{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agustina/anaconda3/lib/python3.5/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pymc3 as pm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "# R-like interface, alternatively you can import statsmodels as import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf \n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.style.use(['seaborn-colorblind', 'seaborn-darkgrid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brain</th>\n",
       "      <th>mass</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>438</td>\n",
       "      <td>37.0</td>\n",
       "      <td>afarensis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>452</td>\n",
       "      <td>35.5</td>\n",
       "      <td>africanus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>612</td>\n",
       "      <td>34.5</td>\n",
       "      <td>habilis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>521</td>\n",
       "      <td>41.5</td>\n",
       "      <td>boisei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>752</td>\n",
       "      <td>55.5</td>\n",
       "      <td>rudolfensis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>871</td>\n",
       "      <td>61.0</td>\n",
       "      <td>ergaster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1350</td>\n",
       "      <td>53.5</td>\n",
       "      <td>sapiens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   brain  mass      species\n",
       "0    438  37.0    afarensis\n",
       "1    452  35.5    africanus\n",
       "2    612  34.5      habilis\n",
       "3    521  41.5       boisei\n",
       "4    752  55.5  rudolfensis\n",
       "5    871  61.0     ergaster\n",
       "6   1350  53.5      sapiens"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'species' : ['afarensis', 'africanus', 'habilis', 'boisei', 'rudolfensis', 'ergaster', 'sapiens'],\n",
    "'brain' : [438, 452, 612, 521, 752, 871, 1350],\n",
    "'mass' : [37., 35.5, 34.5, 41.5, 55.5, 61.0, 53.5]}\n",
    "d = pd.DataFrame(data)\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_6_1 = smf.ols('brain ~ mass', data=d).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4901580479490838"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - m_6_1.resid.var()/d.brain.var()\n",
    "\n",
    "# m_6_1.summary() check the value for R-squared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_6_2 = smf.ols('brain ~ mass + I(mass**2)', data=d).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_6_3 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3)', data=d).fit()\n",
    "m_6_4 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4)', data=d).fit()\n",
    "m_6_5 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4) + I(mass**5)', data=d).fit()\n",
    "m_6_6 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4) + I(mass**5) + I(mass**6)', data=d).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_6_7 = smf.ols('brain ~ 1', data=d).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d_new = d.drop(d.index[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AmZmZfe6uJzp+seMAoN8+s7FMSltbG3w+H8xmM5qbm/sdQ/w+8WprawFEL/LWrl2bcL+v\nvvoKQLQIDhERjYxYLZX44HEgYkF6fEZ3xYoVeP/99/HGG2/g8ssvF8/HMuOxNdVAzzlHo9Hg3nvv\nTfgZsSJtic4DWVlZ4r1SyeFw9Hkudo7Nysrq97X4rHXsnHioHu+xc2msgF/smgdQJzDiFRYW9rmu\niR33jz/+uM/1UO8xVlVVqdbVEyUbg3WiFBjsyTN2wuovwO49Haw/Pp8PAA5ZECXRa7Gswt///vfD\nfkaii5lYdiGVDnWMBjpFMHb89Hp9v/8WRqNR/N3v98NsNov94l+Ld6hj7nK5RGGf/gz2ApKIiAau\nuLgYO3fuHNSNUVmWUVNTA0B9Q/Y73/kOLBYLvvjiCzQ1NSE/Px+yLPeZAg/0BPs+n29I54GxcO4F\n+r92AQZ//jWZTP1uEzvHxtrSxTrc6PX6fq+7DnX+3bRpk8iy94fnXxppDNaJUiB+/XFXV9dh+4zH\n2oskugM9GLETWSgU6nebRO3bLBYLOjs78fjjj/dZcz2ZxC58QqEQwuFwwhsAsQsKALBarQB6jnsw\nGEz4vvH79P6sBQsW4Pnnnx/ewImIaMiOOuoovPnmm/j4448HvM+OHTvg9Xqh1+sxf/588bzZbMYp\np5yCV155BW+++SYuu+wyfPrpp3C5XKK1W0zsHJKbm4t//etfyftC41BsCUKi82VM7Poldv6MBeLh\ncBiKoiS8MdDfNQ8ArFmzBhdeeOHwBk40TKmfG0M0CdlsNhGgx3qX9sfr9YoiK4nWVg1G7DNbWlr6\nLYoSm2oWr7S0FAD6FMSZbIqLi8XJPpYx6S32fG5urrhQiD/uh9onXqx43aH68RIR0ciLFYbbvXs3\nNm/ePKB9/vznPwOIFivtvdb9zDPPBACxbv3//u//AKiz6kDPudftdvd7s3eyiBWoS3SNEhM7l8am\nysemviuKwvMvjVsM1olS5Lvf/S6AaDXRQ3n66acRDAZRXl6esEjLYDidTmi1WgQCAVH0LN4XX3yB\n9vb2Ps8vWrQIQM8FRW9+vx9/+9vf4PF4hjW+gUpV9dX09HSxHv/9999PuM0HH3wAQF1Ub9asWQCi\nReMS+ec//9nnuaOOOgoGgwFNTU3YsmVLwv0+++wzbN++XaypJyKi5MvPz8f5558PALjjjjvEbLf+\nbNq0CRs2bIDBYMCqVav6vH788cfDbrdj69ataGtrw9tvvw2tVosVK1aotps+fTpyc3Mhy3KfgnQx\nO3fuxGeffYZwODzEbzc4qTr/xs6p27dvTxh4BwIBcSPl2GOPBRBdKx+bkZjo/NvU1JQwYRL7rDff\nfDPh91UUBa+//rpqTTzRSGGwTpQiq1atQm5uLjZt2oS77767z11zWZbxwgsv4P7774dWq+23xchg\npKeni2rl69evV7Vp6+7uxj333AO9Xt9nv4svvhgmkwkffvghXn75ZdVr4XAYa9aswQ033IA777xz\nWOM7nNiUwFTe7Y4VBHrsscdEYZ+YLVu2YMOGDZAkCStXrhTPn3baaQCigXzvC4Y33ngjYRXfjIwM\nnHPOOQCAu+++u8/F4Y4dO3D11VfjggsuwL59+4b9vYiIqH833XQTjjjiCFRWVuKSSy4RFcfjRSIR\nvPDCC1i1ahUURcEdd9yBGTNm9NlOp9Ph9NNPRyQSwe9+9zu0tLRg8eLFfZbEaTQaXHbZZQCA3/zm\nN33OfTU1Nbj22mtxySWX4MMPP0zit+0rNjU8VeffmTNnYtGiRQiHw7jrrrtU1y+KouDXv/41Ojo6\nMGPGDHGdA0C0aH3sscdUU+hDoRDuuuuuhOvpv/e97yE7OxuVlZV45JFHVK8pioL169fjpz/9Ka65\n5ppkf02iPrhmnShF7HY7Hn/8caxevRp/+MMf8Nprr+HYY4+F3W5He3s7tmzZgvr6elgsFqxZswbf\n/va3k/K5//mf/4nPP/8c77//PlasWIFjjjkGgUAA//rXv1BWVoYTTjhB1aMUiE4/W7t2LW6++Wbc\nfPPNeP7551FRUYHu7m5s3rwZTU1NKCoqwi233JKUMfZnzpw5AICXX34Z9fX1CIfD/fZ2Hylnn302\nPvnkE7z00ks466yzsHDhQuTm5qK6uhqffvopIpEIbrzxRtUaxXnz5uGss87CX//6V1x++eU4+eST\nkZ2djf379+Ozzz7Dddddh/vuu6/PZ910003YvXs3vvjiCyxfvhwnnHACrFYrqqur8cknn0CWZVx3\n3XVwOp2jeQiIiCYds9mMP/3pT7j11lvx1ltv4cILL4TT6cTMmTNhtVrhdrvx6aefwuPxIDMzE2vX\nrsXpp5/e7/utWLECTz/9tCgc13sKfMyVV16JL774Au+88w7OOOMMfPvb30ZWVhYaGxvx4YcfIhQK\n4bzzzuu3zViyzJkzB5s3b8Z1112HuXPnYt68eaMerN59991YuXIl3njjDezatQvz58+HoijYsWMH\nKisrkZ2djXXr1qmKyV199dV45513sHPnTpxxxhk47rjjIMsyNm3aBL1ej7PPPht/+ctfVJ+TlpaG\n++67D6tWrcL999+PN954A/Pnz0cwGMTWrVtRVVWFzMxM3HXXXaP6/WlyYrBOlEIVFRV45ZVXsGHD\nBrz11lvYtGkTOjs7YbFYUF5ejnPOOQcXXnhhvy2/hmLmzJl47rnn8MADD+Dzzz/HSy+9BIfDgbPO\nOgvXXnstbr311oT7nXnmmZg+fToef/xxbN68GTt27IBOp0NZWRnOO+88XH755arCeSNhxYoV+Pzz\nz/Hmm29i27ZtYj3faLv77rtx3HHH4S9/+Qu2bNmC7u5uZGZmYunSpVi5cmXCfrh33303ZsyYgY0b\nN+K9996D2WzG7Nmz8cQTTyA3NzdhsG6z2fCnP/0Jzz33HF5//XW89957CAQC4rMuueQSVQaBiIhG\njs1mw4MPPojPP/8cr776KjZv3oz3338fPp8PmZmZqKiowEknnYTzzz9f1a4tkfnz56O0tBTV1dWw\nWCw49dRTE26n0+mwfv16vPzyy9i4cSM2bdqE7u5upKWlYeHChTj//PPF7K2RdMcdd+DWW2/F7t27\n8eWXX6puSI+WoqIibNiwAU888QTeffddvPXWWwCi7deuvPJKXHnllX1mJ+Tn5+P555/H/fffL2YH\nZmdn46STTsL111+Pxx9/POFnLVq0CK+88gp+97vf4aOPPsLGjRshSRKKiopw+eWX48orr0zqtRlR\nfyQlVYtPiIiIiIiIiCghrlknIiIiIiIiGmMYrBMRERERERGNMQzWiYiIiIiIiMYYButERERERERE\nYwyDdSIiIiIiIqIxhsE6ERERERER0RjDYJ2IiIiIiIhojGGwTkRERERERDTGMFgnIiIiIiIiGmMY\nrBMRERERERGNMbpUD2Cyc7k6Uz2EUZeZaQEAtLV5UzyS8YfHbnh4/IaOx27oRvvY5eamjcrnTGTr\n1t2HoqJiLF58IiwWa6qHo8LfRTUeD7VEx6O7uwtffbUDsiwDAByOApSWTknJ+EaaLMvYtWs7vN5u\nmEx6WK1WlJdXQKvVpnpoKRf72XC52rF166cAAI1Gg6OOOhYazeTJ31ZVHUBzcyNMJj2mTp0Gm80+\nKp871HPz5PmXISIiIhqgcDiCcDic6mEQDUskEsGBA3tFoG6z2VBcXJbiUY0cjUaDadNmiOCzu7sb\n9fW1KR7V2NLV1ZMotFiskypQBwCj0Sj+HggEUjiSgZlc/zpEREREAxAOhxAKhVI9DKJhqampgs/n\nAwBotVpMnTpjwgdnZrMFJSXl4nFjY70qQJ3suru7xN9ttsk3E8tgMIi/BwL+FI5kYCb2bysRERHR\nEITDYYTDDNZp/PJ4WtHc3CQel5ZOgclkTuGIRk9eXj6ysrIAAIqioKpqv5hdMNnF37iw2WwpHElq\nGAzMrBMRERGNa5FIhJl1GrdCoRCqqvaLx3Z7DnJyclM4otElSRKmT58h1qp7vV40NHA6vKIoqsy6\n1ToZM+sM1omIiIjGNWbWaTyrrq4SN5sMBgPKy6dAkqQUj2p0mc1mlJf3FNKrr6+D19udwhGlXnd3\nNyKRCIDoz0X8lPDJwmAwiN+FYDA45mdcMFgnIiIi6kWWIwgGGazT+ON2u+F2u8TjsrKp0On0KRxR\n6hQWFiItLR1AbDr8ASiKkuJRpU5nZ88UeKs1bdLdwAGisy70+p6bFMHg2M6uM1gnIiIi6kWWZQSD\nY7/4EFG8cDiMffv2isfZ2TnIyhqd1lRjkSRJKC+fJorqdXV1oqXFdZi9Jq7Ozg7x98m4Xj0mfkZB\nMBhM4UgOj8E6ERERUS+KosDvH9sZF6LeKisPiHW4er1+wvZTHwyz2QyHo1A8rq09OGmXuHR0xBeX\nm3zr1WPi27cxWCciIiIaZ5hZp/Gms7MTDQ0N4nFZ2RTo9ZNz+ntvBQVFIkALhUKora1J8YhGXzgc\nFmv2JUmCxWJN8YhSJ34afCjEYJ2IiIhoXFEUZVwUHyICoj+vBw8eEI+zsuzIyspO4YjGFq1Wq+q9\n7nI1qaqiTwbxLdvMZouolD8ZqdesM1gnIiIiGjdiRZfC4dCknS5L40tTU4PImmq1WpSWlk/K4mGH\nkpVlR0ZGJoDYzY3KSVVsLr643GRerw4ABkPPjJPRyKwP5+eMwToRERFRHEmKXh6Fw+y1TmNfMBhA\nXV3PtO7S0lIYjaYUjmhskiQJZWVTVMXmPJ7WFI9q9KgrwU/uYF09DX7k/4+PRMJD3pfBOhEREVEc\njaYns85gnca6mpqDone2xWJBUVFxikc0dplMZuTlOcTj2tqDk2apS1dXz7R/BuujOw0+HGawTkRE\nRJQUPdPgw2O++BBNbp2dHXC7W8Tj6dOni8wxJVZYWASdTgcA8Pv9aG5uTPGIRl4oFITfHy2YqdFo\nYTZbUjyi1Oo9DX6kl0MwWCciIiJKktg0+EiE0+Bp7FIUBdXVVeJxdnYOMjOzUjegcUKn06OwsGf2\nQX197YSvTaHOqlsnfT0DjUYrCuzJsixmpowUToMnIiIiShJm1mk8aGlxiYrmGo0WxcVlKR7R+JGX\n5xDr+sPhMOrr61I8opHl9aqD9clOkiQYDKPXvo2ZdSIiIqIkiU0jjkQiY76tD01OkUgYtbXV4nFB\nQYHoI06Hp9FoUFJSKh43NzciGAykcEQji+vV+4oP1kf6//lQiME6ERERUVLEpkcqiizWeRKNJQ0N\n9SIbaDAY4XAUpXhE409WVrZoYSbL8oTNriuKouopz2A9ymDoubk10sudhpO5Z7BOREREFEerjRaf\nUhQFgYAvxaMhUgsE/GhsrBePi4tLxQ0mGjhJklBU1JNdd7maJuTNuUAgIKZh6/V6tvX7xmhOgw8E\nhj5rg8E6ERERUZxYpehosD5xp8bS+FRXVyPajdlsNmRn56R4RONXenoG0tMzAER/3+vraw6zx/gT\nn1W32WyTvrhczGgG68NZYsFgnYiIiChOfLAeDAZHvFIw0UB5vd2qVm0lJeUMvoYhml0vEY/d7hb4\nfN4Ujij54oP1tLS0FI5kbDEa44P1kZ0GP5w18eM2WG9tbcVdd92FE088EbNnz8aiRYuwevVq7Ny5\ns8+2fr8f999/P5YvX445c+Zg0aJFuP7661FZWdlnW1mW8eSTT2LFihWYO3cujjnmGPz4xz/Gl19+\nmXAcGzduxPe//33Mnz8fCxYswMqVK/Gvf/0r6d+XiIiIRofRaBTt20KhECvC05hRW1stekJnZmYh\nLS09xSMa/9LS0pGRkQkgeoOurq42xSNKLnVmncF6jF4/egXmJl1m3e1245xzzsGLL76I008/HWvX\nrsWFF16Ijz/+GD/4wQ+wa9cusa2iKLj66qvxyCOP4Oijj8bdd9+NH/3oR9i8eTMuuugiVFdXq977\n9ttvx7333ovy8nLcdddduO6661BZWYlLL70UW7duVW378MMP45ZbboHVasVtt92GW265Bd3d3fi3\nf/s3vPnmm6NyLIiIiCi5jEYjNJpotjISCbPXOo0JHR3taGvzAIhmhNmqLXmKi3vWrre2tsDr7U7h\naJInWlyu57sws95jdKfBD/39dUkcx6j5zW9+g8bGRjz44INYtmyZeH7u3LlYvXo1fvvb3+L+++8H\nALz++utO9qEXAAAgAElEQVT48MMPcdVVV+Gmm24S2y5evBjf//738atf/Qrr168HAGzduhUvvvgi\nTjvtNLE/ACxbtgzLly/HmjVrsHHjRgBAfX09Hn74YcyfPx9PPvmkKOxxxhln4IwzzsCaNWuwdOlS\n6PX6ET8eRERElDxGo0lMLQ6FGKxT6imKgtrag+JxdnYuLBZLCkc0sVitNmRl2eHxtAIAGhrqMG2a\nM8WjGj6fzwtZji7jMRqNMBqNE26a/1DFtzoMBkNQFGXElpSEw5NsGnxeXh7OPPNMnHrqqarnTzzx\nREiShN27d4vnXn75ZQDAZZddptp29uzZWLBgAd5//310dHQcctv8/Hyccsop2LVrF/bu3QsAeO21\n1xAKhXDJJZeoKnDabDacffbZaGlpwYcffpikb0xERESjxWQyi2nwzKzTWODxtIpe2RqNRrXOmpKj\nsLBY/L211Q2fb/x3gojPqnMKvJpWq4VGE/1/XpYj4qZGsimKMqxzyLjMrF977bUJn+/q6oKiKKJn\nIgBs374dBQUFcDgcfbafN28etmzZgp07d2Lx4sXYvn07tFotjjzyyITbvvrqq9i2bRtmzJiB7du3\nAwAWLFiQcFsA2LZtG5YsWXLI75KZOfnuiup00V+Myfjdh4vHbnh4/IaOx27oeOzGH7s9HTqdFpGI\nBECG0agZM/9+/HlSmwzHQ1EU7N/fBJMpOluzuLgY+flZCbedDMdjoAZ7LDIzLfB48tHaGs2ud3S0\noKCgYsTGNxpcrpD4ucnMTIdON3b+L0s1nU4Ds9kkOiuYzboRma0SCoXEsqqhGJeZ9f48//zzAIAV\nK1YAiAbvbW1tCQN1ACgoKAAA1NZGC0nU1dXBbrcnnLoe27ampkZsC0Sz7r0VFhaqtiUiIqLxw2Kx\niIurcDjCAnOUUi6XS2RItVotSkpKD7MHDVVpac+xbW5uGvfZ9c7ODvH39HQWI+wtft36cIrAHUoo\nFBpWR5FxmVlP5B//+AcefvhhzJ49GxdffDGAnqkfJpMp4T6xuyex7bq7u0VQPpBttVqt6h85xmw2\nq7Y9lLa2ybduJHZHbzJ+9+HisRseHr+h47EbutE+drm5nOo4XOGwBrIMyHJ0+mJra8eY+dnn76La\nRD8eiqJg9+698Puj02gLC/PR3R0CkHha7UQ/HoMxtGOhh8FgQUdHOwBg9+79KC+fOgKjG3myLMPt\nbhPdA8xmK8JhmT8b38jMtECr1YvfLbe7A4Dx0DsNQWdnBwKBSVYNvreXX34Zq1evRlFRER599NGE\nATQRERHRQBgMBuh00Xo0iqIgEPCneEQ0WUX7fkezu1qtDg5H4qQSJU/82vWWluYRy7iONK+3WwTq\nJpMZOt2EydEmTXyv9WBwZGqThMNhhMNDz6yP+2D9oYcews0334yKigo8++yzyMvLE6/F1q73N4Ul\nlvm2Wq3iz/629Xq9qve0Wq2IRCIJS/H33paIiIjGD71eD602uiROlmX4/QzWafTJsoy6up4llQ5H\nAXQ6dhkaaWlp6aIYmyzLaGxsSPGIhia+v7rVypgkkdFo3zbcafDjOlhfu3YtHnjgASxduhRPP/00\nsrOzVa9brVbY7XY0NjYm3L++vh4AUF5eDgAoKSmB2+1OGIDH1qjHbwsg4XvHti0rY/9LIiKi8Uav\nV2fW/X6/yFARjRa32yVmdeh0OuTnM6s+GiRJQkFBkXjscjUjHA6ncERDow7WrSkcydhlMPRMex+p\nYD0Y9ENR5CHvP26D9Yceegh//OMfce6552L9+vVinXhvCxYsQGNjowjM43322WcwmUw44ogjxLay\nLGPbtm19tv38888BAEcddZTYFgC2bNnS77ZHH330EL4ZERERpZJGo4HBYIzrtR5EJDL+LtZp/FIU\nBfX1deKxw1HIacyjKDMzC2ZzdM17JBKGy9WU4hENnjpYZy2TRAyGnpkqI9WiMxAIDOtm77gM1jdt\n2oQHH3wQp556KtauXavqc97beeedBwB46qmnVM9v3rwZO3fuxOmnny7uNn3/+9+HJEl9tq2qqsK7\n776LhQsXiiqRZ555JkwmE/70pz+p7rZ5PB5s3LgRpaWlWLhwYRK+LREREY02o9EUF6yH2GudRpXb\n3dIrq564sxGNDEmS4HAUisdNTQ2ixdd4EA6HxdJeSZJGpCXZRBCfWU80szoZhhusj8tbdL/61a8A\nAIsXL8Zbb72VcJuTTjoJZrMZS5cuxbJly/CHP/wBXV1dWLRoEerr6/HEE0/A4XDgpz/9qdhn5syZ\nuPzyy/Hkk09i9erVOPXUU9HW1oYnn3wSJpMJt99+u9g2JycH//mf/4lf/OIXuOKKK3D22WcjEAjg\nmWeeQVdXF/73f/8XGs24vBdCREQ06RmNPZn1cDgarPcziY8oqaJZ9Vrx2OEohFY7Li/Zx7Xs7BzU\n1VUjGAwiGAzC7W5Bbm7e4XccA7zeno5UZrPlkInNyUy9Zn1kbsgGgwHI8iQL1nfu3AkAWLNmTb/b\nvPPOOygujlZzXLduHR577DG8+uqreOWVV5Ceno4lS5bghhtuQG5urmq/m2++GcXFxXjhhRdw++23\nw2w249hjj8X111+P6dOnq7ZduXIlsrKy8NRTT2HNmjXQarWYP38+7rrrLjFdnoiIiMYfs9kMSdIA\niCAcDrPXOo2a1lY3/P5oVlSn0yEvj1n1VNBoNMjPL0BNzUEAQGNjPXJycsVNvLEsfgo8C173T6fT\nQaPRQJZlRCJhRCKRpN/YCAaDky+zvnv37kFtbzAYcM011+Caa6457LaSJOHSSy/FpZdeOqD3PvPM\nM3HmmWcOajxEREQ0tplM5rjMepjT4GlUKIqChoaerHp+voNr1VMoNzcf9fW1iEQi8Pm8aG9vQ2Zm\nVqqHdVhdXT3BusXCYL0/kiRBrzeIJSehUBBabXKnUAWDgWEVmONvPxEREVEvFosFGk00WI9EGKzT\n6PB4WkULYK1WywrwCciyDK+3G36/H4GA75s1wYBGI0GSNDAYDLBYrLBYdKppzkOh0+mQm5uPxsZo\noerGxoZxEax7vcysD5RerxfBejAYhMmU3GA9FApNvsw6ERER0UgymSzfTIMHIpGIuJgjGinRrHpP\nBfi8PAf7qn8jEomgvb0NHk8r2ts9A2qlVl29D2azGRZLOrKzc4cchOXnO9DU1ABFUdDR0Qav1zum\nC7aFQkEEAgEAgEajFVXtKTG9fuTWrSuKwmCdiIiIKNmMRqOYfizLilhDTDRSOjraxVpjjUajqkY+\nWYXDITQ1NaKpqWFIvc59Ph88ng7U1dUiLS0dhYXFyMjIHNR7GI0mZGXZ0drqBhCtDD9lyrRBj2W0\nqKfAW8bFGvtUUrdvS25tktg6eAbrRERERElkMBhEBW5FkeH3M7NOI6uhoV78PTc3D3r95M2qRyIR\n1NfXorm5EZFIpM/rRqMRFosNJpMJJpMJkqSBosiQZRl+vw/d3d1QlJBq387ODuzevQsZGZkoKSmD\nxWId8Hjy8wtEsO52u1BcXDpm/304BX5w4jPryW7fFg6HEYkM/iZTPAbrRERERL3o9Qbo9bFgnZl1\nGlldXZ3o6GgD0LfH92TT1ubBwYMHxFTuGKPRhJycHGRm2mGxWA+bMU5PN8HtdqOqqgZtbR6R3Wxv\nb0NHRzvy8x0oLi4bUKtlmy0NVqsN3d1dkGUZLlcTCguLh/4lR1B8Zt1qZbB+OPE3XYYye+NQQqEw\nwuG+N5sGg8E6ERERUS+SJKnWuPr9PsiyPKALe6LBihUwAwC7PQdGoymFo0mNcDiMgwcPwO1uUT1v\nsVhQUFAEuz1nUFO6NRoNcnNzoddbEQwGUFdXi5aWZiiKAkVR0NjYgI6ODkybNuOw67olSUJ+fgEO\nHNgLAGhuboTDUTjm/j9QFEXVto3B+uHF14UIh5O7Zj0cDkGWhxesj62fMCIiIqIxIr59WygUYq91\nGhHRddWt4nFBweTLqnu93di160tVoK7X6zF16gzMnj0P2dnD629uMBgxZco0zJ49T7Vm3evtxs6d\n29HS0nzY97Dbs8WU6WAwCI/HPeTxjJRAICCywzqdblLe9Bms+Mx6sgvMhUKhYU+DZ7BORERElIDJ\nZBaZs1AohGCQ7dso+Rob68UU7czMrEGtpZ4I3G4Xdu3aoaoLkZOTizlz5iMnZ3hBem8WiwVO5yyU\nlU0Rv9uyHMGBA/tQX197yEJgGo0GeXn54nFTU2PSxpUsvbPqLC53eCOZWY+2FRx6cTmA0+CJiIiI\nEjKbTeJiNxwOM7NOSRcKBeF2u8TjgoKiFI5mdCmKgvr6GtTV1YrntFotpkyZDrs9e8DvE4lE4PN5\n4fP54PN5IcsRKApgtRqg0+kQiWhhNpthNlug1WrFlPa0tAwcOLBH9LWvra1GMBhEWdmUfoPcvLx8\nNDTUQZZldHV1wuvtHlM3VzgFfvB6Z9YVRUnaTY5AwM9gnYiIiGgkmM3xvdbDCAYDh9mDaHCamxsh\nyzKAaBGztLT0FI9odCiKgurqKjQ1NYjnTCYzZsyoGFBf8HA4DI+nFa2tLejoaE8YEHV2RoMwvz+a\nLZUkCRkZmbDbc5CVlQWLxYJZs+Zg7949orhfc3MjwuEQpk6dkXA9ul5vQFaWXUzXb25uRHn52Gnj\nxmB98LRaLTQaLWQ5AlmWIcsR0QlkuILBAGSZwToRERFR0pnNVmg00QxLJBJJelsfmtwikQiam5vE\n48lSAV5RFFRW7kNLS8+MgoyMTEyf7jxskOT3+1BfX4fW1hZxk2Mwn9vW5kFbm+eb4nP5KCwsgtM5\nE5WV+0QA3trqhiRpMHXq9IQZ1rw8h9i2paUFxcVl0OlSH1JFi8t1i8cM1gdOr9cjEIgWgguFQkkL\n1jkNnoiIiGiEmEwmaDRaANHAir3WKZnc7hZR0MpoNCEry57iEY08RVGwf/9etLb2FJKz23Mwder0\nQ1ZWDwT8qK+vRUuLK2HwE53mboXZbP5mWrOE9HQTAoEAXK62b6bJe8X2siyjqakBLlczHI4ClJZO\ngV6vR2NjNNPvdrug02lRWtp3SrzNlgaLxQqvtxuyHEFLS/OYuNESWwIAAAaDAQaD4TB7UIxer0Os\nU2AoFIYpSXX5QqEgFGVwN5V6Y7BORERElIDBYBQZs2ivde9h9iAaGEVR0NTU064tP98x4YuBKYqC\ngwcPqAL1vLx8lJVN7fe7R49TA2pra/q0wLJabbDbc2C3Z8NoNAKIBkd+vx/BYAChUBiSpEFaWhoy\nMzMhywqCwQDa2jzweqMZaFmOiJsAU6ZMgywraG6OFo5ramqETqdDUVGp6nMlSUJeXj6qqg4AAJqb\nm5CfX5Dyfz9m1YdupIrMBYNBZtaJiIiIRoLBYBDBuizL8Pl8KR4RTRTt7W3i50mr1SE3Ny/FIxp5\ntbXVqmn/+fmOhJnrGL/fh8rK/ejs7FA9n56egaKiEqSlpUNRFHR2dqCpqQEdHW2iWBwAmEzqNetA\nNNA2mcywWq3w+wOirVYwGMDu3bvgcBQiK8suWunV1dXCaDQjJydXNYbs7FzU1FQjEgnD7/eho6Nd\n1RIuFbhefehGqn1bNLPOYJ2IiIgo6bRaLQwGo3gcP42WaDhi060BIDc3L2lrZMeqhoZ6NDTUicc5\nObmHDNRbW1tw4MB+VTbdYrGitLQc6ekZiEQiaGpqQFNTw6CWpyiKIn6PY0FUMBiAwWCEJElobKyH\nxWKBzZaGrq5OAEBV1X6YzWZVAKzVapGTkyPatzU3NzFYH8dGIrOuKAqDdSIiIqKRZDKZxd8DgWgm\nbqIHVjSyvN5uUX082kbMkeIRjSyPpxU1NVXicVaWHeXl0xIG6oqioK6uBvX1Pe3cJElCYWERCgqK\nAQANDXVoaKhDOBzus79Go4HZbIHBYITdng6NRkJ7uzeuvZtXBE+xz9fp9OjsbIfBYITJZIbX64Ve\nr4dOp0M4HIYsy9i7dzdmz54Lvb5nHXhenkME621trQiFgqrXR5Msy2JqPwBYrWOnndx4MBKZ9Ugk\nLH5Gh7NEgmcbIiIion6YTGZoNBrIsoxwOIRgMASzmZdPNHTxWfWsrGwYjUmqZjUGeb3dOHBgr3ic\nlpbeb1u0SCSC/fv3oK3NI54zmcyYPt0Ji8WK9vY2HDxYCb9fvRxFp9PBbs9BZmYW0tLSodVGi0Jm\nZkZbwLW1eeM+I4yuri643S60trohyzK0Wi3S0jLQ3d0Ft7tFlSGP3ZwLBgPYt28PKiqOEGM3my1I\nS0tHZ2cHFEVBS4sLBQVFSThqg+f1doubECaTWZUppsMbicx6OMxgnYiIiGhEmUwmcaEVCoURCgVh\nNpsPsxdRYsFgUFVgzeEoSOFoRlYoFMLevbsRiUSnshuNJkyfXiGC6XjhcBh79nwlpp4D0XZu06Y5\nIUnA/v174Xa7VPuYTCbk5xcgJycv4XsmotXqkJGRiYyMTJSWlsPlahZZepstDTqdDi0tLmRkZMJs\nNiMSiSASicBgMKKzswN1dTUoKSkT75ebmyfW1LtcTXA4ClNSaE49BZ5Z9cEaicx6OBwWP/sM1omI\niIhGgNlsgSRpAEQQiYTZa52Gpbm5UfQHt9nSYLOlpXhEIyPaom0PAoHoenKtVosZM2aqgqKYUCiE\nPXu+UgWcBQVFKC4uhc/nxb59e1TZdK1Wh6Ki4mFXYNfp9CgoKEJOTh7q6mrgcjXBZDLDbteitbUF\nwWAA6ekZYo27wWBEY2M9MjIykZ6eASA6M0Krrfqm0JwfnZ0d4rXRxPXqwxP/c5nMzHoygvX+GxoS\nERERTULxBYHMZgs0muiFVjgczawTDUUkEhFtwQCMid7cI6WurgYdHe0AooHK1KkzYLFY+mwXCgWx\ne/dOVbBZVjYFJSVlaGlxYdeu7apAPTs7B0ceOT+pGWy9Xo/y8qk44oi5MBpNMBgMyMnJg8/nhdvd\nApPJjO7ublEsrLJyn5jerNVqkZ2dI97L5WpOypgGSx2sT8wbQCMpfhp8sjLrwWBAFEiM3vAdGgbr\nRERERHHiC1dZLBZxoRWJRBAIBFI1LBrn3G6X+NkyGk3IyrKneEQjo729TVX5vaioJOF3DYfD2L37\nK9FuTZIkTJkyHXl5DtTVVaOycp+YhaDVajF16gxMm+YcsSJuVqsNs2cfiawsO3Q6HbKzcxEKheB2\nu2Cz2eDxtCIcDiMQCODgwQNiv/i2ex6PO6l9ugciHA6LNoCSJCW8KUKHFmvRCUSP53AruAOA3x+A\nLEffR6tlsE5ERESUFPGtoEwmk1gPK8tyn+JWRAOhKIqqsFx+viMla5tHWjAYxIEDe0Wwk5GRmbDo\nWiQSwd69X4sK5tHs+3RkZ+egqmo/6up6qsFbLBYcccSRfXqdjwSdTofp0ytQXFwKvV6P7OwchMNh\neDytsFptoiid290i1tBbrTYx9Tz22miKrwJvNlsGvH6femg0GhGwK4qSsNPAYAWDAfF7oNEM/d+E\nwToRERFRnECgJyA3GAxxF3Eye63TkLS3t4kbPVqtTpWNnSgURcGBA3vFNGK93oCpU6f3uSkR2y5W\nmA0ApkyZhqysbOzbt1s1lTwjIxOzZs0d1aKO0VZxxSgvnwaj0Qi7PRvhcLSKvE6nRVubB4qioLq6\nSnzXnJyef0+XqzkpmdmBip8Cb7NxvfpQJbvIXDDoFz8Hw7mBwmCdiIiIKE58Zl2n04uLOEVRGKzT\nkMSvVc/NzYNWO/FqPDc21qvWqU+bNqPPlHVFUXDwYCU8nlbxXElJOez2HOzbt1vVti0nJw8zZsxM\nWaY4Ly8fU6fOgMlkRkZGJsLhkJgG39XVhVAohNragwCia+lj2VOvt1sVQI+0rq6ez7JYGKwPVbLb\ntwUCQShKzzKOoWKwTkRERBQnPliXJAkmU09Wz+v1jmrWjMY/v9+H9vY2ANGfp7y8/BSPKPm83m7U\n1dWIx4WFRQmrojc3N6luXBQUFCE/34H9+/eqAvWCgiJMmTItYT/20ZSdnYNp05yw2dJgtdoQCoUg\nSUBHRxv8fj9crmZ0dLR/0+s9W+zX0jJ6hea8XmbWkyHZmfVAgJl1IiIioqTrvS7dZDLH9VoPJmU9\nI00ezc1NqjXc8Td/JgJZllFZuV8Ug7NabSgoKO6zXUdHO6qrK8Xj7OwcFBWVoLJyHzwet3i+sLAY\nxcWlY2ZNv92ejdLSKcjIyITRaEQkEoEsy/B4WhGJRFBVdQCyLKuWNrjdLaJt10gKhYKi6KVGo4XZ\nzOJyQ5XszHqse0Dv9x4sButEREREceIzIgBgNvcE62zfRoMRiURUWda8PEcKRzMyGhpqxbRvjUaD\nqVOn98mI+/1+7N+/R/xeWa02lJdPQ21ttaogm8NRiKKikjETqMfk5zu+qWqfDa1WC0mSEAwG0N7u\ngc/nRUNDLWy2NLG2PhKJoLXVfZh3HT71FHjLmDtu40nyM+uBuGB96MteGKwTERERxYlEIqqAPNpr\nPXrJFAqFEAwyWKeBaW1tUbVry8jITPGIkqu7uwv19T1t2oqLS/tkd2VZxr59u1WF52bMqEBLSzMa\nG+vFdvn5DpSUlI3ZgLOoqAT5+Q5kZmZBkiQoioKurq5vgvV6BIMB5OT0LHFwuZpGfEycAp88ycys\nK4qCYLCnzafBYBzyezFYJyIiIuol1rcYACwWa1xmncE6DYyiKGhq6lmfPdHatcWmv8eyh2lp6cjP\nL+izXXV1lWgvptFoMH16BbxeL6qrq8Q2WVnRqeZj+fhIkoSysqmw23Ngtdqg1+sRCgXh8bQiGAyi\npqYaOTm54sZeV1en6CE/UuIz67H2cTQ0ycysR4sRRpdBSJIEg4HT4ImIiIiSJn7dutVqgyRFL5nC\n4YgqY0LUn2iwFgtStaPSJ3w0NTU1qL7flCnT+gTbra0tqoJyJSXl0Go1qinxNpstYYu3sUir1WL6\n9ArY7dnQ6/XQ6w3w+bzweFrhdrvg9/uQmWkX27e0jFx2XVEUVdV5BuvDk8xgPRQKIRKJzqiRJGlY\n3R8YrBMRERH1Eh+sm0wmUc1XluURz5bRxBAfpGZn5wyryNRY4/f7UVdXKx4XFRX3KZzn9/tRWXlA\nPLbbc2C3R3upx4qvGY2mlLZnGwqTyYRp05zIzLRDp9NBkiSRRa+urlLdlGlpcYnCe8kWCATEEgud\nTgej0TQinzNZJHMafDRY78mssxo8ERERURL5fD3t24xGk7iQUxSZvdbpsILBoKrAWH7+xCksF+2V\nfgCyHA1GLBZrn+nvsizjwIE9IrtoNJpQVjYFVVX7RWtErVaLGTNm9unFPh5kZdlRVjYFNlsajEYT\ngsEA2tpa0dHRjlAoKALncDisakmXTL2z6uNhZsJYptf3ZL9DoeF1/AgGAyJYH277QQbrRERERL3E\nZ9Z1Oh0MhmhAoSiKqqgTUSIuV5NqLbfFYk3xiJLH7W5R9Y0vL5/aJyBpaKgT66k1Gg2mTZsBl6sJ\nHk+r2GbKlGmwWMZvq7GiohLk5Tmg1+thMBjg9Xajvb0NdXU1yMrqmQrvdrtG5PM5BT65tFqduOER\niYSH1XrP5/OJ33+NRjusGykM1omIiIh6iWZGetYcxle47u7uVrV2I4ony7KqEvhEatcWiYRRU3NQ\nPM7Pd8BmS1Nt09XVifr6+CnyJYhEIqirqxHPORyFsNtzRn7AIyhaLM+JrKxsGAxGyLKM9nYPOjo6\nVFPf29o8SWkF1huD9eSSJEm1bj22xGAoou0/oz8DOp1uWOcLButERERECcSm6wL4JliPZkcCgcCw\nsi40sbW1tYqOAQaDQZVlHe/q6mpFW0ODwYCiohLV65FIBJWV+1SzCrKzc1XPpadnoKSkbHQHPkIs\nFiumTp0Oi8UKo9GIQCCA1lY33G6XmE2hKApaW1sO806DEy0u1y0eT8Zg3e/3obu7K6k1AZK1bt3v\n94ufd61WO6wxDr00HREREdEE5vP5xEWw1WqBRiNBlhWEQkEEgwHodLyMor7i27Xl5uYPe83qWOHz\nedHU1CAel5SU9alyXVdXI9oearXRCvEHDx4QNy/0ej2mTp0xodZXFxQUiUrwwWAQ3d2daGvzqOoU\ntLS4Era1GyqfzytqBhgMBrFMZ6KL3gxpgdvdoupEYLPZkJ6egbw8x7D+X05WRfhgMABZjgXrumHd\n3OVZhoiIiCiB+HXr0V7rGgAywuEwgsHghFqHTMnh9Xajs7MDQHRabW5ufopHlBzRonJVqox572ns\nnZ0dfYL59vb2XuvUp0+4wFKSJEyb5kRLiwuBQAB+vw8ejxtWa/T/DEmS0N3dBZ/Pq1pOMxyTMave\n1NSA6uqqPlPKZTmCjo52dHS0w+1ugdM5C0ajcUifkazMeiAQENPgh5tZnxi3+oiIiIiSTN1r3QqN\nJpoNjAbr7LVOfTU396xVt9uzJ0xg6vG0oqOjp6hcWdkUVXZclmVUVe0XgVRGRiZstjTU1FSJbfLz\nC5CZmTWq4x4t0enwM2CxWKDRaODz+dDe3iYCNiCaXU+WybReXVEU1NQcxMGDlapAXaPR9mlX5/N5\n8dVXO4bcXjOZmfWeAnOaYc0kYWadiIiIKIHYdF4AsFhsYjpzJBJWrWcnAqI/F/GVvydKYTlZllVF\n5fLyHH1mlTQ01Kmmv5eVTcX+/XtERtFisU6Yder9KS4uQX19dBmAz+eFx9MKg8EIk8kMrVYLt7sF\nxcWlSVkCMFmC9dhNoPgbHRaLFQ5HIbKysqDV6hAMBuDxtKKm5iBkWUYwGMDXX+/AjBmzkJaWdoh3\n7ytZwXog0HN+0Ov1rAZPRERElGzRir7R7IjRaIzrta4u7kQERFuaxdamms2WPlXSx6umpgYRfOj1\nehQVFate9/m8aGioE4+Li0vR2toiAkqNRoOpU2dMmLX7/dFqdXA6Z8FqtUGr1SIYDKKrqxM+XzTL\nGwwG0NHRPuzPkWVZrNcGorN+JqqqqgOqQD0zMwuzZs1BTk6uqJdgMBiRn1+AGTNmiufC4TD27ds9\n6FC8JRUAACAASURBVIruyZgGH4lEEAz27KvTaYf0PjET+7eGiIiIaJBiU5dlWRZBisFgFIWLohfL\n7LVOPRRFUU2Bz8vLnxBF1EKhEOrrewLxwsJiVUCjKAoqK/eLDLrNlgabLb1P67bx3E99MOz2HBQX\nl4rp2R0dHfD7/eL4JKPnutfb0zrSZDKr/j0mkpYWF1pamsXj3Nz8bwLyxMFvRkYmZs48QmTHQ6Eg\namurB/WZycish8OhuLafmj5FGAeLwToRERFRHJPJLP4em9qr0WhUz8dPQyXq7u6Kq06tQXb2+O4h\nHlNfXyMCD7PZ3KdgnsvVjK6uTgDR711WNhVVVfHBuw0OR+HoDjqFJEnCjBkVSE9Ph1arRSQShs/n\nFUUHPZ7WYbd9VE+Bn5hZdb/fh4MHK8Xj7OxclJdPPewNMKvVhrKyqeKxy9Ukfj4HIr6S/FD7rIdC\nIbGvRiNBkphZJyIiIkoas7knKO9dET7G5/P1qUpMk5fLFV9YLmdCZDt9Pp9qtkBxcZlqKnsoFEJt\nbc9adoejEB0dbarp71OmTJ8QMwwGw2y2fFP1Pppd9/m86OqK9gOPRCKq6vhDMdHXq8uyjAMH9oqb\nRCaTCeXlUwb8c5SVZReFDBVFQVXVgQH/X52sYD3WVk+SJFGYdKgYrBMRERHFic+g923fFr3wCgYD\nwypARBNHOByG2+0Wj/PyJka7ttragyLISU/P6FPJva6uWgQ0RqMJdrsddXU14vWiopKktSobb8rL\npyIryw6NRvPNchofurqi2fXhVoVXB+sToy5CvPr6WnR19dzwmTbNOaip5JIkobR0CjSaaEbb6+1W\ntRQ8lGQE6/HLHjQa7bBv6jJYJyIiIoqTaBo8EM24R3utR7MnbN9GQHQdciyTZrFYJ0S2s7OzQ2SA\nJUlCSUmZKrPZ1dUJl6tnPXFJSRlqaqpFkGK1Tq7p773p9QbMmFEBkymaXQ8Gg2hri7Zy6+xsH/L/\nHeFwTycKSZImXC0Av9+nKlZYVFQ6pN8nk8mkKoRYV1c7oOBbq9WJn/NIJDyk/uh+f8+sK62WwToR\nERFRUvWeBh+72Orbaz2YkvHR2DERC8vF+lrH2O05qoBJURRVz+vMzCzIsoz29p4+7ANZXzzRFReX\nIjs7Tywd8Pl86OrqgqIocLtbhvSe8cXlzGZLv8XWxqva2mrx/dLS0uFwFAz5vfLzC8T/5b3bKvZH\nkiRVdj02FX8wopn1nmB9KAF/PAbrRERERHH0eoO4CA6HwyIjY7HYRGY9EgmreunS5BTfmkur1U6I\nwnJtbR5V0biiohLV6y5Xs2pdelFRMWpqqsTr+fmOCTG7YLi0Wi1mzTpCZNcjkTA8nlYoioKWFteQ\nMq7xU+Bttol1jDs7O9Ha2rOcpPdsjsHSaDTIy+sJ9pubGwd0zOOD9VBo8MF6MOiHokQDdEnSDLtl\n4fBqySfg9/tRWVmJxsZGeL1eKIoCi8UCh8OBKVOmqO5WExEREQ3WSF9rSJIEk8ksLox9Pi/0+gyY\nzWZotRqEQtFeuvHr2Wlyis+qZ2fnDLtNU6opioK6up52V7m5+SLYBKI3r+JfLygoQnNzs6jfYDAY\n+wT3k1leXgFycx2orq6EJEnfdA3wQpIkeL3dg76pER+sWywTJ1iPzuaoEo+zs3Ngsw1/PX5OTg5q\na6u/qcrvQ0dHOzIyMg+5T7Q4ZPT/9qFk1gOBgLgpIEnSsGc/JOV/FJfLhQ0bNuCtt97Cnj17+m1J\noNVq4XQ6sWzZMpxzzjnIz58YBTiIiIhoZI32tYbZHB+s+5CengGTyfTNhZwfiqKgu7t7qF+HJoBQ\nKASPpycT2Lut2XjkdrfA6+2ZKVBYWKx6vaGhTgTmRqMRaWnp2L17l3i9rGzKuL9hkUwajQYVFbPQ\n1FQPv9+PcDgMj8cNq9UKt9s1rGB9ImXWPZ7WXrM5SpPyvlqtDjk5OWhqagQQvbl2+GA9PrM++CKi\ngYBfBOsajZTazHp7ezt+85vf4MUXX0Q4HBYDy8jIgMPhEEUPvF4vGhsb0d7ejl27duGrr77CQw89\nhPPOOw/XXXcdMjMPfdCIiIhockrVtUZ8Feueac46GAwGAPgmWGev9cmspcWl6ic+3qd+y7Ksqubu\ncBRCr+9pQef3+1RVtYuKSlUV4zMzs5CVZR+9AY8T+fnR7HpNzUFoNBp0dHTA7/ehtdWNkpLyAU/1\nDgaDCASihek0Gu2EqbSvKIqqBWBenkM1m2O48vIcIlhva2tFMBiAwWDsd/vhVoSPFQCMvtfwWzgO\nOVj/xz/+gf/6r/+C2+2G0WjEGWecgVNOOQVHH300cnNzE+7T0tKCLVu24O2338bbb7+N5557Dm+9\n9RbWrl2LJUuWDHUoRERENAGl8loj/kI4Nt1dkqRvno9mU5lZn7wURVH1Vs/NdaRwNMnhcjWLOgx6\nvb5Pca/4au82WxpkOaJqsVVaWj6q4x0vJEnCzJmz0dzcgEAggGAwiNZWN0wmMzo7O5CenjGg91G3\nbLNOmAJ+ra1uEeDqdDoUFhYl9f3NZgvS0zPQ0dEuCkIWF/efuY8PsAc7DV5RFFWl//jAf6iGlJd/\n7LHHsGrVKnT9f/bOPEiSssz/38ys++66q4+5h+F0DsCRlQVDWQ7Hg0UJYRlYDBbYQAmJXRZQOZZx\nNwTWA1bEAwQGxBBBwVXBCJVlV0D9IcOOo8AczD3ddd9VmVV5/f7Izrcyu2t6urqru6t63k/ERExV\nZVdmXZnP932e5/tUq7j++uvx8ssv4ytf+QouvPDCo148Aa1v4Pzzz8d//Md/4OWXX8Y//uM/olar\n4YYbbsB3v/vdGb2AZrOJ++67DyeeeCKuvPLKSY9/4xvfwJo1a47679///d9N2yuKgsceewwf/ehH\ncdppp+GMM87Addddhz/96U9t9//cc8/hE5/4BNatW4f169fjyiuvxCuvvDKj10KhUCgUCkVjoWMN\nY987z9dJ9tDlcpP7BaE+a6dfSn9SqZTJIo7FYkEwGFrgI5odiqJgbOwwuZ1IDJnK2cvlkqnkf2ho\n2JSFTyQGTSMPKWbi8QTC4SgAbWGjVCpCFMWOXOHNYr2/qzh0VFVFMjlKbsdi8a5koycSjbYW0zKZ\n9JTn7dmUwcuyTP6GYWZfAg/MMLP+ta99DevXr8e9996LJUtm1lMQCARw00034ZOf/CRuvfVWfP3r\nX8d1113X0XPs3bsXN998M/bt23dMd78bb7wRq1atmnT/smXLTLfvuOMOPPvsszj//PNxzTXXoFKp\n4IknnsDmzZuxdetWrF+/nmz70EMP4YEHHsDGjRtx++23Q5ZlPP3007j22mtx//3344ILLujo9VAo\nFAqFQtFY6FjDZrOD4zgSfEmSCKvVNp7RYqGqyvis9WZXSzYp/YHZWC7S9yO00ukkGUVotdpM/fcT\nR7mFQhEUiwVD77oD8Xh3s6GLDYZhcPLJpyGdTkJRFIhiE4VCHk6nE0uXLp+WqFuMYr1SKZsmCxjd\n27tJIDAAm82GZrMJUWyiXC4hGGz/Hs6mDF6SROKnwjBMV6ofZiTWN2/ejM9//vNdOTENDw/jySef\nxJe//OWO/q5UKuGSSy7B0qVL8eMf/xgXXXTRlNufeeaZ2Lhx45TbvPnmm3j22Wdx4YUX4oEHHiD3\nn3/++bjggguwZcsWPPfccwCA0dFRPPTQQ1i3bh0ee+wx8l5s2rQJmzZtwpYtW/DBD37Q1OtDoVAo\nFApleix0rKGVvDtJmS/P87BabXC5tFnrsoxxsd6gYv04QxRFFIt5crvfjeVkWcbYWCu7OTg4ZPrd\n5fM5k6AKhSLYvftt8vjIyNK+X6yYD+LxQQSD4fG+fwaFQhbhcASlUvGYvf4TPTIWi1g3ZtXD4eic\n6SaWZREMhsn+tN9v+wUmY0VJp2XwzWaT/I0m1Gcv1meUm7/99tu7+qNkWRZf/OIXO/obURTx8Y9/\nHD/60Y+wYsWKrhzH888/DwC46qqrTPfHYjGcd955eOutt7B7924AwM9//nOIoogrrrjC9F54PB5c\nfPHFyGazePXVV7tyXBQKhUKhHG/0QqzRzmTO4/GSWeuSJJJsJOX4wWws5yUmh/1KOp2CKGrfY5vN\nblp80EznWqPaYrEEUqlRUtHq8wWoqdw00XvXOY4Dy7JoNLQM73RK4XUneUDzE7Dbj26Q1i/U63UU\niwUA2nsz0SOh2xi/p4VC4ahV2cYFg07L4BuNBmS5NWMdaI1wmyldma3w+uuvd7Q9wzDwer1Yvnw5\ncVXtlHA4jLvvvrvjv9Mvqu32u2PHDnAch/e85z2THlu7di1+9rOfYfv27Vi9ejV27NgBAKayeOO2\nALB9+3ZqnEehUCgUShdYiFjD4TCKda0/2e32gONYSBIgSTLq9RqAo/fQUxYXqqoimzUay/V/Vj2Z\nPEJuJxJDppLsTCZtMv9yOl0YG9O2ZxgGS5YsXTRGZ/PByMhS+Hx+5PM5yLKMbDaNYDAEWZamHHk3\nMau+GN5zY1Y9EAjOueeBx+OF1WqFKIoQxSYqlQp8Pt+k7WZTBt9o8KaxbfpzzfQaBHRJrF955ZUz\n+tJYLBacd955uO222+Z85vqLL76ILVu2YM+ePQCAE044Addccw0uvvhiss2RI0cQDAbblmAkEtpq\nz6FDh8i2ANoe9+DgoGnbqQgE+ns1diZYLNpF4Hh87bOFvnezg75/M4e+dzOHvnfdYT5jDf2zUpQg\nMhldmEgIBFxwuSxwOOwQxSYYRoWqNuf1s6XfJzPz/X6USiWoqgSHwwqLxYIVK0Z6qgS80/fj8OFD\n4DiA46xwOBxYvXoZEeuyLOOdd1JwOLS4eNmyZUinM+R2IpHA4GDvLlT16m9l/fr1+J//+W/IMgtB\n4NFo1CHLPEKho5+fslmRvO+xWHhGr6mX3o9Go4F6vURe05o1K+Dzzf1xDQ3FkUy2xrgFg4FJ74d2\njteOy2plOnq/RkdVMExLqDuddjgcVvj93hkf8+wt6gCcfPLJWLNmDdxuN1RVhaqqcLvdiEaj8Hg8\n5D6n04lEIoFEIgGv1wtRFPHiiy/isssuQz6fP/aOZsH//u//4rLLLsPDDz+ML37xi6hUKrj11ltN\nzrC1Ws3k/mpEL3HSx7TUajVwHNd2pUR/DjrShUKhUCiU7rAQsYaxvLle1xzhjSWoqqqiXC539XVS\neptksjVnPBKJ9pRQ7xRZlnH4cMsBfmRkxJRVP3LkMKlItdvtYFl2vJJEWwRbunTZvB7vYmHNmjXw\neDxgWRaKoiCZHEM6nZ7ybyqV1nnG65258OsVUqkUaSXx+XzTHl83W4xTG3K5XNttJmbWj2VibkQQ\nBKiqXgbPkPPDVHPdj0VXMus/+clP8JWvfAVPP/00/umf/gkf/ehHSSYa0D6Qn/70p3jsscfw6U9/\nmoxY279/P+677