{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- Al2O3 <= 0.18\n",
      "|   |--- TiO2 <= 0.18\n",
      "|   |   |--- T <= 32.50\n",
      "|   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |--- T <= 22.50\n",
      "|   |   |   |   |   |   |--- value: [1.06]\n",
      "|   |   |   |   |   |--- T >  22.50\n",
      "|   |   |   |   |   |   |--- value: [1.06]\n",
      "|   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |--- value: [1.09]\n",
      "|   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |--- T <= 27.50\n",
      "|   |   |   |   |   |--- T <= 22.50\n",
      "|   |   |   |   |   |   |--- value: [1.09]\n",
      "|   |   |   |   |   |--- T >  22.50\n",
      "|   |   |   |   |   |   |--- value: [1.09]\n",
      "|   |   |   |   |--- T >  27.50\n",
      "|   |   |   |   |   |--- value: [1.08]\n",
      "|   |   |--- T >  32.50\n",
      "|   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |--- T <= 55.00\n",
      "|   |   |   |   |   |   |--- T <= 47.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.05]\n",
      "|   |   |   |   |   |   |--- T >  47.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.04]\n",
      "|   |   |   |   |   |--- T >  55.00\n",
      "|   |   |   |   |   |   |--- T <= 62.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.04]\n",
      "|   |   |   |   |   |   |--- T >  62.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.03]\n",
      "|   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |--- T <= 60.00\n",
      "|   |   |   |   |   |   |--- T <= 52.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.07]\n",
      "|   |   |   |   |   |   |--- T >  52.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.06]\n",
      "|   |   |   |   |   |--- T >  60.00\n",
      "|   |   |   |   |   |   |--- T <= 67.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.06]\n",
      "|   |   |   |   |   |   |--- T >  67.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.05]\n",
      "|   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |--- T <= 50.00\n",
      "|   |   |   |   |   |--- T <= 37.50\n",
      "|   |   |   |   |   |   |--- value: [1.08]\n",
      "|   |   |   |   |   |--- T >  37.50\n",
      "|   |   |   |   |   |   |--- value: [1.08]\n",
      "|   |   |   |   |--- T >  50.00\n",
      "|   |   |   |   |   |--- T <= 67.50\n",
      "|   |   |   |   |   |   |--- T <= 62.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.06]\n",
      "|   |   |   |   |   |   |--- T >  62.50\n",
      "|   |   |   |   |   |   |   |--- value: [1.06]\n",
      "|   |   |   |   |   |--- T >  67.50\n",
      "|   |   |   |   |   |   |--- value: [1.06]\n",
      "|   |--- TiO2 >  0.18\n",
      "|   |   |--- T <= 40.00\n",
      "|   |   |   |--- T <= 30.00\n",
      "|   |   |   |   |--- value: [1.22]\n",
      "|   |   |   |--- T >  30.00\n",
      "|   |   |   |   |--- value: [1.21]\n",
      "|   |   |--- T >  40.00\n",
      "|   |   |   |--- T <= 60.00\n",
      "|   |   |   |   |--- T <= 52.50\n",
      "|   |   |   |   |   |--- T <= 47.50\n",
      "|   |   |   |   |   |   |--- value: [1.20]\n",
      "|   |   |   |   |   |--- T >  47.50\n",
      "|   |   |   |   |   |   |--- value: [1.19]\n",
      "|   |   |   |   |--- T >  52.50\n",
      "|   |   |   |   |   |--- value: [1.19]\n",
      "|   |   |   |--- T >  60.00\n",
      "|   |   |   |   |--- value: [1.18]\n",
      "|--- Al2O3 >  0.18\n",
      "|   |--- T <= 35.00\n",
      "|   |   |--- T <= 22.50\n",
      "|   |   |   |--- value: [1.19]\n",
      "|   |   |--- T >  22.50\n",
      "|   |   |   |--- T <= 27.50\n",
      "|   |   |   |   |--- value: [1.18]\n",
      "|   |   |   |--- T >  27.50\n",
      "|   |   |   |   |--- value: [1.18]\n",
      "|   |--- T >  35.00\n",
      "|   |   |--- T <= 52.50\n",
      "|   |   |   |--- T <= 42.50\n",
      "|   |   |   |   |--- value: [1.17]\n",
      "|   |   |   |--- T >  42.50\n",
      "|   |   |   |   |--- T <= 47.50\n",
      "|   |   |   |   |   |--- value: [1.17]\n",
      "|   |   |   |   |--- T >  47.50\n",
      "|   |   |   |   |   |--- value: [1.16]\n",
      "|   |   |--- T >  52.50\n",
      "|   |   |   |--- T <= 65.00\n",
      "|   |   |   |   |--- T <= 57.50\n",
      "|   |   |   |   |   |--- value: [1.16]\n",
      "|   |   |   |   |--- T >  57.50\n",
      "|   |   |   |   |   |--- value: [1.15]\n",
      "|   |   |   |--- T >  65.00\n",
      "|   |   |   |   |--- value: [1.14]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "\n",
    "model = pickle.load(open(\"data/dtree.model.sav\", \"rb\"))\n",
    "features = (\n",
    "    pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
    "    .drop([\"Density\"], axis=1)\n",
    "    .columns.values.tolist()\n",
    ")\n",
    "\n",
    "rules = tree.export_text(model, feature_names=features)\n",
    "print(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 55.0) and (T > 62.5) -> 1.033,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 55.0) and (T <= 62.5) -> 1.038,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 55.0) and (T > 47.5) -> 1.045,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 55.0) and (T <= 47.5) -> 1.051,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T > 60.0) and (T > 67.5) -> 1.053,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T > 67.5) -> 1.056,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T > 60.0) and (T <= 67.5) -> 1.057,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 22.5) -> 1.06,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T <= 67.5) and (T > 62.5) -> 1.06,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 22.5) -> 1.062,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T <= 67.5) and (T <= 62.5) -> 1.064,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T <= 60.0) and (T > 52.5) -> 1.064,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T <= 60.0) and (T <= 52.5) -> 1.069,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T <= 50.0) and (T > 37.5) -> 1.078,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T <= 50.0) and (T <= 37.5) -> 1.081,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T > 27.5) -> 1.084,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) -> 1.088,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T <= 27.5) and (T > 22.5) -> 1.088,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T <= 27.5) and (T <= 22.5) -> 1.091,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T > 65.0) -> 1.144,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T <= 65.0) and (T > 57.5) -> 1.152,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T <= 65.0) and (T <= 57.5) -> 1.157,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T > 42.5) and (T > 47.5) -> 1.161,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T > 42.5) and (T <= 47.5) -> 1.166,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T <= 42.5) -> 1.17,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T > 60.0) -> 1.178,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) and (T > 27.5) -> 1.179,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) and (T <= 27.5) -> 1.184,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T > 52.5) -> 1.187,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) and (T <= 22.5) -> 1.189,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T <= 52.5) and (T > 47.5) -> 1.193,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T <= 52.5) and (T <= 47.5) -> 1.198,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T <= 30.0) -> 1.219]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import get_rules\n",
    "\n",
    "\n",
    "rules = get_rules(model, features)\n",
    "display(len(rules))\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.045,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.051,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.06,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.064,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.064,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.069,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.078,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.081,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) -> 1.144,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.152,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.157,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.161,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.17,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.179,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.184,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.187,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.198,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import normalise_rules\n",
    "\n",
    "\n",
    "rules = normalise_rules(rules)\n",
    "display(len(rules))\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.048,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.062,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.067,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.079,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084,\n",
       " if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088,\n",
       " if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) -> 1.144,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.155,\n",
       " if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.182,\n",
       " if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,\n",
       " if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import delete_same_rules\n",
    "\n",
    "\n",
    "rules = delete_same_rules(rules)\n",
    "display(len(rules))\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['(Al2O3 <= 0.175)', '(Al2O3 > 0.025)', '(Al2O3 > 0.175)', '(TiO2 <= 0.175)', '(TiO2 > 0.025)', '(TiO2 > 0.175)']\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(Al2O3 &lt;= 0.175)</th>\n",
       "      <th>(Al2O3 &gt; 0.025)</th>\n",
       "      <th>(Al2O3 &gt; 0.175)</th>\n",
       "      <th>(TiO2 &lt;= 0.175)</th>\n",
       "      <th>(TiO2 &gt; 0.025)</th>\n",
       "      <th>(TiO2 &gt; 0.175)</th>\n",
       "      <th>consequent</th>\n",
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       "      <th>rule</th>\n",
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       "    <tr>\n",
       "      <th>if (Al2O3 &lt;= 0.175) and (TiO2 &lt;= 0.175) and (T &gt; 32.5) -&gt; 1.033</th>\n",
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       "      <th>if (Al2O3 &lt;= 0.175) and (TiO2 &lt;= 0.175) and (T &gt; 32.5) and (T &lt;= 62.5) -&gt; 1.038</th>\n",
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       "    <tr>\n",
       "      <th>if (Al2O3 &lt;= 0.175) and (TiO2 &lt;= 0.175) and (T &gt; 32.5) and (T &lt;= 55.0) -&gt; 1.048</th>\n",
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       "      <td>0</td>\n",
       "      <td>1.0478999999999998</td>\n",
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       "    <tr>\n",
       "      <th>if (Al2O3 &lt;= 0.175) and (Al2O3 &gt; 0.025) and (TiO2 &lt;= 0.175) and (T &gt; 32.5) -&gt; 1.053</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.05291</td>\n",
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       "    <tr>\n",
       "      <th>if (Al2O3 &lt;= 0.175) and (TiO2 &lt;= 0.175) and (TiO2 &gt; 0.025) and (T &gt; 32.5) -&gt; 1.056</th>\n",
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      ],
      "text/plain": [
       "                                                   (Al2O3 <= 0.175)  \\\n",
       "rule                                                                  \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...                1   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...                1   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...                1   \n",
       "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti...                1   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti...                1   \n",
       "\n",
       "                                                   (Al2O3 > 0.025)  \\\n",
       "rule                                                                 \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               0   \n",
       "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti...               1   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti...               0   \n",
       "\n",
       "                                                   (Al2O3 > 0.175)  \\\n",
       "rule                                                                 \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               0   \n",
       "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti...               0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti...               0   \n",
       "\n",
       "                                                   (TiO2 <= 0.175)  \\\n",
       "rule                                                                 \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               1   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               1   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...               1   \n",
       "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti...               1   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti...               1   \n",
       "\n",
       "                                                   (TiO2 > 0.025)  \\\n",
       "rule                                                                \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...              0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...              0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...              0   \n",
       "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti...              0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti...              1   \n",
       "\n",
       "                                                   (TiO2 > 0.175)  \\\n",
       "rule                                                                \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...              0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...              0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...              0   \n",
       "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti...              0   \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti...              0   \n",
       "\n",
       "                                                            consequent  \n",
       "rule                                                                    \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...  1.0333299999999999  \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...             1.03826  \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T ...  1.0478999999999998  \n",
       "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (Ti...             1.05291  \n",
