Final Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbfca213",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9e6ecbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "603332ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b46bf429",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "data = pd.read_html(\"https://fbref.com/en/comps/8/Champions-League-Stats\")\n",
    "df = data[8].drop([4,9,18])\n",
    "df = df.drop(columns = ['Notes','Last 5', 'Attendance', 'Top Team Scorer', 'Goalkeeper'])\n",
    "df = df[df['MP'].notna()]\n",
    "#print(df)\n",
    "x = df['Squad'].to_numpy()\n",
    "y = df['Pts'].to_numpy()\n",
    "\n",
    "\n",
    "#print(type(x))\n",
    "#print(x[i])\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "for i in range(len(x)):\n",
    "    x[i] = str(x[i])[3:8]\n",
    "\n",
    "for i in range(len(y)):\n",
    "    y[i] = str(y[i])\n",
    "    #x[i] = x[i][3]\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "fig, ax = plt.subplots(figsize = (30,10))\n",
    "    \n",
    "        \n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c35aa1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rk         object\n",
       "Squad      object\n",
       "MP        float64\n",
       "W         float64\n",
       "D         float64\n",
       "L         float64\n",
       "GF        float64\n",
       "GA        float64\n",
       "GD        float64\n",
       "Pts       float64\n",
       "xG        float64\n",
       "xGA       float64\n",
       "xGD       float64\n",
       "xGD/90    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58c3cf22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = pd.read_html(\"https://fbref.com/en/comps/8/10096/2020-2021-Champions-League-Stats\")\n",
    "df2 = data2[8].drop([1,3,6,11,20])\n",
    "df2 = df2.drop(columns = ['Notes', 'Attendance', 'Top Team Scorer', 'Goalkeeper'])\n",
    "x2 = df2[\"Squad\"].to_numpy()\n",
    "y2 = df2[\"Pts\"].to_numpy()\n",
    "\n",
    "for i in range(len(x2)):\n",
    "    x2[i] = x2[i][3:8]\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (30,10))\n",
    "plt.scatter(x2, y2,)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b869d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rk         object\n",
       "Squad      object\n",
       "MP        float64\n",
       "W         float64\n",
       "D         float64\n",
       "L         float64\n",
       "GF        float64\n",
       "GA        float64\n",
       "GD        float64\n",
       "Pts       float64\n",
       "xG        float64\n",
       "xGA       float64\n",
       "xGD       float64\n",
       "xGD/90    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "507ee557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data3 = pd.read_html(\"https://fbref.com/en/comps/8/2900/2019-2020-Champions-League-Stats\")\n",
    "df3 = data3[8].drop([1,3,6,11,20])\n",
    "df3 = df3.drop(columns = ['Notes', 'Attendance', 'Top Team Scorer', 'Goalkeeper'])\n",
    "x3 = df3[\"Squad\"].to_numpy()\n",
    "y3 = df3[\"Pts\"].to_numpy()\n",
    "\n",
    "for i in range(len(x3)):\n",
    "    if pd.isna(x3[i]):\n",
    "         x3[i] = \"\"\n",
    "\n",
    "for i in range(len(x3)):\n",
    "    x3[i] = x3[i][3:8]\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (30,10))    \n",
    "plt.scatter(x3, y3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc98fb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rk         object\n",
       "Squad      object\n",
       "MP        float64\n",
       "W         float64\n",
       "D         float64\n",
       "L         float64\n",
       "GF        float64\n",
       "GA        float64\n",
       "GD        float64\n",
       "Pts       float64\n",
       "xG        float64\n",
       "xGA       float64\n",
       "xGD       float64\n",
       "xGD/90    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()\n",
    "df3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67709cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data4 = pd.read_html(\"https://fbref.com/en/comps/8/2102/2018-2019-Champions-League-Stats\")\n",
    "df4 = data4[8].drop([1,3,6,11,20])\n",
    "df4 = df4.drop(columns = ['Notes', 'Attendance', 'Top Team Scorer', 'Goalkeeper'])\n",
    "x4 = df4[\"Squad\"].to_numpy()\n",
    "y4 = df4[\"Pts\"].to_numpy()\n",
    "\n",
    "for i in range(len(x4)):\n",
    "    if pd.isna(x4[i]):\n",
    "         x4[i] = \"\"\n",
    "\n",
    "for i in range(len(x4)):\n",
    "    x4[i] = x4[i][3:8]\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (30,10))    \n",
    "plt.scatter(x4, y4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f574eb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rk         object\n",
       "Squad      object\n",
       "MP        float64\n",
       "W         float64\n",
       "D         float64\n",
       "L         float64\n",
       "GF        float64\n",
       "GA        float64\n",
       "GD        float64\n",
       "Pts       float64\n",
       "xG        float64\n",
       "xGA       float64\n",
       "xGD       float64\n",
       "xGD/90    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()\n",
    "df4.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93e8ced6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/H4DmzZvz5JNP8rnPfa7estY3m28kSZIkSZIkSZIkSWrkFryxjXvmr6O4tByAbXuKuWf+OoAm04BTUlLCZZddxsqVKwE4//zzmTt3LkEQVDn+hBNOqHRvbYqLi6PHrVq1ijnXvffey89//nMATj75ZF544YWYtrmqDwsWLKj2WsuWLbn00ktjmmfmzJnRLbMyMzN5/vnn6d69e50yLVy4kGuvvZYwDElJSeHJJ5/kggsuqNNcx4rNN5IkSZIkSZIkSZIkNXJTl2yINt4cUlxaztQlG5pE883Bgwe54oorWL58OQBf+MIXeP7558nMzKz2ntatW0ePP/7441qfY9euXVXeW5Mf/ehH/Pd//zcA7dq1Y+nSpfTo0SOme+vD5ZdfXu21Ll268P7779c6x5w5c/jWt74FRLbeys/P57zzzqtTnqVLlzJ69GhKS0tp1qwZv/3tb7nyyivrNNex1CzRASRJkiRJkiRJkiRJUsPavqc4rnpjUlpaypgxY1i0aBEA/fr1Y/HixZx44ok13nd4E0xtTShlZWXRrawyMzPp2LH2hqYHHniA++67D4A2bdrwwgsv0Lt371rvSyZPPfUU48aNIwxDWrRowYIFC7j44ovrNNdLL73EZZddRklJCUEQ8Jvf/Iavf/3r9Zy4Ydh8I0mSJEmSJEmSJElSI9ehdUZc9cairKyMr371q/zxj38EoE+fPrzwwgu0adOm1nsPb4RZvXp1jWPffPNNyssjKwudeeaZ1W5ldci0adP4j//4DwCysrJYsmQJZ599dq2Z6lsYhtX+qq3h6JlnnuHaa6+loqKC5s2b8+yzz8a8TdVnvfrqq3zlK1+huLiYIAj45S9/ybhx4+o0VyLYfCNJkiRJkiRJkiRJUiM3cUhPMtJSKtUy0lKYOKRnghI1vPLycq655hqee+45INIUs3TpUtq2bRvT/UOGDIkeL1mypMaxixcvjh4PHTq0xrE///nPufPOOwFo1aoVixYt4pxzzokpU7JYsGABX/va1ygvLyc1NZW5c+fyla98pU5zvfbaawwbNoyioiIAHnroIW655Zb6jNvgbL6RJEmSJEmSJEmSJKmRG9WvIz++og8dW2cQAB1bZ/DjK/owql/t2yMdjyoqKrjxxhuZO3cuAD179mTZsmW0b98+5jm6d+9Ov379ANi0aVN026rPKikp4ZFHHomejx07tto5H3nkEW6//XYgsj3V888/z7nnnhtzpmTw/PPPc9VVV1FWVkZKSgpPPvkko0aNqtNcq1evZujQoezduxeABx98kO985zv1mPbYSE10AEmSJEmSJEmSJEmS1PBG9evYaJttDheGIbfccgtz5swB4PTTT2f58uVkZ2fHPdf9998fbSy59dZb+fOf/0znzp2j1ysqKrjtttv44IMPABg9enSl7aoON2fOHG655RbCMKRly5b8v//3/7jgggvizpRIS5cu5corr+TgwYOkpKTw+OOPM2bMmDrNtXbtWi699FIKCwsB+P/+v/+PCRMm1GfcY8bmG0mSJEmSJEmSJEmS1Gjce++9/OY3vwEgLS2NvLw8Vq5cWet9l156KS1btqxUGzlyJFdddRVz585ly5Yt9O/fn1tuuYU+ffqwa9cu5syZE507JyeHBx98sMq5Fy1axI033kgYhgDceOON7NmzhwULFtSYqX///pWafQ7305/+lN27d1d5bc+ePXz/+9+vVDv11FP5xje+UePz1eTNN99k5MiRlJSUAHDllVeSkZFR62vo1asXvXr1qlTbunUrX/7yl/nnP/8JwMCBA+nRo0etc3Xu3Jn+/fvX+TU0lODQb2yi5ObmhqtWrUpoBkmSJEmSJEmSJEmSjtb69es544wzEh2jyRs4cCAvv/xy3Pe99957dO3a9Yj6gQMHuOGGG3j66aervfe0005j/vz59O3bt8rrkydP5gc/+EHcmWbNmsUNN9xQ5bWuXbuyZcuWmOe66KKLeOmll+LOcMjs2bMZN25c3Pfdf//9TJ48uVLtpZde4uKLL457ruuvv57Zs2fHfd8hR/M1GgTB6jAMc6u61qzOiSRJkiRJkiRJkiRJkhq5Fi1a8NRTT7Fo0SLGjBnDKaecQosWLWjXrh3nnnsuDz74IH/729+qbbxR4+fKN5IkSZIkSZIkSZIk1QNXvpGSmyvfSJIkSZIkSZIkSZIkSUnG5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJkiSpjmy+kSRJkiRJkiRJkiRJkuooNd4bgiDIBc4BOgItgf3ANmBVGIav1288STrOrZ0Hy6ZA4VbI6gSDJkHfsYlOJUmSJEmSJEmSJDVqhYWFLFmyhBdffJE1a9bw97//nU8++YQTTjiBzp07c/755zNu3DjOOeecmOdcvHgxs2bN4rXXXuOjjz7ixBNPpHv37owePZqbb76ZzMzMGu8vKyvjpZdeYunSpaxcuZL169fzz3/+k7S0NE4++WTOOeccrr76akaMGEFKSkrMud566y1+/etf88ILL7B161ZSUlLo3Lkzw4cP59Zbb6VLly4xz1WbMAxZsWIFS5cu5X//9395++23+cc//kGzZs046aST6N+/P1dccQVjx46lRYsWtc63a9cuVq9ezapVq6KPH3zwQaXnOx4EsQYNguAS4OdADyCoYkgIbARuD8NwaawBcnNzw1WrVsU6XJKOH2vnQf4dUFr8r1paBox4yAYcSZIkSZIkSZKkRmj9+vWcccYZiY7R5D3wwANMmjSJAwcO1Dr2mmuu4de//jUtW7asdsyBAwe44YYbePrpp6sdc9pppzF//nz69u1b5fUXX3yR0aNH889//rPWTOeccw5PPfUUp512Wq1j/+d//of//M//pLS0tMrrrVq1YubMmVx99dW1zlWb9evXM3jwYLZv317r2B49evDkk08yYMCAasesW7eu2vfrkPpuvjmar9EgCFaHYZhb1bWYVr4JguBiYDGwHbgfeI3IajclQDqRVXDOBb4JPB8EwZAwDF+sU1pJaiyWTanceAOR82VTbL6RJEmSJEmSJEmSGsjGjRujjTfdunVj8ODBnH322bRr147du3ezbNkynnvuOcrLy3niiSfYuXMnixYtolmzZlXOd/311zN37lwA2rZty80330yfPn34+OOPeeKJJ1i5ciXvvvsuQ4cO5a9//SunnHLKEXNs27Yt2njzuc99jsGDB/OlL32JnJwcSktLWblyJXPmzOGTTz7h9ddf5+KLL2blypVkZ2dX+zp/9atfMXHiRADS0tK49tprueiiiygtLWXJkiU8++yz7N27l2uvvZbWrVszdOjQo3pfd+3aFW28yczMZPDgwZx33nl06tQJgLVr1zJ79mw++ugjNm7cyKBBg/jLX/7CWWedVeV85eXllc5TUlLo1asXmzdvpri4uMp7klVMK98EQfBnIBO4MAzDohrGnQC8AnwShuFFNYy7GbgZoHPnzgO2bNkSb25JSn6TWxNZFOyzApi859hmkSRJkiRJkiRJUoNz5ZvkcNNNN1FQUMDEiRO56KKqWxdeeeUVhg0bxr59+wB47LHHGDdu3BHj/vCHPzBq1CgAOnfuzCuvvELnzp2j1ysqKvjmN7/JrFmzABg9ejTPPPPMEfM88cQTPPDAA9xzzz1cccUVVW7JtG3bNoYMGcLbb78NwHXXXcdvf/vbKvMXFBRw+umns3//flJTU1m0aBGDBw+uNGb27NnR13TKKaewceNG0tPTq5wvFq+++irXXnstd999N1/72tdo1arVEWP27NnDqFGjePnllwG48MILo8ef9e677zJlyhQGDBhAbm4uZ599Ni1btqRr164c6iM5Xla+ibX5Zh/wvTAMfxXD2FuBqWEYnhBLOLedktRoTesNhR8eWc86BSa8dezzSJIkSZIkSZIkqUHZfJMcdu/eTZs2bWod9/DDD3P77bcD1TeJ9OvXjzfffBOAhQsXMmzYsCPGFBcX06tXLz744AMgsp1S7969K43Zs2cPWVlZBEFQY6a33nqLPn36AJCRkcHHH39c5ZZYEyZMYPr06QBMnDiRBx54oMr5xo4dG20Gevjhh7nttttqfP6a7Nu3jxYtWpCWllbjuJ07d3Lqqaeyf/9+ADZv3sypp54a8/Mcj803Va+ZdKSDwIkxjj3x0/GS1LQNmgRpGZVraRmRuiRJkiRJkiRJkqQGEUvjDcCYMWOix+vWrTvi+qZNm6KNN927d6+y8QYiTTI33XRT9HzevHlHjGndunWtjTcAvXv3plevXkCkqefvf//7EWPCMIw21ARBEG0gqsodd9wRPT60dVZdnXDCCbU23gC0b9+eCy+8MHpe1Xvb2MTafPNnYEIQBH1qGvTp9QlA1WsGSVJT0ncsjHgostINQeRxxEORuiRJkiRJkiRJkqSEOnzbpOLi4iOuL1myJHo8ZMiQGucaOnRo9Hjx4sUNmuvtt99m27ZtAJx11lmccsop1c513nnnceKJkbVW/vKXv7B3796jyhar2l5DY5Ma47i7gP8F1gRB8CLwGrANOAC0ADoC5wIDgULg7npPKknHo75jbbaRJEmSJEmSJEmSktBbb70VPe7SpUuN1wcMGFDjXGeffTYpKSmUl5fzzjvvEIZhTCvdfNbBgwfZtGlTveVq1qwZ/fr14+WXX6aiooL169fzhS98Ie5c8Xr77bejx1W9hsYmpuabMAw3BkFwDvATYAQwuIphB4DfA/eEYfhu/UWUJEmSJEmSJEmSJEmqXzNnzoweDx8+/IjrGzdujB537dq1xrlSU1Pp2LEjH3zwAUVFRWzbto1OnTrFnWnevHns2bMHgP79+5OdnX1UuaBy88vGjRsbvPnm1Vdf5Z133gHgpJNO4pxzzmnQ50sGsa58QxiGm4GxQRC0BPoCHYCWwH5gO7AuDMOiBkkpSZIkSZIkSZIkSZJUT1asWMGsWbMASE9PZ8KECUeMOdQEA9CuXbta52zbti0ffPBB9N54m292797NXXfdFT2/5557qhxXl1xV3dsQDh48yG233RY9v+uuu0hJSWnQ50wGMTffHBKG4X4i205JkiRJkiRJkiRJkiQdV3bs2MHYsWOpqKgA4Ic//GGVjTL79u2LHqenp9c6b0ZGRvR47969cWUqLy/n6quvpqCgAIisxDN69Ogqxx7LXPH69re/zdq1awHo168fd9xxR4M+X7JoFuvAIAjaBUHwvSAI/isIgvMPq/9HEARbgiDYGwTBn4Ig6NswUSVJkiRJkiRJkiRJUp2tnQfTesPk1pHHtfMSneiYKyoqYuTIkWzbtg2INLl897vfTXAqyMvL409/+hMAnTt3Zvbs2YkNVAdTp07l0UcfBSArK4u5c+fSvHnzBKc6NmJa+SYIgmxgFZGtpgDuDoLgGqAd8APgDWA3cAnwYhAEnw/DcGsD5JUkSZIkSZIkSZIkSfFaOw/y74DS4sh54YeRc4C+YxOX6xgqKSnhsssuY+XKlQCcf/75zJ07lyAIqhx/wgknVLq3NsXFxdHjVq1axZzr3nvv5ec//zkAJ598Mi+88EKN20nVZ64FCxZUe1/Lli259NJLa50fYObMmdEtszIzM3n++efp3r17TPc2BrFuOzURyALGAh8AvwJ+BOwBzgnDcB1AEAQXA4uB7wHj6zmrJEmSJEmSJEmSJEmqi2VT/tV4c0hpcaTeBJpvDh48yBVXXMHy5csB+MIXvsDzzz9PZmZmtfe0bt06evzxxx/X+hy7du2q8t6a/OhHP+K///u/AWjXrh1Lly6lR48eNd5Tn7kuv/zyau/r0qUL77//fq3zz5kzh29961tAZIur/Px8zjvvvFrva0xi3XZqGPBYGIbPhmG4ErgbOBX446HGG4AwDF8EngZia32SJEmSJEmSJEmSJEkNr7CazWuqqzcipaWljBkzhkWLFgHQr18/Fi9ezIknnljjfYc3wdTWhFJWVhbdyiozM5OOHTvWmuuBBx7gvvvuA6BNmza88MIL9O7du9b74skFsGXLlirvrQ9PPfUU48aNIwxDWrRowYIFC7j44ovr9TmOB7E233QG1h12/vanj29UMXY10OVoQkmSJEmSJEmSJEmSpHqU1Sm+eiNRVlbGV7/6Vf74xz8C0KdPH1544QXatGlT672HN8KsXr26xrFvvvkm5eXlAJx55pnVbmV1yLRp0/iP//gPALKysliyZAlnn312rZnizVVRUcEbb0RaO5o1a8YZZ5xR6XoYhtX+qq2x55lnnuHaa6+loqKC5s2b8+yzz8a8TVVjE2vzTSmVt6g6tGnYvirGlgA1/ymSJEmSJEmSJEmSJEnHzqBJkJZRuZaWEak3UuXl5VxzzTU899xzQKQpZunSpbRt2zam+4cMGRI9XrJkSY1jFy9eHD0eOnRojWN//vOfc+eddwLQqlUrFi1axDnnnBNTJoCzzjqLTp0iTVNvv/02W7dWv3rRihUr+OSTTwA4//zzadWqVczPU5MFCxbwta99jfLyclJTU5k7dy5f+cpX6mXu41GszTfbgcPb3fYBtwP/V8XYLsA/jjKXJEmSJEmSJEmSJEmqL33HwoiHIOsUIIg8jngoUm+EKioquPHGG5k7dy4APXv2ZNmyZbRv3z7mObp3706/fv0A2LRpU3Tbqs8qKSnhkUceiZ6PHVv9e/rII49w++23A5HtqZ5//nnOPffcmDMBBEHAmDFjgMjKNT/72c+qHfvQQw9Fj6+66qq4nqc6zz//PFdddRVlZWWkpKTw5JNPMmrUqHqZ+3gVa/PNGuBLh07CMDwQhuHPwzDcXsXYS4C/1Uc4SZIkSZIkSZIkSZJUT/qOhQlvweQ9kcdG2ngThiG33HILc+bMAeD0009n+fLlZGdnxz3X/fffHz2+9dZb+eCDDypdr6io4LbbbovWR48eXWlbqMPNmTOHW265hTAMadmyJf/v//0/LrjggrgzAXzve9+jZcuWADz44IMsW7bsiDGzZ8/mmWeeAeCUU07hG9/4Rp2e63BLly7lyiuv5ODBg6SkpPD4449HG4GastTahwAwhcor31QpCIKTgQLgyaMJJUmSJEmSJEmSJEmSVBf33nsvv/nNbwBIS0sjLy+PlStX1nrfpZdeGm1oOWTkyJFcddVVzJ07ly1bttC/f39uueUW+vTpw65du5gzZ0507pycHB588MEq5160aBE33ngjYRgCcOONN7Jnzx4WLFhQY6b+/fvTuXPnI+odOnTgpz/9KbfeeitlZWX8+7//O9dddx0XXXQRZWVlLFq0iGeffRaA1NRUZs6cSXp6eq3vQU3efPNNRo4cSUlJCQBXXnklGRkZtb6GXr160atXryqvPfroo7z33nuVanv27Ikef//73690rU2bNnz3u9+NP3wDCw79xiZKbm5uuGrVqoRmkCRJkiRJkiRJkiTpaK1fv54zzjgj0TGavIEDB/Lyyy/Hfd97771H165dj6gfOHCAG264gaeffrrae0877TTmz59P3759q7w+efJkfvCDH8SdadasWdxwww3VXv+f//kf/vM//5PS0tIqr7dq1YqZM2dy9dVXx/3cnzV79mzGjRsX9333338/kydPrvJavL9XXbp04f333487wyFH8zUaBMHqMAxzq7oW67ZTkiRJkiRJkiRJkiRJTU6LFi146qmnWLRoEWPGjOGUU06hRYsWtGvXjnPPPZcHH3yQv/3tb9U23jSk733ve6xZs4bbbruNHj16kJmZSatWrejduzf/8R//wbp16+ql8UY1c+UbSZIkSZIkSZIkSZLqgSvfSMnNlW8kSZIkSZIkSZIkSZKkJGPzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiQ1EoX5+Wy6ZBDrzziTTZcMojA/P9GRJEmSJEmSJEmSGr3URAeQJEnS0SvMz6fgvkmEJSUAlG3fTsF9kwDIGjEikdEkSZIkSZIkSZIaNVe+kSRJagR2Tpsebbw5JCwpYee06YkJJEmSJEmSJEmS1ETYfCNJktQIlBUUxFWXJEmSJEmSJElS/bD5RpIkqRFIzcmJqy5JkiRJkiRJkqT6YfONJElSI9B+wniC9PRKtSA9nfYTxicmkCRJkiRJkiRJCVRYWMi8efO49dZb+eIXv0jbtm1JS0ujTZs2fP7zn+fb3/42r7/+elxzLl68mKuuuoouXbqQnp5O+/btOf/885k2bRpFRUUxzfH+++/z3HPPcc8993DppZfStm1bgiAgCAIGDhxYp9c5depUBg4cSPv27WnevDk5OTlcdNFFPPTQQ+zfvz/uOWsShiF/+ctf+MEPfsDQoUM55ZRTSE9Pp2XLlnTp0oXLL7+cxx9/nAMHDsQ175YtW7j77rvp06cPWVlZnHDCCfTq1Yvbb7+dt99+u15fQ0MIwjBMaIDc3Nxw1apVCc0gSZLUGBTm57Nz2nTKCgpIzcmh/YTxZI0YkehYkiRJkiRJktRkrF+/njPOOCPRMZq8Bx54gEmTJsXUAHLNNdfw61//mpYtW1Y75sCBA9xwww08/fTT1Y457bTTmD9/Pn379q12zM9+9jPuuOOOaq9fdNFFvPTSS7VmPmTJkiV8/etfZ9euXdWO6datG/PmzWPAgAExz1ud9evXM3jwYLZv317r2B49evDkk0/G9Ly/+93v+Na3vsW+ffuqvN68eXN+8pOfMGHChLgzf9bRfI0GQbA6DMPcqq6lHlUqSZIkJY2sESNstpEkSZIkSZIkNXkbN26MNt5069aNwYMHc/bZZ9OuXTt2797NsmXLeO655ygvL+eJJ55g586dLFq0iGbNqt486Prrr2fu3LkAtG3blptvvpk+ffrw8ccf88QTT7By5Ureffddhg4dyl//+ldOOeWUKucpLy+vdJ6RkUH37t1Zu3Zt3K/xxRdfZMSIEZSWlgLwpS99iauuuooOHTqwc+dOfv/737N8+XI2b97M0KFDWbFiBd27d4/7eQ63a9euaONNZmYmgwcP5rzzzqNTp04ArF27ltmzZ/PRRx+xceNGBg0axF/+8hfOOuusaudcuHAh119/PeXl5QRBwOjRoxkyZAhpaWm8/PLLPP744xw8eJA777yTVq1a8c1vfvOoXkNDceUbSZIkSZIkSZIkSZLqgSvfJIebbrqJgoICJk6cyEUXXVTlmFdeeYVhw4ZFV1t57LHHGDdu3BHj/vCHPzBq1CgAOnfuzCuvvELnzp2j1ysqKvjmN7/JrFmzABg9ejTPPPNMlc+5YMECli5dyoABAxgwYABnnXUWH374IaeeeioQ+8o3Bw4coEePHnzwwQcA3H333fz4xz8+Ytz06dOjq8VccsklLFu2rNa5a/Lqq69y7bXXcvfdd/O1r32NVq1aHTFmz549jBo1ipdffhmACy+8MHr8Wfv376d79+7Rhp7Zs2dz/fXXVxrzwgsvMGzYMMrKyjjhhBP4+9//zsknn1zn19BQK9/YfCNJkiRJkiRJkiRJUj2w+SY57N69mzZt2tQ67uGHH+b2228Hqm8S6devH2+++SYQWaVl2LBhR4wpLi6mV69e0WaYdevW0bt375iyvv/++3E338ydO5err74agNzcXFauXEkQBFWOHT58OM8//zwAS5cuZdCgQTHlqsq+ffto0aIFaWlpNY7buXMnp556Kvv37wdg8+bN0dd4uBkzZjB+/HgAxowZw7x586qc76677mLq1KkAfO9734se10VDNd9UvWaSJEmSJEmSJEmSJEnScSiWxhuINHwcsm7duiOub9q0Kdp407179yobbyCyfdRNN90UPa+uiaS+LF++PHp8zTXXVNt4A3DddddFj5988smjet4TTjih1sYbgPbt23PhhRdGz6t6b4HoVl4AeXl51c53++23R19jQ7+3dWXzjSRJkiRJkiRJkiRJanIO3zapuLj4iOtLliyJHg8ZMqTGuYYOHRo9Xrx4cT2kq97WrVujxz179qxxbI8ePaLHh1bAORZqe28/+eQTXnvtNQCysrI499xzq53rlFNO4cwzzwTggw8+4J133qnntEfP5htJkiRJkiRJkiRJktTkvPXWW9HjLl261Hh9wIABNc519tlnk5KSAsA777xDGIb1lPJIdZ17x44dfPzxx/Wcpmpvv/129Liq9/bw9+jss8+mWbOa21dyc/+129Phvy/JwuYbSZIkSZIkSZIkSZLU5MycOTN6PHz48COub9y4MXrctWvXGudKTU2lY8eOABQVFbFt27b6CVmF7Ozs6PHhGavy2esbNmxokEyHe/XVV6Or05x00kmcc845Neaq7b2Fyg08tb3mRLD5RpIkSZIkSZIkSZIkNSkrVqxg1qxZAKSnpzNhwoQjxuzZsyd63K5du1rnbNu2bZX31rfzzz8/evzEE0/UuBLO448/Xum8IXMBHDx4kNtuuy16ftddd0VXBKouRzK9t3Vl840kSZIkSZIkSZIkSWoyduzYwdixY6moqADghz/8IZ06dTpi3L59+6LH6enptc6bkZERPd67d289JK3a6NGjad26NQCvv/46kyZNqnLcww8/zMKFCyvVPvnkkwbLBfDtb3+btWvXAtCvXz/uuOOOKscl63tbV6mJDiBJkiRJkiRJkiRJkhrews0LmbFmBjuKdpCdmU1e/zyGdztyu6XGrKioiJEjR0a3hRo+fDjf/e53E5wqPllZWUybNo1x48YB8KMf/Yjly5czduxYOnTowD/+8Q9+//vfs3TpUjIyMmjdujUFBQUANGvWcGu0TJ06lUcffTSace7cuTRv3rzBni+Z2HwjSZIkSZIkSZIkSVIjt3DzQiavmExJeQkABUUFTF4xGaDJNOCUlJRw2WWXsXLlSiCyfdPcuXMJgqDK8SeccEKle2tTXFwcPW7VqtVRpq3ZDTfcwL59+7jzzjspLS1lxYoVrFixotKYVq1a8bvf/Y77778/2nzTpk2bSmMWLFhQ7XO0bNmSSy+9NKY8M2fO5K677gIgMzOT559/nu7du1c7Ppnf27qw+UaSJEmSJEmSJEmSpEZuxpoZ0cabQ0rKS5ixZkaTaL45ePAgV1xxBcuXLwfgC1/4As8//zyZmZnV3nNoayeAjz/+uNbn2LVrV5X3NpTvfOc7DBs2jIcffpilS5fy/vvvU1paSqdOnRg2bBgTJkyga9eufOc734nek52dXWmOyy+/vNr5u3Tpwvvvv19rjjlz5vCtb30LiGwPlZ+fz3nnnVfjPcn+3sbL5htJkiRJkiRJkiRJkhq5HUU74qo3JqWlpYwZM4ZFixYB0K9fPxYvXsyJJ55Y4309evTgxRdfBKi1CaWsrCy6lVVmZiYdO3Y8+uAx6NatGw8++GC11/ft28eHH34IRFayOfPMM+v1+Z966inGjRtHGIa0aNGCBQsWcPHFF9d6X48ePaLHsTT4bNmypcp7k0XDbeYlSZIkSZIkSZIkSZKSQnZmdlz1xqKsrIyvfvWr/PGPfwSgT58+vPDCC0dsv1SV3r17R49Xr15d49g333yT8vJyAM4888xqt7I61v785z8ThiEA5557LqmplddoCcOw2l+1NcU888wzXHvttVRUVNC8eXOeffbZmLepOvPMM2nWLNKy8uabb1JRUVHj+FWrVkWPD/99SRY230iSJEmSJEmSJEmS1Mjl9c8jPSW9Ui09JZ28/nkJStTwysvLueaaa3juueeASMPH0qVLadu2bUz3DxkyJHq8ZMmSGscuXrw4ejx06NA6pG0Ys2bNih5/4xvfqLd5FyxYwNe+9jXKy8tJTU1l7ty5fOUrX4n5/hNPPJEvfelLABQWFvLaa69VO/bDDz/knXfeAaBz5871vnpPfbD5RpIkSZIkSZIkSZKkRm54t+FMPm8yOZk5BATkZOYw+bzJDO82PNHRGkRFRQU33ngjc+fOBaBnz54sW7aM9u3bxzxH9+7d6devHwCbNm2Kblv1WSUlJTzyyCPR87Fjxx5F8vrz4osv8uyzzwKR7amuvPLKepn3+eef56qrrqKsrIyUlBSefPJJRo0aFfc8V111VfR4xowZ1Y772c9+Fl29J1ne28+y+UaSJEmSJEmSJEmSpCZgeLfh/Gn0n1h7/Vr+NPpPjbbxJgxDbrnlFubMmQPA6aefzvLly8nOjn+Lrfvvvz96fOutt/LBBx9Uul5RUcFtt90WrY8ePfqYbIv03nvvHZHlcC+88AKXX345AEEQ8Oijj9K8efOjft6lS5dy5ZVXcvDgQVJSUnj88ccZM2ZMneb65je/SYcOHQCYN28ev/3tb6t8vmnTpgFwwgkn8L3vfa/u4RtQau1DJEmSJEmSJEmSJEmSjg/33nsvv/nNbwBIS0sjLy+PlStX1nrfpZdeSsuWLSvVRo4cyVVXXcXcuXPZsmUL/fv355ZbbqFPnz7s2rWLOXPmROfOycnhwQcfrPE5vv/971c6LywsjB6/9957R1zv378/V1xxxRHzrF69mquvvpp/+7d/Y+DAgZx++umkpqaydetWFi1axLJly4BI483DDz/MwIEDa339tXnzzTcZOXIkJSUlAFx55ZVkZGSwYMGCGu/r1asXvXr1OqLesmVLZs6cyciRIykvL2fcuHEsXLiQf//3fyc1NZWXX36ZOXPmUFZWBsC0adM4+eSTj/p1NITg0NI8iZKbmxuuWrUqoRkkSZIkSZIkSZIkSTpa69ev54wzzkh0jCZv4MCBvPzyy3Hf995779G1a9cj6gcOHOCGG27g6aefrvbe0047jfnz59O3b98anyMIgrgyXX/99cyePfuI+rPPPlvrijMnn3wyDz30UL1t1TR79mzGjRsX9333338/kydPrvb67373O771rW+xb9++Kq83b96cn/zkJ0yYMCHu5/6so/kaDYJgdRiGuVVdO6qVb4IgaAmcB7QDPgJWhGF44GjmVIKtnQfLpkDhVsjqBIMmQd/k3DPtWCvMz2fntOmUFRSQmpND+wnjyRoxItGxJEmSJEmqdws3L2TGmhnsKNpBdmY2ef3zGu1S5JIkSZIk1aZFixY89dRTXH/99Tz22GO89tpr7Ny5k1atWtG9e3fGjBnDzTffTGZm5jHLdPHFF/OrX/2KF198kbVr1/LRRx+xb98+TjrpJHr16sXIkSO57rrryMrKOmaZ6urrX/86F1xwAb/4xS9YuHAhH3zwARUVFXTs2JEvf/nL3HrrrZx11lmJjlmjmFa+CYLgTmBhGIYbDqtdB0wDWh829GPg1jAM58cawJVvksjaeZB/B5QW/6uWlgEjHmryDTiF+fkU3DeJ8NPlswCC9HRyfjjFBhxJkiRJUqOycPNCJq+YTEn5v34GTk9JZ/J5k23AkSRJkqRauPKNlNwaauWbZjHOMRUYcNiEQ4FZwAHgB8A44MdEVtJ5OgiCAVVNoiS3bErlxhuInC+bkpg8SWTntOmVGm8AwpISdk6bnphAkiRJkiQ1kBlrZlRqvAEoKS9hxpoZCUokSZIkSZKU3GLdduqzm47dB2wH+oZhuDs6KAh+DawF7gKuqnayILgZuBmgc+fO8eRVQyrcGl+9CSkrKIirLkmSJEnS8WpH0Y646pIkSZIkSU1drCvfRAVBkAJ8AZh+eOMNQBiGHwKPAhfUNEcYhjPDMMwNwzD3pJNOijeCGkpWp/jqTUhqTk5cdUmSJEmSjlfZmdlx1SVJkiRJkpq6uJtvgJZACrChmuv/B7SrcyIlzqBJkJZRuZaWEak3ce0njCdIT69UC9LTaT9hfGICSZIkSZLUQPL655GeUvln4PSUdPL65yUokSRJkiRJUnKLddspgLZBEHQmsgXVXqBNNePaAPuONpgSoO/YyOOyKZGtprI6RRpvDtWbsKwRIwDYOW06ZQUFpObk0H7C+GhdkiRJkqTGYni34QDMWDODHUU7yM7MJq9/XrQuSZIkSZKkyoIwDGsfFAQVwOEDA+DXYRjeWsXYx4ABYRh+PpYAubm54apVq2KMK0mSJEmSJEmSJElSclq/fj1nnHFGomNIqsbRfI0GQbA6DMPcqq7FuvLND6qo7aniiU4ERgHPxhpOkiRJkiRJkiRJkiRJOl7F1HwThmFVzTdVKQJOBYrrnEiSJEmSJEmSJEmSJEk6TsS68k1MwjAsBwrrc05JkiRJkiRJkiRJkiQpWTWLdWAQBC2CIPh2EAS/CILg/iAIulczbnAQBMvrL6IkSZIkSZIkSZIkSZKUnGJa+SYIgpbAX4C+QPBp+T+DILgvDMMHPjP8ZOCi+osoSZIkSZIkSZIkSZIkJadYV74ZD3we+G8iDTjDgdXAj4Mg+EXDRJMkSZIkSZIkSZIkSZKSW0wr3wBjgLlhGN736flbQRAsAWYAtwVBkBaG4U0NklCSJEmSJEmSJEmSJElKUrE235wG/PLwQhiGFcDtQRDsAe4NgqBZGIbfqOd8kiRJkiRJkiRJkiRJUtKKtfmmBEir6kIYhvcFQVAOTAqCoBmwvL7CSZIkSZIkSZIkSZIkScks1uabd4EvAT+v6mIYhpODIACYBAysl2SSJEmSJEmSJEmSJElSkmsW47gXgMuCIGhV3YAwDCcDk4EuRx9LkiRJkiRJkiRJkiRJSn6xrnzzOJFtp7oDa6obFIbhlCAIdgG59ZBNkiRJkiRJkiRJkiRJSmoxNd+EYbgJuCfGsVVuTSVJkiRJkiRJkiRJkiQ1NrFuOyVJkiRJkiRJkiRJkiTpM2y+kSRJkiRJkiRJkiRJkurI5htJkiRJkiRJkiRJktSoFBYWMm/ePG699Va++MUv0rZtW9LS0mjTpg2f//zn+fa3v83rr78e15yLFy/mqquuokuXLqSnp9O+fXvOP/98pk2bRlFRUUxzvP/++zz33HPcc889XHrppbRt25YgCAiCgIEDB8b9Onfv3s2DDz7I4MGDyc7OpkWLFrRs2ZLOnTszYsQIHn30UUpKSuKe91i+hsLCQqZOncrAgQNp3749zZs3Jycnh4suuoiHHnqI/fv312v+hhCEYZjQALm5ueGqVasSmkGSJEmSJEmSJEmSpKO1fv16zjjjjETHaPIeeOABJk2axIEDB2ode8011/DrX/+ali1bVjvmwIED3HDDDTz99NPVjjnttNOYP38+ffv2rXbMz372M+64445qr1900UW89NJLtWY+ZPHixVx33XX84x//qHHcqaeeyrPPPkv//v1jnrs69f0alixZwte//nV27dpV7Zhu3boxb948BgwYEE/UKh3N12gQBKvDMMyt6lrqUaWSJEmSJEmSJEmSJElKIhs3bow23nTr1o3Bgwdz9tln065dO3bv3s2yZct47rnnKC8v54knnmDnzp0sWrSIZs2q3jzo+uuvZ+7cuQC0bduWm2++mT59+vDxxx/zxBNPsHLlSt59912GDh3KX//6V0455ZQq5ykvL690npGRQffu3Vm7dm3cr3HlypWMHDmSgwcPApHmn2uvvZZTTz2VsrIyNm3axGOPPcbOnTt57733GDx4MOvWraNjx45xP1dDvYYXX3yRESNGUFpaCsCXvvQlrrrqKjp06MDOnTv5/e9/z/Lly9m8eTNDhw5lxYoVdO/e/ajyNxRXvpEkSZIkSZIkSZIkqR648k1yuOmmmygoKGDixIlcdNFFVY555ZVXGDZsGPv27QPgscceY9y4cUeM+8Mf/sCoUaMA6Ny5M6+88gqdO3eOXq+oqOCb3/wms2bNAmD06NE888wzVT7nggULWLp0KQMGDGDAgAGcddZZfPjhh5x66qlAfKvGDBo0iOXLlwMwbtw4Zs6cSWpq5fVX9u/fz8iRI1m6dCkAeXl5TJ8+Pab5q1Nfr+HAgQP06NGDDz74AIC7776bH//4x0eMmz59OhMmTADgkksuYdmyZUeVv6FWvrH5RpIkSZIkSZIkSZKkemDzTXLYvXs3bdq0qXXcww8/zO233w7AhRdeyMsvv3zEmH79+vHmm28CsHDhQoYNG3bEmOLiYnr16hVtJFm3bh29e/eOKev7779fp8aVzMxMysvLSU1N5eOPPyYrK6vKsW+99RZ9+vQBoH///qxevTqmXPGoy2uYO3cuV199NQC5ubmsXLmSIAiqHDt8+HCef/55AJYuXcqgQYPqnLWhmm+qXjNJkiRJkiRJkiRJkiTpOBRL4w3AmDFjosfr1q074vqmTZuijTfdu3evsvEGIlsv3XTTTdHzefPmxZE2frt27Ypu/9S+fftqG28AevToET0uKipq0FzxOLRqD8A111xTbeMNwHXXXRc9fvLJJxs0V13ZfCNJkiRJkiRJkiRJkpqcVq1aRY+Li4uPuL5kyZLo8ZAhQ2qca+jQodHjxYsX10O66n3uc5+LbjH1j3/8g8LCwmrHbtq0KXqcTKsybd26NXrcs2fPGsce3kB0aAWcZGPzjSRJkiRJkiRJkiRJanLeeuut6HGXLl1qvD5gwIAa5zr77LNJSUkB4J133iEMw3pKeaT09PRoM1BpaSl33nknZWVlR4zbv38/EyZMACAIAvLy8hosU7zq+v7s2LGDjz/+uJ7THL3URAeQJEmSJEmSJEmSJEk61mbOnBk9Hj58+BHXN27cGD3u2rVrjXOlpqbSsWNHPvjgA4qKiti2bRudOnWqt6yfNW3aNF5//XV27tzJY489xssvv8y1115Lt27dKCsrY+PGjcyaNYuPPvqIFi1a8Mtf/pKBAwc2WJ54ZWdnR483btxYaeWgzzr89wFgw4YNtGvXrsGy1YUr30iSJEmSJEmSJEmSpCZlxYoVzJo1C4isJHNohZjD7dmzJ3ocS7NH27Ztq7y3IXTv3p2VK1cyePBggiDg3XffZfLkyVx33XXceOON/OQnP+Gjjz7i1ltv5a233mLcuHENmide559/fvT4iSeeqHElnMcff7zSeUO/t3Vh840kSZIkSZIkSZIkSWoyduzYwdixY6moqADghz/8YZWr1Ozbty96nJ6eXuu8GRkZ0eO9e/fWQ9KadenShQcffJDLLrus2jFz5szhgQceOCZ54jF69Ghat24NwOuvv86kSZOqHPfwww+zcOHCSrVPPvmkoePFzeYbSZIkSZIkSZIkSZKagML8fDZdMoj1Z5zJpksGUZifn+hIx1xRUREjR45k27ZtQGS7qe9+97sJThW/srIyvvOd79C3b18WLlzIXXfdxVtvvUVJSQl79+7llVde4fLLL6eoqIhHHnmEf/u3f2Pnzp2Jjh2VlZXFtGnTouc/+tGPOP/885kxYwbPPPMMv/jFL/jyl7/M7bffTkZGBjk5OdGxzZolX6tLaqIDSJIkSZIkSZIkSZKkhlWYn0/BfZMIS0oAKNu+nYL7IquNZI0Ykchox0xJSQmXXXYZK1euBCJbH82dO5cgCKocf8IJJ1S6tzbFxcXR41atWh1l2ppdf/31PPnkkwA899xzlVa/adGiBRdccAEXXHABd955J9OmTeNvf/sbt912G88880x03P79+/nTn/5U7XO0a9eOCy64oMFeww033MC+ffu48847KS0tZcWKFaxYsaLSmFatWvG73/2O+++/n4KCAgDatGnTYJnqyuYbSZIkSZIkSZIkSZIauZ3Tpkcbbw4JS0rYOW16k2i+OXjwIFdccQXLly8H4Atf+ALPP/88mZmZ1d5zaFskgI8//rjW59i1a1eV99a3lStXRhtvhg4dWuO2U//1X//F7Nmz2b17N8899xzbt2+nQ4cOAOzcuZPLL7+82nsvuugiXnrppXrN/lnf+c53GDZsGA8//DBLly7l/fffp7S0lE6dOjFs2DAmTJhA165d+c53vhO9Jzs7u0Ez1YXNN5IkSZIkSZIkSZIkNXJln64aEmu9MSktLWXMmDEsWrQIgH79+rF48WJOPPHEGu/r0aMHL774IgDvv/9+jWPLysqiW1llZmbSsWPHow9ejYULF0aPBw8eXOPYjIwMzjvvPBYuXEgYhqxatarGZp1E6NatGw8++GC11/ft28eHH34IQMuWLTnzzDOPVbSYJd9GWJIkSZIkSZIkSZIkqV6l5uTEVW8sysrK+OpXv8of//hHAPr06cMLL7wQ09ZFvXv3jh6vXr26xrFvvvkm5eXlAJx55pnVbmVVH7Zv3x49rq2BCCArKyt6XFRUFD3u2rUrYRhW+6uhV72J1Z///GfCMATg3HPPJTU1+daZsflGOg4V5uez6ZJBrD/jTDZdMojC/PxER5IkSccpv6+QJEmSJEmSmob2E8YTpKdXqgXp6bSfMD4xgY6B8vJyrrnmGp577jkg0hSzdOlS2rZtG9P9Q4YMiR4vWbKkxrGLFy+OHg8dOrQOaWN3eMPNoRVharJly5bocayvPZnMmjUrevyNb3wjgUmqZ/ONdJwpzM+n4L5JlG3fDmFI2fbtFNw3yX8okyRJcfP7CkmSJEmSJKnpyBoxgpwfTiG1QwcIAlI7dCDnh1PIGjEi0dEaREVFBTfeeCNz584FoGfPnixbtoz27dvHPEf37t3p168fAJs2bYpuW/VZJSUlPPLII9HzsWPHHkXy2h2+Is8zzzwTXXGnKu+//z5//etfAWjWrBn9+/dv0Gz17cUXX+TZZ58FIttTXXnllQlOVDWbb6TjzM5p0wlLSirVwpISdk6bnphAkiTpuOX3FZIkSZIkSVLTkjViBN2XL+OM9e/QffmyRtt4E4Yht9xyC3PmzAHg9NNPZ/ny5WRnZ8c91/333x89vvXWW/nggw8qXa+oqOC2226L1kePHl2pOaYhjBgxgszMTAD+7//+j9tvv73KBpx//vOfXH311ZSVlQGRFXnatWvXoNni8d577x3xfh7uhRde4PLLLwcgCAIeffRRmjdvfqzixSX5NsKSVKOygoK46pIkSdXx+wpJkiRJkiRJjdG9997Lb37zGwDS0tLIy8tj5cqVtd536aWX0rJly0q1kSNHctVVVzF37ly2bNlC//79ueWWW+jTpw+7du1izpw50blzcnJ48MEHa3yO73//+5XOCwsLo8fvvffeEdf79+/PFVdcUanWrl07/uu//ovx48cD8Mtf/pI///nPfO1rX+P000+ntLSUN998k9/+9rf84x//ACArK4uf/vSntb4HsaiP1wCwevVqrr76av7t3/6NgQMHcvrpp5OamsrWrVtZtGgRy5YtAyKNNw8//DADBw6sl/wNIQjDMKEBcnNzw1WrViU0g3Q82XTJoMjWEJ+R2qED3ZcvS0AiSZJ0vPL7CkmSJEmSJKl+rV+/njPOOCPRMZq8gQMH8vLLL8d933vvvUfXrl2PqB84cIAbbriBp59+utp7TzvtNObPn0/fvn1rfI4gCOLKdP311zN79uwqr/30pz/lP//zPzl48GCNc3Tt2pWnn36aL37xi3E9d3Xq6zU8++yzjBkzpsZ7Tz75ZB566KF628rraL5GgyBYHYZhblXX3HZKOs60nzCeID29Ui1IT6f9hPGJCSRJko5bfl8hSZIkSZIkSbVr0aIFTz31FIsWLWLMmDGccsoptGjRgnbt2nHuuefy4IMP8re//a3Wxpv69t3vfpeNGzcyadIkLrjgAk466STS0tJIT0+nU6dOjBgxgkceeYR33nmn3hpv6tPFF1/Mr371K6666irOOOMMPve5z9G8eXM6duzIoEGDeOihh9iwYUO9Nd40JFe+kY5Dhfn57Jw2nbKCAlJzcmg/YXyj3Y9RkiQ1LL+vkCRJkiRJkuqPK99Iya2hVr5JPapUkhIia8QI/1FMkiTVC7+vkCRJkiRJkiTp6LjtlCRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IkiRJkiRJkiRJkiRJdWTzjSRJkiRJkiRJkiRJklRHNt9IanzWzoNpvWFy68jj2nmJTiRJkiRJkiRJkiRJaqRSEx1AkurV2nmQfweUFkfOCz+MnAP0HZu4XJIkSZIkSZIkSZKkRsmVbyQ1Lsum/Kvx5pDS4khdkiRJkiRJkiRJkqR6ZvONpMalcGt8dUmSJEmSJEmSJEmSjoLNN5Ial6xO8dUlSZIkSZIkSZIkSToKNt9IalwGTYK0jMq1tIxIXZIkSZIkSZIkSWpgYRgmOoKkKjTk16bNN5Ial75jYcRDkHUKEEQeRzwUqUuSJEmSJEmSJEkNqFmzZlRUVCQ6hqQqVFRU0KxZw7TJpDbIrJKUSH3H2mwjSZIkSZIkSZKkYy4jI4OioiJOPPHEREeR9BlFRUVkZGTUPrAOXPlGkiRJkiRJkiRJkqR60KpVK/bu3ZvoGJKqsHfvXlq1atUgc9t8I0mSJEmSJEmSJElSPTjxxBPZv38/u3fvTnQUSYfZvXs3+/fvb7BVqdx2SpIkSZIkSZIkSZKkepCSkkKXLl3YsmUL+/fvp1WrVmRmZtKsWTOCIEh0PKnJCMOQiooKioqK2Lt3L/v376dLly6kpKQ0yPPZfCNJkiRJkiRJkiRJUj1p3rw53bp145NPPmHPnj0UFBRQUVGR6FhSk9OsWTMyMjJo1aoV2dnZDdZ4AzbfSJIkSZIkSZIkSZJUr1JSUmjTpg1t2rRJdBRJx0DczTdBEOQC5wAdgZbAfmAbsCoMw9frN56URNbOg2VToHArZHWCQZOg79iERFm4eSEz1sxgR9EOsjOzyeufx/BuwxOSRWosCvPz2TltOmUFBaTm5NB+wniyRoxIdCxJkiRJ+HOwJEmSJElKbjE33wRBcAnwc6AHUNVmdGEQBBuB28MwXFpP+aTksHYe5N8BpcWR88IPI+dwzBtwFm5eyOQVkykpLwGgoKiAySsmA/jBo1RHhfn5FNw3ibAk8nVVtn07BfdNArABR5IkSUowfw6WJEmSJEnJrlksg4IguBhYDGQA9wOXAmcBp336eCkwmchKOM9/Ol5qPJZN+VfjzSGlxZH6MTZjzYzoB46HlJSXMGPNjGOeRWosdk6bHm28OSQsKWHntOmJCSRJkiQpyp+DJUmSJElSsot15ZsfAOuAC8MwLKri+npgaRAE04BXiDTivFjdZEEQ3AzcDNC5c+d48kqJUbg1vnoD2lG0I666pNqVFRTEVZckSZJ07PhzsCRJkiRJSnYxrXwD9AceqabxJioMw33ATGBALeNmhmGYG4Zh7kknnRRjBCmBsjrFV29A2ZnZcdUl1S41JyeuuiRJkqRjx5+DJUmSJElSsou1+eYgcGKMY0/8dLzUeAyaBGkZlWtpGZH6MZbXP4/0lPRKtfSUdPL65x3zLFJj0X7CeIL0yl9XQXo67SeMT0wgSZIkSVH+HCxJkiRJkpJdrNtO/RmYEATBojAM11U3KAiCPsAE4OX6CCcljb5jI4/LpkS2msrqFGm8OVQ/hoZ3Gw5E9rzfUbSD7Mxs8vrnReuS4pc1YgQAO6dNp6yggNScHNpPGB+tS5IkSUocfw6WJEmSJEnJLgjDsPZBQdAD+F8iq9q8CLwGbAMOAC2AjsC5wECgEDg/DMMNsQTIzc0NV61aVZfskiRJkiRJkiRJkiRJUoMLgmB1GIa5VV2LaeWbMAw3BkFwDvATYAQwuIphB4DfA/eEYfhuXcNKkiRJkiRJkiRJkiRJx4tYt50iDMPNwNggCFoCfYEOQEtgP7AdWBeGYVGDpJQkSZIkSZIkSZIkSZKSUMzNN4eEYbifyLZTkiRJkiRJkiRJkiRJUpPWLNaBQRC0CILg20EQ/CIIgklBEHSvZtzgIAiW119ESZIkSZIkSZIkSZIkKTnFtPLNp1tN/YXIdlPBp+V7gyC4LwzDBz4z/GTgovqLKEmSJEmSJEmSJEmSJCWnWFe+GQ98HvhvIg04w4HVwI+DIPhFw0STJEmSJEmSJEmSJEmSkluszTdjgLlhGN4XhuFbYRguAi4AfgF8KwiCRxosoSSp3hT+/F425Z7B+l692JR7BoU/vzfRkSRJko7aws0LufTZS+n7275c+uylLNy8MNGRpOPf2nkwrTdMbh15XDsv0YkkSZIkSZKSVkzbTgGnAb88vBCGYQVwexAEe4hsQdUsDMNv1HM+SVI9Kfz5vRT84jnC8gAIKNsHBb94DoCs2/4rseEkSZLqaOHmhUxeMZmS8hIACooKmLxiMgDDuw1PYDLpOLZ2HuTfAaXFkfPCDyPnAH3HJi6XJEmSJElSkop15ZsSIK2qC2EY3gdMAcYFQTArjjklScfQzlnzP228+ZewPGDnrPkJSiRJknT0ZqyZEW28OaSkvIQZa2YkKJHUCCyb8q/Gm0NKiyN1SZIkSZIkHSHWlW/eBb4E/Lyqi2EYTg6CAGASMLBekkmS6lXZvhAIqqlLkiQdn3YU7YirLikGhVvjq0uSJEmSJDVxsa5S8wJwWRAEraobEIbhZGAy0OXoY0mS6lvqCUc23tRUlyRJOh5kZ2bHVZcUg6xO8dUlSZIkSZKauFibbx4HfgF0r2lQGIZTgNuBOUeZS5JUz9qPu4IgpfIqN0FKSPtxVyQokSRJ0tHL659Hekp6pVp6Sjp5/fMSlEhqBAZNgrSMyrW0jEhdkiRJkiRJR4hp26kwDDcB98Q4tsqtqSRJiZV1238BsHPWfMr2haSeENB+3JXRuiRJ0vFoeLfhAMxYM4MdRTvIzswmr39etC6pDvqOjTwumxLZaiqrU6Tx5lBdkiRJkiRJlQRhGNY+qgHl5uaGq1atSmgGSZIkSZIkSZIkSZIkqTpBEKwOwzC3qmuxbjslSZIkSZIkSZIkSZIk6TNsvpEkSZIkSZIkSZIkSZLqyOYbSZIkSZIkSZIkSZIkqY5svpEkSZIkSZIkSZIkSZLqyOYbSZIkSZIkSZIkSZIkqY5SEx1AyWXBG9uYumQD2/cU06F1BhOH9GRUv46JjiVJkmKwcPNCZqyZwY6iHWRnZpPXP4/h3YYnOpYkNby182DZFCjcClmdYNAk6Ds20akkSZIkSZIkNRE23yhqwRvbuGf+OopLywHYtqeYe+avA7ABR5KkJLdw80Imr5hMSXkJAAVFBUxeMRnABhxJjdvaeZB/B5QWR84LP4ycgw04kiRJkiRJko4Jt51S1NQlG6KNN4cUl5YzdcmGBCWSJEmxmrFmRrTx5pCS8hJmrJmRoESSdIwsm/KvxptDSosjdUmSJEmSJEk6Bmy+UdT2PcVx1SVJUvLYUbQjrrokNRqFW+OrS5IkSZIkSVI9s/lGUR1aZ8RVlyRJySM7MzuuuiQ1Glmd4qtLkiRJkiRJUj2z+UZRE4f0JCMtpVItIy2FiUN6JiiRJEmKVV7/PNJT0ivV0lPSyeufl6BEknSMDJoEaZ/5HwbSMiJ1SZIkSZIkSToGUhMdQMljVL+OAExdsoHte4rp0DqDiUN6RuuSJCl5De82HIAZa2awo2gH2ZnZ5PXPi9YlqdHqOzbyuGxKZKuprE6RxptDdUmSJEmSJElqYEEYhgkNkJubG65atSqhGSRJkiRJkiRJkiRJkqTqBEGwOgzD3Kquue2UJEmSJEmSJEmSJEmSVEc230iSJEmSJEmSJEmSJEl1ZPONJEmSJEmSJEmSJEmSVEc230iSJEmSJEmSJEmSJEl1ZPONJEmSJEmSJEmSJEmSVEc230iSJNXV2nkwrTdMbh15XDsv0YkkSZIkSZIkSZJ0jKUmOoAkSdJxae08yL8DSosj54UfRs4B+o5NXC5JkiRJkiRJkiQdU658I0mSVBfLpvyr8eaQ0uJIXZIkSZIkSZIkSU2GzTeSJEl1Ubg1vrokSZIkSZIkSZIaJZtvJEmS6iKrU3x1SZIkSZIkSZIkNUo230iSJNXFoEmQllG5lpYRqUuSJEmSJEmSJKnJsPlGkiSpLvqOhREPQdYpQBB5HPFQpC5JkiRJkiRJkqQmIzXRASRJko5bfcfabCNJkiRJkiRJktTEufKNJEmSJEmSJEmSJEmSVEc230iSJEmSJEmSJEmSJEl1ZPONJEmSJEmSJEmSJEmSVEc230iSJEmSJEmSJEmSJEl1ZPONJEmSJEmSJEmSJEmSVEc230iSJDUWa+fBtN4wuXXkce28RCeSjnsvPTqFv3yxN2/3OoO/fLE3Lz06JdGRVIXC/Hw2XTKI9WecyaZLBlGYn5/oSJIkSZIkSZKakNREB5AkSVI9WDsP8u+A0uLIeeGHkXOAvmMTl0s6jr306BRaT3+KFqWR888VlnNg+lO8BAz8xqRERtNhCvPzKbhvEmFJCQBl27dTcF/k9ydrxIhERpMkSZIkSZLURLjyjSRJUmOwbMq/Gm8OKS2O1CXVSdrMedHGm0NalEbqSh47p02PNt4cEpaUsHPa9MQEkiRJkiRJktTk2HwjSZLUGBRuja8uqVatC8vjqisxygoK4qpLkiRJkiRJUn2z+UaSJKkxyOoUX11SrfZkpcRVV2Kk5uTEVZckSZIkSZKk+mbzjSRJUmMwaBKkZVSupWVE6pLqpPTmsRxIq1w7kBapK3m0nzCeID29Ui1IT6f9hPGJCSRJkiRJkiSpyUlNdABJkiTVg76fNgMsmxLZaiqrU6Txpq9NAlJdDfzGJF4C0mbOo3VhOXuyUii9eSwDv2FTWzLJGjECgJ3TplNWUEBqTg7tJ4yP1iVJkiRJkiSpoQVhGCY0QG5ubrhq1aqEZpAkSZIkSZIkSZIkSZKqEwTB6jAMc6u65rZTkiRJkiRJkiRJkiRJUh3ZfCNJkiRJkiRJkiRJkiTVkc03kiRJkiRJkiRJkiRJUh3ZfCNJkiRJkiRJkiRJkiTVkc03kiRJkiRJkiRJkiRJUh2lHu0EQRCcDrQDPgrD8L2jj6REKszPZ+e06ZQVFJCak0P7CePJGjEiIVkWvLGNqUs2sH1PMR1aZzBxSE9G9euYkCzJmEfVS6Y/x5IkSZIkSZIkSZKkxi2m5psgCK4AVoZhuPWw2iXAL4HTD6u9A9wchuH/1ndQNbzC/HwK7ptEWFICQNn27RTcNwngmDcuLHhjG/fMX0dxaTkA2/YUc8/8dQAJaXhJtjyqXjL9OZYkSZIkSZIkSZIkNX6xbjv1DHDhoZMgCL4ALAI6Ar8FfgA8BXQH/hQEQY96zqljYOe06dGGhUPCkhJ2Tpt+zLNMXbIh2uhySHFpOVOXbDjmWZIxj6qXTH+OJUmSJEmSJEmSJEmNX6zbTgWfOf8RsA/4YhiGf48OCoKfAiuAu4Ebq50sCG4Gbgbo3LlzPHnVgMoKCuKqN6Tte4rjqje0ZMuj6iXTn2NJkiRJkiRJkiRJUuMX68o3UUEQBERWwZl+eOMNQBiGbwCzgEtqmiMMw5lhGOaGYZh70kknxRtBDSQ1JyeuekPq0DojrnpDS7Y8ql4y/TmWJEmSJEmSJEmSJDV+cTffACcAzYG/VXP9TcB/5T4OtZ8wniA9vVItSE+n/YTxxzzLxCE9yUhLqVTLSEth4pCexzxLMuZR9ZLpz7EkSZIkSZIkSZIkqfGLddspgBAgDMO9QRDsBTKrGZcJuBfPcShrxAgAdk6bTllBAak5ObSfMD5aP5ZG9esIwNQlG9i+p5gOrTOYOKRntN7U86h6yfTnWJIkSZIkSZIkSZLU+AVhGNY+KAgq+LT55jDTwjD8XhVjfwEMDMPwzFgC5ObmhqtWrYplqCRJkiRJkiRJkiRJknTMBUGwOgzD3KquxbryzW+rqB2s4olaAKOAF2JOJ0mSJEmSJEmSJEmSJB2nYmq+CcNwXIzzpQD/DuyocyJJkiRJkiRJkiRJkiTpOBHryjcxCcNwP/C3+pxTkiRJkiRJkiRJkiRJSlbNEh1AkiRJkiRJkiRJkiRJOl7Ve/NNEATXBEGwvL7nlSRJkiRJkiRJkiRJkpJNvW479akuwEUNMK+kTy14YxtTl2xg+55iOrTOYOKQnozq19E8kiRJjdnaebBsChRuhaxOMGgS9B2b6FSSGqnC/Hx2TptOWUEBqTk5tJ8wnqwRIxIdS5IkxWDh5oXMWDODHUU7yM7MJq9/HsO7DU90LEmSpEatIZpvJDWgBW9s45756yguLQdg255i7pm/DiAhDS/JlkeSJKlRWjsP8u+A0uLIeeGHkXOwAUdSvSvMz6fgvkmEJSUAlG3fTsF9kwBswJEkKckt3LyQySsmU1Ie+e94QVEBk1dMBrABR5IkqQHFtO1UEASbY/0F3NnAmaUmbeqSDdFGl0OKS8uZumSDeSRJkhqrZVP+1XhzSGlxpC5J9WzntOnRxptDwpISdk6bnphAkiQpZjPWzIg23hxSUl7CjDUzEpRIkiSpaYh15ZuuwG5gewxjW9Y5jaRabd9THFe9oSVbHkmSpEapcGt8dUk6CmUFBXHVJUlS8thRtCOuuiRJkupHTCvfAO8Bq8Iw7FPbL+DhBswrNXkdWmfEVW9oyZZHkiSpUcrqFF9dko5Cak5OXHVJkpQ8sjOz46pLkiSpfsTafLMa6B/j2LCOWSTFYOKQnmSkpVSqZaSlMHFIT/NIkiQ1VoMmQdpnmpvTMiJ1Sapn7SeMJ0hPr1QL0tNpP2F8YgJJkqSY5fXPIz2l8n/H01PSyeufl6BEkiRJTUOs2069AYwOgqBrGIbv1zJ2C/Dno0olqVqj+nUEYOqSDWzfU0yH1hlMHNIzWm/qeSRJkhqlvmMjj8umRLaayuoUabw5VJekepQ1YgQAO6dNp6yggNScHNpPGB+tS5Kk5DW823AAZqyZwY6iHWRnZpPXPy9alyRJUsMIwjCxC9Xk5uaGq1atSmgGSZIkSZIkSZIkSZIkqTpBEKwOwzC3qmuxbjslSZIkSZIkSZIkSZIk6TNsvpEkSZIkSZIkSZIkSZLqyOYbSZIkSZIkSZIkSZIkqY5svpEkSZIkSZIkSZIkSZLqyOYbSZIkSZIkSZIkSZIkqY5SEx1Aqs7CzQuZsWYGO4p2kJ2ZTV7/PIZ3G24eoDA/n53TplNWUEBqTg7tJ4wna8SIhGRJRgve2MbUJRvYvqeYDq0zmDikJ6P6dWzyWSTVv5cenULazHm0LixnT1YKpTePZeA3JiU6liRJkhpQMn0+IEmSJEmSkoPNN0pKCzcvZPKKyZSUlwBQUFTA5BWTARLygVYy5SnMz6fgvkmEJZEsZdu3U3Bf5B96bcCJNLvcM38dxaXlAGzbU8w989cBHPOml2TKIqn+vfToFFpPf4oWpZHzzxWWc2D6U7wENuBIkiQ1Usn0+YAkSZIkSUoebjulpDRjzYzoB1mHlJSXMGPNjCafZ+e06dHGm0PCkhJ2Tpt+zLMko6lLNkSbXQ4pLi1n6pINTTqLpPqXNnNetPHmkBalkbokSZIap2T6fECSJEmSJCUPm2+UlHYU7Yir3tCSKU9ZQUFc9aZm+57iuOoNKZmySKp/rQvL46pLkiTp+JdMnw9IkiRJkqTkYfONklJ2ZnZc9YaWTHlSc3Liqjc1HVpnxFVvSMmURVL925OVElddkiRJx79k+nxAkiRJkiQlD5tvlJTy+ueRnpJeqZaekk5e/7wmn6f9hPEE6ZWzBOnptJ8w/phnSUYTh/QkI63yP3xnpKUwcUjPJp1FUv0rvXksB9Iq1w6kReqSJElqnJLp8wFJkiRJkpQ8UhMdQKrK8G7Dgche6juKdpCdmU1e/7xovSnnyRoxAoCd06ZTVlBAak4O7SeMj9abulH9OgIwdckGtu8ppkPrDCYO6RmtN9UskurfwG9M4iUgbeY8WheWsycrhdKbxzLwG5MSHU2SJEkNJJk+H5AkSZIkSckjCMMwoQFyc3PDVatWJTSDJEmSJEmSJEmSJEmSVJ0gCFaHYZhb1TW3nZIkSZIkSZIkSZIkSZLqyOYbSZIkSZIkSZIkSZIkqY5svpEkSZIkSZIkSZIkSZLqyOYbSZIkSZIkSZIkSZIkqY5svpEkSZIkSZIkSZIkSZLqKDXRASRJx86CN7YxdckGtu8ppkPrDCYO6cmofh0THUuSJEmSarZ2HiybAoVbIasTDJoEfccmOpUkSZKUEIX5+eycNp2yggJSc3JoP2E8WSNGJDqWJDVpNt9IUhOx4I1t3DN/HcWl5QBs21PMPfPXAdiAI0mSJCl5rZ0H+XdAaXHkvPDDyDnYgCNJkqQmpzA/n4L7JhGWlABQtn07BfdNArABR5ISyG2nJKmJmLpkQ7Tx5pDi0nKmLtmQoESSJEmSFINlU/7VeHNIaXGkLkmSJDUxO6dNjzbeHBKWlLBz2vTEBJIkATbfSFKTsX1PcVx1SZIkSUoKhVvjq0uSJEmNWFlBQVx1SdKxYfONJDURHVpnxFWXJEmSpKSQ1Sm+uiRJktSIpebkxFWXJB0bNt9IUhMxcUhPMtJSKtUy0lKYOKRnghJJkiRJUgwGTYK0z/xPA2kZkbokSZLUxLSfMJ4gPb1SLUhPp/2E8YkJJEkCIDXRASRJx8aofh0BmLpkA9v3FNOhdQYTh/SM1iVJkiQpKfUdG3lcNiWy1VRWp0jjzaG6JEmS1IRkjRgBwM5p0ykrKCA1J4f2E8ZH65KkxAjCMExogNzc3HDVqlUJzSBJkiRJkiRJkiRJkiRVJwiC1WEY5lZ1zW2nJEmSJEmSJEmSJEmSpDqy+UaSJEmSJEmSJEmSJEmqI5tvJEmSJEmSJEmSJEmSpDqy+UaSJEmSJEmSJEmSJEmqI5tvJEmSJEmSJEmSJEmSpDpKTXQASapvCzcvZMaaGewo2kF2ZjZ5/fMY3m14k8+SbBa8sY2pSzawfU8xHVpnMHFIT0b162geSZIkSclt7TxYNgUKt0JWJxg0CfqOTXQqSZKSlp+RSo1bYX4+O6dNp6yggNScHNpPGE/WiBGJjiVJx5zNN5IalYWbFzJ5xWRKyksAKCgqYPKKyQDH/Ae6ZMqSbBa8sY175q+juLQcgG17irln/jqAhDS8JFseSZIkSUlq7TzIvwNKiyPnhR9GzsEGHEmSquBnpFLjVpifT8F9kwhLIl/jZdu3U3DfJAAbcCQ1OW47JalRmbFmRvQHuUNKykuYsWZGk86SbKYu2RBtdDmkuLScqUs2mEeSJElS8lo25V+NN4eUFkfqkiTpCH5GKjVuO6dNjzbeHBKWlLBz2vTEBJKkBLL5RlKjsqNoR1z1hpRMWZLN9j3FcdUbWrLlkSRJkpSkCrfGV5ckqYnzM1KpcSsrKIirLkmNmc03khqV7MzsuOoNKZmyJJsOrTPiqje0ZMsjSZIkKUlldYqvLklSE+dnpFLjlpqTE1ddkhozm28kNSp5/fNIT0mvVEtPSSevf16TzpJsJg7pSUZaSqVaRloKE4f0NI8kSZKk5DVoEqR9pkk/LSNSlyRJR/AzUqlxaz9hPEF65a/xID2d9hPGJyaQJCVQaqIDSFJ9Gt5tOBDZS3hH0Q6yM7PJ658XrTfVLMlmVL+OAExdsoHte4rp0DqDiUN6RutNPY8kSZKkJNV3bORx2ZTIVlNZnSKNN4fqkiSpEj8jlRq3rBEjANg5bTplBQWk5uTQfsL4aF2SmpIgDMOEBsjNzQ1XrVqV0AySJEmSJEmSJEmSJElSdYIgWB2GYW5V19x2SpIkSZIkSZIkSZIkSaojm28kSZIkSZIkSZIkSZKkOrL5RpIkSZIkSZIkSZIkSaojm28kSZIkSZIkSZIkSZKkOrL5RpIkSZIkSZIkSZIkSaqj1HhvCIKgPTAA6Ai0BPYD24DVYRjurN94kiQdGws3L2TGmhnsKNpBdmY2ef3zGN5teMLyLHhjG1OXbGD7nmI6tM5g4pCejOrXsclnScY80vEu2f7+kyRJcVo7D5ZNgcKtkNUJBk2CvmMTnUqSJEmSpCYl5uabIAjOAn4KDAaCT38dEgJhEATLgO+GYfhWvaaUJKkBLdy8kMkrJlNSXgJAQVEBk1dMBkjIP0AveGMb98xfR3FpOQDb9hRzz/x1AMe8ySSZsiRjHul4l2x//0mSpDitnQf5d0BpceS88MPIOdiAI0mSJEnSMRTTtlNBEHwe+F/gXGA2cAswHBj06eMtwG+BLwErPh0vSdJxYcaaGdF/eD6kpLyEGWtmJCTP1CUbos0lhxSXljN1yYYmnSUZ80jHu2T7+0+SJMVp2ZR/Nd4cUlocqUuSJEmSpGMm1pVvfgJ8BFwUhuH2asb8JgiC+4A/Az8GhlU3WRAENwM3A3Tu3Dn2tJIkNYAdRTviqje07XuK46o3pGTKUtPzJiqPdLxLtr//JElSnAq3xleXJEmSJEkNIqaVb4DzgIdraLwB4NPrDwPn1zJuZhiGuWEY5p500kkxRpAkqWFkZ2bHVW9oHVpnxFVvSMmUpabnTVQe6XiXbH//SZKkOGV1iq8uSZIkSZIaRKzNNwEQxjg2/HS8JEnHhbz+eaSnpFeqpaekk9c/LyF5Jg7pSUZaSqVaRloKE4f0bNJZkjGPdLxLtr//JElSnAZNgrTPNKKnZUTqkiRJkiTpmIl126mVwO1BEMwLw7DaNeiDIMgGbgf+Wh/hJEk6FoZ3Gw7AjDUz2FG0g+zMbPL650Xrx9qofh0BmLpkA9v3FNOhdQYTh/SM1ptqlmTMIx3vku3vP0mSFKe+YyOPy6ZEtprK6hRpvDlUlyRJkiRJx0QQhrUvaBMEwReBl4ADwDzgNWDbp+ctgI7AucCYT88HhmEYUwNObm5uuGrVqrpklyRJkiRJkiRJkiRJkhpcEASrwzDMrepaTCvfhGH41yAILgYeAr756a/Du3YObTO1CsiLtfFGkiRJkiRJkiRJkiRJOp7Fuu0UYRi+BnwhCILTgHOADkBLYD+wHXg9DMN3GySlJEmSJEmSJEmSJEmSlIRibr455NMGG5tsJEmSJEmSJEmSJEmS1OQ1S3QASZIkSZIkSZIkSZIk6XhV7803QRBcEwTB8vqeV5IkSZIkSZIkSZIkSUo2DbHyTRfgogaYV01MYX4+my4ZxPozzmTTJYMozM9PdKTksXYeTOsNk1tHHtfOS3QiSWqSFryxjfN/spxT717I+T9ZzoI3tiU6kqT65vddkiRJko4zfrYuSZJ07KUmOoBUlcL8fArum0RYUgJA2fbtFNw3CYCsESMSGS3x1s6D/DugtDhyXvhh5Byg79jE5ZKkJmbBG9u4Z/46ikvLAdi2p5h75q8DYFS/jomMJqm++H2XJEmSpOOMn61LkiQlRhCGYe2DgmBzHHNmAa3DMEyJZXBubm64atWqOKZXU7DpkkGUbd9+RD21Qwe6L1+WgERJZFrvyD/8fFbWKTDhrWOfR5KaqPN/spxte4qPqHdsncFf7r4kAYkk1Tu/75IkSZJ0nPGzdUmSpIYTBMHqMAxzq7oW68o3XYHdwJHfsR2pZYxzStUqKyiIq96kFG6Nry5JahDbq2i8qan+/7N35/FWV9X/x19LIMEJMjVQf6U2OGappGVWKg4lUQ6pfdMcyzIHQMMhFRG1VFSgzHLKqdLQnBAT59Rswgk1cyItBcIsUAkUcP3+WPvce+7hnHMH72eA+34+Hjzwfs65fJb3nnM+n7332muJyDJI910iIiIiIrKM0dy6iIiISDFW6ODz/g5Mc/ePtfcHuCDDeKWH6D1oUKeO9yj91+3ccRERycTaA/p16riILIN03yUiIiIiIssYza2LiIiIFKOjyTcPA1t28Lnt97ESacdaI0dgffu2OWZ9+7LWyBHFBFQmQ0ZDn5qF3T794riIiORm1K4b0q9P2y6b/fr0YtSuGxYUkYh0O913iYiIiIjIMkZz6yIiIiLF6GjbqUeBr5rZeu7+YjvPfQm4/11FJT1e/2HDAJgzfgKLZ82i96BBrDVyRMvxHm3zfeLvu8dGy4P+68YCUOW4iIjkYvct1gFg3NRnmDl3AWsP6MeoXTdsOS4iywHdd4mIiIiIyDJGc+siIiIixTD3YgvVDB482KdNm1ZoDCIiIiIiIiIiIiIiIiIiIiIijZjZw+4+uN5jHW07JSIiIiIiIiIiIiIiIiIiIiIiNZR8IyIiIiIiIiIiIiIiIiIiIiLSRUq+ERERERERERERERERERERERHpIiXfiIiIiIiIiIiIiIiIiIiIiIh0kZJvRERERERERERERERERERERES6SMk3IiJSjOmTYPxmMGZA/D19UtERSQPzJk/muR2H8PTGm/DcjkOYN3ly0SGJdJ4+c0RERESyp3suERGpQ3NLIiIi0hP0LjoAERHpgaZPgslHw6IF8fW8f8bXAJvvU1xcspR5kycz65TR+MKFACyeOZNZp4wGoP+wYUWGJtJx+swRERERyZ7uuUREpA7NLYmIiEhPoco3IiKSv7vHtk7IVixaEMelVOaMn9AyOVLhCxcyZ/yEYgIS6Qp95oiIiIhkT/dcIiJSh+aWREREpKdQ8o2IiORv3sudOy6FWTxrVqeOi5SSPnNEREREsqd7LhERqUNzSyIiItJTKPlGRETy13/dzh2XwvQeNKhTx0VKSZ85IiIiItnTPZeIiNShuSURERHpKZR8IyIi+RsyGvr0a3usT784LqWy1sgRWN++bY5Z376sNXJEMQGJdIU+c0RERESyp3suERGpQ3NLIiIi0lP0LjoAERHpgTbfJ/6+e2yUIO+/bkzIVo5LafQfNgyI/tyLZ82i96BBrDVyRMtxkWWCPnNEREREsqd7LhERqUNzSyIiItJTmLsXGsDgwYN92rRphcYgIiIiIiIiIiIiIiIiIiIiItKImT3s7oPrPaa2UyIiIiIiIiIiIiIiIiIiIiIiXaTkGxERERERERERERERERERERGRLlLyjYiIiIiIiIiIiIiIiIiIiIhIFyn5RkRERERERERERERERERERESki5R8IyIiIiIiIiIiIiIiIiIiIiLSRUq+EZF37b7LxvL7bTbjqY025vfbbMZ9l40tOiQR6UZTZkxhl+t3YfMrN2eX63dhyowpRYckDdz06Ct85qx7WP+EKXzmrHu46dFXig5JGijV+2r6JBi/GYwZEH9Pn1RcLCIiOZs3eTLP7TiEpzfehOd2HMK8yZOLDklEREQ6qFTjKhERKQ2N80SkKL2LDkBElm33XTaWAROuYcVF8fXq85bw1oRruA/Y/tDRRYYmIt1gyowpjHloDAuXLARg1vxZjHloDABDNxhaYGRS66ZHX+HEG55gwaIlALwydwEn3vAEALtvsU6RoUmNUr2vpk+CyUfDogXx9bx/xtcAm++TbywiIjmbN3kys04ZjS+Mz+PFM2cy65QYw/QfNqzI0ERERKQdpRpXiYhIaWicJyJFUuUbEXlX+lw8qSXxpmLFRXFcRJZ9Ex+Z2DKRVbFwyUImPjKxoIikkXFTn2lJvKlYsGgJ46Y+U1BE0kip3ld3j21NvKlYtCCOi4gs5+aMn9AyIVvhCxcyZ/yEYgISERGRDivVuEpEREpD4zwRKZKSb0TkXRkwb0mnjovIsmX2/NmdOi7FmTl3QaeOS3FK9b6a93LnjouILEcWz5rVqeMiIiJSHqUaV4mISGlonCciRVLyjYi8K3P79+rUcRFZtgxceWCnjktx1h7Qr1PHpTilel/1X7dzx0VEliO9Bw3q1HEREREpj1KNq0REpDQ0zhORIin5RkTelUWH7cNbfdoee6tPHBeRZd/wLYfTt1ffNsf69urL8C2HFxSRNDJq1w3p16dt4mO/Pr0YteuGBUUkjZTqfTVkNPSpSdDq0y+Oi4gs59YaOQLr2/bz2Pr2Za2RI4oJSERERDqsVOMqEREpDY3zRKRIvYsOQESWbdsfOpr7gD4XT2LAvCXM7d+LRYftw/aHatFOZHkwdIOhQPRSnz1/NgNXHsjwLYe3HJfy2H2LdQAYN/UZZs5dwNoD+jFq1w1bjkt5lOp9tXlKlr17bLSa6r9uJN5sriRaEVn+9R82DIA54yeweNYseg8axFojR7QcFxERkfIq1bhKRERKQ+M8ESmSuXuhAQwePNinTZtWaAwiIiIiIiIiIiIiIiIiIiIiIo2Y2cPuPrjeY2o7JSIiIiIiIiIiIiIiIiIiIiLSRUq+ERERERERERERERERERERERHpIiXfiIiIiIiIiIiIiIiIiIiIiIh0kZJvRERERERERERERERERERERES6SMk3IiIiIiIiIiIiIiIiIiIiIiJdpOQbEREREREREREREREREREREZEu6l10ACIi0jPNmzyZOeMnsHjWLHoPGsRaI0fQf9iwosOSeqZPgrvHwryXof+6MGQ0bL5P0VHJMuCmR19h3NRnmDl3AWsP6MeoXTdk9y3WKSSWn591PB++4Vbe9/o7vLbaCjy/55c45ISzC4kFyvWzKVMsAFNmTGHiIxOZPX82A1ceyPAthzN0g6GFxSMiIiIiIiIiIiIi0h4l34iISO7mTZ7MrFNG4wsXArB45kxmnTIaQAk4ZTN9Ekw+GhYtiK/n/TO+BiXgSFM3PfoKJ97wBAsWLQHglbkLOPGGJwByT+z4+VnHs8UvbqHv4vh6zdffYdVf3MLPoZAEnDL9bMoUC0TizZiHxrBwSVwfZs2fxZiHxgAoAUdERERERERERERESkttp0REJHdzxk9oSbyp8IULmTN+QjEBSWN3j21NvKlYtCCOizQxbuozLQkdFQsWLWHc1Gdyj+XDN9zaknhT0XdxHC9CmX42ZYoFYOIjE1sSbyoWLlnIxEcmFhKPiIiIiIiIiIiIiEhHKPlGRERyt3jWrE4dlwLNe7lzx0WSmXMXdOp4lt73+judOp61Mv1syhQLwOz5szt1XERERERERERERESkDJR8IyIiues9aFCnjkuB+q/bueMiydoD+nXqeJZeW63+LW+j41kr08+mTLEADFx5YKeOi4iIiIiIiIiIiIiUgZJvREQkd2uNHIH17dvmmPXty1ojRxQTkDQ2ZDT0qVmE79Mvjos0MWrXDenXp1ebY/369GLUrhvmHsvze36Jhb3bHlvYO44XoUw/mzLFAjB8y+H07dX2+tC3V1+Gbzm8kHhERERERERERERERDqid/tPERER6V79hw0DYM74CSyeNYvegwax1sgRLcelRDbfJ/6+e2y0muq/biTeVI6LNLD7FusAMG7qM8ycu4C1B/Rj1K4bthzP0yEnnM3PgQ/fcCvve/0dXlttBZ7f80sccsLZuccC5frZlCkWgKEbDAVg4iMTmT1/NgNXHsjwLYe3HBcRERERERERERERKSNz90IDGDx4sE+bNq3QGEREREREREREREREREREREREGjGzh919cL3H1HZKRERERERERERERERERERERKSLlHwjIiIiIiIiIiIiIiIiIiIiItJFSr4REREREREREREREREREREREekiJd+IiIiIiIiIiIiIiIiIiIiIiHRRl5NvzGwlMzvRzB40s7+Z2e/M7FgzW7E7AxQRERERERERERERERERERERKaveHXmSmb0OHOru16WvVwPuBzYH3gZmAp8GtgP2NLMd3P3tbEIWEREREZHl1U2PvsK4qc8wc+4C1h7Qj1G7bsjuW6yjeEoWC8CUGVOY+MhEZs+fzcCVBzJ8y+EM3WBoYfGIiIgsZfokuHsszHsZ+q8LQ0bD5vsUHZWIiIiIiIgshzpa+WYVoE/V12cQiTcnAau4+wbAqsD5RBLOyO4MUkREREREln83PfoKJ97wBK/MXYADr8xdwIk3PMFNj77S4+MpUywQiTdjHhrDrPmzcJxZ82cx5qExTJkxpZB4REREljJ9Ekw+Gub9E/D4e/LRcVxERERERESkm3W17dS+wLXu/kN3Xwzg7m+5+yjg3vS4iIiIiIhIh42b+gwLFi1pc2zBoiWMm/pMj4+nTLEATHxkIguXLGxzbOGShUx8ZGIh8YiIiCzl7rGwaEHbY4sWxHERERERERGRbtbp5BszWxlYE7itwVNuAz7Szr9xmJlNM7Npr776amdDEBERERGR5dDMuQs6dTxrZYqnTLEAzJ4/u1PHRUREcjfv5c4dFxEREREREXkXulL55m1gCfB6g8ffBHo1+wfc/WJ3H+zug9dcc80uhCAiIiIiIsubtQf069TxrJUpnjLFAjBw5YGdOi4iIpK7/ut27riIiIiIiIjIu9CZ5JvDzOznwEXAAuBDDZ73AeC1dxuYiIiIiIj0LKN23ZB+fdrm8ffr04tRu27Y4+MpUywAw7ccTt9efdsc69urL8O3HF5IPCIiIksZMhr61CSp9ukXx0VERERERES6We9OPPdz6U/FPsD4Os/bAXjy3QQlIiIiIiI9z+5brAPAuKnPMHPuAtYe0I9Ru27Ycrwnx1OmWACGbjAUgImPTGT2/NkMXHkgw7cc3nJcRESkcJvvE3/fPTZaTfVfNxJvKsdFREREREREupG5e/f9Y2arAycD97n7LR35nsGDB/u0adO6LQYRERERERERERERERERERERke5kZg+7++B6j3Wm8k273P0/wDHd+W+KiIiIiIiIiIiIiIiIiIiIiJTVCkUHICIiIiIiIiIiIiIiIiIiIiKyrFLyjYiIiIiIiIiIiIiIiIiIiIhIFyn5RkRERERERERERERERERERESki5R8IyIiIiIiIiIiIiIiIiIiIiLSRUq+ERERERHJ2/RJMH4zGDMg/p4+qeiIRDrtpkdf4TNn3cP6J0zhM2fdw02PvlJ0SCIiIuWme8CGpsyYwi7X78LmV27OLtfvwpQZU4oOSUSWY/MmT+a5HYfw9Mab8NyOQ5g3eXLRIZWGfjYi3UvvKZGepXfRAYiIiIiI9CjTJ8Hko2HRgvh63j/ja4DN9ykuLpFOuOnRVzjxhidYsGgJAK/MXcCJNzwBwO5brFNkaCIiIuWke8CGpsyYwpiHxrBwyUIAZs2fxZiHxgAwdIOhBUYmIsujeZMnM+uU0fjC+MxZPHMms04ZDUD/YcOKDK1w+tmIdC+9p0R6HlW+ERERERHJ091jWxddKhYtiOMiy4hxU59pSbypWLBoCeOmPlNQRCIiIiWne8CGJj4ysSXxpmLhkoVMfGRiQRGJyPJszvgJLQvhFb5wIXPGTygmoBLRz0ake+k9JdLzKPlGRERERCRP817u3HGREpo5d0GnjouIiPR4ugdsaPb82Z06LiLybiyeNatTx3sS/WxEupfeUyI9j5JvRERERETy1H/dzh0XKaG1B/Tr1HEREZEeT/eADQ1ceWCnjouIvBu9Bw3q1PGeRD8bke6l95RIz6PkGxERERGRPA0ZDX1qEhT69IvjIsuIUbtuSL8+vdoc69enF6N23bCgiEREREpO94ANDd9yOH179W1zrG+vvgzfcnhBEYnI8mytkSOwvm0/c6xvX9YaOaKYgEpEPxuR7qX3lEjP07voAEREREREepTN94m/7x4bbQb6rxuLLpXjIsuA3bdYB4BxU59h5twFrD2gH6N23bDluIiIiNTQPWBDQzcYCsDERyYye/5sBq48kOFbDm85LiLSnfoPGwbAnPETWDxrFr0HDWKtkSNajvdk+tmIdC+9p0R6HnP3QgMYPHiwT5s2rdAYREREREREREREREREREREREQaMbOH3X1wvcfUdkpEREREREREREREREREREREpIuUfCMiIiIiIiIiIiIiIiIiIiIi0kVKvhERERERERERERERERERERER6SIl34iIiIiIiIiIiIiIiIiIiIiIdJGSb0REREREREREREREREREREREuqh30QGIiEjPNGXGFCY+MpHZ82czcOWBDN9yOEM3GFp0WCLSnaZPgrvHwryXof+6MGQ0bL5P0VGJyHLopkdfYdzUZ5g5dwFrD+jHqF03ZPct1lE8JY1HRESKd9/ZB9LnN39mwOswdzVYtNfWbH/8lcUFVKaxQ5liEZFMaF5SRHqqeZMnM2f8BBbPmkXvQYNYa+QI+g8bVnRYIssNc/dCAxg8eLBPmzat0BhERCRfU2ZMYcxDY1i4ZGHLsb69+jJm2zEa6IosL6ZPgslHw6IFrcf69INhP9LEtYh0q5sefYUTb3iCBYuWtBzr16cXP9zzY4UkmCgeEREpu/vOPpABV/+ZFRe3HnurN8z9RkEJOGUaO5QpFhHJhOYlRaSnmjd5MrNOGY0vbP38s759GXT6WCXgiHSCmT3s7oPrPaa2UyIikruJj0xsM8AFWLhkIRMfmVhQRCLS7e4e23bCGuLru8cWE4+ILLfGTX2mTWIJwIJFSxg39RnFU8J4RESkeH1+0zbxBmDFxXG8EGUaO5QpFhHJhOYlRaSnmjN+QpvEGwBfuJA54ycUE5DIckjJNyIikrvZ82d36riILIPmvdy54yIiXTRz7oJOHc+a4hERkbIb8HrnjmeuTGOHMsUiIpnQvKSI9FSLZ83q1HER6Twl34iISO4GrjywU8dFZBnUf93OHRcR6aK1B/Tr1PGsKR4RESm7uat17njmyjR2KFMsIpIJzUuKSE/Ve9CgTh0Xkc5T8o2IiORu+JbD6durb5tjfXv1ZfiWwwuKSES63ZDR0KdmYbdPvzguItKNRu26If369GpzrF+fXozadUPFU8J4RESkeIv22pq3erc99lbvOF6IMo0dyhSLiGRC85Ii0lOtNXIE1rft55/17ctaI0cUE5DIcqh3+08RERHpXkM3GApEj+XZ82czcOWBDN9yeMtxEVkObL5P/H332CjR3n/dmLCuHBcR6Sa7b7EOAOOmPsPMuQtYe0A/Ru26YctxxVOueEREpHjbH38l93EgfX7zZwa8HhVvFu21Ndsff2UxAZVp7FCmWEQkE5qXFJGeqv+wYQDMGT+BxbNm0XvQINYaOaLluIi8e+buhQYwePBgnzZtWqExiIiIiIiIiIiIiIiIiIiIiIg0YmYPu/vgeo+p7ZSIiIiIiIiIiIiIiIiIiIiISBcp+UZEREREREREREREREREREREpIuUfCMiIiIiIiIiIiIiIiIiIiIi0kVKvhERERERERERERERERERERER6SIl34iIiIiIiIiIiIiIiIiIiIiIdJGSb0REREREREQkFzc9+gqfOese1j9hCp856x5uevSVokMSEREpt+mTYPxmMGZA/D19UtERiUg3mjJjCrtcvwubX7k5u1y/C1NmTFE8ItJjzJs8med2HMLTG2/CczsOYd7kyUWHJPKu9C46ABERERERERFZ/t306CuceMMTLFi0BIBX5i7gxBueAGD3LdYpMjQREZFymj4JJh8NixbE1/P+GV8DbL5PcXGJSLeYMmMKYx4aw8IlCwGYNX8WYx4aA8DQDYb2+HhEZPk2b/JkZp0yGl8YnzmLZ85k1imjAeg/bFiRoYl0mSrfiIiIiIiIiEjmxk19piXxpmLBoiWMm/pMQRGJiIiU3N1jWxNvKhYtiOMissyb+MjElkSXioVLFjLxkYmKR0SWe3PGT2hJvKnwhQuZM35CMQGJdAMl34iIiIiIiIhI5mbOXdCp4yIiIj3evJc7d1xElimz58/u1PGslS0eEVm+LZ41q1PHRZYFSr4RERERERERkcytPaBfp46LiIj0eP3X7dxxEVmmDFx5YKeOZ61s8YjI8q33oEGdOi6yLFDyjYiIiIiIiIhkbtSuG9KvT682x/r16cWoXTcsKCIREZGSGzIa+tQkqfbpF8dFZJk3fMvh9O3Vt82xvr36MnzL4YpHRJZ7a40cgfVt+5ljffuy1sgRxQQk0g16Fx2AiIiIiIiIiCz/dt9iHQDGTX2GmXMXsPaAfozadcOW4yIiIlJj833i77vHRqup/utG4k3luIgs04ZuMBSAiY9MZPb82QxceSDDtxzecrynxyMiy7f+w4YBMGf8BBbPmkXvQYNYa+SIluMiyyJz90IDGDx4sE+bNq3QGEREREREREREREREREREREREGjGzh919cL3H1HZKRERERERERERERERERERERKSLlHwjIiIiIiIiIiIiIiIiIiIiItJFSr4REREREREREREREREREREREekiJd+IiIiIiIiIiIiIiIiIiIiIiHSRkm9ERERERERERERERERERERERLpIyTciIiIiIiIiIiIiItK+6ZNg/GYwZkD8PX2SYilrPCLLuCkzprDL9buw+ZWbs8v1uzBlxhTFIyKZmTd5Ms/tOISnN96E53YcwrzJk4sOSZZBvYsOQERERERERERERERESm76JJh8NCxaEF/P+2d8DbD5Pj03ljLGI7KMmzJjCmMeGsPCJQsBmDV/FmMeGgPA0A2G9vh4RKR7zZs8mVmnjMYXxnt88cyZzDplNAD9hw0rMjRZxqjyjYiIiIiIiIiIiIiINHf32NbkkopFC+J4T46ljPGILOMmPjKxJdGlYuGShUx8ZKLiEZFuN2f8hJbEmwpfuJA54ycUE5Ass5R8IyIiIiIiIiIiIiIizc17uXPHs1SmWJqdt6h4RJZxs+fP7tTxrJUtHhHpXotnzerUcZFGlHwjIiIiIiIiIiIiIiLN9V+3c8ezVKZYmp23qHhElnEDVx7YqeNZK1s8ItK9eg8a1KnjIo0o+UZERERERERERERERJobMhr69Gt7rE+/ON6TYyljPCLLuOFbDqdvr75tjvXt1ZfhWw5XPCLS7dYaOQLr2/Y9bn37stbIEcUEJMus3kUHICIiIiIiIiIiIiIiJbf5PvH33WOjnVL/dSO5pHK8p8ZSxnhElnFDNxgKwMRHJjJ7/mwGrjyQ4VsObzne0+MRke7Vf9gwAOaMn8DiWbPoPWgQa40c0XJcpKPM3QsNYPDgwT5t2rRCYxARERERERERERERERERERERacTMHnb3wfUeU9spEREREREREREREREREREREZEuUvKNiIiIiIiIiIiIiIiIiIiIiEgXKflGRERERERERERERERERERERKSLupR8Y2a9zWw1M+vd3QGJiIiIiIiIiIiIiIiIiIiIiCwrOpx8Y2ZfM7NbzexfwFvAf4G3zOxf6fj/ZRaliIiIiIiIiIiIiIiIiIiIiEgJtVu5xsxWAm4BdgT+BzwG3AcsBPoC6wDbA180s28Cw9z9f9mEKyIiIiIiIiLSPW569BXGTX2GmXMXsPaAfozadUN232KdHh+LiIiIiIiIiBRn3uTJzBk/gcWzZtF70CDWGjmC/sOGFR2WtKMjbaPGAp8DjgYucfe3ap9gZisChwHnAacBo7ozSBERERERERGR7nTTo69w4g1PsGDREgBembuAE294AiD3pJcyxSIiIiIiIiIixZk3eTKzThmNL1wIwOKZM5l1ymgAJeCUXEfaTu0D/MjdL6iXeAPg7m+5+4+BHwNf684ARURERERERES627ipz7Qku1QsWLSEcVOf6dGxiIiIiIiIiEhx5oyf0JJ4U+ELFzJn/IRiApIO60jyzZrA0x389/4KrNHek8zsMDObZmbTXn311Q7+0yIiIiIiIiIi3WPm3AWdOp6lMsUiIiIiIiIiIsVZPGtWp45LeXQk+eZF4Asd/Pd2S89vyt0vdvfB7j54zTXX7OA/LSIiIiIiIiLSPdYe0K9Tx7NUplhEREREREREpDi9Bw3q1HEpj44k31wM7GVmk8xsWzPrU/2gmfUxs8+Y2XXA7un5IiIiIiIiIiKlNWrXDenXp1ebY/369GLUrhv26FhEREREREREpDhrjRyB9e3b5pj17ctaI0cUE5B0WO8OPGcCsB5wJLAX8I6Z/Rt4C1iRaDNVSeL5SXq+iIiIiIiIiEhp7b7FOgCMm/oMM+cuYO0B/Ri164Ytx3tqLCIiIiIiIiJSnP7DhgEwZ/wEFs+aRe9Bg1hr5IiW41Je5u4de6LZJsDXgcHA2sBKwP+AmcBfgF+7+5OdDWDw4ME+bdq0zn6biIiIiIiIiIiIiIiIiIiIiEguzOxhdx9c77GOVL4BwN3/CpzcbVGJiIiIiIiIiIiIiIiIiIiIiCzjVmj/KSIiIiIiIiIiIiIiIiIiIiIiUo+Sb0REREREREREREREREREREREukjJNyIiIiIiIiIiIiIiIiIiIiIiXaTkGxERERERERERERERERERERGRLlLyjYiIiIiIiIiIiIiIiIiIiIhIFyn5RkRERERERERERERERERERESki5R8IyIiIiIiIiIiIiIiIiIiIiLSRUq+ERERERERERERERERERERERHpIiXfiIiIiIiIiIiIiIiIiIiIiIh0kZJvRERERERERERERERERERERES6SMk3IiIiIiIiIiIiIiIiIiIiIiJdpOQbEREREREREREREREREREREZEuUvKNiIiIiIiIiIiIiIiIiIiIiEgXKflGRERERERERERERERERERERKSLlHwjIiIiIiIiIiIiIiIiIiIiItJFSr4REREREREREREREREREREREekic/diAzB7FXip0CCknjWAfxcdRFKmWKBc8ZQpFlA8zZQpFihXPGWKBRRPM2WKBcoVT5liAcXTTJligXLFU6ZYQPE0U6ZYoFzxlCkWUDzNlCkWKFc8ZYoFFE8zZYoFyhVPmWIBxdNMmWKBcsVTplhA8TRTpligXPGUKRZQPM2UKRYoVzxligUUTzNligXKFU+ZYgHF00yZYoHyxSPwQXdfs94DhSffSDmZ2TR3H1x0HFCuWKBc8ZQpFlA8zZQpFihXPGWKBRRPM2WKBcoVT5liAcXTTJligXLFU6ZYQPE0U6ZYoFzxlCkWUDzNlCkWKFc8ZYoFFE8zZYoFyhVPmWIBxdNMmWKBcsVTplhA8TRTpligXPGUKRZQPM2UKRYoVzxligUUTzNligXKFU+ZYgHF00yZYoHyxSPNqe2UiIiIiIiIiIiIiIiIiIiIiEgXKflGRERERERERERERERERERERKSLlHwjjVxcdABVyhQLlCueMsUCiqeZMsUC5YqnTLGA4mmmTLFAueIpUyygeJopUyxQrnjKFAsonmbKFAuUK54yxQKKp5kyxQLliqdMsYDiaaZMsUC54ilTLKB4milTLFCueMoUCyieZsoUC5QrnjLFAoqnmTLFAuWKp0yxgOJppkyxQLniKVMsoHiaKVMsUL54pAlz96JjEBERERERERERERERERERERFZJqnyjYiIiIiIiIiIiIiIiIiIiIhIFyn5RkRERERERERERERERERERESki5R8IyIiPZKZjTazzZo8vqmZjc4zJhEREZHljZl9wMz6NXm8n5l9IM+YRERERERERESWNWa2TtExSHPm7kXHIFJqZvY5YBfg/cB57v43M1sF2BKY7u5zc4hhCLCTu5/Y4PEfAne4+71ZxyJtdXWhwN3/0d2xSOeY2TvA/u7+qwaP7wv8yt175RuZiCzPzOz9wGDgvdRJhHf3q3IPSmQZZWYrAesB7wOs9nF3vz/neFYGjgP2ADZIh2cANwDj3H1+nvGUhZktAb6he66ldWAs4cAC4DXv4ZM3ZvZhYkz+pLvPKzoeEREpTtoodYO7P9ng8U2Bvdx9bL6RybLEzHoDWwPrAH9196cKDklKyszWdfeX23nODlqbkQqN85ZmZl8Hfu/uL1UdWx2Y5+5Lap67OfBVd9fG6CRds74CHArs7O59Cg5JmuhddABSrDRBfCzwJ3efWnQ8ZWJmvYBfAV8lJvMduAb4G7AYuAk4F/hBDuEcDzSbYFw/PUc3ePl7kXhtdFaPW1xYBvUl3us9UvoMXAd4093/U3Q8Iss6M1sB+AnwTZpXn8wl+cbMNnH3v7bznD3c/cac4lkFeBz4sbtPyOOcTWKZAYxw91saPP4l4EfuvkG9xyV7KenmfOBg6o9pK/fuud1vpUmjB4CNgVeBR9NDHwVGA3ub2Wd76DV1qcSoGivQtfvpd8XMVgN2om2i1J3u/kaOYbxIx/7f/2dmdwOnuvvj2YZULukzdyKRaAewM3CPma0FPASc4O7XFxSeiHQTM9uHpZNXb3T3ScVFVZ+Z9Xb3HjtXUBJjgOeBusk3wGbAqYCSb0qgyIR5M9se2BM4w93nVB1fn5jb36zq2JXufkhWsZSdxsFN3W5mn2mUAJ42b98CrJpvWFJiL6JxXq2rgW8ALwGY2fuAOaTxXc1zPwacRMyl9Gipc8MhwP7EdXQxcHehQUm7lHzTw7n7fDP7PnBk0bGU0PHAXsAxwO3A05UH3H2hmd0I7EY+yTcfB85p8vifiF22kr+xFLBY0FVmtgN1dmO7+32FBZWjtMgyoOrQ+xpkoq8O7Af8M4+4SqoP8fo4ERhXcCylZWYfAT5C40mkXKuYmNl6xELi+4FfuvuLZvYeYCAw293fzjGWwivH1cSzEo1/T3lUI/se8G3gF8AdRJLN8cAbwAgiybZuhbuM/NnMRrj7pbUPmNmKxELnt8gpecHd30wD7zfzOF871gNWafL4ysAH8wml3MxsZxp/Brq7n57RqScSu31uIyZpXsvoPJ0xFtiIGFddVNm5lZJZDwN+TCwUHV1UgAVrdr+8MTA3pzgAMLNvAucR7/XKa9eBN83sGHe/LKdQxgJDgS2AqcAz6fhGxDX0EeB36euhwBAz+5y7P1rn38qcmW1F3Cc/4O4Lczjf9sCNwGPAlcR7CAB3n2NmLwBfA3JJvjGz9xKfPdtQv4Kdu/uQPGKpZWYfInZCVo/zbnb3F4qIR6Sj0qbAm4Adic/juemhTwL7mNm3gS/nVT3OzL7n7uc2ebw3cB0xr9LjpPua/Wgd5x3n7o+mz8dhwN3u/kqRMSaFbKYyMyPG40XcH5dOSRLmDwI+7e619+BXEIu7vyfm1XcFDjSz37n7lRnGU2brUcJxcEnmudYAJpvZzu7+Vk182wK3As/mEEfh1AWgw0o/zktzf2sAr+b0Pqq3Kae9jTq5MbMjgD3cfacGj98B/MbdL8ohllWBrxNJN4PT4QeBy4Bb8p5Tl85T8o0AvEDcrBQilQrtrDwGKwcAV7n7xLQYVOtpIvkmD/2BZhMNC4jJv0yZ2QHpP692d6/6uqnluYWGu48pOoaOSBUXriQu2ga8kx5aATjCzH4JHNgDyhyOpDVj2oEJ6U89RgFJbWkQ822aT9ZkPqmfkgz/TfPPnh7LzAYR76nK76LeYMHJqYpJiulsImG0Vzr3H4idFn2BvwIn0/j13p1xlKZyXPrsOw44iub3OnkkmBwI3O7uB1TdVzzs7veY2dXAdGArlt7tkZVHgItS8sS33P11aCnRfi2wKXBhTrFU/JEYVC6VEFQy7wf+l9U/bmY/78K3ubsf2u3BNGBmGxEL4h+l8WSJA1ndr+8BXOPu+2X073fFl4FL3b3N+yYl4fzUzLYAdifn5Ju0ID+S5skCH8rgvAcSn3sVJ5vZt+o8dXVix3EuVbYAzOzLwMVEcsIpQKXFwKbE9eJiM5vj7pNzCOevwBHAx2tbHZjZx4jKpue4+6hUevv3xL1spou+ZvY94PPuPqzq2K+AfdOXM8xsO3f/V5ZxEP+vj9P6+h1T8/gfiHF75szsg8TPf20iYXY14D+0vq8Ku282s9OBE1j6fuYcM/tBnuXazewzRDJx5XdWbyyT21xkSuz4Os3HVrlcP1Op/SOqYqmV68+mRM4kxlQ/As5y99kAZjaQeF0fnZ4zIqd4zjazV9z9mtoH0tjiWiLJJBNdnXcjxllzgD9klaiUEinuALYlPu9WonUu8nXgLODnxLgzi/OXejNV2phzE7GQWsT9cW08nyaSwpt9/nX7PWCNMiTMb028blukscxngfvdfft07BSicuUBxFxPt0vjPAcOc/clHRz35TrOa0em4+B6yjLPRawB3Qf8ysy+Wpk/N7NtgN8CfycShHJTYFL4i6gLQEeUcpyXzr8lMS+7HfF7qa4seg3wQ3e/K+s4SuggYFqTx58lkmEyS74xs88T7+s9ifusx4iiDMcDE939hqzOLd2rJw7qZGkXAseZ2U/dvYib4DFd+J48BivrEbshG5lLDgkvySvEglwjWwGzc4jjCuJnfy3wdtXXzTJUc118loaOJSYfriMmriqVnDYmJkf3Iya2m73mlwf3pb+NuKG9kVjwruZE5YU/uvtD+YUGZvbFFNN7UgxF7+S/DfgS+S/ALwsuBnYgBvkPAP8tMpi0I3QUMWl9K1WTS+7+upndQkwST8ghnDJVjjuLqDjzFPAbin1PbUDrAK2SANkHWioRXk60pMqr0tT2xD3Y94HBZrY/8AniOrAA2NPdb8oplooTiAH/n4Ar8kwITZWatq86tKeZfbjOU1cnqiw8lmE4B3Xhe5wYoOflIuD/EQthRXwG9qX1ml4W76e11VQ9j9A2GSVzaVLvQWBFYqfdBsTn4fuIhMQXgJczOv0AojUuxOtzTWLyqFrlnuvnREnpvBxHXJu2cffqalt3p8/iPxLXsjySb74P/KR2QhbA3Z8wswuJRYXr3X26mV1CPskmXyN2gQNgZjumY9cAT6SYjiPGGFn6JDDa3d+JYgJLeZn8NhKdQbyuhxA/gzlEMtIfidfv14DP5xRLCzM7JJ3/IWJitjqZbBRwkpnNcPcrcojlc8BdRHLSn4j7vXuIXfRbEz+3R7KOoyqerYn74jWaPC2X66eZnQycBvyL+F0VOnYomX2B69x9RPXBlIQzwszWSc8ZsfS3ZuIS4PKUhNlS0j8l3vyKWBQ7LMPzX0Hn592qvW5mX3L3BzOIbQyRKL8H8TpuScBMiQQ3ENVDMkm+ofybqX4MfIi4hyi0MmNK2rocWEQsFhZVcaIMCfMDgedqjm1PvIZaNn24+4KUaHxUhrEclM57OLCEjo37Mr1OlWwc3EaZ5rnc/REz25toLXUBsZF1K2LO62VgiLvndm0vOCl8meoCUKBSjvPM7BPEHM6/iTW7g6vimmNm/Yh5i56YfPMR4trZyFNEUn+3M7OTiGvCh4hx5kXAlem1Ubm3kGWIkm8Eot3Bf4BnzOxK4oZ0qSzmDKuXrN/+UwrxBnFj2ciHgVdzimUK8B0z+3Vt1qmZDSEuiHnsEt8BoKoM3Y7oZmspJS3DexBwh7vvW3N8OvB/KbZDWM6Tb9z9d0RJx8pA5Wfu/qfm35WrHxI3v7u7e7NM67wcB9yZrg3nAs/l0WKgVpo4ut/dX2zw+HrA53KusrUjkXH+vRzP2cx3gRvdfUSDam3Tya/FZJkqx+1PVJvJ63zNLCAmPyEWmx1Yq+rx2UQyQy7c/R1gtJndQ/RdrkzQPwDs7+5ZLcg3cz6xGHUpsVv/BZa+J81q99YOwKmVcxC7TPZs8Nznicn/TLh77Y61Mtqa2J3+44LOP42YGCmTfxElpRvZgqpFqpyMJRbutiYWgOYAw1PFrW8RSZBfyeLE7j6R2O2Mmb0DjHD3X2Vxri74ODC2JvEGAHd/I933nJJTLBvSfEw5Jz2n4mlg1UwjCusRC74VuwOziOuDm9kaRLWnrJNvVgDeavL4GsRrPA9DgEvc/d6q+xtz9/8RCS6bAmcT48A8HUEkumzv7tUtVl4ws9uI6/pRtP19ZuUk4nUymLiWzgF+kD5zdiHag303hzgqzic2NewD3OPu/8nx3LW+SySNfsHdF7Xz3J5mNWL3dyP3kN/YAeJ3NRC4IbV/eNwi++9K4rX0Xc+2NWHtvNsOHfy+XsA6xGLf+cS1v7vtDVzs7jc3GOc9T2uFtCzcl/4u5WYqopLKBG/StixHJxGJ1zu5+8wC4yhDwvyKxFi82ifT37+rOf5Povp8JmrHeSUZ95VmHFxHmea5cPepFq1rr0hz/18lxnc7uvu/84ojKSwp3JeRLgCNmNmn3P2POZyqrOO8scBMYn6iL7EeVO1u4n6nJ+pD/Ewa6dvO4+/G6cRn7JeB36YKyrIMU/KNQNtJmEY3UJlVL3H3l7L4d7vBg8D+ZnZO7QNViQq35xTLmUQVgalm9ltas8w/AXyRWLDLo2zpB4H7K1+4+305nHMpnSi720YeC/NFl+FtYgOaVy+ZTCRXZM6in+j2KSaIkv+/KyCp4+802QWQJtD3cvex+YXERsDJJUm8gRgIOLFItT9AnV3HeZRHvxz4BlHatJ5t0nPyTL55k7gpLouPAj9t8virNN/1253WozyV494L3JzTudrzErGDAXdfZGbPA18gEl8gShTnvTAPkdyyiNbdtM8TSYBF2ID4zKnszHx/jueeQNwTG3FdGsHSrx0H3ix48a4sXqO41wlElaTJZjapRNfMycC3zewRYoH+HWjZKf9NMi5P3MB2xCLZM9XJAgDufomZfZa4N/1ylkGUZGGhWnvVA/LcXPAvIrHlJ7UPpMXePWh7bViT2DiTtZVpu1C1I3BXVUWyvxK7trP2NLGY2Wgc8yWiemce3gc8mf67kjzRr+rxO2ldvMrTxsCJNYk3ALj7YjO7lkjwz8PWwPnu/qqZVTYyrZBiucOizebpxOspD1sRyT/X53S+ZlYDJpUl8SZ9vmxB2zH5o3lWHawyneYJtR8hFhZzkSptfY1I+rktXStPIRLrRrr7zzI+/++afd2e1Grt/G4NqtXaNP/M/R8ZLhwuA5up3iLmmcrgg8CoghNvoBwJ8/8gqsFV2w6Y4+61rclWIuYrepIJlHccXKZ5LgDc/WozW5u4t3oe2MGzb8NaT1mTwkvJzNYkNgoeQsy959ECq6zjvM8SbaXeTGs0tf5BXO+zUu9esyyb+58lWnA1uo/ahagenIVZRLGH84GPm9lVda5RsgxR8o1Ax3dRFCLtqqOADOIziQSce2hNUPp46uF7AjEheVYegbj7v8xsW+KG84u07vpxoq/oke4+K4dQ2iyAm9kS4BsF7GK9gs6V3YX82l+NodgyvI3Mp/ki5kCyK0fZIiVOnU8siFd+fw7MNbNj8yiHXuVUYqD0ZIPHN0vPyTP55lXy273bEVdRjhvg9t7rfWht45OXW4lkiUwnXzthIXFdauSD5DeJVKbKcU8Ag3I6V3vuIa4NlWpJVwNj08SNEQPgXHdJmtnxxGfcbGKA+RVi59g2Zravu/81z3jcfb08z1dz7nlEuWbMbAfgaXefU1Q8y4BraTCRlJPDiPLefzSzPxATxbU7g9zd82zFNZp4H10InGZmz6TjGxITac+T/8L8qrRODlXuL6qvFb8nv0X5MnkcOMjMLnT3Nve/ZrYKUTEyr4SOy4jXyxSiUtCz6fiGwHBaWwRWDCWfcv+vAB+DlgXOTWg7Cflemlek6S6XAT8ys7uIMv8AnjY8nAV8mnzacEHcu1Tub94g7r3Wq3r8PbRNxsnL20Rbp0ZWJb/xxYrEawdaXx/VC/GPkRL6c/I6xbfxrXiUHCscNmNmXyCuVR+seehFM/uuu0/NOaSTgRvN7D53b9Puz8y+QiSw7p5nQB6tcocSczrTiff2camqW67M7OfARY2STFJ7te+4e2Xn+g1Utf3tZq8R1XUa2ZTYTZ85dz+4/WflbirwGfJPtq7nZeIzuWhlSJh/ADjAzC519yfNbA8iIeiKOs/9GK3XscyZ2flEu+XaCk65qTMO/qu75zVf057C5rnSZ28zbxDrI2dWbVLMc/xZyqTwNJYaQEq+rubuuba/S5tgdiMSboYSc8dziPaSeSjrOK8v6T3fwGoZn/8sMzsx/XcvUgtAM6tdl8qsClkT1wA/NLPTgdMrVQjNrA9xv7oL2a3p/T/i9Xoo8d49zczuJ65Vj2V0TsmQkm+k07so8lCVQfwV0mSNmb1OZF6flEfLHnefZmZ7Ea0PKr3+ziUWyOYAe+S5KJUqBO2Wqu5Ueq8+7zn2E6W1kktFZ5JfulOZE8aKLsPbyAPAkal1WZteo2a2CVGu/L4sAzCzfYkbhn8Q76XK+2dT4DvAZWa2wN1/nWUc1SG183hfYKkdpBm7mqhy9aOcz1uXux9UdAxV6iYBmdkAYoCSRwJitWOBu81sPNHb/e8F7RSt+DOR2LFUxRkz60skTv4+p1jKVDnuNOKz5bIS7Bg4F7jDzFZ097eI+5y1iEWoJcDF5Dg5YmZTiUSBW4CD0/3E3WmB8+fAX8xspLtfnFdMZVF9b2zR7/79wJNpYjJzZjaa+Mw7M+2+Ht2Bb3N3z6MKYsVJwPVmdiNxzXqRpZNfspxgO6jqvz+T/ix1emLiIhfu/pqZDSZ6ce9Oazn7GaRWau7+el7xJP8iEqwr7ZTmEztIK95LBrv+Uju7znLPpqVcPeOIxclHzOxHtL0nPYoYazUqt9/dziR2Fn6bqIZWzYhrwxnQcj2/Cngkh7gmA981s95EhcG3iFbIFZvRuCJht3H3n5rZZ4hJ8vOI9/U1xIJDL+Byd/9l1nEkTxHVIEmtt/5M/IxuIRYYDgP+llMs1f5CVN26tHbntZmtleLKqzLELGBdAHefb2ZzidfKjenxdcl3fFXZ+NKsAmxeTgZ+Y2a/cfdHiwoivZ9uIeZ2JhKva4jPv4OAW8xsh5zb9exHVAu5KSWuVhJHNiYWqJ4gxhbViVvdtsBpZh9o8vC3ic/Di4BJ1c/NcRHxIOAuGr+P1yda0R8CkJLHs0ogvxs42MyW2jBgZuunGK5e6ru6QeVnX/m5t/N7a5HzYu8xwP1mdizw46rWYUX4GbCfmY0vuHVFGRLmf0h8zjxuZq8R9xBvUzN3YtFG6MvAbzKMpdYIYLiZPUHMl/6qyA0gJVwjKnKe66AOPGenmq/zHH+WKik8VYw7mbh2N5JHtRnSxvlDiAT9gelwZd7igRznbss6znuBqA7ZyI60jo+72z+I98mqNcdWYOnKee/QWhU7L+OJwgcnAYebWWVstxHxfnuA5pXeuyxVTr4VuDWN4Q4EDiauDW8TP7cPm9kKlSrLUm5W7DqRlE0qNbYG8GpRg4Q0gPojcXF8jNbJgE2IsrizgE/ltYiWfiY7EzcPBjwHTE1l/HqUNFBaidjZ+18iWeMMYiKgIXe/v9njyxMze4vUAzwl37xK9Fi+Jz1+GPAjd8+qP2SjuD5GvK/6EEls1YsMw4iL+Lbunlk5ZzN7PJ3/U7WLT2bWn5hMesvdP55hDKsRGfgQCwbDqd+SZnXgHGADd/9wncczYWYfJXrJzyEmQ/9OvouZpWFmpxKfNR11nrsfl1U89ZjZ0cSNeSPu2bfkqsSyE7HT7ldE4sTdRFLHa0QCylbA59z9DznEMphIwPkDMUi4nEhW+h+x