AitzazTahirCh_MuhammadAbeer_FD_Assignment_04(Portfolio Optimization).ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "b97ab081",
   "metadata": {},
   "source": [
    "## Aitzaz Tahir Ch 19p0012\n",
    "## Muhammad Abeer 19p0061"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d6bb21b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt # so that pandas can recognize dates properly\n",
    "import matplotlib.pyplot as plt # for visualization\n",
    "from matplotlib import style\n",
    "import pandas_datareader.data as web # to collect data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "839bd71e",
   "metadata": {},
   "outputs": [],
   "source": [
    "style.use('ggplot')\n",
    "tickers = ['VOO', 'RDBX', 'SIGA']\n",
    "start = dt.datetime(2018, 1, 1)\n",
    "end = dt.datetime(2021, 12, 31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "350dce6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = pd.DataFrame()\n",
    "portfolio_return = []\n",
    "portfolio_risk = []\n",
    "sharpe_ratio = []\n",
    "portfolio_weights = []\n",
    "rf = 0\n",
    "weights = 0\n",
    "annualized_return = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b426122c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VOO</th>\n",
       "      <th>RDBX</th>\n",
       "      <th>SIGA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.005949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.004385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.006409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.002070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>0.014261</td>\n",
       "      <td>0.006274</td>\n",
       "      <td>0.003755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>-0.000752</td>\n",
       "      <td>-0.013716</td>\n",
       "      <td>-0.048629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>0.001209</td>\n",
       "      <td>-0.045512</td>\n",
       "      <td>-0.009174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>-0.002825</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.011905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>-0.002741</td>\n",
       "      <td>-0.027559</td>\n",
       "      <td>-0.016993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1008 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VOO      RDBX      SIGA\n",
       "Date                                    \n",
       "2018-01-02       NaN       NaN       NaN\n",
       "2018-01-03  0.005949       NaN  0.006250\n",
       "2018-01-04  0.004385       NaN  0.024845\n",
       "2018-01-05  0.006409       NaN  0.004040\n",
       "2018-01-08  0.002070       NaN -0.024145\n",
       "...              ...       ...       ...\n",
       "2021-12-27  0.014261  0.006274  0.003755\n",
       "2021-12-28 -0.000752 -0.013716 -0.048629\n",
       "2021-12-29  0.001209 -0.045512 -0.009174\n",
       "2021-12-30 -0.002825  0.009271  0.011905\n",
       "2021-12-31 -0.002741 -0.027559 -0.016993\n",
       "\n",
       "[1008 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ticker in tickers:\n",
    "    data = web.DataReader(ticker, 'yahoo', start, end) \n",
    "    data[ticker] = data['Adj Close'].pct_change()\n",
    "    \n",
    "    if returns.empty:\n",
    "        returns = data[[ticker]]\n",
    "    else:\n",
    "        returns = returns.join(data[[ticker]], how = 'outer') \n",
    "returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55b5d6c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_portfolios = 50000\n",
    "for portfolio in range(number_of_portfolios):\n",
    "    # Generate random portfolio weights\n",
    "    weights = np.random.random_sample(len(tickers))\n",
    "    weights = np.round((weights / np.sum(weights)), 3) # round-off to 3 decimal points\n",
    "    portfolio_weights.append(weights)\n",
    "    #Generate annualized portfolio return\n",
    "    annualized_return = np.sum(returns.mean() * weights) * 252\n",
    "    portfolio_return.append(annualized_return)\n",
    "    # Generate Portfolio risk\n",
    "    covariance_matrix = returns.cov() * 252\n",
    "    portfolio_variance = np.dot(weights.T, np.dot(covariance_matrix, weights))\n",
    "    portfolio_standard_deviation = np.sqrt(portfolio_variance)\n",
    "    portfolio_risk.append(portfolio_standard_deviation)\n",
    "    #Generate Sharpe Ratio\n",
    "    shrp_ratio = (annualized_return - rf) / portfolio_standard_deviation\n",
    "    sharpe_ratio.append(shrp_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa657a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_weights = np.array(portfolio_weights)\n",
    "portfolio_return = np.array(portfolio_return)\n",
    "portfolio_risk = np.array(portfolio_risk)\n",
    "sharpe_ratio = np.array(sharpe_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be7d4c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_metrics = [portfolio_return, portfolio_risk, sharpe_ratio, portfolio_weights]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c30bfc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_df = pd.DataFrame(portfolio_metrics)\n",
    "portfolio_df = portfolio_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78fdb113",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Return</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Sharpe</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.064702</td>\n",
       "      <td>0.569631</td>\n",
       "      <td>0.113585</td>\n",
       "      <td>[0.223, 0.71, 0.067]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.108959</td>\n",
       "      <td>0.455989</td>\n",
       "      <td>0.238951</td>\n",
       "      <td>[0.076, 0.512, 0.413]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.142829</td>\n",
       "      <td>0.333527</td>\n",
       "      <td>0.428237</td>\n",
       "      <td>[0.277, 0.312, 0.412]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.123558</td>\n",
       "      <td>0.386061</td>\n",
       "      <td>0.320049</td>\n",
       "      <td>[0.213, 0.416, 0.37]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.094545</td>\n",
       "      <td>0.466534</td>\n",
       "      <td>0.202655</td>\n",
       "      <td>[0.221, 0.561, 0.218]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>0.103633</td>\n",
       "      <td>0.455291</td>\n",
       "      <td>0.22762</td>\n",
       "      <td>[0.139, 0.528, 0.333]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>0.166772</td>\n",
       "      <td>0.265873</td>\n",
       "      <td>0.627263</td>\n",
       "      <td>[0.449, 0.165, 0.386]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>0.071471</td>\n",
       "      <td>0.532638</td>\n",
       "      <td>0.134182</td>\n",
       "      <td>[0.316, 0.662, 0.022]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>0.173087</td>\n",
       "      <td>0.258586</td>\n",
       "      <td>0.669361</td>\n",
       "      <td>[0.478, 0.129, 0.393]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>0.104438</td>\n",
       "      <td>0.404785</td>\n",
       "      <td>0.258008</td>\n",
       "      <td>[0.409, 0.484, 0.108]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Return      Risk    Sharpe                Weights\n",
       "0      0.064702  0.569631  0.113585   [0.223, 0.71, 0.067]\n",
       "1      0.108959  0.455989  0.238951  [0.076, 0.512, 0.413]\n",
       "2      0.142829  0.333527  0.428237  [0.277, 0.312, 0.412]\n",
       "3      0.123558  0.386061  0.320049   [0.213, 0.416, 0.37]\n",
       "4      0.094545  0.466534  0.202655  [0.221, 0.561, 0.218]\n",
       "...         ...       ...       ...                    ...\n",
       "49995  0.103633  0.455291   0.22762  [0.139, 0.528, 0.333]\n",
       "49996  0.166772  0.265873  0.627263  [0.449, 0.165, 0.386]\n",
       "49997  0.071471  0.532638  0.134182  [0.316, 0.662, 0.022]\n",
       "49998  0.173087  0.258586  0.669361  [0.478, 0.129, 0.393]\n",
       "49999  0.104438  0.404785  0.258008  [0.409, 0.484, 0.108]\n",
       "\n",
       "[50000 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_df.columns = ['Return', 'Risk', 'Sharpe', 'Weights']\n",
    "portfolio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b636d43f",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_risk_portfolio = portfolio_df.iloc[portfolio_df['Risk'].astype(float).idxmin()]\n",
    "max_return_portfolio = portfolio_df.iloc[portfolio_df['Return'].astype(float).idxmax()]\n",
    "max_sharpe_portfolio = portfolio_df.iloc[portfolio_df['Sharpe'].astype(float).idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0cf26f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Risk Portfolio\n",
      "Return                  0.176455\n",
      "Risk                    0.204934\n",
      "Sharpe                  0.861032\n",
      "Weights    [0.893, 0.048, 0.058]\n",
      "Name: 6855, dtype: object\n",
      "['VOO', 'RDBX', 'SIGA']\n",
      "\n",
      "Maximum Return Portfolio\n",
      "Return                  0.213135\n",
      "Risk                    0.451854\n",
      "Sharpe                  0.471691\n",
