f2laS9evICvry9q1qwJfX199OnTB0lJSUrLJSQkwMPDA7q6ujA3N8fEiRPx8uVLpTJRUVFo2bIl5HI5HBwcEBISkmf7QUFBsLW1hba2NlxdXXHq1Km3qQ4RERERERFRmSp10v3XX3/hxx9/RLNmzZSmjx8/Hnv27MG2bdsQHR2Nu3fv4pNPPpHmZ2dnw8PDA5mZmTh+/Dg2bNiAkJAQTJ06VSoTHx8PDw8PdO7cGXFxcfD398fw4cNx8OBBqcyWLVsQEBCAadOm4cyZM2jevDkUCgWSk5NLWyUiIiIiIiKiMlWqpPvp06cYNGgQ1qxZgxo1akjTU1NTsXbtWixatAhdunSBi4sL1q9fj+PHj+PEiRMAgEOHDuHSpUv49ddf4ezsjB49emDWrFkICgpCZmYmACA4OBh2dnZYuHAhGjduDD8/P3z66adYvHixtK1FixZhxIgR8PHxgaOjI4KDg6Grq4t169a9zedBREREREREVGZKlXT7+vrCw8MD7u7uStNjY2ORlZWlNL1Ro0aoW7cuYmJiAAAxMTFwcnKChYWFVEahUCAtLQ0XL16Uyry5boVCIa0jMzMTsbGxSmXU1dXh7u4ulclPRkYG0tLSlF5EREREREREqqJZ0gU2b96MM2fO4K+//sozLzExETKZDMbGxkrTLSwskJiYKJV5PeHOnZ87r7AyaWlpeP78OR4/fozs7Ox8y1y5cqXA2AMDAzFjxhgyquoAAHjsSURBVIziVZSIiIiIiIjoLZXoSvft27cxbtw4bNy4Edra2qqKSWWmTJmC1NRU6XX79u3yDomIiIiIiIiqsBIl3bGxsUhOTkbLli2hqakJTU1NREdHY9myZdDU1ISFhQUyMzORkpKitFxSUhIsLS0BAJaWlnl6M899X1QZQ0ND6OjowNTUFBoaGvmWyV1HfuRyOQwNDZVeRERERERVEUcaIqoYSpR0d+3aFefPn0dcXJz0atWqFQYNGiT9X0tLCxEREdIyV69eRUJCAtzc3AAAbm5uOH/+vFIv4+Hh4TA0NISjo6NU5vV15JbJXYdMJoOLi4tSmZycHEREREhliIiIiIiqK440RFRxlCjpNjAwQNOmTZVeenp6qFmzJpo2bQojIyMMGzYMAQEBiIyMRGxsLHx8fODm5oY2bdoAALp16wZHR0d8/vnn+Pvvv3Hw4EF8++238PX1hVwuBwCMGjUK//77LyZNmoQrV65g5cqV2Lp1K8aPHy/FEhAQgDVr1mDDhg24fPkyRo8ejfT0dPj4+JThx0NERFR1rFq1Cs2aNZPu9nJzc8OBAwek+bwCRlQ1cKQhooql1ON0F2Tx4sX48MMP0adPH3To0AGWlpbYsWOHNF9DQwN79+6FhoYG3NzcMHjwYAwZMgQzZ86UytjZ2WHfvn0IDw9H8+bNsXDhQvz0009QKBRSmf79++OHH37A1KlT4ezsjLi4OISFheXpXI2Iio8n5ERVW+3atTFv3jzExsbi9OnT6NKlC3r37i2NHsIrYERVA0caIqpYStx7+ZuioqKU3mtrayMoKAhBQUEFLmNjY4P9+/cXut5OnTrh7NmzhZbx8/ODn59fsWMlosLlnpDXr18fQghs2LABvXv3xtmzZ9GkSROMHz8e+/btw7Zt22BkZAQ/Pz988skn+PPPPwH83wm5paUljh8/jnv37mHIkCHQ0tLC3LlzAfzfCfmoUaOwceNGREREYPjw4bCyspJ+WMs9IQ8ODoarqyuWLFkChUKBq1evwtzcvNw+H6LKrlevXkrv58yZg1WrVuHEiROoXbs21q5di9DQUHTp0gUAsH79ejRu3BgnTpxAmzZtpCtghw8fhoWFBZydnTFr1ix8/fXXmD59OmQymdIVMABo3Lgxjh07hsWLF0tt/PUrYMCrq2b79u3DunXrMHny5Hf4iRBVPRxpiKjiKfMr3URUefXq1Qs9e/ZE/fr10aBBA8yZMwf6+vo4ceJEpbgljb+QExVfdnY2Nm/ejPT0dLi5uVX4K2AA2zhRUTjSEFHFxKSbiPJVGU/IAwMDYWRkJL3q1Knzdh8CURV0/vx56OvrQy6XY9SoUdi5cyccHR3f2RWwBw8eFHgFLHcdBWEbJyocRxoiqpiYdBORksp8Qs5fyImK1rBhQ8TFxeHkyZMYPXo0vLy8cOnSpfIOq1jYxokKx5GGiCqmt36mm4iqltwT8tTUVGzfvh1eXl6Ijo4u77CKRS6XS6MgEFH+ZDIZHBwcAAAuLi7466+/sHTpUvTv31+6Avb6j2tvXgF7s1PDkl4B09DQKNUVMIBtnKgouSMNve71kYYASCMNmZiYwNDQEGPGjClwpKEFCxYgMTEx35GGVqxYgUmTJmHo0KE4cuQItm7din379knbDQgIgJeXF1q1aoXWrVtjyZIlHGmIqi1e6SYiJbkn5C4uLggMDETz5s2xdOlSWFpaVuhb0oiodHJycpCRkQEXFxdeASOqBjjSENG7xyvdRFSo/E7I+/TpAyD/E/I5c+YgOTlZ6mU8vxPyN0cvKOiE3NPTU4ohIiKCoxUQvaUpU6agR48eqFu3Lp48eYLQ0FBERUXh4MGDMDIy4hUwoiqIIw0RlT8m3UQk4Qk5UdWWnJyMIUOG4N69ezAyMkKzZs1w8OBBfPDBBwBeXQFTV1dHnz59kJGRAYVCgZUrV0rL514BGz16NNzc3KCnpwcvL698r4CNHz8eS5cuRe3atfO9Anb//n1MnToViYmJcHZ25hUwIiKqsph0E5GEJ+REVdvatWsLnc8rYERERGWPSTcRSXhCTkRERERUttiRGhEREREREZGKMOkmIiIiIiIiUhEm3UREREREREQqwqSbiIiIiIiISEWYdBMRERERERGpCJNuIiIiIiIiIhVh0k1ERERERESkIky6iYiIiIiIiFSESTcRERERERGRijDpJiIiIiIiIlIRJt1EREREREREKqJZ3gFQ1WI7eV+pl705z6MMIyEiIiIiKtjbnLcSlQSvdBMRERERERGpCJNuIiIiIiIiIhVh0k1ERERERESkIky6iYiIiIiIiFSEHakREZUSO2AhIlLG/SIRUV680k1ERERERESkIky6iYiIiIiIiFSESTcRERERERGRijDpJiIiqiYCAwPx3nvvwcDAAObm5vD09MTVq1eVyrx48QK+vr6oWbMm9PX10adPHyQlJSmVSUhIgIeHB3R1dWFubo6JEyfi5cuXSmWioqLQsmVLyOVyODg4ICQkJE88QUFBsLW1hba2NlxdXXHq1KkyrzMREVF5Y9JNRBKekBNVbdHR0fD19cWJEycQHh6OrKwsdOvWDenp6VKZ8ePHY8+ePdi2bRuio6Nx9+5dfPLJJ9L87OxseHh4IDMzE8ePH8eGDRsQEhKCqVOnSmXi4+Ph4eGBzp07Iy4uDv7+/hg+fDgOHjwoldmyZQsCAgIwbdo0nDlzBs2bN4dCoUBycvK7+TCIiIjeESbdRCThCTlR1RYWFgZvb+//196dh1VRt/8Df7Odw6IHRD2AiohLImqSoHjUXJIko4XEJzMzXNI0sBBzocdcK8xKrUQxTfCbmkullhhKuKWiGWIiKo8Whi0HNAVcQeHz+8PfmRzZl8NZeL+ua67LM3OfmfuMc8/Mh1k+6Ny5M7p164b4+HhkZ2cjNTUVAJCfn4/PP/8cixcvxmOPPQZfX1/ExcXh8OHDOHLkCABg9+7dOH36NNatWwcfHx8MGTIECxYsQExMDIqKigAAsbGx8PT0xEcffYROnTohPDwcw4YNw5IlS6RcFi9ejPHjx2PMmDHw9vZGbGws7O3tsWbNmvpfMURERHrERjcRSUz9hLywsBAFBQWygYjKl5+fDwBwdnYGAKSmpuLOnTsICAiQYry8vNC6dWukpKQAAFJSUtC1a1e4uLhIMYGBgSgoKEBGRoYUc/88dDG6eRQVFSE1NVUWY2lpiYCAACmmLKxxIiIyReynm4jKVd0T8l69epV7Qj5p0iRkZGTgkUceKfeEPCIiAsC/J+RRUVHS9KqckEdHR2PevHm1/t2moDZ94V5YGFSHmZCpKikpQUREBPr06YMuXboAALRaLRQKBZycnGSxLi4u0Gq1Usz99a2brptWUUxBQQFu3bqFq1evori4uMyYs2fPlptzQ6pxIiIyH7zSTURlMuQJ+eXLl8s9IdfNoyxRUVHIz8+XhosXL1b/hxM1EGFhYTh16hQ2btxo6FSqjDVORESmiFe6iahMuhPygwcPGjqVKlMqlVAqlYZOg8johYeHY8eOHThw4ABatWoljXd1dUVRURHy8vJkf1zLycmBq6urFPPgSw11L1O8P+bBFyzm5ORApVLBzs4OVlZWsLKyKjNGN4+ysMaJiMgU8Uo3EZWiOyHfu3dvuSfk93vwhLysE2ndtIpidCfkzZo1q9EJORFVTAiB8PBwbN26FXv27IGnp6dsuq+vL2xsbJCcnCyNy8zMRHZ2NjQaDQBAo9EgPT1d9lLDpKQkqFQqeHt7SzH3z0MXo5uHQqGAr6+vLKakpATJyclSDBERkblgo5uIJDwhJzJvYWFhWLduHTZs2IDGjRtDq9VCq9Xi1q1bAABHR0eMGzcOkZGR2Lt3L1JTUzFmzBhoNBr06tULADB48GB4e3tj1KhR+OWXX7Br1y7MmjULYWFh0lXoiRMn4rfffsP06dNx9uxZLF++HJs3b8aUKVOkXCIjI7Fq1SqsXbsWZ86cwaRJk3Djxg2MGTOm/lcMERGRHvH2ciKShIWFYcOGDdi+fbt0Qg7cOxG3s7OTnZA7OztDpVJh8uTJ5Z6QL1q0CFqttswT8mXLlmH69OkYO3Ys9uzZg82bNyMh4d+Xg0VGRiI0NBR+fn7o2bMnli5dyhNyolpasWIFAGDAgAGy8XFxcRg9ejQAYMmSJbC0tERISAgKCwsRGBiI5cuXS7FWVlbYsWMHJk2aBI1GAwcHB4SGhmL+/PlSjKenJxISEjBlyhR8/PHHaNWqFVavXo3AwEApZvjw4bh06RJmz54NrVYLHx8fJCYmlnqXAxERkaljo5uIJDwhJzJvQohKY2xtbRETE4OYmJhyYzw8PLBz584K5zNgwACkpaVVGBMeHo7w8PBKcyIiIjJlbHQTkYQn5EREREREdataz3RHR0ejR48eaNy4MdRqNYKDg5GZmSmLuX37NsLCwtC0aVM0atQIISEhpV6GlJ2djaCgINjb20OtVmPatGm4e/euLGbfvn3o3r07lEol2rdvj/j4+FL5xMTEoE2bNrC1tYW/v3+pt6kSERERERERGVK1Gt379+9HWFgYjhw5gqSkJNy5cweDBw/GjRs3pJgpU6bgu+++w5YtW7B//3789ddfGDp0qDS9uLgYQUFBKCoqwuHDh7F27VrEx8dj9uzZUkxWVhaCgoIwcOBAnDhxAhEREXjllVewa9cuKWbTpk2IjIzEnDlzcPz4cXTr1g2BgYGylzcRERERERERGVK1bi9PTEyUfY6Pj4darUZqair69euH/Px8fP7559iwYQMee+wxAPeeBe3UqROOHDmCXr16Yffu3Th9+jR++OEHuLi4wMfHBwsWLMCMGTMwd+5cKBQKxMbGwtPTEx999BEAoFOnTjh48CCWLFkiPfO5ePFijB8/XnqpUmxsLBISErBmzRrMnDmz1iuGiIiIiIiIqLZq1WVYfn4+AMDZ2RkAkJqaijt37iAgIECK8fLyQuvWrZGSkgIASElJQdeuXWUvQwoMDERBQQEyMjKkmPvnoYvRzaOoqAipqamyGEtLSwQEBEgxZSksLERBQYFsICIiIiIyF3wclMj41LjRXVJSgoiICPTp0wddunQBAGi1WigUCjg5OcliXVxcpK6HtFptqbcP6z5XFlNQUIBbt27h8uXLKC4uLjNGN4+yREdHw9HRURrc3d2r/8OJiIiIiIwUHwclMj41bnSHhYXh1KlT2LhxY13mo1dRUVHIz8+XhosXLxo6JSIiIiKiOpOYmIjRo0ejc+fO6NatG+Lj45GdnY3U1FQAkB4HXbx4MR577DH4+voiLi4Ohw8fxpEjRwBAehx03bp18PHxwZAhQ7BgwQLExMSgqKgIAGSPg3bq1Anh4eEYNmwYlixZIuVy/+Og3t7eiI2Nhb29PdasWVP/K4bIgGrU6A4PD8eOHTuwd+9etGrVShrv6uqKoqIi5OXlyeJzcnLg6uoqxTx4+4ruc2UxKpUKdnZ2aNasGaysrMqM0c2jLEqlEiqVSjYQEREREZkrU3oclI+CkrmqVqNbCIHw8HBs3boVe/bsgaenp2y6r68vbGxskJycLI3LzMxEdnY2NBoNAECj0SA9PV12W0lSUhJUKhW8vb2lmPvnoYvRzUOhUMDX11cWU1JSguTkZCmGiIiIiKghM7XHQfkoKJmrajW6w8LCsG7dOmzYsAGNGzeGVquFVqvFrVu3AACOjo4YN24cIiMjsXfvXqSmpmLMmDHQaDTo1asXAGDw4MHw9vbGqFGj8Msvv2DXrl2YNWsWwsLCoFQqAQATJ07Eb7/9hunTp+Ps2bNYvnw5Nm/ejClTpki5REZGYtWqVVi7di3OnDmDSZMm4caNG9LbzImIiIiIGjJTexyUj4KSuapWl2ErVqwAAAwYMEA2Pi4uDqNHjwYALFmyBJaWlggJCUFhYSECAwOxfPlyKdbKygo7duzApEmToNFo4ODggNDQUMyfP1+K8fT0REJCAqZMmYKPP/4YrVq1wurVq6XuwgBg+PDhuHTpEmbPng2tVgsfHx8kJiaW+msaEREREVFDo3sc9MCBA+U+Dnr/1e4HHwd98C3j1X0c1MrKqtqPgyqVSukiHJE5qVajWwhRaYytrS1iYmIQExNTboyHhwd27txZ4XwGDBiAtLS0CmPCw8MRHh5eaU5EREREZL7azEyo1fcvLAyqo0wMTwiByZMnY+vWrdi3b1+Fj4OGhIQAKPtx0HfffRe5ublQq9UAyn4c9MHz+fIeBw0ODgbw7+OgPH+nhqZajW4iIiIiIjJeYWFh2LBhA7Zv3y49DgrcewzUzs5O9jios7MzVCoVJk+eXO7joIsWLYJWqy3zcdBly5Zh+vTpGDt2LPbs2YPNmzcjIeHfP4BERkYiNDQUfn5+6NmzJ5YuXcrHQalBYqObiIiIiMhM8HFQIuPDRjcRERERkZng46BExqdG/XQTERERERERUeXY6CYiIiIiIiLSEza6iYiIiIiIiPSEjW4iIiIiIiIiPWGjm4iIiIiIiEhP+PZyIiIiIiKietRmZkLlQeW4sDCoDjOh+sAr3UQkc+DAATz99NNo0aIFLCwssG3bNtl0IQRmz54NNzc32NnZISAgAOfOnZPFXLlyBSNHjoRKpYKTkxPGjRuH69evy2JOnjyJRx99FLa2tnB3d8eiRYtK5bJlyxZ4eXnB1tYWXbt2rbTrEiKqHGuciIiofrHRTUQyN27cQLdu3crtu3PRokX45JNPEBsbi6NHj8LBwQGBgYG4ffu2FDNy5EhkZGQgKSkJO3bswIEDBzBhwgRpekFBAQYPHgwPDw+kpqbigw8+wNy5c/HZZ59JMYcPH8aIESMwbtw4pKWlITg4GMHBwTh16pT+fjxRA8AaJyIiql8WQghh6CQMpaCgAI6OjsjPz4dKpTJ0OkajNre71AZvlSnN0NuohYUFtm7diuDgYAD3roC1aNECU6dOxZtvvgkAyM/Ph4uLC+Lj4/HCCy/gzJkz8Pb2xrFjx+Dn5wcASExMxJNPPok//vgDLVq0wIoVK/Df//4XWq0WCoUCADBz5kxs27YNZ8+eBQAMHz4cN27cwI4dO6R8evXqBR8fH8TGxlYp/8rWn6G2dUNjrRkP1rh+a5zqn6nuV/W1X+Q2Wjv6Xn/cXqm2qrqN8ko3EVVZVlYWtFotAgICpHGOjo7w9/dHSkoKACAlJQVOTk7SyTgABAQEwNLSEkePHpVi+vXrJ52MA0BgYCAyMzNx9epVKeb+5ehidMspS2FhIQoKCmQDEVUda5yIiKjusdFNRFWm1WoBAC4uLrLxLi4u0jStVgu1Wi2bbm1tDWdnZ1lMWfO4fxnlxeimlyU6OhqOjo7S4O7uXt2fSNSgscaJiIjqHhvdRGQ2oqKikJ+fLw0XL140dEpEVIdY40REZIrY6CaiKnN1dQUA5OTkyMbn5ORI01xdXZGbmyubfvfuXVy5ckUWU9Y87l9GeTG66WVRKpVQqVSygYiqjjVORERU99hPtxky1ZdCkPHz9PSEq6srkpOT4ePjA+DeCySOHj2KSZMmAQA0Gg3y8vKQmpoKX19fAMCePXtQUlICf39/Kea///0v7ty5AxsbGwBAUlISOnbsiCZNmkgxycnJiIiIkJaflJQEjUZTT7+WqOFhjRMREdU9XukmIpnr16/jxIkTOHHiBIB7L1Y6ceIEsrOzYWFhgYiICLzzzjv49ttvkZ6ejpdffhktWrSQ3n7cqVMnPPHEExg/fjx++uknHDp0COHh4XjhhRfQokULAMCLL74IhUKBcePGISMjA5s2bcLHH3+MyMhIKY833ngDiYmJ+Oijj3D27FnMnTsXP//8M8LDw+t7lRCZFdY4ERFR/eKVbiKS+fnnnzFw4EDps+4kOTQ0FPHx8Zg+fTpu3LiBCRMmIC8vD3379kViYiJsbW2l76xfvx7h4eEYNGgQLC0tERISgk8++USa7ujoiN27dyMsLAy+vr5o1qwZZs+eLevnt3fv3tiwYQNmzZqFt956Cx06dMC2bdvQpUuXelgLROaLNU5ERFS/2E+3GfadaKq3l7PPwdLMdRutL+ynu2ysNePBGq8drj/jY6r7VfbTbZzYT3fZeBw3Huynm4iIiIiIiMjA2OgmIiIiIiIi0hM2uomIiIiIiIj0hC9SIyIiIiIApvuMKxGRMWOjm4ioganNSTVf3kJERERUPWx0k9Go7V/X2RggIiIiIiJjw2e6iYiIiIiIiPSEjW4iIiIiIiIiPWGjm4iIiIiIiEhP2OgmIiIiIiIi0hO+SI2IiIioDHzTPxER1QU2uomIiMhssd9pIiIyNDa6iYiIiMwI/9BARGRc2OgmIiIiMjJsOBNReWq7f+DjL/WPjW4iIiKiOsZGMxER6bDRbaR4sCYiIiIiIjJ97DKMiIiIiIiISE94pZuIiKqMz5ERERERVQ+vdBMRERERERHpCa90k9mozRU4Xn0jIiIiIiJ94JVuIiIiIiIiIj0x+UZ3TEwM2rRpA1tbW/j7++Onn34ydEpEVIdY40TmjTVOZN5Y40Qmfnv5pk2bEBkZidjYWPj7+2Pp0qUIDAxEZmYm1Gq1odMjolpijZsfPgZC92ONE5k31rhx4rG4/lkIIYShk6gpf39/9OjRA8uWLQMAlJSUwN3dHZMnT8bMmTMr/X5BQQEcHR2Rn58PlUpV5/mxr+2GQZ87H31vo8ZO3zXOGm04jPUkgTWu/+M465z0raL9C2uc5+rmxliPp4ZS1W3UZK90FxUVITU1FVFRUdI4S0tLBAQEICUlpczvFBYWorCwUPqcn58P4N7KKkuXObvqMGMyV62nbKnV90/NCyx3mm7bNOG/jdVYfdR4SeHNOsyYjJk+67Q2WOP6rXGAdU76V9H2xxrXf43zfL1+1eZ4qq9jqSFVtcZNttF9+fJlFBcXw8XFRTbexcUFZ8+eLfM70dHRmDdvXqnx7u7uesmRqCocl1Yec+3aNTg6Ouo9F2PCGidjUpU6rQ3W+L9Y42RqeBwvG2ucHqTvY6khVVbjJtvoromoqChERkZKn0tKSnDlyhU0bdoUFhYWstiCggK4u7vj4sWLDfJ2oOri+qq66qwrIQSuXbuGFi1a1FN2pq06Nd7QsWarTp/rijVePdWtcVPdzk01b8B0c9dX3qzx6jH247ipbt+G0FDWVVVr3GQb3c2aNYOVlRVycnJk43NycuDq6lrmd5RKJZRKpWyck5NThctRqVRmvaHUNa6vqqvqumpofxnXqa8ab+hYs1Wnr3XFGtd/jZvqdm6qeQOmm7s+8maNm99x3FS3b0NoCOuqKjVusl2GKRQK+Pr6Ijk5WRpXUlKC5ORkaDQaA2ZGRHWBNU5k3ljjROaNNU70L5O90g0AkZGRCA0NhZ+fH3r27ImlS5fixo0bGDNmjKFTI6I6wBonMm+scSLzxhonusekG93Dhw/HpUuXMHv2bGi1Wvj4+CAxMbHUCxtqQqlUYs6cOaVucaGycX1VHddV1emzxhs6bodVx3WlP/qucVP9vzPVvAHTzd1U8zZ25nYc53ZSdVxXcibdTzcRERERERGRMTPZZ7qJiIiIiIiIjB0b3URERERERER6wkY3ERERERERkZ6w0U1ERERERESkJ2x03+fKlSsYOXIkVCoVnJycMG7cOFy/fr3C7wwYMAAWFhayYeLEifWUcf2JiYlBmzZtYGtrC39/f/z0008Vxm/ZsgVeXl6wtbVF165dsXPnznrK1DhUZ33Fx8eX2oZsbW3rMVsyB3Pnzi21HXl5eUnTb9++jbCwMDRt2hSNGjVCSEgIcnJyZPPIzs5GUFAQ7O3toVarMW3aNNy9e7e+f4peHDhwAE8//TRatGgBCwsLbNu2TTZdCIHZs2fDzc0NdnZ2CAgIwLlz52QxVTlGnDx5Eo8++ihsbW3h7u6ORYsW6funUTmqe9wyhLrYLg0hOjoaPXr0QOPGjaFWqxEcHIzMzExZTFX2OYawYsUKPPzww1CpVFCpVNBoNPj++++l6caaNxkHU9iv1KW6qvWqnF/s27cP3bt3h1KpRPv27REfH6/vn1e/BEmeeOIJ0a1bN3HkyBHx448/ivbt24sRI0ZU+J3+/fuL8ePHi7///lsa8vPz6ynj+rFx40ahUCjEmjVrREZGhhg/frxwcnISOTk5ZcYfOnRIWFlZiUWLFonTp0+LWbNmCRsbG5Genl7PmRtGdddXXFycUKlUsm1Iq9XWc9Zk6ubMmSM6d+4s244uXbokTZ84caJwd3cXycnJ4ueffxa9evUSvXv3lqbfvXtXdOnSRQQEBIi0tDSxc+dO0axZMxEVFWWIn1Pndu7cKf773/+Kb775RgAQW7dulU1fuHChcHR0FNu2bRO//PKLeOaZZ4Snp6e4deuWFFPZMSI/P1+4uLiIkSNHilOnTokvv/xS2NnZiZUrV9bXz6T/r7r7YUOpi+3SEAIDA0VcXJw4deqUOHHihHjyySdF69atxfXr16WYyvY5hvLtt9+KhIQE8b///U9kZmaKt956S9jY2IhTp04JIYw3bzI8U9mv1KW6qPWqnF/89ttvwt7eXkRGRorTp0+LTz/9VFhZWYnExMR6/b36xEb3/3f69GkBQBw7dkwa9/333wsLCwvx559/lvu9/v37izfeeKMeMjScnj17irCwMOlzcXGxaNGihYiOji4z/vnnnxdBQUGycf7+/uLVV1/Va57GorrrKy4uTjg6OtZTdmSu5syZI7p161bmtLy8PGFjYyO2bNkijTtz5owAIFJSUoQQ907+LS0tZX/wWbFihVCpVKKwsFCvude3Bxs3JSUlwtXVVXzwwQfSuLy8PKFUKsWXX34phKjaMWL58uWiSZMmsvU1Y8YM0bFjRz3/InpQdffDxqAm26WxyM3NFQDE/v37hRBV2+cYkyZNmojVq1ebXN5Uv0xxv1LXalLrVTm/mD59uujcubNsWcOHDxeBgYH6/kn1hreX/38pKSlwcnKCn5+fNC4gIACWlpY4evRohd9dv349mjVrhi5duiAqKgo3b97Ud7r1pqioCKmpqQgICJDGWVpaIiAgACkpKWV+JyUlRRYPAIGBgeXGm5OarC8AuH79Ojw8PODu7o5nn30WGRkZ9ZEumZlz586hRYsWaNu2LUaOHIns7GwAQGpqKu7cuSPbLr28vNC6dWtpu0xJSUHXrl3h4uIixQQGBqKgoMDst8esrCxotVrZ+nF0dIS/v79s/VR2jEhJSUG/fv2gUCikmMDAQGRmZuLq1av19GuopvthY1OV7dJY5OfnAwCcnZ0BVG2fYwyKi4uxceNG3LhxAxqNxmTypvpnLvuV2qpJrVfl/KIhtB3Y6P7/tFot1Gq1bJy1tTWcnZ2h1WrL/d6LL76IdevWYe/evYiKisIXX3yBl156Sd/p1pvLly+juLhYVigA4OLiUu560Wq11Yo3JzVZXx07dsSaNWuwfft2rFu3DiUlJejduzf++OOP+kiZzIS/vz/i4+ORmJiIFStWICsrC48++iiuXbsGrVYLhUIBJycn2Xfu3y7Lq1vdNHOm+30V1W1VjhENeR0ak5rsh41RVbZLY1BSUoKIiAj06dMHXbp0AYAq7XMMKT09HY0aNYJSqcTEiROxdetWeHt7G33eZDjmsl+pjZrWelWOjeXFFBQU4NatW/r4OfXO2tAJ6NvMmTPx/vvvVxhz5syZGs9/woQJ0r+7du0KNzc3DBo0CL/++ivatWtX4/lSw6HRaKDRaKTPvXv3RqdOnbBy5UosWLDAgJmRKRkyZIj074cffhj+/v7w8PDA5s2bYWdnZ8DMiMichYWF4dSpUzh48KChU6myjh074sSJE8jPz8dXX32F0NBQ7N+/39BpERk1U6x1Y2L2V7qnTp2KM2fOVDi0bdsWrq6uyM3NlX337t27uHLlClxdXau8PH9/fwDA+fPn6/R3GEqzZs1gZWVV6i2EOTk55a4XV1fXasWbk5qsrwfZ2NjgkUceMZttiAzDyckJDz30EM6fPw9XV1cUFRUhLy9PFnP/dlle3eqmmTPd76uobqtyjGjI69CY1MV+2BhUZbs0tPDwcOzYsQN79+5Fq1atpPFV2ecYkkKhQPv27eHr64vo6Gh069YNH3/8sdHnTYZjLvuVmqpNrVfl2FhejEqlMpsLB2bf6G7evDm8vLwqHBQKBTQaDfLy8pCamip9d8+ePSgpKZEa0lVx4sQJAICbm1td/xSDUCgU8PX1RXJysjSupKQEycnJsquz99NoNLJ4AEhKSio33pzUZH09qLi4GOnp6WazDZFhXL9+Hb/++ivc3Nzg6+sLGxsb2XaZmZmJ7OxsabvUaDRIT0+XNSyTkpKgUqng7e1d7/nXJ09PT7i6usrWT0FBAY4ePSpbP5UdIzQaDQ4cOIA7d+5IMUlJSejYsSOaNGlST7+G6mI/bAyqsl0aihAC4eHh2Lp1K/bs2QNPT0/Z9Krsc4xJSUkJCgsLTS5vqj/msl+prrqo9aqcXzSItoOh3+RmTJ544gnxyCOPiKNHj4qDBw+KDh06yLqD+eOPP0THjh3F0aNHhRBCnD9/XsyfP1/8/PPPIisrS2zfvl20bdtW9OvXz1A/QS82btwolEqliI+PF6dPnxYTJkwQTk5O0lsIR40aJWbOnCnFHzp0SFhbW4sPP/xQnDlzRsyZM6fBdRlWnfU1b948sWvXLvHrr7+K1NRU8cILLwhbW1uRkZFhqJ9AJmjq1Kli3759IisrSxw6dEgEBASIZs2aidzcXCHEvS49WrduLfbs2SN+/vlnodFohEajkb6v69Jj8ODB4sSJEyIxMVE0b97cbLoMu3btmkhLSxNpaWkCgFi8eLFIS0sTv//+uxDiXtdMTk5OYvv27eLkyZPi2WefLbPLsIqOEXl5ecLFxUWMGjVKnDp1SmzcuFHY29uzyzADqGw/bCzqYrs0hEmTJglHR0exb98+WTeFN2/elGIq2+cYysyZM8X+/ftFVlaWOHnypJg5c6awsLAQu3fvFkIYb95keKayX6lLdVHrVTm/0HUZNm3aNHHmzBkRExPDLsPM2T///CNGjBghGjVqJFQqlRgzZoy4du2aND0rK0sAEHv37hVCCJGdnS369esnnJ2dhVKpFO3btxfTpk0zu366hRDi008/Fa1btxYKhUL07NlTHDlyRJrWv39/ERoaKovfvHmzeOihh4RCoRCdO3cWCQkJ9ZyxYVVnfUVEREixLi4u4sknnxTHjx83QNZkyoYPHy7c3NyEQqEQLVu2FMOHDxfnz5+Xpt+6dUu89tprokmTJsLe3l4899xz4u+//5bN48KFC2LIkCHCzs5ONGvWTEydOlXcuXOnvn+KXuzdu1cAKDXoarGkpES8/fbbwsXFRSiVSjFo0CCRmZkpm0dlxwghhPjll19E3759hVKpFC1bthQLFy6sr59ID6hoP2ws6mK7NISycgYg4uLipJiq7HMMYezYscLDw0MoFArRvHlzMWjQIKnBLYTx5k3GwRT2K3Wprmq9KucXe/fuFT4+PkKhUIi2bdvKlmEOLIQQot4uqxMRERERERE1IGb/TDcRERERERGRobDRTURERERERKQnbHQTERERERER6Qkb3URERERERER6wkY3ERERERERkZ6w0U1ERERERESkJ2x0ExEREREREekJG91EREREREREesJGNxEREREREZGesNFNREREREREpCdsdBMRERERERHpCRvdRERERERERHrCRjcRERERERGRnrDRTURERERERKQnbHQTERERERER6Qkb3URERERERER6wkY3ERERERERkZ6w0U1ERERERESkJ2x0ExEREREREekJG91EREREREREesJGNxEREREREZGesNFNREREREREpCdsdBMRERERERHpCRvdRERERERERHrCRjcRERERERGRnrDRTURERERERKQnbHQTERERERER6Qkb3URERERERER6wkY3ERERERERkZ6w0U1ERERERESkJ2x0ExEREREREekJG91EREREREREesJGNxEREREREZGesNFNREREREREpCdsdBMRERERERHpCRvdRERERERERHrCRncNtWnTBqNHj66z+e3btw8WFhbYt29fnc2zNkaPHo02bdoYOo0GY8CAARgwYICh06BysN7pfnW9PRAZwty5c2FhYYHLly8bOhUiMmM8Zt7DRjfVqe+++w79+/eHWq2Gvb092rZti+effx6JiYkAgMWLF8PCwgI//PBDufNYtWoVLCws8H//93+wtrbGSy+9VG7stWvXYGdnh6FDhwIA4uPjYWFhgZ9//rlufxgRlcJ6JzJ+7733HrZt22boNIj0LiMjAy+99BJatmwJpVKJFi1aYOTIkcjIyJDF6Y4dusHa2hotW7bE6NGj8eeff5Y573379mHo0KFwdXWFQqGAWq3G008/jW+++UaKuXDhgmy+NjY2aNasGXr37o233noL2dnZZc73/u9YWVlBrVZj2LBhOHPmTJm5nDhxAi+99BLc3d2hVCrh7OyMgIAAxMXFobi4uErr6rXXXoOlpSWuXLkiG3/lyhVYWlpCqVTi9u3bsmm//fYbLCws8NZbb1VpGXWtZ8+esLCwwIoVKwyy/Npio5vqzIcffohnnnkGFhYWiIqKwpIlSxASEoJz585h48aNAIAXXngBlpaW2LBhQ7nz2bBhA5o2bYoRI0bg8ccfx/bt23Hz5s0yY7/55hvcvn27whN1Iqp7DbneMzMzsWrVKoPmQFRVbHRTQ/DNN9+ge/fuSE5OxpgxY7B8+XKMGzcOe/fuRffu3bF169ZS35k/fz6++OILxMbGYsiQIVi3bh369+9fqrE5Z84cDBw4EKdOncKrr76K2NhYTJs2DdevX0dISEipY9yIESPwxRdf4PPPP8fbb7+Ntm3bYunSpejUqZN0fHzQ66+/ji+++AKrV6/GyJEjkZCQgEcffRRarVYWt3r1avj5+WHv3r0YOXIkli9fjtmzZ8POzg7jxo3D+++/X6X11bdvXwghcOjQIdn4w4cPw9LSEnfu3Cn1B21dbN++fau0DJ26OGaeO3cOx44dQ5s2bbB+/fpazctgBNWIh4eHCA0NrbP57d27VwAQe/furbN5VuTWrVuiuLi43OmhoaHCw8OjyvO7c+eOUKlU4vHHHy9zek5OjvTvQYMGCUdHR3H79u1ScX/88YewtLQUEydOFEII8cUXXwgA4ssvvyxzvoMHD5bNKy4uTgAQx44dq3LuxqB///6if//+hk6DysF6l2O9Exm3kpIScfPmTSGEEA4ODmXuv+bMmSMAiEuXLtVzdkR16/z588Le3l54eXmJ3Nxc2bRLly4JLy8v4eDgIH799VchRPnHjhkzZggAYtOmTdK4LVu2CABi2LBhoqioqNSyExMTxXfffSeEECIrK0sAEB988EGpuAsXLoiHHnpIKBQKceLECWm87nxgy5YtsvgVK1YIAOL999+XxqWkpAgrKyvRt29fUVBQUGoZx44dE3FxceWtJpnff/9dABDTp0+XjZ85c6Z45JFHhJeXl4iOjpZNmzBhgrC0tBRXr16t0jLq0uzZs4VarRZff/21sLCwEFlZWfWew/Xr12v1fV7pLsO+ffvg5+cHW1tbtGvXDitXrpSefSrPlStX8Oabb6Jr165o1KgRVCoVhgwZgl9++aVU7B9//IHg4GA4ODhArVZjypQpKCwsrHaeVV2m7taVjRs3YtasWWjZsiXs7e1RUFAAANi2bRu6dOkCW1tbdOnSpcy/Blbm8uXLKCgoQJ8+fcqcrlarpX+/9NJLyM/PR0JCQqm4jRs3oqSkBCNHjgQAPPfcc3BwcCjzSllubi6Sk5MxbNgwKJXKaueso1s/mzdvxrx589CyZUs0btwYw4YNQ35+PgoLCxEREQG1Wo1GjRphzJgxZf5/rVu3Dr6+vrCzs4OzszNeeOEFXLx4sVTcZ599hnbt2sHOzg49e/bEjz/+WOPcqfZY76z36tb7g8+n6W5VPHToECIjI9G8eXM4ODjgueeew6VLl2qcK5kv3T7m/PnzGD16NJycnODo6IgxY8bI7vS4e/cuFixYgHbt2kGpVKJNmzZ46623ytwmn3rqKezatQt+fn6ws7PDypUrYWFhgRs3bmDt2rXS7asPPluZl5dXYQ4AYGFhgfDwcGn/oVQq0blzZ+lRkvv9+eefGDt2LFxcXKS4NWvWlIr79NNP0blzZ9jb26NJkybw8/OT1f61a9cQERGBNm3aQKlUQq1W4/HHH8fx48drssrJjH3wwQe4efMmPvvsMzRv3lw2rVmzZli5ciVu3LiBRYsWVTifRx99FADw66+/SuPefvttODs7Y82aNbCxsSn1ncDAQDz11FOV5ujh4YH4+HgUFRVVmkd5ucybNw8WFhZYv349GjduXOo7fn5+VX52unXr1nB3dy91pfvQoUPo06cPevfuXea0zp07w8nJCQBQWFiIOXPmoH379lAqlXB3d8f06dMrPWYCwMmTJ9G/f3/Y2dmhVatWeOeddxAXFwcLCwtcuHChVL4bNmzAsGHD8NRTT8HR0VG2r/jqq69gYWGB/fv3l/qebj946tQpadzZs2cxbNgwODs7w9bWFn5+fvj2229l39Md1/fv34/XXnsNarUarVq1Knd9VoV1rb5thtLS0vDEE0/Azc0N8+bNQ3FxMebPn1+qiB/022+/Ydu2bfjPf/4DT09P5OTkYOXKlejfvz9Onz6NFi1aAABu3bqFQYMGITs7G6+//jpatGiBL774Anv27Kl2rlVdps6CBQugUCjw5ptvorCwEAqFArt370ZISAi8vb0RHR2Nf/75B2PGjKn2hqVWq2FnZ4fvvvsOkydPhrOzc7mxQ4cOxaRJk7Bhwwbp2UydDRs2wMPDQzqZd3BwwLPPPouvvvoKV65ckc1306ZNKC4ulk7Yays6Ohp2dnaYOXMmzp8/j08//RQ2NjawtLTE1atXMXfuXBw5cgTx8fHw9PTE7Nmzpe++++67ePvtt/H888/jlVdewaVLl/Dpp5+iX79+SEtLk3ZQn3/+OV599VX07t0bERER+O233/DMM8/A2dkZ7u7udfI7qOpY76z3mtR7eSZPnowmTZpgzpw5uHDhApYuXYrw8HBs2rSpTnIm8/P888/D09MT0dHROH78OFavXg21Wi3dIvrKK69g7dq1GDZsGKZOnYqjR48iOjoaZ86cKfUHs8zMTIwYMQKvvvoqxo8fj44dO+KLL77AK6+8gp49e2LChAkAgHbt2lUrB52DBw/im2++wWuvvYbGjRvjk08+QUhICLKzs9G0aVMAQE5ODnr16iU10ps3b47vv/8e48aNQ0FBASIiIgDce5fD66+/jmHDhuGNN97A7du3cfLkSRw9ehQvvvgiAGDixIn46quvEB4eDm9vb/zzzz84ePAgzpw5g+7du9f5/wWZru+++w5t2rSRGqoP6tevH9q0aVPmH3/vp2vsNWnSBMC9W5rPnj2LsWPHltnIrS6NRoN27dohKSmp0tgHc7l58yaSk5PRr18/tG7duta5APduE//mm29QWFgIpVKJoqIiHDt2DJMmTcLNmzcxffp0CCFgYWGBq1ev4vTp05g4cSIAoKSkBM888wwOHjyICRMmoFOnTkhPT8eSJUvwv//9r8JHWv78808MHDhQejzNwcEBq1evLvcP6kePHsX58+cRFxcHhUKBoUOHYv369dKz5UFBQWjUqBE2b96M/v37y767adMmdO7cGV26dAFw77n/Pn36oGXLlpg5cyYcHBywefNmBAcH4+uvv8Zzzz0n+/5rr72G5s2bY/bs2bhx40ZNV/U9dXPB3Xw8/fTTwt7eXvz555/SuHPnzglra2tx/+p68HbT27dvl7p9MysrSyiVSjF//nxp3NKlSwUAsXnzZmncjRs3RPv27at9u2lVl6m7daVt27bS7WY6Pj4+ws3NTeTl5Unjdu/eLQBU63ZTIe7d+gFAODg4iCFDhoh3331XpKamlhn7n//8R9ja2or8/Hxp3NmzZwUAERUVJYtNSEgQAMTKlStl43v16iVatmwpWwc1ud1Ut366dOkiu3VoxIgRwsLCQgwZMkQWr9FoZOvmwoULwsrKSrz77ruyuPT0dGFtbS2NLyoqEmq1Wvj4+IjCwkIp7rPPPhMAeHu5AbDeWe86Va13IUpvD7o8AgICRElJiTR+ypQpwsrKSra+iYT499busWPHysY/99xzomnTpkIIIU6cOCEAiFdeeUUW8+abbwoAYs+ePdI4Dw8PAUAkJiaWWlZlt5dXlIMOAKFQKMT58+elcb/88osAID799FNp3Lhx44Sbm5u4fPmy7PsvvPCCcHR0lPZJzz77rOjcuXOpnO7n6OgowsLCKowhysvLEwDEs88+W2HcM888IwCIgoICaZ/9ww8/iEuXLomLFy+Kr776SjRv3lwolUpx8eJFIYQQ27dvFwDEkiVLqpRLRbeX6zz77LMCgHQ81B2T1qxZIy5duiT++usvkZiYKNq3by8sLCzETz/9JIT4t97eeOONKuVSFTExMQKA+PHHH4UQ925fByB+//13cfr0aQFAZGRkCCGE2LFjhwAg1q9fL4S49ziYpaWl9F2d2NhYAUAcOnRIGvfgMXPy5MnCwsJCpKWlSeP++ecf4ezsLACUunU8PDxcuLu7S8dX3XnL/d8fMWKEUKvV4u7du9K4v//+W1haWsrOkQYNGiS6du0qe+StpKRE9O7dW3To0EEap9tG+vbtK5tnbfD28vsUFxfjhx9+QHBwsOyqUfv27TFkyJAKv6tUKmFpaSnN559//kGjRo3QsWNH2a1QO3fuhJubG4YNGyaNs7e3l/4CXR1VXaZOaGgo7OzspM9///03Tpw4gdDQUDg6OkrjH3/8cXh7e1c7n3nz5mHDhg145JFHsGvXLvz3v/+Fr68vunfvXuoNjC+99BJu374te+uj7laRB69kDR48GM2bN5fdSpKVlYUjR45gxIgR0jqorZdffll265C/vz+EEBg7dqwszt/fHxcvXsTdu3cB3Ht5R0lJCZ5//nlcvnxZGlxdXdGhQwfs3bsXAPDzzz8jNzcXEydOhEKhkOY3evRo2fqn+sF6v4f1fk9V670iEyZMkD2W8Oijj6K4uBi///57neRM5kd31Ujn0UcfxT///IOCggLs3LkTABAZGSmLmTp1KgCUumrn6emJwMDAOs3hfgEBAbKr5A8//DBUKhV+++03AIAQAl9//TWefvppCCFkx8PAwEDk5+dL+yonJyf88ccfOHbsWLl5OTk54ejRo/jrr7+q/Zuo4bh27RoAVHolWjf9/u06ICAAzZs3h7u7O4YNGwYHBwd8++230t1futi6uMqt06hRI1neOmPHjkXz5s3RokULPPHEE8jPz8cXX3yBHj166C0X3QvRDh48CODe7eMtW7ZE69at4eXlBWdnZ+kW8wdforZlyxZ06tQJXl5eslp/7LHHAEA69y1LYmIiNBoNfHx8pHHOzs5l3sl29+5dbNq0CcOHD5eOr4899hjUarXshWrDhw9Hbm6urCvWr776CiUlJRg+fDiAe4/p7dmzB88//zyuXbsm5fzPP/8gMDAQ586dK/X2+vHjx8PKyqrylVkFbHTfJzc3F7du3UL79u1LTStr3P1KSkqwZMkSdOjQAUqlEs2aNUPz5s1x8uRJ5OfnS3G///472rdvX+p50Y4dO1Y736ouU8fT01P2WXci2KFDh1KxNckHuPfGxh9//BFXr17F7t278eKLLyItLQ1PP/207G2QQ4YMgbOzs+zE+ssvv0S3bt3QuXNn2Tytra0xfPhw/Pjjj1IxlHfCXhsP3q6ja5g8eNu3o6MjSkpKpHV87tw5CCHQoUMHNG/eXDacOXMGubm5AMpf3zY2Nmjbtm2d/Q6qGtZ77fIBGma9V2eeutsCr169WptUyYxVtM38/vvvsLS0LLU/cnV1hZOTU6k/5jxY83WRQ0Vxulhd3KVLl5CXlyc9V3v/MGbMGACQjoczZsxAo0aN0LNnT3To0AFhYWGlnh9dtGgRTp06BXd3d/Ts2RNz586VGvhEOrpG6ION2AeV1TiPiYlBUlISvvrqKzz55JO4fPmy7BZnlUpVpXlXx/Xr10vlAQCzZ89GUlIStm7dipdffhn5+fmyPzLrI5cuXbrAyclJ1rDWPe5lYWEBjUYjm+bu7i7tB86dO4eMjIxStf7QQw8B+LfWy6I7N3pQWeN2796NS5cuoWfPnjh//jzOnz+PrKwsDBw4EF9++SVKSkoAAE888QQcHR1lj3Nt2rQJPj4+Uk7nz5+HEAJvv/12qbznzJlTZt413a+Whc9015H33nsPb7/9NsaOHYsFCxbA2dkZlpaWiIiIkDYIQy/z/qte+qZSqfD444/j8ccfh42NDdauXYujR49Kz1rY2Njg+eefx6pVq5CTk4Ps7GycO3eu3JdLvPTSS1i2bBm+/PJLvPnmm/jyyy/h7e0t+ytZbZX3l6zyxgshANxrDFlYWOD7778vM1b3V00yH6x3uYZU7zWZZ1W+Sw1TVbaZil7qeL+a1nxVt9uqHAuBe/UbGhpaZuzDDz8MAOjUqRMyMzOxY8cOJCYm4uuvv5a6Ppo3bx6Ae8+aP/roo9i6dSt2796NDz74AO+//z6++eabSu9GoobD0dERbm5uOHnyZIVxJ0+eRMuWLaXGK3Cv32c/Pz8AQHBwMPr27YsXX3wRmZmZaNSoEby8vAAA6enpdZbvqVOnoFarZXkAQNeuXREQECDlcvPmTYwfPx59+/aFu7s72rdvD2tr6zrNxdLSEhqNBocPH5a6D7u/D+7evXtjzZo10rPewcHB0rSSkhJ07doVixcvLnPedfWeIt3V7Oeff77M6fv378fAgQOhVCoRHByMrVu3Yvny5cjJycGhQ4fw3nvvyXIGgDfffLPcu4IebPjX5bkUG933UavVsLW1xfnz50tNK2vc/b766isMHDgQn3/+uWx8Xl4emjVrJn328PDAqVOnpBcT6GRmZlY736ouszweHh4A7v216kE1yac8fn5+WLt2Lf7++2/Z+JEjRyI2NhabNm1CVlYWLCwsMGLEiDLn4e/vj3bt2mHDhg14/PHHkZGRgXfffbfOcqyNdu3aQQgBT09P6a9pZbl/fetuvwGAO3fuICsrC926ddN7rvQv1vu/WO9ExsfDwwMlJSU4d+4cOnXqJI3PyclBXl6eVNOVqWqjvbaaN2+Oxo0bo7i4WGo8VMTBwQHDhw/H8OHDUVRUhKFDh+Ldd99FVFQUbG1tAQBubm547bXX8NprryE3Nxfdu3fHu+++y0Y3yTz11FNYtWoVDh48WGYf0j/++CMuXLiAV199tdx5WFlZITo6GgMHDsSyZcswc+ZMPPTQQ+jYsSO2b9+Ojz/+uNYXUVJSUvDrr7/ipZdeqjR24cKF2Lp1K959913ExsbC3t4ejz32GPbs2YOLFy/WWaO2b9+++P777/Htt98iNzdX1itJ79698d///hc7d+7ErVu3ZOu2Xbt2+OWXXzBo0KBq72M8PDyqdO5148YNbN++HcOHD5c9pqfz+uuvY/369Rg4cCCAe7eYr127FsnJyThz5gyEENKt5QCku0ptbGyqtI+qa7y9/D5WVlYICAjAtm3bZM8QnT9/Ht9//32l333wr8Jbtmwp9WzAk08+ib/++gtfffWVNE7XzUFN8q3KMsvj5uYGHx8frF27VnbrZFJSEk6fPl2tXG7evImUlJQyp+nW3YO3sPbp0wdt2rTBunXrsGnTJvTv37/CtyiPHDkSaWlpmDNnDiwsLKQ3nBra0KFDYWVlhXnz5pX6/xBC4J9//gFwrzHSvHlzxMbGoqioSIqJj49HXl5efaZMYL3rsN6JjNOTTz4JAFi6dKlsvO7KUlBQUJXm4+DgUC/HGCsrK4SEhODrr7+Wdc+jc3/3ebrjoo5CoYC3tzeEELhz5w6Ki4tLPdKhVqvRokWLGnW5SOZt2rRpsLOzw6uvvlpq27py5QomTpwIe3t7TJs2rcL5DBgwAD179sTSpUulR6TmzZuHf/75B6+88kqZ7/bYvXs3duzYUWmOv//+O0aPHg2FQlFpHsC9Rm1ISAji4+Oh1WoBAHPmzIEQAqNGjZJuU79famoq1q5dW+m876drSL///vuwt7eX3VHWs2dPWFtbS3el3d/ofv755/Hnn39i1apVpeZ569atCt/0HRgYiJSUFJw4cUIad+XKFdkz2gCwdetW3LhxA2FhYRg2bFip4amnnsLXX38t7RMCAgLg7OyMTZs2YdOmTejZs6fs9nC1Wo0BAwZg5cqVpS4MANB7F5+80v2AuXPnYvfu3ejTpw8mTZqE4uJiLFu2DF26dJFtHA966qmnMH/+fIwZMwa9e/dGeno61q9fX+pZ3fHjx2PZsmV4+eWXkZqaCjc3N3zxxRewt7evdq5VXWZFoqOjERQUhL59+2Ls2LG4cuWK1HdmWQVdnps3b6J3797o1asXnnjiCbi7uyMvLw/btm3Djz/+iODgYDzyyCOy7+hOpHW3fsyfP7/CZbz00kuYP38+tm/fLp3AG4N27drhnXfeQVRUFC5cuIDg4GA0btwYWVlZ2Lp1KyZMmIA333wTNjY2eOedd/Dqq6/isccew/Dhw5GVlYW4uDg+020grHfWO5Gx6tatG0JDQ/HZZ58hLy8P/fv3x08//YS1a9ciODhYurpTGV9fX/zwww9YvHgxWrRoAU9PT/j7++sl54ULF2Lv3r3w9/fH+PHj4e3tjStXruD48eP44YcfcOXKFQD3Xpjo6uqKPn36wMXFBWfOnMGyZcsQFBSExo0bIy8vD61atcKwYcPQrVs3NGrUCD/88AOOHTuGjz76SC+5k+nq0KED1q5di5EjR6Jr164YN24cPD09ceHCBXz++ee4fPkyvvzyy1Ld5ZVl2rRp+M9//oP4+HhMnDgRw4cPR3p6Ot59912kpaVhxIgR8PDwwD///IPExEQkJyfL3lcCAMePH8e6detQUlKCvLw8HDt2DF9//TUsLCzwxRdfSI9ZVCWXzZs3Y+nSpVi4cCF69+6NmJgYvPbaa/Dy8sKoUaPQoUMHXLt2Dfv27cO3336Ld955p1rrrmfPnlAoFEhJScGAAQNgbf1v09De3h7dunVDSkoKnJycpG63AGDUqFHYvHkzJk6ciL1796JPnz4oLi7G2bNnsXnzZuzatUu6df9B06dPx7p16/D4449j8uTJUpdhrVu3xpUrV6Qr5+vXr0fTpk3Ru3fvMufzzDPPYNWqVUhISMDQoUNhY2ODoUOHYuPGjbhx4wY+/PDDUt+JiYlB37590bVrV4wfPx5t27ZFTk4OUlJS8Mcff+CXX36p1vqrljp5B7qZSU5OFo888ohQKBSiXbt2YvXq1WLq1KnC1tZWiimrC6GpU6cKNzc3YWdnJ/r06SNSUlJE//79S3UF9fvvv4tnnnlG2Nvbi2bNmok33nhDJCYm1qgLoaosU9cdwZYtW8qcz9dffy06deoklEql8Pb2Ft98840IDQ2tVhdCd+7cEatWrRLBwcHCw8NDKJVKYW9vLx555BHxwQcfyLrIul9GRoYAIJRKpbh69Wqly+nRo4cAIJYvX17m9Np0IfTg+ilvXrpuVi5duiQb//XXX4u+ffsKBwcH4eDgILy8vERYWJjIzMyUxS1fvlx4enoKpVIp/Pz8xIEDB8rcTqh+sN5Z7xXNq6x6L6/LsAe/q1tWdf6fqWEo7zii25Z0XebcuXNHzJs3T3h6egobGxvh7u4uoqKiZN3dCHFvmwwKCipzWWfPnhX9+vUTdnZ2AoC07VY1ByHudRlWVvddD9aCEELk5OSIsLAw4e7uLmxsbISrq6sYNGiQ+Oyzz6SYlStXin79+ommTZsKpVIp2rVrJ6ZNmyZ1o1RYWCimTZsmunXrJho3biwcHBxEt27dyt0XEAkhxMmTJ8WIESOEm5ubtO2NGDFCpKeny+IqOnYUFxeLdu3aiXbt2sm6ikpOThbPPvusUKvVwtraWjRv3lw8/fTTYvv27VKMrssw3WBtbS2cnZ2Fv7+/iIqKEr///nup5VV2zB4wYIBQqVSyridTU1PFiy++KFq0aCFsbGxEkyZNxKBBg8TatWtLdS1aFRqNRgAQb731Vqlpr7/+ugBQqjtNIe51hfv++++Lzp07C6VSKZo0aSJ8fX3FvHnzZF2ElrWfSEtLE48++qhQKpWiVatWIjo6WnzyyScCgNBqtSInJ0dYW1uLUaNGlZv3zZs3hb29vXjuueekcUlJSQKAsLCwkLp+e9Cvv/4qXn75ZeHq6ipsbGxEy5YtxVNPPSW++uorKaYm5xeVsRCCb3ipiuDgYGRkZJT5PCQRmRfWOxEREVH9iYiIwMqVK3H9+vU666bLmPCZ7jLcunVL9vncuXPYuXMnBgwYYJiEiEhvWO9ERERE9efBc69//vkHX3zxBfr27WuWDW4A4JXuMri5uWH06NFo27Ytfv/9d6xYsQKFhYVIS0srs4/bunbr1q1K+4R1dnaGQqHQey7AvZcb3P/irwdZWVmhefPm9ZJLdRQVFUnPj5XH0dGxXrtWIuPDepdjvRMREZkuUziO+/j4YMCAAejUqRNycnLw+eef46+//kJycjL69etn0Nz0ps5uVDcjo0ePlp5TVKlUIjAwUKSmptbb8nXPEVQ01Oczgv37968wl+o8C1qfdM/JVDTExcUZOk0yMNa7HOudiIjIdJnCcTwqKkp06NBB2NnZCXt7e9G3b1+RlJRk6LT0ile6jdDff/+NjIyMCmN8fX3RpEmTesknNTUVV69eLXe6nZ2drF8/Y3H16lWkpqZWGNO5c2e4ubnVU0ZEpbHe6wbrnYiIyHSP4+aOjW4iIiIiIiIiPWnQ/XSXlJTgr7/+QuPGjaU+4YiMiRAC165dQ4sWLWBpyfceVhdrnIwda7x2WONk7FjjtcMaJ2NX1Rpv0I3uv/76C+7u7oZOg6hSFy9eRKtWrQydhslhjZOpYI3XDGucTAVrvGZY42QqKqvxBt3obty4MYB7K0mlUhk4G6LSCgoK4O7uLm2rVD2scTJ2rPHaYY2TsWON1w5rnIxdVWu8QTe6dbepqFQqFjIZNd5SVTOscTIVrPGaYY2TqWCN1wxrnExFZTXOh0uIiIiIiIiI9ISNbiIiIiIiIiI9YaObiIiIiIiISE8a9DPdxqzNzIQaf/fCwqA6zISI9KE2NQ6wzolIP7hvIlPDc2YyBbzSTURERERERKQnbHQTERERERER6Qkb3URERERERER6wkY3ERFRA7Vw4UJYWFggIiJCGnf79m2EhYWhadOmaNSoEUJCQpCTkyP7XnZ2NoKCgmBvbw+1Wo1p06bh7t27sph9+/ahe/fuUCqVaN++PeLj40stPyYmBm3atIGtrS38/f3x008/6eNnEhERGRQb3URERA3QsWPHsHLlSjz88MOy8VOmTMF3332HLVu2YP/+/fjrr78wdOhQaXpxcTGCgoJQVFSEw4cPY+3atYiPj8fs2bOlmKysLAQFBWHgwIE4ceIEIiIi8Morr2DXrl1SzKZNmxAZGYk5c+bg+PHj6NatGwIDA5Gbm6v/H09ERFSP2OgmIiJqYK5fv46RI0di1apVaNKkiTQ+Pz8fn3/+ORYvXozHHnsMvr6+iIuLw+HDh3HkyBEAwO7du3H69GmsW7cOPj4+GDJkCBYsWICYmBgUFRUBAGJjY+Hp6YmPPvoInTp1Qnh4OIYNG4YlS5ZIy1q8eDHGjx+PMWPGwNvbG7GxsbC3t8eaNWvqd2UQERHpGRvdRFQu3npKZJ7CwsIQFBSEgIAA2fjU1FTcuXNHNt7LywutW7dGSkoKACAlJQVdu3aFi4uLFBMYGIiCggJkZGRIMQ/OOzAwUJpHUVERUlNTZTGWlpYICAiQYspSWFiIgoIC2UBERGTs2OgmojLx1lMi87Rx40YcP34c0dHRpaZptVooFAo4OTnJxru4uECr1Uox9ze4ddN10yqKKSgowK1bt3D58mUUFxeXGaObR1mio6Ph6OgoDe7u7lX70URERAbERjcRlcJbT4nM08WLF/HGG29g/fr1sLW1NXQ61RYVFYX8/HxpuHjxoqFTIiIiqhQb3URUCm89JTJPqampyM3NRffu3WFtbQ1ra2vs378fn3zyCaytreHi4oKioiLk5eXJvpeTkwNXV1cAgKura6lHSnSfK4tRqVSws7NDs2bNYGVlVWaMbh5lUSqVUKlUsoGIiMjYsdFNRDK89ZTIfA0aNAjp6ek4ceKENPj5+WHkyJHSv21sbJCcnCx9JzMzE9nZ2dBoNAAAjUaD9PR02aMeSUlJUKlU8Pb2lmLun4cuRjcPhUIBX19fWUxJSQmSk5OlGCIiInNhbegEiMh46G49TUpKMtlbTyMjI6XPBQUFbHgT3adx48bo0qWLbJyDgwOaNm0qjR83bhwiIyPh7OwMlUqFyZMnQ6PRoFevXgCAwYMHw9vbG6NGjcKiRYug1Woxa9YshIWFQalUAgAmTpyIZcuWYfr06Rg7diz27NmDzZs3IyEhQVpuZGQkQkND4efnh549e2Lp0qW4ceMGxowZU09rg4iIqH6w0U1EkvtvPdUpLi7GgQMHsGzZMuzatUu69fT+q90P3nr64FvGq3vrqZWVVY1vPdWd9BNRzSxZsgSWlpYICQlBYWEhAgMDsXz5cmm6lZUVduzYgUmTJkGj0cDBwQGhoaGYP3++FOPp6YmEhARMmTIFH3/8MVq1aoXVq1cjMDBQihk+fDguXbqE2bNnQ6vVwsfHB4mJiaXucCEiIjJ1bHQTkUR36+n9xowZAy8vL8yYMQPu7u7SrachISEAyr719N1330Vubi7UajWAsm893blzp2w55d16GhwcDODfW0/Dw8P19vuJGqJ9+/bJPtva2iImJgYxMTHlfsfDw6NUDT9owIABSEtLqzAmPDycNU1ERGaPjW4ikvDWUyIiIiKiusVGNxFVC289JSIiIiKqOja6iahCvPWUiIiIiKjm2GUYERERERERkZ6w0U1EREREZCb+/PNPvPTSS2jatCns7OzQtWtX/Pzzz9J0IQRmz54NNzc32NnZISAgAOfOnZPN48qVKxg5ciRUKhWcnJwwbtw4XL9+XRZz8uRJPProo7C1tYW7uzsWLVpUKpctW7bAy8sLtra26Nq1a6V3wRGZKza6iYiIiIjMwNWrV9GnTx/Y2Njg+++/x+nTp/HRRx+hSZMmUsyiRYvwySefIDY2FkePHoWDgwMCAwNx+/ZtKWbkyJHIyMhAUlISduzYgQMHDmDChAnS9IKCAgwePBgeHh5ITU3FBx98gLlz5+Kzzz6TYg4fPowRI0Zg3LhxSEtLQ3BwMIKDg3Hq1Kn6WRlERoTPdBMRERERmYH3338f7u7uiIuLk8Z5enpK/xZCYOnSpZg1axaeffZZAMD//d//wcXFBdu2bcMLL7yAM2fOIDExEceOHYOfnx8A4NNPP8WTTz6JDz/8EC1atMD69etRVFSENWvWQKFQoHPnzjhx4gQWL14sNc4//vhjPPHEE5g2bRoAYMGCBUhKSsKyZcsQGxtbX6uEyCjwSjcRERERkRn49ttv4efnh//85z9Qq9V45JFHsGrVKml6VlYWtFotAgICpHGOjo7w9/dHSkoKACAlJQVOTk5SgxsAAgICYGlpiaNHj0ox/fr1g0KhkGICAwORmZmJq1evSjH3L0cXo1tOWQoLC1FQUCAbiMwBG91ERERERGbgt99+w4oVK9ChQwfs2rULkyZNwuuvv461a9cCALRaLQCU6n7TxcVFmqbVaqFWq2XTra2t4ezsLIspax73L6O8GN30skRHR8PR0VEa3N3dq/X7iYwVG91ERERERGagpKQE3bt3x3vvvYdHHnkEEyZMwPjx403mdu6oqCjk5+dLw8WLFw2dElGdYKObiIiIiMgMuLm5wdvbWzauU6dOyM7OBgC4uroCAHJycmQxOTk50jRXV1fk5ubKpt+9exdXrlyRxZQ1j/uXUV6MbnpZlEolVCqVbCAyB2x0ExERERGZgT59+iAzM1M27n//+x88PDwA3HupmqurK5KTk6XpBQUFOHr0KDQaDQBAo9EgLy8PqampUsyePXtQUlICf39/KebAgQO4c+eOFJOUlISOHTtKb0rXaDSy5ehidMshakhq1eheuHAhLCwsEBERIY27ffs2wsLC0LRpUzRq1AghISGl/sqVnZ2NoKAg2NvbQ61WY9q0abh7964sZt++fejevTuUSiXat2+P+Pj4UsuPiYlBmzZtYGtrC39/f/z000+1+TlERERERCZrypQpOHLkCN577z2cP38eGzZswGeffYawsDAAkM7b33nnHXz77bdIT0/Hyy+/jBYtWiA4OBjAvSvjTzzxBMaPH4+ffvoJhw4dQnh4OF544QW0aNECAPDiiy9CoVBg3LhxyMjIwKZNm/Dxxx8jMjJSyuWNN95AYmIiPvroI5w9exZz587Fzz//jPDw8HpfL0SGVuNG97Fjx7By5Uo8/PDDsvFTpkzBd999hy1btmD//v3466+/MHToUGl6cXExgoKCUFRUhMOHD2Pt2rWIj4/H7NmzpZisrCwEBQVh4MCBOHHiBCIiIvDKK69g165dUsymTZsQGRmJOXPm4Pjx4+jWrRsCAwNL3Q5DRERERNQQ9OjRA1u3bsWXX36JLl26YMGCBVi6dClGjhwpxUyfPh2TJ0/GhAkT0KNHD1y/fh2JiYmwtbWVYtavXw8vLy8MGjQITz75JPr27Svrg9vR0RG7d+9GVlYWfH19MXXqVMyePVvWl3fv3r2lRn+3bt3w1VdfYdu2bejSpUv9rAwiI2IhhBDV/dL169fRvXt3LF++HO+88w58fHywdOlS5Ofno3nz5tiwYQOGDRsGADh79iw6deqElJQU9OrVC99//z2eeuop/PXXX9IbDWNjYzFjxgxcunQJCoUCM2bMQEJCAk6dOiUt84UXXkBeXh4SExMBAP7+/ujRoweWLVsG4N6LI9zd3TF58mTMnDmzzLwLCwtRWFgofS4oKIC7uzvy8/ON7pmRNjMTavzdCwuD6jATMqSCggI4Ojoa5TZqCox5/dWmxgHWubkw5m3UFHD91T3um+oWt9Haqcr64zkzGVJVa7xGV7rDwsIQFBRUqu+91NRU3LlzRzbey8sLrVu3lvX917VrV1kXAoGBgSgoKEBGRoYUU1G/fkVFRUhNTZXFWFpaIiAgoMK+/9gNAREREREREdWnaje6N27ciOPHjyM6OrrUNK1WC4VCAScnJ9n4B/v+q2m/fgUFBbh16xYuX76M4uLiavf9x24IiIiIiIiIqD5ZVyf44sWLeOONN5CUlCR77sNUKJVKKJVKQ6dBREREREREDUS1rnSnpqYiNzcX3bt3h7W1NaytrbF//3588sknsLa2houLC4qKipCXlyf73oN9/9W0Xz+VSgU7Ozs0a9YMVlZW1e77j4iIiIiIiKg+VavRPWjQIKSnp+PEiRPS4Ofnh5EjR0r/trGxkfXJl5mZiezsbFnff+np6bK3jCclJUGlUsHb21uKqahfP4VCAV9fX1lMSUkJkpOT2fcfERERERERGY1q3V7euHHjUq/5d3BwQNOmTaXx48aNQ2RkJJydnaFSqTB58mRoNBr06tULADB48GB4e3tj1KhRWLRoEbRaLWbNmoWwsDDp1u+JEydi2bJlmD59OsaOHYs9e/Zg8+bNSEj49+2EkZGRCA0NhZ+fH3r27ImlS5fixo0bGDNmTK1WCBEREZEpq+0byImIqG5Vq9FdFUuWLIGlpSVCQkJQWFiIwMBALF++XJpuZWWFHTt2YNKkSdBoNHBwcEBoaCjmz58vxXh6eiIhIQFTpkzBxx9/jFatWmH16tUIDAyUYoYPH45Lly5h9uzZ0Gq18PHxQWJiYqmXqxEREREREREZSq0b3fv27ZN9trW1RUxMDGJiYsr9joeHB3bu3FnhfAcMGIC0tLQKY8LDwxEeHl7lXImIiIiIiIjqU4366SYiIiIiIiKiyrHRTURERERERKQnbHQTERERERER6Qkb3URERERERER6UudvLyciIv2rTZdAFxYG1WEmRERERFQRXukmIiIiIiIi0hM2uomIiIiIiIj0hI1uIiIiIiIiIj1ho5uIiIiIiIhIT9joJiIiIiIiItITNrqJiIiIiIiI9ISNbiIiIiIiIiI9YaObiIiIiMgMLVy4EBYWFoiIiJDG3b59G2FhYWjatCkaNWqEkJAQ5OTkyL6XnZ2NoKAg2NvbQ61WY9q0abh7964sZt++fejevTuUSiXat2+P+Pj4UsuPiYlBmzZtYGtrC39/f/z000/6+JlERo+NbiIiIiIiM3Ps2DGsXLkSDz/8sGz8lClT8N1332HLli3Yv38//vrrLwwdOlSaXlxcjKCgIBQVFeHw4cNYu3Yt4uPjMXv2bCkmKysLQUFBGDhwIE6cOIGIiAi88sor2LVrlxSzadMmREZGYs6cOTh+/Di6deuGwMBA5Obm6v/HExkZNrqJiIiIiMzI9evXMXLkSKxatQpNmjSRxufn5+Pzzz/H4sWL8dhjj8HX1xdxcXE4fPgwjhw5AgDYvXs3Tp8+jXXr1sHHxwdDhgzBggULEBMTg6KiIgBAbGwsPD098dFHH6FTp04IDw/HsGHDsGTJEmlZixcvxvjx4zFmzBh4e3sjNjYW9vb2WLNmTf2uDCIjwEY3EREREZEZCQsLQ1BQEAICAmTjU1NTcefOHdl4Ly8vtG7dGikpKQCAlJQUdO3aFS4uLlJMYGAgCgoKkJGRIcU8OO/AwEBpHkVFRUhNTZXFWFpaIiAgQIopS2FhIQoKCmQDkTmwNnQCRERERERUNzZu3Ijjx4/j2LFjpaZptVooFAo4OTnJxru4uECr1Uox9ze4ddN10yqKKSgowK1bt3D16lUUFxeXGXP27Nlyc4+Ojsa8efOq9kOJTAgb3XrUZmaCoVMgIiIiogbi4sWLeOONN5CUlARbW1tDp1NtUVFRiIyMlD4XFBTA3d3dgBkR1Q3eXk5EREREZAZSU1ORm5uL7t27w9raGtbW1ti/fz8++eQTWFtbw8XFBUVFRcjLy5N9LycnB66urgAAV1fXUm8z132uLEalUsHOzg7NmjWDlZVVmTG6eZRFqVRCpVLJBiJzwCvdREQ1xLtZiIjImAwaNAjp6emycWPGjIGXlxdmzJgBd3d32NjYIDk5GSEhIQCAzMxMZGdnQ6PRAAA0Gg3effdd5ObmQq1WAwCSkpKgUqng7e0txezcuVO2nKSkJGkeCoUCvr6+SE5ORnBwMACgpKQEycnJCA8P19vvJzJWvNJNRETUQERHR6NHjx5o3Lgx1Go1goODkZmZKYthH75Epqtx48bo0qWLbHBwcEDTpk3RpUsXODo6Yty4cYiMjMTevXuRmpqKMWPGQKPRoFevXgCAwYMHw9vbG6NGjcIvv/yCXbt2YdasWQgLC4NSqQQATJw4Eb/99humT5+Os2fPYvny5di8eTOmTJki5RIZGYlVq1Zh7dq1OHPmDCZNmoQbN25gzJgxBlk3RIbERjcREVEDsX//foSFheHIkSNISkrCnTt3MHjwYNy4cUOKYR++ROZtyZIleOqppxASEoJ+/frB1dUV33zzjTTdysoKO3bsgJWVFTQaDV566SW8/PLLmD9/vhTj6emJhIQEJCUloVu3bvjoo4+wevVqBAYGSjHDhw/Hhx9+iNmzZ8PHxwcnTpxAYmJiqZerETUEFkIIYegkDKWgoACOjo7Iz8/XyzMjhrr19MLCIIMsl+qevrfRB0VHR+Obb77B2bNnYWdnh969e+P9999Hx44dpZjbt29j6tSp2LhxIwoLCxEYGIjly5fLDqLZ2dmYNGkS9u7di0aNGiE0NBTR0dGwtv73iZZ9+/YhMjISGRkZcHd3x6xZszB69GhZPjExMfjggw+g1WrRrVs3fPrpp+jZs2eVf4+51nhtcR9hPOq7xh906dIlqNVq7N+/H/369UN+fj6aN2+ODRs2YNiwYQCAs2fPolOnTkhJSUGvXr3w/fff46mnnsJff/0l1X1sbCxmzJiBS5cuQaFQYMaMGUhISMCpU6ekZb3wwgvIy8tDYmIiAMDf3x89evTAsmXLANy79dTd3R2TJ0/GzJkzy8y3sLAQhYWF0mfdS5YMtf6MlSH3Tdy/yBm6xk1dVdZfbbZ3bq9UW1WtcV7pJiIJr4IRNSz5+fkAAGdnZwDG34dvdHQ0HB0dpYFvNSYiIlPARjcRSRITEzF69Gh07twZ3bp1Q3x8PLKzs5Gamgrg3gn6559/jsWLF+Oxxx6Dr68v4uLicPjwYRw5cgQAsHv3bpw+fRrr1q2Dj48PhgwZggULFiAmJgZFRUUA7l0V8/T0xEcffYROnTohPDwcw4YNw5IlS6RcFi9ejPHjx2PMmDHw9vZGbGws7O3tsWbNmnLzLywsREFBgWwgorKVlJQgIiICffr0QZcuXQDUXx++ly9fLrcPX908yhIVFYX8/HxpuHjxYvV/OBERUT1jo5uIysWrYETmKywsDKdOncLGjRsNnUqVsTshIiIyRWx0E1GZeBWMyHyFh4djx44d2Lt3L1q1aiWNd3V1Neo+fImIiEwRG91EVCZeBSMyP0IIhIeHY+vWrdizZw88PT1l0319faU+fHXK6sM3PT1d9n6FsvrwvX8eupiy+vDV0fXhq4shIiIyF9aVhxBRQ6O7CnbgwIFyr4Ldf7X7watgD/a1W92rYFZWVrwKRqQHYWFh2LBhA7Zv347GjRtLd444OjrCzs5O1oevs7MzVCoVJk+eXG4fvosWLYJWqy2zD99ly5Zh+vTpGDt2LPbs2YPNmzcjIeHftwxHRkYiNDQUfn5+6NmzJ5YuXco+fImIyCzxSjcRSXgVjMi8rVixAvn5+RgwYADc3NykYdOmTVIM+/AlIiKqW7zSTUQSXgUjMm9CiEpjbG1tERMTg5iYmHJjPDw8sHPnzgrnM2DAAKSlpVUYEx4ejvDw8EpzIiIiMmXVutIdHR2NHj16oHHjxlCr1QgODkZmZqYs5vbt2wgLC0PTpk3RqFEjhISElLpFNDs7G0FBQbC3t4darca0adNw9+5dWcy+ffvQvXt3KJVKtG/fHvHx8aXyiYmJQZs2bWBrawt/f/9St7QSUfXwKhgRERERUd2q1pXu/fv3IywsDD169MDdu3fx1ltvYfDgwTh9+jQcHBwAAFOmTEFCQgK2bNkCR0dHhIeHY+jQoTh06BAAoLi4GEFBQXB1dcXhw4fx999/4+WXX4aNjQ3ee+89AEBWVhaCgoIwceJErF+/HsnJyXjllVfg5uYmnZRv2rQJkZGRiI2Nhb+/P5YuXYrAwEBkZmZCrVbX5ToiajB4FYyIiIiIqG5Vq9GdmJgo+xwfHw+1Wo3U1FT069cP+fn5+Pzzz7FhwwY89thjAIC4uDh06tQJR44cQa9evbB7926cPn0aP/zwA1xcXODj44MFCxZgxowZmDt3LhQKBWJjY+Hp6YmPPvoIANCpUyccPHgQS5YskRrdixcvxvjx46VbTWNjY5GQkIA1a9Zg5syZtV4xRERERERERLVVqxep5efnAwCcnZ0BAKmpqbhz5w4CAgKkGC8vL7Ru3RopKSkAgJSUFHTt2lV2i2hgYCAKCgqQkZEhxdw/D12Mbh5FRUVITU2VxVhaWiIgIECKKUthYSEKCgpkAxEREREREZG+1LjRXVJSgoiICPTp0wddunQBAGi1WigUCllXQgDg4uIivZBJq9WWeiZT97mymIKCAty6dQuXL19GcXFxmTG6eZQlOjoajo6O0uDu7l79H05ERERERERURTVudIeFheHUqVPYuHFjXeajV1FRUcjPz5eGixcvGjolIiIiIiIiMmM16jIsPDwcO3bswIEDB9CqVStpvKurK4qKipCXlye72p2TkwNXV1cp5sG3jOvebn5/zINvPM/JyYFKpYKdnR2srKxgZWVVZoxuHmVRKpVSl0VERERERERE+latK91CCISHh2Pr1q3Ys2cPPD09ZdN9fX1hY2OD5ORkaVxmZiays7Oh0WgAABqNBunp6cjNzZVikpKSoFKp4O3tLcXcPw9djG4eCoUCvr6+spiSkhIkJydLMURERERERESGVq0r3WFhYdiwYQO2b9+Oxo0bS89POzo6ws7ODo6Ojhg3bhwiIyPh7OwMlUqFyZMnQ6PRoFevXgCAwYMHw9vbG6NGjcKiRYug1Woxa9YshIWFSVehJ06ciGXLlmH69OkYO3Ys9uzZg82bNyMhIUHKJTIyEqGhofDz80PPnj2xdOlS3LhxQ3qbOREREREREZGhVavRvWLFCgD3+te9X1xcHEaPHg0AWLJkCSwtLRESEoLCwkIEBgZi+fLlUqyVlRV27NiBSZMmQaPRwMHBAaGhoZg/f74U4+npiYSEBEyZMgUff/wxWrVqhdWrV0vdhQHA8OHDcenSJcyePRtarRY+Pj5ITEws9XI1IiIiIiIiIkOpVqNbCFFpjK2tLWJiYhATE1NujIeHB3bu3FnhfAYMGIC0tLQKY8LDwxEeHl5pTkRERERERESGUKt+uomIiIiIiIiofGx0ExERERGZiejoaPTo0QONGzeGWq1GcHAwMjMzZTG3b99GWFgYmjZtikaNGiEkJKRUr0DZ2dkICgqCvb091Go1pk2bhrt378pi9u3bh+7du0OpVKJ9+/aIj48vlU9MTAzatGkDW1tb+Pv7l+rFiKghqFGXYURERERED2ozM6HyoHJcWBhUh5k0XPv370dYWBh69OiBu3fv4q233sLgwYNx+vRpODg4AACmTJmChIQEbNmyBY6OjggPD8fQoUNx6NAhAEBxcTGCgoLg6uqKw4cP4++//8bLL78MGxsbvPfeewCArKwsBAUFYeLEiVi/fj2Sk5PxyiuvwM3NTXoP06ZNmxAZGYnY2Fj4+/tj6dKlCAwMRGZmJtRqtWFWEJEBsNFNRERERGQmEhMTZZ/j4+OhVquRmpqKfv36IT8/H59//jk2bNiAxx57DMC9lyJ36tQJR44cQa9evbB7926cPn0aP/zwA1xcXODj44MFCxZgxowZmDt3LhQKBWJjY+Hp6YmPPvoIANCpUyccPHgQS5YskRrdixcvxvjx46XehWJjY5GQkIA1a9Zg5syZpXIvLCxEYWGh9LmgoEAv60iHfySi+sLby4mIiIiIzFR+fj4AwNnZGQCQmpqKO3fuICAgQIrx8vJC69atkZKSAgBISUlB165dZb0CBQYGoqCgABkZGVLM/fPQxejmUVRUhNTUVFmMpaUlAgICpJgHRUdHw9HRURrc3d1r+/OJjAIb3UREREREZqikpAQRERHo06cPunTpAgDQarVQKBRwcnKSxbq4uECr1UoxD3bDq/tcWUxBQQFu3bqFy5cvo7i4uMwY3TweFBUVhfz8fGm4ePFizX44kZHh7eVERERERGYoLCwMp06dwsGDBw2dSpUolUoolUpDp0FU53ilm4iIiIjIzISHh2PHjh3Yu3cvWrVqJY13dXVFUVER8vLyZPE5OTlwdXWVYh58m7nuc2UxKpUKdnZ2aNasGaysrMqM0c2DqKFgo5uIiIiIyEwIIRAeHo6tW7diz5498PT0lE339fWFjY0NkpOTpXGZmZnIzs6GRqMBAGg0GqSnpyM3N1eKSUpKgkqlgre3txRz/zx0Mbp5KBQK+Pr6ymJKSkqQnJwsxRA1FLy93AzV5k2MAN/GSERERGSqwsLCsGHDBmzfvh2NGzeWnp92dHSEnZ0dHB0dMW7cOERGRsLZ2RkqlQqTJ0+GRqNBr169AACDBw+Gt7c3Ro0ahUWLFkGr1WLWrFkICwuTbv+eOHEili1bhunTp2Ps2LHYs2cPNm/ejISEf89DIyMjERoaCj8/P/Ts2RNLly7FjRs3pLeZEzUUbHQTEREREZmJFStWAAAGDBggGx8XF4fRo0cDAJYsWQJLS0uEhISgsLAQgYGBWL58uRRrZWWFHTt2YNKkSdBoNHBwcEBoaCjmz58vxXh6eiIhIQFTpkzBxx9/jFatWmH16tVSd2EAMHz4cFy6dAmzZ8+GVquFj48PEhMTS71cjcjcsdFNRERERGQmhBCVxtja2iImJgYxMTHlxnh4eGDnzp0VzmfAgAFIS0urMCY8PBzh4eGV5kRkzvhMNxEREREREZGesNFNREREREREpCe8vZyIiIjIyNT2pahERGQ8eKWbiIiIiIiISE/Y6CYiIiIiIiLSEza6iYiIiIiIiPSEjW4iIiIiIiIiPWGjm4iIiIiIiEhP2OgmIiIiIiIi0hM2uomIiIiIiIj0hI1uIiIiIiIiIj1ho5uIiIiIiIhIT9joJiIiIiIiItITNrqJiIiIiIiI9MTa0AkQEREREbWZmVDj715YGFSHmRAR1S1e6SYiIiIiIiLSE17pplL4l2YiIiIiIqK6wUY3ETVYtfkDkynjH9aI9K+h7l+IiKg03l5OREREREREpCe80k1ERERERFQNtb2bhXeONSwmf6U7JiYGbdq0ga2tLfz9/fHTTz8ZOiUiqkOscSLzxhonMm+scSITv9K9adMmREZGIjY2Fv7+/li6dCkCAwORmZkJtVpt6PQaJD4rSnWJNU5k3ljjROaNNU50j4UQQhg6iZry9/dHjx49sGzZMgBASUkJ3N3dMXnyZMycObNUfGFhIQoLC6XP+fn5aN26NS5evAiVSlUqvsucXfpLnko5NS/Q0CkYnYKCAri7uyMvLw+Ojo6GTqfescbND+tcjjWu3xqvLe4jGg597ZtY4/qv8YZYpzyWGo8q17gwUYWFhcLKykps3bpVNv7ll18WzzzzTJnfmTNnjgDAgYPJDRcvXqyHqjIurHEODWlgjf+LNc7BHAfW+L9Y4xzMcaisxk329vLLly+juLgYLi4usvEuLi44e/Zsmd+JiopCZGSk9LmkpARXrlxB06ZNYWFhodd8q0v3VxN9/fVen0w1d2PMWwiBa9euoUWLFoZOpd6Ze40DxrnNVZWp5m5sebPG9V/jxvZ/buy4vqqvonXGGq/bGm8o22dD+Z2A6f/Wqta4yTa6a0KpVEKpVMrGOTk5GSaZKlKpVCa5AQKmm7ux5d0Qb0erKVOsccD4trnqMNXcjSlv1njV1abGjen/3BRwfVVfeeuMNV51Va3xhrJ9NpTfCZj2b61KjZvs28ubNWsGKysr5OTkyMbn5OTA1dXVQFkRUV1hjROZN9Y4kXljjRP9y2Qb3QqFAr6+vkhOTpbGlZSUIDk5GRqNxoCZEVFdYI0TmTfWOJF5Y40T/cukby+PjIxEaGgo/Pz80LNnTyxduhQ3btzAmDFjDJ1arSmVSsyZM6fULTamwFRzN9W8zZk51zhg2tucqeZuqnmbq/qocf6fVw/XV/VxnZWvrmu8oazrhvI7gYbzW026yzAAWLZsGT744ANotVr4+Pjgk08+gb+/v6HTIqI6whonMm+scSLzxhonMoNGNxEREREREZGxMtlnuomIiIiIiIiMHRvdRERERERERHrCRjcRERERERGRnrDRTURERERERKQnbHQbqZiYGLRp0wa2trbw9/fHTz/9VK/LP3DgAJ5++mm0aNECFhYW2LZtm2y6EAKzZ8+Gm5sb7OzsEBAQgHPnzslirly5gpEjR0KlUsHJyQnjxo3D9evXZTEnT57Eo48+CltbW7i7u2PRokU1zjk6Oho9evRA48aNoVarERwcjMzMTFnM7du3ERYWhqZNm6JRo0YICQlBTk6OLCY7OxtBQUGwt7eHWq3GtGnTcPfuXVnMvn370L17dyiVSrRv3x7x8fE1zpsaJkPXOGCadQ6w1qlyVdkuHzRgwABYWFjIhokTJ9ZTxvWruvufLVu2wMvLC7a2tujatSt27txZT5kaj+qss/j4+FLbkq2tbT1ma76M4dhZl+bOnVtqW/Hy8pKmV+VYZozq6/zCpAgyOhs3bhQKhUKsWbNGZGRkiPHjxwsnJyeRk5NTbzns3LlT/Pe//xXffPONACC2bt0qm75w4ULh6Ogotm3bJn755RfxzDPPCE9PT3Hr1i0p5oknnhDdunUTR44cET/++KNo3769GDFihDQ9Pz9fuLi4iJEjR4pTp06JL7/8UtjZ2YmVK1fWKOfAwEARFxcnTp06JU6cOCGefPJJ0bp1a3H9+nUpZuLEicLd3V0kJyeLn3/+WfTq1Uv07t1bmn737l3RpUsXERAQINLS0sTOnTtFs2bNRFRUlBTz22+/CXt7exEZGSlOnz4tPv30U2FlZSUSExNrlDc1PMZQ40KYZp0LwVqnylW2XZalf//+Yvz48eLvv/+Whvz8/HrKuP5Ud/9z6NAhYWVlJRYtWiROnz4tZs2aJWxsbER6eno9Z2441V1ncXFxQqVSybYlrVZbz1mbH2M5dtalOXPmiM6dO8u2lUuXLknTKzuWGav6OL8wNWx0G6GePXuKsLAw6XNxcbFo0aKFiI6ONkg+DxZLSUmJcHV1FR988IE0Li8vTyiVSvHll18KIYQ4ffq0ACCOHTsmxXz//ffCwsJC/Pnnn0IIIZYvXy6aNGkiCgsLpZgZM2aIjh071kneubm5AoDYv3+/lKONjY3YsmWLFHPmzBkBQKSkpAgh7u0kLC0tZQfHFStWCJVKJeU5ffp00blzZ9myhg8fLgIDA+skbzJ/xlbjQphunQvBWie5qmyXZenfv79444036iFDw6ru/uf5558XQUFBsnH+/v7i1Vdf1WuexqS66ywuLk44OjrWU3YNhzEeO2trzpw5olu3bmVOq8qxzBTo6/zC1PD2ciNTVFSE1NRUBAQESOMsLS0REBCAlJQUA2b2r6ysLGi1WlmOjo6O8Pf3l3JMSUmBk5MT/Pz8pJiAgABYWlri6NGjUky/fv2gUCikmMDAQGRmZuLq1au1zjM/Px8A4OzsDABITU3FnTt3ZHl7eXmhdevWsry7du0KFxcXWU4FBQXIyMiQYu6fhy7GWP5/yLiZQo0DplPnAGud5KqyXZZn/fr1aNasGbp06YKoqCjcvHlT3+nWq5rsfxp6HdR0n339+nV4eHjA3d0dzz77rLRfoZoxlWNnTZw7dw4tWrRA27ZtMXLkSGRnZwOo2rHMFNXV+YWpYaPbyFy+fBnFxcWyE0EAcHFxgVarNVBWcro8KspRq9VCrVbLpltbW8PZ2VkWU9Y87l9GTZWUlCAiIgJ9+vRBly5dpHkqFAo4OTlVmHdlOZUXU1BQgFu3btUqbzJ/plDjgGnUOcBap9Kqsl2W5cUXX8S6deuwd+9eREVF4YsvvsBLL72k73TrVU32P+XVgTHtr/SpJuusY8eOWLNmDbZv345169ahpKQEvXv3xh9//FEfKZslUzl2Vpe/vz/i4+ORmJiIFStWICsrC48++iiuXbtWpWOZKaqr8wtTY23oBIj0ISwsDKdOncLBgwcNnQoR6RFrveGYOXMm3n///Qpjzpw5U+P5T5gwQfp3165d4ebmhkGDBuHXX39Fu3btajxfang0Gg00Go30uXfv3ujUqRNWrlyJBQsWGDAzMjZDhgyR/v3www/D398fHh4e2Lx5M+zs7AyYGdU1Xuk2Ms2aNYOVlVWpNxPm5OTA1dXVQFnJ6fKoKEdXV1fk5ubKpt+9exdXrlyRxZQ1j/uXURPh4eHYsWMH9u7di1atWsnyLioqQl5eXoV5V5ZTeTEqlYo7SKqUKdQ4YPx1DrDWG5qpU6fizJkzFQ5t27at0nZZFf7+/gCA8+fP1+nvMKSa7H/KqwNj2l/pU13ss21sbPDII4+Y1bZU30zl2FlbTk5OeOihh3D+/PkqHctMUV2dX5gaNrqNjEKhgK+vL5KTk6VxJSUlSE5Olv3V1JA8PT3h6uoqy7GgoABHjx6VctRoNMjLy0NqaqoUs2fPHpSUlEgnMhqNBgcOHMCdO3ekmKSkJHTs2BFNmjSpdl5CCISHh2Pr1q3Ys2cPPD09ZdN9fX1hY2MjyzszMxPZ2dmyvNPT02WFnpSUBJVKBW9vbynm/nnoYozl/4eMmynUOGC8dQ6w1huq5s2bw8vLq8JBoVBUabusihMnTgAA3Nzc6vqnGExN9j8NvQ7qYp9dXFyM9PR0s9qW6pupHDtr6/r16/j111/h5uZWpWOZKaqr8wuTY+g3uVFpGzduFEqlUsTHx4vTp0+LCRMmCCcnp3rtbuLatWsiLS1NpKWlCQBi8eLFIi0tTfz+++9CiHuv+ndychLbt28XJ0+eFM8++2yZr/p/5JFHxNGjR8XBgwdFhw4dZK/6z8vLEy4uLmLUqFHi1KlTYuPGjcLe3r7GXQlNmjRJODo6in379sm6Xrh586YUM3HiRNG6dWuxZ88e8fPPPwuNRiM0Go00XdeN0ODBg8WJEydEYmKiaN68eZndCE2bNk2cOXNGxMTEsBshqhZjqHEhTLPOhWCtU+Uq2y7/+OMP0bFjR3H06FEhhBDnz58X8+fPFz///LPIysoS27dvF23bthX9+vUz1E/Qm8r2P6NGjRIzZ86U4g8dOiSsra3Fhx9+KM6cOSPmzJnTILsMq846mzdvnti1a5f49ddfRWpqqnjhhReEra2tyMjIMNRPMAvGcuysS1OnThX79u0TWVlZ4tChQyIgIEA0a9ZM5ObmCiEqP5YZq/o4vzA1bHQbqU8//VS0bt1aKBQK0bNnT3HkyJF6Xf7evXsFgFJDaGioEOLe6/7ffvtt4eLiIpRKpRg0aJDIzMyUzeOff/4RI0aMEI0aNRIqlUqMGTNGXLt2TRbzyy+/iL59+wqlUilatmwpFi5cWOOcy8oXgIiLi5Nibt26JV577TXRpEkTYW9vL5577jnx999/y+Zz4cIFMWTIEGFnZyeaNWsmpk6dKu7cuVNq/fj4+AiFQiHatm0rWwZRVRi6xoUwzToXgrVOlatsu8zKyhIAxN69e4UQQmRnZ4t+/foJZ2dnoVQqRfv27cW0adPMsp9uISre//Tv31/aB+hs3rxZPPTQQ0KhUIjOnTuLhISEes7Y8KqzziIiIqRYFxcX8eSTT4rjx48bIGvzYwzHzro0fPhw4ebmJhQKhWjZsqUYPny4OH/+vDS9KscyY1Rf5xemxEIIIerhgjoRERERERFRg8NnuomIiIiIiIj0hI1uIiIiIiIiIj1ho5uIiIiIiIhIT9joJiIiIiIiItITNrqJiIiIiIiI9ISNbiIiIiIiIiI9YaObiIiIiIiISE/Y6CYiIiIiIiLSEza6iYiIiIiIiPSEjW4iIiIiIiIiPWGjm4iIiIiIiEhP/h+UM2BOEuLIMwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Explore the raw data\n",
    "# data = data.to_numpy()\n",
    "n_plots_x = int(np.ceil(np.sqrt(predictors_sel.shape[1])))\n",
    "n_plots_y = int(np.floor(np.sqrt(predictors_sel.shape[1])))\n",
    "fig, ax = plt.subplots(n_plots_x, n_plots_y, figsize=(10, 10), dpi=100, facecolor='w', edgecolor='k')\n",
    "ax=ax.ravel()\n",
    "for idx in range(20):\n",
