{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Лабораторная 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вариант 7. Экономика стран"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Бизнес-цели:\n",
    "1) прогнозирование уровня инфляции на основе данных за года\n",
    "2) определение факторов, значительно влияющих на показателль ВВП на душу населения"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Технические цели:\n",
    "1) Разработать МО для прогнозирования уровня инфляции на основе исторических данных\n",
    "2) Проанализировать взаимосвязь между экономическими показателями и ВВП"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n",
      "       'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n",
      "       'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n",
      "       'tradebalance', 'USTreasury'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\".//csv//EconomicData.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подготовка данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stock index             0\n",
      "country                 0\n",
      "year                    0\n",
      "index price            52\n",
      "log_indexprice          0\n",
      "inflationrate          43\n",
      "oil prices              0\n",
      "exchange_rate           2\n",
      "gdppercent             19\n",
      "percapitaincome         1\n",
      "unemploymentrate       21\n",
      "manufacturingoutput    91\n",
      "tradebalance            4\n",
      "USTreasury              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заполним пустые значения медианами:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    if (column != \"stock index\" and column != \"country\"):\n",
    "        df[column].fillna(df[column].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+0AAAK9CAYAAABRvo1QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADX6klEQVR4nOzdd5hdVb3/8c8+vUzPJFPSQyopJCQkhK4GAgKK8JNyvRdELnhFFIggoBJAkQgCItIEC6hwQa8KFohIJNSQQEIINaRMemYyJTNn5vSz9/79EXNgmJLps2fyfj1PHjJr7bPO90wJ8zlr7bUM27ZtAQAAAAAAx3H1dwEAAAAAAKB1hHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AOCgtWbJEhmHo6aefbtFXVVUln8+nz3/+8/1QGSTp9ttvl2EY2rJlS3+XAgBAvyK0AwAOShdeeKE8Ho9+8YtftOj71a9+pXQ6rUsuuaQfKgMAAPgIoR0AcFAqLS3Vaaedpr/+9a+qrKzMttu2rV/84hcaOXKkTjnllH6sEAAAgNAOADiIXXzxxcpkMnr44Yezbc8995w2b96sr3zlK3K59v1v8uGHH5ZhGHrxxRf11a9+VUOGDFFeXp7OP/987d27t8W49913n6ZOnSq/36/y8nJ9/etfV319fbNrTjjhBBmGkf1TXFysU089Ve+8806L8X73u99p9uzZCgaDKioq0rnnnqvt27e3GG/atGktHtvaMvMxY8botNNO68Rn6iNjxozRl7/85WZtf/jDH2QYhsaMGZNt27JliwzD0O23395ijGnTpumEE05o1rZ69WrNmjVLoVBI5513nhKJhCTpnXfe0YwZMxQOh3X++ecrFou1GO/GG29s9rnc/+eTdT7//PM69thjVVhY2Oy6yy67rMVYNTU1zR77xhtvyDCMZt8r+1/jx9sk6etf/3qL59//PfTxr4NlWZoxY0arYwAAsJ+nvwsAAKC/nHzyyRo5cqR++ctf6pprrpFhGHrooYfkcrl00UUXtbj+sssuU0FBgW688UatX79e999/v7Zu3arly5fLMAxJ+0LfTTfdpAULFuhrX/ta9rrXX39dr7zyirxeb3a8yZMn67vf/a5s29amTZt055136rOf/ay2bduWveaHP/yhrr/+ep199tn67//+b1VXV+tnP/uZjjvuOL355psqKCjo9c9TezKZjL773e92a4xIJKKTTz5ZwWBQN998s958803dfffdkvZ9zv/nf/5HlZWVuvvuuxUMBvXzn/+81XF++9vfZv9+5ZVXNuurqKjQqaeeqrKyMi1evFhDhw6VJP3Xf/1Xt2r/pI0bN+qhhx7q0LW//e1v9fbbb/fo8wMABh9COwDgoOVyufSVr3xFN910k5YvX65p06bpqaeeyob5T/L5fFq2bFk2eI8ePVrf/va39de//lWf+9znVF1drSVLluikk07SM888k52pnzx5si677DL97ne/04UXXpgdr6SkRP/5n/+Z/TidTuuWW25RdXW1hg4dqq1bt+qGG27QzTffrO985zvZ684880zNmjVL9913X7P2/vDQQw9p27Zt+tSnPqXNmzd3aYxf/vKXqq2t1fvvv69JkyZJkr74xS/q//7v/3Tbbbfp7LPPliSFQiHdeeed+v73v6+SkpLs4zOZjAzDaPa5/N73vtfsOf75z38qHo/r0Ucf1ZFHHplt7+nQ/t3vfleTJk1SQ0NDu9clk0ktXrxYp5xyip555pkerQEAMLiwPB4AcFC76KKL5HK59NBDD+nXv/61UqlUmxvQXXLJJc1myr/2ta/J4/Fkd6B/7rnnlEqldMUVV2QDu7RvGX5eXp7+/ve/NxsvnU6rpqZG1dXVWrFihf785z9rxowZKi4uliT96U9/kmVZOvvss1VTU5P9U1paqgkTJuj5559vNp5pms2uq6mpaXU5+cefu7a2VplMpvOfOEmxWEzf//73ddlll2nUqFFtXvPJmkzTbHbNsmXLdOihh2YDuyTNmzdPkjR37txs25lnnqlkMqmXX3652eNTqZT8fn+7tTY2NkqShgwZ0vEX2EmrV6/WH/7wBy1ZsqTZ17819957r2pra3XDDTf0Wj0AgMGBmXYAwEFt5MiROvnkk/WnP/1Jr776qsrLy3Xqqae2eu2ECROafZyTk6OysrLsfcpbt26VpGbhU9o3Qz9u3Lhs/36vvvpqdpn2/vGffPLJ7FL7DRs2yLbtFs+738ffQJCkDz74oNl47Xn22Wez17rdbs2YMUM/+tGPdNJJJ3Xo8ZJ05513KpFI6Dvf+Y4WLVrU6jU33HBDq8H04zPl27dv1/Dhww/4fPuv+eT9/PX19crJyWn3sfPnz5ckXX311VqyZEmHP0+dce211+rYY4/Vaaed1uw++U9qaGjQLbfcokWLFjX7PAAA0BpCOwDgoHfxxRfr6aef1tatW/Xd735XHk/f/O9xxowZuuOOOyRJ1dXVuvvuu3XCCSdozZo1Ki0tlWVZMgxDzzzzjNxud4vHfzKojhkzpsX91H/4wx/04IMPtnjsvHnzdPPNN0uSdu3apVtvvVVf+MIX9O677zbbUK4tNTU1+vGPf6zrrrtORUVFbV53ySWX6Itf/GKztosvvrjZx/s3neuoeDze7OPKykqVlpa2+5ijjjpKP/7xj3XTTTfp0EMP7dTzdcSzzz6r5557TitWrDjgtbfeeqtcLpeuvvpq1dbW9ngtAIDBhdAOADjonXbaaRo2bJiqq6v13//9321et2HDBn3qU5/KftzU1KTdu3frs5/9rKR997hL0vr16zVu3LjsdalUShUVFVqwYEGz8QoLC5u1nXDCCSovL9evf/1rXXfddTrkkENk27bGjh2riRMnHvB1hMPhFs+xdu3aVq8tLi5udu348eN19NFH68UXX+xQaL/55puVm5uryy+/vN3rJkyY0KKmcDjc7OOysjLt2rXrgM+5c+dOSVJ5eXmz9vfee0+HH374AR9/1VVXacOGDfrjH/+o3/zmN/L5fDrxxBMP+LgDsW1b1157rb7whS80u1++Nbt27dJPf/pTLVmyRLm5uYR2AMABcU87AOCgV1dXp4aGBp144ontBtYHH3xQ6XQ6+/H999+vTCaTPc99wYIF8vl8uvvuu2Xbdva6X/7yl2poaGhz2f1++2eQk8mkpH33cLvdbt10003NxpP2BcWeDHyWZUlSqzP6n7Rlyxbdf//9uvHGGxUMBrv93Mcdd5zeffddffjhh9m2lStXSpJWrVqVbXvyySclSccee2y27Y033tCmTZv06U9/+oDP89e//lUPPvigfvGLX+izn/1sizcTuurxxx/XunXrtGTJkgNee9NNN6mkpET/8z//0yPPDQAY/JhpBwActNatW6f/+7//0zPPPKNkMtnufcjSvhnzz3zmMzr77LO1fv163XfffTrmmGP0uc99TpI0dOhQXXfddbrpppt08skn63Of+1z2uiOOOKLZ7uaSVFVVpd/97neS9i03//nPfy6Px5M9Q/2QQw7RzTffrOuuu05btmzRGWecodzcXFVUVOjPf/6zLrnkEl111VVdeu3V1dVaunSpJGn37t269dZblZ+f32wlQVteeOEFTZkypdlO+N1x6aWX6mc/+5lOPPFEXXHFFVqzZo1efPFFSfvuE6+oqMge+XbuuedmVzF8//vf109/+lONGzdO559/frvPUVlZqYsuukj//d//rTPOOOOANf3rX/9SXl5e9uMNGzZIkt5++229/fbbmj59erbv2Wef1cUXX9xiL4PWPPvss3r00Ufl8/kOeC0AABKhHQBwEFuzZo1uueUWlZeXa/HixTr99NPbvf6ee+7Ro48+qsWLFyudTuu8887T3Xffnd04Ttp3TvvQoUN1zz336Morr1RRUZEuueQS3XLLLa1uHLf/yLGCggJNnTpVd955p+bMmZO95tprr9XEiRP1k5/8RDfddJOkfZvnnXTSSdk3C7pi1apV2RUCxcXFOvzww/XII4+0WHrelltuuaVDs/IdUVpaqqVLl+qSSy7Rd77zHZ1xxhn65je/qe9973v62c9+puuuu04bN27UOeec0+z+/IceekhnnHGGbr75ZoVCoTbHt21bF154oQoKCnTXXXd1qKZzzjmn1fY777xTtbW1evjhh7NtwWBQN954Y4fGnTlzps4777wOXQsAgCQZ9ifX2wEAgGYefvhhXXjhhXr99debBWr0nttvv11XX321KioqOnSPfV/58pe/LEnNQjsAAL2Je9oBAAAAAHAolscDAAB00MfvZQcAoC8Q2gEAADroW9/6Vn+XAAA4yHBPOwAAAAAADsU97QAAAAAAOBShHQAAAAAAh+KedkmWZWnXrl3Kzc1tdtYuAAAAAAC9wbZtNTY2qry8XC5X2/PphHZJu3bt0siRI/u7DAAAAADAQWb79u0aMWJEm/2Edkm5ubmS9n2y8vLy+rkaAAAAAMBgF4lENHLkyGwebQuhXcouic/LyyO0AwAAAAD6zIFu0WYjOgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKO5pBwAAAIBBzLZtZTIZmabZ36UcVNxutzweT7ePFSe0AwAAAMAglUqltHv3bsVisf4u5aAUCoVUVlYmn8/X5TEI7QAAAAAwCFmWpYqKCrndbpWXl8vn83V71hcdY9u2UqmUqqurVVFRoQkTJsjl6trd6YR2AAAAABiEUqmULMvSyJEjFQqF+rucg04wGJTX69XWrVuVSqUUCAS6NA4b0QEAAADAINbVGV50X0987vnqAQAAAADgUIR2AAAAAAAcitAOAAAAAAehE044QVdccUWHr//ggw905JFHKhAIaObMmdqyZYsMw9DatWu7VUdPjTNYsREdAAAAAByE/vSnP8nr9Xb4+htuuEHhcFjr169XTk6OGhsbO/2cX/7yl1VfX68nn3wy2zZy5Ejt3r1bxcXFnR6vN7RWY3/q15n2F198UaeffrrKy8tlGEaLT4pt21q8eLHKysoUDAa1YMECbdiwodk1dXV1+tKXvqS8vDwVFBTooosuUlNTUx++CgAAAAAYeIqKipSbm9vh6zdt2qRjjjlGo0eP1pAhQ3qsDrfbrdLSUnk8vTunnE6ne3X83tKvoT0ajeqwww7Tvffe22r/bbfdprvvvlsPPPCAVq5cqXA4rIULFyqRSGSv+dKXvqR3331X//znP/W3v/1NL774oi655JK+egkAAAAAMCB9fHn8mDFjdMstt+grX/mKcnNzNWrUKD344IPZaw3D0OrVq/X9739fhmHoxhtvbDGeaZq66KKLNHbsWAWDQU2aNEk//elPs/033nijHnnkET311FMyDEOGYWj58uWtLo9/4YUXNHfuXPn9fpWVlenaa69VJpNpVvs3v/lNffvb31ZRUZFKS0tb1GQYhu6//3597nOfUzgc1g9/+MMu1yhJ27dv19lnn62CggIVFRXp85//vLZs2dLlz3+H2Q4hyf7zn/+c/diyLLu0tNT+8Y9/nG2rr6+3/X6//b//+7+2bdv2e++9Z0uyX3/99ew1zzzzjG0Yhr1z5842nyuRSNgNDQ3ZP9u3b7cl2Q0NDT3/wgAAAACgH8Tjcfu9996z4/F4q/3HH3+8ffnll9u2bdujR4+2i4qK7HvvvdfesGGDvWTJEtvlctkffPCBbdu2vXv3bnvq1Kn2t771LXv37t12Y2OjXVFRYUuy33zzTdu2bTuVStmLFy+2X3/9dXvz5s327373OzsUCtlPPPGEbdu23djYaJ999tn2ySefbO/evdvevXu3nUwmW4yzY8cOOxQK2Zdeeqn9/vvv23/+85/t4uJi+4YbbmhWe15enn3jjTfaH374of3II4/YhmHYzz77bPYaSfawYcPsX/3qV/amTZvsrVu3drnGVCplT5kyxf7KV75ir1u3zn7vvffs//iP/7AnTZpkJ5PJLn0NGhoaOpRDHXtPe0VFhSorK7VgwYJsW35+vubNm6cVK1bo3HPP1YoVK1RQUKA5c+Zkr1mwYIFcLpdWrlypL3zhC62OvWTJEt100029/hoAAAAAYKD47Gc/q0svvVSSdM011+gnP/mJnn/+eU2aNCm7fD0nJ0elpaWSpJqammaP93q9zXLW2LFjtWLFCv3+97/X2WefrZycHAWDQSWTyewYrbnvvvs0cuRI3XPPPTIMQ5MnT9auXbt0zTXXaPHixdmzz2fMmKEbbrhBkjRhwgTdc889WrZsmU488cTsWP/xH/+hCy+8sNn4Xanxd7/7nSzL0i9+8QsZhiFJ+vWvf62CggItX75cJ510Usc/0Z3k2N3jKysrJUklJSXN2ktKSrJ9lZWVGjZsWLN+j8ejoqKi7DWtue6669TQ0JD9s3379h6uHgAAAAAGlhkzZmT/bhiGSktLtWfPnk6Nce+992r27NkaOnSocnJy9OCDD2rbtm2dGuP999/X/Pnzs+FYko4++mg1NTVpx44drdYrSWVlZS3q/fgEb3dqfOutt7Rx40bl5uYqJydHOTk5KioqUiKR0KZNmzr1+jrLsTPtvcnv98vv9/d3GQAAAADgGJ/cSd4wDFmW1eHHP/7447rqqqt0xx13aP78+crNzdWPf/xjrVy5sqdLldSxesPhcI/U2NTUpNmzZ+vRRx9t0Td06NAuvoKOcWxo378UoaqqSmVlZdn2qqoqzZw5M3vNJ99JyWQyqqura3e5BQAAAACgZ73yyis66qijskvsJbWYhfb5fDJNs91xpkyZoj/+8Y+ybTs72/7KK68oNzdXI0aM6JcaDz/8cD3xxBMaNmyY8vLyulVDZzl2efzYsWNVWlqqZcuWZdsikYhWrlyp+fPnS5Lmz5+v+vp6rV69OnvNv/71L1mWpXnz5vV5zQAAAABwsJowYYLeeOMN/eMf/9CHH36o66+/Xq+//nqza8aMGaN169Zp/fr1qqmpafUYtksvvVTbt2/XN77xDX3wwQd66qmndMMNN2jRokXZ+9n7usYvfelLKi4u1uc//3m99NJLqqio0PLly/XNb36z2ZL93tCvob2pqUlr167Nbu1fUVGhtWvXatu2bTIMQ1dccYVuvvlm/eUvf9Hbb7+t888/X+Xl5TrjjDMk7XsH5uSTT9bFF1+sVatW6ZVXXtFll12mc889V+Xl5f33wnqBlckoFU8oWd+oRF29kvURpePJLo2VjsUVr4soUd8oM5mSZZpKRpqU2BtRqjHWw5VjoLEtW/H6JjVV1yu+t1HWAd4JBQAAACTpq1/9qs4880ydc845mjdvnmpra5vNaEvSxRdfrEmTJmnOnDkaOnSoXnnllRbjDB8+XE8//bRWrVqlww47TP/zP/+jiy66SN/73vf6rcZQKKQXX3xRo0aN0plnnqkpU6booosuUiKR6PWZd+PfW+H3i+XLl+tTn/pUi/YLLrhADz/8sGzb1g033KAHH3xQ9fX1OuaYY3Tfffdp4sSJ2Wvr6up02WWX6a9//atcLpfOOuss3X333crJyelwHZFIRPn5+WpoaOjzpQ4dkW6KyUynFdu9R9Vr3lUmlpDL61HhlPEqOnS8XH6fPH7fAccxM6ailbXa/MyrilbWSoahokmjNOLoGdry9CtK1DXIlxvSiOPnKG/scHmD3Pd/sEk2xbX7rY1a/8wqJRtj8gR9OuSEWRp91DQF8kL9XR4AAAA6IZFIqKKiQmPHjlUgEOjvcg5K7X0NOppD+zW0O4WTQ3s6FlesqkbphkZVrVrXoj93zAiVzJ0hf8GB645W1uqtXz4l22r+JfflhnTIKUdp01PPZ9vGnHyUiqeNl9HN5ScYOMx0Rpuef1Mf/P21Fn0j503RtC8cyxs5AAAAAwihvf/1RGgnkTlcqqFR/oI8Vb/5fqv9jVt2yDZNpZraX9aeSaa0dfnqFoFdklKNMcXrIgoWF2TbdrywWqmmeLdqx8CSjMS04dk3Wu3bvup9JRv5fgAAAAD6GqHd4WK7q2WlM7Ja2aBhv2RD4wGPYjCTaUW2tn12fWRHlYLFhdmPM/GkrGSq8wVjwErFEjLTmdY7bSle39i3BQEAAAAgtDudJxyUy93+l8nt98s4wE0OhsuQN9z2khhvKCAz1fyNAcPj7nCdGPjc3vZPgPQGDrxvAgAAAICe5dhz2rFPuGyYzGRSobJh8gT9yj9kpAzDkJnOaO/7m5Wsj8gbDsoTav8eFV9OSKM+NVuZeFL+vJAMGdq7eaf2vLVRZiqtkpkTlYnFVThhlPa8tb5DY2Jw8eUEVTBqmOq37WnRF8gPy58X7oeqWpeOp5Roiss2TXkDfgULnFMbAAAA0JMI7Q7nCQdly9LwE+aq/oMNql3zlmzTlNvvV8GEcQqVlcrweQ44S2qlMwrkhVT5zgeqa2iUDEM5I8s05ezPKFbXoL0fbFbduxvlzQlp2Jxpyhtd1qEd6TF4+HOCOvy/FurVe/+sRH1Ttt0b9GveJacrWNDxExl6U1NNg1Y/8YK2r9ko27aVO6xAR/znpzVs/HBWAwAAAGDQIbQ7nJVKSaat2nXrFK/6aAbUTCa19533JcNQeOTwA44Tr92rbc+88FGDbatp2y4l6xpUduwR2vTKGkn7jpfbuXyVPCcfK39+bo+/HjhbzrACHXvlF9W4u1YNO2uUM6xQ+SOGKljojMAe3duof/74D2qqbsi2Ne6p17/u/JNOuuZslUwa2Y/VAQAAAD2Pe9odLt0YlWy7WWD/uIYPNkimqUwy2eYYmURSVa+tbX38pqhSkUb58pqHsl0vrz7gjvQYnIIFORo2ZbQmLJitshnjFCrKlWEY/V2WJKlu655mgf3j3nh8uRKNfM8CAABgcCG0O1w6GlOqse1du610WlYmIztjtnmNnTEV31PbZn+8ulaBovzmz9sUk9XWTuJAP9n97pY2++q27pGZ4nsWAAAAgwuh3eEMj1tuv7/9a9xuqb2ZUMOQO9j2GJ5gQOYnjncz3K4D7loP9LXwkPw2+3zhgAyXM1YEAAAAAD2FVOZwgcICeYJBudrYFC5YMlQyDHmCbe/07gn6VTx9cuudhqFQ6TBFK2uaNRdOHsfu8XCckbMOaXOp/qELZyuQzy7yAAAA/cFMpZRubFJy716lm5pkplIHflAPuPfeezVmzBgFAgHNmzdPq1atavf6P/zhD5o8ebICgYCmT5+up59+ulm/bdtavHixysrKFAwGtWDBAm3YsKE3X8IBEdodzh0MKGOZKj36SLm83mZ93twcDZk1Q26ft917jg2XS/kTxyhnVPknOgwNP2Ge6t7fJNkfHfQeLBmi0nnT5fKwTyGcJVSQo+O+fnqLVSDl08fokGOmyeXinzQAAIC+lonHVbP6Te1+/gVVvfSqdv/rBdWsflOZeLxXn/eJJ57QokWLdMMNN2jNmjU67LDDtHDhQu3Z0/p+YK+++qrOO+88XXTRRXrzzTd1xhln6IwzztA777yTvea2227T3XffrQceeEArV65UOBzWwoULlUgkevW1tMew7Y+ltYNUJBJRfn6+GhoalJeX19/ltGCbpjKplOx0RqmGiNJNUfkLC+QJh+TyeuUJtL98fr9MPKF0NK54VY1cfq9Cw4rlDviVSSQV31OndDSuUMkQ+XLD8oaDvfyqgK7JpDOK1zepZtNupaIJDZ0wXKHCHAVyQ/1dGgAAgKMkEglVVFRo7NixCgR6ZxWtmUqpZvWbSlbXtOjzDy1W8exZcvt651jeefPm6YgjjtA999wjSbIsSyNHjtQ3vvENXXvttS2uP+eccxSNRvW3v/0t23bkkUdq5syZeuCBB2TbtsrLy/Wtb31LV111lSSpoaFBJSUlevjhh3Xuued2usb2vgYdzaFMpQ4AhtstbzAoBSVfXtePYfMEA/IEAwoWFzZrd/u88uc540gv4EA8Xo9yhxYod2hBf5cCAABw0LOSqVYDuyQlq2tkJVO9EtpTqZRWr16t6667Ltvmcrm0YMECrVixotXHrFixQosWLWrWtnDhQj355JOSpIqKClVWVmrBggXZ/vz8fM2bN08rVqzoUmjvCawlBQAAAAB0iZVJd6u/q2pqamSapkpKSpq1l5SUqLKystXHVFZWtnv9/v92Zsy+QGgHAAAAAHSJy+PtVj8OjNAOAAAAAOgSl98n/9DiVvv8Q4vbPAWru4qLi+V2u1VVVdWsvaqqSqWlpa0+prS0tN3r9/+3M2P2BUI7AAAAAKBL3D6fhsyc0SK4+4cWa8jMGb22CZ3P59Ps2bO1bNmybJtlWVq2bJnmz5/f6mPmz5/f7HpJ+uc//5m9fuzYsSotLW12TSQS0cqVK9scsy+wER