       "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (Ti...             1.05601  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import get_features, vectorize_rules\n",
    "\n",
    "features = get_features(rules, [\"T\"])\n",
    "print(features)\n",
    "\n",
    "df_rules = vectorize_rules(rules, features)\n",
    "df_rules.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (6). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (7). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (8). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (9). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (10). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{2: 0.5483575964237912,\n",
       " 3: 0.602943554055511,\n",
       " 4: 0.8221763769597347,\n",
       " 5: 1.0,\n",
       " 6: 1.0,\n",
       " 7: 1.0,\n",
       " 8: 1.0,\n",
       " 9: 1.0,\n",
       " 10: 1.0}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'The best clusters count is 5'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.cluster_helper import draw_best_clusters_plot, get_best_clusters_num\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "X = df_rules.copy()\n",
    "X = X.drop([\"consequent\"], axis=1)\n",
    "\n",
    "clusters_score = get_best_clusters_num(X, random_state)\n",
    "display(clusters_score)\n",
    "\n",
    "draw_best_clusters_plot(clusters_score)\n",
    "\n",
    "clusters_num = sorted(clusters_score.items(), key=lambda x: x[1], reverse=True)[0][0]\n",
    "display(f\"The best clusters count is {clusters_num}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кластер 1 (5):\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.048;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062\n",
      "--------\n",
      "Кластер 2 (5):\n",
      "if (Al2O3 > 0.175) and (T > 35.0) -> 1.144;\n",
      "if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.155;\n",
      "if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166;\n",
      "if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.182;\n",
      "if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189\n",
      "--------\n",
      "Кластер 3 (6):\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.062;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.079;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088;\n",
      "if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091\n",
      "--------\n",
      "Кластер 4 (4):\n",
      "if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178;\n",
      "if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193;\n",
      "if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208;\n",
      "if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219\n",
      "--------\n",
      "Кластер 5 (4):\n",
      "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053;\n",
      "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057;\n",
      "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.067;\n",
      "if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088\n",
      "--------\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cluster\n",
    "\n",
    "from src.cluster_helper import print_cluster_result\n",
    "\n",
    "kmeans = cluster.KMeans(n_clusters=clusters_num, random_state=random_state)\n",
    "kmeans.fit(X)\n",
    "\n",
    "print_cluster_result(X, clusters_num, kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.06250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Density\n",
       "0  20    0.0   0.0  1.06250\n",
       "1  25    0.0   0.0  1.05979\n",
       "2  35    0.0   0.0  1.05404"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.04158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.08438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Density\n",
       "0  30   0.00   0.0  1.05696\n",
       "1  55   0.00   0.0  1.04158\n",
       "2  25   0.05   0.0  1.08438"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "density_train = pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
    "density_test = pd.read_csv(\"data/density_test.csv\", sep=\";\", decimal=\",\")\n",
    "\n",
    "display(density_train.head(3))\n",
    "display(density_test.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 70) -> 1.033,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 47.5) -> 1.038,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 43.75) -> 1.048,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 27.5) -> 1.06,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 20) -> 1.062],\n",
       " [if (Al2O3 = 0.3) and (T = 70) -> 1.144,\n",
       "  if (Al2O3 = 0.3) and (T = 50.0) -> 1.155,\n",
       "  if (Al2O3 = 0.3) and (T = 43.75) -> 1.166,\n",
       "  if (Al2O3 = 0.3) and (T = 28.75) -> 1.182,\n",
       "  if (Al2O3 = 0.3) and (T = 20) -> 1.189],\n",
       " [if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 70) -> 1.056,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 50.0) -> 1.062,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 41.25) -> 1.079,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 30.0) -> 1.084,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 27.5) -> 1.088,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 20) -> 1.091],\n",
       " [if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 70) -> 1.178,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 50.0) -> 1.193,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 35.0) -> 1.208,\n",
       "  if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 20) -> 1.219],\n",
       " [if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 70) -> 1.053,\n",
       "  if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 50.0) -> 1.057,\n",
       "  if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 46.25) -> 1.067,\n",
       "  if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 20) -> 1.088]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import simplify_and_group_rules\n",
    "\n",
    "clustered_rules = simplify_and_group_rules(density_train, rules, clusters_num, kmeans.labels_)\n",
    "clustered_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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5Qee5cHknHPhWdRohclVyejKjI0ZTwbMCH1b7UHWclyIFUB4JLOtFrwYlmbzuNJfvJqqOI0Tu2RoKD65ClwVga686Td4o3QzqfAybx2rLfQhhoeYemcuNhzcICwzD3sa8728pgPLQiLYV8XZzYviSKDKkK0xYoqt7IHIevDoaCptv03iOtBwP+b0hvK+27IcQFubQ7UP8fOpn+tfoT1mPsqrjvDQpgPKQi4MdM7oHcDj6Ad/tvqQ6jhDGlfJI+8ffty406K86Td5zyAdd5sO1/VoRKIQFSUpLInh3MAGFAuhVuZfqOEYhBVAeq+PnyUeNSjFj4znO336oOo4QxrN5LDy8rRUBNraq06hRsgE06Kd1A8aeUZ1GCKOZdWgWdx/fJbRRKLYWcn9LAaTAsFYVKOHpwrAlUaRlyKgRYQEubtMeAG45AQqWUZ1GrVfHgIef9iB4RprqNEK8tMibkfx59k+G1BpCSbeSquMYjRRACjjZ2zKzewAnbyYwf7s8MCnMXHI8rOgPfo2hzkeq06hn7wRd58OtY9pUAEKYsYepDwnZE0Jdn7q8WfFN1XGMSgogRQJ8C/BpszLM2XKekzfjVccRIuc2jNKKoM7zwEb+pABQrBY0HqpNBnnrmOo0QuTY9APTeZj6kAmBE7DRWdb9bVnfjZkZ8Go5ynm7MmxxFCnpMmpEmKGz6+HIL9A6DDwsp2ncKJp8DoUqacuBpKeoTiNEtu24toPlF5bzWe3PKJa/mOo4RicFkEIOdjbM7B7AxTuPmLPlvOo4QmRP0n1YNRDKtoSaljEqxKjsHLSusLvntJYgIcxIXHIc4yLH0ahYI14r95rqOLlCCiDFKhd1Y1DzcszffpEj0Q9UxxEi69Z+BunJ0Okr0OlUpzFNPtWgWZD2LND1g6rTCJFlk/ZPIiUjhfENx6Oz0PtbCiAT0KdpGaoVc2fYkiiS06QrTJiBk+FwYim0mwFuRVSnMW2BQ6BIda0rLO2x6jRC/KONVzay7vI6RtUbRWGXwqrj5BopgEyAna0NM3sEcP3BY6ZvOKs6jhAv9ugOrBkKFTtAte6q05g+WzvoukBbLX7LRNVphHihe4/vEbo3lOYlmtO+VHvVcXKVFEAmomxhVz5vXYHvIy6z79I91XGEeDaDAVYP1v67w2zp+sqqQhWgeQjs/RquRKhOI8QzGQwGJkROAGBM/TEW2/X1H1IAmZD3A0tRu6QHw5dGkZiSrjqOEE87thjOrIYOX0D+QqrTmJf6faFEfW25kJRHqtMI8ZTVl1az9dpWxjQYQ0Hngqrj5DopgEyIrY2OGd0DuPswlcnrTquOI8STEm7Cus+0bq/KnVWnMT82ttDla0i8A5tCVKcR4gm3E28zef9k2pVqR8uSLVXHyRNSAJmYkgXzMapdRX7ZG82u83dUxxFCYzDAygFg5wxtp6lOY748S2vLhRz8Di5uVZ1GCEDr+hobORYnWydG1RulOk6ekQLIBPWsV5JGZb34fOkxEpJlLSFhAg7/DBc2Q6c54OKpOo15q/0hlG6mLR+SLLPAC/X+Ov8XETciGNdwHO6O7qrj5BkpgEyQjY2Oqd38eZSczoRVp1THEdbuwVVtuYsa70D51qrTmD8bG+g0F1IewvqRqtMIK3fj0Q2mHZhG17JdaVK8ieo4eUoKIBNVrIAzYzpWZumh62w+dVt1HGGt9HpY0Q+cPaD1ZNVpLEcBX2gzGY7+CmfXqU4jrJTeoCckIgR3R3c+r/O56jh5TgogE9a9VnGaVyzMiL+O8yAxVXUcYY0OLIIru6DzXHByU53GslTvCeXbwMqB2rIiQuSx38/8zv6Y/UwInEB+h/yq4+Q5KYBMmE6nY/Jr1UjL0BOy8qTqOMLa3LsIm8ZCnY+1Z1aEcel00PFLyEiFtcNVpxFW5mrCVWYfms2bFd6kfpH6quMoIQWQiSvs5sSEzlVYFXWTNcduqY4jrIU+Q1u6wdUHWo5XncZyufpA+5lwYhmcXK46jbASGfoMRu8eTSGXQgypNUR1HGWkADIDnQKK0q6aD8Hhx7nzMEV1HGENIufC9QPQZT445FOdxrJVfV2bV2n1UHgUqzqNsAI/n/qZY3eOERoYiou9i+o4ykgBZAZ0Oh0TO1fFRqdj1PLjGAwG1ZGEJYs9DVtDoUE/KNlAdRrLp9NB+1mgs4FVg7U5l4TIJRceXOCrI1/Rq3IvanrXVB1HKeUF0Lx58/Dz88PJyYl69eqxf//+F+4/e/ZsKlSogLOzM76+vgwZMoTk5OQ8SqtOwfyOTHqtGptO3Wb5kRuq4whLlZGmdX15lIJXx6hOYz3yeWnPA51dA8f+VJ1GWKg0fRqjI0bj6+rLgJoDVMdRTmkB9OeffzJ06FDGjh3L4cOHCQgIoHXr1sTGPrsZ+LfffmPEiBGMHTuW06dP89133/Hnn38yapR1zFzZuooPXWsUY+zKk9yKf6w6jrBEu2ZBzHHoOh/snVSnsS6VOoD/G7D2c4iXDznC+L49/i1n758lrFEYjraOquMop7QAmjVrFh9//DHvv/8+lStXZsGCBbi4uPD9998/c/89e/YQGBjI22+/jZ+fH61ateKtt976x1YjSzKuYxVcHGwJWiZdYcLIbkXBzmnQeCgUq6U6jXVqOxUcXLRlR+T+FkZ0+t5pFkYt5MNqH1LVq6rqOCZBWQGUmprKoUOHaNGixX/D2NjQokULIiMjn/mehg0bcujQocyC59KlS6xdu5Z27do99zwpKSkkJCQ88TJn7i72TH3dn53n7vD7/muq4whLkZ4Cy/tCoUrQxPomRDMZzh7aLNEXt8ChH1WnERYiNSOV0RGjKVOgDH38+6iOYzKUFUB3794lIyMDb2/vJ7Z7e3sTExPzzPe8/fbbTJgwgUaNGmFvb0+ZMmVo1qzZC7vAJk+ejLu7e+bL19fXqN+HCs0qFOatur6ErTnFtftJquMIS7B9Ctw9p3V92TmoTmPdyrWAmu/BxmBtGRIhXtL8qPlcjr9MWKMw7G3tVccxGcofgs6O7du3M2nSJL7++msOHz7MX3/9xZo1a5g4ceJz3zNy5Eji4+MzX9euWUaryej2lSng4sBnS6PQ66WpXLyE6wchYjY0CwKfaqrTCIDWYeDsqS1DoterTiPM2LE7x/j+xPf0DehLBc8KquOYFGUFkJeXF7a2tty+/eQ6V7dv38bHx+eZ7xkzZgzvvvsuH330EdWqVaNr165MmjSJyZMno3/OHwlHR0fc3NyeeFmC/I52TO/uz95L9/kp8orqOMJcpT3WRn0VqQ6B1jshmslxdIUu87RlSPYvVJ1GmKnk9GRG7x5NZc/KfFD1A9VxTI6yAsjBwYFatWqxZcuWzG16vZ4tW7bQoMGz5x5JSkrCxubJyLa2tgBW+UBwwzJe9G7ox9T1Z7h055HqOMIcbZkIcdHQdQHY2qlOI/5XqSZQ91+weRzcvaA6jTBDc47M4eajm4Q1CsPORu7vv1PaBTZ06FAWLVrETz/9xOnTp+nbty+JiYm8//77APTq1YuRI0dm7t+xY0fmz5/PH3/8weXLl9m0aRNjxoyhY8eOmYWQtQlqU5Ei7s4MXxJFhnSFiey4EgF7v4bmIVBImsZNUotx4FYUwvtqy5MIkUUHYw7yy6lfGFhzIKULlFYdxyQpLQnfeOMN7ty5Q0hICDExMVSvXp3169dnPhgdHR39RItPcHAwOp2O4OBgbty4QaFChejYsSNhYWGqvgXlnB1smdHdn+4LIlm06xJ9mpZRHUmYg5RH2j+qJepD/b6q04jncXDRliP5oQ3s+QoaDVadSJiBpLQkgiOCqVG4Bu9Uekd1HJOlM1hZ31FCQgLu7u7Ex8dbzPNAAJPXneaH3VdYNaARFXxcVccRpm71EIj6A/pGgKd8OjR5m0Jg73z4ZAd4V1adRpi4iZETWXVpFcs6LsPXzfxHPucWsxoFJp5vSIvylCzowrAlR0nLkFEj4gUubIGD30PLCVL8mItmo7RrFd5HW65EiOfYc2MPi88tZmitoVL8/AMpgCyEk70ts3pU5/Sth8zbJg9Miud4HKfNMly6GdT+UHUakVX2TtqD6jEnYNdM1WmEiUpITSBkTwj1i9SnR4UequOYPCmALEi14u70e6Usc7de4MSNeNVxhCnaMApSHmqzDdvI7W9WitaAJsNh53S4eVR1GmGCpu2fRmJaIhMaTsBGJ/f3P5GfkIXp/0pZynu7MnTxUVLSZdSI+B9n1sLRX6HNZCggTeNmqfFwKFxJm7spPUV1GmFCtkVvY8XFFXxe53OK5C+iOo5ZkALIwjjY2TDrjQAu301k9ubzquMIU5F0H1YNgvJtoHpP1WlETtk5QNdv4N4F2D5ZdRphIuKS4xgfOZ6mxZvSpWwX1XHMhhRAFqiijxuDW5Tnmx0XORz9QHUcYQrWDIOMVOj4Jeh0qtOIl+FdBV4ZCRFfwrUDqtMIExC2L4w0fRpjG4xFJ/d3lkkBZKH+1aQ0/sULMHxxFI9TpSvMqp34C07+Be1nguuzl5kRZqbhIChaUxsVlioLIluz9VfWs/7KekbXG00hl0Kq45gVKYAslJ2tDTN7BHAj7jHTNpxRHUeo8ihWa/2p3Bmqvq46jTAWWzttVFj8ddgyQXUaocjdx3cJ2xtGy5ItaVuqreo4ZkcKIAtWplB+Pm9TkR8irhB58Z7qOCKvGQzacz86G2g/S7q+LI1XOWg+FvbNh8u7VKcRecxgMDA+cjw2OhuC6wdL11cOSAFk4d5v6EfdUp58tjSKRynpquOIvBT1B5xdqz33k89LdRqRG+r1gZKBsOJTbXoDYTVWXVrF9mvbCWkQgqeTp+o4ZkkKIAtnY6NjRrcA7iemMmntadVxRF6JvwHrgsD/DajUQXUakVtsbKDzPEi8BxvHqE4j8khMYgxT9k2hQ+kONC/RXHUcsyUFkBUoUdCFUe0q8du+aHacu6M6jshtBgOs7K8tpNl2quo0Ird5loJWE+HQD3Bhs+o0IpcZDAbG7hmLs50zI+qOUB3HrEkBZCV61itB43JeBC09RvxjWUvIoh36ES5u1WZ7dvZQnUbkhdofQOlXYMUAbbkTYbGWnl/Knpt7GB84HndHd9VxzJoUQFZCp9Mx9XV/ElPSGb/qpOo4Irc8uAIbRkPN96BcC9VpRF7R6aDzXEh9BOulVcBSXX94nekHpvN6uddpVKyR6jhmTwogK1K0gDNjO1Xhr8M32HgyRnUcYWx6PYT3A5eC0DpMdRqR19yLa12eUb/DmTWq0wgj0xv0jIkYg4ejB5/V+Ux1HIsgBZCVeb1mMVpUKsyo5ce5n5iqOo4wpv3fwNXd0GUeOLqqTiNUCHgLyrfVpj9IlKkvLMlvp3/j4O2DTAycSD77fKrjWAQpgKyMTqdj0mvVSNcbGLPihOo4wljunofN46Duv6BUE9VphCo6nTbtgT4d1g5TnUYYyeX4y8w+PJu3K75N3SJ1VcexGFIAWaHCrk5M7FyVNcdusSrqpuo44mXpMyC8L7gVgxbjVKcRqrl6a8uenFwOJ5apTiNeUoY+g+CIYHzy+TC41mDVcSyKFEBWqmNAUdr7F2HMihPEPkxWHUe8jD1z4MYh6DJfG/ouRNXXoUpXbRmUh7dVpxEv4ceTP3Li7glCA0NxtnNWHceiSAFkxSZ2roqdjY5Rfx3HYDCojiNy4vYp2DYJGg6AEvVUpxGmpN1MsLHTngeS+9ssnX9wnnlH5/FelfeoXri66jgWRwogK+aZz4HJr/mz+XQsyw7fUB1HZFdGGiz/F3iWhmajVKcRpiZfQeg4B86t00aGCbOSpk9j9O7RlHAtQb/q/VTHsUhSAFm5lpW9ea1mMcavPMnNuMeq44js2DkDbp/UVgW3d1KdRpiiiu20kWHrgrSV44XZWHRsEecenCOscRiOto6q41gkKYAEYztWIZ+jHUHLjklXmLm4eQR2zYAmw6FoDdVphClrMwUc8sOK/tIVZiZO3jvJomOL+Nj/Y6oUrKI6jsWSAkjg7mzP1G7+7Dp/l1/3RauOI/5Jegos7wuFK0Pj4arTCFPnXAA6fwWXtsHB71WnEf8gNSOV4N3BlPMoxyfVPlEdx6JJASQAaFq+EG/XK8GktaeJvpekOo54kW2T4N4FrevLzkF1GmEOyraAWu9rK8bfv6w6jXiBeUfncSXhCqGNQrG3tVcdx6JJASQyjWpXCc98DgxfGoVeL03lJunafm3Y+yujwFuaxkU2tJqoPRi9op+2bIowOUdjj/LjyR/pV70f5T3Kq45j8aQAEpnyO9oxo3sA+y/f54c9V1THEX+XmgTL+0DRmtBwoOo0wtw4umpzRV2NgH0LVKcRf/M4/THBEcFULViV3lV6q45jFaQAEk+oX7og7wf6MW39GS7eeaQ6jvhfW8ZDwg2t68vWTnUaYY78GkG9vtrv0t3zqtOI//Hl4S+JSYwhtFEodjZyf+cFKYDEUz5vXZFiBZwZtjiK9AxpKjcJl3dqn9qbjwWvcqrTCHPWPERbOX55H8hIV51GAPtv7efX078yqOYgSrmXUh3HakgBJJ7i7GDLjB4BHLsexzc7L6mOI1Ieas9tlGwE9fqoTiPMnYMLdFkANw/Dni9Vp7F6iWmJhOwJobZ3bXpW6qk6jlWRAkg8U80SHvyraRlmbz7HmZgE1XGs28ZgSLwHneeCjdyywgh860DgINg2WZtMUygz4+AM7iffZ0LgBGx0cn/nJflpi+ca3KIcpb3yM2xxFKnp0hWmxIXNcOhHaB0KntI0Loyo2UitO3V5H0hPVZ3GKkXciGDpuaUMrz0cX1df1XGsjhRA4rkc7WyZ2SOAszEPmbvtguo41udxHKwYAGVe1eZwEcKY7By1UWGxp7RZxUWeSkhNIGRPCA2LNqR7+e6q41glKYDEC1Ut5k7/V8syb9sFjl+PVx3HuqwfAamJ0Okr0OlUpxGWqGh1aPKZtq7czSOq01iVqfun8jjtMeMbjkcn97cSUgCJf9TvlbJUKuLK0MVHSU7LUB3HOpxZo63g3XaKNmJHiNzSeBj4VNW6wtKSVaexClujt7Ly4kqC6gbhk89HdRyrJQWQ+Ef2tjbM6lGdq/eS+GLzOdVxLF/iPVg1CCr8/0reQuQmW3vo+g3cvwTbJ6lOY/EeJD9gfOR4mvk2o1OZTqrjWDUpgESWlPd2ZWir8izceYlDV++rjmPZ1gwFfTp0mC1dXyJvFK4Er4yGiDkQvU91GosWujeUDEMGYxuMla4vxaQAEln2cePS1PAtwLDFUSSlygRqueLEMjgVDu1ngau36jTCmjQcAMXrQHgf7dkzYXTrL69n49WNBNcPxsvZS3UcqycFkMgyWxsdM7oHEJOQzLT1Z1XHsTwPb8OaYVClK1R9TXUaYW1sbLVRYQm3YPN41Wkszt3HdwndF0prv9a08WujOo5ACiCRTaUL5SeoTUV+3HOFPRfvqo5jOQwGWDUQbOyh3UzVaYS18ioLLcbB/m+05VeEURgMBsbtGYedzo7R9UarjiP+nxRAItvea+BH/dKefLbkGA+T01THsQxHf4Nz66Hjl5CvoOo0wprV/QT8GkN4P0iWWeCNYcXFFey4voOxDcbi4eShOo74f1IAiWyzsdExvVsAcUmpTFp7WnUc8xd/XZvzJ+BtqNhOdRph7WxstGVXHt/XlmERLyUmMYap+6fSqUwnXinxiuo44n9IASRyxNfThdHtK/P7/mtsOxurOo75MhhgRX9wyA9tJqtOI4TGww9ahcLhn+D8JtVpzJbBYCAkIgQXexeC6gapjiP+RgogkWNv1fWlSflCjFh2jPgk6QrLkYPfw6Vt2idu5wKq0wjxX7V6Q5nmsHIAPH6gOo1ZWnJuCZG3IpnYcCJuDm6q44i/kQJI5JhOp2Pq69VISs1g3CpZUTrb7l+GjWO0db7KNledRogn6XTaMiypSbBOWi+y69rDa8w4OIPu5bvTsFhD1XHEM0gBJF5KEXdnxneqwvIjN1h/IkZ1HPOh10P4p5DPC1pNVJ1GiGdzLwbtpsGxP+H0KtVpzIbeoCd4dzCeTp4Mqz1MdRzxHFIAiZfWtUYxWlb2ZvTy49x7lKI6jnnYNx+i90CXr8HRVXUaIZ7P/w2o0B5WDYZEmfoiK3459QuHYw8zMXAi+ezzqY4jnkMKIPHSdDodk7pWQ28wEBx+AoPBoDqSabtzDrZMgPqfgl8j1WmEeDGdDjrOBoMeVg/RHtwXz3Up/hJzjszhnUrvUMenjuo44gWkABJGUcjVkdAu1Vh3IoaVUTdVxzFdGenaUgPuxaF5iOo0QmRN/sLQYRacXqkt1yKeKV2fTvDuYIrkK8KgmoNUxxH/QAogYTTt/YvQMaAoIStOEpuQrDqOadrzJdw8Al0WgL2z6jRCZF2VrlD1dW25lofyvN+z/HjyR07eO0loo1Cc7JxUxxH/QAogYVQTOlXBwc6GEX8dl66wv4s5AdsmQ+Ag8JWmcWGG2s0AO0dYOVC6wv7m7P2zzDs6j/ervE9AoQDVcUQWSAEkjMojnwOTu1Zj65lYlhy6rjqO6UhP1bq+vMpBs5Gq0wiRMy6e2nIt5zfA0V9VpzEZaRlpBEcE4+fmx6fVP1UdR2SRFEDC6FpU9qZbreJMWHWKG3GPVccxDTunQ+xp6LpA+wQthLmq0Baq94R1IyDumuo0JuGbY99w4cEFJjWahIOtg+o4IoukABK5IqRjZdyc7Ahaegy93sqbym8chl0zocnnUESaxoUFaDMZnNxhZX9tTisrdvLuSb49/i2fBHxCpYKVVMcR2SAFkMgVbk72TO3mz+4Ld/l131XVcdRJS4blfcCnGjQeqjqNEMbh5A6dv4JL2+Hgd6rTKJOSkcKo3aOo4FmBj6p