time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Read Historical Daily Yen Futures Data\n",
    "In this notebook, you will load historical Dollar-Yen exchange rate futures data and apply time series analysis and modeling to determine whether there is any predictable behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-08-02</th>\n",
       "      <td>3398.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3398.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-03</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-04</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-05</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-06</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle  Volume  \\\n",
       "Date                                                                 \n",
       "1976-08-02  3398.0  3401.0  3398.0  3401.0     NaN  3401.0     2.0   \n",
       "1976-08-03  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-04  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-05  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-06  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1976-08-02                         1.0  \n",
       "1976-08-03                         1.0  \n",
       "1976-08-04                         1.0  \n",
       "1976-08-05                         1.0  \n",
       "1976-08-06                         1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Futures contract on the Yen-dollar exchange rate:\n",
    "# This is the continuous chain of the futures contracts that are 1 month to expiration\n",
    "yen_futures = pd.read_csv(\n",
    "    Path(\"yen.csv\"), index_col=\"Date\", infer_datetime_format=True, parse_dates=True\n",
    ")\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6954.0</td>\n",
       "      <td>6954.0</td>\n",
       "      <td>6835.0</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>48336.0</td>\n",
       "      <td>51473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6877.0</td>\n",
       "      <td>6910.0</td>\n",
       "      <td>6865.0</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>38206.0</td>\n",
       "      <td>53860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6937.0</td>\n",
       "      <td>7030.0</td>\n",
       "      <td>6924.0</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>49649.0</td>\n",
       "      <td>55699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6952.0</td>\n",
       "      <td>6985.0</td>\n",
       "      <td>6942.0</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>29944.0</td>\n",
       "      <td>53111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6936.0</td>\n",
       "      <td>6972.0</td>\n",
       "      <td>6936.0</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>19763.0</td>\n",
       "      <td>52072.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle   Volume  \\\n",
       "Date                                                                  \n",
       "1990-01-02  6954.0  6954.0  6835.0  6847.0     NaN  6847.0  48336.0   \n",
       "1990-01-03  6877.0  6910.0  6865.0  6887.0     NaN  6887.0  38206.0   \n",
       "1990-01-04  6937.0  7030.0  6924.0  7008.0     NaN  7008.0  49649.0   \n",
       "1990-01-05  6952.0  6985.0  6942.0  6950.0     NaN  6950.0  29944.0   \n",
       "1990-01-08  6936.0  6972.0  6936.0  6959.0     NaN  6959.0  19763.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1990-01-02                     51473.0  \n",
       "1990-01-03                     53860.0  \n",
       "1990-01-04                     55699.0  \n",
       "1990-01-05                     53111.0  \n",
       "1990-01-08                     52072.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim the dataset to begin on January 1st, 1990\n",
    "yen_futures = yen_futures.loc[\"1990-01-01\":, :]\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: Initial Time-Series Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Start by plotting the \"Settle\" price. Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28e3f2b6208>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot just the \"Settle\" column from the dataframe:\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "yen_futures.Settle.plot(figsize=(12,6), legend=True, title='Yen Futures Settle Prices')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Answer*\n",
    " * Based on the plot above, we can see that the Yen Futures Settle Prices experienced two large spikes that were immediately followed by large drops-these each occurred years 1996 and 2012. After each of these spikes and rapid drops, the Yen Futures Settle Prices was fairly range bound for the next few years. The overall long term trend has shown the Yen appreciating relative to the US Dollar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decomposition Using a Hodrick-Prescott Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Using a Hodrick-Prescott Filter, decompose the Settle price into a trend and noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Apply the Hodrick-Prescott Filter by decomposing the \"Settle\" price into two separate series:\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "noise, trend = noise, trend = sm.tsa.filters.hpfilter(yen_futures[\"Settle\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Settle</th>\n",
       "      <th>noise</th>\n",
       "      <th>trend</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>1990-01-02</th>\n",
       "      <td>6847.0</td>\n",
       "      <td>-61.503967</td>\n",
       "      <td>6908.503967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6887.0</td>\n",
       "      <td>-21.799756</td>\n",
       "      <td>6908.799756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>7008.0</td>\n",
       "      <td>98.942896</td>\n",
       "      <td>6909.057104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6950.0</td>\n",
       "      <td>40.776052</td>\n",
       "      <td>6909.223948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6959.0</td>\n",
       "      <td>49.689938</td>\n",
       "      <td>6909.310062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Settle      noise        trend\n",
       "Date                                      \n",
       "1990-01-02  6847.0 -61.503967  6908.503967\n",
       "1990-01-03  6887.0 -21.799756  6908.799756\n",
       "1990-01-04  7008.0  98.942896  6909.057104\n",
       "1990-01-05  6950.0  40.776052  6909.223948\n",
       "1990-01-08  6959.0  49.689938  6909.310062"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of just the settle price, and add columns for \"noise\" and \"trend\" series from above:\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "df=pd.DataFrame(yen_futures['Settle'])\n",
    "df['noise']=noise\n",
    "df['trend']=trend\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28e41964f08>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAIBCAYAAABkwFgrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeZhkZXn38e9de3dXLzPdPTuz4AAybKOsAlEQ2dSIJiAiCZgYiTFR82picEmMCUQT88YlQhJUgokKKGrgFRARISCyzbA5MCzDMEszS/f0Mr3Xdp73j3Oqunt6q67ume6q/n2ui4vqc05VPTXXTPWvnrqf+zHnHCIiIiIiMjWh2R6AiIiIiEg5UpAWERERESmBgrSIiIiISAkUpEVERERESqAgLSIiIiJSAgVpEREREZESKEiLiMxRZva3Zvbd2R7HbDGzFjM7a7bHISIyHgVpEZESmdmZZvZrM9tvZh1m9rCZnVzkfZ2ZrR3281lm1nLwRjs1ZvbvZtYb/Jc2s8ywn++e7fGJiMwFCtIiIiUwszrgp8C/AguB5cAXgNRsjmumOOc+7JxLOueSwD8At+Z/ds5deOD1ZhY59KMUEZldCtIiIqU5EsA5d7NzLuecG3DO/dw592z+AjP7QzPbbGadZnaPma0Kjj8YXPJMMMN7JXA3sGzYrO+yA5/QzE4LZsC7zOyZ8coezOxqM7vtgGNfM7OvB7c/YGZbzazHzF41s8un+uLNbG0wq/4HZrYD+Hlw/AwzezQY49Nm9uZh9/mVmX0heA09ZvYzM1s47PwHzGy7me0zs6unOiYRkUNNQVpEpDQvATkz+46ZXWhmC4afNLN3A58BfgdoBh4CbgZwzuXD5QnBDO93gAuBXcNmfXcd8HjLgTuBa/BnwP8C+JGZNY8xtpuBtwez5phZGHgv8H0zqwG+DlzonKsFTgeensafw5uB1wPvMLPDgDuAzwdjvBr4sZk1Drv+/cCVwGKgBvhEMMbjgG8E55cDy4Al0xiXiMhBpyAtIlIC51w3cCbggG8CbWZ2h5ktDi75Y+CLzrnNzrksfnnE+vysdAl+D7jLOXeXc85zzt0LbADePsbYtgNPAu8ODr0V6HfOPRr87AHHmlmVc263c+65EscE8HnnXL9zbgC4ArjDOXdPMMafAc8AFwy7/tvOuZedc/3AD4H1wfFLgP9xzj3snEvhfwixaYxLROSgU5AWESlREJI/4JxbARyLP4v61eD0KuBrQYlDF9CBHwyXl/h0q4BL8o8XPOaZwNJxrv8+cFlw+/3Bzzjn+oBLgQ8Du83sTjN7fYljAth5wBgvO2CMp+H/ueTtGXa7H0gGt5cNfyznXC/+n5mIyJylIC0iMgOccy8AN+EHavBD4R875xqG/VflnPv1eA8xyVPsBP77gMercc59aZzrfwicZWYrgPcQBOlgrPc4587FD+Ev4M+ol8Q5N3zcO4H/HGOMXy7ioXYDh+V/MLMkfnmIiMicpSAtIlICM3u9mX0yCKoE9cGXAfnyiX8HPm1mxwTn683skmEPsRc4/ICfG82sfpyn/C7w22Z2vpmFzSwRtMxbMdbFzrk24AHgP4FXnXObg3EsNrN3BbXSKaAXyE35D2Bs/w28x8zOHTbGs8daODmGHwIXmdmbzCyOXws+2YcLEZFZpSAtIlKaHuBU4DEz68MP0JuATwI4534C/CNwi5l1B+eGt437W+A7QQnEe4MZ7ZuBrcGxEeHTObcTuAi/drgNf/b3L5n4ffz7wNsYNhsdXP9JYBd+6cRbgI9M+dWPwTm3DX/2+6+DMe4InmvS3zVBt5OPAz8AXsMvAdkz4Z1ERGaZjfxWTkREREREiqEZaRERERGREihIi4iIiIiUQEFaRERERKQECtIiIiIiIiVQkBYRERERKUFktgdQqqamJrd69erZHoaIiIiIVLCNGzfuc841j3WubIP06tWr2bBhw2wPQ0REREQqmJltH++cSjtEREREREqgIC0iIiIiUgIFaRERERGREpRtjbSIiIiIzJxMJkNLSwuDg4OzPZRZkUgkWLFiBdFotOj7KEiLiIiICC0tLdTW1rJ69WrMbLaHc0g552hvb6elpYU1a9YUfT+VdoiIiIgIg4ODNDY2zrsQDWBmNDY2Tnk2XkFaRERERADmZYjOK+W1K0iLiIiIyJxx7bXXcswxx3D88cezfv16HnvssXGvvemmm9i1a1fh569+9av09/cXfl69ejX79u07aGNVkBYRERGROeGRRx7hpz/9KU8++STPPvssv/jFLzjssMPGvX6yIH2wKUiLiIiIyJywe/dumpqaiMfjADQ1NbFs2TI2btzIW97yFk488UTOP/98du/ezW233caGDRu4/PLLWb9+PV/72tfYtWsXZ599Nmefffaox/7ud7/LKaecwvr16/njP/5jcrnctMerrh0iIiIiMsIX/t9zPL+re0Yfc92yOj7/28dMeM15553H3/3d33HkkUfytre9jUsvvZTTTz+dj370o9x+++00Nzdz66238tnPfpYbb7yRb3zjG/zzP/8zJ510EgBf+cpXuP/++2lqahrxuJs3b+bWW2/l4YcfJhqN8pGPfITvfe97XHHFFdN6TQrSIiIiIjInJJNJNm7cyEMPPcT999/PpZdeyuc+9zk2bdrEueeeC0Aul2Pp0qVTetz77ruPjRs3cvLJJwMwMDDAokWLpj1eBWkRERERGWGymeODKRwOc9ZZZ3HWWWdx3HHHcd1113HMMcfwyCOPlPyYzjmuvPJKvvjFL87gSFUjLSIiIiJzxIsvvsjLL79c+Pnpp5/m6KOPpq2trRCkM5kMzz33HAC1tbX09PQUrj/w57xzzjmH2267jdbWVgA6OjrYvn37tMerGWkRERERmRN6e3v56Ec/SldXF5FIhLVr13LDDTdw1VVX8bGPfYz9+/eTzWb58z//c4455hg+8IEP8OEPf5iqqioeeeQRrrrqKi688EKWLl3K/fffX3jcdevWcc0113DeeefheR7RaJTrrruOVatWTWu85pyb7mueFSeddJLbsGHDbA9DREREpCJs3ryZo48+eraHMavG+jMws43OuZPGul6lHSIiIiIiJVCQFhERkTF96rZn+J3rH57tYYjMWaqRFhERkTH9YEPLbA9BZE7TjLSIiIiISAkUpEVERGSUcm1GIHIoKUiLiIjIKN2D2TGPt/WkOPnaXzCYyR3iEYnMPQrSIiIiMornjT0j/favP0RbT4pr79x8iEckla6rq4vrr7/+oD1+Mpmc8cdUkBYREZERfrBhJ9c/sGXMc28+ohmAY5fXHcohyTwwXpDO5ebutx/q2iEiIiIAvLS3h/beNJ+67dlJrzWzQzAimU+uvvpqXnnlFdavX080GiWZTLJ06VKefvppnn/+eb773e/y9a9/nXQ6zamnnsr1119POBwmmUzy8Y9/nJ/+9KdUVVVx++23s3jxYl599VXe//73k81mueCCCw7KmBWkRUREBIDzvvLgpNc4/JKPlGqkK9vdV8Oe38zsYy45Di780rinv/SlL7Fp0yaefvppHnjgAd7xjnewadMm1qxZw+bNm7n11lt5+OGHiUajfOQjH+F73/seV1xxBX19fZx22mlce+21fOpTn+Kb3/wmn/vc5/j4xz/On/zJn3DFFVdw3XXXzexrCUxa2mFmN5pZq5ltGnbs783sWTN72sx+bmbLguNmZl83sy3B+TcOu8+VZvZy8N+Vw46faGa/Ce7zddNHXBERkVkVJ80fhO/mvNATI46vvvpOfvzkawDs3j84G0OTeeSUU05hzZo1ANx3331s3LiRk08+mfXr13PfffexdetWAGKxGO985zsBOPHEE9m2bRsADz/8MJdddhkAv//7v39QxljMjPRNwDeA/xp27MvOub8GMLOPAX8DfBi4EDgi+O9U4N+AU81sIfB54CTAARvN7A7nXGdwzVXAo8BdwAXA3dN+ZSIiIlK04YsLr47czB9E7gHgd1OfB95ROPf20KPscQvJeYcf6iHKoTTBzPGhUlNTU7jtnOPKK6/ki1/84qjrotFoodQoHA6TzQ51nDnY87OTzkg75x4EOg441j3sxxog/6/vIuC/nO9RoMHMlgLnA/c65zqC8HwvcEFwrs4594jzG1b+F/Duab8qERERmZLNe7pZay18OfLv/EHkHu7KnUKvS/Du8MP0pfxgss62cX3s6/xn7J/w1GdaZlhtbS09PT1jnjvnnHO47bbbaG1tBaCjo4Pt27dP+HhnnHEGt9xyCwDf+973ZnawgZK7dpjZtWa2E7gcf0YaYDmwc9hlLcGxiY63jHFcREREDqFL/+NR/jLyAy6JPEi/i/Ov2fewya3hmNA2jvn8PezqGuDNIX8RYr31E8r0zvKIpdI0NjZyxhlncOyxx/KXf/mXI86tW7eOa665hvPOO4/jjz+ec889l927d0/4eF/72te47rrrOPnkk9m/f/9BGXPJiw2dc58FPmtmnwb+DL90Y6z5c1fC8TGZ2VX4ZSCsXLlyqkMWERGRcfSmMpwa38yPcmfyt5kP0EM1L3iHcXH4QQyPV9p6OSI0NPdV17cdOGX2BiwV6fvf//645y699FIuvfTSUcd7e4c+1F188cVcfPHFAKxZs4ZHHnmkcO7qq6+ewZH6ZqKP9PeB3w1utwCHDTu3Atg1yfEVYxwfk3PuBufcSc65k5qbm2dg6CIiIgJQywAN1sfz3ip6qAbgRXcYSRtkubXTl8pypLUw4GIAxLOakRYpKUib2RHDfnwX8EJw+w7giqB7x2nAfufcbuAe4DwzW2BmC4DzgHuCcz1mdlrQreMK4PZSX4yIiIiUZrH5y6HaXEPh2IuePwd2lO2gZyDFWtvFRs+PAPGcgrTIpKUdZnYzcBbQZGYt+CUcbzezowAP2I7fsQP8rhtvB7YA/cAfADjnOszs74F8H52/c87lFzD+CX5nkCr8bh3q2CEiInKIrbE9AGx3iwH4+DlHcOMvB8g546TQS4S6tlNlaTa6IzmT54hpRlpk8iDtnLtsjMPfHudaB/zpOOduBG4c4/gG4NjJxiEiIiIHz2LrBGC3awTgrKOayeSO5YGH1/Pe8AP8et+ZADzpHQlMbUb6Ww9tpTeV5c/fduQMj1pmmnNu3u5a6UroRDMTNdIiIiJS5uroB2A/fu/eo5fWETLjltzZNFoPizb/JwBPeWsB2BO0ISvGNXdu5qu/eJnHX+2Y/GKZNYlEgvb29pICZblzztHe3k4ikZjS/bRFuIiIiFBvvaRclBT+YsJENAzA/3on0O2qOTX0Ai2uiW5q6HNxwiWUdrz3Px5h25feMfmFMitWrFhBS0sLbW1tsz2UWZFIJFixYsXkFw6jIC0iIiIstk72uga++DvHcXiTPyud8TzSRPlZ7mTeG/lfng12M+ylitpgBlsqRzQaLWzJLcVRkBYRERGWWCd7WMiqhdWcerhfJ53L+V/x/0funTTZfv4lezG/88bl9G6qojGans3hiswJCtIiIiJCkgF2u4XUhoYWmrX2pAB4xS3nDzOfAuDeS05g99Yk8ZSCtIgWG4qIiAg1DNBPgsiwIH3HM6P3SDMzsqE4MZcade7FPT381yPbAPj+YztYffWdtPemaGQ/X41+gzfaSwdr+CKzQkFaREREqLGUv4gwNLr12Wfe/nreuLKBf7v8jQBkw3FijJ6Rfs/1D/M3tz9HznN85ie/AeDEa37BByN38+7wr/mLyA/4wRM7D+4LETmEFKRFRETmuVzOo5Z+eqkmEhqKBh/6LX/h2ftPXcWPP3IGFx63FIBsKE7cjQ7SmZwHQPdAZsTxs0LPAHBUaCev7NNGLlI5FKRFRETmufbODqoszT5Xx7AczdUXHs0znz+PZHzkkqpcKEF8jBnpmuC67sGhIL2ITtaFtrPfVdNoPdSYaqulcihIi4iIzHN9HbsB6KBuRGlHOGTUV0VHXZ8Lx8cM0qFgR7ys51hhrfw09hmuifqbGt+aOxuA2sHRddci5UpBWkREZJ7LDfYA0OOqC2F4wutDCRJjBOn8jnie57go9GuODW3jvPBGBlyMn+dOBKB2QEFaKoeCtIiIyDznpfzNVfqJM8Zaw1Fy4TgJ0uNuJf37336cN4Re9h/bGT/IvYXtbjEAtandMzNokTlAfaRFRETmuVzKXwA44GJYMTPSkSoSpMl5jkh46Pr8ffd0D7I2touf5k7j2szl7GYhhiPloiQ1Iy0VRDPSIiIi89zO3XsA6KamqNIOLxwnajlyuZHdOfIz1CE8ltk+drpmdtMIGGsX1dFqjVQP7p3x8YvMFgVpERGReeCVtl6O+tzd7GjvH3Xul0/7ZRj7XQ3xyOTRIBdKAOClBgrHegYzdPb7wXoJHcQsxw63CIBzXr+Iez/xFvpCtcSz3dN+LSJzhYK0iIjIPPDDDS2ksh7/79nRpRX19AFwytGrWdZQNeljuYgfpHPpvsKxr9z7Mgvo5vzQExwe8uug94aW+NcFM9V9oSSJjIK0VA7VSIuIiMwjY1VuLI4NkvHCXPveU4t6jHSsAQDX1wGNywvHr4neyDvCj7PJWw3A6mNOhad7yXl+kO6ONnN4asP0XoDIHKIZaRERkXluTTJDXyhJXVWsqOsH400AuN6heudkzDgn9BQAx4a2scctYK9XC0AiGgagI7aMBV4HR139k8IuiCLlTEFaRERknqvK9dJnNUVfn0v4M9Jef2fh2IKBHSQswxZvGQC/8dbw0EttANQl/E1dUvEFADTQy6+27JuRsYvMJpV2iIiIzAOOsXs+A1R5ffSFkkU/Vm2NH7p7+/tpCI4t7NkMwCcyf8KbQ8/yP94ZHL+qgV9t