820t4JNZJLA2SFIYStxD3Ej991TeSQulYGYLiFLkF9R5bG3itfRZd89lgqTm/OsRE1nvB37p7i+a2XuIe8PZedyjmtmXiM/h9dKhnd39njQIfgg4Ias2Fmb2DjGw7ufub6ev2+N5/q7SLrIfAKOaPa+I14+0MrObgSXuvmf6+lYieWA/YoLrF8AMd/9cN5/3RbpQOc/d1+/OOJoxs+8SpdhXpjVWIxakj3P3ZiXus4hnE6KFUuVn8CIwOe8KZFXxvBe4ntj08BYwwt0vSo/1I8bkl7n7sTnFswdxX7MRrWPyq9w9twWy9Jo5FtjM3ReY2Y7EvVdlZ68De7r7LY3+jYzi+hxx3/cGscO2epx3MDGRPcTdH8ghlmuB97r7rlVf70IsLq5ATFb/yd13a/iPdG88qxG/o2lEW40ZRSbLpyouk4hx+YsUULEtJV9vDGxTWz3ZzAYRY/K/unvtIlGWMXVlAaHb7nuq7rsaPqXe43nd46T49vcGFafN7BDgwjzmmFJS+jSiMsg1xDjzPOK1/J309xZZzNemn8M7wEpV98ftvp9zvj+eQdxXrEHEOpP67/MP5RDLDsS9+nuISpWN5pYyna8tyzgmzVGcSiRYvwCc4e5/rHnOTsT8zkh3bzrP3Y1xfZRYYN0P+ACRoDqVmBe8pYi1mZR4vTuRfP1elq5gklt1lzLNc5VNup6/6u77p6/vI+bavkD8zn5LtAr7ZMN/pPti2Z1IeH6WqPj8HeJ3VnktTQemuPtpGcbQD9iHSH76DPFempLi+RHwVXe/IavztxNb2cZ53yfaaQ4jKjO+SowV7k3Jo+cAw+vNFfYEFlVuRgJfp7Vt4rPAL4GJnnMLWYtuKIcSG/5XJlqT3Qzc4O635RmLdI6SbwQAM9uS2JG9HZGFWr3AcA3RBzCvG88riYvlXrUfIGb2ReJi/uuSVWTIhUV57fWILP2lqnbkMGjagdgB0NGyb0bOi0FFM7OZRN/psQ2SbyYCw9x9g2b/TkaxDSYWEj9d89BDxE3VwxmffyFwiruPa/D48cAYd88sM7+TCR1GLL7k1gamagKp7gRfRc4TSL2IiYBdiIXw49z90bQoMwy42zOoRpYmp3cnfhYHENnlM2qe5sCbxAT2NXlOppvZccQOqn8Ru3HqViHzHMthp+S+icQEW/Vr6G3gcM+xrZtFmfZLaW13V3ldzwEOcPc7MjpvoZP3yxIz+4S7P9bkcQNOzjsxyczOJnaNVsrPVu5JVyMmsE929wndeL4vAg+6+xtVx7YnyjQ/Rux2HkPba/lU4A13/2p3xVET0wehpephy9ftqTw/D2nH80hisuZBGn8GZjbBVhZmtjIxrn6zyXNWIT5rMm/xWXPefYnqhrumZIEtgN/RWr59AfAFd38wz7jKwqJ63s60ToTOAO70nKpcLQvSZ++C6knGNLn9UeCf7v6fwoKLWDYCNnf3SQWdfzAxMbsEuNHzrRZSHccw4AKWbmv0D6JF9a05xbEzsVP8m+kzZwPiHr7SAnQ2sIu7N2r7293xdGRxPpdkeTPbhlhAbFbKP48F6LnAue5+RoPHTwGOdfcBWcZRJmY2hq4ljGa5iPgBWhPA76PxprfVid3Z/dx9s6ziqYltK2IR/GM1Dz1JtKXPpG1j1e/pdI/KkJWvm8rzXjQtfnckpsyredcZF9fG1ePma8suzbkfQFRfXJVop3QtcHVeySVmtjpwL1G1rjKnVFl/aJmrzHlOsjTzXGVSpqRwM3uQSNTaimiFOoc0f2JmmxHViQ5w93qbX7vj/JcQa4mrEvMTVxBVpF4zsw8RSfuFJd+UTdrYNhX4HFG1cyOiyuCaxIa3O4HdXNVVSiXNO+1LJOJ8Gl3DS0/JN4KZfYK4CP6b+HA9mLTQkR5/CHjB3b+RUzyziAtk3V10Fv1Qv+7uA+s93o1xPELrxfrfWZ6rA7GsBJxP/G7qTQzldvObJqk/SUygXUGUyGs6CHD3K7OOC8DM3g8Mpn5mPu5+VQ4xXE0ksW1KlHdsSb6xKMM7nRg4fTfrWJrEuCatiwx/95x6+aZd0D9pknxzHFE1aL0MY/g80VPViCScG4nfSbWWhI68J9DLNoGUPnvuALalte1cZSG8F/BP4OfunlW/00oc9xI7k+7O8jydYWb/JDLfv5B31nszZjaQyIav3hk+KYsEqQ7EknvluI4mKdTKKmkh7ZhYFZjnNeW+06L4ocA6RJW/0z3D6mPLAjP7NvBTYmfSrcTnT3XSy6+A93s3tqaxqJY0jPhsm5uO3UMsjm1N3FPUJtKOISaPck+kLQsz+xdRsjmTBKROxLEyMfHwfuAur2m5ksP5K+0wznX37zd53pnA94BN3P2FvOJrEMv/I8q3LwF+6+61ia3LvbSo+aq7L2jweD9gTe8BlQaXdWZ2EjBWE49gUZFsK9omkz1S9MR5+pweQnzmPJhncpuZXUHHxlaZJ8ub2R+BDYh7vwcq9xx5M7P/ASe6+8QGjw8nNt+tVO9xyUfaNHQq7b9+jaiwcrC7Z9LuqeGJY0G1ZZznBbZTk6WZ2YEdeV5e87XScek+dE+ipdIQYv0ur4rKFxLXqe8QiX8vEO0b/0FUyvgIkdQ/N494quIqzTxXmRWVFG5mrxNztuekBK5/E6+TO9Pj44iqyp/K6PzvAM8TVeL+XPOYkm/qsKhwdRSx2bZ6zvYqorpLnm1ipZPSBpRDPOcOANI5Sr4RLHqUb0y0dOpLVXZqevx0YB933zCneBYSZa1/1uDxw4HxnnFJVTN7mejLuIgo1XcVURIu90XWlMF7KHAbUS7vtXrPy3vQZGZ/Jyqm5Fpau04cKxBlVL9JnaSbipySkworw9tOXAcA97v7iw0eX48o1ZlZglJarNwH2Lp2d3jaVfsn4NocE0suJ6oUNeqd3uOZ2TnA0URm9UNElZfq68MFRMnyzMuYlo2ZzQeO8dSCQZZdZtY7q4GlmZ1GtOVZx93/W3X8GGActKliNw/YqicuhFdYtCd83t33svrV404gdvCv243nfC9R1e99RKndf5vZG8Bodx/fII5vEi0kc1mYsmhxV3gyeLX0MzrW3S8uMIbDiQpkq9G2StJaxATxUe5+ScYxjCfubTZw97eaPK8vMSF4jbs3bdW1PCs6WaoqjiXE7vxGLTz2Jd5zebUU+TRwJLGgUa/CqXvGLSpSQlJLa9PK1+0pOkFJyTeyrEhJL2Pc/ZyC43iYqBbw2dr737QQcz+wortvVUR8Eszs48AniOvBz6m/6a2yaegvec8viXRWWe4BlxUWlVgPBL5KtFrL6570JeAOd/9Wg3HwfcDT7n54HvGUTRnu2csozY8e7e6XpeSx+cRa4vXp8W8CE9x9lYzO/ydio/jrwHXAlZ4quxadfFOG10x7a0KytDJs8JdlXy5Zs1J6nyV2tryZdqnX+geRhJKXl4nKFHWTb4iSaC/nEMf/A3YiSj7uTuyK/q9F3/Ir3f0vOcRQsQcxWb9fjudsl7uv3/6zcvE94NvAL4hd8lcBxxN970cQC5on5hGIuz9vZkOICZKxVfFBaxneIiZGLid2TbzY4PFt0nO67ebBzD5Xc+h+osfpE2k3xd/S8Y2Bw4nM+Ae66/ztyWOH43Jgb+Bid785DbxrPU8k5uTCCm69V+Nxoid3KaTKWpu5++QGjw8DnshjsJWqIq1YXeEmVU07lCiNfm1eFV7M7HvepHVcWmS4jrjOZuGzRAuT6sSbfsRO1jeBvYi2aXsQbbq+B+RWGc3MPkNcHyv93OtNBOQ5XvgoUfmmkVeBNbrzhO7+X4s2GT8irkWnE4PrhokUKYa3uzOOdkwAxpnZbcCVwK0l2In0R6LKXyHMbC8i8fpmojXYpZXH3H2Omd1O3L9nmnxDjBWub5Z4k2JaaGbXES0cc0u+Kdm1YalkKeBfeSZLVYfTzuMr0IUWJF0KJCZDLyc2fDxL/CyK8CLwjpmt5O5vp6878jNYbpNeUmW0znLPuV2jLDPmkO+9QyM/JRI57k4bLf6ajm9KXJ+2AQ7LOygz25Zok1iaBc00ptmIxgsvmY09PVo3PZ7i+CDwG8+pXVtHpbF5o3F5bteyks0RVGL6HK1tu89z979ZtCDdEpheVOWropTsHrC0zOyjxBrEfsRc0xJiM3CeG20HApX1jsp4s3rz803EtaLHJd+U6J69jF4GKq2zF5jZHKIa4/Xp8Q2JhJxMuPs2ZrYpsSl7P+AQi+r7VxIbSQtRotdMmzWh9jai9GQd3eBPN66fyfJLyTcCcRPVrPRvs57UWbgOOC5VVTmrUpY4VcY4gdhdelbWQXiUhboTuDNl6H+VuAn+DnC4mT1LJOFkHgvxO7ovh/N0SQkGuwcCt7v7AVUJAg+n3c9XE22FtiKqBmXO3R8GPl6yMrztLTL0IcoVd6f7qN9XGuDsqscqxz5IvOfy7B28AvH62YMoAw5Rov0G4CovqEx7kRN9NdYmTfo18D+ipU6m0mfwebTTeo98F4BOAiaZ2Y3uPi3H8zZyJpE0WneBlegF/Q/iOpa1i4BPEX3CK62Xfk98HgIcY2afdvfHcojlbDN7xd2vqX0gvf+vJZJrs/IRqpICkiHE++YH7n5XOna1me2SHstFmhS+i7gH/BOwG3GdXIVot/QE8Ehe8SQLgZWbPP5BYG53n9SjJdgRFuWsAZ4mEqcubPAtX6L5Z2N3+yLx3v0K8GUiGfwa4jqVZzJ4tWOBO8zsdwWVbx4F3Ovue6R7v9r32TTgWznEsT7RNq4jniafmKqV4tpQomSpas0SSzYmg8+aBk4CniF2Fc/M6Zz1jCV+Jotrvu7JxnThe5xI4sxMmid5B9jI3ReZWUcq5mWSuGDRprGz3LuxfWQzqSrth9399qpj2wAnEwnhV+ZYwe3nwP5mdkGRCbTufqmZfYRI+N6uzlPGuftlecZkZt8iNt+9TXweFr6gaWbHE/OPzeZDcxl75lUZuCPS+Ok4olXFwCZPzaPq9ErA+ZRojiDN4/yKmD+unP8aYuPZYiJx4VzgBznFszLx+6o33zXO3TNbDK+KoYz3gKWRqrD+H3Ev/knidfMYsfniV+7+as4h/YfW8fgbRNLA/6t6fBExT5kJM/t5F77N3f3Qbg9maWW5Z2+RkjMPo3niah73XA8Rm1Iqieu3ACPMbAExp30Ejcej3cLdnwJGmtlxxGfKISmeymfxtmnuom43iYyU5TUzH6iu2tzeGlFPVpoN/rLsU/KNQPTvbFZSdkdad8Pk4XRiweN44HtmVrk4rU0Mmn4PnJFjPKQByZXAlWa2LpEtenyKI4/km2nEjVSplGhBfANisRdaE0j6QPzuLNoLfZNo8ZGbtDOpTLuT6k6gp4oUQ4FZ3Xy+UleWSdUnbiOqaTmt//+7ET+PA8xsN3dfmHNcpZnoI1rcrdPk8U2BPAYQE+hA672cfYNoL/dHM/sDMYm1pOY5eU0CQEyeN1s8uIP8drFuR0zoVXyVWMg8AniUSHg5AfhaDrFcAlxuZnPc/e7KwTRx/CtiIjLLn8saQG21s22Iz5zbao7/iaiEk5eTiM+9wSmeOURC0D0pEeh6cqzCk/yZ+J2cV/uARduebxD3gZlw99npPy8DfmRmdxETRwCeJvnPIkqm55HIVolrKjA17ZjdO537u8B3zewZ4Argl55vz/sJxATEdWb2CvB36n8GZjXZ9zHiXryRWcBaGZ272gp0PHn5HZrvnspCWa4NhSdLmdmBRMJ1xclpwbfW6kTy6I1ZxlPlg8Cooifx3X1Ms697qLJUea31EnHdrozt/kFxiVIbFHjujjibeE/fDmBmaxBVBFYBFgA/TfeIN+UQy4NE8u4fUxXYetfNXDZZuPvxZnYZkdRbeZ3PAG5x92ezPn8d3ycWm3f1ErTYNLNDiSodvyOuk2cC44lF50OJn1WjBO3uOH+X7jFzaoFwFrEw9RTRtrXIcflEyjdHcDwxnjuG+Nx5uvJAqoJ4IzHflHnyjZmtTlSW3pioHlrZDPhRYkF6bzP7rLv/J+NQCr8HLLnZxHz6v4hksisLrnL1LLAJgLu/Y2aPAgeZ2RXEPOQBxGdgVg7qwvc48VmQtVLcs1eY2ZeJDex9iHZL/23+HZm6ENjDzPq5+wJirmlrWhPZn6K1K0Cm3H0R8XO5Lq3hHUy8ro4BjjazB4hqcpldx6uU5TXzFHCUmb1K6+tkI1u6Y0EbRVSOK4FSbfCXZZuSbwRi8ekUM5tE6824A5jZscAXgOF5BePu/7PobXowkalamQyYSuwSuKKonUJmtgFxo7k/sTC+KKdTnwBMNrNJJamwUDGBcgx2F9D6u3iTeP1WL7jMpm2mfi6KLsNrZqfSmnXuwC/M7BdNvmWpxc53w93zLI3aFScDnyd2Hv2w0hYmJSOdSEwSnASckldARU/01XE3cLCZLdW2x6KVxSHA1TnEUcbWewdV/fdn0p9aeU0CQHzmzW7y+Byi7HUeBhELChVDgafc/acAZnYxsZMgD98ldmXeYGafc/fHzcyIhNp9gO9mvLt3LkvvDNuG2Pn4cM3x+eS7gLU1cL67v5omZyElBrj7HWlgeTqRhJ2XcUSSydXEDnGAgWa2K3AasC7w9ayDcPefWrTkuoS4NlZ2rb6PmHS83N1/mXUcdeJ6kygZfLmZfYBIRtqfuG6cAbwnx3AqC66V+5m82/AtoXkiy9pkWNq6yizSBHUHbEL3Jzq3pyzXhjIkSw2gdVzpwJq03QFYOf4m8flzUsbxVLwM1Gv9XJiU6Pcj4Lfufl3R8RTF3V8qOoZ63H37Zl/nyd3XK+rcHTSYtgmI/0fM43yCWGC8j5jruimHWO6q+u9LqV8hNvONS2a2g7vfm5Js6m5OMrPj3f3sLOOo8X6iCkfhiTfJ4cAf3X2HtPByJjAlLbxMJBKFsvw9XUG8FqrnkapfL1bnGOTTAmF/YlFqtxzO1Z4yzhEcQFSmnGj123Y/TSTf5GEsUU35SOCiVOmzUp3nMODHxML40RnHUYZ7wDK7kZibmFpU5e0adxAboY/0aKt7PrF56j/EZ04/Mkzcd/e8Nyp0Rtnu2c8mNnrt4Tm1dW8kVeP9S9XXrwKfMLPNiXH700W8vt39ZWJO63Qz25GYn90D2J585tbL8pr5PpEwW9kk6cRYt9F4N7N7UjP7lLv/sbv/3W5Uyg3+smxS8o1ALDzvTCS3/I34cB1vZmsSC1Z3ku9iLym55hJKUHbSzPoD+xKDqE8TF6DpRJn2vBZeDiMu2GWpsFBRlsHuS8CHIDKczex5ImmskhSwE7GLIHNlKsNLTApdRbxmDyB2vdTuUKgsMvyRWFjsSfYFJrn7cdUHPfpvH5/Kd/4fOSbfUPxEX63TiJ1IfyFeHw58wcx2JlrwvUUs+matdK33SjgpMJf0OdjAh4kqFXkw2r5Ot6dtJZzcJtjSbq2vEQmit5nZZ4n39H7ASHf/WcYhPE/sKj4PWpL7tgUeSZNZ1dYlp2tVsiJRvQnivQxt28g9Rkyw58bd7zKzw4mdrJUkm8q1/G3gW+7+h5xi2d/MfkP8DDYiXtd/IibUf5NHDM24+z/M7JfEeG4kObQArDn/enmer47HgV2JBIE20r3Y3lRNAGboAeDrZjY6JUfVlZIZvk5UXMjTXMpxbSg8WcrdJxKfLZjZO8AIL0ef+58B+5nZ+MrCWNHc/c107cys0lgzZvYGHU9G7ZNlLLLMW5O2VTq/APy+UlXAzK4lv0S7slSFrSSk112oM7NjiKoceSbfPE2GbUy6YGNiow60fhb1AnD3WWkjwXBaE8W72w41X/chfh/vI64ZlcrkmxIbGv5N8+SG7vReon1QGZRujgBYj+ab2uaS32v9y8CltdUd0r3GT81sC2LDa9bJN4XfA5aZu+dRDbgzfgCcW5mrcPdJZraYGBMvAa53918XGWCBynbPvh5wfNGJN824+/Q8zmNm67RXBdjd7wHuSet8eRUZKMVrxt3vTQUFPklslryCSE7PZW6txkNm9lfiHupqz7+1XnsK2+BvZn1S5aaOPv/z7v67LGKR7qHkG8Hd304LqUcRi1ELiTKUzxEZzhOLzL5OrWFIZevyPO+XiISFLxGDujnEhO2V7v54nrFQvgoLFWUZ7N5DJAJVShheDYw1s7WJxbLPEklmeShNGV53v5k0MZISSc6obruSt46WT86pXDLEQnez18XviMmIPBU90deGuz9vZkPS+camw5X32ZPAN9y9tqVOFkrZeq9kHgC+ZWYTvbV1DgBmNpDIzM+rZOjfiUXxn6XqIYOAe6seX5vok5uLVOJ7KNGHejqxW+u4tBCbtYuJlpXXET+DvdP561WM2p6q0uQ5mEV8DlZ2cMylbauVdYkKPbly94vN7BbiZ1VJenmOSJbMs60S7n4j+bWe6RAzW42o2nQArfeDTxI7JnuSC4BrzOx0Wnd6r2BmGxKTxpuSz0LUBUQFohvNbN96pfvN7L3AJKIN3Y9ziKlaWa4NZUmWquhXJwGyjY5M4naTh4kWFX82s59QYCuaGn8lJvWL8DAlbmVkZoOJKnbvZekFRXf303OO5x5iIviISiXPmsd3Ar7v7nlWsiuL+UTVq0q1h+1o+zm0gOatfrtNiarC/g34rZl9unYcZ2ZHE+PjK3KO6Uzgx2Z2eQlaM0B8BleSASp/V1cxeZEMx6a1CylmNpaYe/uYu1cnzN6Srht/JOa88pjreYIY25VBGecI3iBa3TXyYaIFVB7eT2t1+3oeoW07zqwUeg9oZl2ZOytic2spuLvTujGncuwG2m6m6qnKds/+d8pRVaWNgroA3Glm29Ubi9fxLeBUWue4s1SK10yqmvyqu9+Zvj4NuM3db2n+nZm4mviZnAv80MxuJdYcfluS6l9FbvC/xsz2Tp/DTaWWYbeS8yY86Rwl3wjQUmlmfPpTODNbiyh/uTupFLqZzSEWQE5z9zx2ht9C3HBOJhY0bi8qS7WEFRYqyjLYPRe4w8xWTBPpPySyUiuZ+RcTN1Z5KFMZXqBlt/WLNJ8EyMMVLF0+GYoplwyx6+jDTR7/cHpOngqd6KvH3R8GPm5mmxHJQQY85+7NJnK6W+la75nZEiL5qO6ueTPbF/iVu+dVqehMYBjwqJmdR1QtgSirfyywCjn0lk8uB843syeBdYjk1alVj29DTPx3uzSobOTbxDX9ImBS9XMznAT4JZHEuzcxwKQqhhZm9iGiDV4ufbCTv9A2mfcOYKSZvUQsJh5JVHrJXUoSyDtJobTSxPQXiISbYUQC16vEJPaV7v5YcdEVw91/bWYfIyoWnJgO305cowwY4+6ZV5lx92lpUexU4O9mdgOxyPA6MRFS2VW8GnCquz+SdUw1ynJtKEuyVMXVRBJbXWY2iEju3zCHWKoXSwtrRVPHOcCFZna1R3ua3HiBbZSaSZuCbgB2ofX3Ut3+pXIs1+QbInkXYCszG+ruz9c8/n7iHiMTZtabuGZP99YWo6sT45ZaM4CtcpxXeQo4wMyuIu7FViEqO1d8kPwWwstiKLHT+fa0UFVpvXw40Vb8l+5+SJ4BufsNabHur2Z2M/HaqVfpOa/31j9IbQrd/S0z+yeR3HJtevyTRAuWvBwE/Kgm8QYAd389tUA4kphDzdppwGVmdllOm3CaKd0cAfAgsL+ZnVP7QErGPoS4X83Dv4j70Ea2IJ+qq0XfAx5U51jlXqve3GTlWp5b8k3asHQirYm9S8Xl7j1iDc/MRnfh2/K6PpTtnn0CcKyZXejuhVaPKkEXgA8R9zU7tlON9hhijJPXmLwsr5m/ExuGKvPXL1JQxTF3P9DMjiC6ERxCbKbfHZhtZlcSLd6fKyK2pMgN/nsSa5jfavYkM9sOmEJNoqSUT4+4cEtzacD9YNFxVJjZ+sSAZRDwDLGLA2LR9zvAV8zss+5e2z6nu30XuNajBY3UV4rBrrvPInbwV75eQpROzbp8aj1lKsMLtJSO35d4XxWptnwyxHXoQ8T77X/kV/YbYuL1CDO7092rEwMws12IFlDX5RgPlGyiL2VSP+3ur6by7E/WPL4GsEkOOzvK2HpvqV0cnXy8W7n7Y2b2VSLx5RzaTij9G9g7x8/picSi8+7Ebrvvu/v/ACzaqX2K7AYrL9J8t7wRSTjfrjmeyWA37dzY18zOJhLnXmjwezBi8JlnZYPLgIPMrF+qLvh94vPmivT4bGICJTcpceEKYhdOblV3OlqZrVaOldpmEq0zFtGaFP7bostdp6SxkTSvANGs5dG74u4np9fMfrStknR1nvel7n5aul6fSesO4upF+dlEm7vL84qpKrZSXBvKkixVZfdUDWipkuNpE8g9tE1+zlJZWtHU2gj4J/BE2pH4HHGvXi33Ki8FG00k3pxJTKbfS7zn5xCv635EkmQRLiASyv5kZnvmXIL8a8S9evUirxGJLtOBSjWeFYjKM/uQX7vjccTYfE76+lGiIljFLuS3AAOAmb0fGEz962bm9xbu/h8z+wJREXJyqoy0P/EamkQBr2Ez+yixA301YnGonjwT2+4nkpQq16vrgBEpAW8F4ueVSyXaZE2aj1V6kVNLX2ArYlf4X83sRurv4s/r2lDGOYIziXm3e2gdU33czD5CzJ+uTFTLzsNk4Ntm9ghwSaWiQFok/yax6HlRk+/vFkXfA9ZuZjWzNdP5XyLujavbuB1HtBP5Qlbx1EpzbncRlYH/BOxGvH5WAbYmqk1ldp0q4Th4TBe+J6/rQ6nu2VPF4NWAp1LSwovUr6qSx5xF0V0AKoklt5jZF+tVODWz4cQ85KNE9ZI8lOU1s4i2rXo/D1xSUCykBKnLiGTejxDXo28Q18njzezB9Ph1nnMnFIrd4P8j4Ggzm+vuo+o9ISVr/hZ4m/xex9JF1oEqRrKcs+h5/zfiQ+0qL7jXXppE3w34mrvfVPPYHsREzW3uvmcB4UmVVL5zC+BjxO6pMgx2C2VmfyZen2OKjqWamU0jFupOKTqWesxsReDPxGdQsx7Z7/Y8M4AR7n6LRSuuvxCLK48SgwSIQfcWxKLU1u7+Ulbx1InvQmA7d988fX0uMILYIdQy0efuh+UUTymqu6TrVHs8xyozlZj2b/KzOY5IOhmQV0zpvP2Iss6VCknPAncUMGAphJmNoQutKtz9tO6PZtljZisDQ4hr+YPunlt7sHT+BcB7iMmaXxFJFA/ncN53qF+ZrZncPnPM7E/EBP61XqelSBHSJPqDRKnrZ4h7waeIa+pA4AXgZXevl3i7XDKzPkQ1qc2IBcTXiaTV33snendnFFsprg1mtiUFJ0ulOA4kEpJOdPezq46vSSRUrAMMKaBSUWmU8d6raGb2HPCwu38tJRO/Cuzk7vek6i9/ISqgntj0H+r+uN4hxgi/J3ZBfhj4jrtfkR7fjxhjZfK7MrPfAGu4++erjrX5+VQdv58oe7/X0v9SNtLC5leIhc0LPLUkSDFeQvxsbsohjhWAnxCL3g0rG+d4b7EF0UL8BWBzIklpnyISe83sLiI5/0QiOaruvU5e4/JUkWN74rWxIN0fX0Mk5EBUi9zPO9beojvieZTY2PDJ2vvAVGXqL8A8d98yh1hKc20oUyzVLNodX0qq4k7rGGMOcIC735FTHO8j5mk/RHweP5Me2pBI6Hoe2Nbdc1kkL9E94NXENeuLDR6/HZjj7rkkIprZVOJnMph4rcyh9d5iF+B64Ivu/vuMzl+qcXCap+20POdtyyIl895IXD8byevzeCbwmBfYBcDMDgN+RnSy2NOrWhhZtNWcQFSBHVKWOZW8mNnTRKLhIe4+L73v93P3vJLh22Wt1Z4PJir39gHeBH6d1xpIGZjZFUQi0inu/oOax7YlkkcXE6/jPDsSSBco+UYws1HEB9tGtO6ovRSY6gW8QMxsHnCpux/b4PHxxMWifzef9wPQ2nrCmreuaOHZtapow6JE6aE03108JI9YqmIqzWDXzFYldl/vQgxyD3D3P6TKHN8FJrl7Jm1OauIYSiSyfdKLL8Pbwsz2AS4kBte5lo7vqPRZ9J0sd8nXJkyk9/kPiRu7VdLT3iA+B7+f1/u7Kr6yTfS1l2DydaLlSZ96jy9vzOwrxOQ9RPni+4mkw1qrExnoD7p75rumLFrL3UKUib8s6/N1IJZ5RHuVM4qMRZY9aefWvsSu623T4aeJxJNfelS6y+K8XWrHkXNlgVJJu523S39eo+1E8beIMvKfd/e/Nvln3s35zweucPfpWfz7svwzs+8Tu2QPdPdfpAXMe4H1gJ3d/c9Fxle0ji5+9KTFDjNbSFSx+qmZDSCqUe7m7renx48DDnf39XOOq+V+PV1HrycSac929+/nkHzzInCZV1W6aJJ8czLwTXdfL4tYyiy9Ps4CfkGM6a4iWq28QWy2mEckBN7T6N/IIKYhwG3ERP6eRSTepDjmA+PKtoGplpn1B5Z4k5YWGZ33K0TLu9eIijuVJIqNiLnc1YGv5pREpmtDB6TNZTtT1babmGOvrSCXdRyrEZ8zu5MqLBPzFzcB57j763nGUwZm9hqxqHlhg8ePAE5z9zVyiue/wPnufnq6F/03sIu735Ue/wmwsbvvmNH5NQ5eRqXx+JeIahnNElcz/12lTVQj3D3zalrtxNFyr1VJoDOzI4mf0ePAjj0t8QbAzL5LVDjszDqve0Ht7tJn4U+JlrE9bbPHCsRY7ivAEe7+s3T808BUIvFmp568SWhZorZTgruPA8alslWHEh9sewCvWPQOvtzdX8wzJGJg0sizdGFXewe8CLxjZiu5+9u037qiIo/Ekg8SO9nWJiZlViMm+ypJOP+mgF6NXlO+syhpl+qDwAbE7o0NiLLfuPu/0+7WAcAxGZy7Xj/aspThrbYslI5/i9hpnJuUXLOfmRmx+wdiN2Yhmanu/gytk2l49O39clETfZUwmjy2LfH501N8gtae4Q58Lv2p9SZRyv3IPILyaC33SeCXeZyvA7HMJRZcRDolTQBfAlxiZusR7Tz2J0qC/zDtjL7S3a9t/K906bzLxORh+pnsRCQZ/9LdXzSz9xCVZman+9e8bAdc7O7PpEVWSDsm3f0SM/ssMfH15YzOPwIYbmZPEMlZv3L3OU2/owdKlQ22dfefNHj8CKIyz2MZnf8LwEJ3v6/Jc7YH3pPXTvAKd/+Bma1DlLteDIwixhBfKCLxxgpuRVPnfD164bSBN2idQ3sDeIcYn1fMIz6PC+Pur5vZF4kJ6xMsSrln/d56P9H6pdpbRNuBf9Ucn0V+7XHK5kCiMtIBVdfNh1PS6tVEi66tiHYj3cai8mszS4AtgediSAxk3DayjnksA2MHz7kiZNV5b7ZoITmRpVvCvgzsm0fiTYpF14YG6myIuTX9KUwaW51Evu3dy25FYN0mj6+bnpOXFYFX0n9XWuWsWvX4Y8R4OBPLyji4LMzs/wGnERt/1yLGDfekdYmzgZ+6+19yCmcIMNHdv5fT+Zp5AhhUdBDufk7avH58Smx7lki8mU4kLOSaeGMdbOuW9TjP3S80s78SSaGDiHvSB6m/mbQwZlaJ7SBaK/bm9X6qjeUjKYb3UacyWFa/M3d/x8y+RlQzvSDNr79IJMovITYJKfFmGaHkG2nhUcLw96kU29eIRJxTgJPM7B6iGs6Nnn3Z9N8BOxCl4urZniiP293GEoupi2u+LoMziOSRIcQNzRxiZ/gfiUHU14h+jT3VGcRE5zbAP2jt6V5xM/Gzy8KYJo81GiDl2a+8YkzVf+/R4DlFxAW03GB9h0hWyl1Ktintol2eE30WfXCHVx2aYGZn1nnqe4lEwDz73RfKozXRadB+VaACPEbsrCuDe4lrUqG7XqqZWS8iCbHRoub9uQeVs3Qv58Cu7r44fd0Ri4nPx9vd/ReZBVgjJX6fBpyWyqseAHydmDDo1uSbWhZtNS9y9z81eHxrolLbIVnGUXPOs4kk4l7E7/EPxCC8L1FC+GSilHJeViVaVUD0mwZYuerx3xOV5bKyETExsx9wPnCORdn2K4Fbck5EKrNTiVZudZNvgC8S98jd3s7XzHYkJo2+0c5TBwG/MLPt3f2B7o6jHUem