Assignment-05-Multiple Linear Regression-2-ver-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment-05-Multiple Linear Regression-2"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Prepare a prediction model for profit of 50_startups data.\n",
    "Do transformations for getting better predictions of profit and\n",
    "make a table containing R^2 value for each prepared model.\n",
    "\n",
    "R&D Spend -- Research and devolop spend in the past few years\n",
    "Administration -- spend on administration in the past few years\n",
    "Marketing Spend -- spend on Marketing in the past few years\n",
    "State -- states from which data is collected\n",
    "Profit  -- profit of each state in the past few years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.regressionplots import influence_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41\n",
       "49       0.00       116983.80         45173.06  California   14681.40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import dataset\n",
    "data=pd.read_csv(\"Database/50_Startups.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   State            50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RDS       ADMS       MKTS       State     Profit\n",
       "0   165349.20  136897.80  471784.10    New York  192261.83\n",
       "1   162597.70  151377.59  443898.53  California  191792.06\n",
       "2   153441.51  101145.55  407934.54     Florida  191050.39\n",
       "3   144372.41  118671.85  383199.62    New York  182901.99\n",
       "4   142107.34   91391.77  366168.42     Florida  166187.94\n",
       "5   131876.90   99814.71  362861.36    New York  156991.12\n",
       "6   134615.46  147198.87  127716.82  California  156122.51\n",
       "7   130298.13  145530.06  323876.68     Florida  155752.60\n",
       "8   120542.52  148718.95  311613.29    New York  152211.77\n",
       "9   123334.88  108679.17  304981.62  California  149759.96\n",
       "10  101913.08  110594.11  229160.95     Florida  146121.95\n",
       "11  100671.96   91790.61  249744.55  California  144259.40\n",
       "12   93863.75  127320.38  249839.44     Florida  141585.52\n",
       "13   91992.39  135495.07  252664.93  California  134307.35\n",
       "14  119943.24  156547.42  256512.92     Florida  132602.65\n",
       "15  114523.61  122616.84  261776.23    New York  129917.04\n",
       "16   78013.11  121597.55  264346.06  California  126992.93\n",
       "17   94657.16  145077.58  282574.31    New York  125370.37\n",
       "18   91749.16  114175.79  294919.57     Florida  124266.90\n",
       "19   86419.70  153514.11       0.00    New York  122776.86\n",
       "20   76253.86  113867.30  298664.47  California  118474.03\n",
       "21   78389.47  153773.43  299737.29    New York  111313.02\n",
       "22   73994.56  122782.75  303319.26     Florida  110352.25\n",
       "23   67532.53  105751.03  304768.73     Florida  108733.99\n",
       "24   77044.01   99281.34  140574.81    New York  108552.04\n",
       "25   64664.71  139553.16  137962.62  California  107404.34\n",
       "26   75328.87  144135.98  134050.07     Florida  105733.54\n",
       "27   72107.60  127864.55  353183.81    New York  105008.31\n",
       "28   66051.52  182645.56  118148.20     Florida  103282.38\n",
       "29   65605.48  153032.06  107138.38    New York  101004.64\n",
       "30   61994.48  115641.28   91131.24     Florida   99937.59\n",
       "31   61136.38  152701.92   88218.23    New York   97483.56\n",
       "32   63408.86  129219.61   46085.25  California   97427.84\n",
       "33   55493.95  103057.49  214634.81     Florida   96778.92\n",
       "34   46426.07  157693.92  210797.67  California   96712.80\n",
       "35   46014.02   85047.44  205517.64    New York   96479.51\n",
       "36   28663.76  127056.21  201126.82     Florida   90708.19\n",
       "37   44069.95   51283.14  197029.42  California   89949.14\n",
       "38   20229.59   65947.93  185265.10    New York   81229.06\n",
       "39   38558.51   82982.09  174999.30  California   81005.76\n",
       "40   28754.33  118546.05  172795.67  California   78239.91\n",
       "41   27892.92   84710.77  164470.71     Florida   77798.83\n",
       "42   23640.93   96189.63  148001.11  California   71498.49\n",
       "43   15505.73  127382.30   35534.17    New York   69758.98\n",
       "44   22177.74  154806.14   28334.72  California   65200.33\n",
       "45    1000.23  124153.04    1903.93    New York   64926.08\n",
       "46    1315.46  115816.21  297114.46     Florida   49490.75\n",
       "47       0.00  135426.92       0.00  California   42559.73\n",
       "48     542.05   51743.15       0.00    New York   35673.41\n",
       "49       0.00  116983.80   45173.06  California   14681.40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data.rename({'R&D Spend':'RDS','Administration':'ADMS','Marketing Spend':'MKTS'},axis=1)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [RDS, ADMS, MKTS, State, Profit]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1.duplicated()] # No duplicated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73721.615600</td>\n",
       "      <td>121344.639600</td>\n",
       "      <td>211025.097800</td>\n",
       "      <td>112012.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45902.256482</td>\n",
       "      <td>28017.802755</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>40306.180338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>51283.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39936.370000</td>\n",
       "      <td>103730.875000</td>\n",
       "      <td>129300.132500</td>\n",
       "      <td>90138.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73051.080000</td>\n",
       "      <td>122699.795000</td>\n",
       "      <td>212716.240000</td>\n",
       "      <td>107978.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>101602.800000</td>\n",
       "      <td>144842.180000</td>\n",
       "      <td>299469.085000</td>\n",
       "      <td>139765.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>182645.560000</td>\n",
       "      <td>471784.100000</td>\n",
       "      <td>192261.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RDS           ADMS           MKTS         Profit\n",
       "count      50.000000      50.000000      50.000000      50.000000\n",
       "mean    73721.615600  121344.639600  211025.097800  112012.639200\n",
       "std     45902.256482   28017.802755  122290.310726   40306.180338\n",
       "min         0.000000   51283.140000       0.000000   14681.400000\n",
       "25%     39936.370000  103730.875000  129300.132500   90138.902500\n",
       "50%     73051.080000  122699.795000  212716.240000  107978.190000\n",
       "75%    101602.800000  144842.180000  299469.085000  139765.977500\n",
       "max    165349.200000  182645.560000  471784.100000  192261.830000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RDS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.241955</td>\n",
       "      <td>0.724248</td>\n",
       "      <td>0.972900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADMS</th>\n",
       "      <td>0.241955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>0.200717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKTS</th>\n",
       "      <td>0.724248</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.747766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.972900</td>\n",
       "      <td>0.200717</td>\n",
       "      <td>0.747766</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             RDS      ADMS      MKTS    Profit\n",
       "RDS     1.000000  0.241955  0.724248  0.972900\n",
       "ADMS    0.241955  1.000000 -0.032154  0.200717\n",
       "MKTS    0.724248 -0.032154  1.000000  0.747766\n",
       "Profit  0.972900  0.200717  0.747766  1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cb63e48e50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lJVi6dClqamqwYcOGRGeHiGIk9yeYw60hG83GC5Q6YjnvgWL9sbtnYP+H57kONomGsRu9hF2NpkyZgoaGBgCDK0QEsmLFCqxYsWLUa3q9Htu3bx/32blz5/qPN1JNTQ1qamoESDERJZTMn7wPt4as7Kd/UELEdN6HYn33stvx7ikrPF6g/sg5XLzqlNXdEUptjN3osSVPQ8YcPTK08Z96p3sAtqt9AqSI0sbQuqv+BjnSDrEIc3mHR0jGzike7rhzo4/0NHzeJ2RqUVY8BQoFoFIAXzLqQv+hD8g3aDDDbAgaU0QJowAMGRo8ds90eH1AY+v5yDu2aRy77BSnoQytGg+83Bz3cRoftcAmQHqIQorkATghjBnJHrv6RLhOM6Ums16Fl5bOxdlLDrx04JT/3E+P5KEjmd8doRQVoM187O4ZePODDqy596bIYnAodn+3yoILV3rTJnbZKSYiWQs2Ly4hS7mNGMket0QSOzjpyQfk52Tge28ejS0GY707QhSjQG3m9oOnol9WzQdMzc1EjvLa/1OdKKtPEBHFKtwDcKLitrtpSVYxSBRGsHi1O/vZZoXBTjERyZrcl3Kj1McYpGTCeI0dO8VEJGtyX8qNUh9jkJIJ4zV2LCEikjfO5SWpMQYpmTBeY8ZOMRHJHx9WIqkxBimZMF5jwukTRERERJT22CkmIiIiorTHTjERERERpT12iomIiIgo7bFTTETiUgBW5wCOX+6F1ekBFFIniNIeY5KI9QBcfYKIIqEY3Dp0cHkfHcx6VWxPMyuAI+d7/FuQDq+fWTolm09HkzRCxSQEinsiORtq38909eL0F3Y0fHAeV3rdeOGBInzNqJc6daJK2EjxsWPHUF1dDQA4d+4cKisrUVVVhY0bN8LrHdx+sKGhAWVlZSgvL8ehQ4cAAE6nEzU1NaiqqsKKFSvQ1dUFADh69CiWLFmCiooK1NbW+r+ntrYWixcvRkVFBdra2hKVHaL0NdRpKNt5BA/+4gOU7WzBkfM9MY0iWPsG/J0PYHDr0bWNbbD2DQicaKLIBIvJLpdHsLgnkq0R7XvNnqN4teksqkunYkKmFmsb29DZ3Sd1CkWVkE7xrl27sH79erhcLgDAf/zHf+D73/8+du/eDZ/PhwMHDsBqtaK+vh579+7Fa6+9hm3btsHtdmPPnj0oLCzE7t27sWjRItTV1QEANm7ciK1bt2LPnj04duwY2tvb0d7ejvfffx/79u3Dtm3bsGnTpkRkhyitCdmRveRw+48zzNnvxSWHW5C0EkUrWEx29fXzBxylvEDt+/aDp1BWPAXOfi++sLkkTqG4EjJ9oqCgADt27MATTzwBAGhvb8dtt90GAFiwYAHee+89KJVKzJs3D1qtFlqtFgUFBThx4gRaW1uxfPly/2fr6upgt9vhdrtRUFAAALBYLGhpaYFWq4XFYoFCocDkyZPh8XjQ1dWF3NzckOlTqRQwmTKDvKcM+p4cCJU+tVolQGowLi3pUn7xpWF8/MkhXcGcOnclYKeh2+XBjEnZ4z4fKi+TvYNbjo48XoZGickTMmWXfzmfk3gEa/9SNb8jBcpjsJjs6/dGFfdykAznMNT1V7w0yK+cpEpTsPZdoRisB1/KzoDJJK8pFCpV4h6HS0ineOHChTh//rz//z6fDwrF4D2nrKws2Gw22O12GI1G/2eysrJgt9tHvT7yswaDYdRnOzs7odPpYDKZRr1us9nCdoo9Hh+6u3sDvmcyZQZ9Tw6ESJ/ZbMTAgEeQ9IxNSzqU3zCz2Rj+QwEEij85l5tJpw7YaTDpVAHTHCovOSrghQeKxs3fzFGNjyWpyfmcAMLGHyD//AohUB6DxeQkoy6quJcDMc+h0PEnJjnGulRpCta+KxWD9WJKToYsy0qpFGZgbyxRHrRTKq/16h0OB7Kzs2EwGOBwOEa9bjQaR70e6rPZ2dnQaDQBj0FEwjHrVQE7DWa9OvqHjnxA6ZRs7F9ZOvTwkja24whFqAcIKXkFi0kE7iyPi1fGEMlFDLEYqH3fsuhWzJ5kQK5OBaUyvSbRi9IpvuWWW/DnP/8Zt99+O5qamlBaWoqioiL85Cc/gcvlgtvtxpkzZ1BYWIji4mIcPnwYRUVFaGpqwvz582EwGKDRaNDR0YH8/Hw0Nzdj1apVUKlUeOGFF7Bs2TL87W9/g9frDTtKTERREroj6wPMGWqYM9T+/0dNiI4IV8KgYUFisjQ/G7uX3Y7PbU5MMmZgilEDjLzTzBgiuYglFofa0ZwMNfYsux3O/gHkZGiute9pGMOidIrXrVuHZ599Ftu2bcO0adOwcOFCqFQqVFdXo6qqCj6fD6tXr4ZOp0NlZSXWrVuHyspKaDQabN26FQCwadMmrFmzBh6PBxaLBXPmzAEAlJSUYOnSpfB6vdiwYYMY2SFKP0J0ZIUSaeMfpuMc7AHC/StLr+WT0pcCONLZgxf/cBL3F92A40obSgomYGae3t8xZgyRXISNxbHtYaYKRzrHt6PTJ+jTsjM8LGG1dsqUKWhoaAAAfPnLX8brr78+7jPl5eUoLy8f9Zper8f27dvHfXbu3Ln+441UU1ODmpoagVJNRKKLctQ3oo5IBB3nUCthsEND1r4BvPiHk1haUoDtB0/54+j5b92KOwpyAN/4GLo+JwNlxVNwrrsPMGVyKgWJ5qqzH8ss0zD0+BYaW8/j4lXnYHumV49rD5//1q34zwP/wx90Y3BHOyKSTgxrIEeyrFsky8jlZQ0+SDVShkaJvCytABmjZHfJ4cb9RTf4O8TAYBw9+euP/HE0Moauz8lAdelUvNZ8Fo+88X+5rjGJRwF8buvHa81nUXvwNH72p8G1hqdep0deljZge/jkrz/C/UU3jDoMl8dkp5iIJBTLGsiRdGYj6TgPP2AyfKxRD1JR2svL0kGlRMg4GhlDZcVTxnWgua4xicHaN4Anfz1+reEffGM2zHp10PZw7MpmHBSIYPrEyHV/3333XWi1Wnz1q19NeMKIKPXFMoUhktUwhjvOY5cZGtXgy20lDJIVs16FkoIJoeNoRAyd6+7jdBySRLB2VOHzAb7g7eH8EfEd16pCKSRkTf3tb3+L7du345133sGrr76KP/3pT8jLy8PRo0fx3e9+V6w0ElGKiqjzOlYEndmIl5GL5AHCVF9yayh/p85dgUmnTr38xcoHzMzT4/lv3Yonf/1R8DgaiiGYMqOPZSIBfMmow2P3TId3KCYbW8/jSq/bH3vB2sNb8vQcFBgjZKe4sbERv/nNb6DRaLB3717s378feXl5qKioYKeYiOIW8xrI4TqzQo0Cp/qSW6mev3h5gTsKciKKI0HX8yaKlAL4H6sDO5vO+uPue/fMwLS8rFFLqwVsD70yWlVIJkJ2ilUqFTIzM3H69Gnk5uZi4sSJAEZvxkFEFLNETmEQYBm5VF9yK9XzJ4hI44jTcUgCgerwSwdOYf/K0ujvilHoTrHH44Hdbsfvf/97LFiwAADwt7/9DQMDfHCAiAQi48Y61ZdtS/X8iU7GsUypiXVYWCGHfB966CF84xvfQFNTEx566CG0tbWhqqoKjz76qFjpIyKSTKov25bq+SNKdazDwgr5M+Lv//7vcfDgQf//tVotGhoakJeXl/CEScWnAMxmoyDHcg14oFOrBD+WUOkjotBSfZ5oquePKNWxDgsr7Nj6u+++i3feeQdXrlzBpEmT8M///M8p3SnWqlV44OVmQY7V+KhF8GOp1SoMDHjiPhYRRSDV54mOyF+3ywOTTpVa+SNKdaneRoksZKf4jTfeQFNTEx588EFcd911uHDhAn7605/i3LlzWLp0qVhpJCKSiRTcnmxoHuyMSdno7u7lxVQIqb6MH4krXDxxLrtgwq5T/MYbb0ClGrxtf/PNN8NiseDhhx9mp5iIxCFlB4NLltFIkcQiY4aEFG088QdZXEI+aKfRaPwd4mFarXbca0SU5hSA1TmA45d7ca6rN7oB1RF/a3V6Rv/t0AWhbOcRPPiLD1C2swVHzveINmAbyzbUJLBQ8SFyOiKJRcYMCUYBdNr7ccpqx/I7p+H6nIxR8TSuXkjcXqaCkCPFCkXgkvT5+LODiIbEMzIW5m8Tuo5uBCMqXO5IQkPn50xXL05/YUfDB4O7dEk16hpRLCqAM129jBmK3Yh2CQoFnn3rY5y73IcMjRKP3T0D9UfO4eJVJ8509WLtr0a3m4XmLK47HqeQpdTe3o6KiopRr/l8Ppw5cybqL9q/fz9+/etfAwBcLheOHz+OvXv34jvf+Q5uvPFGAEBlZSXuu+8+NDQ0YO/evVCr1XjkkUdw1113wel0Yu3atbh8+TKysrLw4x//GLm5uTh69Ci2bNkClUoFi8WCVatWRZ02IopdPB3XcH873Cm9PicDZcVTMPw7/aqzP75GPsKOfEzbUFP8Apyf4Q6BVBf5SH4gWfsGcPoLOzI0SkzI1PpjVqUY3IqXKKQQcX/xqhPbD57Cqrumo/bQaZz6wj6u3fz5v5XwB1mcwm7zfPDgQeTk5KC0tBQAYLVa8fOf/zzqLyorK0NZWRkAYNOmTXjggQfwySef4KGHHsLDDz/s/5zVakV9fT0aGxvhcrlQVVWFO+64A3v27EFhYSFqamrw9ttvo66uDuvXr8fGjRuxY8cO5OfnY+XKlWhvb8esWbOiTh8RxSae0dRwf5uXpcPU6/RYWlKA7QdP+S8U0/NuxfQJ+phHCyPtyHO5I2kEOj/bD57CMss0vHzotCQX+Uh+IF1yuNHwwXk89U83o7ffg5cOjIhZs4HziimkcHHv7PdiUk4GXlw8B1veOT7qb539XvS6PfwRH6eQrcpPfvITqFQqWK1W9PX1YcqUKXjmmWfw4IMPxvyFH330EU6fPo2NGzdi48aN+PTTT3HgwAFMnToVTz/9NNra2jBv3jxotVpotVoUFBTgxIkTaG1txfLlywEACxYsQF1dHex2O9xuNwoKCgAAFosFLS0tYTvFKpUCJlNm0PfVAq0tnIhjKQQ6plDpGluOKpUyZNlKTQ7pCxR/ckhXrCZ7EbAhnjwhM2yewv1ttteHzd+8FSvrPxh1oXjy1x/hnRoLvD7gC5sLE4065Jv0UCojmzx36tyVgJ3xbpcHMyZlAxg6JzmZ+JpRj5mrLDF9jxwFa//kFIPBzo9CEXlsBRJPHrO9PmxdMheP7zvq7+huXTIXX5lo9MfDZC9wpdcNm2sAtUOdmOG0r21sw+9WWTA1N/T3e70+dHb3xRRvcjqHwYS7/oqTBvmVk0qlRLfLM+oOAwA0tp73/ztDo0RHVy/uuWkirvS6R/19hkaJgtyssDEaS7rkWFaJErJT3NHRgf3798PtduOBBx6ARqPBL3/5S3zlK1+J+QtfffVV/454RUVFWLJkCWbPno1XXnkFL7/8Mm6++WYYjdc2p8jKyoLdbofdbve/npWVBZvNBrvdDoPBMOqznZ2dYdPg8fgGlx4KwGw2xr0O8EhCH0uIdYqHjyWEseVoMmUGLVs5EDJ9sW6iEij+5F5uoeSoEHA0NUc1Pj5i+luvd1wHaUKmFv+38yqe+T8fxfSEv0mnDtgZN+lU/u8deU5ylEBOzuDt756evkiLJqGEjD9AXjEY7PwoFYg4tgIeN8483jbZMG492JHxMBzPp6z2gJ36C1d6kRPqeh7nyhVinkOh409Mcor1YSZTJq7Tq/Hg/zN11B2G790zAwBGTaUoyTcFbDdN6vAxGku65FhWSmViFnwI2Ske7nBqtVp4vV7813/9F0wmU8xf1tPTg7Nnz/qnYtx7773Izs72/3vz5s0oKSmBw+Hw/43D4YDRaITBYPC/7nA4kJ2dPeq1ka8TkYjGLB4/eUImclSI7DZxBAvPB7ptvaRkir9DDET/QAmnRchboPOzZdGtmD3JgFydhEtMhVsPdiiep5j02Nl0Nurb2Al9sJRkz+P1+TvEwOD5f+nAKfxn+Vwss0xD/ZFzuNI72E7OzMsM2m5yzeLYRTwGfd1118XVIQaAv/zlL/jqV7/q//+yZcvQ1tYGAP5pD0VFRWhtbYXL5YLNZsOZM2dQWFiI4uJiHD58GADQ1NSE+fPnw2AwQKPRoKOjAz6fD83NzSgpKYkrjUQUg6HOwszrMgdvD4KHfjQAACAASURBVEfTEI/4W3PG+E7pcAcpQzPYXGVolJgx0RB0LnKk3zncGf/lt0uwf2Up53vKSYDzs2BqDnK1SbDmqg+YYtCMi1n/j64QQs2xp9QX7Pwf/1sPXj502r/6ynAHOFS7SbEJWUNPnz6Nxx9/HD6fz//vYVu3bo36yz799FNMmTLF///nnnsOmzdvhkajQV5eHjZv3gyDwYDq6mpUVVXB5/Nh9erV0Ol0qKysxLp161BZWQmNRuP//k2bNmHNmjXweDywWCyYM2dO1OkiogSKdzH5AKPJUCjif6CEu0DJWzKfHx9Qmp+N3ctux+c2JyYZMzDFqAG8of+Mq52kt2Dn/7Ybc7FnxW3IzdBIe6ckDYR90G7Y2KXZYjH8oNywWbNmYe/eveM+V15ejvLy8lGv6fV6bN++fdxn586di4aGhrjTRkQJINTuXmM7SIrAc5FTZvpDOu5KlUp5VgBHOqOPe07rSW/mTBVe/df5aO24Aq8P+O2xz1DxdwVY+6u20Wt0U8KE7BTfdtttYqWDiFJQwuZIRjAXWRBSdNTSaZvgEBsVJHOeY457seKa5EUB/PVyL0583oPTX9ixb2ijmmfvvwVvvt+Bi1edAMD55SJgyRJRwiR0R7hob69H2cH1en2SdE7T5mGrMBsVDOcZQNKNHscV98k8bYSiF6IebP7dJ3hh8Ryc/NyGxtbzuHjVyY04Eixxi70RUdobniM3kqBzJBWA1TmA45d7YXV6gGBLcQ5deMp2HsGDv/gAZTtbcOR8T/DPA+js7gvYObX2DQiT9iCS4mGrSMs9hGAbFZQVT/H//0xXb1TnTC4SHveU/Ibq0LEvHDhttWNC5mBsjKwHzn4vTn5uw8/+dBbVpVMx9To9YyjB2CkmooQJtHJE0Kfwo+1oRdHRDTb6GqqD+4XNJUnnVPYdqhh+YAz/3cjze9XZH3SDDmAwz4G2sk30jxIhRBX3lH5G1KHlv2zFq01nseru6Xjyf92E63MyRm1U4/Nd6yj/4BuzGUMJxtIlosSJdI5kDPNoo5lmEMvt7InGwS2m7y+6wd9R++2xzxLeOZX7w1YxTe9QAp9c6hv1ANHqe27C1Ov0OHf52sYCw52A4XWJX/zvk6MOI9jUm0Tj3GAKIVAd2vy7T7BywTRUl07Fmx90QKmAfxrF8GcUPh9jKMFk3rIQUdKLYI5kLB2taDq6sSx1NSUnA6vumoFnf/Oxv3O6+ZuzYc5Uh11aKy4y71BF/QNDAbx37iqe/PW13Qcfu3sG/vPASfzgG7Pxv19v9b/+/LduxZeMWvzzLaVQKRUBt7KVzYh5OJwbTEEEq0NqpRLbD57CK/9SjFOf2/Dz/++c/yG7pIr9JMbpE0QkuVjm0UYzzSCW29nnrzr9HeLh9Dz7m49h7RXh9r2MF+aPdnqHtW/A3yEGrt0Kvr/oBih8vlEbdNxRkIPpJj3MGWrk6jgFgVKTMUMTsA7dmJcFZ78XKgDX5+j9PwoZ++JhCZMsGHP0yNAKE45O9wBsV2Pf653EF8tIblTTDGIYfQ01p1j2t+8TKNrpHcF+8KiUGDwPwUZTZT5iThQrZ/8AHrt7BrYfPDXq7smF7l5/uxdqG2dKnPRt2UlWMrRqPPBysyDHanzUApsgRyKxxDSPNtpOU5S3sycaubtYQFGWe7AfPPMLJoS/0HMKAqWgnAwt3vygA8ss06BQAD4f8OYHHfjm3BvGbePM2BcXO8VEJLxoN72IdVQwgReOfJNe1g+8SSqKcg/0g+f5b92KW/L0iZ2bHUoq7Z5HScesV+Hp/zUT7Rd74PUBaiXwxMKbMf26TG7jLDF2iolIUDFveiGzkRGlUsHb90II9oNHwg5x2uwYSLLlHvBiZ9NZfwxuXTKHHWIZ4IN2RCQoqTa9SAgZP/CWVGRUjrGsWU0kpEAx+Pi+Y4xBGWCnmIgEJdWmF0SRSIodAymlMQbli51iIhLU8ANqI/EBNZIL2e8YSCmPMShfonaKFy1ahOrqalRXV+Opp57CuXPnUFlZiaqqKmzcuBFe7+Avp4aGBpSVlaG8vByHDh0CADidTtTU1KCqqgorVqxAV1cXAODo0aNYsmQJKioqUFtbK2Z2iCiA4QfUuL4syRG3YCapBYrBrUvmMgZlQLQz4HK5AAD19fX+177zne/g+9//Pm6//XZs2LABBw4cwNy5c1FfX4/Gxka4XC5UVVXhjjvuwJ49e1BYWIiamhq8/fbbqKurw/r167Fx40bs2LED+fn5WLlyJdrb2zFr1iyxskVEY/ABNZI1rn9MUgsQg1+ZaERPD9fXl5poneITJ06gr68PDz/8MAYGBvDv//7vaG9vx2233QYAWLBgAd577z0olUrMmzcPWq0WWq0WBQUFOHHiBFpbW7F8+XL/Z+vq6mC32+F2u1FQUAAAsFgsaGlpYaeYSGoyW0mCaBTGJ0ltTAwqlQqJE0SAiJ3ijIwMLFu2DEuWLMFf//pXrFixAj6fDwrFYCBkZWXBZrPBbrfDaDT6/y4rKwt2u33U6yM/azAYRn22s7MzbFpUKgVMpsyg76vVqlizmfBjKQQ6plDpGluOKpUyZNmGIlSa3ANemM3GoO+Hem/8sTxQCHzBDBR/8ZSbkLxeHzq7+/CFzYWJRh3yTfqoG2u55CVewfIhRBlJKVj7lyrnLRiv14eOK33421UnDBkauAY8yM3UJt35CyUZzmG46684aZC2nIZjsfNKLzJ1akw0aDFBIX25BCJ1WQWiUiVu5q9oneIvf/nLmDp1KhQKBb785S/DZDKhvb3d/77D4UB2djYMBgMcDseo141G46jXQ302Ozs7bFo8Hh+6u3sDvmc2GzEw4Ik1m+MIfSy1WiXIMYVK19hyNJkyg5ZtKEKWu1atDLo7XrTl1/ioBVZr4P3xoulcjxQo/mItN0EJtH6rLPIigID5kNEat0LGH5A65y2gAOftsbtn4M0POrDm3ptSZo1iMc+h0PEnJkljPUAsfu+eGZg+0YC5E7NkF4dybBdMpkwolcINOI4k2oN2v/rVr/D8888DAD7//HPY7Xbccccd+POf/wwAaGpqQklJCYqKitDa2gqXywWbzYYzZ86gsLAQxcXFOHz4sP+z8+fPh8FggEajQUdHB3w+H5qbm1FSUiJWloiEoQDOdfXi+OVeWJ0eQKJBK67fGh7LKAkpgE57P05Z7Vh+5zRcn5MBZ78X2w+ewv1FN/D8UeIpAKtzAMcv9+K8vR8v/uHkqDbkpQOn8NFnVxmHMiDaSPHixYvx1FNPobKyEgqFAj/60Y8wYcIEPPvss9i2bRumTZuGhQsXQqVSobq6GlVVVfD5fFi9ejV0Oh0qKyuxbt06VFZWQqPRYOvWrQCATZs2Yc2aNfB4PLBYLJgzZ45YWSKKn4xGHkOtnemfe5nmWEZJJsgIcf2Rc7h41QmFguePEixMDAKDMej1gXEoA6KVvlar9XdkR3r99dfHvVZeXo7y8vJRr+n1emzfvn3cZ+fOnYuGhgbhEkokomAjj/tXloreOA6vnTmy08e1M0djGSWXQPVr+8FTWGaZhteaz8Ln4/mjxAoVgy8fOg1gMAaVCjAOZYCbdxBJSE47G3H91vBYRsklWP1SKYHH7p6B37V9xvNHCRUqBgH45xTfekMO41AGeAaIJCSrkUeu3xoeyyipBKtfd07Pw8CAB/8wfR7PHyVUsBj8hxlmzLkhB5laFXL1GtyYZ+A6xTLAkWIiCclm5HH4QZBLvQAUmJmXOTh9g52F8YbWF515HctIVkY8zDT8wGqw+nWjUYvpJj3PHwknQPwBwWMw36BBkTkL03MykKtVpcyygMmOI8VEUhoaefzdKgsuXOmVZuRRRg/7EcUkRAyPHNmfPCETOSowrklYYdpQ3l1KHhwpJpKaD5iamynZyCOXGaNkFzKGR4zsT83NZGeEBBe2DeXdpaTBkWKKWbCd42Jd1J2kwWXGKNkxhklKjL/UwbNFMQu0c1ysO+41PmoRKlkUJVk97EcUA8YwSYnxlzo4fYIozcnmYT+iGDGGSUqMv9TBM0aU7vggCCU7xjBJifGXMtgpJiL/gyD++W9szCnZMIZJSoy/lMDpE0QkP0HW/Iz4fUovEcbLX85dYbyQsCJpi9heJQ2OFBPRIMXg0kKDt/90MOtV0ox2hFs3mesq07ChmD3T1YvTX9jR8MF5XOl1M15IHArgw7/Z0X6xB14foFIAt1yfjeJJhmuxxfhLKhwpJiJ/w1228wge/MUHKNvZgiPneyQZ0Qi35ifXVSYAo2K2Zs9RvNp0FtWlUzEhU8t4IVF0uTw4e8mBnU1nUXvwNF5tOouzlxzocl1bgYnxl1zYKSYiWTXcodb8jOR9Sg+BYnb7wVMoK57CeCFRdPX146UDp0bF4EsHTqGrr9//GcZfchFt+kR/fz+efvppfPbZZ3C73XjkkUcwadIkfOc738GNN94IAKisrMR9992HhoYG7N27F2q1Go888gjuuusuOJ1OrF27FpcvX0ZWVhZ+/OMfIzc3F0ePHsWWLVugUqlgsViwatUqsbJElDLktPh8uDU/uSYoAcFjVqFgvJA4HG5PwBjsdV8bKWb8JRfRRorfeustmEwm7N69G7t27cLmzZvxySef4KGHHkJ9fT3q6+tx3333wWq1or6+Hnv37sVrr72Gbdu2we12Y8+ePSgsLMTu3buxaNEi1NXVAQA2btyIrVu3Ys+ePTh27Bja29vFyhJRyhhuuEcK23An6OGRcGt+ck3QNDUm3r5kDByzSgUYL5QYY2Iw35QRMAavN+r8/2f8JRfRzso//dM/YeHChf7/q1QqfPzxx/j0009x4MABTJ06FU8//TTa2towb948aLVaaLVaFBQU4MSJE2htbcXy5csBAAsWLEBdXR3sdjvcbjcKCgoAABaLBS0tLZg1a5ZY2SJKCcMN99iHQYKutRnq4ZF4hVvzU4o1QeXyEKIU5JD3IPH20tK5+N6bR/2vbVl0K2ZPMiBXpwoYL90uD0w6FdeQpfDGxn2mCkc6w8fguHaTaxgnFdE6xVlZWQAAu92Oxx57DN///vfhdruxZMkSzJ49G6+88gpefvll3HzzzTAajaP+zm63w263+1/PysqCzWaD3W6HwWAY9dnOzs6waVGpFDCZMoO+r1arYs1mwo+lEOiYQqVr7HHiSZ8Y5R5L+kLFSiwCxZ9KpRT8e6L1NaMeM1dZ8IXNhYlGHfJNeiiVgYd/z3X1BpyD/LtVFlwnUF5MOcCMON6P1/A58Xp9+ONJKx7fd+3Ct3XJXPzjTeag5SNnwdq/QDEol7wHi7ffPmrB7yKMWVPOYB49Hm/A91OBHNqRcMJdf8VJQ+hyChT3Py4rwrY/now5BsO1V3I9d3JMl0qVuEkOoo7fX7x4EY8++iiqqqrw9a9/HT09PcjOHhxZuvfee7F582aUlJTA4XD4/8bhcMBoNMJgMPhfdzgcyM7OHvXayNfD8Xh86O7uDfie2WzEwIAn4HuxEPpYarVKkGMKla6xx4knfWKUeyzpCxUrsQgUfyZTZtDvCUngUbwcJZCTM3jrr6enL+jnLlzpDTiX7sKVXkzNjTEvMjN8TqzOAf/FERjM5+P7jmL/ylLR51uPJGT8AYFjUC55DxZvF7t7MfO6zPExG6RexFzPkoSY+RM6/sQUrpwCxf26/W1YZpmGlw+d9n9uVAyadLD2DeDPn16OqS2Wa2zKMV0mUyaUSuEG0UYSbU7xpUuX8PDDD2Pt2rVYvHgxAGDZsmVoa2sDAP+0h6KiIrS2tsLlcsFms+HMmTMoLCxEcXExDh8+DABoamrC/PnzYTAYoNFo0NHRAZ/Ph+bmZpSUlIiVJSJpSbiMWkxzkJNUOj89Lpe8RxVvMlpekJJTsLgfO0Dpj0HGXMoQrVP805/+FD09Pairq0N1dTWqq6vx5JNP4kc/+hGqq6vx4Ycf4rvf/S7MZjOqq6tRVVWFf/u3f8Pq1auh0+lQWVmJU6dOobKyEm+++aZ/lYlNmzZhzZo1WLx4MW655RbMmTNHrCwRSUrKZdTS6eGRdPoBMJZc8h5NvMlpeUFKTsHifn7BhIAxyJhLHaJdwdavX4/169ePe33v3r3jXisvL0d5efmo1/R6PbZv3z7us3PnzkVDQ4NwCSVKEpIuo5ZGD49E/RBiCpFN3qOIt1D1IpFz0Cl1BIv7W/L0AWNQTktaUnx4toiSlOTrX/oAc4b6WqMvZidJzBUR0ugHwDhj8m7I0MDVPwBrn0f8VSgijDfJ6wUlvwB1XqVU4Li1F3lZOszMyxyMv6EYZMylDu5oR5Sk0mkKwyhSzN8b6pDNvC5zsFOWDh3iYT7ArFfjqnMAVa/9GZWv/UXWcybTtl6QsIbrfF4mzlzuxTd/2hK0vWHMpQ6eMaJklaYjmMHm70m9GkQqS6oyT9N6QYkRUewz5lKGzFozIoqKlFMYJML5e+JLujJPw3pBiRFx7DPmUgKnTxBRUpHLigjphGVO6Yqxn17YKSaipML5e+JjmVO6YuynF55VIkounL8nPpY5pSvGflphp5iIkg/n74mPZU7pirGfNjh9goiIiIjSHjvFRERERJT22CkmIiIiorTHTjERERERpT12iomIiIgo7XH1CSIZ8Hp9sDoHhpb80cGsV6XfE86KwS1Vg5ZBuPcpNkPl+tHpS9BrlcjN0CBXx7KNGeNUXJGW94jPTfYCOSqEPi88j2kpJTrFXq8Xzz33HE6ePAmtVosf/vCHmDp1qtTJIoqMAvjjSSse33cUzn6vf3H40inZ6dMIK4Aj53vw4h9O4v6iG6BSAiUFEzAzTw94r72/trEt8jLiRS28AOX67/cWYmpuJoonGVi20YolTtNdPLEUrLzzs2HtHXHMTBWOdEZxXnge01ZKTJ/44x//CLfbjTfffBOPP/44nn/+eamTRBQxa9+Av0MMAM5+L9Y2tsHaNyBxysRj7RvAi384iaUlBXit+Sy2HziNla+34r1zV/0XzeELFBBBGQ1d1Mp2HsGDv/gAZTtbcOR8D6AQMVNJIFC5bvvD/+DsJQfLNgZRx2m6izOWgpX3J5f6Rh3zvXNX8eIfTkZ8Xnge01dKdIpbW1tx5513AgDmzp2Ljz/+WOIUEUXuksPtb3yHOfu9uORwS5Qi8V1yuHF/0Q3YfvDUqAvRk7/+yD+KFE0Z8aIWmQs2V8Byzc3UsmxjwLocnXhjKVh5t3ZcGdeO3F90w7jPBTsvPI/pKyWmT9jtdhgMBv//VSoVBgYGoFYHzp5KpYDJlBn0eGq1SrC0CX0shUDHFCpdY48TT/rEKPdY0hcqVmIxNv4me4EMjXJUI5yhUWLyhEzBv1sMKpUy6nRP9gKqzu6AF6JulweTJ2RGVUanzl0JeqwZk7ITlo9kMDL+DDZ3wHLN0qkTWrZSSfQ5lbouJ0PMjoy/eGMpWHl7Rh8Szn4vVGOGAEOdFynOo1zPnRzTpRp7MgWUEp1ig8EAh8Ph/7/X6w3aIQYAj8eH7u7egO+ZzUYMDHgES5vQx1KrVYIcU6h0jT1OPOkTo9xjSV+oWInF2PjLUQFbl8wdN6c4RxX8u+XMZMqMOt05qsE5xIEuRCadCjkq4IUHisbN8QtWRiadOuixIk1bLPkQkxDxN0GnwvfumYGXDpzyl+v37pmB3CxNQstWKok+p9HGqdDEjFkh4i/eWApU3s9/qwj/eeDkqM9laJSYP6J9CXdepDiPcm1v5JgukykTSqVwg2gjpUSnuLi4GIcOHcJ9992Ho0ePorCwUOokEUXOB/zjTWbsX1k69GCIFma9Or0e6PABM/P0eP5bt+LJX3806kI0XBalU7IjLiOzXhXwopZ25RpGrk6FaXlZWLlgGrw+QKkAbszNxIwJGYMPOAbAsg0hyjhNd3HHUqDyzlRDde9N4455S57e/7nJEzJDrz7B85i2UqJTfO+99+K9995DRUUFfD4ffvSjH0mdJKKoKJUKmDPUMGcMVcl0bHy9wB0FOcEvRD5EXka8qEXGBxRPMiA/JwPdLg9MOtVgOQXpEA//Dcs2hGjiNN0JEUtjy9sb5Jjea5+LaPST5zEtpUSnWKlU4gc/+IHUySCieAl5IeJFLTJD5TRjUvZgRyHCETqWLQkiEbHE+KQYpcTqE0RERERE8WCnmIiIiIjSnsLn8/HGAhERERGlNY4UExEREVHaY6eYiIiIiNIeO8VERERElPbYKSYiIiKitMdOMRERERGlPXaKiYiIiCjtsVNMRERERGkvJbZ5jpbbPYCrV/sCvmcw6GC3u0ROUeSYvvgImT6z2RjT3wWKP7mXWzRSJS9yz4eQ8QfIP79CSPU8ipk/oeNPTHKMAzmmCZBnugwGHfR6bUKOnZYjxQqFIuh7arVKxJREj+mLjxzSFyj+5JAuoaRKXlIlH2MFa/9SNb8jpXoekyF/oa6/YpFjOckxTYA805XINKVlp5iIiIiIaCR2iomIiIgo7bFTnMoUgNU5gOOXe2F1egDp71oREQXG9orSHeuA5NLyQbu0oACOnO/B2sY2OPu9yNAo8cIDRSidkg34pE4cEdEIbK8ozXm9PtYBGeBIcYqy9g34KxcAOPu9WNvYBmvfgMQpIyIaje0VpbvO7j7WARlgpzhFXXK4/ZVrmLPfi0sOt0QpIiIKjO0VpbsvbC7WARlIWKf42LFjqK6uBgAcP34c5eXlqKysxFNPPQWvd/DENzQ0oKysDOXl5Th06BAAwOl0oqamBlVVVVixYgW6uroAAEePHsWSJUtQUVGB2tpa//fU1tZi8eLFqKioQFtbW6Kyk3TysnTI0Iw+vRkaJfKyErO2HxFRrNheUbqbaGQdkIOEdIp37dqF9evXw+UaXPC5trYWjz76KPbs2QO32413330XVqsV9fX12Lt3L1577TVs27YNbrcbe/bsQWFhIXbv3o1Fixahrq4OALBx40Zs3boVe/bswbFjx9De3o729na8//772LdvH7Zt24ZNmzYlIjtJyaxX4YUHivyVbHh+klnPaeREJC9sryjd5Zv0rAMykJDSLigowI4dO/DEE08AAGbOnInu7m74fD44HA6o1Wq0tbVh3rx50Gq10Gq1KCgowIkTJ9Da2orly5cDABYsWIC6ujrY7Xa43W4UFBQAACwWC1paWqDVamGxWKBQKDB58mR4PB50dXUhNzc3ZPpUKgVMpswg7ymDvicH0aTva0Y9Zq6y4AubCxONOuSb9FAqE/s4ayqVX+LSMD7+5JAuoaRKXlIlH2MFa/+kzq8Y7ZXUeUy0ZMhfqOuveGmQXzmpVEp8beaXRL9mR5IuOZZVoiSkU7xw4UKcP3/e//8bb7wRP/jBD/DKK6/AaDTi9ttvx+9//3sYjde2iczKyoLdbofdbve/npWVBZvNBrvdDoPBMOqznZ2d0Ol0MJlMo1632WxhO8Uejw/d3b0B3zOZMoO+JwfRpi9HCeTk6AAAPT2J31oz1covlFi3OQ0Uf3Ivt2ikSl7kng8h4w+QR34T3V7JIY+JJGb+hI4/MckxDkymTPT09Il+zQ5HrmWlVCZmVztRHrTbsmUL3njjDfz+97/HokWL8Pzzz8NgMMDhcPg/43A4YDQaR73ucDiQnZ0d8LPBXh/Z0SYiIiIiioQoneKcnBz/SO/EiRPR09ODoqIitLa2wuVywWaz4cyZMygsLERxcTEOHz4MAGhqasL8+fNhMBig0WjQ0dEBn8+H5uZmlJSUoLi4GM3NzfB6vbhw4QK8Xm/YUWIiIiIiorFEmcH9wx/+EKtXr4ZarYZGo8HmzZthNptRXV2Nqqoq+Hw+rF69GjqdDpWVlVi3bh0qKyuh0WiwdetWAMCmTZuwZs0aeDweWCwWzJkzBwBQUlKCpUuXwuv1YsOGDWJkh4iIiIhSjMLn86XdXin9/Z60mVMstnRKX6xz6gLFn9zLLRqpkhe550PI+APkn18hpHoek2FOcajrr1jkGAdyTBMgz3SZTJnQaJJ4TjERERERkZyxU0xEREREaY+dYiIiIiJKe+wUExEREVHaY6eYiIiIiNIeO8VERERElPbYKSYiIiKitMdOMRERERGlPXaKiYiIiCjtsVNMRERERGmPnWIiIiIiSnvsFBMRERFR2lNLnQAiSh/GHD0ytMI0O073AGxX+wQ5FhERETvFRCSaDK0aD7zcLMixGh+1wCbIkYiIiBI4feLYsWOorq4GAFy+fBmPPPII/uVf/gUVFRXo6OgAADQ0NKCsrAzl5eU4dOgQAMDpdKKmpgZVVVVYsWIFurq6AABHjx7FkiVLUFFRgdraWv/31NbWYvHixaioqEBbW1uiskNEREREKSwhI8W7du3CW2+9Bb1eDwB44YUX8PWvfx333Xcfjhw5grNnz0Kv16O+vh6NjY1wuVyoqqrCHXfcgT179qCwsBA1NTV4++23UVdXh/Xr12Pjxo3YsWMH8vPzsXLlSrS3twMA3n//fezbtw8XL15ETU0NGhsbE5ElIiIiIkphCekUFxQUYMeOHXjiiScAAB9++CFuuukmfPvb38YNN9yAZ555Bi0tLZg3bx60Wi20Wi0KCgpw4sQJtLa2Yvny5QCABQsWoK6uDna7HW63GwUFBQAAi8WClpYWaLVaWCwWKBQKTJ48GR6PB11dXcjNzQ2ZPpVKAZMpM8h7yqDvic3r9aGzuw9f2FyYaNQh36SXVfoCYfoiScP4+JNDuoQSLi9qtUqw70pkmaXSORkpWPuXqvkdSaVSIjtbP65dVSoVUidNEMlwDkNdf8VLg/TlNPb6PkEhfbkEIoeyGkulStwaEQnpFC9cuBDnz5/3//+zzz5DdnY2fvGLX6C2tha7du3CjTfeCKPR6P9MVlYW7HY77Ha7//WsrCzYbDbY7XYYDIZRn+3s7IROp4PJZBr1us1mC9sp9nh86O7uDfieyZQZ9D1RKYAj53uwtrENzn4vMjRKvPBAEb4280vo2XH6aAAAIABJREFU6ZHvw0Uxl58CsPYN4JLDjbwsHcx6FeCTUfoCMJuN4T8UQKD4k03cCSBUXsxmIwYGPIJ9VyLLTO7nRMj4A+SfXyFkZ+vx38c/H9eulk7JDt3eiNQ+xUvMcyh0/IlJ8lgPcH3fumQubptsED6u4oxdycsqAJMpE0qlcIMrI4myJJvJZMLdd98NALj77rvx8ccfw2AwwOFw+D/jcDhgNBpHve5wOJCdnR3ws8FeH9nRTmbWvgF/hQEAZ78Xaxvb0Nkt3w5xzIYaiLKdR/DgLz5A2c4WHDnfA6TG4A0RyURnd1/AdtXaNxD8j9g+kcACXd8f33c0dBzGgrEbNVE6xfPnz8fhw4cBAH/5y18wffp0FBUVobW1FS6XCzabDWfOnEFhYSGKi4v9n21qasL8+fNhMBig0WjQ0dEBn8+H5uZmlJSUoLi4GM3NzfB6vbhw4QK8Xm/YUeKEUgBW5wCOX+6F1emJK/AuOdz+CjPM2e/FFzZXnImUn2A/AARvIIgoegK2a1L7wuYK2K5ecriD/g3bpzSVwLgPdn0PFYexYOxGT5Ql2datW4f169dj7969MBgM2Lp1K3JyclBdXY2qqir4fD6sXr0aOp0OlZWVWLduHSorK6HRaLB161YAwKZNm7BmzRp4PB5YLBbMmTMHAFBSUoKlS5fC6/Viw4YNYmQnsCDTHcLelgsiL0uHDI1yVMXJ0Cgx0agTMNFDJL41GKqBMGdw1UAiyQjcrkltojFwu5qXpQ36NzG1T0ky3YKCSHDcB7u+B43DGOOJ19boKXw+X9pV1f5+j+Bziq3OAZTtPDIuyPevLI0t+GKZUzyy4hh0UCmAz22u0JUoVOVH9BUxlvKzOj0o29kiXNkJnL5gYp1TFyj+5DhvK1bh5hQLuU6x1Zq4lYrlfk4Ei7+hdqPb5YFJpx5VzwVr14Jd1EXuPMYypzjq9knCHxLJMKc41PVXLOHKSfDr+VgBYuTHDxRhYpYGORna0fUgkngKUo+EuLbKsR00mTKh0SRmTjF/KghE8F9kPqB0Sjb2rywdCnQtzHp18KekA1Sc790zA79sOYcrve6gjXKw2yv7V5bizOVeURp2s16FFx4oGvddZr2aoytEiRTmgitIuxbsO/KzcaRT3M6jUqkI2K6G+r5o26dQbSpH55KDGCOsWrUSKxdMg9cHKBXA1V43nv9/T4y7XoeNpxB1mNfW6LGGCiTq2yGR8AHmDPW1ShgiiANVnJcOnMIyyzS8fOh00EY5WOW/aHOJ17AH+QEgSaXlbU9KI+EuuEK0a8G+Y/ey26XpPEbRrg6/H037lBS3rNnOhZSQ6/kI1r4BfO/No+OOH+h6HS6ewtVh2VxboyFhfIryoF06GP5FlqEZLNJRv8hEEKziKBTX/h1oEv9w5R8pQ6NEplYlyoMAfkMXqpnXZQ5eOCTqEPNJXUon4R74EaJdC/Ydn9uc4rYx8YiifQrWpgrVoYob27mwEn09j+Z6HS6ewj60J4drazQkjk92ioUyYjThl98uwf6VpaI+jBKs4gzPGA/WKAer/Ll6jbwb9gTgk7qUbsJ24ARo14J9x5eMGSnZxkg9QBIO27kIJPh6Hs31Olw8yf5HWJSkjk92ioUk4S+yQBXne/fMwP4Pz4dulINU/lydyA27DJZ9EmuZHCK5iKgDF2e7Fuw78o0aWXceYxagTS00Z+H4JXksacd2LkIJvJ5Hdb0O00FP6I8wBXCuq1fU67LU8ZnkrQ/5BZj3plIqMGuSMfw8oiBz7ESbiySTZZ8SPY+MSHZGtBuDq0+ohK/nwebkemX0LIHQhttUvVoWbdtIbOdkIECdyNCpg1+vQ82DT9QzORJdl6WOT44Up5Ixv2xztar4fuWKNPId0+2SBIwsy/22J1FCDNXzv5s6IXH1PFhbEuh1Gdw1EorUt4IDYTsnE2NiP39CZuzX2gRcq4PF7uluZ0LrpdTxyVpAkovoae0xazB3dvf5n94V7BesnFbBIEpHMrlrJJRQq/tItvID2zmKQLDY7bjShwvdXbjl+mwUTzIIHzcSxydHiik2Ao7mhH1QYOzTqK+24OwlByZkDr4v6OhLsj2pS5RC5DiyGlaItjBY23bss6vSrvzAdi41iXBd7ujqw6tNZ3H2kgNdLk+cCQ5CwvhkpzhdxVN5BF4yJdztkmBrMJcVT/Efgw+KECW/kaNT1+dk4NG7pmP5ndNw1T0gz2kUYdrCYA9U7fvgPIAk6fSTuGK9NotwXX7s7sGHAYevwV19/bEdXMY4fSIdxXmLMtRi4aacGNIT5nZJuDUdAT4oko7cA96Yt5odyzXggU49ftvQWI7vdA/AdjXIVuwU0vDo1IRMLapLp2L7wVNw9nvxsz/JcxpF2N3GxrRtGrUS/76vDRevOv3HkN3GHiSdOK7Ngu+kOBS7v1tlwcnPbfjkog31R875Y9fZ70WvO0EjxRJiLUxD8VaeUHOAZ8SaqBBP1wZ7GnV4x2tuXZmetGolHni5WZBjNT5qGXcstVqFgYHoG/3GRy2wCZKq9DM8OnXKavd3iAH5bpUc0fMQI9o2q9ODK72j72jxBz0Ni+fanJCdFH3A1NxMuPs9eHzfsXHX4OuNutiOK2OcPpGG4l0HUOzFwoNNr/jnW74kyUYpRJQgQ6NTRTfkJMVautG2hVI/WU/yFs+1OZHXZXNG+sRt6uWIwop3HcDhhn3sLZ6EVZAQ0ytyr8v0f4aIUoAPmDy0253c19IN2RYGapO48gOFEM+1OepYjEYaxW3COsXHjh3Diy++iPr6ev9rv/3tb/H666/jzTffBAA0NDRg7969UKvVeOSRR3DXXXfB6XRi7dq1uHz5MrKysvDjH/8Yubm5OHr0KLZs2QKVSgWLxYJVq1YBAGpra/Huu+9CrVbj6aefRlFRUaKylFxGLmE2ZtmfuCuPFBUk1OLlRJRSArVRz3+rCObMwU0/ZCOWtpBtGQURKO63LLoVKqVi8IG5MHGV0OtymsRtQjrFu3btwltvvQW9Xu9/7fjx4/jVr34F39Dm3larFfX19WhsbITL5UJVVRXuuOMO7NmzB4WFhaipqcHbb7+Nuro6rF+/Hhs3bsSOHTuQn5+PlStXor29HQDw/vvvY9++fbh48SJqamrQ2NiYiCwll3CT9YWoPGlSQYhIAj6gND8br/7rfLR2XIHHC/zngZNQ3XuT/KZKsS0koYy4Np/p6sWpL+x48b9P4kqvO7IH7hiLcUvInOKCggLs2LHD//8rV67gxRdfxNNPP+1/ra2tDfPmzYNWq4XRaERBQQFOnDiB1tZW3HnnnQCABQsWoKWlBXa7HW63GwUFBVAoFLBYLGhpaUFrayssFgsUCgUmT54Mj8eDrq6uRGQpqUS01ifXqSQiGbP2DuB/v96K7QdO4+VDp3Huch+XL6PUN3QtXvurNmw/cBoXrzq5dJ+IEjJSvHDhQpw/P7gOo8fjwTPPPIOnn34aOt21JxXtdjuMxmvLHWVlZcFut496PSsrCzabDXa7HQaDYdRnOzs7odPpYDKZRr1us9mQm5sbMn0qlQImU2aQ95RB35ODSNJ36tyVgJP1u10ezJiUncjkpUT5AYDX60Nndx++sLkw0ahDvkkPpVKYhVIDxZ/cyy0a4fKiDrD0WawSeSxFHMeX87kM1v7JLQYT0Y7JLY9CEyp/Yrd/YpNjHIxMk5TX8FDpEku4+FOpErdGRMIftGtvb8e5c+fw3HPPweVy4fTp09iyZQtKS0vhcDj8n3M4HDAajTAYDP7XHQ4HsrOzR7028nWNRhPwGOF4PD50d/cGfM9kygz6nhxEkj6TTh1wsr5Jp0p43lKh/CJdKzLWNXIDxZ/cyy0aofJiNhtjWuYsmEQeK9Yl2QCIci6FjD9AfjGYiHZMbnkUmiD5k6D9E5sc42BkmqS8hodKlygiiD+TKRNKpXADIiMlfEm2oqIivP3226ivr8e2bdswffp0PPPMMygqKkJraytcLhdsNhvOnDmDwsJCFBcX4/DhwwCApqYmzJ8/HwaDARqNBh0dHfD5fGhubkZJSQmKi4vR3NwMr9eLCxcuwOv1hh0lDmpoF5m/nLsS9/aIUkvYsj8CbiEpZ0m51SxRrGTa9slm+bI0afeGpUT7l+TnLCGxnyRlInX8SbYkm9lsRnV1NaqqquDz+bB69WrodDpUVlZi3bp1qKyshEajwdatWwEAmzZtwpo1a+DxeGCxWDBnzhwAQElJCZYuXQqv14sNGzbElpg4d3iTnUQ8hZpqZRRCQhZBJ5IjOddrOSwDJefySZCkb/9S4ZwJHftJVCZSx5/CN7wcRBrp7/eMuh1gdQ6gbOeRcbcq5LZ7EiDdbZ9Iy0iOt6VGiiR9VqcHZTtbwuY11tuHY+Mv0nQli3DTJxK5C52Qx4pnRzurNfF72gkRf8nU9gkh2nqWbOUjRDsiRfsnpEjOmRzb20SmKZ44FrusIok/kykTGk2STp9IBvHu8JYO0qmMZHPblijB0qlexyIdyyfZ2790PGfhJFOZSB1/yRHlCRbvDm/pIK3KSA63bYlEkFb1OgZpWT5J3v6l5TkLI6nKROL440gxpP9lkgzSroy4jjOlgbSr11FK2/JJ4vYvbc9ZCElXJhLGn0xLRGQjfpl0uzww6VRJ9ctYFEk+ekBEAbDtC43tXvLhORuPZRIxdoqHDf0ymTEpe3BS+f/P3t2HR1Xe+eN/z3PmMUNkoiCEGk1a1AYIWaTryPqwlbZrdxF5SjTWX0W+yyVBWEGo5eFid/kqP5psBWQrlK0/QwmGDW1d2e7366KC0YAlLsSmpSLsJkFQAgkwk6dJZub3R5Ihk8yZp5xz5szM+3VdXpecOXNyn3M+53Pfc8597juZg0XVP6xJf/Ab4DBqxNkfTiFJlHriyX1S5RglYt5LPslwzoZdQzafxIVMhmOiAGwUp5pIQ6+kU2VGROJLouGdkhJzdOoLcQ2Vz5+KGeP7Z+7l+U8c9imOVywDYcs4aHbYga8HLsS5u47hyddPYO6uOhw7fz328iTJIOBEJKKB6/7UpQ583urGGFP/SzpJMblDvBOUyJ3rxMrRJK7RxsGw77f1eEfU088fOInWrj55zz/r8hF4pzgesdwpkfmuSqShV0I1mGMac5N3iYjST4jrfvmDeag81oSL17qVPblDvDkrAblO6KaGUsdFTgujjYMQ398855sYY9Lj4rXuwGrdvT5cdPXId/5Zl4cU9Z3iM2fO4H/+538kLEryiGUaQrmnLBwcemWowaFXxBirMNFTMBKR/EJd99vePYO5hRMAKHh4J8SfsxKR65JpPNl0Mdo4CPX9H//6U8wvmhC0XoZODZNeI9v5Z10emmCj+MMPP8T999+P3t5eVFVVYenSpXjhhRdw4MABOcsnnxger8WSuOROcuGGXgnXYI4WkzZRClIBTW2dgo9Rha57lUr5wzvFm7MSkevEyNEkkoE2QdPVLiy+LxfjMjMCH8USB0JxlJdtCaqny+dPRZZRJ9v5Z10emmAW27NnD6qrq6HT6bB792784he/wLhx41BaWor58+fLWUbpxfgYIZaBsGUfNDvM0CuDDebh+xnL0CxJNQg4EUUWRf4Tuu7vzc3C9yZnK3p4p3hzViJynRg5mkQQobtQLHEgFEe3Z5mC6unbs6247uqS7fyzLg9N8E6xSqVCdnY2WlpaoNPpMGnSJOj1emg00sw3nUixPkaIZSDshAyaLTTw9ZAG854np+MXPyhCvsMc06ZF2x928CdShGjyn9B1f4c9Q/GTO8Sbs4Z/b9JNRrz2xHRc7vBEl7PiyXFDcvQbTxXh4JKZad/HMxHCdReKtc4LF39D62m1WhX6/E+0obUrjroyQvwl3YQeMhHc+76+PvT19eH999+H0+kEAFy/fh1dXV2yFU4u4R4jhOzcHstA2AocNPvslc6gX6IvP/pN3GzVIzNDH3n4FzH2hx38iRQjqvynwDwWtXgnKBnyvWvdvfjK5cGGt36PRwpuhUYNFOWMweSxRsAX4rujyXEcTzbhhK6Ju8db+196iyX2Y712hp5/FXCsJcY4GhjS72xbJz6/5Eb1ifNo7/SM/F4yX9MSErxTPGfOHHzve9/D66+/jqeeegqfffYZnnjiCTz++ONylk8WcfXjimUaQgVNmRnqF/DaX32K2nPt0Q//Msr9YQd/IuWIOv8pKI/FbKDsfzZpTGxlH/heZoYO/3T4MywsysGe2nPYdvhzLNlbjw+broXMl8xxyU3omphkN8YX+3FeOzHH0ZAh/cqqTuK1o+dQOnMSxpj0ob+XzNe0RAQbxY8++igOHjyId955BxMnToTdbsfLL7+MefPmRbXhU6dOobS0FADwxz/+ESUlJSgtLcXTTz+Ny5cvAwCqq6sxd+5cLFiwAO+99x4AoLu7G2VlZSgpKcEzzzyDtrY2AMDJkycxf/58LFq0CDt27Aj8nR07dmDevHlYtGgRGhoa4joI6fQYIdwLM3IlbnbwJ1KOdMp/8brc4cEjBbdi27tnRtxQCJUvmeOSm1KuiVjjKFy3D8ZfdATP8K9//euQyz/77DPMmTMn7EZ3796Nt956C0ajEQCwefNmrF+/HpMnT8b+/fuxe/duLF68GJWVlaipqUFPTw9KSkpw7733oqqqCvn5+SgrK8OhQ4ewc+dOrFu3Dhs3bsT27dsxceJELFmyBI2NjQCAjz/+GAcOHMDFixdRVlaGmpqa2I9CvI/XoqWgGYqEOtf7B8ojx3ijsnTwV9AxJ1K0gfz39jInLrR3JudjVImv97FmAzRqRN3NTtIcx9wmvVi6Fkh4PmKNo0ijxCjqJTqFxrFgy2fdunUYP348HnjgARgMBvj90Zc2JycH27dvxwsvvAAAqKioQHZ2NgDA6/XCYDCgoaEB06ZNg16vh16vR05ODk6fPo36+nosXrwYADBr1izs3LkTbrcbHo8HOTk5AACn04m6ujro9Xo4nU6oVCqMHz8eXq8XbW1tyMrKiv1IDDxGyLvFhqtXO0VtECup/2yot5sH36oF5LlwJH/DOtwxJ6KR/MCkLBMy1Tf+nTRkyLEOowZFOWOibqBIluMUVp+ktGj6dkt8PmKNI6FGtFoFZY1iouA4FmwUHz16FIcOHcL777+PcePG4fvf/z7uueeeqDY6e/ZsnD9/PvDvwQbxJ598gr179+KXv/wlPvjgA1it1sA6ZrMZbrcbbrc7sNxsNsPlcsHtdsNisQSt29LSAoPBALvdHrTc5XJFbBRrNCrY7aagZT6fHy1Xu/B5czscFgMm2o39b4OOUlNbZ8g+QW8vc2JSlinCt0OVXT2i7LF62GrE5GVOXLrejT4/sP43nwaGmSmfPxW3Z1vj3vdoyxcog6sH2VbxjjcQ/pjfJMLxG61Q8SfGeVWKSPui1Yo3go2U21KNYvtKPpeh4q9/efQxOJgvpbh+YxVLjh3NdTbDkoEtcwuw5uCNijxcvpQix0Xa12TII8PjLxGxJNZxErN+FypTLHFk8/lRPn8qnj9wMhCj//vRb2LqhEzcmhnfcZUipkZ73DSaqOedi5lgozgrKwulpaUoLS1Fc3Mz3nrrLbz22mu466678Pzzz8f8h/793/8d//zP/4xdu3YhKysLFosFHR0dgc87OjpgtVqDlnd0dMBms4Vc12azQafThdxGJF6vv/9u8CAJf7VcaO8M+TjjQnvnjbsyMbDbTcFlj1OmGsi0ZwAq4J8XTQt6RHT9evwjjMRSvkw1kJlpAIBR/c3hwh3zSVniHD8AcDgix1ooI+IP4p1XJQi3Lw6HFX19XtH+lpTb0mo1cW9fjnMpZvwBMcSgwu7yxJJjR3ud/fnEkY/Uw+UusXNcpH2VM4+IEn8JiiWxjpOY9Xu4MsUSRzPGW0J2+4g3/qSIqdEeN7vdBLVamuGBozptarUaOp0ObrcbTU1NMf+R3/zmN9i7dy8qKysxceJEAEBBQQHq6+vR09MDl8uFs2fPIj8/H4WFhThy5AiA/rvV06dPh8VigU6nQ3NzM/x+P2pra1FUVITCwkLU1tbC5/PhwoUL8Pl8cXWdkPJNYVlnKIpzXMxUe/uUs0KJy5pphMNhjfo/nU4j+Bklv7jzpURjk8t6vUeTLyUcgz3Vcpuso3QMOS9NbZ2inBdRzkcMs+lGJQnqdCXHseCd4tbWVvz2t7/Ff/zHf8BoNOKv/uqv8C//8i9B3Rii4fV6sXnzZowbNw5lZWUAgD/7sz/D8uXLUVpaipKSEvj9fqxcuRIGgwHFxcVYs2YNiouLodPpUF5eDgDYtGkTVq1aBa/XC6fTiSlTpgAAioqKsHDhQvh8PmzYsCGugyDUOf1sWycct47uF6tsMxTJ8YtboR3jhwt7zClmGXotHnu1Nur1w91hrXnWKVaxKEFiHtcdkDQ/KWoWODXwYdM1rP3Vp4roY6p0ccVSPCSKv1GfDzHLlST1M6DsOFb5Bd6gu/POO3Hbbbfhu9/9LsaOHQuV6sbPl4ULF8pWQCn09nqDHge0dnsxd1fdiM7pS2bl4jvfyB79xRkUrKN7s1voUUZrdx/m7jo2Yh8OLpkZtqKK+iKK8uJVTDcAgWMuZvnivfM5PP4ABR23EBwOq6iN4li2FY7U24q3+0TNs060trpEKVc4YsYfEH0MCuXLcLkmrvwUiyhzrKTXmQr4w5Uu/K+99dLt58DfEdrXZOg+MTT+4omleEgaf6Oo30dVrqF/12JAy9UuPPfmSdF/jEkWU6M4bna7CTqdzN0nli5diu9+97sAgMuXL6O1tTXwX6pxGDXYPOebQWMSLn8wDwdOnBdnXD8ZHmfEPC7mkEG+n3z9RMSJO5JuMPokeIRElIziGcNV8nF7FXC9t3b14dMvrkk/PrEC9lUsco0HLGn8jeJ8xF2u4fX3a3U4d7kDY0z6wDYUXT8Dio1jwcgb7OrQ0tKC9vZ23Hzzzbj55ptlK5is/MDdt1iwZFYufH7A7wcqjzWhvdOjiD4u0Yh1PEOhRq7QL1TZHnMRkbLFMT2sLGOTJ9jlDg8m3WQOuZ+WDF0CS6ZgMk01rNT4i7dcoervVw6fwdPOXLz63ueBZayfYyd4p/j8+fOYN28eXnjhBezatQtLly7F448/jkuXLslZPtlkGTTIc1jw8w/O4dX3Pg/MFZ4s/VBj/cUd6y/UqDrGq/qHWpHiBRMiUpAY7/IoZYYwQSK8HDfWbMDFq51Y/mDeiKeOPb0KvmOXaDLcMVRq/MVbrnCTdAyKq9Ev4UuiyULwyL/88stYu3YtioqKAss+/PBD/P3f/33QNMspI9lndYrxF3esv1AjdoxX2DBNRKQgMt0RjItIucth1OAbt9iw/q3f42lnLlSq/qeOb55oxv13TJOu/BTZsPgbP8aETA0SH39xzqYbbpKOwf+P+cU1gevgYasxvn1LUoKN4ra2tqAGMQDce++92L17t+SFSphkntUJiG4GngExv/0ZoVKLtTtGWEn0Fi0RRSmG/CQn0XKXH5g81oiVD+UHjT6xec43oVGr+u+6KWSf09KQ+FPUi81xzKYrVH/nO8wommiP60en0HUw8ZmZuNWkkB+wMhC84rXa0B/5fL6QyynJxHPnJkylJlqfY95xJiIZifq+hA+4NycTB5fMxNm2Tpy55MZP/u+fAt3xmMdIFGHq76ybTIF1YiF0Hbz7p0vIc1jSJnYFr/irV6+itjZ4iCK/349r165JXiiSiYh3bsR6kUHUO85ERBGI/hLWQB5d/a8NQdtkHiNRifzkReg68PrSK3YF9/Cuu+7CoUOHgpa1t7fj3LlzkheKko9Yg3FzlAsikpMUEwkwj1GyCXUdLH8wD5XHmtIqdgX38KWXXgr8f0NDA/bu3YtPP/0U8+bNk6VgJBK5+ueK9KKiUofOIaIUJcFLgGPNBky6yYhHCm4NjAjwb6e+YB6j2Mj5fs3AdbDv6Xvw/plWeH39Q9NevNadVnWwYKPY4/Hg0KFD2LdvH3Q6HdxuNw4fPoyMjAw5y0ejIXf/XBFeVFTy9I9ElKJEfhTtMGmw7IE8rP/N7wN57B/+5m44TFqAr+VQNBLxfo0fmGjRIc9hSds6WLBR/OCDD+KRRx7B1q1b8bWvfQ2LFy9mgzjJJGX/XCUP3UREFIXWzr5Agxjoz73rf/N7ZedeUpSE1d9KHb5OJoJH9sknn8Tbb7+NL774AvPmzYPfnyZHJFlE8Vglafu1KXToJiJKERI/lk7a3EvyGRaDNl9wACY0hpQ6fJ0MBI/skiVLsGTJEnz88cc4cOAAfv/732Pr1q34m7/5G+Tn58tZRhouyscq7J9LRDSMDI+lmXsprBAxWD5/KmaMtwRikDGUGILTPA+aMWMGtm7dinfeeQe33HILXnjhBTnKRWEIPVZp7QqeSlSpU1smHKeyJEpb0ebP0UhU7vX5/MxtSSBUDD5/4GRQDKZt/Z3g+jnqo2uz2VBaWorS0lIpy0NRiPqxSir2zx3lY0+fz8/JQYjSmCyPpcXKvbHkOxXwn39qxfMHTjK3KVxUMajE+lvq0TAUMHlXxDvF8Tp16lSgAd3U1ITi4mKUlJRg48aNgVnxqqurMXfuXCxYsADvvfceAKC7uxtlZWUoKSnBM888g7a2NgDAyZMnMX/+fCxatAg7duwI/J0dO3Zg3rx5WLRoERoaGqTaHUUZfKwylOBjlYG+QZNvMvVfbEMDK9numA5cMHN3HcOTr5/A3F11OHb+ekzlbrnaJfldIiISkch5Kqb8ORrhcm80Ysx3rV19gQYxwNymZPHFYIIraBHq30jkeIoTiSSN4t27d2PdunXo6ekB0D/m8YoVK7Bv3z74/X4cPnwYra2tqKysxP79+7Fnzx5UVFTA4/GgqqoK+fn52LdvH+bMmYOdO3cCADZu3Ijy8nJUVVXh1KlTaGxsRGNjY6DPc0VFBTZt2iTF7iiOKI9VZAhwsYlxwVxy9QjT5cmmAAAgAElEQVT+QicihZEgTyXLY+lY8124u4+kLKFisHz+1OAYVFgdLUeDVQkxLEkWyMnJwfbt2wP9jxsbGzFjxgwAwKxZs/Dhhx9CrVZj2rRp0Ov10Ov1yMnJwenTp1FfX4/FixcH1t25cyfcbjc8Hg9ycnIAAE6nE3V1ddDr9XA6nVCpVBg/fjy8Xi/a2tqQlZUVtnwajQp2u0ngM7XgZ0qg0ahhzzThYasRk5c5ccnVg2yrARPtRqjV0V8tTW2dIQP87WVOTMqKf/+lPH5nmtpDXjBXe7zIu8UW1TZu9qtCvrwwfoxJtvMeKv6UHndarSbqdVUR1o9lW5FIua1I+xGOks+lUP5TYgyKnafEyp9yiDXfjfch4bktGuHqX/nKkPhYHx6DX7vJHDTKl1R1dKwGj5UY9W8k0cawRiNZJwdpGsWzZ8/G+fPnA//2+/1QDUzrYzab4XK54Ha7YbVaA+uYzWa43e6g5UPXtVgsQeu2tLTAYDDAbrcHLXe5XBEbxV6vX3CIEVGGH5Gw383Q8mWqgcxMAwDg+vWumLZzob0zZIBfaO+8MfnGKMsnNrtBG/KCsRs0Uf/NCZnGkJODZGoQc7kdDmvklUIIFX9KHvbG4bCir88b9fparSbs+rFsKxIptxVpP8KR41yKGX+AMmNwaJ4al5mBuYUToFIBre7uuMZOFSt/yiHWfJepAcrnTx3Rpzie3BYNseNPTnHHush1+9AY9PuDj4tUdXSsBo+VGPVvJJkaRFU/2+0mqNXi3RAZSpbnRWr1jTPY0dEBm80Gi8WCjo6OoOVWqzVoebh1bTYbdDpdyG0klAI6ikcjGYd7EWO2O7VapbyXF4gopME8NcakR+nMSdj27hl09/rw8w+UmVfFFHO+8wN/+XUHc5tUZK7blVZHyzLbrAJeLpTl98add96J48ePAwCOHj2KoqIiFBQUoL6+Hj09PXC5XDh79izy8/NRWFiII0eOBNadPn06LBYLdDodmpub4ff7UVtbi6KiIhQWFqK2thY+nw8XLlyAz+eLeJdYakroKB6NZOlXF2TIBfPGU0U4uGRmfAlptC/AEJEsBvPU/KIJgQYxoNy8Kqo48p1arWJuk4jcdbvi6mix6t8o/k4iY1iWo7tmzRqsX78eFRUVyM3NxezZs6HRaFBaWoqSkhL4/X6sXLkSBoMBxcXFWLNmDYqLi6HT6VBeXg4A2LRpE1atWgWv1wun04kpU6YAAIqKirBw4UL4fD5s2LBBjt0JK2lmMlLAL7K4cLY7ovQxkKeMek1y5FWxMd8phux1uxLr6DSIR8myyYQJE1BdXQ0AuO2227B3794R6yxYsAALFiwIWmY0GrFt27YR606dOjWwvaHKyspQVlYmUqlHT2mPPMJKgwAnoiTnB8ZbM5Inr1JKSkjdzjpadjJ2104PinvkIZeBsUR/19SeHGMeE1HSSFheTbax3EkyKV23M84DUuBsKowSH3lITUkvF0o94w4RyS8ReVUJeY35TDlStW4XK85TJFbZKJZCmj3yEHoB4eCSmfL291NCJUZE0pA5ryY8rzGfKU8K1u2ixHkKxSq7T9CoKWEWGiB5Rv4gIuVLdF5jPiM5iBHnqRSrbBTTqMU3j7v4El2JEVHqSHReYz4jOYgR56kUq2wU06gp5QWERFdiRJQ6Ep3XmM9IDmLEeSrFKvsU0+gNeQHhao8XdoMmIS8gyDLjDhGlhwS/WMV8RrIQIc5TKVbZKCZxDLyAkHeLrX+O8kRcCKn6djARJUYiX6xiPiO5jDbOUyhW2SiW2miGKQn1XQovBd8OJiIBseRXFdDU1okL7Z3JM2QU85nyxVPHp2LdniKxykaxlEYzTInAdx+2GmUpOhGRosWSX1NoyChSkHjiinW7ovFFOwlFPUxJiNlkhL7bcrVL7t0gIlIcoRz5+dXuEbNypdKQUaQcgvW0u1dwVjjW7crGRrGEohqmZOBX49xdx/Dk6ycwd1cdjp2/jmvdvSG/e8nVI0fRiYgUTSi/fniuLZBHBxsmqTRkFCmHUFy9f6Y1KP6i+Q7rdmVgo1hC0QxTIvSr0aDThvxuttUgfcGJiBROKL/6/SPvBKfSkFGkHEJx5fVB8EmE0HdYtysDG8USimb8P6Ffje7u3pDfnWhnvyMiolD5dfmDeTj4yXkAwXeCEz3mMKWmcDEo9CRCKBZZtyuDbBmht7cXa9euxRdffAG1Wo1/+Id/gFarxdq1a6FSqZCXl4eNGzdCrVajuroa+/fvh1arxdKlS/HAAw+gu7sbq1evxpUrV2A2m7FlyxZkZWXh5MmT2Lx5MzQaDZxOJ5YtWybXLkUWxTAlg78ahzaMB+9gTB5rGvFdtVqgoxIRUToZkl8vunpw6otrqDzWhIvXugEMuxM8sO7by5wDo08k75BRpCADcbXv6Xvw/plWeH0IxKDgkwiBdgHrdmWQ7U7xkSNH0NfXh/379+PZZ5/FT3/6U7z00ktYsWIF9u3bB7/fj8OHD6O1tRWVlZXYv38/9uzZg4qKCng8HlRVVSE/Px/79u3DnDlzsHPnTgDAxo0bUV5ejqqqKpw6dQqNjY1y7VJ0BoYpmXyTqX+okmFJOOwdjAjfJSJKawM5siDbjDyHBe2d/XfmQt4J9gOTskzMpyQuPzDRokOew4I9tecCDeKwTyJYtyuWbHeKb7vtNni9Xvh8Prjdbmi1Wpw8eRIzZswAAMyaNQsffvgh1Go1pk2bBr1eD71ej5ycHJw+fRr19fVYvHhxYN2dO3fC7XbD4/EgJycHAOB0OlFXV4e77rorbFk0GhXsdpPAZ2rBz6TysNWIycucuOTqQbbVgIl2o+CvxkSULxYsXzRlGBl/SihXOFpt9ONoqiKsH8u2IpFyW5H2Ixwln0uh/Kf0GIwkmjya7PsYSTLsX7j6V74yiH+cYqnH5SqTGJRYLo1Guvu5sjWKTSYTvvjiC3z3u99Fe3s7fvazn+F3v/sdVKr+oDGbzXC5XHC73bBarYHvmc1muN3uoOVD17VYLEHrtrS0RCyL1+vvn3UtBLvdJPiZlDLVQGZmf0f769eFh2ZJVPmilU7lcziskVcKIVT8Kfm4ORxW9PV5o15fq9WEXT+WbUUi5bYi7Uc4cpxLMeMPUHYMRitSHk2FfQxHzv0TO/7kJNVxirYel7NMo6XEctntJqjV0kx4Iluj+PXXX4fT6cTzzz+Pixcv4gc/+AF6e3sDn3d0dMBms8FisaCjoyNoudVqDVoebl2bzSbXLhERERFRipCtT7HNZgvc6c3MzERfXx/uvPNOHD9+HABw9OhRFBUVoaCgAPX19ejp6YHL5cLZs2eRn5+PwsJCHDlyJLDu9OnTYbFYoNPp0NzcDL/fj9raWhQVFcm1S0RERESUImS7U/zUU0/hxRdfRElJCXp7e7Fy5UrcfffdWL9+PSoqKpCbm4vZs2dDo9GgtLQUJSUl8Pv9WLlyJQwGA4qLi7FmzRoUFxdDp9OhvLwcALBp0yasWrUKXq8XTqcTU6ZMkWuXiIiIiChFyNYoNpvNeOWVV0Ys37t374hlCxYswIIFC4KWGY1GbNu2bcS6U6dORXV1tXgFJSIiIqK0w8k7iIiIiCjtsVFMRERERGmPjWIiIiIiSnuc+J1IIayZRmToxbkke/q8MIg4sQUREVGqY6OYSCEy9Fo89mqtKNuqedYp6raIiIhSHbtPEBEREVHaY6OYiIiIiNIeG8ViUQGt3X3445VOtHZ7AVWiC0RElKaYj0lujLmUwD7FYlABx85fx+qaBnT3+pChU2PrYwWYOcEG+BNdOCKiNBIuHxNJgW2AlME7xSJo7eoLXAwA0N3rw+qaBrR29SW4ZERE6YX5mOTGmEsdbBSL4HKHJ3AxDOru9eFyhydBJSIiSk/MxyQ3xlzqYKNYBGPNBmTogg9lhk6NsWZ9gkpERJSemI9Jboy51MFGsQgcRg22PlYQuCgG+xM5jOyyTUQkJ+ZjkhtjLnXwjInBD8ycYMPBJTNxucODsWZ9/8XADvZERPJiPia5MeZShqyN4tdeew3vvvsuent7UVxcjBkzZmDt2rVQqVTIy8vDxo0boVarUV1djf3790Or1WLp0qV44IEH0N3djdWrV+PKlSswm83YsmULsrKycPLkSWzevBkajQZOpxPLli2Tc5du8AOODC0cGdrAv4mIKAGYj0lujLmUIFv3iePHj+O//uu/UFVVhcrKSnz55Zd46aWXsGLFCuzbtw9+vx+HDx9Ga2srKisrsX//fuzZswcVFRXweDyoqqpCfn4+9u3bhzlz5mDnzp0AgI0bN6K8vBxVVVU4deoUGhsb5dolIiIiIkoRsjWKa2trkZ+fj2effRZ/+7d/i/vvvx+NjY2YMWMGAGDWrFn46KOP0NDQgGnTpkGv18NqtSInJwenT59GfX097rvvvsC6dXV1cLvd8Hg8yMnJgUqlgtPpRF1dnVy7REREREQpQrbuE+3t7bhw4QJ+9rOf4fz581i6dCn8fj9Uqv5pX8xmM1wuF9xuN6xWa+B7ZrMZbrc7aPnQdS0WS9C6LS0tEcui0ahgt5sEPlMLfqYELN/oKKF8oeJPo+n/farVakT7O4nalirC+smyj5H2I5xEx1g4QvlPCdeG1FJ9H5Nh/8LVv/KVQXnHSYllApRZrsH6UgqyNYrtdjtyc3Oh1+uRm5sLg8GAL7/8MvB5R0cHbDYbLBYLOjo6gpZbrdag5eHWtdkiz1rk9fpx9WqnQDlNgp8pAcs3OmKWz+GwRl4phFDxZ7eboFZr0NfnFaNoAJCwbWm14fcjWfYx0n6EI8c1IGb8Acq/dsWQ6vso5/6JHX9yUmIcKLFMgDLLNVhfSkG27hPTp0/HBx98AL/fj6+++gpdXV341re+hePHjwMAjh49iqKiIhQUFKC+vh49PT1wuVw4e/Ys8vPzUVhYiCNHjgTWnT59OiwWC3Q6HZqbm+H3+1FbW4uioiK5domIiIiIUoRsd4ofeOAB/O53v8O8efPg9/uxYcMGTJgwAevXr0dFRQVyc3Mxe/ZsaDQalJaWoqSkBH6/HytXroTBYEBxcTHWrFmD4uJi6HQ6lJeXAwA2bdqEVatWwev1wul0YsqUKXLtEhERERGlCFmHZHvhhRdGLNu7d++IZQsWLMCCBQuClhmNRmzbtm3EulOnTkV1dbV4hSQiIiKitMMZ7YiIiIgo7XFGu0EqoLWrD2ea2mE3aOEwahI3+PZAWfpnxjEktixEFBNPny/ul5CG6/b0wXWtS5RtCVJS7pPCkHw63gdkapBa+5fukqG+TIYyEgA2ivupgGPnr2N1TQO6e32BectnTrDJH7hKKgsRxUyvVeOxV2tF2VbNs064RNmSgFTPN6m+f+kuGc5vMpSRAth9Av2/4AYDFgC6e31YXdOA1q6+tC4LEaW2VM83qb5/6S4Zzm8ylJFuYKMYwOUOTyBgB3X3+nC5w5PWZSGi1Jbq+SbV9y/dJcP5TYYy0g1sFAMYazYgQxd8KDJ0aow169O6LESU2lI936T6/qW7ZDi/yVBGuoGNYgAOowZbHysIBO5gnx+HUf4u10oqCxGltlTPN6m+f+kuGc5vMpSRbuBZAQA/MHOCDQeXzMTVHi/sBk1/wCaiE/yQsvS/qapPXFmIKLUpKfdJYVg+HT/GxNEnUkky1JfJUEYKYKN4kB9wZGiRd4utf57vRAbsQFkcGdrAv4mIJKGk3CeFIfnUbjf17yOljmSoL5OhjASA3SeIiIiIiNgoJiIiIiJio5iIiIiI0p7K7/ezdwsRERERpTXeKSYiIiKitMdGMRERERGlPTaKiYiIiCjtsVFMRERERGmPjWIiIiIiSntsFBMRERFR2kvLaZ49nj5cu9YV8jOLxQC3u0fmEkWP5RsdMcvncFjj+l6o+FP6cYtFquyL0vdDzPgDlL+/Ykj1fZRz/8SOPzkpMQ6UWCZAmeWyWAwwGvWSbDst7xSrVCrBz7RajYwliR3LNzpKKF+o+FNCucSSKvuSKvsxnFD+S9X9HSrV9zEZ9i9c/SsXJR4nJZYJUGa5pCxTWjaKiYiIiIiGYqOYiIiIiNIeG8VERERElPbYKCYKRQW0dvfhj1c60drtBRLfDY7iwfNIRJRcEpi303L0CaKwVMCx89exuqYB3b0+ZOjU2PpYAWZOsAH+RBeOosbzSESUXBKct3mnmGiY1q6+wAUJAN29PqyuaUBrV1+CS0ax4HkkIkouic7bbBQTDXO5wxO4IAd19/pwucOToBJRPHgeiYiSS6LzNhvFRMOMNRuQoQu+NDJ0aow1SzNYOEmD55GIKLkkOm+zUUw0jMOowdbHCgIX5mCfJoeRXfCTCc8jEVFySXTeZu1ANJwfmDnBhoNLZuJyhwdjzfr+C5IvZyUXnkciouSS4LzNRjFRKH7AkaGFI0Mb+DclIZ5HIqLkksC8ze4TRENxXFsa4PP5GQtERHLjOMVECsBxbWmQCvjPP7Xi+QMnGQtERHLhOMVEypDo8RFJOVq7+gINYoCxQEQkh0TXw2wUEw1I9PiIpByMBSIi+SU697JRTDQg0eMjknIwFoiI5Jfo3MtGMdGARI+PSMrhMGpQPn8qY4GISEaJroeZ4YkGcVxbGuQH/vLrDsYCEZGcOE4xkYJwXFsaoFarGAtERHLjOMVERERERInDRjERERERpT02iomIiIgo7bFRTOmHUzlLRwU0tXXy2BIRUewSXD/zRTtKKz6fn1M5S4XTZBMRUbwUUIfwTjGllZarXZzKWSKJnp6TiIiSlxLqEMkaxVeuXMFf/MVf4OzZs2hqakJxcTFKSkqwceNG+Hz9O1xdXY25c+diwYIFeO+99wAA3d3dKCsrQ0lJCZ555hm0tbUBAE6ePIn58+dj0aJF2LFjR+Dv7NixA/PmzcOiRYvQ0NAg1e5Qirjk6uH0vRJJ9PScRESUvJRQh0jSKO7t7cWGDRuQkZEBAHjppZewYsUK7Nu3D36/H4cPH0ZraysqKyuxf/9+7NmzBxUVFfB4PKiqqkJ+fj727duHOXPmYOfOnQCAjRs3ory8HFVVVTh16hQaGxvR2NiIjz/+GAcOHEBFRQU2bdokxe5QCsm2cvpeqSR6ek4iIkpeSqhDJGkUb9myBYsWLUJ2djYAoLGxETNmzAAAzJo1Cx999BEaGhowbdo06PV6WK1W5OTk4PTp06ivr8d9990XWLeurg5utxsejwc5OTlQqVRwOp2oq6tDfX09nE4nVCoVxo8fD6/XG7izTBTKRLuRUzlLJNHTcxIRUfJSQh0i+l86ePAgsrKycN9992HXrl0AAL/fD5Wq/xVCs9kMl8sFt9sNq9Ua+J7ZbIbb7Q5aPnRdi8UStG5LSwsMBgPsdnvQcpfLhaysrLBl1GhUsNtNAp+pBT9TApZvdDQaNR6efDMmL3PikqsH2VYDJtqNUKvle8U1VPwp/bhF62GrEXeW3Yevrncn5NiKKVXOyXBC+S9V93eoVN/HZNi/cPWvfGVQ3nFSYpkA+cv1sNUYsX7WaKR7HU70RnFNTQ1UKhXq6urwxz/+EWvWrAm6e9vR0QGbzQaLxYKOjo6g5VarNWh5uHVtNht0Ol3IbUTi9fpx9WpnyM/sdpPgZ0rA8o2O3W7C9etdyFQDmZkGAMD1611xbcvhiBxroYSKP6Uft1jkjDHBpup/VTjeY6sESj8nYsYfoPz9FUOq76Oc+yd2/MlJiXGgxDIBiSlXpPrZbjdBrdZI8rdFb27/8pe/xN69e1FZWYnJkydjy5YtmDVrFo4fPw4AOHr0KIqKilBQUID6+nr09PTA5XLh7NmzyM/PR2FhIY4cORJYd/r06bBYLNDpdGhubobf70dtbS2KiopQWFiI2tpa+Hw+XLhwAT6fL+JdYkoTHIs4ufH8ERGlLoXmeFk6aqxZswbr169HRUUFcnNzMXv2bGg0GpSWlqKkpAR+vx8rV66EwWBAcXEx1qxZg+LiYuh0OpSXlwMANm3ahFWrVsHr9cLpdGLKlCkAgKKiIixcuBA+nw8bNmyQY3dI6cKNdUjKp4CxKomISCIKzvEqv9+fdtVMb6+X3SckooTytXb3Ye6uY0FDu2To1Di4ZCbybrGJVr54Hx+Gij8lHDexjHZfwp0/R4Z8L1wo/ZyIGX+A8vdXDKm+j8nQfSJc/SsXJcaBEssESFOu0eZ4u90EnS5Juk8QJZoSxjqk+PH8ERGlLiXneDaKKeUoYaxDih/PHxFR6lJyjmejmFKLCgD82DqvAM89dAfGZWZwvFwxyPhShBLGqiQiohjEUEcoOccnvgREsVD1z49+ucODsWYDHEbNjY75ITrvb57zTdx9iwVZBk3CO/AnrWhfihg4N2ea2mE3aIPPTSz8wMwJNhxcMnPgPOv7kyXPHxFR4gjVv7G+OKfgHM9GMSWPCBdea1df4DOgv4/Sj3/9KQ4umamIiy1ZhTquq2sagl+KEPttYj/gyNDe2D7PHxFR4oTJ8VHVEcMpNMez+wQlDaELr7WrD4CyO+8ns2iOa6RzQ0REyStcjk+lupeN4nSj0AGzoxHpwlNy5/1kFs1xTUhSTOJYJiIKSaF5LVyOT6W6l43idDLw+GPurmN48vUTmLurDsfOX1fMRRdJpAtPyZ33k1k0x1X2pJjksUxENIKC81q4HJ9KdS8n7xhGqQNoDxpN+eSYFEHS4xdNv9WgFwFGdt4Xs3xpNXlHhOMq9wxFSR/LIuDkHbFL9X3k5B3RUWIc2O0mnPnyuiImLhperqtXOyPn+Eh1hMhlkmryjuRrxlPcwj3+SNQFF5No3lhVaOf9pBfpuA45N1d7vLAbNJImxaSPZSKiYRSd1yLVvylS97L2SCODjz+G/wpNqn4/KXLhpaSBcxOYSlvCc5MSsUxENITi81oa1L/sU5xGUqnfD6U3xjIRpRrmtcTjkU4nCh4wmygmjGUiSjXMawnHRnG6SYPHH5QmGMtElGqY1xKK3SeIiIiIKO2xUUzJQaEDmlMKYGwREYWXJnmS3SdI+WQeA5fSSLjYIiKitKqDeaeYFC/cnOtEo8HYIiIKL53yJBvFpHjhBjQnGg3GFhFReOmUJ9koJmUI018p3JzrRKMxPLbGZWZg+UN3wOPzo6mtM2X7zRERRaQCmto64fH58dxDd2BcZkbgo1Stg9mnmBIvQn+lwQHNh3/O8RtptIbG1hiTHk9+axJeOXwm5fvNERGFFaJefu6hPLxR14T2Tk/K1sFsFKc6VX9/oP6BwA1wGDWKC+Lh/ZXGmPQ40+qGUa/BeGsGHEYNBzRPZkNj0GKARgV85epRRjwOGSz/Wk8f/p//78SIfnMHl8y8MWYoEZFcElV/q4AWdy/OtLqx+L5c1NSfx8Vr3Xjl8Bn84gdFyDRoU7YOZqZPZUJ3YCfa0NqpnIby0P5K4zIzUDpzEra9O/JuHQc0T0JR3G2I6k6slJXDwGD54frNsVFMRLIKkTtffrQA906yAb7IXxfz7y5/MA+Vx5pw8Vo3evt8cGSmZoMYkKhPsdfrxY9+9CMsWrQIjz/+OJqbm9HU1ITi4mKUlJRg48aN8Pn6z2p1dTXmzp2LBQsW4L333gMAdHd3o6ysDCUlJXjmmWfQ1tYGADh58iTmz5+PRYsWYceOHYG/t2PHDsybNw+LFi1CQ0ODFLuUlITeGP3D5S7M3XUMT75+AnN31eGD5mu40JW48QeH9uucWzgh0CAeWuZUfMs1HYSKwVcOn8HcwgmBc9vW4w0//uVAkh4as8fOXxc9ToX6rt9sNaTF+JxEpByhcufaXzWg8XJXf8tNonGDQ/3dbe/25+xU7Uc8lCS3PwYbt/v378fx48fx0ksvwe/3Y8WKFbjnnnuwYcMGHD58GFOnTkVlZSVqamrQ09ODkpIS3HvvvaiqqkJ+fj7Kyspw6NAh7Ny5E+vWrcPGjRuxfft2TJw4EUuWLEFjYyMA4OOPP8aBAwdw8eJFlJWVoaamJr6CD3Qqv9DeqYg7qKMldOervrk9KOBf/NWnWDIrF9sOfx6+H6VEd+uG9utUqcC7dSlEKAZVAwl8jEmPxq/cePFXn2KMSY/5RROQl23B7TeZ4cjojy+hH3did2sI1Xf9lYVT8VlrR1qMz5kO/CrA4bCKsq2ePi8MWo0o2+r29MF1rUuUbZGAIfXXeB+QqYGir2Gh3PlJcztUALp7vXjuzZNBeSnfYR511zShv6tRI2X7EQ8lSSvjL//yL3H//fcDAC5cuICxY8fi/fffx4wZMwAAs2bNwocffgi1Wo1p06ZBr9dDr9cjJycHp0+fRn19PRYvXhxYd+fOnXC73fB4PMjJyQEAOJ1O1NXVQa/Xw+l0QqVSYfz48fB6vWhra0NWVlZshU7BwakH73wNDfAMnRreYY9eunt98Plv/H/IBoeUx2dov05PH37+wcgyp/qv01QlFIP+gZh58luTAg1ioW4zsnVrGBKHlzs8GD/GBI+nD3N3HWM/4xSh12rw2Ku1omyr5lmnqNtyibIlCikJ6/dw9Xd9czsAjMhLUd3civPv3p/nwESLTrHHSyySZXWtVos1a9bgnXfewbZt2/Dee+9BNXB7yGw2w+Vywe12w2q98avdbDbD7XYHLR+6rsViCVq3paUFBoMBdrs9aLnL5QrbKNZoVLDbTUHLmto6Q96NenuZE5OyTKE2kxAajXpE2YXYfH6Uz5+K5w/c+DW5ZW4BKv7zT0HrDW2kAP37frXHi7xbbszqFe3xiaV8w9kzAV+IMpfPn4rbs61Qq0f/fGg05RNLqPhTQrnEMnRfQsXgYJ/iDJ0aX7vJjO5en2C3mbeXOTF+jClkkh4/xiTJMbNnAnkD+3Hs3JWQDfLh10cyCRV//ctTJwbD0Yp0d1fsbYlx7JPhHArFn5SSoX4ffu5sPj+2zC3AmoPBfXvfPNGMRwpuDTxtGxTq5lY8+xcqZ5fPn4q7xtlEqYPFoNFIN52hVAcAACAASURBVJqwpLc6tmzZglWrVmHBggXo6ekJLO/o6IDNZoPFYkFHR0fQcqvVGrQ83Lo2mw06nS7kNsLxev24erUzaNmF9s6Qld+F9k5kKmg0Z7vdNKLs4cwYbwketcGkhfrbXw/54tOgDJ0adoMm6O9Ee3xiLV9UZTZqcf26OI8WxSjfoHgfw4aKPzHLlWjD92X4+dSoVbjrFivGmvW45ulDhk4t2G3mQnsnJo81hRySL1MDSY+Z3W6C3aAN2SAffn0kgpjxB6RWDApxOKzo6/OKtj0xtyXGsZfzHIodf1JKhvo91Ln78xwbfvbEdHzS3A6vD3jzRDMWFuXgzRPN+JuptwatG+rmVrz7NzRnjx9jQqYGotXBYrDbTVCrxftBOpQk4fDrX/8ar732GgDAaDRCpVLh7rvvxvHjxwEAR48eRVFREQoKClBfX4+enh64XC6cPXsW+fn5KCwsxJEjRwLrTp8+HRaLBTqdDs3NzfD7/aitrUVRUREKCwtRW1sLn8+HCxcuwOfzxd51Aik8QcTAm/WTbzL1P+713XhEvL14Kp57KA8mnQbtnf0z0wSNATyE0PHRadXiv3w0vMwp/rgm5Q07n1l6zY3/z9DhuYfyoFFB+Pob0q3hjaeKcHDJTPEee0Z4WWWwn/Fg2YSuDyJSrqSt333AXWONmJ4zBho18EjBrXjzRDNWffvruGucLSgvPfdQHg5+cj7w1VHt35CcPSnLlFZ1sCSZ/eGHH8aPfvQjPP744+jr68OLL76I22+/HevXr0dFRQVyc3Mxe/ZsaDQalJaWoqSkBH6/HytXroTBYEBxcTHWrFmD4uJi6HQ6lJeXAwA2bdqEVatWwev1wul0YsqUKQCAoqIiLFy4ED6fDxs2bIirzGk1QcRAwDtutaE1y4Rr3b3Y9/Q9cHf3Co4BHOr4PPdQHv7uQEPw0FpEMcgyaJA71oymKx1Y/8id+Ie3/xD6+huMWTGH5Iumn+GwfsYcI5so+SR1/e4D7rzJCIdJh8sdHvzVndmBH+VD81LLte6gm1vPPZSHlmvdcBgtyt9HBVH5/f60O1y9vd7Qj29UwDUvBkafUGbll9BHnANv71509eDUF9dw4ET/gN5A/0V4cMlM5N1iU/QjWCV0nwgVf6n06DrmfRmIq2vdvTDotGF/nImptTv4JTrgRhw7MrSKPydixh+QWjEoxOGwKvZFu9bW0b9qlwzdJwTrX6kNHX1ioEuAkur30Z67No8Xh/7wFXx+wO8HDn5yHu2dnlG/FKzEvGC3m6DTSdN9gs8Ah/IDk7JMN/rgKOiCUYQhkxxsO/x50EeDowHkJaholMSG3wU26wLLpcTJOojSyJA8o8SG3mh95eoZUS8DYD6LkUK6mFMySdr+WURDMI6JKFUwn4mDjWKKGV8+olTAOCaiVMF8Jg4erXQXzyx1fPmI5CTRTIqM4+RgzTQiQ8+qiiisIfnsoqsHJr0GWUZdokuVdGLKNGfOnIFOp8PXvvY1iYpDshrNLD+jHQ1AqoYOpZYQMfryowW4d5IN8EX+ekRSjGpBosrQa0V9oY0olZ290hmULzfP+SbuvsWCLAPr2GiE7T7x4Ycf4v7770dvby+qqqqwdOlSvPDCCzhw4IBc5SMJtXb1hZzlp7WrT9o/PNDQmbvrGJ58/QTm7qrDsfPX0ebxCo4XS+kpVIyu/VUD/nC5KzExEmFcYyKiRAmVL3/8609x6A9f4WjTNbR5mLMiCdso3rNnD6qrq6HT6bB7927s2bMHe/fuxb/+67/KVT6SULi376Uk1Bg/9IevghrJvHhJKEbrm9ul//E2nMCPOcYpESmBUL70+RFoHDNnhRe2UaxSqZCdnY2WlhbodDpMmjQJer0eGo0048ORvGJ+W3XIXbLzHb34/GrXjbtlA5/9rqk94h20cBfu4P/LcseaopegO6RCMer1ARddPeHLI3KZE/ZkhYgoCkL50u+/UceurmlAi7s3qN4OWaenqbB9ivv6+tDX14f3338fTmd/X6zr16+jq0s5c2BT/GKa5SdE387lD+YFppzUa9V47s2TUfVNHrxwh0+aMHzedo6vqBCj6Xs+Sg6jBi8/WoC1vxoZdxo1sO3w56HLI0GZOa4xESlZqDp9+YN5qDzWFNQ4fv9MK/IclhH19tA6XY78rkRh7xTPmTMH3/ve9/D666/jqaeewmeffYYnnngCjz/+uFzlIykNeVv1jaeKcHDJTMELIdRdsm3vnsEjBbdidU0DGi9ej/oOWqihY0Sdt51EldA7pH7g3kk2vPbEdCx/6A487czFmyeasejPcnDgxHnB8khRZo4DSkSKNqRO3148FUtm5aLyWBPaOz1Y/mB/HTv4pC1UvT20Tk/