2ccZaxsBGIzU+9daDw1j7JgoUm4UpEVEROYRY3SRdI3XQ88UgvTCOv/a3r6hDiAvvvAcRGGLW86zudcBcM/vn0h7b5qVjdUA/PjVCH8Yh1W2F2+czVxEyomCtIiIyHwwQW6t8nppjSwq+qGqqvxgnE2nAPjfl9pYF9rODq+ZfhKF62riEWriQ1Fjm/O7eKy0vahEWiqBaqRFRETmkeFdOx7b2s6tT+ygZoqlHaGovyjR5dIAfOuhraywfYWgPJ5equh3cZptv2akpSJoRloqVntvihf39HD62qbZHoqIyJx06Q2PAo53x3von0qQjsQBsCBIe86x3Pbxkre+cM31l79x1P1WN9bQ1lNPs3XheQrSUv40Iy0V6/3ffIz3f+uxwpa1IiLic84V2s/FyRC3LINTCNLhSNAmLwjStbkemm0/L7kVhWvG2mp8xYJqWmmgmf2FTVpEypmCtFSsF/f2ABQ2AhAREd9nfrKJIz57NwAL8N8rU9G6ou8fjsbJOSOU9bcIb3AdAOxxC1lU689Wh0Ojg3QkbLS5BhZZF3prlkqgIC0VK/8erlkPEZGhtYYG3Pz4jsLxI0MtAHRUrSr6scLhEH1UEc74W4Sf3OTPbrdTxxGL/Znt8BgJ40u/czxtrp5F1qnSDqkICtJSsfJfK3paGS4i84DnOT7wn4/zyxf2jnk+X+Z2YMXF8bYVgBeZQpAOGT1UsXnba/SlsjzxlL/t9w0f+13+6eITuOTEFZy5tnnU/ZbUJ9jpFlFv/YQG2ot+PpG5SkFaKlY+SGtGWkTmg750lgdebOMPb9ow6bWxiP/r/3X2Gh+O/D8e945iR39x24MDREJGn0sQ8/p5aW8Py20fOWdEGg5jeUMVX77khMJzHOiKC98MQLR/7MAvUk4UpKXiabGhiMwHU6mUWLXQ7wP9wfDdhHB8LP1nxMcJvmMJh4x+EtQwyN7uQRropYsk0ejkzcC8YHvxSKqr+AGLzFEK0lKx8tvhKkaLyHyQKXKHk18831oo73hr+Cke8E5gD41EQlML0imixMjy4e8+SaN10+WSYy4wHCUI0uF0d9HPJzJXKUhLxdOEtIjMBzf+6tUJz+ffCx/f1kE667HWWlhinWz0jigcL1Y4ZKRdhJhlAH/L7x1uETZGy7tR40jU+4+R2l/084nMVQrSUvkUpEVkHtjR0T/h+WRiqOxiTVMNfxS+i4wLc0fu9Ck/VyQUIk2UGH6QXmyd7HLFbX7lqoLSjrSCtJQ/BWmpeFm17RCReeCnz+4e8fO/3PsSq6++s/DzmqYaAJLxCFtf+g3vizzALbmzaWPBlJ8rHDLSRIiTYXl9ggZ66aS4DV0sVkvWhYhkFKSl/ClIS8XK5Pyp6H/82QuzPBIRkYNvCe38T+yvOc62ksl5fP2+l4GhTanypR0rF1ZzZmgTAN/OXVi4/xlrG6f0fPka6RMXh4iYR6crLkhHIyG6qSY8qCAt5W/y5bUiZe6xV4uv+xMRKVfvCT/M+tArfChyJznvI4Xjj25tJxyywgJszzlWWBtpF2abWwLALz7xZpbWV03p+QZdjKpQigXpXQC0uNF9o8eyuC7BayTJ9XdO6flE5iIFaal4WmwoIvPBIvODaZIBsp7DzH//u/xbjwHwL+89AYD+dI7lsT46c7WAkYxHWLuodsrP100NdfTz2o5XIAa7XXEz2tFwiC5qqU9rkkPKn0o7pOJ5StIiMg8st30ANNl+cjlH5IBWdPm3wo6+NPWZfexxCwG44YoTS3q+bldNlaVZZH4/6AVNS4q+bwf1VGXUR1rKn4K0VDzlaBGZD1YEQbrRusl63qiezn/xw6dYYa30prIss3Z2BTPIhy2oLun5uvHvt9JaAfiPq84t+r5dVkdNVkFayp+CtFQ87WwoIpXOOUe99QHQxH5yOW/UBiufjtzMr+J/zjH2KvXWR1ewODBUzCYqY+gnAcDKSDs5QiSSDUXft93VEk938vIebcoi5U1BWireVLbNFREpR+mcRw0DAMQtS26we9SM9G+FfgPA6aHnqGaQviAIZ4vcEfFA7zv9KAAWuXZ6rBaK2Iwlb082ScxyfO7Wh0t6bpG5QkFaKp5qpEWk0r28p4caBukM+T2h3UDXiG/jQnissr2AX/qRtEH68Lt01CaiJT3ngvo6ABa7DrqK7CGd1+X8xY2hQZV3SHlTkJaK19qTmu0hiIgcVIN9+4lajv3xZQC4wf0jvo1bbm1UWyq47ddS97k4L/z9BSysiZX0nKGYXyO9xDpoy9VM6b75+upIpqek5xaZKxSkRUREyly43w/H7YlVgD8jnRuWpF9nQ7seHmktAJx49BEkouGSn9NV+bPfUcsV6q2L1e384J3t14y0lDf1kRYRESlzoYF2AFpsCScCPV3teK6BFdbGpyK3sNb8TVNe8pZzVMgP0se94bRpPadrPKJwu9VNbZvx/Ix0HX3TGoPIbNOMtMwLn/7xswxmcrM9DBGRgyPtl0i0RxYDsGnrTnLZNN+K/jPvCj/CutB2Bl2UrW5Z4S51TcvGfKhihWPVpJ0/o72ryM1Y8rqdH6QPT2anNQaR2aYgLRVrle3h5ug1LKeNmx/fyQ837JztIYmIHBShIEivXO3PEj+/dQdnhjbx+tBO/j3722RdiOuzF9E5rAQjXtc0reeMhI2Y+RMUu4PNXYp172feBUBzdHBaYxCZbQrSUrGuqN3Im8LP87vhhwDoHtTMh4hUpnyQtoYVANQywOmh5xh0Uf4lezFvTP07X8+9p9BdI+WiROJTWyB4oOF9qncxtVBenWzAw6j2eqc1BpHZpiAtFavK+TMdISutR6qISLkIpf1a43htI30uTr31sdr2sM0tIU2UbpKAsT9Y5DdIDAtNLwJEwsafpj/GI7l1POWtneKAQ/SG6qn3tNhQytuk/4rM7EYzazWzTcOOfdnMXjCzZ83sJ2bWMOzcp81si5m9aGbnDzt+QXBsi5ldPez4GjN7zMxeNrNbzay0Pjwiw2RzHlWDewBIBpsUaIdDEalE33poK3dvfBmAmtp6eqgmyQCrbC873KIR1+6jHoCYTf8bumgoxJ3eaVyW+Rwppv6ruzPSTGNu37THITKbivk4ehNwwQHH7gWOdc4dD7wEfBrAzNYB7wOOCe5zvZmFzSwMXAdcCKwDLguuBfhH4CvOuSOATuCD03pFIsALe3pYHWw+UB+sCleOFpFKdM2dm0naAP0uzoJkNW2unsXWyUprZbtbPOLarqqVAFQx/f764fDQToZP/825U77/YLiGhOuf9jhEZtOkQdo59yDQccCxnzvn8h9nHwVWBLcvAm5xzqWcc68CW4BTgv+2OOe2OufSwC3ARWZmwFuB24L7fwd49zRfkwh1iWjhF0W9+UH6/977EquvvnM2hyUiclAkGaCXKmriEVpcM28MvUyVpUcF6RNOPIOUi3Jn5JxpP2dk2BbkDdVTn5FOhxIknDbMkvI2EzXSfwjcHdxeDgxvjdASHBvveCPQNSyU54+LTFucNDAUpEVEKlXSBuh1CZLxCLtcE3Xmz/Rud4v52vvWF67LRWs4KfVvfKXqY9N+zmh4ehEiE6oi7tS1Q8rbtP4VmNlngSzwvfyhMS5zJRwf7/muMrMNZrahra1tqsOVecThiFsGUMN/Eal8SQbotyqqYmG2DZuFftvpp9GcjBd+DpvRQzWRaYZggHBorF/hxdvS6RHzBqY9DpHZVPK/JDO7EngncLkbWsXVAhw27LIVwK4Jju8DGswscsDxMTnnbnDOneScO6m5ubnUocs84BzE8L/oyM/MiIhUqgbrJRX1FxI+6x1eON6TWMJphw9tlhIKwq9fWTm7+khQzeCIrcxFyk1JQdrMLgD+CniXcyNWCtwBvM/M4ma2BjgCeBx4Ajgi6NARw1+QeEcQwO8HLg7ufyVwe2kvRWSIA2L4M9L1B8xI9wxmZmFEIiIHzwJ66A37Qfp5t5r7cm/gK5nfJRaLFcIzQCgI0NOcTJ4R61YuoYoU+wf0nizlKzLZBWZ2M3AW0GRmLcDn8bt0xIF7g0+1jzrnPuyce87MfgA8j1/y8afOuVzwOH8G3AOEgRudc88FT/FXwC1mdg3wFPDtGXx9Mk8554gHQTppg0TIkg3+uncPZqlNRGdzeCIiM2qh9fBCEKQzRPhg5i8BuHHR0E6GyxuqyFd0hGZoRvqWq05jTVNpG7vU1NYRsxx9/QMsrFHnWylPkwZp59xlYxweN+w6564Frh3j+F3AXWMc34rf1VvQlbgAACAASURBVENkxjjniFuW/nAd1blu6uingzoAnnttP8sbqmZ5hCIi0+d5jjA56q2f/kj9qPN1waTBnR87k6X1Vfz4yRYAZqqyY3jZyJTFqgHIDPYCo8cuUg60s6FUpqzfUqknshCARdbFTdF/5JLwA/Roq3ARqRD/8/RrNOBvsz0QaRh1Pr8g8Jhl9SysiTGYyQGwo2P2145YzJ/JTg9om3ApXwrSUpmCIN0bbQLgraEnOSv8DF+O3kDW05bhIlIZPvGDZ1hgPQCkYn6QroqGC+cjB2wDfszyoPwjO/vvgy29fsi/9dcvzPJIREqnIC2VKef3kO6LLgDgjNBzhVOZnFaIi0jlWGt+s6vNfbUAPPRXZxfOHZCjqQ5C9kzVSE9HW8ofy4s79szySERKpyAtlakwI+3X750a2lw4ZWn1lRaRynFueCNdroaVx78ZgKZknG9ecRLxSIjVjSMXAhZ6P89+jub01/vblZ9/ZN0sj0SkdJMuNhQpRy7nB+m+mF/aEbGhrzETKW3mIyKVYQHdXBB6nLu9U2lIDi2iPnfdYl685sJR1xf6SB+yEY5vcVOwhiWRm+WRiJROM9JSkXIpf9vZdKKpcOw1589OJ1LtszImEZGZdkroRWosxa3Zs4oq1wgH18Qi4UmuPPhi1X4pikvpW0IpXwrSUpEyKX9FuosPfWX4lHcEoBlpEakcJ4deYNBFeca9rqggnV8h0lwbn/C6QyGaCHpcZ2a/g4hIqRSkpSJlMn5pRziaKBx71lsDQCzTMytjEhGZaYdZG9vdYtJEC5utTKQ72EVwYc0c2JQq6tdvh7IDszwQkdIpSEtFyqX8N+ZwbChI1x7/TgCiOc1+iEhlOMJa2OaWAMV14sg5f046fGA7j9kQbMgSzuo9WcrXHPiXJDLzshm/Rjoar+Li1N/wmcwHufi8twB60xaRyvDgC3tYZXt5ya0AhnXkmEhQ2zEXFhsS9YN0RO/JUsYUpKUi5dJ+kI7EEmxwr+f7uXMIh6MMuijRrBa2iEj5++RN9xE2R6vzN2IprkbaT9JzoI00hMIMEsNyg7M9EpGSKUhLRfKCGelYfKgdFAZ9JIjmVI8nIuVvtfkbmex0zcBQa7uJHL/CD91/dObhB29gU5AiRkRBWsqY+khLRfKCxYbR2FCQDpnR7xJEVCMtIhVgdcgP0lvdMgCKqexoSsbZ9qV3HMxhTUnK4oQ1uSFlTDPSUpEKM9KJkUG6l4QWG4pIRWikG4B9rh6Amlj5zY2liBP1NCMt5av8/tWJFCPYIjwyrGtH2Ix+EtSoRlpEKsAS66DXJViztJm3H7+MNx/ZPNtDmrKsxQgHO9GKlCMFaalIYZf2b0SGNh2IRowBEuraISIV4XDbzVa3lI+ecyQXHLtktodTkqxFCbvMbA9DpGQq7ZCKFM7lg3SscMww0qEqBWkRqQiLrIs9bmFxbe/mqFQoQUzldlLGFKSlIoVcmqwL4Q370sUMUpEk8ax2NhSRuWNLaw9P7eic8v2abD/7XD21ifL9cnkgXEuV1zvbwxApmYK0VKSIlyZNlN5UdsTx7vBCarOd4HmzNDIRkZHe9i8P8p7rfz2l++zp7GUhPbTRwKlrFh6kkR18qUgt1TkFaSlfCtJSkcJemhRRssMCsxkMRuuJkIWMFhyKSPnq7thDyBxtrh6bE7urlCYdrSfpFKSlfClIS0UKuwxpIrzp8MbCMcPY1evv6tXTpyAtIuXrF48/C8Cbjj96lkcyPdloDVUM6ltCKVsK0lKRwkFpx4EzNV2ZMADPbts7G8MSEZkRj/7mBQAWLloxyyOZHgsHnZXUAk/KlIK0VKR8kB7ODFLOP7ZpR+tsDEtEZEasNP897JVs+dZHA1g0CNJZBWkpTwrSUpEiYwTpkBmp4NiPHntlNoYlIjIjDrNWUi7KjnT9bA9lWkJBi9JcNj3LIxEpjYK0VKSwSxdC8/f/6FSufNMqwqGhIB1HGwCISPlaSA/t1FIdL9/WdwBE/N1nd7VNvf2fyFygIC0VKZceJOX8XzCnr23iCxcdCzAsSGv2Q0TKk+c5FloPna6Wmlh5B+mn9/otSq/7+dOzPBKR0ihIS0Xq6esr1EMPlz8WN81Ii0h5Ovwzd7HK9rLTLSIaLt/WdwADoRoAolm1wJPypCAtFSlOpjD7PFyKWOG8iEi5WmId7HKNhMp4e3CAgVA1AAlP24RLeVKQlorjnCNGljSjv/JUjbSIzFXZXHG9lCNkSdogXS5JqIw3YwFIh/0Z6UROQVrKk4K0VJyc54iRGdW1AxSkRWRmvNY1gHNuRh/zX3+5pajr6vE3lNpPTdkH6VzQRzrsqf2dlCcFaak4Wc8Rt8yYNdLhqL9CvC6SPdTDEpEK8dLeHs740i/51kOvzujjbt1X3I6rS60dgN1uIb99wtIZHcOh1pfzN8lKhPSeLOVJQVoqTibnjTsjff7xKwFIhHKHelgiUiG2t/tlCI9ubZ/Rxy12hntZEKSPW3cMtYnR73PlpCfYbbYmrPdkKU8K0lJxBjPeuIsN/+IdJwBQpdkPESlTdeYH+f5w7SyPZPo+89vrAVhZF57lkYiURkFaKs63frV13MWG4VgVoD7SIjL3DJ+Pbu9Ncd/mvWNel2QAgFTQOq6cvf6wJgCiTutWpDwpSEvF6ekfJGo50mPUSBOK4BEi6hSkRWR6prPO7/4XWrnpYb/G+pLwA/xe+F7ufHZ34fzvf/txPvidDQxmRpc8NFsXWRdiw57y/2YtHImRc8aGV3bN9lBESlLeWyKJjOH0VbXwG8Ys7cCMbChGzFOQFpHZ8wc3PQHAFacexpejNwDw3dy5hfNb2vwNSsYqmz7SWtjilvP5d68/+AM92Mzoo4paBsh5jnCZ98WW+Ucz0lJxokFru7EWGwJkLVa4RkSkVH2pHKuvvpNvPbS15MfItr9SuF0TlGyA38YTwDE6SS+KDBBONnPiqgUlP+9c0k01ddbPvc+PXcoiMpcpSEvFcVm/H+nC+rEX4mRDMWIq7RCREuW7a/Sk/A/k19y5ueTHGmwdCuGLrbNwOx+kg//xrYe28quX9wFQTT+pcPnXR+f1uGpq6YcxPjSIzHUK0lJxXMYP0u9709oxz2dDcW3IIiLTtum17mk/xj/86NeF28ODdJ4XhPZr7tzM7337MQCq3QDpSOUE6V4SJBkgElIkkfKjv7VSeXJ+kLZIfOzTFiOmrh0iMoM8r7TZ1Hhmf+F2M/7tv73jucIxN8au4TX0V0THjryUixKzLNGIIomUH/2tlcoTlHaEolVjnw7FiGlGWkRKNFZk7hksrYPGAusp3K4xv0b6pl9vKxzzDlhtOJjOUuMGSIWrS3q+uShFjDhpolpoKGVIQVoqTr5G2qKxMc/nQjFi6lkqIiWqG2M3wQMDb7Ea6CXr/F/Fi+Ojw/iBj9u5fz9Ry+HF60p6vrkoRZQYmpGW8qS/tVJxUoP+rE4okhjzfDYUV2mHiJQsVgh8jlNtMzEyZLwxajCKsMB6ec35m5LU0D/q/IEVI7kBfwa7oaGxpOebi1JEiZNR6zspSwrSUnF+9LjfTioUGztIe6GoFhuKSMnyXTveGXqUW+N/zyciP5xSw4n9/UPvPwvooZNaelwVNW6Abz7od/Goo496egvPldfV5Xfu8GLJab6KuWPQxaiy1Jg9s0XmOm3IIhUnX/8cGm+xobp2iMg05PPeyaEXAFhpraNmjifyjftfLtxusF7aXR29VkU1/Xzmrs0k6efu+NUAeN67Rtz373/0OLcauNjY7T3LUSsNNLGflpy+KZTyoxlpqTj5kByOjj0jnQvFtSGLiJQs36Fjje0BoNG6x9w4ZTz7etN8MHwXl4QfoNn20049va6KGueXdvxV5BaWWzvLrZ1bf/noiPuGMsGOh/HKCdK7XBNhc4T7Wmd7KCJTpiAtFSc/Ix2Nj9+1I26ZUV+ZiogUI//OkQ/SSQZ44MW2ou8fyvbx19Hv8uXoDSy1DrZ7i+ilimoGAMdF4Ydpd35QfvCJp2jvTRXuW0eff6OCgvTxa5b6N7IDE18oMgcpSEvFiZm/8j0UHbu0wwtKO5SjRaQUnnMkSLHM/HrlJAN87n82FX3/tzV1jfh5u1tMr0tQ4wZYYfuoswHuyJ0OwHJrp3tYa71m83tN56oXT/dlzBknvc4P0pZNTXKlyNyjIC0Vp1D/PE7Xjlw4RpxMye2qRGSec/DO8KOEzfGKt5SkDRS29C7Ggr6tI37e4RbTQzVJ+jjWXgXgZ7lTAAphPW+xdZJ1IVxN5XTtcMF6FssOzvJIRKZu0iBtZjeaWauZbRp27BIze87MPDM76YDrP21mW8zsRTM7f9jxC4JjW8zs6mHH15jZY2b2spndamZjN/8VKVIhSIfH/qvkz0inp7Q4SEQkL+cc7wr9mle8pdzrnUSS4ksSMjmP+5/cPOLYdreILpdkAT2FbcJfdsvpdEmW274RH/oX00kbDUQjFdQrIOKX4VlOM9JSfoqZkb4JuOCAY5uA3wEeHH7QzNYB7wOOCe5zvZmFzSwMXAdcCKwDLguuBfhH4CvOuSOATuCDpb0UEV+M4GvQ8bp2hGPELIeXK20nMhGZ3wbTWdaHtvCIt45uV0XcskXvlvrY1g7q6SHrQvxn9nzuzb2RTupop44GejjMWhl0UTpJsss1stz2jVjPsdg62esayORK61s9F7mw/179+EstszwSkamb9COtc+5BM1t9wLHNAGajmqdfBNzinEsBr5rZFuCU4NwW59zW4H63ABeZ2WbgrcD7g2u+A/wt8G8lvBYRAOIWtFAKj9/+DmBgoJ9EXF+AiMjUZPo6qLMBtrplZIP5qJoiZ6Vr4mGWWAd7WMgXslcCcNzyerr2JAmb44TQK7zqluIIsc/Vs9C6R3x7tsg62ekWkeuvnM5D+51fhvfMlp2zPBKRqZvpGunlwPB/CS3BsfGONwJdzrnsAcfHZGZXmdkGM9vQ1lb8CmmZX2JkSbswhMb+652f/Xhld/uhHJaIVAhvwF8suN/V0Ov8soSkFRekq2Jhltu+wm6GAD/5yOkcf7j/q2+ltdLm6gHooZpaBkYsjF5snbS6Bs44oolK0Rfytzuvs75ZHonI1M10kB5rf09XwvExOeducM6d5Jw7qbm5ucQhSqWLkyFNdNzzhy1aCMDgoN60RWTqvH6/c0YPVfTiB+naImeknYM6+ulyQzsTRsIhslH/58XWRSd+a7tuV0WtDRRqpBvZz0LrZadbRF1i/Pe4cnPW+iMBOHtl5bwmmT9mOki3AIcN+3kFsGuC4/uABjOLHHBcpGQxMqQmCNLhYOvwzKBWiIvI1EUG/QWB7a6uEKSLXXDonH9t/n55ncm1hdubvNVAfka6vxCkzwo9A8AT3lHTGv9cE47GSbkoCac+0lJ+ZjpI3wG8z8ziZrYGOAJ4HHgCOCLo0BHDX5B4h/NXUNwPXBzc/0rg9hkek8wzMbITzkjnFyGGtEJcREoQT/kt6dqpK5R21FhxH8w956izPnrcyCDdX7WscPv4404AoNdVUWVpXM6vh74o/DC73UKedYdP+zXMNSmLEc5pckPKTzHt724GHgGOMrMWM/ugmb3HzFqANwF3mtk9AM6554AfAM8DPwP+1DmXC2qg/wy4B9gM/CC4FuCvgE8ECxMbgW/P7EuU+SZu6UId9Fgs37NUb9oiUoJo2q+R7nS1Uy7tIDNIvfXT5hpGHO7JDX34b69a4x+jGoBQqocQHm8IbeG+3BvITt4noOwMEiPi6T1Zyk8xXTsuG+fUT8a5/lrg2jGO3wXcNcbxrQx19hCZtrqIR3icXQ0BXH6jFu2iJSIlCAUfwgeJFWaWa62/qPuG+/cC0MrIIP3DJ1/jt3LH8ebwb2hPrAS20uP8IG2pbprpotYG2OxWzdCrmFtSxInoW0IpQ9rZUCpOlAxZG7+0w4IgrdIOESlFOJvCc0aKKIP4H9rjRfaRdoPdgN/xY7jPvv1oPpT5JCcPXs9pr2vm6KV1nPMGv27aUt00mz8Lvi/o6FFpBokRyQ3Q1Z+e7aGITImCtFScGBmyofFnpCmUdugNW0SmzrzBYEGzkQ6+2I1S3AZP2YFeAPpJjDh+7rrFpIjRRgMN1VHu/vhvsWzxYv/a7g5WWisAO11ldqzqtjr69rex/u/upbNP781SPhSkpeLEipyRVo20iJQinB1kEH8zp0wQpGsixe00+PhL/pYK/W7kh/1waKgbbDzi/2rOxfz+yv/x86dYFQTpbW4J//eSE6Yx+rmpy+powm8r2K4gLWVEQVoqTowMuQmCNOFgaYCnLcJFZOpC2QEGgiC9prmWHCGWJcf/dfqxm5/igq8+CMCzL28D4AvvfRMnrBgq04gM20CqKua/R2Wr/E1Xmmw/zdZFj6uinwSJaHhGX89c0JqtocF6g5/G3U5CZM6pvKW/Mu9FXYbMBKUdFvZ/AYa8ytliV0QOnX37Wqk2v8bZOchZBC87/vvJHc8MbY/wutBr5Jyx8LCjuO1P6skF+38Pn5GuiflBORVvxHPGIuui2bpoDTp9eK7yguZ+L0Ey7H9LaDbWXm0ic5NmpKXiTDYjbeFgNsfLHaIRiUglqaWfbvwg7TlHzqKEXXEfzJvoppNaqqpqiIZDhdnlyLAgXRUE6Rda++mhinp6WWRd7MOfwa68GA29LkHcMkTJUoGfE6SCKUhLxfEXG8bGPZ+fkTaVdohICeqsv9D2LuccWYsUHaRrg/tWHVCeERoWpGNh/1dz1nP+7obWTxP7C72nVy6snomXMad0Bz2z6+kjk/PY1aVdDqU8KEhLxYmSwU0YpFUjLSKlqyLFQND2zvMgbQmqXXF9pGvpp4fqwoLCseRLG65402p6XDV19LPYOml1Dfzyk29h/WEN4963XL3m/Hrww6yVL979Aqd/6Ze0dmtBuMx9CtJScWIugxeeKEj7ZR+ptFaGi8jUxS1DGv99xDlHb2QB9a570vs556i1AXpc9YgZ6PEk4xFec42sD71C0gbZ6Zo5vDk57fHPRa8Fbf2WWjsPvtQGQGe/1rHI3KcgLRUnRoZcEVuEP7S55VANSUQqSIwsXvAe4znYMVhFdaZj0vtlco4666c/VHxpxla3jGbz28Jtc0tKG3AZyG80k3+tAGElFCkD6tohlcU5YpOUdoSifh/pBJqRFpGpS5BmycI62OUvNuzORVliGZxzE3acSOc86m2A+vqmMc9v+sL5o47tcIvGvF1p2qml1TVwcugFvpPz/xyGtwQUmav0t1QqipfNEDaHm6C0oyFZTdqFWdc8/jUiIgd6eMs+2nsGqWaQbNifVfacvylLlGyhld149nYPUu362dQ+9nXJeIRkfOT81i9ybyTnjFe8pTSuXDczL2QOcoR4zlvFattbOBaPKqLI3KcZaako2Uyw31gkMeF1aYuTDKv+TkSKk8l5XP6tx3jD0jg/MY9sxK9Vds6RJULEcmQ9R2SCvVIu+7df8bgNFDpUFGMPjbwl/VVSLsoaq7yNWPI+dcFRbP7FKn4r9FPipEkRUxs8KQv6uCcVJZv2V3lPtNgQIEWMqKcV4SJSnP6033d+d+s+ALLRoT7SGRcmSpZMbuJtwr1+v4766NcdPqXnbnHNtNFAJVc6fOSstbziLSNiHkvM/3NSjpZyUMH/LGU+ygdpN8FiQ/CDdMRTjbSIFGcgCNINwe57XtSfka6ORYou7VhofmePSLK5pDE8unXyBY3l7E1vPB6AZdYO+LP9InOdgrRUFC8TzDJPMiOdtihRL3UIRiQilaA/7fedrw/77xux6jo++/aj+d4fnUqWMBFyZHJDwc85N2qGujEI0i2ZymxhN10Xn30aAEvJB+nZHI1IcRSkpaK4rP9LbqLFhgBZIoTQhiwiUpx0EIqTlp+RruFDbz6c1U01pIkQI0vWGwrO//izFznis3eTznospZ2Ph3/EIjoB6KD20L+AclC7FKBQ2pHX2j3I//35i3iTzPiLzAYFaaksGT9Ie+GJFxvmCBPWzoYiUqR8Rk54/g6GudhQGM6Xdlx2w6Pc+7zfdeLmx3cA0JvK8tfR/+b/RH/EReFfA9CWKy1INyUnLlkre7FqOlyyUNqR9+kf/4Z//eUWnthW2aUtUp4UpKWiuGy+Rjo64XUZixJyCtIiUhwXLH1LeAP+z7Gh8oyT1y4jbI7X2rv50H9tAKA65nfYGMjkWB3zNxl5a/hpPGese92qksbQlKz8lp3b3BKOCL0GDJV25L8NSGUnXswpMhvU/k4qS760IzTxzE2OMHGVdohIkfKhLpLrh5Bf2pGXrK0DoJpB9pNfhOgH6b5UlmrXX7i2kySXv2lqXTuS8QgfP+cILjyucnc2zHvRO4xzwxuBoQ8vkWA79cm6oojMBgVpqSj5GumJtggHyBLWjLSIFC0fpJP4M9LEhoK0F6kCoJpUIUjXBBur9PQPsM5rK1zbE66ncYLdDw+06QvnEzK/O8h8sMMtpsm6aaBn6MNLsFf48MWcInOFSjuksuSClnaTLDbMWYSwyx2CAYlIJcjPjtYQdAYaVtrhRfwNVqptqDf9sy1+OUffrpepYpAO51/fli1+MxbwZ6PnS4gGeNI7AoD1oVcKfaRjkXyQ1oy0zD0K0lJRXCa/IctkpR0RwpqRFpEi5WdHq22QARcjHBlah5ELyjyqGN1S8/o7/QWGd+VOBSDr5k8oLsU2txiAFdZW6CMdD2akVSMtc5H+RUtlyeW7dkzS/s7ChJ22CBeR4nhBqEsyQB8JwqGh8oz8BlBxRr+nNNMFwM25cwjhcUvurdxxCMZbrlppIO3CLLd9hRnpaBCk0wrSMgdpRloqigVBerLSjhRxok47G4pIcfKhrsYG6XMJNr22f+hcyJ+djtnob7mazb9up2viM9kP8ax73UEfazlzhNjlmoIZaf/YFErKRQ45BWmpKEMbskzcR3rAqogHbaxERCaTD3V19NNDNSsXDqt1Dmako2N0Amq2LlIuQjc1o87J2F5zTSy3feQ/vuSDtEOLDWXuUZCWylLkzoYpixNTkBaRIuXrdRdYD50uyYmrFgydDOqlY2MG6f200QBoWrVYLa6ZFbZv2Bbh/p+dtgyXuUhBWiqKFYL0xIsNBy1Bwg0ObVcmIjKBfIZroJcukmPWSI85I00X+1w9v3VE06EYZkV4zTWxyLoKa16GZqRF5h4FaakshRrpiXc2TFlQ+pHVrLSITM7z8jPSvXS42hFBOr8mIzZsseEKa+Wzke9yZKiFNtdAzlMMLFZn0IvbUj0AFP6oNSUtc5CCtJSdTa/tZ/XVd/KNX748+mQuTcpFCYUm/qvd0uPPRG/d034whigiFcYBITzq6aOLWkI2ekY6YX6QfnlvDx8I38OHInex1Dpoc/VkgyA9IoDLmHqdv8GNpbv9/+dLO4D9Axn+9HtP0tGnxeIyNyhIS9n54YadAPzzz18afTKbIkWEyX5XpfFnrB