analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'output/test/wsga/user_sigam_test/nn_user_sigma_exp.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m nn_df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(\u001b[39m'\u001b[39;49m\u001b[39moutput/test/wsga/user_sigam_test/nn_user_sigma_exp.csv\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      4\u001b[0m lg_df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39moutput/test/lg/user_sigam_test/lg_user_sigma_exp.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m lc_df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39moutput/test/lc/user_sigam_test/lc_user_sigma_exp.csv\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[39m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    946\u001b[0m     defaults\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mdelimiter\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\"\u001b[39m},\n\u001b[1;32m    947\u001b[0m )\n\u001b[1;32m    948\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    602\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[1;32m    604\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    607\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[1;32m    608\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1439\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m   1441\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[1;32m   1734\u001b[0m         mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[1;32m   1736\u001b[0m     f,\n\u001b[1;32m   1737\u001b[0m     mode,\n\u001b[1;32m   1738\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1739\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1740\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[1;32m   1741\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[1;32m   1742\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m   1743\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1744\u001b[0m )\n\u001b[1;32m   1745\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    851\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[1;32m    852\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    853\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    854\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[1;32m    855\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[1;32m    857\u001b[0m             handle,\n\u001b[1;32m    858\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[1;32m    859\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[1;32m    860\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m    861\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    862\u001b[0m         )\n\u001b[1;32m    863\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[1;32m    865\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'output/test/wsga/user_sigam_test/nn_user_sigma_exp.csv'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "nn_df = pd.read_csv('output/test/wsga/user_sigam_test/nn_user_sigma_exp.csv')\n",
    "lg_df = pd.read_csv('output/test/lg/user_sigam_test/lg_user_sigma_exp.csv')\n",
    "lc_df = pd.read_csv('output/test/lc/user_sigam_test/lc_user_sigma_exp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_sigma</th>\n",
       "      <th>balance_fitness</th>\n",
       "      <th>duration_fitness</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.164686</td>\n",
       "      <td>70.622928</td>\n",
       "      <td>MERL-LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.412133</td>\n",
       "      <td>66.069451</td>\n",
       "      <td>MERL-LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.754874</td>\n",
       "      <td>67.119401</td>\n",
       "      <td>MERL-LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.301580</td>\n",
       "      <td>69.348519</td>\n",
       "      <td>MERL-LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.819003</td>\n",
       "      <td>67.786722</td>\n",
       "      <td>MERL-LB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_sigma  balance_fitness  duration_fitness   method\n",
       "0         0.0        19.164686         70.622928  MERL-LB\n",
       "1         0.0        16.412133         66.069451  MERL-LB\n",
       "2         0.0        16.754874         67.119401  MERL-LB\n",
       "3         0.0        18.301580         69.348519  MERL-LB\n",
       "4         0.0        18.819003         67.786722  MERL-LB"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import seaborn\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Apply the default theme\n",
    "# sns.set_theme()\n",
    "# import scienceplots\n",
    "\n",
    "# plt.style.use('science')\n",
    "\n",
    "ga_df2 = pd.read_csv('output/test/wsga/user_sigam_test03/wsga_user_sigma_exp2.csv')\n",
    "ga_df2[\"method\"] = [\"MERL-LB\"] * len(ga_df2)\n",
    "lg_df2 = pd.read_csv('output/test/lg/user_sigam_test/lg_user_sigma_exp2.csv')\n",
    "lg_df2[\"method\"] = [\"Least Duration Gap\"] * len(lg_df2)\n",
    "lc_df2 = pd.read_csv('output/test/lc/user_sigam_test/lc_user_sigma_exp2.csv')\n",
    "lc_df2[\"method\"] = [\"Least Connection\"] * len(lc_df2)\n",
    "rr_df2 = pd.read_csv('output/test/rr/user_sigam_test/rr_user_sigma_exp2.csv')\n",
    "rr_df2[\"method\"] = [\"Round Robin\"] * len(lc_df2)\n",
    "df2 = pd.concat([ga_df2, lg_df2, lc_df2, rr_df2], ignore_index=True)\n",
    "df2[\"user_sigma\"] = df2[\"user_sigma\"]  * 12 /  60\n",
    "df2[\"duration_fitness\"] = df2[\"duration_fitness\"]  * 12 / 60\n",
    "df2 = df2.drop(columns=\"Unnamed: 0\")\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_sigma</th>\n",
       "      <th>method</th>\n",
       "      <th>fitness_type</th>\n",
       "      <th>fitness_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>19.164686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>16.412133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>16.754874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>18.301580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>18.819003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_sigma   method     fitness_type  fitness_score\n",
       "0         0.0  MERL-LB  balance_fitness      19.164686\n",
       "1         0.0  MERL-LB  balance_fitness      16.412133\n",
       "2         0.0  MERL-LB  balance_fitness      16.754874\n",
       "3         0.0  MERL-LB  balance_fitness      18.301580\n",
       "4         0.0  MERL-LB  balance_fitness      18.819003"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df2 = df2.melt(id_vars=[\"user_sigma\", \"method\"],var_name=\"fitness_type\", value_name=\"fitness_score\")\n",
    "# df2.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f23107e4d00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 931x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.relplot(\n",
    "#     data=df2,\n",
    "#     x=\"user_sigma\", y=\"fitness_score\",\n",
    "#     hue=\"method\", col=\"fitness_type\",\n",
    "#     kind=\"line\",\n",
    "#     height=5, aspect=.75, facet_kws=dict(sharex=False),\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2 = df2.melt(id_vars=[\"user_sigma\", \"method\"], var_name=\"fitness_type\", value_name=\"fitness\")\n",
    "# df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f61dc906310>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "plt.scatter(20, 400)\n",
    "plt.xlim(0,500)\n",
    "plt.ylim(0,500)\n",
    "x = np.linspace(0, 500, 100)\n",
    "y = 420 - x\n",
    "plt.scatter(x, y)\n",
    "plt.scatter(x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TT+epp57inHPOYfjw4RxxxBF87nOf4+ijjy553fwFULEJEyYU7i8+n9v7HRFCCLF3iMfjVFZW7vDzu07jzHv88cf59a9/zSeffEImk9nu47dn/fr16Lpe8kEKQHV1NWVlZSX/FsPWr1W6/tvc1dq1a6mpqenx+qq/xhIOhwG6XQPkPyzp+m+xruuMGTOmZF/++qf4tfpyndnbP4fLLruMhQsXcswxx7D//vszd+5cTj755MJ4tnU9NmHCBN54442SfT1dd/Xmz0WI/iBBhhDboes6hx56KA888ABr1qxh8uTJvP7665x//vkcfPDBXH311VRXV2NZFo8++ihPPvnkNo935513csstt3DKKadwySWXEIlE0HWdH//4x90aPeVfvyc9PXZHGIYxoMcfKD2dl/78WWzb5swzz6StrY2zzz6bCRMmEAgE2Lx5MwsXLsRxnJLHF1fabEtlZSVPPPEEL730Ei+88AIvvPACjz32GCeffDI33HBDn8cJA/87IoQQYve3adMm2tvbu70h78nWmjH21N/pj3/8IwsXLuT444/nrLPOorKyEsMwuOuuu3a68q83laOw9X/f+9POjqU/r0H6ep3Z21VqPv/5z3PQQQfxt7/9jZdffpl7772Xu+++m9tuu425c+f2eZy74s9FiK2RIEOIXsj/gx+PxwF4+umn8Xq93HvvvSUl/Y8++uh2j/X0009z6KGH8uMf/7hkfzQaLTTX7It8ir5ixQrmzJnT5+dvT2VlJV6vt8dVMHqzMkZvxpevKPjkk0+63bdq1SqGDRtWUo3RH7qO3XVd1qxZU2hMtmLFClavXs0NN9zAySefXHjctqaE9JbH4+G4447juOOOw3EcrrnmGn73u99xwQUXMG7cOEaNGrXVcwHdKzCEEEKIP/7xjwAceeSRhX2RSKTHsCH/yXtvPP3004wZM4Zf/OIXJW/2uza97m0QADB69Ggcx2HNmjVMnDixsL+xsZFoNLrVZpV9NXbsWF566SVaW1u3WpWxq8aS5zgO69atK6l6yP+bn3+tnbnO3J6amhrOOOMMzjjjDJqampg3bx533nknc+fOLbkey69CUzxGuf4QuxPpkSHEdmQyGV5++WUsyyr8A2cYBpqmlXyiUV9fz9///vftHs8wjG7p/FNPPbXV+Yrbs//++1NbW8sDDzzQrXyxPz6RNwyDOXPm8Pe//71kjGvWrCks77Wz46upqWHffffliSeeKHnMihUrePnll3foU4LteeKJJ0qmh/z1r3+loaGhMMUjX+VQfA5d1+WBBx7YqddtaWkp+V7X9UJ4ki8dnTt3Lu+++y5vvfVW4XHxeJzf//73jB49ulfzYIUQQuw9lixZwi9/+Utqa2tLVlkbM2YMq1atKqzGBvDBBx/w5ptv9vrY+U/di/89fOedd3j77bdLHuf3+4HeTWvM/7vedenx++67r+T+nXXCCSfgui6/+MUvut2X/3l21ViK/e///m/JOP73f/8Xy7IK4cHOXGdujW3btLe3l+yrrKykpqamcP0xbdo0Kisrefjhh0ums/zzn/9k5cqV21yVT4hdTSoyhOjihRdeKHzy3dzczJ///GdWr17NueeeW5jvOHfuXO677z7OPvtsvvCFL9DU1MRvf/tbxo4dy4cffrjN4x9zzDHcfvvtXH755cyaNYsVK1bw5z//udt8yd7SdZ1rrrmG888/n5NPPpn58+dTXV3NqlWr+Pjjj7n33nt36LjFLrzwQl566SVOO+00TjvtNBzH4aGHHmLy5MksX768X8b3ve99j3POOYf/+I//4NRTTy0svxoOh3ts0rWzIpEIp59+OvPnzy8svzpu3LjCknUTJkxg7Nix3HDDDWzevJlQKMTTTz+9030nrrzyStra2jjssMMYPnw4GzZs4KGHHmLfffctBGXnnnsuf/nLXzjnnHP42te+RiQS4YknnqC+vp7bbrutV1NYhBBC7Jny1ym2bdPY2Mirr77Kyy+/zKhRo7jjjjtKmjSeeuqp/OY3v+Gss87i1FNPpampiYcffphJkyb1uinoMcccwzPPPMO3vvUtjjnmGOrr6wvHyFeqgpreMGnSJJ566inGjx9PeXk5kydP7rFH1tSpU5k3bx6/+93viEajHHzwwSxdupTHH3+c448/nsMOO2znTxSqAeeXvvQlHnzwQdasWcNRRx2F4zi88cYbHHrooXz1q1/dZWPJ83q9vPjii3z/+99nxowZvPjii/zjH//gvPPOK/Sb2JnrzK3p6Ohg7ty5fOYzn2Hq1KkEAgH+9a9/sXTpUhYuXAiAZVlcdtllXH755Xz1q1/lxBNPLCy/Onr0aL7xjW/012kQYqdJkCFEF8Wlkl6vlwkTJnDNNdfwla98pbD/8MMP50c/+hF33303P/7xj6mtreWyyy5j/fr12/0H5rzzziORSPDnP/+ZxYsXs99++3HXXXdx00037fCYjzrqKO6//35uv/12fv3rX+O6LmPGjNnhdeS7mjZtGnfffTc33ngjt9xyCyNHjuTiiy9m1apVhdBnZ8c3Z84c7rnnHm699VZuvfVWTNPk4IMP5rvf/e4Ohzzbct555/Hhhx/yq1/9io6ODg4//HCuvvrqwidKlmVx5513ct1113HXXXfh9Xr59Kc/zRlnnMGXvvSlHX7dk046id///vf89re/JRqNUl1dzec+9zkuuuiiQkBRVVXFww8/zE9/+lMeeughUqkUU6ZM4c4775RPQ4QQYi+Xv06xLIvy8nLq6upYtGgR8+fPL1mNC1TD7BtuuIFbb72Vn/zkJ0yaNIkbb7yRJ598kn//+9+9er358+fT2NjI7373O1566SUmTZrET3/6U/761792O8Z1113Htddey09+8hMymQwXXnjhVpt9X3fdddTW1vL444/z7LPPUlVVxTe/+c1+//DiJz/5CVOmTOEPf/gDN954I+FwmGnTppWsPLKrxgKq2uKee+7hmmuu4ac//SnBYJALL7yQb33rW4XH7Mx15tb4fD5OO+00Xn75ZZ555hlc12Xs2LFcffXVnH766YXHzZ8/H5/Px913383PfvYzAoEAxx9/PN/97ne7rT4nxGDSXOkGJ4TYQRdccAEff/xxt6Vpd2evvvoqCxYs4JZbbuGzn/3sYA9HCCGEEHuJhQsX8vTTT5dMHRVC7BipTxZC9EoymSz5fvXq1bzwwgsccsghgzQiIYQQQgghxN5IppYIIXrl+OOPZ968eYwZM4b169fz8MMPY1kWZ5999mAPTQghhBBCCLEXkSBDCNErRx11FH/5y19oaGjA4/Ewc+ZMvvOd7zB+/PjBHpoQQgghhBBiLyI9MoQQQgghhBBCCDFkSI8MIYQQQgghhBBCDBkSZAghhBBCCCGEEGLI2ON7ZDiOQzabRdd1NE0b7OEIIYQQuxXXdXEcB9M00XX5fGMgyTWJEEIIsXV9uSbZ44OMbDbL0qVLB3sYQgghxG5t+vTpeDyewR7GHk2uSYQQQojt6801yR4fZOSTnOnTp2MYRr8c07Ztli5d2q/HHOrknHQn56Rncl66k3PSMzkv3Q3EOckfU6oxBp5ck+w6cl66k3PSnZyTnsl56U7OSc/6+7z05Zpkjw8y8qWbhmH0+y/dQBxzqJNz0p2ck57JeelOzknP5Lx0NxDnRKY6DDy5Jtn15Lx0J+ekOzknPZPz0p2ck57193npzTWJfPwihBBCCCGEEEKIIUOCDCGEEEIIIYQQQgwZEmQIIYQQQgghhBBiyJAgQwghhBBCCCGEEEOGBBlCCCGEEEIIIYQYMiTIEEIIIYQQQgghxJAhQYYQQgghhBBCCCGGDAkyhBBCCCGEEEIIMWRIkCGEEEIIIYQQQoghQ4IMIYQQQgghhBBCDBnmYA9AiD2O60I2BZkEZqoV4k3gDYLpB12yQyGEEEIIIcTQ4rouZLO4to2btSGbIZtOQzI5KOORIEOIHWFnwU6pwMJOq6/ZFKRjkO4AOwPZFMGWj2BtHDw+MH3gKwNvGVh+tZl+MD2D/dMIIYQQQggh9iKlwUS2cNtJpbGbm8g2NZPesoVM0xayjY1kW1qw29rItLWSibbitndAJkP8xBPgsMN2+fgHNciIxWLccsstPPvsszQ1NbHffvuxaNEiZsyYAaiTe+utt/LII48QjUY58MADueaaaxg/fvxgDlvsDRxHBRR2CrLpztupDhVWZFPgZFRg4TrqOZoOhgmGBaYXrAAZbxWUjQA3C9kktG+C1nWqaqPwOD/4ysEbUsGG5ZPqDSGEEEIIIUSvbTWYyGRxo21km5rINDaSaWgg29hIprkJO9qG3daGHY2SjUZxYzH0eBLN3fZrFYcIHza/w5wB/cm2P4Zd7sorr+Sjjz7ixhtvpKamhj/96U+ceeaZLF68mOHDh3P33Xfz4IMPcv3111NbW8stt9zCWWedxeLFi/F6vYM5dLEnsDOd1RT5r5mECioyic77nWzncwwTdEuFEFYYdBN0o+fjOw5oWi7gyFVkdHv9XBVHvEmFGxpgeKV6Qwgh+tlrr73Gvffey7Jly2hoaOD222/n+OOPL9z/zDPP8PDDD/Pee+/R2trKE088wb777ltyjFQqxfXXX8/ixYtJp9MceeSRXH311VRVVe3qH0cIIcQerhBM5AKJ4pDCjcfJNjeT2aJCiWxTI9mWZuzWVrK5YMKJtuPE2nFjHWhZe7uv1/Uj1KgfogFoC0I0oNEWgLagRjQAsaCOGwpgB30cXLPrqzFgEIOMZDLJM888wy9/+UsOPvhgAC666CKef/55fvvb3/Ltb3+bBx54gPPPP79woXHjjTcyZ84cnn32WU488cQ+vZ5tb/8Pr6/H6s9jDnW75Tlxc1UV2XTnNJBsGjK5qor8fU5GhQigqiDyQYVugRVQYYWmbf11HKfn3bZT8rUbzQAzoLbiMeeDlbYNYK8BXDA8arP84C8HK5gLN3zqqzZ0qjd2y9+VQSbnpGdyXrobiHOyt5zfeDzOlClTOOWUU7jwwgt7vP/AAw/kc5/7HFdeeWWPx/jxj3/MP//5T37+858TDoe59tprufDCC3n44YcHevhCCCH2AK7jqGqJTEYFEtksbtbGTSWxW1sLwYTd1Ijd0kK2tY1stBW7LYodjeLG2nHaY5BM9fo18+9iEh5oC6hwIhrQcgEFtOVutwcgGwqgBwPowSBlRpAy/EQ0P2H8DDP8jNGDlJkBfLofzWvhWhaxlDUwJ2s7Bi3IyGaz2LbdrbLC6/Xy5ptvUl9fT0NDA3PmdBaqhMNhDjjgAN56660+BxlLly7tl3EP9DGHul1+TpwsupNBc9Jo+dt2EiOTQLeTaG4WzcmiuepC3QXQTVzNxC36OpBBwMcff7zzB3Gi6E4G3Umj2WnAxQVcw4Ore7DNALYVwjF8OIYH1/Di6oPzl0pvyf8/3ck56Zmcl+7knPTd3LlzmTt37lbvP/nkkwGor6/v8f729nYeffRRfvazn3H44YcDKtj4/Oc/z9tvv83MmTP7e8hCCCGGANd1VTCRr5bIZnEzGdxMRoUTGzeR3aL6TNjNzWRbmsm2tmK3teHkKifcjo7OD1Z7Kat3Vky0BbTS6olgPrTQSIUs9GCIoBUkogcI58MJzU+VHmCCGSCoBQiZQQyvF7weDK8X3ePFMD0Ylhev14/HG8DnCWJ6vBiWB8uw0FyNFe+tGKAzu22DFmSEQiFmzZrFL3/5SyZMmEBVVRVPPvkkb7/9NmPHjqWhoQGAysrKkudVVlbS2NjY59ebPn06hrGVKQB9ZNs2S5cu7ddjDnUDek7sTG6qR6qoqWYHZGK5pppdqyq8oIdUVYXhyVVX7Ppfdcd2+Pjjj5k0aRK6MQBBSXH1RjalzgUJMLJqswK56o1A0fQU36BXb8j/P93JOemZnJfuBuKc5I8ptm3ZsmVkMpmSD1gmTpzIqFGj+hxkSJXowJPz0p2ck+7knPRMzovSWTGRxU6noa2NZP16tHgHmS1byG7egt3YQLapqRBOOK1qWofb3o4bi8HWKrO3Ij+dI1o0jaOtuHoiqKZ4xAMGlj9ARA8S0QKU4VebHmCUHqDO8BM0ApSZISx/ENc00HydwYRhWpgeH16vH68niMfrxzK9mLqJqZsYmlG4bW7jfZRt25ia2W+/K305zqD2yLjxxhtZtGgRRx99NIZhsN9++3HiiSfy3nvv9ftrGYbR7xfCA3HMoa7fz4mdgc3vqh4STtEvtpGb/mFa4Amo27vp9Ard0DEGpHFnrrkowdLd+d4b2Q5oae7svWH6VP+NQu+NQGdj0UHovSH//3Qn56Rncl66k3Oy6zU2NmJZFmVlZSX7KysrCx++9JZUie46cl66k3PSnZyTnu2R58VxwLZLNs22IZFEa2mBlha01ha01lbc9na09nboiKF3dODtiLM6HkfvRb+JYu0+aA1Ba1CjNaiqJlpD6rb6Xt2O+cGn+ShzfYQcL2HXR8j1EnS9RPAzEi8BvPj0AF7Lh2t5VEBhmOimhWaa6LqFYXmxTC+W4cMxPWQ1A0Mz0DUDAxMjY6BndUjppNohRQJI7NRpHYzflUENMsaOHctDDz1EPB4nFotRU1PDt7/9bcaMGUN1dTUATU1N1NTUFJ7T1NTE1KlTB2vIYldrXq1W+ggPV9UVYvvyIY8n1LmvUL2RgvaNauUUUJUqphc8QbVyiiegAg5fZOtNTIUQQuwUqRIdeHJeupNz0p2ck54NtfNS0hQzv1pHNouTSqnpHFsaSG/eRLZhi1qpo7UVpy2K3daaq56IoSfTfXrNuCcXSJSEElq3ffGgQcgMEtZUxUQ4N6UjpPuZqPsJ6QGCpp+QFSboD2N4vLiWiWaaGJYX0/SgWx5My4PPF8Sy/PhMX0m1hKmZvaqeGAj9/bvSlyrRQQ0y8gKBAIFAgLa2Nl566SW++93vUltbS3V1NUuWLCl0DY/FYrzzzjucdtppgzxisUt0NELLKghUDE6I4TqQiUM6rpqDpjtKb2fyt+O5+2K5feq2no5Tl0mgLx0BkVqIjIay2tztWghW7boqEk3vnF5SzM6oZWFTUehoUCm1boA3DGWjITAMvBFZClYIsderqqoik8kQjUZLqjKampoKH770llSJ7jpyXrqTc9KdnJOe7Q7npdBvoqj3hBOPk928mcyGDaQ2biC9ZTPZpmbs1haybW1ko23Q3rtlRDU6m2GmjXzlRGeVRD6UaCsEFRpOKIDPG6BM81Om+QnhLQQUo3Q/QcNPwAgQ9oTxegK4Pg+ax0I3LXSPB91QX03Li9cTwPL58Ok+LMMqmdJh6Vbhtr6bVp7nDcbvyqAGGS+++CKu67LPPvuwdu1abrzxRiZMmMD8+fPRNI0FCxZwxx13MG7cuMLyqzU1NSXLpYk9VDYFjR8BrqoW2JHnF0KFoi3T0X3f1vZn4jv1I2iAAdC6Rm1dGd6icGN0Z8ARqQV/xbZXSukv+eqNYk4WUu2w5X0VavjKoWwU+IepgGNXjEsIIXYz06ZNw7IslixZwmc+8xkAVq1axYYNG6TRpxBC9JFr250BRSYD2SxOOk22sZHMhg1kN28mvWkTmcYGss3NuRU8WnDaoujx5DaPXfx22taKKifyFRNF0zxaQxrpoBe3LIDlC6hAAh9lqICiQvczRvcR0PzEG9vZp3YfNJ8XPF4Mnxfd40EzTAyPB8P04vUGMH0+vFYAj+UtBBM9fTWk+nmnDGqQ0d7ezs0338ymTZsoLy/nhBNO4NJLL8Wy1Burc845h0QiwVVXXUU0GmX27Nncc8893VY6EXsY14XmT1SFQHgE1L8OybYuVRBF1RFFVRCF6ggn03/j0U0VpniCatlTTw9bD/tt08/qNesZX+3DaN8AbfXQth7a1qnpHXYKmleprSvLX1S9kQs58t/7IgMbJuimCi38w1Qj0VQ7bHpXBS/+YVA2Un3dkYBJCCEGSUdHB2vXri18X19fz/Lly4lEIowaNYrW1lY2btzIli1bAPjkk08AVYlRXV1NOBzmlFNO4frrrycSiRAKhbjuuuuYNWuWBBlCCJHjOk5uxY4sZDsrKexolPSGDWQ3blLVFIUmmS3YLS04bW040XY0Z9vNMfN1CWkTmkPQHIaWkEZzuDOc6Agb2OEAbiiIHgwSxlfYQnip0bzsowcIGX5CRhDDsNA8FprXi+73oVueXMWED4/Hh+n1YxoePl75CXUzZuGxPJiaiaEbJRUUu3vVxJ5mUIOMz3/+83z+85/f6v2apnHJJZdwySWX7MJRiUEX26KCDH85PPU92PDWjh/LCnQJHAKqd4QnqPpB5G8X9ucfH+p8rrmDwZnjkG7IQm1d96kZTlb1/mir79yi69XX2Ga1SkvTR2rryhMsDTaKN294x8a6NYYHArmVg7JJSDSrEMYTgEAVhIarUMPy9e/rCiFEP1u2bBkLFiwofP+Tn/wEgHnz5nH99dfz3HPPcfnllxfuv/TSSwG48MILueiiiwBYtGgRuq5z8cUXk06nOfLII7n66qt34U8hhBCDw3Uc1X+ieJpHNouTSJDdsoXMhg1kNm3OreLRTLY5F1Lk+lGQSm33NTTAQVVQNIehOazRElJfVWABybAPrbyMgK+MSj3EMDdIBQFG4KVODxIyAngNHxg6hmWh+XwqoPD5sTxeLMuP6fWqZUS9XtWHwuPD0q1tBhO2bdO2IcHw4PBBn24jlN2iR4YQBZkENK4Aw4AP/qJCDNMH1VM7A4Z82LC16oj8Zvp334aVutkZPnRlpyG6MRdu1BdVctRDxxZVcdLwodq68paVBhtlRVNWdraCwvRByKcqZjKJzqahnhCEqiFUo6ahDMIKKEIIsT2HHnooH37Yw9+bOfPnz2f+/PnbPIbX6+Xqq6+W8EIIsUcpTPPIhRR2Mom2ZQvxpUtxN20is2kT2S0NZJub1BSP5mbs1lbs1ja1xKi7nUYUOXFvvooiH1BAc0grVFW0hDW0shCVRhmVWphhBKnQgtRqQaYZQcqNMnyGDzwmus+P6Qtg+QN4fUEC3jCW15cLJryYlg/T8kjFxB5Mggyx+3BdaFoJiRbVhPK1e9X+ORfB1BMHd2y7kuGBYePU1lU21Vm50bWSI96kmnZueV9tXfmHdankGN0ZdnRtArotmpYLkwLqzywdg9a1qg+IJ6SqNIJVKtQw5K8YIYQQQojB4tp2Z8PMTAY3ncHNpMk2N5NZV6/6UTRsIdvYhN3UpKZ7tLbga2ml3u7dMqNZXVVLlFZPaLmqChVWtITA8VpU6WGqtDIqtDCVWpCxepgZRpiIESRihtG9XvB60P1+DMuD6fXh9QYJ+MrUqh1eP17Lj8fwFJphatK/ba8k7zLE7qN9k3oz7C+HJ7+j+lyMPQymbH360V7H9ELFBLV1lYmryo1C0LGus5Ij2aoCokQLbOphSaNAVWflRs1+sM9RvZumomnqcd4wOLYKNZo/huaVqjok3yTUVy4rnwghhBBC9KPOfhSdG5kM2ZZWMutzIcXmLWSbGtV0j6ZG7Nx0D9Lb7ieXjwZifo2mcGc/iuYQtIS1zsqKMEQD4GoaYc1HlVZGlR6mUi9jjF7GAWaYYUaYMm8YnycEfi8YpupB4fFheDx4PQF8gTABbxiP4VGb7sEyLCzdkkoK0SMJMsTuId2hppSYXlj6iOoN4S2Do78rq2T0lhWAqslq6yodK52iUqjmqFfNPOONatv4NnzwJLx0M9QeDBOPg3FHqOqL7dEN1YjUF+lc+WTz+6oqw1fe2STUlCahQgghhBDbku9Jka+gIFdVkY1GydTXk9m4keymzWQbG7Gbmsg0qWkfTmsrbnLbq3rkxQIajWFoKIOmMDSVaTSWqa8qsICMqa7DdTQqtBBVehlVRhljjXJmmmUMM8uI+IdR7i3H8PlwdB3X0sEwMb0+LMuH5fER8IYImAFVSWFYhYoKj+6R1TvEDpEgQww+x1FTSpJRSLfDWw+p/Ud9p7PRpNg5npDqM1I9tft9ybbOkKN1Laz9l1pJZe0StRkeVRkz8Tj11exFY8+eVj7Z+K56rjeCFW9S4Yo/0v8/qxBCCCHEbsx13UL1RHE1hROPk16/nsz69YWQQlVTqMaZTksrbjzeq9eI+1QlRUMZNJVBY1ijqeh2c1lnSAEQxEulXkalEWaUUca4Do19qsdREaig3F9FyBvENQxsHVzLANNUvShML6ZhEjACBKwAXtOLR/cUgop8VYUQ/U2CDDH42jeoaRC+Mnj6cnAdmPgpmHBM6eMKjYTc0ts93V/yfU/73KKn9nTc4mN2Pe5Wjtd1HI6NmWpRvStMD+gWGJZ6k787VZnkqyiG76++P+RsaFkNK5+Hlc+pP5tPXlCb6YPxR6pQo/YgFXJsT37lk0BlbuWTFgJtH8JaE8LVRSuf9KFPhxBCCCHEbqqzH0W60J/CSSbJrF9Pev0Gsps2qkqKxiYy+ekeLa2qcWYvJD2lIUVTLqTIV1M0hSHl6bzWDOClUg9TaZQx3CxjPzPCME855f5hlPsrKPOWY3g8ZA1wdA3H0Fm1dg2TJk3Ga3oxdROv4SVgBfCb/pKgIv9V+lSIXU2CDDG4Uu3Q+JF6E/vm/epNc6ASjsgtues6EN1Q9Ma/+KtWugut++OK/1LVenhuyTG29pyur5Wbp6fpnY/Vivblx+G6ZLwtaoqMk1aNOtMxVaFA0fMMMxdw5EIOw+o83mAZNh4OOhNmfwOaPlaBxsrn1NKwHz+rNk8Qxh8Nk46DUbPU2LfH9EHQQ8bfov7M2zdBa33nyifB6tz0E1n5RAghhBC7H9d1SyopnHQaN50m29RMeu1aMhty1RQNDSqkyK3w4Ubbe7W6R9qE5jKNhnA+pIDGsnw1hQop4l4K16B+PIWQotosY4rRGVJE/MOIeMsxPV4VUhg6rqGDZWIYaiUPQzOwdAuf4cNn+vCaXgzXwGj2MrNmJn7LX1iaVIjdiQQZYvA4tnqTnO5Qy4oue1TtP/p7qjoDoKNBNaKsmtz9zb1WHDAUBRM9hhn98Li+Js22TWIzUDtTPddO57YM2Cl1O5NUTTozHZDNgBNXTU7z/9BpWme4Ufy1N6FBf9C0zr4bh5wLDcvh4+dg1fOq0mTFU2rzRWCfuapSY8T03i176wmALyQrnwghhBBit5EPKpxcbwo3k8FJpclu2Uxm7Voy6zeQ2byZzJbNZBobcZqbsZtbIJXa7rGzOrSUaTSE3c5+FIVqChVSxPwUrjl9WFTqYSr0MJVmhIlmhGFWhHLfMCKBYZT5yvFYPrImqjeFaYBlYBhqNQ9TU2GFz/DhN/2F6or8ah/5qR+mVrryh23bbDQ3UuYpwzAkwBC7J3mHIAZPflUNTxD+cr3aN/WLMPZQdTsdA3JvpINVgzbMfqHroPvA2kp/CdctCjgynaFHJqVCjnSH2p+NgZ0Ft2g5LCMXbOSnruTDjv6maWpFk5r94LDz1eonq56HVf9QfTaW/0ltgSqYkAs1avbbfgC01ZVPVql9svKJEEIIIfpJvj+Fm85gJxNoLS2k167F3rQptxzpetKbNpFpaMBuasRubsFpboFsdrvHbgtqNJS5NERUSNFY1DyzKQzRoFrdA8CLqSop9DIqjDLGmWUM85QT8ZZTHqgg4otgWX5sS8PRNVzLBNPANFRzTFNTQYTX9OI3VEhRCChy4YT0pxB7MgkyxOBItKoGn94QvHqXmrIQHqneIINa9SLeAtX7Dv0Qozc0TU2n2NaUiuKAI387m4J0vqIjBZmEChWcoqBD11WfikLYkfu6M3MZdQNGzVTbnItgw1uqp8YnL6jVT5Y9qrbwCJhwrAo1Kidt/zW7rXwSgy3Lc/vLO1c+8ZbtXn1GhBBCCLFb6LYkaTqtmmjW16upH+vXk96ipn5kGxvxNDSyOtahms9vg4NaxaMhAg1lGo0R2BJRX/PBRdpS1yYWRiGkqDTKmGGWUWFFiPiGFaopPKaPrKnhmqqSQjMtDNMqne5h+fAb/pLGmV0rKoTYW0mQIXY9O6umlGSTqqnkh4sBDY65vHOZz1iDehNcMX4QB7qbyVdbsJXlSx27M+jIpouqOhK56Svxzj4dTqazP2m+T0c+5DA8vWvimaebaqnW2oPhyG9D/euqn8bql1QPjHf+T22RMSrQ2Gdu74/rL1db15VP/MPU74d/mArDhBBCCLFXKAQV6XQhrLCjUdJr15Jet47U+vVktnQuS+o0t0B7e0lf9mL5iRNZXfWkaIhoNJRRCCjywUVTGdiGhoFOhRYq9KUYZ0aYaZRR7i2n3F9BJDgMnyeAbWi4polj6WimiWF6CtM9LCNXSWH68RrekuqJ4qBCCLF18n+I2PVa16oGnp4AvPBTtW/Gl2HkDHU7GVWVCVWTc2/cRa/oBuj+ra/+4TjdKzq6Bh12RlV42OncUqlhML29H4PhgXFz1JZNwtpXVKix9hU1lejN+zHevJ99AqPREp9VjULLRvfuuIWVT1KQbIHYplzz0CpZ+UQIIYTYQ7i2XRJSuJkM2ZYW0qtXk1y3jvSG9WQbtpBtbMRpasFpaYGO7S9JmjbVKh/5cKIxF1g0lKuvLSFA1xmmBanUwlQaYSqNCLVmGcOsslzzzAoCvjCOqWObGpgmrmlgenyYRdM5ikOKkn4UudsSUgix8+T/IrFrxZuheaWaOvDiTZBoya2Q8Z/qfjujgowR09QbU9F/etunI5tQU39imyDRpgIJKx9qbOW5PTF9agndCceocGTNy7DyOdz61/DF18Pr96qteoqq1JhwLIRqenFcL5jV6nYmrqYltdWDFVTPD9WAv0KahAohhBC7ma4rfuSXJU1s3EB67RpS9fVkt2wh29CA09yUCypa0VLp7R477iVXSaGxpTioyAUX0QBE9CCVRdUUY/QyRkczTBgxgfJgBaFgBNcw1OoepolmmWgeD6bH29mTwvAWGmd2ne4hlRRC7Dryf5rYdbJpaPpI9T7Y+DZ88k/QDDh2Ueen/rEtEBkN5WMHdah7peI+Hb6I+jNItUOyVU0RSbZBrBEsr1pZpC/VD54ATP40TP40TqKNza/8gZHx99E2vAUNH6rtlTtg+LRcqDFXVV9sjxVQm+uqPiGta9V0JV8EIrXqGPkVcIQQQggxoFzHKammyKaSpNtaSK5dQ3rdOjIbN2I3NGI3NpJpbsJtacWIdqA5W1+WNN8RK+qnUEmxJaIaaTYUTf/QfL5cX4p8A80wB5hllJthIr5hlPmHoXk92IYGpoFrmbiWydp16xlXNxWP6Sk0zfSZvpJgIh9YSE8KIXYfEmSIXadlDbRvVtNFXvq52nfgAqiqU7cTLWoFk8pJvVu+UwwsTVMhgK9M9bcohBqb1deORhVAecN9CzW8YdpGHMHwKWdipNpUg9CVz6n+F5uXqW3JL2DkzFxPjaNUMLG9sXpCanOyqqpn01I1rmCVaiTrr9h2M1UhhBBCbFNxQJFJJ8kkO0ht3Eh6nWqimdq0kWTDZjLNjWjNUTxtcTzJnlf7KL7SszVyS5DmVvqIdK740RDRaC+zCPnKqNTCVOWWI601y5hmhol4yoj4hmH6A9imhmMaqnmmx8Lw+DAtL6Zp4TN8eE0vASNQqKIwXANfy/vMrpmN1/KWLEEqhNi9SZAhdo2ORjWlxD8MnvuhajhZPRVmnaHuz6+4MfIA+QR9d9Q11EjHctNPtkCiGeJNqo+FN6QqJHrLPwz2+5LaOhrUUq4rn1MrlWx4U20v/Q/UHqRCjfFHqLBiW3QTAhVqy8ShfaNa5tcbLqrSiMiqJ0IIIUQR13HIplNkU3EyqSTZVIJ0tIXE2rVE160ktn4t6YbNOE0tmK0xfG0JgtE0RpfFPny5rVjMR2EZ0nxY0RCBjoiXTHkQPRSizAhSrgeJaAEiepAxZpCwGaLMG8HjC2J7DVzDBI8BlgopDI8H0/Kq6R6WH5/uUyt8dGmcaelWjyGFbdv4DB+W0fP9QojdlwQZYuBlU9CwQtUGrnwO1v1bvek99nL1ptN11ZvY8vEQHjXYoxXbo2kqFMgHA11DjY6mXKVGH0ONYDVM/39qi26EVc+r35emj2Hdq2ozLBhzqAo1xh6+/UqQ/NQTx1YVJVvezzUOrVJLuQYq+9bMVAghhBiCXNcllYqTiLfS0liPnUkRjTXTuGEl0bUrSWxcR3bLFvTmKJ6WDgJtKcqiGYIJNeXDA1Rs5di2Bs3hzmqK5ohOvNxHujyIUx7EjUQIeIOUuX4imp+Rup86I0TQDGBYFrbXAp8X12OCaaJ7vBgeD5rlwfB48Fr+wpSPwjKkXXpT6Jq+y86lEGL3IEGGGFiuC82rIN6ogoxXfqn2H3IOlI9Tt+NN4I1A5QTVkFIMHcWhRvkYSMWKpp+0QEczmBZ4ctNPevtpR9lImHm62lrXwsrnYeXf1e3VL6nN9KkwY9JxUHvItgMJ3ehcyjWbhHiDWjnHV6amnQSrwFcuv39CCCGGHNd1sdMp0qkEmXSCdCpOU2wLmzaupKl+BR0b1pFubEBracNq6aCxPUt5m01Fu0u5DeXbOX6+iWZbxCRW7iVZ7idTHsSJhNEjZXiCZYR1PyF8TMDLvoYXzTBxDB08Fvi96H4/hseL7vGieT14PH68vhAeb275Ud3E0Az1VTcwNbOwuoeEFEKInkiQIQZWbAs0r1ZTCP66UL2JHDkTpp2i7s8kVE+D4dNUfwwxtHlDaovU5kKNNrWqSKJZrVhjWmAGVcDVW+VjYfbXVT+V5lW5UOM5aN+gqjZWPa9WLBl/JEw+HkbPhm1d9Jg+CPnAdVSVRuMKddxAZWeVhizjKoQQYjfgui5uOk0mncwFFXGSiRhb1n9Mw5oPad+4hlTDJpzmVqy2DnzRFOH2LBXtLsOzMHw7x3c0aA3rtJd7iJf7SJUHyJaHcCNlmOVlWKFygp4gQcdDWDdA01T/CdNEtyw0nw8jEMDw+fF4A5g+Pz5vCNPrU0uSWqqComtIIdM4hBA7S4IMMXAyCfUm0TDggydVE0crAMd8X73RdB3VO6NyEoRHDPZoRX8rhBqjId2hpp90bIFYI1ZyC7RHwB9RvxO9uaDRNKicqLaDz1Irnax6TgUbHQ3w0dNqi9TC/vOg7rPbDsc0XfXK8EXU9KdEs1py1hNSv4/BGhXASZWGEEKIAZBfirSkmiIeI7ZhLVvWfkDb+k9IbN6I3dKM3taBry1JqD1NpN3F60BtL14jFtDpiHhIlvvJRkLELYPAyOEYkXJ8ZeVYQdWXLAxENF2FE6aFblpY/gBWIIQVDOPxBzEtH6bXh+H1Ynn9mJbqLWFoBoY0aRdC7GISZIiB4brQtFKtRGJn4LV71f7DL1Sl/KBCjEAVVEyQxot7Ok9QbZHRkIgS32JDuEZNP0m0qF4p3nDfQo2aqWo79DwVkn38HHz0N2irh3/dBq/dA5M/o0KNYeO2fTzTC6Ea9XubjqnGtM2rITAMykarKg1PH/p9CCGE2Ku5joObzeKmM2TTSdLpOOlYO8n164iuXUnLhk+Iba4n09KM1tqON9c4M9zhoLtQhtq2xtEgGtKJlVlqqkckiBsJY5RF8JRFCIYrMEJhNMCrmwQ0HU3TaWhoZvw+E/H4Apj+AGYghBUIYnp8GF4flteH6fFjef3oprxNEELsvuRvKDEw2jdC6xr1ifaTl4KTUf0MpnxO3Z/uUG8aqyaD1bW3tdijeYJk/NVqhRo7pXpqxBpUr5R4i6rg8YbVdJFehRo6jJihtkPPVWHGe49Dy2p4/wm1jZ4N+8+HsYdte2nf4p4fdkZNjdnwlqrSCA2H8PBclYZ88iSEEHsr13Fyy5BmcdNp0uk4qbZWkvX1JDesI7p+Fe2b15NubsRtacOKdhCIpgnGO5f3GJbbepLVoSWslhxNRHxkygO4kTBaJIwnXI4/VE4wMAxD1wmgEdYNDMPEML14PD683iCeYAjLr0IKy+vH4wmgWx7eW/4Bk2fPxvT5ZHqHEGJIkyBD9L9UDBo/Up9yL/09NH0E3jI4+jL1RtGxVb+E6n0hVD3YoxWDyRNQW9koNRUp0aqmiXQ0Qnx930MNK6CWct33JLV067LHYO0SWP+G2sIj1f1TPr/9ZX4NSzUBdV3IdEDrahXO+cpVZUmgUk2dEUIIMaS5jgPZLK5tqy2TwclmyKZTpFuayDQ1km7YQraxiWTTFtobN5Bs3ILb0ooR7cAfTeFLdoYUHqByK6+VNqEpDG1lBvGIj3QkgBMJoUfKsMIRfKEIoUA5QbxE0Bim6RiWhWF6MCwvPm8ATyCMFQzjDYSwvH4sy4flDWB5vOgeD1hbX2oUvx/d45EQQwgx5EmQIfqX66gpJckopKLw1kNq/1HfUW/8QH36Hh6+/XJ/sXex/GorG9kl1GiCxAZVeeENqSkq2+tgrmmqCmP0bFUd9P6f4IO/qNuv3gmv3weTP62mnVRO3P6xPCG1OVlVpbHxHRXABGtUPw1/BRjy16kQQuwOXNfFzWQglcKOxcB1cW2bTCpJtq2FdGMD