XJ2Bhb288+uij+Pa3vw2TyQS1Wo1Lly7h5Zdfxp133ilX+UhqUb59L3SXTKUK7vow9DPBO2ghhsIaPm970sxLnwYSfofUB9x5kxEOkw6XOzxw3p6FvzvQEJhiPFR5pChzTE9WiIgSYbBOv9WG27NMyMu24Mwld1DjuPJYk2C9PVinp+sTsLB7vGLFCvz0pz8N/Ds7OxvZ2dmSF4qUJ1yXhwydGuphfZAi3kEb1hh3GC0cL1ahhM69rHdIh8RLa7c38ANKqDySlJnjGhNRshjSOL410wgA8PqAymNNuHitW7DeHqzT0/UJWNjuE21tbXKVgxQuVJeH5Q/m4e2GL7D1sQLcNc42upl0Bi7gyTeZ+hvKbGgohtJmSoqmPJKVmXFKRMnED0y06JDnsGBP7blAgzhUvT20Tk/XmfDC7nVLSwsqKipCfvZ3f/d3khSIFGrYXTJLhg49vX24/45pgYvn4JKZuNrjhd2g4R20VKK0O6TRlEdpZU4CfhXgcFhF2Va3pw+ua3whm0gRhPIhIFynp2muDNsozsjIwG233SZXWUjphvc/hi6wHAAcRi30ei0utHcCUHGWulQi5sxvYsxmGE15OFtdTPRajagzx7lE2RIRiUIgHwYtU+nQ2tWHP17uvJGb00zYRvHYsWPx6KOPylUWSmYJHLaLkgjjhIhIeQRy88NWY6JLJquwfYrvvvtuucpBSY4TG1A0GCdERMojlJtbrqZXN6iwjeJQQ695PB68+OKLkhWIklOipoym5MI4ISJSHqHcfMnVk6ASJUbYRvHu3bvx7rvvBv793//933jsscc4zbMSJGjaXSGc2ICCCMQn44SICCNypG/4oMEyE8rN2VZDgkqUGGH7FO/ZsweLFy+GyWTC5cuXUV5ejrVr12L27NlylY9CUWC/TE5sQIN8Pr9gfDJOiCjthajDy+dPxYzxFsXV4RPtRly/nj5dKMI2ih0OB3bt2oUf/vCHyMjIQFVVFW655Ra5ykah3tKHcN+fg0tmJm4GmoEhX95e5sSF9k4OgZXGWq52hY3PpB8qTYzRM4gobbV29eEn7/wJTztzoRp4ivb//p8/4p8XTUt4HT48N6uHz/CR4sIefY/HgzFjxuDVV1/F8uXL0dbWhqysLACAXs/HnZJSAZ986Ubjxevw+QGNCrhznA33W42Jn3ZXiB+YlGVCpvrGvynJiNDgu+TqCR+fyTxUmgKf0hBRcuno7cOa70zG6S/76/d/O/UFFhbl4Fp3b8Lr8KTNzSIJe/S/853vQDXwM8bv96OsrCzw2eHDh6UtWZpr6/Hi3OUO7Dp6LlD5PvdQHvKyLcqYdpdSj0gNvmxr6sanIp/SEFHyUAHnr/bgR7/6NJBnlz+YhzdPNGPmbVMTXbq0FzaL33PPPTFvsLe3Fy+++CK++OILeDweLF26FHfccQfWrl0LlUqFvLw8bNy4EWq1GtXV1di/fz+0Wi2WLl2KBx54AN3d3Vi9ejWuXLkCs9mMLVu2ICsrCydPnsTmzZuh0WjgdDqxbNkyAMCOHTvw/vvvQ6vV4sUXX0RBQUF8R0JKcdx9a+vqxSuHzwRVvq8cPoNpOWNwu03PfpkkOrEafBPtRunjM0FdGBT7lIaIlGtIvrJm6PDTw58F5dlt757B085cuLt7AbMuwYVNb2GzeGNjI7q7u/H9738f06ZNg98fudZ56623YLfbsXXrVrS3t+PRRx/FN77xDaxYsQL33HMPNmzYgMOHD2Pq1KmorKxETU0Nenp6UFJSgnvvvRdVVVXIz89HWVkZDh06hJ07d2LdunXYuHEjtm/fjokTJ2LJkiVobGwEAHz88cc4cOAALl68iLKyMtTU1IhzZMQSz903FeD1A4vvywUA1NSfx8Vr3eju9aGzpw/w65O/XybdoAKa2joH+mInro+qWA0+tVolbXwmsAsDn9IQUUxCdIX84Z/fhp8dPYeL17oB9OdZjRrMIwoQdki2t956Czt27EBPTw927dqFkydPIicnB/fdd5/gd77zne/gueeeC/xbo9GgsbERM2bMAADMmjULH330ERoaGjBt2jTo9XpYrVbk5OTg9OnTqK+vD2x/1qxZqKurg9vthsfjQU5ODlQqFZxOJ+rq6lBfXw+n0wmVSoXx48fD6/Wira1NjOMimpgnKxio8Be/cQI73v0cP//gHEpnTsK4zAxk6NSYOMbUv95A35/JN5kC/TQpCQ2c70d21OLJ109g7q46HDt/PSFD7Ik6XJqE8ZnICUAG39AePE5Bd8GJiIYZ2hVyx7uf47Wj59DZ68WT35oUWCdDp0bRpCzmEQWIeAby8/OxatUqAMDvfvc7lJeX48svv0R1dXXI9c1mMwDA7XZj+fLlWLFiBbZs2RLom2w2m+FyueB2u2G1WoO+53a7g5YPXddisQSt29LSAoPBALvdHrTc5XIFXgYUotGoYLebBD5TC34WjzNN7SHvvl3t8SLvFtuI9ZvaOkdU+NvePYMls3Lx9ZutuG2sOao79oki9vETmxLKNzT+Qp3v1TUNeHuZE5Oy5C2nzedH+fypeP7AyaBhgm7Ptsb0BrLUxzjWaypeQvvxsNWIycucuOTqQbbVgIl2Y1K9oR0u/2m14o1BL2YMiFkupW5LjOOlhPwWSbj4k68M8h2nsy1XQ3aF/Mm8KQD6G8Rb5hZg5teyAiNRKIkSY0qjCXs/d1Si+lnidrvxzjvv4O2330ZXVxf++q//Ouz6Fy9exLPPPouSkhJ8//vfx9atWwOfdXR0wGazwWKxoKOjI2i51WoNWh5uXZvNBp1OF3IbkXi9fly92hnyM7vdJPhZWAJ9HO0GbcjHrXaDJuTfudDeGbLCn3JrJgqy+xvEcZVPJnEfP5mIWT6HI3KshTI0/oTO94X2zhujeIQjct/aGeMtI7o9xDpGpdQxEOs1FfffCbMfmWogM7N/UPtEjeEpRvwN315fn3e0xQoQ8zoTs1xK3ZYYx0vO/Ct2/MlJkuMkkIvd3b0hc7xBq8YbTxUF8qxKJd41IyYl1ul2uwlqtTSTyIWtdn/7299i2bJlePLJJ/HVV19h06ZN2LdvH5544gnB71y+fBk//OEPsXr1asybNw9A/3TRx48fBwAcPXoURUVFKCgoQH19PXp6euByuXD27Fnk5+ejsLAQR44cCaw7ffp0WCwW6HQ6NDc3w+/3o7a2FkVFRSgsLERtbS18Ph8uXLgAn88X8S6xJAYegc/ddWzEI/BYH7cKPcIeZzWwi0QKGlWXhTBxF7ck6JbDLgxEpChhcvF4W0bIHH+rzaDoPJuuwtYiK1euRG5uLr7xjW/gs88+wz/90z8FPisvLw/5nZ/97Ge4fv06du7ciZ07dwIAfvzjH+Mf//EfUVFRgdzcXMyePRsajQalpaUoKSmB3+/HypUrYTAYUFxcjDVr1qC4uBg6nS7wdzZt2oRVq1bB6/XC6XRiypT+Rw9FRUVYuHAhfD4fNmzYIMpBiVWkt/ZjeemIM36ll9Gc77QdHkxgkHleH0SUCGFzsVHLOj2JhK0533jjjZg3uG7dOqxbt27E8r17945YtmDBAixYsCBomdFoxLZt20asO3Xq1JD9mMvKyoLGT06EiG/txzIgNiv89DKKmQDTengwDjJPRAoRKRezTk8eYWvOwREjKLyIwzRF6vcZ4nNW+GkkzpkARR0eTGlTFyutPEREoagAS4YOqx7Ox6SbzPjiaifcPV7826kvbuRi/ohPGil+O0keYR+BI8KYqpw2luIkWlcbpcWg0spDRBRKiFy1/ME8/NupL7DsgTw4TFrAF3kzpBzSjWuRToZ0eXjjqSIcXDIzUIFHGlM1kWOuUpILE3exUFoMKq08REShhMpV2949g0cKbsX63/werZ3MWcmGd4rFIvB45Fp3L5525gbGHxycnW6wr1Fa9wul0RPhsdxgDI7LzMDcwgmBWL3W3ZuQGOQ1kfw8fb64h+wiShaXOzwYY9IH5c2a+vNQqZizkhXPlpRUwFeuXuypPRf0aOXNE82BvkacNpYSbazZgEk3GbGwKAfb3j0TiNU7xn4Td4wxyt5lgddE8tNr1Xjs1VpRtlXzrFOU7RCJ7WarAU9+a1Jgco4MnRrPPZQHgDkrWbH7hIRau/qw9lcjH638/V/fHehvzDFXKdEcRg3+/q/vDjSIgf5YXfurTxPSZYHXBBElA6/PH3K2OgDMWUmKZ0xCQo+BVX7/jbtvHIKNEs0PqPx+5XRZ4DVBRElAqI7Pz7agINvMnJWE2CiWUNSPgTlcCyWY4ros8JogIoUTypucgTZ5sfuEFFRAa3cfLnf0YNcT0zHpJiMAPgYm5Yqry8JAnP/xSidau72jm16aiEjphuU8h4ldvVINz5zYQoxb+PKj38TNVj0yM3R8DEzKFGuXhXBjCRMRpRqhnDeRXb1SCe8UiyzUuIVrf/Vpf4M4Q2EXC+/00VADXRYm32SKGKtCYwl/frUbTW2djCUiSimC46d39kWdN2PGOlp2bBSLLNwYq7KKdDEN/Oqdu+sYnnz9BObuqsOx89d50VFUhOL8w3NteGRHrTJjiRUMEcVJ9rpdDfzhShf+4/QlfNx8FUv3f6LMvJpi2CgW2WDH+6Fkf2EpigZvW48XZ1rdWHxfLpY9eAfGmPScNYyiJhTnfr9CZ6ALcU188qWbjWQiiopsdfvAj/e6luv4pLkdB06cx88/OIeFRTn4yTt/UlZeTUFsFItMCWOsRpwmVwX8/ks3dh09hx3vfo6ff3AOpTMnYYxJL/8dbUpKoeJ8+YN5OPjJeQAJejoSxvBrYoxJj3OXO/ikhIiiIkvdPuTH+/L9J/Ha0Rt18+D00UrKq6mIL9qJTQFjrEaaJre1qw8//vWnIyYVWTIrN3lm4FH1N3T6j7EBDqNGWf21U92QOL/o6sGpL66h8lgTLl7rBqC82ZyGXxNzCyeMGHR/dU0DfvGDImQadP3xREQ0SIa6PdQNrW3vnsHTzly8+t7n0KiRuLyaJnUuG8VSSPAYq5HGnBVqNOdlW5LjzdlwIx8oveypZDDOjVp0erxo7+y/gxF0B0Uh52P4NaFSQbBP9M8/OIetjxXgYasxEUUlIqWSuG4XnPBL1Z9Xp+eMSUxeTaM6l90nUlCkxzxCfaNuzzIlRYBH7B5C8hpyB+WNp4rw9jKn4pLl8GtCM1DJDDW8T3TL1a5EFJWI0pRQ3axWAS8/+k3cOdaYkLyaTnUu7xSnooFGym/+9lto6+pFh8eL8baMwMeDDYThv/qUdGcvnEjdQygBhtxBsdtNuHq1M9ElCjbs0efNVgPucFiCroHlD+ah8lgTgP54uuTqQWamITHlTZNHlUR0g8OkwWtPTEd9czt8fuDfTn2B5Q/m4+5bLMgyaABf5G1IIZ3q3NTaGwryWWuH4OOORPd7Hg3FTUlMyWHYo89IfaKzrYlrEKfLo0oi6ufz+XGsZeTEX/dOyuxvDCfw2k+nOpfdJ1JUxMcdMUzUoDRKGOGDUsDANVCQbUaewzKiT/REe2L6FKfTo0oi6tdytSvkxF+tnYm/7tOpzk29PSIAKf64I8nvdJPCCMSTWp2Y8dlS+tolopAuuXqUe92nUZ3LDJuibrYasPyhO+AbCNqa+vNo7/SkzuOOBI/wQSlGKJ4S0Lc3nR5VEhEAFWDSawJ1dk39eVy81q2s6z5N6lw2ilORqr8/8a6j5wJ9k557KA+5Y80p++uOSGw+nz8hfXuT/UVYIopBiHcIlj+YhzdPNGPVt7/O615mkjWKT506hZ/85CeorKxEU1MT1q5dC5VKhby8PGzcuBFqtRrV1dXYv38/tFotli5digceeADd3d1YvXo1rly5ArPZjC1btiArKwsnT57E5s2bodFo4HQ6sWzZMgDAjh078P7770Or1eLFF19EQUGBVLuUNEL1SXzl8BkcXDKTFxdRlEL18Vtd04CDS2ZK+zgzjR5VUj9Pnw8Oh1WUbVkzjXBd43CCyUJowo59T9+DiRYdr3uZSZLZd+/ejbfeegtGY/+LKi+99BJWrFiBe+65Bxs2bMDhw4cxdepUVFZWoqamBj09PSgpKcG9996Lqqoq5Ofno6ysDIcOHcLOnTuxbt06bNy4Edu3b8fEiROxZMkSNDY2AgA+/vhjHDhwABcvXkRZWRlqamqk2KWkwj6JRKOX0D5+afKokvrptWo89mrtqLej1Wrw5v/6FlwilInkIVRfu7t7AbMuQaVKX5KMPpGTk4Pt27cH/t3Y2IgZM2YAAGbNmoWPPvoIDQ0NmDZtGvR6PaxWK3JycnD69GnU19fjvvvuC6xbV1cHt9sNj8eDnJwcqFQqOJ1O1NXVob6+Hk6nEyqVCuPHj4fX60VbW5sUu5RUhAYAV0zfJKIkkG3ldURE0mJ9rSyS3O6YPXs2zp8/H/i33++HStX/JrfZbIbL5YLb7YbVeuNxkdlshtvtDlo+dF2LxRK0bktLCwwGA+x2e9Byl8uFrKyssOXTaFSw200Cn6kFP1OCcOXz+fxoudqF9u5e7CotwvrffIqmK13I0KlRPn8qbs+2Sv5GfTIfP/nKMDL+lFAuscS7L4Pxe8nVg2yrARPtxoSNAAEAY1QqlM+fiucPnAz09ZPrOpJSuPyn1WpE+zvclvzbGoxKJeeScPEnXxmUk2+tXh92lRbhRFNbYMKOF2ZPVkyeUdKxGqTRSDeasCzP0tXqGzvQ0dEBm80Gi8WCjo6OoOVWqzVoebh1bTYbdDpdyG1E4vX6BWfcUuRsXEMIlk8FfPKlG40Xr8Pn75/Gdu3sb8Bu1CIzQweHUYvr16XvZ5a0xy8O8fYBDBV/Sj9usYhrXxQ4YYXdbsKMWy3Y9/Q9+MrVjVusGZhg1clyHUVDzPgb3F5fn3e0xQrgtuTf1mDDWo5cInb8yUkR+XZgZJuzbZ34/JIbB070jxD18qPfxIxbLYrJM4o4VsPY7Sao1eL9IB1Klsk77rzzThw/fhwAcPToURQVFaGgoAD19fXo6emBy+XC2bNnkZ+fj8LCQhw5ciSw7vTp02GxWKDT6dDc3Ay/34/a2loUFRWhsLAQtbW18Pl8uHDhAnw+X8S7xKmqrceLc5f7R5zY8e7neO3oOfxPWyeyTPqkm5yD0o8SJ6wYnGGqZM9xLP3lf6F4z3Eca7l+43YcEVE8Bm4CzN11DGVVJ/Ha0XMonTkJY0x6xUzYka5kuVO8Zs0arF+/HhUVFcjNzcXs2bOh0WhQWlqKkpIS+P1+rFy5EgaDAcXFxVizZg2Ki4uh0+lQXl4OANi0aRNWrVoFr9cLp9OJKVOmAACKioqwcOFC+Hw+bNiwQY7dUaS2rl68cvjMiBEnpk7IRJZeml9URGJR4suhCRt9gohSmtCIE087c/Hqe5/zpfgEkuyoT5gwAdXV1QCA2267DXv37h2xzoIFC7BgwYKgZUajEdu2bRux7tSpUwPbG6qsrAxlZWUilTp5dXi8IRsVnR7xHu8RSUWJE1YoeoYpIkpaQjcBVKrE5710J0v3CZLeeFtGyDdYx1kNCSoRUfQGJ6wYjOGgCSsShKNPEJEU/n/27j6+yfLeH/gnz2mTtGklTLAtE2gnggVKpj2HwJluDvXgVJ5bVzaBdTAoyqSiDEHGw+SFuCPFuukezqw7Ilh25hHPzjlDgV9d0VlWGJ04QFdAUEsLNEmbpk3u3x9tQ9ImadLeSe4kn/frtddskqbXdd/f67q/3Pf1EGjFCbkMMe/3kh2T4gRh0kovqSAKmdeGFS9/14x9pYUxnWQHANnGFLYpIhKdv5sAWx+4Bf9685di3u8lO/buiYK7YFG8k9iGFXK5jG2KiMTn53o9Zrihe8UJ9i8xxaQ4kUgsqSCKe2xTFGfE3DLa4eziltGR0qdvkcKaxMSkmIiIKGGItWU0AFQvt3DLaEoqHFNMREREREmPSTERERERJT0mxURERESU9JgUExEREVHSY1LcSwY0Obrw58bLaHK4gHieCNpTlw+b2+K/LpRcwo1dxjpRfEvWNtyn3m43l7aRAq4+AQAy4Mj5Vs9e5L2L9MflItqJVBdKLuHGLmOdKL4laxv2U+8dcyfh1pH6xK53HOCdYgBN7V2e4AS69yAvrz6OpvauGJcsfIlUF0ou4cYuY50oviVrG/ZX70f31id8veMBk2IAl+xOT3D2cnS6ccnuDPxLEn3kM6i6EElAWLErA652dGHJtNFYccdYjEjXBv88EUlO3FyvRL7ex029kxCHTwAYptNAq5L7BKlWJccwndr/L0j4kU/YdSGSiJBj10/7W3lHLqqONOJym5OxThQn4uJ6FYHrfVzUO0nxTjEAU4oC22fnQ6vqPhy9QW9K8f9vhkCPfFo6XDG/exxuXYikItTY9df+dr59CnPNWZGLdYk+GSKKZ5K+XvW0+WNf2HG6yYaM1O6EVYwhHv7qvWPuJGnUO8nxDACAABRmpWFfaSGudLhg1Ci6gzPAvwL9PfrISFXjxGc2/Og//xrbu8dedblk775rFqwuRJIRYuwGevQ48YZ05A/XiR/rEn4yRBTXpHq9CvI06uJVh2eog0k7yBTKT73HDDegtbVd3HpQ2HinuJcA9MRAIgAAIABJREFUmLRKfHVURnegB2mUvY8+vM01Z3kSYiDGEwZ66jLuutQB60IkKSHErr/2p1XJMcKgiUisJ+tkIKKokOD1KtDTqFkFWQBEGurQp95yOR8/SQGT4kHw9+gjd7ieA+eJoiDaj1w5KYYouQRq8zKZxIZ4kOh4VgfDz6MPyGQcOE8UDVF+5MpJMUTJJVCbnzo6E/eMGy6NIR4UEbxTPFh9Hn2YtBKeMECUaKL4yFXSk4GISHSB2vxYo1YyQzwoMtiri0WqEwaIaGjYtomSC9t80mJSLKaeu1eeGalsQESJgW2bKLmwzSclDp/o1bMm4Z8bL3MdUiIp4lrBRBQt7G+SUkLcKXa73Xjqqafw0UcfQa1WY/PmzRg1alToXyADTlyyw97hhr2jCy1aJT63yzFhWATWPI0nsu6labofH2lgSlEk9/GIILdbQJODxzqgwawVLFb8sh0QSZe/9okhttl4W5ucfZRoEiIp/uMf/win04nXXnsN9fX1ePrpp/HCCy+E/PtXOl34rNWJjf/V4GkAG+4dj6x0LYxKRQRLLmHx1inEMxnwx4+a8Ojeeh7rAAKtFbyvtND/AvpixS/bAZF0BWifaqUcD782+P407P4mlthHiSohhk/U1dVh2rRpAIBJkybhxIkTYf1+S3uXJyEGuhvAxv9qQEsSL87PDQuip6m9y5MQAzzW/oS7VrBY8ct2QCRdgdpnw8XWIbXZeFqbnH2UuCT2T57Bsdls0Ov1np8VCgW6urqgVPqvnkIhg9GY6vm5+aLVbwNotjtRkJ0RmUIPkkIh9yl7pJxqvOz3mFzpcCH3+rSAvxet8g2WFMrXN/4Ge6ylKhLHeKQbftcNHZmR6vdviXFMFQo5rnS4EurcAP3jz5tSxCdj/K7of1fvsFcxyyV2Ww4Wf+EK1M7dfe6Q9m2zA/VR4fY3Yhhsvxnp64cUrpl9KRSRu5+bEEmxXq+H3W73/Ox2uwMmxADgcgm4cqXN8/OIdK3fBnC9QevzOSkwGlOjUiajRun3mBg1iqB/P1rlGywxy2cyGQb1e33jb7DHWqoiEQPpCmD77Px+jwjTFfD7t8Q4pkZjqqTPjVjx5/19XV2uoRbLg98V/e/qTYbFLFegOBc7/gYjUPvsu2Ny3zY7UB8Vbn8jSl0G2W9Guo+S4jXdaEyFXB6Zoa0JMXyioKAAhw8fBgDU19cjLy8vrN/P0quw6b4JPgt1b7pvArINKtHLGi+4YUH0mFIU2DF3Eo91MF7rhr78XTP2lRYGHTMnVvyyHRBJV6D2OX5E2tDabJj9TSyxjxJXQhy1O++8E++++y4WLFgAQRCwdevW8L7ADXztRiP+Y/Ft+MLWgeF6TXdC7B74VxMWFy+PHgH4xldMPNYDCWfdULHil+2ASLoCtU9AlLYfF+sUs48SVUIkxXK5HD/+8Y+H9iVuIFunwi03pHc/KkjmhLhXvHQKCUAul/FYi02s+GU7IJKuAO0zqdos+yjRJMTwCSIiIiKioWBSTERERERJTyYIAm+0ExEREVFS451iIiIiIkp6TIqJiIiIKOkxKSYiIiKipMekmIiIiIiSHpNiIiIiIkp6om/e0dnZibVr1+LTTz+F0+nEsmXLMHbsWDz++OOQyWTIzc3Fhg0bIJfLsWfPHuzevRtKpRLLli3D7bffDofDgfLycjQ3N0On02Hbtm3IzMxEfX09tmzZAoVCAYvFghUrVgAAdu3ahYMHD0KpVGLt2rXIz88Xu0pERERElOBET4rfeOMNGI1GbN++HZcvX8YDDzyAm266CY888ghuu+02rF+/HgcOHMCkSZNQVVWF6upqdHR0oLi4GFOnTsWrr76KvLw8lJWVYf/+/aisrMS6deuwYcMGVFRUIDs7G6WlpWhoaAAAvP/++9i7dy8uXryIsrIyVFdXi10lIiIiIkpwoifFd911F2bMmOH5WaFQoKGhAbfeeisAYPr06Xj33Xchl8sxefJkqNVqqNVq5OTk4OTJk6irq8OSJUs8n62srITNZoPT6UROTg4AwGKxoLa2Fmq1GhaLBTKZDCNHjoTL5UJLSwsyMzODltHp7MLVq+1+39PrNbDZOsQ4FBHB8g2NmOUzmQyD+j1/8Sf14xaORKmL1OshZvwB0q+vGBK9jtGsn9jxF01SjAMplgmQZrn0eg1SUtQR+W7Rk2KdTgcAsNlsWLlyJR555BFs27YNMpnM877VaoXNZoPBYPD5PZvN5vO692f1er3PZ8+dOweNRgOj0ejzutVqHTApVirlMBpT/b6nUMihVCoGV/koYPmGRgrl8xd/UiiXWBKlLolSj74C9X+JWl9viV7HeKhfsOtvtEjxOEmxTIA0y6VQRG46nOhJMQBcvHgRy5cvR3FxMe69915s377d857dbkdaWhr0ej3sdrvP6waDwef1YJ9NS0uDSqXy+x0DcbkEXLnS5vc9ozE14HtSwPINjZjlG+ydEn/xJ/XjFo5EqYvU6yFm/AHSr68YEr2O0ayf2PEXTVKMAymWCZBmuYzGVMjlkUnURU+3L126hEWLFqG8vBxz5swBANx888147733AACHDx+G2WxGfn4+6urq0NHRAavVijNnziAvLw8FBQU4dOiQ57NTpkyBXq+HSqXC2bNnIQgCampqYDabUVBQgJqaGrjdbly4cAFut3vAu8RERERERH2Jfqf4Zz/7GVpbW1FZWYnKykoAwI9+9CNs3rwZzz77LEaPHo0ZM2ZAoVCgpKQExcXFEAQBq1atgkajQVFREdasWYOioiKoVCrs2LEDALBx40asXr0aLpcLFosFEydOBACYzWbMnz8fbrcb69evF7s6lKxkQFN7Fy7ZnRim08CUogCEWBeKwsbzSBQ+thtKUjJBEJIu1Ds7XRw+ESFxUb6rbYE7/J6LwZmWNpz+woY9H5zH5TYnts/OR2FWms+FYbCPD/3Fn9SPW8hkwFUXcOFyW+wvpjLgyPlWlFcfh6PTDa1K7vc8BpKWloIzX1glmxiIGX9AAsVgEIlex5BjNljSG2K7ETv+okmKcSDFMgHSLJfRmAqVKjLDJyIypphIqtxuIXCHj/4Xg5V35KLqSCPKq49jX2khTFo2mYCGmISKram9y1MWAHB0ukM/jzLgjx814dG99ZKoC9GAQo3ZAdrpkNoNUZzjjnaUVM5daffb4Te1d/m9GOx8+xRmFWTB0enGJbszlkWXvEAX06b2rpiU55Ld6SlLr1DPY1N7lye56P29WNaFaCChxuxA7XQo7YYo3vGffZRUvrB2BO3w/b0nkwFalRzDdJFZFzFRBLuYxuIO0zCdBlqV3KdMoZ5HqdWFaCChxuxAnxtKu6HgDOkp0KoD9x/hDElxOLtgjfF6z4mIvTslleGGYB2+zO97chmwfXY+TClKPjoPQmoXU1OKAttn5/d7TBzKeZRaXYgGEmrMDvS5obQbCk6rVmL28zV+31MqFejqcoX8XdXLLbCKVTDy4PAJSirZxhRsn50Prao79L07/N6Lgfd7W+6/Bf9685c4ljQE/o6f52IaCwJQmJWGfaWFePm7ZuwrLQz5PJpSFNgxd5J06kI0gFBjdsB2OoR2QxTvuPpEH1KcaemN5Rua/qtPqH3vgPjMylYHvTvC1Sf88Fl9IvjxkzrfmfzSqwtXnwhfotcx5JgNo58LhKtPhM9kMoh6p7ipKfL3iqXYZrj6BFG4gi05JAAmrfLaODvvi0Gw92hgAjAqMxXp8ms/D4oE1kmVy2WMBZKeIG0j5JhlP0fkF5NiSjzBlhwi6ZPY0m5EksG2QRRRHFNMCUdqS4NReHj+iPxj2yCKLCbFlHC4zmZ84/kj8o9tgyiymBRTwuldcsgbl9OKHzx/RP6xbRBFFpNiSjiSWxqMwsLzR+Qf2wZRZLElUeLxWmdTqstpURA8f0T+sW0QRVTEkuJjx47hmWeeQVVVFVatWoVLly4BAD799FNMnDgRP/3pT7F582YcPXoUOp0OAFBZWQmVSoXy8nI0NzdDp9Nh27ZtyMzMRH19PbZs2QKFQgGLxYIVK1YAAHbt2oWDBw9CqVRi7dq1yM/Pj1SVKJ5wyaH4xvNH5B/bBlHERCQpfumll/DGG28gJSUFAPDTn/4UAHD16lUsXLgQTzzxBACgoaEBv/jFL5CZmen53V//+tfIy8tDWVkZ9u/fj8rKSqxbtw4bNmxARUUFsrOzUVpaioaGBgDA+++/j7179+LixYsoKytDdXV1JKpERERERAksIklxTk4OKioq8Nhjj/m8XlFRgW9/+9sYPnw43G43GhsbsX79ely6dAlz5szBnDlzUFdXhyVLlgAApk+fjsrKSthsNjidTuTk5AAALBYLamtroVarYbFYIJPJMHLkSLhcLrS0tPgk2f4oFDIYjakB3pMHfE8KWL6hkUL5/MWfFMollkSpS6LUo69A/V+i1tdbotcxHuoX7PobvTLE7jgplf53YpMFeS+QaNRBijGlUERuOlxEkuIZM2bg/PnzPq81NzejtrbWc5e4ra0N3/72t/HQQw/B5XJh4cKFmDBhAmw2GwyG7u0jdTodrFYrbDYb9Hq957t0Oh3OnTsHjUYDo9Ho87rVah0wKXa5BG7zHCHJVL7BbnPqL/6kftzCkSh1kXo9xIw/QPr1FUOi1zGa9RM7/qIplts8B9rKOdxtngFEpQ5SbDNGYyrk8jjf5vkPf/gDZs6cCYWiuyIpKSlYuHChZ4hFYWEhTp48Cb1eD7vdDgCw2+1IS0vzec37dZVK1e/13oSaiIiIiChUUVuSrba2FtOnT/f8/I9//APFxcVwuVzo7OzE0aNHMX78eBQUFODQoUMAgMOHD2PKlCnQ6/VQqVQ4e/YsBEFATU0NzGYzCgoKUFNTA7fbjQsXLsDtdg94l5iIiIiIqK+o3Sn+5JNPkJ2d7fl5zJgxuPfeezFv3jyoVCrcd999yM3NRVZWFtasWYOioiKoVCrs2LEDALBx40asXr0aLpcLFosFEydOBACYzWbMnz8fbrcb69evj1Z1iIiIiCiByARBSLoFXTo7XRxTHCExL58MaGrv6lnDUwNTisJnySIpjCn2F38xP24DGeC4epN8XUIk9XqIGX+A9OsrhrirYxjtDoiPMcXBrr/REssxxbOfr/H7XrhjiquXW9DUZBWraAFJsc0YjalQqeJ8TDFRxMmAI+dbUV59HI5Ot2e3p8KsNK7lORQ8rkTRx3ZHFHXc5pkSRlN7l+cCAgCOTjfKq4+jqb0rxiWLbzyuRNHHdkcUfUyKKWFcsjs9F5Bejk43LtmdMSpRYuBxJYo+tjui6GNSTAljmE4Drco3pLUqOYbp1DEqUWLgcSWKPrY7ouhjUkwJw5SiwPbZ+Z4LSe8YPFMKh84PBY8rUfSx3RFFH1sXJQ4BKMxKw77Swp7Z2uruCwgnpQwNjytR9LHdEUUdk2KKLwMtUSQAJq0SJq3S8zOJIJTj2nNuTjVehlGjHHD5KCIKhyzWBSBKeEyKKT70JFxnWtpw+gsb3jn5Bb5203DkDtdjzHU6mLRMwGIqnpeP6omtC9YO6DQKZGpVyNREZg1MCkGYa/PGnVDrF89tiihOMSkm6fNzcdgwczx+dvg0GpvbebGQgEDLR+0rLbx2d1mK/MTWw1/PxehhOnzNkBLr0iWfRE8Ew6hf3LYpojjGiXYkef4uDhvfbMDM/Bs8P3P9ztiK1+Wj/MXWcwdOoeFiK85daY9x6ZJPoq/NG0794rVNEcUzJsUkDTKgydGFD5vb0ORw+QyfC3RxkMl8f+bFInbidfmovrE1Il2LxZbRuCE9pTueOIwzqhIyEfTq2646u5CR6tsmAtUvXtsUUTzjMxiKvQEeKfZeHLwvllqVHILX40ZeLGKrd/movudQ6rPlvWNrRLoWJYWjsPPtU4n56D4OBGrrcdu2AwzPebm2ERevOgAErl+8timieBaxO8XHjh1DSUkJAKChoQHTpk1DSUkJSkpK8NZbbwEA9uzZg1mzZmHevHl45513AAAOhwNlZWUoLi7G9773PbS0tAAA6uvrMXfuXCxYsAC7du3y/J1du3Zhzpw5WLBgAY4fPx6p6lAE9X2kmJGqxqkmG459YUeTwwVTav/1On983wS8efxTz89cvzPGvJaP+o8lt2FfaaH/ZDLIE4FY8F4LdlZBlichBhLv0X08SKi1eWXAOVsnTjXZsGTaaIxI13qG58w1ZwEYoH5eberl75oDtykiEk1EepqXXnoJb7zxBlJSuieq/O1vf8NDDz2ERYsWeT7T1NSEqqoqVFdXo6OjA8XFxZg6dSpeffVV5OXloaysDPv370dlZSXWrVuHDRs2oKKiAtnZ2SgtLUVDQwMA4P3338fevXtx8eJFlJWVobq6OhJVogjyfmQa8G5ddp/1OlOVmPClyVy/U0p6lm3LvT4NV660+U2IJTeJyivx+ORye8BH95zYFCWJsjavn1hfeUcuqo503yGeeEM6Xv6ueeD6cYlJoqiKyJ3inJwcVFRUeH4+ceIEDh48iAcffBBr166FzWbD8ePHMXnyZKjVahgMBuTk5ODkyZOoq6vDtGnTAADTp09HbW0tbDYbnE4ncnJyIJPJYLFYUFtbi7q6OlgsFshkMowcORIul8tzZ5nih/fYuYB369q6YNIqMe661O4LhBu+P/NiIXmSnUTVk3jcmJHKMZxSIMR/2/YX6zvfPoVZBVnQquQYYdDEdf2IElVEbn/MmDED58+f9/ycn5+PuXPnYsKECXjhhRfw/PPP46abboLBYPB8RqfTwWazwWazeV7X6XSwWq2w2WzQ6/U+nz137hw0Gg2MRqPP61arFZmZmUHLp1DIYDSmBnhPHvA9KZBy+dxuAWcvt+Pz1g4MN2iQbUyBXD7w8/E0t4Adcyfh0b31kMng927dlQ4Xcq9PG3IZpXD8/MWfFMollkB1OdV42TN2d1ZBlmeipK3LjVwJ1N07Dnvv7u2YOwljhhtCiuN4Eaj/S6QYDCRQHd1uAeeutOMLa3h9VyC9se7N0emGQo6IxlQ8nMNg19/olSF2x0mp9L8GuizIe4FEow5SjCmFInJrRETlmeCdd96JtLQ0z39v2rQJZrMZdrvd8xm73Q6DwQC9Xu953W63Iy0tzec179dVKpXf7xiIyyV0P971w2hMDfieFEi2fEN8NH7rSD32lRbiqrMLv/h//SfaGDUKUeot5vEzmQaONX/8xZ9kz+sgBKqLUaPEqOtSMN+c4zM8ZuwwHW40qCVxx6w3Di/ZnRiZkYp0BdDaKs2l2cSMPyCxYjAQv3WMwLAeo0bpd8Lg13JNyNarIhZT0TyHYsdfNMUq1k0mA7q6XH7fUyoVAd8LJBp1kGK/YDSmQi6PzAZLUVmSbfHixZ5JcLW1tRg/fjzy8/NRV1eHjo4OWK1WnDlzBnl5eSgoKMChQ4cAAIcPH8aUKVOg1+uhUqlw9uxZCIKAmpoamM1mFBQUoKamBm63GxcuXIDb7R7wLjFFRkuHyzOhZMUdY5GRqg7v0XjPI9Ox6drEmWiTSESYIGdKUeDH35rQb3jM47/7a+yHUPTyenQ/KjNVEok6RVbQYT2DjPtAEwaz9SrGFJGERSXTeOqpp7Bp0yaoVCoMGzYMmzZtgl6vR0lJCYqLiyEIAlatWgWNRoOioiKsWbMGRUVFUKlU2LFjBwBg48aNWL16NVwuFywWCyZOnAgAMJvNmD9/PtxuN9avXx+N6lBfMuDEZza8ePjjfpNKwp6klCgTbRKJWHfSBEAmCJzMRpISaG3kq45OnGluG1zcsx8jiksyQRCSrpl2dro4fGIoZN13V7o7ew0UcuC+n9X2e1RYOn007rppuKSSHSkMn/AXf5I4rwE0Obow68Uj/c5voO1mg9WlyeHCrBf7x0rYW9f2iUFTikL0hEPK5wQQN/4A6ddXDP7qGCgm/2PxbVi1tx4z82/wjH//r2Of4oUFkyXVp3mLh+ETwa6/0RLL4ROzn6/x+164wyeql1vQ1GQVq2gBSbFfMBpToVJFZviENFs2SZefu4Zb7r8FGalqz2L0QPedltzhet4dSQDBdhkLNzkQZUMCKS7tRnErUEx2dHb1G/++8o5cXHV0SjYpJqKhYcumsPgbf/ej//wrSqePxs4Dpz2f06rkGMMxmQlB1F3GRHisHGgMaNh3m4mAgDF5ztbZb/z7zrdP4T8W3xbjAhNRpERloh0ljkB3DXOH6zk5LkGJvsvYENehDXbnmmhQ/MSkzdHpN85sjs4YFZKIIo1ZC4Ul0F3DMZmp2FdaiCsdLhg1Cg6bSCQSmzQk6p1rogAYZ0TJh3eKKSzB7hqatEp8dVQGd2lKRBLaZUz0O9dEfjDOiJIPWzeFrmfGf7pWiVcX3wZHZxfStSreFaboCvXOdRRWqKAE1hNnv1/6T2hp74Td6cLING2sS0VEEcSkmEITYMb/2IwUcRINJjAUjp47156JdX4S4oisUME4TTp/b7Ljmf/7CDPzb8Bx+VWYczIwblgK4B74d4kovjApppBEdMY/l9gikUUkXhmnSaepvQvP/N9H/ZZme/qBWzA1J53nnSjBcEwxhSSSM/6DbrNKNAiRiFfGafK5ZHdiZv4N0t6anIhEwzvFFBLRZ2J7PYZWqeR+N//g1r/kEeawhUisHCDmJiYUH3p37HR0ujEiXYtZBVme3e24iQdR4mGLppCIshNZLz+PoR/+ei5erm30JMZc+og8BjFsQdR47cElupKPKUUBc04GRl2X0m8Ixdhht4g3p4KIJIFJcbIL9Q6ciGvV+nsM/dyBU55d8cRIYChxtHS4cKrJhiXTRgMAquvODzw+OAJrK0ci0SaJE4Bxw1Kw8VsTsPSVOmSkqj13iz9ptmPcl/TIVCtiXUoiEgmT4mQW7h047xn/Q5iFH+gx9MQb0vHyd80x3xyCJEQGnPjMhhcPf+yJ0ZV35KLqSOPAwxYGWqGi5/tDjmOJbWJCUeIG5IKAjFQ1SgpH+dwtzsnUYfqX09HUxhVJiBJBxJLiY8eO4ZlnnkFVVRU+/PBDbNq0CQqFAmq1Gtu2bcOwYcOwefNmHD16FDqdDgBQWVkJlUqF8vJyNDc3Q6fTYdu2bcjMzER9fT22bNkChUIBi8WCFStWAAB27dqFgwcPQqlUYu3atcjPz49UlRJO2DP0vRIIyGR48o0TaGxu902mQxDoMfQIgyZ4AkOJKUhi2tTehR/95199YnTn291PFYY8bGEwq0mEkmhTwhmm02CuOavfhLudb/8diq/n4fHfdcfoqOtS8ONvTYBMEJggE8WhiCTFL730Et544w2kpKQAALZs2YInn3wS48aNw+7du/HSSy/hiSeeQENDA37xi18gMzPT87u//vWvkZeXh7KyMuzfvx+VlZVYt24dNmzYgIqKCmRnZ6O0tBQNDQ0AgPfffx979+7FxYsXUVZWhurq6khUKSENOHHIO1nRa3DuSjsefq2+3x27i1cdnmTamD7w3+VjaPIYIDENFKO5w/VDjpeQ/1GY6GsT99TvVONlGDXKxKufCEwpCuQO1/eLxZn5N3gS4hHpWsw35+D7r9RxyT6iOBWRJdlycnJQUVHh+fnZZ5/FuHHjAAAulwsajQZutxuNjY1Yv349FixYgNdffx0AUFdXh2nTpgEApk+fjtraWthsNjidTuTk5EAmk8FisaC2thZ1dXWwWCyQyWQYOXIkXC4XWlpaIlGlhNR7x9abZ+JQT7Iy68UjWPjvH2DWz2vx8SU7MlK778713rGbVZDl+Tnk5a68HkO//F0z9pUW8sKRpAZa5ixQjI7JTB1yvIS0bFvfdvBiLY6cbwVkQ/vbkuFVv+JfvJd49ROLAIy5TtcvFntXpgCAWQX97yRzyT6i+BKRO8UzZszA+fPnPT8PHz4cAHD06FG88sor+O1vf4u2tjZ8+9vfxkMPPQSXy4WFCxdiwoQJsNlsMBgMAACdTger1QqbzQa9Xu/5Pp1Oh3PnzkGj0cBoNPq8brVafe48+6NQyGA0pgZ4Tx7wPSkQs3xpbgE75k7Co3uv3f3dMXcSxgw34NyVdr+T4RZbRuP5d057XutdnkirkmNkRmpY5TOmA7mi1CR0Uji//uJPCuUSSzh1OdV42W9ieqXDhdzr04LGqFw+tMxtpBt+h/GMzEiF0dgdy1dd8Ju0v7nCglGZ8Xm+vOOvsaUt4eoXzFDamb9YNI/K9MSQTIagsRwN8dCPBLv+Rq8MsTtOSqX/iZmyIO8FEo06SDGmFIrIbbERtYl2b731Fl544QW8+OKLyMzM9CTCvUMsCgsLcfLkSej1etjtdgCA3W5HWlqaz2ver6tUqn6v9ybUwbhcAq5cafP7ntGYGvA9KRC7fLeO1PebONTa2o4Ll9v8dvAyrzxEq5JDEOB5TJiuAFwud9IcP5Np4Fjzx1/8ST3uwhFOXYwapd/E1KhReL4jUIwOVboCfofxpCuAK1faYDSmBmwHFy63IT3GWx+JEX9Srl8kDLWd9YvFVKUnhgD//8jyjuVIi2Y/Imb/F22x6m9NJgO6ulx+31MqFQHfCyQadZDitcloTIVcHplVX6KSFP/+97/Ha6+9hqqqKs+d3X/84x9YtWoVfve738HtduPo0aN44IEH0NLSgkOHDiE/Px+HDx/GlClToNfroVKpcPbsWWRnZ6OmpgYrVqyAQqHA9u3bsXjxYnz22Wdwu90D3iUmL0HGSgaaDCf3ujP89AO34EsGNf715kKOB6ZBMaUo8Nz8SWi42Aq3AChkwM0j0nzjKVKT20JYTSLR1yZO9PqJrm8suq/F0FVHJ8YOuwU/PfB3zMy/AQo5YM7JgClVCbiDfy0RSUPEk2KXy4UtW7ZgxIgRKCsrAwB89atfxcqVK3Hvvfdi3rx5UKlUuO+++5Cbm4usrCysWbMGRUVFUKlU2LFjBwBg48aNWL16NVwuFywWCyZOnAgAMJvNmD9/PtxuN9ZK8frTAAAgAElEQVSvXx/p6iSOASY4BZoMl2fSwZxt7J9AMCGmQXJ2uX2WXNs+O4oryAyQcCf6pNBEr19UeMXQ2MwUwGs1Ck62I4ovMkEQkq6pdna6kn74RJOjC7NePNLvDpHPzHufO8mhrcmaLMcPGPzjQ3/xJ/XjFo5w6hJSHMaIpx6DaAfRIFr89dTvSocLRo1CMvWLhEi3s1jHczwMnwh2/Y2WWA6fmP18jd/3wh0+Ub3cgqYmq1hFC0iK1yajMRUqVRwPnyDpGXA5NoBrslLEhRSHsZbo7aCnfrnXp3Vf/BKtflEUF/FMRAGxlSYpjiUkKWAcUiJhPFM8MqSnQKsOnA6G81TA4eyC9erQJ0LHCpPiJMWxhCQFjENKJIxnikdatVLUYR2RH9QROSElxe+88w5uv/12z89vvfUW7rnnnogViqJA8J01rVEpYXN0oqndxR2tKHpCWAEi5hJ9RzsST088/37pP6GlvRN2pwsj07SxLhURhShoUvzOO+/g6NGj2L9/P/7yl78A6F5N4u2332ZSHM/6bN/c2uHCwy9za1KKkVDG7MYqMR1glRYif/7eZMcz//cRZubfgOPyqzDnZGDcsBQuzUYkcUGT4ptuuglXrlyBRqPBjTfeCACQyWSYOXNmVApHIuibTKQqcORc90U+I1WNueYs5GSmouyOsXjlyFlcvOpAefVxScz+pzjiFWcj3d0bY3iSxqEmtDFMTANtQ832EUVxdqe+qb0Lz/zfR1j0zzeiuc0JtwDUnb0Me0cXzCMNki47UbIL2qubTCY88MADuPvuuyGXJ+D2Roms50JypqUNp7+wYc8H53G5zYmnH+heXD4jVY2SwlHY+fYpT6Kx8o5cVB1pxMWrDs6WptAFS1ox9IRWtMR0EMkVVxOIMTnwt0vtqDt7GW4B+K9jn2L1nV+R9J36S3YnFnw1B22dLp/1tx/+ei5GX5eKTHVklpIioqELmumuWbMGAHDPPffg7rvvxt1334277roLd999d1QKR4PUk6TMevEIyl6tx88Pf4ySwlHISFXj8d/9FTPzb8DCfxoFR5cLS6aNxoo7xiIjVY2db5/CrIIszpamsARKWpvau4K+F6pgiWnIvNrEwn//ALNerMXRz2xocnThw+Y2NDlcgKz/r/WuJuCN7SNK5EBDn4R4vjkHz/zfR2HFT7QN02mQnZGK5w6c8on75w6cQkt7Z4xLR0TBBL3VkZWVBQBYu3YtvvGNb0SlQDR0/hKRnW+fwmLLaOw7eh63jExDe5cL/7hkR3Vd9x3k3rvECjk4W5rCMlDSOtQ7rYGWufqSQYMmR2h3fvu2iYxUNT6+ZMfDr9UHvYPN1QRiRAa823jVZ2e4lXfk4rUPzmJm/g2SvlNvSlGgUS7zG/dtzhBm8cfZcBGiRBK0Vzlw4ACGDx+OqqoqNDc3+7w3f/78iBaMBq83SRmRrsWsgizIeu6ADTeosfCfRuGRPfX9hkzsfPsUSqePxtdyTcjWq9gJU8iCr80qG/K6rf4S0+fmT8Lfm+whD8vom7jPKsjqdyfP75CMWK2OkYyJkVedDVoVfnrg737/Ya+QQ9p36gVgVEaK37gfYdAE/11O7CSKqaDDJ7Zu3YrW1lY4nU40NTX5/I+ka5hOg1HXpaCkcBR+WfMxdr19Gr/4fx8jRaXE7j+f7XehmVWQBUenG7nD9UyIKWy9SWvvMAPvu6nB3guZV2L68nfN2FdaiOx0bVjDMvoOg5DJAt/B9vf3TVolxl2X2p0wRyEh7jvU48j5Vr/DOxJGnzoX/fI9zDfnYET6teXMHJ1uKOTAlJyM8OInBkzawcW9GMONiGjwgrbQ/Px85OfnY+rUqcjJycHZs2eRlZWFzMzMaJWPBsGUosBPHrgFRz5pwZJpowEA1XXn8eTvT2CxZTSef+e057OOTjdksu5Oe0xmKhNiCl+fu6kjM1J9Vp8Q5U5rn2XbPmxuC2tYRt+7zYqemB/wDnYM7tgm1YoXPcf3grUDp5tsyEhV4+JVh8+d4d7+SquSoyAnAzfHw9Jmg3zCwImd0jPQbm+x4uxyh7XTHIUmpDN9/vx5lJeXY8yYMTh16hRWrFiB++67L9Jlo3B5XcDdkOH39Z+isbndZ5iEos+zAa1KDrmM44hpiLySVqMxFVeutPl9r/dnv8JIQMPeTrdPkvIlgwZjTfqgY4XdbiEmj7ITNjEKsjykvxVweu8MA93n9ukHbsH4eEiIe4Ua9164TbT0BNvtLVzVyy2ifA8AqJVySZYr3oXUw/7mN7/Bvn37oNPpYLPZ8J3vfGfApPjYsWN45plnUFVVhcbGRjz++OOQyWTIzc3Fhg0bIJfLsWfPHuzevRtKpRLLli3D7bffDofDgfLycjQ3N0On02Hbtm3IzMxEfX09tmzZAoVCAYvFghUrVgAAdu3ahYMHD0KpVGLt2rXIz88f+lGJR37GonlfYHrHDE/JyfB0ulqVHFvuvwUTrtcjU5MEYxZJusIcSzmoCXB9kpSB7uSdu9Iekzu2CZkY+Tm/vctD+hs3/Pw7p6FVyfG1XBPM2cZr5ydeEuJB4sROotgKqWeXyWTQ6XQAAL1eD40m+GSBl156CW+88QZSUlIAAD/5yU/wyCOP4LbbbsP69etx4MABTJo0CVVVVaiurkZHRweKi4sxdepUvPrqq8jLy0NZWRn279+PyspKrFu3Dhs2bEBFRQWys7NRWlqKhoYGAMD777+PvXv34uLFiygrK0N1dfVQjkfc6l0wfrFlNAxaBW4wpqKx2Y6194zD1rc+xMWrDuQO1+PmYSn+EwF2uBRDYQ8ZEGM73QHu5H1h7UBGqtpnsmp13fmI37GNi8QozGElLR0unGqy+Qznevx3fw06nGv77PzuOQ46VfebUql7JMXDtudECSyknj0nJwdPP/00zGYzPvjgA+Tk5Az4+YqKCjz22GMAgIaGBtx6660AgOnTp+Pdd9+FXC7H5MmToVaroVarkZOTg5MnT6Kurg5LlizxfLayshI2mw1Op9Pzdy0WC2pra6FWq2GxWCCTyTBy5Ei4XC60tLQMOOZZoZDBaEwN8J484HvR5nK5cfILGz5rdWBEegq+YtJ5yud2Czh3pR1fWDsw3KCBwy1gvjkHr31wFvPNOSh//Vi/pYxu+lIajGmpMKYBuREqs5SOnz9SKJ+/+JNCuQD0i6tsYwrk8vBmeCkUcqSlpYT1PacaL/sdMnClw4Xc69MClvX9C014dO+11VR2zJ2Eb3zFFHaZ/RkBGRb+0yjPKhW9GzDkXJeKq24M6RgN5JuGFIxbYYnI3wjU/4Uag263gD9+FPpxd7sF/L+Gz3w2sgg2nMsydhjunzhS9OPqdgs4e7kdn111QKdRwepwQqVUYLhejRvSxT+HQ2FMH1wfLZV+JJhg19/olSG846RUirfhSqDvkg3i70i1XJE+v4q+HYeIQkqKN2/ejL179+JPf/oTxowZg0cffTTo52fMmIHz5897fhYEAbKeWy06nQ5WqxU2mw0Gw7VB4r1DM7xf9/6sXq/3+ey5c+eg0WhgNBp9XrdarQMmxS6X4Dvm0Uu/8ZCxIgcOfnIFT/7+hOdCsum+Cbh3wvWw2hz9HkX+5IFbPGt49u5SB1x7JPnzb0+BUYmI100yxy8AMcs32EkO/uJPEsdNpOWg0tJS8L8ffh7W9xg1Sr9DBowaRcDj0uTo8iRmQHesP7q3XrThDV0u9Fu2bfefz2JEmtZn/dxIjTNOlwPp6d1P5Vpb2/u9L2b8AaHHYLjHvcnRhbU9x6v384GGc22fnY8xaWpA8F/nQQswvOy1D85iwVdzMHqYDgXX6+P+jmw0+xGx4y+awjlOJpMBXV0hrC8dokDfpVQqwv47Ui1XNPIMuTwyO0OGlG4vXboUDz74INavX48HH3wQKpUqvD/itUW03W5HWloa9Ho97Ha7z+sGg8Hn9WCfDfYdieCctdOTEAPdF5Inf38CJ7+w+X3U/ETPTnWBlpqSCULcd/gUWWItBxVoLG6w7xnM0m2i7HQXRJOto9/3z8y/wZMQ9/69ZFsyK9zjHujz3sO5epfai9QkxkAbGs3MvwHPHTiFhoutSXUOici/kJJig8GAP/7xjzhz5gw++eQTfPLJJ2H9kZtvvhnvvfceAODw4cMwm83Iz89HXV0dOjo6YLVacebMGeTl5aGgoACHDh3yfHbKlCnQ6/VQqVQ4e/YsBEFATU0NzGYzCgoKUFNTA7fbjQsXLsDtdifMcnGfWx1+LySftToCXmS8Z2p7i/tJOhQVYiWZX1j7J5MDfo+ftYgHSpAivQXzcEP/71fIw1jfOEGFe9wDfX5MZirgjs4a0IFiu/cmgltAUp1DIvJvwGeMNpsN58+fx29+8xvPazKZDC+//HLIf2TNmjV48skn8eyzz2L06NGYMWMGFAoFSkpKUFxcDEEQsGrVKmg0GhQVFWHNmjUoKiqCSqXCjh07AAAbN27E6tWr4XK5YLFYMHHiRACA2WzG/Pnz4Xa7sX79+nDrL1nXG7R+Hydfn6aFWuZ/l7ApORlY/8YJrLwj1zOEQpKTdEiSxFr1oDeZDPt7wlzCKtIT0rKNKf2+3+z1uL9Xsv2jM9zjLoWJg4FiWxCuLUuZTOeQiPyTCYIQsFt65ZVX8Ktf/QoKhQJPPvkkpk+fHs2yRUxnpyu6Y4rD3QBA1j0O79SlNnzcZMOeD87jcpsz6Jji7bPzUZidhqa2Llx1dEKjUsLm6Iz67GVJjI0NQgpjivvFnwy46gIuXG6L7Za+MRxTPJiyem8J7OjsQrpWJWqsG42puHK1zavtqmFKVfZbWzdW2/CKFn89wmobPn1aCH1MuJ8XU09/+o/LDrgFAecvt+HV9896JiZzTPHgiB1/0RTumGIx1wMO9F3hjt0N9l2xLldTk1WUcgViNKZCpYrMmOKgd4rffPNN/OEPf4DNZsNjjz2WMElxVIWbaPj5/Ob7JyDXpMdInbJ71mWgZXvcfe60JdNSRvFKpERUFCItByWXyyK7rFSAYzY2I0X8Y9b37rWbS2YBCH9jikFsZCEKP7Hy8NdzsWbGTchIVWJy9i3ITFFxnXYiAjDAmOLe5dIyMzPR2dkZrTIllHAnL/n7/Lr/PAGVDL4L1wvRGYtHkSXW5DbRiBVXEYzPmB8ztr