sportowe_wizualizacja/MovingAvg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('fcb_xg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>xG</th>\n",
       "      <th>xGA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-08-25</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>17:00 (20:30)</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-17</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>16:00 (19:30)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-10-19</td>\n",
       "      <td>13:00 (16:30)</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>21:00 (00:30)</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-10-29</td>\n",
       "      <td>21:15 (01:45)</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-11-02</td>\n",
       "      <td>16:00 (20:30)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>18:55 (23:25)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-11-09</td>\n",
       "      <td>21:00 (01:30)</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-11-23</td>\n",
       "      <td>13:00 (17:30)</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>21:00 (01:30)</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>21:00 (01:30)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-12-07</td>\n",
       "      <td>21:00 (01:30)</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date           Time  GF  GA   xG  xGA\n",
       "0   2019-08-16  21:00 (00:30)   0   1  1.1  0.5\n",
       "1   2019-08-25  21:00 (00:30)   5   2  2.0  0.2\n",
       "2   2019-08-31  17:00 (20:30)   2   2  0.5  1.6\n",
       "3   2019-09-14  21:00 (00:30)   5   2  2.0  1.3\n",
       "4   2019-09-17  21:00 (00:30)   0   0  0.6  2.6\n",
       "5   2019-09-21  21:00 (00:30)   0   2  0.5  1.7\n",
       "6   2019-09-24  21:00 (00:30)   2   1  0.7  0.7\n",
       "7   2019-09-28  16:00 (19:30)   2   0  1.4  0.7\n",
       "8   2019-10-02  21:00 (00:30)   2   1  1.2  1.1\n",
       "9   2019-10-06  21:00 (00:30)   4   0  2.4  1.8\n",
       "10  2019-10-19  13:00 (16:30)   3   0  2.2  0.4\n",
       "11  2019-10-23  21:00 (00:30)   2   1  2.3  1.4\n",
       "12  2019-10-29  21:15 (01:45)   5   1  3.4  0.7\n",
       "13  2019-11-02  16:00 (20:30)   1   3  2.2  0.9\n",
       "14  2019-11-05  18:55 (23:25)   0   0  2.2  0.2\n",
       "15  2019-11-09  21:00 (01:30)   4   1  1.9  0.4\n",
       "16  2019-11-23  13:00 (17:30)   2   1  1.8  0.4\n",
       "17  2019-11-27  21:00 (01:30)   3   1  2.6  1.2\n",
       "18  2019-12-01  21:00 (01:30)   1   0  1.2  1.5\n",
       "19  2019-12-07  21:00 (01:30)   5   2  3.6  1.3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change this cell to drive the whole IPYNB\n",
    "graph_var1= 'GA'\n",
    "graph_var2= 'xGA'\n",
    "pand_out1 = 'RollingGA'\n",
    "pand_out2 = 'RollingxGA'\n",
    "hue_disp1 = 'GA vs xGA'\n",
    "hue_disp2 = 'GA and xGA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def movavg(pandas_df,col1,col2,out1,out2):    \n",
    "    pandas_df['X_axis'] = df.index\n",
    "    for i in range(0,pandas_df.shape[0] - 4):\n",
    "        pandas_df.loc[pandas_df.index[i+4],out1] = (pandas_df.iloc[i][col1] + pandas_df.iloc[i+1][col1] + pandas_df.iloc[i+2][col1] + pandas_df.iloc[i + 3][col1] + pandas_df.iloc[i+4][col1])/5\n",
    "        pandas_df.loc[pandas_df.index[i+4],out2] = (pandas_df.iloc[i][col2] + pandas_df.iloc[i+1][col2] + pandas_df.iloc[i+2][col2] + pandas_df.iloc[i + 3][col2] + pandas_df.iloc[i+4][col2])/5\n",
    "    \n",
    "    return pandas_df[['X_axis',out1,out2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df = movavg(df,graph_var1,graph_var2,pand_out1,pand_out2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_axis</th>\n",
       "      <th>RollingGA</th>\n",
       "      <th>RollingxGA</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   X_axis  RollingGA  RollingxGA\n",
       "0       0        NaN         NaN\n",
       "1       1        NaN         NaN\n",
       "2       2        NaN         NaN\n",
       "3       3        NaN         NaN\n",
       "4       4        1.4        1.24"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def melt(pandas_df,x_axis):\n",
    "    pandas_df = pandas_df.melt(x_axis,var_name=hue_disp1, value_name=hue_disp2)\n",
    "    return pandas_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_for_disp = melt(main_df,'X_axis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_axis</th>\n",
       "      <th>GA vs xGA</th>\n",
       "      <th>GA and xGA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>RollingGA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>RollingGA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>RollingGA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>RollingGA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>RollingGA</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   X_axis  GA vs xGA  GA and xGA\n",
       "0       0  RollingGA         NaN\n",
       "1       1  RollingGA         NaN\n",
       "2       2  RollingGA         NaN\n",
       "3       3  RollingGA         NaN\n",
       "4       4  RollingGA         1.4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_for_disp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9PrvP6/bt29O+ffsSYzRo0MDzYQKU/P264447+Prrrz0XwW7BwcGEh4dTq1YtAPbu3ev52e7du0skBO65e/ToQY8ePTzfN5vN1KtXj507d174Al1Gr169qF69Ojk5OSQnJ2O328nKyqJ69er069ePb7755qrHdJ+/ULxc8dzndP5zcb/XUHyOtWnT5oIOde5j3OeLezx3U4aQkJASx2dkZHh+l9znbXR0tGdci8XiSUDdLvZ37HpczXO82O/E9OnTadWqFSNHjuTRRx/Fbrfz7bffMm7cuEqxB1VERCqOSltpctu6dSt5eXlYLBYaNGjAHXfcQXR0NMeOHaNNmzbUq1eP7OxsgAv2oxQWFnq+PnjwIABBQUEl9o9YLBZOnjxJbm4uUHyRFR0d7fnPvSTufO7x3J++V6tWzbNk5/xPnx0OR6ljuC9Yx40bR+3atT1L7i62ryYhIQEovkByd7WaPn068fHxF4zr3gfxzTffkJmZ6dnXMHDgwIsmBe6L88GDB9OvXz/OnDnjSVYcDgePP/44TZo0oVu3biQnJ9OjR48Se2POVaVKFZ5//nlsNht2u53//d//9SzZSUpKoqioCMMw+MMf/uCZ+/yuXD169OCmm24C4P333yczM9OzNK1bt26en11MfHw848aNY8qUKVgsFl555RUCAgI4efIkeXl5ADRp0gTAsw/j6NGj172J331Be/5Fn3v/k7sSeX7lpjQNGjQo9ftOp7PU77uTzC+++IJ69eoxduxYoOQ59fXXXwPFlZ1OnTqxd+9ez7It93n90Ucflfg9uOOOO1i6dGmJuYqKijxf33PPPRiGQWJiIo0aNSpRCTSZTBw9evSC53P+83fP/eKLL14w97UkTPBbpTIsLIyMjAwOHTrk+T290r1x51u/fj2nTp0C4Jlnninx2tatWxfDMC74+3Du15f7+3CxvxduDRo08CSn7jHd3Uah+O9eq1atPK/fzTff7Kmgus+by3WgdMdwqeOu5jle7HciOzub3/3udzRp0oRevXqxc+dOBg8eTKdOnS4Zn4iIyNWqtJWmESNG0KVLF9q0aUNERAR5eXns2LHDc9EVGRnJK6+8Qv369S/Yy1SarKws5s2bx+DBg5k9ezZLliwhIiKC9PR0XnvtNT799FPGjRvHV199RWJiIiEhIbRv3561a9fyzDPPXDDep59+Su/evXnqqaeoX78+bdu2xWq1snLlSvbt23fJ+524HT9+nKpVqzJ69Gh69ep12SVnmzZt4tNPP2XUqFG8++67DB48mPT09AuW3LkvBtPS0jxL/qB4z0j16tXp06fPBYkWFH9KvHnzZm677TYA/v3vf1NQUAAUVzDGjRvHli1byM3NpXnz5sBv1bPz/fWvf6VOnTpMnToVq9XKuHHjeP311/nDH/7AkSNH+Nvf/sakSZOYMGEC3bt3Jy0t7YLlle7nkZGRUaLCcccdd1CtWjUGDRpU6l6b83388ceMGTOG6Ohohg0bxpdffslnn33GE088wQcffEBSUpInOS5tuVJZmTt3Lt27d+fpp5/2fABwOe+88w4RERH89NNPREREeCpxF6uyubvq9e7dG8Mw6N279wXHrFy5kqNHj3ouTM9d1jlz5kx+//vfM3r0aOrVq0dWVhZNmjShQ4cO1KlT56Jxuudt3749r7322gVt4xMTE8nOzqZx48bMmjULh8NxQYv8Tz/9lD59+jBx4kTat29PQUEBLVu2pFq1alf0Wp0vLCyMmJgYoHj56dmzZ4Hiyk337t3p2bPnRau6l5Kfn8+LL77I3//+d+Li4mjevDk//fQTNWvWpHv37rRt29ZTte3atSufffYZAQEBtGnThl9++aXUfURXo3r16sybN49jx44RGxuL0+lk3rx5nDx5km+++Yb777+fRYsWsWrVKs9r98UXX/DOO+9w+PBhoPjDoddee421a9eW2C/q5t7TNGbMGFq2bMlXX311wTFl8RzHjRtHv379SEtLw2azeapX7oqUiIhIWam0lSb3nqZmzZrx448/8vDDD3Py5Ek2btzItGnTOHv2LD169GDBggWeT7EvZ8KECUyZMoWsrCwGDx7MrbfeSnp6OlC8VG7y5MlkZ2czePBgunbtyt69e/n2229LHWvZsmU8+uij7Nq1i4EDB1KlShW++OKLEt3nriSe3bt307x5c0JDQ/nyyy8v+5gXXnjBs/+hQ4cODBs2jNDQUObNm8eUKVOoV6+eJ4l6/PHHGTVqlOe/BQsWAJf+hN29TAt+q0hA8ZKhrKwsevXqxYgRI7DZbEydOrVExz63++67j8GDB7Nz506mTp3KW2+9xa5duxg8eLCnuvbhhx/y8MMPs3r1apo3b86wYcOIjo4mPj6eP//5z4SEhHiqYxMnTizxPD755JPLPo9z5ebmeh7zP//zP5jNZv72t7/xxhtvkJ+fz+DBg8nOzuaVV17h/fffv6Ixr8Xs2bOZMmUKubm59OzZs8Rc51ZtzjV37lwKCwu55557PMnlU089ddEbur7zzjusXr2a6tWr07ZtW/7+979fcIz7Itv99bnL/3bs2MGwYcNYt24dd9xxB/fffz+hoaG89957l3xuM2bMID4+nqCgIO68805P8xG3M2fOMHLkSNLS0ujQoQMnT570VE7dFeHly5czatQoduzYQe/evRkwYABOp9Pz3l2tgQMHEhwc7Olg6T5/hg8fTnp6Olar9aJNVy5n3rx5xMXFsXTpUurUqcOQIUNo0qQJ//rXv8jPz+fYsWPExcWxYsUKOnbsSLt27UhMTCQuLs5TGb9W69evZ8OGDXTv3p2DBw/y9NNPs337dqC44cJ7772H0+lk6NChdOjQgY0bN3r2JLn3xoWEhDB69OiLdhH8xz/+wfbt22natCmPPvpoqcv7yuI5pqamYrfbPUuHjx8/zsSJEy/Y7yYiInK9TLVq1dLCbxE/YDabCQwM9OwBa9++Pd999x12u53GjRtfNHGqLMLDwz3VHpPJxMqVK2nSpAnPPffcNd8wddq0adx8883XvNTOnwwdOpR3332XNWvW3BDPV0REpCxV2uV5IpVNWFgYSUlJLFy4kMLCQk87+y+//LLSJ0xQXAWz2+3s3r2bzp0706RJEzIzM0tdHiYiIiJSlpQ0ifiJoqIi0tPTGT58OAEBARw8eJDPPvus1Ht7VUapqamMHDmSAQMGeO6f9sYbb2j/ioiIiJQ7Lc8TERERERG5hEpTaXI6ndhsNiwWi69DERERERGRCsrhcGC1Wi97C41zVZqkyWazee59IiIiIiIicjHVqlUjMDDwio+vNEmTO1OsVq2aqk0iIiIiInIBh8PBqVOnrqrKBJUoaTKZTABYLBbP3e5FRERERETO584drlSlvbmtiIiIiIhIWVDSJCIiIiIicglKmkRERERERC5Bm39ERERERMpI1apVGT16NHXr1r3qfTNSdlwuF4cOHWLGjBmcPn36usdT0iQiIiIiUkZGjx7N1q1beffdd3E4HL4O54ZlsVjo1asXo0ePZsqUKdc9npbniYiIiIiUkbp165KUlKSEycccDgfLly+nbt26ZTKekiYRERERkTJiMpmUMFUQDoejzJZIKmkSERERERG5BCVNIiIiIiKVlGEYjB8/nuTkZFasWMHSpUuZPn06TZs2LXHcxIkT2b9/P5GRkT6JMyoqimnTprFu3ToSExNZtmwZf/nLXwgPDy9x3KJFi0hMTPR6fEqaREREREQqqWnTptGyZUvuueceevbsSZ8+ffjqq69o3Lix5xiz2UxcXBwpKSnExcV5Pcbg4GDmz59Peno6d911FzExMcTGxpKRkUFUVJTnuKZNm1KjRg1CQkJo27atV2NU0iQiIiIiUgk1bNiQ2NhYnnvuOc6cOeP5/rJly4iPj/f8u3fv3uzfv5+33nqL4cOHlzpWcHAw27dvp3r16p7vTZo0ieeee47g4GA+/vhjVq5cydKlS/noo48ueHxQUBDLli2jX79+AHTp0oXk5GRCQ0MZNGgQ2dnZvPvuuzidTgCKioqYPn066enpnjFGjBjBnDlzmDNnzkXjLC9qOS4iIiIiUgm1bt2affv2XfY+RSNGjOCrr74iJSUFq9XKbbfdxubNm0sck5+fT3x8PIMGDWLGjBlYLBYGDRrEvffeS8+ePQkPD6dHjx5A8b2qzldQUMBjjz3GV199xbFjx5gyZQqjR48mNzeXNm3aXDDf+QzDYPDgwdx3333YbDYSExN55ZVXKCwsvMpX5dqo0iQiIiIicgNo2rQpiYmJrF69msmTJwMQGRlJ586dWbhwIQBz5sxhxIgRpT5+9uzZDBs2DIBevXqxZ88eDh06xPbt22nSpAmvv/46AwcOpKioqNTH79mzhzfffJOFCxfy0UcfsW3btlKPGzJkCImJiaSkpHDfffcBEBMTw759+9i/fz9HjhwhNTWV2NjY63o9roaSJhERERGRSmjbtm00atSIKlWqALBr1y5iYmKYMWOGp8FCXFwcVquVpKQkUlJSePjhh7n33nsJCgq6YLyUlBRCQ0Np3rw5w4YNY9asWQAcOHCAHj16sGrVKrp3787SpUsJDAwsNaY2bdpw8uRJoqOjS8TZrl07z7/nzJlDTEwMW7Zs8cQxfPhwmjZtSkpKCikpKbRu3dqrS/SUNImIiIiIVELp6eksWbKEt99+u0QXupCQEM/Xw4cPZ9SoUXTq1IlOnTrRvn17fvrpJwYOHFjqmHPmzOHxxx/nzjvvZNGiRQBER0fjdDpZsmQJL7/8MpGRkURERFzw2NjYWO644w569uxJTEwMvXr1AmDevHlERUUxbtw4zObf0hN3wnTTTTfRuXNnT4zuONu1a0edOnWu/4W6AkqaREREREQqqWeeeYY9e/YQHx/PihUr+Oabb2jbti0zZszgtttuIyIigtWrV5d4zLx58y5axZkzZw5xcXEsWbKE/Px8AFq0aMHChQtZunQpixcv5r333uPYsWMlHle3bl0mT57M448/TnZ2No899hhvvvkm0dHR5OfnM2jQIJo0acLatWtJSEjg22+/JSMjg6SkJIYOHcry5cvJzc31jFdYWEh8fLxnuWB5M9WqVcvllZnKmd1u58SJE0RFRWEY6m8hIiIiIt43depUnn32WV+HIb86//241pxBlSYREREREZFLUNIkIiIiIiJyCUqaRERERERELkFJk4iIiIiIyCUoaRIREREREbkEJU0iIiIiIiKXoKRJRERERETkErySNE2aNIkff/yRzMxMmjVrdtHj7r33XpYvX05SUhLLly8nKirKG+GJiIiIiFRKKSkpJCcns3TpUlatWsXvfve7yz5m2rRpjBo1CoDx48czadIkAPr27ctLL7103TFFRUUxbdo01q1bR2JiIsuWLeMvf/kL4eHhJY5btGgRiYmJ1z1fWfDKXWCXLFnCJ598woIFCy56TLt27ZgwYQJxcXEcP36c8PBwioqKvBGeiIiIiEilNWbMGHbu3EmzZs1ISEhg2bJlHDt27KrHSUhIICEh4bpiCQ4OZv78+Xz99dc899xzOJ1OAgICGDlyJFFRUZw9exaApk2bUqNGDYqKimjbti1bt269rnmvl1eSppSUlMseM3bsWD788EOOHz8O4HnBxPvusFg463Kxw+n0dSgiIiIifutOi4UuRvlcbv9gt7PO4biqx+zcuZPTp09Tq1Ytjh8/zosvvsjdd98NQFJSEq+99hrOS1z/DR06lJiYGB599FE6d+7Mq6++yubNm2nfvj0ul4v/+Z//Yffu3QD86U9/4r777uPUqVOsWbOGbt260b9/fwYNGkR2djbvvvuuZ9yioiKmT59eYq4RI0YwZ84cioqKGD58uM+Tpgqzp6lJkybUr1+f+fPnk5CQwDPPPOPrkG5IESYTIwMCGBUYiNXXwYiIiIhImenYsSNZWVns2LGDhx56iFatWtG3b1/69u1L69ateeihh65qvGbNmvHFF1/Qu3dvFi5c6Ll+j4mJoU+fPvTu3ZuBAwfSqFEjz2PatGnD5s2bLzmuYRgMHjyYWbNmMXv2bO6//34CAwOv/gmXIa9Umq6ExWKhRYsWDBs2DKvVyr///W8OHz7MnDlzfB3aDaWvYWAGqppM3GWiua3xAAAgAElEQVQYrLTbfR2SiIiIiF9a53BcdTWoPHzyyScANGzYkLFjx2Kz2ejevTuzZ8/GZrMBMGvWLGJjY/niiy+ueNy9e/eybds2ADZu3EhMTAwAXbp0YeHCheTn5wMwe/Zsnn322VLHGDJkCGPHjqVq1aq89tprfPvtt8TExLBv3z72798PQGpqKrGxsZfc6lPeKkyl6fDhwyxatIiioiJyc3P5/vvvufXWW30d1g0lDOhuGKxzONjjcNDPMLD4OigRERERuS5jxoyhW7duPP7440ydOrXMmq0VFhZ6vnY4HBhXsBRx27ZttGvXzvPvOXPmEBMTw5YtWwgKCgJg+PDhNG3alJSUFFJSUmjdujXDhw8vk5ivVYVJmubPn0+PHj2A4pJct27d2LFjh4+jurH0sVoxgCU2G/E2G1FmM50sSptEREREKoPvvvuOlStX8tRTT7Fq1SqGDBmCYRgYhsGQIUNYtWpVmcyzZs0a7rnnHoKDgzGZTMTFxXl+Nm/ePKKiohg3bhxm82+piDthuummm+jcuTOdOnXy/Ne+fXvatWtHnTp1yiS+a+GVpGny5Mls3LiR6OhoZs+ezYoVKwCYOXOmJ9NcsGABJ06cYNWqVSxdupSdO3fy73//2xvhCRAM3G0YbHY4OOpykep0csDpJNZqxeTr4ERERESkTLz++usMGzaMhIQE0tLSSExMJDExkbS0NGbOnFkmcyQkJLBixQqWLVvGokWLOHr0KGfOnAEgPz+fQYMG0aRJE9auXUtCQgLffvstGRkZJCUlMXToUJYvX05ubq5nvMLCQuLj4xk2bFiZxHctTLVq1XL5bPYyZLfbOXHiBFFRUVdUGpSSYg2DQQEBTM7P56Cr+JRob7HwWGAgHxUWsrECrMcVERERqeimTp160f07N5LQ0FByc3MxmUy88847HDt2jDfeeMPrcZz/flxrzqDsQgigeGleqsPhSZgANjkcHP212qSkSURERESu1HvvvUfdunUJDg5m69atfPDBB74O6booaRK6GgbhJhOLf+2e4uYC4m02RgUG0tpsZpvu2yQiIiIiV+CRRx7xdQhlqsI0ghDfMIB+hsFOh4O9pSRFKQ4HJ51OBlh11yYRERERuTEpabrB3WmxUM1sJv68KpObA/jebucWi4Wm5op/uljrVyPs7saoe4WIiIiIlJWKfxUs5cYM9LdayXA42HGJpXc/2O2cdrkqfLXJWi+CiAdaE3J7XYJa1fJ1OCIiIiJSSShpuoF1sFioYTaz2G6/5HE2YKnNRkuLhfoVtNpkrVOFiAda4ziVh+3oWULvqg8WlZtERERE5PpVzCtgKXcmINZq5bDTyZYr6Iy3wm4n1+ViQAVs527UDKfqoDY4zhZy6uut5KzahyU8iOBbfXcDNBEREZGKICUlheTkZJYuXcqqVav43e9+d9nHTJs2jVGjRgEwfvx4Jk2aBEDfvn156aWXyjXeBx54gCVLlvDDDz/w/fffM2/ePPr06VPimF69epGZmUlsbGy5xnKuincFLF7R1mKhjtnMJ4WFXMmNugqB5XY791qt1LbZOOKqGLf3skSFEvFgG1z5NrLnbMGVZ8OWl01hehahd9SjIDUTV5HapYuIiMiNa8yYMezcuZNmzZqRkJDAsmXLOHbs2FWPk5CQQEJCQjlEWOx3v/sdjz32GKNHj2bPnj0ANGvWjO7du5c4bvjw4SQnJzN8+HDi4+PLLZ5zqdJ0gxpgGBx3OtlwFfdfWm6zUeByEVtB9jZZqgdTLa4tLruTU3O24swp8vwsd3U65mArIR1u9mGEIiIiIhXHzp07OX36NLVq1cJsNjNp0iSSkpJISkpi0qRJmC+zDWPo0KFMnz4dgM6dO5OYmMibb77JsmXLWLp0KU2aNPEc+6c//Yk1a9awaNEiJk6cyJIlSwB48MEHWbRoEYZhYDKZmDVrFg8//DBQXNV6+eWXPQmTO2b3nADVqlWja9euPPHEE3To0IGbbrqpzF6fS1Gl6QbUwmymocXCl4WFXM2dl3KBlXY7MYbBtyYTx31YbTJXDSIirh0A2XO24DxTUOLn9l9yKPj5F0I61CXvp8O48krvDigiIiJSXoJa1iSodfk0pyrYdpSCHVdXLerYsSNZWVns2LGDhx56iFatWtG3b18A/vWvf/HQQw/xxRdfXPF4zZo149lnn+V///d/efrpp3nmmWf4wx/+QExMDH369KF3794UFBTwySefeB4zd+5c7rrrLiZOnMiZM2fIzs7miy++IDIyktq1a7Np06ZLzvnggw+ydOlSTpw4QXx8PEOHDvXKjXNVaboBDbBaOeV0svYqqkxuS202HEA/H1abzOGBVBvSFpNh5tTXW3Gcyi/1uNwfMsBsIvTO+t4NUERERKQC+eSTT0hOTmb+/Pm8+eab2Gw2unfvzuzZs7HZbNhsNmbNmnXBMrjL2bt3L9u2bQNg48aN1K9ffM3VpUsXFi5cSH5+Pi6Xi9mzZ5d43MSJE+nVqxdxcXGMHz/+ouN/++23JCUllViCN2zYMGbNmgXArFmzGD58+FXFfK1UabrBNDKbaWaxMLuoiEv3zCvdaYpbkHc1DL4zmcj2crXJFGIlIq4tpiAr2XO24DiRe9FjHdn55KceJbhtNHkbD+E8XXDRY0VERETKWsGOY1ddDSoP7j1NAwcOZOrUqaSkpJTJuIWFhZ6vHQ4HxhU2DLvpppsIDQ3F5XIRHh5OTk4OJ0+e5MiRI9x6662sWrUKgPvuu49mzZrx5ZdfAtC2bVuaNWvG1KlTPWPVrFmTjh07sn79+jJ5ThejStMNZoDVSo7LxarLtBm/lO/tdkxAXy930jMFGVSLa4slPJDT81KxH8u57GPy1u0Hp4uwuxqUf4AiIiIiFdh3333HypUreeqpp1i1ahVDhgzBMAwMw2DIkCGeZOV6rVmzhnvuuYfg4GBMJhNxcXGen1mtVj766CMmT57M22+/zT/+8Q8sFgsAU6dO5ZVXXqFRo0ae40NCQjxfDx8+nA8++IBOnTp5/nv77be9Um1SpekGUtdkoq3FwoKiIoouf/hFnXS5+NHhoLthsNhm4/Kpy/UzBViIeLAtlmohZM9PxXbkzBU9zplbRN6mw4R0uhljw0Hsxy9emRIRERGp7F5//XW+//57evToQcOGDUlMTARgxYoVzJw5s0zmSEhIoEOHDixbtozs7Gw2btxI1apVAXjppZfYvn0733zzDQBdu3blj3/8I6+//jozZ84kPz+fDz/8kLCwME6cOEF+fj5//vOfCQwM5IEHHuD+++8vMdf8+fNZtmwZL774Ivn5pW/ZKAumWrVqVYze0dfJbrdz4sQJoqKirrg0eKN5NCCA1hYLf8rP53pPqVomE38OCiLebucbW/k2WTBZzVR9sC3WWuGc/mY7RelZV/f4QIPIMZ2wHT7D6QXbyilKERERkeJqybPPPuvrMHwuNDSU3NxcTCYT77zzDseOHeONN97wehznvx/XmjNoed4NoqbJRHuLhSS7/boTJoCjLhebHQ7uNgyCy2C8izLMVL2/NdboKpxZlHbVCROAq9BOXspBAhtHYq1TtRyCFBEREZFzvffeeyQkJLBq1SoCAwO90uGuPKkkc4Pob7ViB5aVYVVosc1Ge8Ogp2EQfx17pC7KbKLqvS2x1ovgTPzPFO4+cc1D5W0+TPBtdQjt1pDsr34qwyBFRERE5HyPPPKIr0MoU6o03QCqm0zcabGQbLdztgzHPehykepw0MdqJaAMxwXABFXuaUFgo0jOJu6mMO2X6xvP7iR37X4C6lQloFFk2cQoIiIiIjcEJU03gH6GgQtIKIdq0GKbjXCTia5lvI+sSv/mBDW9ibNJeyhIzSyTMQu2H8WelUdY1wZgKpMhRUREREpwuVyebnDiWxaLBVcZ3R5HSVMlVwXoahisdTg4VQ73VNrrdLLL4aCfYZTZWs/wPk0IalmTnOR08jcdLqNRAaeL3B/SMW4KI6h5jbIbV0RERORXhw4dolevXkqcfMxisdCrVy8OHTpUJuNpT1Ml18dqxQJ8X44d7hbbbDwTFMSdFgurHY7rGiusZ2OC29Umd91+8lIOlFGEvyncdQLb0bOEdmlIwa7j4KgUzSNFRESkgpgxYwajR48mNjYWk0lLW3zF5XJx6NAhZsyYUSbjKWmqxEKAnobBBoeDX8qhyuS2w+kkw+Ggv9XKGocD5zWOE9qlASHt65K38RC5P2SUZYgl5KxOp1pcW4Lb1iZ/cxlWskREROSGd/r0aaZMmeLrMKSMaXleJXa3YRBkMhFfzvdRAlhst1PDbKb9NZaiQzrVI/TO+uRvPULOir1lHF1Jtv2nKNp/itA762GyqnQuIiIiIpempKmSCgR6W638ZLdzuByrTG5bHA6OOJ0MsFqvusdC8O11COvWkIIdxzi7dHe5xHe+nOR0zCEBBLev65X5RERERMR/KWmqpLobBmEmU/ncP6kULiDeZqOO2Uzbq6g2BbWJJvzuWyjYdZwzS34uHsgL7MfOUrDrOCEd62IKtnpnUhERERHxS0qaKiED6Gu1kuZwkO681h1GV2+9w8Fxp5MBV9h+PLBFDcJjmlC47yRnFqV5LWFyy/0hHZNhIfSOet6dWERERET8ipKmSqiLYVDVZGKxF/YyncsJLLHZaGix0MJ86VMrsEkUVfo3x3Ygm9MLd4DT+13sHFn5FGw/SnC72pjDA70+v4iIiIj4ByVNlYyF4pvZ7nU42OnFKpPbWoeDU7/ubbqYgIbVqXJPC2yZZzj9zTawez9Ot9w1GQCE3tXAZzGIiIiISMWmpKmS6WixEGU2e73K5GYHEu12mlksNCql2mStF0HV+1phP57L6XmpuGy+S5gAnDlF5G0+TFDLmlgiQ3wai4iIiIhUTEqaKhETEGu1ctDpJNUHVSa3VXY7OS7XBdUma+0qRDzQGsepPLLnbsVVdH03wi0reSkHcNkchHVt6OtQRERERKQCUtJUidxmsRBtNnvlvkyXUgQstdloa7FQ99c7YRs1w6g6uA2Os4Vkf70VV4F3uvpdCVeBnbz1Bwm8JQojuoqvwxERERGRCkZJUyUywGrlmNPJRofvKzhJdjv5LhexViuWqFAiHmyLq8BG9pwtOPN8m9SVJm/jIRy5RYR1U7VJREREREpS0lRJtDabqWc2s8Rm83bn7lLlAyvsdupHhhIZ1xaX3cmpOVtx5hT5OrTS2Z3krd1PwM0RBDSo7utoRERERKQCUdJUScRarWQ5nayrAFUmt2XBFj69rzkBJhPZX2/BebrA1yFdUn5qJo7sfEJVbRIRERGRcyhpqgSamM00sVj43m6noqRM5rAArEPakm+YGL3wZ6qeqtgJEwBOFzk/ZGCtEUZg85t8HY2IiIiIVBBKmiqBAVYrZ1wuVtsrRnMFU4iViCHtMAVbKZibSs2T+fQzDF+HdUUKf/4F2y85hHVpCGaTr8MRERERkQpASZOfq28208piIdFmoyK0VzAFGVSLa4slPJDT81PJOnqWtQ4HXQ0Df+lLl5ucjiUimOA20b4ORUREREQqACVNfi7WMMh1uVhZAapMpgALEQ+2wVIthOwF27AdPgPA9zYbFqDPefdtqqiKMrIoOphNSOf6YOhXRERERORGpytCPxZtMnG7YZBkt+PzHUOGmaqD22DcFMbpb7djO5Dt+dEvLhcbHA56GgYhPgzxauQkp2MJDSDk9rq+DkVEREREfExJkx+LtVopcLlY5uOb2WIxEfFAa6zRVTizOI2i9KwLDom32QgymbjbT/Y22TPPULjnBCGdbsYU5B8xi4iIiEj5UNLkp6JMJjpaLKyy28n1ZSBmE1XvbUVA/Wqc/X4nhbtOlHrYYZeLLXY7va1WAr0c4rXKWZ2OKcBCSKd6vg5FRERERHxISZOf6m8YOIFEX+5lMkGVAS0IbBzJmcRdFOw4dsnDF9vthJlMdPeTapPjZB4F248RclsdzGEBvg5HRERERHxESZMfijCZ6GwY/GC3c9rl8lkc4f2aEdTsJs4m7aFga+Zlj093OklzOOhrteIfaRPkrskAILRzA5/GISIiIiK+o6TJD8UYBmbgex9WmcL7NCG4VS1yVqeTv+nwFT9usc1GVZOJLn5SbXKeLSR/yxGCWtfCUj3Y1+GIiIiIiA8oafIzYUB3wyDF4eCkj6pMYT0aE9yuNrk/HiDvxwNX9didTid7HQ76GQaWcoqvrOX+eACX3UFol4a+DkVEREREfEBJk5/pZbUSaDKxxEcd80LvakBIh7rkbTpE7ur0axpjsc1GlNlMR4t/pE2ufBt5Gw4R1PQmjJrhvg5HRERERLxMSZMfCQZ6GQab7HYyfVBlCul0M6Gd65O/NZOcpL3XPE6q08lBp5NYqxVTGcZXnvI3HMKZV0RYN1WbRERERG40Spr8SA/DIMRkYrEPqkzBt9UhrFsjCtKOcXbpruseL95mI9ps5jZ/qTbZHOSuO0BA/WpY61fzdTgiIiIi4kVKmvxEANDHamWbw8EBL1eZgtrUIrzXLRTsOs6Z+J+hDKbf6HBwzOlkgNV6/YN5Sf7WIzhOFxDWVdUmERERkRuJkiY/0dUwqOKDKlNg8xqExzSlcN9JzixKK5OECYqHWWKzUc9sprXZT05Dh4vcNRlYa4UT2DTK19GIiIiIiJf4ydXqjc0C9DUMdjkc7HE6vTZv4C1RVIltju1gNqcX7gBn2Va41jkcZP26t8lfFKQdw348p7iTntlfdmSJiIiIyPVQ0uQH7rRYqG42E+/FKlNAw+pUGdgC+9EznF6wDexln6w5KL7XVBOLhSb+Um1yQc7qDIzqIQS1quXraERERETEC/zkSvXGZQb6W61kOBxs91KVyXpzBFXvbYn9RC7Z81Jx2cpv3tV2O2dcLr/a21S07yRFh08T2rk+GPoVEhEREansdMVXwbW3WKhpNhNvt3tnQhNUiW2O40wB2XO34ip0lOt0NiDRZqOVxUJ9f6k2AbnJ6VjCAwm5rY6vQxERERGRcuY/V6k3IBMQa7VyxOnkJ0f5Ji9uAY0isYQHkpOcjivfO4naSrudXJeLWMPwynxlwXb4NIX7ThLS6WZMgf4Tt4iIiIhcPSVNFVhbi4W6ZjNLbLayalp3WcFtonHkFFK076SXZoQCIMlu53bDINrkP80VclenYwo0COl4s69DEREREZFypKSpAhtgGJxwOlnvpSqTOTyQgIbVKUg9Wmatxa/UcpuNQpfLrzrp2Y/nUpj2CyG318EcGuDrcERERESknChpqqCam800tFhYYrfjnZQJgtvUAhPkp2Z6acbf5FC8TK+jxUKUH1WbctZkgNlEyJ31fR2KiIiIiJQTJU0VVKzVSrbTyRovNoAIah1NUXoWzrOF3pnzPIl2O06gnx/tbXKeLiB/aybBbaOxRAT7OhwRERERKQdKmiqghmYzLSwWEu12vJQyEdCwuAGEL6pMbqddLtbY7dxlGET4UbUpb91+cDgJ7dLA16GIiIiISDlQ0lQBDbBayXG5WOWtKhMQ3NbdACLLa3OWZondjhmI8adqU56NvA2HCGpeA6NGmK/DEREREZEypqSpgqlrMtHOYmGZzYa3Fsl5GkBsOwpOL3eAOM9Jl4sUh4PuhoE/pR95Gw7izLcR2rWhr0MRERERkTKmpKmCibVayXe5SPJmlam17xpAlGaJzUagyUQvP+qk5ypykPvjAQIbVsd6c4SvwxERERGRMqSkqQKpYTLR3mJhhd1OnrcmNUFQm1oUZZzCecY3DSDOl+lysclup5dh4E+tFfJ/OozjbAFh3VRtEhEREalMlDRVIP2tVuzAUpvNa3MGNKyOJTyI/K1HvDbnlVhssxFiMtHDj/Y24XCRu2Y/1ugqBNwS6etoRERERKSMKGmqIKqbTHS2WFhtt3PWi/NWlAYQ5zvgcrHN4aCP1Yo/3Ta2YPtR7CdzCevaEPynAaCIiIiIXIKSpgqi768VlQQv7mUyhwUQ0DCyQjSAKM1im40qJhNd/ana5ILc1RkYkaEEtazp62hEREREpAwoaaoAwoGuhsE6h4Msl/eSl6A20b82gDjqtTmvxh6nk90OB30NA4uvg7kKhXtOYMs8Q+hdDcCicpOIiIiIv1PSVAH0sVoxgHgv7mXCBMGeBhAF3pv3Ki222ahuNnOnxZ/SJshJTsdSJYjgW+v4OhQRERERuU5KmnwsBOhpGGx0OPjFi1WmgAbFDSAKtlaMNuMXs93pZL/TSX+r1a+2CNkOZlOYkUXoHfUwBfhXwiciIiIiJSlp8rG7DYNgk8m7VSZ+bQCRW0ThvpNenfdaxNts1DSb6eBn1abc5HTMwVZCOtzs61BERERE5DooafKhQKC31coWh4NDXqwymcMCCGhUcRtAnG+zw0Gm00msn1Wb7L/kULDzF4Lb18UU4j836hURERGRkpQ0+VA3wyDMB1WmoNbRmMwm8lMr9tI8NxfF1aa6ZjNt/K3atDoDk2Em9M76vg5FRERERK6RkiYfMShuM/6zw8E+p9N7E//aAKIwIwvn6YrbAOJ86x0OTjidDPCn9uOAIzufgtRMgttGY64a5OtwREREROQaKGnykbsMgwizmcVerjIFNKiOpUrFbwBxPgewxG6nkcVCc7N/nba5a/eD00XYXQ18HYqIiIiIXAP/uvqsJMxAf8Mg3eHgZ29WmTinAcTeit8A4nxr7Hayf93b5E+cuUXkbT5MYIsaGDeF+jocEREREblKSpp8oJPFQpTZzGK73avz+lsDiPPZgUS7nRYWCw39rNqUl3IQV6Gd0C4NfR2KiIiIiFwl/7ryrARMQH+rlUNOJ1sdDq/OHdS6FiaziQI/aQBRmlV2OzkuFwP8rNrkKrSTl3KQwMaRWOtU9XU4IiIiInIVlDR52a0WC7XNZuJtNrxa6zFBcJtoijKycPhRA4jzFQLLbDbaWSzUNflTA3LI23wYR04hod1UbRIRERHxJ0qavGyA1coxp5MNXq4yBdQvbgCR72cNIEqTZLeT73L53d4m7E5y1+4noE5VAhpF+joaEREREblCSpq8qKXZTH2zme+9XWWiuAGE008bQJwvD1hht9PeYqGGn1WbCrYdxZ6VR1jXBvjVnXpFREREbmBKmrxogNVKltPJWi9XmcyhAQQ0jiR/u382gCjNUpsNO8X7w/yK00XuDxkYN4UR1LyGr6MRERERkSugpMlLbjGbaWqxkGC3492UqXI0gDjfWWC13U5ni4XqflZtKtx1HNuxs8Wd9Cz+FbuIiIjIjUhJk5cMsFo543Kx2sttxj0NIPafwpHtvw0gSpPw62sZYxg+juTq5SanY6kaRHDb2r4ORUREREQuQ0mTF9QzmWhtsbDUZqPIy3MH1K+GpWoQ+VuPeHnm8pflcrHO4aCbYRDu62CuUtH+UxTtP0XonfUwWS2+DkdERERELkFJkxcMsFrJc7lY6e0qExDUNhpnXhGFe/y/AURp4m02DKCPv+1tAnJWp2MOCSC4fV1fhyIiIiIil6CkqZxFm0zcbhjFbbK9PLc5NIDAxlHkb6s8DSDO94vLxUaHg56GQYivg7lK9qNnKdh1nJCOdTEF+1/SJyIiInKjUNJUzvpbrRS6XCyz2bw+d2VsAFGaeJuNYJOJu/1xb9MPGZgMC6F31PN1KCIiIiJyEUqaylGUyUQni4Vku50cH8xfWRtAnO+Qy8UWh4PeViuBvg7mKjmy8ijYfpTgdrUxh/tb9CIiIiI3BiVN5aivYeDkty5v3vRbA4jKXWVyi7fZCDOZ6OaP1aa1+wEIvauBbwMRERERkVJ5LWmaNGkSP/74I5mZmTRr1uySxzZu3Jh9+/YxadIkL0VX9qqaTHQxDNba7WS7vL+f6LcGECe8Prcv7HM6+dnhoK9h4G9pk/NsIfk/HSaoZU0skf62M0tERESk8vNa0rRkyRIGDRrEwYMHL3mc2WzmzTffJD4+3kuRlY8Yw8ACfO+DKlNxA4hI8rcfq7QNIEoTb7MRYTZzlz9Wm348gMvmIKxrQ1+HIiIiIiLn8VrSlJKSwpEjl79X0JNPPkliYiL79u3zQlTlIwDobhikOBwc90WVqVUtTBYzBTfI0jy3NKeTdIeDfoaBydfBXCVXgZ289QcJvCWKkM71MYWom56IiIhIRVGh9jS1bNmSnj178vHHH/s6lOviANbY7SzwQcc8gOC2tX5tAOHtJue+t9Ju5yazmWiTv6VNkLfxEIV7TxJ2VwOixt5JlQHNMWpX8XVYIiIiIje8CrOOyTAM3nrrLZ599lmcTqevw7kuDuArHyVMxQ0ggslJTvfJ/L6269dzp4nFwhEfLI28LnYnpxdsw1ItmOBbaxPUqhZBLWpi++Us+T8doSDtF7D79++GiIiIiD+qMElTzZo1adCgATNnzgSgSpUqmEwmwsPDef75530cnf+40RpAnO+Ey8Upp5MmZjMrfR3MNXKcyicnaS+5q9MJbFGTkFtrU6VvM8K6N6Zg+1HyfzpyQ1YRRURERHylwiRNhw8fplWrVp5/jx8/ntDQUF599VUfRuVfzCFWAhtHkrfpMDhunAYQ59vza9Lk71w2JwVbMynYmom1TlWCb61N8K21CWlfl8KMLPJ/OkLRvpNw477VIiIiIl7htSvLyZMns3HjRqKjo5k9ezYrVqwAYObMmbRr185bYVRqQa1/bQCRemM1gDjfLqeTamYzkX64r+libIdPc2ZRGic/XkfOD+kYkaFEPNCayNF3ENLpZkzBahwhIiIiUl5MtWrVqhSfU9vtdk6cOEFUVBSGH7acLguRozvhOFNI9pwtvg7Fp+qYTLwcHMynhYWsdTh8HU75MEHgLVEE31qbgHrVcNmdFO46Tt5Ph7FnnvV1dCIiIiIV0rXmDDdmdlEJWetXwxIRTM7qG7MBxLmOuFzkulzcYrFU3qTJBYW7T1C4+wSW6iHFjSNa1iSoZU1sxz5t+mkAACAASURBVH5tHPGzGkeIiIiIlAUlTZVEcJtonPm2G7YBxLlcwB6Ho1Lsa7oSjqw8cpbvITc5naCWNQi+tQ5V+jUjrHuj3xpHnC7wdZgiIiIifktJUyVgCrESeEsk+Ztv7AYQ59rtdNLOMKgC/H/27jxIrqu+G/733KWX2STNSJpNkjXSjEaLtdjyJmS8yYukJMTGMRADKUySx0/lrVSFAEkqjgkESHhCgD8wBbYpeF7KeSF6KAKPjSRvsmywLFs2WoxG0vQsGkmz9Ox7L/fec94/pmeQjTTqGXX37b73+6lylWQPmh/uds/53d853zPqdjE5oiwHsePdiB3vhrksFRxxXS2KbliORPsgYsc6kWwfZHAEERER0RyxafKA8IapAIjYCX8HQFwskrqvqV7X8RuvbtGbhXVhBNaFEWjFAYQ3VSO0qRoLH9gIZziG2IluxN7thooX2D1WRERERC5h0+QB4U3VSJ4fhjPEu3umnZMSCaXQoGm+bJqmyYkkJt7owMSb52aCI0puW4XiD6xE/HQvYse6YEcZHEFEREQ0GzZNBc5csXAqAOJ1BkBczAHQJiUadB2wLLfLcZ9USDT3IdHcB73id8ER4WurYHWPTgVHnOnl9k4iIiKiS/DHSXkPC29KBUBEGADxfi1SYpkQCLtdSJ5xBiYx/nILBp48jLGXIxABHWW71mLxo9tQfNsqaGUht0skIiIiyiucNBWwqQCIxQyAuIxmx8EfmSZWaxp+Kxm9/X4q6SB2rAuxY10wly9EeEsNirYuQ9ENy5BsG0TsWBeSZwfdLpOIiIjIdWyaCthMAMS7DIC4lHYp4SiFBl1n03QF1vlhWOeHoZUEEN5UMxUc8eBG2EMxxI53IX6yh8ERRERE5FtsmgpYaGMqAGKQARCXkgTQIaVv7mvKBDmexMShs5g43IFgw2KEt9Si9I7VKNm+EvEzvYgd7YLdO+52mUREREQ5xaapQJkrFsJYFMbIobNul5LXIlLiLsOACYBxEHMgFRJn+pA40wdjSTHCm6eDI6phdY0idqwT8eY+bgslIiIiX9BLSkq+6HYRmSClxOTkJIqKiqD5YLJQ8sE6aMUBjL1whpeVzsIUAtsMA6ccBwOK/6LmQ05aqTNOnZDjyamLczfVILypGiJkwhmKQSX9G+tOREREhWO+PQMnTQVIhKcDILr4pP8KWlJ3NNVrGpp5rumqqISD2NFOxI52wlyxEEVbalF043IU3bgcybaBqeCIjiG3yyQiIiLKODZNBSi8oZIBEGmaBNA5fV+TzSCDTLHODWPk3DC00iDCm6oR3lSNYP1ixM/0YvS5U26XR0RERJRR3t/H5kGhTdVIXhiGMzjpdikFodlxsFrT+GbPAjmWwMTrZ9H/1GFMvHkOocalCKyqcLssIiIiooziOrLAmMsXwlhUhNhxTpnS1SIlQkJguQ/OurnGUZg4dBb2wARK71wNGPx3TURERN7BlU2BCW+qhoxZSET63C6lYERSZ5kYPZ5lUmHs5RboC8MounG529UQERERZQxXkQVEhE0EGxYj3hRlAMQcDCuFPt7XlBPW+WHET/ei+Mbl0BaE3C6HiIiIKCO4iiwgoekAiBPcmjdXESlRr+tul+EL46+2QilMbdMjIiIi8gA2TQUkvLEayQsjDICYh4jjoFQIVAnhdimeJ8eTmDzcgeDqxQjUlbtdDhEREdFVY9NUIMzlC2GUFyF+osvtUgoSzzXl1uQ7F6ZCIe6qB3Q2qkRERFTYuIIsENMBEPFIv9ulFKRepTCi1NR9TZR9UmHsAEMhiIiIyBvYNBUAETanLg5tigK2dLucgtXiOJw05ZB1bhjxM70ovmkFtDKGQhAREVHh4gqyAITWV0IYGmLvMgDiajRLiQpNQznPNeXM+EGGQhAREVHhY9NUAMKbqpHsHIEzwACIqxFxHAA815RLM6EQ9QyFICIiosLF1WOeM5ctSAVAcMp0tTqVQkwpNk05NvnOBdiDkyi5k6EQREREVJi4esxz4U3VkHEL8eY+t0speApAC+9ryr1UKISxKIyiGxgKQURERIWHTVMeE2EDwYYlDIDIoIjjoEbTUOJ2IT5jdQwhfqYPxTevgFYadLscIiIiojlh05THQuurpgIguDUvY6bva+K0KffGX20FAJQwFIKIiIgKDJumPMYAiMzrkBIWzzW5Qo4lMHG4A6GGJQisXOR2OURERERp48oxTzEAIjtsAG1SsmlyyUwoxF0MhSAiIqLCwZVjnmIARPZEpMQKTQNP1rjAURg/0AJjURGKtjIUgoiIiAoDm6Y8JEIMgMimFseBJgRWc9rkimTHEOLNfSi+haEQREREVBi4asxDofWVUwEQ7/a4XYontUoJRyk0MAzCNeMHU6EQdzAUgoiIiPIfm6Y8FN5cA6trBE7/hNuleFICwHmea3LVVCjEOYTWLEHgGoZCEBERUX7jqjHPmLVTARCMGc+uiJSo0zQYbhfiY5PvnGcoBBERERUENk15ZioAwkb8DAMgsikiJUwhsJLTJvc4CuOvtMAoL0LR1mVuV0NERER0WVwx5hERMhBcswTxUwyAyLYWxwEA1LNpclXy7BDikT4U33INQyGIiIgob3HFmEdmAiC4NS/rxgF0SckwiDww/koqFOJ2hkIQERFRfmLTlEfCm6phdY0yACJHIo6Dek0DT9O4S44lMPHmOYQal8BkKAQRERHlITZNecKsXQCjohixE11ul+IbESkRFgLLBNsmt02+fR720CRK76oHNL4eRERElF/YNOWJ8KZqyAQDIHKpRU6dG+MWvTzgKIwfaGUoBBEREeUlNk15YCYAookBELk0qBT6eV9T3kieHUSipR9FDIUgIiKiPMPVYh4IrZsKgIi/ywCIXGthGEReGXulBUIAJbevcrsUIiIiohlsmvJAePNUAITdxwCIXIs4DsqEwFKea8oLcjSBibfOIdS4FOaKhW6XQ0RERASATZPrzNqyqQAITplcEZk+18Qtenlj8sh52EMxhkIQERFR3uBK0WWhjakAiNO9bpfiSz1KYVQpbtHLJ47C+CstMCqKUXR9rdvVEBEREbFpcpMIGQg1LkX8FAMg3NTiOJw05ZlkeyoUYttKaCUBt8shIiIin+NK0UUzARAnuDXPTS1SYommYSHPNeWVsVdaU6EQq90uhYiIiHyOTZOLwpuqYXUzAMJtzY4DgOea8o0cjWPiyHmE1i6FuZyhEEREROQerhJdYtaUwVhcjBinTK67oBTiSrFpykOTR87DGY6hdAdDIYiIiMg9XCW6JLRpKgAicYYBEG6TAFp5X1N+siXGUqEQYYZCEBERkUvYNLlABA2E1ixB/FQUymIARD6IOA5qNQ3FbhdCvyfZNohE6wCKt10DrZihEERERJR7bJpcEFq/FMLUGQCRR6bva6rntCkvjb3SAqFpKLl9ldulEBERkQ+xaXJBeFMNAyDyTLuUsJRCPc815SU5EsfkW+cQWlfJUAgiIiLKOa4Qc8yYDoB4l1OmfGIDOCsl1rBpylsT06EQdzEUgoiIiHKLK8QcC29MBUCcZgBEvolIiRWaBp6ayVO2xNjBVhiLixG+jqEQRERElDtsmnJIBA2EGpcgfqqXARB5qMVxoAuBVZw25a1k6wASbQMo/gBDIYiIiCh3uDrModC6VAAEt+blpVYpIZVi9HieGz/AUAgiIiLKLTZNORTeVA2rZxR277jbpdAlxDB10S0vuc1vzkgck0fOT4VCLFvgdjlERETkA1wd5ohRXQZjSQlijBnPaxHHwSpNA2dN+W3irXNwRuIMhSAiIqKcYNOUI+FN1ZBJG4nTfW6XQrOISImAEFjBaVN+syXGXmmBsaQE4S01bldDREREHseVYQ6IoI5Q4xIkTvVCWY7b5dAsIs7U68Po8fz3u1CIlQyFICIioqziyjAHQusqIUydW/MKwBiAHilRzzCIgjD+SguErqHkNoZCEBERUfawacqBqQCIMQZAFIiIlKjXNPCkTP5zhlOhEOsrYdYyFIKIiIiyg01TlhnVpQyAKDARx0GxEKgRbJsKwUwoxI56sNMlIiKibGDTlGVTARAOEqd73S6F0hSRUxcP876mAmFLjB2cDoWodbsaIiIi8iA2TVkkAjpCjUuROB1lAEQBGVAKg1LyvqYCkmwZQKJ9EMXbV0IrMt0uh4iIiDyGq8IsmgmAOM6teYUmwqap4IwfmAqFKGYoBBEREWUYV4VZFNpUDSvKAIhC1CIlFmoalvBcU8FwhmOYfPs8whuqYNaWuV0OEREReQibpiwxqkphLmUARKGavq+pntOmgjLx5jk4o3GU3NXAUAgiIiLKGK4Is4QBEIWtWymMK4U1DIMoLLbE+CutMJcyFIKIiIgyh01TNhgaQmuXInG6FyrJAIhCpAC0OA4nTQUo0dKPxNmpUAjBUAgiIiLKAK4Is0EqJFr6MXnknNuV0FWISIlKTQOvTC084wdaIAwNJR9kKAQRERFdPTZN2SAVRveehjMcd7sSugrT9zXVc4tewXGGYph8+wLC11bBrGEoBBEREV0dNk1El3FeSiSUYvR4gZo43DEVCrGDoRBERER0dbgaJLoMB0CblGjgpKkw2RLjB1OhEJtr3K6GiIiIChibJqJZNDsOaoVA2O1CaF4SkX4kO4ZQvL0OIsxQCCIiIpofNk1Es2iREpoQTNErYGMHIhCmhpLb6twuhYiIiAoUV4JEs2iXErZSDIMoYM5gDJPvXED42moY1QyFICIiorlj00Q0iySADimxhpOmgjZ5uAPOWBylO+oZCkFERERzxpUg0RVEpMQ1mgaeiClcykqFQlSWIryJoRBEREQ0N2yaiK4g4jgwhEAdp00FLdGcCoW4dSVDIYiIiGhOuAokuoJWKSF5X5MnjB1ogTB1lHyQoRBERESUPq4Cia5gEkCnUryvyQOcwUlM/qYT4Y3VMKpL3S6HiIiICgSbJqI0tDgOVmka/4PxgMk3OuCMJVC6o4GhEERERJQWrgGJ0tAsJUJCYDm36BU8ZTkYf3U6FKLa7XKIiIioAHAFSJSGFscBAEaPe0TiTN9UKMT2Ooiw4XY5RERElOe4AiRKwwiAXikZBuEhYwdaIAI6Sm5d5XYpRERElOe4AiRKU0RK1Os6j8F4xEwoxKZqGFUMhSAiIqLLy1nT9IUvfAFvvvkmuru70djYeMmv+cxnPoODBw/i5ZdfxvPPP4877rgjV+URXVHEcVAiBKoE2yavYCgEERERpSNnTdP+/fvxwAMP4Pz585f9mqNHj2LXrl3YsWMHPvOZz+B73/seQqFQrkokmlVESgBg9LiHKMvB+GutMKtKEdrIUAgiIiK6tJydgH7rrbeu+DUHDx6c+XVTUxOEEFi0aBG6u7uzWBlRevqUwkjqktvX3C6GMiZxug/JjcMoubUOiVNRKEu6XRIRedg6TcNfBoPg4zd3HHMc/DCZdLsMKkB5Gxv10EMPoaOjgw0T5ZVmx2EYhAdNHGrHoo9dh2DDEsSbom6XQ0Qedr9pwlIKb6RSWSl3GjQNW7hbhOYpL5umbdu24e///u/x0Y9+1O1SiN6jRUrcaBioEAIDSrldDmWI1TkKZziG0PpKNk1ElDVrNQ11uo5nkkm8Zttul+M7dxgGHtZ1LBQCw/wZTnOUd4/Mt27dim9/+9t45JFH0Nra6nY5RO8RST0Z5LTJe2JNUZgrFkIrDbpdChF51G7TxLCUOMSGyRXdqbPJ1Qx0onnIq5Xf5s2b8eSTT+Iv//Iv8e6777pdDtHv6VQKE0oxDMKD4k1RCCEQWrfU7VKIyINWaRrW6jpesG2wZXJHT2q6VMUHnzQPOXvXfPnLX8Y777yD6upq7NmzZyb04ZlnnsHmzZsBAF/72tcQCoXw7//+73jxxRfx4osvYu3atbkqkeiKFIBWXnLrSXIkjuSFYYTWV7pdChF50C7TxLhS3JbnohGlEFOKkyaaF1FVVeWJTZ22baO/vx+LFy+GYeTlUS3yiPsMAw8GAvjs5CTG3C6GMiq0sQpl9zZi8D9/A7uHry4RZcYyIfCFcBg/Tyaxl02Tq/4hGIQF4BuJhNulkEvm2zPwcTnRHPG+Ju9KnOmDshxOm4goo3aZJmJK4SAbJtd1K8XteTQvfNcQzVGHlEgqhXp+6HqOSjpItA4gtHYpoHH7BhFdvaVCYKuu46BtY9LtYgg9UmKBEChyuxAqOFz1Ec2RA6BdSk6aPCp+MgotbCKwqtztUojIA3aZJmwAL1mW26UQpiZNAMMgaO74jiGah2YpsVwIhNwuhDIu2TEIZzyB8Poqt0shogJXLgRu0XX8yrZ5BjZPMHac5otNE9E8tDgONCGwmk+qvEcBidO9CKwqhwgzVIaI5u/e1CHzF3iWKW/0KwWL55poHviOIZqHNinh8L4mz4qdjELoGkKNvLOJiOanFMCthoE3HAdDyhNBxZ6gAPQqhWo2TTRHfMcQzUMCwDkpGQbhUU7/BKzecaboEdG83W2aMADs51mmvNMtJbfn0ZxxxUc0TxEpUadp4AYub4qf7IFZXQa9nBlLRDQ3RQDuMAy87Tjo5ZQp73QrhQohYLpdCBUUNk1E8xSREqYQWMlpkyclTvdCScVpExHN2Z2GgbAQ2McpU17qkRKaEKjktInmgKs9onlqcRwAQAObJk+SkxaSZwcRWr8U4M9VIkpTEMAO08Rx20Ynp0x5aTpBj2EQNBd8txDN0wSATt7X5Gnxk1HopSGYyxe6XQoRFYjbDAMlQmAvE/PyVlQpSIZB0Bzx3UJ0FSKOg9WaxkGERyXaBiDjNrfoEVFaDAD3miZOOQ7aU9MMyj8WgAGlUMXteTQHbJqIrkKLlAgLgeX84PUmWyLR3IvQmiUQJj8uiWh2HzAMLBACe3mWKe91c9JEc8R3C9FViKSeJHKLnnfFT0YhTB3BhiVul0JEeUwHsNMw0OY4OMMpU97rlhKVQnCnCKWNTRPRVRhSCv1SMgzCw6yuUTjDMW7RI6JZ3ajrWKxpPMtUIHpSCbiLuVOE0sSVHtFVikiJek6aPC3WFIW5YiG00qDbpRBRHhIAdpomzkuJE6lkVcpv3alkQ27Ro3TxnUJ0lSKOgzIheKDUw+JNUQghEFq31O1SiCgPXafrqNE07OdZpoLRk9pCWc2f3ZQmNk1EV2n6XFM9n1Z5lhyJI3lhGKENVW6XQkR5aJdpIiol3uaUqWBMAhhRinc1Udr4TiG6SlGlMKoUwyA8Lt4UhVFeBKOq1O1SiCiPbNA0XJOaMvEq28LSIyV3iVDa2DQRZUDEcRgG4XGJM31QlsNACCJ6j92miUEpcZhTpoLTLSXPNFHa+E4hyoCIlFisaVjEJ1aepZIOEq0DCK1dCuh8nYloalt2g67jedsGW6bC060UioTAArcLoYLApokoA1pSTxg5bfK2+MkotLCJQF2526UQUR7YbZoYVQqvM2a8IE2HQfBcE6WD7xKiDDivFGJKsWnyuGTHIJzxBMLrGQhB5HcrhMC1uo6XLAtJt4uheWHsOM0F3yVEGaAAtErJMAivU0DidC8Cq8ohwobb1RCRi3abJiaVwkFOmQrWcOqBJ2PHKR1smogyJOI4qNE0FLtdCGVV7GQUQtcQauSdTUR+VS0ErjcMHLBtxN0uhq5Kj5Tcnkdp4buEKENm7mvitMnTnP4JWL1jTNEj8rGdpomEUjjAy2wLXrdSjB2ntLBpIsqQs1LC4rkmX4ifjMKsLoNeXuR2KUSUY4uFwE26jldtG+NuF0NXrUdKLNI0hN0uhPIeV3dEGWIDaJeSTZMPxE/3QknFaRORD91nGJAAXuRZJk+YDoPgFj26Er5DiDIoIiVWaBqCbhdCWaUmLSTbBxFavxTgrg4i31ggBD5gGDhk2xhJLbapsHVPx45zix5dAZsmogyKOA50IbCKT6w8L94UhV4agrl8odulEFGO3GMY0AA8zymTZ/QrBVspxo7TFfEdQpRBbVJCKsXocR9ItPZDxm1u0SPyiRIAtxsGjjgO+jll8gwJoJex45QGNk1EGRTH1EW3PNfkA45C4kwvQmuWQJh8vYm87i7TRFAI7GNinud0M3ac0sB3CFGGRRwHdZoGXn3qffGmKISpI9iwxO1SiCiLQgDuNAz8xrZnggPIO7qVwhIh+HObZsWmiSjDIlIiIARW8KmV51ldo7CHYtyiR+RxtxsGioXAXk6ZPKlHSmhCYCm36NEsuKojyrAWxwEAbtHziXhTFOaKhdBKmZlI5EUmgHtME791HJzjlMmTphP0GAZBs+G7gyjDxjD1AbyGYRC+ED8VhRACoXWcNhF50a2GgTKeZfK0qFKQSjF2nGbFpokoCyJSYrWm8QofH5AjcSQvDCO0gU0TkdfomLrMNuI4iKSmEeQ9SQCDjB2nK+C7gygLWhwHRUKglk+tfCF+MgqjvAhGVanbpRBRBt2i6yjXNJ5l8oFuNk10BXx3EGVBc+qJJO9r8odEcx+U5XDaROQhAsBO00SHlDjJKZPn9UiJSiG4Q4Qui00TURYMKoVBKRkG4RMq6SDROoBQ41JA549cIi/Yquuo1DSeZfKJ7lTybQV3iNBlcEVHlCURKTlp8pH4yR5oYROBunK3SyGiDNhlmuiWEkdTiajkbT2pZMRqNk10GWyaiLIkIiUW8N4H30h2DMEZTyC8ocrtUojoKm3UNCxPTZkYMu4P07HjVdwhQpfBdwZRlkR4X5O/KCBxuheBunKIMO+VJypku00T/VLiCKdMvjEBYFQpNk10WXxnEGVJt1IYVwr13KLnG7GTUQhdmzrbREQFqVHTsFrX8bxtgy2Tv/RIye15dFlsmoiyKOI4WMOnVr7h9E/A6h1jih5RAdttmhiWEq/bttulUI51S8nYcbosvjOIsigiJZZoGhbwyZVvxE9GYVaVQS8vcrsUIpqjOk3DOl3Hi7YNtkz+060UioUAb9yjS2HTRJRF0zfI81yTf8RP90JJhdB6TpuICs1uw8C4UniNUyZf6kn9zOa0iS6F7wqiLDovJeJKsWnyETVpIdk+ONU0ccBIVDBqhcBmw8DLloWE28WQK2Zix/kzmy6B7wqiLJIA2nhfk+/Em6LQS4Mwly90uxQiStMu00RcKbzCKZNvDSqFuFKo4pZ6ugQ2TURZFnEc1AgBnnDxj0RrP2Tc5hY9ogKxVAjcoOs4aNuYdLsYclUPwyDoMviuIMqyiJTQhMBqfgj7h6OQONOL0JolECanjET57j7ThA3gRctyuxRyWQ8nTXQZXMURZVm7lLCVwhpu0fOVeFMUwtQRbFjsdilENItFQmCbruN128aY28WQ67qlRLmmIeh2IZR3eG09UZZZAM5KiXpOmnzF6hqFPRRDaH0l4k1Rt8vJW8E1ixGsL9zGUiYcJE5FYXWNul0KzdO9hgEB4HmeZSJMxY4DQJWmoSOVpkcEsGkiyomIlLjXMBAAkHS7GMqZeFMUxR+4BlppEHKMeVzvZywtQdnudZBxGypZmAtWrTiAoi01sHrHEDvWhfipXsDmQqtQlAL4oGHgsONgMLVYJn+biR0XAh0u10L5hU0TUQ5EHAe7TBN1moYzfHLlG/FTUZRsX4nQukpMvnXO7XLyiy5Qtmst5KSFwf/3bahEYTZNMDSE1lUivKUGZfc2ouS21Yif7EHsWBec4Zjb1dEV3G2aMADs51kmSulTCo5SU2EQjuN2OZRH2DQR5UCrlJCp+5rYNPmHHIkjeWEYoQ1smt6v5NY6GIuLMfzTE4XbMAGALRF/txvxd7th1pYhvKUW4S01KNq6DImzg4gd60KybQDgECPvhAHcYRh4x3EQ5ZSJUhwAvUqhmmEQ9D5smohyIAagU6mp+5q4b95X4iejKLuvEUZVKeweHjMHALN2AcJbl2HyWBeSHUNul5MxVucorM5RaEUmQpuqEd5Ug4X3XwtnNI7Y8S7E3u2BinGikS/uNAyEhcA+TpnofbqlRA3PIdP78B1BlCPNjoNVmgZm6PlLorkPynIQ2sA7mwBAmDrKdjXCGY5j4rVWt8vJCjlpYfLwOQw8fRgjvzgJZyiGkg+uwuL/cQvKdq2FUV3qdom+F8TU1rwTjoMLnDLR+/QohSVC8Oc1vce8myYhBO66665M1kLkaS1SIigEVvDpla+opINE6wBCjUsBnds9Su5YBa0shLH9p6Esj29VVUCipR/DPz2BgR8eQezdbgRWV6D84eux6BPXI3RtFWDw88ANHzQMlAiBvZwy0SV0SwldCCzlFj26yJy3561btw4f+chH8OEPfxi6ruPaa6/NRl1EnhNJHSit1zS081yTr8RP9iC0dimCdRVItPS7XY5rAnXlCG+qwcRb53wX0e0MTmL8QAsmftWO0PqlCG+pRdl9jSi5bdVUcMTxLjjDcbfL9AUDwD2GgTOOgzZ+FtMldE8n6GkauhkGQSlpNU0VFRV48MEH8Sd/8idYv349lFL4p3/6J/zkJz/Jdn1EnjEKICol1mgaXnS7GMqpZMcQnPEEQhsqfds0iZCB0nvXwO4bx8Shs26X4xplOYgd70bseDfMZQsQ3lKD8HW1KLphORLtg4gd60SyfZDBEVm0TdexSNPwv+NsUunSeqbvauKkiS4ya9P0h3/4h3jooYdw5513IhKJ4L//+7/x6U9/Gs899xyee+45JBK8d4RoLiJS4jpdhwDXRL6igMTpXoSvq4UIG1Ax/4WBlO5ogBY2MfTfvwUcvvsBwLowAuvCCLTiQCo4ohoLH9gIZziG2IluxN7thor7772STRqAnaaJdsfBKU6Z6DKSAAaknIodJ0qZtWl