0AAAAAoMs8waCKZ8+SlUzJyqTl8njl8vt6LbDvt2jRIl1wwQWaM2eO5s6dq7vuukvRaFQXXnihJOn888/X8OHDtWTJEknS5ZdfruOPP1533HGHTj31VD3++ON644039OCDD0qSDMPQFVdcoZtvvlkTJkzQ2LFjdf3116u8vFxnnHFGr76W9hDaAQAAAADd4vb1fkj/pHPOOUfV1dVavHixKisrNXPmTC1dujS7kdy2bdvkcn20uPyoo47SY489pu9973v6zne+owkTJujJJ5/UtGnTstd8+9vfVjQa1SWXXKL6+nodc8wxWrp0aa8dmdcRnNMu55/TDgAAAACd1RfntKN9PXFOO/e0AwAAAADgUIR2AAAAAAAcitAOAAAAAIBDEdoBAAAAAHAoQjsAAAAAAA5FaAcAAAAAwKEI7QAAAAAAOBShHQAAAAAAhyK0AwAAAADgUIR2AAAAAAAcitAOAAAAAOiWTDypeG2DmnZVK17boEw82SfPe++992rMmDEKBAKaN2+eVq1a1e71f/jDHzR58mQFAgFNnz5dTz/9dLP+L3/5yzIMo9mfk08+uTdfwgF5+vXZAQAAAAADWioSVcXSVxTZsivbljemXGNPPlq+vHCvPe8TTzyhRYsW6YEHHtC8efN01113aeHChVq/fr2GDRvW4vpXX31V5513npYsWaLTTjtNjz32mM444wytWbNG06ZNy1538skn69e//nX2Y7/f32uvoSOYaQcAAAAAdEkmnmwR2CUpsmWXKpa+0qsz7nfeeacuvvhiXXjhhTr00EP1wAMPKBQK6Ve/+lWr1//0pz/VySefrKuvvlpTpkzRD37wAx1++OG65557ml3n9/tVWlqa/VNYWNhrr6EjCO0AAAAAgC5JxxItAvt+kS27lI4leuV5U6mUVq9erQULFmTbXC6XFixYoBUrVrT6mBUrVjS7XpIWLlzY4vrly5dr2LBhmjRpkr72ta+ptra2519AJxDaAQAAAABdYiZT3ervqpqaGpmmqZKSkmbtJSUlqqysbPUxlZWVB7z+5JNP1m9+8xstW7ZMt956q1544QWdcsopMk2z519EB3FPOwAAAACgS9x+X7f6nebcc8/N/n369OmaMWOGDjnkEC1fvlyf+cxn+qUmZtoBAAAAAF3iDQWUN6a81b68MeXyhgK98rzFxcVyu92qqqpq1l5VVaXS0tJWH1NaWtqp6yVp3LhxKi4u1saNG7tfdBcR2gEAAAAAXeIJ+jX25KNbBPf9u8d7gr2z87rP59Ps2bO1bNmybJtlWVq2bJnmz5/f6mPmz5/f7HpJ+uc//9nm9ZK0Y8cO1dbWqqysrGcK7wKWxwMAAAAAusyXF9Yhpx+vdCwhM5mS2++TNxTotcC+36JFi3TBBRdozpw5mjt3ru666y5Fo1FdeOGFkqTzzz9fw4cP15IlSyRJl19+uY4//njdcccdOvXUU/X444/rjTfe0IMPPihJampq0k033aSzzjpLpaWl2rRpk7797W9r/PjxWrhwYa++lvYQ2gEAAAAA3eIJ+ns9pH/SOeeco+rqai1evFiVlZWaOXOmli5dmt1sbtu2bXK5PlpcftRRR+mxxx7T9773PX3nO9/RhAkT9OSTT2bPaHe73Vq3bp0eeeQR1dfXq7y8XCeddJJ+8IMf9OtZ7YZt23a/PbtDRCIR5efnq6GhQXl5ef1dDgAAAAB0WyKRUEVFhcaOHatAoHfuLUf72vsadDSHMtM+QNi2rUwyLSuVkmxbMgy5fF4ZliV3MCDDMLo8diaZUiaelCR5Aj55Av33LhK6zjItpaNxSZLb75VngO3UCQAAAKAlQvsAkI7GZabSSu5tUPWad5Sqb5Q3L0dDZx2qwJB8NW7bqdzRI+QJdu7dM8s0Fa+p144X16hx2265/T4NnTFBxdMOkTccGnDHMxzMkpGoKld/oMrV78tMZVQ4frhGf2qOAkX5crnZbxIAAAAYqPht3uEy8YTidXvVtH2Xdjz3ipJ1DbItS6n6iHY+/5oaNu+QJxTUntfXKpNIdmrsePVevf/bvytSsVO2aSkTS2j3a29r899fUbI+ItuyeulVoSelGqN67/FntePltcrEk7JNU3Xrt+mtXzylRF1Df5cHAAAAoBsI7Q6XaozKlxNW9RvvtNpfu/Z9+fJy1bRlh8x4osPjZhJJbX/+9VaDeXR3tZL1Tcp0Yjz0n6bKOsWq6lq0WxlTW59fLTOZ6oeqAAAAAPQEQrvDRXdWyUqmZGUyrfbblqV0LC65pURNy+DWFjOVVuP2qjb7I1t3yzbNTteLvlfz7qY2++o37VAmme7DagAAAAD0JEK7w7l9XsnV/pfJ5XZLpuTydnyLAsMw5PK42x7T75W6sbkd+k57R2u4fF6JLyMAAAAwYBHaHS48olQuj0fe3HCr/Z5gYF/AdhnyDynq8LieYEDFMya02V8wdrhcHvYpHAiGHTaxzb7S2VPkDQX7sBoAAAAAPYnQ7nDeUFCZeEIjPj2/xUy64XZrxGfmq379ZpUdM69Tu8e7PG6Vzp2mQFF+i77hR8+UNzfU6d3o0T8C+TkaceysFu3h0iEqPXwSu8cDAAAAAxhTqQ7n8noUHFqkdCqlcV84SY3bdilRvVf+onzljRmudCKhwsnj5QkF213u3hp/Xo4mnn2SYpU1qvtwq7xBv4qmjJUvJ9TmzD6cxxP0a/i8qRoyebT2rNugTDypoVPHKVwyRL7cUH+XBwAAAKAbCO0DgNvn3XdvuyR/QV6zPr9azpR3hj8vLH9eWIUTR3drHPQvT9CvnKBfOaVD+rsUAAAAAD2IdbMAAAAAgG5JRhOKVNaptmK3IpV1Skb75vjoe++9V2PGjFEgENC8efO0atWqNq999913ddZZZ2nMmDEyDEN33XVXt8fsC8y0AwAAAAC6LFbXqNd/+09Vvb8t21YyZbSO+K8FChXl9trzPvHEE1q0aJEeeOABzZs3T3fddZcWLlyo9evXa9iwYS3rjMU0btw4ffGLX9SVV17ZI2P2BWbaAQAAAABdkowmWgR2Sap6f6te/+1zvTrjfuedd+riiy/WhRdeqEMPPVQPPPCAQqGQfvWrX7V6/RFHHKEf//jHOvfcc+X3t35scmfH7AuEdgAAAABAlyQbYy0C+35V729VsjHWK8+bSqW0evVqLViwINvmcrm0YMECrVixwjFj9gRCOwAAAACgS9Lx5AH6U73yvDU1NTJNUyUlJc3aS0pKVFlZ6ZgxewKhHQAAAADQJd5g68vMP+r39VElgxehfYCxbVuWZcm27R4Zq72PcfDhewBdxfcOAAAHJ39uSCVTWj8+umTKaPlzQ73yvMXFxXK73aqqqmrWXlVVpdLSUseM2RMI7QOAmUor2dik5N4G1bz5jipfWqn69zcq1RBROhbv9HjpWFxNOyq14/nXtPOFlYru3qOm7bu0+8XX1LBxi9JNvXPfCZwpk0qrfned3vjjy3ru7qf03rK1aqxuIIShQ8xEQvE9e7R33TrVv/++UpGIzHS6v8sCAAB9xB8O6Ij/WtAiuJdMGa0jzl8gfzjQK8/r8/k0e/ZsLVu2LNtmWZaWLVum+fPnO2bMnsCRbw5npdJKNUSUicW1+8WV0r+DVNPWnapd59WIE4+TDMkbDHZovHQ0ru3LXlW8sjrbVr++QnmHjFJ4WJGqXn1D7oBfIxeeIF9+7x3PAGcwM6Z2vbtV/7jzT7Ktfd9bFavWyx8O6PTF/6GiEUP7uUI4WSaRUO2aNco0NWXbotu3K2fcOOWMHi2319uP1QEAgL4SKsrVkf99ipKNMaXjKXmDPvlzQ70W2PdbtGiRLrjgAs2ZM0dz587VXXfdpWg0qgsvvFCSdP7552v48OFasmSJpH0bzb333nvZv+/cuVNr165VTk6Oxo8f36Ex+wOh3eEyiYTcXq92vPJiNrDvZ6XSqnptjUqPOUK23y/DdeCFE41bdjQL7PtFNm1T7uhyuQN+mYmkqla9qfLjj5Tbxz0og1lsb5Oe+9lfsoF9v2Q0oeUPPK3PXvNFBXppSRMGNtuyFN22rVlg369p82YFS0oI7QAAHET84UCvh/RPOuecc1RdXa3FixersrJSM2fO1NKlS7MbyW3btk2uj2WkXbt2adasWdmPb7/9dt1+++06/vjjtXz58g6N2R8I7Q4Xr90rt8cjO2O22p+s3SvbtJRJJOUNtT/bnoklVPfuhjb7I5u3K1ReosbN2xTfvUdmIkVoH+QaKuuUSba+lLmmolKJxjihHa0yUylFd+xosz+2a5d8kyb1YUUAAOBgdNlll+myyy5rtW9/EN9vzJgxHboFtL0x+wOh3eFs05JlZ9q/xrJazMK3ep1sWZm2x7LSpgy3+2MP4J7mwa6twL6fZVp9VAkGItts/c1ESbLb+bcGAAAAHcdGdA4XKMiTvzC/zX53MCCX1yO3/8Az4m6/T7ljRrTZnzOyVInqOkmSNzdHLh9LWwe7wuHFktF6X6ggp8+XOGHgcHk88hcXt9kf7MclZAAAAIMJod3hPDlhWaap/InjWu0fOmeGDI9HLs+BF0243G4NmTax1YDvy8uRLy+sVH2DJGnYvFnyBAlsg10wP6xpJ81pte/oLy9QqDCnjyvCQOHyeJQ/YUKre2l48/PlzeF7BwAAoCewPN7hPAG/ZFkqPHSCAkMKVffeBlmJpLx5ORoy81B5w+EO7xwvSd7csMaecaJq316vhg1bZbhdKpgwRrmjy1X9+loFS4eq+PDp7Bx/kPCF/Jp1xnwNPaRUa/78qppqIyoeU6K55xyvIaOHyTDamIYHJHlCIQ098khFNm1SsqZGhsejnJEjFSovlzvAm34AAAA9gdA+ALiDAdnxuIIlQ1WSlysrnZY3JyR5PHJ7vTJcHQ9WmVhcViqt3NHDVXToeLm8XsnlUjrSpKLDpsmXlyNPOMiuzweRYF5IE46equHTxsjKWPL4PQrkdPyNoM5IxRJKNsYVr4vIGwookB9WsIAZ2YHKcLnkzclR4dSp2XvYXX4/b/YAAAD0IEK7w9m2rWRTVHY8oV3LV8hMJLN9uWNHqeiwKbJM84A7x0tSYm+Dti19UenGaLYtZ1SZCicdooq/v7hvQztJBRNHa/ixc+QN905wgzOF8sO9On4iEtU7f3xRu9ZuzLYFCnI075LTlVc+hKA3gLk8HqkDt+gAAACg87in3eEy0ZgMy9LOZS83C+yS1FixTU0V25RsjGYDd1vS0Zi2Pr28WWCXpKZtuxXZskMFE8dk2+o/3KrqtR/IamdnaKAzzIypzcvXNgvskpSob9KKe/+seH3Ls74BAAAAENodLx1PKFFdKyvd+vFJez/YJK/fq3Qs3u44qUiTMtHWr4ls2qb8Q5rvKl+zbr0y0UTXigY+IRmJqeKlt1vtS0UTatxd28cVAQAAAAMDod3pbFuphsY2u61kSrL3nefennRTrO2nsCzpE0eyW+kMM+3oMVYmIzPV9pnw0ZqGPqwGAAAAGDgI7Q5nGIYCQwrb7Pf8+152l8fd7jjt7Qbv8noku3lq9wT9BxwT6Ci31yNfO2e+55UP6cNqAAAAgIGD0O5wnlBQvsL8Ns9ML5oxRWY6kw3vbfHmhOQvzG+1r2DyIdq7vqJZW8ncGft2qAd6QCA/rAknHdFqX2hInsJDC/q2IAAAAPSoeGNcNdurtWP9dtXsqFa8sf3bd3vKvffeqzFjxigQCGjevHlatWpVm9e+++67OuusszRmzBgZhqG77rqrxTU33nijDMNo9mfy5Mm9+AoOjO1+Hc4bDikVjWn4gmNV9eobStTulSQZHo+Kpk1SsKRYbr/vgDtve0NBjTr5OO16cZWiO6v2jeF2qWjqRAWKC1X1+ruS9s3YDztimgomjmY3b/QYw+XSiDkTlUmktPG51TL/vUdD0bgyzfrSiQrmc+wbAADAQNVQ3aC//vRJbX7zo02HDzl8vE775hnKH9r6xGFPeOKJJ7Ro0SI98MADmjdvnu666y4tXLhQ69ev17Bhw1pcH4vFNG7cOH3xi1/UlVde2ea4U6dO1XPPPZf92NPPp+QYtv2JddEHoUgkovz8fDU0NCgvL6+/y2mVmUjKTKVkZ0xZpimXzyuX1yO33y+Xu+PL2DPJlMxEQlbalNvnlScUkJUxlYklZJmm3H6fvOFgp8YEOspMZ5RsjCkdS8rt88gXDra7bB4AAABdl0gkVFFRobFjxyoQ6J3fueKNcf3x1t83C+z7HXL4eJ357bMVzO2do6TnzZunI444Qvfcc48kybIsjRw5Ut/4xjd07bXXtvvYMWPG6IorrtAVV1zRrP3GG2/Uk08+qbVr1/ZIje19DTqaQ5lpHyDcAb/cAX+3x/H4ffL4fc3aXB6PPD0wNnAgbq9HoaI8qai/KwEAAEBPiNY3tRrYJWnTmo2K1jf1SmhPpVJavXq1rrvuumyby+XSggULtGLFim6NvWHDBpWXlysQCGj+/PlasmSJRo0a1d2Su4x72gEAAAAAXZKItX9MdPIA/V1VU1Mj0zRVUlLSrL2kpESVlZVdHnfevHl6+OGHtXTpUt1///2qqKjQscceq8bGtk/06m3MtAMAAAAAuiQQan/Zvf8A/U5zyimnZP8+Y8YMzZs3T6NHj9bvf/97XXTRRf1SEzPtAAAAAIAuCRfk6JDDx7fad8jh4xUu6J0Nh4uLi+V2u1VVVdWsvaqqSqWlpT32PAUFBZo4caI2bmz9FoC+QGgHAAAAAHRJMDeo0755Rovgvn/3+N7ahM7n82n27NlatmxZts2yLC1btkzz58/vsedpamrSpk2bVFZW1mNjdhbL4wEAAAAAXZY/NF9nfvtsReublIwl5A8FFC7I6bXAvt+iRYt0wQUXaM6cOZo7d67uuusuRaNRXXjhhZKk888/X8OHD9eSJUsk7du87r333sv+fefOnVq7dq1ycnI0fvy+Nx2uuuoqnX766Ro9erR27dqlG264QW63W+edd16vvpb2ENoBAAAAAN0SzA32ekj/pHPOOUfV1dVavHixKisrNXPmTC1dujS7Od22bdvkcn20uHzXrl2aNWtW9uPbb79dt99+u44//ngtX75ckrRjxw6dd955qq2t1dChQ3XMMcfotdde09ChQ/v0tX0c57RrYJzTDgAAAACd0RfntKN9PXFOO/e0AwAAAADgUIR2AAAAAAAcitAOAAAAAIBDEdoBAAAAAHAoQjsAAAAAAA5FaAcAAAAAwKEI7QAAAAAAOBShHQAAAAAAhyK0AwAAAADgUIR2AAAAAAAcitAOAAAAAOiWpkhUO7bs0odvb9LOLbvVFIn2yfPee++9GjNmjAKBgObNm6dVq1a1ee1DDz2kY489VoWFhSosLNSCBQtaXG/bthYvXqyysjIFg0EtWLBAGzZs6O2X0S5COwAAAACgy2oqa3XHtffqG1+4Vtecf5Mu+8I1uuO6+1RTWdurz/vEE09o0aJFuuGGG7RmzRoddthhWrhwofbs2dPq9cuXL9d5552n559/XitWrNDIkSN10kknaefOndlrbrvtNt1999164IEHtHLlSoXDYS1cuFCJRKJXX0t7DNu27X57doeIRCLKz89XQ0OD8vLy+rscAAAAAOi2RCKhiooKjR07VoFAoFeeoykS1R3X3qu1K95p0TfzqOn61pJLlZMX7pXnnjdvno444gjdc889kiTLsjRy5Eh94xvf0LXXXnvAx5umqcLCQt1zzz06//zzZdu2ysvL9a1vfUtXXXWVJKmhoUElJSV6+OGHde6553a6xva+Bh3Nocy0AwAAAAC6pL6uodXALklrX31b9XUNvfK8qVRKq1ev1oIFC7JtLpdLCxYs0IoVKzo0RiwWUzqdVlFRkSSpoqJClZWVzcbMz8/XvHnzOjxmbyC0AwAAAAC6JNYY71Z/V9XU1Mg0TZWUlDRrLykpUWVlZYfGuOaaa1ReXp4N6fsf150xe4On354ZAAAAADCghXKD3ervLz/60Y/0+OOPa/ny5b1260BPYaYdAAAAANAlBUX5mnnU9Fb7Zh41XQVF+b3yvMXFxXK73aqqqmrWXlVVpdLS0nYfe/vtt+tHP/qRnn32Wc2YMSPbvv9xXRmzNxHaAQAAAABdkpMX1tev/0qL4D7zqOn6+vUX9domdD6fT7Nnz9ayZcuybZZladmyZZo/f36bj7vtttv0gx/8QEuXLtWcOXOa9Y0dO1alpaXNxoxEIlq5cmW7Y/Y2lscDAAAAALqsuHSIvrXkUtXXNSjWGFcoN6iCovxeC+z7LVq0SBdccIHmzJmjuXPn6q677lI0GtWFF14oSTr//PM1fPhwLVmyRJJ06623avHixXrsscc0ZsyY7H3qOTk5ysnJkWEYuuKKK3TzzTdrwoQJGjt2rK6//nqVl5frjDPO6NXX0h5COwAAAACgW3Lywr0e0j/pnHPOUXV1tRYvXqzKykrNnDlTS5cuzW4kt23bNrlcHy0uv//++5VKpfT//t//azbODTfcoBtvvFGS9O1vf1vRaFSXXHKJ6uvrdcwxx2jp0qX9et8757SLc9oBAAAADD59cU472sc57QAAAAAADGKEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKEI7AAAAAAxi7D3ef3ric09oBwAAAIBByOv1SpJisVg/V3Lw2v+53/+16ArOaQcAAACAQcjtdqugoEB79uyRJIVCIRmG0c9VHRxs21YsFtOePXtUUFAgt9vd5bEI7QAAAAAwSJWWlkpSNrijbxUUFGS/Bl1FaAcAAACAQcowDJWVlWnYsGFKp9P9Xc5Bxev1dmuGfT9COwAAAAAMcm63u0cCJPoeG9EBAAAAAOBQhHYAAAAAAByK0A4AAAAAgEM5OrSbpqnrr79eY8eOVTAY1CGHHKIf/OAHzQ6ot21bixcvVllZmYLBoBYsWKANGzb0Y9UAAAAAAPQMR4f2W2+9Vffff7/uuecevf/++7r11lt122236Wc/+1n2mttuu0133323HnjgAa1cuVLhcFgLFy5UIpHox8oBAAAAAOg+w/74tLXDnHbaaSopKdEvf/nLbNtZZ52lYDCo3/3ud7JtW+Xl5frWt76lq666SpLU0NCgkpISPfzwwzr33HM79DyRSET5+flqaGhQXl5er7wWAAAAAAD262gOdfRM+1FHHaVly5bpww8/lCS99dZbevnll3XKKadIkioqKlRZWakFCxZkH5Ofn6958+ZpxYoVbY6bTCYViUSa/QEAAAAAwGkcfU77tddeq0gkosmTJ8vtdss0Tf3whz/Ul770JUlSZWWlJKmkpKTZ40pKSrJ9rVmyZIluuumm3iscAAAAAIAe4OiZ9t///vd69NFH9dhjj2nNmjV65JFHdPvtt+uRRx7p1rjXXXedGhoasn+2b9/eQxUDAAAAANBzHD3TfvXVV+vaa6/N3ps+ffp0bd26VUuWLNEFF1yg0tJSSVJVVZXKysqyj6uqqtLMmTPbHNfv98vv9/dq7QAAAAAAdJejZ9pjsZhcruYlut1uWZYlSRo7dqxKS0u1bNmybH8kEtHKlSs1f/78Pq0VAAAAAICe5uiZ9tNPP10//OEPNWrUKE2dOlVvvvmm7rzzTn3lK1+RJBmGoSuuuEI333yzJkyYoLFjx+r6669XeXm5zjjjjP4tHgAAAACAbnJ0aP/Zz36m66+/Xpdeeqn27Nmj8vJyffWrX9XixYuz13z7299WNBrVJZdcovr6eh1zzDFaunSpAoFAP1YOAAAAAED3Ofqc9r7COe0AAAAAgL40KM5pBwAAAADgYEZoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChPfxeAjrMtS5l4QummqKx0Rr68HLkDfrl9vk6PYyaTMuNx2aYpdzAoK2MqE4vLmxPu0pgY/OKRqOL1UcX2NipYkKNQQY6C+eH+LguDjJlOy0qlZMZiMjweuQMBuQMBGYbR36UBAAD0C0L7AGGbluI1tdr1/KuyUulse/6EcRoy81B5goEOjWOZplJ1dapbt062aWbbQyNGynB7VPXyawqPGqEhM6fJE+jYmBj8mmoatPzev2jv1j3ZtoLhQ3TCN89Q7tCC/isMg4qZTKrhww8V37072+byelU0a5Z8eXkyXCwOAwAABx9+Axog0rGYdj73UrPALkkNGzarcesO2bbdoXHMREK1a9c2C+ySFNuxXZ6AT97cHEW37VDjpi2yLavH6sfAlWyK65VfPNMssEtS/c5avXjfX5WIxPqpMgwmtm0rtnNns8AuSVY6rdrVq2UmEv1UGQAAQP8itA8QsV1Vss3WQ3Td2x/IjHfsF9rYrl1SGwE/unOncseOkiQ1fLiJX5IhSUo0xrTnw52t9tVt3aNEI6Ed3Wclk2raurXVPts0laqv79uCAAAAHILQPkCkGhrb7DPjCdnWgWfabctSJhpte5xEQu6AX9K+2a2OjInBL51It9ufiqf6qBIMZrZlyUq3/b2WbuffLgAAgMGM0D5ABIcNabPPm5crw33gL6XhcslXWNhmvyccVrpp3y/G7mCwQ2Ni8POF/O1uAubPYe8DdJ/hcskdDLbZ78vP78NqAAAAnINUNkAEiodkZ8E/aejs6R3eiC44dKgMT+v7D+aMGqXGzfuWpxZNnyI3G9FBUiAvpDFHTm61b+Th4xXIDfVxRRiM3IGA8saPb7XP5ffLm5vbxxUBAAA4A6F9gPDmhDRy4fHyD/loptzl82rY/NkKDivu8DjuYFDFc+bIk5PzsXF8yp88WdEdlbJMU0NmTVeorJQjliBJ8gX9OvyLx2nC8dOzqy8Ml6FxRx2quV/6jPxh3txBz/APGaL8yZObvbHoy8/f929WO7PwAAAAg5lhd3Tb8UEsEokoPz9fDQ0NysvL6+9y2pVJJGUmk7JNS26/T55goEvHIJnJ5L77R21bcrtlW7bsTEYur1eeQICl8WghnUwrEYkqHU/JG/ApkBeSN+Dr77IwyNiWte/fp0xm35J5r1cuH99nAABg8OloDuWc9gHGE/DL08Yy+c5w+/1y+7s/Dg4eXr9XXs5kRy8zXC5m1QEAAD6G6VQAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEN5uvNg0zT105/+VGvXrtX06dN15ZVXyuPp1pAAAAAAAODfujXTfu211+oHP/iBEomEfvKTn+jKK6/sqboAAAAAADjodSu0P/XUU/rNb36j3//+9/rrX/+qP/3pTz1VFwAAAAAAB71uhfaqqiodeuihkqSpU6eqqqqqR4oCAAAAAADdvKfdtm25XPtyv2EYsm27R4pCS1bGVCaZlJXKSLYlw+2W4fXI7XbJ7fd3b2zTlJlIyspkZBiG3AG/3D5fD1UOp0vH4jKTackw5A0H5fayLwUAAADgFJ3+7bywsFCGYUiSmpqaNGvWrGxwR+9Ix+MyE2lVvvaW6jdtkyxbwWFDNPzYw2Wm0/LlhOQvzJfL7e782LG4Mk1R1b71vmK7962UCJUN09A5h8lXkJf9WmPwMdNpRSvrtHnpCkUra2W4XCqeNk6jjj9cgYLc/i4PAAAAgLoQ2n/yk58Q5PpQOhaXlUpr81PLlIpEs+3xPbXa+KfnNOGLC1W18k2VHTVH/sL8To2dSSSVbmzSrudflZVKZ9tju/do+9LnNerUBfLl5fTYa4GzxKrr9fYjf5f+vULGtixVr9uoxu1Vmnb+qfLnhfu5QgAAAACdDu1f/vKXe6EMtMVKpRXdXdMssGfZtipfe0tl8w9T7dp3VXL0EXL7vB0e20ym1LRtZ7PAnn3edEb1H25W8aypXZrBh7Ol40ltee71bGD/uMTeRjXtriG0AwAAAA7Q6XXtbrdbe/bs6Y1a0ArLshXZsrPN/qZde+TyehSrrJaVznRq7Ew8rnhVTZv9sV2VstItAz0GPiuVVmRbZZv9dR9u68NqAAAAALSl06Gdzeb6luEy5AkF2+z3BPySLbkDPhmuzt224PK45Q60vYmd2++XwX4Fg5PLkDfY9teeWXYAAADAGbqUyLinve+4XC4VTRnbZn/xjIlq2l2pwkMnyhMMdGpsTzCovLGj2uwvnDqRXeQHKV84qLJ5U9