    "    ax[idx].hist(predictors_sel.iloc[:, idx].to_numpy().flatten())\n",
    "    ax[idx].set_title(predictors_sel.columns[idx])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7560d75b-2bfe-4e65-8079-895d825ff39b",
   "metadata": {
    "id": "7560d75b-2bfe-4e65-8079-895d825ff39b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "tree_height = predictors_sel['hm'].to_numpy()\n",
    "data = predictors_sel.drop(columns=['ID','h','X','Y', 'hm','forestheight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e7fb69-1866-4009-a954-70394fadf2d0",
   "metadata": {
    "id": "85e7fb69-1866-4009-a954-70394fadf2d0"
   },
   "source": [
    "Now we will split the data into training vs test datasets and perform the normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a2c23e6-fcd5-43d4-b330-6e39e8b1d078",
   "metadata": {
    "id": "0a2c23e6-fcd5-43d4-b330-6e39e8b1d078",
    "outputId": "10310550-6978-4d65-8ede-b63025c6a726",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape:(15000, 18), X_test.shape:(5000, 18) \n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data.to_numpy()[:20000,:],tree_height[:20000], random_state=0)\n",
    "print('X_train.shape:{}, X_test.shape:{} '.format(X_train.shape, X_test.shape))\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0147955b-50e0-4001-8843-57b91bee188f",
   "metadata": {
    "id": "0147955b-50e0-4001-8843-57b91bee188f",
    "outputId": "f0a793c8-b2cd-43df-9bbd-56ebab4214a7",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the normalized data\n",
    "n_plots_x = int(np.ceil(np.sqrt(X_train.shape[1])))\n",
    "n_plots_y = int(np.floor(np.sqrt(X_train.shape[1])))\n",
    "fig, ax = plt.subplots(n_plots_x, n_plots_y, figsize=(10, 10), dpi=100, facecolor='w', edgecolor='k')\n",
    "ax=ax.ravel()\n",
    "for idx in range(18):\n",
    "    ax[idx].hist(X_train[:, idx].flatten())\n",
    "    # ax[idx].set_title(predictors_sel.columns[idx])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d064bba1-fbb6-41b3-a799-1007f9686d0e",
   "metadata": {
    "id": "d064bba1-fbb6-41b3-a799-1007f9686d0e"
   },
   "source": [
    "Now, we will build our SVR regressor. For more details on all the parameters it accepts, please check the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8d9f7d40-a999-4a3e-95e1-4c9aee9bb3b0",
   "metadata": {
    "id": "8d9f7d40-a999-4a3e-95e1-4c9aee9bb3b0",
    "outputId": "daf996d1-54de-4061-e795-72d881da5a90",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVR on training set: 0.23972\n",
      "Accuracy of SVR on test set: 0.23232\n"
     ]
    }
   ],
   "source": [
    "svr = SVR()\n",
    "svr.fit(X_train, y_train) # Fit the SVR model according to the given training data.\n",
    "print('Accuracy of SVR on training set: {:.5f}'.format(svr.score(X_train, y_train))) # Returns the coefficient of determination (R^2) of the prediction.\n",
    "print('Accuracy of SVR on test set: {:.5f}'.format(svr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939f77e2-ea7d-45b8-b177-5c14457b5bc4",
   "metadata": {
    "id": "939f77e2-ea7d-45b8-b177-5c14457b5bc4"
   },
   "source": [
    "Now, let's test the fitted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1d69ff6-cd11-4f9e-a0d8-3f65cbd429a9",
   "metadata": {
    "id": "d1d69ff6-cd11-4f9e-a0d8-3f65cbd429a9",
    "outputId": "3a97c1f5-c651-4eee-c578-279bca06e3b5",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = svr.predict(X_test)\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(5,5))\n",
    "ax.scatter(y_pred, y_test)\n",
    "ax.set_xlabel('Prediction')\n",
    "ax.set_ylabel('True')\n",
    "ax.set_title('slope: {:.4f}, r_value: {:.4f}'.format(slope, r_value))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8dd204f-fe75-4b6e-8513-06498eeff02a",
   "metadata": {
    "id": "b8dd204f-fe75-4b6e-8513-06498eeff02a",
    "outputId": "a56c4d39-e41d-49d7-c674-20b7fd7c1c5f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVR on training set: 0.23964\n",
      "Accuracy of SVR on test set: 0.23212\n"
     ]
    }
   ],
   "source": [
    "svr = SVR(epsilon=0.01) # Let's change the size of the epsilon-tube\n",
    "svr.fit(X_train, y_train) # Fit the SVR model according to the given training data.\n",
    "print('Accuracy of SVR on training set: {:.5f}'.format(svr.score(X_train, y_train))) # Returns the coefficient of determination (R^2) of the prediction.\n",
    "print('Accuracy of SVR on test set: {:.5f}'.format(svr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "811c62b8-689f-4f2c-b4d0-6b31d386171f",
   "metadata": {
    "id": "811c62b8-689f-4f2c-b4d0-6b31d386171f",
    "outputId": "5b18e5aa-0c37-4836-ec7d-a5f636af5670",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVR on training set: 0.24863\n",
      "Accuracy of SVR on test set: 0.23657\n"
     ]
    }
   ],
   "source": [
    "svr = SVR(epsilon=0.01, C=2.0) # Let's add some slack to this model\n",
    "svr.fit(X_train, y_train) # Fit the SVR model according to the given training data.\n",
    "print('Accuracy of SVR on training set: {:.5f}'.format(svr.score(X_train, y_train))) # Returns the coefficient of determination (R^2) of the prediction.\n",
    "print('Accuracy of SVR on test set: {:.5f}'.format(svr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6142049-878b-48b9-b21e-7dd2a164622a",
   "metadata": {
    "id": "c6142049-878b-48b9-b21e-7dd2a164622a"
   },
   "source": [
    "Let's do I quick plot to show how this model is performing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "65da7012-caa5-4a8b-8558-9db9c6d140fa",
   "metadata": {
    "id": "65da7012-caa5-4a8b-8558-9db9c6d140fa",
    "outputId": "13218a8f-832c-4bd4-aa1e-7e2e3b6035cd",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAHWCAYAAADttCmyAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAdyNJREFUeJzt3Xl8E2X+B/BPWtr0Ti/apNiWclNOubsgCBRBTgUPUBYQFhYoyiGroj8ERKyou8AKorIIriyoqAgI4lJO6bYgR4VahFJbQGgKtDQtLT1ont8fNSF3ZnLNJPm+X6++XjCZzDxJJvnOc30fCWOMgRBCCCEAAB+hC0AIIYSICQVGQgghRAcFRkIIIUQHBUZCCCFEBwVGQgghRAcFRkIIIUQHBUZCCCFEBwVGQgghRAcFRkIIIUQHBUZi0pQpU9C8eXOhi0E8zNKlSyGRSIQuBiEWUWAkHmnjxo1o3749AgIC0Lp1a7z//vucn5ufn4/x48fjgQceQFBQENq1a4c33ngD1dXVevs9/PDDkEgkRn/Dhg0zOuapU6cwbNgwhIWFITQ0FI888ghycnIslqO8vBwxMTGQSCT46quvOJef8GfP9aJrxYoVkEgk6Nixo9Fj9fX1WLZsGVq0aAGpVIoWLVrgzTffxL179/T2O3z4sMnrSiKRIDs72+y56XpxnCZCF4AQR/voo48wc+ZMjBs3DgsWLMCPP/6IF154AdXV1Xj55ZctPvfq1avo1asXZDIZ5syZg8jISGRlZWHJkiU4deoUdu7cqbf/Aw88gPT0dL1tcXFxev8/ffo0+vXrh/j4eCxZsgRqtRoffPABBgwYgBMnTqBt27Ymy/L6668bBWPiePZcL7p+//13vPXWWwgODjb5+MSJE7F9+3ZMnToVPXr0QHZ2NhYvXowrV67g448/Ntr/hRdeQM+ePfW2tWrVyuz56XpxIEaICZMnT2aJiYlCF4O36upqFhUVxUaMGKG3/dlnn2XBwcGsrKzM4vNXrFjBALDc3Fy97ZMmTWIA9J4/YMAA1qFDB6tlGj58OIuIiGC3bt3Sbrt+/ToLCQlhY8eONfmcc+fOsSZNmrA33niDAWDbt2+3eh6u1Go1q66udtjx+FiyZAkT08+OvdeLrqeffpoNGjTI5HVx4sQJBoAtXrxYb/uLL77IJBIJ+/nnn7XbDh06xPszd+b14o2oKdULVVZWYt68eWjevDmkUiliYmIwZMgQnD592uLzqqqq8OKLLyI+Ph5SqRRt27bFe++9B2awQItEIsGcOXPwn//8B23btkVAQAC6d++Oo0ePGh3z2rVrmDp1KmJjYyGVStGhQwd88sknRvtduXIFv/76q9XXdujQIZSWlmL27Nl629PS0lBVVYU9e/ZYfH5FRQUAIDY2Vm+7QqGAj48P/P39jZ5z79493Llzx+wxf/zxR6SmpiIqKkrveAMGDMB3331n8rlz587F448/joceeshieblo3rw5Ro4ciR9++AE9evRAYGAgPvroI6vPe++99yCRSHD58mWjxxYtWgR/f3/cvn0bQONrfPLJJ5GQkACpVIr4+HjMnz8fd+/etXiOoqIiSCQSbN682egxiUSCpUuX6m0T2/WicfToUXz11VdYvXq1ycd//PFHAMD48eP1to8fPx6MMXzxxRcmn1dZWWnU1GqKI68XQn2MXmnmzJlYv349xo0bhw8++AALFy5EYGAgzp8/b/Y5jDGMHj0aq1atwrBhw/CPf/wDbdu2xd/+9jcsWLDAaP8jR45g3rx5mDhxIt544w2UlpZi2LBhyM3N1e5TUlKCPn36ICMjA3PmzMGaNWvQqlUrTJs2zegHZtKkSWjfvr3V13bmzBkAQI8ePfS2d+/eHT4+PtrHzXn44YcBANOmTUNOTg6uXr2KL774AuvXr8cLL7xg1Ex28eJFBAcHIzQ0FHK5HIsXL0Z9fb3ePrW1tQgMDDQ6V1BQEOrq6vTeEwDYvn07/ve//+Gdd96x+nq5unDhAiZMmIAhQ4ZgzZo16Nq1q9XnPPXUU5BIJPjyyy+NHvvyyy/xyCOPICIiQlvm6upqzJo1C++//z6GDh2K999/H5MmTXLYaxDj9QIADQ0NeP755/GXv/wFnTp1MrlPbW0tABhdB0FBQQAa+6ANPffccwgLC0NAQAAGDhyIkydPmjy2M64XrydwjZUIQCaTsbS0NIv7GDalfvvttwwAe/PNN/X2e+KJJ5hEImGXLl3SbgPAALCTJ09qt12+fJkFBASwxx9/XLtt2rRpTKFQ6DUxMsbY+PHjmUwm02vuGzBgAKcmuLS0NObr62vysaZNm7Lx48dbPcby5ctZYGCg9nUAYK+99prRflOnTmVLly5lX3/9Nfv3v//NRo8ezQCwp556Sm+/Tp06sTZt2rB79+5pt9XW1rKEhAQGgH311Vfa7dXV1SwhIYEtWrSIMWZbs5qhxMREBoDt27eP93NTUlJY9+7d9bZpmgX//e9/65XbUHp6OpNIJOzy5cvabYZNqYWFhQwA27Rpk9HzAbAlS5Zo/y/W62Xt2rVMJpOxGzduaM9t2JT69ddfMwDss88+09v+4YcfMgCsY8eO2m2ZmZls3LhxbOPGjWznzp0sPT2dRUVFsYCAAHb69Gm95zvjeiGMUWD0QomJiaxHjx7s2rVrZvcxDIwzZsxgvr6+rKKiQm+/rKwsBoC9//772m0AWEpKitExn376aRYUFMTu3bvH1Go1Cw8PZzNmzGA3b97U+9u0aRMDwI4dO8b7tU2dOpUFBgaafCw+Pp6NGTPG6jE+++wzNnToUPbxxx+zr7/+mk2dOpVJJBK912jO9OnTGQCWlZWl3bZ+/XoGgE2ePJn98ssv7Ny5c+zpp59mfn5+Rj+Wr7/+OlMoFKyyspIx5rjAmJSUZNNzV69ezQDo3fi8+OKLTCqVMpVKZfI5d+7cYTdv3mRHjhxhANi3336rfczWwCjW6+XWrVssMjKSvffee9ptpgLj3bt3WWJiIouNjWVff/01KyoqYl988QWLiopiTZo0YS1btrR4nvz8fBYYGMiGDh2qt90Z1wuhwOiVvvjiCxYQEMB8fHxYz5492ZIlS1hBQYHePoaBcejQoSw+Pt7oWOXl5QwAW7hwoXYbADZp0iSjfRcvXswAsOLiYlZSUqJXIzP198033/B+bfbWALZt28YCAwPZ1atX9bZPmTKFBQUFGdVWDP36668MAFu+fLne9ldffVUbCAGwHj16sNdee40BYDt27GCMNQaJwMBA9sknn2if56jAOGjQIJuee+3aNebj48NWrFjBGGsMUAkJCeyxxx7T2+/y5cts8uTJLCIiwuhz/PTTT7X72RoYxXq9zJw5k7Vq1YrV1tZqt5kblJWbm8uSk5O15ZVKpWzNmjUsJiaGdenSxWpZx48fz/z9/bUtD866XghjNF3DCz311FN46KGHsGPHDvz3v//Fu+++i5UrV+Kbb77Bo48+6pIyqNVqAI1D2CdPnmxyn86dO/M+rkKhQENDA27cuIGYmBjt9rq6OpSWlhpNpTD0wQcf4MEHH8QDDzygt3306NHYvHkzzpw5g9TUVLPPj4+PBwCUlZXpbV+xYgUWLlyIX375BTKZDJ06dcKrr74KAGjTpg2AxuH2zZo1w8MPP4yioiIAgFKpBADcvHkTRUVFSEhIgI8P/6EBpvo4uYiLi8NDDz2EL7/8Eq+++iqys7Nx5coVrFy5UrtPQ0MDhgwZgrKyMrz88sto164dgoODce3aNUyZMkX7WZtibrJ/Q0OD3v/FeL3k5+fj448/xurVq3H9+nXt9pqaGtTX16OoqAhhYWGIjIwEAHTo0AG5ubnIy8vD7du3kZycjMDAQMyfPx8DBgywWtb4+HjU1dWhqqoKYWFhTr1evB0FRi+lUCgwe/ZszJ49Gzdu3EC3bt2wYsUKs4ExMTERGRkZqKysRGhoqHa7ZuRfYmKi3v75+flGx7h48SKCgoLQtGlTAEBoaCgaGhosBhq+NINKTp48ieHDh2u3nzx5Emq12uqgk5KSEu2AEl2aATXWRgj+9ttvAKB9jboiIiLQr18/7f8zMjLwwAMPoF27dgAaR1JeunQJLVq0MHquZtTk7du3ER4ebrEMjvb0009j9uzZuHDhAr744gsEBQVh1KhR2sfPnTuHixcv4tNPP9UbbLN//36rx9a81+Xl5XrbDUfCNm3aVHTXy7Vr16BWq/HCCy/ghRdeMHo8KSkJc+fO1RsYJJFI0KFDB+3/9+7dC7Vazek1/fbbbwgICEBISAgA8V4vHkHoKitxrXv37rHy8nKj7T179mQ9evTQ/t/c4Ju33npL73lPP/202cE3p06d0m67cuUKCwgI0GuCmzJlCvP392fnzp0zKo9mIIPG5cuX2fnz562+vurqahYZGclGjhypt33ixIksKCiIlZaWarfdvHmTnT9/nlVVVWm3jRw5kvn7+7MLFy7oPf+xxx5jPj4+2n5ZlUrFampq9PZRq9Xs6aefNnrtpnz++ecMgF7f1I8//sh27Nih97d8+XIGgL300ktsx44drK6uzup7YCgxMdFonh4fJSUlzNfXly1ZsoTFxcUZDS46e/YsA8A2b96s3aZWq9mIESOMmklNzWOMjo7WG5TFWGM/JgwG34jterl586bR57Vjxw7WoUMHlpCQwHbs2MHOnj1r8dzdunVjCoVCr+/e8LUwxlhOTg7z8/Njo0eP1m5z1vVCqI/R69y+fZsFBwezyZMns3/84x/s448/Zk899RQDwP7+979r9zMMjA0NDWzgwIFMIpGwGTNmsHXr1rExY8YwAGzevHl658Afo+yio6PZG2+8wVauXMkSExNZQECA3kRmpVLJEhMTWVBQEJs7dy776KOPWHp6OnvyySdZRESE3jG5jjJkjLF169YxAOyJJ55gGzZs0E7O1/STaWh+pA8dOqTdduTIEebr68tiYmLYG2+8wdatW8ceffRRBoD95S9/0e536NAhJpfL2fz589m6devYe++9x/r27csAsBkzZuid58iRI2zw4MFs5cqV7F//+hf7y1/+wnx9fdmwYcNYfX29xddiqc8IABswYIDV98PewMgYY6mpqSw0NJQBYF9//bXeY3V1daxly5YsOjqarVixgr3//vvs4YcfZl26dOEUGF955RUGgE2bNo2tX7+eTZgwgXXv3t0oMIrxejHFXB/jk08+qS33u+++y9q3b8+kUinLyMjQ22/gwIFs+PDh7M0332Qff/wxmzdvHgsKCmIymYzl5eVZPDf1MToGBUYvU1tby/72t7+xLl26sNDQUBYcHMy6dOnCPvjgA739TGW+qaysZPPnz2dxcXHMz8+PtW7dmr377rtMrVbr7QeApaWlsS1btrDWrVszqVTKHnzwQZM/KCUlJSwtLY3Fx8czPz8/JpfL2eDBg9nHH3+stx+fHzrGGPv4449Z27Ztmb+/P2vZsiVbtWqVUTnN/dAdP36cPfroo0wulzM/Pz/Wpk0btmLFCr0g9ttvv7Enn3ySNW/enAUEBLCgoCDWvXt39uGHHxqd59KlS+yRRx5h0dHRTCqVsnbt2rH09HS9ARvmmPuhq6ysZAA4TSdwRGDcsGEDA8BCQ0PZ3bt3jR7Py8tjqampLCQkhEVHR7Pp06ezn3/+mVNgrK6uZtOmTWMymYyFhoayp556it24ccMoMDImzuvFkLnAuHLlStauXTsWEBDAIiIi2OjRo9mZM2eM9luzZg3r1asXi4yMZE2aNGEKhYJNnDiR5efnW30dFBgdQ8KYQdoSQuwkkUiQlpaGtWvXCl0Uj7V3716MHDkSP//8s9lJ5YQQ29BwJULc0KFDhzB+/HgKioQ4AY1KJcQNvfvuu3Y9X6VSWc1jKpfL7ToHIe6KAiMhXmju3Ln49NNPLe5DvSzEW1EfIyFeKC8vT29SuimOnC9IiDuhwEgIIYTooME3hBBCiA6P72NUq9W4fv06QkNDzeZlJIQQ4tkYY6isrERcXJzV/LEeHxivX7+uTexMCCHEu129etVokQBDHh8YNQmvr169irCwMIFLQwghRAgVFRWIj4/XWwTBHI8PjJrm07CwMAqMhBDi5bh0qdHgG0IIIUQHBUZCCCFEBwVGQgghRAcFRkIIIUQHBUZCCCFEBwVGQgghRAcFRkIIIUQHBUZCCCFEh6CBsXnz5pBIJEZ/aWlpAICamhqkpaUhKioKISEhGDduHEpKSoQsMiGEEA8naGD86aefUFxcrP3bv38/AODJJ58EAMyfPx+7d+/G9u3bceTIEVy/fh1jx44VssiEEA/UoGbIKijFzpxryCooRYOaVuPzZqJaj3HevHn47rvvkJ+fj4qKCjRt2hRbt27FE088AQD49ddf0b59e2RlZaFPnz6cjllRUQGZTAaVSkUp4QghRvblFmPZ7jwUq2q02xSyACwZlYxhHRUClow4Ep9YIJo+xrq6OmzZsgVTp06FRCLBqVOnUF9fr7eKeLt27ZCQkICsrCyzx6mtrUVFRYXeHyGEmLIvtxiztpzWC4oAoFTVYNaW09iXWyxQyYiQRBMYv/32W5SXl2PKlCkAAKVSCX9/f4SHh+vtFxsbC6VSafY46enpkMlk2j9acooQYkqDmmHZ7jyYajLTbFu2O4+aVb2QaALjxo0b8eijjyIuLs6u4yxatAgqlUr7d/XqVQeVkBDiSU4UlhnVFHUxAMWqGpwoLHNdoYgoiGLZqcuXLyMjIwPffPONdptcLkddXR3Ky8v1ao0lJSWQy+VmjyWVSiGVSp1ZXEKIB7hRaT4o2rIf8RyiqDFu2rQJMTExGDFihHZb9+7d4efnhwMHDmi3XbhwAVeuXEFKSooQxSSEeJCY0ACH7kc8h+A1RrVajU2bNmHy5Mlo0uR+cWQyGaZNm4YFCxYgMjISYWFheP7555GSksJ5RCohhJjTKykSClkAlKoak/2MEgByWQB6JUW6umhEYILXGDMyMnDlyhVMnTrV6LFVq1Zh5MiRGDduHPr37w+5XK7X3EoIIbby9ZFgyahkAI1BUJfm/0tGJcPXx/qK78SziGoeozPQPEZCiCU0j9E78IkFgjelEkKIkIZ1VGBIshwnCstwo7IGMaGNzadUU/ReFBgJIV7P10eClJZRQheDiITgfYyEEEKImFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0UGAkhBBCdFBgJIQQQnRQYCSEEEJ0NBG6AIQQ4s0a1AwnCstwo7IGMaEB6JUUCV8fidDF8moUGAkhRCD7couxbHceilU12m0KWQCWjErGsI4KAUvm3agplRBCBLAvtxiztpzWC4oAoFTVYNaW09iXWyxQyQgFRkIIcbEGNcOy3XlgJh7TbFu2Ow8NalN7EGejwEgIIS52orDMqKaoiwEoVtXgRGGZ6wpFtCgwEkKIi92oNB8UbdmPOBYNviF2oRF1hPAXExrg0P2IY1FgJDajEXWE2KZXUiQUsgAoVTUm+xklAOSyxhtN4nrUlEpsQiPqCLGdr48ES0YlA2gMgro0/18yKplaXwRCgZHwRiPqCLHfsI4KrJ/YDXKZfnOpXBaA9RO7UauLgKgplfDGZ0RdSsso1xWMEDczrKMCQ5Ll1E8vMoLXGK9du4aJEyciKioKgYGB6NSpE06ePKl9nDGG119/HQqFAoGBgUhNTUV+fr6AJSY0oo4IpUHNkFVQip0515BVUOoRrRK+PhKktIzCmK7NkNIyioKiCAhaY7x9+zb69u2LgQMH4vvvv0fTpk2Rn5+PiIgI7T7vvPMO/vnPf+LTTz9FUlISFi9ejKFDhyIvLw8BATRiSwg0oo4IgQZ7EVeRMMYEu+V65ZVXkJmZiR9//NHk44wxxMXF4cUXX8TChQsBACqVCrGxsdi8eTPGjx9v9RwVFRWQyWRQqVQICwtzaPm9VYOaod/Kg1ZH1B17eRDd/RKH0Az2MrzeNFcX9ckRa/jEAkGbUnft2oUePXrgySefRExMDB588EFs2LBB+3hhYSGUSiVSU1O122QyGXr37o2srCyTx6ytrUVFRYXeH3EsGlFHXIkGe1nmic3LQhM0MP72229Yv349WrdujR9++AGzZs3CCy+8gE8//RQAoFQqAQCxsbF6z4uNjdU+Zig9PR0ymUz7Fx8f79wX4aVoRB1xFUqfZt6+3GL0W3kQEzZkY+7nOZiwIRv9Vh6k6VJ2ErSPUa1Wo0ePHnjrrbcAAA8++CByc3Px4YcfYvLkyTYdc9GiRViwYIH2/xUVFRQcnYRG1BFXoMFepplrXtbMJaYbVNsJWmNUKBRITk7W29a+fXtcuXIFACCXywEAJSUlevuUlJRoHzMklUoRFham90ech0bUEWejwV7GqHnZuQQNjH379sWFCxf0tl28eBGJiYkAgKSkJMjlchw4cED7eEVFBY4fP46UlBSXlpUQIgxN+jRzt1wSNI5O9ab0adS87FyCBsb58+cjOzsbb731Fi5duoStW7fi448/RlpaGgBAIpFg3rx5ePPNN7Fr1y6cO3cOkyZNQlxcHB577DEhi04IcREa7GWMmpedS9DA2LNnT+zYsQPbtm1Dx44dsXz5cqxevRrPPvusdp+XXnoJzz//PGbMmIGePXvizp072LdvH81hJMSL0GAvfdS87FyCzmN0BZrHSIjnoGXOGtFcYv74xALKlUoIcRuawV7eTtO8PGvLaUgAveDorc3LjiR4rlRCCCH8UfOy81CNkRBC3BTNJXYOCoyEEOLGqHnZ8agplRBCCNFBgZEQQgjRQYGREEII0UGBkRBCCNFBgZEQQgjRQYGREEII0UGBkRBCCNFBgZEQQgjRQYGREEII0UGBkRBCCNFBgZEQQgjRQblSCSHEDdBalK5DgZEQQkRuX24xlu3OQ7GqRrtNIQvAklHJtLyUE1BTKiGEiNi+3GLM2nJaLygCgFJVg1lbTmNfbrFAJfNcFBgJIUSkGtQMy3bngZl4TLNt2e48NKhN7UFsRYGREEJE6kRhmVFNURcDUKyqwYnCMtcVygtQYCSEEJG6UWk+KNqyH+GGAiMhhIhUTGiAQ/cj3FBgJIQQkeqVFAmFLADmJmVI0Dg6tVdSpCuL5fEoMBJCiEj5+kiwZFQyABgFR83/l4xKpvmMDkaBkRBCRKJBzZBVUIqdOdeQVVCKBjXDsI4KrJ/YDXKZfnOpXBaA9RO70TxGJ6AJ/oQQIgLWJvEPSZZT5hsXkTDGPHoCTEVFBWQyGVQqFcLCwoQuDiGEGNFM4jf8MdaEPaoZ2o9PLKCmVEIIERBN4hcfCoyEECIgmsQvPhQYCSFEQDSJX3woMBJCiIBoEr/40KhUQoggaH3BRppJ/EpVjcl+Rgkap2bQJH7XocBICHE5Wl/wPs0k/llbTkMC6AVHmsQvDGpKJYS4FK0vaIwm8YsL1RgJIS5jbWqCBI1TE4Yky72uhkST+MWDAiMhxGX4TE1IaRnluoIJzLC/dWTnOAqIAqLASAhxGZqaYIz6W8WH+hgJIS5DUxP0UX+rOFFgJEQAplZR8Aa0vuB9lApOvKgplRAX8+amM5qacB/1t4qXoDXGpUuXQiKR6P21a9dO+3hNTQ3S0tIQFRWFkJAQjBs3DiUlJQKWmBD7UNMZTU3QoP5W8RK8xtihQwdkZGRo/9+kyf0izZ8/H3v27MH27dshk8kwZ84cjB07FpmZmUIUlRC70FSF+2hqAvW3ipnggbFJkyaQy+VG21UqFTZu3IitW7di0KBBAIBNmzahffv2yM7ORp8+fVxdVELsQk1n+nx9JF7xOs2hVHDiJfjgm/z8fMTFxaFFixZ49tlnceXKFQDAqVOnUF9fj9TUVO2+7dq1Q0JCArKysswer7a2FhUVFXp/hIgBNZ0RXZr+VgBGg5G8rb9VbAQNjL1798bmzZuxb98+rF+/HoWFhXjooYdQWVkJpVIJf39/hIeH6z0nNjYWSqXS7DHT09Mhk8m0f/Hx8U5+FYRwQ01nxBD1t4qToE2pjz76qPbfnTt3Ru/evZGYmIgvv/wSgYGBNh1z0aJFWLBggfb/FRUVFByJKFDTGTGF+lvFR/CmVF3h4eFo06YNLl26BLlcjrq6OpSXl+vtU1JSYrJPUkMqlSIsLEzvjxAxoKYzYo6mv3VM12ZIaRlF14DARBUY79y5g4KCAigUCnTv3h1+fn44cOCA9vELFy7gypUrSElJEbCUhNiOms5cy1sTKRD7CNqUunDhQowaNQqJiYm4fv06lixZAl9fX0yYMAEymQzTpk3DggULEBkZibCwMDz//PNISUmhEanErVHTmWt4cyIFYh9BA+Pvv/+OCRMmoLS0FE2bNkW/fv2QnZ2Npk2bAgBWrVoFHx8fjBs3DrW1tRg6dCg++OADIYtMiEN4+1QFZ9MkUjCsH2oSKVDtnFgiYYx5dNtCRUUFZDIZVCoV9TcS4gUa1Az9Vh40O2dUM8jp2MuDqJbuRfjEAlH1MRJC+KN+NH18EikQYorgmW8IIbajfjRjlEiB2ItqjIS4KUpIbholUiD2osBIiBuitfzMozUfib0oMBLihqgfzTxKpOBc3tCnTX2MhLgh6kezTJNIwbD/Ve7l/a/28pY+bQqMxOs1qJnbTbanfjTrKJGCY3nT3FAKjMSruesdMCUk54YSKTiGty2yTX2MxGu586hO6kfzTkL173lbnzbVGIlX8oQ7YOpH8y6mWjcig/3xWNc4DEmWO7WZ2Nv6tCkwEq/E5w5YzE1x1I/mHcz175VV1eGTzCJ8klnk1C4Ab+vTpqZU4pU86Q6Y1vLzbJZaN3QVO7ELwNvmhlJgJF7J2+6Aifuy1rphyBmJHbytT5sCI/FK3nYHTNyL7iCbzEs3OT/PmYNgvGmRbepjJF5Jcwc8a8tpSAC9ZipPvAMm7sPUIBu+nNUF4C192hQYideiUZ1EbPaeLcbsraftPo4zuwC8YW4oBUbi1bzlDpiI396z1zFn2xm7jkGJHRyDAiPxet5wB0zEbV9uMWZvtT8oAtQF4AgUGAkhRECa6RhczRnYCnfr7mFHzjWUVdVrt1MXgONQYCSEEAHxnY7Rt1U0UlpG4dURydQF4CQUGAkhXkGsq6jwGUGqO4WIugCchwIjIcTjuXoVFa5BuEHNcKuylvNxqf/QNSgwEkI8mqvXEeQahPnMV/SRAGsndMOQZDmyCkpFV+v1NBQYCSEey9WrqHANwub2M2fthAfh4wP0W3nQ7dYOdUeUEo4Q4rFcuY6gtSAMNAbhuntqTknBgcbA9+HEbvDxkbjt2qHuiGqMhBCP5cpVVLgG4c+yijg1ny4e0R5T+iYBaKwpuvPaoe6GaoyEEI/lylVUuAbXy2XVnPaLDpXC10fi0lovaUSBkRDisVy5igrX4Fpxt976TjrH86S1Q90FBUZCiMdy5TqC1oKwxrc51zkd73ZV4zQOWjvU9SgwEkI8mqvWEbQUhG2xfM95NKgZrR0qAAljzLFLPYtMRUUFZDIZVCoVwsLChC4OIUQgrsp844j1FDW2Te+DlJZR2ukdgOm1Qx0Z4MWaIchefGIBjUolhHgFV6VQ013KLPPSLaw9dMnmY2n6Dc2tHRoR7IfHuzaDLNAfDWpmdwBzdYYgsaIaIyHEKk+tRTjbzpxrmPt5js3P19QYNTSfw/48Jb7NuY6yqjrtY/YGMGtJBz50cIYgV6MaIyHEYagWYTtbB8SYW3DY10cC1d06bMoscmiKO0vJCTRe+eac18yVpME3hBCzNLUId8q40qBmyCooxc6ca8gqKEWD2jWNYqbOy3Wkqi5Lo2WtZddhaJzsz/c1c1n6qry6HmsP2t4s7E6oxkgIMcnVeUYdQajaraXzLhmVjFlbTkMC44EzDEB4kB/Kq7ktOMwlgGkm+/PpT+U6B/LjowWYM6iVaD5vZ6HASAgxiU/GFTGsC+jqVTT4nNfUwBlNANQM1OHSf6us4BbAdPfj0j/Mtcm3qq4Baw/mY25qG077uysKjMQlaPCG+3GnjCtC1W65nve9J7rgpWHtUHanFpHB/pDLAvW+A1xvLMrucFu7MTP/JkZ3icPag/nYlFmEcp1sO6Zq0L2SIhEe6Ke3nzmbMoswZ1Brs++jJ3zXKTASp6PBG+7JnTKu8K3dOurHm+t5n914XLstMtgfb47pqHc+c+Ux3B4e6MepXN+dLcZ/85SoqGkwesxUDdrXR4J+raPx3Vnrfcbld+vNthJ4ynedAiNxKqGat4j9NANHlKoakzUicyMn+XBUgOJTu+X7422pjLbUlsuq6jB762n89fckLBqebLY8o7sosOvnYr3tkcH+nM5Rc0+NmnumHzNVg96XW4w9HIKihqnX7UnfddGMSn377bchkUgwb9487baamhqkpaUhKioKISEhGDduHEpKSoQrJOGF6/p0rho1SPhxdp7RfbnF6LfyICZsyMbcz3MwYUM2+q08yGmkq+EI0OgQKadzFt2q4jXK1loZuZ7XlI+OFmLFnl9MlqdYVYOPjhYabb+tM2/RHpqabPYfo2e5rg+pYfi6Pe27LooJ/j/99BOeeuophIWFYeDAgVi9ejUAYNasWdizZw82b94MmUyGOXPmwMfHB5mZmZyPTRP8hZNVUIoJG7Kt7mc4iZmIizOax8zVLrikODNVHnmYFDX31FBV15v9gY8M9oOfjwQllaaDi6YGfOzlQdpalKUyzuifhJ0516Gs4NbvZ+6cQv4Ahwf64bm+zbEqI5/X8+RhUiwd3UH7GbnDd92tJvjfuXMHzz77LDZs2IA333xTu12lUmHjxo3YunUrBg0aBADYtGkT2rdvj+zsbPTp00eoIhOO3GnwBjFPN8WZIwZU2DNQxlywKqmo1W4zF2zKqiwPLNHth+yVFGm1BvTR0UKLx+NC6FpJ+d163kERaHy/dZtHPe27LnhTalpaGkaMGIHU1FS97adOnUJ9fb3e9nbt2iEhIQFZWVlmj1dbW4uKigq9P2LMFZOg3WnwBrFMk2d0TNdmSGkZZdcoQ1sX3uUSUMOD/BAbZt/1dKOyhtN8QW9m2Dzqad91QWuMn3/+OU6fPo2ffvrJ6DGlUgl/f3+Eh4frbY+NjYVSqTR7zPT0dCxbtszRRfUorho55orBG8T92Fq74BJQy6vr8dlzD2LW1tO4U2tm9IkVMaEBblOzEZJhDduTvuuC1RivXr2KuXPn4j//+Q8CAhx3F7Fo0SKoVCrt39WrVx12bHdkWDPce9Z1Kb5cuUgscR+21i64BqvswlKbgqLuuobuUrMRgxuVNR73XResxnjq1CncuHED3bp1025raGjA0aNHsXbtWvzwww+oq6tDeXm5Xq2xpKQEcrnc7HGlUimkUttHinkSUzVDH4npfg1nTYI2t1yOpbRXxLPZWrvgGqyul9/lXSbDH29rZST3aT4XT/quCxYYBw8ejHPnzulte+6559CuXTu8/PLLiI+Ph5+fHw4cOIBx48YBAC5cuIArV64gJSVFiCK7FXODFCx1JTorxZejB28Q96apXZjLHwqYrl1wDahxEYG8yxQZ7I8Vj3fUm/BurozuQh4mxZ3aBpublK0xdQPjKd91wQJjaGgoOnbsqLctODgYUVFR2u3Tpk3DggULEBkZibCwMDz//PNISUmhEalW2DIvSZcz+ldctUgscQ+21C64BtRQqR/WHSrgVZ5uCTLkXlMhVOqHPn8MLjJXRjF7bXg7xIQFaAPS/jylxTUWbWXpBsYTvuuCT9ewZNWqVfDx8cG4ceNQW1uLoUOH4oMPPhC6WKJn74g66l8hrmBL7YJLQG1QM6MVK6zZf/4m9p+/ibWHChAe5Ie3x3bCsI4KvTJmXrqFtYfEu+xSeJAfpvZroff+WQvuClkAxvdMwKqMi7zO5Y7No3yIYoK/M3njBH9bVw03nOBMiFhZSyW3L7cYM7ectusc0/o2R2qyXHvsN3b/gk8yi+wsufN8OLGbNogrVXdRVlWHyBAp5GEB6J4YgVOXb0NZUWOUyBwA+q08aLWJ+r0nuuBWVa3bNo+61QR/4ni21PjENnLMEzL0E8fic00M66jAhxO7YemuX2zOTLMxswgbM4u0eUvFGhQ1WWiAxgBnrma4ZFQyRneJ076HGlybqPu2jjZbBq5J0N3le0w1Rg/UoGYW7wCBxtGpugNxxJQB31My9BPHsfWaaFAzZP9Wir98+hPu1qttOrfYB9/Iw6QY0zUOHx8ttFpOwyZm3ffQ1veYTxJ0Ib/HfGIBBUYPpRmVCpi+A1z3zIOICJaK7k7OnhyaxDPZe01wzeMpJn6+EtQ3OP+n2fA95FvDM/fZcD2fK/GJBYKnhCPOoel0l8v0m1XlsgCsn9gNwzvHOSzFl6N4WoZ+sXNFWkB7OeKacMcsNvUNDGMfdH7gYH/8ad5DPqn/bBn97i7fY+pj9GDuNqeI72KzxHbu0lztiGvCXUdZXynjn6jAVly/V7o1yluVtTaNfneH77FNgfHHH3/ERx99hIKCAnz11Vdo1qwZPvvsMyQlJaFfv36OLiOxgzvNKfK0DP1i5U4LytpyTRg2B3ZPjEBsqBQllbYvDyWEk5fLXXq+/XlKi78Vpm6m7CHm7zHvptSvv/4aQ4cORWBgIM6cOYPa2saLTaVS4a233nJ4AYn38LQM/WLkbs3VfK8JUwsL91qRgZ7N3SN5tZC+PPm72c9dczPlyEQHYv4e8w6Mb775Jj788ENs2LABfn5+2u19+/bF6dP2zRsi3k2T8stcQ69ukmdiG1uXfBIKn2vC3I93+d16fHfOccnxPdWd2ntYe9A4gUGDmmHprl8cNjLXHb7HvAPjhQsX0L9/f6PtMpkM5eXljigT8VK6GfrNEcs8S3sJNfDF3Zqrua7aAMCuNIik0ab/FRpdi2sPXrJ5Lqghsc2XNod3YJTL5bh0yfiu4tixY2jRooVDCkW817COCszonwTD74wEwMjOcgxJNr+yirsw1dzXb+VBhy75ZY47NldbG2E9rKOCFhZ2kPLqer3Wgn25xbzTxQGNNcK/9k+CwsJnxpUQN5G8B99Mnz4dc+fOxSeffAKJRILr168jKysLCxcuxOLFi51RRuJF9uUWm5yozADsPqvEj5f2a/NYuiOhB764y4KyhgNohiTLLY6wFksN1xNo3ktNfzRXQ9rHYHjnOMjD7n82Lw1rb9eoeKFGT/MOjK+88grUajUGDx6M6upq9O/fH1KpFAsXLsTzzz/vjDISL8FlXlR5db3oRk5yZW3gizPWwzRk65JPrmTLj6GYarjuTvNe8q2F7z9/A7nXK/SuH3tGxQt5E8m7KVUikeC1115DWVkZcnNzkZ2djZs3b2L58uXOKB/xIly/iLoTkt2JWAa+cGmadCXdprI1GfkmB9BofgzNNTf3SopEeKCfyccIN4aDYmyphVv7nLgSevS0zRP8/f39kZxseaAEIXzw+SKKfYKwKWIa+CKW5A9c58Zpfv5e25GLQe1i4d9E/57e10eC5/om2dQfRhoxAKO7NN4UZRWUIr/kjk3HMNfyYSrdHADttuhgKSABbt2ptZo8wNlJAngHxoEDB0IiMf/lOXjwoF0FIt6Lb3OYu/UriW3gi9DJH/jm2QSA0qo69EnPwFuPG/czzxnUCpv+V8hrHUai76Ojhfji5O92vYeGQatBzbD2YD42ZRah/O7944YHNdbw7TmXs34DeAfGrl276v2/vr4eOTk5yM3NxeTJkx1VLuKFNANDuPZruFu/krsMfHEG3dpCdLAUasbwytfnbJpeUVZVj5lbTmN+amvMGdRarz/r7bGd7F6H0ds56sbiRmUN9uUW45Vvzpk8piPO46zfAN6BcdWqVSa3L126FHfu8K96E6KhGRhi7YfNXQOIOwx8cQZHpxLTWJWRj20nrmLp6PuDcu6vw5gHZYV7tSh4mqJb1VidcdEpc0ud/RvgsNU1Jk6ciE8++cRRhyNeSvPDpmlmMeTuAURsA1+czRmpxHQpK4wHewzrqMDfn+zilPN5uxCpL+YNbg15mNRiNiJ5mBTbTlxxWlAEnPsb4LDVNbKyshAQ4F5NW0ScNAND1h68hE2ZhXr9EnIRrgDBl1gGvjibLcsS2eq1Hbm4W9cAuSwQvZIicavKvRKGu4s7tQ1YcyAfM/on4eOjhWZbPib0SsCqjHynlMEVvwG8A+PYsWP1/s8YQ3FxMU6ePEkT/InD+PpIMDe1NeYMauWRAUTogS+u4KpsNAyNg3Lmf/kzgMbaSl8Pf2+FxNCYcHzdMw9i+Z7zep+xJmjV3lM79JyLR7RHdKjUZb8BvAOjTCbT+7+Pjw/atm2LN954A4888ojDCkYI4B0BxFMJNWpYWVGLr89cF+Tc3uJ2dT3yb9zBsZcHaW9cIwP98WtJJf5XUArGHNNOoOlLnNI3yaU3xLwCY0NDA5577jl06tQJERERzioTIcQDuNuoYcLPpswizBnUGikto5C+Nw8bfiyEI+fbCzmegNfgG19fXzzyyCO0igYhxCprS0YR91Z+tzHhePrePHx0lH9QDPL3NTvIDhB2QBrvptSOHTvit99+Q1JSkjPKQwjxEJampxDPcP12NTb8WMjrOeFBfnjuT0mYM6gVANOZb4QeT8A7ML755ptYuHAhli9fju7duyM4OFjv8bCwMIcVjhDi3jTTU17dkYuyqjqhi0Mc7MzV25xqin/uk4AezSNNBjwxjiHgHBjfeOMNvPjiixg+fDgAYPTo0Xqp4RhjkEgkaGhocHwpCSFua1hHBe7WNWhHjRLPUVLJbVqMRCLBmK7NnFwax+EcGJctW4aZM2fi0KFDziwPIcQDyWWBQheBOEGwP7cQkhgZ5OSSOBbnwKgZfjtgwACnFYYQ4pm45ImNCPLDhF4J+O1WFQ7+esPhc+GI443r9gB2/XzdYnOqjwT4c0pzl5XJEXiNSrW0qgYhxDzdNQezCkrdbi1JvgxfL9A47B6AyVGqDEBZdT3WHS7A97lKCooip1m78U+tojH9IcsDMac/lGS0TJjY8Rp806ZNG6vBsazMuYusEuJubFmR3t3orp5ReLMK/86+rDfYRvN610/s5pSE4sR1DOcXLhreeMNjOI/RR9IYFF8a1h5ZBaVulb1KwjimKPDx8cHq1auNMt8YEtvSUxUVFZDJZFCpVDRilricuTUHNT8LYk8cbmpxWcMfNa6rZ0jQ+HqHJMuR/Vsp/ldwC5szi1BVRwP23InmJscw32/X+HBsPX4Zl8uqkRgZhD+nNMfBX0tEc1PIJxbwCoxKpRIxMTEOKaSrUGAUBy4/sJ6mQc3Qb+VBswFDk+7q2MuDRPlecKnp8l1sWCELwOIR7Y1ybPLVv3UUjuaX2vx8YpuhHWLwwbM9sD9PafO1IdRNoVMCo6+vL4qLiykwEt68oSnRlKyCUkzYkG11v23T+4huLtfes9cxe+sZo+26P2pDkuUWA78zTewdjy3Hr7r8vAQY2LYpDl24abRdk8Rhat/mGNw+Fi9+mQNlhenpHELcFPKJBZx7RB2VFJZ4F3Pr8SlVxuvoeRquSbSFSrZtzt6zxZizzTgoAvez1yzbnYfsglLB+gpDA/ygkFEuViGYCorA/Wvjk8wiPPuv42aDombfYlUNThSKc0wK58CoVqvdrrZIhGVpPT7dH1hPHaHJNYm2mJJt78stxuytpy0Ov9f8qGUWmP6BdAWJRIIeieGCnZ84hthuCjXcawwtcSvW1uMT+12jvawl0dYMee+VFOnKYpmluZHh6nq5cD9qn2Vfxu6zSsHOTxxDTDeFuigwEqdx16ZER9Ek0QaM5+4JuaSOOXwXFm4WIVw2m8qae4Kdm9hPbDeFhigwEqdxx6ZER9Mk0ZYb9IfZu6SOMxIG8LlBUcgC8KeW0Xafk3gfMd4UGuK9ugYhXHFJAyYX4V2jo6aWaI5Te0+N957sAjDgVpX9S+pwGeVry2soulXFuQxLRiWjT4soKGQBNFmf8CJ3wLXqbBQYidNYWo9PrHeNjppaYuk4tkzN0Px4ZOQpsTGzyOhxzSjf9RO7AQDv17AvtxirMvKtlkMC4P3xXbXH0Xy+AL/1FkMDfFFZQxP7PZ3m5ve9J7qYvCkU61QuzvMY3RXNYxSeWC9+Q46akOzoic18MsvIgvygqq7ndW5riQgMGX52e88W4/928ltv8c99EjG8kwKZl25h7aFLnJ9H3Ie1693VCQCcMo/RGdavX4/OnTsjLCwMYWFhSElJwffff699vKamBmlpaYiKikJISAjGjRuHkpISAUtMbDGsowLHXh6EbdP7YM34rtg2vQ+OvTxIVEHRUVNLHD1Fxdw8UFMYgHITQdHaufkOuilW1WDmltNYk5GPvWevY/mePN6LECdEBiGlZRTaxYbyeh4RryA/X73/W+pHF/tULkGbUh944AG8/fbbaN26NRhj+PTTTzFmzBicOXMGHTp0wPz587Fnzx5s374dMpkMc+bMwdixY5GZmSlksYkOrv0Dvj4S0WV30cVnaoml1+GI42jeU6XqLpbvOc+ridISc+e2dVTwqoyLNpelTUwIGtQMr+/OtfkYRFyq6xubxsMD/fBc3yTMGdTKbDeJo75vziJoYBw1apTe/1esWIH169cjOzsbDzzwADZu3IitW7di0KBBAIBNmzahffv2yM7ORp8+fUwes7a2FrW19zMuVFRUOO8FeDl3aSLlwlFTS+w9DtdmU3tk5Cn1fmyEGBU894scPNe3Ocqq6l1+buJcqrv1WJ1xEW3lIWZ/B8Q+lUs00zUaGhrw+eefo6qqCikpKTh16hTq6+uRmpqq3addu3ZISEhAVlaW2eOkp6dDJpNp/+Lj411RfK/jaaneHDW1xJ7j8Gk2tceOnGt6TVTWEhE4Q/ndek6DfYj74dIUKvapXIIHxnPnziEkJARSqRQzZ87Ejh07kJycDKVSCX9/f4SHh+vtHxsbC6XSfMaLRYsWQaVSaf+uXqVEw44m9v4BWzgqS42tx7H0njpaWVW9XrYhS4kICLGFtaxWYs8KJXhgbNu2LXJycnD8+HHMmjULkydPRl4e97RUhqRSqXYwj+aPOJYnpnpzVJYaW4/DdwCMvfbn6d9cmktEQIg9zDWFij0rlOCB0d/fH61atUL37t2Rnp6OLl26YM2aNZDL5airq0N5ebne/iUlJZDL5cIUlgAQf/+ArRyVpcaW4/B9rzQ/F1P+lIjIYH9ezwUaV0BYvvsXvaw5Q5LleLoHdT0Qx7HUFOqsrFCOILoJ/mq1GrW1tejevTv8/Pxw4MABjBs3DgBw4cIFXLlyBSkpKQKX0ruJvX/AHsM6KoxWJrclEwff4/B9rzTZQ2SB/tj8v8u8nquxMbMIGzOLoJAFYHQXBXbmXLe4VBAhXHHNauWo75ujCRoYFy1ahEcffRQJCQmorKzE1q1bcfjwYfzwww+QyWSYNm0aFixYgMjISISFheH5559HSkqK2RGppJGzUyy5a6o3rhw1tYTPcay9pwAQGeyHxSM7QB52/zNdvvsXu8tZrKrBR0cL7T4OIQD/plAxTuUSNDDeuHEDkyZNQnFxMWQyGTp37owffvgBQ4YMAQCsWrUKPj4+GDduHGprazF06FB88MEHQhZZ9FwxhcIVqd7EmD/Rmbi8p2893knvM9yXW2wyPRwhruQjgd76nYa5UN0RpYTzIK5OsWRvEDYX/DxpfiRfXF873zRuhNjL3A3bume6ISLYHzcqaxAdLAUkwK079ifLBxx7g8wnFlBg9BDWfig1zZvHXh7k0JqXrReuuQAwuosCHx8tdFlwFyPdzDdlVXWIDJHqNZ8CQFZBKSZsyBa4pMRb/LV/Enb9XGzxhs3RN7SOPh4FRh3eEhi5/lBum95H8PZ8czVba5wV3MXI0o/CkGQ5Vu2/SMm3iUs80a0ZHmrT1GJt0BmJ8x3d+sUnFohuVCqxjbtMobBnIrvQ+RNdxdyPgvKP5N3hQX4or3ZtKrXIYH/eicKJ+/ORAF+dvoavTl8DYHrpNGsJPyRoTPgxJFnO6YbW0cezheDzGIljuMsUCkdMZBc6uDsTl6xCrg6KozrLsXhEe5eek4iDYfIqzc3ZGzpzYLkm/PjkWCGnbFhiSCBCNUYP4S5TKBwR1GJCAxzSKc/lGK4eHevqDDhcnLxcjvE9E4UuBnEhw5GmGppNn2QW4ZM/5sAO78gt4cqKveex7vAlvD22k8VmUDG0flFg9BCumELhCPbUWDXB/XZVrdFAI76d8lw69oUYHSvG2nCxqgZqxhAZ7EerYXgJrmmOlaoaXlOGyqvrMXPLaXxooY9QDK1f1JTqQcScYkmD60oO5vInju6iQNrWM3at6sFlZRBnrB7SoGbIKijFzpxreqnYdEWHSHkf1xWe33aGgiIxormCfST8EtAv3fWL2WZVMSQYpxqjhxFriiUNLjXbGSaGhstlAVg8IhnL99jXKc+lY3/prl8ASBza+c+59inSMeLldykoEvP4LqSjrKg1O4hODK1fFBg9kBhTLOnS1GwNA4VuxoyXhrU3Cu6OWPWbyzGs5QvlOzp2X24xZm45bbRdU/vUrc3fqqJcpcQ9DWgTjZNFt1FV18Bpf0vdBlx+I5yJAiMRhLWarang7ohOeUf24XE5VoOa4ZVvzpl8zFTtU8hRw4Z354TwuSaOXLzF69jWrnUhW78oMHohseQh5VuzdUSnvCMDD5djrT14yeL0CsPaJ5dk4s4wP7U1Nv2vyOVTQYg4KWQBWDyiPZbvOe+UUdLyMCmnPkKhWr8oMHoZseQhtSU4O2JKCpdjxIZJAUhQUmHf1JcGNcOmTG6rVmhqn5b6V5xB81pmPdwK205cBUCBkQBP94hHRLAULw1ti9NXbuOz7CsOPf7S0R1EM+7BFAqMXsRSRhXDvi5H0w2ERbeqsO3EFb2+PC7B2RGd8rrHMIUBGNM1Dg8mRNjd+X+isIzzoBXd2qe5/pUgf19U1zWYLBND40T83WeVnM6na3zPBPzzwEUoK8Q3VYQI44PDl7D6QL7Djxse5Gd1HqMYUK5UL2FrknFHNLuaqqWaOj/ALQeiI2q96XvzLK5B+MEzD8LHR2LXeXbmXMPcz3Os7hce6IcTr6Xi1OXbeu8z0NgUuymzUC/AGk6+Dg/0w3N9kzBnUCvsz1Nafa91aYItIc6QNrAlJH/cuqW0iEafllGC1RQpibgOCoyNwW1zZiGW7zlvdV/dJOOOCEB8EobzSRJuT8DmsmSTjwRYO6Ebhna0vfOfa2L3UZ3lOHm5nPNKI+boJhk/UViGH34pxqdZl+HZ33AiZmJYtECDkogTLS61NV2avi5HNLvyTRjOZxqEPZ3yXNKuqRkwe6vlDB3WcBlIEyz1Ndn8WayqsVijNaXYxGez+X+X+RabELuJJQWlrSjzjQczl73FEk0eUmuJrJftztPLXGEqq4uteT/5TKngkk3GnuMbvk4+59X0ZwKms4JIAPj5OvYryHTKLMb0csTziSkFpa2oxuih+NbWdO/w+E6kN9fkyjW5sCGuUypsberlM2XDVA2Wz3nNDaRRyAIwvmcCVmVc5FwWPmVetf8iIoL8HH5sQgwZLknmqkn4zkSB0UPxqa0Z3uHxmUhvqcmVT3JhTTm4Nr/Y09SraeLk27xs63nNTVResSeP0/ltoVnE2NwqCYQ4ypqnu6KJr4/g86IdiZpSPRSfZjTDJONca1TRIVKrTa5ckwvzaX6xpalXl24TJxea98Oe82r6RMd0baatfX558nfOZbAVBUXibGXVdXrXtrsHRYACo8fiGtwWj2iPYy8P0qvlcM1uDwZOg1g0qc8s4bMCiCMWMh3WUdE4JcNCwQyz+DtyAdXsglLcqb1ndT9CxE7oxc+dgZpSbSSWtGrmcM0SM6VvklG5uU6k55rwemrf5vg+V2myj615dBDv989RC5kO7xyHtZBg9lbjyf6marCOXEA16zd+eSUJERt3H3lqCQVGG4glrZol9maJ4ZLdPquglFNZhiTL8dqIZIfdSDhyIdPhnRX40IdbFn/OTczBUmQVlFp5reK5iSLEFgyNLU5iqhA4Ck3w58nc4As+mVtcyd4gbqlmrJkob61WymXCPh+2ntfaazlRWAZlRQ3K7tQiMtgfclmg3j5199Tok55hdsFeCQBZkB8CmvjqpVcz9X5nXrqFZ/913O73ghAhia1CYAllvtHhyMBoa1o1oTmz2VdzowCYrpU660aB73m53CBY2geAxUQJlhJ+a8q07pluiAj2x43KGkQHS/GXf/+Eu/Vqri+ZENERa4XAFAqMOhwZGLmm+BJTGiRXEKppmet5raWl++CZbvDxgdmWAC5fEHmYFDX31BaXbTKcOuHfxAd19ygwEvcm1gqBIUoJ5ySOHHzhSexZUNSe2iyX83JJdDBn22mEBjSxOA3Dkqhgf7z7RBf8+ZMTFvcznDpBQZF4As1o7OzfStG3VbTQxXEICow8OHLQh6exJXepI2qa1s7LNS+q6q7tUydKq+pwnMMUDUI82ez/nMLKcZ1F36TKBc1j5IHr/D5PHL7saObyuGoyyOzLLXbIeVxVe//tZqVLzkOIWKnu3sNMB353hUSBkQdLSaHFljjXluTarmJv5ho+XFV735tbgvAgP5snYdBkDuIpXvnmnKh+b2xBTak8cZnfJzSxzbM07EdUqxmvJOX24JsX1RG4DtjR9e+pvXCxpBKXy6pRXXsPX52+5oyiEeJ05dX1yC4oRd/W7tvfSIHRBvYMNnE2R6yjaIqtg2RMBekgP19O53REM6imlj9zi3F2G0crr67H/NQ2+PynK3qv11Iib82Ivj+1isZDbZpqpwQR4s6yfrtFgdEb2bNQrrNYa6KUoLGJckiynFcQt7UGai5IV9c3cDrvrcpaNKiZ3TccQ5LlmDe4NdYcyLc419DU5Hy+q1M0jw7Ckb8NxGdZRX/U/hqw59x1i/MVdZvfswtKHVa7lUgAz56MRcRL+EqCPSgwehC+6yhyYWsNlO96kKYs33Me/zpWaFcTsKmgbkjzFX57bCcMaherDWqJkUGIDQ3A85+f4fw6im5VYcC7hzgHtxn9k/QSDLz89TmOZ7JsVGc5dp9VOuRYxLOFB/qh/O79+bfhQX4W5+NyIbZKA18UGD2Io+dZ2lMD5bMepCXFqhrM3HIa81PbYM6gVkYp3Cw17Vqb2K8h18luYxjUFLIAzOifhJ0516GsMJ80XYLGH5RVGfm8Xt+XJ39HO4UMV0qreD/XlKhgfywf0xG1DWoKjISTtIEtobpbD0CC3kmR+FVZgRV7f7X5eBFBfujTggIjEQlHz7O0pwbq6GkSqzIuYtuJy1g6ugMA4/Rshk27XGqs4UF+WDehG/q0jML+PKXZmvHHRwux7pluyL9xB6syLhodRxOObakd366ux/wvcmx45n2RQX7o3SISLZuGIqVlFPq0iMLmzEK7jkm8gwTQC4IfHLZ/Dc/0sZ0sdn+IfWUigAKjR+G61BSXeZYNaobMSzc5nddUEHTGNAllRa3ZQTSamuW0vs2RmiyHmlke+Qo0Dpbx+eMLaa1mvHxPHo69PAht5SFYuitPrx9S/scSWqaCprOFSH2hBvB9bgmAEqw9dAkKWQAeSY5xeVmI+zG85vkExRBpE701RbmOOxDTiHlzKDB6EHuXmtLg0i+nyzAINqgZ1GqGEKkv7tRyG2jjKBszi7AxswjhgX6c9r9RWcN7AWLG9AfS3K27hzNXhMl80/j+6r/HxaoafJp1RZDyEO8RGtAEH03sjltVtZxqfs4aMe8MFBg9jL3zLLn2ywGma6B8g6qz6A4msCQmNIBzs29GnhIbM4tMnOseDl+khYeJdylW1cDHR4IxXZtZ3ddZI+adhQKjB7J1niWfkaSmaqB8gqqhyGA/3K6qt2sUKx+6QT2b44LL236iWhghurjeVDpjxLwzCZoSLj09HT179kRoaChiYmLw2GOP4cKFC3r71NTUIC0tDVFRUQgJCcG4ceNQUlIiUIndh2ae5ZiuzZDSMorTXRifkaRyWYBe04c90zMUsgC8OaajDc+0jVFQ53iDWl1Hq2EQoovrWAJ3W5lI0MB45MgRpKWlITs7G/v370d9fT0eeeQRVFVVafeZP38+du/eje3bt+PIkSO4fv06xo4dK2CpPRfXi3LOwFY49vIgvWZZe6ZnjO8Zj9RkOealtoaMY98gH4b9jRHB/pg7uDVq76mRVVCKGxXi+DIS4i74LphQdKvK+k4Qz8pEgjal7tu3T+//mzdvRkxMDE6dOoX+/ftDpVJh48aN2Lp1KwYNGgQA2LRpE9q3b4/s7Gz06dNHiGJ7LK4XZd9W0UY1UHvu9FZl5GPNgXy7h4mbM7hdU+z7pQRVdY2DVMqq6rD6wP05gyFS6lEghCu+Cybsyy22OkeXz4h5VxDV6hoqlQoAEBnZ+OacOnUK9fX1SE1N1e7Trl07JCQkICsry+QxamtrUVFRofdHuLFnWS177/ScmYz/6zPXtUHRFN0h54QQywy7USzRdLFwIZaViQARBUa1Wo158+ahb9++6Nixsb9JqVTC398f4eHhevvGxsZCqTSd1SM9PR0ymUz7Fx8f7+yiewzdZbUMWbtL7JUUifAgxzeDEkLE44luzYy6USzh2sUyL7WNaKZqACIKjGlpacjNzcXnn39u13EWLVoElUql/bt69aqDSug9ZCYCXHiQn8W7xP15SrvzKxJCxO3r09ewP497qkGuXSzNo4NsLZJTiKJzZc6cOfjuu+9w9OhRPPDAA9rtcrkcdXV1KC8v16s1lpSUQC6XmzyWVCqFVCp1dpE9kqXpFrctBD0+zSWEEPe2bHceQqV+nCb2OzpNpasIGhgZY3j++eexY8cOHD58GElJSXqPd+/eHX5+fjhw4ADGjRsHALhw4QKuXLmClJQUIYrssaxNt3BFwnBCiLhp5hs+u/G4dpullG6OTFPpSoI2paalpWHLli3YunUrQkNDoVQqoVQqcffuXQCATCbDtGnTsGDBAhw6dAinTp3Cc889h5SUFBqR6mB806LpEsvcI0KI62lSuu3LLTZ6THfcgmGdku/oVlcSNDCuX78eKpUKDz/8MBQKhfbviy++0O6zatUqjBw5EuPGjUP//v0hl8vxzTffCFhqz2TPBFyuzSAPxoe7/dQICQB5mBT/+UtvTO3bXOjiECI4TU1w2e48NJgYXq5JUymX6f9O8Bnd6mqCN6VaExAQgHXr1mHdunUuKJH3sqcvoFdSJORhUovrFQLAmavlthRNVBiAEZ0av8h7zxnfIRPijayldLM1TaVQ3Pv2nTiMPX0Bvj4STOiVYNdCuz4S585ldCTNCh6EEH2WWp40aSrdgWimaxBh2dsX0Dw62K7zu0tQJISYJ7bRpbaiwEi07OkLcMQXYlrf5lDIPOOLRYgnmT2gJSKCzDcw8s2dKnbUlEr02NoXoMl8Y88k/9RkOV4dkaw9d3SwFHnFFVix97zNxySE2O+z40WorDGdVlHMo0ttRYGRGOHSF9CgZnrBs3tihM3n0+2/NDx3z6RIpH9/nppaCRGQuaAIcF8E3Z1QYCS87cstxrLdeXrzHiOD/W2qLVq72zx1+TYFRUJEKirYH0f+NhD+TTyrV86zXg1xOk3aOMNkAGVVdTYdz1r/pauSB4zs7Dl3u4Q4ApdW0dKqOpy6fNv5hXExqjF6EMPmTXvmCZk6FgCLaeO4mjOwFVrHhnAqoytGuSlkARjeUYFjl265XSL0AD8f1NSrhS4G8RCRwX5YPLIDyu7UYvkebn37npj5igKjhzDVvGkphyHfY8nDpOjXKtohOVH7tormPJ/J2vxKR+jYLAxpW00nTxe7x7vG4YuTv1NzM3GIsqp6yMMCONUWNTxlioYuakr1AOaaNy3lMOR9rIpafHX6mt1l5Tuk29L8SkfZn3fDLYMiAGz7iYIicawblTWIDuG2QlFksJ/HTNHQRYHRzVlaFcNaDkM+x3IECWwb0m1ufiUhxPGKblXjxS9zOO375piOHjNFQxc1pbo5Pqti9EqKtNgH6czlo2xt1tUwNb/ydlUdlu/JoyWvCHGQEKkvVmVc5LTvX/snYXjnOCeXSBgUGN0c147v/XlKLPgyx2IfpLM60RePaI8pfZOs3llaGzxkan7l0I6NwVKpuovFO3/Bndp7TnkNhIiZBODV0mNu/zu15ucrap8rAf7SLwmLhifzOKN7oaZUN8e14/uTzCKrfZCO7kTXpIniEhT35Raj38qDmLAhG3M/z8GEDdnot/Kg1f5RTbC8UnaXgiLxWnJZAFLbx1jdTyELwAfPPGhXtwRjwL9+LOQ1dsHdUGB0c5pRm5bCjrmYZNgHyeVYXPFJE2Xv4KEGNcOmzEJ7ikuIW3v78U44+OsNq/u99mh7DO8ch2MvD8K26X2w6qkuiAz2t+mcXMcuuCMKjG6Oy6oYlq5d3T5IR44A5boIqSMGD50oLEP5Xfeaf0iII607fInT6OSSP7pLNC0tclmgTck5dH83NBrUDFkFpdiZcw1ZBaVuHTSpj9EDaEZtGs09lAVgeEc5p7UDNf2L5o5ljQSNaeH+b0R7yGWBnJML8Bk8pOlfNOyLVFaId/BNsNQXVRz6bQixRw7HRcCLSqv0/m/vuALN8x05j1oMKDB6CHOrYpwoLOMUGHX7FzXHyi4oRdrW05xqYwyN6aHkskBei5Fy/WJa+gIG+/tyPp+rzRvcGiv2/ip0MYiHq2vgVju7XFqt9/+iW9Vm9uQmJjRA2xViWAJNVwiXliOxocDoQUyN2rSWOUZ3ZQvDY/VtHY23x3UyedGbk3nplsWUdIa1vehgbhOJLX0Bq+rEWSNTyALwe/ldoYtBiNbR/FvYl1uMYR0V2JdbjNUcp2YY0vxudE+MwIB3D5ntCpGgsStkSLLcreY7UmD0cJp+w1lbThsN0eYyQEbTtPrqjnMoq7Jec1x76JL234ZNKaZqe+GBfpBIGke6mcLlCyhWIzsr8K9jNCiIiMuy3XkY1C7W5mQeur8bpy7f5t0V4g5o8I0XMJc5hssAmQY1gyzQH68NT0ZoAL/7KN1RpeZGnpbfrTcbFDW4fAHFRCIBhiTHYMOPhVZfGyGuVqyqwWdZxtO3TBnZWQGFwe9GZLA/nuvbHLJAf879++6WaJxqjF7CXB+kpeYNUzU8PjRNKUt3/QJAwvvu1EcCrJ3wIIYky7Fq/wWbyiAI1ph/lRCxulzGrW9xSHIs1ox/ECcKy5CRp8SOnGsorarDJ5lF+CSzCJHBfpyO426JxikwehFTfZC6dPv/im5VYVVGvt3nZGhMQG4LNQMullTi/3bmcmrG5Ss80A/1DQ2oqnPssk2eUkkM9vfFjP4tobrb+ENIPEdiZBCn/WJCA+DrI9FeA4bXtrXvpbkxDGJHgZEAsL926CyrD1yyvpONnuubxDkvpDf68Nnu6N0yCh2X7BO6KMSBIoP98OeU5vjXsUJOg/K4Li5gyxgGsaI+RmK2/89T+UiAD57phubR3O6avdXKH35F56U/cJ4KQNxDg5rh4K8lVhODaAIa18UFIgwy6HBN8iFGVGP0Yg1qhuyCUrzy9TmnNv+FBfiisrZBNANRhnWI1SYfJ+blXq8QugjECSru3tPOLzSXGMSWxQUW/5Hcg+sYBjGjwOilXNl0WlEjrnmGe3NLcHjpD3jvic6Qh0lt7gMlxB1p7k+X7voFma8Mtjooj+vAGU3GK82xNEvduWNwpMDohcxNlOdKAiA8yA+3q903P2l1XQNmbz2DUZ3l2H1WKXRxCHE5ZUUt1h68hLmprS0OyuOaJOR2VS36rTzoEWnhqI/Ry3DtSLeEAUgf2wl/7Z/kqGIJ5ujFW7yf4+eGd8CEmLIq4yKnpd2s9UeO7qJA2tYzNq+QIzYUGL0M1450LhYNT8YHz3TjPfFfTFQ1/NdwrHfjVQMIMcRl+ShLSULWPdMNu34utmuFHLFx3180YhNHZKDQzX84vLMCtQ1qzP8ix+7jCiVE6stp5XJCPBHXlG2WFirwtLRwFBjdlGEybq6d3I7IQGF4ocvDXJ/VwnDOlD0oKBJvx/WG2VSSEL4r5LgDCowCsDWoadiz9pm1jnQAkDbxQe0969lgNBc6l2Pqigz2Q8/ESPyQV8Jhb9Pcp1GGEPGz54aZ63PdKS0c9TG62L7cYvRbeRATNmRj7uc5mLAhG/1WHuTUOd2gZliTcREzTUzG59rJbakjXYNLUATuX+hcjqn7mJrBbFAMlop3bUVCPJHCzpRtmhtjc999iQPO4WoUGF3IXIYZLkFtX24x+r590Gz+Uj6d3OY60rnSXOjdEyOQeekW3vvhAnKvqTB3cGvEWmhWDQ9qTDhcbmaax6jOcq9Y7T4iyA8jO7vX8HXiuUZ3Udg115DLqFV3SwsnYUws+Uico6KiAjKZDCqVCmFhYYKVo0HNjOb46NLMBTr28iCjC4jvvMNt0/voTbS1tmiwsqIGy7/7hVOibs0RZvRPwhcnfzcKcrLAJpjaNwnNo4MbFyGWALfu1CI6RIoXv8yxOJnekf2GYjWqsxx/f+pBDHj3EK/Rwe3kofhVWenEkhFvpTDzu8OXPV08rsAnFlAfo4vYOnLLlnmH+/OUWPBljtULVNORnlVQynn1CrksAKO7KPDRUdML8Kru3sOqjHx8OLEb+raO1m7PzL9lNcOMpwdFAMgsKMXxglLeU2YoKBJncdSIUVuWthMrakp1EVtHbtky7/CTTONFSHWbaxvUDFkFpdiZcw1ZBaW4fpvb2mxzBrbEkb8NxLdnrlvdd+muX7RNuvtyi5G29TSv1+CpyqrqMWvrKaGLQbzEoLbR1neC40aMam62x3RthpSWUW4ZFAGqMbqMrSO3+F6wPpLGwS2GNIsGL/rmHJbu+kWv9ibheO32bdUU6w8XoKTSem5RZUUtNmcW4nr5XWyktfz00PQQ4mw+EmD6Q0l4uG0sDl6wnt3JnUaMugIFRhfhmm/QcOQW3wvW0rgbBpjMb2qtl1lTtuzfSrHmAPfFi5fvOc95X0KI4zQN8ceDCRE2/+54O0GbUo8ePYpRo0YhLi4OEokE3377rd7jjDG8/vrrUCgUCAwMRGpqKvLz7V9VXgi2jtyyNhTaFRiA6tp6XkGRiE/agBZ4bXh7PNyGW/MacV83Kuswa8tp7M9T2vS7Y9jd4k7p3BxB0MBYVVWFLl26YN26dSYff+edd/DPf/4TH374IY4fP47g4GAMHToUNTXuk0FBl6V8g+YW9OQ6R9DZVCJbOorwt+7Ib1h3+BLG90rAB888KOj1RJxLd/rWkGQ5r98de+ZaewrRTNeQSCTYsWMHHnvsMQCNtcW4uDi8+OKLWLhwIQBApVIhNjYWmzdvxvjx4zkdVyzTNXTZkvnGlesnip1mWkd4kJ/ZOZHEsg8ndsPx38qw6X9FQheFONm26X2Q0jKK0++Oualhmr1MBVJ7M3m5ikdM1ygsLIRSqURqaqp2m0wmQ+/evZGVlWU2MNbW1qK29v7gkIoK8a1CbirfoDWaodCr9l/A2kMFTiqZOIUH+qH87v0AKAv0w3N9m2PWw63w6jdn8dXpawKWTlwCmvhA2sTH6qohS3f9gr8/1ZUCoxfQDOCz9rtjaWqYZvCepgaqCXxin7toK9FO11AqGxePjY2N1dseGxurfcyU9PR0yGQy7V98fLxTy+lKvj4S9G3VlNO+kcH+ev9XyAIQHuTnls1n657phvmpbSAL/CNzzt16rMrIR/93DmFvLi0yrKvmnprTUlrKilqAQfD+a+J8XAfw8ZlrDdiXyUvsRBsYbbVo0SKoVCrt39WrV4UukkNxzUuY+fIgLB7RHpNSErF4RHsc+dtAvD22kyuL6hARQX5Q3a3HqoyLUN3VbzZVVtSguo76Pm11q6pWFP3XxHk0qRu5DKThM9faWu0ScL81GHWJtilVLpcDAEpKSqBQ3K+Sl5SUoGvXrmafJ5VKIZVKnV08wWgG48zactpkCjUGoHtiBAa+dxjKivsX+rrDl/B412YY0VmB7866z53c7ep6/O2rn4UuhkeKCQ1ASssorJ/Yjfqv3ZSfrwT1DeaDT8dmYUbpB801dfKZa+2JazDqEm2NMSkpCXK5HAcOHNBuq6iowPHjx5GSkiJgyYRnLQn4d2eL9YIi0JhxZWNmkVsFRY0qqhU6nDxMqp27NqyjAsf+aGEg7iPY3weRQf4W99mfd4NzUyefVTI8cQ1GXYIGxjt37iAnJwc5OTkAGgfc5OTk4MqVK5BIJJg3bx7efPNN7Nq1C+fOncOkSZMQFxenHbnqzTQ/ZvNTWwtdFOKGlo7uoB1AoRlVGBkiRcQfK6AQ8Zv+UEtOWagMmWvq5DPX2hPXYNQlaFPqyZMnMXDgQO3/FyxYAACYPHkyNm/ejJdeeglVVVWYMWMGysvL0a9fP+zbtw8BAe75ZjvD5z+5pg810M8Hg9vHumWN0xNFBvtxTvyuS7P6Se09NbIKSnG7qhbL95ynZlQ3tNmOEcXmmjo1rVGGTetyg+ZXT8+oI5p5jM4ixnmMjpJVUIoJG7Jdcq5Qfx+oIaFmTQGlPdwSbeShiAltHFDBd+kqAAj296XPkGitGd8VY7o2M9rOZ84joD/WwdKcRyHxiQWi7WMk1rmy/b6yTk0/qALr17qpdtUC/yY+WDIqmfdoUvoMia5blbUmR6tyWSXDlkxe7kK0o1KJde7afk/4MdcspflheuWbc5QBiOjhuui3bqJ/Wybme9IajLqoxujGxJBgnNwXGuC8+0xTiZ6Bxh+mU/83BPNTWyPIz9dp5yfuJVjK/1q0dWK+p6zBqIsCows4K1O97igyIhwJGgfDVHLIOMOXgkOzlK+PBHNT22DDpB4OPz9xT3dq+V+LnjAx31GoKdXJ+OYSNNXpDcBsU4WmOW3h9p9pAVwBPd61mc0LMitkAVg8Ihn5NyrxybFCvZRu1bX3cEF5B0OSmdU78T4to2werUoI4P4T8x2FAqMTmctUr2myMKwJmAqi4X/MK9PtQzIMrEOS5fCRnHXeCyEWzUttg15JkbwC4/zU1mgeHay90dmfp8TqjHyja0VVcw+rMi7i4x8L8O64zhjeOc7sMX19JHhzTEfM3nrGthdCyB/cdWK+o1BTqpPwzSVoLiFveXW90cAKw76AtQcvocIJzXj2CPL3RY/EcHhAd4NVzaODcLuqltNrlYdJ8eHEbpib2gZjujZDr6RIZP9Wile+PmdxsERVbQNmbz2D9L15AMw3zw/vHIchyTEOeFX6fCTA9IeSoDCTbYl4Fm8f2Ec1Rifhk0uwV1Kk2SBq7rmaJWDUamBVxkUHlNixqusacPJyudDFcImiW9VYnXHR6ucXIvXF6yPv1/RtWWPzo6OFULPGtH+mmucBICPvBu/XYIqfjwR/ahmF/m2a4s8pzeHfxAfdEyMw84+5a8R1woP88NZjHfFLcQXWOXnZOR8JcLuKf0YdT0IT/J1kZ841zP08x+p+a8Z3RUxogM0T9SOD/VFWVWfTcyVoHEkpttqmu5AAiA2TApAY5aY1tz/QOPEZgMlmdns5cvHmsIAmOPl/Q+Df5H7DUoOa4f0DF7H+yG+ovad2yHmIdRIAM/onYfup313ShyyB8QR9d1mQ2ByPWKjY3fHJJWhPe749QREA3h7bGcv30MoKfGnevwm9ErAqI5/TczRBcNE3Z6FmEocHRQAOnc9YUXMPvd/KQPrYThjWUYF9ucU0Z1IgDI2tBa6kuyjx3rPX8X87c/WCsicsSGwOBUYn4ZNLULPwpzOFSJvoDeHWzX3o4wNqHuNJ8/7ZUmu6Xe0+NfTb1fWYueU0RrrZcmXEPrpdPYcvlJgMysVmBhF6AgqMTmJp3URNbWN8zwR8d/Y6okOkkIdJUVJRy7kWIQEQwXFo/vzU1pgzqLXFKR/zU1tzrvl4syB/XwzvKEff1k0hC/SH2rN7IrQoKHqn/+YpscnCaGsG/Zqlp6A+RiczNcAiIsgPDPrNXpq+IS6pnDSX3/vjH8Tru3+x2JyqkAXg2MuDrF60DWqGTkt+QHU9zYXkIzZUiht3auHZ3yLirUIDmnBKXLFteh/Rz3ukPkYRMcwlWHSrymTNTBMkZQaDJ0zNY5TLAjC6iwIrvj9vtY9xfM8EzmWV0OQd3mxZD48QsePTIgV43rxHCowuoMkl2KBm6LfyoMV9A/18sW5aN9yqqjXKfKOsqEHZnVpcvV3NuSN+VcZFfP7TFSwe0R4RwVKzI8pOFJahijLneCV/Xx/UNdAIU3IfA9AnKRJ7c0s47e9p8x4pMLqQtbmNQGOHto+PxGiNNNXdOryz71ebRo8Wq2qMsqHojihrUDNkXrrJ+7hEGNMfSsLGY43zGR3hub7N8dHR3xxzMOIxuAbFyGA/t12Q2BwKjC6kVN21aT9zqeXsK0vjiLIZ/ZOw6+dimq7hBiQApvwpEQ+3iUFgEx/800ETvb88edUhxyHeaVKfRKGL4HAUGF2I65xD3f0spZazh+Z4rp4bRWzHAGz632Vs+t9lhx73Ns1LJHZYfeASvjj5u0fNaaThFi4UGSLlvR+X5lfiOYKlvogN5XadEMLHyM7OC1q2ruUoVhQYXUgexq2DuuBGpTYxtKeM9vKcGU7OVVXbAIlEgvmpbbDqqS6IDPYXukjEQ5y6fBsfPPOgUSL48EA/Ts8f0j4GIVLTi2F72lqO1JTqQppsONZqgGsPFWDtoQIoZAG8pluImft/VVynpKIGqzMuYl5qG5tT/hFiqFhVg4hgKY69PEgv2YeaMTz7r+NWn7//vOXk9J60liMFRhfy9ZFg8Yj2nNfLK1Y1/kCGB/lBVV1PwcUDSCSwmgxAs3rKpv9R/y9xrBuVNdrpYxoNamYxfaUt53B31JTqQvtyi/Hqt7m8nqP5kRRjUAw206xC7ls7viu2Te+DaX2bA7AeFDUMMyN5O2qKNxbkz//7Z2q+oSZ9JeCY99kT5jRSYHQRzZQLW37sblfXI21gC0hE9uvQv3VToYsgek2a+KBXUiT25ipter60ifd+RbvFy7T9X2K8MRTS/NQ2OLd0KOantubURyhB49xlc/MNh3VUYP3EbpDbsRC1tXO4E2pKdQFHTLlYd0h8E7BbNg0Wugii9+qOcwj2b2LHyGLhQkKQvy8kEgiWEen0VZUg53UHzaOD4OsjwdzUNtoFAvbnKfGJmYTfDMCwDrHahdFN5U7WTV/5fW4x/p3FfVqQ5mhLRiV7RDJx770ddSFPnHIhAXD9drXQxRC9sqp6/HXLSZufX3tPuMBYXdeAqtoGhEjp/llsdJsrNX2GvZIitbmVTdn0v8uYsCEb/VYeNDutwtdHgl5JkUiMDOJVHrkswKOWn6IrngN7Vq5uTLd2y8kldD0G4Jscz5iz5GzVde6dh1R3HU8iDrer9JPX88mOZWkdRVOrAZkTFtAEY7rGoXlUMP6c0hz+HtTsT8tOWWHqQuG6cjWfi4wQQrjSXU5OszgB398ZwyXp7Ek9yfU3UUh8YoHnhHgn0FwohhcclywP5p5LCCH2KlbVYHNmobY1y9bFBTTHsHccBGW+8RKWLhRrWR7q7qnx6o5cXheZ+3dXE0Jcafme8+i38iAy8mwb8ax7jLUHL3EKrqEB3pH5hgKjGdbuwnSzPOjal1uMPukZnDOWBPn74oluzbB6fFc7SksI8UZKVQ02mhmJylWxqgarMi5y2reyxvwIZXO/ie6IBt+YwTV7g+5+trTRV9c14KvT1/D16Ws8S0gI8Xaa3xqfPzIqiaGuRplvPBjX7A2a/extoxfDBU0IcU9qdj9LljNIAERxTGhPmW88mCbht7kLzTDLgyfOVSSEuEZogP2Nd1P7Nrcrc405mt/A5WM68vpNdGfUlGqGJn/grC2njXKVmsryYE8HOCHEu70xugMKbt7B2kMFNh9jSLIcr41I1s65jg6RAgzY90sxPsu+wvk4PpLGGqiGXGcqho8POP8mujMKjBZo8gcazkWUG8zZaVAz7MihPkJCiG3kskDIZYE2B8aoYH9t4hHDJZ98fCS8AqNmZvvUvs0xJFmul9CE62+iu6PAaIVu/kBzmW9OFJahrEqYlRAUsgAsHpGMiGB//PBLMb4+fQ2VNZSphDhfZLA/rRfpAD4SoHtiBHx9JDYv/zSma5zZmhrXdWA1NH2V3+cq8doI4xogl99Ed0d9jBxo7sLGdG2GlJZRRheAEKOwHu8ah23T++DYy4MwvLMCKS2jsHR0R+S8/gheG97e5eUh4uCKFVgkf/y9OaYjIizk5iTcqBlw6vJtu5Z/GpIsN/uY5rh8jmlt6oW130R3R4HRARwxCovPZeUjAVY+0cXkBenrI8HUfkkWkwk7WvfEcKceX/NDPCQ5xqnnESMJgHmDW8Pfl9sV8v7TD+KJbs0cWgbD3zxNwujhnRVY8Vgnh57LVnyu9xARriOqubm2ZfknLgNeNMdV8Byc4wlTL2xBTakOoGmq4NIEopAFYHQXBXb9XGyyjX5Qu1hM2piN7MLbZo8x/aEkiwl7fX0keHtsJ8zccprza+CysrzRc9BY7i//+ie8/f15/OvHQrOvXwLghcGt0aBWgzFg64kruM1xbUrd/ou9Z6/j/3bm6jVd21J2Llo2DcJvN6sFm0oTLPXF35/sgmEdFWinCLX6ef61fxJGdo3Do50VyPj1hsW1PxWyALw6vD2W7PrFbHOoJh6undANEcH+JpvNhndW4K+/J+Gjo4U2vUZ7DW7XFH95qCV6JUVif56SU27id8Z1xvI9520eRR4e5Ie3HuuIiGApblTWoOhWNbaduAJlhX4+5dFdFNh+6ndO3Sy6N9eGTZVFt6qwKiPf7gEvusfNvHSTU3+mJ0y9sIVbJBFft24d3n33XSiVSnTp0gXvv/8+evXqxem59iYR50ozuR8wPSdxWt/mSNXpyLa2Ykf63jxs+LFQb3SYj6QxKC4ansy5TEt35el9YYP8fdApToZuiRGIDJYiOlQKeVgAuidG4KeiMmQVlAJg6N08Cj6+EmTkKbHpf8brsmlKqpuhv+6eGq9+cxZ7zylRXX8/Q4apBMPm3i/Nl39+ams0jw42+d4YvneGZU9pEY2eSZFYf7gAmzILUX73/g8Tl4AAAPIwKTJfGYwGNWt8TblKVNfdf03hgX5IbR+Dvq2icbm0Gv/Ovqx3PMORfXyN6izH6vHd9F73vtxivPLNOaOAFyL1xTvjOmN45zi9fc0lm5Dg/uemeS/35ynxbc51vdfAJzH03rPX8dq353C7+n7/tjxMiqd7JuBegxr/OvabTUtozU9tjYq79diRc00vwEQG++HNMR31XjNw/9rIyFPimzO/G5Vn6egOGNZRYVMyjhCpL9Y/0x1/ah1tFIjMfZ/r7qnRJ/2AxZsPuUEyb1PsWczAFE3icXM381zL5U74xALRB8YvvvgCkyZNwocffojevXtj9erV2L59Oy5cuICYGOtNa64KjIDjL966e2p8llWEy2XVSIwMsmlpF3uWzNLg+7q4ntPR75cp5spiKTADMFqSx9prMnz8dlUt0raeMTq+IYUsAK892h4llTWcPucGNUN2QSmyfrsFoLGfp08L0308zvrczLH0fL6BKDzID2+P7aQ38ptv2aw9h+vqN+auCa74XmvmOOK77IxyuQuPCoy9e/dGz549sXbtWgCAWq1GfHw8nn/+ebzyyitWn+/KwAg4/uIVC2e9LiHfL2cHZnPHH98zAc2jg1zyesV0PZp7PxaPaA9ZkL9ejb+PiwZ0GN/Q1GH5HsdfE664CfSkcjmDxwTGuro6BAUF4auvvsJjjz2m3T558mSUl5dj586dRs+pra1Fbe39RTwrKioQHx/vssBI3IuzA4eYApMYuMP74Yk3ge5YLkfjExhFPfjm1q1baGhoQGxsrN722NhY/Prrryafk56ejmXLlrmieMQDmJoQ7U7Hdzfu8H44q4xife1iLZeQPG66xqJFi6BSqbR/V69eFbpIhBBC3Iioa4zR0dHw9fVFSUmJ3vaSkhLI5aYntEqlUkilUlcUjxBCiAcSdY3R398f3bt3x4EDB7Tb1Go1Dhw4gJSUFAFLRgghxFOJusYIAAsWLMDkyZPRo0cP9OrVC6tXr0ZVVRWee+45oYtGCCHEA4k+MD799NO4efMmXn/9dSiVSnTt2hX79u0zGpBDCCGEOIKop2s4gqvnMRJCCBEfPrFA1H2MhBBCiKtRYCSEEEJ0UGAkhBBCdFBgJIQQQnSIflSqvTRjiyoqKgQuCSGEEKFoYgCX8aYeHxgrKysBAPHx8QKXhBBCiNAqKyshk8ks7uPx0zXUajWuX7+O0NBQSCT8M8ZrVue4evWqx073oNfoGbzhNQLe8TrpNToeYwyVlZWIi4uDj4/lXkSPrzH6+PjggQcesPs4YWFhHnuBatBr9Aze8BoB73id9Body1pNUYMG3xBCCCE6KDASQgghOigwWiGVSrFkyRKPXsqKXqNn8IbXCHjH66TXKCyPH3xDCCGE8EE1RkIIIUQHBUZCCCFEBwVGQgghRAcFRkIIIUQHBUYL1q1bh+bNmyMgIAC9e/fGiRMnhC6SQy1duhQSiUTvr127dkIXyy5Hjx7FqFGjEBcXB4lEgm+//VbvccYYXn/9dSgUCgQGBiI1NRX5+fnCFNZG1l7jlClTjD7XYcOGCVNYG6Wnp6Nnz54IDQ1FTEwMHnvsMVy4cEFvn5qaGqSlpSEqKgohISEYN24cSkpKBCoxf1xe48MPP2z0Wc6cOVOgEvO3fv16dO7cWTuJPyUlBd9//732cbF+hhQYzfjiiy+wYMECLFmyBKdPn0aXLl0wdOhQ3LhxQ+iiOVSHDh1QXFys/Tt27JjQRbJLVVUVunTpgnXr1pl8/J133sE///lPfPjhhzh