z689NJLeOSRR3DLLbd043BMfOxjH8PatWuxfv168uU+55xzsGnTJlx00UX45je/\nicsuu6xtGcR8UCzWF2S/C4m+QnU8vvbZQt+72UHfv5lD37uZM9/vXSTS/4FcO+Yz1tA/K1VVIYoq\nZFmCIIjIZEqw2Wyw2ZxQFC2Ay2Ry8/q7oL9FM/P5fkiShEOHxqAomtuzy+Xvuc+hk/cjlRpDuayJ\nb5vNDrvdR/5OFEXs3r2PmGVFo0PYvXsv6eEdGRlEvS6hXu+sTHg+6eXfytKlq1AsvgFV1ao1Dhw4\njFhsSdvqXkVRkMsVibiVZW5Gr6lX3g9VVbFv30E0Gtp3aXAwOG/HxDB2NJvK+G+4ikqlhkZjshiX\nJJWUwOdy5WmPkysWy5AkBYA67vqvQJZFyPLM2xa6kll/5plnsHXrVjz++OO47rrrTBdPQCsVv+66\n6/Dd734X9913H/77v/8bgFZO841vfANDQ0N4+eWXu3Eok1i6dCnOOeecSatQoVAIF1xwAQRBwLZt\n2wAAbreb9KRNpF7XjWU8ZFtZltsay0zclkKhUCgUyuxYiFhDd4TX0U3mjE7MxhnslMVNLpc1CHU3\nXC73Ah/RzNHmqrd6hhOJQVNWfWzsCBHqTqdzvM+3NaotFptbM7DFzoknngKHwzE+WUJBJpNEPt8+\n01uv14hQdzgcfT9pqlwuodFo+SDMp0Ehx3Hw+1tZ/KOZ+xn9AzrpWxcEHrqpHMuy5Dc1m8x6V8T6\nU089hQ984AM45ZRTptzutNNOw7nnnovvfe975D6O47BhwwaMjo5O8ZdzQyiklULoY1lGRkaQy+Xa\nCnC9R12fyz4yMgIApO+h3bZLly7t+jFTKBQKhXI8slCxxtEd4bVMSbPZRKPR6Ph5Kf1HNtsqVY5E\non1t8pXLZdBsat9bq9WKcDhKHms0GkinW/Gt5gDfuj00NDKtueCUo+NwODA0tAQsq5VJF4tFk3Gh\nkcU2si2Taf2OQqHIvLeSBAID5P9HK4W3WltiXRSnL9Z5XiD/Z5jW65qNe39Xfmn79+8nwvdYRCIR\n7Nq1y3Sf1WolA+S7iSiKeOGFF/Diiy+2fXzfvn0AWuZx69evh6Io2L59+6Rt33jjDQDAhg0byLYA\nSFa+3bann376LF8BhUKhUCgUYOFiDXNmXau+83i8RKyIYpOIHsripVarTpg1Hl7gI5o5qqoSR3dA\nm/9tFEyjo4cMo+k8qNVqpoqCfn7tvcQpp7yHZMlFUcTBgwfbLvwtJrEuiuL4jHONSCQ6xdZzQyAw\nQBbayuVy2/FsxrL3TmatNxqtCm2Oa8nsBRfrdrudCNRjsX37dlM5gSzL+MMf/jAnbvBWqxX/+Z//\niVtvvRX79+83PbZnzx785je/QTweJ6PaPvGJT4BhGDz++OOmbffv34+XXnoJGzduxJIlSwAAH/nI\nR+BwOPDkk0+aXk+hUMBzzz2HJUuWYOPGjV1/TRQKhUKhHI8sVKzRLrPu9XqIWJckebz0kbKYMWYD\ng8HQtHtYe5FcLmsaxxaNtn4XPF9HNpshtyORmKmiYHh4SV9XFPQSAwNBhMMRci7J5zPI5TKTtjOL\n9f72JMlmM4aFIO+CtJJYrTay6KGqKkql4qRtzGXw05u1riiKqTpbm7OuseBl8OvXr8eePXvwz//8\nzxgbG2u7TT6fx1133YW33noLJ510EgBttNmNN96I0dFR/M3f/E1H+9yzZw9++ctfkn/6Poz38TyP\nu+66C7Is4+/+7u/w4IMP4vnnn8fXv/51fOpTnwLDMPjSl75EVrVOPPFEXH311fj1r3+Nz3zmM3j+\n+efx+OOP4+///u/hcDhwxx13kP2Hw2HcfPPN+POf/4xPf/rT+PGPf4wf/OAH2Lx5M6rVKrZs2UJL\nhCgUCoVC6RILEWsAE8W6NkPX5fKQTKSiyMQhm7I4kWXZ1Nvaz7PVJ2bVY7GESZgcPnyQeDD4/YHx\nUXXabZ/PD78/ML8HvMg5+eTTyLmE53kcPLjf9LgkSaSih2EY04SKfkNVVVOp/0Jk1XWMpfBGp32d\nmZTBi6JoWiTW9aXNZpuVJuyKG/xnP/tZvPbaa3jhhRfwwgsvYHh4GPF4HHa7Hc1mE9lsFgcOHICi\nKGAYBtdddx0A4I9//CNeeuklhMNhXH311R3t88UXX8SDDz5oum/Pnj343Oc+R27/5je/wVlnnYUf\n/ehH+Na3voUnn3wS1WoVgUAAf/3Xf43rr7+eXMx1br31VgwPD+Ppp5/GHXfcAafTife+97246aab\nsGrVKtO2V155JQYGBvD4449jy5Yt4DgO69atw5e+9CVSLk+hUCgUCmX2LESsAWgBl8VigSRJkGUJ\nzWYTNpsdVqsNgiBAURRUKpUuv1pKL1Eo5EgprMPhhMfTv9nNQiFPKkQ4zoJYLE4eq1YrKBRaJcrB\nYBj79u0ht2lWvfuMjCyD1+tDPp+Dqqo4dGg/1q7dQBYJjVl1l8vd16MCq9UKWXjgOAuCwYVrp/B6\nfchmx8aPa7JYNy5gTbcMvtlsmLbVBf9ssupAl8T6qaeeisceewxbtmzBzp07cejQIRw6dGjSdoOD\ng7jllltw7rnnAgBWrlyJD33oQ/iXf/mXjkvTbrzxRtx4443T2vaUU06ZJOyPBsMw2Lx5MzZv3jyt\n7T/ykY/gIx/5yLS2pVAoFAqFMjMWItYAdEd4F8m+CEIddvsAnE4nua9dZoayeDCWwPezsZyqqhgd\nbc1Vj0ZjpnJ+42PBYMhUkh0Mhvp6kaJXYRgGq1adgD/+8Q9k4S+ZHMPy5SsBLK5+deP3KRQKLejC\ng9vdmnPP8zxEsQmr1UYeN/4uplsGry/eAgDLMuA47Tlm068OdEmsA5qZ2k9/+lPs27cPb731FlKp\nFARBgM1mQygUwurVq3HKKaeYTnDvec978M1vfrNbh0ChUCgUCmURs1CxhlGs1+t1+P0DcLk8ALSS\nzmqVZtYXKzxfJ589wzAm1/R+o1QqkpYNluUQjw+Sx6rVCorFAgDtdXq9Phw4sI/cHhpaMv8HfJxw\n4omn4k9/+j8IAg9ZlrFz51tYtmwFGIZZNGJdURTTaLpQKLKAR6OZRHq9XpRKJQBApVJBMNgyMJ1J\nGTzP16EoWssIw7BkMcJud8zqWLsi1nmeJ26py5cvx/Lly6fc/k9/+hMxdaNQKBQKhUI5FgsZaxj7\n1ut13WSulWXUg+x+LlGltMeYVR8YCPbtjOvJWfWo6bUYHxsYCE0wmYuapiJQuovdbsfw8AjefXcX\nVBVIp5Oo1apwuz1kvDTQ32K9VCqSfm673dETVRo+n98g1ssmsT6TMnhB4KGqyvgthlwPZlsG3xUH\ntKuvvhrl8vRKwB555BFcccUV3dgthUKhUCiU44SFjDWMpk7GWeu6aVCz2TS5AFMWB4qimEp3F9IQ\na7aUyyVSAcKy7JRZdafTaRhTx2FwcGT+D/g447TT1pGZ681mA3v37kGz2YQoaucVjuP6esHEaNAY\nCoV7opXE7/eT/0+sjjIuZE2/DJ4nZowMA0NmvQfE+vbt23HFFVcgnU4fdZt8Po9/+Id/wFe/+tU5\nmalOoVAoFApl8bKQscZEsa4oCtxuDxnNI0kSGg2ha/uj9AbFYp7MYLbb7fD5+tcJfWxslPw/HI6a\nsn1HjrS8HwYGgqasejyegM3W6uWlzA3hcJSIR1UFdu16xyQgtfPNwgvcmSDLkmm2+kIayxnx+Xzk\nPa3XayYnd4vFOLptumXwLbHOsgxZzO0JsX7xxRdj9+7duPzyy3HgwIFJj//ud7/Dxz72MbzyyisI\nhUJ4+OGHu7FbCoVC6Un2F3k891YKT20fw3NvpbC/SGcwUyizZSFjDY6zkL5DVVUhCLwpsy7LrfFK\nlMWDsQQ+HO5fY7larYpyWZslzTDMpKy6PmeaYRjY7Q6y8GSxWEzbUuaWNWtOhv4VKxQLeOHNndiR\nquKdTA1V9O+CSaGQJ8ZrLpe7Z8bPWSwWuN3anHdVVU2LI+Y56xIR4VPB860RngzTaonqCTf4e+65\nB/F4HN/+9rdxxRVX4OGHH8ZJJ50ERVFw//3345FHHoGiKDjnnHNwzz33IBgMdmO3FAqF0lNsGy3j\nqe1j2DZWRrUpQ1ZUcCwDj43DhoQPV6xNYMOgb6EPk0LpSxY61nC5XETEaCZzAVgsFohiE4qioFaj\nJnOLCUEQTCK2n43lkslWVj0YDMHhaBlembPqIeTzrXLlRGLIlGGkzC1r1pyC3/+/30NoNCCrEvbv\nehsp+MAAeHbMhtX7lL6MIyaWwPcSfn8A2azWAlKplMn8dY7jwLIcFEWGoihQFNkk4NshCNr1QVVV\nsu1sZ6wDXXSDv+mmmzA0NIS7774bV111Fe644w784Ac/wPbt22GxWHDLLbfMaL4phUKh9AMv7sri\nK6/uR6raQF1U4LZxsLAMBElBri4iUxPxf8kK/uXsZbhwdW9drCiUfmEhYw2Xy01mUNfrNYRCYdjt\ndvB8HaqqTrufntIfZLOtrLrfH5h1KetC0WgIJhduY6a8UjFn1W02G/L5BgCtZzcajYMyf/x6Xwl7\nmm7EFAEqgKBaQ5L1QlaBXWUVe/lc38URothEuVwit3ulBF5n6r51CxoNrZ1KkqQpxbpWcdUS63rr\nyGyz6kAXxToAXHrppYhGo7jppptw6623AgCWLVuGr33tazjppJO6uSsKhULpGbaNlvGVV/fjQFGA\n18Zi+YADrKFcUlFV5OsiDhQF/Mcr+xF12/puZZxC6RUWKtaY6Aivz1/XjbmqVSrWFwuqqpr6tvs7\nqz5GSnh9voDJUXx0tJVVDwSCk7LqdLrB/KHHEVU+gstsOXAMwDAKwkwDZasPQ04X8rzUd3FEPp8j\n3z+v19dzi14+X+s9rNWqpqkemjjXFq8kScJUhy5Jkqm33Wbrzox1oEs960bOPfdcPPnkk6T87IYb\nbqBCnUKhLGqe2j6GVLUJr41F2G0zCXUAYBkGYbcNXhuLVLWJp7aPLdCRUnqJZrOBI0cOYfv2N/DH\nP/7elP2iTM1CxBpmk7kaVFWF290aP2Sch0zpb0qlIprNVoZZL43tN0RRNPXdJxIJ8v92WXV9ooHN\nZkMkEpvfgz3O0eOIps0HntXaFBioCKpV8IxWSt2PcYR5tnpvZdUB7buuu+xr7Uyt87jFMn1H+Gaz\naRrxpv/tbGesAzPMrL/++uvH3Obmm2/Gli1bcOedd8Jut2NgwHyiO/PMM2eyawqFQukp9hd5bBsr\noy7KWD4w9Ul5wGnF/qKAbWNl7C/yWBbo3zEslJmhqipKpSIymRSKxYLJtKZQyJnmvB7v9FqsYbc7\nwHEcZFmGKIoQRXHCrHUBkiTRHt9FQCaTIv8Ph6Oz7jldKDKZFBRFK+N1udwmN3tjVn1gIGgSVTSr\nPr/ocQREASf6JNRFG7yogwHgRANWSQDLKVAYtq/iCFFsktJyhmEwMNCb1zev10cMQiuVMnw+rTS+\nE0f4RoOHLCvktv63C1YGf+WVV07bEVNVVXzuc58z3ccwDN56662Z7JpCoVB6ijdHNTM5t42bCcuy\ndwAAIABJREFUlFGfCMcycFtZVJsy3hwt9/RFltJdGo0GRkcPI5NJodFoTHrcarUhFku0+cvjl16L\nNfSydz345PkacYRXFAWSJKLZbFCx3uc0m03S2gD072x1WZaRSrWyr/H4IPk9TcyqWyw2Ms/bZrPT\nrPo88/q7oxhqHMEyewMehUMJbsRQAqCChYpBJQebBBRYL3Ksv2/iiHw+byqBN84u7yU8Hi/SaW2B\nrl5vObp3Ita1sW2aWGcYhvS3d6MMfkZXlMFBOsaBQqFQAKAuKpAVFRZ2eqLCwjJQFBV1UTn2xpS+\nRjMdK2J0dB9yuRx4vjlpG5/Pj2g0hkAg2LfZu7miF2MNl8tNxHq9XofP5yNiXRRF8HwdLpd7gY+S\nMhuy2bShx9sPh6N3xdBU5HIZ04x4Y9XO2Nhh8v+BgSAKhVav+uDgED0XzROKouDw4YMoj74Lh9qE\nqi9OsixkmYEF40IXAvKqgpBcglvhkWcHoChsz8cRhUKrWmNgoHcngRnP2UaxbrUaxfrUZfC60Sig\ni3WtMmXBxPpLL7006x1TKBTKYsBlZcGNu75PB0lR4bCycFlpMLRYEcUmMpk0Mpk0Gg0BDoc5m2C1\nWhEKRRCNxvpWCMwHvRhrGPvW6/UaBgZC4DgLJEmCLMuoVqsIhSILeISU2aCq6qTZ6v2IqqqmcW2x\nWIII8Hq9RioH9AxgS9Q7+vY19xvNZgO7dr2Der0GK8uAASAqDMqsG03GAr9cQQxa9YMVMjhZhMxZ\n4VCbWKWmcZCN9XQcIYoiKpWW6WYvi3WHw0kWXRuNBkRRhNVqnTRrfSp4niez5IGWWF+wMvhCoTCp\nL2y2zMVzUigUylyzftAHj41Dri5CUdUpS+FlRUVNVBB227C+D1xcKdNHy6KXkMmkUCjkTb3oOl6v\nD9FoDAMDIZq5mga9GGs4na0MDM/XYbfbYbPZ0GgIxI+A0r9UKmU0Gtr4JYvF0tMCYyqy2SwZI2Wx\nWExl7WNjR8j/fT6/Kfs5ODhMz03zQLPZwDvv/IV8RnGvHU2rG7+vuDHsdiOslFHivAjLFbBQoAJw\nMCJqjA2qooJTRJxiy+DU8KkL+0KmoFhsXQc9Hm9XROtcwbIsXC4XqlXNXK5er8HvD0wwmJtarNfr\ndQBaLGCxcMS0sRu/pxk9w6WXXopdu3bNeuc6u3fvxqc+9amuPR+FQqHMF8sCTmxI+OCycsjXpy6T\nKvAiXFYOGxK+nu4z63dEUUSxWCBuznO9r7GxI9ix403s3PmWaUwNoAXKQ0PDOP30M3DSSaciFIrQ\nYHia9GKsYXaE58cd4VsCnor1/sZoLBcKhfvSZE1VVRw+3DKPi0bj5HUIAm8ykrNYLESEOByOnnTr\nXmxMFOosy2LtmtUID62E1WpDvi7CqTYBhgEPG1RoCYCAWsMRNoy6pMDKMljisUDKHW67MNwLFAp5\n8v9eNZYz0q4U3lgGL4rHyqxrf6OJ9e6ZywEzFOvFYhGXXnopHnnkkWOuNEyFLMt4/PHHcemll6JY\npBc4CoXSn1yxNoGYx4ZKU0G21oSsmC+esqIiW2ui0lQQ89hwxVpqJDYXCIKA/fv3Yvv2N7Br19t4\n++0/z0kgo2fR3313F7ZvfwOHDh0ggZeO1+vDihWrsXbt6Vi5cqVJ0FGmRy/GGhzHweHQpj6oqgqe\nr8PjaVXJ6P3slP5DFEWTwOhXk7VyuYRKRfsesiyLWCxOHhsbGzUYfnlNi0uDgyN0IXGOEUVxklBf\nufIExGIJXLFucDyOkKE0alBVIMd4iVh3qE3UhDq2N4Nw2Sw4LeZBpVI2tTv0CpIkoVwukdv6iM1e\npp1YN5bBG8eytUP/TFVVJUZ63ZopP6Nf5Q9/+EOEw2F89atfxaZNm/DDH/6QpP+nA8/zePrpp/Hh\nD38Y9957L0KhEH7wgx/M5FAoFAplwdkw6MPN71+GpQEHRAXYXxSQrDSQrTWRrDSwvyhAVIClAQf+\n5exl2EBL4LtKrVbFu+/uwo4dbyKdTpK+MUmSuyrWtSz6KHbs+D+8885fkMtlDT1qWpYqFkvg1FPX\n4aSTTkU4HOnLzFyv0KuxxsRSeL+/NQ5LEOrHDOoovUkulyG/Z4/H07dGgYcPt8zjwuEIrFYbAC2j\nm8tlyGMWi5UsgjmdTppVn2NUVcXevbsnCXW91UKPI1b6reBUGcWGhIxsRU21QlUBRVVxspJCIBDA\neWvXIOHVhOCRI4dMpmi9QLFYIL8lt9vTlVnjc43x916rae9nJ3PW9fYZLbOu/ea6lVmfUc/6qlWr\n8NOf/hRbtmzBf/3Xf+Huu+/GPffcg/e97304/fTTsXr1asTjcVIuVq/XkUwmsWfPHrzxxhv4/e9/\nD0HQ+rs++tGP4s477zTNKqVQKJR+46ITwoh5bHhq+xi2jWnj3JRxM7mw24YNCR+uWJugQr1L6Nnt\nsbFRlMuTs6VutwdLliybdaZIVVVUqxWk00kUCnmTONfxeLykF71Tca4oCorFAiRJRChExb2RXo01\nXC4X6fOt12vw+fzEnKjZFCEIAtxuz6z3Q5k/VFVFNms0luvPrDrP88jltO8mwzCIxVoTFZLJMYOA\ncpsyn4nE8LTHJFJmxuHDB02VDCtWrJ7kiXDRCWF4lRpe3pbBWKWBvGLDEdWP1ciABbDMVscV563A\nhqEA3n77z6jVqlAUBXv37sHJJ5/WM5UR/eICb8TlcoNhGKiqCkHgIcsSLJbW9ViS5KP+rSzLZPQh\nAJJZ16uwZsuMh4F6PB7cd999uPzyy/HQQw/hlVdewcsvv4z/+Z//mfLv9CzH2WefjRtuuAEbNmyY\n6SFQKBRKT7Fh0IcNgz7sL/J4c7SMuqjAZWWxfpD2qHcLVVWRz2cxNjbaNpvg9wcQjw/C5/PPKviU\nJBHZbAaZTBo8Pzmby3EWhEJhRKOxGWXgeJ5HJpMyjVfi+TqWLl0x42NejPRirDExA5NIDJtmrdfr\ndSrW+4xarUqqNjiOQyjU+z227TDOVQ8EBuB0atcdURTJHGkAsFptJHvocNCs+lyTz+dMxn5DQ8Om\nUXpGlrhUnL8qhKIgIc/5ITA28H/+DVio4FgF3noSLBvEihWr8Ze/bIeiKKjXa8hmM4hGF36RSZZl\nlEqthaB+6FcHtEoHp9NF4op6vQ6Px0sEvCxLUFW1bVwhiqJBzKuw2bTMercqCmYs1nXWr1+Phx9+\nGMlkEr/+9a+xbds27N69G6lUipz4XC4XYrEYVq1ahdNPPx0f+tCHkEjQnk0KhbI4WRZwUnHeZWRZ\nRjabRjI5RsrNdBiGQTAYQjw+OCuRpGfRM5kU8vncUbLoHkQiMQSDnZtPybKMQiGPTCZlGmljfB2U\n9vRSrGH0H6jXa3C5XKSkWJYVVKtlRCJ0/FU/YRzXFgyGTL2q/YK+wGizadnVWKz13U+nx6Aomphw\nOJwmb4XBwSF67plDGo0G9u17l9wOBAYwODhy1O31zybgsODM1SMIBAbw7JE3UCwWoarAzp1vYc2a\nk+F0OjE0NIJDhw4A0Fz+w+GFNzCtVMrku+Z0OsmCUT/gcrmJWK/VqvB6feA4jrSLSJJEsuZGGo0G\nec0AQwzmekas68TjcWzevBmbN2/u1lNSKBQK5ThHywiNIZVKTjIZY1kOkUgEsdjgrMrNJElCLpdB\nJpNq2xOtZdoiiESiM1oMqFarSCaTOHjwcFujNLvdjnA4ikRiaEbHfzzRC7GG1WqD1WqDKDYhyzJk\nWYbD4YAg8ABUapjbZ8iyhHw+S273q7FcOp0aFwwsPB4PvF6t5UqWJaRSSbKdzWYb/65qYiIYpFn1\nuUJVVezf/y7xsbDbHVixYtVRF0f0LLmOntldtmwl/u//3gAA5PNZ8HwdTqcL0WgcyeQoRFFEoyEg\nl8su+EJhsdgyafT7+2skt3Hah9FkTr9uy3J7sV6v16AoKqnoslgsYFmWZNhnS/8tHVIoFApl0SMI\nApLJUWSz6UkZbqvVimg0jmg03vbCOR1UVUWtVkU6rWfRJ/ejud1aFj0U6jzTJssScrkcstkUJEkb\nIWcU6gzDIBAIIhKJwu8P0MxWH8EwDNxuN4pFrUexXq/C5fKgWCwAAEqlwkIeHqVDcrkcZFn7/btc\nrr5sYVAUBel0S5APDbWy5el0mpx7bDa7SQwmEkMLnoldzGQyadKnzjAMVqxYZTItm4gm+rTrncPh\nINe3NWtOxp//vB2SJEGSJLz99p+xYcN7wXEc4vFBkl0fHT2MUCi8YJ+pqqrkPAj0T7+6jvG3ry/c\nWywWNManwB5tKgnP16GqClRVBcsyYFkOdruja9d1KtYpFAqF0jPUalUkk6OT5pUDWlYiHk8gHI7O\n2IhNy6Jnx7Pok3veWZYjveidBu36AkAmk0Y+nyUCwOFoBWcOhwORSMzk0kzpP1wuNwlKa7UafD4f\nRscnKFWr1QU8MkqnZLOtXu5wONqXC2f5fA7NprZ4ZLPZEIlEUS4LUBQFqVRrtJfdbictOFpFT2RB\njvd4oNEQcOjQfnI7FkuQaoejYWxPcLtbZpherw+BwACy2QxUFdi7dw82bHgvACAajWFs7AgkSUKj\nISCfzyIcXpjseq1WJd9Dq9UKj6e/zMONmXWer0OW5WmNbxMEfjxeUcGynGnEZzegYp1CoVAoC8p0\nnN3j8UEEg6EZB9J6Fl0btzY5i+5yucdFdLjjLLokieMLAOmjLACwCIfDcLkC8Hp9ptegqioqlTJk\nWYbfH6BZrj7BnIGpIRBolXs2GgIkSZwyg0bpDer1GllcYVkWoVD/iVdVVU3GcoODg+Q8ks2mTeJJ\nL38HgHicZtXnCq38fa9hwVbrLz8WtVproc/jaZ1jGIbB6tVrkM1qo/cqlRLy+RzxV4jHB3H48EEA\nenY9siCLTsaqon6sGOM4CxwOJxHfPF+fliO8vsiiqipJJHRrxjpAxTqFQqFQFoi5dnbXS9EzmZQp\nCNLRsughRCJaFr2TfbRGuqVQKLQ3o3M6XYhEoli5cimsViuKxTr520qljHw+h0IhR9zgE4khjIws\n7fh1UuafyY7wg8QRXhSbEAQBHg8V671OJtPKqg8MBGfcVrOQVKsVcn5jWRaJhDauTVVVjI2Zs+r6\nwoSefafMDcViwVT+vnz5qmlVgxmrciZmpZcuXYlt215Ho9GALMt4++0/4/3vPxcAEItpveuSJEEQ\nBFQqZfh8/i6+oulRKLTEeiDQXyXwOm63myxq1es1YhYHTF0GD+gz1rtrLgdQsU6hUCiUeWaund31\nUvRcLkMyG0ZcLtd4L3rEdCGeDqKoOS5nsynwPD/pcZblEAxqCwAej7YAYLVaSfVAoZBDPp83zWSV\nZRmqqkKSxM5fLGVBsNlssFqtEEURsizBbneA47jx8W0S6vVa35WAHm/Isoxcrv+N5ZLJVlZda6/R\nFhzy+Sw5v3IcB0FokO3i8UGaVZ8jZFnGwYP7ye1IJAav99jngmazST4vfYyYEY/Hg2AwhLGxUagq\ncOjQATJKTB8lqhsJZjLpeRfrjYZAFt1ZloXfP/+LBd3A5XKT80K9XptmGbz2uamqStrbaBk8hUKh\nUPqOYzu7RxGLJWZ0kZNlGfm81overmeYZVmDiPZ2nEUvl0vIZNIoFvNts+hutwfhcBShUJgsAOgZ\n9Gx2FNlsBuVyq3pAlmUIAo9arQZB4GGz2WhWvY9gGAYul5tkz1RVgcWiiXdFUVAqlRCNxhf4KClT\nUSjkyXnIbnccs5+4FxEEweS+rY9r07LqrbnedrsT9bp2XrRabX27MNEPJJOjRHRbrVYMDx+7/B0w\nl8C73Z5JiykMw2DNmpORTI5BVVXU61Ukk0eQSAwD0PwWdLGuf7c7XYyeDUZjOW3kWX9KTGPVVL1e\nN5nkHS2zrn/eqgpYrTSzTqFQKJQ+Yy6d3ev1GulFb7fq7XQ6iaFbpz3EzWYT2WwamUx6UgUA0H6k\n28QS92azSQzmZFkCz7cEur5ewHEWyLKMw4cPUsHeR7jdHiLWeZ6H0+ki5ZDUEb73yWZbs9Ujkf40\nlkunx4gRp98fINnYfD5P3KxZlkWzac6qz9SgkzI1giCYFkmGhpZM+7pjNJc7WlXO4OAwHA4HeJ6H\noih45523iFh3udxkTriiyCgUcvO6KGMU6/1aAg/ANBdeEHjTokm7Sj1VVdFoNKCq6nhm3Q6GYWCz\n0Z51CoVCofQ4c+XsLstaIJJOp0wBjg7LshgYCCIajc8oi14qFZHJpFAsFiYdN6AFUpFIFMFgGBzH\ntRXoOpIkoVzmUamUUanUwDBahsSY8dAv7PF4oqP3gbKwmPvWq/B4PGRed7lcWqjDokwDnufJZ8Qw\nzIK5Z88GSZKQybQWHPTzh6qqOHToILnfbneQRSRtcZRm1eeKw4cPkgVpbfTn9L9XZif49i1gLpcb\n4XAEhw4dhKoCo6NHxh3LufHvcQQHD2oVXJlMet7EuizLZMoAAJPhZr9htdrGF9ClSZn0dpn1ZrNB\nEgUMo41HtNnsXW0z6ZpYTyaT2Lp1K6699loEg60VlSeeeAJPPfUUUqkUTjrpJNx2221Yu3Ztt3ZL\noVAolB5iLp3d6/U6MpkUcrlM24umw+E0jEXrLIveaAjjWfSMKQulY7FYSBbd5XITgzm9B934N5rJ\nD49KpYJGgwfHcWBZ1nRMukB3Ol2IRmOIRuN0jNI06KVYw+02lktq49t0qtUK6Sel9B5GY7lAIAib\nrf/GKGazaZLpczpd8PkCAIBSqYRyWRNODMOY/DFisQTNqs8R1WqFLNYBwJIly6f9+9fGfrbapI6W\nWWcYBieccBKOHDkERVHRaPA4eHA/li9fCQAIhSKkl71arUAQeDgczrbP1U3K5RJZpHC53F11Qp9v\nGIaB0+kg7XTGWKNd9R7P88R3hmFYWCyWrvarA10S6+l0GpdffjmSySQuvPBCcgH9/ve/jy9/+csk\nM/Hmm2/i05/+NJ5//nksWbKkG7umUCgUSg8wV87uWhY9j0wmZVq519Gz6JqJj6+j51YUBcViAZlM\nCuVyqW0W3efzIxyOYmAgCJZlUa1WcfDgvrYCnefr4wGSburEwmq1kdEvsqzAZrPD5XIjFosjEokh\nEAjQEV/TpNdiDZvNDovFAknSMjD6d1svi5QkkZgNUXoHRVGQy2XI7Wi0/7Lq2ri2JLkdiyXIuc+c\nVbeT85HFYkEsRn0U5gJVVcnoNAAIBkPTMpXT0UvXAe0zm2rxKB4fhNPpRq1WhaIo2LNnJxHrVqsV\ngcAACgXNxyCbzWB4eO711