      "Weights    [0.002, 0.001, 0.997]\n",
      "Name: 42056, dtype: object\n",
      "['VOO', 'RDBX', 'SIGA']\n",
      "\n",
      "Maximum Sharpe Ratio Portfolio\n",
      "Return                0.185928\n",
      "Risk                  0.210737\n",
      "Sharpe                0.882276\n",
      "Weights    [0.904, 0.0, 0.096]\n",
      "Name: 4348, dtype: object\n",
      "['VOO', 'RDBX', 'SIGA']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Minimum Risk Portfolio')\n",
    "print(min_risk_portfolio)\n",
    "print(tickers)\n",
    "print('')\n",
    "\n",
    "print('Maximum Return Portfolio')\n",
    "print(max_return_portfolio)\n",
    "print(tickers)\n",
    "print('')\n",
    "\n",
    "print('Maximum Sharpe Ratio Portfolio')\n",
    "print(max_sharpe_portfolio)\n",
    "print(tickers)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ad1be82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AITZAZ~1\\AppData\\Local\\Temp/ipykernel_21872/3179639514.py:12: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  plt.colorbar(label = 'Sharpe Ratio')\n"
     ]
    },
    {
     "data": {
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saAR8r1q1Cp999lmxc958803s3r0bAPDQQw85Zq/quFWy589Vq1YNrVu3Np3T//7K2kJU1hQWFmLAgAE4evQoGjZsiM8++8xIkHHD7/cDCH/vH374Ydg1zvT5JScnG7Fmixcvdh23adMm43s52q89gaCyI8SZ4JzgzjvvNEo+PProo6CU2sa8++672LhxIwDg3nvvLfG19HZOAJCZmVnidQDVuqRnlI0dO9YxcPnbb781xMaIESPCJg5Ew4gRIwAA+/fvx9SpUx3HPProowgEAiCEGOOj4dVXXzUSA+655x6sXbvWdeySJUsMl9xFF12Ef/3rX2HXnj59Ovbu3Ws7/uuvv2L+/PkA1K8Lr9ccWquXXTjTv7vyZtiwYfjjjz/g8/mwZMkS1K5du9g5egzfjh07HH+JWbNmDd57772wa5TG89O/ln7++WcsWLDAdj4YDGLMmDEA1PCG2267rcTXEggqI0KcCc4J6tSpg2eeeQYA8PXXX6Nfv374+eefcfz4cezcuRPjxo3DP/7xDwBAx44dz0icJSQkGC6h6dOn488//0RhYSFkWY46gDouLg6vvvoqALW3Zvfu3fHdd98hOzsb+/btwyuvvIKbb74ZgGqJ0jsOlAY33XSTUb7j6aefxqOPPoqtW7ciJycHv/76K2655RajvtTo0aNx8cUXR32NtLQ0LFiwAAkJCcjNzUW3bt0watQo/PTTTzh69CgyMzPx/fff46677sLAgQMRCARQq1YtLF68uFjLZkpKCq6++mrMnz8fR44cweHDh/G///0Pffv2hSzLSEpKMr4meDp06ABAFQaffvopTpw4YfzdVZbA8f/85z9YuHAhAODtt99GkyZNkJeX5/jiBf/NN98MSZIQDAbRr18/fPXVVzhy5Aj27duHqVOnom/fvmFL0QCl8/xGjRplWM/uvvtuPP/889izZw+OHz+O1atXGx0+ALVjR7isXYHgnKScit8KBI5E0iHAjdLqrcn3B3Tj7bffdr2G0/24dQjQiaS3pluF/jPprXn8+PFie2vefvvtJeqtybN+/XrWokWLYnszXn755Wz37t2u6/D3s27dOlatWjXHdXw+H1u1apXjGhkZGUYFfeurJL01w32dRvI1Fe7vz61DgH48kpd17qRJk1zH1q1bl23dujXsvkvj+TEWWW/NJ554wrFLQXFf1zp6L9xnn3027DiBoKIhLGeCcwZJkvDmm2/i+++/x6233op69eohJiYGNWrUwFVXXYXp06djw4YNYesiRcpDDz2E999/H1dddRWSkpIgSWf2rfT0009jw4YNuPvuu9GoUSPExcWhWrVq6NSpE1588UVs3bq1RJar4khOTsaPP/6IGTNmoGfPnkZB0jp16mDAgAH48ssvMX/+/KirzFu57LLL8Oeff+KDDz7ALbfcgsaNGyMhIQE+nw9NmjTBnXfeic8//xzr1q1D06ZNI1rz8ssvx2+//Ybhw4ejQYMGiI2NRb169TB8+HBs3rzZNZmgbt26WLNmDW6//XakpaWd8b1VNsaPH49FixbhyiuvhM/nQ0JCApo3b47HHnsM6enpxdYeK63n16hRI2zcuBGvvfYarrjiCiQlJSE2NhZpaWkYOnQofv75Z/z73/8uNTe+QFCZIIxVEju+QCA475kzZ47RWkv86BIIBOcqwnImEAgEAoFAUIEQ4kwgEAgEAoGgAiHEmUAgEAgEAkEFQogzgUAgEAgEggqEEGcCgUAgEAgEFQiRrSkQCAQCgaDCwpRDIJ608t7GWUWIs1IgIyPjjOanpqYiOzu7lHZzbiCeiR3xTJwRz8WOeCZ2xDOxU9JnUq9evTLYTXjkI5HVP+Tx1nHvs1zR8RY/RCAQCAQCgaD8UJi9X3JxVGaBU5n3LhAIBAKB4DyA4vxy8glxJhAIBAKBoEJDEb3lrDIjxJlAIBAIBIIKjXKehccLcSYQCAQCgaBCI9yaAoFAIBAIBBUIRYgzgUAgEAgEgoqDsJwJBAKBA4wxHC0swJcH9uDz/XtQpMioGhOLZzt0Qfuatct7ewKBQHDOIMSZQCCwkS8H8d6Ozdhz+gTiPV5cWbsevvz7L/x16hT8VDHGFSqFeGTdSrzX7Ro0T0opxx0LBIJzGZEQIBAIzgvSc7IwMf1n5MoBVPHE4MpaaTicfxpZBQXICfhRIMvG2LVZGQADnDwLMmN4/o91mN/zhrO3eYFAcF5xfhXSEOJMIDhnyAkUIoZ4UDUmFgDgVxQcKjiNmvEJ8HljsCJzP744uAd7Tp9AUFFQRCkAAgAoVBQsObgHYAAxBBiJ+NonivylezMCgUDAIRICBAJBpWJv7gm8vH09MoryUCTLkCkFYwxU+1lGADBGIFPuh5vTzzldi1EAxEGYEZd5AOok+Eq8f4FAICgO5fzSZkKcCQQVkVzZj88P7cSW00fRtnod5MpB7N+eC6/C0Ld2E7SrUQc+bwxkSvHMnz9if8Fps9uR12FM/w9Rj5t0l+UAg8s4uAqzWEnCuPaXlfxmBQKBoBiEW1MgEJxVKGPICRaiqjcWcZIX7x/YhJn7N0JhDAzAzzmHAQaofX8Jvj/6N2KIhJTYeCRIXuwvyLULM8K9B0IHCPfRSWxRyzoS994yngC4qFoNjG9/GZpWr1HS2xcIBIJiUaIIszgXEOJMIDgLnAgWIqsoD7+dOIStednIKMzFvsKTht7xgIBRQGbEJIJsGksjSCmOFBVoJy2WL/5nmNsC+jn+OHUY5/LraiyRcO/Fl+Cui1o5DxAIBIJShAq3pkAgOBOy/Hk4HixAg/jqOFR4Gq/s/Qn7Ck6o2odpHkYAvIqSNSuZ9bhxhACQdOuZrsCIXUy5hoo5+SlhtpIpACnmt1MPIWjgq4or66ThzqYtw44VCASC0kJYzgQCQVj+LsxBIZXRpEoKgoxi/cm/kS/7caDgJJYd2408GgwNZrwgczJp8WINNrFlzNVfhBvIIvthxfh1ieWEnrAZ4W+l7VJq4T9desBDpOIHCwQCQSkhxJlAcJ6jMIpDRSeQ6InDYf9JpOceQvOE2qgbVx2P7/wCeYofjKlmdgLJ8PwxBlAtLkyF6P83zhvHwQsv1QKmiqiQNcwQdIzzTTIHa1norFoGg/dy6gKMErvhTLOUMRZZ0Yya8VVwf4tLhDATCARnHRrhL6PnCkKcCc5r/FTGabkABBJqxCTg22Nb8F7mT5ANtQQoFGCQwJjmSoQqzBiIoZMohXbewRBlxOIzMEbAGAMhxAjw50WcbuXS1zJbxzRRRJjpAoyLFTOsZHxZDH1f2rVD2yLcn86KL4ZIaJxYDbUTfBjWvBVa1kh1HCcQCARlibCcCQTnKJRR5ClFmHf0J/yZdxAFShB5SsgFyZgWkM8JJkCN96JUszARVaxZ3ZK8cFPPMsc4L9VSJoFSLm6MgxCASQAUWLC6RFU/JzMsaRZzmctvmWFjyjiN5iEEtav4MLx5a/RreKH7HIFAIDgLKDi/LPZCnAnOGXKVAvhpENWkBOTSQuTLfsw5ugJ7io5CZgooI6CMqBYvpmf/SNCFDSEAGAlZrTSoJrxUkQY4iSrnNEmzNYpSgNHQ9dxi9COCEE2E6deynotyPW6dOBDM7H4dLqhaHcSpGK1AIBCcZcrKrblx40bMnj0blFL06tULAwcONJ3Pzs7Gm2++ifz8fFBKcfvtt6NDhw5lsheechdnhw4dwqxZs7Br1y74fD707NkTgwYNgiS5q+Q9e/bgu+++w/bt23HixAmkpKTgyiuvxIABAxAbG2sau2PHDrz//vs4cOAAkpKS0K9fP1x//fWmMcFgEB999BF+/PFH+P1+tGzZEvfeey9q1apVJvcsKD0oozgVLMC7Rz/HPv8RyFDAGBBQAMo8mn3J7DqklIFB0vSNqmIoJaCaaFPhrGDGOBJai4MQQJJ04WZHD+hn1MPNdRFo2lhCNKtYWAVXjAizGOcYWMhyZtQt067B/eBrlVITF1ZLCrOwQCAQnF3Kwq1JKcXMmTMxfvx4pKSk4Omnn0anTp2QlpZmjFm8eDGuuOIKXHPNNTh06BBeeumlc1+c5eXlYdKkSUhLS8PYsWNx5MgRzJs3D4wxDB061HXe2rVrcfToUQwYMAB169bFgQMHsGDBAhw4cACPP/64Me7IkSOYPHkyOnbsiNtvvx179uzB+++/j7i4OPTq1csYN3v2bKxfvx733HMPqlWrhkWLFuGFF17A1KlTbWJPcPY5reThgP8wYpgHW4r2Ykf+fmQGcxBgZuEiU0BhkhYLplm6eDclVCHlkQCFUjB4QAggK9CEmUU4cZ+IJm2ighGXTE3rONgMbYyLFTNO8FYs/d4J995tbQ2TMKP8gNDc6jGxePWK7u57FQgEgnJAYaXv1tyzZw/q1KmD2rVrAwC6dOmCDRs2mMQZIQQFBWpNyYKCAtSoUaPU9+FEuYqz5cuXIxAI4LHHHkNCQgLatGmDwsJCLFq0CP3790dCQoLjvIEDB6JatWrG51atWiE2Nhb/+9//cOzYMdSsWRMAsGTJEiQnJ2P06NHweDxo3bo1srOz8cknn6Bnz54ghOD48eNYuXIlRo4ciauvvhoA0KhRI/zjH//ATz/9ZBJxgrKFMYZNhZvxe346MjOO4oScpwXQa1YtEASpBECCbLgkAV3ZeLU6YJQBEiGcKHK2dCmaQKGca5MxYmsrSfQlmLtI00WYPpcxQFH4WDRbLQzLAua11MAzXiyqFwiV1SD2ubzA08fw1jyTtcx8nABoXK06nmrXGXGecjeoCwQCgQlaBjFnOTk5SElJMT6npKRg9+7dpjGDBg3CCy+8gGXLlsHv9+OZZ54p9X04Ua4/hTdu3Ii2bduaRFjXrl0xf/58bNu2DZ06dXKcxwszncaNGwMATpw4YYiz9PR0XHnllfB4PMa4Ll264LvvvsPBgwfRsGFDbNq0CQBw2WWh3oDJyclo0aIF0tPThTgrAxQq47tTX+KPovUIMqYGyGuZjzIlkOEBZaoIA6D5ItXfnAiILVYsNIjBSxhkEFBGwbhvZkpV65g6U1UwiqJnXer70rMaVZHFCy2zy5JpQowYYV/6XL2umZq9Gbq+bpVjirNA46+lJgNYx2hik/LHLTFn/B5pSJgZ2ZhhYjZGtWyHIU1bwBsmnEAgEAgqG0899ZTxvnfv3ujdu3dU89esWYPu3bvjxhtvxK5du/DGG2/g1VdfDRt6VRqUqzg7fPgwWrUyt39JTU1FXFwcMjIyolpr165dIIQY5smioiIcP34c9evXN43TzZWHDx9Gw4YNkZGRgZSUFMTHx5vG1a9fH9u2bYv2lgQObCv4A0tz50MGBRiBAgJFC84HJIAQSNDj2yUj8JMvTUEIgQcMCtSSEM6EXJeGvYyCu5Z6XtFMSDJnnVI4YcO0vfBtJWUlJPB16xozhJh2H+CMUzALLkKISzIBM3cNoE7JBjCNN1nUeGuZ/p6qL1NmptWKBoD/RdRPFSHMBAJBhaWkMWdTpkxxPZecnIzjx48bn48fP47k5GTTmJUrV2LcuHEAgGbNmiEYDCI3NxfVq1cv0X4ipVzFWX5+Pnw+n+24z+dDXl5exOucPHkSn376Kbp162Y8MN1HbHWN6tfLz88HoMa9OblPExMTjTGCyChU8vBT/nwcDRxEPvIgQ4HMGGTqASMSCANkgBNFakB7kOnB+2ZXJO9elDRRolBAMZXU52GGdQwAgooEmToF8ZsD/52SAAC1ITkAyLK2AyO2i59jThjQV2G8aNL25iy6tIGUM9WdKVbxasSYWaxuNHSoTbKoXyYQCCouZRFz1qRJE2RmZiIrKwvJyclYu3YtxowZYxqTmpqKLVu2oHv37jh06BCCwaCj9660qfTBJbIsY9q0aYiPj8c999xzVq65YsUKrFixAoCqylNTz+wfNq/Xe8ZrnE1k6seGnAX4K/9XFNDTKJTzEQSDzCiCVIIMLxTm0SxkHkN0qKUoQsKMECDIJFBtDIFq5XKzHBHC4JHU2IOgYtcyjHFFYRnRvpnNQkrfB1+XzC1gnzG95AYf21a8gGLGf5xOuAk06xyLmHOLF7NOoQ4jHLWs7uoEasTH49qLW1fashmV7fvnbCCeiR3xTOxUpmdCI/jZGy0ejwcjRozA5MmTQSlFjx490KBBAyxYsABNmjRBp06dcPfdd+Pdd9/F119/DQAYNWrUWflZWa7izOfzGRYunvz8fCQmJhY7nzGG6dOn4+DBg5g0aZJpjm4Ns66vW8N0C1piYqLjHvLy8hyteoDdb52dnV3sXsORmpp6xmuUNYxRHAyswY7AV8ihh0GZ11BHjKiuRgK1VIT6jhqxTgxeMABFlJiCOlXhQ6Ir98UAv6KWyJC44Hc9/p0x1cbml9W9UAdB42p4i+TyWnX/4vZIFU3IERYSkcVlVprWcLB+GarPQbTpJTisrk4D52vGSATPdrzcZNqvbFSG75+zjXgmdsQzsVPSZ1KvXr0y2E14yqoIbYcOHWylMYYMGWK8T0tLw6RJk8rk2uEoV3FWv359HD582HQsOzsbfr8/or/8OXPmYMOGDXjmmWdssWXx8fFISUmxxa7p19PH16tXD8ePH0dRUZEp7iwjI6NcvgDLmyJ6FCeVzYhBKk4oe5AR/AmnkQ2FMSNjUoJkiksnRHU7BhUJCrzQrWIeqMeLFBlFUJ+tRHRRxozSF3xslIcwzZ1oEVRQY7sKFY/hplQYoJgsW9pYxrseQ+8pnxHJiRyrW9KMFMq11NazZmXyljfGAKqo8/SNM21CqOsSM423uxzthKxxTqJNXduEIQSND7a5MZIH97dojc61zr+vc4FAULkoC7dmRaZcxVm7du2wZMkSFBYWokqVKgDUGmaxsbFo2bJl2LmfffYZli1bhn/+859o0aKF45j27dvj119/xdChQ43MirVr1yIlJQUNGjQAALRt2xYA8Ouvv6Jbt24A1PTa7du347777iuV+6wMBOlp/O7/B/zsJBiTIDMJMiOg8IIyQEGMNpLASxgkJiOgyS/9uAy+yKp2lABFLFaLRQ/FZFFGIMNriDUehRJIFuNQoewBg0fNitSUkTXQHwitJYFpsWlqIL41mFTvcWmKE9MyGplpjKadWGgv/BuqW6sQEmrM9ENESwTgG5bzli2nzExiFoDqhQBQLnvVCWZ5bxNzoftNjInBAxe3wa1NmrksJhAIBBWHsiilUZEpV3HWp08fLF26FFOnTsWAAQOQlZWFRYsW4YYbbjAF6Y8ePRotW7bEyJEjAQA///wzPvroI3Tv3h3JycnYtWuXMbZOnTpGsF7//v3x008/4Y033kCvXr2wd+9erFixAvfdd5/hmkpJSUHPnj0xd+5cADCK0NasWRNXXXXV2XoUZ5Wc4Nc4Qt+BggKAEcjMhwIma5YxCZSpcWMMqqXLXHNLRSKAl1EUUQ+KqBpjRkA0ixRgsiTBYz4GhiDzQhd2vBUqQCUo8EBhADHsTAQeiUCmBB4JCGp9Ka3CTF9LFza6tUtxiBUjmqlJMcpN6FmX+idecIUyMUP3oFrI9GvphipqybZULWNczJohmrg/rQKKhe5DrdNhUWPUPsVWUsPam5MbnxIXj+c7d0X7VNEBQyAQVA6UMmrfVFEpV3GWmJiICRMmYObMmXj55Zfh8/nQr18/DB482DSOUgrKFZrSa5OtWrUKq1atMo0dNWoUunfvDkAVav/6178wd+5cvPTSS0hKSsJdd91lq102fPhwxMXFYe7cuQgEAmjZsiUeeeSRc6Y7QEA5hBz6AvxsGwJgkDUhJQEIQgIjBfAwD2QWqwbbazXEiPFvvfNvLARAAY1T3YxaQL8uOIKUwE9jEGSSZrDhBQsxGXl4ZBayvjkVe5UIQBiDn6pfuk7hX3oB2CD1wGM/ze0+9N4uKhkok7T6ZfzemVEWg3efMgCMcoJKM/AxxTzfsJpRmK7nvC8Ywoxwws6acED4edzahkVPn6QN6ZXWUAgzgUBQqTjfGp8TxqxOJUG