vsL546rg+rAQAAANCWLoX20tJSud3uVv+gZ3lCQbm8Hg0/bnaLvpyRpSoYP1rJPbXKGTm882MHAvIV5it/Qss3BfInjlOguKhLNcP5DJdLJYdNVP7oshZ9408/Vr7cUD9UBQAAAOCTDLuT691dLpf++Mc/qqio9UB3/PHH90hhfSkSiSg/P18NDQ3Ky8vr73JasDLmvl3k0xk1bt0lM5lS7uhyeUIByTTlCYf2LZPvytimqUwsLjOR3Hfkm2EoPLxM3nBIbj+z7INdKhpXsr5J9Zt3yBMMqGBsuXy5oU5taAgAAACg8zqaQzsd2t1ut3bv3q1hw4Z1u0incHpoBwAAAAAMLh3NoWxEBwAAAACAQ3U6tD///PNtLo0HAAAAAAA9x9PZBxx//PGqr6/X//7v/2rjxo2ybVuHHHKIzjvvPMI8AAAAAAA9qNMz7c8884xGjRqlJUuWaN26dXr77bd16623avTo0frb3/7W4wXu3LlT//mf/6khQ4YoGAxq+vTpeuONN7L9tm1r8eLFKisrUzAY1IIFC7Rhw4Yer8MJbMtSJpGUmU732HhWJiPbtmXb9r6/W1aPjA0c7PiZAgAAQE/o9Ez7pZdeqm984xu6+eabs+e127at7373u/r617+u0047rceK27t3r44++mh96lOf0jPPPKOhQ4dqw4YNKiwszF5z22236e6779YjjzyisWPH6vrrr9fChQv13nvvKRDo3LnlTpVKpqRkUtGdu5WorpE7GFTeuNFy+/3y5oQ7PZ6VySjVGNXe9zcp1dCoUEmxwiNKVb9hi1xulwonjZMnJyy3t9PfHsBBz7ZtpSJRNWzapsbtlfLn52jItIny5YXZlR8AAACd1und44uLi7VixQpNmDChWfuGDRs0f/581dTU9Fhx1157rV555RW99NJLrfbbtq3y8nJ961vf0lVXXSVJamhoUElJiR5++GGde+65HXoeJ+8eb6bSysRi2r38ZVmp5jPsQ2ZNV6istFPB3TJNNW7dpR3PvdKs3eX1aPin56vylTVKR2MaedIxyhlRKpfb3SOvAzhYxGvrtfEP/5D5iZ/XUScepfzxo3gzDAAAAJJ6Yff4SCSiSCSiG2+8UTfccIP27t2bbaurq9ONN96oH/zgB9m2SCTS7Rfxl7/8RXPmzNEXv/hFDRs2TLNmzdJDDz2U7a+oqFBlZaUWLFiQbcvPz9e8efO0YsWKNsdNJpPN6uyJWnuLmUqpds26FoFdkmrXviPb3LfEvaMysbh2Pv9ai3YrndGeVetUOn+mckcP164XVykTS3SrdjiXZVqcBNELMvGEtj+3okVgl6Tty15TJhbvh6oAAAAwkHV4yqegoCC7HF6SnnjiiRbXPP7447rssstk27YMw5Bpmt0qbvPmzbr//vu1aNEifec739Hrr7+ub37zm/L5fLrgggtUWVkpSSopKWn2uJKSkmxfa5YsWaKbbrqpW7X1GdNUoqa29T7bVqK2TgHPMPk8HftSphqaZLfxdUnubZDL55U35NPw446QmUpJ6vzyezhXqjGqxh1Vati4TZ5QQMXTJ8iXlyNPwN/fpfW4TCIlM5mW4XbJlxPss+eMVbX+82pbluI19fLn5/ZJLeh9mURSdsaU4XEPyp8hAADgDB0O7c8//7wk6ZVXXtFf//pX3XzzzfL8Oyim02ktXrxYn//853XkkUf2WHGWZWnOnDm65ZZbJEmzZs3SO++8owceeEAXXHBBl8e97rrrtGjRouzHkUhEI0eO7Ha9veFAm1jZtiWZGaVjcXlDbQcT27KVjjYpE29/9txKpdW4eZsaN29T0WGHypcbltvn61LtcJZkpEkb//hPpRuj2ba6dzeqdP5hGjp9ktyBwfF1NtMZxWrqteVfb6hha5V8OUGNOnamiiaM7P3wbh/g5zXTvTcy4QxmMqVE7V5Vvb5OqfpG+fJyNHTOdAWHFcnjJ7wDAICe1eHQfvzxx0uSvvzlL+vvf/+7Dj300Gb9ZWVlOu2003TNNdf0WHFlZWUtnmfKlCn64x//KEkqLS2VJFVVVamsrCx7TVVVlWbOnNnmuH6/X/4B8ouV4fXIm5erdKSx1X5ffp5st0vJ2jp5Q8PbHCfd2KRtTy9T6TFzJcOQWlka7QkHZaU/Wmpf99Z7yh09gtA+CFjpjCpfe6tZYN+vcsVbKjhk1KAJ7Y27avTWr/4q29r3PZ6JJ/XBn5arZNZEjT/5SHlDvbdBpdvvly8/R6mGplb7g8M4FnOgs0xTkS07tOuFVdm2eHWdtj3zgkrnz1LhlPFyedgLBAAA9JxOH/kWjUZb3RjupZdeUlNT67+odtXRRx+t9evXN2v78MMPNXr0aEnS2LFjVVpaqmXLlmX7I5GIVq5cqfnz5/doLf3G41PhtCn7gvYnhEeNUNOOKiljyeVyKZNofRbdSmdU+9Z7stIZNW7bqSHTJrR63bA501W/fmOztuiO3d1/Deh3mURS9R9ubbO/YfOOPqym96Sa4trwl5eygf3jqt78UKmmWK8+vzcc1MhPH9nqz2vxzMm9+oYB+kYmnlDlq2+22le1ap0ycfYtAAAAPavT2xjffPPNuvTSS/Xoo49qypQpkqT3339fr776qu65554eLe7KK6/UUUcdpVtuuUVnn322Vq1apQcffFAPPvigJMkwDF1xxRW6+eabNWHChOyRb+Xl5TrjjDN6tJb+YicSiu6q1rD5c9VYsUWpvfVyBwLKGT1KZsZUzdoPlDt6+L6Z8zb2FTNTKTXt2CVJaty0VUMOO1Tlxx2x78i3pqgCRYUqnjFJkc1bldrb0OyxndnkDg5m2+3eamGmW26cNhBlEilF9+xts79+S6XCvTzbHSot1sRzT1Hla+sUq6qRNxxSyRHTFB4+TG7/4FjNcDAz40lZbfy82KapTCwhX25OH1cFAAAGs06H9ksuuURHH320fv3rX2vjxn2zsnPmzNG9996r6dOn92hxRxxxhP785z/ruuuu0/e//32NHTtWd911l770pS9lr/n2t7+taDSqSy65RPX19TrmmGO0dOnSQXNGeyaRlK8gV9v/+aryJ4xW7vjxMpMpVb+1Xqn6iAqnHCLD7ZLh9bazjN2Q4XZn76etfes9eXPCyhs7Su6AXy6fR42bt6lpa8vZ1vCIshZtGHhcPq9yRpTsW5nRivyxI/q4ot5huFrOcH+c29f7x625vR6FhhZp9MKjZKYycrld8gQHx79HkHSA7zHD1ekFbAAAAO3q9Dntg5GTz2lP1kckw9D2f7y07+8f4w74NeqkY2S4DLmDgTZnd2zTUs1b72rvO+tb7R+x8Hjtev7VFsfKhYeXquSoOQSOQSJWvVcbfv+MbLP5jHvu6HKNOvGoQbF0Ox1P6t0nnlP9pp0tOw1D8644R8EiZ/2MY2BJR2OqePI5paMtb7VwB/wad+ZJ8uVw6gYAADiwHj+n/eNeeukl/ed//qeOOuoo7dy575fj3/72t3r55Ze7Vi3a5AkHlUkkNOLEozVkxiR5wiG5A34VTBqrkQuOksvnkeH1tLsc03C7VDDpEPlaOWoqf+I4xarrVXrMXIVHlsvl98mbm6Ohc2eq5KjZBPZBJFCUp0nnnaqCCaPlDvjky8/ViE/N1agF8wdFYJckb9CviacdLW+45euZ+Llj5O2jo98weHnDIY1YcJQMd/PN5gyXSyM+c1S7p3gAAAB0Radn2v/4xz/qv/7rv/SlL31Jv/3tb/Xee+9p3Lhxuueee/T000/r6aef7q1ae42TZ9qlffekZxJJGdp3zrZMU7Yh+fPyJI9b3mCgQ0sy09GYEtV1ilRsldvrU/6kcfKEQ0pH46p7b5P8+bkKlxbLE/LLl8dZ0oOVmUrLTKVluIxBGzASextVt2G7ajdsV6AgR2WzJytQmCsP95SjB9iWpXRjVA2btylWVatgcaHyx4+WNzcsl5ud4wEAQMd0NId2OrTPmjVLV155pc4//3zl5ubqrbfe0rhx4/Tmm2/qlFNOUWVlZbeL72tOD+37ZeLxfUeyuVxyB/xye7p2f65tWbItO7uZkicYkJlKycqYcnu9cnl7/75foC9YpiXDZchoZTf3tpiptKxMRi63m43jcECWacnl5j52AADQeR3NoZ1OZ+vXr9dxxx3Xoj0/P1/19fWdHQ4dkIknZCYSqnvnQ8X31MgT8Kvg0AkKFBfJGw51euOjdDSuhg2b1bRlhwyPW/kTxsqXn6u6t96VNy9XhVMnypuTI1cX3xQAnKIzYcpKZ5Ssj6h69TtK1DXIm5ejYbOnKlCUL7ff34tVYiAjsAMAgN7W6VRWWlqqjRs3asyYMc3aX375ZY0bN66n6sK/ZRJJpZui2vGPF7JHdmWiMVW+tEp5h4xR0fRJnVrKnm5s0vZnnpeZSGbbql9/S4HiIhVMPkQ1b6xVbNculR57lEKlw3r89QBOZNu2mnZWavuzH+3LkW6KasuuKpUdPVv5k8Z2eWULAAAA0B2dniK4+OKLdfnll2vlypUyDEO7du3So48+qquuukpf+9rXeqPGg5qVTqv69bdaPWM7smmLrHRGmWSqY2OZpva+v7FZYN8vUVMn27TlCQUlW6p5401l4vFu1w8MBOloXLtefL3VvsrX3pQZT/RxRQAAAMA+nZ46uvbaa2VZlj7zmc8oFovpuOOOk9/v11VXXaVvfOMbvVHjQc02TSVq6trsj+3eo9xDRndoLDOZUmMrZ7HvF92xW/7iIcps26FMLC4zlZYnODg3KgM+zkokW30zS9p3ZGK6KdbuCQ0AAABAb+l0aDcMQ9/97nd19dVXa+PGjWpqatKhhx6qnBx+oe0d7W+g1Zn72Q1Jhqud8Vwu6WP7EnZ86y5ggDvARnWd2cgOAAAA6Eld3kHH5/Pp0EMP1dy5cwnsvcjwuBVs597yUNmwDh8x5A74lXfImDb7c0aVK76nWpLkzc2Ri52zcZBwB3zy5oRa7XN5PfKEW+8DAAAAelunZ9rPPPPMdvv/9Kc/dbkYtOT2eTX0iBna8Y8XZKXSzfqGHHao5HHJ7fN2aCzD5VL+hLFq3LJd6UhTs77wiDJZqZSsZEqG262hc2fLEwj02OsAnMwbDmn4p+Zr69+fb75/hGFo+AlHyhPiZwEAAAD9o9OhPT8/P/v3xx57TKeffrpyczu+ezk6x+3zybSlUad8Wo1btitWWS13wK+CyYfIEwrJlxvu1HjecEgjTjxOsco9imzaKpfbrfxJh8gwpIYNm1QwZaJyx4xiZhEHneCwIh3yxVO09/1NilfXyl+Qp6KpE+TNDXd4NQsAAADQ0wzb/thNzJ2Um5urt956a8Af9dbRQ+37WyaTkZ1KyXC55Pb7u32frZVOS4Yhl8cj27Jkm6YMt7vT574Dg4ltWbIypgy3i7AOAACAXtPRHMrBwwOIx+ORevCsaJf3o2X1hstFWAe072fB7eNnAQAAAM7Ab6YAAAAAADhUp6dt77777uzfM5mMHn74YRUXF2fbvvnNb/ZMZQAAAAAAHOQ6fU/72LFj2x7MMLR58+ZuF9XXBso97QAAAACAwaHX7mmvqKjoVmEAAAAAAKBjuKcdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcKhO7x6/XywW07Zt25RKpZq1z5gxo9tFAQAAAACALoT26upqXXjhhXrmmWda7TdNs9tFAQAAAACALiyPv+KKK1RfX6+VK1cqGAxq6dKleuSRRzRhwgT95S9/6Y0aAQAAAAA4KHV6pv1f//qXnnrqKc2ZM0cul0ujR4/WiSeeqLy8PC1ZskSnnnpqb9QJAAAAAMBBp9Mz7dFoVMOGDZMkFRYWqrq6WpI0ffp0rVmzpmerAwAAAADgINbp0D5p0iStX79eknTYYYfp5z//uXbu3KkHHnhAZWVlPV4gAAAAAAAHq04vj7/88su1e/duSdINN9ygk08+WY8++qh8Pp8efvjhnq4PAAAAAICDlmHbtt2dAWKxmD744AONGjVKxcXFPVVXn4pEIsrPz1dDQ4Py8vL6uxwAAAAAwCDX0Rza5XPa9wuFQjr88MO7OwwAAAAAAPiETof2RYsWtdt/5513drkYAAAAAADwkU6H9jfffDP795dfflmzZ89WMBiUJBmG0XOVAQAAAABwkOt0aH/++eezf8/NzdVjjz2mcePG9WhRAAAAAACgC0e+AQAAAACAvkFoBwAAAADAoTq9PP4vf/lL9u+WZWnZsmV65513sm2f+9zneqYyAAAAAAAOcp0+p93lanty3jAMmabZ7aL6Gue0AwAAAAD6Uq+d025ZVrcKAwAAAAAAHcM97QAAAAAAOFSnZ9rRfzKJhGzTkmTL5fHK7fd1faxkUnbGlMvjkdvvUyaekG3Zcnk9cvu8PVc0MIiYyZTMVFqGyyVvONjf5QAAAOAgQGgfADKJhFINjbISSTVs2KR0U5O8uTkqnHao/IX5cvs6Ht7NZEqJ2jrVrn1PqcYm+fJyVTR9spK1e7X3g40KDhui4sOmypufK5fb3YuvChg4zHRGiboG7Xz5TUV3VcsbDqps3nTljSknvAMAAKBXdXojusHIyRvRmYmkGjZXyJChve+836K/eM5M5Ywe2aGAbWVMNWysUPWqtS3HmT1Dsd1Viu2qkgxDI08+QcGhQ3riJQADXuP2Sn3w+D+kT/xzWTxjgkYeP0eeoL+fKgMAAMBA1dEcyj3tDpeJx+XPy1P9+x+22l+79h2ZiWSHxjITCdWsfrvVvrp17yl/wth9H9i29ry2RpkOjgsMZuloXFv+saJFYJekmnUblI7G+6EqAAAAHCw6vTx+27Zt7faPGjWqy8WgpVjlHvlyc2S3cZSencnITCblDYcOOFYmnmhzHCudafZxcm+DrFRKCjCDiINbJplSoq6hzf6mXXsULC7ou4IAAABwUOl0aB8zZowMw2jRbtv2gD2n3clcHo/Uyuf741r7erR6nav9hRUtxunguMBgdqCfL5eXrUEAAADQezr92+abb77ZG3WgDcHSoUrW7pU74G91Gbw7GJDb37HZcHfA3+Y4nlBQZiqV/Tg0vLRbu9MDg4Un6FfuqFI1bqts2WkYCpcO7fuiAAAAcNDo9D3thx12WPbPtGnTtGzZMt1xxx169tlnNXXq1N6o8aDmCQRk29KQmdOlT8yUGy6Xhh05R+5goGNjhYIqO26e5Go+c2i4XBp6xGHa+94GSfvC/bAjDuvUrvTAYOUJ+DX6xPmtbjY3ZuFR8uZ07OcPAAAA6Ipu7R5/9dVX6xe/+IVOPPFEvfzyyzrrrLP0s5/9rCfr6xNO3j1eksxUSmY8ISudVnTnbqUjjfIVFihn9Eh5QyEZ7o6/92KZpjLRmCKbtylZWy9/cYFyR49Q084qJaqqFR5RqnB5qbw54V58RcDAk2xoUv2m7Wqo2Cl/fo6GHjZRvrxcefze/i4NAAAAA1BHc2i3QvvEiRN1xx136PTTT9fq1av1uc99Tjt37uzqcP3G6aF9P9u2Zdu2ZEuuTgT1tlimlR1n/9iuA9z3DhzsLNOU4XJ1eC8JAAAAoDUdzaHd2kGpqqpKhx56qCRp6tSpqqqq6s5wOADDMHo0KHw8+Pf02MBg5XK7+7sEAAAAHES6Na368ZlZwzDUjUl7AAAAAADwCZ2eaS8sLMzOyDY1NWnWrFksqQYAAAAAoBd0OrTfddddvVAGAAAAAAD4pE6H9gsuuKA36gAAAAAAAJ/Q6dAeiUTa7Xfy7usAAAAAAAwknQ7tBQUFre4ybtu2DMOQaZo9UhiaM9Ppf5/VnpEMQy6vV55QoEs7WVvptDKJpOyMKZfXI5ffLyuZlJXOyOVxyx0IyOXt1sECwKBn27bS0bjMZEqGyyVP0C9PwN/fZWGAMBNJmamUZNty+bzyBIP9XRIAAHCoTiez559/XtK+X1g/+9nP6he/+IWGDx/e44XhI6nGJiXrGlT9+lplYnFJkn9IoUqOPFzuUFDeYKDDY6WjMVW/sU5N23ZKti3D41HhlPFyB4OqXvWmZBjKHTdKxbOmyRvil0igNWYypciWndr50mplYglJUrh8qEZ+Zr4Chaw2Qtts21aqIaLqVWuUqm+QJHlCIRXPOUyBIUN4wxQAALRg2N04py03N1dvvfWWxo0b15M19bmOHmrfHzKxmFKNUe34xwst+lw+r0ae8il5w2G5PAeecc/EE9q1fIUS1bUt+gqnTlIq0qjo9l2SpNDwEpUdM1duPzOHwCdFtuzS5r/8q0W7JxzUxLNPli833A9VYSBIN0W149nnZWcyLfqGLzhB/qKCvi8KAAD0i47mUM5qczgrY6n2rfda70ul1bRtl6xWfvlrTSaeaDWwS1L9+k3KGzsy+3FsZ5Uy8WTnCwYGuXQsoV2vrGm1LxONK1pZ08cVYaCwbVvRHTtbDeySVPfO+zJT6T6uCgAAOF23Q3tr97ej59i2pWRdfZv9iZpaqYOLJdKRxrafJ5OR1PxrmYnFOjQucDCxTVOJ2vo2+6M79/RdMRhQbNNUfE/bb+qk9ta3GegBAMDBq9M3z82aNSsb1OPxuE4//XT5fL5s/5o1rc9Aoes8oaBSbcy+eMNhGe6Ovffibm+jI8OQ4Woe2lkaD7TCMOQJBbL3sn+SvzC3jwvCQGG43PLm5ihe2fobO+5QsMP/ngMAgINHp0P7GWeckf375z//+Z6sBa1w+XwqPHSiql59o2WnIeWNH9Phsbw5IbmDAZnxlmEjZ2SZorurPro2Nyx3Jza4Aw4W3nBQw2ZP1a6XVrfoM1wu5Y1mY060znAZyhs3RpENm1vtL5wykTdLAQBAC93aiG6wcPJGdLZlKR2Nae97H6ph/Ue/6Blul0rmz1Fw2BB5czq+6VVyb4N2PveSMh8L7oHiIhXNmKLdL7wm2zTlCYc0YsEx8uU763MBOEU6Fteul9/U3g8++pl0eT0ae9rxCpcP69JRjDg4WJmMYrsqVb1qjWzLyrbnT56ggknjCe0AABxEOppDuxTa6+vr9X//93/atGmTrr76ahUVFWnNmjUqKSkZkMe/OTm0S5Jt2UpHo7JNU8m6ehluj3wFuTJ8Pvm6MBuejsaUborJjMflzcuV2+eVmUwp3dgkTygkT06I496AA8gkksrEEkrU1svl9ylQkCdPOEBgxwFZpikznlC6sUm2acqXnydXwC+319vfpQEAgD7U0Rza6eXx69at04IFC5Sfn68tW7bo4osvVlFRkf70pz9p27Zt+s1vftOtwtGS4TLky82RJPkL8rs9njcckjccat6WE1ZgSGG3xwYOFp6AX56AX4Gi7v9M4uDicrvlygl3apUUAAA4eHV6x5tFixbpy1/+sjZs2KBA4KNZ3s9+9rN68cUXe7Q4AAAAAAAOZp0O7a+//rq++tWvtmgfPny4Kisre6QoAAAAAADQhdDu9/sViURatH/44YcaOnRojxQFAAAAAAC6ENo/97nP6fvf/77S6X3nhhuGoW3btumaa67RWWed1eMFAgAAAABwsOp0aL/jjjvU1NSkYcOGKR6P6/jjj9f48eOVm5urH/7wh71RIwAAAAAAB6VO7x6fn5+vf/7zn3r55Ze1bt06NTU16fDDD9eCBQt6oz4AAAAAAA5aXTqnvTWxWExvvPGGJCknJ0eHH354TwzbJ5x+TrskWZmMMvGE7ExGhsslw+vt1lnqViYjM56UbZkyPB55gkEZLqMHKwbQGWbGVKoxJiuTkdvrlS83JJe704uhAAAAMED0+DntrW0+93Fvv/22PvWpT2nUqFGaOnWq/va3v3W8WrQrHY0qWbtXtW+9KzMelwxDofJSFU2bIk8oKJfX27nxYnHVvfWeIpu2yrYsuf0+DZk5VbmjR8gd8PfSqwDQlmRjTDteXaedK9+Tlc7I7fdq1DGHqWzOFPlyuv7mHAAAAAa+Dof2goICGUbbM7G2bcswDFVUVPRIYdgnk0gqWVevPa+98VGjbSu2c7fSkUYNm3+E/AX5nRqv8qVVildVZ9vMZEp7Vr4p27JVMGmcDBeze0BfSceT2rT0Ne1ZtzHbZibTqlj2hjLJlMZ8eo7c3k7fyQQAAIBBosO/CT7//PPt9m/YsKHV89vRPVYyobq332u1L93YpFRDo9yBgDwdnCHPxOLNAvvH1b71rnJGlcsbDnW5XgCdk44mmgX2j9ux4h2Vzz1UwUJn3rYDAACA3tfh0H788ce3219QUNDdWtAKy7SUaYq22Z/au1fBYcUdHi/V0PZtDlYqLSuVlsKdKhFAN6Qa2/75tk1LmXhSKuzDggAAAOAorIN2OMMwZHjafm/FHQh0ajxPsJ3rDcnwuDs1HoDucQd87fa7WBoPAABwUCO0O5zL51fuuNFtdLoUGDpEbn/7v/R/nDc3R642rs8ZOVxuPxvRAX3JlxNUoDC31b68EcPk68YpEQAAABj4CO0O5wn4lHfIGAWGNl8Cb7jdGjZvtjyhUKc2jvOEghrxmWPk8jXfcd5XmK+hRxwmt69zO9ED6B5/bljT//PkFrvEBwpzNeWLn5Y33LnVNAAAABhcOnxO+5lnntluf319vV544QWZptkjhfUlp5/TbpuW0vG4rGRSybq9cvl88hfky+Xztb/cva3xbFuZaFyphojS0Zj8hfnyhkPyMKMH9JtEQ5NiNQ1K1EUUGlqgYFGe/HlsMAEAADBY9fg57fn57R8rlp+fr/PPP7/jFaLDDLdLvpywlBNWYEhR98czDHlzQvLmsEs84BSB/BwF8nOkQ4b3dykAAABwkA6H9l//+te9WQcAAAAAAPgE7mkHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKEI7AAAAAAAORWgHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQ3n6uwB0TDqekJVKy0wkJduWO+CX4XHJMFzyBAMyXN17/8W2baWjcWXiCcmy5QkF5A0FZbh5X2cwa/Z1t215ggF5w8Fufz8BAAAA6BmE9gEg1dikVEOTdr6wUploXJLk8nlVeuQs+fLDSlTXKFReJrfP26XxLdNUdHeNtv7j5ez4br9PIz41V3mjh8vt79q4cDbLNBXdVa2tz77yia/7POWNKe/y9xMAAACAnsN0msOlmqKy0hltW/piNlhJkpVKa9eLq2TbUsOmCqUaIl1/jkiTNj+5rNn4ZjKlrUtfVqKuvjvlw8FSDU3a/NS/Wvm6v6REXUM/VgYAAABgP0K7w1mWpb3rK2RbVqv9NWveVdGMqdr77vsyU6lOj29blmrf2djm+JUr18lMdn5cOJttWqp9Z0PbX/dV62Sm0n1cFQAAAIBPIrQ7XcZSsp3Z7mR9RC6XW+lIo2zT7PTwVsZUvLquzf5EXYOsTOfHhbNZZkaxdr7uydoGWWlCOwAAANDfCO1O53HJX5DXZrcvL1e2ZcmTE5bhdnd6eJfbrcCQgjb7/YV5cnk6Py6czTjA191XmCeXhy0vAAAAgP5GaHc4l+FS4eRDJMNotX/orEO1990PVDR1itw+X6fHN9wuDZk+sc3xS+dOl9vf+XHhbC63W8V83QEAAADHI7Q7nDcnJHndGnnSMc1ClOF2q3T+LLn8XoVHlMlXmN/l5/DlhjXu9BOaje/yuDXy0/MULC7sVv1wLl9ejsae9omvu9ejkZ85UsF2ZuEBAAAA9B3Dtm27v4vob5FIRPn5+WpoaFBeXttL0ftTOhqXld53Trv973PU5XLJ5XbJHfDL1YWl8R9nW9a+87pjcdmWLW84KE842O1x4Wx83QEAAID+0dEcyk2rA4Q3HJQU7LXxDZdLvtywfLnhXnsOOA9fdwAAAMDZWB4PAAAAAIBDEdoBAAAAAHAoQjsAAAAAAA7FPe0DRCaWkGWZstKmzERShtslt88rw+eT1++T4e7c+y9mKq1UU1xmMrVvHLdLViott98rTygot5dvjcHEzGSUisSUjifl9nrkDQflCwf6uyzAsTLxuKxkSrZty+33yx0MyGjjiEQAAIDeRDIbABJ7I8rEE0rujWjXK2tkJlKSJH9BnkadOF8pw1CgIFeegL9D46UaY9r6/Bvas26j9O/DA4omjlLZ7Mna/tyrKpg8VqVHTP/35ncY6JJNMW1/9R1tWb5WVsaUJOWPKdH0cz6j0JCuHxUIDEa2aSm5t157Vr6hTDQmSXL5fRo6e6aCJcPk4g1NAADQx1ge73CpxqgqV74tK53R9mWvZQO7JCXrI9r05DJ5fF7Fqmo7NF4mmdKWZau0560N2cAuSXUfbtOO197RkMMmq3bdh9qz+j1ZmUyPvx70Lcu0tHvNh9r83OpsYJekhi1VWv3Q35RoiPZjdYDzpGMx7X7h5WxglyQrmVLVq6uUamzsx8oAAMDBitDucPHaeg2dNVmVK9e12m+lM2rYtF2pppgy8cQBx0tHE6p+Z3OrfQ0VuxQaViRJqnl7vdLRA48HZ0s1xlTxrzWt9sXrIorV1PdtQYCD2Zatpi3bZJtWq/1733lfZirdx1UBAICDHaHd4eJ76uT2uJWoa2jzmtieWnnDoWYzqW3JJFLNZthb6zdcLtmmJYtfTgc8M51WOpZss7+psq4PqwGczTZNJWv3ttmfaojINlmBBAAA+hah3eECRfmyLEu+vHCb1/gL82Qmkx3ajM7j97bb7/b7ZFuWZBhy+bh3c6BzeTxyt/N1DBVzTzuwn+F2yZuf22a/NxyW4Xb3YUUAAACEdscLDStS7TsbVTJ7aqv9hsulwoljZUjyhg68cZwnFFD+uOGt9oVLhyhZH5EkFUwcLU+Q3cUHOl9uSCOPmt56XziocElRH1cEOJfhcilv3BipjU3iC6ZOktvn69OaAAAACO0O580Na8jU8fIXFWjYnKmS66PfJt1+n8acepwyyaRyRpV1bLygXxNOO0a5I0uatYdLh2jsgiNUveY95Y4uV/nRh8vta39WHs7n9rg1+pjpKp05oVl7oCBHs796uoIFOf1UGeBMnnBIJUfNk+H52AoVl0tFM6fJX1jQb3UBAICDl2Hb7dzg7DA/+tGPdN111+nyyy/XXXfdJUlKJBL61re+pccff1zJZFILFy7Ufffdp5KSkvYH+5hIJKL8/Hw1NDQoLy+vl6rvOtu2lW6Ky7ZM2aalVKRJLo9HnnBAhtcjj8/X6YCdjiaUisaVborJEwrI7fXIjCfkCQX2/eng8XEYGNLxpFJNcSXqm+QJ+uTPDSuQ3/YtF8DBzDYtZRIJZeJxybLkCYXkDvjl8nDLEAAA6DkdzaED5jeQ119/XT//+c81Y8aMZu1XXnml/v73v+sPf/iD8vPzddlll+nMM8/UK6+80k+V9jzDMOTLDWU/DhR1/z5kbzggbzggDSv8WCv3Nw9W3qBf3qBf4aEF/V0K4HiG2yVvOCRvOHTgiwEAAHrZgFge39TUpC996Ut66KGHVFj4UchsaGjQL3/5S91555369Kc/rdmzZ+vXv/61Xn31Vb322mv9WDEAAAAAAN03IEL717/+dZ166qlasGBBs/bVq1crnU43a588ebJGjRqlFStWtDleMplUJBJp9gcAAAAAAKdx/PL4xx9/XGvWrNHrr7/eoq+yslI+n08FBQXN2ktKSlRZWdnmmEuWLNFNN93U06UCAAAAANCjHD3Tvn37dl1++eV69NFHFQj03PFj1113nRoaGrJ/tm/f3mNjAwAAAADQUxwd2levXq09e/bo8MMPl8fjkcfj0QsvvKC7775bHo9HJSUlSqVSqq+vb/a4qqoqlZaWtjmu3+9XXl5esz8AAAAAADiNo5fHf+Yzn9Hbb7/drO3CCy/U5MmTdc0112jkyJHyer1atmyZzjrrLEnS+vXrtW3bNs2fP78/SgYAAAAAoMc4OrTn5uZq2rRpzdrC4bCGDBmSbb/ooou0aNEiFRUVKS8vT9/4xjc0f/58HXnkkf1RMgAAAAAAPcbRob0jfvKTn8jlcumss85SMpnUwoULdd999/V3WQAAAAAAdJth27bd30X0t0gkovz8fDU0NHB/OwAAAACg13U0hzp6IzoAAAAAAA5mhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQb8Oe0Hk3QsLtmWJMkdDMrl6vx7LrZlyUwkJduW4fHI7ffJtixlYgnZsuXyeOQJ+Hu6dMCxbMtSOhqXbe/7/veGAv1dEtDnLNOUlUxJ+vf/G3y+/i4JAAD8G6F9AMgkEkpHIops3KhMU5NcPp/CI0cpVFYmTyjY8XHicUU2bVFkw2ZZ6bT8RYUactg0JfY2averq2Wl0goOLVLpUYfLX5Qvt9fbi68K6H+pppiq3/pQVavfk5lIKVw6RCM/PVehYUVy+/j+x8EhE4up/sNNaty8RXbGVGBYsYYcNk3evFy53O7+Lg8AgIMey+MdzrIsJevqVLd2rTJNTfvaUik1btqohg/XKxNPdGicTCKhqhWvq/699bLSaUlSsm6vdi1/SW7/vhl3SYpX16niqeeUqNnbOy8IcIh0NK7Nf3tJu15ZKzORkiRFK2v1wWPPKLq7pp+rA/pGJh7X7hdXKPLhJtkZU5KU2FOjncteULqxqZ+rAwAAEqHd8cx4QpENG1rtS+zZIyud6tA4maaYkjV1LTtsqWH9BhUdOr5Zc+Wrazr8hgAwECUjUTVu291q39bnViodjfdxRUDfS9VHlI40tuywbNW99a7MVLrviwIAAM0Q2h3OzqRlJZNt9qcjkQ6NE6va02Zfqr5BvvycZm2J2npZ6UzHigQGoMbtlW32JWrrCSs4KER37GyzL76nWnaGnwMAAPobod3hjANsNufq4H3nbn/bm8sZLpdk2y3bDKNDYwMDkTfY3s+EIcPF9z8GP1c7G4+6vB5J/BwAANDfCO0OZ3i98hUVtd7ndsuTk9Nq3yeFSoa2+btXaES5Ilt2NWvLnzBannZCDTDQ5YwoafONqcLJY+VhF3kcBHJHjWyzL2/8OLk5TQQAgH5HaHc4TyCggsmT5frkTLlhqHD69HZn0D/OFfBr6NzZLdq9uTkKjxiuyOZt2TZffq6Gzp4ml4fDBTB4eXNCOuT041oEd39hrkYcezinJ+Cg4A4GVTRzWot2X1Gh8g4Zc8DVXgAAoPcZtv2JddEHoUgkovz8fDU0NCgvL6+/y2lVJhpTsqFeqfp6uQNBBYcNlcvv71SwsDIZZeJxxXbsViaRUKi0RN68XFkZU5GK7cpE48oZWabAkAJ5w6FefDWAM5jptNKNMe3duF2pSJPyx45QaFihfLnh/i4N6DNmOi0zFld0xy6ZqZTC5WXy5uXKE2S1CQAAvamjOZTQroER2gEAAAAAg0dHcyjr3gAAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAABzK098F4MAs01Q6GlOmKaZYZY08oaBCpcUyDMnl9coTCnZ5bNu2ZaWSyiQSkiUlampl21KopFjuQFBuv68HXwkGglRTTMm9EcX21MpfkKfg0EL5csMyDKO/SwMAAAAOOoR2h7NMU5loTDuee1WJmr3ZdsPt0sgTj5GVSipUOkzenHCnx7ZtW5lYVMmaGqUa46p/b322r05S7thRKpo+Ve6AvydeCgaAZEOjNj25TKmGpmyb2+fVIV9YoOCwIoI7AAAA0MdYHu9wZiKlPWvebRbYJck2LW3/58sKFA9RYm9E6Vi8zTGSsYTqd9epekuVItX1MtMZSZKVTqlx44dy+QLNAvt+jRXbFK+u6dkXBMfKJJLa9txrzQK7JJmptDb/5Xmlm2L9VBkGs2Q8qbrddarcXKm9lXuVSaX7uyQAALok1RhTrLpO8Zp6paJt/24OdBYz7Q5npdOKbNzWap9tWopV1ajq3a0yJI1ZMFf+vOYz7o3VDVr+q3+oYs0GyZY8fq9mf+5ITT9ptrwuU55wjhq3bG/z+Rs+2CBPfr4CeTk9+bLgQJl4UtGdVW30JZRuismX2/kVHUBbIjUR/fOXS/Xey+/Itmy5vR4dcepcHfX/jlVOIf/mAAAGBjOdVtOOPdqy9FWlGqOSpEBRvsaeeqxCw4rkcjNPiu7hO8jhbNOUbVlt9mdicQXyc1Tz7mZt/NvLSscS2b5ofZP+dvsfVLF6X2CXpEwyrZV/eEnvLlsry7JkuF0yk8k2xzeTSdVtr1Y6yezXYGdnMu32ZxJtf58AnRWLRPWXu/6kd198W7a17x8oM53Ra0++qld+/6LSyVQ/VwgAQMck9kb04f89lw3skpSoa9D6/31GqUhTO48EOobQ7nCG2y1vO7OboZJi1a3fKkmq37RD6Y8txWmqjai6ovWZ09VPrVAiYcmMxxUYOqTN8b2FBVr/yvuK1fMPzmDn8vvk8nnb7Pfn5/ZhNRjsovVRbX5zU6t9bzy9Sk17+TcHAOB8Ziqt3a+uk2y7RZ+VMVXz9oZ2J+CAjiC0O5xp2SqZO6PVvkBxodyBgOK1Ddm21MfuO967s7bNcVPxpDLJjFxer0IlQ+Xytdwl3nC55CsdrvdffFvJGLOsg503HFTp3Omt9uWPHyVPKNDHFWEwi1Q3tNlnZkwlo/ybAwBwPjOVVrSy7d+5m3bukZVufzUjcCCEdoer3bpHvqICjfjM/OyMu+F2KX/iWI34zFF693+fbXa9J/hRsMopzmtzXJfbJY/fq/DocbLSSZUeM1fBkqHZfl9RofJmztQLv31emXRG3gBHvw12LrdbRVPGacSn52WPEXR5PRo2e6pGHD9HHk4RQA8KFbSzP4JhyBtoe9UHAABO4fJ45Mtr+/9p/sI8GR62EUP38B3kcEWjSvSPmx/TrHNP0KhTjpcsSzIMxWsjevOhJ2XGP7rvM1icL1/OR2e255cUKlSQ0+rS9knHTVeoIEdun1fBkjJl4gnlTJ6svCmTFWuIaccH2/Xm7U+qqa5RYw4fr2BeqE9eL/qXJxjQkKnjlTdmuOxMRobbLU8oIJfb3d+lYZDJKcxVUfkQ1e1qOTsxfs4EhQvYiA4A4HyegE/lRx2mD3//bKv9JYdPYSM6dJth263cgHGQiUQiys/PV0NDg/Ly2p6d7g+WZWnv1j166Z4nlYolNPfCU5QzJFc7/rWq2VKcQFGeDj3vJAWL8ps9vnbbHj11y+NqqmvMto2cPkYnXnq6coY0f617d9fpL7c8roaqj46XKx1frlMWnanc4ubjAkB31e6s0WM3/FZ7d9dl24ZPGqH/d925yh/KvzkAgIEhHU+q5q0PteOlNdl72w2PW2NPPloF40fI3cptqIDU8RxKaJezQ7sk2ZateH2TorURpWIJ5ZYNkcvYdwxXqjGmQF6O/AVh+dvYsK6xNqKm2ojikZjyhxUoVJDT5sx5095GRWsb1bS3UXlDCxQuzFEon2O+APSOxrqIIjURNdY2qqCkQLlFucyyAwAGHDOVVjqWUKK2QYbbpUBhrjzhkNweViuibYT2TnB6aAcAAAAADC4dzaHcYAEAAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4lKe/C8CBZZJpmfG40tG4bNOULzdHchlyuV1yBwNyubt3lISZSsnKpCXLlpVMypbkCQbl8vu7PTYGl3Q8oUw0oXQ0Jm8oIE84KG8o2N9lAegnZiolK5WSmUjI5fXK7ffLHQj0d1kAAAwqhHaHS8fjiu2u0a4XVspKZyRJhsul4lmHKnd0ueLVtcoZXiaXz9ul8c1EXPGq3TI8PjV8sF62ae7rMAzlTZig0PDhcnu7NjYGl1RjVFuWvqzY7upsW2BIgcaedrz8+bn9WBmA/mAmEqr/4AP9//buOz6us8wX+O/0qZpR77Ity72X2HGcxA5JnIQECAllIRcIy8JlCSwdlksLdwvlXhaWernsZQNLC4GEkkBItdPsFPduucrqVps+p98/FI89mRlZbtJI+n0/H39A533PmXeio5nznPO+z5Pu7c1sk7xelC9bBiUQGMeRERERTS6cHl/k7GQa7U++kAnYAcB1HJzaugdmPInYiQ4Y8cSFHdswED18EHIghKG9+84E7ADguogeOgQzGr3Yt0CTgJXW0fbklqyAHQDS/UM4/sgzMJPpcRoZEY0Hx7YRO3YsK2AHADuVQt/WrbDS/EwgIiK6VBi0FzHbsjF44Cjgunnb+3bsR9mC2RjcdwiOZeftMxLHNCBqHqS6ugr2iR09Cts0z/vYNLlYKR3xtvznSapvEBaDdqIpxTEMJDo68rfpOuxkcoxHRERENHkxaC9irm3BiMQKthuxOARRghmNZz8lH+3xLQuiosBKpQr2sVMp4AKOTZOLc44bN1ZaH6OREFExcG0bcJyC7SN9rxAREdH5YdBexARZhqeyrGC7pzQMx7aglZdCkM8/YZyoqHD0NJRg4fXIcjAIgcnopjxJVQBBKNiu+Jh4imgqESQJglw4LY7s94/haIiIiCY3JqIrYpIkIdQyDf27DuZ9kl65YgFs3YKvrhZmLAnF74WoFP6V6vEUjEQalmnB1C3YpgWPrxyaX0OivT3vU5PAtOmIdvVDDfigBryQLzDhHRUvSzfg2g5kTYUg5b+PJ/u8KJszAwMHjua0BZpqITNoJ8rimCZcx4GoKBDEM39XicE4kpEE9KQOX9gPX8gPj3/i/f1ImobA9OmIHT6c0yb7/ZCZQZ6IiMaBlUzDSqdh6yZkrwbJ64GsqeM9rIvGoL3IyQEfpt26Hp0bX4QRjQMAJI+G2rUr4NguDvzqUQCAIImov3oZKhbPguLNvVhKDMSw4/fPo2n5LDx33+OInYoM7ycImPu6xVh22yrED+4dng4PQFRV+Kc3o/Wxreja1gpBEjHzhpVouHI+VD9LfE0GZjKFRGcverfth2NaCM1sRNm8mdBCuVmfJVVB7dqlgChgYP+reRYEAaHmBtSvWwnZo439GyAqQrauwxgaQvzECTiWBU9lJfz19ZC8Xgx29uOhr96P/va+TP+5Vy/A6953EwKlE6sCgyCK8NfXw7UsxNvaMjd91dJSlC5cyLJvREQ05vRIDCcfew76YCSzLTijEbVXLYcyweMXwXULZDmbQqLRKEKhECKRCEpKSsZ7OFn0SAzdL2xFoKkOis8HwIVj2Yi1daJqxULs+r8PZvVvueN1KG1pytpmJHU89+M/o/mq+dj0H3+FHs9da3jF267FgusXA7YF17YhSDL2P7wFPTuPZPVb8q6bUL2o+ZK/TxpbZjKFk0+/hOiRk1nbJa+G2W+7uWAJN9s0YSXTsA0TkiJD9nkgqRP/7iXRpWAbBiIHDiDV3Z21XVQU+BcsxS+/8FNEeyM5+y1//RVYf/cGyOrEu4/u2DYcw4BjmhAkCaKqskwoERGNOTORwvE/PZl5yHm20nkzUbNmGcQRlnWNl9HGocU3csoSb+tA/GQ34ie7c9p81RWouXIRurfszmzreG4HAnWVUHxn7ialo0kMtvUitWhG3oAdAHY98hJmLG2CExnOEC6qKqoXTMsJ2g8/9hJKZ9RADfguxdujcWIMxXICdgCwUzp6XtqNhvWr8i61kBQFUogX5ET52KlUTsAODJfpHOoeyBuwA8DOx7dh5ZvWIFxdermHeMmJkgTR6wW8E/sJBhERTWxmPJE3YAeAoYPHULFkHtSS3NmkEwUT0RUxI5bAUOuJgu2R1uMonZX9VF0fjMKxs9emm2kD3nAAsZ6hgsfSE2k4Z825cAwDvrLcuz3Jvggcq3DGYJoYBg4eL9g2eOgEs8ETXYB8ATswfBM00j1YcD/btGHpLK1JRER0ocx44VKjruPAsawxHM2lx6C9qLkQRsjYnS+bt7c8BOk1meQVr4rkYAyh2hEy0Zf4IAivWSmR56UDVaUQLyBTPRWX8zytiGg0CvzxOIaBUHW44G6KpkDxcJkJERHRhVKChauWCJI0YrLuiYBBexFTgwGEZk0v2B6ePQP9+49lbau/dgXk1ySi85T4UDW7AapHgS+cf1rIsjesgmiemVIiqhoSp3KncrbcshpqgNMgJ7rSuYXzEoTnzIDsZWI5ovPlra7Ou921bQRCXpTVl+dtX37bKvhLJ+6UPSIiovGmBHzQSkN520rnz8yJjyYaBu1FLtBUC095OGe7p6IU/roq9L6yDwAgqgqm33QV/DW5F4WqV8PKt69H/4lerP/vt6CsqTLTJikSlr5hFZoW1MFOvhq0CwJ8dU3oeOXQmX6agnlvvhbhpvwXpTSxqKEAwnOm52yXfV5Ur1xQlIk6iIqd5PXCV1+fs11UVZRUleItX7oLdXMbzmyXRCy75QqsvO1KyBP8CQAREdF4UnxeNN18DXzVFWc2CgJK581E5ZJ5E/7altnjUdzZ44HhDPKJjh4MtR6HgOEnob7aKji2BQEibMOE7PVACXghSsNT113HgZlMwU7pgCjAtR3YaR2yzwsHAlJJHY5pwxP0QfPKMPq74ZgGJK8fnvJKSJoHxqt13R3bhur3QivxZY5/qaWjCejRJPR4Ep5QAFrQB41P9C8rM5lCsncAp7bvh2OYCLc0ITxr2oRO0kF0vlzHgR5LQo8mYOsmPKVBqH4v5Aucrm7rOsxYDPHjx+FYFrxVVfDW1kJ+NVFbMppEKpqAmTahBTwIhAOcGj8JuK4LK5mCmUjBMQwowQBkjwZpEtQGJiKaSKy0Djulw7EsSJoKyasVdVWT0cahDNpR3EG767pI9vQj3nkK4ZYG2LqByKHjGNjbCgDw11ejatVSqCUByNrwCenYNlK9/eh6bitqrlyKrhe2wYjEMscMNNSgbt0qKH4fYj2D2PL//oLSpir4Sv2IdA7ATBtY+d9ugG+M6gYn+iJ46T8eQayrP7OtrLkWy99905iNYSqzDROu40DS1JFzKBBNMo7tINbeiz2/fAxmMj28URBQv3oBpq1bdlFLgRzLguu6EGWZf1eTnOu6SPcNou3RZ2Cl0pnt4dkzULVqcVY1FyIiorONNg7l9PgiZ0QTOPjrv0LxeXDi4Y049tDjmYAdABIdPejbuR9G4kwpNzOexIk/b0T5otnofG5rVsAOAPH2bvS8uBPpaALPfu/3GGzrxdHn9mDPn17Eya2t6N57Ajt/9yzMtHHZ358eS+Ll/5cdsAPAwNEu7PrNRphJZjG/3CRVgezRGFjQlKNH4tj500fOBOwA4Lro2LIHp/Ydw8Xc0xZlGZKi8O9qCjDjSRx/+OmsgB0Ahg4dw9CBo3AdVlwhIqKLw6C9yCW6++C6LmSvBjOeyNsnduwkrEQSjm0DAOJtnQAEiKoCM5a/XmHkSBscw0A8T7I5AGjf1go9Vrh0wqWSjiYR7ezP29a773jBuvJERBdr4HA7HNPO29a2aTuMEcrHEJ2WOtUPx8xfsq9/98Hsm0JEREQXgEF7kUv3RyBpKuzUCF/6rgvHtOC+Wp89PTAESVVy7vrn7GMUrlfoOu6Y1A0+e4ZAPpZ++Z/2E9HUlOgdKNimRxNw7Sm/eoxGwRiKFWyzdQOw898YIiIiGi0G7UXOV1MOK6VD9vsK9hFkCZKqZuqn+2oqYesGlJH2kSSIWuGkDKIsXXAipvPhKRmhpqIoQGHpMSK6TEJNNQXbvBUhCDK/IuncPJWlBdtknxeCfHkSuBIR0dTBK5Ii56sohezVkB6I5i39BgDh2c2QA14I4vCv019XDVGRYcYS8FTkv5goWzALkqaibPrwRausKZmgHwBa1i8ZMaC+VLSAFxWzG/O2Na6aBy3IBD5EdHmEmqqh+PLXbW2+cRW0QOEbn0SneUrDkP35v6uqVi6CzER0RER0kZg9HsWdPR4AUgMRdL+yB/Vrl8FJ6zCTKXQ8+zKclI7SeS0Iz54BAJB9Hig+L1zXhT4YQeczL6Ny+QL07zmERHs3AEAQRZTOb0HF0nlwHMBM6UhHEtDjSSgeDYpPg5k2UFJXAU9g+FjJwTgSAzEYSR3ByhA8JT5o/vwXuhf0/obi2P27Z9C9+wjgDo+xcfU8zL1lNTyhy3/jYCpwHQeWbkAURZYgIjpLoncQ+x94CvHu4dwaskfFjBtXoWphc8GAnshMG0hFk4idikCURPhL/YgeaEXk4DEAgKjIqFi+AKWzZ0D28jwiosnDMU1YaR1WPAFBkiD7fJB9nszDQzo/LPl2Hoo5aHddF0Y8AVc3MLi/FUYkBqUkiLL5syDIMk5t34fI4RMQFRlKwI+mDVdn6mybyTRsXYcAAa7rwnVsSKoK2edBOpZC3+FORNq6cezZ3cCrp4Hi07Di3RvgL/VCC5VgqDeGv3zjN0gNnUmCN/uahVj1N+vhC1+6gNpM6dDjKVi6AcWjQQv6MiXs6OLokTj69h3BUGsbJFVBzRUL4K+pgFLgyRDRVGPEUzCTaTiWDcWnQQ36IUq8+KD80vEUDjy1Ey//9tlMLhnFo+K6D92G6hlVwKslNGWfB6LEqfFENHlYaR1D+1sxsOcgIAxXSoEL1K67Et7qCn7mXYDRxqHyGI6JLoCZSsHoH0TXMy9mtukDQ4gfP4naa1cjPLcZoZlNsFNpSB4Vyd5+iIoM2euB4vPkfVJkpg20btyJYHkJjj2zK7NdVCQ0r1sKuECiPwk9bkD1eyGdNW1e9WmomF6FVP8QEl190Ep80Er80IIXN41U8Wpcv34ZpAej2P+LP8M6K3txrK0bZfOb0fS6VXySSARADXgvqiY7TS29R7rw0v2bsraZaQOPf+shvOXrf4vS+opxGtnlYVs2UkNxxPuicCwbwaowPCU+KGOQ94aIikuq+xRix0+ies1yiIoCxzAheT1I9fZBCfiglgTHe4iTFoP2Ymfa6Hlxe96mni3b0HjLdTjxh8cy25SSILwV4RGn46VjSfhKAzi68cxxRUnEynffhPYte3Hiqa2Z7Z7SIG685w144oePIB1LYsNH34QTT25F51OvZPoEasqw9O5b4CsrrlkKU51tmuh8YUdWwH7awL6jqFkxn0E7EdF5SMdT2Pq75/K2ua6Lgxt3YfU7roMgCmM8ssvD1E107TmO5//jL5mKMoIkYumb12LWtYug8WYX0ZRhpdKIHDmOqlXL0LNlK6yzKkD562tg19eO4+gmP87/K3K2rsMpUPbMMcycNjMaQ9emLSOWe7N1E4HyEiQHoplt9SvnoHfvMQwc7sjqmx6MYd8DT2HVW6/GkluuQMdzOxHrOJXVJ949gJ3/9VfWNC4yVsrAwIHjBdv7Dxwbu8EQEU0CtmEhfipSsH2gvQ+2Vbic6kST6IvgmR/8KasErGs72P7bZ9F/vGccR0ZEY811HJQ0T0P38y9nBewAkOjoRuTwMTiT6POv2DBon4SMSAxWqnD9c9mjIhVJoKS2PLOtduEM9Ow6kre/HklA1WTUzqnH4NGuvH1iHX0w4iPUhafxMVLGCqazICI6L7JHQVlTZcH2mtkNkJTJMYnRsR0c2rgLhVIf7frDZugJfu8TTRWCJAFwYaf1vO2xoycKttHFY9Be5CRNg6jmT8gmqgrEApnAT//R2LoBIxqDPjAEM56AY9vwlPiRjqUw99YrM/1d180k1MnHiJ77KbqZ4h/q+XAdF6mhOCKdfYh1D0CPFb7RciFkr4rSOdMKtpfNm3FJX4+ICABsw4QeiSHVNwg9EodjTp4nL5rPg5VvvTZvm6wpmHnVPAjC5Jga79g2ol0DBdvj/RHYxuT53RLRyGSPVnD2LzA8C8ex7DEc0dQyOW4HT2KuIKBq9TJ0P/tSTlvVqmVwHQfe6kqkerKnrMt+H8xYAt2bX0Gqe7hNkGWUL56HkpZpmHHVAjiWhcVvX4/9f9wM13UhaQrss6bAnc1TGoTjAhCEgk9o1UtYBm6yM1M6evefwO7fPQMjPhysl9RXYPm7NiBYU3ZJLvokRUH91csQPd4J6zU3VMrmzoAWClz0axARnc2IJ9H1wnYMHjwOuC4EUUTZghbUrFo0aSpWlNaX48aP3Y5nf/IY0q/e0A7VlOK6D70BwYrQOI/u0pFkGZUtdejadyJve2lTFWQPq7wQTSVqabhgm6gqECfJTKNixP+yxc51oZWF0fj612Fw7yEYkRjUUACl82fD0XV0PfkMqteuRvsTfZlg2ltdCVGS0P74szBj8TOHsiz0bdsNUZERmt2M9GAMwbIArvnEW+FYNqZdvRhHn9yaM4RAbTlK6irg2A5ql81C17ZDOX0q509j9uXzEGk/ha0//WvWtmhHH57/zu+w7tN/c8mS+nlKSzD/Xbehb89hDLa2QdLU4ZJvdZVQfPx9EdGlY6V1tD/1IqLHz+RGcR0H/bsPwXUc1F+