EDA-PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>game_date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>home_win</th>\n",
       "      <th>attack_diff</th>\n",
       "      <th>bup_dribbling_diff</th>\n",
       "      <th>bup_passing_diff</th>\n",
       "      <th>bup_speed_diff</th>\n",
       "      <th>...</th>\n",
       "      <th>last_year_avg_diff</th>\n",
       "      <th>previous_points_diff</th>\n",
       "      <th>rank_change_diff</th>\n",
       "      <th>rank_diff</th>\n",
       "      <th>three_year_ago_avg_diff</th>\n",
       "      <th>three_year_ago_weighted_diff</th>\n",
       "      <th>total_points_diff</th>\n",
       "      <th>two_year_ago_avg_diff</th>\n",
       "      <th>two_year_ago_weighted_diff</th>\n",
       "      <th>gdp_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-03-03 00:00:00+00:00</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>USA</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-62.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-05-24 00:00:00+00:00</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.295569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-06-20 00:00:00+00:00</td>\n",
       "      <td>Italy</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-73.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-06-04 00:00:00+00:00</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-06-24 00:00:00+00:00</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-47.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   game_date  home_team    away_team       country  neutral  \\\n",
       "0  2010-03-03 00:00:00+00:00     Mexico  New Zealand           USA     True   \n",
       "1  2010-05-24 00:00:00+00:00  Australia  New Zealand     Australia    False   \n",
       "2  2010-06-20 00:00:00+00:00      Italy  New Zealand  South Africa     True   \n",
       "3  2010-06-04 00:00:00+00:00   Slovenia  New Zealand      Slovenia    False   \n",
       "4  2010-06-24 00:00:00+00:00   Paraguay  New Zealand  South Africa     True   \n",
       "\n",
       "   home_win  attack_diff  bup_dribbling_diff  bup_passing_diff  \\\n",
       "0         1         13.0                -0.0             -25.0   \n",
       "1         1         11.0                -0.0              -0.0   \n",
       "2         2         23.0                -0.0             -40.0   \n",
       "3         1         10.0                -0.0              -0.0   \n",
       "4         2         15.0                -0.0             -30.0   \n",
       "\n",
       "   bup_speed_diff    ...     last_year_avg_diff  previous_points_diff  \\\n",
       "0            -5.0    ...                   -0.0                 531.0   \n",
       "1            -0.0    ...                   -0.0                 489.0   \n",
       "2            -3.0    ...                   -0.0                 771.0   \n",
       "3            -0.0    ...                   -0.0                 447.0   \n",
       "4            -5.0    ...                   -0.0                 409.0   \n",
       "\n",
       "   rank_change_diff  rank_diff  three_year_ago_avg_diff  \\\n",
       "0              -3.0      -62.0                     -0.0   \n",
       "1              -2.0      -58.0                     -0.0   \n",
       "2              -0.0      -73.0                     -0.0   \n",
       "3              -2.0      -53.0                     -0.0   \n",
       "4              -1.0      -47.0                     -0.0   \n",
       "\n",
       "   three_year_ago_weighted_diff  total_points_diff  two_year_ago_avg_diff  \\\n",
       "0                          -0.0               -0.0                   -0.0   \n",
       "1                          -0.0               -0.0                   -0.0   \n",
       "2                          -0.0               -0.0                   -0.0   \n",
       "3                          -0.0               -0.0                   -0.0   \n",
       "4                          -0.0               -0.0                   -0.0   \n",
       "\n",
       "   two_year_ago_weighted_diff  gdp_diff  \n",
       "0                        -0.0  0.000000  \n",
       "1                        -0.0  1.295569  \n",
       "2                        -0.0  0.000000  \n",
       "3                        -0.0  0.000000  \n",
       "4                        -0.0  0.000000  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "train = pd.read_csv('data/train_merged.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = train.iloc[:,6:]\n",
    "\n",
    "# scale data\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X_scaled = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pca decomposition\n",
    "dim = X.shape[1]\n",
    "pca = PCA().fit(X_scaled)\n",
    "scores = pca.transform(X_scaled)\n",
    "\n",
    "# correlation between PCs and data\n",
    "pc_cors = np.corrcoef(X_scaled, scores, rowvar=False)[:dim, dim:] # only want cor(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/22348229/matplotlib-legend-for-an-arrow\n",
    "from matplotlib.legend_handler import HandlerPatch\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "def make_legend_arrow(legend, orig_handle,\n",
    "                      xdescent, ydescent,\n",
    "                      width, height, fontsize):\n",
    "    p = mpatches.FancyArrow(0, 0.5*height, width, 0, length_includes_head=True, head_width=0.75*height )\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='ongshengsiong', api_key='fC66C9lv4st6AKGfBWYV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'growth_diff'"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns.values[13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.12743375, -0.35232221,  0.24534996,  0.30661297,  0.2419436 ,\n",
       "        0.23818307,  0.11533429,  0.29154681,  0.37071885,  0.12966129,\n",
       "       -0.05617904, -0.78484336,  0.06816692,  0.74895819, -0.05261285,\n",
       "       -0.00468216,  0.11904756, -0.73286876,  0.06737507,  0.01127724,\n",
       "       -0.08727227,  0.09708455, -0.00326455, -0.00326455,  0.00796387,\n",
       "        0.00796374, -0.00875358,  0.02067707,  0.03035724,  0.15366251,\n",
       "        0.15366526,  0.04320467,  0.10687435,  0.10687552, -0.15609754])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_cors[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6aN12Ea+got4OKuoi9mzZVct7WaUs3LiTtUW7AejfK5QTRyZwwogEJg2KIShAU1pEDtU3/6YWZJeysawGY2BSSgwzMxM5ZVRf4iN1MaqILSrq7aCiLtK1qutbeCerhHkri1njKedjBvTihBHui0HTEiK1HJ1IB8jdUcOC7FLmZ5WSt7MWPwNTBsdyekY/ThnVV6shiXQxFfV2UFEX6XytbS6+yN3F6yuLWbh+J81tLob3jeS8cUmcOSZRW6qLdCLHcdi8o5YFWSXMzyolv7wOfz/DUUNiOT29HyeP6qtpZSJdQEW9HVTURTrPhtI9zFtZzFtrSiivbSImPIgzMxM5f3wSoxKjNHIu0sUcx2FDaQ0Lst2lvbCingA/w9FD4zg9ox8nj+xLdJiWNxXpDCrq7aCiLtKxKmqbeHtNCfNWFbOuZA8BfoYZw/tw3vgkjk/roznnIl7CcRzWlexhflYpC7JLKKpsINDfcOywPsyaPJBjhsXj76f/TIt0FBX1dlBRFzlyza0uPt24k3mrivls405aXQ6j+0dx/rgkzhzTnxi9rC7i1RzHIXt7NfOzSnlj1XbKa5tI6h3KJZMGcuGEAboIVaQDqKi3g4q6SPs4jkPO9j3MW1XM22u2U1XfQnxkMOeM7c9545JI6xtpO6KItENzq4uP1pfx4tJtLMmvINDfcPKovvx4SjKTB8VoyppIO6mot4OKusjh2bmnkTdXb2feqmI276glKMCPE0cmcP64JKanxmmHUJFuJG9nLf9Zto3XVxaxp7GVoX0imDV5IOeOSyI6VHPZRQ6Hino7qKiL/DDHcVhZWMWTX27lo/VluBwYO7AX549PYmZ6oi4+E+nmGprbmJ9VwovLtrGmaDchgX6cmZnIrMnJZA7oZTueiE9QUW8HFXWRA2ttc/F+ThlPLtrK2qLdRIcGcvGkAVw4YQBD4iNsxxMRC3K2V/Pism28vWY79c1tpPePZtbkgZw5JpGwoADb8US8lop6O6ioi3zfnsYWXvm6iGcXF7B9dwMpsWFcMW0Q541P0i9iEQHcPyfeXr2dF5ZuY9OOGiKDAzh3XH9mTUlmWIKuURHZl4p6O6ioi/xPUWU9z3xVwCvLt1HX3MaUwTH8bNpgZgzvg5+WaROR/fhmatwLSwt5L7uM5jYXk1JimDVlIKeM7ktwgL/tiCJeQUW9HVTURWBlYRVPLcrng5wy/IzhjMxErpg2iNH9o21HExEfUlnXzGsrivjP19sorKgnJjyIy6cmM+eoFHqFaZlW6dlU1NtBRV16qtY2Fx+sK+PJL7eypmg3USEBzJqSzOypKfSNDrEdT0R8mMvl8NWWcp5bXMDCDTsJD/Lnsqkp/Gz6IOIitCa79Ewq6u2goi49zZ7GFl5dXsQzX/1v/vlPpw3ivHFJhAdr/rmIdKyNZXt45LMtzM8qITjAj4snDuTnxw6mX3So7WgiXUpFvR1U1KWnKKqs59nFBbyyvIjaplYmDYrhZ9MG8aMRCdomXEQ6Xf6uWv75+RbeXL0dY+D88Un88tihDIwNsx1NpEuoqLeDirp0d2uLdvOvL/J5P6cUP2OYmdGPK6YNJj1J889FpOsVV9Xz+H/zeWVFEW0uh7MyE7n6+CEM7aOVYqR7U1FvBxV16a42lu3h7x9uZuGGHUSFBHDp5GRmH5Wsl5tFxCvs2NPIE1/k8+KybTS2tnHq6L5cc/xQRiVqEEG6JxX1dlBRl+5ma3kd9328mXezSogIDuDnxwxmztGDiND8cxHxQpV1zTy9aCvPLS6gpqmVGcP7cM3xQxmf3Nt2NJEOpaLeDirq0l2U7G7goU9zeXVFMUH+fvzk6BSuOmawlkQTEZ9Q3dDC80sKeGrRVqrqWzhqSCxzZwxl6uBYjNF1NOL7VNTbQUVdfF15bROPfraFF5YV4jgOsyYnc/XxQ+gTqSUWRcT31DW18tLX23j8i3x21TQxPrk3c48fynFp8Srs4tNU1NtBRV18VXVDC098kc/TX22lsaWN88cnce2PUknqrRUURMT3Nba08drKYh77fAvbdzcwKjGKG05KU2EXn6Wi3g4q6uJr6ptbeXZxAY99voU9ja3MzOjHb04cxpD4CNvRREQ6XEubi7dWb+eRz/IoqKhnemocN58+guF9o2xHEzksKurtoKIuvqKptY2Xlm3j4c+2UF7bxIzhffjtScO0QoKI9AjNrS5eXFbI/QtzqWls4aKJA/jNicM0zU98hop6O6ioi7drbXPxxqrtPPBJLtt3NzB5UAw3nZLG+OQY29FERLrc7vpmHvo0j38vKSDI34+rjx/KFdMGERLobzuayEGpqLeDirp4K5fLYUF2Kfd9vJn88joyk6K54eQ0pg2N0/xMEenxtpbX8df3N/Dhuh0kRofwf6cO54yMRPy007J4KRX1dlBRF2+0KLecu97bwIbSPQxLiOC3J6Vx0sgEFXQRkX0s2VLBnQvWs65kD5kDenHr6SOYkKJXHMX7qKi3g4q6eJPS6gbuXLCBBVmlDIgJ5foTh3FmZn/8NUIkInJALpfDG6u3c8+HG9mxp4nT0/vxf6cMZ2CsVsES76Gi3g4q6uINWtpcPPPVVu5fmEuby+Hq44by82MHa86liMhhqG9u5V9f5PP4f/Npczn85OgUrj5+KNGhgbajiaiot4eKuti2NL+C297OYfOOWmYM78MfzxilUSARkSNQVt3I3z/axLxVxfQOC+I3J6RyyaSBBPj72Y4mPZiKejuoqIstO2sa+ct7G3lz9Xb69wrlj2eO4sSRCbZjiYh0Gznbq7lzwXqW5lcytE8EN582QhsmiTUq6u2goi5drbXNxfNLC/nHR5tpanXx82MHc/VxQwkN0jQXEZGO5jgOH6/fwV/e38jW8jptmCTWqKi3g4q6dKWVhZXc+tY61pfuYXpqHH86cxSDtaOoiEina2518cLSQh74JJfaplZ+cexgfjUjVdcCSZdRUW8HFXXpChW1Tfz1/Y28trKYftEh3DpzJKeO7quXX0VEulhVXTN3vbeB11cWMzg+nLvPy9ByjtIlVNTbQUVdOlOby+Glr7dxz4ebqGtq5Yrpg7h2RirhwQG2o4mI9GhfbN7F79/IpqS6gcunJHPjKcOJ0M9m6UQq6u2goi6dZW3Rbm59O4es4mqmDI7hjrNGk5oQaTuWiIh41DW1cs+Hm3huSQGJ0aHcdc5ojkvrYzuWdFMq6u2goi4dbXd9M3d/uImXvt5GfEQwN58+gjMzEzXNRUTES60srOSm17PYsquOc8f159bTR9I7PMh2LOlmVNTbQUVdOorjOLy9poTb56+nuqGF2VNT+M2JqUSGaKMNERFv19TaxiOf5vHo51voFRbIn84czWnpupZIOo6KejuoqEtHqKpr5pa3cliQXcq4gb2465x0RvTT0l8iIr5mQ+kebno9i+zt1Zw4MoE7zx5NQlSI7VjSDaiot4OKuhypzzft5KbXs6iqb+Y3Jw7j58cMwd9PIzAiIr6qtc3F019t5d6PNhMU4MfNp43gookDNLouR6Sjirr21xU5BPXNrdzyVjZznllOr7BA3rrmaK4+bqhKuoiIjwvw9+OqY4bw4XXHMLJfFL97I5tZTy6jsKLOdjQRjaiL/JDV26q4/tW1FFTU8bNpg/jtSWnaNENEpBtyuRxeXl7EX97bQIvLxQ0npfGTowdpUEYOm0bURTpZS5uLf3y8mfMfW0Jzq4v//GwKN58+UiVdRKSb8vMzXDp5IB9dfwxHD4njzgUbOPefi9lUVmM7mvRQKuoi+5G3s5bz/rmYBz/J5awxibx/3XSmDom1HUtERLpAv+hQnpw9gQcvGUtRZT0zH/qSRz7Lw+XqObMQxDtoWy6RvbhcDs8vLeTP720gLMiff84ax6np/WzHEhGRLmaM4czMRKYNjeO2t3O458NNLNlSwT8uyqRPpFaGka6hEXURj7LqRmY/8zV/eGcdRw2J5cPrjlFJFxHp4WLCg3jokrH89dx0lhdUctoDi1iUW247lvQQKuoiwLtrSzj5/i9YUVDFXeeM5uk5E+mjtXRFRAT36PrFkwbyztxp9AoL5LKnl3HvR5tobXPZjibdnIq69GjV9S1c+9JqfvXSagbHh/Per6cza3Ky1s8VEZHvSesbyTtzj+b8cUk89Gkelz6xjNLqBtuxpBtTUZcea1FuOSff/wXvZZdyw0nDeO3nUxkUF247loiIeLGwoADuuSCT+y7KJKekmtMe+JJPN+6wHUu6KRV16XGaW13c/u56fvzUMiJCAnjz6qOZOyOVAH/9cxARkUNzztgk5v9qGn2jQ/npsyu4a8F6mls1FUY6llZ9kR5lZ00j17y4iuUFVcw5KoXfnTpc66KLiEi7DI6P4M2rj+KuBRt44sutfF1QxcOXjGVATJjtaNJNaAhReoyVhVXMfHAROdv38NAlY/njmaNU0kVE5IiEBPpzx9mj+eesceTvquW0B7/kvexS27Gkm1BRl27Pcdxro1/8ryWEBvnz5jVHcUZmou1YIiLSjZya3o/3rp3O4PgIrn5xFbe+lUNjS5vtWOLjVNSlW2tsaeOm17O49a0cpg2N45250xjeN8p2LBER6YYGxITx2s+ncuX0QTy/tJBzHl1M/q5a27HEh6moS7e1fXcDFzy2hNdWFnPtj1J5avZEokMDbccSEZFuLCjAj5tPH8nTcyZQVt3AzIcW8ebqYtuxxEepqEu3tDivnDMeWkRBeR1PXj6B608chp+f1kYXEZGuMWN4Au/9ejqjE6P5zStrufG1tdQ3t9qOJT5GRV26Fcdx+NcXW/jxU8uIDQ/i7blHc8LIBNuxRESkB+oXHcp/rpzMr2YM5fVVxVzw2BLKqhttxxIfoqIu3UZdUytzX1rNn9/byCmj+/LmNUczOD7CdiwREenBAvz9+O1JaTw1ewIF5XWc9cgisourbccSH6GiLt1CQXkd5z66mPezS/ndqcN55NJxRARrmwAREfEOM4YnMO/qowjw8+OCxxfzQY6WcJQfpqIuPu/TjTs44+FF7Khp5LmfTuIXxw7BGM1HFxER7zK8bxRvXXM0I/pF8YsXVvHo53k4jmM7lngxFXXxWS6Xw/0LN/PTZ1cwMCaMd+dOY3pqvO1YIiIiBxQfGcxLV07hjMxE7v5gEze+nkVzq8t2LPFSmhsgPqm6oYXrX1nDJxt3cu7Y/vz53HTtMioiIj4hJNCfBy8ew5D4cO5fmMu2ynoe+/F4YsKDbEcTL2N1RN0Yc4oxZpMxJs8Y87v93H+fMWaN522zMWb3Xve17XXfO12bXGzK21nL2Y98xX837+JPZ47i3gszVdJFRMSnGGO47oRhPHDxGNYU7eacR78ib6c2R5LvMrbmRhlj/IHNwIlAMbAcuMRxnPUHOP5XwFjHcX7q+bzWcZzDWtJjwoQJzooVK44suFi1alsVP312OQF+hkdnjWfSoBjbkURERI7Iqm1VXPXvFTS1uvjnrPFMS42zHUmOkDFmpeM4E470cWyOqE8C8hzHyXccpxl4GTjrIMdfArzUJcnEK32yYQeXPrGU6NBA5v3yKJV0ERHpFsYN7M1b1xxNYnQos5/5mheXFdqOJF7CZlHvDxTt9Xmx57bvMcYkA4OAT/e6OcQYs8IYs9QYc/aBvogx5irPcSt27drVEbnFgleXF3HV8ytJ7RPJvF8eRXJsuO1I0h7V1eDSRVMiIvtK6h3G67+cyjGpcdz8Zg63v7ueNpdWhOnpbBb1/a2fd6C/kRcDrzuO07bXbQM9LylcCtxvjBmyvxMdx/mX4zgTHMeZEB+vFUF8jeM4PPxpLjfNy+KoIbG8fNUU4iKCbceS9qqtha8WgZYjExH5nsiQQJ6cPZGfHj2Ip7/aypX/XkFtU6vtWGKRzaJeDAzY6/MkoOQAx17MPtNeHMcp8bzPBz4HxnZ8RLGpzeVw29vr+PtHmzl7TCJPzZ5IuDYx8m1RkbB+HWRn2U4iIuKV/P0Mt50xkjvPHs1/N+/i/H8upriq3nYsscRmUV8OpBpjBhljgnCX8e+t3mKMSQN6A0v2uq23MSbY83EccDSw34tQxTc1trQx9z+reH5pIVcdM5h/XDiGoAAt++/zwiPAGFiyGLbm204jIuK1fjxMJ726AAAgAElEQVQlmWd/MpHtuxs4+5HFrN5WZTuSWGCt+TiO0wrMBT4ENgCvOo6zzhhzuzHmzL0OvQR42fnu8jQjgBXGmLXAZ8BfD7RajPie6oYWLn/6a97PKeOW00fw/04bgZ+fdhrtFvz8IMKzWNOnn8DOHXbziIh4semp8bx59dGEBflz0b+W8n52qe1I0sWsLc9og5Zn9H5l1Y3MeeZrtuyq5e8XZHLWmP1eXyy+7N13oGQ7hIbCsDSYNNld4EVEZL8q65q58t8rWL2tir9fkMm545JsR5If0B2WZxT5jrydNZz3z8UUVdbzzJxJKundVUwMjBoNTU2QnqGSLiLyA2LCg3j+iklMHRLLb19by3+WbbMdSbqIfkOKV1hZWMX5jy2hqbWNV34+VZs9dGeTJsOUqRAUBOtybKcREfEJYUEBPDV7Isen9eH/vZnNU4u22o4kXUBFXaz7ZMMOZj25lF6ejYxG94+2HUk6U2AgBATAyFHuFWBaWmwnEhHxCSGB/jz24/Gclt6XO+av5+FPc21Hkk6moi5WvbJ8G1c9v5JhCZG8ro2MepZRo6G1FTZvsp1ERMRnBAX48eDFYzlnbH/+/tFm7vlwIz3pesOeRotSixXujYzyuPfjzRwzLJ5/zhqnNdJ7mrAwSE11r6k+cpR72UYREflBAf5+3HtBJiGBfjzy2Rbqm9u4beZIjH6OdjtqRtLlXC6HP767jn8vKeScsf3523kZWiO9p0rPhI0bobAAUgbZTiMi4jP8/Ax/PiedkEB/nvmqgMYWF3edPVrLGXczKurSpRzH4da3c3hx2TaunD6I35+qNdJ7tJgYSBoAWVkq6iIih8kYw20zRxIW5M8jn22hsaWNe87PIMBfg1/dhf4kpcs4jsOf3l3Pi8u28fNjB2sjI3HLzITSEti1y3YSERGfY4zhxpOHc8NJw3hz9XZ+9dJqmltdtmNJB1FRly7hOA5/fm8Dzy4u4Ippg/jdKcM1l07c+ie5R9az1tpOIiLis+bOSOXWmSN5P6eMX7ywksaWNtuRpAOoqEuncxyHuz/cxBNfbmX21GRuOX2ESrr8jzHuuer5W6C21nYaERGfdcW0Qdx1zmg+27STK55bTn1zq+1IcoRU1KXT3bcwl39+voVLJw/kj2eOUkmX70tNheBgyMm2nURExKfNmpzMvRdksmRLBZc/9TV7GrVXhS9TUZdO9dAnuTz4SS4XTkjizrNGq6TL/vn7w+h02LBeGyCJiByhc8cl8dAl41hTtJsfP7mM3fXNtiNJO6moS6d57L9buPfjzZw7tj9/OTdDF47KwY0YCW1tsHGD7SQiIj7v9Ix+PH7ZeDaW1XDxv5ayq6bJdiRpBxV16RRPfpnPX9/fyBmZidxzQSb+KunyQ0JDYViaewMkl1YsEBE5Uj8akcDTsydSWFHPrCeXamTdB6moS4d7bnEBdy7YwKmj+3LfhSrpchjSM6CmBgoKbCcREekWpqXG8dTsCRSU13PFcytoaNZqML5ERV061IvLCvnDO+s4cWQCD14yVpsuyOHp3RsGJkO2lmoUEekoRw2N44GLx7B6WxW/fHElLW161dJXqEVJh3l1eRE3v5nDjOF9ePjSsQSqpEt7ZGRCWRns2GE7iYhIt3Fqej/uOiedzzft4sbX1uJyObYjySFQk5IO8caqYv7vjSymp8bx6KxxBAf4244kvioxEWLjtAGSiEgHu2TSQG48OY231pRw+/z1OI7KurcLsB1AfN/ba7Zzw2trmTo4licun0BIoEq6HAFjICMDPv8MavZAZJTtRCIi3cbVxw2horaZp7/aSmx4EL/6UartSHIQGlGXI/JedinXv7qWCSkxPDlbJV06yJChEBoG2doASUSkIxljuOX0EZwztj/3fryZF5YW2o4kB6GiLu22cP0Orn1pNWMH9OKZORMJC9ILNNJB/P1h9Gj3mupNWvtXRKQj+fkZ7j4/gxnD+3Dr2zksyCq1HUkOQEVd2mVN0W7mvrSKUYlRPPOTiYQHq6RLBxsxEhwHNm20nUREpNsJ9PfjkUvHMX5gb657ZTWLcsttR5L9UFGXw1ZUWc/PnltOfGQwT82ZSGRIoO1I0h2FhEDacG2AJCLSSUKD/Hlq9kSGxEdw1fMrWFu023Yk2YeKuhyW6oYWfvrscppbXTwzZyJxEcG2I0l3lp4BtbWQn287iYhItxQdFsi/fzqJ2Igg5jzzNXk7a21Hkr2oqMsha2lzcfWLKymoqOOxy8YztE+k7UjS3UVHQ8og91KNWkZMRKRT9IkK4fmfTsbfz4/Ln1pGye4G25HEQ0VdDonjONz8ZjZf5VXwl3MzOGpInO1I0lNkZMCunbCjzHYSEZFuKyUunOd+OpGaxlYuf/prquqabUcSVNTlED36+RZeXVHMtTOGcv74JNtxpCfp2w/i47UBkohIJxuVGM0TsyewrbKeOc8up66p1XakHk9FXX7QO2tLuOfDTZw1JpHfnDjMdhzpaYyBjEzYuhWqq22nERHp1qYMjuXhS8aSXbybX7ywkuZWXcxvk4q6HNSKgkpueG0tk1JiuPv8DIwxtiNJTzRoMEREQI42QBIR6WwnjerLX8/L4Mvccm54bS2OrhGyRkVdDqigvI4r/72C/r1Cefyy8QQHaNdRscTfH0anawMkEZEucuGEAdx0ShrvrC3h0c+32I7TY6moy35V1TXzk2eXA/DMnIn0Dg+ynEh6vOEj3NNgNqy3nUREpEf45bFDODMzkb9/tInPNu60HadHUlGX72lqbePnz69ke1UDT1w+gZS4cNuRRCA42F3Wc7Khrc12GhGRbs8Yw9/Oy2BkvyiufXk1+bu0xnpXU1GX73Ach5tez+Lrgkr+fmEmE1JibEcS+Z/R6VBfD/l6GVZEpCuEBvnz+GXjCfT348p/r6CmscV2pB5FRV2+476Fuby9poQbT07jzMxE23FEvisqCgZpAyQRka6U1DuMRy4dR0FFPb95ZQ0ul37+dhUVdfnW6yuLefCTXC6ckMTVxw2xHUdk/9IzobwcSkttJxER6TGmDonltpkjWbhhJ/cv3Gw7To+hoi4ALN5Szu/fyOLoobHcdU66lmEU79W3LyQkQNYa20lERHqUy6cmc+GEJB78NI8PcjRY0hVU1IWt5XX84vmVpMSG8+gs9zw0Ea+WkQmFhbB7t+0kIiI9hjGGO84ezZgBvbj+1bVsKquxHanbUyPr4Rpb2vjlCyvx9zM8PWci0aGBtiOJ/LCUQRAZCdlZtpOIiPQowQHui0vDgwO48t8r2F3fbDtSt6ai3sP98Z11bCyr4R8XjWFATJjtOCKHxs/PvQLM5k3Q2Gg7jYhIj5IQFcJjPx5PWXUjv3ppNa1tLtuRui0V9R7sjVXFvLy8iGuOH8LxaX1sxxE5PMNHuAv7+nW2k4iI9Djjk3tzx9mj+DK3nLs/3GQ7Trelot5D5e6o4eY3c5g8KIbfnDDMdhyRwxcU5C7r63K0AZKIiAUXTRzI5VOT+dcX+by1ervtON2SinoPVN/cytUvriI82J8HLxlLgC4eFV81Oh0aGiAv13YSEZEe6daZI5k0KIb/m5dFdnG17TjdjhpaD+M4Dre8lUPerloeuHgsCVEhtiOJtF9kJAwZAllZ2gBJRMSCQH8/Hp01jtjwIH7+/ArKa5tsR+pWVNR7mNdWFPPGqu1cOyOVo4fG2Y4jcuQyMqGyArbrZVcRERviIoJ5/LIJVNQ1c/WLq2jRxaUdRkW9B9lQuodb387h6KGxXPujVNtxRDpGfB/o2w+y1tpOIiLSY6UnRXP3+Rl8vbWS299dbztOt6Gi3kPUNrVyzYuriAoN5P6LxuLvp51HpRvJyISibVBVaTuJiEiPddaY/lw5fRDPLy3UzqUdREW9B3Ach9+/kU1BRR0PXTKW+Mhg25FEOlZyMkRFu+eqi4iINTeePJz0/tH87o1syqq1z8WRUlHvAV5cto1315bw25PSmDI41nYckY7n5wfpGZC7GRrqbacREemxggL8uP/iMTS1uLjhtbW4XLrQ/0ioqHdzOduruf3d9Rw7LJ5fHjvEdhyRzpOWBgEBsE4bIImI2DQkPoJbZo5gUV45zywusB3Hp6mod2N7Glu4+sVVxEYEcd9FY/DTvHTpzgIDYcRI9wZIra2204iI9GiXThrICSP68LcPNrKxbI/tOD5LRb2bchyHm17LomR3Aw9fOpaY8CDbkUQ63+h0aG7WBkgiIpYZY/jreRlEhQTw65fW0NiiHaTbQ0W9m3p2cQEfrCvjplPSGJ8cYzuOSNcID4chQ91LNWoDJBERq+Iigrnn/Ew27ajh7g822Y7jk1TUu6E1Rbv583sbOGFEH66cPth2HJGulZEBVVVQVGQ7iYhIj3f88D5cPjWZp7/ayhebd9mO43NU1LuZuqZW5v5nFX0iQ7j3gjEYo3np0sPExUNiImRrAyQREW/w/04bwdA+Edzw2loq65ptx/EpKurdzN8+2Mj23Q3cf/EYosMCbccRsSNjDBQXQ0WF7SQiIj1eSKA/D1w8hqr6Zn7/RhaOpiYeMhX1bmRpfgX/XlLInKNSmJiieenSgw0cCL16ueeqi4iIdaMSo7nhpDQ+XLeDV1doauKhUlHvJhqa2/i/eVkMjAnjxpPTbMcRscsY9wZIeblQV2c7jYiIAFdOH8zUwbH86d31bC3Xz+ZDoaLeTdzz4SYKK+r523kZhAUF2I4jYl/qMAgKgvXaAElExBv4+RnuvTCTAD/Dda+soaXNZTuS11NR7wZWFFTyzOKtXDYlmalDYm3HEfEOgYEwcpS7qLe02E4jIiJAYq9Q/nxuOmuLdvPQJ9rz4oeoqPu4xpY2bno9i8ToUP7v1OG244h4l1Gj3Rsg5W62nURERDxmZiRy7rj+PPxZHisLK23H8Woq6j7uvoWbyS+v46/npRMRrCkvIt8RFuaeAqMNkEREvMqfzhxF/96hXPfKGmoa9arngaio+7A1Rbt54ot8Lp44gOmp8bbjiHin9AyoroZthbaTiIiIR2RIIPddOIbtVQ388Z31tuN4LRV1H9XU2saNr60lISqE/3f6CNtxRLxXbCwkDdBSjSIiXmZCSgzXHD+UeauK+a92Ld0vFXUf9dAneeTurOXP56YTFaKNjUQOKiMDSkpgl34RiIh4k7kzhjI4Lpzb3s6hsaXNdhyvo6Lug3K2V/PP/27hvHFJHJ/Wx3YcEe+XNAB694bsLNtJRERkL8EB/txx9mgKK+p59PMttuN4HRV1H9Pc6uKG19YSGx7EbTNH2o4j4huMgYxM2JIHdbW204iIyF6OHhrHWWMSeezzLeTv0s/ovamo+5hHP89jY1kNd52TTnSYpryIHLKhqRAcDDk5tpOIiMg+bj59BMGBftz6dg6OVun6loq6D9lQuoeHP83jrDGJnDgywXYcEd8SEOBeV10bIImIeJ0+kSHcdHIaX+VV8M7aEttxvIaKuo9obXNx0+tZ9AoL5I9njLIdR8Q3jRwFbW2waaPtJCIiso9LJyeTkRTNnQs2UN2gARVQUfcZj3+RT/b2au44azS9w4NsxxHxTaGh7g2QsrPA5bKdRkRE9uLvZ7jr7HQqapu496NNtuN4BRV1H5C3s4YHFuZyWnpfTk3vZzuOiG/LyIQ9e6CwwHYSERHZR3pSNJdPTeH5pYVkFe+2Hcc6FXUv5zgOf3p3PSGBftx+1mjbcUR8X+/eMHCgNkASEfFS1580jLiIYG5+M4c2V8++sNRqUTfGnGKM2WSMyTPG/G4/988xxuwyxqzxvP1sr/tmG2NyPW+zuzZ51/ls006+zC3nuhPcf2lFpAOkZ0JZGezcYTuJiIjsIyokkNtmjiR7ezUvLC20Hccqa0XdGOMPPAKcCowELjHG7G9h8FccxxnjeXvSc24M8AdgMjAJ+IMxpncXRe8yLW0u7py/gcHx4Vw2Ndl2HJHuo39/iImFLG2AJCLijWZm9GN6ahx//3ATO/c02o5jjc0R9UlAnuM4+Y7jNAMvA2cd4rknAx87jlPpOE4V8DFwSifltOb5JYXkl9dxy+kjCPTXLCWRDmMMZGZC/haoqbGdRkRE9mGM4fazRtPU5uLOBRtsx7HGZvvrDxTt9Xmx57Z9nWeMyTLGvG6MGXCY5/qsyrpm7l+4mempcRyf1sd2HJHuZ8hQ9yowOdm2k4iIyH4Migvn6uOG8M7aEr7M3WU7jhU2i7rZz237XjHwLpDiOE4GsBB47jDOdR9ozFXGmBXGmBW7dvnOH/L9CzdT19zGrTNHYsz+nq6IHBF/f/cGSBs3QHOz7TQiIrIfvzh2CCmxYdz29joaW9psx+lyNot6MTBgr8+TgO9sReU4ToXjOE2eT58Axh/quXs9xr8cx5ngOM6E+Pj4Dgne2TbvqOHFZduYNXkgwxIibccR6b5GjnKvp76x576sKiLizUIC/bnj7NFsLa/j8f/m247T5WwW9eVAqjFmkDEmCLgYeGfvA4wxey8afibwzW/TD4GTjDG9PReRnuS5zec5jsMd89cTHuTPdScMsx1HpHsLCYFhaZCdrQ2QRES81PTUeM7ITOSRz/MoKK+zHadLWSvqjuO0AnNxF+wNwKuO46wzxtxujDnTc9i1xph1xpi1wLXAHM+5lcAduMv+cuB2z20+75vlGH99wjBitAOpSOdLz4DaGti61XYSERE5gFtPH0Gwvx+3vbPOdpQuZRyn5ywkP2HCBGfFihW2YxxQS5uLk+/7AoAPrjuGoACt9CLSJT54Hxoa4JxzbScREZEDeGrRVu6Yv54XrpjMtNQ423EOyhiz0nGcCUf6OGqCXuTb5RhnjlBJF+lKGZnuzY/KymwnERGRA/jxlIH07xXK3R9upKcMNKsNeokqLccoYk+/fhAXD1lrbScREZEDCA7w5zcnDiOruJoPcnrGwIqKupe4b+FmaptatRyjiA3GQEYGFGyFPXtspxERkQM4Z2x/UvtEcM9Hm2ht6/6LAKioe4H/LceYrOUYRWwZPATCwiA7y3YSERE5AH8/ww0np5G/q443Vm23HafTqahbtvdyjL85Ucsxiljj7w+j02HTRmhq+uHjRUTEipNGJjBmQC/uW7i522+CpKJumZZjFPEiI0aC48CG9baTiIjIARhjuOmUNEqrG3lhaaHtOJ1KRd2iljYXd87fwOC4cC6bkmw7jogEB8PwEZCTDW3de5RGRMSXHTUkjumpcTzyWR41jS2243QaFXWLvlmO8ebTtRyjiNdIz4C6Otja87aqFhHxJTeenEZVfQtPftl9N6xTO7SkvrmVRz7L46ghscwYruUYRbxGVBQMGuReqrGHrNMrIuKLMpJ6cVp6X578Mp+K2u55bZGKuiX/WbaNirpmfnPiMC3HKOJtMsbArl1QWmo7iYiIHMRvT0qjsdXFI59tsR2lU6ioW9DY0sa/vshnyuAYJqbE2I4jIvtKSIA+fSBbGyCJiHizIfERnD8uiReWFlJcVW87TodTUbfg1RVF7Kxp4toZqbajiMj+GAMZmVBQANW7bacREZGD+PUJqWDggYW5tqN0OBX1Ltbc6uKxz7cwPrk3U4fE2o4jIgcyaDBEREKWNkASEfFmib1CuXxKMvNWFZO7o8Z2nA6lot7F5q0qpqS6kV/NGKq56SLezM8P0tNh8yZobLSdRkREDuLq44cSFhTAvR9tth2lQ6mod6GWNhePfp5HRlI0xw6Ltx1HRH7I8BHuaTDaAElExKvFhAdx5fTBfLCujDVF3WfKoop6F3p7TQlFlQ38akaqRtNFfEFQkHu3Um2AJCLi9a6YPojY8CDu+XCj7SgdRkW9i7S5HB79LI8R/aI4YYTWTRfxGaPToaEBtuTZTiIiIgcRERzANccP5au8ChblltuO0yFU1LvI/KwS8svrNDddxNdERsLgIdoASUTEB8yaMpD+vUK5f2H3mKuuot4FXC6HRz7LI7VPBKeM6ms7jogcrowMqKiAku22k4iIyEEEB/hzxbRBrCisYvW2KttxjpiKehf4cF0Zm3fUMnfGUPz8NJou4nP6JEDfvlqqUUTEB1w4cQCRIQE8+eVW21GOmIp6J3Mch4c+zWNwXDgzMxJtxxGR9krPhG2FUOX7IzQiIt1ZRHAAl04eyPs5pRRV+vZupSrqneyTDTtZX7qHq48fir9G00V8V0oKREVBtkbVRUS83ZyjUvAzhqe/8u1RdRX1TuQeTc9lQEwoZ43RaLqIT/Pzg/QM9wZIDQ2204iIyEH0iw7ljMxEXl1eRHVDi+047aai3om+yC1nbXE1Vx83lEB/fatFfF7acPD3h/XrbCcREZEf8LPpg6hrbuOlr7fZjtJuao+dxHEcHvwkl8ToEM4bl2Q7joh0hMBA9wZI63KgtdV2GhEROYhRidEcNSSWZ78qoLnVZTtOu6iod5IlWypYWVjFL44bQlCAvs0i3cbodGhqgrxc20lEROQHXDl9MGV7GlmQXWI7SruoQXaShz/Lo09kMBdOGGA7ioh0pIgIbYAkIuIjjh0Wz9A+ETzxxVYcH/yZraLeCXJ31LB4SwVzjk4hJNDfdhwR6WgZme5lGouLbScREZGD8PMz/GzaINaX7mHJlgrbcQ6binoneHHZNoL8/bhIo+ki3VN8PPRLdI+qi4iIVzt7bH/iIoJ44st821EOm4p6B6tramXeymJOTe9LbESw7Tgi0lkyMqG4CCp9b4RGRKQnCQn057IpKXy2aRd5O2tsxzksKuod7J21JdQ0tXLZlGTbUUSkMyUnQ3Q0ZGkDJBERb/fjKQMJDvDjyS99awMkFfUO5DgOLywtZHjfSMYn97YdR0Q6kzHuDZByN0O9b29RLSLS3cVGBHPe+CTeWL2dXTVNtuMcMhX1DrSmaDfrSvYwa0oyxhjbcUSksw1Lc6+tvi7HdhIREfkBV0wbRHOri+eXFtqOcshU1DvQC0u3ER7kzzlj+9uOIiJdITAQRo5y71SqDZBERLzakPgIThiRwAtLC2lsabMd55CoqHeQqrpm3s0q4eyx/YkIDrAdR0S6yqjR0NwMmzfbTiIiIj/gyumDqKxrZt4q31heV0W9g7y+spjmVhc/1kWkIj1LeDgMTYVsbYAkIuLtJg2KISMpmqe+3IrL5f0/s1XUO4DL5fDiskImJPdmRL8o23FEpKtlZMDu3bBtm+0k0kM0NTXRqulWIofNGMMV0waRX17Horxy23F+kIp6B/hqSzkFFfUaTRfpqWLjoH+SNkCSLrF48WKuuuoq/P2187VIe5wyui/RoYE+Mf1FRb0DvLC0kJjwIE5N72s7iojYkpEBJduh3PtHaMQ31dbW8utf/5pp06Zx0kknaXUxkXYKDvDnzMxEPsgpY09ji+04B6WifoRKqxv4eP0OLpwwgOAAjW6I9FgDBkKv3u656iId7KOPPmL06NE8+OCD9O3blwsuuMB2JBGfdv74JJpaXSzIKrUd5aBU1I/QS18X4QCzJg+0HUVEbDLGPaqelwd1dbbTSDfy5ZdfcvXVV1NY6F77+ZprriEoKMhyKhHflpEUzdA+Ecxb6d3TX1TUj0BLm4uXv97GscPiGRATZjuOiNiWOgyCgiAn23YS6UYmTpxIQkICISEhhISEcNVVV9mOJOLzjDGcPz6JFYVVbC333sEVFfUjsHD9DnbWNPHjybqIVESAgAD3uuob1kOLd897FN/gOA5XXHEF2dnZLFq0iOuvv574+HjbsUS6hXPG9sfPwBtefFGpivoReGFZIf17hXL88D62o4iItxg5yr1L6eZNtpNIN3DHHXfw8ssv8+qrrzJ+/HjuuOMO25FEuo2EqBCmp8Yzb2Wx166prqLeTlt21fJVXgWXTh6Iv5+uvBcRj7AwSE2FrCxwuWynER/28ssv84c//IEHHniAU045BQA/P/3aFulI549PoqS6kSX5Fbaj7Jf+xbfTf5ZtI9DfcOGEAbajiIi3Sc+EPdWwrdB2EvFRS5YsYc6cOcydO5e5c+fajiPSbZ04MoHIkACvvahURb0d2lwOb6/ZzgkjEoiPDLYdR0S8TUwMDBigDZCkXQoKCjjrrLOYMWMG9913n+04It1aSKA/Z2Qm8n5OGbVN3rfbr4p6OyzbWkF5bTNnZCbajiIi3iojE0pLYddO20nEh1RXVzNz5kwSEhJ4+eWXCQgIsB1JpNs7f3wSDS1tvJftfWuqq6i3w/ysUkID/Tk+TReRisgB9E9yj6xrVF0OUWtrKxdddBG7du1i/vz5REVF2Y4k0iOMHdCLwXHhvO6F019U1A9Ta5uLD3LK+NGIPoQGaSdSETkAY9yj6lu2QG2t7TTiA6677jo+//xz3n77bZKTteyvSFcxxnDe+CS+3lrJtop623G+Q0X9MC3Nr6SyrpmZGZr2IiI/YGgqhIRoAyT5QQ899BCPPPIIzz33HFOmTLEdR6THOXdcf4yBeV62prqK+mFakF1CeJA/x6VpwwkR+QH+/jA63b0BUnOz7TTipRYsWMB1113H7bffzkUXXWQ7jkiP1C86lGlD45i3yrvWVFdRPwwtbS7ezynjhJEJhARq2ouIHIIRI93rqW/aaDuJeKGsrCwuvvhiLr30Um655RbbcUR6tPPHJ1Fc1cCyrZW2o3xLRf0wLN5Swe76Fk17EZFDFxoKw4ZBtjZAku8qKytj5syZZGZm8uSTT2KMNs8TsemkkX2JCA7wqukvKuqHYUFWCZHBAUxPjbMdRUR8SXoG1NRAwVbbScRLNDQ0cNZZZxEYGMibb75JcLD25BCxLTTIn5kZ/Xgvu5Q6L1lTXUX9EDW3uld7OVHTXkTkcPXqDQOTtVSjAOByuZg9ezabNm1i/vz5xMfrmicRb3He+CTqm9v4IKfMdhRARf2Q/X/27jwsqrJ94Pj3zD7MsAu474IrWlm+lS2vW+ZumqZmZrmVVpq5Zqi5pZlaZmWL/kzFpfR1K3u1xcryNbXcDTrtpvcAACAASURBVAU1FRQVWWSY/ZzfH4OIO8LAsDyf6+ICZs6cc4M43NxzP/fzW/xFMmwuOjSu4OtQBEEoiRo3huRkOFc8nvwF34mJiWHt2rV8/fXX1KtXz9fhCIKQS9NqwVQMNLDlcPF4rhaJeh5t2n8Wf4OG5rVF5UMQhHyoUBHKlfP0qpdCVqsVWfTg39GXX37JtGnT+Oijj2jVqpWvwxEE4TqSJNGiXji/HruIzen2dTgiUc8Lu8vNlsPneKJBeXQa8S0TBCEfJAkaNYYTx+Fyhq+j8boVK1aQkJDg6zCKtV9//ZUBAwbw+uuvM2jQIF+HIwjCLbSsF0GWw83/jqf4OhSRqOfFr0cvctnmon20aHsRBKEAatUCox8cKF0bICmKwoIFC/jrr798HUqxFR8fT9euXXnyySeZNWuWr8MRBOE2HqwZip9OzfdHkn0dikjU8+KbA2cJNGppXltMexEEoQDUamjUCP4+Ana7r6Pxmp07d/Lnn3+KRP0WUlNT6dChA1WqVGH58uWo1WIggSAUZwatmua1y/HjkfMoim83PxKJ+h3YnG62Hk6mbYPyaNXi2yUIQgHVqw+K4knWS4mPPvoIgL179/o4kuLH6XTSvXt3MjIy2LhxI2az2dchCYKQB63qRZCUbuPwWd+2KorM8w5+PnqBTLtoexEEwUv0eoiqCwcPlIoNkC5cuMCqVasASn1F/ezZs3d1vKIoDB06lB07drBx40YqV65cSJEJguBt/64bjiTBD0fO+zQOkajfwTf7zxLsp+WhWqG+DkUQhNKiUTRkZsLxkr/4cuXKlTRq1AjwVI/vNpktCex2O0OGDGHfvrubgz9nzhw+++wzli1bxn333VdI0QmCUBjC/PU0qRLEDz7uUxeJ+m3YnG6+P5JM24YV0Ii2F0EQvCUwEKrX8GyA5OP+x4J6+eWXefHFFwkICODvv//2dThed+bMGR577DFWr15Ny5Yt8/y49evXM2rUKN555x2eeuqpQoxQEITC0qpeBPvOpJOcYfNZDCL7vI1tcRfIcrjpINpeBEHwtujGcOFCid8ASa1Wk5SURKVKlQgLC6NChdLzfPnTTz9x7733snPnTrp27YpWq83T4/766y969+5N//79GT16dCFHKQhCYWlZLxyAH//2XfuLSNRv49djFzDrNTxQI8TXoQiCUNqULw9h4Z6qegmXmJhIxYoVfR2GVy1atIjWrVtz4cIFAJ5++uk8PS4xMZGOHTvywAMP8PHHHyNJUmGGKQhCIYqK8KdSkNGn7S8iUb+N7fEX+VfNEDHtRRAE75MkiI6GkycgPd3X0RTIlYp6adK/f38ee+wxJEkiODg4T20vFouFTp064efnx5o1a9DpdEUQqSAIhUWSJFrVC2d7vO92KRUZ6C2cvpTFPylZPCxmpwuCUFhq1gKzGQ7s93UkBVIaK+off/wxP/30Exs2bGDgwIF3bHuRZZlnn32WEydO8M033xASIl6JFYTSoFX9CGxOmd/iL/rk+iJRv4Ur/yBikyNBEAqNSgUNG0Hc3yV6A6TSVlE/cuQII0eOZOzYsXTo0IFp06bd8THjxo3jm2++Ye3atdSpU6cIohQEoSg0qxGKWa/hex+NaRSJ+i1sj79IRICe2uFicwpBEApR3XqeNpgjh30dSb5YrVYuXbpUairqDoeDZ599lgYNGjBp0iQANBrNbR/zxRdfMGvWLBYuXMjjjz9e+EEKglBkdBoVj0aW44cjychy0U/pEon6Tciywu8JKTxcu5xYCCQIQuHS6z3J+oED4PZND2RBXJmbXloq6hMnTuTIkSMsW7YsTz3mP/30E0OGDGHMmDH079+/CCIUBKGotawbwfnLdg4mFf16IpGo38SRcxlcsjhE24sgCEWjUSOwZkFCydsAKTExEaBUVNR/+eUXZs6cyXvvvUfdunXvePzRo0fp1q0bnTp1Yvr06UUQoSAIvvDvuuGoJHzS/uLTRF2SpLaSJMVJkhQvSdLYm9z/uiRJhyVJ2i9J0g+SJFXLdZ9bkqS92W8bvBnX9mOe/nSxkFQQhCLhHwA1asKBkrcBUlJSEpIkUb58eV+HUiDp6en07duXdu3aMWTIkDsen5KSQvv27alZsyZLly5FpRJ1L0EorUJMOu6tGuyTMY0+e2aRJEkNLACeBOoDvSRJqn/dYX8BTRVFiQa+Bmblus+qKEqT7LdO3oxte/xF6oSbiQgwePO0giAItxYdDRcvwtkkX0dyVxITEwkPD8/zZkDF1dChQ7FarXzxxRd3bHl0OBx069YNm83Ghg0b8PPzK6IoBUHwlVb1IziUlMHZdGuRXteXJYAHgHhFUY4riuIAVgKdcx+gKMpPiqJkZX/6P6ByYQdlc7rZdfISzeuIarogCEUoojxERMC+krUBUmmY+LJixQqWL1/OokWLiIiIuO2xiqIwePBgdu/ezcaNG0tFy48gCHfWyke7lPoyUa8EnM71+Zns227lRWBzrs8NkiTtliTpf5IkdbnVgyRJGpR93O4rO8zdzp+nUrE5ZdGfLghC0YtuDKf+gbRUX0eSZyV9hvqpU6d46aWXGDJkCB06dLjj8TNnzmTJkiWsWLGCJk2aFEGEgiAUB7XCzIT769l14lKRXteXifrNXlu8aXOmJEnPAk2Bd3PdXFVRlKZAb2CeJEm1bvZYRVE+VRSlqaIoTcPCwu4Y1G/xF1GrJJrVDL3jsYJQ0qWmpnLo0CFkWfZ1KAJA9Rrg71+iNkAqyRV1t9tNv379iIiIYPbs2Xc8fs2aNYwbN47Zs2fTsWPHIohQEITiQpIkmlYPZtfJoi2k+DJRPwNUyfV5ZeCG5kxJkloBbwKdFEXJ2RFEUZSk7PfHgW3APd4Iavuxi9xTJQiz/vZzcwWhNAgKCmLhwoWEhYXRpUsX5syZw+7du3G5XL4Orcyw2WxXP1GpoFE0HD0K1qLtg8yvklxRnzNnDtu3b2f58uWYTKbbHrt792769u3LoEGDGDFiRBFFKAhCcXJftRAS06ycS7fd+WAv8WWivguoI0lSDUmSdMAzwDXTWyRJugdYiCdJP5/r9mBJkvTZH5cDHgYKvFtIepaT/YnpYtqLUGZIksS8efNo374969evZ+TIkdx///08+uijpKSk+Dq8MuGXX37h66+/vnpDVF1Pwl5IGyApXpwqoyhKia2o7927lzfffJPJkyfTtGnT2x57+vRpOnbsyMMPP8yHH34o9tcQhDLq/urBAOz+p+jaX3yWqCuK4gKGAf8FjgCrFUU5JEnS25IkXZni8i5gBr66bgxjPWC3JEn7gJ+AdxRFKfBvtR3HL6Io8IhYSCqUISqVii+++IJOna4OTzKZTORlTYdQcA0aNKBXr1588803nht0OqhXHw4WzgZIP//8M8nJ3hkxlpaWhtVqLXEVdavVSu/evXnggQcYM2bMbY/NzMykY8eOBAUF8dVXX5X46TaCIORfvQoBGLVqdhdh+4tPB78qivKtoiiRiqLUUhRlWvZtMYqibMj+uJWiKBHXj2FUFOV3RVEaKYrSOPv9F96IZ3v8RUw6NY2rBHnjdIJQYmi1WlatWsW///1vevToQXJyMo0aNWLkyJGkpxf9TmxlScWKFQkICKBbt2789NNPnhsbNgKbDeKPef16Bw4cYN68eV45V1KSp1uxpFXUx4wZw5kzZ1i6dClqtfqWx7ndbnr16sWZM2fYtGkTQUHid4MglGVatYomVYLKRkW9OPotPoV/1QxFqxbfFqHsMRgMrF+/ntdff50///yTefPmsXjxYiIjI1m0aJFYcFpIJEkiOjoau91Ox44d2bFjB5jNUKsW7Pf+BkjHjh3jo48+8sofYCVxV9LvvvuO+fPn8+GHH1KjRo3bHjtq1Ci2bNnCunXrqFXrpvMKBEEoY+6vHszhpAwy7UWzlktkpNnOpGZx4qJF9KcLZZq/vz/NmjVDo9EwdOjQnC3SBw4cyL/+9S/+97//+TrEUik6OhqArKwsJk6cyPnz5z2jGi9dgsQzXr1WfHw8GRkZfPTRRwU+V1JSEjqdjtDQkjEl6+LFi/Tv358ePXrQt2/f2x67cOFC5s6dyxdffEHz5s2LKEJBEIq7+6qHICuw91RakVxPJOrZfou/CCA2OhKEXMqVK8dHH33En3/+idFo5MEHH6Rfv36cPXvW16GVKtHR0TRr1gxFURg7dizh4eEQFg4VKniq6l4UHx8PwNy5c8nKyrrD0bd3ZeJLSVhcqSgKAwcORK1W8/HHH9825q1btzJ06FAmTJjAs88+W4RRCoJQ3N1TNQhJKroFpSJRz/ZbfArh/nrqhJt9HYogFDuNGzdm27ZtrFy5kh9//JHIyEhmzZqF3W6/84OFO2rZsiVbt26lRYsWxMTEXJ3MEt0YTp/2VNa9wOVyceLECcCzoHLRokUFOl9JmviyaNEi1q1bx5IlSwgJCbnlcYcPH6Z79+5069aNyZMnF2GEgiCUBAEGLXXLB7Dnn6JZUCoS9Wz7zqRxX7XgElEZEgRfkCSJnj178vfffzNixAhiYmJo2LDh1WklQr5Vr14df39/Jk+ezG+//cbWrVs9d1StBgGBcMA7VfULFy7w8ccfU6lSJd588026detWoPOVlBnq8fHxvPbaa4wcOZKWLVve8rgLFy7QoUMH6tWrx//93/+hUolfkYIg3KhptWD+/CcVl7vw126JZyEg3erkn5QsGlYK9HUoglDsmUwm3n77bY4cOUJ0dDQdOnSgXbt2xMXF+Tq0Eq958+a0bt36alX9ygZIx45BAdtUACpUqMCAAQOIiooiLi6OChUqFOh8JaGi7nQ6efbZZ6lZsybTpk275XE2m40uXbrgdrtZv349RqOxCKMUBKEkaVo9GIvDzd/nLhf6tUSiDhxK8kw/EIm6IORdjRo1WLNmDd9//z2nTp2iUaNGjBo1ioyMDF+HVqJNnjyZnTt3snnzZs8NUVGg0cDhQ167xpVEvaBKQkV92rRp7N27l9jYWPR6/U2PURSFF198kQMHDrBp0yYiIiKKOEpBEEqSptU97XNF0f4iEnXgYKInUW8kEnVBuGstW7Zk7969vPfee3z++edERkayZMkSMc4xnx588EGefPJJJk6c6Kmqa7WeDZAOHQSXd8aBeSNRd7vdnDt3rlhX1Hfs2MHUqVOZOXMmDRs2vOVxU6ZMYeXKlaxatYpGjRoVYYSCIJRElYKMVAg0sOtk4S8oFYk6cDAxg0pBRkJMOl+HIgglkkaj4ZVXXuHo0aN06dKF/v378+CDD/LHH3/4OrQSafLkyezevZtNmzZ5bmjYCBwOOHbUK+ePiori0qVLXLx4Md/nSE5ORpblYltRv3z5Ms8++ywtWrTglVdeueVxK1euZOLEicybN48nn3yyCCMUBKEku69asKioF5WDiek0qBjg6zAEocQLCwvjk08+Yc+ePeh0Opo1a0b//v05d+6cr0MrUe6//346dux4tVfdZIJatWH/fq9sgBQVFQVQoKp6cd+VdPjw4aSlpd12UeiOHTt4/vnnGTp06G2TeUEQhOvdXz2Es+k2EtOshXqdMp+oX7Y5OX7RItpeBMGL7rnnHn755RdiY2PZunUrkZGRzJ49G4fD4evQSozJkyezd+9e1q1b57khujGkpcLpUwU+d9WqVdHr9QVK1IvzrqRr165l0aJFfPrpp7eM7+TJk3Tu3Jl///vfzJs3r4gjFAShpLuvWjAAuwu5/aXMJ+qHkzwL38RCUkHwLkmS6NWrF3Fxcbz66qtMmDCBRo0aXV0kKdzWPffcQ9euXZk4caKn379cOahYyVNVLyC1Wk2dOnUKXFEPDAzEZDIVOB5vSkpKYuDAgfTv3/+W4yfT09Pp0KEDERERrFq1Co1GU8RRCoJQ0tUt749Jp2b3ycJtfynzifqBRDHxRRAKk8lkYurUqRw+fJj69evTrl07OnTowLFjx3wdWrE3adIkDhw4wJo1azw3RDeGxDOQkv/e8isKuqC0OE58kWWZ559/nqCgIN5///2bHuNyuejZsycXLlxg06ZNBASItkdBEO6eRq3i3mrB7C7kPvUyn6gfTEynfICBMP+bj+0SBME7atasyX/+8x+2bNnC8ePHadCgAWPHjuXy5cKfQ1tSRUdH8/TTTzNp0iTcbjdUrQpBQV6pqhc0US+OM9Q//PBDfvjhB5YuXYq/v/9Njxk+fDjbtm1j/fr1VKtWrYgjFAShNLmvWjB/n8sgw+YstGuIRD0pg4aVREVFEIpK69at2bdvH++++y6ffPIJUVFRfPnll2Kc4y1MnDiRI0eOsHr1apAkaNQY4o+BxVKg80ZFRZGQkIArnyMfi1tF/dChQ4wePZoJEybw0EMP3fSY+fPns2DBApYsWcK//vWvIo5QEITSJrpyIIoCx5ILr+BUphN1i91FwoVM0fYiCEVMq9Xy2muvcfToUdq3b8/zzz/Pww8/zK5du3wdWrHToEEDnnnmGSZNmuRJqiMjQafzzFUvgKioKJxOJydOnMjX44tTRd1ut9O7d28aN27MhAkTbnrMt99+y/Dhw3n77bfp2bNnEUcoCEJpVCfc88rdseTMQrtGmU7UD5/NQFHERkeC4Cvh4eF89tln7Nq1C5VKRbNmzXjxxRdJTk72dWjFSkxMDPHx8axYscKzS2n9Bp6dSp35f7m1oCMai1NFfcKECSQkJLBs2TK0Wu0N9x84cIBnnnmG3r173zKRFwRBuFuVgowYtCqOnReJeqE4KBaSCkKxcN9997F9+3aWLl3Kd999R2RkJHPmzBHjHLPVrVuXPn36MHnyZE9VvUFDT5J+NP895kFBQYSHh+crUbdaraSmphaLivqPP/7Ie++9x7x586hTp84N9587d44OHToQHR3N559/jiRJPohSEITSSKWSqB1u5qhofSkcBxLTCfPXExFg8HUoglDmSZJEnz59iIuLY+jQoYwbN47GjRvz3//+19ehFQtvvfUWJ0+eZOnSpeDnB3Ui4UDBNkDK74LSK5sd+bqinpqaSr9+/ejUqRMvvvjiDfdbrVa6dOmCRqPhP//5D3q9GBogCIJ31Qn3J15U1AvHwcR00fYiCMWM2Wxm+vTpHDp0iMjISNq2bUvnzp1JSEjwdWg+VadOHfr27cuUKVNwOp0QHQ3p6fDPP/k+Z0ETdV9W1BVFYciQIbhcLj777LMbKuVXRjX+/fffbNq0ibCwMB9FKghCaVY73MzZdBuXC2nyS5lN1K0ON/HnM2lYUUx8EYTiqHbt2qxfv57vvvuOuLg46tevz/jx48nMLLzKRXH31ltvcerUKZYsWQIhoVC5Cuzfl+/zRUVFcfTo0bt+XGJiIpIkERERke9rF9Ty5ctZvXo1ixcvvmkSPnHiRNasWcPXX39NvXr1fBChIAhlQWSEZ0FpYVXVy2yifvhsBrIi+tMFobh74okn2L9/PzNmzGDBggVERUWxfPlylAK0fJRUNWvWpH///kyZMsXTvx8dDWeT4MKFfJ0vKiqKc+fOkZGRcVePS0pKIiIi4qYLN4vCyZMnGTp0KMOGDaNt27Y33L906VKmTp3KggULaNWqlQ8iFAShrKgTbgYotAWlZTZRv7KQtFFlkagLQnGn0+l4/fXXOXr0KG3btqVv3740b96cPXv2+Dq0Ivfmm29y9uxZFi1a5KmoBwfnu6qe38kvvpz44na76du3L5UqVWLmzJk33L99+3YGDBjAiBEjGDx4sA8iFAShLKkS4odOoxIVdW87mJhOqElHebGQVBBKjIiICL744gt27tyJLMvcf//9DBw4kPPnz/s6tCJTvXp1XnzxRaZNm4bNbofoxnA8AfLRElSjRg00Gk2+EnVf9afPmjWLnTt3snz5cvz8/K65LyEhgS5duvDEE0/w7rvv+iQ+QRDKFrVKolaYudA2PSqzifqx85lElfcXo7oEoQS6//77+e2331iyZAmbNm0iMjKSefPmeRZZlgHjx4/n/PnzfP7551C7Duj1cPDAXZ9Hq9VSq1atu07Uk5KSfFJR37NnDzExMUydOpV77rnnmvvS0tLo0KEDlStXJjY2FrVaXeTxCYJQNtUJN4vWF287fSmLaqF+dz5QEIRiSaVS0bdvX44ePcqQIUMYPXo0jRs3ZuvWrb4OrdBVqVKFQYMGMX36dKxOp2eu+pHD+doAKT+TX3xRUc/KyqJPnz48/PDDjBw58pr7nE4n3bt3Jz09nY0bN2I2m4s0NkHID5fLRfXq1dHpdJjNZsLCwqhevTrR0dF8+OGHvg5PuAt1ws2cSbWS5XB5/dxlMlG32F2kWBxUCRGJuiCUdP7+/rzzzjscOnSIWrVq0aZNG7p27crx48d9HVqhGjduHJcuXWLhwoWenUrdboj7+67Pc7eJuqIoPqmov/HGG5w7d44vv/zymmq5oigMGzaM33//nQ0bNlClSpUijUsQ7tbJkyeZMmUKLVq0ICUlBafTicVi4eLFi5w9e5Zu3brx8ssv+zpM4S7UifAUBxLOW7x+7jKZqJ9OzQKgSrBI1AWhtKhTpw4bN27k22+/5fDhw9SvX58JEyZgsXj/ibM4qFixIi+99BIzZszAIssQGeXZAEmW7+o8UVFRHDt2DDmPj0tNTcVmsxVpRf2bb77h448/5uOPP6Zq1arX3Dd37lw+/fRTli1bRtOmTYssJkHIC1mW2bx5My+88AJ169bFYDBQo0YNYmJi2LFjB+XKlQM8G7717NmT1NRUJk6ciEpVJtOzEqt2uGdE47Hz3u9TL5M/CacvWQGoKirqglDqPPnkkxw4cIBp06bxwQcfEBUVxYoVK0rlOMcxY8Zw+fJlPv74Y2gUDRkZcPLkXZ0jKioKq9XK6dOn83R8Ue9Kev78eV544QV69+5Nr169rrlvw4YNvPHGG8yYMYOnnnqqSOIRhNtJTk5m7ty5tG7dmoiICDQaDe3atWPx4sUkJSXRqFEjRo0axV9//YXT6WTjxo00bdqUEydOsHLlyhsWSAslQ7VQP7RqqVD61Mtkon7qUnZFXSTqglAq6XQ6Ro4cydGjR2ndujW9e/fm0Ucf5a+//vJ1aF5Vvnx5hg4dysyZM8nUaqFqVThwd6Ma73ZEY2JiIlA0u5IqisKLL76IwWBgwYIF19y3d+9eevfuzfPPP8+YMWMKPRZBuJ4sy2zfvp1hw4YRHR2Nn58f5cuX5/XXX+fHH39Eq9XSsWNHPvvsM9LT08nIyGDXrl3MmjWLJk2aANCwYUN27dpFtWrVfPzVCAWhVauoUc7EsWSRqHvF6UtZmPUagv18s1mHIAhFo3z58ixevJj//e9/OBwO7rvvPgYPHsyFfG4QVByNHj0aq9XqWXwW3RjOnYPk5Dw/vly5cgQHB+c5UU9KSkKv1xMSEpLfkPPs008/5ZtvvuHLL78kKCjomhg6dOhA06ZN+eSTT8T0LqFIZGRk8Omnn9KpUycqVaqEVqvlkUceYcGCBcTHx1O7dm2GDRvG9u3bcTqdnDlzhvXr1zNgwAACAsQu6KVdnXB/0friLacvZVE52Cie3AWhjGjWrBk7duxg8eLFrF+/nsjISObPn4/L5f0V+kUtLCyMYcOG8e6775Jh9ofQUE+veh5JknRXC0qvbHZU2M+fcXFxjBgxgtGjR/PYY4/l3G6xWOjUqRN+fn6sWbMGnU5XqHEIZde+ffsYNWoU999/PwEBAQQGBjJ48GA2bdqEy+WiZcuWzJ07l+TkZLKysti/fz/z58/n4YcfFj3mZVDtcDOnLmVhc7q9et4y+ZN0OjVL9KcLQhmjUqno168fR48eZeDAgYwcOZImTZrwww8/+Dq0AnvjjTdwOBzMv1JVP54Al/Ne2bmbRL0oJr44nU769OlDVFQUb7/9ds7tsizz3HPPcfz4cTZt2kRoaGihxiGUHTabjdjYWHr06EGNGjXQarU0adKE2bNnc+DAASpVqkT//v359ttvcblcJCcns2XLFoYPH054eLivwxeKgToRZhQFEi54t/2lzCXqiqJw+pJV9KcLQhkVEBDArFmzOHjwINWqVaNVq1Z069aNk3e5CLM4KVeuHK+99hqzZ88mvVwYGI13tQHS3VbUC7s/ffLkyRw6dIjY2NhrKubjx49nw4YNrFmzhmrVqrF9+/ZCjUMoveLj45k0aRIPP/wwwcHBGI1G+vTpw1dffUVGRgYPPfQQU6ZM4fjx49hsNo4cOcKiRYt48sknRbVcuKnICM/kl3gvLygtcz9tFzMdWJ1uUVEXhDIuMjKSb775hk2bNnHgwAHq1atHTEwMWVlZvg4tX15//XVkWWbe/PnQsJFnAySHI0+PjYqK4vTp03kaZVnYFfXt27czY8YMZs+eTb169XJuX7x4MTNnzmThwoVYrVYaNWpUKlqXhMLncrnYuHEj/fr1IzIyEr1eT506dZg8eTJ//PEHISEh9OrVi1WrVmG320lJSeHnn39mwoQJ1KhRw9fhCyVE9VATkgTHL3h3JHCZS9SvTnwx+jgSQRCKg/bt23PgwAEmT57M3LlzqVu3LqtWrSpx4xxDQkIYMWIEc+bMIa1CRVAU+PtInh57ZfLLsWPH7nhsYVbU09PTefbZZ2nTps01G75s27aNQYMGMXDgQNauXUv79u2JiIi4pnddEK44e/Yss2fPpmXLloSFhaHT6ejUqRNffvklycnJNGnShHHjxnHw4EGcTicJCQk5bS9izUP+OB0uHDZXiXve9CadRkWIn47zl+1ePa/Gq2crAc5kb3YkKuqCIFyh1+sZPXo0ffv2ZezYsTzzzDN89NFHfPDBBzRu3NjX4eXZ8OHDef/993lvwQKmtGntWVTasBHc4aX62rVro1KpiIuLyxkbdzNXenMLq6L+6quvYrFYWLx4cc5i1aNHj9K1a1dq1qzJkiVLcGS/ShATEyMGAgjIsswvv/zCqlWr+PXXX0lISMBmswGedSmVKlWic+fOdOrUrUmePwAAIABJREFUiaeffhqz2ezjiEsnCYl9PyfgdskYzTqMZj0Gsw6jWUdQmBmNTn3nk5QC4QEGLly2efWcZS5RP5XiSdQri11JBUG4ToUKFViyZAkvvfQSr7zyCvfeey+DBw9mypQpJWLhYlBQECNHjmTmzJm8/uKLBB8+BCeOQ63at32cXq+nevXqd+xTT05ORpblQqmor169mi+//JJ169ZRvnx5AFJSUujQoQM1a9bkgw8+oFWrVoBnis+Vj4WyJS0tjRUrVrBp0yb++uuvnJ9JAJPJRN26dXn00Ufp3bs3zZo183G0pY/bLWO3OLBZnFgtdmwWJ7ZMO1aLE4fN04qWmWYjM81GULiZgFA/1Nqy07wR7q8XFfWCOp2aRbi/HoO2bPx1JwjC3fvXv/7Fzp07WbJkCWPHjmXlypVMmTKFwYMHo9EU76fNV199lblz5zJr4UJmPP4o7N8HNWvBHarPeVlQWli7kp45c4YhQ4YwcOBAOnfuDIDD4aBbt25kZWWxfv16evXqRcWKFalSpQqjR48u0mr6hQsX2LVrF3/88Qd//fUXw4cP59///neRXb8s27NnD7GxsWzbto2jR4+SmelZqCdJEhEREbRp04Z27drRq1cvypUr5+NoSwe3W8ZmcXjeMj3vrdmfO6xX14Vo9RoMJi0Gk47AMBMZKVbSLmQSVimQirVDMQUafPhV+Ea4v564c96dpV68f+MUglOXxGhGQRDuTKVS0b9/f5566immTJnC8OHDWbhwIe+//36xTtICAgIYNWoUU6dOZXTfZwn+9RdIPgflK9z2cVFRUfz666+3PebKrqTeTNRlWaZfv36EhoYyZ84cwDOda/DgwezatYutW7cycOBAzp8/z/bt27FardSvX99r178VRVGYMWMGn332Wc5EIJVKxfLly4v1v39JlpWVxZo1a1i/fj27du0iMTERt9szk9pgMFCjRg2aN2/O008/TcuWLcvM9JUzVjsmtYpgnfc2aXS7ZGxZVxPxnGQ805FTGQdPMm406zCYPC0sBpOnncXgp0V9XcEz+Z9UajYuj8Gv7Pb5hwfouZBpxy17r1e/zCXqpy9ZeaBG4e+oJwhC6RAYGMjs2bMZMGAAw4cPp0WLFnTv3p3Zs2cX222/hw0bxnvvvcf0xf/Hu/9q5qmq5yFR//zzz1EU5ZbV6qSkJAIDAzGZTF6Ldd68efz888/8/vvvOf3DM2fOZMmSJXz99ddMmzaNQ4cO8csvv1CzZk2vXTcvatSowZkzZwBPkr506VKeeeaZIo2hNIuLi2P58uV8//33HD58mPT09Jz7QkNDeeSRR3jiiSfo3bs3VatW9WGkvnXe7mT3+XQcsoJVUZBUEiaNmjCDlnblg/FT37xDwO2Sr03Cc1XIcyfjOoMGgylXMm7WYcz+XK3J+x9DEdWCC/y1lnTh/gbcssIlS94mbuVFmUrUFQXOposZ6oIg3L26deuyefNmNm3axIgRI6hbty5jxoxh9OjR+PkVr+cUs9nMmDFjiImJ4c2ePQjasxvS0yEw8JaPiYqKIjMzk7Nnz96yYu7tiS/79u1j3LhxTJw4kQceeACANWvWMG7cOGbNmsXXX3/Ntm3b+P7772nUqJHXrnsn27dvZ/To0ezYsYPmzZvz+++/8+WXX9K7d+8ii6G0uTIice3atezYsYNTp07hdDoB0Gq1VK1alQ4dOtCtWzfat29fJqevOGWZ4xYbCZk2ztodZDjcuGQZHRCgVqNVS5gAt6KQ5nLzoCkAvSJhSbd5kvFc1fHbJuMR5pxE/G6TceH2wv31AJz34oLSMpWoO90ysgJVgkvuaEZFVnBdtKIO1KPSiz57QShKkiTRsWNH2rRpw9y5c5k6dSqLFy9m9uzZdO/evVhNIXn55ZeZPXs2U5bH8t5993g2QHq4+S2PvzKiMS4u7paJujdnqNtsNvr06UPTpk0ZN24cALt376Zv374MGDCAEydO8PXXX7Np0yYefPBBr1zzTo4cOcLYsWPZsGEDjz32GDt37iQ0NJTff/+dPn36FEkMpcWZM2dYtmwZ//3vfzlw4AApKSk59wUGBnLffffRqlUrevfufc28/LLgnM1BXKaV01l2Uu0ubG43asBfpUKT/RzikGXcsqd6rlWrSHG6CVCpSHO4uN+pIizLjfVEIjvt1yXjZh0Gs47gCE+bisGsw+AnkvGiEh5wJVH33oLSMpWoO9yeleElpUddtrtwnrXkvDnOWnCds6CtaCZscLSvwxOEMkuv1zN27Fiee+45xowZQ48ePXj88cf54IMPirTyezt+fn6MHTuWsWPHErPlvwT+fQSa3g96/U2Pr1ChAmazmbi4uFv2YHuzoj5u3Dj++ecf9u3bh0aj4fTp03Ts2JGHHnqI8PBwZsyYwcqVK2nTpo1Xrnc7SUlJTJo0iS+++IJ69eqxadMm2rVrl/OHV61atQo9hpJMlmV+/PFHVq9ezfbt2zlx4kTOiES1Wk2lSpV4/PHH6dSpE927dy92r0AVBovTTZzFygmLjQt2J5lON8gKRpWEX3ZvvUtRcLplkECtVqGRVJRTVFRyyZitEnaLE5vFid3u5LdwHZUuOblXo8kZeyiS8eIn3N+zgPZChkjU88Xh8iTqlUtIoo4MjtOZZP4vCXfK1ZdRHP9kkDTlf2gCdagD9Z63gFwfZ9+uMpSpf15BKHIVK1Zk6dKlvPTSS7z66qs0adKEl156ibfffpuQEN+vhRk8eDCzZs1i+tr/MLNRA89upU3uuemxkiTdcfJLUlISTZs2LXBcW7ZsYd68eSxevJiaNWuSmZlJx44dCQoKomXLlowfP56FCxfSo0ePAl/rdjIyMpg1axZz5swhJCSEzz//nOeeew71LXp+BY+UlBRiY2P59ttv2bt3L8nJyTkb3ZhMJurXr8/jjz9Or169vPLzUly5ZZnjWXYSMm0k2eykO9243J5WFbNKhepKddwto6CgVkmoJRUmBco7Icym4MpyYbM4cNrdOeeVjRqcJh1+/npCKvhjMOm416RDb9KhVotkvDgLE60vBeOWFSQg1FQyet9URg3+j1TC/HBFbMdSsfyehC0uFb97wtFV9cedbsed7sB10Yo9IQ1XugOy/xgBkPTqnKRdHXA1gc+d2Kv8NMXq5XpBKIkeeughdu7cyeLFixk/fjwrVqxg6tSpDBo0yKdJn9FoZNy4cbzxxhtM2LAe/4MHoFE03CKmOyXq3qiop6Sk8Pzzz9OtWzf69euH2+2mV69enDlzhrFjxzJq1ChmzJjBoEGDCnSd23E4HHzyySdMmTIFh8NBTEwMr732GkZjyW2LLEx//PEHsbGx/Pzzzxw7dgyLxbNFuiRJlC9fnieffJIOHTrQs2fPYvEHqrddsDn4O9PKaauDFLsTm8uNSvG0qmhVnt+f9uxWFSRQSRJaBco5FSrYFDRZzhuScZ1Ri2zS4RdgIKRCQHbPuFYk4yWcQasmwKARrS/55ZYV/DSqEjdDXVJJGKNCMEaF4LpoxXHmMn5Nwm84TlEU5CyXJ4HPcGQn8p5k3p1ux3EqA3e6A8Vx9ckCjerayvxNEnuVSYukEsm8INyOWq1mwIABdO/enbfffptXX32VTz75hA8++MCnW90PHDiQmTNnMmvzZqbUrwfHE6BO5E2PjYqKYsmSJTe9Lysri7S0tAL1qCuKwqBBg5AkiYULFyJJEqNGjWLLli1MnjyZsWPH8sYbbzBmzJh8X+NO11+9ejXjx4/nzJkzDB06lDfffLNEbGZVVDIzM/nqq6/YsGEDu3fv5uzZszkjEo1GIzVr1uSRRx6hZ8+ePProo6VmRKLV7Sbuso2TWTaSbQ4ynW5kWcEvV6uKW1FwyTKKAioAWSHAqVDermC2OLFbnLhy/X7VG7XozDoMAQZCKwTkLN40mLSoRDJeakUEGDgvWl/yx60oBBq9N4fUFzTljGjK3bzqI0kSapMWtUkLt/ldKttc1yTwuRN759lMXOkOlFybGqCWPBX466vyuT8265DUIpkXhKCgIObMmZMzzvHxxx+nZ8+evPvuu1SpUqXI4zEYDLz55pu89tprjF29CtP+fVC7zk03QIqKiuLkyZPY7Xb01/WyX9nsqCAV9SVLlrB27Vq2bNlCaGgoCxcuZO7cuYwfP55JkybRr18/Zs2aVSiv8v3000+MHj2a3bt307t3b6ZOnUqNGjW8fp2S5tChQyxfvpwff/yRI0eOkJGRAXh+n5QrV47HHnuMtm3b0qdPH69vdFXUZFnmH6udY5k2kqwO0pwunG4ZLZ7qeO5WFTm7lUdxK2gdbsLsMuEWN86s65JxP8+GP4ZAI2GVAq8m434iGS+rwgP0ovUlv9yyQoDo20Zl0KAyaNBG3HoWsuxwX5vMZ1z92H48HXe6HTnTefUBEqj9dTcm8IFXEnxPu40kFrsIZUT9+vX573//y4YNG3j99deJiorKae0o6haLF154gXfeeYf5P//C2LqRcPYs3CTpioqKQpZl4uPjadCgwTX3FXRX0oSEBF555RWGDx9O69at2bp1K0OHDuXFF19k/vz5tG/fPqfK7k0HDhxg7NixfPvtt7Rs2ZI9e/Zw7733evUaJYXD4WDDhg2sXbuWnTt3cvr06ZwRiTqdjmrVqtGlSxe6detGu3btiv0uvLdyyeHk78tWTmXZuWh3Ys1uVTGrVOhytaq4ZMXTW68ATjdBDoVwixvt9ZVxP62nNSXIiKGySMaF2wv3N7Dr5CWvna9k/i/MJ7dc8ivqRUWlU6MK80MbduuFt4pLvmmLjTvdjv3UZdzpF5EvOzxPglfOa9becvHrldtVupLVmiQItyJJEp07d+aJJ55gzpw5TJs2jUWLFvHee+/x1FNPFdn6EL1ez4QJE3j55ZcZvnQJhv37bpqo16lTB/CMaLw+UU9MTMzpSb5bLpeLvn37Ur16dWbMmMGRI0d4+umnad26NevXr+eBBx4gNjbWq4nh6dOniYmJYcmSJURHR/Pdd9/Rpk2bMrUm559//mHZsmVs2bKFgwcPcunS1eQhKCiIBx54gFatWtGnT5+cf/uSwu6Wicv0TFVJtjm57HQhywpGScKkvtqq4pRlFFlBUcDtcqN3yIRlufG/7ER2Xl3TpffTenbcDDJiyK6MG7MXcKpE66dwF8L99aJHPb9Eou5dkkaFJsSAJsRwy2MUt4z7sjO7In9tMu88a8H29yXcGQ7Itd2uyk9zbZuNmGgjlHAGg4Hx48fnjHPs3r07LVq04P3336dhw4ZFEsPzzz/PjBkzWLR7Dy/XqQ1paRAUdM0xJpOJKlWq3HRBaVJSEhEREflKpmfMmMGePXvYtWsXly9fpn379tSoUYODBw9So0YN/vOf/9zQapNfaWlpvPPOO7z//vuEh4ezZMkS+vTpU2p6qW9FlmW2bNnCV199xe+//86JEyew2z3JgkajoVKlSrRs2ZKuXbvStWtXDIZbP28XF7Isc9rm4FimjUSrnVSHp1VFg6dVRZ2rVcUty6CA2y2jOJ2EWt2EZjhRObN/t0hgyGlT8cNQ0TPi0GDSofcTybjgPWH++pwpg95QprIdt6wQIBL1IiWpVWiC9GiCbv1LWJEVZIvzppV55wUrtoQ03Hcz0eZKZV5MtBGKmcqVK7N8+fJrxjm+/PLLTJ48meDgwt1+W6vV8tZbbzFk8GAGfrYQ7YH98MijNxx3q8kv+Z34snPnTiZPnsysWbOIioqiZcuWOBwOMjMz8ff3Z/Pmzfj7++fra8rNbrezYMECpk2bhqIoTJ06laFDh5aIhDQ/zp8/T2xsLJs3b2bv3r1cuHAhZ0Si2WymYcOGtGjRgt69e9OkSRMfR3t7aQ4nf2d6WlUu2F1YXZ6Z42aVhD77DyyHrOCWZWRZRpbB6XRhtsqUu+zAz547Gc9uSwkwYqyg9cwYF8m4UITCA7z7nFO2EvVSsJi0NJJUkqe/3V8HlW/+CztvE23sKI5cf8WKiTZCMdW8eXN27drFokWLGD9+PLGxsUybNo0BAwYU6jjHvn37Mm3aNFYf+Zs+Lhfc/wBcl8hGRUWxZ8+eGx6bn11JMzMzefbZZ3n88cd57bXXeP7559m3bx9Vq1bFYrHw008/FXjiiizLxMbGMmHCBM6dO8err77KuHHjCv0Pn6IkyzI7duxg5cqV/PLLL8THx5OVlQWASqWiQoUKtG/fno4dO/LMM88QEBDg44hv5HDLHLPYSLBYc1pV3G4FgyRhzm5VkbM3AHLLCpKs4HDKGGwyIZlOQqxuVEhIEuizk3FjgBFDheCcnnG9n7bUJeOZqZf4a/MGwmvUJqJmbQLDI25fgEpPhx9/gNBQCAuDsHAIDr7lSFbB+8L9vfPq4BVlK1EXrS8lVl4m2iiKgmJ331CVz9NEmxs2jhITbYTCpVarGThwIN27d2fSpEkMHTqUTz75hPnz59O8efNCuaZGo2HixIkMHTKEZ96fi/rwIbj3vmuOiYqKIjY2FkVRrkkIEhMT77pN5/XXXyclJYWffvqJ6dOns3z5cho1asS5c+fYvn07lStXLtDXs3XrVsaMGcPevXt57rnnePvtt6latWqBzlkcZGRksHr1ajZu3MiePXs4e/YssuwpQhiNRmrXrs0jjzxCr169eOihh4pNW48syyTZnRy9bOWM1UGqw4XD7UaDZyGnJvvnye6WcckyslvB4ZJx2GSCMp1EWN3olFzJuFmH0d+IoXz2xyYdemPpLq4oioLDmoUlLRVL6iUsaakc++N3/lj/NQAGk5nwGrWoWKMW9z7SEqNaBVlZYLVefZ+RDueT4Uj2SdVqqN/AszOxrmTsI1OSiUS9gESiXnpJkoRU0Ik22W02siWPE22uVOjFRBshn4KDg3n//fcZNGgQr732Wk4CNmvWrAInsjfTq1cvpk2bxuaT/9BBr4fGTa6ptkVFRZGamsrFixcJCwvLuT0pKYk2bdrk+Trr1q3js88+Y/Xq1fz222/ExMTQsGFDTp48ybZt2wq0ePGvv/5izJgxbN26lbZt27J3716io6PzfT5f279/f86IxLi4OC5fvgx4ntPCwsJo2bIlbdu2pXfv3vlazOttGU43cZlZnLDYuGh3kZXdqmJSSRiy/2hwKgoulxu3rKA4ZawOmUCLiwiLmwDZMx00Z3qK2YghIlfPeClMxt0uF1kZaVhSUz1JeNql7PdpOQl5VtolLGlpuJyOax6ryfX/UyfLVLXaaZiagfG7bz03ShIY/cDP6Hmv04HN5nlfJxLq1fdU2IUiIVpfCijAIBL1sk5MtBGKowYNGrB161bWrVuXM85x/PjxjBw50qt91leq6sNeeYX206YgxR+DqLo590dFRQGeyS9XEnVFUe6qR/3s2bMMGDCA5557jipVqvDYY49Rt25d4uPj2bJlC/fcc0++Yj958iRvvfUWy5Yt47777uOHH36gRYsW+TqXr9hsNtatW8e6devYuXMnZ86cweXyvMqn1+upXr06Dz30EN27d6dNmzY+G5HolGUSLDYSMm2ctTnIcLpxyzJ6JPxztao43DIutwxuGatdRp3lolyWmzCngkalurqA02zEEK7L3oHT06ZS0tcQXal+Z6ZeIistlcy0VLLSUq+phl95b828fM1jNRoNZqMfJr0Bk1ZHebUGk38wfqHlMev1+OkNmAIC8QsMYtOu35FV0LjJ/VSr1xCVyXRtYm4wXN0XQVHgh+/hnnuhVm3QipynqJn1GtRe/EOz7CXqoqIu5MFdTbS5PpnPuIuJNjdptxETbcouSZLo2rUrbdu25b333mPatGl88cUXzJkzh86dO3stsenRowdTp05lR/J5Htq/HyKjcn7RV6lSBaPRSFxcXE4LTmpqKna7PU896oqi8MILL+Dv788bb7xBy5YtqVChAvHx8axbt45HHnnkruNNSUlh+vTpfPjhh1SqVInY2Fh69uxZbFo+buf48eMsW7aMrVu3cujQIVJTU3PuCw4O5sEHH6RNmzb06dOnyDdgkmWZc3YXRzOzOG11kGp3YXe7USvgr76uVcUt43bJKE4ZVZaLYKubCnYZo5SdjJt1GExGjGFXquTZlfESmIy7XS6y0tOyK965qt+puZLw1BSyMtJxOZ3XPNbPYMRkMGLS6jBrtETodJgqVPEk5Do9poAATEHB6AICPUm20Qh+ftcm3n5G0Bsg++e7Res2mIND8v4FtGrtzW+HkA8GL77CXuYyAtH6IniL9yba2MF1NZm/00QbTaAOySgm2pRmRqORCRMm0K9fP0aPHk3Xrl1p1aoV77//PvXr1y/w+dVqNRMnTmT4mNH8MXYMJCZCdpuNSqWiTp0610x+SUxMBPK2K+mCBQvYsmUL3377Lb1790aSpJx53u3bt7+rOK1WK/Pnz2f69OloNBpmzZrFkCFDvDbK0dtcLhffffcda9as4ffff+fkyZM4HJ42Bo1GQ5UqVXjiiSfo2rUrXbp0QVdE/cKXnW6OZlo5kWXjgs2JJbtVxU8lYczVquJ0uXG5ZNxOmcs2F8FWmfJ2mSBZym5T0WI0GTCE6q5OUykhybiiKNizLNktJler31eq4ZbUS1gupWBJT8NqybzmsVqNxpN86/SYNFrK6/SYAkMxhVXEpNfjZw7AHBSEMSgYlcmcnXgbr3ufXfnOxx+Xd5Wkl4B/i7JAr/Xeq+dlLlEPMJa5L1nwocKaaCNpVTdf/Com2pQqVapUYcWKFTnjHKOjoxk2bBiTJk0i6LoZ6Here/fuTJkyhcOpqdQ/sC8nUYcbRzTmdVfSw4cPM2rUKMaMGcO8efM4efIkmZmZzJ8/nz59+uQ5NrfbzdKlS3nrrbdISUlhxIgRjB49msDAwLv8KgvXuXPnWL58OZs3b2b//v1cvHgxZ0RiQEAATZo0oUWLFvTp06fQ5+W7ZZmELDvxmVZPq4rDjUuW0eFZyKm6MnPc5cbllpGdbhSbG8kqE2aXKe9SMJn0ntYUsxFD6NU2FV0xLgxcW/3OXfm+dDX5TkslKyMDl+tq9VuSJPz0Bvx0esxaHWadjgidHlPFqp7qt8mMKSj4avX7dsm3mKgiXEcvKur5JyrqQnFToIk26Q5ceZ1oE6jLTuRzJfZiok2J8Oijj7Jnzx4+++wzJkyYwPLly5k+fTovvPBCvsc5qlQqJk+ezJtT3uY/wcGQmuoZ44YnUf/qq69yjk1MTESv1xMScuvKnsPhyElI09PT+f7773G73UyaNIlhw4blKSZFUdi8eTNjx47l0KFD9O/fn8mTJ+drfru3ybLM9u3bWblyJb/++isJCQlYrVbA872sWLEinTt3pkOHDvTs2ROz2VwocSTbHMRlWjmd5eCSw4nN5WlVMatVaK+0qsgyTpeMy+nGbXcj2dyE2GUqORSC/fTZrSkGDMHZybhZh85QfJLx3NVvS2oqlvSrvd5ZqZfITLnoqYJnpGG1WK55rFajyen7Nun0nup3UDlM4ZUwmUyYAoMwBYVgDA7J7vXO3XqSnYQbjSL5Luay3DJ+6uLb+mYQFfX8E4m6UBJ5ZaJNvJhoU5Kp1WqGDBlCjx49mDhxIi+99BKffPIJH3zwAQ8//HC+ztmlSxemTZ3KWYuFCvv3wWOPA55EPSEhAafTiVarzZmhfrtELiYmhqNHjzJy5EimTJmCWq3mlVdeISYmJk+x7Nq1i9GjR7Nt2zY6duzIihUraNCgQb6+Lm9IS0tj1apVbNy4kT///JPk5OScEYl+fn5ERkby2GOP8cwzz9CsWTOv9stnud38fdnKSYuN83YnFqcbRVYwqiT8sq/jUhQcLjdOp4zD4Uaxuwmyy1RwKJQ3aPEz6TGY/DAGXe0Z93Uy7nY5yUpP9yTd6alXE/FLKVhSLnqq4OlpZF3OyFlgC9nVb53eU/3W6fDX6amgM+BXsZqn8u3v6fs2hYReW/3OnXz7+YnkuxT5MSWD6cfP8lCQmYeCzTwYZKKCvviMnhQV9QIw6crclyyUIXmaaOOUrybwGXc50eZWs+bFRJsiExISwvz583PGOTZv3pw+ffowc+bMu648q1QqJk6axLS5c/jAbEb1QDMwGomKisLlcnHixAkiIyPvOPFl27ZtzJo1i6FDhzJ16lS0Wi09e/Zk7ty5d0wMExISePPNN1m1ahXNmjXj559/5tFHb9wxtbD9+eefxMbGsm3bNuLi4sjM9PQpS5JEeHg4rVu3pn379vTq1Yty5coV+HpuWeZklp1j2VNV0pwuXG4ZLeB/XauK0yXjcrhw22VUdpkwp0J1rQazSY/RZMRwJRk3FX0ynlP9vrLQMj1Xv3fKRSypl8hKTyUzPR2bNeuax2o1Gk/ft1aPSZ9d/Q4Ow1SpGib/QExBuarfZvN1Cy6zE3AfTcUR8s8pK1jcbixuOdebm6zrPr/p/S7P55luN8etdo5b7Sw7mwJALaOeEdUj6BYR7PNXh0Sink9qlVTqdi0ThLslaVVoQo1oQo23PMZrE22uT+bFRBuvadSoET/88ANr167NGef45ptvMmLEiLsa59ixY0fenT6dLLsd8+FDcF/Ta0Y0RkZG3nZX0rS0NJ577jkeffRRFi1ahEajoW3btixatOi2VeYLFy4wZcoUPvnkE6pVq8ZXX31Ft27diuQXbFZWFmvXrmX9+vXs2rWLM2fO4Ha7ATAYDFSvXp3mzZvTo0cPWrZsWaBq+QWbgziLlVNZDi7ZnVhdblSKp29cp8rdquLG4XDjsstIdjehLoUaag2hfnqMZgOGctm7cZp0aIsgGXe7nFjS0jwtJunZle/UFCwXL2K5dNGTlGekY7l8Gbc7V/UbCT+9Lif59tfpqaD3w69aOcz+AfgFBl2tfgcGXW07yd3/LUYKFguKomCTlTwn0ha3nOsYd05Sff3jHYpy2+ua1Kpcb2pMahV+2Z9H6LWY1Cr0Kok4iw0ZaGg20jUimM7hQVQ2FI+qulhMmk/qYtLzKn8KAAAgAElEQVR/V9iu31FQEO6WmGhTMkiSRLdu3WjXrh3vvvsuU6ZM4fPPP2fu3Ll07NgxT99HSZIYFxPDh599yut+fugaNyEgIIDy5csTFxdHx44dSUxMvGWVe+jQoVitVuLi4nA6nTRr1oxVq1ahvUWyZbFYmDdvHjNnzsRgMDBv3jwGDhx4y+O94dixYyxdupQffviBw4cPk5aWlnNfaGgozZs354knnqBPnz752tnU6nZz9LKNE1k2km1OMp0uZFnBKEmYsvto3TmtKm4UhxvFLhPsUKiqVlPZqMPPbMAQkp2Mm3Vo9d7/f6AoCnaL5dqxg5cuYUm5kN16kp2UX758Y/VbrcGc3X6SU/0uVx5TjTqYAoI81e+Qcreufovku1DJioL1Jolz5u0S6Ssfu26diMu3uaZaAnN2In01mb76eTmj5oZEO/f9JrUKk+baxxtzvZp0O7vTLehVKrpGBBNl8u4GQ94gKur55M0B9MWJ220nPX03l1J3kJr6P+rVnY7ZHOnrsIRS7q4n2tywEDavE21yL4QVE21uxmg0EhMTw/PPP8+oUaPo3Lkzbdq0Yd68edSrV++Oj3/yySf5ePZs3nA4IP4Y1K13zeSXW1XUY2NjiY2NpVatWvzzzz/Uq1ePTZs2YTTe+GqNy+Xi//7v/4iJiSEjI4ORI0fyxhtv4O9/85+d/HK5XHzzzTesWbOGHTt2cOrUqZwRiVqtlqpVq9K+fXu6du1Kx44d8zwiUZZl/rF6WlWSrJ5WFYcre6qKWpVTCHK4PZVxt9ON7JBRORQqq1TU0OsJMOsxBOkxmrUYTHq0erVXkvHc1e/MtFSyUlOwXLzgqX5f6Qe/nIEl83LOKwcAEngS7+zkO0Cnp4IpAFNEpZzNdszBofiFlsuufhuvXXyp1YpxgPnglBWy8lKRvov7s9y3S6nBoJJuSKSvJM7BWjWVDNqbJ9K5E3HNtZ/rJMlnRZWmgSaaBt56vZav6TWiop4vefkrraSQZTtnEmO5ePEH0tP3IMueX0RqtYmE47NRqfSoVQZUagMqlQG1So9KZUCl1md/bvAck32/SmXI/lif6+MrbzpR4RTy5dqJNjefgnGniTaOpEzc6Q4UW94n2mgC9aj8dWUuma9atSqrVq26ZpzjK6/8P3tvHiZVeeb9f55zaq/eFzZZGmUTXAOKYlRQMTrJaJxhRCfjT3DXJIZkTOKaqL8saBw1w0xURh31NSYZjLuvJu4at4DKuEEUEQSBbrqq19rrnOf941RVV/UOvdP357rqqrPVOU81DXzPfb739/kuP/3pT7uNNlRK8d0rr+R3v3uIf/D7Cc6cxcyZM9mwYQPpdJra2toOHvWtW7dy6aWXMnXqVD7//HMmT57M888/3+E6WmuefPJJrrzySj755BMuvPBCfvrTnzJu3Lh++c7bt2/noYce4tlnn+X9998nFArl9pWWlvKVr3yFk046iX/+53/u1U1LOJliY0uML2IJ6uMpoikLA8c3nm9VSaYskimbdNJCpWzGYXKAx011ka+tgTPo2WsxXlj9zkQN1u8mUl/nVMGbGog0NRFpbSEejxV81mM63u9c9dvrcybdKSkhWFJOsKLcqX5XVnWc5dLjEfGdQWtNIs/60RchnV+xTth7b/0Y43F3uz9omgRdhfsDhoFrlP1bONT43FJRH/UYhpcJ48/E467ENIOEQq+idRLD8GCaQWw7TjJZj2UnsO04thXHsuPYdgLLijvb7Hgvr6baCfhObgJyy9mbgbwbgnafyd4ktC1nbxS8eTcH8qs5WhiwRBsDzKLRmWizcOFC3n33XVavXs11112Xi3Ncvnx5l37rxYsXc/5/rOKcRAK2b2PmzJk89thjubST/Iq6ZVmce+65uFwuPv/8c6qqqnj55ZcZM2ZMwTnfeustfvjDH/KXv/yFM844g0cffTTnf98bbNvmpZdeYs2aNbz22mts3ryZeNz5d8w0TSZMmMDxxx/P6aefzpIlSwgEOm+qTlg2n7TG2ByJUxtP0pzMWFUMg6I8q0oqbZFOWeikjUrZVGIw1e1iStBLoKQol6bi9vRejGer3474biBaX0fr7jqi4bAz+U5zI5GWFiLR1u6r314f48sqCU6qcawn5RUEKyoIVo3BXVpaGDk4CsR3V9aPvlasrW40dU/Wj0r3wFk/hOGNVNQFAFyuIOPGnca4caeRTrewe/fz7N79J6ZPuwqvd0yPn9daY9vJnGhvE/AJR+Bn1i07jm0l8paz2wtvAiwrQioZytwQxLEyn2k7dwKtUz2OC0ApV0awt1X9zU6eCBSst7txyN0EmN685a5uFLzy1GCYs3eJNm2V+cTWZqzmZPeJNu2aX0dyoo3L5eKyyy5j6dKl/OQnP+Giiy7KxTkeffTRHY5XSvEvK77PS089yaGBIDNnzqSuro6NGzcChbOS3nLLLbz66qtorQkGg7z66qtMmTIlt/+TTz7hqquu4pFHHuGYY47h9ddfZ8GCBXv8HcLhMA899BD/9//+X9577z1qa2tzEwoFg0EOPPBAFi5cyNlnn80RRxxR8FnbtvkimuCT1hhfxhKE4ymSlo1HKYrzrCoJyxHj6aSNTtl4gYmmi+lBH6VFQXxFTgOnqxsxrrUm3tpaWPneXZdLPYk0NeasJ/FEYYEkW/0Oep0K+LhAkGDNGIIlJRSVVRAoryBYWY2/sjLP+x0Ar3fEiu90F6kfQ2X9KHOZTPCOLOuHMLzxSkVdaI/LVcz48WcwfvwZvf6MUgrT9GKaXmBwZvzT2iq4Iciv8LfdBCQ63hB0cUwq1ZC7Cehws2Flnxp0/5gxS+FTgO6fCnR+Q5BvMerihiD/GEOaq/qbPifa7IgQ37AXiTalHgzv8PzntLKykv/8z//k4osv5vLLL2fBggWcc845rFy5soPvfNGiRVx9990c39rCQQcdDMA777wDtM1K+u6773L11VcDjuf7pZdeyllKamtrueGGG1i9ejXTp0/nscce47TTTuu1mFm7di0PPfQQr7zyCp988gmRzGQ2SinGjRvHKaecwje+8Q3OOuus3ORLjckUf2uN8fttu9kdTRKzLBSOb9yXeXqQtDXptEUqaWOlLNxaMd40meb3Mr6kODcDZ3sx7lS/G6j/osER3rvriIR2O5PuZGa7bG1tJhqNYNl5fRaQs50EPT5K/AHGV48jeMBMZ8Kd8gqClZVO9busvM337fMNK/GttSapNa3pwbV+BAqEcqE4FuuHMBLwDWZFXSlVAlRrrT9rt/0QrfX7/TYSYVSglInLFQQGpwlEa43WqV7eEHT25KDtGCuzPZVqzD01sO0EtpXIW4/n+gV6QikzdzPQoV+gUzuRL88+1P6GoLMnB4V2JcPwodS+Z/PYU3qdaNOaKmh8tZqTvUi06dj4OlwSbQ455JCcbeSKK65g5syZXHvttaxYsQKv1/lZKKU45aKLuGblL7nA58ftdvPhhx9SVlZGIBAgGo2yZMmSXLLU008/zRFHHEFrayu33HILt9xyC8XFxfzmN7/hvPPOw9VNxnU0GmXNmjU8/vjjrFu3jh07dhREJB5wwAG5iMQFxx3HZ9Ekn7XG2BFJcM+uVuydrfhNRXFmEhtba9KWTTplo9MWcRuKDZP9fR72L/ITLPbhK/JgugwS0QjRhjCR+i8Jfbwr03QZciIIm5uc6ne0lXgiUTBmj+nKiG8vAX+A8UXFBMY7jZdF5ZUEKiooqh6Dr7IaoyjYNsV8P06G1BW21sRsuzC5I733Yjq7nu7B+tGlWDYNKtyubvd3JsT9plg/hJFPf1bUle4mz1IpdSZwO1AHuIFlWuu1mX3vaq2/0m8jGQQqphyow1s3DPUwhH0cre0OFf5eW4lyvQTxdjcA2X0dew5sO47WVs8DA5TyFPQCFDQQ97afoFMrkadDE7JpelFq321E7jbRprltuVeJNvmV+cDAJ9pEo1FuvvlmbrrpJiZOnMhtt93G17/+9dyf1UmHH4YnnabZ7UH7/TQ2NvLRRx9x4YUXcvfddwNO4suSJUu4++67ueGGG4hGo/zoRz/i+9//PsFgxxvxDRs28OCDD/LCCy+wceNGmpqacvuqqqo4+JBDWHjGEuYsPoVml4+GRJqkzlhVXCauzNiilk1LyklV8diacsNkssfNDL8br46TjjaSaMhYT8Ihx3rS3OS8Iq1EY9HC6rdSBN0eAl4vQV/AmWa+qIRgSamT911ZSbCymmB1XvXb7++T+E7bmqjdeUTe3lato5bd7bNDr6E69VHviZBuv99riPVjb4lEIixbtoz58+dzyimnMGfOHPlZ7kPc/vwnfH/xzHe01vP6eq6ehPp64FSt9U6l1JHAA8DVWutHlFLvaa0P7+sABhMR6sK+im2nerQJdWclsjI2pA43Ad3cOPQOI+9pQftEoXb9Bz1YiXI3ED2kFSk1fPzkPSXaZJeHKtFmy5YtXHHFFfzxj3/k1FNP5bbbbmPmzJnc+NOf8tMbb+SIAw6gNpVi5syZfOc73+H0008H4Fe/+hX7778/V111FZs3b+aSSy7huuuuyzWTJpNJnnzySR555BHeeusttm3bRirl9KcEq6s5+Gtf58BjT2Di7IMIlFVgKkXQ5TTSgRNf15y2SKQsjHSaYCJJWbSZsY11FDXWkWgJE29uJNLipJ5EolHiyS6q315/RnwXEyguoaisPGM9qSY4Jq/67fN3mGI+a/0oqDinOwrnPa1Yx/fS+rGnQrqtSdHELdYPbNsmlUqRTqdzr/z17vYNxPrzzz/Ppk2bAJg4cSKnnHIKp5xyCosXL6akpKTH7/PXv/6Vv/71r8yaNYv58+f3e9SpsPfc8fJnXLZo2qAI9Q+01gfnrY8HngLux6mu96mirpQ6Bfg1YAJ3a61Xttvvxbk5mAuEgKVa6y2ZfVcB5wMWcLnW+k89XU+EuiD0D85Tg+SeWYm6eyrQxQ1Bfv+B1umeBwYo5e5EzPei32AI40vthFXQ/Nq+Kt9pok1xJ42ve5lo8+KLL/K9732PjRs3smLFCs4880yOPPJIALxuN6d985s89thjpFIplixZwpdffsmbb77JmWeeyc9//nM8Hg8PPvggf/7zn/nggw9oaG6m+uDDmPCVI5l88KFMnD6L6rFjKQ8EKHG1WVVa0haRRJJ0LIbZ3IgvtIui7ZvxfrGJVLSFaLyL6rfPT1EgQCBY5Ew1X1pGoLwCs7IaKqrQFVUki4qJuNxENANm/TCAIlffhfRQWD+cMAG7V6JysAXsQF07q3cMw8AwDFwuV27ZNM3cK39b/ntn21wuFx6PB5fL1eFlmmbuPfvKXtM0TZqbm4nH4yil8Pv9TJw4kQkTJlBZWZn7M8r+OeW/5y/v2rUr92fq9/uZPHkyc+fOZdq0aX2aUVfoG//9+uec99X9B0WovwGck+9PV0oVA48BX9Vad23y7OnCTtnrE2AxsB1YC5yttf4475jLgEO01pcopc4CztBaL1VKzQZ+BxwJTACeB2boHp7/i1AXhJGLbafzUoTyEoU63CgkurhRaPdUoIubhgGPL+00rajn+FLD9qAjCloUdovVaWXebu19oo0rs01lprpOp9Pceeed/OQnP8HtdhMOhUhnGjNdbjepVIqxY8dSW1vLwQcfzNSpU9na3Ep6zASqD5zD+AOmM3a/iYyprKTS589VcGOWTVMiTry1hXS4HnZ+gfHFZ3h2fYFlW6RcXrTPjxEowggWQ7AIgsXYmVc6ECTlD5D0BUi4PUSUQbRdtnV/WD8ChkHAVPjQ+AGf0ni1xqttPNrGazvvbiudeVmQSmFZ/S9QLcvCsqzcsm3buW22befWs4It+wIKtmXJ/j+f//+91hrDMDKhAmbBu1IqJ0yzx2S35e/Lbu9sX/tztz+u/QvosJz/3n654G/gCLWMZP88uvqOnS23f88+qcpuGzNmDIceeihHHnlktz0iwsDy0Ntf8K2jpvSLUO/pT/FSnAb2HFrrlkwl/Mw+XvtIYJPWejOAUur3wOnAx3nHnA5cn1l+GPgP5fx2ng78XmudAD5XSm3KnO/NPo5JEIRhimG4MIwioPOJk/qbERFfWu7FrCq8CVDag7LcqLQblXKjki5Imqi4C74wIGY6+2w3huXBcPtweYO4AkG+OamGbzzwW55//XX+//t/DbaiqHgsJUcdT9GMWZROrqF6wgTGVlRSFQwQzFhF0loTSqZojMf5vLmF9bW1NERjNCbiRFGk3Z7Mq4jU/odiTZ/b7fdzpR0x7LJSuONpXJEmXOkQLiuFK515pdIUWynK0ilcqRQuK53Zl8aVTmbe27YbeSK1vbBTSpEGWpSitRcCsivR1Jm47Gy9/b5stTXb1Dsc6Y1o7M3PLFvl7Uys9yTo828GetqWf7OQv97Vtu5e3VXWu9vfvkqffQfHhpP/c+mMVCLFJ+99zJyjDu10//vvv88TTzzBfvvtx7x585gzZ45U0YcJdjdF8D2lJ6EeAcYCm9ptPwp4q4/X3g/Ylre+HZjf1TFa67RSqgmozGx/q91n90MQBKGfGNr40kTbTUEf4kttO066k/hSy8rcXOgE0DF/eupiuHex4zf/yXNnMm/qDA5btJjGtMXutMWOdJr3W6LUp9I0p9MkLQsfGh84724PwVI3lRTjRTvbMtVpL3F8lo0XG49u2+7RNj40bm2jMo/3AbSp0YYGN2gNWrtAu9DonA0ADbZuqyi3f+X2a8e2pQve2x1vO5V5bWe32dhao20NuWO0sy2zX9tkPpf5vAbb1mDbbcfZmZe2c59Haywrs80CTWafrcHO+5zWaEsXnt/SuQjR7DastuMBFAq0AjQKhdIKlAbtLCulMJRCZUUuGVFsKAxVKJTJiWFQqlAUo5Sz3TBQ4HzeaC+ynevknxdDYRqF6waZ62cq8YbKG5NpoDBQRuZaRp5QV8569lqGYTrrhuFcGzBcJoYywHS+q5E5p2EYbT8Dg7aX6aRl2obt/A6aGm0Y2IbCNizn3wiXSdrM/lyy53Nuvgwzc86Mncl0mSgzb2zZJw5G23dUme9hmAZur5vvXfQdtuzaxt8d9zW+ufQMjvvmSbjcjnTLJjcJvSe8M8L7L26j5pAqJs4qx+UemJ6mtzaHej6ol/Qk1G8Hru5keyyz7+/7cO3ObiPb34J0dUxvPuucQKmLgIsAisYfsCfjEwRBGFSGS3ypZcVJRyPs2rSJxmdXctdn2zjv6hsZEwhQZFlUYeGzLTyQ56fOvGvQGM4/1FqhtUKjQBvZLwkZMabQoBRppbAUxPNEWU505USici6h8iq0yrmukW2UVJnxqIzPO7s5T0iRE4/kiaN8MZp/jOpwTJsYNHLrw7WKaVsZW4x23rMiPt8uo7XGtqzczYhtO8u2ZWd6UbKv/H3Z422s7P7MNtu2Msfa2LaFZdtYVtsYOrPsONfSuX2dHtN+zF1s7+kcvTl/d9fo7XV6O9besuqPq1n1x9VU+Es5dvpxnHj0coLFVc7vKjh/L6CtyVw5N2q5RN7Mcdn92b+2hettx7c9DSF3I5ZdV/l/t7L7sp9XeZ/JjiNzvKL9uTLb8z6bOz537cw1MufK3RRmz+W1MFwaHXPlXTPvXORdI7P/k7W1fPTaDlxek8mzK5h6SBVTDq7EX+Tp9Z9HT8wc23+NvT0J9ZrOstK11uuUUjV9vPZ2YFLe+kRgRxfHbFdKuXDKWuFefjY71tXAanA86n0csyAIwj6D85+sB8Pw4HK1+4+lBLZuNzm7Zj9u/2wrzR+u5TcPPghAIm2xLRxja30rX9Q2sDvURGNTM7HWFoxUjGp3mgpXmgq3psSw8Ks0+VJWAxYuUrZJ2lIkUwappItkwkUq5iYd9ZBKeEinTdrXZZTLRnnB9IHLBy6/gdtr4vGbeP1uvH4XXr+HQMCLP+AjGPDhC3jx+l14Mi+X2+jWcrA3tPeBd/bem2O6+kx/nKMv71353Hv8jAZN5l1nngpkl+3sUwINeU9ItKbt6QWZ5bxzQN45M2vZN91dp8IwtbJnfw6575d718QScVLpFGhwm248Hi9er5e2OTHabpBz6+1+BLqzffnHZH+OmacvaJVZz9vf7nPOn1G7/QX7dMFy4f7MNbIX0ThPpfI/b5Mbg51/jsxxzgM0jUpB2cw02oZ4WBEPGcTDCjvd+VicG1NnPZ2w+OLjcO4GdsaR4zB72YDfE9XF/Wdj60mo+7rZ1/XUf71jLTBdKTUV+BI4C/jndsc8AZyL4z1fAryotdZKqSeAh5RSt+I0k04H/trH8QiCIAh5NDQ04ve4+eFpf8+Vv/0tZ599Nl//+tfxukymjSli2pgimD2u4DOJtMX2hhhb6iNsCUX53/oIW0Kt1IWbibVGKDEtykyLCX7NxKBijF9TVmRRppOY6Raw86qMSqE8bnC5sU0XaWWS1gZJ2yCZhkTSIJFQtCRMrJDCjitImJA0nPdUF4+1DY2RE/sKt8/A7Tfx+FyO0A+48fu9+PxeggFvB6Hv8bvweM2CiMyu/OnC8GKwbmT64xzpVIrn//gsMw+axaSZU3O/bwN9Mzgcfi57ijIgMEYTGGPhcrmYNGkSY8aM6fD3sbEuytP/+T6TZldQc3Al+00vx+zHyYmyJNIdLYV7S09Cfa1S6kKt9X/lb1RKnQ+805cLa8dz/h3gTzjxjPdqrT9SSt0IrNNaPwHcA/yfTLNoGEfMkznuf3AaT9PAt3VvZ3wRBEEQekU4HAbgxDmzOeuss7j44ov56KOPKC3t2rPvdZkcUF3EAdUdm36TaZttDVG2hiJ8Xu+8r62PsLU2yvaGKLbW+A3NlCLFjHIXNcUGE/xQ5bEpMtIE7SR2IkYqEcHKpl24nZe7yo8nEMTl92H4vRg+E+01SRsGKSBuQzxtk0hYxKNpkvE0qZhFKm4Tj2sicY3dpNB1piPyEyYkMoK/s1Ks0hgeMH0qI/adqr4j9t34MmLfH/DiC3hyNwGO0Ddzgt80h6dtZl9lpN1Qnfu9C4Z6CIPOntw4JJNJPvzwQ3w+H2VlZZSVlVFSUtKlHa2o3Ms/Xz9/wP/8E+n+k6Q9xTOOBR4FkrQJ83mABycqcVdXnx2OSDyjIAhC77nrzjtpfekpFh40j8mXfJvZs2dzxhlnsHr16n6/VjJts70hypZQhC31mfdQlC31kYyId44rC7ipqQwyrdLHAaUGk4sMxvo05aaNmY6TjEZIxqLOezSK1W4iJJfXhycQwOMP4g4E8ASCznogiNsXQAXcaK9B2rBI2AkS6RjReJxoNEEsFiceTRGPpUjGUqRiaZJxm3TMJh3XWHEg6Yh83V7s250LB8MNpjcj9v15Fh6fC1/A41T2Ax5H2Hch9gfCyiMII4FYLIZSCp+vOwPI4LPqhU+5/KQZAx/PqLWuBRYopRYBB2U2P621frGvFxYEQRCGNw0NYdyATiaprq5m1apVnH322SxdupQTTzyxX6/lcRnsX13E/l1U4rc3RNkaivJ5fcSpyIeirN3WzPaGGFZGxWdFfE1lFTVVQWr2DzKl3Mt+AfDpFMmoI+BTsUhmOUokVE8yFiEdL8zNNz0ePP42EV8VCODxF+MpCeAZF3SEvT+Ay9PWgKa1JqmTJO04STtBwk6QtOPErTjxZIJoNE48liQeTZKIJUnGLJIxi1TcIh23icdxRH3chKZ8sW86NwCpzsW+MhwLjytr4fGb+PxuvD43voAXn9+dZ9sx2wn+zq08gjAS8Pv76sIeGOL9WFHvVqgrpXzAJcA04APgHt3b6QEFQRCEEU0oFGYcjlAHWLp0Kb/73e+48MIL+eCDDwgGByedJl/EL2q3L5m2+bIx64mP5Lzx7733ZQcRP6UyyNTKAFMqK5laNZkpNQGmVgUpC3iwLYtUXiW+rSofIRWNEm0Mk4xGScWiBdc3XK5MVT6Ixx/AnVehDwQClPkr8QQCmEVeVGX3QlhrTdJOZER+vth31uOpOLFYkng0QSKWJB5Lk4ylScUdC08ironni/uwid5pohImJFyQMNAJg86tPODyOmLf4zPxZBpz/QGPs+xz9SD2TbHyCEKGRGrwPOr3AyngNeBU4EBgRb9dXRAEQRi2hMOOULdTjlBXSnHHHXcwe/ZsrrnmGm6//fahHSCOiJ9aFWRqVcebhpRlO42tGQGfrciv3/Yl2/JEfKnf7VTgKwNORb4qQE1lOTWTg5QHCyPbtG2TjMXyqvKRnN0mFY3QUrszt54Xt4Fhmo6I97ez2+TWM9u8Prymj+K9yO63tU3KTnYp9JN2gng6QTyeqexnLDzJjFc/HbdJJk0S2ap+wqnsU+dCJZyJs4gb2AkD7M5vOky3wu03cGcbc/1ufH43br8rI/bbLDti5RH2VQazmXS21vpgAKXUPUiyiiAIwqghFA6BF3SybcbUCRMmcOutt3LBBRdw5plnsmDBgiEcYfe4zTwRP7NwX8qy+bIhxuehCFszVfgtoQiPr+9ExFcGqKkKOhX5qkCmMl9GeWV1l4JSa00qHiOVFfN5dptkNELr7rpc1V7bbf+pK8PIE++BAvuNO2/d7fOj2jXMGcrAaw6s0E/YcRJWgngyU9WPpjKNuWlScY1OmCQTJsmkSWumsq+iLlSjO1fZ1wkDO6HQqc5/dsqgTbj73Dkx7/W7MtX+nqr7YuURhpZ4apCsLzjVdCCX0tJvFxYEQRCGN+FwA4wvwc5YX7IsX76c3//+95x33nmsX79+2DVy9Qa3aThV9G5EfL6VZksowpP/u4Nt4SjpjIgv8bmYmhHwuYp8VZCayiDlAbdTPfcHCFZWdTkOrTXpRCLjnc+z3mTEfbQhROOXX5CMRrGtPOepUhmrTaGY9wQCuPPX/YEOgr4r+kvoJ9p59NuL/ey+RCpOPJYikWnMTSXsnC8/lXm1JkyMpAuV8KBCTmVfZyw8dlxhJzvmhjs/H3B7zTYB78uKfbNgPbvNLVYeoR8ZzIr6oXb8a/AAACAASURBVEqp5syyAvyZdQVorXVJv41kEOh2IgRBEAShgFAo5Aj1VKFQV0qxevVqDjroIG688UZ+8YtfDNEIB4aeRPyOxlimqbWtubUzEZ8V7YUVeUfE58cEun0+3D4fUNnlmLTWWKlkgd0mX9jHmhpp2rWDVDQvujL7fXz+vKp81/Ybw+zbdOr5Qn9v2FOh7zTqJkjGM2I/bhUk7lgJk2jCJJZ0YyQ9GAk3qsUF9c4xOuFk71vx7AQ6HXG5jTyrjtlW2c+JfbHyCB3pz3jGnlJf+va3dpiRfZQpCIIg9Ew4HAY9FZ3qmCFQU1PDypUrWbFiBUuWLOErX/nKEIxw8HGbBlMqHdHdnrSVaWzNxEpmK/JPvb+TL/JEfHFeJd5pbm2ryFcEPZ2KOqUULo8Xl8dLoKy82zFaqZRjtYlEScYibfabaIREawstdbtIRaOkO0RXejNV+I7RlfmNsqbL3YefYNf0p9BvL/I7Cv5I2zFWgkQymRP6JEx00vHp2wmTZNKDlfSQSLpRCbdT1W82M5V9hRVXWHGNnep8XIahCsW7z9VO8Jt51X0Xbp8pVp4RTnwQm0n3KUSoC4Ig9A6ttSPUFR0q6lkuu+wy/vCHP7B8+XLWrl2Lx+Pp9LjRgitPxB8/o7pgX9qy2dEYdzzxoUiuIt+ZiK/Jt9LkmluDXYr49phuN353Gf6Ssm6Ps9LpDnabgujKcD3JaPfRlTlBX2C/6RhdORj0h9BvX7HvWuhHc8fkjk8nczn6xJ1ITZ0wMRMeSHpJJz3YSTeJpNs5psFE73L8+lYcrLgmndBdWnk83sLqfXdWno7bXLj9plh5BolBq6jva1i2M7OVPIISBEHonkgkQjKZdHyOyc6FumEY3HPPPRx66KHcdNNNXHfddYM7yBGEyzSYXBlgcmUA6FzEO5M8tU349PQHO9kWjpKyCkX8lMpAW0U+09xa2UsRn4/pcmEWl+Ar7t7FmouujEVJRtomlMpW6psaG5zoynisMOmmm+jK/EZZ0+MdFv8vG8rAZ/rxmXuXzb0nQj9hxwpEvrbjWDqFqYGkkTc7romR9OJKejGTHsykFzvpIp5wkUi4aI4Y2GFVIPZTMbt3Vp5MNd/bbj1f3LeJ/TbbjylWnh4ZTI/6PoXGeRzh9+xTjh5BEIR+JxwOZ5YUOtXFM31gxowZ3HDDDVx77bX8wz/8A3PmzBmcAe5D5Iv44zoR8Tub4m0TPdVH2dqZiPe6mJKpvNe0a27dGxGfj2GaeIuK8RYVt7/HKKDT6MqcqM9EV8aipKJRdJ6S7Dm60nl3eX3DWiD2l9Bv79HvPH0nUrDPsuOktPP31NBAWuWEvpn04kr6cKV8mEmPY99JujOC3yAWN7BbDewEWHFIxzWpmIWV6tyFsKdWnk7tPXth5and0oxSUD25eFj/HoBYX/pEczwlQl0QBKEHQqEQAErRrVAH+MEPfsCaNWs477zzeOONNzD72JQotOEyDSZVBJhU0bESb9maHY3t0mnqIzzzoWOnaS/iHU98YUW+qqhvIj4fZRh4g0G8PUyEVRBdGYvkWW+ipGK9ia4stNu421XoO4uuHAkMpdBP5gl9cBJDTAtImpgJH56UDzPpw5XytjXmJlyQdGEnDKIJg0iTwqoFKwGpuE0qZpNKWL2y8jje/DbbjulTaE+KsqLSnNhPxNK8cN8GyscFmDF/HDPnj6O4YngmTon1pQ80xVKMLRmef7CCIAjDhVxFXSnsHoS6y+Xi3nvvZe7cudx+++3867/+6yCMUDANlRPxx07vRsRnBPzWkCPit4VjJC1HABd5XUzJxUoG8qrx/Svi81GZaEmPP0CQHqIrkwmSkXbRlRlxv2fRldkqfX7F3o9h7Ds3lX0V+pa22qXudJe+E+mwL607/jthanCnvLhTAdxJH2bSi5nMiP2kC5UAndCQtEjHkyTjCqvRqeo3tTajEzud5t68AnXDrihvP76Ztx/fzITpZRy2eDJTD+n692goGMyZSfc5mmLd/4cjCIIgtAl1pVTBhEddcfDBB3P11Vdz7bXXctpppzF9+vSBHqLQDYUivnBfVsRvDUXzJnyK8KePavkiFO0o4jMNrdl4ySmVAaqLBt5XrpTC7fXh9u5ZdGWbqM9EVzY39Rhd6c7z0g9EdOVIwFQmZh+Fftaj31uhH+9G6GefiWgNKm1Qtnk69Y8WUVzpZcK0csZPK2XC9DLKxgb6+M37H6mo94FmEeqCIAg9EgqFKC0tdYS6baEtC9WDWLn66qv54x//yAUXXMBLL72EMQLtB6OBfBH/1emFlUjL1uxsiuUaWrOWmvYiPugxC4R7LjO+anBEfD57FV0ZLWyITcaiPUdX+oNtM8RmRX1wcKIrRwKmMvGbAfzm3gnnfKEfsyI8U/tHkjpJmbucscX7EZg4hq/eMIEJY8YNe4+6VNT7gFTUBUEQeiYcDlNRUYHjVgUdj6N68B57PB7uvfdejjrqKO666y4uvfTSQRip0J+YhmJieYCJ5V2L+PyJnj6vj/Lcx7VsDUdJpoefiG9Pb6Mr7XQ655PPeufzvfSRcD2pbNJN/vl7iK7MVugHO7pyJJAv9G1tc3zVqYzz7UfAzPy7M7zcLd0iqS99QIS6IAhCz4RCISoqKnKiyo7HMXoQ6gBHHHEEV1xxBT/60Y/4u7/7O6ZMmTLQQxUGiXwRf8y0jiJ+V3O8YKKnLaGuRXxNJwk11cXDI6YRnGhJ355GV0bbVepjvYiu9OfNGJv11AeHX3TlYFPuqaTc07XdaTiTSFu5J0/9wagT6s2xjjPsCYIgCIWEw2EqKytRhkLjVNR7y/XXX8+jjz7KxRdfzDPPPDMqhcZowzQU+5X52a/M30HE27ZmZ3OcrfWRzIRPTkX++Q21bAm1ifhArhLfMaFmOIn4fAqiK7tB2zapeKxAzDtRlU6lfk+iK9tiK9vEvcs3vKMrRxO7WxI9H7QHjCqhbiglFXVBEIRekLO+qBSgsPdAqPv9fu655x6OO+44HnjgAc4999yBG6gw7DHyRPyCTkR8WyW+zRf/woZatoaiOQtBVsTXdJJQM2aYivh8lGHkxHV3dIyuLLTftNbvzjXKdhld6W8T8R1E/giNrhxJ1IlQ33tMQ4S6IAhCbwiFQkyaNAnVWotWYMd6L9QBjj32WL7zne+wYsUKTj75ZMaPHz9AIxVGMoahmFDmZ0KZnwXTCvflRHxmtlbHEx/hxQ11bAlFciLe7zY7na116ggR8fnsaXRlKloo5jtEV8ai2OnC6Eq3318g5p0qfbBd2s2+FV05mNQ1i1Dfa0ypqAuCIPSKXEU9shsA3a5prjf88pe/5Mknn+Syyy7jkUceGVGCSRh6CkT8AYX7bFtT25KdsTWa88a/tLGO/+5ExLefrbWmMsjYkpEl4vPJj64MlPcUXZnKVeE7i65s3rWDZE/Rle3sNvl59KMhunJP2N2yZ0WNnhhdQt1QNMdFqAuCIPREKBRyPOrbs82ke14lKioqYvXq1Xzta19jzZo1nHnmmf09TGGUYhiK8aV+xpd2LeJzEZMZO83Lf3Mq8dnp3X1uI9fQOqUqkPHEt1XijT2c4n444kRXenB5PH2Lroy00rK7ds+iKwOFjbLdRVdqrWnYtpXyiZNHvDWnriVBf/7qjD6hLhV1QRCEbtFa5yrqLYaRaSbd84o6wMknn8x5553Hd77zHU444QSqqkZQxpowIskX8UcfUFhttm1NXUuiLV4yFGFrfZRX/rab+0NbOoj4gnjJTFrN2GLfPiHi29O36Mq29S6jK92ePO98oJ39Jsj299/ls9dfZuzM2YydOQdvL1KmhiN1zQmqirx83k/nG3VCXawvgiAI3dPa2ko6nXaEulJgGHvsUc/n3/7t33jmmWdYsWIFDz74YD+OVBD2DMNQjCv1Ma7U10HEa62pbU4UxEtuqY/wyt9280BoK7GUM9ukz20wpSIvYrKqLZ1mXxXx+fRfdGWjs94uunLbe2vZtn4dlVP2Z9yBcygdP3FEWZTqWuKMKfH22/lGnVCXirogCEL3hEIhACorK/lCKZTLhb2XFXWAsrIy7rzzTk4//XTOOussvvGNb/TXUAWh31CqTcQftX9HEV9Qic80t77yyW4eeLOjiM9vbs0K+nEl+76Iz2dPoisTkVbef+JhUvEYvuISgpXVFFVVo22NnU5jukfOjK91LQnGlvj67XyjTqhHkhYpy8ZtjmwPlCAIwkARDocBMjOTAh43ei886vmcdtppnHXWWVx88cV89NFHlJV1/3hdEIYTSinGlvgYW9K1iM9N9pSpxL/6aT1b8kS812W0a2xta24dbSI+n6wnfcaikymqrMLl7T+ROxTUtSQ4eL/Sfjvf6BLqypm4ozGaorq4/x5LCIIg7EvkV9RVP1TUs/z7v/87s2fP5oc//CH/9V//1efzCcJwIF/Ez+9ExO/OVeKjmQmfIrz2aT0PvrWVaLJQxGebWadUZppbq4KMHwUivjdWmpFA2rKpb00wph815qgS6m6XIgnsaIyJUBcEQeiCbEW9rKzM8Ya6Peg+eNSzVFdXs2rVKs4++2yWLl3KSSed1OdzCsJwRinFmBIfY/ZYxEdyIt7jMphSEegQL1kzSkT8SCIUSaI1VIv1Ze/wmAZJYFtDlEMnyWNXQRCEzgiFQpSVlWGaJmQr6on+yQZeunQpv/vd77jwwgv54IMPKCoq6pfzCsJIozcifkteRvyWUITXN4X47dtfdCni8yvyE0r9IuIHmexkR1JR30s8LscH9UU4OsQjEQRBGL6Ew2EqKzPCQSlwu/qlou6cTnHHHXcwe/ZsrrnmGn7961/3y3kFYV8iX8QfObWiYJ/Wmt2tibac+ExF/vVNIR56+wsieSJ+ckWgwAufjZgUET8w1GUmOxKhvpcYSlEZ9LBNhLogCEKX5GYlBRQK5XJjx/tvtr0JEyZw6623csEFF3DmmWdyzDHH9Nu5BWFfRynFmGIfY4q7FvFbQ9FcQs2W+ihvbg7xu7/2TsSPL/VjiojfK+paMhV1sb7sPZMqAmwL970pShAEYV8lOyspgDIyFfV+aCbNZ/ny5fz+97/n/PPPZ/369fh8IzvpQRCGA/ki/oiajiK+vjWZlxPvJNS8uTnE79duozWRBhyb8OTKgCPgMw2tUzOTP00oExHfHVnrS3WRVNT3mkkVAf53W+NQD0MQBGHYkl9RBwUuF3Yf4xnbo5Ri9erVHHTQQdxwww388pe/7NfzC4JQiFKK6mIv1cXeLkW8kxHf1tz61ucdRfykCn9eRnwwJ+hFxDvWl4qgJ2e17g9GnVCfXOHnmQ92krZsXJKlLgiC0IFQKERNTQ3gWNRx9X9FHaCmpoaVK1eyYsUKlixZwty5c/v9GoIg9Ey+iJ/XiYgPRZIFs7VuCUV4+/MQf+hExNfkC/iMpWa0iPi6lv6NZoRRKNQnlQdI25qdTXEmVQSGejiCIAjDjvbNpMrlwo5FBuRal112GX/4wx8477zzWLt2LR6PZ0CuIwjC3qGUoqrIS1VR1yI+f7bWz+sj/PXzMP+zdhstGRHvNhWTKjLZ8JVBpla1JdS0F/GN0STPfLiLMw7fD5/bHNTv2lfqWhL9Hv896oT65Iw439YQFaEuCILQCR2aSd2ufm0mzccwDO655x4OPfRQbrrpJq677roBuY4gCP1PvoifO6WjiA9Hsp74aM4Tv3ZLmDXrOop4p7HVaWi9/flPufW5T1h+TA3fmj+FUr97KL7eHlPXHGdadVW/nnPUCfWsON8WjsIBQzwYQRCEYYbWuqCirgwFpgs9QEIdYMaMGdxwww1ce+21nHHGGRx00EEDdi1BEAYHpRSVRV4quxXx0Uy8ZITPQ1HWbQ2z5p1ttMQdEX/zs3/jNy99xj/Pn8x5x0xlXOnwbTq3be3MSloiFfU+Mb7Uh2koSX4RBEHohObmZizL6qSZdOCEOsAPfvAD1qxZw3nnnccbb7yByzXq/nsShFFDoYgvL9j30sY6lt+3ljHFXvavDjK1qogxxV4+qW1hTLF32Oa/f9kYI2VpJpb7+/W8o+5fQpdpMKHMJ5MeCYIgdEIoFAIonPDIZaJjA1vccLlc3HvvvcydO5fbb7+dK664YkCvJwjC8GTamCI+uP5kin0jw+6SZVNdKwAzxhb363lHZezJ5IoA2xpEqAuCILQnHA4D5HnUQZku7ET/xjN2xsEHH8w111zDddddx6effoplWQN+TUEQhheTKgIjTqQDfFrXAsD0MUX9et5RKdQnlQdkdlJBEIROaC/U8yvqWusBv/5VV13F9OnTOf/887n11lv5/PPPB/yagiAIfeXT2laqi72UBfo3uWp0CvWKAPWtSSKZjmNBEATBIRQKoZSirKwMcLykZPziehCq6m63m1tvvZXXXnuNK6+8kqeffnrArykIgtBXPq1r7fdqOoxioQ6wvUEaSgVBEPIJh8OUl5djGM5/D0oplOlkGdsD7FMHqKur46qrrnKuZ9s89dRTA35NQRCEvqC1ZpMI9f4jm6UuDaWCIAiFhEKhtkZSyFhfBq+iPnbsWF555RWWLFkCwEsvvURra+uAX1cQBGFv2dUcpzWRZlo/N5LCKBXqkzLROeJTFwRBKCR/siNwmkkZxIo6QCAQ4A9/+APXXHMNyWSSF154YVCuKwiCsDd8WusUE6Si3k9UBD0EPaZU1AVBENoRCoUKhDrKmfAIGNBJj9pjGAY/+9nPeOCBB3juuecG7bqCIAh7yqd1AyfUR12OOjiey0kVAbZLRKMgCEIB+bOSAihloEynpmPHBk+oZznnnHNYsGDBoF9XEASht2yqa6Ei6KGyqH9nJYVRWlEHmFoVzN0BCYIgCA7trS8octYXnRh8oQ5wwAEHDMl1BUEQesMnta1MG4BqOoxioX7QfqVsDUVpiqWGeiiCIAjDhvbNpEqpPI/60Ah1QRCE4YrWmk9rWwbE9gKjXKgDfLSjaYhHIgiCMHzoUFGnTajruETaCoIg5LO7JUFzPC1Cvb85aEIJAB9+KUJdEAQBnNzyhoaGwtQXBaBRPh92fODjGQVBEEYSuUbSAYhmhFEs1CuLvEwo9fHhl81DPRRBEIRhQVNTE7Ztd2gm1RoMrxdbKuqCIAgFfFrbAgxM4guM0tSXLPMml3JUtcWGDRuGeiiCkGP8+PG56dsFYTAJh8MAHZtJtUb5/WjxqAuCIBTwaV0rpX431cX9n/gCo1yof3OGj9KSEqZP2w9XxoMpCENJLBbjyy+/FKEuDAmhUAigw8ykGo3h80lFXRAEoR2f1rUyfUyR03g/AIxa6wtAuRdcgWLiaT3UQxEEAHw+H6mUJBEJQ0NnFXWFAo1TURePuiAIQgGb6lqZPnZgbC8wyoW62zRQShFLWkM9FEEAGLA7ckHoDaFQCNM0KS0tzW1ThkJrO+NRF+uLIAhCllBrgnAkybQxA9NICqPc+mIaCtM0iKcsaq58us/n27Ly6/0wKkEQhKEhHA5TXl7e7oZRoTUov0/iGQVBEPLIJb4MUCMpjPKKOoDfbRIdgop6Y2Mj//M//5NbX7169R6fY968eXt0/FFHHQXAfffdx5tvvgnARRddxDHHHMPHH39csCwIwuijY4Z65imP1hg+v0x4JAiCkMfHO5zkwBkDFM0IItTxe0wS6ZEp1PeWZcuWcfTRRwPw3nvv8frrrzN79uyCZUEQRh/tZyUFnNQXNMrnRSdEqAuCIGR5Z2sD+5X5GVfqG7BrjGrrCzgV9cGgtraWb33rWySTScaMGcPUqVN55ZVXWLhwIaeeeip/+9vfWLhwIZdeeinJZJL77ruPhoYGvv/973POOeewe/duli9fTktLC5MmTeLBBx/Mnfvee+/l3XffZdWqVR08zvfffz+rVq1i9uzZRCIRAK6//nrmzZvHq6++ysaNG1m4cCFHHHFEbvmFF17AlBQcQRh1dFpRR6EzFfX07t1DNDJBEIThhdaadVvDzJ9a2fPBfUCEumdwBGl5eTnPPvssLpeLH/zgB8yYMYPjjz+ehx9+GIA1a9bw8ssvAxCNRjnnnHNIJBJ89atf5ZxzzuEXv/gF559/PmeccQa2befOe+edd7Jp0yb+4z/+o8M1Lcvitttu4+233yYSiVBTU1Ow/+abb+bFF1/MXfell17KLQuCMPoIhUKMGTOmYJvTTKox/BLPKAiCkGV7Q4za5gRH1JQP6HVGvVB3mwYuc+AdQOFwmEsuuYSGhgZ27txZkKrQnueee45bb70VgE8++QSAjRs3cs011wBgGM54Y7EY//7v/87bb7/d6Xl2797NpEmT8Hq9eL1eDjjggP78SoIg7GOEw2FmzZrVbqvjUVc+iWcUBEHIsm6rE2c7d0pFD0f2jVEv1AECbpM/f/+4AW0G+O1vf8vJJ5/MZZddxg9+8ANcLheW1eaNz7es3HDDDbz00ksF4vrAAw/k9ddf5/TTT8e2bQzDwO/38+tf/5qzzz6bhx9+GJ+v0CNVXV3N9u3bSSaTRCIRPvvsswH7foIgjHxCoVAH6wvKSX0xfF6pqAuCIGRYt6WBYq+LmeMGTjuCNJMC4HObJFIWtj1wEx+deOKJ3HHHHZx++uns2rWL8ePHE4vFWLJkCZs3b2bRokWcdtppPPnkk/zjP/4jixYt4tvf/jbl5c4jlauuuorVq1dz/PHHs3z58tx5jznmGFasWMG3vvWtDhPlmKbJihUrWLBgAd/97neZMmXKgH0/QRBGPuFwuEMzqVNEyFTUJfVFEAQBcIT64VPKMY2Bnf9EaT16ZuWcN2+eXrduXW59w4YNHHjggTTFUmwNRTiguoigVx4yCENL9vdSEAYTy7Jwu9089NBDnHXWWbntD//8OorKKzmyqJL6u+5ixut/GcJRCoIgDD1NsRSH3fhnvn/SDC4/cXqnxyil3tFa71mOdieIKqUt+SWWska0UF++fDmff/55bn3ZsmUsW7Zs6AYkCMKIobGxEa11x4q6YQA6M+GRVNQFQRDe/aIBrWHelIFtJAUR6gC4TYXLMIgNwcRH/cl///d/D/UQBEEYoYTDTmNUx3hGMvGMPux4HK11hxhYQRCE0cS6LWFMQ3HY5LIBv5Z41HE8mH6POeKFuiAIwt4SCoWAjkIdlU198YFlQbteGEEQhNHGui0NzJlQQsAz8PVuEeoZgl6TeNoiZdk9HywIgrCPka2od9ZMqgHD5wfATkhEoyAIo5dk2uZ/tzcydxBsLyBCPUdxxpvemkgP8UgEQRAGn3A4jMvlori4Y9SYtm0MnxcAOyYRjYIgjF4+2tFEPGVzRM3A5qdnEY96hgd23AbAm3V7f46La37Yq+M2btzIypUrue+++zrsW79+PcuXL2fx4sXcfPPNez+YveCoo47irbfeYuXKlSxdupSamhpOO+00Ghsbeeyxx1i2bBmNjY08/vjjHR+PC4IwoslmqLf3n6vMBGsqU1GXhlJBEEYz72xtAAankRREqA87nnnmGX7yk59wxhlnDNkYrrzySgB27tyJYRi89tprBcuCIOx7hMPhLm7AldNM6ncmVLMlS10QhFHMui0NTKrwM6bE1/PB/YAI9UEinU5z1lln0djYmMvIfvbZZ/n5z3+OZVl897vf5dBDD+Wuu+6iuLiYcDjMokWLuPTSS0kkEhx++OHcdttt3HfffTzxxBMkEgnq6up4/PHHmTBhAvfeey+rV6/G4/Fw/fXXs2DBAi644AJ27NhBUVERDz74ICUlJR3G9Ytf/IInn3ySuXPn5mZKXbZsGVdccQU33ngjb7zxBv/0T/+EUiq3vGbNmkH92QmCMPB0OispTi+p00yaqagnRKgLgjA60VqzbmuY46ZXD9o1xaM+SDz22GPMmDGD559/nkMOOQTbtrnxxht54YUX+Mtf/sKdd97JzJkzWbZsGb/85S85//zz+fGPf8xvfvMbXn75ZdLpNNnJmsrKynj66ae56KKLePjhh9m9ezf33HMPr732Gq+++ioLFy7k7rvv5oQTTuDFF1/k3HPPZfXq1R3GtGvXLv70pz/xxhtvcPnll+dSH7LcfPPNHH/88axZs6ZgWRCEfY/OZiV1yDaTZj3qItQFQRidbA1FqW9NMrdmcGwvMEQVdaVUBfAHoAbYApyptW5od8xhwB1ACWABP9da/yGz7z7geKApc/gyrfX6wRj73rJp0ybmzp0LwJFHHsmjjz7Kp59+ysknnwxAfX09u3fvLvjM3/72N84//3wAWlpaOPHEEwE4/PDDAZg0aRJvv/02mzdv5vDDD8ftdgNgGAYff/wxa9eu5YEHHiCVSnHsscd2GNOWLVs45JBDUEoxY8YMSktLB+bLC4Iw7AmHw4wbN67DdmUosO08j7o0kwqCMDpZl/GnD1YjKQyd9eVK4AWt9Uql1JWZ9R+3OyYK/H9a60+VUhOAd5RSf9JaN2b2/1Br/XB/DSjbCLq9IUpTNMXsCSX9OqnHtGnTeO+99/jHf/xH1q1bR1VVFQceeCDPPfccbrebVCqVE9pZZs6cyS233MKUKVPQWmNZFg8++GDBuLTW7L///qxfv550Oo3L5cK2bWbNmsXRRx/NOeecA0Cqk+zjmpoaPvzwQ7TWfPbZZzQ1NXU4RhCE0UEoFGLOnDkdtisUGi0VdUEQRj3rtoQp8bmYVl00aNccKuvL6cD9meX7gW+2P0Br/YnW+tPM8g6gDhhwU1Cx14WlNdF+nvzom9/8Jhs3buTEE0/k3XffxTAMrrnmGk466SQWLVrEt771rQ6fuemmm7jkkks44YQTWLx4MTt27Oj03NXV1SxfvpxjjjmGE044gZdffpmLLrqI5557