0RFuTzUppB6rKyl8ownzIbAuKkAOZqUkQQUZQyGJgduUBRUy1fBVSL2TEIMBC7krGAD/zcNavEAEq4YTiA2CN+QJy5RjIiIFMiS2AnjGgiHod5+XLMQCxfxNSBshU3UOR7EGQegEwh4xNoEiWoFCvmt1j+AftKo4xQKH6ferqhUBWAIXrg2m4JbU5RO+jySRjeUrBFYgN+S2ZW90yU3smbk+mdVQ3qxGT5yTorEVt+cQAfl1tP2mJVfFOt95Ijq9i31MlRQR62xHPxI54JnYqU0LAgj2XRj1nSNMNZbCTs0OlL6UhCMHYKeTSW0BxEjIjkBmBAo8hjopoDBjfqkitLIZYosAPD6pIMnIVD/QviwBVy2Ewh4ByqgmsGKIgQAG9Gr9EVPNzEDGahYwYbjodww1o3T/UGmgemMerIkqLM6OAn4bEG2GapCKhcZSpSQnqrx0SwJxFnEK1Sv7QckyJel+8tcywhPHWMwqt0n9oLXvxWO0OnX710Y1c1qKw1iK1jr82cdeIIJEgNIugSbXqeK5z13NKmAkEgvOD881yJsRZZYYVArgTwAGAAYWMQdFisQjRy1Zo1h9m/qxidy/GSjICNFZro2QeT1moQXgRjYEMLwgYYiSKAPUAYKAMKFBiYc6WNF+FMcALWRNwIfdloSyBMQIFzPRtqFA1I9OwrBn7Ut2RDMzIhAzSkEs2lB1pr3Umy4BMzV/+BFSLYwtZCQlRL8eoJcOSF7lWkcWtqN4+ZwkDAEW3qOnPh39EFhVr9kiacRN+DsclQvDvLlejToJzeRiBQCCoyIiYM0ElQAHYGgD/B8N9BrX6PtWDnaDFRGnWoAJqz6TU0dsl8bYfPvMyQCUE4YWi1RBTuL6XqnAwpyFaiwVaY6QoAwppLCgIZKrGv1HGNIGn7sJc9kKNb5epZLL8ha4Jo2Za+BgudVFKdRepeSxlodZR4XAsWuugoEJWN24vjlmUYSCwt12KYA5jzOTa7FGvgRBmAoGg0iKyNQUVllj8D7GYDQUy/GCgYKakvDgCyJQhCEmN0QKBQiUE4dFMwmYFIVPJGKsaeAisQfgBKsEPNU4t1LOSQnap5M8Y0Sxs4Kw4kqFRGNPaNxEJEgCvRDULllqiI0DNkWgMajN0CglBF4HJGIFftn8pq5fn75m3fDmJI1U8UYaQtctSQIyYLGHObk/7LiwfXaxbjugB/uZ8BffLOeznvhaXYNjFrVwmCAQCQcVH1DkTVEACSMT1IFD7jUpMAoNsrpZA1L/MBInhJFUtaJQBQXjAuEKqgCoo8mgcgkadMvVfdEIVBOEFYwQeKKAgCGpFZU2XIoDEqJEkoH/TUAacluOgcG5BiVDDoqVQApl5tGQAAknrNSRTIEi9odJfTLXG6TFgQUUCA3E1IFmLwPL7BAtlWOrnZWq1vpnXYlwQPqMASMgKpeYNMDBK7JmiVsHlFBNms6bpm9WPOwf429yf/J8Wryl/1SZVq2F4y9YOFxQIBILKQ1m0b6rICHFWwSHYjOp4EJQr9iCDOv77TrV/yD3Q3ICaMAO0IHymruFnMRbRpcaRBZkHp2k8Zz5WY8icjUxMEz5Ea2QO5MlxWoeAEPqu/YqkxaKFfvuhkEAVIEDNApAxhiLFCwrJJLzslrBQxiVj9pgEoikbSiXN1Ue07EyL5Yt/hlQyZZcSoj4byitDLnMzVOHfuGioRpklicBVmMF6XPtAAShctqbbPP2RWOLWYgjBiJaXuM8VCASCSoKwnAkqCEVIxXUgREZQ+0dXImpV+1jigcyoYTlTLWESZC3wXWbQ3JVEE3WqdUwCIBGGAHO2hnnAECcpKNQC5QOKZFjdePT4LoWpZSuKEAeFebQEAjMEBAojKFL4QP7QOn5q34tfkYzkBb5Abci4xAz3q6yoDc71GDXdrapfHVpOqqLYrWWUmls/6eU0rNY3QlQ9xoyyG9CEKezlMxgnxPR1XWuaQZtDQq5LTmSRSAJgmfqMrW5PLyF4sFVbdK/fsPg1BAKBoIIjsjUFFYAgapFrAGhde6BKjCALWcxiISGgmWzytNgxmREEmKRazLT2SJQLcqJQq/qHM95ImupgIAggxvC08SKJQg2oL6BeFLJ4S26lM9SxtROBV6JahmUIr8QQoHZhwphaqoMyoEj2mkpgMM3659TonOpCyronS+cDQogmwszX1O+dEKa6PHUtZBNd4QL+HYQWA4hegsNqgQPg+heln9OFmeV4zSpVMK7jZehcu26YBQQCgUBQURHirAKSihuMf3S9RIIEggIWhAzZGCNThiLtPdXcjzIk7bcLolrOTPmXWuYl8yAIr1ofjA9tYqHfTIqYF4xbi4Fp2X/QylV44Gcxmj0uZGmy9YOEKozygnGu9yo5KBCnqv+EqG5LtQQF06PkVEHJXd9hpraeeVFr5X/+Ogz8eOaQdal9dkqKiDTQn9ufyd3JCzs+U9MaV2axzumkJSbio2tugOT0EAUCgaCSEkkm/bmEEGcVDIL18JAi4zNjDBKRUAUx8DNVnAUUhnzAcO2ptfIpAvAax+w1zaAdVzMyFaa6MXUUSKDwIMg8oLYvC1WEMGgB9Zp40wu28pYzdb/MaNGUF4jVEgScv7GcvuGcRBZfKoNRYrKU6U3NzaLKvHs+ct6IFYsIXZKGAvKpbs4MOycCdGuZSfwR8zmnOZowdLrKDY2aCGEmEAjOOYRbU1Cu1MRY1RLGCSfCAAkSYuBBEAqKYDaayIygEF4oUC1RLKz5Rs2SpERCAfPy5VYhMwmnlVD1eAnUJvIULXEAAAKaSLO7EdXrB6gHQcTYzofWUi15/FkGLbEBDJQSrQVTaEfaVjmBFXLbhlYwX8+pdIZTcoEx3cUaxhgBlflruuGURWE5xoCQIdSSiclvwbityARXu5o1IxonEAgElQkqEgIE5QXBHlBijwljABRQUEYhM3NtM8qAPMQYGZZ6+YhYBFEEtVI/PzbAPPAQ1WkZZF7IatQWGIBTShVQeIxaZEHmgUQoJDDITILCJPi53pzOdccIKPMgQD1qrJfDCEArBKtZ19RyGR6uGC5FgOqFbt1EiV6Tzey2Nacv6tmcLu5L3RRIiOG6pBZrFWNQLXWUcALJOX7MOKXdlzGKAMZfmq4jZVUmqw/D4fZMXwSRJQYkx8ejdXJq8WMFAoGgkqFE8nPwHEKIswpECkaEDdbPZYohePR/sAuZ11Y5mRDAC4YYJiPIvIYrM8A8kLm/8njiRyGLg8KIUUOGMaCQxhjxZgpT2zIFqMcihsKHV1EQx0xM/RoK1BZThDIUyXrPT20uU+PsnOraMAYEFYA6XFwVXJrgo+p7PkuTMa2sBifkKNX0EiNmCxtv5FL0+yaWkhW2HXCTYCRTQLaIOuoQyB8pmrtTYmZjGgHw4TX9bE3mBQKB4FxAWM4E5QYj7oFMipZlqRaAhVF/LFxhvpNUbdejhjURGC2XtFZJ2UoiZHhAmQQFHii6oHFokeQB1Vo6qcqEslAMmjWxgEIrPqsLLusYYz9az0yry5Go7lnKzHY3xgBZUQvWhuqlmccQovbNVBSvaV1GGRjXdip0gjh80xsKGJRywiz0OFwFmhHLRqGW2XDIqGREH0jCx65ZXayy5W+Fs9b5vF5UjY0Ns5hAIBBUXoTlTFAuJGBC2POKpgYYAwqZWnvMwxi8UBBwcP8FIAFMrWmmBuRTeAkDYQzHlUT4WYxayoKpa1NQMHi1rgH2bwKJQCtiSxCkBPlKPIxcSxZyE+pCkJNLoGCGkFDFnybMqFrTLOIAeuiuVF5MhQL2GQNkmUAxrHz8MK0EhqmqrpsljEsCULTPpmxKbhg33yhIqwszFGMhMyUCcGvaxjGtGK11vdDkeI/4VhYIBOcuwnImKBfi8L0mB6zB7AwyKE7SIADAz4hW7JUgCPWfZwlMM8Coc2VGkEfjEWBerhOAhAADCmgMApowA2AUn1WYWl+MKtRYn4cCCGqxZAEaazrOAK1hgCof1Gr9EjyEQmGSVmVfqzcGAsqIWuCWAQo8juH1trg7pvbYdK6ppl1TARSX/ptGjBkx+wPdjWAEVAEAyVmY6Z8N6xXRgwO1TEqHpALtRQCHGmks9AeBeW3qJMy43TNgTLsOjnchEAgE5wKiQ4DgrMMYAwOFxOVOGucA5NGgkb1ptZIRAsQwCkWLKStiMShgcaBaMVqrUAkyD6yFxFSBRqEwD2IkCtkhKN6vqGurKIC2V7VVFDHi4NSCr6oAA2NQKN8wHSiSPQgyb2hhPR6M26ku5HTRBajCLKDYn48xnhb/mxUJaRnTXLc1oe/M5pMlDtmUulRyabfk1GfT4jo2CTQFRm+GcLF9AEE9nw+9GjQKO0ogEAgqM6K3puCsI+etBAAwi0OQMYZCKkPW7EX5lCAI3V0WQo1DU+WdzDyaq1PPZjRT3Jc3A1BEPYghaolXBkBhHlDiMSxOHqLGwAGSKuwYU1tGMQmMERQpXq2RuSbJmFrItVDxALwA1axNlAGMAkSC0ceSAZAkvRuALgpDSQvWuHfGSHEqxri/0Bw9psy+ninpUxeRxOGcdQ/8fKuAs763EfIN802zuK8Ip4uiRY3kcIsKBAJBpUdYzs4yhw4dwqxZs7Br1y74fD707NkTgwYNgiS5/0XIsoyPPvoIu3fvxt69exEMBrFw4ULbuMGDBzvO93q9+PDDDwEAWVlZePjhh21junTpgkcffbRkNxUlzD8eBNCqikkgIChSZJxE0KjAQClQYLRjUtFD6fXsxyIWYwg1MArKCCSilpLwwwsCwAsFQeYxZfXppTMAIEBjwOBFwCIiePuWHrCvW9gURiAzDwgIZIUgQM21zRgj8CvO7kZKVfHHGAFfI4QBoApDUNG/DszWQr4rAdOK0+ptrqzjjTUpQBU99kx9zws+fYraG9MSUMatAWo+ropK/Yb4wfwSxQT/c3Pc4tQcpBlACEa1aRfBwgKBQFB5KasOARs3bsTs2bNBKUWvXr0wcOBA0/k5c+Zg69atAIBAIIBTp05hzpw5ZbIXnnIVZ3l5eZg0aRLS0tIwduxYHDlyBPPmzQNjDEOHDnWd5/f7sXLlSjRt2hTNmzfHli1bHMe98MILtmMvv/wymjdvbjt+1113mY5Xq1atBHdUMmRkQQKBhxEoRDUb8cIMAHK5WmY6FKoQU6CKI91SpjAJp6kPCjyQKVBkqvqvijZeeshM0npf6gVk7TJAr9BPoIojygj8RnyXdl0K5NM429xQqQ77mgozNzjXjxPN8mctgqtbu/R6ZEQP9Ae0bEzOXMWIIaEYA5QgAeDhaplZ9mOIMmcYF+jPX4dRzaqmJw+Y9sBtwDG6zvJA3AScVSdq74dc1Ax1fD73NQUCgeAcoCw6BFBKMXPmTIwfPx4pKSl4+umn0alTJ6SlpRljhg0bZrxfunQp9u3bV+r7cKJcxdny5csRCATw2GOPISEhAW3atEFhYSEWLVqE/v37IyEhwXGez+fDrFmzQAjBsmXLXMVZs2bNTJ/37NmD3NxcXHnllbax9erVs40/WzAwyIxCBqAwNbif/ydcZgRyGGsK1QrGAkCB4sVp6tMcpAx+5oUCvqyEBx5C1WxNaPHrTAKFGld2WqkCLwm5+RgD8pUYo86ZGpLPzEYhqO7H08E4kyuVUrWNVOg3Hifbj5tYsUbG69mYejdO9RylBIoWz6VbAw13pVa7LFRAlpgvyZz2Y/FH6lYvBkBxElfaZ024ma1eZqHokoppCvwP/fgx74HpW9P3x4BH2rXHIIdfNAQCgeBcoywsZ3v27EGdOnVQu3ZtAKrHbMOGDSZxxrNmzRpXj1xpU67ibOPGjWjbtq1JhHXt2hXz58/Htm3b0KlTJ9e5JSm2uWbNGsTFxaFjx44l2m9ZwRggGxmXKrpjUM/IdJ0LIAgJRcyLU0o8ilicWjZCO+klFAGTr15dTYIqnIKKB3k0FjLzaq5NAplRI5WgUPGYiskyEMhanBjRTFYKA04HYqG3clLdpFwFf4JQKQ3bTiJHoeY4OsaIqYG57lrk3ZXqFs3CyBoKZhgL+Q0RFgr+t55zghJT3TEz4axlRCtKG3pGJsGnFkULrcBgVBJJjo8vZlMCgUBwbmD1HEXKU089Zbzv3bs3evfubXzOyclBSkqK8TklJQW7d+92XOfYsWPIyspC69atS7SPaClXcXb48GG0atXKdCw1NRVxcXHIyMgo1WsxxrBu3TpceumliIuLs51/6623kJeXh+rVq6Nr16647bbbEHuWinpSUJs3ixACD2MIQq32r5bLsLsFs2Qf/CwGfhar9rHkRKvaKYBCYorhtuRFFmVAHo2DAo/ltxIJiqZ0irhirvrSevalXqWfQjK5H4OGlcgeJyZTBg+xiCmqujd1m5UECkLU5uzgBJ+iWNyl1F7ygzl2TbcfMk4pnNrhbYGWlk9OyRXG2jKxH+MVIC/uHJZxtCtqc4hlEgPUhAEJqJso3JkCgeD8QCmh5WzKlCmlcv01a9bg8ssvDxsPX5qUqzjLz8+HzyFexufzIS8vr1SvtX37duTk5KBLly6m4zExMbj22mvRtm1bVKlSBVu3bsUXX3yBo0ePYuzYsaW6BzdkwJapCYSsKQxAHBTkU6JW9AeBBwoKWDzyWYIREO+ERNQ6ZlbxR0FMbZrsqFmXCvclwhiDZIoN40xiXNyZW2wVIarolJkECRQyJZAVmIQdgxZbwBiCQQkMEghhkPkWSnz9iojdpG5YY8i09RknyAyBRUKX1/9UEAriZ5Zl9SUpQLj4NxNW/3CENKpWDRcnpxQ/UCAQCM4BysKtmZycjOPHjxufjx8/juRk5+z3tWvX4t577y31PbhR7tmaZ4s1a9bA5/OhXbt2puM1atQwPfBWrVohKSkJM2bMwP79+9G4cWPbWitWrMCKFSsAqKo8NfXMmk1nZTHVrmX52qMg8GuWIEaB0ywe+r/6DEBAC8gnhEBi6lGrhYcyS9sLxiAzPbNRvWiQEsiMqK5MbWhAkVBkybpU12Oa1Uzdg0IlLaOSD3h3iY/TMisZIygIxoKBcNqEGaUoCCGglIBpX56KQiwCVP1TIvYem4Q46Bxd4bqIUEeBR2C3iOkiSg8K5OPMrBfVx3FWMwItqcB0F9wWGD/Z/QcRYUCL1FqoWbOm65jzCa/Xe8bfg+ca4pnYEc/Ezvn+TJo0aYLMzExkZWUhOTkZa9euxZgxY2zjDh8+jPz8/LMal16u4szn86GgoMB2PD8/H4mJiaV2HUVR8Msvv+Cyyy6D11v8LV9++eWYMWMG/vrrL0dxZvVbZ2dnn9H+ZO1PDzMH4vsZgV+reHWCqe2SdAiAGKKAUIoAYjhRFvqHXY1lkziXpmoNCiAGhUoM/DQWsqJ2HGAgUMDg0cSg30GY6e5FCi0DkxHkcUkAan2yUAC/Fb21U1CRoFf6J9o+rfFkBCzUVYASk7vWjPlaTl5NIqnuS3Vk6Nkwt8xIl3VCGyYgvNszjNWLWGqfWaVgKNAf4BQz3J6hfiQBZ/51d66QmpoqnoUF8UzsiGdip6TPpF69emWwm/CURfsmj8eDESNGYPLkyaCUokePHmjQoAEWLFiAJk2aGHHva9asQZcuXUoU615SylWc1a9fH4cPHzYdy87Oht/vL9W//D///BOnT592zNIMx9n6i2AACqH+ZXg0I4+fERTq9bsYtGr/ZiSiCrRCFm9ZTW2bFGRerqq/KjiCTEJuMBYBFmu0U9ItaAwEQcbgV7yuactqQoD6XAplLRqO8eUyHOZoIktmqqWPUvPaMrU3P1etcuq+3IWZ6mrlszQZZyHTxVeo20Co44A5e9JB4GnuSoOSfCm4iDb+aobBjAGEhsSj6zW1NUUygEAgOJ8oq8bnHTp0QIcO5vZ3Q4YMMX0+WxmaPOVacrddu3bYtGkTCgsLjWNr165FbGwsWrZsWWrXWbNmDWrUqBHxmuvXrwcAXHjhhaW2h3DorjkZwCkq4ST1oJBZ6ns5zNN7U5pRXWwylWzCjALIDvoQgJrRKUkSvBJDHFFAGQNlgJ/qjdLt3wjmtkpEE1vWOmZm2xBlauslmfEZn+Y1ne9NlynhvyGpIqklOyhsdcpU65gEwiTV2qVf3BKjp4s6w5om28eYwtwixTo2nPFPtggzp/nc579Pn45iIwKBQFC5oZq3JppXZaZcLWd9+vTB0qVLMXXqVAwYMABZWVlYtGgRbrjhBlN5jdGjR6Nly5YYOXKkcSw9PR1+vx/79+8HEBJUTZo0McXiBINBbNiwAd27d3fMsli4cCGKiorQvHlzVKlSBdu3b8eSJUvQuXNnNGrUqIzu3IwCLa7e5WtJrchPITPJZERSQFDA7JmnCiQUsDgQbVGFAkHqwQklAQqLUS03fONzAsQyigDzaqZjZ2Gm2uTU87pIAwCnArP6HPU8N58Sk7WLMphciCYjmUOgPD9W0WO/HL4JGQMYX8WXhParDtD+MBWWNQ3WBhDzBIpQzBkXT2bfgMtxh3syLGaOKhVmYahtp2WqSAYQCATnD2Xh1qzIlKs4S0xMxIQJEzBz5ky8/PLL8Pl86Nevn82ESCkFpeYAoRkzZuDYsWPG59deew0AMGrUKHTv3t04np6ejoKCAluWpk79+vXx5Zdf4vvvv0cgEEBqair69++Pm2++uZTusngk9IDCflDjyEDht2RQyowgj8bCSxgkPSAfBKeUeMcSDzJT654RpsarFcheFCEWMlPdh05eQolYMhQtUM5KxyyCSj+mo68vU4Ig9UBiFEEWEnUKk9Q6a5QYHQJ0BcL3zXSN+4LeE5PYxuklO5gCgHhCPkOmukANi5juMnVqSK5/NLk2VfFE9LpnxbViisTCxokuxhzkLXcdoqV76HPa1aodwQUEAoHg3OB8a3xOmGNhKEE0nGlNtioJARw43QUeTQAUMi88hBoi7IiciABioYsYXcooVEIh9OKvKgpTRZvMvFAYQYB6NesaQZBKrnF0MiUoojHwK15bsT81Vo0AWnxZQUCCHzFaMD9BkHpNJmSPagpCkewFZR4AzGgwTikQoN5Qo3LHjuNaMoPi0cSaPaaMaUKSyqrr0joXnJA0BdobDdKJqrecxJm+lEzMAf06xnpWN6RujlNfjs9a2w/hP3OFaJ3H2tdZfefgsxqcWpERgd52xDOxI56JncqUEDB8w/Co58y+dHYZ7OTscN6U0qjI+BLqgZ6SUMC4eC8ua0+BFwqTDPeh2kKJwiMxeKiiWaXUKaeVOCjwGs3JCedGs1dS045riQIAECvJ8FO98Kxq6aKMqC2emN6iSSsMayxNwX8pKUzS4tNChW/Vfpyqe9WQIQ7iQs/cVKhH33So3Abj9Q8v7kLh9cYY/fnZemHyF+Muat0LhbMwM+Y5nVPNdsSIa3PATdg5od+a1Q0KICu/ALVFEVqBQHCeINyagnJBLXnh/A++GnDPF2olUEBAGYWfizmjLDQCIJAIIEFBqP6ZWkvM2mS8QPaikMZCrSymIIbICFAP/DQGiiawgpSgUPECFpcrIYBHAqjCB80RyLYK+6p1zuw2tQfaKYrEuTqh6x2jRpppTQZQGeZjRlKAg6rR1wNU0cZbusBNYYCp63xUhPZNoK7D6Wwt9C1Ci1eYmDbvWapSLRAIBBWByh7gHy1CnFUQauIzHMYgOFlUnLMnQ9mSOhTEsGkxzYpECIEEBoUxeEAhwwOJ0yQFshf5SqgsgwwP/LJHK5ERShoIUrswC+3ELoWIg6jg+2C6oYRJSKCKuUeoqq040WqIGc5aZmzMIgQp99kadK/NderaoC/rJpoIf56LFwud5M4RyzF+TBgkQpCSUKX4gQKBQHCOcL7FnAlxVkGIi6kLFtBVgvmL0KXxj/HFqpfJ8DOP2iKJMSiMoECJAWUSYhFEHotXrVxMgaIJr1OBOMiwFptl8EoUQaquJWtuuOICE/nzlAFBhRj6Q5KglbvQ9kdCgfuhODH3b7xQ1X+r5cwi9qKOnrSLJuOtmwCzWrOYReRZx4b5gUI4N60pBg0wskINscftsXNdkQwgEAjOL4TlTFBuUCRBwintky68mGpNcog9L6IxCDK116ZRpJYxFMixyKVVuEQBpgmyUJslBjgIMxXePkUIUKjEIpSeaBaP1sKval0zCTL0umYMCmWgiv06IYGmr+X8zafXK7PMDmVmRiPKDIuWy7W4M5a2oS6E4gPNQf4Ibw2j9lO6HiSK/tlqEVQH3XHJxeE2JBAIBOccIuZMUG5c4P0YuwI3QmZAvERBGXBMqQ4/vIhh1Gg6rgfw5xu9NmGcoExCHo03ZXACBB4wBLX6YpQBfsXtr14Vbn7Fo33SI9glSISZelkyRgwxpu+rKOixuGF10aaaf6zJwaFYMr12GgX4dlMu8WaAao2LJDaMgQvu512fvIiyCCBTWBjv8nSwhtmsXkwTdnYjaOg8zFmYTLcNalYyPhmB93xKABpXrx72fgUCgeBcQ1jOBOWGV6oKgjYoYLtRQNWMR79WKiMACV6mNrCgIChgTk3JCfJpjK0UBn+eUTVYv4i5/9UHlFA7JQYJEpiRKaroJTGYGh9GuGuFaqHZ4+MkCaCKJuioKgAlzr0Z6q1J1Gr/WgIlo+qebfdCAShOMWwhH6zhNVUIGOESIQg0XzDRhJKDWZIhlOnJizIn0caP0V2jimWMNd7M9oRIaO9u8WwMiPVIiPXaW3kJBALBuYyIOROUKxfHv471BX0dXHVEcxVqaOUq+IKtqovTC9MJfTgAmXqQp4QalYfOhD4rFCiisabrEm4cA9GSA8wbpBQIcNY4hcLooSlpZiRFAWfRI1A0f51uOTPi6BUCtRCIMVTVLUwdyxTiKmDUmmHcQ9Gvp2s2TTwx2Xx3/HV4VyWMOcS8jn7cYrnjG6IbesxinSNOghDc+TDnYpgkMjUFAsF5x/lmORM/5SsgTfC8YVyRHFQIZUAR86r/XjNAYUAhjUERiwVjBBKxm2aCVEKuEqpRpr8YI5CpWrw2SAnyZL3YrTMeopq1GCMIyh4UBb0oCnrhVyRQptZgUxRdmOnuSrUTgPna0K7Pj9MPO1xf10ZKaO/h9qkWuZVCdcX0npkKAIVA/1/xEE2rhcxxxBBY+gjtf3p2K78zBrU9k6L9aSzjrC6NDgSmmwm9LQjKWLZ7XwT7FggEgnOH8623phBnFZCaCZ0RZB4EQRCDIBf3pYqoIhqrWsigCh6ZehHk3JweMDCtkTnVxNtpOR5Of92EAH4ag0IlFkWKuduADoNqCQsqBIWyFzIlCMgeKEwruQEJjHmgUNVlqceXcVdRLVoO/jxCVKubLBMosgdyUAq5FS1QB3egM1w8GYjhvlQ/ufkli0Gr4m96KS5WLstWzMY5Thha3aK8+9YaB8dC2Z3rDmZGvm+BQCA4BxDiTFAhuDJuIQppLAKIAYFa0ytficFppQqKWMjtyJja6Nz4DICBwEOYKvCYB4VyjK0mGo9eqoMQ/X1INejxYAXBeBTKcaBMbclk7cFJiBpXFlT0rFDLNbThlOodAIghtgy3phGDRkAVNUZNLcGBUBum0KZtMKa6K5nRE9MsymwuTNsClj/5wZo+Jpb/RYxVEyqwiT17pJ720q102l9Nnt8f+XUFAoFAUOkQMWcVFK83HvVwHfbTFdA1tMzU6vl81qbaP9NjtEfyU703pup6LFBi4aceuOlwxgAJFFSrfeaRAIWqTjfVHam7JEOxabb6YiZckhEotJZMfAIBgRLkYrKgtV8yYsa0bAFobkBrSREjRkz7U4Y6JhLRZAS48YF7DuM0ISUx4qyebNYv7pzbNlhoiGvwP3dt/iAhwKlCIc4EAsH5RWW3hEWLEGcVmEsS78fB0+uhkHwwTYQVshjEEgUSYQgoHpxQ4sGgWrLU8HcGD2GQqYSAIiFQjDBTC+VLkBhDkKkxaLogc1MXhOhB/O7YkhVkzbXJG6+ItjWqxZsZwsyuwJjWeN1W9AvQBIzuOo3wG1hfhnJ7IpZzWvyXU7cDN/RKcraEAquLsrh9caKMj18DATJz8yPfkEAgEJwDnG/ZmsKtWcG5ruoM5CsSAkxCkMWAwQM/i0UhjUMBjYWCGFB4QJnqTlSYBIUR5MtxKKB6AoAZSoHCoAd5cgwK5VhQqjroJEIRoFosGSMokj0oCMSgUI5RRZtCEJQlUGo1GWmxbVyTcYVzScoyAWMex0B/tS2TLqygaizGLMVledVkSQYw3Jcu37jhvp8NseQQT0YJJAaLw9hlvuUzgRb8L6sv3oVJFId5+lzddcqvYx1HgYBCcbygMMyNCQQCwbmFiDkTVCgIIegZPxGFNBay5a8rqIkomeqiTILCPAgqEoLQa5XZRVSeHIsiGgM/jUER86KAxiBf8cJPPVAUQFaAwmAMZM1FqmdZMmg9O3X3I6OhpANKtHEMisJAmQey4oWseDV3pm0r6n4s2ZeEAESCkRTA9Hgz0zeaOduUyUQtjeEmerQ/rCUtdMsYGDOyLQkLRZNZ7HO2AH3rdQwLmyU+TUJoXeiGOm4skblsTl0choMCs37dXMwggUAgOHcQ4kxQ4agV3wwtPNdpZTVCqiAIL+d+VIVKEfUaHQI8hMvypECh7EVuMA4K82hJBLwwkqBQD4LMi4DitZmQCVF7ZJoli1prLahImuVOPUcdXaL2bxRKUYzFi7OOGbfOzMf0NkiEhMSWDvfeKGOhizJKtCD8SL+BLZmU/Hu9VAZ3zLau02U0MWaWp+7DeZbu3I/M03kR7l0gEAgqN0KcCSokl1YbjHpoA2guP6pZlXiKqAcyV0+MEMBDGCijKKIxULjYNCeIEX4VPnZLz+AMKgRFQQ9kRUJQlhAMehCUQz01+XVtosqsM2374D6FXJf6spb5uhAivGDjxhDD8kZC63FlMFwFGm8lc2rgyVvKSkJxyQAuz0jf7fhvfy7hhQUCgaByIcSZoMJybfIYdIy9RYs/k0zWLcb0gEn7F2SQehxiz+zjQrPdlRNjBEHFA5lKoMxjtGBS65uZrXi6ngklBqjCUpEJ5AABlZ2/eZzdmPpJgClQS2YEYeutaUgwZpGYumjTi9FGih7mpr/nj+tWMz7gP8LkAd2SV2xiADN/5sdn5ebjdJHI3BQIBOc+jJGoX5WZcs/WPHToEGbNmoVdu3bB5/OhZ8+eGDRoEKQwLWpkWcZHH32E3bt3Y+/evQgGg1i4cKFt3JtvvonVq1fbjk+bNg3169c3PhcUFGDOnDnYsGEDKKXo2LEjhg8fjqpVq5bOTZYi7atei9oxzfHxqWlGX0pA/7fbQXAR3eWnftaLYphVR2gNqos83YWoKSvG1Fi0INd3U12faBY8szDU96a/KNUD/0Ptm4yL8h8Ni6B2XQXm4qyUGcLN1FiK2dfiHgw3Xx/i8I2rPxJOdIUeDDGvq9clM9bWJlK470P/k+u7yeCYJxEyFOrjSWjv+rp+WdFaYAkEAsG5TVlla27cuBGzZ88GpRS9evXCwIEDbWPWrl2LRYsWgRCCRo0a4ZFHHimTvfCUqzjLy8vDpEmTkJaWhrFjx+LIkSOYN28eGGMYOnSo6zy/34+VK1eiadOmaN68ObZs2eI6tn79+hg5cqTpWM2aNU2fp02bhoyMDDz44IOQJAnz58/HK6+8gueff/7MbrCMqBffGO2LeuA3/6rQFywjXIPyEJRpljPGjEKxjDJQpou0kPgKygQBxWuIL90ByijgV7SMUIdQ+XB1z1RRpicE6HPdLWZqz0vJ+AxraQ1NaPLiymQd0//krV38b1Bmj6sdLbPS5CrVp/BB/xwEWvyaQ4N28w2q65tC9/j9cu5YI/ZM4q7Nj4daj27bkePoekF9CAQCwblMWbgpKaWYOXMmxo8fj5SUFDz99NPo1KkT0tLSjDGZmZn4/PPPMWnSJCQmJuLUqVOlvg8nylWcLV++HIFAAI899hgSEhLQpk0bFBYWYtGiRejfvz8SEhIc5/l8PsyaNQuEECxbtiysOIuLi0OzZs1cz+/atQubNm3CxIkT0bJlSwBAcnIyxo0bh82bN6NNmzZndpNlRI+kgZBzGNYUrYOXMMiG8AkpE6YJMz3+TK895pEAMIrCoFe1fEH9wi9SYkzuTwbVKuaXJeM4X7tMv5YkMSjUWXQxo+yGxWJmG6dZyZgUElTMYTzRlnKK8+JNUBZ3oFUTOu6WQe1taRVwfLC/1RqmlcawylPj8fAXCreObD8MQBV9hHtvWfKl5evwvyHXol71imflFQgEgtKiLNyUe/bsQZ06dVC7dm0AQJcuXbBhwwaTOPv+++9x7bXXIjExEQBQvXr1Ut+HE+Uac7Zx40a0bdvWJMK6du2KQCCAbdu2hZ1LHH1B0ZOeno7q1asbwgwAmjZtilq1amHjxo2lco2yok/yTejm640A9UKB16hPBqgWqyCV4KexRqA/A4NMCfyKhIJgLCg8UJhkxIpZv/j1fppO7lIr5rpkIVdmKMvA3VylCjMJoJLWB9PaIN1pksOSfIC+/tIFlIJQ43OmxnzZBJxbYD+BUWZDX4vIgKRoZTKcp6gxZXompwJILpY3fk64e9WfiMQdLwoq+PD37e6LCgQCwTlAWSQE5OTkICUlxfickpKCnJwc05iMjAxkZmbimWeewb/+9a+zpgvK1XJ2+PBhtGrVynQsNTUVcXFxyMjIKJVrHDp0CPfccw+CwSCaNGmC2267zSTEDh8+bIo/06lfvz4OHz5cKnsoS3pWvwZVPUlYcupzEE18BZgXCvPaRBUBUCCHGqTzUGqOWQsqWtsmwyJnho8pCyqayTkUoqads5iHuNg346gu6vTxejxZuO8rq0sQCAkxCts1bO5GzS3KTGoyTNYmYJis9IblxOGckzWuNH/XIy7vj4qOAQKB4BynpJazp556ynjfu3dv9O7dO6r5lFJkZmbi2WefRU5ODp599llMnToVPp+vRPuJlHIVZ/n5+Y436PP5kJd35jWcLrjgAlx00UVIS0vD6dOn8eWXX2LSpEmYNGkSmjZtauzByX3q8/mQlZV1xns4G1ya2BltqrTFs4enQGYKgtTraFlUGFG7ARCLB5ABAcUDylR5Qhks7ZucxVxAJmDUq1nMiIMg02ZzLlUjgUH/Uwv+Z4pZ2rjGuTtZuKylM0zrMLOwAhCKttOVpHlta6sl07XhIricNCzvUuXHRYN+Tcp9tqy3/Uh2lIsKBAJB5aKkMWdTpkxxPZecnIzjx48bn48fP47k5GTbmIsuugherxe1atVC3bp1kZmZaWiIsqLcszXLkuuvv970uX379vi///s/fPrppxg7dmyJ112xYgVWrFgBQP2LT01NPaN9er3eM14DAJ6vNg5T97yDgwUnIVl6hCsMkBVPKKCfE0uMATLVgvChijhdbAEhYaWjNjEnALxaNX+AucSc6fMN0UbU2DLGJPMAOBWu1c/DVXxBL2SrCyurtdAhDs3IXrUIL6JlUeriTL0jTsCFcUk67pmz4hn5CU4CztLc3LpfQ4xayoboY4r8FBn5QbRpVDeKDZ47lNb3z7mEeCZ2xDOxU5meSVkkpjdp0gSZmZnIyspCcnIy1q5dizFjxpjGdO7cGT///DN69OiB06dPIzMz04hRK0vKVZz5fD4UFBTYjufn5xvBd6VJXFwc2rdvj99//920h9zcXMc9uJktrabR7Owzs1ykpqae8RoAEIsYjKs7GktzfsQXJ342rEaqHCNQDJFEwECNf/wpDRWu1aHUbAULVf9Xe2wyeIxzenF+cGLOOK6JP0YJ17PcHOoYLtsTABBUF7MZA3nrlmIXZlbU5lMkFI9m3ADMIsp4T8xjED5mDIA549LB0ka0MfqzcEpKMPbDC0LKzbeMZwDumfkp/tmzE65v3aSYDZ57lNb3z7mEeCZ2xDOxU9JnUq9evTLYTXjKopSGx+PBiBEjMHnyZFBK0aNHDzRo0AALFixAkyZN0KlTJ7Rt2xabNm3CP//5T0iShDvvvPOslNkqV3HmFNeVnZ0Nv99fZn/5hBCTNaV+/fr4/vvvbeMyMjJw6aWXlskeyprrkruhmlQdc499jSBTQ9YpAwqVGEN8EKIKJKqVyVD05udaJqSTYGJQrW/6cUqhZWMCICxUo0wTGEyfRKSQSAwd1OLMECoK6/S9FwQIJE7xWc4XZ9Hiz3FxaSYDlaNQCmPFczrFWcCKFXAk5KZ01Ju8e5U74fajST/+9k/puK7VhaWWLCMQCAQVhbIqKtuhQwd06NDBdGzIkCHGe0II7rnnHtxzzz1lcn03yjVbs127dti0aRMKCwuNY2vXrkVsbKwpaL+0CAQC+OOPP3DhhRcax9q3b4+TJ09ix44dxrG9e/fi6NGjaNeuXanv4WzRNakt3mjyBBjVffWaoAJRDUdMjRnLD8YiSL1gUDsOKNQab6YTCtYHeGFGuHO6IpOglsSQwJhktJky6pYpxKjyD9mUf2jGwfVoeukFaiPtkekgzKDfgS4si2vFZFyX24fR6DxCwog3orlXJb3vJtd/03Ed7hkVBRRknBL9NgUCwbmHaN90FunTpw9iYmIwdepUbN68GStWrMCiRYtwww03mIL0R48ejbfffts0Nz09HevXr8f+/fsBAOvXr8f69etx7NgxAGrV/wkTJmD58uX4888/sXbtWjz33HM4ceIEbrrpJmOdZs2aoW3btpg+fTp++eUX/Prrr3j99dfRokWLClvjLFLipBjMaf4UvMwLxRAdBAwSioJeFCmxFnemWjaDhhMPAADGCTPrGVXk6C9dPLAgARTuWqaSGRpW8SWDE4ScCGREFXUKCS+mbIH+Lt+shrjixSYzuz85EcSXyJBkGKUyeFcm90jMWiySuAnOkqcLR919adqzVSACGPfp6gguIBAIBIKKTLm6NRMTEzFhwgTMnDkTL7/8Mnw+H/r164fBgwebxlFKQan5X+EZM2YYQgwAXnvtNQDAqFGj0L17d3i9XlSrVg2ffvopTp06hZiYGDRr1gwTJ05EkybmuJxHH30Uc+fOxdtvvw3GGDp06IDhw4eX0V2ffWY0/z/8mPMnpmcuV71yYX+hIJCIs0CzZlw6Qq1WN32wlnBArSrDBRcrl3HOmhjgMsYkvNysW05rEU5ZKbDt2RybFhpqQ8+/4ISdaY9uLlLLgrpLmCEk1mxPmQKZp/Lx466/0a1ZQ6fdCAQCQaXkfOtURxg732659DnTmmxnK1B1S+7fmHjgU4AAikIQpF44qQNF0ZuYq+hfIYGgWqhWbXRud30qMrG3W9LnKxKXEABVNIVTiRQgsrYHp8AsOYw448QN4fp0urof3ZIJGGcR05fVLVqRfNdQmLsC8Pei781qu1acxZd+W27nAE08M6BO9QS8f++NEWzw3EAEetsRz8SOeCZ2KlNCQJsvJ0Q9Z/ONFbMFYySUq1tTcHZpXbUhPm45Gm2qNAQBAXGIpmfMXE+GMbVTgD9AQKkHjEmaMINtrvrRWTAZliPjKy6MsKIIJQlYXZ2aJYsY7Z2ccc3/tM6JIKHA5FiNJIxB7yKgcK5J6/X1hABqGc+sE0IQy5+O5xlw9FQBqPidSyAQnEMwRqJ+VWbO6TpnAjteyYMJF94MAHh21xfYkHcQkqZzGCNQFBghV3ozdEr1shkOFjEWGs+lZxpjKAUg261wqq9PS+GkCJmmZC07k4d3K+p9NR1iu/RljTmK5STlxI9+yohFY2blFc6tGg5dhDpkYxrWN1n7rUi/vssavKXMJNic3KGcC5cxYPKXa/FM/67R7l4gEAgqJJUpwH/r1q1YvXo1Tpw4gRo1aqBbt25o3bp1VGsIy9l5zHPNBqCtLw1BhagvmUBhHsgyQUAmCAQ9YEwvneH8jUGpFOqLSRzEm8z3yXRYx7CGERBZMgsz3arEx4RpYspWHFYXJ7wlig/x14QZATH6ZBLKr0PMa7GQwHKEcGP5e9Hv0OXniOkJUPMxq4WN8G5YTnyZ7tvyDPQ11u4+hOVb9oW5AYFAIKg8GDUzo3iVB99//z2mTZuGpKQkdO7cGTVq1MB///tfo3B9pAjL2XnOS80HYt7hX/F+xh/Qq8mGemoCxdqOtCB1RrkYeqpaoZhTRXtAFULU8tkJS4kM1RHLTJ8BhHpk6lawEli8DAFkEVzW7giO4ghhjlmJ9AeGxfJmWN347FH+hCUOjTLg7ZV/oE/rCyK8oEAgEFRcKoubcsmSJRg/fjwaN25sHOvSpQteffXVqPp6CsuZAHfV74zP2g9HPPECYCDE6jd0Rm/LpNYHk8AUD5jiAZhHtaQVU/nfyKBksJe5cLm8U+A+0f8XLg6NOc91vIZmgTMC/60vvi2TZuEKWeaMTTnuwTVj1HZP9nFGzBtnISPMcl2OfL+M6/69AO+t3BjBFQUCgaDiUlliznJzc5GWlmY6Vq9evaj7hQtxJgAAJHrj8GWne7G4/d2oLsWaRI61SbreCYAGJa0IrMuXkWGOssAJJUK4uDNr7S4nDJHEHI/zYVnuc617sW9bAsxN0HVXKQWkkljInK7tQnGZoBElJWjXYgxYvGEn/r1kbQSTBAKBoGLi9Htyca/yoEWLFnj//ffh9/sBAEVFRZg3bx6aNWsW1TpCnAlMVI+pgs87D0PDuGpgjBluRF0QUIWAyhIU2YNQEVn1xSjUyv/ay+aC4y1PPLw1Si8ya5lqG8/HiFlKVuh6j8+MJJaXcT19nxTmyv9c+yTdKiaxkFgz1nHbn1aklsjqSxd2TmU1mPUnCldOw37f5rmu1+eeBQD8sP0gRNUcgUBQWakslrP7778fBw4cwLBhw3D//fdj+PDhOHDgAB544IGo1hExZwJH5nUajL9yj2NY+heghjBDSDgxwFAIRBNj/DEdWTtuBE3B3PtRFxyG+tNPmAUab8Ey3JNaZqnjtyAndvQSFdYCsAwOgom/NLP8CYfjDPYFdGHGnwoTq+aUIGC7lvbe9Zw+n9mvqX9+9/t0PNS7AwQCgaDSUUl+t6xRowaee+45ZGdn4+TJk6hRowZSUlKiXkdYzgSuXFg1BauvGo7b6rZS2zVRS0ajKXIecHQQEgICCYRKanakQ1ZkSP1pAopzcerWJ8iApBBIujRzi7XSrVbQX6HsTMvO1MZVnIIyWdV0oWMROMYH7WWN+yJwEGb6dfR980uYnoPFqqfDxcCZ7pNfX+H24PJsvtiwG3/sy7Q+MYFAIKjwVGTLGe+V0DsaJScn48ILL0SNGjUcuxwVR4ksZ9u2bcOSJUuwZ88e5OfnO16UEIKPP/64JMsLKhCEEIxqehkG1muJh9K/wXG/1qQ+Evea7bwuzLTJFDB+P7AInJBAsVjQjH3BZI0zlbbQvxwpit+fw3aZRZBFvAQ3z2kOv7Y5tRImi5cxjrthW/gebwnUx0Twm+X4j3/E1Rc3wJMDuxQ/WCAQCCoIFTkqY9iwYZg7dy4A4LbbbnMdt2DBgojXjFqc/fHHH3jllVdAKUVqairq1asHj8dT/ERBpaZeQlUs6ToEO05n46HfvkFA9wuGwxpfZogNa20KYnfHWS1xmviyeRH5ArbWODBuTSfvoxXDIlfMOEd4d6WLq9N2jxZrmPW9bpy07cct3o0Xq0770+as3noQbRvtQd/2TV0GCwQCQcWiIpfSePXVV43306dPL5U1oxZnixYtgsfjwVNPPYW2bduWyiYElYcW1VKxqufdmLdvC97enQ7GWCgGjC8KZrjreNMPLOYvblwkFl+r+OAC9AHY6qKFFSuAWUxF+1uZVYxpsW3G5zCWMUB7VPwwhzRTR0sbc46TK3b73PUYgNe/+R29LrkAMV7xi5VAIKgEVGBxlpqaarxft24d+vfvbxvz1Vdf4YYbboh4zahjzv7++2906dJFCLPznLsuaI01fe5EDW+8KiD07xvKTFmHJgjMpTJ065EWLxWJwDC9N5qaE7Obz4puabLO1/9kDuet++bGOr70Tga6CLOIN4dovOLh11K4lxNcNwU3TG5aBjy34MdIdiEQCATlTmXpELB48eKojrsRteUsPj4eiYmJ0U4TnIMQQvBNz8HYe/oERv26HKeVgNnNCIsbT5+nJQAwrqUSr1YYmPbRwT/Il7owT3KHOcRx8eLQOtfJLGURcKZwN6PFlGUOv9+S/tLHWclMfTYt1zHuwWpp4/Zv7Ec7nf5XFvpPWoAX7uqONo1rl3CDAoFAcBaowDFnALBlyxYAakKA/l7n6NGjqFKlSlTrRS3OLrnkEuzatSvaaYJzmCbVauDb3oNxOuBH/+WfwG+rY8YAWVULBASMMq0/JhyFiyE0qFm0GfXKHM3bxasfm1B0iPsCP8awBnIWMV2VKeb1bMLM4p6MSqCZ3LSWfTutYRWM1h9iilY4l99P6BTGvb8KM0b3Q50a4pcugUAgKAlvv/02ACAQCBjvAdWIkZSUhBEjRkS1XtTi7I477sC4ceOwePFi3HzzzeaaVYLzmmqxcfjh+tvx5K+r8PPRDM5qpAf8awKNhYSas2Ahof/yQkMvzgouto3H6TcrB5ei9ZzNEMVbyuAwX5tEqEUvcaLMIYTMbLVz26/mtrTFGzjF5Dm4Yol2nL+UFOYZ6Mfve+NrzBh9PerUqOoyUiAQCMqPipwQAABvvvkmADUh4OGHHz7j9UqUEJCWloaFCxfihx9+QOPGjZGQkGAbRwjByJEjz3iDgsoFIQT/vqwHjhbm4x+rv0NGYaFjrFfYPpd8vJol3kttfg670OG7ETiUlrAZnVysZsaSvDvSyc3pJtwsx2xWL4vVzmmeq17ln6NDkoFVHPJbj8RwN+ad7zDn//ojIS6mmJECgUBwlqngbk2d0hBmQAnE2erVq433x44dw7Fjx1zHCnF2/lK7ig+f9L0JW3OO4dGfViKfKlosWTEWL6vLkDsucRY1xgkbU8FVXfRIsIsZq/s0zP4lXkTxgw2R6ICjuQxmIaYrJqvLMlLCJBnoAs7teFiBxoACv4ypn6zFhDuujnZXAoFAUKZUdMuZTkFBARYtWoRt27YhNzfXVKCWd3cWR9TirLRqeOgcOnQIs2bNwq5du+Dz+dCzZ08MGjQIkuSeSCrLMj766CPs3r0be/fuRTAYxMKFC01jKKVYsmQJ/vjjDxw6dAgAcOGFF2Lo0KFo2tRc32nw4MG2a1x00UWYPHlyKdzh+U2r5JpYPmAIAOC5X9bg24N/Owov3VLmeA4wt4HSvKFhi65a6p9JmquPjyNzFSsMJiuUzQRVwt/gJHBJCWFws/IZeylN9LWpmoSxYWcmZiz9HcOvbQ9PmO9BgUAgOKuUkeVs48aNmD17Niil6NWrFwYOHGg6v2rVKsybNw/JyckAgL59+6JXr16u682YMQM5OTm49dZb8cYbb2D06NFYsmQJLrvssqj2FbU4O3bsGBISEtC4ceNop9rIy8vDpEmTkJaWhrFjx+LIkSOYN28eGGMYOnSo6zy/34+VK1eiadOmaN68uS0zAlCD8r744gt0794dAwcOBCEEy5Ytw4QJE/DCCy/gwgsvNI2/4YYbcPnllxufo82sEBTPs5d1xYTOXXDD55/gpCyr4kqLRwPCCCVZs2TpL80qVqyrTisvoUsMWwwbHJIfGdR2UVZr3Bn+YOBdliZDoVP4nNXKZ7UsEttb+zgnrK5a/b40Yaav9cXa3di8Lwv/HdlXxJQKBIIKQun/LKKUYubMmRg/fjxSUlLw9NNPo1OnTkhLSzON69KlC+69996I1ty8eTOmTZuGqlWrQpIkXHrppWjSpAlefvnlqOqcRS3OnnvuOfTp0wf33XdftFNtLF++HIFAAI899hgSEhLQpk0bFBYWYtGiRejfv79jLBsA+Hw+zJo1yxBcTuIsNjYWb7zxhqnsxyWXXIJHHnkEy5Ytw6hRo0zja9WqhWbNmp3xPQnCQwjB1zcNwqqDf2P8mrXqwTAZkyQYcmfq4oM51U/Tz+uiKlzNL4tI4TMcnfpiRgwv4vjsSIdOAEaLKH4cf1/FWQWjzf7kM05Z6PpgzHa/hAD7Mk9hwuwfMGlEzwgvIhAIBGVIGVjO9uzZgzp16qB2bbWUUJcuXbBhwwabOIsGxpihXeLj41FQUICkpCQcOXIkqnWi9ltUq1YNsbGx0U5zZOPGjWjbtq1JhHXt2hWBQADbtm0LO7e43+glSbLVY/N6vUhLS8OJEydKvmlBqdC9QUP8PHQonutyhWE8M7739A8KJ8w4jGB33prGCzPuvW22JlIkpokmRfuTmxv2K8tqxeJfFPZG5k7xc/p1dNGmmEWh9eV474S7V6pZ+7R7MQlE3W1pua/i7pEwYNNfWZi99I9wIwUCgeDs4PDzvthXMeTk5CAlJcX4nJKSgpycHNu4X375BY8//jheffVVZGdnh12zUaNGhn5p0aIFZsyYgRkzZqBu3brFb4gjastZy5YtsXPnzminOXL48GG0atXKdCw1NRVxcXHIyMgolWvwBINB7Nu3z+S+1Fm0aBHmzJkDn8+Hjh074u677xbFds8CvRo2Qq+GjfDroUN4bPXPptiqYht5c645a0KAo/igISHGTze+iUM92It3l+pjZLurUo+JMxazxq4xcxKDIbKcskJJyNKmCzgjdo6Z78V2PzS0dsSWQD7RAsBnP+/CDxsPYOYT/UWrJ4FAUH6UMCHgqaeeMt737t0bvXv3jmp+x44d0bVrV8TExGD58uV488038eyzz7qOf/DBB40kgOHDh+Ojjz5Cfn5+1FmcUYuzoUOHYty4cfj4449x6623wuuNegmD/Px8+Hw+23Gfz4e8vLwSr+vGp59+iry8PPTt29d0/Oqrr0bHjh1RrVo1/PXXX1i8eDEOHDiAl156KWxigqD06JyWhp/uGIrPduzEtA3phkgwaRabCtKO8S5M6hDDBRg10og+R1vcNDTIrel0LasQtAgZ0zguI9PYOifUIv4xY1gQLXNcBKixbct5N8FJCAEYM8W+WV2cp/L8mDL/JzxzT/dIdy0QCASlSknbMU2ZMsX1XHJyMo4fP258Pn78uBH4r1O1aqj2Y69evfDBBx+EvZ7uIgWA6tWr46GHHgKgtr6MhqiV1WeffYaGDRvis88+ww8//IBGjRohKSnJNq6i1Tn7448/8Omnn+Luu+9GvXr1TOf+8Y9/GO9btmyJ+vXr46WXXsJvv/2Gzp0729ZasWIFVqxYAUD9i+ebnpYEr9d7xmucK9x/ZSruv7IrXlu9BjM2bAyfUQkYrjyTgHOAFGc9slrfdPj6aQ7m8nClKWyxXAiJJKbtKVy9NP36buu4Yr1XR6HJTFZEfZ4jBPht5xEs+20fBvVqj7iYkv9CVhaI7x874pnYEc/ETqV6JmUQc9akSRNkZmYiKysLycnJWLt2LcaMGWMac+LECdSoUQMA8Ntvv4WNRysoKMCRI0eQmpqKatWqAQD279+PTz75BOnp6Zg/f37EezujOmcnT57EyZMnXccWJ858Ph8KCgpsx/Pz80vVpbhnzx5MmzYNffr0Qb9+/Yod365dO8THx2Pfvn2O4sxqGi3OB10cqampZ7zGucb/Xd0V/S9shBd+WoffMrJUyw4nyAx7ptVFyR8n5jE2bKmT3Hu9tAdnUWJRuAfdXLKGm1GxHHPas9VSx6/DZ1taBhBAy/hkJtenCT7Oznp9l5t8e/EvmL1kA4b2ao2brm7pPKgcEN8/dsQzsSOeiZ2SPhOrgeOsUAZ1zjweD0aMGIHJkyeDUooePXqgQYMGWLBgAZo0aYJOnTph6dKl+O233+DxeJCYmGhLJtT5448/8J///Ad+vx9erxejR4/Gtm3b8NNPP6Fnz5544403otpbudY5q1+/Pg4fPmw6lp2dDb/fX2p/+RkZGZgyZQouueSSiHtb6ckGooxA+ZJUJR5Tr+kBANhzPAf3fb48lLEJLlaLhxNwushwtZpZhZklho1YhA9xtEChWFHjdt1i9xTFkgaU2YSdTYC5xeTx98cPoKE5/gDF3KWbIcsUg3q1jnZ3AoFAUCKKjUEuIR06dECHDh1Mx4YMGWK8v/3223H77bcXu87HH3+Mu+++G926dcPKlSvx5ptvomPHjraqEZEStTirWbNm1Bdxo127dliyZAkKCwuNumJr165FbGwsWrY889/MT5w4gcmTJ6N27dp45JFHIo4f27hxI4qKimy10ATlR9OUZKy6dwh+O5SJsct+dIw/MOK6wIkrN5jLn9b3ThexjNFFInOyfDkF+UdCcdlGNjerg5WMs/pFdU3LfZjKfWjvP/xuC37ZegivjL5W/BIjEAjKnjISZ6VFVlaW4VG75pprMHfuXIwcORJxcXElWq9cg0f69OmDpUuXYurUqRgwYACysrKwaNEi3HDDDabyGqNHj0bLli1NbtL09HT4/X7s378fALB+/XoAqg+5Zs2aCAQCePHFF5Gfn497770XBw4cMObGxMTgggsuAKDGj+3duxeXXHKJkRDw6aefomnTpjY1LSh/OqXVxff3Dsa7v6Rj4Z+7wSxZnbxAs6HHePEWIqsVKxKdEca6xRjX+kmBPZI/CtFlsvhZA9b4dTgBZb2UDbdkB77Pp8Wd6pZYsPfQScz4/Hfcf1OnMDckEAgEpUAFb9/Et2mSJAnx8fElFmZACcRZNP7p4gINExMTMWHCBMycORMvv/wyfD4f+vXrZ2unRCkFpeYu0TNmzDD19XzttdcAAKNGjUL37t1x8uRJQ5BZszVq1qxpdJCvXbs2Vq9ejV9++QWFhYVISkpCt27dMHToUJGpWUEhhOChyztgWKc2+OzP3Zj722YE9Bix4uLM3GKwuPM28eameNySA/hAf2rZipbF6egKZTBKc5i2Y51jixVjjlYyR4MWtzcrNoHqYp3k+WbtbqRUr4Kbe7ayDxYIBILSooJbzvx+v6nERlFRka3kxnPPPRfxeoSx6BJUeV9s2IUJwccffxzN0pWWM63JJgJV7UT7TPL9AQyY/RkoV9tLh/BCSwnj6mPceE54GQH2MGLsDZEkgRvLzLFwNhGonTS0j1X4KZzVzTzFEHWOW6fO4sy0gBLGuuYWA+cUn+bgpiWMoX+35hh2Y/lYmsX3jx3xTOyIZ2KnMiUENH57atRz9o98vAx24syqVauKHdO9e/eI14vactatWzfHGJP8/Hzs378f2dnZaNmyZanGpgkExeGLi8XyBwfjrZ/+wKdb9gAwW7JMgo0Pere6N7kK+8Z8p3IWDhY0w2NosWy5uUwJN9dm0SKWMY6R+ghZzeCAPkV2z1cwHoFTfJw2iVAWWs5JcAJYsnonVm/4CzMn3gJJqtjuB4FAIChtohFekRC1OONrglmhlGLx4sVYvnx52HECQVlACME/unXEP7p1xIa/M/H0Fz+GD4q3WNeIU80xB6uSIcA4d6PpvCXr03Bl2jYMUyaksbZlb6AsJAZ105pWPNYYR2Hu8K5bvdysYg5bsaHVQnMbw5hZ9J3KD2LQEx/jjusuwc29RSanQCAoRSq4W7O0KdWgKkmSMGjQINSsWRMffvhhaS4tEETFpQ3r4ruHB6NnszRbZiPhRIWe1UkcrGMGTrFXVgHmBh9/Bss1FPN1nQLw+esb7lUG1ZVJYRaVmtCDAlNHhGKFmeX5hCx5JOxcmzVR+zD/mz/x+KtLi7mqQCAQRAEj0b8qMWUS8d68eXNs2rSpLJYWCCJGIgTj+nbF8jFD8PGIGw0zMbOJkDCLhEkscI0B43FRR45uT6djzC7cDDHpkIxgEnqW2DlHeAubbsWjgES1Hw5uZke3NbXh+w6dxL9nrQ5zYYFAIIgcwqJ/VWbKRJzl5eXB7/eXxdICQYlISUzA0keG4IUBXUPuSF2MhPsmjiJz0cDFosYAk2UrJJ6YFtTPzJ8pAykuXyecSAqnHHUxxvXtJHDo4VlCCIBfN2dg7NSloLSS/5QUCATlj5uFP9yrElPq4mzz5s1Yu3YtGjRoUNpLCwRnzGUXpuG7fw7Bt/83GC1qJxXvkuTf85Y2J2sbf97pBwMXn6ZawDTxxaBZqEJxZASadUwXj04/bMIds+7BwVpIqPkHgOo2ZeaXEwzmZvNueyLAX4dOYPA/P8bHX292XksgEAjOIYLBID766CM8/PDDuOeeewAAmzZtwrJly6JaJ+qEALc6HZRSZGdnG2m5t956a7RLCwRnDUIIXr/zWuT7Axj9wXc4nJMf0kiAWWQw+zHDbciP561wLmUtQhmgzF6rDAgF+jvEwDGHa5q8jhYroPGWSxAIiybGTMZCvh2UfkJ35/JJCDoKlwTB3dsny7Zgxc+78NbzNyE2xlPMRgQCgcBMZXFTzp07Fzk5ORgzZgxefPFFAECDBg0wd+5c9O3bN+J1ohZn27Ztcz2XmJiIdu3a4cYbb0Tr1iJbS1Dx8cXFYta9NwAAXvjiZ/y8k+v16pS9CQAMYLoQ4oUSLNmWxgeYhRkziyDA2QgV1m1qFYJujcy5++B1FDPqfRDXPRlZqVyNN9tWeAsdg9kNK0ELylWF6Km8AO56bAEevusKXHXpBW53JxAIBHYqSYD/r7/+itdffx3x8fFG2bHk5GTk5OREtU7U4mzBggXRThEIKgXjB1yJfH8AD81aimOniwBwFjIdXQQx+zk90cDQZJxaI7xic4lfY8YizhCExJBR/Fb/0yHGzbFRO78WLy6t96kN0u+RmCaGxoamM9MxI74OzCTqKANen7sOcbEedG7b0O1WBQKBwEwlsZx5vV5bR6PTp0+jatWqUa0j+hMJBBy+uFjMGzkAix65CU1rV3cMMnX7/U0PGwO4jEouZozoMWVwXwOAPVbMUh5DsrpcI/ih5ZoRaiQfFLMPHWvcnGWsNWuUP85nUE3938945LkvcSq3qPjNCwQCgcPP4oqYEHD55Zdj+vTpyMrKAgCcOHECM2fORJcuXaJaJ2px9txzz2H16vAp8j/++GNUPaQEgopG1fhYTB/eF0ufHoL3HuyLGMnitnTC8gOBFyRWEeO4BgOIVvsMTNNxfEapZZJVINnWLO4HFAUgI1RvzWlsGDHK74P/02mP1ucCAJlZuXjgqU+xZeeRYq4gEAjOdypLKY3bb78dtWrVwmOPPYaCggKMGTMGNWrUwKBBg6Jap0QxZ61ahW9ynJ2dHTY2TSCoTKSlVMeSp4ag0B/EiDe/xKmCoCk+3g3bGKvVzBKXphekNc5Z6qhZLVE8RtYng16gzLIZfaIeYwZT0oHhdTXmh8ZYMbI6rcfcHgjlB3HvGQNjwKT/fo8Fb93hMlkgEAhQqdyaw4YNw7Bhwwx3plPLy+IoE7dmIBCAxyMysgTnFlXiYvDR/92M2f/oh1rVYtWqF3zQPf/Dw4iz1wZZe2DqQoivt+ZwTWJ5OcJ3C9D3QkPXJQoLXUcJuTH5Nd3Wd0w65USmk2sT/GcHq6FRpoMb/+IbK9zuTiAQCCqNWxMAMjMz8emnn2LhwoX47LPPkJmZGfUapSrOGGM4duwY0tPTkZKSUppLCwQVhto1EjHnkZsw55F+8Ib1c8JIBrBlUmqEa7EU7phxSYfG54aFTY95g0VIWUWTy75NgtNUGNfBcsfP416m3p6MAQozux2016ZtRzHkofm4//GFyM0TcWgCgcBMZXFr/vzzzxg7diwOHDiA+Ph4/P3333jyySfx888/R7VORG7NIUOGmD4vWrQIixYtCjvnpptuimojAkFlo2b1RHzxzBAolGLHoWw8OesHzlUZEk1uLtAzTgwP89uhLcuyuGvzZUOoZVmFmSxsYfejD3IQceGasOvncvOCuP/xxXj5mevQqH5yuKsJBILziUpSSuPjjz/G008/jZYtWxrHtm/fjunTp+PKK6+MeJ2IxNnFF19s+Ey3bduG1NRU1KpVyzZOkiQkJibikksuQc+ePSPehEBQmfFIElo1rIWvJg7Bl+u3439LN4eEk5tlTfM6Wmul8eedYLroO4PfCvX4MKPUmVM9N2YugVHsj0XKbHuKRJCa4t24A08+9w06tqmH0fddhfj4mOKuLhAIznUqScxZYWEhmjVrZjp20UUXoagoOo9AROJs4sSJxvshQ4agR48eogOAQODAjZdfjBsvvxgAcNOzH0PmMy31MhuA2bWo/dDRa5jxsaOMf8Ps1ig3q1xYONGov7XXSFPVW1ivrZEsYHetlhguFu33TRkYPnoBru/TAncN7lQaqwsEgkpKZekQcMMNN+Cjjz7CkCFDEBsbi0AggIULF+KGG26Iap2oszWnT58On88X7TSB4Lzjs+eG4of0fZi2+BdDDLlmXHJxacxiRTLGWkSebQ1ubVexxKBauaxZmE7ZRPyeXa5D9P+aNh4lVF+QOF7vm+92YN++bIx/4lpIUuVwbQgEgvOT7777DidPnsQ333yDxMRE5OXlAQCSkpLw3XffGePefvvtsOtELc5q1qxpvC8qKkJmZiaKiopw8cUXR7sUAODQoUOYNWsWdu3aBZ/Ph549e2LQoEGQJPdcBVmW8dFHH2H37t3Yu3cvgsEgFi5c6Dh2w4YN+Pjjj3HkyBHUqlULgwYNshWDKygowJw5c7BhwwZQStGxY0cMHz486oq+AoGVHu0vQI/2F4AxhvVbD2LKh2vVE1Ydw2VrumY/wllDmcZxRWIdi6DJLHzmp7EmMycEWCcZvkheKZLwAk231PGWQcYJPMUlKI0A23dn444H5uPx0d3QUXQWEAjOPyqJ5Wz06NGlsk7U4gwAjh8/jtmzZ+P3338HpRSEEHz88ccAgB07duDdd9/FfffdV2w9tLy8PEyaNAlpaWkYO3Ysjhw5gnnz5oExhqFDh7rO8/v9WLlyJZo2bYrmzZtjy5YtjuN27NiBV199Fddccw2GDx+O9PR0/Pe//4XP50Pbtm2NcdOmTUNGRgYefPBBSJKE+fPn45VXXsHzzz9fgqcjENghhOCK1g3x+eQGWP7bX3jr0w0haRM225M7yQsl0xioxWsd1mGcy9G4lnN9DH2joWQGLqHBlHxguGejsGJxuotR9Y0pU5UxMBJGNGrzp77+Ix5+oAu6XnZh5NcWCASVnrJya27cuBGzZ88GpRS9evXCwIEDHcetX78er732Gl566SU0adLEcQylFCtXrsSDDz6ImJgzi5WNWpydOHEC48aNw6lTp9CpUyecOnUKu3btMs43bdoUp0+fxtq1a4sVZ8uXL0cgEMBjjz2GhIQEtGnTBoWFhVi0aBH69++PhIQEx3k+nw+zZs0CIQTLli1zFWeLFy/GxRdfjBEjRgAAWrdujUOHDuGTTz4xxNmuXbuwadMmTJw40ciuSE5Oxrhx47B582a0adMm2kckELhCCME1lzbBNZc2wbxlG7F41Q7jHLPEmxlQrYe4MRAhFaO1dnIM3o+kVZS+njXLlHMv2mLcIvkhaR1jXc/qvuTrvDmIz9B7hunvrkHu6SJc0+ti4eYUCM4XykCcUUoxc+ZMjB8/HikpKXj66afRqVMnpKWlmcYVFhZi6dKluOiii8KuJ0kSNm/eXKKis7a1op2waNEinD59GuPHj8fjjz9uEy9erxctWrTAzp07i11r48aNaNu2rUmEde3aFYFAoNgOA8XdfDAYxJYtW3DFFVeYjnfp0gW7du1CQUEBACA9PR3Vq1c3pb02bdoUtWrVwsaNG4u9B4GgpNzVtx0+nzIUc8cPRNP61exWNM216FjGguu3KXEWKKc4NhNh3KKmGkEue7bHuDHze4aQa1VhILL20gvP6i/F8rLux/Iy7k2zrr3/0e94+PHFLrsUCATnHA4/F860CO2ePXtQp04d1K5dG16vF126dMGGDRts4xYsWIABAwZEZA3r168fFi5cCFmWI70zR6K2nKWnp6Njx45o3bq165jU1FTs2LHD9bzO4cOHbda11NRUxMXFISMjI9qtmTh69CgURUH9+vVNx+vXrw/GGDIyMtC0aVMcPnzYNkYfd/jw4TPag0AQCdUT4zF1zPXIOV2IR6d9g9z8oNnqZc30BFRrmX7OCcNN6XQOIX8lBYjq/IShfCJFF2N6MJlV1FmSIAyvaJSWN6vg1M+dzCnE3fd+gLZtGqBTh/q4ssuF8HjKpOmJQCAoZ8rCrZmTk2MqmJ+SkoLdu3ebxvz111/Izs5Ghw4dsGTJkmLXXLZsGU6ePImvv/4a1apVM50rLgmAJ2pxdurUKdStWzfsGI/HE1FNj/z8fMfMT5/PZ2Q4lBR9vnX9xMRE49r6n07uU5/PZ3SVFwjOBsnVquD9Z28BAKz4dQ/eWhj6DY5ZhU+kuMWYUYAwpsWNEc2jGQoqY9DPOa0ZUlp8nbSwezBWjpAwmaK8KFUUhj/S/8Yf6X9j6bfb8eLz/cImEwkEgvOLp556ynjfu3dv9O7dO+K5lFK8//77GDVqVMRzyi0hIDExEcePHw87JjMzE0lJSSXdU4VnxYoVWLFC7QU4ZcoUpKamntF6Xq/3jNc41zjfn8nQ61Mx9PrLsXj5Rry7cA38fs2/WZwQcoIXaIwBjHDCLAQhJNQLVJ9IYE+vVMKIrDDxYlGJS6ffkrU6b25C9eDBExj9z8WYP/cBeL3nd2/f8/37xwnxTOxUqmdSQsvZlClTXM8lJyeb9Mzx48eRnBzqTFJUVISDBw/iueeeAwCcPHkS//73vzF27FjXpAA+ROpMiFqcNW/eHL/99htOnjzpKMAyMzOxceNGXHXVVcWu5fP5jNgvnvz8fMPCVVL0+db1rRY1n8+H3Nxcxz241XOzqu/s7Owz2mtqauoZr3GuIZ6JytXt03B1+yHwB2SMf2MF/jp0Qj1B1KxHvlKGKRGAz7QE94YirMvTGt/GKNSgNi6OQ09OsFfW4MZpByMWZG4WPuN8KFHBuBZgFo6E4OTJQtww4L94dEx3dOp4/pbcEN8/dsQzsVPSZ1KvXr0y2E14ysKt2aRJE2RmZiIrKwvJyclYu3YtxowZY5xPSEjAzJkzjc8TJ07EXXfd5SrMdPbv34/t27cjNzeX80jYW2GGI2r7f//+/REMBvHss88iPT0dfr8fgKow09PT8fLLL0OSJNx4443FruUU15WdnQ2/33/Gf/m1a9eGx+OxrZ+RkQFCiLG+W2xZRkaGYyyaQFAexMV6MfulOzF70s24qWcLLWhfU0KMOQozwJIooIkbCeF1ELG8JwrUBuos9APDVopMr4vGdUSI+IcpJ+bUFxdvB6gJAzR0jvBJCNbkAm0P//nPD3jiyc/w99/hrfwCgaCSUAYJAR6PByNGjMDkyZPxz3/+E1dccQUaNGiABQsW4LfffivRNlesWIFnnnkGW7ZswRdffIG///4bX331FY4cORLVOlFbzi666CLcf//9mDFjhslceM899wBQb3bkyJFo0KBBsWu1a9cOS5YsQWFhIapUqQIAWLt2LWJjY8/YNBgTE4PWrVtj/fr16NOnj3F87dq1aNasmRFn1r59eyxevBg7duxAixYtAAB79+7F0aNH0a5duzPag0BQ2lRLjMOdN7bH7f3a4ds1OzHzk3QADIwXaODKcvBuRWoeY6vdwcWShSZpfzCXMXopC2YXdcViuZYhunRrnUO/TvN4+xr8DjIzTmPcuK9wwQXJmDDhesTEnN+uToGgUlMGljMA6NChAzp06GA65mbh4ltZuvHFF19g3LhxuPjiizF8+HA88cQTSE9Px5o1a6LaV4mK0Pbs2RMXX3wxvv32W+zevRt5eXlISEjARRddhL59+0Zs9erTpw+WLl2KqVOnYsCAAcjKysKiRYtwww03mIL0R48ejZYtW2LkyJHGMd1qt3//fgBqgThANVPqXQxuueUWTJw4EXPmzMGll16K9PR0pKenY9y4ccY6zZo1Q9u2bTF9+nTcddddIIRg/vz5aNGihahxJqiwSBLBdVe1wHVXtcDxEwV48t9f4lSeYh5kiU+zl8Ew/mN8DiuqqIN4o0wVaE4/ON0W0z2S1m4CXAN2ptg7GRiji+tEYGH/XzkYMewDpKRUweSXBsDni4t4rkAgqBhUlt6ap0+fNjomEUJAKUX79u3x+uuvR7VOicQZANStWxfDhg0Lu0FrGqmVxMRETJgwATNnzsTLL78Mn8+Hfv36YfDgwaZxlFJQav6XZsaMGTh27Jjx+bXXXgMAjBo1Ct27dwcAtGjRAv/3f/+HBQsW4LvvvkOtWrUwZswYU3cAAHj00Ucxd+5cvP3222CMoUOHDhg+fHhxj0AgqBCk1EjAjJeGQJYVPPnvpfg743RkViw3y5NVEemuRscgfYQXSnosmSVmzPaD1mL5c8vSNE0r9oe1WcQdP16Ih+7/CC+8dCMaNUoJM08gEFQ4Kok4S05ORlZWFmrVqoW6devit99+Q9WqVeH1Rie3CGNR/AoaAQUFBfj888/x7bffYu7cuaW5dIXlTGuyiUBVO+KZ2In0mVDKsH1vFp77z/faEUvpC2Ogm3DTfyQQrk4ZC5Mpqp1zyuzU488s12bM2WXJC7Aw9XLV82770d21THONOgy5sEkKnniyDxIT410WqdyI7x874pnYqUwJAS3HT4t6zrYX/lkGOwnPqlWrUL16dbRv3x7p6el47bXXIMsyhg8fjmuuuSbidaKScseOHcNff/0Fj8eDpk2bmrI1A4EAvv76a3z55ZfIz89HbGxsNEsLBIJSQpIIWl1UGwvfvB2BoIIf1u7FrAUbIv/NUw/sl1xi0ngRxmeGurlJLcsAIcsZn21qFX+M2OeZ9hguxVNxUG7c8L/2HMeoBz/Gy1NvQt261V0uIhAIKgyVxHKme+4ANaZ99uzZkGUZ8fHR/SIYsTibNWsWvvvuOyMt1Ov14u6778a1116LrVu34s0338Tx48fh9Xpx3XXX4aabbopqIwKBoPSJjfHg2qub4dqrm+G/M1dj7W+H1BNu8WV8ticvtuRQBiXTXZlWwaWf4z671Sszzut7YWaLmaGjOEFlLb9m8o9aLHu2rgLWg1opkrH//BQtW9XCyId7IKmGcy9fgUBQAagk4kzn1KlTpmL8p06dQu3atSOeH5E4W7VqFb799lsQQoyGoIcPH8bs2bMRFxeH9957D5RS9OnTBzfffLOpiJtAIKgYPHLv1XjkXuDY8Vy8OXcdduzUYjZ58xXjMzP1l1lMGa5HzmWpn7AnHehuxTCpBlaXqz6HgbPQWQUXF8NmicxwFGZOEPU/27dmYczIBbjznstwzXWlU0BSIBCULpUlIWDjxo14++23cfLkSdu5BQsWRLxOROJs9erV8Hq9ePbZZ9GsWTMAwLZt2/DCCy/gnXfeQUpKCp588kk0bHj+Fn0UCCoLNVOqYuL/XQN/QMaL/12BnXuOQ49Lc/wB6GBlI9xx3qJlnOSSAEKLOAg0x4QELpDfcKVaMjSjKN1hSyQw1mCcAAU+mP0Ltm3JwNU9mqFthwaQpHCrCgQCgZ2ZM2filltuQffu3c8ovCsicXbgwAFceumlhjAD1BYFl156KdavX4+RI0cKYSYQVDLiYr147om+YIxh7scb8O3KXaGTkf6W6iDcDLcnU8uVhfIRmNn8FtV1mGZRI/bjYfZm+2zdrCU07Y8NB5G+4SAaNq6Bp569TpTdEAgqCpXEcpaXl4c+ffqAuAbMRkZEHQIKCgpQp04d23G9ATov2gQCQeWCEIJht3XGR+/diddesHf2cI5NC7MeZSAKINHQOKNTAWVGVX+36hwGlIXGhxnsXiqEOca3gUHbQ6hbgqmTAoC/9+VgwuOfw++Xw+1QIBCcLVgJXuVAz5498cMPP5zxOhFZzhhjjjU6PB614rbIzBQIzg3q1q6Oj2bciWPH8/DxJ79j7a8HQ95Kyw87p/gyvpCsdtA8khCuEwDn6mR2AWZyWxquU13tEfN4R/MdVwaEcDY3bZ/F/V6bfSwfD9zxPp54pg9aty2+44lAICg7KnLM2YQJEwxLGWMM33zzDb744gtb/3G9gXoklLgIrUAgOHepmZKI0Q9ejYcfYPh940H8960focihn47hrGnmc8UlAmiWMZg9nY7xZPwPZ058Mes5PX4O9vEAQm2hrGVCjAtyO2HAK89/BwCIr+LFM5NvRFojkfAkEJx1KrA469mzZ9jPJSGiIrTRdFI3FiYEH3/8cYk2VdkQRWhLH/FM7JT3Mzl1ugDjnv0aJ07o6eF8gVcCKM4FX22YrFpA2Lgxyxx1HrGc4gL79TG8j9LpvGkBh+vZEhpCh9t2TMPDT/Su0L06y/trpSIinomdylSE9pLHp0U958+pZ78IbWlRZpazUm48IBAIypnq1RLw5rRBAID9B47jX89+45Cq6QDfYP1Mfi5YXZhcOQ39MHOK+teEmcktUlxCAl/vTfusD93020FMeeYrPDNlQAlvRCAQRE0FlxR//fUXvF6vkRx5+vRpzJkzBwcPHsRFF12Eu+++O6pCtBGJs2hqcwgEgnOfxo1SMH/OXTh67DT+Nf4rFBa6BM7zJTE4ospjsgolHVt8W8iqZnQrIMS5REhxP+gdrsd/3rvzGJ4Y9RH+Nbk/kmr4ir8HgUBwZlRwcTZnzhzceuuthjh75513cOLECfTq1Qtr1qzBBx98gPvuuy/i9SLK1hQIBAInateshhnv3o45M29HgwbVtaN6cD8zPhL9BbM1ytWSxtSMTiJrSQbWeDOFwtos3QTV54VLCQ1z/eL2SIBjGfl4/MGPMW3yt/h68SYUFQbDryUQCEqMNas6ktfZ5PDhw7j44osBAPn5+UhPT8fo0aPRt29fPPLII/j999+jWk+IM4FAcMbExHgx5cUB+GDe3bjqqgvUg7o7kR+ol8WgnOhi3HG9Wbmi1kgj0P7k2zMpLpY0HU20EQYQJdy40HjrfGOvWp9R42XsVx0iByg2/fo3Fr3/Kx4d/gG2bToc/kEJBIKS4VQqo7jXWURRFKOqxe7du5GUlGTE5qWmpiI/Pz+q9YQ4EwgEpcqDD3TDBx/cgwnP9kWoyH7IigZqtqIZL9265lDmgsDcIcBkfbNSkh/KVvcrdbG4WX/oc++LCoJ45Zmv8eCgWdjx55klCQkEAjPGz4coXmeTBg0aYN26dQCANWvW4JJLLjHO5eTkICEhut69QpwJBIIyoVmz2nj/g3sw5/070bJVHZOQijYEzEBxEmPOcW2h2mkucxhTLXKGxU5zleqlNlwxr6vXUtMFY8Av4+V/fYXpL30X6V0JBILiqOCWszvuuAPvvfcehg8fjj/++AMDBw40zq1duxbNmzePaj1R50wgEJQpHo8HT4/rC0oZ5n/wK35ZuxenTgXAOEETanTuvg4JN0SPQ3NDzxi19uiE9hs2gVFvLXQRt82EunUa+3dY9/e1+3DvTf9Dj+ta4fb7uohenQLBmVDBEwJatGiBt956C5mZmahbty6qVKlinOvQoQO6dOkS1XpCnAkEgrOCJBHcdfdluOvuyxAMypj5vzX4Zd2+UHFb/YcvgSbc7EVije4C1r51nDBzkkBMX8Pazkk/q+iTSfGRxJRqQy0DHQQalRm+/3ILfvp2G16ZdSeqVa8CgUAQPRW5Q4BOlSpVcOGFF9qOl6QunHBrCgSCs05MjBcP/eNqzP5gGN6bexfq1qsWMkgZXkqLW7KYZpzhMrTUvp7aS19D6/FpJBpE0qjYUdwVT8BP8fjwecg9XRjlTIFAAKDCuzVLm3K3nB06dAizZs3Crl274PP50LNnTwwaNAiSFF43FhQUYM6cOdiwYQMopejYsSOGDx+OqlWrGmMGDx7sONfr9eLDDz8EAGRlZeHhhx+2jenSpQseffTRkt+YQCCIiLg4L15+7RZkHc3Fd0u34LtvdhSTjWn8R/sc5U9hhpALE6o1jrkJM7dMTv29WwsoHc7TGvQzjLntfVSvUQWjnu6DZq3qRrdvgeA8pjJYzkqTchVneXl5mDRpEtLS0jB27FgcOXIE8+bNA2MMQ4cODTt32rRpyMjIwIMPPghJkjB//ny88soreP75540xL7zwgm3eyy+/7BiYd9ddd5mOV6tW7QzuTCAQREut2lVx57ArcNtdl+F/01dj/c/7HDMj9fg0wwsayQ9t0zoW61c4YabFxTHr8ZJ0PNDmnTpRiJfGfoGqVeMx8uk+uLht/cjXEAjOV8pInG3cuBGzZ88GpRS9evUyBfIDwHfffYdvv/0WkiQhPj4eDz74INLS0spmMxzlKs6WL1+OQCCAxx57DAkJCWjTpg0KCwuxaNEi9O/f3zX1dNeuXdi0aRMmTpyIli1bAgCSk5Mxbtw4bN68GW3atAEANGvWzDRvz549yM3NxZVXXmlbs169erbxAoHg7OPxSBj5SA+MfKQH9uzIxKTxS9UTWkyXHq9vElfhRJKDi8M1qcBlPcKfd5trFWzW/ASTCCTIPV2Ef4/7Epd1a4IHx/a2x7AJBAKDsrCcUUoxc+ZMjB8/HikpKXj66afRqVMnk/i68sorcc011wAAfvvtN8ydOxf/+te/Sn8zFso15mzjxo1o27atSYR17doVgUAA27Ztc52Xnp6O6tWrG8IMAJo2bYpatWph48aNrvPWrFmDuLg4dOzYsVT2LxAIypamLepi7icj8O/pt6BWnUTjONN7XYbrMmCcD3NOwxBfXEkNxw4EjJvrGBPHQrFskUAZfl21B/de/w7GDJmFnX+KIrYCwdliz549qFOnDmrXrg2v14suXbpgw4YNpjG8PikqKjprv0SVq+Xs8OHDaNWqlelYamoq4uLikJHhXsTx8OHDqF/f7gqoX78+Dh92/uHGGMO6detw6aWXIi4uznb+rbfeQl5eHqpXr46uXbvitttuQ2xsbJR3JBAIyoLadarjlelqDOnObRl46ZlvIncpWmPDKA1Vy9DXkCSzEOPFFW+m42t58NcPU8UjLJyVLu9UEV5+4gtUrR6P598Ziuo1oitaKRCc05SB5SwnJwcpKSnG55SUFOzevds2btmyZfj6668hyzImTJhQ+htxoFzFWX5+Pnw+e9Ngn8+HvLy8sPOcXJ4+nw9ZWVmOc7Zv346cnBxbrZGYmBhce+21aNu2LapUqYKtW7fiiy++wNGjRzF27Ngo70ggEJQ1zVvWw5zF9+GvXUfx6gvfIv90wC6YLJmfIVcjNU6boNQ9w8s4rlnhrL0+Yb6kEaNmFYWmNS2lQQBDqOWeLMTjd83Fy7PvRHLNqs7zBYLzjRKKs6eeesp437t3b/Tu3TvqNfr27Yu+ffvi559/xuLFix2TCEubcs/WPFusWbMGPp8P7dq1Mx2vUaMG7r33XuNzq1atkJSUhBkzZmD//v1o3Lixba0VK1ZgxYoVAIApU6YgNTX1jPbm9XrPeI1zDfFM7IhnYiY1NRULvmkFr9eLH777E1MnLYES0EWPZXCxDc5R/A9/FvozXMkO/U9DoLmux1wzPpUgxeN3vo/GTWuixw3tcP2gSxEXH1PMBkOIrxU74pnYqUzPpKQxZ1OmTHE9l5ycjOPHjxufjx8/juTkZNfxXbp0wXvvvVeyjURJuYozn8+HgoIC2/H8/HwkJiY6zAjNy83NdZznZIlTFAW//PILLrvsMqMxaTguv/xyzJgxA3/99ZejOLOq7+zs7GLXDEdqauoZr3GuIZ6JHfFMnElNTcXFbWpi5qJ7EQwqmDV9Fdat+qvENckcBZUmoAi0+mvcMTcIY6axhBC1uC7lfKDFrLF/9zHMnrYcs/+zHDcPvwzXD+oAyVN8qLD4WrEjnomdkj6TkhRVPWPKwK3ZpEkTZGZmIisrC8nJyVi7di3GjBljGqNX/AeAP/74w3hf1pSrOHOKEcvOzobf7w/7l1+/fn18//33tuMZGRm49NJLbcf//PNPnD592jFLMxwie0ogqFzExHjw4D974cF/9kLWkVN4ZvQnCPiV4ida3J/Eesqpu0A4kWZNKNASDewJBsUkNHATPp31C1Z+8Sf63dYJV/RqhgSfPXZWIDhXIZHGmEaBx+PBiBEjMHnyZFBK0aNHDzRo0AALFixAkyZN0KlTJyxbtgx//vknPB4PEhMT8Y9//KPU9+FEuYqzdu3aYcmSJSgsLDT6UK1duxaxsbGmTEwr7du3x+LFi7Fjxw60aNECALB3714cPXrU5rYEVJdmjRo1wq7Js379egBwbMMgEAgqB7XqVMe7i+7Frm2ZmD19NY4cOhVmdCjSP6pfyZwEWnGCK+x6MFsIeCMZYzh5LB/zX1+ND6evxs0jrkC/oSLzXHCeUEZ1zjp06IAOHTqYjg0ZMsR4P3z48LK5cDGUqzjr06cPli5diqlTp2LAgAHIysrCokWLcMMNN5gC/kePHo2WLVti5MiRANT6ZW3btsX06dNx1113gRCC+fPno0WLFkaNM51gMIgNGzage/fujl0HFi5ciKKiIjRv3hxVqlTB9u3bsWTJEnTu3BmNGjUq2wcgEAjKnGYt6+Klt9Si1kWFQTx5/3ycPuVXA/tNJjLnn/66K5M4Bfg71VjT3ZbRWt6dMj4pYPyrxBfhpcCnM9bhz1/245pb26Hd5RdE5O4UCCorokPAWSQxMRETJkzAzJkz8fLLL8Pn86Ffv362tkuUUlBq/sn16KOPYu7cuXj77bfBGEOHDh0cFW56ejoKCgpcO8LXr18fX375Jb7//nsEAgGkpqaif//+uPnmm0vvRgUCQYUgvkoM/vvBMBw/loePZqzF7z/v44Ly3X/6EyC82KJR1NJwSj5wKs9hOu5s0du1OQO7/8xEnQbV8ehLA1CzjuhsIjhHOc/EGWGsDBy55xnharJFgghUtSOeiR3xTJw5k+eiKBTjRy3EkYMnQwcZC8Wb6sKNd1VaRZqbMLOO0wvi2n7iOljGLMNMK1nLb2jH4uJjMOLJXuh4VVPUrFlTfK1YEN8/dipTQkDnYa9FPefXOf9XBjs5O5w3pTQEAoHAiscj4aV3VZfnlj/+xnuvrETuyUJ7g3OnhufFlufg50DtHKCa4CwDQ2uZGzw56TgWEnnGwgAkAn9REG9PXApvjISX592HpFrx4fcnEFQmzjMzkhBnAoFAAKB1h4b470fDwBjDx++twfLP/nQfHGnQP2ctI9rHaDIOTKU7AFXgOaELP0IgBykeG/wuWnRMw7W3dsAllzcWmeeCSo+IORMIBILzGEIIbnvgStz2wJU4sOcY3pu6EtlHTiNQFIwuC5OZ3ZWm4xGKJa7uLYibMNNH6AkOkmpz2/H7Iez4/RB8VePw+LSb0bBprcj3LhBUNIQ4EwgEAgEANGpaEy+8o6bVZ2Wewmvjv0RW2JIcGjTkejR6eBLi3NopTLFco7aTQkMuUdciuZo1z9J0Pf90EZ6790O07XoB7n68N5KS7YW6BYKKzvlmORO51wKBQBABtepWx5SZd+I/Hw9HtaQqZrOW6cUMN2ZY+5hWmBb8OCOmTHtR3n0aJs4tnDVOO75pzT48dtN7eKD369i1+bDzWIGgomL93ojkVYkR4kwgEAiioFqNBEz7eDge+tc1SKgaZxZmVHUvOmZX6l0D+Bc065hVkOkv6/ySwlnplICClx9eiJHXvIHtfxws+ZoCwVmEsOhflRnh1hQIBIIoIYSg89UXofPVF6GoMIAfv9mGbxdvxIlj+QDXU7PYQHxdcDnFkxmuSpiFWUmC+61lQBhDoEjG1Ec/QbN29fHEtFtFEVtBxaaSi61oEeJMIBAIzoD4KrG45pZ2uOaWdqAKxb5dWZjz2kpk/HU8vLWLOQgv07ko/zVyKvfhBlcKZNfGw/i/m/6Hnje1wY3DrhCZnQJBBUD8qiQQCASlhOSR0OTiOnj+f7fh9oevCp1wjImBuzALh/W8W6xNJLE3mhs1N6cAX8xcj/uumoapj36Cw/tEsVZBxYLQ6F+VGWE5EwgEglKGEIJeN7VDr5vaIScrFz8s2Yxv5v9+5gs7CbPQRSM/B5i7FXBjt2/Yj+eGzcOlvZrhzsf7oEpC7BltWSAoFc4zt6awnAkEAkEZklyrKm65ryveW/EwbhvdDVJp/NR1sohZ3Zp8YoF1PKUAVQDGNWnXXwAUWcH6b7fj4T6v4/UnP0Vhvr8UNi0QlJzzLSFAiDOBQCA4C0geCb1vbof3vh+Df701CM3b1Uf9C1NQJbGULFOUqfXQFGpPMNAFGqWALJv7gTLKuUK140ZJEIZNP+3F6L5v4MevNiEYlEtnrwJBtJxnpTSEW1MgEAjOMhdeXBdjp91ifN6y4QDemvAV/AVB+2C3huu2MQxGEQ/jMz8m3I7CxMABYArD3Be/w9yXvsO1t3fC4H/0CLeYQFDqVHZLWLQIy5lAIBCUM60vbYS3lv4D494agova1FMPOjVcd4NywqyksAgiqBnw7fzf8OWctWd2LYEgWlgJXpUYIc4EAoGggtCkVV089cZgzFz9KDr2vKj4CVyXAcuJ6C4cZc/Qz9/5GY/2fQMzn/8awYBwdQrKnvMt5ky4NQUCgaACMmriDQg+LeP3H3fjp2+2YMfvh8wDTOU4zsBqxlvoinOdcv/g5Z4owNpvtmLrL/swbsZdSK1bveR7EAiKo5LHkEWLsJwJBAJBBSUmzovL+1yMJ6YNwnNz70L15AT1hEUoASxq65et5lqx4y3HtEK2p7Lz8fpji7FpzV5QpZIXlxJUWITlTCAQCAQVjrQLUvHa5w8CALKPnMIzd85BoEjhRkQgsgD3llFM+w9vPdNLcuhYLWuaQDu8Jwuv/3MRiETQsHltxMV70aFHc/S8tSM8XmEDEJQClVxsRUu5i7NDhw5h1qxZ2LVrF3w+H3r27IlBgwZBKqYYUEFBAebMmYMNGzaAUoqOHTti+PDhqFq1qjHmzTffxOrVq21zp02bhvr160e1lkAgEFQUUutUx9srHoEcVPDSqI+wf9sR7UyEFjS3Mcz4jz1707GQrdkdyijDge3qXnalH8LXs9fh6Zl3oXZajeL3JBCEobJbwqKlXMVZXl4eJk2ahLS0NIwdOxZHjhzBvHnzwBjD0KFDw86dNm0aMjIy8OCDD0KSJMyfPx+vvPIKnn/+edO4+vXrY+TIkaZjNWvWLNFaAoFAUJHwxnjwzHt3Yt2yrfjwvytRcLoUisW6/SPo1q8zTAuq3BMFGHfzu6jdMAn/9/pQpNZLOvP9Cc5PrJbec5xyFWfLly9HIBDAY489hoSEBLRp0waFhYVYtGgR+vfvj4SEBMd5u3btwqZNmzBx4kS0bNkSAJCcnIxx48Zh8+bNaNOmjTE2Li4OzZo1c91DNGsJBAJBReSKvq1wRd9WAADGGF566EPs/TMD2gH1zzNpaO4kzCLt3Qng6IETeHLA2+jYsxkenHyTcHUKoqeMtNnGjRsxe/ZsUErRq1cvDBw40HT+q6++wvfffw+Px4Nq1aph5MiRNgNPWVCu3yEbN25E27ZtTSKsa9euCAQC2LZtm+u89PR0VK9e3RBTANC0aVPUqlULGzdujGoPpbmWQCAQlDeEELy2+BH8d+nDeOLNIbj2zkvhjfHYEwCKa7rON1DX/6T0jKqw/75yF6aOmg85qBQ/WCDgKIuEAEopZs6ciXHjxmHatGlYs2YNDh0yZ0U3btwYU6ZMwdSpU3H55Zfjgw8+KKM7NFOulrPDhw+jVatWpmP/396dx0VV748ffw07DIoCAgpagoqBCxaZSwopat8otY3splZ2b90izbTUqJRS1H511TbrVmqYVmpqYospopVi7mtoCpgLqMimMOzM/P7wMjLMsDjMwAy+n4/HFHPmnDPv83aAN5/teHp64ujoSGZmZp3HVR8zVsXX15eMjAydbefPn+fJJ5+kvLycgIAAHn/8cZ1C7EbOJYQQ1sLVzZnufTrRvU8noqLvoVhVysuRH1FeXG1dsuqtajWLsdo0dL9anNx/lskRCwjuH8CQqFACb+9k1HnETcYMS2mkpqbi4+ODt7c3AAMGDGDv3r34+flp9+nRo4f2665du/L777+bPA5DmrXlTKVSoVQq9bYrlUoKCwvrPM5Ql6dSqUSlUmmfd+7cmfHjxzN9+nQmTZqEWq1m9uzZpKam3vC5hBDCmjkrHVm89WWGP3EnCtsaXZQ32iJmzC/KagvmFheWsW/Lcf7fv74i5qFPyM+u/ee9EGCelrPc3Fw8PDy0zz08PMjNza11/6SkJEJCQkxwNfVr9tma5nTffffpPO/Tpw9Tpkxh3bp1TJs2zejzJiYmkpiYCMD8+fPx9PRsVJx2dnaNPkdLIznRJzkxTPKir66cRL8dRfTbUZxOyeDT2PWUlVRQVFDMxTM5ujuauqWilvNd+juHqcMX0aqNC+Nm3E/EY/2wtTV9u4F8TvTdDDmZMWOG9uuIiAgiIiKMOs9vv/1Geno6sbGxJoqsbs1anCmVSoqKivS2q1QqXF1d6zyuoKDA4HGGWuKqODo60qdPH/bv39+oc9X8B87Ozq71PRvC09Oz0edoaSQn+iQnhkle9DUkJ628HHl18fVZ8Rs+/42Ez01wz8yqyQP1LcNR/TWNhoL8IhbPWM2mlTuYseQpk08akM+JPmNz0qFDBzNEUw8j/1aYP39+ra+5u7uTk3P9j5KcnBzc3d319jty5Ajr168nNjYWe3t74wK5Qc3arWloXFd2djalpaV1/uPXNh4sMzPT4Pix6hQKBYpqPygacy4hhGgpRv1rMK8vG49NVZen3l0ENNe7Jqu6QWuqvk9tXaS1nfd/j/QjGbw46B3m/zOew7+dMv+FC6ug0Ghu+FGfgIAALly4QFZWFhUVFSQnJxMaGqqzz+nTp/n888+ZNm0abm5u5ro8Pc3achYSEkJCQgLFxcU4OzsDkJycjIODg86g/Zr69OnD2rVrOXHiBN27dwcgLS2NS5cu1dkfXFZWxoEDB/D392/0uYQQoqXxD+7A57umcyW3kM/e2EjqoXNUVFReb7UwVGg1ZDJBZaXuvgpFnfuXlVRwav9ZTu0/i0IBgXfeyr/mjKZNO1kY/KZlhjuD2draMmHCBOLi4lCr1dxzzz107NiRVatWERAQQGhoKCtWrKCkpIQFCxYA11obp0+fbvpgalBoNM13N9HCwkKmTJlCx44dGTVqFFlZWcTHxxMZGamzCO3EiRMJCgrSWUw2Li6OCxcuMG7cOBQKBStXrsTNzU27cGxRURHz589n0KBB+Pj4UFBQwI8//sjp06eZPXs2AQEBDT5XfeqaWdoQ0tyuT3KiT3JimORFnylzUpBfzLqPt7Fv6wmKCkr1W8NqdmEaojbwm7Wu42q536dPZw+GjOlLuBG3hZLPiT5r6tYcOmTeDR+zNek1M0TSNJq1OINrS10sWbJE5/ZNUVFROrdvio6OJigoiOjoaO02lUpFfHw8e/bsQaPRcPvtt/P000/TunVr4For2QcffEBaWhpXrlzB3t6ebt268eijj+otSlvfueojxZnpSU70SU4Mk7zoM2dOyksriHn4E3IvXNV9obbFbo1YD+36cYZfcm3rwn82T8bOoeGdP/I50WdVxdk9RhRn26Q4u6lJcWZ6khN9khPDJC/6zJ0TdaWa9Yt/JfHbPZSVNHLdtNrUUZwBBPTyJearZxp8Ovmc6LOq4ix87g0fs3V7jBkiaRoteikNIYQQpmdja8PDE+/h4Yn3ANfGiKkKinlnQjyXM/KvdWM2pLvzBmg0VV2j1yZ1pR4+z4TesXi0d+OW29ozNiZSxqS1YHLjcyGEEOIGODjZ4eDUijnrnuePn4+x7K2NDb9RtVoDCnS7Q2u0mul28Gi4dsC1L3Myr5CTeYUDW0+gdHNm0gdj6NrnlkZekbA4N1knn9x9VgghhEnY2dty98jefPz7NG4J8ql7fTON5noBV3Wvz+r3/qyDRrtkh+5DlVfEvCeXsmbRZvKyrtZ9EmFVFOobf1gzaTkTQghhUk4uDsxc+S8Ass7lMmfsElRXS67vYKgA01ulo2ZrWY2da2tJUcPPX+xk87Jd3BXZk3/OfciYSxCWRlrOhBBCCNPw6ujO+9tf4bUvn6LvvcHXNtZZiGm3gkatvzPU32Wq0VBZUUnyhkPs+fmoMWELS6Mx4mHFpDgTQghhVgqFgi69O/LcvIf4fN/rTHh7JK5tXLCxVaCwAdCg0eg+uLZZt8XEiGU5Pn1lDZ9N/47KSivv57rJmeMOAZZMujWFEEI0GRtbGwaOCmHgqBDttsqKSp69Y47hFjSN9j8Nbw2pMVP0j42HebBDNKCgtacSNw9XHp48jF6Du9V+DmFZrLzYulFSnAkhhGhWtna2fPD7NGIf+5ScjCv6Oxjs9axj0dvqt4dSKHT2uZqt4mq2ikXPf4WtrQKfzp4MGz+QQQ/drnPfZWFhbrKGT+nWFEII0eyUrZ159+eXeW/zywT388fZ1VG7Ykadat48vWqboVtG6VBQWa4m42QWX76xnjcf+IDysop6jhHNRbo1hRBCiGbi7uPG1M/GA1BaVMbbj/+XC2lGrOxfo8VMq3rLWtVzIDM1i+dCYrnz/3ryz3kPY38Dt4YSTcDKi60bJZ8+IYQQFsnRxYG4DRNRq9Xs25zCktfXU15aT+tWzdtGGSrQKisNH6vWsPfHI+z/5RiBobfyxMwH6ODvZfwFCGEk6dYUQghh0WxsbOh7bw8W747h4SkRBA/swpOxI3F0dtDdsWb3Zk0N6u4EdYWa43+kM/OBD0hcnmyCKxCNVrP7uiEPKybFmRBCCKtga2dL5IRBTP3vOMIeDeX/bX6ZjoE+2NjZoLCppQtTrb7+y7oBhVl1lRVqVsZt5J1xn1GiKjXRVQijqI14WDHp1hRCCGGVWrkreWvdC9rn+VlXmXHvAsqKq3V9NqIVRaFQoNHAid2neb5PLF6d3Hnt6+dwdnXC3tEOG1tp32gq1j7A/0ZJcSaEEKJFaOPVmk8PxJJ1Locd3x/gx09+NWnvVtbZXF4eOBcAOwc7wh/vyz9ef0CW4GgKN1lxJmW/EEKIFsWrowcPTRzGix89gb2TfeNOVr1btFqBUFFWQWJ8Mt9/sKWR0YoGucnGnEnLmRBCiBapz5Db+O+hWNKPnGfuPz5FXWH4F7ZGo/lfF+a117VfN2CMWsJHSRzflU63O29l6NgBtPVubdJrEP9j5cXWjZKWMyGEEC2afy8/1l/4hLGzRuKkdNDfQaNBU62FrEGFmUZz7Qbsag2n9p7mx8XbmDIgjn/3fJN9m46Y50JuZjIhoGmdP3+epUuXcvLkSZRKJUOGDOHRRx/FxqbuurGoqIgvv/ySvXv3olarueOOO3j66adp1aoVAGq1moSEBA4cOMD58+cB8Pf3Z8yYMXTp0kXnXFFRUXrn79q1K3FxcSa6SiGEEM1JoVAw5PG7GPL4XWSkXuJQ0gn8unqjAT6b+i3Fhf+bjdmQLjGNptb7fJYWlfFx9EoefPkyD0QPkfFoJiITAppQYWEhs2fPxs/Pj2nTpnHx4kW++uorNBoNY8aMqfPYhQsXkpmZyXPPPYeNjQ0rV67k3Xff5e233wagrKyMDRs2EB4ezujRo1EoFGzatImZM2cyZ84c/P39dc53//33069fP+1zZ2dn01+wEEKIZufbxRvfLt7a5w+9PJwty5O5fD4XTS1dnzpq26XawrfrF27m+0WbsXWwI3xMX8bE3I+tnW3jg79ZSXHWdLZs2UJZWRlTp07FxcWFXr16UVxczJo1axg5ciQuLi4Gjzt58iSHDx8mNjaWoKAgANzd3YmJieHIkSP06tULBwcHPvzwQ1xdXbXH9ezZk5deeolNmzbxwgsv6JzTy8uLbt26me9ihRBCWKSIcQMY8kQ/SovKOHsik/eeWkpFfXciqFVVy5sCDQoqSitI/HInSSt28diM+xg+YbApQ795qM1TnB06dIhly5ahVqsZOnQoo0eP1nk9JSWF+Ph4zpw5w+TJk3UaccypWcecHTp0iN69e+sUYQMHDqSsrIyUlJRajzt48CBubm7awgygS5cueHl5cejQIeDaitLVCzMAOzs7/Pz8yMvLM+2FCCGEsGo2NjY4uzoRGOrPop0xhN7bAzevVro7VY0zq1eN+3oqFKgr1HwT9wNblv