244S9WnjtwCic/tyJdo8LYdC0y1UyIiahbyIu9BRllQUGINVObk0ASE893+HjMKFSBYoUT64jIn6DDJ06fPo1HH30UgiB4/rtX7wQ4Ci7cyUsJucUrBcTzHT4eMwpVoFjhxDoi8idoUvxv//Zvnv9esGBBxAuTiOJxpjZFD893+HjMKFT+YuXhr+di9DAd44WI+gmaFPduzUxDEO7kJZEmO1Gc6Dnfb66w9Kw+wfM9ILYRCpVXrFy0dkCnVSFDo+DEOiLya+h7odLA4mWmNsWGAIzKTEW6/NrPNAC2EQqVV6x4luJivBCRH0yKxRLuWsRi/S4lN7FihzFI8c47hvUaKGTA59YOxjMRhYxJsRiGstaslNappfgiVuwwBineBViP+OXaRlxuc16LZyKiIEJeko0CC7puas9uSsea7Djd6kCL0wXIQvxdoiDEip3e78lIVWP57WOxZNponG6yoaUj9F2MRNfTbj5sbkOTw7fNEHn0xMnxL+yQyWV45Bu5WHHHWGSkqvHcgVOYVZDFPpWIQsY7xSIItBbmVUcnzjS3+Z353LulaLA1Vz3jJYn8ECt2LtmdyEhVo6RwFHa+fcoTqzmZOkwflR79u8W8c02h8BMnK+/IxX8d+xQlhaNQdaQRsp5/TPW2i9zYlpiIJI53ikXQuxamN61KDo1K6Xc3pYaLrZ67FoF+l2to0kDEip1hOg3mmrM8CTHQHas/+s+/xuTuGp+eUCj8xcnOt09hZv4N2Pn2Kcw1Z6F3zyn2qUQUiqglxfv27UNJSQlKSkowb9483HLLLWhoaMC0adM8r7/11lsAgD179mDWrFmYN28e3nnnHQCAw+FAWVkZiouL8b3vfQ8tLS0AgPr6esydOxcLFizArl27olUdH71rYfYmKL13tmyOzgF3Uwr0u6YU3iWm4MSKHVOKArnD9ZLZJY471lEoAsWJTNb9/zmZqdh39Dz7VCIKWdR6iVmzZmHWrFkAgI0bN2L27Nn429/+hoceegiLFi3yfK6pqQlVVVWorq5GR0cHiouLMXXqVLz66qvIy8tDWVkZ9u/fj8rKSqxbtw4bNmxARUUFsrOzUVpaioaGBowfPz5a1eoWYN3UpnbXwLspcc1VGiyxYkcAxlynk8wucdyxjkIRKE4Eofv/xwzTYdsDE9inElHIoj584q9//StOnz6N+fPn48SJEzh48CAefPBBrF27FjabDcePH8fkyZOhVqthMBiQk5ODkydPoq6uDtOmTQMATJ8+HbW1tbDZbHA6ncjJyYFMJoPFYkFtbW20q9StZy3Mcdeldo/nFPzfyXv467kYPyLN966Fn98lColIsWPSSueJBZ+eUCj8xcnKO3Lx5vFPsX12PrJ0KvapRBSWqF9lfv7zn2P58uUAgPz8fMydOxcTJkzACy+8gOeffx433XQTDAaD5/M6nQ42mw02m83zuk6ng9Vqhc1mg16v9/nsuXPnBiyDQiGD0Zga4D15wPdC5XYLOHelHV9YO3DT9WnYv8KCxpY2pGqUGK5X44b0FMjlg5tOL0b5IonlC6UM/eNPCuX6piEF41ZY8IW1A8MNGmQbg8epd5x7f16MuoRblkiQwjmJhED9n9Tr6y/evjmuJ05aHUjVKOF0ufHNm78UMF6kXsehiof6Bbv+Rq8M4R0npVIh2t8O9F2yQfwdqZYr0udXoYjc/dyoJsWtra34+OOPUVhYCAC48847kZaW5vnvTZs2wWw2w263e37HbrfDYDBAr9d7Xrfb7UhLS/N5zfv1gbhcQveuRn54djwaSKDNDkKYOd/a2j7w9wcQcvliJJnKZzIZBv6QH/7iTyrHLV0OpKdrAAwQp0Hi3JguTl2CliUKm41I5ZwEImb8ASHUN5YbvASJt3Q5kG7U+nw8UOxK/ZwOVTTrJ3b8RVM4x8lkMqCrS7zlKQN9l1KpCPvvSLVckT6/RmMq5HLx/kHgLarDJ/785z/jn//5nz0/L168GMePHwcA1NbWYvz48cjPz0ddXR06OjpgtVpx5swZ5OXloaCgAIcOHQIAHD58GFOmTIFer4dKpcLZs2chCAJqampgNpsjX5GeDnrWi0ew8N8/wKwXa3HkfKvnosGZ85ToYhrnQdofRUiMjzn7VSKKhqgmxZ988gmysrI8Pz/11FPYunUrSkpKcPToUfzgBz+AyWRCSUkJiouL8Z3vfAerVq2CRqNBUVERTp06haKiIrz22mtYsWIFgO5Je6tXr8acOXNw8803Y+LEiRGvR7AOmjPnKRnEMs6ZIEVfrI85+1UiioaoDp9YsmSJz8/jx4/H7t27+31u3rx5mDdvns9rKSkp2LlzZ7/PTpo0CXv27BG3oAMI1kFz5jwlg1jGOTe8ib5YH3P2q0QUDdy8o1fPdqF/brw84LaygTZN+JJBA0DA9jn5ePjrYzEiXcuZ85SQYrJCRE8bdboFT/vqFfEEKdG3nZYBjS1t/usnA/RaFVZ+fSxW3HHtuEczKeWKJEQUDexRgLC3le3toL0//9z8Sfh7k93ntS3334IJ1+uRqYnihBSiaIj2+tp+2ujDX8/Fy7WNuNzmvJYgReLvJ/q208HqB/9bKb/2wVmsvvMr0Vv/l+u5E1EUMClG4PFy+0oL/T8a9NNBA8CsF4/02yZ3X2khO25KTD1rJHvaSATj3F8bfe7AKfz6O2aka5QRTZDC7h/iTLD6AfC7lfJ/LL4N2XpVdPu2KMYbESUnDp/AICdx9Nk0gRNBiCInUPvq7HJHfHOGRG/bweoX6D2bo5NJKRElHCbFCDxGOJzxcmJ8BxH5F8v2lehtO1j9Er3uRETemBRDnEkcnAhCFDmxbF+J3raD1S/R605E5I09G+AzRvhKhwtGjSL8MYqcCEIUObFsX4netnvq9+YKCy5cbutXv4SuOxGRFybFvXrGCOden9a9ReFgOn1OBCGKnFi2r0Rv2wIwKjMV6fJrP3u/l9B1JyLqweETRERERJT0mBQTERERUdJjUkxERERESY9jiomIiChhGNJToFUHT29MJkOUSpNcnF1u0Y6tw9kF69V2Ub4rVEyKiYiIKGFo1UrMfr4m4PtKpQJdXa6Qvqt6uUWsYiUFtVIe9NiHo3q5BVZRvil0UU2K77//fhgM3f+CyMrKwtKlS/H4449DJpMhNzcXGzZsgFwux549e7B7924olUosW7YMt99+OxwOB8rLy9Hc3AydTodt27YhMzMT9fX12LJlCxQKBSwWC1asWDH4AsqAxpa2nmWJNDClKDjTmihaZN1bDncv/cX2R3GKcUwUt6KWFHd0dAAAqqqqPK8tXboUjzzyCG677TasX78eBw4cwKRJk1BVVYXq6mp0dHSguLgYU6dOxauvvoq8vDyUlZVh//79qKysxLp167BhwwZUVFQgOzsbpaWlaGhowPjx48MvoAw4cr4V5dXH4eh0exapL8xKY4dGFGlsf5QIGMdEcS1qSfHJkyfR3t6ORYsWoaurCz/84Q/R0NCAW2+9FQAwffp0vPvuu5DL5Zg8eTLUajXUajVycnJw8uRJ1NXVYcmSJZ7PVlZWwmazwel0IicnBwBgsVhQW1s7YFKsUMhgNKb6vNbY0ubpyADA0elGefVxvLnCglGZqf6+JiYUCnm/sksJyxdKGfrHnxTKJZbB1EWK7S+Rzok3f/HX/Xpi1tdbpOsY6ziOh3MYKP7EplQqAr4nG+D9cL4rXIG+K9wyBfuuwZBquQL1VZEStaRYq9Vi8eLFmDt3Lv7xj3/ge9/7HgRBgEwmAwDodDpYrVbYbDbPEIve1202m8/r3p/V6/U+nz137tyAZXG5hO4NOrxcuNzm6ch6OTrduHC57dqC9hJgNKb2K7uUJFP5BjuZwF/8Sf24hWMwdZFi+5P6OREz/gDp11cMka5jrOM4mudQ7PgTk8lkCDpmOJwxxQDC+uxgvyvcMgX7rsGQarkC9VVyuXiJt7eoJcU33ngjRo0aBZlMhhtvvBFGoxENDQ2e9+12O9LS0qDX62G3231eNxgMPq8H+2xaWtqgyjdMp4FWJffp0LQqOYbp1IP6PiIKHdsfJQLGMVF8i9o9mNdffx1PP/00AODzzz+HzWbD1KlT8d577wEADh8+DLPZjPz8fNTV1aGjowNWqxVnzpxBXl4eCgoKcOjQIc9np0yZAr1eD5VKhbNnz0IQBNTU1MBsNg+qfKYUBbbPzodW1X1IeseCmVK4QAdRpLH9USJgHBPFt6i11Dlz5uCJJ55AUVERZDIZtm7dioyMDDz55JN49tlnMXr0aMyYMQMKhQIlJSUoLi6GIAhYtWoVNBoNioqKsGbNGhQVFUGlUmHHjh0AgI0bN2L16tVwuVywWCyYOHHioMs45rpUVBYXIEUtR6ZWhUwNZw0TRYUAFGalYV9pYc+sfXV3IjGU9sdVAELTc5xONV6GUaPkcRqKSMQxEUVN1JJitVrtSWS9vfLKK/1emzdvHubNm+fzWkpKCnbu3Nnvs5MmTcKePXuGVrhgM4aJKDoEwKRVwqRVen4eNK4CEBoeJ/GJGcdEFFUSmkIWO03tXX5nDDe1d8W4ZEQ0GGzToeFxIiK6hkkxgEt2p98Zw5fszhiViIiGgm06NDxORETXMCnGtRnD3jhjmCh+sU2HhseJiOgaTolF94zh5+ZPQsPFVrgFQCEDbh6RxgkSFDVut4AmByeFiaV3FYC+Y2XZpn2x7yMiuoZJcQ9nlxsvHv7Y5wJKFBUy4I8fNeHRvfWc7CQWrgIQMvZ9RETdOHwCnGxCsdXU3uVJiAHGn2h6VgEYd11q90oATIj7Yd9HRHQNk2JwsgnFFuOPYoWxR0R0DZNicLIJxRbjj2KFsUdEdA2TYnBrTootU4oCO+ZOYvxR1LHvIyK6hj0f4DMp50qHC0aNgpNyKHoE4BtfMXFSGEUf+z4iIg8mxb16JuXkXp+GK1faeFGgqJLLZdwalmKDfR9JgCE9BVo1UxKKLUYgERERxZRWrcTs52tE+a7q5RZRvoeST9SS4s7OTqxduxaffvopnE4nli1bhuuvvx5Lly7Fl7/8ZQBAUVER7rnnHuzZswe7d++GUqnEsmXLcPvtt8PhcKC8vBzNzc3Q6XTYtm0bMjMzUV9fjy1btkChUMBisWDFihXRqhIRERERJYioJcVvvPEGjEYjtm/fjsuXL+OBBx7A8uXL8dBDD2HRokWezzU1NaGqqgrV1dXo6OhAcXExpk6dildffRV5eXkoKyvD/v37UVlZiXXr1mHDhg2oqKhAdnY2SktL0dDQgPHjx0erWkRERESUAKK2+sRdd92Fhx9+2POzQqHAiRMncPDgQTz44INYu3YtbDYbjh8/jsmTJ0OtVsNgMCAnJwcnT55EXV0dpk2bBgCYPn06amtrYbPZ4HQ6kZOTA5lMBovFgtra2mhViYiIiIgSRNTuFOt0OgCAzWbDypUr8cgjj8DpdGLu3LmYMGECXnjhBTz//PO46aabYDAYfH7PZrPBZrN5XtfpdLBarbDZbNDr9T6fPXfu3IBlUShkMBpTA7wnD/ieFLB8QyOF8vmLPymUSyyJUpdEqUdfgfq/RK2vt0SvYzzUL9j1V6lUiPZ3gn2XLMy/FY1yhVumYN81GFItV6C+KlKiOtHu4sWLWL58OYqLi3HvvfeitbUVaWlpAIA777wTmzZtgtlsht1u9/yO3W6HwWCAXq/3vG6325GWlubzmvfrA3G5hO5Z1n4YjakB35MClm9oxCyfyWQY+EN++Is/qR+3cCRKXaReDzHjD5B+fcWQ6HWMZv3Ejj+TyYCuLtdQi0GFqU8AAA+qSURBVOUR7LuUSkVYfysa5Qq3TMG+azCkWq5AfZVcLl7i7S1qwycuXbqERYsWoby8HHPmzAEALF68GMePHwcA1NbWYvz48cjPz0ddXR06OjpgtVpx5swZ5OXloaCgAIcOHQIAHD58GFOmTIFer4dKpcLZs2chCAJqampgNpujVSUiIiIiShBRu1P8s5/9DK2traisrERlZSUA4PHHH8fWrVuhUqkwbNgwbNq0CXq9HiUlJSguLoYgCFi1ahU0Gg2KioqwZs0aFBUVQaVSYceOHQCAjRs3YvXq1XC5XLBYLJg4cWK0qkRERERECSJqSfG6deuwbt26fq/v3r2732vz5s3DvHnzfF5LSUnBzp07+3120qRJ2LNnj3gFJSIiIqKkE7XhE5InA5ocXfhz42U0OVzdo8uJKLp62uGHzW1sh1LG80RECYg72gGADDhyvhXl1cfh6HRDq5Jj++x8FGalcctTomhhO4wPPE9ElKB4pxhAU3uXp4MHAEenG+XVx9HU3hXjkhElD7bD+MDzRESJikkxgEt2p6eD7+XodOOS3RmjEhElH7bD+MDzRESJikkxgGE6DbQq30OhVckxTKeOUYmIkg/bYXzgeSKiRMWkGIApRYHts/M9HX3vGDlTCodcE0UL22F84HkiokTFXgwABKAwKw37SgtxpcMFo0bR3cFz0ghR9Hi1w0t2J4bp1GyHUsTzREQJiklxLwEwaZXIvT6te1tBdvBE0dfTDk1apednkiCeJyJKQBw+QURERERJj0kxERERESU9JsVERERElPRkgiBwNBgRERERJTXeKSYiIiKipMekmIiIiIiSHpNiIiIiIkp6TIqJiIiIKOkxKSYiIiKipMekmIiIiIiSHpNiIiIiIkp6ylgXQArcbjeeeuopfPTRR1Cr1di8eTNGjRoV8b97//33w2AwAACysrKwdOlSPP7445DJZMjNzcWGDRsgl8uxZ88e7N69G0qlEsuWLcPtt98Oh8OB8vJyNDc3Q6fTYdu2bcjMzER9fT22bNkChUIBi8WCFStWhF2uY8eO4ZlnnkFVVRUaGxsjVqZdu3bh4MGDUCqVWLt2LfLz88MuX0NDA5YuXYovf/nLAICioiLcc889MS0fIN1zGyqpx8Bg6yLVeImlWPV/4ers7MTatWvx6aefwul0YtmyZRg7dmxcx6Y/zc3NmDVrFn71q19BqVQmXP3EJqW+Vqr9ppT6QMm3Y4GE//mf/xHWrFkjCIIg/OUvfxGWLl0a8b/pcDiE++67z+e173//+8KRI0cEQRCEJ598Uvjf//1f4YsvvhBmzpwpdHR0CK2trZ7//tWvfiXs3LlTEARBePPNN4VNmzYJgiAI3/rWt4TGxkbB7XYLS5YsEU6cOBFWuV588UVh5syZwty5cyNaphMnTgglJSWC2+0WPv30U2HWrFmDKt+ePXuEX/7ylz6fiWX5BEG65zZUUo+BodRFivESa7Ho/wbj9ddfFzZv3iwIgiC0tLQI//Iv/xLXsemP0+kUfvCDHwjf/OY3hdOnTydc/cQmpb5Wqv2m1PpAqbdjDp8AUFdXh2nTpgEAJk2ahBMnTkT8b548eRLt7e1YtGgRFi5ciPr6ejQ0NODWW28FAEyfPh1/+tOfcPz4cUyePBlqtRoGgwE5OTk4efKkT5mnT5+O2tpa2Gw2OJ1O5OTkQCaTwWKxoLa2Nqxy5eTkoKKiwvNzpMpUV1cHi8UCmUyGkSNHwuVyoaWlJezynThxAgcPHsSDDz6ItWvXwmazxbR8gHTPbaikHgNDqYsU4yXWYtH/DcZdd92Fhx9+2POzQqGI69j0Z9u2bViwYAGGDx8OIL7bXjRIqa+Var8ptT5Q6u2YSTEAm80GvV7v+VmhUKCrqyuif1Or1WLx4sX45S9/iY0bN2L16tUQBAEymQwAoNPpYLVaYbPZPI+Gel+32Ww+r3t/1rseva+HY8aMGVAqr42qiVSZBlvWvuXLz8/HY489ht/+9rfIzs7G888/H9PyAdI9t6GSegwMpS5SjJdYi0X/Nxg6nQ56vR42mw0rV67EI488Etex2de+ffuQmZnpueAD8d32okFKfa1U+02p9YFSb8dMigHo9XrY7XbPz2632yeIIuHGG2/Et771LchkMtx4440wGo1obm72vG+325GWltavbHa7HQaDwef1YJ9NS0sbUjnl8mshImaZAn1HuO68805MmDDB899/+9vfYl6+eDm3oZJ6DIRDivESa7Ho/wbr4sWLWLhwIe677z7ce++9CRWb1dXV+NOf/oSSkhJ8+OGHWLNmjc9drXivXyRIua+VamxKoQ+UcjtmUgygoKAAhw8fBgDU19cjLy8v4n/z9ddfx9NPPw0A+Pzzz2Gz2TB16lS89957AIDDhw/DbDYjPz8fdXV16OjogNVqxZkzZ5CXl4eCggIcOnTI89kpU6ZAr9dDpVLh7NmzEAQBNTU1MJvNQyrnzTffHJEyFRQUoKamBm63GxcuXIDb7UZmZmbY5Vu8eDGOHz8OAKitrcX48eNjXr54ObehknoMhEOK8RJrsej/BuPSpUtYtGgRysvLMWfOHACJFZu//e1v8corr6Cqqgrjxo3Dtm3bMH369ISpXyRIua+VamzGug+UejuWCYIghHw0E1Tv7Ou///3vEAQBW7duxZgxYyL6N51OJ5544glcuHABMpkMq1evRkZGBp588kl0dnZi9OjR2Lx5MxQKBfbs2YPXXnsNgiDg+9//PmbMmIH29nasWbMGTU1NUKlU2LFjB0wmE+rr67F161a4XC5YLBasWrUq7LKdP38eP/zhD7Fnzx588sknEStTRUUFDh8+DLfbjSeeeCLkjse7fA0NDdi0aRNUKhWGDRuGTZs2Qa/Xx7R8Uj63oZJ6DAy2LlKMl1iLRf83GJs3b8Z///d/Y/To0Z7XfvSjH2Hz5s1xG5uBlJSU4KmnnoJcLo/rthdpUutrpdpvSqkPlHo7ZlJMREREREmPwyeIiIiIKOkxKSYiIiKipMekmIiIiIiSHpNiIiKi/9/evYVEtf0BHP865i3r0KDhWE7kBYvmKQq6ieSkJWOjpBkKzWQy9ZLZVA4oaBhBUT0kBUpWGmoUaojmJTDLLoKGLwnS3TBLMW9Y4mXSPA+R/D3ZqX+UHpvf523PWnuxFvw2+zdrr72XEMLmSVIshBBCCCFs3n/zC+3it2loaMBsNuPn5wd8/pi1l5cXBw8eJCoqCo1Gw/j4OFarlfDwcHbu3AnA3bt3ycnJQaFQMDY2xvbt2wkPD5/JoYhZJjs7m7y8PGpqanByciI5OZnm5mYWLFjA6OgoSqWSlJQU1Go1586dIzMzk9raWjw8PADo6ekhMDCQY8eOERkZSUlJCSUlJdjb2zM+Po7JZCIgIGCGRyn+axoaGjAajZw5cwadTjfxu16vR6PR8PDhQ6qqqnBycqKrq4v4+HhMJhOPHz+mubmZrq4uhoeHUavVKJVKzp49K7En/m//vPeOjIyg1+sxGAzfPbepqYmUlBS0Wi1v3rzh5MmTdHd38+TJE7Ra7e/uuk2RpNgGrV27ljNnzkwcHz58mNu3b+Pn50d+fj4AHz9+ZN++fSxatAitVkt6ejqlpaX89ddfDAwMEBERwYYNG3Bzc5upYYhZ5saNG+h0OioqKoiMjATAYrEQGBgIQGNjI2azmevXrwOwdOlSqqqqiIuLA6CyshJPT08APnz4QGZmJhUVFTg6OtLZ2Ul0dDS1tbWTdkcSAsDHx4fy8vKJpPjp06cMDQ1NqtPZ2cmePXs4cOAAwcHBREREAJ+3X25paSEpKQmQ2BM/73/vvVarldDQUCIiIr67Y96DBw+IiYmZlEDX19fT0tIiSfEvJlewjbNarbx79+6ri9LBwQGj0UhlZSUAbm5u5OXl8fz5c1xdXamqqpKEWPywhoYGlixZQkxMDFeuXJmyzurVq3FwcKC1tRUAnU7HzZs3J8rv3LlDUFAQAHPnzmVsbIyrV6/y+vVrPDw8uHXrliQlYkrLly+no6OD9+/fA1BWVoZer58ob29vZ/fu3SQnJxMcHPyvbUnsiV9hYGAAhUJBXFwciYmJxMXFYbVasVgsxMTEEB0dTWVlJU1NTRQXF1NQUEB1dTVarZbBwUGys7MpLy+npqZmpofyR5Gr2AbV19djMBjQ6XRERkYSEhLCunXrvqrn7u5OX18fAFlZWQwNDXHo0CECAgI4f/48su+L+FFFRUVER0fj4+ODo6Mjjx49mrKem5vbRMy5u7vj4uJCW1sbra2tqFQqnJycALC3tyc3N5fW1lZMJhNBQUEUFxdP23jE7BMSEkJ1dTXj4+M0NTWxcuXKibLExEScnZ3p6en5bjsSe+Jnfbn3Go1GLBYLaWlpuLq6otfruXz5MoWFhSiVSq5du0Zubi4ZGRl4eXmxbds24uLiCAkJAT7H4N69e9m6dSubNm2a4VH9WWT5hA368ginr6+P+Ph4vLy8pqz39u1bVCoV/f39tLe3Y7FYsFgsdHZ2sn//fjQajTy6Ed/V39/PvXv36O3tJT8/n4GBAQoKCrC3t/+qbnt7OyqVauI4LCyMiooKRkdH0ev11NXVAZ8fdQ8PD3PkyBEAXr16hclkYtWqVSxbtmx6BiZmFb1eT3p6Omq1+qutXo8fP467uzuxsbGsWLHiX7e5ltgTP+ufSxcBLl68iLe3NwAvX75k/fr1AMybNw9fX1/a2tqmvZ+2TGaKbZhSqeT06dOkpqbS1dU1qcxqtZKXl0dYWBhWqxWz2UxHRwcACxcuxN3dHUdHx5notphlysrKiIqKIicnh0uXLlFYWEhdXR29vb2T6tXV1eHs7DwpKd6yZQs1NTU0NjayZs2aid+7u7tJSkqiv78fgMWLF6NUKnFwcJieQYlZR61WMzg4SH5+/lcvCfv7++Pp6UlycjJms5nh4eFvtiOxJ341Ozs7AHx9fWlsbAQ+L6949uzZNyetFAoFnz59mrY+2gqZKbZxfn5+GAwGcnNzefHiBQaDATs7u4mZuS//WlNTU0lISGDOnDmMjY2xceNGedta/JCioiJOnTo1cezi4sLmzZspLi6mo6ODCxcuoFAocHV1JSMjY9K58+fPR6VSoVarJ63Z1Gg0GI1Gdu3ahbOzM2NjYxPLM4T4Fp1OR2lpKd7e3lPOwIWGhnL//n2OHj3KiRMnpmxDYk/8Ljt27CAtLY3Y2FhGRkZISEj45rs7/v7+ZGVlodFoCAsLm+ae/rnsxmVhqBBCCCGEsHGyfEIIIYQQQtg8SYqFEEIIIYTNk6RYCCGEEELYPEmKhRBCCCGEzZOkWAghhBBC2DxJioUQQgghhM2TpFgIIYQQQti8vwEGb7SKRk91rQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style='darkgrid')\n",
    "sns.pairplot(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols(\"Profit~RDS+ADMS+MKTS\",data=data1).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    50122.192990\n",
       "RDS              0.805715\n",
       "ADMS            -0.026816\n",
       "MKTS             0.027228\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Coefficient parameters\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept     7.626218\n",
       " RDS          17.846374\n",
       " ADMS         -0.525507\n",
       " MKTS          1.655077\n",
       " dtype: float64,\n",
       " Intercept    0.00000\n",
       " RDS          0.00000\n",
       " ADMS         0.60176\n",
       " MKTS         0.10472\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding tvalues and pvalues\n",
    "model.tvalues , np.round(model.pvalues,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9507459940683246, 0.9475337762901719)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding rsquared values\n",
    "model.rsquared , model.rsquared_adj  # Model accuracy is 94.75%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build SLR and MLR models for insignificant variables 'ADMS' and 'MKTS'\n",
    "# Also find their tvalues and pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept    3.040044\n",
       " ADMS         1.419493\n",
       " dtype: float64,\n",
       " Intercept    0.003824\n",
       " ADMS         0.162217\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_a=smf.ols(\"Profit~ADMS\",data=data1).fit()\n",
    "slr_a.tvalues , slr_a.pvalues  # ADMS has in-significant pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept    7.808356\n",
       " MKTS         7.802657\n",
       " dtype: float64,\n",
       " Intercept    4.294735e-10\n",
       " MKTS         4.381073e-10\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_m=smf.ols(\"Profit~MKTS\",data=data1).fit()\n",
    "slr_m.tvalues , slr_m.pvalues  # MKTS has significant pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept    1.142741\n",
       " ADMS         2.467779\n",
       " MKTS         8.281039\n",
       " dtype: float64,\n",
       " Intercept    2.589341e-01\n",
       " ADMS         1.729198e-02\n",
       " MKTS         9.727245e-11\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_am=smf.ols(\"Profit~ADMS+MKTS\",data=data1).fit()\n",
    "mlr_am.tvalues , mlr_am.pvalues  # varaibles have significant pvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Validation\n",
    "## Two Techniques: 1. Collinearity Check & 2. Residual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>Vif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RDS</td>\n",
       "      <td>2.468903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADMS</td>\n",
       "      <td>1.175091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MKTS</td>\n",
       "      <td>2.326773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variables       Vif\n",
       "0       RDS  2.468903\n",
       "1      ADMS  1.175091\n",
       "2      MKTS  2.326773"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) Collinearity Problem Check\n",
    "# Calculate VIF = 1/(1-Rsquare) for all independent variables\n",
    "\n",
    "rsq_r=smf.ols(\"RDS~ADMS+MKTS\",data=data1).fit().rsquared\n",
    "vif_r=1/(1-rsq_r)\n",
    "\n",
    "rsq_a=smf.ols(\"ADMS~RDS+MKTS\",data=data1).fit().rsquared\n",
    "vif_a=1/(1-rsq_a)\n",
    "\n",
    "rsq_m=smf.ols(\"MKTS~RDS+ADMS\",data=data1).fit().rsquared\n",
    "vif_m=1/(1-rsq_m)\n",
    "\n",
    "# Putting the values in Dataframe format\n",
    "d1={'Variables':['RDS','ADMS','MKTS'],'Vif':[vif_r,vif_a,vif_m]}\n",
    "Vif_df=pd.DataFrame(d1)\n",
    "Vif_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# None variable has VIF>20, No Collinearity, so consider all varaibles in Regression equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2) Residual Analysis\n",
    "# Test for Normality of Residuals (Q-Q Plot) using residual model (model.resid)\n",
    "\n",
    "sm.qqplot(model.resid,line='q')\n",
    "plt.title(\"Normal Q-Q plot of residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([49], dtype=int64)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.where(model.resid<-30000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test for Homoscedasticity or Heteroscedasticity (plotting model's standardized fitted values vs standardized residual values)\n",
    "\n",
    "def standard_values(vals) : return (vals-vals.mean())/vals.std()  # User defined z = (x - mu)/sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(standard_values(model.fittedvalues),standard_values(model.resid))\n",
    "plt.title('Residual Plot')\n",
    "plt.xlabel('standardized fitted values')\n",
    "plt.ylabel('standardized residual values')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test for errors or Residuals Vs Regressors or independent 'x' variables or predictors \n",
    "# using Residual Regression Plots code graphics.plot_regress_exog(model,'x',fig)    # exog = x-variable & endog = y-variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9PR2LxcKoUaPo2rUr69atY+3atZ7dEAsXLmTChAmcO3cOgC5dujB69GjWrVvHZ5995gkuXPz5119/zdy5cz2vi/785z/Ts2fPYq39pTp27Ej37t1ZsWIFUVFRnDlzhmuuuYasrCx8fHwIDg4mJiaGoKCgKz6eSGWjAIZIFde8eXPGjBnD5MmTcbvdzJ49m+Dg4MuuO3HiBF988QXvv/8+vr6+xMfHs3jxYrp37w7A119/zYcffkhAQABPPvkkK1eu5Mknn+S5554jOjqaDh068PHHH7N69eoCx/Luu++yYcMGz+ePPPJInu2NQ4YM4dNPP/W84HG73Xz22We8/PLLHDlyhEWLFvG3v/2N2rVrc/DgQYYPH87GjRtZvHgxvr6+fPrpp5w7d46HHnrosgCG1Wqlb9++rFu3jhYtWpCUlMSuXbuYOXMmL7/8Mt26dWPkyJGcOXOGOXPmMGjQICyWwtMENW/enHHjxvHss88yZcoUWrZseVVfExEREW8zbNgwAM6dO4fD4aBr1668/PLLAHz44Yf89NNPrFmzBpvNxqpVq5gyZQpLly5l3rx5REdH07p1a3bs2MHu3bv54x//6On3wIEDLFmyhP/7v/+jbt26nmDFlfztb3/j2WefpX379qSmpnL33Xezb9++PG/Kly1bdsXn7/79+9O/f38mTJiAj48P69evJyIigqSkJF544QWWLVtGSEgIiYmJRERE0Lx5cyBnF+jmzZsJCAjg9ddfJyQkhHfeeYe0tDQmT55McnJyoeOPiYlh+PDhhIeH88MPP7Bq1aoCAxiFrX1Bbr75ZrZu3QrAqFGjeO6557jzzju5/fbbadOmDffddx+hoaFXXmiRSkYBDJFqYOjQoXz22Wc0adKELl265HvNDTfcwLx58/joo49ISEjg22+/JTU11XN7u3btCAgIAODWW28lKSmJn376CZvNRocOHQC4//77C31hUtARkquxc+dO/t//+388+uijnjbDMDh69Ci7du1i0qRJGIZBnTp1PEGXS/Xt25d+/foxceJEPv74Y7p160ZgYCDdu3dnwoQJfPfdd3To0IEpU6ZcMXiR69///jd169bl888/Z8iQIVd9PxEREW+Se4xh//79jBw5kvbt23PNNdcAsGXLFr7//nv69u0L5ORhSE9PByA8PJxRo0bRpUsX7rrrLkaMGJGn3127dnHXXXdRt25dAAYMGMCOHTuuOJ65c+eybds23nzzTX7++WecTidpaWl5AhhX8/wdGhpK8+bN2bx5Mx06dODf//43s2fP5uuvv+bMmTM888wznmsNw+DHH38Ecv5Ikfu6p1OnTowcOZJTp07RsWNHxo4dS2BgYKHj79WrFzNmzGDz5s107NiR559/vsBrC1v7wvj6+gI5wYxPP/2U/fv389VXX7Fz507efPNNXnvtNbp163bFfkQqE73SFqkmQkJCaNCgQYG379+/nwEDBpCSksJdd93FE088kef23CdJyHmCN00TwPN/LputbOKibrebDh068H//93+ef6tXr+amm266bBz5HY+BnCDNrbfeyueff866devo168fAF27duWzzz6jV69eHDhwgN69e3P69Okrjmnp0qX8/PPPbNiwgVOnTvH666+XwkxFREQqzm233cYLL7zAxIkTOX78OJDzHPzEE094nn/j4uJYsWIFAGPGjOGDDz6gRYsWrFu3Lt8jmAU9R1/8egIgKyvL8/EjjzzC1q1bufHGG3nmmWe47rrrLnvNcbXP3xEREXz44Yd8/PHH3HPPPfj7++NyuWjSpEme1xWrVq3y5Izw8/Pz3L9ly5Zs2rSJAQMGcOLECfr378++ffsKHf/AgQPZsGEDd911Fzt27KBPnz44nc4ir31B9u3bR7NmzcjOzmbq1KkkJSXRokULhg8fzl/+8heeeuqpYucvEfFmCmCICABfffWV54mvXbt2bNq0CZfLVeh9mjdvjmmani2MmzZtIikpqUTjsFqtZGdnX/Zxhw4d2LlzJ4cOHQJyzrD26dOHjIwMOnXqxNq1a3G73SQlJbFp06YC+4+IiGDp0qWkp6d7jpmMHTuWf/zjH4SHhzNt2jQCAgI4evRooeP84osv+Mtf/kJMTAx16tRh8eLF/PWvf2X79u0lmr+IiEhFu//++2nZsqXnGENYWBhr164lJSUFgNdee43x48eTnZ1Nt27dSE9PZ9CgQUybNo0ff/yRzMxMT1933XUXO3fu9AQW1q9f77mtdu3a7N+/H9M0SUlJYcuWLQBcuHCB77//nqioKHr06MHp06c5evToZXm2rvb5u3v37uzfv5/Vq1cTEREBQOvWrUlISOCrr74Cco669OzZM9/k4dHR0cTGxnLPPfcwefJkmjZtysGDB6lTpw4HDx7E6XSSlZXFZ5995rnPwIEDOXDgAA8//DAzZ87kwoULnDlzpshrn5+tW7fy+eefM2DAAGw2G4cPHyY2NtYTQMnOzubQoUPceuutV3w8kcpGR0hEBMh5wty4cSO9evXC7XbTtWtXkpKSPC9W8mO323n99dd56aWXWLhwIbfccstVbXksTLdu3Vi4cCFZWVm0adOG119/nVGjRrFkyRJmzJjB888/j2ma2Gw23njjDfz9/YmMjGTatGn06tWLOnXq5EkQll//06dPz7PF9emnn2by5MmsWrUKq9XKPffcwx//+EcSExMZOXIkb7/9dp68IcePH+f5559n9uzZnl0tTZs2ZerUqYwbN47169dTr169Eq2DiIhIRXrxxRfp06cP27dvp3///p4cEYZhUK9ePebOnYvNZmPSpElERUVhs9kwDIM5c+bg4+Pj6Sc3X9SwYcPw9/fPky8qt/8ePXoQHBxMu3btME2TmjVrMnLkSB566CH8/PwIDg6mTZs2JCQk5MnrUNDz96V8fHy47777+OKLLzyPn/vHh3nz5uF0OjFNk3nz5hESEsKXX36Z5/7Dhg1j4sSJ3H///fj4+NC8eXPCw8OxWCz88Y9/pFevXtStW5f27dt7jqBERUUxZ84cXn31VQzDYNSoUZeVmL+atYecPGQPPPAAkLNr5brrrmPZsmWeYzmvvfYa8+fPp2fPntSoUQO320337t3zHI8RqSoM89K9WCIiIiIiIiIiXkZHSERERERERETE6ymAISIiIiIiIiJeTwEMEREREREREfF6CmCIiIiIiIiIiNdTAENEREREREREvF61LKPqdrtxuUq/+IrVapRJv5WF5l+95w9ag+o+f9AaVMf52+3Wih6C1yiL1xfV8XvqYtV9/qA1qO7zB61BdZ8/VM81KOj1RbUMYLhcJufPp5V6v0FBfmXSb2Wh+Vfv+YPWoLrPH7QG1XH+desGVvQQvEZZvL6ojt9TF6vu8wetQXWfP2gNqvv8oXquQUGvL3SERERERERERES8ngIYIiIiIiIiIuL1FMAQEREREREREa9XLXNg5MflyubcuTNkZ2cWu4/ERAPT9P7kKjabD7Vr18Vq1ZdfREREREREKge9g/3NuXNn8PX1w9//egzDKFYfVqsFl8tdyiMrXaZpkpp6gXPnznDttfUqejgiIiIiIiIiV0UBjN9kZ2eWKHhRWRiGgb9/TVJSzlf0UEREROQqfHIgkdjtR0hMdhIc6ODpTo3odUtwRQ9LRESk3CmAcZGqHrzIVV3mKSIiUtlt+PYkczYeJCM7Z4fn6WQnczYeBFAQQ0REqh0FMLzIN998zdSpL9CoUWMMw8DpdNKjx7306zfwivf973/3MWfODMLCOnPq1AmmTJnBr7/+yv/+9xNhYZ3LYfQiIiJS2hb88ydP8CJXRrab2O1HFMAQEZFqRwGMYoqPtxEd7cOxYxZCQ91ERWXSp0/J81/ccUdbpk9/GYDMzEwGD+5Lz57hBAYGFnq/L7/8Nw8++HCeYMc333xFQsIRBTBERASAuLjV9O0bUdHDkCI4lZSRb3tisrOcRyIiIlLxFMAohvh4G2PHOjBN8PU1OXnSYOxYBxZLJr16lV4Sz7S0NCwWC6NHP029evVJTk5m/vxXeeWVmZw4cQKXy8XAgUOoV68+H3/8f9hsdurWDSYmZiHvv7+a5cvfJSMjgz/8oSVhYV1KbVwiIlI5rV+/VgGMSqZeLV9O5hPECA50VMBoREREKpYCGMUQHe2DaYKPT87nPj6QmQnz59vp1SurRH3v2fM1o0aNxGKxYLPZGDNmHH//+9/o3v1eunTpSlzcKmrVCuLFF2eSlpbKY489wptv/pVeve7nmmuuoUuXrsTELMRisfDII4/+tgNDwQsREZHKaGz3Zkz+cF+eYyS+NgtPd2pUcYMSERGpIApgFMOxYxZ8fc08bXY7HD1qKXHfFx8hyfX3v/+NBg0aAnDkyBHatm0HgJ+fP40aNebEieMlflwREakadEykaunTqj6paU5VIREREUEBjGIJDXVz8qTh2YEBkJUFDRqU3vGRS1ksOcGRRo0a8d13/6FLl66kpaVy6NAh6tevn+99DMPANMtuTCIi4n10TKTq6XVLsAIWBVCJWRGR6qXkWwaqoaioTAwj59iIaeb8bxgwblzJjo9cjT59HiYpKYmnnnqcUaP+zGOPjaB27Tr5XtukSVO2b9/Kv/71WZmPS0RERKQ8fXIgkTkbD3I62YnJ7yVmPzmQWNFDExGRMqIdGMUQHp4NcFkVkvvvd+NyFb/fNm3a0qZN28valyx52/Ox3W5nypTpl13z+ON/9ny8du1HADRrdjMrVqwr/oBEREREvFTs9iMqMSsiUs0ogFFM4eHZnkDG77ShRURERKQ8FFRKViVmRUSqLr3jFhEREZFKp6BSsioxKyJSdZXJDoysrCwmTZrEiRMnyMzM5KmnnqJp06ZMnDgRwzC46aabmDZtGhaLhdWrV7Ny5UpsNhtPPfUUXbt2JSMjg3HjxvHrr7/i7+/PK6+8Qp06ddi7dy+zZ8/GarUSFhbGqFGjAFiyZAmff/45NpuNSZMm0bJly7KYloiIiIh4iac7NWLOxoMqMSsiUo2USQBjw4YNBAUFMX/+fM6dO8dDDz3EzTffzOjRo2nfvj1Tp05l06ZNtG7dmvfff5+4uDicTieDBw/mrrvuYsWKFTRr1ozIyEji4+OJjY1lypQpTJs2jZiYGEJDQxk5ciT79+8H4Msvv2TNmjWcOnWKyMhI4uLiymJaIiIiJaYypyKlIzfPhaqQiIhUH2USwLj33nvp2bOn53Or1cr+/ftp164dAJ07d2bnzp1YLBZuv/12fHx88PHxoUGDBvzwww/s2bOHJ554wnNtbGwsKSkpZGZm0qBBAwDCwsLYtWsXPj4+hIWFYRgG9evXx+VycfbsWerUyb8yh4iISEVSmVOR0qMSsyIi1UuZBDD8/f0BSElJ4dlnn2X06NG88sorGIbhuT05OZmUlBQCAwPz3C8lJSVP+8XXBgQE5Ln22LFjOBwOgoKC8rQnJycXGsCwWg2CgvzytCUmGlitJU8JUhp9lAfDuHwNSspqtZR6n5VJdZ8/aA2q+/xBa3A187fZrGW6RoX1X9aPLSIiIlKWyqwKyalTp3jmmWcYPHgwvXv3Zv78+Z7bUlNTqVmzJgEBAaSmpuZpDwwMzNNe2LU1a9bEbrfn20dhXC6T8+fT8rSZponL5S7gHvmzJBzBsWYlzv4DcTdshNVqKXIfv4/Jxbhxz3H48M+MHPk0vXrdT1zcKvr2HYDT6WTjxk/o3fvBq+pr2rQXeOCBvvmWZM1lmpevQUkFBfmVep+VSXWfP2gNqvv8QWtwNfPPznaV6RoV1n9ZPHbduoU/54qIiIiUljLZLvDLL7/w2GOPMW7cOPr16wfArbfeyu7duwHYtm0bbdu2pWXLluzZswen00lycjKHDh2iWbNmtGnThq1bt3quveOOOwgICMBut3P06FFM02THjh20bduWNm3asGPHDtxuNydPnsTtdpfL8RFLwhH8YhZhPXYUv5hFWBKOlKi/X3/9hfPnz7N+/T/o1et+AN577x0Azp79lY8++rCkQxYRERERERGptMpkB8abb77JhQsXiI2NJTY2FoDJkycza9YsFi5cyI033kjPnj2xWq0MHTqUwYMHY5omY8aMweFwMGjQICZMmMCgQYOw2+0sWJigzzYAACAASURBVLAAgOnTpxMVFYXL5SIsLIxWrVoB0LZtWwYMGIDb7Wbq1KllMaU8coMXpsOBeV0wRtJ5/GIW4Rw9FldIg2L1OW/ebI4fP8a8ebNp1qw5SUlJXLiQRHT0XFyubI4cOcxf/7qU/v0HMXfuDJKSkgAYPXocTZo0JS5uNR9//CHXXHMt586dK83piohIFWBJOMLgQ//DknAEd8NGFT0cEZEq75MDiUoyK1LKyiSAMWXKFKZMmXJZ+/Llyy9ri4iIICIibzKzGjVqsHjx4suubd26NatXr76sPTIyksjIyBKM+OrlCV7Uysm9YdYKgqTz+L62ANeoMcV6YTh27ESmTZvENddcC8CwYY8TF7eaqKiJnDp1kkOH/sfw4SOIjV3MHXe046GH+nHs2FHmzJnO/PmvsWbNSv72t5VYLBYef/yR0pyyiIhUcrnPXcHpafjFLCItsnjPVSIicnU+OZCYp8zv6WQnczYeBFAQQ6QEKkfGSS/iWLMSsrI8wYtcZq0gjKzsnNvL0M8//4/4+A2MGjWSV16ZRXJyMgkJR2jc+EZ8fHyw2WzccsttZToGERHxPvHxNrp29WPz5jV07epHfHzO3ygsCUf4ZdxrrP04gC9PDWPtxwH8Mu61Eh99FBGRgsVuP+IJXuTKyHYTu/1IxQxIpIpQAKOInP0Hgt2OkXQ+T7uRdB7Tbsu5vZSYppnTt2HBNHN+ATZs2IiIiMEsWfI2M2fOpUePe6lf/waOHPkZpzMDl8vFTz/9WGpjEBGR0lFQgKG0+h471sHJkwYWi5OTJw3GjnWw9d3j/DLuNTZ/4c/JtNoYRjYn02qz+Qt/BTFERMpQYrKzSO0icnUUwCgid8NGpEWOwXA6PUEMI+k8htNJxnNjS3VLbqNGjZkx40Vq165NVlY2sbGL+dOfHmPLln8yatRIxo6N5MYbm1C7dm2eeOJJnnzyMaKinqVGjRqlNgYRkeooLu7y44olUVCAobSCGNHRPpgmNLUeZpJrPk2thzFNODV/Ffv3urhgCcJqBcMAqxUuWILYv9dV5rsGRXJ9ciCR3m/vpt2CbfR+ezefHEis6CGJlKngQEeR2kXk6hhm7p/5q5GsrMvLyJ0+ncD11ze86j5yzxOTlQV2e05Q48Ybi11GtbwVdb5XQ+UTq/f8QWtQ3ecPlW8N4uJW07dvxGXtjzwSwfLlRQ9iFDT/rl39OHnSwMcHzp8/R1BQbTIzoX59ky1bSr5eTZsG0Mz+M5Fpr+B2pmBxBBDjNwGn02C08xWyrL4kW4PIyEjH17cGga7z2F0ZPPDFqBIH3lVG9Xf5vb4oqcr2M5WfS3MBAPjaLEzqcdMVcwFUhfmXVHVfg8o6/5J831+qsq5Baanu84fquQYFvb7QDoxiyt2J4QptoGRoIiKV1Pr1a8vlcY4ds2C3522z23PaS8OdwYd4OvkVMqjBcSOEDGrwdPIr1KvnZlWjcfi406npztk1WNN9Hh93OqsajdNzl5QL5QKQ6qjXLcFM6nET1wc6MIDrAx3FCl6ISF5lUoWkunA3bER61MSKHoaIiHi50FC3ZwdGrqysnPaSsiQcYfENr7E5wZ8LZhCmmc45M4iawOIb5vL9fWOZPXsCz6a/wg3uM+AKYHGNCTw3qT6QXeLHF7kS5QKQ6qrXLcGlHrBQaVap7rQDQ0REpIxFRWViGJCZCaaZ879h5LSXlGPNShrUc9KhVwCBgSamaSMw0KRDrwAa1HNy7y/Lee7V+qxsOJ4EbmRlw/E892p9wsMVvJDyoVwAIqUj91jK6WQnJr+XZlVOGalOFMC4SHVJB1Jd5iki4i3Cw7NZsMBJ/fombreD+vVNFixwlkoQIbc6VrPrzjJsWBaNG69m2LAsml13Fux2nP0HEh6ezfKdwezqvoflO4OrZPDi22+/ZejQoQAkJCQwaNAgBg8ezLRp03C7c3a6rF69mocffpiIiAi2bNkCQEZGBpGRkQwePJgRI0Zw9uxZAPbu3Uv//v0ZOHAgS5YsqZhJVRFPd2qEry3vS05fm4WnOzWqmAGJVFI6jiWiAIaHzeZDauqFKv/m3jRNUlMvYLP5XPliEREpNeHh2WzZkka3bv3ZsiWt1IIIhVXHqi45mpYuXcqUKVNwOnOOJLz88suMHj2aDz74ANM02