/avGumhyciFcg5qKeXkDk6XXLEOS/mbxGexN8K/NyvPMixoW2F8zvdokKFdFgtKCb0wF+cxVnH+51NQqneEec8z3HL4zu48ze7+ff/fWU2hicyioK0lJ30BLtb/fyZHaSI8ujWjokfI2ihXhXW7oYiMjnnHAvMD3adLjmiXtfFasg5o976CseWMvRt11NuLVdf+HoAJvmybN5b3VTD0kV+Pbml/T/v/GePnINkwn/v7h7QPgAyN+iftMxZzjn++5Ft9KdHLuB57NXxQ3LEpUkT5ZhldRM+djrYXSwR0u6GIjI5h7/QEKCL2kIXDwALhemhmnqGgvQC6+FHuTP5ePojvOnsd7Kwxq+j1oz05LLRWgBChdIOn+c5EpEwMLTL4fO7uulUmYfMIgVpmbN+sbmVv779Of7pZy8CfrD+wRM7SWU8xlu/vcB62O+SI3u8jiETzEhbTrMaIjI55/z3F/BnpJc3VBXOhQx6qSJpgwDUMECdDbDFW8Ht3plEItFCnW9INdKTykWDxYaFGWn/zyznHDk3stb87V9/iHdd96tZGKWIT0Fa5qzOfn+WoWfQnzW+57k9fOpHzzLYtYen41fx0fCPCyvp89ZEO3nNNXLS6oUTPna+Rvq+TTt5ZmfXQRi9iMxlP3mqhcu/9WjR13vDSztIFsId+OG438Wpwu8atMLaANjpmv3zZoXyhIiC9KTyQTqc8f+8BzN+CZ7nXOE9/7aNLYXdJXd2qPuSzB4FaZmz8l/dJaL+X9P+tP9menLoRRqsj09GbyvMTuQlXS/1CxdNujI+FQTpltYOLrru4ZkeuojMcf/n1md4eEvxXXscsAB/RrrL1Y44Z0A/CWrwZ6SXWCcAu53/gT5kQ53bqmOR6Q18HsgGQfq/H9hEz2CGW57wF5h7nhvxnv/5O54r3N7SOnJhosihoiAtc1YqmIXY1eXPNsSD2rjVtqdwzW+2t424T8ylyU6yPThAj/NLP5KmmQwRmVx+sWHGhemhasQ5M6PPJQrvJ83mf8vVSgMAWc8xELyf1cTDh3DU5cmFE2RcmKT1c9aXHwAgTpqcx4h+3Ds7+gu33/mvDx3qYYoACtIyh+VnpO9/sY3/+N9XePiVfQCstL2Fa/r37RxxnzhpvNDEuxoC/NPvnQlAHQrSIjI556CRbrpIcuGxS0ecCxnsco2Fko5m/JKDfa4egNuffq3wLdnrl0y8EFr8Dya9VFHLAO19aU6yF3gx8QES2385IkgvXzD0gWYwo11qZXboOyaZs/J1cQBfvPuFwu3Vw4J0s2sduoOXI0GKbGTkbNFYli3yaxc1Iy0ixXA4jgrt5CVvBe88ftmIc2bGdreYxdZFnDTN1kWPq2IA/9uxl/b2snZRkm9ecRJnrG2cjeGXlXDI6HC1NJr/geTEkL/5VuzV+/i7l5fxwfDP+JV3LAPpWi4JP8Cx9ir/kL18Nocs85iCtMxZ//rLLWMe///snXd4XMXV8CotAAAAIABJREFUh9+52ySttOpWcZNlG/eGcSP0XkwgISEhlYQESCeNQEIKCQRCQggQ+IAUAiEkQAoJvRhjOqbZBhs32Zat3qXt5d75/phtslbVKiv5vs/jx6vduavZ1e69Z878zu9M05p4X8xmodxFR/1eQGV8XnlvFzciCdry+n1uLUNpHJ1mRtrE5LBGStmtcLD3cSojXUUZ2Zbu45vdwXhh4SxRyyTRQXM0Gw0JH+RT55cM38QnMHarRoMsiGvNs4Qq4izIEKzxbePHtvvZbZRzdsMveSrjbgA+kNN5bMuqHoscE5ORxpR2mKQxkgst6ygh4RttJ0w5rbyrLQTgxTc3AfCtf2xi4zYVeIdsuT2f6iA0u9JIOzDt70xMDmd0I7WV5sFIQ1IguuiQOUzJ777rFYoYvGwsAuAY7X0miXaaSSzos80Cw0Fhs2i04ooXdxZGpTJFopNKUQ/ALK2OeWJ//JjFooqNffQYMDEZKcxA2iRtWSm2c73tT9xk+7/4ffNFNZqQ7NZm0CJdlIvWuAQkL9oMQXP2v3UqLFYiUsMhVCBd22Fmpk1MDkfer+sa0Dgt1IVTBKmTBcye1N21Q5eSVnI5YBSzUNvLbFFLlZHIjJ44d9KwznmiY7dquGUWOUIVExYJ9TfK1duYLFri406wqERKo8xjtlaLaSxoMhaYgbRJ2rJQ2wfAdKF00BkEudl2OwFpY4tjGfWygHLRwsrrngMgL+rx6sjpP5DWhCCIjQyUV/Utz+0cgVdgYmKS7rR6ggMa5/Art6BGWcDB7polOarAeaus4BjtffKFh91ycvzxX3988fBM9jDBZhG4ycKFD5BxrXSB0Ua5SFgWnqW9QVDaWK8vZYZoGJBEx8RkuDEDaZO0ZYpDZSNiWYlTtHeYoTVyRfhSZkydRp0soly00hVt2BJr35uVW9Tvc8cC6Zi046G3akbiJZiYmKQ5kQFKOyxhtePlIbOHT/2qSrV432ZMjzdtqZaTOGFOMY9/85i4dafJwHBYNRplHg4RoQA32VF/7kI6mCxa2GZMB+AIrZYqWU69LKRIdLG/uXMsp21ymGIG0iZpyzRZC0Ce8OLEz4e09+mQTh41VlPgtFM8ZSZlSdmJUqH0cc6C0n6fWxPglZm4hHdkJm9iYjIuGKhG2hJRC3qfdPSa+dwhp8RvV8sSCp0OFpT3X7Nh0h2bReOAVHKYyaKFrGgg7SDMXLGfD+Q0GqXSoO+Uk+N69KWFZs2LyehjBtImacsUmWi8Mlm0sFSrYrMxE4lGxJBkF1fgEn5yUBe4hdpeDhjF5BUU9/vcQgiabWVx2QhAQ2dg+F+EiYlJWrO9fmAa6bvXvQ+Aj9596vfJxCL+gJyEJ2gGdkPBbtVoi3aPzBduskSArngTrQC1spAOqbofbjUqWD5/DgDL8kNjM2GTwxozkDZJWzJkMJ6VWKV9wBxxgE1yFgDuQAQjV2kQy0ULp2sbOUN7k9eNeRQ47QN6fkdeKXnRqnCAT9z92jC/AhMTk3Tn1l5sNg8m1v7bR++dU3fIqTymr+L+yMkEsfODM+YOyxwPN2wWjU6cgJLsOQmyN2mRUieL+GXk07xrzOIR/RhWLlbvsy3QnPL5TExGEtOTxyQtMQxJBgF2iEqm0sQXLU+iCcn/9DUA2K0Cka08WUtEOz+3/YVtcjrXRD7HxxwD+1hHLJlxf1KA6lZfH6NNTEwOZ2LFzO3RTGhqBF8Pfyv+U2VxX2NNesNu1aiXhehSMFOrJ0sE2apPZ4m2B1BdJF80lrAhtAQAV0klAOGmgS2KTEyGEzMjbZKW/N+GKrIIsidShF/amaE1stWYTpVUllJCCCJO1aZ3rfY6JaKDeyJn4CFrwJXbEUsmmQysYt/ExGRicbDzRn/kCw+GFHSSOjje/osz+PuXV8d/XlNpdjAcKnaLho8M2nBRKeoA2CsTbdm3GJXx259ZPY3c4ikEpA2rp27U52pi0m8gLYT4sxCiSQjxftJ9BUKIZ4UQu6L/50fvF0KIW4UQu4UQW4QQRyYd8/no+F1CiM8n3b9cCPFe9JhbhelfYwJs3t9KNn66cNIULSp5VF8DUadQAURcU3HLTC6wbsAnHXRNO4k7P3Nk7096ELolkyyCHKO9x2xhunaYmBxODKzEUNHiCeLCi5tMjF4umxk2Cw5b4rG/X7I65TiT/rFZ1PvYLrPjvtFeMvl5+LPcFjmPDhI+3p9bUwFC4BbZWMOesZiuyWHOQDLSfwHOOOi+K4F1UsrZwLrozwBnArOj/y4B/g9U4A38FFgFrAR+Ggu+o2MuSTru4N9lchiSpXvQhKRdZnOb/hF2G+X8Sz+W445QhYSaEAhNo1oqecdD+vEsO2IGZyws6+tpu2HLKcQqDO63X89T9h8gMLjjBXNr0MTkcEAOIpKO6JIMQgSws7KioNdxdou5yTscxOwF28lJBNLSwZ/1M7kpcgFOe8JOMPaee4QTW3hghaPjkaauAHe/WIUczAfXZFTo91svpXwROLjv5rnAvdHb9wLnJd1/n1S8DuQJIcqA04FnpZRtUsp24FngjOhjLinla1J9Ou5Lei6Tw5j2FtUGtkNm80/9eE4J/YZm8vEEVBV8fpYNTQh+Hv4sz+pHcnvkXML6IE8wroRVlUVIKkU9vx9g4ZGJicnhg0UTZIgQAWnnhvMX9Touw2YG0sNJh8ymRHQAKiMdw5lUBxMLut04ifg6RneCo8g3/v4uv3xiO+s+aKLiysd5YUdT/weZjApD/daXSCnrAaL/x/qfTgYOJI2rid7X1/01Ke5PiRDiEiHEW0KIt5qbzerciYxDV4V/8yundLv/2NkqIz23zIUQsFHO48vh79FM/oD9YGMEM0u6/TxFtAz6OUxMTMYnNiLcbvsdq7Vt/Y41pMRBmCC2Pmsw7Baz8cpwsfmnp1E5bWr8Z2+SW0qqQLo1koE17J5w5/ADbT6qmj00u1U9z4//q1S2j7xbO5bTMkliuJfPqc4wcgj3p0RKebeU8igp5VHFxf17BZuMXzKjVlNBkdnt/m+ePJt7vrCCMxf2bLoSNoxB/Y5QtlqzRaT6GkwWLQQjg3sOk94xDMkDb+wnENbHeiomJj04QdvE2ZaNXGl9oN+xhkxIO/oqUnSYGelhIzfTRsSRF//ZJxP+3b/8SGJXIBZId+HEhY+wPrHO4cfeuJ6Tb9rA9ELlo10f7Xdg0czPWrow1L9EY1SWQfT/2B5DDTA1adwUoK6f+6ekuN/kMCcLPwABLRFI33/xKiya4MQ5kxBCIA5ah4Ujg8tEhJ2lfDP0NS7JuJEIFqYItcvx0FsH2G9a4R0ylT98gh/+5z1uX2/KZUzSj6U5Sk+bj4fq1r47nOqGJEf48MrMHuedZBxWM7gZTkL2/PjtjiS3lMVTcrnzM8uZWpAZ7xswa+pkXMI34Jbv4425Za6xnoJJLwz1W/8/IOa88Xngv0n3fy7q3rEa6IxKP54GThNC5EeLDE8Dno4+5hZCrI66dXwu6blMDmOOLFUnxyNnJdZZH5rV3U5qfrmLb5w0i48eqTLL+iAz0poQ/M/4EDvETJpEUbyo5Yp/buG4X68/lOmbJOEORMZ6CiYmPSizKYeHHOHjsvvf6XOsYSjpVy1F9OUrZY8G0oO11jNJTcieaK/eaEnsQjodVs5YWMpLV5wUd/gI23Nw4UWfoLuKr1a1dvvZMIsO04aB2N/9HXgNmCOEqBFCXAzcAJwqhNgFnBr9GeAJYA+wG/gD8FUAKWUb8Avgzei/n0fvA/gK8MfoMVXAk8Pz0kzGG/6QzuKfPc26DxqpcKmTxIKK8vjjqbSJ3z1tDj9du4CjZxZy2QkzB/X7tOjVTkpJi1ZIqTi4ptZkOMiwJXSjUkp2NLj7GG1iMjrk6er7nosXXyDQ51hDSgroolnm9jnOYbV0+9/k0EiuY/nkygoAlkzNSzk2YnNhFzqRkNpdqG71cuJvXqDJ3fffdryw+UD3Qsqy3N47bJqMLv22gJNSXtjLQyenGCuBr/XyPH8G/pzi/reAhf3Nw2T80NgVoL4zwNJeTni9sbvJQ1cgwo1P7eD68qi0wu7s97jcLBsPfHnwnq2xrJEEarQprBGvdns8rBvxbIfJ0LEnbXf/d1Mdlz+4iT9+7ihOmV/Sx1EmJiNLrq4CE4uQ5Mi+/Yd1PYJDRPBLR58ZaYsmuOKMOZw4Z1Lvg0wGjNs1q8d9jl7OybpdSR+krwPy87nnlX3sbfHy2OZ6vnjMjBGd51jgHGAHX5ORx4wSTIadU27awHm3vzLo4258ejsAOxrdWCPRQNrWfyA9VCzRK6IhJa0ZU8kXHp60X8kj9qvJJBCvkjYZPMmV88mSmw8alC51Z5OZlTYZW+wykanMMzr7HhxRNRt+7Gj99Az76gmzmGfqWYeFYGYZz+tLuS78qfh9y6anTtCEHEpP3dnW2O1+CbyxpzVe9BwI6zz1fgOGIfn6A+9w3I3r8YfSvyC6kE6+a30oXojvCZqSuXTBXNKYDDvuIX7Bq5oSWSEt7CUobVhsdi49vpKttcNvtC/igTR05i0EN8zT9gNwqvY2Ef3MYf+dhwNt3hC/enJ7/GdLUuARu23K+0zGGrsMEpIW7EInX/YdSMtQLJB2mNrUUURogi+GrwDg3rmTWDYtj7MWpW669U6rnWOBvz77Oj9btDq+c/DCjiZ+8dg25pbm8IMz5vKnl/fy8u4WfnHuAh7bovoVrL3tJdZ994RReEVD58vWx7nM+hgtMpd79dPZ32YWxKcLZiBtkjZML3RSF7X20cI+vDhw2DSuOnPeiPw+S5JG+oBrOdeGP81+OYlbbb9nkbaX0ASzURotfv30Dh586wCzRQ2t0sWM4sSuQiybN9G8XscbhiHZ3uBmbmlOvFbgcMMuQ9TIYipFA/myvc+xMqwC6SA2U/88iiRn/w1Dcu7SXttMEMlSlrjS3dDt/pd2qSLy7Q1uvvCXN+P3723xcaL2Lmstr3Fzy8eAE4Zv4iPAErEHgM9anqVctLAl/PUxnpFJDFPaYZIWNHQGWF2pXDk+vnwKWsSLj4wRbbkb10hL0CyCP+pn84yxgu1yKvNF9YTzIx0tAmGdUlp51nEFf7bf2O2xWNBmZvXGlv9uruWsW1/isffqx3oqY4YM+9ktJ2NIQVnkQN+DQyr7d+6KWRTnOPoeazJsZCW1Au/P1m7+7EoAHCGlfe/NptCCTjHtPPfq69xt+y3nW17me9aHhmnGw8sTSd/P4miHx1laHZdaH2eB942xmpbJQZgZaZO0YPX16+K3XZk2fO4uhMygfCQD6W5BXeKkWyeLOELU4DMD6SFR3+lnkbYXgKXaHqr0hP4wtngxzIz0mLKnWTkb7G3u2z95IpMpQnQY2bSRQ67ed0Y6ppHOduaMwsxMYuRm2uK3+7M3PXrOdALSRpFQMkBfqKfE0EaEf9t/wnxRTQMFCCQv6os4UduELxAkKyO9Fkl/fa06frtIdLLZqCSLILO1WiaF9o/hzEySMTPSJmlHuy+Ex92Bj4wR3XaObRsmh3THzi6iSpZTIRrQA8Ovyz4cmFmczcJoIA3gCLTEb8c00rqZkR4T/rupljte2M1tz6smObL3RrITGiklOfhwk0W7zCGPfopfw9HCRFtm3+NMhhVXUiDdXyc/u81CvSygTCi/5X+8eYAiOlmrvYadMACfsTzLIm0fFiGZLFr5h34i/9SPJ1f4uOr3943cCxkiHX41bxde8oSXx/VVnBr6Na0yh4JIYz9Hm4wWZiBtMmY8s7WB425c30NC8e93askSQbxyZLMDcdcOQ8YzpGctKsOTPQOrMMDd1NfhJr2wfHo+5SSaB2T6E81KE7sAoz4tE+Bb/9jEjU/tiP/84s7mMZzN2BEOh8kWAbpkFm3kMNXRd+GWDKvHDasZSI8mGUl69JPm9m0paLMIGilgUlQCYSPCv+w/5ff223jAfh3LxQ6+Yf0PbxhzOTpwKzeGP8ENkQt5wVgMwJT2jSP3QoZI7LoUk3U0yAIAamURhWYgnTaYgbTJmHH1I++zv81HmzfU4zEnAXyMrOG8SPKRjmVILUJwwTELAIj4+tnuNemVEtFOSKqLYIY/sSARprRjzJApdgHe2d+RYuTEJ+BRr9tNFmF7Prmy790nLVpsiM1sgjGalOUl3m9LP7uTFk3QJbNwoeRKc8V+pmtNPK8vZY44wL8c1+AkyNXhL1JHEXfo53Ljp4/hktOOpFHmUSEa6AqER/T1DJbYjlFu9DV1Rtuk18oiisxAOm0wA2mTMSMeVEUv8JWijostT6BhkEUA7wgH0lqSFVsssNM0QVZuEQChrsMzW3eoGBJmanVslqrTpDWcsDVM9u42GV1Mu6wEIZ8KnD1kUB3IIjPSgbuvICqqkcaaNQqzM4kxmIZYQgi6cOISPn77zA6O1HYB8MPwxXwj/A280sEtkY+wS06JH1OU7WBzTSfVsoRpWhMNnenVBTFWMFkglPSoVSqNfrUsZVKkAXTTSzodMANpkzEjdpKIJSf/YLuJH9vu58L8D8jWgsgRbMYCiQyHIWU8sLNokDVFbfXZWz8Y0d8/UfAEI1Rc+Tg//M97ABiGwSQ62GFMBcAaTuhPNdFT2vGDf27hqGufG70JH6Ykux6sENt5xP5jKkVdH0dMXPSoC4dfOujESS5eFv3s6V7Hi2hGWpoa6bSmReZSRCe3Pb+TMtFKUNpooIAXjKUsDP6JO/TzeOjSNVxynHL4WDmjAF8own5ZwnTRiC/NGrPEkk350UC6PRpIV8kybIS576mXaOxKr+D/cMQMpE3GjNhOXUQ3yMbHTE1Z/Sy2VJNFgNzcwbUYH+rvlxICYaXTtlss5OQW4Jd2hK9tRH//ROGa/20F4IE3VBW5LezBJnQOSKVp7BZIp7C/e/CtA7R4zC6SI80fX0oUgF5tu5+lWhUfsbw8hjMaO0J+tUvix0GXzMIudDIIUXHl4ynHi3hG2pR2pDMHZDEOEaGEdkpEO83ksqA8FwAZDXdWzijgh2fNY98NZwPw3dPmUG1MolS0E/Knl4tNLPFQEC2GbUMF0s1SXRsfeXkTl93/9thMziSOGUibjBidvr71ZrHOgmHdYIZImOiXROrJJEjEMrLbqMlBXYdf6bTzsmxomsBDJraIp6/DTaK0+7pr3O0BJYlplPm4ZSa2bhlp9f89r+zreyvdZNj5+0a10JkpalmiqeYOy8XOlNrpic4jG5VrSQA7Xaidr5gONSURlfUTdlPakc40SxU0F4ouykUr/szSfvsBHDktn4+cfAwAWmd1n2NHm2MrXfzZdiNfs/6XgLThRxXg10eLDqeI5n6vsyYjjxlIm4wYf3p5T5+Px9yMghGDxdELe5PMo8hXBYBuG+FAOhrIZ9gs8ZbV+Vl2ALxmID1ksny1ADhLKnGTiS2iAml/SOeaR7fFxz2/3XRFGU2+csJMCunkLtvNGFKw0ZjDVNFMiyfE01sbqGo+fD7vgVhGWtppimb3ykTqHSgpJfvqo/USZrHhqLN4Si6nzCsZ0Njl81RdRr7wUEob7dZJLJuaH388ucFLMtKlOiaKg7oijjXlkRpOsmzCJXy04SLW76BKlqNLwSyt1qw3SQPMQNpkxOjPAzpWeLaz0c1MUYdXOnjJWMhCbR8AEXvuyM4vOr1Mm4WbLljCd049gnllauusVeSTFWrp42iTGCKpjW/FlY/z3GuqDe+XzjmeLunEFi02fPDN7g0EDu5a2ek3MysD5cWdzZz/f68OqtW6BbjTfjOztDqeMFbxpjGHUtFGXZuHS//6NifftGHkJpxmWHWVYQ7gYI8sA2CGSN3lcf2OJnbWthCQNjTNbA8+2vzv68fwx88fNaCxl579IQCmiiZKhQqkf/rh+Tx06RoAInrq74slSwXbQXd6nfNFV6LjplcmFnERrLThoojOw9QJPr0wA2mTYeczlmd50P5zGlo7+xwXC7S//eBmZoo69ssSamTCKzSQUTyi84wVX2XYNKbkZ/HNk2fHg8IaPR/R1bMQS0rJ89sbTfu2PpgqmglKK+SU4SYTeyR1swu7tfvp5471u0djehOCyx/cxNvV7T1kNX3h8u9nhbaTF/Ql/CD8ZeplITahE+pMryzcaKBFA+nZU4pZtnAh0HtGurEriBM/Phzxxb9JmpI3nRBWzrO8gkNE2OuYQ5bdyqLJKikT6kXmYXWqQPqxjdtHbaoDYfOORF2DcVDL8xbpolC4zYx0GmAG0ibDzrW2e1ilbad283N4g73b8yRflOZq+3nfqOCATATP4REOpANhVaGdYeuZZWqX2eQJD1JK/vV2DdvqlF3WI5tq+eJf3uJvG832rDEOXlRMEc3UyiKE0HDLLNpaW+j0h7FEM9ArxHZy8PXYsejtImfSk6GEc4VeZQd2U+TjeMmkThYCcN3fDz/HlGWlSsJ13opZWB1O6mQBC5K6cSajG5Ji0UmzzMOMo9MczUKzzGWVpgLi/fZZgEqWAEwrSC0XtGar70KfOvkxwCXUfHYb5dwY+US3x9qki0LRRT+d001GATOQNhlWIknB0CxRy5m3vNTr2Jj9nJ0wRXRSSxEHjERGOpQ5soH09EIndqvGFWfM6fGYmyyy8ROOGHz34c2cdat6HX95VRWjvL3PdPSIcXBGZIpopkYWo0sZfR99XPPoVrIdFo4S23nY8XOusv4N/aBt1sHIFA53RJLjzEDJ96vP7m5ZDkCdVH7pU0VCq97uVc4VL+yY2Pr1XKta4FeWFdPpD/OGMY+V2g4s9LQ/k1JSILpok65+m4KYjD310QViSFposypttRCCe76wgocvW5PymOL8PILSRq5Ir0A6B+UWc0boBtYZy3n+u8dz92eXc8ysIlpxUUjnqBYLt3lDePpIjh2umIG0ybBy55NvxW9Xivo+m0C0eNS29AxRj0VIdhuT4xd5AD2zaOQmCmQ7rOy89kxOmtuzkKVLZmERkn113XWTmw+ojmiPbDo8/XdTkRz/2ohQIRqokUXkOKx0SCcFoot/v1ODJgRLNFVIeqrlnR4Z6H++XTOa0x7XiHgzoYFfRDPDHXilA3+00dEeWUZYWpirJXZXttWrnZe7NvRdKDzeiflCaxlZnLu0nPX6MopFJ4vFnh5uMrohcRLAQ0a8QNkkfXnbOAKAVnJ56oOE5vnEOZMocaUuFrVogi7hxEV6FdzmCB9e6SCCFYDK4mxOW1DKny9aQWssIz2K+Ycjf/Esx924fvR+4TjBDKRNhpUnX9kYvz2zn2YPR5SodqezhXJ52C0n00oul4e+yhdC38dqtY7cRPuhoGwGAN+889Exm8N4ITkj/VHLS+QJL08bK8nLsrNbTsYl/JSiMvjzNZUVdZDIbBQ61TZ7ujVDSGdiW9XeQbxnjnAXHdEWwwAhbNTIIqYnZaR3Nio9uz7BdZcxX2irLYtT55fwhjEXgCVaFXduqKLDF4rbiukSnNFOq01us/lFumNbfD5haeEZfTmZKWR7vWE4cimxpdff14UPNz3lKHarhnQW4xJ+LMbA6ySGgzbv6P6+8YAZSJsMKy6hMtBVRhkLtb04CPWaNYud5GZrtehSsFeWAvCIcQzrjWUEImMn/rIWTAOgTLSO2RzGCxFdkkGQI8VOfmD9O5uMmWwwFmO3ajRJVcRTINwIIZiEyui7hJ/mVnV7frkLgMuOnzk2L2Ac4rSrReZgvLidkXbaop3RYmyT01lreZ1/2n9GLp64PeHupvTKzA03mu7HJx1YrRoWTdBIAQ0ynyVaFYaEpT9/liU/fwZQNQBOEcAnM8jNtI3xzE36oyN/IUcHb+MXkc/y7HeOG/BxQasLp0yvz32O8OGWqXXdOz0qu657mkdzSiYpMANpk2ElBxVIP2ssxyX8TBVNzLjqiZRj7VaNLAJcYHmB/XISQexMzku04B1LOWJuoQrq80k4Thxo83HWotKxmlLaokvJjba7+bfjZxQIDz8JX0SsHM6N+nvGtH6FIvF+elqUlGNKYBfftv4TQ1eZjmZ3kO8+tDleDGrSk5gfrjswcL2iM9xGS7RhxeS8TN6++hT+FDmLRpnHUdpOPmx5lY8sU366H5o1srKqscYS9uIlA5umxWUym42ZLBFVPcbqMibtyGRGkXO0p2oySHTDoJk8IliZkj/wXgTVfge5RkefBfKjjQsvXSky0kB8UTwjs3f55HhHSjkuGkaZgbTJsHLidNV5aatRASRkG6mwWTR+Y7uTMtHGa8YCAP5xyWoqoxersfz+nHf0YkB1yPqN7U7+YLuJ425ch9+UH/RANyTHaVsAuDr8BbbIRGY5lk3JFj50w6BctFAdLSi1BZSk4PMdt/Mt678p7XoPgOse38a/3qlh7o+fGs2XMa6wRrsZhQaxa+OMtMcD6ePnFFOY7eAdeQSrgnfQKbOYJWqpKFTfPc8E7zppDXtwy0wslsRqfZsxnUqtgWc2d3fkkUaELBHEhwObxbxkpjue6OLywpVTB3Xc1lAZlaKevU1dIzGtIZEj/L1mpNuFaiS0rDB9Av/h5rfP7mTGVU9Q3ZpeRaAHY54VTA6ZNm+Io659li01HeTLdgDWG0vxSztHaTt7Pc5u1VitbeM5fRk/iVzE+u+dwNSCLM5erBokjGVdjz2ngBbp4nzLS3zM8iKnWt5mlbadvTu3sNXxBT6R9Vb/T3KY4A+GycbP7ZEPc79+arfHgpq6CJQ4wmSLIHnCy7tSWVJlBps50OajOKSaDpR40svDNd14dXcLx974PO3eEBujrjGX3f/2wA6WkqxwOy2oQPqcxeXdHt4vJzFNNHHzc+r7un7HxNwu7gqE2dvixRbx4CETa3Tb6wdnzKURJUMKtHev7dCihYlemdGjiZBJ+hFb7Disg2ue0yjzcIgIr21Lj0JbKSU5+OK7egdz/rFLAMjR20dzWqPKbc+r3gLH//qFsZ1IP5hnBZND5pXdLbR4Qty1YQ/Z4Va6ZCazY19SAAAgAElEQVQesnjHmM3x2mYAGjoDHDjIwaM0UkuB8LDBWEIEa9wb94KjpnLWolLOWVLOWLJPljJXS3SWOl17k7O0N3CKIOcYz4/hzNILq78ZqzDitlPJPPCN0wBYWAgioE74OwylP3cGW/j0H16Py4Hyg0rqsaclvbMPY4EvFOFTf3yDA21+7noxcaEPDjAjXVd3AIeI0CjzeemKE1kzs/vfKhZIx6icoBKGj97xKif+5gWsES8emQikv3LCTNqECqSLRfdGUjKkdLNeMvvt1moy9lx11jx+cMZcvn96T1vTvvjI6nkAHFU6dkXuyRhS1Rx1ydTfxU8eq3ZNHfroSDtueW5X/Pbz2xtH5XeOF8xA2uSQiUQd4S2aIDPSRXtUu7XeWMosrY6popHV16/j2INsc0p01VFtmzEdSGSgpxZkccenl1OWm3olPlrsNJRedKMxh5f1BazStrPIqgJrB2blcoyZGSrwaJAFPR4rKlRa2wzDBwG1ZbpfTiIsLbj0VmSgE7tQcpmckMqCbqnpuyPm4cj+Nl/0MyfZ3eQmxzG4i/3TL78BwAFZzNQUTSkmT51JsehgmdjFc/bvcXR26nbZ451YEaU94sVDZjdf6C+dqTyGJ4nuGT4ZjAbS0jFKszQ5FCya4CsnzMQ5yO+IM1edv/RAepx/IroezUj3ovO2qeujVfeP+FyCEZ2bn9vJWdrrrBDb49a1JgozkDY5ZCLRxhpWiyBT98SLI9YbSwFYKXakPC5DVxeoLtSKu8OXXrrM2yIfZasxndsj5/GasYD5WjVLUK+lRJpuHjFOmKbs6zpSZU6sGUSw4NC9iOgFqhMnzeSSp7cxiURjm0BnzyzHm2bjG9q8Ib59y/2867iU71ofpsSV0aO9en+8tFnJZmIuKjEevmwNj379GISzCJfw80Xrk8zS6ljtndg7Ljbdg5eseKEhwMpF8wGYJDq6Dw4mMtImExctQ8meDH9nWhS5RYJ+HCJC6aSefQ4AsCrXDqsRHPm56JKlYjd32G/lfvsvcTnM0DEZ890wOWQiUUf4f79Ti7erJb4VtU+WEpRWZmupG204IipD2RUtpki3Xgf1FHJ26Ho2GEvYGPWZLRcqsCulNV4NuaPBTcWVj3P1I++N2VzHEmtEbS36SNHsQAi8wkmm4eXRjWoR0iWzaJUusvVOCqJyjv1GMYX0LPJ5dHMd9Z3+Mb+ojSW3rtvFN63/JksE+bhlA4aExaFNrLN/V30OB4ArKp852AFgRUUBi6bk4i5cBMA5ltcBmBzaN3wvIA2xR7z4xEGBsbOYCJYelpcirKRGjizXaE3PZAywZqnivYC7nRlXPcHdL46tVlr3q8SD4chJPUDTCAkHdmPkM9Iv7mxmreU1ABwiQpF7dOpZbES40LKOEtp4cWf61m2YgbTJIZNsF+TCF79Y61jYI8s4QqQOpJ1hFZQ+/aOP8rNz5rNocu7IT3aI7JCJCvBqYxJ2EQGv6pr1+h514b3/9f0pj53oWMOxnQX1d3/uO8fx2DeOiT/uF5lkGj6IZqQf+MapWDJc6AE3k4V6DzfJWRQKFUhPE428YP82P7Xey32vVbPm+uf59dOpdzUOB0ozDY7V1CKtiE6EEeazlqeYqdWzUhvYBS1PRP9GvTgAGDnd6xEK9YncIlxii3jxiYPeC4uVOq2USlHfrVW4JRpIa45sTCYuVqe6/gQ9Strzx5f3jsk8dEOyp9mDEZXC4VALuGRr2BhBLQOrMfJNZL7yt3c4SttJs4wuJlt6NxEYLgxDcor2Ntfb/sQvbPfQ7B75zPtQMQNpk0MmueApVhxx3xdXArBTTuWIXjLS2eFWOsghL8fJRR+a0W2bNd147ocfpkYqve9/DBUk3vDQOhb97Om0k6SMNnZdBRoeqU70syblsDBpUeQTWWQYHnKizXqsWfn4tSxc+CgV7QSlld3GZHKFD7/fz5csT1ChNfIF69NMF0pHf8cLPf19Dxcmh/aQLQI8pa/AKgxy/LUUGmoRelzJwC4u06ztBKSNdlJnt4Qz4RsdkRqFejN7J2jRZyZBbELHr/VcVDRpJZxpeZPtjos4X3sRKSVz8tV56fKzl432VE1GEVuWOmdpIeV1P1a7YNc+vo2TbtpAa0t0MRuVnMRqkZIJiwzsoyDtcBBisdjD//QPAbBu46YR/X0v7mzGE4qwUFOLmRO0TXz/4XepuPLxtNydNANpk0NGNxIf7JiBfEG07fNOYwpTRAtZ9Fw1O3RvXB+djly4clr89iRXBheFruDq8Bd4XlcX1KrdO3AHIvF2zYcr1ojKdmoZOfz83AU9Hu8ULgxva1xeYM/Ko9NSQLHooES00SjzaY7asl374AvM16qpMsqISI1PWtb3eL7Djcy6VwF4Rl8OQNW+arJjDW4iDQN6jslaW7QYNPViNWDLi99+xViIU3pZ+5sneXlXyyHMPL2I6CoQWaqpRVmVmN5jTKMW9TgXOudaXiEYMTA6VSKgsKzneJOJg8OpvgMiqDLByde10eSRd1Xvhd0H1P+26LxitUjJRCwZ2OXIZ6SniiY0IXnPmEGHdFJG64i9Pzc8uZ3P/Xkj1z+xnQWiGgC70Fmtqa6rnf70S1wd3hGAybBgRFeIViI4RZAumcXskmwqCrPIKFHNOVK12rYZfoKkbyX8NR9ewJ8vOoq3rz4FgMVLV3G/fiqukgoASqN66eufVNvrsZbnhxv2iJegtPHOz87mc2sqejy+P+ikkC5yhI+AtKHZM+iwFFIkupgqmmmggNpotn/njm1MFw1sNOay3ljGxywvYmPiNhwYEPvfoMooY49U8guX8OESKltcEElIMHY3edjTnLrFcYbupbOPRWuLN8zzuioOftZQAXu5aGV3k7vXY8YbhlTSmPtsNwDwum9yjzF3u48mIFUb8BmigUBYZ/eeKnzSgZaZvtIzk0MnIzOLkLRASH232sdop9Ebbfr1v9fVdWVyaSkXHzOD+y5e2WNsWHOMSkZ6gdgHwHY5jXpZSJlopdUzMr/3zg1qobu/zct8rZodTnU+miOUY9afx0hy0xdmIG1yyNR3qBVxzA94xbwZOKwWXvj+icyfq7w5p4uejgw2PYhfpChQSxPsVo2T5pZQmK2C/Zid0ooFcwhJC+VJi4OlYjfZ4cPTycOmKyux3miTORQId3S3Ito5z6K0dkvFbg7ISfFA+githmLRRbUs4X79FIpFJ5dZ/jfyLyJNeb+mgxXaDjYac+OBcI7sIhd1sc80EvKLU367gZNu2tDjOUIRAyc+3LL3v9FR0/O5PPxVTg7+mk2GWvzOFjXd7OHGO4aUXGB5AZvQuT9ycrwBSzKb5SwWBv/ELZGPUi5aCPj9uPDRiTPuOW0yMbFqAh8Z+Nwd/Q8eSaKJ3pgUzplbwI/XzmdBec+FXMSSiSPFbu9wMyVayxLOm0G9LKBctA7Ywz4Zf0gnGOm7O/DHlk8BoKOphkmig735x3DAKGa19gEAi6fk9XX4mGAG0iaHhJSSB99SK8XcaJbMsCd0mNduVBefOSkKDm0ykNYZ6YNxRC3HpBA0yoJ4RnqKaOIRx0+4z349gfDh10LcHvH2aQ3mJYNMgrhEIphzR4NCqzDYalRw6pFqwbVYqEr5fbKUDcYSntJX8GXrE2SOwsUiHfnm7Q+TK3xsljP59sdPIYSN+frOuPd2htF/M4ZARMeFFzdZfOKo1G2TZ5fk0EU2VXIyu6S6kFWK+rjv8kRASpinVdMg87k6cjG9yVwiWKk2JmERkve2vY9L+OiUzgm1qDDpiRCCZpmH5htbd4hQVIIUS0xlu3r688fQLZk45MhnpKeLRtplNksqSvE4SigVbfiHcK2b95OnOON3L/U5JmbtWexRBeayZBEbjMWcqr3NVy3/xTZI68/RIP1mZDKuaPGEKKad66x/igdBHltx/PErzl1Bu8xmajQj/c2/vxsPNjN0T08LqjSmObqVdc8r+6ijMJ6RPll7F4B52gFOve7fYza/scKue/H01jQA8EkHNqFTJLriGelXGhPNErYaFXz73JUYUnCUpk6e1VJ5p/4pciYu4WNt1JbtcGOuUE4wm42ZfPjIClpFPhUkFqWZAwikwxGDEtHOjIpKfvWxxf2On1SQS7N0MUNr4N7XqtOyuGco6FKyWOzhLeOIfsc2o7Jedz3xerzuw6qZl8uJToPMp0yMrXd9RaE6l7qED10K8vJ67pzEiFgyyBiFQHqxtoe3jdmcsbCUBfPmUyjc+H1DK0bur4i5ONvBArGXY6JORZ25c7g28hnWG0u5wvYgGc3pZzNrnhlMDolAWOd8y0t82rqOX9n+AECrrTT++JmLynjLOIJPWdfzRcuT/G9zHU+8p7qm2UMddIrx482an6UKKD+9ahoNsoDSaDORWaI2PmZqaGy9Rw+VFk9w0IGTXffi7WNBFPOXLqEtnpHebiQKObfJ6VgsNvbKUmZq6rOxX6qirzflHFplDsvE7gkT0A0UKSUzoq4l37lAtVr3iGymSKWLbpJ52CL9Z4wjQR+5wkcws5fGDj1+L7xtzGG5UBZX4RRFTuOJ65/4gD++tIcDbT7yhIcW2b/WOTamSHRFnYiyMBPShwHOIvJI1AWM9jnHMCRWiwrLcvDhIRNnhr3X8RGrk2zhH/HCyALhpknmcdqC0rhVpt5xYMjPF9Z7l4WUt2/kcceP+JL1Sd42ZhOx5xLAweXhrxGQNkr3PDzk3ztSmIG0ySERMWS8Cj5TqLahL9Tbuo25O7IWgJ/Y/koebmwWjbf3teEyuqgJjp+M9MXHzOCYWUVccNRU6mUhpaINgcEc7QB7DLV4mC+qqbjy8X51YOnI1rpOjrr2OR56a3AnyLCvkw69d6372lWqY9wMrTGekW7DRY0sYqcxGTdZWDTBPqnew0aZxyePmR89WlAjiykXrfHGP4cLXYEIS7UqdhmTOWmJ0i17tGwmC7X1XCcLcRKINwaKETpIu6h71M5JJKP3zFYyZy4sZYtRSYXWSAltKW23xhN3vbiHax//gLNv2UAO/j6LLmPcfLFauBSJzri2P53tOU2GB5GZxwytkW9Z/kUhnVT1Urw7UnzmT2/E5VQu4SNg6aUZS5SQPY88PH0Gpv1xwZ2v8e0H+7Cz08MU0EVr1FlJ5CrpF521vR/TD/e+ui/l/YYhKa9/DoCHI8fx4/AX4pIqN1lcF/k0+0tOGfLvHSnMQNrkkNANGdcKAwSlDa9h7TbmTTmXjwZ/BsAabRs2i0Z7Zwc2odOZqq10mjK1IIv7v7SKiiIn9bIAh4hQiJt5Yj/VeauolwXM15Rdz48feX+MZzt4djWqE/irVYMrmlQa6d4DaVt+wh2hrCSRFT07+Es+HvopoPSJ26SyF9tszGTJ1ERBSb0spFy0pLR/mqg0u4MsueYZZokadsgp8YtJm56Q0NTIImxCh0h3/fgRVz9JbUei25nhU99P3TGwQLqyOJtXDGVjuFjbQzA8vgPpGIV0oglJl9b/+1BQXIYuBcWiM56RNpn4eGyFAHzb9i8+aVmP3TK6TkzJ594cfHhF39fHiD2XbBEgHBq6vGPjvjb+824fQXHHfqzCYNZcJQuz5KnzuXDX0ewOYgwhweELpU40/W3jfopa32aDvpjvRy5jm6zAkrSA/at+Gp9fn34GBWYgbXJI6IYkHzfP6Mt5RD+a74S/wns1nT3GbZGVBKSN5dpO7FZBZrStaV8BWDpTL1UByBJtNznCTzBvFh8Y05gbtegZj/67MRtDbZCZN6cIxJuxpEI6E8FzxJbIsHSSTSeJbnH3Rk7ncX0lv4ucjwDe/NEpXHR0BXVS6dFD4zDLPxSqW72suO45HISYJpqokomFSGM48T7XRZ1OvG7Via2MVr5seQwbEfYl6RANn3rcyOi72v3S4yo5d2k5n1wxlaqo1d4sUYc7MDHsB0uEeh9OX7OMT62axnPfOa7XsXabjTZymEQ7OfgoLRmYLMZkfNOQNTd+O1+4x3Q3xiV8KZsGJSOt6voZDvbeJnzBT57inNteHvI8ZFcdAF6H+g7Y8lVGOth6gBXXPcfvnht8l8OKotQLhN88vpkZor5bJ+E2X6jbmHTcmDQDaZNDwgh0MlU0s01O5/Lw13ncWE2WvecqPoKVzXImy7VdaEIgom2l+wrA0pmdcgqGFFxjuxcAd/YMMotnMEU0A3JcyhBiUx7sDnYO/j6LDWVSABe0qkC6KDuh+/vhWeri1UIuXwtfzjZZwdrFZRTnOPjJ2vnUyUKyRYCqmrrBTWwcEozoXHTPm4DyMbYISaM90QikK2nhEbMMfK+qBiklN9vv4Ee2B/iw9mrcYQYAvwogZWbfmdirzprHLZ9chhACD1k0yHxmanV0BdKvAcJQcEXtxGRGPr/8yCJmTeq5bX77p45kdWUB2Q4r/qxy5moHsAhJQUFxj7EmE49a12J2GCpQLBRdY1ofkIsXXz8Zaazq+hkJ9V507A3pvFfbM7k1UL71h6cA+G+VWlRkZGXTJTMJdalajXXbm3o99mCmiGZutt1Ozc7UUpK5Wi0ZIsw7xuz4fTc8uZ2XrjiRpy8/jmyHtVujtHTBDKRNDgmLux5NSKqM8vh9XzxmRsqxe4wypohmdEMiQiqQHq8Z6X2yjN/r58b9NTty5rBNVOISPhaIffHs7nhiSBnpSBCHCPfpUdyuJx7z2VQm/56LEs0FLjluZo9jYnpUTROsWDgHgIxQ+8DnNU55c297vKr9ZO0dAE45bW388a6k97khuity7b/foNkTpAh1sTxK29FNzyujgTQD1EgDXHp8JVVGObNE7bgOpJOLxZxRC0VfH+ecsxeX8Y9L1mC1aLTby1mm7QbAm1MxovM0SQ86jUxOD93Iu8YsCuk6JO3xUFkpPuA+2/XM1Q5QY6voe7BNfZYjgf7de1LR2NW/rWismdrShapuJctuoUnmY3Er96DB2EKu1V7jI5ZXKNvye9anCMDPn6EkKtWyhAcvWQ3Ac985nqkFWcwpzWF+uYsCp63HcWONGUibHBp+pb9sJ5HdufS4ym5D1n/vBK46cy4NsoBi0Uk4FGB7tXJn6KuRRzrzseVTuDnyMa4Nf5rvhi4jnFnAPxuU08RMUc/5R04Z4xkOHhkPpAd+TMSv2un29Xd0ZCay1e2ZKpswp7RnNvAf0RPnwZSUqffSOsb+rqNBZnQ3p4hOLrY+wYv6IiI5ic+SOynz79VUdjpH+An4/VREHT6mi8ZuAYC7Xb1v/WWkk/n+aXOokuVUijrc/jAH2nz89tmd4845JblpRFY0kPbKgXnXxxZ9AMUVi4Z3YiZpyUNvqeCwRbooEl1jsrP4XdvDHGdRFm8N1p7dN5MR0UBaD6WWdvhCvcuyqpo9rPrluj6fP1YD1SWzOGmxSnhkWC3skpMpCSprzi01nVxw52t9Pk+MOZqSPq7WPqCmvWfwX+7bTlDa+OYn17KqspB9N5zNrEmJXbiHLl3D90+f2+O4scYMpE0OCS2gsl3tMvFhj9n3xJhR5OTS42dShyrkuPvxV3hpq2rz6ZXjMyN97OwiJBp/1M/mX8Zx2C0aQVclbpnJCm07rsz0WzX3Ryz2GkxGets+VaTSl0Tn6JmFPBxRetRnmlVwYrdqfOmYGXx4SWInY3VlIdeet5CLD97RcKptdemd+IF0RDdYIPbxjOP7OAnwq8iF8QYFAB1J37P5lWpRkouX/2x4E6tQf8BpWhONXQHer+3kd8/t5I0t2/DIDAL0bqN1MBZNUCXLcQk/wY4GPnHXa9y6bhfN7pH3rB1OkouaSjNUUHHk7IEtcpMDaWth+m0nmww/x85WcqkOmU2ecI96RjqTAEeKXfGfW219a/NFVNqhh1Jnlrv8vQfSAymID+sGZaKNelkQ7+yraYI6WRSvOQBVsNgfUkoqhUqglYk2Cizdg/839rTirt9NvSjmzCXj6/tmBtImh4QWzUgnX+B7o16qQDrL30h2NDtk2MaPa0cyk/O6B45t3hA/PGcJ7xqzOEHbTHgcFsbFpR2DSElbohIdTx/b5UII/uD6GkcG7mT9/oRM4Oq187n1wmXdxn5m9XR+vHZ+9+Oz1cVEHAYZ6bAu+ZL1cRyEOSd0HVtlBfakhenupMLDjqgHe4Fw09qsstHvGRWU08r3/vEWa297md89t4ty0UK9LORDsweu8xVCxAtqrd4GGqJbwEPJ0OmG5Ef/eW/UrcSAbt3XZmWrRUB52cAC6frMhE7Tahs/HVhNhs41H1ZuNT4cZBIa9UB6qVaFTejcHvkw90RO5+Gmvj+rwh4LpFNLO/qav92qUU4LC0XvvQ8ihqRUtNKQFEgD7JMl5Ah/tCZoYIR0gzw8cQecSYG9BMI679V0IqXkE3e/xtHaVt7VU0tD0xkzkDY5JGIZ6Q76D6S/fu6xABTozTiFWo3+9GOrRm5yI8iyad23yauaPSydlsdTxkqmas1ke/eP0cyGzlCkHZZILJDuW6LjNey0MbTmO5bsInQpsHgah3T8eCJsGCwXO3neWMbOaOV68sJmp5xCjSzib5GTCdvVZzAfN7U1+wB4x5iNJmS3C1yZaKVeFnQLyAdCm1DPb/M3xwtRj77heXY1uvs4qic7Gtz87Y39fP2Bdwd13HDgT9raztE78IhssA4sM3/ffrXwf1o/alwWD5sMnljTLT8OMgmOeKOTg1ko1E7tXZG1XBP5PJ2hvk/G/Uk7kucvpeTvG/ezvUHJ8ezoPOX4AY85rqaU1JankeSMdJKJwGZDyTwWiH3x+/qzwQuEIpSKdl4wlgCwf+vr3L5+N+f8/mW2N7jJw0Ou8PG+YQbSJocZlmAHIWkZUNHg6qXKh7JMtMULf+xZ46ezYTIWTfDZ1Qk3haJsB0XZjnj74dKuzWM1tSETOw8ORtoRz0j3475yKNraDIedFnIxuia+a4fwtjJZtLDbSGSeK4sTuzZl+dmcEPwtP4pczCUnzSMorWQLP9OEKtx5xVgIKJ30idq7nK5tZLJopVYWDbqhyD3fUEWOloN2Av75Tk2q4b2yYac6/oP6rkEdNxz4Q4mMnFPvwqv13eAimepAFscGb+b74UspdJoZ6cOBfKcKpAPYyRBh5Cjb35WLVtwys5s7T19YbOq8a/QSSCfb99W0+7nq3+9xxu9eAmCafxuuaELrQ9rWlMeHQyGK6aSBArLsiYz0R08/GY/M4HrbHzhXU9Z6oX6y989v3IRDhMmdeyLtMht/7fu8Xa0Scc3uIEs0lRnfa6no72WnHWYgbTJkdjd5EP42OsgBBnCRduTgFU6W5/nIjn6BhWNgJ4x05BfnLYzfLsxWF9pdcjJdMpPSzi1jNa0hE5N2DCbcsoQHlpE+lIyew2qhXhZQv79qyM8xXsjorMIiJO/KWQDsu+FsJuUkFqm3f+pIIqgLmtNuxY+DDEIUiU7C0sKi5WrX5xTtbe6x/5q77L+jWHTGZVWDwZqjJDVvbd2OwOAK6z/4vOVp7trQ+1ZwKmKfq2XT+vaxHgmSi63CAQ9BbeA1GULAAVlCF06Kc8xA+nDCHy1IFXr/rhbDySxRyx5ZNuDxmkOdd2U49TzDumSmqMWRQqYia9+J316hbU95fEfTATQhqZeF3Wo1LjphAbdEPoqdCLfY7+Bs7XX2t/XtHPKP9er31em5VMsSKi1NcatcbzBCpVCJkndD469Q3wykTYZEVbOHU367ge27q2iViSzPTR9f0udxjaII2VVLNgG80kFZ3vjUSMd4++pTOHFOMRcdXQGARGObrIDmD0Z9W/BQiRVmDSZzGQuk3f10fou9E59bM73PcalwWDUaZQElon3cuUYMlphUqk2mzpwWJQV0Qqgt6CyC5OOmg2zmz5uPR2bwGWv3avw35ZxBz8WakUWHdFIq2jla28pXrf/jGtu9FNMxqOdxZajAf0H56O8+vVfbicAAJE7DjU8b+MJ9Sr7ZzfBw5OnLj+OUJUpeoIWHZis3VMpFKwfkwGsZNLv6jMpw6ox0cO9rrHN8nx9b/9qtkUms8K9LZvG2MTu+o3UwV9/3NJCw2kzmD/pajgzexQfGVC6xPsZpN7/I/a9XU3Hl4wTCPWuEYr0jLJk5+HIqmGlpJDOa5e7wh5kvqmmT2eQXlg749acLZiBtMiSaulThzixRF++CBnD+8r5Xk/vDeZSJVpYURmgnZ9xnegqzHdzzhZUUOBO6y1pZRJlo5ZYhdHwaS3799A5gcA1ZtLDSy/aXkY5lQ85YOPiTpMNqoVnmUiw6eHRL/aCPH0942lTRYHsvgXRyu1xNE/ikgywRoFC4aZUudCnYL1Um+UV9EecEr+Wq8MW8ZsxP+Xx9YdUE1bKECtHAcVpih+U0y1uDagscija1sGqjf7m57vGtPGm/ittst+HCh28Q0o6bL1gKQIlrfJ+jTAbHnNIcJhWo3RMRGd2MdIFw0yYHvuC0RDXSqQLpTn+Y9Y8/CMAKbQehJCtIf1hnrrafrUYFtbKIctGaMklRZCjt9Jplqe0fQ9h4VF/DEm0PRXRyddQJpM0b6jE2j2ixcUY+73Y6KdBb2VajEgfb67tYoFWz2ZhJ9jh0vDqkM5sQ4ltCiPeFEFuFEJdH7ysQQjwrhNgV/T8/er8QQtwqhNgthNgihDgy6Xk+Hx2/Swjx+UN7SSajgUSSQZBpooldxsC3YupkAWWijUBbHU1y9Ld6R4NaWUgpbdzx/HZW/3IdwXHm4DEYjfSz7+zGkAIffQcbHT7l1tHq6XmC7Q+HTaNOFlEgPOyrmdiB9OZtH2BIQROpPZ+Tmx9oAkRWPnl4KBBdtMkcdEPyrqFkIRuMxbwnK/m7fjKDE+wohBDUyiJKRRvzxH62GDPwSgeVop6XdrcM+HliF/DkreHRolLUM1c7wDmW15kk2vFYB37OqShS2b5vnjy7n5EmEw1pU397LTKKGWk9Qr7w0MbAF3tWRzQjnSLg39fiZaam5BK5wsv9b1QT26Z8DMcAACAASURBVBv0BCKUiHbqKKAumvhJ5TQVa8ZSHcrtdQ6vGcrpJFke4g32tN2bHG1eFsgqo8ooxyZ0XG1bmCv2c/9re5gh6tktJ7Nm5uBlaGPNkM9sQoiFwJeBlcASYK0QYjZwJbBOSjkbWBf9GeBMYHb03yXA/0WfpwD4KbAq+lw/jQXfJunL0+83MFPUoQnJTjnwQLpBFlIsOpksWmiSE+/P/NeLV1Ini7AISQntNHQF+N+mCVwkF/JEre8GFqgNpeOj3aKxL5plfeLljYM+fjwxSXTQRg5hrCkft3YLpAVa9iSKRBcFuGkjh4gh+U3k41wfvpAH9JMPeT6t0kWx6GS2VssuOYX9soRpojHl1m1vxMYOqmPmMDFHHIjfLhRu3NaBX6RzMmzsu+FsPr1q8HIkk/GNjPoza6OYkZY+FbS2RjPSOQ4rxx/Rt8zDGrW/I0VGOhgxmC1UYXAxHfzzjSqetv+AX1nvZnd9GyW0Uy8LWTB/AQ4RIdxR2+M5ykSbamCU0XsgvVVWEJRWlmq7OUt7nUssj/LWvp4uIGWiFZ90MGvaFN4w5gFwo+1unnJcydXW+8kUIbInz+OKNGy40h+HkiKYB7wupfRJKSPABuAjwLnAvdEx9wLnRW+fC9wnFa8DeUKIMuB04FkpZZuUsh14FjjjEOZlMgrMKHJyRPRLOqhAOpppq9QaJmRG+tjZxRx9pNoSLo+u5mOSif5o84bGxNngYAYT62bjH1B3yrMXD7yA5mA0TdAq1Ym8QHQNqK3teCVLBPpsUmSxJIJRiyYI2fMpEF3xLWHdkLTj4i79HJzZLuaXHZoueY8sI194KBNt7DImR6Ueg7MhDIR1TtLe4cENo29/VyC6W/W5beMv22Uy+kir+g5qkdTa45HA8KrrRUzW9d41p3PvF1f2eYw1I6rjT1FsGAjrFIlOACxCco72GnO0Gj5hfYGf/OVRbEJn5Yqj8eWoTsR6k7pO1Xf6ufwf7xII63EP6R+tXdDrHJ753qnst8/io5aXucN+Kz+0/Z2n/3t/j+LGxQUGbeRw9Oxibrz4LFplDrM1Fbx/waq02GUzFw+q5Xi6cCiB9PvAcUKIQiFEFnAWMBUokVLWA0T/nxQdPxk4kHR8TfS+3u43SWOmFWaxWvuALplFtSzh1guXcfunjuz3uPPOPid+eyIG0gAfPn4NANM1FXA0DbAb3Jm3vMiZt7w0YvMaKJKBR9LZwt+v9R3Ap1aqTlUrZ/QsWhkILVEP6kl0jLsizsHgJICvDyvJ5Ix0odOObnOSh4d84aFVunpo0H/9cWU5OVTHjK1GRfz2e3IG1XIS00QTwXDvHdMOprBjC3+2/4Zf2v40pDkcCi683X72mIG0yQCINQobzUD6xU0fANA6CL/9TLuNoLQSCfaUoIRCIfLx8IGh/OiPtyQsWZdEm7B4s8p5z6t+3wPr1G7fmuuf55FNdWzY2cxMRxfBrFKyHT13yB6+bA3fOGkWFUVOdtvnUBwN2gEutKynxdP9upepu/Fb1CLhQ7Mnxf2kX9QT+uv/NI2/QkOgl/3DASCl/EAI8StUBtkDbAb6OrumWmbIPu7v+QRCXIKShTBt2vhqITnRCOuSeVo1m4yZRLB2a/XcFxUVlfHbjb3oQMc9BTMIYotvqw2Uxq70aL/sCUQI6wa2ATTwiGWkn/32cX2O+9Cs/2fvvMPjqM79/zkzW7XSrrpkFVvuHReMMQaDMT2EEiBAbgoQQhoEUuCXmwRCgCQ4gZDc9JBLGrkJEAi9hQ6m2NjYxsZNcrfVu7TaOnN+f5zVrtaSpVW37Pk8jx+vZmdmz7bZ97zn+37fXPasPH/AY3r81k8T/em3maRVDkgeMhY42BzATQg/Ll74+jK8ru5FN12zNUIIojYPTqEuuxeffhIue6JpQmaag9lFPtbdemZSV7L+sElOpFJm4yLMGnMmE0QtThHhkVdWc+H81FaiMtsrAFisbUNK2W8/68GQK1oJSjsuoTT6La6xZ61lMfIkpB0jo5EORgz+9eYGTncc3rGnJ9KdNoI4eiyK1Fr3YxMm7xmzmKnt50L93fh9J+uqKDCUVsiu/Wqy0FST/HvlsuvkGPWUu3tOkJ1Qls0JZSox8qj7coraNvFvYxmTRSVX6q/y6EcH+MvqSu7/7CLKcj24jTbaRMI159bI53nCOIV3zVl8Qz7KJnMSk/PGZnJtUNUfUsoHpJQLpZSnAo1AOVATk2wQ+7/TV+UAKmPdSQlQ2cv2nh7vfinlIinlory81C1iLIaeaCTIdHGAbbJ/ExpPZn78dt1RmpFG06k2sygQqiJ5aT+LJ9bvayI6wq1pu/KvdQe47m9rU9rXpwVpl26m5A+vH7jX46GOTMbR2C997liipSNCpminTbqZUeilKLN7pv9Q5wvDnrCP9BRMSrrvvstVxicn3ZkUYPeHAC4uDP2Ic0I/JYKNuXPUOT9Wkrq8xh1SS9Yakl+/WjGgcQyUBa4qKsQEOmK+wM2esdc1zWLkMZ1KSmYPt/Sx5+AJRQ1++/rOuBSwXh5ej3woNk0QxIHWxe+6JRChLRhBi3WCnbYkkcDozE6frG3GlIKwO5+PnzCNWpnJVO0gr29P2OC5NJNs2UiLvaDPcdSRyUXhH/JX4xzeMWfjEAYPP/0cO2raWX7v64DKSPu7BNIBXLxlHkcUG/dEr+QFczFfWzE2C3sH69qRH/t/PHAJ8E/gKaDTeeMq4MnY7aeAz8XcO5YALTHpx4vA2UKIrFiR4dmxbRZHMDa/ykxVyNQy0Z143Q62mKp457/OP2s4hnZEUEUOE0QtmbT1WA3dG5/47Tvc+NDI60mBmOcuvL69ro89FT4tQEZm9ohkGeull1zRQlswdVnBWENV7R9+afdQ+WDQlZiYklWWdN9xJYOfqG658xzq8VFHJtMLMrhwhWr4UixTd0/xhpWlnxc/z23s3yrNYPGZLQSceZwTXslFoTux2VNrD25xbGO41WqpLdQ/z/SB8MCq3fzylXLmaTvZb+ZRNqGMF7/e+wpfJ5omCOBA7xJIz7vjPyy86yVEUI19XOkk2mLyu3XmNII4KBDN1JLJvAl5TCtIZ6dZxARRzdV/fj/RCtxfi45JmyO/2+MeysEmldX+yvLJFM5cCsDxWnnSPmlmKx1679n2gU74R5vB+hE9JoTYAjwNXB8rFlwJnCWEKAfOiv0N8BywC6gA/gh8FUBK2QjcBbwf+3dnbJvFEYwWULPnRullbnHqM2ghBNdHbuSG8NfIKRmbs89U2GROZIFWwQbXl1jR/lS/j998cOSLDqeLfWx0XsfpWupBvJsAYW1kGlfUSx+5ogV/6OjMSDtsgmzamNSLbO3QCUswLTGR1bwDL+g8HF3bAj9w9SI0XwkRqROuS727oS+iJmW6kNTXdncGGE480k/U4WW/LGCjnDIqFnwWYxDdjSkFujH8GunWgEoM5ItmqsjmW2dPY3ph6vKODtzYo+1J2yKG5IXVShOdmZ+QM5XOOZmDmrpO7GMcE3LUitZ+mcckUYXAxB9rzLXy4VcB+KCp7xqYhphvdHGmmxs+sZyd5jiWapuT9vEY7QS7BNKdMrXXbl7OpNyx3ZhtsNKOZVLKWVLKeVLKV2LbGqSUZ0gpp8b+b4xtl1LK66WUk6WUc6WUa7uc509Syimxf38e3FOyGAkcHSrLVCOz+L/rTuzXsbvlOJ4xTxpz/sr9oXjagvjtZW0vcPdzW3vtylfVknzBHo3C5cW2crwiwFdtT/a9cwyPDBDWR+YiqALpVtpDkRF5vJEmEomSJkKk+3qvHZhb7GPlJapAJ+xJBM92m9JUP/aVpTx347IhG5fPbeeSBcWUZKWh2XQayaChJvWA2CUTOtNOudNI4ZF+AnpiOTkUGT3JlMXYQWiCMDY0o/++9/1lW3UrIJkqDrDTLMJp619WtlW6aWjsbjdXKJowpMCdVcSfjXMAcE07ne125cBRqQQFpDlsrJPTyBFtSR0O58QKEqv1vqUduelqpUcCWWkONsjJnKmv517775km9lN+sA4HYYL2xGrb5h+cw8bvn83EXA