dynamic_rfm_III.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import datetime as dt\n",
    "from datetime import date\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "sns.set(font_scale=1.5)\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from linearmodels.panel import PanelOLS\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_location = 'C:\\\\Users\\\\Tan\\\\Desktop\\\\GA\\\\Capstone\\\\know_customer_monthly.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.read_csv(file_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dateindex'] = pd.to_datetime(df['trans_date'], format='%Y-%m-%d')\n",
    "df.set_index('dateindex', inplace=True, drop=True)\n",
    "df.index = df.index.to_period('M').to_timestamp('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>id</th>\n",
       "      <th>invoices_no</th>\n",
       "      <th>categories</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unitprice</th>\n",
       "      <th>gross</th>\n",
       "      <th>days_since_last</th>\n",
       "      <th>trans_90d</th>\n",
       "      <th>revenue_90d</th>\n",
       "      <th>avg_revenue_90d</th>\n",
       "      <th>trans_date</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_avg</th>\n",
       "      <th>recency</th>\n",
       "      <th>rfm_equal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dateindex</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",
       "      <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>2011-03-31</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>146</td>\n",
       "      <td>3.106667</td>\n",
       "      <td>310.78</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1009.31</td>\n",
       "      <td>336.436667</td>\n",
       "      <td>2011-03-01</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>12823.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>459.00</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>535.50</td>\n",
       "      <td>267.750000</td>\n",
       "      <td>2011-03-30</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>13069.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>398</td>\n",
       "      <td>1.771667</td>\n",
       "      <td>273.98</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>595.97</td>\n",
       "      <td>297.985000</td>\n",
       "      <td>2011-03-24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>13842.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5.070526</td>\n",
       "      <td>311.07</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>596.96</td>\n",
       "      <td>298.480000</td>\n",
       "      <td>2011-03-11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>13911.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>208</td>\n",
       "      <td>2.452222</td>\n",
       "      <td>282.28</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>559.27</td>\n",
       "      <td>279.635000</td>\n",
       "      <td>2011-03-20</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  invoices_no  categories  quantity  unitprice   gross  \\\n",
       "dateindex                                                                   \n",
       "2011-03-31  12747.0            1        12.0       146   3.106667  310.78   \n",
       "2011-03-31  12823.0            1         1.0        60   7.650000  459.00   \n",
       "2011-03-31  13069.0            1        48.0       398   1.771667  273.98   \n",
       "2011-03-31  13842.0            1        19.0       115   5.070526  311.07   \n",
       "2011-03-31  13911.0            1        18.0       208   2.452222  282.28   \n",
       "\n",
       "            days_since_last  trans_90d  revenue_90d  avg_revenue_90d  \\\n",
       "dateindex                                                              \n",
       "2011-03-31             40.0        3.0      1009.31       336.436667   \n",
       "2011-03-31             35.0        2.0       535.50       267.750000   \n",
       "2011-03-31             48.0        2.0       595.97       297.985000   \n",
       "2011-03-31             36.0        2.0       596.96       298.480000   \n",
       "2011-03-31             34.0        2.0       559.27       279.635000   \n",
       "\n",
       "            trans_date  frequency  monetary  monetary_avg  recency  rfm_equal  \n",
       "dateindex                                                                      \n",
       "2011-03-31  2011-03-01          2         2             2        1          5  \n",
       "2011-03-31  2011-03-30          2         2             2        1          5  \n",
       "2011-03-31  2011-03-24          2         2             2        1          5  \n",
       "2011-03-31  2011-03-11          2         2             2        1          5  \n",
       "2011-03-31  2011-03-20          2         2             2        1          5  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 7892 entries, 2011-03-31 to 2011-12-31\n",
      "Data columns (total 16 columns):\n",
      "id                 7892 non-null float64\n",
      "invoices_no        7892 non-null int64\n",
      "categories         7892 non-null float64\n",
      "quantity           7892 non-null int64\n",
      "unitprice          7892 non-null float64\n",
      "gross              7892 non-null float64\n",
      "days_since_last    7892 non-null float64\n",
      "trans_90d          7892 non-null float64\n",
      "revenue_90d        7892 non-null float64\n",
      "avg_revenue_90d    7892 non-null float64\n",
      "trans_date         7892 non-null object\n",
      "frequency          7892 non-null int64\n",
      "monetary           7892 non-null int64\n",
      "monetary_avg       7892 non-null int64\n",
      "recency            7892 non-null int64\n",
      "rfm_equal          7892 non-null int64\n",
      "dtypes: float64(8), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xwF5lAqcnDgc+AaxKUWn0eERcDjxKUZG0KbBxZfzXM/OFSpwvRcRoiqqpoeX4p8s5ngTmBTYB1qrMcWFmnlsTx/coqp3mAtYBnomIK4B/UCS6lgR2AhYsxz8DnNjDd+0PXwDWpEgMLgHcX24h2JFUWgfYls7tEZ8EvjStg5QkSZIkSZIkaVow6dS6y4F36EwQPZiZf2/iub2AF4GvUCQl1i4/tR4Fdi3PWOqRzHwvIjah2M5ta4pqm93LT9UE4GuZeWadOS6PiO2Bc4CFKJJr+9RZrh04HfhynTleiIhtgEuBhSkSbbUxdLgf2CMz3+j+DftXZr4VERtQbLO4FcXvuF35qXUxcFBmvjMNQ5QkSZIkSZIkaZpxe70WZeY4Jt8KrmGVU+W5SZn5DYoqpJMokktvU5wR9QrwZ+BzwFq9SThV1nkzM7ehSIT8HngOGAe8Dzxerr1iZv66wRzXA8tQJJSuA14GJlJUKf0dOANYNzMPzcwPu5jjHopqq68CN1TmeB/4J8UWfLsD61XOg5ruZeZ/M3NrYBvgXOBZineaSFGxdQ6wWWbubsJJkiRJkiRJkjQjG9LeXvfoIUkDzGuvjfV/zJIkSZIkSVIfGjVqjiH9HcNANfGFhwfMv18OX2x1f+destJJkiRJkiRJkiRJLTPpJEmSJEmSJEmSpJYN7e8AJEmSJEmSJEnSDK5tUn9HoGnApJOmWxGxGLDH1JovM4+fWnNJkiRJkiRJkqTJmXTS9GwZ4BdTcT6TTpIkSZIkSZIk9RGTTpIkSZIkSZIkqW+1t/V3BJoGTDppupWZtwJD+jsOSZIkSZIkSZLUvZn6OwBJkiRJkiRJkiQNfCadJEmSJEmSJEmS1DK315MkSZIkSZIkSX2rzTOdBgMrnSRJkiRJkiRJktQyk06SJEmSJEmSJElqmdvrSZIkSZIkSZKkPtXe7vZ6g4GVTpIkSZIkSZIkSWqZSSdJkiRJkiRJkiS1zKSTJEmSJEmSJEmSWuaZTpIkSZIkSZIkqW+1eabTYGClkyRJkiRJkiRJklpm0kmSJEmSJEmSJEktc3s9SZIkSZIkSZLUt9rdXm8wsNJJkiRJkiRJkiRJLTPpJEmSJEmSJEmSpJa5vZ4kSZIkSZIkSepbbZP6OwJNA1Y6SZIkSZIkSZIkqWUmnSRJkiRJkiRJktQyk06SJEmSJEmSJElqmWc6SZIkSZIkSZKkvtXe1t8RaBqw0kmSJEmSJEmSJEktM+kkSZIkSZIkSZKklrm9niRJkiRJkiRJ6lttbq83GFjpJEmSJEmSJEmSpJaZdJIkSZIkSZIkSVLLTDpJkiRJkiRJkiSpZZ7pJEmSJEmSJEmS+la7ZzoNBlY6SZIkSZIkSZIkqWUmnSRJkiRJkiRJktQyt9eTJEmSJEmSJEl9q83t9QYDK50kSZIkSZIkSZLUMpNOkiRJkiRJkiRJapnb60mSJEmSJEmSpD7V3j6pv0PQNGClkyRJkiRJkiRJklpm0kmSJEmSJEmSJEktM+kkSZIkSZIkSZKklnmmkyRJkiRJkiRJ6lvtbf0dgaYBK50kSZIkSZIkSZLUMpNOkiRJkiRJkiRJapnb60mSJEmSJEmSpL7V5vZ6g4GVTpIkSZIkSZIkSWqZSSdJkiRJkiRJkiS1zKSTJEmSJEmSJEmSWuaZTpIkSZIkSZIkqW+1e6bTYGClkyRJkiRJkiRJklpm0kmSJEmSJEmSJEktc3s9SZIkSZIkSZLUt9om9XcEmgasdJIkSZIkSZIkSVLLTDpJkiRJkiRJkiSpZW6vJ0mSJEmSJEmS+lZ7W39HoGnASidJkiRJkiRJkiS1zKSTJEmSJEmSJEmSWmbSSZIkSZIkSZIkSS3zTCdJkiRJkiRJktS32jzTaTCw0mmAiohh/R2DJEmSJEmSJElSByudBpiIGAkcAYwFju7ncKZbEXEk8KPycv/MHNN/0UiSJEmSJEmSNOMz6TSARMSqwGXA0sBR/RyOJEmSJEmSJEnNaXd7vcHApNPAsjZFwkmawmfW+HR/h6Bp7NKHLuvvECRJkiRJkiTpIyadNEPKzCOBI/s5DEmSJEmSJEmSBo2Z+jsASZIkSZIkSZIkDXxWOkmSJEmSJEmSpL7V5plOg4FJpx6KiI8DBwGbAEuU3S8DdwFnZOZfGjy7OPB5YDNgOWC+8tabwOPAtcCZmTm25rkjgR/VTPejiOjo2z8zx9RZb2ngi8DWwOLArMB/gHuBC4A/ZmZ7E++8IvAVYEtgMWAc8Hfgd8DpwPzldwBwTmaObjDXJsD+wEbAwkB7+eztwHmZeUuDZ8dQfH8TMnNERKwJHA+sD7wLPAKclJlX13xndb+fyrw7AHsDGwALAROBF4AbgdMyM7t6tnx+JmA3YHdgXWABYDzFd30XcElm/rnRHJIkSZIkSZIkDXQmnZoUEbNSJFj2rXN7qfKzT0ScBXwxMz+sPDsE+DHwbWB4nedHUiRgtgK+ExE7ZOZ9Lcb7feCIOustVn4+C/w1InbNzJcazPMF4FdM/rcyC0WCZgPgQOCAJuIZBZwNbF/n9rLlZ/+IuALYLzPf7ma+ZYCbgbnLrlkpkmLndhdLZY4FgAuBTWtujQBWLj//ExHHAj+ol6Ar3+sKisRX1XBgzvK99ouIO4GdMvONZuOTJEmSJEmSJGkgMenUhIgYClxJUaHU4TbgDopKnXUpqomgSMJ8ABxaGftz4Dtlux24CbgPeJsiabIeReJjCEWVzB8jYoXMfL985nqKSp51gD3KvhvKfsq5qvGeTFGZVI31TuB9YBlgJ4oqq/WBuyNincz8T533Pgw4pdL1EHANMKF8508BawKX1z5bM89c5TuvWnZNAq4D7i+v1wG2pThjbCfg9ojYMDPfbTDtmXQmnDpMpEgAdSsi5qeoQlqm7BpbvkdSJIzWo0gCzgx8j6Ka6wt1prqIzoTTCxR/Jy8AswOrADuU77VROXbLZuKTJEmSJEmSpBmK2+sNCiadmvN1OhNObwG7ZuZN1QERsSNwKcV3+sWI+G1m3hcRywPfLIdNBD6VmTfXLhARG1MkdGajqETagSJJQWbeBdwVEaPpTDrdlZnH15nns3QmnF4BdsvMO2rGzAX8Bti1XGsMsF3NmMWAX5SX7cC3KLaua6+M+SRwGcXWfY2cRmfC6Xlg58x8qGa9dcq5FinHngZ8rov5ZqH4Pf5OsX3g/RTJow27q5CqOJvOhNOfgdG1VUjlVoqXUVShHRIRN2fmhZX7n6CzSuo2YNvMHF9njpspklBbRMT6mfnXJmOUJEmSJEmSJGnAmKm/A5jelef1fLPStVdtwgmgPLPnp5Wug8v/7kNncu/kegmn8vnbKZI/HdbuRaxDKjG0UyR37qgdVyZm9gKeKLs+FRHr1gz7EcWWdVAkm06s3V6uPL9qt25iWgXYs7wcB2xTm3Aq57qfotppQtm1T3mWVFc6Enh/ycz3M/PRzDy9USyVmDagSOoB/I0iiTjFtnflFofV9zui/I47VL+z/6tNOFXmOKG8nERRQSVJkiRJkiRJ0gzHpFP3NgAWLNv3ZOa1DcaeSVF9cwXFNm1QVLl8h+I8qDO7WeuRSnuOnofKRsAKHetm5r1dDSzPnDq20rVPR6NMtH26vJwA/KzBPDdRbPXXlT0otg0EODszn2ww12MUFUiUz4xuMO9VmfmvBvcbObDSPjEzJzaI6S7glvJyJWCNyu0PK+0NGqx3MhDAyMw8uYexSpIkSZIkSdKA194+acB81Htur9e9rSrtqxoNzMx/UyQmqn23UWy91lBEzAYsXeka1oMYO3yy0n6wifF3VdobVdrrUpz5BHBnvSqgGpcy+fdUtWnNuO5cQrFlHsDGDcbd3cRcXenN99SxveJGFNVRMPnv+u2IWAI4D7ilch4Xmfkm8Gbvw5UkSZIkSZIkafpn0ql7i1Xaj7c6WUTMSlH1sgywbNleneIco+rvMWTKp7tVTXgdHhGH9+DZ6rlMy1TajzXx7CMN7i3V5LgOD1faS3c5Cv7ZxFxTiIihwHKVrscjoidTfPQ9ZebDEXE+sG/ZtUf5mRARdwLXUlRkPTHlNJIkSZIkSZIkzVhMOnVvgUr7rd5OEhGfAb5KUSkzcxfDJjW414x5ptKzC1bazbzzfxvcm6/S7q5iCiavCGr0Pr39LVr5juo9fxDF+/8Pnb/dLMDm5ee4iEjgt8D/y8xxLa4vSZIkSZIkSdJ0yaRT93qzzd1HImIYcC6wZ53bL1FUT90P3AosCZzRwnLV33MMva/MmqXSbubcr95UZXWlul57g3EfNrjXSPU7Ggcc0cPnJ6v8yswJwNci4gSK33gnivOdqsnDoDg/65CI2DQzX+xx1JIkSZIkSZI0kLW19XcEmgZMOnWvWlEzdy+e/wGdCacJwCnAFcDfMvOd6sCIOKxXEXaqVgndnZm9TWBVK5KaqQxq9L28CXysbM8LvNbNXNXKqF5XlnUTT4cRFNVHE1qdNDNfAH4B/CIi5qQ4N2priiTUEuWwZYCzgG1aXU+SJEmSJEmSpOmNSafuPVtpr9Dd4Ig4liKZ8QzwG4ot9TrslZl/avD4qEq7N9VD1Vg/TjdVUxExMzBHZtYmd6pnEK1E9xqNeYbOpNNqwE3dzLV6pf1sl6N6KTPHR8TLZUxDgHWAOxs9ExFzABMyc2KTa7wDXAlcGRFfBb4E/L/y9tYRsWBmvtrbd5AkSZIkSZIkaXrUzNZpg93dlfbWjQZGxAjgMOArwE+BFYG5yttvdJNwAtis0q732zTabg7gjkp75zKeRnYB3oyINyPi95X++4H3y/aGEdFdhdeOTcb0mW7mAfhspX13l6NaU42p3raHtf4AjIuI5yNit47OiPhNRNwXEf/p6rvOzPbM/BXw90r3Ir2KWpIkSZIkSZIGqva2gfNRr5l06t4NwOtl+5MRsVGDsfsBs5ftq5j8XJ85GiWBImJ7YJNKV72zpCZV2jPXuX8j8HLZHgV8r8F6swBHlpdzU6luysxxwIXl5SzAtxvMsyawQ1f3gfPoTJbtHxFdVotFxCoU32GHCxrM24pzK+2DImLlBjF9Atie4n8rCwP3VW6vQFEpNYoigdfVHDMx+baBL/UiZkmSJEmSJEmSpmsmnbqRmeMpzuqBYju2iyNi7dpxEbEJcEJ52Va2nwQ+KPuGAT+LiCE1zw2JiM/RmeTpMGudcMZW2ovXiXUiRYVVhx9ExLfLpEd1zbmA84GOZMsbwK9qpjua4gwqgO9ExKG160XEahTnU3W5TWNmPgFcXF6OBK6NiDXqzLUWcC1Fkgvg/My8r3bcVHIVcG/ZHlHGtE6dmNYBLqp0nZuZz1Wuz660T42I9erMMQQ4Flig7LrTrfUkSZIkSZIkSTMiz3RqzvHAVsCWFGcB3RsR11BUvbQD6wPb0nkO088z836AiBgDHFz2fwPYMiJuokj0LAJsAyxV3v+AzgqnamVMh39V2vtGxFvAq8BfMvMOgMw8NSI2BXYr4zmWoprnKuC/wJIUVTnzlvNMAkbXnuuUmf+IiO8A/0eRnDw1Ig4CrgPGAWtQbKs3jGIrvo4kWb3awy8AawLLAUsA90fEdXRWDa1D8f11VG89SXEOUp/IzPaI2BO4h6JKaVHgnoi4niIZNXMZ76fo/E2fAr5eM9UYinf7OEW12F8j4kbgEeA1ikTT1nQm98YD3+ybt5IkSZIkSZIkqX+ZdGpCZrZFxI7AmcC+FEmY7ctPVRvws8w8otL3dWB5OrfOW6381HoMGA3cBQwHVo+ImTOzuqXeIxSJkvXKGL5S9v+Syc8p2gt4AfgqRQJlOeBrddb8L3BgZv65i/c+OSJmA46i+FtZq/xU3UFRyXRyeT2h5j6Z+VZEbEBxNtJWZUzblZ9aFwMHZeY79WKaWjLzn2Vl0sXA2hTf57blp9YdwG6Z+XbNHJPKbRGvoEg8QpGY3LLOHC8D+2fmPVPpFSRJkiRJkiRp4GjzrKTBwO31mpSZ4zPzc8CGwFnAPygqfMaX7TOANWoSTmTme8AWFNVON1MkeiYB75bP/RH4HLBmZj4A3FI+Ohc1ZyVlZhtF9c2vgRcpKqPeoWYrvsyclJnfpKiwOQF4iKKy6sNy/TuA7wKRmZd3895HUyRlToOdeJcAACAASURBVAeeoahyehu4EziEIpk2rvLIe13M89/M3Jqisutc4FmK729iOe85wGaZuXtfJ5wqMf2TokrpsxTnR/2zjGkC8DxwCUVV2Ccz85Uu5ngN2AjYvRxffa+XKM7Z+grFd31dX76PJEmSJEmSJEn9aUh7e3t/x6ABLiIOB44rL7+dmb9oNF59Y+NFtvB/zIPMpQ9d1t8hSJIkSZIkDSqjRs0xpPtRqmfcTWcMmH+/HLnFIf7OveT2eqqrPM/pJeCJzHywm+HV7QKz76KSJEmSJEmSJA1I7W6vNxi4vZ66cgBwHnBfRMzX1aCIWAjYtbycBPx1GsQmSZIkSZIkSZKmMyad1JWOs6VmAn4VESNrB0TEx4ArgRFl1wWZ+Z9pFJ8kSZIkSZIkSZqOuL2eunIssC8wG7AnsHlE/Bl4HhgJLAfsAMxSjn8J+Eo/xClJkiRJkiRJmt61ub3eYGDSSXVl5j8jYlvgQmBhYAHgwC6G3w3slplvTKv4JEmSJEmSJEnS9MXt9dSlzLwDWAH4MnAD8CrwAfA28ATwB+BTwCcy86X+ilOSJEmSJEmSJPU/K53UUGaOBX5VfiRJkiRJkiRJkuoy6SRJkiRJkiRJkvpWu2c6DQZurydJkiRJkiRJkqSWmXSSJEmSJEmSJElSy9xeT5IkSZIkSZIk9a02t9cbDKx0kiRJkiRJkiRJUsusdJLUsgUWHsUh3z2QtTZck9nmnI2nH/sHZ590Lg/c/mDTc6y89kocdPhoYtXlaW9v54E7/8ZpPzuTl59/eYqxSy63BId890BWWWclhg0fxuMPPMEZx/yWpx57uqXYhs8yjL0P3ZOtP7slCyy8AK+/8jp33nAXZ594Lu++816XsS+78jKcefVpHHHIUdx+3Z1Nv7MkSZIkSZIkzUisdJLUknnmn4dT/nQym+2wCffedh9X/v4qFl1qEU78/bFstNUGTc2x+nqr8suLT2CpWIprLr6O26+7kw233IAzrjqFhRZdcLKxSyy7OKdedjJrbrg6t171F66/9CZWXnslTr3sZFZYPXod28xDZ+YX5x/DgYeP5vVXXuePv/0T//n3f9j94F05/nfHMHRY/Rz9vKPm4Sen/4ihQ2fuwbcmSZIkSZIkSTMeK50kteSgw0ez0KIL8p3Pf5+7bvwrAL8/7SLOuuY0vvHzr3LvbffzwcQPGs5x+HHfYMK4CRy83aG89vLrANzwp5s48Q/H8aUjvsgPDznqo7Ff+fGXGDnbSA7e/jD+8fgzAFx27hWcfuWv+MbRX+GQ7b/Uq9h2O/AzrLXhGvz+1As57WdnfDTH1376ZT67/6fZYufNuO6SGyaLe5mVlubo3xzFwkss3NuvT5IkSZIkSRocPNNpULDSSVKvjZx1BNvsujVPPpwfJXUA/vvqf7nkt39igY+NYv3N1m04xzobr80Syy7OlRdc81HCCeCBO/7G/X95gE9ssxFzzjMnAIsutQjrbrIOd1x/10cJJ4B/5nNcf+lNrLjGCiy78jK9iu0z+3+afz//Mmcce9Zk8V3w64u45qLrmDB+wmT9X/zewZxx5SnMu8C8PHzPo81+ZZIkSZIkSZI0wzLpJKnXVlprRWYZMZy/3fXQFPceLPvW2GC1hnOsvv6qAF3OMXTozKz28VWKsesVcz1455Rj/1b2rbH+6j2ObcnlluBjiy3EndffzaQPJ0029pUXX+Xorx/HrVf+ZbL+fb60J08+nBy07aE8cEfzZ1dJkiRJkiRJ0ozK7fUk9VrHtnIv/evfU9x75YVXAFhs6UUbzrFIxxzP1Zvj1cnmWGTJrtd7+cXJ1+tJbEutsCQA/3zqOdbffF32+8o+LLfKsrz79rvcePktnPWLMYwfN36yOQ7/3P/y15vvbfhukiRJkiRJkkrtbq83GFjpJKnX5iq3vXv37femuPfu2KJvtjlma26Od97teo45iznmbDD2vXeKsbOX6/UktvkXnA+AjbbagF+c93PefeddLj/vSt547U32/MJuHP+7Y5h56MyTzWHCSZIkSZIkSZImZ6WTpF4bOrT4v5CJEydOce+DCR8AMHyW4Q3nmHlYxxwfTDlHOW/HHEPLxE/H3FUdzw8f0TG2+dhGzDoCKJJOxx1+An/+/dUAzDTTTPzo1O+z+Y6bssvnd+aSsy5t+C6SJEmSJEmSNJhZ6SSp1yaMnwDAsOHDprg3bJaib/z746e4VzWxY45hdeYYPnyyOSaMn1j2T5kvH17GMO6jsc3H1t7WDsBTjz79UcIJoK2tjVN/cjoAm++4ScP3kCRJkiRJktRAW9vA+ajXrHSS1K1lV16GjbfZaIr+115+Hai/hV7HNncdW9l1ZexbxVZ5s885G2++/mbdOd4r5xj79tgu1+vYgu+9cuu9sW+/23RsHf996tGnpxj76kv/YexbYz86I0qSJEmSJEmSVJ9JJ0ndWm7lZTngm5+fov9/PvM1ABZebKEp7n1s8Y8B8MIzLzSc+4VnXyzGL7bQR+3OOYp5ny/n+GhsOfdkYxfrGPtiOfaFpmN78dmXABhap4IKYOahMzOhTHhJkiRJkiRJkuoz6SSpW9dcdB3XXHTdFP0jRo5g/LjxrL7B6lPcW7Pse+yBJxrO/ch9jwKwxgarc+9t908xx6RJk/j7354sxt77WDF2/dW44vwrJx+74RoAPF6ul4883XRsf3/oSSaMn8ga66/OTDPNRFulhHbxZRZj1tln5YE7/tbwPSRJkiRJkiRpsPNMJ0m9Nn7ceP5y9R2sus7KbLTVBh/1z7fgfOx6wC689vLr3HXjXxvO8dDdj/DKi6+y0747sNCiC37Uv/Yn1mSdT67N7dfeyVtvvA3Ay8+/zCP3Psom221MrLb8R2OXiiXZ+jNb8PeHnuSpx57ucWzvjX2PW668lYUWXZB9/mfPj8bOPHRmDv3BIQBcdcE1vf2aJEmSJEmSJLW3DZyPes1KJ0ktOf2Ys/j4Jmvz0zOP5MbLb+btN95hy503Y+755+YHBx3Jhx98+NHYjrOh/vH4M9x+3Z0AtLW1ceL3Tubo3/6EM685jRsuvYmRs41kq1224O033ubUn5w+2XonH3EKv7r0JH55yYlcf+mNtE1qY+vPbMGQIUM46fu/7HVsp/z4dFZeeyUO+c6BrLnBGvzjiWdY+xNrsvwqy3HT5bdw5w1399l3KEmSJEmSJGnwiIilgK8AWwNLAm3Ai8B1wK8z88mptM5IYH/gM8BqwNzAu8A/yrVOy8x/T421Ogxpb2+fmvNJ6icbL7JFv/2PeeElPsYXv3cw63xiLWaaeSb+8cSzjDnpPO6//YHJxn1q92343knf5pqLruPorx832b21N16L/b++H8uvuizj3hvPw/c8whnHnMWL/3xpivWWX2U5DvnuAaz68VX48IMP+ftDT3LmcWeTjzzV69gA5pxnTkZ/7XN8crtPMPe8c/PKi69w1R+u4cIzLplsy71a+39jPw745uf53gFHfJRMmxYufeiyabaWJEmSJEmSYNSoOYb0dwwD1bjLjxswyYiRO3+7z37niNgHOAOYtYshE4HDM/OXXdxvdp1VgD8ByzYY9i7wxcz8XStrVZl0kmYQ/Zl0Uv8w6SRJkiRJkjRtmXTqvXF/OmbA/PvlyF2+2ye/c0RsD1xB59FH9wA3lO3NgQ0rw0dn5jm9XGcx4D6g4zyTt4HLgGeBhYFtgSXKe+3AHpl5cW/WquX2epIkSZIkSZIkSX0oImYHfkNnwunrmfl/lSE/jIiDgdOBIcCvIuLqzHytF8sdS2fC6Tpg38x8vRLLCOA44MvlWqdGxFWZ+X4v1prMTN0PkSRJkiRJkiRJUgsOARYq2xfUJJwAyMwzgePLy9mBw3u6SETMRnGGExTb5+1ZTTiV64wHvgo8WHbND2zV07XqMekkSZIkSZIkSZLUtz5faR/bYNwxwISyvXdE9HSrv2WBWcr2o5n5Vr1BmdkO3FjpWqaH69Rl0kmSJEmSJEmSJPWt9raB85nKImIhYLXy8uXMfKirsZn5BsVZTwCLAB/v4XKTKu2Fuxk7f6X9epejesCkkyRJkiRJkiRJUt9Zs9K+p8tR9ces18O1ngEmlu0lImLveoMiYmlgt/JyInBrD9epa+jUmESSJEmSJEmSJEl1LVdp/7OJ8c9X2sv2ZKHMHBcRZwGHll1jIuLjwFnAs8ACwKeAI4E5yjFHZebztXP1hkknSZIkSZIkSZLUt9qm/rZ1A8jHKu0Xmxj/70p7wV6s9x1gVeATwDDga+Wn1ovAEZl5di/WqMvt9SRJkiRJkiRJkvrOXJX2+02MH1dpz9nTxTJzLLAlRaKp0Xp3Anf3dP5GrHSSJEmSJEmSJEkqRcRoYHQPHhmTmWMa3J+l0h7fxHzVpNMsXY5qbA/gq8CswKvAZRTb9s0HbAusVI7ZJSK+0E38TTPpJEmSJEmSJEmS+tbA2l5vSWCTHoy/tZv7kyrt9ibmG1Jp9/iLi4iT6NxO71zgsMx8r3L/W8BhwP8Bw4GzIuKlzLyhp2vVMukkSZIkSZIkSZLU6Tngth6Ob+TdSntEE/NVx0zoQRxExI50JpxuAvbPzMkSV5nZDpwSEbMAJ1AcxfTLiFipvNdrJp0kSZIkSZIkSZJK5VZzY6bilNWk06xNjB9Zab/dw7W+VWl/vzbhVOOXFFvwLQ6sAKwD3NfD9SYzUysPS5IkSZIkSZIkqaGXK+2Fmxi/SKX9SrOLRMRwYIPycixwb6PxmfkhcGOla61m1+qKlU6SJEmSJEmSJKlvtbe0a9tA93ilvWQT4xevtJ/qwTrzA8PK9ltNbpVXTWrN1YO16rLSSZIkSZIkSZIkqe88BnQkgNZtYvz6lfaDPVjn/Up7wYiYuYln5qu03+jBWnWZdJIkSZIkSZIkSeojmfkG8NfycvGIWLmrsRExH52Jqf8CD/RgnbeA18rL4cBmjcZHxJCaMQ83u1ZXTDpJkiRJkiRJkqS+1dY2cD5948JK+8gG4/6XImEEcH5m9jSgKyrtH0dEozzQ54Hly/ZzwP09XGsKJp0kSZIkSZIkSZL61m+AF8r2rhHxk7LS6CMRcQjwjfJyHHB8L9Y5GphQtjcALoyIOWsHRcQewKmVrh80eQZUQ0NbnUCSJEmSJEmSJEldy8z3IuIw4HKKgqAfADtHxJ+BD4EtgI0qj3wtM1+snScixlBUKAGck5mja9Z5NiIOBs4tu3YFto6Iy4CngdmALYF1Ko/9JjN/19obFkw6SZIkSZIkSZKkvtV329YNGJl5ZUTsC5xJkfxZtfxUfQh8OzPPaGGd8yLiPeAsYG5gTmC/OkMnAccAP+ztWrXcXk+SJEmSJEmSJGkayMw/ACsCvwAeB96l2A7vGYpk1JqZedJUWOdSYEngW8AtwKvAB8DbwMPAicCKmTlVttXrMKS9farNJakfbbzIFv6PeZC59KHL+jsESZIkSZKkQWXUqDmGdD9K9Yz73Q8HzL9fjtznJ/7OvWSlkyRJkiRJkiRJklrmmU6SJEmSJEmSJKlvtXum02BgpZMkSZIkSZIkSZJaZtJJkiRJkiRJkiRJLXN7PUmSJEmSJEmS1Lfa3F5vMLDSSZIkSZIkSZIkSS0z6SRJkiRJkiRJkqSWmXSSJEmSJEmSJElSyzzTSZIkSZIkSZIk9a329v6OQNOAlU6SJEmSJEmSJElqmUknSZIkSZIkSZIktcykk2Z4ETFsephDkiRJkiRJkgattraB81GvmXTSDC0itgdu6OLe6IhoLz9HdjFmnog4E9irD8OUJEmSJEmSJGnAM+mkGVZEXAxcCSzZy+c3A54EDsL/rUiSJEmSJEmS1NDQ/g5A6kO7tvj8JsACUyOQaeGoD+fr7xA0DT0xfDgXrnVof4ehaWyPB0/r7xAkSZIkSZJ6x23rBgWTThq0MnMMMKafw5AkSZIkSZIkaYbglmGSJEmSJEmSJElqmUknSZIkSZIkSZIktczt9Qa4iBgGjAb2BtYARgDPA5cDJ2bmKxFxJbA98K/MXLLy7Bjg8+XlZpl5a4N1mhobEfOX8WwOrAzMR/F39ibwNHAjcFpmvtbF888BSwDXZea2ETE3cBiwC7A0MBJ4Ebge+H+ZmTXPLwn8s2baJSKivWzflpmblmNHA2eX/Udl5pF13rXD2RHRMXYzYCXglPL6yszcsd77VOIaCfwHmB14GVgsMyc1ekaSJEmSJEmSZhjtnuk0GFjpNIBFxELAX4EzgE2BuSmSTssDhwMPRcR60zCewygSPr8APgUsDswGzAIsBGwMHAU8GxHbNTHfx4FHgZ8B6wDzUiSdlgO+BDwaEQdM/TdpygXAxLK9TUTM0834nSkSTgB/MOEkSZIkSZIkSZrRWOk0QEXEXMDdwJJl1/vAH4EEFgN2BRakqHh6aRrE8wU6K38oY7sDeIMi8bQmsA3F39zswAURsUJm/ruLKRcFrgJGAf+meI8XKd5td4oE1DDgzIi4NzMfK597gyLhBkXyC4oqq6PL9gtNvM4FwGPA1sBWZd+FwP1l+5nMfCMirgY+XcbxWeA3Debcu9I+v4kYJEmSJEmSJEkaUEw6DVw/pTPh9BTwqcx8tuNmRHwPuJhim7sF+zKQssrn2ErXfpl5Xp1xK1Nsr7cQMAewL3BcF9OuXP73FOCbmTmhMs8PKLbXW4uiWu+rwMEAmfkOcHw5riPp9E5mHt/s+2TmtcC1ETE7nUmnazNzTM3Q8yiSTgB70UXSqfx+tikvH8/MvzUbiyRJkiRJkiTNCNrb2rsfpAHP7fUGoIhYADi0vJwA7FhNOAFk5hvATsAz0yCkTwNzle2L6yWcypgeB06odK3dzby3Al+uJpzKef4LfL3StXmPop16rqSorALYtNzusJ7dgOFl2yonSZIkSZIkSdIMyaTTwLQjMHPZvjgzn6o3KDPfA340DeJ5GPgmRVXSSd2MfaTSnqObsb/OzK7S33cDH5TtrpI9fSozJwIXlZczUWz7V88+5X/bgd/3dVySJEmSJEmSJPUHt9cbmLaotK/pZuyfgA/pw986Mx8EHuxuXEQMB5avdA3r5pF7G6z5QUS8RXHm04hm4uwj5wFfLNt7Ar+s3oyIRYGNy8u/ZObz0zA2SZIkSZIkSZKmGZNOA9MilfbfGw3MzPcj4ilgpb4NqVOZXApgmfITwKrAGkyeIBrSzVSvdnP//fK//Vaxl5l3RcQ/gGWBDSJiicz8V2XIXnS+Z91tByVJkiRJkiRphtfW1t8RaBow6TQwLVBpv93E+P8wDZJOEbEZ8C1gK7quYppE59aADWXm+92Pmi6cDxxZtvcEjq3c27v873jgkmkYkyRJkiRJkiRJ05RnOg1MPU0JT5gKazasSoqI44Gbge2YPOH0H+BW4ARgZ2CnqRDL9Ob8SnvPjkZErEhR3QVwZWY2kyCUJEmSJEmSJGlAstJpYPpvpT0v8Gw34+dqYs7utrrr8tykiNgf+GZ52Qb8FrgYeCAz/1szdrsmYhlQMvOZiLgL2BBYIyKWy8yngT0qw86v/7QkSZIk6f+zd9/hVVf3A8ffCTvsvQRFlAOoOMBBnXWPOrDuiT9HtbZWrYqrVqtWba171VG1TrSiIkPFAW5R3IhHEZC9ZW+S3x/3JtwkN/MSQuT9ep775NzzPePzzfcGePLhnCNJkjYBeW6vtykw6VQzfQvsmSxvB3xaRvtQQn3qT3m9MsZoX8q1ASnlS2KMt5fStnVKuaxEV03yBImkE8DhwG2sW9U1DxhWHUFJkiRJkiRJkrShuL1ezfROSvmo0hqGEHYDmpdwOfXMpNYltCGEUAfYvoRrzSic1HqktHiAX6eUN/bPX14F2g4EViXLh4cQOrBua73nYoyr12tkkiRJkiRJkiRtZDb2X/orvRdZt8Xeb5KJpZJcU8q16Snl/Utpdx7QpIRrdYu8b1XSICGEHYGTUqrqlNR2PVmb/Forw/5ljhFj/BkYkny7B3Ay61ZyubWeJEmSJEmSpE1bbl7NeanSTDrVQDHG5cANybfZwIshhN6pbUIIdUIIdwKHlDLUGynlE0MIBxdtEEI4Ebi5lDHmADNT3t+cXBlVdJyDgdconGjKKWXc9WFx8murEEKDDPoDdC5H+yeSX2sDVyfLE2KMH1RibkmSJEmSJEmSahTPdKq57iSxVd0RQDvgoxDCK8BnJFYl9QO2AlYA9dMNEGMcHUJ4H9idRDJoWAhhCDAGaAzsx7ot4v4HHJNmjLwQwt3AjcmqY4GdQgjDgFlA22Sc2yavr2Hd565lpe68/H4CmpG4/+EhhFeBJTHGeyrQP98lIYS6JLYkHBRjHJum/TASK9Basm5lmKucJEmSJEmSJEmbBFc61VAxxjwSSaDHklW1SSSargcuJZFwWgacWMZQxwPjkuUs4HDgWuDPJBJOK4HfA0+VMsYtJM40ytcV+COJ1Vh/ZF3CaTKJbfwmJd93CiGUeJbUevBgSnlv4CbWrRArjxGsizUHuBz4G3BgusYxxlUU/j6ASSdJkiRJkiRJ0ibCpFMNFmNcHWM8g8RKooHANGAVieTOv4FeMcaXyhhjGonk0vnAu8B8EqujxgP3AzvFGO8vY4y1McYTSKxyGgbMJrGiaRmJpM0QEomr7jHGUSS22YNEkuukYgOuJzHG+4CzgS+ApSS+N/NDCCWeO1Wk/1ISyaqnSazaWgP8TGIVWElSk3OjY4w/VCJ0SZIkSZIkSfplyc2tOS9VWlZenodi/dKFEPIf8k8xxi2qM5ZfuhDCb0lsRQjwxwps5Zext9oe5w/zJuTbunWrOwRVg+M/K/X/AEiSJEmSpCrWunXjrOqOoaZadvfva8zvL3P+eJ/PuZJc6SStX6ckv64Cnq3OQCRJkiRJkiRJ2pBqV3cA0i9FCKETcFjy7csxxrnVGY8kSZIkSZIkbTTctm6T4EonqZJCCA1DCNnJcmfgOaBO8vIG21ZPkiRJkiRJkqSNgUknqfIOBZaEEGYDk4DdkvXDYozvVFtUkiRJkiRJkiRVA7fXkypvMtAg+Uqt+131hCNJkiRJkiRJUvUx6SRV3nhgDLANsBB4Fbg6xji1WqOSJEmSJEmSpI1NXl51R6ANwKTTJiDGmFXdMfwSxRjnAX2qOw5JkiRJkiRJkjYGnukkSZIkSZIkSZKkjLnSSZIkSZIkSZIkVa3c3OqOQBuAK50kSZIkSZIkSZKUMZNOkiRJkiRJkiRJypjb60mSJEmSJEmSpKqVm1fdEWgDMOkkqdpk1cpms7MOocPJ+1G/cxtWzf6ZGc+M5Ke7XyJvzdoy+zcMm9FlwPE07dONWo0asOSbSUx5YAhzho0uaFO/U2t+9em9ZY71QZ/zWTFlTkb3o7Jl1cqm1xkHss2Jv6ZJ59YsnbWAcc+/w2f3vkJuOZ556+22YOc/HUWHXQJ1GjZgyYx5jB8ymk/ufIk1y1cWanvAnecRjt497Thj7n2FD28euF7uSZIkSZIkSVKCSSdJ1abbzWfS8bQDWPDROOa+/ilNdw5sefnxNNpmc74567ZS+zbquTm9h14PZDHr5Q9Yu3gZrQ7eme0evYTx1z3B5PteAWDNwqVM/Ofzacdo0LU97Y7eg6U/TGPVnAXr+/aUxt439GfbU/Zl+ujIxBGf0X7nbux2yTG06tGZV8+9q9S+Hfv24IgnLgPgx+GfsHTWz3TYtTu9zz+czXbvyaBjbmDtytUF7Vv26MTS2QsY++Rbxcaa/klcvzcmSZIkSZIkyaSTpOrRpE83Op52ALMHf8g3Z99eUN/jrvNpf/zetDxgJ+aN+KzE/uEfZ5FVuzZjDruKxV9NBGDCTc+y85v/oMtlxzP9mbdZ8/MS1ixaxsRbiyedsmrXos/wG8lduZpvzr6N3BWri7XR+tWu99Zse8q+jB/yMa+ed3dB/f63/Y7ux+7JFvvtwKQ3vyix/9439ofsLF44+m/M/mJCQf2vb/4/tjl5X7Y7bX++eGg4ANm1a9G8awcmvfk5o28fVGX3JEmSJEmSJGmd7OoOQNKmabP/OwiAif/6X6H6H298mrzcXDqcvF+JfWs1akB2Tj3mjhhTkHACWLtsJXNfH0OtBnVpvF2XUuff4sKjadxrSybd+SJLx03J4E5UXtudvj8Ao+94sVD9h7cMJC83l54n7lNi3+Zbd6DF1h2Z+PpnhRJOifFeAqDzPr3Wtd+qA7Xq1maez1aSJEmSJGnjkJdbc16qNFc6SaoWzXbrwaq5i1j6XeGkwKpZP7Psxxk069ujxL5rlyznk30vS3stZ6sOiXFK2S6vbtvmdD7/cFZMmcPke16uRPSqjA67dmf5vEXMj1ML1S+dtYAFE2bSYdeSn/mqxSt4/8ZnivUFWLsqsUqtbsP6BXUte3QGYO64yesjdEmSJEmSJEnlYNJJ0gaXVbc29Tu2YuGY79NeXzFlDg237kidlo1ZPW9x2QNmZ9Ggcxs2O/tQWu2/E3NfH1Pq6qUulx5LrZz6fH/lo+SudFu9DSG7bm0ad2jJzM/Gp72+aOpcmm/VgfotGrNifvFnvnTmfD5/YGjavl0P7gPAvO+nFdS16tEJgGZbtue3g66hZY9OrFmxip/e+pKP/vEcS2d5hpckSZIkSZK0vpl0krTB1WnWCIA1C5elvb5mcaK+duOcciWddnrxWprtllgls+Dj7/jmd3eUPHfLxrQ7di9WzpzPzP+9U9HQVUn1k8985aL0z3xV8pnXa5KTNulUkgatmrDLn38LwNin3yqoz1/ptPOFRzFh+KfM/Hw8bXfoSo/j9qLTntvy/BHXsnTm/ErdiyRJkiRJkiohN6+6I9AGYNJJ0gaXVacWALmr0q8yyl99lF2/brnGW/jJ9yz6bDxNd+5Gs127s+ML1/DliX9nzYKlxdp2PO0AatWvy6RHXiVv9dpK3oEqKrt24pmvLeGZr00+81r16pR7zLqNG3D445fQsE0zvnzktUJnPa1ZsYoFE2YwXmBisgAAIABJREFU7Ow7mJ+yAqr3H4+g72XHsdffTmX4OXdW5lYkSZIkSZIklcCkk6QNLnfFKgCy66T/Iyg7mXhYu2xFucb78YanCspd/3Iym//hSLYccALfX/FIsbbtjt2LvLW5TH/m7YqGrQysST7zWiU88/xk0+plK8s1Xv0WjTniicto06sLE0d8xnvXP1Xo+vCz0692G3PPK/Q8fh+22H9H6uTUK/d8kiRJkiRJksqWXd0BSNr0rFm0jLy1udRukpP2eu3GOQXtKmrCzc+ydtkKWiXP+UmVs1UHcrp2YMFH41g9Z2GFx1blrVq8jNy1udRt3CDt9brJZ56/zV5pmmzehmNevpY2vbow4fUxDD/3LvLW5pYvkLw85o6bTK06tWnYvkW545ckSZIkSZJUNlc6Sdrg8lavZcXUOdTv3Cbt9fqd27Bq7qK02+MB1G7WkKa7dGfF1Dks/XZysbFXzlpAvTQJhZb77wTAnKEfZ3gHqqjc1WtZPHUuTTqlf+ZNOrVm+bxFrCzhmedr1bMzRzw5gJzWTRn3/Du8denDxRJOtevXpWWPTqxZsZp54yYXG6N2ctvG/C39JEmSJEmSVPXycsv5n4ZVo7nSSVK1WPDxd9Rr25wGW7YvVF+3bXNytmzHwjHfl9i34dabsf0TA+jy52OLXavVuAH1N2vF8kmzil1r2nvrxNwfjsswelXGjE8iDds2o1mXdoXqE3VtmfnZ+FL7N92iLUc8lUg4ff7gMN68+MG0K5xy2jTl2MHXccCd5xa7Vrt+XVpvuznL5i5k8dS5md2QJEmSJEmSpEJMOkmqFjOffweArleeCFlZBfVdrzqJrOxspj/xRol9F475nhVT5tDq4D403SUU1GfVyibcfBbZdWozI82ZTY2268LaZStZ8l3x1S+qet+98B4Auw04rtAz7zvgeLKysxn71Fsld87K4sB7zienVVO+ePhV3r/+6RKbLpo8h9lfTaRVj850O+pXha71veJ4clo15Zsn3szsZiRJkiRJkiQV4/Z6kqrFz+98zayX3qftUbvTe9gNLHh/LE37dKNZ357MHvwh80Z8VtC2yyWJFU0Tb30+UZGbx7iLH2D7Jy9nx/9dw+zBH7Jq/mJa7L0djbp3Zu6IMUx9aFixOet3as3yH2dAbt4GuUcVNvW9sXw/+EO6HdGXY17+K9M+GEe7PlvTcdfujB/yMZPe/KKg7S4XHQ3A6NsHAbDlQb1pu/2WrFmxitXLVhRcT7V0zgLGPplIXL19+SP0e+4qDrjzXLoeujOLp86l/S6BtttvybSPxvHpPYM3wB1LkiRJkiSpgL+T2ySYdJJUbb49/x6WfjeV9ifszWZnH8rKaXOZcPNAfrr35ULtulxaJOlEImk15jdX0+XS42h5YG+y69Vh+cSZ/HDN40x5aFixv8RqN2tIdu1arJwxr+pvTCV6408PMP/7afQ4dk+2P/Mglkyfx0e3/o/P7h9SqN0uFxdOOnXYtTuQ2B5v5wuOSjv2nLE/FSSd5nw9ied+cw27/vkYNtu9J3X3rc+iqXML5spdtaaqblGSJEmSJEnaZGXl5ZldlH4J3mp7nD/Mm5Bv69at7hBUDY7/7P7qDkGSJEmSpE1a69aNs8pupXSW3nhajfn9ZcOr/utzriRXOkmSJEmSJEmSpKqVl1vdEWgDyK7uACRJkiRJkiRJklTzmXSSJEmSJEmSJElSxkw6SZIkSZIkSZIkKWOe6SRJkiRJkiRJkqpWbl51R6ANwJVOkiRJkiRJkiRJyphJJ0mSJEmSJEmSJGXM7fUkSZIkSZIkSVLVys2t7gi0AbjSSZIkSZIkSZIkSRkz6SRJkiRJkiRJkqSMmXSSJEmSJEmSJElSxjzTSZIkSZIkSZIkVa3cvOqOQBuAK50kSZIkSZIkSZKUMZNOkiRJkiRJkiRJypjb60mSJEmSJEmSpKqVl1vdEWgDcKWTJEmSJEmSJEmSMmbSSZIkSZIkSZIkSRlzez1JkiRJkiRJklS1cvOqOwJtAK50kiRJkiRJkiRJUsZMOkmSJEmSJEmSJCljJp0kSZIkSZIkSZKUMc90kiRJkiRJkiRJVSovN7e6Q9AG4EonSZIkSZIkSZIkZcykkyRJkiRJkiRJkjLm9nqSJEmSJEmSJKlq5eZVdwTaAFzpJEmSJEmSJEmSpIyZdJIkSZIkSZIkSVLGTDpJkiRJkiRJkiQpY57pJEmSJEmSJEmSqpZnOm0SXOkkSZIkSZIkSZKkjJl0kiRJkiRJkiRJUsbcXk+SJEmSJEmSJFWtvNzqjkAbgCudJEmSJEmSJEmSlDGTTlpvQgh1qjsGSZIkSZIkSZJUPdxeT6UKIWwBTEy+HRVj3CdNm1rAhUAX4A8bLLhShBD6A48m314XY7y2+qKRJEmSJEmSpE1cbl51R6ANwJVOykgIoSPwKXAr0Kiaw5EkSZIkSZIkSdXElU7K1NbADtUdhODvdRdWdwjagGau8nlvaupn1+GxXodWdxjawIZ/Nay6Q5AkSZIkSSo3k04qVYxxEpBV3XFUVIzxMeCxag5DkiRJkiRJkqRNhkknSZIkSZIkSZJUpfI802mT4JlOkiRJkiRJkiRJypgrnTZCIYRJwOYAMcZSt7YrqW0I4Vrgr8m37WOMM0MI/YD+wE5AG2A+8CnweIzxfyWMvwUwMfl2VIxxn2R9f+DRIs1PDyGcnixfF2O8Ntl2JLA38FOMcYsQQlPgYuAYYAsgF4jAi8DdMcZFJcRS6J6ABsDtwH7ASuA74N8xxieKxFcQS5oxs4EjgZOBXYG2ybEmAW8A98QYf0zXN2WMJsA5wOFAAFqQ+N6OBQYDD8UYl5U2hiRJkiRJkiRJNZ0rnTYN9UMILwGDgCOAzYC6QDvgN8DzIYShIYR6VR1ICKErMAa4BugJ5ACNgN7ADcB3IYRe5RiqBfA2iYRRI6AlsDuJJFZFYvmIxPfltyS+L3WS420LXAiMDSH8rpQxDgPGA/8E9iKRtKqT/LovcAfwQwhhz/LGJUmSJEmSJEm/OLl5NeelSnOl06bhUWAfYDUwBPicRLLnABLJHoBDgb8BA8o55ifApUBX4Nxk3afAwGT5gzR9GgCvJPssJ5HsGUci2XMsicRRe2BUCGHPGOM3pcz/L5IrvIpIu2KrqBBCR+Dd5HwAK0h8b74GmpL43mwH1AMeCCEsiDEOLDLGMcCzQK1k1TjgNWA2iYTeocBWQAfgjRDCQTHGkeWJT5IkSZIkSZKkmsak06ZhH+BboF+M8fuU+iuKbFn3+xDCNTHGlWUNGGMcS2IV0D6sSzqNjTHeWkq3NsnXeODQGOMP+RdCCFeRSBj9GmgG3EtiS76SHAxMB35HYsVTB+A3McZxZcWe9CDrEk5fAUfFGPO3Eczfdu8G4Ipk1T0hhMExxuXJ612A/5BIOK0B/gA8GGPMSxnjYuAy4EYSK8ueCSF0jzEuLGeMkiRJkiRJkiTVGG6vt2lYCxxZJOGU729A/plFjYBdqjiWpcBBqQkngBjjfBJb5U1JVu0VQjiojLF+G2McEmNcGmP8IcZ4e3kCCCFsQ2IVEsDPwMGpCadkPLkxxitJJLQAWgH9UppcDjROlq+IMf47NeGUHGNtjPEm4OFkVTsSSTJJkiRJkiRJkn5xTDptGkbEGMenuxBjzCWxzVy+dlUcy90xxgklxLIYSF0p1S9du6TPY4wfVTKGo1LKD8YYZ5TS9h4S2xEOBBYDhBDqAqckry8F7i5jvhtSyidXLFRJkiRJkiRJ+gXIza05L1Wa2+ttGkaXcX1OSrl+VQYCPFfG9SHAncnyAaW0+zCDGFLHHVpawxjjIBJnT6XqQ+JMLIBvy9qOMMY4OYQwDegIbBtCaBJjXFTBmCVJkiRJkiRJ2qiZdNo0zCrj+rKUclWuflsNfF1agxjjhBDCcqAB0CmEkFV027qkiWnqyqtTSnlsJfr3TCnvHEJIF19JsoHNSJyxJUmSJEmSJEnSL4ZJp03DsrKbFMiqsijg5xjjmnK0W0Ai6VQHaA7ML6FNZbVJKS+sRP/mGcy9PvpLkiRJkiRJUs2SW5H/u6+ayqRTzVeVSaL1bXU529VKKa8qoU15klclqZNBXyj8c/MuMLiC/SdnOL8kSZIkSZIkSRsdk04buVK2l8tX1WcwrU9Ny2oQQshOabc0xrikCuJYALRNiSndSqrS/JxSnhpjvHW9RCVJkiRJkiRJUg1m0mnjlJtSrgesSNcohFAHaLlBIlo/GoUQNosxTi2lzdYk7hngxyqKYwLrkk7dgQ9KahhCqAs8mezzVYzx6WQ5387lmTCE0ILE9oKuIZUkSZIkSZK06XF7vU1CdnUHoLRSz2BqXUq7nSi8FV11qOifFAeXcf2IlPIrFRy7vD5MKR9YRttdgGOBAcDJKf3XJstbhRB6lzZACKE9MBNYGkL4OoTQsOIhS5IkSZIkSZK0cTPptHGanlLev5R2A6o6kHJYm1IuTwLskhBC2i0BQwjNgItSqp7PJLBSPJ1SPjeE0LyUtr9PKQ8GiDEuBl5Kqb81uS1gSf5G4hypBsCsGOPSCsYrSZIkSZIkSdJGz6TTxumNlPLfQgibp14MIdQJIfwT6Ldhw0prcUq5cznaB2BgCKFJocoQ2gJDgfbJqsdijF+unxALizGOAYYn37YFXg4htCkWaAgXAScm304Hnkq5fAOwOlneB3g6zT3VCiEMAM5Kqb4+4xuQJEmSJEmSJGkj5JlOG6f/AFcCTYHNgG9DCANJnCXUHjgK6AAsBEYDB1RTnACTSWyxlwXsFUJ4FPge+CbGmG57vNUkttD7IYTwPIlkzpbAMSTuF2A8cGkVx/1/wBgS38c9k/G8BESgEXAIsEOy7RrgrBjjkvzOMcYvkkmpe5JVxwP7J8eYSCKZdSCJJFu+f8QYR1XdLUmSJEmSJEnSxikvzzOdNgUmnTZCMca5IYR+wCCgGZADnFGk2QzgOBLJmmpLOsUYfw4h/I/EuUcA/ZNfB5P+TKYzgLuANsD5aa6/C/SLMc5bz6EWEmOcGULoS+J73BtoApyWpulC4IwY4/CiF2KM94YQFgF3k0iYtQTOTDPGGuCGGON16yt+SZIkSZIkSVLNFELoAlxAYuHCFkAuMBV4DXggxvjdepxrS+BsEgstOgMNgTnAx8B/YoxD19dc4PZ6G60Y49tAN+BG4CtgCYmt7L4E/gpsF2N8r/oiLOQ04BYSK3xWAUuBuiW0fR/YFriDxMqtVcB84FUSW9ntU9UJp3wxxsnALsl5XyTxQ72SxPf5MxJb4YUY44uljPEEiZVaVwCjgFkkVnPlP6vbgW1NOEmSJEmSJEmSQggnA98AFwI9SSw6aQR0B/4EfBlCuGA9zXUpMBa4HNgeaE7id/cdgaOBISGEF0MIDdbHfABZLmlTVQshjAT2Tr7tEmOcVH3R/HLt3+kgf5g3ITNXLazuELSB1c+uU90hqBoM/2pYdYcgSZIkSUrRunXjrOqOoaZadPaBNeb3l00eer1KnnMI4TASu4TlLwj6GBiRLO8L/Cqlef8Y4+MZzPU34C8pVe8CHwDLk/McmHJtUIzxt5WdK5Xb60mSJEmSJEmSJFWhEEIj4GHWJZwuijHekdLkLyGEs4F/A1nAPSGEYTHGOZWYa3fg6uTbZcDxMcYhRdrsT+KInPrA0SGEg2KMr1V0rqLcXk+SJEmSJEmSJKlqnQO0S5afLZJwAiDG+BBwa/JtI+DSSs51D4nEFUC/ogmn5FxvADelVPWv5FyFmHSSJEmSJEmSJEmqWqenlG8ppd3NwMpk+aQQQoW2+gsh7AjskHz7bIzx9VKa/wf4gcQ2f4srMk9J3F5PkiRJkiRJkiRVrdwac6TTehdCaAf0Sr6dEWP8oqS2Mcb5IYSPgb2AjsDOwOgKTHdCSvlfpTWMMU4FulVg7DK50kmSJEmSJEmSJKnq7JhS/rgc7VPb7FrBufomvy4ExlSwb8Zc6SRJkiRJkiRJklR1tk4pTyxH+8kp5a0qONe2ya/jYox5IYRs4ETgFBKrrVoCs4GRwN0xxk8qOH6pTDqpysUY96nuGCRJkiRJkiRJ1SdvE95eD2ifUp5ajvbTU8ptyztJCKEN0Dz5dkYIoTPwDPCrIk07AacCp4QQ/glcHmNcLw/IpJMkSZIkSZIkSVLVaZpSXlaO9stTyk0qME+zlHJd4FWgR3K8l4GxQCPgQBJb/mUBlwF1gIsrME+JTDpJkiRJkiRJkiQlhRD6A/0r0OWxGONjpVyvl1JeUY7xUpNO9UpsVVyjlPJhya+fA0fFGFO37Ls8hPB74G4gG7gohDAsxvhGBeZKy6STJEmSJEmSJEmqWjVre70tgL0r0H5kGdfXppTL843ISinnViCOnCLvZwEHxxhnF20YY7wvhNARuDJZ9RfApJMkSZIkSZIkSdJ6NAkYVcH2pVmSUq5fjvFS26ysQBxF296WLuGU4h/ARUADYI8QQosY4/wKzFeMSSdJkiRJkiRJkqSk5FZ5j63HIVOTTkVXI6XTIKW8sALzLC7y/rXSGscYF4YQPgX2JLHN3vbA2xWYrxiTTpI2iOxa2fQ740gOPfEQ2nVux/xZ83n1+dd59t6BrF2ztsz+jZs15vQ/n8Zu++1Cs1bNmPzDFJ574HlGvlL6fzjo2nNL7ht6D9edez0fvPZhsetNWzSl/yWn0ffAvjRq0pCpE6Yx9KlhDHlyKHl5NWrJ70arXYc2/OnK89hlj940btKIcV9/z/3/eoSP3vmk3GNs32db/jDgd/TsFSAvj4/e/ZTbb7iXqT9NL9SuTt06jJ7wNnXqpP/r7fDdj2fi+J8K3r/x+WDadWiTtu3vTriQ99/+qNwxqmxtO7Th/CvOoc/uO9GoSUPiNz/w0L8eZfS7Y8o9xna9t+Hcy86iR69AXl4en7w3hrtvuJ9pk2cUa7v7fn0544JT6Bq6sHZtLp999AUP/etRfvj2x/V5W5IkSZIkSWVJ/cVFh3K075hSnlmBeeYVeT89bavCpqWUW1ZgrrRMOknaIC644Q/85pTD+Hr0N3w44iO22XkbzrjkdLr22JK/nXtDqX3rN6jHLU/dxFbbdmXUkHeYPW02ex66B1ffdyVNWzTl5ccHp+3XvHVzrvn3X6hVu1ba681aNuXul++k/ebtGffZOEYO/patt92KP/39j/TabTtuPP+mjO97U9eydQv+O/jftGrTkqGDXmPxoiUc2u9AHhx4Jxf0H8DI194tc4zeu+3AQ8/dxaKFi3l54FAaN2nEof0OZJfde3P8QWcwfcq6v7O3CltSp05t3nvrQ74aM7bYWD/PX1BQbtK0Me06tOHLMd/w/lvFk0uTJ06t5F0rnRatmvPwy/fSsnULXh00giWLl3LgUftxz7O3cckZV/LO6++XOcaOu23Pvc/exqKFixny3HAaNW7IQf32p8/uO3LqQWczY+q6f4MddfLhXH3rZcycNoshz79K/Qb1OfDI/dhjv76cd+yFfPnJ11V5u5IkSZIkqaiKnEz0y5P6i6otytG+c0r5+/JOEmOcE0KYA7ROVjUD5pTRLTVPVHSlVIWZdJJU5Xr27slvTjmMUUPe4frzbiyov+y2Szjw2APYdb9d+fjNj0vs3+/MfnTrtTV3XX0Pgx9/BYAn73yau16+g7OuPJNRQ0axYF7hVaZb9tiS6x66hvabty9x3LOvOov2m7fnxf+8xL1/vX9d/ZVncvx5x/HJyE95/fkRlb1tAX8YcA4dOrXn/FP+zKgRiaTCo/c+xXMjHuPqmy/l/bc/YvWq1aWO8ddbL2f58hUcf2B/Zs1I/B055IXXeOi5u7jkr3/k4rOuLGjbredWADz72KAyE1phm60BGDbodZ56+LlK36PK59wBZ9F+s3ZceOoA3nvjAwD+e9/TPPnaIwy46WI+HDm6zM/CVf+8lBXLV3LawWczO/lZGD5oBPcOvI0L/3o+A87+CwANG+VwyfV/YupP0zn5gP9j6eKlAAx85AX+O/xBLv7bHzn9kHOq8G4lSZIkSZIK+QbIA7KAXcrRfreU8mcVnOsrYL9keTvghzLad0kpT6ngXMVkZzqAJJXlyNMPB+CJO54sVP/ILf8hNzeXQ088uNT+R5z2G+bPns+QJ4YW1C1fupyn736GBjn12feofQu1P+uKM7nnlTtp3qY5X4/+Ju2Y2bWy2fOQPVj08yIevuk/ha499q//snTxUn571tHlvkcV1yCnAUccewhjvxhXkHACmDNrLk89/BztOrRhz/36ljpG3712Ycutt+DFp18pSDgBfPzup3w4ajT7HrIXTZs3KajPTzp9/+34MuOrSFtlpkFOAw475iC+/fK7goQTwNxZ83j2kf/RtkMbfrXvbqWMALvu1Yctttqcl58ZUpBwAvjkvTF8/M6n7H3wHgWfha232Yq5s+by/KODChJOAOPH/ciE7yfRo1egdglbMEqSJEmSJK1vMcb5QP5WO51DCNuU1DaE0JJ1ial5QPnPJUgYklI+obSGIYT2JM5xApgFxArOVYxJJ0lVbrtdt2PBvAVMij8Vqp83az5TJ0yj167bldi3/ebtad2+NV+P/obc3MJrcL/44EsAeu1WuP8Jvz+O77/8nt8f+gc+f+/ztOM2a9mMnEY5TPxuIitXrCx0bfXK1UydOI0u3bcgp1F5zvVTOr16b0O9+vUY/X7xvxfz6/r03bHUMXr33aFQ+6Jj1K5dm5123b6grlvPrVi8aEmhLfdKkp90iiadqty2O/WkXv16fPp+8Z/HT99P/Ged/Gddkh132z7ZPv0YtWvXZoddegHwxcdfcVTfE3jq3wMLtatbry7tOrZl0YLFrFm9plL3IkmSJEmSKicvN6/GvKpI6i8qri2l3RVA3WT5yRhjRTcmfAbI307mmBDC7qW0vZZ1O+I9G2NcW8G5ijHpJKlK1albhzYdWjPjp/RJgFlTZ9G4WWOatmia9nqH5PZ46fr/POdnVq5YyWZdNitUf+VpV3Phb//M5PElrwbN38arTt06aa83bNyQ7Oxs2nRsnfa6ytZpi8R5h1MmTSt2bdrkxPPcYsvOxa6Vd4z8xFLqGN16dGXm9FlccMW5DP3wecb8NIqX33mGU84+rlj/bj278vO8Bfz2pMN5adTTfDppJMNHv8D5l55V4udClbPZFonzMaf9VPw5zpiSOIep85adSh9j88RnYWolxqhTtw49t+/O7Y/fTLMWTXn0rifKH7wkSZIkSdL68TDrtq87JoRwfQghK7VBCOEc4OLk2+XArRWdJMY4C/hH8m0W8FLRxFMIISuEcAWQf/7AosrMlY57y0iqUo2bNQZgyaKlaa/nb33VsElDFs5fWOx6k+R2WUsWLUnbf9niZTRsUng10ui3PykzrsULFjPjpxl03aYr7Tq1ZeaUWQXXNu+2Oe07t0vE1bhhmWMpvWbNE4nExYuKnz+4JPncGzVpVK4xFi0sPsbiRYXHaNm6RcGrfv16vP3auzTIacBe+/+Ky2+4mO7bBq7+0/UAZGVl0TVsSU5OA04790TeGDqS0e+P4Vf77Mp5l5zFDrtsz7knXMjatRn/5w4BTfM/CwuL/xwvWZyoa9Sk9J+1/MT0kgqOUatWLd6bMIJatWoB8PRDzxVbASVJkiRJklTVYoxLQwi/B14msSDoauDIEMIrwBoS5zClJocujDFOLTpOCOEx4PTk28djjP3TTHc9sAewN9AKeC+EMILEFn+1gMNYt60ewAXp5qoMk06SqlTt2olf9OavLCpq9cpEfd166VeW1K5du/T+q1ZTr0G9SsX2/EMvcMENf+D6/1zHHVfcxY/fTmCrbbpy0S0XsmrFKho0bEBWVlbZAymt/Ge3amXxZ7dq5SoA6tWrW+xaoTHqlPz8V60qPEar1i344bvEmT2Xn39tQZ/GTRrx0HN3cdQJh/HGsJGMfO1dWrRqzuQJU1i0cDF/6j+AxcmkZt16dbnt4b+zz4F7cMIZv+Wph5+rzK2riILPQvKZpVpV8GdAGZ+F5J8lFR2jYeMcXn5mKKtWrGKP/fty0tnHUb9Bff5+6T8rdhOSJEmSJEkZijEOCSGcAjwENAS2S75SrQEuizE+mME8K0MIhwAPAqckqw9IvlKtBP4vxvh0Zecqyu31JFWplSsSvyDOTx4UVSeZbFqxbEUJ/Vcm+6dPStWpW6fEvmUZ/PgrvPDwi2zebXPufPF2hsSXuWPQbfzw9Q+8MejNRFzLV5YxikqS/+zSbVWXnxxYtmx56WMsL2WMuokxlifHiN+Op9/eJ/Pns68qlKRavGgJ/7r+HgAO7Zf4e3XenPkcs99p/N/R5xcknCCRDLvpqn8BcEi/on8Hq7IKPgtpfo7zE87Ly/g5zv+zpKJjLFqwmL9f+k9u/cudHL/v6Yz54HOOPuUI9vvNPhW6B0mSJEmSlKHcvJrzqkIxxmeAHsA/gbHAEhLJnx9JJKN2jDHevh7mWR5jPJXE6qmHgfHAMmAFMA64A+ixPhNO4EonSVVs6eKlrF27tsRt6vLrly5elvZ6/lZaDUvYeiuncQ4/z/250vHdf90DvDrwVXbaY0fIyuLrj7/m+69+4C/3XwWQ0dibirDN1ux3yN7F6mfNmA2k3/KsUfK552+zV5L8bfUaNW7IvDnzC11rnBx3cQlbN6Ya91UEoGPnDmW2nTZ5Bgt/Xshm5WirwrptsxX7HLxnsfpZM+YAJX0WEtsjLi1hC818BZ+FJo2YX+TnsmCMMj5PK5ev5L6bH+KRwfex98F78uaQkaW2lyRJkiRJqgoxxinAZclXRfv2B/pXoP0HwAcVnaeyTDpJqlJrVq9h9tTZtOvUNu31dp3asmDeAhYvKH5mD8DUCVML2hXVok0L6tWvx5QfM9tudOJ3k5j43aRCdd16dWPJwiXMmzkvo7E3Bd237cbvLz2rWP3pR54LkDZ5s9nmibpJ438qdexJP04GEsminyYQLriaAAAgAElEQVRMKXQtP4E06cfEGG3bt6Zzl0788N2PLChyPli9+oktGFclV8u0aNWcLbp2ZvqUGcycPrvYvHXr1WNJCYlQlazbNltzziX/V6z+7H5/AKBDms9Ch87tAZj045Ri11JNTl7v0Lk9k4t8FgrGGJ//eWlP916BMR9+wYJ5Cwq1nTF1JgDNkmdESZIkSZIkaf1xez1JVe6bT8bSsm1LOnbpWKi+ZdsWdOzSkW8/G1di39nT5zBr6iy23XnbYucrbd+3F0Cp/Utz5T2X8+wnT5GdXfiPwq226Ur7zu0Y885nlRp3U/PywKFs23a3Yq9vv/qO5ctW0KfvjsX67PyrnQD48tNvSh37s9FfJtunH2Pt2rV8/fm3ABxz6lE8+uJ9HHHsocXa7rRr4lzEsV8mPit7H7A7/x38b844/5RibbfZvjsNcuoXtFX5DXluOH3a71nsNe7LyIrlK9hpt+2L9emdfLZfjyn9s/DF6K8A2KnvDmnHWLt2LWO/SDyzg/rtzy0PXc9BR+5XrO3WPbcCYOqkaRW7OUmSJEmSlJncGvRSpZl0klTlRrzwBgBnDjijUOLozAH/R3Z2NkOfGl5q/zcGvUmbDq05sv8RBXUNGjbgpD+eyIrlK3jjhTcrFdeU8VNo1a4Vvz5yn4K6ho1zuPgfFwHw7P0DKzWuEpYvW8Gbw0ayw8692OegdVuutW7bipPPOo5ZM2YzcsR7pY7x6QefM33KDI49tR8dOrUvqN91zz703XsX3hw2ip+TK1lGDHmb3NxcTj/vRFq0al7QtlWblvzpyvNYvWo1zz/xEgCjRrzP8mUrOOqEw9iia+eCtg0b5XD5DRcD8Myj/8v8myAAVixfwVvD3mH7nbdjrwN3L6hv1bYlJ5x5DLNnzOHdEaWv8v7swy+YMXUmR59yBO03a1dQv/Mevdl1rz6MHP5uwaqmN4eOYs2aNZx2/kk0TVnR1LRFUy64+lxyc3MZMrD0P3ckSZIkSZJUcVl5eVV7KJakDWP/Tgdt1D/MV917Bb8+Yh/Gff4dX37wJT379KTXrtsxasg7XH/ejQXtTrsosfLkv7c/WVCX0yiH+4bezWZbbsa7w95j+k/T2fPQPeiweQfuvvpeXn58cInznnbRKZx28alcc9a1fPDah4Wu5TTK4d+v3U+rdi0ZOTiRvNjj4F/RYfMOPHrr4zx153o9Q2+9mrlqYdmNNgLtOrZl4OuP0qRpE4a/+Do/z1/Iof0OoEWr5lz4f5fz9qvvFrTNPxvqu7Hf89bwdwrq99r/V9z1+D9YvHAJQwe9Rk7DBhx29EEsWbKUkw45k2mTZxS0vfCq8zjrgtOZN3c+rw9+i7r16rLPQXvQslULrr/sHwx8fFBB2+NO68c1/xzA0iVLefXlN1m1chV7H7A7HTq15+G7/8sdN9y3Yb5J5VQ/u051h5CRth3b8MSrD9OkaWNee+kNFsxfyEFH7U/zVs247MyrGfXaugRk/tlQcex4RqV8Rnbfry//euzvLFm0hOGDRpCT04CDjz6ApUuW0f/Q3zF9yrrPwlkX9efcy85k7ux5vDVkJLXr1mHvg/agZesW3H3jAzx+z1Mb9P4ra/hXw6o7BEmSJElSitatG2eV3UrpLDj+1xv17y9TNRv4ts+5kkw6Sb8QG3vSqVbtWpxw/vEcdOwBtGrbitnTZzNi0Js8d//zrF61uqDdG1NeA2D/TgcV6t+sVTPOHHAGu+2/G/Vz6jNl/BSe+/fzjBw8qtR5S0s6QWKLv7OuOJMdd9+BBg0bMPG7SfzvwRd479X3M7/pKlRTkk4AnTbvyIVX/57d9tqZWrVqEcf+wAP/+g8fvjO6ULsjjz+MG+/6Cy89O5Sr/3R9oWu77bUz5/35THr0CixfupxPP/ycO//+AJMnFj8H6De/PZiTzz6Wrbt3Zc2atYz9chyP3P0EH4z8uFjbPfbty5l/OJVtdugOWVn8MO5Hnnr4OYYNen39fhPWg5qedALouHkH/njVueyyZx+ya2Xzw7fjefi2x/j4nU8LtfvNcYdw7Z1X8srA4Vx34d8LXdtlz96c/ecz6L5dN5YtXc7nH33JvTc9yJSJxc92O+CIfTnpnOPYuudW5OauZdyXkSfuf5b33thgZ2dmzKSTJEmSJG1cTDpV3s/H7rNR//4yVfPnR/qcK8mkk/QLsbEnnbR+1aSkk9aPX0LSSRVn0kmSJEmSNi4mnSrPpNOmwTOdJEmSJEmSJEmSlDGTTpIkSZIkSZIkScpY7eoOQJIkSZIkSZIk/cLlVncA2hBc6SRJkiRJkiRJkqSMmXSSJEmSJEmSJElSxtxeT5IkSZIkSZIkVam83LzqDkEbgCudJEmSJEmSJEmSlDGTTpIkSZIkSZIkScqYSSdJkiRJkiRJkiRlzDOdJEmSJEmSJElS1cqt7gC0IbjSSZIkSZIkSZIkSRkz6SRJkiRJkiRJkqSMub2eJEmSJEmSJEmqUnlur7dJcKWTJEmSJEmSJEmSMmbSSZIkSZIkSZIkSRlzez1JkiRJkiRJklS13F5vk+BKJ0mSJEmSJEmSJGXMpJMkSZIkSZIkSZIyZtJJkiRJkiRJkiRJGfNMJ0mSJEmSJEmSVKXyPNNpk+BKJ0mSJEmSJEmSJGXMpJMkSZIkSZIkSZIy5vZ6kiRJkiRJkiSparm93ibBlU6SJEmSJEmSJEnKmEknSZIkSZIkSZIkZcykkyRJkiRJkiRJkjLmmU6SJEmSJEmSJKlK5Xmm0ybBlU6SJEmSJEmSJEnKmEknSZIkSZIkSZIkZczt9SRJkiRJkiRJUpVye71NgyudJEmSJEmSJEmSlDGTTpIkSZIkSZIkScqY2+tJkiRJkiRJkqQq5fZ6mwZXOkmSJEmSJEmSJCljJp0kSZIkSZIkSZKUMZNOkiRJkiRJkiRJyphnOkm/EPc2NIe8KXmbrao7BG1gp9+/U3WHoA3s1+cM5vDtD6/uMLSBvfLlK9UdgiRJkiRVjbys6o5AG4C/pZYkSZIkSZIkSVLGTDpJkiRJkiRJkiQpY26vJ0mSJEmSJEmSqlRebnVHoA3BpJPWmxBCFnAMcAbQB2gGzAHeBe6OMb4fQrgcuCnZ5dcxxpHJvv2BR5P1fYHxwG1A/mEW44GnY4y3F5kzB+gPHAHsADQHFgMTgFeB+2OMM8qIux5wKnA0sBPQAlgKzADeAZ6JMY4qY4xewJnAr4EuQB1gHvAtMBx4JMa4sLQxJEmSJEmSJEmqyUw6ab1IJn9eAA4ucqkDcDxwXAjhFqA8iZe6JBJGvVPq+gBfFZlzP+C/yTlStUy+dgb+HEK4LMZ4bwlxdwWGAd2KXGqWfPUAfhdCeBE4Kca4Is0YfwWuofh2lR2Sr/2Bq0MIJ8cYh6e9Y0mSJEmSJEmSajiTTspYCKEO8Dqwe7JqNTAY+IJE4uYIYGvgcmByOYa8gsIJp3z/S5nzQGAo6z7Dc4CXgElAK+BQIAA5wD0hhFYxxuuKxF0/OUZ+wimSSHbNSsa9E4mEEUA/4B7grCJj9AeuTb5dBQwBvkmWOyf7tSaxAuvFEMI2McYfy/E9kCRJkiRJkiSpRjHppPXhAtYlnKYBh8YYC1YlJbfUuwEYQCIRU5aDSWyRdx6J5FVz4DjgjeR4rYDnWff5fQo4L8a4OGXOS4BLSWzllwX8NYTwfozxjZR5jiGRmAJ4Gjg1xlhoZ9EQwuHAoORc/UMIf40xTktpcnXy61Jgnxjjp0X6X5y8h32BesmYzi3H90CSJEmSJEmSfjHycrOqOwRtAEW3A5MqJITQEPhL8u0a4IjUhBNAjHFNjPFy4MkKDH1OjPGpGOPiGOPkGOOtMcbVyWuXAU2S5bdIJIsWp3aOMebGGG8B/p6syiKR+Eq1S0r5n0UTTslxXiGR1ALIBXbNvxZCaAF0Tb59o2jCKdl/KYmznvLtnP52JUmSJEmSJEmq2Uw6KVP7A02T5RdjjJ+V0vZKEombsswDnivl+gkp5ctjjHmltL0R+DlZ3jWE0CPl2pqUct9Sxrgc2BJoEGMclFK/OqW8XQihUbrOMcZJQC+geYwx3baBkiRJkiRJkiTVeCadlKlDUsqvlNYwxjgF+KQcY36cbtURQAihK9Ap+XZqjLHU8WKMy0mc25Rvz5TyqJTy3SGEf4cQfh1CqFtkjJkxxokxxrVF6heTOLcKEkmpT0IIvwshdKKIGOPXMcYFpcUqSZIkSZIkSb9Uebk156XK80wnZaprSvmbcrT/ipQt6kowsZRrXYqMVR5fAqcky1um1L9CIvG0N1ALOCf5WhpCGAm8CgyNMZYWzwBgWLJ/d+ABgBDCd8BrwHBgZIxxZTljlSRJkiRJkiSpRnKlkzLVNqVcnpU888rRprRxWqaU55djrKLtmucXkqupjmLdmU35GgKHAXcDE0IIY5IrmIolaWOMrwP9gBlFLnUH/kQicTU7hPBoCKF7OeOVJEmSJEmSJKnGMemkTNVLKZfn85RVjjZrSrmW2r+0s5xS1SqpT4xxQYzxFBJJomuBz9KMuxOJFUzvhxCaFB08xvgKiRVYxwHPsu4MqXxNgP7AVyGE08oZsyRJkiRJkiT9YuTlZdWYlyrPpJMylXYVUSmarcf5WpbYqrDUdmlXUcWE62KMvYE2JBJIDwOzU5rtAvyzhP4rY4zPxxhPBFoBfYDLgXdZl8SqAzwcQuhczrglSZIkSZIkSaoxTDopU9+mlHuWo3152pTmx5Ryr3L22T6lPKGsxjHGuckE0tlAR+CWlMunhBBKTXXHGHNjjGNijLfEGPcCtgGmJi/XAU4oZ9ySJEmSJEmSJNUYJp2UqVEp5cNKaxhCaAXslslkMcYfWXd+0mYhhJ3LmDMHOCSl6sNkfeMQwjMhhC9CCN+m7w0xxjXAVcCSZFUOyRVdIYSTQwhvhBCmlbZtXoxxHInzofJtVlrMkiRJkiRJkiTVRCadlKlBwOJkuV8IYZtS2l5BYqVPpp5MKd9Uxsqjq4GmyfK3McavAWKMi4E9SayC6hFC6FvKGA2BBsnyctad2dQA2A/oAJxURsytU8rTymgrSZIkSZIkSb8oebk156XKM+mkjMQYlwD/SL6tA7wSQuie2iaEkBVCuAi4aD1NewfrzmbaD3gihNCoyJzZIYRLSZyrBIlzlS4sMs6jKeWnQwjdik4UQqgDPAjUSlYNijHmn9H0POtWQB0UQrguhFA7zRi7Ar9Pvs0FXirj/iRJkiRJkiRJqnGK/YJcqoR/AIcDuwBdgC9DCC8DXwGNSGxvl3/+0nLWrRqqVM44xjg9hHA68AKJz/DJwIEhhJeASUBL4FAgNfl1U4xxRJq4TwU2B7aA/2fvvqOjqtY+jn/TCylAEiCU0DmA9I4IghQBwQ7YBVHUa8GC9fqq13KvYhdU7OWKAmIDkaZelN6LtI303gJJCOmZef+YSTJJJkPKJAH5fdaa5ZldnrM3k5m45snem82WZf0MbMGxmqkOji0DGzrbxwNPu4wj0bKsfwJvOYuexnHm0y/AXiAQ6AAMJO+99pYxxpRm3iIiIiIiIiIiIiIiZzMlnaTMjDEZlmUNAn4ELsKRbBnmfOTIBp7EkYDp4yxLL8M9Z1iWdRnwBVATx/Z1d7hpmgrcZ4z52E2MU5Zl9QdmAhaO1UxDnY+CtgE3GGN2FojxtmVZ1XFs4+cHNALGuOmfDbwNjCveDEVERERERERERET+Puw2T6ekyN+FttcTrzDGnAB64Vg5NBc4DGQCB4Gvga7GmPFAkEu302W85zygMY5t834BjjjveQpYCTwHNHGXcHKJ8ReOc51GAz8B+4A0HAmxvc6y0UBrY8zqImI8C7TDseJpDY6t/7KAEzhWe70GtDPGPGSM0Y6gIiIiIiIiIiIiIvK3pJVO4jXOs46+dD6KUtXl+ohL38+Az0pxz9M4kj1vnamthxjpwCfOR2ljbKTwmVEiIiIiIiIiIiIiIucNJZ2kTCzLaonj/KRtwEpjzCEPbavg2H4O4KQx5lgFDFFERERERERERERERCqAkk5SVpHAK87rH4ErPbS9HQh2Xi8qz0GJiIiIiIiIiIiIyNnDbq/sEUhF0JlOUlargSTn9RDLsq5x18iyrKHAyy5FE8p7YCIiIiIiIiIiIiIiUnG00knKxBiTYVnWizgSSn7AdMuylgPLgGNATaAb0Nml23vGmPkVPlgRERERERERERERESk3SjpJmRljxluWFQE8jiPx1NX5KMgOPA/8qwKHJyIiIiIiIiIiIiKVzG7zqewhSAVQ0km8whjzlGVZX+E4t6k30BgIxbHaaT8wH/jEGLOj0gYpIiIiIiIiIiIiIiLlRkkn8RpjzGbgocoeh4iIiIiIiIiIiIiIVDwlnUREREREREREREREpFxpe73zg29lD0BERERERERERERERETOfUo6iYiIiIiIiIiIiIiISJkp6SQiIiIiIiIiIiIiIiJlpjOdRERERERERERERESkXNntlT0CqQhKOolI5fHzpdpNlxM5bBABdWuSdewESd/NJ/7DaZCVXbJYPj7Eff06Ie2aY5oPKlQdO/4RIi6/xG3X+A+mcfz1T0szAykhHz9fWo8aQPPr+xARF0PKkQS2fvMH696Zia0Yr3l06wZ0HHslsV0sAqqEkHwonp0/rWD1Wz+QlZqe/16+PlxwSz9a3NCHyIa1SI1P4sCiTax45RtSjiSU1xSlgKxsG18v2sj3y7dwIP4U0RGhXNHFYtQl7Qjw8ztj/20H43l3zkpW7zxEemY29WMiGdHjAq7t3rJQ2+2HT/DunJVs2H2U0+kZWLWjufni1vRt06g8piYuatSO4e7H76Bjj/aERYSxbeNffPz656xcuLrYMVp1bMmdj47Gat0Mu93OqkVreOfF9zm495DHfvc9fTeXX38Z/VsMcVsfFhHGbQ/cTJ8hF1MtqhqHDxxh7nfzmfzeVDLSM0o0TxERERERERHxTEknEak0NZ++h6ojBpOyaiPJvy0jpENLosfeQlDzRhwc+2KJYlUbeRUh7ZoXWR9kNSTr2AkSpv5cqC519aYSj11Kp+cLI2l50yUcWmH4c/4aanVuRpdx1xLVIo75d73tsW/t7i247L+PArBz9kpOHzlJ7a7NaX/PUOr0aMmP175Adnpmbvs+b9xFs6t7cGzDLjZ+Np+qjWNpPuJiandrwfTBT5GRlFKucxWH/3y3iG+XbaF9w1pc3LIB63Yf5t05q9h2MJ5Xbx3gsa85GM/ICT+QkZVN/7aNiQoPYcHG3bwwfSH745N4YEi3fG1vnfAD2OHSdo2pEhzAgk17ePjz+TwwpCsj+7Qr76met6pHV+P9HyYQFVOdud//QnLSaQZc2Ze3vn6Fx257ioXzlpwxRruubXh7yqucSkxm1rQ5hEVUYcCV/ejYoz2jBt7Jof2H3fbrd3kfrrvjWlKSU93Wh0WE8f4PE2jYrD5Lfl3O7u0L6Hhhe0dyq1VTHr/96TLNXURERERERETyU9JJRCpFcPsWVB0xmFNzFnLwgX/nltd66WEir+xHld5dOL1gRbFiBdSLJfr+m4tu4O9HYMO6JC9YQfzEyWUdupRSzY5NaXnTJez4aTnz756QW97n9TuxhvXE9G3H3l/XFdm/54sjwdeHH69+jqPrduaW93rpNlreeAkX3NKPDR/OBqDR4M40u7oHf/24lF/vezd3/XbbOwfT/akbaD1qAKvf+qF8Jiq51u06zLfLttCvTSNeuaUfPj4+2O12/m/KAn5atY0/Nu+hV8v6RfZ/Z/YKUjOyeH3kAC5p3RCAewZ25vo3vuWLBRsY1r0ldaIiAHhx+kKysm18cd+VtKwXA8C9g7ow4vXpvDdnFVd2aU7VKsHlP+nz0JhHbyO2bi0evuUJFv+yFIDJ703hszkf8Mi/H2TZgpVkZmR6jPH4K+NIS01n5KA7OXboGABzv/uFt6e8yn1P382TY54p1GfE7ddy3//dhZ+HFXN3Pjaaxs0b8soTb/Dt5z8C4OPjw/hPX6D34F6069qGdcs3lHbqIiIiIiIiUgJ2m09lD0EqgG9lD0BEzk/VbhgKwPF38ieBjr3+KXabjchrLy12rFovjCXr6Akydu13Wx/UqB4+gQGkb9tV+gFLmbW6tR8Aq978Pl/58penYrfZaHF97yL7Vmtam2pN67B73pp8CSeA1W86kkdxvdvk3WvUADJOpbLoqc/ybRi86YtfMdMXkhqfVMbZSHFMXexYRXjXgI74+Dj+x9LHx4f7B3fBxwe+W7bVY/9N+44RERKUm3ACCA0KYGD7JtjsdjbuPQpAcloGaRmZ9GoZl5twyml7ccv6pGdls/XAcW9PT4CQ0BAGXXspW9ab3IQTwPEj8Uz7+Ftq1I6h+yVdPcbo0qsjDZrEMXPKz7kJJ4BVi9aw4o9V9Bp4ERHVInLLa8fF8u70N3nwuXvZsXUnJ+Pdb5cZHBLMkBEDWb/iz9yEE4DdbueTN75g5pSf8Q/Q31+JiIiIiIiIeJOSTiJSKUI6tSLrRCIZf+3JV5599AQZuw8Q2rl1seJUve4yQru25cjTb2NLS3fbJshyfGGdbpR0qkyxXZuTGp/ESZM/OZhyJIGEnYep3bVFkX0zTqWx7MWvMVN/L1SX7VxB4e9cxeIfGkStzhYHlm4mPeF0vrZZqen878H32fzlb2WdjhTDmp2HqFYlmCax1fOV14isQv3oqqzeedBj/8jQYE6nZ5CUkv+9fTTR8bpWCwsBICw4kGnjhvH6yMLJ6l1HHQmJqPCQUs9DinZBhxYEBQeyesnaQnWrlzhWLrbv1tZjjHZdHfWrF7uP4e/vR9sueb8T2ndrS+tOF/Dle1O44/J7SUtJcxu3ffe2hISG8L9ZhT83tqw3vPjQeFYtWuNxbCIiIiIiIiJSMvrzThGpcD4BAQTExpC6zv0qh6wDRwhqVA+/apFkn0wsMo5/rWiix91GwjdzSFm+vsh2OUmnwAZ1iPvqVYKshtjSMjj9+wqOvfk52UdPlG1Ccka+gf6E1Y7iyJrtbutP7T9OtSa1Ca4eTtqJU4XqTx8+wbpJs9z2bTiwEwAntx0AoHqzOvj6+XJy2wFqtG9Ml3HXUqNDE7Izstg9bzXL/zPV7T3EuzKysjmSeJrWcTXc1teuHsbuYwmcSE6lepj7hNCw7i15+YfFPDH5Vx67qgdRYSHMX7+TmSu30aJuNB0bx7rtl22zceDEKb5euJHFW/fRq2UcTWOjvDY3yVOnfm0ADuwunEA8tM9xDlNco7qeYzRwxthTvBib1mxmeM+bc+uK0ri547N/57bdDLymP9ffOZz6jeM4efwkM76exRcTviI7O9tjDBEREREREREpGSWdRKTC+VYNA8B2KtltffYpxyoG3/BQj0mnmv+6H3tKGsfGf+TxfjlJp6h/3EDy/CWkrt9KcJvmRF49gNALO7B3xINkHdHWW+Up2PmapyeluK3POOUoD4wILVFCKCQ6gs4PXwPAlq8cq5dCa1YDILpVA9reOZgja7az5esFxLRpSIvrehPb2eK7oU+TcSq11PORM0t0rj4JDwl0Wx8W7ChPTssoMul0fc9W+Pn5MP6HJVz+nym55d2a1eXlm/vi5+t+wfbt785k7S5HQqJdg1q8fFO/Us9DPIt0bnt3Kqnw5/lp52d8WERY8WIkuonhjBsWnhdj9/a9xRpbdM1oAIbfdjXd+nRlwc9/sG7Zerr16cKdj46mkdWQ/7v7uWLFEhERERERkbKz23Wm0/lASScRqXA+/o6PHlsRB8vbneU+Qe6/rAaIuKIvYRd35sD9L2A7dbrIdgC2tAwydu/nwL3Pk+HyZWX1O68j5sFbqfHPuzh4/wslnYaUgK+/H5C3FV5B2emOcr+ggGLHDAwPYdDn4witUZU/P56be9ZTQEgQ4Djjac2EH1kx/pvcPt3/7wbajhlMp4euZsm/JruNK96RlW0DIND52heUU56RWfRKkw17jvDJr+sI8PNlYPsmhIcEsnzbfpb/tZ93Zq/kiasvyj0rylXbBjVpHVeDdbuPsG73Ye6Y9BPv3DGIyNBgL8xMXOWciZSZnlGoLsP5vg708FkO4O/8nZCZ4SaG8zMjMNhzDHdCnK/3hX278cCNj7Li91UABIUE8dZXr9D/ikuY98OvLJy7uMSxRURERERERMQ9nekkIhXOnub4YtGniAPcfQIdiQd7Eed0+EVVpcYTYzg1bzHJ8878ZeHB+55n18A78iWcAE58MJWMfYcI69MVH30ZXa6ynK+5XxGveU6yKSvF/blcBQVXD2folCep0aYRu+evYcnzeQkku90OQMqxRFa9/l2+fitf/ZbM02k0HtK1xHOQkgnKSUY4k08FZWQ5kk0hge5/JpLTMrjvo9mcTs9gykPX8ML1fXjsyh5Mfeha+rZuxLQlm5m2ZLPbvg8M6cZDl3fni/uv5Nbebdm49yjvzF7phVlJQenOs/QCAgsnjAOd7+vUFM+rCnNjBLiJEVi8GO7YbI6fvT/mLs5NOAGkp6bz/ssfA9BvaO8SxxURERERERGRommlk4hUuOzk09izs/ELr+K2Pqc8O9n9CqaaT98Dvr4cef6dsg3Ebid96y4C68USUDOajF37yxZPipRxKgVbto3AcPfbqAWGh+a2O5OI+jW47MvHiGxQk13zVjP/7gnYXRIbGc4t/E5s3YctK/8qmqzUdBJ3HSa6VQP8ggJyV1iJ94UHB+Lr48Op1MKrV8CRVAIIK2L7vd837SYxJZ0x/TtQP6ZqbnmAvx9PXn0Rv2zYyYyVhhE9LvA4jnsHdWbqkk0s2LSHJ6/pWcrZSNMLmnDxwIsKlR89dAyAKm4+z6s4t8Q7fYbVqDnb6lWJqMKJ4yfzx3BuzXc6yXMMd3LuazZsK1S3bZPjfLk69euUOK6IiIiIiIiUjt3936XK34ySTiJS8TKzyDx4lIA6tdxWB9StRdaJRGxuzvcACL/U8cWzIgYAACAASURBVMVnk4Vfua23ts4m88ARdvYdiU9wEEFWQ+xp6aSbXYXa+jq3bLK52dZJvMeWmU3y/uOE16vhtj6iXgyp8UmkJ3j+YjmqZRyXffkYoTGRmG/+YMEjH+VLOAEkOs/y8S1iBY1vgB/ZmVlkZ2SVYiZSXAH+fsRWC+NgEWd0HThximpVgovc8u6w82ehkfOMLlfVw0OoViWYwwmOz4jElDTW7TpMbLVwmtWOKjSOmPBQjiSWPGkheZpd0ITbHx5ZqPyuq+4HoHZcbKG62nGOz/g9O/Z5jL13p6O+dr1Y9u3Mn/yvXa94MdzJieXv5rMgZ1vAtFT3K2pFREREREREpHSUdBKRSpG6ehORV/YjoEEdMncfyC33q1GdgPq1Ob1gRZF9j0/80m151RGD8Y+pzvGJX2Jz/lW8f3Q16k99g3Szi91X/CNfe5/gIIJaNiErPoGsA0e9MCvx5NBKg3VtTyIb1spNDAGE1qxKZMOa7Pl1ncf+EQ1qMmTyY4RER7L+g59Z+rz7pGPinqOcPnKSmDYNCagSTObpvC+VAyNCiahfk5PbDoBzGz4pP+0b1uKn1X+x51hCvtVKRxNPs/d4Ir1a1C+yb1SYY1XcnmOJheqSUtJJSEmjaawjwbTzSAJjP5lL39YNeW3kgHxtT6Wmc+hkMnExkd6Y0nlr1rQ5zJo2p1B5cEgwaalptO/etlBdh+7tANi4apPH2OtX/AlA++5tWf57/m0QO1zYjuzsbDav3VLiMa9zxu3UowMfvvJpvroWbSwAtm/ZWeK4IiIiIiIiIlI0nekkIpUi6cdfAYh5cCT4+OSWxzw0Ch9fXxKmzS6yb/zEyW4fWc5tmeInTubkFz8AkLn/MGmb/iLIakj4kD754sQ8PAr/qKokTJnl5dmJO9u+XQRAl8eG53vNuz42Ah9fXzZP/q3ozj4+9Jt4DyHRkWz4aE6RCScA7Ha2fL2AgJAguv/fDfmquj42HP/gQLZO/b1Mc5HiGdKpGQATfl6BzeZI8tntdt7+eQV2O1zTvUWRfXu1rE9woD9TFm1kf3xSbnm2zcarM5Zgt8Og9k0AaFO/BrHVwliwaTdrdx7KbZuVbeM/3y0iy2bjyi5WeUzxvJeWmsaCnxfSplMreg64MLc8umYUw0dfw9FDx1j0y1KPMdYuXc+h/Ye56qahxNbNWwHb6aIOdOnVid9nLyLhROHk45ns2b6XtcvW07ZLay69ql9ueUhoCGMevY3s7Gy3iTQREREREREpHza7zznzkNLTSicRqRQpS9eRNOt3Ii67mLgpr5OyfAMh7VsQ2rk1p+YszLfSKereGwFHMqk0Dj/9NnFfvEzs+HGED+hB5sEjhHRsRUjrZqSs/JMTk6Z6ZU7i2YFFm9g+YylNLu9O2I/PcHDJFmp2akrtrs3Z8dNy9rqsdOr04NUArHrjOwAaXtqRGm0bkZWWQWZKWm69q5RjCWz+0pG4WvvODOr2bEXLGy8hqkUch1YYanZoQmwXi0MrDJu++KUCZizdmtXl0naNmbtuB7dM+IHOjWuzfs9h1uw8TL82jejZIi637XtzVwFw96WdAMcWeo9f1YPnpv3BiNem069NI8JDAlmx/SDbDsbTsXEsN/ZqDYCfry/PDL+Y+z6azZhJPzGgXWOqVglm2bb97Dh8kp4t4rihZ+uK/wc4T7z30od0ubgT//nwOeb98CuJJxLpf2VfqkVX5fHbnyYrM28ry5yzobZt2s4fcxyJaJvNxitPvMn4T1/k0znvM/e7+YRUCeHSq/qTcCKRCc+/V+qx/eeRV5n0/ds8/fYT9BlyMYf2HaZH327ENa7HFxO/4i/n2U4iIiIiIiIi4h0+dm0vJPK3YJoPOvfezP5+RN0xnIir+uNfM4qsQ0dJ+vE3Tnw0HXtmZm4za6tj1ZNpPshjuPrfTyS4RWO37QIb1iXq/psJ7dYW3yqhZB04QtKMwvc6V/zvdNSZG52FfP39aHfPUKxhPalSsxrJB+PZ9t1i1r33EzaXM5bu2ufYQnFSvZsAuPCZm2hz+0CPsY9v2sP0gf/Mfe4fHEi7e4bS9MoLCYutzukjJ9k+Yxmr3/qe7LRz7zW/9b0OlT2EUsnMzubT39YxY+U2jiaeplbVMIZ0asrIPu0I9PfLbdfu4fcBWPfanfn6r9x+gE9+Xcefe4+SnplF3agIBndoyq192ubrD7B53zEmzVvFmp2HycjKJi46kiu6WNzQsxV+vufe4u4+Y2ZU9hCKrU792vzjyTF07tkRPz9f/tq8g0/e+JwVf6zO1+6y4QP5vzcfZ9bUOTz/4Ev56jr37Mjoh27Fat2U1NOprF22gUkvfci+XQfw5PvlUwiLCKN/iyFu62vWqcEd40Zx4SVdCQ2rwt6d+5j28bf8NKXoFbWVaeb6mZU9BBERERER8SAmJlzLYEppW4uB58z3l822zNHrXEpKOon8TZyTSScptXM16SSld64mnaT0zqWkk3iPkk4iIiIiImc3JZ1KT0mn84O21xMRERERERERERERkXJl11lJ54Vzb68ZEREREREREREREREROeso6SQiIiIiIiIiIiIiIiJlpu31RERERERERERERESkXNlt2l7vfKCVTiIiIiIiIiIiIiIiIlJmSjqJiIiIiIiIiIiIiIhImSnpJCIiIiIiIiIiIiIiImWmM51ERERERERERERERKRc2e2VPQKpCFrpJCIiIiIiIiIiIiIiImWmpJOIiIiIiIiIiIiIiIiUmbbXExERERERERERERGRcmW3+VT2EKQCaKWTiIiIiIiIiIiIiIiIlJmSTiIiIiIiIiIiIiIiIlJm2l5PRERERERERERERETKlc2u7fXOB1rpJCIiIiIiIiIiIiIiImWmpJOIiIiIiIiIiIiIiIiUmZJOIiIiIiIiIiIiIiIiUmY600lERERERERERERERMqVXWc6nRe00klERERERERERERERETKTEknERERERERERERERERKTNtryciIiIiIiIiIiIiIuXKbq/sEUhF0EonERERERERERERERERKTMlnURERERERERERERERKTMlHQSERERERERERERERGRMtOZTiIiIiIiIiIiIiIiUq5sdp/KHoJUAK10EhERERERERERERERkTJT0klERERERERERERERETKTNvriYiIiIiIiIiIiIhIubJre73zgpJOIiIiIiIiIiIiIiIiFcSyrIbA/cAAoAFgA/YDc4FJxpit5Xz/H4ArAIwxXs0Gans9ERERERERERERERGRCmBZ1o3ARuABoCUQCoQBzYGxwHrLsu4vx/uPxplwKg9a6SQiIiIiIiIiIiIiIuXKbq/sEVQ+y7IuA74gb0HQcmC+8/oS4EIgEHjLsqxEY8znXr5/Q+ANb8YsSEknERERERERERERERGRcmRZVhjwEXkJpweNMW+6NPk/y7LuAN4HfICJlmX9bIw55qX7+wKfA+HeiFcUba8nIiIiIiIiIiIiIiJSvsYAtZzXUwoknAAwxnwIvOp8GgY84sX7PwL0dF5nejFuPko6iYiIiIiIiIiIiIiIlK9bXa5f9tDuJSDdeX2DZVk+Zb2xZVltgeecTz8ADpY1ZlG0vZ4XWZbVD8fhXx2BKCAN2AcMNMbsq8yxiYiIiIiIiIiIiIhUFpu9zLmTc5ZlWbWANs6nh4wx64pqa4w5YVnWcqAXUAfoDKwow72DgP/iOCtqF/AwcGlp452JVjp5iWVZNwDzgMtwLJELwLE3YhPgcCUOTUREREREREREREREKk97l+vlxWjv2qZrGe/9ItAasAEjjTHJZYznkVY6ec94HId7AWwEZgCpgM0YU277I4rkqFortbKHIBVo/MZNlT0EqWC2uyt7BFLRAn38KnsIUsHus9ViQZu7KnsYUsF6b5hU2UMQEREREZHy19Tlelcx2u91uW5S2ptaltULeND59A1jzB+ljVVcSjp5gWVZ1XEscwM4CXQv72yhiIiIiIiIiIiIiMi5wn4eb68HxLpc7y9Ge9czl2qW5oaWZUUAX+DY8W4z8FRp4pSUttfzjjCXa6OEk4iIiIiIiIiIiIiIOEW6XKcUo73rtlYRpbzn20B9IAu4xRiTVso4JaKVTt7hmrxLr7RRiIiIiIiIiIiIiIhImViWNRIYWYIunxljPvNQH+RyXZzkj2vSKajIVkWwLOsq4Fbn0xeMMatLGqO0lHQqA8uyFgAXFyi+2LIse84TY4yPSztjjGluWdYlwH+ANsAJYDXwb2PMsgLxawJ3AYOBRjgymseBdcC3wBfGmKxijLMlcD/QD6gHJDpjvGeM+d6yrE7ASmfzUa5vDsuyGpC3x+TvxpjeHu5TkralnpvzDf+p82l3Y8wy57/pHUB3oBaQBGwAvsbxhs8uaizOmL7AFcCNOA5mq4kjgbgb+AWYaIzZ4dK+GnAIxxs+G6hjjDlyhnt8AoxyPh1sjJntqb2IiIiIiIiIiIjI34Xt3NperwGFv/v3ZMEZ6l2/n7YX2SqP6z+WrQTjyPnu/QPn09XAiyXpX1baXq+CWZbVHZgNdAGCgdrAUCCgQLvRwHbgWWfbaCDQ2X4w8DGw0ZlQ8nS/f+BIvtwJNHbGiAH6A99ZlvUxFfxz4K25OflalvUu8CtwHY7lgkE45tgX+AhY5kwSFTWexsAy4DvgGqAujtcjDGgFPABssizrzpw+xpiTwE/Op37AsDPMOQi42vn0KDC/GHMTERERERERERERkYq3G/i9BI/dZ4jneiRPcDHu79qmpLurfYzjO/c0HNvqnXHhijdppVPZvIcj8VANeNJZttNZ7k4A8BmOBIurw8DinCeWZT0IvO5Svwr4H5CAY6XSUKAOYAFLLMvqbozZUvBmlmXdD7zlUrQe+BnIwJGl7Q3cRv5DzMqVt+bm4kUc87DjSOQsw5EF7kVeJroTMAkY4WY8dYCF5P0bpOF4Tf/Esc9mf6A1jkTWJMuyEowxU51t/4sjSQWOhNdED+O8jLx9O6dU9BtdRERERERERERERIrHuRvYZ14M6Zp0Ci1G+xCX68Ti3sSyrDE4vosG+KcxZnNx+3qLkk5lkJN8cG4rl5N02meMebWILo2c/10C3AdsxbGSpokxxuaM1RUY72yXjCMT+b1rEGfiZrwzRiQwzbKsdq5byFmWVQ/4t0u3fwL/McbYXdoMx5E4GVSCaZeat+ZWQG/gIHCVMWZFgVijgE+cT4dZlvWwMWZ/gf4fkJdw2gBcaYzZ5RLDF3gBeMJZNNGyrBnGmFQcCbx4IAq40LKsOGPM3iLGeYPL9X+LaCMiIiIiIiIiIiIifz+HXK5rF6N9HZfrw8W5gXO3r5wFH6cAf8uyxhXRPMKln2ubqcaYfcW5X1GUdKp4R4FBxpgk5/MVzkeO58h7XUYVTMoAGGPSgPsty7KAATgSV9cA01yaPQpUcV5/aoz5NwUYY6Y5fxAnlWE+JeGtuRV0Y8GEkzPWp5Zl3Qz0wbH6qQ8uCR/Lsi7AsZ0fwElgoDHmUIEYNuBJy7K6OftHA1cBXxljMi3LmgLc44w/Anil4Dgsy4ogL7tsjDGrPMxFRERERERERERE5G+nOAcZ/Y1tcrluUIz2cS7X24p5j0jycgLhwMvF7Of6nfYqoExJJ53pVPEmuySc8nGuThrgfLrdGDP9DLFcDwC7sUDdFS7XhRJOLj4A/jrDfcrMy3NztdUYs8BD/e8u17UK1F3pcv1BwYRTAROBtcBUHFniHK6rlq4vou815O3B+aWHe4iIiIiIiIiIiIjI389G8vJuXYrRvpvL9RrvD6f8aKVTxVvqoa6ny/XaYsRaCWTiOCuqR06hZVnNcJyPBPCXMWZ7UQGMMXbLsqaTt31cefHK3NwotMKpgGMu1wUPaOvvcj3LUxBjzHfAd27Kl1uWtQ1oBrS3LKuZMaZg5jlnaz07MPkM4xURERERERERERGRvxFjzAnLspYB3YE4y7IuMMZsctfWsqwo8hJT8cDqYt5jN44duc7IsqzdQH1nv2L1KS4lnSreLg91LV2uh1mWVZIVh1GWZYUaY1LIv9/jlmL0LU4SqKy8NbeCjpyhr2ufgiv76rlcu32DF9OXOLYOBMdqp3/lVFiWVRPHtnwAi13PixIRERERERERERE5X9jsXs1tnIum4kg6ATwLDCui3RNAoPP6S+cRMOcMba9X8RI81FUrY+yc/jVcyhKL0e9oGe9bHN6aW0HuElFFKfipVtJ/p6J8Sd7SyOsK1F0H+Lm0ExEREREREREREZHzz0fknZd0rWVZz1uWle87a8uyxgAPOZ+mAq9W4Pi8QiudKl6WhzrX1+NHYFEJY+ecNVTSzGd6CdsXxVOq2ltz86YAbwQxxuyyLGsRji0Em1uW1dYYs95ZnbO1XgYwzRv3ExEREREREREREZFzizHmtGVZ/8Dx/bgv8BRwhWVZM3HkDfqS/6iZB4wx+wvGsSzrM+BW59PPjTEjy3PcJaWk09nlpMv1ZmNMabOY8S7X1YvRPrKYcc+0/rHgmUmuvDU3b0oAajqvI4ETZYj1X/LOrRoOrLcsqyF5e2/+bIw56baniIiIiIiIiIiIiPztGWN+sizrJuBDoArQ2vlwlQU8aoz5oKLH5w3aXu/sstPlunNxOjgPFStos8t1wR9Yd1p4qHNdNRV0hjixHuq8NTdvch1T8zOMJdCyrGmWZb1kWdYNbpp8A6Q5r4cW+C84klIiIiIiIiIiIiIi5yW73eeceZQnY8zXOL6TfwXYBCTj2I1sB45kVHtjzBvlOohypJVOZxfXLed6WZYVa4w5VFRjy7I6Aqssy0oG1hpjegEYYw5blvUX0BSIsyyrvTFmrYf7DvRQ53pmUswZxu8pmeSVuXnZUvIObhsALPHQtgt5B7v9DHzlWmmMSXAugxwGtLYsqz4wxFmdAMzy1qBFRERERERERERE5NxljNkHPOp8lLTvSGBkGe/foCz9PdFKp7OIMWYzsNr5NBB4uai2zgPG/uN8GgZsK9DkI5fr5z3E6Q709zCsE+St4GloWVbjIuJUAe4pKoiX5+YtromjuyzLquah7T9crmcU0cZ1NdN1wMXO62+MMd46N0tERERERERERERE5KykpNPZ5xmX65sty3rDsqx829pZlhUMvEVesigDeKlAnEnAXuf1ZZZlTbQsK7BAnHbAt3j4OTDG2ID/OZ/6AO857+8aJxrH4Wf1KmhuXmGMWQ3Mdj6tCfxoWVaNgu0sy3oQuN759CAwuYiQc4DjzusncSTXAL70yoBFREREREREREREzlG2c+ghpaft9c4yxphZlmW9CoxzFj0AXGtZ1gwcCY+6OLZtq+vS7QFjzPYCcZIsy7oRmAeE4FiFNNiyrO9xrF5qC1wJBOBYyZQvkVTA68Ag53V/4C/Lsr4F4gELuAoIBdbhWJnUpDzn5mW34ViBVRvoiWNuPwAGx1wGAe2cbbOA240xye4CGWMyLcuaAtwLRDiL9wALy2/4IiIiIiIiIiIiIiJnByWdzkLGmEcsyzoKPIcjGVSX/Nu75UgBHjTGfFBEnEWWZV0KTANqAQ2Bhwo0+wVYg4e9I40xv1iW9QiOFUd+zvGMLdBsOY7k02w88NbcvMV5/lV34DugI45k0S1umiYCo4wxHucHfIEj6ZRjsjHG7pXBioiIiIiIiIiIiIicxbS93lnKGPMK0BTHeUzLcWzbloUj+bESeAGwzpSUMcYsxLEa6SkcyaVTzscSHKt8BgCpxRjPq0AH4ENgJ5DuHNPvwB3ARcaYQxU5N28xxuwFuuDYQu97YD+O+Z3C8W/2vHM83xcj1krgL5ciba0nIiIiIiIiIiIi5z07PufMQ0rPx27XIozznWVZz5J33tIoY8xnlTeac5tlWQHAYaA6sNoY06mi7n2kd2+9mc8j3TcmVvYQpIKNC7mgsocgFWyKrVh/yyF/I/fZalX2EKQS9N4wqbKHICIiIiIlEBMTroxEKf1Ra9g58/1lr8Pf6HUuJa10EvGuwTgSTgCfV+ZAREREREREREREREQqkpJOIt51t/O/acDkyhyIiIiIiIiIiIiIiEhF8q/sAYicyyzLCjPGJFuWFQQ8BlzqrPraGHOiEocmIiIiIiIiIiIictawnTOb60lZKOkkUjYHLMtKAyKBIGfZaeC5yhuSiIiIiIiIiIiIiEjF0/Z6ImVzEKhBXsLJDtxjjNldaSMSEREREREREREREakEWukkUjY/kZd0Wg/82xgzq3KHJCIiIiIiIiIiInJ2seFT2UOQCqCkk2CMeRZ4tpKHcU4yxjwCPFLZ4xARERERERERERERqWzaXk9ERERERERERERERETKTEknERERERERERERERERKTNtryciIiIiIiIiIiIiIuXKrjOdzgta6SQiIiIiIiIiIiIiIiJlpqSTiIiIiIiIiIiIiIiIlJm21xORyuPnR+hVVxEyZAh+sbFkx8eTNns2p7/6CrKzz9i92oQJBLZu7bYu6fXXSZ0xI/e5b40ahN12G4Ht2+MbGUnWnj2kzphB6qxZXpuOuBdbuybjnrqX7j27EB4exuY/t/L2Kx+w+I/lxY7RvlMbHnriH7Rq2wK73c6SP1bw8nNvsW/PgUJt+w/qzZ33j6LFBU2x2eysX7uRCa9+wPLFq3PbjH30TsY+epfHey5bvIobrrij+BMVt3z8fGk9agAtr+9DRFwMp48ksPWbP1j7zkxsWWd+n8e0bkDHsVdSu4tFQJUQkg/Fs+OnFax66weyUtPzte331t00u7qH2zhr3pnJspememVO57OQ0GA++/Uj8PFhRLcbi93v8puHcPmNQ6jfJI5sm40dm3cw9f1v+GP2onIba7e+Xbll7E00shqQnpbOkvnLeP8/H5EQn1Cobb3GdRk9biQderQnMCiQPdv3MvX96fw243/lNr6/Ex8/X5qMHkCjGy+hSr0YUo8msHvq72ydMBN7Md7nEc3qcMFjw4jq2ISAsBASNu1h2/s/c+DnVR77RV5Qn35znmfpHW9xcM5qj21FRERERKTy2Sp7AFIhlHQSkUoTPnYsoZdfTsaGDaQvWUJAq1aEjR6Nf5MmJD7zzBn7+zdsSNbevaT99luhukxjcq99Y2KoPmkSvhERpP/+O9nHjxPYuTMRjzyCf5MmnHrrLa/OS/JEx1Rn2qxPia4RxYzpP3PqVDJDrxrI59Pf5c5bHuLXOb+fMUbn7h34Yvp7JCUk8e2UGYSHh3H5NYPodlEnruh3Iwf2HcptO/ymq3jpzadJOJnI9K9n4Ofvx+XXDOLL797nzpsf5Ld5CwFHQonxk9zeb/AVA2hqNWLFEn2B6Q29XhjJBTddwsEVht3z11CrczO6jruW6BZxzL3rbY99a3dvwdD/PgrAjtkrSTlyktiuzelwz1Dq9GjJD9e+QHZ6Zm77qBb1SDmawKYvC38mHFppCpVJyY154nZi42I5tO9wsfs8Mv4hht54GQd2H+Snr38mIDCAXoN78sJH/2Liv95j2gfTvT7Ovlf04Zl3n+LA7oP88MVMatapwcDhA2jbrQ1jBt9NctLp3LbNWjXlzW9exT/An99mLCDlVAq9LuvJs+89RfWYakz/+Duvj+/vpv1/RtL45r4cW76Vg3NXE9WlGa0eHUbVlvVZeofn37GRLeO4ZOYz4OPDvh+WkpmcSp1LO3Lhxw+y/rmv2Pae+z8OCYqJ5MIP78fX3688piQiIiIiIiKl5GO32yt7DCLiBUd69z6n3swBF1xA9XfeIW3BAhKffTa3POLxxwkZOJCTTzxBxtKlRfb3rVWLmClTOD1tGsnvvuvxXhFPPUVIv34kPPkk6UuWOAr9/Kj2xhsEtmnD8VGjyN61yxvTqjDdNyZW9hCK5cXXn+L6W67h9hvuz0341KgVw4xfJ5OdbaNPp6FkZGR6jDF/6XdEx0QxqOcwDh86CsCFPbvwxbfvMfen37jntkcA8Pf3Z/nm+fj5+XFZ7xG5ySirRRN+mP8lRw4fo3enoR7v1a5ja6bN+oR1q/5kxNDRnE2/I8eFXFDZQyixWh2bcvUPz7D9p+XMu3tCbvklr99J82E9mTXyVfb8uq7I/tf/9jIRDWry/dXPcXTdztzyi1+6jQtuvITFz01m/YezAfD19+MO8zF7fl3LnDF/j0TyFNuhMzeqQK06XcDE79/E19eXQ/sOF2ulU8sOLZg0cyIbV2/mweHjSE9zrE6rFl2ND+e8R2S1SK7rfiPxR094bZwhocFMW/E1yYnJjL70TlKSUwAYPGIgj7/+CFMmTePd598HwMfHh4/nvU+dBrUZe+3DbF3vSE6GRYbx2S8fEl41nKGtriIj3fPnlLfcZ6tVIffxpqhOTblk5rPsm7mcZWPyEsmd37qTBsN7sejmVzn0y9oi+/eZ8QzV2zbi16HPkLBhNwB+oUH0n/9vQmOr81PH+8g4mZyvT2TLOC785EHC6tcAYPGo18/plU69N7j/IwgREREROTvFxIT7VPYYzlXza444e75oOYP+R6bqdS4lnekkIpUi5MorAUj+/PN85ckffojdZiPksss89g9o1AiArJ07PbYD8IuOJnPr1ryEE0B2NmkLFgAQ2LJlCUYuxRVaJYSrhw/hz3WbcxNOAEcPH+PzD6YQW7smF/d1vxVajosu7krjpg2ZNvmH3IQTwJKFK1i0YBn9B/emarVIAOIa1KVa9aosXbgi3+ons2U7a1f/SVyDukTHVC/yXgEB/rz27vNkZ9t4bOyzZ1XC6VzV6tZ+AKx68/t85ctenordZqPF9b2L7FutaW2qNa3D7nlr8iWcHPF+ACCud5u89k1q4xfoT/yWfV4avbgKDArgsdfG8efKTSQnJZ+5g1OvQT0B+O/bk3MTTgAnj59kxn9nEhQcSIce7b061r5XXkJktQi++XB6bsIJ4Oepc9izfS8Dh1+Kr6/jf4HbdW9Lk5aN+ebDb3MTTgDJicl8SdVuDAAAIABJREFUNP5T5n/3K1Wjqnl1fH83jUf1B2Dza/lXhP35b8f7vOGNvYvs6x8Wgn9oEAd/WZubcALITknn0Pw1+IUEUrVV/Xx9Wv/zOvr+/BzBNSI5tnyr1+YhIiIiIiLlz47POfOQ0lPSSUQqRWDbttgSEgqtMLLFx5O9fz+Bbdt67O/fuDEAWTt2nPFeJx94gBN3FT6/xz8uDoDsE977C3vJ065Da4KCg1i6aGWhupyyrj06eozRubujfpmbGMsWrcLf359O3RxfWCcmOFZ/1a4XW6htjZrRZGRkkuThy/KbbhtOw8b1+ez9yezasdfjuKR4andtTmp8EifM/nzlKUcSSNh5mNpdWxTZN+NUGkte/JotUwtvwZjtXB0XUCU4tyyqheP9HL9Fr115uO3hkdSqW4tXHnmNkuRjV/2xmk9f+5yt6wpvb5izeiikSki+8g492vHa1+P5ecuPzP3rJ96dMYGLL+tV7Hu27eZIRq5ZUngV3bql66laPZKGzRsA0LVPFwB+n7WwUNs538zj1cfe4OjBo4XqJE9M1+akxyeRVOB9nnYkgVM7DxPTrej3eVZyKvP7PcnS0W8WqgtvUtsR51hSvvLm9w7l5Pqd/HLpUxxduMkLMxARERERERFv0plOIlLxAgLwq1GDjM2b3VZnHz6Mf1wcPpGR2BPdbyPn36gRdpuNgFatHGcz1auHLTmZtN9/5/Snn2I/fdptP3x88I2OJmTgQEKGDiVz2zYyVqzw1szERVzDugDs3b2/UN3+fQcBaNi4fqE6V/WdMfbs8hTDmWw4fpI5M39l4NC+PPp/9/PRu//FxwfuGzeGRk0a8OmkyWSkZ7i9T5WwUO556HZOnUrm3Tc/KeYMxRPfQH/CakdxeM12t/Wn9h+nWpPaBFcPJ+3EqUL1pw+fYN0k92e5NBrYCYAT2w7klkW1qAdAZKNYrvruaaJa1CMrLYM9v61n+fhppBxJKOuUzlvNWjdl+J3D+Oz1z9m7o2QryVYtXM2qhe63Pes50LHScZfZnVt22fWDeWT8gyTEJ/LbjAWkpqRx0YALef6DZ/jgpY/5csJXZ7xnnfqOZMXBvYW3JzzsPIuqXqO67Ni8k0ZWAwAO7DnIbeNGcuk1/aheI4q9O/byxVuT+X3WHyWZ7nnHN9Cf0DpRxK92/z5P2XeMiCa1CYwKJyO+8Pu8cEAfqsTVoOnoS4nt246D89aQtDX/z9zCG8dz+Lf13hi+iIiIiIiIlAMlnUSkwvmGhwNgT3a/6iSn3DcsjOyikk6NG+Pj60vY6NGk//47GRs2ENi2LVWuvZbADh04ee+92FNSCvWLeOIJQgYMACBr714SHnsMsrO9MS0poFq1qgAkJRb+ovGUc8VReHiYxxg5W+clJRUvxoN3/5MTJ05y19hR3DV2VG75+29/yvjnJxSKkePa66+gelQ1Pnr3v7lxpWyCqzpel4ykwu9DgIxTjvLAiFC3SaeihERH0OXhawDY/NVvueU5K506PXAlu2av4sja7dRs15gWw3tRr2crvr38WU4f1qrGkvLz9+Px1x9hz7Y9TH5nitfiDhw2gNadW7Fzy042rnKsVomJjeaBF+5j7/Z93Hv1AySddKxw+fDlj3ljyiuMfmQki+ctyZekcieiWgTpaRlkpBVOMiefcvxBQhXn50ZUrSjS0zJ4/oNnaNamGQtnL8Jut3Px4J48/8EzvPbEm/z4xUyvzfvvJtD5Ps9Mcv+HHplJqQAEhIcWK+nU+7uniOnaHIDjKwzL7ppYqI0STiIiIiIiImc3ba8nIhXP35Hvtme4X3Viz3Qe2B4Y6L6/jw+25GQy//qL+JEjSXrlFZInTuTEmDGkzJhBQKNGVBk50m3XrF27OD1lCukrVuAfF0e1CRPwrXXuHdx+LvAPcLzO7lYX5ZQFBQeVPkZG4RhXjxjC5dcM4sC+Q3zx0RSmfvk98cdPcvPo67hq+JAi73Pz6OFkZmby6aTJZ5iVFJevvx+QtxVeQdnOrdX8gwKKHTMwPITLPh9HaI2qbPh4br6znrLSMkjYeYjpQ57ml7HvseT5r/j+mudZPn4aYbHV6fnczWWYzfnrpntvoKHVgPGPvEp2lncS9B17duDhlx4kMyOTlx95Lff8tAFX9yMoOJCPX/0sN+EEkJGWwSevfY6fnx8Dhw04Y3x/fz8yi/j9kun8uQty/tyFhAYTFBxIo+YNGT1gDOMfeY1XHn2d2wfeReLJJO59+m6qRetMp6L4BDjf5+lZbutz3v9+xXyfx6/8C/PeLI6v3EZ0F4uLpz9JQNUq3hmsiIiIiIhUOts59JDS00onEalw9nTHYfI+Ae6/hMopt6emFhHAzsl//MNtefJ77xHSvz/BffuS/O67hZqkfP117nXIFVcQ8eCDRIwdS8ITT5RwFnImaWlpAAQEFn6dA4McCcWUlCJeY6f0NMfPSqC7GM6kZKozRqeu7XjxtadYu2oDt1x7N6eTHStpXouZyNSfPuHlt59h4/rNbNua/xywDp3b0qhJA36b9weHDh4pyRTFgyznKhPfAPf/q5HzJXRmSnqx4gVXD2fIfx+lRpuG7J6/hsXP508Qzrmj8JkwAKsnzqT5iN7U79ce/9Agsop5P4EGzepz8/03MP3j79ni5kym0ujerxvPTXoa/wA/Xrz/Jbas3Zpb16xNMwA6XtQ+d9u7HDnnPjW5oMkZ75GelkFAEb9fApw/d6kpjs8nm82R8Jr8zhSOHjyW2+7w/iN8+/F33DZuJD0GdOenr34u5gzPL9k57/PAIt7nzs/u4r7v/nwxbzVd66eup/k9Q2j16DDWPvlZ2QYqIiIiIiIiFUZJJxGpcPbTp7FnZ+NTxf1fL/uEheW2K3Hs1FSy9u8noGlTx0qpIv7aHSD1xx8JHTaMwC5dHKuvstz/pbZ41qJVMwYM7lOo/NDBowCERxTeQi+n7Exb2SUmJOW2P34s/9ZoBWNcc/3lALzywoTchBPA8WMneO3f7zDx4/FcNWIIL//rrXxx+g28GIDZM37xOBYpmYxTKdiybQSFh7itDwwPzW13JhH1azD0y8eIbFCTXfNWM/fuCdizi/l3R3Y78Vv2Elm/BmGx1UnYUficn/Ndkwsa0/PSHvnKfP186dyrE/FH4vl4/Kdeuc9l1w/m4ZceALudfz8wnl9++C1ffZjzPX3lLZcXGSOiqmN71oHDLyW2bs18dX9t2sGiuYs5lXiKoJAGBAQGkFlgpV1YuOP3zmnnNnunndvCmQ3bCt1r+yZHgjrnjCgpLDMpBXu2jYAi3ucBEY7yzGK8zwva+PI0mozqR+1LOyrpJCIiIiIicg5R0klEKl5WFtlHjuAXG+u22q9WLWwJCdhPuT//wScsDP/69bElJpK9f3/h+qAg7NnZjiRScDCBbdpgP32azE2bCrXNPnIE/7p18Y2IwHZC572URstWFmMfvatQ+YihowGoF1enUF1O2c7tuz3G3rVjLwB14+rkXhcVI7a24wvo7dt2FYrzl3N1U+26hX/m+vS/iMzMTObPXuBxLFIytsxsTu0/Tni9Gm7rw+vFkBqfRHqC5+RyVMs4hn75GKExkWz95g/+98hHhRJO/sGBRLWoR1ZaJvFb9haK4R/sWBWXs6Wf5NfkgiaMevjWIuvnbZ9VqCw8Mow/DvzK2iXrGDvs4TPe46b7bmDM46NJT03n2bufZ/H8pYXapJ52rFoc0f0mDu31nBwcNGwA7S9sl69s9rS5LJq7mH0799OmS2tq1avJvh35f0fExjk+A/bu2AfA/l37adG+OQFuVur4OVfppaVqdVxR7JnZnN5/nCpxMW7rq9SLIT0+icwi3ucBVasQ3bkZKfuPk7hlX6HYaUcSCImt7vVxi4iIiIhI5dC2decHJZ1EpFJk/vknIZdeil/duvkSR75RUfjVrUvG0sJfSObwb9qU6m+8QfrixST885/56nyrV8cvNpas7dvBZsM3LIxq48eTaQwn7rwzfyA/P0fyKjkZW2KiV+d3Pvl2yky+nTKzUHlIaDCpKal0ubBjobpuPToBsHblBo+xVy1fC0DXCzuy8H/5fya69uhIdnY269c4kok5K6EaNq7P8aPx+do2aBTnaHPkeL7y0CohNLEasXXTXyQlnvmQeymZwysN1rU9iWxYi8Rdh3PLQ2tWpWrDmuz+dZ3H/hENajJ08mOERkey7oOfWfL8V27bhdaI5JoZ/yJ+y16mDngyX51/cCDRreqTcjyRU/uPu+1/vpszbS5zps3NVxYWUYVht1/jtv0N91xPRnoG0z/6lkP7z7wl5TW3XcWYx0eTnJTM47c+xYYVf7ptt2PLTnoNuojmbZsVSjrVbViHy28awrplG1gyf6nHRNefKzZy2XWDaNetbaGkU7vubTmVmMyevxzJyfUr/qT/1f3o0KM9axbn/3ls7tzub8eWnUjRjq8wNBjWk7BGtUjemfc+D65ZlbBGtTg0f22RfSOa1uGiL8axf9YKlt6efxWqf3gIoXWjOeUSU0RERERERM5+vpU9ABE5P6XNmwdA2B13gI9PbnnYHXfg4+tLyk8/Fdk3888/yY6PJ7BbNwLats2r8PcnfOxYfAICSPn+ewBsx4+T8eefBFgWQZdcki9O2OjR+EVHO8aSne3F2Qk4zkyZO+s3OnZpS1/nFnYANWrFcOuY6zh86Ci/zfvDY4zli1dzYN8hrr/1GurUy1uldGHPLlzUuxvzZv2PE/EnAfj5R8fP1Lgn7yE4JDi3bXhEGA8+fjcAM7/P/8V6y1YWfn5+bFhXeBWclJ35dhEA3R4bnu993u2xEfj4+rJ58m9FdQUfHwZMvIfQ6Ej+n737Do+q6Ns4/k1vhBZ6DXXovVpAAQVRREVFrGB7RFQUH8XefURFFBX7qygqggKCotKxIIKAiLShdwglkEr6vn/sJtkkm96I3J/r2iuzM3Nm5uzmZOH8dmb+/uinXANOANH7jnF0w27CWjeixRXnZCnr9ehwgmtUYdO0JcU7mbNMbHQcn0z6zOMjKTGJ2OhYPpn0WY5gVXYt27VgzFN3kZiQxIPXj8814ASwaPZiUlJSuWP8rVSvWS0j38fHm7HP38N1d11L5WqV8x37rwtWEBcTx4jRwwl1LccHMHj4IBo1a8j86T/gcDj3clr23c/EnIph2K1X0rh5o4y6DZrUZ+jNQzgRcYJVy1bn2+fZbO/XvwLQ/tHhWa7z9o85r/Ndny/L9dgTa7cTd+A49QZ2JaxHy4x8Lx9vurw0Em8/X/ZM/7n0Bi8iIiIiIiIlTjOdRKRcJK1dS8LSpQT264fPlCkkrV+PX9u2+HfsSMLy5VlmOoWMHAlA3NSpzoyUFKInTqTq889TbeJEEpYvxxEVhX+3bviGh5OwZAkJP/2UcXzMpElUe+stqjz+OIl9+5J65Ah+7dvj36YNyVu3Evvhh2V45meXiS+8zXkX9OadT17lu9kLOBl5iiFXDSSsRnVGj/wvycmZ+2il7w21+R+bsdRdWloaTz38P96f9jpzF3/B3G9+JCQkmKFXX0LkiVO89MzrGccvXfgrX3/xLdfccAULV8xi0Q/L8A/wp/+gvtSpW4t33/iY9Wuz3vBuFN4QgL27sy7rJCXjwG+b2D5vJS0u782wuU9z8Pct1OnWgno9W7Hj+1XsdZvp1P2BqwD48/XZADQd2JVaHZuSkpBEcnxCRrm7+GOn2PS5M3D18yP/x9CZjzNg8l00G9yd6APHqdfDUKtjUw7+sYW1b88rgzM+u6XvDZW+txLAqAdvxtfPl51bttHrwh70urBHjuNWLf+Tzeu2cGD3Qd578QPueXo0ny77mN8WrCA2KpaeF/YgvGVjVixayaJZi/IdR8ypGN598QP+O+EBPl74Psu++5kadWpw4ZC+7Nu5n2lvfZml7qvjJ/HUlCd4f/4UlsxdhsPh4IJL+xAYFMgL903IsS+UZHX0103s+3Ylja7oTb/vn+HYis2EdW9JzV6t2P/dKg4vzpzp1OZB53W8+TXndU6agzXjPuC8aQ/Rd+ZjHPhuFYmRMdTu044qrRpyaNFfbP/oJ0/dioiIiIiIyBnKK/2bniJSsUVccEHFu5h9fAi5/noCBw3Cp2ZNUiMiSFi4kLivvoLkzJt8tZcvByDigguyHO7Xpg0hN92EX/v2ePn7k7JvH6fnz+f0t99Ctr9tPvXrU+nWW/Hv1g2v4GBSjxwhYckS4qZPh8SKt19H740VZznARuENePjJ+zi3b0+8fbzZunEbb038gN9+XpWl3rDrhvDq28/xzfR5PHzv01nKzu3Tk/seupO2HVoTHxfP6pXrmPji2+zZlXP/nutuvooRNw+jecsmOBywdfN2pn7wJd/PyTkj47bRN/L48w9y/38eZd6sM/vG5n+D2pb3EIrE29eHzmOG0Oqa8wmpXY3YQyews1fw17vfk5aUGXS8e//nALzT8EYAzn36RjrePijPto9v2svMQZlLbFZtVpceD15N/XPb4B8SSPSB42ybk7OviuKrtLz3NSov8zfPJTY6luG9bsiSP+jagTz2+sP8OHMBLz3wirPupm+zzDby5K2np/D1R7Mznvfq14Ph/7kG06El3j4+HNp7iJ++XsicqXMLFQDqd/kFjBg9nPAWjYk+FcPqn//ko5c/5sTRnPv3tevWllvuv5F23ZzX2Zb1W5k6aVqeM7NKw71pdcq0v5Li5etDq3uHEH5tH4LqVCP+4An2zvoNOyXrtXfN4S8A+Lpu1t+dqh3CafvgMGr0NPgE+BG7O4LdXy1n+0cLIC33f960efAq2v53GCtGTeLQT2tL5+TKwAUb3ivvIYiIiIhIIdSsGeqVfy3xZH7tERXm/uWlEdP1PheRgk4i/xIVMugkRVaRgk5SMipq0EmK7kwNOknpqahBJykeBZ1EREREKhYFnYpOQaezg/Z0EhERERERERERERERkWLTnk4iIiIiIiIiIiIiIlKq0jR36KygmU4iIiIiIiIiIiIiIiJSbAo6iYiIiIiIiIiIiIiISLFpeT0RERERERERERERESlVaWh9vbOBZjqJiIiIiIiIiIiIiIhIsSnoJCIiIiIiIiIiIiIiIsWmoJOIiIiIiIiIiIiIiIgUm/Z0EhERERERERERERGRUuUo7wFImdBMJxERERERERERERERESk2BZ1ERERERERERERERESk2LS8noiIiIiIiIiIiIiIlKq08h6AlAnNdBIREREREREREREREZFiU9BJREREREREREREREREik1BJxERERERERERERERESk27ekkIiIiIiIiIiIiIiKlKs3Lq7yHIGVAM51ERERERERERERERESk2BR0EhERERERERERERERkWLT8noiIiIiIiIiIiIiIlKqHOU9ACkTmukkIiIiIiIiIiIiIiIixaagk4iIiIiIiIiIiIiIiBSbltcTEREREREREREREZFSlVbeA5AyoZlOIiIiIiIiIiIiIiIiUmwKOomIiIiIiIiIiIiIiEixKegkIiIiIiIiIiIiIiIixaY9nUREREREREREREREpFSleZX3CKQsaKaTiIiIiIiIiIiIiIiIFJuCTiIiIiIiIiIiIiIiIlJsWl5PRERERERERERERERKVRpaX+9soJlOIiIiIiIiIiIiIiIiUmwKOomIiIiIiIiIiIiIiEixKegkIiIiIiIiIiIiIiIixaY9nUREREREREREREREpFQ5ynsAUiY000kqFGOMAqUiIiIiIiIiIiIiImcgBZ2kwjDG9AbWlfc4REREREREREREREQkJ80akQrBGPMa8ADgVd5jEREREREREREREZHCSdOd3bOCgk5SUQxDAac8nTwUXN5DkDIU7JNQ3kOQMmaSkst7CFLGHPpX2lmnVWB0eQ9ByljzSefCb1PKexhSxmLPG1PeQxARERERKTVaXk9ERERERERERERERESKTd+hFRERERERERERERGRUpVW3gOQMqGZTiIiIiIiIiIiIiIiIlJsmulUyowxe4DGwHxr7WXGmNrAPcBVrvwEYCfwKfChtTbZdVwIcBcwAmiB873aAXwNvG6tjcujz2rAHcBgoC1QBTgJWGA+8L619lQux4YDu11PH7XWTjDGtATGAAOBBkCKa8xzgTdzaytbu01d53Mx0AgIBo4Cq4GvgFnWWoeH4/LK22utDfdQ3gi4BbgQ52sX5io6CWwCfsL5WsfkMtb09t+31t5ljLkbGAfUBw4Dy3G+Fy+66n1vrR2Sx+ljjAlynW8lVxsNrbWpeR0jIiIiIiIiIiIiIlKRKOhUhowxfYGZQC237BCcQZEewBBjzOVAE+BboHW2Jjq4HsOMMX08BU2MMSOAd3EGmtzVcj3OBx42xtxprZ1VgDGPAqYAQdmKurge9xhjBlpr/8qjjceBpwD/bEUNXY9hwB/GmKuttQfzG1Me/XgBzwEPe+gLnOdQD7gIGG+Mucxa+2c+bT4ITHTLaoLzdTTA065+BhpjqllrT+bR1FCcASeA6Qo4iYiIiIiIiIiIiMi/jYJOZacJzplBVYBdwDwgBuiFMwgCMAh4ArgRaApsd9WLAroBQwAvoBPwLM7ZNxmMMbcC/+eWtQ/4DjiEM9hyOc4gT3Xga2PMbdbaT/IY8yXAeTiXYfwbWOgaczvgSsAPqAl8a4xpaa1NzN6AMWYycJ9b1s/ACiAeaOYaU5jrdVhpjOlmrT3qVv8h18/HgGrZ8qKydfcSMN6VdgBLgD9d9aoCPYELcL6GtYBZxphW1tr4XM6/BTDKQ/58a+1BY8wPwBWu12EY8FEu7QBc75b+PI96IiIiIiIiIiIiIv86OZa0kn8lBZ3KThvXz9eBh9xnuhhjXgAedz19yvXzDeC/2eqNBNKDRCONMQ+mL0lnjGkLvO/W30TgcWttktvxDwKv4AwCeQHvGGNWW2s35TLmPjiX0rvNWjvVvcAY0xlYijOY0wi4GvgiW51hZAacjgDXWGt/y1anCs5gzdU4A2JTcS4LCIC1dqKr3j24gk7pednaaQk86HqaBFxirV3qod75wI84Z5g1BC7DOfvMk36unx8Bz+MMXvXBuUwfwDScQSdwLoPoMejkWu5woOvpprxmhYmIiIiIiIiIiIiIVFTe5T2As8xKa+04D0urvYpzb6d0fwA56rkCP9b1tBrOmULpniYziPiJtfYh94CT6/hEa+1YMoNDgcCT+Yz5xewBJ1dbf+EMoKXr517uWuruBddTBzA0e8DJ1U4UzoDNZlfWJcaYHvmMyZMbyDz/yZ4CTq7+fsUZ2ErXNZ92f7LW3mGt3WetjbLWfud2Ht8Dka70BcaYOrm0cQ2Zy/1plpOIiIiIiIiIiIiI/Csp6FS23vKU6Qq87HPL+r/0GUwebHZL1wQwxgTh3DMInDOTHs9+UDbjgfSA1tXGmNA86r6bR9nPbunsAZdzgVau9FJr7ercGrHWpgAvu2XdkEefuVmK87zeBz7Mp+4Gt3Re5w55nL8rqJc+S8obuDaXqunn4wC+zKc/ERERERERERERkX+dNK+K85Ci0/J6ZWtNHmURQEtXen0e9WLd0gGun+eQOZPmD2vt4bwG4dqPaCXO/Zp8gN4492vKbp+1NiKPpo65pQOzlfVxS6/Lazwuv7ulzy1A/SystT+TNQjmkTEmBOd+Wen88jlkZT7l04C7XOnrgDez9dcAON/19BdrrXtwUURERERERERERETkX0NBp7J1KI+yNLf0yVxrZa2XrolbeoOHck/+xhl0gqxBGHd5BZwA4t3S2WfNtXFLP2SMeaiA4wLnHlHFYowJBgzOJQibu9IdgfZk/b3PK24dZ609lkc51trfjTE7XH30NsY0ttbudasywq2PaYU7CxERERERERERERGRikNBp7IVn38VIHPpu4IKc0tH5lorK/d61XKpU9DxQs7gTW5tFkSRjzXGXAWMxTlbyieXaql5lLk7VcBuPweecaWvI+tSgde7fiYA3xSwPRERERERERERERGRCkdBpzKUxz5NxeUe8CloH+5Bl9IYl/vv1lRgUyn0kcEY4wd8hjPok91BV/9rgOVAOPBBAZpNKWD3HoNOxpjWQCdX/veuvbtEREREREREREREzjqelvCSfx8Fnf4d3GctheVaKyv3egWd0VMY7ksErrTWFiTIUxxPkBlwSgSmAPOAv6y10e4VjTF3l2TH1tqdxpjfce6t1ckY08Jaux0Y7lbt85LsU0RERERERERERETkTKOg07/DTrd0hwIe09EtvasEx+Kpze7kM7PIGOMDhFprCx0AM8YE4lxSL90Ia+2cPA6p6ZbOa0+nwpiGM+gEMASYBFzuen4C+KGE+hEREREREREREREROSN5l/cApESsInMpuF7GmLp5VTbGNAR6uJ6mAatLYUy/uaWHugJDebkSOGmMOWmM+dJDeV5LALYCqrjSkfkEnAAudEuX1DUwA0hypYcYY+qRubTeTGttcgn1IyIiIiIiIiIiIlLhpFWghxSdgk7/AtbaWCA90OILvJjPIRPIfO9/KsrsogJYDBx2pWsCj+VW0RgTQOaeSFWBzR6qpbrV98lW5u+WDs0rwGWMuRTo65bll1vdwrDWngS+dz09D7iBzFlUWlpPRERERERERERERP71FHT693gRSJ9NM8oY84oxxj0YgzHG3xjzBnC9KysBGF8ag7HWJgEvuGU9YYx52BiT5XfOGFMFZ1CmrSsrEnjbQ5MxbulG2cq2knnufsCLxpgsy+YZY7yMMTfhnJHkLji/cymEaa6fvjj3mALYZa39vQT7EBERERERERERERE5I2lPp38Ja+3fxpgHyAzYPAQMN8bMwznjqA4wlMyAjQMYa63dWIpjescYcwFwDc5ZPy8Dtxtj5uPc5ygc57J61V2HpAIjc5l5tZfM5epmG2NmAj7W2hestdHGmKnAHa7yccAAY8wSnEGs+sBAoImrPJnMGU5hJXAscDoGAAAgAElEQVSq6X7AeV5hQGVXnmY5iYiIiIiIiIiIyFnP4ZV/Han4FHT6F7HWTjHGROMMPFXGGWC6x0PVSOBma+38MhjWCGA/MBbwAVoA93uodwK4zVr7XS7tfIgzaAbO4FMnAGPMFNfSdg8ALclcOq+D65HdRmAk8DvOZfk6GmN8rLWpHuoWirU2yRgzA7jbLVtBJxERERERERERERE5K2h5vX8Za+00oCnwOLACOAakAKeA34CHgeZlFHDCWptqrX0Q5/J5rwHrcQa9UnAGmn4DHgGMtXZuHu3MxzkraiXOpfaSgQNAQ1d5HNAf52ynpa62U4FYYAcwC7gJ6GytXQssczVdBbis5M6YL9zSq62120uwbRERERERERERERGRM5aXw+Eo7zGI/GsYY4YB37ie3mut9bQ/VanY2nKwLuazyNVRkeU9BCljk2lc3kOQMvaM7/HyHoKUsXf8S3KrSakImk86t7yHIOUg9rwx5T0EERERKYaaNUO1SFwRvdfwxgpz//Ku/Z/rfS4iLa8nUrJudP1MAr4qz4GIiIiIiIiIiIiInCnSynsAUia0vJ5ICTHGNAQudT2da63VV9RFRERERERERERE5KyhoJNIERljQowx3q50I2Am4OcqLrNl9UREREREREREREREzgRaXk+k6AYDnxpjYoEaQPo6nz9Ya38pv2GJiIiIiIiIiIiInFm0vN7ZQUEnkaLbBwS5Hu55/ymf4YiIiIiIiIiIiIiIlB8trydSdDuAtUACEAF8CpxrrT1QrqMSERERERERERERESkHmukkUkTW2hNAt/Ieh4iIiIiIiIiIiIjImUBBJxERERERERERERERKVWO8h6AlAktryciIiIiIiIiIiIiIiLFpqCTiIiIiIiIiIiIiIiIFJuW1xMRERERERERERERkVKV5lXeI5CyoJlOIiIiIiIiIiIiIiIiUmya6SQi5cfHm2o3XU7Vawfh16A2KcciiZq1mBMfzISU1MK15eVF4xmvEdSpFVtbDs5RXPfV/1JlaD+Ph574YCbHJk4twglIQdSpV4uxj42mx3ldCa1ciS3/bOPd1/6PP375s8BtdOzWjnvG/4c2HQw4HPzx6xpef2EKB/YeylLPz9+P1buW4efn+eNtyLnD2b1jb8bzXn26c9s9N9G2U2sCAvw5sPcQ877+ganvfElqaiF/B8UjLx9vGtw+iPo39CewUS2Sjp7k0PTl7H1rLo4CXOchpgFNxw+nSrcW+FQKInbjHva9N59jP6zOWdnbiwajBlLvxn4EN6lL8oloIn/9h50vzSAp4mQpnN3ZqVa9mtz5yG10OaczIZVD2L5xB5+8/hlrf11X4Dbadm3D7Q+NxLRvicPhYO2Kv3j3xQ85vO9wjrrhLRpz5yO30a5bG/z8/di0djMfTPiYbRu356jbrlsbRo27hZbtmuPr58vmv7Yy7a0vWb/y7xx1p8x5gw492nsc38RHXmfutO8LfD5nPR9vwm4ZQvXrLsa/YW1Sjp7k5DeLOfruN0X6PG826xWCO7finyZDchT71atJ7XE3ENKrA77VK5O4Yz8nvviRkzMWltDJSEGkpKYx/fctzPnTcjAylhqVgxjatQWjLuiAn0/+32vcdjiSdxauY+3uCBJTUmlcozLDe7fm6p4mz+N+2bKf+z5dzFf3XU6remEldToiIiIiIlKCFHQSkXJT++m7qXbdYOLXbCR26R8EdWlDzftvIqBVEw7d979CtVV91BUEdWqVa3mAaULKsUhOffVjjrL4tZsKPXYpmLCa1fls3vvUqBXG/NkLiImOZfCVF/PBjMncN3I8yxf8mm8bXXt14sOZbxIdFcPcGfMJrVyJwVdeTI9zuzJ84CgO7c+8Sd3cNMXPz5fflq5kg4f39WTkqYz0ZcMG8b+3nyIuNp7F85cRGxPHOX178sATY+jYrT333fJwybwIZzkz4Tbq3zyAU39s4djCNVTtbmj2yHBC2zbmn9tfz/PYSm0a023+c4AXEXN/JyXmNDUHdaPDJw+y/dnP2ffOd1nqt3lrDHWvPp/ov3dx4OOfCG5Rn3ojLqTaOW1YPeARUqLjS/FMzw7ValRjypzJVK9ZjUVzlhAXE0f/of2Y9OXLPHbrU6xYtDLfNjr2bM+k6a8QExXLj18vICQ0hAFX9KfzOZ2445LRHDkQkVG3cfNGvPPtZLy8vVg0ZwkOB1x8VX/e+XYy9wx7gK1/24y6Pfp245Vp/yMuOo6l3y0nNTWNfkP6MnnmRJ4d8yJL5y3PMo4mpgl7d+xjydxlOca49e9tRX+RzkL1nhtN2PWDiPtzE8cXryakW2tqj7uRwNZN2Hf3hEK1VeO2oQR39vx57lsnjGbfvoZv1VCiflxBckQkoed3psGEewlq25RDT71XEqcjBfDS3JXMWr2NzuG16du6Eev3HuWdRX+x7XAkE2/0/CWfdPZQJCPfm09SSioXtW9CWKVAlm/ZxwtzfudAZDT3X9Ld43G7jp7i6W/y/3eDiIiIiJy50sp7AFImFHQSkXIR1Lk11a4bTPSPv3Jo7EsZ+XVfHkeVKwcQdUEP4pZ7mMnggV/DOtQYe1PuFXx9CGjagNjlqzn+1hfFHboUwj3j76Rew7qMufFBfl60AoBPpnzBzEVTeWLCQ6xY9gfJScl5tvH0xEc4fTqB4RePJOLwMQC+n7WAD2e+yX+fvpdxtz+WUbdlm+YAfDV1dp4BrYDAAB598QFiY+K4ZsDNHHTNrvD19eHNT1+l36A+DBh8AYt/WF6c0z/rVenWkvo3DyBi3h9svCMzwNTmzbupO7wvYRd14cSi3GfHmFduw8vXlzWXPkHMht0A7HrpK3oseZmmD1/LoenLSDkZC0Cty3pS9+rzOTJnBZtGvwUOBwCNRl9Gi2duouHtl7B70qxSPNuzw+0PjaROg9qMv+Vxfl/8BwBfvjuT//vxXca9NJbVP6/J95p+6JVxJJ5O5I7Bozl2+DgAi+YsYdL0Vxjz1F08eeezGXXve24MQSFB3HHp3ezYtBOAbz+bx/vfv824/93HnZeOAcDLy4vxr/2X+Jh4Rl38H44eOgrAtLe+5NMlH3Hv06P5ef4vpKY6/4tTp0FtQqtU4ocZP/HJpM9K9kU6ywR3aUXY9YOI+uE39o15OSO/wcT7qTasP6H9uhOztGAzW/0b1aH2uBtzLa/7yCj8alZjzx3PE7PY+W+EIz7eNJ3+P8JuupQTn/9A4rZ9xTshydf6vRHMWr2NAe3DefX6C/Dy8sLhcPDk17/y/bqd/LJlP31aN8z1+CkL13E6KYVJN/WjX9vGAIy5uAsj3prHZ79s4pqerahfPTTLMX/uPMzDXy7jZFxiqZ6biIiIiIgUn/Z0EpFyUfWGywA4/vaXWfKPvTYVR1oaVa8dWOC26rw4lpSjkSTuOuCxPKBZQ7z8/Ui0e4o8Xim8oOAgLr/mEjat35IRcAI4FnGcLz6aSZ16tTi/f+882+jdpwdNW4Qz58vvMgJOAKt+XcPKn1fT75I+VKlWOSM/Pei0bfOOPNvtcW4XqlSrwuwv5mUEnABSUlL5cPKnAJyXz9gkfw1udV7Hu1/7Jkv+jhen40hLo/4NuX8b3qdSED7BgRxftC4j4ASQGp/I8YVr8QnyJ7R9k8y+bhtESkw89pH/ywg4ARz4dBGHZ/5C0vGokjqts1ZQcCADr76YrX/bjIATwImIE3zz8Rxq1a1Jrwt75NlGt/O70rh5I77/6seMgBPA2t/+Ys0vazlv4LlUdl3TDZrUp0ffbvy28PeMgBPAbruHhbOX0LpTK5q3bQZAvcZ1OR13mp++WZQRcEof2/qVf1OjTg3qNKyTkd+sdVMAdm7ZVYxXRADCbroUgIjJ07PkH3nlMxxpaVQbfnGB26o/4V6SI07k+nnuWyeM+L+3ZwScAEhNI+oH52dMbjOkpGTNWLkVgLv6d8LLy7kTtJeXF/cN6oaXF8z+M++ZgpsOHKdykH9GwAkgOMCPQR2bkuZwsHF/5ud9QnIKz37zG//5aAEOB7SuryX1RERERETOdAo6iUi5CO7ejpTIKJK2782Sn3I0kqQ9Bwnu3q5A7VQdMZiQXh058uRbOBI9f/s1wDhvTCfY3R7LpXR06NqWgMAAVq9Ym6MsPa9b7855ttG1d6cs9bO34evrS5eeHTPyWrZpTkx0bJYl9zw5uP8wb7z4DovnL89RlpyUBEBwSFCebUj+qvZqRdLxaOK27s+SnxRxkvidh6nau3Wux6bGnmZ1v4f559bXcpQFN6/vbOeYc7lEn+AAqvZsxckVm0g5FZelblp8IpvvncLBzxYX93TOem26tCYg0J+/fl+fo2ydK69T7w55ttGxl3MPpdza8PX1oYPr73/Hns621q3IWfcvV16nXs7r/+CeQ9zYdxRvPj0lSz0vLy8aNm1ASkoqUSejM/KbtXEFnTYr6FRcwT3aknIiKscMo5SjkSTuPkSlngX7PK9+wyVU6t2Bg49NIS0hyWOd3dc9ys4rxuXID2jawNnnce3dVhbW7T5CtZAAmtepliW/VuVgGteowtpdR/I8vkpwAHGJyUTHZ/1321HXEqjVQgIz8k7EnGbOmu2c36oBM8YOpXntrH2KiIiIiMiZR8vriUiZ8/Lzxa9uTU6v3+qxPPlABAFNG+JTrTKpbjcJs/OtU4OaD93Kqa8XEP9Hzk3i06UHnfzD69No+kQCTDiOxCRil//J8dc/JeVoZPFOSDxqGO4MDOzfczBHWfrsovCmjYrcRnpgyb2Nlq2bceRQBPc9ehcDL+9PnXq1OLD3EF9Pm8PnH87MqLdr2x52bdvjsc/+g/sCsENBymLx8vclsH4NotZu91iesP8YIS3q4xcWSvKJmPwb9PYiqFEtGt5xCTUGdObYwrXEbXEGs0JMA7x8vImzB6jctQVNx19Lla4tSEtM5viCNex44cuC9SF5qte4HgAH9x7KUXZkv/Mmc0PXzf/c1E9vY4+nNiKytFE/PPf+Dh/Iuz8fXx8aNWvILWNvpGmrJsz6eA6xUbEZ5c1aNyUtLY323dsyfuKDNGzWgJioWJbP/4WPJ35KXEycx3YlKy9/X/zr1ST+r9w/zwObNcCnemVSI3P/PPerW4M640cSOWMhcSs3FLBzL3xrV6f6NQOofv1ATm/cSczynF9QkJKVlJJKRFQ87RvW9Fher1ol9hyLIjI2geqVAj3Wuaan4eXvVvHoVz8z/vJehIUGsWjDbr5bu53W9cPo2jRzVmLl4AA+uWswncNrl8r5iIiIiEjZ0p5OZwcFnUSkzHlXda7Tnxod67E8Ldb5TVfv0JA8g051nr8XR3wCRyd8lGd/ASYcgBpjRhCz8HcS/t5KYEdD1WEXEXJuZ/Ze8wApESeKcCaSl6rVqgAQE53zZn+s64ZupcqVCtRGdFTONmKis7YRVrN6xiMwMIBlC34lKDiIPgPO4ZEXxtGqneGJsc/n2V/TFuHccMdwEhMSmTdjfj5nKHnxq+p8X1KiPN+8T4lxXue+ocEFCgh1nfM0VXs5Z0adWrWVjf+ZnFHm7/q2fWj7JjS6ewhRa7Zz6PMlhHZsRr3r+1GlRyv+HPQYqTGni3VOZ7v0pSxjPbyn6dd0SGhIwdrw8Pc/o43KzjYq51E3Lv36z6W/GSs/p3a9WgAsn/8Lbz79TpbyZq2b4u3tzW0PjeLn+b+wftUGOvXqwLW3D6PruZ25+4qxxLs+iyR3PlXSP889X+epruvcJzQkz6BT/RfHkHY6gcMvflzgvhu89gDVrrwQgISdB9gz6hlI1X9hS1uUa3ZSaJC/x/JKgX4AxCYk5Rp0GnFuG3x8vHnlu1VcPjFzr71eLerx8ogL8PHOXIwjNNBfAScRERERkQpGQScRKXNevs4/PY5cNptPz/cK8HxDA6DyFf2o1Lc7B+95kbR8vpHuSEwiafdBDox5nqQdmcv/hN01nJrjbqH2k3dx8J4XC3sakg9f1/uclJjzfU5KdC6dFJDHewzg6+dsI9nD70pSUtY2atSszvatO9m1bQ+PjHkm45jQypX4cOabXHHdpSz+YTnLF/zqsa/adWvyzpeTCA4O4uWn3uCI274wUnhefj4ApOVynaclpgDgHZj370C6U39uI2rdDqp0b0nVnq3oMutJ1o94iZRTcfgEO29shvXrxO43ZrPrpRkZxzV/5iYaj76Mpg9dw/anPivOKZ31Mq7ppJxLnyW7rnP/fK5pH7/0NnL+XqQvbZnehq+vT5a23aUf75/L78+qpatJOJ1A13O7cMGlffjfx8/x1H+eJSkxGS8vL2KjY9m2cTvjb3mC40ece0t5eXkx7qWxXHHTEG598BbefvbdPM9FnDOXoSCf5365tlH1qgsJvbAbe0e/lO/nubsEu5djH8wmsFU4oX260HTmy+y+6UmSD+pvd2lKSXMG9vx9PK/S7u/jvG6TUlJzbWPDvqN8vGwDfj7eDOrYhNBAf1btOMSqHYeYsnAdjw7tlbFXlIiIiIiIVDwKOolImXMkOL8l6+Xv+SZUer7jdILHcp+wqtR+9E5iFq4gZuGKfPs7OOYFj/kn3p9JlWsuptKFPfEKDsQR77k/KZpE1/vs5+F9Tr+pHB+f98yTxNN5tOHvbOO0qw27eQdX9r0hR72Y6Fhee/5tPpn9DoOvvMhj0KlheAM+/PpNGjSqx8xPZzPt/a/yHJfkL31PlvSb0tl5BzjzUwt43e184cuMdPMnb6DxPZfTbPxw7KMfg+smaNKxU+x+9Zssx+16ZSb1bx5Arct7K+hUTHld036uoEJCPu9nUnobfh7acF3T6W0kun6H/Pxz/g75u8ZwOpf+Xh3/OgA+Pt48PvkRLrqyP8NGXcn092bicDi4a8i9OY5xOBy889x7DBw2gAFDL1TQqQDS0j/PPbyfkPl5nhbvec9F3xpVqfvE7UT99DvRP/1eqL6Pv585Q6b6jYOp//xo6j13F3tve65Q7UjhBLiCz8m5zCpLSnUGm4I8XLfgnAF179TFpKU5+Orey2lc0zmjOTkllUdn/MLMP7bSrHZVhuex55+IiIiIVFyO8h6AlAkFnUSkzKXGxuNITcW7UrDH8vT81Fy+8Vz76bvBx5sjz77jsbzAHA4St+7Gv2Fd/OrUIGnXgeK1d5YybVvQ/5K+OfIjDju/bV6pcs7lr9KXxIrN51vt6cvqVQoN4cSxrHtvhbrajcllWSd3WzZYAOo3qpejrF2n1kz54jXCalRnxtTZPD/+lXzbk/ylRMfjSE3Dt7Ln69w3NDijXmHtnPAVDW69mBqDumEf/Tijjdgt+3Fk+3Z9Wnwip3cdJrR9E7wD/EjzMGtGsmrethnnDzw3R/6xw84ZQZ6W0CvoNR1zyrlUXqXKIZw8ftJjG+n7KcW4rn9P/aUvwReXyzKt6VJT03jnhQ+46Mr+nDfwHKa/NzPP+qfjE9i/6wAt27XAP9CfpIScs7okU1qM6/M81PN17uPKz20GU73n7sLLx5tDT71XrHFEfv4DNW4dSmjfLnj5+eJITilWe5K70EA/vL28iMnl2ohNcP6NrZTLLMSft+wnKj6RO/t3ygg4Afj5+vDY0F4s/mcP89buUNBJRERERKQCU9BJRMpecgrJh47i36COx2K/BnVIiYwiLcrzzcTKg84DoMWKLzyWt9r2A8kHItjZbxRegQEEmHAciUkkbt2do276En6ORN1YLKpW7Vpy90O358i/ZehdADTwEOhp0NiZt2fH3jzb3rPTuRxi/Ub12Ltrf5ay9ADSnp3ONmrXrUmjJg3ZvnUnpyKjstQNCAwAyHEDuXefHkyeOoHgkGA+eGMqb75UvBufksmRnErCgWMENarlsTywUS2SjkeTcsrzzWjfqiFU7WFIOHCc2M37spQ5klNJjDhFQN3qAMTvOgLkPqvKy8+XtOQU0pJ0I7ogWrRtzq0P3pIj/56r7gegXsOcf7vrNqoLwP6d+3OUudvvCu7XbVgnI53ZhrPdfa42Muq62s5St2F6XWedsNphtOncih2bd3F43+EsdY8fOU5yUjJVqjtvcFeqHEJ4y8ZERUbnGAM4/16kpqaSot+XfDmSU0g+eAz/hp733PFvWJuUE1Gk5vJ5XuUSZ3Cz9WrPsxDb7/6OpAMR2PNvxysogJAe7UiLiSN+3dYcdZMPHiWgST18qlQi5fipIp6R5MfP14e61UI4dNLze3owMoZqIQFUCQ7wWH7E9Te/aa0qOcqqVwqiWkhARh0REREREamYFHQSkXJxeu1mqlzRH7/w+iTvOZiR71urOv7h9YhdtjrXY4+/5TnYVPW6S/CtWZ3jb31Bquvb7741qxH+9eskbN3NnsvHZKnvFRhAYNvmpJw4pT0gimHujPnMnTE/R35QcCCn4xPo1rtzjrLu53QB4O81G/Nse93qv131O/P78lU52khNTeWfvzYDcPVNVzD6wdt45anJfPb+9Cx1u/TsCMCmv7dk5HXo2pbJn75MYGAAE56YxOcf5j0DQgrv1CpL3Wv7ENS0Lqd3ZQYC/GtXI7hpHY4vWpfrsSEtGtBx2niOfr+Kf26blKXMJzSIwAY1iN/pbPP0nggSj0RSuWNTfEICSY3LXHLNt3IwQY1rE2cPgEMT+Qvix5kL+HHmghz5gUGBJJxOoGPvjjnKOrvyNq7dnGfbG/78B4BOvTuy+uc1OdpITU1ly1/OgMKG1c6/D516dWDe599nrXtOJwA2ufrrck4nnnr7Mb56/2umPJc1eBzesjF+/n4c2nsIgJbtWzB55mv8tvB3Hh31ZJa6YbWqU69RXbZv3EFamuflwySruDWbqXZVP/yb1CNp96GM/PTP85ilf+Z6bMQbX3rMr37DJfjVrEbEG1+S6prN6lM5hCZTn+H0PzvYcfkDWQ/w8SagRUNSo+NIORld/JOSPHVuXJvv/9rJ3mNRWWYrHY2OZ9+JaPq0apjrsWGVnHvw7T2W832Kjk/kVHwiLepUL/lBi4iIiIhImfG8A6yISCmL+nYJADXH3QJum0XXfHAkXt7enJrxU67HHn/rC4+PFNdSTcff+oKTn84FIHn/ERI2biewVRMqD7kgSzs1/zsS37CqnJqeM2AixXc6PoElPyynU/cOXDDw/Iz8mrVrcMPt1xJx+CjLF/2WZxtrfv+LQ/sPc81NV1KvYeZsh57nd6N33x4s+eFnTp5wfqN90ffLSEtL45bRI6heo1pG3Rq1whj72GiSk5L5etq3AAQFBzHx/RcIDg7ilacnK+BUSg5//QsAzR+7Lst13vzxEXh5e3Nw2pJcj41au43T+49RY1A3qvQwGflePt6YCbfh7efL4enLnZkOB4e+WIpPcAAtnr0pSzvNHhuBT5A/h6YvK7kTO0slnE7glx9+o323tpx7Ue+M/LDaYVx965UcO3yc3xf/kWcb61du4MiBCC6/8TLqNMicHdP1vM5069OVX39akTFT8fC+w2xY/Q99B5+P6dAyo24TE87FV/Vny/qtbNu4HYCVS1cRHxvP5TdcSoMm9TPqBgYFcv8Lzv2b5n/l/FzZsHojJyJO0KtfTzr16pBR19fPl/tfuBc/fz/mTJ1b1JfprHNy9lIA6jx0c5brvM7DN+Pl7U3k9JwBzHRHJ0/3+Eg5djKj/MQn8wBIiYgkbs1mgto3p8pl52dpp/aDN+FXO4yTc5ZCLnsNScm5rEtzAN5asJa0NGcw3+Fw8OZPa3A4YJjb3+zs+rRuSKCfL1+t3MyBEzEZ+alpaUycvxqHAy7p2KR0T0BEREREyk2aV8V5SNF5OfStX5F/ha0tB1e4i7ne6+OpfGlfTv9tiV/1N0Gd2xDcvR3RP/7KobEvZdSrce8NQO4znNKFz32LwNbN2NpycJb8gLbNaTRtAt7BgcQuXknywaMEdWtLUPuWxK/+h/2jHq9w+z9cHRWZf6UzQJ36tZmx8BMqV6nMj3MWcjIyisFXXkT1GtW4/9ZHWPbTrxl10/eG2rppG0t//CUjv8+Ac3jz01eIiYpl/uwFBIcEcelVA4mNjeP6S27joNtSWvc/Pprb77uFE8cjWThvKf4B/lww8DzCalTn+YdfYcanswG4+T8jePi5sZyKjOLL//va49h379jDj98uLqVXpvAm07i8h1Ak7d4fS+0rziFq3XZOrthElW6Gar1bEzHvDzbe8XpGvSb/vRqA3RO/ycir1qc9nT4fD0DEvJUkR8ZQvW8HKrVqyPFFa9lwy0QcrhvM3oF+dJn1FFW6tSRq7XZO/bGFKl1bULVXa079sYV1Vz2XUbeieMb3eHkPIYda9Wrx0Y/vEFollMVzlxIVGc2AoRdStUZVnrj9GX5b+HtG3fS9oXZs2smvC1Zk5Pfu35P/ffw8sdGxLJq9hKCQIC66sj/xsXH857J7OLz/SEbdlu1b8Pbs13E4YOHsxaSlpnHxVf3x9fPlvqvHsWW9zag76JqLeXTSQ5yOO83SectJSkqmd78e1Gtcj28/m8drj07OqHvOgF68+NGzOBwOln33M1Eno+nepyvhLRuz+NulPDvmxdJ9IXPxjr/nvZHOdA3ffIiqQ/oQv94Su/IfQrq2IqRHO6J++I19Y17OqFdr7AjAGUzKS/P5kwlq05R/mgzJkh9gGtNs5gS8Q4KIXriKpIMRhHRtTXDnVsT/vZ3d1z9GWnxCLq2emZpPyrl/WkUw/svlLNiwm3YNa9C9aV3+3nuUdXsiGNA+nFevvwAvVwDy3UV/ATD6osxZz9+u2c5zs1YQ5O/LgHbhhAb5s3rnYbYdjqRrkzq8e9vF+Pv6eOz3yZm/8t26HXx13+W0qhdW6udZWmLPG5N/JRERETlj1awZqpBEEb3S+MYKc//y4b2f630uIgWdRP4lKmLQCV8fwu68lipXDcC3dhgph44RNXcpkR9+nSUI1GrbDwA5gknZ5RZ0AvBv2oAa991EcO8OeIcEk3wgguh5y3L0VVFUlKATQMPG9Ru4L5wAACAASURBVLn/ibvp1ac7Pj4+2E3bee+1j1n5S9YlFIcOv5QX33ySb7+azxNjn89S1qtPd0Y/eButOxhOx51mzcq/mPy/99i3O+f+MZcNG8QNd1xDi1bNSElJZdPfW/i/t6ZlWZ5v8tSX6X9J3zzHveTHnxk7cnwxzrxkVdSgk5evD43vHUrd6/oSUKc6iQePc/jrX9k7ZR4Otz1z+kfMAGBJ7eFZjg/t0IQmD11D1Z6t8A7w4/TuIxyavoz9H/4IaVn/7HkH+RN+71BqX3UegfXCSIw4ScScFeyeNIs01+b2FcmZGHQCqNe4Lnc9dgfdzuuCt483OzbvYurr01jz69os9S65diCPvf4wP85cwP8eeCVLWdfzuzDqgZtp2b45p+MS+HvVBj6Y8H8c2H2Q7Fq2a8Gdj9xK++7tSElOYcv6rXz4yifYDdty1O16fhduuvd6WndqhY+3Nzu37mbOp3P56euFOeq26dKakfffSLtu7fAP8Gf/rv1898V85nw6j/L693FFDTrh60Ot0VdTdVh//OqEkXzoGKfmLOPY+7OyXOftd38HkCOYlF1uQScA//C61H7gBiqd1xnvSkEkHzzKqXm/cOy9WTgSEkv2vMpARQ06Jaem8cnyDcxbu4Oj0fHUqRrCZZ2bMbJv+ywBo06PfALA+gmjshz/587DfLx8A//sP05icgoNqocyuFNTbsl2fHYKOomIiMiZQEGnolPQ6eygoJPIv0SFDDpJkVWkoJOUjIoadJKiO1ODTlJ6KmzQSYqsogadpHgUdBIREanYFHQqugkVKOj0iIJOReZb3gMQERERERERERERERE5WxhjmgD3ARcD4UAacABYALxnrd1aQv1UAW4DBgPtgOpAvKuv5a6+NpZEX+m8S7IxERERERERERERERER8cwYcwOwEbgfaAMEA5WAVsBY4G9jzH0l0M8lwA7gNaA/UBvwA6oAbYExwAZjzGvGmNzXuS4kzXQSEREREREREREREZFSVWHW1itFxphLgc/InBC0CljkSvcDzgH8gcnGmChr7adF7GcAMBdnkAngIPAdsB+ojDMI1Q3wAsbhDHr9pyh9Zaegk4iIiIiIiIiIiIiISCkyxlQCPiIz4PSAtfYNtypPGmPuAN7HGQx62xjzg7X2WCH7CQI+JjPg9D4w1lqbmK3eDcAnrnp3GmNmW2sXFPa8stPyeiIiIiIiIiIiIiIiIqXrTqCOK/1VtoATANbaD4GJrqeVgIeK0M9woKEr/QswOnvAydXXF8ATblnjitBXDgo6iYiIiIiIiIiIiIiIlK5b3NIv51FvApAeJLreGONVyH6GuKVft9bmtbLhu0CKK93XGFPsmJGCTiIiIiIiIiIiIiIiUqrScFSYR0kzxtQBOrieHrbWrs+trrU2EudeTwD1ge6F7K6DW3pNXhWttTFA+vJ9AUD1QvaVg4JOIiIiIiIiIiIiIiIipaezW3pVrrU81+lZyL66Ae2Ai4EjeVU0xmQPNMUWsq8cfIvbgIiIiIiIiIiIiIiIiOSqhVt6dwHq73NLNy9MR9baKCAK2FSA6oNxznAC2G2tTShMX54o6CQiIiIiIiIiIiIiIqUqrbwHUL7quqUPFKD+Ibd07RIeCwDGGD/gWbesOSXRrpbXExERERERERERERERKT1V3NLxBah/2i1duYTHkm4C0N6VjgdeK4lGNdNJRERERERERERERETExRgzEhhZiEOmWmun5lEe4JYuyBJ27kGngFxrFZEx5l5gnFvWI9baQ7nVLwwFnURERERERERERERERDKFA30LUX95PuWpbmlHAdrzckuX6MqExph7gMluWdOttW+VVPsKOomIiIiIiIiIiIiISKkqSKTlDLIH+LmQ9fMS65YOLEB77nUSCzGOPBljniLrPk4/UbgZXflS0ElERERERERERERERMTFtVTe1BJs0j3oFFyA+kFu6ajidm6M8QPeB0a5ZX8PXG2tTSpu++4UdBIRERERERERERERESk9h93S9QpQv75b+khxOjbGVAVmAf3csj8HRllrU4rTticKOomIiIiIiIiIiIiISKkq0Y2JKp5NbunwAtRv5JbeVtROjTENgIVAa7fsV4Hx1tpSWfFQQScREREREREREREREZHSsxHntlZeQI8C1O/lll5XlA6NMeHAcqCxKysNGGutfbso7RWUd2k2LiIiIiIiIiIiIiIicjaz1kYCf7ieNjLGtM2trjEmjMzA1AlgbWH7M8bUApaQGXBKxLl/U6kGnEBBJxERERERERERERERKWVpXhXnUUpmuKWfyaPeo4C/K/25tbZQKxMaY7yAaUBTV1Y8MNhaO6cw7RSVgk4iIiIiIiIiIiIiIiKl6yNgvyt9tTHmeVeAKIMx5k5gnOvpaWBiEfq5E7jY7fnt1tqlRWinSLSnk4iIiIiIiIiIiIiISCmy1sYZY+4G5uKcEPQEMNQY8x2QAvQHznU75H5r7YHs7RhjpgK3uJ5+aq0d6VbmBzzmVn0XUN8Y898CDvMDa210Aet6pKCTiIiIiIiIiIiIiIhIKbPWfm+MuRH4EAgB2rse7lKAh621HxShi35AI7fnTYFXC3H8N0Cxgk5aXk9EREREREREREREREpVGo4K8yhN1trpQGucwaBNQCyQCOzEGYzqbK19vYjNZw9glTnNdBIRERERERERERERESkj1tr9wMOuR2GPHQmMzKVsIkXbB6rEaKaTiIiIiIiIiIiIiIiIFJtmOomIiIiIiIiIiIiISKkq3UXr5EyhmU4iIiIiIiIiIiIiIiJSbAo6iYiIiIiIiIiIiIiISLEp6CQiIiIiIiIiIiIiIiLFpj2dRERERERERERERESkVKWV9wCkTGimk4iIiIiIiIiIiIiIiBSbgk4iIiIiIiIiIiIiIiJSbFpe7wxkjPG11qaU9zhEREREREREREREREpCGo7yHoKUAc10OsMYY3oD68p7HCIiIiIiIiIiIiIiIoWhoNMZxBjzGrACaF/eYxERERERERERERERESkMLa93ZhkGeJX3IKRi+jSpankPQcqQn1dUeQ9BytgOX7/yHoKUMR/9k+CsszWhcnkPQcqY44EV5T0EKWPNH2tOyJynynsYUsbirnyuvIcgIiJyRtDiemcHzXQSERERERERERERERGRYlPQSURERERERERERERERIpNQScREREREREREREREREpthLZ08kYswdoDMy31l5mjKkN3ANc5cpPAHYCnwIfWmuTXceFAHcBI4AWrvHsAL4GXrfWxuXRZzXgDmAw0BaoApwELDAfeN9aeyqXY8OB3a6nj1prJxhjWgJjgIFAAyDFNea5wJu5tZWt3aau87kYaAQEA0eB1cBXwCxrbY6lK40xeeXttdaGeyhvBNwCXIjztQtzFZ0ENgE/4XytY3IZa3r771tr7zLG3A2MA+oDh4HlON+LF131vrfWDsnj9DHGBLnOt5KrjYbW2tS8jikqY0wl4GZgANAJ5/kH4Tz/PcAy4D1r7Z5sx10MLHA9/cNa2zuffryBA0BdIBmoa609ka1OdeBeYCjQEue+XHtw/u5MstYeN8ZsBQy5vJ8iIiIiIiIiIiIi/2Zp5T0AKRMlPtPJGNMX2AA8AbQBQnAGBHoAU4B5xhg/V5DnT2Ai0BWojDNI0wF4HvjNGBOaSx8jcAaNXgb6AjUAP6AWcD4wAdhpjBlWwDGPAtYD9+EMDITgDGJ1AZ4FthljOufTxuPAFuAhoCNQDQgAGgLDcAbSfjfG1C/ImPLox8sY8zywHXgOZ9CpAc6ASxBQD7gIeA3YYYzpXoA2H8T53jQDAoEmwP+zd9/xURXrH8c/6YWEBEgghCJIGUCQJs2CUgRRsVAsYEHRe/3Zr4jdq9d29dqvBbEXRFDxKggWEEERBKSpIANKDSVACISQBNJ+f5yTZJPsphfK9+0rL2bnzJkzs7uH4D77zFyCEyQ87DYb7Ab6SnIhTsAJ4KNqDDgNw3n9XwEuxhlvXQreAz2Bu3Fet7FFTv8OJyAG0NsYc0IplzsTJ+AE8JWXgFMvnEDnw0BXnPdOOM57/15gtTHm9HJOUURERERERERERETkqFPVQaeWONkdDYENwAs4AaTZHm3OwQlIfQW0xwmePAv8E5gO5GXgdMEJ+BRijLkWmIwTFALYghN8uN/9c6tbXx/4xA0olWQI8CZOwGYV8LQ7lo9xMlsAYoHPjTEh3jowxrwIPAYEu1XzgSfceb4D5AUqegOLjDENi3Qx3v1J9lL3eJG2/3b7DcZ5rua4dffgBNu+p+A5bAhMM8aElzD/Nu5Yi5pprd0GzHIfB+EEz0oyyqM8qZS2FWKMORcngBfjVv0GPI8T4PkX8CmQ7h4LAiYaY7rkne8Gwj7y6PKSUi55uUe50JyMMT1xnv+8sWwHXsJ5fT4ADuK8BjOBRqXPTkRERERERERERETk6FUly+t56OD++Tww3jPTxRjzGE5gCJygDjhBqTuLtBuDE6gBGGOMGZe3JJ0x5iRgosf1ngHut9Ye9jh/HPAfnKwlP+BVY8wSa+1qH2Pui7OU3lhr7bueB9zsprlANM5yeSOAD4u0Ge5eC2AnMNJau6BImyicwNYInMynd3GWBQTAWvuM2+5mnAyp/Loi/bQFxrkPDwNDrLVzvbQ7AyeoV8e93vk4QTRv+rt/vokTINyP85zkBcA+AC5yy5e77Ypxs6AGuw9XW2tX+LhehRljAoCXKQiWPmCtLRqUwxjTFGcJvQ5AAPA34EaPJh/gLCUIcClOoNHb9TwDbSnADI9j/jjvxbzMrik476E0jzb34wRS84NeIiIiIiIiIiIiIsejHIrtMiPHoCpfXg9YZK29w8vSak/j7O2U52egWDs38GPdh/VwlnzL8xAFgbJ3rLXjPQNO7vmHrLW3URAcCgUeLGXMjxcNOLl9rcAJoOXp73ncGOOHk+EETnbRhUUDTm4/+3ECNmvcqiFulkx5jaZg/i96Czi51/sRJ7CVp3sp/X5trb3eWrvFWrvfWjvDYx5fAnvd8lnGmDgffYykINOrWrKccIJhLd3yUm8BJwBrbQLOeyVP9yLHVwK/5x0zxrT2cb3BOBlzAJ9aaz3fv6MoCCYtBa7wDDi519nq9lFoST4RERERERERERERkWNRdQSdXvJW6QZetnhUvZWXweTFGo9yLIAxJgxnzyBwMpPuL3pSEXcDeQGtEb72h3JNKOHYfI9y0YDLaUA7tzzXWrvEVyfW2iycPajyjC7hmr7MxZnXROCNUtr+6lEuae5QwvzdoF5elpQ/vpejy5tPLs7yh9VhG3AbTobcY6W0LW3+noGxS3304XNpPQovNfiwr/2rrLW7gOd8DVJERERERERERERE5FhR1cvrAfxSwrFEoK1bXllCu1SPct4+SqdSkEnzs7V2R0mDsNZuM8YsAk7HWWKtD/Ctl6ZbrLWJJXS126McWuRYX4/y8pLG41roUT6tDO0LsdbOp3AQzCtjTB3gRI+qoFJOWVTK8Q+AG9zyZcB/i1yvKXCG+/AHa61ncLHKWGvXAetKa+cuw9feo8rb/D/E2cvKHyfoVChryg1yXuA+TMDjeTfGBAID3YeHKLxnmTefFe1fRERERERERERERORYUx1Bp+0lHMvxKCf7bFW4XZ6WHuVfvRz3ZhVO0AkKB2E8lRRwAvBcMq1oZlgHj/J4Y8z4Mo4LnD2iKsUYEw4YnCUIW7vlzkAnCr+2fiV0c9Bau7uE41hrFxpj/nSv0ccYc4K1drNHk8s9rvFB+WZRcW5wqQ0F828LdAS6UbDXEniZv7U2wRgzD2fJxE7GmPbW2j88mgz16GOytdbzPdnI45i11maWMtR1OMGpkFLaiYiIiIiIiIiIiByTtKPT8aE6gk5ppTcBCpa+K6sGHuW9PlsV5tmuno82ZR0vFA9e+OqzLCp8rjFmGM4yc6fhZHF5k13CMU/7ynjZScDDbvkyCi8VOMr9MwP4tIz9VZgxpitwD05gKMxHs7LMfxIF+3RdSsH8oOSl9Rp5lEt9/qy1OcaYZIovzygiIiIiIiIiIiIicsyo8qBTCfs0VZZnwKes1/AMOlTHuDyfv3eB1dVwjXzGmCDgfZygT1Hb3Ov/AswDWgCvl6HbrDJe3mvQyRjTHuji1n/p7t1VbYwxtwLPUzzrLBn4A2eZwx9xsotWlNLdp8ArOIGr/KCTMSYKGOK2+dVa+1uR8zwzlsq6L1pJ2WYiIiIiIiIiIiIiIke96sh0qi6eWUsNfLYqzLNdWTN6ysNzicBF1tqyBHkq4wEKAk6HcAIm04EV1toUz4bGmBur8sLW2r+MMQtx9tbqYoxpY61djxOsyVM0I6hKGWP6Ay9QEMD5FGc5v6VF9/gyxnSgFNbaA8aYL3Ce03bGmJOttb8CwygILHmbU1ky6DzH4gdEldZORERERERERERE5FjlbU8dOfYcTUGnvzzKJ5fxnM4e5Q1VOBZvffaglMwidw+iSGttuQNgxphQnCX18lxurf1fCafEepSrKsvmA5ygEzhL2z0HXOA+TgJmVdF1fLmbgrn811p7Wwltyzr/SRQE8i7G2S8sL5CWA0z2cs4GnKUEQ4E2xpigUvZ1aum2FRERERERERERERE5ZpV1abAjwWIKloLrbYxpXFJjY0wzoKf7MAdYUg1jWuBRvtANDJXkYiDZGJNsjPEWzChpCcB2FGTL7C0l4ATQz6NcVa/zVOCwWx5qjImnYGm9j0sJvFSFXh7lt0ppW9b5fwPscstDjTH1KNjnaZ61dlvRE9x5LnIfBgMDSxnL0FKOi4iIiIiIiIiIiIgc9Y6aoJO1NhXIC7QEAo+XcsqTFMzv64pkF5XBHCBvWbdY4D5fDY0xIRTsiRQNrPHSLNujfUCRY8Ee5ciSAlzGmPOAMz2qgny1LQ9rbTLwpfvwdGA0BVlE1bq0nsvzOYjx1cgY05TCWWE+52+tzQKmuA+7Add7tC9pTu96lO93l9DzNpYI4B8l9CMiIiIiIiIiIiJyzMs9iv6Tijtqgk6ux4G8bJprjDH/McZ4BiIwxgQbY14ARrlVGTjLslU5a+1h4DGPqgeMMXcZYwo9r8aYKJwAxklu1V7gZS9dHvAoNy9ybC0Fcw8CHi8a6DDG+BljrsTJSPIUXtpcyuED989AnD2mADZYaxdW4TV8+dWj/C9jTJ2iDYwxPYH5OIG9PKXNPy+45Ac86JYzgGklnDMF+MMtnwa8VTQQaIxpAHwBnFDK9UVEREREREREREREjnpH055OWGtXGWP+QUHAZjxwqTFmOk7GURxwIQUBm1zgNmvt79U4pleNMWcBI3GCFk8B1xljZuLsc9QCZ1m9+u4p2cAYH5lXmylYru4zY8zHQIC19jFrbYox5l2cTByAO4CBxpjvcIJYTYDBOPsHgROgysvYaVAFU80zC2deDYC6bl1NZDkB/Bf40C2fDvxpjPkcSMDJfOrt/uTJwnmPRxljAt2spmKstUuNMWtxljCMcKunW2tTfA3EWnvYGHMt8D3Ofk3XAGcbYz4DEnFeh+FAPSCNgsCX9ssTERERERERERERkWPS0ZbphLX2FeAqIC8g0By4GScL6hYKAk57gaHW2tdrYFiXA89RsDxeG+B24FFgLAUBpyRguLV2ho9+3vAodwGeAB519xkCZ5m2+R5tTnbrHgVuoCDg9DvQh4L9lzp7Wa6vQtzsrqKZVDUSdLLWTgae9aiKw5n3YzjPd17AKQkYQcGeW/4UBPN8KTqHUudkrf0ZGIbzXgNoCtyK8168DifgtB24zOO0Q6X1KyIiIiIiIiIiIiJyNDrqgk4A1toPgBOB+4GfgN04WS37cAINdwGtrbUza2g82dbacTjL5z0LrMQJRGThBEAWAPcAxlr7RQn9zMTJilqEs9ReJk4WTzP3+EFgAE6201y372wgFfgTZzm4K4Gu1tplOFk4AFHA+VU34/xsI4Al1tr1Vdh3iay1dwJn48x1G85zlOGWv8VZSrGNtXYa8I3HqaNL6XoS5C/WmQR8XcbxfAW0xwn8rQL2A+nAareuA87SiHkOlqVfERERERERERERkWNJzlH0IxXnl5urTbGkfIwxw4FP3Ye3WGu97U8lLmNMD2CJ+3CWtfa86rjOvS1G6WY+jnydsbm2hyA17IbAlqU3kmPKR+ys7SFIDbspu1FtD0FqmAnxuZqzHKNa39e6tocgteDgxY/U9hBERKQKxcZG+tX2GI5WN7e49Kj5/PLlTVP1OlfQUbWnkxwxrnD/PAxMqc2B1BZjzCigDrAOWOQuO+jLyR5lW60DExERERERERERERGpJQo6SbkYY5oBeZk6X1hr99TmeGrRmcDf3PLFwOfeGhlj/IH/86ha4K2diIiIiIiIiIiIyLEsh6Mm0Ukq4ajc00lqjjGmjhs4wRjTHPgYCHIPH8/L6n3vUX7CGNO4aANjTBjwOtDdrfoLmFUDYxMRERERERERERERqXHKdJLSnAu8Z4xJBWKAvLUsZ1lrf/B1kjGmLgWZQFXhdWvtkbTo/afAvThL57UHNhpjpuMst5cFNAfOB2Ld9lnAGGttRi2MVURERERERERERESk2inoJKXZAoS5P551fy/lvPrA01U4jk+BIyboZK3NMsacA/wP6AWEACN9NN8OXGKt/ammxiciIiIiIiIiIiIiUtO0vJ6U5k9gGZABJALvAadZaxNqdVRHAGvtDuBUYATwCbAJSAfScJ63b4BrgLYKOImIiIiIiIiIiMjxLPco+pGKU6aTlMhamwScUoHzNlGwFN8xy1qbA0xzf0REREREREREREREjlvKdBIREREREREREREREZFKU6aTiIiIiIiIiIiIiIhUqxwtXHdcUKaTiIiIiIiIiIiIiIiIVJqCTiIiIiIiIiIiIiIiIlJpWl5PRERERERERERERESqVU5tD0BqhIJOIlJr/AP86TNmMD0u60f9Zg05sGsfyz6Zz7wJ08nJyi5XX+36d+Xqt8fz33PvZceazfn10U1juHvBf0s9/6nTb2Vfwp5yz0FK1yi+ITff+3d6nt6diLp1WPvbel5/9m0W//hLmfs4uftJ3Hj39bQ/2ZCbC0sW/MKLj05g25btJZ4XGxfDtPmTsKvXc/2wW0pse+m1w7nniTs4o+1gUlNSyzw2KZlfgD8drxlE+1H9iGweS1riPuzHP7DylRllus9jOrWg220X0biXIahOGKnbk9gwcwnLX/icrPRDha/l70eHqwfSflQ/olrGkZ6UwrYfV7P06U9IS9xXXVM8LgQE+HPxNRdz/qhzadw8jqTEvXz18ddMfmUK2WV4HSOjI7n2zqvpM6A30THRbF6/hSkTPub7GfOKtQ0JDWH0zZcz4KJ+xMTFsGPLTj5/7ws+f296oXbzEuaUet3bR45j5aJVxepDw0J59/u3WPD1Al5+eEKp/UjJ/AL8aT12ECeO7k+dZrGk79rHpqnzWfvSDHLL8P6o27YJJ909kgbdWxMUEca+1ZtZN3EW22aV/Hsi6qQTGPj1oyy6/kW2f72sqqYjZRHgT8yYodS/bBDBzRqRuSuZ5E/msGvCp1DOf8Ph50erz/5Dna7t+LXF0GKHg+JjaTRuNBG9Tyawfl0y/tzK3klfsXfqt1U0GSmLrJwcpqzYzGe/bmV7ShoxdUK44KSmXNOzFUEBvhdQ+WVrEtd/vLjU/leMOze/fP+slcz6w/u/8cb0OJHb+rYr/wREREREpMYo6CQiteaCR6+h16gBbFyylj/mLOeE7m05e9xI4to3Z/KNL5a5n9hW8Qx/+u9ej2WkpDHnhWlej8W0jKPLhaex66/tpO7eX6E5SMnqx9TjnekTaBBbn1mffUtqykHOuXggr059njvG3Mv8bxeU2ke33p2ZMPUFUvYfYPrUr4ioW4chF5/NKad2Y/Q5Y9mxdafPc+9/ajyRUZGlXqNrr87c9sCN5ZqblM3pj4+hwxX92bHYsmn2cuJOaUuP8SNo0KE5s/9eckA4/tT2nPvBXQBs+GopaTuTady7HV1vGkqT0zowffhjZB/KzG/f74UbaDPsNHb/upHV784mulVj2l12JvF92jNtyAMcTkmr1rkey257/FYuuOJ8fl38GwtnL6LjKScxdvw1tO7Qiof+/kiJ54aGhfLM5Kdo07E182bMJ3H7LvoOOYOHJjxAdIMo/vfuF/lt/f39eXjiP+kzoBeLvlvM/Jk/0rNfD25//FYaN4tjwmOv57d997n3vV4vukE0F119AXt3J7Plzy3FjgcE+PPAS/cS17RRBZ8NKarrv8fQ6soB7F68lu3fLKNBz7Z0vGsk0R1OYNH1Jf8+j+rQnP4zHgI/P7Z+vojM1HSaDO7OqW/9g1WPTGbdhJlezwuJjeLUN27FPzCgOqYkpWjy6P/RYNQ5HFyymj1zllCne3vixl1BaPuWbLnxyXL1FTP2Qup09R5ECIprQOsvniUwOpL9X/1E5s69RPTtStOnbiH0pBPZ/s/XqmI6UgZPfreaab9upWuTepzZqgWrticzYeF61u0+wDMXdPN5XnzdMP7ep7XXY7/v3M9PG3fTtUm9QvXrdx+gQXgwIzo3L3ZOlyb1KzcREREREal2CjqJSK1o3q0NvUYN4LeZi5l8U8EHUiOfvYFuw/vSrn9X1s5dUWo/J/bpwOUv30pEg7pej2ekpPGdl6CTf2AAN37+CFmHMpl844tkeXxwLVXnxruvp3HTOG69cjw/zl4IwPuvTubDb9/i3ifHsXDeYjIPl/zcP/D03WSkZzB68Fh27dgNwFfTvmXCxy9wx0M3M/66B7yed87FZ3Pm4NNLHePgCwfw0PP3EhYeWs7ZSWkadW9Dhyv689eXi5lzw0v59Wc9/3fMyDNoPqALW75b6fP80x8fA/5+fHHxI+xeuSG/vu9T19J+dH86XDWQ3974CoCW5/WgzbDT+PPzRXx3y6uQmwvAyX8/TNH1dgAAIABJREFUlz4PjqLjtYNY/sLn1TPRY9xJ3TtwwRXnM+/L+Tx8w6P59fc8fxfnjByUHyDyZfjYizEnt+WF+/+bn630/guTePWL//K3+67j+xnz2ZfkZKL1u+As+gzoxZTXPuY1N8D01tPv8J9J/2bk30bw9aez2bh2I+A76PTEO84Y/337U+zdnVzoWGR0JP985X56nHlKBZ8NKarBKW1odeUAts5YzM9/Kwgk93jx77S4pC+NB3Zlxxzfv8+7PXkN/oGBfDf0Ifb9ugmA35/8hLNnP0HH8SPYNGU+h5MLZ59GdWjOqW//g4gTGlbLnKRk4d3a0WDUOeybuYAtNz2VX9/02dupP3wAyf17cGDu0jL1Fdw8jrhxV/g8HnfvNQTF1mPTdY+SMmeJU/mUP60+eoKYq84jadIsDq0rHlyWqrVyWzLTft3KwLZx/Of8rvj5+ZGbm8s/v/6VL9ds44e/EunbynsgPz4qnBtObVus/kBGJiPf+5Go0CCeOr9rfn1mdg4b96bS98SGXs8TERERkSOf7zx4EZFq1OeqQQB892LhgNDXT00hJyeHUy7rV+L5gSFBDHvyesZOug8/Pz+2/baxXNfvd/NFNOnYknmvfkGi3Vq+wUuZhIWHcf7Ic1izam1+wAlgd+Ieprz5KY3iG3Ja/94l9tGrbw9atjmBzz/6Mj/gBLBkwTJ+nr+Us845g6h6xQOO9RpEc9djt/HjnIXFjuWJrh/FM289wZMTH2HvnmS2bND7oKqdNGYgAMue/1+h+iVPTiU3J4d2l5/l89zoNvHUa9OEzd8uLxRwAljmBo+a9zs5v67jNYM4fCCdBQ+8mx9wAljz/nes+/RH0vekVHI2x6+LxlwIwHvPf1Co/o0n3yInJ4fzLj/X22kF5199AXt37WX6B1/m16UfTOeDlyYTFh7GwIv6F2qblZnFpJcm59dlZ2Xz1n/ewd/fn/MuO6fEa50zchCnnt2Hr6Z+zdL5hZdm639hP977/m16nHlKsWNSca2uORuANc9+Vqj+tyec+7zl6LN8nhsYEUZgeAjb56zIDzgBZKcdYsfs5QSEBRPd8YRC53S6/zIGzHqE0IZR7F68tsrmIWXX4KrzAEh88aNC9Tufep/cnBzqXzaozH01feoWMhOTOPRXgtfjQXENSFu1viDgBJCdw75ZPwH4zJCSqvXxSmfp6r/3aYOfnx8Afn5+3HKGwQ/432/eX7+SPD1vDYmpGdxxVntiIwq++LNxbypZObm0iS09U11ERESOPrlH0X9ScQo6iUitaNGzHalJKSSuK/w/qQd27WPPxp2c2Kt9iedHxEbR47J+rJ27gv8OuYedtuzfco1sGE3fv51HcsJu5r82o0Ljl9J16taBkNAQlv60vNixvLrufboWO+ape+8uhdp7+uWn5QQGBtK158nFjt39xB0APPOg72WdWrU7kX5DzuCLKTO5bOA17Nq522dbqZjGvdqRnpRCsi18n6cl7mPfhp3E9/Z9n2emZvDz4x+xdur8YsfyltQLquN8SBUYHkJcD8P2hWs4tO9gobZZ6Yf4/vaJ/DFpbmWnc9zq3KsT+5L2sdFuKlSflJhEwoYEOvcufg/miT+hMbGNY/l1yW/k5BTeMnblQifLrXMf5/yg4CDad2nHn6v/InV/4cyWtSst6WnpdO7d2ee1QkJDGHv3taSlpjHxiTeLHb/givM5nHGIe6++nw9f/shLD1IRsb3acSgphZQi93lG4j4ObNhJbAn3eVZqOrMH3seisS8UOxbZOt7pZ3fhgHG7m4eSvGoDcwY/wK4fV1fBDKS86vQ8iayk/cUyjLJ27eXQxu3U6dWxTP3Uv2IIEX1OJuHeV8g5dNhrmw2X3sufF95RrD6kVVMAMvckFzsmVW95wl6iw4JpHVM4ENQwIpQT6tVhWUJSufpbm7ifL1dvo1PjaIZ2aFLo2PrdBwBoE+t9FQMREREROfJpeT0RqXEBwYFExzdgy4r1Xo/vS9hNw1bx1KkfycG9B7y2Sd9/kNeGP8zmZevKff2Bt48gODyU6Q+9p2X1qlHTFs6HCAmbthU7tn3rDgBOaNWslD7iS+2jeavC6/2fNfh0Bl84gPtv+hd73SW7vEnYtI1L+1/Nn2s3+GwjFecfHEhEfAMSl//p9Xhqwh7qtY4ntH4kGV7u84M79rLKx14uLYc4S6PtXee8L+q1bYJ/gD/J67bRsFsretw5gobdWpN9OIvN3y5j8RNTvV5DShcUHETD+IasWf6H1+M7ExJp3ro5UfWj2L+3+N548Sc49/D2zTuKHdu7O5lDGYdo2tL58LhRk4YEBgWyfXPxzeNzcnLYvX03TU9sUuxYnhHXDSM2Lob3Xvggf7k+T+89/wGrl63m8KFMuvTxHbySsvMPDiS8SQOSlnm/z9O27qZu63iCG0RyOKkM96C/H3WaN6TN2ME0HtCF7d8uJ2Vt4SzUH0f/h51zV1XF8KUC/IIDCY6P5eAK71lmmQmJhLZqSkD9umTv9Z1hGtQ4hsZ3j2HvlG85uOjXMl7cj6BG9ak3ciANRg0m/fe/ODBvWUWmIeVwOCubxNQMOjWO9no8PiqMTckH2Zt2iPrhIWXq88UfLbnAzae3zc+cypMXdNq89yBjPlrE+t0phAQGcMaJsdx0uqFhhJZDFhERETnSKegkIjUuPCoCcPZb8ibjgFMfGhnuM+h06EB6hQJOdepH0nX46aQkJrPy8wXlPl/KLrpeFAAH9hd/DVMPOFkMEZF1SuwjKr+P1GLHUg8cLNZHRN0I7ntqPD/NXcSsad8SUTfCZ9+J23eRuH1XKbOQigqNdp77wz7u87z64Lrh5QoIhcXU5ZRxwwH440Mne6lOI2cD8phOLTj5hnPZtexP1k6eR+zJLWl32VnE9TD87/x/cvhAekWnc9yKjHa+1Z6aUvwedOrd+7BuHa9Bp7ru8pe+zj94II2IunXK1Db1wEGahzcnIMCf7OzCWVOBQYEMu+YiDmUc4rO3ve/dtWKh7/3DpGKC3fs8M+Wg1+OZKc49FxQZXqag01mfPUBsL2e5tD1LLD/f8HKxNgo41a6AKOfvhBwfr3m2+2+4gMg6JQadmjxxEznpGWx/4u0yX7vZs/+g3jBn+eWMvxLYOOZhKPJ3gVS9/RnOF7QiQ7x/dBARHARA6qGsMgWd1u9O4efNe2jfqC49m8cUP+4uh/vGz+vp3yaOTo2j+X3HPqav3sbPm5N4f1QfGkWGVXQ6IiIiUsv0r7fjg5bXE5Ea5x8UAEDWYe9ZRlmHsgBn36aq1nPUAIJCgln43jdkZ2ZXef9SIDDI+XDisJfX+bCbYRYSWvKHEyX3cdjtIzi/bty/bqFOZDiP3/V0xQYtVcY/0LnPs31kE2a7r2lAOe7z4Mgwhrx3J+ENo/ntrW/y93oKdD/kanbWyayaMJPpIx5j0SMfMn3EY6yaOIvoVo3pfsewykznuBUY6N6DPl7HTPd1DA4J9nq8LOfnnVvS/Q6Qecj3tfoNPZMGjRrwzaezvQa/pHr4BeXd51lej5f3Pk9auh47YSZ7lq4jpqfhzE/vIyi65C8nSM3yc+/THB/3aa57n/qV8JpHD+tH3X6nsO2fE30Gr7zJWLeZ3RM/48APywlt1ZRWnzxFUNOG5Ri9VERWjrOfQVCA948OggKd+sNlDABOXr4JgCu7n+j1eEhgAM3rhTNp9Gk8fm4Xxp3Vnncu78NNp7VlV2oG/5m7ppwzEBEREZGapqCTiNS4rAwnWBAQ5P0bk4HuNykPpx2q8mt3G3YGOdk5LPt4XpX3LYVlZDivX1Bw8Q+egt0Po9LTSs48OZTXh5f3St4Hz+lpGQD06tuDiy4/n5efmMiOhMSKD1yqRN597h/s/T4PcN8XWWW8z0PrR3L+1PuI7Xwim2cvZ9EjH+Yfy3U/EEvbvZ9lz31W6LxfnplG5sEMWg3tVe45iMc96ON1zLu/8+7Dipyfd+4h9z0TFOT9w+qgkCBycnLISC/+nhk0/GwAZk6e5fVcqR7ZVXyf//b4FH59ZDLfX/Av1r7yJQ26tabjXSOrZrBSJXLce9rPx32aF2zK8fGaB8ZEE//gdez/eiEpXy8s17V3vzaNHf9+h41XPcS2ByYQ0jKeJo/cUK4+pPxC3KBSZrb3zbQzs5xgU5gbhC7J4axsvrU7qBsaxMC2cV7bPHdhd7649ixaFdk/6tperWgSFcYPG3aRdth7oFtEREREjgxaXk9EalzGgTRysnMIjQz3ejyvPm+ZvaoS2yqemBMbs+HnNaTu8b3ki5RP25Pa0G/IGcXqd23fDXhfQi8i0lmSKbWUbzin7HOWY4qoG8HeIpuF5/WbmpJKWHgY/3z2bn795XemvD2t/JOQKnfYvc+DfSyBE1w3PL9daeqe0JBzP7ybqBaN2PTNMmb/30vkenyjOq+PvWu3kpNVOIMxK/0Q+zfuJKZjCwJCgnxmXol3Bw8cJDs7mzo+lsLMWxrv4AHv93KquzSmr/PrRIaT7N7bqe5SnHXq+rhWZB3SD6aTm1v4g8/wiHC69OnMji07sL+Wf9lVqbjMlDRys3MI8nGfB9V16jMr8Pv896c+pvU1A4kf3J0V971bmWFKFco5kEZudjYBPv4Nl1ef4+PvhPhHbsAvwJ9tD75WqXEkTZpFzNgLiTyzG35BgeRmKghRXSJDgvD3g1Qfvz9T3ay3CB/BZ09Lt+4lLTOboW3jfGZO+eLv50fb2Lps259O4oEMWjbwvYSyiIiIiNQuBZ1EpMZlZ2azb9se6jWL9Xq8XtNYUpNSSN9f9iVXysL06wLA718tqdJ+j3emYxtuuHNssfqxF90IQJPm8cWONWneGIBNf20pse8tG7bmt88rF/QRn99Hhy7tiG/WmPhmjVm+o/heXaec2o0VO39i+tRZPHTb42WYlVRWTmY2qQl7iGzmfemjyGaxpCelcGhfyfd5gw7NOffDuwmPjcJ+/APzx79ZKOAEsH/jTsB39qR/YADZmVlk65vR5ZaVmUViwi4aN/P+jfS4ZnHsS9rHgX3e9+vZuiEBwOv59RvWJyQ0hK1/Off2zq07OXzosNe2/v7+xMbHsnld8b8zTunbnaDgIH74Svv01bTczGwOJuyhTnPvv8/rNIvlUFIKmT7u86DoOsT0aEtawh72/1H47/jczGwyEvcR1rh+lY9bKi43M4vD23YT3KyR1+PBTRuRlbSfbC97MQJEn3saAB2Wvu/1+MmbZnA4IZG1p1+HX1gIEb06kp1ykLTla4u1PbxtFyEt4wmIjiBr974KzkhKExTgT+O6YWxP8Z6dvm1/OtFhwUSFeV9m1dOPG5y9NAe08f47JT0zm/W7UwgJDMA0rFvs+CH3iyV52VciIiJy9MnFe/a0HFsUdBKRWrFpqaXb8DOIaRnHHvcDY4DIhtE0aBnH2u9WVPk1m3dtA8DGJcU/uJCKmzF1FjOmFl/SKjQ8lPS0DLr36VLs2CmndgPg119+L7HvFYudDeO79+nKonmFg4XdT+1KdnY2q1f8QWh4KK8981ax80NCgrnmlivZvnUH06fOwv6+vszzksrbudTSdsQZRLWMyw8MAYQ3iiaqZSM2z1lZ4vl1WzTivMl3ExYTxaqJs/j50cle26Vs2sXBncnEdG5JUJ1QMg8WLPUWXDecui0akbxuG+TqH7cV8dvS3xk84myatmxCwsZt+fUNGjWgacsmLJrzs89zd23fxc6ERDr17Iifn1+hLKUufToDsHqZsz9HdnYOf6xYS/su7QirE0b6wYIPONt1MYSFh7F6efG9PDp0aw/Ar4t/q9xEpUL2LLG0GHkGESfGkbqh4D4PbRRNxIlx7Jjt+/d53TZNOP39O0mYuYRF171Y6FhgZBjhTWM44NGnHBnSlq6h3vD+BLeM5/DG7fn1gQ3rE9wyngPfLfV5buIL3v8erz96CEGx9Uh8YTLZbhZ0QN06tHz3YdJ++5M/h/6j8AkB/oS2aUZ2ykGy9ip7vbp1aVKfmWu2sXlvKifUL8gw2pWawZbkg5xxYtn21vptxz78gG5NvAeTkw4e4uqPFtEmJpKPry6cRZ+emc0fiSnUCwumcV3v2ZUiIiIicmTQV4REpFYs/+xHAAaNvxQ/P7/8+nPuvgx/f3+WfjS3yq8Zf9IJHE4/RKLdWnpjqbSMtAzmzppP5x6dOHPQ6fn1sY1iuOy6EezasZsfZv9UYh/LFq1kR8JOhl95YaHsh56nd6f3mT34/qsfSE7ax46tO5n4zNvFft5+aRIA27fuYOIzbzPv6x+rZ7Li1bpPncyTnvdcAh73ec97LsXP358/PizhPvfzY8DLNxEWE8Vvb37tM+AEQG4ua6fMIygshD7/HFXoUM+7LyEwNBg7dX6l5nI8+/bT2QBcf8/YQn9fX3/PWPz9/fnyw5klnj972hwaxjfk4msuzK8LqxPGlbeMIiM9g2+nzSm41rTZBIcGc824q/PrAgIDGDv+GgC+9LJnU5uTWgOwdpWtwOyksjZ/4vy92uneSwvd553uc+7zDZO+93lu0rL1HEzYQ/zg7jTo2Ta/3i/An27/HoN/UCCbPtK9e6RJ/sz5uztu/FWFXvO4u6/Cz9+fpI++8Xlu4gsfef3J2p2cf3zP29MByErcy8Ff1hDeqTVRQwsHIOLuvJKgRg2csRTJfpWqd36HJgC8tGAdOe6XB3Jzc3npR0suMPzkZqX2kZWTw19JB2hWL5zIUO97gjWNDqd9w7qs33OAWX8UfMkhNzeX//64luT0w4zs3LzQ7yIREREROfIo00lEasVfP/3OqhmL6Dy0D9H/+xd/LVrDCd3a0LJXe36buZi1cwu+GT3g9uEAfPdC5fbqiW4ay56NO8jNUbZDTXn53xPpc1ZPnn7rcb75fDb7kvYz+OKB1I+px53X3keWxx4MeXtD2d/X5weHcnJyeOKeZ3j+3Sf58Ou3+OqzbwmvE8aQYYPYt3c/zz/ySm1NTcpg24LV/PnFIlpf2IeLpj/E9oV/ENe9DY17t+OvLxez5buCTKfudwwDYNlznwHQ8pzuNOxyIlkZh8k8mJF/3FParn38Mcn58HPFy9NpekZH2o/uT/32zdm5xNKwa2sa9zLsWGxZ/d6cYudL2SxbsJy5X3xP/wv78cr0/7Jy4SpO6t6Bzr1PZt6X81n03eL8tmPuuAqAd58rWDrrowlTOWvomdz6yM107t2Z7Zu303fIGTRpEc+LD7zE/r3789t+NfUbzrlkMJf8bQQntmvJut/W0/OsU2h9UmumvPYxG9duLDa++BMak5GeQVJiUjU+C+LLrh9Xs+XzRTS/qA/9v3yY3T+toUGPtsT2bsfWGYvZMafg93mHcc59vOZZ5z4nJ5df7nid0z8Yz5kf30fCjMUc2nuARn07EtWuGdtnr2D9m1/XxrSkBKk/rWLfjB+IHtqX4P89Teqi3wjv1o6IXh3ZN3MBB+YWZDo1uv1ywAkmVcS2+1+l1SdP0vyFcaScezqHExIJP6U9dbq2I23Venb+x/syfVK1ep8QwyDTmG/tDq6evJBTmjVg1fZkVmxLZmDbuEKZTq8tdPbWu+HUtoX62JV6iENZOTSL9r5vX54HBnXi+qk/88CsVXy3bieNo8JYkZDMmsT9dGtan7G9WlX9BEVERKTG6OtCxwcFnUSk1nz8j1fZtS6BbiP6ctq157B/WxKzn/2E+RNnFGo3sAqCTmFRdQgIDCBlx95KjVnKZ+e2RK4+7+/c+sAN9D37NPwDAli3+k8evOUxFv9QePmdvL2hpk+dVSgjacGcRdx8+Tj+Nu5aLh49lLSD6fww+ydefmIi27fsqOkpSTl9f9trJK/bRtuRZ9Bp7GBStyex9OlPWTnhy0LtTikSdGrcqx0AgaHBdLvtIq9971m9OT/olJ2RyZeX/psuNw+l9UWn0vGaQRxMTGbFS9NZ9uL/iu0DJeXz+G1PsnHdJs4ZOZgRY4eRuH0Xbz39DlMmfFyonbegU1pqGrcOu53r7hnLqQN70/OsU9jy51YeufEx5k6fV+j8nJwc7rriXq4ZdzX9hp5Jp54d2b5pOy/c/1++eL/w74Y8devVZfeOPVU7YSmXJbdMIGVdAi0u6Uub688hbVsSv//nE+wrhe/zk+50fp/nB51wglZzL3iYk8YNp/HZXQkICSJ1YyIrH/qA9W9+A/qiyBFpyz+eI2PdFuqNGEDMtReQuW03O5+dxO6Jhf+t1uh2J/u0okGnDLuZ9RfcQdwdo4k4vSuRA3qQmbCLxBc/YteEaeRmHKr0XKRsHhvSmVYNIpixehuTl28irm4o/3dqG8b0OLFQ5tHERX8CxYNO+9MPA9AoIrTE63RoFMWk0acxYeE6lm5J4uDGbOLrhuVfKzgwoIpnJiIiIiJVzS9X+xuIHBPubTFKN/Nx5OuMzbU9BKlhNwS2rO0hSA37CO1lc7y5KbtRbQ9BapgJ0X5Ex5vW97Wu7SFILTh48SO1PQQREalCsbGRWuu1gq5uMfyo+fzyvU3T9DpXkDKdRERERERERERERESkWuUoAea44F/bAxAREREREREREREREZGjn4JOIiIiIiIiIiIiIiIiUmkKOomIiIiIiIiIiIiIiEilaU8nERERERERERERERGpVtrR6figTCcRERERERERERERERGpNAWdREREREREREREREREpNK0vJ6IiIiIiIiIiIiIiFSrHC2wd1xQppOIiIiIiIiIiIiIiIhUmoJOIiIiIiIiIiIiIiIiUmkKOomIiIiIiIiIiIiIiEilaU8nERERERERERERERGpVrna0+m4oEwnERERERERERERERERqTQFnURERERERERERERERKTStLyeiIiIiIiIiIiIiIhUq5zaHoDUCGU6iYiIiIiIiIiIiIiISKUp6CQiIiIiIiIiIiIiIiKVpuX1RERERERERERERESkWuWQW9tDkBqgTCcRERERERERERERERGpNAWdREREREREREREREREpNIUdBIREREREREREREREZFK055OIiIiIiIiIiIiIiJSrXK1p9NxQZlOIiIiIiIiIiIiIiIiUmkKOomIiIiIiIiIiIiIiEilaXk9ERERERERERERERGpVjm1PQCpEcp0EhERERERERERERERkUpT0ElEREREREREREREREQqTUEnERERERERERERERERqTTt6SQiIiIiIiIiIiIiItUqNze3tocgNUCZTiIiIiIiIiIiIiIiIlJpCjqJiIiIiIiIiIiIiIhIpWl5PRERERERERERERERqVY5aHm944EynURERERERERERERERKTSFHQSERERERERERERERGRStPyeiIiIiIiIiIiIiIiUq1yansAUiOU6SQiIiIiIiIiIiIiIiKVpkwnkWPEbc231/YQpAY9/fOm2h6C1LCV8U1qewhSwyJzQmt7CFLDfgnU9/6ON+ecm1XbQ5Aalvnjr7U9BKlh238Ogacvre1hSA2LWTi1tocgIiJSa5TpJCIiIiIiIiIiIiIiIpWmTCcREREREREREREREalWueTW9hCkBijTSURERERERERERERERCpNmU5HKWPMJuAE4Btr7TnGmJHAP4HWwG5gIfCQtdZ6nHMicAMwCGgOhAO7gCXAFGCatbbUcLMxpj5wPTAUOAmIAPYDq4BPgXestRml9NEZuA4YADQBgoCdwALgA2vt7BLOfRe4GjhkrQ01xvgDVwGjgY5Affc5+Al4w1o7pwxzqgOMAYYBJwPRQCqwBpgOTLTW7vNofy4w0334u7W2Uxmu8SfQCjgExHn2JyIiIiIiIiIiIiJytFOm0zHAGDMC+Bgn4BIKNAMuBdI92twP/AGMBzoD9YAQt+1w4BNgoTGmxJ3qjTFXABuAJ4HTcIIzgUADoD/wKrDKGNPWx/mBxpiXgOXAzUB7oC4QBrQErgS+NcbMMMZElWHuMcAPwDvAQCAOCMYJZF0CzDbGvGGM8Suhj0HAeuBldw4x7pyigVPdua42xvTxOO0bINEtdzTGdCxlnL1xAk4AMxVwEhERERERERERkeNJDrlHzY9UnIJOR7/6wGte6pdYa7cAGGNeBB7DCcYAzAeeAB7ACdYkufW9gUXGmIbeLmSMGQt8AOQFg7YArwAPAm8DKW59W2CumxHleb4fTnDrZpz3XjbwFfAITpbWR8BBt/n5wDxjTHgJc/cHvsAJfqUBk92xPAOs82h3HU6Gl7c5DQa+BBq7VbuAN91+JlIQWIoHvjHGtAaw1ma7481zWQnjBBjlUZ5USlsRERERERERERERkaOOltc7+vVw/5wO3AVsc+vqABhjhgO3um12AiOttQs8O3Azit4ERuBkPr0LnFukTXOcTKA8zwL3WWsPe7S5H/gaJ5OqCU4w6WaPc+4ALnLLFhhurV1d5DpxwFSgL9AFJ4B0o4+5B+FkIi0ALrHW7vDo5x7gdeBaj2tPKHKtCOA9tx9wgkh/s9amerS5C2fJwLOBSOAFnIAYOAG4293yZThBvGKMMQE4WVcAyRQsyyciIiIiIiIiIiIicsxQptOx4XdghHWkWmu/t9Z+6WYWPea2yQUuLBpwArDW7gcux9m/CGCIMaZnkWa34CzdBzDVWnunZ8DJ7WcnzlJ92W7V1caYYAA3Y+l+tz4NGFw04OTRxwU4ezIBXGeMaVrC3JOBCzwDTm4/2TjBtgNuVWtjTLMi514NNHLLi4ArPANObj8pOEsVJrtV5+YtQWitXQ7kzaGVMaYH3g3wuM7HRZ83ERERERERERERkWNdbm7uUfMjFaeg07HhDWttppf604B2bnmutXaJrw6stVnAUx5Vo4s0udCj/HAJ/fwFTMFZwm8yzvJ/AMNw9pEC+Mhau7mEPvZTkFUVBIz01RYnAJbs7YC19iCwzKMqrkiTizzKj1hrc3z0kwy8AfyEkwVW1+PwBx7ly32M0fO51NJ6IiIiIiIiIiIiInIKvnFDAAAgAElEQVRM0vJ6x4ZFPur7epSXl6GfhR7l0/IKxpgWQBv34QZr7dqSOrHWXlENY3neRzufgTTXbo9yXqYWbgbWme7DQ8Cckjqx1t7t49CHOPtj+QOXGGPu9AxeGWNCKQhubcIJXImIiIiIiIiIiIiIHHMUdDo2bPRR38GjPN4YM74cfTb3KHsuS1dsSbwy8hzLK8aYVyo4lqISSzk3zaPsmdnXiIK9nNa5mV7lZq1NMMbMA/rj7GN1Bk6WV56hFGRGTbLWKjdTRERERERERERERI5JWl7v2LDPR309H/Vl4XluwzJcqzz9VeW5aSUcK8rPo1wVc8rzvkf5siLHRnmUtbSeiIiIiIiIiIiIHJdyjqIfqThlOh0DSsjS8Xx936XiWUpBpTcpledYngN2lOPc/VVw/aKqYk55pgGvAuHACGPMLdbaLGNMNDDEbfOLtdZW4TVFRERERERERERERI4oCjod25I9yousta9XsB/PTKDoKhjLN9babyvYT1WpijkBYK1NNcZ8jpPVFAP0A2bj7OUU4jZTlpOIiIiIiIiIiIiIHNMUdDq2bfAo9wBKDDoZYwKASGtt0eXmPPtpV9pFjTFDgJHAX8CX1tpVbh+9PMZSYtDJGBMCBFlrU0u7XgVtAbKBAKC1MSbAWptdwni6A7fhzGmutfbHIk0+oGApvaE4QacL3MdZwEdVOHYRERERERERERGRo0ou2u7+eKA9nY5tCzzKFxpjQktpfzGQbIxJNsZMzqu01q4DktyHrY0xLUvpZzhwDfAY0NbLWC4tdeRwC3DAGLPbGPN0GdqXi7U2DVjlPgwDzijllCHAlcDDQG8vx2cDO93yUGNMMDAw75i1dlelBiwiIiIiIiIiIiIicoRT0OnYNoeCvZNigft8NXQzix52H0YDa4o0ycvU8QPuLaGfGJwsJ4B0nGAMwCdAhlvuZIy5toQ+6gPj3YcxFASHqtpkj3JJcwoBPMc7o2gbN0sqr78WwE1ApPtYS+uJiIiIiIiIiIiIyDFPQadjmLX2ME62UZ4HjDF3GWMKve7GmCicwMhJbtVe4OUi3T0HpLnl640x93jppx7wKVDXrXojb6k+a+1u4CWP5q8aY64qOmZjTBzwP6ChW7UOmFLaXCvoTQqykwYZY15yM5Q8xxMKvA/kZXdNt9au9dHfBx7lh90/U4HPq2a4IiIiIiIiIiIiIiJHLu3pdIyz1r5qjDkLJ/vID3gKuM4YMxNnybwWOMvq1XdPyQbGFN3XyVq70RhzI/CO28+/gauL9DPCo58/gAeLDOcB4FTgNCAEeM8Y8w+c/Z0OAm2AYUC42z4NGG2tzarUk+CDtXa/MeYK4CsgCLgZuMAY8wVOhlhjdzxN3FN2um189bfSGPMb0ImCwNtn7lJ+IiIiIiIiIiIiIsetHO3pdFxQ0On4cDmwFbgNCMAJ7tzupV0SMNZaW2z5OABr7XvGmMPAazhBlXbuT1GLgBHW2pQi5x82xgwCXgdGu9Vd3J+itgCXW2t/KWVulWKt/c4Ycw7O0niNgOY4+0kV9Qcw3Fq7tZQuPwD+4/FYS+uJiIiIiIiIiIiIyHFBy+sdB6y12dbacTjL5z0LrMRZQi8LJ9C0ALgHMNbaL0rp6yOgFfAQ8LN7fhawC5iFE0w6w1q73cf5adbaK4CewKs4e0ftBzLdPubgZBN1sNYurMS0y8xaOxdoDYwD5gO73fHsBb4H/g/oZq39owzdee4TtQP4rmpHKyIiIiIiIiIiIiJyZPLLzVVKm0hVMcZ0B/Kys5611t5ZU9fe2fcs3czHkaY/r6/tIUgNuz7+tNoegtSwhBytznq86eAfWdtDkBr2wLn7Sm8kx5ScA4drewhSw7b/HFLbQ5BaELNwam0PQUSqUWxspF9tj+FoNaDpoKPm88vvEr7V61xBWl5PpGpd4VF+r9ZGISIiIiIiIiIiIiJHJGNMS+BWYBDQAsgBEoBvgNestWur6Dr+wGXAlUB3IBpnpa8/cLaGmWStzaqKa+XR8noiVcQYE4Fz8wIstdb+VpvjEREREREREREREZEjizFmNPA7cDvQAQgHIoB2wG3AKmPMrVVwnWhgLvAhcA4QCwQB8cAA4B3gJ2NMs8pey5MynUQqyBgTCmRZa7OMMTHAW0AD9/DLtTcyERERERERERERkSNLDkfN6nrVxhhzHvA+BQlBi4HZbrk/cCoQDLxojNlvra3QalrGmEDgSyBvv4b9wKfARpzMqpFAFNATmGmMOdVam1qRaxWloJNIxXUCfjTG7McJNgW49auAybU2KhERERERERERERE5orgrZb1JQcDpH9baFzyaPGiMuR6YCPgBLxtjZllrd1fgcrdTEHBaDQy21m7zGMuDwAzgFJzPuR8E7q7AdYrR8noiFbcFCAEaUhBwSgauqup1MEVERERERERERETkqPY3IM4tTykScALAWvsG8Iz7MAIYX96LGGOCKQgg5QCXeAac3OvsBM4HDrhVN7ureVWagk4iFWStTQR+AA4CScD/gNOttb/W6sBERERERERERERE5EhztUf5qRLaPQkccsujjDF+5bzOeUBeAGmGtXaNt0bu59sT3YfhwLByXscrLa8nUgnW2jNrewwiIiIiIiIiIiIiR7rc43hPJ2NMHHCy+3CHtXalr7bW2r3GmMVAX6AJ0ANYUo7LDfIof1VK21nAnW75IuD1clzHK2U6iYiIiIiIiIiIiIiIVJ+uHuXFZWjv2aZXOa/VzaP8cyltPYNZ5b2OVwo6iYiIiIiIiIiIiIiIVJ82HuWNZWi/xaPcurquZa3N2zoGoL4xpl45r1WMltcTEREREREREREREZFqlZN7/C6vBzT2KCeUof12j3Kjsl7EGBMC5AWODlhrU8p4rQYe10ou6/W8UaaTiIiIiIiIiIiIiIhI9YnyKKeVoX26R7luNV6nMtfySplOIiIiIiIiIiIiIiIiLmPMGGBMOU5511r7bgnHQzzKGWXozzMQFOKzVeWvU5lreaWgk4jUnoAAwoddTPjQ8wlo3JjspCTSZ33FwQ8nQ3Z2qafXf/klgk/u5PXY/meeI3369IJLNW1CxJgxBHfvjn/dSHKSkzm0cBEH3nqb3P37q2xKUnZNm8bz2KP30O+sU4mKqsvKlb/z6GPP893cHyvU39Qpr9OqVQtO6THI6/GLLhrC+HE30qlTe/bvT2He/IU8+tjzrFv3V2WmIaXwD/DnrDFDOP2yATRo1pCUXcks/GQe30z4nJys0u9zTx37d+Omt+/h8XPHk7Bmc7Hj9eIbcMG4y2jb+yQi6key889t/DDpW36aOreqpiNF+Af4M3TMUAaNGkyjZo1I3pXMnI/n8Omrn5Bdhtc3IiqC0eOuoMeAHkTHRLH1zwSmvTaNBTNK/3vgntfu5bRzT2Psqdfy/+zdd3wUxf/H8deV9ITQAqHXsFQBQZoC0qRIFRBUREC/9t71Z+8dC/ZeUVCRKr0LiDQpyiKdhBIIkBDSLnf3++OOkJC7FNJE3s/HIw82szOzM2z2bu8+OzPxsfE59pWrWI5r7htF+8vaE14ujLid+5n97a/M/nY27vN7OocSYbVZ6TSmN+1Gdqei9zpfO3kJi96fVujrvHH31oz97EHe7PcwB7Jd5xVqVubh5e/kW/6lS+7gWOyRQvdBCslqJeDSgQRc3Bdrpaq4k47iWDmPjDmTwFWAc24PILDXcALadcNSsQru4wlkblxF+qxvIfVkjqyWClEEDRiNrdEFWMLL4Tq4D8fSmThWzCmhzolPViuBlw0h8NLLsUZF4z6eQMbSOaTPmFig+/awx97Ebvi+b0/9fDwZC2dk/W6JiiZ42FjszdtiCQzCGbuLjNk/4fh9cXH1RgrCZqXitQMpP6IPATWrknn4KIk/zefIh5OgkK/tWCzUnfQ6Ia0a83dMv3yz13jnUcr1uYTtl47BERefb34REZESUhfoWoj8i/PZn/0NtCAfTC3Ztl2FaEf2vAX9AHy2x/JJQScRKTPl7rmL0IEDyfhzI2m/rSCweXMibriegIYNOf7Ek/mWt9erR+aevaQtzP2FssM0s7ZtdepQ6b13sYSGkP7bCpxxcdiNRoQOHkRgu4tIuOkWBZ5KWZUqlVmyaArR0VF8N3EKSYknGDFiEL/O+o4rho1jxox5harv3ntuYugVl7Phzy0+99937828/NLjJCWdYNLkaWRkOLhiSD/69ulO/wHXsur3tcXRLfFh5LPX0/nqXmxf/Tcb56+hQRuDgfeNoGaTOnx86xsFrie6QQ1Gv3qr3/3loyvy0NQXCS8fzrpff+f4waM07dKSUS/fTK1m9fj+iU+Loztyhlueu4U+1/Rly+otrJ73O03aNmXU/aOo17QeL938Yp5lg0KCePa756jfrD7LZyzn8P7DdOrbiYfefYjIipHM/HKG37Id+3Tk4n4X+9wXWSmS16a+TnTtaLau28qyacto0LwBt75wG807tODV218pUp8lt8HPjqX91T3ZtXorf81fS902BpfddyXVmtThm1vfLHA9VRpUZ/irN/vcl5qUwrw3f/S5r3K9arQedDHxO+I4cVjv56UhaMRtBHbuR+b2zWRsXIWtQVOCBozGWqM+aZ88n3dhq42Q25/F3qglmds24tq4Emsdg8AeQ7DVb0LKGw+AMxMAS/nKhD74JpbwcmSuW447MQFbkwsJHnU31loNSP/hvVLorQAEX3cXQd37k2luIn3dCuyNmhM8bCy22g1IeefpfMvbatbDuX8vjlWLcu1z7tqWtW2tVouwx9/GGlGOzM1rce7bhc1oQejtj5NerxFp339UrP0S/6KfupUKI/uR8sdmTixYReiFTYm6+1qCGtcj7o4XClVXxbGDCWnVuEB5Iy7rRLk+l5xNk0VE5Bxwjj0CuBtYUsj8eUnOth1cgPqy50kvRDsKe5yiHMsnBZ1EpEwENG9G6MCBpC1azPEnn8pKj3z0YUL69CGoY0fSV670W94WHY01IpzUWbNI/vyLPI9V7vbbsEaEc+yxx0lfevrp+bDR1xJxw/WEXzeaE2/n//S0FJ+nn3qAOnVqMmjwdcycNR+A1954n9WrfmXC2y8yd+4SMjIy8q3HarXywvOPcP99/oMRDRrU5fnnHuHo0WNc3Hkg//yzE4AXX3qL1atm89lnb9KyVXccDkfxdE6y1L+wEZ2v7sXamSv55LbxWenXvX4bHYZ2pXn3C9m8cF2+9TTq2IwbJtxDRCX/0wpf8cgoIqPK8/4NL7NxvieI+MvL33LPxCfpOro3S7+Zy/5t+4reKcnSuE1j+lzTl+Uzl/PyLS9lpd/9xj30GNaDi3pcxB8L/vBbfuC4gTRs0ZD3H3ufWV/NBOCHt77n1V9eY8wjY1g+YxmJCbkDCGGRYdzynP9rfuyjY4muHc30z6fx0ZOnv5wc8+hYht48lHWL17LgxwVn02Xxoc6FMbS/uicbZ67i29veykq/8vVbaDO0C427t2brwvX51lO/Y1OumXAX4X6u87SkFOa/+VOudKvdxu2/PEtmuoNvb32LzHS9lpc0a/0mBHbuh2PdMtI+Of3Fc/Do+wjo0BNH83Y4N6/2Wz6g+2DsjVqSMW8y6VM+y0oPuvIWAi8diL1tVzJ/91yjQUPGYY2sSMr7T+Hc9Lu3AVZC7n6ZwK4DcCydietA7pGvUrxsMU0J6t4fx+olpLzzDOD5JiLkxocI7HwZ9lYdyNywym95S+WqWMLCyVw6m/QpX+V5rJCxd2ONKEfqxA/ImDXZW4GVkFseIejyETg2/I5z65/F1TXxI6R1EyqM7EfSr8uIu/P0QyTVXr6X8lf0JLFbO5IX+b/OswuoHU3U3dcWKK+1XDjRT/l/jxcRESlN3qnyvijGKrMHg0ILkD8k23Zhnq4r7HGKciyfrEWtQETkbIQOGQxA8hdf5kg/8eHHuF0uQvpfnmd5e4P6AGTu2JlnPktICIFt2uDYauYIOAGc/PY73OnpBHVoX9jmSxGEhYVy7ahhrFn7Z1bACeDAgUNMePczatasRp8+3fKtp3Wr5qz+fTb333cr8+b5f/Bk6BWXY7fbee3197MCTgD79u3nrbc/plFMffr161G0TolPXUf3BmDWWzlHJ/zy8ne4XC4uHpn3/3tAUACjXrqJu755HIsF9m7yf71HRldk95/bswJOAC6ni3WzPF+C1Wsdc7bdED8uH90fgInjJ+ZI/+rlL3G5XFw20vdUl6f0G305x+KPMfubX7PSUk+mMumdHwgODabrYN+zGPzvyRuxB9rZunZrrn1Wm5WOfTuRdCyJL1/M+f7y7evfkHIihUE3DC5Q/6RgOo72nOf5b+UMCP368ve4XC7ajcz79dweFMDQl/7H/775PywWC7F5XOe+dL99MDWa12PRe1M5aCqwXBoCuwwAIGPmtznS06d+jtvlIuDi3nmX7zoA15GDpE/NeY1mzP8Jx8p5kHH6wUpL+co492w7HXACcLnIXOe5p7PVK9jICSmawJ6DAEg7I2CUNukT3C4XgZfmPV2arZbnvt25L5/79sgK2Ju0whm353TACcDtIu27DwAI6jO0sM2Xs1BhlOc9/sg73+VIP/z6F7hdLsoPz/s6z67a83eRGX+U9J2x+eat+n83YgkIIGX934VrsIiIyLnhQLbt6gXIXyPb9sGCHsQ0TSdwan7acoZhhJXUsfxR0ElEykRgy5a4jh8nc9euHOmuhASc+2IJbNUyz/L2Bg0AcOzMZz0eq5UTH3zAyUmTcu9zOnE7nVhCQnLvkxLTvt2FBAcHs2Txilz7FnvTunbumG89AwZcRsMGdXn4kee4fMAov/nq1q0NwO+/5x5Rs2mT5wPtJZ3aFajtUjgN2zXhREJSrhFGifHHiN91gEbtm+RZPiKqPBeP7MHmhet4ru8DxJl7/eYdP+IpXh70aK70qg0893FJRzTlVnFr1r4ZiQmJ7N2Wc5TB0UNH2b8zjuYdfK/dARBdJ5rK1Sqz5Y8tuFw5p4veuHIjAM3b5y5/YdcL6TGsB58+8wnHjxzLtT+yUiSh4aHs2bqH9LScMwI40h3E7YqjTuM6hITrdb+41GvXhOSEJA5ty/ll4on4YxzZdYB6+V7nkbQb2Z2tC9fzZt+HChU4iqhSga439udY7GEWfzAt/wJSLGwxzXGdSMw1wsideBRXfBz2GP/XvjW6NtZKVcnctCrX2k/uo/Gkff0GmeuXZ6Wljn+QlJfv8lFPLQBcJ44XpStSQHbjAlxJx3HF7s6R7j6egOtgLPbGF+RZ3lbbG3Tam3fQyRpVzZNvZ+6HCtzHE3CdSMTWyP/flxSf0Iuak3k0kfR/cl7nmfFHydgVR2i75gWqp/zV/Qjr0JIDj72DOz3vmXrCOreh/BU9OfTixziP6NoWEfmvcuE+Z35KQPY1IeoWIH/tbNvb/OYq4rEMwwgHKnp/PWia5olCHisXTa8nIqUvIABblSpkbPnL527nwYPY69TGEhnpd60le/36nicrmzcn8sEHsNWqhftEMmlLlpD82ee4T3oWoXafPEnKpMk+6wi8qC3W0FDS/9KTdKWpfv06AOzYmXs6nN17PF82xsTUz7eeGTPm8cGHXxEfn/eC8eneD7hBQYG59pWLjACgdp2a+R5PCsceaKdi9crsWu/7vigh9jDRDWoQXjGC5KO+72dSEpN5dejj7Fxr+tzvj8ViIbJqBToO70bnq3uxd/NOtizOf3ovKTh7oJ2o6lFsXZf7i0GAQ7Hx1GxYi3IVy5F0NCnX/mq1PV8sHtxzINe+44ePk56WTo36OR/8CgkL4faXbmf9svUs+HEBHXp3yFXW4Z1aLSAwwGe7wiLCsFqtRFWvkitYJoVnC7RTvnol9q7/x+f+Y7FHqNKgBmEVIzjp5zpPTTzJe0OfZM/awn6Ggl53DyUwNJipT36pafVKiz0Aa4UonLt8X/vuhENYomthCY/EnZz7Hs5a3XMP4Nq/F1uziwjqMwJrrQa4U06SuWYx6TO+zjHSKQeLBUtkJQI69iLgkr44927HucX/FJ5STOwBWCtVIXO77/t215GD2KrXxhIRifuE7/t2ay3Pfbu9UTMCb7gPa7WauE8m41i9lLSfv4RU7337qamO7T5ewy0WLEEhWAIDISgY0tOKpXuSmyXQTkC1KFI3+L7OHXGHCGpQC1vFcjh9vMefYo+uTJUHxnF80hxSVuU9JaI1LIRqz91B8m/rSfx5PhE9838ATURE5By0Gc+yVhagIE8/Z//Qm//aBDltAk5NO9GOnEGoM2WfAqqwx/FJI51EpNRZy3m+6HcnJ/vc7zrpSbeGh/utI6BBAyxWK+HXX49j2z+kzpiJ6/hxwoYPo+K772AJzWfK0qAgIm67DYDU6dPPohdytipVqgDAcR8BxcREz5eSkd5gUF7Wrd+Ub8AJYO06z6iJwYNzT/0yoL9nWqjIcvkfTwonNNJz/aYkpfjcn3rCkx4S4f9aTTuRWuiAE3jWjHpx1QcMvG8E8bsPMGHMi7icrvwLSoFFlPdcMyeTTvrcn3LCkx4W4XsUf0SF/MqnEHpG2TGPjiWiQjnefWSC33YlJyZzcO9B6jWrR9VaVXPsq92oNlVre9LCyhV0WmvJy6nrPNXPdZ7mvc6D87nOzybgFFYxgguHdibp0DHW/7Is/wJSLCyhnnPuTvF9D+dO817TIb7PuSWyEgD2C9oTetszuFNP4lg2C3fSMQJ7DiXk9ufAavNZNnj0fYS/8DVBA0bjit9P6ruPg0uv7SXNEua9b/d3zlM859wS4n/WFlut+lisVoKHjsW5+x8yFs3CnXScoD5DCX/8TQj2/L249u/GnZ6GvWlrOKM+e4uLPAEnTv8dSsmwee/DnUl+Pqt5X9ut4XnP1FPt2Ttwp6Rx6KVP8j1mlQfHYStfjoOPa51dERH57zJN8yhwaiHM2oZhNPOX1zCMSpwOTCUAa/3l9WNmtu2850LOuX9uIY/jk4JOIlL6bJ5Blm5Hhu/9GZ6nHE99sMzFYsGVnIxj2z8cuW4MSa+8yol3JpDwvxtJmTqNgPr1CR87xv/x7XbKP/MUAfXrkbZ8OWmLFp99X6TQAgI85z89Pff5zxqVFBxUbMf78ccZ7NkTy/XjruLxx+6hatUoqlaN4skn7qNvn+6AZ2SMFC9bgOdLw8wM36MPTo1KCPAxAq2o9m/bx7wPp/HX0g1EN6jB/ZOfoWLNqGI/zvnMbvdcxw4/59eRkQn4P7827+uAw8/olMwMB4FBp590b96+OX2u6cO3r3/Lob2H8mzblI+mEBQcxGOfPk6Ttk0IDg2m6UVNefj9R8hI87zu6JovHgW9zu1BvkeeFUX7q3sSEBTIb1/Owelw5l9Aisepe7hMPyPLvOkWu+9r3xIUDIC9RXvSvn2L1PeeJP2nj0l5+S4ca5dib9icgK79fZZ17d9DxrwfyfxrLbboWoTe9xqWilWK2CHJl/f1Hkfe55w87tvdKck4d//DiUeuJ/XT10n79j2Sn7iF9AXTsdWqT/AV12UdI2P+VKyRFQi79zmstRtAUDD2lu0Iuf4+3Gmpxds38c17zt1+Xttd3nRrHvdwkYO7E37pRRx85n1cJ3w/YHJKaLvmlB/Zl8NvfY1jX5GXkBARkX+5sp4yr4yn1wP4Idv2U3nkewQ49Wb7jWmahX3aaiFw2Lt9hWEYPudDNgyjGnCj91cHMNFXvsJS0ElESt2p+bwtvqbOAPBOi+T3g6XbzdFbbiXhhv/hOnIkR/qJ997HlZpKcM8ePotagoOp8OILBHfsSMbff5P47PNn3Q85O6mpnulQAn18OREU5Ak2pZz0/dT82UhLS2PwFWOIjT3Ak0/cT9y+DcTt28BNN45mzDjPOhEpqfoSo7g5vF/u2wN8z+R76kvo9JTinx5n7gdT+fnFb3hn9AtMfOwTqtSrxshnri/245zPTq2XFODn/AYEeoPLqb7Pb4a3vD3Qz99HYABpKZ48gUGB3PHKnezYtJ1pn07Nt22zvprJ1E+nUrtRbV75+VUmb/2Rl396he2btrPo50XeduW9roQUTEGv84yU4v//vvCKzricLtZMWlzsdUsevA8MWex+Zmn33tu5M/y8trs9n5Wde7fj+G12jvT0KZ8CEHBhZ59FM+ZNJn3Kp6ROeIy07ydgrVKD4JG3nUUnpFBOTXfo77791Dn3N92d283Jp+8g+fGbcR/Led+eNvED3OmpBHTslpWcNvkzMlYtwt74AiKe/4jIT2YSeu/zOFbMJ/PvDTnbJCUi67NagO9zbvV+VnP5eY+3VSpPlUdvJGnub5yY81uex7IEBVLt+btI27ydo1/k/x4vIiLyH/AJcGoh22GGYTxrGEaOpyINw7gRuNf7ayrwWmEPYppmJvCc91cr8IthGI3OOE40MB04NYz8A9M04wt7LF+0ppOIlDr3yZO4nU4sfqZksIZ5XutcyXk/Feez7tRUnPtiCWgU43niMuP0aBpLZCQVXnmJwCZNyNi8hWMPPoRbwYYS07JlMwYN7J0rPTbWs4aLryntTk2rl5hU5DULc9i06W+aX9CVAQMuo17d2uyL3c+0aXOoXbsGAIcOHc6nBims1BMpuJwuv9NqnZpW79Q0eyVl6Tdz6XH95TTr2gpbgE0jIopJyokUnE5nrinwTjmV7m/6vOTE5Bz5cpcPZf+R/QCMun8UVWpW4Z7LX8BVwKm0Pnn6Y+Z9P5dWl7QCi4UtqzezfeN2Hnr/YQCOHdYC5cUhLZ/r/FR6WjFf51ENqhNVvxo7Vv1F8hHfa8hIyXCnnsTtcvqdSs0SHJaVz195AOe+7bn3HY3HnXICS+Vq+bbDsXQmgd2HYGvaxjP6yplZ0C5IIblTvOc81M85P5WeUvj7dtLTcB2IxVY3BgICPUFNZyap7z5HxpyfsTVqDpkOMjetwXVgH2FPv4vbkYH7ZPHeJ0pOzhMpuJ1OrH5e20+lO2mF5B8AACAASURBVP2MYIp+6lYsNiuHnnov32NF3X0tATWqEnv7nZouU0REzgumaZ40DONWYCqeYNBjwCDDMKYDmUAP4OJsRe42TTP2zHoMw/gC8A4X50vTNMf4ONy7wAigE1AP2GAYxo+ACdQGrgTKn2oa8H9F6lw2CjqJSOnLzMR56BC2ar6/VLBVi8Z1/DjuE74/UFrCw7HXrYPreCLO2Fyvu1iCgnA7nZB5+gsIa9WqVHzjNey1apG+ejXHH3sCd5oWIC5JLS9oxhOP35crvVv3KwCoV69Wrn316tYGYJu5o9jbk5qaxqRJ03KktWnTEoC///6n2I93vnM6nByNO0zlWr6nPqpcswonEpJISTyLL6nOEBgSREz7JqQmpbBzXe61YY7GHaZKvWqElQ8n6bC+oC4OmY5MDscdzloj6UxVa1UlMSExK7h0pv0792flO1OFKhUICg4ibmccAJ36XYw9wM47c32v5fTpis8AGFA755Rce8w97DH35EiLadGQ5MRkjh5KyKN3UlBOh5NjcYepUMv39JUVa0aRnJBEajFc59k17tYagM2/ri7WeqUAnJm4j8ZjqRTtc7e1cjSuE4ngZ/0fV7zn2vc7UspqA4e3bGAQtoYtcKedxLXz79x1HY3HXqUGlrAI3EnHCt8XKRhnJu4jh7BG+TnnUdVwJR33HwgKDcNWow7uE0m4Dua+bycwCLcr5307gHP7Xzi3/3U6ISAAW816uA7sQ0qYIxPH/ngCa/o+5wE1o8k8mojLz3t8uT6XABCz4luf+5v8M4uM2EPs6DaWiD6XYAmwU3+G7wBVw8VfAPB3TH5LUYiIiJw7TNOcYRjGKOBjIAxo4f3JLhN40DTNj4pwHKdhGP2AKUA3IAS41kfWP4H+pmkW25M9CjqJSJlwbNxESJ/e2GrWzBE4slaqhK1mTdJXrvRbNqBRDBXfHE/ab79x/JGcQXhrpYrYqlcj85/tWU/LWSIjswJOqQsWkvjc8+DUaIeS9tXXk/jq60m50kNDQ0hJSaVL54659nXt6klb+Xth10f076K2rZjy8+e8+tp7vPX2xzn2DRncF4C58xYX2/HktO1/bKXD0K5UqVeN+F0HstIjq1Qgql40mxesK5bjhJQL5fYvHmXvpp28OODhHPusNivRMTVJTUoh+aiejC5Of/3xF92Hdqd6vers37U/K71i1YpUr1edPxb84bfs4f2HiY+Np+lFTbFYLLjdp+fLbtHBc6+9de1WAKZ9NpWwcrkXje8yoDM1G9Zi6qdTc4youv+dB2jevjnjOozNMTKqfrP6VK0dzfIZy86+05LL7j9M2gztQuV60RzZdXotjogqFahUL5qtC9YX+zFrt24IwK7VuQMRUvKcO7YQ0L4nlio1cMfHZaVbIitiiaqOc7P/YKBzt4nbkYGtYQuwWLOm2wOwVq2JJTiUTNMzhZolJJzQ25/FufcfUl66M2dFVivW6NqekVfJepigpGVu20zgJZdhja6ZI3BkKV8Ja9UaZG5Y5besrW4M4Y+8jmPdClLGP55jnyWyItYq1XDu3p71txD22JsQEMDJJ3NOnWi/oB2WwCAyN60pxp6JPylr/qL8kB4E1q1