68sknMTQ0hEcffRT79u3LVU1EnhVxHNxqGKgWAl08fOwrsZNRFN2wHKG1SxE72uV2OTkVbFyC0NqlGP91O+zecbfLyTtyIonJNzow+eY5BOsrEN5Si5LbVqH4AysRP92L2LEu2FEmL2bCjbqOJZqGPXzoX3k1oQAAIABJREFUS1fQPX1XE1HKrE3TZz7zGTz00EN46qmncPz4cfzsZz/DL37xCygu9ojmJZJ6stmg6+hi9LivOP0TsHrHEFpf6aumSSsOoHRHA6zuUd5VdSVSIdHcj0RzP/SKIoS31CC0vhLha6tg9YwidqwL8TN9gM0JyXwIALtME51S4gTPqdAV9EiJNYbBnSE0Y9YWes+ePXjooYewbds2vPzyy/j0pz+No0ePoqKiAjt27IDGDpxoTvqVwpCUaOB/O74UPxmFWVUGvbzI7VJypvTeNRCGhtF9p7nymANnYBLjL7dg4MnDGHs5MhXVvnMtFv+PW1B821QCIc3NZl1HjaZhr2XxrUhX1KMUAkKgnOeaKCWtlduFCxfwrW99C7feeisefPBB/Nd//Re+9KUv4e233852fUSe08Kmybfip3uhpPLNnU2hjVUIrqrA+K/a4QzF3C6nIKmkg9ixLgz+77cxtOc4kueHUbR1GSr+4iYsuP9aBFaWu11iwdhtGOiVEu9wykRpmE7QYxgETZtz5PiRI0dw5MgRPPbYY9i5c2c2aiLytGYpcaNhYLEQ6OdWV19RkxaS7YMIravExK/bPT150cpCKLljNZLnhhA72ul2OZ5gnR+GdX4YWkkA4U01CG2qxsIHN8IeiiF2vAvxkz0MjriM9ZqGlbqOHyUS4OZGSsfFseMnGRpCuELTZJomampq0NHRAQAzdzNNe/bZZ7NbHZEHtaSecjZoGvr5xNN34k1RLFhdAXP5Qljnht0uJ2vKdjYCChjdf8btUjxHjicxcegsJg53INiwGOEttSi9YzVKtq9E/EwvYke7GLjxPrtNE0NS4jA/cylN4wDGlUIVd4ZQyqxN01/8xV+gpqYGjz/+OADg61//Ot59910AwOLFi1FeXo4nn3wy+1USeUiXUphQCvW6jjf4A9x3Eq39kHEb4Q1Vnm2awluXIbB8IUb3n4YcY0pZ1kiFxJk+JM70QV9cjKKZ4IhqWF2jiB3rRLy5z/cR7/WahjW6jp8kk+AcjuaiW0pUc3sepczaPj/wwAP4wQ9+MPP7ZDKJ+++/H/fffz8++clP4sMf/nDWCyTyGgWea/I1RyFxphfBhsUQpn7lry8wenkRSm6tQ6KlH/GTUbfL8Q2nfwJjL0XQ/703MHagBSJkoGz3uqngiFvroJUF3S7RNbtME2NK4ddMLKU5Yuw4XWzWd0JNTQ3a29tnfv/KK6/M/Lq9vR21tbXZq4zIwyKOgypNQ5nbhZAr4k1RCFNHsGGx26VkliZQtmstVNLG6IvNblfjSyrpIHa0E4M/PIKh/3McVucoim5cjoo/vxkL7t+AwDWL3C4xp1YIgY26jpcsC0m3i6GC0yMlSoRAiduFUF6YdXteIBBAWVkZRkdHAQB/9Vd/NfPPysrKEAgEslsdkUdN39dUr+v4Dbfo+Y7VNQp7KIbQhkrEm7wzjSm6eQXMqlKM/N+TUJOW2+X4nnVuGCPnhqGVBhHeVI3wpmoEVy+GPTiZCo6IQiW8PX3ZZZqYVAoHOWWieei5KAwiwjAI35t10nT8+HHs3r37kv/sD/7gD2bONxHR3JyTEkmluEXPx+JNUQRWLIJW6o1tU0ZlCYpvXoF4UxSJSL/b5dBF5FgCE6+fRf9ThzHyy1OQMQuld9Zj8aO3wFy+0O3ysqZKCFyn63jFtsHAe5qP7lTCLbfoEXCFSdO3v/1tPPXUUygtLcXevXvR29uLyspK7N69G3/7t3+LRx99NFd1EnmKA6BVSjToOmDxibwfxZuiKNm+EqF1lZh865zb5VwdQ0PZrrWQkxbGDrS4XQ1djqOQON2LxOleGEtLsODDGxHeXA3rvDcDSXaaJiwAL/MzluZpUCkklOJdTQTgCk3Ta6+9hs997nP44he/iH/+53+e+fs9PT34/Oc/j1dffTXrBRJ5VYuU+APDQBjgU1AfkqNxJM8PI7Sh8Jumku0rYVQUY/inJzy/3csr7N5xJFsHEGxcAmgCkN5K2KsQAjenpkwMX6f5UgCijB2nlCtebvvcc8/hueeew+rVq1FeXo6hoSG0tPBJItHVijgONNPEak3Db7lX2pfiTVGU3dcIo6oUds+Y2+XMi7lsAcJbl2HyWCeSHUNul0NzkGgbQHhTNcxlCzwXf3+fYUABeIFnmegqdUuJejZNhCucaZp2yy23oLW1FUeOHHlPw3T//fdnrTAir2uTEo5SU1v0yJcSzX1QloPQhkq3S5kXYeoo29kIZziO8Vfb3C6H5ijZMQRlSwRXVbhdSkYtALDdMHDIcTCsvDVBo9zrlhIVmgZvnD6lq5FW0/T000/jscceg2FMDabKysrwve99D5/73OeyWhyRlyUBdPC+Jl9TSQeJln6EGpcCeuHtmS+5YxW0shDG9p8GbE5LC44tkTw3hIDHmqa7TRM6gOd5lokyoCfVeFfyXJPvpbVau/vuu3Httddi3759+NM//VMcOHAAo6OjuOeee7JdH5GnRaTENZoG0+1CyDXxpii0sIlgXWEtXAN15QhvqsHkkfOwukbdLofmKdE6AGNRGHp5kdulZEQxgNsNA0ccB32cMlEGdF8UO07+ltY7IBqN4pFHHoGmafj617+OV155BX/3d3+HWIzH14muRkRKmEKgjh/GvpXsGIIzniioLXoiZKD03jWw+8Yxceis2+XQVUi2DwIAgqvKXa4kM+4yTYSEwD5OmShDepWCoxSbJkqvadqwYQP27duHc+fO4VOf+hS2b9+O73znOygrK8t2fUSe1pK62JaHTH1MAfFTvQjUlUOEC2PmWLqjAVrYxOi+04DDp/mFTI4lYPWOIbC6sCadlxIEcJdh4Khto4tTJsoQB0AfY8cJaTZNe/bswVNPPYVHHnkEL730Enbs2IF4PI4DBw5kuz4iT5sE0Dl9XxP5VrwpCqFrCK1d4nYpVxRsXILQ2qWYeKMDdt+E2+VQBiRbB2HWLIAIXTFQN6/dYRgoFgJ7mZhHGdYjJWPH6cqR4wCwa9cunDv3u3tEYrEYPvvZz+Lee+/NWmFEftHsONhmGNAA8Ci9Pzn9E7B6xxBaX4nY0S63y7ksrTiA0h0NsLpGC/5uKfqdRNsAirddg8DKciRO97pdzryYmAqAaHIcdPAKB8qwbqWwUQjomJo8kT+l1TZf3DBd7IUXXshoMUR+1CIlQkJgOZ9i+Vr8ZBRmVVleH8gvvXcNhKFhdP/pqVsfyRPsnjE44wkEC3iL3nbDwAIhsJdnmSgLuqWELgSWcIuer3GVRuSySOqpKKPH/S1+uhdKqrwNhAhtrEJwVQXGf9UOZ4ghQF6TbB9EYGU5oBXeolDH1GW2LY6DZk6ZKAu6U2fkuEXP3/jqE7lsWCn08b4m31OTFpLtgwitqwTybN2qlYVQcsdqJDuGEDva6XY5lAWJtgFoIQNm7QK3S5mzm3QdFZrGKRNlTc907DgnTb7GVRpRHohIiXqGQfhevKkHemkQ5opFbpfyHmU7GwEFjD5/xu1SKEuSZ4egbIlggV10KwDsMk2ckxK/5ZSJsiQBYFBKxo773GVffSFEWn8R0dWLOA5KhWCkqc8lWgcg4zbC6/Nni1546zIEli/E+CstkGMJt8uhbLElkueGEFhdWPc1Xa/rqNI03stEWdfD2HHfu2x63oULF6DSuOdg2bJlGS2IyI+mzzWt0XX0MC7XvxyFxJlehNZXQrykQ1nu5jTp5UUoubUOiZZ+xE9GXa2Fsi/ZNojSuxugLwoXzLm13aaJHinxG4eZZpRd3VJiu2FAgDk4fnXZSdPNN9+MW265Bbfccgsee+wxvPHGG3j44Ydx22234eGHH8brr7+Of/zHf0zrm3zhC1/Am2++ie7ubjQ2Nl66EE3Dv/7rv+KNN97AoUOH8PDDD8/v/xFRAepVCiNK8ZJbQqwpCmHqCK5Z7G4hmkDZ7rVQSRujLza7WwvlRKJtAAAK5qLbjZqG5akpExexlG09SiEkBBZy2uRbs06apj366KPYuXMnRkdHAQBtbW04fvw49u/fjx/96EdX/Cb79+/H97//ffz85z+/7Nc8+OCDqKurwwc+8AGUl5fjhRdewGuvvfaeOoi8rMVxGAZBsLtGYQ/FEFpf6ep0p/jmFTArSzHyf09CTXLrkx/IsQSs3nEEV1Ug9nb+/+zdbZrolxJvccpEOdB9URjEUBo7sch70rrctqysDOFweKZpAoBwOIyysrK0vslbb711xa/50Ic+hGeeeQZKKQwMDGD//v34oz/6I3z3u99N63sQFbqIlNhqGCgXAoP8QPa1eFMUJdtXQisNunKOyKgsQdEt1yDeFEUi0p/z70+5EQIQet9Tc61tENpNy7EwZAKJ/N0qfI2mYbWu4z+TSV42Sjkx3TRVaRqaGDoyZ164GDitpmnPnj3Ys2cPnn76aXR1daGmpgZ//ud/jj179mSskNra2vdMlTo7O1FTU5OxP58o3zWnnpZeq+t4jeeafG26aQqtr8Tkm5e+XDxrDA1lu9ZCTiQxdiCS2+9NORMC8L/CYYTf1zSd7xzHk5rAxxsrsTky6E5xaRpRCof4WUk5MgZgQinGjs+DDuAroRCetSwcKuDJcFpN05e//GWcPXsWH/rQh1BVVYVoNIof/vCHeOaZZ7JdH5FvXFAKHVLiHsPAr2ybe/R9TI7GkTw/7ErTVLJ9JYyKYgz/9ARUonB/uNHs6jUNYSGw17LQf9FTc3U+AX0iif3LSnH8t90uVnhlZ6UEN45SLnUzdnxeVmgaKjQNhZ6/mlbTpJTCj370o7TOL81XZ2cnli1bhuPHjwP4/ckTkR/ssyz8z2AQN+g6jhTw0xi6evGmKMrua4RRVQq7Zywn39NctgDhrcsweawTyY6hnHxPckeDrsNRCnstC8n3/bPS9gFY9YvRqiQg+fiGaFq3UtjMOxXnbPq8dqTA1zVpNU0AcPvtt2PDhg0oLi5+z9//+te/npFCnn32WXziE5/A3r17UV5ejp07d+L+++/PyJ9NVCiOOg66pMQu08TbjsNpk48lmvug7qpHaEMlxnPQNAlTR9nORjjDcYy/2pb170fuatA0dEj5ew0TMHVfWPjaapg1ZbAujOS8NqJ81SMlPmgYKAYw4XYxBaRB0xCVEqNX/tK8ltaM8atf/SqeeOIJbNq0CTU1NTN/VVdXp/VNvvzlL+Odd95BdXU19uzZg4MHDwIAnnnmGWzevBkA8NOf/hQdHR04dOgQnnvuOXzrW9/C+fPn5/f/iqhAKQD7LQvLNA0b+TTL11TSQaKlH6HGpYCe/T30JXeshlYawuj+04DNQ85eZmIqSCFymcPsVscQlC0RLJDocaJc6ZlO0OMWvbQJAPW6ftnPm0KS1qTpgQcewN13342urq55fZPHH38cjz/++O/9/U984hMzv5ZS4h/+4R/m9ecTeckRx8GHpMRuw8CJAh9l09WJN0URWleJYF0FEi3ZS7EL1JUjvKkaE2+dg91V6M8C6UrqNA2mEJddxChLInl+GIFVFQCnjkQzulPJtlVCoMXlWgpFjRAoFqLgt+YBaU6aBgcHMTLCET1RLjgA9ts2Vuk6Gvk0y9eSHUNwxhMIbajM2vcQIQOl966B3TeOiUNns/Z9KH9MX6LdMssiJtk2AKO8CPqicK7KIsp7A0ohqRSq+LM5bfWpXTNemDSl9ao/+eST+M53voOtW7dixYoV7/mLiDLvkG1jWErsNk23SyE3KSB+qheBunKIcHbeC6V3N0ALmxjddxpweIrODxp0HRekxOQsX5NoHQCAqWkTEQGY2kIfVYrb8+agQdMwJCX6PXD/ZFrb8772ta8BAO655573/H2lFJYtW5b5qoh8zgbwom3joUAAdZqGdg88oaH5iTdFUXzjcoTWLkHs6Py2SF9OsHEJQo1LMf7rdth9PNbsBxqA1ZqGN65wv5EcS8DuG0dwVQVi7zDJlmhat5RYxaYpbQ2ahhaPrGHSappqa2uzXQcRvc9rto1dpondhoHvJC+VcUV+4PRPwIqOIbS+KqNNk1YcQOmOBlhdo5h8K8cX6JJrlmsaQkKktYhJtA6g6KYVEEEDKsFLZImAqabpBl1HALhk+iT9zmIhsEjT0OyRS6jZKhPlqQSAly0Lmw0DtbyB3NfiTVGYVaXQK4oy9meW3rsGwtCm0vIKf9cEpWnmvpR0mqa2QQhNILByUbbLIioYPUpBEwKV/Ll8RQ1pnJ8sJGlNmnRdx6c+9Sls27YN5eXlEBe9UR544IGsFUfkd6/YNu41Tew2TTzNaZNvxU/3ouT21Qitr8TEr9qv+s8LbaxCcFUFxg5E4AzFMlAhFYp6TUOflBhO43yB3TMKOZlEcHUFEmf6clAdUf7rvih2/LxHmoFsqdd1TCiFLg+cZwLSnDR96Utfwic/+UkcPnwYmzZtwi9/+UssXrwYv/71r7NdH5GvTQI4aNvYqutYyqdavqUmLSTbBxFaVzl16cVV0BaEUHLHaiQ7hjJ+RoryX8Nc7ktRU9OmwMryq37fEXlFr1KQTNBLyxpNQ4vjeGYzQ1qv+O7du/Hxj38c3//+92HbNr7//e/jkUcewfbt27NdH5HvvWRZsAHsZJKer8WbeqCXBmGuuIqtUgIo29kIKGD0+TOZK44KQpUQKJ3jfSnJtgFoYRNmzYIsVkZUOGwAfUqhmg8yZ1UGoHKWS7QLUVpNUzgcRmdnJwAgHo8jHA6jpaUF1157bVaLIyJgDMCvbRvbdB3l/JD2rUTrAGTcQnj9/O9sCl+/DIFlCzF+oAVyLJHB6qgQzOU807Tk2SEoRyKwmtHjRNN6pOSk6Qq8dD/TtLRe8Ugkgi1btgAAjh8/js9+9rP4m7/5G/T09GS1OCKa8kIqeeYeI61jiORFjkLiTB+CDYshTH3O/3O9ogglt9Yh0dKPeFM0CwVSvmvQdYwohd45nC9QlgPr/DCCq8qzWBlRYelWCpVCME1tFg2ahoRSOOe3punxxx+Hkxrnf/GLX8TGjRtxzz334POf/3xWiyOiKYNK4bDj4IOGgVK3iyHXxJqiEKaO4JrFc/sfagJlu9ZCJW2MvtCcneIo7zVo2py25k1LtA3AqCiGvjCUhaqICk+3lDCEwGLu/risBl1Hm5TwUlRGWo+tjx8/PvPr9vZ2fPSjH81aQUR0afssC9t0HXebJv7bstwuh1xgd43CHppEaH0l4ifTnxYV37wCZmUpRn5xEirG944flQuBCk2bmVrPRaJ1EKV3AYFVFYj9pjML1REVlp7UtLZa09DLBL3fEwawTAj80mP/bjhZJCoQvUrhHcfBHYaBzN3WQ4Um3hRFYMUiaKXBtL7eqCxF0S3XIN4URaKlP8vVUb6aOc80j0WMHI3D7p9AcBXPNREBU2eaADAM4jJWaxq0OYbOFAI2TUQFZJ9lISwE7uTZJt+KN/UCAELpBEIYGsp2NUJOJDF2IJLlyiifNWgaJpVC5zzvS0m0DsBctgAiMPfzdEReEwMwxDCIy2rQdThKoe2i80x6RRGEWdj/vgq7eiKfuaAUjjsOdpgm0pszkNfI0TiS54fTappKttfBqCjG6PNnoBLeeuJHc1Ov62iVct73pSTaBiB0DYE6BkIQAVNb9DhpurQGTUOHlEimfq+VBlH+ya0Irpt/+ms+uKqmac2aNZmqg4jStM+yUCIEPshpk2/Fm6IwyotgVF0+FsRctgDhrbWYPNYJq2Moh9VRvikBUDPPEIhpdvco5GSSW/SIUhg7fmkmgJXvu58pvKUGEALJ9kH3CsuAOb/aixYtwqc//Wns378fL7zwQjZqIqJZtEmJ046Dew0jvSQX8pxEcx+U5SC04dJP7URAR9nORjjDMYy/2pbj6ijfZOS+FAUk2genJk18uE6EbqUQFgILOW16jzpNgyHE7z5vDA3hjdVItPQX/P2AaTVNuq5j165d+MEPfoCjR4/iX/7lX/Daa6/hpptuynZ9RHQJey0LCzUNH+C0yZdU0kGipR+hxqWA/vs/sEvuWA2tNITR/WcA2zt3ZND8NGgakkqh4yrvS0m2DkALmzBrFmSoMqLC1c0wiEuqT03fWlKT7dD6SmhhE7HfXHCzrIyYtWnavHkzvvKVr+DEiRP4j//4D/T19eFjH/sYBgYG8OSTT