bRpE2fOnOH9999n5cqVLFu2jIULF5KZmcmKFSto1qwZH3zwAQ8++CCxsbEATJs2jQULFrBixQq+/fZb9u/fX5FTrNSUC0DKQnWsbKPjWCLKgeFRu3Zdzp07Q0rK+WL3YRhGpQiA2Gw+1K5dt6KHISLideLjbURH+/Djj2vo2rUGUVGZpbpbwZJwhMGH/ocl4UipBhZygxh+MYuom5FerYIXAA0aNCAmJobx48cDsH//ftq1awdA586d2blzJxaLhdtvvx0fHx98fHxo0KABP/zwA3v27OGJJ57wXBsbG0tKSgqZmZk0aNAAgLCwMHbt2sVtt91WMRP0QkU9h18WuQCk+rq0wkfuUQqgSn+fBQc6OJ1PsELHsaQ6UQDjN1arjWuvrVeiPqpjeRsRkaoiPt7G2LEOTBMsFicnT/oxdmzOi8LSCGLklt8OTk/DL2ZRqQcYcoMYiTu3V6vgBUDPnj05fvy453PTNDEMAwB/f3+Sk5NJSUkhMPD3kmz+/v6kpKTkab/42oCAgDzXHjt2rJxm4/2q65tH8R6FHaWoyt+DT3dqlG9pVh3HkupEAQwREREgOtoH0wQfH0hLy/k/MzOnvaQBjNzghelwcMa3BqbDUWZBjA+aNOW+ahS8yI/F8vsJ2dTUVGrWrElAQACpqal52gMDA/O0F3ZtzZo1r/i4VqtBUJBfKc4ErFZLqfdZUm/uTMj3zeObOxMY1KFxqT6WN86/vFX3Nchv/oUdpaiKa5W7BoM6NMbfz8GCf/7EqaQM6tXyZWz3ZvRpVb+ih1imKvvPwIZvT5b4a1bZ16A0KYAhIiJeJS5uNX37RpT74x47ZsHXN+8xQLs9p70kLg5emLWCAHL+TzpfJkEMgVtvvZXdu3fTvn17tm3bxp133knLli159dVXcTqdZGZmcujQIZo1a0abNm3YunUrLVu2ZNu2bdxxxx0EBARgt9s5evQooaGh7Nixg1GjRl3xcV0us9R3Ynrj7s5TSRkFtleH+Ze36r4G+c2/sKMUVXGtLl6Dzg2D6PxEuzy3V8U5X6wy/wxcumPtZFIGkz/cR2qas0i7hQpbg6paWrdu3cB825XEU0REvMr69Wsr5HFDQ91kZeVty8rKaS8Jx5qVkJXlCV7kMmsFQVZWzu1SqiZMmEBMTAwDBgwgKyuLnj17UrduXYYOHcrgwYMZNmwYY8aMweFwMGjQIA4ePMigQYNYtWqVJ1Axffp0oqKi6NevH7feeiutWrWq4Fl5D5VFlYpW3SrbbPj2ZLVLWFpVlHXlmOpYWlc7MERERICoqEzGjnWQmQmmmXN8xDBy2kvC2X8gfjGLMJLO5wliGEnnPWVOpeRCQkJYvXo1AI0bN2b58uWXXRMREUFERN7dPTVq1GDx4sWXXdu6dWtPf5KXzuFLRcv963JV/KvzpT45kMicfx4kI0s5Zyqjsq4cUx3zwSiAISIiwu+JOnOqkDioX98slSokF1cIoZqWOZWqpTq9eRTvVV0q28RuP+IJXuSq6m9Qq5KyrhxTHUvrlmkA49tvvyU6Opr333+fMWPG8MsvvwBw4sQJWrVqxaJFi5g1axbffPMN/v7+AMTGxmK32xk3bhy//vor/v7+vPLKK9SpU4e9e/cye/ZsrFYrYWFhnm2eS5Ys4fPPP8dmszFp0iRatmxZltMSEZFSVNScF2WZIyM8PJvw8GweeSSC5ctL76/v1b3MqVQ91eXNo0hFq45vUKuSst6xVh1L65ZZAGPp0qVs2LCBGjVqALBo0SIAkpKS+NOf/sQLL7wA5NRq/8tf/kKdOnU89/3rX/9Ks2bNiIyMJD4+ntjYWKZMmcK0adOIiYkhNDSUkSNHsn//fgC+/PJL1qxZw6lTp4iMjCQuLq6spiUiIqVs/fq1RQpIFPX6orIkHGHwof9hSTiiMqciUiaqatI9KX3V8Q1qVVLWO9aq45G+Mkvi2aBBA2JiYi5rj4mJ4ZFHHuG6667D7XaTkJDA1KlTGThwIGvX5iRu27NnD506dQKgc+fO7Nq1i5SUFDIzM2nQoAGGYRAWFsauXbvYs2cPYWFhGIZB/fr1cblcnD17tqymJSIiVcTFgYqL2/xiFhGcnoZfzKI8t5WG3DKnCl6IVF/VMemeFN/TnRrha68+CUurol63BPPRyPZ8ObYzH41sX6rByl63BDOpx01cH+jAAK4PdDCpx01VOiBaZjswevbsyfHjx/O0/frrr+zatcuz+yItLY1HHnmE4cOH43K5+NOf/kSLFi1ISUkhMDCnbIq/vz/JycmkpKQQEBDg6cvf359jx47hcDgICgrK056cnJxnR4eIiMjFLg1UpEWOAfCUOz3jWwPT4VCZUxEpddUx6V5l4m27Y3rdEoy/n4P5n/3oNWMS71LdjvSVaxLPTz/9lPvvvx+r1QrkZP7+05/+5Dlmcuedd/LDDz8QEBBAamoqAKmpqdSsWTNP28Xtdrv9svbc4EdBrFaDoCC/0p4eVqulTPqtLDT/6j1/0BpU9/lD8dbAZrPmuc+ln1/p+iI7fBjLWzFQM4Bf/fzxqRmAT/QcwIS610FQEBaLBUdwXTh/Hp+3YnCPGw+NG1+x66uZf4nHfwWF9V/Wjy0iV6acBt4rd3dMboCpKBU/yjLw0adVfTo3DLryhSLVQLkGMHbt2sVTTz3l+fzIkSOMGTOG9evX43a7+eabb3jooYc4e/YsW7dupWXLlmzbto077riDgIAA7HY7R48eJTQ0lB07djBq1CisVivz58/n8ccf5/Tp07jd7ivuvnC5TM6fTyv1+QUF+ZVJv5WF5l+95w9ag+o2//ySaRZnDbKzXXnuc+nnV7q+KHJ3XpgOB6avP263mwxff+wHd2FJOk9m13swM7Jy2jOywNcfy6/ncC37K+lRE6/Y/9XMvyTjvxqF9V8Wj123buF/NBCRvJTTwHsVd3dMSQIfIlI05RrAOHz4MKGhoZ7PmzRpQu/evYmIiMBut/PAAw9w0003ERISwoQJExg0aBB2u50FCxYAMH36dKKionC5XISFhdGqVSsA2rZty4ABA3C73UydOrU8pyQiUm2VdTLNsuBYsxKysjCvy/uCMvuOtvhs/he2/3xNVtd7PO1G0nmw23H2H1jeQy0TDz3Ur6KHIFLtVceke5VFcXfH6FiQSPkp0wBGSEgIq1f/XoYuPj7+smtGjBjBiBEj8rTVqFGDxYsXX3Zt69at8/SXKzIyksjIyFIYsYiIVGXO/gPxi1mEkXQes1be7bhZLVtj8FvQ4rf/q1q508oWcBKpqhw2i+cNb02Hlai7m+qNrhco7u4YHQsSKT9lVoVEREQqv7i4y4PGlVluKVPD6bw8UDFpKqmTpmI4ndTNSK9ywQsRqXi5Rw2SMrI9bZkuswJHJBd7ulMjfG1Fr/hRUIBDx4JESp8CGCIiUqD169dW9BBKnbthI+JbjOPjtS6yf+7Ix2tdxLcYh7thI0+AI7GGX6UNXuiYiIj3KuyogVS84pakLG7gQ0SKrlxzYIiISOVR1XZf5IqPtzF2bnNCXRN4yLKU9a4RHJvbmAV1nYSHZ+Nu2IgPmjTlvkoYvAAdExHxZlXhqIG3lRktbcUpSZl7fVVeFxFvoQCGiIjkqyJ2X1gSjjD40P+wJBwps90P0dE+mCacrtGYl53jCKpRGzMzpz08PPvKHYiIFFNlr0CiahsFK07gQ0SKTkdIRETEK+SWOA1OT8MvZhGWhCNl8jjHjlmw2/O22e057SIiZamyHzXQERgRqWh6tSYiUsVVhqMgucEL0+HgjG8NTIejzIIYoaFusrLytmVl5bSLiJSl4uZY8BZV4QiMiFRuCmCIiFRx3pyI05JwhJE/HMB/zgxMh8NT2tSsFVRmQYyoqEwMAzIzwTRz/jeMnHYRkbLW65ZgPhrZni/Hduajke0rTfACVG1DRCqeAhgiIlIhcnddtDx3Ftt3e+GSSoJmrSDIysKxZmWpPm54eDYLFjipX9/E7XZQv77JggVO5b8QEbmCyn4ERkQqPwUwRESk3F18ZGRfUG1Mux37jq0Y5897rjGSzoPdjrP/wDz3y03yWRLh4dls2ZJGt2792bIlrVyDFypzKiKVVWU/AiMilZ+qkIiISLm6OHhh1goixW4nq1MX7Nu3Yt+xlYCsLIyk8xhOJ2mRYzzVSC5N8nnxbZWJypyKlJ6qXtLTG6nahohUJO3AEBGRcuVYsxKysjz5LiDnuMjBkK4c+9lFw2O1+Xiti/gW4y4LXpRHkk8RqRxyS3qeTnZi8ntJz08OJFb00EREpIwogCEiIkVWkqMczv4DwW7POSLym4MHLWzdZuMbsw3bjTuZ45rAn+c2Jz7e5gleHD5Vg3c/rMvhwxE5/5+qoSCGSDX1yYFEXvrkR5X0FBGpZhTAEBGRIrn0KEdRAwjuho1IixzDsYNZrHsnlcOHI9j+UQp2Vzrzas1igs9sTtdojGlCdLQPjjUrOf5zNh9tv5bkZAPDyCY52eCj7ddy/OfsUk/yKSLeLXfnhdvM/3aV9BQRKR2fHEik99u7abdgG73f3u0VO9wUwBARkatWWkc5PtrXlIH/eYHslAxCOYLdncEc10R+ymrsucZuh2PHLDj7D+Q/+32p6T6P1ZpT8tRqhZru8/xnv2+eJJ8iUvXFbj9y2c6Li6mkp4hIyXnrMT0FMERE5KpcmnwTcnJXFCeIER3tQ4KlMW/UnMBRSwMWWCdwmMakphqea7KyIDTUjbthI2ZlTcTPkk5Nd86xk5ru8/hZ0pmVNbFSJvIUkeIrbIeFSnqKiJSO/ILF3nBMTwEMERG5THy8jRM7JtDhn3fwyF2JxMfb8k2+CTlBDLKyinSU49gxC3Y7HLU25mXbOH4JzNl5kZ0NpgmZmTk7LaKiMgFwN2zIIp8J+JJOiHkcX9JZ5DMBd8OGpTdpEakUCtphYTFQSc8qzBu3sotUZQUFiyv6mJ4CGCIipSwubnW+H1cW8fE2Xht9kmfT36MhPzMwYR6vjT7Jp9c+clnyTSDnc7u9SEc5QkPdZGX9/rnDYRIQAA4HuN0O6tc3WbDASXh4NpATyDhma8xC+wQSaMBC+wSO2Rp7Ahyl7aGH+pVJvyJSck93aoSvLe9LWF+bhZd6NVfwopyVV1DBW7eyi1RlBQWLK/qYXpkGML799luGDh0KwP79++nUqRNDhw5l6NCh/OMf/wBg9erVPPzww0RERLBlyxYAMjIyiIyMZPDgwYwYMYKzZ88CsHfvXvr378/AgQNZsmSJ53GWLFlCv379GDhwIN99911ZTklE5IrWr1+b78eVxYo5J5iUNpmW5vekGgFkWWvwbPorLF1qvyz55rp3Ujl2MIu0yDFFOsoRFZWJYeTstMjdceHjY/Lmmxl069afLVvSPMELgPDwbBYscJId2ohZlilkhzbKE+AobX37RpRJvyJScr1uCWZSj5u4PtCBAVwf6NDOiwpQnkEFb93KLlKVFRQsruhjeray6njp0qVs2LCBGjVqAPDf//6X4cOH89hjj3muOXPmDO+//z5xcXE4nU4GDx7MXXfdxYoVK2jWrBmRkZHEx8cTGxvLlClTmDZtGjExMYSGhjJy5Ej2798PwJdffsmaNWs4deoUkZGRxMXFldW0RESqNEvCER4/PIfb3PvJBDqYu/mGLmRaajDgyHy2bBnN7P+8wLPprxDKL2SnBDDwPy/w3L76hDe8+mBCbuAhOtqHH3/M2XERFZVJeHg2K1YUfJ/w8GweeSSC5ctLZ2eLdlqIVE69bglWwKKCFRZUKO2vjbduZRepynJ/jmO3HyEx2UlwoIOnOzWq8N+9ZbYDo0GDBsTExHg+37dvH59//jlDhgxh0qRJpKSk8N1333H77bfj4+NDYGAgDRo04IcffmDPnj106tQJgM6dO7Nr1y5SUlLIzMykQYMGGIZBWFgYu3btYs+ePYSFhWEYBvXr18flcnl2bIiISNH4Ln2TtsY3pOLHBaMm2aaNdlk7Md0mQf6ZnJq/Kk/yzTdqTiDB0pjoaJ8iP1Z4eDZbtqTlu+OivGinhYj3UI6DyqU8gwreupVdpKrrdUswH41sz5djO/PRyPYVHryAMtyB0bNnT44fP+75vGXLlvTv358WLVrwxhtv8Prrr3PzzTcTGBjoucbf35+UlBRSUlI87f7+/iQnJ5OSkkJAQECea48dO4bD4SAoKChPe3JyMnXq1ClwbFarQVCQX2lO97d+LWXSb2Wh+Vfv+YPWIHf+NpvVsw4Xf1whDh/mkcOHCDqXCI0bX/FaS9JZ6gTbOXsKTLdBBr5YzWw6Z2/B/7bbmP7dn3D4Gxw3buRl2zjq2OvgsMHx4zm/V4vzPXDpGl1pzSp8TQtR3X8GRIrikwOJnr/s1fS1kerMJtvMuS33OIK/n4PODYMK70hK3cVfm4L+6hoc6OB0PsGKsggqPN2pEXM2Hsyz48MbtrJL0V3N95ZIYcosgHGp7t27U7NmTc/HM2fOpG3btqSmpnquSU1NJTAwkICAAE97amoqNWvWzNN2cbvdbs+3j8K4XCbnz6eV5vQACAryK5N+KwvNv3rPH6rWGsTFrS7yX+dz55+d7eLvf8/87XjESm6/Hc/xiPKUW/a0bmoq2bPnXDFPRY1lf8VqseHTqxONNu7m56NO0s1A7HaoX8uJz23X4DrfEOdJE5/fNly43SaZmRASkvN7tTjfA9nZrjz3ufTzK13vTarSz8DVqlu38Odckfzk5k/IfUOalHH578eMbDcL/vkTnZ9oV97DKzWV8c3apV+b3GASkGfs5RlU8Nat7FI0V/u9JVKYcqtC8vjjj3sSbO7atYvbbruNli1bsmfPHpxOJ8nJyRw6dIhmzZrRpk0btm7dCsC2bdu44447CAgIwG63c/ToUUzTZMeOHbRt25Y2bdqwY8cO3G43J0+exO12F7r7QkQE8q8OcnFbSZJvJiZ2ZOxYBydPGlgsTk6eNBg71kF8fLnFjD3BC9Ph4IxvDUyHA7+YRVgSjhR4H2f/gWC3A+DXoz1BAT9zQ8Bebm2UjKNjK9JHPJlv8s2Ly52KVBVut5upU6cyYMAAhg4dSkJCQkUPqUrJL39Cfk4lZZTDaMpGZa2ccbUJM8s7mao3bmWXolEyVikN5fZq+qWXXmLmzJnY7XauvfZaZs6cSUBAAEOHDmXw4MGYpsmYMWNwOBwMGjSICRMmMGjQIOx2OwsWLABg+vTpREVF4XK5CAsLo1WrVgC0bduWAQMGeF5siIhcyfr1ay/bYZFfW1F8+CFMn+7H/v2TsFoNAgJMDAN8fHLe6EdH+5TLLoyLgxdmrZyt12atIEg6j1/MogJ3YrgbNiItcoznvvuDatM86TzZt9xG2qSpuBs28iTqzC/5pkhV8q9//YvMzExWrVrF3r17mTt3Lm+88UZFD6vKuNo8CfVq+ZbxSMpOeSa5LC2fHEjM91gI5P81UzJVKQolY61aKmqHWZkGMEJCQli9OucvmrfddhsrV6687JqIiAgiIvK+YahRowaLFy++7NrWrVt7+rtYZGQkkZGRpTRqEakuinNMpCDx8TbGjbPgcuUc4Ha74cIFA6s1Z0eD3Q7HjpXPpjfHmpWQlYV5Xd4nEbNWEMaJ4zjWrCQ9amK+9704iFHDlc3hwJoE/xa8yFUW1UBEvM3FCcVbt27Nvn37rnifQ4cO8vjjI0p1HDabheyr2KlQ2fx68gKZrsLnZTEM/K/158GPy2/3Wmnae+x8vu2ngQf/cfV5Pcrre+BsWiYJZ9Nxm2a+t/tYLTy4vWaZj+NSVfVnoCiqyhoU9HN/pe+tqjL/kvC2Nbj098Up4Mm3DRrWqUEdv6Inds/Pzp3b822vnM8IIiKloKQ7Li4WHe2DaebstjAMN2AFwOXKSeiYlQWhoeXzxOPsPxC/mEUYSec9OzAAjKTzYLfnHBUpRG4QI3HndjbVq0+7QvJmiFRVlyYPt1qtZGdnY7MV/tLJZivdQKVhGKXepzcIrePH4V9S87xZNgwDqwHZbhOHzUJIbT/qBjowC3hD7e0cNgvOfN5wOGyWIn1Ny+t74GRSRoHBC4thEFrHL884fknJ5Pi5NJzZbs/X69qA0nnjcrGq+jNQFFVlDfL7uc/ve+tSVWX+JeFta5Df7wu3aXIyKYPrapbtzjkFMESk2ouPt/12JGINXbvWKFY+h2PHLPj55eSFsFrTcbkCyPm9bin3PBEX76IgKecvgEbSeQyn84qJPC/u44MmTct4pPl76KF+FfK4Ihe7NHm42+2+YvCiSZObWLv241IdR1VODHs1248r8/wvTVgIOUkui5onoqhrUNxt3e0WbKOgUNGM+5rn6SN3brUvmpvLZuHxMsiBUZm/B0pLVVqD4nx/VqX5F5e3rUFBvy8MYO3YzmX62ApgiEi1Fh9vY+xYB6bJbwk3/Rg71kFoaMci9RMa6ub0aQs2G1itmQQEQHJyTtWj+vXLvwrJxUGMuhnpRQpeVLTS2hUjUhJt2rRhy5Yt3Hfffezdu5dmzZpV9JCqnKqeP6EiKmeUpMpDQWVRrw90XHbfgvJ7LNh8SJVCpFBV/ee+uijPMsqXUgBDRKq1i49+pKX9nnDz0KHBReonKiqTceN8PdU5DMOkVi0IDZ3DP/85uoxGX7iLj4JUluCFiLfo3r07O3fuZODAgZimyZw5cyp6SFIJlfebtZIkDi2oLOpdN9am99u78wQlCkq6mJSR7SmJqxKZIlVXeZZRvpQCGCJSrR07ZsHXN+8mOLsdkpODocDNtJcLD8/G39/N9Onkqc6xYsUXQMUEMOD3oyD3Chi0QQAAIABJREFUeXHwQkdGxBtZLBZmzJhR0cMQKZKSVHnIb8fIXTfWJn7//7tsR0egw8oFp+uKfXp71RURKZ6K2GGWSwEMEanWQkPdnDxp4HNR3rGsLPD1TQSuK1JfDz4I3Rr/l48HPsz9767D3bARK1aU7nirIh0ZEREpHSXd1n3pjpHeb+/Od0eHw2bD12a57Lb8qESmeLOKKgVaFVTUcSDvSWUqIlIBoqIyMQw8Rz9yE242afJB0Ts7fBi/mEUEp6fhF7MI2xc7GHzof1gSjpT6uEVERC71dKdG+F5SqaAk27oLCj5cyMhmUo+buD7QgUFOnoyaDmu+15bHmXiR4sjNGXM62YnJ7zuMPjmQWNFDk0IogCEi1Vp4eDYLFjipX9/E7c45+rFggZPg4C+K1I8l4QiW+fMwHQ7O+NaATCeB40bTKPkCfjGLFMQoRTpyIiKSv163BF8WWChq1ZOLFRR8CP4tsedHI9vz5djOfDSyPVF3Ny3V4IlIWSssZ4x4LwUwRKRcxcWtrughEB9v44svlrB58xq6dvUDYMuWNLp168+WLWlFrhZiSTiSU7LU1xezVhABWVnY/vtfTB8f6qWng9OpIEYp0pETESlPnxxIpPfbu2m3YBu9397t9X+dvTSwUJIt3kXZ0VHawRORslaSnDFScRTAEJEykV+gIi5uNevXry3zxylMbtlUp/Pa38qmGowd6yA+vvgpgRxrVuYkzggKwkg6///Zu/fwKKt77ePfZ445QqBglJIDolTAIgJVq8EWuq0bo29FDBBEX5WtWypRkcQgRZBykkLACsZWd20tlKMUe4j62i0qUqgWVKwBVJCEIBIRDGRymOPz/jFNTCCEhExmcrg/1+WVzMozz6y1EoS5s9ZvMbDsa7BZMbt0xTDBWnIQ0+lsNyFGQYGNESNi2Lx5A9u2rTjj3GglhIh0dJ19iXlzQ4lQhicira2xFUbSdinAEJFW0VBQEerw4lzuWXNsqsXiwzCCx6aaZrD9XLkzxgePLikrw1b4ERbTxHRGQXU1pgG+AZdidk0ArzcYdrRhNQHP4cMGFosbt7vHGQMerYQQkY5OS8wVSkjHFeqaMRIeCjBEpMnOtKqiPSkpsWC312+z24PtNSzFRc0qvhlISaUyaypUV+NPTiZgGBgnT2D4fRQmdMP898oM7PZg2NGG1QQ8DkewmKnF4mtxwCMi0l5piblIx6VtT+2TAgwRabJzWVXR1gKOpKQAF1Qf4FHfYlLNYiC4++OqxP1M2L8P27at9U4SaU6IEch5BBxOvoiOwfB48PUfiMtuxzhRhuF2U5k1lUBKausN7hR1t4KMGBHTpG0ypwY8UVFRpwU8ItL6auou9Hvs1XZRd6Gj0hJzkY5NK4zaH/2LVERaVWtsG2mJx//vJzxUvYiB/r2s8tzNkIotpAQO8NS3nyC1/CTxOQ+Bx83RqOjm163o04fKrKkUxcdTvvhJcDrpWV0VsfCi7laQptb6SEoK4PV+8zgqKhqvN9guIuHR2esutCVaYi7S/rS3wrvSPAowRKTTsBQXkf7RYq75XhUXGvuIxs0z7kn8KfUBkhOruKCqCtPhwLZ7N3FeL2bXhGaHGIGUVFb3vQjf1WlUZk2lNDom7OEFnL4VpKm1PrKzPRgGeDzB6z2e4POzsz1h6rmIqO5C26El5uF36pvPP+86HOkuSTuiALjjO/ey+yIiDdi4cf1pxR0LCmwsWeLg4483EBVVSkGBrdlHlbZU7VGnHjep5bvZF18CQG+LFYq24fclYZgETw5xVzOw7GuME2WYXRMwPj+Ec8NaqrKnN+s1a8KMG8IcXkBwy0dUlFmvrSlbQWq+L0uWOPjsszh69TLJzvaE/fsl0pmp7kLbMqp/ogKLMKl581kT4B0pd/OzP33EjOvafmj0yp5S8t8uorTcTWK8k58OT23zfe6IGguA9f3oGFo1wNi1axdLlixh5cqV7Nmzh7lz52K1WnE4HCxatIgePXowb9483nvvPWJjYwHIz8/HbreTk5PDsWPHiI2NZdGiRXTv3p0PPviA+fPnY7VaSUtLY8qUKQCsWLGCN998E5vNxowZMxg0aFBrDkukQ2kocGiJTZterHe/0tKrmTbN+e+TP7451QII65ti54a1GF8fx1pcDDYrHouVxOoqiO8Cfj/WI4exmYHgySFRUVhME1vhR/gGXtouim+eKikpwOHDBo46Cy6auhUkPd1X53ujlRci4ZYY7+RIA2GF6i5IR9fgm09vgFkvf0z+20W1W3faWlDQUPCy4LVPgWAApnAjfBQAd3yttoXkueeeY+bMmbjdwR+W+fPn89hjj7Fy5Uquu+46nnvuOQAKCwv5n//5H1auXMnKlSuJj49nzZo19OvXj9WrV3PzzTeTn58PwOzZs8nLy2PNmjXs2rWLwsJCCgsLeffdd9mwYQNLly5lzpw5rTUkkQ6ptWtU7N8/oU2cauHOGI/1YDFUVwWPOAW+djggEADDINC9B1UWKyeP+9m308UJ10X8tbAvJZ96I7IFpKW0FUSk/VLdBemsGnuTeaTczdxXP+Hnr3zc5rYHNPZbf21paLpQ1K5Q4d2Or9UCjOTkZJYvX177eOnSpfTv3x8Av9+P0+kkEAhQXFzMrFmzGD9+PC++GHwjtXPnToYPHw7Atddey/bt23G5XHg8HpKTkzEMg7S0NLZv387OnTtJS0vDMAx69eqF3+/n+PHjrTUsEWmm6urEsx5bGg6BlFRccxbgKod9O12UlQ2kvLwPJ5znEejaFawW3nf259XDl2HxuTlgJHOiysH49x/lLx9dFNa+hkJ6uo+8PDe9eplYrcGtIHl5bm0FEWkHVHdBOquzvcn0Bkx89XdHton6MI391l81bZomVEGPAuCOr9W2kFx//fUcOnSo9vF5550HwHvvvceqVav4wx/+QGVlJRMnTuSuu+7C7/dzxx13cOmll+JyuYiPjwcgNjaW8vJyXC4XcXFxtfeLjY2lpKQEp9NJQkJCvfby8nK6d+/eWkMTkWaIiirF6+1zTlsZGlO3rsaIEdFNqtPwp69/yAbXr8jzTaYHVVSb0bx89HtcPaSKXr0C+P9s4rc6mN71GS4/+SoFXe6h2N+HJUvMdvnGX1tBRNqvmroLCQkxlJVVRro7ImHx0+Gp9bZiNFWktwc0tu1LWxqaprGgJ/P7fZp8n5qgV1t2Oq6wFvF8+eWXeeaZZ3j22Wfp3r17bWgRHR0NwFVXXcXevXuJi4ujoqICgIqKCrp06VKvrW673W4/rb0m/DgTq9UgISEm5OOzWi2tct/2QuNvn+O32axN7ndD19Zts1ottY9rPvbrt5aDB2fg8wW3MpimDavVYPZs85zn66WXICfHUltX48iRWHJyooiNDXDzzWd+3rJlFj53/IDp9l8x8/iDFBnJ+CxOppyYxZ9+H+Czl37Hy93u5pC9D69Wfpfu9u44bXDoUNP+n1HzM1B3Tpozvx1Be/1zECqdffwiIi1R981nQ4HAmZjATc++E7E3qg0FLzW/9T/TWLSlob5QBj2RKryrWifh0aQA44033mDEiBG1j19++WVuuOGGZr3Qn/70J9atW8fKlStrV0wUFRUxdepUNm3aRCAQ4L333mP06NEcP36ct956i0GDBrFlyxaGDh1KXFwcdrudgwcPkpSUxNatW5kyZQpWq5XFixczadIkjhw5QiAQOOvqC7/fbJXfZnT235Jo/O1z/D6fv8n9bujaum0JCTG1j2s+9uixlaysapYscXDihJOoqFIWL3bwwx/6KCs7tz7PmROD32/W1tWw2Uw8HpgzB374wzOPpagojqgok38Yw5noeJ5M/3r+GncPnxzuQ1k3F3mxd2H3p+KwBtenBgLB+/bu3bT/Z9T8DNSdk+bMb0fQXv8chEpnHH/Pno3/0kBEpDlq3nyeWhgTwG4xMM3Tt5HA6YUzw+lsv/U/U7gh32jvxYvPVshVQqfRAOONN97gvffeo6CggPfffx8I1q/YvHlzswIMv9/P/PnzueCCC8jKygLge9/7Hg888AA33XQTY8eOxW6385Of/ISLL76Y3r17k5ubS2ZmJna7nby8PADmzJlDdnY2fr+ftLQ0LrvsMgCGDRvGuHHjCAQCzJo165wmQkRaT81WhokTx/778foW3e9cjwitezJHkZHCQlsOMYFutdtZ+vZdTUnJz1T4UqQVHT58+Ixf69WrVxh7IiJtWc2bvl/9vZgvTlTXhgJw5hUakTwu80y/9deWhqZpbBVLS4VjZYSObw2fRgOMSy65hLKyMpxOJ336BPceGYbBjTfe2KSb9+7dm/Xrg29U3n333Qavueeee7jnnnvqtUVHR/PUU0+ddu3gwYNr71dXVlZWbTAiIo0L9bGppyotvZoRI2Jqa1PYbFfXfi2xspIffXEYS3FRi071ONcjQrOzPUyb5qwNKEzTVi+gSEzcxkMPuf9dW8NJr15mk2priEjTTZ06FYCysjIqKiq4+OKL2bdvHz169GDTpk0R7l3bpGXJ0lmN6p9I5vf7nLaybVT/RK7I20IDCzHaZG2JSG1paE9aK+gJ18oI1ToJn0YDjJ49ezJ69GhGjRqFxRLeEwNEJDTqBhYbN65n06YXWy3AeOkl2L07iwHRRcwMPMemknvYXZVFQYGNn3R7k5/tep8j0THELF/WoqNJTw0imrpSoiaIqAkooqJKyctz1Aso6q4WWbWqZStFROR069atA+D+++9n0aJFxMXFUVlZycMPPxzhnrVNf951WMuSRRrQ3rccyOlaI+gJ18oI/TyGT6OpRG5uLgA33HADo0aNYtSoUfznf/4no0aNCkvnRKTlNm16scHPW8O8eRZSzWIe9i4ihYM87F1EqlnM5jn/ID7nIZx+PxdUVYLHTczyZViKi87pdWqOCP1ej8941LuM7/X4rMlHhKan+3jjjUpGjszg6qunaHWFSIQcOXKk9nSxmJgYvvzyywj3qG3K+9snOoJRpAE6LlOaIlwrI/TzGD6NrsDo3bs3ADNmzOA//uM/wtIhEWm+U7eFtMY2EUtxERP276u3/eO0tgMHyAkspZoEvjQSOA83eb5cBhR/ijkghhMOJ46AH9vu3fgGDGjRSoybLt3HuO8v462K/0fu9w9TeelUAjT/PiISGWlpaUycOJFLL72UDz/8kJ/85CeR7lKb9MWJ6gbbtSxZOjvVlpCmCNfKCP08hk+jAcbrr7/Oeeedx8qVKzl27Fi9r40bN65VOyYiTXfqtpBQbxOxFBcRs3wZiVWVtaEDUK+t+pYMZtpf4gszBo8lAaiiS6CMH7IF07SBox8AHosVbFZsu3fjT0nBuWEtVdnTz6k/ptPJ0ahoTKezxdtSRCS8pk6dyqeffsqnn37KzTffzCWXXBLpLrVJF3SN4nADIYaWJYuotoScXWsWBz2Vfh7Do9EtJAsWLODkyZN4PB6OHj1a7z8RCZ2NG8+t1sK5Pq85GgwLFvyc2AU/r9cWN3sGV158jBNGV/x+6Boo4weev+HDTozNjaXkYO09TWcUVFdhPViMO2P8OffH7Bo8ktnsmlAbYpzrtpRQGj361gY/FxHYsGEDAHl5efzlL39h7969vPzyyyxdujTCPWubpl3XT8uSRUTO0aj+icz48cWcH+/EAM6PdzLjxxcraGjHGl2BMWjQIAYNGsQ111xDcnIyBw8epHfv3nTv3j1c/RPpFM51xURrFuSEhsMCANueQgACaT8AggGCr/8Aer//Ht/tuo9yYzj9q//J147zSPR9ihEdBV4vMT4flXY7xskTGKZJ+ZwFzV4x4dywFrxezPPq/8Vjdk3A+PzQOa3oCLW635PW/P6ItEfnn38+ABdeeGGEe9I+/J/LelFR6dayZBGRc6SVER1LowFGjUOHDpGTk0Pfvn359NNPmTJlivaqinQCDYUFtsKPwGYDE2y7P6ptD/ROxiw7zrWfvU58kotPvUe5+OKLOfSxQddvf5vyEwESvviSAAkcrPTx8cMr+N7VVzW7T+6M8cQsX4ZxoqxeqGKcKAO7vdkrOkQkvIYPHw7A9ddfz8mTJ7Faraxfv56bb745wj1ru/SPbxERkaAmnY36wgsv8Mc//pH8/Hw2bdrE73//+9bul4iEQd0inA1xZ4wHuz0YDvybb+Cl4PNRXuZn3d7BHDgwlhdesHP4H4cwDhRRYbNhPfolMX4f+Px80P1bfNJ7BMWlsZQTRxyVZDme4fZnf0RBQZMy1HoCKalUZk3FcLtr+2WcKMNwu1UDQ6Qdyc7O5qOPPmLx4sXY7XZmzZoV6S6JiIhIG9ekAMMwDGJjYwGIi4vD6VThKJH27tTCnA2FGA2FBQAlXb/L5i+/i8tlwTB8dCk7SGDbDorOv5IdPXri73keXbxefP0H4LLbeXPXtyi0fpfjRnfusD/He7HXYpqwZInjnPpet189q6sUXoi0QydPnuRHP/oRR44c4d5778Xj8US6SyIiItLGNenXn8nJyTzxxBMMGzaMHTt2kJyc3Nr9EpFWZCku4qucX/Luh3Ec+Pr/cvSvLq44+Et6LH7wtBCgJiyIWb6sNiyYfHw+pdEGD3sX0dN7gO8GPmWH9UpO7kkhLs6O90fXceyFjZibdvG158cEOIHb7mCi/XmKjBQSALsdSkqalKE2qKZfpX9/u1XCCxXfFGldXq+X559/noEDB7Jv3z4qKioi3SURCbNX9pSqvouINEuT3j3MmzePpKQktm3bRlJSEnPnzm3tfolICxUU2BgxIobNmzcwYkRM7XaNxMpKvsr5JZu3xXK4shuG4eNwZTc2b4vlq5xfNroSozQ6hsqsqfyjtC9fRPVhRWwu3fmaj60DOWxP4cSJ4PWffNmdV723gNdLGm8TY1Qzzzud/YGLau/p9UJSUuC012qOQEoqq/teFLLwom5ooeKbIq0rNzeXY8eOMXnyZN555x0ef/zxSHdJRMLolT2lLHjtU46UuzGBI+VuFrz2Ka/sKY1010SkDWtSgHHfffdx2223MWvWLG677Tbsdntr90skIho6ljQcR5WGWmnp1Uyb5uTwYQOLxc3hwwbTpjkpLb2aH31xmMIP/Jy0JGC1gmGA1QonLQkUfuAPFu5sQN2wICkpgNcLB619+Kn9l5RZuxPnK6Nr1+C127ZZMTH4yDGEty3X8HRcLgfog88Xg2mCxxN83ezsyC0Zb2iFhUILkfAZMmQIaWlpvPrqqwwdOpTvfOc753yvv/3tb0ybNq328QcffEBGRgbjx49nxYoVte0rVqzg1ltvZfz48Xz44YcAHD9+nLvvvpsJEybw0EMPUVVVBcDmzZsZM2YM48aNY/369vf3gEhbl/92EdW++r/IqPYFyH+7KDIdEpF2oUkBRnx8PP/7v//L/v37OXDgAAcOHGjtfolExKZNLzbY1t5CjP37J2Ca4HAEgwKHA0wz2P76Bb0oq3CQQFm95yRQRlmFo0mneGRnezCMYBBxgBSW2nNxBKpwVpVx4MBYPEdPEEU1T8TPI9c+n9KY1H+HGwaBgJNevUzy8tykp/taZwKaYMyYsdomIhJBS5cu5aWXXmLdunXs2bOHRx999JzuM2/ePPLy8ggEvnkjNHv2bPLy8lizZg27du2isLCQwsJC3n33XTZs2MDSpUuZM2cOAPn5+dx4442sXr2aAQMGsG7dOrxeLwsXLuT5559n5cqVrFu3jqNHj4Zk3CISVFrubla7iAg0IcBwuVwcOnSIF154gccff5xZs2Yxe/bscPRNpM1oKNhoy6qrEzl1oZTdHmwvjYlhXWoOjkAVXQLBEKNLoAxHoIp1qTkNbsc4dTsKQF6em169TAIBJ8e6pLLMMR2Lp5okiogxqllozuQTb5/ae1gsJnFxRYwcmcEbb1RGNLyooRUXIpGzc+dOfvGLXxATE8Po0aM5dOjQOd1nyJAh9bafuFwuPB4PycnJGIZBWloa27dvZ+fOnaSlpWEYBr169cLv93P8+HF27txZe7Trtddey7Zt29i/fz/Jycl07doVh8PB0KFD2bFjRyiGLSL/lhjf8KEAZ2oXEYGzFPFctWoVzz//PFarlQcffJBrr702XP0SkRaIiirF6+3DRdYDTPY9R4H/Hgor+xAI2Nm8eQMXXBDDEmsu2f5FfDtwFPxxPBWdy4MzegH1g4WCAhvTpjkxTf69HSWGadOc5OW5eeONSiZOHMvnn/+VwxWpPGPL5Ybjz/LHmHspKk/F6jKwWr/ZMtK37+rITIiItDl+vx+3241hGPj9fiyWxn+nsmHDBl544YV6bQsWLOCGG27gnXfeqW1zuVzExcXVPo6NjaWkpASn00lCQkK99vLyclwuF/Hx8Wdsq2l3uVxnHZPVapCQEHPW65rDarWE/J7tSWcfP3TcOci5/jv87E8fUe39ZvVUlN1CzvXfqTfejjr+5ujsc9DZxw+ag7oaDTD++te/8uqrr+JyuXjkkUcUYIi0E337rsYomsDkikUQcHHv14v4vf82RvMiFtPLb0/ksN96Ic91fYQfl/6O7Sl38uCMXg2uiliyxFG7HaWyMvjR4wm211xfUmIhKsqkxNqHhbYcEmK6Ya2swDTjareMZGd7WLNmW7inQkTaqDvuuINbbrmF48ePk5GRwZ133tno9RkZGWRkZJz1vnFxcfVONKmoqKBLly7Y7fbT2uPj42uvj4qKqr22oXvUDTTOxO83KSurPOt1zZGQEBPye7YnnX380HHn4NqUBGZcd/Fpp5Bcm5JQb7wddfzN0dnnoLOPHzrnHPTs2fDfu40GGA6HA4fDQffu3fF6va3SMREJvUHx/8vPLi/m3Q9jOPB1D1ICh3neuBuL6cNr2klyl/Fz53xOfqsP2wftZNWqXE5deVGjJpyo69QjUJOSAhw+HFxtUcNiCdCvX4BvfzuDVauCNUTWrAn5UEWknfrDH/7AmjVrKCoqonfv3nTv3j0k942Li8Nut3Pw4EGSkpLYunUrU6ZMwWq1snjxYiZNmsSRI0cIBAJ0796dIUOG8NZbb3HLLbewZcsWhg4dSt++fSkuLqasrIyYmBh27NjBpEmTQtI/EfnGqP6JOjZVRJqlSUU8AUzTPPtFp9i1axe33347AMXFxWRmZjJhwgRmz55dW2xr/fr13HLLLYwdO5Y33ngDgOrqarKyspgwYQL33HMPx48fB5pXVVyks7AUFzFh/77a409t27bys13vk5xYxS13x/Ld3n9gqLmDrmYZsVRRSSzf8RbyWPXPsBQXn/X+NSeO1HXqEah1i3rWnDICZkRPGRGRts0wDB599FFee+01fve737F06dKQ3XvOnDlkZ2dz6623MmDAAC677DIuvfRShg0bxrhx48jKymLWrFkATJ48mYKCAsaPH8/777/PxIkTsdvtTJ8+nUmTJjF+/HjGjBlDYqLeZImIiERaoysw9u3bx7Rp0zBNs/bzGnl5eY3e+LnnnuPPf/4z0dHRACxcuJCHHnqIK6+8klmzZvH6668zePBgVq5cycaNG3G73UyYMIFrrrmGNWvW0K9fP7KysigoKCA/P5+ZM2cye/Zsli9fTlJSEvfeey+FhYUAtVXFv/jiC7Kysti4cWNL50XauY0b13eKAo3Wg/15/cfLCXz9fV7/8XLi/juD616ZQYXPh21PIT5g4JfVdKUSN1FY8ZPACUoDiXzHW8jCqBmsq2x8OVp2todp05z1wolTj0Ct2UqybFkUu3cHt4zYbMtJT39Iqy5EpEFjxowJ2b2uvPJKrrzyytrHgwcPbvDo06ysLLKysuq19ejRg9/85jenXTty5EhGjhwZsj6KiIhIyzUaYDz55JO1n48ff/ajFetKTk5m+fLlPPLIIwAUFhZyxRVXAMEq33//+9+xWCxcfvnltVtVkpOT2bt3Lzt37uS//uu/aq/Nz8+vV1UcqK0q7nA4GqwqHqqlqNI+bdr0YrsJMOquoGjoBJAz+efSfzBn7xt8bL+MQ5bz6eP6kr6/eIBDl1+C17Yf/H4cb75OgttLAAcAJgalJFJNFE7TzZUJe/nqi4pGX6cmnFiyxMHHH39Tz+LUehnp6T5uuy3AjTcGt4xMnLgNeKg5UyEincjo0aMj3QURERFpZxoNMGoCh3Nx/fXX1zsSzTRNDMMAzl7l+0wVwZtTVVwBhrQHluIiYpYvI7Gqkpjly6jMmtqkEOOfS/9BnycewI/BJd5/4eMSvmvZS1XAiWvnQf7F/8G7fw+9uiVgpRQ71Vhw8BmpVBNFFNXYDT9xQy/h9aNfcsNZXi893Ud6uo+JE8fW1rMQEREREREJp0YDjFCqezza2ap8121v7NrGqoo3pjWOOQvet3Mfb9OWxm+zWc+pL3Wf99JLkJNjobj4rwAMG2ZhyZIAN9/c8HObPf4DBzj46HJeeT+ez47/X74qqOD7h5eT/PQj0KfPGZ/22rMH6LX4UfwYfEVPoqjmOjZzwteNL+lJF06QShFbzTSu/HIrXYmjKy5OWhIwAn6icRFv8XAg/lK+88Rcjk19qMn9bmhe67ZZrZbax6d+rLkWCOnPybl+r1tDW/ozECmdfQ46+/hFREREWlPYAowBAwbwzjvvcOWVV7JlyxauuuoqBg0axJNPPonb7cbj8bB//3769etXWxF80KBBtRXBm1tVvDGtccwZdM7jbepqS+P3+fzn1Jea5xUU2MjKisLlAgiuHCopgbvuMqiocDd43Ghzxm8pLuKrnF+yeVtsMFgwqvi8IoHXtsDI/36CHosfbHAlhqW4iIrHlrPbcimX+P9FFNVUE8WXJNKbQ5hANdF8ZBlItS2BwsB3+U7gfebG/IIbPH/m0sA/AYOPrEPhZ7Mo65bYrLlq6Nq6bQkJMbWPT/0IcNNNo9m06cWQ/pyc6/e6NbSlPwOR0tnnoDOO/0zHnImIiIiEWtgCjNzcXB577DGWLl3KhRdeyPXXX4/VauX2229nwoQJmKbJ1Kn1rE9wAAAgAElEQVRTcTqdZGZmkpubS2ZmJna7vbZgaE1Vcb/fT1paGpdddhlAbVXxQCBQW1VcpCWWLHFQVRUsVln3AJ7qaoMlSxwNBhjN4dywlsIP/Jy0JGC1Bl/HaoWTJFD4wUl+vGEtVdnTG3xedbmXUkcKJ42ufM+zlSiqOUFXYnDRk6O8Zr+RskBXzguU4bM6yAz8gZ+t6MtzC77HqH3LsFkrOX/+I/zgzt4tGsPZjB5962lt7aUuiYiIiIiItD2tGmD07t27tgp4nz59WLVq1WnXjB07lrFj67+piY6O5qmnnjrt2uZUFRdpiZISC34/WCz1A4xAIPi1lnJnjKds2QoSrGWU800NlwTKKKtw4M5ouGiuO2M8Uc8tJ66ijAp7Atu8V3CN+S5xHMWPnWzHcv7Dupne7kNYiGOpI5cjtpoaFolMnPgZAKtaObyAM4cVCjFERERERORctPydmEgHlJQUwGqtH15AMNBISgq0+P6BlFTWpebgCFTRJVAGQJdAGY5AFetSc85YyDOQkorl0alEU0Wst4wTRjx77d+lGif/7+blFMSNZak9l2KSWWrPpcTWh759V7e4vzUaWlXRUFs4Rfr1RURayyt7SvnBkje5Im8LNz37Dq/sKY10l0RERCJKAYaExcaN6+t9bOuysz1ER0OKeYCZzCWVAwBERQWPEA2FzBnf5qnoXOz+Kr4dOITdX8VT0blkzvh2o8/7wZ29iZs3lW/FVtE7cAR7nINZl4xg4rNXkpfnxpeUyjzLTHxJqeTluUlM3BaS/kLDqyfOtqKitQMGregQkY7olT2lLHjtUw6fqMYEjpS7WfDapwoxRESkU1OAIWHxq18dZsSIGLKyxjFkiIeCgrCVX2mSggIbE68p5ft/G8rEa4L/OPztrL3MjX2CS/kX6xjH6G+9yYoVDRfwPBfp6T4efLIXa1MeoZgLWZvyCA8+2atJ9//Bnb350WtZWPpu50evZeFP3lN7zzfeqGTkyAzeeKMyZH1tCQUMIiLNl/92EdW++iv+qn0B8t8uikyHRERE2gAFGNLqCgps7N6dxeHDBhaLm9JSG9OmOdtMiFFQYOOXDx1mfPEvSOEzxhf/gg0PvMN3X85jzE0VXB7/GufF7GFdt/v4Sbc3Q/ra6ek+Vv09ke3X7WTV3xObFTgEUlJZ3feiM243ERGR9qu03N2sdhERkc5AAYa0uiVLHICBwxE8bcNi8WGaNe2Rt2bB5zxQtQivNZrPLb2JMjzkVUzmwM6vse3ejd8wOOFwYjocxOc8hG3b1kh3OaRCvcVDNSlERFouMd7ZrHYREZHOQAGGtLqSEguGUX9lgd0emtM8WspSXMS4osV4LNGctARPA0kOFGE3vQwrfwv8PjwWKwBml65gGMTNnoGluCiCvQ6tUG/x0JYREZGW++nwVKJs9f+ejLJZ+Onw1Mh0SEREpA2I/DtIabPOVnCzqQU5k5ICmGb97SJeb2hO82gp54a1JMR6KKtzlOlu2yBizEochg/jxIlvLq6uxoyKwp+cinPD2gj0VkREOotR/ROZ8eOL6dU1CgM4P97JjB9fzKj+iZHumoiISMQowJAz2rTpxRZ9vUbw1A4Tjyd4LGkgYMMwCNlpHi3hzhjPwMFWugTK8PuD/TtuduMd+9U4o8CMjgbA7vdj+H34+g/E7NYNd8b4CPdcREQ6ulH9E3kr+4e8O+1a/nLvlQovRESk01OAIa0uPd3HgAHL6dXLJBBw4nR+RV5e6E7zOJWluIgJ+/c1aZtHICWVHosfZOTVFfSK+RrTtNEr5mv6X5NA9ZPLMICuHjc2THz9B4LTSWXWVBXOFBERERERCTMFGBIWiYnbao/3vPrqKa0aXsQsX0ZiVSUxy5c1K8S49UYXV1zwArfe6KLH4gfxjL6V8sVP4rZa+SI6WuGFiIiIiIhIBCnAkA6jJrwwnU6ORkVjOp3NCjEqs6ZSGh1TL6TwXZ3G/Msupyi+i8ILERERERGRCFKAIacpKLAxYkQMmzdvYMSIGAoKvinA2dTCneFWE14c+CKa373UkwMHxgY/fhHdrBBjdd+LTgspSmNiGmwXEREREelMXtlTyk3PvsMVeVu46dl3eGVPaaS7JJ2MAgypp6DAxrRpTg4fNrBY3Bw+bDBtmrM2xGhq4c5wc25Yy6HPfPzl7R6UlxsYho/ycoO/vN2DQ5/5OvWpIaNH3xrpLoiIiIhIO/fKnlIWvPYpR8rdmMCRcjcLXvtUIYaElQIMqWfJEgemCQ4HGEbwo2kG29syd8Z43i+MokugDKs12HerFboEyni/MKpTnxoyZszY2s9Hj75VgYaIiIiINFv+20VU+wL12qp9AfLfLjrrc7VyQ0LFdvZLpDMpKbEQFWXWa7Pbg+3noqDAxpIlDj7+eANDh8bw5ZfP4/fHM2JENNnZnpAV8wykpDLPO51pliewBqAaJ10CZTgtVczzTmdESs+QvE57VzfMEBERERFpqtJyd7Paa9Ss3KgJP2pWbgA6HlqaTSswpJ6kpABeb/02rzfY3lx1t6OYpklJiYHbnYhpBk7bmhIKgZQUljlyiaKK3uYhoqhimSOXQEpKyF6jrdKqChERERFpTYnxzma112jJyg2RUynA6CSaWnwzO9uDYYDHE9w64vEEt2NkZ3ua/Zo121Eush5ghj+P4WxhCQ/zC/9jXGQ9EPKtKdnZHkpsfVhqz6WYZJbacymx9TmnvodbSwOIU1dWKNAQERERkVD66fBUomz13z5G2Sz8dHhqo88715UbIg0J6xaSP/7xj2zatAkAt9vNnj17WLt2Lffddx+pqakAZGZmcsMNN7B+/XrWrl2LzWZj8uTJjBgxgurqanJycjh27BixsbEsWrSI7t2788EHHzB//nysVitpaWlMmTIlnMNqFzZterFJ2wdqtnQEt3046dXLPOetHiUlFvrZP2NKxSKcHOd2VmJg4sNOavmXLIybxyclFzb7vmfveyrzPp7Jd5Kiyct2h2ybSms6160dZwoqtFVEREREREKpZrtH/ttFlJa7SYx38tPhqWfdBpIY7+RIA2HF2VZuiDQkrAHGLbfcwi233ALAnDlzGDNmDLt37+auu+7i7rvvrr3u6NGjrFy5ko0bN+J2u5kwYQLXXHMNa9asoV+/fmRlZVFQUEB+fj4zZ85k9uzZLF++nKSkJO69914KCwsZOHBgOId2mo0b17fbN5Hp6T7S031MnDiWVavO/djUqxL3M774F9gNDwPYSwJlBLBymF5c4vsXuSdn8lzqXCB0e99C1ff2or3+jImIiIhI+zOqf2Kz61b8dHhqvRoY0LSVGyINicgWkn/961/s27ePcePG8dFHH/Hmm29y2223MWPGDFwuFx9++CGXX345DoeD+Ph4kpOT2bt3Lzt37mT48OEAXHvttWzfvh2Xy4XH4yE5ORnDMEhLS2P79u2RGFY9bfW40XCxFBfx1LefwIGHQd4dnEcpbqLwYieRI3gDNgb4/sUz3X+Gpbgo0t0VEREREZFWMKp/IjN+fDHnxzsxgPPjncz48cUq4CnnJCKnkPz617/m/vvvB2DQoEFkZGRw6aWX8swzz/D0009zySWXEB8fX3t9bGwsLpcLl8tV2x4bG0t5eTkul4u4uLh615aUlDT6+larQUJCTMjHZbVaau9rs1lb5TXO1bn0p6Hn1LYdOMDEA/tJ+LoU+vQB6o/fsmIjCSl+un1ZhPHxV/iwgNWG3x/Aio9vWcuI/XYCXas/wfzLRgIzHwvNQBvp+7k+z2azApz1fnXH31l19jno7OMHzUFnH7+IiEhDzmXlhkhDwh5gnDx5ks8++4yrrroKgOuuu44uXbrUfj537lyGDRtGRUVF7XMqKiqIj48nLi6utr2iooIuXbrUa6vb3hi/36SsrDLUQyMhIab2vj6fv1Ve41w1tz+W4iLGfvwxJ3ftJpCSWu8+f3ryEwILl+H7+io2XfULLI9O5Qd39q43fstNY4hZvoz4Qd/GHviS8v376PKtHhw/fgzDhB79zgO/B/eFA6m8aQyBEM/Vuc5/Q8/z+fwAZ71f3fF3Vp19Djr7+EFz0BnH37Nn/NkvEhEREQmBsG8h+ec//8nVV19d+3jSpEl8+OGHAGzfvp2BAwcyaNAgdu7cidvtpry8nP3799OvXz+GDBnCW2+9BcCWLVsYOnQocXFx2O12Dh48iGmabN26lWHDhoV7WG1WQYGNESNi2Lx5AyNGxDTp2FJLcRExy5eRWFVJzPJl9bZ4WA/2xzVzGccqojlkOZ9jFdG4Zi7jrd8dqnePQEoqlVlTweHEO/hyyhwOqK7GGjApczrA78fXfyCVM2bVC0hEREREREREGhL2FRgHDhygd+/etY8ff/xx5s6di91up0ePHsydO5e4uDhuv/12JkyYgGmaTJ06FafTSWZmJrm5uWRmZmK328nLywOCBUGzs7Px+/2kpaVx2WWXhXtYbVJBgY1p05yYJlgsbg4fjmHatGC13zOdzFETXphOJ0ejojGdTmKWLwuGEcCYfV9QZTmPCnsChr+KCnsCeCFm4TL4ySPQ7ZulYTUhRszyZRyMjSMpJoaq6iocCi9ERERERESkmcIeYPzXf/1XvccDBw5k7dq1p103duxYxo6tf8JCdHQ0Tz311GnXDh48mPXrO/6JE821ZIkD0wSHAyorgx89nmB7QwFG3fDC7JoAEPx4oizYHhWNxWfHFZOAUed5LlsCseUnsfxhFUyZVu+eNSFG0d/fZlDeU/zjgckA/LAdhRejR9/a6Yuyioi0ReXl5eTk5OByufB6vUyfPp3LL7/8jMerr1ixgjfffBObzcaMGTMYNGgQx48fJzs7m+rqas477zwWLlxIdHQ0mzdv5umnn8ZmszFmzJjT/k0iIiIi4ReRU0g6so0b206QUlJiwW6v32a3B9sb4tywFrze2vCihtk1IdgOBGxe4nxl9b4e5ysjKt5O4LaJDd43kJLK6r4X4bs6jWcv6c+zl/Rvs+HF6NG3ntamo0pFRNqm3/72t1x11VWsWrWKhQsX8vOf/xyA2bNnk5eXx5o1a9i1axeFhYUUFhby7rvvsmHDBpYuXcqcOXMAyM/P58Ybb2T16tUMGDCAdevW4fV6WbhwIc8//zwrV65k3bp1HD16NJJDFRERERRghFxb+k19UlIAr7d+m9cbbG+IO2M82O0YJ+oHFMaJMrDb8dxwI46uRfQ0S4n1lmGaEOstI5oqLI9OrT2NpD07U1jRULAhIiKRdeeddzJ+/HgA/H4/TqfzjMer79y5k7S0NAzDoFevXvj9fo4fP37aEe3btm1j//79JCcn07VrVxwOB0OHDmXHjh2RHKqIiIigACOkXnoJtm1bwUUXxbFt24omFcxsTdnZHgwjuG3ENIMfDSPY3pCa7R6G210bYhgnyjDcbt7olcnf7tqE79jF2GwG51tK6R04wrdiq4ibFzyFpCPTKgwRkcjasGEDN954Y73/ioqKiIqK4ujRo+Tk5PDwww83eLz6mY5dr2lv6Ij2ho5zFxERkciK7DvsDqSgwMYDD3hxu3sQHV2J293jrAUzW1vN6y5Z4uDjj5306mWSne1ptD91C2/2rK6qDS++XLqBKqL5ypJAtd9NT/NLbD0+5UevPEsgpWOHFyIiEnkZGRlkZGSc1v7xxx/z8MMP88gjj3DFFVfgcrkaPF7dbrc3ekR7VFRUo0e01w00zsRqNUhIiGnhSE+9pyXk92xPOvv4QXPQ2ccPmoPOPn7QHNSlACNElixxUFl5EpstgNvtw2IJrno4U8HMcElP95Ge7mPixLGsWtW0+hw1IUbp39+m+pYMXHcFw4tTTx5xlJ39XiIiIq1l3759PPjggzz55JNccsklAPWOV09KSmLr1q1MmTIFq9XK4sWLmTRpEkeOHCEQCNC9e/faI9pvueWW2iPa+/btS3FxMWVlZcTExLBjxw4mTZp01v74/SZlZZUhHWNCQkzI79medPbxg+ags48fNAedffzQOeegZ8+Gf3GgACNESkosBAJu4JuqmY0VzGzragpvjtm2lepyLy7HBaedPNKj8mucG9ZSlT09Yv08E9WsEBHp+PLy8vB4PMyfPx8IhhfPPPPMGY9XHzZsGOPGjSMQCDBr1iwAJk+eTG5uLuvXr6dbt27k5eVht9uZPn06kyZNwjRNxowZQ2Ji4hn7ISIiIuGhACNEkpIC7N5d/8iPxgpmhpOluIgJ+/dhKS5q9ukf7ozxRD23nLiKsuDKi3+L85URsHmDhT/bINWsEBHp+J555pkG2890vHpWVhZZWVn12nr06MFvfvOb064dOXIkI0eODE1HRUREJCTa5/KANihYGNMkELBhmhAI2BotmBkuluIiYpYvI7Gqkpjly7AUFzXr+YGUVCyPTiWaqtNOHtl40QVt9jhUERERERER6VgUYIRIerqP+PhsnM6vCAScOJ1fkZfnjmj9i5rwwnQ6ORoVjel0nlOI8YM7exM3byrfiq2qd/KIP3lP63RcRERERERE5BTaQhJCUVGvMHhwae3j9PSmFc1sDXXDC7NrcOuH2TUBTpQRs3wZlVlTm7V64gd39sYyIou/jr+FH639I4GU3jz3v63UeREREREREZFTaAVGK0isrKytOREpzg1rweutDS9qmF0TwOsNfr2Zagp7atuIiIiIiIiIhJsCjBBLrKxk3IHPzrnmRKi4M8aD3Y5xov5Zp8aJMrDb22zxTREREREREZGGKMAIoWS/n3EHPsNtsbSo5kQoBFJSqcyaiuF214YYxokyDLe72dtHRERERERERCJNAUaIWIqLmFJRgdtiocIePE7V7JrQZkKMntVVCi9ERERERESk3VKAESLODWuxm2ZteFGjJTUnQqEmxCiNjoloeDF69K21H2s+FxEREREREWkqBRgh4s4Yj9cwiPV667W3hZoTbaH45pgxY2s/1nwuIiIiIiIi0lRhP0b15ptvJj4+HoDevXtz3333MX36dAzD4OKLL2b27NlYLBbWr1/P2rVrsdlsTJ48mREjRlBdXU1OTg7Hjh0jNjaWRYsW0b17dz744APmz5+P1WolLS2NKVOmhHtYBFJSWREby+OBAHi9VPy7gKa2bYiIiIiIiIi0XFgDDLfbDcDKlStr2+677z4eeughrrzySmbNmsXrr7/O4MGDWblyJRs3bsTtdjNhwgSuueYa1qxZQ79+/cjKyqKgoID8/HxmzpzJ7NmzWb58OUlJSdx7770UFhYycODAcA6t1vHq3jjKeuDzd+OvX/uxPJrDD1J6R6QvIiIiIiIiIh1FWLeQ7N27l6qqKu6++27uuOMOPvjgAwoLC7niiisAuPbaa9m2bRsffvghl19+OQ6Hg/j4eJKTk9m7dy87d+5k+PDhtddu374dl8uFx+MhOTkZwzBIS0tj+/bt4RwWALZtW/n11/CtY3H09+8Hw88Cfy7//cR3KCgI+0KXsFAtCxEREREREQmXsL6zjoqKYtKkSWRkZFBUVMQ999yDaZoYhgFAbGws5eXluFyu2m0mNe0ul6tee91r4+Li6l1bUlISzmFh27aV+IfuJzngJYUdHKcbV5n/5E/WEg6afViyxEF6ui+sfQoH1bIQERERERGRcAlrgNGnTx9SUlIwDIM+ffqQkJBAYWFh7dcrKiro0qULcXFxVFRU1GuPj4+v197YtV26dGm0H1arQUJCTGgGtWUL1oenYHx9nBg8mJh0wcpXdGfRycnkdvkVbx66NnSvd45sNmuz+3Dbbbed9pzG7mO1WiI+zkjq7OMHzUFnHz9oDjr7+EVERERaU1gDjBdffJFPPvmExx9/nNLSUlwuF9dccw3vvPMOV155JVu2bOGqq65i0KBBPPnkk7jdbjweD/v376dfv34MGTKEt956i0GDBrFlyxaGDh1KXFwcdrudgwcPkpSUxNatW89axNPvNykrq2zxeCzFRXSZMgW+PIrp9+HDAliIJRiouMwEZp2YRlXftZSVJbb49VrC5/M3e8yjRv3ktOc0dp+EhJiQzGt71dnHD5qDzj5+0Bx0xvH37Bl/9otEREREQiCsAcatt97Ko48+SmZmJoZhsGDBArp168Zjjz3G0qVLufDCC7n++uuxWq3cfvvtTJgwAdM0mTp1Kk6nk8zMTHJzc8nMzMRut5OXlwfAnDlzyM7Oxu/3k5aWxmWXXRaW8UQ/9yuMygrw+8CEAF4gGrARTzlGwMKH9sE8MfAF4JGw9ElERERERESkIwprgOFwOGpDh7pWrVp1WtvYsWMZO7Z+jYXo6Gieeuqp064dPHgw69evD11Hm8gEsNvx907CWnIQG15MiwUCBgZwwv4tBlyTQI+fjSUQ9t6JiIiIiIiIdBxhPYWko6m+5z58gwZTXmnjs0AqBlFYAyY2w8txp0HSjZfSY/GDBFJSI91VERERERERkXZNAUYLBFJS+ev3f87mL7+L22fjcy4ADL40E9kV1Y+KGbM6XHiho1NFREREREQkEhRgtNDjL/RjXvR8PrYPxEUsrxs/ZJPxEx7xPtXhwgvQ0akiIiIiIiISGWGtgdERlZRYiIrqwxOO+Yxy/5p19vEcIIWAx8m/q2SIiIiIiIiISAtpBUYLJSUF8HrhoLUP8ywPU2SkYJo2oqJKI901ERERERERkQ5DAUYLZWd7MAzweMA0IRCwASZ9+66OdNfqUe0KERERERERac8UYLRQerqPvDw3vXqZQDRO51cMGLCcxMRtke5aPapdISIiIiIiIu2ZamCEQHq6D4B77imhujqV/fsnRLhHIiIiIiIiIh2LVmCEQEGBjWnTnPj9F2CxuHG7e7B7dxYFBcqHREREREREREJBAUYILFniwDTBMLwYBlgsPsBgyRJHpLsmIiIiIiIi0iEowAiBkhILdnv9tuhoGyUlml4RERERERGRUNA77BCoOUq1Lqs1mqSkQGQ6JCIiIiIiItLBKMAIgZqjVE3TXnuUqmEE20VERERERESk5VRlMgS+OYXkCwKBVKKiSsnLc9S2i4iIiIiIiEjLKMAIkfR0H927/4jBg4f8+/H6CPdIRESkY6usrGTatGmcOHGC6OhoFi9eTPfu3fnggw+YP38+VquVtLQ0pkyZAsCKFSt48803sdlszJgxg0GDBnH8+HGys7Oprq7mvPPOY+HChURHR7N582aefvppbDYbY8aMYezYsREerYiIiGgLiYiIiLRL69evZ+DAgaxevZr09HTy8/MBmD17Nnl5eaxZs4Zdu3ZRWFhIYWEh7777Lhs2bGDp0qXMmTMHgPz8fG688UZWr17NgAEDWLduHV6vl4ULF/L888+zcuVK1q1bx9GjRyM5VBEREUEBhoiIiLRTd955J5MnTwbg8OHD9OjRA5fLhcfjITk5GcMwSEtLY/v27ezcuZO0tDQMw6BXr174/X6OHz/Ozp07GT58OADXXnst27ZtY//+/SQnJ9O1a1ccDgdDhw5lx44dkRyqiIiIEMYtJF6vlxkzZvD555/j8XiYPHky559/Pvfddx+pqakAZGZmcsMNN7B+/XrWrl2LzWZj8uTJjBgxgurqanJycjh27BixsbEsWrSo0WWikXDBBb0YPfrWiL2+iIhIR7VhwwZeeOGFem0LFixg0KBB3HHHHXzyySf89re/xeVyERcXV3tNbGwsJSUlOJ1OEhIS6rWXl5fjcrmIj48/Y1tNu8vlOmsfrVaDhISYlg71lHtaQn7P9qSzjx80B519/KA56OzjB81BXWELMP785z+TkJDA4sWL+frrrxk9ejT3338/d911F3fffXftdUePHmXlypVs3LgRt9vNhAkTuOaaa1izZg39+vUjKyuLgoIC8vPzmTlzJrNnz2b58uUkJSVx7733UlhYyMCBA8M1rHoefngao0b9JCKvLSIi0pFlZGSQkZHR4Nd+//vfs3//fv77v/+bl156iYqKitqvVVRU0KVLF+x2+2nt8fHxxMXFUVFRQVRUVO21NW2nXns2fr9JWVllC0Z5uoSEmJDfsz3p7OMHzUFnHz9oDjr7+KFzzkHPng3/vRu2LST/+Z//yYMPPlj72Gq18tFHH/Hmm29y2223MWPGDFwuFx9++CGXX345DoeD+Ph4kpOT2bt372lLPLdv337GZaKRkpmZGbHXFhER6Wx+/etf89JLLwEQExOD1WolLi4Ou93OwYMHMU2TrVu3MmzYMIYMGcLWrVsJBAIcPnyYQCBA9+7dGTJkCG+99RYAW7ZsYejQofTt25fi4mLKysrweDzs2LGDyy+/PJJDFREREcK4AiM2NhYAl8vFAw88wEMPPYTH4yEjI4NLL72UZ555hqeffppLLrmkwWWbZ1ri2dAy0bNpjSWewft27qU9Gn/nHj9oDjr7+EFz0NnHH25jxowhNzeXjRs34vf7WbBgAQBz5swhOzsbv99PWloal112GQDDhg1j3LhxBAIBZs2aBcDkyZPJzc1l/fr1dOvWjby8POx2O9OnT2fSpEmYpsmYMWNITEyM2DhFREQkKKzHqH7xxRfcf//9TJgwgZtuuomTJ0/SpUsXAK677jrmzp3LsGHDGl3iWdN2piWeNfdrTGss8YTOubSnLo2/c48fNAedffygOeiM4z/TEs9w6NGjB7/5zW9Oax88eDDr159+nHlWVhZZWVlNusfIkSMZOXJk6DorIiIiLRa2LSRfffUVd999Nzk5Odx6a7DQ5aRJk/jwww8B2L59OwMHDmTQoEHs3LkTt9tNeXk5+/fvp1+/fg0u8TzTMlERERERERER6VjCtgLjV7/6FSdPniQ/P7/2nPbp06ezYMEC7HY7PXr0YO7cucTFxXH77bczYcIETNNk6tSpOJ1OMjMzyc3NJTMzE7vdTl5eHnDmZaIiIiIiIiIi0nEYpmmake5EuHm9fm0haQUaf+ceP2gOOvv4QXPQGccfyS0kbU1r/PuiM/5M1dXZxw+ag1SqmrUAACAASURBVM4+ftAcdPbxQ+ecg4ifQiIiIiIiIiIicq4UYIiIiIiIiIhIm6cAQ0RERERERETaPAUYIVBQYGPEiBh69rQwYkQMBQVhPZ1WREREOphX9pRy07Pv0O+xV7np2Xd4ZU9ppLskIiIScXqn3UIFBTamTXNimhAdDYcPG0yb5gQgPd0X4d6JiIhIe/PKnlIWvPYp1b4AAEfK3Sx47VMARvVPjGTXREREIkorMFpoyRIHpgkOBxhG8KNpBttFREREmiv/7aLa8KJGtS9A/ttFkemQiIhIG6EAo4VKSizY7fXb7PZgu4iIiEhzlZa7m9UuIiLSWehddgslJQXweuu3eb3BdhEREZHmSox3NqtdRESks1CA0ULZ2R4MAzye4NYRjye4lSQ72xPpromIiEg79NPhqUTZ6v8TLcpm4afDUyPTIRERkTZCRTxbqKZQ55IlDg4dMujd2yQ726MCniIiInJOagp15r9dRGm5m8R4Jz8dnqoCniIi0ukpwAiB9HQf6ek+EhJiKCurjHR3REREpJ0b1T+RUf0T9W8LERGROrSFRERERERERETaPAUYIiIiIiIiItLmKcAQERERERERkTZPAYaIiIiIiIiItHmGaZpmpDshIiIiIiIiItIYrcAQERERERERkTZPAYaIiIiIiIiItHkKMERERERERESkzVOAISIiIiIiIiJtngIMEREREREREWnzFGCIiIiIiIiISJtni3QH2rtAIMDjjz/Oxx9/jMPhYN68eaSkpES6Wy3i9XqZMWMGn3/+OR6Ph8mTJ3P++edz3333kZqaCkBmZiY33HAD69evZ+3atdhsNiZPnsyIESOorq4mJyeHY8eOERsby6JFi/4/e3ceEFW5P378PTPMsMyAiAvmBVRM3HHNFb1pGWqZVpq797pc0dTSfppLKnpNy9BuaaJg2lfRJNO6WkpaViLFtcIwRdByx2VQVGQQGJg5vz9UAgEXdpjP6y/nmTNnns8zp87D5zznc3BzcyM2NpbFixej0Wjw8/Nj8uTJ5RvoAwwYMABnZ2cAPDw8mDBhArNmzUKlUtGoUSMCAwNRq9VVcgw+//xzvvjiCwAyMzOJj48nPDzcZo6Bw4cPs2zZMsLCwjh79myp/e4ffvghP/zwA3Z2dsyZMwdfX99yjvy23PHHx8ezaNEiNBoNOp2OpUuXUrNmTd566y0OHTqEXq8HIDg4GK1WW+Xij4uLK7XjvqLGLyoGmV9UvXML2PbcAmx7fmHrcwuQ+YXML0qIIoplz549ysyZMxVFUZTffvtNmTBhQjn3qPi2bdumvPXWW4qiKMq1a9eUv//978rWrVuVdevW5dkuKSlJee6555TMzEzl5s2bOf9ev369smLFCkVRFOWrr75SFi1apCiKojz//PPK2bNnFavVqowbN045evRo2Qb2CDIyMpT+/fvnaQsICFD+97//KYqiKPPmzVP27t1bpcfgrgULFijh4eE2cwyEhoYqzz33nDJo0CBFUUrvdz969KgycuRIxWq1KhcuXFBefPHF8gn4HvfGP3z4cOXYsWOKoijKli1blCVLliiKoihDhgxRkpOT83y2KsZfWsd9RY1fVBwyv6ha5xZFkbnFvWxpfmHrcwtFkfmFzC9KjtxCUkwxMTF069YNgNatW3P06NFy7lHx9e7dm9deey3ntUaj4ejRo/zwww8MHz6cOXPmYDKZ+P3332nTpg06nQ5nZ2e8vLxISEjIMybdu3cnOjoak8mE2WzGy8sLlUqFn58f0dHR5RXiAyUkJJCens6YMWMYNWoUsbGxxMXF0aFDB+B2XD/99FOVHgOAI0eO8OeffzJ48GCbOQa8vLxYuXJlzuvS+t1jYmLw8/NDpVJRt25dLBYL165dK5eYc7s3/vfee4+mTZsCYLFYsLe3x2q1cvbsWebPn8+QIUPYtm0bQJWMv7SO+4oav6g4ZH5Rtc4tIHOL3GxtfmHrcwuQ+YXML0qOJDCKyWQyYTAYcl5rNBqys7PLsUfFp9frMRgMmEwmXn31VaZOnYqvry9vvPEGmzdvxtPTk1WrVmEymXKWQd79nMlkytOu1+tJTU3NN0532ysqBwcHxo4dy7p161i4cCHTp09HURRUKhWQN66qOgYAISEhTJo0CcBmjgF/f3/s7P66u660fveKOh73xl+7dm0ADh06xKZNm/jnP//JrVu3GDFiBEFBQXz00Ud88sknJCQkVMn4S+u4r6jxi4pD5hdV69wCMrfIzdbmF7Y+twCZX8j8ouRIAqOYDAYDaWlpOa+tVmueg7OyunTpEqNGjaJ///7069ePXr160aJFCwB69erFsWPH8sWelpaGs7Nznva0tDRcXFwK3NbFxaVsg3oEDRo04Pnnn0elUtGgQQNcXV1JTk7Oef9+cVWVMbh58yanTp2iU6dOADZ3DNylVv/1v8mS/N0L20dFtHv3bgIDAwkNDcXNzQ1HR0dGjRqFo6MjBoOBTp06kZCQUCXjL63jvrLEL8qPzC+q3rlF5ha3yfxC5hZ3yfxC5hdFIQmMYmrbti2RkZEAxMbG4uPjU849Kr6rV68yZswYZsyYwcCBAwEYO3Ysv//+OwDR0dE0b94cX19fYmJiyMzMJDU1lZMnT+Lj40Pbtm3Zv38/AJGRkbRr1w6DwYBWq+XcuXMoikJUVBTt27cvtxgfZNu2bbzzzjsAGI1GTCYTXbt25eDBg8DtuNq3b1+lx+CXX36hS5cuOa9t7Ri4q1mzZqXyu7dt25aoqCisVisXL17EarXi5uZWnqEWaMeOHWzatImwsDA8PT0BOHPmDMOGDcNisZCVlcWhQ4do3rx5lYy/tI77yhK/KD8yv6h65xaZW9wm8wuZW4DML2R+UXQqRVGU8u5EZXa3SviJEydQFIUlS5bQsGHD8u5Wsbz11ltERETg7e2d0zZ16lSCgoLQarXUrFmTRYsWYTAY2Lp1K59++imKohAQEIC/vz/p6enMnDmTK1euoNVqWb58ObVq1SI2NpYlS5ZgsVjw8/Nj2rRp5Rjl/ZnNZmbPns3FixdRqVRMnz6d6tWrM2/ePLKysvD29uatt95Co9FU2TH46KOPsLOz45///Cdw+37NRYsW2cQxkJiYyOuvv87WrVs5ffp0qf3uK1euJDIyEqvVyuzZsyvMhOtu/Fu2bKFz58489thjOVeznnjiCV599VXWrl3L119/jVarpX///gwdOrTKxb9169ZSPe4ravyiYpD5RdU7t8jc4jZbnV/Y+twCZH4h84uSIQkMIYQQQgghhBBCVHhyC4kQQgghhBBCCCEqPElgCCGEEEIIIYQQosKTBIYQQgghhBBCCCEqPElgCCGEEEIIIYQQosKTBIYQQgghhBBCCCEqPLvy7oAQwvYcPHiQqVOn8vjjjwOQlpaGh4cH06ZN46WXXqJ58+YoioLZbOb5559nxIgRAOzfv5/169ejVquxWCwMHDiQ559/vjxDEUIIIUQFIHMLIWyDJDCEEOWiU6dO/Oc//8l5/f/+3//ju+++4/HHHycsLAyArKwsJk2aRN26denZsycLFixgx44duLi4YDKZ6N+/P127dqVGjRrlFYYQQgghKgiZWwhR9cktJEKIcmc2m0lKSsLFxSVPu1arZdSoUezevRuAGjVqsHHjRv744w/0ej0REREywRBCCCFEPjK3EKJqkgSGEKJc/O9//2PkyJH07duXF198kV69etG5c+d829WsWZPr168DsHr1atLT03n99dfx8/MjJCQERVHKuutCCCGEqIBkbiFE1ScJDCFEuejUqRNhYWFs3rwZrVaLh4dHgdtduHCBOnXqkJKSwsWLF5kxYwZffvkln3/+OQcOHOD7778v454LIYQQoiKSuYUQVZ8kMIQQ5ap69eoEBQUxd+5crly5kuc9s9nMxo0befbZZzGbzUydOpVLly4BUKtWLWrWrIlOpyuPbgshhBCigpK5hRBVlxTxFEKUu8cff5yRI0fy8ccf8+effzJy5EhUKhXZ2dn069ePLl26ADB37lwmT56MnZ0dFouFJ598Ej8/v3LuvRBCCCEqGplbCFE1qRS5yUsIIYQQQgghhBAVnNxCIoQQQgghhBBCiApPEhhCCCGEEEIIIYSo8CSBIYQQQgghhBBCiApPEhhCCCGEEEIIIYSo8CSBIYQQQgghhBBCiApPEhhCCCGEEEIIIYSo8CSBIYQQQgghhBBCiApPEhhCCCGEEEIIIYSo8CSBIYQQQgghhBBCiArPrrw7IIR4dImJifTq1QsfH5+cNkVRGDVqFAMHDnykff3www8cPnyY1157jX379hEdHc3cuXPv+939+vXjt99+K3L/y0v//v0JCwvDxcWl1L7jYcfns88+w2w2M3z48FLrixBCCFHRWSwWNm7cyJdffonFYiErK4sePXrw2muvodPpADAajfznP/8hLi4OlUqFvb09AQEBPP300wD07NkTrVaLg4MDKpWKrKwsunbtyqxZs1Cr1fneN5vNqNVq3njjDbp3756vT/fb38WLF+U8L0Q5kgSGEJWUg4MDO3bsyHltNBp57rnnaNGiBU2aNHno/Rw5coSUlBQAnnrqKZ566qkS72tFkXu8yltMTAyNGjUq724IIYQQ5WrBggWkpKSwYcMGnJ2duXXrFtOnT+fNN98kKCiIa9euMWTIEF577TXefvttVCoVCQkJjB49GkdHR7p27QrAsmXLaNmyJQBms5mRI0fyySefMGLEiHzvA3z99dfMmTOHqKioAvtV2P6efPLJh4pLzvNClA5JYAhRRbi7u1OvXj3OnDmDl5cXCxYs4OzZs9y4cQO9Xs+yZcvw9vZm5MiRVKtWjVOnTtG3b1/Cw8OxWCw4OztTr1499uzZQ0hICLGxsQQFBWE2m7ly5QpdunRhyZIlhX5/YmIiw4cPp2HDhly4cIGwsDASExNZtmwZ6enpqNVqJk+eTI8ePbBYLLz77rt89913ODs74+vry8mTJwkLC8vTv6FDhzJgwAAWL17MiRMnyMrKonPnzrzxxhvY2dmxYsUKvvnmG7RaLdWrV+ftt9+mdu3ahbY3btyY6Oho3NzcWLVqFbt27UKj0dCgQQPmzZtHrVq1GDlyJK1bt+bQoUNcunSJzp07s2jRItTqvHfcjRw5kmbNmhETE8P169fp378/r776ap5tsrKyeOedd4iOjkaj0eDr68vs2bOJjo7mu+++48cff8TBwUGuzgghhLBJiYmJfPnll0RFRWEwGABwcnJi4cKFHDp0CIBPPvmEtm3bMmDAgJzPNWnShBUrVhS6olKn09GuXTtOnTpV4PuKopCYmEi1atUeqp+595c7gSHneSHKniQwhKgifvvtN86dO0erVq2IjIzExcWFTz/9FID58+ezefNm5s2bB4CLiwu7d+8Gbp/Er1+/zrRp0/j8889z9rdx40ZeffVVOnbsSFpaGk899RRHjx7F1dW10D5cvnyZ5cuX0759e1JSUpg9ezbr1q3Dw8MDo9HIyy+/TOPGjYmMjCQuLo6vvvoKlUrFxIkT8+wnd/9mz55N8+bNeeedd7BYLMyaNYuPP/6Y5557jg0bNhAdHY1Op2P9+vX8/vvvNG/evMD2u8tMAbZv386BAwfYtm0bTk5OrFy5klmzZrFu3ToAzp07R1hYGLdu3aJPnz78/PPPdOrUKV+8p0+fZsuWLaSnp/Pyyy/TsmXLPFdbVq9eTVJSEjt27ECj0fDmm2/y7rvv8u9//5t9+/bRqFEjmdQIIYSwWXFxcTz++OM5yYu7atWqhb+/PwBHjx6lW7du+T77xBNPFLpfo9HI999/z9SpU3Papk+fjr29PTdu3ADAz8+PNWvWPFQ/C9ofyHleiPIgCQwhKqmMjAz69+8P3L5/tHr16gQFBfHYY4/x2GOP4enpSVhYGGfPnuXnn3+mTZs2OZ9t3779A/f/zjvvEBkZyZo1azh16hSZmZncunXrvgkMOzs7WrduDUBsbCxXrlxh0qRJOe+rVCqOHz/O/v376d+/P/b29gAMHjyYsLCwAvv3ww8/cOTIEbZt25YTN9xecdKkSRNeeOEFunfvTvfu3encuTNWq7XA9twiIyN58cUXcXJyAmDUqFGsWbMGs9kMQI8ePVCr1RgMBurVq5dzi829Bg8ejFarRavV0rt3b6KiovIkMCIjI5k2bRparRa4vWoj93gIIYQQtkytVmO1Wu+7jUqlQlGUB+5r+vTpODg4YLVa0Wq1DBo0KCcJAn/dEnL+/HlGjx5N06ZN8fT0fOT9JSYm5mwj53khyp4kMISopO6tgZHbJ598wtatWxk+fDj9+vXD1dU1zwn37h/u9zNixAgaN25Mt27d6NOnD4cPH37gBEKn02Fnd/t/KxaLhYYNG/LZZ5/lvG80GnFzc8uz0gPId3tG7v5ZrVY++OADGjZsCMDNmzdRqVSo1Wo2bdrEkSNHiI6OZsmSJXTr1o033nij0Pbc+1SpVHleZ2dn57x2cHDI+ff9Jk53Y4XbK1nujaOg78nKyipwX0IIIYSt8fX15dSpU5hMpjyrMIxGI/PmzWPFihW0bt2a2NjYnFoWd4WHh5Oens7o0aOB/DUuCuPp6cm7777LqFGjaNWqFb6+vgVu9zD7k/O8EGVPHqMqRBUUFRXFCy+8wKBBg2jQoAHfffcdFoulwG01Gk2eP97hdpLgyJEjTJ8+nWeeeYbLly9z7ty5B14lya1169acPXuWX375BYD4+Hj8/f0xGo38/e9/Z+fOnZjNZrKzs/niiy8K3Y+fnx//93//h6IomM1mJk6cyKZNm0hISOC5556jYcOGBAQE8M9//pMjR44U2p5bt27d2L59O7du3QIgLCyMJ554Iqfa+cPauXMnVquVlJQUIiIi6NmzZ77v2bJlC1lZWVitVjZv3pxTbKygcRdCCCFsibu7O/369WPOnDmYTCYATCYTCxYswNXVFQcHBwYPHszPP//Mzp07cy4oHD16lBUrVuR5GtujuFtTY8GCBY80t7mXnOeFKHuyAkOIKmjMmDHMnz8/57aL1q1bc+LEiQK37dSpE9OnT2fRokU0b94cuF2DYvz48bzwwgs4OTnh7u5O27ZtOXv27H2XW+bm5ubGihUrePfdd8nMzERRFN599108PDx48cUXOX36NAMGDMDJyQkPDw8cHR0L3M+bb77J4sWL6devH1lZWXTp0oVx48ah1Wrp06cPL730Ek5OTjg4ODB37lyaNGlSYHtuAwcO5NKlSwwaNAir1Uq9evVYtmzZww5vjoyMDAYOHEhaWhrDhg2jc+fOeVa6TJw4kaVLlzJgwACys7Px9fXNqUPSvXt33nnnHQACAgIe+buFEEKIqiAwMJDg4GCGDBmCRqPBbDbz9NNPM2XKFABcXV0JCwsjKCiIkJAQ1Go1jo6OLF68OCdZUBSvv/46ffr0YevWrQwZMqRI+5DzvBBlT6U8zE1lQghRgqKiokhOTs6p4fHWW29hb2/PjBkzyrlnD2/kyJEMHz6c3r17l3dXhBBCCCGEsAlyC4kQosw1atSI//73v/Tr149nn32W69evM2HChPLulhBCCCGEEKICkxUYQgghhBBCCCGEqPBkBYYQQgghhBBCCCEqPElgCCGEEEIIIYQQosKTBIYQQgghhBBCCCEqPJt8jKrVasViyVv6Q6NR5WuzJbYeP8gYSPwSv8Qv8d+lKFZu3LjBlStJpKenY2dnR40aNalZsyZ2dto8n9VqNWXd3QqroPlFccmxadvxg4yBrccPMga2Hj/Y5hgUNr8o0yKeISEhfPfdd2RlZTF06FA6dOjArFmzUKlUNGrUiMDAQNRqNVu3biU8PBw7OzsmTpxIjx49yMjIYMaMGSQnJ6PX61m6dClubm7ExsayePFiNBoNfn5+TJ48+YH9yMqycOPGrTxtrq5O+dpsia3HDzIGEr/EL/FL/FevXmXDhnV8/PFHJCUZady4CQEBk3jppZdxdHQs8LO1ajmXcW8rroLmF8Ulx6Ztxw8yBrYeP8gY2Hr8YDtjYFUUDl+4yaHEG8zq16LAbcpsBcbBgwf57bff2LJlC+np6axfv563336bqVOn0rFjR+bPn8++ffto3bo1YWFhbN++nczMTIYNG0bXrl3ZsmULPj4+TJkyhV27dhEcHMzcuXMJDAxk5cqVeHp6Mn78eOLi4mjevHlZhSWEEEJUenFxcSxf/h7btn1KRkYGPXs+TUDAGp58sicqlaq8uyeEEEKIKuzstVvsjk/i62NGLt7MRK/TlH8CIyoqCh8fHyZNmoTJZOKNN95g69atdOjQAYDu3bvz448/olaradOmDTqdDp1Oh5eXFwkJCcTExDBu3LicbYODgzGZTJjNZry8vADw8/MjOjpaEhhCCCHEAyiKwvfff8uaNav44YfvcHBwYNCgoYwfP5HGjZuUd/eEEEIIUYXduJXF3uNJ7D6WRNzlVNQq6FCvOhP86vPk4zUL/VyZJTCuX7/OxYsXWbNmDYmJiUycOBFFUXKu7Oj1elJTUzGZTDg7/7UcVa/XYzKZ8rTn3tZgMOTZ9vz58w/si0ajwtXV6Z42db42W2Lr8YOMgcQv8Uv8thF/eno6mzdvYsWKFSQkxPPYY4+xaNEixo79FzVrFj5hEEIIIYQojsxsK1GnktkVZ+SnM9exWBUa1dLz2t+96d2kFjUN9g/cR5klMFxdXfH29kan0+Ht7Y29vT2XL1/OeT8tLQ0XFxcMBgNpaWl52p2dnfO0329bFxeXB/bFYlGkBsY9bD1+kDGQ+CV+ib9qx280Xmb9+lA2bFjPtWvXaNmyFR9+GMKAAS9Ru7YrN27cKtIYSA0MIYQQQhTmbl2L3ceMfHviCqZMC7UMOoa1/Rt9m7nzeC39I+2vzBIY7dq1Y+PGjYwePZqkpNtVzTt37szBgwfp2LEjkZGRdOrUCV9fX95//30yMzMxm82cPHkSHx8f2rZty/79+/H19SUyMpJ27dphMBjQarWcO3cOT09PoqKiHqqIpxBCCGErjhw5zLyly4ne9xWKxUL1pp2ZHfgKU4f0k/oWQgghhChURLyR4ANnMKZm4u5szyvd6tOnqftDffbeuhaOWjU9G9WkTzN32nu6olEXbQ5SZgmMHj168MsvvzBw4EAURWH+/Pl4eHgwb9483nvvPby9vfH390ej0TBy5EiGDRuGoihMmzYNe3t7hg4dysyZMxk6dCharZbly5cDsHDhQqZPn47FYsHPz49WrVqVVUhCCCFEhWSxWNi792tCQlbx009RqLQOGFr3wbldP7TV6/J5kpomCUkPPQkRQgghhG2JiDeyZO8fZGRbAbicmsmSvX8A0Kepe4HJjc713Nh7/AoR8UaOXrpT18KrOgFd69OjUU0cS+DR62X6GNWKQh6jmp+txw8yBhK/xC/xV/74TSYT4eGbCA1dzZkzp/Hw8ETdog9K456oHQx5tq3jbM+X4zsCxYtfbiH5izxGteTZevwgY2Dr8YOMga3HD/cfg+KskriffqEHuZyama+9zp3vyJ3cALi7oMKqQKNaevo2c8e/SS1qPURdi4IUNr8osxUYQgghhCgd58+f46OPQti8eSM3b6bQvn0H5s5dQN++/ejywU8UdKXCWMCkRAghhBCVx4NWSRRHYfMEY2omqyJP50lewO3EhZNWw0dDW9GolqHAz5YESWAIIYQQldQvvxwkJCSYXbt2AtCvX38CAibRrt0TOdu4O9sXeAXF3bloV0SEEEIIUTEEHziTL5GQkW0l+MCZYicwCps/OOk0GE3mAj+TnmUp1eQFSAJDCCGEKJbSWrpZmOzsbL76agchIauIifkVF5dqTJgwmbFjx+Ph4Zlv+4KWeTrYqXmlW/1S66MQQgghSt/9VkkUV0HzB4BbZgs6jQqzJf/6zrK4OCIJDCGEEKKISnPp5r1SUm4QFraBdetCuHAhkQYNvHn77SAGDx6OwVD41Y67/SjLJIsQQgghSl9prbI0Z1ux16ipX8OJBKMJADu1ip4+NZn6d29+PX+j3C6OSAJDCCGEKKLSXLp516lTJ1m7djVbtmzm1q00unbtxttvL+OZZ3qjVqsfah99mrpLwkIIIYSoYkpylaWiKPx+8Sa7jhn59vhVUjOzqanXMaK9B32b1c5za0h5XhyRBIYQQghRRKW1dFNRFH76KYqQkFXs2ROBnZ0dL7wwkICASbRs6VusfQshhBCiaiiJRML56+nsPmYkIj6JCykZONip6dGoJn2b1eYJr+po7j5epIDvLo+LI5LAEEIIIYqopJdums1mvvhiGyEhwRw9+js1atRg2rTpjB79L9zd6xS3u0IIIYSoYoqSSLiRnsU3x68QcczIkUupqIAO9VwZ36UeTz5eEyedpnQ6WwIkgSGEEEIUUUkt3UxOTmbDhnWsX7+WpCQjjRs3YfnyFQwcOBhHR8cS7nXldvjwYZYtW0ZYWBjx8fEsWrQIjUaDTqdj6dKl1KxZk7feeotDhw6h1+sBCA4ORqvVMmPGDJKTk9Hr9SxduhQ3NzdiY2NZvHgxGo0GPz8/Jk+eDMCHH37IDz/8gJ2dHXPmzMHXV1a+CCGEeHRlXey7MOZsK1GnrxFxzEjUqWtkWxUer6nn1e4N8G9Sm9qV5OlkksAQQgghiqi4SzcTEuIJDQ1m27ZPycjIoGfPpwkIWMOTT/ZEpSp4yaYtW7t2LTt37sxJ6ixevJh58+bRtGlTwsPDWbt2LbNnzyYuLo6PPvoINze3nM9+/PHH+Pj4MGXKFHbt2kVwcDBz584lMDCQlStX4unpyfjx44mLiwPg559/5rPPPuPSpUtMmTKF7du3l0vMQgghKq+yLPZdkLt1LXYfS+LbE1e4mZFNDb2OwW3+Rt9mtfGpXbqPPC0NksAQQgghiuFRl24qisL333/LmjWr+OGH73BwcGDQoKGMHz+Rxo2blGJPKz8vLy9WrlzJ9dDSZAAAIABJREFUG2+8AcB7771H7dq1AbBYLNjb22O1Wjl79izz58/n6tWrDBw4kIEDBxITE8O4ceMA6N69O8HBwZhMJsxmM15eXgD4+fkRHR2NTqfDz88PlUpF3bp1sVgsXLt2LU9CRAghhHiQsij2XZDz19OJiDey+9hfdS2ezFXXwq6QuhaVgSQwhBBCiDKQnp7OZ5+FExoazIkTx3F3r8Ps2fMYNWoMNWrUKO/uVQr+/v4kJibmvL6bvDh06BCbNm1i8+bN3Lp1ixEjRjB69GgsFgujRo2iRYsWmEwmnJ2dAdDr9aSmpmIymfI8glav13P+/Hns7e1xdXXN056amvrABIZGo8LV1akkQ0ajUZf4PisTW48fZAxsPX6QMajM8d+v2PejxPQwY3DjlpldRy+zI/Yiv52/gUoFnb1r8NpTjXimmTt6+6rxp3/ViEIIIYSooIzGy6xfH8qGDeu5du0aLVu24sMPQxgw4CV0Ol15d6/S2717N6tXryY0NBQ3N7ecpMXd20w6depEQkICBoOBtLQ0ANLS0nBxccnTlrtdq9Xma7+b/Lgfi0Xhxo1bJRqfq6tTie+zMrH1+EHGwNbjBxmDyhz//Yp9P0pMhY2BOdvKj6evsTtXXYuGNZ3y1bXISjdzI91c9EDKQa1aBZ93JYEhhBBClIIjRw6zZs0q/vvf7WRnZ+Pv35cJEybRuXNXqW9RQnbs2MGnn35KWFhYzoqJM2fOMG3aNL744gusViuHDh3ihRde4Nq1a+zfvx9fX18iIyNp164dBoMBrVbLuXPn8PT0JCoqismTJ6PRaAgKCmLs2LFcvnwZq9Uqt48IIYR4ZCVV7Du3u3UtIuKT+Ob4X3UtXm5Tl77N3PGppa/S8wxJYAghhBAlxGKxsHfv14SErOKnn6JwctLzj3+MYdy4CXh7Nyzv7lUpFouFxYsX89hjjzFlyhQAnnjiCV599VX69evHyy+/jFarpX///jRq1AgPDw9mzpzJ0KFD0Wq1LF++HICFCxcyffp0LBYLfn5+tGrVCoD27dszePBgrFYr8+fPL7c4hRBCVF7FLfadW+KNdHYfMxIRn0TijapV1+JRqBRFUcq7E2UtK8uSbwlOZV6aVBJsPX6QMZD4JX6Jv+jxm0wmwsM3ERq6mjNnTuPh4cnYsQGMGDGKatVcH7yDclac+Atb4mmLCppfFJf8t2nb8YOMga3HDzIGthx/SnoW3564wt4TVzl07gYqoL2XK32b1aZHo5rodVV3PYLcQiKEEEKUsMTE83z0UQibNm3g5s0U2rV7gjffDOTZZ5/Hzk5OsUIIIYR4NLnrWvx4+hpZFgWf2gamdGuAf9PauN+pa2GrZHYlhBBCPKJff/2ZkJBgvvpqBwD9+vVn/PhXaN++Qzn3TAghhBCVjaIoHLmUyu5jRr49foWUjGzcnLQMan27rkWHRrVISUkv725WCJLAEEIIIR5CdnY2X321g5CQVcTE/IqLSzUmTJjM2LHj8fDwLO/uCSGEEKKSSbyRTsSxJCLijZy/kYG9nZonH69xO2lR76+6FlW5KOejkgSGEEIIcR8pKTcIC9vAunUhXLiQSIMG3rz9dhCDBw/HYDCUd/eEEEIIUYnczMji2+NX2HUsid8v3kQFtPNyZUwnrypf16IkyOgIIYQQBTh16iRr165my5bN3LqVRteu3Xj77WX06uWPRqMp7+4JIYQQopLIslj58dQ1dscnEXUqmSyLgncNJyZ3a0BvqWvxSMo8gZGcnMyLL77I+vXrsbOzY9asWahUKho1akRgYCBqtZqtW7cSHh6OnZ0dEydOpEePHmRkZDBjxgySk5PR6/UsXboUNzc3YmNjWbx4MRqNBj8/PyZPnlzWIQkhhKgiFEXhp5+iCAlZxZ49EdjZ2fHCCwMJCHiFli1blXf3hBBCCFFJ3LeuRVN3fGrr5daQIijTBEZWVhbz58/HwcEBgLfffpupU6fSsWNH5s+fz759+2jdujVhYWFs376dzMxMhg0bRteuXdmyZQs+Pj5MmTKFXbt2ERwczNy5cwkMDGTlypV4enoyfvx44uLiaN68eVmGJYQQopIzm818+uknhIQEc/To79SoUYNp06YzevS/cHevU97dE0IIIUQlkXgjnYj4JCKO5a1r0aeZOx1z1bUQRVOmCYylS5cyZMgQQkNDAYiLi6NDh9sV27t3786PP/6IWq2mTZs26HQ6dDodXl5eJCQkEBMTw7hx43K2DQ4OxmQyYTab8fLyAsDPz4/o6GhJYAghhHgoycnJbNiwjv/7v4+4fPkyjRs3YfnyFQwcOBhHR8fy7p4QQgghKoG7dS12H0vicK66FqM73q5rYbCXyg0lpcxG8vPPP8fNzY1u3brlJDAURclZNqPX60lNTcVkMuHs7JzzOb1ej8lkytOee9vcBdT0ej3nz58vq5CEEEJUUsePJxAaGsxnn4WTkZHBM888w/vvB9Ojx1OynFMIIYQQD5RlsfLT6WvsPpbEgTt1LRrUcGKSX316N61NHReH8u5ilVRmCYzt27ejUqmIjo4mPj6emTNncu3atZz309LScHFxwWAwkJaWlqfd2dk5T/v9tnVxcXlgXzQaFa6uTve0qfO12RJbjx9kDCR+ib+qx68oCt98s5cVKz5g7969ODg4MHz4CKZMeZWWLVtgsVjLu4vlxhZ+fyGEEKK4FEXh6J26Ft/kqmsxsFVd+jarTePaBrkQUsrKLIGxefPmnH+PHDmSBQsWEBQUxMGDB+nYsSORkZF06tQJX19f3n//fTIzMzGbzZw8eRIfHx/atm3L/v378fX1JTIyknbt2mEwGNBqtZw7dw5PT0+ioqIeqoinxaJw48atPG2urk752myJrccPMgYSv8RfGvFHxBsJPnAGY2om7s72vNKtPn2aupf499xPeno6n30WTmhoMCdOHMfdvQ6zZ89j1Kgx1KhRAwCLxSq/fxHjr1XL+cEbCSGEEJXYhZR0Io4lERGfxLnr6djbqfl7wxr0bS51Lcpaud6MM3PmTObNm8d7772Ht7c3/v63H003cuRIhg0bhqIoTJs2DXt7e4YOHcrMmTMZOnQoWq2W5cuXA7Bw4UKmT5+OxWLBz8+PVq2kSrwQQlQEEfFGluz9g4zs2ysbLqdmsmTvHwBlksQwGi+zfn0oGzas59q1a7Rs2YoPPwxhwICX0Ol0pf79QgghhKi8bmZk8e2Jq0QcMxJ74U5dC89q/KODJz2lrkW5USmKopR3J8paVpZFVmDcw9bjBxkDiV/iL+n4+4Ue5HJqZr72Os72fDm+Y4l+V25HjhxmzZpV/Pe/28nOzsbfvy8TJkyic+euhS7rlN9fVmCUhILmF8Ulx6Ztxw8yBrYeP8gYlGX8t+taXGf3MWOeuhZ9m9Z+YF2L0lx1aovHQGHzC0kbCSGEKBXGApIX92svDovFwt69XxMSsoqfforCyUnPP/4xhnHjJuDt3bDEv08IIYQQVYOiKMRdTmX3sST2JiTl1LV46U5diyYPUdeivFed2hJJYAghhCgV7s72Ba7AcHe2L7HvMJlMhIdvIjR0NWfOnMbDw5PAwLcYMWIU1aq5ltj3CCGEEKJquZCSzor9p9j/5zUsd25KaPmYM2M71aNjPVfsNOqH3lfwgTM5yYu7MrKtBB84IwmMEiYJDCGEEAUq7lLIV7rVz3M1AsDBTs0r3eoXu2+Jief56KMQNm3awM2bKbRr9wRvvhnIs88+j52dnNqEEEIIkV9qRjbfnrhCxDEjv124me/9P66kcTMz65GSF1C2q05tnczyhBBC5FMSSyHvbleS94P++uvPhIQE89VXOwB47rn+BAS8Qvv2HYq8TyGEEEJUXXfrWkTEGzlwMhmzRaGBmxMGnQaT2ZJn26KumiiLVafiNklgCCGEyKewpZDzdx8n+MCZh05E9GnqXuylk9nZ2ezatZM1a1YRE/MLLi7VCAiYxLhxAXh4eBZr30IIIYSoehRF4diduhZ77tS1qO6o5cVcdS06vnegwM8WZdVEaa46FXlJAkMIIUQ+9zt5l1VhqpSUG2zatJF160JITDxP/foNWLLkXYYMGYHBYCi17xVCCCFE5XQxJYOIeCO7jyVx7no69nZqujesQd9mtelUr3qeW0NKctVEaaw6FQWTBIYQQoh8Cjup31WahalOnTrJ2rWr2bJlM7dupdG1azeWLAmiVy9/NBpNiX+fEEIIISqvgupatPOsxj+e8KSnT00M9gX/yVvSqyZKYtWpeDBJYAghhMinoJP6vUqyMJWiKPz0UxQhIavYsycCOzs7XnhhIAEBr9CyZasS+x4hhBBCVH7ZFis/nblOxDEjkXfqWtR3c+QVv/r0blqbx1wcHrgPWTVROUkCQwghbNC9TxiZ4d+Y7vX+euxo7pN6YSsxSqIwldls5osvthEaupojRw7j5ubGtGnTGT36X7i71yn2/kXVc/jwYZYtW0ZYWBhnz55l1qxZqFQqGjVqRGBgIGq1mq1btxIeHo6dnR0TJ06kR48eZGRkMGPGDJKTk9Hr9SxduhQ3NzdiY2NZvHgxGo0GPz8/Jk+eDMCHH37IDz/8gJ2dHXPmzMHX17ecIxdCCNuWu67F3uNXuJGeRXVHLS/4PkbfZu40dTegUqmIiDcyPvzwQyUlZNVE5SMJDCGEsDEFPWHkzR1HmdOrUZ6T+N2T+r3bQ/ELUyUnJ7NhwzrWr19LUpKRxo2bsHz5CgYOHIyjo2OR9yuqtrVr17Jz586cY+Ttt99m6tSpdOzYkfnz57Nv3z5at25NWFgY27dvJzMzk2HDhtG1a1e2bNmCj48PU6ZMYdeuXQQHBzN37lwCAwNZuXIlnp6ejB8/nri4OAB+/vlnPvvsMy5dusSUKVPYvn17eYYuhBA268KNdD793zl2HzNy9no6Oo2K7g1r0rdZbTrXz1vXoqA5zqKvT7Bs35+kZlpklUUVIAkMIYSwMQU+YSSr8JoWJbnE8vjxBEJDg/nss3AyMjLo0eMpVqxYTY8eT6FSqYoWkLAZXl5erFy5kjfeeAOAuLg4OnS4/Qjd7t278+OPP6JWq2nTpg06nQ6dToeXlxcJCQnExMQwbty4nG2Dg4MxmUyYzWa8vLwA8PPzIzo6Gp1Oh5+fHyqVirp162KxWLh27Rpubm7lE7gQQtgYU2Y23x6/wu74JH5LTAGgrUc1Rj7hwVM+tQqta1HQHCfLqpCVeftxqWVViFyUHklgCCGEjSmsdsX9aloUZ4mloih8//0+QkJW8f33+3BwcGDQoCGMH/8KjRs3KdI+hW3y9/cnMTEx57WiKDmJL71eT2pqKiaTCWdn55xt9Ho9JpMpT3vubXM/0Uav13P+/Hns7e1xdXXN056amioJDCGEKKZ7b2HNfUEk22Il+sx1dh9LIvLkVcwWhXrVHZn2VCOebFCdutUeXNfiYepzlWYhclH6JIEhhBA2piQfG3Y/6enpbNv2KaGhwRw/nkDt2u7MmjWXf/xjLDVq1CjR7xK2Sa3+a9lwWloaLi4uGAwG0tLS8rQ7Ozvnab/fti4uLmi12gL38SAajQpXV6eSCC3XPtUlvs/KxNbjBxkDW48fqs4Y7Dx8kSXf/EFG1l+3dyzee4LkDAvXbpnZdeQy19LMVHfSMvgJTwa0+hst/+aCnZ0Gi6XwouK5PVbNgYspGQ/czpiaWanGtKocAyVBEhhCCGFjCnxsmLZ4NS1yMxov8/HHa9mwYT3Jycm0aOHLypVrGDDgJeztC0+S3O+qjBAFadasGQcPHqRjx45ERkbSqVMnfH19ef/998nMzMRsNnPy5El8fHxo27Yt+/fvx9fXl8jISNq1a4fBYECr1XLu3Dk8PT2Jiopi8uTJaDQagoKCGDt2LJcvX8ZqtT7U6guLReHGjVslGqOrq1OJ77MysfX4QcbA1uOHqjMGQXuO5yQv7srMVvjguz8LrWuRkpL+SPFP6FrvgU9Rg9sXbSrTmFaVY+BR1KpV8IUDSWAIIYSNKaimxb1PISmKI0cOs2bNKv773+1kZ2fj79+XCRMm0blz1wfWtyio6JbcoyoeZObMmcybN4/33nsPb29v/P390Wg0jBw5kmHDhqEoCtOmTcPe3p6hQ4cyc+ZMhg4dilarZfny5QAsXLiQ6dOnY7FY8PPzo1Wr24/tbd++PYMHD8ZqtTJ//vzyDFMIIaqEwp5qBvD1hM44OxT/T9N75zguDnakZWaTrfy1TXELkYvypVIURXnwZlVLVpYlXwbLFrNaudl6/CBjIPFL/EWJ32Kx8M76cNavXU3qmd9R6xx46rlBLHrjdby9Gz70fvqFHixwYlPH2Z4vx3d85H49Kvn9ix5/YVdIbFFB84vikmPTtuMHGQNbjx8q9xjkrmvx7YkrBW5z91xf2ErM4sZfFVZ4VuZjoKhkBYYQQogSYzKZCA/fxPurPiTpwjk0LrVwfXIMzq2e4ZzBheOZBrwfYX9FKSwqhBBCiIpHURTijSZ2HzOyN+EK19OzcHXU0rGeK78lpmC2/HX9/O5qiPutxBzauUGx+lOcQuSi4pEEhhBCiIeWmHiejz4KYdOmDdy8mYLBsyk1n5+JU+MuqNQaoGjVvcuqsKgQQghRmVXk1QSXb2YQEZ/E7mNGzlxLv1PXogZ9mrnT5U5di8L63y/0YP5HvN+ZTxQ3gSGqFklgCCGEeKBff/2ZkJBgvvpqBwDPPdefgIBXeGV/BgXdh/ioKycKLCwq96gKIYQQOSpivShTZjbfnbjK7ngjMedTAGjzNxeG9/LgKZ9a+epaFLYaQlZiioclCQwhhBAFys7OZteunaxZs4qYmF9wcalGQMAkxo0LwMPDEwD3QwXXrlCpbk+0HnZCVVBh0Yp0VUmUrNOnTxf6XoMGcqVNCCEKEnzgTKGrFMryfJltsfK/s7frWkSeTCYz24pXdUcmdK1H76a1+Vs1x0fep6zEFA+rzBIYWVlZzJkzhwsXLmA2m5k4cSKPP/44s2bNQqVS0ahRIwIDA1Gr1WzdupXw8HDs7OyYOHEiPXr0ICMjgxkzZpCcnIxer2fp0qW4ubkRGxvL4sWL0Wg0+Pn5MXny5LIKSQghqqSUlBts2rSRdetCSEw8T/36DViy5F2GDBmBwWDIs21BKycArAqPfFVI7lG1HXPmzOH8+fN4e3uTu5a4SqVi48aN5dgzIYSoeHYevkjQnuOFPsWjLFYpKIpCQpKJXXF/1bWo5mBH/xZ16NusNs3qOD/wiWP3IysxxcMqswTGzp07cXV1JSgoiOvXr/PCCy/QpEkTpk6dSseOHZk/fz779u2jdevWhIWFsX37djIzMxk2bBhdu3Zly5Yt+Pj4MGXKFHbt2kVwcDBz584lMDCQlStX4unpyfjx44mLi6N58+ZlFZYQQlQ6Bd1/OrRzA06fPsXatav55JNN3LqVRpcufixe/C7PPNMbjUZT4L7uJhwWRBzHes+9JOVxVUhUDuvXr2fEiBEEBQXh7i7HhxBCFCYi3siSb/4gI8ta6DaluUrhbl2LiGNJnL52C51GRbeGNejT1J0uDaqj1ahL5HtkJaZ4WGWWwOjduzf+/v45rzUaDXFxcXTo0AGA7t278+OPP6JWq2nTpg06nQ6dToeXlxcJCQnExMQwbty4nG2Dg4MxmUyYzWa8vLwA8PPzIzo6WhIYQghRiHvvn710M4O5az/nw/n7+DXyG+zs7Bgw4CUmTJhEy5atHmqffZq6E7j7eIHvyb2roiCOjo4sXLiQixcvSgJDCFFllUTBzeADZ+6bvCiNVQq561ocOp+Cwu26FnN6NeLpAupalBRZiSkeRpklMPR6PXD70XuvvvoqU6dOZenSpTlLjfR6PampqZhMJpydnfN8zmQy5WnPvW3u5cx6vZ7z58+XVUhCCAFU7Irg97p7/6xiySItPpLUX3diNp7kkpML06ZNZ/Tof+HuXqfAz94vTrl3VTyqFi1aFPret99+y9NPP12GvRFCiJJVUgU373choE4JzjmyrQoHz1xn9zEj+3PVtRjfpR59mhWtroUQpaFMi3heunSJSZMmMWzYMPr160dQUFDOe2lpabi4uGAwGEhLS8vT7uzsnKf9ftu6uLg8sB8ajQpXV6d72tT52myJrccPMgYSf9Hi33n4Yp6lnZdTM1nyzR/onex5vlXdku5msV00JnEzNgLToV1Y0q6jreGFm/9kDM178M47/Qv93IPinOHfmDd3HM1zlchBq2aGf+NKcVzJ8V+x4t+4caMkMIQQlVpJFdws7AJBHWd7vhzfsVh9vFvXYvexJPYmJHHt1u26Fs/fqWvRvJh1LYQoDWWWwLh69Spjxoxh/vz5dO7cGYBmzZpx8OBBOnbsSGRkJJ06dcLX15f333+fzMxMzGYzJ0+exMfHh7Zt27J//358fX2JjIykXbt2GAwGtFot586dw9PTk6ioqIcq4mmxKNy4cStPm6urU742W2Lr8YOMgcRftPiD9hzPt7QzI8tK0J7jdK/nWlLdK7bjxxMIDQ0mccsnKNlmHBq0pUb7qTg0aItKpaJuNYf7xv+gOLvXc2VOr0b5Vmh0r+daKY4rOf6LHn+tWs4P3ugR5S7sKYQQlVFJPRb0lW7189XAKO5tI/fWtdBqVHTzrkHfZrXp0sCtxOpaCFEayiyBsWbNGm7evElwcDDBwcEAvPnmm7z11lu89957eHt74+/vj0ajYeTIkQwbNgxFUZg2bRr29vYMHTqUmTNnMnToULRaLcuXLwdg4cKFTJ8+HYvFgp+fH61aPdw920IIURIq8nPLFUXh++/3ERKyiu+/34eDgwPde7/A2bo9UKp75GznYKfm//Xyue++HiZOuXdVlBS54ieEqOxK6tbKPk3d0TvZE7TneLFuVTVlZvPdH1eJOGYk5k5di9Z/c2F2r0Y87VMTFwftI+1PiPJSZgmMuXPnMnfu3HztmzZtytf28ssv8/LLL+dpc3R0ZMWKFfm2bd26NVu3bi25jgohxCOoiLUf0tPT2bbtU0JDgzl+PIHatd2ZNWsuo0aNoWbNmgXWsni+Vd37XoGviHEKIYQQFVVJPhb0+VZ1i7Sqs6C6Fp6uDozvUo/eTWvj4Sp1LUTlU6Y1MIQQoqqpSM8tNxov8/HHa9mwYT3Jycm0aOHLypVrGDDgJezt/0o0FGWlREWKU1R9cguJEKKyK6/HgiqKwvE7dS325Kpr0a+5O32budPiMalrISo3SWAIIUQxVITnlh858jshIav44ottZGdn4+/fh4CASXTp4vfQk5Sdhy/ed3lqRYhTVH1JSUnUrl2b0aNHl3dXhBCi2Mry1srLNzP4Oj6J3fFJnE6Wuhai6pIEhhBCFFN51H6wWCx8880eQkJW8eOPB3By0jNq1Gj+9a+JeHs3fKR9RcQb8z9hpIBHvUmNC1HSPvjgA7Zs2UJWVhYZGRnUr1+fXbt20bNnz/LumhBCVHhp5my+O3GV3bnqWrSq68Lspx/n6ca1pK6FqJIkgSGEEJWIyWQiPHwToaGrOXPmNB4engQGvsWIEaOoVq1oTz0JPnAm/xNGivCoNyEe1YEDB4iMjGTJkiWMHj2ahQsXlneXhBCiSAqqL1Ua59Bsq8LBs9eJOGbkhz//qmvxry716CN1LYQNkASGEEKUg0ed6CQmnuejj0LYtGkDN2+m0K7dE7z5ZiDPPvs8dnbF+195RX6SiqjaXF1d0el0pKWlUa9ePdLT08u7S0II8cgi4o156kQVtpKxqBRF4bjRxK5jxjx1LZ5r7s6zlaSuRVkleETVJwkMIYQoY48y0fn1158JCQnmq692APDcc/0JCHiF9u07lFh/5AkjorzUqVOHbdu24ejoyPLlyzGZTOXdJSGEeGTBB87kKXINJbOS0ZiaydfxSew5foU/kkxoNSr8vGvQt2ltunpXnroWpZ3gEbZFEhhCCFHGCpvozN99nOADZwjo7IHl1EHWrFlFTMwvuLhUIyBgEuPGBeDh4Vni/XmlW/08NTBAnjAiysa///1vLl26RO/evfniiy94//33y7tLQgjxyEpyJWNOXYv4JGLO3UAB2nq5Mvvpx3nKpxbVHCtfXYvSSvAI2yQJDCGEKGOFTWisGSaOH/yc8e9+SfbNK9Sv34AlS95lyJDhGAzOpdafPk3d0TvZ3/cpJEKUJIvFgsVi4fXXX+c///kPiqIwaNAgxo8fz8aNG8u7e0II8UiKu5Ix26rw89nr7M5V18LD1YF/da5Hn2a1aVG/Bjdu3CrpbpcZuVVVlCRJYAghRBm7d6KTdf0SqTE7Mf3+DUpWBvaeLWjQbzKRy15Fo9GUSZ+eb1WX7vWKVgRUiEe1fft21qxZw9WrV+nduzeKoqDRaGjXrl15d00IYeOKUqvhlW7189wiAQ9eyagoCieS0tgdb+Tr+Nt1LVzu1LXo28ydlpWgrsXDkltVRUmSBIYQQpSxV7rVZ/GeE6Sc/p2bv+4g/Y+DoNagb9oNlycGoHNvSBaUWfJCiLL28ssv8/LLL7Nt2zYGDhxYrH19/vnnfPHFFwBkZmYSHx9PeHg4EyZMoH79+gAMHTqUvn37snXrVsLDw7Gzs2PixIn06NGDjIwMZsyYQXJyMnq9nqVLl+Lm5kZsbCyLFy9Go9Hg5+fH5MmTixu2EKKCK2qthrvvPUziw5iayZ74JHbHGzl59RZ2ahV+3m70beZO1wZu6Owqdl2LskrwCFEYlaIoSnl3oqxlZVnyLcNydXWq1EuzisvW4wcZA1uOPyLeyJofz3IpJSPfybikq2abzWb++9/tBH3wAWf/OIba0QVD6z44t30WO4NbznYu9hqcdHZldkuHLf/+IPEXJ/5atYp+e9Ply5dZtmwZ169fx9/fn8aNG9OqVasi72/hwoU0adIEtVpNamoqY8aMyXnvypUrjBkzhu3bt5OZmcmwYcPYvn07mzdvxmQyMWXKFHbt2sVvv/3G3Llz6d8LAoE6AAAgAElEQVS/PytXrsTT05Px48czdepUmjdvft/vL2h+UVxybNp2/CBjUJbx9ws9WOBKgTrO9nw5vmOR95tmzub7P66y+1gSv96pa+Fb14W+zWrz9EPUtagox8C9CR64nYiY80yjB85RijOfqijxlydbHIPC5heyAkMIYdPud7UFKLGq2cnJyWzcuJ7169diNF7Gx6cxy5Z9QDXfnizbfz7PZMBOBelZVm5mZhb7e4WoyObPn8/o0aMJDg6mffv2zJo1i61btxZpX0eOHOHPP/8kMDCQwMBATp8+zb59+6hXrx5z5szh999/p02bNuh0OnQ6HV5eXiQkJBATE8O4ceMA6N69O8HBwZhMJsxmM15eXgD4+fkRHR39wASGEKJyK8laDdlWhV/OXWdXnJH9fyaTcaeuxbjOXvRp6o5ndcfidrfMFacYZ5+m7jKHESVCEhhCCJt2v5Px3X8X9N69J+GIeCPL9v3JzUwLANUc7Ph/PRvirb5OaGgwn30WTkZGBj16PMUHHwTTo8dTOfe26uwd8lyVSM+ykJKR/VDfK0RllpmZSefOnVm9ejXe3t7Y2xf9fuiQkBAmTZoEgK+vL4MGDaJFixasXr2aVatW0aRJE5yd/7qao9frMZlMmEymnHa9Xk9qaiomkwmDwZBn2/Pnzxe5b0KIklfSKySh+LUaFEXhxJU0dh8zsifhCslpZlwc7Hi2uTt9mtbGt65Lpa5rIcU4RUUgCQwhhE0rysn43vci4o38O+I42XduyFMUhcvHDjJx4xxunT6Eg4MDgwYNYfz4V2jcuMkD+3Rv8uJh+iREZaTT6Thw4ABWq5XY2Fh0Ol2R9nPz5k1OnTpFp06dAOjVqxcuLi45/160aBHt27cnLS0t5zNpaWk4OztjMBhy2tPS0nBxccnTlrv9QTQaFa6uTkWKofB9qkt8n5WJrccPMgYFxb/z8MU8j/++nJrJkm/+QO9kz/Ot6hb5u2b4N+bNHUfzPlZcq2aGf+P7/gaXb2aw8/BFdsRe5ESSCa1GxZM+tRjQ+m/83acW9sWsa1FRjoHHqjlwMSWjwPbS7F9Fib88yRj8RRIYQgib9qCrLQ9zJWbZvj/JVsCalUla3Pek/rqTrORzaPTV8Xjqn+xdOZ+aNWsW+P0F3cJyv76WptK4miXE/SxatIilS5dy/fp11q9fz4IFC4q0n19++YUuXbrkvB47dizz5s3D19c359YPX19f3n//fTIzMzGbzZw8eRIfHx/atm3L/v378fX1JTIyknbt2mEwGNBqtZw7dw5PT0+ioqIeqoinxaJIDYwSZuvxg4xBQfEH7TmeJ8kAkJFlJWjP8WI9Uat7PVfm9GqU71zYvZ5rvj7cMlvu1LUw8suduhYtH3Nh5lOP83TjWrjeqWuRbsrg82KeXyvKMTCha70Ca2BM6FqvVPtXUeIvT7Y4BlIDQwhRpirLH8MPqox973u361NY6LA8Endne7p6V+da8hVMh3aRGhuBNf0m2tre1Hh2Gvom3VHbaQtNXkDBt7AUpLSrde88fLHE6n0I8bDq1KlDQEDA/2fvzgOiKtcHjn+HAYZlQEAWtcAdtyuumSaaWuaSlWmoWFZqQZrmnl77pZmp3WuYlSKg2HXJcqushEozRcx7Xe6lUtyXJBdQEGQQHJg5vz9UAp1hhwHm+fwFhzNnnuc4cg7Ped/n5fz58zRv3hxfX98yHefcuXM8+OCD+d+/8847zJ8/Hzu72///5s+fj1arZdSoUYwcORJFUZgyZQoajYbg4GBmzpxJcHAwdnZ2hIWFAbcbgk6fPh2DwUBgYGC5mosKISpWZU5lKKpXg8GocODCdWISU9h96ho5eUYeqFN0X4uyrmxSHZVmtRUhKousQnKHNVa1CrL2/EHOQUXmX54u1ZZQ0lVIXDRqsnON5Bpv/9rUJ5/lxqGvyUqMA6MBx2ZdcH3oGTS+bfPnuBbXubxLWBzmfgnXc9FU2Q3CM6sOmBwWWt7O6zWF/P+3zCok4eHh7N27l7Zt2/Lbb7/Rv39/Xn755TIfz9JkFZKKZ+35g5wDU/lX1moh5pxM0RGTmML3x1NIzdLjorGlbwsvBrb+q6+FuQc3FRGrfAasO3+wznNQrhEYOp2OjIwMPDw8cHSseR1zhRBVqzxdqi1hQCsfgrs1NnlhKPgk5qmo/5CRk0326YPcOPQ1ty78jsrOAZf2/XHp/DR27oXn3drZqIodNWFuCktVFw4umyhegPTdEJUrLi6ODRs2YGNjQ15eHiNHjqzRBQwhRNUobvRkRUjJvMUPx1OISUzh9LUsbG1UBDbxYEBrHwIbe2BfoK9FUaMspPGlEBWryALG119/zYYNG0hPT8fDw4PMzExcXV0ZOXIkTz31VFXFKISoYcp7sa6O0090Oh2ndm/hxuFvyLt+GbWLF269RuPSrh82Dtr79lcBb/f3LzbuqrgJKwlzjbkqu++GsG4eHh5kZ2fj7OxMbm4uHh4elg5JCFEDVNZUhpt6A7tPX2P70YJ9LVzu62txr6Ie3JR3ZRMhRGFmCxizZs2iY8eOrFq1qlDn7czMTL799ltmzJjB4sWLqyRIIUTNUp6LdXWbK/rnn0lER0exfv0aMjLSsa/fAs+nR+HUojsqG7XJ15Rmukx1mU86ra8/b319xOKFFGEdhg8fjkqlIjU1lX79+tGiRQvOnDmDm1vZm+8JIaxLUb0qSsNgVDh4p6/Fz3f6WjSo48DYrn4MaO2Dn4m+Fvcq6sHNvIEtqsWDCiFqC7MFjHnz5plcj93FxYWRI0cydOjQSg2sNIxGI++88w4nTpzA3t6e9957j4YNG1o6LCGsVnlGFVSX6SeHDh0gMjKc777bBsCgQc8Q0G8Emy5q78vryTbe7Dt7vcwFiIq6CSuPp9s1IOvmLYsXUoR1WLJkiaVDEEJYuVNXdWw/msIPx1O4dqevxcDWPoX6WpRUUQ9uqsuDCiFqC7MFjBUrVjBp0qRC/3lTU1N58803iY6ONlncsJSdO3ei1+vZuHEjCQkJvP/++6xYscLSYQlhtUp6sTY1VcSSc0Xz8vLYvv0bIiKWc/jwQVxd6xAa+jpjx4bg6+sHQLNqOL2lolSHQoqwDiqVin379tGwYUO6dOli6XCEEFbiqu4W3x9LIfZYCqeu3u5r0b2xBwNbe9O9SV00BfpalEZxD27k+ipExSmyB8bo0aNZsmQJHh4exMfHM2fOHMaOHVtVsZXY4cOH6dGjBwDt27fnyJEjFo5ICFHcxdrcVBEXjZobtwz37V+Zc0UzMtJZvXoFy5Yt488/k2jUqDELF/6TESOeR6st3AFZbkKEKL+IiAh27drF7NmzLR2KEKKWu9vXIibxdl8Lo3K7r8WbjzWjbxF9LUpDRlkIUXXMFjAmT57Mzz//zMsvv0z79u357bffiIiIwN/fvyrjKxGdTodW+1cTPbVaTV5eHra2JVpkRQhhAeamimhsbXGwtamSuaLnzp1l5coVfP75Z2Rl6XjkkUAWLPgnTzzRH7XadH+LqlQdm5kKURHGjRtHbm4uZ8+etXQoQogaojTXRINR4dCFdGKOJfPzqWtk597uazH6YT8GtPKmoYdThccnDziEqBpF/oXfrFkzHB0d+eWXXxgwYADNmjWrqrhKRavVkpWVlf+90WgssnihVqtwc3O6Z5vNfdusibXnD3IOqjp/c1NCbuTkEdzFl40HkzAooFbBkI4PENytcYW8r6Io7N0bx0cffcR3332Lra0tw4YNZ/LkybRr175C3qMifPPrJRbuOEVOboERKjtO4eyk4el2DfL3CdtxkssZOdSv48C0vv75Pyst+fxL/lWZv7u7O1OnTsXLy6vK3lMIUXOVtMH3qas6YhJv97W4qrvd16J/K28GtvKh3QOl62shhKiezP6Vv23bNj7++GNmz57No48+ygcffMCoUaNYsmQJPj7Vq7rYsWNHfv75ZwYOHEhCQkKxo0QMBoX09JuFtrm5Od23zZpYe/4g56Cq8zfX8MrVwZYv/3sRg3L7e4MCX/73Ii09ncr1ZEOv1/P111uJjAzn999/xcPDg8mTpzF69KvUq1e/2v37L/7hRH7x4q6cXCOLfzhBz4Zu993MXcrI4a2vj5B181aZzlN1y7+qSf5lz9/Ly6X4ne6Rk5PDypUrmTVrFqdPn2bWrFloNBoWLFhAkyZNyhSHEKL2KqrB90O+bnx//CoxicmcupqF+k5fi2m9vQksR18LIUT1ZLaAsWXLFjZs2JBfrJg1axY7duzg+eefZ+fOnVUWYEn07duXffv2MWLECBRFYeHChZYOSQhRDHMNrxRFqdBVSFJTU1m7djWrV68kOfkK/v4t+OCDjwgKGoGjY/FLo1lKcc1Mq8tqLUKUxbx58+jQoQMA7733HqNGjcLf358FCxYQHR1t4eiEENWNuWvilcxbPBn1H4wK/K2+CzP6NOOJFl64OZW/r4UQonoyW8BYu3btfcOs+vbtS8uWLSs9qNKysbHh3XfftXQYQohSMNfwam7MCZP7l3YVkpMnTxAZGc7mzZ+Tk5ND796P8dFHy+nd+/EaMYS0qCXZoPgChxDV2Y0bN3jxxRfR6XScOHGCwYMHo1KpyM7OtnRoQohqyNw1Ua2Clx72Y2Al9bUQQlQ/ZgsY5m7wfX19Ky0YIYR1MdXwKnzveZM3KSoVdAmLK7Jxl6Io/PzzT0RFhbNr104cHBwIChrBq6+Oo2XLVpWWR2Uobkm24gocQtQEBw8epHPnzvn3HFLAEELc68SVTJp6Od13zbNXq5j9RHOebF3PQpEJISxBlukQQlQrpv5wBzDe6YlhqnFXdnY2W7ZsJCoqnBMnjuPt7cOsWf/Hiy+OwdPTs0rjryjFLclWXIFDiOrM29ubJUuWEB8fz/jx49HpdKxatYoWLVpYOjQhRDVwTXfrvr4WLb21XLmRQ3pOHvVkZS4hrJYUMIQQZlliGc97/3BXqf4qXtx1t9dDRw/49NMo1qxZTWpqKn/7WwCffBLB4MFD0WjuH4lQ05YlLWpJNllzXtRk77zzDlu3bmXSpEk8+uijJCQkoNPpmDNnjqVDE0IUo7Kupdm5BnafvkZMYgoH/riOUYE29VyY82QrAv3q4O5kX+j958acIGzXGRRFIfOWQa6DQlgJlaIoiqkfjBo1yuw0EkVRUKlUrF27tlKDqyy5uQZZheQe1p4/yDm4N/97V7mA20/4Zz/RvEpvDrqExXHvLyl98lluHPoa/Ym95OXl0a/fAEJDX+eRRwLN/t4qLh/595f8Jf+qW4XkrqSkJK5fv46Pj0+1W+GsLEzdX5SXfDatO3+oXuegou8NDEaFQ0npxCYm8/OpVG7mGmjgqqF/ax8GtPKmkYdTofxNvX9BlrhPqQrV6TNgCdaeP1jnOTB3f2F2BMa6desqLRghRPVXXVa5uNvrQVGMZJ8+yI1DX3Prwu/Y2Dsw+sXRvPrqazRp0qzY41SXfIQQ8OeffzJ58mTs7OyoW7culy5dwtHRkQ8//BBvb29LhyeEMKOirqWnr2URm5jM98dSSNHp0WrU9G3pxZOtfWj3gCs2dx5GxB5LJmLfH1zOyMHHRcNNfZ7Z4kVZYxFC1CwyhUQIYVJ1WeViTCcv3v4wkrSDX5N3/TJqFy+8+oxh3tTxPNfFv8THqS75CCHg/fffZ9asWXTu3Dl/2759+3j33XdZtmyZBSMTQhSlqOVMn4r6T5FTOK5l6fnhWAoxicmcvNPX4pFG7kzp5UOPpnXR2NoAf00Rubdpp6nG1aWJUQhRO0gBQwhhkqVXubh48U9WrYpk/fo1ZGSk4/hAC9x6jMKpRXfcnDQ4u9Qp1fEsnY8Q4i9paWmFihcA3bt3Z+XKlRaKSAhREuaupWC6yXZOroHdp1PZnpic39eidT0XZvRpSt8WXvl9Le4qbopISWMUQtRexRYwfv/9d9q2bZv//YEDB+jSpUulBiWEsDxLrHIReyyZf26I5dyezdw8uQ8blYqnBj1DQL8RbLqozY8lIyfvvpuk4siqHUJUH7a2pm8/jMay/9EihKh85lYKuysnz8jcmBPMiTmBg60NRkVBb1Co76rh5S6+DGjlQ6O6TmaPb2qKSmnIdV2I2s9sAePQoUOcPn2af/3rX4wePRoAg8HAhg0b+O6776osQCGEZZR1lYuydCfPy8tjQdR6oleuIOfiMVQaZ1w7D8azy1MMCQq8c0NT+IlPaee5yqodQlQf6enpxMfHF9qmKAoZGRkWikgIURIFr6XmRmLcbbydk2dErYIxXX0JfaRRfl+LopRk+kcdB1sc7dQkZ97C1cFWViERwsqYLWC4urpy7do19Ho9V69eBUClUjFjxowqC04IYVlFLeNpyr1DP00NJy0oIyOd9evXEh0dyZ9/JmHrVh/3x0PR/u0xbDROGPir4GBKaee5ljYfIUTlaNOmDdu3b79ve+vWrS0QjRCiNO5eR+fEnCh2X4MCMUdTGNe9cYmOXdQUFbg9wmJan6ZyLRfCipktYPj7++Pv78+wYcOkI7gQokTMdSefE3OC8L3n85+MnDt3lpUrV/D555+RlaXjkUcC0Xd5CYemD6GyURd6/ZXMW9iowNSCzzLPVYiaadGiRSa3nzhR/B9EpgwePBgXl9vLrT344IO89tprzJo1C5VKRfPmzZk7dy42NjZs2rSJL774AltbW8aNG0fv3r3JyclhxowZpKam4uzszD/+8Q88PDxISEhgwYIFqNVqAgMDmTBhQpnzFaKmKWo0ZU6ugbBdZ0p8rNI8bChqiko9GWEhhKCIAsYbb7zBxx9/zJAhQ+772b3DPoUQ1s1cx/CCLt/I4f9WfslH53fwv30/YWtry+DBQwkNHU9AQHueivqP2dcbTRQvZJ6rEDXX3r17+ec//0mdOnVYtGgR9evXZ8mSJcTExLB79+5SHevWrdu/Nwou//7aa68xefJkHn74YebMmcNPP/1E+/btWbduHVu3buXWrVuMHDmS7t278/nnn+Pv78/EiRPZvn074eHh/N///R9z587lk08+wdfXl5CQEI4ePUqbNm0q8jQIUS2ZGk05505fC0dbGwx3+lqUVGkeNpia7jmjXwt6NnQrXRJCiFrLbAHjbqPOZcuW0b59+yoLSAhRsxTXMVwx5JJ1bC+Zh7ahTz7DZSdXJk+exujRr1KvXv38/YprDFaQjQpmP9FcnsIIUUMtXryYjz/+mIsXLxIWFkZqair169dn27ZtpT7W8ePHyc7OZsyYMeTl5TF16lSOHj2afx/Ts2dP9u3bh42NDR06dMDe3h57e3v8/Pw4fvw4hw8f5pVXXsnfNzw8HJ1Oh16vx8/PD4DAwED2798vBQxR68UeS+ad2BMmHxwAZN/pa+FkZ8PN3OKv18U9bDA30qPg9d3NzYn09JulTUUIUUuZLWBs3LiRBx98kA8//JA333wTpcD47cDAwCoJTghR/ZnrGG64mYEu4Xsy/7cdgy4Nu7q+ePSbgLZNL/4+64n79jf11MVsgzCl5KuPCCGqH1dXVxo3bkzjxo156623eP311xk2bFiZjuXg4MDYsWMJCgri/PnzvPrqqyiKgupOw0BnZ2cyMzPR6XT500zubtfpdIW2F9xXq9UW2jcpKakcGQtR/d19IGGueHGXQQE7tQ0OCvet7PVkG2/2nb1eombZpe2bJYQQUEQBY9KkSezcuZPU1NT7Vh2RAoYQ4q5757bmXkvixqFtZB3dhZKnx6FRB+oOmIRD4w6oVDbUK2Io6b1PXcxNK5HeF0LUbDY2Nvlf169fv8zFC4DGjRvTsGFDVCoVjRs3xs3NjaNHj+b/PCsrC1dXV7RaLVlZWYW2u7i4FNpe1L6urq7FxqJWq3BzM79EZFmo1TYVfsyaxNrzh6o5B9l6A0t+PlviJUxv5OTxwXMBhO04yeWMHOrXcWBaX3+ebtegxO8Zse8Pk32zIvb9QXC3v5p+ymdAzoG15w9yDgoyW8B4/PHHefzxx9m1axd9+vQhLS0NNze3QjcdQgjh46Lh8o0ccs7/jxsHt5Fz7jAqW3vcAvpQp/MzKO6++fuWtm+FqWkl0vtCiJovOzub8+fPYzQaMRqNnD9/Pn+kZ+PGJVut4K4tW7Zw8uRJ3nnnHZKTk9HpdHTv3p3//Oc/PPzww8TFxdG1a1cCAgJYunQpt27dQq/Xc+bMGfz9/enYsSN79uwhICCAuLg4OnXqhFarxc7OjgsXLuDr60t8fHyJmngaDEqFD3W39uHz1p4/VN45MCoKh5PSiUlM4edT18jSG0r8Wh8XDT0butHzlS6FtpcmzssZOWa3FzyOfAbkHFh7/mCd58DLy8XkdrMFjLucnJx47LHHcHFx4caNG8yfP5/u3btXeIBCiOqpqE7k2dnZtM44wP/+FYX+6h+ond2p0+MFPDs9yZxnOwOYfW1JmJpWIh3Ihaj5HBwcePvttwHQaDT5X6tUKtauXVuqYz333HP8/e9/Jzg4GJVKxcKFC3F3d+ftt99myZIlNGnShH79+qFWqxk1ahQjR45EURSmTJmCRqMhODiYmTNnEhwcjJ2dHWFhYQDMmzeP6dOnYzAYCAwMpF27dhV7EoSwkLOpWcQkpvD9sRSSM2/hbK/mMX9P9p5J43p2brGvr6gHCeamisooSyFEUVSKYmpxwr8EBwezdOlSfHx8SE5OZsKECWzevLmq4qsUubmG+ypY1ljVKsja8wc5B6byN9Wg08HWhvGd3Pgj/mvWrIkmNTUVv2atsG33FIZG3ajnrq2RRQb595f8Jf+y5W/uCYk1MnV/UV7y2ayd+Rf1cOBeFXEOUrP0/HA8hdjEFI6n6FCroFtjDwa08qZn07o42KnNXvNL09eiNMy9371NumvrZ6A0rP0cWHv+YJ3noMwjMNRqNT4+t3+J+Pj4oNFIVVQIa3Fvg0598lmuHfqaye/HgdFAv34DCA19nUceCcxvmCeEEEII8yq7eWXBpc3rONji7aLh7LUsDAq08tEytXdT+rX0wsPJvtDrqnrUo4yyFEKURbEFDK1Wy7p163jooYc4ePAgderUqYq4hBDVQHLmLRTFSPbpg9w49DW3LvyOys4Bbbv+7Ah/lyZNmlk6RCGEEKJG+eCn0yabV4bvPV/uP963J15hwQ+nyL2zlEhGTh4ZOXn0aOrBxB5NaFy36CaA9zbTrmxV/X5CiJqv2ALG4sWLCQ8P58MPP6Rp06YsXLiwTG+UmZnJjBkz0Ol05ObmMmvWLDp06EBCQgILFixArVYTGBiY3yRr2bJl7N69G1tbW2bPnk1AQABpaWlMnz6dnJwcvL29WbRoEY6OjuzatYvly5dja2vL0KFDy9XNXAhxm06nQzkay6V9X5J3/TJqFy/ceo1G264fD3jVleKFEKJCnDp1iubNm1s6DCGqROyxZG7cMt0s895VvUrjbl+LdQeTTC6Deiolq9jihRBC1ATFFjDeeeed/IZW5fHpp5/StWtXXn75Zc6ePcu0adP46quvmDt3Lp988gm+vr6EhITkL3124MABNm/ezOXLl5k4cSJbt24lPDycQYMGMWTIEKKioti4cSPPP/88ixYtYsuWLTg6OhIcHEzv3r3x8vIqd8xCWKOLF/9k1apI1q9fQ0ZGOg4NWuDWYxRO/o+gUtvKKiBCiAo1f/78UjfuFKKmCt973uzPStu8Mu2mnh+OXyU2MZljybf7WpgqXkD5iiNCCFGdFFvA0Ov1HD9+nMaNG+fPcbe3ty/mVfd7+eWX819nMBjQaDTodDr0ej1+fn4ABAYGsn//fuzt7QkMvD2nvkGDBhgMBtLS0jh8+DChoaEA9OzZkyVLltC1a1f8/Pzyp7Z06tSJQ4cOMWDAgFLHKERJlab5Vk1x4MB/+OCDML79dhuKojBo0DOEho7nmrZRrctVCFE97N27l4yMDOLj44Hb9wFC1GZFFRJK8nAgJ9dA3JlUYhJT+Pf5tEJ9LZ5o4cXLn/1PVvYQQtRqxRYwzp07x/jx41GpVCiKgkql4qeffiryNZs3b2bNmjWFti1cuJCAgACuXr3KjBkzmD17NjqdDq1Wm7+Ps7MzSUlJaDQa3NzcCm3PzMxEp9Ph4uJidtvd7TqdrmTZC1EGld18qyp99/tFFq78jKS4Ldy6dBxHZy0hIeN55ZVQfH1vFxZjjyVbOEohRG0VExNDamoq27dvB6SAIWo/c0uH1nGwNXsPYTQqHE5KJyYxmZ9OXiNLb8DHRcMLD/kysLU3Teo65+87vkcj06uHychJIUQtUWwB47vvviv1QYOCgggKCrpv+4kTJ5g6dSpvvvkmXbp0QafTkZWVlf/zrKwsXF1dsbOzu2+7i4sLWq2WrKwsHBwc8ve9u+3efYuiVqtwc3O6Z5vNfdusibXnDyU/BxH7/jDZfCti3x8Ed2tcWeFVqIyMDKa99yEb/hVFXkYKtm71cX88FM8OfXlsWBfatm0AwDe/XmLhjlPk5BYo1uw4hbOThqfbNbBkChXO2v8PSP6SvyXyX7RoEaNGjWLRokVV/t6idqhpIyLNFRim9Wl6377nUm8Sk5jMjyeucikjB2d7NX2aezKwtQ8dfetgY2L1L1nZQwhR25ktYMTGxvL+++/j4ODA4sWLCQgIKNcbnT59mkmTJrF06VJatmwJ3F7hxM7OjgsXLuDr60t8fDwTJkxArVazePFixo4dy5UrVzAajXh4eNCxY0f27NnDkCFDiIuLo1OnTjRt2pQ//viD9PR0nJycOHToEGPHji0yFoNBuW8dXWtcW7cga88fSn4OLmfkmN1e3c/huXNnWbUqgg0b1pOVpUPj+zfcHwvBselDqGzU6IHFP5ygZ8PbI6AW/3Aiv3hxV06usdA+tYW1/x+Q/CX/suZvbp32kurbt2+5Xi+sV00cEVlcgSHtpp4fj18lpkBfi8Bmnrwe2IieTeviYKcu0XtU1/yFEKK8zBYw1qxZwzfffASoCuEAACAASURBVMONGzdYsGABERER5XqjsLAw9Ho9CxYsAG4XL1asWMG8efOYPn06BoOBwMBA2rVrB0Dnzp0ZPnw4RqOROXPmADBu3DhmzpzJpk2bcHd3JywsDDs7O2bNmsXYsWNRFIWhQ4fi4yO/tEXlMTf8s7Lnl5b1KZOiKOzfv4+IiOX88EMMtra2DB48lHinrtjXu38lkYLzc83N1ZVmYEKIivLiiy9aOgRRQ4XvPV9py5FWpnsLDDm5Bn48nkLssRT2n7vd16Klt5YpvZrQr6U3TR9ws+oCqxBCFGS2gGFvb0+dOnWoU6cO2dnZ5X6jFStWmNzevn17Nm3adN/2iRMnMnHixELbPD09iY6Ovm/fPn360KdPn3LHKERJWGJ+aVmeMun1er7+eiuRkeH8/vuveHh4MHnyNEaPfpV69erzVNR/ii3EWKpYI4QQQhSnJhTZzT18MCoK//szo1BfC2+tvcm+FkIIIf5SbA8MuP0EVwhxmyXml5bmKVNqaipr165m9eqVJCdfwd+/BR988BHPPTccJ6e/5rh3b+LO1l+vFHrtvYUYaQYmhBCiuqruRXZTDx/e++EkO09c5WRKFlcyb+Fkp6aPvycDW3vTydfNZF8LIYQQfzFbwEhKSmLJkiUoipL/9V1Tp06tkuCEqK6qen5pSZ4ynTx5gsjIcDZv/pycnBx69erDRx8tp1evx7CxsSn0uthjyWw/mnLf8Z5s410oL2kGJoSoDBs3bjT7s+HDh1dhJKImq+5FdlMPH/QGhbgzaTzS2J0JPRrzaLOS9bUQQghxm9kCxhtvvGHyayFE1TP3lMlba8/PP/9EZORydu3aiUajIShoBCEh42nZspXZ45m6qQLYd/b6fdukGZgQoqJdvXrV0iGIWqA6F9lzcg0mr9t3fTSkbRVGI4QQtYfZAkaLFi1o3bq12RcePXqUNm3aVEpQQtQ0lb2M271PmYy5t9Af38PFY7EMP3cKLy9vZs58i5deGounp2exx6sJ84aFELXXhAkT8r9OSUkhLy8PRVFISbl/ZJgQRalORfa7fS1iE1PYedJ8ka5eNZniIoQQNZHZAsbevXuJjo6mf//+tGjRgrp165KZmcmvv/5KTEwMLVu2lAKGEFTNMm53j7M05r+c3fsVWQmx5N3M4G9/C2DWJxEMHjwUjabkN0TVfd6wEMI6zJ49m4SEBLKzs8nJycHX19dkY28hqrPzqTeJOZbM98dSuHzjdl+L3v6eeDrb8cXhi9wy/NVLrjpNcRFCiJrIbAEjNDSUK1eu8PnnnxMdHc3169fx9PSkS5cuzJw5kwYNGlRlnEJUW1WxjNuRI78TE7mco19tITc3l379BhAa+jqPPBKIqgwNv0zOG7aTmyohRNU6e/Ys27dvZ86cOUyZMoVJkyZZOiQhSuT6TT0/Hr9KzLEUEq9kYqOChxu6Mz6wMb0K9LVo6ulcLae4CCFETVXkKiT16tVjypQpAKSnp1OnTp0y/bEkRG1WWdMxjEYjP/74PVFR4cTHx+Hk5MyoUS/z6quv0aRJs3Id29S84Rn9WtCzoVu5jiuEEKXh7OyMSqXi5s2beHh4kJuba+mQhDDrVp6RuDOpxCQms//8dQxGBX8vZ6b0asITLb3xdLa/7zXVaYqLEELUBsUuo3rw4EHmzZuHwWCgf//+NGjQgKCgoKqITYgaoaKnY+h0OjZu/IyoqBWcO3eWBx54kDlz5vPCCy/i5uZe3nDz3XtT5ebmRHr6zQo7vhBCFKdNmzZER0fj7e3NlClTyMvLs3RIQhRiVBQSLmYQk5jCzhNXydIb8Nba83ynBxjQ2odmns6WDlEIIaxKsQWMpUuXsn79eiZOnMhrr71GcHCwFDCEKKCilnG7ePFPVq2KZP36NWRkpNOpU2f+/ve3efLJp7Gzs6vgqIUQwvKmTp2KTqfDwcGBuLg42rVrZ+mQhADgfNpNYhOTib3T10IFKIC7ox3jezTiydb1LB2iEEJYpWILGDY2Nri5uaFSqdBoNDg7S6VZiILKu4zb4cMHiYxczrffbkNRFAYNeobQ0PE89NDDlRm2EEJY3LJlywp9n5iYWGiFElF7VPZqXRUh/WYuP55IYXviX30tmtR1ws5GRa7xdiPO69m5vL/jNDYqVbWLXwghrEGxBQw/Pz/CwsJIT08nKipKmncKYUJp57jm5eURE/MtERHLOXToAC4uroSEjOeVV0Lx9fWrxEiFEKL6uLvss6IoJCYmYjQai3mFqIkqc7Wu8hZGbuUZ2Xunr8UvBfpaTH60Cf1aejF6Q0J+8eKuim7ULYQQouSKLWDMmzePzZs306lTJxwdHZk/f35VxCVErXTjRgbr168lOjqSpKQLNGzYiAUL/kFw8AtotS5mX1cTnlwJIURpjRgxotD3r7zySqmPkZuby+zZs7l48SJ6vZ5x48ZRr149XnvtNRo1agRAcHAwAwcOZNOmTXzxxRfY2toybtw4evfuTU5ODjNmzCA1NRVnZ2f+8Y9/4OHhQUJCAgsWLECtVhMYGCgjQ8qhMlbrij2WTNiuM2Tk/NU3paSFEaOi8OvFG2xPTOank1fR3TLgpbVnZMcHGNjah2Zef402rqxG3UIIIcqm2AKGra0twcHBVRGLELXWuXNnWbUqgg0b1pOVpeORRwKZP/99+vUbgFqtLvK1lfnkSgghLOncuXP5X1+9epXLly+X+hjffPMNbm5uLF68mOvXr/Pss8/y+uuvM3r0aMaMGVPo+OvWrWPr1q3cunWLkSNH0r17dz7//HP8/f2ZOHEi27dvJzw8nP/7v/9j7ty5fPLJJ/j6+hISEsLRo0dp06ZNheRtbSq6CHDvdbGgogojf6TdJOZYCt8nJnPpxi0c7Wzo09yTAa196Ozrhtrm/pX2KrpRtxBCiPIptoAhhCgbRVH4979/ISJiOd9/vx1bW1sGDx5KaOh4AgLal/g4lfHkSgghqoM5c+bkf63RaHjzzTdLfYz+/fvTr1+//O/VajVHjhzh3Llz/PTTTzRs2JDZs2fz22+/0aFDB+zt7bG3t8fPz4/jx49z+PDh/JEfPXv2JDw8HJ1Oh16vx8/v9pS+wMBA9u/fLwWMMqroIoCp62JBBQsjd/taxCSmcPROX4sufu68FtiIXs08cbQr+iFCRTXqFkIIUTGkgCFEBdPr9Xz99Vaiolbw228JeHh4MHnyNEaPfpV69eqX+ngyfFUIUVutW7eu3Me421xcp9PxxhtvMHnyZPR6PUFBQfztb39jxYoVLF++nJYtW+Li4lLodTqdDp1Ol7/d2dmZzMxMdDodWq220L5JSUnljtVaVXQRoLjrn7fWnp9OXiUmMYV959IwGBWaezkz6dEm9G/phae25IWT8jbqFkIIUbGkgCFEBUlNTWXt2tWsXr2S5OQr+Pu34IMPPuK554bj5ORU5uPK8FUhRG3Tp08fVKq/huvb2tqSl5eHvb09sbGxpT7e5cuXef311xk5ciRPPfUUN27cwNXVFYC+ffsyf/58OnfuTFZWVv5rsrKycHFxQavV5m/PysrC1dW10LaC24ujVqtwcyv773vTx7Sp8GNWhG9+vUTYjpNczsihfh0HpvX15+l2phu9B3drjLOTpsT7F2Qq//p1HLiUkWN6fxVk3Mpj1rfH8HHRMPqRRgxu14AW9cz3mSpOcLfGBHdrXObXl1d1/QxUFWvPH+QcWHv+IOegIClgCFFOJ0+eIDIynM2bPycnJ4devfrw0UfL6dXrMWxsbMp9fBm+KoSobb7//nsURWHevHmMGDGCgIAAEhMT2bBhQ6mPde3aNcaMGcOcOXPo1q0bAGPHjuXtt98mICAgf+pHQEAAS5cu5datW+j1es6cOYO/vz8dO3Zkz549BAQEEBcXR6dOndBqtdjZ2XHhwgV8fX2Jj48vURNPg0EhPf1mqXMoipubU4Ufs7zu7UFxKSOHt74+QtbNW2ZHJvRs6EbPV7oU2laSvEzl/1r3hmZ7YKhtVPRp7snAVj509vurr0V1O4elUR0/A1XJ2vMHOQfWnj9Y5znw8jJdeJYChhBloCgKu3fvIjJyObt27USj0RAUNIKQkPG0bNmqQt9Lhq8KIWobe3t7AJKSkggICACgdevWhZp6llRERAQ3btwgPDyc8PBwAGbNmsXChQuxs7PD09OT+fPno9VqGTVqFCNHjkRRFKZMmYJGoyE4OJiZM2cSHByMnZ0dYWFhwO1V2KZPn47BYCAwMJB27dpVUPY1n6V7M3Vr6MFj/p78ePxq/hKnTT2dePEhX3o3L76vhRBCiJpLpSiKUvxutUturuG+CpY1VrUKsvb8oWTnIDs7m61bNxEVFc7x48fw8vJmzJhXeemlsXh6elZRpJXD2j8Dkr/kL/mXLX9zT0hKYvz48fj7+xMQEMD//vc/kpOT+ec//1nm41maqfuL8qqOn80uYXGYunlUAQem9SzzcU0tGR7crTHp6TfR5xmJP5vK9nv6Wgxo5U3/Vt54laKvRU1THT8DVcna8wc5B9aeP1jnOZARGEKUQ3JyMp9+upI1a6JJTU2lTZu2fPzxCp599jk0mtp70ySEEJXpgw8+4KuvviIuLo6mTZsyefJkS4ckSqAyejOZWjJ8wQ8nSb6Zx6W0LHaeuEbmrTw8ne0J7vgAA1t709xLW8xRhRBC1DZVXsA4c+YMw4YN45dffkGj0ZCQkMCCBQtQq9UEBgbmzzFdtmwZu3fvxtbWltmzZxMQEEBaWhrTp08nJycHb29vFi1ahKOjI7t27WL58uXY2toydOhQhg0bVtVpiVrqyJHfiYxczldfbSE3N5d+/QYQEjKe7t17FGpAJ4QQouR+//132rZty3//+18aNmxIw4YNAdi/fz+BgYEWjq76MTUywZLTCCuqN1PBvFQqMN4zrOOWQWH57jM42NrQu7knT7Yu3NdCCCGE9anSAoZOp+Mf//hH/txXgLlz5/LJJ5/g6+tLSEgIR48eBeDAgQNs3ryZy5cvM3HiRLZu3Up4eDiDBg1iyJAhREVFsXHjRp5//nkWLVrEli1bcHR0JDg4mN69e+Pl5VWVqYlaxGg0smPHD0RGLic+Pg4nJ2dGjXqZV199jSZNmlk6PCGEqPH2799P27Zt2b59+30/kwJGYd/8eum+kQkLfzwFYLEiRkX0Zrp3xEVRE5p/GNcNJ3vpayGEEKIKCxiKovD2228zdepUxo8fD9wuaOj1evz8/IDbNy379+/H3t6ewMBAVCoVDRo0wGAwkJaWxuHDhwkNDQWgZ8+eLFmyhK5du+Ln50edOnUA6NSpE4cOHWLAgAFVlZqoJXQ6HdHRK4mKWsG5c2d54IEHmTNnPi+88CJubu6WDk8IIWqNkJAQABYtWoTBYEBRFBISEvIbeoq/hO04adGGmeYMaOVTrvc31QjUlAZ1HKR4IYQQIl+lFDA2b97MmjVrCm1r0KABAwcOpGXLlvnbdDodWu1f8xednZ1JSkpCo9Hg5uZWaHtmZiY6nQ4XFxez2+5u1+l0lZGWqKUuXvyTVasi+eyzNaSnp9OpU2f+/ve3efLJp7Gzs7N0eEIIUWstXrwYX19fLl26xNGjR/Hy8uL999+3dFjVyuWMHJPbk030oKgJFEXht0s3TPbQuJeDrQ3T+vpXQVRCCCFqikopYAQFBREUFFRoW9++fdm6dStbt27l6tWrjBkzhsjISLKysvL3ycrKwtXVFTs7u/u2u7i4oNVqycrKwsHBIX/fu9vu3bcoarUKNzene7bZ3LfNmlhj/gcO/IePPvqIL7/ciqIoDBkylDfeeIOuXbtZOjSLsMbPQEGSv+Qv+Vd9/ocPH2bGjBmMGjWKdevW8dJLL1V5DNVd/ToOXDJRxChPw0xLSLqeTUxiMrHHUriYkYMKTK5kYqO6PZ3k7rSUp9s1sLrO+0IIIcyrsikkO3bsyP+6T58+rF69Go1Gg52dHRcuXMDX15f4+HgmTJiAWq1m8eLFjB07litXrmA0GvHw8KBjx47s2bOHIUOGEBcXR6dOnWjatCl//PEH6enpODk5cejQIcaOHVtkLAaDIsuo3sNa8s/LyyMm5lsiIpZz6NABXFxcCQkZzyuvhNK2bUvS029axXkwxVo+A+ZI/pK/5F/1y6gajUZ+++03HnzwQfR6PWlpaWU+Vm01ra8/b319pNwNMy0hPTuXnSeuEpOYzO+XM1EBD/m5EfJIQ/QGI2G7ztyX1+wnmlt0aowQQojqzeLLqM6bN4/p06djMBgIDAykXbt2AHTu3Jnhw4djNBqZM2cOAOPGjWPmzJls2rQJd3d3wsLCsLOzY9asWYwdOxZFURg6dCg+PnLhE4XduJHB+vVriY6OJCnpAg0bNmLBgn8QHPwCWm3Zb76FEEKU3TPPPMP8+fNZuHAhixcv5sUXX7R0SNXO0+0akHXzVrVahaQo+jwj8efSiE1MJv5sGnlGhWaezrzRszH9WnrjXWDkiMbWpsbkJYQQonpQKUpRfZ9rp9xcg4zAuEdtzf/cubOsWhXBhg3rycrS0a1bd0JDX6dfvwGo1YWbgpX1HFS35e3KqrZ+BkpK8pf8Jf+qH4EBkJmZyaVLl/D19cXJqWZP4zF1f1FeNeGzebevRUxiCjtPXuVGTh51ne3p39Kbga298ffWFn8QM2pC/pXN2s+BtecPcg6sPX+wznNg7v7C4iMwhKhoiqLw73//QkTEcr7/fjtqtZrBg4fy2muvExDQvkLf695l4KrD8nZCCFFT/PDDD6xYsQKDwUD//v1RqVT5K5WJ6i/pejaxx273tfgzPQcHWxt6NfdkYGtvHvJzx9ZGZekQhRBC1DJSwBC1hl6vZ9u2L4mMDOe33xJwd3dn0qRpjBnzKvXq1a+U9zS1DFx1WN5OCCFqgk8//ZRNmzYxduxYxo8fz9ChQ6WAUc1lZOey48RVYhJT+P3yjfy+Fq90bUiv5nVxtpdbSyGEEJVHrjKixktLS2Xt2k+Jjo4iOfkK/v4t+OCDj3juueGVPhzZ3DJ2NXV5OyGEqEo2NjbY29ujUqlQqVQ4OjpaOiRhgj7PyL5zacQU6GvR1NOJiT0a06+Vd41bEUUIIUTNJQUMUWOdPHmCqKgVbN78OdnZ2fTq1YePPlpOr16PYWNjUyUx+LhoTK5lLzdzQghRvM6dOzNt2jSSk5OZM2cObdu2tXRI4o67fS1ij6Ww48RffS2GdWjAwNY++Hs5o1LJFBEhhBBVSwoYokZRFIXdu3cRGbmcXbt2otFoCAoaQUjIeFq2bFXl8Yzv0ahQDwyoOcvbCSGEpU2dOpW4uDhatWpFkyZN6NOnj6VDsnp/pmcTm5hCzLFk/kzPQWNrQ69mdRnY2ocuDaWvhRBCCMuSAoaoEbKzs9m6dRNRUeEcP34MLy9vZs58i5deGounp6fF4rrb56I2rEIihBBVJS8vj127duHq6krPnj3p2bMnV69eZfLkySxdutTS4VmdjOxcdp683dfit0u3+1p09nNjbFc/ejf3lL4WQgghqg25IolqLTk5mU8/XcmaNdGkpqbSpk1bPv54Bc8++xwaTfWYpjGglY8ULIQQohSmT5+OWq3m6tWrnD59mgcffJC33nqLF1980dKhWY2CfS32nUsj16DQpK4TE3o0pr/0tRBCCFFNSQFDVEtHjvxOZORyvvpqC7m5uTzxRH9CQ1+ne/ceMudWCCFquAsXLvDll1+i1+sZOnQodnZ2rF27lqZNm1o6tFpNURR+v5xJTGIyO09cJSMnDw8nO4LaN2BgKx/8vaWvhRBCiOpNChii2jAajezY8QORkcuJj4/DycmJF154iZCQcTRp0szS4QkhhKggWq0WAHt7e4xGI6tXr8bNzc3CUdVed/taxB5LJkn6WgghhKjBpIAhLE6n07Fx4wZWrlzB2bNnaNDgAd5++11GjXoJNzd3S4cnhBCiEtWtW1eKF5XgRk4uO0/c7mvx652+Fp383Bj9sB99/KWvhRBCiJpJrl7CYi5e/JPo6CjWrfsXGRnpdOzYicjI1Qwa9Ax2dnaWDk8IIUQlOX36NNOmTUNRlPyv7woLC7NgZDVbrsHIvrNpxBxLIf5saqG+Fv1aelHP1cHSIQohhBDlIgUMUeUOHz5IVFQ433zzNYqiMGjQM4SGjuehhx62dGhCCCGqQMGVRkaMGGHBSGo+RVE4cqevxY4CfS2ea9eAJ1tLXwshhBC1ixQwRJXIy8sjJuZbIiKWc+jQAVxcXAkJGc8rr4Ti6+tn6fCEEEJUoS5dulg6hBrvz/RsYo+l8P2xFC5cz87vazGgtQ8PS18LIYQQtZQUMESlunEjg/Xr1xIdHUlS0gUaNmzEggX/IDj4BbRaF0uHZ7VijyUTvvc8yZm38HHRML5HI4K7NbZ0WEIIUW0YjUbeeecdTpw4gb29Pe+99x4NGza0aEw3cnLZefIaMUeT/+pr4VuHl7v40ru5J1qN3NYJIYSo3eRKJyrFuXNnWbUqgg0b1pOVpaNbt+7Mn/8+/foNQK1WWzo8qxZ7LJmFP54iJ88IwJXMWyz88RTOThp6NpRGekIIAbBz5070ej0bN24kISGB999/nxUrVlR5HPo8I3tOXyMmMYW9d/paNK7rxOuBjejfylv6WgghhLAqUsAQFUZRFP7971+IiFjO999vR61WM3jwUF577XUCAtpbOjxxR/je8/nFi7ty8oyE7ThJz1dkWLcQQgAcPnyYHj16ANC+fXuOHDlSZe9dsK/FT6eucf1mbn5fi4GtvWnhrZW+FkIIIaySFDBEuen1erZt+5LIyHB++y0Bd3d3Jk2axpgxr1KvXn1LhyfukZx5y+T2yxk5VRyJEEJUXzqdDq1Wm/+9Wq0mLy8PW1vzt05qtQo3N6cyv2fS9ZtsS7jEtl8vcT71JhpbG/q29uGZdg0IbFoXW7VNmY9dU6nVNuU6p7WBtZ8Da88f5BxYe/4g56AgKWCIMktLS2Xt2k+Jjo4iOfkKzZv7s3jxUoKCRuDkJP/BqisfFw1XTBQx6teRYchCCHGXVqslKysr/3uj0Vhk8QLAYFBIT79Zqve529ciNjGZhIs3AOjsW4dRnf3p09yTB31cSU+/iS7TOovMbm5OpT6ntY21nwNrzx/kHFh7/mCd58DLy3S/RClgiFI7deokkZHhbN78OdnZ2fTq1YelS5fRu/fj2NhY39OhmmZ8j0aFemAAONjaMK2vvwWjEkKI6qVjx478/PPPDBw4kISEBPz9K+53ZK7ByC/nrhN7LJm4M3f6Wng4MT6wEQPu9LWIPZZM8JrDhZotD2jlU2ExCCGEEDWRFDBEiSiKwp49PxMZuZyfftqBRqMhKGgEISHjadmylaXDE6Vw9wb43lVInm7XwOoqu0IIYU7fvn3Zt28fI0aMQFEUFi5cWK7jKYrC0SuZxCSm8OPxFDJy8vBwsmPonb4WLQv0tTDXbBmQIoYQQgirJgUMUaTs7Gy2bt1EVFQ4x48fw8vLm5kz3+Kll8bi6elp6fBEGQ1o5SM3wUIIUQQbGxvefffdch/nYkY2sYkpxB5L4cL1bDS2NvRsWpeBrb3p2tDdZF8Lc82W58ScIHzveRmNIYQQwmpVWQHDYDCwaNEijhw5gl6vZ+LEifTu3ZuEhAQWLFiAWq0mMDCQCRMmALBs2TJ2796Nra0ts2fPJiAggLS0NKZPn05OTg7e3t4sWrQIR0dHdu3axfLly7G1tWXo0KEMGzasqtKqtZKTk/n005WsWRNNamoqbdq05eOPV/Dss8+h0WgsHZ4QQghRbWXm5LHz5FViE5P5352+Fp186/DSQ7708fdEqyn69stcs2WQ0RhCCCGsW5UVMLZt20ZeXh5ffPEFycnJxMbGAjB37lw++eQTfH19CQkJ4ejRowAcOHCAzZs3c/nyZSZOnMjWrVsJDw9n0KBBDBkyhKioKDZu3Mjzzz/PokWL2LJlC46OjgQHB9O7d2+8vLyqKrVa5ciR34mMXM5XX20hNzeXJ57oT2jo63Tv3kOWbBNCCCGKoLuVx6xvE9l7JhW9QaGRh2OhvhYlZa7Z8l05eUbC956XAoYQQgirU2UFjPj4ePz9/QkJCUFRFN5++210Oh16vR4/Pz8AAgMD2b9/P/b29gQGBqJSqWjQoAEGg4G0tDQOHz5MaGgoAD179mTJkiV07doVPz8/6tSpA0CnTp04dOgQAwYMqKrUajyj0ch3333LkiUfEh8fh5OTEy+88BIhIeNo0qSZpcMTQgghaoSk9Gz+m5TBswH1Gdjah1Y+2jIV/001W75XUaM0hBBCiNqqUgoYmzdvZs2aNYW2ubu7o9FoiIyM5ODBg/z9738nLCys0Brrzs7OJCUlodFocHNzK7Q9MzMTnU6Hi4uL2W13t+t0uiLjM7VOuzWuravT6Vi3bi2ffPIJp0+f4sEHH2ThwkWMHfsK7u7ulg6vylnjZ6AgyV/yl/wlf1E+vm6OxIQ+bLKvRWkUbLZsbiSGj4tM5xRCCGF9KqWAERQURFBQUKFtU6ZMoVevXqhUKrp06cL58+fvW2M9KysLV1dX7Ozs7tvu4uKSv7+Dg0P+vqaOUbCgYYqpddqtaW3dixf/JDo6inXr/kVGRjodO3Zi3brP6NOnP3Z2dgBWcy4KsqbPgCmSv+Qv+Uv+ZWFunXZrpNXYkpddMcuJ3222HPdHOm99feS+pa/H92hUIe8jhBBC1CQVc5UtgU6dOrFnzx4Ajh8/Tv369dFqtdjZ2XHhwgUURSE+Pp7OnTvTsWNH4uPjMRqNXLp0CaPRiIeHBx07dsw/RlxcHJ06daJp06b88ccfpKeno9frOXToEB06dKiqtGqU//73EKGho+ncuS3h4R/Ts2cvvvtuB7Gxuxg+fHh+8UIIIYQQ1cPT7Row+4nm1HPRoALquWiY/URz6X8hhBDCKlVZD4xhw4Yxd+5chg0bhqIozJs3D4B58+Yxffp0DAYDgYGB1+NXqQAAHDNJREFUtGvXDoDOnTszfPhwjEYjc+bMAWDcuHHMnDmTTZs24e7uTlhYGHZ2dsyaNYuxY8eiKApDhw7Fx0cu6nfl5eURG/sdERHLOXjwP7i4uBISMp6xY0Pw82to6fCEEEIIUQxZ+loIIYS4TaUoimLpIKpabq6h1k8huXEjg/Xr1xIdHUlS0gUaNmxESMg4goNfQKu9f7hvbcu/LKz9HEj+kr/kL/mXhUwh+Yup+4vyiD2WTMS+P7ickYOPi4bxPRpZXSHD2v9vgpwDa88f5BxYe/5gnefA3P1FlY3AEFXj3LmzrFoVwYYN68nK0tGtW3fmz3+ffv0GoFarLR2eEEIIIUog9lhyoZVIrmTeYuGPpwCsroghhBBC3CUFjFpAURT+/e9fiIhYzvffb0etVjN48FBCQ8fTrp30AxFCCCFqmvC95+9bRjUnz0j43vNSwBBCCGG1pIBRg+n1erZt+5LIyHB++y0Bd3d3Jk2axpgxr1KvXn1LhyeEEEKIMko2s3yque1CCCGENZACRg2UlpbK2rWfsnr1Sq5cuUzz5v4sXryUoKARODk5WTo8IYQQQpSTj4uGKyaKFT4uGgtEI4QQQlQPUsCoQU6dOklkZDibN39OdnY2jz7amw8//ITevR/HxqbKVsQVQgghRCUb36NRoR4YAA62Nozv0chyQQkhhBAWJgWMak5RFPbs+ZnIyOX89NMONBoNzz03nJCQ8bRq1drS4QkhhBCiEtztc2Htq5AIIYQQBUkBo5rKyclh69ZNREWFc+xYIl5e3syc+RYvvjgGLy8vS4cnhBBCiEo2oJUPwd0aW93SeUIIIYQ5UsCoZlJSUvj005WsWRPNtWvXaNOmLR9/vIJnn30OjUbmvQohhBBCCCGEsE5SwKgmjhz5ncjI5Xz11RZyc3N54on+hIa+TvfuPVCpVJYOTwghhBBCCCGEsCgpYFiQ0Whkx44fiIxcTnx8HE5OTrzwwkuEhIyjSZNmlg5PCCGEEEIIIYSoNqSAYQFZWVl88cVnrFy5grNnz9CgwQO8/fa7jBr1Em5u7pYOTwghhBBCCCGEqHakgFGFLl78k+joKNat+xcZGel07NiJyMjVDBr0DHZ2dpYOTwghhBBCCCGEqLakgFEF/vvfQ0RGLuebb75GURSefPJpQkNf56GHukh/CyGEEEIIIYQQogSkgFFJ8vLyiI39joiI5Rw8+B9cXFx59dVxvPJKKH5+DS0dnhBCCFHjZWZmMmPGDHQ6Hbm5ucyaNYsOHTrw448/8s9//pP69esDMHHiRLp06cKyZcvYvXs3tra2zJ49m4CAANLS0pg+fTo5OTl4e3uzaNEiHB0d2bVrF8uXL8fW1pahQ4cybNgwC2crhBBCCClgVLAbNzL47LN1rFoVQVLSBfz8GvHee+8zcuQotFoXS4cnhBBC1BqffvopXbt25eWXX+bs2bNMmzaNr776iqNHjzJjxgz69euXv+/Ro0c5cOAAmzdv5vLly0ycOJGtW7cSHh7OoEGDGDJkCFFRUWzcuJHnn3+eRYsWsWXLFhwdHQkODqZ37954eXlZMFshhBBCSAGjgpw/f45VqyL47LN1ZGXp6NatO+++u4j+/QeiVqstHZ4QQghR67z88svY29sDYDAY0Gg0wO1ixbFjx1izZg0BAQFMnz6dw4cPExgYiEqlokGDBhgMBtLS0jh8+DChoaEA9OzZkyVLltC1a1f8/PyoU6cOAJ06deLQoUMMGDDAMokKIYQQApACRrkoisK///0LERHL+f777ajVagYPHkpo6Hjatetg6fCEEEKIWmPz5s2sWbOm0LaFCxcSEBDA1atXmTFjBrNnzwage/fuPP744zz44IPMnTuXL774Ap1Oh5ubW/5rnZ2dyczMRKfT4eLiYnbb3e06na7YGNVqFW5uThWRboFj2lT4MWsSa88f5BxYe/4g58Da8wc5BwVJAaMM9Ho927Z9SWRkOL/9loC7uzuTJk1jzJhXqVevvqXDE0IIIWqdoKAggoKC7tt+4sQJpk6dyptvvkmXLl0AGDp0KK6urgA89thj/PDDD7Rs2ZKsrKz812VlZeHi4oJWqyUrKwsHBweysrJwdXXN33bvvsUxGBTS02+WN9VC3NycKvyYNYm15w9yDqw9f5BzYO35g3WeAy8v09ddmyqOo0ZLS0tl6dIP6Ny5La+/HkJ29k0WL17K//53jNmz50jxQgghhKhCp0+fZtKkSYSFhfHoo48Ct0dHPv3001y5cgWA/fv306ZNGzp27Eh8fDxGo5FLly5hNBrx8PCgY8eO7NmzB4C4uDg6depE06ZN+eOPP0hPT0ev13Po0CE6dJCRlUIIIYSlyQiMEjh16iSRkeFs3vw52dnZPPpobz788BN6934cGxupAQkhhBCWEBYWhl6vZ8GCBQBotVpWrFjBe++9x4QJE3BwcKBp06YMGzYMOzs7OnfuzPDhwzEajcyZMweAcePGMXPmTDZt2oS7uzthYWHY2dkxa9Ysxo4di6IoDB06FB8fH0umKoQQQghApSiKUhVvlJmZyZQpU8jOzsbOzo7Fixfj5eVFQkICCxYsQK1WExgYyIQJEwAqdamz3FzDfUNw7h2WoygKe/b8TGTkcn76aQcajYbnnhtOSMh4WrVqXfEnyMKscVjSvaz9HEj+kr/kL/mXhbkhntbI1P1Fecln07rzBzkH1p4/yDmw9vzBOs+BxaeQfPnll/j7+/PZZ58xcOBAoqOjAZg7dy5hYWF8/vnn/Prrrxw9erTQUmdLlixh3rx5APlLnW3YsIHWrVuzceNGcnNzWbRoEatXr2bdunVs3LiRq1evljnOnJwcPvts7f+3d+9hVVX5H8ffRzhocikvmamZotn1MSXyZ4pmM/VoZNqjUV6AmrTU8YIXSCO8FF5yDJ0wdbCSZ0IHRi1//WrUrKcxNM0ZZdQkkdDxkhpqKnoIOFzW7w8fT6JgqBzZHj6vvzx773Pc37XWXq7n61p70aPHIzz33DPs3LmDV1+NJSPje+bNe9cjkxciIiIiIiIiVnfdEhjt2rVzvRDL4XDg7e2Nw+HA6XTSsmVLbDYbISEhbN68+bJbnXXr1g04t9XZpk2b2Lt3r2urMx8fH9dWZ1cqNzeX2bNnEBR0H+PGjcJmq0Ni4iIyMjKJjp6kvd9FREREREREapBb3oFR0VZnU6ZM4ZtvviE0NJS8vDyWLVuGw+HAz8/PdY2vry+HDh2ibt26bt3q7MJtznbs2MH8+YmkpaXidDoJDX2KqKgoevR4DJvNVqV4/2/HERK+yOZoXiG331yPCU+0o8+Dzar0XavQ1jwqA8Wv+BW/4hfrWLM7l4Ub9pN7tojb/Ovyx26tePJevYdDRERqN7ckMCra6mzUqFEMHTqUAQMGkJWVxejRo0lNTb1km7KAgADsdrtbtzorLS1j+fJz26Bu2PA19evXZ8iQoURGDqFNm7sAyMsrqFKsa3bnMnPdDxSWlAFwJK+Q1/93F/m/FN1QA43auK7qYrW9DBS/4lf8iv9q6B0Y1e/iscVPZ4uYue4HgBtqbCEiIlLdrtsSkoCAAFdioVGjRuTn5+Pn54fdbufgwYMYY9i4cSPBwcFu3+osK2s34eHPs3dvDpMnv8n27bt5551EV/LiSizcsN81wDivsKSMhRv2X/FviYiIiGhsISIiUrHrto1qVFQUcXFx/O1vf6OkpIT4+HgA3njjDaKjoyktLSUkJIQHH3wQwK1bnfn4+JCUtITevftit9uvKa7cs0VXdFxERETkcjS2EBERqdh120bVSqqyjWpVPb14Cz9VMKBo6l+XT1/5n6u+x+uttk+fBpWB4lf8il/xXw0tIflVdW2j6ilji+pQ259NUBnU9vhBZVDb44faWQY1vo2qp/pjt1bU8y5fjPW86/DHbq1q5oZERETkhqaxhYiISMWu2xIST3X+ZVp6U7iIiIhUB40tREREKqYERjV48t7bNKgQERGRanN+bFEbpw2LiIhURktIRERERERERMTylMAQEREREREREctTAkNERERERERELE8JDBERERERERGxPCUwRERERERERMTybMYYU9M3ISIiIiIiIiJyOZqBISIiIiIiIiKWpwSGiIiIiIiIiFieEhgiIiIiIiIiYnlKYIiIiIiIiIiI5SmBISIiIiIiIiKWpwSGiIiIiIiIiFied03fgLt88cUXrF27loSEBAC2b9/OjBkz8PLyIiQkhFGjRgHw7rvvsn79ery9vYmNjaV9+/acPHmS6OhoCgsLadKkCbNmzeKmm27iq6++YsGCBXh7e9O/f3+ee+45ysrKmDZtGnv27MHHx4fp06dz55131mToLosXL2bDhg0AnDlzhhMnTvDNN9+wbt06/vSnP3H77bcDMHr0aDp16nTNZWE1xhi6d+9Oq1atAOjQoQMTJkxwW1uwmrNnzxITE4PD4aC4uJhJkybRsWPHWlP/VWHl5/dqFRcXExsby+HDh3E6nYwYMYKmTZsyfPhw17MwcOBAQkNDWb58OWlpaXh7ezNixAgee+wxCgsLiYmJ4eeff8bX15fZs2fTsGHDSp8bK3rmmWfw9/cHoEWLFgwfPpxJkyZhs9m46667mDp1KnXq1PHI+D/++GNWrVoFQFFREbt37yYtLa1W1b+nUP/kme2zNvdPULv7qB07dvD222+TkpLCgQMH3FbvFY3nrOLCMti9ezfx8fF4eXnh4+PD7Nmzady4MdOnTycjIwNfX18AFi5ciN1u94gyuDD+zMxMt7V7q8ZfbYwHio+PNz179jRjx451HevTp485cOCAKSsrM0OHDjW7du0yu3btMhEREaasrMwcPnzY9OvXz/X9jz76yBhjTFJSkklOTjZOp9M8/vjj5vTp06aoqMj069fPHDt2zHz++edm4sSJxhhj/vOf/5jhw4df/4Cr4JVXXjHp6enGGGPmzp1r1q5dW+58dZSF1ezfv98MGzbskuPuagtW884775jk5GRjjDF79+41zzzzjDGm9tR/Vdwoz++VWLlypZk+fboxxpiTJ0+aRx991Cxfvtx88MEH5a47duyY6d27tykqKjJnzpxx/XnJkiUmMTHRGGPMZ599ZuLj440xFT83VlRYWGj69u1b7tiwYcPMt99+a4wxZvLkyWbdunUeG/+Fpk2bZtLS0mpV/XsS9U+e1z7VP5VXm/qoxYsXm969e5uwsDBjjPvqvbLxnBVcXAaDBw8233//vTHGmNTUVDNz5kxjjDEDBgwwP//8c7nvekIZXBy/u9q9VeOvTh65hCQoKIhp06a5PjscDpxOJy1btsRmsxESEsLmzZvZtm0bISEh2Gw2mjVrRmlpKSdPnmTbtm1069YNgO7du7Np0yb27t1Ly5Ytufnmm/Hx8eGhhx5i69at5a7t0KEDu3btqomQL2vdunUEBAS47jMzM5OPPvqIQYMG8dZbb1FSUlItZWE1mZmZ5ObmEhERwcsvv8y+ffvc2has5sUXX2TAgAEAlJaWUrduXaD21H9V3AjP75Xq1asXUVFRrs9eXl7s2rWL9evXM3jwYGJjY3E4HOzcuZOOHTvi4+ODv78/LVu2JCsr65I637x5c6XPjRVlZWVRUFDASy+9RGRkJNu3byczM5NOnToBv7ZjT43/vO+++46cnByef/75WlX/nkT9k+e1T/VPv6ptfVTLli2ZP3++67O76r2y8ZwVXFwGc+fO5d577wV+HaeWlZVx4MABpkyZwoABA1i5ciWAR5TBxfG7q91bNf7qdEMvIVmxYgV//etfyx2bOXMmoaGhbNmyxXXM4XDg5+fn+uzr68uhQ4eoW7cut9xyS7njZ8+exeFwuKb3VXTs/HGHw3HJb3t5eVFSUoK39/Ut2srKon379iQlJTF37lzX8a5du/L444/TokULpk6dSlpaGg6H45rLoiZVFP+UKVN45ZVXePLJJ9m6dSsxMTEsWLDAbW2hJl2u/o8fP05MTAyxsbGAZ9b/1bLK81udzk+5dDgcjBkzhrFjx+J0OgkLC+OBBx5g0aJFLFiwgHvuuafSPq2iOq/oubGievXqMWTIEMLCwti/fz8vv/wyxhhsNhtQtT79Ro7/vKSkJEaOHAlA+/bta039exL1T57XPtU//aq29VE9e/bkxx9/dH12V71XNp5t2LChu0P8TReXQZMmTQDIyMhg6dKlLFu2jF9++YXw8HD+8Ic/UFpaSmRkJA888IBHlMHF8bur3Vs1/up04/4rCISFhREWFvab1/n5+ZGfn+/6nJ+fT0BAAHa7/ZLj/v7+ruvr1avnurai37jw2vPKyspqZHBRWVnk5OQQEBBQbt1s//79CQgIAOD3v/89n3/+Offcc881l0VNqij+goICvLy8AAgODiY3NxdfX1+3tYWaVFn979mzh/Hjx/Pqq6+6Mv2eWP9XyyrPb3U7evQoI0eOZNCgQTz99NOcOXPGVedPPPEE8fHxBAcH/2afdrk6P/97VtO6dWvuvPNObDYbrVu35pZbbiEzM9N1/kr69Bsxfjj3zqN9+/bRuXNn4Fyd15b69yTqnzyvfap/Okd9FNSp8+sk+Oqs98rGs1a1evVqFi1axOLFi2nYsKEraXHTTTcB0LlzZ7KysjyyDNzV7m+U+K+FRy4huZifnx92u52DBw9ijGHjxo0EBwcTFBTExo0bKSsr48iRI5SVldGwYUOCgoL4+uuvAUhPT+ehhx6iTZs2HDhwgNOnT+N0Otm6dSsdO3YkKCiI9PR04NyLQtu1a1eToV5i06ZNdO/e3fXZGEOfPn346aefANi8eTP3339/tZSF1bz77ruuWQlZWVk0a9YMf39/t7UFq8nJySEqKoqEhAQeffRRoHbVf1VY/fm9GidOnOCll14iJiaGZ599FoAhQ4awc+dO4Nc6b9++Pdu2baOoqIizZ8+yd+9e2rVrV2GdV9aHWtHKlSt56623AMjNzcXhcNC1a1fXrLz09HSCg4M9Nn6Af//733Tp0sX1uTbVvydR/+R57VP90znqo+C+++5zS71XNp6zok8++YSlS5eSkpLCHXfcAcD+/fsZNGgQpaWlFBcXk5GR4RqneloZuKvd3yjxXwubMcbU9E24w5YtW0hLS2PevHnAuX/8Z86cSWlpKSEhIYwbNw6A+fPnk56eTllZGa+99hrBwcGcOHGCiRMnkp+fT4MGDUhISKB+/fqunReMMfTv35/Bgwe73hKenZ2NMYaZM2fSpk2bmgy9nDfeeMO1ZOC8jRs38uc//5l69erRpk0b4uLisNvt11wWVpOXl0dMTAy//PILXl5eTJkyhTZt2ritLVjNiBEj2LNnD82bNwfOJfIWLVpUa+q/Kqz+/F6N6dOns2bNGgIDA13Hxo4dy5w5c7Db7TRu3Jj4+Hj8/PxYvnw5f//73zHGMGzYMHr27ElBQQETJ07k+PHj2O12EhISuPXWWyt9bqzG6XTy2muvceTIEWw2G9HR0TRo0IDJkydTXFxMYGAg06dPx8vLyyPjB3j//ffx9vbmxRdfBM6ttY6Pj68V9e9J1D95XvtU/3RObe2jfvzxR8aPH8/y5cv573//67Z6r2g8ZxXnyyA1NZVHHnmE22+/3TUL4eGHH2bMmDG89957rF27FrvdTt++fRk4cKDHlMGFbcCd7d6q8VcXj01giIiIiIiIiIjnqBVLSERERERERETkxqYEhoiIiIiIiIhYnhIYIiIiIiIiImJ5SmCIiIiIiIiIiOUpgSEiIiIiIiIiludd0zcgIp7pL3/5CyEhIWRnZ5OYmOja4/vMmTMEBQUxdepUtmzZwtixY2nbti3GGEpKSoiMjCQ0NBSAVatWsWrVKry8vDDGMHToUEJCQsr9PZMmTWLUqFG0aNHCdWzevHmcOHGCGTNmAOe2Dk5KSiI5OZkZM2YwcuRIGjdufJ1KQkRERNztwjEFQH5+Pi1atGDcuHH079+f+++/H2MMTqeTPn36EB4eDsDXX3/NkiVLqFOnDqWlpTz77LP06dOnJkMRkctQAkNEqt3Ro0fJzs5m+PDhZGdn07t3b6KjowEoKytj0KBBfPfddwB07tyZefPmAecGGxEREbRu3ZoWLVqwcOFC/vGPf+Dj40Nubi5hYWGsX7+eOnUuP3ls9OjRhIeHs3r1ajp06MCsWbNITk7G29ubiIgIEhISmDVrlnsLQURERK6rC8cUABMmTOCrr76ibdu2pKSkAFBcXMzIkSNp1qwZv/vd75g2bRqffPIJAQEBOBwO+vbtS9euXWnUqFFNhSEil6ElJCJy1YqLi4mNjWXw4MEMHDiQLVu2AJCamkrPnj0r/E5+fj5nz57F39//knO+vr48//zzrF27lvr161NaWkpqaioHDx7ktttu48svv/zN5AWAt7c3CQkJzJ07lwkTJhAXF0eTJk0ACAwMZN++fZw6deoaIhcRERErczqdHDt2jICAgHLH7XY7kZGRrF69GoBGjRrx4Ycf8sMPP+Dr68uaNWuUvBCxMCUwROSqrVixggYNGrBs2TIWLlzIm2++CcC//vUv7r77btd1n332GeHh4fTs2ZMXXniB4cOH06pVqwp/s1GjRpw6dQovLy+Sk5M5cOAAQ4cO5bHHHmPlypVVvrfmzZvToUMH8vLyePjhh8udCwwMJCMj48oDFhEREcv69ttviYiIIDQ0lH79+vHEE0/wyCOPXHJd48aNXf+RsWjRIgoKChg/fjwhISEkJSVhjLnety4iVaQEhohctezsbNLT04mIiGDMmDGUlJRw6tQpTp06Ve4dE71792bp0qW8//775OfnV5q8ADhy5AhNmzYlNzeXwsJCpkyZwrp161iyZAkffPABe/bsqdK9ffnll+Tm5tKxY0cSExPLnbv11ls5ffr0VcUsIiIi1tS5c2dSUlJYtmwZdru93PuxLnT48GGaNm1KXl4eR44cISYmhk8//ZSPP/6YDRs28M9//vM637mIVJUSGCJy1QIDA3nqqadISUnhvffeo1evXtx88800bNiQM2fOXHL9HXfcwdSpU4mKiqKgoOCS8w6HgxUrVtCrVy9OnDhBdHQ0eXl5wLkZFQ0aNMBut//mfR06dIjZs2czZ84cXn/9db744gs2b97sOp+Xl6fpoSIiIh6qQYMGzJkzh7i4OI4fP17unNPp5MMPP+Spp57C6XQyduxYjh49Cpz7D47GjRvj4+NTE7ctIlWgl3iKyFUbMGAAcXFxhIeH43A4GDRoEHXq1KFTp07s2LGDZs2aXfKdLl260KVLFxITE+nRo4druuf5t3+PHj2awMBAACIjI3nhhReoV68epaWlhIWFuc5V5vxgZNKkSTRt2hSAt99+m1GjRrFy5UoaNWrE7t27XS8VFREREc/Ttm1bIiIiSE5OJicnh4iICGw2GyUlJTz99NN06dIFgLi4OEaNGoW3tzelpaX06NHjkh3PRMQ6bEaLvESkmh0+fJjZs2dfsnTDHSraRvVycnJyXNupioiIiIjIjUNLSESk2jVv3py7777btVWqlaSkpBAVFVXTtyEiIiIiIldIMzBERERERERExPI0A0NERERERERELE8JDBERERERERGxPCUwRERERERERMTylMAQEREREREREctTAkNERERERERELE8JDBERERERERGxvP8H6giwoGbA2SsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15,8))\n",
    "sm.graphics.plot_regress_exog(model,'RDS',fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15,8))\n",
    "sm.graphics.plot_regress_exog(model,'ADMS',fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15,8))\n",
    "sm.graphics.plot_regress_exog(model,'MKTS',fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Deletion Diagnostics (checking Outliers or Influencers)\n",
    "### Two Techniques : 1. Cook's Distance & 2. Leverage value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.21825244e-05, 3.27591036e-03, 3.23842699e-02, 2.17206555e-02,\n",
       "       1.44833032e-02, 1.17158463e-02, 2.91766303e-03, 3.56513444e-03,\n",
       "       4.04303948e-05, 4.86758017e-03, 1.51064757e-02, 1.63564959e-02,\n",
       "       1.15516625e-02, 4.01422811e-03, 6.12934253e-02, 3.40013448e-02,\n",
       "       8.33556413e-03, 3.30534399e-03, 2.16819303e-03, 4.07440577e-02,\n",
       "       4.25137222e-04, 1.09844352e-02, 2.91768000e-03, 2.76030254e-04,\n",
       "       5.04643588e-03, 3.00074623e-03, 3.41957068e-03, 2.98396413e-02,\n",
       "       1.31590664e-03, 1.25992620e-04, 4.18505125e-05, 9.27434786e-06,\n",
       "       7.08656521e-04, 1.28122674e-04, 2.09815032e-02, 6.69508674e-03,\n",
       "       5.55314705e-02, 6.55050578e-05, 5.61547311e-02, 1.54279607e-03,\n",
       "       1.84850929e-03, 1.97578066e-03, 1.36089280e-04, 2.05553171e-02,\n",
       "       1.23156041e-04, 9.03234206e-02, 5.45303387e-02, 5.33885616e-03,\n",
       "       1.90527441e-01, 2.88082293e-01])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Cook's Distance: If Cook's distance > 1, then it's an outlier\n",
    "# Get influencers using cook's distance\n",
    "(c,_)=model.get_influence().cooks_distance\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-099d41a6999a>:3: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  plt.stem(np.arange(len(data1)),np.round(c,5))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the influencers using the stem plot\n",
    "fig=plt.figure(figsize=(20,7))\n",
    "plt.stem(np.arange(len(data1)),np.round(c,5))\n",
    "plt.xlabel('Row Index')\n",
    "plt.ylabel('Cooks Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 0.28808229275432634)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index and value of influencer where C>0.5\n",
    "np.argmax(c) , np.max(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Leverage Value using High Influence Points : Points beyond Leverage_cutoff value are influencers\n",
    "influence_plot(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leverage Cuttoff Value = 3*(k+1)/n ; k = no.of features/columns & n = no. of datapoints\n",
    "k=data1.shape[1]\n",
    "n=data1.shape[0]\n",
    "leverage_cutoff = (3*(k+1))/n\n",
    "leverage_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>116983.8</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RDS      ADMS      MKTS       State   Profit\n",
       "49  0.0  116983.8  45173.06  California  14681.4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1.index.isin([49])] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RDS       ADMS       MKTS       State     Profit\n",
       "0   165349.20  136897.80  471784.10    New York  192261.83\n",
       "1   162597.70  151377.59  443898.53  California  191792.06\n",
       "2   153441.51  101145.55  407934.54     Florida  191050.39\n",
       "3   144372.41  118671.85  383199.62    New York  182901.99\n",
       "4   142107.34   91391.77  366168.42     Florida  166187.94\n",
       "5   131876.90   99814.71  362861.36    New York  156991.12\n",
       "6   134615.46  147198.87  127716.82  California  156122.51\n",
       "7   130298.13  145530.06  323876.68     Florida  155752.60\n",
       "8   120542.52  148718.95  311613.29    New York  152211.77\n",
       "9   123334.88  108679.17  304981.62  California  149759.96\n",
       "10  101913.08  110594.11  229160.95     Florida  146121.95\n",
       "11  100671.96   91790.61  249744.55  California  144259.40\n",
       "12   93863.75  127320.38  249839.44     Florida  141585.52\n",
       "13   91992.39  135495.07  252664.93  California  134307.35\n",
       "14  119943.24  156547.42  256512.92     Florida  132602.65\n",
       "15  114523.61  122616.84  261776.23    New York  129917.04\n",
       "16   78013.11  121597.55  264346.06  California  126992.93\n",
       "17   94657.16  145077.58  282574.31    New York  125370.37\n",
       "18   91749.16  114175.79  294919.57     Florida  124266.90\n",
       "19   86419.70  153514.11       0.00    New York  122776.86\n",
       "20   76253.86  113867.30  298664.47  California  118474.03\n",
       "21   78389.47  153773.43  299737.29    New York  111313.02\n",
       "22   73994.56  122782.75  303319.26     Florida  110352.25\n",
       "23   67532.53  105751.03  304768.73     Florida  108733.99\n",
       "24   77044.01   99281.34  140574.81    New York  108552.04\n",
       "25   64664.71  139553.16  137962.62  California  107404.34\n",
       "26   75328.87  144135.98  134050.07     Florida  105733.54\n",
       "27   72107.60  127864.55  353183.81    New York  105008.31\n",
       "28   66051.52  182645.56  118148.20     Florida  103282.38\n",
       "29   65605.48  153032.06  107138.38    New York  101004.64\n",
       "30   61994.48  115641.28   91131.24     Florida   99937.59\n",
       "31   61136.38  152701.92   88218.23    New York   97483.56\n",
       "32   63408.86  129219.61   46085.25  California   97427.84\n",
       "33   55493.95  103057.49  214634.81     Florida   96778.92\n",
       "34   46426.07  157693.92  210797.67  California   96712.80\n",
       "35   46014.02   85047.44  205517.64    New York   96479.51\n",
       "36   28663.76  127056.21  201126.82     Florida   90708.19\n",
       "37   44069.95   51283.14  197029.42  California   89949.14\n",
       "38   20229.59   65947.93  185265.10    New York   81229.06\n",
       "39   38558.51   82982.09  174999.30  California   81005.76\n",
       "40   28754.33  118546.05  172795.67  California   78239.91\n",
       "41   27892.92   84710.77  164470.71     Florida   77798.83\n",
       "42   23640.93   96189.63  148001.11  California   71498.49\n",
       "43   15505.73  127382.30   35534.17    New York   69758.98\n",
       "44   22177.74  154806.14   28334.72  California   65200.33\n",
       "45    1000.23  124153.04    1903.93    New York   64926.08\n",
       "46    1315.46  115816.21  297114.46     Florida   49490.75\n",
       "47       0.00  135426.92       0.00  California   42559.73\n",
       "48     542.05   51743.15       0.00    New York   35673.41"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discard the data points which are influencers and reassign the row number (reset_index(drop=True))\n",
    "data2=data1.drop(data1.index[[49]],axis=0).reset_index(drop=True)\n",
    "data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Deletion Diagnostics and Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thus model accuracy is improved to 0.9613162435129847\n"
     ]
    }
   ],
   "source": [
    "while np.max(c)>0.5 :\n",
    "    model=smf.ols(\"Profit~RDS+ADMS+MKTS\",data=data2).fit()\n",
    "    (c,_)=model.get_influence().cooks_distance\n",
    "    c\n",
    "    np.argmax(c) , np.max(c)\n",
    "    data2=data2.drop(data2.index[[np.argmax(c)]],axis=0).reset_index(drop=True)\n",
    "    data2\n",
    "else:\n",
    "    final_model=smf.ols(\"Profit~RDS+ADMS+MKTS\",data=data2).fit()\n",
    "    final_model.rsquared , final_model.aic\n",
    "    print(\"Thus model accuracy is improved to\",final_model.rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9613162435129847"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RDS       ADMS       MKTS       State     Profit\n",
       "0   165349.20  136897.80  471784.10    New York  192261.83\n",
       "1   162597.70  151377.59  443898.53  California  191792.06\n",
       "2   153441.51  101145.55  407934.54     Florida  191050.39\n",
       "3   144372.41  118671.85  383199.62    New York  182901.99\n",
       "4   142107.34   91391.77  366168.42     Florida  166187.94\n",
       "5   131876.90   99814.71  362861.36    New York  156991.12\n",
       "6   134615.46  147198.87  127716.82  California  156122.51\n",
       "7   130298.13  145530.06  323876.68     Florida  155752.60\n",
       "8   120542.52  148718.95  311613.29    New York  152211.77\n",
       "9   123334.88  108679.17  304981.62  California  149759.96\n",
       "10  101913.08  110594.11  229160.95     Florida  146121.95\n",
       "11  100671.96   91790.61  249744.55  California  144259.40\n",
       "12   93863.75  127320.38  249839.44     Florida  141585.52\n",
       "13   91992.39  135495.07  252664.93  California  134307.35\n",
       "14  119943.24  156547.42  256512.92     Florida  132602.65\n",
       "15  114523.61  122616.84  261776.23    New York  129917.04\n",
       "16   78013.11  121597.55  264346.06  California  126992.93\n",
       "17   94657.16  145077.58  282574.31    New York  125370.37\n",
       "18   91749.16  114175.79  294919.57     Florida  124266.90\n",
       "19   86419.70  153514.11       0.00    New York  122776.86\n",
       "20   76253.86  113867.30  298664.47  California  118474.03\n",
       "21   78389.47  153773.43  299737.29    New York  111313.02\n",
       "22   73994.56  122782.75  303319.26     Florida  110352.25\n",
       "23   67532.53  105751.03  304768.73     Florida  108733.99\n",
       "24   77044.01   99281.34  140574.81    New York  108552.04\n",
       "25   64664.71  139553.16  137962.62  California  107404.34\n",
       "26   75328.87  144135.98  134050.07     Florida  105733.54\n",
       "27   72107.60  127864.55  353183.81    New York  105008.31\n",
       "28   66051.52  182645.56  118148.20     Florida  103282.38\n",
       "29   65605.48  153032.06  107138.38    New York  101004.64\n",
       "30   61994.48  115641.28   91131.24     Florida   99937.59\n",
       "31   61136.38  152701.92   88218.23    New York   97483.56\n",
       "32   63408.86  129219.61   46085.25  California   97427.84\n",
       "33   55493.95  103057.49  214634.81     Florida   96778.92\n",
       "34   46426.07  157693.92  210797.67  California   96712.80\n",
       "35   46014.02   85047.44  205517.64    New York   96479.51\n",
       "36   28663.76  127056.21  201126.82     Florida   90708.19\n",
       "37   44069.95   51283.14  197029.42  California   89949.14\n",
       "38   20229.59   65947.93  185265.10    New York   81229.06\n",
       "39   38558.51   82982.09  174999.30  California   81005.76\n",
       "40   28754.33  118546.05  172795.67  California   78239.91\n",
       "41   27892.92   84710.77  164470.71     Florida   77798.83\n",
       "42   23640.93   96189.63  148001.11  California   71498.49\n",
       "43   15505.73  127382.30   35534.17    New York   69758.98\n",
       "44   22177.74  154806.14   28334.72  California   65200.33\n",
       "45    1000.23  124153.04    1903.93    New York   64926.08\n",
       "46    1315.46  115816.21  297114.46     Florida   49490.75\n",
       "47       0.00  135426.92       0.00  California   42559.73\n",
       "48     542.05   51743.15       0.00    New York   35673.41"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMS</th>\n",
       "      <th>MKTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70000</td>\n",
       "      <td>90000</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RDS   ADMS    MKTS\n",
       "0  70000  90000  140000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# say New data for prediction is\n",
    "new_data=pd.DataFrame({'RDS':70000,\"ADMS\":90000,\"MKTS\":140000},index=[0])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108727.154753\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual Prediction of Price\n",
    "final_model.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     190716.676999\n",
       "1     187537.122227\n",
       "2     180575.526396\n",
       "3     172461.144642\n",
       "4     170863.486721\n",
       "5     162582.583177\n",
       "6     157741.338633\n",
       "7     159347.735318\n",
       "8     151328.826941\n",
       "9     154236.846778\n",
       "10    135507.792682\n",
       "11    135472.855621\n",
       "12    129355.599449\n",
       "13    127780.129139\n",
       "14    149295.404796\n",
       "15    145937.941975\n",
       "16    117437.627921\n",
       "17    130408.626295\n",
       "18    129129.234457\n",
       "19    116641.003121\n",
       "20    117097.731866\n",
       "21    117911.019038\n",
       "22    115248.217796\n",
       "23    110603.139045\n",
       "24    114051.073877\n",
       "25    103398.054385\n",
       "26    111547.638935\n",
       "27    114916.165026\n",
       "28    103027.229434\n",
       "29    103057.621761\n",
       "30    100656.410227\n",
       "31     99088.213693\n",
       "32    100325.741335\n",
       "33     98962.303136\n",
       "34     90552.307809\n",
       "35     91709.288672\n",
       "36     77080.554255\n",
       "37     90722.503244\n",
       "38     71433.021956\n",
       "39     85147.375646\n",
       "40     76625.510303\n",
       "41     76492.145175\n",
       "42     72492.394974\n",
       "43     62592.049718\n",
       "44     67025.731107\n",
       "45     50457.297206\n",
       "46     58338.443625\n",
       "47     49375.776655\n",
       "48     51658.096812\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Automatic Prediction of Price with 90.02% accurcy\n",
    "pred_y=final_model.predict(data2)\n",
    "pred_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## table containing R^2 value for each prepared model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prep_Models</th>\n",
       "      <th>Rsquared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Model</td>\n",
       "      <td>0.950746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Final_Model</td>\n",
       "      <td>0.961316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prep_Models  Rsquared\n",
       "0        Model  0.950746\n",
       "1  Final_Model  0.961316"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2={'Prep_Models':['Model','Final_Model'],'Rsquared':[model.rsquared,final_model.rsquared]}\n",
    "table=pd.DataFrame(d2)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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