Bxu7T17m9SkUC61YneZH/6/zw276DTRWu6os9qiKH3/4W5wlPwOrol79gi8j9Hl/u8i4ENajF0S9+weln1LSIiJybsn/2O5+ZpjnRMIzlwB1AP6AOEADE4lmP6W3TNDcXw3ESDcPogWdU07VAG6ASkAxsBL4HPjNNM/fC60WgoJOIlInUOXMJ6dObiBv/x/EnnwLvm07ETf/DYrWSOm2G37IZGzfhTEggqH0HAlq2xPHnn54ddjvl7r4LS0AAKVOmZOWPfOA+7LVqkbZkCYnPPJt1LCkbKSmpTPllFtdcPZT+/XsxY8Y8AKpVq8rtt40jLu4AM2fOL7bjbdq8lYiIcMaNu4r33v8Ch3ch7FGjhtH/8l5MnzFXI51KyO8/L6XD0K4MeuAqPrltfNbN5eCHrsZqtbJsYvGc58RDx9ixZisN2jam7YBOrJm+ImvfwPtHUr5qRRZ98Ssup6ZtKU4Lf1pA96HdGf3Qdbx8y0tZ53f0Q9dhtVqZ893sPMsv+nkhI+4cSf8x/Zn++XQAQsJCuPKOEaSnprHo54UATPt0ms/y9ZvWo2bDWkz7dCrxsaennY7dEUvXQV3pMqgLi6csBjzT9d3xiudL6x/f/7FI/Zac1v28jDZDu9DngZF8e9tbWX8HfR8aidVq5feJC4r9mNWb1SUjNZ2DpkY8lAXHqgUEtO9J0MAxpH36QtZ9VdCgsVisVjKW/+q/cFoKmeuWEtC+J4G9ryRj9veedKuNoCGetfccK+YC4E5MwLljC7YGzbC36Urm2iVZ1QQOuA5r+UpkLJ6mKblKQcbyeQRechnBw68nZcIzWec8+MobPOd80Uy/ZZ3mZlzHE7C3bI+t8QU4t2707LDZCbnuDiz2ADLmn17Lx3UojsAufbA1aoFz2yYALBWjCLnmFtwZ6aTP/qnkOipZEn9ZQPkhPYi67zri7nwx65xH3TcGi9XK8R/8v8cfecd30CmiVwfsURVz7D/mZx2n4Cb1s4JOjrhiWVpCRETkX8c0zX3Ag96fwpYdA4wpYF438IP3p1Qo6CQiZSJj7VpSFywkpEd3Kr7/LhnrNxDYvBmBLVuStmhxjpFO4WPHAJD8+ReehMxMkl55jfLPP0vFN14jbdFiXEmJBLVti71uXVLnLyD1V88HIXujGIK7dMHtcuE8eIjwMddxJndGBie//a6kuyzZPPb4S/Tq2ZXJP3zM9z9MJeHIUUaMGESVKpUZduUNWYEhOL021IY/tzBt2pxCHystLY3/e+xF3hz/LH+sns2c2YuoXacmVwzpx969cdxxZ7FNWStn2PrbJtZM/422Ay7mgSnPsW3lFupf2IiY9k1ZO3MlmxeeHul0+d3DAZj55uSzOtZ3//cx901+hrFv3smF/TqSEBtPg7YG9Vo3Yvef25n6iq7x4vbn8j9ZOm0pXQZ24dVfXmPTyo00btOE5u2bs3zm8hwjna6652oAJo4/fR5++uAnLunfmRufvonm7ZtzYO9BOvXtRLU61fjg8Q9IOpp0Vu2a+skv9BzWgztfvYvWXS4k8chxOvTpSLU61fjmtW/Ysan4p+88n23/bTN/Tl9BywGduHXKM+xYuYU6FzaifvsmbJy5iq0LT4906nn3UADmv1m0L40r1IziyK4DuF16iKQsOM0NONYsIaBtV6wV3iBz20Zs9Ztgj2mBY92yHCOdAi+/BoCMmae/ZE7/6RNs9ZoQNPA6bDEtcMXtwma0wlarAY41S3Bu+j0rb9rECYTe+yrBYx8g88JLcCccwtagGbZ6jXHu2Ub61M9Lr+PnMeeWdWSsWkRgh26EPfkOmX9twB7TDHvjC3CsXpJjpFPQkNEApE/5yls4k9RP3yD0rqcJe+gVHL8vwZ2chL15G2w16pCxciGOZafv79J+/pKANhcT9sCLZKxYAI4MAtpfiqVceVI/fhX3cU2PWhpSVmwgccYSIvt3JWDyG5xc9SehFzYl9KLmJP26LMdIp8p3eK5zf8EmEREROf9YNKRN5L/hYJdLz72L2WYj7JqrCenbB1vlKJzxh0idM5eTE7+HbEGH6KWLATjY5dIcxQOaNiXsumsJbN4CS2Agmfv2kjpjJilTfsl6Gi90+DDK3XF7ns1wnUgm/vL+eeb5t6m56twfmVO/fh1eeP5RenS/BJvNxsaNf/Hc8+OZvyDn1Fejr72Szz4dz5dfTeL6G+7xW19mRhwb/txC24su87n/6quv4O67bqRRTH2OHDnKrF8X8PwLb3Lo0OFi7VdJ+V/1i/PP9C9ktdvofctgOg7rSvnoihyNO8LvU5Yx78OpZGacXr/h/d2eqRhvqXul37pGv3YrHYddyvP9HiD2rz259kfVjWbAvSNockkLgsNDSYiNZ83035jz/lQcacU6UrxUxLpSyroJ+bLZbQy7dTg9hvegUtVKHN5/mEU/L+KnD37McX6n7/WMXj1z3aXylctz7YOjadezHcGhwcTuiOXnD35m2fSl+R77/z7+Pzr07sj1ncblGOkEnin+xjwyhgs6tSQkLITd5m5++WgKK2f7n7r136CpNaKsm3BWrHYb3W4ZSJthXSkXXYHjcQmsn7KMxR9Ox5nt7+Dl3RMBeKjuVX7rGv7azbQd1pU3+z3MAR/XeUhkGE/9+Qnblm7k09EvFn9nStlj/Y6XdRPOjtVGYO8rCejQC0v5SriPxuNYvZCMeT9C5ul7uIj3PKOeTtzaN2f5sAiC+l2NvdXFWMIjcSUcwrFiDo4FU3JMuQdgiapOUP9rsTVpjSU4FHfCIRxrlpAxdzI40ku8q8XNdeLcez8CwGYjqP9VBHTujbVCZVwJ8Th+m0f6zB9ynPPIrz2jGxOv7ZGzeIMmBA0ehT2mOQQE4jq4j4xFM8lYMC3XLATW6JoEj7wRW0xTsNpw7f6HtGnf4fx7Q8n3swTsXxVU1k04O3YblW+6ksgremKvWgnH/sMkTV1IwseTcWd7bW/yzywg/3WX6k17h+AmDQq0PlPN9x4noldHtl865pwd6VR5Rak9TC4iZSAqKsJS1m04V7Wr3vWc+f5y9f4lOs9nSUEnkf+IczLoJGftvxB0ksI5V4NOcvbOhaCTFK9zNegkZ++cDTrJWTtng05y1s7ZoJMUiYJOIv9tCjqdPQWdzg/Wsm6AiIiIiIiIiIiIiIiInPsUdBIREREREREREREREZEis5d1A0RERERERERERERE5L/NzTkzu54UgUY6iYiIiIiIiIiIiIiISJEp6CQiIiIiIiIiIiIiIiJFpun1RERERERERERERESkRLndml7vfKCRTiIiIiIiIiIiIiIiIlJkCjqJiIiIiIiIiIiIiIhIkWl6PRERERERERERERERKVEuNL3e+UAjnURERERERERERERERKTIFHQSERERERERERERERGRIlPQSURERERERERERERERIpMazqJiIiIiIiIiIiIiEiJcru1ptP5QCOdREREREREREREREREpMgUdBIREREREREREREREZEi0/R6IiIiIiIiIiIiIiJSolxoer3zgUY6iYiIiIiIiIiIiIiISJEp6CQiIiIiIiIiIiIiIiJFpqCTiIiIiIiIiIiIiIiIFJnWdBIRERERERERERERkRLl1ppO5wWNdBIREREREREREREREZEiU9BJREREREREREREREREikzT64mIiIiIiIiIiIiISIlyuTW93vlAI51ERERERERERERERESkyBR0EhERERERERERERERkSLT9HoiIiIiIiIiIiIiIlKi3Gh6vfOBRjqJiIiIiIiIiIiIiIhIkSnoJCIiIiIiIiIiIiIiIkWmoJOIiIiIiIiIiIiIiIgUmdZ0EhERERERERERERGREuVya02n84FGOomIiIiIiIiIiIiIiEiRKegkIiIiIiIiIiIiIiIiRabp9UREREREREREREREpES50fR65wONdBIREREREREREREREZEi00inUmQYRk/gbqANUAlIA/YBfUzT3FeWbZOSYxjGF8B13l+7maa5uOxaIyIiIiIiIiIiIiJSMhR0KiWGYVwNfANYsiUHAA2Bg2XSKBERERERERERERERkWKioFPpeYXTAafNwDQgFXCZpukos1bJf0ZgJc2Jej6pEBJe1k2QUtY2M7CsmyCl7B9LYlk3QUpZl9SyboGUtsCb7y7rJkgpc6cnl3UTpJRVt79f1k2QUhZ41RWw/uuyboaUsuTW15Z1E0TOCS63vr88HyjoVAoMw6gI1PD+egzoaJqmPm2IiIiIiIiIiIiIiMh/hrWsG3CeyD4kwVTASURERERERERERERE/ms00ql0ZA/upZdZK0REREREREREREREyoAbTa93PlDQqQgMw9gN1AHmmKbZxzCM4cATQEPgMBAIVD2jWFfDMLKuLtM0Ld66FgNdgS2maTY3DKMccAswAmgAuIHdwPfAhFOjpQzDsAPjgNFAEyAU2INnzaiXTdNMKO5++2MYRkvgBqAHnukEA4CDwHLga9M05xWwnt7AjUAnoJK3jqXAG6ZprjMM437gVW/2eqZp7s5WdgzwuffXp03TfCqP4xQor2EYgcBIoA9wEVAZz+i1RCDW27aPTNPcXJD+iYiIiIiIiIiIiIj8FynoVEwMwxgGTMqWVKsIdTUHpgL1z9jV0vszzDCMnkAQ8DOe4EyOKoAHgCsNw7jYNM24s21LAdtrB8YDt5J7ysZ63p9rDcOYAYwyTdPnyuiGYQQAn+AJoGVXC7gGT39uK86258cwjEuAb4HaPnZX8v60BG43DOOZvIJcIiIiIiIiIiIiIiL/ZQo6FY+KwAc+0ncD7wIVgEe9aTuB9/OoqzwwC0+g5SDwE3AEuAAYDFiANsDz3n/bA/vxBJ8OAs2AYXhGGdUB3gGuONuO5ccwDAsw2ds2ACcwF/gDyMQz+mogEAb0BxZ7A2EpPqr7ARji3XYDs4FVeEYVDQIa4fl/nl8inTmDYRit8fQlxJu0w9umODzXTiNgABCJ57w8aRjGGtM0Z5RG+0RERERERERERETOFS63ptc7HyjoVDwu8v47DXgQT1DiIiDMNM0ZhmHU5XTQaZ9pmq/lUVcN77+TgOtM00w7tcMwjBuAj72/3ur9d7I3X2q2fJ8A8/CMOhpkGEalEpxm715OB5xMYKhpmluyZzAMIxpPQKkL0Ap4LVv7T+UZyumA00lvPXOy7X8UeAm4D7is+Lvh05ucDjh9CNxmmqYzewbDMCoAU/BMjQiefinoJCIiIiIiIiIiIiLnnTOnQpOztxkYZnokm6a5qAgjXvYA12YPOHl9hmcNoez5RmcPOAGYprkQT9AJPOe49Vm2I0+GYYQC/+f9NQXofWbAydueg3hGOx32Jt1gGEbNM7I9lW371uwBJ28dmaZp3o9nTasS5w0UdvH+GgfceWbAyduuY8D92ZLalHzrRERERERERERERET+fRR0Kj4fm6bpKKa6PjRNM+PMRNM0XcBf2ZK+8RGYOiV7vqhiateZrsAzdSDARNM09/jL6F3HaYL31wBg+Kl9hmHUB5p7f90LfJ3HMR8CXGfb4EJwALfhGZX1uK/zkc3GbNsRJdoqEREREREREREREZF/KU2vV3xWFmNda/LYdyjb9oY88iVn2w4qWnP86pJte10B8q/Itn0xMN673TNb+hzTNP1O7mma5l7DMH4HOha4lWfBNM044L388nnXtGqWLSmgxBolIiIiIiIiIiIico5yozWdzgcKOhWfXcVY1/489mUf5XOsgPlKStNs2+8ahvFuIcrWzrZdI9v23wUou54SDjqdyRtcqg80zPbTHM90ehWyZbWUZrtERERERERERERERP4tFHQqPseLsa6UAubLtcZQKauQf5YCla2SbTuxAGXji3DcQjEMowHwKDAUiPSTzQnYSqtNIiIiIiIiIiIiIiL/Rgo6FRPTNDOLsbpzZZxh9r+fN4ADhSibPbhU2FFZ6YXM70+eo5IMwxgGfEPu6QmT8YzI2gAsB+YAB4upTSIiIiIiIiIiIiL/OW53aUzOJWVNQScpiuzT+80xTXPuWdaTkG27YgHy+xtxdKb8proL9rfDMIzGwNecDjgtAD4CfjdNc88ZeUML2B4RERERERERERERkf8sBZ2kKHYC7b3bFwF5Bp0MwwgCAkzTTD5j11/ZtlsU4LhN8tiXPVx+5gilM1XLY989nA5KTQWGmKbpbwRaVLZtrekkIiIiIiIiIiIiIucla1k3QM5py7NtjyhA/juAE4ZhHDYM49Vs6cs4PaVgP8MwAvxVYBhGMNA1j2NkXw8rym8uj4vy2Nc+2/ZneQScALpl29Y1JSIiIiIiIiIiIiLnJX1BLkUxGUjzbrcwDGOcv4yGYVQEHvD+Whn489Q+0zTjgNnZ9t2dxzHvAsrnsX9/tu3uhmH4/Bs3DKM10CuPegKzbVf2l8kwjHDgiTPSNIJQREREREREREREJBsX7nPmR86egk5y1kzTPAy8ky3pPcMwRp+ZzzCMaGAKUMWbtA34/oxsTwKZ3u0XDMO4zkc91wDP5tOsP4Ak73Zd4Ckf9bTytseWRz0bs20/bBhGrlFThmE0ApYA9c7YpTWeREREREREREREROS8oxEZUlSPAZ2Ai/GsofSlYRj34Fnf6SQQA1zB6UBMCnCNaZqZ2SsxTfMPwzAeAl7H83f5hWEYNwHz8azT1B3o7M2exun1ljijHodhGO8A/+dNetwwjL7eejKBtkBvPGsvTQaG++nXO8CV3nwxwHbDMCYDu4FIoLW3TafWcHIAp6YFrMTpwJeIiIiIiIiIiIiIyHlBQScpEtM0MwzDuAz4CLjGm9zK+3OmvcBVpmmu8VPXG4ZhZACv4QlgdfT+ZPciniBXXus6PQ0YwDDv7229P9l9DjyIn6CTaZq/GYZxNzAez4jAcsD1PrKmeOu5CDg1OqsNsCuP9omIiIiIiIiIiIicV9xuTVt3PtD0elJkpmmmmKY5CmgHvAf8BSTiGf0Tj2eU0e1AU9M0V+RT1wSgOTAB+AfPqKbDwFSgm2majxagPQ7TNIcDA4BfgANABhAH/AT0Nk1zHKen8/NXz9tAe+BrPCOc0r0/B4HFwDNAjGma73J6TSo4HXwTERERERERERERETlvWBRdlHONYRiLOT3SqZ5pmrvLrjX/HkeHdNXFfB4xFh4s6yZIKXsp4qKyboKUsomWw2XdBCll92RULOsmSCnrOW9MWTdBSpk7PbmsmyClLP2998u6CVLKAq+6oqybIGUgufW1Zd0EKUVRURGW/HOJL7Urtjhnvr/ce3STzvNZ0vR6IiIiIiIiIiIiIiJSolycMzEnKQJNryciIiIiIiIiIiIiIiJFpqCTiIiIiIiIiIiIiIiIFJmm1zuPGIZxfzFW94NpmvuKsT4RERERERERERERETmHKeh0fnm1GOtaAyjoJCIiIiIiIiIiIiL5cru1ptP5QNPriYiIiIiIiIiIiIiISJFppNN5xDRNS1m3oTiYpnlpWbdBRERERERERERERERyUtBJRERERERERERERERKlEvT650XNL2eiIiIiIiIiIiIiIiIFJmCTiIiIiIiIiIiIiIiIlJkCjqJiIiIiIiIiIiIiIhIkWlNJxERERERERERERERKVFutKbT+UBBJxEpO1YbQZdfQXCv/lirVMN1LIH0hb+S9vO34HTmWzzihXcIaHKBz30nP3id9DnTsh3LSlCfwQT16o+tWk1cScfJ3LiWlG8/wX0sobh6JIVQvUY0//fEvXTu0oFy5cLZtPFvXn15AksXrzyr+j776i3q1atDt86Dfe7vdVlX7rr3Jho3icHpcrLitz947aUJbNlsFqUbkg+LzUrTsZdhXN2NiNpRpBw6zj+TlvLnu9NxZ+Z/nVdqUZfWdw2manuDgLAQTu5PYNfM1Wx48xcyU9MBCK9ZmRGr3sy3rh863E1y7JEi90lOs9qsDBk7iH5X9SW6djRHDx1l9uS5fP/uDzgLcH4jykdw3X2j6dCjHeUrl2fvP/uY9MFkFk9fkme5Bk3r897MCTx987OsmJP7NaNc+QjGPjiGjr06EFkxkoRDCSyZsZSv3viG9LT0s+6v+GaxWal7Q29qXdOd0NpVSI8/xr6JS9jxzrQCXefhRk2Mh4ZTvm0M9vAQkjbvZtcHszg46498y174yd1UG9CehW3vIHWfru/Skul0MvHXpfw0fwVx8UepXKEcg7u1Z9zgXgTYbfmW37orlnd/mMW6v3cA0LheTW4Y0ouOLRvnyrvnQDwfTJ7Nqo0mickpVCofQZcLm3HbiH5UjIwo9r6Jb5lOJxPnruTnRX8Qd/gYlctHMKhLG8YN6Fqgc75t7wHe/XE+a7fuIt2RSZ3oyozs1YFh3dvlyrt1z37e+3E+68zdADSuW53rB3SlY4uY4u6WnA2rlcCeQwjs0g9rVDTu40fJWD6b9FnfF+gzHPYAgvqNIKBjT6yVquA6doTM9StJm/oVpJ4s+faLX5lOFxOXbGDKyi3EJSRSuVwYgzo0ZWyvtgTYCnCdxx3mvZmrWLs9znOdVynPiC4tGXZxizzLuVxurhs/iU27D7LhnbuKqzsiIlKGbE899VRZt0FEikHqD188VdZtKKzQm+4ldPi1OOP2krFyMZawCIK798VWsw4ZKxbnX37sbbjiD5I+awqZmzfk+HFs3pAjmBR216OEXHENroTDZPy2EFwugi7pTmD7zmQsmgOOjJLraAmYsCu5rJtQJFFRlfh13g+0aduSWTPm88fq9XS65CLGjLuKTRv/Zsf2XYWq79Y7xnHTrWOIP3SELz//Ptf+UdcN5+PPx2OxWJjy0yx27dpL3749uG7cVSxf+jtxcQeKq2slpmdQjbJuwlm5+MWxtLpzEInbD7Br5mqCyoXR6MoulI+pwa4Zv+dZtlqnJvSb9H+UqxfNvvnr2b9sMyGVy1F/YAdqdGnO9p9+w+10AeBMd3Bg5d+5flIOHaNi41oc376fzR/Nwp3pKo1uF4vNlpSybkK+7nrhDq6582r27Yhl6YxlhEeG03v4ZdSJqc2SGUvzLBscEsRrP7xCx14dWLN0LeuWrSOmRUMuv6YfiUeTMP/0HRCuEFWBF795gciKkSyatph9O2Jz1hsazNtT36Jdt4sw/9zGb3NWEB4ZQbdBl9Ky4wXM/XEebve/8+m6js6Qsm7CWWn+yjhi7h7Cye37OTBjFQHlwqg1sivhjWpwYNqqPMtGNK3Nxb8+Q1i9aA7NXsPxNf9QvnUD6lzXE2dKOsf+2Oa3bHS/i2j04HAAdn30K5lJ//5r5kz1R7cq6yaclec+nsQnU+ZRt0ZVLuvYmhMpqUxbspqdsQfp3enCPMuu2fIPNzw9gZ1xGq4wmwAAIABJREFUh+jUqjFtmzVk6+5Yvp21hPLhYbSIqZOVd8e+A4x6dDx/7dxHhxYGl7RuSoYjk3mrNjBv1Z9c3rktIUGBJd3d4uU8t+45T3nu86l8Om0JdatF0at9C06kpDF9+Xp2xsXTu4Pvh8BOMfcc4LpnPmTX/sN0a9OEi5rU5599B5nx2wbSMhx0bN4wK++av3fyvxc+Zef+w3S6IIa2Teqzdc9+vp2zksjwUFo0qFXSXS12zj/WlHUTilXw6LsIHnANroP7cPyxFEtYOIGX9MZWrQ6OP/J+aASbjbD7XiKwcx9csbvIXLscS3AogR26YW/cCsdv88B97tyr+WNr0aSsm3BWnp+0kE/nrqFulQr0ah3DidQMpq/+m10Hj3LZhY3yLGvGHmbM+MnsOnSUSy+oT9uYmvyzP4GZf2wlzZFJh8a1/Zb9euF6pq76C4Cb+3Uo1j6VpoxqLcu6CVKKwsKCni7rNpyrXnvp3afKug0Fdf/Dt+s8nyWNdBKRMmE3mhHceyAZKxaT/OqTWelhdz5CULc+BLTtiGON/xEv1qhorGERpC34ldQfvsjzWAEduxLU9TLSly3g5PhnwftlY/CgEYSOuZWg/kNJm/RlcXRLCujhx+6iVu0aXH3lTcybsxiACW9/yoIlP/HK60+yaMEyMjIc+dZjtVp57Kn7uOOuG/zmCY8I44WXH2P37n107zyYE0megN0nH37D3EU/8txLj3JZt2HF0i/JqUqbGBqP6s6uGb+z8OZ3stK7jL+JmOGdqdWjFfsWbPBbvtPzY7BYLUwf8gxHNuzMSr/45XE0vqY7TUb3ZPPHv5KRlML6N37OVd5itzFw+tM40x0svPltnGn5/01JwTVt05T+oy5nyYylPHvL81npD75xP5cN70X7Hu35fYH/wOKQ64fQ6IIY3n5sAtO+nA7AN299x9tT3+SGR69nyYwlHE9IzFGmfpP6PP3xE1SrU81vvf1HXU6dmNr89MkU3n/6g6z0h996kJ5X9KDHkO7M+3H+2XZbzlChbQx1RvfkwLRVrPvfW1npLd++hZojulClV2vi5633W77FK9djtdv57fInSNroeeDAfPEHOi94kUYPDmffxMU4juV+0MIeGUazl8cVf4ckXxvMnfw4fwW9OrTitXvHYrFYcLvdPPbut0xfspolazfTtU1zn2WdThdPvP8daRkOXrt3LJd1bA1AWnoGtzz/Pq9+OYVOrRpTp1oVAF79cgonUlJ54/7r6dn+9Jd5H/00hwnfz+TDH2fz8Di9h5e0Ddv28NOiP+jVrjmv3nFV1jl//MMfmb58PUvWb6Vr69yj1E6Z8OM8UtMzGH/3KLq3bQrAbcN6MvKxd/lq5jKGd29HzSoVcbpcPPnxz56/jzuvolc7z+iItAwHt77yOa99O4tOF8RQJ7pyqfRbcrM1bErQpf1x/LGElPeeBSAdCLnhQQIvvgx7y/Zk/un/vT+w1xXYm7QifdYPpE3+OCs9eNTtBPUYTED7bjhWzCvpbogPG3bu56ffNtOzVUNeHdfv9HX+zTxmrP6bpZt30qV5fb/l3525ktQMB2/c0J/uLRsAcFv/jlz1ykS+WrCO4Re3oEblyFzl9h0+znszz26mCxE5N/1bHwCU4mUt6waIyPkpqO8QgFwBo5SvP8LtchHU8/I8y9vqem54M3fvyPdYwZdfgTvlJCkfjc8KOAGkzZ5K+qI5uBOPFbL1UhRhYaFcOXIwG9Zvzgo4ARw6GM/HH35N9RrR9OjVJd96LmjZlAVLfuaOu25g0cLlfvM1b96Ygwfj+fSjb7ICTgB/bTExt/5Dy1bNCAgIKFKfxLcmY3oCsH78lBzpa176AbfLhXHVpX7Llo+pTvmYGuyZuy5HwAlg/Zu/AFCzW95PVre6cxCVW9TlzwnTOLY1Ns+8UniDrhsAwNdvfpMj/dOXP8PlctHvqj55lh84uj9H448y4+uZWWmpJ1P57p2JhIQG031w9xz5b3jkeiZMf4sKVSqwafVmv/UaLT1P4s7+YU6O9F8nzgag6YXn5tPH/1Z1xl0GwLbXf8qRvvX573G7XNS6ppvfsvbwEGyhQcTPW5cVcAJwpqRzaO46bCGBlGtR12fZZs+Oxhpg59ga/yOhpGR8P3sZADcP74PFYgHAYrFw19UDsFgs/LzA/5eHm3fsIfZQAp1aNs4KOAEEBwVy1zUDvNP2eeo/mZrG75u20bR+rRwBJ4Bxg3sSFBDA8vV/F3f3xIcf5ntGLN40pHuOc37niN6eUeSL854Kc8vOWMqFhWQFnABCg4Po2/ECXG43m3fGZuWLjT9KpxYxWQEngODAAO68sjeZTiffz9WX02UpsPsgANKmfp0jPe3HT3G7XAR26Zdn+aAeg3AdPkDaz5/lSE//dTIZy+fgztAUuGXlh2UbAbi5b/uc1/nATlgs8POKLXmW37LnEOVCg7ICTgChQYH0aWN4rvM9h3KVcbvdPD1xAVGRYdSpUr4YeyMiImVNQScRKRP2Zi1xJR7HuTfnNGruYwm49u/D3izv6WZsdTw3s849+QSdgkOwN26BY/N63Mkncu5LT+Pk2y/kXPtJStyFbS8gODiI5ctyPwW5fKknrdPFuef3P1Pvvt2pV782Tz/xKiOH/s9vvlUr19KuVS8+ePeLHOlBQYHUrFWd48cTcTg0AqYkRLdvTGpCEsfMnAGflEPHSdx5kOgO/r/8z0hOY/XzE9n2Q+5pWlzpnvMVEBbst3xo1fK0uLkfybFH2PjejLPsgeSlRfsWHE84zm5zT470hENHid0ZxwXt/c/fX61ONaKqRbFp9WZcrpzT6GxY8ScAF3TIWX7krVey7c9t3NrvdtYv9z9yJulYEgBVa1bJkV4puhJArtFTUjQVOzQh/UgSyWcEdtMPHePkjgNU6uj/Os9MTmVZ94dZO258rn3hDasDkHE49/mK6taSmiO68PeTX5PuY7+UrLV/76BCRBgxtavnSK9SMZI61aJY89d2v2Xj4o8CcEGjurn2NartmUZ2/VbPvZ3L5eaeUQO5tn/uwKXNasVms5KiNdpKxdqtu6kQEUpMregc6VUqlKNOdCXW/J33tMiR4aGcTE0n6WRqjvR47+t1hYgwAOLiPQ+CXdAw9xR6jWp7jr1+255c+6T02I0WuE4cxxW3O0e6+3gCrkOx2A3/DwRZq9fGWjkax4aVudZ+ciccIvXTV8lck/fUvFJy1m2Po0J4CA2r5xxJWCUynDpRFVi7PS7P8pFhwZxMyyApJS1Hevxxz0N/FcJzTyE8efkm1vwTy+MjexAUoImYRET+SxR0EpHSZw/AVrkKroO+b1yd8QexhkdgKZd7+P0ptrr/z959x0lVXn8c/2ylLmWpIipgOagI2AB7V+yxd2OJsURTjMaa2JJfYixJNNYYe++C2LCAHTsokoMo0ov0zrb5/fHcYWd3Z2HZ2dnZYb/v12tfM9zGs6+7987c5zzPOZsTq6ggv+92tLv5P3R84jU6/OcZWp91ITmt21Rut8lm5OTlUT71R/K22oaia26h4+Ov0uGhl2jzqz+s9f+Q9OjVO+Tz/nHy1Brrpk0NfxObb9Frncd5/bV32HngAfz7X/fV6LRem8LCAgbusB2PPnU3xcUd+ect99R5X6m73MJ82vboxNIpc5OuXzZ9Hi06tKFlcfIi8CtmLeDru0Yw/e2xNdZtdvBOACycWPvD7/YXH01B65Z88Y/nKV+toGJDKygsoGuPLsyakrwe2pzpcyjqUET74uT32B5Rerxk+y/8aSGrV62mZ++eVZZfefrV/PaY3zN10rS1tu21p16nZHUJ519zHtvutA0tWrZgwJD+nHPl2SxbvKzGDCipv9zCfFpt3IkVSUYvA6yYNo+CDm0p7JT8Oq95wBxa9+rGNn/5OV333545b3zO0glVz3dem5Zsd/Mv+Gn010x/Sp2Tja2ktJQ58xfRs5b0Zj26FLN0+UoWLF6adH1h1KlYWlpWY93SFSEgMWteCDwUtWnF6Yfvy6F77FRj24/G/Y8Vq1azebUgiDS8ktIy5ixYTM+unZKu79G5I0tXrGLBktrrjR633yDKKyq4/I6nmDp7PstXruaF0Z8x7L0v2LpXD3baujcABQV54f8sK69xjKVRR/aseYtS/ZWkvvILyC3uSsXc5J/9FfPmkNOmiJyi5J/9eRuH81w+Ywr5/QfR5sp/0u7ulym69UlannAuFNY+mEjSq6S0jDmLltEzSfo7gB6d2rF05WoWLK29duJxu29HeUWMKx58jak/LWL5qhJe/Gg8w8dMYOtNurLjllXr085euJR/DfuAo3bZlkGWfbXaRKT+KohlzY/Un4YSiEijyykKnU8Vy5M/nMZWLA/btW5LbEnyEcz5m/UhJzeXViefRcmHoyn7diz52w6k5eHHkd9/R5Zc8StYuYLcjqFTJK/PlrT72YmU+XhWj3yZvM2NFvsfSv7W27HkD+et+T8l/YqLQ+qExYuX1Fi3ZEnopGrXbt0dlOO+WnuKh2Ty8vKYOnsseXmhU+OeOx/irn8/sN7HkXVr0aEtACVLkj+cxpcXtGvNqgXJOyeTadm5HTv8/hgA/vfY28m3KS5ii2N2Z/nshXz//Afr02ypo6IO4RpdtiT5vXP50rC8Tbs2LF5Q8z7ermO7aP/knwMrlq6gTbvWVZZ98s7a0zfFfff1JC475Qqu+vcV/OuFyhk0c6bP4TdHX8yc6ckDJLL+CqLrvHRx8uu8LOqcyi9qTcn8dV/nu7zwJ4qHhLowC8b8jy/Pvb3GNlv/6WQKitvy9dH31bfZkoLFy8I5LWpdc8R64vJlK1ZR3L7mZ/k2fULH4qjPx/OrEw8lP/o8Bhj12dfRvitr7Jdo5eoSbnoopFk9dv9d1/M3kPVVec6TBwTaRsuXrVhNcbu2Sbc5+cBdyc/N5cZHRnD4JbesWT6k3xb8/cITycsNY2G36R06pUd/OYFfHbt/lb+P0V+EVIrLVladRSGNJ6dNuKZjK2p5hlsZPcO1akNsac3P/pwOIXBZMHAIBQN3oXTsGEpGvUx+3wG0GHoceX36svzvl9SYBSXpt3hFmDVa1KpF0vVtWxYCsGxVCcVFrZNuc9JeA8nLzeXvz43miOsr6yUP6bspN55x8JrrPO6GJ9+iVWE+v/vZHg3xK4iISBOjmU4i0uhy8qJ4d1ktsw+iVGc5hYW1HCCH2PJllP3wHYt/fQYr7ryJFff/myWXnMOq118if7M+tD7xzLBpy/AgXLjDYFa9+ARLr/41Kx64g6VX/5qVLz1F3sab0uqEMxry15N1yM8P9ZNKVpfUWLc6WtaiZfIHnlQVtWvLY488y3/ufpjJk6dy7gU/55Z/XZ+W/6u5y80PHUW1zTIqLwnL81vUvZ5WQVErDnzoElp37cD4/75eo9ZTXN9T9yW/ZSHfPvAGFaXquEiH/Oj8lpYkP7+l0XkvrOX85ufnr33/klIKW9TyGbAOHTq15+w/nElx12I+HPkRT9/zLF99OJZuPbvxu7/+mjbt2qz7IFInOdGshIpazmM8FWZuy7pd5ws/ncgPd77Mwk8nUjy4L4Ofu5qCDpXnq3iXrdn09P2YeOOzrKxlFqWkV1k0A6WwljRIBdHy1bWkre3RpZgDhgzk+2mzuPQfD/LjzLksXb6SEe99xm2Pv0zLFoWsrbZ0aWkZl9xyP99Pm8XeO/XjoF13SO0XknUqKw+zyWs754XrOOcA4yZN5b/DR1OQn8fhu2/PyQfuQp8eXRgz/nvueHbkmoLiPTp35IBB/fh++lz+8O8n+XHWPJauWMUrH37FbU+/QcsWBWv9+5A0q+MzHAXJP79zWoTnsoKBu7DigVtZ8c+rWPXk3Sy77leUfDKa/K22o3DfIxq61VIHZVGgrzA/L+n6+PKSJLNU48ZNnsX9Iz+lIC+PwwZtzUl7DaRP92LG+FTuGPHRmuscYPiYCXzw7RQuP25v2rVOz3OfiIhklmY6iUijW1MgNr+WTqiCsDy2qpaRrrEYSy6/IOnyFQ/eRYu9DqRw9/1Y8cAdUBG+3FYsWsDKJ6vOaFn5xP20PPBwCnfbJ2wrjWLVqjBCtbCw5vlvEXUyr1hee+qGVCxauJjf/+ZPANxw7S088cy9nH7GCYx+50OGvfhaWv7P5qp8VQgg5hYm/6qRF53/0hV1q8fRsriIgx79A53792bqyC8Yc/1jtW67xbG7U1FekbQelDSM1dH5za+t4zkKNq1akXxE+uqoDkt+QfLPgYLCglr3XZcrb7+CfoP6ccP5f2H0y5Xp1475xVGcf815XPy333LDBX+p17Glqor4dV7L30Fu9HdQXsfr/H9/fmLN+75/PJnNLzycrS47nvFXPEBuywL633oOi8dOZvK9r6TYcqmvFvF7d5L0Z1CZNq9Vi9o7Ea8570QWLV3GW2PG8taYkEI1Py+PS37+M54d+SGz5i1Iut+KVau5+Ob/8uHY/9Fv8035669PT+VXkTpqEX2O13bOS9ac8+SBhmUrVnHhzQ9RURHjyT9fSK+NOkfHK+OKO5/mqTfHsPnG3TjhgCEA/Onso1i4dDlvfTqetz4Ns9rz8/L4/SkH89zbnyq9XiaVRvfyvFq6keLPcKtr+fyOgg7lU76j9N2E+3isglVP30PhoL0o2HkvSka+0FAtljqK11Oq9TqPlrdK8vwGsGzlai66exgVsRhPXnYSm3XtuOZ4Vzz0Gk+/N47Nuxdzwp4DmL9kOTc//y779t+c/QdumYbfRkREmgIFnUSk0cVWLCdWXl6l9lKi+PJ6pbxbtZLymdPJ77MlFBauSf9QPuWHmqkaVq+ifNaMsG1BIZTWnHkj9ddvu74cfOj+NZbPmjkbgKIkKfTiafWWrKUuQENZuXIV/3fDPxnxxhMMPWQ/BZ0aWMnSFVSUV1BYlDwFU2GUOq1kLbnh44o268rQxy6jXa9uTHn9c94+/3Zi5cnreLXffCPa99mIWR9NYNW8mikcpWEsX7qc8vJy2hQlv4/Hly+v5fwuWxyu8dpmHbUuas3CqK7L+ujcvTM77LE9Yz8eVyXgBPDcfS9w8IkHs/shu9GqTStWLl97Ci9Zt9IlK4iVV1DQLnmqnfwoBU9ZLWk218b/9hSbnXUA3YbuyPgrHsAuO55Wm3Th87OvWjOgRBpfUetW5Obk1JoCL16XqbZUbADt2rTmvmsu4uNxzoTJ02jTqiV77rAtG3Up5p5nXqNT+3Y19lmweCkX/vUevvl+Kv237MWdV51Hm1aq/9IYilq3DOe8lrR2y6IBArWd81FfTGDxspWce9S+awJOAAX5+Vzx8yMY+ck3vPTeF2uCTu3atOK+K3/Bx99MYsKPM2nbqgV7DOzLRp07cO8Lb9OpffIUfpJ+sRXLiVWs5RmuVbR8ZfJnuPizXfmU72qumz+X2PKl5Hbt0TCNlfVS1KoFuTk5LF2V/Hl4WbS8bS3p90Z/M5nFK1bxy6GD1gScAAry87jy+H1486tJDBszgRP2HMBfnxlFeayCK47fp+F/ERHJCjFNW24WFHQSkcZXVkbFT3PI67pR0tV53TaiYvEiYsuS13/Iad2WvE02o2LpYipmTq+5vrCQWHk5lJVRPitaX9usqvw8YmVltaeJkHrrt93W/OGKi2osP+LgUwDYbLOeNdZtGi2bNGlyg7Vjs149GTCwHx+8N4b586t2Yk+bNgOATp06JttVUlBRWs6y6fMo2qRr0vVtN+nCyvlLKFm09uBy8TabMvSxy2jVpT0Tn36X9y+9r9aAE8Am+w0E4MdXPql/42WdykrLmDt9Lt036ZZ0ffdNurFo/iKWLkp+H5/+w/Q121VX3LWYFi1bMO37mvf3denSowsAU7+blnT9lO+m0Ms2o3P3zkz7Pvk2Unex0nJWTv+JVpt2Sbq+9aZdWD1vCaW1XOcFHdrQcZCxcvo8ln47tcaxV89ZRMuNigHofthgcgvy2fOdG5Mea9/PQv2nEd1Oqu+vI3VQUJDPRl2KmTE3+WykGXMX0LGoDe1rCUjH5eTksMuAvuwyoO+aZTN/WsDCpcsZYL2rbDvzpwWce8MdTJn1E7sO6Mutl5xN6zSl4ZWaCvLz2ahzB2bMTT4QYMZPC+lY1Jr2bZMHn+dEdf1696h5n+jUvi0di1oze37V2Us5OTnsst2W7LJd5SyImfMWsnDpCgZsuVl9fxVJVXkZsXlzye3cPenq3M7dqVi6iNjy5J/9FXOiz/W8Wp7LcvNgdfoHnklNBfl5bFRcxMz5yespz5i/mI5tW9G+TfLg8uyF4Zz36V5cY11xUWs6tm21Zps3v5oEwAFXJ6/NOPCif7FRcRGvXnfWev8eIiLSdKimk4hkRNmEr8kt7kRuj6qBh5yOncjdqCdlPr7WffM235J2f7uT1j+vmWIvp2Mxud16UD75O6iooGL2TCoWzCN/C4OWVWdc5LRuS163jSmfNhkliG94Tz7+Al3aW42fsV+NZ8WKleyy28419tltj0EAfPbJlw3WjqOPPYz/PvQvjjrm0Brr+vULnV0/Tp5aY52kbs6nTutuHWjXu2rnROtuHWjfuxtzP5+01v2LenVj6OMh4PT1Pa/w3sX3rjXgBNB1hy0AmP2xp9Z4WadvPh1Pp26d2Dgq/B7XqVsxG/femG+jou/JzJ35E3Omz6Hfzv3Iycmpsm7ALv0B1rp/bRZFs6N69tk46fqNe29MRUUFi+YrPVNDWTDGadmtI236VL3OW0TLFn1ec0R7XNstN2bnRy5ly98fXWNdflErWvXszIofQ+2myf95lYk3PVvjZ9l3YfDA5HvDekm/7fv2Yd6iJfw4s2pdrbkLFjN19k/036p3LXuGVEuHXng9v7upZmdjPNXergO3XrNs4ZJlawJOQ3fdgdsvP1cBpwzY3noxb/FSfpw1r8ryuQuXMHXOfPpvsWmt+xZHM5OmzJ5XY92S5StZtGwlnduHme6lZeUcdvHN/O6fj9bYNp5qb9ftlI4rk8q++4bcDp3I7Vb1czYnWlY+6dva953sxEpLyLf+kFO1Kyq3+ybktGpN+fTk9Tol/bbv04N5S1YwpVqAee7iZUz9aRH9eyUPNgJ0imY2T5lb8/vVkhWrWLR8JZ2iWdHnHjw46U/8GOcePJhT9t6+oX4tERHJEAWdRCQjVo96HYDWp5wDCR2OrU/7JTm5uaweObzWfcsmfE3FgvkU7DiY/G0HVK7Iz6fNOb8lp6CAVa++GJbFYqx+cwQ5LVrS+sxfVTlOq1PPIadFC1a/9WrD/WKyTitWrGTE8JEMGrwDBx2875rl3bp35ZxzT2PWzDm88dqoBvv/hr/0BmVlZVz023MoLq6c0VRc3JE/XncJFRUVPPG4csenw6Rn3wdgp8uPr3Kd73T5CeTk5uKPvV37zjk57PPvX9Gqc3u+ue81Prnh8Tr9n5369aJs5WoWumaxpNvI594E4OzLzqwSODr7srPIzc1lxGNrv7e++fxbdO3RhSPPqCwa3qpNK06+6CRWrVzFm8+9td5tmjV1Nj5uIgN26c+uB+5SZd3QEw5ii20357PRn9c6A0vW3/Rn3gPArjyxynXe96oTycnNZeojtZ/HhZ9/x4ppP9Ft6E50HGRrlufk5dLvb2eRW5DPtCdGAfDjva/y3c3P1fhZNmkmAJPvfYXvbn4uDb+hVHfEXmGAyG2PD6eiIgwEiMVi/Ovx4cRiMY49YNda9y3Iz6N75w68/+UEps76ac3yiVNmcO9zr9OpfRFH7j14zfLr73mSKbN+Yr/BA/jbb06noJYi95Jeh+8eOoBvf/r1Kuf8tqdeJxaLccy+g2rdd8+BfWnZooAn3/iI6Qkz5MorKrjp0RHEYjGGRoMNCvLz6NapPR+MncjU2fPXbDtx6iz+89I7dGrfliP23CEdv6LUUcmHIwFoeczZVe75LY89m5zcXEpGj6h955XLKf1kNLmdu9Hi0BMrl+fl0fL4c8Lx31O660w5bFAI+N8+/EMqojS2sViM24Z9SCwGx+zWr9Z99+zXm5aF+Tz57limz6ucLVVeUcHNz79LLAYH7xg+588/ZEjSn3hQ6vxDhnDqPgo6iWzIKmKxrPmR+lN6PRHJiLJxn7P6vbdoscd+tPvbnZR+8yX51o+CbQdQ8uEoSj/7aM22rU44A4CVTz0Y7VzG8rtuou1lf6bomlso+fAdYkuXUDBgJ/I26cXq996i5O3Kzs6Vzz1KwYCdaHng4eRv1ofSCePI32pbCrbpT+m341gdD1BJo/nL9bey97678cAjt/H8syNYMH8hRx97KJ27dOKMUy+itLQy3WG8NtQ3X0/g1RHr3wk96bsfuPnGO7n8ql/z3sfDGf7S6xQUFjD0kP3o2rUzN1x7M1998XVD/noSmfn+eH546SP6HLkLhw+7hlkfTqDbjlvSfUhfJr88hmlvfbVm2+0vDjMdvrz1eQA2G7ojXQb2oWxVCWXLV61Zn2jl3EX879Gqgau2PTuz+IfZxFTzJe2+eP9L3hk2in2O2JvbXvonYz8cyzY7bUP/wdsx+uV3GfPWmDXbnv67UwF4+B+Vo9efuusZ9jpsTy68/gIGDOnPzCkz2eOQ3emxWQ9uv/oOFi9InuJlXW655B/c8vTfuebeP/Lxm2OY9v10+mzdm0H77My8OfO57arbU/vFpYr5737DzBc/pMfPdmXXV65n/gfj6bjTVnTaZWtmDfuYuSMrZ65ueckxAJXBoYoY4y6+l50f/QODn72KWcM+pmTBUrrstR1FfTdhzsgv+PE/GhjS1AzpbwzddQde+/ALTr3qHwzadku+mjiZLyZ8zwFDBrLnDtuu2fbOp18B4ILjD1mz7Pen/YxTr/oHp111KwfvviOlZeW8+v7nlJSVcftlv6RVi0IAvv1hGm99Mo6cnBx6dO7I3c/W7IxuUVDA2UcdkObfWIb024KDhmzH6x9/zWnX3c3OW/dh7HdT+cIhSKxwAAAgAElEQVR/5IBB/dhzYGXQ+K5oQML5x4S6np3at+WK04/guvue5/grb2f/Qf0oat2ST7/9AZ86i5369ubUobut2f/3Jx/CadfezenX3cXQXQZQWlbOax+NpaSsnNsuPnHN34dkRvm3X1Ay5h0KB+9Dm+LbKPvfWPK32IZ860/pp6MpG1v52d/iyNMBWP3Sw2uWrXrqHvK22JqWx5xFXt8BVEz7nvytdyBvsy0oGfMOZV99VOP/lMYxpO+mHLTDVrz+xUROv/Updt6yJ2Mnz+KL72ey/8At2GPbylmsd73yMRACRBBS6F1+3N5c//hbnHDj4+w/cAuKWrXgk4nTmDhjHjtusTGn7D0wI7+XiIhkRo6Kd4lsGBYctVf2Xcx5ebQ8+hRa7DuU3OLOVMyby+pRb7DqhSeq1FgqfmE0AAuO2qvq7lttQ6vjTid/637kFBRSPmMaq0e+zOrXXqyZLq+wBa2OOYXCPfYjt3NXKhbMp+S9t1j5zENQkrxgalNmb8/OdBNS1qv3Jvzx2kvYc+9dyMvNY/w3/+Pmv9/B6Hc+rLLdiScfxe13/Y0nH3ueiy64otbj/bTY+WbcBPbZ42dJ1//s6IM594Iz2LZfXyrKy/nqy2+44/b7Gfn6qIb8tdLmb0U10xFmg5z8PAb86nC2PG4PWnfvyPKZ85n03AeMu+tlKkrK1mx39vQQjPhvzxCcGHztqfT7xdC1Hnv++Cm8eNBVa/5d2KENp31zDzNGf81rpySv+5JNnsj5ad0bZVhefh4n/uoEDjruADp368zcmXMZ+fxbPH3XM5SWVN7H35wWZrfuv8lBVfbv0LkDZ192JkP2H0LL1i2ZNmkaT9/zDKOGjV7r/3v6707l9ItP40+/uJYPX6/ZQbXRZhtx2m9OYcc9d6B9cXsWzlvImLc+4eF/PMqCWmrRNAW/K6lZCyEb5OTnsflFR9DzxL1o2b0jK2fMZ8Yz7/HDHcOrXOeHznkCqFl3qV3/3mx16TEUD+5LbosClk+ezfQnRjH5P6/BOgLIOz54Md0P3pm3d7qIldNqpu9q6vYfeUamm1AvpWXl3P/iSF4aNYa5CxazUeeOHLbnzpx55H4UFlTWa+l/3K8BGPfMbVX2//q7Kdz2xHAm/DCdvLxcBlpvzjtuKFv33mTNNo+MeIebHlz7TOSi1q344KHsut/HsrRmTWlZOfcPH82w975g7sIldO/UnsN2254zD9uTwoLKsawDTr0SgLGP/l+V/T/59nvuH/4u33w/jVUlpfTsWswhuw7gjEOr7g/w9ffTuP3pN5jw48zw97Hlppx71H5s3atH+n/RNFh9512ZbkLDysujxSEnUrD7QeR27EzF/LmUfjiS1a8+XeUZrv0DIQC5+Mz9q+ye06YdLY48lYIddyenqAMV82ZT8u5rlLz+LMTWnkY5WxSeVHOwVDYoLS/ngZGfMWzMBOYuWkb3jkUcNqgvZ+y3Y5XrdOBF/wLgq9t/U2X/TydO4/6Rn/H1lNmsLi2nZ6d2HLJTX36+3w41rvPqjv/bY0ycMa/GMbPJsu1Py3QTpBF16VKUs+6tJJnioi2zpv9ywdLvdJ7rSUEnkQ1EVgadpN42hKCTrJ9sDTpJ/WVD0EkaVrYGnaT+sjXoJPWXrUEnqb8NLugk65StQSdJjYJOzYuCTvWnoFPzoPR6IiIiIiIiIiIiIiKSVpoA0zzkZroBIiIiIiIiIiIiIiIikv0UdBIREREREREREREREZGUKb2eiIiIiIiIiIiIiIikVQVKr9ccaKaTiIiIiIiIiIiIiIiIpExBJxEREREREREREREREUmZ0uuJiIiIiIiIiIiIiEhaxWJKr9ccaKaTiIiIiIiIiIiIiIiIpExBJxEREREREREREREREUmZgk4iIiIiIiIiIiIiIiKSMtV0EhERERERERERERGRtKpQTadmQTOdREREREREREREREREJGUKOomIiIiIiIiIiIiIiEjKlF5PRERERERERERERETSKobS6zUHmukkIiIiIiIiIiIiIiIiKVPQSURERERERERERERERFKmoJOIiIiIiIiIiIiIiIikTDWdREREREREREREREQkrSpiqunUHGimk4iIiIiIiIiIiIiIiKRMQScRERERERERERERERFJmdLriYiIiIiIiIiIiIhIWsWUXq9Z0EwnERERERERERERERERSZmCTiIiIiIiIiIiIiIiIpIypdcTEREREREREREREZG0iqH0es2BZjqJiIiIiIiIiIiIiIhIyhR0EhERERERERERERERkZQp6CQiIiIiIiIiIiIiIiIpU00nERERERERERERERFJq1hMNZ2aA810EhERERERERERERERkZQp6CQiIiIiIiIiIiIiIiIpU3o9ERERERERERERERFJK6XXax4000lERERERERERERERERSpqCTiIiIiIiIiIiIiIiIpExBJxEREREREREREREREUmZajqJiIiIiIiIiIiIiEhaqaJT86CZTiIiIiIiIiIiIiIiIpKynFhM8UURERERERERERERERFJjWY6iYiIiIiIiIiIiIiISMoUdBIREREREREREREREZGUKegkIiIiIiIiIiIiIiIiKVPQSURERERERERERERERFKmoJOIiIiIiIiIiIiIiIikTEEnERERERERERERERERSZmCTiIiIiIiIiIiIiIiIpIyBZ1EREREREREREREREQkZQo6iYiIiIiIiIiIiIiISMoUdBIREREREREREREREZGUKegkIiIiIiIiIiIiIiIiKVPQSURERERERERERERERFKmoJOIiIiIiIiIiIiIiIikTEEnERERERERERERERERSZmCTiIiIiIiIiIiIiIiIpIyBZ1EREREREREREREREQkZQo6iYiIiIiIiIiIiIiISMoUdBIREREREREREREREZGUKegkIiIiIiIiIiIiIiIiKVPQSURERERERERERERERFKmoJOIiIiIiIiIiIiIiIikTEEnERERERERaRBmdpCZ6TlTRERERKSZ0sOAiIiIZIyZ5WS6DSIi0qBeBWaY2a1mtkOmGyMiIiIiIo0rJxaLZboNIiIi683MugAbAzPc/adMt0fqx8ymA48Dj7n72Ey3R0Qyx8x6AicCPYEZwIvu/l1mWyXry8wqgMSHTAceAR539ymZaZU0BjPLcXd1MIhswMysnbsvyXQ7RESkaVPQSUSyipkZcAawB9AH6Ejo2FgE/AB8BDzp7p9nqo3SMMxsEDDY3W+vtrwH8F/gwITF7wO/d/fPGrGJ0gCqdU6OJ3RMPuHu0zPXKskUM2tLCDh0BKa6+4xoeSt3X5nRxknKzKw3cCmwk7sPqrbuNOA/QEHC4grgn+5+aeO1UlJlZvcDRwHto0WxhNcPCPf5Z9x9cQaaJ2mkgSQiGz4zWwG8DDwGjHD3sgw3SUREmiAFnUQkK5hZa+Au4BQgMR1X/H31m9kjwIXuvqwRmicNyMw6AE8D+wFlQGt3L4/WtQS+JgQcq6dlWwkc6e5vNmJzJUVmthRoE/0zsWPyXeBh4Dl3X5qJtknjMLMi4FzC/b0flemfL3X3W6NtxgKzgRvc/f2MNFRSYmZDgeeBFtGijvGR0tGAknFUDTjFxYBbFXjKLmZWCBwKnBy9toxWxe/zJcArhO9rI9y9tNEbKQ1OA0maHzP7IYXdK4DVwHJgDmFW5PvAK+5e0gDNkzSodp0vBJ4iBJo/zFyrpCGY2Z7pPL67v5vO44tI06Kgk4g0eWbWjtABvR01Aw21iQETgCEKPGUXM3sP2JVwrmOAufukaN2lwI3R8hzCOe4EdI12nwH0dffljd1uqZ8okHgkoWPyIKAwWhX/grKKMJryUUInRHmjN1LSJnq4fQLoHi1KHEiQGHRaTmWn9Y3ufmWjNlRSEn2OTybMYIvbzt3HR+vvJ8xijhHu47cDXYALgNaEAQgD3f3bRmy2NJAosHwM4T6/D5AXrYrf5xcRBps8pqBydtNAkuYnIQBR/Rktfv6rDxCsbbtEk4Az3P2jBmmkNCgzm0QYABgXP4c/EgLNjyk1bnZKkh63IcXcPT9NxxaRJih33ZuIiGTcw0B/wkPKauBuYH9Ch1Q+oZN6I2AocF+0TQ6wNSFVj2QJMzsM2C365zLgeiCxXtNZCe8vcvdtCef+lmhZD+Dn6W6nNBx3X+XuT7n7kYTAw7mEzikI13Er4FjgRWCWmd1uZkMy01ppSGa2O/A64bznRD8zkmzXkTADJr7NZWZ2dSM2VVJ3NpXpcN8GeicEnAqBo6PtYsAB7n6Tu/+ByhmveYSglGQhd1/q7g+6+4GEWoy/BT6h8pruCPwSGG1mP5jZ9dHsN8k+XYCTgOFAKeH85gJ7EVIjzzazp8zscDPLq/0wkkUmRj+LCPfwxOBSCWGW8hwqn89Isl31ny2BUfq+1zS5+xbALoQBInOoPG+9gD8C/zOzMWZ2oZl1zlhDpb6SXZMN9SMizYhmOolIk2ZmexM6qOKjnw+Od1StZZ9+wKuEjo0YsI+mcmcHM3sQOB0oJ8xS+zxh3daEVC0xYIq796m279fANsCb7n5QozVa0sLMNiZ0XJ0MDExYFf/i8gOVoym/b+TmSYrMrBXwHSFQHCMMGPizu09LGGWZONOpO3ADIXgBoTOzn0bSZgcze4MwWGQuIeC0MmHdgcBrhHP+nrvvXW3fYcBhwFh3377RGi1pZ2a9gCMI6ff2ouZM18+BB4HH3X1RY7dPUhOlSz6OkDp1D2rOeJlPZVqujxu/hdJQzGw3YARQBCwAbiWkU53o7rGE7XoTrvnfApsR0uqdSEir14EwyPB8YMdol+mEjAeq6dhEmVkuYYDIKcDPgHbRqvh5LwNGEjIWvOjuqxq9kVJnZvZAOo/v7mem8/gi0rQo6CQiTZqZ/Rc4kxCEGOzuX9Rxvx2AMYTRlQ+5+1nr2EWaADObCGwODHP3o6qtuwT4O+Eh5nZ3/2219bcAvwOmu/umjdRkaQRm1hc4gdBRMZCaHVefEGZEPuXuCxq/hbK+zOzXwD8J5/BKd78xYV2NoFPCusuAv0br/+HulzReq6W+zGw6YVbqve5+frV1/wB+Qzinl7v7TdXWXwv8CZjv7l0ap8XSmMxsU0LH85WETmuoTLELIc3q44SablMbv4WSKg0k2XCZWSfgG0Kq66+Aoe7+0zr2aQMMI6TbnA5sG0+7aGY5hEwVZxH+Ps5w90fS9xtIQzGzFsDhhOv8YCprOMav82XAc4Tr/K3Gb6GIiDQmpdcTkaZuD8IX1dfqGnACiLZ9jdBpsXua2iYNL16b6Zsk64YmvH8jyfq50as6JTcw7v4/d7/O3XcEegJ/AJZQmaphEPBvYIaZPRGlbZOm7YjodRIhmFxXNxFGRAPs26AtknTqFL0mCxgkzkx9M8n6eDH5dknWSZYys+3M7AYz+4pQ7+uvhIBT/L7+HaFAfTzN6lnAN2Z2aoaaLClw9xnufrO770CYlX4d8GW0Oocw4OgaYKKZfWRm55tZcYaaK+vnYqAbYdbSEesKOAFEtVePI1zjG0fHiK+LARdSmV77pIZusKSHu69292fd/WhC6uQzgRcIwaYcwj3+58AbZjbdzG40s20z12IREUknBZ1EpKnbKHr9rB77xvfZpIHaIunXMnpdlrgwSsUVr/VUTmXNn0TxYFNZepommWRmm5vZbwnpOf5CeHCtXg+gBXA8oS7IMDPrkZHGSl1sSzh/rySm3lkXd68gBJ1zqFrEWpq20ui1MHGhmfUE+kb/XOjuX1JTfObqsiTrJIuY2RZm9kczG0+YEXElsB2V9/D5hAEEg9zdCJ2WPyPUfssB2gIPmtnQZMeX7KCBJBucIwmf58+7e426jLWJZqY/Rzjnx1Rbtwp4mcqApGQZd1/s7g+5+zFAZ0JgcUm0OofwjH8JMM7MPjazU6JUfSIisoHIz3QDRETWoSEKTioIkT3mEkY8Vg8U7kMIKMSAj9w9Wedj/+h1dvqaJ43JzDYhpFw6karpeOL3hTX1IAgPr6cSaoMURK/vm9nu7j6z0RotdRUfwV6f63VO9NpyrVtJUzIZ6Bf9JDokeo0RAgtVmFkeoRZUjJB+S7JMFFg8gTBbIbEmV/w+vgoYTkit9pq7r/nOFr0fBgwzsz8B1xIGTV5JmM0uWcrMNiek4TqckJEgn3CdJxabjw8kOd7MRgDn6fO8SeoVvfraNqrF5Og12SCS+MxYDSDKQlEAaR8qr/Ne1TZJfMbfmZAm+2IzO31d9ZslO0R/A60Js913BE6JgpAi0kwo6CQiTd1Mwgi3neqx787Rq4IQ2eMT4GjgMDO72N3jaZVOS9hmWPWdzGwQlR2TdU7DKE2PmXUldDKdCOySsCr+cFpCGP36CGGWTGnCNs9HgaqngcGEItU3AGenu92y3hYQ0ml2XdeGSfROOIZkh3cIM1oOM7Pt3f3LqKZHYm2+55PsdzWhMzIGvJf+ZkpDMLMuhNRZJwK7Unn/TqzH9x7hPv6Muy+uw2H/BlxBmC03cB3bShOkgSQbrPhs5W712De+T0mSdfG+qoYYgCiNxMz2IFzjxxJmOEHVc/g14d7/OGE262mEa70TYWDC6Og6/1+jNVrqzMwOAS4gBJGKCPfn6tdoDsqqJSIo6CQiTd8HwBbAQWa2nbt/XZedzGwAoU5EDHg/je2ThvUMIei0CfCqmf2LEHg4IVpfDjwZ39jMCoGjgDsTjvF04zRVGoqZtSekVjkJ2JvKB5XEh5gPCQ+pT7n7otqO5e7TzOwEKkfPHtzgDZaG4ITOpoOA39d1JzMrIqTbigHqkMge/yXU6CgkdCi9SQhCxQNKs0gYUBDV7TmZynpP5cB9jdlgSclMkt/HJxJSpD7q7j+uzwHdvcTMFhMC1asbopGSfhpI0ixMJAQLjjGzy6PUeOtkZi0I3/ljwI9JNtkqep2TZJ00IWa2I+E7/PGEjBVQ9d4/mxBkesTdxyYsnwl8YWZXR+sPBzoSrvPj0t1uWT9mdg3wp+if9QkG1zmdtohsGBR0EpGm7gFCwdE8QnqVA939u7XtYGZbEYqW5hG+3DyW9lZKQ3maMPJ9MCH4sHfCuhjw72r54qcRRtHFv/h+7O7Ppb+Z0lDM7CVCx3JBtCjxIWYSlR2UdU6t5e5TzWwe4W+jfUO1VRrUy8CewNZm9gt3r2tA4V7CaNgYSq+VNdz9azO7AbiGUJfnyGhVDiGgdG61zua/EWY6xO8Hf3b3bxurvZKyvIT38wmDRR5x90/qe8BoZlwrQrD55dSaJ+mkgSTNzjBC0KkHcJ+ZnVbHWo13EGp6xaiWxcDMugNHROs+bdjmSkMws22onLmYWHcrfp2vAF4kXOcjo5qcSbn7cjM7mzAAJRfYKy2Nlnozs20JAadkwabEGrvJlpcCT6Dv7SLNjoJOItKkufu7ZvYKoe7DpsCXZnY/Iag0jsr0SsWEmj5HA2cSOiZihC+5bzZ6w6Ve3D1mZocTZjxVf+B4DLis2rIpQJfo/adUdmRK9jicyjoOEK7ppwkdlB/V54BmFi84nwMoL3zTdA9wKSEweEc0g+nf1QIPa5jZ9sDNVAaiFxMCUJIl3P06M5sCXEVlB9VXwGXuPrLa5hMIHZjLgKvc/fbGa6k0gNVUnb2Scm1Nd1+OBhE0eRpI0izdRpjJWkwING5uZtcCb7p7eeKGCXX6rgJ2ixYvAP4drc8nPPP9H5W1XJ9K/68g68PMxlK1RmP8Oq8gpNN9GHguum/XibvPM7P5hOc61exsen5JOM8x4F3gEkJfTD/gc0IN7Y0J39u6APsCfySkxC4APnF3XcsizUxOLKYZjiLStJlZZ+Btwpeauty04l98JwK7uPvCdLVN0sfMdgMGEb7Ejnb3cUm2+Q/QlzAj7sG1jaKTpsnMKgjpdV4hdFC+XFvgYT2O2ZrQaTEVmOTuqv3TBJnZoYRRsPFR8MuBbwnXfbzmy3RCfb4tom3iD7ynu7tmsWYpM+sIlLr7slrWn0wYPPKMuy9p1MZJysysw9pmr8iGK/pMT8dAkmWEe8Jn7j6oIdoqDcfMBgMjgTYJi1cTUukuInzOdwS2JKRZhfA3sgo4yt1fj46zJyFoETfG3XdNb+tlfSW5zscTvsM/Vi0jxfocM4/wPbCQ8Ny3T0O0VRqGmX0JDCBcz70TazFGwcIOVPtubmZtgdGEmZDLgS3dXbW2RZoRBZ1EJCuYWTvgbkJtn3XlEI4RZsqcW8fi1CKSIWZ2PvCkgsPNk5kdS6j3UxQtqu2Lafy+Xwr8zt3vrGU7EWlCorotRwFL3X1EkvUnEep3vQQ87u4rGrmJ0oA0kKT5MrNBhFnMAxIWV/9MT3yGmwCc5e5jEo7xCypnMY8DDnJ31XRqYqLrfA4hZdoj7v5lAxyzBaHW41R3n5vq8aRhmdlPhNmMj7r7z6utewPYD/iPu59Xbd1WhGsd4Hp3v64x2isiTYPS64lIVohGOp9sZn8hBJ72JkzX7hRtshD4Hnif8OV3QrLjiEiTMx7Yzsy+X9/RkWa2NXAgUOTuf05L6ySt3P1ZM/sMuAI4BWhdy6blhFlRf65WhFo2QJops2Ews+MIabM6E1Jk1Qg6EVIjH0oIKvwpqvH2RuO1UhrYVYSZ57Ma6oBRIPLZhjqepIe7f2JmOxCCyMcQ0ui1rbbZKuAj4EHCgKPqAcnlhKDTO8Cz1dPzSZNxKPB6Q2aYcPfVwGcNdTxpcPHUphOTrPuGcL0PrL7C3Sea2duEoNSe6WueiDRFCjqJSFZx9/GEIpbSTJjZAELe900JqTmei3dImdkxwKfuPjWDTZTUjCKMhL0UuHU99z0GuJ6QvkdBpyzl7j8C55rZhcBOwDaEaz2fMKDgB+Cj2lKxSXYxs42Awe7+YrXlbYGbgFOB1ma2EngOuCb6G5EsYma/BO6K/pkDbFXLpn0StukJDDez49x9WJqbKOmxBeBm9jKhTt+HmW6QNB53jxFqsD4WpUvrFv20IHxX+2FtNd7c/QnC7Blpwtz91cR/R5/r7dzdq29rZrsDg4Hh7p4sYCHZYRkh8JTs+p0UvW5dy75fEoJOfdPQLhFpwhR0EhGRJikaIf0nQgd0Igfio6BvBTYys4eAi919aSM2UTIvPiumaK1bSVaIRjx/FP3IBsbMCgmzXs4Ays2sddRBGa/l8Cahflc8/VJrQgBqqJkd5O5fNX6rpT7MrA/wLyrP5TuEc5/MqcBtwK+AEwkFxx80M3P3n9LdVmlwexNmt5xACD7IBs7MOiZLkRzNUpoZ/cgGKLrX/5MwU/UB4Jwkm+0LXAP83cxeAi6qb90nyah5hKDTxknWxYNObc1sE3efVm19vNxBx3Q1TkSaJgWdRESkyTGzu4BfRv9MzP8eS9imgPDFNwc4C9jNzPZWHvCmx8zyCZ2JuWvZbCczO72Oh8wjzHy7KPr3/BSaJxmmmi/NxhPAzwj37Pg1PCVadzYwiMp7/DKgVbRdF+BxM+u/thHy0qRcSJjZEAP+6u5X17ZhFGz+APjAzD4FbiF0bF0AqPZD9tko4f27GWuFNKZZZvYKIcg43N1LMt0gST8zGwK8Rhj4lQNYLZtuTuWz3JHAIDPbV7Oess6nhJmsQ80sJz5oKDIp4f1goHrQqVea2yYiTZSCTiKSNcxsF8Joqe5AS0IHds5adwpi7n52OtsmDcfM/gycm7DoHeBDQp2ARC0IDzuHEDq2+gJPE0bZShPi7mVmtiehYzmZHMKo6BPW89A5hHP/VgrNkwxSzZfmIbr+jyJcr2WEzsmVCZv8MuH9jYT7fVtCbY/jCZ1ZpwAPNUZ7JWX7R69fry3gVJ27/8PMTiPUhTgMBZ2y0QxCJzOE4KFmoG/4CgnBhCOBJWb2DPCYu4/ObLMkXcysPaHOWrto0ULC4IFk/kWoA3QWoR5zD+A5M9tRAcqs8hpwEuH+/m8z+018IJC7/2Bmiwl/D+eQUIPPzDoDxxG+/zVYrT8RyQ4KOolIk2dmmxA6IwencBgFnbKAmW0FXEb4YjoTON7dP4rWVQk6RfVdDotyhT9FGF27h5kd7u7DG7flUgd/IHRIdKllfV0CyMlMAa6o576SQar50qyclPD+qMQZbWbWC9iBcN+fC1wVFSdfYmanElLu9QKORkGnbNGLcD5H1mPfVwlBpy0bskHSaP5BZSrFq4HzMtgWaRyzCQMCIQQazwbONrPpRPWdopq8suE4lxA8it/nT3T3Rck2dPcvgC/M7CbgEUIAYhvg58B/Gqe50gCeJNTP7Um4rx9uZn909/j3sueBM4H9zew5QrrFzsDlhGBUDHiv0VstIhmloJOINGlm1pIw06U39e+Ujq17E2kiziekU6oAfubun69rB3d/38yOBMZEi04FFHRqYtx9kZmdAOxVbdU1VD601qWWTwwoJ4yengKMVLq17KOaL83OnoRr980kKRQPTXg/LAo4AWtmSY4gpGvrn/5mSgNpEb0uXutWyS2LXgsbqC3SiNz9zui7+/8B55hZT+Bu4CN3VyrcDdPGhO92JwHHAMXR8p6EgWSXmdk4QsDhCXfXbIfsd0T0Ogc4NhoIuFbuXhKl0d6VELA6AQWdsoa7l5rZKcDrhPTHG1O17vJfCKmwCwmplH9W7RAVhO/yItKMKOgkIk3deYRR7jGgFHgUGA38BKijecOzP1EAoi4Bpzh3/8zMXiWk3xqUrsZJatx9FDAqcZmZXRO9fcPdb23sNknGqOZL8xKv8/JFknVDE94nS5sYLzjerUFbJOk0h9AhtV099o3XBVFAOQuZ2Z3R2/cJKbEPjn4ws+XAIkKKzbWJufvm69hGmoiotssoYJSZ/Qo4iBCAOoKQJhXCoIGbgBvNbBQhAPV8XYIV0iRtQ/j+9uL6nEN3X21mzxNqsg5IV+MkPaKBnrsD/wT2ACYnrPshSo/7MKEMQqIYcHE0601EmhEFnQlvOPEAACAASURBVESkqTs2eq0ADnX3NzPZGEm7TaLXMWvdKrkvCUEndUxml4cJDyPfZLoh0qhU86V5aRO9Vkm/Y2YFVNbhixFmvFXXMWG9ZIcxhBkPh5nZpu4+tS47mVkXwujoGKFouWSf86i8VhOv2RxCAKJNjT2qitdqlCwU1XgZAYyIZrwdTghAHUwYaJJHCEbuC9xlZsMIAwpfc/fyzLRa6iEeTJxej33jM92KGqgt0ojc/UtgLzPbjDAgOHHds2b2NfA7QlmEQmAccLu7f9jojRWRjFPQSUSaur6Eh89XFXBqFuLpdOpTWLa82qtkAXc/I9NtkIzohWq+NCcLgK7UHBSwG6ETOgZ86e4Lkuy7dfSqmS/Z43FC0Kkl8KKZDXX3uWvbISpM/wyVtR+eTnsrJV3Wlg67vqmyJcu4+yrCNf2MmbUj1OU7gRBwKiCk6Do++pmHBo1lk0VAJ2Czeuwbn/mctAaUZAd3n1LLcke1/EQkoqCTiDR1raNXjXhtHmYTHmBsXRsmEU+rN6fhmiMiaaKaL83LF4SR7kOBixOWn5jwflj1ncxsS8IM1hgwNp0NlAb1IvAZsCMhhdK3ZnYP8Aownsrrvj1hcNGBhE6qboRz/bm7K+iUhdw9N9NtkKbH3ZcADxLqMXYAzgH+SBh0kAN0zlzrpB7GEYKHR5jZ7+paWzWa3Xwk4T7/bRrbJyIiTYCCTiLS1M0EeqPOxebiI8IMiCPNrIO712kUnJntSOi0ilG/1HySZmb2p/h7d78+2fJUJB5TsoJqvjQvLxCCTmZmDwM3A7sAZyds82TiDmY2mDBjJo9wb3+hcZoqqXL3mJmdBLwHdCekSLw8+lmbHEINr6PT20IRaWxm1g84ipBybweqznpTSsXs8gIh6NQVuAc4rY773QxsSjjfNQaaiIjIhkVBJxFp6kYBfais+SAbtscJud+LgAfM7LgoP3ytzGxr4Hkgl/AQ80zaWyn1cS2VnQrX17I8FQo6ZRfVfGleHgYuBbYATol+oLJ+y2Pu/l18YzMbT5gBE+fAY43TVGkI7v69me0E/IcQcKyLV4Gz3F0zlkU2AGZmhJR6J1D1nh4POP2PUNPp0UZumqTmAeBKwqCCk82sF+F7+FvuXpG4oZnlAHsAV1A5QHAOcG9jNlhSo0GCIlIfObGYBpWISNNlZjsQOidzgUPc/fUMN0nSzMzeAfYiqvEB3BK9fhstuxS4HdiJkJrpF4S6ERBS8gyqfkzJPDOLP4TG3D0vyfJUVDmmNH1mdhTwHJVp0+pa8+UlYM9ov5OUgit7mNnmhALzW1VbNRo4Mkq/FN/2S0JaNoApwP7u/n2jNFQanJltQwgWHwBsQhgdX0Co6fEDYZbz4+7+RcYaKU1C1EF9mLsPz3RbpH7MrA+VgabE2czxQNNcwszWR939s0ZunjQQMzsQGE7VgeyrgYlU1mvqQBhs0ir6dw6hbu9Qdx/VOC2VhhA9r6XceaznNZHmRUEnEWnyzOxqwuipJcBFhI6J8sy2StIlmsnwEWGGW+KHVHxE/EpCkCknYTmEh9jBtRU2lcwys5/H37v7Q8mWpyLxmNL0RR2LYwg1XwAWElK01KXmCyjAnJXMrAVwLKEGXxkh4DTc3WPVtnsC2J0wmvrv7r6s+rFEpOkys5bAYYR7fBEhyJhTbbMcwqCyAkIN106EYHM7d1dGlixiZpsAxxMCTTsmrIqf85WEQSOPAq/rOW7DYGYHEWp1dUtYXL2DMfG6nw2c6u5vp7lp0sDqOUgwRrU0mgo6iTQvCjqJSJNmZsdHb88jpNiLEYJPXxGm5telcGnM3c9e92bSVJhZJ8JDzKEJi6sHoBJ9Apzo7j+mt2Ui0lCimS/xmi91/UIar/myi7tPT1fbJLPMrK0CTSLZKaqz+RxhVtv6ykEdk1nFzD4AhiQsin9HryCkSX8UeFb39A2TmbUBfk6YzboXIYicqJwwo/1x4L7EWc2SPeo4SDCHMDB0I2BnwuzmXELq+wuAcndfXPvuIrKhUdBJRJq0JFO54w8y63Xz0sNrdjKzIYRC83sCW1ZbPQt4H3jY3Uc0dtuk6TCzYndfkOl2yPozsx6o5ouIyAbBzNoCkwgpFOsqcTR8CfC2ux/S0G2T9Eh4Voufw28IgabH3H1Gxhomjc7MCgiznhJTqE5195UZbZhkhJltT6i7vClwv7ufk+EmiUgjU9BJRJo01XuRODPLAzoScocvdPfVGW6SNAAzi6fYuMvdn1nPfX8F3ADMd/fqQUnJIqr50vxEBeb3IKRS7UjotFxzvt19fAabJw0kCizvQ5jR2JIw6rn6bOWkVHA8+5jZ7wi1OGOEbAQPAuMI1/llhLSaFxDu710Ifxt7RbuXEGaxftm4rZZURM9qs4AngEfcfWyGmyQiTYSZDQQ+j/55kLu/mcn2iEjjUq5kEWnq9sl0A6RpiPK/z8t0O6TB7U3onHq5Hvu2JRQpbrWuDaVpc/dvgW+B/8t0WyS9zGxf4M/A4HVsNx74rWo/ZCczaw/cDRxHHYNMSSjolH2GRq8xYKi7fwBgZq2B3wN5wGR3fyva7nozOxR4klDX6QEz28HdG2LQmTSOg4C3dM5EpDp3/8rMXid8NvwSUNBJpBlR0ElEmjR3H53pNohI02NmLajs3CrNZFtEpG7M7I/ANYQgxLoCEf2AN8zs/9z9T2lvnDQYM8sFRgI7Uv+Ak9JxZKdtCeduVDzgBODuK8zsa2AgYbDJWwnrRpjZBcBDwHbAiYT6L5IF3H1kptsgmWVmG1O/2azvprNd0mR8RnhmG7KuDUVkw6Kgk4g0S2b2e+DvhNR7uhc2MjM7Pf7e3R9OtjwViceUzDOzfEIH5GZr2exKM7uwjofMAzoTHm5jwHeptVBE0i3qVL6OytofC4ERhALjCwjXdSdgAKHGV3tC59VVZvaDuz+YgWZL/ZwB7ERl4OgdYDTwEyHlmmy4OkavY5KsGwtsT/jbqMLdHzGza4FehFSrCjo1A1GAeiBwirv/PtPtkfVjZucCFwNb1GP3GOqPbC7i57lLRlshIo1ON3kRac7qO/pWUvcg4WEjBjycZHkqqh9TMszdy8zsZmA4yc9vDqGjqmOSdbWJX78x4I7UWijpYmZrZqgk1mdJXJ4K1XzJDmbWnTDQI+4vwF/cfVUt27cC/kioAZMD/NvMXnP32WlvrDSEkxLen6WAYbO0KMkyj163rWWf14HzCIFnyTJm1p+QPmtHoIhQt6v6s1YOYTBBASGdYnxwAYT0i5IlzOwe4BfRP/VMLWuzf/Q6P6OtEJFGp6CTiIhkSm0PKHpw2QBF6XOeA46pZZP1Oe/lwFJgCnCvu9+favskba6lMtB4fS3LU6GgU3Y4n9DBGAMud/eb1raxu68kzH5cCNxIqNv2S3S+s0V/wrkerYBTszMf2JgQTKhuUvS6sZm1dfdl1dbPiF67p6txkh5mdhahhltetVWJA4SqL0ukdJpZxMz2A86hcubyUuBzNJtVqolmsMZnPiebASsiGzAFnUREJBOuW8/lsgFw9+OqLzOzCsKDyKXufmvjt0oaQQ61z3BLhTqpske8/pqvK+BUzc3AmYABh6CgU7ZoF72qXkfz8x3QE9g5ybrvE95vD7xXbX1x9NoqDe2SNDGzzYC7SN63FA9KVP+8j39+lwEfEGa5SfY4J+H9/cCv3V3Bpg3YemQoyAFaEFLp7UtImRr33wZulog0cQo6iYhIo3P3pMGl2pbLBk+z2zZcZ67nctkw9SF0Mr6yPju5e8zMXgH6Rj+SHWYDm6LAcHP0DrAPsJ+Z7eXuoxPWOWGmci5wODWDTvtFr8lS80nTdR4hXV4MmABcA4wDtgFeIASWdgJKqeyI/jXQgdAfdbu7v9D4zZYU7EY43xOBc9xd9/oN37XU7zM9/oz3rLuv13dAEcl+CjqJiEiTYWZDCYXkh7n70ky3RxpF7+h1QUZbIWnh7g+tz3LZYBVFr/PqsW+8BkDLBmqLpN9HwGbAkEw3RBrdQ8DVhCDEa2b2T+A+d//e3Vea2QfAnsCFZva+uw8zs0JCnbd4WsaxmWq81Ms+0etK4EB3nwlgZt8Dy4A2wFbu/iwhKPWumd0HjCIMSLjbzN5298WN3nKpry7R6zAFnJqV+gwSXML/s3ffUZJV1dvHv80wAyICQ85RfEiSwQjijyCSBUkiSVERFCQHUYKiIEHBAIJkEIEBJAgSRSQrgkQ3SM7iAILkman3j33q7aLpnk4Vu5/PWrVuzw3FZlXX7XvvPmdv+DmuZmI2KjnpZGZm7WRPcqTrO5J2i4iTWx2QNVZEPNHqGKz5JG1DltyYEBHRz+7W+V4G5uS9ZVYGqnqME9Od4zfAVsDaklaKiDtbHZA1R0Q8Jeko4LvAOGBfsr/TLmWX48mk03TAxZJeIpPSY2ve5tzmRWx1sDCZLPxDNeEEEBFTJP0dWK28JtRse1rSl4DbgNnJcm1HNzNoG5aXgLkAJwpHj8FUKJhC9vZ6FvhrRExqTEhm1u6cdDIzs3ayLDmKahxwS4tjMbPG2Rn4JHCYpD0j4rhWB2QNdTfwOWATSXtFxOsDOUjSB4FNyAea9zYwPqujiLhe0onk9/z3kr4UET1LqdkIFRHfkzSWHEg0Bni0ZttFks4DtiS/17OVTdXZEn8GTmtiuDZ848vy/l623UcmGVfsuSEi7pB0G/AxcsCZk06d4z4y6bRkqwOx5nCFAjMbCiedzMysncxY8/Mjfe5lI46kpYDdyVr/c5OltKYZ4OGViPA1TWdZgkwwV4DLWxyLNd5lZNJpDuBXwPYDPO7Ecox/TzqIpFWBM8nv+RrADZL+CdwBvECOgO5XRBzWqBitsSJif0mnANsAt/fYvC3ZC2Y3chYUZN+f04E9XK6r47xJzlZ7q5dtD5VlX8mJ28gynEs3IC5rnN8Ca5EDSeaMiH+3OiAzM2s/fkBjZmbt5Ha6a8OvBNzcwlisSSTtCJxEd5JpKDXDrbNMV/Pzs33uZSPFacCBwDzAlyXNA+wfEX/vbWdJKwE/Jke/V8hExW+aFKsN3210z1ypkOf0JcprMJx06mAR8TDZfL7n+knAwZJ+RP5OjAP+6V6eHWsimXSas5dt/yrL8ZLmiogXemyv9vmbDeskZwA7kTPWz5e0sXtymZlZT046mZlZO9kbuJ4c+fprSetGxNMtjskaSNJHyNkMY2pWvwu8wgBHw1tHuh7YqPy8BnBl60KxRouINyVtT37OY8hk0l8lPQ3cQ/aHqJbaWhaYvxzaRZ4PtouIN5seuA1Hz8EDgx1M4NkuI1xEvA38o3adpJmBBSLivtZEZUNwN7AIOfOlp9qqBavw/hmr1XP9GKxjRERF0sbAJWT5xEdK2czBzma9sXFRWj1JWrDR/42IeLLR/w0zay4nnczMrG1ExF1lhPvJ5IynhyX9AbgVeIxMRPTbjNQ3MR3l22QD8QpwF7AHcKubzo543wGWBxYETpK0aUT8tcUxWQNFxHWSNgDOB2Yqq+en+6FjrWqC4hVg+4i4tgkhWv0MpuG4jSCSqv2bDo+IUwZ57HfJ2W1PAQvXOTRrnGuBLwAflbR/RBxRs+1hsvze9MCXqUk6SZqB7NkH4PJsHURSNak0DXn9PivZw2/nQbxNBT+P7CSP09jBIP59MBuB/KU2M7O2UXMTA3nxOR15I/uFQbyNL1o7yzpl+RKwdkS83MpgrGneAD4PHAOsC9wm6S4Gn2A+s5FBWn1FxNWSFgP2A7YEFuhj1+eAs4GfRcRzzYrP6sMNx0e1hcnrsJn72a83k8mE89z1DMga7kzgUHKm6uFlBszhEXF5REyRdDmwObC5pGeBU4HZyzFzk78vt7UmdBui6XtZ59LYI58/YzMbFD+UMzOzduKbmNFnfvKBw/lOOI0qtYmEas+XFcproCrkwy7rIBExEdgX2FfSomRZptnI34GXgEci4tGpvIWZjSCSushZr1uUVW+1MBwbpIh4XdLO5CzWaYBVyWoF1VlNPwY2I8/xu5dXTyc2IVSrHw8sGH32Kcu16R4wOIXsx3wXOVvxbXLAwZLAZ4BZyGv1t8jSi2Y2yjjpZGZm7cQ3MaNPtezKU60OxJqqt2SyE8wjlKTdgWWACcC1ETEZoCSXnGAaRSRND4wHXouI/7U6Hhs8SWOAv5Pf6Z6q5ZeOknTUEN6+ArifU4eJiIskbQT8gpzt9ljNtrsl7Qn8lN7/zh8TEdc3JVCri4hwCdVRJiKOkbQT3b3bfgfsFxG93r9Jmo4spX0YeZ93c0Qc1JRgzaxtOOlkZmZtwzcxo9KjwEr03tfFRq5DWx2ANdU25Pf8K+Ro2WNbG441S5nFshn5O/ApclYb1PweSLoBCOBIz3JrfxExucxsuYnGDBb4SQPe0xosIq6Q9GFgdeCZHtuOl3QfOcv1Y8A44B7g+Ig4t+nBmtmgSFoB+CV5zj86Ivad2v4R8TZwpKSngbOAAyRdFRF/aXy0ZtYunHQyMzOzVvo9sDKwsaQ9IuLdVgdkjRcRTjqNLh8mH1RUyBJMNgpIEvl5186Iqf4e1FoRWA3YTtLuEXFSk0K0IYqIWyX9DNi0x6aFyM/3FeDVAbxVhezl9BrwBHByRFxRz1iteSJiCnBDH9uuBwY8o0nSjMCs5dgn6xGfmQ3ZnsBYcoDI/gM9KCLOkfR18m/8dwAnncxGESedzMzMrJV+CXwTmBc4mt5r/ZvZyOHebaOApCWBm8n+DtXZMO+SD61q95sVmJFMPkwHnCBpckSc0sRwbQgiYi9gr9p1kqaUHw+PCM9otOH4BjnrrYKfW7U1SfMCm5OzWRckS6j+pHoel3QkcDdwXklMWudZnfwuXjKEz/B6Mun0qbpHZWZtzX+8zcysbZVR0juQF6qLkjcx1RG0jwK3Ar+LiDtbFaMNT0T8V9IXgD8A35K0NPBrsuHsCxHhhuKjkKRZgHci4o1Wx2J1cTnw5fLzhmQvABuhSs+fi8gm4gBXA4cDfwNer903Il6StArwY7JXRBfwM0lXRsSzzYva6sj9+axe/LvUxkqPvqOBnegeUFCdzTpzza47kqVVvydpm4i4q6mBWj3MXZZD6cU4TVl+qE6xmFmHcNLJzEar88iHH9aGJM0AnED2gKi94az+PFd5fQLYU9JZwLfclLzzSKqW0fk3MAfw2fKqbh/I21Qiwtc0HUzS2sAXgLWBBSgPLyS9DjwCXAZcEBH3tixIG449gKXIMmonlpksF7Q4Jmuc7QCRDx5/HhHfqW7o7ZxeBo6sI+l44FvADMDXgUOaEazVT0RM0/9eZtbpJM0E/AlYnqkkByV9EJid/HuwBHCjpLUi4vamBGr1MpG89/74EI5dqyyfql84ZtYJ/IDGzDqGpGmBZSPi7z3WjwH2BrYF5ieb104AfhoRr/T2XhHxNPB0YyO2oSg3MTcCH6Xvm5ie67cFVpb0cSeeOs66dPf3qC49snWUkLQUWWJx9ZrVtZ//jMCy5fVdSacB+0bES82L0upgMTLxdAD5nf+dpF8CtwOPkbNXJ/X3JhFxWCODtLrZrCyfAababLyHfYBNgPnI35ND6huWmZnVydnACuXnF4FfALcA1/TY713y78DewJzAB4FzJS0dEW82KVYbvlvIHn7rlqThtQM5SNJ25CDRCnBVA+MzszbkpJOZdQRJB5IPI8aRF6u1LgbWLz93kVO3DwK2kvS5iHi8WXFaXZxJPmAGeBs4jUwi/oPsBTINWaJhOeCLZMJpOmBJ4GRg6ybHa8PzJO9vKm+jgKSPkg3HZ+G9iaY3ySTEGLI8y3Q123YEPi3pExHh3kCd4zbem1zuIkc+rzfI93HSqTMsT37Ol0XEOwM9KCLelnQ5sDPwkUYFZ80jaWVy1nptr5cDqv2eJJ0N3A/8MiJebVmgZjZgktYCNiDP8zcCG1e/vz1ns5a/AUdLOpW8Z18NWAjYHjixiWHb8JxMJp26gIskfTMizulr5zIo+DvAj8qqt4HjGh6lmbUVJ53MrO2V0dA7U2pES5ovIp4p27ag+6K3+tCyulyc7BsxlGng1gKS1gA2Ij/PZ4DPR8T9PXabArxA9oi4upTjuZIcGb2FpBMi4sbmRW3DERELtzoGaz5JHwL+SD6AhCy5cTRweUQ8VrNfF3ku35i8eZ2n/PsiasowWkfobQbjYGY1OjndOWYvy6GU0qnOQu85wMg6iKTZgdOBz9esrvZ6qbUeOVhoT0nbR8QVmFm727YsXwe2HEjCuPTv2xL4F/ABsqSyk04dIiKuKuXstyX/Pp8p6Yfk7KUHgf+WXWclq5WsR14LVM/7e0fEo00P3MxaykknM2trkpYjE05VN/Leh1S71vx8Ft1T939FjqRaRdJmEXFho2O1uqjexEwBNukl4fQ+EXGfpI3JMk3TADuQvydm1r6+TSaQKmRPgE16K40ZERXgIeAoSScDfyDLdKwuaXP3BeoYO7Y6AGuqV8kZybMM4dh5y7LX8sjW/iTNS5ZiWoCp93qZmfwdqZC/L7+XtGlEXN6UQM1sqD5Nfm8vjYh/D/SgiHhe0kXAl+muamGd46tkRZlNyr8XBL42lf27yNLJ+0XErxocm5m1ISedzKzdbU/3CJldIuL/j4iSNDfdF73/Bb4eEW8DL0r6PNl8fk6yBJuTTp1hNfLz/GPP3l1TExF/l/RHsszipxsVnJnVzRfKciLwxYH0YouIVyRtCvwTmIlMMDvp1AEi4oxWx2BN9Sg5wnlQsxEljaN7tvMjDYjLmmMC+TAS4AHgSOBWcgBBrdeALYCDgaXJZxOnSpLLp5q1tbnL8sEhHPtwWY6f6l7WdiJiErCppM3I8/YyU9m9QlYiOTgi/taM+Mys/UzT6gDMzPrxWfKi5fbahFOxHt0jKC8vCScAIuIN4NKyfZVmBGp1MU9ZDuXitHrMAnWKxTqApNkkfbvVcdigiTy3XxARA57REBEvkA80u4BVGxSbmQ3PlWW5sqSNBnHcEcD85eer6xuSNUN5GPlxyvkdWC4izoqIf/XcNyKmRMQEYCW6B4fNRo6mN7P29W5ZDmUQe7V06ht1isWaLCIujIhlgcWAb5D9Nk8kK838gCyZukBErO+Ek9no5plOZtbuqg8fbu5l27o1P1/Vy/bHy3KuegZkDTWY/h59mVSH97AmKzMXv0w+fPoQMJb3/z50kQNmxgIzkA+nquV7ft60YK0eqgOfnp7qXr2r1oSfsU6xWINJWn0Yh08hG1C/DrwQERPrE5U10AnAXuTDxbMk7RARF/e1s6S5yNkw1RK7bwEnNTxKa4StynIisFNETO7vgIh4R9JXyYFm44ENyR5/ZtaengGWYGgDO9epeQ/rYKUH68mtjsPM2peTTmbW7mYuyxdrV0qaBlizZtX1vRxbHUnlc13neJYcNbXyEI6t3vg8X79wrBlKOcxzyWTTQNUmpHo2Jrf29ziwZHkNVrXny1ASVtYaN1Cn76mkl4GbgOMjore//dZiEfFvSXuQD6NmBCZI+hdwV81un5Y0K/m3e3VgHN3llL8fEc82OWyrj1Xp7vXy2kAPiohXJV1MznIayt8FM2ue68jv6TqSloiIfw7kIEnbAsuT54gbGheemZm1A5fXM7N2Vy27NFuP9auQoyErwIMR8Vwvx364LD0qunPcTD50+pykjw70IEnLAZ8jfx9ualBs1gCSZgfOI3v0dA3wVVUhkxceEd95LiQ/y00lLTrQgyR9CNic8lCzQbFZYwz0+93fa1ZyJsQ1kjzDsU1FxCnkbKfJ5Of2Ybq/uwAbAwcAawHT0X1uPzoijmlutFZHc5blUHpyPV6WM09tJzNrud+Qs5DHABdKmr+f/ZG0A/DrmlWnNyQyawpJ80raXdL5km6TFGXGanX7kZK2LgOFzWyU8gnAzNrdPeSDiP/rsX6zmp8v63mQpHnobkZ9X8Ois3o7rSzHAJdKWry/AyR9BLi4HANwToNis8b4JjkSvkLOUtuHTCDuVrZPAtYvrx2AM+guoTgJ2D4ivtnEeK0+jiHL5M0AXCFpof4OkDQOOJN8qPkS8JOGRmj1dEB5PUl30mEKcDs5G+YIsrzar4G/0N0vokL2ffgTcAvwAFl6rZqA2kXS95vzv2CDFRE/BVYDrqH/gQR3AhtExH4tCNXqp9qnZYYhHFsdYPZqnWIxswaIiHvIMqpdZJm9ByX9WtLONbvNI2ktSQdIuhs4BZie/Lt+TkTc2fTAbdgkTS/pF+Q1/LHkM5lVyYEltQMGdgTOBu6TtELTAzWztuCSU2bW7i4nE04rSjqcfCj1SWDXmn3Oqz2gjLa6kO4L28ubE6oNV0TcKOkKYD1gQeAuSaeSSaV7yAfNkCPdlwU2JS9qP0B+1tdExLVND9yGY62ynASsGREPAki6gUwqTAeMiYjq9/hMSccDfwRmB86QtGREvN3csG04SimldchBA0sC95bP9QLg3oiYUt1X0rxkD7+9yYcb75BJyekkLTiV/8aTDfxfsEGIiCMl/Yg8r0M+iDgwInotkShpNuBA4Dvk+f3KiDi6bJsB+BqZqJoO+L6ks0pvAWszEXE7OXt5fjIBtRQ5U31a4GXywdVN1XO/dbwngVmATw3mIEld5OCS6nuYWXv7Dtl7eWOypP1OZX11YMme5VVVHWBwM/D1ZgRo9SVpJnIQ0PJMpQ+zpA+S92gV8rr9RklrlesBMxtFnHQys3Z3CrAfObJ9//KqqgBXRcTd1RWS/gisAYwtq57F0/c7zQ5kj65lyIeNu/LeJGNP1Yveh4CtGxqZNcLidH+X//9Dx4h4t4yM/BjZ7+Pymm13SdoeuAJYiPydqS3ZYW1O0qPlxw+U5Yx0z4Z5t/TteZccNTlj2af6XR9LJi2mpoKvc9uGpE+Sf8srwJERceDU9o+IicBekp4mZ8X9SNK1EXF3RLwBHFd+hy4hfy++Anyvof8TNiwlwXhuq+OwhrsaWA74jKRPjSO2IwAAIABJREFURMStAzxuL7qvB65rVHBmVh8RMRn4gqS9yEEi42s2V3h/UuIt4OfAQRHxLtaJzgaqs5ZeBH5BzkK/psd+7wL7koPF5iSTkudKWjoi3mxSrGbWBlxez8zaWmlCvBHZl6lnOZZ/kg2Hay1AdzPq/wKbRsT/mhawDVtE/Af4NPC7sqq//h4V4HzgYxHxctMDtuGq3qTe1cu2e8nPeKWeGyLij3SXzlyvMaFZAy1cXnPVrKt+p8eV9fMDH+K9Jbhq9xto7y9rvW+Tn8nDwEEDPaiUZ7uHLJ+6V49tlwF/Le+7dt0iNbPhOJGcjQowQdL7/n7XkjRO0iHkzEXIHmAnNy48M6un0oNvfmBb8rt7M/AgORjwdnKwwa7AghGxnxNOnUnSWsAG5H33n4HFI+IHEfG+QQIR8U6Znb4UWTIZcpDg9s2K18zag0eAmlnbi4i/SloK+AZZM3gSebFzSkS83mP3B4HFyCREn6V7rL1FxKvAl0pJxS3J2WuL0F3v/2WySfVNwFkuy9PRJpFJhp7fZYAoy6X6OPZP5Iy4ZRoQlzXWjXSXYLGRbzXy8760tnTiAF1JllNdo5dttwGrkA+8zKzFIuIxST8AfgDMDdwu6Xrg7zW7LS1pJ/K7uzEwB92DiI6LiIebHLaZDUOZvXIO7qs7km1blq8DW5Z79amKiJckbQn8i6xs8AVyYIKZjRJOOplZRyizXw4fwK57AtuV8jvW4SLifsBN4ke2iWTD8Vl72fZIWc4paXwvM9leqG5vVHDWGBGxRqtjsKaaoyxfGcKx1dnKc/Sy7YWpbLMWKUmG4apExJp1eB9rsog4XNKcdM9wXLO8qgMNdiivqurM1AvIkkxm1kEkTQvMGhH/7mXbR4GPkH13+01UWNv6NN2Dh973OfclIp6XdBHwZXIAkZmNIk46mdmI4sbxZh3nfmBB8mamp0dqfl4JuLbH9mpptnENiMvM6ucVsqn08kM4tvqQYmIv26rJJg80aS9rMPSZjNUEhGdCdrCI2F3SjeSMpyX62f054IcRcULjI7N6k9QVEf6+jkKSZgEOI8umnQPs0stuG5LngTclnQR836XvO9LcZTmU6iLV2avjp7qXmY04TjqZWUcq5fYWJC9e7q6WV5O0cEQ83srYzGxQrgc+D3xC0pci4rc124JsRjst8EVqkk5lVGW1l9NLTYrV6kRSdQbj1RFx2yCP3YDs5zc2Ijaoe3DWCH8jv+cblUbS9w/kIEkiy29VgH/0ssvHy/KRXrZZaw2lr1qFLMPTW7lV6zARcSFwoaRPAauTpXLHk3/TXwYeJcskX+s+Lx3tKUm/Bc6JiN7O00N1EjChju9ndSRpceA6YL6yqq/k8mLk34MZgN2BtSWtHRHPNz5Kq6N3gekZ2jPkD5alBwiZjTJOOplZx5C0MLA3sDUwS82mfegedXNVPqPisIhwXekOI+nUYRw+BXibfFj1ApmwuK2UZrT2dTpwMHlDcpakDYGfRcTtEfGupGvJh9VflfQAcCo5Y+II8ka2Qj7Qts5yCPnZ/Y/syzMYy5CJCI+U7Rynk9/jscCVkjbs7+GkpCWBy8iZjBXg7B7bNwU+VrbdUP+QbRgWGcA+XeQDrHnI3j67AgsArwHrRcQLUznWOkhE3Azc3Oo4rGHmBfYC9pJ0P3AWcO5w++pGxGvk+cDajKTpyL/P1X6Kk4G+Pu8LyHu0zYGZgKWBCySt7hlyHeUZMrG4yhCOXafmPcxsFHHSycw6gqStgV+TD6ZrR89WavbpAhYiH2qdKWk9YNshNC231tmB+pbUmSzpXGD3iBhKLxFrsIiYKGl/4Bdl1RbAi8Dt5d9Hkw+rpwF+Wl49ndboOK2tfKQsx7Y0ChuwiLhA0leAz5Gjou+QdDH50Oo+svzeNOQsiKWA9cmG09XP+KbqLEhJcwO/Aqqz3CYDv2nS/4oNQEQ8MYjd/wn8SdIvgEuBzwIXSVrN129mHeF1umcyLE0OCvpxKa94JnBhSSDZyLEjeS1WAe4BNouIR3vbMSL+CPxR0gHkzLXVgU+S1/vnNSdcq4PrgCWBdSQtERH/HMhBkrYlSyt7gJDZKOSkk5m1vTKauTrCuQt4E/g78Kkeu85Mjoibrfx7K7J8x7eaEKbVT8+kYn8leqa2z7Rk49I1JH08Ip6rQ3xWZxHxK0mTyQcVM5Mld6rb/iTpWGDPPg4/LyIuakKYNkilBOJP6X4Y1ZstJS0zwLccQ5ZVXZ383nvEZGfZFLiGfNg0lhz1vPlU9q+e1+8ANqlZv0r5d3WAwhEDffhh7Ssi3pC0FfA4WTZxezygwKwTzEHOPv4SObBgHHn+/kx5/VLS5eS93BURMblVgVrdfLEsXwU+FxH/7u+AiPiPpC+Q/X3Gk/dnTjp1jt+QPbvGkGVTP9ffbEZJO5CDhKpOb1h0ZtaWuioVz2g1s/YlaTzZq2EW4B3gUOCYiHhH0hTyodM+EXFs2X86sgTfwWTCYQqwakT8vRXx2+BImotsVHoJWWani3xIeRE5km5iWTcLORp+Q3K0+7TkSMsDgLfK9mXJm6Lpy9vfHBGrNev/xQavNCTehOzTdnePbTsC+wIqq54Hfg4c6dHw7UvSXsBRvH8GYzWhMJQL0a5y3JERceAwwrMmK4nI3YGDyATz1LwG/AT4SW2/l5rfqVeBwyPiqAaFay0g6SxgG+CGiPi/VsdjQyNpLHkN9n/kdd305GzGgfT6qkTEmg0MzxqkXMdtTn6HV+P9f+snkomGcwbbz9Hah6QXgVmBUyPia4M89kTg68ALETFPI+KzxpD0c7IUboXsz/Rb4C4ysVQBjgWuIgcHbQl8lO5r9nMiYrsWhG1mLeSZTmbW7r5JJhAqwNcj4syp7RwRbwOHS3qKHE3TBXytvI+1v/8AfyATTi8DW0TE9X3seztwmqQVyCTVfMAXI2KN6g6S9gUuB1YEPilpnYi4uoHx2zCUEoin97HtNPLznhkYFxEvNjM2G7KfkqNZl+tj+0AeQPZUIUu0HDLEmKxFImIScIykE4C1yNKZiwFzAdMBLwEPAH8GLoqI13t5myvI/jB31iajbMSIsly6pVHYkElaDvg9OTN1sKoPKK0Dleu4k4GTJc1H9uH9ElleC7In5y7ALpIeJfs/nRMRj7QiXhuy6qCRx4Zw7ONlOWt9QrEm+g7Zx2tjsorBTmV99Zy9J++tTFG9xr+ZTDSa2SjjmU5m1tYk3Qx8gpz5sGKPbe+b6dRj+9/IZMMDETHQ8k3WQpJ2IXv7TAHWiIibBnjcssBfycEUX4uIU2u2zUOWaxtHNjb+ct0DN7M+SZoXWLxmVRdwPXn+PhE4fwBvUyF797wGPOkebWYjk6SfkrPh3oqIGVodjw1OGRjyEFlybagqETGmTiFZG5C0BDnzYSMyAdVzBtQdZP+n8yLipeZHaIMh6SUy8fTLiNhtkMceBewF/Cci5mxEfNZYZcb5gWSZxKreyt2/RValOMiDhMxGJ890MrN2tzh5EXPNEI79M5l0GspIS2uNbcjP+8qBJpwAIuIeSZeRPUN2AE6t2fZcqSW/GX3PtjCzBomIZ4Fna9dJ1SqJPBIRf256UGbWdkqJ5M3KP92zrTPtRiacKsB/ydmufwZeJMsx2ShU+u4dChxaBoNtQ5ZZnanssmp5HSvp92QyY8D3AdZ095O9lTeUtGeZxdwvSV3A+uT54eEGxmcNFBHHSPoVed+9Olnyfjz5fPllcrDnTcAFEfGflgVqZi3npJOZtbvqzchQRr1Vjxlbp1is8ZYsyzuGcOzd5MVvb7Paqjc28w0lKGscSavX430i4sZ6vI81zY5l+deWRmFmbUHSIsAJZOmeCnBdayOyIdq4LN8FPhsR/2hlMNY+JC1G9mLdEPg0+SyqOjuiOkNiOmALYAtJfwB2LgNXrL1cTiadFgQOB/Yb4HH7AEtQBhg2JjRrhoh4EzinvIaslOFcrLyn7+XMRhgnncys3f0HmId8CDFYS5TlxPqFYw32gbKcbhjHjutl25tl6VI97ecGht+/oYKvaTpKRJzR6hjMrDEk9dWLsacu8u/9HMAidD94nkyW2rXOsxj5N/liJ5xM0gLAVuW1fM2m6nd9InAe+eB6HrIH5PrkgMH1gZskfdqJp7ZzErA/OTh0b0nzA4dGxEO97SxpIeAAss8ywKvAr5oRqLW9rYCjyNL6vpczG2H8pTazdncvMC+wvqTdI2LKQA6SNBc52rIC3NPA+Ky+niIfWHyeLLsxGJ8ry95uTKslFl0nvj31rAFuZmadaw0GP5ig9u/A/hFxX/3CsSaqPl/w5zdKSZqTnK20FdmXt6r6HX+HnClzFnBFj14vF5VE1fnAx4CFgB8AX2103DZwEfGypG+TfbgqlMSipEfJ0nvVvpuzkINAP0z3jLYp5Aw235NZLd8Lmo1ATjqZWbu7hEwmLAR8l7zxmCpJ0wPnAjOSF8KXNzJAq6tryRuT5UuS8biBHCTpG2T/rkp5j9pt05OlPJyAbE8DmfHSBUxPjoJdjhxZWQEmAL9tXGg2XDUzHioRsWYv64fjPe9pZm1lsA+Q3gL+BBwbES6t17meBj4CfKjVgVjzSJqZ7Me2NZl0nqZsqj0P3EImms6LiFfoQ0Q8JWlL4LGy6vN1D9iGLSLOLn34fk5eowMsWl49VX8P3gB2iYjzmhCimZm1mJNOZtbuTiXrRC8IHCJpduCIiHiu546SpiWTCz+ku1708+U9rDMcD+wEjAGOKXWef9TXzamkmYB96a4lPomacg2SxpMleuYkfx8ubVzoNhQRsWP/e3Ur3/NdgB8DXwSui4iTGhGb1cUa9D7joa/1A9U1zOPNrHEWGcS+U8gHkS9FhL/Tne9qQMA6ZPktG+EkVQcIVnvo1iaa/gWcDZwdEY8O9D0j4klJ/wFmB2auV6xWXxFxiqSrgd2AjYDF+9h1Ijkg9NiIeLxJ4ZmZWYt1VSq+tjez9iZpVeB6unv2ALxIdyLhPuA1cgZEtWdPF9nEeP2IeM/MF2tvZdbSCXQ/UH4buJn8nF8hR0+OB5YCPkn2g6je4B4YEUeU91kBuKPs3wU8AywREa835//EGknS1mQPgLeAFSPiny0OyXohqVoStRIRY3pZPxzveU8zM2stSYsDd5MzH3aIiLNaHJI1WPl7XqH7WvwlsjzeWRFx6xDfswv4H3nv97eIWLUesVpjld5NC5D36GPJ+7ZHI+LhlgZmbUvSXmRPJ1/Tm41AnulkZm0vIu6Q9DlyhNT8ZfUcdCcllinL2pF1r5A3u044dZiI+LWkMcBPyCTi9MD/lVdP1c/8bWC/iDi+ZttS5IwpgNeBLZ1wGjki4lxJ+5DJ5l2Bb7c4JOtdXzMeBjMTwszMOkBEPCxpV+A3wG8kLQycGBEvtjYya7B3gSvI8nmX9+jTNBQfALYHniRnS1kHiIgngCdaHYeZmbUHz3Qys45R6oXvAnwFWKyP3f5N3vD8NCKebVZsVn+S5gcOATYGZutjtzeA88gSfI/0OH5f8nflT8CRLucw8kg6CtgLeDgi1Op4zMzMRrNy7QWwHrA63QPEngReIK/b+uN+fR1E0gHA6b2VPjczmxrPdDIb2TzTycw6RkT8l+zj8mNJ85AzWcaT57KX8fT9ESUingZ2kvQ1YFUy0TgXWU7vJeABsuTGW30c/xNytpSNXK+V5fxT3cvMzJpG0naN/m9ExJmN/m/YkBxBd6Kpuuwie7MuOIDj3a+v83wYCEmXA7+IiFtaHZA1l6SVgW2AT5Hf8/HAARFxbNl+NnA/8MuIeLVlgZqZWVM56WRmHamMpvOIulGgNBa/vbzMaq1UlgMZOW1mZs1xOo1NHFQAJ53aV9cA19nIsAYwI7Al2WvTRglJs5Pn+8/XrO4tcbwesDWwp6TtI+KK5kRoZmat5KSTmXUMSXMDOwDP9NaYWNLOZG+XS4ATIuKZ5kZoZs1S+rxtQN7Y3t/icGwYJI0lZzPOTfZwm4YBPqD0bAeztuUkw+jkfn2jzzw1P9/YsiisqSTNC9wCLMBUzvelPP4s5PX6bMDvJW0aEZc3JVAzM2sZJ53MrCNI2gc4DBgHXEj2berpw8DSZNm93SR9JyJObV6UVm8u1zDySVp9gLt2kaUV5wDWIkdMVkdT/q4x0VkjlWTTYWSvvhmH8Bae7WDWnlYhk8eH0D0C/mFgAnAX2X/zbWBmYElgXWBt8pz+FnB8WVqHiYgnWh2DNd0zdPfanZnu0sc2sk2gu2TmA8CRwK3AQz32ew3YAjiYvE+fFjhVkiLi5SbFamZmLeCkk5m1PUmHAgfRPYpKfew6S1l2kQ8wT5Y0JiJObnCIVmcu1zCq3MDQyjBVzwcPAr+pWzTWTBeT33HPiDAbQSLiTkknk8mkd4DdIuKkPna/GjhO0ifIh5hzAytExLrNidbMhumnwC/KzwcBO7cwFmsCSZsBHyev3ycAX4qIyWXbe/aNiCnABEmXkuUXNyNnPH0VOLqJYZuZWZM56WRmbU3ScsB36X4oeTrwq972jYidJB1MltjbmzzHHSfpao+87Bwu1zAqDTXpcDOwZURMqmcw1njlgcV6dCccHwH+AryIe3SZdTRJm5APFCvAlyNiQn/HRMStktYn+zeuLWk7l88cGSR9EPgEsCg5Y70CvAI8CtzhmeqdLSJ+JWl64EfA1yTND5wI3BoRE1sbnTXIVmU5EdipmnCamoh4R9JXgc+S54ENcdLJzGxEc9LJzNrdLmSJlgqwa0ScOLWdSx+nAyXdTZbcmg74FrBPowO1unG5htHljEHsO4VMSDwLXBsRf21MSNYE29f8fChwWEQMZcabmbWfb5blDQNJOFVFxN2Sfg9sDnwNl8/saJKWBL4HfBEY08duUyRdCewfEQ80LTirG0nVwYA3Af9HzmD+fNn2Oplg7G9wUCUiFutnH2sfq5L35pdGxIDLKUbEq5IuJgclLNmo4MzMrD046WRm7e4z5EXtrf0lnGpFxPmSdgVWAz6Hk04dweUaRp+I2LHVMVhLrEx+z++MiENbHYyZ1dXy5Pf7z0M49h9k0umjdY3ImkrS9mRlgumZ+mzmMcD6wJqSdouIU5oRn9XVznTPWq4dPFItd/7Bfo7vrXy2tbc5y/KRIRz7eFnOXJ9QzMysXU3T6gDMzPoxf1kO5cHFDWW5SH1CsSYYUrkGMtH0Ulm1YYNiM7P6mbUsr2lpFGbWCNWHieOGcOys/e9i7UzSpsCpdCecJpOzYH4J/IAsw/Zr4DZyBnMX8AHg15Lcy6szdfXymtq23vazzlEtgzzDEI6drSxdVtPMbITzTCcza3fV89Q7Qzi2eoxvaDqHyzWYjQ7/BubD/ZvMRqKnyP496wEHDfQgSWOBL5DXAQ83JjRrpNJv8yS6r73PAfaLiGf72H9+sozy1uSA2LMkfTgi/tuMeG34IsIDmUefJ8m+up8azEGSusiZjdX3MDOzEcxJJzNrd0+TM5VWHsKx1dIsL9QvHGswl2sYZSQ92uD/hPsEtKe/k0mnFVodiJnV3bXAN4DlJO0XEUcO8LhjyGu+CnBRo4KzhvoGOVutAhwfEXtMbeeIeBrYRtKLwG7l2K8CxzY6UDMbsquB5YDPSPpERNw6wOP2AhYnzw/XNSo4MzNrDx6VYmbt7mZytOTnJA24vr+kRYGNyYvaWxoUm9WfyzWMPgv38lqovAayfqGpvKr7WPs5gzy3byDJSUGzkeV4sqQawI8knVyuy3olabnSn3FX8rrteeDnjQ/TGqA6i+EpYN9BHLcP3TMfNq5rRGZWbyfSXVFkgqSVprazpHGSDgGOKKsmAyc3LjwzM2sHnulkZu3uDGBbstHwZZLWj4j7p3aApEWAS4HpyIcXZzc8SqsXl2sYfa4gv6fzACuWn7uAScBDZBm2t8kZbALGl+Oq+7l8ZgeKiIvLQ+aNgIslbRwRj7U6LjMbvoh4UNLuwC/Ic/VXgK9Iehh4EKiWTpuVnJW+YPl3Fzn4ZIuI+F9zo7Y6Ed1lkt8d6EER8a6kS4BvA8s0KjhrP+UafoOIuKzVsdjARMRjkn5A9mibG7hd0vXkLPaqpSXtBKxCJpLnIM/xFeC4iHAJVSMijiFnOZvZCOSkk5m1tYi4XtLVwDrAAsCdki4C/gDcD7xSdp0FWKLstwXZkLgCXB0RVzU9cBsql2sYZSJiA0mrApeUVY8CBwMXR8SbPfeX9HHg+8C6wJvA1hFxabPitfqQNCewOzATsAYQkq4B7iBLog6o11NEnNmoGM1s6CLiV5KmBQ4HPlhWL15ePVUHDzwJbBMRNzchRGuM6sCQXns49eP5spyxTrFYE0maHtgAWAn4EDCW9w8M6iKr7YwlqxrMRl73z4SfTXWUiDi8XMt9m/xc1yyvStllh/Kqqv4uXMDgZkFam5E0niyPPQc5yHfAfN1uNrr4D7uZdYIvAzcAS5E3KVuWV1+qF7X3Als1NDKrtxPJB9FjyXING0XEnX3tLGkccCDdjcpdrqHDSJoL+D3Zz+s6YOOI6DPhEBG3AetJOo680T1b0rIR8Xgz4rW6ea7m5wp5TbpueQ1UBfDNq1mbiojjy0Ch/chZjQv0sevDwG+AEyPitWbFZw3xXzKRMO8Qjp2nLF+uXzjWDKW82oX0/R2fmursF+swEbG7pBvJGU9L9LP7c8API+KExkdmjSBpHrL07cYMrVWLr9vNRpmuSsV/382s/UmaETgS2IlMSEzNu+TDi32m9vDa2pOk75I3L5XyqpZr2Lf8+3TgVnov13BsROzT/KhtqCQdSfZy+A/wkYh4pZ9DqsdNCzwALEY+qNy1cVFavUmaUoe3qUTEmDq8j5k1gaTFgfnIQQZTgInAAxHxQksDs7qR9CfgM8ATwOIRMWmAx40lk48LADdHxOqNi9Lqqdyj/Yv8Xg9UtUQyZG+g6yNivXrHZo1RzuVvRsTTNes+BaxODhIdTw4mepmsYHATcO1gSm5ae5E0K3AnWQ53qKXNfd1uNsp4ppOZdYRS23/XkpBYD1ibvDGdk0xCvUJe1N4KTIiI5/t6L2tvLtcw6mxMfrYTBppwAoiISZIuJhNWflDReQ5tdQBm1lylh4f7eIxsV5BJpwWBHwL7D/C4H5ZjKuU9rHN8jbwfq5ClcU8H7gEWJWc5TgJ2Ie/X5gA+S/6OQCacPhERdzU3ZBumw4AtJN1Ozl66opRFdWnUkWs/YCHyez4FuIYc/Pe/8m8zs/dx0snMOkp5KP3b8rIRyuUaRpVqA/mnhnDsq2U5d51isSaJCCedzEah0vdlPPBaGVBkI8vJZNnjmYB9JM0GHNTXbLZSYvdwYMey6lXg180I1OqmWha3Aqxb7ckmaQay5+oY4LGIqPZcPUzS+sDvyL5Op0laMSL84LpzfJIc9Pcx4PUWx2LNsVFZvgt8PiL+1MpgzKwzOOlkZmZtKSIuBC50uYYR7y2yCa2GcOyKZfli/cIxM7N6kdQFbAZsA3yK7PcDOUv12LLPDUAAR0bEoy0I0+okIl6RtAtwDpmE+AqwnaTbyNkvL5X1swHLAh8nr+uqZZK/FRHu6dRZliY/uxuqCSeAiHhD0r3A8sAaZN/O6rY/lN+TM4CPkj14PaCwc8xR8/NfWxaFNVN1ltMEJ5zMbKCcdDIzs7bmcg0j3t3kw4jNJB0cEU8M5CBJKwIbkjdANzUuPDMzGwpJAs4HlqlZXU0u1FoRWI1MTuweESc1KURrgIg4V9IHgBPJ5w1jgU+XV0/VEsnvAntExDnNidLqaHxZ3t7Ltn8AKwAr99wQEWdJOgRYGNgEJ506yb/oPq/PW/5tI9sb5CDB+1sdiJl1DiedzKwtSDq1/FiJiK/2sn443vOe1r4kbVd+/GtEPDjIYz8FfBGYISK+UffgrFHOJZNOMwBXStowIh6Z2gGSVgUuJq9jKuSDLesgkibX4W0qEeFrWbM2JGlJcsDIzLw3sTC2x36zAjOS5/LpgBMkTY6IU5oYrtVZRJxaZjcdQpZlGtfHrpPIv+eHR8Q9TQrPGqO3vpxRlkv3ccxVwM7Acg2JyBrlB8B55eefSNo8IupxXWft60GyrKJLmpvZgPlG3czaxQ50j3z9ah/rh8NJp85wOvl570Ne3A7G6sDuwH8BJ506x2nkA4flyRJ790q6ALiS/B34b9lvVrIEyybABsA05O/K6RFxY7ODtmHr6n8XM+tEksYAFwGzlFVXk317/kaP/h8R8ZKkVYAfA2uR54afSboyIp5tXtRWbxHxALBF6eP1SWARsqxeF1lm7xHg9ohwT5jONhGYj0ww91SdATOfpBl76eP2TFn6QXYHiYgLJE0hB31tDPxd0snArcBjwCvu0TXinEWWyN1U0r4R8XarAzKz9uekk5m1k95KrlTXD0c9klbW/uYqyw+0NAoblIiYVBpK30rWC58O+HJ59aV6TrgU+HpjI7QGeZL+z81dwPTkQ8oxZV0FuIbBJ6XNrHm2IwcRVICfR8R3qhuy4t57RcSdwDqSjge+Rc58/To5S8Y6XES8BVzf6jisYR4G5gdW6WVb7cz1FYC/9Ng+a1n62r2DSLqi/PgoeY22DHBcj336exvPVu8spwDbA58ATpe0fUS80+KYzKzN+SRvZu3is4Ncbx1K0rTA3vRdagXy4dOMA3zLMcCCdCcpXhhGeNYCEfG8pOXIkfA7051g6MtE4IcRcVw/+1mbioiFB7pvOWesCOxPznRbFTgsIm5pTHRmNkybleUzwL6DOG4f8js+H7AuTjp1PEmLAnOQA0oGzDOYO8qfyPu1NSV9JiL+XLMtgMnk7PQNeX/Sac2y7K00n7WvdekeOFRdegb7CBYRkyV9AbgE2AL4mKSzyd68L5Pf84G8j8/tZqOIk05m1hZ63KD0u946V5nZMhOwXx+7dAFrl9dgVGfKXT6M8KxFIuJV4NuSDiMfTKxJPnicE5hCJpoeAG4ELi4jp23f5fwkAAAZ90lEQVQUiIhJwB1kSY+jgL2A8yQtGxEvtzY6M+vF8uTf48sGMxI6It6WdDk5+OAjjQrOGkvSB4FDyRLZ44fwFhX8nKKTnAEcRPZr+6OknwG/iYhHIuJNSTeTJbC/JemmiLhU0jhyoNGy5Of9j1YFb0MykNnqNvK8BdwJfAxYGPjuII/3ud1slPEX3szMWuEwYEuyvn9vhjpa7g7gwCEea20gIl4ETi0vs54OALYG5gV2IR9amVl7mb0snxrCsU+X5QfrFIs1kaQZyMEhy+OZD6NCRDxVBoR8l6xisC/Z32mXssvxZNJpOuBiSS8BHyKTVFXnNi9iG67BzFa3kaEMJriOrDzg2W1mNiBOOplZR5L0AWABcgRlhSzL8ISbWnaGiHhL0sbASj02nUZ+nucBVw3grSrkdP7XgCeAeyLCI+/MRqgyU/L35MOszXDSyawdvUr2+ZhlCMfOW5Yut9WZ9iB791SvxYKcpfw/ctayjUAR8T1JY4E9yRLJj9Zsu0jSeeRgswp5boDu35E/k9f/Zta+vkXet1fIZNPT5Ln9BXxuN7M+OOlkZh2jlGLYAdiRHGXT8xw2RdLd5Gi5EyLizeZGaIMREfcB99Wuk1S96fxbRJzR/KjMrAM8V5aLtjQKM+vLo+Rsp0H15SzXeRuRD7UeaUBc1nibl2UF+Iqv5UaPiNhf0inANsDtPTZvCzwE7EbOggKYBJwO7OEBY2Ztb6uyrABfB07199bM+uOkk5l1BEnLABOAxcuq3qZzjyGTUSsCO0naOiJcI7yzHFqWt7Q0CjNrZ9Vkk8t6mLWnK4FVgZUlbRQRlw7wuCOA+cmHWlc3KjhrqA/T3c/LCadRJiIeBg7pZf0k4GBJPwKWIMvw/TMiXmtuhGY2RAuT5/bfR8QpLY7FzDqEk05m1vYkLUnWh5+Z7oeM75Ij5l4ik02zkTe6Y8r2JYA/S1opIjxatkNExKH972Vmo5WkBeku0fNoP7ubWWucAOxF9mU6S9IOEXFxXztLmgs4kpwNAdms/KSGR2mNMKks/9bSKKwtlTLo7xkQKGlmYIFSAcHM2lP1Gcw9LY3CzDqKk05m1tYkTUPOcKr2BXgI+D5wSc/+TaV58SbAweSMqJmACSXx5FrDo4CkLmCDiLis1bGYWX2UkltzAGsBhwEzUEbStzIuM+tdRPxb0h7AycCM5LXYv4C7anb7tKRZgVWA1cmZD13kd/v7EfFsk8O2+niYrDgwc3872sggqToA5PDBzoCQ9F3y7/pT5EwKM2tPjwDLk6VzzcwGxEknM2t32wBLkg8h/gys31evpoh4A/itpIuBK4DPAMuSo+LPbU64Vg+Spgc2IBuWfggYy/tLaXUB05RtM5Cz3ZYjk43++2bWxiRNHuZbvAIcV49YzKz+IuIUSTORM5imJWejV0uvAWxcXvDev+9HR8QxTQvU6u135LXbRpL2c8+PUWFh8ns9lETjZPL7P3c9AzKzujsfWAHYTNLeEfFOqwMys/bnh3Jm1u42LcvXgK36SjjViog3JW0FBJmwcNKpg0haCbgQWGAIh1dHSZtZextOP6b/AptFxMR6BWNm9RcRP5V0CzmTYe1+dr8TODgirmh8ZNZAJwBfBQQcBezd2nCsHZXKBAsCW5RVb7UwHDPr36+Ar5NJ5tMlbVd6tZmZ9amrUvGzOTNrX5KeBOYDzo6I7Qd57OnAdsBzETFfA8KzOpM0I/AvYM5BHFah+wH2O8D1EbFevWMzs/qR9DgDTxBPAd4AngWuBU51wsmss0iaH1gNWAoYTw5+fJnszXZTRDzYwvCsjiR9hKw4sAhwG3AKcDf5eQ9olmtEPNmwAG3QJI0B/g4s06D/xK0R8ekGvbeZ1YGkpclz+/zAE8Cvyf59z5LX6f1e1/vcbja6eKaTmbW7OcoyhnDsQ2Xp2sOd42tkwqlCXryeTjYsXRTYj2xQvQtZUm8O4LNkGUXIhNMnIuIuzKytRcTCrY7BzBpD0qrA2Ii4ubouIp7Gs85Hi0eAs8kerB8vr8Go4OcUbSUiJkvaGbiJ4c1U7stPGvCeZlYnku4pP3aV18LAjwb5Nj63m40y/sKbWbt7i2wuPeMQjv1QWb5ev3CswdYtywqwbvWBlaQZgL2AMcBjEXFd2e8wSeuTPQRmAE6TtGJETGly3GZmZpa+C2wg6Tlg74j4XasDsuaQNC1wCXk9Vx313ogkhTVZRNwq6Wd0lz6vWoj8rF8BXh3AW1XIGW+vkbMlTnZZTbO2twzvn8nkc7uZTZWTTmbW7h4DlgfWAg4c5LFrlaWncXeOpckL2ht6jJB+Q9K95O/CGsB1Ndv+IGkX4Azgo8BWwG+bGbSZmZn9fyuRD6PmIUvm2ujxNeDzdJc+fou8Dn+BLJVqHSwi9iIHgf1/kqqf6+ERcWzzozKzJrgR9002s0Fy0snM2t31ZKJhJUlbRMT5AzlI0pbkQ48K2QPEOsP4sry9l23/AFYAVu65ISLOknQIOdV/E5x0MjMza5VZa36+r2VRWCvU9l89HDgqIgYy+8U6m2c8mI0AkuYDno+I9/Tfi4g1WhORmXWyaVodgJlZP06ke2TkaZK26e8ASV8GTi3/nAyc3KDYrHFe6WVdta/X0n0ccxV507tcQyIyMzOzgbin5me1LAprhcXIAV/XRMT3nHAa+SJimvLyLCezzncV8D9J+7c6EDPrfJ7pZGZtLSL+JekoYD9geuDMchF0EflQ4yXy5nY2YFmyzvhSZPKhAhwfEdHbe1tbmgjMB8zcy7ZqiZ75JM0YEf/rsf2Zspy7UcGZmZlZvw4ErgDGAj+XtF4vf7NtZJq+LG9raRRmZjYUC5D9tN8zQaGU0ZwC7OsEs5kNlJNOZtYJDgLmBbYt/16qvPpSLfFwAbBPA+Oy+nsYmB9YpZdtj9T8vALwlx7bq+V8PtCAuMzMzGwAIuJ6SWsApwOfAh6WdA5wK9