sSRbf2Ow+EkYt242NUuSVCr1aAoWgk8y2pVc93sVwe6JNa3bt2KsbEx\nbN68GatXrwagvaCHHnoIAPClL30JmzZtwi9+8QvceeedeOyxx3DXXXd1Y9cUCoVCWUDmytmd53lk\nMklks0fLojvGs+jRjrPogsAjk0kjm82YLsQ6VqsV4XAUkUgUdru2wn7kyCHk87kJAl1Eva4JdKPr\nsjHQ0lbpnfB6vRgYiCAcjsLvD9Ds1gzotVhDM5nzkAoSq9UGlmUhyzJEUftu+P1UrPcahUKejEu0\n2+0kI91PFAp5k4lZKBQGoLVjGMdnGc+dWladdr/OBaVS0dRW0alANpqiut1Ti3y9GmvfvipUFUil\nxiCKIrkOhsNRItZzuSyGhkbmtMJHVVUUi60qun4ugdcxOvHr5wqgfc96rVaFqipkxjrHcb2ZWX/5\n5ZexYsUK3H777eS+P/zhD8jn8zjzzDNx6aWXAgAuvfRSPPfcc/jd737Xjd1SKBQKZYHQnN2TSKeT\nposZoDu7RxCLDXZ00VIUhfSit8uiMwxDsuidZuj1585k0m37iRmGgc/nRyQSg98fAM/XyQz1RqMl\n0EVRBM/XUamUx4NlBhYLZzKTYRhm3F3ai3h8CCtWLEEgEEC5PNmkjjJ9ejHWcLvdRKwzTMssUJYl\nVCrlRZFlWmwYS+DD4f40lksmW/PTI5EYWfwzmpvZbHayuMhxFuIUT+kuE7PqkUis49LzWs1oLjf1\nwjbDMFi16gQcPLgPsqyg2Wxg797dWLPmZAAYXwzWeq4bDQE8z8Plck35nLOB5+vke2axWBbFyErj\n5zfRg2Yi1aoWq+hiXb/+d5OuiPVUKoULLrjAdN9vf/tbMAyDD3/4w6b7V65cib/85S/d2C2FQqFQ\n5hlBEJBKjSKTyZCyPZ2ZOrsLAj/u6J6ZJPwBzSApEtEy3Z2WFfO81ud+tAy9zWZDOBxFOByBJEnI\n53M4dGg/Eeja/HNtbnalUjYFv8YLsn6B9vn8SCSGEA5HSFl+IDB3gdLxRC/GGsa+dc3534Fms0Ha\nQuajBJUyfQTBbCzXiQFYr1CtVogZGcuyZEQgz/MoFPKw27XQ3nh+jsXi8zrG63gil8uS1i+O4zA0\nNNzxc0zHCd5INBqHy+VBpVKGoih4992WWNdmnAdI/3ypVJhTsa5PxAAAny/Ql4tfEzE6whur6WRZ\nmuRFonsNqKpKWtp6Uqw3Go1JJwF9Rfvss8823c8wzKL4ICkUCuV4olKp4PDhwzh8eLQrzu5apjtP\n+sUnooncIKLRaMcBwLHc4rXnHkAopBnRFQp57Nz5tmFWqgpJElGrGQX65F40hmHgcDjg9weQSAwj\nHI7A7fbQa9wc0YuxhrG9o1arwe32kO+zcf41pTcwuqcHAgNdHa80X6RSY+T/wWCItN2MjR0h52ab\nzQZBaAlImlWfG1RVxejoYXI7Hk90vKAsiiLxFWBZ1jRl4mg4nS7EYglUKmWoqtabro2P1ERmIDBA\nxHqhkEciMdTRMXWCeWTb4qgkstsdYFkWiqJAFEXyf0DLrhuTEfqIRG1smy7Wu3te6YpYD4fD2Ldv\nH7l94MAB7Ny5E0uWLMHIyIhp2yNHjiyaD5NCoVAWM0Zn92aztXqsMxNn91a/ePooWXQ76UXv1KG5\nVqsik0kfdea63e5AOByBy+VGtVrBoUMHTAJdFEXUalUi0FvOrq1VdoZh4XA4MDAQJBl0p9NFBfo8\n0Iuxhs1mh9VqhSiKkGXJ1G9aLk9eKKIsHIqiTJit3n8jzBqNBvL5HLkdjw+O3y+YTPOMvbWdVjpR\npk8ul4Ug8AB0A7/Bjp+jVmv1q7tc7mmN/GIYBitXrsb+/e9CkiSIYhPvvrsTp566DoBWCq+bXdZq\nVVNPezeRJMm0IL5Y2n70hXhdiOtCHdCy68b3UhB40yKZzWbvuidNV8T6hg0b8OKLL+KZZ57BunXr\nsGXLFjAMg02bNpm227t3L/7whz/gnHPO6cZuKRQKhTIHtHN2dzhaF6dOnd111/V0OtV2nJue6db7\nxTvLokvI5bLIZNKmoGfic3s8PohiE9lsBo3GIfI6NYFeGRfozfEZ6FaTwQzLsrDbHQiFwhgcHEIo\nFJmXcTgUM70YazAMA4/HSwydnM5W5QXP1yBJInX77xGKxZaxnM1m70thkU4niTDw+fwkC5tMtiqe\n9H5lQPMP0QU9pbtoWfVD5HYslphRq4G5BH76RqzhcBRutwelUhGKomDfvr1ErFutVng83vHMu4pS\nqYBwuPstH+VykXzv3G7Popp+4XC4TGJdX0QxttOpqopmUyDvgd1un5OxdV0R69deey1+9atf4c47\n7wSgHXwsFsPVV19Ntvntb3+L2267DbIs45JLLunGbikUCoXSRY7l7B6JROD1hqbt7N5oCMhk0shk\n0m1d1202O+lF76QcVc8WaDPXc5N65wHNLd7r9QFgUC6XiJjSBHoTlUoF1aom0FmWbSvQHQ4nwuEI\nBgeHEQqF+7JkdjHRq7GG2+0h3y+LxQqW5aAoLUd4n88/L8dBmZp02phV7z9jOVmWTeZ4emm7KIqm\n8n5VbWUBo9EYzarPEdlsxjQWLx6fWatBJ07wRpxOJ+LxQdIzXijkUK1WieAPBAaIUWuxODdi3ewC\n33+LX1Nh7FuXZZmIdWPViiiKkCS9j12LabrtBA90SayfeOKJePTRR/Gtb30LR44cwfLly/GFL3wB\nfn/rAlWr1ZDP53H99dfjvPPO68ZuKRQKhdIFpuPsfsIJK+F0OlEs1qd8Lm2MSwHpdLLt7HKGYeD3\nB8ZnjA90FDBLkjieRU+RFW/zsbJwu93gOMu4sVyaHFOz2US1WkalUoEotgS6sT+QZVk4nS5EIlEM\nDo4gGAzRQLeH6NVYw7h4pSgKLBYOzaYMSZJQLpeoWO8BNGM53bWfmRPhMtdks2mS1XM4HGREVio1\nRsp0WZaBLMtgGAYsy9Ks+hyhKMqEXvXBGY3F0xeedTpxUmcYBsuXr8TevbsgihJEUcSePe9g3boz\nAGhi/dChAwCAUqlkKuXuBlrGviXW/f7+H9lmxFg9pyUEtFjAmFnn+TpkWSamc3MxYx3oklgHgDPO\nOAPf+973jvr4X/3VX+EXv/gFVqxY0a1dUigUCmUWdOLsblxlbkej0UA2m0ImkzaNOtGx2WyIRKII\nh2MdlYmpqopKpYxMJo1CIdc24LBYLLBarZAkCZVKhfxdo9FApVJGtaoJdI7j2gp0l8uNaDSOwcFh\nBINBOou4h+nFWMPj8ZD+0GazAbvdQX4DuVyGOsL3AMbMs98fmJNS1blEVVWkUklyOxZLgGEYSJJk\nul9VQRZAI5FYx74flOmRz2dNc+5jsfiMnkcTe5r40/qdO/u8QqHIeBtOAYqi4MCBfUSsOxxO2O0O\nNBoCGSUZDE6/zP5Y1Os1UjFntVqnXXHXLxhjHkmSoJ8yjH441WoVqqqOi3UtAdDTYn0qFEWBJElU\nqFMoFEoPUKtVkUyOIp/PzcrZXc+iZzIplErFtll0n8+PaDTecRZdFEVks1oJvW7gY0SW5fFeYNVU\nitZoCCiXNYEuSSIR6MZAgmU5uN1uxGIJDA4OIxAY6LohDGX+WahYg+MscDqdqNfrUFUVTqfTVH5K\nWVg0Y7mW+Vo/GsuVSkWTkZleGZBOJ03ioZVhZ5FI0Kz6XKCqqmmefSyWmPEC78QS+E6Refx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kG1WiG/F8rcIQg8OY8yDINIpL+M5XR/Ep14vF1WXSFjwwYGgh1VSFGmj6qqZD45AMRi3fFSMU6H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\nAAAAMCPrAAAAMCPrAAAAMCPrAAAAMBNf4nL9nbsmeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f541d79a4e0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 219,
       "width": 501
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(8,3))\n",
    "ax1.scatter(d.mass, d.brain, alpha=0.8)\n",
    "ax2.scatter(d.mass, d.brain, alpha=0.8)\n",
    "for i in range(len(d)):\n",
    "    d_new = d.drop(d.index[-i])\n",
    "    m0 = smf.ols('brain ~ mass', d_new).fit()\n",
    "    # need to calculate regression line\n",
    "    # need to add intercept term explicitly\n",
    "    x = sm.add_constant(d_new.mass)  # add constant to new data frame with mass\n",
    "    x_pred = pd.DataFrame({'mass': np.linspace(x.mass.min() - 10, x.mass.max() + 10, 50)})  # create linspace dataframe\n",
    "    x_pred2 = sm.add_constant(x_pred)  # add constant to newly created linspace dataframe\n",
    "    y_pred = m0.predict(x_pred2)  # calculate predicted values\n",
    "    ax1.plot(x_pred, y_pred, 'gray', alpha=.5)\n",
    "    ax1.set_ylabel('body mass (kg)', fontsize=12);\n",
    "    ax1.set_xlabel('brain volume (cc)', fontsize=12)\n",
    "    ax1.set_title('Underfit model')\n",
    "    \n",
    "    # fifth order model\n",
    "    m1 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4) + I(mass**5)', data=d_new).fit()\n",
    "    x = sm.add_constant(d_new.mass)  # add constant to new data frame with mass\n",
    "    x_pred = pd.DataFrame({'mass': np.linspace(x.mass.min()-10, x.mass.max()+10, 200)})  # create linspace dataframe\n",
    "    x_pred2 = sm.add_constant(x_pred)  # add constant to newly created linspace dataframe\n",
    "    y_pred = m1.predict(x_pred2)  # calculate predicted values from fitted model\n",
    "    ax2.plot(x_pred, y_pred, 'gray', alpha=.5)\n",
    "    ax2.set_xlim(32,62)\n",
    "    ax2.set_ylim(-250, 2200)\n",
    "    ax2.set_ylabel('body mass (kg)', fontsize=12);\n",
    "    ax2.set_xlabel('brain volume (cc)', fontsize=12)\n",
    "    ax2.set_title('Overfit model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6108643020548935"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (0.3, 0.7)\n",
    "-sum(p * np.log(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.924989685887581"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit model\n",
    "m_6_1 = smf.ols('brain ~ mass', data=d).fit()\n",
    "\n",
    "#compute de deviance by cheating\n",
    "-2 * m_6_1.llf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:05<00:00, 338.85it/s]\n"
     ]
    }
   ],
   "source": [
    "# standarize the mass before fitting\n",
    "d['mass_s'] = d['mass'] - np.mean(d['mass'] / np.std(d['mass']))\n",
    "\n",
    "with pm.Model() as m_6_8 :\n",
    "    a = pm.Normal('a', mu=np.mean(d['brain']), sd=10)\n",
    "    b = pm.Normal('b', mu=0, sd=10)\n",
    "    sigma = pm.Uniform('sigma', 0, np.std(d['brain']) * 10)\n",
    "    mu = pm.Deterministic('mu', a + b * d['mass_s'])\n",
    "    brain = pm.Normal('brain', mu = mu, sd = sigma, observed = d['brain'])\n",
    "    m_6_8 = pm.sample(1000, tune=1000) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "theta = pm.summary(m_6_8)['mean'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.48254797793284"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute deviance\n",
    "dev = - 2 * sum(stats.norm.logpdf(d['brain'], loc = theta[0] + theta[1] * d['mass_s']  , scale = theta[2]))\n",
    "dev "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.12 - 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overthinking section corresponding to cells 6.12-14 is not ported because it requires an ad-hoc rethinking package function. Feel free to contribute code to this section.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('Data/cars.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:12<00:00, 161.13it/s]\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m_6_15 :\n",
    "    a = pm.Normal('a', mu=0, sd=100)\n",
    "    b = pm.Normal('b', mu=0, sd=10)\n",
    "    sigma = pm.Uniform('sigma', 0, 30)\n",
    "    mu = pm.Deterministic('mu', a + b * data['speed'])\n",
    "    dist = pm.Normal('dist', mu=mu, sd=sigma, observed = data['dist'])\n",
    "    m_6_15 = pm.sample(1000, tune=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_samples = 1000\n",
    "n_cases = data.shape[0]\n",
    "ll = np.zeros((n_cases, n_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for s in range(0, n_samples):\n",
    "    mu = m_6_15['a'][s] + m_6_15['b'][s] * data['speed']\n",
    "    p_ = stats.norm.logpdf(data['dist'], loc=mu, scale=m_6_15['sigma'][s])\n",
    "    ll[:,s] = p_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agustina/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: DeprecationWarning: `logsumexp` is deprecated!\n",
      "Importing `logsumexp` from scipy.misc is deprecated in scipy 1.0.0. Use `scipy.special.logsumexp` instead.\n"
     ]
    }
   ],
   "source": [
    "from scipy.misc import logsumexp\n",
    "n_cases = data.shape[0]\n",
    "lppd = np.zeros((n_cases))\n",
    "for a in range(1, n_cases):\n",
    "    lppd[a,] = logsumexp(ll[a,]) - np.log(n_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pWAIC = np.zeros((n_cases))\n",
    "for i in range(1, n_cases):\n",
    "    pWAIC[i,] = np.var(ll[i,])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "412.49046436448634"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "- 2 * (sum(lppd) - sum(pWAIC))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.88328369492803"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waic_vec = - 2 * (lppd - pWAIC)\n",
    "np.sqrt(n_cases * np.var(waic_vec))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 9)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv('Data/milk.csv', sep=';')\n",
    "d['neocortex'] = d['neocortex.perc'] / 100\n",
    "d.dropna(inplace=True)\n",
    "d.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a_start = d['kcal.per.g'].mean()\n",
    "sigma_start = d['kcal.per.g'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:08<00:00, 243.55it/s]\n",
      " 87%|████████▋ | 1747/2000 [02:33<00:22, 11.39it/s]/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 1 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
      "  % (self._chain_id, n_diverging))\n",
      "100%|█████████▉| 1999/2000 [02:45<00:00, 12.10it/s]/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 2 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
      "  % (self._chain_id, n_diverging))\n",
      "100%|██████████| 2000/2000 [02:45<00:00, 12.10it/s]\n",
      "100%|██████████| 2000/2000 [00:28<00:00, 69.65it/s]\n",
      "100%|██████████| 2000/2000 [04:31<00:00,  7.38it/s]/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:452: UserWarning: The acceptance probability in chain 0 does not match the target. It is 0.681715916019, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "  % (self._chain_id, mean_accept, target_accept))\n",
      "/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 25 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
      "  % (self._chain_id, n_diverging))\n",
      "\n",
      "/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 5 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
      "  % (self._chain_id, n_diverging))\n"
     ]
    }
   ],
   "source": [
    "import theano\n",
    "\n",
    "mass_shared = theano.shared(np.log(d['mass'].values))\n",
    "neocortex_shared = theano.shared(d['neocortex'].values)\n",
    "\n",
    "with pm.Model() as m6_11:\n",
    "    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)\n",
    "    mu = alpha + 0 * neocortex_shared\n",
    "    sigma = pm.HalfCauchy('sigma',beta=10, testval=sigma_start)\n",
    "    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])\n",
    "    trace_m6_11 = pm.sample(1000, tune=1000)    \n",
    "\n",
    "with pm.Model() as m6_12:\n",
    "    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)\n",
    "    beta = pm.Normal('beta', mu=0, sd=10)\n",
    "    sigma = pm.HalfCauchy('sigma',beta=10, testval=sigma_start)\n",
    "    mu = alpha + beta * neocortex_shared\n",
    "    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])\n",
    "    trace_m6_12 = pm.sample(1000, tune=1000)\n",
    "    \n",
    "with pm.Model() as m6_13:\n",
    "    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)\n",
    "    beta = pm.Normal('beta', mu=0, sd=10)\n",
    "    sigma = pm.HalfCauchy('sigma', beta=10, testval=sigma_start)\n",
    "    mu = alpha + beta * mass_shared\n",
    "    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])\n",
    "    trace_m6_13 = pm.sample(1000, tune=1000)\n",
    "    \n",
    "with pm.Model() as m6_14:\n",
    "    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)\n",
    "    beta = pm.Normal('beta', mu=0, sd=10, shape=2)\n",
    "    sigma = pm.HalfCauchy('sigma', beta=10, testval=sigma_start)\n",
    "    mu = alpha + beta[0] * mass_shared + beta[1] * neocortex_shared\n",
    "    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])\n",