zAlKBmWMTierVMH3FbFQ210KPpyCIIjxBL3zhyXUjVBAFNF81H3sffTn3iboALHnTVVBZ9YVoSlGDfqihIIxILKetbOHcERNh08Vh0F7kjGgcqZ4+iIKD0OwZEFUFrmmi96WtsBPDd/jj7Z0INNQO/29jPSqvWAIzlsgK2M/Wv3Mf/A21iHecwrHHt6Bx/Urs/vVTWPSOGzDt2iU4+cKezPSW8lkNmPfma+F79enBrNdfCVEW0fnK8EUYBAE1S2Zi9q1rmIl8lIxECvv++HzeNjOp49SBNky7auElez0tHETtmiWoWj4PgihA9vAii4guPSuZzgrYzzaw7wiqVyyYFEE7MBzQBspLECif3FVT/GVBbPjM2/Hcj/+MWM8gAMAb8mP1u29AqLZsnEdHRGNN9nlRf/3V6H7hrJm8koTSBbNRMnMaBJErry8XBu1FLn1qAFo4iGjrYUSP5M/2bUaiqFi5FBUrFkPyaJBUBfG2/BdOwPB6d9eyETnZDS0URKx7eM3a7l89gZqlLVj0jhuGpzXKEoaOdQFnzdT2lPgx5w1XY8Z1y2HpJmRNgRrwQi6wtp5y2aaFaEdfwfZTrR2XNGgHhkv6ibypQkSX0UhVS+C6sEdYC0nFSZQlVDTXYMNn3w4jkYLruFD9HvhKA5Nm7T4RnR8l4EfdujXDFa4sG5KqQPJ6IErSeA9tUmPQXuTUUBBmPAnZ5y345FzyeSFqKhTvmenOStBf8JiCLEGQRPgqwhg63I6ycDDT1r3jMLp3HM78LHtUNK7NDiBlTYGsTZ51e2NNEEV4y0qQODWUt72krjzvdiKiYiad4+atqPKSY6Lyhf3whQtfVxDR1CJp6jk/8+nS4hyGIuetKsfAvlaE5rQU7BNqac4K2AFAC4cKZp0PzZ4JyetBaUsjjHgSnrAfkpa/b9PaRdCCvgt/A5TDU+LH7A1X5G0TRBF1S2aO8YiIiC6e7PPAUx7O2xZoqOFaRyIiogvEoL3IKX4vGq6/CumhOMoWLwDEM9PRBFFE+bJFkAM+WMkUrFQ6U09V9vvQcOO1kF6zfjnQVIfS+bMgShK0kB/z33kTul7ai8XvuB7Kq9nf1aAP09cvxdL33IyGKxecc7qLY9nQYwkY8WTBeq6UrWpeE5rXL81aeiBrClb/99vgLZ3caySJaHJSfF7MuHUdtNd8hnkry9B0wxrm06ALZukmUpE49PilLY1KRJeGreuwkik4Zv4qVHTxBJdRFqLRKEKhECKRCEpKii9gMpMpWCkDZioN1a/BiicBAVCDQUAUMLS/FfG2TgiShPCcmQhOb4Ds88J13eFgPpGCretQggFIXg2ydubCyUqloUeTMBJJSKoKCALslI7253ciNRCBryKMxnXL4a8qg+zJngbjui7SgzF0vbQX/QdPQFJk1Kych/J506GNMD2fhpkpHXo8hXjvIGRVga+8BJ4SP0SZa4KIaOIyE0mY8RTMRApq0AfZ72W1CrogjmUj0R9B62Ov4NShdmgBD1quX46K2U3wlHAWINF4s9JppHr6MLD7AKxUGt7KcpQvmQ8lGOD17CiNNg5l0I7iDtqtVBqR4x3o23cCffuGE9EpAR8kVcasN1yN3s0vw3lNch+togz169dAPsdFkm2YOLV9P7pf3AVRlhCcXg81HMbxJ1/O6dvyxmtRubA566l7aiCKXf/vD7DS2a8fqK/CvLdeD5XT6omIiOgCRdpP4dlvPZCpaHNa3fJZWHTnOmgsNUs0bmzdQN/OfYgcOJzdIAho3LAO3uqK8RnYBDPaOJTT44ucEU9ClJVMwA4AZjyJQF0F4idO5gTsAKD3DSA9MHTOY1vJNLpf2g1g+G52qGUa2p7Znrfvsb9uhhE7My3NNi10PL8zJ2AHgHhHLxI9/ed8fSIiIqJ8jEQau367MSdgB4DOba1ID+VPzktEY8NKp3MDdgBwXfRs2TZyRRE6bwzai1zq1CBinadytpc21yHZ2V1wv+jh48N11EeQHowAZ020cF0Xjmnl7WvrJqzkmaDdSunoP3C84LF7drae8/WJiIiI8jHTOgaPFb7O6T3QNoajIaLXSp8aKNhmRKJwDJb5vJQYtBc5QQSEPIngXNuBIBb+9QmSBJyjhuprj3vOmqtnv56Qf1yZrvK5X5+IiIgoHwHCiNcRksL1skTjaaQ4YLgD44BLiUF7kfNVVSBYl7smpO/ACfgb6wvuF57TfM4gXAsHIZyVJMK1LMi+/CV51IAPylltis+L6mWzCx67Zvncc98EICIiIspD8XtQs3B6wfbKuU1jNxgiyuGpKC0YmHuqyiGyjvslxaC9yCl+L1zbRuM1S7O2Dx3pgLemCmoomLNPSXMTlJLc7fmO3XTDVZmf+3YdRPNNV0IQs/8ABVHE7Devy0osJ0oiapbPhbc8lHPcqiWz4C0rroR+RERENHEoHhXz33Q1tDxJbefetgaeElapIRpPsteDqtXLcraLqoLqK1dkVauii8fs8Sju7PEAYKV1GPEkjGgSvbsOw0ykEJ7ZgPJ50yHJIlK9fYgeaYMoSwjPnQk1VALZm/+J+WvZpgUjGkff7kMwhmIIz54Ob0UpenYdRrJnAP7aclQvmQ0tHMhbr12PJhA50YVTuw9DUhXUXjEf3oowVD8zuhIREdHFSQ5E0bP3OLr3HIMW9GHGNYvhrwxBLTAzkIjGjm2YMOMJDB08AiuehK+uCsGmBsgBH2fcjhJLvp2HYg/aT3McB65lA4IAUZay/hgcywJEEeII69xH4joOHNvJHHf4ZxuiJI24dv4027QgiELewJ6IiIjoYliGCVGSIEqcJEpUbFzHges4ECSJwfp5Gm0cKo/hmOgiiaIIqPm/rET54n6VgihCOis4f+3P5yIpPJWIiIjo8pBVZbyHQEQFCKI4qod8dOH4X5eIiIiIiIioSDFoJyIiIiIiIipSDNqJiIiIiIiIihSDdiIiIiIiIqIixaCdiIiIiIiIqEgx5fcEko6nYFs2tIAHrmHBcRyYjgNBEOEP+vL39XsgjyKzu21aMJI6JFmC6mftU6LLQU/qMHUTmk+FoqnjPRwiIiIqco5tw0zqECQRqo/X6FMVg/YJIBlJoPNQO7Y+8hKuevMaSOk0Bvcfhes48E2vh1JTiaPRBKbNaYIsiug81I4XH3weqWgS05fMxIrbViFUXZq3tqnjOIifiuDgE9vRufcEtIAHC265ApUz6+Ap8eUZDRGdr1Q8hZ7j3Xjql09joLsfDbMbsP7t16G8rhyKxjJGRERElM11XaQGY+h4cR969x6DpKmYds0SlM2sgxbkNfpUI7iu6473IMbbaIvaj4dULIVN//UEdj22DXd85i1IHzqCVO9AVh81FEBw1WL0nYpi8Eg3dv51a1a74lHw377+PlROq845/lBHH/7yz7+EpZtZ22etW4Rld14DLeC99G+KaAox0gZe+esr+OP3/5C1XRRFvPdf/xazls8ap5ERERFRsUr2RfDi9x6EmUxnba9cMB3z71wHLcDAfTIYbRzKNe1FLj4Qxa7HtqGsrhya4OQE7ABgROJw+gdRXhnOCdgBwEybeOLHjyIdT2Xvl0jj5V8+nROwA0Drpt1IDsYv3RshmqLig3E88qOHc7Y7joPffvO3iPRFxmFUREREVKwsw8SRJ17JCdgB4NTe40jy2mHKYdBe5I5tPwIAmL60Gcm2zoL93FgCPa2F20/uOY50IvsPX0+m0b2/reA+HbuPnedoiei1+jv7YFt23rbIqSEkY8kxHhEREREVMyupo2fXkYLtXdtax3A0VAwYtBc5QRQAAK7jAIIwqr75G/NsEoS820/LtwaeiM6PII78dySc4++aiIiIpp6Rrg8EidcOUw2jsiI3Y+lMAMDR7Ufgn15fsJ8YCqJqZm3h4yxrgec169NVvwd1i2YU3KduYeE2IhqdstoyyGr+nJ9ltWXwMZkMERERnUXxe1AzQs6bumWzx3A0VAwYtBe5QFkJVt+xFpGeIcRTFvz1VTl9POUh2MEABvojWH3n2px2ze/B6/52AzyvKeWmejWsfPv6vCXeFtxyBbxh/6V7I0RTVLA0iDs+ekfOdkmR8LZPvw0l5cWV/JKIiIjGl6TImHHdcmgludfitStmw1vGa4ephtnjUdzZ4wEgGU2ir60X2/78MpZevwReRcDgviOvlnxrAEoC0C0HddNrIIoi+k+ewst/2IxkJIHmFS2Yv24xQlXhvNNsXNdFoi+KY1v2o33XUXhKfJh34wqUNlQwczzRJaIndfR19uHZ3z2L/o5+NM1vwpW3XomymjJIijTewyMiIqIilBqMoXfPUXTvOgLZo2La1YsRrK/kNfokMto4lEE7ij9oP81Mm7AtC6pXg2tZcBwXluNAkiSoHrVg39GsTXccB2bagCRJkFk3muiysAwLpm5A9WgM1omIiOicXMeFpRsQRJHX6JPQaOPQ/AstqSgpHgUKXv1jlVRIAAr96Wb1HQVRFKH5cqfJE9GlI6tywfXtRERERK8liAIUrzbew6BxVtRr2r/61a/iiiuuQDAYRFVVFW6//XYcPHgwq086ncY999yD8vJyBAIB3Hnnnejp6RmnERMRERERERFdOkUdtG/atAn33HMPtmzZgscffxymaWLDhg1IJBKZPh//+Mfxpz/9CQ888AA2bdqEzs5O3HFHbtInIiIiIiIioolmQq1pP3XqFKqqqrBp0yZce+21iEQiqKysxC9/+Uu85S1vAQAcOHAA8+bNw+bNm3HllVeO6rgTZU07ERERERERTQ6Tck17JBIBAJSVlQEAtm7dCtM0ccMNN2T6zJ07F01NTSMG7bquQ9f1zM/RaPQyjvriuY4DUzchwAEEEVZKh51KQ1QUiKoMxe+DJOdPamWbJmzdgGvZsA0TtmlCDfgh+zyQFAWu6yIVScA2LQiCC0lTIUoiNN/Ey0rp2A7iAzEMdQ8inUihvKES/pAfnuCFvRfbsmEk0hAlCVpg6q73dywLgiBCGEVCQ6KJzHVdWEkdLlwoPg+stA4znkKqPwLJo8FTGoQW9EEQJ/ffgmPbEAThot6nY9lwTBOCJEJS1XPvcCGvYduwkmkYkRgcy4JWGoLs1S7b6xEREY2XCRO0O46Dj33sY1i7di0WLlwIAOju7oaqqgiHw1l9q6ur0d3dXfBYX/3qV/GVr3zlcg73knEdB0YsgWR3PwYPHoMgiShf0AIl6EfvzlaUtTTAdRyofh8kNTvxnK0bMJMpxE+0I36iA4IkIjCtAY5uwDgSR+n8mdCTaYhwILkmXN1E5OAhwHERmN4IT0UZZO/ECN5ty0bnoXY8+C+/gp44c0Nm/rpFuO7uDfCXBkZ9LNd1ETsVwf4nt+P41sPQ/B4sev0VqJ3bCF9o6tSuN6JxRE90InL0JGSfD5WLZ0MNBSB7mAyFJh89Ekff/uPo3XkIEARUL50Nf3UZDvzmCVhpAwAge1TM+5sNCNZXTsrA3UqnYQwOItnVBUGSEGhshBwInFcQ7DoOzFgCg/tbkezuhezxoHThnOHvk0v42WGbFpKdPTj55AtwLTuzvWzhbFQumw/ZO3VvtBIR0eQzYYL2e+65B3v27MFzzz130cf63Oc+h0984hOZn6PRKBobGy/6uJeDEUvg+J+fRerUQGZb5MhJlDQ3oPbKJTjwq0fRcvt1ECDkBO1WOo3Op56DY1pwHReuZUEfGIJWXorQrGYke/ogqjJsS0f8RCeSnWdudCS7uqGWhlCz9krIE+Cpe6w/igfu/Tksw8ravm/TblQ2VeGK268aVek7AIh0D+L3X/oZjOSZ4P/J7/wBM1bNwdXv3QBvie+Sjr0Y6UMxtP72MVjJVGbb4P4jqL1qGcoXzYKs8UkWTR56JI49P/8L0gNnZl0d++sW+KpKMfPWtTj4u6cBAFbawN5fPIplH7wDnnBwvIZ7WdjpNPq3bYMVj2e2pXt64K2rQ2jWLEja6AJufSiCk395Gq7tAADMaBypp/oQnjcL5YvnQbpEnx1WIom2x54DXrPCb2DPIXiryhFumXZJXoeIiKgYTIhHBR/+8Ifx8MMP4+mnn0ZDQ0Nme01NDQzDwNDQUFb/np4e1NTUFDyepmkoKSnJ+leMbNvGUOuJrID9tOjRdhixBEIzG9G7/SBcx8bZ6Qls04Q+MITq1ctRuWIRqq9chtp1V8JbVQG9fxCu62Do0DG4egquI2QF7KcZgxEkOjoxEdIetO0+lhOwn/bS719AYiiet+21zLSBVx54JitgP+3YSwcR6x0acX/XdZEcjKHvaCc6dx1BpKsfejw14j7FxjZMdD6/LStgP63rhe2wEhPr/RCNxHVd9B84nhWwn5bsHUR6KA5fdVlmm2NaiJ6cXBVKXMdBoqMjK2A/LdXZCSuZzNluJtNI9Q1i8HAb4p2nYMQSsHUdvVu2ZwL2sw3tb4WVSl+yMQ+1Hs8J2E/r27YXZvLSvRYREdF4K+on7a7r4iMf+QgeeughbNy4ETNmzMhqX7FiBRRFwZNPPok777wTAHDw4EG0tbVhzZo14zHkS8qKJzGw/2jB9v49h1G/biWO/HETXNuFY9mQlOFfqZ02kOrtQ+TQUeDV6xpBllG1ailERUGyvQtqwA/J60P00JGCrxE9fAz+xnrInuKeajjYmXtj47RULAXnrOmTI9ETaRx7pbVg+5Et+1HVUpe3zXVdDLWfwrPf/T3S0TMXuXWLm7Hina+DNzz6KfrjyU7riBxtL9gea+uEpyw0hiMiunzMZBq9Owv/zQ8cPIHwjDoke858xuQL8CcyxzCQaC/8N59ob4caDkMQBADDM8CO/eV5RI93ZvqoQR9m3XkDbN0oeJxUzylo4Yu/Se46DvShwr8DM54EnNwbB0RERBNVUT9pv+eee/Dzn/8cv/zlLxEMBtHd3Y3u7m6kUsNP+kKhEN73vvfhE5/4BJ5++mls3boV733ve7FmzZpRZ44vai7g2oWDTde2YSWTqL9qMQQpO1FY+lQfIgfPBOwA4FoWejZvRXhOM1wBkANewHXgjnBx4zoOHKv4L37q5hZe3lBaWwZJHf39qdMXpnnbpPwJ/wAgORjDpm8/mBWwA0DnrqM48NgrsK38MwGKjeui4BMsALDN0d0AIZoIBEEARvqbF8Wcv4dAfeXlHtbYG+l7wLYz/w1s00LH8zuyAnYAMGJJHHrgcZQunFv4NUb473w+BFGEv666YLunPAxBKfxZTURENNEUddD+wx/+EJFIBOvXr0dtbW3m3/3335/p861vfQu33XYb7rzzTlx77bWoqanBgw8+OI6jvnQkn4bQjIaC7eHZ0xE/dhKuaQ5nkn81MZKVSmNg94H8O7kuEl29CM+aCX9tNVLdXfDW539yDAByeQVS6cJPTopFdXMNghX5n+Cse88NCJSObv2pJ+DFzDXzCrbPvLLwBWm0cwBGIv+UzKPP7kE6kjvFtBhJqgJfTUXB9pJphc8XoolG8XlQs7zw33X53OkYPHLmKbQWCsBfVVaw/0QkKAo81YWDYF9dXSbxnplIoX9P/tlZZiIF1wVERcnb7qu+dDc7go21BdfHV69eAnmUa/CJiIgmgqIO2l3Xzfvv7rvvzvTxeDz4/ve/j4GBASQSCTz44IMjrmefSGRVRfniOZB9uVPTPWUhBOoqke4fhJ1OZz3AcF0HZiI7QBRkCSUt01G1eik8FaVQS0Mw0gakkhDg9UMO5k7dljwanFApEtHiX8NcUhHC3/zTe9C48EzyIW/Qi1s+8iY0Lpw+6uPImoIVb74K3jxZ4udetxjBisLTwhN9kYJttmnBNkd+0m7pJvRoAsY4r4GXvRoa1q/KW+ItPGsa1ODkT8RHU0vprEb4q3MD8UBDJRS/B6lX/7ZLptdiwX+7BVrJ5KoiIUoSgtOn5w22lWAQ6ll5X1zLGnF2lpU2Iftzk5eWL5kP6RJmdFeCfkx/w/XwVJRmtsk+Lxo3XA2tLHzJXudScCwLZjIFK5WeEDliiIio+Aguv0FGXdR+PCT7BiFKEvp2HUTkyEkIooiyeTNQOmcG+rbvgbeyHL66akgeDcqrWd4tXUfnUy8gfaofAOCtqkD5knmIHj0OvW8AoqogNKcFUrAElm4iljQQCvth9HQj0dYO13Hgq6tFoHk6fv293+NN738DymomxpOlVDyFVDQJ27CgBTwIlAZHnTX+bLG+CI69eBDHXj4E1adh0S1XoHxa1YiZ40+1duDpbz6Qt031e7DhC3fBl+eJv2PbSPVHcWLjNgwe7YTi09B49RKUz26EGhifANmxbBjROLpf2o14ew9kj4rKFfNR0lQHJc8FOdFEp0cTiJzoQs+2g4AA1KyYh5LGasB1YaUNiLIE2eeB4p18T3BtXUd6IAJRkZDq6oI+0D88Bb2xEb6aGkhn5TTRI3Hs/c8/wDbMvMea965boQW8iBw5gWRnDySvB2ULZkMNlVyyzPFns1Jp2LoB13EgaSpkn3fEJU5jyXVcmPE4BvYcRKK9C6KiIDyvBcGm+glRlYWIiC6/0cahDNpR3EG7EUtg6OBhlLTMAFwXru0gdaoPfS/vzPTxN9ai+soVWXVpkz2n0P7XTZA8GmrWrkTv5pdzno5462qglFZCCQXQt20vJBEITq+HIAiId/Qi3tGNuhuuRqCmArJS1DkLLwvXcWGkdYiSBEXLP93zbKmhODZ+63eI9QzmtC17+zrMXLcks4ThbLHOU9j2f/+Qk3G5YuEMzL7taqjjGCTbpglbNyGIQuamENFkZr2aSG2qlDV0TAuD+1vRv2MvIAoINNXDV10J17GhlYXhrarMCoId20b3S3vR8ey2nGN5K0sx5203QvH7hvOhmBYESYQoT73vDwDQI1G0PfIU3NfkM/HWVKL2mtWsJU9ERKOOQ6fmN+kE4hgmhva1IjRrBnq3bEeqty+nT+JkF8yFiawLAK00jNprr4QRi2HoYGve6Yypzm74Gxpgp3Sku3uHj9XZm9UnsucggnmmjU4FgihAy7M0oRBvOIBrP3I7XvnFk+jZ3wZgeLr9vFtWoWnlnLwBu5lMo/WRF/KWSOrbcwzTrlk2rkG7pCiQCqxPJZqMpkqwfpqVTqN/577hHxwX8ePtiB8fXsMveT1ouvV1UHxnZvyIkoTKxbPhOsPBu/Pqsp9Qcz2mbVgDxT/cVxDFy/JkfaKwTRP92/fmBOwAkOo+BSMSY9BORESjxqC9yCW6uiGIIlzLzhuwZ/p1dMNbWZ75WVIVBKbVw4zFEdl/sOB+xuAgXKHwaRBv74ZtmJDUqXvxdT78FSGsef/rocdTsA0LileDJ+SHJOfPZGylDUTbCtd8Hjh8EsG6wknhiIguhhmLF6wWYafScNIG4MtepqP4PahdvRgVC1tg6yZERYbs1SB7Jt/SgQvlGCbiJzsLtseOn4SvZhJWISAiosuCQXuREyXpTOIaQSh4cZVv+qEgCMMlykbYT5AkuCNUdBNEEQKKY33gRKH6PFBH+4ReECCIAlwn/+9HmoLLEoho7Ah5ZgBlEfN//ouyBC00uqocU5UgDd9wz9tW4EYuERFRPkWdPZ4AX13N8Fp2y0agsbZgP399/oz5kqrC31C4RJcSCkPJkzn+tPCsaZA8fMp+uSg+DeXzphdsL2spXH+eiOhiKQF/wQBSDQUhsXTaBZE8GkpaphdsL5nRNHaDISKiCY9Be5GTvR5ULF+Evp37ULZ4Xt41cOVL5uctsQMAoiKjbFH+UjulC+fCTBsYOtaF8NyZOe1KwI+KZQumbBKhsSBrKmbeuApqIPf317xhFdQgk78R0eUjeT2ouXoVXjuhSpAl1KxdxXXXF0iUJJTNnw0lkFseMDxvFuRxqgxCREQTE7PHo7izxwPDJdzMaBy2YUIJ+JHs6kWyowuiR0N4VjOUksCIawlt3YCt60j1nEKyqweyR0OwZQYUvx+2ZSM9GIM+FIMW9CB6pA22bsDbWAtfTSUcRUFJqPCTeAKMlA49lkS8NwJZU+AvL4En5D+vUnPpoRgGWtvRt/841BIf6lfNh6e0ZFKWlyIqNo5tw07rsOIJuK4LJeCH5NGmzA1L27RgJZKIHD4GYygGb3U5gtMbofj9EApMjx9PtmnBSqagD8Xhui484SBkv6cok2aaiSRSPacQO3YSoqYgPLvlnN/ZREQ0dbDk23ko5qDddRyk+gYRO9GBkmn1OPnkC5BUFZ6KMBzDgm3ZqLt6BdRg7t18YLiGbd/2PYgePg6tLAytLAzXdRGe3Qw1HMLgiR5s+9ljqFvWAjSV4fe/exSO42Dn9n1oPXAUG25dj8/e+w+orCrPe/ypLh1LYt/DW3D4mV3Aq39Jik/D2g++AeUzayFJ57du0bEsQBAvqLY8EZ0/x7SQ7OrGqZe3w7VfXX8sCChbPB/BGdOmVBJO99WyooIkFk2t89eydBNDrSdw/K+bM78vQRTR+LorUD6/uWiDYce2h/PMnCuHABERTSks+TZJmIkU2h7dhOm3Xoe2R5+FmUjCRALp/jO1wHtfVlB7zcqcpwyu4yJ69ASih48DAPSBIegDQwCA+Il2NL3+emz54R/gWDbE6hDef/dnEIvGs47x2CMb0dBUh3s+8bdQ1OJ7ijGeXNdFx/bDOLxpV9Z2M6njme88hJu//C4EKsPndcyp8mSPqFiYiQR6t7ySvdF1MbBzL7RwGN7qqZPhWxCEok+Qpg9GcOzPz2Vtcx0HbU+8CF9VGYIN1eM0spGJ53kDl4iI6Gy85VvkUr398DfUDAfriWTePpEjbbAS6ZztViqFwT35y725lo1EVy98r07lbuvqzgnYT/v1zx5C36mBC38Tk1Q6ksC+v7yUt82xbHTtOT62AyKi8+I4DqKtRwu2D+47CNswx3BENBLHstD98t6C7Z2bd/H3RUREkxKD9iJnRGJQAn6Y8VThTq47PK06z3ZbNwruZkZj0II+qAEv2tu7C/ZLJlIwjMLHmapcx0VqMP+NDgCIdvFGB1Exc217uE55AVYyeWbKPI07x7ShD8YKthuROBwzz3chERHRBMegvch5KkuhD0SgjpAMTpAlSHmmrguimDdzbebYFWVI9keRHopj9qwZBfuVVZRCK9J1guNJlEWU1JYVbK9oKVxqj4jGnyhJ0CoK/w2r4TCXrBQRUZXhryu8XMFfU573u5CIiGiiY9Be5DylYegDQ5A8GjwVpXn7lC+cnbfkm+zzomL5wrz7SF4PPBVlsA0TRiKNcr8fDU35g8y//9jdqKquuPA3MUl5SvxY/Oar87ZpAS8qZjJoJypmgiiiZMY0CPkSPwpA6fzZEBUG7cVClCRULZtb4PcloGb1Iv6+iIhoUmLQXuSUgA/Tbr0OgweOon79lQg01mbaBElC+eK5KFswq2CSG29tFapWL4N4VpI6T0UZGjesgxYKYu1H70SooRLtT2zHt/79S1h55dJMP5/fi49/7r9jw63rITLjbV4VLXW44t03QvWdmYlQ2lSF6z75VvjLi6sSARHlkn0+1K6/OmtW0nDt8jVQgix3WWy0cABz3n4TtHAws00N+jH7rTdmbSMiIppMWPINxV3y7TQzlYajG4AowLXs4YzvigxJ06D4PJl+0YEYjLQxPHW7NAhZkeE6DqxkGrZhQJBEiJIE1wUEUYDs9cBIpKHHknAcB5YkIJ5MQdcNBEsCqKwuh3IZat9ahgkrqcN1XcgedULXI3dsB+lIAkYyDVGSoAa88ARzZz4QUfGy0sOfsa7rQtJUSB5P0ZY9O1+WbsJKpQEXkL1q0ZZFOx9GPAkrpQOuC9nrgRr0jWo/2zDhvJqjRVRVTqcnIqJxxZJvk4gRS6D75X3wVobhqyzF0KFjiB4+AceyoJWFUbNmGeRQEEf2teE33/kdOo91wePz4Lo7rsHr3rIO4cowlIAPQlpCoqMHPS/thBlLQNJUlC+eC7WiHJu+9wekI0n4ykuw6I1XYfqCadAClyfwTA1EceyJl3Fq71G4jotwcx1ablkDX2XphKxPLkoifGVB+Mr4lIdoopI9HsDjOXfHCSbZF8HRJ15C//7jcF0XZTMbMPOWK+GrCE/omuFqwAc1MLpAHRgu0WlGYzi1dTcSHV0AAH9DHSqXL4JSEpg0N2iIiGhy4pN2FPeTdjORROuDT8NXXYbQzAYM7NyHdN9gTr/6G6/