jhNOOIETTjiBP//5zx0+N27cOBYvXszRRx/Nrbfe2sVjb0EQRgNdNpMqBRqUy4Vyu8WjLgjCqGXd1gbmTinHMAZvdmaltR60i+UuqlSj1rosb71Ba92l4UcpdSSOoJ+jOJR3GgAAIABJREFUtbYz1pejgQTwAnCl1rrTWTiUUhcBFwFMnjx57tatW3P7NmzYkGvszJK2bD7e2czYEh9jB6mjVxDy6ez3UhAGmrKyMlauXMkll1xSsP3J22/CTqc5/Ypr+NsRRzLmX/+V8rOWDtEoBUEQhob61gTzfvY8P/zaTL69aFqPxyul3tFaz+vrdQfM+qKUeh7oaHiEa/bwPOOB/wOcq7XOTht6FbAL8ACrcWwzN3b2ea316swxzJs3r8e7Epdp4HebtMbTjO0YkjKiOfXUU4nlTVZy5ZVXcsoppwzhiARBGA6k02mampo6T33BsdgBGD4ftnjUBUEYhby40Zlo5/gZg5f4AgMo1LXWJ3W1TylVq5Qar7XemRHinU4zpJQqAZ4GrtVav5V37p2ZxYRS6r+BK/px6BT5XNS3JLFsjTmIjzcGmmeeeWaohyAIwjCkocFpkOpUqBsG4Ah15fej450+vBQEQdineWFDLeNLfcyZMLhV3KHyqD8BnJtZPhd4vP0BSikP8CjwgNZ6Tbt94zPvCsff/mF/Dq7I60KjiSTS/XlaQRCEYUk4HAbosk8lV1H3eqWiLgjCqCOesnjt03pOPHBMvwaN9IahEuorgcVKqU+BxZl1lFLzlFJ3Z445EzgOWKaUWp95HZbZ91ul1AfAB0AV8LP+HFzQ48JQilYR6oIgjAKyQr3zCY8U6LyKuqS+CIIwynhrc4ho0uLEA8cO+rWHRKhrrUNa6xO11tMz7+HM9nVa6wsyyw9qrd1a68PyXusz+07QWh+stT5Ia/0vWuvW/hyfYSgCHrNfhbrWmr//+7/n2GOPzf2nmM+8eU6/wfXXX89TTz3Vb9ftDfF4nIULFwKwYsUKYrEY0WiUY489lsWLFxcsC4Kw75Gd7KzTinpe9cjxqItQFwRhdPH8hloCHpOj9x/8dLyhylEfdmyY1XnKxoY9OMeBG7s+eteuXRiGwWuvvbaHIxtcbr/9dgDefPNNvvKVr/DrX/+6YFkQhH2PcDiM2+0mGAx22KcAbTt9/MrvQ4tQFwRhFKG15sUNdXx1WhU+tzno1xehPkh873vf44033qC6uppzzz2XW265hY0bN7Jy5Uruu+++Xp3jX/7lX9i+fTvpdJqHHnqIyZMnc//997Nq1SrmzJnDu+++ywcffEB9fT0XXHABzc3NTJgwgfvvvx/T7PjL9e1vf5sPP/yQo446Krdt4cKFPPXUU3zve99j586d2LbN22+/nVtetWpVf/1IBEEYJoRCISoqKjr1XirDIBuXZXiloi4Iwuji453N7GiKs+KkGUNy/aHyqI86br75Zo4//njWrFnT88FdsHr1al5++WV+9KMfcdddd5FOp7ntttt44403uO2228hmxK9cuZLLL7+cF198kcMPP5xHH320w7nWrVtHQ0MDr7zyCied1DGg5+abb2bp0qWsWrWqYFkQhH2PcDjczYRnbR51wy/xjIIgjC5e2FCHUrBo1pghub5U1AeZ/IrVnkw2ZVkWP/7xj1m/fj2JRII5c+ZQX1/PpEmT8Hg8VFRUcMABBwDw8ccf8/bbb3PjjTcSi8U455xzOpxv06ZNzJ07F4Ajjzyyj99KEISRTJezkkJGp2eaSX1+dGzXII5MEARhaHlhQy2HTSqjutg7JNcXoT7IlJeXs23bNgDeeeedXn9u/fr11NbW8tprr/HEE0/wyCOPUF1dzfbt20kmk0SjUTZv3gzArFmzOOOMMzj22GMBSKVSHc43bdq0XNPqunXr+vq1BEEYwYRCoS4r6qp9M2lCrC+CIIwOapvj/O/2Jn74tZlDNgYR6hk6awRNWTYbdzZTXeJjXImvX65z8MEHE4/HOemkk5g2recpaLPMmjWLnTt3snjxYmbPng2AaZpcfvnlLFiwgFmzZjFlyhQArrnmGi688EJ++tOfAo6NJZsqk2XevHmUlJRw3HHHMX/+/H75boIgjEzC4TATJ07sYq9qayb1+SSeURCEUUN2NtITDxwa2wuA2hP7xUhn3rx5Or96vGHDBg48sPO0lyybd7eSsjQzxhYNesh9b0ilUrjdbsLhMKeeeipvv/32UA9J6CO9+b0UhP5k7ty5LFq0iFtuuaXDvj+vXkVT7U7+6bpfUH/nnTQ+8ijT/vynIRilIAjC4HLB/WvZsLOFv/x40R5rQKXUO1rreT0f2T1SUe+BUr+bLxtjxFM2fs/gxfKceuqpxGJtTVtXXnklp5xySofj7rjjDh555BFaWlr42c86n/epqamJ008/vWDb7bffzmGHHdbp8YIgjC66ayZVqGwvqVNRl9QXQRBGAfGUxV821bN03qQhLdSKUO+BEr+bHY0xmmKpQRXqzzzzTK+Ou/zyy7n88su7Paa0tJSXX365H0YlCMK+SE/NpLnUF5nwSBCEUcLrm+qJp+whmY00H4ln7AG3aRD0umiKpfYopUUQBGEkkEqlaG5u7raZtC31xYeOSTyjIAj7Ps9vqKPI62L+/l0UMQYJEeq9oDTgJpG2iKesoR6KIAhCv9LQ0ADQTUXdyAl1w+dHp1JoS/4tFARh38W2NS9sqOW4GVV4XYM/G2k+ItR7QanPjULRGOsYcygIgjCSCYVCAN1U1AHaJjwCxKcuCMI+zYc7mqhrSXDirKG1vYAI9V7hMg2CXnPI7C/Lli3jww8/5OWXX+aKK64Y9OsPB/b0Z3DWWWexZcsWnn322dzMrDfeeCPz58/nxRdfLFgWhNFMOBwGuqmo5zeTeh2hLj51QRD2ZZ7fUIcxhLOR5iPNpFnuuqPb3fv35hwXX9ovQxnu2LaNYQzMPV5/nzs/Keepp57ir3/9K+Ck6GSXBWE005NQV0q1NZNmKuq2ZKkLgrAP88KGWr4yuZyKoGeohyIV9cEinU6zZMkSTjrpJL773e+ybNkyHnroIebPn8/8+fN59tlnAfjVr37FCSecwNy5c3nuuec6PVc8Huf/sXfncVHV++PHX2dYZhA3TNM008QFc0VATWVX47qgJooLKP26Wl0rvaZJWW73upFZmn01s8LUXCrJrnZVFAdNjXLXC5riFiUmm8oOM+f3BzqJDIoKDMv7+Xj06MzMmc95z4n0zWfen/cnICCAqKgosrOzCQoKwsfHB39/f27cuAHAvHnz8PT0xMPDg5MnTwKwfft23N3d6dGjB+vXrwcKZqpffvllfHx8GDt2rGn8u8e8ePEi7u7uDBs2zGyv5d9++w0fHx/c3d35xz/+AYC/vz9XrlwBYMWKFXz22WckJSUxePBgfHx8CAoKwmAwoNfrGTBgAP7+/qxZs6ZE98CcnTt34uzsTEBAAFevXgUgPDycZcuWsXjxYuLi4vDy8mLu3Lmm499//73E4wtRFSUnJ6PVaqlRo4b5ExRQ1dsbHtkBoGbLglIhRNV0OTmT//1xg97PWL7sBSRRLzcRERG0bt2aXbt20alTJwwGAwsWLGDv3r3s2LGDt99+G4AJEyYQFRXFjh07mDdvXpFxMjMzGTVqFJMmTcLHx4dVq1bh4+NDVFQUY8eOZeXKlZw8eZIzZ84QHR3Npk2bmDFjBkajkTlz5rB7925+/PFHVqxYgeHWgrAuXboQFRWFVqtFr9ebHRPgjz/+YN26dbz55ptF4lqwYAFvvvkm+/btIysri+joaIYNG8Y333wDwHfffcfzzz/PggULeP3114mKisLZ2dlUlnLjxg22bNnC2LFj73sPijNjxgx2797NunXruHTpUqHXJk+eTJs2bdDr9UyfPt103KRJkxKPL0RVdLs1Y3F9ghVFc7tEHY1OC4AxO6e8whNCiHL1zZEEFAUGdW5s6VAAKX0pN/Hx8bi4uADg5ubGN998Q7NmzdBqtaZ/8vPzWbduHV9++SUajYbExMQi42zZsoXnn3+eXr16ARAbG8svv/zCl19+SV5eHu7u7sTFxXHgwAG8vLwAsLKyIikpibNnz9K3b18AkpKSuHbtGoApLhcXF+Lj482OCdCpUydsbc1/DRQfH4+bmxsA3bp149y5cwwbNoznn3+e4cOHo9PpcHBwIDY2lpiYGObMmUNWVhbBwcHUr18fV1dXU6Jwv3tQHIPBYPr6vlOnTiV+nxDVWXJycrELSaFgManK7faMMqMuhKi6jEaVzUcS6NWyPk/UsbN0OIAk6uXG0dGRo0ePMnToUA4fPkyDBg04fvw4OTk5ZGVlkZubi7W1NYsWLeJ///sfqamppmT8TiNHjsRgMLBixQpefvllnJycePbZZwkODgYKeiLHxcXh6enJqlWrTM9ZWVnRtm1bIiMjsbGxIS8vDxsbGwCOHj2Ki4sLR48eZcSIEWRkZBQZ8/fff79n7XjLli355Zdf8PPzIyYmhhdeeIHatWtTp04dPvzwQ4YNGwaAk5MTQ4YMMSX/eXl57N+/v9DY97sHxbGysiI1NRV7e3tOnDhR4vcJUZ3dc7Mj4FbtCyA16kKIqi3mQgoJqVlMfa6NpUMxkdKXcjJ48GBiY2Px9fUlJiYGnU5HaGgoHh4ePPfcc8ydOxcAb29v3N3dmTt3LrVr1zY71uLFizly5Ahr165l/PjxREZG4uPjg4+PDzt37qRjx460atUKT09PvL29ee+999BoNEyfPp3evXvj7e3N6NGjTeP9/PPPeHt7k5WVhZeXl9kx72fatGmEhYXRq1cv7Ozs8PDwAGDYsGEsW7aMQYMGATB9+nQ++OAD09jHjx8vMlZJ7oE5c+bMwdfXlxEjRtC0adMSv0+I6iw5OfneibryV9cXje5We8YcSdSFEFXPt0cSqKm1pu8zjSwdiolSnXbbdHV1VQ8dOmR6HBcXR9u2bR9ojNTMXH5LycSxQU3stQ/2hcTtWexVq1aRnJzMtGnTHuj9ZSEkJIQpU6bQvn17S4cibnmYn0shHlbv3r1p3ry56Ru4u0Wv/ZzLp44TvGAJqqpyul17Gi9cSJ2BA8o5UiGEKDsZOfm4zd2Ff6fGLBja8ZHHUxTlsKqqro86jpS+PKDaOmsUReF6Vt4DJ+qDBg0iPT0drVbLxo0byyjCsrdx40aWL/+rnWWjRo3YsGFDucbw1ltvcfDgQdNjPz8/QkNDyzUGISqz7OxsFEUhJSWFLl26FHteQXvGv441Oh1GqVEXQlQx/z2VSGaugaEuT1o6lEIkUX9AVhoNtbTWXM/K44k6umI7JZjzww8/lGFkDyc8PPyB3xMYGEhgYGDpB/MA5s+fb9HrC1HZaTQaXF1duXz5MjExMcyaNYtZs2YVPVFRTItJARQ7O1SpURdCVDHfHk6g2WM1cG3mYOlQCpEa9YdQt4YNeQYjmbkGS4cihBAPxdbWllq1anH9+nX27t1L9+7dzZ6ngGkxKRTUqRulRl0IUYX8lpLJwfPJBHR58oEmYMuDJOoPoZbOBo2ikJaVZ+lQhBDioT377LMANGnShD59+pg/SVG4cy2TotPJjLoQokqJOFqw+eGQLhVvbxVJ1B+ClUahls6a65m5GI3VZzGuEKJq6dGjBwBjxozBysrK7DkFNep3zahnS6IuhKgaVFXlm8MJ9HB8jCcditmh2YIkUX9Ij9nbkm9UuS6z6kKISur2jHpISEix5yh3z6jb6WTDIyFElfHLxVQup2QytEvFWkR6mywmve2T5fc/5w41gSLNe156pbSiuS+9Xk/jxo1p3bo1er2erVu3smjRohK/f8SIESxYsIDTp0+TlZXFkCFDmDNnDtu2bWP+/Pn8+OOPpmMfH58y/CRCCEt54oknCAoKonXr1sWfdFe9pkarw5idU8aRCSFE+fj2cAL2tlb8rUPF6Z1+J0nUKwCj0XjPXT/N0ev1uLq63vsv2BLw8/MzHW/dupWff/4ZgNDQUNOxEKLqeu+99+5zhsyoCyGqpqxcA9tOXqFfhyeoYVsxU+KKGVUVlJ+fz4gRI0hLS6Nt27bcvHmTEydO0KNHD65fv86yZcsICgrixo0bNGzYkLVr1/LOO+8QEBBAnTp1cHNzIzU1lVWrVlG7dm3Cw8P59ttv+fbbbxk7diynTp1i0KBBXLx4kbVr19KhQ4ciMezcuZNp06bh6OhIcnIyUNCeMT09ndzcXOLi4vDy8qJPnz6m43Xr1tGkScVbXCGEKB2NGt17FqmgRP3OGnU7jBkZZR2WEEKUuR3/SyQ9J7/C9U6/k9Sol5OIiAhat27Nrl276NSpEwCpqalMmjSJNWvWsHLlSvr37090dDTt2rVj/fr1eHh4sG/fPvbt20fPnj05duwY+/btw8PDg5CQEObPn88XX3wBFOx6umXLFhYtWmR67m4zZsxg9+7drFu3jkuXLhV6bfLkybRp0wa9Xs/06dNNx5KkC1G9KYqm8GJSOx2qtGcUQlQB3xxOoGk9O7o2r2fpUIoliXo5iY+Px8XFBQA3NzcAHBwcaNmypen1289369aNc+fO0atXL/bv389PP/3Em2++yd69e/n999/NJs+dO3cGoGnTpqSmppqNwWAwUK9ePbRaremXBSGEuKeCRup/PdTqMEp7RiFEJfdHWhb745N43vlJNJqK1Tv9TpKol6I7vx6+m6OjI0ePHgXg8OHDAIXq0lu2bMkvv/wCQExMDK1ataJu3bqkpaWRnp6Ou7s73333Hc2aNQPAxsYGg+GvDZfubNBfXBxWVlakpqaSm5vLiRMnHvJTCiGqE+WuGnWNnbRnFEJUfhFHf0dVqbDdXm6TGvXbHqFjS0pGDgmpWbTINVBTa/6WDh48mPXr1+Pr60vLli2xsbEp9Pq4ceMYPXo0X331FY0aNeKtt94CoF27dtjZ2WFtbY2trS0eHh4A+Pj4MG3aNKKjo/H39y9RnHPmzMHX15fmzZvTtGnTh/68QohqRFHunFC/teGRLCYVQlRet3und3u6Hk89VvF6p99JudcscFXj6uqqHjp0yPQ4Li6Otm3bPvK4BqPK6cQb1NLa3PM/eF5eHjY2NqxatYrk5GSmTZv2yNcWVU9p/VwKURpiIjZxMmoHf//oMwCSP/uclLVrabUnysKRCSHEwzl0MYWAFQcJC+jIcNeymbhUFOWwqqqujzqOzKiXAiuNgkMNW5IzcskzGLGxMl9RNGjQINLT09FqtWzcuLFMY3rrrbc4ePCg6bGfnx+hoaFlek0hRBWkKHeuJUXRaWVGXQhRqX2+/wK1ddb07/CEpUO5L0nUS0k9e1uS0nNIzcjl8do6s+f88MMP5RbP/Pnzy+1aQoiqq2D9y13tGaVGXQhRSf2Wksn2U4mM93DEvphy5YpEFpOWEp2NFTW11qRk5N5zUakQQlQ2dy8mVbOz5c85IUSl9NmPF7DSKIT0aG7pUEpEEvVSVM/ellyDkZvZ+ZYORQghSoWi3LUzqa7gG0M1J8dSIQkhxEO5npnHpkO/MbBTYxrVMV/9UNFIol6KatvZYG2lITkj19KhCCFE6SjYmtT0UHMrUTdKnboQopL56ufLZOYa+HuvFpYOpcQqfnFOOfn45UfvYDBhhQ/1atjy581scvMN2FpblUJkQghhOcpdibqiswNAlTp1IUQlkptvJPzABXq1rM8zjWtbOpwSkxn1UlbP3hYFHmhWPTExkZkzZxZ5fsSIEVy8eJHMzEzc3d3p06cP27dvJyIiotixvLy8SE9PL/Tc9u3bmTVr1n3j6N69OwDh4eGmjjHjx4+nZ8+exMbGFjoWQlQPiqLc2UYdjd2tGXVJ1IUQlcjWE39w9UYOf3d/2tKhPBCZUS9lttYaaulsSM3Io2FtHRrl/tvSNmrUiNmzZxf7+vHjx+nSpQtLliwpzVCLFRISYjo+evSoacfUO4+FENXFXTPq2ls16pKoCyEqCVVV+XTfBVo3rIln6waWDueBSKJeBh6racuNpAxuZOVRt4YtAHq9nnnz5mFnZ8fFixd59913+fLLL7ly5Qoff/wxYWFhfPPNN+zcuZNp06bh6OhIcnIyABMnTuTKlSsYjUZcXFxIT0/n1VdfJTw8nM8++wyDwcC///1vfHx8TDGkpaUxfPhwNBoNTZo0KXYn0tWrV/PRRx/xzDPPkJGRAcCsWbNwdXVl7969nD59Gi8vL9zc3EzHu3fvxspKynqEqA4KKl8Kd30BMGZJoi6EqBwOxCcTd+UGYUM73mo5W3lIol4Gamqt0VpruJaeQx07G9MPhaqqbNmyhc8++4x169bx/fffs2TJEo4dO2Z674wZM9i9ezf29vam3SnDwsLYunUrixYtIjw8HICkpCTWr1/P3r17ycrKYuDAgYUS9VWrVhEQEMD48eOZPn262TgNBgMffPABMTExZGRk0Lx580Kvh4WFERUVhV6vB2DPnj2mYyFENVFc15dsWUwqhKgcPt13nvo1tQxybmzpUB6Y1KiXAUVRqF9LS1augfScv1o1duzYEYAmTZoUOk5NTTWdYzAYqFevHlqtlk6dOhV7jfPnzxMbG4u3tzf9+vUjMTGx0Ovnzp3DxcUFgK5du5od49q1azRt2hStVku9evVwdHR8uA8shKiy7l5MqrErWExqzJb2jEKIiu/XqzfRn7nG2Geboa2ETT5kRv2WCSt87n/SA3CoYcufN3K4eiOHmrd2vrrz65Y7j++crbKysiI1NRV7e3tOnDhR7PgtWrSgY8eObN26FUVRyMvLK/R6y5YtOXr0KC4uLhw6dMhsqUqDBg1ISEggNzeXjIwM4uPjH/rzCiGqqLsWkypaLSAz6kKIymHVvvPobDQEdW9m6VAeiiTqZUSjKDSopeWPtCwyckq+AdKcOXPw9fWlefPmxdaVA9SvX58RI0bg6emJlZUVHTp0YOnSpabX//73vzN8+HC+/vprGjduTLNmRX9AraysmDRpEj169MDJycnsOUKI6k25ezGpoqDodFKjLoSo8P68mc13R/9guNuTONjbWjqch6JUp22gXV1d1UOHDpkex8XFmerAy4LRqHLm6k1srTU4NqhZZtcRVUtZ/1wK8SCO7fyBHzes5tXPN5qe+7X7s9R//TXqjRplwciEEOLe3t95hmV7zhH1hhdP17cv12srinJYVVXXRx1HZtTLkEajUL+mlivXC2bV7bWWvd0vvPACFy5cMD0OCQkp1IpRCCHuVlCjftdzOh2qzKgLISqwrFwDa3+6RO+2Dcs9SS9NkqiXscfsbbl2M4erN7JpYeFZ9S+++MKi1xdCVD7KXV1fADQ6HUapURdCVGDfHEkgNTOPce4tLB3KI5GuL2VMo1GoX8uW9Jx8MnNLXqsuhBAVggJ3T6krdnay4ZEQosIyGlU+//ECnZ6sg1tzB0uH80gkUS8Hj9lrsdIo/HlD2pkJISobBdVobkZd/jwTQlRMu+KuciEpg7+7t6h0GxzdTUpfbvn45ahHHqO4Fo9Wt2rVr97IJis3Hztbue1CiMpBURTUu2fUdVppzyiEqJBUVWXl3vM0qWvH39o3snQ4j0xm1MtJ/Zq2BbPqN0t/Fio8PJxly5YB4Op6/wXGK1asIDw8nMTERGbOnAnAjh07cHZ2ZvHixYWOhRDVm7nFpBqdnbRnFEJUSPpfr3HoUiove7bA2qryp7kytVtOrDQaHrPX8ufNbLLyDNjZPPjuWEajEY2m9H7oGjVqxOzZswHYvHkzn3/+Oc7Ozrz00kumYyFENXfXzqQAGjtZTCqEqHiMRpWw7Wd4ql4NAt2esnQ4paLy/6pRSeTn5/PPl1/g/wX0p1+/fixcuJBNmzYBcObMGcaMGYOqqrz22mt4e3vTp08fEhISAHjmmWcYM2YMU6dOJTIyEh8fH7p27cqCBQtKfP3Lly/Tq1cv+vXrx969ewG4ePEiAQEBREVFsWXLFsaPH8+2bdsKHQshqjezpS9aHarUqAshKpitJ68Qd+UGk/u0xta6aqS4VeNTVAIRERE0a9aM/+zYhW+/waRnZJoS9Y0bNxIYGMi2bdtwcHBgz549LFiwwJSIJyQksGTJEt5//3169uxJVFQUMTExfPfdd2RllWxWKywsjBkzZvDDDz8UWVjh4+ODn58fX3zxBf379y90LIQQRRaTyoy6EKKCyTMYeX/nGZwa1cK/U2NLh1NqJFEvJ/Hx8bi5uVG/ppaOXVz53+lfSU1NJT09nV27dtG3b19iY2OJiIjAy8uLyZMnk5aWBkDLli1xcChoL3T06FF69+6Nl5cX58+f588//yzR9c+dO4eLiwsAXbt2LZsPKYSocgp+sb97MamdbHgkhKhQNh36jUvJmUx9rg0aTeXu9HInqVG/pbiOLaWlZcuW/PLLLwwdOpTzscd44qmn6dm9K/Pnz6dNmzbY2Njg5OTE8OHDeffddwHIy8sDKFSXvmDBApYuXYqTkxNdu3YtshHJva5/O8k/dOgQvr6+pf8hhRBVTrEbHuVIoi6EqBiycg0s3X0W12YO+Dg9bulwSpUk6uVk8ODBbN68GQ8PD2rUqME7i1agUwz0cG7Lf//7XwAGDhxIVFQU3t7eAAQFBfHiiy8WGmfo0KEEBgbSoUMH7O1LviXum2++yahRo1i0aBF169YtvQ8mhKjazHR9UXQ6mVEXQlQYqw9e5OqNHD4a2aXS902/m1LSGdmqwNXVVT106JDpcVxcHG3btrVILL+nZZGSnkubRjWxtX7wDjCi6rLkz6UQd4vbH80PHy3ijQ3/MT2X8uUakj75hNb7f7RgZEIIAdez8vAI20OXp+ryxQsVp7RXUZTDqqrev2f2fciMuoU0qKklJSOXazdzaOJQo1TH3rhxI8uXLzc9btSoERs2bCjVawghqgcFirRnVOx0qCVcyC6EEGVp5d54rmflMeW5NpYOpUxIom4httYa6tWwISUjj8dqGtA9RF/14gQGBhIYGFhq4wkhqrFbXyOrqmr6Slmjs8OYk1PoOSGEKG9/3szm8x8v4t+pMe0a17F0OGVCur5YUMPaOjQKXLkutZ5CiIpJUW79NXHHrLqi04LBALcWvAshhCUsizpHnsHI5D7HfVvWAAAgAElEQVStLR1KmZFE3YKsrTQ8XlvHzew8bmbLX3hCiIrn9oT5nZseaXR2ABizZZJBCGEZl5MzWf/zZQLdmtK8fsmba1Q2Uvpyy4EtsY88Ro9Bzzzwex6raUtKRg5/pGXTuqH1fb9GXrlyJePHjwcKWjUGBgby9NNPP1S8d1uxYgU6nQ4/Pz+WL1/O7Nmz2bFjB6GhoQQHB9OuXTvT8eTJk0vlmkKICu526YtRNU3taOx0ABizsrGqXdtSkQkhqrEPdv2KlUbhdd9Wlg6lTEmibmEaReHxWlp+S80iOSOX+jW19zz/zkQ9NDS0TGJq1KgRs2fPBmDz5s18/vnnODs789JLL5mOhRDVg8LtyYM7Sl+0BYm6Kr3UhRAWcDrxBt8d+52XPBxpWFtn6XDKlJS+lBO9Xk///v3p378/Xbt25ezZszzzzDOMGTOGf737FrnpaQQGPI+3tw9BQUEYDAYOHjxI165d8fT0ZMaMGURERHDmzBm8vLzYuHEjISEhnDp1ivz8fAICAujduzevvfYaISEhAGzfvh13d3d69OjB+vXrzcZ1+fJlevXqRb9+/di7dy8AFy9eJCAggKioKLZs2cL48ePZtm1boWMhRDWhub2Y9I6n7phRF0KI8rZox6/U1FrziqejpUMpczKjXo6uX7/Ovn37OHjwIHPnziUhIYH9+/fj4ODApH9OZuQLL9HvuT6s/+z/iIiI4Pjx48yYMYMBAwZgNBrRaDS0adMGvV4PYNooKSIigtatWzNv3jxWrVrFjz/+iNFoZM6cOej1eqytrfH29mb48OFYWRXuLhMWFsaMGTPo27cvo0ePLvSaj48Pfn5+TJkyhfbt2xc6FkJUD6YZ9UKLSQtq1NVsadEohChfhy+lsCvuKlOfa0OdGjaWDqfMyYx6OXJ2dkZRFFxcXIiPj6dly5Y4ODgA8OuZ06z8cCGD/taHDRs3kpiYyIQJE4iMjGTMmDFs37692HHj4+NxcXEBwM3NDYCkpCTOnj1L37598fHxISkpiWvXrhV577lz50zv7dq14mwUIISoIG7XqN+5mPT2jHp2jkVCEkJUT6qqsnD7GerX1PJCz+aWDqdcyIx6OTp27BiqqnL06FEcHR05deqU6TUnJycG+g/i8Vadsdda06SOLfn5+SxZsoTc3FxcXFzo16+f2cWmjo6OHD16lKFDh3L48GEA6tevT9u2bYmMjMTGxoa8vDxsbIr+5tmyZUuOHj1K7969OXToEL6+vmV3A4QQlY7pjxyjmRp1mVEXQpSj6F+v8fOFFP41qB01bKtHCls9PmUJPEzHlgdVq1Yt+vfvT1JSEuvWrWPkyJGm16ZPn864ceO4lpxCTr6R98LCOPrLT2zevJmMjAxT3bm3tzf+/v6MGzfO9N7Bgwezfv16fH19admyJTY2Nmg0GqZPn07v3r3RaDQ0aNCATZs2FYnpzTffZNSoUSxatIi6deuW+T0QQlQ2ZmbUdQWL3qVGXQhRXnLyDczZGkuzx2oQ6PaUpcMpN4p619bQ5XJRRakHbASaAxeB4aqqppo5zwCcvPXwsqqq/reefxrYANQDjgDBqqrm3u+6rq6u6qFDh0yP4+LiaNu27SN9lpLS6/Vs3bqVRYsW3fM8o6py9upNQKF1w5ol3vXv9oz5qlWrSE5OZtq0aaUQtbCE8vy5FOJ+zh/9hYgFs3ktfBO2djVMz5/u0JFG/5pD3cGDLRidEKK6WBZ1lkU7fyX8BTe82jxu6XDuS1GUw6qquj7qOJaaUQ8FdququkBRlNBbj81lllmqqnY28/xC4ANVVTcoirICeBFYXnbhlh+NotCojh2XkjNK1K7xtkGDBpGeno5Wq2Xjxo1mz9m4cSPLl/91mxo1asSGDRtKJW4hRNV0ezHp3XM6ip0dqtSoCyHKweXkTD6KOkf/Dk9UiiS9NFkqUR8EeN06Xg3oMZ+oF6EUTDH7AKPueP8sKnii7uXlhZeXV4nOra2zpqbWmqs3sqlrZ4O11f3X/P7www/3PScwMJDAwMASxSCEEMAdReqFM3WNVotRatSFEGVMVVVmfH8Ka43CuwPKvky5orFU15eGqqpeAbj17+J+PdIpinJIUZSfFEW5/f3qY0Caqqr5tx4nAE3KNtzypSgKT9Sxw2hU+fOmzFgJISzHlKYbCyfqBTPqUqMuhChb208loj9zjcl929CoTtXe3MicMptRVxRlF9DIzEvTH2CYp1RV/UNRlBZAlKIoJ4EbZs4rttBeUZTxwHiAp56qPIsP7GytcLC3JTk9l3r2tuhsrO7/JiGEKG1m2jMCaHQ6jJKoCyHKUHpOPrP/E8szT9Rm7LPNLB2ORZRZoq6qau/iXlMU5aqiKE+oqnpFUZQngD+LGeOPW/8+ryiKHnAGvgXqKopifWtW/Ungj3vEsRJYCQWLSR/281hCw9o6rmfmkXg9m+b17S0djhCiGlKUW1+83lWkruh0qNL1RQhRhj6I/JWrN7NZHtSlRGXAVZGlPvX3wNhbx2OBLXefoCiKg6Io2lvH9YGeQKxa0KZmDxBwr/dXdCtXrrzvOTZWGh6vreVGdh5LP7ZMCf6IESO4ePEi27dvJyIiAoA5c+bQrVs3oqKiCh0LIaog08akMqMuhCg///vjOuEHLjKy61M4P+Vg6XAsxlKLSRcAmxRFeRG4DAwDUBTFFXhZVdW/A22BTxRFMVLwC8UCVVVjb71/GrBBUZR/A0eBzx41oPcDBzzqELyxcWuJz125ciXjx4+/73mP1dRy7WY2K1d+yqv/eBlNCds1ljY/Pz/T8datW/n5558BCA0NNR0LIaoeU4vYu2fU7XSy4ZEQokwYjSrvfHeKunY2THvOydLhWJRFEnVVVZOBIltgqqp6CPj7reMDQIdi3n8eqFT73R88eJCJEydiZ2dHSkoKFy9exMvLi1deeYXc3FzCw8NJTU3ln//8J8HBwcyaNYsLFy7w559/4tPnOS7En8Xdw5N5//4Xnp6ehcZes2ZNkfdfvnyZkSNHUqdOHerWrUvfvn0JCQlh3rx57NixA1VV+fjjj+nQoegt3rlzJ9OmTcPR0ZHk5GQAwsPDSU9PJzc3l7i4OLy8vOjTp4/peN26dTRpUqXW9AohANOGR0Vm1O0wSntGIUQZ2PDLbxy9nMbi4Z2oU6PorurViexMWk5++OEHZsyYwYABAzAajXTt2hW9Xg9AZmYmwcHB5OTk0KtXL4KDg4GCxa+rV68GYM2XX7Jiw39o9XjNImMPHTq0yPsXLlzIzJkz6du3L0FBQQCcPHmSM2fOEB0dTWJiIq+88oqpnOVOM2bMYPfu3djb2xfZeGfy5Ml89dVXptgjIiJMx0KIqqe4L/E0Oi35aWnlG4wQospLSs9h4fbTdG9RjyHOMgEoiXo5mTBhAvPnz2fTpk2MGDGi0GuRkZEsXrwYgF9//dX0vJubm+nYxlqDlaKQkJqJY4PCO5aae398fDwuLi6FxomLi+PAgQOmfu5WVuY7yRgMBurVqwdAp06dHvozCyEqv9uLSe+eUVd0dqhZiZYISQhRhc37IY7M3Hz+Pbh9iXdnr8okUS8nderUYcmSJeTm5uLi4oJO91cv0NmzZ7Nnzx60Wi2Ojo6m5zWav9b6ahSFxnV1XE7J5Fp6Do/Xuvf7HR0dOXr0KL179+bw4cP4+Pjg5OSEp6cnq1atAiAvL89srFZWVqSmpmJvb8+JEydK9T4IISoZUyN1WUwqhChbP51PZvOR35ng7UjLx2tZOpwKQRL1Wx5kIejD+OSTT9i8eTMZGRmEhIRw9epV/P39GTduHEOHDsXb2xtnZ2ccHMyvbG7Tpg0vBo8kaPyrKO27UFtnY+qtbu79U6dOZdSoUbz//vvY29tjY2NDx44dadWqFZ6enmg0Gvr06cPbb79d5Fpz5szB19eX5s2b07Rp07K7KUKIik8xX6NesJhUEnUhROnIzTfyzneneNLBjle9W1k6nApDufsP36rM1dVVPXTokOlxXFxckRrsii7PYOTXqzfRWlvh2MC+2K+F8vPzsbYu+D0sKCiI1157jW7dupVnqOIhVcafS1F1/X46lg0z32Tcx19Qu34D0/NJK1aQtjmCljt3WDA6IURV8fGec7y34wxfhLjh7VTchvWVh6Ioh1VVdX3UcWRGvZL5v2Uf8fW3m8nJM2BrrcHNpQsffvhhkfMuXbpESEgI+fn5dOrUqdgk/a233uLgwYOmx35+foSGhpZZ/EKIykXRmGpfCj+v06FmSXtGIcSj+y0lk4+izuLXrlGVSNJLkyTqlczEiRN5/fXXuZScSXpOvtkuMFBQo75v3777jjd//vzSDlEIUaXc7qNe+FmNzg5jjrRnFEI8GlVVmfn9/9AoCjMGPmPpcCqc6rkfayWnKApN6tqhKJCQllWkdlQIIUqLUlyNuk4rM+pCiEe2/VQiUaf/ZHKf1jSua2fpcCocSdQrKRtrDU/UsSMjJ5/kjFxLhyOEqKpMG5MW3fBIzctDzc+3QFBCiKrg6o1s3o44SfsmtQnp0dzS4VRIkqhXYg41bKilsyHxeja5+QZLhyOEqIKUvzL1Qs9r7ApaxMrupEKIh2E0qkz5+jhZeQY+DHTG2kpSUnPkrlRiphIYICFVSmCEEKVPubWfg1pkMWnBV9RqjrRoFEI8uNUHL7LvbBLT+z9Dy2LW2wlZTGryfuCARx7jUXux6/V6tm7dyqJFi0r8HltrDY3q6Pg9LYuUjFweq6l9pBhK27Fjxzh48CCvvPIKrq6uHDp0iFmzZuHq6oqrqyvLly9n9uzZZXb9ESNGsGDBAk6fPk1WVhZDhgxhzpw5bNu2jfnz5/Pjjz+ajn18fMosDiEqvSKlLwV/1hizJFEXQjyYM4k3mf/f0/g6PU5Qt6csHU6FJol6FVDP3pbrWXkkXs+mls4GW+sH/6LEaDQW2gm1tHTu3JnOnTubfa1Ro0ZlmqTfyc/Pz3S8detWfv75ZwBCQ0NNx0IIM4pdTHprRj1bFpQKIUouJ9/AxA1Hqa2zZmFAx2L3gxEFpPSlnEyYMMGUEG7fvp0hQ4bg4+NDjx49ePXVV4uc7+r6V4/87t27A5CUlMTgwYPx8fEhKCgIg6GgLl1RFJ50sEMFfk/LYu7cuXh6euLh4cHJkyeLHW/WrFmMHTuWv/3tb/zvf/9j0aJFPPvss/To0YPDhw+Tm5vLgAEDTGNlm9mFMDw8nMGDB9O/f3969uzJmjVr6N+/P56enuTk5KDX65kyZYrZe3Lx4kUCAgIA2L17N927d6dbt26Eh4cDEBISwrhx4+jduzeDBg1CVVUOHjxI165d8fT0ZMaMGWbH3blzJ87OzgQEBHD16lVTnMuWLWPx4sXExcXh5eXF3LlzTce///67+f9wQlRzxXV9MdWoy4y6EOIBvLf9DKcTb/JeQCfqV7AqgIpIEvVyMmLECDZu3AjApk2b+Pe//83u3bs5cOAAf/zxB2fPnr3vGAsWLOD1118nKioKZ2dnIiIiTK/ZWlvRqI6OI0ePcTL2NNHR0WzatKnYZPa2p556iv/+9780aNCA77//nv3797N27VqmTZvGb7/9hk6nIzo6mujoaHQ6ndkxGjRowLZt2/Dx8eHIkSNs27aNLl26EB0dXeL78/bbb7N161b27dvH0qVLybrV9s3d3Z1du3ZRs2ZNTp48yQ8//MCMGTOIjo5m1qxZZseaMWMGu3