9pDqOyCYpgESuaVyuEO/UL8GktWe4ei9RdRw1toXBg8ta15etveo0QhhPmVeh9oewKQTuX1KdRol5R+Zx7eE1wgLDsLeR+9vcSAEkctXItpUo5OrI8CVRZFhbV1j0XtjzFbwyGgpL07iwQC0naHMEhX8K+gzVafLUkdgj/HjyR/rX6E9Zj7Kq44gckAJI5Kp8jnZM7+bPwasP+CHisuo4eSc1EcL7QvE60HCA6jRC5A7H/ND5a63Y3ztfdZo8k5SWRPDuYPwL+fNe5fdUxxE5JAWQyHX1Shfkg8BSTNtwlguxD1XHyRubx0HCLa3ry8ZWdRohco9foLasy5YJcOes6jR5Yvbh2cQmxRLWKAxbub/NlhRAIk981roCxT2cGbY4ivQMCx81cmkH7F8ILcdDwTKq0wiR+5qPgQIltAf+M9JVp8lV+27t4/czvzO41mBKupVUHUe8BCmARJ5wsrdlZvcAjt+IZ8GOi6rj5J7kBFjRD/waQ52PVacRIm/YO2utnbeOQsQXqtPkmkepjxgTMYa6PnV5q+JbquOIlyQFkMgzNUp40LdZGb7ccp5TNxNUx8kdG0fD4wfQeR7YyO0lrEjx2tBoCGyfCjHHVafJFTMOziA+JZ4JgROw0cn9be6UX8F58+bh5+eHk5MT9erVY//+/S/cPy4ujn79+lGkSBEcHR0pX748a9euzaO04mUNbF6OMoXyM3TxUVLTLawr7NxGOPwztA4DD2kaF1aoaRB4lde6wtJTVacxqp3Xd7Ls/DI+q/MZxfIXUx1HGEGOC6AtW7bQoUMHypQpQ5kyZejQoQObN2/O1jH+/PNPhg4dytixYzl8+DABAQG0bt2a2NjYZ+6fmppKy5YtuXLlCkuXLuXs2bMsWrSIYsXkl9FcONrZMrNHABdiH/HV1vOq4xhP0n1YOQDKtoCaMipEWCk7R60r7M4Z2DlNdRqjiU+JZ9yecQQWC+T1cq+rjiOMJEcF0Ndff02bNm1wdXVl0KBBDBo0CDc3N9q1a8e8efOyfJxZs2bx8ccf8/7771O5cmUWLFiAi4sL33///TP3//7777l//z7h4eEEBgbi5+dH06ZNCQiQ5QXMSZWi7gxsXo6vt18k6lqc6jjGsS4I0h9Dp69Ap1OdRgh1ivhrLUG7ZsGNQ6rTGMXk/ZNJzkhmfIPx6OT+thg6g8GQ7dnpihcvzogRI+jfv/8T2+fNm8ekSZO4cePGPx4jNTUVFxcXli5dSpcuXTK3v/fee8TFxbFixYqn3tOuXTs8PT1xcXFhxYoVFCpUiLfffpugoCBsbZ89FDElJYWUlJTM/09ISMDX15f4+Hjc3Nyy+B0LY0vL0PP6/D0kpqSzZmBjnOzNeCjp6VXw5zvQdSEEvKE6jRDqZaTBdy21+bD+tQvsnVQnyrEtV7cwePtgJjWaRMcyHVXHEUZkl5M3xcXF0aZNm6e2t2rViqCgoCwd4+7du2RkZODt7f3Edm9vb86cOfPM91y6dImtW7fSs2dP1q5dy4ULF/j0009JS0tj7Nixz3zP5MmTGT9+fJYyibxjb2vDzO4BtP9qN7M2nWNUOzOdKTnxLqwaDBU7gH8P1WlyxGAwkJ5u2UOXzZmtrS025vZAva09dFkA3zSBbaHQKlR1ohy5n3yfCXsn8Krvq3Qo3UF1HGFkOSqAOnXqxPLly/nss8+e2L5ixQo6dMi9XxK9Xk/hwoVZuHAhtra21KpVixs3bjB9+vTnFkAjR45k6ND/LkL5nxYgoV45b1eGtyrP5HVnaFnZmzp+nqojZY/BAKuHAAbo8IVZdn2lp6dz584dctAQLPKQi4sL7u7u5tX9UrgivBqsrRVWoT2UbKA6UbYYDAZC94ZiMBgY02CMef3sRZbkqACqXLkyYWFhbN++nQYNtF/qvXv3EhERwbBhw5gzZ07mvgMHDnzmMby8vLC1teX27dtPbL99+zY+Pj7PfE+RIkWwt7d/orurUqVKxMTEkJqaioODw1PvcXR0xNHRMdvfo8gbHzYqzcaTtxm+JIp1gxrj4pCjX0k1TiyD0yuh+0/aekhmxmAwEBcXh42NDR4eHvIH3gQZDAZSU1NJSNCmjShQoIDaQNnVoB+cWaMtC9M3AhzyqU6UZesur2PT1U3MbDoTL2cv1XFELsjRM0ClSpXK2sF1Oi5dev4qwfXq1aNu3bp89dVXgNbCU6JECfr378+IESOe2n/UqFH89ttvXLp0KbNJ+Msvv2Tq1KncvHkzS5kSEhJwd3eXZ4BMyJW7ibT9chfdaxdnQueqquNkTcIt+Lq+Nuqr23eq0+RIRkYGt2/fxsPDA2dnZ9VxxAs8evSIhIQEfHx8zK877N5FWNAIqveE9jNUp8mS2KRYuq7oSmCxQKY1sZzRbOJJOfq4ffmycRa1HDp0KO+99x61a9embt26zJ49m8TERN5//30AevXqRbFixZg8eTIAffv2Ze7cuQwaNIgBAwZw/vx5Jk2a9NxWJmEe/LzyMaJtRcauPEnrKj4EljXxT1sGA6wapA35bTdddZoc0+u1eZieN4BAmI7/tG5nZGSYXwFUsAy0GA/rPoNKHaB0M9WJXshgMDA+cjwOtg6MrjdadRyRi5T2N7zxxhvcuXOHkJAQYmJiqF69OuvXr898MDo6OvqJm93X15cNGzYwZMgQ/P39KVasGIMGDcryg9fCdL1bvyQbTsbw+dJjrB/cGFcne9WRnu/IL3B+A7z1J7iY2XNLzyBdX6bP7K9RnY/gzCpY0R/67gEn0219D78Qzs7rO5n76lzcHd1VxxG5KMtdYEOHDmXixInky5fviYeKn2XWrFlGCZcbpAvMdF1/kESb2btoX60IU7v5q47zbHHR8HVDqNwZumR9zitTlJaWxp07dyhUqBD29iZccArLuFb/uXeqdIHOc1Wneaabj27y2srXaFmyJRMDJ6qOI3JZlluAjhw5QlpaWuZ/P4/Zf1IRyhT3cGFMh0oELTtO66revFrR+5/flJf0eu0TrJM7tJmkOo0Q5qVACe2+WTkAKnWE8q1VJ3qC3qAnZE8Irg6ufF7nc9VxRB7IcgG0bdu2Z/63EMbUo7Yv60/EMGLZcTYO8aCAy9Mj+5Q5+B1c3gHvhmtFkFCiWbNmVK9endmzZ6uOIrKrxrvaxKErB8KnkSbVhbz47GL23drHwpYLcXVwVR1H5AEze5pOWDqdTseU1/1JSdczduVJ1XH+695FbT6TOh9BmVdUpxHCPOl00HEOpCfDOtNpZYlOiGbWoVm8UeENGhQ1r/mKRM7lqABKTExkzJgxNGzYkLJly1K6dOknXkK8DG83J8Z3qsKKozdZd/yW6jigz4AV/bS5flrIrOJCvBS3ItroyeNL4NTTSx7ltQx9BmMixlDQqSBDa734+VZhWXI0Cuyjjz5ix44dvPvuuxQpUkSe+xFG17l6UdafiGF0+AnqlPLEK7/CySz3fg3Re+H9teCYX10O8ZQHDx4waNAgVq1aRUpKCk2bNmXOnDmUK1cOg8FA4cKFmT9/Pt26dQOgevXq3L59m1u3tMJ69+7dNG/enAcPHuDi4qLyW7Eu1bprk4iuHgIlGkL+Qsqi/HL6F47EHuGHNj/gYi+/A9YkRwXQunXrWLNmDYGBgcbOIwSgdYWFdq1Kqy92Erz8BPPfqamm0L5zFrZM1Ga0Ldkw78+fxx6nZnDxzqM8P2+ZQvlxdsj+fES9e/fm/PnzrFy5Ejc3N4KCgmjXrh2nTp3C3t6eJk2asH37drp168aDBw84ffo0zs7OnDlzhooVK7Jjxw7q1KkjxU9e0+mg/RfwdT1YMwR6/FvJUjKX4i4x5/Ac3q38LrW8a+X5+YVaOSqAPDw88PQ0nYfXhGXyyu/IpK5V6fPLYVYcvUmXGsXyNkBGOizvAx4ltTWNrMDFO4/o8NXuPD/v6gGNqFosew+W/6fwiYiIoGFDrTj99ddf8fX1JTw8nO7du9OsWTO++eYbAHbu3EmNGjXw8fFh+/btVKxYke3bt9O0aVOjfz8iC/IXgg6zYfG7WndYHi8mnK5PZ/Tu0RRzLcaAGgPy9NzCNOSoAJo4cSIhISH89NNP8slJ5Ko2VYvQuXpRQlacoEGZgni7OeXdySO+gFtH4cPNYG8dS0WUKZSf1QMaKTlvdp0+fRo7Ozvq1auXua1gwYJUqFCB06dPA9C0aVMGDRrEnTt32LFjB82aNcssgD788EP27NnD55+bzsO4VqdyJ607bO1w8GusPR+UR74/8T2n7p/il7a/4GSXh39XhMnIcgFUo0aNJ7ogLly4gLe3N35+fk9NzHX48GHjJRRWb3ynKkRevEfQsmP80LtO3nSFxRyH7VOh0VAobj1N484OttluiTFl1apVw9PTkx07drBjxw7CwsLw8fFh6tSpHDhwgLS0tMzWI6FI22lweZc2P1DPJXnSFXb2/lnmR83nw6ofUq1QtVw/nzBNWS6AunTpkosxhHi+Ai4OTH3dn/d/PMDig9d4o06J3D1heqrW9VWoAjSVZVZMVaVKlUhPT2ffvn2ZRcy9e/c4e/YslStXBrRnyRo3bsyKFSs4efIkjRo1wsXFhZSUFL755htq165Nvnzms0K5RXLxhE5fwW/d4ci/oWavXD1dWkYao3aPorR7afoG9M3VcwnTluUCaOzYsbmZQ4gXeqViYd6o7cvE1acJLOtFcY9c7HrdMVV7+PmTbWBnQhMxiieUK1eOzp078/HHH/PNN9/g6urKiBEjKFasGJ07d87cr1mzZgwbNozatWuTP7/W1dakSRN+/fVXPvvsM1Xxxf8q30qbJHH9KG2x1AK59yFnftR8LsVf4o/2f2Bva6bLigijyNE8QNeuXeP69euZ/79//34GDx7MwoULjRZMiL8L7lAJd2d7Pl96DL0+S0vYZd/1Q7D7C63lx0eaxk3dDz/8QK1atejQoQMNGjTAYDCwdu3aJ7rlmzZtSkZGBs2aNcvc1qxZs6e2CcVaTwLnAtqcW3p9rpzi+J3jfH/ie/r496GCZ4VcOYcwH1leDPV/NW7cmE8++YR3332XmJgYypcvT9WqVTl//jwDBgwgJCQkN7IahSyGat4iLtyl57f7GN+pCu819DPuwdMewzdNwCE/fLgJbHM0RsBsWMQCm1bCaq7Vpe3wc2doOx3qfWLUQyenJ9NjdQ/y2eXj3+3+jZ2NZd/f4p/lqAXoxIkT1K1bF4DFixdTrVo19uzZw6+//sqPP/5ozHxCPCGwrBe9GpRkyrozXL6baNyDbw2FB1eh6wKLL36EMEmlm0Gdj2HzWG35GSOae2QuNx7eIKxRmBQ/AshhAZSWloajozYz7+bNm+nUqRMAFStWzJxhVYjcMqJtRQq7OTJ8SRQZxuoKu7oHIudB8zHaw89CCDVajof83hDeV1uGxggO3T7Ez6d+ZmDNgZQuIMs1CU2OCqAqVaqwYMECdu3axaZNm2jTpg0AN2/epGDBgkYNKMTfuTjYMbN7AIejH/Dd7ksvf8CUR9ofW996UP/Tlz+eECLnHPJprbDX9msfSl5SUloSwbuDqV64Ou9UescIAYWlyFEBNHXqVL755huaNWvGW2+9RUBAAAArV67M7BoTIjfV9vPk48almbHxHOdvP3y5g20eC49iocvXYJP95RiEEEZWoj407K91S8eeealDzTo0i3vJ9wgNDMVW7m/xP7LdEWowGChdujTR0dGkp6fj4eGR+bVPPvlEZoYWeWZoy/JsPRPLsCVRLOvbEHvbHNTzF7fBgW+h3QwoWMb4IYUQOfNKMJzbCOF9/n9QQvYf/o68GcmfZ/9kVL1RlHDL5fnDhNnJ9r8YBoOBsmXLEhMT80TxA+Dn50fhwoWNFk6IF3Gyt2Vm9wBO3kxg/vYcPDCZHA8r+kOpplD7Q+MHFELknL0TdJ0Pt45pU1Nk08PUh4TsCaFekXq8UeGNXAgozF22CyAbGxvKlSvHvXv3ciOPENkS4FuAT5uVYc6W85y8GZ+9N28YpRVBneeBTY56g4UQualYLWg8VJuc9NaxbL11+oHpPEx9yMSGE7HRyf0tnpaj34opU6bw2WefceLECWPnESLbBrxajnLergxbHEVKehZHjZxdD0d+gTaToYBv7gYUQuRck8+hUCVteZr0lCy9Zce1HSy/sJygOkEUyZ93C6wK85KjAqhXr17s37+fgIAAnJ2d8fT0fOIlRF5ysLNhVo8ALt55xJwt5//5DUn3YdVAKNcKasioECFMmp2DNirs7jmtJegfxCXHMS5yHI2LNaZL2S65n0+YrRzNBjV79mwjxxDi5VQq4sbgFuWZufEsLSp5U6OEx/N3XvuZ9kmy45w8WXlaCPGSfKpCsxGwLQwqtIPitZ+766T9k0jNSGVcw3Ho5P4WL5CjAui9994zdg4hXtq/mpRm46nbDFsSxdqBjXGyf8aQ15PhcGIpvPYtuEnTuBBmI3AwnF2rdYX12QX2zk/tsvHKRtZdXseUxlMo7CIDcsSL5fjJsIsXLxIcHMxbb71FbGwsAOvWrePkyZNGCydEdtjZ2jCzewA3Hjxm+oazT+/w6A6sGQqVOkG1bnkfUJitjIwM9Lm0QKfIIls76LIA4q/BlolPffne43uE7g2lZcmWtCvVTkFAYW5yVADt2LGDatWqsW/fPv766y8ePXoEQFRUFGPHjjVqQCGyo2zh/HzWugLfR1xm36X/GaloMMDqwYAOOnwhXV9mbv369TRq1IgCBQpQsGBBOnTowMWL2lQIDRs2JCgo6In979y5g729PTt37gQgJSWF4cOHU6xYMfLly0e9evXYvn175v4//vgjBQoUYOXKlVSuXBlHR0eio6M5cOAALVu2xMvLC3d3d5o2bcrhw4efONeZM2do1KgRTk5OVK5cmc2bN6PT6QgPD8/c59q1a/To0YMCBQrg6elJ586duXLlSq78rCxKofLw6hjY+zVcicjcbDAYmBA5AZ1OR3D9YOn6ElmSowJoxIgRhIaGsmnTJhwcHDK3v/rqq+zdu9do4YTIifcDS1GnpCfDl0aRmJKubTy2GM6s1oqffF5qA5qy1CS4eTTvX6lJ2YqZmJjI0KFDOXjwIFu2bMHGxoauXbui1+vp2bMnf/zxBwbDf9eJ+/PPPylatCiNGzcGoH///kRGRvLHH39w7NgxunfvTps2bTh//r8P0SclJTF16lS+/fZbTp48SeHChXn48CHvvfceu3fvZu/evZQrV4527drx8KE2G3lGRgZdunTBxcWFffv2sXDhQkaPHv1E9rS0NFq3bo2rqyu7du0iIiKC/Pnz06ZNG1JTU7P1c7BK9ftCiQba8jUp2ofv1ZdWs/XaVsbUH4OnkwzEEVmjM/zvX4ksyp8/P8ePH6dUqVK4uroSFRVF6dKluXLlChUrViQ5OTk3shpFQkIC7u7uxMfH4+bmpjqOyCVX7yXSZvYuXq9VjNBXC8LX9aFca3h9kepoJiMtLY07d+5QqFAh7O3/f5bdm0dhYdO8D/PJDihaPcdvv3v3LoUKFeL48eN4e3tTtGhRtm7dmlnwNGzYkCZNmjBlyhSio6MzZ7MvWrRo5jFatGhB3bp1mTRpEj/++CPvv/8+R48ezVzq51n0ej0FChTgt99+o0OHDqxfv56OHTty7do1fHx8AG3B6JYtW7J8+XK6dOnCL7/8QmhoKKdPn85sqUhNTaVAgQKEh4fTqlWrp87zzGtlze5fgvmBEPAWt18JouvKrjQp3oQpjaeoTibMSI4egi5QoAC3bt2iVKlST2w/cuQIxYoVM0owIV5GyYL5GNW+EmPCjzP09ig87V2g3TTVsUyfV3mtGFFx3mw4f/48ISEh7Nu3j7t372Y+nxMdHU3VqlVp1aoVv/76K40bN+by5ctERkbyzTffAHD8+HEyMjIoX/7Jc6akpDyxmLODgwP+/v5P7HP79m2Cg4PZvn07sbGxZGRkkJSURHR0NABnz57F19c3s/gBnlofMSoqigsXLuDq6vrE9uTk5MxuPPEPPEtDq4kY1gxjrP46zrbOjKw7UnUqYWZyVAC9+eabBAUFsWTJEnQ6HXq9noiICIYPH06vXr2MnVGIHHmnXglS9v2A562dJHX/AxfnFwyNFxoHl5dqickrHTt2pGTJkixatIiiRYui1+upWrVqZhdSz549GThwIF999RW//fYb1apVo1q1agA8evQIW1tbDh06hK3tkyMF8+fPn/nfzs7OTz1L8t5773Hv3j2+/PJLSpYsiaOjIw0aNMhW19WjR4+oVasWv/7661NfK1SoUJaPY/Vqf8hfp34l4sFpvm4yHXdHd9WJhJnJUQE0adIk+vXrh6+vLxkZGVSuXJmMjAzefvttgoODjZ1RiBzRxUXzwaOFLDW8yt5TRZlRRXUiYQz37t3j7NmzLFq0KLOLa/fu3U/s07lzZz755BPWr1/Pb7/99sQHsxo1apCRkUFsbGzm+7MqIiKCr7/+mnbttFFG165d4+7du5lfr1ChAteuXeP27dt4e3sDcODAgSeOUbNmTf78808KFy4s3fAv4UbiTabZJvJ6QgqNo1ZCqTaqIwkzk6OHoB0cHFi0aBEXL15k9erV/PLLL5w5c4Z///vfT32iEkIJvR5W9MPGxRObNpNYeug6m0/dVp1KGIGHhwcFCxZk4cKFXLhwga1btzJ06NAn9smXLx9dunRhzJgxnD59mrfeeivza+XLl6dnz5706tWLv/76i8uXL7N//34mT57MmjVrXnjucuXK8e9//5vTp0+zb98+evbsibPzf+ejadmyJWXKlOG9997j2LFjREREZH4o/E9rUs+ePfHy8qJz587s2rWLy5cvs337dgYOHMj169eN9WOyaHqDnpCIENydCjC8bhAc/RXOrlMdS5iZl1ohrkSJErRt25bu3btTrlw5Y2US4uUdWARXdkHneXStX5HmFQsz4q/jPEiUUTbmzsbGhj/++INDhw5RtWpVhgwZwvTp05/ar2fPnkRFRdG4cWNKlCjxxNd++OEHevXqxbBhw6hQoQJdunThwIEDT+33d9999x0PHjygZs2avPvuuwwcOJDChf874Z6trS3h4eE8evSIOnXq8NFHH2WOAnNycgLAxcWFnTt3UqJECV577TUqVarEhx9+SHJysrQIZdHvZ35nf8x+JgZOJH+tD6B8G1g5UFvmRogsytEoMND+EHzxxReZw0bLlSvH4MGD+eijj4wa0NhkFJgVuHdRGyFS811op/3DGJuQTMsvdtKkfCG+equG4oCmQUYW5Y2IiAgaNWrEhQsXKFOmTI6OIdfqv64mXKXbym50LdeVUfVGaRsfxsC8elC2OXT7Xm1AYTZy9AxQSEgIs2bNYsCAATRo0ACAyMhIhgwZQnR0NBMmTDBqSCGyTJ+hTZXvVgRajMvcXNjNiYldqjLw9yO0qeJDe39ZBkPkjuXLl5M/f37KlSvHhQsXGDRoEIGBgTkufsR/ZegzGL17NIVdCjO45uD/fsHVB9rPhGUfQqWOUKWrsozCfOSoAJo/fz6LFi16ol+9U6dO+Pv7M2DAACmAhDqRc+H6AfhgPTjke+JLHf2LsP7ELYLDj1O3lCeFXB0VhRSW7OHDhwQFBREdHY2XlxctWrRg5syZqmNZhJ9P/cyxO8f4qe1PuNi7PPnFqq/D6ZWweiiUDIT8shaYeLEcPQOUlpZG7dpPr8Zbq1Yt0tPTXzqUEDkSexq2hkLDAVCi/lNf1ul0TOxcFVsbHaOWHyeHvb9CvFCvXr04d+4cycnJXL9+nR9//PGJ+YVEzlx4cIGvjnxF7yq9qVH4Gd3YOh20nwU2trBqsLb8jRAvkKMC6N1332X+/PlPbV+4cCE9e/Z86VBCZFtGmtb15VkaXhn93N0K5nckrGs1Np26zfIjN/IwoBAip9L0aYyOGE0J1xL0q9Hv+Tvm84IOs+HsGjj2Z57lE+Ypy11g/zvMVKfT8e2337Jx40bq19c+ae/bt4/o6GiZCFGosWsWxByHjzaDvdMLd21dxYfXahRj7MqTNChTkCLuzi/cXwih1rfHv+Xs/bP82u5XHG3/oeu6UgfwfxPWfg5+jcFdVicQz5blAujIkSNP/H+tWrUAMqdu9/LywsvLi5MnTxoxnhBZcCsKdk6DxsOgWM0svWVsxypEXLxL0LLj/PR+HVk9WggTdfreaRZGLeSjah9RxSuLs5m2nQKXd8DKAfDOMq17TIi/yfEweHMlw+AtTHoKLGym9ft/tBXsHLL81u1nY+n9wwEmda3G2/VePP+LJZKh1ebDWq9VakYqb6x+AzsbO35r9xv2ttn43s9vhl9f17rEar+faxmF+XqpiRCFUG77FLh7Hrp+k63iB6BZhcK8VdeXsDWnuHY/KZcCCiFyan7UfK4kXCGsUVj2ih+Aci2g5nuwMRgeXMmVfMK85WgYfHJyMl999RXbtm0jNjY2cyXm/zh8+LBRwgnxQtcOQMRseDUYvHO20Nfo9pXZdf4uw5dE8fvH9bGxkaZyIUxB1J0ovj/xPQNqDKC8R/mcHaR1GFzaBuH94L1VYCOf+cV/5ei34cMPP2TatGmULFmSDh060Llz5ydeQuS61CQI7wNFa0DDQTk+TH5HO6Z182ff5fv8FHnFePlErmnWrBmDBw9+7td1Oh3h4eFZPt727dvR6XTExcW9dDZhHI/THxO8O5gqBavQu0rvnB/I0RU6z4Oru2H/QqPlE5YhRy1Aq1evZu3atQQGBho7jxBZs3UixF+HN38H2xz9GmdqWMaL3g39mLr+DE3LF6J0ofxGCilUuHXrFh4eHqpjiJcw5/AcbiXe4stXv8TO5uXub0o1gXp9YPM4KNsCvMoaJaMwfzlqASpWrBiurq7GziJE1lzZDXvnQ/MQKJTDpvG/CWpTkSLuzgxbEkWG3qrGBVgcHx8fHB1llm9zdSDmAL+e/pWBNQZS2r20cQ7afCy4FdVajfUZxjmmMHs5KoBmzpxJUFAQV69eNXYeIV4s5RGEfwolG0K9vkY7rLODLTO6+xN1LY6FOy8Z7bgid+j1ej7//HM8PT3x8fFh3LhxmV/7exfYnj17qF69Ok5OTtSuXZvw8HB0Oh1Hjx594piHDh2idu3auLi40LBhQ86ePZs334zIlJSWxJiIMdT0rsk7ld8x3oEdXKDLfLhxCPbMMd5xhVnLUdti7dq1SU5OpnTp0ri4uDw1LPP+/ftGCSfEUzaNgcS70GuF0R9orFXSk4+blOaLTed4tWJhKvhYXyvn4/THXI6/nOfnLeVeCme7rE9I+dNPPzF06FD27dtHZGQkvXv3JjAwkJYtWz6xX0JCAh07dqRdu3b89ttvXL169bnPD40ePZqZM2dSqFAh+vTpwwcffEBERMTLfFsim2YenMn95PssarUIG52RH1guUU9bJmfbJCjXGrwrG/f4wuzkqAB66623uHHjBpMmTcLb21smkRN548IWOPi9tt6PZ6lcOcWQFuXZdiaWoYuPEt4vEHtb6xo1cjn+Mm+sfiPPz/tnhz+pXDDr/yD5+/szduxYAMqVK8fcuXPZsmXLUwXQb7/9hk6nY9GiRTg5OVG5cmVu3LjBxx9//NQxw8LCaNq0KQAjRoygffv2JCcn4+T04pnFhXHsubGHxecWM6b+GHxdfXPnJM1GwbmNsPxf8PFWyO7QemFRclQA7dmzh8jISAICAoydR4hnexynzepa+hWo/UGuncbJ3paZ3avT5esI5m27wOAWxnnGyFyUci/Fnx3yfg2lUu7ZK2j9/f2f+P8iRYoQGxv71H5nz57F39//iSKmbt26/3jMIkWKABAbG0uJEtY3SWZeS0hNIGRPCA2KNKB7+e65dyJ7J+g6HxY1h10zodmI3DuXMHk5KoAqVqzI48ePjZ1FiOdbPxJSHkLnubk+rX214u70f6Usc7deoEUlb6oWc8/V85kSZzvnbLXEqPL3bnedTvfUfGQvc8z/tGq/7DFF1kzdP5XEtEQmBE7I/R6FojWgyWewczqUbwNFq+fu+YTJylH7/pQpUxg2bBjbt2/n3r17JCQkPPESwqjOrIWo36DNFHAvnien7P9qWSr4uDJ08VFS0mXUiLmqUKECx48fJyUlJXPbgQMHFCYSf7ctehsrL64kqG4QPvl88uakTYZD4cqwvI+2nI6wSjkqgNq0aUNkZCTNmzencOHCeHh44OHhQYECBWT+DWFcSfdh1SAo3xaqv51np7W3tWFmjwCu3E3ii03n8+y8wrjefvtt9Ho9n3zyCadPn2bDhg3MmDEDQJ5dNAFxyXGMjxxPs+LN6FwmDyfRtbWHrgvg/kXtoWhhlXLUBbZt2zZj5xDi2dYMA30adPwyz1d0rujjxuCW5Zix4SwtK3tTq6QU9+bGzc2NVatW0bdvX6pXr061atUICQnh7bffloebTUDYvjDSDemMbTg27wtS7yrQbKQ2qWrF9uD77GfDhOWS1eCF6TrxFyx9H7p9D1VfVxIhPUNP928iiUtKY+3Axjg72CrJkRusdYXxX3/9lffff5/4+HicnbM+9F4lS7xW66+s57MdnzG9yXTalGqjJkRGOvzQRmtp7rNbmy9IWI0cj/HdtWsX77zzDg0bNuTGjRsA/Pvf/2b37t1GCyes2MPbWutP5S7Kih8AO1sbZnQP4GbcY6ZtOKMsh8i5n3/+md27d3P58mXCw8MJCgqiR48eZlP8WKK7j+8StjeMViVbqSt+QFtGp8t8SLgBWyaoyyGUyFEBtGzZMlq3bo2zszOHDx/OfMAwPj6eSZOkP1W8JIMBVg8GG1ttzh/FyhTKT1CbivwQcYXIi/dUxxHZFBMTwzvvvEOlSpUYMmQI3bt3Z+FCWRhTFYPBwPjI8djobAiuH6w6DniVgxbjYN98uLxLdRqRh3JUAIWGhrJgwQIWLVr0RHNsYGAghw8fNlo4YaWi/oCza7XnfvIVVJ0GgN4N/ahXypPPlkbxKCVddRyRDZ9//jlXrlwhOTmZy5cv88UXX+DiIl0dqqy6tIrt17YztsFYPJxM5Lm6uv+Cko1gxafadBvCKuSoADp79ixNmjR5aru7uztxcXEvm0lYs/gbsC4IAt7SHkw0ETY2OmZ0D+B+Yipha06rjiOEWYpJjGHKvil0KtOJV0u8qjrOf9nYQJd5kHgPNppAq5TIEzkqgHx8fLhw4cJT23fv3k3p0kZavVdYH4MBVvYHh3zanD8mxtfThdHtK/H7/mh2nLujOo7RWNk4CLNkCdfIYDAwds9YnO2dCaobpDrO0zz8oHUoHPoRLmxWnUbkgRwNg//4448ZNGgQ33//PTqdjps3bxIZGcnw4cMZM2aMsTMKa3HoB7i4Fd5ZBs4FVKd5prfrlmD9iRiClh5jw5AmuDub74gcW1tbdDodDx8+xNXVVebFMUEGg4GMjAwSEhLQ6XTY2eXoT7ZJWHJuCXtu7mFBiwW4OZjoCNxa78PpVbBiAHwaabJ/h4Rx5GgYvMFgYNKkSUyePJmkpCQAHB0dGT58OBMnTjR6SGOSYfAm6v5lmB8I/t21Z39M2K34x7T6YictK3szq0d11XFeSkpKCvfv37eIFgZL5uDgQIECBcy2ALr28Bqvr3yd9qXbM7bBWNVxXiz+BnzdACq20yZLFBbrpeYBSk1N5cKFCzx69IjKlSuTP39+Y2bLFVIAmSC9Hn7qCPHR0HcPOLqqTvSPlh26zrAlUSx8txatquTR9P25RK/Xk5Ehy32YKhsbG2xsbMy2hU5v0PPhhg+5lXiLZZ2Wkc8+n+pI/+zo7xDeB978zaSeRRTGla2PEx98kLVVuL///vtshZg3bx7Tp08nJiaGgIAAvvrqq+eu2Py//vjjD9566y06d+5MeHh4ts4pTMj+b+DqbnhvtVkUPwCv1SzGuhMxjFp+nNp+nnjmc1AdKcf+8w+sELnht9O/cfD2Qb5v/b15FD8AAW/C6ZXaMjy+9U1mNKowrmz91fvxxx/Ztm0bcXFxPHjw4Lmv7Pjzzz8ZOnQoY8eO5fDhwwQEBNC6dWtiY2Nf+L4rV64wfPhwGjdunK3zCRNz9zxsHgf1+kIp87mWOp2OSa9VJUNvYEz4CdVxhDBJl+MvM/vwbN6p9A51fOqojpN1Oh10mA36DFgzVHUakUuy1QXWr18/fv/9d0qWLMn777/PO++8g6en50sFqFevHnXq1GHu3LmA1hzv6+vLgAEDGDFixDPfk5GRQZMmTfjggw/YtWsXcXFxz20BSklJeWIl6ISEBHx9faULzBRYwDT0q4/dpP9vR5jzVg06BRRVHUcIk5GuT+e99e8RnxLPko5LcLYzw5m3/7Mcz+vfQbVuqtMII8tWC9C8efO4desWn3/+OatWrcLX15cePXqwYcOGHD1EmZqayqFDh2jRosV/A9nY0KJFCyIjI5/7vgkTJlC4cGE+/PDDfzzH5MmTcXd3z3z5+vpmO6fIJXvmwI1D2oOGZlj8AHTwL0oH/yKErDhB7MNk1XGEMBk/nvyRE3dPEBoYap7FD0DV16DKa7B2uLY8j7Ao2e74d3R05K233mLTpk2cOnWKKlWq8Omnn+Ln58ejR4+yday7d++SkZGBt7f3E9u9vb2JiYl55nt2797Nd999x6JFi7J0jpEjRxIfH5/5unbtWrYyilxy+yRsnwwNB5r9KswTO1fFzsaGkcuOy2gqIYBzD87x9dGv6V2lN9ULV1cd5+W0nwk29rBqoDZXmbAYL/Xk439GJvxnrorc9vDhQ959910WLVqEl5dXlt7j6OiIm5vbEy+hWEYaLO8DnmXglVGq07w0j3wOTH6tGlvOxLL00HXVcYRQKk2fRvDuYEq6laRf9X6q47w8F09tao5z6+Hob6rTCCPKdgGUkpLC77//TsuWLSlfvjzHjx9n7ty5REdHZ3sYvJeXF7a2tty+/WTT4u3bt/HxeXpo8cWLF7ly5QodO3bEzs4OOzs7fv75Z1auXImdnR0XL17M7rcjVNg5A2JPaV1fdo6q0xhFy8revF6zOBNWneJm3GPVcYRQZtGxRZx/cJ6wRmE42Jrv6MgnVGwHAW/D+hEQLx9yLEW2CqBPP/2UIkWKMGXKFDp06MC1a9dYsmQJ7dq1y9EwWgcHB2rVqsWWLVsyt+n1erZs2UKDBg2e2r9ixYocP36co0ePZr46derEK6+8wtGjR+X5HnNw8wjsnA5NPoOi1VWnMaqQjpXJ72RH0LJj0hUmrNLJeydZeGwhn/h/QuWClVXHMa42k7VpOlb0l64wC5GtUWA2NjaUKFGCGjVqvHBSrr/++ivLAf7880/ee+89vvnmG+rWrcvs2bNZvHgxZ86cwdvbm169elGsWDEmT578zPf37t37haPA/k4mQlQoLRkWNgM7B/hoC9ia7zISz7Pz3B16fb+f0C5Vead+SdVxhMgzKRkpvLn6Text7Pm1/a/Y21je/c2FLfDLa9B+FtT550E4wrRlayLEXr16GX020jfeeIM7d+4QEhJCTEwM1atXZ/369ZkPRkdHR8skbZZi+yS4fxE+2WGRxQ9Ak/KFeLteCSatPU2TcoUoUdA8R7cJkV3zjs7jasJV/uzwp2UWPwBlm2vrhW0cA2VeBc9SqhOJl/BSS2GYI2kBUiR6nzbnT/MQaDREdZpc9SglnbZf7qSImzN/fFIfGxvzXMJAiKw6GnuU99a/x8AaA/mwmoW3jKQ81NYtdCsGvdeAfEA3W3LlRO5LTYLwvlCsljbs3cLld7RjercA9l+5z/cRl1XHESJXPU5/THBEMFW9qtK7Sm/VcXKfoyt0+Rqi98C++arTiJcgBZDIfVvGQ8JN6LIAbGxVp8kT9UsX5IPAUkzfcJYLsdmbH0sIc/Ll4S+5nXibsMAwbK3k/savEdT/FLZMgDvnVKcROSQFkMhdl3fCvgXQYix4lVWdJk993qYCxQo4M2xJFOkZetVxhDC6/bf28+vpXxlUcxB+7n6q4+St5iHgXlxbNT4jXXUakQNSAInck/IQwvtByUZQ91+q0+Q5J3tbZvQI4Pj1OL7ZeUl1HCGMKjEtkTERY6jtXZu3K72tOk7es3fWWrVvHoE9X6pOI3JACiCRezaMhsf3ocs8q31QsGYJD/7VtAyzN5/j9K0E1XGEMJrpB6YTlxLHxMCJ2Ois8/7Gtw4EDoJtkyHmhOo0Ipus9LdW5Lrzm+HwT9AqFDz8VKdRanCLcpT2ys+wxVGkpktXmDB/u2/sZtn5ZQyvM5zirsVVx1Gr2UjwKqd1haWnqk4jskEKIGF8jx/Ayv5QpjnU6q06jXKOdrbM7BHAudsPmbv1vOo4QryU+JR4xkaMJbBoIN3KdVMdRz07R21Zn9jT2iz3wmxIASSMb90Ibeh7p6/AyBNnmquqxdwZ8Go55m2/yLHrcarjCJFjU/dP5XH6Y8Y1HGf0iXHNVpEAaPI57JoJNw6rTiOySAogYVynV8OxP6DtVHAvpjqNSfn0lTJULuLG0MVRJKdlqI4jRLZtid7CqkurGFFvBD75nl6w2qo1Hgo+1WB5H23ZH2HypAASxpN4F1YPhgrtIeBN1WlMjr2tDTN7BBB9L4kvNsncIcK83E++z4TICbzi+wodS3dUHcf02NprXWEPLsO2MNVpRBZIASSMw2CANUNBnwEdZ0vX13OU93ZlaKvyLNx1iYNX7quOI0SWGAwGQveGojfoCWkQIl1fz1O4ErwyGvZ8BdF7VacR/0AKIGEcJ5bBqRXQYRbkL6w6jUn7uHFpavgWYPiSKJJSZQI1YfrWX1nPpqubCK4fjJezl+o4pq3hACheR1v+JzVRdRrxAlIAiZf3MAbWDIMqr0GVrqrTmDxbGx0ze1QnJiGZqevOqI4jxAvdSbpD6N5Q2vi1obVfa9VxTJ+NrdYVlnALNo9TnUa8gBRA4uUYDLBqENg6QPuZqtOYjVJe+RjRpiI/RV5lz4W7quMI8UwGg4HxkeOxt7FndL3RquOYj4JloOV42L8QLu1QnUY8hxRA4uUc/RXOrYdOc8DFU3Uas9KrgR8NShfks6XHeJicpjqOEE8JvxDOjus7GNdwHAWcCqiOY17qfAx+jWFFP0iWWeBNkRRAIufirsH6kVC9J1RoqzqN2bGx0TGtmz9xSamErTmtOo4QT7j16BbTDkyjc5nONPNtpjqO+bGxgc7ztIlhN0rrmSmSAkjkjMGgzfbs6AptJqtOY7Z8PV0I7lCZPw5cY9uZWNVxhAC0rq+QPSHks89HUN0g1XHMl0dJaB0Gh3+GcxtVpxF/IwWQyJmD38Gl7dpsz07uqtOYtTfr+NK0fCGClh0jLknWEhLqLT67mL239jKh4QRcHVxVxzFvNd+Dsi1g5QBIkqkvTIkUQCL77l+CjWOg9gdQtrnqNGZPp9Mx9XV/ktMyGLfypOo4wspdS7jGzEMz6VG+Bw2LNVQdx/zpdNoHxfTHsE5a00yJFEAie/QZEN4P8hWClhNVp7EYPu5OjO9chfCjN1l/4pbqOMJKZegzCI4IxtPJk2G1h6mOYzncikLb6XB8MZxaqTqN+H9SAIns2TsfoiOhy3xwzK86jUXpUr0YrSp7M3r5Ce49SlEdR1ihX07/wpHYI4QGhuJi76I6jmXx7wEVO8DqIdqyQUI5KYBE1t05C1smQP1PwS9QdRqLo9PpCOtaDQMwevkJDAaD6kjCilyKu8Scw3N4p/I71PaprTqO5dHpoMMXgEFbM1Hub+WkABJZk5GurXJcoAQ0H6M6jcUq5OpIaJeqrD8Zw8qom6rjCCuRrk9n9O7RFM1flIE1BqqOY7nyF4b2s+D0Kji+VHUaqycFkMiaiNlw66g2xbu9s+o0Fq1dtSJ0CihKyIqT3E5IVh1HWIEfTvzAqfunCGsUhpOdk+o4lq1KF6jaDdYO15bLEMpIAST+Wcxx2D4FAgdDcWkazwsTOlfBwc6GEcuOSVeYyFVn75/l66iv+aDqB/gX8lcdxzq0mw52jrBqoHSFKSQFkHix9FRY3he8ykOzEarTWI0CLg5Mea0a287eYcnB66rjCAuVlpHG6N2jKeVeir4BfVXHsR4untBxDpzfCEd+UZ3GakkBJF5s53S4c1rr+rJzVJ3GqjSv5E33WsWZsPoU1x8kqY4jLNA3x77hYtxFJjWahIOtg+o41qVCG6j+jracUFy06jRWSQog8Xw3DsGumdA0CIpI07gKYzpWxs3Jjs+XHkOvl6ZyYTwn7p7g2+Pf8q+Af1HRs6LqONapzSRtJv0V/UCvV53G6kgBJJ4tLVnr+vKpBo2GqE5jtdyc7JnWLYA9F+/xy76rquMIC5GSkcLo3aOp4FmBD6t9qDqO9XJyh85z4fJObXkhkaekABLPti0UHlzRur5s7VWnsWqNynnxbv2STF57hit3E1XHERZg7pG5XH94nbDAMOxt5P5WqswrUOcj2BQC9y6qTmNVpAAST4veC3vmwqujoXAl1WkEMKJtRQq5OjJ8SRQZ0hUmXsKR2CP8dPIn+tfoT1mPsqrjCIAW47U5gsI/1ZYbEnlCCiDxpNREbcJD37rQoL/qNOL/5XO0Y0b3AA5FP+D73ZdVxxFmKiktidG7RxNQKIBelXupjiP+wzG/trzQtX2w92vVaayGFEDiSZvHwcMY7Wa0sVWdRvyPuqU8+TCwFNM3nuX87Yeq4wgzNPvwbO4k3SG0USi2cn+blpINoUE/2DIRYs+oTmMVpAAS/3VpO+xfCC0nQMEyqtOIZxjeugK+Hs4MWxJFeoaMGhFZt/fWXn4/8ztDag2hpFtJ1XHEs7waDB4lIbyPtvyQyFVSAAlNcgKs6A9+jbUH8oRJcrK3ZWaP6py4Ec/87fLApMiaR6mPCIkIoa5PXd6s+KbqOOJ57J2hywK4FQW7v1CdxuJJASQ0G0bB4wfQeR7YyK+FKavuW4C+zcowZ+t5Tt6MVx1HmIHpB6cTnxLPhMAJ2Ojk/jZpxWtpU4/smAq3jqlOY9HkThBwbiMc+Te0nqQ1vwqTN7B5OcoUys+wxVGkpktXmHi+ndd38tf5v/i8zucUy19MdRyRFU2DoFAFCO+rLUckcoUUQNYu6T6sHABlW0JNGRViLhztbJnZI4ALsY+Ys+W86jjCRMWnxDNuzzgaFWvEa+VeUx1HZJWdozYQ5c4ZrSVI5AopgKzduiBIfwyd5oBOpzqNyIYqRd0Z1LwcX2+/wNFrcarjCBM0ef9kkjOSGddgHDq5v81LEX9oOgJ2z4Lrh1SnsUhSAFmzUyvh+GJoOx3ciqpOI3Kgb7MyVC3mzrDFR0lOkwnUxH9tvrqZNZfWMLLuSLzzeauOI3Ki0RAoEqCNCkt7rDqNxZECyFo9ugOrh0DFDuDfQ3UakUN2tjbM7B7AtQePmbHhrOo4wkTce3yPiXsn0rxEczqU7qA6jsgpWzttVNiDq7A1VHUaiyMFkDUyGGDNEMAAHWZL15eZK+ftyvBW5fku4jL7L99XHUcoZjAYCN0bisFgYEz9MdL1Ze4KV9TmB4qcB1f3qE5jUaQAskbHl8LpVdB+FuQvpDqNMIIPG5WmVgkPhi+JIjFFJlCzZmsvr2Vz9GaC6wdT0Lmg6jjCGBr0A9962qiwlEeq01gMKYCsTcItWDsMqnaDKl1UpxFGYmujY0b3AO48TGHKOplG31rFJsUSti+MtqXa0sqvleo4wlhsbKHL1/AoFjaPVZ3GYkgBZE0MBlg1EOycoN101WmEkfl55WNku4r8e+9Vdp+/qzqOyGMGg4Fxe8bhaOvI6HqjVccRxlawjLZM0YFv4eI21WksghRA1uTIv+H8Ruj0Fbh4qk4jcsE79UrSsExBPl8aRUJymuo4Ig8tv7CcXTd2Mb7heNwd3VXHEbmh9odQqom2bFGyzAL/sqQAshZx0bB+FNR4B8q3Vp1G5BIbGx3TuvmTkJzOxFWnVMcReeTmo5tMOzCNrmW70qR4E9VxRG6xsdGWK0qO1/6ei5ciBZA10OthRT9wcteWuxAWrbiHC2M6VGLJoetsOX1bdRyRy/QGPSERIbg6uPJZnc9UxxG5rUAJaDMJjv4CZ9erTmPWpACyBge/g8s7ofNcrQgSFq9HbV9eqVCIEX8d50GirCVkyf48+yf7YvYxoeEEXB1cVccReaHGu1CulfZMZ5JMfZFTUgBZunsXYVMI1PkIyryiOo3IIzqdjimv+5OarmfsypOq44hcEp0QzReHvuCNCm/QoGgD1XFEXtHpoOMcSE+BtdLql1NSAFkyfQaEfwr5vaHFeNVpRB7zdnNiQucqrIy6ydrjt1THEUaWoc8gOCIYL2cvhtYaqjqOyGtuRaDdDDixFE6Gq05jlqQAsmSR8+DaPm1VYcf8qtMIBToFFKVNFR+Cw09w52GK6jjCiP596t8cjT1KaGAoLvYuquMIFap1g0odYc1QbY4gkS1SAFmq2DPa2jEN+kFJaRq3VjqdjtCuVdEBo5cfx2AwqI4kjOBi3EW+OvIVvSr3oqZ3TdVxhCo6HbT/AtBpazvK/Z0tUgBZoox0bfVgj5LaGjLCqnnldySsa1U2nrpN+NEbquOIl5SuT2f07tEUcy1G/xr9VccRquUvBB2+gDOr4dhi1WnMihRAlmj3F3DrmLaKsL2z6jTCBLSpWoQu1YsSsuIkMfHJquOIl/Dd8e84c/8MYYFhONk5qY4jTEHlTlCth/ZAdMJN1WnMhhRAlubWMdgxBRoNgeK1VKcRJmR8p6o429sStOyYdIWZqTP3z7AgagEfVP2AaoWqqY4jTEm7adoH3pUDpCssi6QAsiTpKdpqwYUqQtMg1WmEiXF3sWfq6/7sOHeHPw5cUx1HZFNqRiqjd4+mdIHS9A3oqzqOMDXOHtoyRxc2w+GfVKcxC1IAWZIdU+HOWei6AOwcVKcRJuiVioV5o7YvoatPce1+kuo4IhsWRC3gUvwlJjWahL2tveo4whSVb6VNkrhhNDy4qjqNyZMCyFJcP6g9+9MsCHykaVw8X3CHShRwceCzpVHo9dJUbg6O3TnGdye+o29AXyp4VlAdR5iy1pO01qAV/bRlkMRzSQFkCdIew/I+UKQ6BA5RnUaYOFcne6Z382fvpfv8HHlFdRzxD5LTkxm9ezSVPSvzQdUPVMcRps7JTVsw9couOLBIdRqTJgWQJdgaqq323nUB2NqpTiPMQMOyXrzXoCRT1p/h0p1HquOIF/jqyFfcfHSTsEZh2NnI/S2yoHRTqPsJbBoLdy+oTmOypAAyd1f3aDM+Nx8DhaRpXGRdUNuK+Lg5MXxJFBnSFWaSDt0+xL9P/ZuBNQdSukBp1XGEOWkxTlsuI7yvtiySeIpJFEDz5s3Dz88PJycn6tWrx/79+5+776JFi2jcuDEeHh54eHjQokWLF+5v0VIeab/cJepD/U9VpxFmxsXBjhndAzhyLY5Fuy6pjiP+JiktieDdwdQoXIN3Kr2jOo4wNw75tGWQrh+APV+pTmOSlBdAf/75J0OHDmXs2LEcPnyYgIAAWrduTWzss9c12b59O2+99Rbbtm0jMjISX19fWrVqxY0bVjjD7eax2vovXb4GG1vVaYQZqu3nyceNSzNr4znO3X6oOo74H7MOzeJe8j1CA0Oxlftb5ESJ+tCwP2wLg9jTqtOYHJ1B8Yxo9erVo06dOsydOxcAvV6Pr68vAwYMYMSIEf/4/oyMDDw8PJg7dy69evX6x/0TEhJwd3cnPj4eNze3l86vzMVt8O8u2mrAdT9WnUaYseS0DDp8tRsnexuWfxqIva3yz0VWL/JmJJ9s+oTR9UbzZsU3VccR5iwtGb5pAvZO8NEWkCkUMin9S5eamsqhQ4do0aJF5jYbGxtatGhBZGRklo6RlJREWloanp6ez/x6SkoKCQkJT7zMXnI8rOgPpZpC7Q9VpxFmzsnellk9Ajh96yHztskDk6o9TH1IyJ4Q6hWpR48KPVTHEebO3kkbIBNzAnbNVJ3GpCgtgO7evUtGRgbe3t5PbPf29iYmJiZLxwgKCqJo0aJPFFH/a/Lkybi7u2e+fH19Xzq3cutHaUVQ53lgI5/WxcvzL16Afs3KMHfrBU7ciFcdx6pNOzCNh6kPmdhwIjY6ub+FERSrCY2Hwc7pcPOo6jQmw6zvrilTpvDHH3+wfPlynJyevSjgyJEjiY+Pz3xdu2bmSwCcXQ9Hf4E2k6GABRRzwmT0f7Uc5b1dGbY4ipR0GTWiwo5rOwi/EE5QnSCK5C+iOo6wJE0+g8KVtIEz6Smq05gEpQWQl5cXtra23L59+4ntt2/fxsfH54XvnTFjBlOmTGHjxo34+/s/dz9HR0fc3NyeeJmtpPuwaiCUaw01ZFSIMC4HOxtm9gjg0t1HzN58XnUcqxOXHMe4yHE0Kd6ELmW7qI4jLI2dA3RZAHfPw/bJqtOYBKUFkIODA7Vq1WLLli2Z2/R6PVu2bKFBgwbPfd+0adOYOHEi69evp3bt2nkR1TSs/Uyr3DvNAZ1OdRphgSoVcWNwi/J8s+Mih6MfqI5jVSbtn0RqRirjGoxDJ/e3yA0+VaHZCIj4Eq4dUJ1GOeVdYEOHDmXRokX89NNPnD59mr59+5KYmMj7778PQK9evRg5cmTm/lOnTmXMmDF8//33+Pn5ERMTQ0xMDI8eWfhstifD4cRSbdSX64tbx4R4Gf9qUppqxQswfHEUj1OlKywvbLyykXWX1zGq3igKuRRSHUdYssDBULQGhPeBVOteEFl5AfTGG28wY8YMQkJCqF69OkePHmX9+vWZD0ZHR0dz69atzP3nz59Pamoq3bp1o0iRIpmvGTNmqPoWct+jWFgzFCp1gmrdVKcRFs7O1oaZ3QO4EfeY6RvOqo5j8e4+vkvo3lBalmxJu1LtVMcRls7WTusKi78OWyeqTqOU8nmA8prZzQNkMMCf70D0Xui3D/J5qU4krMS3uy4RuuY0f3xSn/qlC6qOY5EMBgODtw3m6J2jLO+8HE+nZ0/nIYTRRc6DDaOg9xrwa6Q6jRLKW4DEPzi2GM6shg5fSPEj8tQHgaWo6+fJZ0ujeJSSrjqORVp9aTVbr21lTP0xUvyIvFWvL5RoCOGfQop1zgIvBZApS7ipPfhcrQdU7qQ6jbAyNjY6pnf3596jVCatlWn0je124m0m75tM+9LtaVHy2fOYCZFrbGygyzxIvAsbx6hOo4QUQKbKYICVA8DBBdpNU51GWKmSBfMxsl0lftsXzY5zd1THsRgGg4GxkWNxtnNmZN2R//wGIXKDZ2loNQEO/QAXNqtOk+ekADJVh3/SfiE7fQXOHqrTCCv2Tr0SNC7nRdDSY8Q/TlMdxyIsO7+MiBsRjGs4DndHd9VxhDWr/SGUfgVWDIDHcarT5CkpgEzRg6uwYTTU7AXlWqpOI6ycTqdj6uv+JKakM2HVKdVxzN6NRzeYfmA6r5d7ncbFG6uOI6ydTged50LqI1hvXa2RUgCZGr0eVvTTWn1ahalOIwQARQs4E9KxMssOX2fTqdv//AbxTHqDnjERY3B3dGd47eGq4wihcS8ObaZA1G9wZq3qNHlGCiBTc2ARXNmlLXTqZAbD9IXV6FarOM0rFmbkX8e5n5iqOo5Z+v3M7xyIOcDEwInkd8ivOo4Q/1X9bSjfBlYNgsR7qtPkCSmATMndC7BpLNT9BEo3VZ1GiCfodDomv1aNdL2eMStOqI5jdq7EX2H2odm8VfEt6hWppzqOEE/S6aDjl6BPg7XDVKfJE1IAmQp9hrZKr1sRaDFOdRohnqmwmxMTOldlzbFbrIq6qTqO2cjQZxAcEUxhl8IMrjlYdRwhns3VR1tu6eRyOLFMdZpcJwWQqdjzFVw/AF3mg0M+1WmEeK6O/kVoX60IY1acIPZhsuo4ZuGnUz9x7M4xQhuF4mLvojqOEM9X9XWo3AXWDIOHlv28nxRApiD2NGwLg4YDoER91WmEeCGdTsfELlWxs9Ex6q8TWNlqOtl24cEF5h6ZS+8qvalRuIbqOEK8mE4H7WeBjR2sHqzNSWehpABSLSMNlv9Lm5DqldGq0wiRJZ75HAjrWo3Np2+z7PAN1XFMVpo+jVG7R1HCtQT9avRTHUeIrMlXEDrMhrNrIep31WlyjRRAqu2aBTEntK4veyfVaYTIstZVfHitRjHGrzrJzbjHquOYpG+Pf8u5B+cIaxSGo62j6jhCZF2lDuD/Jqwboa0cb4GkAFLp5lHYOQ0aD4NiNVWnESLbxnasQj4HO4KWHZOusL85de8UC6MW8lG1j6jiVUV1HCGyr+0U7ZnUlQMssitMCiBV0lO0UV+FK0GTz1SnESJH3F3smfJ6NXadv8tv+6NVxzEZqRmpjN49mrIeZfmX/79UxxEiZ5w9tOWYLm7V1guzMFIAqbJ9Mtw9D12/ATsH1WmEyLFmFQrzVt0ShK05TfS9JNVxTMLXR7/mSsIVwhqFYW9rrzqOEDlXrgXU6g0bguH+ZdVpjEoKIBWuHYCIL+GVkeAtTePC/I1uXwnPfA4MXxqFXm95TeXZEXUnih9O/kC/6v0o71FedRwhXl6rUO3B6BX9tOWaLIQUQHktNQnC+0DRGtBwkOo0QhhFfkc7pncLYP/l+/yw54rqOMo8Tn9M8O5gqhSsQu8qvVXHEcI4HF2h89dwNQL2LVCdxmikAMprWydqT9R3WQC2dqrTCGE0DcoUpHdDP6atP8PFO49Ux1FizuE53Eq8RWijUOxs5P4WFqRUY6jXB7aM1x7fsABSAOWlK7th79fQPAQKSdO4sDxBbSpStIAzw5dEkZ5hOU3lWXEg5gC/nP6FgTUGUtq9tOo4Qhhf87HgVgyW94GMdNVpXpoUQHkl5SGEfwolA6FeX9VphMgVzg62zOgeQNS1OBbuuqQ6Tp5JTEtkTMQYannX4p3K76iOI0TucHCBrgvg5mHYM0d1mpcmBVBe2TgGEu9C53lgIz92YblqlfTgkyZlmL3pPGdiElTHyRMzD87kfvJ9JgZOxEYn97ewYL51oeFA2DYJbp9UnealyJ2aFy5s1uZQaDURPEupTiNErhvSshx+Xi4MWxxFmoV3hUXciGDJuSUMrz0cX1df1XGEyH2vjIKCZbWusPRU1WlyTAqg3PY4DlYMgNKvQO0PVKcRIk842tkys3t1zsY8ZO7WC6rj5JqE1ARC9oTQoEgDupfvrjqOEHnDzhG6zofYU7Brhuo0OSYFUG5bPxJSH0Hnudoqu0JYiWrF3en3SlnmbrvA8evxquPkiqn7p5KUlsSEwAno5P4W1qRoDWg8HHbOgJtHVKfJESmActOZtRD1G7SZAu7FVacRIs/1f7UsFX1cGbbkKMlpGarjGNW26G2svLiSoLpB+OTzUR1HiLzXZLg2me/yPpCWrDpNtkkBlFsS78GqQVC+LVR/W3UaIZSwt7VhVo/qXLmbxBebz6mOYzQPkh8wPnI8zYo3o3OZzqrjCKGGrb22nNP9S7B9kuo02SYFUG5ZOwz0adDxS+n6Elatgo8rQ1qWZ9HOSxy6el91HKMI2xdGuiGdsQ3HSteXsG7elbWHoiPmQPQ+1WmyRQqg3HBiGZxcDu1ngqu36jRCKPdJk9IE+BZg+JJjPE41766w9ZfXs+HKBoLrBePl7KU6jhDqNRwIxWtryzylJqpOk2VSABnbw9uwZhhU7gJVX1edRgiTYGujY2b3AG7FP2bq+jOq4+TY3cd3Cd0XSquSrWhTqo3qOEKYBhtbbXmnhFuwebzqNFkmBZAxGQywejDY2EH7WarTCGFSShfKz+etK/LjnivsuXhXdZxsMxgMjI8cj63OluD6warjCGFavMpCi7Gw/xu4vFN1miyRAsiYon6Hs2u1537yFVSdRgiT07uhH/VKefLZkmM8SjGvtYRWXlzJ9mvbGdtgLB5OHqrjCGF66v4LSjaC8H7a8k8mTgogY4m/DutGgP+bULG96jRCmCQbGx0zugcQl5RK2JpTquNkWUxiDFP3T6Vj6Y68WuJV1XGEME02NtBlHjy+DxtGq07zj6QAMgaDAVYOAId80HaK6jRCmDRfTxdGt6/M7/uvsf1srOo4/8hgMDB2z1ic7Z0JqhukOo4Qps3DD1qFwuGf4Pxm1WleSAogYzj0A1zcCp2+AmdpGhfin7xV15cm5QsRtOwY8UlpquO80JJzS9hzcw/jG47H3dFddRwhTF+t3lCmOazsD48fqE7zXFIAvaz7l2FDsHbBy7VQnUYIs6DT6Zj6ejWSUjMYv8p0V5S+9vAaMw7OoFv5bjQq1kh1HCHMg06nNQikJmmPhpgoKYBehl4PK/ppDzy3ClWdRgizUsTdmXEdq/DXkRtsOBmjOs5T9AY9YyLG4OnkyfDaw1XHEcK8uBeDtlPh2B9werXqNM8kBdDL2LcArkZA56/B0VV1GiHMzms1i9Gysjejlx/n3qMU1XGe8OvpXzl0+xATAyeSzz6f6jhCmJ+AN6FCe216mETTm/pCCqCcunsetoyHen2gVGPVaYQwSzqdjkldq5GhNzBmxQkMBoPqSABcjr/Ml4e/pGelntTxqaM6jhDmSaeDjrNBnwFrhmoDhkyIFEA5kZGurX7rVgyaj1WdRgizVsjVkdAu1Vh7PIZVx26pjkO6Pp3g3cH45PNhUM1BquMIYd7yF4YOs+DUCm2ZKBMiBVBO7JkDNw9D1wXg4KI6jRBmr71/ETr4F2FM+AliE5KVZvnx5I+cuHeC0MBQnO2clWYRwiJU6QpVXtOWiXpoOs/7SQGUXbdPwrZJ2uJvvnVVpxHCYkzsXBV7WxtG/nVcWVfYuQfnmHd0Hr2r9KZ64epKMghhkdrPBFsHWDXIZLrCpADKjvRUreurYFl4ZZTqNEJYFI98Dkx5rRpbzsSy5ND1PD9/WkYawbuD8XPzo1/1fnl+fiEsmosndJoD59bD0V9VpwGkAMqeXTMg9hR0nQ92jqrTCGFxWlT2plut4kxcdYobcY/z9NwLjy/k/IPzhDYKxcHWIU/PLYRVqNAWqveE9SMh7prqNFIAZdnNI7BzBjQeDkVrqE4jhMUK6ViZ/E52BC09lmddYSfvnWTRsUV87P8xVQpWyZNzCmGV2kzWpo1Z2V95V5gUQFmRlqx1fXlXgSYyIZoQucnNyZ6pr/uz+8JdftkXnevnS8lIYfSu0ZT3KM/H/h/n+vmEsGpO7tos0Ze2w8HvlEaRAigrtk+C+5eg6zdga686jRAWr0n5QvSsV4JJa05z9V5irp5r3tF5RD+MJqxRGPY2cn8LkevKNofaH8DGMdq/rYpIAfRPovdBxBztoWfvyqrTCGE1RrWrhJerA58tOUaGPneayo/GHuXHEz/Sr3o/ynmUy5VzCCGeoeVEyFcIwvtpEyUqIAXQi6QmQngfKF5bG/YuhMgz+RztmNEtgANX7/NDxGWjHz8pLYnRu0dTrVA1elfpbfTjCyFewDE/dJkP0ZGwd76SCFIAvcjm8ZBwC7osABtb1WmEsDr1Shfkg8BSTNtwlguxj4x67C8Pf0lsUixhgWHYyv0tRN7zC4T6n8KWCXDnbJ6fXgqg57m8E/Z/Ay3GgldZ1WmEsFqfta5AcQ9nhi2JIj1Db5Rj7r+1n9/O/MagmoPwc/czyjGFEDnQfAwUKKENNMpIz9NTSwH0LMkJWr9kyUZQ91+q0whh1ZzsbZnZPYDj1+P4ZufLPzD5KPURYyLGUNu7Nm9XetsICYUQOWbvrC0rdesoRMzO01NLAfQsG4Ph8X3oMg9s5EckhGo1SnjQp2kZZm8+x+lbCS91rBkHZxCXEsfEwInY6OT+FkK54rUhcDBsnwIxx/PstHL3/935TXD4J2gVCh5+qtMIIf7foBblKFMoP0MXR5GanrOusF3Xd7Hs/DKG1xlOcdfiRk4ohMixZiPAqzws76stO5UHpAD6X48fwMoBUKY51OqtOo0Q4n842tkys0cA528/ZO7W89l+f3xKPOP2jCOwaCDdynXLhYRCiByzc9S6wu6chp3T8uSUUgD9r3VBkJqkzVKp06lOI4T4mypF3RnYvBzztl8k6lpctt47Zf8UHqc/ZlzDcejk/hbC9BTxh6ZBsGsW3DiU66eTAug/Tq+CY39C26ngXkx1GiHEc/RtVobKRdwYtiSK5LSsTaC25eoWVl9azYh6I/DJ55PLCYUQOdZoCPhU07rC0pJz9VRSAAEk3oVVg6FCewh4U3UaIcQL2NvaMLNHANH3k5i16dw/7n8/+T4T9k7gFd9X6Fi6Yx4kFELkmK291hX24ApsC83VU0kBZDDA6iFg0EPH2dL1JYQZKO/tyrCW5Vm06xIHr9x/7n4Gg4HQvaHoDXpCGoRI15cQ5qBwJXh1NOyZC1cjc+00UgCdWAanV0KHWZC/sOo0Qogs+qhxaWqW8GDYkiiSUp89gdq6y+vYdHUTwfWD8XL2yuOEQogca9AffOtCeF9tWapcYN0F0MMYWDMMqrwGVbqqTiOEyAZbGx0zugdwOyGZqevOPPX1O0l3CNsXRhu/NrT2a60goRAix2xstbXCHsbAprG5c4pcOao5MBhg5UCwdYD2M1WnEULkQCmvfIxsW4mfIq8SceFu5naDwcC4yHHY29gzut5ohQmFEDlWsAy0nAAHFsGl7UY/vEkUQPPmzcPPzw8nJyfq1avH/v37X7j/kiVLqFixIk5OTlSrVo21a9dm/6THFsP5DdBpDrh45jC5EEK1d+uXpEHpgny+9BgPk9MACL8Qzs7rOxnXcBwFnAqoDSiEyLk6H4FfY1jRX1umyoiUF0B//vknQ4cOZezYsRw+fJiAgABat25NbGzsM/ffs2cPb731Fh9++CFHjhyhS5cudOnShRMnTmTvxJvGQvWeUKGtEb4LIYQqNjY6pnXzJ/5xGqGrT3Pr0S2mHphK5zKdaebbTHU8IcTLsLGBzvPgcRxsGGXUQ+sMBoPBqEfMpnr16lGnTh3mzp0LgF6vx9fXlwEDBjBixIin9n/jjTdITExk9erVmdvq169P9erVWbBgwT+eLyEhAXd3d+LDyuM2dD84uRvvmxFCKPPH/mhG/BVF7XpLiUu/wfLOy3F1cFUdSwhhDId+glUDYVy80Q5pZ7Qj5UBqaiqHDh1i5MiRmdtsbGxo0aIFkZHPHvoWGRnJ0KFDn9jWunVrwsPDn7l/SkoKKSkpmf8fH6/98Bq62GL7c5OX/A6EEKYkX0k9p25nkHrjParv26g6jhDCaPLxlW1VGiUk4OrqapQpLZQWQHfv3iUjIwNvb+8ntnt7e3PmzNOjOgBiYmKeuX9MTMwz9588eTLjx49/avvJIadzmFoIYfqebj0WQpi39gDT3ImNjaVQoUIvfTylBVBeGDly5BMtRnFxcZQsWZLo6Gjc3aX7S6WEhAR8fX25du0abm5uquNYPbkepkOuhemQa2E6/nMtHBwcjHI8pQWQl5cXtra23L59+4ntt2/fxsfn2ev1+Pj4ZGt/R0dHHB0dn9ru7u4uv8wmws3NTa6FCZHrYTrkWpgOuRamw1gzuisdBebg4ECtWrXYsmVL5ja9Xs+WLVto0KDBM9/ToEGDJ/YH2LRp03P3F0IIIYT4O+VdYEOHDuW9996jdu3a1K1bl9mzZ5OYmMj7778PQK9evShWrBiTJ08GYNCgQTRt2pSZM2fSvn17/vjjDw4ePMjChQtVfhtCCCGEMCPKC6A33niDO3fuEBISQkxMDNWrV2f9+vWZDzpHR0djY/PfhqqGDRvy22+/ERwczKhRoyhXrhzh4eFUrVo1S+dzdHRk7Nixz+wWE3lLroVpkethOuRamA65FqbD2NdC+TxAQgghhBB5TflM0EIIIYQQeU0KICGEEEJYHSmAhBBCCGF1pAASQgghhNWx2AJo8uTJ1KlTB1dXVwoXLkyXLl04e/bsE/skJyfTr18/ChYsSP78+Xn99defmmRRvLz58+fj7++fOZFYgwYNWLduXebX5TqoM2XKFHQ6HYMHD87cJtcjb4wbNw6dTvfEq2LFiplfl+uQt27cuME777xDwYIFcXZ2plq1ahw8eDDz6waDgZCQEIoUKYKzszMtWrTg/PnzChNbLj8/v6fuDZ1OR79+/QDj3RsWWwDt2LGDfv36sXfvXjZt2kRaWhqtWrUiMTExc58hQ4awatUqlixZwo4dO7h58yavvfaawtSWqXjx4kyZMoVDhw5x8OBBXn31VTp37szJkycBuQ6qHDhwgG+++QZ/f/8ntsv1yDtVqlTh1q1bma/du3dnfk2uQ9558OABgYGB2Nvbs27dOk6dOsXMmTPx8PDI3GfatGnMmTOHBQsWsG/fPvLly0fr1q1JTk5WmNwyHThw4In7YtOmTQB0794dMOK9YbASsbGxBsCwY8cOg8FgMMTFxRns7e0NS5Ysydzn9OnTBsAQGRmpKqbV8PDwMHz77bdyHRR5+PChoVy5coZNmzYZmjZtahg0aJDBYJD7Ii+NHTvWEBAQ8MyvyXXIW0FBQYZGjRo99+t6vd7g4+NjmD59eua2uLg4g6Ojo+H333/Pi4hWbdCgQYYyZcoY9Hq9Ue8Ni20B+rv4+HgAPD09ATh06BBpaWm0aNEic5+KFStSokQJIiMjlWS0BhkZGfzxxx8kJibSoEEDuQ6K9OvXj/bt2z/xcwe5L/La+fPnKVq0KKVLl6Znz55ER0cDch3y2sqVK6lduzbdu3encOHC1KhRg0WLFmV+/fLly8TExDxxPdzd3alXr55cj1yWmprKL7/8wgcffIBOpzPqvWEVBZBer2fw4MEEBgZmzhgdExODg4MDBQoUeGJfb29vYmJiFKS0bMePHyd//vw4OjrSp08fli9fTuXKleU6KPDHH39w+PDhzOVl/pdcj7xTr149fvzxR9avX8/8+fO5fPkyjRs35uHDh3Id8tilS5eYP38+5cqVY8OGDfTt25eBAwfy008/AWT+zP+zQsF/yPXIfeHh4cTFxdG7d2/AuH+jlC+FkRf69evHiRMnnuhfF3mrQoUKHD16lPj4eJYuXcp7773Hjh07VMeyOteuXWPQoEFs2rQJJycn1XGsWtu2bTP/29/fn3r16lGyZEkWL16Ms7OzwmTWR6/XU7t2bSZNmgRAjRo1OHHiBAsWLOC9995TnM66fffdd7Rt25aiRYsa/dgW3wLUv39/Vq9ezbZt2yhevHjmdh8fH1JTU4mLi3ti/9u3b+Pj45PHKS2fg4MDZcuWpVatWkyePJmAgAC+/PJLuQ557NChQ8TGxlKzZk3s7Oyws7Njx44dzJkzBzs7O7y9veV6KFKgQAHKly/PhQsX5L7IY0WKFKFy5cpPbKtUqVJml+R/fuZ/H2kk1yN3Xb16lc2bN/PRRx9lbjPmvWGxBZDBYKB///4sX76crVu3UqpUqSe+XqtWLezt7dmyZUvmtrNnzxIdHU2DBg3yOq7V0ev1pKSkyHXIY82bN+f48eMcPXo081W7dm169uyZ+d9yPdR49OgRFy9epEiRInJf5LHAwMCnpkk5d+4cJUuWBKBUqVL4+Pg8cT0SEhLYt2+fXI9c9MMPP1C4cGHat2+fuc2o94aRH9Y2GX379jW4u7sbtm/fbrh161bmKykpKXOfPn36GEqUKGHYunWr4eDBg4YGDRoYGjRooDC1ZRoxYoRhx44dhsuXLxuOHTtmGDFihEGn0xk2btxoMBjkOqj2v6PADAa5Hnll2LBhhu3btxsuX75siIiIMLRo0cLg5eVliI2NNRgMch3y0v79+w12dnaGsLAww/nz5w2//vqrwcXFxfDLL79k7jNlyhRDgQIFDCtWrDAcO3bM0LlzZ0OpUqUMjx8/VpjccmVkZBhKlChhCAoKeuprxro3LLYAAp75+uGHHzL3efz4seHTTz81eHh4GFxcXAxdu3Y13Lp1S11oC/XBBx8YSpYsaXBwcDAUKlTI0Lx588zix2CQ66Da3wsguR5544033jAUKVLE4ODgYChWrJjhjTfeMFy4cCHz63Id8taqVasMVatWNTg6OhoqVqxoWLhw4RNf1+v1hjFjxhi8vb0Njo6OhubNmxvOnj2rKK3l27BhgwF45s/YWPeGzmAwGF66ner/2rt/l9T6AI7jn3P7QYOS/RARKhzCKCj6sViTNQgNQgQVDYIODRGSQ/0D0VBDUHNLNbU0RZRDZEj0wwgCIQoEh0CLyKUaCuNOjyBxl+f2PHrveb+m4zl+9ftdDm+O5yAAAMAf5K+9BwgAAOBXCCAAAGA6BBAAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIABlw+v1KhKJlHoaAEyAAAIAAKZDAAEoC8FgUMfHx1pbW5NhGDIMQ+l0WslkUsPDw7JYLHI4HAoEAnp6eiqM83q9CofDikQiqqurk8Ph0Pr6ul5fXxUKhWS1WtXa2qr9/f3CmFgsJsMwtLe3p66uLtXU1Mjj8SiZTJZi6QBKgAACUBbW1tbU39+vqakpZTIZZTIZWa1WDQ0NqaenR5eXlzo4ONDDw4PGx8eLxm5ubqqxsVEXFxcKh8Oanp7W2NiYBgYGdHV1JZ/Pp0AgoLe3t6Jx8/PzWllZUSKRkN1ul9/v18fHx/+5bAAlwr/BAygbXq9X3d3dWl1dlSQtLi4qHo8rGo0W3nN/f6/m5mbd3t7K7XbL6/Uqn88rHo9LkvL5vGprazU6OqqtrS1JUjabldPp1OnpqTwej2KxmAYHB7W9va2JiQlJ0vPzs5qamrSxsfElsAD8fSpLPQEA+JXr62sdHR3JYrF8OZZKpeR2uyVJXV1dhf0VFRVqaGhQZ2dnYZ/D4ZAkPT4+Fn1Gf39/Ybu+vl5tbW26ubn51jUAKE8EEICy9fLyIr/fr+Xl5S/HnE5nYbuqqqromGEYRfsMw5AkfX5+/kczBfCnIYAAlI3q6mrl8/nC697eXu3s7Mjlcqmy8vtPV2dnZ2ppaZEk5XI53d3dqb29/du/B0D54SZoAGXD5XLp/Pxc6XRaT09PmpmZ0fPzsyYnJ5VIJJRKpRSNRhUKhYpC6d9aWFjQ4eGhksmkgsGgGhsbNTIy8vsLAVD2CCAAZWNubk4VFRXq6OiQ3W7X+/u7Tk5OlM/n5fP51NnZqUgkIpvNph8/fv/0tbS0pNnZWfX19SmbzWp3d1fV1dXfsBIA5Y6nwACYzj9PgeVyOdlstlJPB0AJcAUIAACYDgEEAABMh5/AAACA6XAFCAAAmA4BBAAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwnZ/+RI/A80IbSgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wxfDKeChaRXWa/NVqEjh7ahdRZ6SrTiOEwR2JPcL3p7/n41ofU96tvOo4LyTFTT4qYG/DnB5+HIm+zzd7L6mOI4RhJT/U/mH3rg8NP1adJv/ZF9RaS1w9COGLVKcRwqASUxMJDAvEr4gffav2VR3npaS4yWf1fNx5r3EZ5mw5x/nYB6rjCGE424LhQaz2D7yVteo0apRuBA0HwV9T4dYZ1WmEMJjPD3/O7cTbhDQOwdoEjm8pbhQY0aYS3m6OjFgVQWq63F0hzMDFHdrFtK0nQ+FyqtOo9UoguJXWLqpOT1WdRohc239zP7+e/ZWhdYZS2qW06jhZIsWNAg621sztWZNTNxJYslMuPhQmLike1n4MPk20i2otna0jdF2qzc6893PVaYTIlQcpDwgKC6K+V316V+6tOk6WSXGjSE3vQgxsVo4F289z6ka86jhC5NzmcVqB02UxWMlHCgAl60DjYdpEhjePq04jRI7NPjSb+OR4JgdMxkpnOse36SQ1Q5+0rED5ogUZsTKC5DS5u0KYoLOb4diP0HaqNhQj/qvZaChSGdYMgLRk1WmEyLZdV3ex5sIaRtUbRYmCJVTHyRYpbhSys7His541uXj7IQu2n1cdR4jsSbwH6z+B8q2htvHfPZHvbOy0uX7unNPO4AhhQuKS4pgYPpHGJRrzWoXXVMfJNiluFKta3IUhLSuwZOdFjkbfVx1HiKzb9CmkJUHnhaDTqU5jnLxqQPPR2rU31/5WnUaILJt2cBrJ6clMbDgRnQke31LcGIEBzcpRo4QrI1ZFkJQqw1PCBJwKhZOrof0ccCmmOo1xCxgGxWtpw1Opj1WnEeKltlzewh9RfzC2/lg8C3iqjpMjUtwYARtrK+b29OPa/cfM/vOs6jhCvNjDW7BxOFTuCDV6qE5j/KxttLl/4qJh+xTVaYR4obuP7xKyP4SWpVrSsWxH1XFyTIobI1G+qDOj2lbi27AoDly6qzqOEM+m18OGYdp/d5wnw1FZVaQStAyC/V/A5TDVaYR4Jr1ez+TwyQBMaDDBJIej/kOKGyPydkAZ6pZ2Y+TqCB4lp6mOI8TTjq+EMxug4+dQsIjqNKalwUAo1UBrUZH8UHUaIZ6y4dIG/rr6F4ENAinsWFh1nFyR4saIWFvpmNPDjzsPUpi2KVJ1HCGelHBDu4i4eneo2kV1GtNjZQ1dv4BHt7Xu4UIYkdhHsUw/MJ1Xy7xKG582quPkmhQ3RqZ04QKMa1+Znw5Es/vcbdVxhNDo9bBusDb7bvvZqtOYLveyWouKv7+BC9tVpxEC0IajgsODsbexZ7z/eNVxDEKKGyPUx780jct7MPq348Q/lt40wggc+Q4ubIPOC8DJXXUa01b3XSjbXCsWH8epTiMEv53/jbDrYUxqNAlXe1fVcQxCihsjZGWlY2Z3Xx4mpTFlw2nVcYSlu38F/hwPtd6Eim1VpzF9VlbQeREkP4DNY1WnERbu+sPrzD40m27lu9G0ZFPVcQxGihsjVaKQIxM6VWX14WtsPR2rOo6wVBkZsHYQOLpB2+mq05iPQt7QbjpE/AxnNqlOIyxUhj6DCWETcLF34dN6n6qOY1BS3BixHnVK0rJyUcb+foL7j1JUxxGW6NAyuLwHuiwCBxfVacxLzT5QsR2sH6K1shAin/1y5hcOxRxicqPJONs5q45jUFLcGDGdTsf012qQmp7BhLUnVccRlubuRdgaDPXe064REYal00Gn+ZCeAhtHqE4jLMzl+MvMOzyPf1X6Fw2LN1Qdx+CkuDFyRV0cmNylGhuO32Tj8Zuq4whLkZGutQtw9oJWk1SnMV/OXtBhLpz6HU6tUZ1GWIj0jHQCwwIp4lSEYXWGqY6TJ6S4MQGd/YrzanUvAkNPcPtBsuo4whKEL4Jrh7S2AfYFVacxb9VfhyqdYcNwrbWFEHns+9Pfc/z2cUICQnCydVIdJ09IcWMCdDodIV2rY6XTMW7NCfR6vepIwpzdioS/QqDhIChtfqerjY5Op834rLOC9UO1OYWEyCMX7l9g4dGF9K3al9qetVXHyTPKi5vFixfj4+ODg4MD/v7+HDx48IXLz5s3j0qVKuHo6Ii3tzfDhg0jKSkpn9KqU7igPVO71WDr6VjWHL2uOo4wV+mp2nCUWxl4ZYLqNJajgAd0mgdnN8LxFarTCDOVmpHK+LDxeDt7M7j2YNVx8pTS4mbFihUMHz6c4OBgjhw5gp+fH23btuXWrWefmv35558ZM2YMwcHBREZG8s0337BixQrGjRuXz8nVaFfdi261ShC87hQ34x+rjiPM0Z7PIOYEdFsCtg6q01iWKp3AtxdsGgXx8gVGGN7XJ77m7L2zTG08FXtre9Vx8pTS4uazzz7j/fff5+2336Zq1aosXboUJycnvv3222cuv2/fPgICAnjjjTfw8fGhTZs29O7d+6Vne8zJxE7VcLKzZvRvMjwlDOxmBOyeBU2GQ4k6qtNYpldngp2TNnuxHN/CgCLvRvJVxFe8W+NdqntUVx0nzykrblJSUjh8+DCtWrX6bxgrK1q1akV4ePgzX9OoUSMOHz6cWcxcunSJTZs20b59++duJzk5mYSEhCcepszVyZYZr/uy+9xtfjl4VXUcYS7SkrXhqCJVoOko1Wksl6MbdF4IF7fD4eWq0wgzkZKewri94yhXqBwDfAeojpMvlBU3d+7cIT09HU9Pzyee9/T0JCYm5pmveeONN5g8eTKNGzfG1taWcuXK0bx58xcOS02fPh1XV9fMh7e3t0HfhwotKhXlX/W8mbrxNFfvJaqOI8zBzhlw57w2HGVjpzqNZavQGmr3hS2BcP+y6jTCDCyJWMLlhMtMbTwVW2tb1XHyhfILirNj586dTJs2jS+++IIjR47w+++/s3HjRqZMmfLc14wdO5b4+PjMx9Wr5nG2Y3yHKhRysmPkqggyMuT0tciFq4cgbB40HwNeNVSnEQBtpoKjO4QO0lpgCJFDEbcj+Pbktwz0G0gl90qq4+QbZcWNh4cH1tbWxMY+2TcpNjYWLy+vZ75mwoQJvPXWW7z33nvUqFGDbt26MW3aNKZPn07Gcz4A7O3tcXFxeeJhDpwdbJndw5cDUff4Lvyy6jjCVKUkQugAKF4LAoaqTiP+w8EFui6GK3vh4Feq0wgT9TjtMYF7A6lWuBrvVH9HdZx8pay4sbOzo06dOmzfvj3zuYyMDLZv307Dhs+eWyMxMRErqycjW1tbA1jkxbWNynnQv5EPMzef4dLth6rjCFP01xSIvwZdl4K1jeo04n+VaQr1P4RtE+HOBdVphAlacGQBNx/dJKRxCDZWlnV8Kx2WGj58OMuWLeO7774jMjKSgQMH8ujRI95++20A+vbty9ixYzOX79SpE0uWLOHXX38lKiqKrVu3MmHCBDp16pRZ5FiaUe0q4eXiwIhVEaTL8JTIjst7Yf8SbT6bIhVVpxHP0ioYXIppZ9cy0lWnESbkUMwhfor8icG1BlPWtazqOPlOaSnXq1cvbt++TVBQEDExMdSsWZPNmzdnXmQcHR39xJmawMBAdDodgYGBXL9+nSJFitCpUyemTp2q6i0o52Rnw9yefvRYGs5Xuy8xsHk51ZGEKUh+CKEfQamG0GCg6jTieewKaC0wvm0H+xZAY/PsAyQMKzE1kQlhE6hVtBZvVnlTdRwldHoLG89JSEjA1dWV+Ph4s7n+BmD6pkj+HXaZ9YMbU8nLvFrXizywYRhE/AoDw8Dd8r7VmZwtE+DAUvhgF3hWVZ1GGLkp4VNYf2k9v3X6DW8X079DOCdM6m4p8XzDWlekdGEnRqw6Rmq63F0hXuDCdvj7W2g9WQobU9FivPZ3FTpAa5EhxHPsu76PledWMqLOCIstbECKG7PhYGvNZz1rEnnzAYt3yMWH4jkex2mz35ZtAfXeU51GZJWtgzY8FXMS9sxVnUYYqYSUBIL2BdGgWAN6VuqpOo5SUtyYkRolXRnUojyL/rrAyevxquMIY/TnOEh+AF0Wad2ohekoURuajIDds+HGMdVphBGadXAWj1IfMbnRZHQWfnxLcWNmPm5RnoqezgxfeYzkNLm7QvyPM5vg2E/Qbjq4llSdRuRE00+haBWtVUZasuo0wojsiN7B2otrGVVvFMUKFlMdRzkpbsyMnY0Vn/XyI+rOI+ZtO686jjAWifdg/RCo2A5q9lGdRuSUjR10+xLuXoCd01WnEUYiLimOSeGTaFayGV3Ld1UdxyhIcWOGKnu5MLRVRb7cdZEj0fdVxxHGYOMISE+BTvNlOMrUeVaDFmMhbL7WOkNYvKkHppKakUpww2CLH476DyluzNSHTcviW7IQI1ZG8DhFhqcs2snf4dTv0GEuOD+7tYkwMY2GaC0zQgdoLTSExfrz8p9svryZwAaBFHEqojqO0ZDixkzZWFsxt6cfN+IeM+vPM6rjCFUe3tLO2lTtAtVfV51GGIq1jdYyI/4abJ+sOo1Q5M7jO4TsD6F16da082mnOo5RkeLGjJUrUpBR7Srz77DLhF+8qzqOyG96vXadjZU1dPhMhqPMTZGK0DIIDiyBqD2q04h8ptfrmRQ+CSudFYENAmU46h+kuDFzbzfyoX4Zdz5dHcHD5DTVcUR+ivgVzm6CjvOggIfqNCIv+A+E0gGw9iPtFn9hMdZfWs/OqzsJahiEu4O76jhGR4obM2dlpWNOdz/uPUph2qZI1XFEfom/Dn+MBt9/QZWOqtOIvGJlBV0Ww6O7WosGYRFiHsUw48AMOpXtRMtSLVXHMUpS3FiAUoWdGNe+Cj8fiGbXuduq44i8ptfDuo/BzglenaE6jchr7mWgzRQ4/G+4sE11GpHH9Ho9wfuCcbRxZHT90arjGC0pbixEH/9SNKngwejVx4l/LL1pzNrh5XDxL+i8CBzdVKcR+aHuO1pLjbWDtRYbwmytPr+afTf2MSlgEq72rqrjGC0pbiyETqdj5uu+PEpOY9L6U6rjiLxy/zJsCYTa/aBCK9VpRH7R6bSWGikPYfNY1WlEHrn24BqzD83m9Qqv07hEY9VxjJoUNxakeCFHgjtX4/cj19lyKkZ1HGFoGRkQOgic3KHtVNVpRH5zLQmvzoSIn7VWG8KsZOgzmBA2AXcHdz6t96nqOEZPihsL83rtErSqUpRxa05w71GK6jjCkA5+BVf2aheY2jurTiNU8OsNFV/VpgB4JNM/mJOfI3/m79i/mdxoMgVsC6iOY/SkuLEwOp2Oaa/VIC1Dz4S1J1XHEYZy5zxsC4b6H0KZpqrTCFV0Oq3FRkYqbBqhOo0wkKj4KOYdmccbld+gfrH6quOYBCluLFBRZwemdKnOxuM3WR9xQ3UckVsZ6RA6EFxKQKuJqtMI1Zw9tVYbp9bAyd9UpxG5lJ6RTmBYIF4FvBhaZ6jqOCZDihsL1cmvOB1qFGPC2pPcSkhSHUfkxr4FcP0wdF2i3f4tRPXXoWpXrfXGg1jVaUQuLD+1nJN3ThISEIKjjaPqOCZDihsLNqVrdWysdIxbcwK9Xq86jsiJ2NOwYxo0Ggyl/FWnEcakw2dgZaNdfyPHt0k6f/88i48tpl/VftQsWlN1HJMixY0Fcy9gx7RuNdgWeYvfjlxXHUdkV3oqrPkQ3MtC83Gq0whjU6Cwdv3NuT8g4hfVaUQ2pWakMn7veEo5l2JQrUGq45gcKW4sXJtqXrxWuwST1p3iRtxj1XFEduyeA7GnoNtSsHVQnUYYo8odtDuo/hitdRAXJmPZ8WWcu3+OqU2mYm9trzqOyZHiRhDcqRoF7G0Y/dtxGZ4yFTeOwp450HQkFK+lOo0wZu1mgF1BWPuxDE+ZiFN3T7Hs+DLe932faoWrqY5jkqS4Ebg62jKzuy97zt/hpwPRquOIl0lLhjUDoWhVaDJSdRph7BwLQZeFcGkH/P2t6jTiJVLSUwjcG0gFtwp8UOMD1XFMlhQ3AoBmFYvQu34ppm2KJPpuouo44kV2TIO7F7ThKBs71WmEKSjfCur01zqH34tSnUa8wOJji7mccJmQxiHYWtuqjmOypLgRmcZ3qIJ7ATtGro4gI0NOXxulqwe1W79bjAVPOV0tsqFNiHaR8dpBWqsOYXSO3TrG8lPLGVRzEBXdKqqOY9KkuBGZCtrbMLu7Hwej7vHvfZdVxxH/lJIIawZA8drQaIjqNMLU2DtDly/gShgcWKo6jfiHx2mPCQwLpHrh6vSv1l91HJMnxY14QsNyhXk7wIdZm89w8fZD1XHE/9o+CRKua8NR1jaq0whTVKYJ+A/UfpfunFedRvyP+UfmE/MohpDGIdhYyfGdW1LciKeMaluZ4oUcGbEygrR0OX1tFKL2aN+2WwaDRwXVaYQpaxmktepYMwDS01SnEcChmEP8FPkTQ2oPoYxrGdVxzIIUN+IpjnbWzOnhx/FrcXy155LqOCL5Aaz9CEo3Bv8BqtMIU2fnpJ39u3EE9s1XncbiPUp9xISwCdT1rEufKn1UxzEbUtyIZ6pT2o0Pm5Xj863nOBOToDqOZdsSCI/uQpdFYCWHrDAA7/rQ6BPYMV2bCFIoM+fvOdxLuseUgClY6eT4NhTZk+K5hraqQFmPggxfEUFKmgxPKXFhGxxeDm1DwF1OVwsDajEOCpfXWnikpahOY5HCroex+txqRtYdSUnnkqrjmBUpbsRz2dtYM7enH+diH7BoxwXVcSzP4zhYOxjKvQJ13ladRpgbG3tteOpWpDbbtchXCSkJBO0LolHxRvSo2EN1HLMjxY14oeolXPn4lfIs3nGBE9fiVcexLJvHQMoj6LwQdDrVaYQ5Kl4Tmn6q9Sm7fkR1Gosy8+BMHqc+ZlKjSejk+DY4KW7ESw1qUZ4qxZwZvvIYSanpquNYhjMbtU7Or84AVzldLfJQkxHgVV27eyo1SXUai/BX9F+su7iO0fVH41XAS3UcsyTFjXgpW2sr5vaoyZW7iXy+9ZzqOObv0V1YPwQqtdc6OguRl6xtoetSuB8FO6aqTmP27ifdZ1L4JJp7N6dzuc6q45gtKW5EllTycmZY64p8tecSh6/cUx3HvG0cDhlp0HGeDEeJ/OFZVbvAeN9CiN6vOo1ZC9kfQro+neCGwTIclYekuBFZ9kHTstT0LsSIlREkpsjkX3ni5G9wOhQ6fAbOnqrTCEvS6BMoWRdCB2rXegmD2xy1mS1XthDYIBAPRw/VccyaFDciy6ytdMzt4UdMQhKzNp9VHcf8PIiFjSOgWjeo/prqNMLSWFlrw1MJN2HbRNVpzM6dx3cIORBCW5+2tPNppzqO2ZPiRmRL2SIFGd2uMsv3XWbfhTuq45gPvR7WfwJWttB+ruo0wlJ5lIdWE+HgV3Bpl+o0ZkOv1zNx30RsdDaM9x+vOo5FkOJGZFu/hj40KOvOp6uP8yApVXUc83DsZzi3GTrNhwKFVacRlqz+B+DTBNYOgiSZndwQ1l5cy65ruwhuGIybg5vqOBZBihuRbVZWOmZ39yMuMYWpGyNVxzF98de0OW383oDK7VWnEZbOykpr9fH4PmyRswy5FfMohpkHZ9K5XGdalGqhOo7FkOJG5Ii3uxPjO1Tl10NX2XH2luo4pkuvh7Ufg11BaDdddRohNG4+0CYEjnwP57aoTmOy9Ho9QWFBONk6Mbr+aNVxLIoUNyLHetf3pmnFIoxefZz4RBmeypG/v4VLO7Rvyo6FVKcR4r/q9IdyLWHdYEiU6R9yYtW5VYTfDGdKoym42LmojmNRpLgROabT6Zj5eg0ep6Yzcb10Fs62e1GwZYLWN6p8S9VphHiSTqe1/kh9DH/IWYfsuvrgKnP+nkOPij1oVKKR6jgWR4obkSvFXB2Z2Kkaa45eZ/PJGNVxTEdGBoR+pF083GaK6jRCPJtrCXh1JpxYCafXqU5jMjL0GQTuDcTdwZ0RdUeojmORpLgRufZa7RK0rurJ+DUnuPswWXUc03BgCUTvg65LwN5ZdRohns/vX1CpA2wYBo9k+oes+PH0jxy5dYQpAVMoYFtAdRyLJMWNyDWdTse0bjXI0OsJDD2JXq9XHcm43T4H2yeD/0Dwaaw6jRAvptNBp3mgz4ANQ7WL4MVzXYq/xIKjC3izypvU86qnOo7FkuJGGEQRZ3tCutbgj5MxrIu4oTqO8UpPg9ABWqfvlkGq0wiRNQWLQsfPIHI9nFitOo3RSstII3BvIMUKFOOT2p+ojmPRpLgRBtPBtxgdfYsRtPYUsQlJquMYp33z4cZRbZp7OyfVaYTIumrdoNprsGmk1qJBPGX5qeWcunuKkMYhONo4qo5j0aS4EQY1pUt1bK2tGPPbcRme+qeYk7Bjutag0FtOVwsT1GEu2NhrrULk+H7C2XtnWXxsMW9Xexu/In6q41g8KW6EQbkVsGPGazXYcfY2q/6+pjqO8UhL0YajPCpAi3Gq0wiRM07uWouQ81vg6I+q0xiN1PRUxu8dj4+LDx/V/Eh1HIEUNyIPtKrqSfc6JZm84TTX7ieqjmMcds+GW5Ha3VE29qrTCJFzlV6Fmn1g81iIi1adxih8efxLLsZdZGrjqdhZ26mOI5DiRuSRoE5VcXawYdTq42RkWPjp6+tHYM9caPopFK+pOo0QudduOji4as01MzJUp1Hq5J2TfH3iaz7w/YCqhauqjiP+nxQ3Ik+4ONgy83Vf9l28y48HrqiOo05qEqwZAF7VoYlM5iXMhIMrdFkIUbvh729Up1EmOT2Z8XvHU9GtIu/5vqc6jvgfUtyIPNO0YhH6+Jdi+qYzXL7zSHUcNXaEwP0o6PYlWNuqTiOE4ZR7Beq+C1uD4O5F1WmUWHR0EVcfXGVa42nYWsnxbUykuBF5alz7Kng42zFyVQTpljY8Fb0f9i2CFuOhaBXVaYQwvNaTtTlwQj+CjHTVafLVkdgjfHfqOwbVHER5t/Kq44h/kOJG5KkC9jbM6e7H4ej7fLs3SnWc/JPySBuOKlkPGg1WnUaIvGFfELp8AVcPwP4vVKfJN4mpiQSGBeJbxJf+1fqrjiOeQYobkef8yxbmnYAyzN5ylvOxD1THyR/bJsKDGO3uKCtr1WmEyDs+AdBwEGyfArfOqE6TLz4//Dm3E28TEhCCtRzfRkmKG5EvPm1biZJujoxcFUFaupnfXXFpJxz8ClpNBA85XS0swCuB4FYaQgdqLUbM2P6b+/n17K8MrTMUH1cf1XHEc0hxI/KFg601c3v4ceJ6PEt3mfHFh0kJsPZj8GkC9T9QnUaI/GHrqLUUuXkMwj5XnSbPPEx5SFBYEPW86tG7cm/VccQLSHEj8k2tUm4MbF6O+dvPc/pGguo4eePPcfD4PnRZDFZyeAkLUrIONB4GO2dCzAnVafLE7L9nE58cz5SAKVjp5Pg2Zsr/dhYvXoyPjw8ODg74+/tz8ODBFy4fFxfHoEGDKFasGPb29lSsWJFNmzblU1qRW5+0rEC5IgUZvvIYKWlmNjx1bgsc/QHahGin6IWwNM1GQ5FK2sX0aSmq0xjU7mu7+f3873xa71NKFCyhOo54iRwXN9u3b6djx46UK1eOcuXK0bFjR7Zt25atdaxYsYLhw4cTHBzMkSNH8PPzo23btty6deuZy6ekpNC6dWsuX77M6tWrOXv2LMuWLaNECflFMxX2NtbM7enHhVsPWbD9vOo4hpN4D9YNhvKtoE5/1WmEUMPGXruI/vYZ2DVTdRqDiU+OZ+K+iQSUCOD1Cq+rjiOyIEfFzRdffEG7du1wdnZmyJAhDBkyBBcXF9q3b8/ixYuzvJ7PPvuM999/n7fffpuqVauydOlSnJyc+Pbbb5+5/Lfffsu9e/cIDQ0lICAAHx8fmjVrhp+fdGA1JdWKu/JJywos2XWRY1fjVMcxjD9GQ9pj6LwQdDrVaYRQp5gvNBsDez+Ha4dVpzGI6Qenk5SexKSGk9DJ8W0SdHp99vvWlyxZkjFjxvDxxx8/8fzixYuZNm0a169ff+k6UlJScHJyYvXq1XTt2jXz+X79+hEXF8fatWufek379u1xd3fHycmJtWvXUqRIEd544w1Gjx6NtfWzb8dLTk4mOTk5888JCQl4e3sTHx+Pi4tLFt+xMLTU9AxeX7KPR8lpbPykCQ62Jnw75el1sPItbRZiv3+pTiOEeulp8E0rbb6nD3drFxybqG1XtjFs5zCmNZ5Gp3KdVMcRWWSTkxfFxcXRrl27p55v06YNo0ePztI67ty5Q3p6Op6enk887+npyZkzz54r4dKlS/z111/06dOHTZs2ceHCBT766CNSU1MJDg5+5mumT5/OpEmTspRJ5B9bayvm9vCjw8K9zN1ylvEdTLTh3MPbsGEYVO4Ivr1Up8kRvV5PWpp5375ryqytrbEytYvTrW20u6e+bAp/hUDbqaoT5cjdx3eZsn8Kr3i/QseyHVXHEdmQo+Kmc+fOrFmzhk8//fSJ59euXUvHjnn3C5CRkUHRokX56quvsLa2pk6dOly/fp3Zs2c/t7gZO3Ysw4cPz/zzf87cCPUqeDozsk1Fpv9xhtZVvahfxl11pOzR62HjMNBnQMfPTXI4Ki0tjdu3b5ODE7giHzk5OeHq6mpaQyJFK2vz32wNgsodoHQj1YmyRa/XE7I/hAx9BhMaTjCtfS9yVtxUrVqVqVOnsnPnTho2bAjA/v37CQsLY8SIESxYsCBz2U8++eSZ6/Dw8MDa2prY2Ngnno+NjcXLy+uZrylWrBi2trZPDEFVqVKFmJgYUlJSsLOze+o19vb22NvbZ/s9ivzxbuOybDkVy8hVEfwxpAkF7HP0K6nGidUQuR56fKf11zExer2euLg4rKyscHNzkw9vI6TX60lJSSEhQZs6oVChQmoDZVfDQXBmoza534AwrV2DidgUtYlt0duY22wuHo4equOIbMrRNTdlypTJ2sp1Oi5duvTcn/v7+1O/fn0WLlwIaGdmSpUqxccff8yYMWOeWn7cuHH8/PPPXLp0KfM07fz585k5cyY3btzIUqaEhARcXV3lmhsjcvnOI16dv4fudUoypWt11XGyJuEmfOGv3R3V/dkXwBu79PR0YmNjcXNzw9HRdK+JsAQPHz4kISEBLy8v0xuiunsRljaGmm9Ah7mq02TJrcRbdF3blcbFGzOr2SzVcUQO5OhrclSUYRogDh8+nH79+lG3bl3q16/PvHnzePToEW+//TYAffv2pUSJEkyfPh2AgQMHsmjRIoYMGcLgwYM5f/4806ZNe+7ZIWEafDwKMLZ9ZYLWnqJtNS8aVzDyb0l6Paz/BGwcoP0c1WlyLCNDm2foeRfjC+Pxn7PS6enpplfcFC6ndQ/fNFK7Nq1cC9WJXkiv1zNx30Tsre0Z5z9OdRyRQ0rHAHr16sXt27cJCgoiJiaGmjVrsnnz5syLjKOjo584kL29vfnzzz8ZNmwYvr6+lChRgiFDhmT5ImZhvN70L83mkzGMWh3B5mFNcXGwVR3p+Y7+AOe3QO8V4GRi1wk9gwxHGT+T/zuq+y5ErtNak3y0DxxcVSd6rjUX1rDn+h4WvbKIQg6FVMcROZTlYanhw4czZcoUChQo8MQFus/y2WefGSRcXpBhKeN17X4i7ebtoX0NL2Z1N9K5i+Ki4YtGULULdM36nE7GKDU1ldu3b1OkSBFsbY24mBTm8Xf1n2OnWhetPYkRuvHwBq+te43WpVszJWCK6jgiF7J85ubo0aOkpqZm/vfzmPw3DKFMSTcnJnSswujfTtC2mhctq3i+/EX5KSMD1g7SvnW2m6Y6jRCmpVAp7bhZNxiqdIaKbVUnekKGPoOgsCCc7ZwZVW+U6jgil7Jc3OzYseOZ/y2EIfWs683mkzGM+f0EW4a64Vbg6TvglPn7G4jaDW+FGvVpdXPXvHlzatasybx581RHEdlV6y3tDsN1g+Gj/UY1rLvi7AoOxBzgq9Zf4WznrDqOyCUTuzJNmDudTseM131JScsgeN0p1XH+6+5Fbb6Ouu8a/QWRQhgtnQ46LYC0ZPjDeM6ORCdE8/nhz+lVqRcNizdUHUcYQI6Km0ePHjFhwgQaNWpE+fLlKVu27BMPIXLD08WBSZ2rsS7iBptO3FQdBzLSIfQjbS6b1pNVpxHCtLkUg/az4cQqOP10m538lp6RTmBYIIUdCjO8zouvJxWmI0d3S7333nvs2rWLt956i2LFisl1NsLgutQszuaTMQSGnqR+GXc8CiqciHH/F3D1ALy9yaQmIbME9+/fZ8