9Pf+0U1t16Zr+e95GENT23GBBuv/ohvevqj/VYFJUKqRSzjVVqx5/Py8YCNpllzNH28OSb77Nm9+EXWk66+9Wkv0fDc3delgpQ80hRF2iapBEgbBuhQBofObTwlb+npt8ea0TDscmUrfcM0NNfO4UrYy4oobRC6qSPf0RPx25Tn5njJ2QxawjbcW+8/Wzuu0JZOto1jVqZyTiR+qKhEWijLqb7HNFA2jRIp4Ngl0DaaT96rzkWQ4ceC6Qxhz+Qfn17HXk0ky3a2S5LcfZz1aRNuklHXTue+TBRXlZII3Vk4nA4+GX0EhYGf08ovZTnfVey0xzH30NKPlKU6WZzzJ1nsbaN6WIf99t/xldsT3FQ5nBA77sO6tKFKuvttGnomiDnhMsBuEx/kzvtf+GaXz8LQIc94drkduj40lTs4HHayEkfux1EB+zaYXFsIwKNmFIwafzAq+AXTThKXTsA3ZXOFyK3MFvs4VnndzleK0/ZBg/616Z7qMiNqlUGn/D3sWeMiB8NOWIZ6ZAzF4dh4CPF8Y0xzHDsedn7XkrtRNPtnBj6DQLJZSPQglvTBHtkIXPFbkxToqWwdJJGkP0yjzzRwmklwzfG8po23tnZwFVLy9SGkJJHdQ2kbWPQo9Zi5HHoGmHsEB1eJyUzZuU5Q1MOwNvk+H7rhP24SY/5M9zwj4Qsb5xooEZmU6gJothoxIsmBMH0Ys4I/4yrO78nwCnLVlC32scSbQsXaO9yqr4JgPuj55OK2+hNZ07F47TxiQXKubh9wpncsvqLnK2tZYW2nrmx7Pae6NFpP2lNzy0GxOZdB2jHjcPW/7nYn65exH2XzxtRG6yRxhnLtpfLYtqli1O0Tdzx9Ed9HvcF/Vl+ZHsAfRRempLIHkAVvNl6dbKMEVLNLsK24XXs6OTai9TSX+ePztGGEVJZJdGPQFrXwETDQMc+Qh+aWplFjmglmsIvbMQwyRR+tpmlGGjkGTV87k9rqKht6/PY/nL+r1Zx+1NdvmNBtbIS1BPZef0ovuZYDB1Ou0YEGzI6vDIyIyb3myX2ArDNLO339zgoHThF93EWiCbqhApcO/XIQiSC967e0DefM4OD6XM5RdvMSdoWnjaW8PPIpfwq+gnOnt23t3Oaw8aNZ0yNrzJ73Q7+ZSznf6KXoAvJVbb/ALChPfV6qrGElZG2GBDpBGjDTeEA5BkrZvStuRrrdF64wth5z5zJ8doOxmf3XpSXRSu32v8PgDfkJcM+xkNxmQktaw4pFDvGAunICEk7bAXTASimfkQeb6TpzEgLR3+KN5N9pUeCJplOhghQ3dJGYU7vP4xNbX7yRTs1MptGMjGbD/DmwTq+/nCIZ742dDpugHBU6Z/X72tiwfiseCAd6jrRswJpixRw6Bp+qYMxvBnpzuZSZ+nr2GqW0kxGvwtiQ9hxkpCgfMf2f4wXtZSIOtZrqpYizaHTFoyiCcEr25QOurJLXY5d12jNX8R8/yoAHjNO5XVzPg9eu5hTpuT2+3mlu1RouVlO5IDMZYm2FUMKarAy0hYWcXzCT7t0j8l2niOBrUtW4SNZxmRRyfFFh+9YBzBPSzghzDX6zl4PNS4ZoEmqoCNXpKCTDqlKcWOEAmmHR0mB0sXIdRsbSbRAzGrLnbrk6ZWt/WvXPRQclOqH9Wu/ezy+LRgxOOnuV3hozb6kfSNtqgjqtIUzaRUZ2MJqgtbpVDAcfOK371D238+ya7+q4wjZEk4BHaGj1zrRYuhw2JS0QwxzsaFhSrJpZYEo5z/mIj55fAnjfKmvSEFnIB1BSomXdr5ke5bz9PfxiBANmgpcOy1DhYBPxeorDu1WuNm3PH57rTkNUE1XBjJBj0Q7i3oFzxuqzfkacyYheXTmbq1A2mJATBUH2CWH3m7raKHrBGOLWYYuJNnt5b0cQVxHBlBk9NiTaFhxySB7parkzhN9+6e+slE11xgpaYfuUgGRV4xMt7ERJ+b7KvsRSI+Gp3behJkApHckPKFf315HVUuQ//73pqR9127fA0Bj1E29mU6OUIF0bVvqDV0Gyi+eUNk1f5e24KdNtxp5WfSNw6akHQxzIB01JVPFQXQhWWPOYP74/nu/lxVk4yRC2DBZrG0HlOc+wDb7zKR969pCTMhRK16HFjV+0JLB1eFb+FT4e7Sj9hloomxml2Lnn0U/yfcjV3Fr9JqjtpmWFUhb9Jv2UJRC0cR+2bdR+7HKadPycNo0Mpw2PpKqAU1+x45ejxknGmmQGewz88iWw98IYOFdL/GtR5TXKMFW0qWfrbFmOXkpZKQfeXsLAKZ9ZAJpbE6a9RzmuWv73ncMooXUa66npf5jeuXi0r53GmJyC9Vjdl212NOQKAA1u2inX1mvJltbm2CnUcBkoSaIwYhJ2X8/O6TjOlRb2ukQsjfiZffdH2PzHeewbKoVSFv0jUPXCGEbkYz0ZE19J/aYheRn9L5q2RMfVgexCZNd1c1x+7rzQis5J7SSLfY5SfueNauAjJjsovQQqeFXlk/idXMB75qzueWc6Vwwr2jAxbkZsWy3Q9cI4uRvxjnslMX84MLZAzrfkc7RmWe3GFY+3FPDUhGmRY5tE/XhJMNlZ/sPz+O2Jzbz4HsRWmQa9trNSCkPu1RWJqrZJwswEWTLpoTWc5ho9Id57IMD/PSy49CbVbHLWnM6n+I1cmnFMGWvGYmMTomFM/XmAYOl1lFKfriO2tYgdl0jy3MUdaqLaaQ1R+rfq0m5IzSJ6cIXzj0R1sMZpYnPhqfLMvGWqlbmxJo0FTpD4IcMXzZ79xeQJdpJpyOe8RpKjEOKHwtFE+3SxVNb2/ilEN2Wsi0sDocQggBucozhXf2KmiZlopqgtHOQXM6Y0f/kVCvqenH346tZLurokE7q8FEnM6Eu2eHIZde5fFEpNk1w/nHJXYkXdvmtuf70KQN4NgmEEPzgglksnZJLTWuQ8dlpFGe6sR2llrdH57OyGFb0WJHZxBJL2tEXqopZsMUsY3y4gqc2Hl6yMV6rZbcspN2WjTPUyCd++w6rd3U32h9qfvaf7dQ2KF/gOny0Sxe5ooWdde2HPUZKpe0DkK7Bt6JOlZDuIU0GWPzjV1j845dH7HFHAhFRP3qdEpZUiJgj32BEc3pok24qD+yNZ5/D0cQ4uvrDnzZR6T3PWjCVPVIVGXc6FAC9NinqL4eaiBSIJmrk0WuxaTG8BIQbe3R4rTYNU5JJO01kACIlO8lD6axrmZweZoo4SIUsomsR8qHYdY0rThjfbWIphKAky03OECUnrj55ItMKMlg2NY8JOZ6jNogGK5C2GABaSMkOFk6fOMojOfLpXG6ukEVMEDXsbzx8hsOHnyaZwcFoRlyjvL9p+Avr3iyv487HVZOT9HQvTTKDTNHWa2MAw5RMELU0ynQ2jqCJRlBLw22qH7eIMXRB2JGALdxKVGpoztSzzKOlOayXXnJFC/V+5WoQMUyWaR+SG2vms/lgCzf/ayO2sJp0624vRtlyOqST+xy/Y7xQRZKh6NBPBCaIavJoJl80USuzuPX8mX0fZGFxCE2mCy089DaNXYkaEp/w4/bmsGfl+QM6x6nzVGHgtIwIOaKVWtk9sfH0Dafwk0vn9nmuVd9ewbrbzhrQOI5lrEDaot90ajml8+j0hBxKrj1lIosnZlMlc8gS7bjp2U7JQYQMoVwzak0vWSgv54gx/BlHXdMId6gfjBbTgR8XHkLdlsq7YkiJV/hplum0BkauZffGRhtZsgXVjPbowh5poYX+ZW5mjfPy2SUTeO3m5cM3sB6oI5M8WhCxzJe7bQ8POlbyoONuoqbJjf9cz6PrDtDSrFZUNLcPnBl8PnILPvzcY/8DdqK09+Ci8aUH1/Lp/32v32OaW+xjvqjgDec3ecxxO8Winhoye/0cW1gcjnY5/BlpU6rW4B2evrsHHo7T5k5S5wq2kS3aaCaDhYcULc4t8XHFCQN/DIvesQJpi34TD6T74S5wrJLvdfHIl06iUirngPRQdY/7FQuV1q2UOdTJTDQhyaZtRAJpuybi3Qz3tHcG0oFeA5CIIckgQBtpjMvsf4HMQKmUuaSJEFmowH8opQGjjT3cSov09KtSXgjBXRfPYWLuyNYr1MosikQ95bHGKgWN6wCYLvZjmuB1q9a/0Q51rdBcGbSHIrxnzuL/jDM5UdvGH+z34Q9F2dfQQVswMRl78aMa3q7ov6Qp2+PgMzYl9xmv1VEsGqiRWZw6zSowtOg/AZy4GH7XjiLRgD99wsBPEvOdT+vYzzjRyHazhM8sUefLirXgthherEDaot/EA2mXt489LTqpJhuAJ976oMf7C0VMo6zlUS9Vpj9PtLB2T9Owj82mi3hg2kQGfunCI4K9dq4LRgwyRTst0sMIxPpxOpctc2M2ag++t7e33ccU9nALrXiwjUCr78FSbhYzQaulvLIRfyjKpu3K2lETEiMSxGVXz8EWaccvndhtDt7bpT7jv4hewpPGUlboGwjV7+bUe15j7g/+M+gx2QQs0bbwqjE/vm3y1JnMHGddpyz6TxgbTiIwjJN1MxLEJSIYjoF/RjW7mkQ371O9BwLeMi6YV8SdF83m2RuHtumRRc8c+VdsiyMOPdZUQbisjHSqFOSramwv3TXS4ahJRmz7ivlTERkJL+feihMHQ7RL9GvTNLyig6jUOGl6aSwjHew1Ix2MGBSIJmrJYk7xyAUqTSj9cGfg//0nU2tc0xKIcM7P32RbdQodGw9DKGpw/5s7h22VwBGJZaRHoz98P6lHTfam+yK8v6eRmVpiQqP7a9BizjTh9gZaSX5OQZzcH1V60MefeXrIxpQjGygR9bxhzuMdYxYAB3OWDtn5LY4tZpTkoQkJxvBJ18yAuh6ZgwikPRnq2NKY9d1nzjgBu67xuZPKKMrsX3MXi4FhBdIW/aatsRpTCnBbGulUue+zpwKqtfqhRAwTD6pBxfYm+P6VpwMpdhccIHc/vy1+W9MEPluUAE5OnZ6PHzce0Xsg3RGKkkczpeMn8V+LR057V6Ury6arbS8yQfQsk+mJtyvq2V7Txi9e6r0pTm/871u7+fFz2/jrO3sGfI7ecERblUZ6DHQL/cI5iwCIttVhShn3rwXYsL2Cd3YqaYYvUkeNzMKmCW6/YFZ8n3JZQkTqeJq2Jp33cC41q8rreXTdAd6uOHxla75ZB8Bemc/nI7dwZuinBDPKBvT8LCzW7FfJDRkdxoLvWBt70znwQNrlVdKlGdp+AGzegsGPy6JfWIG0Rb+p3L2VarLQ7SOnjR3r6LFJR0YPXfmipowHzfuDTuw+dSHMY/gC6Ze2JFpLCyDXaWLqTkqz3filEw9BAhHjsBncp9/fgU2Y4M4aUAvZgfKNy86kQWbwMX0NDzvuQiO17HCn7tgYxDLtPS+qrmEVtYe3BeyLbdWt3PyvjT1OUpyR/mukRwtPpvqMRtsa+H+PfogPP3tMte3VdVvi+xWJBiplDromOHFiosNgGDs7ZAkLREXSeZ/YcLDHx/vMA6u5+V8b+fT/rmZfQ8/ON8VRlRWvkMV4PBlUyJJenWcsLHojhNIXt3cMo5d0ZyDtGIQXv81Bk/BREquzsQLpkce6ylj0CyklpaKWfbJgTGTOjhgcSpLQU0baNCVTRCV10scHteBMy6BNuoc1I9019hUCdCOIoTlZPi2frKxsPAT47uObOPcXb1HX1t1ppNitljvHF4+sl7hdF9wR+RygGm6UpZiV1mNPeCgcHB5f33Owlwrn/uItHl13gL0NyW4AH+xtxB5powVPfKxHMs5YFkx21NPQHiRHtLJTqtWCzjbgIBknGqmSOehCMKvIy7a7zo2fY605jXnaTjodWAJhA5umMV9UMFUc4HD05PQBkC8bMKWgUubGvayddn2Qz9TiWGXFXLXSFg0MfOLcFyLUmZEe3Opui1DHt0k3LvfIN2k61rECaYuU2VPv57uPb2KCqGGfmT8g8/hjFpuDEA6y7d2D0qgpyRAdNMoMAhEDl02nXnrJE81cMK+oh5MNLQJwmB2E9DQ0TTBvcjEOYbC/Tl3kuzoqdOKR6sclzZs97OPril3XeMo8mYtCdwLEW073RTwjPQSB9FB4Hx9aUPjUmu3YhEmzTB8T3yt3ptLxi45GLpkYJV0EWWWqdsQ5sUY9WbSRJkJUyuz4c3J1CWy3yfGkiyBThZqYXPDrVaxZ/RZPOL/PY47b+z2mdLONNtw8cM2JzC5SS+VjYVJicWSip6lrW8Q/fAXfIhSbdA6ycL9NU8dXy+x4oa/FyGG94hYpc+1f3+fxNRUUiGb2yXwrI91PQno62Xqw23bDlLgJEcDBZceXYNdF3Kd3JJamhRC4zQ7CesxCLZY998Sy5+Eeiuv02A+ANoJdDYF4EduuWPZzul6V2nGxz6p5hNjlhY3kRiqhVqUNbmFkbewGiiMjFwB7qInpXpUh3isLCEgHOaKVmWIv65xfAWC9OTXp2GtPUY2c3jDmAXC5/joCk4radlZoGwDwigChYM9L6od7D9MiTTQLL6dPz48XWbX2MAm0sEgFX7aaLDbUqVWvGbc9z13PbOntkH4jgrHusIPQSAM06ko2tVmWJU1WLUYGK5C2SJmwYVIiVEHPPlkQD2osUiNs8+A0uhv8R00Ttwjj8/q4+5K5CCHYbY5juraPD/Y0Dvu4BJBGIB5Id1aQZwqVdT73F2/xhb++n3SMPxb4ibSRLTjt7OTXRhr10sskW11Kx3W2so6OQjfEyuZA/PE7+fWrydrgQJt6PVvl2Aik0e20yjR27N6DI6KCgWaZTgNeckQLX7A9RxSNlZEr+UAmB9K3fXwWe1aej/SV8Joxj+tsz/E7+/8AcIaesIf8xm8eTTruBv1xvqQ/zd8PY3noC1dRjZKcpLtU++NihxyRCgAAIABJREFUy7XAYoC4vSo4jfrVNTgYMXlg1e4hO/+mAy08tCrmOjTIwv23pLJ83GBOseoCRgHrFbdIGSlhQqy17z6ZT5rDmvn2h6jNg8v0d2siYprgIoTb7YlrO7fK8WSLdloaUsu4DgQvfq7QX0NIE48MELapIC7qLQWgVCSC1Je31iYd++7mnQDY0kZW2tERTmRym2QGrmhqdnYHm1V2PRgdeEvtSXmJIPeuZ7bw3KbEe1PTGqTJ3715w/ef3MzSla/y8taapO1PbEiWpEzzqnGNlYw0QJ30kS+a4u9BCx5qZSZTxUHO1NbxlHkyvzcuBHqecPvcdm6I3MhLxvGcqa1jsjjIIm0HTxjKss7ekHD0mCiquNn+L75j/yez0nvuNpcRaaBBU5/HG1dM5e5L5nLunMIhfMYWxxK6S63MhTraaGjvuSPtYLjg16vwCj+GFIjBFBsCD7SewOWh23jQOGtEi78tFFYgbZEyQhC3udon8/E4baM8orFF1J5BOh3dNLZR0ySDAIYjUSSyXapgdoGWnLkcKgRwm+1BfmL/I5PC20gjQERXHbJMjwo+im1thz3eG3Mf0dwjK+3o+hvRRDpZpFYI9D+vKNu7wbQz16MdfEp/BSdhHli1m6/+XyJ7euKPX2Hxj1/udszf3lXZ03V7lc7SRzuX6W9gI0p5TVs8w76zQjmCNMix0zzkgMyjRNTjjgXSP/n0qWwzx3Octhuf6OCf0dN7PX7lpcfhx83fjLOwCZPbbH8H4NfRiwlLnelaouDwJC2xpD65Y2OP53MZftqF+g65HTqfWjzeCiosBozuVJ+lJ97fyfE/THy3v/v4piF7jEKaaMCHTR9cKCbRWCNnYloh3ahgveoWKePQNSaKatqkm0YGN4M+FgnbvWTT1q1joGFK1RDFngiiNplKR5pqMV1/EUKwUFPBZbZRTzoBwrr64RB2tRxuMw+fhelsLGMbYWnHhV2KL9N8uXH5SV9cFDtuSv7AK9o/EX6Gu+0P8Gn9lR7vj3SRjTy54SC767tnTr9qe5J77X/gi/qznPXzN5lx2ws8t6mKedpOWmUaFXL4i0uHinp85IhWnFFVlLpoxiS2xSaAddLLOjmt1+Pnl2Yya5yX9eYUIlJnub6RzWYZFbKEnbKYBaKC8ho1mZsoqghJG23SzZ51/+me/TcNnKafgDZ2MvoWRzY2p/ospZFc1/KP1fuG7DGmagepMIuGTCZpFRqODtarbpEydl1joVbORnMSh1uutTg87WmllIpajEiyfZchJV46ktrEtpNGo0xPanQx1DTHugTmRStJFwGisVazwqECabvsHkjvbfDznX9/iFf4aZcuNJt92MbXE0mZG1cWvhQDaWfsB2YwtYZzhNJH9mW5t6OmjZse2sDp974e31bdGkRKycma0kR+wfZsvJjznZ31FIl6DpDHF5ZNHvgAR5hmmY4PP85IK35cYHPwmHEqjxmn8O3IF+m8Rtx3+bzDnmNLVSvtpPGuqZq1PGMsAeAtcy6LtO2c/fPXASgW9RyUuawzp3G8toPr/rY26TyBpoNoSHZ0WNZfFkODw+3BlAKP6F4gPliUvE8yWRykXBZjG6Juppt/cM6QnMeif1iBtEXKOG0axaI+7phg0T9CrhwcwsAIJUsmjHAQp4gkSTtAyWdKRW1cFjCURE0TMxbo5EdU1jsS00hrsUDaRXcZxBV/eI9/rtmPlw5aSRvycaXK1UvLCNp9ZNHeTXPeE53GI4Nxv/OaKvM6RfTuIx2KJKQ7TsLMFHt5ckMlv3tjJ8Wink1mGdminZttj9DpoVwsGnDmTOB75886zFmPPDKy88kQATyh2rikwo+bb0W+yqvmQgD2rDyfSxaW9HmuWyJf4tbINfzZOJd5JT72yXzswqBYqCLMyaKSnbKIj+QEJotKtu1Nfg8+fe9jAByUuUP5FC2OYTRdpwEv+TQDMEFUc7ftjxQw+ALwiCHJpxmvCFAhi4esCdNgJSIWA8N61S1SJku0kyXa2S/zWDxxZIvMjgYqY8nT/TXJbZDNSCzjYUvuFHkgFkgf2rxjKFg6KZf02JJldkQVwhk2FQzpdjemFKSJIC5CiC7dA6tb1TFe0TFqDhN7Vp7PDy6cTdiRiVuEkaG+X5+MYCUvO26mLDhw+yqvVFrgKdrh5TYtHREu+PWq+N+/tv+K553fYY7Yxe9fWEeWaOcZ4yT+ET2da2wv8k/7j9h+sIEiUY/fPbLNbQZL6eTZAEzo2EyTGJjE5+kbTgGghmz+bpzFpHG5/PSyeaw1pwNwglCt7PNEM9Uym1XmXOzCYKk9uXYgW6jJ6VjSmFsc2eR4HFTLLAqFCpx/bHuAT9le42u2xwd97mDUYElM918uSwZtJbu4LJscj2PQ47IYGFYgbZEyJVItae+S47jqpLLRHcwYZM0BtZT/+kfJGjszoiQUUnfGt9l1wT6ZT7Gopz0w9BXjE3LT4l0WfWH1voZjxYa6rtFEOtPEAdY6v8Kf7PfgpZ3XtiVkJjm2ABH76Ork21wq8DSaerZD68rs+heYolVyTssjA368PKkmQPmimUIaetynti2IwOTT+suUiSpOikk5ztQ/YEpM775DlvDd6Bf4aeQKTtK3cFzlv/CJDtpdxQMe22iwsU29/wU00CSyBnSOuSU+Xv7mqfG/izNdlOWmsVuqgtc56a1gRMjEz8QJZXxkTgBgmkh+z/OEyhrWy5HV7FscvQghqJVZ5IoWbERZpO0A4ERtW0rHP/NhJdd3KUjuSjBisFjbRqtM431z+qAz0o98+STW3XbWoM5hMXCsQNoiZbxSZX2aZAbnWbZS/UZ3qkA10HGIrtdQgbKwJTIKhT4X+2Q+DmEwxd3G3gY/a3YPnae0lJARc97IM1SA/GGdyjzrmqBe+jhTW0e6CHK6vpHb7X/jxofWx49Pl3782ujqUds0FTSF2nqXvry/pxHnvrcASDMO70TSK2E/GXTwr+ipmFLwiONOvPgJ99Dl8GPaGn5k/xPPOb5LekxfOU0c6OLBng8IfmtcSL30cpX+IgCN7gkDG9to4cmL32zUlHuLYwBLy1PyExOyey6bh9Oms+3ui2nVfJTqTbRU70ETEr+rkPnTJnJQ5jBFJgfSc7zqda7HCqQtho5amUmhaGSO2INTRNhiTmCqdpDqGpV8eG1bLc9+2LNF6Q3/WM+zMYvM93Y18O8PEi40oYjJHG0PW+V4DPRunU4txhbWu2eRMtm6ymBetWLemGhjfKRRkq/kMJn2ZC9jMxzLOHfJSD/8xZOYNFXpZX/56Eucds/rXP6Hd+kIJxcqDhgp4xlpp1BaaL+IZaQ1QaP0ogul333KOIlztfeJdulwmIGfDjG6DgkbatS4/7NhZ3zb9uo2PnX/e7SHEq/Tq9tqmampwCs9MDAXlIq9ahXhfTmdmyNfYrxWxwX6u9S0JhciGVJyQixjlSbU+1ops5ku9scD6YSOV/CuOYvxmtpe4xw/oLGNFpeevoSIVF7ynRnpbXedy84ff6zf57rnsuP4xRXzyYotTwshaNTzyDbq+Ob/PgNAu6uQU6fmssUs4zixK+n4PNFMg8wggmXJaTF0tKeXkSdaOV5T9pT/MFYAcMv//JnXttdyzV/e5/p/fNBrx8PNB1u48v73+OYjCdvGQMRgkqhiq6m+81YcPbax3j6LlMkwVObvoqWHr8K3ODwXLVGa0hm+5GDYNJSVl7AlAumiTDfHz18AQGkX546uhWyDQTeC2ETyuTpQGm1dE/HGIHvNfNaYM/CIEN5IfXxfn2zDr42utOOTS5SOdlp24jL2P6/s4N1dDbwak6FIKflw2w4yY40PikQDmP1vyvKTf78LQItM59/mMvabeZytrY37QHcSjppMElW0yURHvYeNM5isVbFQK6dOegmSeJ/fMWfHb9fp+f0e12iSl5kRLzitR2WkNU2gawKbJnD0o8PaJxeVcvGCZGlLkz2f3Ggt3pDS8PvdRVx7ykQ+0qYxSatGBlvi+2YajSp76E2uM7CwGAwzZ6uOgZfoq2iS6TxlLMWUggWigmv+/D5ZtHK82M4Dq3ax+WBLj+f4+K9WddsWCvjxio64FMnKSI9trHfPIk59e4hV5fXxLnCH4o3UE8YOI9zN7mhBT1ctZ+3hZCmCjBcbJheLmBnFRKWWFEhXtvT83vQXLRa8G10uAS2x4E8XgpZYIeF2WRrXq07U1HKml3YyRIAqRtchwZ6mAvmuxYarylWw749lpFdV1JNeuw6AR43TsAsDWvuflY62qfegXnoBwXvmTKZpB5K8owFCUZNJWhUvmwv5t3EK90Y+ybviOADO1Nez1ZzAnOJEQdybxnH4pZN/Rk+nOTA0k6SR5NmYXd1a5iRtf+Hry3juxmWDOvdBCigwquIdNtsc+QghyC1Tj/X0a2/H9800Gmggk6duOHlQj2lh0ZV2n2pvP1vby9vmHFrxsEOWsEArR2DyD8ePecx5B+dpa3irvL6PsxGXgskmtcJVg1rJGSrXDovRwQqkLeIs+uHLfOaB1Zy88tUe708zWmjVvMnt5SxSx60mIPZQc9JmEVVaZeFIlkpoNgdVMifJS/r8X3bPbgwETapAs7FLkViLEctI64IdUmUHa2Umu01V1DdRKL1fZ2CzrnV0HRKawmoZ/5m15fFtrUH1vDod8fyhqMpCA+/EvIrbqndi9tMHL9NU71kD6jlXkkM+TUSj4ST7vYC/jRJRzy5zHN+MfJVfG59gi5hCpVTv/SpzDr/5r4Xs+OF5ZDht5JRMYXHot3wvei03rJjS35dg1Nl3wvf4eOiHrGrNS9o+JT9jUM1vACiYhVuEWaxtpU56aTXU+72mRWW///PWO/FdM80mREYh+VZG2mIICWaU8dvohYSkjQei5wGwwZzMfG0nJ2sfMVNTAfG5erL0rSsloo5r9Ofx0U5zh0pgOGo/BGC9qb7zg3XtsBhdrEDaImUcRoCQcPe9o0WPdAbKtQ1NzL39RRraY0WGoVjx4SE+0rom2CFLOFtbxx22P/O5WFHakIwlFkjviCaKRj+zfC6gLupPGSfzrLGY30YvoopsgtLORFENSD6rvwTAXjm6BafHTSsDwBFu7nbf1iplVeewacwQ+2mQGWyUqtnJPQ+/xKTvPte9O14vzM5UeuzGmL1alcxRGvK2mqROlRVblQ6yq9e6icaN4Rt41DiVR4zl2HQNh01j0x3n8K2zp+PHjRQaJVmj58s9UKYW5bBZThqWcxeUqiDjBG270pXHXuZ589Rye+fEDinJMptos+cMyzgsjl2EgJ9Gr2Be6I+sl1OZXpDBejmVLNHO921/o1Wm8a4xi2liP5EeJudZtPKE4zZutz/Ij+wPEOrMSAfU5L5OqkmhlZEe21iBtEWP9NTkwm4GCGtWxmeg6LFGJzur6mgLRbn/rV38/o2diIiSJghnckbapgmeNxeTJkJcZXuJO+1/pUz0XCHeXzRTBYblMqFLXTpVBcaaENSRyfWRr1NJLhKN/TFP60/pr3Kl7XXWm1Pi7aBHiwyPh0aZTr7oHkg/+J4qLvQ4bBSLOvbIQiplLqYUZIXVa7i3sSPlx5rqU9+HsxaoYLxBUxlYve0gkS6ZqA83qo57u2SyJ/RaOYObI1+mmQzsXbqYHWxSUp3BdFwcTfqjg+4v9nFKP+4UUfbJgs44ms8vn02VPo4labGmLB2N2InSZrMCaYuhRQgBiHhdgxCJLPI07SAPG8vZJkspFXXYeoiFP6W/Rq5oZZNZxqnaJhrblYxv06YPCUl7vMbAykiPbaxA2qJHwj0sU+nRDgzb2MuaHSloukaHdJKGykT/4Y1drHx+G1pEZaSFM7l4TxOCx4xlfCV8E9eHbwTo5lYwUIRUgfQOWUqHdPLX6FloMclOTxf1RjLIEu18Un+D3WYB/xX+bkod64abRuklS7Txxo46fvRsonL+uJJYEY+ukSdaqJc+wtipISvunmGYqWuS7YYfPy7Onauec72mgjbdX5Wkk55qV+e+8tzl8W3RQ3TU6c6Es0S6a2y7TDh0fdjO7csr4UDM4eQtc2788ymEoNo1hRJjPwCyTU2MNjZbk3yLoeXQS+Gnl0ygokvy4TnjRPbLfNJFEB9qFWxPvT+eiDpbX8tWczx/jp6LV3Tw3fsfRUrJ+MhOPpQTkbEQzMpIj22sQNoiCSdqubunGCPHqCfkyut+h0VK6ELQgRMPyZZpIqKykodqpG26QKLxvHkiL5sLMaWgTNQMyVg0QwXSzdLD/ND93B69Om5p2NNFPSuvmDyamSwqeducQwAXzmHMRqZKOy7SCXLVn9bwx7d2UyJquUx/gxXTVQAmjSgTRA17ZQEAB2QuJUIVBYWjqaeBHVE/HbjpfGXqNHV+W1tlkkQkJ1xJnfRx1WmJVt+dk9KHvriEN25ZTpojETyvmJHPkknZvH7z8n4/9yOB3PTh66aWk+7g+vCN/C56AU8ZS8n22OP3HZC55Bu1YEQJN6tAuja2TG5hMVQculL02SUTeP/Ws/lb9CzeNmazQU5mzmwliXO2HeCDfU0sv/d1/v7eXmxEOU7s4lVzfjz4LjKr+ONbu8iijYYuzYOEVXc0phn9X0KLI4ZTtE185Pw852vvETkkko5Go5RQi98ztrxujyQ0IWiUGfF2xnFiDhq6PTmj1jVQDeGgimzKtGqMfhbK9TgWqWzboujKiQURz74celEXAoK+iUzWqvCJjrj+90i4+PulG49IOJn8r/1n3Gv/A+MbVSGa1lGHS0RiTVDggMyjVFPFm4da1/VGdV0draaLvAy1xFtaWEi7dPH+h5tYfu/r8f1yRFtc93go80szmZCTPFlKd9p46IsnUZY7up7cA6Vz3CtmDL11X4bTxkY5hZ9EP0UIB+nORCD9UnMxbhHmvFvv5/V1mwDILrSuTRZDS9eGS7//zEIAPE4b349ew6cj3+M7H5vFBacp5xqX/wB76pVMb93eJopEA5qQGJkTufkK1XWwVNTxTnkd40QjbbaBdQO1OPKwAmmLOKdrG7AJkyv013h6Y7JFWCjYgSYk0jG63ezGMpqmsmb5Itn+TkRjRYf25OxewSEOBJUyhwKaeuym1190qYJ3FUTHxtdDYHzR/CJ2330+kfSEjKNT/zv6YTS0kYYXpXXOoIMZmlruz25Rrbn1gJJa1MlMrj1lIhVmMUU0kE0r/n40t/EQoB0380oz+ed1S/jO+TOpkjn4InVJ+/lEe9w68FCOhAz+UFPgdfHgtYv5+eXzh/zch07UbF205TkTlH56sqhkwxbVLOPJnf33B7ew6I3O1aQrFpVy7hx13ev6Pb566UTsOWUApHUcjK/mRU3JNKE6GRZOnk/EkUW7dFEqarH5q/GKDtp908nPcGIx9jn6ruwWA2a2tgeAiaI67sPbidGhCrqijtG1PBvL2DWNRrxkktwiPBryE5E6tkMy0i67TppD5+qlZQA0y3QyRfuQBNKuiGoe0DXo60nSYY+1fI56Eg4d68xpwJHhglglsxkXs7c7XUu0MO/Yr+ylVm9SQdZNF53MbR+fxcyTzkMTklO1D+kIpx54pYsA7TGf7ZMm5+C261TJ7Li1Xomoo0TUkkczWQWqCPPFr5/KX645gXsuO44lk7KPiAz+cLBsah6+NHvfOw6AZ288JX77wnkJJ5TSGcfTLl0s0rYzSVRSL72UjRtbDW0sjnw6V//cjkQtQNfvsV0X4PQSRcMWbo1fQ00pyRXqGps9bgKargq2J4gacgy1ItboKOQ/3zg16TNuMTYZ25UuFkNKZxFWsajHDCc3/pABK5AeLJom8Phy8bZ9lLQ9GvITwNljILvlznMBJQvIeC0PX8s+QlEDGFzg4oyqwpgmEisMXeM8IZQ+sDOQ9hcuBlDFkt4sCBl8dknZoMYwFFTLbLwigIcAl+lvUiMzKTeL4wHujp0VYIcOu/Jx7sibz05zHNfZnuX94HUpP06WHiaYngjUbLpGlcxhmnYAHYNVzpuokz7SCbDLo/TY0wszmF6oCkg/uWh0HU7GKrOLfLzz3yvwOG247Ilg5qpl0/jglfEs0CrIoIP15lT+9vnFozhSi6ORy44vobymjW+cNS1pu00TzC/NjAfVfjzYIsmSvXk5JrTCGQumg8PDa86JTAt/xOqICqQbtHwy0xxkpg1fnYHFyGBlpC3iZNFGtcxCExJ3296k+8wOJUeIOnw9HWqRIkE9HR9+LtZWsUxTWVO7EaADZ68WSBcvKCY7p4BM2uNepIPBFQukkzLSXSLp739cFct1LmPa3F4uD93GBeEfUpTpZtMd58SDxNGkETWGOWIPp+qbeMhYQZXMoSAmn8lFZYVqYv7PLpeLvxpnM1vbS1rj5pQfJ012ENITr1W2R2nW82mmTKiOj3miBbcIE7AKcoeUokw3PnfyxFHXBG8ZxzFP28UkrZr3zWlx/bqFxVDhsuvccdGcbp+/8h+dxyNfOin+d4fmwRFtR3QRvHllOxFsCIcHIQS+cZMppJHcqLpe1Omj2xnWYuiwAmkLRUcjHhFirTkdgEDdnqS7ZaAzkLYq4wfDq3U+7MLgF47f8qBjJZm0YTeCBKQj7ppxOAxXJukiSCgU7HW/VOj0kQ6RyIZ01Uh3eiN3eh47bRpr5Ex2ymImZB85FojNUmXUL7e9BsCjxjKqySaPZs645xVyRQsd0klpgQpupZS8aJwAQE7jhpQfJ40Owl0CaZ/bTpXMQROSFV0kJQBBlyUxGAku/ORV8dvvmrNHcSQWxxpCiKTrdbOeTXakkvf3NAJqNc9nNNKkZcWX+gLOXGzCZEpgEw0yAz9HznXUYnBYgbQFALJhJ6BaGAM4/AeS749JOwynJe0YDFvkhKS/l2mbsJkBArj6NOU3XWoSE/U3Jm1vD0X7rZvuDKRv+djcxDatayCttIGd0o6uy+o/+kTimNFmd6y74qX6KnaZheyX+dTILGzCpL2hkkLRRK3M5LgS9doFIwa1ZBKSdvx1e3s7dQIp8RAgeoiHelWs7fdSLVmqE3CPbsfHYwXvpBN4IHoef46eQ6Rg3mgPx+IY5oBzCkWRffzlnT2AKjbMjNbT1CXr3OSdAcDp+gYqZPGQuC9ZHBlYgbQFAEazcjv40JxESNoIN+zDMCW/ea2C9lAUYoG06bKkHYNhmyzlnsjlfC78bdqkm+O1HdiiStrRpym/S9kldcpsOplz+4t89oHV/RqHFmsRfs2yqT3e35mRtsUCaac9canwOI+c0oqubcrfNucAgupYgFsoGpkkqtgnEkVqs4t8SDSqZDai9cChp+uZYAt2DAL2ZLuqKqmaspygbadO+vhz9By2maU0ZR05E42jGYdN567oZ7kjehXP37RstIdjcQwTtGWSLv1oqOvmxFwPWUY9zXqi22alLWHPWG4WMznfcsA6WrACaQv2Nvi5+6FXALj4tBM5IPMoEzW8tKWae17czo+f24oIquDNdFqB9GCQaPzGuJg3zXmUy2IWaBXYzQAdsu9A2ohlpLVgIpB+fbsqXFm9u7HHYw6HZkaISP2w1hud2ZLOLHlta6hf5x8pItgISaVffNM8DoCDsW5440UtE0UVO4xEu+45xT623HkOlTKHcaKRmta+ZTKN1Spz3eFMlmx0ZqTTRZD9Mo87oldxbnglQreKh0aCru3Jj1ZHFIuxQSgmeVwktvM929/J1TrINuppsiXqJWqiHvaZ6u93zNnxOhSLsY8VSFuwdk8T40QDHdJJxOFjqxzPDLEPf0jZg/1j9T4qq6tpkWnY9CMnGzkWKfAmCqLWmtOZLvZjMwLKtaOPYEDGAmkRWx0AqG4ZmF5alxEiMdOeZ288hVvPn5l0f2dxTbZHBYULJxyZzQNWf/cMbo58iReME+KB9E5ZRETqrNDX4xZhKmRR0jFpDhtV5FAk6nlla22v52/piHDTH58DwO9MLiJsJ40GqYodD8i8WOtv0aMft8XQ0yk7srAYbVo9SrL3iPMurrM9h/+t3+AmSIs9Mfl+blMVX43cxLcj1/GceWKSXM5ibGNdiY5C3iqvY1V5fcr772vsoFA0UiWz0TSNAzKfcaKBv7y9M75P+d79tEhP3/IDi1759X8tjN+ukVm4RARvpJ4gKRQbOlUwq4USGelAP7rzdUWTUaKoC/nsIh9fWDYp6f6rl5bxk0vn8qnFajnSfYRe9Au8Lp42l/LlyDdi3e9sLJ4yjmqZzXJtIwA7zaJuxx2MNbdZtb261/O3BiNxB5DAIUWEVy8t4wNTSWM+MKcqCRSwrbp10M/Lom/6qimwsBgpWtOTr59X2V4EoMWemHz/47oT2Swn8bBxercOpxZjGyu9eJSxbm8jn31gDQB7Vp6f0jFGrAvTQZmLELDVLMVpi2JWbQImkpVmxxfx04LH+vEaJF3bUtdIFRinm60Ehetwh8RxeZXeLtzWEN+Wn9H3cT2hm4mMdE/YdI0rTkho+sbCBOoPnz2ec2YrzfSzt03kfE19D7bJ7q2jq2QOupD4G/YDh/cffmpjJQWoQDp4iLTjBxfO5pR3P8cuOY7HjFPj2y2ZwcgghGBynodlUy27QYvRJeIpZIM5mTSCvGou5Mu2pwFodySuGZPy0rnyBOUnv/LS40ZlnBbDgxVIH2Vc+rt3+31MmggzTTvIU5Gl+ISgLW8htMC3bP9iryzgno4r8Dn8tEgPaQ7rIzMYui77f/asJfDGrwAI0LcHbnFBAYYUSYH09f/4AACPo38ZY032HkiPRbp6vf7LWM75+hqeMZbg8HS3bKyM6aiXF4Z7Pec9L27nLlsjTTKdkOiufT4g87k7+mml1405p1y/fMpgnoZFP3jlW8tHewgWFjhsdi4J34GdKAu0Cr6MCqRrXckuTVYAfXQyKGmHEOIbQoiPhBCbhRD/FEK4hBAThRCrhRDlQoiHhVC/PkIIZ+zvitj9ZV3O853Y9u1CiHMG95QsOunNXqe2Lch42bgvAAAgAElEQVRND62nIxylQKhCtWqy8ThtTJ8xh1XGbFboG7jG9iKX2d/Gh59m0uOaWYuBsXRyDlPy01l5yVxsWYkiuFQ8RW02nRY8OCLdpQP9NVLSTCMu7UiVlZfM5YWvH7nuCEsmJSrkbdPP5szQT7k58qUe6ymNdCX3yDPr+jxvoWiiWmbxq1crut3XqS3vOpHRdSsjbWFxLPFmeR0mGiEcvGfO5OHocu6NfJKHNvtHe2gWI8CAA2khRDFwI7BISjkH0IErgZ8AP5dSTgWagGtjh1wLNEkppwA/j+2HEGJW7LjZwLnAb4UQR6Ygc5C8s7OeK/7wLlFj8J3pUiHSy+OsfG4bT26o5OH397N7u+qwt8cswGnTuPT4Eq6N3MLFoTvZZ+ZxDu+RL5pokF687qMriznSCCF4+ZunceXi8YiMRCC9z8ju81hdEzTLdOyRlqTtXvwg+/eZ0geQkb5y8XhmFI4NH3FdE1TIEoI4yeqhBe+9X/gYAA5/VZ/nGi9qqJI5zC7q/tznlapsd1NHJPHYlrTDwuKYous1Zs33zuTb0S/ya+MTozgii5FksMWGNsAthLABaUAVsAJ4NHb/X4GLY7cviv1N7P4zhBITXgQ8JKUMSSl3AxX0Jlocw3z9oQ2s3t1IfXvvy8lDRW+tpDdXqmDsjqe30FpVDsAeOY7d9X6m5GcQwsEGOYUnzJM5Wf8IrwiwW46j0DswTa5Fd4JdugpWypxe9lTYNEEL6djDiUC6iHrWO7/IV8WjvRzZnYEE0mOJTscZgPs/t6jb/YX5+XTgwhHo3bXDSZjp2gE+lJO46YzuntuhSPfvmGaVcFtYHFPkpieu5V3rVn5wgWVxdyww4Eu+lPIgcC+wDxVAtwDrgGYpY90e4ABQHLtdDOyPHRuN7Z/TdXsPxxxVxL15R2jpt7eMdGfnOoBJjhbCUqeBDJZNVdrR335auUs8ZyyJ77dLjrMKqYYQu67xqjEf6NlZ4lBURtpDS2Mi+Jup7UUXkmvF0/w11lUrFTQz2m9px1iiLagyxE/dcDITc3uukG8XHpxGe6/n+eQkdSnbZ+b3+NnvdOo4rsTHJQvVZctmRdIWFscU15+eXBcxIUdJ9azehccGg5F2ZKGyyROBIsADnNfDrp2fpZ4iMNnL9p4e84tCiLVCiLV1dX1rG480oiPUEvSr+hPcanuw1yYau+sT2q2Zaa3U63k8/bVTOTGmM/3Y3HFs/P7ZkD+LVukGoN4zfXgHfoyha4JbIl/iitBtVJLb5/5CCJpJJxM/9/1nO5sOtJAvlKe0W4S566mNKT/20VRsWOB1diu2/Oll87h0YQmzxh1eitIhPLj6CKTnRDcBsE5Ow5Tdv78nT8nh+AlZ3Hf5fH566XF8cNtZY8LhxMLCYujIitUOdX73/37ticwt9nHhvL4TJBZjn8H8kp4J7JZS1gEIIf4NLAUyhRC2WNa5BKiM7X8AKAUOxKQgPqCxy/ZOuh6ThJTyfuB+gEWLFo25yV6nNtocxoC6TFTx/+yPAHDqrx7lzbs/3+N+y7X1fN32b26M3IA7UEWdlse84uSuhb40O/ddsYCLf3UX2bRy35fPGrZxH4s4dI0GfDTI1LtFNst0MrV2fvlqBb98tYKb9ERzlnyaezkyGV1GiR4lgfTb317Rbdv0wgx+dvm8Xo/r0NJxRHsPpO0dtRhSsF/m93h/hsvOY19ZGv/bKsa1sDj26Jw7l2SppFNpdhpPf+2UURyRxUgymDXIfcASIURaTOt8BrAFeA24LLbPVcCTsdtPxf4mdv+rUkoZ235lzNVjIjAVWDOIcR2xdGakhzMxvUzbFL89S+w97H632f7OfG0nn9P/Q45RS0Wou0UYwKwiL7tkEWvlDDLdVpAwlMwu8vLl0yb365gWPHjpQENNyjoz0gDjRMPhDuuGLqNHTUbapmvYBtDlLmLPwBFt63Wf7HAVjXouV588mRUzeg6mLSwsjm3SHDZ+culc/nndkr53tjjqGIxGejWqaPADYFPsXPcD3wa+KYSoQGmgH4gd8gCQE9v+TeC/Y+f5CHgEFYS/AFwvpRxYu7YjHIdNvdzNgeEpNjRMyUyxL/53sehZ/mIGWpmsKbeCRdp2CmnkgOxbWtC1oMJi8Agh+Pa5/ZPLNMt0NCHJoAOAPNGMKVU6ZFzMxjAVNDNy1GSkB4zLh8toR/Yg2eik2DhArXMC379gltWS2sLC4rBcccJ4ijLdoz0Mi1FgUL+kUsrbgdsP2byLHlw3pJRB4JOHOc+PgB8NZixjgdx0J23BKOU17cNiI9boDzNT28d75kxmiT2UiJ7bhL/5ylMsB3bI8czXdgGJBhU98eiXT2L17sYBZf0sekcIwX+fN4P5pT2vCBxKs0wHIFO00yLTyRfNfCgnMV/s7GdGOkJEHNsXfc2dSXqjn46wgcfZ86Uw16xni92qDbCwsLCw6BkrMhpBOgv8vvbP9UmtooeKYCjMdLGfreZ4Dso8SnrISEspee/dtwB4Ujsjvr1WHD6QXlSW3a0q2WLo+PJpk5OaifTG1Wcol49MlLY3TzSziyJaZRrjRS3hXiwPu3I0aaQHiuHIIIMODDPxmlXUtrOrTr22a3dWk2m20Gq3WlBbWFhYWPSMFUiPEve/uWvIz2k27iZNhNgiJ3BA5lHaQyC98vltLNTK2WUW8mp4dnz79ZecOeTjsRh6XF414ckUfkCSRzNhVz7lspip2kGm3fp8SpM07SjSSA+UiD0DmzAxgomCwzPve4MVP3sDgJv++AKakDTpfcueLCwsLCyOTaxAepS476Ud7G/sGNJzbly/GoBys4QDMpdiUU/4kKBq08EWxotadspithuFHJQ5fGROQGZNHNKxWAwPaZkqqPPRTibtOIRBk55NtcwiF9WoJRDuO5DWZQTjGA+kayJK2vL+lu6tvwEKRBMA5cGMERuThYWFhcXYwgqkRxgnYTptsntrmDIQdu7YCsA+mU+VzCZDBOjwJ7eTdokIk0Ul5bIYXdc5K3QPl4Vvx247eptzHE2k+5TMIFu0xR07mkQWtTIrFvhJUumZYzPDhLAP40iPfPbEAuQXVn/Y4/1FMc35u3XOERuThYWFhcXYwgqkRxAnYd5yfp2f2X+XtF1KyQ+e+ohV5T0XB6bCxv3NZIQq6ZBOGsmgBVWUFmxLdnI4b7yBXRiUm8WcMiWXDlwEcPXqXGBx5JCWmUdI2ikUjfFA+v+3d+dxcpVV/sc/p5be93Q6OwkJgQRICBA2g4DIjoAbbiMwKIKOiriiuKCoo/74jduoDLggLogMOsKoAyIiCgzKjoQEAiEh6ezpdNLprbrrnvnj3u500ks6XZWq6u7v+/XqV1Xdurfy1El117nPfZ7zlE2Yxks+lUprZwrDq9yRDDroYHwniOcvCetMn3HAwGcec2Nr6fYYq3xyLpslIiKjiBLpHDql9CUarJk3xR8kRtBbT3rjjk5+/PAqPnz7UyN+7Qu++xAH2gZWewNgvBItILF15e6vWdUd9rJd+/ZTdqvCodXYRofiZIJGn8B028LEaAGWg2bPYa2HPdVTbCvDOSdKBp2kbHyXM6ypDSd4lnp7v+fcnem2hQ3U0cn4jpOIiAxOiXQOLSzZNflvnr3CzQ+9DEB7NI65YxhjW4cy19aywqcD8GIwDYCdG3ef1FjWEi7SEpswh8+ffxivXzSVGy86moXTh1d+TfLLzNhCNRPY0dsjHa+ezEavBcLhCK/79weHnnCY7iJOetz3SBeVRSUoU/1XN+xKO/VsZ6tnv0yliIiMHUqkc6gkvWsVtTm2jufW7wCyM1a6hE6m25beBLotESYA3a27X+pPN60i7Uai7gCm1ZTyzbcdyZmH6dL1aLLVq2iwbTRYM61ezOT6el7yqaQ8zvzYahqb23llqImsXWEPbKeN70S6uDxKkrta+z2XSgfMsg284g3c+f4lOW6ZiIiMFkqkc6gsCHu+Ajdm23qCIDuTDt2dObaemDkrPEyk44kidnoJmzZu6N1v/fZ21jQ20kwFyeT4nmg2mj0XzGRObD2zbR2bvIa68iIuPG4OG7yud4JcbKgZh1Ei3THOhyxYspyUxynq7D+uvKW1jem2mekHLeSIYS6WIyIi448S6RyqCrbT6BNY6/XMjTVy2vxJQHgZORN3PL6WOdYIwIs+jTMPm8T3L15MMxWkW3etdrelJcUM28xmryGhMdGj1jM+B4DXxJ9mE7Uk4jFKknFW+yQOtrUAfOi2Jwd/ge6eRHp890gTi7HeJlLRvr7fUw8+9RxxczpKdbVGREQGp0Q6h2qCZrZ4Nc/4bBbYSuZOCitrjLRH+p6lGzj5+vv5+B3PsCS2lA5PsiExlRsvWsxxsyew3cupZtf4z+4gYH5sNf8IDsSGUyNNCtKDweEsCw4AYI03kIwZV5w8m6U+i7m2FiNg6bodg79A1CM93icbAmy2eipSm/ptv+fhJwB4QTWkRURkCEqkc6jKW2j2CtZ6A5OtadfQjmEu67ynK376OKu3tjGVLbwh/ld+mT6Fuz50au/z27yCWtvJ1p2dANz29zVU08pWNIFqNEsT5+Ndl/OX9AJu6T6DRDxGQ2UJyQkHUmRpptleyij2jJEe7z3SQFusguJ0/8mGRW1hL/WtyzKbACwiImObEukcKqGDVkpo9AkUWzdFbeH45a4gs6Ed7038NwD/0X0+M2pLe7c3U0ENO+mIEvXfPPYSJdbFDi/P6N+T/HvWZ3Nx16f4h8/uLV0477jTATjOlg99cO9kQ/VIt8cqKOvu33vfczLyvfedl+smiYjIKKJEOkeCwCnxFO0U8WI0IbC8JSx/F7S38L3kNzma5/b5daeyhbfG/8wd6ZNYz4TdakMfOnsmtdbS2+P9L8eGJdKao8VaZGxIxsNEOlVzMGu9nk8kb2MKWwfd/75/rAr3t5JcNK+grWESNemttLY077Z9lm2kySs4YNrUPLVMRERGAyXSOZJKB5RaJx1ezGavBuDW+8NxmDXrHuCc+N/5ILft02uW0cHtxdcBcEP6/P7/ZtkkJlgL6VRYCm1ueXh75fkq5zWafeOtR+z2OBELf42LipK8K/Vx6tnO2xJ/4sVN/YcsANz+vysAtNAI8HRreHL5tdv+GG1xDrCNzLQNrPbJmpQrIiJDUiKdI6l0QCmdHH7gFG664kwA6iysK13WtBSAbt+3L+1Lav/BdNvCFV0fZo1P6vd8V/kUAIId4XjPko5wUlVJnXrZRrPzFk7lzUdP733ck+wl4zFe8Bm84NNZaCv52SOr+x3r7pQQjplPjfM60gBNhJMJl69cBcB74r/jL8Uf5sT4Ulb5JE3KFRGRISmRzpFUV5pSUpAsJVZWR+BGnYVjMzc0vgLAbGvEg+FPPJzVvYpOT/CXYCFvO2ZGv4UjgpK68E57eNm6uCNaWbFCJb1Gs0Q8xnUXHNb7ONabSIe3z/ks5sXW4AOsFe4OJdYFQGdMifQGD39HesZEnxl/rPe5nsooIiIig1EinSNdqTZi5niyjFg8QTPl1NFCW6qbdMtGACbaDpat7V+KazAHBi/zok8jIMbVZ83rt3CEl4RDSLwjTKTLOjaSdsMq+vdey+hSFO//q5uMtm3wWibSPOBJWeBOaW+PtIZ2NMycT+DGrFj4Ozg91kTK4zyQXsh/pk/Oc+tERKTQKZHOke72aBniRBlm0ORV1NkOHl21jXrb3rtfaZ9lxPdmSrCRlz0cvjHQSnYbu8sAeHzpCp54ZRsV7evYQB0xrWo46iUGSKSLEuG2LV5NwgKKurf326c7cEpIAdCJJhte/9aj2UolE2nm0E/+isls4Vvdb+KSrk/yqgWH5Lt5IiJS4JRI50h3R5RIF0WJNJVMsBbueHwt9baddg97B39+/zPDfs0a3947cTE2wP/kogULAVj38nO88XsP07TuJRq9vrdcmowtPT3SW6LPxANP9K8Cc8/SDZRamEh3uE6oJleV0Oj1zLINzLF1ALzo4RyCtx+roR0iIjI0JdI5EkSVM0iWEjMLe6TZwd3PrmMCO3gp+vJ+asWq4b1gWxOVtNHo9YPuMmlCHVu9kkm2DQjHgTZ6/YC91zI6TavZVTe8t0eaMJHue6WjR3NbF8WkaPeijOuXjwXxmPFcMIt5sVc4yBoBestT6tdERET2Rol0rnSFPdLpRBltqW4avZ7ptoVLFtVQbN29iXSVtQ3r5Vo3vBDuP30+33jrEVSWDNy7uNWrmGjbiZNmMk1KpMeQO9+/hNvfe0Lv457Jhj1XKY6ekOp3TFsqTSmdtFPExh2duWloATMzDjt8EXW2k1PiT9PlcVZHFXC2tfWPn4iISF9KpHPEotXkSJbSHTirvYEy6+QfT/wvACuDcKxzNa3Der2f3v0wAA9sKOINR04fdL+VPpV59goNNJOwQEM7xpAjZtTs3iO9x9COiq5t/Y5pS3VTSooO1ZDu1Tz9VAAuiD/MCz6ds484gEUzajjlkIY8t0xERAqdEulc6Qp7moNEGTEztkbJztzYWgDmLzgagGobXiK9Zf0qAFZ2Vg2537PBLGbGNrE49jwAq32SEukxqmeM9HbK6fI4wc7+FWDaUmlKLEW7q/Rdj6B+LquCsBf6xu7zOH52Hb95/xIqihN5bpmIiBQ6JdK5EvVIe7KUuQ0VvQtBzLOwhnTJlLBCQBV7H9rh7tT7Vjo9ybbodQbz12ABAP9e9B1avZjHg4NH/BaksPUk0k6MrVRRT/8x0h1RPXOtarhLWVGSC1Of45LU1dwVnDBgaUEREZGBqMslR6y7J5Euw8x6h3IcF1sOwNw5B9Pyp1KOnbL3L/G2VJrJ1sQGr+WyE2cPue/TfhBXpt7PibFnuTs4hj9+4swM34kUqmTcePeJB/K6hVPo+nk9M7r7LxHenfZwsqES6d1sppYHgnC58MGWVhcREdmTEukciXWGqxh6sgKAjdTS6Qnmxhrp9hhBSS2tVk5J9w7aU2mWbdjBUQfUDvhagTvTbAsbqOO18/e+uMpdwRLuCsJVD7+WjGfpHUmhMTM++7pDAXgiqKSsaxtfu3s5QeB86pz5AHQFAaXWSYcrke4R22OoU7mGdIiIyDDpGmaOJFo3hONWSydEW4xNHibKq30SFo+zM1ZBUXcLn/r1M7zxew+zcUfHgK+1cXs7h9gang9mcMKcCQPu0+Plr5yz2+PipP7Lx4M1HSVU0coNf36JG/+ysnd7OnCqaGM75XlsXWE5dMru8wxeo0mGIiIyTMqqciTe2Uwz5Vgs7BG+7MQDuSdYDMAGr8PMaLUKStMtPLsu7L3e3t414Gv9bekKqqydldGqhkMxMz5z7vzex+VF6m0bD3Z42YATV7sDp85a2OZDj60fT8qLE6z66rm9PwumV+e7SSIiMkooq8qReNdOdnppb8WMz7zuUJY8eBbHxZbxH+nz+BrQFqugLr2JF7eFYzQHq61xSHETAOeddPyw/u3LXj2bdx4/k80tnarYMU5spzyauOr0/SQF6YBaWthGRd7aJiIiMlYokc6RWNdOdlJK3zy2kYmcl/pXIFxFbW17EQfEd1Va2HPdlK50wFW/fIq6lQ+zGKiZNnfY/35JMs6MurJM3oKMIju8jIQFlNNBK7tqTZNOkbCANi/hnqtOyl8DRURExgAN7ciReGonrZRig6wqaBjNVFDLTsJeREgHu+/z/IYWfvfMemraw5J55ZOGrtgh41dxZTh2fs8FfhLpcNx9dXUVh0zW8A4REZFMKJHOgfuXb6Jx4yZavJTixMAhT7uzwesos06qouSnO9g9k+6pE3xM7HmWBzOYPLF+/zZcRq0LTzwc6L/ATzwIE+numBZkERERyZQS6Rz45h9fiC6xl1DSp2rGjRcd3Xs/1R2w3usAmGLhGOhzv/1g7/M/e2Q1Z37zLyToZnHsBR4Jdk0gFNlTUVVYFnFS9Fn63TPrAYhHPdKdVpKfhomIiIwhSqRzIB4zKqydnV5KcWJXHeczD5vcez8dBL2rFNZa/wUhPvObZwFYEltKmXXyNyXSMpRJ4edjrjUC8MlfPwPAlKIwkdbVDBERkcwpkc6BqTWlVNLOTsooGWRBlDkTK2j2sJJCLS2926+67cne+w1s4/rkjawJJnJ/sGj/NlpGtURZLWk3Ki1ccr6loxuAQ4q3AHDhaa/OW9tERETGCiXSOdCV6qDYutjpuw/tAHj2C2ey7LqzMDPWx8LL8e+M/5Gbkv/GRJr5zVPraGxup6wozlWJX1HHDt7T9VGueO3h+XgrMkok43FaCU/gerSn0hSnwqowyaq9r4gpIiIiQ1MinQPF6bBXcCel/XqkK4oTlBaF2+IlVTT6BJbEl3JG/HE+n/wxAK//7kOUp7ZyYfwBfpE+leV+AOcu3PtiLDJ+VZcl2UEZVX0mG17+08dIRp9FilQKUUREJFNKpHPg8GgV752U9lbeGEhDVQnPBbMAWOv1nBv/O6+PPcjmlk5Ojz9O0tLcmn4tAJ1dwaCvIwLhiplTaOp9/NcVW0gEUQ91onSQo0RERGS4lEjnQL2FS35v9poh97v0VbO4uus9fCT1Xk7t/Df+FszjS8kfUUkbJ8WeYa3Xs8xnAlBRorV0ZGjrfALTbEvv45iFdaTbvBhi+tUXERHJlL5Nc6C0M0xm3n3WsUPu95ZjZtBEFb8OTiJFkuu73kKFdfDG+F95VexZ/ppeAMDdV72aA+vL93u7ZXTb6lXMjG3it0XX8JHE7QQOyXQ77aYa0iIiItmgRDoXOsMJXiccOmevu85tqOi9/5jPY63Xc1n891RZOw8HhwEwb3LV/mmnjCkbvRaAw2OruDLxG5J0k0y304ESaRERkWxQIp0DjyxfA0CsuGIve4Y1p/t6MjiIGbHN4esE83nqc6dnv4EyJj0SHLrb4yUVG0gG7bSjxVhERESyQYl0DpTRCUCseO+VEvZMpFdXHgWEkw+3xydQU1aU/QbKmPSaU8/ii13v5MrUBwCYkWgike5Qj7SIiEiWaMZaDpRbO2k34snhlxy77MQDef9rDuLWh2dx6wPL+W1wPLe/74T92EoZa3737AZeSJ9DPeHQImtZT1F1O9u1PLiIiEhWqEc6BxrirTRRCWZ73TcdOABnL5hMbXkRxSWlXNN9Ga3TTmTRjKGrfoj0NaE87HneSiUpjzPZtpEMOujU0A4REZGsUCKdAwcUt9JRVDesfXsS6dJkeLGgoysNwNNrmvdP42TM+sgZBwPgxNhELZMsHNrRGVMiLSIikg1KpHOgImihLTa8ShtpDxPpokT4X/NKU9t+a5eMbSWJcMXMoniMjV7LFJpIpNtxLcYiIiKSFUqkc6DIO0gNsxewp0c6EU06bE2FPdKTqjRBTPZNIh5+htLubEtMZJJtoyhoJ0hoeXAREZFsGHEibWaHmNlTfX52mNlVZlZnZvea2Yrotjba38zs22b2opk9Y2ZH9XmtS6L9V5jZJdl4Y4Wk2Dvpig8vkf7kWfMoL4ozuTrc/1Nnz2PxzFru/tBJ+7OJMgYlexLpwJk07UCm2lbKaCeV0GI+IiIi2TDiRNrdn3f3Re6+CDgaaAP+C/gkcJ+7zwXuix4DnA3MjX4uB24AMLM64FrgOOBY4Nqe5LsQtaW6uf