6S1byDY2YLe0kGpuItHaSCbaht0eRYslMONJvB1ZDKd7c+pgbusq4VEhRXNYoyPiIRUJYJcHIVKGJxzBEx5G2BtmmBugWjfR0NBME8PyYJgeLK8PT7AMfzCC5Q9ien1Ylg+PL4jH48Py+sGURplCCAESZIj+Ft0AbetUE8enL1fBxsRPwYRj1P2pdvWGr3IymJ5BHarYjZWEGkk1/aSjUYVg0Y19CzXCI+HQb6qVTz7+O7z3mArbPnhSbSMPUIHG+CNVr45t0U0VyAUqIR2HtvVqSVhfRDUZDVRuv9JDCCFEn7iu21kxkbUhm8G1bexMhmxrC6mGLWSaGsg0bCHb1Izd0kKmrZXMxvUstbO4sQ6MjgRWR7rHYALAm9t6kvBAWwCiAWgPaLSFDFLlPrKREFokjBkuw1M2jIgnTMT1M4oAuqbjGjq6x4NpetC9HjyBMrzBMvz+sKqk8OQrKXxqxQ/PNpYRF0IIUUKCDNFv9EwHNLWoN5dv/EYFGoFKOOIS9QAnC4k2GDENAhWDOlYxhFg+sEaoyofiUKOjj6GG6YOpJ6ppJZuWqkDjkxdUdcXGdyBYraadTP0C+Mu3P678tBgnqwK6TcvUWINVKjzxV0hYJ4QQRVzX7Vx+O98MM2uDncXNZnEyWey2VtJNjaS3bCLT2ECmqRm7pRknGiXbFiUTbcWOtaPF4pjxFLq99eW8I1vZn/CoUKItH0zkvmZCXuyQH4IBtGAQIxjCEwgSNIOE8RFyPYzAxzjNA4aBbpnolgfD48UMhPCGwvh8YTy+gKqk8AZVlYU3gMeSnhRCCNGfBjXIsG2b2267jT/96U80NjZSU1PDvHnzuOCCCwp/2buuy6233sojjzxCNBrlwAMP5JprrmH8+PGDOXTRlWPji62DcBkkGmDZo2r/3O91fkId26x6IUTGDN44xdBWHGpkU7npJ41qWdf2TYCmqiO2tcKIpsHIGWrraFDTTpb/Wd1+7R54836YeJyq0qieuv0x6aZqAOofpqbEtG9SlRrecFGVRkRW5hFCDJyODrJbtuDkl4vOhwVFwYHq/+Dg2jY4Nm42WwgTsNX3rm1D1sZ1bNXI0s4Weka4jqqGcO0sTn5/NptbnSMDuWO7du54uQ3bUc+11XEL98U6sKPtOO1RstEobkcHekcS3Xa28kMqJt0vXvPBRNQP0aDWGVL4NbIhH07Ir4KJUBDTHyLkyQUT+Ai7XoYbAby6F80wcHU13UP3eDE8Hozcah4ejx+Px4/XE8C0PFi+QG66RwCP5cXUZMqHEELsSoMaZNx999383//9HzfccAOTJk1i2bJlXH755YTDYRYsWFB4zIMPPsj1119PbW0tt9xyC2eddRaLFy/G65USvN1G6zqsRAN4RsBT16t9+34RxhyqbidawQyqVUqkl4DoD6ZX9VoJD4fspM6eGu0bVTNZf0T1tdjWhWWwGg4+Cw78Gqz8h6rSaPgAVjyttpr9YNp82Geuav65PfkpMY4N6XbYslw9L1ClpskEKtW4hRCin7Q9+ijem29mlW2D46jgwnFym6saWboO2tYLFwZd13Wgkha0BXPBREAjGsxXUGi0BzTsoBcnFMAIBjCDYYJWiDLNTxk+QnqAkVqASZqXloYWRowaheaxcC0TzeNB93pVTwrLg2l68Xr9eK0AnlzTTDO33zIsTN0sbEIIIXYvg/o381tvvcWnPvUpjjnmGABqa2v5y1/+wrvvvguoaowHHniA888/n+OPPx6AG2+8kTlz5vDss89y4oknDtbQRbFEKzSvxLGCaK/9SlVehEfBYeer+7MpyMTV8pjSP0AMhOJQo3xsriqiXm2+sFo1Z1vTTgwP1J2gti3vq9VOVv1D3X7uffD/UgVz+53U2bR2W3RDrW7iK1e///FG1T/GG1ZVScEqdZ++nf4eQgixHcv/+TgVrW1bvb83NQK2Bo4Otg5O7raj5b7f1v78vsJ+rXC/W/S8kufn9nf4VEihpnVANujLTecIEvSGiBAkogcI637CeoDheoCQGSCg+3A9Jq5lgmWBZaKbFrplYZgWuuXBY/nwmD7SK9cy/oCZ+CxfSShh6iamZmLIUtpCCDFkDWqQMWvWLH7/+9/zySefsM8++/DBBx/wxhtvsHDhQgDq6+tpaGhgzpw5heeEw2EOOOAA3nrrrT4FGbZt99u488fqz2MOWXYGtnyIk0ngj65CX/FXXDScud8Dwwe2De2b1XSS0Aj1/V5Cfk96NuDnxQpCxUT1+xbbonq1tNarpVl9ERUybEvVVDhmERxyHtqHf0Fb/ie0eBO8eT/uWw/hTpiLu988Va3RmzJiPVeR4TpqaeItH+SahlaoXhqBSmxd9dKQ35VS8v9QdwNxTuT8Dm3P/Mck3hi3FCgNG5yuQULutqbratMMMHR0TcPQTAxNx0THKN40A4Pcfq34PqPweF0zMDUd3dXVV83AQFPP1Uofr+ePo+mM0P1M1oOEPCECniCuZeKYOpgGrqGjGSqgUCt6qK9ejx+vx49P93WrmDA1E0tX+zRNw7ZtEpsdasO1GIYEFkIIsacZ1CDj3HPPJRaL8bnPfQ7DMLBtm0svvZSTTjoJgIaGBgAqK0s/Aa2srKSxsbFPr7V06dL+GfQAH3Oo8cTW429fiWP4mfCRWmq1efTxbGnzQdsKzHQbjmYQj1XibH53kEc7OOT3pGe76rxotg8rFceKr8PMvIere8haZdsPNAACh8Ksgwg3vUXFhucJRFeirXwOVj5HIjSWllHHEK0+GFfvxbST4jE5GYzMajQnhWMGSPsqMTwVLHv7dVzN7N3Y9iLy/1B3ck5E3pVzruLeTV7KQl78Hr8KDHQDXdMxdBPDUFGCpuvomg6ahourglhNAxfQwNW0XI8HDVfL3Z+r59B0LfcwDVfPP0XPPU+NQ9PU95qm5x6voakdaHrua9H3mqZjml5M3cRrePGaXnxGD5UTRUGF9KAQQgiRN6hBxlNPPcWf//xnbrrpJiZNmsTy5cv5yU9+Umj62Z+mT5/eb4m8bdssXbq0X485JMWbYX0DjJ6F9tJN6Jkobvl4yj/9X5SbHsgmId4Co2aqT573MvJ70rNBOy92RvXQaKtXUz00vQ8ri+wLnI7d+BHa+4+jrfw7/tha/CseYOTaP+JOORF335MgVNO3MbkupGM48VZWr32P8RMno5seVcVhesDwqp4bhkeFG7qlqjkMU33Nf7+HXtzL/0PdDcQ5yR9TDE26plM7eia1k2pVNYKuAgMdXb3x10DP/aflwgoNDV3Tc8GCCijyIYGW/6/oe6DwvOIwIf991+eUPL7L8/O3dU2X6R1CCCF22KAGGTfeeCPnnntuYYrIlClT2LBhA3fddRfz5s2juroagKamJmpqOt8gNDU1MXVqL1YTKGIYRr9fCA/EMYeMbBpaVoLmwIbXYPULuJqOc8zlGB6fKqOPN6kS/8joPfaNVm/s1b8n27DLz4thgGcMREap383WdWq1k6SjVhyx/Ns/Rs0UqFkIh50HHyyG9/+IFtuM9s5v4d2HYfyRsP98GHlA73/n/RHwhsluiqF7Qxg4aknXdAqcFtU41C3q0qdpoBkqzNAMFWSY3tzmVwFIccjRNfQYgn055P+h7uSciGIjfSOZXiWBnxBCiL3HoAYZyWSyW5mgYRhqnXFU88/q6mqWLFnCvvvuC0AsFuOdd97htNNO2+XjFUVaVqv+A4YFL/0cgMaxJ1JRNVnd39GomiJWTtyrQwyxG9INVTkRrIZEi1oqtetKJ9vjK4eZp8OML8OaJWq1kw1vwScvqK1iglq+ddLxvQtI8uOy/NsPGlxHhRtOFtzc11QKEnYu9ChaulCjM+zQTBXmGEWVHvnqDyMfcuS2wvfypkgIIYQQQux+BjXIOPbYY7nzzjsZNWpUYWrJfffdxymnnAKo0sMFCxZwxx13MG7cuMLyqzU1NYVVTMQgiDVA8ypVlv/3ayAdw62eQuOYz1IBkI6rT5CrJoPlG+TBCrEVmqYabgYqoHyMCjOiGyBevNLJdkI43YR9jlJb8yp473H46G/q9os3wat3wdTPw34nq9VK+mXcOhh675aDdd3OsCMffqQ7wGnL7Xdyc+RzFR8llR650MP0gulTXw2rtNLD8KgqkCFY5SGEEEIIIYauQQ0yrrzySm655RZ+8IMfFKaP/Md//Aff+ta3Co8555xzSCQSXHXVVUSjUWbPns0999yD1+sdxJHvxTJJaPxIvflZ+SzUvwaGB2fu5bA5qd4sxZugemrf+wUIMVj85WqLjFHLB7euU700PEG1f1tLt+ZVTICj/gsOORc+/KsKNdo3wLu/h3cfgbGHw7T5MHr2rqtS0jRViaH38q/6QuCRCz2yCUi3d+5DQ7X5IxeoWCrk8EXUZgVUpYcVkHBDCCGEEEIMmEENMkKhEFdccQVXXHHFVh+jaRqXXHIJl1xyyS4cmeiR60LLJ7lGicArd6j9h5wL5WNh8wrVTDE8HIaNH8yRCrFjvCG1lY1SU6da10LbBlVZ5C/vXSDgDcOM/wfTT4F1r8Kyx1Tgt/ZfaouMUdNO6j4LnsCA/0h9ovcl9LDByaimvu0b1LmCzn4dPYYbMlVFCCGEEELsvEENMsQQE9sMzZ+oxoh/XajewIyapT5ldkHPdIBeAZWTe7kShBC7KcsPw8ap1XY6GtSb9NgW9UbcP0xNqdgeTVdVGGMPV89/7wlY8VdoWwf/uhVeu0eFGfvPg7LRA/4j9Ts9N/3ELJo+5rpgp1UlR/tGVdmiaZ1TVCTcEEIIIYQQ/UCCDNE76biaUmJY8MGTsHmZehMy9/vqDZudxsjGYNgE1XNAiD2B6VGr7oSGq0qktnoVaAAEhpW+id+W8rFwxMVw8NkqzHjvcRVovPcYvPcYeu3BhEMzoVKDYGXvqz92N5rWWZGRVwg3ktC+SYUbsO1wwxiCP7sQQgghhNhl5GpRbJ/jqOaFyVa17Opr96r9h18I4RHqdvsWMr5qGDZ20IYpxIAxTPW7HqyBRLNa6SS2CZwmtYKJJ9i743gCqoJp/3mw/g017WTtErT616jlNfjg7twDNfXmPlChmur6h3U2Ju36vbesdz08Bsv2wo3YZhUQQWe44S1TYY7hVZVeTlatuCKEEEIIIQQSZIjeaN+ollv1lcOT31bz4sceDlM+p+5PtILlIxkqG5qfIgvRW7oOwSq1tHByrOqf0b5BLePqyy3d2ptGnpoGtQepLboB573HSa96Ba8bR0u0qCVUk61qY9V2jmWo6hB/LuTIhx35fYGi/VZg91gOuadwAyCbUlvHFoiuB8ch1LwO1thqaVx/ea5yIyCVG0IIIYQQezG5ChTblopB08eq2eG7v1O3vWVw9GXqzYidVss51kzDaW8a7NEKsWtomqqK8A/LLd2aqypoq1fNPn19qJIoG4V76Pl8Uv4p6qbUYeBCKgrxZhWQxJtVFUj+a/52vFk9zrWho1Ft22N4SoMNf1HYUaj2yN3uGjLsCoVwo0x97zjYVqu63dGglseFztVSvOFcRUzxtJReLEsrhBBCCCGGNAkyxNY5DjSthFQ7JNvg7f9V+4/6jvpE2nUh1qBWYYiMBiTIEHshX5naIqNzS7euVVNPPAFVpdHXKqV8Q1H/sO0/1sn2EHb0EH7EWyDToYLH9k1q2x4r2Bl2BLZS7REZrapQBpCrW+r8Fi/naqdV5Ua8UVXEOG5puJGv3PAEJNwQQgghhNgDSZAhti66XjUk9JWpVUpcByYdDxOOUfcnmtWbhsqJu/ccfSF2BU8AKvYpWrp1narUMKzcSicD8GZaNyFYrbbtySZVoNG1qqM4AMnfttMq+GjrUH8HbEuwCsrHq1VehuW+lo9Xf28MFMOjNm+4c5+dWwo20QSxjeCizo/pU0vq+oeVNhSVlZWEEEIIIYYsCTJEz5JRaPxYNTF84zfqzUygCo64RN2fTapPREftp94k2PagDleI3YbpVdNN8ku3tq1V0z40VBXDYEzZAPWGvmyk2rbFdVWIEW/pUtXRpdqjo7Hza0cjrH+99Dj+is5gY9h4KM999ZcPzM9nWGrrMdxoyVWhuJ3hhuVXYYhuqSoYw6O+anrnV63r91u7bzfoOyKEEEIIsReRIEN0Z2fVUqvZDvWJ8rJH1f6531VvElwHYo3q0+fwdt4UCbG3MkwVGoSGQ7xJhYGxzeDYamqGFRjsEfZM09R0EU9IBTLbko5Byxq1ta7O3V6tfs585ceGN0uf44vkAo7xneHGsHEq+OjvQGBb4UY6pv4uc13VZ8R1VBVHMU1T92saoKvpLdpWNsNSIYluFd0uDjt6CEW2ep8EI0IIIYQQ2yJBhuiubZ1qqucLwT9vUPv2/SKMOVTd7mhUb8QqJ8oFtxDbo+sQqlZTMBIt6v+t9o3Q0axW4ih+kz3UeEIwfH+1FUvHVa+Q4nCjZY36uZNtsPEdtRXzhkuDjWH7qP47btd0YSflw42+KIQdbi7w6HLbyUA2kfu+eMsfQKMkJdluMGKoIEzPBSL5MWs6uBpWokEdH1mSVgghhBB7JwkyRKlECzSvVPPbX7lDfbIaHgWHna/uT8fVBXRVnSrNFkL0jqZ1rg5SPgaiG1Wo0bpOBQL9/YZ9MHkCUDNVbcWySRVw5IONltXQukadh1Q7bF6mthwDqDN86CsmlPbfGDZOVbrsqiBV00Drx38uS8KOriFJNheMdA1FcsGIbeNr3wiZOJiR/huTEEIIIcQQIkGG6GRnVF+MbBpaPoQVTwEaHLtQlcE7tiqRr5rSu+aCQoie+SJqyy/d2rIaK7kZ2ss6P33XzdJtT6h+Mn0qBK2qK92fTamla1tWd4Ycratx2+ox7CRseV9txSx/roJjXGmz0fCI3b/5cL7yYkekk2ju+v4djxBCCCHEECNBhujUulY1xPP44YWfqn0z/gNGzFC3O7ZAsAYqxu8Zb6qEGGyeIFROgNBw4huTajqFnYRMQn0qn4mrnjVOBjU9Ifcl36CyMPUgH3bs5m/gt8b0qqlqlRNLdjuZFKvfeZnx5WC0re0MOtrq1Tlq+EBtxQxvLtwYV9psNDxSnTMhhBBCCDHkSZAhlI4maFqp5uy/8FM1xWTYeDjoTHV/ql29aaqaPHirLgixpzJ9ZALDoboOjNyb7XyAYafVbTudm3KQVm/iMx1qXzYF6Q51n+N0hoy6kavsKAo88g0ohwrDIh0cBRPqVD+JPCerlocu7r/RulqFsXYKGleorcuxiIztDDaC1SpIym9W0W35O04IIYQQYrcmQYZQb4waP1LztNe+Ap+8oJrNHXuFuqB3spBoheHTIFg52KMVYu9g5CotttWLxnE6Aw47v+W+TydUA8pMQu3PJFQg4hYtlazpndUc+c2wcqto7MZVV7qpKi7Kx8E+R3fud2zVULRrD46WNSrgaF6ptu0e3yoNOTwh1fejcLvrfUE1/a74PtO3e59DIYQQQoghTIIMoS72O7aoNzAv36L2zf66qr4ANYe/bBSUjx20IQoheqDroPsA39Yf47oqyChUdxQFHtmUmr6SDzvsOCQzKhDI07QuQUf+trX7vVHXDYjUqm38kZ37XUf9Pda6GppXq6+JNrUEa7qjc8vEAVedq2Sr2naUpncPPaweApAet3w44h+604WEEEIIIQaQBBl7u9gW9QmlvwL+fo26sK/eF2aeru5PtqpPGisnqTcwQoihRdPA9AAeILj1xxVPXykJO9KQjasVi+w0ZJKqSsvJ5FYUddWb7Xw1R/FXfTf5O0PToWyk2sYevvXHuY4KMwrhRkz93IXbHV3uKw5Bim7nVxlJRdW24wNXlSBFAYhu+SnXa4AzduK4QgghhBBD225ylSkGRSap5pFrOqx8FupfA8OjVinRTfWmJRVTzT795YM9WiHEQMpPZdkWxy6avpILPuw0ZFK5N/K5sCMb6z6NpbiSwzA7+3bsTlUdhSqK0I4fw3XVlJ5thR7bvC/3vWsDbuf+jtwQgUrdwrUz/fETCyGEEEIMSRJk7K1cVzX3jDer71+5Q3095Fw179x1IdYAkTGqTFsIIXRDbdY2prLkg47CNJbc7XRcvUnPplSIarerMAQAt7MpaUnIMYQak+ZpmqpiswI7vky16+bOWfeQw4m3Ut+aZrRh9e+4hRBCCCGGEAky9laxzdC2FvzD4K8LIZuEUbNg2nx1f6JZfSpZOWlorXIghBhchqW2rU1jceyeg45MsrNPRTYDThyyKazEFmgLqtVcdufpK/1J01SjZdMLgdIGy246Ser9twZpYEIIIYQQu4cdugKMRqM8/fTTrF27lrPOOovy8nLee+89qqqqGD58eH+PUfS3dFytUqKbsPzPsHmZ+vRw7vdVaXU2pbaR+4J3J0qshRCiK90A3b/11VjyzUntFKSTdLR4YURdbunZOKQ6hvb0FTFoXnvtNe69916WLVtGQ0MDt99+O8cff3zhftd1ufXWW3nkkUeIRqMceOCBXHPNNYwfP77wmNbWVq699lqef/55dF3nhBNO4IorriAY3Eb/GSGEEEL0uz4HGR988AFnnnkm4XCY9evX8+Uvf5ny8nKeeeYZNm7cyI033jgQ4xT9xXHUlJJkqworXv+12j/nIgiPyE0p2QIVEyA8clCHKoTYC+Wbk5oeMANkfZVQPkZVZOTlG5PaqS7TVxK56SvJHqavkFtutjjkMNT0Fd2Q1UH2AvF4nClTpnDKKadw4YUXdrv/7rvv5sEHH+T666+ntraWW265hbPOOovFixfj9XoBuOyyy2hoaOC+++4jk8mwaNEirrrqKm666aZd/eMIIYQQe7U+BxnXX3898+bN43vf+x6zZs0q7J87dy6XXXZZvw5ODID2jdC6Bnzl8OS31aecY+dA3WfV/fFGNd2kcqJa2lEIIXY3hcakgZ7vd5yeg45MsnNVEjujAg/Hzi036xYdwAX0zp4gWpev+eBDwo8hZe7cucydO7fH+1zX5YEHHuD8888vVGnceOONzJkzh2effZYTTzyRlStX8uKLL/KHP/yB6dOnA3DllVdy7rnn8r3vfU8qUoUQQohdqM9BxtKlS/nhD3/Ybf/w4cNpaGjol0GJAZJqV1NKLD+8+zto+hi8ZXD0f6lPQTNxsG1Vxr21sm8hhNjd6Trovq03Jc1PX3GyRZtd+r2dUVVrdm6qXX6VFjf3WNdWxymmbS/8MGSay26qvr6ehoYG5syZU9gXDoc54IADeOuttzjxxBN56623KCsrK4QYAHPmzEHXdd59910+/elP9/r1bNve/oP6eKz+POaeQM5Ld3JOupNz0jM5L93JOelZf5+Xvhynz0GGx+MhFot127969WoqKir6ejixqzi2mlKSjqlpJW//r9p/1HdUMznHho4mqKqDUM2gDlUIIQZUfvoKnt4/p2vQ0e37LGTTncGHnc41Nk2p0MPJ9fMoyT7cnsOOrl8l/Bhw+Q9iKitLm6tWVlbS2NgIQGNjY7frHNM0iUQiff4gZ+nSpTsx2l13zD2BnJfu5Jx0J+ekZ3JeupNz0rPBOC99DjKOO+44br/9dn7+858X9m3YsIGf/exnnHDCCf05NtEf7KyaPhLbDG3rwFemVilxHZh0PEw4Rj2uo0EtFVixj1w0CyFEV/mgAW/vn9Ob8COT6iH8yHROeXGzQNHfycU9P8SQNX36dIzivi87wbZtli5d2q/H3BPIeelOzkl3ck56JuelOzknPevv85I/Xm/0OchYuHAhF198MXPmzCGVSvG1r32NxsZGZs6cyaWXXtqnYx133HGsX7++2/7TTz+dq6++mlQqxfXXX8/ixYtJp9MceeSRXH311VRVVfV12Hse1+1cvtDJ5OaB5+aCZ1NqmkgmkbtYzs0F94bgjftVoBGogiMuUcdKtatP/arq1HJ/Qgghdl5fww/X3X74kUmRanDB3Mq0GbFDqqurAWhqaqKmprMqsampialTpwJQVVVFc3NzyfOy2SxtbW2F5/eWYRj9fiE8EMfcE8h56U7OSXdyTnom56U7OSc9G4zz0ucgIxwOc9999/HGG2/wwQcfEI/H2X///UvmlfbWH/7wh5J5MB999BFnnnkmn/2sajz54x//mH/+85/8/Oc/JxwOc+2113LhhRfy8MMP9/m1hpR8FYWd7nI7o5ZOzSZyn95l1ad1dkZVWGiauhDW9KJlCE21tKovAhvfhWWPqteY+z3whtWFcaIVhu8PwcptDksIIcQA0rSiRqZbYdukwu3q73bRb2pra6murmbJkiXsu+++AMRiMd555x1OO+00AGbNmkU0GmXZsmVMmzYNgFdeeQXHcZgxY8agjV0IIYTYG+3wldDs2bOZPXv2Tr1417mmv/rVrxg7diyHHHII7e3tPProo/zsZz/j8MMPB1Sw8fnPf563336bmTNn7tRrD4p8gzk7XVpF4WRy3fQTuSqKTGezOTdbNKfaVUsH5gMK0wLdn/t+OwlYOgb/vEHd3vckGHOIuh3bopZZLR87UD+1EEIIMeg6OjpYu3Zt4fv6+nqWL19OJBJh1KhRLFiwgDvuuINx48YVll+tqakprGIyceJEjjrqKP77v/+bH/zgB2QyGa699lpOPPFEWbFECCGE2MX6HGRcd911jB07lgULFpTsf+ihh1izZg1XXHHFDg0knU7zpz/9iTPPPBNN01i2bBmZTKak0mPixImMGjVqh4KMAe8Qng8eSqZ55Kd65Jb8y6Zy9+eavjlFz9f1zoBCN8HwghVUt3vTs8Jxtnm39q9foMc244ZH4hzyTfX4ZBvoHhg2AdDViiU7SDr5difnpGdyXrqTc9IzOS/dDcQ52VvO77Jly0quXX7yk58AMG/ePK6//nrOOeccEokEV111FdFolNmzZ3PPPffg9XZODfrZz37Gtddey9e//nV0XeeEE07gyiuv3OU/ixBCCLG363OQ8fTTT3PHHXd02z9r1ix+9atf7XCQ8eyzz9Le3s68efMA1R3csizKyspKHldZWblDy7z2ZydVzU7jSTWx4l+r0e00up1CczJoro2W60yfjx5cAE1VTLha0aabagrIANKcDGaqmWDrh4z8+K+4aKzZ5wwSq9aBk8VKNZEom0S6fXW/vaZ08u1OzknP5Lx0J+ekZ3JeupNz0neHHnooH3744Vbv1zSNSy65hEsuuWSrjykvL+emm24aiOEJIYQQog/6HGS0trYSDoe77Q+FQrS0tOzwQB599FGOPvroASvP7NcO4W0bWPuvVxk3fgJ6vmqi6zbQK384NsSboGMLWqwBOjZDrAGtY4uaLtLRgJZsLXmKO/3LjDnk8+qbtvVQth+MmL79aSm9IJ18u5Nz0jM5L93JOemZnJfuBuKc9KVD+GDKZDI0NjaSSCSoqKigvLx8sIckhBBCiEHS5yBj3LhxvPjii4wbN65k/wsvvMCYMWN2aBDr16/nX//6F7fddlthX1VVFZlMhmg0WlKV0dTU1Ofu4NDPnVQNA9DQy0dj6ANQVeG6atpHPpTIb8XfxxtVg8/tMX1qWdVRs9AP/k81hSXerJZhrakDy9OvQ5dOvt3JOemZnJfu5Jz0TM5Ld3vLOYnFYvzpT39i8eLFvPvuu2QyGVzXRdM0RowYwRFHHMGXv/xlabYphBBC7GX6HGR84xvf4Nprr6W5uZnDDjsMgCVLlnDfffexaNGiHRrEY489RmVlJcccc0xh37Rp07AsiyVLlvCZz3wGgFWrVrFhw4ah2eizWDqeCyVUFUX321tUb43t0QwIVkFoOIRqVGDR9bY3XFodkk2phqKjZqr7hBBCiN3Qfffdx5133smYMWM49thj+eY3v0lNTQ0+n4+2tjZWrFjBG2+8wVlnncWMGTP47//+b8aPHz/YwxZCCCHELtDnIOPUU08lnU5z55138stf/hKA0aNHc80113DyySf3eQCO4/DYY49x8sknY5qdwwmHw5xyyilcf/31RCIRQqEQ1113HbNmzdq9gww7DR0NucqJnkKKzZDu6N2x/BW5UKJGfS3egjXgH9a3aSGuq8ZWPh7Co3boxxNCCCF2haVLl/LQQw8xefLkHu+fMWMGp556Kj/4wQ949NFHef311yXIEEIIIfYSO7T86umnn87pp59Oc3MzXq+XYDC4wwP417/+xYYNGzjllFO63bdo0SJ0Xefiiy8mnU5z5JFHcvXVV+/wa/Wblk8INi9De3cDxBtyIcVmFV4ketknxBPqHlKU3K4Co3+nfRBvBF85VE5UU0yEEEKI3dTNN9/cq8d5PB5OO+20AR6NEEIIIXYnOxRk5FVUVOz0AI488sitdhH3er1cffXVu0d4kbdpKfpvTmQkLqzaymMMT/dwomtg4Qns0mGTiavlVUdM3vWvLYQQQvSjWCzGK6+8wj777MPEiRMHezhCCCGE2MX6HGQ0NjZyww03sGTJEpqbm3Fdt+T+5cuX99vgdktlo2HMISRaNuKtmYie70lRvHkjA79qybY4WbAzua9pddtOQ1Wd6pshhBBCDCGXXHIJBx98MF/96ldJJpOccsoprF+/Htd1ufnmmwu9tIQQQgixd+hzkLFw4UI2btzIBRdcQE1NzUCMafcWqMA59Tesf/ER9pl55OBM0XCdXFCRyYUUudv5TEkzwLRAt9QUFk8IPEEoGzW4AYsQQgixA15//XXOP/98AP72t7/hui6vvfYajz/+OHfccYcEGUIIIcReps9BxhtvvMFvf/tb9t1334EYjwDVlLNQVZEGO3/bVkGEpqmQwrDA8IJvmFqBxPSqaS2mR+03PGDs1OwhIYQQYtC1t7cTiUQAePHFFznhhBPw+/0cc8wx/PSnPx3k0QkhhBBiV+vzu9yRI0d2m04idkA+qCiurHDszvt1MxdEWOAPq6oKy58LKvIhRS60EEIIIfZgI0eO5K233iISifDiiy8WGoFGo1E8Hvl3UAghhNjb9DnIWLRoETfddBM/+MEPqK2tHYgx7Rn6PP0jrJpwdg0qDI+sMCKEEGKvtmDBAr773e8SCAQYOXIkhx56KACvvfYadXV1gzw6IYQQQuxqfQ4yLr30UhKJBJ/+9Kfx+XxYllVy/7///e9+G9xuz05D1slN/8ioKSCuA7ig6bkQwlTTPAKVKrDoVlHhBd0Y7J9ECCGE2G2dccYZzJgxg02bNjFnzhz0XMA/ZswYvv3tbw/u4IQQQgixy+1QRYYAV7cg0ZoLJSwIRMAKqOkfXYMKw9ru8YQQQgixddOnT2fKlCnU19czduxYTNPkmGOOGexhCSGEEGIQ9DnImDdv3kCMY2jxV9BRPhXGzuqcDiKrgQghhBADIpFIcO211/LEE08A8PTTTzNmzBiuvfZahg8fzrnnnju4AxRCCCHELrVTzRdSqRSxWKxk2ysYFrY3At4yVXEhIYYQQggxYG666SY++OADHnjgAbxeb2H/4YcfzuLFiwdxZEIIIYQYDH2uyIjH4/zsZz/jqaeeorW1tdv9y5cv749xCSGEEEIA8Pe//53/+Z//YebMmSX7J0+ezNq1awdnUEIIIYQYNH2uyPjpT3/KK6+8wjXXXIPH4+G6667joosuoqamhhtuuGEgxiiEEEKIvVhzczOVlZXd9icSCTSpihRCCCH2On0OMp5//nmuvvpqPvOZz2AYBgcddBAXXHABl156KX/+858HYoxCCCGE2ItNmzaNf/zjH932P/LII92qNIQQQgix5+vz1JK2tjbGjBkDQCgUoq2tDYDZs2fzgx/8oH9HJ4QQQoi93qWXXso555zDxx9/jG3bPPDAA6xcuZK33nqLBx98cLCHJ4QQQgwNjgNu8WZ3+d4Fp+u+osc5DjhZtdkZsNNY8aZB+VH6HGTU1tZSX1/PqFGjmDBhAk899RQzZszg+eefJxwOD8QYhRBCCLEXO+igg/jjH//Ir371K+rq6nj55ZfZb7/9ePjhh5kyZcpgD08IIYToH67bQ4jgdAkX3K2EELnHOXYubMh9dbNg5766bi6scIDcc7JpSHdAOgaZDkjHIZOAbELtzyQgk9tXcjuO7mQpG/8V4DO7/FT1Ocg45ZRT+OCDDzjkkEM499xzOe+883jooYfIZrMsXLhwIMYohBBCiL3c2LFjue666wZ7GEIIIcT2uW5n5UJhKw4Zcls2pTYnkwsb7KKwwqUQNhSHGDjg5l7DTkEmmQsX4p0hQzap9mdz36fjuWAiocKKTDwXWHSoyoodpAG+jvX9dNL6ps9Bxje+8Y3C7Tlz5vDUU0/x3nvvMXbsWKZOndqfYxNCCCGEYN999+Wll17q1vCzpaWFOXPmyIppQgghBk4ulNDstKpawO0eSBSHEnZKhQOOrYKJfIDhZlFv/XOcTGfAkEnmvsZV2JBNdAYQmXhRpUQ8Vz2R21y7/35Oyw+eIHhCua9BsIKdt/P3WX6wAmD5sG2bxmQN1f03il7rc5DR1ejRoxk9enR/jEUIIYQQohvXdXvcn06nsSxrF49GCCHEkOX2FEL0IpSwM4SaVsCa1txxikIJ1wUn1VntUAgaYpCK5b5G1e1km9pSURVU9AdNV8FC1xCi25a7zwp03jb9YPnA9KpjFYcvrt3ZU6Pk9TTQDNANVRmysaN/fo4+6lWQ8cADD/T6gAsWLNjhwQghhBBC5OWvPzRN45FHHiEQCBTucxyH1157jQkTJgzW8IQQQgwmx+5S9VDcF6IonMjkAolsCux053Ncp/OxxaFEusvUi3QMLRWjYtM6tFYL0u2QaodkFFJt6qud3rGfQdNVqOANdQ8hrB5CCE+g++NMf27s9laCiKLvC58LaICrQgknC1lXhROaDoYPvB4wvCrgMCzQTRVc6GbpbVcjFX1/5/4cd1Cvgozf/OY3vTqYpmkSZAghhBCiX+SvP1zX5eGHH0bXO1eNtyyL2tpaWTFNCCGGoq4hROGr0/P+XFWE6iWRVl+7rsCRDyXyb9YdJ9cPIjctI527XaiSaC/aorlqiWiP0zV0oGp7P5Nuga8MfBHwlhXdjnTe9pXl7ouozRNU4UGxknPgdD8X+fOUTauNFpVLaEZnpYSWCxo8VlEg4QHDLAojzNLHFja9p5+uZ7atwpBB0Ksg47nnnhvocQghhBBClMhff3zta1/jF7/4BZFIZJBHJIQQosc31/k33YVqiL6GEPkml0XVEZqWO2ZGVUhkU519IrLx0p4RqeJgIhdIpNopKkHoG9NXEkg43jJaEw7lI8ah+8u7BBJlKqyw/N3f1G+rQiITV2PMT58sfm4hkMgFC4YFZigXRnjB9HQGD4UgomsgYQxayLAr7HSPDCGEEEKIgfTggw8O9hCEEGLP5bq5sCGlPuHPJPDEN0HzJ0A+iEh3hhElS4FuJYToPLjaZ6c6V9RIJzpX2ChM48j1lEh3qDf3hYqJmHrNneEJllZFFFdL5LeSCoqyzp4R+Z/Ccdj8wXIikyaC5napjrBzFR0tudwkN20DVLWFXlwlYYDhL6qQ8PZcJdHTVA5RYoeCjE2bNvH3v/+djRs3ksmU/mJdfvnl/TIwIYQQQgjY/rXFT37yk100EiGEGKIcu7N5ZTatgolsSoUFmQ7I5kOKNNgO/rZPYIude5Ot5yoIcitpFC/1mY7nAoj811hndUQ+jEh3sMOVEXnFvSS84dztoq/eUPdpHPlwQu/yltd1t1JRktsSzblgpvj8OZipFki2qmqIfCjh8RVVSWwrlCiunOjD1A2xVX0OMpYsWcL555/PmDFjWLVqFZMnT2b9+vW4rst+++03EGMUQgghxF4sGo2WfJ/NZvnoo4+IRqMcdthhgzQqIYTYjbhuZzhR/DUdV80psykVVGQznT0gNABdvTnvaITYZohtQY+uZ1TLJvSPs50VEnZq58doeLsEESHwhIv25W+HigKK3GOtQM/TJIorQroFE1noaFDTOYrl+0nopgpICv0k/J2BhOktDSTQ6IiWwdiZYBXdtwdP3djd9TnIuOmmm/jP//xPLr74YmbNmsVtt91GRUUFl112GUcdddRAjFEIIYQQe7Hbb7+92z7HcbjmmmsYM2bMIIxICCEGQX6KR3FYkU11Tsko9KHIVczn37C7NsSb1Zv69s0Q2wTRDdC2Xt3usrymBgS6vXjuHk+wNIAoCR+2E0oYnq3/bD02/MxNV0l3qKkbXaskoHQp0MLUDROMYK5Cop/6Sdg2ticXrBgyzWN30OcgY+XKldx8883qyaZJMpkkGAxyySWXcMEFF3D66af3+yCFEEIIIYrpus43vvENFixYwDnnnDPYwxFCiJ3nOLleFPnpH7nAIpXrH5GvqrAzneFDvgdDNqmqKjoaoH0TtG9QYUV0A8Sbtv26hgfKRkF4FJSNwgmPZENripHj6zD8ZZ2hhBXYdq+GHqds5EKJ/Gog+SafxX0kcDubWuoGYOQaXHpyTS69oHuKAgmjKJQweg4lxB6vz0FGIBAo9MWorq5m7dq1TJ48GYCWlpb+HZ0QQgghxFasW7eObDY72MMQQojey/enKFRWpCCTVEFFJl60wkfR321G7k17KqrCivZNnVUV0Q0QXZ/rQ7ENnhBERufCitEquMhvwaqSJUBd26b9g+WMHD4BcIvCiNbSSokeqyP0LlM2dDUVo9DcMhdOlIQR+RCj+HvpJSG2rc9BxgEHHMAbb7zBxIkTmTt3LjfccAMrVqzgb3/7GwcccECfB7B582Z++tOf8uKLL5JIJBg3bhw//vGPmT59OqDWjr/11lt55JFHiEajHHjggVxzzTWMHz++z68lhBBCiKGnazNP13VpaGjgH//4B/PmzRukUQkhxFbY2VwDzVTnlsktEVq8+kd+2U1dB91StxNFU0Ci63PbBmjf2DllZGsCVUUBxejSr76y0se6budyqMm2zgAld5+ZiarxGp7O5T+NYGcYYXp7CCOKp3jsHUuAisHT5yDj8ssvp6NDJX4XXXQRHR0dLF68mPHjx7Nw4cI+HautrY3TTjuNQw89lLvvvpthw4axZs2aknXi7777bh588EGuv/56amtrueWWWzjrrLNYvHgxXq93G0cXQgghxJ7g/fffL/le13UqKipYuHAhp5xyyiCNSggh6AwtMgnVWDPZqppjZpNFvSo09abesNS+jgaIbVHhRHFVRWwL21zdQzMgPKJLUJG/PRJMX+njC2FFBhItnWGF66oxGVbnihuBSjWFxPSCZhKLRmDcrKLAQqZriN1Ln4OM4qZagUCAH/7whzv84nfffTcjRowo+aSl+Piu6/LAAw9w/vnnc/zxxwNw4403MmfOHJ599llOPPHEHX5tIYQQQgwNDz744GAPQQghehdamB4VFEQ3qFVA8iFFPrBItm77NUzfVoKKURCq6XkpUSerKisSrZ0VH3mGlesvkQ8rQuo18lM9TJ96THHVhG3jWPWqJ4Y0thRdOI5LPGMTS2ZpjiVp7BicKZ59DjLyli5dysqVKwGYNGkS06ZN6/MxnnvuOY488kguvvhiXnvtNYYPH87pp5/Ol7/8ZQDq6+tpaGhgzpw5heeEw2EOOOAA3nrrrT4FGbZt93l82ztWfx5zqJNz0p2ck57JeelOzknP5Lx0NxDnRM6vEEL0oLehhWNDWz00r4LGFdD4EbSuZZuVFb5Iz9M/ykaBv6LnqRh2JtdfIzc9xckWTU0xc6tzWBCoyIUV3lxI4ekMLWSKh9gBruuSyNjEUlmiiQxNsTTrW+O8U9/G6sYO5lQNkSBj06ZNfOc73+HNN9+krEzNtYpGo8yaNYv/+Z//YcSIEb0+1rp16/i///s/zjzzTM477zyWLl3Kddddh2VZzJs3j4aGBgAqKytLnldZWUljY2Ofxr106dI+PX6wjjnUyTnpTs5Jz+S8dCfnpGdyXrrb285JY2MjN9xwA0uWLKG5uRnXLX2DsHz58kEamRBij+BkIRPbfmhhZ1RI0fxJLrRYoaoteuKvgGHjeuhZMUoFDT2xM2oMTlqFFk5G5SH5JUaN3ModvnLV86KwvKhXwgrRr5K54CKWzNAYS9PckWLp+igfbIyyfFM79S2JwmONtI+zB2GMfQ4yrrjiCrLZLIsXL2bChAkArFq1ikWLFnHFFVdw77339vpYrusybdo0vvOd7wCw33778dFHH/Hwww/3e/Ou6dOnY/RTaZRt2yxdurRfjznUyTnpTs5Jz+S8dCfnpGdyXrobiHOSP+bubOHChWzcuJELLriAmpqawR6OEGIoK660SLTjb/kA1kRVeFAcWmRT0LqmNLRo39TzMUPDoWoyVNXltslqGkdPnKwKS/KvZ6c7izd0s3NVj2A5eMNg+XJhhaezwkLCCtHP0lmHjlSWWCpLc0ea1nialQ0x3tsQ5YNN7azY3E7G7vwQQQMmDw8xuSbEEZWpQRlzn4OM1157jYcffrgQYgBMmDCBK6+8kjPOOKNPx6qurmbixIkl+yZMmMDTTz9duB+gqamp5MKlqamJqVOn9um1DMPo9wvhgTjmUCfnpDs5Jz2T89KdnJOeyXnpbm87J2+88Qa//e1v2XfffQd7KEKIoWRb00OyaXBdrHQbJHwQrS8NLToaej5meJQKKqpzoUXlZPCXlz7GddRr2qnSBpugmmYaXhVWBCKqwably/Wt8HRWWEhYIQZQ1nboSNnE0lmaO1K0JbJsbk2ybEMbH25u5/0NUVoTpavkVIU8zBo7jFljyjmgtpyg12RDawephnWD8jP0OcgYOXJkj2u2O47T509JDjzwQD755JOSfatXr2b06NEA1NbWUl1dzZIlSwoXL7FYjHfeeYfTTjutr0MXQgghxBA0cuTIbtNJhBCixPZCi/wqHan23PSQVeiNK9hn03KMf0d7PmZkTGeFRf6rN1z6mHxokU1BNgG2rV4rX0kRiKipJJa/aAqIhBVi13Icl460qrhoi2do7kjTlszw4cZ2PtjczvKNUdY0xUue4zV1po+OqPBibDm15X5SWYd42qY9lSGetgn7TDRrcH6P+xxkfPe73+Xaa6/lqquuYvr06YCaq/ujH/2I73//+3061te//nVOO+007rzzTj73uc/x7rvv8vvf/76wEoqmaSxYsIA77riDcePGFZZframpKaxiIoQQQog926JFi7jpppv4wQ9+QG1t7WAPRwgx2HobWiTbctNDVqkmnI0r1L4cDfVmyNV0tPKxRaHFFKicCJ5g6eu6DmTiudAiCY6TO0iuoiJYA76wWu3D8oPpB13fhSdGCMV1XeJpm45UlrZEhqaONPFUhjVNcT7YFGP5JjVlJJ11Sp43sTrIrDEquNh3ZBm6phFPZ+lI22yMJvCaBiGvyfiqAGGfRcDSeK+9flB+xl4FGQcffDBaUWIYj8f58pe/XChrtW0bwzBYtGhRnwKGGTNm8Itf/IKbb76Z22+/ndraWhYtWsRJJ51UeMw555xDIpHgqquuIhqNMnv2bO655x68Xm+vX0cIIYQQQ9ell15KIpHg05/+ND6fD8uySu7/97//PUgjE0IMuF6FFibEW7qHFulY9+NpBlSMh8o6nKrJrO3wMWbmXAxvl9DCsVVokUmq4MK1QdM7Q4vQiFwPi1xoYfmlwkIMqmTGpj2Za9DZkaYjlaUplubDXGixbEOU5o50yXMqAh5mji1n1phyZo4ppzzgIZmxiadttrQnMTSdgNdg9DAfFQEvYZ9JwGMUsoHBXPmsV0HGokWLBmwAxx57LMcee+xW79c0jUsuuYRLLrlkwMYghBBCiN3XQF6HCCF2Q+kOVTkRa1DBRXFooRvQ0ViYHkLjCmj6SAUdXekWVOyjKizy00Mq9lHTOgDXcUh8uEI10EzHc6+TUpUXmpYLLfwQHtkZWngCar+EFmKQpbI2HSlVddEYS9GezNKezLCqoYMVm9t5f2OUVQ0dJQsBewyd/UeVMWtsOQeOHcbYigCOS6HqYkNbvFvVRdhnYhm7X2VRr4KM/l5BRAghhBCit+Q6RIg9nOvmwotWiG2BRIuqhgCIN6tKi6aVKrBoWqkCh64MD1ROKu1pMWy8mmJSzMmq18omIZ3ASmyGWFgFFFZALZPqDRVND5HQQuwe8g0621MZWuJp2uIZ4imbTdEkKza3s3xjO+9tbCOZKZ0uMr4yUGjSud+oMrymUai62NCW2G7Vxe6qzz0y8pqammhqasJxSk9UX1cTEUIIIYToKhaLEQqFCre3Jf84IcQQ4rpqikiyLRdeNKtpHHYKNi2Dda/A+jfU8qRdmb7uy52Wj1XLlxYrDi0KlRaGer4VhNAo4s0mjD1YhRfSgFPsRuxcg86OVJaWeJrWDtVgsy2ZZuWWDj7Y1M7S9W00tJcufxrxW8waU86sseXMHDOMiqAH23GJp7O0xjPYbmrIVF1sS5+DjGXLlrFw4UJWrlzZrYO4pmksX7683wYnhBBCiL3TwQcfzEsvvURlZSUHHXRQj58Mua4r1x5CDCWOA+l2SLRCbBMko50rfmx+F9YsgfVvqn4UeVawdLnTqslQVqummJQcO6t6YmRTudDC7QwtPCGIjFXNOy1/rtrCB7ZNZn0C/MNgL1rSWuye8g06Y6ks0XyDznSWZMZhTVMHH22OsWxDGx9vieEUvQ03dY39RpUxa8wwDhxbzviqILqmFaou1rfGC1UXo4ZY1cW29DnIWLRoEePHj+dHP/oRlZWVQ/qHF0IIIcTu6f777ycSiQDwwAMPDPJohBA7zHEg1aYqL6Kb1G07rQKMTe/Cmpdhw9uqWiKvYgLsMxf2OUpND9G6fFJcHFpkkiq00E0VWnjDKrTwhjpDC1MWCRC7p3TWIZrMqCVR42k6kllStkNzLF3oc7F0fRvxdGlTzTHD/IVlUaeNiuCzjELVRUN7Ctt19oiqi23pc5Cxbt06brvtNsaNGzcQ4xFCCCGE4JBDDunxthBiCHBsFVwkWqE9F144WRVebHhLhRcb34XiNoSVk2HCXNjnaDVNJM/OQLajs9ICVDWG6QNvGUTGgTcooYUYMjK2Q1siQ0tHmi3tKTpSWeJpm08aO1ieCy42tpX2gQl7zaLVRYZRHVa/5/mqi6aO1B5ZdbEtfQ4yDj/8cD744AMJMoQQQgixy6RSKT788MMe+3N96lOfGqRRCSEK7KwKL+LNatpIKpYLL+Kw4U1Y/RJsXlb6nOp9VXAx4WjVZDMvE1fPz6ZUs07TD75yNQWkeHqI6dmlP6IQOypjO0QTqknnlmiKjnSW5liad+vbeHNdKx9uipZMFzF0jakjwoUmnROrQxi6Vqi62BxNqqoLwyDk23OrLralz0HGddddx8KFC/noo4+YPHkypll6CLmYEEIIIUR/euGFF/j+979PS0tLt/ukR4YQg8jOqKqLREsuvGjvbOK54U1Y/SJs6fL/5/BpndNGwiPUPtdVy5+mY2qZVcsHgSoI1XQueyqhhRhisrZDNJmluSNFQzRNLJ2hNZ5h2fo2/r26heUboyWPHxXxMWus6nMxbXSEgEe9z05mbNoSGRKZ7F5XdbEtfQ4y3n77bd58801eeOGFbvfJxYQQQggh+tt1113HZz/7Wb71rW9RVVU12MMRYu+WTeUqL5rUaiPpGOCqffWvq8qLxhVFT9Bg5IzO8CJYrXbnl1xNtavKDcuv7gvVqOoLT1BWEBFDTj68aOlI09Ceoj2ZJZrK8P76KK+taWbZ+raSyov9R5Vx5KQqDhpfwYgyH4BUXfTSDlVknHTSSVxwwQVyMSGEEEKIAdfY2MiZZ54p1x1CDJZMEpKt0NEEHQ2Q6VD7482w/nX45EVoXtn5eE2HkTNVz4vxR0KgUu13XRV8pNrBtlVYER6pAgx/ufpeiCHGdlyiiQyt8TSb21PEklliySzLN0V5fXULb9e3YhelF3XDQxw1uZojJ1VRFersddHckZaqiz7oc5DR0tLCN77xDbmYEEIIIcQu8ZnPfIZXX32VsWPHbv/BQoj+kY6rKouOBhVg5MOLRDPUvwafvAAtqzsfr+kweraqvBh/pAomQK1GkmpXPS9cRy2nWlarwgtfBDyBXf2TCbHTbMelPakadubDi3g6w4rNHby2upk317aQsTvDiwlVQY6cXMVRk6oZEVGVF4m0TUN7ioxtqxVGpOqiT/ocZJxwwglyMSGEEEKIXeaqq67ikksu4Y033qCurq5bf64FCxYM0siE2MOkO1TPi44GNXUkHQddV9+v+7cKL9rWdT5eN6H2IBVejJujgglQq5YUhxeeoFqJJFilHmP5B+XHE2JnOK5LazxNe8pmU1SFF8lslo82d/DGmmZeW91CKtvZjLp2mJ+jJ1dz5OQqxgxTgV0yY9MYS5G2HbymTmXIQ3XYS5nfIihVF33S5yBj/Pjx3HTTTXIxIYQQQohd4sknn+Tll1/G4/Hw73//u+Q+TdPk2kOIHZWf6pFoVf0uki0qvDAsaN8Ia19V4UX7hs7nGBbUHqKmjYw9XDXjhM4lV9Md6rieEJSPU+GFv1yWRRVDkuO4tCezNMUSfNCYptXXSsaBlQ0x3ljTwr9XNxNP24XHjyjzcdTkKo6aXM34ygCappHM2DTFUqSyKrwYFrCoLvNR7rcIevv8dlzk9PnMPfLIIwQCAf7973/LxYQQQgghBtzPf/5zLrroIs4991x0feBKbWOxGLfccgvPPvssTU1N7LfffixatIgZM2YA4Lout956K4888gjRaJQDDzyQa665hvHjxw/YmITod26uMWeyDdo3q94X2aSqroiuh7WvqPAitrnzOYYXxh6qKi/GHt45HcTJquen4+p7TwgqJqieGL5yWWlEDEmO49KeytIaT7OlPUVbIkMqleHDpgz/2LSOf3/SQnsqW3h8VcjDkZOqOWpyFZNrQmiaRiqrel4kszZeU6c8oCovygMeqbzoJ30OMp577rmBGIcQQgghRI8ymQyf//znBzTEALjyyiv56KOPuPHGG6mpqeFPf/oTZ555JosXL2b48OHcfffdPPjgg1x//fXU1tZyyy23cNZZZ7F48WK8Xvm0Wezmsik88U1Qn8otc5pU1RWta2Hdq/DJP6GjsfPxpk+FFhPmwphDO6eDOFlVwZHuUH0xPEGonAT+CjVtRMILMQS5rks0mSWayLApmiSazJDK2GxoSfLG2haWrGyiNZEBYgCU+y2OnFTFkZOr2HdkGbqmkc46tMTVMqkeUyfit5gYDlEesAh5TQkv+pnUsgghhBBit3byySezePFizjvvvAF7jWQyyTPPPMMvf/lLDj74YAAuuuginn/+eX7729/y7W9/mwceeIDzzz+f448/HoAbb7yROXPm8Oyzz3LiiScO2NiE2CmOA7FN0Pgx/taPoKoOomthzb9U5UWipfOxVkD1upgwV00fyU8HsTPqcem4Ci+8YaiaAoFhKrwwrMH52YTYCa6rKi/a4hk2F4UXm6Mp3lzbyr9WNtIYSxceH7A0jphczdy6GqaNimDoGhnboS2eIZ7JYpk6EZ/FhOogkYBFWMKLAdXrIOMnP/lJrx53+eWX7/BghBBCCCG6chyHe+65h5deeokpU6Z068/VH9ce2WwW27a7VVZ4vV7efPNN6uvraWhoYM6cOYX7wuEwBxxwAG+99Vafggzbtrf/oD4eqz+PuSeQ85KTaIGWTyC6Cbf5EyrX/w39vf9R00FyXE8Id9wRuPscrVYdMXIVFXYaYo2QiatpJ94yqKwD/zDwlal9eUP0PMvvSc/25PPiui6xlE00kWFze5K2RJZ0xqYhluLtdW38a1UTm6OpwuP9lsFhEyqYM6GCYKqBqXX7YLvQGk+SSNuYuk7YbzJmWJDygIeQ10TXVXjhOM7WhrHH6O/flb4cp9dBxvvvv7/dx0jiJIQQQoj+9uGHH7LvvvsCsGLFipL7+uvaIxQKMWvWLH75y18yYcIEqqqqePLJJ3n77bcZO3YsDQ0NAFRWVpY8r7KyksbGxp4OuVVLly7tlzEP9DH3BHvredGySTyJTXjjmzGTTVRseJ5I4xsMy92fNYO0V82kvepAOiJTVCiRAG3FR+jZOLqdwtV0bCtExluJ7QlgW15oaQfaB/NHGxB76+/J9uwp58V1XZJZl/a0S2vSJpZ2SNvq+4+aMixtSLOlo/MNtKXDftUeDhjuZUqlhWU4ZFMNJDIur7z7IboGQUujwqfj8RoYSY3mVo3mQfwZB9tg/K70Osh48MEHB3IcQgghhBA92lXXIDfeeCOLFi3i6KOPxjAM9ttvP0488UTee++9fn2d6dOnYxhGvxzLtm2WLl3ar8fcE+y158XJQnSjqsKIu2htH6C9/zu0bBJX02mrOZTQzHloo2dRphuUAWRTaqnUbFJVY3jDEBrRWXmhDWxvmsG01/6ebMeecF5c16Ujnau8iKZIJTK4to2eyPDJujaWfNLMJ43xwuNNXWP2uHKOmlTFQeOH4bMMsrZDLGWTSNtouGzZsIajD9yPipCPsM/C0OVD/P7+XckfrzekR4YQQgghBDB27Fgeeugh4vE4sViMmpoavv3tbzNmzBiqq6sBaGpqoqampvCcpqYmpk6d2qfXMQyj398cDMQx9wR7zXlxXdWos2UVtG+Bxg/h9fsgWq/uHz4NZ87FbGx0qBtTh+GkVcPObEqtSOKPQHiKWmnEWwYD3Fh3d7PX/J700VA8Lx2pLG2JDA3tKVriaZIZh3gqy7v1bSxZ1cSHmzsrigxdY+aYco6eXMWh+1QS9JrYjksslWVzNI2ha4R8JuMqg4R9BqvSG5lQUzbkzsmuMBi/KxJkCCGEEGK3c9VVV3HBBRcwYsSI7T528eLFZLNZTjrppH557UAgQCAQoK2tjZdeeonvfve71NbWUl1dzZIlSwrTXGKxGO+88w6nnXZav7yuEDsk1Q4ta6B1HXRshncehrVL1H3+CjjsPJj0abCzmOtfV4/zBlVoER6hmnV6y0CmiIshyHFcOtJZ2pPZovDCJpVxWLpehRfvb4ji5h6vazBtdISjJ1dz+IRKyvxWIbzY0JZA1zTCXpNJNSGGBT2U+UxMQ8e2bQz5f2S3IkGGEEIIIXY7FRUVnHjiiRx44IEce+yxTJs2jeHDh+PxeIhGo3z88ce88cYbLF68mJqaGn74wx/u9Gu++OKLuK7LPvvsw9q1a7nxxhuZMGEC8+fPR9M0FixYwB133MG4ceMKy6/W1NQUVjERYpfKpiG6Hpo/gXgzrPo7LH1ErTCiGTD9FDjw62olkmQbJKPYVhBGzVKrjXjDEl6IISmZsWnPLZXa1JEilsqSsV3SGYf3NrTxyifNvFvfiuN2Pme/kWUcNbmKIyZWMSzowXZcOlJZNrYl0DQIekwmVgcZFvAQ8VuYxt5VlTQUSZAhhBBCiN3Ot7/9bb761a/yyCOP8H//9398/PHHJfcHg0HmzJnDD3/4Q44++uh+ec329nZuvvlmNm3aRHl5OSeccAKXXnoplqWWljznnHNIJBJcddVVRKNRZs+ezT333NNtpRMhBpTjQMcWaFoFHQ2w5X144z6IbVb3j54Ncy6GYeMgk1BhhycMIw6gI74FIrUgpfFiCMlXTLQnMzTF0kSTGeJp1ZyzI5X9/+zdd4AU5f348ffM9r293rg7+tGLAkIUxK5RQf2q6C+WWLBFY4tGo6AGEQRE7FFjxZIEY4vGqGAhVogNQUCK9HKF6237zPz+mN293bujHBzcHXxeZrM7M8888+yze8s+n30Kv5TW8/3mSn7cUk04LnrRN8fDMX2zGNsnm+xkB7phBi9Kan0YBiQ5rPTKSiI9yQxe2CR40ansUSDjhhtuYNasWXg8Ht555x3GjRuH3W7f32UTQgghxCEsKyuL6667juuuu46amhqKi4vx+/2kp6fTvXv3Nl8tbdy4cYwbN26nxxVF4eabb+bmm29u0+sKscd81ZHlVIvMwMWSV6FoiXnMkwtH/R56HQuGBnXFgArpvSGjJ1icoFa0Y+GF2HO+oEadP0SNL0RFQ5D6QJiwrhMOG2yqaODn4lqWba1he7Uv4byemW6O6ZvNMX2zyEt1oRsG3oBGaa0f3TBwO6z0yGgMXtitErzorPYokPHZZ5/h9XrxeDxMmjSJY445ptnyY0IIIYQQ+0tqaiqpqantXQwh2kfIb85tUb0J/NWwZj6s/JcZsLDY4PCLYNiF5sSd/hoI1ENyLmT0BnemOYRE03Z3FSHaTUjTqfeHqQ+EKa8PUOsL4Q/r6IZBcbWf1SW1LN9Ww5rSuoQhI6oC/XKTGdE9naP7ZNE9w20GL4IaJbV+dF0nyWGla7qLTI9DghcHkT0KZPTq1YuHH36YI488EsMw+PDDD/F4PC2mPfvss9uyfEIIIYQQ1NbW8tNPP1FRUYFhGAnH5LuHOGjpGtSVQOUGcx6MkmXmaiS+SvN4j6Nh9PWQkg8hb+MwkvzDITkfLDKKXHRMRiTYUOcPU+0NUtkQxBvUCGk61d4gv5TWs6KoluXba/CFEoNw+alOhnVPZ1i3NA4rSCXJYY3lV1rrRzMMkuwWCtKdZEWCFw6rDKc62OzRp9vUqVOZNWsWn332GYqi8Oijj7bYnVNRFPkyIYQQQog2tXDhQm677bZY79D47yDy3UMctBoqzGEkdSXmMJEfXoLSFeax1K4w+kbofiTo4cZhJBmF5twY9qT2LLkQLQqGder8Ier8Icrrg9T7wwQ0jYaAxoYdDawqqWXZthrK6wMJ5yU7rRzeNY1h3dIY3i2NnBRnLD9v0JzzQjfAbbeQn2YGL1JcNpw2CV4czPYokDFixAhef/11AAYMGMCCBQvaZGjJE088wV/+8peEfb169WL+/PkABAIBZs2axQcffEAwGGTs2LFMmTKFrKysfb62EEIIITqHBx54gAkTJnDrrbficrnauzhC7F/BBqjeYi6p6q+D1f+G1f8BQwerE0ZcAkPPB9UGvioINJjLqGb0BneGrEQiOgxdN6gPhqn3h6lqCFLlC+ELhgmENbZU+FhTUsfyoho2lDUknGdVFQblp0QCF+n0zk5CVRTCmo4v1NjrwmZRcNut5KU5SXHaJHhxiGl1f7NPP/2UjIyMNitA3759mTt3bmzbEjeL8owZM/j888959NFHSU5OZtq0adxwww289tprbXZ9IYQQQnRspaWlXHrppRLEEAc3LWRO4lm5Afy1sP07+OFlCNSaxwtPhCOvBU+OOYykrhQcKTKMRHQo8UujltcHaAiGCYR1Sqr9/LKjnpVFtawqriWo6Qnn9cx0M6xbOsO7pTEoPwWnzYJuGPiCGpUNQQJhDauq4rJbyE93ku62k+Sw4rFbUVUJ3h2KWv2JV1BQQG1tLW+++Sbr168HoE+fPpx33nkkJye3ugAWi4Xs7Oxm++vq6njrrbeYM2cOo0ePBszAxrhx41i6dCnDhg1r9bWEEEII0fmMHTuW5cuX061bt/YuihBtzzDMZVQrI8up1myH71+A8rXm8fRecPRNkD88cRhJVl9I6y7DSES70nSDen+YukDi0qhVDUHW7ahndUkdy7fXUOMLJZyXkWSPDRU5vFsa6W47hmEQCOt4I8ELRQGX3UJGkp2MJDvJTitJDqsskyqAvQhkLF++nKuuugqHw8Fhhx0GwEsvvcRf//pXXnzxRQYPHtyq/DZv3szYsWNxOBwMGzaMP/7xj+Tn57NixQpCoRBjxoyJpS0sLCQ/P3+vAhlaG87UHM2rLfPs7KROmpM6aZnUS3NSJy2Temluf9RJZ6jf4447jgcffJD169fTr18/rNbEry8nnXRSO5VMiH3krzWHkNRsNXterHgbfllgHrMnwcgrYND/gWJpPowkSVYQFO2jpaVR6wMhNuzwsra0jhVFNWyrSlwW1WlTGZKfyvDuaQzrlk63dBeKohAMm8NFovNcOKwqHoeVHpkuPE4bHodVhouIFrU6kDFz5kxOPPFEpk2bFvsiEQ6Hufvuu5kxYwZ///vf9zivww47jJkzZ9KrVy/Kysp48sknufjii3nvvfcoLy/HZrORkpKScE5mZiZlZWWtLTbLly9v9TntkWdnJ3XSnNRJy6RempM6aZnUS3OHWp3cc889ADz55JPNjimKwqpVqw50kYTYN+GA2fOiaiME6mDLIljyNwhF5groPw5+dTW40s1hJA0VkWEkwyA5T4aRiAOqpaVRG4IaWyob+KW0gVXFtawprUOLWxdVVaBvTjLDupmTdPbvkozNoqLpBt5gmLK6AOG4eS56piSR6rLhcVpx2SwtLiwhRLxWfwquWLEiIYgBYLVaueqqq5gwYUKr8jruuONijwcMGMDhhx/OCSecwIcffojT6Wxt0XZp6NChCfNv7AtN01i+fHmb5tnZSZ00J3XSMqmX5qROWib10tz+qJNonh3Z6tWr27sIQrQNXYf6Uqhcby6nWr0FvnsOqjaZx7P7w9E3Q86gyDCSEkCJDCPpAXZ3e5ZeHCIMw8AX1imp8VMXMId5NATCFNf6WVdqDhdZWVRDQzCxR19eqjMWuDisIA2P04puGPhDGjW+UMI8F3kyz4XYR60OZHg8HoqLiyksLEzYX1xcTFLSvo3RS0lJoWfPnmzZsoUxY8YQCoWora1N6JVRUVHR4pwau2OxWNr8i/D+yLOzkzppTuqkZVIvzUmdtEzqpTmpEyE6IW+lGbCoLTJ7Yfz0Gmz4zDzmSIEjrzF7YqCYw0iCXvDkyjASccCENZ0qb4jtVQ2sLgtSrJazrswcLrKyqJYddYnLonocVg7rmsrwbukM655GlxRnwjwXRdU+medC7DetDmSMGzeOu+66izvuuIPhw4cDsGTJEmbPns348eP3qTANDQ1s3bqV7OxshgwZgs1mY/HixZx66qkAbNiwgaKiIpnoUwghhDjEfPvtt7z44ouxicYLCwu56qqrGDlyZDuXTIjdCPnMeTCqt5jDRDZ8BsvmQdgPigoDzzLnwnCmNA4jcaZC3uEyjEQcEP6QRnl9gKJqH+vLGvjqlzKWbqqjqG4FRlw6q6owoEsyw7qbq4sUZnuwqAohzQxclNT60HRzPgyZ50Lsb63+ZPzTn/4Uu49OEGa1Wrnwwgu57bbbWpXXAw88wAknnEB+fj47duzgiSeeQFVVzjjjDJKTk5kwYQKzZs0iNTUVj8fD9OnTGT58uAQyhBBCiEPIu+++y+TJkznllFO45JJLAPNHlMsvv5yZM2dy5plntnMJhWiBFllhpHID+GvM4STfPge1283jXYaaw0gy+5jDSGpLQFVlGIk4IAzDoNYXpqzOT3GNjx82V/HFLxUs3VpF3FQXdM9wx1YXGZyfistuic1zUVHf8jwXSQ4rbrvMcyH2r1YHMux2O3fffTd//OMf2bJlCwDdu3ffq7XdS0pKuPXWW6muriYjI4MjjjiC119/nYyMDAAmT56MqqrcdNNNBINBxo4dy5QpU1p9HSGEEEJ0Xn/961+5/fbbufzyy2P7Lr30UubOnctTTz0lgQzRsRgGeCugcqM5H4a/Bn78mzmhJ4A7E466Dgojq+1Eh5EkdzGXWpVhJGI/Cms6ld4gJTV+tlZ6+fKXMr5YW05RjT+W5rCuqQxICXPaqAFkJ7ti81w0BMNU+YJYFAWX3UKXVCfpSXY8TitJdisWmedCHEB73VfN5XLRv3//fbr4I488ssvjDoeDKVOmSPBCCCGEOIRt3bqVE044odn+E088kYcffrgdSiTETgTqzXkwaraaQ0d++RiWvw5ayFxCdej5MOJSs7eFDCMRB5AvaA4f2V7tY01JLZ+vKWPRhgr8IR0Al83CSQNyGDc0j/w0BytWrUFRiM1z4bQ1znPhcVjxOGWeC9G+5NNSCCGEEB1aXl4eixcvpkePHgn7Fy1aRF5eXjuVSog44aA5ZKRykzmRZ/kq+PZ5aNhhHi8YCUffaA4ZkWEk4gAxDIMaX4gdtWYA438bKvjyl3J+Lq6Npema7uKMoXmcMCAHh9VCrT9EUbUPX0jHZbPQM9NFskvmuRAdjwQyhBBCCNGhTZw4kenTp7Nq1aqEicb/9a9/cdddd7Vz6cQhTdehocycB6OhDPzV8MNc2L7EPO7JhdE3QM+x5ra30pz8M7mLuRqJO6Pdii4OXiFNp6ohSHGNn40V9Xy2uowvfymnoiEIgKrAkb0yGT80j8O6phII69T4QtQYIVJcNgZ2SSbFa2dkj3SsVmkuio5J3plCCCGE6NAuuugisrOzefHFF5k/fz4AvXv35pFHHuHkk09u59KJQ5av2lyNpHY7aAFY9R9Y+S8wNLDY4fALYdhFYHWYc2B4K82VSfIOh5R8UOXXbdG2vMEw5XUBimr8LN1Sxedry/luUyXhyOydKU4rpw7uwmlDupDlcVDvD1NU48NhVclJcZhzXrjtqBhUblVlsk7RoUkgQwghhBAd3imnnMIpp5zS3sUQAkJ+qN4K1Zsg7IOiZfD9C+aknWD2vjjqekjJazKMpI8MIxFtTtfN4SOltX62Vnn5el05X6wtZ0N5QyxNv1wP44fmM7ZPFooCNb4QxTU+kp02+uYkk5XsIMVpjQUuoitTCtGR7VUg45133uG1115j27Zt/POf/6SgoICXXnqJrl27yi8jQgghhBDi4OSrgtKVZu8KbwV89zyUrjCPpXaDMTdCt19FVi6pNCf0TM6TYSSizQXDOpUNQYprfKwtreO/q8v4en05df4wADaLwjF9sxk/NI++OR4aghrlDQEsikKGx0ZeajIZSXYcVukZJDqnVgcy/vGPf/D4449z2WWX8de//hVdN2e6TUlJ4eWXX5ZAhhBCCCH22a9+9Svmz59PRkYGo0aN2mUX52+//fYAlkwcsoINULoKarbD6vfMoSQYYHXCiMtg6HlgsUWGkURWI8kfbgYyZBiJaCMNgTDl9QGKqnx8s6mSz9eUsWxbNZHRI2QnOzh9SBd+PagLSXYLtf4wRTV+3HYLvTKTyE52kOqyocpSqaKTa3Ug429/+xvTp0/n5JNP5tlnn43tHzJkCA888ECbFk4IIYQQh6ZJkybh8Xhij2WstmhXWsgMYqx4E5a/AYHIqg+FJ8FR10JSdpNhJP0grbsMIxFtQtcNqn0hSmt9bC738t+1O/hybTlFNf5YmmHd0hg3NI9f9cwgGNap9YfwhcKkumwU5iSRmeTAZZeAmjh4tDqQsW3bNgYOHNhsv91ux+fztUmhhBBCCHFoO+ecc2KPzz333HYsiTjk6TqUrYUlL8OPr5j7MnrDmJsgf5gMIxH7TTCsU9EQoKjax8rttSxcs4P/bajAHzJ7xLtsFk4akMO4oXnkp7moD4QpqfXhtFnokuqMTd5pkd4X4iDU6kBG165dWbVqFQUFBQn7v/zySwoLC9usYEIIIYQQAAMHDuSrr74iMzMzYX9VVRVjxoxh1apV7VQycUio3gyr3oWlfzO3h18CR1wGqlWGkYj9oj4QprzOz9ZKH1+tK+eLX8pYVVwXO94t3cX4oXmcMCAHi6pQ4wtRUusj1WlnQJdkMj0Okp22dnwGQux/rQ5kTJw4kfvuu49g0FyH+KeffuI///kPzz77LNOnT2/zAgohhBDi0GYYRov7g8EgNpt8WRf7UW0xrPsUFj0Bhg79ToeRV5hLrMowEtGGdN2gyhuktNbPuh31fLp6B1/9Uk5Fg9nmUhU4slcm4w/LY2h+Cg1BjWpfEKtFJcvTuHSq3aq28zMR4sBodSDj/PPPx+Fw8Oijj+Lz+fjjH/9ITk4OkydPZvz48fujjEIIIYQ4BL3yitmNX1EU3njjDdzuxoairut899139O7du72KJw523krY+g18MdscNtLlMBh7i7lyiQwjEW0kENaoqA9SVO1jyeYqFq7ZwfebqghHZu9McVo5dXAXThvShXS3nVpfiOJaPx6HlcJsD9keJykuq8wjJA45e7X86llnncVZZ52Fz+fD6/U26+ophBBCCLGvXnrpJcDskfHaa6+hqo2/NNpsNrp27crUqVPbqXTioBZsgOJl8NksqC81gxa/vg98lWB1yTASsc9q/SHK6wJsrvDy+dodfL6mjI0V3tjxfrkexg/NZ2yfLMK6To0vRGVDkDS3jX5p5tKpTpu8/8Sha68CGeFwmG+//ZYtW7ZwxhlnAFBaWorH4yEpKalNCyiEEEKIQ9PChQsBuOSSS/jLX/5CampqO5dIHBLCQXOFkq8egbJVYEuC02aak35a7JB3mPTCEHtFiwwfKanxs7q4lo9X7eDrdeXUB8IA2CwKx/TNZvzQPAqzPdT6Q5TV+3HZLXTLcJGb4iJNlk4VAtiLQMb27du56qqrKC4uJhgMcvTRR+PxeHjuuecIBoPcd999+6OcQgghhDhEvfrqq+1dBHGo0HUo/wW+fx42fg6KCif/GZKywF8P+YdLEEO0mj+kUdEQZHuVl8XrK/jvmjKWbasmOv1PdrKD04d04deDuuCwqtT4QpTW+Ul12eiVlUpGkp0kx179/izEQavVfxH3338/Q4YM4d133+XII4+M7T/llFO455572rRwQgghhBAAJSUlfPrppxQXFxMKhRKOTZo0qZ1KJQ46VZtg5Vuw7DVze/T1kDcMGsogZzCk5Ldn6UQnYhgGtf4w5XUB1pfVs3B1KZ+vLae4xh9LM6xbGuOG5jGyRzreoEZ9IExIU8hJMSfvzHDbsVpk8k4hWtLqQMYPP/zAvHnzsNvtCfsLCgooLS1ts4IJIYQQQgAsXryY6667jm7durFhwwb69u3L9u3bMQyDQYMGtXfxxMGitgh++QgWPwUYMPBMGHgW1JVARiGk92zvEopOILr6SHGNn6Vbq/j451K+2ViJP6QD4LJZOGlADuOG5pGb4qTGF2JHXYBkp5V+uR6ykh0kO2TyTiF2p9WBDF3X0XW92f6SkhKZH0MIIYQQbe6hhx7iiiuu4KabbmL48OE88cQTZGRkcNttt3HMMce0d/HEwcBbCZv/B18+CGEf5I+AMTdCXSmkFEBWX3OpVSF2IqzpseEjn68tZ8HKElaX1MWOd0t3Mf6wfI7vl4WBQq0vRJUvSGaSnbxUF+lJNhxWmbxTiD3V6kDG0Ucfzcsvv8y0adNi+xoaGnjiiSc47rjj2rRwQgghhBDr16/n4YcfBsBqteL3+0lKSuLmm2/m97//PRdddFE7l1B0asEGKFoKn8+AhnJI7Qon3wsNleDOhJwBYLXvLhdxiAqENcrrg2ypaODTVaUsWFnK1iofAKoCR/bKZPxheQzqkkxdQKPaFyLJYaVXVhI5KQ5SnDJ5pxB7o9WBjDvuuIOrrrqKcePGEQwGue2229i0aRPp6emxLxlCCCGEEG3F7XbH5sXIzs5my5Yt9O3bF4Cqqqr2LJro7MJBKP0ZvpxjTvJp98CpM0EPgc0JuQPBLj2ORXPeYJiyugAbyhr4+OcSPlm1gx11AQCcNpXTBudx5uF5eBxW6vxhKrwh0tw2CnOSyExy4LJL7wsh9kWrAxl5eXm8++67fPDBB6xevRqv18t5553HmWeeidPp3B9lFEIIIcQh7PDDD+eHH36gsLCQ4447jgceeIC1a9fy8ccfc/jhh7d38URnpetQvha+eQY2f22uUHLKVHClQcgHeUPAld7epRQdTK0/RGmNnw1lDcxfWczC1WXU+MxAa7LTylmH5zNuSBcMFOoDIXwhjfw0JzkpTtLddizS+0KINtGqQEYoFOL000/nmWee4ayzzuKss87aX+USQgghhADMVUkaGhoAuPHGG2loaOCDDz6gZ8+e3Hnnne1cOtFpVW2C5W/AijfM7aP/ADmDwFdl3id3ac/SiQ7EMAyqvCFKanysK63ng5XFfLG2HG9QAyDL4+Cc4QWcPDCHYFinLhAm2WljYF4KmR4HHlk6VYg216q/KpvNRiAQ2F9lEUIIIYRIoGkaJSUl9O/fHzCHmdx3333tXCrR6dUWwZr58M3T5vaQCdD/dHOFkqx+kNajfcsnOgRNN6hoCFBc7WdVcS0LVpbw1bpyQpoBmBN4ThjRlaP7ZOELalR5g6S57AzOTyU72YHTJsNHhNhfWh0evPjii3nuueeYPn06VqtEF4UQQgix/1gsFq644go++OADUlJS2rs44mDgrYRNX8NXD0I4AF1HwZHXmkGMtG6Q2UdWKDnEBcM65fUBtlf7WLGthgUrS/h2UyW6Gb+gX66H847oxsge6dT6QlQ0BM35L3LTyPLYZfURIQ6AVkcili9fzuLFi/nqq6/o378/Lpcr4fhf/vKXNiucEEIIIUTfvn3Ztm0b3bp1a++iiM4uUA/bf4DPZpgBjbQecNKfoaEMPDmQPQAs8kPdocoX1Cir81NU7eeHLZV8tLKUZdtqYseHdUvj/CO6MrBLMrX+MDvq/GQk2enXJZnsZAc2iwTAhDhQWv1JnZKSwqmnnro/yiKEEEII0cwf/vAHHnjgAW6++WYGDx6M2+1OOO7xeNqpZKJTCQdhx8/wxYNQuQEcKXDaDHNiT7sHcgaCzbX7fMRBp84fpqLBx/ZqL99urOLjn0tZU1oHgAKMKczkvCO60SPTTbU3RHlDkIwkOwPSU8jy2LFKAEOIA67VgYyZM2fuj3IIIYQQQrTommuuAeC6665DURpn/DcMA0VRWLVqVXsVTXQWumauULL4Kdj6DahW+PU0c2lVLWxO7ulMbe9SigPIMAyqvUE2V4eo3FTJ/zZW8cnPpWyu9AJgVRVOGJDDhOFdyUlxUNUQpKIhQGaSg67pLjI9DlmBRIh21GH6zj377LM89NBDXHrppdx1110ABAIBZs2axQcffEAwGGTs2LFMmTKFrKysdi6tEEIIIQ6UV155pb2LIDozwzBXKFn6D/j5X+a+Y/4ImX3BXw1dDgNPdnuWUBxAmm5Q2RCkuMbH9soGFqz38uP/VrKjzlzQwGlTOW1wHmcPyyfZaaPSG6SyIUiWx05+uovMJAlgCNERtDqQcfbZZyf8GhKlKAp2u50ePXpwzjnncNRRR+1xnj/99BOvvfZabEbyqBkzZvD555/z6KOPkpyczLRp07jhhht47bXXWltsIYQQQnRSv/rVr9q7CKIzqy2C1e/Dt8+a24ddAH1OgvodkNUfUru2b/nEARHSdCrqg2yv9rK9ysfna8v4dNUOqn0hAJKdVs46PJ/xQ/OwWVRqfCGqvEFykh0UpLvIcNtRJYAhRIfR6kDGMcccw7x58+jXrx+HHXYYYE4AumbNGs455xzWr1/PxIkTeeKJJzj55JN3m19DQwO3334706dP5+mnn47tr6ur46233mLOnDmMHj0aMAMb48aNY+nSpQwbNqy1RRdCCCFEJ/Tdd9/t8vioUaMOUElEp+OtgE1fwZcPgR6CHkfDyCugrhTSe0FmIbTwA504ePhDGmV15gokWyu9fL62jIWrd+ANagCkOVTOG9WdUwfnAVDlDWKxKHRJdZKf5iLdbWvxR1whRPtqdSCjqqqKiRMncv311yfsf+qppygqKuLFF1/k8ccf56mnntqjQMZ9993Hcccdx5gxYxICGStWrCAUCjFmzJjYvsLCQvLz8/cqkKFpWqvS70lebZlnZyd10pzUScukXpqTOmmZ1Etz+6NOOkP9XnLJJc32xTcsZI4M0RI17IOiH+G/95tDSDIK4fjJ5golyV0gux+oskzmwao+EKas1s/2Gj+byuv5bE0ZX6wtJ6jpAHRLd3HO8Hy6UEWv3jlUNgSxWxW6prvIT3OR6pIAhhAdWasDGR9++CFvv/12s/3jx4/n3HPPZfr06YwfP565c+fuNq/333+fn3/+mTfffLPZsfLycmw2W7M14zMzMykrK2ttsVm+fHmrz2mPPDs7qZPmpE5aJvXSnNRJy6RemjvU6qRpj4xQKMSqVat47LHHuOWWW9qpVKJDCwdw1qxHXfYyVG8GVzqcej+E6s1JPXMGgtXR3qUUbcwwDGp9YUpr/RTX+FhXVs/CVTtYvKEC3TDT9Mv1cN4R3TiyVwb1/iDLVlXgDWr0yHSTl+oi1W1r3ychhNgjrQ5kOBwOfvzxR3r06JGw/8cff8ThMP9BMAwj9nhniouLuf/++3nxxRd3m7YtDB06FIulbaLumqaxfPnyNs2zs5M6aU7qpGVSL81JnbRM6qW5/VEn0Tw7suTk5Gb7jj76aGw2G7NmzWrxBxZxCIusUNJlw+soJT+AxQa/nm4GLgzDXKHE0fw9JTovXTeo9AYpqfGzo87P6uI6PllVypIt1bE0w7qlcf4RXRlakEpDUKO4xo/DAnkeC0f0SCMtydl+T0AI0WqtDmT89re/ZcqUKaxYsYKhQ4cC5i9Db775Jr/73e8A+Oqrrxg4cOAu81m5ciUVFRWce+65sX2apvHdd9/x97//nRdeeIFQKERtbW1Cr4yKigqys1s/s7TFYmnzL8L7I8/OTuqkOamTlkm9NCd10jKpl+akTkyZmZls3LixvYshOhLDgMqNKEv/TnrJV+a+Y/8E6T3AXw95h0FSZvuWUbSZsKZT0RCkqNpHeV2AFUW1fLSyhFUldQAowJjCTM47ohuF2UnUB8IU1fhw2630yUki22NnnXc7yU7phSFEZ9PqQMbvf/97unbtyt///nf+/e9/A9CrVy+mTZvGmWeeCcAFF1zAhRdeuMt8jjrqKN57772EfZMmTaJ3795cffXV5OXlYbPZWLx4MaeeeioAGzZsoKioSCb6FEIIIQ4hq1evbrZvx44dPPfccwwYMKAdSiQ6rNoiWP0eyg8vAqAPuxi117HQUG72xEjJb+cCirYQCGuU1wfZXuWlvC7A0q01LPi5hM0VXgCsqsIJA3KYMLwr+WlOav1mACPJYaVfbjK5KU6SHNZOMUeQEKJlrQ5kAJx11lmcddZZOz3udO6+a5bH46Ffv34J+9xuN2lpabH9EyZMYNasWaSmpuLxeJg+fTrDhw+XQIYQQghxCIku/W4YRsL+YcOGcf/997dTqUSH01ABGz+HLx9G0cPUZg4nafil5jKrGb0hvaesUNLJeYNhdtT62V7tp7IhwPebqpm/soTSWj8ATpvKaYPzOHtYPulJdur8YYpq/HicVgZ0SSY3xYXLLr3ZhDgY7FUgo7a2lgULFrB161auuOIK0tLSWLlyJVlZWeTm5rZZ4SZPnoyqqtx0000Eg0HGjh3LlClT2ix/IYQQQnR8n376acK2qqpkZGQckDm2RCcRqINt35srlARqMTL7UtTvUvrWl0JqV8jqB6ra3qUUe6nGF2JHrZ/iGj8VDQH+t76SBStLqPaFAEh2Wjnr8HzGD80jyWGlxheiuMZHitPGoLxkclKcOG0SwBDiYNLqQMbq1auZOHEiycnJbN++nfPPP5+0tDQ++ugjiouLmT179l4X5tVXX03YdjgcTJkyRYIXQgghxCFK13UWL17Mxx9/zPbt21EUhYKCAk477TT+7//+T5ZHFBAOQMlK+GwG1GwDdyb6r6djWfcLuPtCzgCw2tu7lKKVDMOg2msGJEpr/ZTXB/hqXQWf/FxKQ9AcEpLlcXDO8AJ+PSgXm0WlxheipNZHqstO7+xUspMdOKwSwBDiYNTqQMasWbM455xz+NOf/sTw4cNj+4877jhuu+22Ni2cEEIIIQ5dhmFw3XXX8fnnnzNgwAD69euHYRisX7+eO++8k48++oinnnqqvYsp2pOuQdla+PoRKF4KFoe5zKqiYljskD0I7EntXUrRSjW+ENurvBTX+imt8fP5mjL+u6aMoKYD0C3dxYQRXTm2XzaqolDtDRLQdFJdNvrkppHtcWC3Sg8cIQ5mrQ5kLF++nPvuu6/Z/tzcXMrKytqkUEIIIYQQb7/9Nt999x0vvfQSRx11VMKxxYsXc/311/POO+9w9tlnt08BRfsyDKjYAN/PhbXzzX0nTIKUAgg04EvuBa60di2iaJ36QJiiKh9F1T42V3r55OdSvl5fjh6ZHqdfrofzjujGkb0y0HWDal+IYFgjw+Ogf7qLLI8Dm+XgCWDouk4wGDzg141Ogur3+2WFrAipk5btTb3Y7XbUNhjq1+pAht1up76+vtn+TZs2kZGRsc8FEkIIIYQAeP/997n22mubBTEARo8ezTXXXMN7770ngYxDVe12+PkdWPKSuT3yCuh2JPiqIHMA4YbqdiycaA1fUKOo2sf2yO2Tn0v575odsQDGsG5pnH9EV4YWpBLWDcrrA2i6QabHTtf0FDKT7FgPogAGQDAYZOPGjei6fsCvbRgGVquVzZs3y/C9CKmTlu1NvaiqSq9evbDb923IX6sDGSeeeCJPPvkkjz76aGxfUVERc+bM4de//vU+FUYIIYQQImrNmjXcfvvtOz1+7LHHNptfSxwiGsph3UL4+lEwNCg8CQ67AOpLIasvpHeHLdXtXUqxG4GwRmmNny1VPkprfXy+upz5K0tiQ0h+1TODC3/VnT45HkKazo66AAaQ5bFTkO4iM8mBRT34GpWGYVBcXIzFYqFbt25t8ut1a6/v8/lwuVzSaI+QOmlZa+tF13WKioooLi6me/fu+1SXrQ5k3Hnnndx0002MGTOGQCDAJZdcQnl5OcOGDeOWW27Z64IIIYQQQsSrqakhMzNzp8czMzOpqak5gCUSHUKgDrZ+B5/PhGA95AyEY2+Dhh2Q1g0y+wAH16/zB5toUGJrpZcdtX4Wra/gPz8VUx8IAzAwL4XLRvdgcH6qGeyILK+anewgP81FZpId9SAMYESFw2G8Xi/5+fm43e4Dfn3DMNB1HafTKY32CKmTlu1NvWRnZ1NUVEQ4HMZms+31tVsdyEhOTmbu3Ll8//33rFmzBq/Xy+DBgxkzZsxeF0IIIYQQoilN07Bad/5VxWKxxMbntsW1nnjiCf79739TXl5OTk4O55xzDr///e9jX84Mw+Dxxx/njTfeoLa2lhEjRnDvvffSs2fPNimD2AMhP5T8BP+dDrVFkJQDv55uDidxZ0P2ALDYoI3eF6JtaZFhIVsqveyoDbBkcxXvLNtORb05D0T3DDeXje7BqJ4ZhDTDDGAokJPioCDNRbr74A5gREU/1/a1670QHVH0fa1p2oENZESNHDmSkSNH7vWFhRBCCCF2xTAM7rzzzp1+mW/LSfCee+455s2bxwMPPECfPn1YsWIFkyZNIjk5mUsvvTSW5tVXX2XWrFl07dqVxx57jCuvvJIPPvgAh8PRZmURO6FrUP4LfPEQlK4AqxNOmwGGDnYP5A4Em6u9SylaoOsGFQ1BtlWZPTBWbK/lX0u3s63KB5jLqF58ZHdO6J+DYRiU1QcwDDOA0TXdTbrbdkj+Cn4oPmdx8Gur93WrAhm6rvP222/LWu5CCCGE2O/OOeec3aZpq4k+f/zxR0466SSOP/54ALp27cr777/PTz/9BJhBlVdeeYXrrruOk08+GYDZs2czZswYPvnkE8aPH98m5RA7EV2h5NvnYP2ngAIn3g1J2aCFzOElztT2LqVowjAMqr0htlaZPTDWltbx7tIi1pTWAZDssHL+yK6MH5qPRY0soxrWyE520i3DfdAPIRFC7L09DmTIWu5CCCGEOJBmzpx5wK41fPhwXn/9dTZu3EivXr1YvXo1P/zwA3feeScA27Zto6ysLGEobXJyMocffjg//vhjqwIZbTUcJj6vtsyzQ6rZBsvfQF36Kgqgj7oaI2+4OaQkdyi4MhOGkxwy9dIKB7pOan0htlf7KKnxs6XCy39WlLAkMgGr3apy1mF5nDM8H7fdQo0viDcYJsNtp29OCtnJ5iSehqHv11FCHfV9omkahmHEbgda9Jrtce2OSuqkZXtTL9H3taZpzf72WvO3uMeBDFnLXQghhBAHq2uuuYb6+npOP/302Nwbt9xyC2eddRYAZWVlAM0mH83MzKS8vLxV11q+fHnbFHo/59lRWAPVpJZ8RbeVT6EYOtU5R1FiG4J15ff4PT0IeMtBqWjx3IO5XvbW/q4TX1in3KtR3qBR1qDxTVGAZaVBDEBVYFS+g5N7u0l1+Pll3ToagjpJdpXcJCsEVEpqFEr2awmb64jvE6vVis/na5flV6N8Pl+rz5kyZQrvvfceEyZM4K677ko4NnPmTN544w3OPPNMpk6dGkvb1OjRo3nyyScBGD9+PMXFxQA4nU66du3KhRdemNBj7/vvv+eaa67h888/Jzk5eY/KOX78eC666CIuvvjiZseKioo444wzYttWq5UuXbpw1llnceWVV8oohBa05r0SCAQIhUKsXr16n665x4EMWctdCCGEEAerDz/8kPfee4+HHnqIPn36sGrVKmbOnBmb9LMtDR06FIvF0iZ5aZrG8uXL2zTPDiVQC5u+RF30dxTNj5E7lOTT7iG5oQLShkLuYFCbf5096OtlL+zvOvGHNIprfNRV+Qkpfn7YXsbHP+8grJu/1I4pzOTiI7tRkObCGwxT4w2RY7fSLd1Fl1QnTtuBf5066vvE7/ezefNmXC4XTqfzgF9/X5YatVgs5OXl8dFHH3HPPffEyh8IBFiwYAH5+flYLBbcbjcWi4VjjjmGGTNmJORht9tjq7UoisJNN93E+eefj9/vZ/78+UybNo1u3bpx7LHHAsTmKHK5XHu8youiKAnXiRct89y5c+nTpw/BYJAffviBe+65h7y8PM4///xW1cnBbG/eK6qqYrPZ6NOnT7P3d/Rvck/scSBD1nIXQgghxMFq9uzZXHPNNbEhIv3796eoqIhnnnmGc845h+zsbAAqKirIycmJnVdRUcGAAQNadS2LxdLmjab9kWe7C/lhx0r47wyoL4XkLiin3IclUA2pXSB3ENh2PcnqQVkv+6it6yQQ1iit8bO1ykdZnZ+v15lLqfpCZhfxw7qmctnonvTLTcYf0iitC+KwqBTmpFCQ7iLJsddrD7SZjvY+sVgsKIoSu7WXvbm+oigMGjSIrVu38vHHH8d6tX388cfk5eXRtWvXhOdmt9sTPlNbyi8pKSmW5pprruGFF15g0aJFHHfccbE0rS1v/Dk7O5aenh67bkFBAW+++SarVq2SHhktaG3dK4qyz393e/zJIWu5CyGEEOJg5ff7m30Js1gssXG/Xbt2JTs7m8WLFzNw4EAA6uvrWbZsGRdeeOEBL+9BTwvDjtXw+WwoWwU2N5w6E/QgOFIhZxDYDvwv1aJRWNPZUWcupVpRH+DbjVW8u3Q71b4QAL2zk7hsdE+Gd0sjrDcupdot3UVBuptU194vu3goMgwjFhw6ENfyBjVcLmOvG+0TJkzg7bffjgUy3nrrLc4991y+/fbbvS6Xrut8/PHH1NTU7NOynXtj+fLlrFq1inPPPfeAXlfs3B4HMg7kWu5CCCGEEAfSCSecwF//+lfy8/NjQ0vmzp3LhAkTAPMXpEsvvZSnn36aHj16xJZfzcnJia1iItqIYUDlBvjuGdj4OSgqnDQFXGnmUqs5g8CxZ+PgRdvTdIPy+gBbK73sqAuwYlsNb/+4nZJaPwBdUpxcclQPxvbNwjCgvD6IZujkRFYiOVSXUt0XhmFw3l8X88PmqgN63ZE90nnj2tF79XqdddZZPPTQQ2zfvh2AJUuW8PDDDzcLZHz22WcMHz48Yd/vfvc7rr322tj2nDlzeOyxxwgGg4TDYdLS0g7I8I4LLrgAVVUJhUKEQiHOPfdcmUahA2nVqiUHai13IYQQQogD6e677+axxx5j6tSpseEjv/nNb7j++utjaa6++mp8Ph9//vOfqa2t5YgjjuD555+Pjc8WbaRmG/z0T1g6z9w+6jroMgT8tZB3OCTtvIew2H8Mw6CiIci2SABjbWkdb/+4nQ1lDQCkuWxcMKobvx7cJbKUagh/WCMzyU73TDdZSQ5ZSnUfdLaay8jI4Pjjj+df//oXhmFw/PHHk5GR0SzdkUceyb333puwLzU1cSnlK6+8knPPPZeysjJmz57NRRddRI8ePfaoHH/961955plnYtvvv/8++fn5e3TuI488QmFhIeFwmLVr1zJt2jQeeughbrvttj06X+xfexzIOJBruQshhBBCHEgej4e77rqr2Sz78RRF4eabb+bmm28+gCU7xNSXwdoFsPgvgAEDzjRvDeXmnBgpe9YAEW3HMAyqvSG2VnnZURtgc3kD7y4r4qft5pByl83CuSMK+L/DC3DaVGr9YeoDIdLcdvrmeshJdmC1qO38LDo3RVF449rRB3ZoiddHZqpnn3rPTJgwgfvuuw8wVzNpicvl2m1QIj09nR49etCjRw8ee+wxzjzzTIYMGUKfPn12W4YLLriA008/Pba9q/k4msrLy4uVrXfv3qxfv56nn36aG2+8UQLYHcAeBzIO5FruQgghhBDiEOOvhc2L4PNZEPZD/nAYc70Z3MjoDek9QYYkHFC1/hDbq7wU1/gpqvbxwfISFq03l7q1qgrjhubx/0Z2I9Vloz4QpqjGh8dpY1BeCrmpThzWjjOBZmenKApu+4GZGNUwDAhb9nkI0DHHHEMoFEJRFMaOHdsmZcvLy2PcuHE89NBDPP3007tNn5aWRlpaWptcW1VVwuEwoVBIAhkdQPtPEyyEEEIIIQ5tIT8UL4OF06ChDFIK4KR7oaHC7IWR1Q9UaRQfKA2BMEXVPrZX+9hRE+Dj1aUsXL0DTTdQgOP6Z3PxkT3okuLEF9TYXu3FZbfQNyeZ/DQXLru8VsKcQ/HDDz+MPW5JMBikrKys2XktDUOJuvTSSznjjDNiS+dGrV27lqSkpNi2oii7XFWqtLSUVatWJeyLH3ZSXV1NWVkZmqaxZs0a5s2bx5FHHonH49lpnuLAkUCGEEIIIYRoP9EVSj6bCRW/gD0JTpsJYZ85wWfOQLC2PEebaFv+kEZxtY9t1T7K64J88UsZHywvJhDWAXPyx0tH96RXVhLBsE5xjQ+bRaFnZhIF6S6SnbISiUi0u0b/l19+2ay3Rq9evZg/f/5Oz+nTpw9HH300jz/+OM8991xs/8UXX5yQzmKx8PPPP+80nxdffJEXX3wxYd/s2bM54ogjALj88stj+WRnZ3P00Udz++237/L5iANHAhlCCCGEEKJ9RFco+d+TsPlrc4WSk6eawQws5gol9qTdZiP2TTCsU1rrZ0ull8qGAP/bUMm/lxVR5w8D0D83mcvG9GRoQSohTWdHrR8DyE9z0TXdRZpbAk3CNGvWrF0ef+qppxLS7i79woULW9z/wgsvxB4feeSRrFmzphWl3Hm+UU3zM+cN8eJ2u1t1HbH/SCBDCCGEEEK0j5qtsOwfsPx1c/vomyG7PwS9kDcY3DvvXi72XVjT2VEXYEull6r6AD9uq+FfS7ZTVh8AoGu6i0uP6sFRvTPRDaioDxDUdHKSHXTNcJOZZJelVIUQ7UICGUIIIYQQ4sCr3wFrPoT/RX6hHXwO9DvVnBcjdzCk5LVv+Q5ium5QXm8GMMrrA6wpqePtJdvZXOkFIDPJzoW/6s7JA3NRFKj2hfAGw2R6HAzMcJPlcWCRpVSFEO1IAhlCCCGEEOLA8tfApq/h8wcgHICuo+DIa83gRlZfSNv1coxi7xiGGcAorgmwoy7ApooG/vVjEauKawFIclg4/4hunHFYHnaLSp0/TF0gTJrLRmHXNHKSHdhkKVUhRAcggQwhhBBCCHHghHxQtBQW3gfeCkjrDifeY65WktYNMvuAKo3ltlbnD7OxOkzl1mqKa4P856civttUBYDdonLm4XmcN6IbHqeVhkCY8jofSU4rA/OSyU1x4rTJSiRCiI5DAhlCCCGEEOLA0MKwYw38935zkk9HirlCSbAe3FmQ1R8ssvJFW9J0g6JqH+t31LG+MsTqLVv54pdydANUBU4emMuFv+pOlseBP6RRVO3DYVMpzPFQkO7CbZfmghCi45FPJiGEEEIIsf9FVyhZ9Chs/QZUK5xyH1jsoNrNZVbtsiJAW6rxhdhU3sDmCi8LVhazYGUtkZVUGd07k0uO6kG3DDchzVy1RFGhW6aLruluUmQpVSFEB9augYx//OMfzJs3j+3btwPQt29ffv/733PccccBEAgEmDVrFh988AHBYJCxY8cyZcoUsrKy2rPYQgghhBCitWq2wY+vwMp/mdtjb4GMXhAOQu4gcKW1a/EOJiFNp6jax6byBpZureYf326htNZciWRwfjKXj+nFgC4pkVVL/OgG5KY46JbuJs1tk5VIhBAdXrsGMrp06cJtt91Gjx49MAyDd955h+uvv55//etf9O3blxkzZvD555/z6KOPkpyczLRp07jhhht47bXX2rPYQgghhBCiNRrKYfV/4JtnzO2h/w8KTwBvFeQdBp6c9i3fQaTaG2RjeQPry+r599IivvilHDBXIjmj0MHZRw9GUSxU1AcIhDWykh10z0giM8mOKiuRCCE6iXYNZJx44okJ27fccgvz5s1j6dKldOnShbfeeos5c+YwevRoAGbMmMG4ceNYunQpw4YNa4cSCyGEEEKIVgnUw8YvzRVKtCB0PwpGTjSDG9kDILVbe5fwoBAM62yr8rK5vIFFGyp4/ftt1PhCKMD4oXlcfGQ3tmxcR60/jC8UJM1tp39eMtkeB1ZZiUQI0cl0mDkyNE1j/vz5eL1ehg8fzooVKwiFQowZMyaWprCwkPz8/L0KZGia1qZlbes8Ozupk+akTlom9dKc1EnLpF6a2x91IvUr9qtwEIqXwqf3ga8K0nvC8ZPNIEZaD8joDTKMYZ9V1AfYWN7A2tI63vh+Gz9urQagW4abm07ow4C8FOr8Acq9GvkKDM5PJTfFid0qAQwhOpJLLrmEAQMGcNddd7V3UTq8dg9krFmzhgsuuIBAIIDb7ebJJ5+kT58+rFq1CpvNRkpKSkL6zMxMysrKWn2d5cuXt1WR92uenZ3USXNSJy2TemlO6qRlUi/NSZ2ITkHXoWyNGcSoiqxQ8uv7IVgHybmQ3R8s7f5VtFMLhDW2VnrZVN7AwlU7ePvH7fjDOlZV4f+N7MZ5R3RFAUpr/YBB1xQrI7qnk+S0t3fRxUHozjvvpLa2lqeeeqpdrt+/f3+efPJJTj755N2mi3K5XOTk5DBixAh++9vfMmTIkP1dTAC++eYbLr30Ur777ruE9u4TTzyB1XpgPhcXLFjA3//+d1atWkUgECAvL48RI0ZwySWXMGjQoANShn3R7v969OrVi3feeYe6ujoWLFjAHXfcwd/+9rc2v87QoUOxWNpm/WtN01i+fHmb5tnZSZ00J3XSMqmX5qROWib10tz+qJNonkK0uapN8PUjjSuU/HqaubSqzQ3ZA8HmbO8SdlqGYVBeH2RjeT3Lt9cw79utrNtRD8DAvBRuPKEP3TLc1PhC1AfCdElx0DXdyWafDadNPk+FmDlzJscccwyBQIBNmzbx+uuv8//+3/9jxowZnH322XudbzAYxG7f+0BhWlraXp/bGg8++CBz587lkksu4aabbiI/P5/Kykq++OILHnroIV544YUDUo590e6BDLvdTo8ePQAYMmQIy5cv55VXXuH0008nFApRW1ubEKWqqKggOzu71dexWCxt/kV4f+TZ2UmdNCd10jKpl+akTlom9dKc1Ino8GqLYcnLsOItc3vsLeZQEkMzl1l1puz6fLFT/pDGlgov68vr+eCnEj5YUYymG7hsFi4f05PThnQxVy2p8eK2WxlSkEKXFCcKBpvbu/Bi7xkGhLwH7lpBH7hcbTr0a+3atcyePZsffvgBl8vF0UcfzaRJk8jIyADgiy++4Omnn+aXX37BYrEwbNgw7rrrLrp37w6YQYJZs2bx0UcfUVNTQ1ZWFhdccAG/+93vYnMvXn/99QAUFBSwcOHCnZYlJSUl1qbs2rUrY8eO5Y477uC+++7jhBNOIDU1lSeeeIJPPvmEd999N3beyy+/zCuvvBLLO9oLZejQofz973/HbrezcOFC3nnnHV555RU2btyI2+3mqKOOYvLkyWRmZrJt2zYuvfRSAEaNGgXAOeecw6xZs5oNLampqeH+++/nv//9L8FgkFGjRnH33XfTs2dPAN5++21mzJjBI488wowZMygpKWHEiBHMnDmTnJyWJ1FeunQpzz//PHfddVesHAD5+fkMGTIEwzBi+7Zs2cLMmTNZtmwZPp+P3r1788c//jFh+ofx48dz/vnns379ehYuXEhycjLXXnstF1988S7fD/uq3QMZTem6TjAYZMiQIdhsNhYvXsypp54KwIYNGygqKpKJPoUQQgghOipfFaz6D/wv0r18yHnQ+3gI1EGXoZCU1a7F66wMw6CsLsCG8ga+21TJvG+2UFTjB+DIXhlce1whGUl2qhqChHSdbuluumcm4XGYX/dlPpxOzDDgxVPN3k0HgAIkAUa3o+CK+W0SzKitreWyyy7j/PPPZ9KkSQQCAebMmcMf/vAHXnnlFQB8Ph8TJ06kf//+eL1eHnvsMa6//nreffddVFXl1VdfZeHChTz66KPk5eVRXFxMSUkJAG+++SajR4+O9bTYm2D/5ZdfzjvvvMPXX3/NuHHj9vi8xYsX4/F4mDt3bmxfOBzm5ptvpnfv3lRUVDBr1izuvPNOnnvuOfLy8njiiSe48cYbmT9/Ph6PB6ez5R5qd955J5s3b+bpp5/G4/Hw4IMPcs011/D+++9js9kA8Pv9vPjii8yePRtVVbn99tt54IEHeOihh1rM8z//+Q9ut5uLLrqoxePxyy97vV6OO+44brnlFux2O++88w7XXnst8+fPJz8/P5buhRde4Nprr+XGG2/kq6++4v7776dnz54cffTRe1yPrdWugYyHHnqIY489lry8PBoaGvjPf/7Dt99+ywsvvEBycjITJkxg1qxZpKam4vF4mD59OsOHD5dAhhBCCCFERxT0wqav4bMZ5gol3X4FI68Ab6XZEyOloL1L2Cn5ghqbKupZW1rPv5Zs57O15nxx6W4bvzu2kDGFmfhCGsU1PtLcdgZlpZCd7EhokIjOrnO/ln/7298YNGgQt956a2zfjBkzOO6449i4cSO9evWK/Xgdf3z06NGsW7eOfv36UVxcTI8ePTjiiCNQFIWCgsbPk2ivjvieFq3Vu3dvALZv396q89xuN9OnT08YUnLeeefFHnfr1o277rqL8847j4aGBpKSkkhNTQXM+R+bzgkZtWnTJhYuXMi8efMYMWIEAHPmzOH444/nk08+4fTTTwcgFAoxderUWM+Viy++eJfzlGzatIlu3bolzMUxd+5cHn/88dj2F198QXJyMgMGDGDAgAGx/X/4wx/45JNPWLhwIb/97W9j+0eMGME111wDmFNHLFmyhJdeeungDWRUVFRwxx13sGPHDpKTk+nfvz8vvPBC7AlPnjwZVVW56aabCAaDjB07lilTprRnkYUQQgghREu0EJT8BB/fA75KcyjJCZPBW2GuTpLRS1YoaSVdN9hRF2BjeT2fry3nn99tocobAuDXg3KZOKYXLruFHXUBLCr0yfHQNd0t82AcbBTF7BlxgIaWGIaB1+vDnZrZZn+zq1ev5ptvvmH48OHNjm3ZsoVevXqxadMmHn/8cZYtW0ZVVVVsiENxcTH9+vXjnHPO4YorruC0007jmGOO4fjjj2fs2LFtUj4gdr3WBgD79evXbF6MFStW8Je//IXVq1dTU1OT8Fz69OmzR/muX78eq9XK4YcfHtuXnp5Or169WL9+fWyfy+WKBTEAcnJyqKioaNVzmDBhAieeeCLLli3j9ttvj5W3oaGBv/zlL3z22WeUlZWhaRp+v5+ioqKE85t2NBg2bBgvv/xyq8rQWu0ayJgxY8YujzscDqZMmSLBCyGEEEKIjswwoHwdfDwFKiMrlJx6PwQaICUfsvqBKo3r1mgIhNlc0cCK7TW8/v02vt9cBUB+qpMbTujD0K5p1PhClNb5yEl20jMzifQkWY3koKUoYE86MNcyDAgrbRp49Hq9nHDCCdx2223NjkV7UFx77bUUFBQwffp0cnJy0HWdM844g1DIDN4NHjyYTz/9lC+++IJFixbxhz/8gTFjxiT0JNgX0eBA165dATOgET9fBJhDRppyuVwJ216vlyuvvJKxY8cyZ84c0tPTKS4u5sorr4w9l7bUdJWTlsodr2fPnvzwww+EQqHY8JSUlBRSUlJiQ3WiHnjgARYtWsQdd9xB9+7dcTqd3HTTTfvlebRWh5sjQwghhBBCdDI1W+HLObD1f6BY4JSpZuDCkWIOKbFKA3tPabpBaa2fdWX1fLSyhLeXbMcb1FAVmDCiK78Z1Q0Fhe3VXpIcVgblpZCX6sJqUdu76ELs1ODBg1mwYAEFBQUtLi9aVVXFxo0bmT59OiNHjgTg+++/b5bO4/Ewbtw4xo0bx6mnnspVV11FdXU1aWlp2Gy2fZoL5uWXX8bj8cQmsszIyKC8vDwhKLBq1ard