8/+SSEof6hmV9W/EOtiKhqiSIcV6AfgogLsvMbOngK8CP0+J/HsSmx0KlaqXHGbRJussouVclpYQnzPVMbxJbOqq1YdIWspNiSoIb0DrvXHlZ9AnBTA/bXz7JjCum8/7D5rPa5UhmWIS8f5pdK3KlZl9GPhwpapVOrYNca+1OpEIfnGeMbn7jWZ2M5GgtUE6PINI4Mr1PVU2qVrwp4lEwLvcvTbpLxdl2hADYGbbufuDBZy33gbF9uS5EfBB4Cgzu712DiBVgTkqPScvs0jJQOlaMJdoYXtjenxdWtcplntVyQpXu7uXJXkBwMzWJ9Zh+qa/W5JN3P1VMxtMXMfzShZ4i9iAXAanERsaLvOCuwC4+4mpQuVRxP3PdKLiTS4V5GtcQf22brX3ZZm/ftNmmPsAzOwgYr6r8Plsi9bdXyEq+e1CzHe9RiQcX+buT+Ucz/uJOaWdK4fqPM3J8Hfm7m+nqod3p1gWEmOHXdz94azOK91PyTeylDRRcqmZTSGydvcndtnuBLxqZucSJRGzKkk7ArjPzM4jSiXPATCztYATiEmT7bv7pLUlbktW8nYIcIm731u9uziVLD3JzDYlflf7FRFcCQa7XwIudPdHqn4+1WbQWq2iu+VaVvxdKDzOJgu9qxOLaO8hFtKy9j7rYFs5yK+1XInMpXUSdj3iprf2Pe3Ak8SAc3x3B5AWvh04LC1udCTBx9390O6OpZ5URvpgIhmyLE4FbjSzKe5+b8GxjAJ+Z2anAed5weWszex44v5htSZP6wllTDcgBmxW9XVHK/xtB/yfmX0oJaHlJt1jfDT9Wbmdp3eXg4C7gLrJN8Q19UAgl+QbM/s28b76EbGDtqWKQapycAvRPnFCHvEk/yJVeHD3NyxaRny06vH3kuH7yqOF5knEffAORHLWnkSbxrlmdi0xafuHrGJYRnySeN008jvaJtx2p4OBJ9ub1HP3a1KJ8G8SJdO7XTsT91OJsdaNwHqpwlQltjwSXr4HfJt2WtHkEMdSzKw30aqiWdJCLvdeJXEXcIiZjUhzIRcROxJfIK6n6wPfLyCul4iqi224+zgze554bWXpFWKhsCM2o3WHf+ZKVkF4AdHmHeL35bStAjQb+H/dfVJ33767/80MXARcaWY3EdesetWD8xyXn03MUVRayq0B/JZoU70A+KmZzcmr2kxFSrL5CwXtAq9IcynfpvGO8LwS2jCzw4lk79WI99TOwL/S/PE/gKPc/ZI8YkkeozwbYu43s78BlxGtB/OqLjWmC9+T50bAY4jkmnvNbBrwt3R8I6IC4evAsTnFAvF+/kzV13cAI83sJeI6dSSNx6SZKbAi4xXE6+FaYoNH5et6C+At4ZBPkvqZxHzKZsS1oHYD523EmDwvU4jPvEab1zPTIMmusC4AZla7keRu4mczkKgGuX1Vdb9KMDdkEUuNHeoc6w18CPgusfmjiMpk61NwxUEz+wQxn/Z14nPGic+/nwM351D8oZELiNfOT4lqlK/ldWKLdlvVvkHciw4kCi+8UPucgpLKpIPMi+msISWVdt59idgZ9EXigvAgcDGRUXsksQDzU3fPpJ1GKou7MlDprzo3/T0g/V1vgiTvsriVMupfdvfv5nCut4DvuvtlFj1756Zz35oePwI41d1zL+Fcb7CbSi7mNtit+fks1V/eorflue6+UpZxSHNm9iJLL/Q6MQH6LHCBuz+UcQzv1ImhGfcMe5x2IR7Ise+qmf0dGO7ut+RxvqrzVn4u/VLGd0d287nn1Ac2LUYtAI5192aLmrlJCUqfBDYhehk/S/3WcpkvkqXr+CrEZCzEZ3K9WDK/bpvZoUQbo98RA7kziYSxRcS9zgwiefPKrGNZFpnZiu7+VtqN8nPgs+6+Xg7nNWLAewCx8LsScf93DbHbONMyq+1VtjKzQ4jXTd8s46g63+PA8+6+V4P7nBOAI929WQn17o7pZqId4p7p61uBjxOJ4CsQi70z3L1ea76sYupHJOB8g0imsLyul2WV7pGPbHQvbmbfAn6cxWs53UNc5e6nduC5Y4BvZHVdqLqvaDZxXyuX+4pUPeUld9+t9v2dkg+nAz/zaBWdmzSxdy+xwGC0/flV/ju3e68ysGgpsg7wgrsvTseOITYrLQGuB8aVrQpF+l2u6hm1izGzS4gdqxt4kxa5aR7jBaKa8mFZxFLnnFcTVV+H0VpBeCdqKgi7e23brCxieRS421N7CDN7Bpjm7vulr68FtnH3wjfN5K3mM7rh2DjHsd5LRGvNM9PXRxHjh08QY6x7iUXhMcBhxCais919Wh7xFcmird2NxP/zmzRYlMrjdWxmewHXEZWuJxPV2qvvj28C+rj70KxjqYppR+Lns3vRG2LMbBSRCL0RMfat/IymeoaLQSnpsdOyukbVkxKtf0Csf6ySDr9JbHI42d1za8ViZjsTGz++6e4LzGwDIhm9UlVlNlHt4Mmc4ulQRcasPo9T9aGWdliVr9vjObTPMrM5xLjp9AZj8sOBs9y9f9axpPNVkkT/SFQJmZHle7vm3F25181s3NBgbr163FJ7vPAxTNrU+WdivHxeQTGsRtwXV1fUu9Pd38jwnEcT16bNid/F34HLgSvyuB9vj0X1sV+6+xEFnLszr2PIcX1Iuka/HAFa+tgdSixyvJ9YDP8xUW3lb1VPvc7MLgT+j0jEyUJ7ZXHLYitit0fmyTfEDV0ls/ENotzYelWPvwfol0McbaTB7k9oO9gFwN3nmNntxKJZ1jtNZhNZw41sQbyuMpOSsRZ6lPFr9Jztgfe4+x2NnpOHNEioTIb83d1zyeLNY8G2g8rU1/QqSvx5V9Tkr7uv0Ozronm07JlN5xbxsnZQ1X9/Iv2p5cS1Pmtluo4fDvzR3XdIn31nAlPSouZEYndij1k47Cgz24p4rewLvM/dF6Vki20zPu9mtLaXGkRMFN9GlFqdUlnszOjcH6DtvdVGZlYvcWR14nWVZ4nnjxI7bxp5ldak9bz8CjjCzPq5+wKiVe3viAUpiATFXCtApAnrV4jy7QuJpK2ebg6waZPHNyPGfVkYRLRG64iXiMoUWam367AsimpF054ziAW7bxIlyl8g2hf9g3i/f4Ts2xktxcw2Iqrgrkq0W+loC8V3LSWWPFNz7Hyi9V1ppR2RWe6K/DExif4bM9vH67TLtWj9NInY2XpBhrHUKlMF4XuIRKDvpa+vBsaa2drEmOKzwLk5xAGAmX2IlDSVDs0gdhq/0Pi7MlOmlu8AawIzq77+AvD7yuK3mf2aqDZ4EXGf+kViXL9Jd5w8Ja++A2yU7sE7MneR14bEHxIJ8buXINloFHCvu++R3t+X1jw+DfhWzjHtT1wn70rJ84VtiElJu+PM7DPEuG5v4jPolXRvcblHm9/uPm8ZWtk1lf6/v542fFQ2sb5aRPKsu98J3Fn19Qwz+yhx/VoCPFjvupqhQisy1ibRtJdUY1Ehdjjd9PnbjtWIcWYj7yHftdZ/E9fOrYAjAGqru5Dd4nzZEoUPLjqAzkqb3H5BrCvmnnxjZt9M512Ftgkeb5rZMe5+WUannkDM1VxDtJUqunJ7rRWIDa1FKPUakXSekm8EM3uAWEwxYtL8WOA37v52g295APhOVvH4slEWN29PEbuJcXc3sz8D37VoM7ACsdvmb02+PytlGezeBhxqZj8mdiC1sOjNfQAZtmJIu1umEDutmxkE/MLMtnf3TMrqN2NmHyfKOG9Xc/wB4Gh3n553TAUpRV9TAHc/qOgYOsrMVqJ+Weme2JoLYpfdPmb24zLscC5TglLJruMbAyen/64MYnoBuPssM7uYmKzpSGuzblXkAmKDeFYnJowPAT5GvNefrTzu7tcTu/qzVLkOTSMm96/x/MqoHky0b/P05yTqlwA2YkEkzwmehTRvufVBWitF5sLdfw38uurrR9Mi5h7ERPFv89o1miaoDyAWTz9QOT+RtNXT3QV808wu8Zp+6Wa2CbEYk1XJ7bfp+OaAfrS2Y+l2eeyGfRcKaUXTAUOJnZiXVyUtLHH3Z4D9zew+4nP68O4+cdp1fR6RWPw2UfnnVDM7hfictvTHzWwq8BXPoTR5qjJ4kbvXbf9gZlsD33H3XFoS1jn/YJq3CMuk1L+7T7doP3AG8JKZ3UBMWr9OLFR9grg2rAaclPOY831Eq1xofZ9Vfy7dSbym8nAucIelqoLE+2ctWisnXZxXLGZ2OtGStTYB/Rwz+4G712snkRkvV8t3iErbAwDMrNJ+tbra6QLis3rjND/3FN2bVPYSrfejUK6NDRsRlUGKTryBGK8c3+TxWbS9nubhoKr//gTFboiJk7n/Hvh9qjbwtXTuU4gExHuIedwb87iOlk2qElLbYr1w7j4fyLX6dJUDgdvd/YCq+7+H0+alq4nx+lZEQmkZrAFsmNO5/knzTQ2fIt8NOoUt1Jctyc6X3UrWbxFVNXNlZl8m7jtnENeDyjzBpsBRwMUW7TUnZ3D6I4nKMnkmFXbGA6Q12LwtS2tE0jFKvhGIm5TzibKqz3Xg+XdR7t2Dy6ObgWOrdhePBaYSpdkgbrZq+1vmoSyD3dOALwOPEgMUBw5MpfT3JHYtnZ3h+Q8GnmwvocPdrzGz44gdpLkm36RqAg8CfYnXU/WN1TDgATPbtnZhRnq2tPhxHHHzPbDJU4su17kSMDCvhd7kUuJaeKeZTQCeY+ldbT01MalMltDaqrLy9/uqHn+R2L2fibIuINbEuCuRcPNlYrfWs8R19TcFXBPOIdpKPZ3zeQFuIl4PRiRjXQz8oeY5TiyO/4Wle7xn6c/EwuVSO6LMrC+R/Pv7HOOpy93/SdvFqcykKgr/RyTdfJL4vT1GJFv/yt1z6WGe4vgtcJe7n9zkeWcSyXZfdPfX84gtOYO4F/5LShx4LB3/BPG+fxvIZEGeGKd8CriwA8/dhtZxTU/zEqmCZ6pw8DxRZeHq9PhOFLMwNJD4rAOoVB2rbk92E7ERo9uTb4jNG8OJseR/gJPNbAnR4uV2Yhzch6iwuitwDNmO9SoOIuZC6ibfELuADyTeW7mxaLl3A7ALTVqEkd17HXf/gZnNJNp4HFRzbogkshHufkVWMTRQmgrC7j6Lqh3z7r4EODr9yY1F68yTgIeI+67qeYFRxIL8jDx/V2b2gZKNmZ4CDjCzq4hqIatQVZmCSHp+JS3cQyTq3N9dJ6/dyFCyjQ2vUrPhrUBLaNIWh6ioN7/J492uTBtiaqXqbZea2RTimrk/cY+xE/CqmZ0LnJ8+m7pdUQmiDWJZFRhJXDffDxzg7n8wszWIKhSTvG0XgJ6krBUZy+AG4Dtmdhmt13OHls4Ae5NfQm+pFurTRq51GyVYm9nmwD/d/b/5RlZeZjaIKG5QxBj4OOBpot1pdcvYu9N7/I/Eel+3J9+4e9O5gVQRch3gOXef293n74BjgHvN7B53/00B55fliJJvBGCdziz0pMnsMu8eXO6kC9OFVV/fY2afJtoyLCF2KTxUQGilGOy6+2wz+xSx2+gQYpLvG8RN8G3A4RnvnN+OyDjviJtpv0JOFsYSu/0+U3sznBJz7k/P2au7TtjB8si18iqXLB1zFlF29ingNzTo6Z4FM3ubmAS5Nn29KvBLYsfsEzVP34N4D+aZBPQkrYsK2zd5nloaFesfpJK4qazrP4my/temxz9Jtq0YSrmAaNHn/hBikXBdomTx9cR9xUnunlUljKbc/YQizpvO/TipvKyZfZBIPnqy9nmpJdeJpJZcOYU3DpiadhtWqjQNTIlTpxG/w6/nFEtZzCbGsv8iNhFcWe/3lYNvE8noX2nneT8mPgsOI8e2Iu7+gpkNAa5g6Va5TwEHd3DzRVdMBY4yszPc/dlGT0qVi/Yhw8QtMzsg/efVqUrBAU2/IXH3jt7fvxulakVT5T+0Vtx6gxhHVFfgWUQsoGXhYKIC2nbu/raZnU1UsfuNu+9d9bxzUwXPr5NP8k17VibDCk5NjCYWEM8E7iba/x1IJImeSCSXdOg1/264+xVm9kvgM0RLu9WI6jdPEi17ivjZlLWCcJGOIBLItve27TxfMLPbiE1CRxHXjbz83czuIDY33OwZthntoHHEvE0l0fpR2m6e2gV4pPKFu59HAS0jCnI1MWeUS7J1Ox4nxk9LxZI2QOxNaxJpj5Z+Hl8iqt58kbiHfpDYbPAWUYngLCKx7MhuPnfhCaI18axJ/L9vQFQp2YCUhOnu/zazA4mEumMyOn9XKsa4uw/p9mDqK2tFxjI4k3gf/YmYQ3fgBDP7AbA1scmhp1wLap0DbJn+1HM58XmcSScNM+tDVJSeV5tAaGb7Ep996xD3hafXmVPORJP3++pEJbn3EPfsefs4MLYm8QYAd3/DzK4kKuJ0u9SVYQgxf/Na1fE1iHuMXdKhxWZ2pruPzSKOJn5KfPZNShsLZhDrn9Uy+Uw2s68TY6aXqo6tTv3X9ebAV/OuVimdo+QboYylJS160p5Ia1Z8bZsT92x6Vi4zUpnXoku9lmawm3Zbf8XMViOqORnwfMZJNxWDiN3yHfESkZSUt88BP6mXhe7uT5rZhXT/TXCZyiOXkpW7nzvEbqjb3X23nM5XrTdtk/veQwx0JxQQSz1jKdnrO1VgOJTmO8pymbQp0XX8fqJ1RqUv+XXAiDQJuALxGs+y5VSpFhDNbD8i6ebzxADyVmKB5TZisnW/rM7dUam8/3607kQ8zqOl0XuJSm13u/srWcbg7qfVxNS0JVfW3P0uMzscmEhrkk2lKsbbwLfcvbZKT+ZSIviRRPWoem0Js7xe3Ui0lZrqxbb+G0YkwTetTJISxW8gknRyTaRIY4bNzOwTtFb6ejYlnGVpApGcdKeZfdPd76x9gpntRCy2vkW21/criGv2tcR7pvL1Uq00qzgdT65/NzrSimZMDnHUehbYBMDd3zGzR4GDzOwKIrH4AGIyMgsfAn7orW2wryAqctRLDL2BqPCUCTP7AG0rpWxkZp+r89TViSpAebYbqPgqcJ27j65qEfFK2rBzNzEeP4jWe6HMpLml+9KfMihdBWEz+wiNr5t5JP1tDJxYL8HF3Reb2bXE51CefkZUs9sFeC1VnLmsoEqIuPsUi9biXwHmARdUqtyk99jLZHx96MICfV7jvCuAHczsZuK+9O8svSiVV/XXC4BrLNqoVX4fK5jZhkQVrk1pXqk7M2a2MvBpYixzV3v3iRnG8RFifuCAFMt/iITwS2oqu1yX5gT/j25OvqEkCaJVziCq+21DzFnWVjO9mVgYzsoGlGweqUZZKzIWzt1fT+Pf04kxuQE7E+2fLyQ2MS3MOy4z24FI5N8gHZpBjE/vzTGMHYBfNHn8FrLdiHwyMVZYB2iprmNmxxAJtZX7rY2Bnc1sK8+ncnq997sTn8U3EPcXRWymbzYGhmw/ow4HdnP382uOX0qsMc4gEtm2A041s+nuflOG8dSq/M4q9zEfyPHcVxPvk5eg5Z5zDvE5U3tf+DGikqWSb0qsRycvSKuSLdh9jijnPI/IJt6N+IBZhcgkfoKqXSbdfO5GGbr1FJFAUTalG+x6lPJvk/CTFoHHZvgafpuOl6vuRzE7Ilcmdig0MovW3a3domTlkSt2IEorlkWZ+7lDXA9uLjqIMnL3MUXHUC1V6vg9cW2aR+w2/g+t1/R/k1PZ7SKv43VMBB6vWnQ5FfgorbtL7gCyrLZSmgXE5GpiMDsCuKZmp0nGp26fRQu5O4BtidfrSrRWVnid2JH5c2JyJY94StOSy90vTjv19yZ2SRnR7m5S1slI9aTKIZcT9zTP0jo5kQt3/1qe52tiE+CaDj73z0QSaSHc/TFa207lcb6ZaffWJOD2VHnsMeK9vCrR+uoDRBuYr3q0ZMnKDkDfqs/i0rRQ9pK0oqnjDuB7ZnZkSgo6n0he+g9xr9qPqBqShffRdmGn0kau3mLPHNq2w+puBxP3DpX79ZPSn1pGJNQfnGEsjfw/4vcDrYvg74GWZIpriInuzJNvyqZMFYTN7P1E0ujOlUN1npZH0t/bxD15I6uSc1shdz8iLZB9lZiXHAmMNLM/AZcAv3b3pdr7ZhzT/dRpJZXunfNI2NqeuMfq6O8irzmEv9GavNrsnibz6q/u/mszqyw+VT7fbqe1re8Yd/9t1nHUSgnzPyTG40685/9lZmsR98tHufslOcTxADGmMqJ6/bHEGObt9Pi3geHuvkn6lgfIpipFaRJEky8BF7r7I1XxVJtBa/vEbufu62X1b3eTslZkLIW05jAcGJ6qKBnwaiVBM09p0/OVtCYCVTakrAAckaoRHphTbGvTfD7gZbJdR/sscKdXtbVKG+5OJaqY7EW0UtqDSPL4HktXhe12JX6/P05sqrjQ3dvME5vZKsRnYFYbdT5NtO2uPucHiTm3x4FPeVQtXxN4mKgmflNGsSyl4N9ZvbFB8RO10mVKvpFSLdglJxETkIOJgcoc4AfpxnwXoi1CVhfIaXR80FopjZkJM+ts5qJ7jj1y0wkLH+ymwdKHgP+4+/M1j32K2N02hNab0Cz8HfgUVRN7TWxDMf08ZxCDzJ80ePxLZLd7tTTcvVQt82oTlEqYsPQEUdlJyu8MojzyEOL3Nodoi/NH4jP6a0S1kzwUeR1vw92fAZ6p+no+8GUz6w8s8TplVrtZmRYQIapLrEfs5P2vmd2QkpLKYgzxutkDeIiqn5O7L0mVQ3Ylw+QbK2lLLojqKcCPzaw3kci2DvG+zz35hnifPwPs5O4zCzh/WaxETOh1xJt0c6Jz2aXqAZ8kdojuRkyqVbxFTKSNzjqZzd1/Z2b/SUkIPy/b/WAzaQJ0ZJ7jvDTZeQ9wU0q8wd0nmdliWivyXO/uv84wDG/w33m7iahwakTy58VAbaUxJ97ff2HpHfR5eIPWub03iHFv9QLHPGKXfybqVOjIbeNWM6mS3jrAm15VDdeLqyB8AbEI/1Pi/ZVbO98afwG+bWaX1lbjSMkBhxHJ87lKnzW/BH5pZusTSTgHEotkE8zs18Cl7v7nvGMryGLic+cuItn51oIr/VWUqvqru5+cxgf70TY5/er0Xs+Vme1FzLndDEwmXr+VWOeY2e1Eu+HMk2+IiuDnAxd7/Raja6TnVNxFNsnJZUsQXYPmVereIfsxeUM147yn3P2vOYfQkYqMp2YZQErG7KjPZBZIO9z91faflaljic++64jKUpWNphsT76f9iGSGPFphzScqKTfyQWLsl5WPUPV5mwwhEop/4O53pWNXpznJwu9TCzaO2Pz3iJn9CKh8zmxKVMb+MNklGq/N0lWkd0x/X1g19nzVzH5BMRsbRLqFkm8EyrVgB3GTeX76kF09HVsBwN3vMLOriQncHRv9A+9CmQaSY2i/HHq13HrktjlpQYPdNJn2E+Cb6ZyknVG7Eztof0a8jt8BfkXciGZlKnCUmZ3h7g3bUJjZR4F9KKY/9lXAD82s8rOolJit3JTvQgbVH9Lv6UzgRXf/WZPnHU4Myk8qYseANHQacJmZXebR2k2qpJ0mB7J0idcbgKtyniAdQpSPvrdqB5elXaInmdmmRDujPNoKFXkd7xB3n5fn6Rr8dxEG0do+6WrgQjO7ntgxVYYEir2JCeKbG+xEfJ64tnc7K1lLLjPbnpjwOMPd51QdX59YDN6s6tiV7n5InvERP5NRRSXemNk7tP9+cs++vd1rNJ9orPZBClhstYLbAKZFg73MbEViYnQ1ovrNc5XJtZzMIxZ3vmNmTwCXAb/0fFrUdlpKujkaOIb4vWU+zkv3NRfSdnz1B2APd381JR/mlYBY7/2d+zXUoz3b49Cyaek37v5k7fPMbCvifbYvkXibpxeIqn6VRNWniEoDP7coa7cnkOV9/Aq0/d2UZYdmH+K+/ERikaFoOwM/c/fubunSWacTrV+eNrPLaLvgcjCxUFVoG1J3/ztwspn9gJjX2Z/4XDrUzKYTC2nXZRlDCaqDr0O04jmIaLU5x6Id18/T5oJCFF39NVVYfaY6cczdH6FOVdW0UXAPdx+bY4ijgHvdfY80lqldDJ5G7OLPwzoerQA7JCUSZJGcXGiCaB2zSW2VGtiCjCt6lnmcV5KKjJ2trJPp/aGZDSI+82enr/tSfzPZP7O+NtU4CLjD3WvnR6YD/5euY4eQT/LNn4ADzWycu79R/YCZrUpcz7JMnl2Dpe91tyFeG7fVHP8TUQknN2b2IWIDXPW88c3u/kKecVS4+01mdiQxR/xjWt9DRiRSHenuWVXBX4Vo1VZt6xRDbau0F4jWvrkzs9WINnvVv7M7a1/fIs0o+UagXAt2ACvSuoO3MiG7atXjjxGD725X9ECyjoXEBOcviJ3XpdRosJuxo4hdWS8TiWIfJqrP/ITYqb41sbB4eg43MxOAbwN3mtk33f3O2ieY2U7EwPut9Py8nQtsSSTTVZKSICaRjGgJkMUN+f7EBMTW7Tzvz8RuwCeJZKkeo85u0fbkuZt0K6I11l/N7Ebq93TPvepWGaQSprcBnyMGCZUJit2AocABZrab59fz+X3E+wdaW9tVt8O7k4x3KFUp7DreSGpntB7xc1pqUcijtHxWSrGACODuc4nP2gssWm0eCvwfMXHzaoqrfxGxJWvTvLzt/2j7WupOZWvJdRDwaXevnei8gujv/Hti4mhXYqLrd+5+ZY7xvUy814tyFUu/j3oTk+nbEJOOj+UQxzRikfu0Djx3L3KutmAlagOYEm2WSlrIi7uvb2Y7EovLexJtCc8xs5uJxcw78ozHzL5GJAV8hKg6ezWRhP6OmR1GbJBZg6i68v2cwjqSGF/NJKq7fIRoWXER+bRZqXaZmV1Uc+xWM6u9D81tTsvd27zPU4JxJaH1Y8T9RcONGBm6CzjEzEakRbKLiOv8C8Tn5AZkWFGghJU7AXD3hWaWdxXnZlYguxL+Hebu95vZnsT94LE1D/+DaFHxQP6RtTKzzYl71P2IRZeXiITJt4l5l2vNbOOskirKUB08JUKcB5xnZlsTnzOHEe0A/0z8PK7NoYpn2dxHJCLt24EqdpsT4988k28+Bhzf5PFZRBWRzHUm8SZjRSeI1rqNSOT7MTVt3cxsGyJJYELGMRxEucd5RStNe1gz25AYv5xMrI1BVDI9l6U3TC82s8caVJrKwgY0r8A/mfxahJ1L3I8+ZGan0ToO/wTxObwukUSblbm0tiuv2IaoIvdwzfH55DgfZ2anE5uda9sxnmNmP3D3zna+6BbufmHaoL0LMVcKrQkmWW5WfJlYw6u2LTDXazpaEOO83O9zzOybxD3YKrS+xx1408yOcffL8o5Jlk1KvhEo14IdxGBkXYj2EGY2l8j6vjE9vi5x8Vze7UVMDu9L7ASfTJS6vr3oyiBm9v+IAdLbxO6/OenY2UQlg1WJxYWTM5y4+QaxYPDplCiGmf2E2NH6GrCdu9eWBM+Eu880s68TCSy3m9k/iRvN14mfxSeADxDJVF9NOwlyk0rHb0BM3F9C/O7WSw/PIErK31X/u9+1fYC73L32ZrcNd3/YzKYSi8A9KvmG8vZzh6jAVdEoWSLLqlu7mVllF9RK6Vx7m9knap63VUbnb+ZkokLGucAPPfUWNrMBxALHKKJ63Ck5xfMqrTsC3iA+b9arevw9tL22Z6k01/GUdHM+cT2td99baSFZOxDuTqVbQITWxNlU1nkvYpFje+BSMxtOtFq60TNuB1PjNWKnbyObkl2FnrK15NoaaJMMYGYbEf3M768sdprZKcCjxERxnpOyPwP2M7PxabE3V+5+UKPHzGxb4BbinjBrVwDXm9lZ7t6wgqCZ/ZB4/eY5poIStQEsA3e/B7jHzL5L3HMeTNyr7m1mLxO/z8vd/cUs4zCzYbTe7/6b2PF9HOBpl+q3iUpfxxHVRPN6jx1AlKz/VGVXn5ldAhxkZgNSAmce7qf4SnENmdmuxEL4l4n7q2eJBLzf5HzNrDiLSN4yaJlM70tri4gTKEfllyLcRrRX7kh76Kw9AHy86CAA3H2ymU0hxlDrp8MzgEdyrtzZIu0w/jpxP7ol8dqdTMxfTK3MgZnZecTn5xFkl1RRqurgHq22/mxmI2idJ7wIGG9mh7v7L/KKBVqqGIwkFuzeDxzg7n8wszWIe4pJ7v63Zv/Gu7QWsfHtOHefkOF5umIJS1dJqrY25UkIzIy1tiyC9hNE1ye/JGOI6/WXifHTLSmGA83sW8Rc6UxakyyyUrpxXpmqu3QgsS5PBxPJl+PrPPY9WjcxrECMqw4hvxZq84nP4EYGktPnTdrQ/11ic0N1W1oj5rqPzHDdAWLc9BXSpuI0L7stcV9TW2l1Xeq3ge92ZnYIcd/wEHAOUBknbEqaMzazGe5+RR7x1Epju0k5n3YasVn1R+4+y8w+TSQd1vtc2YScK3Sb2ZeJ9noziPn86t/ZUcDFZjbH3SdnFEJpNm/Ku2cFr+FLCaSJxonuPi5lnc8HjnP3C9LjI4Gx7p7VTuPaeK4F3uvuu1Z9vQuxE3kF4kL6J3ffLY94imZm7yfamhxEtHWaSez2vTzHbOrqeDYiJh1WJW6i5hCVH35LDJrmEQuIKxM3WJ9pL/Gii3G8AYxx9/Oqjm1G7HQ+zt3zyu6ujmkTIglhN9ruBn+LmPgbneeErNUpHU/sYt3Dc+pNa2azgfPcvd0JXzMbBRzj7oOyj6w8zKySdDOFcvVzr+z6a5e7v5TBuTv7M3B3zzKBog0zex6Y5u5fa/D4tcBgd6/N6M8qnqnAq+6+f/r6PmJi8gvEtfO3wJvu/skcYinNdTwtGh5KfAbfQ4OWL1ntJEu/h07dbLt7YTu9zGw9YsLoQKIV4Duefdue6vNfDWxHDGz7EUllO6VEgfWJa/zV7t7tyQJpgqZSweATRBJbdUuuZ4kE2lxarpjZf4j7nB9VHfsOUeHvwOqFFjMbDRzl7mvmEVs65w7AD4iF559QvzJa1lWlGjKzccDW7p754piZTSbu/f5ALBI+TmsC9hbEZ9C2xPX9K1nHUxPbf4k2gKenKh3/BnapTH6mxPWN3b2wNoBFSztaDyXe/wOJ6pC/Ay5z90wSws3sLiIpdRd3n54Sbm4gdmj2IRJ8z3P3XDecpPHV2Or79lSB4jEiISfLcvGlVnN9XJd4L91BJAvsnde1oSvM7NvA0e6+aUb/fmmreKZkgDuJ+4dziXZ3eVWlrI1lQ6Kc/lHu/psiYuiI9HoZ7u6b5HjOq4lF737E/cSlREWyuotjaePTL9y9WZLDu4lnJvBrdx9pUR38VWBnd787PX4TMN/dC2nPZdFO8lSiHcKYrCoANTj3msCDxOau54mKJjun5FZSQsXN7n5MRud/h/h//ypxHb0W+Ga9hHmLlrJX5TxH8ADwursPrXrtVMYyKxDX01fc/Yt5xdSImZ1EXPO7/eeTxjHXEBtI/0ZcN1+o3NekzR+VBNHrgXPy3GSaNo9eQFQsrnyOVNrTHO7uL2d8/lKN86yquou7n52OVV6/S1V3ATYtYj2iCBaVxh53929VHWvz3q46fiExB9he5ffuiu1GIhH0s7VrDWl94kHgPnfPrXqlma1DbGyozIM+C1zv7q80/q5uOe83iHmb3xD3WnsTa1VHufuFNc+9G1jo7kOzjCmd62FibeyzteM6M+tNJGa/x90z31BqZrd08ls8i7kLM/s4kYDzP+AZYu5vReBz7v5QzXNfIFo5Zlk1qTa+B4kqStt4TXXBlHz8R+C/7r5dBud+h6gEV6k81ItYC36RpRPp+gPr5nmPI52nyjcCkcH3cYhP1XRj8d30obwCUdo0y10LtS4jdtf1SwOo7xPZ31ekx2cTuwB7hDThcA5Rjm5bIuv6COB4M/s98H13fzDHkI4jFlpGEDtpzyBublaialLWzHYmJpFPIG56utvKxGuhWuXrJzI4X7vc/a/AXma2IlGmfTVi8eW5OpnWeShD6fjViQStjqiu3NGTlLKfO2STVNMJpSk128C6NC/h+jtg93xCAeBm4Niqa+dYYCoxgQ0xaZLX+75M1/E9iBZChUyOe0lbMTTiUe1htJmdSpS5zq2/fHIaMRD/CzFh68AX0j3Fd4iJix9mcWIvX0uuFYHahYRK8lztbsB/kn+7sLur/vtSlk4yy6OqVDPPkU/lG4gJxouIRYRP13nciKoU38kpnmqlawNYNule6zgzOwH4IjHO+gIxmZ1VNcYtgAvcfXqK4b9mdjIxATu+svBRgJVZenfhzKrHepy0eHsI8XpYAtxK7Hq8Dfgg+bXmfjfWICZus7I95a3iOSed7+OkzzpbupWk55Ro/FOidP6klNwxg/rtfPNqL9zIGsCGOZ9zH6ICxcVep4V3HQ8R82JZKVt18EpVisqmvI8Qn80/JDbv5OkMIlF1G6JNWe1cz81ExaAsvUC0nL+cuFffxMz28Iyr1nXQBcA1Fm1GrkrHVkjJDT8gFhmbtaV6V1JSS0d9Jqs4iIXDw4l73yeI+YFfEhVEcPfzieq0hXD3fwJfsai4tSFxr/68u/8npxDKNs4rc3WXon2Ejlcd+htRGS0vo4lEgEct2uj+NR3fFBhGXL9yvValJJvxKbFka2K+ewCt49Gs/JKodLg3USEOonpemwrUZvYh4p7+exnHU7ExcGK9DRXuvjhtVMxkfquOL9U5VptcV/tYt3P3x81sD+L/+2PENf3UOok3uxIbSn+bRRxNfJxITF2q3ZW7v2FmV5Jdhft/ED/3VWuOrVBzDGLD0D8yikO6iZJvBMq1YEcabN9Z9fUMM/soMYBbAjzo2fYeLK10IXrIzE4kbix2Ito85Zl883ngEnf/MYCZzScqdhxXvRvS3e9MFQe+nmEstTcCla8L7W2cEm2ebPeJ2StD6fg3iAm8jngfBfTyLJqXrJ97JydsgJbJk27l5So1W89clu5TW+3D6Tm5SLs5Lqz6+p6UsPl1YnfSjbWDmQxjaXQd3ynFkud1vC9wX07nWm6knYe3pz95nvd5MxtC7JKs7OKtTIw8CeyfJkuzjqMMLbn+QUycVdsOmFPnZ7ASOX7eJFkuenWH7Vl6UjsTafx0QKq2sxexE7uSgP0kcEMlyaIApWkDuAzYmmhDsG36uqOJBF0xgJhorFbpcX9vhuftiEbjq0YTs4VKFSDGZpiwcDWRJDGCSOZtqaBXJ4mjp1pMvD7uomRVPInF7/YWD/L6RW5AxFKZKP9ATuddFqzrnajMm5IsXswsmpK08zWzPkQbjYOJKqJLiCSlkUQrriLeZ18CLnT3R1L1h1oziAShTKV7r6+lqgI/AKaZ2dfd/Y52vjXruH5tZh8j2oxUkhNuJz5njKh2kuVCYmcrgGe1uLq+me1IvHb3JFrRnJMSBH5e9O+pwt1fJzZ9tMjhvgLKN87bEbjF3evd+z5ePTdnZr8m+wS7MlmZpeep/0skDfy95vjr5Jis7u5PmNnniffXXrQmnUAkqQ5398w2J5vZ9sT7+wx3n1N1fD1irXGzqmNXuntmG7vS9XBfMzubSJh6wd2n1QubaCOZV3Xet4FVmjy+KtmOOVt4TbXAVB1yDjUVnHKK5VZiQ0Oz50xl6YSTPLQ3LshsE4G7r5fVvy3FUPKNlGrBrhF3n08MMns0M/sUMXjZh8h8/wPRRiNPaxNlmysqN3J/rfPcJ4mEiqzsZmYDq75eibgI7m1mn6h5rrt7vSz+5dmGxKDxjapjPyYWET8K5FE6/ilioui89p4I7ExrL80eycvRz70rEzaF7Voq0J3AEWZ2ZxoUtDCzXYidXpn2wm7P/2/v3uMtn+s9jr8+Y0rEhOJwch4hHKcQTafD0TlG6ZCcEXEqxIxRUxplSISM0FEYlUsYM4zbcDzMmArJJZXK/RZOuVPCcUvm4Vrmc/74fNfsNctaa689s7+/33fv/X4+HvNo9m//5rG+Wbfv5XNx95tp2UyqS/oe/2END30LsfguXkUbfcXzaFX5PotWkv9ELH7vd/fbzWyymV3oFbVCSMG0c4A5LS1HjgSOIO9a6joioGOmu9+dsoPWo6+CVLONyJ9NthjP1KqtV2a2R4dfrUIE+n2MCF6tTNrcrKX6Yhc3s3hm85XAVDN7lMigmgLcWMfASmDR3ncPYr7VyHy+g74M7WwPzRuDnho/v5TxcXtRzPoqHey+G3jO3R9o+d1mxGfxR4jMv1xeJQ7ddwD+bGbzvE17kxGuqCqeZrahu98N4O4Terj/pOyDQhvq3Qwk8KYitVcHN7MTiT3ZlYm5xQFEq62qKnN08g76gkXbWUgkP1TC3Y8zs9uJ9lOXmdnh7l5VFQEAUgLVoqop7n6Ymc0lKqMtWssQrXPbHQYPpmKqB6fD3J+Z2T5EhaLGXvYuZvYYsaY5q8qKRYXMK6C8dV7J1V3q9jywRvOFFOjRbu96dfraxVQifaZsYdEScO10+WF3f9rMtjCzazLuL00ANnf3L7dcP5t43f6aWGduA+xpZr/IvYfQSKLq8vsH6P4dNthuBian9/pirTTNbDViPlHXWrzKSpRvkF6z6wDPuHtrUkqd7iSS1n+Q9rEXMbMViNf9nXUMrJWZLev1dNqQHin4Rtqq+sCulL6DJWrZFN6AaNcxg/pa0rSWx2z8vV3v9Ffp65+bw660r6wzuc01p30JzeGshNLx84iKLju4e8eDdzMbTwTfZOkNPtS4+yvA+Wb2CLHw35qYlFahmA2bwh1GLCIvTxt+jcX3e4l2Es8QZWCzsuhXvhORLTHX3Z9K175DZDCtSASgHObu12Ucx0ADQb2iIJODgR+b2UUVbHZ2VNBG35CRDs5aq8jV0QoBqKUl1zHEpv2dZvYsEcz8Gi3BrGa2DFGtY27m8ZRmNp3LJP+NCJ6YmnsQrQcuBSqpDWARUhn08cTaahtiT+R5oiXMLHe/vaKhrJVa3DU0WgqslyoULSZtJleh9vVV+lw7Bdib9B43sxuJdp6vAKcRWasLiQDJbw32GJqsQbQr2ouogvMDM7uYOFhoXWeNSKVV8QR+amZb9HKgmwIc9iFaiUlFzGxJ1kju7kcN+mDaK6E6+BRir+0C4iBxNPF93un+qpLNniTWNJ1sSsVtENz9ajP7ABH8d3T6+54VDuF64LOkVpXpcO5AohpEuyTFbLzA6sEpGXAGMMOi/dYk4nv1G8ChZvYLYv6Vq9VnafMKKG+dwEmfiQAAFapJREFUV2x1lwLcRSSUfruHe/+DihIxWveX0lzs6fS7zczsfPLvL32QSOxoHtcGxFrzl55asJvZN4DbibOtLME36b3yLeARdz+ty31fJCq/HubuWYJPzOwiYLK7/xk4imjX/Tszm8XircEmEvu1Q6GF7aAxs1FEEYjmz+PrgR0LCcY+jjjLui2tE5qfs32JKveVdYhpx8zGEt+lnyJv0QNZSgq+GcFKObBL2vUd7KbW6MwqmNlO9G0KO6m1E/ATd2/tDT4SKUCgN3WXjj+dqABykZkdT7Qse6Txy1RJYG+irch9tPRjHYms5n7uJW7YlMjdH00be8cQ/ZQbB2gLiE3SQ9w968ZjWtjeQHxfGzDNzP6d6Im7NpF1s5BY/F6VDiJuzTSccUTLv15LpuZa6J7Z5vJjwA1pQfcQUbJ9sbG4+6RM4ylto0+WUtqkyd6Sy90fTmWkpxEL/JuITfzWLLutgGfJXFkqfbbg7r9s/rk/jfszaDcPdOA5ItvvxTa/z6HdgcsMajhwacfLagNYKzPbmFhb7UbfJtW1RGDAvBqyxo5Kf1r9oM01gGUyjqWhlPXVvkTwxmPEPGddYDPi+3RNYrP/XOCo3JmSHm16TwZOTsFSk4gM/gnEIYfTFzg14hVSxXMF4Oo07/2/TjeZ2feIAIdKWmya2dXAGURl6UpaDBTsiCX4N077z8xBV1B18OXoHBDZqqpks8uBSali1GKvYzP7F+Jw9XsVjGMx7v5Ieo5mEp/RGxCHZ1Vo3VtblqgMMpP2FcJHrJRA+jUzO5ioUvklYFtgS9JcOpNi5hVQ3jqPwqu71GwucJKZjXf3jonjZvYJ4nU8JedgCttfWp2o6tVsHPF9NLNxwd1fNrM55A103p0IevxgP/fdRMzr7yHfZ85OwIfM7PPufmk63zuZqGDX7A/AnhWcuZZmCvF5/Dixl7Ie0fb5dGoOagFw9/lmNoU4Hz+Jxc/RXgSmdEsuz8XMVqEvIWSjNJ77qh6HDIxlCvKTwrU5sHsKaD2wG01EM78G5Dyw60mamB4L/DPwuLuvWed4cjOzhUSmyyXAecRz1FUVGZFpXHPoK+O3PPBN4qChddI1Fvi0u1exWSwt2jxX0P35ypItZWbrEr081ycmLS8QAQorAmOIz6B7ge0LKzVYGevcz/0s6uvnLj2ySEFcNf34dK4MijaPeyaxsXcQ8ARwNBEA8w5gh3QAgpl9lNh8vMLdd8k0lsbm62XE6/bSOl636XNvoDzX91Q6fDqBxTf6NiE2cSrf6BvqzOxQojWX5hUVS+8tB5Zz99eafu74T8j43ipF+u+weyNbN2UhPk0NvdOlu6bvhz9SQ6uDlrFMG+i/cfdv5hhLiczsVmIvYnN3fyldO4UI6H8WGO/u19c4vmWJwJJJxCEDRKbzxcShfPY2umY2kGqhWwPb1PF5bNFSc1oawxHufmQFj7klsa91P7BlCqBqvecEYD+imskOVQTDpOoGKxHVBM4jKj1UlSFf1OvFzN61JP/O3R8d7LGUKr2OB6SKJBqLtoS3EgGhPyI+B88D3kwcmj0BvD9XRcDWeVeHe/YnDs1GAeT+7NNccOBS5deJRJDAGOAVd18+4+MVPa+om5ldQ5wRfriHe39GdVWMa5fmfLcTlciPJb67H236/buIQJgDiXZGY3MG9Je0v2RmLwL7ufsZTddmEQHqa7n7H5uuTwROd/c3ZxrLZcBod9+mh3svB1539//MNJYPEWvNtYlKP18hgjbG0tca7CHgtjr3+uv6rjKzW4jg4s1SZTTM7AzidbNqu3l7HcxsJeJ8Zq106SHgqqoTl8ysUXV7PDHXuo9I+J1bxZpTlo6Cb0aokg7sehjrhsTCaVviwP444AQf5r3WmzaIe36TVrGhtgQHm8P+4KVUJT1XZvYW4HPAzkSpvjFEEM7dxCJh5nB/T3dib+znfiZl9HOXLlJm3ceJoLLG6/n3wGXufkNFY3iQCHL5Svr5Y6Qqae5+fMu9JwC7uvvqmcayKpHpOAF4DxEweg71tUgsgjb6BpeCb+pjZhOIOek57u5NP3flg9jXfYCHhxCBrM8Dd+UKUC/xwMXKbQNYq1QCfBZwZVVBsrJkzGwBEagxvenahsBvaTPHqVOq4rkXUbXyH4CF7p69wnRJ67xWHap4ngOc1nwQk3kM2xP7WLcQn8cvNf1uOtGW8EpiHlZJFRozawQnTCIqTZPGNxO4wDO25Sr59VISK6s6eLHSf5OTibVwo828E1Vxvujuj2V87H6Db9J9WwEXAaso+KYMZvZ3xH7BRKKNsAF3EHOz83MeuA6leUUdzGwfosLDjj1Ud5lLVH44taLh1c7M1iH22f6RvoTSF4h9wEZC6e+B7XIH9pe0v2RmvyOSRfdrunYvMMbd12i590vANHdfLdNYngSmu/txPdx7ILB/6xgHeTzLEcFa+xCJH3uV9n1QY/DNAmJP77imaxsT3webNc6kR7KW9eWawDPEumVXYBd3r6qynywltZ0aubYk2r+cBIuiVRsHdos+5Nz9qhR92EuZ00GVFnRHEaXBXwdOJMowPlv1WGpSanZjKeXIpX/FPFfu/gqxmDup7rEUqNR+7tKGmY0hnqtteWOJaYBDUtbFbo0o/oz+ntgwamhkz7Yrb303GXvBevTmnQ5MN7MPEguFzwNfNbObiE21C3MeLPTCzEYT2UDvBO7x/G1h1ic2+l5qunYqsTnyHQXeyFDh7rO7/VyRJd0YdzO7jdiMfGIwB1SocRTQBrA07v5fdY9BevZW4MmWa42fK6kU0qt0yHJ4qmbUyEysQjHrPOhaxXMqNVTx9CjzP4HI/p5vZh9397+a2XFpTFdRUcWbpjG9BlwIXJgy5Rub6qcDJ6QAwTPd/dcZHr6o10snZrY80cbtLy1z5yoeu6R2vkVLQXQ7pHVxI4jiAXd/zsy2MLOzcwX1uvuo/u8Cd7/WzDZK45OapLX3eOK7YRtir+t5Yj08y91vr2goQ2ZeUZNZxL7kRWbWX3WX3xEJgyOGR9veTVk8oXQNIgDnOqLy4cy0751bSftL1wF7mNlMd7/bzHYkgq5nt7l3I+BPGceyCj10jEieTvdnk5KL9zWzucT75Uozm0HMM9rdf07O8UDbRKbliX2AXcxsk/bDynL28FYiML/Z402/q1wpyUtmthuxPtiSWEtdSrRruxx4F3FGLkOIgm9GrmIO7FqZ2crAoUR06LLEIedhuaOHS+OFlhb3CkrZyuDQczWklNjPXdq7mCjH/itik+K39GW9bExsSmwP/A+wXeaxLEsEbjU0/t5u0f8qfZmJWaUg3ptSSdxPEpttpwPfNbMvuvt5OR/fzMYRWatHu/tTTdfXBuYDGzZdO9vdcx6UaaOvHwOsZrJFtoHIgHSo/nUvUf0r16bfQA8PjRjb5kSP9enUkFBQg78R/9+vpsY2gCJLqTUorPHzX6seSC9SNaUr0p8qHq+YdV6bKp4HUEAVT3efY2ZvA04BLjCzh9LYriECb7K1hehhbI8SwR1HAB8lDj0nEAkY9xItomcOVtB6Sa+XVql65oHEmmGtpuuPEBUWjm+ez2f0NaKc/370VQefSxxOLcrEbqoOfjBQS3XwuqQs+XcDz7n7A+7+AnBz+t1mZnYk8BEiSKl27v4kb1yH5bKdRUsuqO9AsxipisFE4qCwcZ5wLbF/Mq+mz98hNa+okru/mirGXQYcBhxqZp2qu2xf5/dnXQpKKC1pf+kY4j1+p0VrzbcTyR/Tm28ys2WIILy5GceygOjm0Yu3A5UkBbr7zy1asN4JTE5/Gkmcnv7uRIXI3DolMk3ucD3n2UOnz+OOWciZjaOM5KVzifZW+xFVMRcVoOiSoC0FU/DNyFXcgZ1FH839iFZYKxHZSAe5+x25H1tEpEZDIhNRFvVa3ZooZ3pgm1tuB842s+OBqWb2UXe/qtJBFiRtUJyfNs8XEv/t1qngoScQZXi/3HJ9NpFx82vgRiL7bk8z+8VgtsZpQxt93Q20msmIqNJRqh6qf309V/WvpTg8/FFq97HnYI6nSWkHLu+krw3gJcBTZjbi2wDKkNP8voLu761hf5BZuGKreLr7qWa2EvAt4vVzDWUdHG5CHEj9G6l6CDFnPgE4yMw+6e6/qW94eZnZZkRg/GrEvPhu+g57NwC+CnzWzD7h7jdmHk7x1cHrkg5OTyGSTCxduxH4BLGHfBrwKeK1O4d4v4007RKp6jjQLMUd6X//SASynVVAQq3mFV0UVt1Fuitif8ndHzazLYFpwLrATUQC3D0tt25FtMT6Ycbh3ENUXpze341E0HPrGLNILRDPJAKDZgB1Vr4u6eyhtM/jUpKXXiUC0XcA/mxm81IVJRmizNXufEQaSE/aVPLqHM/YJ9fMJgFHEBV5bgMOdvdrcj2eiIjIQJnZbGJjdh3vMoEys1HAg8DP3X1ixvE0NjhvS5eWJ1oWzgDub7l9LPDpnN/lLWNbgzjonkCUnn2cyOY4LZUqz/nY/wtc4+77Nl3bgKju90t3H5euLUcETP0pV2n0Ns8RdH+eRtxGX9qwGZCSM7iHOzO7kv6rf20BXOHuuat/9czMPgOc4u6DWmI6vccHwqv6HAZoagP4KeI5KqYNoEgnpb+vZHGlPV8dKurtA6wOfBtobWdU6dwrBQPtBkwC3kccls0HZjT24szsw8AZwAvuvmlVY6uSma1GHH6NBg4BZjcfMKR5+p7AfxOHIhvmrIBjZi8DU9x9Vvp5TeAPRLDW5S337gWc7u5vyjWekqRqpicAjxEtM9YlAsfmAmsSLX3PBY5y9wfrGWV9tJZ5o9RGbxZwZbd9kwrHU9T3lMiS0v5Se+l7ajqwk7t3DPIxs/FEcsr+7v79jONZDjiWaAf2ODBpJCdmNivx8zhVYWwkL72HaGFWefJSWiPsTuzfbEJUdLoYOJt4Hd0H7Ozu86oakywdBd+MUKUd2KXxOHALcBH9ZzWPiMmDiIiUw8zuAq5296k93PtdIqB1o4zjKWrRYmZvIiL0JxJZJ68DPyIyB35aVeaAmT1H9ME+senaF4iMzT2b216Z2eHAvu6+aqaxFPUciSyNVP3rJ3Su/tW473hgKrDtcN9kGioHLmb2FvraAG5FHD5nbwMosiSGyvtKQmnPV6lzLzP7CLGZviPwFmID/Qwi6OTZNvd/jggafXPusdXBzI4FvkJUq7yty33vB34DfN/dD8o4nqISFEtiZrcSQVKbu/tL6dopxKHis8B4z9dyVGSplfY9JbKkSp3j1C0Fu9xBVA05nqhk90jT79cikoS+CjwMbJqrilNqzz2bCFQ9F/iyu/8lx2MNRaV/HpeSvJTmv5OAzwBvI+akqwJ7u/tZVY1Dlo6Cb0ao0r6sSxuPiIhIqxTUcYi7n9bDvV8AjnH3lTOOp5hFi5mdSJTaXpnoNX0mcJ67P5fj8foZy4vAfu5+RtO1WUQWw1rNlXfMbCKRuZrlYKOk50hkaZVW/UsGLvWcn0ZULzrC3Y+seUgiIoOq1LlX2vN6FZhHVLnp+pipVcHh7l5Sm4BBY2b3ANe7+9493DsT+Fd3f0/G8Sj4pgMzW0DMGaY3XduQqH74NXcfaAtZERFZAqXOcUpgZusClwLrE0n9LxCVQ1YkgigMuJeoaJetSpuZ/Q14BpjcrQqPlK2U5CUzWzaNYxIwLl2+i6iIc0mbNm9SkNF1D0BqU9oCvrTxiIiItBpDLN56sQBYIeNYSltETwFeBi4gquqNBiaYWaf7c1aw+wPRo7zZh4Cn2rS8Wh54PtM4SnuORJbWWGB+f+Xj3X2hmc0nAjykZh3aAB5DVCUTERlWCp577U8EbPQUmO7u1wLX5h1SrdYCel0L3Ehk/ua2nZmtnv6+PHF4t4uZbdJy39gKxlKStwJPtlxr/HxXxWMRERmxCp7j1M7dH0jf158Ddib2BFcngnCuI1olzmxucZnJJUSQxjOZH0cySpWRzjezR4CFxN7WOjWM41Wie82cVMFpL2Jv50jgCBTfUTQ9OSNUaV/WpY1HRESkjVH03xax9f6RZDmi+s2uPdzr9L7hPlDXAXuY2Ux3v9vMdiQOnGe3uXcj4E+ZxiEy3LyTyBbrxb1EsIfUoEsbwKlU2AZQREQWuQk4wMzWJwL6XyC+Ky8boS17Xgfe1OO9o9P9ubVbx0zucO9IKyPf+v+38fNfqx6IiIhIOylg4qT0p64x7FLXY8vgKDV5KbVSO9zMpgHbEIE4UjAF34iIiIj0rjkjspuRlhFZUgW7Y4DdgDvN7Fng7cBrwPTmm8xsGWA8kQEjIv0rqvqXtNemDeAB1NQGUERkpDOzMURlyG2Jlgetvm5mlwG7uXuv37HDwf3E+uHUHu4dBzyQdTRlrWVK1LoG7lYZKGeFUxEREZFBNZSSl1Il6ivSHymY9VM1XEREREQAMxvoZNvdfZksg5GuzOwDwDRgXeBB4Gh3v6Hlnq2J6jtT3f3q6kcpMrSkz8Dd3X1OD/fuRrTX0GdgxdLz9DJR8vq2Hv6JDslERDIxsyuJUvW/AmYBvyWq3owBNgb2BrYArnD37eoaZ9XM7BvEXH17d+94eGBm2wCXAt9096OrGp/00RpYREREhqs2yUtnouQlGQQKvhERERHpgZltOdB/o7aKIjJcpMOXOfQW0DEW+LQOX6qnQzIRkTKkwJGfANPd/cAu9x1PZNZu6+5XVTW+OpnZikQg0hpEMPwZ7v5Q0+/XIQKT9geeADYeYZWBiqE1sIiIiAxXSl6SXBR8IyIiIiIiIl0pqGNo0CGZiEgZzGw2sCWwjnfZfDWzUUSlxp+7+8SKhlc7M1sf+DGwHtHCaAHwF6Iq0BiiTdcDwHh3/31d4xQRERGR4Un7XJLL6LoHICIiIiIiIsXbqu4BSP8USCMiUoyxwPxugTcA7r7QzOYT7alGDHe/z8w2ISrc7Ay8l6iE8wJwHTAPmOnuL9U2SBEREREZzrTPJVmo8o2IiIiIiIiIiIjIIDGz54BD3P20Hu79AnCMu6+cf2QiIiIiIiKSy6i6ByAiIiIiIiIiIiIyjIwhWin1YgGwQsaxiIiIiIiISAUUfCMiIiIiIiIiIiIyeEYBAyk3rj1aERERERGRIW503QMQERERERERERERGWa2M7PVe7hvbPaRiIiIiIiISHbmPpAkDBERERERERERERHpxMwWDvCfuLsvk2UwIiIiIiIiUglVvhEREREREREREREZPFvVPQARERERERGplirfiIiIiIiIiIiIiIiIiIiIiIgsoVF1D0BEREREREREREREREREREREZKhS8I2IiIiIiIiIiIiIiIiIiIiIyBJS8I2IiIiIiIiIiIiIiIiIiIiIyBJS8I2IiIiIiIiIiIiIiIiIiIiIyBJS8I2IiIiIiIiIiIiIiIiIiIiIyBL6f50lwqil3dQQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2880x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (40,9))    \n",
    "plt.scatter(x,y)\n",
    "plt.scatter(x2, y2)\n",
    "plt.scatter(x3, y3)\n",
    "plt.scatter(x4, y4)\n",
    "plt.xticks(rotation = 90, fontsize = 18)\n",
    "plt.yticks(rotation = 90, fontsize = 18)\n",
    "plt.legend(labels = ['2021-22','2020-21','2019-20','2018-19'], loc = 1, prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a0790c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(df.corr(),cmap = 'coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5f48f6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = np.linspace(1, len(df), len(df))\n",
    "plt.hist(z)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "838ab92f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     MP    W    D    L    GF    GA    GD   Pts    xG   xGA  ...  Squad_Macca  \\\n",
      "0   6.0  6.0  0.0  0.0  18.0   2.0  16.0  18.0  13.2   5.9  ...            0   \n",
      "1   6.0  5.0  0.0  1.0  20.0   6.0  14.0  15.0  16.2   8.4  ...            0   \n",
      "2   6.0  5.0  0.0  1.0  17.0   6.0  11.0  15.0  12.3   6.8  ...            0   \n",
      "3   6.0  4.0  2.0  0.0  14.0   2.0  12.0  14.0  13.6   3.3  ...            0   \n",
      "5   6.0  4.0  2.0  0.0  16.0   7.0   9.0  14.0  10.6   7.3  ...            0   \n",
      "6   6.0  4.0  1.0  1.0  15.0   6.0   9.0  13.0  13.2   7.6  ...            0   \n",
      "7   6.0  4.0  1.0  1.0  10.0   4.0   6.0  13.0   9.5   4.3  ...            0   \n",
      "8   6.0  4.0  0.0  2.0  12.0   7.0   5.0  12.0  11.9   9.3  ...            0   \n",
      "10  6.0  3.0  2.0  1.0   7.0   4.0   3.0  11.0   6.9  10.7  ...            0   \n",
      "11  6.0  3.0  2.0  1.0   8.0   6.0   2.0  11.0   9.1   5.7  ...            0   \n",
      "12  6.0  3.0  1.0  2.0  12.0   7.0   5.0  10.0   9.0   7.3  ...            0   \n",
      "13  6.0  3.0  1.0  2.0  10.0   7.0   3.0  10.0   8.7   7.1  ...            0   \n",
      "14  6.0  3.0  1.0  2.0   7.0   8.0  -1.0  10.0   6.5   7.4  ...            0   \n",
      "15  6.0  2.0  3.0  1.0  10.0   5.0   5.0   9.0   8.2   7.3  ...            0   \n",
      "17  6.0  2.0  1.0  3.0  12.0  12.0   0.0   7.0   9.9   9.3  ...            0   \n",
      "19  6.0  2.0  0.0  4.0   8.0   8.0   0.0   6.0   8.6   5.1  ...            0   \n",
      "20  6.0  1.0  3.0  2.0   8.0  10.0  -2.0   6.0   3.7  10.9  ...            0   \n",
      "21  6.0  1.0  3.0  2.0   5.0   9.0  -4.0   6.0   8.3   6.9  ...            0   \n",
      "22  6.0  2.0  0.0  4.0  11.0  16.0  -5.0   6.0   8.3  12.2  ...            0   \n",
      "23  6.0  1.0  2.0  3.0   4.0   8.0  -4.0   5.0   9.0   7.5  ...            0   \n",
      "24  6.0  1.0  2.0  3.0   5.0   9.0  -4.0   5.0   9.5   9.8  ...            0   \n",
      "25  6.0  1.0  2.0  3.0   6.0  12.0  -6.0   5.0   5.4  10.2  ...            0   \n",
      "26  6.0  1.0  1.0  4.0   4.0  11.0  -7.0   4.0   4.2  12.5  ...            0   \n",
      "27  6.0  1.0  0.0  5.0   9.0  13.0  -4.0   3.0   8.6  10.6  ...            0   \n",
      "28  6.0  0.0  3.0  3.0   1.0  12.0 -11.0   3.0   4.8  11.2  ...            0   \n",
      "29  6.0  1.0  0.0  5.0   7.0  21.0 -14.0   3.0   6.4   9.8  ...            1   \n",
      "30  6.0  0.0  2.0  4.0   4.0  15.0 -11.0   2.0   9.2  10.9  ...            0   \n",
      "31  6.0  0.0  0.0  6.0   5.0  24.0 -19.0   0.0   5.5  15.1  ...            0   \n",
      "32  6.0  0.0  0.0  6.0   2.0  22.0 -20.0   0.0   4.9  14.3  ...            0   \n",