6e5b1jl4ejxDvoig3vcvrZC6aw9LqzKEmGl+Wn15Zxx/teRW25yt7Jvon3WQa8q6iGUksRJ6A1oUmrIiIi2ZCt8nevBV5y99VmdgFwSrT9FuDPwNXABcBP3N2BR8ysxsymRPve6+5NAGZ2L3AW8IsstS1r7n9+E5fe/CgARfE4Hzpt7t4PCgKqfTvtCa1GKLmV6FOTvLu4uvf+smZVvRQREcmGbI2Rfhu7Et9J7r4eILptiLZPA9b0OWZttG2w7f2Y2eVm9piZPbZ58+YsNX34epJogHXN7cM7aEcjJaTYXDxzP7VKZGDJeN8e6V0XeVa1a7y9iIhINmScSJtZEXA+8J9723WAbT7E9v4b3W9y98XuvnjixIn71tAs6FsGehgloQEI2poA6EgW7GgVGaN6JhsCbEpM6b3f7JX5aI6IiMiYk40e6bOBJ9x9Y/R4YzRkg+h2U7R9LTCjz3HTgXVDbC843ie9v+3RNazfvvde6d8/9jwAy1QGWnIs2WeM9Nbk5N7726jIR3NERETGnGwk0m9n9/HMdwE9lTcuAe7ss/3iqHrH8cD2aOjHPcAZZlYbTTI8I9pW8N7w3Yf3us/KtesB2OmqlCC51bdHOijZdUVkvU/IR3NERETGnIxmHZlZGXA6cEWfzV8FbjezdwOvABdG238PnAO8SFjh41IAd28ysy8CPQOQr+uZeFhoFs+opKHxXj6Y+C9+mD6HO3acvNdjjpoUg03wT6ccnoMWiuzSN5EuLU7ygdQHqbZWFs+ZPMRRIiIiMlwZJdLu3gZM2GPbVsIqHnvu68D7B3mdHwE/yqQtufDhlv/PkqIHAPiU3Urz3Av3cgSUBeHKhDMmT9qvbRPZU9+hHdNqSvhtcAIAd593aL6aJCIiMqaoDtY++K/4Gfw0dSQzbBOfTt7K1OSOvR5jqXCfRLlq90puxfqUv3vNIQ299ytLkvlojoiIyJijJcL3Qef0JdwdHMuzfiAANS0rhty/uS2Ft++gw5MUFw1vQRaR/cH6lJkpixb7ERERkcwokd4HFcVhArIqCMeYTuhsHHL/Rdfdy/JVa2mhjKKEQi2FoaZMPdIiIiLZoOxun4S9euupY1uslpkdy/Z6RKW1scOVSEvhsOEWQRcREZEhaYz0PvjKGxfw+fMPpak1RfN3JlCZ3r7XY+rZQROVzIkpeZHc+8HFi5k9UaUXRURE9gcl0vuoOBFnSnUpz8arKO/e+2TDabaZJ3wux+SgbSJ7Ou3QXdVifnn58aSDARcNFRERkRFQIj1CHYlqJqU2Dvr81+5eToyAKdZEY1Cfw5aJDOy42VqIRUREJJs0cHeEOpPVVPJ+mNEAABNUSURBVAaD90jf8OeXaGAbSUtTN3VODlsmIiIiIrmgRHqEuouqqWQnBMGg+0yzLQCUT5yVo1aJiIiISK4okR6h7pJa4jjeMfiEw4NjawFoL5+Wq2aJiIiISI4okR6hzd1lANz5yNJ+z7k7r4k9yZcTP2KT17CzfGaumyciIiIi+5kS6RFa2V4BwOP/eLZ3W3sqzdadnQQOb43/mZ2U8PrO60gWFeWrmSIiIiKynyiRHqEdJVMAqO3a0LvtTTc8zNFf+iNd6YAjYyu4N1jMOupRBWkRERGRsUeJ9AjFy2oAKO5u69323Pqwikd3V4qJbGeth2XvnlzTnPsGioiIiMh+pUR6hD5y7lEAHD4xDGHQZ6GLJ596gpg5rwThYhiHT63OfQNFREREZL9SIj1CE2qq6fI4yXTYI33f8k29z/3kfx4A4GWfzLuWHMilS2blo4kiIiIish9pZcORMqPVSkmkWgBIde+qJz052AhxWHL0kXz0vEPz1UIRERER2Y/UI52BbVZDSWe46Eo8tmtK4YKSTez0Ei4647h8NU1ERERE9jMl0hlY211Devs6AEqSu0I5I2hkjU2loao0X00TERERkf1MiXQGtlNBFa0AJOMx3h3/HY8Wv5cT/GnWxrWaoYiIiMhYpkQ6A1U1dVRYO+nAWbutjbfF/8xEC0vgNcan57l1IiIiIrI/KZHOQF1tHRW0s7Ojm3R3itm2rve5v+6cmseWiYiIiMj+pqodGUiXTqDcOnlq/UaKOrcRN+cLXRfxgk/noeDwfDdPRERERPYj9Uhn4LnWSgA+fcs9lHaEdaQbvZ6HggWghcFFRERExjT1SGcgUdkAQEnXdiraugBY7ZPy2SQRERERyREl0hmoqKoFoD7RSVl7uDDLep+QzyaJiIiISI5oaEcGTlxwEABvPqycktQWOj3JfdecD8BhU6vy2TQRERER2c/UI52BZG1YK7q8YyMlnU1spprKZJwnPnv6bgu0iIiIiMjYo2wvA0WlVTR5BS+/uIx0y0a2eBVmUFdeRFmRzlFERERExjIl0hmIxYytXk2ttRC0bGKLVxMEnu9miYiIiEgOKJHO0FaqaLBmGmwbm72G6tJkvpskIiIiIjmgRDpDLwTTWRx7gXrbwUqfgpnqR4uIiIiMB0qkM/SS71oKfIVPy2NLRERERCSXlEhn6G/B/N77S4NZ+WuIiIiIiOSUSktkaLkfwKe73kUnSY4+bP7eDxARERGRMUGJdIZOndfAz5efBsD/m9+Q59aIiIiISK5oaEeGbrroaCpLwvORC4+enufWiIiIiEiuqEc6Q4l4jKc/dwZmqGKHiIiIyDiiRDoLYjEl0CIiIiLjjYZ2iIiIiIiMgBJpEREREZERUCItIiIiIjICSqRFREREREZAibSIiIiIyAgokRYRERERGQEl0iIiIiIiI6BEWkRERERkBJRIi4iIiIiMgBJpEREREZERyCiRNrMaM7vDzJab2TIzO8HM6szsXjNbEd3WRvuamX3bzF40s2fM7Kg+r3NJtP8KM7sk0zclIiIiIrK/Zdoj/S3gbnefBxwBLAM+Cdzn7nOB+6LHAGcDc6Ofy4EbAMysDrgWOA44Fri2J/kWERERESlUI06kzawKOAn4IYC7p9y9GbgAuCXa7Rbg9dH9C4CfeOgRoMbMpgBnAve6e5O7bwPuBc4aabtERERERHIhkx7p2cBm4GYze9LMfmBm5cAkd18PEN02RPtPA9b0OX5ttG2w7f2Y2eVm9piZPbZ58+YMmi4iIiIikplMEukEcBRwg7sfCbSyaxjHQGyAbT7E9v4b3W9y98XuvnjixIn72l4RERERkazJJJFeC6x1979Fj+8gTKw3RkM2iG439dl/Rp/jpwPrhtguIiIiIlKwRpxIu/sGYI2ZHRJtei3wHHAX0FN54xLgzuj+XcDFUfWO44Ht0dCPe4AzzKw2mmR4RrRNRERERKRgmfuAoyiGd7DZIuAHQBGwEriUMDm/HTgAeAW40N2bzMyA7xBOJGwDLnX3x6LXeRdwTfSyX3b3m4fxb28GVo+48WNbPbAl340YAxTHzCmG2aE4ZofimB2KY+YUw+zIVRxnuvuAY4ozSqSlMJnZY+6+ON/tGO0Ux8wphtmhOGaH4pgdimPmFMPsKIQ4amVDEREREZERUCItIiIiIjICSqTHppvy3YAxQnHMnGKYHYpjdiiO2aE4Zk4xzI68x1FjpEVERERERkA90iIiIiIiI6BEWkRERERkBJRIi0hWRLXiZYTMrCy6VRwzYGbJfLdhtNNnMDvM7DAzK8l3O0Y7M4tHtwX5uVQiPYoV6odqNDGzRL7bMFqZ2YlmdoOZ/QuAa8LFPjOzmJnVmdkfgI+D4jhSZna8md0GXG9mh+e7PaORmR1nZt8HrjazARefkL0zs4Vm9iDwJWBCvtszWpnZEjO7BfiMmdUV6t9GJdKjjJnNN7MTQF+4mTCzE6IvjGPy3ZbRyMyOAm4AHgfOMbNvRCudyj5w9wDoBqqB2WZ2GugkeV+Z2YWEn8ffAiXAR6LtiuMwmFnczL5CWAHhIeAo4Fozm5Tflo1anwHucPc3uHsj6LO4r8xsNvA94H5gJvBFMzs3v60amBLpUcLMqqPE7zbCD9SXzeygfLdrNDKz9xB+YTwBPNlz2Uj2ybHAo+7+A+AyoI0woa7Pb7NGpUOBDcBfgfPMrFQnyftsLvDf7v4z4BsQDvFQHIctBrwCXOjuPwauAo4HSvPZqNEmusI0B9jp7t+Mtp1uZjVAQQ9PKEDHAMuiz+NHgaeA15nZjLy2agBKpEePjxOWKzwCuILwctGsvLZo9DoA+LS73+DuHe6ezneDCp2ZvcXMPmJmr4o2PQFUmNlkd98A/AmoB5bkrZGjQJ84Ht9n82pgKfACEABnmdnkvDRwlOgTxxOiTc8DbzSzTwD/C0wFvmtmuuI0iGgozMHRwwD4hbu/YGbF7r4OWEv4Oy1D6BvH6ArTJuDVZnaumf0G+BjwbTR0a0hmdp6ZfaDP38ZHgRlmNsPdtxFeKWkG3pC3Rg5CiXQBM7MDzaynR+D7wOcA3P0loAZYkK+2jSZRHIuj+3XA4cDfzexUM7vHzK4xszdGz6u3oI/oku/ngKujTTea2XlAK7AKODna/gCwHZgRHac49jFAHL/f85kDFgHl7v4Xwi+Kfwe+ZGYJxXF3g8TxfODXwIeAk4CL3f0sYDPwJp2U7M7Maszsd8C9wFvMrMLd0+7eDODunWZWCRwIrMtnWwvZAHEsB3D3FuBm4IvAj9z9TOAHwPF7nEALYGZTzOy/CU80aoGbzexMd19JeFL8lmjX54HngAmFNoFTiXQBMrNZZvY/hL98PzOzQ9x9tbuvM7OiaLd24KX8tbLw7RHHW81svrs3AVuBnwOvJxyDtR74nJkdod6C3UW99YcAH3X3rwNfAD4IJAjjtsjMDnX3bsI/dG+IjlMc+xggjtcCV0Y9WeuAVjO7GbiUsGf6GXfvVhx3N0gcPwwc7O73AR2En0OAO4GFhCd9sks5cA/h73E58OoB9jkOWBp951SY2dxcNnCU2DOOJ/V57reEV4xro8ePARuBzhy2b7RYDDzo7ie5+xeBbwHviZ57EFhgZsdGv/uNwBJ378hTWwekRLpA7NHz9DHgb+7+WsKB9l80s8Oi53qGIUwD1kTH6v8xMkQc/0TYy3cg4ZfvAmCdu9/p7jcDvwcuyHmDC5CZXWxmJ0fj+iD8Aqg1s4S730F4Anca0JO4fCnabxrwqKkSCrDXOP6acDjHBcBE4AygBTgCuB440sxm5b7VhWcvcfwVYRzfFvU8vwS8OdrvSMLP57jXJ4ZV0eS3m4DbCeNznJlNjfbr+d2tAdaY2aWEl9g1kZhhxXEagLs/Q9jD+oFo3sg7Ca+Ebs1T0wtKFMdToivF9wE/6fP0VmBFdP8R4Engm2ZWARwGvGJRqdBCoQSscJTAbn/IlgK4+3cIJ3a9w8wa3D1t4STDJnd/0szeB3y2z5fMeDdYHL8LHA1cTnjJ9wfs+sIFaAAezl0zC4uFppjZ/cAlwD8RjjGtALYQnnhURLt/C7gI2OTuXwCao0ucbwN+EPVOj0v7GMfvAO8AngZOdfcr3X074aSaT7j7qpy/gQIxgji+gbCT4Q/AMWb2CHAhcE10qX3cGSSGN5hZfTQ3pA34I2Gv6akAfX53LyBMBE8C3uru/5n7d1AYRhJHAHf/IfAL4PPAm4DL3P2VnL+BAjFAHN8B/Agoc/f1tqv++xSinnx33+Du3yJMqH9EeELytSjmBUOJdJ5ZOKP3XsLap2+J/pA1EfZIHWFmRwDPEpZ/6alHOZvwy+J+4Hzgtp7xbePVMOO4lHCi4QHufg3hme1Xoy/duuj5ccfM4tEQgkqgMerB/xfCMc/fIhz+sgRYaGZl7r6ccPjB26OXuAL4Z3c/xt1fzP07KAwjiOMywp6Xd7j7Dgtn/Mfcfb27b87X+8i3EX4eVxBWnLgPuBh4j7ufFj037gwRwybCXlQA3P0hwrkO88ysKjpRAfgd8BZ3vzTqXR2XRhDHQyyssFUZbf868GF3P9Pdn8v5GygQw4xjEN2eDtwRHdcQbfsE8G53P87dn6fA6BJsHkU9y18C/pWw9NAnostA1xOWH/oy4SW2qwjrop4OLCO8FFwLXOHuf8xD0wvKCOJ4HmGJrH8mnN3/J3f/Q+5bnl9Rr/11QNzMfg9UEQ0dcvduM/sAYVm2rwO3EvY4TwF+CXQR9eC7exdhL/+4lIU4PhLtG/R/9fEjwzimCGua4+47gX/k/A0UgGHE8EpgnZmd7O4PRId9n/Dv533AAWa2yN1vy0PzC0aGcbwXmGlmR7r7uujv47i0r3G0cA7YZuAFM/syYbm7U6KqHQV7ZUk90jnW0+sUPTwOeDwap/sk4eWhfwVKokH3V7r7ie7+GGHpl57LGbe5e914TqIzjGPPL2SLuy8fp0n0yYSJRy3wIuEM8y7gNWZ2LPQmdl8Arnf3Wwgvm19sZk8SnoSPy2SlL8UxOxTHzA0zhk6Y2Hy+z6HnEvYOPgUscPf1OWx2wclCHJ8mjOO4rniyj3H8QnRYCWEH132EvdenRUl0YXN3/eToh3BG/jrgy9HjhcA2YFb0+ArCD97PoscW3V5OWLf3qHy/h0L4URyzEsNXAxf1efw94H2Ef8Qej7bFgMmEl9lmRNsmA7Pz3f5C+VEcFcdC+dnHGN7e5+/lBcBJ+W5/ofwojnmL43TC+WA/ARblu/378qMe6RyJxp5dAHwNONvM5nk49uwW4Ctm9hDhB++fCWekT3J3N7OrCEvBXOHuT+Sp+QVDccyax4Hbbdeqjg8Rjh3/MeFluA962AM4Hehy9zXQO/ljZV5aXJgUx+xQHDO3LzFMezSZ1cMreX/JR4MLlOKYHfsSx8Dd17r73939Ynd/Kk9tHhEl0jni4bi9Kz2cgfoHdl3K+CjwfuBqd38n4YIMm6NbgJs8nMT1aK7bXIgUx+xw9zZ37/Rdqzqezq5xzpcC883st4SzznXiMQjFMTsUx8yNJIZmWvBnT4pjduxjHB+H0RtHTTbMId9V+uabwF0Wrt5zj5ltd/cHo+feSzgWujs6pqDKvBQCxTF7ot4CByYBd0WbW4BrCOuevuxhvVQZguKYHYpj5vYlhh5dX5f+FMfsGA9xVI90Hrj7BuCHhB8kPKwNfayZ3Um4iMB1fc7iZBCKY1YEQJKwNu/CqIfgs4SX2h5U0jJsimN2KI6ZUwyzQ3HMjjEfRxulJwCjWlQrNjCzOwiXWe4krDSxwt217PcwKY7ZYWbHE5ayexi42cOFBGQfKY7ZoThmTjHMDsUxO8Z6HJVI54mFS1zeDRxK2HP67Tw3aVRSHDNnZtMJVyr8urt35rs9o5XimB2KY+YUw+xQHLNjrMdRiXSemNnHCGerXj0WP1i5ojiKiIhIviiRzpOeYQn5bsdopziKiIhIviiRFhEREREZAVXtEBEREREZASXSIiIiIiIjoERaRERERGQElEiLiIxyZpY2s6fMbKmZPW1mHzGzIf++m9ksM3tHrtooIjIWKZEWERn92t19kbsfBpwOnANcu5djZgFKpEVEMqCqHSIio5yZ7XT3ij6PZwOPAvXATOCnQHn09Afc/WEzewSYD7wM3AJ8G/gqcApQDHzX3W/M2ZsQERmFlEiLiIxyeybS0bZtwDygBQjcvcPM5gK/cPfFZnYK8DF3f120/+VAg7t/ycyKgYeAC9395Zy+GRGRUSSR7waIiMh+YdFtEviOmS0C0sDBg+x/BrDQzN4cPa4G5hL2WIuIyACUSIuIjDHR0I40sIlwrPRG4AjCeTEdgx0GfNDd78lJI0VExgBNNhQRGUPMbCLwH8B3PBy7Vw2sd/cAuAiIR7u2AJV9Dr0HeJ+ZJaPXOdjMyhERkUGpR1pEZPQrNbOnCIdxdBNOLvx69Nz3gF+Z2YXA/UBrtP0ZoNvMngZ+DHyLsJLHE2ZmwGbg9bl6AyIio5EmG4qIiIiIjICGdoiIiIiIjIASaRERERGREVAiLSIiIiIyAkqkRURERERGQIm0iIiIiMgIKJEWERERERkBJdIiIiIiIiOgRFpEREREZAT+DxsnN2/rRBV8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Price vs. the Trend\n",
    "# YOUR CODE HERE!\n",
    "df[['Settle', 'trend']].plot(figsize=(12,9), legend=True, title='Settle vs Trend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smoothing with the HP Filter and plotting the resulting trend against the actual futures returns, we can see that there's a lot of short term fluctuations that deviate around this trend. Perhaps these would represent profitable trading opportunities: For example, when the blue line deviates far below the orange, we can see this as a sign that the Yen is temporarily more undervalued than it should be (and, therefore, we'd see this as a short-term buying opportunity)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28e4171c388>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Noise\n",
    "# YOUR CODE HERE!\n",
    "noise.plot(figsize=(12,8), legend = True, title = 'Noise')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Returns using an ARMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using futures Settle *Returns*, estimate an ARMA model\n",
    "\n",
    "1. ARMA: Create an ARMA model and fit it to the returns data. Note: Set the AR and MA (\"p\" and \"q\") parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the ARMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the forecasted returns (the results forecast from ARMA model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Settle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>-0.410601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>-0.369458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>-0.564304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-14</th>\n",
       "      <td>0.151335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>-0.469509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Settle\n",
       "Date                \n",
       "2019-10-09 -0.410601\n",
       "2019-10-10 -0.369458\n",
       "2019-10-11 -0.564304\n",
       "2019-10-14  0.151335\n",
       "2019-10-15 -0.469509"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series using \"Settle\" price percentage returns, drop any nan\"s, and check the results:\n",
    "# (Make sure to multiply the pct_change() results by 100)\n",
    "# In this case, you may have to replace inf, -inf values with np.nan\"s\n",
    "returns = (yen_futures[[\"Settle\"]].pct_change() * 100)\n",
    "returns = returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Estimate and ARMA model using statsmodels (use order=(2, 1))\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "model = ARMA(returns.values, order=(2,1))\n",
    "\n",
    "# Fit the model and assign it to a variable called results\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>   <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(2, 1)</td>    <th>  Log Likelihood     </th> <td>-7894.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.692</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 27 May 2020</td> <th>  AIC                </th> <td>15798.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>16:55:39</td>     <th>  BIC                </th> <td>15832.765</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>15810.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>    0.0063</td> <td>    0.008</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.009</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.y</th> <td>   -0.3063</td> <td>    1.277</td> <td>   -0.240</td> <td> 0.810</td> <td>   -2.810</td> <td>    2.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.y</th> <td>   -0.0019</td> <td>    0.019</td> <td>   -0.099</td> <td> 0.921</td> <td>   -0.040</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.y</th> <td>    0.2947</td> <td>    1.277</td> <td>    0.231</td> <td> 0.818</td> <td>   -2.208</td> <td>    2.798</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -3.3343</td> <td>          +0.0000j</td> <td>           3.3343</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>        -157.1788</td> <td>          +0.0000j</td> <td>         157.1788</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -3.3932</td> <td>          +0.0000j</td> <td>           3.3932</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 7514\n",
       "Model:                     ARMA(2, 1)   Log Likelihood               -7894.071\n",
       "Method:                       css-mle   S.D. of innovations              0.692\n",
       "Date:                Wed, 27 May 2020   AIC                          15798.142\n",
       "Time:                        16:55:39   BIC                          15832.765\n",
       "Sample:                             0   HQIC                         15810.030\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0063      0.008      0.804      0.422      -0.009       0.022\n",
       "ar.L1.y       -0.3063      1.277     -0.240      0.810      -2.810       2.197\n",
       "ar.L2.y       -0.0019      0.019     -0.099      0.921      -0.040       0.036\n",
       "ma.L1.y        0.2947      1.277      0.231      0.818      -2.208       2.798\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -3.3343           +0.0000j            3.3343            0.5000\n",
       "AR.2         -157.1788           +0.0000j          157.1788            0.5000\n",
       "MA.1           -3.3932           +0.0000j            3.3932            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "# YOUR CODE HERE!\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28e018eecc8>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Returns Forecast\n",
    "# YOUR CODE HERE!\n",