iQIaxfv57k5GSaNWvGggULqFChAnq9nqJFi7JkyRK6d+8OQM2aNYmNjeXmTa1o3rt3Ly1btuT+/fs4OTmpfCuWpUYP7e6pDcOgVCMoWERZlB9O/8CxW8f4tu23ONnK74C5yFFx88cff7Bx40YCAgIMnUcIQBueCulWnTaf72bc7yf48q06aoroW2dg+xRtplUTmz4+Jx6npHPx9sN83265IgVxtMv+fDv9+/fn/PnzrFu3DhcXF0aPHk379u05ffo0tra2NG3alJ07d9K9e3fu379PZGQkjo6OnDlzhsqVK7Nr1y7q1asnhU1+0+mgw+faRJgbhkKvH5W0L7kYd5GFRxfyZtU3qetVN9+3L/JOjoobNzc33N2N50IwYZ48CtozrVt1Bvx4hNBj1+lWq2T+BkhPg9AB4FZa65FjAS7efkjHhXvzfbsbBjemeonsXaT9n6ImLCyMRo20wvOnn37C29ub0NBQevToQfPmzfnyyy8B2L17N7Vq1cLLy4udO3dSuXJldu7cSbNmzQz+fkQWFCwCHefByre0ISrfnvm6+bSMNMbvHU/xgsX5pJZMBGtuclTcTJkyhaCgIL777jv5xiPyVLvqxehSszjBa0/RsKwHXq4O+bfxvZ/DzQh4dxvYWkZ7gnJFCrJhcGMl282uyMhIbGxs8Pf3z3yucOHCVKpUicjISACaNWvGkCFDuH37Nrt27aJ58+aZxc27777Lvn37GDXKeC5stThVO2tDVJtGgk9jcCmeb5v+5sQ3RN6L5IdXf8DBJh8/V0S+yHJxU6tWrSeGBS5cuICnpyc+Pj5PTSp15MgRwyUUFm9S52qEX7zL6N+Os/ztevkzPHXzOOyaCY2HQck6eb89I+FoZ53tMyjGrEaNGri7u7Nr1y527drF1KlT8fLyYubMmRw6dIjU1NTMsz5CkVdnQdQeWPcJ9FmVL8NTZ+6dYWnEUt6p/g6+RXzzfHsi/2W5uOnatWsexhDi+Qo52THzdV/eXn6IFYeu8q/6pfJ2g2nJWhfjIpWgmbT2MFZVqlQhLS2NAwcOZBYod+/e5ezZs1StWhXQrt1q0qQJa9eu5dSpUzRu3BgnJyeSk5P58ssvqVu3LgUKFFD5NoSTO3ReCD/3gCPfQ51+ebq5lPQUxu8dT5lCZRjoNzBPtyXUyXJxExwcnJc5hHihFpWL0quuN1M2nCagvAfe7nk4HLprJtw+A+/vABuFd2mJF6pQoQJdunTh/fff58svv8TZ2ZkxY8ZQokQJunTpkrlc8+bNGTFiBHXr1qVgQW34q2nTpvz00098+umnquKL/1WxjTbB35/joGxz7Tq3PLI0YimX4i7xS8dfsLM2oklChUHlaJ6bq1evcu3atcw/Hzx4kKFDh/LVV18ZLJgQ/xTYsQqFnOwYtfo4GRlZaomWfdf+1q61aTYGisnpamP373//mzp16tCxY0caNmyIXq9n06ZNTwyVN2vWjPT0dJo3b575XPPmzZ96TijWdho4umktTv6/Y72hHb99nG9OfsOHfh9S2b1ynmxDGIcsN878X02aNOGDDz7grbfeIiYmhooVK1K9enXOnz/P4MGDCQoKyousBiGNM01b2IU79Pn6ABM7VaV/QBnDrjz1MSxtos1l8+42sM7R9fYmwyyaMVoIi/m7urQTvu+iXYfj/6FBV52UlkSP9T0oYFuAH9r/gK2VGe9HkbMzNydPnqR+/foArFy5kho1arBv3z5++uknli9fbsh8QjwhoLwHfRuWZsbmM0TdeWTYlf8VonUu7rrU7AsbIYxS2eZQ733YGqy1PDGghUcXcuPhDaY2niqFjQXIUXGTmpqKvb12LcK2bdvo3LkzAJUrV86c+VOIvDLm1cp4ujgwYuUx0g01PHVlH4Qv1uazKSqnq4VQpvUkcPaCNQO01icGcDj2MD+c/oHBtQZTrlA5g6xTGLccFTfVqlVj6dKl7Nmzh61bt9KuXTsAbty4QeHChQ0aUIh/crKzYW4PP45ejePrPZdyv8Lkh9rdUd7+2kzEQgh17ApAt6Vw7RCEL8r16hJTEwncG0jNojV5q+pbBggoTEGOipuZM2fy5Zdf0rx5c3r37o2fnx8A69atyxyuEiIv1fVx573GZZi75RznYh/kbmXbguHhLej6BVhlvwWAEMLASjWARh9rQ8W3InO1qs8Of8bdpLuEBIRgLce3xcj2hQV6vZ6yZcsSHR1NWloabm5umT/74IMPZMZikW9GtKnEjrO3GbEygt8/aoStdQ5q9Ys74NDX0H4OFJbT1UIYjRaBcG6LNjz13jawzv51MuE3wllxdgXj/MdRyiWP58cSRiXb/xro9XrKly9PTEzME4UNgI+PD0WLFjVYOCFexMHWmrk9/Dh9M4ElO3Nw8WFSPKz9GMo0g7rvGj6gECLnbB2g2xKIOaFNz5BND1IeELQvCH8vf3pV6pUHAYUxy3ZxY2VlRYUKFbh7925e5BEiW/y8C/FR83Is2H6ek9fjs/fizeO0AqfLYrDK0QitECIvlagDTYZrE2vejMjWS2cdmsWDlAdMDpiMlU6Ob0uTo7/xGTNm8Omnn3Ly5ElD5xEi2wa/UoEKns6MWBlBcloW7644uxmO/QjtpkMh77wNKITIuaajoEgVWDNQa42SBbuu7iL0Qiij6o2ieMH8a8YpjEeOipu+ffty8OBB/Pz8cHR0xN3d/YmHEPnJzsaKuT38uHTnIfO3nX/5CxLvwfpPoEIbqPVm3gcUQuScjZ1299Sdc7BzxksXj0uKY2L4RJqUaEK38t3yIaAwRjmaqWzevHkGjiFE7lQt7sKQlhX4bOs5Wlf1pFYpt+cvvOlT7RtgpwX50oFYCJFLXtWh+RjYMRUqd4CSdZ+76LSD00hJT2Fio4no5Pi2WDkqbvr1y9uurULkxIBm5dh6OpYRqyLY9EkTHGyfcdvnqVA4uRpe+xpciuV7RiFEDgUMhbObtLunBuwBW8enFtlyeQt/RP3BjCYzKOokN7dYshxfZXXx4kUCAwPp3bs3t27dAuCPP/7g1KlTBgsnRHbYWFsxt6cf1+4/ZvafZ59e4OEt2DgcqnSGGt3zP6AwWenp6WTkUTNHkUXWNlprlPirsH3KUz++8/gOIftDaFWqFe3LtFcQUBiTHBU3u3btokaNGhw4cIDff/+dhw8fAhAREUFwcLBBAwqRHeWLOjOqbSW+DYviwKX/uaNPr4cNwwAddPxchqNM3ObNm2ncuDGFChWicOHCdOzYkYsXtekAGjVqxOjRo59Y/vbt29ja2rJ7924AkpOTGTlyJCVKlKBAgQL4+/uzc+fOzOWXL19OoUKFWLduHVWrVsXe3p7o6GgOHTpE69at8fDwwNXVlWbNmnHkyJEntnXmzBkaN26Mg4MDVatWZdu2beh0OkJDQzOXuXr1Kj179qRQoUK4u7vTpUsXLl++nCf7yqwUqQivTID9X8DlsMyn9Xo9U8KnoNPpCGwQKMNRImfFzZgxYwgJCWHr1q3Y2dllPv/KK6+wf/9+g4UTIifeDihD3dJujFwdwaPkNO3J4yvhzAatsCngoTagMUtJhBvH8v+RkpitmI8ePWL48OH8/fffbN++HSsrK7p160ZGRgZ9+vTh119/Ra//b9+xFStWULx4cZo0aQLAxx9/THh4OL/++ivHjx+nR48etGvXjvPn/3tBemJiIjNnzuTrr7/m1KlTFC1alAcPHtCvXz/27t3L/v37qVChAu3bt+fBA22W7PT0dLp27YqTkxMHDhzgq6++Yvz48U9kT01NpW3btjg7O7Nnzx7CwsIoWLAg7dq1IyUlJVv7wSI1GAilGmotU5K1L9YbLm3gr6t/MaHBBAo7SgsgATr9/34CZFHBggU5ceIEZcqUwdnZmYiICMqWLcvly5epXLkySUlJeZHVIBISEnB1dSU+Ph4XFxfVcUQeuXL3Ee3m7eG12iWY2rIwLG4AFdvC68tURzMaqamp3L59myJFimBr+/+zv944Bl81y/8wH+yC4jVz/PI7d+5QpEgRTpw4gaenJ8WLF+evv/7KLGYaNWpE06ZNmTFjBtHR0ZmzrBcv/t/bhFu1akX9+vWZNm0ay5cv5+233+bYsWOZ7WWeJSMjg0KFCvHzzz/TsWNHNm/eTKdOnbh69SpeXl6A1ly4devWrFmzhq5du/Ljjz8SEhJCZGRk5hmGlJQUChUqRGhoKG3atHlqO8/8u7Jk9y7BkgDw+xexLcbQbW03mno3ZUaTl99NJSxDji4oLlSoEDdv3qRMmTJPPH/06FFKlChhkGBC5EbpwgUY174yE9aeZMStcbjbOUH7WapjGT+PilqhoWK72XD+/HmCgoI4cOAAd+7cybweJjo6murVq9OmTRt++uknmjRpQlRUFOHh4Xz55ZcAnDhxgvT0dCpWfHKbycnJTzT+tbOzw9fX94llYmNjCQwMZOfOndy6dYv09HQSExOJjo4G4OzZs3h7e2cWNsBT/fYiIiK4cOECzs7OTzyflJSUObQmXsK9LLSZgn7jCILTr+Fg48DY+mNVpxJGJEfFzb/+9S9Gjx7NqlWr0Ol0ZGRkEBYWxsiRI+nbt6+hMwqRI338S5N84N+439xNYo9fcXJ8we3hQmPnlKszKPmlU6dOlC5dmmXLllG8eHEyMjKoXr165rBOnz59+OSTT1i4cCE///wzNWrUoEaNGgA8fPgQa2trDh8+jLX1k3fUFSxYMPO/HR0dn7p2o1+/fty9e5f58+dTunRp7O3tadiwYbaGkx4+fEidOnX46aefnvpZkSJFsrwei1f3XX4//RNhcWdY3GQWrvauqhMJI5Kj4mbatGkMGjQIb29v0tPTqVq1Kunp6bzxxhsEBgYaOqMQOWIVH807j5axWv8K+08XZ0411YmEIdy9e5ezZ8+ybNmyzGGnvXv3PrFMly5d+OCDD9i8eTM///zzE1+6atWqRXp6Ordu3cp8fVaFhYXxxRdf0L69djfO1atXuXPnTubPK1WqxNWrV4mNjcXT0xOAQ4cOPbGO2rVrs2LFCooWLSpD47lw/dENZlk/4rWEZJoeXw9lX1UdSRiRHF1QbGdnx7Jly7h48SIbNmzgxx9/5MyZM/zwww9PfRMSQomMDFg7CCsnd6zaTWP14WtsOx2rOpUwADc3NwoXLsxXX33FhQsX+Ouvvxg+fPgTyxQoUICuXbsyYcIEIiMj6d27d+bPKlasSJ8+fejbty+///47UVFRHDx4kOnTp7Nx48YXbrtChQr88MMPREZGcuDAAfr06YOj43/nW2ndujXlypWjX79+HD9+nLCwsMwvfP85C9SnTx88PDzo0qULe/bsISoqip07d/LJJ59w7do1Q+0ms5ahzyAoLAhXh0J8Wn80HPsJzmxSHUsYkVx1EytVqhSvvvoqPXr0oEKFCobKJETuHVoGl/dAl8V0a1CZlpWLMub3E9x/JHejmDorKyt+/fVXDh8+TPXq1Rk2bBizZ89+ark+ffoQERFBkyZNKFWq1BM/+/e//03fvn0ZMWIElSpVomvXrhw6dOip5f7pm2++4f79+9SuXZu33nqLTz75hKJF/ztZnLW1NaGhoTx8+JB69erx3nvvZd4t5eDgAICTkxO7d++mVKlSvPbaa1SpUoV3332XpKQkOZOTRb+c+YWDMQeZEjCFgnXegYrtYP0QrbWKEOTwbinQDvLPP/8889bJChUqMHToUN577z2DBjQ0uVvKAty5AEsbQ+23oL32j96thCRaf76bphWLsLB3LcUBjYPcgZM/wsLCaNy4MRcuXKBcuXI5Wof8Xf3XlYQrdF/Xna7luzK+wf/fZv8gBhb7Q7lXoMe/1QYURiFH19wEBQXx2WefMXjwYBo2bAhAeHg4w4YNIzo6msmTJxs0pBBZlpGuzX/hUgxaTcx8uqiLA5O7VGPIr8doV82LDr7SekHkjTVr1lCwYEEqVKjAhQsXGDJkCAEBATkubMR/pWekM37veIo6FWVYnWH//YGzF3SYC7+9C1U6QfXX1IUURiFHxc2SJUtYtmzZE+PYnTt3xtfXl8GDB0txI9TZtxCuHYJ3NoNdgSd+1NmvOH+eiiEw9AT1y7hTxNleUUhhzh48eMDo0aOJjo7Gw8ODVq1aMXfuXNWxzML3p7/n+O3jfPfqdzjZOj35w+qvQ+Q62DgCfBpDQektZclydM1Namoqdes+3ZW1Tp06pKWl5TqUEDlyK1LrGtzoYyjV4Kkf63Q6pnSpjpVOx7g1J8jhiKwQL9S3b1/OnTtHUlIS165dY/ny5U/MnyNy5sL9Cyw8upB+1fpRq+gzhpZ1OujwGeistOtv5Pi2aDkqbt566y2WLFny1PNfffUVffr0yXUoIbItPRXWfAhuZaDF86cjKFzQnmmv1WDr6VjWHL2ejwGFEDmVmpHK+LDxeDt783Gtj5+/YAEP6DRf6x4e8Wv+BRRGJ8vDUv97q6VOp+Prr79my5YtNGigfUM+cOAA0dHRMomfUGPPZxBzEt7bBrYOL1y0bTUvutUqQfC6UzQsV5hiro4vXF4IodbXJ77m7L2z/NT+J+ytXzKcXKUj+PaCP0ZDmabgKrPmW6IsFzdHjx594s916tQByJwu3MPDAw8PD06dOmXAeEJkwY1jsHsWNBkOJWpn6SUTO1Vj38U7jP7tBN+9XU+6CAthpCLvRvJVxFe8V+M9qnlkcSbOV2dC1G5Y9zG8+bs2ZCUsSo5vBTdVciu4mUlLhq+ag5U1vPcX2Ni99CX/sfPsLfr/+xDTutXgDf8Xz29ijuT2YtNhqX9XKekp9NrQCxsrG35u/zO21tl47+e3wU+vQ8d5UPftPMsojFOuJvETQrmd0+HOeei6NFuFDUDzSkXpXd+bqRtPc/VeYh4FFELk1JKIJVxOuExIQEj2ChuACq2gdj/4czzcv5wn+YTxylFxk5SUxOzZs2nfvj1169aldu3aTzyEyBdXD0HYfGg+Bryq52gV4ztUpZCTHSNXRZCRYVEnMYUwahG3I/j25Ld85PcRldwr5WwlbaeCU2EIHaS1ZBEWI0fFzbvvvsusWbMoXbo0HTt2pEuXLk88hMhzKYkQOgCK14KAoTleTUF7G2b38OVA1D2W77tssHgi7zRv3pyhQ4c+9+c6nY7Q0NAsr2/nzp3odDri4uJynU0YxuO0xwTuDaRa4Wq8XT0XQ0r2ztB1MVzZCwe/NFxAYfRyNInfhg0b2LRpEwEBAYbOI0TW/DUF4q/Bv34B6xz9GmdqVM6D/o18mLn5DM0qFaFckYIGCilUuHnzJm5ubqpjiFxYcGQBNx/dZP4r87Gxyt3xTZmmUP9D2DYRyrcCD+mDaAlydOamRIkSODs7GzqLEFlzeS/s/wJemQBFKhpklaPbVaZ4IUdGroogXYanTJqXlxf29jL7tKk6FHOIHyN/ZHCtwZR1LWuYlbYKBpfiWmuWdJlo1hLkqLiZO3cuo0eP5sqVK4bOI8SLJT+E0I+gVCNoMNBgq3W0s2ZOD18irsbx1e5LBluvyBsZGRmMGjUKd3d3vLy8mDhxYubP/jkstW/fPmrWrImDgwN169YlNDQUnU7HsWPHnljn4cOHqVu3Lk5OTjRq1IizZ8/mz5sRmRJTE5kQNoHaRWvzVtW3DLdiuwLaTQfXD8O+BYZbrzBaOTrfV7duXZKSkihbtixOTk5P3Zp47560nRd5ZOsEeHQb+oZqt38bUJ3S7rzftCyfbz3HK5WLUsnL8s5OPk57TFR8VL5vt4xrGRxtsj6Z4nfffcfw4cM5cOAA4eHh9O/fn4CAAFq3bv3EcgkJCXTq1In27dvz888/c+XKlederzN+/Hjmzp1LkSJFGDBgAO+88w5hYWG5eVsim+b+PZd7SfdY1noZVjoD38xbyh8afqzdYVmxLXhmcc4cYZJyVNz07t2b69evM23aNDw9PWUCNJE/LmyHv7/Vuv+6G+h09T8Ma1WRvyJvMXzlMUIHBWBrbVmzJUTFR9FrQ6983+6KjiuoWrhqlpf39fUlODgYgAoVKrBo0SK2b9/+VHHz888/o9PpWLZsGQ4ODlStWpXr16/z/vvvP7XOqVOn0qxZMwDGjBlDhw4dSEpKwsHhxTNeC8PYd30fK8+tJNA/EG8X77zZSIvxcH4LrBkA7/8F2b29XJiMHBU3+/btIzw8HD8/P0PnEeLZHsfBusFQtjnUfTfPNuNga81nPWvS9YswFv11gWGtDXNNj6ko41qGFR1XKNludvj6+j7x52LFinHr1q2nljt79iy+vr5PFCj169d/6TqLFSsGwK1btyhVyvImeMxvCSkJBO0LomGxhvSs1DPvNmTrAN2WwrKWsHsOtBibd9sSSuWouKlcuTKPHz82dBYhnm/zWEh+AJ0X5flU6jVKujKoRXkW77hAqyqe1CjpmqfbMyaONo7ZOoOiyj+HwnU6HRm5nMfkf9f5n7PRuV2nyJqZB2fyKPURkwMm5/1IQPFa0HQk7J4NldppfxZmJ0fn3GfMmMGIESPYuXMnd+/eJSEh4YmHEAZ1ZhNE/AztpkOhPDpd/Q8ftyhPJS9nRqw6RnJaer5sUxhepUqVOHHiBMnJyZnPHTp0SGEi8U87onew7uI6RtcfjVcBr/zZaJOR2jU3awZAalL+bFPkqxwVN+3atSM8PJyWLVtStGhR3NzccHNzo1ChQjK/hDCsxHuwfghUbAc1++TbZu1srJjb04+oO4/4fOv5fNuuMKw33niDjIwMPvjgAyIjI/nzzz+ZM2cOgFwraATikuKYFD6JZiWb0aVcPk4Aa2OnDU/dvQg7p+XfdkW+ydGw1I4dOwydQ4hn2zgC0lOg0/x87+xb2cuFYa0rMufPs7Su6kmd0lK4mxoXFxfWr1/PwIEDqVmzJjVq1CAoKIg33nhDLhQ2AlMPTCVNn0Zww+D8LzY9q2nX3PwVApU6aHdTCbMhXcGF8Tr5O6x+G17/Bmp0VxIhLT2D7kvDiX+cyqZPmuBoZ9jbz1Wy1E7TP/30E2+//Tbx8fE4Omb99nOVzPHvavPlzXy661NmNZ3Fq2VeVRMiPQ2+bQuP78OAvWDnpCaHMLgc3+e6Z88e3nzzTRo1asT169cB+OGHH9i7d6/BwgkL9iBWO2tTtQtUf11ZDBtrbXjqRtxjZm4+oyyHyLnvv/+evXv3EhUVRWhoKKNHj6Znz54mU9iYozuP7zB1/1TalG5DO5926oJY22jDUwnXYfskdTmEweWouPntt99o27Ytjo6OHDlyJPNivfj4eKZNk/FLkUt6PWwYqk3S1+GzfB+O+qdyRQoyql1llu+7zL6Ld5RmEdkXExPDm2++SZUqVRg2bBg9evTgq6++Uh3LYun1eiaFT8JKZ0Vgg0D11z55VICWwXBgKUTtVptFGEyOipuQkBCWLl3KsmXLnjhFGhAQwJEjRwwWTlioiF/h7CboOA8KeKhOA8DbjXzwL+POp6uO8zBZetOYklGjRnH58mWSkpKIiori888/x8lJhh9UWX9pPTuv7iSoYRBuDkZyHZv/ACgdAKGDtCknhMnLUXFz9uxZmjZt+tTzrq6uxMXF5TaTsGTx1+GP0eD7L6jSUXWaTFZWOub08ON+YgpTN55WHUcIkxTzKIYZB2bQqWwnWpZqqTrOf1lZQZfFkHgX/hyvOo0wgBwVN15eXly4cOGp5/fu3UvZsnkzLb6wAHo9rPtYu6jv1Rmq0zzF292J8R2q8MvBq+w8+/RsuKbKwu4pMEnm8Hek1+sJ3heMo60jo+uPVh3nae5loM0UOPIdnN+mOo3IpRzdCv7+++8zZMgQvv32W3Q6HTdu3CA8PJyRI0cyYcIEQ2cUluLwcrj4F/T5DRyN5HT1P7xRvxSbT8Yw5rcT/Dm0Ka5OpnvnirW1NTqdjgcPHuDs7Kz+2gfxFL1eT3p6OgkJCeh0OmxscvSRbRRWn1/Nvhv7WNJqCa72Rjrrd913IHK91urlo31G+zkkXi5Ht4Lr9XqmTZvG9OnTSUxMBMDe3p6RI0cyZcoUg4c0JLkV3EjdvwxfNNJu+e68QHWaF7oR95i283bTuoonn/WqqTpOriQnJ3Pv3j2zODNgzuzs7ChUqJDJFjfXHlzjtXWv0aFsB4IbBquO82Lx1+CLhlCpPbz2peo0IodyNc9NSkoKFy5c4OHDh1StWpWCBQsaMluekOLGCGVkwHedID4aBu4De2fViV5q9eFrjFwVwZdv1aFttXyaMj6PZGRkkJ4uLSaMlZWVFVZWViZ7Zi1Dn8G7f77LzUc3+a3zbxSwLaA60ssd+xlCB0Kvn4zq2j+Rddn6GvDOO+9kablvv/02WyEWL17M7NmziYmJwc/Pj4ULFz63c+//+vXXX+nduzddunQhNDQ0W9sURuTgl3BlL/RbbxKFDcDrtUuw+eRNxq85QT0fd9wL2KmOlGP/+cdTiLzwc+TP/B37N9+0+cY0ChsAv95wep02JUWpBkZz16bIumx9oi1fvpwdO3YQFxfH/fv3n/vIjhUrVjB8+HCCg4M5cuQIfn5+tG3bllu3XnzB5uXLlxk5ciRNmjTJ1vaEkblzHrZNhPofQpmn78AzVjqdjmmv1SAtQ09g6AkZ1hHiGaLio5h3ZB59qvShfrGXf2E1Gjqd1vIlIw02DtdudhAmJVvDUoMGDeKXX36hdOnSvP3227z55pu4u7vnKoC/vz/16tVj0aJFgHaK3Nvbm8GDBzNmzJhnviY9PZ2mTZvyzjvvsGfPHuLi4p575iY5OfmJjsAJCQl4e3vLsJQxSE+Df7fTmmOa6NTn6yNuMPiXoyzoXYvOfsVVxxHCaKRnpNN3c1/ik+NZ1WkVjjYmOCP0yd9g9TtKW8CInMnWmZvFixdz8+ZNRo0axfr16/H29qZnz578+eefOfrmmpKSwuHDh2nVqtV/A1lZ0apVK8LDw5/7usmTJ1O0aFHefffdl25j+vTpuLq6Zj68vb2znVPkkX0L4Pph6LrEJAsbgE5+xengW4ygtSe5lZCkOo4QRmP5qeWcvHOSkIAQ0yxsQGv9Uq0bbBoJD2JUpxHZkO2Bdnt7e3r37s3WrVs5ffo01apV46OPPsLHx4eHDx9ma1137twhPT0dT0/PJ5739PQkJubZv0h79+7lm2++YdmyZVnaxtixY4mPj898XL16NVsZRR6JPQU7p0OjwSbfjXdKl+rYWOkY+7sMTwkBcP7+eRYfW0y/av2oWbSm6ji5034uWNnA+iEyPGVCcnUV4X+u4P/PXAx57cGDB7z11lssW7YMD4+sXeBlb2+Pi4vLEw+hWHoqrBkA7uWg+TjVaXLNvYAd01/zZfuZW6w+fE11HCGUSs1IZfze8ZR2Kc2gmoNUx8m9AoWh0wI4t1m7i0qYhGwXN8nJyfzyyy+0bt2aihUrcuLECRYtWkR0dHS2bwX38PDA2tqa2NjYJ56PjY3Fy+vp22svXrzI5cuX6dSpEzY2NtjY2PD999+zbt06bGxsuHjxYnbfjlBh9xztzE23JWDroDqNQbSu6slrtUswef1prsc9Vh1HCGWWHV/GufvnCGkcgr21veo4hlG5vXYH1eYx2jw4wuhlq7j56KOPKFasGDNmzKBjx45cvXqVVatW0b59+xzdSmpnZ0edOnXYvn175nMZGRls376dhg0bPrV85cqVOXHiBMeOHct8dO7cmRYtWnDs2DG5nsYU3DgKu2dD00+heC3VaQwquFM1CtjbMHr1cRmeEhbp1N1TLDu+jPd936da4Wqq4xhWuxlgVxDWDpLhKROQrbulrKysKFWqFLVq1XrhhFK///57lgOsWLGCfv368eWXX1K/fn3mzZvHypUrOXPmDJ6envTt25cSJUowffr0Z76+f//+L7xb6p9kEj+FUpPgq2ZgbQfv/wXWptu64Hl2nbtNv28PMqVrdd5qUFp1HCHyTUp6Cr029MLWypafOvyErZX5Hd9c2AY/vg4d5kK991SnES+QrUn8+vbta/BZMnv16sXt27cJCgoiJiaGmjVrsnnz5syLjKOjo2WCMXOxcxrcuwQf7DTLwgagWcUivOFfiumbImlawYPShU1k0jIhcmnxscVcSbjCrx1/Nc/CBqB8K6jzNmwJgnKvgLs0ijZWuWq/YIrkzI0i0Qfg27bQMgiaDFedJk89TE6j3bzdFHd15NcPGmBlZZrT5guRVcduHaPf5n4MrjWY92qY+RmN5AewpBG4lIT+G0G+fBsl+VsReS/lEYQOgJJ1odEnqtPkuYL2Nszp4cfBy/f4NixKdRwh8tTjtMcEhgVS3aM6/av1Vx0n79k7a3NzRe+DA0tUpxHPIcWNyHvbJkHCDe0Dwdo0uxpnV4OyhXk7wIdZf57lwq3szf8khCmZf2Q+MY9iCAkIwcbKMo5vfBqD/0Dts+32WdVpxDNIcSPy1qVdWmPMlsHgUUF1mnw1qm1lShZyZMSqCNLSM1THEcLgDt48yE+RPzGk9hDKuJZRHSd/tQyCQt7anF3paarTiH+Q4kbknaQEWPsxlG4M/gNUp8l3jnbWzOnpx4lrcXy5+5LqOEIY1KPUR0wIm0Bdz7r0qdJHdZz8Z+eknY2+eQzC5qlOI/5BihuRd7YEQuJd6LLIYi+6q13KjQ+blWPetnNE3kxQHUcIg5l9aDb3k+8zOWAyVjrLPL7xrg8BQ2DnDIg5qTqN+B8W+hsp8tz5rXDkO2gbAu4Wdrr6H4a2qkBZj4IMXxlBSpoMTwnTt/f6Xn47/xsj647E29nCJ09tPlYbcl8zANJSVKcR/0+KG2F4j+/DusHaPBB13ladRjl7G2vm9vTjfOwDFv11XnUcIXIlPjme4LBgGhVvRI+KPVTHUc/GXhueuh2pzb4ujIIUN8Lw/hgNKYnQeREYeNJHU1W9hCuDX6nA4p0XibgapzqOEDk24+AMHqc9ZlKjSQaf1NVkFa+ptZTZMxeuH1adRiDFjTC0yPVwfAW8OhNcS6hOY1Q+alGOqsVcGLEqgqTUdNVxhMi27Ve2s+HSBkbXH41XgaebG1u0JiPAqzqsGai1mhFKSXEjDOfRHVg/FCq1B79/qU5jdGytrZjb04/ou4l8tvWc6jhCZMu9pHtM3j+Z5t7N6Vyus+o4xsfaFrp9CfejYEeI6jQWT4obYRh6PWwYBvoM6DhPhqOeo6KnM8PbVGTZnkv8ffme6jhCZIlerydkfwjp+nSCGwbLcNTzFK0CLcbDvkUQvV91GosmxY0wjJO/QeQ6rVuus6fqNEbt/SZlqeVdiBGrIkhMkcm/hPH7I+oPtl7ZSmCDQDwcPVTHMW6NBkPJetrdUymPVKexWFLciNx7EAMbR0C116D6a6rTGD1rKx1ze9YkNiGJmX+cUR1HiBe6nXibqQem0tanLe182qmOY/ysrKHbUu1zcdtE1WkslhQ3Inf0elj3CVjbaWdtRJaU8SjAmHaV+S78CmEX7qiOI8Qz6fV6JoZPxNbKlvH+41XHMR2Fy0HrSXDwK7i0U3UaiyTFjcidYz/B+T+h03xwcledxqT0behDw7KFGbX6OA+SUlXHEeIpoRdC2X1tN8ENg3FzcFMdx7TUex98mmgtaJJkdvL8JsWNyLm4q7B5LPi9AZXbq05jcqysdMzq7ktcYgpTN0aqjiPEE24+vMmsQ7PoXK4zLUq1UB3H9FhZQZfF2qSmW+SsV36T4kbkjF4P6z4Ge2doN111GpPl7e5EYMeq/HroKjvO3FIdRwhAG44K2heEk60To+uPVh3HdLmVhrbT4Mj3cG6L6jQWRYobkTN/f6ONJXdeCI6FVKcxaf+q502zikUY/dtx4hKlN41Qb+XZley/uZ8pjabgYueiOo5pq90XyrfWWtIkyvQP+UWKG5F99y7BlglQ9x0o31J1GpOn0+mY+bovSanpTFx3SnUcYeGuJlxl7uG59KjYg0YlGqmOY/p0Oui8ANIea61pRL6Q4kZkT0Y6hA6CAkWg9RTVacyGl6sDk7pUI/TYDTafvKk6jrBQ6RnpBIYF4u7gzoi6I1THMR8uxeHV2XBiJZxepzqNRZDiRmTP/iUQHQ5dvwD7gqrTmJWuNUvQpqon49ec5M7DZNVxhAX6MfJHjtw6wpSAKRSwLaA6jnnx7QmVO2ozuT+S6R/ymhQ3Iutun4Xtk6HBQPBprDqN2dHpdEx7rQZ6IHDNSfR6vepIwoJcirvEgiMLeLPKm9Tzqqc6jvnR6aDj54AeNgzVbsoQeUaKG5E16WnadOKFSkHLINVpzJZHQXtCulZn86kY1kXcUB1HWIi0jDTG7x1P8YLFGVJ7iOo45qtgUejwGUSuhxOrVacxa1LciKwJmwc3j2nTits6qk5j1trXKEZnv+IErT1FbEKS6jjCAvz75L85fe80IY1DcLBxUB3HvFXrCtW7w6aRkCDX1+UVKW7Ey8WcgJ0zIGAolKyrOo1FmNylGnY2Voz57bgMT4k8dfbeWb6I+IJ3qr+DXxE/1XEsQ/vZYGMP6z+R4ak8IsWNeLG0FFgzEDwqQvMxqtNYjEJOdsx4rQY7zt5m1d/XVMcRZio1PZXxe8dTxrUMA/0Gqo5jOZzcodMCOL8Fjv6oOo1ZkuJGvNjuWXA7UhuOsrFXncaitKziSY86JZm84TTX7ieqjiPM0NLjS7kYd5GpAVOxs7ZTHceyVGoHNd/UWtjERatOY3akuBHPd/0w7PkMmo6CYr6q01ikCZ2q4uJgw6jVx8nIkNPXwnBO3jnJNye+4QO/D6hSuIrqOJap3TRwcIW1gyAjQ3UasyLFjXi21CRtOMqrBjQZrjqNxXJxsGVWdz/2XbzLjweuqI4jzERyejLj946nknsl3qvxnuo4lsvBFbosgqjdWksbYTBS3Ihn2xEC9y9rw1HWtqrTWLTGFTx4q0Fppm86w+U7j1THEWZg0dFFXH1wlakBU7G1kuNbqXItoN57sDUI7l5UncZsSHEjnnYlHPYtglfGQ1E5XW0MxrxamSLO9oxcFUG6DE+JXDgSe4TvTn3H4FqDKe9WXnUcAdBqkjYHTuhHWosbkWtS3IgnpTyC0IHgXR8afqw6jfh/BextmNPDj8PR9/l2b5TqOMJEJaYmEhgWiF8RP/pW7as6jvgP+4LQdQlcPQD7v1CdxixIcSOetDUYHsRoB5qVteo04n/UL+POuwFlmL3lLOdjH6iOI0zQ54