      "\n",
      "    Squad_Marse  Squad_Milan  Squad_Napol  Squad_Porto  Squad_RB Sa  \\\n",
      "0             0            0            0            0            0   \n",
      "1             0            0            1            0            0   \n",
      "2             0            0            0            0            0   \n",
      "3             0            0            0            0            0   \n",
      "5             0            0            0            0            0   \n",
      "6             0            0            0            0            0   \n",
      "7             0            0            0            0            0   \n",
      "8             0            0            0            1            0   \n",
      "10            0            0            0            0            0   \n",
      "11            0            0            0            0            0   \n",
      "12            0            1            0            0            0   \n",
      "13            0            0            0            0            0   \n",
      "14            0            0            0            0            0   \n",
      "15            0            0            0            0            0   \n",
      "17            0            0            0            0            0   \n",
      "19            1            0            0            0            0   \n",
      "20            0            0            0            0            0   \n",
      "21            0            0            0            0            1   \n",
      "22            0            0            0            0            0   \n",
      "23            0            0            0            0            0   \n",
      "24            0            0            0            0            0   \n",
      "25            0            0            0            0            0   \n",
      "26            0            0            0            0            0   \n",
      "27            0            0            0            0            0   \n",
      "28            0            0            0            0            0   \n",
      "29            0            0            0            0            0   \n",
      "30            0            0            0            0            0   \n",
      "31            0            0            0            0            0   \n",
      "32            0            0            0            0            0   \n",
      "\n",
      "    Squad_Real   Squad_Sevil  Squad_Shakh  Squad_Vikto  \n",
      "0             0            0            0            0  \n",
      "1             0            0            0            0  \n",
      "2             0            0            0            0  \n",
      "3             0            0            0            0  \n",
      "5             0            0            0            0  \n",
      "6             1            0            0            0  \n",
      "7             0            0            0            0  \n",
      "8             0            0            0            0  \n",
      "10            0            0            0            0  \n",
      "11            0            0            0            0  \n",
      "12            0            0            0            0  \n",
      "13            0            0            0            0  \n",
      "14            0            0            0            0  \n",
      "15            0            0            0            0  \n",
      "17            0            0            0            0  \n",
      "19            0            0            0            0  \n",
      "20            0            0            1            0  \n",
      "21            0            0            0            0  \n",
      "22            0            0            0            0  \n",
      "23            0            0            0            0  \n",
      "24            0            0            0            0  \n",
      "25            0            1            0            0  \n",
      "26            0            0            0            0  \n",
      "27            0            0            0            0  \n",
      "28            0            0            0            0  \n",
      "29            0            0            0            0  \n",
      "30            0            0            0            0  \n",
      "31            0            0            0            1  \n",
      "32            0            0            0            0  \n",
      "\n",
      "[29 rows x 43 columns]\n"
     ]
    }
   ],
   "source": [
    "df=pd.get_dummies(df)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "print(df)\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, df.Pts, test_size=0.25, random_state=5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6aa3dcbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0306096457764417e-15\n",
      "0.06684421219526265\n"
     ]
    }
   ],
   "source": [
    "#Linear regression\n",
    "df\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression().fit(X_train, y_train)\n",
    " \n",
    "# Get the RMSE for the train dataset\n",
    "print(np.sqrt(mean_squared_error(y_train, reg.predict(X_train))))\n",
    " \n",
    "# Get the RMSE for the test dataset\n",
    "print(np.sqrt(mean_squared_error(y_test, reg.predict(X_test))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4e9fc220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5111751167652824\n",
      "1.2046731921977845\n"
     ]
    }
   ],
   "source": [
    "#Random forest machine learning module\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    " \n",
    "rf = RandomForestRegressor().fit(X_train, y_train)\n",
    " \n",
    "# Get the RMSE for the train dataset\n",
    "print(np.sqrt(mean_squared_error(y_train, rf.predict(X_train))))\n",
    " \n",
    "# Get the RMSE for the test dataset\n",
    "print(np.sqrt(mean_squared_error(y_test, rf.predict(X_test))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5be7e88e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00014933186699049483\n",
      "0.8935867789270032\n"
     ]
    }
   ],
   "source": [
    "#Gradient boost machine learning module\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    " \n",
    "gb = GradientBoostingRegressor().fit(X_train, y_train)\n",
    " \n",
    "# Get the RMSE for the train dataset\n",
    "print(np.sqrt(mean_squared_error(y_train, gb.predict(X_train))))\n",
    " \n",
    "# Get the RMSE for the test dataset\n",
    "print(np.sqrt(mean_squared_error(y_test, gb.predict(X_test))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4d80b43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "23e59848",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Significance of Each Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rk_GR</th>\n",
       "      <td>0.377046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>0.224426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pts</th>\n",
       "      <td>0.178052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rk_R16</th>\n",
       "      <td>0.100746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GD</th>\n",
       "      <td>0.051087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0.025166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>0.021806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GF</th>\n",
       "      <td>0.007631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xGD</th>\n",
       "      <td>0.004032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Bayer</th>\n",
       "      <td>0.003474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xGA</th>\n",
       "      <td>0.003342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xG</th>\n",
       "      <td>0.002410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Napol</th>\n",
       "      <td>0.000353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Barce</th>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Vikto</th>\n",
       "      <td>0.000069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xGD/90</th>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Juven</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Lever</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Macca</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Marse</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Shakh</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Milan</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Porto</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_RB Sa</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Real</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Sevil</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_FC Co</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Inter</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Eint</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Dortm</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Dinam</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Club</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Benfi</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Ajax</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_ Tott</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_ Rang</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_ Manc</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_ Live</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_ Chel</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_ Celt</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squad_Atlét</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Significance of Each Factor\n",
       "Rk_GR                           0.377046\n",
       "W                               0.224426\n",
       "Pts                             0.178052\n",
       "Rk_R16                          0.100746\n",
       "GD                              0.051087\n",
       "L                               0.025166\n",
       "GA                              0.021806\n",
       "GF                              0.007631\n",
       "xGD                             0.004032\n",
       "Squad_Bayer                     0.003474\n",
       "xGA                             0.003342\n",
       "xG                              0.002410\n",
       "Squad_Napol                     0.000353\n",
       "Squad_Barce                     0.000296\n",
       "Squad_Vikto                     0.000069\n",
       "D                               0.000061\n",
       "xGD/90                          0.000001\n",
       "Squad_Juven                     0.000000\n",
       "Squad_Lever                     0.000000\n",
       "Squad_Macca                     0.000000\n",
       "Squad_Marse                     0.000000\n",
       "Squad_Shakh                     0.000000\n",
       "Squad_Milan                     0.000000\n",
       "Squad_Porto                     0.000000\n",
       "Squad_RB Sa                     0.000000\n",
       "Squad_Real                      0.000000\n",
       "Squad_Sevil                     0.000000\n",
       "Squad_FC Co                     0.000000\n",
       "Squad_Inter                     0.000000\n",
       "MP                              0.000000\n",
       "Squad_Eint                      0.000000\n",
       "Squad_Dortm                     0.000000\n",
       "Squad_Dinam                     0.000000\n",
       "Squad_Club                      0.000000\n",
       "Squad_Benfi                     0.000000\n",
       "Squad_Ajax                      0.000000\n",
       "Squad_ Tott                     0.000000\n",
       "Squad_ Rang                     0.000000\n",
       "Squad_ Manc                     0.000000\n",
       "Squad_ Live                     0.000000\n",
       "Squad_ Chel                     0.000000\n",
       "Squad_ Celt                     0.000000\n",
       "Squad_Atlét                     0.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances = pd.DataFrame(gb.feature_importances_, index=X_train.columns, columns=[\"Significance of Each Factor\"])\n",
    "feat_importances.sort_values(by='Significance of Each Factor', ascending=False, inplace=True)\n",
    "feat_importances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6d9c15b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances.plot(kind = 'bar', figsize = (18, 5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fea527fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.boxplot(by='Squad', column='Pts',figsize = (10,20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "337ff4ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of    Rk               Squad  MP  W  D  L  GF  GA  GD  Pts    xG   xGA  xGD  \\\n",
       "0   1            pt Porto   6  4  0  2  12   7   5   12  11.9   9.3  2.6   \n",
       "1   2      be Club Brugge   6  3  2  1   7   4   3   11   6.9  10.7 -3.8   \n",
       "2   3       de Leverkusen   6  1  2  3   4   8  -4    5   9.0   7.5  1.5   \n",
       "3   4  es Atlético Madrid   6  1  2  3   5   9  -4    5   9.5   9.8 -0.3   \n",
       "\n",
       "   xGD/90                                     Notes  \n",
       "0    0.43                             → Round of 16  \n",
       "1   -0.63                             → Round of 16  \n",
       "2    0.25  → Europa League Knockout round play-offs  \n",
       "3   -0.05                                       NaN  >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b84b9cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initial visualization\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5b77770-9944-4fce-85f3-e70c9b3ec289",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1db980f-6964-4ad2-95e6-7355ba8e7b81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "0d591c6e422414675974e227c13f5382000c440fedd3c5006ef2be5d887f0ba7"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}