5rNhwwaqq6u57bbbyMvLA8weGvGiAYRdlbewsJBwOMyyZctiQ0ui9bSnvTpaMn78eF599VX+8Y9/cNlll+0y7Y8//sg555zDKaecApg9NFoaerNs2bJm24WFhXtdxj0hgQwhhBBCCLH36svgh5dhxZvm9tg/QFp3QIWcQQfuV+SDQJ0/xMbyBn7cXMU/vtvKmpI6wBwyctOJfeiRmRSbzLNrupueWY2TeQrREdTV1TVr7KelpXHRRRfx+uuvc+utt3LVVVeRlpbG5s2b+eCDD5g+fTqpqamkpaXxz3/+k+zsbIqKippNVjl37lyys7MZOHAgqqoyf/58srOzY3NMFBQUsHjxYkaMGIHdbo/NQ9GS2tpaysrKCAaDbNq0iX/+85988sknPPDAA7H8jjzySO677z6ee+45Tj31VD799FO+/PJLPB7PLusgPz8fm83Gq6++yoUXXsjatWubzVlRUFCAoih89tlnHHfccTgcDpKSEj8re/bsyUknncQ999zD1KlT8Xg8zJkzh9zcXE466aRdvxC7MHz4cK644goeeOABioqKOOWUU8jLy6OsrIw333wTRVFQVTMw2qNHDz7++GNOPPFEFEXh0UcfRdf1ZnkuWbKE5557jpNPPplFixYxf/58nnnmmb0u456QTz4hhBBCCLF3/LXmCiWLnjC3h0wwVygJeiHvcHBntGvxOgtNNyiq9vHLjjreXVrEB8uLCWkGDqvKb4/qwZmH5RMIaxTX+khz2RmYlUK2x4GqypwjomP59ttvOfvssxP2nXfeedx///3MmzePOXPmcOWVVxIMBsnPz+eYY45BVVUUReGRRx5h+vTpnHHGGfTq1Yu7776bSy65JJZPUlISzz//PJs3b0ZVVYYOHcqzzz4ba3TfcccdzJo1izfeeIPc3NxdLr86adIkwJzKIDc3lyOOOII33niDwYMHx9IUFhYyZcoUnnnmGZ5++mlOPPFEJk6cyBtvvLHLOsjIyGDWrFk8/PDDvPrqqwwePJg77riD6667LpYmNzeXG2+8kYceeohJkyZx9tlnM2vWrGZ5zZw5k/vvv59rr72WUCjEyJEjefbZZ2M9OvbWHXfcwdChQ5k3bx5vvfUWfr+fzMxMRo4cyT//+c9YsObOO+9k8uTJXHDBBaSnp3P11VfT0NDQLL+JEyeyYsUKnnzySTweD3feeSfHHHPMPpVxdxRjVwNoDgKaprF06VKGDRu2V8vwHKg8Ozupk+akTlom9dKc1EnLpF6ak3/TOreD7vULB2D9f+Gd68zJPbuOgpPvBV815A42J/dsJ53pfV3jC7GpvIFF6yv4xzeb2VrlA2B4tzR+f0Ifsj0OyusDqCp0S3fTLWPvJvPsTHVyoHTUOvH7/bGVPHa2LOf+ZE726cXtdsvKNxFSJy0zDIMTTjiByy+/nMsvv3yPztnV+7s1f5PSI0MIIYQQQrSOrkHxclgw2QxipHaDE+4yl1nN6muuWCJ2KaTpFFX7WF1cyxs/bGPh6h3oBiQ7rVx9TG+O75dNnT9MaZ2PbI+TnllJZMhknkIIAUggQwghhBBCtIZhQOVGc5nVyvXgSDZXKAnWQ1o3yOwDqkw8uSvV3iAbyxtYuLqUed9upbw+CMDx/bO5amxvXDYLxTV+XHYLA7ukkJ8mk3kKIUQ8CWQIIYQQQog9V1cMXzwIWxaZK5ScPBVUK7gyIHsAWPZt7PbBLBjW2VblZcX2Gv7+zRa+2VgJQE6yg98f34cR3dOo8oaoaAhQkO6iR6abZKfUpxCi43r//fdxu90H/LoSyBBCCCGEEHvGW2muUPLTa+b20TeZvTBUO+QOApurfcvXgVU2BFm/o473lxfz1g/bqQuEURU487B8Lj7SHIpTVGNO5tm/SzI5yTKZpxBC7IwEMoQQQgghxO4FG2D1f+DrR8ztQWebK5SEg2YQw7nzpQ4PZYGwxtZKLz9sruKVRZtZWVwLQM9MNzee2JfCbA8V9QFQoTDbs9eTeQohxKFEAhlCCCGEEGLXtBBsWgSf3GuuVlJwBIy6EgJ10GUoeHLau4QdjmEYlNcHWbejjrd+2MZ7PxUTCOvYLAoXjurOOcML8IU0Smp9ZCc76JXlkck8hRBiD0kgQwghhBBC7JyuQ+lKWHAneCsiK5RMBn8NZPU3t0UCf0hjS4WXr9eX8/KiTWyq8AIwJD+FG07oS06Kgx11AVx2C4PyUshLc2GTyTyFEGKPSSBDCCGEEELsXNUm+OguqFgHdg/8ehoEveYSqxm9QZF5HKIMw6CsLsDqkjr+/s1mPv65FN2AJLuFiUf34uSBOdT4wpTXB8hPc9Ejy02KTOYphBCtJoEMIYQQQgjRsvod8OUc2PQVKCqcfK+5KklSDmT3B4t8lYzyBTU2VdTzyaod/G3xZkrrAgCMKczkd8cW4rJZKKn1y2SeQgjRBuRfHyGEEEII0Zy/Bn54CZb+w9wec6O5QoktCXIGgs3ZrsXrKHTdYEddgOXbq3lp0Wa+XlcOQEaSnWuPK+RXPTOoqA9QZ+j0zvLQPVMm8xRC7F+XXHIJAwYM4K677mrvouw3EsgQQgghhBCJQn5Y/b7ZGwMDBv2fuUKJoUPOIHAkt3cJO4SGQJhN5fX8e1kxr323lRpfCIDTh3ThstE90Q0jNplnz8wkMpLsKDIURxyk7rzzTmpra3nqqafa5fr9+/fnySef5OSTT95t2v/973+88MIL/PTTT/j9fgoKCjj22GOZOHEiubm5B6C0beObb77h0ksv5bvvviMlJSW2/4knnsBqPbib+gf3sxNCCCGEEK2ja7Dlf/DRPZEVSkbAyIkQ9EHe4ZCU2d4lbHe6blBS6+e7zZW88OVGftpWA0BBmosbT+xDv9xkyuoDOG0qA/NSyJfJPIXoMF577TWmTp3K2WefzeOPP05BQQHFxcW88847vPjii0yaNKm9i7jP0tLS2rsI+50EMoQQQgghhMkwYMdq+PB28JZDSgEcPwkCDeZwkpT89i5hu6vzh1hfVs/r323l7R+34w/pWFWFCUd05fwRXfEGNcrrA+SlOumZlSSTeYo2YRgGvrDvgF7LZbjatAfR2rVrmT17Nj/88AMul4ujjz6aSZMmkZGRAcAXX3zB008/zS+//ILFYmHYsGHcdddddO/eHYBgMMisWbP46KOPqKmpISsriwsuuIDf/e53nHjiiQBcf/31ABQUFLBw4cJmZSgpKWH69OlccsklTJ48Oba/a9eujBo1itra2ti+BQsW8Pjjj7N582ays7P5zW9+w+9+97vY8RNPPJH/9//+H5s3b2b+/PmkpqZy3XXX8Zvf/AaAbdu2cdJJJ/HEE0/w6quv8tNPP9GjRw+mTp3K8OHDY/l8//33PPzww6xYsYL09HROOeUUbr31Vtxud+x5P/bYY/znP/+hoqKCvLw8rrnmGkaPHs2ll14KwKhRowA455xzmDVrVrOhJTU1Ndx///3897//JRgMMmrUKO6++2569uwJwNtvv82MGTN45JFHmDFjBiUlJYwYMYKZM2eSk9Mxl9eWQIYQQgghhDDVbIMFk6B8LdiTzBVKQn5zdZL0nofsCiW6blDrD1FWF+D7zVU8/+UG1pc1ANA/N5kbT+xDboqT8voAqW4bfbukkpvslMk8RZswDINLP7yUpWVLD+h1h2cP5+XTX26TYEZtbS2XXXYZ559/PpMmTSIQCDBnzhz+8Ic/8MorrwDg8/mYOHEi/fv3x+v18thjj3H99dfz7rvvoqoqr776KgsXLuTRRx8lLy+P4uJiSkpKAHjzzTcZPXo0M2fO5JhjjsFiaXkemvnz5xMKhbjqqqtaPB4dnrFixQr+8Ic/cMMNNzBu3DiWLFnCfffdR3Z2NhMmTIilnzt3LjfddBPXXnstCxYs4N5772XUqFH07t07luaRRx7hjjvuoEePHjzyyCP88Y9/5KOPPsJqtbJlyxauvvpqbr75ZmbMmEFlZSXTpk1j2rRpzJw5E4A//elPLF26lLvvvpsBAwawbds2qqqqyMvL44knnuDGG29k/vz5eDwenM6W5y6688472bx5M08//TQej4cHH3yQa665hvfffx+bzQy2+v1+XnzxRWbPno2qqtx+++088MADPPTQQ615qQ8YCWQIIUQ7MgwDQ9dB1zE0DaN5gpZO2qN9LaTap/ya7YtsN+42GvcbRuI5sbRGs/NbSm8YBrquo1RVESotRbdYWs5zD8tgRLej6XW9+XNp9vzi8ttFOiOxAJE0NNFS/kZifezBNXXdQNm4AWPQYHDJZIGijTVUwBezYePn5golJ/0ZLA5IzoOsfqAeeu85f0ijsiFIcY2PTRVePv25lI9+LiWsG7hsFi45qgenDu5CtS9IjT9E72wP3TLcuOyHXl2J/auzz63yt7/9jUGDBnHrrbfG9s2YMYPjjjuOjRs30qtXL0499dSEc2bMmMHo0aNZt24d/fr1o7i4mB49enDEEUegKAoFBQWxtNFeHSkpKWRnZ++0HJs2bcLj8ey2l8HcuXMZPXp0rIdHz549Wb16NS+++GJCIOPYY4/l4osvBuDqq6/mpZde4ptvvkkIZFxxxRUcf/zxANx0002MHz+ezZs3U1hYyDPPPMOZZ57J5ZdfHrvOXXfdxSWXXMK9995LUVERH374IXPnzmXMmDEAdOvWLZZ3amoqAJmZmQlzZDR9zgsXLmTevHmMGDECgDlz5nD88cfzySefcPrppwMQCoWYOnVqrAfMxRdf3G7znewJCWQI0ckZTRt38Y931phsmq6ltLt63Gxz5+ma5qFpGjQ0oFXXQGS8cLPG3C7O392xxvqIK9suG8xxiXfV+I1uRxvAcY8b02FOhEekoRzNS9fNh+b/JeQJoGsayrp1+IIh1D2pk53VwS730Vj+6PPRtcbnEQmoxMqt643PS9MTn3/suB63Ha0vHfRIulgeetx1aJYPGBh68/rWNQ1r2Q4qv16EqoChR1+HyDVir1lcWQ0an0/8dRJep7jnHP+6N319W3gPROuJJuftdF/ce2Cn+e1sX9P3WiRPe24u+q9PBZesGCHaUKDeXKFkyavm9ugbILUHOFMhZwBY7e1avANJ1w2qfSHK6wJsr/Hy3YZKFm+oZNm26thHz8ge6Vx3fCFum5Wyej9ZHgc9s5LIlMk8xX6gKAovn/bygR1a4vORkZzRZu/n1atX88033yQMqYjasmULvXr1YtOmTTz++OMsW7aMqqqq2Heh4uJi+vXrxznnnMMVV1zBaaedxjHHHMPxxx/P2LFjW/3c9uQ5bdiwgZNOOilh37Bhw5g3bx6apsV6fPTv3z92XFEUsrKyqKioSDgvPk00yFJZWUlhYSGrV69mzZo1vPfeewll1HWdbdu2sWbNGiwWS2zoyN5Yv349VquVww8/PLYvPT2dXr16sX79+tg+l8sVC2IA5OTkNHsuHYkEMvaBEf9rWmsagXGPE9uCe5nH7hqUe9rQ2cPjLTWwdF2Hujq0qiqMnXTl2uU196Ihm/jLbrMHe1yfLdZfS+c0bQzFksc1PKLpIo0wZcsWAi4Xqqq20GiK5h/X8G2Sp5lvfHniGkUJZWv+/Jv/0tv0vdr8eTcPAjQejz1vXY9rVCU2ABMbtnpjY0xvrCNDC2PZsoXaTZtQUOIa1rqZX9x2QmMwmkek0RttAMfn3fTcWOM6WrZYPpHAgq4npI2VwWjyfFtqbDZtLEdel/h0sbInlC1xvxEpg8Pno9TuaJ5/fIO/6fOMr+MWrtdY/3EN6k7GBtS0dyE6GNXnA01r72KIg0k4CGs+NHtjYMDAM6HXsaBYIXegOcTkEOANhiO9L/ysKa7jy3VlfLOhkurISiQAA7ok83/DCvhVz3QqGoIENY0BXZLJT3Njt8pknmL/URQFt819QK5lGAaE2rYXiNfr5YQTTuC2225rdizauL/22mspKChg+vTp5OTkoOs6Z5xxBqGQ+Tc4ePBgPv30U7744gsWLVrEH/7wB8aMGcPjjz++x+Xo1asXdXV17Nixo03mfmi6MoiiKM2+g0eHbkSPQ6TthFkvF1xwAZdcckmzvPPy8ti8efM+l3FP7clz6UgkkLEXtOoalNWr8QWDqEr8P1q7aESaO3fyOCFRy3nsqqG+izx2liaxMR7X8GvaYIOdNOQiGUYas7oWRt26ldriYiyKGpdeb5KX2RCNb5hGr2EoRH5lbdJAhsYGZuRxLKgQKZtiENeI02NlNOIapk1vBtEGYGP+SqwO9MZGMIll3eNfWXUdW20tZR5PXD23UMctNIZ3lmfThvKurr3b85qk2ek5epPXsA04gLI2y+3goAKh3aY6QFTVHAevquY/uKra+Di6rSgocelQlMa0kXTN0jc9Hvc4If+4W4PPS5InOZZ2l3nsKs/481sq5y7KvdPnsquyN923izRG5EtN0+eCoqBAwnX1UIitVVUoB/mSauIA0nXY+i0suBPCfsgbBiOvMIMb+UPBld7eJdyvNN2gyhukrM7PlkovX6+rYPH6Cn7ZUR9Lk+ayccKAHE4ZmEtBuosaX8iczDPNRY/MJFJdMpmnELszePBgFixYQEFBQYvLglZVVbFx40amT5/OyJEjAXMSzKY8Hg/jxo1j3LhxnHrqqVx11VVUV1eTlpaGzWYze/7uwqmnnsqcOXN4/vnnEyb7jKqtrSUlJYXevXuzZMmShGNLly6lZ8+eO51/Y28MGjSIdevW0aNHjxaP9+vXD13X+e6772JDS+JFgyS7et6FhYWEw2GWLVsWG1oSre8+ffq0wbNoH/JNqJXCVVVsv/56HJs2UWy3o9A04GC0fB99bBjml9JoA7Tpsab5RB63eI2dnRdtcDctR9MARRtzIo3TpqyAt70L0R6aNjSbNAg1w8BitTY/1tL2ro61lLZJ4zuhoRp/LJq2pcdN899Vg75JnrsqZ7PHkfMMYHtxEV27dkWxWBMbsvGNXlVNqN+WGtwtNu6bNsLj8jCi6aExbbydfZbRyoDtrvKLHWr6uadTvWEjGb16oqiWXeYdX2wjfmez68TtU+L3KvFnRg7GBZabXsxoknYnn6tKS9k2Lc/OjiW8Fo2/4Bi61kI+QuylinXwwa3QUG6uSHL8JAj5IGcQJHdp79LtNw0Bs/dFUbWXH7fW8PUv5Xy3uRJ/yPxRQVVgRPd0fj0ol1E9MwhHJvssrvGT4rQytGsauSlOLDKZpxAJ6urqWLVqVcK+tLQ0LrroIl5//XVuvfVWrrrqKtLS0ti8eTMffPAB06dPJzU1lbS0NP75z3+SnZ1NUVFRs0km586dS3Z2NgMHDkRVVebPn092dnZsboiCggIWL17MiBEjsNvtsfkj4uXl5TFp0iSmTZtGfX09Z599NgUFBZSUlPDuu+/idru58847ueKKKzjvvPN48sknGTduHD/++COvv/46f/7zn9u0vq6++mp+85vfcN9993H++efjcrlYt24dixYt4s9//jNdu3blnHPOYfLkydx9993079+foqIiKioqGDduHAUFBSiKwmeffcZxxx2Hw+EgKSmxF13Pnj056aSTuOeee5g6dSoej4c5c+aQm5vbbPhMZyKBjFYKl5biX7oUFTikO/a28MupYRioFkvLDajoDcwGWeSeuHulyXZsf9OGZ3yjLHre7n6B3ZNfgeOu26pzdnINA4PyigqysrNRVUvr8tvT60XrqbXPs2n6luoh7jWLNcCjL39k24i+Pk1f810E1wwtzC/r19OnsLCxR1MLAbldBghbOKfFuUJ2dn5c43K3DeDY4501gOMfNZ5jxAUtY8dbanwboBk6hhbGml/QGOVX4gunJF5sl/sT9ym7ySM+cNFy2qZ/w9H3hNKYT3R//DWbpI89n2ggZSfnxr+PNE3D8Hqx9epl1kt8mZW46ydUaPyTjN/VUromj5ttNz3WNOkuzt1lvi1s72G+mq5jrLCjug9M92JxkKsthg//BGVrwJYEp9wHWhCy+kJay78OdmZhTafKG2JHrZ8NZfV8traMxesrKKrxx9LkpTo5ZWAuJw7IIcVlo94fprTej9NiIcvjIDfFSXqSDYdVJvMUoiXffvstZ599dsK+8847j/vvv5958+YxZ84crrzySoLBIPn5+RxzzDGoke+hjzzyCNOnT+eMM86gV69e3H333QlDLpKSknj++efZvHkzqqoydOhQnn32WXMIN3DHHXcwa9Ys3njjDXJzc1tcfhXMSSx79erFCy+8wA033IDf76egoIDjjz+eiRMnAmYPkkcffZTHH3+cp59+muzsbK699lrOPffcNq2vAQMG8Oqrr/Loo49y0UUXAeZknuPGjYuluffee3n44Ye59957qa6uJj8/P7YMbG5uLjfeeCMPPfQQkyZN4uyzz2bWrFnNrjNz5kzuv/9+rr32WkKhECNHjuTZZ59NGPbS2ShGRx740gY0TWPp0qUMGzaszboB1S/5kXXvvEP3IYPNXwnjG3zQ/Av2Tu6Vpl9647+cN0nfYr7x58Vtt5hv3OOWum/vbH+LaaFZI1LXwqxdu5Z+/fqhKipGCw3T3fYc2cMeKns9MeRepNtpT5g9yE83DDZu2ECv3r2wxAUBQMFo2opu1mg20+3+l+MWGs+7yqcJZVcbTRvHO2ssxzceExrPzRunAIau88v69fQt7BMJfEXSRIMqRN97kX2RfJT442pcgzi2n8SATHyZ4v8udtkAbqyIxF27OaeFBnTzQMJOzok02FesWMHQoUPNOiHu3N19njQt7x6cEytFk8+GFtPuosG9v+2Pz+/Obn/UidTzgdOhXj9fNXxyL/wwFxQVTp0BKQWQ1g1yh4Cl8365hcR68YZ0KhuCbKvysXh9BV+vL+enrTVokX8b7VaVsYVZnDIol4F5yXiDGnWBMBZFIcVlJS/VRZrbhsdhbdfPxH0lf+vNddQ68fv9sZU8drac5v5kGAZerxe3292p3/NtSeqkZXtTL7t6f7fmb1J6ZOwFW34+RvfuqGlpWBS1SZdjmnQjbroj/lDTBmZiA9Voel6L3ZObDmXBHG8dl68SnzK+sWs0HlR2ddyIBmhIPCGuQasbOkp9PVp1deNkn00as7tq3DY/riQ2bCPpW2ystfRLMHHpWmp0K0p8k3On+SUEqZrm3SRd04aypmkY4RCOfv3ifmVv3mBuvnt3jeWd55PwcKfp9zTfuOfcUoOXnbweTR4r8fkqivmLst2Oa9gwLJExkq39B8EwDHTDHNusG+aEqJphPjaP7+rk3eS9iwS7C/vuLiq8syCcpuvUODxU25OaBL32XOwdbexBQRpLFDl37+LZ5kutoCpgURXUyN+oqiiRW/sGQg4GeuQ9rhtmcDQc1ghoB/XvD+JACPngh5dhyUvm9pHXQVp3cGVC9oBOH8QACGk6VT6NFdtrWF1Sz3/X7uCbDZVUeRtnI+qX6+GUgV04pm8WqqJQ5w9RWhcgyWGld1YSmR4HqS6bDB8RQogOqF0DGc888wwfffQRGzZswOl0Mnz4cG677baEdXcDgQCzZs3igw8+IBgMMnbsWKZMmUJWVla7lduSnoZe2BvX4MGJkaKmjZQm2y1P3rmTHbvqCbCL7ebtpObRDyXaoG/W8Gxs8Df7JbiloEOTxqlut+M67DAsFmvjNVo6v4XGbbPjB0HjR9M0jMpK7N26dago/4FmGEYk4GA2xEKagU+DuoCGEmo8FtJ0vMEw9QENbyBMQzBMvd+8bwhoeIMaDcEw3kAYf0jHF9Lwx246gbBGSGsMQxg7+bto8XhL6eJ7DLXyeKwUxm6Ox+0Lh8NYP/0qFhxI+BOKBI2iMT4lGrZQovG5yN82jcGFhO24oNNut2m8fnz+jeVpLIvNomKzKNitKnaLis2qYreqOCLb0ccuuwWH1YLTpuKyWXHbzXunzYLDpuK0WXDZzHNUVUWNBEMMQ8cb0mkIhLFFpg5RFSUSNGn/IEnTIIMRuY/uM+KOxQJuuk5QMwgENQJhPfYe9oY0AmGNQMggEA4TCBsEwjrBkE5Q0wlp5n0wpOEKN3DYUA2P69D9XBH7QAvDmvnw2QzzA2jAeHOFEpvLXKHE5mrvEu41wzCoC4SprA+woayOf61u4Jdvl7M2buLOZKeVE/rn8OtBueSnuajzh6n2BXHaLGSnmENH0twydEQIITq6dg1kfPvtt1x88cUMHToUTdN4+OGHufLKK3n//fdxR8b/zpgxg88//5xHH32U5ORkpk2bxg033MBrr73WbuVWrFZIScGamXlIN1ATaBq43Vg8HqmTTswwDMLxvRyiDTW9sTEW0DR8AZ36SLDBG9AiAQjz1hAI4wvq5rFgfLBBwx/W8QU1auu98N+vCYT1yM0MQhzyAsH2LkG7UhSwW1SsFsUMkqgK6GGSvlyUGCyxJAZKHDYLDquK06bitFpw2i24IvsaAyXRNBacNvMzKhDS8YfNAEIwbBAIm0GxYNi8RbcDkTSBsEEwrBHUDILhSHAhrBPWdEKaQUjTCemRe00nHNkX1g3CmkFYN/ft6zvdqsIN4zU8+17l4lBjGLDtB/jw9sgKJYfDEZcDijm5pzO1vUu4V4JhnSpvkOJqHz9sqeKzNWV8v6kKX8iczUwBhkcm7hzZM51gWKfOb072meq20Ts7ifQkOx6HdFQWQojOol0/sV944YWE7VmzZjF69GhWrlzJqFGjqKur46233mLOnDmMHj0aMAMb48aNi42dEULsnBZpVGm6GaAIaeY44aqGoHnvNe8rG4JUe0PU+EJ4Qxr+SAAiEI409EKN92G9rQIOLU+XqyrEGpuxhqnNgj3SUHVYIw1Uq4o94bEa6/4b+50+fvRPfI+juOPRuxaPR5Mpiccbz2mSj6Hs+jiJx6P3OgZF27eTl18Q62VgRLpzGIZCfB8Ow0js8WG+HOaexjlNzXFhRuRIrCdJ5FyjST6G0XiMyLA2I9qbJHIf64ESSRcd2hNrsOt6rHEf0gyCmtnAN/cZjb0KwpEeBtH7uACWYRAJbEH8+6PMe/Cu/WNRFWwWBZuqmsEbaySIE+nt0vSxqkCy4cVm2bshSOIQV7kR/nMzNJRBcj4cdydoIehyGHiy27t0rWIYBrW+MOX1AX7ZUcenq3eweH0F26p8sTQZLpXThhZw8sAueBxW6vwhKhqC5tCR7CSyPA5SZOiIEEJ0Sh0q9FxXVwcQWypnxYoVhEKhhDVzCwsLyc/Pb3UgY3drCrdGNK+2zLOzkzpprq3rRI8EI6JBCV8wTEVcQKLKG4oEJULU+ILU+ELU+s3hGXUB894b1Pb51+CoaMDBFfdrePSX8fhggyMyZCAabLCpCtWV5RTk5eK0WbFbFBy2aLDCbNBF55ZRFAVLdChDZEhB9NpqZHiBqijmL/iKiqrGzduxF/b3SAWlWYjEpOkav4TL6dM7HUukgRo/322zaS9iUQZiwYoWDsWCIS0faz7crclguBaH3ETPjR6IBkXi41vRHj1GNE2LARQFDAMdg3AYQrpGWIdQWCek64TCBsFwmG1FJWRk5aBFhh+ZvRvMAEhYb+wJEW7aIyIc10tCiw+mmL0pVAWskQCBVU0MGFgtClY10jMkeh+3z6o2prOo5nvaGsnHalHNe1XBYlGxKuYQHItqprVawKoq2C0WrKqC2qQB1ThgSYncRSpSMbd1XaesaAu6rrfZZ4t8bh8i6svMnhhlq8DmhlPuBV2D7P6Q2rW9S7fH/CGNKm+QoiofX64r54u1ZSzbVoMW+RCyW1TGFGZy4oBs1Lpi8rvnENQ0vCFk6IgQQhxEOkwgQ9d1ZsyYwYgRI+jXrx8A5eXl2Gy22NrAUZmZmZSVlbUq/+XLl7dZWfdnnp2d1ElzLdWJYRhoBuZN06kPGdQEDOoCGrUBg7qgRl1AjzzWaQjqNIQMvCEDb1jHFzKILHW/V5wWBbdNwWVTcNtUXFYFp03BZVVwWBSsKpFfiRVsqtnwsqlKJNBgmA0zQFEb505QFIisMQKKjqoYqGiggEUxgw+W6PwGXZ1Y1Bpzv6GghkAJRwITNM6FEM1TjU2joiRsq3GRh/DeV0eH0CPNRqhsE6HdJ91jzXqW7GN+Lc4rAgm9N/TIfXyvjqZpokGU6DF7k/MNA1ANdBv07uFCN2rNYAlg6Oa9bkQDKGYwJBpMMfNWMLA0Xivuek0DNk1770Tn72l83BhGUtBi84Oo0XMUUCMnq7GgWySNHovJxd7XsflHIudHc26Wb+z6jeWMlq1nuo11q1e2+xwhohMJ1MHnD8C6T0BR4cS7wOqC9J6QWbj/o7j7SNcNav0hyuoCLC+q4dOfS1m8oZLKhsbheIXZSZwyqAvH9DHnUKv1B9lcYeC2W+ibLkNHhBDiYNNhPtGnTp3KL7/8wj/+8Y/9kv/QoUPbdKmz5cuXt2menZ3UiUnXzTH0O+qDbC6v59uV63Cn51DjD1PVEKLaG6LaF6LOH6LO3zinxN5OD6Eq4HFYSXZa8TisJDmi9xaS7Fbcdgtuu4UkuwW3w4LHYcNtV7FaLLHAghpZbcKqRru5x/16HPtlWTHTRoILTVeoiJ980RK3WkVLkzLKe6U5qZOW7W29GHE9Qpr2DtHjgizxE53GBzAiD5tNdNoRAgf7470SzVMcpMIBWPIKfP+8uf2r30FaT/DkQnY/UDvuZ44/pFHZEGRTRT3/XV3GF7+Us6akLnbc47ByfP9sThmYS5dUZ+Tf1RAep43CrCRcdXaO6J6OzdZhvu4KIYRoIx3ik/2+++7js88+429/+xtdunSJ7c/KyiIUClFbW5vQK6OiooLs7NaN5bRYLG3eQNgfeXZ2B3udaLpBvT/Mtmov26p8bKvyUlTtZ3uVj5JaHztqA5Q3BAmG47tL1O5R3g6rGgtIRIMSSQ4rSXYLSQ4rbnskQBEXrHDZLFhVFYvaGFCwKCqqCg6rBZtViU2KGB2CYaYxu8erKpHzldjtQDnY3yt7Q+qkZVIvzUmdiD2ia/DLx7BwmtmFqf84c4USRzLkDACro71L2IyuG1T7QpTXBfhmYwWfrCrlu01VeIONE3ce3i2NXw/KZUT39ITVf7qkOslJdpDmtmNRDOqK1GbDt4QQQhwc2jWQYRgG06ZN4+OPP+bVV1+lW7duCceHDBmCzWZj8eLFnHrqqQBs2LCBoqIimehT7BfmZJgBtlT42FLlZXuVj6JqH8U1Popr/JTVBaj2hvZonok0l40kq05WqicWnHDbLbgdVpIiQQlPJFDhcdpw2i1YFQVFBYuqYkHBGglE2Cwqdqs5rr6l4IMl0nPC7FnRfNy9EEKIQ1DRMnj/Vgj5zAk9R1wGFhvkDjaDGR2IN2iuIrK2tI5Pft7Bl7+UsTVu4s6cZAcnD8zlhP7ZuO3W2EpZaW4bhTlJpLvtJMUNHZG5X4QQ4uDWroGMqVOn8p///IennnqKpKSk2LwXycnJOJ1OkpOTmTBhArNmzSI1NRWPx8P06dMZPny4BDJEqxmGgS+kUVTtZ2ull21VXrZX+9he7aO42k9prZ/y+mBsubZdsVkUsjwOMpLsZCbZSXPbyUiyke62k5FkJ8vjwGFV2LxxA/369sFhs5oTXUYmvbRZzF+J4oMP0eEb8YGJjtCVXQghRCdUtQX+fT3Ul0JyFzj+DrNXRs5AcGe0d+kAs5djtTdISa2Pz9eU8981O/hxS3VsdSyrqjC6MJNTBubSN9dDQ0BD03VQoDAnsuqI0ybBeyHEHrnkkksYMGAAd9111369Tv/+/XnyySc5+eSTWzy+bds2TjrpJN555x0GDhy4X8tyMGvXQMa8efMA800Vb+bMmZx77rkATJ48GVVVuemmmwgGg4wdO5YpU6Yc8LKKjs/sjhpka6WXLZU+szdFjS8y7MPPjtoAlQ1BtKZLMLQg2WmNBSoy3HbSkmxkuG1kJjlId9tJ99jMXhKqBYddwWUze1tEVz6wW1RURSep3s4RvTKwWjvEKC4hhBA7ceKJJ7J9+/Zm+y+66CKmTJlCIBBg1qxZfPDBBwnfR7KystqhtLvhrYQPboMdP4PNBSdPjQQx+kNKfrsUyTCMxtWDwjr1gTDLt1Xz4cpSFq0rpyJu4s5eWUmcMjCXo/tkYhjgD5tLN8cPHbFbZQliITqaO++8k3/9618AWK1WcnNzOe2007j55ptxODreULam3n77bSZNmgSY82JlZWUxcuRI/vSnP5GXl9dm18nLy+Orr74iPT29zfI8FLVr62rNmjW7TeNwOJgyZYoELwShsEZJbYDN0d4UkWEfRTV+SiLDPuoDu1+7wqIoZHrMnhQZSXbSk+yku22kJ9nJTHKQlWR2T7VazKUUoxNmOqyWWK8Km0VpDFjs5JcgTdOkV4UQQnQSb775ZsJwhF9++YWJEydy2mmnATBjxgw+//xzHn30UZKTk5k2bRo33HADr732WnsVuWVBr7lCyS8LAAVOuMtcbjW9t7lKyX7SNFAR1MzgQyCs0RDQ8IU0an1Biqv9bKn0sXhDOauK62JDNZPsFo7rn8OJ/XPITXHgDYUJ6QZpLht9Up3Nho4IITqmY445hpkzZxIOh1m5ciV33HEHiqJw++23t3fR9ojH42H+/PkYhsG2bduYOnUqN998M6+//nqbXcNisbR6vkfRnPyLIPY7XTfQDQPNMPAHdeoDIWr8Iep9GnWByOod/hD1wTD1fi22kkd0/GuNL8SO2gAVDQFCe7C8h9tuIdNjBiRiQYpob4okO5ke85ccm6risKm4bOZ8FfG9KWxWM1AhgQghhDg0ZGQkDrd49tln6d69O7/61a+oq6vjrbfeYs6cOYwePRowAxvjxo1j6dKlHWe4qxaCpX+Db58xt391tblCSUo+ZPUFde97MRhGJEihGQmBCn9Iwxs0AxUNgTClNT5KaoOU1/spqwtS0RCkoiFAeV2AhmDzoZuHFaRyyqBchnVLwx/WMQwDi6rQJzuZTI9dho4IEWEYBobPt/uEbXQt3efDcLla/T3YbrfHGul5eXmMGTOGRYsWxY4Hg0Fmz57N+++/T319PUOGDGHSpEkcdthhgNkrYsaMGXz//fexcz755BOuv/762I/gTzzxBJ988gkTJ07k8ccfp6amhmOPPZZp06bh8XgA8Hq93HvvvXz88cckJSVxxRVX7FH5FUWJlT8nJ4fzzjuP6dOnU19fjxr5DP3HP/7Biy++SElJCQUFBVx33XWcffbZCfns2LGDq666im+//Zbs7Gxuv/32WGC86dCSb775hksvvZSXXnqJBx98kPXr1zNw4EBmzJhB7969W1X/hxIJZIhmDMNA081lCnXDIKTp5nKhPs28D4SpC4SojywfWu8PU+cPsaWoGvuqH/EGzS81DYFw7LE/ZH7J2ZNAxK4oCqS77WQl2cnw2M1hHpH5KTKSHGRFvvTsa28KIYQQh65gMMi///1vJk6ciKIorFixglAoxJgxY2JpCgsLyc/P36tARltORBnNSwuHYf0nqB9PQTEM9L6nYvQ4FlxpkNUPFAvs4rpmoML8Nz8Y1glp5s0X1PGFwpF/y3XK6gOU1phzSlU0BCivD1LZEKS8Pki1L7Tb8qa6rOQkOxnWLZXj+mXhtlvxh8y5L3KT7eQkO0h12WJDRwxD31Wxd18vMulnjNRJcx21TjRNiywl3njbcvFv8f344wEth2vEcLr/7W97HMwwIsO3o/dr167lxx9/JD8/P7Zv9uzZLFiwgFmzZpGfn8/zzz/PVVddxYIFC0hLS2uWR0v5GobBli1b+OSTT3j66aepra3llltu4dlnn+WWW26JXee7777jySefJCMjg0ceeYSVK1cyYMCAhLx3Vf6Kigo+/vhjLBZLrA4++ugjZsyYwaRJkxg9ejSfffYZkydPJjc3l6OOOiqW12OPPcYf//hHJk+ezL///W9uvfVW+vTpQ2FhYcJ1ojeARx55hDvuuIOMjAzuvfdeJk+eHJuKoaNq6fXak3MMw0DTtGZ/e635W5RAxl6o84XYUhPCWVKLRbXEukXGXsjYduJ5LR03aPqHSpM0ieewi+MGxk6PhzSDhkCY2kjwocHf2OMh2gOiIRjGG2j8ZcUX0vAFNQIJS4nujn+PUjmsKi6bBafNgstuLiPqsKo4I/ucNhWH1bx32SxkesxhIJnJdpxWCzbVXFI0GqiwWy3Sm0IIIUSb+OSTT6irq+Occ84BoLy8HJvNlrAUPEBmZmZsovLWWL58eZuUM966r9+hzzd3Ygl58aYUUpR6DPrWYrypyWiVazEMg7AOYd0gFLs3gxf+kEFAMwhrOjVBg0qfTrVfpyagU+PXqQ3oVAfMe30331UdFoUMl0qGSyXdaYk8tpDqUEi2W7CoEDYgpDWwbUsDyXaFTLcVm10h1KCyfQc0n6lk7+2Puu7spE6a64h1YrVa8fl86LrZU0nXW/N9vG3omo7X693j79OapvHZZ58xfPhwNE0jGAyiqip/+tOf8Hq9+Hw+5s2bx9SpUxk5ciQAkyZN4uuvv2bevHlcdtllBINBDMPA6/XG8g0EAgCxfaFQCF3X+fOf/0xSUhIAp59+Ol9//TW/+93v8Hq9vPnmm0yfPp3DDz8cgClTpnD66acTCoUS8o4XDAapq6tj+PDhGIaB32+2ay688MJYHbzwwgucccYZsR4Yv/nNb1iyZAnPP/98rFcJwMknn8z48eMBuPrqq/nyyy956aWXmDRpUixfv9+P1+uNPb9rr72WIUOGAHDppZdy0003UVVV1SnmF/G1ordQIBAgFAqxevXqfbqmBDJaaWN5A+Mf/9Jcz/yjRbs/4SBiURXckcBDNODgspnBB7tFIeCtJzcrHafNGhegMAMSrui23UJSZFJMRTGXCbVgLjlqVZVYjwmravaosKoqqkps2Ee0N4XNYi4/KoQQQrS1t956i2OPPZbc3Nz9kv/QoUOxWCxtkpemaaz6diEDVj+GGqhET8oldOwk0lQ7vowh6PYsGoJhApFekbW+ICW1Zk+KivoAFQ1BKhtClNebvSqC2q4bS1ZVISfFQW6yg9wUZ+Sxk5xkc+imw6oS1on06DBQMH9csSoKNquK1aLgsllIdVpJT9p/Q0c0TWP58uVtWtedndRJcx21Tvx+P5s3b8blcuF0OgHoOe8fB3Roic/vx52eHhtOsScsFgtHHnkkU6ZMwefz8dJLL2G1WjnzzDMB2Lp1K+FwmKOOOgq32x077/DDD2fr1q243W7sdjuKoiQcjzbko/tsNhtdu3ZNmGciPz+fhQsX4na72bJlC6FQiFGjRsXOcbvd9OrVC5vNlpB3PLvdTlJSEm+//TbhcJgvvviC9957j9tuuw2Xy4XP52PTpk1ccMEFCXmMGjWKV155JWHfyJEjE7aPOOIIVq1ahdvtjr2mTqcTt9sde36HH3547JyuXbsCZoCgI08KahgGPp8PVyuGIamqis1mo0+fPrG6iIr+Te4JCWS0ksOq4nFY8QU14l+r6AvX9OWLplGIPUhIF0vfJJ2imP/wN0vXLG1jKiUu74RzFVBVJRJ0MJf/dNjAYVWwWw3sVgW7zcBhA6vFwGEFm9XAZjVwWBTsdgOH1cCigqHoQBiMEIaioSg6imJgGBpFRdvo2jUPVVXQ0QAdQ9ExDJ0QOkF0qg0dI6CjGxo6GgY6uqGjo6Hr5mPN0NAMzdyv6xiKgcviwm1147aZtyRbEm6bG5fFFXscPW5VraiKikUxu4GpimreULGoHecfKSHag2EYGBjohh7rxaWjx/bHjus6YSNMQAsQ0kIEtABBPUhQM28hPYSqqFhVK1bVik21Jd4s5r1Vtcb+Fhs/38zHsXsUzP8l7heiPWzfvp1FixbxxBNPxPZlZWURCoWora1N6JVRUVGxVxO2WSyWNm00ZS19ArV8JZrFyfIBt7Bla4jNah7bNzVQWV9FRUOQ8vqdz1MRT1Ug09MYqGi8OchJduBxWtFigQo9tlSqAqAo6JjzT6UnWfA4rDhsFuwWsxelw3rgh3W2dV0fDKROmutodRIdyhC9QaStEel9sL8ZhoGqKKiq2qrebw4WugAAJiFJREFUzYqi4HK56NmzJ2CuRPl///d/vPnmm5x//vkJ6VrKV1EULBYLhmEkHA+Hw7Hj0Xur1ZqQRlGU2Hnx6XZ2nZ2VX1XVWPn79OnD1q1bmTp1KrNnz96j8u9Jmqbli27bbLbY42gAqWlddFQ7e767Sruvf3cSyGilLqkObvm/Oj75+RuSU5MjQzoijW7DbKAb6JGGQaSBbuiNDXZDj6U3j0fu4x83O9cMFOiGEcun8d7AiGybjZPGbT3+mGHQgE59JI8EBhCM3PZVVRvksY/sqh27xY7D4sBpdWK32HFanLFtp8WJy+rCZXPhtjQGR6LBkCRbEk6LE4/dg8saCZRY3dgstliDLP5eVQ7sEnBNG5zRxmhsK+547Jz4BqqhE9bDaLpGSA8R1sOEjTAhLUTYCLeYb3weTfOL3xctX0vpATNQpWmsr11P7bZaVFVNuFbsvCblN/9eml+n6TWh8R8RVVFjjeLoa6QoCioqKKCiJjSko+kSGtg7u6cxbUJecXk0KwNqQtlU1Nhj3dCpDFaytXYrYSVMMBxsDBroQUJaKCGAENDN4EJIC5np9GDsnJAWIqSHYudHX+OQHordwno4dkvYNsIJx6L1u69URcWqWLGoFiyKpfFescQCHRbVEksT26dY8Df4SStPw2oxAyZWpTFwEn9vVa3YVXtCUMVisWBTzKBK7LzI4/hAZ9PXPuG9Ef17b+F9ED23pfOi7zmLakk4jgIWLAnpWvoM2dmXAV3T8WreVo1FFa3z9ttvk5mZyfHHHx/bN2TIEGw2G4sXL+bUU08FYMOGDRQVFbX7RJ/bFj5Hj/Kv0FH4Y/h63vm6e+RITeTWXJrLFgtONA1WpLvtAISjK5BoOpqhowCaDt6AhtWq4rJbyLKbK4nYIwEKu1WNPe4MX7yFEPuXqqr87ne/Y9asWZx55pl0794dm83GkiVLKCgoAMxhIsuXL+eyyy4DID09nYaGBrxeb6x3QmuHIHTr1g2bzcayZcvIzzeXnK6pqWHTpk2MGjWqVXldc801nHLKKVx22WX06tWL3r17s2TJktjQQ4AlS5bQp0+fhPOWLl2aMAHosmXLGDhwYKuuLXZNAhmttLx8OTN++LO5cWB6d7WL+F4MsceRW/wX9vh0oVAIp8PZ7Nz4dPFf3OPzatqYiE9vYJi/COsB81dhLUggbP467A/7CWjm/qhow64+VN+mdWJTbTgsjoTAiMPqwGlxNgZIbJGeI1Y3LquLyrJKfljxAzqNwYOwYTYeo4+j+zVDS0gT1sONPVQi+6KPYz1WIkGJpj1Z4tMk9HCJ3DqELe1dgA5obXsXYOcUlISggVW1JrzXwkbj+7gp3dAJGkGaxlD3WMtDWQ8qu+qtEh8AA+hq78qww4aRbE1ut/IerHRd5+233+bss8/Gam38ipScnMyECROYNWsWqampeDwepk+fzvDhw9s9kFG29EN6ALNDv+Ed7QgAXDZLQpCiS1ygIjfFiVVVzNVHIoGKkG5EVhiDGl8oNowz2WUhye7AbW8erLBZDmwQXwjROZ122mnMnj2bv//971x55ZVceOGFzJ49m9TU1Nhkn36/n/POOw8wh1e4XC4efvhhLr30UpYtW8bbb7/dqmsmJSUxYcIEHnzwQdLS0sjMzOSRRx7ZqwBrXl4eJ598Mo8//jiPPPIIV155JbfccgsDBw5kzJgx/Pe//+Xjjz9m7ty5CefNnz+fIUOGcMQRR/Dee+/x008/cf/997f6+mLnJJDRSgMyBvCbfr9h6balpCSnNBu+kNAIb2k/LTfg47dVtXk+FuJ+1YsLFDRr/KvNAw0WxYJiRNKpkfvItkW1mL8oG3FDMVS12ZfqqIQuU3FpdE1nw4YNFBYWxrpCxX8Bj3UfNzcS8kv4T1ESrhndF2sw0TiLc7RHSrSxHtTMwIYv7DODHXpj0COkh/Br/tiv2vFd5IN6sDFAEgmKBLQAgbB5H/1VOvprNruflD1RaSvTt5No75JY74W41zcq/jVtmibhfRLpqRA7J27oAIbZRTC++1xL14t/D8TntdP8I1rqpdL0sR5pUUd/1dbRm/VqiR5vtq8VvWH2tkdDfG8Di2JpFkCI3SK9DKK9GaL7Y0M+IvtUVY31Qojuj51jscbuLUrcPsx7m2rDrtrNngVNehvEiz7v6N9j06Bc/HZ8QE4ztNgwlvh9mm7mUVpWSnpGOhpaQmBPN8xz4vfF5xU9HkurNw8ENn1PxN4PkWBf09e0xfu4x/uiWe+iXWRXYpQQ1sP7dD3RskWLFlFUVMSECROaHZs8eTKqqnLTTTcRDAYZO3YsU6ZMaYdSJsr8f4/x5L/+Rlp6d57Kt5PWbRBJbjea0ThPRTRgYWBQ2RDAEplzyq5aSHPbSXKYc1jZrSoOiyUWrJC5qIQQ+8pqtfLb3/6W559/ngsvvJDbbrsNwzD405/+RENDA0OGDOH5558nNTUVgLS0NB588EFmz57NG2+8wejRo7nxxhu55557WnXd6ASj1113HUlJSUycOJH6+r37kfPyyy/nN7/5DStWrODkk09m8uTJvPjii8yYMYOCggJmzJjBkUcemXDOjTfeyAcffMDUqVPJzs7moYceatZrQ+wbxTjI+6dqmhZbGq2txr7VB+r5csmXDBw4sMU8Wxrb3TQC2GKaPThvZ1pzzZ01TOMbhM3GeDUJLDS9pq7rLP9pOYcddpg5Zm03Dc6m4+X3VLShFG10xPcyaHFfXLAj1oMh2rjRdUJGKKFHQ3QIQ/x50aCGL+wjoAUaAyJ6pGdINGgSNu9DemQ+gXCQ6vpqPEkeM0igqgnBgujj+H3xNwuWxmNx50a75SsojfnSmH/0saqawS6rYm08Ft+FP9rFP9L1Pb6bfGs0Td/ieMC4NJqusWH9Bgr7FGJRLPGJdn+t3eQd1dKQk7iDiWlaGBLTkt2la3qt+PTR91XsPz0uEAKEtTCbN2+mT+8+2FRbLCDZ0nCG6HOOH9oSDXZGg5Dx7wtFbTxmwdIseJowNCIuiJUwzGYXaVsK3rQYANpJw79pYCB+X1gL8/PPPzNw4MDYMKTY37oeHcSnY/6vyf5IMCL2N42eUJ5oAGpP3wN78vo3m2eEyDw/0X1N5ydRGs+LBdiUuECKbqArjY8NDDRdw1fq44IxF5DsbJseGfvj30nRsv1R15qm8cnbc0lOS8ObPQzN5kEBrHGTY7vtHWO+igNJ3tfNSZ0011HrxO/3s3HjRnr16tVsMsQDIbpqiNvtlmFiEVInLdubetnV+7s1f5PSI2MvuKwucuw5dEvu1qE+9NqTpmnYVTtOq3O/1kl0vLqF/XONWPDDMBJ+td1dcCT+nOgvz0EtyKo1q+jXr18sqKCokQZhNCAUmV8hekyhsWeO+YRJbMy20GshvnEZq6ddndNCl/Wd9ayIr/eE7T0JxLXwWaagoGkathIbh+ccbk5m1cpAyK7SxWs6R0j8vqYN2D0NbuwsWLGrvJud00I6TdPwVHoYkjsEm8WW0DOp6bwdLW4fpP+gappGmb2Mrsld2+RzpcWgyR68B3b3+u/Ja7+7NC0GRlpIo2s6P9f9jNN64L/Yio7LkpROWs9hFGR0kfkqhBBCHBIkkCFEnPihFTZs+5SXpmkE3UGGZnWsJb3am2IojZM87sdVZFoattLKziYHjKZppFhTyHBmyHtlP4oF6jro+2BPaJpGsa34gE8yLDq29Nye9O/dUz4/hBBCHDLkm5AQQgghhBBCCCE6DQlkCCGEEEIIIYQQotOQQIYQQgghhBBCdDAH+ZoM4hDVVu9rCWQIIYQQQgghRAcRne8mGAy2c0mEaHvR9/W+zuskk30KIYQQQgghRAdhtVpxu92UlZVhs5nLsh9IhmEQCARiy8ELqZOdaW296LpOWVkZbrcbq3XfQhESyBBCCCGEEEKIDkJRFPLy8ti4cSObN28+4Nc3DINQKITNZpNGe4TUScv2pl5UVaV79+77XI8SyBBCCCGEEEKIDsRut9O3b992GV6iaRqrV6+mT58+sqxzhNRJy/amXux2e5v0MpJAhhBCCCGEEEJ0MKqq4nQ6D/h1NU0DwOl0SqM9QuqkZe1ZLzLZpxBCCCGEEEIIIToNCWQIIYQQQgghhBCi05BAhhBCCCGEEEIIITqNg36ODMMwgMbxO20hmldb5tnZSZ00J3XSMqmX5qROWib10tz+qJNoXtF/L8X+I99JDhypl+akTpqTOmmZ1EtzUicta+t6ac13EsU4yL+5BINBli9f3t7FEEIIITq0oUOHYrfb27sYBzX5TiKEEELs3p58JznoAxm6rhMOh1FVVdb8FUIIIZowDANd17FarW2yHJrYOflOIoQQQuxca76THPSBDCGEEEIIIYQQQhw85KcXIYQQQgghhBBCdBoSyBBCCCGEEEIIIUSnIYEMIYQQQgghhBBCdBoSyBBCCCGEEEIIIUSnIYEMIYQQQgghhBBCdBoSyBBCCCGEEEIIIUSnIYEMIf5/e/cdFdWZ/3H8jYhBBUEFuzGoYUhoghoEBxXFoBE3dtlVXBWjKBuiYmGN/ixLQI8aC/Yay2I0sawtRrGz9hLbYsOCigaCBSMKCvP7g8MNw4yARhjK93WO5zB37n3u9z48Bz4+89yLEEIIIYQQQogSQyYyhBBCCCGEEEIIUWLIRMYb+ve//03btm1xdHSkZ8+enD9/3tAlGVRkZCQqlUrrX4cOHQxdVpE6efIkgYGBqNVqVCoV0dHRWu9rNBrmzJmDWq3GycmJ/v37c+vWLcMUW4Ty65fQ0FCdsRMQEGCgaovG4sWL6d69Oy4uLri7uzNs2DBu3LihtU9aWhqTJ0/Gzc0NFxcXvvzyS3777TcDVVz4CtIn/v7+OmPl//7v/wxUceGLioqic+fOuLq64urqSu/evTl48KDyflkbIyJvkkv+IJkki+QSXZJJdEkm0SWZRL/imktkIuMN7Ny5k4iICIKCgti8eTN2dnYEBASQnJxs6NIM6sMPPyQmJkb5FxUVZeiSilRqaioqlYqJEyfqfX/p0qWsWbOGSZMmsWHDBipWrEhAQABpaWlFXGnRyq9fADw9PbXGzrfffluEFRa9EydO0KdPHzZs2MDKlSt59eoVAQEBpKamKvuEh4ezf/9+Zs+ezZo1a0hMTOQf//iHAasuXAXpE4BevXppjZUxY8YYqOLCV6tWLUaNGsWmTZvYuHEjLVq0ICgoiGvXrgFlb4yI15NcoqusZxKQXKKPZBJdkkl0SSbRr9jmEo0osB49emgmT56svM7IyNCo1WrN4sWLDViVYc2dO1fzl7/8xdBlFBu2traaPXv2KK8zMzM1LVu21CxbtkzZlpKSonFwcNBs377dECUaRO5+0Wg0mrFjx2qGDh1qoIqKh+TkZI2tra3mxIkTGo0ma2zY29trfvrpJ2Wf69eva2xtbTVnz541UJVFK3efaDQaTd++fTVhYWEGrMrwmjdvrtmwYYOMEaFFcok2ySS6JJfokkyin2QSXZJJXq845BJZkVFA6enpXLp0CQ8PD2VbuXLl8PDw4OzZswaszPBu376NWq2mXbt2hISEkJCQYOiSio27d++SlJSkNW7Mzc1xdnYu8+MGsma+3d3d8fHxYeLEiTx69MjQJRWpp0+fAmBhYQHAxYsXefnypdZ4adSoEXXq1OGXX34xRIlFLnefZNu2bRtubm74+voyc+ZMnj9/bojyilxGRgY7duwgNTUVFxcXGSNCIblEP8kkeZNc8nqSSSST5CaZRFdxyiXlC7X1UuTRo0dkZGRQvXp1re3Vq1fXuXeqLHFyciIiIgIbGxuSkpKYP38+ffr0Ydu2bZiZmRm6PINLSkoC0DtuSvM9hgXh6elJ+/btqVevHnfu3OHbb7/liy++YP369RgbGxu6vEKXmZlJeHg4rq6u2NraAvDbb79hYmJClSpVtPatXr26MpZKM319AuDr60udOnWoUaMGV65cYcaMGdy8eZN58+YZsNrCdeXKFfz8/EhLS6NSpUrMnz+fxo0bExsbW6bHiPiD5BJdkknyJ7lEP8kkkklyk0yirTjmEpnIEH9K69atla/t7OxwdnbGy8uLn376iZ49exqwMlHcderUSfk6+2FJ3t7eyicipd3kyZO5du1ambx/+3Ve1ye9e/dWvlapVFhbW9O/f3/i4+N5//33i7rMImFjY8OWLVt4+vQpP//8M2PHjmXt2rWGLkuIYk0yiXhbkkkkk+QmmURbccwlcmtJAVWtWhVjY2OdB2glJydjZWVloKqKnypVqvDBBx8QHx9v6FKKBWtrawAZNwVQv359qlatyu3btw1dSqGbMmUKBw4cYNWqVdSqVUvZbmVlxcuXL0lJSdHaPzk5WRlLpdXr+kQfZ2dngFI9VipUqECDBg1wcHAgJCQEOzs7Vq9eXabHiNAmuSR/kkl0SS4pGMkkkkkkk2grjrlEJjIKqEKFCtjb23P06FFlW2ZmJkePHsXFxcWAlRUvz549486dO6X+B1xB1atXD2tra61x8/vvv3Pu3DkZN7k8ePCAx48fl+qxo9FomDJlCnv27GHVqlXUr19f630HBwdMTEy0xsuNGzdISEigSZMmRVxt0civT/SJjY0FKNVjJbfMzEzS09PL5BgR+kkuyZ9kEl2SSwpGMolkEskkeSsOuURuLXkDAwYMYOzYsTg4OODk5MSqVat4/vw53bp1M3RpBjNt2jS8vLyoU6cOiYmJREZGUq5cOXx9fQ1dWpF59uyZ1qc9d+/eJTY2FgsLC+rUqUO/fv1YuHAhDRo0oF69esyZM4caNWrg7e1twKoLX179YmFhwbx58/Dx8cHKyoo7d+4wffp0GjRogKenpwGrLlyTJ09m+/btLFiwgMqVKyv3Dpqbm2Nqaoq5uTndu3dn6tSpWFhYYGZmRlhYGC4uLqU2NOTXJ/Hx8Wzbto3WrVtjaWnJlStXiIiIoHnz5tjZ2Rm4+sIxc+ZMWrVqRe3atXn27Bnbt2/nxIkTLF++vEyOEfF6kku0SSbJIrlEl2QSXZJJdEkm0a+45hIjjUajKdQzlDJr165l+fLlJCUl8dFHHzF+/HhlSVFZNGLECE6ePMnjx4+pVq0aTZs2ZcSIEaX6HrHcjh8/Tr9+/XS2d+3alalTp6LRaJg7dy4bNmwgJSWFpk2bMnHiRGxsbAxQbdHJq18mTZpEUFAQ//vf/3j69Ck1atSgZcuWfPXVV6V6aatKpdK7PSIiQvmPR1paGlOnTmXHjh2kp6ejVquZOHFiqZ3pz69P7t+/z+jRo7l27RqpqanUrl0bb29vhg0bVmof3jdu3DiOHTtGYmIi5ubmqFQqvvjiC1q2bAmUvTEi8ia55A+SSbJILtElmUSXZBJdkkn0K665RCYyhBBCCCGEEEIIUWLIMzKEEEIIIYQQQghRYshEhhBCCCGEEEIIIUoMmcgQQgghhBBCCCFEiSETGUIIIYQQQgghhCgxZCJDCCGEEEIIIYQQJYZMZAghhBBCCCGEEKLEkIkMIYQQQgghhBBClBgykSGEEEIIIYQQQogSQyYyhHiHNm3aRLNmzQr9PM+fP+fLL7/E1dUVlUpFSkpKoZ+zOFOpVERHRxu6DMUPP/zAwIED/1Qbd+/eRaVSERsb+46q+nNGjBjBihUrDF2GEEKIPEgOMQzJIYY1Y8YM/vWvfxm6DFHEjDQajcbQRQjxJkJDQ9m8eTMA5cuXx8LCApVKRadOnejWrRvlyhXN/Fzbtm3p168f/fv3V7a9ePGCZ8+eUb169UI9d1RUFJGRkaxatYqqVatiZWWFkZFRoZ6zsKhUKubPn4+3t/dbt5GUlISFhQUVKlR4h5W9nbS0NNq1a8ecOXNo2rTpW7eTkZHBw4cPqVq1KuXLl39n9fn7+2NnZ8fXX3/9RsddvXqVvn37snfvXszNzd9ZPUIIUdJIDpEckpvkkIJ72xySl4cPH+Lt7c1//vMf6tev/87aFcWbrMgQJZKnpycxMTHs27ePpUuX4ubmxjfffMOQIUN49erVW7er0Wj+1PGmpqaFHh4A7ty5Q6NGjbC1tcXa2vqtwkNGRgaZmZmFUF3Rs7a2LhbhAWDXrl2YmZn9qfAAYGxsjLW19TsND3+Gra0t9evXZ+vWrYYuRQghDE5yiOSQnCSHGFa1atVQq9VERUUZuhRRhGQiQ5RIFSpUwNrampo1a2Jvb09gYCALFizg0KFDyqck+pbEpaSkoFKpOH78OADHjx9HpVJx8OBBunXrhqOjI6dPnyY+Pp6hQ4fi4eGBi4sL3bt358iRI0o7/v7+3Lt3j4iICFQqFSqVCtC/pDMqKgpvb28cHBzw8fFhy5YtWu+rVCp++OEHgoKCcHZ25tNPP2Xv3r2vvXZ/f39WrFjByZMnUalU+Pv7A/DkyRPGjBlD8+bNcXZ2ZtCgQdy6dUs5Lru2vXv38tlnn+Ho6EhCQgLp6elMmzYNT09PmjRpQs+ePZX+yXb69Gn8/f1xdnamefPmBAQE8OTJEwAOHTrEX//6V5o1a4abmxtDhgwhPj5eOTY9PZ0pU6agVqtxdHTEy8uLxYsXA1mfJgEEBQWhUqmU17nl1UZ2H+Zc0nnmzBk+//xzHB0d6datG9HR0VpjIfv7fvjwYbp06YKTkxP9+vUjOTmZgwcP0rFjR1xdXQkJCeH58+dKu/ldK8DOnTvx8vLS2hYaGsqwYcNYtGgRHh4eNGvWjHnz5vHq1SumTZvGJ598QqtWrdi4caNyTO7xm13z0aNH6datG87Ozvj5+XHjxg2d8+T0zTffKGMkNDSUEydOsHr1amXc3r17F8hacTFo0CBcXFzw8PBg9OjRPHz4UKstLy8vduzYofd7JIQQZYnkEMkhkkOKNofs2rWLzp074+TkhJubG/379yc1NVV5v23btuzcuVPv90+UTjKRIUoNd3d37Ozs2L179xsfO3PmTEJCQti5cycqlYrU1FRat27Nd999x+bNm/H09CQwMJCEhAQAIiMjqVWrFsHBwcTExBATE6O33T179hAeHs6AAQPYtm0bfn5+jBs3jmPHjmntN2/ePDp27MjWrVtp1aoVo0aN4vHjx3rbjIyMpFevXri4uBATE0NkZCSQ9cvh4sWLLFy4kPXr16PRaBg8eDAvX75Ujn3x4gVLly4lLCyM7du3U716daZMmcLZs2eZNWsWW7dupUOHDlrhIzY2lv79+9OoUSPWr19PVFQUXl5eZGRkAFn3yQ4YMICNGzfy3XffYWRkRFBQkPIpy5o1a9i3bx+zZ89m165dTJ8+nbp16wLw448/AhAREUFMTIzyOre82sjt999/Z+jQodja2rJ582a++uorpk+frnffefPmMWHCBL7//nsePHjA8OHDWb16NTNnzmTJkiXExMSwZs0aZf/8rhWywpajo6POuY4dO0ZiYiJr164lNDSUyMhIhgwZgoWFBRs2bMDPz4+JEyfy4MEDvbVmmzVrFqGhoWzcuBFjY2PGjRuX5/45ff3117i4uNCrVy9l3NauXZuUlBT+/ve/8/HHH/Pjjz+ybNkykpOTGT58uNbxTk5OnD9/nvT09AKfUwghygrJIZJDQHJIXt42hyQmJhISEkL37t3ZuXMnq1evpn379uR8QoKjoyMPHjxQJkZE6Vf81woJ8QYaNmzIlStX3vi44OBgWrZsqby2tLTEzs5OeT18+HCio6PZt28fffv2xdLSEmNjYypXroy1tfVr212+fDldu3alT58+ANjY2PDLL7+wYsUKWrRooezXtWtXfH19ARg5ciRr1qzh/PnztGrVSqdNS0tLTE1NMTExUc5969Yt9u3bx7p163B1dQWyHnzUpk0boqOj6dixIwAvX75k0qRJyrUlJCSwadMm9u/fT82aNQEICAjg8OHDbNq0iZEjR7Js2TIcHByYNGmSUsOHH36ofO3j46NVX3h4OO7u7ly/fh1bW1vu379PgwYNaNq0KUZGRlq/+KtVqwZAlSpV8uzHvNrIbdu2bQCEhYXx3nvv0bhxYxITExk/frzOvsOHD1eWXvbo0YOZM2cSHR2t3F/p4+PD8ePHGTx4cIGuNSUlhadPn1KjRg2dc1laWjJ+/HjKlStHw4YNWbZsGS9evCAwMBCAIUOGsHTpUk6fPk2nTp1ee30jRozgk08+AWDw4MEMHjyYtLQ03nvvvdcek83c3BwTExNMTU21+nvt2rV8/PHHjBw5UuvaWrduzc2bN7GxsQGgRo0avHz5kqSkpDy/B0IIUVZJDpEcIjnk9d42h6SmpvLq1Svat2+v9H32KqRs2eMnISGBevXq5VuLKPlkIkOUKhqN5q3u08w9c/3s2TPmzZvHgQMHSEpKIiMjgxcvXiifhBTUjRs36N27t9Y2V1dXVq9erbUt5w/jSpUqYWZmprOsPy9xcXGUL18eZ2dnZVvVqlWxsbEhLi5O2WZiYqJ1rqtXr5KRkUGHDh202ktPT8fS0hLI+iQk9/s53bp1i7lz53Lu3DkePXqkzI7fv38fW1tbunbtysCBA+nQoQOenp60adMGtVpd4GsD3qiNmzdvolKptH6h6vtkArT7vXr16lSsWFHrIVFWVlZcuHChwNf64sULAL33yTZu3FjrAXBWVlZaQczY2BhLS0uSk5Pz7IucNWeHgOTkZOrUqZPncXm5fPkyx48fx8XFRee9+Ph4ZSLD1NQUQLlOIYQQ2iSHSA6RHPLm8ssharUad3d3OnfujFqtRq1W4+Pjg4WFhbJfdn/nvBVHlG4ykSFKlbi4OGUWNvuHdc5lZ697gFbFihW1Xk+bNo0jR44wduxY3n//fUxNTQkODtZaHvkumZiYaL02MjIqlAdgmZqaagWs1NRUjI2NleWBOVWqVEk5Ji+BgYHUrVuXsLAwatSoQWZmJr6+vkpf2dvbs3fvXg4dOsSRI0cYPnw4Hh4ezJ07t8B1v4s29Mn5ACsjIyOdB1rl/j7kd62WlpYYGRnp/TN0+trO73wFqRlQjjEyMiL3H6IqyEPjUlNT8fLyYtSoUTrv5fzEJPt+5KpVq+bbphBClEWSQ/ImOUSb5JAs+eUQY2NjVq5cyZkzZ/jvf//LmjVrmDVrFhs2bFAmfrIzSvYqG1H6yTMyRKlx9OhRrl69yqeffgr88YMsKSlJ2aegfwv77NmzdO3alfbt26NSqbCysuLevXta+5iYmOT7w75hw4acOXNGa9uZM2do3LhxgeooqEaNGvHq1SvOnTunbHv06BE3b97M81wfffSR8ue1GjRooPUv+z+w2Q920if7HEOHDsXd3Z1GjRopv0hyMjMz47PPPiMsLIxZs2bx888/K/fempiYKPe55iWvNnKysbHh6tWrWs9xyPlpxtsqyLVWqFCBxo0bc/369T99vrdRrVo1rfEOumNe37i1t7fn2rVr1K1bV2ccZAdJyPrkrFatWhIShBBCD8khkkNAckhh5RAjIyOaNm1KcHAwW7ZswcTEROsBq9euXcPExERrlYko3WQiQ5RI6enpJCUl8euvv3Lp0iUWLVrEsGHD8PLyokuXLkDWDH6TJk1YsmQJcXFxnDhxgtmzZxeo/QYNGrBnzx5iY2O5fPkyISEhOj9069aty8mTJ/n1119fu/xy0KBBbN68maioKG7dusXKlSvZs2cPAwcO/DOXr+ODDz6gXbt2TJgwgVOnTnH58mVGjx5NzZo1adeu3WuPs7GxoXPnzowZM4bdu3dz584dzp8/z+LFizlw4ACQdf/jhQsXmDRpEpcvXyYuLo6oqCgePnyIhYUFlpaWrF+/ntu3b3P06FGmTp2qdY6VK1eyfft24uLiuHnzJrt27cLa2poqVaoAWf149OhRkpKS9IaPgrSRU+fOndFoNEyYMIG4uDgOHz7MihUrAN5quW+2glwrgFqt1gmNRaVFixZcvHiRLVu2KMtPr127prVP3bp1OXfuHHfv3uXhw4dkZmbyt7/9jSdPnjBy5EjOnz9PfHw8hw8f5p///KdWuDt9+rTWPdxCCFFWSQ7RJjnkD5JD3n0OOXfuHIsWLeLChQskJCSwe/duHj58SMOGDZV2T506RdOmTfNdwSNKD5nIECXS4cOHUavVtG3blkGDBnH8+HHGjx/PggULtJYmhoeHk5GRQbdu3QgPD9f5KwyvExoaSpUqVfDz8yMwMBBPT0/s7e219gkODubevXt4e3vj7u6utx1vb2/GjRvHihUr8PX15fvvvyc8PBw3N7e3vvbXiYiIUP4EXO/evdFoNCxZskRnuai+47p06cLUqVPp2LEjw4YN48KFC9SuXRvIChkrVqzg8uXL9OzZEz8/P/bu3Uv58uUpV64cs2bN4tKlS/j6+hIREcGYMWO02q9cuTLLli2jR48e9OjRg3v37rFkyRJlye3YsWM5cuQIbdq0oWvXrnprzK+NnMzMzFi4cCGxsbF8/vnnzJo1i6CgIED/PaMFVZBrhayHdR08eJCnT5++9bnelqenJ8OGDWP69On06NGDZ8+eKYE628CBAzE2NqZTp064u7uTkJBAzZo1WbduHZmZmQQEBNC5c2fCw8MxNzdX+jgtLY3o6Gh69epV5NclhBDFjeQQXZJDskgOefc5xMzMjJMnTzJ48GB8fHyYPXs2oaGhtG7dWml3x44dklHKGCNN7huZhBCilNm6dSvjxo3j1KlTRTJTHxwcjL29PUOGDCn0cxWVqKgooqOjlU+VhBBCCFEwkkMK18GDB5k2bRpbt27Vee6HKL3kOy2EKHW2bNlCvXr1qFmzJleuXGHGjBl06NChyJYbjhkzhv379xfJuYqKiYmJ3j8dJ4QQQghtkkOK1vPnz4mIiJBJjDJGVmQIIUqdpUuXsm7dOpKSkrC2tsbb25sRI0boPBVeCCGEEOJdkxwiROGTiQwhhBBCCCGEEEKUGPKwTyGEEEIIIYQQQpQYMpEhhBBCCCGEEEKIEkMmMoQQQgghhBBCCFFiyESGEEIIIYQQQgghSgyZyBBCCCGEEEIIIUSJIRMZQgghhBBCCCGEKDFkIkMIIYQQQgghhBAlhkxkCCGEEEIIIYQQosT4fw3XbI9aBbCwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1300x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# sns.set_theme(style=\"ticks\")\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig = plt.figure(figsize=(13, 4))\n",
    "    ax = fig.add_subplot(1,2,1)\n",
    "    sns.lineplot(\n",
    "        data=df2, \n",
    "        x=\"user_sigma\", y=\"balance_fitness\",hue='method',legend=False\n",
    "    )\n",
    "    ax.set_xlabel('Duration forecast sigma(minute)')\n",
    "    ax.set_ylabel('Degree of Imbalance')\n",
    "    ax.set_title(\"Balancing comparison\")\n",
    "    ax = fig.add_subplot(1,2,2)\n",
    "    sns.lineplot(\n",
    "        data=df2,\n",
    "        x=\"user_sigma\", y=\"duration_fitness\",hue='method'\n",
    "    )\n",
    "    ax.legend_.set_title(None)\n",
    "    ax.set_ylabel('Duration(minutes)')\n",
    "    ax.set_xlabel('Duration forecast sigma(minutes)')\n",
    "    ax.set_title('Duration comparison')\n",
    "    # plt.xlabel('duration forecast sigma(minute)')\n",
    "    # plt.ylabel('balance(resources)')\n",
    "    # plt.title(\"Balancing comparison\")\n",
    "    # # plt.legend()\n",
    "    plt.savefig(f'output/image/user_sigma_comparison.png',  dpi=500, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>balance_fitness</th>\n",
       "      <th>duration_fitness</th>\n",
       "      <th>method</th>\n",
       "      <th>average_load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.358998</td>\n",
       "      <td>64.147687</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.661587</td>\n",
       "      <td>63.306988</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.363000</td>\n",
       "      <td>64.619368</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.665329</td>\n",
       "      <td>61.861065</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.465302</td>\n",
       "      <td>63.217271</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   balance_fitness  duration_fitness   method  average_load\n",
       "0        10.358998         64.147687  MERL-LB           0.3\n",
       "1        10.661587         63.306988  MERL-LB           0.3\n",
       "2        10.363000         64.619368  MERL-LB           0.3\n",
       "3         9.665329         61.861065  MERL-LB           0.3\n",
       "4        10.465302         63.217271  MERL-LB           0.3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga_df3 = pd.read_csv('output/test/wsga/user_load_test01/wsga_user_load_exp2.csv')\n",
    "ga_df3[\"method\"] = [\"MERL-LB\"] * len(ga_df3)\n",
    "lg_df3 = pd.read_csv('output/test/lg/user_load_test/lg_user_load_exp2.csv')\n",
    "lg_df3[\"method\"] = [\"Least Duration Gap\"] * len(lg_df3)\n",
    "lc_df3 = pd.read_csv('output/test/lc/user_load_test/lc_user_load_exp2.csv')\n",
    "lc_df3[\"method\"] = [\"Least Connection\"] * len(lc_df3)\n",
    "rr_df3 = pd.read_csv('output/test/rr/user_load_test/rr_user_load_exp2.csv')\n",
    "rr_df3[\"method\"] = [\"Round Robin\"] * len(lc_df3)\n",
    "df3 = pd.concat([ga_df3, lg_df3, lc_df3, rr_df3], ignore_index=True)\n",
    "df3[\"average_load\"] = df3[\"user_load_rate\"]\n",
    "df3[\"duration_fitness\"] = df3[\"duration_fitness\"]  * 12 / 60\n",
    "df3 = df3.drop(columns=\"Unnamed: 0\")\n",
    "df3 = df3.drop(columns=\"user_load_rate\")\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>user_load_rate</th>\n",
       "      <th>method</th>\n",
       "      <th>fitness_type</th>\n",
       "      <th>fitness_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>10.358998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>10.661587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>10.363000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>9.665329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>MERL-LB</td>\n",
       "      <td>balance_fitness</td>\n",
       "      <td>10.465302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_load_rate   method     fitness_type  fitness_score\n",
       "0             0.3  MERL-LB  balance_fitness      10.358998\n",
       "1             0.3  MERL-LB  balance_fitness      10.661587\n",
       "2             0.3  MERL-LB  balance_fitness      10.363000\n",
       "3             0.3  MERL-LB  balance_fitness       9.665329\n",
       "4             0.3  MERL-LB  balance_fitness      10.465302"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df3 = df3.melt(id_vars=[\"user_load_rate\", \"method\"],var_name=\"fitness_type\", value_name=\"fitness_score\")\n",
    "# # df3 = df3.melt(id_vars=[\"method\"],var_name=\"fitness_type\", value_name=\"fitness_score\")\n",
    "# df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# sns.set_theme(style=\"ticks\")\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig = plt.figure(figsize=(13, 4))\n",
    "    ax = fig.add_subplot(1,2,1)\n",
    "    sns.lineplot(\n",
    "        data=df3, \n",
    "        x=\"average_load\", y=\"balance_fitness\",hue='method',legend=False\n",
    "    )\n",
    "    ax.set_xlabel('Average cloud load')\n",
    "    ax.set_ylabel('Degree of Imbalance')\n",
    "    ax.set_title(\"Balancing comparison\")\n",
    "    ax = fig.add_subplot(1,2,2)\n",
    "    sns.lineplot(\n",
    "        data=df3,\n",
    "        x=\"average_load\", y=\"duration_fitness\",hue='method'\n",
    "    )\n",
    "    ax.legend_.set_title(None)\n",
    "    ax.set_ylabel('Duration(minutes)')\n",
    "    ax.set_xlabel('Average cloud load')\n",
    "    ax.set_title('Duration comparison')\n",
    "    # plt.xlabel('duration forecast sigma(minute)')\n",
    "    # plt.ylabel('balance(resources)')\n",
    "    # plt.title(\"Balancing comparison\")\n",
    "    # # plt.legend()\n",
    "    plt.savefig(f'output/image/user_load_comparison.png',  dpi=500, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret value `method` for parameter `hue`",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mwith\u001b[39;00m sns\u001b[39m.\u001b[39maxes_style(\u001b[39m\"\u001b[39m\u001b[39mwhitegrid\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m      2\u001b[0m     fig \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure(figsize \u001b[39m=\u001b[39m (\u001b[39m11\u001b[39m, \u001b[39m7\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m     sns\u001b[39m.\u001b[39;49mlineplot(\n\u001b[1;32m      4\u001b[0m         data\u001b[39m=\u001b[39;49mdf2,\n\u001b[1;32m      5\u001b[0m         x\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39muser_sigma\u001b[39;49m\u001b[39m\"\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mduration_fitness\u001b[39;49m\u001b[39m\"\u001b[39;49m,hue\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mmethod\u001b[39;49m\u001b[39m'\u001b[39;49m\n\u001b[1;32m      6\u001b[0m     )\n\u001b[1;32m      7\u001b[0m     plt\u001b[39m.\u001b[39mylabel(\u001b[39m'\u001b[39m\u001b[39mduration(minute)\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      8\u001b[0m     plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mduration forecast sigma(minute)\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/seaborn/relational.py:618\u001b[0m, in \u001b[0;36mlineplot\u001b[0;34m(data, x, y, hue, size, style, units, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, estimator, errorbar, n_boot, seed, orient, sort, err_style, err_kws, legend, ci, ax, **kwargs)\u001b[0m\n\u001b[1;32m    615\u001b[0m errorbar \u001b[39m=\u001b[39m _deprecate_ci(errorbar, ci)\n\u001b[1;32m    617\u001b[0m variables \u001b[39m=\u001b[39m _LinePlotter\u001b[39m.\u001b[39mget_semantics(\u001b[39mlocals\u001b[39m())\n\u001b[0;32m--> 618\u001b[0m p \u001b[39m=\u001b[39m _LinePlotter(\n\u001b[1;32m    619\u001b[0m     data\u001b[39m=\u001b[39;49mdata, variables\u001b[39m=\u001b[39;49mvariables,\n\u001b[1;32m    620\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator, n_boot\u001b[39m=\u001b[39;49mn_boot, seed\u001b[39m=\u001b[39;49mseed, errorbar\u001b[39m=\u001b[39;49merrorbar,\n\u001b[1;32m    621\u001b[0m     sort\u001b[39m=\u001b[39;49msort, orient\u001b[39m=\u001b[39;49morient, err_style\u001b[39m=\u001b[39;49merr_style, err_kws\u001b[39m=\u001b[39;49merr_kws,\n\u001b[1;32m    622\u001b[0m     legend\u001b[39m=\u001b[39;49mlegend,\n\u001b[1;32m    623\u001b[0m )\n\u001b[1;32m    625\u001b[0m p\u001b[39m.\u001b[39mmap_hue(palette\u001b[39m=\u001b[39mpalette, order\u001b[39m=\u001b[39mhue_order, norm\u001b[39m=\u001b[39mhue_norm)\n\u001b[1;32m    626\u001b[0m p\u001b[39m.\u001b[39mmap_size(sizes\u001b[39m=\u001b[39msizes, order\u001b[39m=\u001b[39msize_order, norm\u001b[39m=\u001b[39msize_norm)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/seaborn/relational.py:365\u001b[0m, in \u001b[0;36m_LinePlotter.__init__\u001b[0;34m(self, data, variables, estimator, n_boot, seed, errorbar, sort, orient, err_style, err_kws, legend)\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[1;32m    352\u001b[0m     \u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m,\n\u001b[1;32m    353\u001b[0m     data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, variables\u001b[39m=\u001b[39m{},\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    359\u001b[0m     \u001b[39m# the kind of plot to draw, but for the time being we need to set\u001b[39;00m\n\u001b[1;32m    360\u001b[0m     \u001b[39m# this information so the SizeMapping can use it\u001b[39;00m\n\u001b[1;32m    361\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_default_size_range \u001b[39m=\u001b[39m (\n\u001b[1;32m    362\u001b[0m         np\u001b[39m.\u001b[39mr_[\u001b[39m.5\u001b[39m, \u001b[39m2\u001b[39m] \u001b[39m*\u001b[39m mpl\u001b[39m.\u001b[39mrcParams[\u001b[39m\"\u001b[39m\u001b[39mlines.linewidth\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m    363\u001b[0m     )\n\u001b[0;32m--> 365\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(data\u001b[39m=\u001b[39;49mdata, variables\u001b[39m=\u001b[39;49mvariables)\n\u001b[1;32m    367\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimator \u001b[39m=\u001b[39m estimator\n\u001b[1;32m    368\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39merrorbar \u001b[39m=\u001b[39m errorbar\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/seaborn/_oldcore.py:640\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    635\u001b[0m \u001b[39m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[1;32m    636\u001b[0m \u001b[39m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[1;32m    637\u001b[0m \u001b[39m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[1;32m    638\u001b[0m \u001b[39m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[1;32m    639\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_ordered \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mFalse\u001b[39;00m, \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mFalse\u001b[39;00m}  \u001b[39m# alt., used DefaultDict\u001b[39;00m\n\u001b[0;32m--> 640\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49massign_variables(data, variables)\n\u001b[1;32m    642\u001b[0m \u001b[39mfor\u001b[39;00m var, \u001b[39mcls\u001b[39m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_semantic_mappings\u001b[39m.\u001b[39mitems():\n\u001b[1;32m    643\u001b[0m \n\u001b[1;32m    644\u001b[0m     \u001b[39m# Create the mapping function\u001b[39;00m\n\u001b[1;32m    645\u001b[0m     map_func \u001b[39m=\u001b[39m partial(\u001b[39mcls\u001b[39m\u001b[39m.\u001b[39mmap, plotter\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/seaborn/_oldcore.py:701\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    699\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    700\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_format \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlong\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 701\u001b[0m     plot_data, variables \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_assign_variables_longform(\n\u001b[1;32m    702\u001b[0m         data, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mvariables,\n\u001b[1;32m    703\u001b[0m     )\n\u001b[1;32m    705\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mplot_data \u001b[39m=\u001b[39m plot_data\n\u001b[1;32m    706\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvariables \u001b[39m=\u001b[39m variables\n",
      "File \u001b[0;32m/opt/conda/envs/deepjs/lib/python3.9/site-packages/seaborn/_oldcore.py:938\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    933\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(val, (\u001b[39mstr\u001b[39m, \u001b[39mbytes\u001b[39m)):\n\u001b[1;32m    934\u001b[0m \n\u001b[1;32m    935\u001b[0m     \u001b[39m# This looks like a column name but we don't know what it means!\u001b[39;00m\n\u001b[1;32m    937\u001b[0m     err \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCould not interpret value `\u001b[39m\u001b[39m{\u001b[39;00mval\u001b[39m}\u001b[39;00m\u001b[39m` for parameter `\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m`\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 938\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(err)\n\u001b[1;32m    940\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    941\u001b[0m \n\u001b[1;32m    942\u001b[0m     \u001b[39m# Otherwise, assume the value is itself data\u001b[39;00m\n\u001b[1;32m    943\u001b[0m \n\u001b[1;32m    944\u001b[0m     \u001b[39m# Raise when data object is present and a vector can't matched\u001b[39;00m\n\u001b[1;32m    945\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(data, pd\u001b[39m.\u001b[39mDataFrame) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(val, pd\u001b[39m.\u001b[39mSeries):\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret value `method` for parameter `hue`"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1100x700 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig = plt.figure(figsize = (11, 7))\n",
    "    sns.lineplot(\n",
    "        data=df3,\n",
    "        x=\"user_sigma\", y=\"duration_fitness\",hue='method'\n",
    "    )\n",
    "    plt.ylabel('duration(minute)')\n",
    "    plt.xlabel('duration forecast sigma(minute)')\n",
    "    plt.title('Duration comparison')\n",
    "    plt.savefig(f'output/image/user_sigma_duration_comparison.png',  dpi=500, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbcf4c3c7f0>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "x = nn_df['user_sigma'] * 12 / 60\n",
    "y1 = nn_df['duration_fitness_mean'] * 12 /60/60\n",
    "y2 = lg_df['duration_fitness_mean'] * 12 /60/60\n",
    "y3 = lc_df['duration_fitness_mean'] * 12 /60/60\n",
    "fig = plt.figure(figsize = (8, 4))\n",
    "plt.plot(x, y1, label='ga')\n",
    "plt.plot(x, y2, label='lg')\n",
    "plt.plot(x, y3, label='lc')\n",
    "plt.ylabel('duration(hour)')\n",
    "plt.xlabel('duration forecast sigma(minute)')\n",
    "plt.title('Duration comparison')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbd00183b80>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = nn_df['user_sigma'] * 12 / 60\n",
    "y1 = nn_df['balance_fitness_mean'] * 12 / 60\n",
    "y2 = lg_df['balance_fitness_mean'] * 12 / 60\n",
    "y3 = lc_df['balance_fitness_mean'] * 12 / 60\n",
    "fig = plt.figure(figsize = (8, 4))\n",
    "plt.plot(x, y1, label='ga')\n",
    "plt.plot(x, y2, label='lg')\n",
    "plt.plot(x, y3, label='lc')\n",
    "plt.xlabel('duration forecast sigma(minute)')\n",
    "plt.ylabel('balance(minute)')\n",
    "plt.title(\"Balancing comparison\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "nsga_fitness_path = 'output/train/nsga/run02/elite/g3437_0/mean_fitness_record.npy'\n",
    "nsga_fitness = np.load(nsga_fitness_path)\n",
    "nsga_fitness.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "plt.figure(figsize=(12, 8))\n",
    "# 其他方法\n",
    "# 10 - 10\n",
    "name_list = [['RR','LC', 'LG'], [\"DeepJS\"]]\n",
    "fb_list = [[0.0433, 0.0331, 0.1427],[0.0312]]\n",
    "fd_list = [[525.0072, 524.0789, 345.8716],[449.5481]]\n",
    "all_edge_color = ['#009688', '#64b5f6']\n",
    "all_color = ['#80cbc4', '#90caf9']\n",
    "all_ftype = ['Heuristics','DeepJS']\n",
    "for name, fb, fd, color, edge_color, ftype  in zip(name_list, fb_list, fd_list, all_color, all_edge_color, all_ftype):\n",
    "    fb = np.array(fb) * 500\n",
    "    fd = np.array(fd)*12/60\n",
    "    plt.scatter(x=fb, y=fd, c=color, edgecolor=edge_color, label=ftype)\n",
    "\n",
    "    for i in range(len(fb)):\n",
    "        plt.annotate(name[i], xy = (fb[i], fd[i]), color=edge_color, xytext = (fb[i]+1, fd[i]-3))\n",
    "\n",
    "\n",
    "nsga_fitness_path = 'output/train/nsga/run02/elite/g3437_0/mean_fitness_record.npy'\n",
    "nsga_fitness = np.load(nsga_fitness_path)\n",
    "\n",
    "# res_np=np.load(\"output/one_job/fuse/run01/res_np.py.npy\")\n",
    "# # 用拥挤度排序选择100个结果\n",
    "# ga_color = np.array(ga_color)\n",
    "# x = res_np[ga_color==1][:,0]\n",
    "# y = res_np[ga_color==1][:,1]\n",
    "# plt.scatter(x, y, c='#ef9a9a', edgecolor='#ef5350', label='WSGA')\n",
    "# x = res_np[ga_color==0][:,0]\n",