c/53bibUIah2Atx7dxKd0IGg6C7VPg1hnVaUyeFDfivy7thEPLoPUkKFxOdRrxDCPbVsLbzZERqyJIS5e7K0TW7b+5n1/P/srQOkMp7VJadRzxLK8EgltpCB2gtbwROSbFjdAkJcDaj8GnCdR7X3Ua8RwOttbM7VmTk9fjWbJTLj4UWfMw5SFBYUHU96pP78q9VccRz2PrCF2Xws0I2Pu56jQmTYoboflzHDy+D10Wg5X8Whizmt6FGNi8HAv+Os+pG/Gq4wgTMPvv2cQnxzM5YDJWOjm+jVrJOtB4GOyaCTePq05jsuS3XMC5P+HoD9B2mnZKVBi9T1pWoFyRgoxYGUFKmgxPiefbfW03v5//nVH1RlGiYAnVcURWNBsNRSppN3ekpahOY5KkuLF0ifdg3SdQvjXUlrsnTIW9jTVze/px4dZDFmw/rzqOMFLxyfFM3DeRxiUa81qF11THEVllY6/d1HH7jHYGR2SbFDeW7o9RkPYYOi8AnU51GpEN1Yq7MqRlBb7YeYFjV+NUxxFGaNqBaSSlJzGx4UR0cnyblmK+0GwM7P0Mrh1WncbkSHFjyU6vgxOr4NXZ4FJcdRqRAwObl6NGCVdGrDxGUqpM/iX+a+uVrWyK2sQ4/3F4FvBUHUfkRONhUMxPu3sq9bHqNCZFihtL9fA2bBgKlTuCb0/VaUQO2VhbMbenH1fvP2bOn2dVxxFG4u7ju0wJn0LLUi3pUKaD6jgip6xttLun7l+Bv0JUpzEpUtxYIr0eNg7T/rvjPBmOMnHlizrzaZtKfBMWxcGoe6rjCMX0ej0h+7V/CCc0mCDDUaauaGVt/pvwxXBln+o0JkOKG0t0YhVErocOn0HBIqrTCAN4p3EZ6pRyY+SqCB4ly+Rflmxj1Ea2RW9jQsMJFHYsrDqOMISGg8DbX7t7Kvmh6jQmQYobS5NwEzaNhOrdoVpX1WmEgVhb6ZjTw4/bD5KZ8YdM3W6pbiXeYtqBabQv057WpVurjiMMxcoaun4BD2/BtmDVaUyCFDeWRK+HdYPBxgHaz1adRhiYj0cBxravzA/7r7D3/B3VcUQ+0+v1BO8LxsHagXH+41THEYZWuBy0ngyHvoaLO1SnMXpS3FiSoz/Aha3QeSE4uatOI/LAm/6lCShfmFGrI0hISlUdR+SjNRfWsPf6XiY2moirvavqOCIv1H0XyjTVWuUkyezkLyLFjaWIi4bN46DWm1Cxreo0Io9YWemY1d2PhKQ0QjacVh1H5JMbD28w69AsupXvRtOSTVXHEXnFykprkZMUr7XMEc8lxY0lyMiAtYPAwVVrsSDMWolCjgR1rMrKv6+xPTJWdRyRxzL0GQSFBeFi58KoeqNUxxF5rVApaDcNjv4IZzerTmO0pLixBH9/A1G7ocsircARZq9H3ZK0qFSEMb+f4P4j6U1jzlacXcGBmANMDphMQbuCquOI/FDrLajQBtZ/orXQEU+R4sbc3b0IW4Og3ntQroXqNCKf6HQ6ZrzuS0paBsHrTqmOI/JIdEI0nx/+nF6VetGgWAPVcUR+0emg0wJIS4ZNn6pOY5SkuDFnGekQ+hEU9IRWk1SnEfnM08WByV2qsS7iBptO3FQdRxhYekY6gWGBeDh6MLzOcNVxRH5zKQbt58DJ1XAqVHUaoyPFjTkLXwxXD2jdZe3ldLUl6uxXnHbVvAgMPcmdh8mq4wgD+uH0Dxy7dYyQgBCcbJ1UxxEq1OgOVTrBxuHaHDgikxQ35urWGa0XScNBULqh6jRCEZ1OR0i36uiAcb+fQK/Xq44kDOBi3EUWHl1I36p9qe1ZW3UcoYpOp7XQQQcbhmlzmQlAihvzlJ6mdZF1K631JBEWzaOgPVO7VWfL6VhCj11XHUfkUlpGGuP3jqeEcwk+rvWx6jhCtQIe0PFzOLMBjq9UncZoSHFjjvZ+DjePa91kbR1VpxFGoF31YnStWZzgtaeIiU9SHUfkwjcnvuHMvTNMDZiKg42D6jjCGFTtDDV6wh+fQsIN1WmMghQ35ubmcdg1AxoPg5J1VKcRRmRS5+o42Foz+rfjMjxlos7cO8PSiKW8U/0dahSpoTqOMCbtZ4GNo9ZiR45vKW7MSlqy1jW2SGVoNlp1GmFkXJ1smfm6L7vO3WbFoauq44hsSklPYfze8ZQtVJaBfgNVxxHGxtFNa61zYRsc+V51GuWkuDEnu2bC7bPQbSnY2KlOI4xQi8pF6VXXmykbTnP1XqLqOCIblkYs5VL8JaY1noatta3qOMIYVWyjTfD35zi4f0V1GqWkuDEX1/7WrrVpPhq85HS1eL7AjlUo5GTHqNXHyciQ09em4Pjt43xz8hsG+A6gknsl1XGEMWs7TTuLs3aQ1nrHQklxYw5SH8OaAVCsJgQMU51GGDlnB1tmd/cl/NJdvg+/rDqOeImktCTG7x1PVfeqvFvjXdVxhLFzcNGaa17eA4eWqU6jjBQ35uCvEK3rd7elYG2jOo0wAY3Ke9CvYWlmbD5D1J1HquOIF1h4dCE3Ht5gauOp2FjJ8S2yoGwzqP8BbA3WWvBYICluTN2VfdpMxC0nQBE5XS2ybvSrlfFycWDEymOky/CUUToce5gfTv/AJ7U/oWyhsqrjCFPSaqLWomHNAK0Vj4UxiuJm8eLF+Pj44ODggL+/PwcPHnzussuWLaNJkya4ubnh5uZGq1atXri8WUt+qN0d5e0PDT5SnUaYGCc7G+b08OPo1Ti+3nNJdRzxD4mpiQTuDaRm0Zq8WeVN1XGEqbEroLXeuXYIwhepTpPvlBc3K1asYPjw4QQHB3PkyBH8/Pxo27Ytt249u0/Gzp076d27Nzt27CA8PBxvb2/atGnD9esWOPPq1iCtn0jXL8DKWnUaYYLq+rjzfpOyzN1yjnOxD1THEf/js8OfcTfpLiEBIVjL8S1yolQDaPSxdunCrUjVafKVTq94Ni9/f3/q1avHokVaZZmRkYG3tzeDBw9mzJgxL319eno6bm5uLFq0iL59+750+YSEBFxdXYmPj8fFxSXX+ZW5+Bf80E3rClv/fdVphAlLSk2n48K9ONhaseajAGytlX/nsXj7buzjw60fMs5/HL0r91YdR5iy1CT4sqk2W/1728BCphFQ+imWkpLC4cOHadWqVeZzVlZWtGrVivDw8CytIzExkdTUVNzd3Z/58+TkZBISEp54mLykeFj7MZRpBnXl7gmROw621szt4UfkzQd8scMyLz40Jg9SHhAUFoR/MX96VeqlOo4wdbYO0G0JxJyAPZ+pTpNvlBY3d+7cIT09HU9Pzyee9/T0JCYmJkvrGD16NMWLF3+iQPpf06dPx9XVNfPh7e2d69zKbR4HSQna7X5W8i1b5J6fdyE+al6OhX+d5+T1eNVxLNqsQ7N4mPqQKY2mYKWT41sYQIk60GQ47J4FN46pTpMvTPrImTFjBr/++itr1qzBweHZDeTGjh1LfHx85uPqVROfdv7sZjj2I7SbDoXMoFATRmPwKxWo4OnMiJURJKdZ3t0VxmDX1V2EXghldL3RFCtYTHUcYU6ajoIiVbSbUNKSVafJc0qLGw8PD6ytrYmNjX3i+djYWLy8vF742jlz5jBjxgy2bNmCr6/vc5ezt7fHxcXliYfJSrwH6z+BCm2gltw9IQzLzsaKz3r6cenOQ+ZtO686jsWJS4pjYvhEmpRoQtfyXVXHEebGxk6bC+3Oedg5XXWaPKe0uLGzs6NOnTps374987mMjAy2b99Ow4YNn/u6WbNmMWXKFDZv3kzdunXzI6px2DRSq7g7LQCdTnUaYYaqFHNhaKuKfLnrIkei76uOY1GmHZhGSnoKExtNRCfHt8gLXtWh+RgImw9XD6lOk6eUD0sNHz6cZcuW8d133xEZGcnAgQN59OgRb7/9NgB9+/Zl7NixmcvPnDmTCRMm8O233+Lj40NMTAwxMTE8fPhQ1VvIH6fWwMnftLujXOR0tcg7HzYtS42ShRixMoLHKTI8lR+2XN7CH5f/YJz/OIo6FVUdR5izgKFQvBaEDoAU822eq7y46dWrF3PmzCEoKIiaNWty7NgxNm/enHmRcXR0NDdv3sxcfsmSJaSkpNC9e3eKFSuW+ZgzZ46qt5D3Ht6CDcOhSmeo0V11GmHmbKytmNvDjxtxj5n15xnVcczencd3mLJ/Cq1Lt6Z9mfaq4whzZ20DXZdC/DX4a4rqNHlG+Tw3+c3k5rnR62HFmxC9HwYdgAIeqhMJC/H1nkuEbIzkl/cb0LBcYdVxzJJer2fojqEcu32MNV3W4O7w7CkthDC4fYtgy3jovxF8GqtOY3DKz9yIlzi+As5sgI6fS2Ej8tXbAWWo7+POp6sjeJicpjqOWdpwaQN/Xf2LCQ0mSGEj8leDgVCqEYR+BMnmNzu5FDfGLP46bBoFNXpC1c6q0wgLY22lY3YPX+4+TGHaJsuauj0/xDyKYfqB6XQo24FWpZ89T5cQecbKGrouhke3YcsE1WkMToobY6XXw7rB2pTZ7WepTiMsVOnCBRjXoQo/H4hm17nbquOYDb1ez8R9E3G0cWRs/bEvf4EQecG9LLSeDIf/DRe2qU5jUFLcGKsj38HF7dB5ITi6qU4jLNib/qVoXN6D0auPE/84VXUcs/Db+d8IuxHGxEYTcbV3VR1HWLK670LZ5rB2MDyOU53GYKS4MUb3r8Cf46HWW1Cxjeo0wsLpdDpmdvflUXIak9efVh3H5F1/eJ3Zh2bzeoXXaVKyieo4wtJZWUHnRZDyEDabz1lEKW6MTUYGrB2kna1pO011GiEAKFHIkQmdqvLbkWtsPR378heIZ8rQZzAhbAKu9q6MrDtSdRwhNIW8tZY+ET/DmU2q0xiEFDfG5uBXcHmP1hTTwQRuVRcWo0edkrSsXJSxv5/g3qMU1XFM0i9nfuFQzCGmBEyhoF1B1XGE+K+afaBiO1g/BB7dVZ0m16S4MSZ3LsC2iVD/AyjbTHUaIZ6g0+mY/loNUtMzmLD2pOo4Judy/GXmHZ5H78q98S/mrzqOEE/S6aDTfEhPgU0jVKfJNSlujEVGutat1aUYtJqoOo0Qz1TUxYEpXauz8fhN1kfcUB3HZKRnpBMYFkhRp6IMrT1UdRwhns3ZCzrM/W+7HxMmxY2x2LcQrh2CrkvAroDqNEI8VyffYrSv4cWEtSe59SBJdRyT8N3p7zh++zghjUNwsnVSHUeI56v+OlTtAhtHwAPTvb5OihtjEHsadkyFRoOhVAPVaYR4IZ1Ox5Qu1bGx0jHu95NYWAeXbLtw/wKLji6iX7V+1CpaS3UcIV5Mp4MOn4GVDWwYqs25ZoKkuFEtPVXrzupeFlqMV51GiCwpXNCeqd1qsC0ylt+OXFcdx2ilZqQybu84SjmX4uNaH6uOI0TWFPCAjvPg7CaI+EV1mhyR4ka1PXMh5qQ2HGXroDqNEFnWtpoXr9UqwaT1p7gR91h1HKP09YmvOXf/HFMbT8Xe2l51HCGyrkpH8P0X/DFG6yBuYqS4UenGMdg9G5qMgBK1VacRItuCO1XDyc6a0b8dl+Gpfzh99zRfRXzFezXeo5pHNdVxhMi+V2eAnZPWCsjEjm8pblRJS4Y1A6BoFWj6qeo0QuSIq5MtM1/3Zc/5O/x8MFp1HKORkp7C+L3jKe9Wng99P1QdR4iccXTTZi+++JfWf8qESHGjys7pcPcCdPsSbOxUpxEix5pXKkrv+t5M3RhJ9N1E1XGMwhfHvuBywmWmNp6KrbWt6jhC5FyFVlC7H/wZCPeiVKfJMiluVLh6CMLmQ/Mx4Cmnq4XpG9+hKu4F7Bi5OoKMDNM6fW1oEbcj+PepfzOo5iAqulVUHUeI3Gs7FQoUhrUfay2CTIAUN/ktJVG7O6p4LQgYqjqNEAZR0N6GWd19ORh1j3/vu6w6jjKP0x4TuDeQaoWr0b9af9VxhDAMe2etJdCVvXDwS9VpskSKm/y2fbJ25XnXpWBtozqNEAbTqJwH/Rv5MGvzGS7efqg6jhILjizg5qObhDQOwcZKjm9hRso0Bf8BWougO+dVp3kpKW7yU9QeOLAEWgZBETldLczP6HaVKV7IkRErI0hLN43T14ZyKOYQP0b+yCe1PqGsa1nVcYQwvJbB4FJCuxkmPU11mheS4ia/JD+AtR9BqUbgP1B1GiHyhKOdNXN6+HL8Whxf7bmkOk6+eZT6iAlhE6hdtDZvVn1TdRwh8oadkzYn240jsG+B6jQvJMVNftkSqLWR77oYrGS3C/NVp7Q77zcty+dbz3EmJkF1nHwx5+853Eu6R0hACFY6Ob6FGSvlr7UK2jENYk+pTvNcchTmhwvb4PByaDNZa7MghJkb1qoiZTwKMGJlBClp5j08FXY9jNXnVjOizgi8XbxVxxEi7zUfB4XLa8NTaSmq0zyTFDd57XEcrB0MZVtA3XdVpxEiXzjYWjO3R03OxDxg0Y4LquPkmYSUBIL2BdGwWEN6VuqpOo4Q+cPWAbot0c7c7JmjOs0zSXGT1zaPgZSH0GWR1m1VCAtRo6Qrg1qUZ/GOC5y4Fq86Tp6YeXAmiamJTA6YjE6Ob2FJitfSZtffPQduHFWd5ilS3OSlMxu1jqrtZoBrSdVphMh3H7coT2UvZ4avPEZSarrqOAb1V/RfrLu4jtH1R+NVwEt1HCHyX9OR2kS0awZAapLqNE+Q4iavPLoL64dAxXZQ8w3VaYRQws7Girk9/bhyN5HPt51THcdg7ifdZ1L4JJqVbEaXcl1UxxFCDWtbrYXQvUuwc5rqNE+Q4iavbBwOGWnQab4MRwmLVtnLhaGtK/DV7kscvnJPdRyDCNkfQro+neCGwTIcJSybZ1VoMQ7CFkD0AdVpMklxkxdO/ganQ6H9HHCW09VCfNCkLDW9CzFiZQSJKcY9+dfLbI7azJYrWxjvP54iTkVUxxFCvUafQMm6WmuhlEeq0wBS3Bjeg1jYOAKqdoXqr6tOI4RRsLG2Yk4PP27GJzFr81nVcXLszuM7hBwIoU3pNrTzaac6jhDGwcpaaymUcBO2TVKdBpDixrD0elj/CVjZQIfPZDhKiP9RrkhBRrerzPJ9l9l38Y7qONmm1+uZuG8i1jprAhsEynCUEP/Lozy0CtYaa0btVp1GihuDOvYznNsMHedp7eGFEE/o38gH/zLufLrqOA+SUlXHyZa1F9ey69oughoG4ebgpjqOEMan/odQujGEDoIktbOTS3FjKPHXtDltfP8FVTqqTiOEUbKy0jGnhx/3E1OYtilSdZwsi3kUw8yDM+lUthMtS7VUHUcI42RlpbUYenxPazmkMorSrZsLvR7Wfgx2BeHVGarTCGHUvN2dGN+hCr8cvMrOs7dUx3kpvV5PUFgQTjZOjK4/WnUcIYybmw+0CYEj38H5bcpiSHFjCH9/C5d2QOeF4Cinq4V4mTfql6JJBQ9G/3ac+ETjHp5adW4V4TfDmRQwCVd7V9VxhDB+dfpDuZaw7mN4fF9JBClucuteFGyZoP1lVmilOo0QJkGn0zGruy+JKelMWm+8nYWvPrjKnL/n8HqF12lcorHqOEKYBp1O+7Kfkgh/jFESQYqb3MjIgLWDtIuH24SoTiOESSnm6sjETtX4/eh1/jwVozrOUzL0GUwIm4C7gzuf1vtUdRwhTItrCXh1Jhz/FSI35PvmpbjJjQNL4UoYdPkC7J1VpxHC5LxWuwStqngyfs0J7j5MVh3nCT9F/sTh2MNMCZhCAdsCquMIYXr8/gWVOsCGofAof6d/kOImp+6ch+2TwH8AlGmiOo0QJkmn0zHtteqkZ+iZsPYker1edSQAouKjmH9kPn2q9KGeVz3VcYQwTToddJoHGelaS6J8PL6luMmJ9DStC6pLCWgZrDqNECatqLMDU7pWZ9OJGNYfv6k6DmkZaQTuDcSrgBdDag9RHUcI01awKHT8DE6v1VoT5RMpbnJi33y4cQS6LQU7J9VphDB5HX2L09G3GBNCT3IrIUlpluWnlnPy7klCAkJwtHFUmkUIs1CtG1R7TWtN9CB/rq+T4ia7Yk/BjunQaDB411edRgizMaVLdWytrRj7+wllw1Pn7p9j8bHF9K/Wn5pFayrJIIRZ6jAXbOxh/ZB8GZ6S4iY70lJgzYdQuDy0GK86jRBmxa2AHdNfq8H2M7dYdfhavm8/NT2V8XvH4+Piw6Cag/J9+0KYNSd36DRfa1F07Kc835wUN9mxZw7cioRuS7QKVAhhUK2retK9TkmmrD/N9bjH+brtr058xYX7F5jaeCp21nb5um0hLEKlV6FmH9g8FuKu5ummpLjJqutHYPccaDISitdSnUYIsxXUqSoFHWwYvfp4vg1PnbpzimXHl/GB7wdULVw1X7YphEVqN12bOmXdx3k6PCXFTVakJkHoQPCsBk1Hqk4jhFlzcbBl5uu+7L1whx8PROf59pLTkxm/dzwV3Srynu97eb49ISyag6s2e/GlnfD3N3m2GSlusmLnNLh3Cbp9Cda2qtMIYfaaVixCH/9STN8UyZW7j/J0W4uPLSb6QTRTG0/F1kqObyHyXPmWUPcd2BKk/duaB6S4eZnoAxC2AFqMA085XS1EfhnXvgqFC9rx6arjZGTkzenrY7eOsfzkcgbVHEQFtwp5sg0hxDO0ngIFPCB0kNbKyMCkuHmRlEcQOgBK1oVGn6hOI4RFKWBvw5zufhy6co9vw6IMvv7E1ETG7x2PbxFf+lfrb/D1CyFewL4gdF0C0eFwYInBVy/FzYtsmwQJN6HrUrCyVp1GCIvjX7Ywbzcqw6w/z3Lh1kODrnv+kfncSrxFSEAI1nJ8C5H/fAKgwUDt39rb5wy6ailunidqNxz8ElpNBI/yqtMIYbFGtatESTdHRqyKIC3dMKevD948yM9nfmZonaH4uPoYZJ1CiBxoGQSFSmmjJOlpBlutFDfPkpSgjQP6NIH6H6hOI4RFc7C1Zm4PP05ci+PL3bm/+PBhykMmhE2gnlc9elfubYCEQogcs3XUWhndOAph8wy2WilunmVLIDy+B10WgZXsIiFUq1XKjQHNyjFv2zlO30jI1brm/D2HuOQ4JjeajJVOjm8hlCtZFwKGws4ZEHPSIKuUI/ufzm+FI99BmxBw81GdRgjx/4a0qkC5IgUZsSqClLScDU/tubaH387/xsh6IynpXNLACYUQOdZ8DHhUhDUDtFZHuSTFzf96fB/WDYZyLaFOf9VphBD/w97Gmjk9/Dgf+4CFf53P9uvjk+OZuG8iAcUD6F6hex4kFELkmI291trodiTsnp3r1Ulx87/+GA0pidrsiTqd6jRCiH+oXsKVwa9U4IudF4m4Gpet1844OIPHaY+Z2GgiOjm+hTA+xfyg6SjYMxeuH87VqqS4+Y/I9XB8BbSfBa4lVKcRQjzHRy3KUbWYCyNWRZCUmp6l12y/sp0NlzYw1n8sXgW88jihECLHmgwHrxqwZqDW+iiHpLgBeHQH1g+FSh3At5fqNEKIF7C1tmJuTz+i7yby2daXz41xL+kek/dPpoV3CzqW7ZgPCYUQOWZtq909dT8KdoTkeDVS3Oj1sGEY6DOg0zwZjhLCBFT0dGZEm4os23OJQ5fvPXc5vV5PyP4QMvQZBDUMkuEoIUxB0SrwSiDsWwTR+3O0CiluTv4Gkeug42dQsKjqNEKILHqvSVlql3Lj/e//Ztvp2Kd+npiaSGBYIFuvbCWwQSAejh4KUgohcqThx+BdX7t7KiX7zXMtu7h5EAMbR0C116BaN9VphBDZYG2l4+u+dalTyo33vv+byetPZ94ifvbeWXpt6MXWK1uZ2ngqbX3aKk4rhMgWK2ut99SDGNg2Mdsv1+n1+rxpt2ukEhIScHV1JT4uDpcN72uzIg46AE7uqqMJIXJAr9fzbdhlZvwRSSUvZzo0iuLr0/PxcfVhTrM5lHEtozqiECKnDnwJf4yCvmuhbPMsv8woztwsXrwYHx8fHBwc8Pf35+DBgy9cftWqVVSuXBkHBwdq1KjBpk2bsr/R4yvh/J/QeYEUNkKYMJ1Ox7uNy/D9ezW4YfclX5ycTS33tvzc4WcpbIQwdfXe11ohrf1Ya42URcqLmxUrVjB8+HCCg4M5cuQIfn5+tG3bllu3bj1z+X379tG7d2/effddjh49SteuXenatSsnT2ZzyuatwVCzD1R61QDvQgih0vHbx5l05H3snS9SzWYw2/YEEBx6lscpWbtVXAhhpKysoMtibZLdP8dl+WXKh6X8/f2pV68eixYtAiAjIwNvb28GDx7MmDFjnlq+V69ePHr0iA0bNmQ+16BBA2rWrMnSpUtfur3MYampFXEZfhAcXA33ZoQQ+SpDn8F3p75jwZEFVPWoyqymsyheoDgrDl1l4vpTlHJ3YtEbtano6aw6qhAiNw5/B+s/gYnxWVrcJo/jvFBKSgqHDx9m7Nixmc9ZWVnRqlUrwsPDn/ma8PBwhg8f/sRzbdu2JTQ09JnLJycnk5ycnPnn+HhtxzRyssb6+6a5fAdCCJX06EnLSOOtKm/xgd8H2GbY8uDBA9pXLkRFd19Groqg9czN2EgDXCFMXAEWWlencUICzs7OL53WQWlxc+fOHdLT0/H09HzieU9PT86cOfPM18TExDxz+ZiYmGcuP336dCZNmvTU86eGReYwtRDC2Iz7//8JIcxXB4BZrsTHx+Pi4vLCZZUWN/lh7NixT5zpiYuLo3Tp0kRHR+PqKkNSuZWQkIC3tzdXr1596S+beDnZn4Yl+9PwZJ8aluzP7HN2fvkws9LixsPDA2tra2Jjn5yAKzY2Fi+vZ/d/8fLyytby9vb22NvbP/W8q6ur/CIZkIuLi+xPA5L9aViyPw1P9qlhyf40LKUD0XZ2dtSpU4ft27dnPpeRkcH27dtp2LDhM1/TsGHDJ5YH2Lp163OXF0IIIYRlUT4sNXz4cPr160fdunWpX78+8+bN49GjR7z99tsA9O3blxIlSjB9+nQAhgwZQrNmzZg7dy4dOnTg119/5e+//+arr75S+TaEEEIIYSSUFze9evXi9u3bBAUFERMTQ82aNdm8eXPmRcPR0dFY/c+dDo0aNeLnn38mMDCQcePGUaFCBUJDQ6levXqWtmdvb09wcPAzh6pE9sn+NCzZn4Yl+9PwZJ8aluzPvKF8nhshhBBCCEOSyR+EEEIIYVakuBFCCCGEWZHiRgghhBBmRYobIYQQQpgVsyhuFi9ejI+PDw4ODvj7+3Pw4MEXLr9q1SoqV66Mg4MDNWrUYNOmTU/8XK/XExQURLFixXB0dKRVq1acP38+L9+CUTH0/uzfvz86ne6JR7t27fLyLRiV7OzPU6dO8frrr+Pj44NOp2PevHm5Xqe5MfT+nDhx4lO/n5UrV87Dd2BcsrM/ly1bRpMmTXBzc8PNzY1WrVo9tbx8fhp2f1r652eO6U3cr7/+qrezs9N/++23+lOnTunff/99faFChfSxsbHPXD4sLExvbW2tnzVrlv706dP6wMBAva2trf7EiROZy8yYMUPv6uqqDw0N1UdEROg7d+6sL1OmjP7x48f59baUyYv92a9fP327du30N2/ezHzcu3cvv96SUtndnwcPHtSPHDlS/8svv+i9vLz0n3/+ea7XaU7yYn8GBwfrq1Wr9sTv5+3bt/P4nRiH7O7PN954Q7948WL90aNH9ZGRkfr+/fvrXV1d9deuXctcRj4/Dbs/LfnzMzdMvripX7++ftCgQZl/Tk9P1xcvXlw/ffr0Zy7fs2dPfYcOHZ54zt/fX//hhx/q9Xq9PiMjQ+/l5aWfPXt25s/j4uL09vb2+l9++SUP3oFxMfT+1Ou1g7NLly55ktfYZXd//q/SpUs/8x/j3KzT1OXF/gwODtb7+fkZMKXpyO3vUlpamt7Z2Vn/3Xff6fV6+fw09P7U6y378zM3THpYKiUlhcOHD9OqVavM56ysrGjVqhXh4eHPfE14ePgTywO0bds2c/moqChiYmKeWMbV1RV/f//nrtNc5MX+/I+dO3dStGhRKlWqxMCBA7l7967h34CRycn+VLFOU5GX7/38+fMUL16csmXL0qdPH6Kjo3Mb1+gZYn8mJiaSmpqKu7s7IJ+fht6f/2GJn5+5ZdLFzZ07d0hPT8+czfg/PD09iYmJeeZrYmJiXrj8f/4/O+s0F3mxPwHatWvH999/z/bt25k5cya7du3i1VdfJT093fBvwojkZH+qWKepyKv37u/vz/Lly9m8eTNLliwhKiqKJk2a8ODBg9xGNmqG2J+jR4+mePHimf+gy+enYfcnWO7nZ24pb78gzN+//vWvzP+uUaMGvr6+lCtXjp07d9KyZUuFyYSAV199NfO/fX198ff3p3Tp0qxcuZJ3331XYTLjNmPGDH799Vd27tyJg4OD6jgm73n7Uz4/c8akz9x4eHhgbW1NbGzsE8/Hxsbi5eX1zNd4eXm9cPn//H921mku8mJ/PkvZsmXx8PDgwoULuQ9txHKyP1Ws01Tk13svVKgQFStWlN/PF5gzZw4zZsxgy5Yt+Pr6Zj4vn5+G3Z/PYimfn7ll0sWNnZ0dderUYfv27ZnPZWRksH37dho2bPjM1zRs2PCJ5QG2bt2auXyZMmXw8vJ6YpmEhAQOHDjw3HWai7zYn89y7do17t69S7FixQwT3EjlZH+qWKepyK/3/vDhQy5evCi/n88xa9YspkyZwubNm6lbt+4TP5PPT8Puz2exlM/PXFN9RXNu/frrr3p7e3v98uXL9adPn9Z/8MEH+kKFCuljYmL0er1e/9Zbb+nHjBmTuXxYWJjexsZGP2fOHH1kZKQ+ODj4mbeCFypUSL927Vr98ePH9V26dLGoWxkNuT8fPHigHzlypD48PFwfFRWl37Ztm7527dr6ChUq6JOSkpS8x/yU3f2ZnJysP3r0qP7o0aP6YsWK6UeOHKk/evSo/vz581lepznLi/05YsQI/c6dO/VRUVH6sLAwfatWrfQeHh76W7du5fv7y2/Z3Z8zZszQ29nZ6VevXv3ErckPHjx4Yhn5/DTM/rT0z8/cMPniRq/X6xcuXKgvVaqU3s7OTl+/fn39/v37M3/WrFkzfb9+/Z5YfuXKlfqKFSvq7ezs9NWqVdNv3LjxiZ9nZGToJ0yYoPf09NTb29vrW7ZsqT979mx+vBWjYMj9mZiYqG/Tpo2+SJEieltbW33p0qX177//vkX8Q/wf2dmfUVFReuCpR7NmzbK8TnNn6P3Zq1cvfbFixfR2dnb6EiVK6Hv16qW/cOFCPr4jtbKzP0uXLv3M/RkcHJy5jHx+Gm5/yudnzun0er0+f88VCSGEEELkHZO+5kYIIYQQ4p+kuBFCCCGEWZHiRgghhBBmRYobIYQQQpgVKW6EEEIIYVakuBFCCCGEWZHiRgghhBBmRYobIYQQQpgVKW6EEGZn586d6HQ64uLiVEcRQiggMxQLIUxe8+bNqVmzJvPmzQMgJSWFe/fu4enpiU6nUxtOCJHvbFQHEEIIQ7Ozs8PLy0t1DCGEIjIsJYQwaf3792fXrl3Mnz8fnU6HTqdj+fLlMiwlhAWT4kYIYdLmz59Pw4YNef/997l58yY3b97E29tbdSwhhEJS3AghTJqrqyt2dnY4OTnh5eWFl5cX1tbWqmMJIRSS4kYIIYQQZkWKGyGEEEKYFSluhBAmz87OjvT0dNUxhBBGQm4FF0KYPB8fHw4cOMDly5cpWLAgGRkZqiMJIRSSMzdCCJM3cuRIrK2tqVq1KkWKFCE6Olp1JCGEQjJDsRBCCCHMipy5EUIIIYRZkeJGCCGEEGZFihshhBBCmBUpboQQQghhVqS4EUIIIYRZkeJGCCGEEGZFihshhBBCmBUpboQQQghhVqS4+b9260AGAAAAYJC/9T2+oggAWJEbAGBFbgCAlQBuHoPqLkE94QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from skfuzzy import control as ctrl\n",
    "import skfuzzy as fuzz\n",
    "\n",
    "temp = ctrl.Antecedent(density_train[\"T\"].sort_values().unique(), \"temp\")\n",
    "al = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"al\")\n",
    "ti = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"ti\")\n",
    "density = ctrl.Consequent(np.arange(1.03, 1.22, 0.00001), \"density\")\n",
    "\n",
    "temp.automf(3, variable_type=\"quant\")\n",
    "temp.view()\n",
    "al.automf(3, variable_type=\"quant\")\n",
    "al.view()\n",
    "ti.automf(3, variable_type=\"quant\")\n",
    "ti.view()\n",
    "density.automf(5, variable_type=\"quant\")\n",