+/DiCg4MxdOhQ1NTUuLiktrP2GgFg2LBhep/rtm3bXFhC+x05cgRpaWnIzs7G/v37UV9fj0ceeQRVVVXafebPn4/du3dj+/btOHLkCK5fv46xY8cKWGp+uLxGAJg+fbreZ/nOO+8IVGL+HnjgAbz99ts4deoUTp48iUGDBmHMmDH45ZdfAIj4M2TEpF69erG0tDTt/xsaGlhcXBxLT08XsFSOtWTJEtalSxehi+E0ANiOHTu0/1er1Uwul7N3331Xu628vJxJpVK2bds2AUpoP8PXyBhjkydPZmPGjBGkPM5y48YNBoAdOXKEMdb4ufn5+bHt27dr9zl//jwDwLKysoQqpl0MXyNjjA0YMIDNnTtXuEI5QUREBPvXv/4l6s+Qaowm1NXV4dSpU0hNTdVu8/HxQWpqKrKysgQsmePl5+cjLi4OLVq0wLPPPosrV64IXSSnKSwshFKp1PtcZTIZevfu7XGf6+HDhxETE4O2bdti1qxZKC0tFbpIdlGpVACAyMhIAMCpU6dQX1+v91m2a9cOCQkJbvtZGr5Gjf/85z+Ijo5Gx44dsWjRIlRXVwtRPLs1NDTg888/R1VVFVJSUkT9GXp8EnFb3Lp1Cw0NDYiNjdXbHhsbi19//VWgUjle7969sXnzZrRt2xbFxcVYtmwZHnroIeTm5iI0NFTo4jmcUqkEAJOfq+YxTzBs2DCMHTsWSUlJKCgowKuvvopHH30UWVlZ8PX1Fbp4vKnVasybNw99+/ZFx44dATR+lv7+/ggPD9fb110/S1OvEQCeeeYZJCYmIi4uDmfPnsXLL7+MCxcu4JtvvhGwtPycO3cOKSkpqKmpQUhICHbs2IHk5GTk5OSI9jOkwOjFHn30Ue2/O3fujN69eyMxMRFffvklpk2bJmDJiD3Gjx+v/XenTp3QuXNntGzZEocPH8bgwYMFLJlt0tLSkJub6/b935aYe40zZszQ/rtTp05QKBQYPHgwCgoK0LJlS1cX0yZt27ZFTk4OVCoVvvrqK0yePBlHjhwRulgWUVOqCdHR0fD19TUaHVVSUgK5XC5QqZwvPDwcbdq0waVLl4QuilNoPjtv+1xbtGiB6Ohot/xc58yZg++++w6HDh3SWz5OLpejrq4O5eXlevu742dp7jWa0rt3bwBwq8/S398frVq1Qvfu3ZGeno4uXbpgzZo1ov4MKTCa4O/vj+7du+PAgQPabWq1GgcOHEBKSoqAJXOuO3fuoKCgAAqFQuiiOEVSUhLkcrne51pRUYHjx4979Of6+++/o7S01K0+V8YY5syZgx07duDgwYNISkrSe7x79+7w8/PT+ywvXLiAK1euuM1nae01mpKTkwMAbvVZGlKr1aitrRX3Zyjo0B8R+/zzz5lUKmWbN29meXl5bMaMGSw8PJwplUqhi+YwL774Ijt8+DArLCxkmZmZLDU1lUVHR7MbN24IXTSbVVZWsjNnzrAzZ84wAOwf//gHO3PmDLt8+TJjjLG3336bhYeHs507d7KzZ8+yMWPGsKSkJHb37l2BS86dpddYWVnJFi5cyLKyslhhYSHLyMhg3bp1Y61bt2Y1NTVCF52zWbNmMZlMxg4fPsyKi4u1f9XV1dp9Zs6cyRISEtjBgwfZyZMnWUpKCktJSRGw1PxYe42XLl1ib7zxBjt58iQrLCxkO3fuZC1atGD9+/cXuOTcvfLKK+zIkSOssLCQnT17lr3yyitMIpGw//73v4wx8X6GFBgteP/991lCQgLz9/dnvXr1YtnZ2UIXyaGefvppplAomL+/P2vWrBl7+umn2aVLl4Qull0OHTrEABj9TZ48mTHWOGVj8eLFLDY2lkmlUjZ48GB24cIFYQvNk6XXWF1dzR555BHWtGlT5ufnxxITE9n06dPd7obO1OsDwDZt2qTd5+7du2z27NksIiKCBQUFsccff5wVFxcLV2ierL3GK1eusP79+7PIyEgmlUpZq1at2N/+9jemUqmELTgPU6dOZYmJiczf3581bdqUDR48WBsUGRPvZ0jLThFCCCE6qI+REEII0UGBkRBCCNFBgZEQQgjRQYGREEII0UGBkRBCCNFBgZEQQgjRQYGREEII0UGBkRBCCNFBgZEQDzFlyhQ89thj2v8//PDDmDdvnl3HdMQxCHE3FBgJcbIpU6ZAIpFAIpFoVxp44403cO/ePaee95tvvsHy5cs57Xv48GFIJBKjlQ74HIMQT0HrMRLiAsOGDcOmTZtQW1uLvXv3Ii0tDX5+fli0aJHefnV1dfD393fIOQ1XghfqGIS4G6oxEuICUqkUcrkciYmJmDVrFlJTU7Fr1y5t8+eKFSsQFxeHtm3bAgCuXr2Kp556CuHh4YiMjMSYMWNQVFSkPV5DQwMWLFiA8PBwREVF4aWXXoJh2mPDZtDa2lq8/PLLiI+Ph1QqRatWrbBx40YUFRVh4MCBAICIiAhIJBJMmTLF5DFu376NSZMmISIiAkFBQXj00UeRn5+vfXzz5s0IDw/HDz/8gPbt2yMkJATDhg1DcXGxY99QQpyIAiMhAggMDERdXR0A4MCBA7hw4QL279+P7777DvX19Rg6dChCQ0Px448/IjMzUxtgNM/5+9//js2bN+OTTz7BsWPHUFZWhh07dlg856RJk7Bt2zb885//xPnz5/HRRx8hJCQE8fHx+PrrrwE0rodXXFyMNWvWmDzGlClTcPLkSezatQtZWVlgjGH48OGor6/X7lNdXY333nsPn332GY4ePYorV65g4cKFjnjbCHENYRf3IMTzTZ48mY0ZM4Yx1rjs1f79+5lUKmULFy5kkydPZrGxsay2tla7/2effcbatm3L1Gq1dlttbS0LDAxkP/zwA2OMMYVCwd555x3t4/X19eyBBx7QnocxxgYMGMDmzp3LGGPswoULDADbv3+/yTJqlrK6ffu23nbdY1y8eJEBYJmZmdrHb926xQIDA9mXX37JGGNs06ZNDIDe8mXr1q1jsbGx3N4sQkSA+hgJcYHvvvsOISEhqK+vh1qtxjPPPIOlS5ciLS0NnTp10utX/Pnnn3Hp0iWEhobqHaOmpgYFBQVQqVQoLi5G7969tY81adIEPXr0MGpO1cjJyYGvry8GDBhg82s4f/48mjRponfeqKgotG3bFufPn9duCwoKQsuWLbX/VygUuHHjhs3nJcTVKDAS4gIDBw7E+vXr4e/vj7i4ODRpcv+rFxwcrLfvnTt30L17d/znP/8xOk7Tpk1tOn9gYKBNz7OFn5+f3v8lEonZgE2IGFEfIyEuEBwcjFatWiEhIUEvKJrSrVs35OfnIyYmBq1atdL7k8lkkMlkUCgUOH78uPY59+7dw6lTp8wes1OnTlCr1Thy5IjJxzU11oaGBrPHaN++Pe7du6d33tLSUly4cAHJyckWXxMh7oQCIyEi8+yzzyI6OhpjxozBjz/+iMLCQhw+fBgvvPACfv/9dwDA3Llz8fbbb+Pbb7/Fr7/+itmzZxvNQdTVvHlzTJ48GVOnTsW3336rPeaXX34JAEhMTIREIsF3332Hmzdv4s6dO0bHaN26NcaMGYPp06fj2LFj+PnnnzFx4kQ0a9YMY8aMccp7QYgQKDASIjJBQUE4evQoEhISMHbsWLRv3x7Tpk1DTU0NwsLCAAAvvvgi/vznP2Py5MlISUlBaGgoHn/8cYvHXb9+PZ544gnMnj0b7dq1w/Tp01FVVQUAaNasGZYtW4ZXXnkFsbGxmDNnjsljbNq0Cd27d8fIkSORkpICxhj27t1r1HxKiDuTMGr8J4QQQrSoxkgIIYTooMBICCGE6KDASAghhOigwEgIIYTooMBICCGE6KDASAghhOigwEgIIYTooMBICCGE6KDASAghhOigwEgIIYTooMBICCGE6Ph/a6bUodfvx2QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = svr.predict(X_test)\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(5,5))\n",
    "ax.scatter(y_pred, y_test)\n",
    "ax.set_xlabel('Prediction')\n",
    "ax.set_ylabel('True')\n",
    "ax.set_title('slope: {:.4f}, r_value: {:.4f}'.format(slope, r_value))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a53a65-7331-47fd-a77b-3537ae4e0fc5",
   "metadata": {
    "id": "14a53a65-7331-47fd-a77b-3537ae4e0fc5"
   },
   "source": [
    "Since 'C' and 'epsilon' are hyperparameter, how can see be sure we are selecting the best parameters for this task?\n",
    "One way is via testing a large range of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe48af4c-7870-475c-a55d-7bee17176711",
   "metadata": {
    "id": "fe48af4c-7870-475c-a55d-7bee17176711",
    "outputId": "ef16f685-b6df-47d5-dda5-c62628588c8f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C: 0.1, epsilon: 0.01, score: 0.20991995961716992\n",
      "C: 0.5, epsilon: 0.01, score: 0.22612846899651118\n",
      "C: 2, epsilon: 0.01, score: 0.2365668732185091\n",
      "C: 5, epsilon: 0.01, score: 0.24056242242851433\n",
      "C: 10, epsilon: 0.01, score: 0.2429408169988566\n",
      "C: 20, epsilon: 0.01, score: 0.24472596728667861\n",
      "C: 30, epsilon: 0.01, score: 0.243360274955275\n",
      "C: 0.1, epsilon: 0.1, score: 0.2103005921649257\n",
      "C: 0.5, epsilon: 0.1, score: 0.22606989340094064\n",
      "C: 2, epsilon: 0.1, score: 0.23678250721356053\n",
      "C: 5, epsilon: 0.1, score: 0.2404947913592861\n",
      "C: 10, epsilon: 0.1, score: 0.24305086505451956\n",
      "C: 20, epsilon: 0.1, score: 0.24504954391258305\n",
      "C: 30, epsilon: 0.1, score: 0.24348299246368343\n",
      "C: 0.1, epsilon: 1, score: 0.21093892736458641\n",
      "C: 0.5, epsilon: 1, score: 0.226719697213827\n",
      "C: 2, epsilon: 1, score: 0.23803327114830342\n",
      "C: 5, epsilon: 1, score: 0.24207967494903404\n",
      "C: 10, epsilon: 1, score: 0.24520955760079832\n",
      "C: 20, epsilon: 1, score: 0.24611213734158977\n",
      "C: 30, epsilon: 1, score: 0.24634335054722845\n",
      "C: 0.1, epsilon: 5, score: 0.21191731592681273\n",
      "C: 0.5, epsilon: 5, score: 0.23183716657514897\n",
      "C: 2, epsilon: 5, score: 0.24508827723146664\n",
      "C: 5, epsilon: 5, score: 0.2524757704090205\n",
      "C: 10, epsilon: 5, score: 0.25713959246246865\n",
      "C: 20, epsilon: 5, score: 0.26064565425376707\n",
      "C: 30, epsilon: 5, score: 0.2615897511408104\n",
      "C: 0.1, epsilon: 10, score: 0.17929567698442406\n",
      "C: 0.5, epsilon: 10, score: 0.20593053182898224\n",
      "C: 2, epsilon: 10, score: 0.21963851125161138\n",
      "C: 5, epsilon: 10, score: 0.22607429024593917\n",
      "C: 10, epsilon: 10, score: 0.23065631272737752\n",
      "C: 20, epsilon: 10, score: 0.2344238846306863\n",
      "C: 30, epsilon: 10, score: 0.23548594244449417\n",
      "C: 0.1, epsilon: 15, score: 0.08566704852027629\n",
      "C: 0.5, epsilon: 15, score: 0.11609763497355186\n",
      "C: 2, epsilon: 15, score: 0.13043810923772747\n",
      "C: 5, epsilon: 15, score: 0.13330531774189558\n",
      "C: 10, epsilon: 15, score: 0.13719931600961133\n",
      "C: 20, epsilon: 15, score: 0.13834968592898178\n",
      "C: 30, epsilon: 15, score: 0.14039431576260986\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Define a range of 'C' values and 'epsilon' values to test\n",
    "C_values = [0.1, 0.5, 2, 5, 10, 20, 30]  # Logarithmically spaced values from 0.01 to 100\n",
    "epsilon_values = [0.01, 0.1, 1, 5, 10, 15]  # Linearly spaced values from 0.01 to 0.1\n",
    "\n",
    "# Matrix to store scores for each combination of C and epsilon\n",
    "scores = np.zeros((len(C_values), len(epsilon_values)))\n",
    "\n",
    "# Loop over the values of C and epsilon\n",
    "for j, epsilon in enumerate(epsilon_values):\n",
    "    for i, C in enumerate(C_values):\n",
    "        svr = SVR(epsilon=epsilon, C=C)\n",
    "        svr.fit(X_train, y_train)\n",
    "\n",
    "        # Evaluate on the test set\n",
    "        score = svr.score(X_test, y_test)\n",
    "        scores[i, j] = score\n",
    "        print(f'C: {C}, epsilon: {epsilon}, score: {score}')\n",
    "\n",
    "# Plotting the results using a heatmap\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.imshow(scores, interpolation='nearest', cmap=plt.cm.hot, aspect='auto')\n",
    "plt.xlabel('Epsilon')\n",
    "plt.ylabel('C')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(len(epsilon_values)), ['{:.2f}'.format(eps) for eps in epsilon_values], rotation=45)\n",
    "plt.yticks(np.arange(len(C_values)), ['{:.2f}'.format(c) for c in C_values])\n",
    "plt.title('Test R^2 Score for SVR by C and Epsilon')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3d8093-fb48-46cb-9048-209e2ba88b23",
   "metadata": {
    "id": "3d3d8093-fb48-46cb-9048-209e2ba88b23"
   },
   "source": [
    "For a fixed error margin (epsilon), you can perform gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee814271-52f8-4d74-9662-2d3e235ff30b",
   "metadata": {
    "id": "ee814271-52f8-4d74-9662-2d3e235ff30b",
    "outputId": "05d14047-8c48-4e53-bbcf-50b0c2befc53",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
      "Best value of C: 25\n",
      "Best cross-validated R^2 of the best estimator: 0.2546769891813275\n",
      "Accuracy of SVR on training set: 0.29787\n",
      "Accuracy of SVR on test set: 0.26162\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, r2_score\n",
    "# Define your model without setting the C parameter\n",
    "svr = SVR(epsilon=5)\n",
    "\n",
    "# Define the parameter grid: list of dictionaries with the parameters you want to test\n",
    "param_grid = {\n",
    "    'C': [0.1, 0.5, 1, 2, 5, 10, 15, 20, 25, 30, 35, 40]  # Example values for C\n",
    "}\n",
    "\n",
    "# Define the scorer, if you want to change the default scoring method (which is R^2 for regression models)\n",
    "scorer = make_scorer(r2_score)\n",
    "\n",
    "# Set up the grid search with cross-validation\n",
    "grid_search = GridSearchCV(estimator=svr, param_grid=param_grid, scoring=scorer, cv=5, verbose=1)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best model\n",
    "best_svr = grid_search.best_estimator_\n",
    "\n",
    "# Output the best parameter\n",
    "print('Best value of C:', grid_search.best_params_['C'])\n",
    "print('Best cross-validated R^2 of the best estimator:', grid_search.best_score_)\n",
    "\n",
    "# Evaluate the best model on the training set\n",
    "print('Accuracy of SVR on training set: {:.5f}'.format(best_svr.score(X_train, y_train)))\n",
    "\n",
    "# Evaluate the best model on the test set\n",
    "print('Accuracy of SVR on test set: {:.5f}'.format(best_svr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0cdad774-6554-498c-94a1-fea2dbb58590",
   "metadata": {
    "id": "0cdad774-6554-498c-94a1-fea2dbb58590",
    "outputId": "811c938d-e04c-452d-c456-6d59afd358ee",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = best_svr.predict(X_test)\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(5,5))\n",
    "ax.scatter(y_pred, y_test)\n",
    "ax.set_xlabel('Prediction')\n",
    "ax.set_ylabel('True')\n",
    "ax.set_title('slope: {:.4f}, r_value: {:.4f}'.format(slope, r_value))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "807907b9-3c09-48ce-aec8-4080d164d06f",
   "metadata": {
    "id": "807907b9-3c09-48ce-aec8-4080d164d06f",
    "outputId": "5b0b9620-6438-4b2f-96db-87af8098353a",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVR on training set: 0.19898\n",
      "Accuracy of SVR on test set: 0.19392\n"
     ]
    }
   ],
   "source": [
    "svr = SVR(kernel='linear',epsilon=5, C=30)\n",
    "svr.fit(X_train, y_train) # Fit the SVR model according to the given training data.\n",
    "print('Accuracy of SVR on training set: {:.5f}'.format(svr.score(X_train, y_train))) # Returns the coefficient of determination (R^2) of the prediction.\n",
    "print('Accuracy of SVR on test set: {:.5f}'.format(svr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1122962f-5029-4644-85cb-c12b9e24f9db",
   "metadata": {
    "id": "1122962f-5029-4644-85cb-c12b9e24f9db",
    "outputId": "7393b20f-d64c-49a5-9faf-b32adc18c56d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVR on training set: 0.19092\n",
      "Accuracy of SVR on test set: 0.18723\n"
     ]
    }
   ],
   "source": [
    "svr = SVR(kernel='linear')\n",
    "svr.fit(X_train, y_train) # Fit the SVR model according to the given training data.\n",
    "print('Accuracy of SVR on training set: {:.5f}'.format(svr.score(X_train, y_train))) # Returns the coefficient of determination (R^2) of the prediction.\n",
    "print('Accuracy of SVR on test set: {:.5f}'.format(svr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "694f7c02-1fb4-4162-8bb7-34e65601c910",
   "metadata": {
    "id": "694f7c02-1fb4-4162-8bb7-34e65601c910"
   },
   "outputs": [],
   "source": [
    "# import satalite indeces\n",
    "glad_ard_SVVI_min = \"./tree_height/geodata_raster/glad_ard_SVVI_min.tif\"\n",
    "glad_ard_SVVI_med = \"./tree_height/geodata_raster/glad_ard_SVVI_med.tif\"\n",
    "glad_ard_SVVI_max = \"./tree_height/geodata_raster/glad_ard_SVVI_max.tif\"\n",
    "\n",
    "# import climate\n",
    "CHELSA_bio4 = \"./tree_height/geodata_raster/CHELSA_bio4.tif\"\n",
    "CHELSA_bio18 = \"./tree_height/geodata_raster/CHELSA_bio18.tif\"\n",
    "\n",
    "# soil\n",
    "BLDFIE_WeigAver = \"./tree_height/geodata_raster/BLDFIE_WeigAver.tif\"\n",
    "CECSOL_WeigAver = \"./tree_height/geodata_raster/CECSOL_WeigAver.tif\"\n",
    "ORCDRC_WeigAver = \"./tree_height/geodata_raster/ORCDRC_WeigAver.tif\"\n",
    "\n",
    "# Geomorphological\n",
    "elev = \"./tree_height/geodata_raster/elev.tif\"\n",
    "convergence = \"./tree_height/geodata_raster/convergence.tif\"\n",
    "northness = \"./tree_height/geodata_raster/northness.tif\"\n",
    "eastness = \"./tree_height/geodata_raster/eastness.tif\"\n",
    "devmagnitude = \"./tree_height/geodata_raster/dev-magnitude.tif\"\n",
    "\n",
    "# Hydrography\n",
    "cti = \"./tree_height/geodata_raster/cti.tif\"\n",