mr8xVg0gDexz05O88TwJLAh1odiLWGpJWBbcjv/oJk6ewDqg+qJZ0N3A/80jPhzNpOdeBAb/fTLqNpZoPi8npm1vYiYnJEbA/sDDxNXvBM7fUMsEtEbBURbnjZWf5UlmtK+kyPbUGWSwTYsJdj1yzL3krzmZmZWRNIeoAsczyGvC6bC9gDOB/4KznA5LF+Xo82PXCrh4vJz3wjSX5AOYpIml3S5WRf1t3I3rpz8v6BzusBPwQekbRec6M0s35MLMuNJHkgp5kNS1el4uexZtY5JI0FVgPWABYhy+p1kWX2HgFuAq6PiH5H0Fr7kbQA+TBqLPAO8DPgNxHxSNl+A7A68BawVURcKmkc2ax6L7Kk4vURsXYLwjczMxv1Shme6k3mUBMPlYgYU6eQrEkkzQncC8wOHBsRrjgwCkiaF7iFLM3V8ztfAfaJiGMlzQy8XNZ1kTMeN42Iy5sZr5n1TtJlwPrkd/RlctDn2+Szlwo5IOSpIb59JSLW7H83MxspXF7PzDpKRLwLXF9eNsJExFOlh9d3yXrS+5L9nXYpuxxPJp2mAy6W9BJZwmVszduc27yIzczMRidJ8wHPR8TkHptupDvpZKNIRPxb0ubAJcCekj4O/AL4G/BsRLzZ0gCtUSaQpfQAHgCOJMtpPtRjv9eALYCDgaXJ51GnSlJEvNykWM2sb8eRsxEhS2N+vGZbF7BoeQ1Wtd+2mY0inulkZm2tJCDOioh7Wh2LNY+kI4A9ydI8+0XE0TXbzgW2pHuUJDU/3wCs6bKKZmZmjSXpPmAx4NCIOKLV8VjrSbq0/Lgo2X91KNdjlYjw4NgOIWkzso9uhUw+famaiK6Z9bhPtadTWT8OOAfYrGx/z7W+mbWOpC+RieP56vzWnsFsNsq4p5OZtbu9gLsk3Stp31J+zUa4iNgfWAb4AVkbvta2ZX1t8+FJwMnABk44mZmZNcUC5Kzk99xTSposaZKkPVoTlrXQBmRppiV5b4nFwb6sc2xVlhOBnXqZ+fg+EfEO8FWyPDr03qvVzFogIn4bEQuQbQwWontmU4Usab/IEF9DmSFlZh3MI4jMrBN0kaMlfwz8SNJfgLOACRHx6lSPtI4VEQ8Bh/Sy/v+1d/cxlp11HcC/s22l8tJSmhZ5DS2FH1AiIIKtSEEbkGJt5S2ABIsoL0IoQQMSAaG8JYiWIEhBKVFKo4hNbaVVKRqtCOVVMYD8KC10wUoFWVp5KX3Z6x/nTmZYdrt7Z/bOubP7+SQn586Zc26+yc7duXO/53mem5K8cjoa6r4ZRkN9vrv/b2MTAsB+7eDpfsfFxpd22LP/2BpTKO1vHprh3/zCWd6Ld/d1VXV+hvLpvvMKB6zNdMrLbUlSVcuHt3X3VaOFAjYVpROw6J6fYSq1h2fl7scTpttbq+qiJO9JctG0jGAfUVUPTvKsJN3db9rJKb+eYcTT+5P8QZJ/28B4ALC/+98kd0xySlW93no9dPc9xs7Ahjtyur9iDdd+ebo/dO9EAebkmgyv9edX1ee7++KxAwGLT+kELLTuPivJWdPFqp+c5KlJHjz99sFJHj/dtlXVXyY5t7v/dZSw7BVVtSXJW5I8d3rowiQ7K53umeSQDD8TT55+4PXKjUkJAPu9T2aYSu3+Sb5SVZ3k+6u+/xtVdfIan3vS3SeuNyAwd9/NMM3mrddw7eHTvZkrYLHdkOHm33sk+c64UYDNQukEbArd/V9JzkxyZlUdk6FoeEpWpmO4Q5LnJHlOVV2VYfTTud3dY+RlXd6R5JlZmZbnXrs47/ok305y2wxT7L28qrZ39xnzjwgA+703J3ns9PFhSY5b9b2lDOs3rGUNh6WYog02i61Jbp/kYbNcVFVLGUrr5ecAFtcRqx5/fLQUwKayZfenACyW7v5id7+mu49N8sAkb0hyVVam37tHkpcl+VxVfbyqTh8tLDOpqodnmNs9SW7KsKbTo3Z2bne/NMMb4OdmKJ+WMhRPx84/KQDs37r7g0menuTqrLwHW72O09IaN2Dz+ECG1+0jqur4Ga77rQw3lk2S/MM8ggF7zRdXPb7zaCmATWVpMnETGbBvqKrjkjwhyalJjln1rUl3HzBOKmZRVeckeVqGP0BP6e6L9vC6RyT5x+mX7+ju580pIgCwg6o6LCsjj6/M8Hv89UneudbntFg5LL6qOirJfyY5KMnXMrx//+T0e9sz/F/w4u4+c3rsR5L8TpKXZ7gJ+qYkx3b35SPEB/ZAVT0pyXszvJ4vSPKk7r553FTAolM6AfuMqrpVkhOTnJLkl5PcJtMpWpROm0NVXZFhpNoHuvukGa+9KMlJSS7v7ppDPABgN3b2QTOw76qqlyV5TYbX/STDjWCfSvKS6dd/muQjSR6S4ebAI7IyjeaZ3f3ijU8NzKKqnpDk7RmWNfhMkj/J8Lr+UpJvdff2EeMBC8iaTsCmVlU/muTkJE9O8vNZWcR2eXqW65KcN0I01uZO0/3H1nDtZRlKp7vuvTgAwIwuzfBh8lfGDgLMX3e/rqqOTPKCDH+DnTjdlu9wfsZ0W7b8d9r7MhRTwAKrqounD69McniS+2dY13H1Obt7mkl3+wwa9iNe8MCmM52W4bEZiqaT88NF000Z5hc/J8kF3X39hodkvdaypsPyNe6yAoCRdPcjx84AbKzufmFVXZphxNN9dnP6fyd5bXefNf9kwF7wmKyUyMt7azACt0jpBGwKVXVgkkdnKJpOTXK76bdWv9n5RIai6c+7+xsbm5C95Kok907yM2u49iHT/dV7Lw4AALA73X1ekvOq6mFJTkhyvySHZfjcaVuGURIfSvLB7r5xtKDArLZmpWwC2CPWdAIWWlU9KkPR9Lgkt58eXl00fTnJuUnO6e4vbGw69raqenuSZ2d4U3tid//THl73Exmm5FtKcnZ3P3tuIQEAAACAnTLSCVh0f5+hgFhdNH0rwxzg53T3h0ZJxby8M0PplAx3Sj6luy+5pQuq6vgMPw9bMvysnD3fiAAAAADAzhjpBCy0qlpen+fGJBdnmD7v/d19w3ipmKeqOifJ07IyhP+jSS5K8tkMhWMyjHq7T4YpF0/ISin5nu4+bePSAgAAAADLjHQCFt1HMhRN7+3ubWOHYUM8O8ldkjxy+vVPTbddWS6cPpjkWfOLBQAA7KiqDkryxCQ/l+THkhycYRaCpVu6bmrS3SfOMR4AsMGMdAJg4VTVAUl+c7rdcTenfyPJG5P8fnf7pQYAABukqh6Q5K+T3H0Nly9lKJ0O2LupAIAxKZ0AWFhVtZTk+CSPSnK3JEcmOSjDNHtXZhgJd0l3f3+0kAAAsB+qqkOTfCHJEet4GqUTAOxjTK8HbBrTaRsemtmnbEh3v3uO0ZiT6cilD083AABgcZyeoXCaJLk2yZuS/HOSryf57oi5AIARKZ2AhTctm16d5HlJbruGp5gkUToBAADsPadO9zcm+dnu/vSYYQCAxaB0AjaD85OclD0c1QQAAMDc3TPDDX7nK5wAgGVKJ2ChVdUTkjw2wx8zSXJFkn+JKRsAAADGtPyZ0mdGTQEALBSlE7DoTlv1+Iwkr56u8wMAAMB4vprk3kluN3YQAGBxbBk7AMBu/GSGUU6f7O4zFE4AAAAL4QMZpkB/9NhBAIDFoXQCFt0dpvtLRk0BAADAam9N8r0kD6iqp48dBgBYDEonYNH9z3Rv/SYAAIAF0d2XJ3l+hpkp3llVr6iqI0aOBQCMzJpOwKL7VJK7JHnQ2EEAAAAYVNVLpg8/lOSEJK9K8qqq2prkmuzZjYOT7j5xPgkBgDEsTSaWRwEWV1U9Lsl5SW5Icmx3XzFyJAAAgP1eVW3PMMpptaWdHNuVpQyl0wF7NRgAMCrT6wELrbvPT3Jhkh9Jcn5VHTVyJAAAAAZLO2w7O7arDQDYB5leD1hoVXVkkhcmOSTJI5N0VV2S5GPZ8ykb0t3vnldGAACA/ZAbAgGAH2J6PWChVdXNOxyaZbqGZZPuVrIDAAAAAMyRD2GBRbezaRdMxQAAAAAAsGCUTsCiO2PsAAAAAOxeVd0myfFJjk5yWIZZKr6V5MokH+vu60aMBwBsANPrAQAAALBmVXXfJK9I8sQkB+zitO1J/jbJS7v7cxuVDQDYWEonAAAAANakqk5L8rYkB2f3U6FPklyf5PTuPnve2QCAjad0AhZCVd19+XF3b93Z8fVY/ZwAAACsX1U9Psn7pl8uJbk5yUeSfDrJNzOMejo8yQOSPDTJlum525Oc3N1/t6GBAYC5s6YTsCi+nOGut0l+8P+m5ePrseNzAgAAsA5VdWiSP87K6KZzk/x2d1+9i/PvmuQNSZ6aoXw6p6qO6e5rNyIvALAxfAgLLJJdTcWwuykaAAAA2FjPSXKHDDf5/WF3v+iWTu7uryZ5WlV9Pcnp02t/LcmZ8w4KAGwcpROwKC7Nzkc07eo4AAAA4/mF6f4rSV4yw3UvTvJLSe6W5NQonQBgn2JNJwAAAABmUlVfS3JEkj/q7tNnvPbNSV6QZFt3Hz6PfADAOLbs/hQAAAAA+AGHTfc7XcNpN7423d92L2UBABaE0gkAAACAWV073d95DdfeabrftpeyAAALQukEAAAAwKw+m2QpyS9W1R6vGV5VByU5JcPavV+YUzYAYCRKJwAAAABmdfF0f/ckr53hutdOr1n9HADAPmJpMpmMnQEAAACATaSqbp/kS0kOmR56V5KXd/c1uzj/jklel+RXM4yQujbJ0d1tij0A2IconQAAAACYWVU9Ncm5GabKS5KbklyW5D+SfHN6/PAkP57kuCQHZiicJkl+pbvP3ejMAMB8KZ0AAAAAWJOqemaSt2colJKVAmpHS9P9jUle1N1vm3c2AGDjWdMJAAAAgDXp7ncleWCSv0pyQ4ZyaWfbTUnel+QhCicA2HcZ6QQAAADAulXVwUl+OslRGabVW8owzd4VST7a3d8ZMR4AsAGUTgAAAAAAAKzbgbs/BQAAAAB2raqOTnJEklvNcl13XzqfRADAGJROAAAAAMysqm6T5Iwkz0hy2BqeYhKfTQHAPsUvdgAAAABmUlW3TnJpkgdmWLsJAEDpBAAAAMDMXpTkQRlGKyVJJ/lckm8n2T5WKABgXEonAAAAAGb1pOl+kuSZ3f1nY4YBABbDlrEDAAAAALDpHJOhcPobhRMAsEzpBAAAAMCsbpruPzFqCgBgoSidAAAAAJjV5dP9oaOmAAAWitIJAAAAgFn9RZKlJKdU1dLYYQCAxaB0AgAAAGBWZyX5fJJ7JXnjyFkAgAWxNJlMxs4AAAAAwCZTVfdOcnGSo5JcluTsJP+eZFuSm/fkObp769wCAgAb7sCxAwAAAACwKV2R5D1JfjfJcdNtFpP4bAoA9il+sQMAAAAwk6o6MMkFSR6ToTxKhjWeAID9mNIJAAAAgFk9K8lJGQqnpSTXJ9ma5Jok20fMBQCMSOkEAAAAwKxOW/X4dUne2N3XjRUGAFgMSicAAAAAZnXPDKOcLunuV4wdBgBYDFvGDgAAAADApnPwdH/ZqCkAgIWidAIAAABgVldN97cbNQUAsFCUTgAAAADM6vwkS0lOqaqlscMAAItB6QQAAADArN6S5OtJjk7yeyNnAQAWxNJkMhk7AwAAAACbTFWdkOSCJIck+XCStyb5RJKru/t7Y2YDAMahdAIAAABgJlV14fTh0Unul2QtHzBNuvvAvZcKABibX+wAAAAAzOrkrBRNy3trOwHAfk7pBAAAAMCstmZto5sAgH2Y6fUAAAAAAABYty1jBwAAAAAAAGDzUzoBAAAAAACwbkonAAAAAAAA1k3pBAAAAAAAwLopnQAAAAAAAFg3pRMAAAAAAADrpnQCAAAAAABg3ZROAAAAAAAArJvSCQAAAAAAgHVTOgEAAAAAALBuSicAAAAAAADWTekEAAAAAADAuv0/Wf163e4O/OAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 569,
       "width": 846
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The transformed RFM variables show similar strong positive correlations to the resultant \"rfm_score\" and \"cust_class\"\n",
    "M = df[['invoices_no', 'categories', 'quantity', 'unitprice', 'gross', 'frequency', 'monetary', 'monetary_avg', \n",
    "        'recency', 'rfm_equal']]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "mask = np.zeros_like(M.corr(), dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "sns.heatmap(M.corr(), mask=mask, annot=True, ax=ax)\n",
    "ax.set_yticks(np.arange(0, df.shape[1])+0.1)\n",
    "ax.set_ylim([M.shape[1], 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming the \"gross\" target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>gross</th>\n",
       "      <th>invoices_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7892.000000</td>\n",
       "      <td>7892.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>459.108215</td>\n",
       "      <td>1.548784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>547.206488</td>\n",
       "      <td>0.942002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1000.370000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>170.520000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>318.320000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>553.620000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5540.540000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gross  invoices_no\n",
       "count  7892.000000  7892.000000\n",
       "mean    459.108215     1.548784\n",
       "std     547.206488     0.942002\n",
       "min   -1000.370000     1.000000\n",
       "25%     170.520000     1.000000\n",
       "50%     318.320000     1.000000\n",
       "75%     553.620000     2.000000\n",
       "max    5540.540000     6.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['gross', 'invoices_no']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 343,
       "width": 618
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of \"gross\" is decidedly not symmetrical, much less close to normality\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.distplot(df.gross, bins=30, kde=False, ax=ax, color='forestgreen')\n",
    "plt.title('Monthly Gross Revenue', fontsize=15)\n",
    "plt.xlabel('£', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "639"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gross[df.gross <= 0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The existence of negative observations preclude taking the natural log, so instead I take the cube root to transform \"gross\"\n",
    "df['gross_cubic'] = np.cbrt(df.gross)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 343,
       "width": 618
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The cube root of \"gross\" is much more symmetrical, though a bit bimodal\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.distplot(df.gross_cubic, bins=30, kde=False, ax=ax, color='forestgreen')\n",
    "plt.title('Monthly Cube Root of Gross Revenue', fontsize=15)\n",
    "plt.xlabel('£', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating month dummies\n",
    "df['month'] = df.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using FE model to derive the weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the first half of the dataset as the training set to generate the coefficient weights...\n",
    "# then use the second half of the dataset as the test set for hypothesis testing. \n",
    "# Need to be done manually to ensure the monthly sorting is correct\n",
    "df_upper = df[:3947]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_upper[['id', 'month', 'gross_cubic', 'frequency', 'monetary_avg', 'recency']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure that each \"id\" has at least two observations in the dataset\n",
    "countdf = X.groupby('id').count()\n",
    "X = X.loc[X['id'].isin(countdf[countdf['gross_cubic'] >= 2].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3217, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>id</th>\n",
       "      <th>month</th>\n",
       "      <th>gross_cubic</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_avg</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dateindex</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>2011-03-31</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.773571</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>17965.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.793853</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>13148.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.173164</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>14426.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.261505</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>17213.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.846915</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>17869.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4.894037</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>13394.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6.744971</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>15856.0</td>\n",
       "      <td>8</td>\n",
       "      <td>11.655089</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>13003.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3.914868</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>14057.0</td>\n",
       "      <td>8</td>\n",
       "      <td>10.663620</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3217 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  month  gross_cubic  frequency  monetary_avg  recency\n",
       "dateindex                                                                \n",
       "2011-03-31  12747.0      3     6.773571          2             2        1\n",
       "2011-03-31  17965.0      3     6.793853          3             1        1\n",
       "2011-03-31  13148.0      3     7.173164          3             1        1\n",
       "2011-03-31  14426.0      3     9.261505          3             1        1\n",
       "2011-03-31  17213.0      3    -1.846915          3             1        1\n",
       "...             ...    ...          ...        ...           ...      ...\n",
       "2011-08-31  17869.0      8     4.894037          0             0        0\n",
       "2011-08-31  13394.0      8     6.744971          1             2        1\n",
       "2011-08-31  15856.0      8    11.655089          3             2        4\n",
       "2011-08-31  13003.0      8     3.914868          1             1        1\n",
       "2011-08-31  14057.0      8    10.663620          3             3        3\n",
       "\n",
       "[3217 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.set_index(['id', 'month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tan\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "features = sm.add_constant(X[['frequency', 'monetary_avg', 'recency']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(X.gross_cubic, features, entity_effects=True, time_effects=False)\n",
    "fe_reg_ini = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>gross_cubic</td>   <th>  R-squared:         </th>    <td>0.0607</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>-0.2463</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>3217</td>       <th>  R-squared (Within):</th>    <td>0.0607</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>-0.0959</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:55</td>     <th>  Log-likelihood     </th>    <td>-7249.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>47.231</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1023</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>3.1447</td>      <th>  Distribution:      </th>   <td>F(3,2191)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>6.0000</td>      <th>  F-statistic (robust):</th>  <td>30.376</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>6</td>        <th>  Distribution:      </th>   <td>F(3,2191)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>536.17</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>457.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>620.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "        <td></td>       <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th>  <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>7.8462</td>    <td>0.2102</td>   <td>37.319</td>  <td>0.0000</td>   <td>7.4339</td>   <td>8.2585</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>frequency</th>     <td>0.2957</td>    <td>0.0956</td>   <td>3.0924</td>  <td>0.0020</td>   <td>0.1082</td>   <td>0.4832</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>monetary_avg</th>  <td>-0.8439</td>   <td>0.0950</td>   <td>-8.8854</td> <td>0.0000</td>   <td>-1.0301</td>  <td>-0.6576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>recency</th>       <td>-0.1864</td>   <td>0.0801</td>   <td>-2.3278</td> <td>0.0200</td>   <td>-0.3434</td>  <td>-0.0294</td>\n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 1.9004<br/>P-value: 0.0000<br/>Distribution: F(1022,2191)<br/><br/>Included effects: Entity<br/>id: 0x2baf7c2a748"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            gross_cubic   R-squared:                        0.0607\n",
       "Estimator:                   PanelOLS   R-squared (Between):             -0.2463\n",
       "No. Observations:                3217   R-squared (Within):               0.0607\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):             -0.0959\n",
       "Time:                        17:58:55   Log-likelihood                   -7249.9\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      47.231\n",
       "Entities:                        1023   P-value                           0.0000\n",
       "Avg Obs:                       3.1447   Distribution:                  F(3,2191)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       6.0000   F-statistic (robust):             30.376\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       6   Distribution:                  F(3,2191)\n",
       "Avg Obs:                       536.17                                           \n",
       "Min Obs:                       457.00                                           \n",
       "Max Obs:                       620.00                                           \n",
       "                                                                                \n",
       "                              Parameter Estimates                               \n",
       "================================================================================\n",
       "              Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "--------------------------------------------------------------------------------\n",
       "const            7.8462     0.2102     37.319     0.0000      7.4339      8.2585\n",
       "frequency        0.2957     0.0956     3.0924     0.0020      0.1082      0.4832\n",
       "monetary_avg    -0.8439     0.0950    -8.8854     0.0000     -1.0301     -0.6576\n",
       "recency         -0.1864     0.0801    -2.3278     0.0200     -0.3434     -0.0294\n",
       "================================================================================\n",
       "\n",
       "F-test for Poolability: 1.9004\n",
       "P-value: 0.0000\n",
       "Distribution: F(1022,2191)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7c2a748"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"frequency\" and \"monetary\" are statistically significant at the 95% confidence level, and \"recency\" at the 90% level\n",
    "fe_reg_ini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis testing on composite RFM on \"gross_cubic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rfm_gross'] = 7.8462 + df.frequency*(0.2957) + df.monetary_avg*(-0.8439) + df.recency*(-0.1864)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lower = df[3947:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = df_lower[['id', 'month', 'gross_cubic', 'rfm_gross', 'invoices_no', 'quantity', 'categories']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "countdf = P.groupby('id').count()\n",
    "P = P.loc[P['id'].isin(countdf[countdf['gross_cubic'] >= 2].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2873, 7)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = P.set_index(['id', 'month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_feature = sm.add_constant(P[['rfm_gross']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(P.gross_cubic, single_feature, entity_effects=True, time_effects=False)\n",
    "fe_gross_single = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>gross_cubic</td>   <th>  R-squared:         </th>    <td>0.0473</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>-0.2215</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>2873</td>       <th>  R-squared (Within):</th>    <td>0.0473</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>-0.0940</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:56</td>     <th>  Log-likelihood     </th>    <td>-6492.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>88.386</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1091</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>2.6334</td>      <th>  Distribution:      </th>   <td>F(1,1781)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>5.0000</td>      <th>  F-statistic (robust):</th>  <td>62.348</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>5</td>        <th>  Distribution:      </th>   <td>F(1,1781)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>574.60</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>134.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>866.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "      <td></td>      <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th> <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>      <td>1.1507</td>    <td>0.7367</td>   <td>1.5620</td> <td>0.1185</td>   <td>-0.2942</td>  <td>2.5955</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rfm_gross</th>  <td>0.8830</td>    <td>0.1118</td>   <td>7.8961</td> <td>0.0000</td>   <td>0.6637</td>   <td>1.1024</td> \n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 1.8734<br/>P-value: 0.0000<br/>Distribution: F(1090,1781)<br/><br/>Included effects: Entity<br/>id: 0x2baf7f4fe08"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            gross_cubic   R-squared:                        0.0473\n",
       "Estimator:                   PanelOLS   R-squared (Between):             -0.2215\n",
       "No. Observations:                2873   R-squared (Within):               0.0473\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):             -0.0940\n",
       "Time:                        17:58:56   Log-likelihood                   -6492.2\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      88.386\n",
       "Entities:                        1091   P-value                           0.0000\n",
       "Avg Obs:                       2.6334   Distribution:                  F(1,1781)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       5.0000   F-statistic (robust):             62.348\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       5   Distribution:                  F(1,1781)\n",
       "Avg Obs:                       574.60                                           \n",
       "Min Obs:                       134.00                                           \n",
       "Max Obs:                       866.00                                           \n",
       "                                                                                \n",
       "                             Parameter Estimates                              \n",
       "==============================================================================\n",
       "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "------------------------------------------------------------------------------\n",
       "const          1.1507     0.7367     1.5620     0.1185     -0.2942      2.5955\n",
       "rfm_gross      0.8830     0.1118     7.8961     0.0000      0.6637      1.1024\n",
       "==============================================================================\n",
       "\n",
       "F-test for Poolability: 1.8734\n",
       "P-value: 0.0000\n",
       "Distribution: F(1090,1781)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7f4fe08"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"rfm_gross\" is statistically significant at 95% confidence level, and with a positive sign and a coefficient close to 1\n",
    "fe_gross_single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logically, \"invoices\", \"quantity\" & \"categories\" should have some effect on \"gross\", and also as exhibited in the correlation\n",
    "# matrix above...\n",
    "multi_features = sm.add_constant(P[['rfm_gross', 'invoices_no', 'quantity', 'categories']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(P.gross_cubic, multi_features, entity_effects=True, time_effects=False)\n",
    "fe_gross_multi = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>gross_cubic</td>   <th>  R-squared:         </th>    <td>0.4230</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>0.3895</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>2873</td>       <th>  R-squared (Within):</th>    <td>0.4230</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>0.4120</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:56</td>     <th>  Log-likelihood     </th>    <td>-5771.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>325.84</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1091</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>2.6334</td>      <th>  Distribution:      </th>   <td>F(4,1778)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>5.0000</td>      <th>  F-statistic (robust):</th>  <td>114.32</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>5</td>        <th>  Distribution:      </th>   <td>F(4,1778)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>574.60</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>134.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>866.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "       <td></td>       <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th>  <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>-0.7223</td>   <td>0.6363</td>   <td>-1.1352</td> <td>0.2564</td>   <td>-1.9703</td>  <td>0.5256</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rfm_gross</th>    <td>0.6543</td>    <td>0.0942</td>   <td>6.9439</td>  <td>0.0000</td>   <td>0.4695</td>   <td>0.8391</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>invoices_no</th>  <td>0.7124</td>    <td>0.0910</td>   <td>7.8317</td>  <td>0.0000</td>   <td>0.5340</td>   <td>0.8907</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quantity</th>     <td>0.0032</td>    <td>0.0008</td>   <td>4.0485</td>  <td>0.0001</td>   <td>0.0016</td>   <td>0.0047</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>categories</th>   <td>0.0521</td>    <td>0.0083</td>   <td>6.2672</td>  <td>0.0000</td>   <td>0.0358</td>   <td>0.0684</td> \n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 1.5055<br/>P-value: 0.0000<br/>Distribution: F(1090,1778)<br/><br/>Included effects: Entity<br/>id: 0x2baf7f60988"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            gross_cubic   R-squared:                        0.4230\n",
       "Estimator:                   PanelOLS   R-squared (Between):              0.3895\n",
       "No. Observations:                2873   R-squared (Within):               0.4230\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):              0.4120\n",
       "Time:                        17:58:56   Log-likelihood                   -5771.9\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      325.84\n",
       "Entities:                        1091   P-value                           0.0000\n",
       "Avg Obs:                       2.6334   Distribution:                  F(4,1778)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       5.0000   F-statistic (robust):             114.32\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       5   Distribution:                  F(4,1778)\n",
       "Avg Obs:                       574.60                                           \n",
       "Min Obs:                       134.00                                           \n",
       "Max Obs:                       866.00                                           \n",
       "                                                                                \n",
       "                              Parameter Estimates                              \n",
       "===============================================================================\n",
       "             Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "-------------------------------------------------------------------------------\n",
       "const          -0.7223     0.6363    -1.1352     0.2564     -1.9703      0.5256\n",
       "rfm_gross       0.6543     0.0942     6.9439     0.0000      0.4695      0.8391\n",
       "invoices_no     0.7124     0.0910     7.8317     0.0000      0.5340      0.8907\n",
       "quantity        0.0032     0.0008     4.0485     0.0001      0.0016      0.0047\n",
       "categories      0.0521     0.0083     6.2672     0.0000      0.0358      0.0684\n",
       "===============================================================================\n",
       "\n",
       "F-test for Poolability: 1.5055\n",
       "P-value: 0.0000\n",
       "Distribution: F(1090,1778)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7f60988"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"rfm_gross\" remains statistically significant at 95% confidence level\n",