    "density.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[IF (al[low] AND ti[low]) AND temp[high] THEN density[lower]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[low]) AND temp[average] THEN density[lower]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[low]) AND temp[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF al[high] AND temp[high] THEN density[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF al[high] AND temp[average] THEN density[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF al[high] AND temp[low] THEN density[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[average]) AND temp[high] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[average]) AND temp[average] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[average]) AND temp[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[high]) AND temp[high] THEN density[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[high]) AND temp[average] THEN density[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[low] AND ti[high]) AND temp[low] THEN density[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[average] AND ti[low]) AND temp[high] THEN density[lower]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[average] AND ti[low]) AND temp[average] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (al[average] AND ti[low]) AND temp[low] THEN density[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import  get_fuzzy_rules\n",
    "\n",
    "fuzzy_variables = {\"Al2O3\": al, \"TiO2\": ti, \"T\": temp, \"consequent\": density}\n",
    "fuzzy_rules = get_fuzzy_rules(clustered_rules, fuzzy_variables)\n",
    "\n",
    "fuzzy_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
    "\n",
    "sim = ctrl.ControlSystemSimulation(fuzzy_cntrl, lenient=False)\n",
    "\n",
    "display(len(fuzzy_rules))\n",
    "fuzzy_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: al                      = 0.0\n",
      "  - low                             : 1.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "Antecedent: ti                      = 0.0\n",
      "  - low                             : 1.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "Antecedent: temp                    = 20\n",
      "  - low                             : 1.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF (al[low] AND ti[low]) AND temp[high] THEN density[lower]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - temp[high]                                             : 0.0\n",
      "                      (al[low] AND ti[low]) AND temp[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            density[lower] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF (al[low] AND ti[low]) AND temp[average] THEN density[lower]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - temp[average]                                          : 0.0\n",
      "                   (al[low] AND ti[low]) AND temp[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            density[lower] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF (al[low] AND ti[low]) AND temp[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - temp[low]                                              : 1.0\n",
      "                       (al[low] AND ti[low]) AND temp[low] = 1.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 1.0\n",
      "\n",
      "RULE #3:\n",
      "  IF al[high] AND temp[high] THEN density[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[high]                                               : 0.0\n",
      "  - temp[high]                                             : 0.0\n",
      "                                   al[high] AND temp[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          density[average] : 0.0\n",
      "\n",
      "RULE #4:\n",
      "  IF al[high] AND temp[average] THEN density[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[high]                                               : 0.0\n",
      "  - temp[average]                                          : 0.0\n",
      "                                al[high] AND temp[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             density[high] : 0.0\n",
      "\n",
      "RULE #5:\n",
      "  IF al[high] AND temp[low] THEN density[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[high]                                               : 0.0\n",
      "  - temp[low]                                              : 1.0\n",
      "                                    al[high] AND temp[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             density[high] : 0.0\n",
      "\n",
      "RULE #6:\n",
      "  IF (al[low] AND ti[average]) AND temp[high] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[average]                                            : 0.0\n",
      "  - temp[high]                                             : 0.0\n",
      "                  (al[low] AND ti[average]) AND temp[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #7:\n",
      "  IF (al[low] AND ti[average]) AND temp[average] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[average]                                            : 0.0\n",
      "  - temp[average]                                          : 0.0\n",
      "               (al[low] AND ti[average]) AND temp[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #8:\n",
      "  IF (al[low] AND ti[average]) AND temp[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[average]                                            : 0.0\n",
      "  - temp[low]                                              : 1.0\n",
      "                   (al[low] AND ti[average]) AND temp[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #9:\n",
      "  IF (al[low] AND ti[high]) AND temp[high] THEN density[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[high]                                               : 0.0\n",
      "  - temp[high]                                             : 0.0\n",
      "                     (al[low] AND ti[high]) AND temp[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                             density[high] : 0.0\n",
      "\n",
      "RULE #10:\n",
      "  IF (al[low] AND ti[high]) AND temp[average] THEN density[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[high]                                               : 0.0\n",
      "  - temp[average]                                          : 0.0\n",
      "                  (al[low] AND ti[high]) AND temp[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           density[higher] : 0.0\n",
      "\n",
      "RULE #11:\n",
      "  IF (al[low] AND ti[high]) AND temp[low] THEN density[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[low]                                                : 1.0\n",
      "  - ti[high]                                               : 0.0\n",
      "  - temp[low]                                              : 1.0\n",
      "                      (al[low] AND ti[high]) AND temp[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           density[higher] : 0.0\n",
      "\n",
      "RULE #12:\n",
      "  IF (al[average] AND ti[low]) AND temp[high] THEN density[lower]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[average]                                            : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - temp[high]                                             : 0.0\n",
      "                  (al[average] AND ti[low]) AND temp[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            density[lower] : 0.0\n",
      "\n",
      "RULE #13:\n",
      "  IF (al[average] AND ti[low]) AND temp[average] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[average]                                            : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - temp[average]                                          : 0.0\n",
      "               (al[average] AND ti[low]) AND temp[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "RULE #14:\n",
      "  IF (al[average] AND ti[low]) AND temp[low] THEN density[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - al[average]                                            : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - temp[low]                                              : 1.0\n",
      "                   (al[average] AND ti[low]) AND temp[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              density[low] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: density                  = 1.0774975002635008\n",
      "  lower:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  low:\n",
      "    Accumulate using accumulation_max : 1.0\n",
      "  average:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  high:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  higher:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(1.0774975002635008)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.input[\"temp\"] = 20\n",
    "sim.input[\"al\"] = 0.0\n",
    "sim.input[\"ti\"] = 0.0\n",
    "sim.compute()\n",
    "sim.print_state()\n",
    "display(sim.output[\"density\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "      <th>DensityPred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.06250</td>\n",
       "      <td>1.077498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05979</td>\n",
       "      <td>1.076593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05404</td>\n",
       "      <td>1.069156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05103</td>\n",
       "      <td>1.061106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.04794</td>\n",
       "      <td>1.045833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.04477</td>\n",
       "      <td>1.046360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.03826</td>\n",
       "      <td>1.047642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.03484</td>\n",
       "      <td>1.046360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.03182</td>\n",
       "      <td>1.045833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.08755</td>\n",
       "      <td>1.077498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.07105</td>\n",
       "      <td>1.067145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.06760</td>\n",
       "      <td>1.067145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.06409</td>\n",
       "      <td>1.067988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05691</td>\n",
       "      <td>1.062538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>70</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05291</td>\n",
       "      <td>1.047191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     T  Al2O3  TiO2  Density  DensityPred\n",
       "0   20   0.00   0.0  1.06250     1.077498\n",
       "1   25   0.00   0.0  1.05979     1.076593\n",
       "2   35   0.00   0.0  1.05404     1.069156\n",
       "3   40   0.00   0.0  1.05103     1.061106\n",
       "4   45   0.00   0.0  1.04794     1.045833\n",
       "5   50   0.00   0.0  1.04477     1.046360\n",
       "6   60   0.00   0.0  1.03826     1.047642\n",
       "7   65   0.00   0.0  1.03484     1.046360\n",
       "8   70   0.00   0.0  1.03182     1.045833\n",
       "9   20   0.05   0.0  1.08755     1.077498\n",
       "10  45   0.05   0.0  1.07105     1.067145\n",
       "11  50   0.05   0.0  1.06760     1.067145\n",
       "12  55   0.05   0.0  1.06409     1.067988\n",
       "13  65   0.05   0.0  1.05691     1.062538\n",
       "14  70   0.05   0.0  1.05291     1.047191"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fuzzy_pred(row):\n",
    "    sim.input[\"temp\"] = row[\"T\"]\n",
    "    sim.input[\"al\"] = row[\"Al2O3\"]\n",
    "    sim.input[\"ti\"] = row[\"TiO2\"]\n",
    "    sim.compute()\n",
    "    return sim.output[\"density\"]\n",
    "\n",
    "result_train = density_train.copy()\n",
    "result_train[\"DensityPred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
    "result_train.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "      <th>DensityPred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.05696</td>\n",
       "      <td>1.073918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.04158</td>\n",
       "      <td>1.047642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.08438</td>\n",
       "      <td>1.076518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.08112</td>\n",
       "      <td>1.073918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.07781</td>\n",
       "      <td>1.069156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.07446</td>\n",
       "      <td>1.067145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.06053</td>\n",
       "      <td>1.067988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.17459</td>\n",
       "      <td>1.172492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.14812</td>\n",
       "      <td>1.136460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.07424</td>\n",
       "      <td>1.067145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.07075</td>\n",
       "      <td>1.067145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.06721</td>\n",
       "      <td>1.067988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.22417</td>\n",
       "      <td>1.204157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.21310</td>\n",
       "      <td>1.202348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.20265</td>\n",
       "      <td>1.203630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.18265</td>\n",
       "      <td>1.176072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.17261</td>\n",
       "      <td>1.172492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     T  Al2O3  TiO2  Density  DensityPred\n",
       "0   30   0.00  0.00  1.05696     1.073918\n",
       "1   55   0.00  0.00  1.04158     1.047642\n",
       "2   25   0.05  0.00  1.08438     1.076518\n",
       "3   30   0.05  0.00  1.08112     1.073918\n",
       "4   35   0.05  0.00  1.07781     1.069156\n",
       "5   40   0.05  0.00  1.07446     1.067145\n",
       "6   60   0.05  0.00  1.06053     1.067988\n",
       "7   35   0.30  0.00  1.17459     1.172492\n",
       "8   65   0.30  0.00  1.14812     1.136460\n",
       "9   45   0.00  0.05  1.07424     1.067145\n",
       "10  50   0.00  0.05  1.07075     1.067145\n",
       "11  55   0.00  0.05  1.06721     1.067988\n",
       "12  20   0.00  0.30  1.22417     1.204157\n",
       "13  30   0.00  0.30  1.21310     1.202348\n",
       "14  40   0.00  0.30  1.20265     1.203630\n",
       "15  60   0.00  0.30  1.18265     1.176072\n",
       "16  70   0.00  0.30  1.17261     1.172492"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test = density_test.copy()\n",
    "result_test[\"DensityPred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_train': 0.009765373953597112,\n",
       " 'RMSE_test': 0.009031443610368107,\n",
       " 'RMAE_test': 0.08581225574298121,\n",
       " 'R2_test': 0.978451748357252}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "rmetrics = {}\n",
    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_train[\"Density\"], result_train[\"DensityPred\"])\n",
    ")\n",
    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
    ")\n",
    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
    ")\n",
    "rmetrics[\"R2_test\"] = metrics.r2_score(\n",
    "    result_test[\"Density\"], result_test[\"DensityPred\"]\n",
    ")\n",
    "\n",
    "rmetrics"
   ]
  }
 ],
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