1O7sV8KitquYWUMMwMszXVajVLliwhJiaGhQsXsnPnTu0wqCqenp688MIL3H333ea6MoOateUsIyOD4OBgnW2enp44OjqSmZlZ53G+vr562319fcnIyKj1uPLyck6fPm2w8l2zZg1ffvklSqWSO+64g/Hjx+sVd0IIIVo+pZsL0R+OBaCkqJT3n43nxJ70ugeZazTU3t8JKBRo1GpWvrWBlW9tuPY+bVx4dfm/uLVXR9MF31KZoVszNTUVHx8fvL2vdXEPGDCAvXv34ufnp93Hy+vavVWbeuxgsxZnKpUKpVKpt12pVFJYWFjncYa6PJVKJVlZWbUet27dOgoLC7n33nt1toeFhXHHHXfQunVr0tPTWbt2LWfOnGHevHn1TkwQQgjRcjm5ODJ9xbMAbPt2N6vifqCsuFy77Mb1oqEBkwhq7KPKLyJ25Pt069uZmNXRpg28pTFDcZabm4uHh4f2uYeHB6dOnTL5+xij2WdrNpUDBw6wbt06xo8fT4cOHXRei46+/k0RFBSEr68v8+bNY9++ffTt21fvXImJiSQmJgIwf/58PD09GxWbnZ1do8/R0khO9ElODJO86JOc6DNFTh59MZJHX4wkL+sKOZn5fDZ9Jcd3Gfpl/r/ZnFWtLdpCzvB5T+45zcz/W0DwwEDufSqMgJBbGxVnQ1nV58TIMWczZszQfh0REUFERISpIjKrZi3OlEolRUVFettVKlWdXYpKpZKCggKDxxlqiUtNTWXhwoUMGzaMyMjIeuMKCQnBycmJ06dPGyzOav4DZ2dn13vOunh6ejb6HC2N5ESf5MQwyYs+yYk+k+bEBtr4KXl1xb/4YXESCR9uoaKs5hiy6sttaK63tNXi7PFMzqZkkPjVb9zWvwvPvf8ESjfDk+JMxdic1GzgaBIa4xYumz9/fq2vubu7k5OTo32ek5ODu7u7Ue9jas3aZ2dojFh2djalpaV1/uPXNrYsMzNTbyxaZmYm8+fPp2fPnkyYMKFBcVX1Lcv6NEIIIWqjUCh4IHoon5+Yz5vrJ3JnZC+UbasXVNW6MhvY8FNeUs6RbceZNjiODe//woW0LEpUpaYO3fqYYUJAQEAAFy5cICsri4qKCpKTkwkNDW2Ci6lfs7achYSEkJCQQHFxsXZdseTkZBwcHHRmYtbUp08f1q5dy4kTJ+jevTsAaWlpXLp0iZCQEO1+eXl5xMXF4e3tzUsvvdTg8WOHDh2ipKREby00IYQQwhD/3p144cNxAGSfzyXj5EXsHO1Y9MxSyktufFkOVX4x6xf8wvoFvwDg2dGdWRsn06qtTFQzFVtbWyZMmEBcXBxqtZp77rmHjh07smrVKgICAggNDSU1NZX33nsPlUrF/v37Wb16NQsWLDB7bApNfW2tZlRYWMiUKVPo2LEjo0aNIisri/j4eCIjI3UWoZ04cSJBQUE8//zz2m1xcXFcuHCBcePGoVAoWLlyJW5ubjqL0L7++utcvnyZSZMm6XST2tvb07lzZ+Da+LG0tDR69uypnRCwbt06OnTowJw5cxpU0NU1s7QhpAtCn+REn+TEMMmLPsmJvubKiepKEcteW8O+n4427IAa3XeaancjcG7tyFNzo7gzsrdJFrS1pm7N/+s0+YaP+fnsIpPH0VSateXM1dWVmTNnsmTJEt555x2USiWRkZF6t1NSq9Woa9znbPLkycTHx/PJJ5+g0Wi4/fbbefrpp7Wv5+fnc+bMGUC/z7ldu3Z8/PHHAHh7e/Prr7+ye/duiouLadOmDYMHD2bMmDEyU1MIIUSjKN1ceHHxk1z8+zL/Gf8Zl8/Wsc6mobaSapuKr5by6aQVfP3W99z7bBgRTw3Gwcne9EFbopvsDgHN2nLWUkjLmelJTvRJTgyTvOiTnOizlJxUlFdSkFPIj//dxvaVu3QnEtRYbqN6q5kh7bt48cJH4+kUrL/uZ0NYVcuZ36QbPubn8x+YIZKmIU1DQgghRBOxs7elrY8bY2eNZvIXEwjs508rT1ccnO1p8KyB/7mQmsXyN9aaJ1BLY4YJAZbsplnnTAghhLAkPQYH0mNwIADqSjXr/vMze386zOWzOVSWN2zpiOzzuRQXlODcysmcoTY/tXFLaVgraTkTQgghmpmNrQ2PTIvkne0xLPhjFnfe3xuFTf3LOdk52GFr3/jJARZPWs6EEEII0VzaeLXmxU+e4vxfF/li6jdcSLtIWXEF6kr91iP/Pp1qnRSwc+1efl+1m4ryCjoF+/FYzAM4ujiaO3zzsPJi60ZJcSaEEEJYIL9AH2ZtnEz2uVwUNgoun8thzbwfyb2Qj52DHf4hnfjnu2MMHvvDx1vY+FEiJQUlAJzae5pzKRm89t1E61yJwMjbN1krKc6EEEIIC6VQKGjX6drNuT393JmZMJmiq8XYOdjV2mKmVqvZtW6/tjCrcvrIOQ4npdAnoofZ4zY1jZG3b7JWVlg+CyGEEDcvl9bOda5vVqoqQ3VV/77V5SXlpB04Y87QzEetufGHFZPiTAghhGhBnFwdaeWuf5snJ1dHegwKbIaITOAmmxAgxZkQQgjRgigUCiKeHkwrd6V2m42tgsC+AQT2C2jGyBpBrb7xhxWTMWdCCCFECxM2ph/tA7zY9Nk2ykrKCb67G8OfCUOhqH95Dotk5S1hN0qKMyGEEKIF6nanP93u9G/uMExCY+UtYTdKijMhhBBCWDZpORNCCCGEsCBWPvvyRklxJoQQQgjLJuucCSGEEEKI5iItZ0IIIYSwaBrp1hRCCCGEsCA3WbemFGdCCCGEsGjSciaEEEIIYUluspYzhUZzky0eIoQQQghhwWS2pgWYMWNGc4dgcSQn+iQnhkle9ElO9ElO9ElOLJcUZ0IIIYQQFkSKMyGEEEIICyLFmQWIiIho7hAsjuREn+TEMMmLPsmJPsmJPsmJ5ZIJAUIIIYQQFkRazoQQQgghLIisc2ZG58+fZ+nSpZw8eRKlUsmQIUN49NFHsbGpvSZOTU1l8+bNHD9+nLy8PDw8PLj77rsZNWoUDg4OTRi9eRiTk3PnzrF8+XLOnj1LQUEBbm5u9O7dm8cee4y2bds2YfTmY0xeqlOr1cTExJCens706dO54447zByx+RmTk6ysLF588UW97QMGDGDy5MlmjLZpNOZzsnv3br7//nvOnj2Lo6MjAQEBTJ06FScnpyaI3HyMycnq1av57rvvDL72+OOP8+CDD5or3CZh7OckLS2Nb775hrS0NAD8/f0ZM2YMXbt2bYqwRTVSnJlJYWEhs2fPxs/Pj2nTpnHx4kW++uorNBoNY8aMqfW45ORkLl26xKhRo2jfvj1nzpxh1apVnDlzhldeeaUJr8D0jM1JUVERXl5ehIWF0bZtW7Kysvjuu+9IT09n3rx52NraNuFVmJ6xeakuKSmJnJwcM0fadBqbk3HjxhEYGKh93rp1a3OG2yQak5OtW7eydOlSRo4cydixY1GpVBw7dgy12roX9jQ2J0OHDiUkJERn2969e9mwYQN9+vQxc9TmZWxOsrOzmT17Np07d2bixIkAJCQkMGfOHN577z3atWvXVJcgkOLMbLZs2UJZWRlTp07FxcWFXr16UVxczJo1axg5ciQuLi4Gjxs9erTOL5Lg4GAcHBz47LPPuHz5slV/gxibk8DAQJ1ftMHBwXh4eDBnzhzOnDmDv79/U12CWRiblyqFhYV88803PPHEE3z66adNFLV5NTYnHTp0oFu3bk0UbdMwNidXr14lPj6ep59+WmcAeN++fZsqdLMxNiceHh54eHjobFu7di2+vr7ceuutTRC5+RibkwMHDlBcXMyrr76q3adbt24888wzHDx4kOHDhzflZdz0ZMyZmRw6dIjevXvrfCMMHDiQsrIyUlJSaj3O0F/4VT8s8vLyTB5nUzI2J4a4uroCUFFRYdIYm0Nj87Jq1SoCAwPp0aOHOcNsUqb8rLQUxuZk165dAISHh5s7xCZnqs9JQUEBR44cYeDAgeYIs0kZm5PKykpsbW1xdHTUbnNycsLW1haZN9j0pDgzk4yMDDp06KCzzdPTE0dHRzIzM2/oXCdPnkShUODt7W3KEJtcY3OiVqupqKggMzOTr7/+moCAALp06WKucJtMY/Jy5swZtm3bxvjx480ZYpNr7Gdl8eLFPPbYYzz77LPEx8dTVlZmrlCbjLE5OXXqFB06dCApKYl///vfPP7448TExPDXX3+ZO2SzM9XP2d27d1NZWdkiijNjc3LXXXfh6OjI8uXLuXLlCleuXCE+Ph6lUkn//v3NHbaoQbo1zUSlUqFUKvW2K5VKCgsLG3ye/Px81q1bx+DBg3FzczNliE2usTmZN28ehw8fBq4NVH3ttdcaPGDekjUmL0uXLuXee+/Fx8eHrKwsc4XY5IzNib29PSNGjKB37944Ozvz559/smHDBi5dusS0adPMGbLZGZuTK1eukJmZydq1axk7diytWrViw4YNzJ07l/fff582bdqYMWrzMtXP2Z07d9K5c2fat29vyvCahbE5cXd3Z9asWcyfP5+ff/4ZgLZt2/L666+3iDGb1sb6f7O1YBUVFSxcuBAnJyeefPLJ5g6n2U2YMIG4uDhefPFFSkpKmDt3botoETHWzp07yczM5KGHHmruUCxG27ZteeaZZwgNDSU4OJioqCiefPJJ9u3bx99//93c4TULjUZDSUkJzz//PIMGDSIkJIRXX30VGxsbNm3a1NzhNbu8vDxSUlJaRKtZY+Tl5bFgwQL8/f2JiYkhJiaGzp07M3/+fLKzs5s7vJuOFGdmolQqKSoq0tuuUqm046XqotFo+Oijjzh37hyvvfZag46xdI3NSfv27enatSuDBw/m9ddf5++//2bHjh3mCLVJGZOXiooKVqxYwahRo9BoNKhUKoqLiwEoLS3Vfm2tGvtZqa5fv34ApKenmyS25mJsTpRKJQqFgqCgIO02FxcX/P39OX/+vFlibSqm+JxUjckbMGCASWNrLsbmJCEhgcrKSqZMmUJISAghISG88sor2NjYkJCQYM6QhQHSrWkmvr6+ZGRk6GzLzs6mtLRUbzyAIV9++SV79+7lzTffxNfX11xhNqnG5qS6du3a4erq2iK68ozJS2lpKTk5OSxfvpzly5frvLZo0SK8vb358MMPzRazuZnys1JFoVCYIrRmY2xOfH19DQ7o1mg0Vj8swBSfk507d9K9e3c8PT3NEWKTMzYnmZmZ+Pn5YWd3vSyws7OjY8eOXLp0yWzxCsOs+zvTgoWEhHD48GGdFozk5GQcHBx0/oI1ZP369WzatImJEyfSvXt3c4faZBqTk5oyMzMpKCjAy8vL1GE2OWPy4uTkxKxZs3QeL730EnBtEc1JkyY1SezmYsrPyh9//AFg9UuuGJuTqgWJjx07pt1WVFREeno6t9xyi/kCbgKN/ZxkZWVx6tSpFtWlaWxOPD09OXfunM4M+PLycs6ePWvVSzhZK2k5M5Nhw4bx888/89577zFq1CiysrJYs2YN999/v84U54kTJxIUFMTzzz8PwI4dO/jmm28IDw/H3d2dkydPavf18fGx6oGZxuZk+fLl2Nra0rVrV1xcXMjIyCAhIQFvb+8W0RVhTF5sbW0JDg7WOU9VK2KnTp2sfkVvYz8rq1evpqSkhMDAQJydnTl+/DgJCQn07dvX6gsRY3MSEBBAaGgon376Kf/4xz9o3bo1GzZswNbWlhEjRjTX5ZiEsTmpkpycjK2trbbruyUwNidDhw4lKSmJd999lxEjRqDRaPjll1/Iz8+XG6Q3AynOzMTV1ZWZM2eyZMkS3nnnHZRKJZGRkURFRensp1ardVbprpqNuH37drZv366z7wsvvGDVaxUZm5OAgAA2bdpEYmIi5eXleHp6ctdddzF69Girv/UMGJ+XlszYnPj6+rJx40a2bt1KWVkZnp6ejBw5skVMmmjM52TSpEl89dVXLF++nNLSUrp3786sWbOsfixrY793du7cSY8ePaz6j96ajM1J1USA7777TjskolOnTrzxxhtWvzCvNVJoZHU5IYQQQgiLIWPOhBBCCCEsiBRnQgghhBAWRIozIYQQQggLIsWZEEIIIYQFkeJMCCGEEMKCSHEmhBBCCGFBpDgTQjSp2NhYvTWXzCEqKorY2FidbatXryYqKoo///yz3n2FEKK5SHEmhND64IMPiIqK4pdffql33zlz5hAVFcWePXuaIDJ90dHRREdHm+38tRVyQghhbnKHACGE1tChQ9mxYwdJSUl13tonKyuLo0eP0rZtW+29Gy3NwoULcXR0NPm+QghhblKcCSG0goODad++PadPnyY9Pb3Wm4UnJSWh0WgIDw/H1ta2iaNsGF9fX7PsK4QQ5ibFmRBCx9ChQ1mxYgVbt241WJyp1Wq2b9+OQqFg6NChABw9epSEhARSU1MpLS2lXbt29O3blwcffFDnZsu1qaioIDExkYMHD3Lu3Dny8/NxdHTE39+f+++/nz59+mj3/fPPP3nrrbe0z6uPXwsLC9N2dUZFRREUFNSgsWQ1942Ojuby5csAOu8F17o7Fy1aRHJyMrGxsQQFBemd748//mDBggWMGDGCZ555pt73F0KI6qQ4E0LoCA8P59tvv2Xnzp2MHz9er7vv4MGD5Obm0qtXL7y8vNiyZQtffPEFjo6O9OvXDzc3N1JSUtiwYQP79+9n9uzZKJXKOt+zsLCQZcuWERgYSK9evWjdujV5eXns37+fefPm8dxzz2kLwXbt2vHII4/w008/AXDfffdpz2OqGzTfd9997N27l5SUFMLCwmjXrp3O68OHDyc5OZnExESDxVliYiIAw4YNM0k8QoibixRnQggdrVu35s4772TXrl3s2rWL8PBwnde3bt0KQEREBJcvX2bZsmU4OTkxd+5cne7BL774gs2bN7NixQqee+65Ot9TqVSyePFiPDw8dLYXFRXx5ptvsmLFCgYNGoSDgwNeXl5ERUXx66+/Aphl5mdkZCQqlYqUlBTCw8MJDg7WeT0oKIiOHTuye/duCgoKaNWqlfa1S5cucfToUQIDA+nUqZPJYxNCtHwyW1MIoSciIgK4XohVycvL4+DBg7i5uREaGsrvv/9ORUUFI0aM0Bu3NWbMGJydnfntt98oLy+v8/3s7e31CjMAFxcX7rnnHlQqFampqY28KtMaNmwY5eXlbN++XWf71q1b0Wg02hwKIcSNkpYzIYSeHj164O3tzV9//cX58+fx8/MDYNu2bVRWVhIWFoadnR3p6ena/WtydXXl1ltv5fjx42RkZNTb5Xju3DkSEhI4fvw4eXl5egVdbm6uaS7ORMLCwvj6669JTEzkgQceAK6Nndu+fTtKpZL+/fs3c4RCCGslLWdCCD3VB/snJSUBoNFo2LZtGwqFQtsqVFxcDEDbtm0Nnqdqe1FRUZ3vd/LkSV577TV27NhBhw4dGDZsGA8//DCPPPIIoaGhwLXCx5I4OzszaNAgLly4wLFjxwDYt28f+fn5hIWF4eDg0MwRCiGslRRnQgiDqpbJ+PXXX6moqODYsWNcunSJ4OBgfHx8gGsFCkB+fr7Bc+Tl5QHUO2Nz3bp1lJWV8cYbbxATE8NTTz3FY489RlRUFF27djXdRZnY8OHDgesTAKr+L12aQojGkOJMCGFQmzZtCA0NpaCggD179uhMBKjSuXNnAIOr6KtUKv7++2/s7e3rXUfs4sWLuLq66g28B0hJSTF4jI2NDWq1usHXc6NsbK79eKzrPW655RYCAwPZs2cPp06d4ujRo9x2223abmAhhDCGFGdCiFpVdW3+8MMP7N27l1atWtG3b1/t64MGDcLW1pZNmzZx8eJFnWNXrVpFcXExgwYNwt7evs73adeuHYWFhZw5c0Zne1JSEocPHzZ4jKurK1evXqWsrMyYS6tX1QzM7OzsOvcbPnw4FRUV/Oc//0Gj0cjyGUKIRpMJAUKIWvXu3Zt27dppZ0qOGDECO7vrPza8vLx46qmnWLJkCdOnT6d///60bt2alJQUTp48ia+vL2PHjq33fSIjIzl8+DAzZ86kf//+uLi4kJaWxokTJ+jXrx9//PGH3jE9e/YkLS2NuLg4brvtNuzt7bnlllu0Y9QaKzg4GIVCwddff825c+e0a7U9/PDDOvv179+f+Ph4cnNzadWqFXfddZdJ3l8IcfOS4kwIUauqiQHffvstYHgs1YgRI/Dx8WHjxo3s3r2b0tJSPDw8GDlyJA8++GC9C9AChISEMH36dNatW0dycjI2NjZ06dKFWbNmkZWVZbA4e+ihh1CpVOzfv5+//voLtVpNWFiYyYozPz8/oqOj2bhxI7/88ot29mjN4szOzo67776bn376ifDw8HpbCYUQoj4KjUajae4ghBDCmsXGxnL8+HEWLVpE+/btmzscIYSVkzFnQgjRCKmpqaSkpNC7d28pzIQQJiHdmkIIYYTNmzeTm5urXfvNHLeREkLcnKQ4E0III2zYsIGcnBy8vb2ZOHEiXbp0ae6QhBAthIw5E0IIIYSwIDLmTAghhBDCgkhxJoQQQghhQaQ4E0IIIYSwIFKcCSGEEEJYECnOhBBCCCEsiBRnQgghhBAW5P8D2amKJ65xbcYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "plt.scatter(portfolio_risk, portfolio_return, c = portfolio_return / portfolio_risk) # c for colorbar based on sharpe.\n",
    "\n",
    "plt.title('Portfolio Optimization', fontsize = 26)\n",
    "\n",
    "plt.xlabel('Volatility', fontsize = 20)\n",
    "plt.ylabel('Return', fontsize = 20)\n",
    "\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "\n",
    "plt.colorbar(label = 'Sharpe Ratio')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc56b63d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}