G//7cT9Bzsjdr++xls/Df/+lvEQz5MXjgKLqeeyVn38D0BsSSwL5Hz7Qtfes6zFy3GNIlrtmbHoph2//9A4xYdvk6UZGw8u/vgK8y/7p9IiI6P6nBGLb96CGYrykJKqkKVnzoDvjKQ+M0srFnxOJoe/hJOGZ2SThRVdB06/VQuRSCiIjGwWjj0Il7m32KSA/Gkeg6hXBLI2DbeQN2AOh7ZTduevv6nO2Htreiv3sAZjKFnpd25t03frwdtQumAWc9aNjzxxeQjiTOa6xGMo1EXwSJvgiMpJ63T/+hkzkBOzBcyufEsztgs1wPEdFFcx0Xp/YczQnYgeEp4h2b98Cxzq+cnRFLID0YhR6Jn/e+48m1HURaj+UE7ADgGCaiR07AdZxxGBkREdHocHp8kYud7IIgS3AcF3p//oAdAPTBCGqWTcvbdmzfcdRUBOEYuRcsp1mJJFTf8Pp2YHgNpJFIjyqZmmM7iHUPYPdvN2HgaCcAoGJWAxbeeS2C1WUQxOG7AbZpoW//8YLHGTzcASulQ2L2XyKii2IbJvoPnijYPtB6Ek3rlkEbxVpwK6UjcrwD7Ru3woglIMoSKhbPRu3qhVCDhcuKFgvbNJHs6C7YnmjvQnheC2Rt4q/1JyKiyYlP2ouc7PPAtR3ImgpJVQv2EyQRtp3/ScH8pS2wdWPE15E0BbaR/ZRbHOXU+ORAFM99+7eZgB0A+lrb8dy3f4vkQPTMGEURir/wmlHFp03oNZZERMVCkETIvsKft7JPG1UOEdd1MXS0HUf/9AyM2PDsK8ey0bttP44+8hzMROqSjflyEUQR4gjfn5KmQhAv7VIwIiKiS4kRUpEL1FUBACRVhlZeChRIlhOY0YjNj2/L2e7xeRDwa0j29MFbXZ53X0lTYep21tT00qYqKNq5n3jbpo1jz+6CnecpvpU20Pbifjj28DRKURLRsHpBwWM1XLUY6mVKfkdENJVIiozGNQsLtjdevSSr8kghZjyJkxtzc6EAQKytC0Y8d7lTsZFUBaULZhdsL50/mzO8iIioqDFoL3Kp/kFMv2kNjFgcgiSi5qrlOYG7VhpC+eK5ONHanrVdkkR84Mvvht4/iIG9rahavhCyPzsoFmQJDTdchd2PvJTZ5inxYemdV8O1z71m0Urr6Dt4smD7qQNtsNJnAnpveRjTrlue069i/nSUzWo85+sREdHo+KvL0LB2Uc72qiUtCE+rGdUxbN2ENcLT9GTvwAWPbyx5KkpRMmtGzvbQ7ObhG+JERERFjLeWi5zguIid6EDVigVo3/Qy6q5eiRm334BUzwCsZAre6nLIPh92/dejeM+H3oShRBqH9x5HuKwEsxfNQPTAccj1c2CndfS8tBO1a1fCSqWgD0SgBHzQwiWQPB7MvGYhUtEk/KUB+Ep8aHt8C+b9zYZzjk+UJahBL1BguaBW4oWonJl2qPg0NKxZhKqFM9F34Dgcy0b5nCZ4wkGofj5lJyK6VBSfB9PWLUPNsjnoP3Acju2iYt40aCUBqCMsVTqbIEvDN4oLFJpRfBPjc1v2eFCxbCHCc2YicbITEAQEGmoh+b1cy05EREWPQXuR89dXoW9PK0RZQmhGAxSPCjOVAgQB/vpqOKaFZG8/VJ8XJx55HmrAi5aKMKxIBMd++xTUoA/aLavRdPO1sFNpWMkEtFAJXNvGwL4j0MJBhObOgubTIIkiAhUhxDt7Ifu9o5o6qXg1tFy/Av2tHXnbZ163HLKq5OyjeDX4qybH0w09loA+FEc6Eoe3NDh8QTyK5E5ERLZhwE7rMIYiEBUFSkkQskeDIF2aNdaK1wPF60GguuzC9vd5UDq7CYN5ktqJqgxvRbjgvmYiNZxxfiACJeiHJxSEWjJ+ietkjwbZo8FTFh63MRAREV0IBu1FTvZ7Ubd2GVL9gwjNqMexP2+CEYln2rXSEsy4bT3mvvV12PmTh6FH4jDiw1MZZa+GJe+7DcZABD3PvwT3rER1weYmVC6bD608jE3f/G1mHwCoWTQDC9+8blRBOwCEG6vQvG4Jjm7KLik368aVCNZc2IXiRJHqj2DvLx+FPnTmd+KtCGP+OzbAEw6O48iIqNhZ6TQGdu1D/HhbZpsgSahacwW81ZUQL1HgfjEkVUHjdVcg1RdBun8os11UZMy+80Yogfw3KPVoAod//zSS3X2ZbUrAi9lv3QBf5eS4YUtERDRWBNctMOdtChltUfvxYMTiOP7w06h/3RqcfGJL1kXTab7qcjS/8TpYpoVk7xDiXf3wVYQQqC0HXAdtDz8O5PktV6xahkTKxfb7/pLTNvfWK9Fy/YpRZRcGhmu0p6MJ9B1qhyAIqJjdAC3oh+qbvNMOjXgSe/7rL0j1DeW0BRuqMO/tN476xgcRTS2u6yJ65Bj6t+3KbRQENNx8PdRgYOwHVoARTyI9GEWiqw9qiR+BmgooQX/e7whbN3D0kecwdLgtp00J+DD/XbdOiFJxREREl9to41A+aS9yqZ5+BKfVwzHMvAE7ACR7+mEl0/CUh+EpCaCspSHTdmrr7rwBOwBEDrSidOVyQEBOnyNP70Djqnnwhkd30aj6PFB9HpTU5M9QPxmZiXTegB0AYu29MJNpBu1ElJed1jG0vzV/o+si2d4JdV7hjOdjTQ34oAZ8KGk8dwI7M5nOG7ADw9no9WiCQTsREdF5YPb4ImdE45C9Wt6Samc7u1zbaa7rwozF8/QeZiVTgJC/HruZTMN18td9p2GWbozY/tq690REp7muAztdOCv7SJ/dxc6xRv7sGykbPREREeVi0F7kvFXl0IdikD0jPLEVBEgeNc9mAd6ayoK7qaVhuI4Lx8wt7RaoKs0bzNMZI2W7F0QBsjf3d0JEBACiJEENhwu2e6oLf3YXO0lVIY5Q91wrZb4PIiKi88GgvchppSVI9w1AVCSEZuavY142f2bBsjv++hqIWv7gMThnFk4d7szbNv+NV8Ezjll+JwLF50H5vOl526qXz2UJOyIqSNI0lC9ZkL/No8FTMXGXGil+L2pWLczbFmyshuJndQ0iIqLzwaC9yCl+H5puuhaR4+2oWb0YZQtaILya+EeQJVQsnYvaKxdDek1ZtdPUYAANN14LreJMFnfZ70PFlSvhKQ2hflEzKuecuRmgBX1Y9q4NKJtZf3nf2CQgezXMuOlK1KyYm/mdiLKE+qsWofGapQV/J0REAKCVhlF91SpI3jMzqTyV5ai97uoJHdiKsoSqpXNQf/UyiK9+DgqigPIFM9F827XM9UFERHSemD0exZ09/jRbN2AbBlwXcC0bjmVDUmUoAf+oprGbyRSstA7HdiDIMhS/F8qrF1NmUoeeSMGxbCheDZ4SPwRRuNxvadKwTQtmPAXbNCGpCtSAj0sLiGhUXNeFnUrDMU0IoghRVSEVmB010Ti2nflsFGUZis/Dm5lERERnYfb4SUbSLu5CTvF5C06hV3walElcmu1ykxQZEtdoEtEFEAQBss8LYPItpxElCVqoeMrWERERTVScHk9ERERERERUpBi0ExERERERERUpTo+fIFzHgZlMA64LQRLh2sNl2iSPBkk5s0YwOhiFaViQJAnhilDWMRzbhp3WARcQVRmSema6vR5LwrUdiLIINXDxCZAc24YeSwGuC9mjQvFy+j0R0eVkpXXYhgVBEKD4PRDE4rovb6XScF0HoihCGqmMKREREWVh0D4BGPEkBg4chac8DG84gP4d+5Hq6YUoyQhMa0RodjMMV0Tr7iO4//sPov1oJyprK/Dmv7sNS9YsRElpEGY8iYF9rRjYdxiOaSHQWIPK5QuhDw5BkBTYtot9v38Ooiyh5aZVKG2ugzrKDL96LIlkfxR9hzugBX0on1mHky8fwPFnd8FM6aiY1YD5b1yLQHUZJIUJ2oiILiXbtJDuH8LJja8gdrIHsldD9Yr5qFjUckluwl4sK60j1d2Dwb0HYCWTUEtKULZ4PrTysqybx+PJsWykInEMHutGcjCGshm1CFSG4Qmx9OlYsXUDViqFVM8pCKIAb3UVJA+TFxIRAcweD6C4s8ebiSROPPY8yha0wFdRiq6nn808ZT9NKQnCM38+Pv7WL+fsf+t/24A3v+dmtP/1WeiDkaw2QRLReOPVaH9yM0RZQvXaK7Dzl09AjyYx541r0XDlAkjnyIKejsSx9WePof9wBwBg8duuQ9uL+zB0oif7tUQR13zirQg3Vl3IfwYiIiog3tGL/b/8C/Car/NgUy1mvuFaKP7xS3LnmCYG9x1E5ODhnLaKlUsRnN407jMCHMtG/9FOvPijP8Gxzny/BmvLsPoDb4SvjIlGLzcrrWNw917EjrVlbQ8vmItQS/OkqahARPRao41Di2vuHOUwogk4hgWtJIChfQdzAnYAMKMxmJEoKmvLc9q2PPEK9P6hnIAdAFzbwcDeVoRnT4eVSqNv2y7MWL8UAHD40RehR+Ijjs2xbRx/bk8mYFf9HsiakhOwA8PT+/c89ByMZHo0b5uIiEbBTKZx4okXcwJ2AIi1dZ3zc/xys3UDkUO5ATsADOzcCys1/t8J6UgCL/3fh7MCdgCIdQ1g/8MvwNLNcRrZ1JHu68sJ2AFgaO8BmLHxPYeJiIoBg/YiF2vrQuns6RAlEaneUwX7ibEImudPz9k+Y04TosfbC+6X6OyBt2o42NcHIghUlwIAbMOCkRj5YkqPJXH02Z2Zn0vqKzBwtLNg/4EjHbDSvPghIrpUbMNEsqe/YHv0eOHP5LFgJhJAgfl8jmnCMYyxHVAekY5TsE0rb1vn9sPQ48kxHtHUYusGIgdaC7ZHWg/DyfPAgohoKmHQXuREVYGkqXBsG6JcOAWBK0rQ07kXP4ZujrgeTFSU7Kf3rnOmTRr59HAdF1bqzGvahgV5hClskiJDEEY8JBERnQdBEEacXi55xndasSiNnDpnvKfGA4AeLRyUu44Dx3IKttPFcx0Htl745o2d1gGHvwMimtrG/9uSRhRsrEHy1ABSpwYRnNFUsJ9QVo59rxzM2X7ycAdCs6YX3C88axqiR08OH0MS4brDUbWvInTOjO+SqiB01hr1obYelM+qL9i/ac18aMHxT4pERDRZyD4PyuY3F2wPTS/8mTwWJJ8HYoEbx0pJEGIRrFUON1UXbPOEA5A1JkK7nERFhqeqsmC7r7YawggPLYiIpgIG7UVO9mjwlJVALQnAV18HtTSc06dkdgu0kgBkNftLTVFlfOif/g5aSQCVKxfm7OcpD8NXW4V4ezcAIDxnJjq3tULSFCx823XQzpE1Vwt4sfDN1wCvPj13HRddO49gzi2rc/oGakrR8rrlEM+R2I6IiEZPUmTUr10KLZybLG36zWuhBMYvCR0AyB4vqteuznmiLioKqq5cCbkISr95SwMom1mXt23B7VfDGw6M8YimFlGWEZ7TAkHKvT4QVRX+xgYInKZHRFMcs8ejuLPHA4ARS8AxLdiGAdmjvTpdz4QgCBBVDaIsQfZ50dfdj32vHMTBHa1oamnAsmsWo7y6DLIiD5dSSeuw9TTguIAowYzG0fnsyxBkCRVL5yPQUAtBBAQBEGQFiu/cF3uWbmCorRe7f/cMYl39ECURC+9ch9Lp1ejcfhjpaBJ1S2aipL6CFz5jIBlJwEjpECUR3hI/FD4hIpoSjFgCie5+DB1ugxL0oXxuM5Sgb8QlS2PFtR1YqRQSnd0whiLwVJbDW1UB2efLCsZc14WdSsO1bQiiCMk7drXmU0NxHHlqG05s3gvbsOArL8GCN12N8lkNUH0jzzq7VKxUGo5pQRAFSB5txCVxk43rODCiMfRv24V033COBm9tNcqXLIRawuz9RDR5jTYOZdCO4g/azUQS/bv2I9BYB1GWMLBrL4yhCCAI8NXVoGzxAqjBwgHx6S/Dvm07ofcNAAC81VUoW7IQrgBY8RQkj4LEiePQ+/oAAEowiNC8eVCCQYh57n6/lh5LwtINCKIINeCFzLqqY8pIG+g+1IFN//lXDHYM3zyZc/VCXPn2axGsCI338IiIRmTrOhLtnRjcewB2WoeoKCiZ3YySmTPG7Gm8bdrQYwm4jgNJUcasRrtjmEj1DaD3pR0wozEIoojgjEaUL10AxT+1lpTZugHHHE5YK6oqa7QT0aTHoP08FHPQbiXT6HjqWWgV5QjNaETXMy/klPaRvF7Uve5qKP78FxhGLI6Ox57OKRcnKgqq1lwB2zARP3YYjq5n7ygIqFy9GmqR/TehXO17T+DBr/w8Z3tpQzne/IW7EGCdYSIqUo5tI3LoCAZ378tpC0xrRPmyxZM6eEt296L9sWdytquhIOpvvHZUs96IiGhiYp32ScKIxaEPRBBqmY7B/Yfy1uK1UymkT+Uv+TN8MXQ4b313xzSR6umFIAm5ATsAuC6ihw/DsfKXwqHikIom8exPH8/bNtjej4GThUsFEhGNNzudxtC+3ESqABA/cRJ2vu+nScJK6+h9eWfeNiMSgxmJjvGIiIioGDFoL3KpnuGASxAF6P0DBfslO7uRb9KEY5hI9xQO2tJ9A8AI9U+NwUEG7UXO1A2cOt5TsL1t9/GxGwwR0XlyDDPvjeXTrMTkrZPuWhaMwUjB9kRX7xiOhoiIihWD9iInn54W5wKSVjgZjuTz5s2uKkgixJH207RM9vd8RFVl1tYiJ4giVG/hZFOBck6NJ6LilS9r+NlEZfJOjYcgjFjOTObUeCIiAoP2ouetrgBEAUYkNmKd9uC0/G2SqiI8b1bB/fyNdRDkEQK+adMgquOffZgK84X9WLRhRd42QRAwfenMMR4REdHoSZoGraw0f5vXA9k7/mXhLhfJoyE8pzl/oyDAX1cztgMiIqKixKC9yMk+L+rWX4W+HXvgq6+Fp6Yqp0/58iVQAoUzzGplZQjOnJ6zPTx/DmS/D5HDbQg05wZ2nspKeKur+aS9yEmShCW3XIG6uY1Z2wVRwC2fuAN+JqEjoiImaSoqV6+A9JqnyqKioObqKyFN4qBdlCSE582Cp7I8u0EQULvuSj5pJyIiAMweD6C4s8cDw8nkrGQK+kAEaigAx7SQ7j0FUVXgra6C7POes56rrRuw02kke3ohCCK81ZWQPBoESYKVSMGIxaF4NZixGBzHhqe8HJLHA4lP2SeM5FAc0VMRdOxrg7fEh/r5TfCVBqBM4qzLRDR5WMkUjGgM+uAQ1GAQamkIcoGlX5ONlUrDjCeQ6umD5NHgra6A7PVClM9dcpWIiCYulnw7D8UetBMREREREdHkwpJvRERERERERBMcg3YiIiIiIiKiIsWgnYiIiIiIiKhIMWgnIiIiIiIiKlIM2omIiIiIiIiKFIN2IiIiIiIioiLFoJ2IiIiIiIioSDFoJyIiIiIiIipSDNqJiIiIiIiIihSDdiIiIiIiIqIixaCdiIiIiIiIqEgxaCciIiIiIiIqUgzaiYiIiIiIiIoUg3YiIiIiIiKiIsWgnYiIiIiIiKhIMWgnIiIiIiIiKlIM2omIiIiIiIiKFIN2IiIiIiIioiLFoJ2IiIiIiIioSDFoJyIiIiIiIipSDNqJiIiIiIiIihSDdiIiIiIiIqIiJY/3AIqB67oAgGg0Os4jISIiIiIioqngdPx5Oh4thEE7gFgsBgBobGwc55EQERERERHRVBKLxRAKhQq2C+65wvopwHEcdHZ2IhgMQhCE8R5Olmg0isbGRpw8eRIlJSXjPRyivHie0kTBc5UmAp6nNBHwPKWJopjPVdd1EYvFUFdXB1EsvHKdT9oBiKKIhoaG8R7GiEpKSoruJCN6LZ6nNFHwXKWJgOcpTQQ8T2miKNZzdaQn7KcxER0RERERERFRkWLQTkRERERERFSkGLQXOU3T8OUvfxmapo33UIgK4nlKEwXPVZoIeJ7SRMDzlCaKyXCuMhEdERERERERUZHik3YiIiIiIiKiIsWgnYiIiIiIiKhIMWgnIiIiIiIiKlIM2omIiIiIiIiKFIP2Ivb9738f06dPh8fjwerVq/HSSy+N95Boirv33nshCELWv7lz52ba0+k07rnnHpSXlyMQCODOO+9ET0/POI6YpoJnnnkGb3jDG1BXVwdBEPD73/8+q911XXzpS19CbW0tvF4vbrjhBrS2tmb1GRgYwF133YWSkhKEw2G8733vQzweH8N3QZPduc7Tu+++O+fz9eabb87qw/OULrevfvWruOKKKxAMBlFVVYXbb78dBw8ezOozmu/6trY23HrrrfD5fKiqqsKnP/1pWJY1lm+FJrHRnKfr16/P+Uz94Ac/mNVnIp2nDNqL1P33349PfOIT+PKXv4xt27ZhyZIluOmmm9Db2zveQ6MpbsGCBejq6sr8e+655zJtH//4x/GnP/0JDzzwADZt2oTOzk7ccccd4zhamgoSiQSWLFmC73//+3nbv/GNb+A73/kO/s//+T948cUX4ff7cdNNNyGdTmf63HXXXdi7dy8ef/xxPPzww3jmmWfwgQ98YKzeAk0B5zpPAeDmm2/O+nz91a9+ldXO85Qut02bNuGee+7Bli1b8Pjjj8M0TWzYsAGJRCLT51zf9bZt49Zbb4VhGHjhhRfw05/+FPfddx++9KUvjcdbokloNOcpALz//e/P+kz9xje+kWmbcOepS0Vp1apV7j333JP52bZtt66uzv3qV786jqOiqe7LX/6yu2TJkrxtQ0NDrqIo7gMPPJDZtn//fheAu3nz5jEaIU11ANyHHnoo87PjOG5NTY37v/7X/8psGxoacjVNc3/1q1+5ruu6+/btcwG4L7/8cqbPX/7yF1cQBLejo2PMxk5Tx2vPU9d13fe85z3um970poL78Dyl8dDb2+sCcDdt2uS67ui+6//85z+7oii63d3dmT4//OEP3ZKSElfX9bF9AzQlvPY8dV3XXbdunfvRj3604D4T7Tzlk/YiZBgGtm7dihtuuCGzTRRF3HDDDdi8efM4jowIaG1tRV1dHZqbm3HXXXehra0NALB161aYppl13s6dOxdNTU08b2ncHDt2DN3d3VnnZSgUwurVqzPn5ebNmxEOh7Fy5cpMnxtuuAGiKOLFF18c8zHT1LVx40ZUVVVhzpw5+Pu//3v09/dn2nie0niIRCIAgLKyMgCj+67fvHkzFi1ahOrq6kyfm266CdFoFHv37h3D0dNU8drz9LRf/OIXqKiowMKFC/G5z30OyWQy0zbRzlN5vAdAufr6+mDbdtZJBADV1dU4cODAOI2KCFi9ejXuu+8+zJkzB11dXfjKV76Ca665Bnv27EF3dzdUVUU4HM7ap7q6Gt3d3eMzYJryTp97+T5PT7d1d3ejqqoqq12WZZSVlfHcpTFz880344477sCMGTNw5MgR/I//8T9wyy23YPPmzZAkiecpjTnHcfCxj30Ma9euxcKFCwFgVN/13d3deT9zT7cRXUr5zlMAeOc734lp06ahrq4Ou3btwmc/+1kcPHgQDz74IICJd54yaCeiUbvlllsy/3/x4sVYvXo1pk2bht/85jfwer3jODIioontb/7mbzL/f9GiRVi8eDFmzpyJjRs34vrrrx/HkdFUdc8992DPnj1ZuWuIik2h8/TsfB+LFi1CbW0trr/+ehw5cgQzZ84c62FeNE6PL0IVFRWQJCknE2dPTw9qamrGaVREucLhMGbPno3Dhw+jpqYGhmFgaGgoqw/PWxpPp8+9kT5Pa2pqcpJ8WpaFgYEBnrs0bpqbm1FRUYHDhw8D4HlKY+vDH/4wHn74YTz99NNoaGjIbB/Nd31NTU3ez9zTbUSXSqHzNJ/Vq1cDQNZn6kQ6Txm0FyFVVbFixQo8+eSTmW2O4+DJJ5/EmjVrxnFkRNni8TiOHDmC2tparFixAoqiZJ23Bw8eRFtbG89bGjczZsxATU1N1nkZjUbx4osvZs7LNWvWYGhoCFu3bs30eeqpp+A4TuZLnmistbe3o7+/H7W1tQB4ntLYcF0XH/7wh/HQQw/hqaeewowZM7LaR/Ndv2bNGuzevTvrJtPjjz+OkpISzJ8/f2zeCE1q5zpP89mxYwcAZH2mTqjzdLwz4VF+v/71r11N09z77rvP3bdvn/uBD3zADYfDWRkOicbaJz/5SXfjxo3usWPH3Oeff9694YYb3IqKCre3t9d1Xdf94Ac/6DY1NblPPfWU+8orr7hr1qxx16xZM86jpskuFou527dvd7dv3+4CcP/t3/7N3b59u3vixAnXdV33a1/7mhsOh90//OEP7q5du9w3velN7owZM9xUKpU5xs033+wuW7bMffHFF93nnnvOnTVrlvuOd7xjvN4STUIjnaexWMz91Kc+5W7evNk9duyY+8QTT7jLly93Z82a5abT6cwxeJ7S5fb3f//3bigUcjdu3Oh2dXVl/iWTyUyfc33XW5blLly40N2wYYO7Y8cO99FHH3UrKyvdz33uc+PxlmgSOtd5evjwYfd//s//6b7yyivusWPH3D/84Q9uc3Oze+2112aOMdHOUwbtRey73/2u29TU5Kqq6q5atcrdsmXLeA+Jpri3v/3tbm1trauqqltfX+++/e1vdw8fPpxpT6VS7oc+9CG3tLTU9fl87pvf/Ga3q6trHEdMU8HTTz/tAsj59573vMd13eGyb1/84hfd6upqV9M09/rrr3cPHjyYdYz+/n73He94hxsIBNySkhL3ve99rxuLxcbh3dBkNdJ5mkwm3Q0bNriVlZWuoijutGnT3Pe///05N+p5ntLllu8cBeD+53/+Z6bPaL7rjx8/7t5yyy2u1+t1Kyoq3E9+8pOuaZpj/G5osjrXedrW1uZee+21bllZmatpmtvS0uJ++tOfdiORSNZxJtJ5Kriu647dc30iIiIiIiIiGi2uaSciIiIiIiIqUgzaiYiIiIiIiIoUg3YiIiIiIiKiIsWgnYiIiIiIiKhIMWgnIiIiIiIiKlIM2omIiIiIiIiKFIN2IiIiIiIioiLFoJ2IiIiIiIioSDFoJyIiojH13e9+F//0T/8EAPj+97+PT33qU+M8IiIiouLFoJ2IiOgyu/vuuyEIQuZfeXk5br75ZuzatWu8hzYu3va2t+GnP/0pFEXBP//zP+N973vfeA+JiIioaDFoJyIiGgM333wzurq60NXVhSeffBKyLOO2224b72GNi+rqahw6dAgnTpzAyZMnMW/evPEeEhERUdFi0E5ERDQGNE1DTU0NampqsHTpUvzjP/4jTp48iVOnTmX6fPazn8Xs2bPh8/nQ3NyML37xizBNM+s4x48fz3pqf/rf0NAQAODee+/F0qVLM/0feeQRrFixAn6/H42NjfiXf/kXuK4LAFi/fn3eYwmCgHvvvRcAMH36dHz729/OGsPdd9+N22+/PfPz+vXr8bGPfSzv+/7Yxz6G9evX5/QVRRF1dXU4cuQIFEXJGvNrbdy4Mes9Dg4OYvHixXj3u98N13Xxs5/9DOXl5dB1PWu/22+/He9617uy/rv86Ec/QmNjI3w+H972trchEolk7fOTn/wECxYsgKZpqK2txYc//OGC4yIiIhoLDNqJiIjGWDwex89//nO0tLSgvLw8sz0YDOK+++7Dvn378O///u/48Y9/jG9961tZ+54OuJ944gl0dXXhd7/7XcHXeeGFF/DGN74RN954I7Zt24b//b//N77xjW/ge9/7HgDgwQcfzDz9X7NmDT75yU9mfh6rdeaf/vSn4fF4Rt0/Ho/j9a9/PZqbm/GTn/wEgiDgrW99K2zbxh//+MdMv97eXjzyyCP427/928y2w4cP4ze/+Q3+9Kc/4dFHH8X27dvxoQ99KNP+wx/+EPfccw8+8IEPYPfu3fjjH/+IlpaWS/NGiYiILpA83gMgIiKaCh5++GEEAgEAQCKRQG1tLR5++GGI4pn751/4whcy/3/69On41Kc+hV//+tf4zGc+k9l++sn76af2ZWVlBV/z3//937FixQp87WtfAwDMmTMHR44cwde//nV85CMfydpXVVUEAgHU1NRcmjc8Ck8//TReeOEF/N3f/R2efvrpc/bXdR1vectb4PP5cP/990OWhy9jvF4v3vnOd+I///M/8da3vhUA8POf/xxNTU1ZT/nT6TR+9rOfob6+HsBwQrxbb70V3/zmN1FTU4N//ud/xic/+Ul89KMfzexzxRVXXMJ3TEREdP74pJ2IiGgMXHfdddixYwd27NiBl156CTfddBNuueUWnDhxItPn/vvvx9q1a1FTU4NAIIAvfOELaGtryzpONBoFAPj9/oKvtXv3bgQCAfz2t7/F2rVrs9rWrl2Ljo6OzHFG47Of/SwCgUDm3y9+8YucPj/4wQ8QCARQXl6O1atX409/+tOIx3RdF5/85Cfx5S9/GaFQaFTjuOuuu/Dkk09i3bp10DQtq+39738/HnvsMXR0dAAA7rvvvkwCwNOampoyATsArFmzBo7j4ODBg+jt7UVnZyeuv/76UY2FiIhorDBoJyIiGgN+vx8tLS1oaWnBFVdcgf/4j/9AIpHAj3/8YwDA5s2bcdddd+H1r389Hn74YWzfvh2f//znYRhG1nE6OzshiuKIT8TnzJmDHTt2YOXKlQX7nB3MnsunP/3pzA2HHTt24I1vfGNOn7vuugs7duzAM888g2uuuQZvectbMgF0Pj/72c+QSCTwwQ9+cNTj6O7uxu9+9zv867/+K3bv3p3VtmzZMixZsgQ/+9nPsHXrVuzduxd33333qI/t9XpH3ZeIiGgscXo8ERHROBAEAaIoIpVKARhefz5t2jR8/vOfz/Q5+yn8aS+//DLmzp074jpwVVXR0tKCJUuW4IUXXshqe/7559HQ0IBgMDjqsVZUVGSt7Q4Gg5mkcKeFQqFMn6985Sv45je/if379+c9XjKZxOc//3l873vfg6Ioox7HH//4RzQ3N+P9738/3vve92LLli2ZKfIA8Hd/93f49re/jY6ODtxwww1obGzM2r+trQ2dnZ2oq6sDAGzZsgWiKGLOnDkIBoOYPn06nnzySVx33XWjHhMREdHlxiftREREY0DXdXR3d6O7uxv79+/HRz7yEcTjcbzhDW8AAMyaNQttbW349a9/jSNHjuA73/kOHnroocz+hmHgv/7rv/Bv//ZveO973zuq1/yHf/gHbN26Ff/4j/+IgwcP4v7778fXv/71rDXyl4pt20in04hEIvjRj34ERVEwZ86cvH1/+ctfYubMmVkZ6Efj9Br8r33taxgcHMys1T/tne98J9rb2/HjH/84KwHdaR6PB+95z3uwc+dOPPvss/iHf/gHvO1tb8vMWrj33nvxzW9+E9/5znfQ2tqKbdu24bvf/e55jZGIiOhSY9BOREQ0Bh599FHU1taitrYWq1evxssvv4wHHnggkyjtjW98Iz7+8Y/jwx/+MJYuXYoXXngBX/ziFzP77969G/feey+++MUv4hOf+MSoXnPhwoV48MEH8cgjj2DRokX41Kc+hc985jOXpYzZ9773PXi9XlRVVeEnP/kJfvGLX+Q86T4tmUzim9/85gW/lt/vx09+8hP8y7/8C/bs2ZPZHgqFcOeddyIQCOS9IdDS0oI77rgDr3/967FhwwYsXrwYP/jBDzLt73nPe/Dtb38bP/jBD7BgwQLcdtttaG1tveBxEhERXQqCe7p2DBEREdEEd/3112PBggX4zne+k7X93nvvxe9//3vs2LFjfAZGRER0gbimnYiIiCa8wcFBbNy4ERs3bsx6ek5ERDTRMWgnIiKiCW/ZsmUYHBzE17/+9YJr6YmIiCYiTo8nIiIiIiIiKlJMREdERERERERUpBi0ExERERERERUpBu1ERERERERERYpBOxEREREREVGRYtBOREREREREVKQYtBMREREREREVKQbtREREREREREWKQTsRERERERFRkfr/PBmxPZ8aFsQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "ax = sns.scatterplot(x='exchange_rate', y='oil prices', hue='inflationrate', data=df)\n",
    "plt.title('Уровень инфляции')\n",
    "plt.xlabel('Валютный курс')\n",
    "plt.ylabel('Цены на нефть')\n",
    "plt.legend(title='inflationrate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q1 = df['oil prices'].quantile(0.25)\n",
    "Q3 = df['oil prices'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "threshold = 1.5 * IQR\n",
    "outliers = (df['oil prices'] < (Q1 - threshold)) | (df['oil prices'] > (Q3 + threshold))\n",
    "\n",
    "median_rating = df['oil prices'].median()\n",
    "df.loc[outliers, 'oil prices'] = median_rating\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "ax = sns.scatterplot(x='exchange_rate', y='gdppercent', hue='inflationrate', data=df)\n",
    "plt.title('Уровень инфляции')\n",
    "plt.xlabel('Валютный курс')\n",
    "plt.ylabel('Цены на нефть')\n",
    "plt.legend(title='inflationrate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение данных на выборки и оценка сбалансированности выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 221\n",
      "Размер контрольной выборки: 74\n",
      "Размер тестовой выборки: 74\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# обучающая и тестовая\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# обучающая на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конструирование признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Кодирование категориальных признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.head of     stock index    year  index price  log_indexprice  inflationrate  \\\n",
      "0        NASDAQ  1980.0       168.61            2.23           0.14   \n",
      "1        NASDAQ  1981.0       203.15            2.31           0.10   \n",
      "2        NASDAQ  1982.0       188.98            2.28           0.06   \n",
      "3        NASDAQ  1983.0       285.43            2.46           0.03   \n",
      "4        NASDAQ  1984.0       248.89            2.40           0.04   \n",
      "..          ...     ...          ...             ...            ...   \n",
      "364      IEX 35  2016.0      9352.10            3.97            NaN   \n",
      "365      IEX 35  2017.0     10043.90            4.00           0.02   \n",
      "366      IEX 35  2018.0      8539.90            3.93           0.02   \n",
      "367      IEX 35  2019.0      9549.20            3.98           0.01   \n",
      "368      IEX 35  2020.0      8073.70            3.91            NaN   \n",
      "\n",
      "     oil prices  exchange_rate  gdppercent  percapitaincome  unemploymentrate  \\\n",
      "0         21.59           1.00        0.09          12575.0              0.07   \n",
      "1         31.77           1.00        0.12          13976.0              0.08   \n",
      "2         28.52           1.00        0.04          14434.0              0.10   \n",
      "3         26.19           1.00        0.09          15544.0              0.10   \n",
      "4         25.88           1.00        0.11          17121.0              0.08   \n",
      "..          ...            ...         ...              ...               ...   \n",
      "364       51.97           1.11        0.03          26523.0              0.20   \n",
      "365       57.88           1.13        0.03          28170.0              0.17   \n",
      "366       49.52           1.18        0.02          30389.0              0.15   \n",
      "367       59.88           1.12        0.02          29565.0              0.14   \n",
      "368       47.02           1.14       -0.11          27057.0              0.16   \n",
      "\n",
      "     ...  USTreasury  country_China  country_France  country_Germany  \\\n",
      "0    ...        0.11          False           False            False   \n",
      "1    ...        0.14          False           False            False   \n",
      "2    ...        0.13          False           False            False   \n",
      "3    ...        0.11          False           False            False   \n",
      "4    ...        0.12          False           False            False   \n",
      "..   ...         ...            ...             ...              ...   \n",
      "364  ...        0.02          False           False            False   \n",
      "365  ...        0.02          False           False            False   \n",
      "366  ...        0.03          False           False            False   \n",
      "367  ...        0.02          False           False            False   \n",
      "368  ...        0.01          False           False            False   \n",
      "\n",
      "     country_Hong Kong  country_India  country_Japan  country_Spain  \\\n",
      "0                False          False          False          False   \n",
      "1                False          False          False          False   \n",
      "2                False          False          False          False   \n",
      "3                False          False          False          False   \n",
      "4                False          False          False          False   \n",
      "..                 ...            ...            ...            ...   \n",
      "364              False          False          False           True   \n",
      "365              False          False          False           True   \n",
      "366              False          False          False           True   \n",
      "367              False          False          False           True   \n",
      "368              False          False          False           True   \n",
      "\n",
      "     country_United Kingdom  country_United States of America  \n",
      "0                     False                              True  \n",
      "1                     False                              True  \n",
      "2                     False                              True  \n",
      "3                     False                              True  \n",
      "4                     False                              True  \n",
      "..                      ...                               ...  \n",
      "364                   False                             False  \n",
      "365                   False                             False  \n",
      "366                   False                             False  \n",
      "367                   False                             False  \n",
      "368                   False                             False  \n",
      "\n",
      "[369 rows x 22 columns]>\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "df = pd.get_dummies(df, columns=['country'])\n",
    "print(df.head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Дискретизация числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min = 27.0\n",
      "max = 65280.0\n",
      "10880.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"min = {df['percapitaincome'].min()}\")\n",
    "print(f\"max = {df['percapitaincome'].max()}\")\n",
    "print(df['percapitaincome'].max()/6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.head of 0       низкий\n",
      "1       низкий\n",
      "2       низкий\n",
      "3       низкий\n",
      "4       низкий\n",
      "        ...   \n",
      "364    средний\n",
      "365    средний\n",
      "366    средний\n",
      "367    средний\n",
      "368    средний\n",
      "Name: percapitaincome_level, Length: 369, dtype: category\n",
      "Categories (5, object): ['незначительный' < 'низкий' < 'средний' < 'высокий' < 'очень высокий']>\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "\n",
    "bins = [0, 11000, 22000, 33000, 44000, float('inf')]\n",
    "labels = ['незначительный', 'низкий', 'средний', 'высокий', 'очень высокий']\n",
    "\n",
    "df['percapitaincome_level'] = pd.cut(df['percapitaincome'], bins=bins, labels=labels)\n",
    "print(df['percapitaincome_level'].head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Ручной синтез признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\featuretools\\synthesis\\dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n",
      "  agg_primitives: ['max', 'mean', 'min', 'std', 'sum']\n",
      "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n",
      "  warnings.warn(warning_msg, UnusedPrimitiveWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сгенерированные признаки:\n",
      "      stock index    year  index price  log_indexprice  inflationrate  \\\n",
      "index                                                                   \n",
      "0          NASDAQ  1980.0       168.61            2.23           0.14   \n",
      "1          NASDAQ  1981.0       203.15            2.31           0.10   \n",
      "2          NASDAQ  1982.0       188.98            2.28           0.06   \n",
      "3          NASDAQ  1983.0       285.43            2.46           0.03   \n",
      "4          NASDAQ  1984.0       248.89            2.40           0.04   \n",
      "\n",
      "       oil prices  exchange_rate  gdppercent  percapitaincome  \\\n",
      "index                                                           \n",
      "0           21.59            1.0        0.09            12575   \n",
      "1           31.77            1.0        0.12            13976   \n",
      "2           28.52            1.0        0.04            14434   \n",
      "3           26.19            1.0        0.09            15544   \n",
      "4           25.88            1.0        0.11            17121   \n",
      "\n",
      "       unemploymentrate  ...  oil prices * year  percapitaincome * USTreasury  \\\n",
      "index                    ...                                                    \n",
      "0                  0.07  ...           42748.20                       1383.25   \n",
      "1                  0.08  ...           62936.37                       1956.64   \n",
      "2                  0.10  ...           56526.64                       1876.42   \n",
      "3                  0.10  ...           51934.77                       1709.84   \n",
      "4                  0.08  ...           51345.92                       2054.52   \n",
      "\n",
      "       percapitaincome * tradebalance  percapitaincome * unemploymentrate  \\\n",