6K3tzdXdRLRRU5LiXbHwX2GwYOJPhU/GYUWNhGse+92qUBdOcIbqzH59nnYXaMuVUf5pEHT0C4l5h42/l7JjiEoRyLAi26JZhL0uEXvvdboOjqlxGTq90XX18KKjsHqLPyfR7M+pt67dy+Ghobw2GOP4dlnn5254FbNM32HiDJnr23j/wkGcaOu402PxXrSlSXPDcEZTyC0oXImSlyEDJTeuwZ23zgmDp11t0DKC0EAKzQNe+dxP9P7qaQD68IIgqsrMPGr9qsvjqiAjQCYZBjEe2gAVmka3kh93gSuWTQVRrT3lLuFZcis7fE3v/lNjI2N4Rvf+AaeeOIJ3HPPPdB1nU0TUR444TjolBK7TJNHDPxIAfFTvQjUlUOETQBA6d0N0MImRvedBhx+TlPm70tJtA7AqCiGviCUkT+PqJB1S4lqTppmLNc0hIRAS2oKF76+Fs5EEvHmPpcry4xZX+lvfOMb2LZtGz7+8Y9jYmICTzzxBE6cOIGFCxdi3bp1uaqRiC5BYepsU42mYYvOu1P8KH6yB0LXEFq7BMHGJQg1LsXEoQ7YfRNul0Z54lL3pVyNRNsAACDAFD0i9CjF7XkXmblEW0roi8IIrqpA7FiXZx7ipfVKHz58GJ/73OewefNmPP7443jzzTfx4x//GHv37s12fUQ0i7cdB1EpsYuBEL7kDEzCio4hvLkGpTsaYHWNYvLIObfLojzSoGk4LyUylVklR+Kw+ycQXM2miahbSiwQAkVuF5InGjQNfVJiWCmEr6uFsiViJ7rcLitj5tQex+Nx/OxnP8PHPvYx3HjjjfjlL3+ZrbqIKA0KwPOWhZW6jvV82uVL8aYojIpiCEPD6P7TmPftpeQ5Bqbif5szNGWalmgbgLlsAUSAE27yt57UcRVOm6bU6zoiUkIEdYQ2VCF+uhdq0nK7rIy54uNpwzBgpw503XTTTdAuemN873vfy15lRJSWNxwHfygldpsmmhKFfQcCzV38VC+Kb16B8dfPwhmKuV0O5ZFrNA3mRecLMiXZNoDim1YgsHIREs39Gf2ziQrJxbHjfr8Rr0oIlKbOT4a2VEML6Igd7XS7rIyatWn6sz/7M9x4443467/+awDAT37yEwwOTt3mGw6H8ZWvfAU//vGPs18lEV2WA+AF28bHAgHUa1rGF0iU31TMQv9333C7DMpDDe+7LyVTrK5RyJiFwKoKNk3ka/1KwZo+1+TzFNs105doK4mi62qRPD8Mu3fc5aoya9Z54kMPPYTvfve7M79PJBK44YYbcMMNN+AjH/kIHn744awXSERX9mvbxqhS2G2abpdCRHmiQdfRJSUyvmxRQLJ9EMFVFWB0J/mZAhBVigl6mLrUdkQpjKyqgL4ghEkPXGb7frO+yitWrEBTU9PM75ubm2d+ffLkSVxzzTXZq4yI0pYE8JJl4VpdxwreGUHkewJTi5hMRY2/X6J1AFrYhFldlpU/n6hQdEvJu5owNdlucRyEt9bCGYkh2TrgdkkZN2vTVFxcjHA4PPP7P/7jP575dVFREYqKmBdClC9etW1MKoVdnDYR+d4yIRAWApEsbddNnh2EciQCTNEjn+tRChVCwM8/ecuFQIWm4XhFGIFlCzF5tMuToUSzNk2nT5/GHXfcccl/duedd+LMmTPZqImI5iEG4BXbxnW6jio+9SLytYbp8wVZappU0oF1YWRqix6Rj3VLCU0IVPr45+70+cmzm6shkw7iv+12uaLsmLVpevrpp/Fv//Zv2LlzJ0TqzSCEwK5du/DVr34VTz/9dE6KJKL0vGxZsADs5LSJyNcaNA39UmJIZe9xb6JtAMbiYmhloax9D6J8N5Og5+NzTQ2ahr6QDqdxCeIne6AS3gzFmDU97xe/+AWqqqrwxBNPwDRNDA4Oory8HMlkEt/85jfx85//PFd1ElEaxgH8yrZxp2HgWSEwkMUFExHlrwZdR1OW07ySrQPAnfUIrq7wXLQwUbp6lYL0eYJeg67jpfVLIAzN058FV7yn6cknn8R//ud/4oYbbkB5eTmGhobw9ttvY2xsLBf1EdEcvWDbuN0wcJ9h4P+zvHOpHBGlZ6kQKBMCzVlewDkjcdgDEwiuKvf0QoloNhaAAaV8uy2+BMASQ8eZjZVItA54+r7AKzZNADA+Po6DBw9muRQiyoRhpfCGbWO7YeCXloURtwsiopyauZ8pB3e2JdoGUXR9LURAh0r68yk7UbePY8frdR3v1pfDLgpg0uMPT/z5ChN53PO2DR3APTzbROQ7DbqOUaXQk4PtucnWAQhdQ+CaRVn/XkT5qltKVArhy2vLGjQNr2+qhN0/AatjyO1ysopNE5EH9SmFtxwHtxkGit0uhohyavq+lFywukYgYxYCTNEjH+uREqYQWOzDLXqltQvQs6QYk7/x9pQJYNNE5Fn7LAshIXAXp01EvrFQCCzRtKxFjf8eBSTbBxFcVQ5fPmYnwtT2PMB/CXpBAG1bqmHELMRPRd0uJ+v89eoS+Ui3Ujhq27jLMMBAYCJ/mD7PFMlhileibQBaUQBGdVnOvidRPpmJHffZpGnlwjBO1S2C+W4PYOfoQY2L2DQRedhe20axELjdSCvzhYgKXIOmIa4ULuTwuoHk2UEoR/KiW/KtGICR6dhxHwldVwsBIHr0gtul5IS/Xl0in+mQEk2Og7tNE9ykR+R9DbqOFimRy2e+KuHA6hyZ2qJH5FM9UvoqdlyYGvo3VGJlywDi40m3y8kJNk1EHrfXsrBACGzntInI04oB1OYwBOJiibZBGEtKoJUFc/69ifJBt5S+OtNUtKEKVtDAgmNdbpc71o57AAAgAElEQVSSM/55dYl8qllKtDgO7jMM6G4XQ0RZU69P/ReesxCIiyRbBwCAW/TIt7qVQpEQWOB2ITlSel0tlkXHEe0adbuUnGHTROQDey0LFZqGm3W2TUReVa9psJRCuwtNkzMcgz04icBqNk3kTz2p/+78cK4pUFcOWV6EbSeirky23eL9V5aI8FspcU5K7DRNpgITedQaTcNZKWG79P2TbQMILFsIYfLhDPmPn2LHw9fXIjyeRHnLAMbdLiaHvP/KEhGAqWlTlabhek6biDwnAGBFLu9nuoRE6wCEoSFwzSLXaiByy7BSiCnl+dhxvbwIwZXluPm3UbRZbj2icQebJiKfOOo46JESu3nZLZHnrNI06ELk9H6m97M6RyDjFrfokW/1SOn57XlF19cCtoNtp/pdfUjjBm+/skQ0QwHYZ1lYrmnY6PEPdSK/adB1SKXQ6uYiRgHJ9kFGj5NvdXt80iRCBkLrK7HodB+K4zZa2DQRkVe95Tjo57SJyHMaNA0XlELc5ToSbQPQigIwqktdroQo97qlxEJNQ9jtQrIkvLEawtRx3Yko+qXEYA4v0c4HbJqIfMQB8LxtY7WuYw2nTUSeoGNqe15zHqRYJduHoKRi9Dj5Uk+qifDkFj1NIHxdDZIdQ7hhOOG7KRPAponId163bYwoxWkTkUes0DQEhMiLRYxK2LA6RxBg00Q+1P3/t3cnwXFe5/3vf+e8b3ejMZMgCZDgIJJo2CJFUaJly/lbtiXPkv9ywtgupepmc7XIXaUSV5KrnRep60pZWWR3s8lNqSpyNhnkRLEGW5Zjh4kjyZYsidRADCRBggAHgBgINHp4z7kLAIwkgiCG7n57+H42toAG+qlq4j3v8z7nPM9y2/E63KKX6tumoK1JyTdG1R7z+cm4kDQBDaYo6SeFgg4Fge6ox6dhQINZrhpXy01MbmhCiR2tsm2puEMBKuqq9yp4X5dtx5s/0avitawyZ6ckxTNEO27196kCuK2fF4ua816PhGHcoQDYpL4g0Lhzmo07kCX54QlJUoouemgwTtLlOmwGEfa0KbGrQ9k3RpUJAs14f2MrYiMhaQIaUE7STwsF3ROG6q2zizvQSIykvpjnM31UdC2r4uQ8W/TQkOqx7XjzsV65XFELp8aVsVaDVVLVrrT6+lQBrNnLxaIWvNfXONsE1KxdxqilCs8X5IcnlNzTKZPgNgONZcx7bTdG9bKPw7YklerfroWT4+ooOG2vsoc0lcTVDGhQ81rcpvfJINB2qk1ATcoEgaTqO1+QG56UCa0S+7bEHQpQUWPOyRqj7jpZV9P37JKs0fwbo8pU2fnJSiNpAhrYS4WCIolqE1CjMtZq0jlNVNn5gsLotNxCkdbjaDg3OujVwxa9wCh9907lhybkpheUsVYL3utClV1vKqUOPlEAGzUt6USxqN8KAm2pk6diQCPJVOtWGeeVPzvJuSY0nMvey3lfF23Hm+7slm1Oav71UUmLle1B51SFV5yKIGkCGtyLxaKMpC/TSQ+oKduNUae1VTGfaSW5oQkFLUmFPW1xhwJUTF7SZJ20HW8+1qvC5esqnJ9Si6TeBm4CIZE0AQ1v0nu9EkX6XBiKWxugdvQt3ZSdrtKbmPzZSXnn2aKHhjNWB0lTYk+nwu2tyr6xWGXqq9Lzk5VU258ogJJ4vlBQKOmLnG0CakZ/EOh6Fc9L8QtFFUanlWReExrMmHPqNka1vEGv+Viv3HxeC+9ekrS4Fbjgvc6QNAFoZJe81+tRpIfCUOm4gwGwJn1LW2WqM2ValB+eUGJHq2xbKu5QgIoZd05JY9RVo+eago4mJQ92KfvmmBQtXmEy1uqscyrGHFucSJoASFqsNqWN0UOcbQKqXoek7mptAvEBueEJSVLqwNaYIwEqZ2yp+ruzRpOm9L29kvPKvnlRkpSUtLcGrjflRtIEQJJ03nu9HUX6YiKhZNzBAFhVrZwviCazKl7L0kUPDaWW246bZKCmu3qUe/+K3FxeknTAWgXGNHQTCImkCcAHPFcoqM0YfZZqE1DVMtYq573OV3nSJC1u0Uvu3SKF3HKgMcxLmvG+JpOmpsM9sqlQ869fuPG1TBDIea+hGrjelFPtfZoAymbIOb0fRfpKGIq0CahemSDQkHOqhee+uaEJmdAquW9L3KEAFTPuXO1tzzOLW/Pyo9MqXrp+48sZa3XBe2VjDK0akDQB+JDnCgVtsVafXtr+A6C6pCX1GqOBGtkqUxidlssVlaKLHhrImHM113Y8eaBL4Za0sh+oMgVa3J5XK9ebcqqtTxNA2b3rnM5GkR5OJLhAAFWoz1pZY6p2qO1NnFf+7KSS+2kGgcYx5r1ajKmp+YfN9/YqmllQbuDqja/ts1ZJY6r+/GQlcE8E4CbPFYvabq3uo9oEVJ2+IFCxxual5IYmFLSmFHbX0i0ksHHjS3+ftVJtCra1KLlvi7K/uagPzjHILMVPpYmkCcAK3owijTqnhxOJmh7OB9SjjLU655zycQeyDvkzk/LOs0UPDeNG2/EaSZqaj/XKFyJl3x770Nf7gkDjzmk2priqSW18kgAqymtxblOvtTpKtQmoGglJd9TgvBS/UFTh4oySzGtCg7jmvRa8V08NNIMw6YSa7uxW9tQl+YX/GV9rtLgduNauN+VC0gRgRb+KIl1xTg/TfhyoGvutVVhDTSA+KD88oUR3m2wrk+DQGMZrpBlE+u6dMqFV9o3RD319lzFqqdHrTTlU/ycJIBZO0guFgvYHge6sgYs+0Agy1tbsvJTc0IQkMegWDWOsFipN1ih9zy7lzkwqmpz/0LcyNTJEu1K4EwJwS7+MIl1zTo8kEnGHAkCLNzGj3mv+9i+tOtHkvKKprFIkTWgQ485pq7VKxR3IKlL92xW0ppR9ffSm72Ws1aRzmvB+hZ9sPCRNAG6pKOnHxaI+FgQ6SLUJiJXV4ryUwRreKpMbmlByb6cUcj1B/VtuBtFTxetn87FeFSfnlT87edP3Mpxn+pDq/RQBVIX/KBY16z3VJiBme6xVkzE6XcM3MbnhCZlEoOS+LXGHApTdjbbjVbpFL9zZrsTO9hWrTNuNUae1tTMPrgJImgCsKi/pp4WCjgSBdlfphR9oBP1LT6trudJUuDAtlysqRRc9NIAr3ivyvmqbQTR/olduoaiFd8Zv+t7yfKbTNXy9KbXq/BQBVJWfFYvKUm0CYpWxVped03TcgWyG88qfvUYzCDSESNIl76uy0mTbUkpltiv79ph84eZqUiYIdN17jXOe6QaSJgC3ldVi4nQsCKq/ExBQh4wWh0zWw/mC/PCEgtaUwh2tcYcClN24c1V5pil9zy5JuqnN+LK+pfOTpEz/o/o+RQBV6aVCQUVJX6XaBFRcjzFqrZN5KbkzE/LeK3WQahPq35j32m6MqmpMfGiVPrJTucGrcrO5m77dIambJhA3IWkCsCbXJf2iWNSng0BbqTYBFVVP81J8tqjCxRklSZrQAMacU2CMdlTRutl0qFs2nVixAYS0WNWW6uN6U0okTQDW7MfForykr4Zh3KEADSVjraac05U6OV+QH55QortNtjUZdyhAWd3ooFdFW/Sa7+1V4dKsCqMrn5DMWKuc9zpP0vQh1fMJAqh6U97rl1GkB8JQ7XEHAzSQepuXkhuakCQl91NtQn1bbqRQLeeBE/u2KNzWcssqk7RY2R52TrW/Gbi0SJoArMsLhYICSV/mbBNQEV3GaGudzUuJJuYVTWc514S6l5c04VzVVJqaj/Uqmstr4f3LK34/LanXGFqNr6A6PkEANeOK93otivT5MFRz3MEADaBe56XkhiaU3NsphdyKoL6NVcmspmBLWqkDXcq+eVGKVt7q22etrDF19ZCmVOL/BAHUnBcKBTUZoy9wtgkou74g0Jz3ulgn55mW5YYnZRLBYuIE1LFx59RjjOLeoJe+t1e+6BaTplvoCwIVvdcZkqabkDQBWLdR7/WbYlFfTCSUijsYoM71W6sh5+puXkrhwpRcvqgUg25R58acU9KYWDvPmlSgpsM9Wnjvsvx84Zav67dW55xTvoKx1QqSJgAb8lyxqBZj9DmqTUDZtEnqsbYu5jPdJPLKn72mJEkT6tzYUpV4Z4xJU9NdO2WTwS2H2UpSQtK+Oms6U0okTQA25KxzeieK9JVEQqRNQHnU+7yU/NCEgraUwh2tcYcClM1y2/GeuM41mcU24/nzUypevn7Ll+23VmGdDNEuB5ImABv2fKGgDmP0GapNQFlkrFXee52r06Qpd2ZS3nsG3aKuXZc0631sSVPq4DYFHU2af/3Cqq/LWCvnvYbq9HqzWSRNADbsfec0FEX6ahgqiDsYoA5lgkBn6nheis8WVBybUerA1rhDAcpq3LnYtuelj/Uqms4qvzQf7VYyQaBR7zVfobhqTcWSpgMHDujZZ5/ViRMn9Oyzz2r//v03vaarq0t/93d/p5/+9Kf6xS9+ob/4i79QEHArBlSz5woFbbNWn+JvFSipJkl7jNHpOn/qmxuaVKKnXbYlGXcoQNnE1XY83NGq5J5Ozb9xUat1kwkkHbBWg2zNu6WKfXrf//739dRTT+mBBx7QU089pSeffPKm1/zRH/2RBgYG9MUvflFf+MIXdPfdd+uRRx6pVIgANuBt53TeOT2cSMTeThWoJweX56XU+U1Mbnjx6XeSahPq2LhzajVGlT69l763Vy4faeHk2Kqv22OtmhrgIc1mVOQgQldXl44cOaLHHntMkvTMM8/oe9/7nrq6ujQx8T+lQu+9WltbZYxRMplUIpHQ+Ph4JUIEsAnPFQr6v1IpfSUMdb5GL7jT3mu0zubgoLZlgkCR9xqu0b+ptYquzimaXlDqQJcW3mbNR30aW/o73lnB7nSmOaGmj+9Q9u0x+dzqD1+Wh2jX+0OazahI0tTb26vx8XG5pX8kzjldunRJu3bt+lDS9Fd/9Vf6m7/5G7355ptqbm7W3/7t3+q1116rRIgANuH1KNKYc/pmsna310Te6//OZjUbdyDAkj5rNeKccnEHUgG54Qml7+qRQisV6ztJRGO60Xa8gklT+ugumdCu2mZ8WcZaXXZO0xWIq1ZVVcurRx99VO+++66+/e1vq7W1VT/4wQ/09a9/XT/60Y/iDg3AKrykv1xY0I642qluUrcx+j9TKWWCQK/zlA1VINRi+9+fFYtxh1IR+aEJNd/bq+SeTuXPTMYdDlBy17xXznv1VKoZRGDUfHSXckMTiq5lV32p0eJ4gzdZ/1ZVkaRpdHRUPT09stbKOSdrrbq7u3Xx4sUPve7xxx/Xd77zHXnvNTs7qxdffFGf+cxnSJqAGnBd0vUa3UZ0TtL/4b0y1pI0oSrcYa0SxtTtfKaPyl+YkstHSh7oImlCXfKSxivYdrzpYztkW5KaX0OVqccYtTKf6bYq8slNTEzo1KlTOn78uCTp+PHjOnny5Ie25knSyMiIHnroIUlSIpHQZz/7Wb333nuVCBFAA4skDTmnDB0AUSUa7nxB5JU/N6nUQZpBoH6NVbDtePpYr4pX51Q4d+22r83U+RDtUqnYXponnnhCjz/+uE6cOKHHH39cTzzxhCTp6aef1tGjRyVJ3/3ud3X//ffr5Zdf1ksvvaTh4WH94Ac/qFSIABrYoHPabYzScQcCaGleinOaizuQCsoPTShoa1K4vSXuUICyGHdOXdYqVeb3SfR2KNHdpvnXb19lkhYf0kw5pys0Q1pVxc40DQ4O6utf//pNX//93//9G///3Llz+r3f+71KhQQANwxEkWwioYPW6iRP2xAjo8V24680yHmmZbkzk/LeK3mwS8UrjZQuolGMLyUl3cZopIwJSvpYr1y2oIV3L63p9ZkKNqeoZbV5ahsASmzYOUXeq48teojZHmOUbqDzTMv8fEHFsVmlDnTFHQpQFh9sO14utj2lVN82Zd8aW1Mnyi5jtNVaDTbY9WYjSJoAQFJe0jnnbpwlAeLSyOcLcsMTSuxsl21OxB0KUHKXvVfkfVmTpuZ7eyVJ2Tcv3uaVi5bXvNONcn5yE7g7AIAlA87pDmuraxYDGk7GWl11TlMNeL4gN7TYICpJtQl1KJJ0pYxtx03CqumuncqdviI3u7YJb5kg0Jz3utiA15v1ImkCgCWDzilhjPZTbUKM+oJApxuwyiRJ0dU5RTMLSh0kaUJ9GnOubJWmpsM9sk3hmhtASIsPaYacEynT7XFnAABLlmdUsEUPcek2Ru3GNE6r8RXkhieU3LdFCio0BBSooDHvtcMYleP0bPreXhXGZlQcm1nT69sk9VjLfKY14s4AAJbMSxplXhNitJywN+J5pmX5oUmZRKDkni1xhwKU3LhzCozR9hJv0Uvu36pwa/P6qkwNfH5yI0iaAOADBqJIB63l4ohYZIJAM97rUgOfL8ifvyaXj5Rk0C3q0NjS33ZPiXc0pI/1KprNKXf6ypp/ps9a5b3XOZKmNeG+AAA+YMA5NRmj3RWa2g58UIatMlLkVTh3jdbjqEvjy23HS7jGBFublbpj62LHPLf2By6ZINAZ59TgV5w1I2kCgA9Y3qbQzxY9VNgWY7SNIZOSFs81Be1NCre3xB0KUFI5SZMlbgbRfKxXvhAtzmZaoyYtzoRr1KYzG0HSBAAfMOW9rjinPppBoMKWzzM1chOIZflhWo+jfo2XsO24aQrVdKhbC+9dls8W1vxzB62VbfCmM+vFXQEAfMSAc+qj0oQK67NWWe91voHPMy1z8wUVxmbYooe6NOaceqxVKdKm9JGdMolgXQ0gpMWteZH3GqbStGYkTQDwEQNRpHZjyjaAEFhJfxAwL+UDcsMTCne2yTQn4g4FKKkx79VkjLZsdo2xRul7dyl/7pqiq3Pr+tGMtRpxTmsbgQuJpAkAbrJ8poR5TaiUFkm7aALxIfmhCRljlNpPFz3Ul+VmEJt9MJfq26agrWndVaZQ0h2cn1w37ggA4CMue69p75nXhIrpY17KTYpX5hTNLih5kC16qC9jy0nTJh/MpY/1qngte+MM4FrdYa0SxnC9WSeSJgBYwWAU0QwCFZOxVgXvdZabmA/JDU0quW+rFLBVFvVjVtJ17zfVdjzsblOyt0PZN9ZXZZKkfprObAh3BACwggHntM1abeVcEyogY63OOKdi3IFUmfzwhGwyUHJ3Z9yhACU1vsm2483HeuVyRS2cGl/3z/YFgUad0/pOQYGkCQBWcHrpCRzVJpRbStJezhesKD9yTb4QsUUPdWfM+w1vz7MtSaU+tl0LJ8fl8+urFlktthvn/OT6cTcAACsY9V5Z72kGgbI7YK0CY7iJWUnklT93TSmSJtSZcefUbow2Mr45fXSXZI3mN7A1b7cxSnOeaUO4GwCAFXhJg87RDAJllwkCOeal3FJueEJBe5OCbRu5vQSq03IziHVv0QuM0kd3Kj80ITe9sO73zdB0ZsNImgDgFgaiSLusVWvcgaCuLc9LWf/tT2PID09KEoNuUVfGloZYr7fteNOd3bLNyXW3GV+WsVZXndMUQ7TXjaQJAG5h+UlcH9UmlEkoab+1GuSp7y25ubwK4zNs0UNdmfReee/XXWlqPtarwuXrKpyf2tD79gWBTnO92RCSJgC4hXPOqcC5JpTRXmuV5HzBbeWGJhXubJNJJ+IOBSgJL2l8nc0gEns6FW5v3VCbcWmxqtVuDK3GN4g7AQC4haKkM86RNKFslv9t0QRidfnhCRljlDqwNe5QgJIZc25ds5qaj/XKzee18O6lDb3fcjdYHtJsDHcCALCK085pj7VKxR0I6lJ/EGjMOV2PO5AqV7x8XdFsTknONaGOjDunrcYouYbXBlvSSh7sUvbNMSna2HmkTBBoxntd4jzThpA0AcAqBqNIgTE6SLUJJWa0NC+Fp75rkh+eUPKOLVLAwGnUh3HvZY1R9+2qTaFV+9fvlM9Fyr55ccPvl2E+06ZwFwAAqxhyTs57mkGg5HqNUTPnC9YsNzwhmwyV3N0ZdyhASay17Xj7l/sV7mjVzHPvys3lN/ReW4zRNh7SbApJEwCsIidphHNNKIPleSl0slqb/MiUfCFSki56qBOXvZe7TTOI9L29ajrUrbn/PKv8mckNvxfnJzePuwAAuI0B53TAWoVxB4K6krFWk85pkvMFa1N0yo9coxkE6kZR0hXvb9kMIrGnU60PHlRu4KrmXxnZ1HtlrFXWe13gerNhJE0AcBsDzilhjPZRbUIJZYKArTLrlBuaVNCRVtDVHHcoQEmMObfi9jzbllLH/75T0bV5zTz/3qbfJxMEGnJOpEwbxx0AANzG8pkTtuihVLYbow7mM61bfnhCkhh0i7ox5r12GPPhG/LQquMbh6XAavpfTskXNrelrlXSLppAbBp3AABwG9clXXTuxhkUYLP6OV+wIW4ur8L4LK3HUTfGnVNojLZ9YIte25cySvS0aea59xRdy276PZYbGfGQZnNImgBgDQajSH3WimbHKIW+INB17zXG+YJ1yw9PKLGrXSadiDsUYNPGl64By1v00vfuUvpwj67/59kbldXN6rNWBe91lqRpU0iaAGANTjuntDHqXcf0duBWmJeycbmhCRljlNpPQwjUvvHltuPGKLG7Q60P9ik3eFXz/32uZO+RsVZnnFOxZL+xMdEMCgDWYHBpYesPAl0osvRg4zqM0Q5r9e/8O9qQ4uXriq7n1PLAHWo61B13OBviFoqaffE9+QJP/htdVtI159TR3qSORw8pmsqWpPHDspSkvdbqBa43m0bSBABrMOm9JpbmNb0cdzCoaTfmpbBVZsPmfnlOTXd2S0HtbZgxVmrat12FsRllf30h7nBQBUat9PYjH1tq/HBSPl+6KvQBaxUYQ2W7BEiaAGCNBpzTnTSDwCZlrNWC9zpP0rRhC2+NaeGtsbjD2LDOb92t5vt2K/ubUSniXFuj+/fP79fV7lbN/PCkosnNN374oEwQyHmvYa43m1Z7j2gAICYDUbS4tYpzTdiETBBo2DlxC9O45l4dUdCaUtPhnrhDQczS9+zS+KEdeui1UTUPT5b892es1YhzWij5b248JE0AsEbL26mY14SNapa0i60yDa8wMqXCxRm1fGqPaMnZuBK9HWp98KASQxN66FcXtbPED+RCSfutvXEmF5vDyg8AazTuvWa9Z14TNuygtbIMtYWkuVdGFHSk1fTxHXGHghjY1uRi44fpBc0+966spJ4SP5Dba62SXG9KhqQJANZhIIqoNGHD+oNARe91hpuYhpcfnlDxynU137837lBQaYFRxzcOSwmr6X85pZl8pHnvS15pyjBEu6RY+QFgHQad03Zr1cm5JmxAn7U665wKcQeCqjD3yojCrhal+rbFHQoqqO2LGSV2tmvm+fcUTc5LksacuzHgtlT6g0Bjzul6SX9r4yJpAoB14FwTNiop6Q5r2SqDG3Knr6h4bV7Nn6ba1CjSR3cqfWSn5n55TvnBiRtfH/e+pNvzjBa3A3O9KR1WfQBYh/POacF79ZE0YZ32My8FH+Wl+VfPK9HdpuQdW+KOBmWW6G1X60N9yg1PaO6/zn7oe2POqcMYNZfovXqNUTPXm5Ji1QeAdXCShpxTP80gsE4Za+W81xBPfvEBC+9cUjS7wNmmOmdbk2p/9LCi6QXNPPfuTd8fW7oulKratNywiEpT6ZA0AcA6DUSReq0t2RNBNIZMEGjUe5V2dCVqnvOaf+2Ckrs7lejtiDsalENg1PHoYZmlxg8+d3P1Z9wvDjkuVTOIjLWadE6TnuHJpULSBADrtPzkro9qE9YokHTAWp1mqwxWkH17TG4+T7WpTrV9IaPErnbNPv/+jcYPH3XVexVKeK4pEwQ6TZWppEiaAGCdzjingvc0g8Ca7bVWKWMYMomVFZ3mf31Bqf1bFe5ojTsalFDT3TuVvnun5v77nHKDV2/5Oq/FalMpOujtMEYdXG9KjhUfANapKOmccyRNWLM+5qXgNrK/uSi3UKTaVEfCXe1q+8LKjR9WMu5cSbbnMZ+pPFjxAWADTjunfdYqGXcgqAkZa3XJOc3EHQiqls9Hyr4xqqb+7Qq2cmKy1tmWpDoePaRoJqeZ595bLCXdxrj36jJGiU2+d18QaNZ7jXGeqaRImgBgAwajSIExOkC1CbdhtHi+gC5WuJ35Ny7IFyI1f2pP3KFgMwKjjm8ckkmGmv6Xk/K54pp+bMw5WWPUvclqU7+1GqTKVHKs9gCwAUPOyXl/o60rcCs7jVEL81KwBj5bVPbNMTXd2S3b0RR3ONigtof6lNjVodkX3lM0sXLjh5Ustx3fzLmmDmO0naG2ZUHSBAAbkJV0gSG3WAPmpWA95n91XnJezfdRbapFTUd2Kn10l+ZeGVFu4NaNH1ZyyXu5TXbQu3GeietNybHaA8AGDUSRDlorak1YTcZaXXNOVzlfgDVwc3ktnBpX+q4e2RZOTdaScGe72r7Yp9yZSc3955l1/3xRi63HN9MMImOtFrzXeZKmkiNpAoANGnBOSWO0l2oTVpGxlta/WJe5185L1qj5vt1xh4I1si1JdXzjkNxsTjPPvbumxg8rGdtspSkINOycuOKUHis9AGzQ8hkVWo/jVrqM0RZrGTKJdXHTC8q9d1npo7tkmsK4w8HtWKP2Rw/JpkJN/csp+YW1NX5Yybhz6jZGG6k1NUvaxfnJsmGlB4ANmtXiAkczCNxK/1JCTScrrNfcKyMyiUDNx6g2VbvWh/qU7O3QzAvvK7o6t6nfNeacEsZo2wa26PUFgawxnGcqE5ImANiEAefUZ+2Gngqi/vUFgea810XOM2Gdosl5LQxcUfreXTJJHsxUq6YjPWq+Z5fmXh1R7vSVTf++8aVrxUY66GWsVdF7nSFpKguSJgDYhMEoUosx2lWCKe6oP5mleSmkTNiI+VdGZJsSSh/dFXcoWEHY06a2L2SUOzupuRPrb/ywkhttxzdSabJWZ51ToSSR4KR+0I4AAB3FSURBVKNImgBgE5bPqrBFDx/VLqmHeSnYhOKl68qdnVTzJ3ZLIbds1cQ2J9TxjcNy13Oa+dHGGz98VFbS9AaaQSQl3cH1pqz4CwSATZjwXtecoxkEbtLHfCaUwPx/j8i2JJW+qyfuULDMGrU/eli2afONH1Yy5ty6K037rVVAE4iyYpUHgE1aPtcEfFDGWuW91whJEzahMDqt/IVpNX9yj2TZBlwNWh88qOTuDs28uPnGDysZc27dZ5oy1sp5ryGuN2XDKg8AmzTgnLZYu6FuR6hfmSDQkHPiuS82a/6VEQXtTWo61B13KA2v6XC3mu/t1dxr55V7f/ONH1Yy7r3SxqhjHT+TCQJd8F7ZskQEiaQJADaNeU34qLSk3cYw1BYlkT87qcKl2cVqE89mYhP2tKntS/3Kn7umuf8YLtv7LDeDWOu5pkDSAWvZmldmrPAAsElj3mvOe5pB4IaD1i7OS+EmBiUy/8qIwq3NSvVvjzuUhmSWGz/M5TT9b++UrPHDSsbW2XZ8r7VK8ZCm7EiaAGCTvBarTVSasKwvCBR5r2FuYlAiuYGrKk7MqflTe+MOpfFYo45HD8k2hZouQ+OHj5r2Xlnv19wMYvlMLQ9pyosVHgBKYMA5dVur9rgDQVXot1bnnFM+7kBQV+ZfPa/EjlYlD2yNO5SG0vr5A0ru7tTMj0+reKX0jR9WMu7cmrfn9VurS85ppswxNTqSJgAogUHmNWFJKGkf81JQBgvvXVY0nVXL/fviDqVhNB3uVvOx3Zr/1Xnl3rtcsfcdW2OlyWixss31pvxImgCgBEacU857Wo9D+61VwhhuYlB6zmv+tfNK7GpXYk9n3NHUvbC79Ubjh+u/KF/jh5WMOadOa5W+zet2GqMWzk9WBKs7AJRAJGnYOfVTaWp4y2fbBrmJQRlkT44rup5Ty/2cbSonk06o47cPy83lNf2j8jZ+WMn4UjOI223RyzBEu2JImgCgRAaiSL3G3PbJIOpbJgg06pzm4w4E9Snymv/1BSX3bVHY0xZ3NPXpRuOHhKb/9ZR8tryNH1Zyo+34bbboZazVNed01Vc4q2tAJE0AUCIDzskao4Ns0WtYVovtxk9TZUIZLbx5US5boNpUJq2fO6Dknk7N/OS0ipevxxLDVe9V8P62bccznJ+sGFZ2ACiRM86pyLymhrbHWjUxLwVl5gtO86+PKtW3TcG2lrjDqStNd+5Q8yd2a/7XF5R7t3KNHz7KSbp8m2YQ24zRFpKmiiFpAoASyUs65xzzmhrY8mfPTQzKLfubUbl8kWpTCYU7WtX25X7lR6Z0/edDcYdz27bjnJ+sLFZ2ACihQed0h7VKxB0IYtFnra44pynOF6DM/EJR2d9cVKp/u4JOTlJu1o3GD9mCpv+t8o0fVjLmvbYbo/AW3+8LAs15r4tcbyqCpAkASuh0FCk0RvupNjWkDPNSUEHzv74gRU7Nn9oTdyi1zUgd//uQbDqh6X85JZ8txB2RpMVmENYYdd9ii16/tRqMomrI7xoCqzoAlNCQc3Les0WvAfUYozbmpaCC/HxB2ZPjajrULduWijucmtX6uYNK7u3UbIyNH1Zyo4PeCutJu6RuzjNVFKs6AJTQvKSLNINoSJxnQhzmXzsvSWq+b3fMkdSm1J071Hzfbs2/fkELMTZ+WMkl7+Vu0Qyij/lMFUfSBAAlNhBFOmAtF9gGkwkCTXuvy5wvQAW52ZwW3r2k9JGdMmlOU65HuKNV7V/uV/78lK7/fDjucG5SkDTh/YqVpoy1ynmvEZKmimFNB4ASG3BOTcZoD1v0Gkpm6XwBUGnzr56XQqvmT1BtWiuTDtXxjcNyCwVNP/uO5KrzYcf4LWY1ZYJAw86JK07lsKIDQIktb5fgXFPj2GqMujhfgJhE17LKnb6i9D27ZFJsDb4tI3V8/ZBsS7KqGj+sZMw5dRujD27QS0vazTy4imNFB4ASm/Zel51TP0lTw1hOkE9TaUJM5l8ZkU2FSt/TG3coVa/1cweU3LdFsy+dVvFS9TR+WMmYc0oao64PnGs6aK2sMVxvKowVHQDKYMA59QWBbj3LHfUkY62y3muU80yISfHKnHJDE4tb9EJu724l9fEdar5vj+bfGNXCqUtxh3Nb40vXlA82g8gEgSLvdYZKU0XxVwUAZTAYRWo1Rj23mK+B+tIXBBp0jnkpiNXcKyOy6YTSd++MO5SqFG5vUftX+pW/MKXr/z4UdzhrslLb8Yy1Ouec8nEF1aBImgCgDE4vn2ui9Xjda5W0y1rmMyF2xbEZ5Uem1HzfHinggc0HBZ1N6vjdI3ILxapu/PBR85JmPtAMIiFpH+cnY0HSBABlcMV7TTPktiEwLwXVZO6VcwraUmo61BN3KFXDtqXU+e2jMoHV1D+9JT9fvY0fVjLm3I3tefutVcIYrjcxYDUHgDIZiCKSpgaQsVYF73WOmxhUgcLIlApjM2r51B5xqFKyLUl1fvtumWSoqX98S9HEfNwhrduYcze25/Ut/S/jDSqP1RwAymTAOW21Vls511TXMtZq2DkV4w4EWDL3yoiCzrRSH9sRdyixMumEOr91t2xLSlP//LaKl6u7U96tjHuvFmPUpsUt3xecU+2lfrWPpAkAymT5jAutx+tXStJezhegyuSHJlS8cl0t9++NO5TYmFSgzm8eUdDRpOln3lZxbCbukDZsuRnEbmt1kPOTsWElB4AyGfVe897fOPOC+rM8L4WtMqg2c6+OKNzWomRfV9yhVJxJBOr83bsVbmvR9L+eUuHCdNwhbcrYUtvx+8JQTQy