    "# y = res_np[ga_color==0][:,1]\n",
    "# plt.scatter(x, y, c='#ce93d8',edgecolor='#ab47bc', label='NSGA')\n",
    "\n",
    "nsga_x = -nsga_fitness[:,-1]\n",
    "nsga_y = -nsga_fitness[:,-2] * 12 /60\n",
    "plt.scatter(nsga_x, nsga_y, c='#ef9a9a', edgecolor='#ef5350', label='Our')\n",
    "x= np.mean(nsga_x)\n",
    "y= np.mean(nsga_y)\n",
    "plt.annotate('Our',c='#ef5350', xy = (x, y), xytext = (x - 5, y - 8))\n",
    "\n",
    "# ns_x = np.mean(res_np[ga_color==0][:,0])\n",
    "# ns_y = np.mean(res_np[ga_color==0][:,1])\n",
    "# plt.annotate('NSGA', c='#ab47bc', xy = (ns_x, ns_y), xytext = (ns_x - 0.006, ns_y - 30))\n",
    "\n",
    "plt.legend( loc='upper right')\n",
    "plt.xlabel(\"balance\")\n",
    "plt.ylabel(\"duration\")\n",
    "# plt.xlim((10, 80))\n",
    "# plt.ylim((40, 120))\n",
    "\n",
    "# 展示\n",
    "plt.title(\"Validation Set Objective Space\")\n",
    "plt.savefig(f'output/image/validation_set_objective_space.png',  dpi=500, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "plt.figure(figsize=(12, 8))\n",
    "# 其他方法\n",
    "# 10 - 10\n",
    "name_list = [['RR','LC', 'LG'], [\"DeepJS\"]]\n",
    "fb_list = [[0.0433, 0.0331, 0.1427],[0.0312]]\n",
    "fd_list = [[525.0072, 524.0789, 345.8716],[449.5481]]\n",
    "all_edge_color = ['#009688', '#64b5f6']\n",
    "all_color = ['#e0e0e0', '#e0e0e0']\n",
    "all_ftype = ['Heuristics','DeepJS']\n",
    "all_markers = ['H','p']\n",
    "for name, fb, fd, color, edge_color, ftype, mk  in zip(name_list, fb_list, fd_list, all_color, all_edge_color, all_ftype, all_markers):\n",
    "    fb = np.array(fb) * 500\n",
    "    fd = np.array(fd)*12/60\n",
    "    plt.scatter(x=fb, y=fd, c=color, edgecolor='gray', label=ftype, marker=mk)\n",
    "\n",
    "    for i in range(len(fb)):\n",
    "        plt.annotate(name[i], xy = (fb[i], fd[i]), xytext = (fb[i]+1, fd[i]-3))\n",
    "\n",
    "\n",
    "nsga_fitness_path = 'output/train/nsga/run02/elite/g3437_0/mean_fitness_record.npy'\n",
    "nsga_fitness = np.load(nsga_fitness_path)\n",
    "\n",
    "nsga_x = -nsga_fitness[:,-1]\n",
    "nsga_y = -nsga_fitness[:,-2] * 12 /60\n",
    "plt.scatter(nsga_x, nsga_y, c='#e0e0e0',edgecolor='gray', label='Our')\n",
    "x= np.mean(nsga_x)\n",
    "y= np.mean(nsga_y)\n",
    "plt.annotate('Our', xy = (x, y), xytext = (x - 5, y - 8))\n",
    "plt.legend( loc='upper right')\n",
    "plt.xlabel(\"balance\")\n",
    "plt.ylabel(\"duration\")\n",
    "# 展示\n",
    "plt.title(\"Validation Set Objective Space\")\n",
    "plt.savefig(f'output/image/validation_set_objective_space_grey.png',  dpi=500, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=5140x3534>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image \n",
    "image_path = 'output/image/validation_set_objective_space_grey.png'\n",
    "path_list = image_path.split(\".\")\n",
    "image = Image.open(image_path).convert('L')\n",
    "image.show()\n",
    "image.save('{}_bw.{}'.format(*path_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3333333333333335"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19,  7, 12,  3,  6,  2, 15, 17,  5, 18, 24,  4, 11, 21, 20, 22,  1,\n",
       "       10,  8, 13, 14,  0, 16,  9, 23])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "root_path = \"output/train/nsga/run05/elite/g20000_0\"\n",
    "file_names = os.listdir(root_path)\n",
    "file_names.remove('mean_fitness_record.npy')\n",
    "scores = [item.split('.pth')[0] for item in file_names]\n",
    "scores = [\"\".join(item.split('-')) for item in scores]\n",
    "scores = [list(map(float, item.split('_')[1:])) for item in scores]\n",
    "b_score = np.array(scores)[:,1]\n",
    "index = np.argsort(b_score)\n",
    "for i in index:\n",
    "    file = file_names[i]\n",
    "    score = scores[i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 值排序\n",
    "def sort_by_values(values):\n",
    "    # 升序排序\n",
    "    sorted_index_list = []\n",
    "    for value in values:\n",
    "        sorted_index = np.argsort(value)\n",
    "        sorted_index_list.append(sorted_index)\n",
    "    return sorted_index_list\n",
    "\n",
    "def crowded_distance(values):\n",
    "    distances = []\n",
    "    sorted_index_list = sort_by_values(values)  # 升序排序\n",
    "\n",
    "    for value, sorted_index in zip(values, sorted_index_list):\n",
    "        distance = np.ones(len(sorted_index)) * 1e5\n",
    "        for i in range(1, len(sorted_index) - 1):\n",
    "            pre_index = sorted_index[i - 1]\n",
    "            curr_index = sorted_index[i]\n",
    "            after_index = sorted_index[i + 1]\n",
    "            distance[curr_index] = (value[after_index] - value[pre_index]) / (\n",
    "                max(value) - min(value)\n",
    "            )\n",
    "        distances.append(distance)\n",
    "\n",
    "    distances = np.array(distances)\n",
    "    distance = np.sum(distances, axis=0)\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  8, 14,  9, 11,  1,  2, 24, 23,  0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = 'output/test/nsga/nsga_run05_g20000/nsga_mean_std.csv'\n",
    "import pandas as pd\n",
    "data = pd.read_csv(data_path)\n",
    "values = [data[\"balance_fitness_mean\"].to_list(), data[\"duration_fitness_mean\"].to_list()]\n",
    "distance = crowded_distance(values)\n",
    "sorted_index = np.argsort(distance)  # 升序排序\n",
    "sorted_index[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data.loc[sorted_index[-10:]].sort_index()\n",
    "data2.to_csv(\"_show_.\".join(data_path.split('.')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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atGgR5s2bp31c981ydHRkkCIiIiIioma3/LTrIOXl5YWqqioUFhZKZqXy8/Ph5eXV6OsUCgUUCkUbVEhERERERB1Ruz6QNzo6GpaWlti1a5f22qlTp3DhwgX069fPhJUREREREVFHZtIZqZKSEpw5c0b7ODs7GxkZGXBxcYG/vz9u3ryJCxcu4MqVKwBqQxJQOxPl5eUFpVKJxx9/HPPmzYOLiwscHR3xj3/8A/369dO5Yx8REREREZG+TNr+fM+ePYiPj693ffLkyUhMTERiYiKmTp1a7/kXXngBL774IoDaA3mfe+45fPXVV6isrMSIESPw4YcfNrm073YqlQpKpRJFRUXcI0VEREREEqIooqamBmq12tSlkAHI5XJYWFg0ugdK12xgNudImRKDFBERERE1pKqqCrm5uSgrKzN1KWRAtra28Pb2hpWVVb3ndM0G7brZBBERERGRsWg0GmRnZ0Mul8PHxwdWVlbNdnIj8yaKIqqqqnDt2jVkZ2cjODi4yUN3m8IgRURERETUgKqqKmg0Gvj5+cHW1tbU5ZCB2NjYwNLSEjk5OaiqqoK1tXWL7tOuu/YRERERERlbS2csyHwZ4veUfyqIiIiIiIj0xCBFRERERESkJwYpIiIiIiIyqMTERDg5ObXqHl26dMHbb79tkHqMgUGKiIiIiKiDmTJlCsaMGVPv+p49eyAIAgoLC436/hMmTMDp06d1GttY6EpLS8MTTzxh4MoMh137iIiIiIiMTK0RkZp9E1eLK+DhYI3YABfIZR2zlXp1dTVsbGxgY2PTqvu4u7sbqCLj4IwUQa0RkXT2Br7PuIykszeg1tzxZzQTERERGcy2Y7kY+Nqv+NsnyXhmQwb+9kkyBr72K7YdyzV1adi7dy/uuusu2NjYwM/PD08//TRKS0u1zwuCgO+++07yGicnJyQmJgIAzp8/D0EQsHHjRgwePBjW1tb48ssv680yHT58GPHx8XBwcICjoyOio6ORnp6OPXv2YOrUqSgqKoIgCBAEAS+++CKA+kv7CgsL8eSTT8LT0xPW1tYIDw/H1q1bAQA5OTkYPXo0nJ2dYWdnhx49euCnn34yxrdMizNS7YSxfoqx7Vgulv14ArlFFdpr3kprvDA6DAnh3q2+PxEREdGdbNuxXDy17iBu/zF1XlEFnlp3EKsmRpns31xnz55FQkICXnnlFaxevRrXrl3DnDlzMGfOHKxZs0avey1cuBBvvvkmIiMjYW1tje3bt0uef/TRRxEZGYlVq1ZBLpcjIyMDlpaW6N+/P95++20sXboUp06dAgDY29vXu79Go8HIkSNRXFyMdevWITAwECdOnIBcLgcAzJ49G1VVVfj9999hZ2eHEydONHgfQ2KQageMFXbM+S82ERERUXun1ohY9uOJev/WAgARgABg2Y8ncE+Yl1GW+W3durVemFCr1dpfr1ixAo8++ijmzp0LAAgODsa7776LwYMHY9WqVXodVDt37lw89NBDjT5/4cIFzJ8/H927d9e+Vx2lUglBEODl5dXo63fu3InU1FRkZmYiJCQEANC1a1fJ/ceOHYuIiIh6zxkLl/aZubqwc2uIAv4KOy2dEm7uLzZQ+xeby/yIiIiIWiY1+2a9f8PdSgSQW1SB1OybRnn/+Ph4ZGRkSL4+/fRT7fOHDx9GYmIi7O3ttV8jRoyARqNBdna2Xu8VExPT5PPz5s3D9OnTMWzYMPz73//G2bNn9bp/RkYGOnXqpA1Rt3v66afxyiuvYMCAAXjhhRdw5MgRve7fEgxSZsyYYcfUf7GJiIiIOrqrxY3/W6sl4/RlZ2eHoKAgyZevr6/2+ZKSEjz55JOSoHX48GFkZWUhMDAQQO0eKVGU/luzurq6wfdqyosvvojjx49j1KhR+PXXXxEWFoZvv/1W58/SXOOK6dOn49y5c3jsscdw9OhRxMTE4L333tP5/i3BIGXGjBl2TP0Xm4iIiKij83DQbWmcruMMLSoqCidOnKgXtoKCgmBlZQWgtnNebu5fK6CysrJQVlbWovcLCQnBs88+ix07duChhx7S7sOysrKSLDlsSM+ePXHp0qUmW6r7+flh5syZ2LJlC5577jl88sknLapTVwxSZsyYYccc/mKzWyARERF1ZLEBLvBWWqOx3U8Cave9xwa4tGVZWgsWLMD+/fsxZ84cZGRkICsrC99//z3mzJmjHTNkyBC8//77OHToENLT0zFz5kxYWlrq9T7l5eWYM2cO9uzZg5ycHOzbtw9paWkIDQ0FUNudr6SkBLt27cL169cbDGqDBw/GoEGDMHbsWPzyyy/Izs7Gzz//jG3btgGo3aO1fft2ZGdn4+DBg9i9e7f2/sbCIGXGjBl2TP0X25zbgBIREREZglwm4IXRYQBQ799cdY9fGB1msvOkevbsid9++w2nT5/GXXfdhcjISCxduhQ+Pj7aMW+++Sb8/Pxw11134e9//zuef/552Nra6vU+crkcN27cwKRJkxASEoLx48dj5MiRWLZsGQCgf//+mDlzJiZMmAB3d3e8/vrrDd5n8+bN6NOnD/72t78hLCwM//znP7UzWWq1GrNnz0ZoaCgSEhIQEhKCDz/8sIXfGd0I4u2LHu9AKpUKSqUSRUVFcHR0NHU5WmqNiIGv/Yq8oooG90kJALyU1ti7YEiL/gLWNbIAILl/3Z2M1bWvsW6Bxn5fIiIiIn1UVFQgOzsbAQEBenWwux2PmzE/Tf3e6poN2P7cjNX9FOOpdQchoOGw05qfYiSEe2PVxKh6f7G9jPgX29RtQImIiIjaWkK4N+4J8zLKmaBkOgxSZs7YYaet/2Lr00CjX6CrUWogIiIiamtymcB/23QwDFLtgLHDTlv+xWa3QCIiIiLqCBik2omO8lMMc+gWSERERETUWuzaR23K1N0CiYiIiIgMgUGK2pS5twElIiIiItIFgxS1uboGGl5K6fI9L6U1W58TERERUbvAPVJkEmwDSkRERETtGYMUmUxHaaBBRERERHceLu0jIiIiIiLSE4MUEREREVEHM2XKFAiCAEEQYGlpCU9PT9xzzz1YvXo1NBpNm9cjCAK+++477ePffvsNQ4YMgYuLC2xtbREcHIzJkyejqqqqzWtrKQYpIiIiIiJj06iB7D+Ao9/U/q9GbfS3TEhIQG5uLs6fP4+ff/4Z8fHxeOaZZ3DfffehpqbG6O/fmBMnTiAhIQExMTH4/fffcfToUbz33nuwsrKCWm3874uhMEgRERERERnTiR+At8OBtfcBmx+v/d+3w2uvG5FCoYCXlxd8fX0RFRWFxYsX4/vvv8fPP/+MxMREAEBhYSGmT58Od3d3ODo6YsiQITh8+LDkPt9//z2ioqJgbW2Nrl27YtmyZZIgJggCVq1ahZEjR8LGxgZdu3bFN99802hdO3bsgJeXF15//XWEh4cjMDAQCQkJ+OSTT2BjY2OU74UxMEgRERERERnLiR+ArycBqivS66rc2utGDlO3GzJkCHr16oUtW7YAAB5++GFcvXoVP//8Mw4cOICoqCgMHToUN2/eBAD88ccfmDRpEp555hmcOHECH330ERITE/Hqq69K7rtkyRKMHTsWhw8fxqOPPopHHnkEmZmZDdbg5eWF3Nxc/P7778b9sEbGIEVEREREZAwaNbBtAQCxgSf/vLZtYZss87tV9+7dcf78eezduxepqanYtGkTYmJiEBwcjDfeeANOTk7aGaVly5Zh4cKFmDx5Mrp27Yp77rkHL7/8Mj766CPJPR9++GFMnz4dISEhePnllxETE4P33nuvwfd/+OGH8be//Q2DBw+Gt7c3HnzwQbz//vtQqVRG/+yGxCBFRERERGQMOfvrz0RJiIDqcu24NiSKIgRBwOHDh1FSUgJXV1fY29trv7Kzs3H27FkAwOHDh/HSSy9Jnp8xYwZyc3NRVlamvWe/fv0k79GvX79GZ6TkcjnWrFmDS5cu4fXXX4evry+WL1+OHj16IDc313gf3MB4jhQRERERkTGU5Bt2nIFkZmYiICAAJSUl8Pb2xp49e+qNcXJyqi2tpATLli3DQw89VG+MtbV1q+rw9fXFY489hsceewwvv/wyQkJC8N///hfLli1r1X3bCoMUEREREZEx2HsadpwB/Prrrzh69CieffZZdOrUCXl5ebCwsECXLl0aHB8VFYVTp04hKCioyfsmJydj0qRJkseRkZE61+Xs7Axvb2+Ulpbq/BpTY5AiIiIiIjKGzv0BR5/axhIN7pMSap/v3N8ob19ZWYm8vDyo1Wrk5+dj27ZtWLFiBe677z5MmjQJMpkM/fr1w5gxY/D6668jJCQEV65cwf/+9z88+OCDiImJwdKlS3HffffB398f48aNg0wmw+HDh3Hs2DG88sor2veq22c1cOBAfPnll0hNTcVnn33WYF0fffQRMjIy8OCDDyIwMBAVFRX4/PPPcfz48Ub3VZkj7pEiIiIiIjIGmRxIeO3PB8JtT/75OOHfteOMYNu2bfD29kaXLl2QkJCA3bt3491338X3338PuVwOQRDw008/YdCgQZg6dSpCQkLwyCOPICcnB56etbNkI0aMwNatW7Fjxw706dMHffv2xVtvvYXOnTtL3mvZsmXYsGEDevbsic8//xxfffUVwsLCAEB7ALCFRe0cTmxsLEpKSjBz5kz06NEDgwcPRnJyMr777jsMHjzYKN8LYxBEUWwoHt9RVCoVlEolioqK4OjoaOpyiIiIiMgMVFRUIDs7GwEBAa3bD3Tih9rufbc2nnD0rQ1RYfe3vlATEwQB3377LcaMGdPg83l5efD29kZaWhpiYmLatrhGNPV7q2s24NI+IiIiIiJjCrsf6D6qtjtfSX7tnqjO/Y02E2UuRFFETk4O3njjDXh6eiI8PNzUJRkUgxQRERERkbHJ5EDAXaauok0VFRWhW7duCA0NxYYNG1rd5c/cMEgREREREVGLNbZTyMnJCZWVlW1cTdthswkiIiIiIiI9MUgRERERERHpiUGKiIiIiIhITwxSREREREREemKQIiIiIiIi0hODFBERERERkZ4YpIiIiIiIiPTEIEVEREREZGRqjRppeWn46dxPSMtLg1qjNur7TZkyBYIgYObMmfWemz17NgRBwJQpUyRjb/9KSEjQvqZLly7a67a2toiIiMCnn34que+ePXsgCAIKCwubre/kyZMQBAHJycmS63379oW1tTUqKiq01yoqKmBtbY3PPvsMAHDt2jU89dRT8Pf3h0KhgJeXF0aMGIF9+/bp+u0xCB7IS0RERERkRDtzduLfqf9Gflm+9pqnrScWxi7EsM7DjPa+fn5+2LBhA9566y3Y2NgAqA0l69evh7+/v2RsQkIC1qxZI7mmUCgkj1966SXMmDEDZWVl2LRpE2bMmAFfX1+MHDlS79q6d+8OLy8v7NmzB3379gUAFBcX4+DBg/D09ERycjLuvvtuAEBSUhIqKysxZMgQAMDYsWNRVVWFtWvXomvXrsjPz8euXbtw48YNvetoDc5IEREREREZyc6cnZi3Z54kRAHA1bKrmLdnHnbm7DTae0dFRcHPzw9btmzRXtuyZQv8/f0RGRkpGVs3s3Prl7Ozs2SMg4MDvLy80LVrVyxYsAAuLi745ZdfWlxffHw89uzZo328d+9ehISEYPTo0ZLre/bsQefOnREQEIDCwkL88ccfeO211xAfH4/OnTsjNjYWixYtwv3339/iWlqCQYqIiIiIyAjUGjX+nfpviBDrPVd37bXU14y6zG/atGmSmabVq1dj6tSprbqnRqPB5s2bUVBQACsrqxbfJz4+Hnv37kVNTQ0AYPfu3bj77rsxePBg7N69Wztu9+7diI+PBwDY29vD3t4e3333HSorK1v1OVqLQYqIiIiIyAgOXj1YbybqViJE5JXl4eDVg0arYeLEidi7dy9ycnKQk5ODffv2YeLEifXGbd26VRtS6r6WL18uGbNgwQLY29tDoVBg3LhxcHZ2xvTp01tcW3x8PEpLS5GWlgagduZp8ODBGDRoEFJSUlBRUYHy8nKkpqZqg5SFhQUSExOxdu1aODk5YcCAAVi8eDGOHDnS4jpaikGKiIiIiMgIrpVdM+i4lnB3d8eoUaOQmJiINWvWYNSoUXBzc6s3Lj4+HhkZGZKv2xtVzJ8/HxkZGfj1118RFxeHt956C0FBQTrVsXz5cklIu3DhAoKCgtCpUyfs2bMHKpUKhw4dwuDBg+Ht7Q1/f38kJSVp90fVBSmgdo/UlStX8MMPPyAhIQF79uxBVFQUEhMTW/W90hebTRARERERGYG7rbtBx7XUtGnTMGfOHADABx980OAYOzu7ZkORm5sbgoKCEBQUhE2bNiEiIgIxMTEICwtrtoaZM2di/Pjx2sc+Pj4AgLvvvhu7d+9Gz549ERwcDA8PDwDQLu8TRRFBQUHw8/OT3M/a2hr33HMP7rnnHixZsgTTp0/HCy+8oO1E2BY4I0VEREREZARRHlHwtPWEAKHB5wUI8LL1QpRHlFHrSEhIQFVVFaqrqzFixAiD3NPPzw8TJkzAokWLdBrv4uKiDWFBQUGwsKidz4mPj8f+/fvxyy+/aLv0AcCgQYOwZ88e7NmzRzIb1ZiwsDCUlpa26LO0FGekiIiIiIiMQC6TY2HsQszbMw8CBEnTibpwtSB2AeQyuXHrkMuRmZmp/XVDKisrkZeXJ7lmYWHR4DLAOs888wzCw8ORnp6OmJgY7fWjR4/CwcFB+1gQBPTq1avBe9Ttk1q9ejU++eQT7fXBgwdr91/NmjVLe/3GjRt4+OGHMW3aNPTs2RMODg5IT0/H66+/jgceeKDRWo3BpDNSv//+O0aPHg0fHx8IgoDvvvtO8rwoili6dCm8vb1hY2ODYcOGISsrSzLm5s2bePTRR+Ho6AgnJyc8/vjjKCkpacNPQURERETUsGGdh2Hl3SvhYeshue5p64mVd6806jlSt3J0dISjo2Ojz2/btg3e3t6Sr4EDBzZ5z7CwMAwfPhxLly6VXB80aBAiIyO1X9HR0Y3eIyAgAJ07d0ZxcTEGDx6sve7v7w8fHx9UVVVJZqrs7e21+7MGDRqE8PBwLFmyBDNmzMD777/fzHfBsARRFOv3Y2wjP//8M/bt24fo6Gg89NBD+PbbbzFmzBjt86+99hpWrFiBtWvXIiAgAEuWLMHRo0dx4sQJWFtbAwBGjhyJ3NxcfPTRR6iursbUqVPRp08frF+/Xuc6VCoVlEolioqKmvwDRkRERER3joqKCmRnZyMgIED7b8+WUmvUOHj1IK6VXYO7rTuiPKKMPhNFjWvq91bXbGDSIHUrQRAkQUoURfj4+OC5557D888/DwAoKiqCp6cnEhMT8cgjjyAzMxNhYWFIS0vTTidu27YN9957Ly5duqTdxNYcBikiIiIiup0hgxSZF0MEKbNtNpGdnY28vDwMG/bXdKdSqURcXBySkpIAAElJSXBycpKsyRw2bBhkMhlSUlIavXdlZSVUKpXki4iIiIiISFdmG6TqNrt5enpKrnt6emqfy8vL07ZIrGNhYQEXF5d6m+VutWLFCiiVSu3X7e0UiYiIiIiImmK2QcqYFi1ahKKiIu3XxYsXTV0SERERERG1I2YbpLy8vAAA+fn5kuv5+fna57y8vHD16lXJ8zU1Nbh586Z2TEMUCoW2c0lzHUyIiIiIiIhuZ7ZBKiAgAF5eXti1a5f2mkqlQkpKCvr16wcA6NevHwoLC3HgwAHtmF9//RUajQZxcXFtXjMREREREd0ZTHogb0lJCc6cOaN9nJ2djYyMDLi4uMDf3x9z587FK6+8guDgYG37cx8fH21nv9DQUCQkJGDGjBn473//i+rqasyZMwePPPKIzh37iIiIiIiI9GXSIJWeno74+Hjt43nz5gEAJk+ejMTERPzzn/9EaWkpnnjiCRQWFmLgwIHYtm2bpEXhl19+iTlz5mDo0KGQyWQYO3Ys3n333Tb/LAahUQM5+4GSfMDeE+jcH+D5AkREREREZsdszpEyJbM4R+rED8C2BYDqyl/XHH2AhNeAsPtNUxMRERHRHYznSHVcHfocqTvKiR+ArydJQxQAqHJrr5/4wTR1ERERERFRgxikTE2jrp2JQkMTg39e27awdhwRERERtUuiWo3SlFQUbf0fSlNSIaqN+2+7KVOmQBAEzJw5s95zs2fPhiAImDJlimTs7V8JCQna13Tp0kV73dbWFhEREfj0008l992zZw8EQUBhYaFONd68eRP/+Mc/0K1bN9jY2MDf3x9PP/00ioqKWvy525JJ90gRavdE3T4TJSECqsu14wLuarOyiIiIiMgwVDt2IH/5CtTk5WmvWXh5wXPxIjgOH2609/Xz88OGDRvw1ltvwcbGBkDtkrb169fD399fMjYhIQFr1qyRXFMoFJLHL730EmbMmIGysjJs2rQJM2bMgK+vL0aOHNmi+q5cuYIrV67gjTfeQFhYGHJycjBz5kxcuXIF33zzTYvu2ZY4I2VqJfnNj9FnHBERERGZDdWOHbj8zFxJiAKAmvx8XH5mLlQ7dhjtvaOiouDn54ctW7Zor23ZsgX+/v6IjIyUjFUoFPDy8pJ8OTs7S8Y4ODjAy8sLXbt2xYIFC+Di4oJffvmlxfWFh4dj8+bNGD16NAIDAzFkyBC8+uqr+PHHH1FTU9Pi+7YVBilTs/c07DgiIiIiMguiWo385SuAhnq7/Xktf/kKoy7zmzZtmmSmafXq1Zg6dWqr7qnRaLB582YUFBTAysqqtSVK1DV4sLAw/4VzDFKm1rl/bXc+CI0MEABH39pxRERERNRulKUfqDcTJSGKqMnLQ1n6AaPVMHHiROzduxc5OTnIycnBvn37MHHixHrjtm7dCnt7e8nX8uXLJWMWLFgAe3t7KBQKjBs3Ds7Ozpg+fbrBar1+/TpefvllPPHEEwa7pzGZf9Tr6GTy2hbnX09CbZi69ScWf4arhH/zPCkiIiKidqbm2jWDjmsJd3d3jBo1ComJiRBFEaNGjYKbm1u9cfHx8Vi1apXkmouLi+Tx/PnzMWXKFOTm5mL+/PmYNWsWgoKCdKpj+fLlkmB24sQJyT4tlUqFUaNGISwsDC+++KIen9B0GKTMQdj9wPjPGzlH6t88R4qIiIioHbJwdzfouJaaNm0a5syZAwD44IMPGhxjZ2fXbChyc3NDUFAQgoKCsGnTJkRERCAmJgZhYWHN1jBz5kyMHz9e+9jHx0f76+LiYiQkJMDBwQHffvstLC0tdflYJscgZS7C7ge6j6rtzleSX7snqnN/zkQRERERtVO2MdGw8PJCTX5+w/ukBAEWnp6wjYk2ah0JCQmoqqqCIAgYMWKEQe7p5+eHCRMmYNGiRfj++++bHe/i4lJvhguonYkaMWIEFAoFfvjhh3Z18DGDlDmRydninIiIiKiDEORyeC5ehMvPzAUEQRqmhNotHJ6LF0GQG/cH53K5HJmZmdpfN6SyshJ5t+3nsrCwaHAZYJ1nnnkG4eHhSE9PR0xMjPb60aNH4eDgoH0sCAJ69epV7/UqlQrDhw9HWVkZ1q1bB5VKBZVKBaB2SWJjtZoLBikiIiIiIiNxHD4ceOft+udIeXoa/RwpSR2Ojk0+v23bNnh7e0uudevWDSdPnmz0NWFhYRg+fDiWLl2Kn376SXt90KBBknFyubzBduYHDx5ESkoKANRbVpidnY0uXbo0WbOpCaLY0DzjnUWlUkGpVGrbLRIRERERVVRUIDs7GwEBAa1eciaq1bVd/K5dg4W7O2xjoo0+E0WNa+r3VtdswBkpIiIiIiIjE+Ry2MXFmroMMiCeI0VERERERKQnBikiIiIiIiI9MUgRERERERHpiUGKiIiIiIhITwxSREREREREemKQIiIiIiIi0hODFBERERERkZ4YpIiIiIiIiPTEIEVERERERKQnBikiIiIiIiPTaERcPlWA02l5uHyqABqNaNT3mzJlCgRBwMyZM+s9N3v2bAiCgClTpkjG3v6VkJCgfU2XLl20121tbREREYFPP/1Uct89e/ZAEAQUFhbqXOfdd99d730bqtkcWZi6ACIiIiKijuzsoav4Y2MWSgsrtdfsnBS4a0IwAiM9jPa+fn5+2LBhA9566y3Y2NgAACoqKrB+/Xr4+/tLxiYkJGDNmjWSawqFQvL4pZdewowZM1BWVoZNmzZhxowZ8PX1xciRI1tV54wZM/DSSy9pH9va2rbqfm2FM1JEREREREZy9tBVbPvomCREAUBpYSW2fXQMZw9dNdp7R0VFwc/PD1u2bNFe27JlC/z9/REZGSkZq1Ao4OXlJflydnaWjHFwcICXlxe6du2KBQsWwMXFBb/88kur67S1tZW8r6OjY6vv2RYYpIiIiIiIjECjEfHHxqwmx+z9Osuoy/ymTZsmmWlavXo1pk6d2qp7ajQabN68GQUFBbCysmptifjyyy/h5uaG8PBwLFq0CGVlZa2+Z1tgkCIiIiIiMoLcrMJ6M1G3KymoRG5WodFqmDhxIvbu3YucnBzk5ORg3759mDhxYr1xW7duhb29veRr+fLlkjELFiyAvb09FAoFxo0bB2dnZ0yfPr1V9f3973/HunXrsHv3bixatAhffPFFg/WZI+6RIiIiIiIyglJV0yFK33Et4e7ujlGjRiExMRGiKGLUqFFwc3OrNy4+Ph6rVq2SXHNxcZE8nj9/PqZMmYLc3FzMnz8fs2bNQlBQkE51LF++XBLMTpw4AX9/fzzxxBPaaxEREfD29sbQoUNx9uxZBAYG6vNR2xyDFBERERGREdg5KpofpMe4lpo2bRrmzJkDAPjggw8arsHOrtlQ5ObmhqCgIAQFBWHTpk2IiIhATEwMwsLCmq1h5syZGD9+vPaxj49Pg+Pi4uIAAGfOnGGQIiIiIiK6E3kHO8HOSdHk8j57ZwW8g52MWkdCQgKqqqogCAJGjBhhkHv6+flhwoQJWLRoEb7//vtmx7u4uNSb4WpIRkYGAMDb27u1JRodgxQRERERkRHIZALumhCMbR8da3TMwPHBkMkEo9Yhl8uRmZmp/XVDKisrkZeXJ7lmYWHR4DLAOs888wzCw8ORnp6OmJgY7fWjR4/CwcFB+1gQBPTq1ave68+ePYv169fj3nvvhaurK44cOYJnn30WgwYNQs+ePfX6jKbAIEVEREREZCSBkR5IeDK83jlS9s4KDBxv3HOkbtVcS/Ft27bVmwXq1q0bTp482ehrwsLCMHz4cCxduhQ//fST9vqgQYMk4+RyOWpqauq93srKCjt37sTbb7+N0tJS+Pn5YezYsfjXv/6ly0cyOUEUReMeq9wOqFQqKJVKFBUVtZu+9URERERkXBUVFcjOzkZAQACsra1bdS+NRqzt4qeqhJ1j7XI+Y89EUeOa+r3VNRtwRoqIiIiIyMhkMgG+3ZybH0jtBs+RIiIiIiIi0hODFBERERERkZ4YpIiIiIiIiPTEIEVERERERKQnBikiIiIiIiI9MUgRERERERHpiUGKiIiIiIhITwxSREREREREeuKBvO2AuroG2f9LRUm+CvaejggYFQu5JX/riIiIiIhMhTNSZu7Yml+wZuaP2L6tAvsOWWH7tgqsmfkjjq35xdSlEREREZGONBo1Lh4/gsx9v+Hi8SPQaNRGfb8pU6ZAEATMnDmz3nOzZ8+GIAiYMmWKZOztXwkJCdrXdOnSRXvd1tYWERER+PTTTyX33bNnDwRBQGFhoV61JiUlYciQIbCzs4OjoyMGDRqE8vJyvT9zW+O0hhk7tuYX/JYsAywcJdcrLRzxWzIA/ILwqfeYpDYiIiIi0k1Wyn78mvgxSm5e116zd3HDkClPIDiuv9He18/PDxs2bMBbb70FGxsbAEBFRQXWr18Pf39/ydiEhASsWbNGck2hUEgev/TSS5gxYwbKysqwadMmzJgxA76+vhg5cmSLa0xKSkJCQgIWLVqE9957DxYWFjh8+DBkMvOf7zH/Cu9Q6uoaJO8tqX0gCNIn/3ycvLcE6uqaNq6MiIiIiHSVlbIfP6xcLglRAFBy8zp+WLkcWSn7jfbeUVFR8PPzw5YtW7TXtmzZAn9/f0RGRkrGKhQKeHl5Sb6cnZ0lYxwcHODl5YWuXbtiwYIFcHFxwS+/tG6V1LPPPounn34aCxcuRI8ePdCtWzeMHz++XogzRwxSZir7f6motFTWD1F1BAGVlkpk/y+1bQsjIiIiIp1oNGr8mvhxk2N2r/3YqMv8pk2bJplpWr16NaZOndqqe2o0GmzevBkFBQWwsrJq8X2uXr2KlJQUeHh4oH///vD09MTgwYOxd+/eVtXXVhikzFRJvsqg44iIiIiobV3OPF5vJup2xTeu43LmcaPVMHHiROzduxc5OTnIycnBvn37MHHixHrjtm7dCnt7e8nX8uXLJWMWLFgAe3t7KBQKjBs3Ds7Ozpg+fXqLazt37hwA4MUXX8SMGTOwbds2REVFYejQocjKymrxfdsK90iZKXtPRwAVOo4jIiIiInNTUlhg0HEt4e7ujlGjRiExMRGiKGLUqFFwc3OrNy4+Ph6rVq2SXHNxcZE8nj9/PqZMmYLc3FzMnz8fs2bNQlBQkE51LF++XBLMTpw4AY1GAwB48skntbNkkZGR2LVrF1avXo0VK1bo9VnbGoOUmQoYFQvFjz+i0sKx4eV9oghFdRECRt3f9sURERERUbPsnZybH6THuJaaNm0a5syZAwD44IMPGhxjZ2fXbChyc3NDUFAQgoKCsGnTJkRERCAmJgZhYWHN1jBz5kyMHz9e+9jHxwdqde2SxttfHxoaigsXLjR7T1Pj0j4zJbe0QN+B9rUPRFH65J+Pg09/jdLdv7ZxZURERESkC9/QHrB3qT/7cysHVzf4hvYwah0JCQmoqqpCdXU1RowYYZB7+vn5YcKECVi0aJFO411cXLQhLCgoCBYWFujSpQt8fHxw6tQpydjTp0+jc+fOBqnTmDgjZcZ6TBqCgg1P4JT3vai0/usnFYrKAgSf+QYeN44gf/kKOAwdCkEuN2GlRERERHQ7mUyOIVOewA8rlzc6Jn7yE5DJjPvvOLlcjszMTO2vG1JZWYm8vDzJNQsLiwaXAdZ55plnEB4ejvT0dMTExGivHz16FA4ODtrHgiCgV69e9V4vCALmz5+PF154Ab169ULv3r2xdu1anDx5Et98841en9EUGKTMWFn6Abjl7IdrThIKnYJQaeUIRZUKToVnIKB2VqomLw9l6QdgFxdr4mqJiIiI6HbBcf1x/7zF9c6RcnB1Q/xk454jdStHx6b31W/btg3e3t6Sa926dcPJkycbfU1YWBiGDx+OpUuX4qefftJeHzRokGScXC5HTU3DR/bMnTsXFRUVePbZZ3Hz5k306tULv/zyCwIDA5v7SCYniOLt68buPCqVCkqlEkVFRc3+IWtLRVv/hyvPP9/sOJ833oDyvlFtUBERERHRnaOiogLZ2dkICAiAtbV1q+6l0ahru/gVFsDeyRm+oT2MPhNFjWvq91bXbMAZKTNm4e5u0HFEREREZBoymRx+PXqaugwyIAYpM2YbEw0LLy/U5OdDFIGyTj2gsXWGrKwAtpeOQxAAC09P2MZEm7pUIiIiIqI7CoOUGRPkcnguXoST73wF5+7j4GX519RieS8VCk5+g+7P/I2NJoiIiIiI2pjZtz8vLi7G3Llz0blzZ9jY2KB///5IS0vTPi+KIpYuXQpvb2/Y2Nhg2LBh7eIkZF1dK7GDd/hUWFs4SK5bWzjAO3wqrpbYmqgyIiIiIqI7l9kHqenTp+OXX37BF198gaNHj2L48OEYNmwYLl++DAB4/fXX8e677+K///0vUlJSYGdnhxEjRqCiosLElbeeuqYGmmQVgNr2kLfSPk4qRVbKpbYujYiIiIjojmbWQaq8vBybN2/G66+/jkGDBiEoKAgvvvgigoKCsGrVKoiiiLfffhv/+te/8MADD6Bnz574/PPPceXKFXz33XemLr/Vrvx2BDYy+3ohqo4gCLCW20L45jgO/HdHG1dHRERERHTnMusgVVNTA7VaXa8loY2NDfbu3Yvs7Gzk5eVh2LBh2ueUSiXi4uKQlJTU6H0rKyuhUqkkX+ao/JpudSlkNvDItsaZLXuNXBEREREREQFmHqQcHBzQr18/vPzyy7hy5QrUajXWrVuHpKQk5Obmak9f9vT0lLzO09Oz3snMt1qxYgWUSqX2y8/Pz6ifo6Uq1CU6jaubsdIkFUPdyGFnRERERERkOGYdpADgiy++gCiK8PX1hUKhwLvvvou//e1vkMlaXvqiRYtQVFSk/bp48aIBKzYcCz87lNWooMuZyYIgwFZujyu/HWmDyoiIiIiI7mxmH6QCAwPx22+/oaSkBBcvXkRqaiqqq6vRtWtXeHl5AQDy8/Mlr8nPz9c+1xCFQgFHR0fJlzlycHXFwRu7AECnMAUAFTeKjVkSERERERGhHQSpOnZ2dvD29kZBQQG2b9+OBx54AAEBAfDy8sKuXbu041QqFVJSUtCvXz8TVmsYvqE9UGR9E/uufodKTZlOr7F2dWh+EBERERG1KVEjouJsIcoyrqLibCFEjW4/JG+pKVOmQBAEzJw5s95zs2fPhiAImDJlimTs7V8JCQna13Tp0kV73dbWFhEREfj0008l992zZw8EQUBhYaFONZ4/f77B9xUEAZs2bWrxZ28rZn8g7/bt2yGKIrp164YzZ85g/vz56N69O6ZOnQpBEDB37ly88sorCA4ORkBAAJYsWQIfHx+MGTPG1KW3mkwmx5ApT+CHlcuRe+EMRvvPgkJm22AXP1EUUSGWouvg9h8giYiIiDqS8mPXUfjjWaiLqrTX5EorOI0OhE24m9He18/PDxs2bMBbb70FGxsbAEBFRQXWr18Pf39/ydiEhASsWbNGck2hUEgev/TSS5gxYwbKysqwadMmzJgxA76+vhg5cmSL68vNzZVc+/jjj/Gf//ynxfdsS2Y/I1VUVITZs2eje/fumDRpEgYOHIjt27fD0tISAPDPf/4T//jHP/DEE0+gT58+KCkpwbZt2+p1+muvguP64/55i2Hr4oL067Utzm9f5lf3WNbXEXILs8/GRERERHeM8mPXcWNdpiREAYC6qAo31mWi/Nh1o713VFQU/Pz8sGXLFu21LVu2wN/fH5GRkZKxCoUCXl5eki9nZ2fJGAcHB3h5eaFr165YsGABXFxc8Msvv7S4PrlcXu89v/32W4wfPx729vYtvm9bMfsgNX78eJw9exaVlZXIzc3F+++/D6VSqX1eEAS89NJLyMvLQ0VFBXbu3ImQkBATVmx4wXH9MeODzzDg+SnI7XQZFZpSyfMVYimq4uQIfGiAiSokIiIiotuJGhGFP55tckzhj+eMusxv2rRpkpmm1atXY+rUqa26p0ajwebNm1FQUAArK6vWlqh14MABZGRk4PHHHzfYPY3J7IMU1ZLJ5PDr0ROx//gbuq64B+I9diiP0kC8xw5dl9/DEEVERERkZiqzi+rNRN1OXVSJyuwio9UwceJE7N27Fzk5OcjJycG+ffswceLEeuO2bt0Ke3t7ydfy5cslYxYsWAB7e3soFAqMGzcOzs7OmD59usFq/eyzzxAaGor+/fsb7J7GxHVg7ZDcwgJ+Q6NMXQYRERERNUFT3HSI0ndcS7i7u2PUqFFITEyEKIoYNWoU3Nzq78uKj4/HqlWrJNdcXFwkj+fPn48pU6YgNzcX8+fPx6xZsxAUFKRTHcuXL5cEsxMnTkj2aZWXl2P9+vVYsmSJPh/PpBikiIiIiIiMQOag27I3Xce11LRp0zBnzhwAwAcffNDgGDs7u2ZDkZubG4KCghAUFIRNmzYhIiICMTExCAsLa7aGmTNnYvz48drHPj4+kue/+eYblJWVYdKkSc3ey1wwSBERERERGYEiQAm50qrJ5X1ypQKKAGWjzxtCQkICqqqqIAgCRowYYZB7+vn5YcKECVi0aBG+//77Zse7uLjUm+G61WeffYb7778f7u7uBqmvLTBIEREREREZgSAT4DQ6EDfWZTY6xml0Vwiy+kfbGJJcLkdmZqb21w2prKxEXl6e5JqFhUWDywDrPPPMMwgPD0d6ejpiYmK0148ePQoHh7/ONhUEAb169Wr0PmfOnMHvv/+On376SafPYy4YpIiIiIiIjMQm3A2uE0MbOEdKAafRXY16jtStHB0dm3x+27Zt8Pb2llzr1q0bTp482ehrwsLCMHz4cCxdulQSggYNGiQZJ5fLUVNT0+h9Vq9ejU6dOmH48OFN1mhuBPH2Q4nuQCqVCkqlEkVFRc3+ISMiIiKiO0NFRQWys7MREBDQ6jNKRY2IyuwiaIqrIHOwgiJAafSZKGpcU7+3umYDzkgRERERERmZIBNgHehk6jLIgHiOFBERERERkZ4YpIiIiIiIiPTEIEVERERERKQnBikiIiIioiZoNBpTl0AGZojfUzabICIiIiJqgJWVFWQyGa5cuQJ3d3dYWVlBENhprz0TRRFVVVW4du0aZDIZrKysWnwvBikiIiIiogbIZDIEBAQgNzcXV65cMXU5ZEC2trbw9/eHTNbyBXoMUkREREREjbCysoK/vz9qamqgVqtNXQ4ZgFwuh4WFRatnFxmkiIiIiIiaIAgCLC0tYWlpaepSyIyw2QQREREREZGeGKSIiIiIiIj0xCBFRERERESkJwYpIiIiIiIiPTFIERERERER6YlBioiIiIiISE8MUkRERERERHpikCIiIiIiItITgxQREREREZGeGKSIiIiIiIj0xCBFRERERESkJwYpIiIiIiIiPTFIERERERER6YlBioiIiIiISE8MUkRERERERHpikCIiIiIiItITgxQREREREZGeGKSIiIiIiIj0xCBFRERERESkJwYpIiIiIiIiPTFIERERERER6YlBioiIiIiISE8MUkRERERERHqyaMmLsrOz8ccffyAnJwdlZWVwd3dHZGQk+vXrB2tra0PXSEREREREZFb0ClJffvkl3nnnHaSnp8PT0xM+Pj6wsbHBzZs3cfbsWVhbW+PRRx/FggUL0LlzZ2PVTEREREREZFI6B6nIyEhYWVlhypQp2Lx5M/z8/CTPV1ZWIikpCRs2bEBMTAw+/PBDPPzwwwYvmIiIiIiIyNQEURRFXQZu374dI0aM0OmmN27cwPnz5xEdHd2q4tqKSqWCUqlEUVERHB0dTV0OERERERGZiK7ZQOcZKV1DFAC4urrC1dVV5/FERERERETtSYu69h08eBBHjx7VPv7+++8xZswYLF68GFVVVQYrjoiIiIiIyBy1KEg9+eSTOH36NADg3LlzeOSRR2Bra4tNmzbhn//8p0ELJCIiIiIiMjctClKnT59G7969AQCbNm3CoEGDsH79eiQmJmLz5s2GrI+IiIiIiMjstChIiaIIjUYDANi5cyfuvfdeAICfnx+uX79uuOqIiIiIiIjMUIuCVExMDF555RV88cUX+O233zBq1CgAtQf1enp6GrRAIiIiIiIic9OiIPX222/j4MGDmDNnDv7v//4PQUFBAIBvvvkG/fv3N2iBRERERERE5kbnc6R0UVFRAblcDktLS0Pdsk3wHCkiIiIiIgKMcI5UQ6qqqnD16lXtfqk6/v7+rbktERERERGRWWtRkDp9+jQef/xx7N+/X3JdFEUIggC1Wm2Q4oiIiIiIiMxRi4LU1KlTYWFhga1bt8Lb2xuCIBi6LiIiIiIiIrPVoiCVkZGBAwcOoHv37oauh4iIiIiIyOy1qGtfWFgYz4siIiIiIqI7VouC1GuvvYZ//vOf2LNnD27cuAGVSiX5IiIiIiIi6sha1P5cJqvNX7fvjWqvzSbY/pyIiIiIiAAjtz/fvXt3iwvTh1qtxosvvoh169YhLy8PPj4+mDJlCv71r39pQ5woinjhhRfwySefoLCwEAMGDMCqVasQHBzcJjUSEREREdGdp0VBavDgwYauo0GvvfYaVq1ahbVr16JHjx5IT0/H1KlToVQq8fTTTwMAXn/9dbz77rtYu3YtAgICsGTJEowYMQInTpyAtbV1m9RJRERERER3Fp2X9h05cgTh4eGQyWQ4cuRIk2N79uxpkOLuu+8+eHp64rPPPtNeGzt2LGxsbLBu3TqIoggfHx8899xzeP755wEARUVF8PT0RGJiIh555JEG71tZWYnKykrtY5VKBT8/Py7tIyIiIiK6wxl8aV/v3r2Rl5cHDw8P9O7dG4IgoKEMZsg9Uv3798fHH3+M06dPIyQkBIcPH8bevXuxcuVKAEB2djby8vIwbNgw7WuUSiXi4uKQlJTUaJBasWIFli1bZpAaiYiIiIjozqNzkMrOzoa7u7v2121h4cKFUKlU6N69O+RyOdRqNV599VU8+uijAIC8vDwAgKenp+R1np