fvZt26dVy6dKnQa5MnT6ZNmzbo9XqmT59uOm7SpEmJYxWiWrprCYxy688DqVEXQpTUj2eTWPXjBYK7N5ONjUpIEvVy0qtXLw4cOEBOTg7x8fFoNBr69euHp6cnhw4d4o8//ij2vbdnsmJjY5k5cyZeXl5s2rSJxMTEQuc9Zm/L75fiOXjgAJ6eXowYMYIbN24UOx6Am5sbUDC73alTJzQaDS1atOD69es4Ojri6elJSEgI77zzjmkG/24dO3YEoEmTJoWOU1NTS3x/jEYj9evXx9bWltatW5vuh7OzMwBNmzYlNTWVCRMmEBkZyZgxY9i+fbvZsQwGA/Xq1UOr1dKpU6cSxyCEKEoxlcTdXaMuM+pCiJJLzcjlja+P4djAnrf7tbV0OJWG1KiXE0VR6N69O7Nnz6Zv37783//9H6+99hr9+vXj+eefL/K1spWVlSnJvj3b7uTkxJAhQ3B3dwcgLy+vyDV6OHcguntP3lv6fzxd3578Wz2OzY0HmOrSmzdvzrFjxzAajVy8eJG6deuSk5PDhAkT0Gg0jB8/nv379+Ph4WH2s5k7fpAuNBqNhqSkJGrXrs2vv/5K48aNzY5Xp04dlixZQm5uLi4uLvTr16/IWFZWVqSmpmJvb8+JEydKHIMQoqjb/wcW7aN+uz2j1KgLIe5NVVXejjhJSkYun411w87WytIhVRqSqN/yqB1bSiIwMJBevXpx5swZ4uPj+ec//8lnn31mdqb61VdfxcPDg3bt2pmS1unTpzNu3DhmzpwJQFhYWKHacwBXF2fat23D8IHPobW1pr/fc7z99ttmx7tTo0aNGDRoED179kRRFD766CMuXbrEiy++iNFopHbt2nTp0qUM7kqBefPm0b9/fwBef/117OzM7072ySefsHnzZjIyMggJCTF7zpw5c/D19aV58+Y0bdq0rEIWonoopkYdGxuwskKVPupCiPv4+nAC/z2VyDQ/J9o3qWPpcCoVpTr13nZ1dVUPHTpkehwXF0fbtlXv6xdVVfktNYvrmbk8Xd+emjobS4ckHkBV/bkUlVNywmXC3/gHY8I+okGzpwu9dqaLC4+/FYrDsGEWik4IUdFdSs6g35J9tG9Sh6/GdcdKUz26vCiKclhVVdf7n3lvMqNeiV2/fp1BgwYVeu7DDz+kc+fONKlrR1augcspWbRqaIVNKez4FR0dbZrNv02v1z/yuI/irbfe4uDBg6bHfn5+hIaGWjAiIaqaYmbUAcXODlVq1IUQxcg3GJm08RgajcLiwM7VJkkvTZKoV2J16tQpNlG20ig0e6wG5/5M57eUTJ6ub//IvUo9PT0tnpjfbf78+ZYOQYgqTbnHX6wanQ6jmbatQggB8FHUOY5eTmPpSGea1DVf0iruTbq+VGE6Gysa17UjPSefqzekjlQI8TDuNaOuQ5VEXQhhxuFLqSzbc44hzk3w71R0bZwoGUnUq7h69rY41LDlz5vZ3MzOu/8bhBDiDspfbV+KvKbRyoy6EKKo9Jx8/rnxGI1q65g9qJ2lw6nUJFGvBprUtUNnY8VvKVnk5RstHY4QojIprusLt2fUpT2jEKKw2d//j4TUTD4I7ExtaWjxSKRG/Zb9n338yGP0fHFCic9NS0tj586dDB8+HICVK1cyfvz4B7qeq6srd3axudc5T9UrqFe/nJJJiwaPXq9eEt27d+enn34y+5qqqvj7+5OWlsaWLVuoV69emcdT3kJDQ/Hz86Nu3bocPHiQV155hS+++IJly5bxxhtvkJOTYzoeNWqUpcMVwixFuTWfY25GXWcnGx4JIQrZduIKXx9OYIK3I12frnp/t5c3SdQtJC0tjU2bNj1Sov4gdDZWPOlgx+WUTBJvZPNEHcsu6khMTESj0bBv3z6LxlEeOnfuTOfOnQHYsGEDu3btwsHBgeeee850LERFdft3ehUzM+o6LWqOJOpCiAKnE28w9ZvjdG5al0m9W1s6nCpBSl/KydWrV+nduzceHh4EBATw8ccfEx0djZeXFwsXLuTMmTN4eXmxceNG1qxZg6+vL126dGHNmjUAXLt2jQEDBuDp6UlQUFChsT///HNeffVVs19NG41GXnnlFbp168byJe9Tz96Wk2fi8fDyxt3dnX/84x8AhIeHM3jwYPr370/Pnj1Zs2YN/fv3x9PTk5ycHFRV5bXXXsPb25s+ffqQkJBg9nOuXr0aV1dXxowZQ0ZGBgBJSUkMHjwYHx8fgoKCMBgMTJw4kQMHDjBs2LBix27bti2jR4/G2dnZdB/OnTuHr68vnp6eTJ061RS7u7s7PXr0ICoqymxc5u5p7969MRgMfPLJJ6bNlkaOHMmVK1d477338PHxwcXFhcjISPLy8ujVq5dpvMDAQM6fP1/kOseOHcPNzY2BAwcSFxcHFLSwnDJlCl999RUxMTH4+/szd+5c0/Hhw4fNxixExXCr9MUoM+pCiOKlZeYy/svD2Gut+STYpVTaQguZUS83Dg4ObN++HWtrayZPnkzr1q3x9PTkm2++AeDrr782tT7MzMwkODiYnJwcevXqRXBwMPPmzePFF19kyJAhGI1/1ZmvWLGCc+fOsWzZMrPXTUtLIzQ0lKZNm+Ls7My0aaFMW76E0eNe5YXAIbw07kWio6MBaNCgAZ9++invvvsuR44cYdu2bfzzn/8kOjqa3NxcHBwc2LNnD4cPH2bBggVFrmkwGPjggw+IiYkhIyOD5s2bA7BgwQJef/11fHx8eP/994mIiCAsLIwpU6bw9ddfs3XrVrNjJyYmsnz5cjQaDX369CE4OJipU6eyaNEinJ2dMRqNJCUlsX79evbu3UtWVhYDBw7Ex8enyH0YOnRokXvauXNnjh07RkxMDFqtFqPRSGJiIk888QQTJkxg6tSpJCUlMWzYMPr06YOzszOHDh2idevWpKSk0KJFiyLXeeedd1i7di2tW7fGw8Oj0GujRo1i5cqVbN26lZo1axIZGWk6FqKi+qtMzkyibqcjPzmpfAMSQlQ4+QYjr60/SuL1bNaP707D2jpLh1RlSKJeTlJSUnj55ZdJTU3lypUr1KlT/Ba6kZGRLF68GIBff/0VgNOnTzN9+nQANJqC31KzsrJYunQpMTExxY7l4OBAs2bNALCzs0OjUbj2x2Xad+rC5ZRM3Lp25dy5c1hZWdGxY0cAmjRpglarNR2npqZy6dIlIiIi2Lt3L6qq0rRp0yLXunbtGk2bNkWr1aLVanF0dAQgNjaWmJgY5syZQ1ZWFsHBwYXeFxsba3bsFi1aULt2beCvhWwJCQk4Ozub7sP58+eJjY3F29vbFENJ76mHh4cpwe/evTsRERGm5HvdunV8+eWXaDQaEhMTARgzZgxr166lU6dODB061Ox1rl69Sps2bYCC9QFCVHq3S1/MbGKt6OxQs6X1qxDV3cLtp9l3NomFQzvg0kzKOUuTfC9RTtatW0ffvn2Jjo5mwIABWFtbYzAYTK/fubhz9uzZfP/99+zYscM029q2bVv2798PYJpRt7Oz49NPP2XkyJFkF9Mizdyi0datWnHl3Ckyc/OJ/vEArVq1KnLunceqquLk5MTw4cPR6/VER0fzxRdfFBm3QYMGJCQkkJubS2pqKvHx8QA4OTkxb9489Ho9MTExvPTSS4XeV9zY5mJv2rQpx48fN92HFi1a0LFjR/bs2YNer+fYsWNm74O5e9qrVy/WrVtHs2bNcHd3Z968ebi7uwOwaNEi9uzZY/rGA8DNzY0TJ06wYcMG09qCuzVs2JCzZ8+iqipHjhwxe44Qlcm9F5NqpT2jENXcd0d/59N9FxjzbDMC3Z6ydDhVjsyo3/IgHVsehq+vL8HBwezYsQN7e3vat29PVlYWAQEBhIWF4e3tjb+/P+PGjWPo0KF4e3vj7OxsWmj41ltvERISwuLFi2nevDmrV68uiLtnTyZNmsTo0aPZsGEDNjb3b4M0bdo0xo4dS3rmfJ5u3ZZObs+arbe+08CBA4mKijLNXAcFBfHiiy8WOsfKyopJkybRo0cPnJycTDP506dPZ9y4ccycOROAsLAw6tev/0Bj3xYWFsa4ceNQVZVu3bqxcOFCRowYgaenJ1ZWVnTo0IGlS5cWeZ+5e1qvXj1ycnLw8PDA1dWV06dPm8pVvL0Lavi7detmmtUH6NOnDz///HOxXWr+9a9/MWrUKB5//HHq1q17z3sqRKVwz8WkOtQsac8oRHV1MuE60749Qden6/HugGcsHU6VpJhbgFhVubq6qne2M4yLi6Nt27YWjMiyjKpK/LV0cvONtHy8JlprK0uHVOEtXLiQ1q1bM2TIkDK7RnX/uRQVS3pKMp+8MpbAmQt48pn2hV5L/uxzUtasoZV+j4WiE0JYSlJ6Dv4f/QjA96/1on5NrYUjqlgURTmsquoj18DKjHoV8sILL3DhwgXT45CQEEJCQoo9X6MoNKtXg7N/pnM5ORPHBjXRaEreX/1Br1delixZQkREhOlx586d+fDDDx953Dlz5rB//37eeOONMr2OEBXK7Q2PzM2o2+lQpfRFiGonz2DkH2uPkJKZyzcv95AkvQxJol6FmKsbvx9bayuedKjBpeQMElKzaFrPrsSbIT3M9crDxIkTmThxYqmPO2PGjHK5jhAVienPA3M16lqd1KgLUQ3N+U8sP19MYcmIzrRvUnxzDPHoZDGpoI6dDY3q6EjLyuXqDengIIT4y+1E3VyVpObWjLp6R8tYIUTVtuHny6z56RLjPVowqHMTS4dT5UmiLgBoUFNLvRq2/Hkzm5SMXEuHI4SoKO7RR13RFexwrObIL/hCVAeHL6Xw7pZTuLeqzzQ/J0uHUy1Ioi6Aglmzxg521NRa83tqFunZeZYOSQhRgZjfmbSgLlXKX4So+hKvZ/Py2iM0rmvHRyOdsXqANW3i4UmN+i0JofseeYwnF7iX+Ny0tDR27txp6se9cuVKxo8f/0DXc3V15c4uNo9Koyg0e6wG8dcyuJRSsLhUZ/PgnWC6d+/OTz/9ZPY1VVXx9/cnLS2NLVu2FNvmsLSEhIQwZcoU2rdvf/+TS1loaCh+fn7UrVuXgwcP8sorr/DFF1+wbNky3njjDXJyckzHo0aNKvf4hCgJ5V6LSW/PqEuiLkSVlp1n4KW1h8nIyWfti92oW8PW0iFVG5KoW0haWhqbNm16pES9LFhpNDR/rAbn/szgYlIGjo/XxMaq9L54SUxMRKPRsG/fo/9iVFl07tyZzp07A7BhwwZ27fvuCcMAACAASURBVNqFg4MDzz33nOlYiArrXotJ7Qq2CTdmSaIuRFWlqirvfHeK47+lsSLIhTaNalk6pGpFSl/KydWrV+nduzceHh4EBATw8ccfEx0djZeXFwsXLuTMmTN4eXmxceNG1qxZg6+vL126dGHNmjUAXLt2jQEDBuDp6UlQUFChsT///HNeffVVzPXEnzJlCl5eXnTt2pVjx45x8+ZN/P39ARg1ahRhYWEAhTYb6tvbl78P+xu//XaZH/QHTb9AGAwGnn32WbPXWb16Na6urowZM4aMjAwAkpKSGDx4MD4+PgQFBWEwGJg4cSIHDhxg2LBhqKrKa6+9hre3N3369CEhIQEo2IV19OjRODs7mz7/uXPn8PX1xdPTk6lTpwIQHh6Ou7s7PXr0ICoqqth7v2TJEnr37s2gQYNQVZX8/HxGjRqFp6cn/fr1IyUlhYsXL/Lss88SEBDAM888w+bNm3n++efp2LEjcXFxJb7esWPHcHNzY+DAgab36fV6pkyZwldffUVMTAz+/v7MnTvXdHz48OFiYxfC0u7V9UXRFSTqao4k6kJUVasPXOSbwwm87tsKv/aNLB1OtSMz6uXEwcGB7du3Y21tzeTJk2ndujWenp6mLeq//vpr9Ho9AJmZmQQHB5OTk0OvXr0IDg5m3rx5vPjiiwwZMgTjHR0WVqxYwblz51i2bJnZ686ZM4caNWpw4sQJFi5cyLp168jIyMBoNJKfn8+JEyc4d+4cLVu2BApm9mvUqMH333/Pjm/WMub1UI6fiiUnJ4e9e/fi6+tbpH2jwWDggw8+ICYmhoyMDJo3bw7AggULeP311/Hx8eH9998nIiKCsLAwpkyZwtdff83WrVtxcHBgz549HD58mAULFrBs2TISExNZvnw5Go2GPn36EBwczNSpU1m0aBHOzs4YjUaSkpJYv349e/fuJSsri4EDB+Lj42P2Hri7u/Ppp58yevRoTp48yZkzZ3jqqaf46quvWL16NR999BFjx44lNTWVH3/8Eb1ez5tvvskvv/zCf/7zH9asWcPkyZNLdL133nmHtWvX0rp1a9Mup7eNGjWKlStXsnXrVmrWrElkZKTpWIiK6q+uL+Zq1GVGXYiq7EB8Ev/aFkfvtg2Z5NvK0uFUS5Kol5OUlBRefvllUlNTuXLlCnXqFN93NDIyksWLFwPw66+/AnD69GmmT58OgEZT8EVIVlYWS5cuJSYmptix3n//fXbs2IFGo8HKqqDevFOnTkRERNCqVSsuXryIXq/Hw8MDg8HAtGnTOHbsGDk5ObRr144n6tjR06cv6775jr2R/yU0NLTINa5du0bTpk3RarVotVocHR0BiI2NJSYmhjlz5pCVlUVwcHCh98XGxhIREcHevXtRVZWmTZsC0KJFC2rXrg38lRwkJCTg7Oxs+vznz58nNjbW9E3AtWvXir0Ht9/XtGlTUlNTiY+Px83NDYBu3bqxa9cuANq1a4eVlRVNmjShffv2aDQamjRpQmpqaomvd/XqVdq0aQMUrCEQovK7V436rRn17KxyjUgIUfZi/7jBS2sO83R9ez4I7PRAGyKK0iOlL+Vk3bp19O3bl+joaAYMGIC1tTUGg8H0+p2z1LNnzy6Y0d6xwzTb2rZtW/bv3w9gmlG3s7Pj008/ZeTIkWSbWcyVnJzM1q1b2bdvH8uWLTMlve7u7sybNw93d3fat2/P8uXLcXd359ixY1y9epV9+/bxzjvvoKoq9WvaEhQUxJrVq4m/cBEnp6LtmBo0aEBCQgK5ubmmRBjAycmJefPmodfriYmJ4aWXXir0PicnJ4YPH45eryc6Otq0gZK5DZeaNm3K8ePHTZ+/RYsWdOzYkT179qDX6zl27Fix9/7O8VRVpWXLlvzyyy8AxMTE0KpVqyLn3f2ekl6vYcOGnD17FlVVOXLkSLExCVFZ3KM7Ixq7gsWk0vVFiKrlcnImY7/4mZpaa1b/v67U0tlYOqRqS2bUb3mQji0Pw9fXl+DgYHbs2IG9vT3t27cnKyuLgIAAwsLC8Pb2xt/fn3HjxjF06FC8vb1xdnY2LTR86623CAkJYfHixTRv3pzVq1cD0LNnTyZNmsTo0aPZsGEDNjZ//c/k4OBAw4YN8fb2pkePHqbn3d3dOXHiBD179qRWrVosX76c5s2b06BBA65cuUKfPn145plngIKE1bnN0xjycunh48fN7Lwi/8NaWVkxadIkevTogZOTE82aNQNg+vTpjBs3jpkzZwIQFhZG/fr1Te8bOHAgUVFRherjX3zxRbP3LywsjHHjxqGqKt26dWPhwoWMGDECT09PrKys6NChA0uXLi3Rf4vBgwezefNmPDw8sLe3Z926ddy4ceOe76lfv36Jrvevf/2LUaNG8fjjj1O3bt0SxSNERaYoBfM55kpfFG1Be0bp+iJE1fHnzWyCPoshz2Bk/bhnaVLXztIhVWuKuT98qypXV1f1znaGcXFxtG3b1oIRVR4DB/ozff6H1HR4DMcGNbGzffC2jaJk5OdSVCR5OdksHRPA4DffxdGlW5HXT3d2ptG771J36PMWiE4IUZquZ+UR+MlBLqdksu7v3XB+SrqSPSxFUQ6rqvrINbAyo16FvPDCC1y4cMH0OCQkhJCQkEced/DgwTg5tcHFqTnnrqUzKngsKYkJpq/ES+s6j2rJkiVERESYHnfu3JkPP/ywylxPCIu4vZjUzIZHULCg1Cg16kJUetl5BsatPkT8tXQ+G+smSXoFIYl6FXK7xru0fffdd6bj5o/Z86/FH6O11tCiQc0KtTPZxIkTmThxYpW9nhCWoNxjMSmAYmeHmp1TniEJIUpZvsHIq18d4ZdLKSwd4YxH6waWDkncIotJxQOxs7XiqcdqkJ1n4LeUTLN1q0KIKuQefdQBNFqtzKgLUYkZjSrTvj3Jrrg/mePfjoGdGls6JHEHSdTFA6uts6FxXTtuZOfxR1q2JOtCVGF/bXhUzOt2drKYVIhKSlVV5v83jm+PJDCpdyuCn21u6ZDEXaT0RTyUx2pqyTUYuXYzB40GGtXWmW2rKISo3EwbHhWTqWt0OtnwSIhKakX0eT7dd4GxzzZjomxoVCFJon5LQui+Rx6jrFs8VjSNauswGuHazRwUoOFDJuvHjh3j4MGDvPLKK2ZfnzVrFq6urgwYMMD0XFpaGjt37mT48OElukZoaCh+fn54eXkVe052djZ+fn7o9XomTZrE/PnzUVWV5557Dp1Ox5YtW0zHkZGRD/QZhai0bk+oF7eY1E6HmiOJuhCVzcZfLrNw+2kGdmrMzIHtZLKtgpLSlwrq9qZGFXlsRVFoXFdHPXtb/ryZw583H25BWefOnYtN0ouTlpbGpk2bHup6JfHhhx9iZ2fH8ePH6dKlC5GRkYWOhagubi8mLa72RdHKjLoQlc32U4m8tfkkHq0b8P4w2XW0IpNEvZyoqsqECRNwd3fH09MTf39/Tp06BRTM9ur1evR6PQMGDMDf3581a9YUen9eXh69evUyPQ4MDOT8+fMcOnQIb29v3N3dWbRoEQCRkZH4+PjQtWtXFixYAEB4eDiBgYH079+fXbt2FYnvvffew8fHBxcXFyIjI8nNzaVnz56m10eOHEl8fDw7d+7E2dmZYcOG4e3tzaX/z96dh8d0tg8c/55JJivZRBJLbFntSyaLJRuiCKWlKOpNW4QqumhL9aX62ktL661aWmqr2ELLr7GkJrETTYiWSELEFmTft5nz+wPzSpNYiiR4PtflcmbOnOfcM5lwzz3PuZ9Ll2hgYYyliQE3sgu5mV3Ixx9/zPHjx4mLi8PMzAyNRsOyZcvYuHEjqamp9O/fn65duzJ8+HA0Gg1qtZpJkyYB8NNPP6FSqfjXv/5F69atdef/+eef6dWrFz4+PuTn57N06VIiIiLw8/MjLi6OsLAwvL296dSpEz///DNwu1Lv7u5O3759OXv2bKU/m3HjxuHr66tbmAnAz8+P3NxcJk6cyLZt2xg/fnyZbUF4Ydyd+lLZxaTGoj2jIDxLjiSmMWFjNG0aWvD98A4Y6ItUsCYTU1+qyC+//IK+vj4HDtyeYjNixIgKH5ednU1ERES5r6CUSiXt27cnKioKZ2dn0tPTadasGaNGjWLbtm1YWlryyiuv8MYbb9C5c2d+//13ZFmmY8eOuhaCBgYG7Nq1q8Lzjhs3jo8++ojU1FRee+01AgICcHV1JTY2FkdHR1JSUnBwcGDo0KGEh4djampaZvXShpa3Vy5LyS6krcqLAwcOYG5uTufOnYmJieHAgQPMmzePuXPnMmHCBLp27crChQsJDQ3VrVZaWlrK119/zfHjx8nNzaVJkya6+FxcXJg2bRpTp05l3759jB07lsTERLZs2YJWq+XNN99ErVajr6+Pv78/gwYN4rPPPmPdunU4Ozvj4+NT4fOOiooiIyODiIgI9u7dy7Fjx8rsnz9/Pjt37mTBggWo1WrdtiC8KKQHdH2RjIyRs+6/sq8gCDXDmatZjFoTRSMrE1YFuWNiINLAmk78hKrIuXPn8Pb+3xx2Pb3/rex5b6VKpVJVOk9sxIgRrFu3jrZt2zJgwAAAYmNjeeWVVwDIyMjg8uXLFBUVMX36dEpKSrhw4QI3b94EwN3dvdL41q9fz5o1a1AoFKSkpAC3q/YhISG0bdtWNz9cq9ViZWUFQJs2bXTH303WZVmmcUs3fl63Bjsbaz7++GMiIyO5evUqDRo04K+//uLYsWN88cUXFBQU8MYbb+gS9dTUVOzt7TEwMMDKygoHBwfd+O3btwfA3t6ejIyMMrGnpqYSHx9Pjx49dLdv3brFjRs3cHFx0b2uFUlISMDNzQ0ADw+PSl8fQXihSVLlFXUjI7Rijrog1HgXU/P414/HMTdWsvZtDyxNDao7JOEhiES9ijRv3pz9+/czcOBAACwsLLh8+TKtWrXijz/+oFevXgAoFJV/BeXu7s4nn3zC2bNnddM72rZty5YtWzA3N0ej0aBQKHj55Zf55ptvcHV1xcPDQ/cf7P3GXrBgAX/++ScZGRm6KTZdu3ZlxowZnD9/noULFwK3P2BkZGRgampKbGxsmTEkSaKhlQky1txMS0dpYIC3tzeff/45TZs2BcDV1ZVXXnlF96GlpKSEQ4cOAVC3bl2uXLlCcXEx+fn5XLhwoczYd8myjFKpRKPRAGBtbU3z5s3Zu3cvSqWSkpISlEoltra2xMfH4+joyB9//EG/fv3KPW9HR0d27twJ3K6uC4JQnoR0n4q6EbKYoy4INVpKViHDVx5DBta87UE9c+PqDkl4SCJRv+Npd2zp27cvv/32G126dMHAwIDp06czYcIEli5dilKpfOhxAgICOH78uK6qPXfuXF599VW0Wi0GBgZs376dAQMGMHjwYFq3bo2pqelDjXt3nrunpydmZmYA6Ovr07p1a+Li4rC3twdud2Dp2rUrzZo1w87OrlzsCknC3sqE5s1boFAakFWkwcDAQDf1ZOrUqYwaNUo3H3z+/Pm6Y/X09JgwYQKdOnXC1dWVxo0bVxpvvXr1KCgoYODAgcyfP5+pU6fSvXt3FAoFdevWZdOmTfznP/9h6NCh2NjYYGFhUeE4KpUKMzMzfHx88PT0fKjXShBeOFKlbdRvV9RFH3VBqLFuZhcy/IdjZOYXs3F0Rxzq1qrukIRHIL1Ii9WoVCr53qrp2bNnad68eTVG9OjmzZuHs7OzbrpLVbtbrS4qKsLT05OTJ0+WmcZzl1aWuZSWT05hCQ0tTbB6yK/Y7o6fnp5Or169ys0ZfxE8i+9L4fn29dB+vDRmIi18upbbl7piBZkbQ3AML3+RuiAI1etqZgHDVhzlZk4RPwa549WsTnWH9MKQJOmkLMsVz7t9BKKiXkMtXryY0NBQ3e127dphZWXFoUOH+PDDDx9r7ClTpnDkyBHd7Z49ezJ58uSHOnb79u0sWbKE3NxcJkyYQEJCAsHBwWUes3HjRuzs7GhsZUJSWh5XMvKRJLA0eXCyvnTpUrZt20ZOTg4zZ858tCf2AFlZWeWmvyxatIh27do90fMIwvNGut8cdUNRUReEmigpNY9hK4+RXVjCupGedGhkWd0hCf+AqKiLyuVTpdXKJKXlkVdUir2VCRYPkay/6MT7UqhpFg1/he4jx9HKr3u5fRmbN3Nzzlxc/jhZDZEJglCR+Bs5DFt5jBKNlrVve9KqgXl1h/TCeVIVddE8U3iqFAqJxnVMMTHQ53J6AVkFxdUdkiAIj+h+F5MqjIzRFhZWWnEXBKFq/Xkti8HLjyIDIcEdRZL+jBOJuvDU6SkkmlibYmygR3JaAdkFJdUdkiAIj0KSkCu5nFRhbARaLZSI32tBqG7RyRm8vvwoRvoKNgV3xNm2dnWHJDwmkagLVUJPIdHU2gRjAz0upeeLZF0QniGSVHnbF8nQCEDMUxeEanbsQhrDVx7DwsSATWM60tT64bq+CTWbuJj0js8///ypjvEoq1rGxMRQXFz83C3Ao6dQ0MTahIu38riUnk/jOiaYGf2vvePcuXMZPHiwruf636lUqnK9ztVqNfXr18fZ2fmhYvDy8uLo0aP3fcz333+PkZERPXv2ZOnSpcyYMYPdu3czefJk3njjDVq2bKnb/uCDDx7qvILwLJJl+XaSLknIsvZ/t++hML6TqBcUonentasgCFUr8vwtRq+NooGFMetHemFnblTdIQlPiEjUa6CYmBhyc3OfaqKu1WrvuwDS06KvUNDU2pSLqXlcSs2noaWxbnW0h+08cy+1Wo1KpXroRP1R2NnZMWPGDAC2bdvGjz/+SPv27QkODtZtC8LzLCftFodC1iFrNCRGHaMwNxePfgPLPEYyur1wilxYUB0hCsILb+9fNxi3/g8cbGqx9m0PrGsZVndIwhMkpr5UsUmTJuHn54eHhwcxMTEABAUF4e3tjY+PD0lJSSxdupTFixfrViu9V2hoKHPmzAEgMzOTHj16ADB79mx8fX3x8fHRrRha0bn8/Pz48MMPKxxbq9XSo0cP/Pz8CAgIIDs7m82bN+sWJcrOziYgIACAd955B19fXz755BP8/PwqfK7+/v7A7XaQ48aNA24v2ISs5fzJA7w1sBf+vl34/sefkGWZoKAgzpw5Q2lpKQMHDqR79+6MHz+eoKAgXXxjx47F09OTOXPmUFBQwOrVq5kyZQpvvvkmsiwzfvx4/P39CQgI4MqVK7rXpmPHjrz77ru61Uz/Ljk5mS5dutC7d28iIyMBSEpKYuDAgfz+++/s2LGD0aNHs2vXrjLbgvA8M7O24ebFREpLirnwxwnqNi7/bZeuol5YVNXhCcIL79dT1xi77iTN69Xm51GeIkl/DolEvYp98cUXqNVqVq5cyZdffklJSQlnz54lMjKSyMhIGjVqxNixY5k4cSK//fZbueMDAwMJCwsDYPPmzQwaNIjY2Fji4uKIiIhg06ZNTJs2rcJz3dW7d292795dbmyFQsGOHTtQq9X07duXkJAQ+vTpo0tIt2/fTv/+/Tlx4gSZmZlEREToEveKWFtbk5qaSlJSEunp6RQUFGBgYIBCoWDWzJkciNjP9t9+Z+Xy5VxJz9MdFxoairOzM/v27aNt27a6+zMzM5k8eTJHjhxh48aNGBsbExQUxJw5c1i1ahW7du3C0tKS/fv3M3fuXObOnUtKSgq7d+/m8OHDTJgwgbS0tApjnT9/PtOmTeP//u//yn2137VrV3r27MmqVasIDAwssy0IzzsH1e0Ve/UNDbFv0brc/rtz1EVFXRCq1paTV5i4MZoOjSxZN9JTtD9+TompL1Vs4cKF7N69G4VCgZ6eHkqlkokTJ/LWW29hbm7+wEV+DAwMaNWqFTExMWzZsoWQkBD27NnD4cOHdZXtuyuF/v1cd7m7u1c4dl5eHsHBwSQnJ5OZmcmAAQMwNjamQYMGJCQksHXrVpYvX87+/ftxc3O771gA3t7e7N27F2NjY0xMTNi1axdeXl6kpqYSHx9Pz5deAiAnK4P4S1fJKypFK8skJiaWGf/gwYMAWFpa0rhxYwCMjY3Lne+vv/4iNDSUyMhIZFnG3t6epKQk2rRpgyRJODs7Y25ecZuqhIQE3Tmft2sDBOFxOLh5cix0E41bt0PfoHwicO8cdUEQqsbao5f49/YzeDtZs+wNN0wMRDr3vBIV9SqUlpbGzp07OXDgAEuWLEGWZTQaDa+99hqrVq3CxsaGbdu2oVQqK52iATBixAjmzJmDhYUFFhYWuLq64uvri1qtRq1WExYWVuG57qpsbnpYWBj169cnMjKSkSNH6o4ZPHgwy5Yto7S0FFtbWxwcHIiOjgbg5MnKFznx9vZmwYIFeHh40LlzZ+bMmYO3tzfW1tY0b96cvXv3olariT0VQ2unxhSXarmaUUDTpk0rHP/vlW6gzGvl6urKoEGDUKvVREREsGrVKpo0acKZM2eQZZmEhASysrIqjNXR0VF3zr9fsCoILzI7BydMzC1o1qHiD+UKozsV9SKRqAtCVVgReYF/bz9D9+Y2rBihEkn6c078dO94El1fHsTS0hJbW1v8/f3p1KkTADk5Obz88su6izs3btxIYWEhI0aMICoqirVr15Ybx9PTk1OnTjFv3jwA2rRpg5OTE76+vigUCgICApg8eXK5cz2Il5cXs2bNIjAwkHr16tGwYUMAevbsyVtvvaWbq+7u7o6pqSm+vr506NABpVJZ4Xht27YlMTERHx8fzM3NCQoKwtPTE4VCwdSpU+nevTsKhYK6deuyadMmTA31KSjW0LpzAD9v3Ei3bt1wdHSsdHy4PS3lk08+ISIigoULF/L777/r5sYPHz6ct99+m4CAADp27EiHDh2oU6dOheN8/PHHDB06lAULFmBhYfFQr5cgvAgkhQIHlSdN21e8wJ5kJCrqglAVZFnm298T+GrveQJb12PRkHYo9US99XknvUiryalUKvneaumzvFS7j48P4eHh901in6aSkhKUSiX79u1j69atLF269ImMm1NYwqW0fGRNKc71LFj70yrS0tL45JNPnsj4z4Jn+X0pPJ8yU65jYVev0v3nWrfB7j9fYNG/fxVGJQgvDq1WZt7ucyyLuMCrHRowf0Ab9EWSXqNJknRSluWKKxyPQFTUa7hevXpRUPC/i7TGjBnDihUrGDhw4GMl6VlZWfTr16/MfYsWLaJdu3YPdXxwcDCJiYloNBrWrFnD4sWLCQ0N1e1v164dixYteuS4ahspaVbXlJ69epOfl4eZqTFbNm965HHuJyQkpMwHCzs7OzZu3PhEzyEIz5P7JekAkrExsljwSBCeisISDR9uPsWu09cZ5tmI//RrhUJRfiqo8HwSFXVRuayRiko0XEzLo1Qj0+hvCyM978T7UnjWxPv4YvXWm9S500pVEIQn42ZOIaPWnOT0lUw+6elKsE+zCq/XEmqeJ1VRr5bvTSRJspIkaa8kSfF3/ras4DH+kiTF3POnUJKk/nf2rZYk6eI9+x6uDCw8MwyVejjUrYWhvoJLqflk5BVXd0iCIFRCMjISFXVBeMLOXs+m/5JDnE/J4fvhbozxdRBJ+guouiY4TQbCZVl2AsLv3C5DluX9siy3k2W5HdAVyAf23POQj+7ul2U5pkqiFqqUUk9Bs7qmmBrqcTkjn5s5hbxI3wAJwrNCYWQkLiYVhCco/OwNBi49jEaW2TymIy+1tKvukIRqUl2Jej/gpzvbPwEPugJpIPCbLMv5TzUqocbRUyhoYm2KhbEBKVmFXM8Sybog1DSSsZFY8EgQngBZlll54AIj10TRrG4tdozrQqsGFa//IbwYqitRt5Vl+TrAnb9tHvD4IcDPf7tvliRJpyVJ+lqSpErXzJUkabQkSVGSJEXdunXr8aJ+DGq1mkmTJj3UY2NiYjh+/PhTjqhqTZ48GbVaTUxMjO5CzlWrVuHm5saGDRvKbP+dQpKwtzLGupYhqblFJKfnoxXJuiDUGApDI7SFRdUdhiA800o0Wj4NPcPMXWd5qYUdm4I7YmduVN1hCdXsqXV9kSRpH1DRdzVTH3GcekBr4N4176cAKYABsBz4BPiiouNlWV5+5zGoVKpKs7sn0Uf9SfVij4mJITc396mukHm3b3tVa9euna6zzMaNG9m3bx+Wlpa89NJLuu2KSJJEfQtjlHoS17MK0aTm0cjKRLSnEoQaQFTUBeHxZOWX8M6GkxxKSOMdPwcm9XARnV0E4ClW1GVZ7i7LcqsK/uwAbtxJwO8m4jfvM9QgIFSW5ZJ7xr4u31YErAKemTXfJ02ahJ+fHx4eHsTE3J5aHxQUhLe3Nz4+PiQlJbF06VIWL15Mr169yh0fGhrKnDlzAMjMzKRHjx4AzJ49G19fX3x8fIiNja30XH5+fnz44YcVjq3VaunRowd+fn4EBASQnZ3N5s2bdQsdZWdnExAQAMA777yDr68vn3zyCX5+fhU+15iYGNzd3enbty9nz54F/vfNwoYNGzh27Bgvv/wys2bN0m3fb6VTgLq1jbC3MiGvWEPCrVwKiitfwVUQhKqhMDIWc9QF4R9KSs3jle8OcfxiOgtea8vHPV1Fki7oVFc58hfgX3e2/wXsuM9jX+dv017uSfIlbs9vP/MUYnwqvvjiC9RqNStXruTLL7+kpKSEs2fPEhkZSWRkJI0aNWLs2LFMnDiR3377rdzxgYGBhIWFAbB582YGDRpEbGwscXFxREREsGnTJqZNm1bhue7q3bs3u3fvLje2QqFgx44dqNVq+vbtS0hICH369GHXrl0AbN++nf79+3PixAkyMzOJiIjQJe4V+eyzz1i3bh2//PIL6enpZfYNHTqUdu3a8dtvvzF16lTdtpub2wNfQ0sTA5pZmyLLkHgrl8x80RFGEKqTwsgIbZFI1AXhUR29kEb/7w6RkV/M+pFeDHRrWN0hCTVMdS14NBfYJEnS20Ay8BqAJEkqYIwsyyPv3G4C2AMRaot9EwAAIABJREFUfzt+vSRJdQEJiAHGVE3Yj2/hwoXs3r0bhUKBnp4eSqWSiRMn8tZbb2Fubs7MmTPve7yBgQGtWrUiJiaGLVu2EBISwp49ezh8+LCusq2np1fhue5yd3evcOy8vDyCg4NJTk4mMzOTAQMGYGxsTIMGDUhISGDr1q0sX76c/fv36xLqysYCuHHjBi4uLgCoVI/dSrQMU0N9HG1qkZyeT3J6PvnFGuzMjVCI1lWCUOUkIyPkgkJkrRZkGemef28EQajYpqjLTA2NpZGVCT8GudO4jml1hyTUQNVSUZdlOU2W5W6yLDvd+Tv9zv1Rd5P0O7eTZFluIMuy9m/Hd5VlufWdqTTDZVnOrern8E+kpaWxc+dODhw4wJIlS5BlGY1Gw2uvvcaqVauwsbFh27ZtKJVKNJrKp3SMGDGCOXPmYGFhgYWFBa6urvj6+qJWq1Gr1YSFhVV4rrsqm5seFhZG/fr1iYyMZOTIkbpjBg8ezLJlyygtLcXW1hYHBweio6MB7jtVxdbWlvj4eGRZ5o8//vgnL9l9KfUUNLU21V1kejE1jxKN9sEHCoLwxNyYM5eiuDiKk5NJfvttEB+WBeG+tFqZub+d4+Mtp/FqVodt73QWSbpQqeqqqNc4T+pC0PuxtLTE1tYWf39/OnXqBEBOTg4vv/yy7uLOjRs3UlhYyIgRI4iKimLt2rXlxvH09OTUqVPMmzcPgDZt2uDk5ISvry8KhYKAgAAmT55c7lwP4uXlxaxZswgMDKRevXo0bHj7K7iePXvy1ltv6eaqu7u7Y2pqiq+vLx06dECprHjV0P/85z8MHToUGxsbLCwsHvn1ehiKOxeZGhvocTWjgISbuTS2MsHEULy1BaEqGDRtQvpPt7vtGrdujVQNF6kLwrMiv7iU9zbGsOevGwzzbMTnL7dEKZoiCPchvUg9qVUqlRwVFaW7/Swv1e7j40N4eHilSfLTVlJSglKpZN++fWzdulXXcrE6FRSXcik9nxKNTH0LI+qYVtq1s0Z7lt+XwounND2deG8f0GiwnfZvrIYOre6QBKFGSskq5O2fTnD2ejb/7tOCoE5NxEqjzzFJkk7KsvzY835F2bGG69WrFwUF/2t7NmbMGFasWMHAgQMfK0nPysqiX79+Ze5btGiRrnXigwQHB5OYmIhGo2HNmjUsXryY0NBQ3f527dqxaNGifxzfP2FsoI9j3VpczijgakYBBcUa6lsYi3nrgvAU6VtZYerlRd6hQ5h6eVV3OIJQIx29kMaEn6PJL9bww7/c8Xd90PIxgnCbqKiLyuVzR5ZlbmQXcjOnCBMDfRpZmWCg/+x8tSjel8KzJnPrVm4tWoxjZISoEArCPTRamSW/J7A4/DxN6piydLgbLna1qzssoQqIirogVEKSJOzMjTE20OdKej4JN3NpVMeEWmLeuiA8FbW7dyf/jz9Eki4I97iZXcjEjTEcuZDGq+0b8J/+rTAV/w8Jj0i8Y4TnlrmxEkObWlxKy+firTzszI2wrmUgkglBeML0zM2pO25cdYchCDVGxPlbfBASQ36xhgWvtRX90YV/TCTqwnPNSKmHo40pl9MLuJ5VQEGJhoYWxmLVN0F4wpT161d3CIJQ7Uo0Wr7ae56l6kRcbGvz32HtcbQRU12Ef+7ZmbgrALd7nd970WZVmzt3LhcvXqx0//Lly//RuIWFhboFm9577z0KCgrIz8/H29ubgICAMtuPSk+hoHEdE+zMjMjMLybhVi5FpZX3qRcEQRCER3U1s4Ahy4+yVJ3I6x6N2PFuZ5GkC49NVNTvOHr06GOP4fUPOx7c7aH+MHr27PmPzvGkTJ48+b77ly9fzujRox/rHHe7xRw5coQOHTqwePHiMtv/hCRJ2JgZYWygR/Kdeev2liaYGVdPe0tBEATh+bHnzxQ+2nIajVbm29fb07et+IZJeDJERb2KqNVqAgMDCQwMxMPDg/j4eFq0aMGIESP46KOPSE1NpX///nTt2pXhw4ej0WgYN24cx48fB25X0v/973+zevVqlixZAsCCBQvo2LEjnTp10q0QqlL97wLjux8cli5diru7O76+vpVW41u0aMEbb7xB+/bt2b59OwDh4eF4eXnh6enJ6tWrAQgKCuLMmTOo1Wp69uxJv379aNu2LbGxsYSGhhIXF4efnx8hISF89tlndOzYER8fn0o/CI0bNw5fX1+mT5+uu8/Pz4/c3FwmTpzItm3bGD9+fJntx1HbSImjTS0M9BQkpeVxJSMfjfbF6XwkCIIgPDlFpRpm/Pono9eepJGVCTvHdxFJuvBEiYp6FcrKyuLAgQMcOXKEWbNmceXKFQ4dOoSlpSWTJk1iwoQJdO3alYULFxIaGsqQIUMICQnBw8ODTZs28eGHH3LixAkAUlJS+OWXXzh06BBJSUmMHj2affv2VXjekJAQ9u3bh7m5OVqttsLHJCcnc/DgQZRKJX5+fvTv359PP/2UXbt2YWZmhpeXF4MHDy5zTElJCWFhYezdu5dVq1bx1Vdf4eLiglqtBm5/kDhy5Aj6+voVnjcqKoqMjAwiIiLYu3cvx44dK7N//vz57Ny5kwULFqBWq3Xbj8tQXw8Hm1rcyC7kVk4RuUWl2FuaiKvxBUEQhId2KS2PdzdEE3s1izc7N2FyL1cM9fWqOyzhOSMq6lWoffv2SJKEm5sbiYmJODo6YmlpCcBff/3F9OnT8fPzY9OmTaSkpNClSxcOHz5MUVERiYmJtGzZUjdWUlISbdu2RaFQ0KxZM7Kyssqd726P/Pnz5zNp0iTefPNN4uPjK4ytadOmWFlZUbt2bQwNDSktLUWr1WJtbY2BgQHOzs5cu3atzDF3F0eyt7cnIyOj3JgzZ84kODiY4OBgbt68WW5/QkICbm5uAHh4eDzMS/jEKCSJeubGNKtbC2S4cCuXlKxCtC/QugKCIAjCP/PrqWsEfnOQ5PR8lr/hxvS+LUWSLjwVIlGvQjExMciyTHR0NA4ODmXmpbu6ujJ79mzUajXHjh0jODgYSZLw8vJixowZ9OjRo8xYTZo0ISYmBq1Wy4ULF7CwsABAT0+P7OxssrOzdUl569atWbFiBaNGjWLevHkVxpaUlERmZiZ5eXkUFRWhr6+PQqEgNTWV4uJizp8/T/2/dXW4t83h3Q8F997n4+PDDz/8gK+vb4UXmTo6OhIdHQ3crq5Xh1qG+jjZ1sLCxICbOYUk3sylsERcaCoIgiCUV1iiYcq2WMb/HI2zbS3+b6I3PVraVXdYwnNMfNdfhWrXrk1gYCCpqamsX7+e119/Xbdv6tSpjBo1SjdXe/78+ahUKgYPHkyXLl2Ii4srM5adnR39+vWjc+fOSJLEt99+C8C7776Lj48PLVu21CXWY8aMISkpidzcXL788ssKY7O3t+edd97h3LlzTJs2DYDZs2cTGBgIwIQJEzA2Nn7gc/T39+fll19m1KhRLFmyhIKCAoqKili5cmW5x6pUKszMzPDx8cHT0/OBYz8tegoF9la3Lyy9mlFA/M1c6pkZUUf0XBcEQRDuSLiZw7sbojmXksNYPwc+CHBGqSfqncLTJckv0Ff9KpVKvrdyW5VLtT/JOdZPg0qlqraqdk1SotFyNaOA7MISahnq09DSBAP9qv2HuCrfl4IgCML9ybLM5pNXmL7jT0wM9Fg4qC1+LjbVHZZQw0mSdFKWZdWDH3l/oqL+AurVqxcFBQW62w9qufgkZGVl0a9fvzL3LVq0SDfPvaZQ6t3uuZ6RX8y1zELib+bQwMIYc2OlqK4LgiC8YK5mFvDptlgizt/Cq5kVi4e0x9bMqLrDEl4goqIuKpdCJYpKNVxOLyC/uBRzYyUNLIzRr4KvOcX7UhAEoXpptTLrjl1i3m/nkIFPerryhldjsaq18NBERV0QnjJDfT0c6ppyK6eIG9lF5Bfn0tDSmNpGYpEkQRCE51XirVwmbz3NiaQMfJzrMvuVVjS0NKnusIQXlEjUBeE+7q5oWttIn8vpBVxMzaNOLUPqmRmJyoogCMJzpESjZXnkBRaHx2Os1GPha215tUMDMe1RqFYiUReEh2BsoI+jTS1SsgtJzS0it7AUeytjTAzEr5AgCMKz7szVLD7ecpq/rmcT2Loen7/ckrq1Das7LEEQibogPCyFQqK+hTFmRvpczigg8WYedc0MsallKKrrgiAIz6DCEg2Lw+NZHnkBK1MDvh/uRs9Woi+6UHOIRP2O8N8dHnuMbl0Tn0Ak//OoLR3PnTvH3LlzWb16NcHBwSxbtoyrV6/Sr18/WrRowZw5c3Tba9aseaKxvkhqGSlxstXjemYhN7MLycovpr6FmLsuCILwLDl+MZ3JW09zITWPwSp7Pu3dHHMT8e+4ULOITv3VQKvVPvVzLFu2DIDIyEiGDRvGmjVrymwLj0f/ziJJTa1NAYmLqXlcSsujuPTp/2wFQRCEfy6nsITPtscyaNkRSrRa1r3tybyBbUSSLtRIoqJeRdRqNQsWLEChUODm5kZERARZWVnMnTuXgIAAgoKCUCqVXLx4EVNTU7Zv3647trCwkOHDh/POO+/QtWvXMuOWlpYyZMgQMjMzy7T0U6lU7NmzhxkzZqDRaMjPz2ft2rW67alTp1bZc3+e1TZS4mSrT2pOETdzisgpzMHGzBDrWoYoxAVIgiAINcr+czf5NDSWlOxC3u7SlA97OItrjYQaTbw7q1B2djYREREUFBQwffp0UlNTee211wgICADA29ubFStWMGzYMGJjYwHIz89n6NChfPDBB3Tp0qXcmNu3b8fZ2ZnZs2ezYsUKDh06pNtnZWXF5MmTyc3N5d1336VBgwa6beHJUdzpDGNhouRaZiEpWYVk5JXQwMKIWmI6jCAIQrVLzyvmi1//ZHvMNZxsarF1bCc6NLKs7rAE4YFEol6FVCoVkiSxfv161qxZg0KhICUlRbe/ffv2ANjb25ORkQHAjh07ePXVVytM0gESEhJwc3MDwMPDo0yiLlQtA309mlibkl1QwrWsAi6k5mFhbEA9cyOU+mKWmSAIQlWTZZlfT1/n81/+JKewhIndnHjH3wFDfb3qDk0QHorIHqqQQnH75V6wYAH79+9ny5YtZfbf26v17oqxr7/+Ovr6+nz//fcVjuno6Eh0dDQA9666KlQfM2Mlzja1sTUzIquwhLgbOdzKKUL7Aq0CLAiCUN3iUnIY8eNxJvwcjb2lMb+O78L7Ac4iSReeKaKifseT7thyP/7+/nh7e+Pp6YmZmdkDH//VV18RHBzMunXrGD58eJl9/fv3Z+PGjXTr1g1XV9enFbLwiBQKCdt7psNczyog4053mFqG4tdOEAThabmVU8TX+86z8XgytQz1+XefFgR1aoKeaKMrPIMk+QWq8qlUKvneqvPZs2fLXIApCE+DLMtkF5ZyPbOAYo0WSxMD7MyNUOpV/IWWeF8KgiA8usISDT8eush3+xMpLNEw3KsxE7s5YWlqUN2hCS8gSZJOyrKsetxxRGnvGRIXF0dwcHCZ+zZu3IidnVicoSaTJAlzYyW1DPW5lVPIrdxisgtLsDUzoo6pgVieWhAE4THcnYc+77dzXM0soHtzW6b0dsWhbq3qDk0QHptI1J8hLi4uqNXq6g5D+If0FBJ25sZYmBhwLbOAa5kFZOTdng5jKqbDCIIgPLKTlzKYuesvopMzaV7PjC8HtqGTo3V1hyUIT4zIDgShihkp9WhqbUpWQQnXswpJvJWLpYkBtmZGGIjuMIIgCA90OT2feWHn2Hn6OnVrGzJ/YBsGdGgo5qELzx2RqAtCNZAkCQsTA2obKbmZU0hqbjGZBSXUMTVAo31xrhsRBEF4FNmFJXy3P5EfD11EIcGEbk4E+zQT30oKzy3xzhaEaqSnkKhnbkwdUwNuZBeRllvEzexCdu+JY6R3M8yNxYJJgiAIpRotG09c5uu950nLK+bVDg346CUX6pkbV3dogvBUiUT9Drv9MY89Rop/u0r3JSUlcf78eXr06FHh/uXLlzN69OhKj//8889RqVT06dPnH8c3d+5cBg8eTNOmTf9RDJUpLCykZ8+eqNVq3nvvPebMmYMsy7z00ksYGRmxY8cO3fbevXv/cfzPMwN9PeytTKhb25DsFD2+/T2BNUcuEezbjKBOTcQS14IgvLDUcTeZtess8Tdz8WhqxarA5rRpaFHdYQlClRATYqtIUlISe/bsqXT/8uXLn3oMkydPrjRJf1IxLFq0CGNjY06dOkWHDh3Yu3dvmW3h/oyUeliZGrBzfBfcGlsyPywOn/lqVh+6SFGpprrDEwRBqDJ3FywKWnWCYo2W74e7ETLaSyTpwgtFlOmqyNKlSzl8+DBRUVH4+/sTFhaGJEl8++23JCcnExcXh5+fH2PHjqW4uJjVq1eTkZHB+++/zxtvvHHfsVu0aIGbmxtnzpxh+vTp9O/fn/DwcKZOnYosy4wdO5agoCCCgoKYNGkSqampzJ07F0NDQ5KSkli3bh0JCQllYoiNjSU8PBylUsn8+fPx8vIqd95x48Zx5syZMvv8/PzYuXMnEydO5Pr162i1Wo4dO6bb/vbbb5/4a/s8atXAnB+D3Dl5KZ35YXF8/utfrDhwkYndnXi1fQP0K+nBLgiC8Ky7nJ7Pd+oEQk5cppahPp8FNmdExybiYnvhhSQS9SoyduxY7O3tmTRpEoMGDeLQoUMkJSUxevRo9u3bV6b1Yn5+Pm+88QZFRUV06dLlgYl6cnIyBw8eRKlU4ufnR//+/fn000/ZtWsXZmZmeHl5MXjw4DLHlJSUEBYWxt69e1m1ahVfffVVmRgWLFjAkSNH0NfXR6vVljtnVFQUGRkZREREsHfvXo4dO1Zm//z589m5cycLFixArVbrtoVH49bYio2jvTiYkMqXu+P4eMtpvo9I5IMAZ3q3qodCdDgQBOE5ceFWLt+pE9kefRVJghEdm4gFi4QXnkjUq1hSUhJt27ZFoVDQrFkzsrKyyj1m7969fPXVVwCcP3/+gWM2bdoUKysrAAwNDSktLUWr1WJtfbuXrLOzM9euXStzTLt2t+fT29vbk5GRUW7MmTNnEhwcjL6+PjNmzCi3qFJCQgJubm4AeHh4PDBG4Z+TJAlvp7p0cbRmz183WLgnjnc3RNO8XiIfveSMv4uNWDRJEIRn1rmUbP67P5Fdp6+h1FMw3Ksxo32aUd9CXCgqCCJRryJKpRKNRkOTJk2IiYlBq9WSlJSEhcXtuXb3JlozZsxg//79GBoa4uDg8MCxk5KSyMzMRKlUUlRUhL6+PgqFgtTUVMzMzDh//jz169cvc8y955Nludx9Pj4+vPTSS2zYsIHly5czbdq0Msc7Ojqyc+dO4HZ1XXj6JEnipZZ2dG9uyy+nrvL13njeWh2FW2NLJvVwoaNDneoOURAE4aGdupzJkv0J7P3rBqYGeoz2ceDtLk2pW9uwukMThBpDJOp33K9jy5PQunVrpkyZwrvvvkuPHj3o3Lmzbo46gL+/Py+//DKjRo1iwIAB+Pv70759eywtLR84tr29Pe+88w7nzp3TJdSzZ88mMDAQgAkTJmBs/ODKxL0xLFmyhIKCAoqKili5cmW5x6pUKszMzPDx8cHT0/NRXgrhMekpJF5p35A+beqzKeoy34Yn8PqKo3g7WTOphwtt7cWFVoIg1FzHLqSxZH8CB+JTMTdW8l53J4I6NcHCRExxEYS/k+5WU18EKpVKvrf6e/bsWZo3b16NET0ZKpVKVLWfI4/6viws0bDu6CX+uz+BjPwSurraMNK7KR2b1RFTYgRBqBFkWeZAfCpLfk/geFI61rUMGOndjOFejaklFisSnkOSJJ2UZVn1uOOI345nTK9evSgoKNDdnjx58lM/Z1ZWFv369Stz36JFi3Tz3IXqZaTUY6R3M4Z4NGLVwYusPpzE0BXHaFHPjJHeTenTpr7oliAIQrXQamX2nb3Bkv0JnL6ShZ2ZEdP7tmCIeyOMDfSqOzxBqPFERf05qKgLz5fHfV8WlmjYEXOVlQcuEn8zF5vahvyrUxOGeTYSXy0LglAlNFqZXbHX+W5/AudScmhkZcJYPwde7dAAQ32RoAvPP1FRFwShQkZKPQa7N2KQyp7I+FRWHrjAl7vj+Pb3eAa6NeStzk1pVrdWdYcpCMJzqESjZXv0VZaqE7mQmoejTS2+HtyWvm3qi/UfBOEfEIm6IDynJEnC17kuvs51iUvJ4YeDF9h04grrjyXTzdWGt7s0w6uZlZjHLgjCY8stKmXbH1dYFnGBq5kFtKhnxtJhHXippZ1Y70EQHoNI1AXhBeBiV5v5A9vy0UuurDt6ibVHL7FvxVFa1r89jz2wtZjHLgjCozt7PZv1xy6xPfoauUWltG9kwX/6txTrOwjCEyIS9Tvs9sc89hj3a/GYlJTE+fPn6dGjx2OfRwAvLy+OHj3K6tWrcXFxoWPHjowePZo///yTFStWsGjRIt12ixYtqjvcGqNubUPeD3BmrJ8D26OvsvLgRd4POcXc384xoqOYxy4IwoMVlmj4v9jrrDt6iT+SMzHUV9CnTX2GeTWivb2FSNAF4QkSiXoVSUpKYs+ePTUmUddqtSgUz34FNSgoSLcdHR3NiRMnym0L5Rkp9Rji0YjB7vZEnL/FDwcv8uXuOJb8nnB7HnuXpjS1Nq3uMAVBqEEupuax4dglNp+8QmZ+Cc2sTfkssDkD3RqKD/iC8JQ8+5naM2Lp0qWEhITg4eHBjBkzAKhfvz7Hjx8nLi6OMWPGUFpaytChQ/H19aV3796kp6eXGyc0NJQ5c+YAkJmZqUv8Z8+eja+vLz4+PsTGxgIwadIk/Pz88PDwICbm9jcGfn5+fPjhh/Tq1avc2Fqtlh49euDn50dAQADZ2dm6sTt27Mj48eNxd3cH4PTp03Tu3JlOnToxa9asCp9zbGwsXbt2pVOnTrz77rsAjBs3juPHjwMQFhbGv//9b93z6NmzJ2+//Taff/55heP99NNPqFQqRowYQV5eHgCff/45O3fu5OOPP+bcuXP4+fnx0Ucf6bY1Gs0DfjIvNkmS8HOxYe3bnoS9503ftvUIOXGZrgvVjPwpisOJqWi1L05nKEEQyirRaPkt9jrDVx7Df4GaVYeS6OxgzYaRnoR/6MtI72YiSReEp0gk6lVk7NixDB48mH379hEdHU1CQgJt27blwIEDHDhwAG9vb0JDQ2nUqBEREREMHjxYt2rpvQIDAwkLCwNg8+bNDBo0iNjYWOLi4oiIiGDTpk261Um/+OIL1Go1K1eu5Msvv9SN0bt3b3bv3l1ubIVCwY4dO1Cr1fTt25eQkBBSUlLYvXs3hw8fZvz48aSlpQHw6aefsnLlSg4dOsT+/ftJSkoqN56joyPh4eEcPnyYa9euER8fz5AhQwgJCQFg06ZNDBkyhBUrVjBw4EDCwsKoV69eha+fRqPh66+/5tChQyxatIhLly6V2T9//nxcXFxQq9V8+eWXum09PdEG7GG52pkxf2BbDk3uyviuTvyRnMHQFcfwXbCfr/eeJzktv7pDFAShilzLLOCrPXF0nvs7Y9f/wcXUPCb1cObw5K78d1gHOjlaiykuglAFxNSXKmZmZkZOTg5qtZoJEyawYcMGFAoFM2fOZP369bqKtaenJ/v27St3vIGBAa1atSImJoYtW7YQEhLCnj17OHz4MH5+fgC65HThwoXs3r0bhUJRJmG9e46/y8vLIzg4mOTkZDIzMxkwYABJSUm0adMGSZJwdnbG3NwcgBs3buh6fatUKhITE2nSpEmZ8ZKSkvjggw/Iz8/n4sWLXLt2DR8fHz7++GOKiopITEykZcuWfPvtt4waNUoXW3R0dLnYbt26hb29PYaGhhgaGuLg4PAIr7rwKOrWNuSDAGfe8XMg7EwKW05e4Zvf41kcHo9HUysGdmhI7zb1xGqCgvCc0WplIuJvsf5oMr+fu4EM+DnXZY5XY/xcbNAT3VsEocqJinoVUSqVumkYrVq1YsWKFXh7e6PRaLh06RL29vY4Ojrq5lUfO3YMJyenCscaMWIEc+bMwcLCAgsLC1xdXfH19UWtVqNWqwkLCyMtLY2dO3dy4MABlixZwr0LW1U2Nz0sLIz69esTGRnJyJEjkWWZJk2aEBsbiyzLJCQkkJWVBYCtrS1nz55FlmWioqIqTJy/++47xo8fT0REBCqVClmWkSQJLy8vZsyYoZu24+DgoEvOT548WWFsdevW5cqVKxQXF5ORkUFiYuLDvOzCYzBS6tG/fQPWjfTk0Cdd+eglF1Jzivh462lUM/fyfkgMB+NT0YipMYLwTEvNLeI7dQI+X+7nzVUniLmcwVg/ByI/8mfVmx50a24rknRBqCaiJHbH/Tq2PAmtW7dmypQpDBw4kL59+3LkyBFq1apFy5Yt0de//WPo378/27Ztw8fHB1NTU9avX1/hWJ6enpw6dYp58+YB0KZNG5ycnPD19UWhUBAQEMDkyZOxtbXF39+fTp06PVSMXl5ezJo1i8DAQOrVq0fDhg2xs7OjW7dudOrUifbt22NtbQ3ArFmzGDlyJFqtlj59+pSrpgP07duX999/nx9++KHMXPHBgwfTpUsX4uLiABg5ciSvvfYamzdvxsbGBldX13Jj6enp8d5779GpUydcXV1p3LjxQz0n4cmob2HMOH9H3vFzIPpyJltPXuHXU9cIjb5KPXMjXu3QgAEdGoqFlAThGVGq0XLkQhqboq4QduY6JRoZr2ZWTO7lSo8WdqJdqyDUENK9ldbnnUqlkqOionS3H3ep9urk4+NDeHg4SqXyqZ+rpKQEpVJJfHw87733Hrt27Xqi42u1WmRZRk9Pj88++4zWrVszePDgJ3qOZ8mz8r4sLNGw7+wNtp68QsT5W2hlaN/IggEdGtK3TX3MTZ7+e1MQhIen0cocu5DGztjrhJ1JIT2vGDMjfQa4NWSYZ2McbcQHbUF4UiRJOimaNQIQAAAZF0lEQVTLsupxxxEV9RquV69eFBQU6G6PGTNGd/Hl4yTpWVlZ9OvXr8x9ixYtol278t8sTJ8+nUOHDlFQUMB3331X4XgRERFMnz69zH1qtfqhYikoKKBnz57IsoytrS3Tpk3jzTff5OLFi7rHBAUFlWnFKFQ/I6UefdrUp0+b+tzMLmR7zFW2nrzKZ9vP8MXOvwhoYcvADg3xdrIWS4cLQjXRaGVOJKWz6/R1fjtzndTcYkwM9OjW3JbA1vXwc6mLkVJcdC8INZWoqD8DlUvhxfIsvy9lWebPa9lsOXmFHTFXycgvoW5tQ15pf3tqjItd7eoOURCee1qtzMnkDHadvs7/xV7nZk4RRkoF3VxtCWxTD38XG4wNRHIuCE+TqKgLglDjSJJEqwbmtGpgzqe9m/P7uZts/eMKPx68yPLICzSvZ0ZAcxu6t7ClVX1zFOICNUF4IrRamejLmbrkPCW7EEN9Bf4uNgS2qUe35jaYGIj/8gXhWSN+awVBeCoM9BX0bGVHz1Z2pOUWsSPmGr+duc6S/Ql883sCtmaGdHW1pXtzGzo7Wouv3wXhEcmyzKkrWew6fY1dp69zLasQAz0Fvi51mdLGlW7NbUUbVUF4xonfYEEQnro6tQx5q0tT3urSlPS8Yvafu0n4uRv8EnOVn48nY6RU4O1Ul+7Nbejqakvd2obVHbIg1EiyLHPmajY7Y28n51cyClDqSfg41WXSSy50b2GLmZG4kFsQnhciUb9jVfy1xx7jTaf6le5LSkri/Pnzut7hQnlhYWEUFBTwyiuvVLg/KCiISZMm0apVK919j/q6DhkyhLlz51bYTvKuc+fOMXfuXFavXk1wcDDLli3j6tWr9OvXjxYtWjBnzhzd9po1ax7pOQpgZWrAALeGDHBrSFGphmMX0tl39gbhZ2+y968bSFIsbRtaENDClm7NbXCxrS1WQBReaAXFGo4npXMw/hZ7/rrBpbR89BUSXZysmdjNiR4t7TA3Fsm5IDyPRKJeRZKSktizZ0+NSdS1Wm2lCx9Vl549ez7yMU/7dV22bBkAkZGRDBs2jPfff5+ff/5Zty08HkN9PXyc6+LjXJcZL8ucvZ5D+Nkb7Dt7gy93x/Hl7jgaWhrTvbkt3Zvb4tHUSvR3Fp57Wq3MmWtZHExI5WB8KlFJGRRrtBjoKfBsZsU7fg681NIOCxOD6g5VEISnTCTqVWTp0qUcPnyYyMhIAgMDmT59OvXr12f79u2Ym5vz9ddfs2TJEkaMGMHVq1cxNTVl3bp1WFlZlRknNDSUc+fOMWXKFDIzMxk0aBB79uxh9uzZ7N69G1mW+e9//0vr1q2ZNGkSUVFR5Ofns3z5ctq1a4efnx9ubm6cOXOG3bt3lxlbq9XSs2dPiouLUSqVbN26FTMzM2bPns2vv/6KSqXi6NGjnDhxgtOnTzN27FhkWSYwMJCpU6eWe86DBw/m22+/JTo6mg8++IA///yTKVOm0K9fP2xsbBg7dixFRUW0b9+er7/+mtWrV5Obm8u7775b4TkBFi9ezMWLFzE1NWX79u261zUqKoodO3YQGhqqW2Bp5syZdO3alT179vDJJ5/g4OBAWlpahT+f0tJShgwZQmZmZpmOKyqVij179jBjxgw0Gg35+fmsXbtWt13R8xb+GUmSaFHfjBb1zRjfzYmb2YWEn7tJ+Nkb/Hw8mdWHk6htqI+Py+0pMv4uNiJREZ4bl9PzdYn5ocRUMvNLAHC1q82/OjWms6M1nk3riG4tgvCCEYl6FRk7diz29vZMmzaNESNGkJCQQNu2bTlw4ADm5uZ4e3sTGhpKo0aN2LBhAz/99BPffvttud7kgYGBLFq0iClTprB582YGDRpEbGwscXFxREREkJKSwtixYwkNDeWLL77AxMSE06dPM2/ePN1Kp71792bhwoXlYlQoFOzYsQNjY2O++eYbQkJC6Nu3L7t37+bw4cPEx8frFjv69NNPWblyJa6urgQEBDBs2LBy00m8vb05cOAA0dHRNG7cmNTUVE6ePMkXX3zB0KFD+e6773BwcGD8+PHc2zYzJSWlwnPeHXPFihUMGzaM2NhY3eu6YMECUlNT+fnnn4mMjKSgoIC+ffvStWtXpk2bRnh4OKamppW2Pdy+fTvOzs7Mnj2bFStWcOjQId0+KysrJk+erPsQ0aBBA9228PTYmBnxukcjXvdoREGxhoMJqYSfvUH4uZvsOn0dPYWEWyNLPJtZ4d7Eig6NLcWFc8IzI6ughCOJaRxMuMXB+FSS0vIBsDUzpJurLd5O1nRyrINNbaNqjlQQhOok/lerYmZmZuTk5KBWq5kwYQIbNmxAoVAwc+ZM1q9fj7u7OwCenp7s27ev3PEGBga0atWKmJgYtmzZQkhICHv27OHw4cP4+fkBoKd3u+KycOFCdu/ejUKh0N0H6M7xd3l5eQQHB5OcnExmZiYDBgwgKSmJNm3aIEkSzs7OmJubA3Djxg1d0qtSqUhMTCyXqPv4+PDDDz9w8+ZNgoKC2Lt3L/r6+iiVSuLi4nj77bcByMnJoVu3brrjKjsnQPv27QGwt7cnIyOjzPkuXLjAX3/9hb+/PwC3bt0CQKPR6L6ZaNu2bYXPPSEhATc3NwA8PDzKJOpC9TM20COghS0BLWzRamVOX80i/OwN1HG3+O/+BLQyKCRoUd8M9ya3E3dVE0uR5Ag1RolGS3RyJgfjb3EgIZVTlzPRymBioIdXszqM6NgEbydrHG1qiWsyBEHQEYl6FVEqlWg0GgBatWrFihUrCA8PZ+3atVy+fBl7e3scHR05ceIEAwYM4NixYzg5OVU41ogRI5gzZw4WFhZYWFjg6uqKr68vK1euBKCkpIS0tDR27tzJ0aNHiY2NZcKECbrjK5ubHhYWRv369Vm3bh3ffPMN6enpNGnShNjYWGRZJjExkaysLABsbW05e/Ysrq6uREVFMWbMmHLjtWrVipMnT+Lk5IS3tzd9+vShb9++ALi4uLBgwQIaN26MLMtoNBrWrVsHUOk5gTL/gcmyXOZ1bdasGW3atGHnzp1IkkRJye2vjvX09MjIyMDU1JTTp09X+NwdHR2Jjo5mwIABZar7Qs2jUEi0s7egnb0FH/ZwIbeolOjkDE5cTOdEUgY/H09m1aEkAJrUMdEl7u5NrWhSx0QkQUKV0GplEm/l6qazHL2QRl6xBoUEbRpaMM7fkS6O1rRvZCmuuxAEoVIiUb/jfh1bnoTWrVszZcoUBg4cSN++fTly5Ai1atWiZcuW6Ovf/jH079+fbdu24ePjg6mpqW6qyt95enpy6tQp5s2bB0CbNm1wcnLC19cXhUJBQEAAkydPxtbWFn9/fzp16vRQMXp5eTFr1iwCAwOpV68eDRs2xM7Ojm7dutGpUyfat2+PtbU1ALNmzWLkyJFotVr69OlTYRcVhUKBpaUl7u7u1KtXj+zsbHx8fAD4//buPTiu8rzj+PfRZSWvJMuSbMvCMpYNNoZiYhhjQltwwSRA/sCmdVo3k5S0tMHpZYbJdKakdAaGmU7j9o9QpqEEEkjazqAAHaZuA2XAYMowIVwyxBcYXwAT5CuWLFnW/fL0j/NKOZJ3rZVZaVfa32dm55zznvccvfvMu0fPnn3POdu2bWPr1q309fVRVFTE448/Prpdur85UVyfeOIJtmzZwvr16ykuLmb16tU89NBDPPDAA2zYsIGmpiaWLFmScj+bNm2iubmZDRs2sGrVqoziJfmhsqyE61Ys4LoVCwDoHxxm75EO3joUJe4vvX+cp99pAWB+ZRlXN9WwtqmWdU21XNpQRUmxkiT5bNydT9p62HW4nd0tHexq6WDP4Q46+wYBWFqXZNOVi7luxXyuXT6f6qTu0CIimTF3z3Ubps3atWs9frZ0Jj+q/frrr2fHjh2Ulk79AX9gYIDS0lIOHDjA3XffPWbM+Gz6m/liJvfLfDQ87Hx48gxvfnSKtw+18eahNlpO9QBQkSjmqqU1rF1ay9XLarhySY0u1pNzcneOdvSyq6WD3Yfbw7Rj9OLPRHERlzZUsbqxmisWz+Pai+pYUpvMcatFZLqZ2Tvuvvaz7kdn1PPcrbfeSk9Pz+jy1q1beeyxx9i8efNnStI7OjrYuHHjmLIHH3yQNWvWnFX3vvvu4/XXX6enp4eHH3445f5effXVsy583blz53m3L5O/eb727dvHXXfdNaasubmZRYsWZfXvSH4oKjIuXljFxQur+Mo1FwJwtKOHtw6FxP2jNh7csR93KCkyVjVUsbK+ikvqq1i5KJo2VJdryEyBOtHZO3qWfPfhaHryTB8AxUXGJfVV3PIbi1jdWM3nGuexsr5KQ1lEJGt0Rl1nLiXPqF9Ov46eAX7x8SnePNTGnsMd7D/eyfHTfaPrq8pKWLloJIGvHE3g6yr1BNXZpK2rn92HO9jdEp0p39XSwbHTvUB0sfLFCytZvXgeVzRWc0VjNZc2zKW8VL/AiMjZdEY9S9xdZ8okbxTSF+d8Uj2nlBtWLeSGVQtHy9q7+9l//Az7jney/1gn+4538vyeozz55sBonfmVCVbWhwQ+JPIr6yup0iPc81Z3/yAft3aHVxeHRqYnuzjS0Ttab/n8Cq5ZXsvqxdV8bsk8LmuYS4Vu/yki06ygjzrl5eW0trZSV1enZF3yQm9v77RcdyATm5dMsG5ZLeuW/fqhY+7Op2f62H9sbAL/1Nuf0N0/NFpv8bw5rIydeW+aX0FDdTkLKst08eo0ON07wMcnuznU2sWv2ro5dLKLj1uj5ROdfWPq1lYkWFqXZN2yWlY1zOWKxdVc3ljNXH3ZEpE8UNCJemNjIy0tLaP32xbJBw0NDblugqRhZiysKmdhVTm/veLXdyMaHnYOt/ew/3hnLIE/w+sHW+kfGh6tV2SwsKqc+upyGuaWs6i6nIbqaLpobjkN1XOory6jrETDKc7F3TnVPcCh1q5wNrw7Sshbo4S8rat/TP2FVWU01VWwfuUCmuZXcGFtkqa6Ci6sS1I9Rwm5iOSvnIxRN7MvA/cDlwLr3D3ljavN7Bbgn4Fi4Afu/p1QvgxoBmqBXwBfc/f+VPuIGz9GXURkKg0ODXOotYtP2no42tHLsY4ejp3uDfPRa+QWfnF1FYnRJL5+7kgyP2dMUj+bhmH0Dw7T3t3Pqe4B2rr6OdXdH027orLR5TBt6+of8wuGGVxQPYeldUmW1lXQFKbRcpJkYvbESkRmhpk+Rn0P8LvA99NVMLNi4HvAF4AW4C0z2+7u7wHbgO+6e7OZPQLcCfzr1DdbRCRzJcVFo3ecSaezd4Dj45L3o6ej6eH2Xt75+BSnugfO2q68tIiKRAnJsuJomiimoixMEyXMGbccr5dMs11ZSRHDDoPDwwwNO4PDztBQmA772PJhZ3AoTfmwMzQ8zNBw9ETO9niyPZJwdw9E8139Kb+sjKgsK6GmopTaZIKaZIKLFlRSk0ywuGbOaELeWDNHF3WKyKyUk0Td3d8HJhoXvg446O4fhrrNwEYzex+4EfhKqPdjorPzStRFZMapKi+lqrz0nMl878DQmGT+aEcvbV19dPcP0d0/RFffID0D0fTTzj66+gfp7huiq3+Q3oHhtPudbslEMTXJBLUVCWoqEjTVJccs1yYT1FSUjpbNS5ZqGJCIFLR8/j1wMfBJbLkFuAaoA9rdfTBWvjjdTszsG8A3wmKfme2ZgrYWqvnAyVw3YpZQLLNL8cwuxTN7FMvsUjyzS/HMnkuysZMpS9TN7CUg1RNk7nX3/8pkFynK/BzlKbn7o8CjoU1vZ2O8kEQUz+xRLLNL8cwuxTN7FMvsUjyzS/HMHjPLykWRU5aou/tNn3EXLcCS2HIjcITom948MysJZ9VHykVEREREZo18vqHvW8AKM1tmZglgC7Ddo9vUvAJsDvXuADI5Qy8iIiIiMmPkJFE3s9vNrAW4Fvipmb0Qyi8ws+cAwtnyvwReAN4HnnL3vWEXfwN8y8wOEo1Z/2GGf/rRLL4NUTyzSbHMLsUzuxTP7FEss0vxzC7FM3uyEsuc3EddRERERETOLZ+HvoiIiIiIFCwl6iIiIiIieWjWJepm9mUz22tmw2aW9hZDZnaLme0zs4Nmdk+sfJmZ/dzMDpjZT8KFrAXJzGrN7MUQixfNrCZFnRvM7N3Yq9fMNoV1PzKzj2Lr1kz/u8gfmcQz1BuKxWx7rFx9MybD/rnGzH4Wjgm7zOwPYusKvn+mOw7G1peFvnYw9L2m2Lpvh/J9ZnbzdLY7X2UQz2+Z2XuhL+4ws6WxdSk/94Usg3h+3cw+jcXtT2Pr7gjHhgNmdsf0tjz/ZBDL78biuN/M2mPr1DdjzOxxMzthaZ7LY5GHQqx3mdlVsXWT75fuPqtewKVEN5nfCaxNU6cY+ABYDiSAXwKXhXVPAVvC/CPAN3P9nnIYy38E7gnz9wDbJqhfC7QBybD8I2Bzrt9HvrwyjSdwJk25+uYk4wmsBFaE+QuAo8C8sFzQ/fNcx8FYnT8HHgnzW4CfhPnLQv0yYFnYT3Gu39MMiOcNsePjN0fiGZZTfu4L9ZVhPL8O/EuKbWuBD8O0JszX5Po95XMsx9X/K+Dx2LL65tj4XA9cBexJs/5LwPNEz/35PPDzUH5e/XLWnVF39/fdfd8E1dYBB939Q3fvB5qBjWZmwI3AM6Hej4FNU9favLeRKAaQWSw2A8+7e/eUtmrmmmw8R6lvpjRhPN19v7sfCPNHgBPAgmlrYX5LeRwcVyce42eADaEvbgSa3b3P3T8CDob9FbIJ4+nur8SOj28QPQdEUsukf6ZzM/Ciu7e5+yngReCWKWrnTDDZWP4h8OS0tGwGcvf/Izopmc5G4N888gbRs38aOM9+OesS9QwtBj6JLbeEsjqg3aNbQ8bLC1W9ux8FCNOFE9Tfwtkf7r8PP/1818zKpqKRM0im8Sw3s7fN7I2RYUSob6Yyqf5pZuuIziZ9ECsu5P6Z7jiYsk7oex1EfTGTbQvNZGNyJ9FZtxGpPveFLNN4/l74DD9jZiMPSVT/HCvjeIThWMuAl2PF6puTky7e59Uvp+zJpFPJzF4CFqVYda+7Z/LwI0tR5ucon7XOFctJ7qcBWE103/sR3waOESVHjxLd//6B82vpzJCleF7o7kfMbDnwspntBk6nqDer+yZkvX/+O3CHuw+H4oLrn+NkcrzTsTJzGcfEzL4KrAXWx4rP+ty7+wepti8QmcTzv4En3b3PzLYS/fpzY4bbFpLJxGML8Iy7D8XK1DcnJ6vHzRmZqLv7TZ9xFy3AkthyI3AEOEn0E0VJOHs0Uj5rnSuWZnbczBrc/WhIdE6cY1e/Dzzr7gOxfR8Ns31m9gTw11lpdB7LRjzDEA3c/UMz2wlcCfwnBdY3ITvxNLO5wE+Bvws/Q47su+D65zjpjoOp6rSYWQlQTfSTbybbFpqMYmJmNxF90Vzv7n0j5Wk+94WcDE0YT3dvjS0+BmyLbfs747bdmfUWzhyT+bxuAf4iXqC+OWnp4n1e/bJQh768Bayw6C4aCaKOud2j0f6vEI21BrgDyOQM/Wy1nSgGMHEszhrTFpKnkfHVm4CUV0gXkAnjaWY1I0MwzGw+8FvAe+qbKWUSzwTwLNF4wafHrSv0/pnyODiuTjzGm4GXQ1/cDmyx6K4wy4AVwJvT1O58NWE8zexK4PvAbe5+Ilae8nM/bS3PT5nEsyG2eBvRU8wh+mX3iyGuNcAXGftrb6HJ5LOOmV1CdJHjz2Jl6puTtx34o3D3l88DHeHE0Pn1y1xfPZvtF3A70beWPuA48EIovwB4LlbvS8B+om+F98bKlxP9wzkIPA2U5fo95TCWdcAO4ECY1obytcAPYvWagMNA0bjtXwZ2EyVA/wFU5vo95Xs8gd8MMftlmN4Z2159c/Lx/CowALwbe60J6wq+f6Y6DhIN/7ktzJeHvnYw9L3lsW3vDdvtA27N9XvJh1cG8Xwp/F8a6YvbQ3naz30hvzKI5z8Ae0PcXgFWxbb9k9BvDwJ/nOv3kuvXRLEMy/cD3xm3nfrm2bF8kugOYgNE+eadwFZga1hvwPdCrHcTuwPh+fRLCxuKiIiIiEgeKdShLyIiIiIieU2JuoiIiIhIHlKiLiIiIiKSh5Soi4iIiIjkISXqIiIiIiJ5SIm6iEgBM7MhM3vXzPaY2dNmlgzli8ys2cw+MLP3zOw5M1sZ1v2vmbWb2f/ktvUiIrObEnURkcLW4+5r3P1yoB/YGh4C9Syw090vcvfLgL8F6sM2/wR8LTfNFREpHErURURkxGvAxcANwIC7PzKywt3fdffXwvwOoDM3TRQRKRxK1EVEBDMrAW4lepLe5cA7uW2RiIgoURcRKWxzzOxd4G3gV8APc9weEREJSnLdABERyaked18TLzCzvcDmHLVHREQCnVEXEZHxXgbKzOzPRgrM7GozW5/DNomIFBwl6iIiMoa7O3A78IVwe8a9wP3AEQAzew14GthgZi1mdnPOGisiMotZdDwWEREREZF8ojPqIiIiIiJ5SIm6iIiIiEgeUqIuIiIiIpKHlKiLiIiIiOQhJeoiIiIiInlIibqIiIiISB5Soi4iIiIikof+HxF8KSTulvphAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # https://stackoverflow.com/questions/37815987/plot-a-correlation-circle-in-python\n",