    "fe_gross_multi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Composite RFM indicator targeted at number of transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_upper[['id', 'month', 'invoices_no', 'frequency', 'monetary_avg', 'recency']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "countdf = X.groupby('id').count()\n",
    "X = X.loc[X['id'].isin(countdf[countdf['invoices_no'] >= 2].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>id</th>\n",
       "      <th>month</th>\n",
       "      <th>invoices_no</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_avg</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dateindex</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>2011-03-31</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>17965.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>13148.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>14426.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>17213.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>17869.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>13394.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>15856.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>13003.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>14057.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3217 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  month  invoices_no  frequency  monetary_avg  recency\n",
       "dateindex                                                                \n",
       "2011-03-31  12747.0      3            1          2             2        1\n",
       "2011-03-31  17965.0      3            1          3             1        1\n",
       "2011-03-31  13148.0      3            1          3             1        1\n",
       "2011-03-31  14426.0      3            1          3             1        1\n",
       "2011-03-31  17213.0      3            1          3             1        1\n",
       "...             ...    ...          ...        ...           ...      ...\n",
       "2011-08-31  17869.0      8            2          0             0        0\n",
       "2011-08-31  13394.0      8            1          1             2        1\n",
       "2011-08-31  15856.0      8            4          3             2        4\n",
       "2011-08-31  13003.0      8            1          1             1        1\n",
       "2011-08-31  14057.0      8            3          3             3        3\n",
       "\n",
       "[3217 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.set_index(['id', 'month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = sm.add_constant(X[['frequency', 'monetary_avg', 'recency']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(X.invoices_no, features, entity_effects=True, time_effects=False)\n",
    "fe_reg_ini2 = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>invoices_no</td>   <th>  R-squared:         </th>    <td>0.1891</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>0.4119</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>3217</td>       <th>  R-squared (Within):</th>    <td>0.1891</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>0.3163</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:56</td>     <th>  Log-likelihood     </th>    <td>-3004.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>170.28</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1023</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>3.1447</td>      <th>  Distribution:      </th>   <td>F(3,2191)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>6.0000</td>      <th>  F-statistic (robust):</th>  <td>151.78</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>6</td>        <th>  Distribution:      </th>   <td>F(3,2191)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>536.17</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>457.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>620.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "        <td></td>       <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th>  <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>1.0563</td>    <td>0.0475</td>   <td>22.231</td>  <td>0.0000</td>   <td>0.9631</td>   <td>1.1495</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>frequency</th>     <td>0.0705</td>    <td>0.0248</td>   <td>2.8466</td>  <td>0.0045</td>   <td>0.0219</td>   <td>0.1190</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>monetary_avg</th>  <td>-0.1031</td>   <td>0.0197</td>   <td>-5.2258</td> <td>0.0000</td>   <td>-0.1418</td>  <td>-0.0644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>recency</th>       <td>0.3714</td>    <td>0.0195</td>   <td>19.018</td>  <td>0.0000</td>   <td>0.3331</td>   <td>0.4097</td> \n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 1.3538<br/>P-value: 0.0000<br/>Distribution: F(1022,2191)<br/><br/>Included effects: Entity<br/>id: 0x2baf7fb41c8"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            invoices_no   R-squared:                        0.1891\n",
       "Estimator:                   PanelOLS   R-squared (Between):              0.4119\n",
       "No. Observations:                3217   R-squared (Within):               0.1891\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):              0.3163\n",
       "Time:                        17:58:56   Log-likelihood                   -3004.4\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      170.28\n",
       "Entities:                        1023   P-value                           0.0000\n",
       "Avg Obs:                       3.1447   Distribution:                  F(3,2191)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       6.0000   F-statistic (robust):             151.78\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       6   Distribution:                  F(3,2191)\n",
       "Avg Obs:                       536.17                                           \n",
       "Min Obs:                       457.00                                           \n",
       "Max Obs:                       620.00                                           \n",
       "                                                                                \n",
       "                              Parameter Estimates                               \n",
       "================================================================================\n",
       "              Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "--------------------------------------------------------------------------------\n",
       "const            1.0563     0.0475     22.231     0.0000      0.9631      1.1495\n",
       "frequency        0.0705     0.0248     2.8466     0.0045      0.0219      0.1190\n",
       "monetary_avg    -0.1031     0.0197    -5.2258     0.0000     -0.1418     -0.0644\n",
       "recency          0.3714     0.0195     19.018     0.0000      0.3331      0.4097\n",
       "================================================================================\n",
       "\n",
       "F-test for Poolability: 1.3538\n",
       "P-value: 0.0000\n",
       "Distribution: F(1022,2191)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7fb41c8"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All RFM variables are statistically significant at the 95% confidence level\n",
    "fe_reg_ini2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the composite RFM indicator\n",
    "df['rfm_invoices'] = 1.0563 + df.frequency*(0.0705) + df.monetary_avg*(-0.1031) + df.recency*(0.3714)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis tests on composite \"rfm_invoices\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lower = df[3947:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = df_lower[['id', 'month', 'invoices_no', 'rfm_invoices', 'categories', 'quantity', 'rfm_equal']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "countdf = R.groupby('id').count()\n",
    "R = R.loc[R['id'].isin(countdf[countdf['invoices_no'] >= 2].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = R.set_index(['id', 'month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_feature = sm.add_constant(R[['rfm_invoices']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(R.invoices_no, single_feature, entity_effects=True, time_effects=False)\n",
    "fe_invoices_single = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>invoices_no</td>   <th>  R-squared:         </th>    <td>0.1431</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>0.4007</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>2873</td>       <th>  R-squared (Within):</th>    <td>0.1431</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>0.2724</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:56</td>     <th>  Log-likelihood     </th>    <td>-2926.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>297.44</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1091</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>2.6334</td>      <th>  Distribution:      </th>   <td>F(1,1781)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>5.0000</td>      <th>  F-statistic (robust):</th>  <td>311.32</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>5</td>        <th>  Distribution:      </th>   <td>F(1,1781)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>574.60</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>134.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>866.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "        <td></td>       <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th> <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>0.0401</td>    <td>0.0910</td>   <td>0.4402</td> <td>0.6598</td>   <td>-0.1385</td>  <td>0.2186</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rfm_invoices</th>  <td>0.9734</td>    <td>0.0552</td>   <td>17.644</td> <td>0.0000</td>   <td>0.8652</td>   <td>1.0816</td> \n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 0.9814<br/>P-value: 0.6334<br/>Distribution: F(1090,1781)<br/><br/>Included effects: Entity<br/>id: 0x2baf7c2c948"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            invoices_no   R-squared:                        0.1431\n",
       "Estimator:                   PanelOLS   R-squared (Between):              0.4007\n",
       "No. Observations:                2873   R-squared (Within):               0.1431\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):              0.2724\n",
       "Time:                        17:58:56   Log-likelihood                   -2926.5\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      297.44\n",
       "Entities:                        1091   P-value                           0.0000\n",
       "Avg Obs:                       2.6334   Distribution:                  F(1,1781)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       5.0000   F-statistic (robust):             311.32\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       5   Distribution:                  F(1,1781)\n",
       "Avg Obs:                       574.60                                           \n",
       "Min Obs:                       134.00                                           \n",
       "Max Obs:                       866.00                                           \n",
       "                                                                                \n",
       "                              Parameter Estimates                               \n",
       "================================================================================\n",
       "              Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "--------------------------------------------------------------------------------\n",
       "const            0.0401     0.0910     0.4402     0.6598     -0.1385      0.2186\n",
       "rfm_invoices     0.9734     0.0552     17.644     0.0000      0.8652      1.0816\n",
       "================================================================================\n",
       "\n",
       "F-test for Poolability: 0.9814\n",
       "P-value: 0.6334\n",
       "Distribution: F(1090,1781)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7c2c948"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"rfm_invoices\" is statistically significant at 95% confidence level, and with a positive sign and a coefficient around 1\n",
    "fe_invoices_single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_feature2 = sm.add_constant(R[['rfm_equal']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(R.invoices_no, single_feature2, entity_effects=True, time_effects=False)\n",
    "fe_invoices_single2 = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>invoices_no</td>   <th>  R-squared:         </th>    <td>0.0909</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>0.4495</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>2873</td>       <th>  R-squared (Within):</th>    <td>0.0909</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>0.2718</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:57</td>     <th>  Log-likelihood     </th>    <td>-3011.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>178.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1091</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>2.6334</td>      <th>  Distribution:      </th>   <td>F(1,1781)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>5.0000</td>      <th>  F-statistic (robust):</th>  <td>229.17</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>5</td>        <th>  Distribution:      </th>   <td>F(1,1781)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>574.60</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>134.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>866.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "      <td></td>      <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th> <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>      <td>0.8739</td>    <td>0.0510</td>   <td>17.127</td> <td>0.0000</td>   <td>0.7738</td>   <td>0.9740</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rfm_equal</th>  <td>0.1423</td>    <td>0.0094</td>   <td>15.139</td> <td>0.0000</td>   <td>0.1239</td>   <td>0.1607</td> \n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 0.8070<br/>P-value: 1.0000<br/>Distribution: F(1090,1781)<br/><br/>Included effects: Entity<br/>id: 0x2baf7f653c8"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            invoices_no   R-squared:                        0.0909\n",
       "Estimator:                   PanelOLS   R-squared (Between):              0.4495\n",
       "No. Observations:                2873   R-squared (Within):               0.0909\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):              0.2718\n",
       "Time:                        17:58:57   Log-likelihood                   -3011.5\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      178.00\n",
       "Entities:                        1091   P-value                           0.0000\n",
       "Avg Obs:                       2.6334   Distribution:                  F(1,1781)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       5.0000   F-statistic (robust):             229.17\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       5   Distribution:                  F(1,1781)\n",
       "Avg Obs:                       574.60                                           \n",
       "Min Obs:                       134.00                                           \n",
       "Max Obs:                       866.00                                           \n",
       "                                                                                \n",
       "                             Parameter Estimates                              \n",
       "==============================================================================\n",
       "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "------------------------------------------------------------------------------\n",
       "const          0.8739     0.0510     17.127     0.0000      0.7738      0.9740\n",
       "rfm_equal      0.1423     0.0094     15.139     0.0000      0.1239      0.1607\n",
       "==============================================================================\n",
       "\n",
       "F-test for Poolability: 0.8070\n",
       "P-value: 1.0000\n",
       "Distribution: F(1090,1781)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7f653c8"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"rfm_equal\" is also statistically significant with a positive coefficient, but much weaker relationship with \"invoices_no\"\n",
    "fe_invoices_single2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's add other regressors to test is \"rfm_invoices\" is still a good predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_features = sm.add_constant(R[['rfm_invoices', 'categories', 'quantity']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(R.invoices_no, multi_features, entity_effects=True, time_effects=False)\n",
    "fe_invoices_multi = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>invoices_no</td>   <th>  R-squared:         </th>    <td>0.2658</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>0.4844</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>2873</td>       <th>  R-squared (Within):</th>    <td>0.2658</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>0.3768</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:57</td>     <th>  Log-likelihood     </th>    <td>-2704.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>214.63</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1091</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>2.6334</td>      <th>  Distribution:      </th>   <td>F(3,1779)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>5.0000</td>      <th>  F-statistic (robust):</th>  <td>130.85</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>5</td>        <th>  Distribution:      </th>   <td>F(3,1779)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>574.60</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>134.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>866.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "        <td></td>       <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th>  <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>-0.0362</td>   <td>0.1062</td>   <td>-0.3412</td> <td>0.7330</td>   <td>-0.2445</td>  <td>0.1720</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rfm_invoices</th>  <td>0.8838</td>    <td>0.0534</td>   <td>16.543</td>  <td>0.0000</td>   <td>0.7791</td>   <td>0.9886</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>categories</th>    <td>-0.0051</td>   <td>0.0020</td>   <td>-2.6035</td> <td>0.0093</td>   <td>-0.0090</td>  <td>-0.0013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quantity</th>      <td>0.0011</td>    <td>0.0002</td>   <td>6.1221</td>  <td>0.0000</td>   <td>0.0007</td>   <td>0.0014</td> \n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 0.9286<br/>P-value: 0.9120<br/>Distribution: F(1090,1779)<br/><br/>Included effects: Entity<br/>id: 0x2baf7fc6108"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            invoices_no   R-squared:                        0.2658\n",
       "Estimator:                   PanelOLS   R-squared (Between):              0.4844\n",
       "No. Observations:                2873   R-squared (Within):               0.2658\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):              0.3768\n",
       "Time:                        17:58:57   Log-likelihood                   -2704.6\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      214.63\n",
       "Entities:                        1091   P-value                           0.0000\n",
       "Avg Obs:                       2.6334   Distribution:                  F(3,1779)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       5.0000   F-statistic (robust):             130.85\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       5   Distribution:                  F(3,1779)\n",
       "Avg Obs:                       574.60                                           \n",
       "Min Obs:                       134.00                                           \n",
       "Max Obs:                       866.00                                           \n",
       "                                                                                \n",
       "                              Parameter Estimates                               \n",
       "================================================================================\n",
       "              Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "--------------------------------------------------------------------------------\n",
       "const           -0.0362     0.1062    -0.3412     0.7330     -0.2445      0.1720\n",
       "rfm_invoices     0.8838     0.0534     16.543     0.0000      0.7791      0.9886\n",
       "categories      -0.0051     0.0020    -2.6035     0.0093     -0.0090     -0.0013\n",
       "quantity         0.0011     0.0002     6.1221     0.0000      0.0007      0.0014\n",
       "================================================================================\n",
       "\n",
       "F-test for Poolability: 0.9286\n",
       "P-value: 0.9120\n",
       "Distribution: F(1090,1779)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7fc6108"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"rfm_gross\" is statistically significant at 95% confidence level, and still with a coefficient close to 1\n",
    "# Shows that the RFM model is very robust as a predictor of customer engagement\n",
    "fe_invoices_multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's do the same with \"rfm_equal\" instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_features2 = sm.add_constant(R[['rfm_equal', 'categories', 'quantity']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_mod = PanelOLS(R.invoices_no, multi_features2, entity_effects=True, time_effects=False)\n",
    "fe_invoices_multi2 = fe_mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PanelOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>invoices_no</td>   <th>  R-squared:         </th>    <td>0.2277</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PanelOLS</td>     <th>  R-squared (Between):</th>   <td>0.4122</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>2873</td>       <th>  R-squared (Within):</th>    <td>0.2277</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, Jan 08 2020</td> <th>  R-squared (Overall):</th>   <td>0.3207</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:58:57</td>     <th>  Log-likelihood     </th>    <td>-2777.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>       <td>Clustered</td>    <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>174.83</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>1091</td>       <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>2.6334</td>      <th>  Distribution:      </th>   <td>F(3,1779)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>2.0000</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>5.0000</td>      <th>  F-statistic (robust):</th>  <td>96.604</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>    <td>0.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>             <td>5</td>        <th>  Distribution:      </th>   <td>F(3,1779)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>574.60</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>134.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>866.00</td>      <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>       <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "       <td></td>      <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th>  <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>0.6876</td>    <td>0.0885</td>   <td>7.7717</td>  <td>0.0000</td>   <td>0.5141</td>   <td>0.8611</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rfm_equal</th>   <td>0.1328</td>    <td>0.0092</td>   <td>14.377</td>  <td>0.0000</td>   <td>0.1147</td>   <td>0.1510</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>categories</th>  <td>-0.0053</td>   <td>0.0021</td>   <td>-2.4954</td> <td>0.0127</td>   <td>-0.0095</td>  <td>-0.0011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quantity</th>    <td>0.0011</td>    <td>0.0002</td>   <td>5.9472</td>  <td>0.0000</td>   <td>0.0007</td>   <td>0.0015</td> \n",
       "</tr>\n",
       "</table><br/><br/>F-test for Poolability: 0.9839<br/>P-value: 0.6155<br/>Distribution: F(1090,1779)<br/><br/>Included effects: Entity<br/>id: 0x2baf7ffeb48"
      ],
      "text/plain": [
       "                          PanelOLS Estimation Summary                           \n",
       "================================================================================\n",
       "Dep. Variable:            invoices_no   R-squared:                        0.2277\n",
       "Estimator:                   PanelOLS   R-squared (Between):              0.4122\n",
       "No. Observations:                2873   R-squared (Within):               0.2277\n",
       "Date:                Wed, Jan 08 2020   R-squared (Overall):              0.3207\n",
       "Time:                        17:58:57   Log-likelihood                   -2777.2\n",
       "Cov. Estimator:             Clustered                                           \n",
       "                                        F-statistic:                      174.83\n",
       "Entities:                        1091   P-value                           0.0000\n",
       "Avg Obs:                       2.6334   Distribution:                  F(3,1779)\n",
       "Min Obs:                       2.0000                                           \n",
       "Max Obs:                       5.0000   F-statistic (robust):             96.604\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                       5   Distribution:                  F(3,1779)\n",
       "Avg Obs:                       574.60                                           \n",
       "Min Obs:                       134.00                                           \n",
       "Max Obs:                       866.00                                           \n",
       "                                                                                \n",
       "                             Parameter Estimates                              \n",
       "==============================================================================\n",
       "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "------------------------------------------------------------------------------\n",
       "const          0.6876     0.0885     7.7717     0.0000      0.5141      0.8611\n",
       "rfm_equal      0.1328     0.0092     14.377     0.0000      0.1147      0.1510\n",
       "categories    -0.0053     0.0021    -2.4954     0.0127     -0.0095     -0.0011\n",
       "quantity       0.0011     0.0002     5.9472     0.0000      0.0007      0.0015\n",
       "==============================================================================\n",
       "\n",
       "F-test for Poolability: 0.9839\n",
       "P-value: 0.6155\n",
       "Distribution: F(1090,1779)\n",
       "\n",
       "Included effects: Entity\n",
       "PanelEffectsResults, id: 0x2baf7ffeb48"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shows that the RFM model is very robust as a predictor of customer engagement\n",
    "fe_invoices_multi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 525,
       "width": 515
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_lower[['rfm_equal', 'rfm_invoices', 'rfm_gross']]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}