1jQ1JEwCUiZc06BznmupY39K8lCFuYlBlcu9fUfFatvGqTaFVx+/cpbCnTdP/9o7yZ6/FHdGmTXmvBe/1KZrOxKpiK/mBAwf07LPP6sSJE3r22We1f//+FV/36KOP6uWXX9bPfvYzvfzyy9q2bVulQgSAkhuIIu20Vm1xB4Ky6LdW551TLu5AgI/y0vxrI0r0tCu5b0vc0VRGYNTxjcNK7OnQzPPvKj84EXdEJTPunFLG6IpzmmKIdiwqljR9//vf11NPPaUHHnhATz31lJ588smbXnP06FH96Z/+qR577DE99NBD+u3f/m3Nzs5WKkQAKLnlJ4Js0as/oRbb//LUF9Vq4dQlRbMLam6EapM16vj6IaX2b9Xsj08r996VuCMqqeUtelxv4lORpKmrq0tHjhzRM888I0l65plndOTIEXV1fXif7R/8wR/or//6r3XlyuI/9NnZWeVyPL8DULvOOac885rq0j7mpaDaOa/51y4ouadTid72uKMpHyO1f+1jSmW2afblAS2cHI87opJbbgZBE4j4VGQV7+3t1fj4uNzSB+6c06VLl7Rr164PvS6TyWjfvn165pln9OMf/1h//Md/XInwAKBsIklnnFOGSlPd+WQQqOg9NzGoatm3x+Tm82r+VP1Wm9q+3K+mO7t1/RfDyr5xMe5wyuK0c8p6r3d4SBObqnr0GQSB7rzzTj322GM6fvy4HnroIX3729+OOywA2JQB57THGDXFHQhKpk3SA2GoX0aR5uIOBlhN0Wn+16NKHehSuKM17mhKrvULfUof2am5X57T/Gvn4w6nbIad0x9ls7rGeabYVCRpGh0dVU9Pj+zS9hRrrbq7u3Xx4sWbXvejH/1I+Xxec3NzevHFF3XPPfdUIkQAKJuBKJI1RgfYolc3vpxIKJT0YqEQdyjAbWV/Myq3UKy7alPLZ/er+d5ezf/qvOb+62zc4aDOVWQFn5iY0KlTp3T8+HFJ0vHjx3Xy5ElNTHy4q8kzzzyjz3/+85KkMAz12c9+Vu+8804lQgSAshl2TpH36meLXl1olvRgGOpXUaTLPPVFDfD5SNnfjCrVv03B1ua4wymJ5k/vVcun9mr+Nxd1/efDcYeDBlCxx55PPPGEHn/8cZ04cUKPP/64nnjiCUnS008/raNHj0qSfvjDH+rq1av6xS9+oZdeeknvv/++/v7v/75SIQJAWeQkjTinPipNdeGhpQGTz1NlQg2Zf31UKjo1f3JP3KFsWvoTu9X6mf3KnhzX9Z8OxB0OGoTp6empi8dkxWJRV69e1bZt2xSGYdzhAMCHfCuR0ENhqD/KZlWMOxhsWErSX6TTGowi/b/5fNzhAOvS+uBBpe/t1cT/94rcTG12J04f3am2L/Vr4f3LmvnRu4tTxIF12GjOwGNPAKiAAeeUMEZ3UG2qaZ8LQ7Uao+eLpL6oPfO/Oi95X7PVpqZD3Wr7Ur9yQxOaee49EiZUFKs3AFTA4FJbauY11a5Q0lcSCb0bRTpD21/UIHc9r4VT40rftVO2JRl3OOuS6t+utq9+TPlz1zT97CnJkTGhsli9AaAC5iRdZF5TTftMGKrDGD3HWSbUsPlXz0vWKP2J3XGHsmbJA11qf+TjKlyc0dQPT0oRCRMqj6QJACrkdBTpoLUycQeCdQskfTUMNRRFep8qE2pYNL2g3HuXlT66S6ap+s+AJ/ZtUcejh1S8fF3Tz7wtFfn7QzxImgCgQgadU9oY7TGkTbXmk0GgbdZSZUJdmHt1RDYZqPne3rhDWVWit0Odv31Yxcl5Tf3z2/L5KO6Q0MBImgCgQgaWKhR9bNGrKUbSw4mEzjunt6kyoQ5EE/PKDVxV+t5emUR1Xo/CnjZ1HL9L0cyCpv7xLfkFmq8gXiRNAFAh17zXVefUTzOImnJvEGintcxlQl2Ze2VENp1Q+p5dcYdyk3B7izq/eUQ+W1hMmLL87SF+rNwAUEEDzlFpqjGPJBK65Jx+HbE1CPWjeGlW+bOTiw0hwuq5HQy2NqvzW3fLFyJd+4c35a4zDw3VoXr+SgCgAQxEkdqNUTfnmmrCYWu111q9UCgwEgZ1Z+6VEQUtSaXv6ok7FElS0NG0mDB5aeof3qrZAbyoTyRNAFBBg0tnYpjXVBseSSQ06Zz+myoT6lDhwrTyo9OLw25tvA9ybFtKnd8+KhNaTf3Dm4quZWONB/goVm0AqKBx7zXjPfOaakDGWmWCQC8WiyJlQr2af2VEQXuTmu7cEVsMtjmhzm/dLdMUauof31I0MR9bLMCtkDQBQIUNRhGVphrwcCKhGe91okjXLtSv/JlJFS7PqvlTexXHEDmTDtX57aOyrSlN/fPbKl6+XvkggDVg1QaAChtwTtus1RbONVWtfdbqriDQTwoF0bcL9W7+lRGFW5uVymyr6PuaVKDOb96toKNJ0z88qeLFmYq+P7AeJE0AUGEDS+djqDZVr4fDUHPe6+dUmdAAcgNXVZycV/P9+yr2niZh1fm7RxRua9H0v76jwvmpir03sBGs2ABQYee9V9Z79ZE0VaWdxuhYGOpnxaIW4g4GqAS/WG1K7GhVcv/W8r9faNXxO3cp7GnXzL+9q/zZyfK/J7BJrNgAUGFe0pBzNIOoUg8nElrwXj9lmC0ayMJ7lxVNL6jl03vL+0aBUcejh5TY06mZ599TbvBqed8PKBGSJgCIwUAUqddatcQdCD5kmzH6ZBDoF8Wi5uIOBqgk5zX/2nkldnUosbujPO9hpPZH7lTqQJdmf3Jaufcul+d9gDIgaQKAGAwszWvqo9pUVb4ahnKSfsJZJjSg7MkxRXN5tXy6DGebjNT+8MfV1L9dsy8PauHt8dK/B1BGJE0AEIOzzqngPc0gqkinMfpfYaj/KhY17X3c4QCVF3llf3VeyX1bFPa0lfRXt32pX013duv6fwwr+8ZoSX83UAms1gAQg6KkM86RNFWRL4WhrKQXqDKhgWXfHJPLFtRyf+nONrU+dFDpu3dq7pfnNP/q+ZL9XqCSWK0BICaDzmmvtUrFHQjUKunzYahXo0gTVJnQwHwhUvaNUaX6tinoat7072t5YL+aj+3W/K8uaO6/zm4+QCAmJE0AEJOBKFJgjA5QbYrdFxIJpYzRC3TMAzT/xqhcvrjpalPz/XvVcv9eZd+8qOs/HypRdEA8WKkBICZDzsl5T+vxmDVJeigM9XqxqDGqTID8QlHZN8eU+tgOBZ1NG/od6WO9an1gv7KnxjX70kCJIwQqj6QJAGKyoMVBt5xriteDYagWY/QcVSbghuyvL0jOq/mTe9b9s01371TbQ31aeP+KZl98vwzRAZXHSg0AMRqIIu23VtSa4pGU9KVEQiejSCNUmYAb3Fxe2ZNjajrcI9uaXPPPNR3qVtuXMsoNTWjmuXcXp3kDdYCkCQBiNOCcksZoH9WmWHwmDNVOlQlY0XKnu+b71lZtSvVvU9tXP6bCyJSmnz0lOTIm1A9WaQCI0WAUSRJb9GIQaHGY7UAUaXBp2DCA/+Fmc1p497LSd++USSdWfW3ywFa1P3KnChdnNPXDk1JEwoT6wioNADGalTTuHM0gYvDpINBWa6kyAauYf3VECq2aj/Xe8jWJvZ3qePSwileua/qZt6UiDyFQf0iaACBmA86pz1qZuANpIEbS1xIJnXNOp6gyAbcUXcsqd/qK0vf2yqRufriT6G1X5+/cpejavKb+6W35fBRDlED5kTQBQMwGokjNxqjXkDZVyn1BoG5r9TxVJuC25l89L5sKlT764WpT2N2mjuNHFM3mdO0f35JfKMYUIVB+JE0AELOBpUoHW/Qq5+FEQhed0xsRT8WB2ylevq7c8ISaP9ErhYu3jsG2FnV+84h8tqCpf3hTfp4HEKhvJE0AELMJ7zXpHM0gKuTuINBua/VCoUA3ZGCN5l4ZkW1OKn1kp4KtaW351t3yxUjX/uEtuev5uMMDyi6MOwAAwGK16eNUmirikTDUVef0GlUmYM2KF2eUPz+l5k/tuTHwduof3pKbWYg5MqAyeKwJAFVgwDl1GKPtnGsqq49ZqwNBoBeKRZEyAesz98qIgtaUTGh17R/fUnQtG3dIQMVQaQKAKjCwVPXot1ZXqICUzSOJhKac038VObAOrFfh3DXN/nRA+QvTiq7OxR0OUFFUmgCgCox7r+veq48temWz31rdGQT6SbEoUiZgY7K/uUjChIZE0gQAVcBLGowimkGU0SNhqOve6xdUmQAA68TqDABVYsA57bBWHZxrKrleY3Q0DPXTQkG5uIMBANQckiYAqBKnl+c1UW0quYcTCWW918+oMgEANoCVGQCqxHnntOA9SVOJ7TBG9wWB/r1Y1HzcwQAAahIrMwBUCSdp2DmaQZTYVxMJFSW9VCjEHQoAoEaRNAFAFRmIIvUao+a4A6kTW4zRbwWBThSLmo07GABAzSJpAoAqMuCcrDE6yBa9kvhKGMpI+jFnmQAAm8CqDABV5IxzKnqvDFv0Nq1N0mfDUP8dRZr0Pu5wAAA1jKQJAKpIQdJZ52gGUQJfSiQUSnqes0wAgE1iVQaAKjPgnO6wVsm4A6lhzZIeDEP9Oop0mSoTAGCTSJoAoMoMRpECY7SfatOGPRiGShtDlQkAUBKsyABQZQadk2Ne04altLg1780o0gWqTACAEmBFBoAqk5U0SjOIDftsGKqVKhMAoIRImgCgCg1EkQ5YK9Km9Qm12Gb8vSjSsHNxhwMAqBMkTQBQhQacU8oY7WWL3rr8rzBUp7V6jioTAKCEWI0BoAoNRpEkqY+kac2spK+Goc5Ekd6jygQAKCFWYwCoQtOSLjGvaV0+GQTabq2eKxbjDgUAUGdYjQGgSg06p0wQyMQdSA0wkh5OJDTqnN5aqtIBAFAqJE0AUKVOR5FajNFOQ9p0O0eDQLuWzjLRZBwAUGokTQBQpQaXzuXQevz2HglDXXJOv6LKBAAoA5ImAKhSV7zXFOeabuuQtbojCPQiVSYAQJmwEgNAFRsgabqtRxIJTTqnX1JlAgCUCSsxAFSxAee0xVp1ca5pRX3Wqj8I9ONiUaRMAIByIWkCgCo2sFQ96afatKKHEwnNeK8TtBkHAJQRqzAAVLGL3mvOe/XRDOIme43RkSDQS4WC8nEHAwCoayRNAFDFvJbmNVFpusnDiYTmvdfPqTIBAMqMVRgAqtxgFKnHWrXFHUgV6TFG9waBflYsKht3MACAukfSBABVboB5TTf5WiKhgqSfFgpxhwIAaAAkTQBQ5c45p7z3bNFb0mWM7g8C/UexqOtxBwMAaAiswABQ5SJJw85RaVry1TCUk/RjzjIBACqEpAkAasCAc9ptjNJxBxKzDkmfCUP9sljUlPdxhwMAaBAkTQBQAwaiSNYYHWzwLXpfTiQUSHqRKhMAoIIae/UFgBox7JyiBp/X1CLpc2GoV6NIV6gyAQAqiKQJAGpAXtKIc+pv4ErTFxIJNRmjF+iYBwCosMZdfQGgxpx2TvusVRh3IDFISfpCGOqNYlEXqTIBACqMpAkAasSgc0oYo/0NWG16MAzVYoye4ywTACAGjbfyAkCNGowiSWq4eU0JSV9KJPROFOnc0qBfAAAqqbFWXgCoYXOSRhtwXtNnwlAdxug5zjIBAGJC0gQANWQginTQ2oa5eAdaHGY7GEU6TZUJABCTRll3AaAuDDinJmO0p0G26N0fBOqylioTACBWjbHqAkCdGFiqtjTCuSYj6WuJhEac00mqTACAGNX/qgsAdWTKe11xTn0NkDQdCwL1WKvnqTIBAGJW/6suANSZgQZpBvFIIqFx5/T6UtdAAADiQtIEADVmIIrUZox6jIk7lLI5Yq32LFWZGGULAIgbSRMA1JhGONf0SCKhq87pVapMAIAqUL8rLgDUqcvea8b7ut2i12+tDgaBXiwWRcoEAKgGJE0AUIMGoqhuK02PJBKa9l7/WSzGHQoAAJJImgCgJg04py5rtbXOzjXdYa0OBYF+UiiIlAkAUC1ImgCgBg0snfWpt2rTI2GoOe/1c6pMAIAqUl+rLQA0iAveK+t9XSVNvcbonjDUTwsF5eIOBgCAD6if1RYAGoiXNOic+uqoGcTXEgkteK+XqTIBAKoMSRMA1KiBKNIua9UadyAlsN0YfTII9PNiUfNxBwMAwEeQNAFAjRpcmtdUD9WmryUSiiS9VCjEHQoAADchaQKAGnXWORXq4FxTpzH6rSDQiWJR03EHAwDACiq20h44cEDPPvusTpw4oWeffVb79++/5WsPHjyo4eFhffe7361UeABQc4qSzjhX80nTV8JQRtKLnGUCAFSpiq203//+9/XUU0/pgQce0FNPPaUnn3xy5YCs1ZNPPqnnn3++UqEBQM0acE57rVUq7kA2qE3S58JQr0SRJr2POxwAAFYUVuJNurq6dOTIET322GOSpGeeeUbf+9731NXVpYmJiQ+99g//8A/1k5/8RC0tLWppaalEeABQswaiSF9PJPR4MqnZuIPZgB5jFEp6nrNMAIAqVpGkqbe3V+Pj43JLh5adc7p06ZJ27dr1oaTp0KFDevDBB/XNb35T3/nOdyoRGgDUtEHnNOKc9tfwFr2fFYu6RJUJAFDFKpI0rUUYhvrLv/xLfec737mRXAEAVpeX9P8sLMQdBgAAda0iSdPo6Kh6enpkrZVzTtZadXd36+LFizde093drTvuuENPP/20JKm9vV3GGLW1tenP/uzPKhEmAAAAANykIknTxMSETp06pePHj+uf/umfdPz4cZ08efJDW/NGR0d1+PDhG//9J3/yJ2ppadGf//mfVyJEAAAAAFhRxTbBP/HEE3r88cd14sQJPf7443riiSckSU8//bSOHj1aqTAAAAAAYF1MT09PXZy+LRaLunr1qrZt26YwrJqjWgAAAACqxEZzhtpttwQAAAAAFUDSBAAAAACrIGkCAAAAgFWQNAEAAADAKkiaAAAAAGAVJE0AAAAAsAqSJgAAAABYBUkTAAAAAKyCpAkAAAAAVkHSBAAAAACrIGkCAAAAgFWQNAEAAADAKkiaAAAAAGAVJE0AAAAAsAqSJgAAAABYBUkTAAAAAKyCpAkAAAAAVkHSBAAAAACrIGkCAAAAgFWQNAEAAADAKkiaAAAAAGAVJE0AAAAAsAqSJgAAAABYRRh3AKXivZckRVEUcyQAAAAAqtFyrrCcO6xV3SRNzjlJ0rVr12KOBAAAAEA1W84d1qpukqZEIqEtW7YoCIK4QwEAAABQpaIoUiKRWNfP1E3SZK1VKpWKOwwAAAAAVSwM158C0QgCAAAAAFZB0gQAAAAAqyBpAgAAAIBVkDQBAAAAwCpImgAAAABgFSRNAAAAALAKkiYAAAAAWAVJEwAAAACsgqQJAAAAAFZB0gQAAAAAq/j/AeCgak7wRjfsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "a4_dims = (14 ,12)\n",
    "bg = \"#181818\"\n",
    "\n",
    "flatui = flatui = [\"firebrick\", \"seagreen\"]\n",
    "palette = sns.color_palette(flatui)\n",
    "sns.set(rc={ 'grid.color': '#5c5b5b','grid.linestyle': ' ','axes.edgecolor': '#000000','axes.facecolor':bg, 'figure.facecolor':bg,'ytick.color':'white','xtick.color':'white' ,'axes.labelcolor': 'white','text.color': 'white'})\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "#with plt.xkcd():\n",
    "cur_axes = plt.gca()\n",
    "cur_axes.axes.get_xaxis().set_ticks([])\n",
    "plt.title('Barcelona ' + hue_disp1 + ' Rolling 5 average | All Competitions',loc = 'left',color='white',weight = 'semibold')\n",
    "\n",
    "ax = sns.lineplot(x='X_axis', y=hue_disp2,hue=hue_disp1, data=df_for_disp,palette = palette)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}