    "# https://stackoverflow.com/questions/4971269/how-to-pick-a-new-color-for-each-plotted-line-within-a-figure-in-matplotlib\n",
    "from matplotlib.pyplot import cm\n",
    "\n",
    "\n",
    "# get top 20 \n",
    "vec_len = [np.sqrt(pc_cors[i,0]**2 + pc_cors[i,1]**2) for i in np.arange(dim)]\n",
    "top20_vec = sorted(range(len(vec_len)), key=lambda i: vec_len[i], reverse=True)[:20]\n",
    "\n",
    "x = np.zeros((35,1))\n",
    "y = np.zeros((35,1))\n",
    "u = pc_cors[:,0]\n",
    "v = pc_cors[:,1]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "color = iter(cm.tab20(np.linspace(0,1,dim)))\n",
    "\n",
    "for i in np.arange(dim):\n",
    "    c = next(color)\n",
    "    if i in top20_vec:\n",
    "        ax.quiver(0, 0, pc_cors[i,0], pc_cors[i,1], \n",
    "                  scale=1, \n",
    "                  angles='xy', \n",
    "                  scale_units='xy',\n",
    "                  width=0.002,\n",
    "                  label=X.columns.values[i],\n",
    "                  color=c)\n",
    "    else:\n",
    "        ax.quiver(0, 0, pc_cors[i,0], pc_cors[i,1], \n",
    "                  scale=1, \n",
    "                  angles='xy', \n",
    "                  scale_units='xy',\n",
    "                  width=0.002)        \n",
    "\n",
    "\n",
    "an = np.linspace(0, 2 * np.pi, 100)\n",
    "plt.plot(np.cos(an), np.sin(an))  # Add a unit circle for scale        \n",
    "\n",
    "ax.set_xlim(-1,1)\n",
    "ax.set_ylim(-1,1)\n",
    "ax.set_title('Circle of Correlation')\n",
    "ax.set_xlabel('PC1')\n",
    "ax.set_ylabel('PC2')\n",
    "plt.legend(loc=3, fontsize='small');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only highlighted the top 20 predictors with largest correlation magnitude as these are likely to be the more important predictors. \n",
    "\n",
    "`start_age_diff` and `full_age_diff` are naturally correlated, and seem to explain a substantial amount of variation in the data. `growth_diff`, which is on the other side of the correlation circle, seems to explain variation that is almost the opposite of `start_age_diff` and `full_age_diff`. This makes intuitive sense as we would expect younger players to have more room for growth.\n",
    "\n",
    "`attack_away_defence_home_diff` and `rank_diff` also seem to be explain similar variation in the data. The rest of the predictors are all clustered at the 3 o'clock region of the circle, right across the predictors `attack_away_defence_home_diff` and `rank_diff`. This suggests that these predictors explain similar variation in the data and might be correlated.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>team</th>\n",
       "      <th>date</th>\n",
       "      <th>overall</th>\n",
       "      <th>attack</th>\n",
       "      <th>midfield</th>\n",
       "      <th>defence</th>\n",
       "      <th>prestige</th>\n",
       "      <th>start_age</th>\n",
       "      <th>full_age</th>\n",
       "      <th>bup_speed</th>\n",
       "      <th>...</th>\n",
       "      <th>cc_passing</th>\n",
       "      <th>cc_crossing</th>\n",
       "      <th>cc_shooting</th>\n",
       "      <th>d_pressure</th>\n",
       "      <th>d_aggresion</th>\n",
       "      <th>d_width</th>\n",
       "      <th>wage_euros_thousands</th>\n",
       "      <th>value_euros_millions</th>\n",
       "      <th>growth</th>\n",
       "      <th>goalkeeeper_overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018-11-15 00:00:00+00:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.64</td>\n",
       "      <td>29.48</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.869565</td>\n",
       "      <td>15.795652</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018-11-13 00:00:00+00:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.64</td>\n",
       "      <td>29.48</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.869565</td>\n",
       "      <td>15.795652</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018-11-05 00:00:00+00:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.64</td>\n",
       "      <td>29.48</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.869565</td>\n",
       "      <td>15.795652</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018-11-01 00:00:00+00:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.64</td>\n",
       "      <td>29.48</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.869565</td>\n",
       "      <td>15.795652</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018-10-29 00:00:00+00:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.64</td>\n",
       "      <td>29.48</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.869565</td>\n",
       "      <td>15.795652</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team                      date  overall  attack  midfield  defence  \\\n",
       "0  Brazil 2018-11-15 00:00:00+00:00     81.0    84.0      80.0     80.0   \n",
       "1  Brazil 2018-11-13 00:00:00+00:00     81.0    84.0      80.0     80.0   \n",
       "2  Brazil 2018-11-05 00:00:00+00:00     81.0    84.0      80.0     80.0   \n",
       "3  Brazil 2018-11-01 00:00:00+00:00     81.0    84.0      80.0     80.0   \n",
       "4  Brazil 2018-10-29 00:00:00+00:00     81.0    84.0      80.0     80.0   \n",
       "\n",
       "   prestige  start_age  full_age  bup_speed         ...           cc_passing  \\\n",
       "0      10.0      29.64     29.48       52.0         ...                 61.0   \n",
       "1      10.0      29.64     29.48       52.0         ...                 61.0   \n",
       "2      10.0      29.64     29.48       52.0         ...                 61.0   \n",
       "3      10.0      29.64     29.48       52.0         ...                 61.0   \n",
       "4      10.0      29.64     29.48       52.0         ...                 61.0   \n",
       "\n",
       "   cc_crossing  cc_shooting  d_pressure  d_aggresion  d_width  \\\n",
       "0         44.0         68.0        75.0         54.0     64.0   \n",
       "1         44.0         68.0        75.0         54.0     64.0   \n",
       "2         44.0         68.0        75.0         54.0     64.0   \n",
       "3         44.0         68.0        75.0         54.0     64.0   \n",
       "4         44.0         68.0        75.0         54.0     64.0   \n",
       "\n",
       "   wage_euros_thousands  value_euros_millions    growth  goalkeeeper_overall  \n",
       "0             43.869565             15.795652  0.043478                 82.0  \n",
       "1             43.869565             15.795652  0.043478                 82.0  \n",
       "2             43.869565             15.795652  0.043478                 82.0  \n",
       "3             43.869565             15.795652  0.043478                 82.0  \n",
       "4             43.869565             15.795652  0.043478                 82.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "sofifa = pd.read_csv('data/sofifa_final.csv')\n",
    "sofifa['date'] = pd.to_datetime(sofifa['date'], utc=True)\n",
    "\n",
    "sofifa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "teams = ['Russia', 'Saudi Arabia', 'Egypt', 'Uruguay', 'Morocco', 'Iran',\n",
    "       'Portugal', 'Spain', 'France', 'Australia', 'Argentina', 'Iceland',\n",
    "       'Peru', 'Denmark', 'Croatia', 'Nigeria', 'Costa Rica', 'Serbia',\n",
    "       'Germany', 'Mexico', 'Brazil', 'Switzerland', 'Sweden',\n",
    "       'Korea Republic', 'Belgium', 'Panama', 'Tunisia', 'England',\n",
    "       'Colombia', 'Japan', 'Poland', 'Senegal']\n",
    "\n",
    "pre_wc_rating = sofifa[(sofifa.team.isin(teams)) & (sofifa.date == '2018-06-16')]\n",
    "teams_wc = pre_wc_rating.team.values\n",
    "pre_wc_rating = pre_wc_rating.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# check that we have 32 teams\n",
    "assert pre_wc_rating.shape[0] == 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# scale data\n",
    "scaler_wc = StandardScaler()\n",
    "scaler.fit(pre_wc_rating)\n",
    "pre_wc_rating_scaled = scaler.transform(pre_wc_rating)\n",
    "\n",
    "\n",
    "pca_wc_mod = PCA().fit(pre_wc_rating_scaled)\n",
    "pca_wc = pca_wc_mod.transform(pre_wc_rating_scaled)\n",
    "n = pca_wc.shape[0]\n",
    "d = pca_wc.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax.set_xlim(-3, 3)\n",
    "ax.set_ylim(-3, 3)\n",
    "\n",
    "for i in np.arange(n):\n",
    "    ax.text(pre_wc_rating_scaled[i,2]+np.random.uniform(0,0.1), \n",
    "            pre_wc_rating_scaled[i,7]+np.random.uniform(0,0.1), teams_wc[i])\n",
    "\n",
    "    \n",
    "ax.axhline(0, color='gray', alpha=0.5)\n",
    "ax.axvline(0, color='gray', alpha=0.5)\n",
    "ax.set_title('Overall VS Starting Age')\n",
    "ax.set_xlabel('Overall')\n",
    "ax.set_ylabel('Starting Age');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax.set_xlim(-3, 3)\n",
    "ax.set_ylim(-3, 3)\n",
    "\n",
    "for i in np.arange(n):\n",
    "    ax.text(pre_wc_rating_scaled[i,3]+np.random.uniform(0,0.1), \n",
    "            pre_wc_rating_scaled[i,5]+np.random.uniform(0,0.1), teams_wc[i])\n",
    "\n",
    "    \n",
    "ax.axhline(0, color='gray', alpha=0.5)\n",
    "ax.axvline(0, color='gray', alpha=0.5)\n",
    "ax.set_title('Offense VS Defense')\n",
    "ax.set_xlabel('Offense')\n",
    "ax.set_ylabel('Defense');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax.set_xlim(-7, 7)\n",
    "ax.set_ylim(-7, 7)\n",
    "\n",
    "for i in np.arange(n):\n",
    "    ax.text(pca_wc[i,0], pca_wc[i,1], teams_wc[i])\n",
    "\n",
    "    \n",
    "ax.axhline(0, color='gray', alpha=0.5)\n",
    "ax.axvline(0, color='gray', alpha=0.5)\n",
    "ax.set_title('PCA plot')\n",
    "ax.set_xlabel('PC1')\n",
    "ax.set_ylabel('PC2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>overall</th>\n",
       "      <th>attack</th>\n",
       "      <th>midfield</th>\n",
       "      <th>defence</th>\n",
       "      <th>prestige</th>\n",
       "      <th>start_age</th>\n",
       "      <th>full_age</th>\n",
       "      <th>bup_speed</th>\n",
       "      <th>bup_dribbling</th>\n",
       "      <th>bup_passing</th>\n",
       "      <th>cc_passing</th>\n",
       "      <th>cc_crossing</th>\n",
       "      <th>cc_shooting</th>\n",
       "      <th>d_pressure</th>\n",
       "      <th>d_aggresion</th>\n",
       "      <th>d_width</th>\n",
       "      <th>wage_euros_thousands</th>\n",
       "      <th>value_euros_millions</th>\n",
       "      <th>growth</th>\n",
       "      <th>goalkeeeper_overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.344615</td>\n",
       "      <td>0.297234</td>\n",
       "      <td>0.330777</td>\n",
       "      <td>0.346562</td>\n",
       "      <td>0.314398</td>\n",
       "      <td>-0.088694</td>\n",
       "      <td>-0.033031</td>\n",
       "      <td>-0.305547</td>\n",
       "      <td>-0.102811</td>\n",
       "      <td>-0.305913</td>\n",
       "      <td>-0.273772</td>\n",
       "      <td>-0.076316</td>\n",
       "      <td>-0.230965</td>\n",
       "      <td>0.175877</td>\n",
       "      <td>0.072288</td>\n",
       "      <td>0.004841</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.042762</td>\n",
       "      <td>0.293134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.092605</td>\n",
       "      <td>0.132644</td>\n",
       "      <td>0.047415</td>\n",
       "      <td>0.032724</td>\n",
       "      <td>0.074232</td>\n",
       "      <td>-0.482917</td>\n",
       "      <td>-0.555090</td>\n",
       "      <td>0.080062</td>\n",
       "      <td>0.240904</td>\n",
       "      <td>0.081064</td>\n",
       "      <td>0.062521</td>\n",
       "      <td>-0.048824</td>\n",
       "      <td>0.139404</td>\n",
       "      <td>-0.193788</td>\n",
       "      <td>-0.068769</td>\n",
       "      <td>0.264955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.462669</td>\n",
       "      <td>-0.019433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    overall    attack  midfield   defence  prestige  start_age  full_age  \\\n",
       "0  0.344615  0.297234  0.330777  0.346562  0.314398  -0.088694 -0.033031   \n",
       "1  0.092605  0.132644  0.047415  0.032724  0.074232  -0.482917 -0.555090   \n",
       "\n",
       "   bup_speed  bup_dribbling  bup_passing  cc_passing  cc_crossing  \\\n",
       "0  -0.305547      -0.102811    -0.305913   -0.273772    -0.076316   \n",
       "1   0.080062       0.240904     0.081064    0.062521    -0.048824   \n",
       "\n",
       "   cc_shooting  d_pressure  d_aggresion   d_width  wage_euros_thousands  \\\n",
       "0    -0.230965    0.175877     0.072288  0.004841                  -0.0   \n",
       "1     0.139404   -0.193788    -0.068769  0.264955                   0.0   \n",
       "\n",
       "   value_euros_millions    growth  goalkeeeper_overall  \n",
       "0                  -0.0 -0.042762             0.293134  \n",
       "1                   0.0  0.462669            -0.019433  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadings = pd.DataFrame(pca_wc_mod.components_, columns=pre_wc_rating.columns.values)\n",
    "loadings.iloc[:2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that PC1 is greatly influenced by `overall`, `defence`, and `midfield`, while PC2 is greatly influenced by `full_age` (negative), `start_age` (negative), and `growth`. This gives us a good way to think about the 2D representation of our dataset summarized by the first two PCs. Teams with higher PC2s generally have lower ages and thus more potential for growth. They are likely to have lots of young, promising players. Teams with higher PC1s are statistically better all-round teams. \n",
    "\n",
    "While this PCA plot is not a measure of how likely a team will win the World Cup (since PCAs only involve the predictors), it can still give us an intuition into the various World Cup teams. \n",
    "\n",
    "We immediately see that France is probably a strong all-round team that also has young, promising players. Nigeria is probably a below-average team but with young, promising players. Costa Rica is probably a below-average team with older players near their peak. Spain is probably a strong all-round team but with older players near their peak. These largely corroborates what we know about those teams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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