      "index                                                                       \n",
      "0                          -164229.50                              880.25   \n",
      "1                          -174979.52                             1118.08   \n",
      "2                          -288246.98                             1443.40   \n",
      "3                          -802692.16                             1554.40   \n",
      "4                         -1758840.33                             1369.68   \n",
      "\n",
      "       percapitaincome * year  tradebalance * USTreasury  \\\n",
      "index                                                      \n",
      "0                  24898500.0                    -1.4366   \n",
      "1                  27686456.0                    -1.7528   \n",
      "2                  28608188.0                    -2.5961   \n",
      "3                  30823752.0                    -5.6804   \n",
      "4                  33968064.0                   -12.3276   \n",
      "\n",
      "       tradebalance * unemploymentrate  tradebalance * year  \\\n",
      "index                                                         \n",
      "0                              -0.9142            -25858.80   \n",
      "1                              -1.0016            -24802.12   \n",
      "2                              -1.9970            -39580.54   \n",
      "3                              -5.1640           -102402.12   \n",
      "4                              -8.2184           -203816.32   \n",
      "\n",
      "       unemploymentrate * USTreasury  unemploymentrate * year  \n",
      "index                                                          \n",
      "0                             0.0077                   138.60  \n",
      "1                             0.0112                   158.48  \n",
      "2                             0.0130                   198.20  \n",
      "3                             0.0110                   198.30  \n",
      "4                             0.0096                   158.72  \n",
      "\n",
      "[5 rows x 207 columns]\n",
      "\n",
      "Описание:\n",
      "[<Feature: stock index>, <Feature: year>, <Feature: index price>, <Feature: log_indexprice>, <Feature: inflationrate>, <Feature: oil prices>, <Feature: exchange_rate>, <Feature: gdppercent>, <Feature: percapitaincome>, <Feature: unemploymentrate>, <Feature: manufacturingoutput>, <Feature: tradebalance>, <Feature: USTreasury>, <Feature: country_China>, <Feature: country_France>, <Feature: country_Germany>, <Feature: country_Hong Kong>, <Feature: country_India>, <Feature: country_Japan>, <Feature: country_Spain>, <Feature: country_United Kingdom>, <Feature: country_United States of America>, <Feature: percapitaincome_level>, <Feature: index price_scaled>, <Feature: log_indexprice_scaled>, <Feature: USTreasury + year>, <Feature: exchange_rate + USTreasury>, <Feature: exchange_rate + gdppercent>, <Feature: exchange_rate + index price>, <Feature: exchange_rate + index price_scaled>, <Feature: exchange_rate + inflationrate>, <Feature: exchange_rate + log_indexprice>, <Feature: exchange_rate + log_indexprice_scaled>, <Feature: exchange_rate + manufacturingoutput>, <Feature: exchange_rate + oil prices>, <Feature: exchange_rate + percapitaincome>, <Feature: exchange_rate + tradebalance>, <Feature: exchange_rate + unemploymentrate>, <Feature: exchange_rate + year>, <Feature: gdppercent + USTreasury>, <Feature: gdppercent + index price>, <Feature: gdppercent + index price_scaled>, <Feature: gdppercent + inflationrate>, <Feature: gdppercent + log_indexprice>, <Feature: gdppercent + log_indexprice_scaled>, <Feature: gdppercent + manufacturingoutput>, <Feature: gdppercent + oil prices>, <Feature: gdppercent + percapitaincome>, <Feature: gdppercent + tradebalance>, <Feature: gdppercent + unemploymentrate>, <Feature: gdppercent + year>, <Feature: index price + USTreasury>, <Feature: index price + index price_scaled>, <Feature: index price + inflationrate>, <Feature: index price + log_indexprice>, <Feature: index price + log_indexprice_scaled>, <Feature: index price + manufacturingoutput>, <Feature: index price + oil prices>, <Feature: index price + percapitaincome>, <Feature: index price + tradebalance>, <Feature: index price + unemploymentrate>, <Feature: index price + year>, <Feature: index price_scaled + USTreasury>, <Feature: index price_scaled + inflationrate>, <Feature: index price_scaled + log_indexprice>, <Feature: index price_scaled + log_indexprice_scaled>, <Feature: index price_scaled + manufacturingoutput>, <Feature: index price_scaled + oil prices>, <Feature: index price_scaled + percapitaincome>, <Feature: index price_scaled + tradebalance>, <Feature: index price_scaled + unemploymentrate>, <Feature: index price_scaled + year>, <Feature: inflationrate + USTreasury>, <Feature: inflationrate + log_indexprice>, <Feature: inflationrate + log_indexprice_scaled>, <Feature: inflationrate + manufacturingoutput>, <Feature: inflationrate + oil prices>, <Feature: inflationrate + percapitaincome>, <Feature: inflationrate + tradebalance>, <Feature: inflationrate + unemploymentrate>, <Feature: inflationrate + year>, <Feature: log_indexprice + USTreasury>, <Feature: log_indexprice + log_indexprice_scaled>, <Feature: log_indexprice + manufacturingoutput>, <Feature: log_indexprice + oil prices>, <Feature: log_indexprice + percapitaincome>, <Feature: log_indexprice + tradebalance>, <Feature: log_indexprice + unemploymentrate>, <Feature: log_indexprice + year>, <Feature: log_indexprice_scaled + USTreasury>, <Feature: log_indexprice_scaled + manufacturingoutput>, <Feature: log_indexprice_scaled + oil prices>, <Feature: log_indexprice_scaled + percapitaincome>, <Feature: log_indexprice_scaled + tradebalance>, <Feature: log_indexprice_scaled + unemploymentrate>, <Feature: log_indexprice_scaled + year>, <Feature: manufacturingoutput + USTreasury>, <Feature: manufacturingoutput + oil prices>, <Feature: manufacturingoutput + percapitaincome>, <Feature: manufacturingoutput + tradebalance>, <Feature: manufacturingoutput + unemploymentrate>, <Feature: manufacturingoutput + year>, <Feature: oil prices + USTreasury>, <Feature: oil prices + percapitaincome>, <Feature: oil prices + tradebalance>, <Feature: oil prices + unemploymentrate>, <Feature: oil prices + year>, <Feature: percapitaincome + USTreasury>, <Feature: percapitaincome + tradebalance>, <Feature: percapitaincome + unemploymentrate>, <Feature: percapitaincome + year>, <Feature: tradebalance + USTreasury>, <Feature: tradebalance + unemploymentrate>, <Feature: tradebalance + year>, <Feature: unemploymentrate + USTreasury>, <Feature: unemploymentrate + year>, <Feature: USTreasury * year>, <Feature: exchange_rate * USTreasury>, <Feature: exchange_rate * gdppercent>, <Feature: exchange_rate * index price>, <Feature: exchange_rate * index price_scaled>, <Feature: exchange_rate * inflationrate>, <Feature: exchange_rate * log_indexprice>, <Feature: exchange_rate * log_indexprice_scaled>, <Feature: exchange_rate * manufacturingoutput>, <Feature: exchange_rate * oil prices>, <Feature: exchange_rate * percapitaincome>, <Feature: exchange_rate * tradebalance>, <Feature: exchange_rate * unemploymentrate>, <Feature: exchange_rate * year>, <Feature: gdppercent * USTreasury>, <Feature: gdppercent * index price>, <Feature: gdppercent * index price_scaled>, <Feature: gdppercent * inflationrate>, <Feature: gdppercent * log_indexprice>, <Feature: gdppercent * log_indexprice_scaled>, <Feature: gdppercent * manufacturingoutput>, <Feature: gdppercent * oil prices>, <Feature: gdppercent * percapitaincome>, <Feature: gdppercent * tradebalance>, <Feature: gdppercent * unemploymentrate>, <Feature: gdppercent * year>, <Feature: index price * USTreasury>, <Feature: index price * index price_scaled>, <Feature: index price * inflationrate>, <Feature: index price * log_indexprice>, <Feature: index price * log_indexprice_scaled>, <Feature: index price * manufacturingoutput>, <Feature: index price * oil prices>, <Feature: index price * percapitaincome>, <Feature: index price * tradebalance>, <Feature: index price * unemploymentrate>, <Feature: index price * year>, <Feature: index price_scaled * USTreasury>, <Feature: index price_scaled * inflationrate>, <Feature: index price_scaled * log_indexprice>, <Feature: index price_scaled * log_indexprice_scaled>, <Feature: index price_scaled * manufacturingoutput>, <Feature: index price_scaled * oil prices>, <Feature: index price_scaled * percapitaincome>, <Feature: index price_scaled * tradebalance>, <Feature: index price_scaled * unemploymentrate>, <Feature: index price_scaled * year>, <Feature: inflationrate * USTreasury>, <Feature: inflationrate * log_indexprice>, <Feature: inflationrate * log_indexprice_scaled>, <Feature: inflationrate * manufacturingoutput>, <Feature: inflationrate * oil prices>, <Feature: inflationrate * percapitaincome>, <Feature: inflationrate * tradebalance>, <Feature: inflationrate * unemploymentrate>, <Feature: inflationrate * year>, <Feature: log_indexprice * USTreasury>, <Feature: log_indexprice * log_indexprice_scaled>, <Feature: log_indexprice * manufacturingoutput>, <Feature: log_indexprice * oil prices>, <Feature: log_indexprice * percapitaincome>, <Feature: log_indexprice * tradebalance>, <Feature: log_indexprice * unemploymentrate>, <Feature: log_indexprice * year>, <Feature: log_indexprice_scaled * USTreasury>, <Feature: log_indexprice_scaled * manufacturingoutput>, <Feature: log_indexprice_scaled * oil prices>, <Feature: log_indexprice_scaled * percapitaincome>, <Feature: log_indexprice_scaled * tradebalance>, <Feature: log_indexprice_scaled * unemploymentrate>, <Feature: log_indexprice_scaled * year>, <Feature: manufacturingoutput * USTreasury>, <Feature: manufacturingoutput * oil prices>, <Feature: manufacturingoutput * percapitaincome>, <Feature: manufacturingoutput * tradebalance>, <Feature: manufacturingoutput * unemploymentrate>, <Feature: manufacturingoutput * year>, <Feature: oil prices * USTreasury>, <Feature: oil prices * percapitaincome>, <Feature: oil prices * tradebalance>, <Feature: oil prices * unemploymentrate>, <Feature: oil prices * year>, <Feature: percapitaincome * USTreasury>, <Feature: percapitaincome * tradebalance>, <Feature: percapitaincome * unemploymentrate>, <Feature: percapitaincome * year>, <Feature: tradebalance * USTreasury>, <Feature: tradebalance * unemploymentrate>, <Feature: tradebalance * year>, <Feature: unemploymentrate * USTreasury>, <Feature: unemploymentrate * year>]\n"
     ]
    }
   ],
   "source": [
    "# pip install featuretools\n",
    "import featuretools as ft\n",
    "\n",
    "es = ft.EntitySet(id='economy_data')\n",
    "es.add_dataframe(\n",
    "    dataframe=df,\n",
    "    dataframe_name='economy',\n",
    "    index='index',\n",
    "    make_index=True\n",
    ")\n",
    "\n",
    "# Автоматическое конструирование\n",
    "feature_matrix, feature_defs = ft.dfs(\n",
    "    entityset=es,\n",
    "    target_dataframe_name='economy',\n",
    "    agg_primitives=['mean', 'sum', 'max', 'min', 'std'],\n",
    "    trans_primitives=['add_numeric', 'multiply_numeric'],\n",
    "    max_depth=2 \n",
    ")\n",
    "\n",
    "print(\"Сгенерированные признаки:\")\n",
    "print(feature_matrix.head())\n",
    "print(\"\\nОписание:\")\n",
    "print(feature_defs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Масштабирование"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler_minmax = MinMaxScaler()\n",
    "df[['index price_scaled', 'log_indexprice_scaled']] = scaler_minmax.fit_transform(df[['index price', 'log_indexprice']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка качества наборов признаков:\n",
    "Набор данных достаточно полный, но требует предварительной обработки (заполнение пропусков, удаление выбросов, нормализация). После обработки он может быть использован для анализа и построения моделей."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Scripts",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}