6e2ucasmjRIsybN0/7uG5GioiIiIiISBc6B6nOnTs3+Gtj+vrrr/Hll19i/fr16NGjBzIyMjB37lz4+Phg8uTJLb6vQqGAQqEwYKVERERERHQnaVGzCQC4cuUK9u7di6tXr0Kj0Uieq2sE0Vrz58/HwoULtUv0IiIikJOTgxUrVmDy5Mnw8vICAOTn58Pb21v7uvz8fPTu3dsgNRAREREREd2uRUEqMTERTz75JKysrODq6io5T0oQBIMFqbKyMu2ZVXXkcrk2uAUEBMDLywu7du3SBieVSoWUlBQ89dRTBqmBiIiIiIjodi0KUkuWLMHSpUuxaNGiekHHkEaPHo1XX30V/v7+6NGjBw4dOoSVK1di2rRpAGpD29y5c/HKK68gODhY2/7cx8cHY8aMMVpdRERERER0Z2tRkCorK8Mjjzxi1BAFAO+99x6WLFmCWbNm4erVq/Dx8cGTTz6JpUuXasf885//RGlpKZ544gkUFhZi4MCB2LZtG8+QIiIiIiIio9H5HKlb/fOf/4SLiwsWLlxojJranK694tuLqvIKHP/oJ1TcKIW1qx16PHkvrGwYLImIiIiImqNrNmhRkFKr1bjvvvtQXl6OiIgIWFpaSp6vO+epvehIQSrlpS/hrHKGrYWD9lpZTTEKHAsQt/RRE1ZGRERERGT+DH4g761WrFiB7du3o1u3bgBQr9kEmUbKS1/Cp9QPkEuv28jtYVNqj5SXvmSYIiIiIiIygBYFqTfffBOrV6/GlClTDFwOtVRVeQWcVc6AvH6YFQQBoijCWeWMqvIKLvMjIiIiImqlFnWLUCgUGDBggKFroVY4/tFPsLVwaHRGUBAE2Fo44PhHP7VxZUREREREHU+LgtQzzzyD9957z9C1UCtU3Cg16DgiIiIiImpci5b2paam4tdff8XWrVvRo0ePes0mtmzZYpDiSHfWrnZAno7jiIiIiIioVVoUpJycnPDQQw8ZuhZqhR5P3ovzS36Fjdy+weV9oiiiXF2CHk/ea4LqiIiIiIg6lhYFqTVr1hi6DmolKxtrFDgWwKbUHqIoSsJUXYf76+J5hLDRBBERERFRq7VojxSZp7ilj+JizTGUq0sk1ys15cjPPwS3lO0o+mQZkP0HoFGbqEoiIiIiovZP5yCVkJCA5OTkZscVFxfjtddewwcffNCqwkh/oloN9wObUL11Ps4d/wq5+YdQU10Ka7ktvLyiYDvweRQd742yz14F3g4HTvxg6pKJiIiIiNolnZf2Pfzwwxg7diyUSiVGjx6NmJgY+Pj4wNraGgUFBThx4gT27t2Ln376CaNGjcJ//vMfY9ZNDShLP4CavDzIAHiXFMI6rHe9MYLCGTerF0MoWAGbrycB4z8Hwu5v81qJiIiIiNoznYPU448/jokTJ2LTpk3YuHEjPv74YxQVFQGoPaMoLCwMI0aMQFpaGkJDQ41WMDWu5tq1P38lQNFzQu2vGjicF6IGhdUzYC1LgbBtIdB9FCCTt3G1RERERETtl17NJhQKBSZOnIiJEycCAIqKilBeXg5XV9d6LdCp7Vm4uwMA5G7BkNm41Hv++xO7UFRZjGifHujmHoBKTRisVUeBnP1AwF1tXS4RERERUbvVoq59dZRKJZRKpaFqoVayjYmGhZcXYOHU4PO/Zafi2xM7UKNRw9bSBjE+tujfqQJ9Xbai74QQeHp6tm3BRERERETtlCDW9cbWU1ZWFnbv3o2rV69Co9FInlu6dKlBimsrKpUKSqUSRUVFcHR0NHU5raLasQN5L30I24HPNfh8eXUFjuafxsHLx3Es72ukXrqJ3JLaPwIBAQHo168f+vbti759+6JXr16wsrJqy/KJiIiIiExK12zQoiD1ySef4KmnnoKbmxu8vLwk+3AEQcDBgwdbVrWJdKQgBQBF23egaFsVBCvHBg/nBTSQ4wa8FNMBR09cHPsTklPTkJycjKSkJBw8eBBVVVWwtrZGdHS0JFz5+vq2+echIiIiImorRg1SnTt3xqxZs7BgwYJWFWkuOlqQAoCyI1dxc/0pACKAW8OUBoAAV8sVsJEnNdi1r7KyEhkZGUhKStKGqwsXLgAA/Pz80LdvX224ioyMhLU1D/klIiIioo7BqEHK0dERGRkZ6Nq1a6uKNBcdMUgBQPmx6yj88SzURVXaa3Jcg5Plx7BxvgAk/Fvn1udXrlxBSkqKNlylp6ejvLwcVlZWiIyM1M5Y9evXD/7+/o3MhBERERERmTejBqnHH38cffr0wcyZM1tVpLnoqEEKAESNiMrsImhUFZCVZUFhnw/B0RPo3L9VLc+rq6tx5MgR7YxVcnIyzp49CwDw8vKSzFrFxMTA1tbWUB+JiIiIiMhojBqkVqxYgZUrV2LUqFGIiIio1/r86aef1r9iE+rIQaotXb16FSkpKdpwlZqaitLSUsjlcvTq1UsSrgIDAzlrRURERERmx6hBKiAgoPEbCgLOnTun7y1NikHKONRqNY4dO4bk5GRtuDp16hQAwM3NTbIcsE+fPnBwcDBxxURERER0pzNqkOpoGKTazs2bN7WzVsnJyUhJSUFRURFkMhnCw8Ml4SokJAQymczUJRMRERHRHaTNglTdy9vzMi0GKdPRaDQ4efKkZK/V8ePHIYoinJycEBcXp10OGBcXBycnJ1OXTEREREQdmNGD1Oeff47//Oc/yMrKAgCEhIRg/vz5eOyxx1pWsQkxSJmXoqIipKWlScLVzZs3AQChoaGSvVZhYWGQy1veNIOIiIiI6Fa6ZgOLltx85cqVWLJkCebMmYMBAwYAAPbu3YuZM2fi+vXrePbZZ1tWNREApVKJYcOGYdiwYQBqZz2zsrIke63Wrl0LjUYDBwcHxMbGasNVXFwc3NzcTPwJiIiIiKija3GziWXLlmHSpEmS62vXrsWLL76I7OxsgxXYFjgj1f6UlJQgPT1dEq6uXr0KAAgODpbstYqIiICFRYt+ZkBEREREdxijLu2ztrbGsWPHEBQUJLmelZWFiIgIVFRU6F+xCTFItX+iKOL8+fPapYDJyck4dOgQampqYGtri5iYGO1ywL59+8LLy8vUJRMRERGRGTJqkAoPD8ff//53LF68WHL9lVdewcaNG3H06FH9KzYhBqmOqby8HAcPHtTOWCUlJeHKlSsAgC5dukj2WvXu3RtWVlYmrpiIiIiITM2oe6SWLVuGCRMm4Pfff9fukdq3bx927dqFr7/+umUVExmYjY0NBgwYoP0zCgAXL16ULAfcsmULqqqqoFAoEB0dLQlXnTp1MmH1RERERGTOWty178CBA3jrrbeQmZkJoLab2nPPPYfIyEiDFtgWOCN156qsrERGRoYkXOXk5AAAOnXqJNlrFRUVBWtraxNXTERERETGxAN59cAgRbfKzc3VBqvk5GSkpaWhvLwclpaWiIyMlISrzp07t+sz1IiIiIhIyuBBSqVSaW+kUqmaHNvewsidEqRqampwZGcqiq5eg9LDHT2HxbKbnQ6qq6tx9OhRSSOLM2fOAAA8PT0lywFjYmJgZ2dn4oqJiIiIqKUMHqTkcjlyc3Ph4eEBmUzW4E/hRVGEIAhQq9Utr9wE7oQg9ceGn5H+w+fQqIu112RyB8TcPwl3PTLShJW1T9euXUNKSoo2XKWmpqKkpARyuRw9e/aUhKugoCDOWhERERG1EwYPUr/99hsGDBgACwsL/Pbbb02OHTx4sH7VmlhHD1J/bPgZqd9+0OjzsQ/OZphqJbVajePHj0v2Wp08eRIA4OrqKlkO2KdPnw7554yIiIioIzDqHqkLFy7Az8+v3k/ZRVHExYsX4e/vr3/FJtSRg1RNTQ3em/SYZCbqdjK5A/7x+Rdc5mdgBQUFSElJkey3KioqgiAICA8Pl4Srbt26QSaTmbpkIiIiojueUYPUrcv8bnXjxg14eHhwaZ8ZObhtP3avWd7suPipixGV0L8NKrpzaTQanDp1SrLX6tixYxBFEUqlEnFxcdrlgHFxcXB2djZ1yURERER3HKOeI1W3F+p2JSUlbA9tZoquXjPoOGo5mUyG0NBQhIaGYtq0aQBq/6KmpaVpw9X777+PZcuWAQC6d+8u2WvVo0cPyOVyU34EIiIiIvqTXkFq3rx5AABBELBkyRLY2tpqn1Or1UhJSUHv3r0NWiC1jtLD3aDjmiKq1ShLP4Caa9dg4e4O25hoCPyHf5McHR0xdOhQDB06FEDtDynOnDmj3WeVnJyML774Amq1Gvb29oiNjdWGq7i4OLi7t/73jYiIiIj0p9fSvvj4eAC1jSf69esHKysr7XNWVlbo0qULnn/+eQQHBxu+UiPqyEv72mqPlGrHDuQvX4GavDztNQsvL3guXgTH4cNbfF8CSktLkZ6erg1XSUlJuHr1KgAgKChIstcqIiIClpaWJq6YiIiIqP0y6h6pqVOn4p133ukwoaMjBynA+F37VDt24PIzc4Hb/yj9ufzT9523GaYMSBRF5OTkaGeskpKScOjQIdTU1MDGxgYxMTHa5YD9+vWDl5eXqUsmIiIiajeMGqQ6mo4epADjnSMlqtU4M3SYZCZKQhBg4emJoF07uczPiMrLy3Ho0CFJI4tLly4BADp37izZaxUZGSmZTSYiIiKivxg9SKWnp+Prr7/GhQsXUFVVJXluy5YtLbmlydwJQQqoXeZ3ZGcqiq5eg9LDHT2Hxba65XlpSiouTJ7c7Dj/tWthFxfbqvci/Vy6dElyrtWBAwdQWVkJhUKBqKgoSbjy8/MzdblEREREZsGoXfs2bNiASZMmYcSIEdixYweGDx+O06dPIz8/Hw8++GCLiybjsrCwMHiL85prunX703UcGU6nTp0wbtw4jBs3DgBQVVWFjIwMbbj69ttv8dZbbwEAfH19JXutoqKiYGNjY8ryiYiIiMxai2akevbsiSeffBKzZ8+Gg4MDDh8+jICAADz55JPw9vbWtm9uL+6UGSlj4IxU+5aXlyc5MDgtLQ1lZWWwsLBA7969JXutunTp0uCxB0REREQdiVGX9tnZ2eH48ePo0qULXF1dsWfPHkRERCAzMxNDhgxBbm5uq4pvawxSLafdI5WfX7/ZBAAIAuQeHvD59wqob9xkW3QzV1NTg6NHj0r2WmVlZQEAPDw8JMsBY2JiYG9vb+KKiYiIiAzLqEv7nJ2dUVxc27TA19cXx44dQ0REBAoLC1FWVtayiqldEuRyeC5eVNu1TxCkYerPx2JlJS5Onaa9XNcW3WHoUJ47ZWYsLCwQGRmJyMhIzJo1CwBw/fp1pKSkaMPVq6++ipKSEshkMvTs2VMSroKDgzlrRURERHeEFs1I/f3vf0dMTAzmzZuHl19+Ge+99x4eeOAB/PLLL4iKimKziTtQQ+dIyZycoCksrD/4z4B1+/M8d6p9UKvVOHHihOTQ4MzMTACAi4uLZK9VbGws/04RERFRu2LUpX03b95ERUUFfHx8oNFo8Prrr2P//v0IDg7Gv/71Lzg7O7eq+LbGIGUYolqtnWGSu7oid+HC2iV/uuK5U+1WYWEhUlJSJPutCgsLIQgCwsLCJHutunfvDplMZuqSiYiIiBpktCBVU1OD9evXY8SIEfD09Gx1oebgTg9SGo2I3KxClKoqYeeogHewE2Sy1i3P0rUJRT08d6pD0Gg0OH36tGSv1bFjx6DRaODo6Ii4uDhtuIqLi4OLi4upSyYiIiICYOQZKVtbW2RmZqJz586tKtJc3MlB6uyhq/hjYxZKCyu11+ycFLhrQjACIz1afN+irf/Dleefb/Hr2eWv4ykuLkZaWpokXF2/fh0A0K1bN8leqx49erT6jDMiIiKiljBqs4nY2FhkZGR0mCDVXqk1ahy8ehDXyq7B3dYdUR5RkMt0n8U5e+gqtn10rN710sJKbPvoGBKeDG9xmLJwd2/R6+rw3KmOx8HBAUOGDMGQIUMAAKIo4uzZs5K9VuvWrYNarYadnR1iY2O14SouLg4eHi0P9kRERESG1qIgNWvWLMybNw8XL15EdHQ07OzsJM/37NnTIMVR43bm7MS/U/+N/LK/9iB52npiYexCDOs8rNnXazQi/tiY1eSYvV9nIaCXe4uW+dnGRMPCy6vxtujNaG0QI/MnCAKCgoIQFBSEiRMnAgDKysqQnp6uDVerV6/GihUrAABdu3bVzlj17dsXvXr1gqWlpSk/AhEREd3BWrS0r6GN4oIgQBRFCIIAtVptkOLaSntb2rczZyfm7ZkHEdLfOgG1gWfl3SubDVOXTxXgu7cONfteY56NhG+3ljUPUe3YUdsWHdA9THGPFN1CFEVcuHBBshzw4MGDqK6uhrW1NWJiYiThysfHx9QlExERUTtn1D1SOTk5TT7f3pb8tacgpdaoMWLzCMlM1K0ECPC09cS2sduaXOZ3Oi0Pv3x2otn3Gza1O2ztr6OksAD2Ts7wDe0BmR7LB5tsi97QuVNg1z5qWkVFBQ4dOiQJVxcvXgQA+Pv7S/ZaRUZGQqFQmLhiIiIiak+MGqQ6mvYUpNLy0jBt+7Rmx60esRp9vPo0+rwuM1LqqixYyPehXHVTe83exQ1DpjyB4Lj+Otd8a1v0uoN3i3ftqheweI4UtdTly5cle63S09NRWVkJKysrREVFScKVn58fDw0mIiKiRhk1SH3++edNPj9p0iR9b2lS7SlI/XTuJyz4Y0Gz41676zXc2/XeRp/XaER8vni/pFvfrdRVWagu/bHR198/b7FeYaohDQUsLucjQ6iqqsLhw4cl4So7OxsA4O3tLTnXKjo6GjY2NiaumIiIiMyFUYPU7QfuVldXo6ysDFZWVrC1tcXNmzcbeaV5ak9BylAzUkDjXftEUYPKok8BsaTR1zq4umH6+5/ptcyPyJTy8/O1SwGTkpKQlpaGsrIyWFhYoFevXpK9Vl27duWsFRER0R1K12xQv2uEDgoKCiRfJSUlOHXqFAYOHIivvvqqxUU3pEuXLhAEod7X7NmzAdTul5g9ezZcXV1hb2+PsWPHIj+/4f1DHUGURxQ8bT21jSVuJ0CAl60Xojyimr1XYKQHEp4Mh52TdA+JwuZqkyEKAIpvXMflzOO6F05kYp6ennjggQewYsUK7NmzB0VFRTh06BDeffdd9OjRA9u3b8fEiRMRFBQET09P3H///VixYgV2796NkpKm/z4QERHRncege6TS09MxceJEnDx50lC3xLVr1yRdAI8dO4Z77rkHu3fvxt13342nnnoK//vf/5CYmAilUok5c+ZAJpNh3759Or9He5qRAv7q2gdA0rlPn659t9JoRORmFaJUVQk7RwWKrh7Gz++/0ezr7n16PkIHDNazeiLzdePGDaSkpGiXA6akpKC4uBgymQwRERGSvVYhISGctSIiIuqATNJsIiMjA4MGDYJKpTLULeuZO3cutm7diqysLKhUKri7u2P9+vUYN24cAODkyZMIDQ1FUlIS+vbtq9M921uQAho+R8rL1gsLYhfoFaIacvH4EXz90uJmx41fuhx+PVp+ZpioEVGZXQRNcRVkDlZQBCghtODMKiJjUavVyMzMlOy1OnGittuls7Ozdilgv379EBsbC6VSaeKKiYiIqLWMGqR++OEHyWNRFJGbm4v3338ffn5++Pnnn/WvWAdVVVXw8fHBvHnzsHjxYvz6668YOnQoCgoK4OTkpB3XuXNnzJ07F88++2yD96msrERl5V9NFlQqFfz8/NpVkAJqW6EfvHoQ18quwd3WHVEeUU22PNeVRqPGJ7MfR8nN642Oae0eqfJj11H441moi6q01+RKKziNDoRNuFuL7knUFgoLC5GamioJV4WFhRAEAaGhoZK9VmFhYQ2eu0dERETmS9cgZdGSm48ZM0byWBAEuLu7Y8iQIXjzzTdbckudfPfddygsLMSUKVMAAHl5ebCyspKEKKB2L0TeLW21b7dixQosW7bMaHW2FblM3mxDiZaQyeQYMuUJ/LByeaNj4ic/0aoQdWNdZr3r6qIq3FiXCdeJoQxTZFRqjYjU7Ju4WlwBDwdrxAa4QK7jbKiTkxOGDx+O4X+26ddoNMjKytKGqqSkJKxZswYajQaOjo6IjY3Vhqu4uDi4uroa86MRERFRG2lX50iNGDECVlZW+PHH2rbc69evx9SpUyWzSwAQGxuL+Ph4vPbaaw3ep6PMSBlbVsp+/Jr4sWRmysHVDfGT9TtH6laiRkTea6mSmajbyZUKeC3ow2V+ZBTbjuVi2Y8nkFtUob3mrbTGC6PDkBDubZD3KC4uRnp6uiRcXb9e+/coJCREstcqPDwcFhYt+pkWERERGYHBZ6TmzZun85uvXLlS57G6ysnJwc6dO7FlyxbtNS8vL1RVVaGwsFAyK5Wfnw8vL69G76VQKKBQKBp9nmoFx/VHYJ84XM48jpLCAtg7OcM3tEerWp5XZhc1GaIAQF1UicrsIlgHOrX4fYgasu1YLp5adxC3//Qor6gCT607iFUTowwSphwcHBAfH4/4+HgAtcufz507J1kO+OWXX0KtVsPOzg59+vSRhCsPD49W10BERETGpXOQOnTokOTxwYMHUVNTg27dugEATp8+DblcjujoaMNW+Kc1a9bAw8MDo0aN0l6Ljo6GpaUldu3ahbFjxwIATp06hQsXLqBfv35GqeNOI5PJW9VQ4naa4qZDlL7jiHSl1ohY9uOJeiEKAEQAAoBlP57APWFeOi/z05UgCAgMDERgYCAeffRRAEBZWRkOHDigDVeJiYn497//DQAICAiQ7LXq1asXrKysDFoTERERtY7OQWr37t3aX69cuRIODg5Yu3at9nDegoICTJ06FXfddZfBi9RoNFizZg0mT54sWQKjVCrx+OOPY968eXBxcYGjoyP+8Y9/aP8BQuZH5qDbPwZ1HUekq9Tsm5LlfLcTAeQWVSA1+yb6BRp/H5OtrS3uuusu7X8zRVHExYsXJcsBN23ahOrqalhbWyM6OloSrnx9fY1eIxERETWuRXukfH19sWPHDvTo0UNy/dixYxg+fDiuXLlisAIBYMeOHRgxYgROnTqFkJAQyXMVFRV47rnn8NVXX6GyshIjRozAhx9+2OTSvtu1x/bn5kyjUTe6HJB7pMhUvs+4jGc2ZDQ77p1HeuOB3uYRUioqKpCRkSEJVxcvXgQA+Pn5SZYDRkZGwtra2sQVExERtX9G7dqnUqlw7dq1etevXbuG4uLiltyyScOHD0djec/a2hoffPABPvjgA4O/L+mvoQYV9i5uGDKltkGFIBPgNDqwwa59dZxGd2WIIoPzcNAtZOg6ri1YW1trZ6DqXL58WXJo8OLFi1FRUQErKytERkZKzrby9/fnocFERERG0qIZqUmTJuGPP/7Am2++idjYWABASkoK5s+fj7vuugtr1641eKHGxBkpw8hK2d9ky/T75y3Wdvtr+BwpBZxGd2XrczIKtUbEwNd+RV5RRYP7pAQAXkpr7F0wxOB7pIypqqoKR44ckTSyOHfuHIDahjy3LgeMiYmBra2tiSsmIiIyb0Y9kLesrAzPP/88Vq9ejerqagCAhYUFHn/8cfznP/+BnZ1dyys3AQap1mvJIb6iRkRldhE0xVWQOVhBEaDkTBQZVV3XPgCSMFX3p85QXftMLT8/HykpKdpwlZaWhtLSUsjlcvTq1UuyJDAwMJCzVkRERLcwapCqU1pairNnzwIAAgMD212AqsMg1XoXjx/B1y8tbnbc+KXLDdoFkEhfbXGOlLmpqanBsWPHkJycrA1Xp0+fBgC4ublJlgP26dMHDg4OJq6YiIjIdNokSHUUDFKtl7nvN/z07n+aHXfv0/MROmBwG1RE1Di1RkRq9k1cLa6Ah4M1YgNc2tVyPkO4ceMGUlNTtcsBU1JSoFKpIJPJEB4eLglXISEhkMlkpi6ZiIioTRi12QTR7eydnFs1jsv8qC3JZUKbtDg3Z66urhg5ciRGjhwJoPaYiczMTO2M1b59+/DJJ59AFEU4OzsjLi5OG67i4uIkh6ATERHdiTgjBc5IGUJL9kjVabjxhBWcRgd2yMYTolqNsvQDqLl2DRbu7rCNiYYglzf/QqI2VlRUhNTUVEkji4KCAgBAaGiopJFFWFgY5PxzTEREHQCX9umBQcowTiXvxda3/t3o87d27atTfux6k63QXSeGGi1MmSLQqHbsQP7yFajJy9Nes/DygufiRXAcPtyo703UWqIoIisrS3Ku1dGjR6HRaODg4IDY2FjtcsC4uDi4uXW8H4QQEVHHxyClBwap1mvo/Kg6Dq5uiJ/8RL0QZcrDeU0RaFQ7duDyM3OB2//K/dkxzfedtxmmqN0pKSlBenq6JFzVnTMYHBws2WsVEREBCwuuKCciIvPGIKUHBqnWae78qNFzFyKk38B61yvOFuL6J0ebvb/bjAhYBzq1pkQJUwQaUa3GmaHDJMHt9ve28PRE0K6dXOZH7ZooisjOzpYsB8zIyEBNTQ1sbW3Rp08fSbjy9PQ0dclEREQSDFJ6YJBqudbsjSrLuIqbG041+x4uj3SDbW+PVtcKmC7QlKak4sLkyc2O81+7FnZxsQZ7XyJzUFZWhoMHD2rDVVJSEnJzcwEAXbp0key16t27N6ysrExcMRER3cnYtY/axOXM402GKAAovnEdlzOP1zs/Suag2z+WdB2ni7L0A42HKAAQRdTk5aEs/YBBA03Nn0udDDWOqD2xtbXFwIEDMXBg7cy0KIq4ePGi5FyrzZs3o6qqCgqFAtHR0ZJDgzt16mTiT0BERFQfgxS1SklhQYvHKQKUkCutmt0jpQhQtri+25kq0Fi4uxt0HFF7JggC/P394e/vj/HjxwMAKisrkZGRoV0O+M0332DlypUAgE6dOkmWA0ZFRcHa2tqUH4GIiIhBilqnNedHCTIBTqMDm+za5zS6q0EbTZgq0NjGRMPCyws1+fn192YB2iWFtjHRBn1fovZCoVAgLi4OcXFx2mtXrlxBSkqKNlwtWbIE5eXlsLS0RGRkpCRcde7cGYLAs+eIiKjtcI8UuEeqNVqzR6pOw+dIKeA0uqvBW59r90g1E2iM0fRB2+QCkL43u/YR6aS6uhpHjhyRNLI4e/YsAMDT01Oy1yomJgZ2dnYmrpiIiNojNpvQA4NU6zTXta+h86NuJ2pEVGYXQVNcBZmDFRQByhbNRGk0alw8cRyXMy8BMlv4hUXAt5sLZLfcy5SBhudIERnW1atXkZKSog1XqampKC0thVwuR8+ePSV7rYKCgjhrRUREzWKQ0gODVOs1dI5UY+dHGbOGHZ+sQkXxX/uxBMEBfp73Iya+D7wj3LQBzZSBxhQHARPdKdRqNY4dOyZpZHHqVG13UFdXV8lywD59+vC/+UREVA+DlB4YpAxDo1HXdvErLIC9kzN8Q3s0upzP0BqaFfO1DUGU61DYWvz1eypXWsFpdCBswt0YaIjuEDdv3tTOWiUnJyMlJQVFRUUQBAHh4eGScNWtWzfIZDJTl0xERCbEIKUHBqn2rXaf1jSU3LyhveZrG4IBHmMAoMGlPK4TQw2+/4qI2geNRoOTJ09K9lodP34coijCyckJcXFx2nAVFxcHZ2fdmuoQEVHHwCClBwap9u3i8SP4+qXF2scCBNznNxM2codG90PIlQp4LegDQSZAoxGRm1WIUlUl7BwV8A52kuypIqKOr6ioCGlpaZJwdfPmTQBA9+7dJXutevToATlnr4mIOiweyEt3jNvPqHKz7iRZztcQdVElKrOLcFlVhT82ZqG0sFL7nJ2TAndNCEZgpIdR6iUi86NUKjFs2DAMGzYMQO2hwVlZWZK9Vp9//jk0Gg3s7e0RGxurDVdxcXFw5xlwRER3HM5IgTNS7d3tM1L+dqHo53F/s6+r7uuNn7ZdaPT5hCfDGaaISKukpATp6emScHX16lUAQFBQkGSvVUREBCwtLU1cMRERtQRnpOiO4RvaA/Yurto9UuXqEp1edyQ5r8nn936dhYBe7lzmR0QAAHt7e9x99924++67AdTOWp0/f167FDA5ORkbNmxATU0NbGxs0KdPH0m48vLyMu0HICIig+KMFDgj1RHc2rVPlz1SsLPE95fLmr3vmGcj4duNG82JSDfl5eU4ePCgdsYqKSkJV65cAQB07txZcmhwZGQkrKysTFwxERHdjs0m9MAg1THceo5Uc137Kvt6Y1sTy/rq3PN4GEL68KfIRNRyFy9elCwHPHDgAKqqqqBQKBAVFSVpZOHn52fqcomI7ngMUnpgkOo4NBo1Lp44jsuZl2BTaAu3i5YQqjSSMTJbC2jivPH9t+eavZ85z0iZ8twuImq5yspKZGRkSMJVTk4OAMDX11eyHDAqKgo2NjYmrpiI6M7CIKUHBqmOqfzYddxYl9no84ch4HxhVaPP2zsr8Nir/c1yj1RWyn78mvgxSm5e116zd3HDkClPIDiuvwkrI6KWyM3N1Qar5ORkpKWloby8HBYWFujdu7d2xqpfv37o0qVL48uWiYio1Rik9MAg1fGIGhF5r6VCXdR4UBJtLPBDbnmjz5tr175b94M15P55ixmmiNq56upqHD16VHKu1ZkzZwAAHh4ekuWAffr0gZ2dnYkrJiLqOBik9MAg1fFUnC3E9U+ONjuuKt4Pv+++LDlHyt5ZgYHjzfMcKY1GjU9mPy6Zibqdg6sbpr//GZf5EXUw165dQ0pKijZcpaamoqSkBHK5HBEREZJwFRwczFkrIqIWYvtzuqNpihufibqVl6ctJi3vj9ysQpSqKmHnqIB3sJNZLucDULsnqokQBQDFN67jcuZx+PXo2UZVEVFbcHd3x3333Yf77rsPAKBWq3H8+HHtcsA9e/bgv//9LwDAxcVFstcqNjaWPygkIjIwBinqkGQOurUUljlYQSYTzLahxO1KCgsMOo6I2i+5XI6ePXuiZ8+eeOKJJwAABQUF2lmr5ORkvPnmmygqKoIgCOjRo4ckXHXv3h0ymczEn4KIqP1ikKIOSRGghFxp1eQeKblSAUWAsg2raj17J90Cn67jiKhjcXZ2RkJCAhISEgAAGo0Gp06dkuy1+uyzzyCKIhwdHREXF6ddDhgXFwcXFxcTfwIiovaDe6TAPVIdVXNd+1wnhsIm3K0NK2o97pEiotZSqVRIS0uThKsbN24AALp16ybZaxUeHg65nP8tIaI7C5tN6IFBquMqP3YdhT+elcxMyZUKOI3uarYhSqMRm9yz1RZd+0S1GmXpB1Bz7Ros3N1hGxMNgf+YIuqQRFHE2bNntaEqKSkJR44cgVqthp2dHWJjYyXhyt3d3dQlExEZFYOUHhikOjZRI6Iyuwia4irIHKygCFBCMNNmEmcPXcUfG7MkXQTtnBS4a4K0i2BD50g5uLohfnLrz5FS7diB/OUrUJOXp71m4eUFz8WL4Dh8eKvuTUTtQ2lpKQ4cOCAJV/n5+QCAwMBAyV6rnj17wtLS0sQVExEZDoOUHhikyBycPXQV2z461ujzt59rpdGoa7v4FRbA3skZvqE9Wr2cT7VjBy4/Mxe4/T8Lf7ZR9n3nbYYpojuQKIrIycmRLAc8dOgQqqurYWNjg5iYGEm48vb2NnXJREQtxiClBwYpMjWNRsTni/dLZqJuZ++swGOv9jdaa3ZRrcaZocMkM1ESggALT08E7drJZX5EhIqKChw8eFASri5dugQA8Pf3lywHjIyMhEKhMHHFRES64TlSRO1IblZhkyEKAEoKKpGbVWi0Vu1l6QcaD1EAIIqoyctDWfoB2MXFGqUGImo/rK2t0b9/f/Tv/9dy4kuXLmlbryclJWHhwoWorKyElZUVoqKiJOHKz8+PhwYTUbvGIEVkBkpVTYcofce1RM21awYdR0R3nk6dOmHcuHEYN24cAKCqqgqHDx/Wzlh99913ePvttwEAPj4+kuWA0dHRsLGxMWH1RET6YZAiMgN2jrotedF1XEtY6NiJS9dxRERWVlbo06cP+vTpg6effhoAkJeXh5SUFG24evHFF1FWVgYLCwv07t1bEq4CAgI4a0VEZot7pMA9UmR6De2RcrUQYC0AFSJwo0Zsuz1S+fn1m00AZrVHiu3ZiTqOmpoaHD16VLLXKisrCwDg4eGhDVZ9+/ZFnz59YG9vb+KKiaijY7MJPTBIkTmo69rnbSkgwkYOm1sCU7lGhMVdvggYHWjUGrRd+wBpmDKjrn1sz07U8V2/fh0pKSnacJWamori4mLIZDJERERo91n17dsXISEhnLUiIoNikNIDgxSZi+wfz8Ji3xVAFCX/MBABCABcJ4Ya/SBhcw4qbM9OdGdSq9XIzMyUnGuVmZkJAHB2dpYsB4yNjYVSqTRxxUTUnjFI6YFBisyBqBGR91oq1EVVjY6RKxXwWtDH6AcKm+PSObZnJ6JbFRYWIjU1VRuukpOTUVhYCEEQEBYWJglXoaGhkMlkpi6ZiNoJtj8namcqs4uaDFEAoC6qRGV2EawDnYxaiyCXm12Lc7ZnJ6JbOTk5Yfjw4Rj+5yy0RqPB6dOnJXut1qxZA41GA0dHR8TFxUn2W7m4uJj4ExBRe8cgRWQmNMVNhyh9x3U0bM9ORE2RyWTo3r07unfvjilTpgAAiouLkZaWpg1Xq1atwssvvwwACAkJkey1Cg8Ph4UF/1lERLrjfzGIzITMwcqg4zoatmcnIn05ODhgyJAhGDJkCABAFEWcO3dOstdq3bp1UKvVsLOzQ58+fSThysPDw8SfgIjMGfdIgXukyDyY0x4pc9Se2rMTUftRVlaGAwcOSMJV3p/LiLt27SrZa9WrVy9YWlqauGIiMjY2m9ADgxSZi/Jj13FjXWajz7dF1z5z1h7asxNR+yaKIi5cuCDZa3Xw4EFUV1fD2toaMTExknDl4+Nj6pKJyMAYpPTAIEXmpPzYdRT+eFYyMyVXKuA0uusdHaLqmHN7diLqmCoqKnDo0CFJuLp48SIAwM/PT7IcMCoqCgqFwsQVE1FrMEjpgUGKzI2oEVGZXQRNcRVkDlZQBCjvyOV8jTHH9uxEdGe5fPmytu16UlISDhw4gIqKClhZWSEyMlISrvz9/XloMFE7wiClBwYpIiIiao2qqiocOXJEstcqOzsbAODt7a1dCti3b19ER0fD1tbWxBUTUWMYpPTAIEVERESGlp+fj5SUFG24Sk1NRVlZGSwsLNCrVy/JXquuXbty1orITDBI6YFBikh3Go2I3KxClKoqYeeogHewE2RcdkhE1KyamhocO3ZMstfq9OnTAAB3d3fJgcF9+vSBg4ODiSsmujMxSOmBQYpIN2cPXcUfG7NQWlipvWbnpMBdE4IRGMnzVoiI9HXjxg2kpKRow1VKSgqKi4shk8kQHh4u2WsVEhICmUxm6pKJOjwGKT0wSBE17+yhq9j20bFGn094MpxhioioldRqNU6ePCnZa3XixAkAgLOzM+Li4rThKjY2Fk5OTqYtmKgDYpDSA4MUUdM0GhGfL94vmYm6nb2zAo+92p/L/IiIDKywsBBpaWnacJWcnIyCggIIgoDQ0FBJI4vQ0FDI2cWUqFV0zQYWbVgTEbVTuVmFTYYoACgpqERuViF8uzm3UVV3NraAJ7pzODk54Z577sE999wDoPbQ4NOnT0varycmJkKj0cDBwQFxcXGS/Vaurq4m/gREHZPZL7S9fPkyJk6cCFdXV9jY2CAiIgLp6ena50VRxNKlS+Ht7Q0bGxsMGzYMWVlZJqyYqOMpVTUdovQdR62j2rEDZ4YOw4XJk3Hl+edxYfJknBk6DKodO0xdGhG1AUEQ0K1bN0yePBmrVq1CRkYGioqK8Ouvv2LRokWwtbXFRx99hPvuuw9ubm4ICQnRjj106BBqampM/RGIOgSznpEqKCjAgAEDEB8fj59//hnu7u7IysqCs/NfP/F+/fXX8e6772Lt2rUICAjAkiVLMGLECJw4cQLW1tYmrJ6o47BzVBh0HLWcascOXH5mLnDbquya/Pza6++8Dcfhw01SGxGZjr29PeLj4xEfHw+g9gfN2dnZkuWA69evR01NDWxtbdGnTx/JkkBPT08TfwKi9ses90gtXLgQ+/btwx9//NHg86IowsfHB8899xyef/55AEBRURE8PT2RmJiIRx55RKf34R4poqa1do+UqBFRmV0ETXEVZA5WUAQoIbRyL5VGo8blzOMoKSiAfaUDnF18YOFobZB7mytRrcaZocNQk5fX8ABBgIWnJ4J27eQyPyKqp6ysDAcPHpQ0ssjNzQUABAQESM616tWrF6ysrExcMZFpdIhmE2FhYRgxYgQuXbqE3377Db6+vpg1axZmzJgBADh37hwCAwNx6NAh9O7dW/u6wYMHo3fv3njnnXcavG9lZSUqK//6B6FKpYKfnx+DFFETWtq1r/zYdRT+eBbqoirtNbnSCk6jA2ET7taiWrJS9uPXxI+hrHBBlOtQ2Fr89fe2tfc2Z6UpqbgweXKz4/zXroVdXGwbVERE7Zkoirh48aLkXKuDBw+iqqoK1tbWiI6OloQrX19fU5dM1CZ0DVJmvUfq3LlzWLVqFYKDg7F9+3Y89dRTePrpp7F27VoAQN6fP5W9fTra09NT+1xDVqxYAaVSqf3y8/Mz3ocg6iACIz2Q8GQ47Jyky/fsnRVNhqgb6zIlIQoA1EVVuLEuE+XHrutdR1bKfvywcjmUFS4Y4DEGNnLpgZU1rbi3uau5ds2g44joziYIAvz9/TF+/Hi89dZbSEpKgkqlQlJSElasWIFOnTph06ZNePjhh9GpUyf4+flh/PjxWLlyJfbv34+KigpTfwQikzLrPVIajQYxMTFYvnw5ACAyMhLHjh3Df//7X0zW4aeyjVm0aBHmzZunfVw3I0VETQuM9EBAL/faLn6qStg5KuAd7NTocr7CH882eb/CH8/BOsxV56V4Go0avyZ+DAEColyHAqj9h8Ct6h7pe+/2wMLd3aDjiIhup1AotLNQda5cuaLdZ5WcnIz/+7//Q0VFBSwtLREZGSk5NLhz5871/rtM1FGZdZDy9vZGWFiY5FpoaCg2b94MAPDy8gIA5Ofnw9vbWzsmPz9fstTvdgqFAgoFN8UTtYRMJujU4rwyu6jeTNTt1EWVqMwugnWgk07vfTnzOEpuXoe7tZ9kOZ8h7t0e2MZEw8LLq9k9UrYx0W1bGBF1aD4+PnjooYfw0EMPAQCqq6tx5MgR7XLArVu3ardTeHl5SZpYREdHw87OzpTlExmNWQepAQMG4NSpU5Jrp0+fRufOnQHUboz08vLCrl27tMFJpVIhJSUFTz31VFuXS0S30BQ3HaL0HQcAJYUFAAAbub3B790eCHI5HEfdi5ufrW50jOfiRc02muAZVETUGpaWloiOjkZ0dDTmzJkDALh69SpSUlK04eqll15CaWkp5HI5evXqJdlrFRgYyFkr6hDMOkg9++yz6N+/P5YvX47x48cjNTUVH3/8MT7++GMAtUt65s6di1deeQXBwcHa9uc+Pj4YM2aMaYsnusPJHHTr9qTrOACwd6qdCStXl7To3tpOf4UFsHdyhm9oD8hk7SdAqHbswM3Vaxp93mXa1GZbn6t27ED+8hWSWS0LLy94Ll7EtulE1GIeHh4YPXo0Ro8eDQBQq9U4duyYdjngrl278OGHHwIA3NzcJAcGx8bGwsHBoanbE5kls+7aBwBbt27FokWLkJWVhYCAAMybN0/btQ+o7Tjzwgsv4OOPP0ZhYSEGDhyIDz/8ECEhITq/B9ufExmeqBGR91pqk8v75EoFvBb00WuP1CezH0fpzRu4z28mbOQOjf5U8/Z713X6K7n5VxMKexc3DJnyBILj+uvxyUzDEK3PGzuDCn9+D315BhURGdHNmzeRkpKiDVcpKSkoKiqCIAgIDw+X7LXq1q0bZDKz7olGHViHaH/eVhikiIyjrmtfY1wnhurdpryua5+vbQgGeIwBIG04IaK24cSt9657za0ECHCz7gQbuT2iHhqDrvf2bzDQmcssVmtbn/MMKiIyNxqNBidPnpQcGnz8+HGIoggnJyfExcVpw1VsbCycnZvfn0tkCAxSemCQIjKehs+RUsBpdFcjnSMlvbdGo8aqJ6agorhAO8bXNqTe62SOVnC+X3r+1K2zWHXBy1npjbDhQxsNXsZStPV/uPLnweNN8XnjDSjvG1XvOs+gIqL2oKioCGlpaZKzrW7evAmgtuHYrY0swsLCIOcPfsgIdM0GZr1HiojaP5twN1iHuaIyuwia4irIHKygCFC2KoQEx/VHYJ+42pmiggKIlXZwdvGBhaN1vXun/rCvXoiqm8m6lVpVe/5U3UzWrbNY9YLXXg0upu+F+zj9Z9RaqrWtz3kGFRG1B0qlEsOGDcOwYcMA1G7hyMrK0s5YJSUlYe3atdBoNHBwcEBsbKxkv5WbW8c7jJ3MF4MUERmdIBMM3oZcJpPDr0fPJsdoNCIO7zz5Vx06nj9l1d0JvybWNrVpLHgJ5aIkeBmbtvV5fn79PU5As63PeQYVEbVHgiAgJCQEISEhmDRpEgCgpKQE6enp2nD1ySef4NVXXwUABAUFSfZa9ezZExYW/OcuGQf/ZBFRh5WbVYjK8r/OjHOz7qTT+VNXfjuiXc7XaPASBIhou4N/BbkcnosX1TaLEARpmPqztqZan7c2iBERmQt7e3vcfffduPvuuwHUzlqdP39estfqq6++Qk1NDWxtbRETEyMJV3XnkBK1FoMUEXVYpapKyCx8AcEeEEt0Pn+q4kYxgOaDl4C2PfjXcfhw4J2367cv9/Rstn15a4MYEZG5EgQBAQEBCAgIwN///ncAQHl5OQ4ePKhdDvjFF1/gtddeAwB06dJFsteqd+/esLLS/SgOojoMUkTUYdk5KiAIMljaxqO69Eedz5+ydq09z8QcD/51HD4cDkOHtuhA3dYEMSKi9sTGxgYDBgzAgAEDtNcuXrwo2Wu1ZcsWVFVVQaFQIDo6WhKuOnXqZMLqqb1gkCKiDss72Al2TgqUFgYDGI3rZXtQVqNq9vwpj8E9Yf+NG8rLWnbwr7EJcnmLO+u1JogREbVnfn5+8PPzw8MPPwwAqKysREZGhjZcbd68GStXrgQA+Pr6SpYDRkdHw9ra2pTlkxli+3Ow/TlRR3b20FVs++gYAEAUNfCSFSDO0RNA/X1PACRd+35cuULvg3+JiKj9ys3N1Qar5ORkpKWloby8HJaWlujdu7ckXHXp0qXR/2+g9o3nSOmBQYqoYzt76Cr+2JiF0sJKAIC3pYCedha49WeLDZ1tlZWyHyfWbUeUbcMNJ4CWHSosakSDtoPvqES1mjNnRGRS1dXVOHr0qKSRxZkzZwAAnp6ekuWAMTExsLOzM3HFZAgMUnpgkCLq+DQaEblZhShVVcLOUQGvQCWqc1TNhhmNRo3L2zMgppRCVvHX8y09VLjhA4qt4DQ60Kht1G///N7BTpCZcXhT7dhRfy+Xlxf3chGRyV27dg0pKSnavVapqakoKSmBXC5Hz549JeEqKCiIs1btEIOUHhikiKg5hphFKj92HTfWZTb6vLHOpLp9Rg4A7JwUuGtCMAIjPQz+fq2l2rGjtrvg7f/39Oc/RnzfeZthiojMhlqtxvHjxyWNLE6erD3D0NXVVbsUsF+/fujTpw//rdkOMEjpgUGKiIxN1IjIey1VMhN1O2Pst7p1j1hDEp4MN6swJarVODN0mGQmSuLP866Cdu3kMj8iMlsFBQXaWau6r6KiIgiCgB49emhnrPr164du3bpBJpOZumS6ha7ZgF37iIjaQGV2UZMhCjD8mVQajYg/NmY1OWbv11kI6OVuNsv8ytIPNB6iAEAUUZOXh7L0Ay3uXEhEZGzOzs5ISEhAQkICAECj0eDUqVOSvVaffvopRFGEUqlEXFycNlzFxcXB2dnZxJ+AdMEgRUTUBnQ9a8qQZ1LlZhVKlvM1pKSgErlZhfDtZh7/p11z7ZpBxxERmQOZTIbQ0FCEhoZi2rRpAGpnPdLS0rTLAd9//30sW7YMANC9e3fJXqsePXpAzll4s8MgRUTUBnQ9a8qQZ1KVquqHKFHUQFNzGRBLAcEOMgvfBseZioW7u0HHERGZK0dHRwwdOhRDh9Z2hhVFEWfPntXOWiUlJeGLL76AWq2Gvb09YmNjteEqLi4O7vzvoMkxSBERtQFFgBJypVWze6QUAUqDvaedo0LyWF2Vheqy3YB4y0HDgj1uXpoG9PEy2Pu2hm1MNCy8vFCTn1+/2QSg3SNlGxPd9sURERmRIAgICgpCUFAQHnvsMQBAaWkpDhw4oA1Xn332GZYvXw4ACAwMlOy1ioiIgKWlpSk/wh2HQYqIqA0IMgFOowOb7NrnNLqrQRtNeAc7wc5JgdLCytoQVfpj/UFiCfZteBeuPvYIjutvsPduKUEuh+fiRbVd+wRBGqb+7NrnuXgRG00Q0R3Bzs4OgwYNwqBBgwDUzlrl5ORoZ6ySk5OxceNGVFdXw8bGBjExMZJw5eVlHj8k66jYtQ/s2kdEbafhc6RadiaVLs4euoqf/3sElUWfSmeibuPg6obp738GmUxuFgcG8xwpIiLdlJeX49ChQ5JwdenSJQBA586dJXutIiMjYWVluCXkHRXbn+uBQYqI2lJbB5Xk737Hvq9eb3bc+KXL4Sb6NHpgsHWYa5vWLarVtV38rl2Dhbs7bGOiORNFRKSDS5cuSc61OnDgACorK6FQKBAVFSUJV506dTLKocElJSX48ssvMXnyZFhbWxv8/sbEIKUHBiki6sgy9/2Gn979T7Pj7n94PmzSGz/LRGZrAU1ZjfZxXcAyxkwaEREZTlVVFQ4fPixpZHH+/HkAgI+PjzZU9e3bF9HR0bCxsWn1e65atQqzZs1CVHQMfvj+O/j6+rb6nm2FQUoPDFJE1JFdPH4EX7+0uMkxAgQ83GMBhDL9/y/BdWIowxQRUTuTl5eHlJQUbbhKS0tDWVkZLCws0Lt3b0m4CggIaHjWSqMGcvYDJfmAvSfQuT8gq105MHnyZGz4YTsgaqBUyPDdt1vQv7/p9+LqgkFKDwxSRNSRaTRqfDL7cZTcvN7omM4e4ehrN6pF95crFfBa0KfN91EREZHh1NTU4OjRo5K9VllZtYe6e3h4SJYDxsTEwP7Cr8C2BYDqyl83cfQBEl4Dwu5H18BgXHfqBmX/R3Dj+3+jOvc0Vq36ENOnTzfRJ9Qdg5QeGKSIqKPLStmPH1Yub/T5Bx9eCKv0lv/fgduMCFgHOkGjEWsPAlZVws5RAe9gJ8jukIDFPV1E1NFcv34dKSkp2nCVmpqK4uJiyGQy9PQA+vrK0c9Pjr6d5Ah2kUEQapeH3xj+PtwGPAbX+56DfY94iOpq3Nz5MUoyfsZTTz2Fd955R9qqvYmZLVNgkNIDgxQR3QmyUvbj18SPJTNTDq5uiJ/8BPzcwnD9k6MtvrfLI92QKwJ/bMxCaeFfB/zaOSlw14RgBEZ6tKr2ttCaEMgug0R0J1Cr1cg8fgxJryQgOesGki6pkXldAwBwsRHQt5McfX3lkNk44l8/X4PPE5/A0tlb+/rijJ9RsPO/6N+vP7Zs/gYeHh7AiR+anNkyBQYpPTBIEdGdQqNR43LmcZQUFsDeyRm+oT20Lc/zXktt8sDgplTF++Hnb881+nzCk+EtClNtNcN19tDVFodA1Y4dtede3f5/p3/uJ/B9522GKSLqOLL/ANbep31YWCEi9bIaSRfVSL5cg+RLahRWADJre3R6+qt6e6sqLh1Hwff/hpuTPba+PQ+RR18EcHsc+fM14z83SZhikNIDgxQRUe0ZV00dGNwYmdIKO4qqUVrYeAizd1bgsVf76xWCWhNu9HH20FVs++hYo883FQJFtRpnhg6TzERJCAIsPD0RtGsnl/kRUcdw9Btg8+ONPq0RRfRfXY6j1lFwH7u0wTE1qmu4/u0rwPVsrLlfgb9FWDYwSqidmZp7tM2X+emaDRrvc0tERHcUm3A3uE4MhVwpPaxRZmvR5Os0UZ5NhigAKCmoRG5Woc611IWbW0MUAJQWVmLbR8dw9tBVne/VFI1GxB8bs5ocs/frLGg0Df/MsSz9QOMhCgBEETV5eShLP9CaMomIzIe9Z5NPiyJw9KoGVj7dGx1j4egO1wcWQW3vgb9vKceU78oauhOguly7d8pMNf3/jkREdEexCXdr8ODdihM3GjioVwGn0V1xsbymiTv+pVRV2fwg6B5uAnq5t3qZX25WYb2wdru6EOjbzbneczXXrun0PrqO0webWxCRSXTuXztTpMpF/SV5QOZ1EWVVGjjcFqTUpQWouHAUFReOQn35GMqvXQQAKG0s4GrTxH/LS/INWb1BMUgREZGEIBNgHegkudZYwBJkAuxOFeh0XztHhU7j9Ak3PsFODdakK13DXWPjLNzddXq9ruN0xeYWRGQyMnltI4ivJ6F2L9OtYUpA8iU1BEEGudIDpSf31ganS0dRfu0CACAwKAT3jB2Fu8M8cXfOm/C0b2aBXDMzYKbEIEVERDppKGABgHewE+ycFE2GH3vn2kYRutA13FRk3kDeltOSWTLRVoDLmBDY9dRtD5Wu4a6sqAqn0/LqNbywjYmGhZcXavLz6zebALR7pGxjonV6H1001tyiJj+/9jqbWxCRsYXdX9sIooFue0mWvhDFnbjyUe15UQFdgzBszAjEx8fj7rvvhrf3n138NGrg7Y2Nzmxp90h1Nt9DfBmkiIioVWQyAXdNCG6yYcPA/2/vzqOjKu//gb/vTJLJMslMkslOoCEECITwA4QYFUGCBIqglV+liDW0KmBBUFwonLpUzwGqPaj117q0VvgWa5VvW2VpkR2FhCBIVBbjJEajkEUCk32deX5/hBkymTtbMpPJJO/XOTknc+9z7zxzuST3k+f5fJ670lyehudKcJMQKEF1vAIdsNR26tRowuW3v0RliR6pd97o/DwuBIGSBBz735Jr/etS8EJSKhG3fl1nACNJ1sHN1UpVcevXeWzKnTAaUbVho3zQJgQgSajasBHhOTmc5kdE3jVmPjB6rs36T7OS/heKqB9h+vTpmD59OpKSkuSPdzKyBQCYvcmn60k5w6p9YNU+IiJPkKuyp45U4aa73KuyZzIJ/M/6fIfBTa42ECp0C6KuEkKgyVgPxf/VIu1658GUs6p99nSt5tdXU+0aC0+gPC/PabuhW7ciLGuKx96XiMhrZNeRSuoMovr5OlIckSIiIo9InRCLlPExvV73ydkIV3SAhGAHx0uShLCACBx/eydSp1wPhZO/ZqZOiMXsZRmWIFAIE0wdFwDRCEhhUAQkQZJs5/B3LXgRMWsWwnNyvF78of7gAZfaeaO4BRGRV9gZ2erPI1FmDKSIiMhjFApJtrqdu7oHN2bqSBWysuKA4xVOzyEaOxcfTh6b6dL7pYyPwYkP9uHUrr+hvaFLAQ1JjcDQW6AMSrM6pns1P0mp9OooUN3evbiy9X9cauvp4hZERF6lUAIpU33dC7cxkCIion7J3ghXW1ktLrkQSDUbG9BgcK2iIACUflKAY//4g+0O0YD2xp0A5tkEU64WxugtS26UM14obkFERPIYSBERUb8lN8KlStFAhEpAowmSZDtt0Jwjdanle6i1ro2OmUxGHNzyhsM27U2HoQhMtZrm52rVv95yuvCvmRAeLW5BRET2OSncTkRE1L9Iis4S50Bn0NSV+fXpmgNQR0cjKX2sS+e8cP4sGi5fctxI1HfmTl3lTkn33ur44QcIADVhwbioVaMmLFi2WHBkXh5LnxMR9RGOSBERkd8Jy4xFZYkexnwDQgOuVVRqMtbjdM0BXGj6CvOXr3daaMLM5SmAotHyrVxJd5NJ9LrYhpxywyV8lD4MLUHXfm0Ht3VgzMVLiK+91qfwGTN6/V5EROQaBlJEROSXUu+8EfrEYyjYthNoMqLZ2NA5nS86GvOXr0daluuLOLo6BRBSmN2S7nLl38O0Kkz96QgkaVUw1bdBER4EVYoGkhvBlb4wH3t3bgcCrYPClkAlPh0Wh4nfViG+rom5UUREfYyBFBER+a20629E6pTrO6fmGa5ArY1EUvpYl0eizJLSx0IdpXM4vS84PBLzH70NSaOibEaZ7K1FFdHYBtO7xbjUpb1SEwTtvFSEZOic9ssqd6t7PtjVBYDPJeoQV1fO3Cgioj7GHCkiIvJrCoUSyWMzkX7jNCSPzXQ7iDKfY8aSpQ7bzHrgQSSnR8tO5/v4Xb1N+4RACZNDlQjuFv8Ya9tQs+08ms84ycmCC7lbkoSWoAAofv2Y3+VGCaMRjYUnULtrNxoLT0AYjb7uEhGRWzgiRUREBCAt6wbMX7MeB7e8YRW8hEfrcEveUrtTBSv0BqvpfGbjQjoDOrnKggBg2Pk1gsdEO5zm52rulhiR6nC/t3K3eqpu715UbdhoVYkwID4ecevX+V1ASESDFwMpIiKiq9KybkDq5Cy3pgrKrSUVHSAhxEmgYqxtRWtZLYJTtXbbuJq75aid3dythbZ5Xn2hbu9eXFj9MNCt4mJHVVXn9pdfYjBFRH6BgRQREVEX5qmCrpJbS6r7dD57TPVtDve7krsVHq2zW+bdXu5Wo6EVe14/g9nLMvo0mLIsLCxkirdf3Vbx9DMQLa2W4hnM+yKi/oo5UkRERL2QkKZFmNY6mGqRW+RJhiI8yPF+O7lbEiTEBCdjaFg6cn78ACSZX+f2cre6OvqeHiaTi531AFcWFjZduYKLTzyB8rw8lOTMRN3evU7Py3wrIvIFBlJERES9oFBImLowzWpbTYdAs0nYLBjclVKjgipF4/T85twtdVRnlb+k0JG4LXk5ZiTcjezY+VAdNaHydydsilfYy93qquFKKyr0Bqd98JSOH35wr/3V6X6Ogqm6vXtRkjMT5Xl5uPjYY24FYEREvcFAioiIqJdSJ8Ri9rIMq5GpL5qNgCTBXiilnTfc5fWk0rJuwAN/fBN3Lfktboy7AyFdFiEG5CsByuVuyXG1nScExMS4d8DVQLRqw0bZUSZzvlX3US5XAjAiot5ijhQREZEHpE6IRcr4GKvqeNrWDtTt/hrG2mu5UEqNCtp5w11aR6orCQoEFHXACPvBV9dKgHK5W3JcbecqYRJoLauVXYA49LpJCIiPR0dVlXyelOwJBToqK9F08hTCsqZc2+ws30qSULVhI8JzcphnRURewUCKiIjIQxQKCUmjrCvohWbo7AYW7mgtq7UKyOR0rQRozt1yNL1PHdlZCr0rk8nY4wWOm89cgmFnabfA8doCxJJSibj16zqr811dUNhV3acFOs23shOAOSOMxs5z//ADAmJiWPCCiOxiIEVERORFkkJyWOLcVc4q/HVvZ87dkqvaZ3bTXWlW60npC/Nt1tFSR+kwY4n9dbTMms9cQs228zbbzdMOo+9JR0iGrrO0+csv2awj5Uz3aYGu5lu5k5fF9a2IyB3MkSIiIvIDzir8ybWTy90COkeiupc+1xfmY8fmDTal1hsuX8KOzRugL8y3+57CJGDYWeqwX4adX0NcrRAYMWsWRhzYj6FbtyLh+eehjIzsHKGSI0kIiI9H6HWTrDa7mm/lajvmWxGRuzgiRURE5AdUKRooNUEOp/fJVQKUy91KSNNajUSZTEYc3PKGw/c/tPUNpE7Okp3m5+60QwCQlErLlDtFsEp+ut/V4Cpu/Tqb6XVO860kybIWlTPMtyKinuCIFBERkR+QFBK081IdtrFXCdCcuzVycjySRkVaBVEAOnOiHCz6CwD1NZdw4fxZ2X3uTjsUJoGWUgOaiqrRUmpA+MxbkfTySwiIi7O0FQAMQxLQvOpB1CbFw2SyrtpnzrfqfNHtMzsIwOS4mm/1wyv/j+tUEZEFR6SIiIj8REiGDtH3pMsUdOhZJUCzBsOVXrVzZ9qh/YIUEzHiwH40nTwF/alCHD9VgMaGOuDwh8DhD2VztezlWwXExbmV1+RqHlXNa6+h5rXXmDdFRAAYSBEREfmVkAwdgsdEe6QSoJlaG+m8kYN2rk47NDW24/Lfv7TZ17UgxUV04MCRD23amHO15q9ZbxNMhefk9KrSnrvrW5nzpvDySwymiAYxTu0jIiLyM+ZKgKH/JxbBqdpeBVEAkJQ+Fuoox6NZ4dE6JKWPtdsfZ9MONXNTULv7a4dtruwsxcEtf3bY5tDWNyzT/EwmgQvFV6D/9AcYtGkI//GPEZY1xe08JnO+ld2CF905WSiYiAYHjkgRERENcgqFEjOWLMWOzRvstrklb6nD9aScTTuUQgKcFqQw1bYhpCkYDQ7amHO12tri8fG7eqt1ssK0KkxdmGZVjdAVPVrfqofrVBHRwNHvR6SeeeYZSJJk9TV69GjL/paWFqxYsQLR0dFQq9VYsGABqqqqfNhjIiIi/5OWdQPmr1lvMzIVHq2zmU5nT0iGDvFrp0D3wDhE/WwUdA+MQ/zayQjJ0LlckCJEqXba5uuib7Dn9TM2iw03Glqx5/UzKD1d7dJ7dRUxa5ZNwQtXuLNOFRENLH4xIjV27Fjs37/f8jog4Fq3H3nkEezevRvbt2+HRqPBypUrceedd+LYsWO+6CoREZHfSsu6AamTszqr+BmuQK2NRFL6WIcjUd3ZW4DY1YIUzUZH41GdvjrRAMB+wHX0PT1SxsfYVCc0M5mMsp+xa75VY0EBal57zWlf3M2vIqKBwy8CqYCAAMTHx9tsr62txZtvvom///3vmDFjBgDgrbfeQnp6Oo4fP47rr7++r7tKRETk1xQKJZLHZnr8vK4UpFBogtBc2wK02D9PaEQUWltiHaYzNVxpRYXegKRRtsUx9IX5OLjlDaty710rAprXtwq9bhJq33/fI+tUEdHA1O+n9gGAXq9HYmIihg8fjsWLF6O8vBwAcOrUKbS3t2PmzJmWtqNHj8bQoUNRUFBg93ytra2oq6uz+iIiIiLvcaUgReS8VMxY8oDDNmOmL4QkOX98aaxrtdmmL8zHjs0bbNbMMlcE1BfmX+uvB9epInJGGI1oLDyB2l27uVaZH+n3gVRWVha2bNmCPXv24NVXX0VZWRmmTp2K+vp6VFZWIigoCFqt1uqYuLg4VDpYWG/jxo3QaDSWr+TkZC9/CiIiIjIXpFBqrKf5KTUqRN+TjpAMndNcrRHXOc/VAoCwCJXVa5PJiINb3nB4TNeKgID9vKmAuDgksfQ5eUjd3r0oyZmJ8rw8XHzsMZTn5aEkZybq9u71ddfIiX4/tW/OnDmW7zMzM5GVlYVhw4bhvffeQ0hISI/OuW7dOqxZs8byuq6ujsEUERFRH3BlHSxHuVomk0CYVmVTaKIrdaQKCWlaq20Xzp+1GYnqzlwRsOvURk+sU+WMySRQoTegsa4VYRGdfbeX30UDS93evZ3VIrtNH+VaZf6h3wdS3Wm1WowcORIlJSW49dZb0dbWBoPBYDUqVVVVJZtTZaZSqaBSqezuJyIiIu+xV5CiK3u5WgqFhKkL07Dn9TN2j73prjSbQKTBcMWlvsm1M+dNeUPp6WqPlXEn/yKMRlRt2CifgycEIEmo2rAR4Tk5nELaT/X7qX3dNTQ0oLS0FAkJCZg0aRICAwNx4MABy/7i4mKUl5cjOzvbh70kIiIib0mdEIvZyzIQprX+o6g6UoXZyzJkAxC11rbwhBxX23lC6elqj5dxJ//RdPIUOhykonRdq4z6p34/IvXYY49h3rx5GDZsGC5evIinn34aSqUSixYtgkajwX333Yc1a9YgKioKEREReOihh5Cdnc2KfURERANY6oRYpIyPcXlKXFL6WKijdA6n94VH65CUPtZbXbZiMgl8/K7eYRtnZdzJv7m6BhnXKuu/+n0g9f3332PRokWoqalBTEwMbrrpJhw/fhwxV9dtePHFF6FQKLBgwQK0trYiNzcXf/rTn3zcayIiIvI2hUKSLXEu31aJGUuWYsfmDXbb3JK31K01s9zRPQ/KJITDPC/AcRl38n+urkHGtcr6L0kIuYmZg0tdXR00Gg1qa2sRERHh6+4QERGRl8itIxUercMteZ3rSHmDXB6UKjQArU0dTo+99b4xGDnZft53fyaMRq8W6fB3wmhESc5Mp2uVjTiwn9etj7kaG/T7ESkiIiIiT3FUEdAbzHlQ3bkSRAG2Zdz9Rd3evajasNEqByggPh5x69exCt1V5rXKLqx+uHNtsq7BFNcq8wt+V2yCiIiIqDfMFQHTb5yG5LGZXp3O5ywPyhG5Mu6uva8R3539HOePHcF3Zz+3WhurL5hLencvpGAu6c31ka7hWmX+jSNSRERERF5QoTc4zYNyRK6MuzNyUxfVUTrMWOK9qYtdsaS3+/pirTLyDo5IEREREXlBY51rQZQqzPrv2o7KuDuiL8zHjs0bbCoTNly+hB2bN0BfmO/W+XqCJb17xrxWmea2uQjLmsIgyk9wRIqIiIjIC1zNb5p9fwYkheRSGXd7TCYjDm55w2GbQ1vfQOrkLMtUxu6VBHvyvt2xpDcNJgykiIiIiLwgIU2LMK3K4fQ+daQKiaMiex3AXDh/Fo2XaxATnIwQpRrNxgZcavkeAtem2NXXXMKF82eRPDZTtpJgmFaFqQvTHI6EmUxGh4U6WNKbBhMGUkREREReoFBImLowTbZqn1lP8qDktJy/gtuSlyM04Fqp5qaOOnxacwAXmr6ybGswXLFbSbDR0Io9r5+xO63Qlfyr0OsmISA+3mlJ79DrJvXm4xL1C8yRIiIiIvKS1AmxmL0sA2Fa62l+Pc2DktN85hKCTyoQogy32h6iDMeNsXcgKXSkZVuoJtJpJcGj7+lhMlkHQa7mX5lLene+6BYgsqQ3DTAckSIiIiLyotQJsUgZH+PxfCQAECYBw85SSIBN4CJJEoQQmBCdg4tNeqijo6FQJqHR8JnDczZcaUWF3oCkUZEAgI6ODuz7y2sOj+mafxUxaxbw8ku260jFxXEdKRpQGEgREREReZlCIVkCE09qLauFsbbN7n5JkhAWEAFd8BBk5/0czQ3tLp3XXHGw9HQ1Dm7Zj+a6yw7bd82/Anxf0ttZLheRJzCQIiIiIvJTpnr7QVRXN81bjOFZN+BC8RWX2odFqCy5VMY2g0vHNBisz20u6d3XfL2WFg0ezJEiIiIi8lOK8CCX2iVOyABwrZKgI+pIFeJSNddyqaQwl95DrfX8iJu7+sNaWjR4MJAiIiIi8lOqFA2UGsfBlFKjgipFA+BaJUFHbrorDVWltZbS6IqAJEBSOzwmPFqHpPSxbvTcMWESaCk1oKmoGi2lBgiTTAXAblxdS8tkMnqqmzTIMZAiIiIi8lOSQoJ2XqrDNtp5wyF1KWzhSiVBc44UAEiSAoGhtzh8j1vylnosB6n5zCVU/u4ELv35C1z+RzEu/fkLVP7uBJrPXHJ43IXzZ21Goroz53IReQJzpIiIiIj8WEiGDtH3pMOws9Sq8IRSo4J23nCEZOhsjnFWSTAswjrIUgalAZiH9qZDgGiwbA+NiMLM+5d7LPeo+cwl1Gw7b7PdWNuGmm3nEX1PuuznAWxztOxxtR2RMwykiIiIiPxcSIYOwWOi0VpWC1N9GxThQVClaKxGorpzVEnQnEtlnt4HdAZTisBUmDouAKIRIRFa/OL3dyEgwDOPk+ZS7o4Ydn6N4DHRsp/L1RwtT+dysULg4MVAioiIiGgAkBQSglO1HjmXOZdqz+tnrN9DUkAZmAwAmJGX4bEgCnBeyh0AjLWtaP3aAEiSTcCYlD4W6iidw+l9ns7lYoXAwU0SQjjP3hvg6urqoNFoUFtbi4iICF93h4iIiKhfKD1djY/f1VuNTKkjVbjprjSkToj16Hs1FVXj8j+KnbaTQpQQzdcKRig1QdDOS0VIhs5Stc+e+WvW9zjAMZmE1VTIhivnsOvFjXbbz121Fu3lCrRcqkOwLgKjF0xBYFBgj96b+parsQEDKTCQIiIiIrKnewDRNZfKk1pKDbj05y96fLw5f0pulCg8Wodb8no+StQ9oBTChPb6N2Ey1su2TwodiYnROQgNuPZc2dTRgPoRSkxaPqtHfaC+w0DKDQykiIiIiHxLmAQqf3fC6fQ+e5QaFeLXToakkDyat2RemLgrY/t3aG/YLts+KXQkboy9AwAgSdcCTvMjd3VKC4Opfs7V2IA5UkRERETkc+ZS7nJV+1xhrG1Fa1ktglO1UCiUSB6b2es+mUwCh/72pe0O0SjbXoKEidE5nd9L1qN2kiRBCIHwkg60t7Vzmt8AwHWkiIiIiKhfMJdy777IsBTq2t/+TfU9G82y59R/vkFrU4ftDilMtr0ueAhCAyJsgijLYZKE0IBwfPnPE57sJvkIR6SIiIiIqN+QK+UuTAI1b55xeqwiPMhpG1eZTAKfHfpO/n0CkgBJbbWmFgCEKNUunbvlUl2v+0e+x0CKiIiIiPqV7qXchUlAqQlymD+l1KigStF4rA8VegNaG2VGo9BZBj4w9Ba0N+602t5sbJBt312wjjn5AwGn9hERERFRv2bOn3JEO2+4wwWI3dVY1+pwvzIoDYFh8yApwy3bLrV8j6aOOtir5SaEQFNHPUYvmOKxfpLvcESKiIiIiPo9c/6UYWep1ciUUqOCdt5whGToPPp+YREqp22UQWnI+kkOEoc3WSoEVn1UgZBvOoMmuap99SMCWGhigGAgRURERER+QS5/SpWi8ehIlFlCmhZhWpXVYsTdBYcFYvLcVKt1tZLHZuLUa3sRXmJEaMC1nKlmYwPqRwSw9PkAwkCKiIiIiPxG9/wpb1EoJExdmGazhlRX0+8ZJbs48aTls9De1o4v/3kCLZfqEKyLwOgFt3IkaoDhgrzggrxEREREJK/0dDU+fldvNTKljlThprvSkDoh1oc9I2/hgrxERERERL2UOiEWKeNjUKE3oLGuFWERKiSkaWVHomhwYSBFREREROSAQiEhaVSkr7tB/QzLnxMREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbkpwNcd6A+EEACAuro6H/eEiIiIiIh8yRwTmGMEexhIAaivrwcAJCcn+7gnRERERETUH9TX10Oj0djdLwlnodYgYDKZcPHiRYSHh0OSJF93p9+rq6tDcnIyvvvuO0RERPi6O4MOr79v8fr7Fq+/b/H6+xavv2/x+vtWX15/IQTq6+uRmJgIhcJ+JhRHpAAoFAoMGTLE193wOxEREfxB4kO8/r7F6+9bvP6+xevvW7z+vsXr71t9df0djUSZsdgEERERERGRmxhIERERERERuYmBFLlNpVLh6aefhkql8nVXBiVef9/i9fctXn/f4vX3LV5/3+L1963+eP1ZbIKIiIiIiMhNHJEiIiIiIiJyEwMpIiIiIiIiNzGQIiIiIiIichMDKSIiIiIiIjcxkCJZGzduxOTJkxEeHo7Y2FjccccdKC4utmozffp0SJJk9bV8+XIf9XhgeeaZZ2yu7ejRoy37W1pasGLFCkRHR0OtVmPBggWoqqryYY8Hlh/96Ec211+SJKxYsQIA731P++ijjzBv3jwkJiZCkiS8//77VvuFEHjqqaeQkJCAkJAQzJw5E3q93qrN5cuXsXjxYkRERECr1eK+++5DQ0NDH34K/+Xo+re3t2Pt2rUYN24cwsLCkJiYiHvvvRcXL160Oofc/5lNmzb18SfxT87u/yVLlthc29mzZ1u14f3fc86uv9zvAkmS8MILL1ja8P7vOVeeN1155ikvL8fcuXMRGhqK2NhYPP744+jo6PB6/xlIkawjR45gxYoVOH78OPbt24f29nbMmjULjY2NVu0eeOABVFRUWL6ef/55H/V44Bk7dqzVtT169Khl3yOPPIKdO3di+/btOHLkCC5evIg777zTh70dWD755BOra79v3z4AwE9/+lNLG977ntPY2Ijx48fjj3/8o+z+559/Hn/4wx/w2muvobCwEGFhYcjNzUVLS4ulzeLFi3H27Fns27cPu3btwkcffYSlS5f21Ufwa46uf1NTEz799FM8+eST+PTTT/Gvf/0LxcXFmD9/vk3bZ5991ur/xEMPPdQX3fd7zu5/AJg9e7bVtX3nnXes9vP+7zln17/rda+oqMBf//pXSJKEBQsWWLXj/d8zrjxvOnvmMRqNmDt3Ltra2pCfn4+tW7diy5YteOqpp7z/AQSRC6qrqwUAceTIEcu2adOmidWrV/uuUwPY008/LcaPHy+7z2AwiMDAQLF9+3bLtvPnzwsAoqCgoI96OLisXr1apKamCpPJJITgve9NAMS///1vy2uTySTi4+PFCy+8YNlmMBiESqUS77zzjhBCiHPnzgkA4pNPPrG0+e9//yskSRIXLlzos74PBN2vv5wTJ04IAOLbb7+1bBs2bJh48cUXvdu5QUDu+ufl5Ynbb7/d7jG8/z3Hlfv/9ttvFzNmzLDaxvvfc7o/b7ryzPOf//xHKBQKUVlZaWnz6quvioiICNHa2urV/nJEilxSW1sLAIiKirLa/vbbb0On0yEjIwPr1q1DU1OTL7o3IOn1eiQmJmL48OFYvHgxysvLAQCnTp1Ce3s7Zs6caWk7evRoDB06FAUFBb7q7oDV1taGbdu24Ze//CUkSbJs573fN8rKylBZWWl1v2s0GmRlZVnu94KCAmi1Wlx33XWWNjNnzoRCoUBhYWGf93mgq62thSRJ0Gq1Vts3bdqE6OhoTJgwAS+88EKfTKsZLA4fPozY2FiMGjUKDz74IGpqaiz7eP/3naqqKuzevRv33XefzT7e/57R/XnTlWeegoICjBs3DnFxcZY2ubm5qKurw9mzZ73a3wCvnp0GBJPJhIcffhg33ngjMjIyLNvvvvtuDBs2DImJifj888+xdu1aFBcX41//+pcPezswZGVlYcuWLRg1ahQqKirw29/+FlOnTsWZM2dQWVmJoKAgm4eYuLg4VFZW+qbDA9j7778Pg8GAJUuWWLbx3u875nu66y9I82vzvsrKSsTGxlrtDwgIQFRUFP9PeFhLSwvWrl2LRYsWISIiwrJ91apVmDhxIqKiopCfn49169ahoqICmzdv9mFvB4bZs2fjzjvvREpKCkpLS7F+/XrMmTMHBQUFUCqVvP/70NatWxEeHm4zlZ73v2fIPW+68sxTWVkp+zvCvM+bGEiRUytWrMCZM2escnQAWM2/HjduHBISEpCTk4PS0lKkpqb2dTcHlDlz5li+z8zMRFZWFoYNG4b33nsPISEhPuzZ4PPmm29izpw5SExMtGzjvU+DUXt7O+666y4IIfDqq69a7VuzZo3l+8zMTAQFBWHZsmXYuHEjVCpVX3d1QPnZz35m+X7cuHHIzMxEamoqDh8+jJycHB/2bPD561//isWLFyM4ONhqO+9/z7D3vNmfcWofObRy5Urs2rULhw4dwpAhQxy2zcrKAgCUlJT0RdcGFa1Wi5EjR6KkpATx8fFoa2uDwWCwalNVVYX4+HjfdHCA+vbbb7F//37cf//9Dtvx3vce8z3dvUJT1/s9Pj4e1dXVVvs7Ojpw+fJl/p/wEHMQ9e2332Lfvn1Wo1FysrKy0NHRgW+++aZvOjiIDB8+HDqdzvLzhvd/3/j4449RXFzs9PcBwPu/J+w9b7ryzBMfHy/7O8K8z5sYSJEsIQRWrlyJf//73zh48CBSUlKcHlNUVAQASEhI8HLvBp+GhgaUlpYiISEBkyZNQmBgIA4cOGDZX1xcjPLycmRnZ/uwlwPPW2+9hdjYWMydO9dhO9773pOSkoL4+Hir+72urg6FhYWW+z07OxsGgwGnTp2ytDl48CBMJpMlyKWeMwdRer0e+/fvR3R0tNNjioqKoFAobKacUe99//33qKmpsfy84f3fN958801MmjQJ48ePd9qW97/rnD1vuvLMk52djS+++MLqDwrmP/iMGTPG6x+AyMaDDz4oNBqNOHz4sKioqLB8NTU1CSGEKCkpEc8++6w4efKkKCsrEx988IEYPny4uPnmm33c84Hh0UcfFYcPHxZlZWXi2LFjYubMmUKn04nq6mohhBDLly8XQ4cOFQcPHhQnT54U2dnZIjs728e9HliMRqMYOnSoWLt2rdV23vueV19fL06fPi1Onz4tAIjNmzeL06dPW6rCbdq0SWi1WvHBBx+Izz//XNx+++0iJSVFNDc3W84xe/ZsMWHCBFFYWCiOHj0q0tLSxKJFi3z1kfyKo+vf1tYm5s+fL4YMGSKKioqsfh+Yq2Hl5+eLF198URQVFYnS0lKxbds2ERMTI+69914ffzL/4Oj619fXi8cee0wUFBSIsrIysX//fjFx4kSRlpYmWlpaLOfg/d9zzn7+CCFEbW2tCA0NFa+++qrN8bz/e8fZ86YQzp95Ojo6REZGhpg1a5YoKioSe/bsETExMWLdunVe7z8DKZIFQPbrrbfeEkIIUV5eLm6++WYRFRUlVCqVGDFihHj88cdFbW2tbzs+QCxcuFAkJCSIoKAgkZSUJBYuXChKSkos+5ubm8WvfvUrERkZKUJDQ8VPfvITUVFR4cMeDzwffvihACCKi4uttvPe97xDhw7J/rzJy8sTQnSWQH/yySdFXFycUKlUIicnx+bfpaamRixatEio1WoREREhfvGLX4j6+noffBr/4+j6l5WV2f19cOjQISGEEKdOnRJZWVlCo9GI4OBgkZ6eLjZs2GD1oE/2Obr+TU1NYtasWSImJkYEBgaKYcOGiQceeMCqzLMQvP97w9nPHyGEeP3110VISIgwGAw2x/P+7x1nz5tCuPbM880334g5c+aIkJAQodPpxKOPPira29u93n/p6ocgIiIiIiIiFzFHioiIiIiIyE0MpIiIiIiIiNzEQIqIiIiIiMhNDKSIiIiIiIjcxECKiIiIiIjITQykiIiIiIiI3MRAioiIiIiIyE0MpIiIiIiIiNzEQIqIiPq96dOn4+GHH+7x8YcPH4YkSTAYDB7rExERDW4MpIiIiIiIiNzEQIqIiIiIiMhNDKSIiMgvdHR0YOXKldBoNNDpdHjyySchhAAA/O1vf8N1112H8PBwxMfH4+6770Z1dbXdc9XU1GDRokVISkpCaGgoxo0bh3feeceqzfTp07Fq1So88cQTiIqKQnx8PJ555hmrNgaDAcuWLUNcXByCg4ORkZGBXbt2WfYfPXoUU6dORUhICJKTk7Fq1So0NjZ67qIQEZHPMJAiIiK/sHXrVgQEBODEiRN4+eWXsXnzZvzlL38BALS3t+O5557DZ599hvfffx/ffPMNlixZYvdcLS0tmDRpEnbv3o0zZ85g6dKl+PnPf44TJ07YvGdYWBgKCwvx/PPP49lnn8W+ffsAACaTCXPmzMGxY8ewbds2nDt3Dps2bYJSqQQAlJaWYvbs2ViwYAE+//xzvPvuuzh69ChWrlzpnQtERER9ShLmP+cRERH1U9OnT0d1dTXOnj0LSZIAAL/+9a+xY8cOnDt3zqb9yZMnMXnyZNTX10OtVuPw4cO45ZZbcOXKFWi1Wtn3uO222zB69Gj8/ve/t7yn0WjExx9/bGkzZcoUzJgxA5s2bcLevXsxZ84cnD9/HiNHjrQ53/333w+lUonXX3/dsu3o0aOYNm0aGhsbERwc3JtLQkREPsYRKSIi8gvXX3+9JYgCgOzsbOj1ehiNRpw6dQrz5s3D0KFDER4ejmnTpgEAysvLZc9lNBrx3HPPYdy4cYiKioJarcaHH35o0z4zM9PqdUJCgmXKYFFREYYMGSIbRAHAZ599hi1btkCtVlu+cnNzYTKZUFZW1uPrQERE/UOArztARETUGy0tLcjNzUVubi7efvttxMTEoLy8HLm5uWhra5M95oUXXsDLL7+Ml156CePGjUNYWBgefvhhm/aBgYFWryVJgslkAgCEhIQ47FdDQwOWLVuGVatW2ewbOnSoOx+RiIj6IQZSRETkFwoLC61eHz9+HGlpafjyyy9RU1ODTZs2ITk5GUDn1D5Hjh07httvvx333HMPgM58p6+++gpjxoxxuT+ZmZn4/vvv8dVXX8mOSk2cOBHnzp3DiBEjXD4nERH5D07tIyIiv1BeXo41a9aguLgY77zzDl555RWsXr0aQ4cORVBQEF555RV8/fXX2LFjB5577jmH50pLS8O+ffuQn5+P8+fPY9myZaiqqnKrP9OmTcPNN9+MBQsWYN++fSgrK8N///tf7NmzBwCwdu1a5OfnY+XKlSgqKoJer8cHH3zAYhNERAMEAykiIvIL9957L5qbmzFlyhSsWLECq1evxtKlSxETE4MtW7Zg+/btGDNmDDZt2mQpGGHPb37zG0ycOBG5ubmYPn064uPjcccdd7jdp3/+85+YPHkyFi1ahDFjxuCJJ56A0WgE0DlideTIEXz11VeYOnUqJkyYgKeeegqJiYk9+fhERNTPsGofERERERGRmzgiRURERERE5CYGUkRERERERG5iIEVEREREROQmBlJERERERERuYiBFRERERETkJgZSREREREREbmIgRURERERE5CYGUkRERERERG5iIEVEREREROQmBlJERERERERuYiBFRERERETkpv8PTlN7lTb2oF4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x1 = [30.14529, 22.14783, 16.26578, 71.35000]\n",
    "y1 = np.array([531.32005, 535.59092, 534.92086, 345.87160 ])/5\n",
    "\n",
    "ax = plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x1, y1, label='Heuristics')\n",
    "\n",
    "x2 = [14.43254, 20.35518, 193.22472365514568, 197.76272]\n",
    "y2 = np.array([492.52471, 384.84853, 276.4934642262895, 274.64717 ])/5\n",
    "plt.scatter(x2, y2, label='DeepJS')\n",
    "# plt.arrow(x2[1], y2[1], x2[3] - x2[1], y2[3]-y2[1], width=0.2, head_width=5)\n",
    "prop = dict(arrowstyle=\"-|>,head_width=0.4,head_length=0.8\",\n",
    "            shrinkA=0,shrinkB=0)\n",
    "\n",
    "plt.annotate(\"\", xy=(x2[3], y2[3]), xytext=(x2[1], y2[1]), arrowprops=prop)\n",
    "\n",
    "x4 = [17.530897585317124]\n",
    "y4 = np.array([353.8849450915141])/5\n",
    "plt.scatter(x4, y4, label='MERL-WS')\n",
    "\n",
    "data_path = 'output/test/nsga/nsga_run05_g20000/nsga_mean_std.csv'\n",
    "data = pd.read_csv(data_path)\n",
    "x3 = data[\"balance_fitness_mean\"].to_list()\n",
    "y3 = np.array(data[\"duration_fitness_mean\"].to_list())/5\n",
    "plt.scatter(x3, y3, label='MERL-2')\n",
    "\n",
    "data_path = 'output/test/nsga/nsga_run05_g50000/nsga_mean_std.csv'\n",
    "data = pd.read_csv(data_path)\n",
    "x3 = data[\"balance_fitness_mean\"].to_list()\n",
    "y3 = np.array(data[\"duration_fitness_mean\"].to_list())/5\n",
    "plt.scatter(x3, y3, label='MERL-5')\n",
    "\n",
    "data_path = 'output/test/nsga/nsga_run05_g60000/nsga_mean_std.csv'\n",
    "data = pd.read_csv(data_path)\n",
    "x3 = data[\"balance_fitness_mean\"].to_list()\n",
    "y3 = np.array(data[\"duration_fitness_mean\"].to_list())/5\n",
    "plt.scatter(x3, y3, label='MERL-6')\n",
    "\n",
    "data_path = 'output/test/nsga/nsga_run05_g70000/nsga_mean_std.csv'\n",
    "data = pd.read_csv(data_path)\n",
    "x3 = data[\"balance_fitness_mean\"].to_list()\n",
    "y3 = np.array(data[\"duration_fitness_mean\"].to_list())/5\n",
    "plt.scatter(x3, y3, label='MERL-7')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"balance\")\n",
    "plt.ylabel(\"duration(mins)\")\n",
    "plt.title(\"Overall Comparison\")\n",
    "plt.savefig('output/image/overall_comparison.png', dpi=500, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.5])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "w = np.array([0.5, 0.5])\n",
    "a = np.array([0.5, 0.5])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.25])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "random.randint(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
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       "      <th>0</th>\n",
       "      <td>41562</td>\n",
       "      <td>41618</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>Terminated</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1279.0</td>\n",
       "      <td>Terminated</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41562</td>\n",
       "      <td>41617</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41561</td>\n",
       "      <td>41617</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41557</td>\n",
       "      <td>41610</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0      1      2      3       4           5   6   7     8     9   10  11\n",
       "0  41562  41618  120.0  686.0   299.0  Terminated   1   1  1.50  0.29 NaN NaN\n",
       "1  41561  41619  120.0  686.0  1279.0  Terminated   1   1  0.89  0.28 NaN NaN\n",
       "2  41562  41617  120.0  686.0   828.0  Terminated   1   1  0.94  0.29 NaN NaN\n",
       "3  41561  41617  120.0  686.0    95.0  Terminated   1   1  1.00  0.31 NaN NaN\n",
       "4  41557  41610  120.0  686.0   545.0  Terminated   1   1  1.37  0.29 NaN NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('data/alibaba_trace2017/batch_instance.csv', header=None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16094656"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "header = ['start_timestamp', 'end_timestamp', 'job_id', 'task_id', 'machineID', 'status', 'seq_no', 'total_seq_no', 'real_cpu_max', 'real_cpu_avg', 'real_mem_max', 'real_mem_avg']\n",
    "data.columns = header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_timestamp</th>\n",
       "      <th>end_timestamp</th>\n",
       "      <th>job_id</th>\n",
       "      <th>task_id</th>\n",
       "      <th>machineID</th>\n",
       "      <th>status</th>\n",
       "      <th>seq_no</th>\n",
       "      <th>total_seq_no</th>\n",
       "      <th>real_cpu_max</th>\n",
       "      <th>real_cpu_avg</th>\n",
       "      <th>real_mem_max</th>\n",
       "      <th>real_mem_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>41562</td>\n",
       "      <td>41618</td>\n",
       "      <td>120.0</td>\n",
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       "      <td>299.0</td>\n",
       "      <td>Terminated</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>41562</td>\n",
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       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>828.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41561</td>\n",
       "      <td>41617</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41557</td>\n",
       "      <td>41610</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
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       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start_timestamp  end_timestamp  job_id  task_id  machineID      status  \\\n",
       "0            41562          41618   120.0    686.0      299.0  Terminated   \n",
       "1            41561          41619   120.0    686.0     1279.0  Terminated   \n",
       "2            41562          41617   120.0    686.0      828.0  Terminated   \n",
       "3            41561          41617   120.0    686.0       95.0  Terminated   \n",
       "4            41557          41610   120.0    686.0      545.0  Terminated   \n",
       "\n",
       "   seq_no  total_seq_no  real_cpu_max  real_cpu_avg  real_mem_max  \\\n",
       "0       1             1          1.50          0.29           NaN   \n",
       "1       1             1          0.89          0.28           NaN   \n",
       "2       1             1          0.94          0.29           NaN   \n",
       "3       1             1          1.00          0.31           NaN   \n",
       "4       1             1          1.37          0.29           NaN   \n",
       "\n",
       "   real_mem_avg  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Failed', 'Interrupted', 'Ready', 'Running', 'Terminated', 'Waiting'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(data['status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1294, 12952, 80554)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['machineID'].unique()), len(data['job_id'].unique()), len(data['task_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['real_cpu_avg'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['real_mem_avg'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>start_timestamp</th>\n",
       "      <th>end_timestamp</th>\n",
       "      <th>job_id</th>\n",
       "      <th>task_id</th>\n",
       "      <th>machineID</th>\n",
       "      <th>status</th>\n",
       "      <th>seq_no</th>\n",
       "      <th>total_seq_no</th>\n",
       "      <th>real_cpu_max</th>\n",
       "      <th>real_cpu_avg</th>\n",
       "      <th>real_mem_max</th>\n",
       "      <th>real_mem_avg</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41562</td>\n",
       "      <td>41618</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41561</td>\n",
       "      <td>41619</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41562</td>\n",
       "      <td>41617</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41561</td>\n",
       "      <td>41617</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41557</td>\n",
       "      <td>41610</td>\n",
       "      <td>120.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start_timestamp  end_timestamp  job_id  task_id  machineID      status  \\\n",
       "0            41562          41618   120.0    686.0      299.0  Terminated   \n",
       "1            41561          41619   120.0    686.0     1279.0  Terminated   \n",
       "2            41562          41617   120.0    686.0      828.0  Terminated   \n",
       "3            41561          41617   120.0    686.0       95.0  Terminated   \n",
       "4            41557          41610   120.0    686.0      545.0  Terminated   \n",
       "\n",
       "   seq_no  total_seq_no  real_cpu_max  real_cpu_avg  real_mem_max  \\\n",
       "0       1             1          1.50          0.29           0.0   \n",
       "1       1             1          0.89          0.28           0.0   \n",
       "2       1             1          0.94          0.29           0.0   \n",
       "3       1             1          1.00          0.31           0.0   \n",
       "4       1             1          1.37          0.29           0.0   \n",
       "\n",
       "   real_mem_avg  duration  \n",
       "0           0.0        56  \n",
       "1           0.0        58  \n",
       "2           0.0        55  \n",
       "3           0.0        56  \n",
       "4           0.0        53  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terminated_data = data.loc[data['status'] == 'Terminated']\n",
    "terminated_data = terminated_data.fillna(0)\n",
    "terminated_data['duration'] = terminated_data['end_timestamp'] - terminated_data['start_timestamp']\n",
    "terminated_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4905990"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terminated_data = terminated_data.loc[terminated_data['real_cpu_avg'] <= 1]\n",
    "len(terminated_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13697990"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terminated_data = terminated_data.loc[terminated_data['real_mem_avg'] <= 0.1]\n",
    "len(terminated_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5331864"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terminated_data = terminated_data.loc[terminated_data['duration'] <= 3600]\n",
    "len(terminated_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5341331"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terminated_data = terminated_data.loc[terminated_data['duration'] >= 60]\n",
    "len(terminated_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5331864"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# terminated_data1 = terminated_data.loc[terminated_data['real_mem_avg'] >= 0.01]\n",
    "# len(terminated_data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "terminated_data['duration'].hist(bins=50) # 映射到0-120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "terminated_data['real_cpu_avg'].hist(bins=50) # 映射到0-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "terminated_data['duration'].hist(bins=50) # 映射到0-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机打乱 按顺序选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000 * 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = terminated_data.sample(100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data['duration'].hist(bins=50) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data.to_csv('data/alibaba_trace2017/sample_batch_instance.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deepjs",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0f3850b99803bf3dc3262c2ab4f238762f0d772c85a32b8bf66071eed9f8caf1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}