    "outlet_dist_dw_basin = \"./tree_height/geodata_raster/outlet_dist_dw_basin.tif\"\n",
    "\n",
    "# Soil climate\n",
    "\n",
    "SBIO3_Isothermality_5_15cm = \"./tree_height/geodata_raster/SBIO3_Isothermality_5_15cm.tif\"\n",
    "SBIO4_Temperature_Seasonality_5_15cm = \"./tree_height/geodata_raster/SBIO4_Temperature_Seasonality_5_15cm.tif\"\n",
    "\n",
    "# forest\n",
    "\n",
    "treecover = \"./tree_height/geodata_raster/treecover.tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aff6954b-582d-4d64-b4d9-d544b08c2e70",
   "metadata": {
    "id": "aff6954b-582d-4d64-b4d9-d544b08c2e70",
    "outputId": "e8910e90-95a0-4888-b5e2-c227e0868b40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./tree_height/geodata_raster/glad_ard_SVVI_min.tif', './tree_height/geodata_raster/glad_ard_SVVI_med.tif', './tree_height/geodata_raster/glad_ard_SVVI_max.tif', './tree_height/geodata_raster/CHELSA_bio4.tif', './tree_height/geodata_raster/CHELSA_bio18.tif', './tree_height/geodata_raster/BLDFIE_WeigAver.tif', './tree_height/geodata_raster/CECSOL_WeigAver.tif', './tree_height/geodata_raster/ORCDRC_WeigAver.tif', './tree_height/geodata_raster/elev.tif', './tree_height/geodata_raster/convergence.tif', './tree_height/geodata_raster/northness.tif', './tree_height/geodata_raster/eastness.tif', './tree_height/geodata_raster/dev-magnitude.tif', './tree_height/geodata_raster/cti.tif', './tree_height/geodata_raster/outlet_dist_dw_basin.tif', './tree_height/geodata_raster/SBIO3_Isothermality_5_15cm.tif', './tree_height/geodata_raster/SBIO4_Temperature_Seasonality_5_15cm.tif', './tree_height/geodata_raster/treecover.tif']\n"
     ]
    }
   ],
   "source": [
    "predictors_rasters = [glad_ard_SVVI_min, glad_ard_SVVI_med, glad_ard_SVVI_max,\n",
    "                      CHELSA_bio4,CHELSA_bio18,\n",
    "                      BLDFIE_WeigAver,CECSOL_WeigAver, ORCDRC_WeigAver,\n",
    "                      elev,convergence,northness,eastness,devmagnitude,cti,outlet_dist_dw_basin,\n",
    "                      SBIO3_Isothermality_5_15cm,SBIO4_Temperature_Seasonality_5_15cm,treecover]\n",
    "print(predictors_rasters)\n",
    "stack = Raster(predictors_rasters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "df97b80c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'crs': CRS.from_wkt('GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'), 'transform': Affine(0.00025, 0.0, 6.0,\n",
      "       0.0, -0.00025, 50.0), 'width': 16000, 'height': 8400, 'count': 18, 'dtype': dtype('float64')}\n"
     ]
    }
   ],
   "source": [
    "print(stack.meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "484c8435-30ea-4ffd-857d-b97ea81c885b",
   "metadata": {
    "id": "484c8435-30ea-4ffd-857d-b97ea81c885b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2079 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|▏         | 27/2079 [06:41<8:28:58, 14.88s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m result = stack.predict(estimator=svr, dtype=\u001b[33m'int16'\u001b[39m, nodata=-\u001b[32m1\u001b[39m, progress=\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/art/geocomp/.venv/lib/python3.13/site-packages/pyspatialml/raster.py:1365\u001b[39m, in \u001b[36mRaster.predict\u001b[39m\u001b[34m(self, estimator, file_path, in_memory, driver, dtype, nodata, progress, constants, **kwargs)\u001b[39m\n\u001b[32m   1363\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[32m   1364\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m rasterio.open(file_path, \u001b[33m\"\u001b[39m\u001b[33mw\u001b[39m\u001b[33m\"\u001b[39m, **meta) \u001b[38;5;28;01mas\u001b[39;00m dst:\n\u001b[32m-> \u001b[39m\u001b[32m1365\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mw\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mpbar\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mzip\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mwindows\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mmap\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpredfun\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdata_gen\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcounter\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m   1366\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mnp\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mma\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfilled\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfill_value\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mnodata\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1367\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mdst\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mwrite\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mindexes\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m:\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m:\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mastype\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mdtype\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mwindow\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mw\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/art/geocomp/.venv/lib/python3.13/site-packages/pyspatialml/_prediction.py:111\u001b[39m, in \u001b[36mpredict_output\u001b[39m\u001b[34m(img, estimator, constants, names)\u001b[39m\n\u001b[32m    108\u001b[39m     flat_pixels = stack_constants(flat_pixels, constants, names)\n\u001b[32m    110\u001b[39m \u001b[38;5;66;03m# predict and replace mask\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m111\u001b[39m result = \u001b[30;43mestimator\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpredict\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mflat_pixels\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    112\u001b[39m result = np.ma.masked_array(data=result, mask=flat_pixels_mask.any(axis=\u001b[32m1\u001b[39m))\n\u001b[32m    114\u001b[39m \u001b[38;5;66;03m# reshape the prediction from a 1D into 3D array [band, row, col]\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/art/geocomp/.venv/lib/python3.13/site-packages/sklearn/svm/_base.py:446\u001b[39m, in \u001b[36mBaseLibSVM.predict\u001b[39m\u001b[34m(self, X)\u001b[39m\n\u001b[32m    444\u001b[39m X = \u001b[38;5;28mself\u001b[39m._validate_for_predict(X)\n\u001b[32m    445\u001b[39m predict = \u001b[38;5;28mself\u001b[39m._sparse_predict \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._sparse \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m._dense_predict\n\u001b[32m--> \u001b[39m\u001b[32m446\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mpredict\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mX\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/art/geocomp/.venv/lib/python3.13/site-packages/sklearn/svm/_base.py:465\u001b[39m, in \u001b[36mBaseLibSVM._dense_predict\u001b[39m\u001b[34m(self, X)\u001b[39m\n\u001b[32m    457\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    458\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mX.shape[1] = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[33m should be equal to \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[33m, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    459\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mthe number of samples at training time\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    460\u001b[39m             % (X.shape[\u001b[32m1\u001b[39m], \u001b[38;5;28mself\u001b[39m.shape_fit_[\u001b[32m0\u001b[39m])\n\u001b[32m    461\u001b[39m         )\n\u001b[32m    463\u001b[39m svm_type = LIBSVM_IMPL.index(\u001b[38;5;28mself\u001b[39m._impl)\n\u001b[32m--> \u001b[39m\u001b[32m465\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mlibsvm\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpredict\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    466\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mX\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    467\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msupport_\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    468\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msupport_vectors_\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    469\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_n_support\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    470\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_dual_coef_\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    471\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_intercept_\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    472\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_probA\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    473\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_probB\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    474\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43msvm_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msvm_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    475\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mkernel\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mkernel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    476\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdegree\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdegree\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    477\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcoef0\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mcoef0\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    478\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mgamma\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_gamma\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    479\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcache_size\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mcache_size\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    480\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    }
   ],
   "source": [
    "result = stack.predict(estimator=svr, dtype='int16', nodata=-1, progress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47af2c97-628f-46c2-b37c-b01853a994d6",
   "metadata": {
    "id": "47af2c97-628f-46c2-b37c-b01853a994d6",
    "outputId": "c808d852-0992-4760-ff1f-35a06071148a"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# plot regression result\u001b[39;00m\n\u001b[32m      2\u001b[39m plt.rcParams[\u001b[33m\"figure.figsize\"\u001b[39m] = (\u001b[32m12\u001b[39m,\u001b[32m12\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m result.iloc[\u001b[32m0\u001b[39m].cmap = \u001b[33m\"plasma\"\u001b[39m\n\u001b[32m      4\u001b[39m result.plot()\n\u001b[32m      5\u001b[39m plt.show()\n",
      "\u001b[31mNameError\u001b[39m: name 'result' is not defined"
     ]
    }
   ],
   "source": [
    "# plot regression result\n",
    "plt.rcParams[\"figure.figsize\"] = (12,12)\n",
    "result.iloc[0].cmap = \"plasma\"\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c9b04e-3d17-4df3-81ab-2207ebd20b59",
   "metadata": {
    "id": "b6c9b04e-3d17-4df3-81ab-2207ebd20b59"
   },
   "source": [
    "## Exercise: explore the other parameters offered by the SVM library and try to make the model better. Some suggestions:\n",
    "- Better cleaning of the data (follow Peppe's suggestions)\n",
    "- Stronger regularization might be helpful\n",
    "- Play with different kernels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e80d594-9fd4-4115-9cd4-5a32a68095d5",
   "metadata": {
    "id": "8e80d594-9fd4-4115-9cd4-5a32a68095d5"
   },
   "source": [
    "<span style=\"color:red\">**For the brave ones**</span>, try to implenent the SVR algorithm from scratch. As we saw in class, the algorithm is quite simple. Here is a simple sketch of the SVM algorithm. Make the appropriate modifications to turn it into a regression. Let us know if your implementation is better than sklearn's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2d5a461-6cfe-476f-9568-197038326bbe",
   "metadata": {
    "collapsed": true,
    "id": "b2d5a461-6cfe-476f-9568-197038326bbe",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "1473c607-9203-42ce-edd8-01008aa810e6",
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[24], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m## Support Vector Machine \u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m train_f1 \u001b[38;5;241m=\u001b[39m \u001b[43mx_train\u001b[49m[:,\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m      5\u001b[0m train_f2 \u001b[38;5;241m=\u001b[39m x_train[:,\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      7\u001b[0m train_f1 \u001b[38;5;241m=\u001b[39m train_f1\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m90\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x_train' is not defined"
     ]
    }
   ],
   "source": [
    "## Support Vector Machine\n",
    "import numpy as np\n",
    "\n",
    "train_f1 = x_train[:,0]\n",
    "train_f2 = x_train[:,1]\n",
    "\n",
    "train_f1 = train_f1.reshape(90,1)\n",
    "train_f2 = train_f2.reshape(90,1)\n",
    "\n",
    "w1 = np.zeros((90,1))\n",
    "w2 = np.zeros((90,1))\n",
    "\n",
    "epochs = 1\n",
    "alpha = 0.0001\n",
    "\n",
    "while(epochs < 10000):\n",
    "    y = w1 * train_f1 + w2 * train_f2\n",
    "    prod = y * y_train\n",
    "    print(epochs)\n",
    "    count = 0\n",
    "    for val in prod:\n",
    "        if(val >= 1):\n",
    "            cost = 0\n",
    "            w1 = w1 - alpha * (2 * 1/epochs * w1)\n",
    "            w2 = w2 - alpha * (2 * 1/epochs * w2)\n",
    "\n",
    "        else:\n",
    "            cost = 1 - val\n",
    "            w1 = w1 + alpha * (train_f1[count] * y_train[count] - 2 * 1/epochs * w1)\n",
    "            w2 = w2 + alpha * (train_f2[count] * y_train[count] - 2 * 1/epochs * w2)\n",
    "        count += 1\n",
    "    epochs += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf8ada22-a71b-46ab-b867-ab1ad10edcc4",
   "metadata": {
    "id": "cf8ada22-a71b-46ab-b867-ab1ad10edcc4"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}