    "pd.DataFrame(result.forecast(steps=5)[0]).plot(title = '5 Day Return Forecast', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Based on the p-value, is the model a good fit?\n",
    "\n",
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No. Based on the ARMA model, the p values are well above the 0.05 threshold that we typically use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting the Settle Price using an ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. Using the *raw* Yen **Settle Price**, estimate an ARIMA model.\n",
    "     1. Set P=5, D=1, and Q=1 in the model (e.g., ARIMA(df, order=(5,1,1))\n",
    "     2. P= # of Auto-Regressive Lags, D= # of Differences (this is usually =1), Q= # of Moving Average Lags\n",
    " 2. Output the ARIMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    " 3. Construct a 5 day forecast for the Settle Price. What does the model forecast will happen to the Japanese Yen in the near term?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Owner\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:218: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "C:\\Users\\Owner\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:218: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# Estimate and ARIMA Model:\n",
    "# Hint: ARIMA(df, order=(p, d, q))\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "model2 = ARIMA(yen_futures['Settle'], order=(5,1,1)) \n",
    "\n",
    "# Fit the model\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "result2 = model2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>D.Settle</td>     <th>  No. Observations:  </th>    <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(5, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-41944.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>64.281</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 27 May 2020</td> <th>  AIC                </th>  <td>83905.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>17:28:21</td>     <th>  BIC                </th>  <td>83960.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th>  <td>83924.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td>    0.3162</td> <td>    0.700</td> <td>    0.452</td> <td> 0.651</td> <td>   -1.056</td> <td>    1.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Settle</th> <td>    0.2826</td> <td>    0.699</td> <td>    0.404</td> <td> 0.686</td> <td>   -1.087</td> <td>    1.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.Settle</th> <td>    0.0007</td> <td>    0.016</td> <td>    0.043</td> <td> 0.965</td> <td>   -0.030</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.D.Settle</th> <td>   -0.0126</td> <td>    0.012</td> <td>   -1.032</td> <td> 0.302</td> <td>   -0.037</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.D.Settle</th> <td>   -0.0137</td> <td>    0.015</td> <td>   -0.889</td> <td> 0.374</td> <td>   -0.044</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.D.Settle</th> <td>   -0.0011</td> <td>    0.018</td> <td>   -0.064</td> <td> 0.949</td> <td>   -0.036</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Settle</th> <td>   -0.2977</td> <td>    0.699</td> <td>   -0.426</td> <td> 0.670</td> <td>   -1.667</td> <td>    1.072</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.8923</td> <td>          -1.3782j</td> <td>           2.3410</td> <td>          -0.1002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           1.8923</td> <td>          +1.3782j</td> <td>           2.3410</td> <td>           0.1002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>          -2.2725</td> <td>          -3.0191j</td> <td>           3.7788</td> <td>          -0.3527</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th> <td>          -2.2725</td> <td>          +3.0191j</td> <td>           3.7788</td> <td>           0.3527</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th> <td>         -11.1522</td> <td>          -0.0000j</td> <td>          11.1522</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           3.3594</td> <td>          +0.0000j</td> <td>           3.3594</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:               D.Settle   No. Observations:                 7514\n",
       "Model:                 ARIMA(5, 1, 1)   Log Likelihood              -41944.619\n",
       "Method:                       css-mle   S.D. of innovations             64.281\n",
       "Date:                Wed, 27 May 2020   AIC                          83905.238\n",
       "Time:                        17:28:21   BIC                          83960.635\n",
       "Sample:                             1   HQIC                         83924.259\n",
       "                                                                              \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const              0.3162      0.700      0.452      0.651      -1.056       1.688\n",
       "ar.L1.D.Settle     0.2826      0.699      0.404      0.686      -1.087       1.652\n",
       "ar.L2.D.Settle     0.0007      0.016      0.043      0.965      -0.030       0.032\n",
       "ar.L3.D.Settle    -0.0126      0.012     -1.032      0.302      -0.037       0.011\n",
       "ar.L4.D.Settle    -0.0137      0.015     -0.889      0.374      -0.044       0.016\n",
       "ar.L5.D.Settle    -0.0011      0.018     -0.064      0.949      -0.036       0.034\n",
       "ma.L1.D.Settle    -0.2977      0.699     -0.426      0.670      -1.667       1.072\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.8923           -1.3782j            2.3410           -0.1002\n",
       "AR.2            1.8923           +1.3782j            2.3410            0.1002\n",
       "AR.3           -2.2725           -3.0191j            3.7788           -0.3527\n",
       "AR.4           -2.2725           +3.0191j            3.7788            0.3527\n",
       "AR.5          -11.1522           -0.0000j           11.1522           -0.5000\n",
       "MA.1            3.3594           +0.0000j            3.3594            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "result2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28e437d4248>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Price Forecast\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "pd.DataFrame(result2.forecast(steps=5)[0]).plot(title=\"5 Day Futures Price Forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the model forecast will happen to the Japanese Yen in the near term?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model predicts that the Japanese Yen will appreciate in the near term. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volatility Forecasting with GARCH\n",
    "\n",
    "Rather than predicting returns, let's forecast near-term **volatility** of Japanese Yen futures returns. Being able to accurately predict volatility will be extremely useful if we want to trade in derivatives or quantify our maximum loss.\n",
    " \n",
    "Using futures Settle *Returns*, estimate an GARCH model\n",
    "\n",
    "1. GARCH: Create an GARCH model and fit it to the returns data. Note: Set the parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the GARCH summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "# YOUR CODE HERE!\n",
    "\n",
    "model3 = arch_model(returns, mean=\"Zero\", vol=\"GARCH\", p=2, q=1)\n",
    "\n",
    "# Fit the model\n",
    "# YOUR CODE HERE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "result3 = model3.fit(disp='off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Zero Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Settle</td>       <th>  R-squared:         </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>         <td>Zero Mean</td>     <th>  Adj. R-squared:    </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -7461.93</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   14931.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14959.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Wed, May 27 2020</td>  <th>  Df Residuals:      </th>    <td>7510</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>17:41:22</td>      <th>  Df Model:          </th>      <td>4</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Volatility Model</caption>\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>omega</th>    <td>4.2896e-03</td> <td>2.057e-03</td> <td>    2.085</td> <td>3.708e-02</td>  <td>[2.571e-04,8.322e-03]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th> <td>    0.0381</td> <td>1.282e-02</td> <td>    2.970</td> <td>2.974e-03</td>  <td>[1.295e-02,6.321e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th>   <td>0.0000</td>   <td>1.703e-02</td>   <td>0.000</td>   <td>    1.000</td> <td>[-3.338e-02,3.338e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beta[1]</th>  <td>    0.9536</td> <td>1.420e-02</td> <td>   67.135</td>   <td>0.000</td>      <td>[  0.926,  0.981]</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Covariance estimator: robust"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       Zero Mean - GARCH Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                 Settle   R-squared:                       0.000\n",
       "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:               -7461.93\n",
       "Distribution:                  Normal   AIC:                           14931.9\n",
       "Method:            Maximum Likelihood   BIC:                           14959.6\n",
       "                                        No. Observations:                 7514\n",
       "Date:                Wed, May 27 2020   Df Residuals:                     7510\n",
       "Time:                        17:41:22   Df Model:                            4\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      4.2896e-03  2.057e-03      2.085  3.708e-02  [2.571e-04,8.322e-03]\n",
       "alpha[1]       0.0381  1.282e-02      2.970  2.974e-03  [1.295e-02,6.321e-02]\n",
       "alpha[2]       0.0000  1.703e-02      0.000      1.000 [-3.338e-02,3.338e-02]\n",
       "beta[1]        0.9536  1.420e-02     67.135      0.000      [  0.926,  0.981]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "# YOUR CODE HERE!\n",
    "result3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-10-15'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the last day of the dataset\n",
    "last_day = returns.index.max().strftime('%Y-%m-%d')\n",
    "last_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<arch.univariate.base.ARCHModelForecast at 0x28e0168fa48>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "# Start the forecast using the last_day calculated above\n",
    "# YOUR CODE HERE!\n",
    "forecasts = result3.forecast(start='2019-10-15', horizon=forecast_horizon)\n",
    "forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h.1</th>\n",
       "      <th>h.2</th>\n",
       "      <th>h.3</th>\n",
       "      <th>h.4</th>\n",
       "      <th>h.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>7.434048</td>\n",
       "      <td>7.475745</td>\n",
       "      <td>7.516867</td>\n",
       "      <td>7.557426</td>\n",
       "      <td>7.597434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 h.1       h.2       h.3       h.4       h.5\n",
       "Date                                                        \n",
       "2019-10-15  7.434048  7.475745  7.516867  7.557426  7.597434"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualize the forecast\n",
    "intermediate = np.sqrt(forecasts.variance.dropna() * 252)\n",
    "intermediate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2019-10-15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>h.1</th>\n",
       "      <td>7.434048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.2</th>\n",
       "      <td>7.475745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.3</th>\n",
       "      <td>7.516867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.4</th>\n",
       "      <td>7.557426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.5</th>\n",
       "      <td>7.597434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2019-10-15\n",
       "h.1     7.434048\n",
       "h.2     7.475745\n",
       "h.3     7.516867\n",
       "h.4     7.557426\n",
       "h.5     7.597434"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the forecast so that it is easier to plot\n",
    "final = intermediate.dropna().T\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28e016dae08>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the final forecast\n",
    "# YOUR CODE HERE!\n",
    "final.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on your time series analysis, would you buy the yen now?\n",
    "\n",
    "Is the risk of the yen expected to increase or decrease?\n",
    "\n",
    "Based on the model evaluation, would you feel confident in using these models for trading?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Based on the time series analysis I would buy the Yen now. According to the Arma model, the 5 day return forecast predicts positive returns for each day. According to the Arima model, the 5 day futures price forecast is predicted to increase from 9224 to 9228. \n",
    "\n",
    "2. According to the Garch model the risk of the yen is expected to increase. This is evidenced by the standard deviation showing an upward trend in the plot.\n",
    "\n",
    "3. I would not feel  confident using the models for trading. The coefficients in the ARMA and ARIMA models all show high p values, which indicates that these models are not strong. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "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": "3.7.6"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}