    "    trace_m6_14 = pm.sample(1000, tune=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/stats.py:220: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "WAIC_r(WAIC=-17.054227823698863, WAIC_se=4.8600477062566103, p_WAIC=2.9504889764816951)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.waic(trace_m6_14, m6_14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WAIC</th>\n",
       "      <th>pWAIC</th>\n",
       "      <th>dWAIC</th>\n",
       "      <th>weight</th>\n",
       "      <th>SE</th>\n",
       "      <th>dSE</th>\n",
       "      <th>warning</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m6.14</th>\n",
       "      <td>-17.05</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>4.86</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m6.13</th>\n",
       "      <td>-9.22</td>\n",
       "      <td>1.88</td>\n",
       "      <td>7.84</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m6.11</th>\n",
       "      <td>-8.63</td>\n",
       "      <td>1.36</td>\n",
       "      <td>8.43</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.58</td>\n",
       "      <td>4.61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m6.12</th>\n",
       "      <td>-7.18</td>\n",
       "      <td>1.93</td>\n",
       "      <td>9.88</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.15</td>\n",
       "      <td>4.76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        WAIC pWAIC dWAIC weight    SE   dSE warning\n",
       "model                                              \n",
       "m6.14 -17.05  2.95     0   0.96  4.86     0       1\n",
       "m6.13  -9.22  1.88  7.84   0.02  3.99  3.25       1\n",
       "m6.11  -8.63  1.36  8.43   0.01  3.58  4.61       1\n",
       "m6.12  -7.18  1.93  9.88   0.01  3.15  4.76       1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_df = pm.compare([trace_m6_11, trace_m6_12, trace_m6_13, trace_m6_14], \n",
    "                        [m6_11, m6_12, m6_13, m6_14], \n",
    "                        method='pseudo-BMA')\n",
    "\n",
    "compare_df.loc[:,'model'] = pd.Series(['m6.11', 'm6.12', 'm6.13', 'm6.14'])\n",
    "compare_df = compare_df.set_index('model')\n",
    "compare_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WFqbs7GwtXLjQaZ8FAFC1xcdvU0TEONlsthLXs9lsGj8+TE2bBtEbfYPrz2F4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53fc0c5630>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.compareplot(compare_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.69650384567058254"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = np.random.normal(loc=6.7, scale=7.26, size=100000)\n",
    "sum(diff[diff<0]) / 100000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.27"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare function already checks number of observations to be equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m6_11</th>\n",
       "      <th>m6_12</th>\n",
       "      <th>m6_13</th>\n",
       "      <th>m6_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.657464</td>\n",
       "      <td>0.342740</td>\n",
       "      <td>0.706000</td>\n",
       "      <td>-1.074019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.466021</td>\n",
       "      <td>-0.031884</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta__0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.095749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta__1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.774855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.188901</td>\n",
       "      <td>0.192944</td>\n",
       "      <td>0.183208</td>\n",
       "      <td>0.139930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            m6_11     m6_12     m6_13     m6_14\n",
       "alpha    0.657464  0.342740  0.706000 -1.074019\n",
       "beta          NaN  0.466021 -0.031884       NaN\n",
       "beta__0       NaN       NaN       NaN -0.095749\n",
       "beta__1       NaN       NaN       NaN  2.774855\n",
       "sigma    0.188901  0.192944  0.183208  0.139930"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeftab = pd.DataFrame({'m6_11': pm.summary(trace_m6_11)['mean'],\n",
    "                        'm6_12': pm.summary(trace_m6_12)['mean'],\n",
    "                        'm6_13': pm.summary(trace_m6_13)['mean'],\n",
    "                        'm6_14': pm.summary(trace_m6_14)['mean']})\n",
    "coeftab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "traces = [trace_m6_11, trace_m6_12, trace_m6_13, trace_m6_14]   \n",
    "models = [m6_11, m6_12, m6_13, m6_14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53ec793cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 532,
       "width": 650
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "pm.forestplot(traces, plot_kwargs={'fontsize':14});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kcal_per_g = np.repeat(0, 30) # empty outcome\n",
    "neocortex = np.linspace(0.5, 0.8, 30) # sequence of neocortex\n",
    "mass = np.repeat(4.5, 30)     # average mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:37<00:00, 264.13it/s]\n"
     ]
    }
   ],
   "source": [
    "mass_shared.set_value(np.log(mass))\n",
    "neocortex_shared.set_value(neocortex)\n",
    "post_pred = pm.sample_ppc(trace_m6_14, samples=10000, model=m6_14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code 6.30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:28<00:00, 355.45it/s]\n"
     ]
    }
   ],
   "source": [
    "milk_ensemble = pm.sample_ppc_w(traces, 10000, \n",
    "                                models, weights=compare_df.weight.sort_index(ascending=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jituTl+HHGMOXa2p5evYqPvy+iuumL+DJs/bjkK75qS5bJH2tn4896Tys2jUA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BAAAg\nAElEQVTrMjMLrS7hsMJ5+bIij698hm4fNqWIjQjNsmJHS5cvd1bi8x9xubOcUi95pV7KfCaJrtAM\noQ+1kC0lsn0FxvvXo3Rg2TPz7OHoq/9TvccQISXLzIjDkfMjTGz5gbxp93Cv6++sd3QMNg/u3oRH\nLmxTPbcYHSM5N0RV5NwQVdFOO06bQaQ2q22i4FAIu+XLhg4dimmaLF26tN4G8XDn9vlRhFdI1FqT\n7w4E8IIyP1F2Iyx6lCstd+b1k1nspbR8ubMYpy345lDoDsyQXuLxkxhZ+yZnC6mifRgz7gqGcN3i\nTPQVT1tbkxBC1DVpC9jz+WPcHf0UW+wtgs2jzm7JiDOby+eSEEKEiZAF8YiICEpKSjCqGL4rrOX1\nmxQWeQI9vcVuIuwGTpuBy25YcqUcwG9qcssC64MXe/zEOo/ufuyaZDMUcRF2vHaTIo+fMq+HMp+N\naEcgpBeU+Sgo8xHvstfoMPraQK35GFW0FwAdmYTZfyLYnBZXJYQQdUjqPDbPfJa7Y/6PvbaGACjg\niYvbMuDkJtbWJoQQopKQBfGePXvy9ddfs2LFCq655ppQHUYcJ1ODv3witOJiL04bOO0GEYaBw27g\nshk4y8N5TfD4TXJLAyHc7dMkuBxhPcu4w2aQWL7cWUGpjzKvn2iHPTA5m7P2T84WCvqCBzAdkaiv\n/w/zL29BvEyAKIQQ1WbHKtbOepW/xj5HgREDgMOA53t35Ir2DSwpqcTrZ8nmHHYWuIlw2WmZEMl5\njWPCYqSbEEJYLWT3iG/dupXbbrsNj8fD2LFjOfPMM0NxmLAWzveIu30mpYai2GOi3V48fhOP38Tr\n19gNhdOucNoCQTzCbhBR/ncoestLvH7yy5cmU0DcCazbbQWtNcVeP6VeE5c9sLRabRfS+7UK90Cs\n9MzUVnIvnzgcOT+s8+3mLP6xIBV3eR9LlF0xpk8XzmkRX+O1FHv8vPnTDmam7qPQ46/0XIzTxnWd\nkxl1TktinLX/81KcOHnfEFWp6/eIhyyIFxUVkZOTw7vvvsvs2bM5+eST6dmzJ82aNSMpKemoZi0/\n66yzQlFajaktQdzm9QXbtdZ4/DoYzDWqUm+5szyUV0dvudaaQrefAreX/DI/TpuqEyG2LpAPRVEV\nOTfE4cj5YY1Zqft4Zkk6/vJvdEkug/HXdqNro5garyWvzMuI2amkZQXWLO/ROIazmsfjctn5cVsu\nv2QUANChQRQTrutKUqSjxmsU4UXeN0RV6noQD1nqOTBEa61ZvXo1q1evPur9lVKkpKSEojRxGEop\nIuwqeG+2zwyE8jKvSZHpx2YonDZFhM3AcQK95abW5JUF1gcvcvuJdtiIDON/YOI4+NyoFW+hz70b\n7DJhoxBCVDetNe/9sosxK7YH21rERfDWtV1olXB0y7RWdz0PLdxEWlYxreJdvHhFB7qVXwzYH7ZW\n/JHJo1/9zu/ZJfxtwUYm3dCtVo2CE0KI6hKyIP7njvYQdbyLELMbCrsRmBX8wN7yfK+vyt7yCLtx\n2PVJvX6T3DIfhW4vpR6TeJdd1jOtg9SXT2L8PAmdMhdzwHuQ0MrqkoQQos4wN8zhlXWlTMmteG/t\n1CCKN6/tQsNoaybC/HlXAasyCkiMtDPx+q40iYk4aJuuyTG8e303Bk7/lTW7C1m5M5/zWiZYUK0Q\nQlgrZEH8hhtuCNVLC4scsrfcd2y95WU+P3mlXvLdPvwmJEY6ZHbxOkj9+inGz5MC/717HWrTIvTZ\nd1pclRBC1A3e9XN46qsU5kdcHGw7q3kcr1/dydJbvKb9FlgZY+DJTQ4ZwvdLjnYyqHsTxq3cwbTf\n9kgQF0LUSyF7t37++edD9dIiTNgNhd1pI4pD95ZH2BQOuwr2ltsNRZHbT77bh10pEl02Wc+0Lsrc\niJr3UPCh2bUf+qxhFhYkhBB1R8m6OTy4eCfLDwjhvdvG8Z8ruli+5OdPGfkA9OucfMRt+3VOZtzK\nHazcWRDqsoQQIizJzFiiWhxVb7mhKPGaRDtssnRJXeUpwph+B8pbAoBu0A7d73WQCy5CCHHCctbM\n4a9L81jvPC3YNqBjLI/16hoWo8uKymdIT4468tD4/dsUe/1oreXCvBCi3pEgLkKiqt7yeJdd1tiu\nq7RGzX0QlfV74KE9EnPAJIg4vpkkhRBCVMj4eS4jl3vZZu8QbLv31ETu7tkpbEJsjNNGXpmPfcUe\nWsYffpLOfcWe4D7hUr8QQtSkGgniWmtWrVrF0qVL+f3339m3bx8lJSV8+eWXlbbbuHEjnTp1qomS\nRA36c2+5qJvUqvcw1s8MPtZ9X4FGXSysSAgh6oZNK+dxz08GmbZmABiYPHFuI246o8MR9qxZ57WM\nZ8Hv2cxK3cd95x5+gs5ZqfsAOLdlza9zLoQQ4SDkQfznn3/m6aefJj09Pdh2qCFI6enp3HDDDfTv\n358nn3wSh0PWlRSi1sj4BfXlk8GH5um3ok8ZYGFBQghRN6xeNp/7f3FRaEQD4MDHi5c05/JuJ1lc\n2cEGntyEBb9nM339Xm7s2ojmcYfuFd9T6Gbq+j0ADDq5SU2WKIQQYSOkXZRLlixh2LBhpKeno7UO\n/jmUH374AdM0mTFjBs8880woyxJCVKeSHIwZd6JMLwC6aQ/01c9ZXJQQQtR+i79byN1rYoIhPIZS\n3rqyZViGcIDTmsbSs2U8+W4fd85KYfWugkrf+7TWrN1dyLBZG8gt9XFW8zjOah5nYcVCCGGdkPWI\n5+bm8uijj+LxeLDb7dxwww1cccUVtGrViiuvvPKg7S+//HKWLl3KsmXL+Oyzzxg4cCDdu3cPVXlC\niOric0NMY8jfiXbFY970HtgPf2+gEEKIw/t0w17+vT4es3wAYUMKePParnRq1dLawg5DKcXLV3bk\n7jmprN9XxB0zN9CpQRRntYgnIsLOim25bNhbBEDX5Gheu6qj3B8uhKi3QhbEZ8yYQX5+PtHR0Uya\nNIkePXoAUFJScsjtmzdvzptvvsmAAQPYuHEjn3/+uQRxIWqDuKaYd8xBffU0+qSLIbG11RUJIUSt\npbVmwuoMxq3cEWxrpbJ564bTaNG0uYWVHZ3YCDvvXt+VCasz+CxlLxuzS9iYXfHdL9Fl58aujRh+\nRgtZQUUIUa+FLIgvXrwYpRR33313MIQfidPp5I477uCRRx5h9erVoSpNCFHdbE701f+xugohhKjV\n/KbmxR+2MPW3vcG2rsnRvNHnDBpEH3lJsHAR6bBx/7mtGHlWC5ZuzWVngRuXy0GLeBdnNYqWyVuF\nEIIQBvGdO3cC0KtXr2Pa75RTTgFg165d1V6TEKKamH4wpCdDCCGqi8dv8sTnS/lyX0Sw7ZwW8bx+\ndSeia2nPsdNmcHm7BgDExUcCUJBfamVJQggRNkJ2STIvLw+A5OTkY9ovMTERgLKysmqvSQhRDfxe\njMk3oJa/AVVMviiEEOLoFXl8jJpaOYRf1b4Bb/TtXGtDuBBCiMMLWRCPjg7M8Ll3794jbFlZVlYW\nALGxsdVekxDixKmvn0Vt+xFj0dMYn94pYVwIIU5AdomHOz9Zzsq8ihB+s3MNL1zRAadNhnALIURd\nFbJ3+DZt2gDw008/HdN+X3/9NQCtWrWq7pKEECcqZS7GireCD3Wz00BmvBVCiOOyI7+MWz9ZSWqR\nI9h2Pwt4ZHB/DHlvFUKIOi1kQfzcc89Fa80bb7xBRkbGUe2TlpbGO++8g1KKnj17hqo0IcTxyE7H\nmH1/8KHudBW65ygLCxJCiNorNbOYW6f9zI6ywHQ9hvbzNJ9y1+33oWIaWVydEEKIUAtZEB88eDAO\nh4Pc3FwGDhzIjBkzKCoqOuS2O3bsYNy4cdx8880UFxfjdDoZNGhQqEoTQhwrbwnG9GEoT+DfsE5o\njXndWFAybFIIIY7VTzvzGfbZWrK9gfu/I7SbMcaH3Hj7oxB9bHPrCCGEqJ2U1qG7wfOTTz7hmWee\nQZUPr1JKERsbS35+PkopWrduTU5ODoWFhUBg7UyAp59+uk4E8czMQqtLqJLbZ1JqKIo9Jjavz+py\nRJgJzm67bRNq3TTU2k9QedsB0LYIzDvnQ9NTrCxRWERmPhaHI+fHkX31RzaPfbURrw58N4ozixhr\n+5DTbnu1TodwOTdEVeTcEFXRTjtOm0GkNolyhO/ElcnJxze3WciWL4OKXvHnnnuO0tJStNYUFBQE\ng/m2bds48DpAZGQkjz/+ODfddFMoyxJCHAX3nMcxvh2LovK1On318xLChRDiOExbv4f/fLcZTeB7\nUCN/Nm/ZJ9P+tvEQ3dDi6oQQQtSkkAZxgP79+3PZZZfx8ccfs3TpUlJSUvD5KnpgXS4XnTt35uKL\nL+amm26iYUP5IBIiHBiNO1cK4dqVgL5wNPr0oRZWJYQQtY/WmvE/7eTtn3dCeQhv69vJ244PaXLb\nexLChRCiHgrp0PSqFBQUUFpaSnR0NDExMTV9+BojQ9NF2CvJRv36KWrrD5gDJwdnQI+Lj0S7iyh+\nugO0OBN92s3oTleB3WVxwcJqMoRQHI6cHwfzm5r/LN3CjA0Vy7l2d2Qy3jee+Num1JsQLueGqIqc\nG6IqMjQ9BOLi4oiLi7Pi0EII0wfpSzDWfgJpC1GmN9C+bTm0OT+4mYqIwRy9Flzyb1UIIY6H12/y\n6KLfWZSeE2zr2Sqe1644gyjzIohMsLA6IYQQVqrxIJ6VlUVBQQEejweXy0VCQgIJCfJBJETIZacH\nJl1bNw1VuOegp9W6aegDgjggIVwIIY5Tmc/PQws38f22vGBbn44NefaydjhsBuC0rjghhBCWC3kQ\n93g8TJs2ja+++or169dTVlZ20DaxsbGcfvrpXHXVVVx77bXYbOE79ECIWsVThEqZi1rzMWr7ikNu\noluciT71ZnS362q4OCGEqJtKPH7u/yKNnzIKgm1DejThHxe0wSi/BUgIIUT9FtIgnpaWxr333svu\n3bsBqOp29IKCAr777ju+++47JkyYwPjx42ndunUoSxOiXlA/vo3x7QsHtevoZHSPAejTBkNyJwsq\nE0KIuqnQ7WPUvDTW7qmYJ+bukmmMMjqCutfCyoQQQoSTkAXxnJwchg0bRm5ubjCAO51OmjZtSkJC\nAg6HA6/XS05ODrt37w7OpJ6ens4dd9zB7NmziY09vhvfhaiXSnMhMrFSkz5lAPrbF1FotLJBx96Y\np94MHS4Hm8OiQoUQom7KK/Myck4qKZnFwbbRxZO5Kz4N87QnLaxMCCFEuAlZEJ80aRI5OTkopTjj\njDMYNWoUZ511Fg7HwV/+PR4PK1as4M0332TNmjXs3r2bSZMmcf/994eqPCHqBr8Xfl+EseZj+OMb\nzFHLIOmkiucTWqLPuBWd2AZ9yk0Q09i6WoUQog7LLvEwfHYKf+RUzPz8aNEEhiZux7zl84MulAoh\nhKjfQhbElyxZglKKCy64gLfffhvDMKrc1ul0ctFFF3HBBRcwfPhwli1bxqJFiySIi7pDa9B+8HvA\n5wn87feC313+d/ljmwOadK+8b3Z64P5uvxd8B2xftA+VMgdVnBncVK2dir7s8cqH7vtKTfyEQghR\nb+0pcjN8dgrb8gLz4Cht8q+i8fRvmIU59DNwxVtcoRBCiHATsiC+a9cuAO65557DhvADGYbBvffe\ny7Jly4L7C1Er/LEYY9lY8Lsxh33xp+e+wfhoMIpDz5FwIN24G+bIbyu1qe0rMOaMPro6crce3XZC\nCCGqxc6CMobPTiGjwA2ATft5rmgMfZOLMIfMkNUnhBBCHFLIgvj++8JPOumkI2xZWbt27UJRjhCh\noTVqxVuor/4VuA/7UL0ehv2oQjgQ6On+M9vhl7jRsU3QpwxCnzoIGsi/HyGEqClbcksZPjuFfcWB\n92679vJy4Sv0buzHHDIdImSuGyGEEIcWsiCenJzMjh07cLvdx7Tf/u2bNm0airKEqD5+D2r+wxhr\nPqpocxcdvF35pGha2QKh2u4M/G1z/OlvJzqh5UG766S2mKcOPmhb7BHoZqdCu0vBkCX/hBCiJm3K\nLmbE7FRySr0AOLWHMQXPc2EzJ+bN08AZY3GFQgghwlnIgnjPnj2ZNm0aq1ev5pprrjnq/datWwfA\nhRdeGKrShDhxJTkY0+9AbVsebNLNz8C84pmDt211Lv4n9xx/WG5xJrrFmcdZqBBCiOq2YV8RI+ek\nku8OrPjissG4wlc5t3kk5uAp4Iy2uEIhhBDh7uhu3j4Ot912Gy6XizFjxpCXl3dU+xQXFzN27Fji\n4+O55ZZbQlWaECcmcxPGxCsrhXDzlAGYt8+GVuccvL0ypMdaCCHqiDW7Cxg+OyUYwmOcNt6+rhtn\n3/oC5s0fSQgXQghxVEIWxNu2bcv//vc/cnJy6NevH9OmTSM3N/eQ25aWljJ//nz69+9PQUEBY8eO\npVmzZqEqTYjj98dijHevQh0wKZrZ65/o68aBPcK6uoQQQoTcyp353D0nlSKPH4D4CDsTruvKaU3j\noHFXcERZXKEQQojaQun9s6pVs3/84x+UlZWRmZnJ2rVrUUoB0LhxY5KSkoiKisLr9ZKbm0tGRgam\naQLQtWtXYmJiOFxZSik++OCDUJRdrTIzC60uoUpun0mpoSj2mNi8PqvLqRXU6smo+f9A6cC5qh1R\nmDeMhy59LK6s+sXFRwJQkF96hC1FfSPnhjicunx+fL81l78t3IjHH/h+0iBC8fYN3enYQHrAj0Zd\nPjfEiZFzQ1RFO+04bQaR2iTKEb6jS5OTj29izpDdIz537txg+N7/t9aavXv3snfv3krbaq2D26Sk\npBz2dQ/cVoiapGOboMovEOm4ZpiDPoSmPSyuSgghRKgtSs/mka9+x2cGPgMa+7OYmP8arY33AQni\nQgghjl3IhqZDIDQf+OdQbYdrr2pbISzR8Qr0Fc+gm5+OedeXEsKFEKIemLcxk4e/3BQM4S38e5ic\n/xhtW7SGOFnhRQghxPEJWY/4N998E6qXFqJmaDMw0dqBTeeORJ995xHX9hZCCFH7fbphL//37Wb2\ndwO08e3k3YInadT5fMwb3wouTymEEEIcq5AF8ebNm4fqpYUIvS3fY3z5JObQaRDTuKJdKQnhQghR\nD0xZt5uXftgafNzRt5UJ+U+R1O1S9A3jwQjZVyghhBD1QEiHpgtRG6nVkzGmDEDt3YAx7XbwlVld\nkhBCiBo04eedlUJ4N+/vTMp/gqTuvSWECyGEqBYSxIXYz/ShFj6BMe8hlFk+k3zeDsjPsLYuIYQQ\nNUJrzdgV2xm7ckew7TRvCu8WPElcj2vQ142VEC6EEDVh11qMSTcGV9aqiySICwFQVoDxyVCMle8E\nm3TTHpjDF0GDdhYWJoQQoiZorXl52TYmrK64+HqOZx3v5P+L6FNvQF/3PzDCd/kcIYSoMxY/jzHh\nCow/lsCno6yuJmTC7rJur169gMCSZ19//bXF1Yh6IXcrxidDUZkbg026S1/M68eBU5alEUKIus7U\nmue+28KMDRXLq17oyuD1rP/DecZgdJ+XDpq8UwghRDUrycX44DrUvtSKtl8+xuz1MDQ+ybq6QiTs\ngnhGRuBKtKwVLmrEtuUY0+5AleYEm8wLH0Rf+oh86RJCiHrAZ2qeWvwH8zZmBdsub5fEi73OxJmq\n0d37BybqFEIIETobF2LMuAvldwebtDJQff6DkdTawsJCJ+yCuBA1Ra35GDXv7yjTC4C2RaD7vY7u\n0d/iyoQQQtQEr9/k0UW/syi94mJs344NebZXe+yGQve4ycLqhBCinkidjzH9dg685KljGuMfsQBn\no3aBJYXroLAL4rL+uKgRWsOWpRUhPDoZc+AH0PIsiwsTQghRE9w+k4cWbmTptrxg21+6JvPkJe0w\npAdcCCFqTqerIaohlGShAX3yjegb3gRX3V4yOOyCuKw/LmqEUuhr/4vO2Qq+MsxBH0JCS6urEkII\nUQNKvH5Gf7GRFTvzg21DS+fwcLEfeNG6woQQoj4yDMzbZ2NM6ovZbwx0vtrqimpE2AVxIWqMIxJz\n8Idgj4SIGKurEUIIUQOKPD5GzUtjze7CYNuIkuncXzIFHfU3tIW1CSFEnecrQ826D93vv+A84Pt3\nckfMhzdZV5cFZDYqUT/sWIX6+tnAkPQDRSdLCBdCiHoiq9jDnbNSKoXwB4o/5IGSKeiLH0Zf+phM\nzCaEEKGyfSXGy10wNszCeP8Gq6uxnPSIizpP/ToDNWc0yu/BjGmEPnek1SUJIYSoYVtyS7lnbiq7\nCitm5H2kaCK3ls3BvPRR9EUPWVidEELUbWrhE6iV7wQnZFO718LqyXDGrZbWZaUTDuL71/2ubrKO\nuDhh2kQtfh7jh9eDTeqH/6FPGwIRsRYWJoQQoiat3V3IffPTyHf7ALBpP08Vjae/exFmr3+iL3jA\n4gqFEKKOKsrEeP9aVHZ6sEkD+qSL4ZRB1tUVBk44iGdkZFT7mt9aa1lHXJyYsnyMOQ+gUucHm3Ry\nJ8zBUySECyFEPfLN5mwe/ep33P7ArUmRuoxXC17iYu/PmL2fQfe81+IKhRCijlo/E2PmqOAqRQDa\nsGH2eRlOv8XCwsJDtQxN13++71YIq2Sno1ZOQK39BOUtCTbrDpdj/uUdCeFCCFGPTP1tD88v3RKc\ngC3JzGN8wbN09/2BedV/0OcMD8lxs0s8zN+Uxc6CMhSK5nER9OnYkAZRdXspHiGEAMA0UdNvR21c\nUHlt8PgWmHfMg3hZJQuqIYinpaVVRx1CnJiMXzC+exn1+8G3M5jnjkT3fhoMW83XJYQQosaZWvO/\nFdt575ddwbZWMQZv7XuOVv4tmNe/gT5lQLUfN7vEw8s/bOOr9Gx8ZuVOitd/3M6V7Rvw9/Pb0CDK\nUe3HFkKIsJC3A2NCb1RJdrBJA/q0Iei+r4Ehc4XvJ5O1ibqhcM9BIVwnd0Zf9CD6ZJmVUQgh6guv\n3+SpxenM35QVbOveOIaxfTqTlD0GszgLOl1Z7cfdU+jmjlkbyChwYyi4rG0iZzaPB2BVRj7fbc1l\n/qYs1u0p5L0butEkJqLaaxBCCMtFJcKBo1LtkZgDP4D2l1pYVHgKuyCem5tL//79Ofvss3n++eet\nLkeEo/ydENsEjANO345XohNaQ9526Ngb85y7oe2FsgyNEELUI4VuH39bsJGfMgqCbRe3SeSlKzoQ\n6bBBizNCcly/qbn/izQyCtx0TY7m1as60jzOFXx+6ClN2VlQxt8XbiIls5j752/kk5u6YzPkM0oI\nUcc4YzAHfoAxZQA0PQXz1s/BFWd1VWEp7IJ4fn4+GRkZLF682OpSRDjRGnasxFj5DqR+gdl/AnS9\ntuJ5w4bZ73WIbwZJJ1lXpxBCCEvsLXIzal4am7IremJuamPnsas7YQ9x4P1hex5pWSU0iXHydr8u\nxLsOHnreIs7F2/26cNO0X0nLKmb59jwubJMY0rqEECLkdq6GZqdVHnLe7lLMkd9B467W1VUL1EgQ\nz8/P59dffyU3NxfTNA+5jWmaZGdnM3v2bAA8Hk9NlCbCnc+N2jA7sO7g7nXBZmPlO5gHBnGAthfU\ncHFCCCHCwR/ZJdwzL5W9RRXfHR4o/pC7Uhajz5kLDTuE9PjTftsDwM09mh4yhO8X73IwqHsTXv9x\nO1PX75EgLoSovUwTNf/vqF8+RHfugx74fuXnJYQfUUiDuNvt5t///jeff/55lQH8UJRStGvXLoSV\nibBXnIn6+QPUqvdQxZkHP2/YwVMMzuiar00IIUTY+Dkjnwe+2Eihxw+AXft4tmgs17mXoBt2QDui\nQl7Dmt2FAPTt2PCI2/btlMzrP24P7iOEELVOwS6M9/qi8ncEHqfNR2+YBd2ut7auWiakQfzee+9l\n+fLlR7W8mVIquF1SUhKPPfZYKEsT4Wr3r4He7/UzUf7KoyK0LQLdo39guZnG3SwqUAghRLhY+HsW\nT3z9B97yGcqjzBJeL3yB871r0c1Oxbz5E4g+cjg+UaW+wEWAeNeRv1Yllm9T6vWHtCYhhAiJXz7C\nmP93lOmraDNkJYjjEbIgvnDhQpYtWwZAdHQ0F110ES1btsTpdDJu3DiUUowYMQKAlJQUli1bRqNG\njXjmmWc455xziIyMDFVpIlyVFWC81wflK6vUrGOboM8ahj7jVohqYFFxQgghwsnktbt4Zdm24OOG\nZg5v5T9DF/8WdJsLMAd9CBExNVJLXISdvDIfGYVu2iQc/vvLzgJ3cB8hhKg1MjdhzBkNO1dVXhs8\n6STMO+ZATGPLSqutQvYpMHfuXAA6duzI+++/T1JSUvC5cePGATBy5Mhg4E5LS+OBBx7gtddeY8KE\nCRLE6yNXHLp7f9SaKQDo5megzxmB7not2ORKmxBCiMAa4a8s28aUdbuDbSf5dvB2wdM0MzPRna/B\n/MvbYHcd5lWq14WtE5m7MZPPU/bxYM/Wh932s5S9AFwk94cLIWqDLd9jLHgcMtMqB3BAnz0cffV/\nrKqs1gvZiuopKSkopfjb3/5WKYRXpXPnzkycOJFdu3Zxzz33yGRtdVnWH6j5D6O+fOqgp/S5IzBP\nvhH/nQsx71qI7n6jhHAhhBAAuH0m//hyU6UQfrp3Ax/mP0IzMxPz1EGYN71boyEcYFD3QE/Qpxv2\nsjmnpMrt0nNK+GzDvvJ9mtRIbUIIcdzSFmBMvhH15xDujMa8bbaE8BMUsiCek5MDQI8eParc5s/3\njrds2ZJbbrmF1NRU5syZE6rShBW0hvQlGB8NwvbGeRg/T0L9/D6U5lberlEX9F/eDtlar0IIIWqn\n/DIvd89JYVF6TrCtt3sZE/OfIkEXYZ57D7rfmMBknjXs5EYx9DopiSKPn2GzUvg6PRufWfEdx2dq\nFqVnM2zWBoq9fnq3S6Jbo5oZNi+EEMet45VwwISX2u7CPPsuzH+kQZueFhZWN4Ts02r/LOk2m+3g\ng9rt+P1+CgsLiYqqPJtp7969efPNN5k1axb9+/cPVXmiJvm9qNn3Y/z2aaVm5cqmSvMAACAASURB\nVCtFbZiDPvM2iwoTQghRG+wqcHPvvFQ255YG227u3oSHCzNx/OzFvOxx9AWjQYV2vfCqKKX4z+Xt\nGb1gIz/uyOfBhZtoEuPkjGZxAKzeVcCe8qXVeraM59+92ltSpxBCHJKnCLXwn+ioBnD5kxXthoF5\n5m0Ya6dhnn8fnHdv5fXCxQkJWRCPjY0lNzeXPXv2kJCQUOm5pKQkMjMzycnJoXHjyjf2N2vWDIBN\nmzaFqjRRk7wlGDPuQv2+KNikUdDpSsxzRkAbWftbCCFE1dIyixk1L5XMEm+w7aGerbn11KYonsff\n+Wpod4l1BZaLdNgY16czU9fvZdpve9ieX8b8TVnB51vHuxjYvQkDT26MwyZfZIUQYSA/AzXv76j0\nxShtog0H5mWPVR5ZdMWzmFc8a12NdVjIgnjbtm3Jzc3liy++oHPnzpWea9iwIZmZmSxfvpwuXbpU\neq6wMLCuZklJ1fdYiVqirADjkyGo7SuCTWb3/uhLHoakthYWJoQQojb4cUceDy7YRHH5Ul8OQ/Hv\ny9tzdYf9S5KpsAjh+zlsBrec0pQhPZqwZnchGeUzpDePi+C0prEYFvXYCyFEJbvWYsx/GHatqXTv\ntzK9sPj5yr3iImRCdkn2/PPPR2vNxIkTGTduHLt27Qo+1759e7TWTJ8+ndLS0kr7LViwAICYGLl3\nqlYrzsKYfEPlEH7BaPQN4yWECyGEOKK5GzMZNS8tGMJjdTFvx3/B1e3Cf7ZxQynOaBZHv87J9Ouc\nzBnN4iSECyGsl7YAY+zZGBN6o/4UwrUyMNtdCmfcall59U3IesQHDBjAe++9R3FxMW+88QYrVqxg\nypTAslS9evVizpw5bN++nf79+zNw4EDi4+NZs2YNM2bMQClFt27dQlWaCDWtMabfjtr9a7DJ7P00\nuucoC4sSQghRG2itee+XXYxZsT3Y1tjM4u38p+mQvR3zGxu6978srFAIIWqZVZMwvn0JVZJ10FPa\ncKC734i+6j/girOguEPTWuM1NS47KH3k7WujkAXxhg0b8tJLL/Hggw9SVlZGgwYNgs/17t2bzp07\nk5aWxubNm3n++eeDz2mtUUoxePDgUJUmQk0pzCufw/jgevCWoPu+ij59qNVVCSGECHN+U/P891uY\nvn5vsK2DbytvFTxDEzMbHZ2M7iETuQohxLEw1k09KIRrZwzmOcPhkoctWW3icLTW5JX5SHI5iHLa\ncfh8VpcUEiGdLeSyyy5jzpw5DBkyhO7du1cc1DAYN24cbdu2RWtd6Y9SipEjR3L55ZeHsjQRas1O\nwRw8BbP/BAnhQgghjqjU6+fBhRsrhfCzPb/yYf6jgRCe0Bpz2HxoLCPmhBCiSr6yg5rMPi+jIfAn\npjH+Pi9jPrYFLns8LEN4vtuHzYCESCcNo5119tYepf+8mHcN8ng8LFq0iPXr11NcXEyzZs24/PLL\nad++bizrkZlZaHUJVXL7TEoNRbHHxOathqtMvjKwu078dURYiIuPBKAgv/QIW4r6Rs4NcTjHe37k\nlnq5b34av+4tCrZdU/YdzxWNwYkP3agL5tDpENukWusVNUfeO0RV5NyoJtnpqHn/QG1bhnn7bGh1\nbqWn1dwH0Z37QIdeFhV4ZFprCtw+UJDgctCuaTx2m0FeXnhP4p2cHHtc+1kaxEMtLy+PcePG8c03\n35CZmUlCQgIXX3wxDzzwAI0aNTri/h6Ph3feeYc5c+awe/duEhMTueSSSxg9ejRJSUlH3L/eBPFN\nX2HM+zvm0GnQqMuRtxdhTz4URVXk3BCHczznx878Mu6Zm8q2/IpenGEln/G3kskYaHSLMzFv/hgi\nw3+SNlE1ee8QVZFz4wRtXY6x8DHYmxKcfE037oY58lsrqzouBWU+TDSJkQ6SIh0kNwhM3l1Xg3iN\njUUwTROjigXgPR4PWmsiIiKq7XhlZWXccsstbNmyhSFDhnDyySezbds23n33XVasWMHnn39OfHx8\nlfv7fD5GjBjBqlWrGDJkCN26dWP9+vV89NFHrF69mpkzZ+J0Oqut3tpK/fYZatZfUaYP48ObMIfN\ng8Q2VpclhBCiFticU8Lw2SnBNcIVmseK3mFI2XwAdLtLMAe8D85oC6sUQogw5CnCePca2JfKQQO3\ns34HnwfstSerFJT58KNJdNlJinTgsIX0DuqwEPIgvnPnTsaMGcP69euDS5P9WWpqKkOHDuXyyy/n\nwQcfpGXLlid83A8++IBNmzbx1FNPMWTIkGB7586dGTVqFOPHj+exxx6rcv+pU6fy448/8uKLL3L9\n9dcDcN1115GYmMhnn33Gr7/+yplnnnnCddZmatV7qC8eRVE+qMLugro7wEIIIUQ12pRVzPA5KeSW\nBkZlRdgUL7jf4YryEG527RdY8tJefRfphRCiTsjbgfH2paiy/ErN2u5CnzoY3fvp2hXC3T78WpMY\naScpylkvQjiEeGj6L7/8wogRIyguLiYiIoK1a9cecrt169YxcOBAlFJER0fzzjvvcPrpp5/Qsa++\n+mr27NnDypUrK/Vca6255JJL8Hg8LF++HFXFzf9XX301pmmycOHCKrc5kjo7NF1r1Pf/xVhywGz3\nyZ0xb5kh9+/VETJMTFRFzg1xOEd7fqRkFnH37FTy3YHPnyiHwbg+nTkzIhNj0rXozteg+7wEhi3k\nNYuaIe8doipybhyj7SsxJt+I8nuCTdoVj9nzr3D+/VDFCORwVej24TMDITwx0kmEvaL+hIQooO4O\nTQ/Z/6mCggLuv/9+iouL0Vpjt9vxVTH1fHx8PM2aNUNrTVFREQ888ABFRUWH3PZoFBUVsXnzZrp2\n7XrQ8HGlFD169CAnJ4edO3cecv89e/awefNmzj///GAId7vd1OHb6Y+e1qhFT1cO4c1PD0wKISFc\nCCHEEfy6p5Dhs1KCITzGaePtfl05s3k8NGyPefc36L6vSAgXQog/WzcD4/1+lUK42fYizH9sggtH\n17oQXuzx4zU1CYcI4fVByIamf/LJJ2RlZeFwOHj44YcZNGgQdvuhD9emTRsWL17MnDlz+Oc//0lW\nVhYff/wxI0aMOK5jZ2RkANCkyaGDYdOmTQHYsWPHIYfBb968GYBWrVrxwQcf8MEHH5CRkYHT6eTC\nCy/kkUceoXXr1kesY/9VnHDk9vnxlHiw+zWxUZFHtY/2+/DMuB/fT5ODbUaHS3ANm4qKiAlVqcIC\n9vIhQfuvUguxn5wb4nCOdH78tD2Pu+emUuzxAxDvVHw45DR6NI2r2Ci+XcjrFDVP3jtEVeTcOHpl\nO5bi12bwsb3ncCL6/9fCio5fkduHw26jcaSDhtFOXI6DL77ay4N5OGeqExGyyw6LFy9GKcWwYcO4\n5ZZbcDgcR9ynX79+3HHHHWit+eabb4772MXFxQC4XIdeTisyMrLSdn+Wl5cHwMyZM5k2bRojR45k\n/PjxDBw4kCVLljB48GD27dt33PXVRtrnxj351koh3Nb9WlzDP5MQLoQQ4oiWb83htmlrgyE8SRXz\nXu6jdCvbYHFlQghRO7iGTMRodRagcF73Yq0N4SUeP2U+k6RIBw2qCOH1Qch6xPf3Kvfp0+eY9uvb\nty9vv/02W7ZsCUVZR8XrDczemp2dzdy5c0lMDCyZ0qtXLxo2bMh///tfJk2axCOPPHLY1wnn+xnc\nPhO/ofD59VHdk6NWT8b4bU7wsXnqIPzX/hdPsQnIPT11jdyvJaoi54Y4nKrOjx+25fK3BRtx+wO3\neDVURbyb8wjt/Tsofes6zPtWyu1NdZy8d4iqyLlxjG6bAxlrKG15FqW18HdW4vVT4jVJdNkx7Ap3\nscZdxbZyj/hxKisLrAfauHHjY9ovOTkZgNLS4z+xYmJiDvsaJSUllbb7s+jowDIpl112WTCE79e/\nf38AVq5cedz11Ub69FswTx0MgHnuSHS/MWDU2Op3Qgghaqklm3N44IuKEN5YFTI5+yHa+3cAoC95\nREK4EEL8WUkuxsSroGhv5XbDDi3PsqamE1TmqwjhCZEOouppT/h+IQvi+wN1VlbWMe23a9cugIMC\n8LFo0aIFSin27Nlz2GNUdZ938+bNAfD7/Qc9l5iYiFKqymHtdZZS6Gtfw99/IvqKZ0HVr8kUhBBC\nHLuv/sjmoS834TUDIby5UcDk7Adpbe4GwLz0MXTPe60sUQghws++NIwxp6EyVmO8eQn4yqyu6ISV\n+fwUuv0kuGzER9qJdtbvEA4hDOKdOnUCYN68ece030cffQRA+/btj/vYUVFRdOrUiZSUFNzuyoMd\n/H4/a9asoWnTpjRr1uyQ+7dr147Y2FhSU1MPem737t1orY+5p7/WKdgN5p8uRBh26HYdHOdybkII\nIeqPeRszefirTfjKQ3grWz7vZ42mhRno3TEvfBB90YNWliiEEOHn928Ca4R7Ap1+qiQLFj9/hJ3C\nm9tnBkJ4pJ0El4MYp4yqhRAG8SuvvBKtNRMnTmTWrFlH3N7tdvPqq6/y2WefoZSid+/eJ3T8/v37\nU1paytSpUyu1z5kzh+zs7OAQc4D09HR27NgRfOx0Ounbty8bNmxg8eLFlfbff6HgsssuO6H6wtq+\nVIwJvVHzHgJZsk0IIcQxmpmyjye+/oPyDE5bez4fZD5AMzMwSs7s+Vf0pY9aWKEQQoShnyZifDwY\nZQaWd9SA2bkPXPGMtXWdAI/fpNDjqwjhERLC91M6RItjezwerr32WrZt24ZSirZt23L++efTpk0b\n4uPjsdlsFBQUkJubS3p6Ot9//z35+florWnbti1z5sw5qpnWq+L1ehkyZAgbNmxg6NChnHzyyfzx\nxx9MmjSJ1q1bM3369ODs6Z06daJt27YsXLgwuH9OTg4DBgxg7969jBgxgubNm7NixQpmz55Nly5d\nmDp1apWzsu+XmVl43PWHmttnUmooij0mNu8B67vv/Bnjo8GossDM8eYFD6B7/dOiKoVVZOIUURU5\nN8ThxMVH8uHqnTz55aZgWwdHHu/uuY8GOh8A8+zh6Kuek9FV9Yy8d4iqyLkRoL54FLXqXfa/M2oC\n38Opxd/DvX6TfLePOJedhAgHca5jC+F1fbK2kAVxCPQ033777WRmZqKO8IG7v4zk5GQ+/PBD2rRp\nc8LHLyoqYuzYsXz11VdkZmaSlJRE7969ue+++0hISAhud6ggDoEw/vrrr7N48WLy8vJITk7myiuv\nZNSoUcTGHvkXXuuC+ObvMKbeivIGTnbtjMEcPAXanG9hpcIK8qEoqiLnhjic6Rsz+ffXfwQfd4n1\nMXHrbSTowOehecZt6D4vSwivh+S9Q1Sl3p8bpon6aCDG5m+DTVopzH7/g1MHWVfXCdofwmMj7CS4\n7MS7jr2DVYL4CcrJyeHVV19l7ty5eDyeKrdzuVz069eP0aNHk5SUFMqSakytCuKp8zA+uxvlD/w/\n0lENMIdMg2anWFypsEK9/1AUVZJzQ1Tl3dUZjFmxPfi4e+MY3uzbmfhlL2L8MAbz1EGBFTdkss96\nSd47RFXq9bnhKcF4pxcqu+ICprY5MW/9DFqda2FhJ8brNylw+4iOsJHgcpBwHCEcJIhXm6KiIlas\nWMGmTZvIysrC4/HgcrlITk6mU6dOnH322URFRdVEKTWmtgRx+0+TUXP/htImADquGeYtn0LDDhZX\nKaxSrz8UxWHJuSH+TGvNm6t28taqncG205rG8kbfzoEJebSGtPnQ6WowZJbc+kreO0RV6vO5Ybx7\nDWrnquBj7YrDHLEYEg+9slNt4DM1eaVeYiJsxLscJLjsRxwZXRUJ4sdp+fLl9OzZ87j2zc/P59FH\nH+XNN9+s5qpqVm0I4qVLxmFf8ESwXSedFAjhCS0trE5YrT5/KIrDk3NDHEhrzf9WbOfdX3YF23q2\nTuC1KzvW+/VhRWXy3iGqUq/PjdxtGG+ch/J70QmtMe/5FpwxVld13HymJr/MS7TTRpzLQeIJhHCo\n+0E8ZOPDRo4cyddff33M+61du5brr7+eb7/9tvqLEkFaa/xf/rtyCG9yMuaweRLChRBCHJHWmpeX\nbasUwi+ML2C8byxRhmlhZUIIUUsktsYc+ilmu8sw7/upVodwf3kIj3LaiI2wn3AIrw9CFsQ9Hg+j\nR48+qqXL9ps4cSJDhw5l9+7doSpLlNPFWZg/Ta543OoczNtmQ3SyhVUJIYSoDUyteW7pFqasq/i8\nviQ+n/+l345j3QyMT+8Cn9vCCoUQIgxtX3FwW5ue6KHTwKi982f4TU1emY9Ih424CDtJkQ4J4Uch\nZP/HGzZsiM/n4/HHH2fKlCmH3TY3N5cRI0bw6quv4vMFZvAePHhwqEoTgBGTjP2umWhXPLp9L8yh\n08EVZ3VZQgghwpzf1DyzJJ3p6/cG23onFvDf9DtwUr4KR1kBaOkVF0KI/dTn92BMuhb1xSNWl1Kt\nTB0I4S6HIs5lJ1FC+FELWRCfOnUqrVu3xjRNnnvuOcaPH3/I7X7++Weuv/56vv/+e7TWxMXFMWbM\nGJ566qlQlSbKqabd8I74EnPQZHDUrYnyhBBCVD+fqfnnN38wMzUz2HZNw0Je+f3WYAg32p4XWPrS\nEWlVmUIIET5MH8bEqzB++xQFqFXvwU8Tra6qWuwP4RF2RVyEg6RIB4aE8KMWsiDeokULpk6dSvfu\n3dFaM3bsWF588cVK27z11lvcfvvt7Nu3D601p556KjNnzuTKK68MVVnizxp1ApvT6iqEEEKEOa/f\n5JGvNjF/U1aw7frGxTyfdit2Ar3fRquzcA3/DJzRVpUphBDhoywP439noTJWV7TZI6HlOdbVVE20\n1uSX+XDaFPEuCeHHI6Q3IyQmJvLhhx9y0UUXobXm/fff55///CeZmZnceeedjBkzJjgU/a677uKj\njz6iefPmoSxJCCGEEMfI4zd5aOEmFqXnBNsGNCvh2ZSh2PEDoJv2wHX3TJTc5iSEEJCdjvH66aj8\niqUddVRDzAdWQdPuFhZ24nR5T7jdUMSVh3CbISH8WNXIOuJ+v58nnniCWbNmoZTC4XDg9XrRWtOg\nQQNefPFFLrjgglCXUeNqw/JlxR4Tm9dndTkizNTrpUTEYcm5Uf+U+fyMXrCR5dvzg21DWpbx6NrB\nGLo8hDfqinnbTOKaBi6my/kh/kzeO0RV6uS5seV7jCkDUaY32KSTO2OOWAR2l4WFnTitNfluH4aC\nhEgnSZF27CGaaE6WL6sGNpuNF154geHDh6O1xuPxAHDeeecxe/bsOhnChRBCiNquxOtn1Ly0SiF8\nWFsvj64bUhHCG3bAvPVTiEqyqkwhhAgfqydjTP5LMIRrwOx4JebI7+pECC9w+1CK8uHooQvh9UGN\n/uYeeughnnjiCYzy/2FRUVE0bNiwJksQQgghxFEo8vi4Z24qqzIKgm0jz2rBA+c0R8U0AkAntcW8\n9XNZ+lIIIQAWPY0x7yEUgQHHGjDPuxc9eEqtXp5sv0K3Hw0klE/MJiH8xNT4b++WW27htddew+Fw\nsHjxYl577bWaLkEIIYQQh1FQ5mPE7FTW7K64xer+c1tx79ktUQ3aYd4xB932IsxbZ0JsEwsrFUKI\n8KFKK0YPaRRm31fhimcsrKj6FJT58KNJcNlJjHLgsEkIP1EnfI94ly5djntfrXWV68wppUhJSTnu\n1w4Hco+4qK3q5P1aolrIuVH35ZV5GTE7lbSs4mDbP85vzS2nNjvivnJ+iKrIuSGqUtfODTXv76i1\nH2Pe/AmcdLHV5VSL/SE80WUnKcqJs4ZCeF2/R9x+ogc+3hyvlEIpddz7CyGEEKJ67Sl0c8/cVNJz\nK74QP36Kg0GJ24AjB3EhhKjvdN9X0Fc8C84oq0upFlaF8PrghIN4s2bywSyEEELUduk5JYycm8re\nosCEqgr41+kR9F86EPwezMFToO2F1hYphBDhZPl46HotJLSs3C4hXByFEw7iixcvro46hBBCCGGR\ntbsL+ev8VArcgZnQ7Ybi32dF0XfJAFRZHgDGrL9i3rey1s/6K4QQ1WLJixhLX4FvXwjMiJ7U1uqK\nqpWE8NCT36gQQghRj327JYcRc1KCITzKYfDGhXH0/XYQqiQbAO2Kxxw0WUK4EEIALH0VY+krKEB5\nSzHev87qiqpVgVtCeE044R5xIYQQQtROM1P28ey36fjLp2tJjLTz5kXxnDznL6jiTAC0MwZzyDRo\neoqFlQohRJj4YQzGkhfYP920NuyYAyZZWlJ1KnD78GsJ4TVBgrgQQghRz2itmbA6g3ErdwTbWsRF\n8NZZftrMur4ihDuiMId8Ai3OsKpUIYQIH8vGYXzz74oQrmyYt82qM++RBW4fflOTGGknMVJCeKhJ\nEBdCCCHqEb+peemHrXzy255gW+eGUbzZLp3kT+9H+d0AaLsLc/BH0Opcq0oVQojwseItjK+fqRzC\nb/0cWp1jaVnV5c8hPMIuITzUJIgLIYQQ9YTHb/L4oj/4Kj072HZ28zjGxC4kbv4LwTYdmYQ5cBK0\n7mlFmUIIEV5+mojx5ZMHhHAD85YZ0KZuvEdKCLeGBHEhhBCiHih0+xi9YCOrMgqCbVe2b8Bzl7cn\n4te1wTZvUkc+OvkVfktrgD91I41jIujbqSFdk2OsKFsIIay1ahLGgscqh/AhU+vMco6FEsItI0Fc\nCCGEqOOyij3cOy+VtKySYNvN3Zvw8IVtMJRCnzaEkl2pbPl9HXeq0RSmAFT0mk9Zt5vujWN46pKT\n6NQwuuZ/ACGEsIJpYiz+T+UQPvhjaHeppWVVl0K3D5+EcMtIEBdCCCHqsG15pYycm0pGgTvYdv85\nLbjzjBYoFfh6mVnsYVj2deywX4XNZqdP+wZc0CoBm6H4dW8Rs9P28dveIm7/fANv9evCKU1irfpx\nhBCi5hgG5shvMcafD94SzMFToEMvq6uqFhLCrSdBXAghhKijNuwr4t55qeSW+gCwKfhX+1xuXPMv\nzO6fQ0QMWmv+/uUmthX66Jwcx9g+nWgcExF8jas6NOSv57Tk6cXpLPwjm/u/SGPukNOIi5CvEEKI\neiC+Oeao5bA3VUK4qFbyWxdCCCHqoOXb8xg2c0MwhLvsBmOar+MvP96G2rUG4/O7wfSzbk8Ra3YX\nkuiy81a/LpVC+H5RDhvPXd6eU5rEkFvqY05aZk3/OEIIUTPyMw5ui2tW50J4QqSdhEiHhHALyW9e\nCCGEqGPmb8rkr/PTKPWZAMRH2JgY/TmXrn2yYqPcbVCaw/T1gWXMbuzamKRIR5Wv6bAZ3H5aMwCm\nr9+D1jp0P4AQQlhhwyyM109Dzb7f6kpCotDtw1sewhMjHbjsNqtLqtdkXJkQQghRh0xeu4tXlm0L\nPm4SZePtspdpn/FtsE2374XZfwJExLJ+304ArurQ4IivfXGbJFx2g615ZZR4TaKd8iVOCFFHpM7H\n+HQECo1a+wkmoK/7n9VVVZsiTyCEJ0oIDxsSxIUQQog6wNSa13/czvtrdgXb2sXC2/v+RtPiTRXb\nnTsS3ftpMAJfwsrKe81jjiJU2w2Fy25Q5jMp80kQF0LUERsXYky/A0VgpI8GdJvzra2pGhV5fHj8\nEsLDjQRxIYQQopbz+k3+tSSdeRuzgm2nx7kZu/1uEnw5AGjDge7zEvr0oZX2TYx0sKfIw5bcUprH\nuQ57nOwSLwVuH4aC2Aj5IieEqAN+X4Qx7dZKIdy89nU4ZaC1dVWT/SE8wSUhPNzIPeJCCCFELVbi\n8XP/FxsrhfBLYzKZsHlIRQiPTMK89dODQjhAr5OSAPgsZd8RjzUzdR+mhotaJ+K0yVcIIUQt98cS\njE+GonRFCF975tMsb9gHj9+0trZqcGAIT4qSEB5u5FNUCCGEqKVyS73cNTuFZdvzgm39uybzX/Uh\nLjwA6OROmMO/hNY9D/kaN3ZphN1QLNmSw/dbc6s81ta8Uj4oH/Y+sHuTavwphBDCApu/w/h4EEoH\nArcG/h09kqFbT2fk3FR6f7CaMT9uI7fUa22dx0lCePiTIC6EEELUQhkFZdz6+XrW7ysKtt19Zgue\nvKQdxk0T0EknoTtcjnnnAkhsU+XrNIx2cufpzTE1jF6wkfd+yaCgzBd83uM3mbcxk9s/30C+28eF\nrRM4r2V8KH80IYQIrS3fY0wZUCmEvxQzgl1dhnBVhwZ0aBBFbqmPd3/Zxc2f/sb2vFJr6z1GxR6/\nDEevBZSW9UdCJjOz0OoSquT2mZQaimKPic3rO/IOol6Ji48EoCC/dn3wiNCTcyM8bMoq5p65qWSW\nBHpqFPDExW0ZcPIBPdWFeyA6OTgp2+ForXnph6189GtgKTOX3aBH4xjshiI1s5jc8mB+bot4Xr+6\nE1FVTNIm54eoipwboio1fm6U5WG83AVlBt7XNPBDl4fo1u8h4l2BJRy11vy6t4gXvt/Chn3FtIyL\n4JMBPYiLOPT0Wlpr3H4Tj0/jNU1shsKmFHaj4o9SqkZ+vGKPnzKfGZyYLdJRe0N4QkIUAHl5JRZX\ncnjJybHHtZ/0iAshhBC1yM8Z+dw+c0MwhDuV5rWWaZVDOEBsk6MK4QBKKR65sC1j+3TmvJbxlPlM\nfsooYPmOfHLLfHRqEMVTl5zEG307VxnChRCiVnAl4D/jtsDM6MCv3R+i54BHgyEcAu+JpzSJ5d3r\nutG5YRQ7Ctx8umFvpZfRWuP2mRSU+cgq8VLqM3HYFfEuO5EOG4YBHtOk0OMnq8QbmOyyzEexx4/b\nZ+Izq78vtC6F8PpAZk0XQgghaolF6dk8tuh3PP7AF7gYw8e4/2fvvsOrqNIHjn9n7tyam5seQu8Q\nAoqouIoIKhZcC9a1gLqufdW1/dTVdXV11V3Lqmtb7KuuoqAo2LCgrrqIoqj03gMkIb3dMjPn98cN\nNwSSUEzuTXk/z+PzOGfO3LzocDPvnHPeU/JnRhYtxu6Xihp22i/6/LF90hjbJ438iiAbyoOYtqKL\n38XAdF/cRnOEEKK1fTjg/yheWITDn8m5p93SZD+fy8E1v+rFVe8vY9riSNbr3AAAIABJREFUAi4Y\n3hVLRWeWhiwbw6Hhduj43U7chh79x6FjKYVpR/+JWArLVpi2HWurNW1MO5rMG7qGo27U3Fn37/o+\nfN9KEt7+SCIuhBBCtANvLNrKff9dy/YxlCytmqeLb2WwtQ4A7ZunUEMngPbLJ7t1D3h2u5WZEEK0\nV1+sK2GW/xL+eESf3b5kPKxnCtlJLvIrQny/uYLBmUm4jWjy7TJ0PIaO13Dg0Os/x7nTZ6gdEnPT\nVkS2/7tlYyqFWZesbx8p1zViybmxwzT3pmKVJLx9kkRcCCGEaMOUUjz13Sae/n5TrK0PRTxTfCvd\n7eiWY2rgsdhnPN0iSbgQQnQoWxaivz4J+4LpkNEfgIpQdH14zyZeOCoVTZaDpk3YtMnxuyisDqNp\nkOV34alLwA19z75zNU3D6dDYOT+2VXTE3NxpxNy0FBFlR5Nzy8a0wFIKhxYdNd9xFD1k2pKEt1OS\niAshhBBtVMSyuffLtUzfYY/v/ew1/Kv0z6SpaEFQ+7Dfo465Y4/XgwshRKdRsBj9+ePRrAj65LHY\nl30GWYPw1lURLw/VFyzeOfl26BpuQyfJ66QmYgHQLeAm0+dqsfB0TcNtaLgBqP8OjyXl1q6j51bd\ncci0sexosU5JwtsnScSFEEKINqgyZPJ/H63gm43lsbYjIvN5uPxv+AihdCfqpAdRIyYmMEoh2peQ\naaNr4HTI7JEOybZh9Wy0H15B2zAXakuJTeY2Q7D0Xci6kQNykpm9poQPVmzjuAEZDZJvl6GR5o1O\nO/caOqtKalhVUkuy20HePlbH3luGrmPoNMjUtk9vj9Ql6KZSRKzo9mvJbkOS8HZIEnEhhBCijdlS\nGeKq95ayqqR+O58JwdncVfUETiyUNx377Beh96gERilE+7C+rJZpiwp4d3lRbCu+bsluzsjL5vS8\nbDJacIRTJEBNMXz7HPrS96B4VWxbsh0pwD7sShhzIwAnDMrg8W838L8NZXyfX8Hwrv4GybfHGZ12\nrpTi8bkbATh7WA5JCdw1osH09h0WoSulpJhmOyWJuBBCCNGGLCms4ur3l7GtbnsygKuC07iy6hU0\nQGXlYp/7H0jrnbgghWgHlFI8P38zj8/dECtyaOgaSik2V4Z4/NuNPPtDPveMG8BxAzISGqvYd/oz\nx6KVb2zyvDLc2KOuITLmJoIhk5BpY+g6E3KzmLa4kNs+XclTJw1hTN+0Bmu+ayIWd32+mneXF+Fz\n6lxyUPd4/HH2miTh7Zck4kIIIUQb8fmaEm75ZCVBMzrd0Klr3HV0f07ST4Apr6IGjIsWZXPHZ3qk\nEO3Zsz/k88S3G9GACblZnLNfDnlZSShg7sZy/vPzFr7eUMZNH63AoQ9iXD9JxtusmmL47nm0ii2o\nUx5pcEr1PxJt/iv1xwD+Lqi+Y7AOuYTqrP0JWzZa2MJtaKR6DdyGg7vHDWBbTYTP15ZyzrSFHNQt\nwCm5WSS5HKzYVs0bCwsoD5m4HBrPTMijX7ovvn9m0eFpSqmW301eAFBUVJnoEJoUMm1qdY3qsI0j\nsusUHtG5BVK8AFSU1+6mp+hs5N5oPf/5eQsPfr0uNnIXcDt49Ne5HNwtEG1Y9z/odWibLsom94do\nSrzvjdUlNZw25Wc04IHjBnL8wMxd+uy4I0Gy28EnFx6ET9bZxl2T98bqz9G+fwltwzdQUxKdEYSG\n/adNYOywnKBwGfozx0D2EOz9ToeDzgeXH4CyYASHpuF16rgMR3TauaHHagRELJv7v1rHv3/MpyJk\n7RLbgV2TuWvcAH7VI6VV/uyieamp0ZcfZWU1CY6keVn7WDtARsSFEEKIBLJsxYNfr+O1hVtjbT2S\nXTx5ch5907z1HfscnoDohGifpi4qAOD0vOxGk3CITun9/SE9mLOxjIUFVXy4chtn5HWJZ5hiRzWl\n8P0L6EtmQtGKRtd6ayhYMA0O3KFIZXYu9u2bdukbNC1sVV9R3NVIgT6nQ+f2I/tx/ajezFhayIKC\nSkKmTYbPxcm5WQzPkdlHovVIIi6EEEIkSE3Y4uZPVvLlutJY2wG+Ch6reJBUx8uAt+mLhRCNspXi\nveVFAJyzX06zfTVN45z9clhYsIqZy4okEU8A2zSp/esg9MpCmlvtrBwuyB4CqT12/5lKURWyCHgN\nkt1Go0n4jpJcDs4b3pXz6LqX0Qux7yQRF0IIIRKgsDrM1e8tY9m26ljb8Wll/G3lxbiJoF48Gfu3\nMyGlbRYIEqKtqgpbVIYtkpwOBmcm7bb/gV2jo56bK0OtHZoIV0F1cYNik7phQCS4SxKuAJKyUH1G\now75XXRpzh6qDlvRPcCdDlluINosScSFEEKIOFuxrZqr3l9GQVU41nZJtxKuXXAR+vZV4ul9IUmK\nRwmxt7YndLZSe7S10/ZqSbKzeCta81/02ffB5h+h2wjsSz9qcFrvNRJ7xWyU7oyu9R56Chx8IXhS\n9/pHRSyboGmT4XMScEuqI9ouuTuFEEKIOPp6fSk3fbSS6ki0MJBDgz8PKOPMby6Krn8EVM9DsM9+\nCQxPIkMVol3yuxyke52U1EZYUFC123W+czaWAdAzRf6+tahwFdpnf0P7eSpasCzWrAoW79LVPeE+\nqgo3Q5/Rv/jHVoYt/G4HyW4jVpRNiLZIEnEhhBAiTqYtKuC+L9dg1Y3A+V0O/pFXxuGzd0jCuw7H\nPm8KuHY/pVYIsStN0zhtSBbPz9/Mqwu2NJuIW7bi9bpCiafL+vCWscPot0YjmzPZEagqAH/9f2+9\n61Dw9fvFP7omYqEBSS4Dv0umpIu2TRJxIYQQopXZSvHInPW89NOWWFtXv4snhlcy+L2L0FR0dFxl\n5WJPmgqeQKJCFaJDOHNoF176aQuzVhazf5ctTBq+axEuy1bc8981rCqpJdPn5Jj+6QmItIMwgzD7\nXvSfXm8w+r3d9vXe9kEXwBE3NNx+rIXYSlETtkj1GiS7HLtdkiBEokkiLoQQQrSioGlx26er+HR1\nSawtLyuJx4dX0+XNC9Cs6Dpxld4X+/xp4JNkQIhfqnvAw5/G9uWuz9fwwNfr+Gp9KecMy2H/nGRs\npfh6fRlTFm5l2bZqXA6NB48ftNvK2qJ5+rfPoCm7QZvSdFSvw1DH/Bl6HNSqP78qbOFx6vhcBl4p\n0CbaAUnEhRBCiFZSXBPmDx8sZ2FBVaztqL5p/G24hf/l89DMWgBUSg/sC6ZDcvNbLQkh9twZeV0w\nNI17v1zLNxvL+WZj+S590r1OHho/iIO6ySyUPRaugm2roNsB9W2GJ1rVfP2cutHvTOyDLoQjrotL\nrYuIZRO2FJk+g4BbknDRPkgiLoQQQrSCNSU1/P69ZQ22RJo0vCs3juqNwwpCr1/Bqtkofzb2+W9C\nyu73xhVC7J0JQ7I5qm86M5YV8u7yIrZWhdE1jd6pHs7I68LxAzJwGzISvkfWfoU++x7I/xG8Kdg3\nr2xw2j7mDvRP78Y+5o5WH/3ekVKKipCF36XjdxkYuvz/FO2DppRqpIqCaAlFRZWJDqFJIdOmVteo\nDts4ImaiwxFtTCDFC0BFeW2CIxFtjdwbe+a7TeVcP2s5laHo2m9dg1tG9+Hc/XdYp2qG0D64BXXo\n5ZA9JEGRtiy5P0RT5N5op8JV8MUD6D9NQattuPbbOv9N6Df2F/+IX3pv1IQtQrZNps9Fps8pa8M7\nkNRUHwBlZTUJjqR5WVnN78zQFBkRF0IIIVrQjGWF3PX5Gkw7+p7ba+g8cPwgxvZJa9jRcKNOeTQB\nEQohxG6s+xr903tg83y0RsbslKbD5p9aJBH/JSxbUWNapHoMAm5DknDRrkgiLoQQQrQApRRPfbeR\np7/Pj7Vl+Zw8flIueZ4qtB9eQR10fgIjFEKI3fjsPvTv/41WW7rLKQXgy8Q+6HwYc0Nc1n7vTlXY\nxGvo+N2GLDEQ7Y4k4kIIIcQvFLZs7pi9mg9Wbou1Dczw8eSJueTolegvnYlWtBy7qgA15kaQURsh\nRBukL5q+SxKuNB16HhJd+91zZIIi21XYsjFtSPE4SHZJSiPaH3l1JIQQQvwCZcEIl81Y0iAJP7xX\nKi+dPpQcZxD9P79BK1oOgPblP2DbikSFKoQQzbJHXQ1ER7+VLwNr9LXYt63HvujdNpWEK6WoDJn4\n3Q78bgOHLi83Rfsjr4+EEEKIfbShrJar3lvG+vJgrO2soV24dUxfDLMa/bVz0bYuBEChoU57CrIG\nJypcIYSoZ9uwc4XxAy/AXvUZatRV0Z0d2qiaiI3h0PA5HSTJnuGinZJEXAghhNgHP26p4NoPllMW\nrN954oZRvbnwgK5oVgj99QvQNs6LnVOnPIIadloiQhVCiIZsG/2hXFTOMNTp/wJ/l2i7rqPOeTmx\nse2GaStqTYs0j5MUjxRoE+2XTE0XQggh9tKsldu4dMaSWBLudmj8Y/wgfjuiG5ptok+7GG3tV7H+\n9vh7USMmJipcIYRoaPbdaLWl6Gu/Qn94OAQrEh3RHqsKm3idOn63A5dDUhnRfsmIuBBCCLEXXpyf\nzyPfbIgdp3kNHvt1LsNzksG20N6+Em3Fx7Hz9tG3oX51WSJCFUKIXZlB9LnP1B/nDANPIHHx7IWg\naWEr8LsMkt2Sxoj2Te5gIYQQYg8opfjXvE1Mnrcp1tY3zcuTJ+XSI+ABZaO9ex364hmx8/boa1FH\nXJ+IcIUQolHauzei2REgWpTNPuPpxAa0h5RSVIctkj3RJFyXKeminZNEXAghhNgNpRSPfrOBF3/c\nHGs7uFuAR08YTMBT96u0aDnaondi5+1DLkEd/ad4hyqEEE2rKUVb+Gbs0O47hprkPniUavOJbXXE\nwunQSHI68EmBNtEByMIKIYQQohm2Utz/1boGSfjhvVJ56uTc+iQcIHsI9nlTUE4f9gHnocbfK/uF\nCyHaFO3tK9GUDUT3By8+4QlCtk1pbQTTVgmOrmkRy6Y2YuN3GQRkSrroIOROFkIIIZpgK8Vfv1jD\nW0sKY21H9U3jweMHNV4kqO9o7Es/gYz+oMm7biFEG1K6Hm3V7NhhOPcUnIFMAm4nQdOiPBgh2W20\nyQJoVWErume4y8DZBuMTYl9IIi6EEEI0wrQVd3y2iveWb4u1jR+Qwb3HDKh/EIzUgtPb8MKsQXGM\nUggh9oz+1mVsn6OjdIOK4x8i3W2Q4XNSFdbRdY3yWhO/y4G3DU39ro1YAPicDvzuthOXEL+UvFIS\nQgghdhKxbP748coGSfjJg7P427EDY0m4Nv8/6P8aA6XrExWmEELsmc0/Qf782GHNAReQkhwg1evE\n6dBJ8zpJ8zhJ8xrURGyqwmYCg61n1xVo87sdBKRAm+hgJBEXQgghdhC2bP7voxV8vLo41nZmXjZ/\nHdcfhx59CNQWTkd79wa00nXoL54MJWsSFa4QQuyW/v4t9aPhhgdt/F9J8Rh4jPoR5mS3QbrPRZrX\nIGIpKoImSiV23XhV2MJt6CS5jDY1Si9ES5BEXAghhKgTNC2u/WA5n68tjbWdt38Ofz6yX/1IzLIP\n0N7+PRp1D6j+LPBlJCBaIYTYM/b507Bzf41CIzj6BpK9HvyNFD3zOR1k+Fyke50oTVEWNLETlIxH\nLJuwpUh2OwjIlHTRAckacSGEEAKoCVtc88Ey5uVXxNp+d2A3rj20F1pdEq79NAVt5vVoKrpmUWUN\nxp40FTwpCYlZCCH2iCdA+anPo6qLSMvsRqqn6RTAbehkJDnRdI3KYITS2ggpHieGHt9p4ZVhC78r\nOhpu6DJ2KDoeScSFEEJ0epUhk6vfX8aPWypjbVeO7MEVI3tEk3Cl0OY8gf7p3bHzKr0v9vlvymi4\nEKLNq4lYmLYiPT2HNM/u11obuk6mz4lDi34/lgUjBOJYUb0mYqFp4HMZ+F0yGi46JknEhRBCdGrl\nwQhXvruURYXVsbbrDuvF7w7sHj1QNtrHf0Gf+6/YeZUzDHvi6+DvEu9whRBir0Qsm5qIRarHIMXj\n3OPtv3RNI93rxKFrca2obtkqFm/A7YjNSBKio5F5HkIIITqtktoIl7yzpEESfvPoPvVJuBVBe+fq\nhkl4n8OxL5whSbgQom378mH0+wcSmj+lruq4c6+TaE3TSPXEt6J6ddjCU1egbcdickJ0NJKICyGE\n6JSKqsNc/PZilhfXxNr+PLYfk4Z3jR1r819BXzAtdqyGnBgdCfcE4hqrEELsFdtE/+oRtGAZKR9c\nR9K715D8Cwqe7VxRvTwYaZWK6mHLJmwr/K7odmVCdGSSiAshhOh0tlaGuOjtxawurQVA1+Cv4/pz\n1rCGo9zqoAtRQ04CwD7wAuwznwfDE/d4hRBib2izbkczgwAoIOXIq3/xFO8dK6prGpQGTSy75ZJx\npRSVIQu/W8fvNmLbRQrRUcmrJiGEEJ3Kpoogl7yzhM2VIQAcGvzt2IGMH5i5a2fdgX36ZLSFb6EO\nOBdkraIQoq0LV6H98FLs0Oh1CK6eI1rko3euqL69iNuerjtvTk3ExqFDktMgSfYMF51Ah07Ey8rK\neOKJJ5g9ezZFRUWkpqYyduxYrr32WrKzs5u9dvDgwc2enzdvHoGATE0UQoj2ZF1ZLZe8s4TC6jAA\nhq7x4PEDGdevrvJ5+SYIdANth4dKw40acV4CohVCiH3wzrVo9vZ13BqBSc+36MfvXFG9PGT+4orq\nlq2oNS3SPE4CbkMKtIlOocMm4sFgkPPPP5+1a9cyceJEhg0bxvr163n++eeZO3cu06dPJyWl+X1f\nBwwYwDXXXNPoOa/X2xphCyGEaCWrimu4dMYSimsjALgcGo+MH8wRfdKiHTZ9j/7aeaj9zkSNv1dG\nv4UQ7Y5duRVj6buxY9eQ4zHSe7f4z2msonqSy4FvH0eyK0MmXqdOktuB25CVs6Jz6LCJ+EsvvcSK\nFSu44447mDhxYqw9NzeXq666iqeeeopbb7212c9IT09n/PjxrR2qEEKIVrasqJrLZy6hNBgdJfIY\nOo/9OpdDe9a9kF35KfrU36GZtWjfPYsd6Io6vPEXsUII0VapN69Ao27dtuYg+dxnWu1nba+obmga\nDjTKgia2Uvhde5deBM3oHudJTinQJjqXDvvK6Z133sHn83HWWWc1aB83bhw5OTnMnDmzVao9CiGE\naFsWFlRy8YzFsSQ8yelg8slDYkm4tmAa+uvno5nRwm3Kl4Hqe0TC4hVCiH1Rs3kJzg3/ix27DzoH\n3Zfa6j/X7zZI8zlJ9xlE7L2rqK6UojJoEXA7SHY70WUmkuhEOmQiXlVVxZo1a8jLy8PlcjU4p2ka\n+++/PyUlJWzatGmPPk8pRU1Nze47CiGEaFPmb67gshlLqQxZACS7HTwzYQgHdovW+NC++Rf627+P\nradUKT2xf/c+dDsgYTELIcTeCpoWnhlXE0tjHS78pz8St5/vczpI97pI9+xdRfXqiIXLoZHkMkhy\nSYE20bl0yPkf+fn5AOTk5DR6vmvX6B6xGzdupGfPnk1+TmlpKTfffDOffPIJNTU1JCUlccwxx3Dj\njTfSpUuXJq/bLjXVtw/Rx0fItAjXhDEsRbJP1ruLhoy6giuBFLk3REPt6d6Ys66EK99bSm3EBiDN\n6+SVcw9gWE4ySiki791B5PP6B1Wt61C8l72DntK1qY8Uu9Ge7g8RX3JvtB7TtgluXIazcGGsLe24\n60jPTo97LBmWTUlthPLaCDURG7+36Yrqpm0TrHGQ5nOR4Xd3zNFB8YsYdfUC2nJO9Ut0yHu+uroa\nAI+n8b1etxda296vKatWrQLgwQcf5J///CfHHnssM2bM4Oyzz6akpKQFIxZCCNGSvlhdzEVTF8SS\n8MwkF29MGhFNwi2T8OtXNkjC9X6j8F79kSThQoh2xVaKslqT5K6D8V/wInpSBprbT/pp9yQkHsOh\nk+lzkeZzkex2UFobIWhajfatCJr4XTrJHgOXFGgTnVCHHBFvCc8++yzp6ekMGzYs1jZ+/HhycnKY\nPHkyL774IjfeeGOzn1FW1nans4dMG0vXMC1FRXltosMRbcz2EQu5N8TO2sO98fmaEm78aAVm3bTI\n7CQXz52aR47LQcW2YvQ3L0Vb8XGsvxp8ApEzniYSdkO47f652oP2cH+IxJB7o3WUByPomobP58Sz\n3+l49z8Dq3wLFRXBhMblUAo9YqJHTPIrgrtUVA+aFjURG6fPiTc9CdO02/Rzs0iM7SPhbf3eyMpK\n3qfrOuTrJ7/fD0BtbeNf9tvXe2/v15gxY8Y0SMK3O++86F6yc+bM+aVhCtEuLC2q5o1FW3n5p83M\nWFZIcU0k0SEJ0aRZK7c1SMK7Jbv592lD6ZNaNx22cits/D7W3z7gPOzfvABOmS4rhIgfWynm5Zcz\nZcEWXvlpM7NWbqMm3PjIcVOqwxa2goDbIM3jjO297WgDM3u2V1RP9zhJ9zqpjdhUhqK1OGylqApZ\nJLsdJLsNdF0KtInOqUOOiPfo0QNN09i6dWuj5zdv3gxA7957v69ieno6mqZRVVX1i2IUoq2btbyI\nJ79ey8KChve6oWsc1z+DKw7pUZ/cCNEGzFhWyJ2frWZ7faBeKR6em5BHTrK7vlN6P+zzXkN/+XTU\nry5DHX2b7BcuhIgbWymmLirg1QVbWF/WcNQ6yenglNwsLh/Zg3Svs9nPCZk2taZNutcgtW4/77bI\n7zbq9hqHsqBJeTCCpmm4DR2fc9/3HReiI+iQibjP52Pw4MEsWbKEUCiE213/EGZZFj/++CNdu3al\nW7dujV6/fPlyfvzxR8aMGbNLn/Xr16OUavJaITqCR79ay6NfrQUg2eXgqH7pJLscrC8L8r8NZXyw\nchtfbSjlyROHcEDXfZuOI0RLenNxAXd/sSZ23C/Ny7MT8shKcu3aucdB2Ff9D1J6xDFCIURnZ9qK\n2z5dyayVxUB02cyY3qm4HDpLiqr5aWslUxZu5av1pTwzIY8egcZrHZm2oipsEnDpOB/ej2Cvg3Ce\nMzkuW5XtC6/TEU3G0SgPRQiZNhk+JymeDpmGCLHHOuTUdIAzzzyT2tpaXn/99QbtM2fOpLi4mDPP\nPDPWtnr1ajZu3Bg7XrlyJXfeeSdPPvnkLp/79NNPA3Dssce2UuRCJNb0JQU8+tVaHJrGjaN68+lv\nD+KecQO45Yi+PHXyED44fwRH9U2jMmRx9ftLyU/wOjQhXv15S4MkfFCGj+dPHRpNwvPnR//ZmSTh\nQog4e2TOematLCbJ6eDvxw5k1gUHcsdR/fnjmL68fMYwpp29P3lZSWyqCPH7d5dSE9l1qrpSivKg\nidfpwPfVA1BVQHjJBxT/pR92sO3O1nQ5dDKSnKR5XaR5DZLdBobeYdMQIfaIppTa/SZ/7VAkEmHi\nxIksXryYSZMmMWzYMFatWsWLL75I7969mTp1aqx6+uDBg+nbty+zZs0CwDRNLr30UubMmcO4ceMY\nO3YslmXxySefMGfOHEaNGsWzzz6LYTT/Jq+oqLLV/5z7KmTa1Ooa1WEbR8RMdDiijYhYNie8Mp/C\n6gj3/zqXE/qmNdrPshXXvL+MrzeUcd7+OfzxiL5xjlQkSlsruPTcD/k8NndD7HhodhKTTx5CiscJ\nqz5Dn3oRGB7s370HmQMTGGnn0NbuD9F2dPZ7o6g6zPEvz8dWihdOHcqB3QKN9qsKm0x6cxFrSmu5\n48h+nDm04Xa5FUETNEWaE7ivL1ghABxdh5F+Y9uvX2QrRdiy8Rj1U9LbS0EuEX/t5d6QYm07cTqd\nvPDCC0yaNImPP/6YW2+9lbfffpuzzjqLV155JZaEN8YwDCZPnszNN9/MunXruOeee7j//vspKSnh\n5ptv5plnntltEi5Ee/TfdaUUVkfol+7jN8ObLvbi0DX+cGgvAGYuK9rrAjNC/FJKKR6bu6FBEn5A\nTjLPnJJHiseJtvAt9CkT0SI1aLUl6NOvgI753lkI0Q5MX1KIaSuO6pveZBIO4HcZXHpQdwDeWLiV\nHcfLasIWplIE3Aauj/4YS8IBAhOfb73gW5CuaQ2ScCE6sw6dTfr9fm699VZuvfXWZvstX758lza3\n283FF1/MxRdf3FrhCdHmfLOxHICzhneNVV9tSm5WErmZSSzbVs2CgioO7ZkSjxCFQCnFA1+v49UF\n9QU5D+ke4LFf5+JzOdDmPo3+0e31/VN6YJ8+WYqyCSESZs7GMgBOHZK9277HDsjgnv+uZXlxDSW1\nETJ8LsKWTXXEIt3rJKCqqZ73aqy/s/9ojJwhrRa7EKJ1dNgRcSHE3qsMR5cpdN2xynQzuiZHC2FV\nhWV5g4gPy1bc9cWaBkn4mN6pPHFSLj6njjb7noZJeFYu9u/el2npQoiEqqqbOdalsQKSO3E5dDJ8\n0arpFSELy1ZUBE0CHoOAxyDy1lWgts9E00g+r32MhgshGurQI+JCiL2zfRuRbdXhPeq/rW5P8SSX\nTDMTrc+0Fbd/uooPVm6LtR3bP52/HzsQp2ajzbwO/afXYudUz5HY574K3sZrHQghRLz4nNGxr+La\nyG77mraiNBiJXVceMvG6dJLdBt6aLZQu/jDW17XfKW1i33AhxN6TEXEhRMzI7tF1a2/utC6tMWtK\nalhYUIXH0Bma7Y9HeKITC1s2N320okESfvLgLO4/bhBOO4Q+9aKGSfig47DPf1OScCHaMFspimvC\n5FcEO/zMqpHdo8u33l1WtNu+X6wtoTJk0SfVg8fQceiQ7HaS6jGofO0SoO73s+4g+exdd/gRQrQP\nMiIuhIg5tn8GD3y9jmWFVcxaXsThTewRrpTiX/M2AXDioEwCbvkqEa2nNmJxw6wV/G9DWaztrKFd\n+NPYvuhWKFqUbe1XsXP28LNRJz8CDmciwhVC7EZJbYS3lxQybXEBmyvrC44d2iOFs/frwpF90nHo\nHaumw5l5XXhhfj4fry7mom3dGJyZ1Gi/oGnx3A/5AJyWl42lIN1jkOYxsLcsxlz7Tayv55AL0T1N\nF34TQrRtMiIuhIhxOXQuPjBarfXaGUuYuawIy244Ml4RNPnL52vrirZcAAAgAElEQVT4aFUxHkPn\nggO6JSJU0UlUhy2uem9ZgyT8ggO6cvvYvuiaBrqBSukeO2ePugY14XFJwoVoo37YXMEpr/7IP+du\nYHNliGS3gxy/C6euMXdTOdd/uILLZi6hItSxRsi7BdxMyM3CtBWXz1zK3I3lu8w821oZ4pr3l7Ok\nqJoufhdH9U0n4HaQ4nHidOhUTrumvrPhIenUh+L8pxBCtKQOu494WyD7iIv2SCnFP77dyMt1b+S7\n+l0cNyCDZLfB+rIgn6wuJmjauB0aj5wwmNG9ZepvZxLPvYArgiZXvreUhQVVsbYrRvbgypE9Glb1\nVzbarNvBn4U64vpWj0s0rbPvFS2aFkjxsnBLBWe9Mp+gaTOye4CLRnRjVK9UdE2jImgyc3kRz/+Q\nT3FthAO7JvPMhDxcjo4zZhQyba79YBlz6nYoGZjh48g+abgcOkuKqvjvulJsBRleJw8eP5BhOX7S\nPS4CnuisM7uyiIoplxJZ8Rne427Ff1zzuwK1F+1lr2gRf+3l3tjXfcQlEW9FkoiL9io54OG1Hzcz\nec46NlaEdjl/WM8U/nBoL1kb3gnFK9EqrolwxcwlLC+u/+V7/WG9uOjA7o1foJRsT9YGSCIumhJI\n8TLhxXn8vKWSkwZl8tdxAxqdfp5fEeTC6YsprA5z25i+nLNfTgKibT0Ry+bZH/KZuqiAkp0Ktxm6\nxrh+6Vw0ohu9Uz2kep2ke527bCdqlqxHT+2JrneMlxTtJdkS8dde7g1JxNsgScRFe7X9YbqsrIa5\nG8tZWFBF0LRI8Tg5qm8avVO9CY5QJEo8Eq2CqhCXzVzK2tL6nxF7IFcKVnwMg46TxLsNkkRcNGVd\nTYRTXvyegNvBJxcehNfZ9G4bH63cxk0fr6R/mpfp5w7fJRHtCCKWzedrS1lVUoNpK7J8To7pn47b\n4cBGke5zkelzRpfgdHDtJdkS8dde7o19TcSlwpIQokm6pjGqVyqjeqUmOhTRSeRXBLl0xhI21c3E\n0DW46+j+TMjNBkD78mH0L/6OPWIS6qSHQJet84RoD95ZVADAhNzsZpNwgKP7pZPpc7K6tJbl22rI\nzWq8sFl75nToHDcgg+PIAKLLwspDJjaK1LribJ0hCReiM+sYc1qEEEK0e+vKavnt24tjSbiha9x/\n7MD6JHzu0+hf/B0A/cf/oH33bMJiFULsnYK66ujDuux+SZPToceqihdWh1s1rrbAVorSoIlD00jz\nOsnwuXDWrY2v+eIxtt01gNDCdxMcpRCipUkiLoQQIuFWFFdz0fTFFFRFH7pddcUAjx+YCYD246vo\nH90e66/6HYk6+KJEhCqE2Afb14NHrD1bERmxbCD6Qq4js2xFaa2J26GR6nWSuUMSbts21R/fh6os\npOKliVS+cVWCoxVCtCRJxIUQQiTU4sIqLn57CcV1hYs8hs4TJ+Yytk9dRf7FM9DevSHWX/UciX32\nv8FwJyBaIcS+GJgZXev55brS3fYtC0b4eWu0zk6fDlyTJGLZlNZG8Dp1UrxOMnzOBgXsat67HcL1\na2Pdh/42AVEKIVqLrBEXQgiRMPM3V3DVe8uojlgA+F0OnjgxlwO7BaIdVn6KPv1KNBUdHVM5w7DP\nmwKujrdmtC1QSjF/SyVvLSlgXWkQWym6+F2cNDiLI/ukxUbqhNhbZw3vxqNfreOztSXkVwTpHvA0\n2ffNxYWELMXhvVLpFuiYL9wilk1FyMTvdhDwOEnzGA2K0tnhILX/ezp2bPQ4EFfvkYkIVQjRSiQR\nF0IIkRBzN5bxhw+WEzSjSXaK22DyKUPqt8Vb9z/0qReh2dGRcpUxAHvSVPCkJCrkDm1taS23fLyC\nZdsaVqddUlTN52tLyU5ycdfR/TlcijeKfZCT7ObXQ7J4d0kh17y/jMmn5JGd5Nql3+w1xTz13UYA\nztu/Y21dtl3ItKkMmyS7DQJug9SdknCAqjevAat+e7PkSS/EO0whRCuTRFwIIUTcfbG2hP/7aAXh\nuvWiGV4nT08YwqCMupHuzT+hT5mEZgYBUCk9sS94C5KyEhVyh7ampIYLpy+mPGSS4XVyxtBsRvdK\nw6HDgq1VTF1cwNrSWq5+bykPjx/MUf3SEx2yaIf+evxgFm+pZFVJLae99hMn52ZxwsBM/C4H68pq\nmba4gDkbygH47YhuHNE7LcERt7zaiEVV2CLVa5DidhLw7PoobldtI/TjtNixc9DRGJn94hmmECIO\nZB/xViT7iIv2SvYCFk1piXvjo5XbuPXTVZh29NdPF7+LZyfk1a8FtU30Jw9HK1kDgPJnY1/0LqTL\ng2hrsJXi9Ck/s6a0ltG9Unlo/CB8O20vZSvFo99s4N8/bsZj6Lw/aQRZjYxmyneHaMr2e2P91gr+\n+MlKvtlY3mg/t0PjipE9+d2B3Trc/uE1YYsa0ybV4yDV48Tvbnw8rOzZ04gsnx090HQy7lyF7s+M\nY6Tx1V72ihbx117uDdlHXAghRJs3Y2khd36+mrocnB4BN89OyGu4XlQ3sH/zAvorZ4FtYp//piTh\nrWjOhjLWlNbS1e/iH+MHNbrHs65pXH9YL1aX1PDV+jKmLynk8pE9EhCtaO/SvE6ePiWPpUXVTF20\nlcWFVQRNm1SPk3H90jl1SBYpHmeiw2xxlSGTiK1I8xikep0kuRrfS93ctqY+CQfcB5zRoZNwIToz\nScSFEELExesLt3Lfl2tjx/3SvDwzofF1onQZGh0FD1VC9pA4Rtn5vLWkEICzhuU0moRvp2ka5w/v\nylfry3hzSQGXHdy9w41YivgZkpXEnUf1T3QYrU4pRWXIwlKKNK9BqsfZ7N+zsn/9uv5Ad+I/6/E4\nRCmESARJxIUQQrS6F+fn88g3G2LHuZk+Jp+SR7q3mZGvjI7/kN4WrCyOTvk7qu/u1+P+qkcKXkOn\noCpMZdgi0MTUWiFENAkvD0WX/6V5DdK8LtxG8zsPGJn9iJRvBsA7+jJ0l6/V4xRCJIbsQyKEEKLV\nKKV48tuNDZLw/br4eW7C0PokPFiO/p/fwJYFCYqyc9u+Vt+9B1uTaZqGq67f9uuEELuylaIsaKJr\n0en4Gb5oEm6Ha6j57BFKHjqUbXcN3OU67+GXA6D50vGddG+8wxZCxJG8yhZCCNEqlFL8Y856Xv5p\nS6zt4G4BHj8xt359ZLga/bXz0DZ+h77pB+zzXoNev0pQxJ1Tps/J5soQS4qq6ZHS9N7OAPkVQcpD\nJi6HRnITa1yF6OwsO5qEuw2NgMdJariI0GdPUrFwJnbpRqD+JVZ4/bwG+4M7h52MZ9SlJJ30V3Rd\nxsuE6MgkERdCCNHibKW4979rmba4INZ2eK9UHt6xGJgZQn/jt2gbvwNAC1WgFa9GtWAiHjQtPl9b\nSn5FCIDuATdH9U3DY0gSud0JAzNZUFDF1EVbOW5ARrN936z7/3lc/wycezCCLkRnY9qK8mCEpNIV\nJH0/GW3lZ5RXFzXZP/jlk7jO/3fsWNd1kk//RxwiFUIkmiTiQgghWpRpK+74bBXvLd8WaxvXL537\njxsYm9aMbaK/dTnami9ifezj/4oacV6LxFATtpj8/SamLymgImQ1OBdwOzg9rwtXHNwDn4zqcnJu\nFv+cu4Hv8it4c3EBZw7t0mi/n7ZU8uqCrQD8ZlhOPEMUol2IWDYVIZPMyQejV0ZnAjW6gENz4MgZ\nguegc/CMuiSuMQoh2g5JxIUQQrSYypDJrZ+u4st1pbG2Ewdl8tdxAzD0ugrbykabcS3asvdjfewj\nb0EdekWLxFAejHDpjKUs21YNwLBsPyO7BwCYl1/OosJq/v3jZr7dVM4zpwzpkFsl7Y2A2+Dm0X24\n+4s13P3FGpYWVTNx/xz6pUeLRG2rDvPWkkKen59P0LQ5PS+bA7ru256pQnQotgnl+ZDWm5BpUxk2\nSXYb6N4UqNzSsK/hweh1IJ7DLsY9/AyZdi6EkERcCCFEy1hdUsN1Hy5nfVkw1nZGXja3j+2HI5aE\nK7QPb0VfMDXWxz7s96gxN7ZIDEopbpi1gmXbqumV4uG+Ywawf07DpHHB1kpu+3QVS4uquWHWCp6b\nkNfpt+E6c2gXgqbNg1+vY9riAqYtLqBbshtD19hcGYoVZjs1N4s/jemb4GiFSKBwFXz3PPqCN2Hb\nCvClU3PdIipDFilegxS3gXHwOdR88Bc0TwBn/9F4j7gK14AjEh25EKKNkURcCCHEL/bxqmL+PHsV\ntaYda/vdgd249tBeDZJc7bN70ee9EDu2DzwfdexfoIUS4Xn5FczLryDNa/DcqXnk+N279Nk/J5nn\nTs3j7KkLmJdfwfebKxjZPaVFfn57Nml4Vw7pEeCNhQW8t7yIzZXRdfW6Fl1acPawLvyqR0qnf2kh\nOqGKrfDNk+hL34fyjez4N0BVb6OmZCtpmV1J8ThJdhvYo6/APexkjOxdq6ILIcR2kogLIYTYZ5at\neOLbDTw/f3OszWPo3HV0f04YmNmgr/b1P9G//mfs2B52GurEB1ssCQd4Y1F0DfM5w3IaTcK3y/G7\nOXtYDpPnbeL1hVslEa8zKCOJPx/Zj5tG96awKoylIMPnlP3CRee07mv0mddD6Tqa/pbSSMv/kkDP\n82O7QeguH7ok4UKI3ZDfrEIIIfZJWTDCHz9eyZyN5bG2HgE3j54wmEGZSQ07KxVdS7n9cNBxqFOf\nBL1li6XN3RSNZUJu9m77TsjNYvK8TXy7qXy3fTsbj+GgV6o30WEIkRjBCvTnT4BtKxpNwJXmwMrK\nJTj0LNyH/Y605OT63SCEEGIPSSIuhBBiry0rqua6D5fHpi8DjO6Vyt+PHUjA08ivFk1D/fp+bLcf\nLf9H7DOfA0fLF0mrDkcrpGf7Xbvtm53kanCNEEIA4PJD1daGU9ANN3Q/COugC6nofyJK10n1GKR5\nXbgNKbwmhNh7kogLIYTYK+8s2sofP1hGcIf14Jcd3J0rR/asL8rWGE1DHXMHygqDY/eJ8r5Icjmo\nDFkUVoXpFmh6ajpAYXU4do0QohOzTdB3eCTWdeyjb8fxwc0odzL20bfBIZdgK0V50ETXIc3jJM3r\nrN+SUQgh9pIk4kIIIfZIxLK565MVvDhvU6wtyeng3mMGcHS/9F0vKFwGWYNA2+lBtZWScIBDe6Tw\nyeoSZiwr5MpDejbbd8ayorprUlstHiFEG1aej/727yF/PvZNS6Mj4duNvAjL5Yf9zgBdx7IVZUET\nt6ER8DhJ9xoYsgWZEOIXkG8QIYQQu1VcE+HymUsbJOF907y8dtZ+jSfhG79Df+54tHeujo42xcnZ\nw3IAeH3RVrZWhZrst7UqFCvsdvZ+XeISmxCijagqQvvP2eiPjkBbPwfNDKK9+3+79ht+Fug6pq0o\nC0bwODVSPE4yvE5JwoUQv5h8iwghhGjWwoJKzpm2gO83V8Taju6bxqtnDqNvWiMFvbYsQH/1XLRI\nDfqCaWgf3ha3WEd2DzCye4DSWpOL317Mgq2Vu/T5eWslF7+9mNJak5HdAxzcLRC3+IQQCRQsQ3vj\nt+gPD0Vf/RkaKnZKWz270UvClk1ZbQSfy0Gqx0mGz9n8EhwhhNhDMjVdCCFEk6YvKeDe/64lYkcf\nWDXgxrH9mDQ0G72xbcc2zEWfMgktFE3aVVIW6tDL4xavpmk8PH4Ql81cytKiaia9tYih2Umx7cnm\n5ZezuLAagCFZSTw8fpDsiy1ERxeuQXvvRrRF09GU3eCU0g3UAeeiTvh7w3alqApbhCxFsscg2W2Q\n5jHk+0II0WIkERdCCLGLsGVz/1frmLa4INaW7Hbw2KnDOKp/BhXltbtetPwj9DcvQTODAChPCvak\nqZDRP15hA5DicfLiqUN5+vtNTF9SyOLC6ljyDZDiNjg9L5vLD+6BTwq1CdFxmWG0D/+I9tMUtJ2W\nyChNRw07HXXSP8Dla3AubNlUhkycDo1Mn4HfZZDkckgSLoRoUZKICyGEaKCwOswNHy5nQUFVrG1g\nho9HTxjM0F5pjV6j/fga2rs3oKnoVmAqKQt70huQMywuMe/M53Jw/ajeXHlID75YW0p+RXS9ePeA\nmyP7puExJAEXosMr34g2/5WG25ChoXJPQE34J3gaFmq060bBw5Yi2R1NvlM9UpRNCNE6JBEXQggR\nM39zBTfOWkFxbSTWNn5gBn85qj8+Z+PJq/a/x9E/vTt2rNL6REfC0/u2ery74zEcjB+YmegwhBCJ\nkNEfeh0KG+ZGE/D+R6FOfQL8Wbt0DZnRUXC3oUdHwd3RkXAhhGgt8g0jhBACpRRvLCrgga/XYdat\nB3docP2o3pw/vGvjUzKVjfbJXejfPFXf1GVodCTcL5XIhRBxYtvwzZNo6+agJk5peOq0p9BmXoua\n8DikdN/1UqWoCllEbEXAa5DkdJAio+BCiDiQRFwIITq5oGlxz3/XMrNuX22ANI/Bg8cP4pAeKc1c\nWI629P3Yoeo9CvucV8AjVciFEHEy73n02fehhSpQgFr9OfQ/qv58ak/UBdMbvTRoWlSFLDxOnQyP\nQbLbSZLUjRBCxIkk4kII0Yltrghx/azlLC2qL2aWl5XEIycMpmuyu/mLvWnY509Ff+Ek6DkS+4yn\nwfC0csRCCAH89Dr6x3ei1ZbEmjRAf/8m7D983+yllq2oCptYClK8BkkugxS3IduSCSHiShJxIYTo\npL7dVM5NH62gLFhfTXhCbhZ/Gtt3z4uZpffDvvjD6JRPXX6lCCFa2Zr/or99FVpVQYNmBZA5CPvU\nJ5q9vDZiUR2x8Bo6qS6DZI/RZP0LIYRoTfLUJIQQnYxSipd+2sKj36ynbjk4hq5xyxF9+M3QLk1v\n0VOxGatgA45BRzVsT+vdugELIQTA5p/QXzkLDRVrUgBpfbBPeQT6jG7yUstWVIZNbAUpnmghtoCM\nggshEkgScSGE6ERqIhZ/+Ww1s1YVx9oyfU7+MX4QI7o2s7Z720r0//yGYPU2PFfMhIwRcYhWCCHq\nhGvQXzqtYRIe6I590oMw8NhmL62JWNRELLxOPZaAe2UUXAiRYJKICyFEJ7GxPMh1Hy5nZXFNrO2A\nnGQeGj+I7CRX0xfmz0d/9dzYWszgvyfCNfPAldTaIQshBAD6iyejhauA6Ci4Pe52GH1ts9eYtqIy\nFF16k+qJbkkWcBvoTc36EUKIOJJEXAghOoGv1pfyx09WUhmyYm2/GdaFW0b3weloZpue1V+gv3Eh\nWqQueXcl4T7vWWokCRdCxJE97DT0rQvRUKgB43abhNeELWpMC5/TQZIruiXZHte+EEKIOJBEXAgh\nOrhXft7CQ1+vi03odDk0/jSmH6flZTd7nbbobbS3r0KzIwAobzrey97C0XsklNe2ctRCCLGDw6/G\n7jsa/aM/o859rcluEcumMmyhaZDmceJ3O0iWUXAhRBskibgQQnRgry/cyoNfr4sd5/hdPDx+MMO6\n+Ju9TvvuObQPb4utx1SB7tiTpuLoPbw1wxVCiKZ1OwD7oncbPaWUoiZiU2taJLkcsS3J3EYzM36E\nECKBJBEXQogOasbSQu77cm3seETXZB4eP5gMn7Ppi5RC++IB9C8fqm/KHIQ9aWp0izIhhIgH24ay\n9ZDed7ddt4+C63Wj4Mlug2S3o+kdIIQQog2Q14RCCNEBzVq5jTs/Xx073r+Ln6dOGrL7JPyDWxom\n4T0Ojo5ASRIuhIgjbfoV6I//Cr75V5N9lFJUhU3KgiY+p06Gz0WW30XAY0gSLoRo82REXAghOpgv\n1pZw26erYnuE52b6eOrkISS5dlOoSNMgrU/sUA0Yh33W81IdXQgRXz+9jrb4bTRA//gO7FAlHHlz\ngy4Ry6YiZGE4IMMXHQX3u2QUXAjRfkgiLoQQHcjcjWXcOGsFZl0W3i/Ny+RT8gi49+zrXo36PXb1\nNqjaijrln+BoZgRdCCFaWvFq9JnXEUunDQ8ccmnstK0U1WGLoGmT7HGQ5DRI8Ri4mtv9QQgh2iBJ\nxIUQooOYv7mCP3ywnEhdEt4z4OaZCXmke/cumVbH/BlQoMmDrRAijmwT/YUT0VR0m0UF2BOngC+N\niGVTG7EJ2zYuh0aGz0nAbZAko+BCiHZKnrKEEKIDWFxYxVXvLSNo2kC0OvqzE4aSneRq+qLS9Wjv\nXAWRnbYi0zRJwoUQcae98hu0muLYsT36Omq6H0ZxTYSKkIXDAeleJ5lJbrKSXPjdshZcCNF+yYi4\nEEK0cyuKq7li5lKqI9FRpAyvk2cn5NEt4G76oq2L0F89G62qEFVbjn32v0GXXwlCiAT5+p9o676K\nHZrdDqT4sJtw2jZ+twOPoeNzOvA5HTh0Sb6FEO2fPHUJIUQ7tq6slstmLKU8ZAKQ6jF4ZkIevVO9\nTV+0fg76lEloocro8eovYOsi6HZA6wcshBA72/QD+ux7Y+vCbXeAmklvke5x4nE68Bo6HkOX0W8h\nRIciibgQQrRT+RVBLp2xhJLaCAB+l4PJJw9hYIav6YuWfYj+5qVoVggA5Q5gn/uKJOFCiISI1Fbg\nfvkMNKK1LZSmw0UzyU5JwevUMXRZJiOE6JgkERdCiHaooCrEJTOWUFAVBsBj6Dx5Ui552f4mr9F+\nfBXt3RvQVHQdufJnY098A3KGxSVmIYSA6P7fQdMmaNr4PrkHLVIdO+c+6W8E+o+Q0W8hRIcnibgQ\nQrQzxTURLpu5lPyK6Ki2y6Hx2K9zGdE10PgFSqH97zH02ffUN6X3xZ40tcG+4UII0ZpM26YyZBIy\nbQyHhtel45nwADpB7B+n4BpyAiljr0x0mEIIEReSiAshRDtSHoxwxcwlrC2NVjo3dI2Hxw/m0J4p\njV+gbLSP70SfO7m+KWc/7Imvgz87HiELIToxpRQhy8asCWPZoGuQ5nXicep469Z/axOfJnzIORj9\nxyY6XCGEiBtJxIUQop2oDltc+e4ylhfXANEH2r8fO5AxfdKavEb78uGGSXif0djnvAzu5FaPVwjR\neZm2ImhaBCM2DodGjtOBx+kgooPXqeN0NFz77Rp4VIIiFUKIxJAKGEII0Q7URiyufn8piwqrYm13\nHz2A4wZkNHudOvhCVHq/6L8PORF74hRJwoUQrSK69tuiLBihtK6IZJrXSVaSi+xkNznJbvwundop\nF2NXFiU4WiGESCwZERdCiDYubNlc/+FyfthcGWv709i+nJKbtfuLk7KwJ01F++Fl1NG3ge5oxUiF\nEJ2RZStqdxj99ho6qZ66qedOBy6HTpIr+shZ+Z/fEl7wDqGF7xH47Wu4hxyb4OiFECIxZERcCCHa\nsIhlc9NHK5izsTzWduOo3pw9LKfxC8zgrm1pvVHH/FmScCFEiwuaVmwLxVSvQabPRVaSm2y/mxSP\nE9cOU9DLvnyO8IJ3ogdWiOoP7kxEyEII0SZIIi6EEG2UZStun72Kz9eWxtp+f0gPLhzRrfELipaj\nPzEKlr4XpwiFEJ2ZrRRVISuagCe5yPa7yUpykeRyoO+0/Vh4y3KK/nNVfYPTS+qVH8Q5YiGEaDsk\nERdCiDbIVoq7v1jDhyuLY22/HdGNyw/u0fgFG+ehv3ASWvlG9Lcuh3X/i1OkQojOqjps4TI0fE4H\nqTuNfu/INsNs/PsYsK26Fo2US95C96XGL1ghhGhjJBEXQog2RinFA1+v4+2lhbG2s4d14frDeqHt\nNMoEwMpP0F8+Ay1YFj12OHd44BVCiJYXrYpu43cZBNzNlxwqf2YCdnX9S0Xvsbfg6j+6tUMUQog2\nTYq1CSFEG/PY3I28tmBr7HhCbha3junbaBKu/fwG2oxr0VQ08Va+zOge4d2Gxy1eIUTnUxU2SXI5\nSHI5dtmKbEfVnzyAuaZ+ho7R51f4j78tHiEKIUSbJom4EEK0Ic9+v4nn5+fHjo8fkMFfjuq/y3pL\nAG3Ok+if/CV2rFJ7YU+aChn94xGqEKKTCpk2lg1JXgfJzYyGh9fOpeaje2PHui+VlCvej0eIQgjR\n5kkiLoQQbcQrP2/h8W83xo7H9knjvmMG4NB3SsKVQvv0bvQ5T9Q3ZedhT3oDkpuopi6EEC1AKUVV\n2MLvduB3GY2+JASwgxWUP3sqoKINmk6PW76k1nDFL1ghhGjDZI24EEK0AW8uLuDBr9fFjg/tkcJD\nxw/adcqnFUGbcU3DJLzXodgXzZQkXAjR6mojNg4dfE4HPmfTj5F2dQmaoz7pzjrvMdzd8+IRohBC\ntAuSiAshRIK9v7yIv36xJnY8omsyj/56MG6jka/ojd+h/Tw1dqgGnxCdju5JiUeoQohOzLIVNaaF\n3+Ug4DYaLx5Zx8joQ/qdqzD6HIpr6ImkHnVFHCMVQoi2T6amCyFEAs1eU8zts1dtn7zJ0Owknjgx\nF5/T0fgFfQ5HnXAf2oe3Yo+YiDrpIdDlq1wI0fqqIxYeQyfJZTT+onAnuuEi7eqP4xCZEEK0P/L0\nJoQQCfL1+lJu+mglVl0WPiDdy79OHtJs8SMAdcglWJkDoe8YaGZESgghWkrEsglbikyfQbK78ReF\ntmmiG/JoKYQQe0KmpgshRALMyy/n+g+XY9rRLLx3qodnJ+SR6nE27Fi8GmpKdv2AfmMlCRdCxE1V\n2CLp/9m77/CoqvyP4+97MzPplQTpoHREFJWyYqHYRSw0FdC14QKyqOtPZVfFhq5lF10QEUREpUsI\noIio6FoAseEqofcuEALpycy9vz9ChowpEJJMMsnn9Tx5nDn3njvfGY5n7nfOvee48kfDHWbR00fL\nsjjyzw4cmXg1lju3CiIUEQksSsRFRPxszb407v9wPTnHh8IbRAYzpU876oT9YTbhPT9hTr0Wc+Zt\nkJtRBZGKiEBWngeAcJeDCFfxo+Fp7w7GSt2Ne+sKUp5piZV9zJ8hiogEHCXiIiJ+9OmWwwxdlEyW\n2wKgbriTKTe0o15ksO+Om5djTr8JIysFY8+PmAtHVkG0IlLb2bZNRq6H8OAgIlxBxU7QlrXybXJ/\n+9D73Iw8AzMkyp9hiogEHN3IIyLiB7ZtM+3nvby6cqe3LMSOMMkAACAASURBVDbUweQ+7WgcHeKz\nr/HrfIyk+zEsd37d0Disi+73a7wiIpA/QZvLYRDuDCp2Ekn3/nWkJz50osAZRszIz/wYoYhIYFIi\nLiJSyfI8Fs/9dxsL1v3uLWsaHcKE3m1oGhPqs6+x6k3MTx73PrejG+UvTxbf0m/xiogAuC2bbLdF\nXKiz2EkkM5b/m8xlL4BtHS8xiL43UaPhIiKnQIm4iEglOpbj5m9LN/Ld7qPesgsaRDLumta+E7PZ\nNsbysZjfvHaiKKEN1uA5ENXAnyGLiACQkesm1GkS7grCFXTibsbsn+eTvuBv2H+YSDL0ytG4zrrI\n32GKiASkGp2Ip6amMmHCBD7//HMOHjxITEwMl112GaNGjaJu3bplOlZOTg59+vRh+/btvPvuu3Tp\n0qWSohaRmmL30WxGfLSebUeyvGW9W8fzVI/mPie1WG6MDx/G/HmGt8hu3Anr1hkQGuvPkEVEAMj1\nWLgtiA4JIsJ14nQx5eXOeA6sL7J/8Hl9ibjyMX+GKCIS0GpsIp6dnc2QIUPYtm0bgwYNon379uzY\nsYOpU6eyatUqEhMTiY6OPuXjTZw4ke3bt1dewCJSo6zZl8aoj9dzJMvtLbu/S2PuvaCh72RHeVmY\n8+/D2PCxt8hueQVW/7fAGebPkEVEgPw5LdJyPIQHm0QEOwgyT/RZRpjvj4NBCS2JvO0tnI07+jtM\nEZGAVmMT8enTp7Nx40aefPJJBg0a5C1v06YNI0aMYOLEiYwePfqUjrVhwwamTp1Ku3btSE5OrqyQ\nRaSG+HjTIZ74fDO5x5cncwUZPNOzBde2ii+6s21BxkHvU+vcgdjXj4MgZ9F9RUT8IMttYZoQbkL4\nHyZoixr8DinPtcEITyCy338Ibn9tFUUpIhLYauzyZUlJSYSFhdG/f3+f8l69elGvXj0WLVqEbdsn\nPY5lWTzxxBM0aNCAgQMHVla4IlID2LbN5B928+iyTd4kPDbEwVs3nF18Eg7gCse6dQZ2fCusi0Zi\n3zBeSbiIVBnLtsnKyiJm2SPwXCPyNn/psz0ouj6xf1tF/FOblYSLiJRDjRwRT09PZ+vWrVx44YW4\nXC6fbYZh0KFDB5YtW8bu3btp3Lhxqcd6//33+eWXX3jnnXfYt29fmeKIiam+l5XmuD3kZubi8NhE\nhoWevILUKo7j9y9HRattnKoct8Xoj9eT+Ot+b1nzOmFMG3AuTWJP8jlGN8R+6L8YIZGVHGX5qW1I\nadQ+AptlWaR/8hIJX7yC4c4GIGPuCOq+vN13x5gLynxshyO/bVTncyOpGmobUpKa3jZq5Ij4nj17\nAKhXr16x2+vXrw/Arl27Sj3Ovn37GDduHDfccAN/+tOfKjZIEakxUrPyGDJrjU8SflHTWBJvv6BI\nEm7tW4v7h9lFjhEISbiI1Fx5P80j64mmBH36nDcJB3Cn7iH3wJYqjExEpGaqkSPiGRkZAISEhBS7\nPTQ01Ge/kjz11FO4XC4ee+z0ZgFNTc08rXr+kOO28JgGbo/NsaNZJ68gtUrBaJbaxsntSM3i/g/X\ns+PoiRPXm9rW5fHLzsTIdXMs98Rkbez9BfP9/pCVip3twT7n5iqIuHzUNqQ0ah8BaOcqzAUjMFJ3\nFtnkOPNPRA56m8zg+mSW85ymYESrOp8bSdVQ25CSBErbSEg4vcGUGpmIV4SPPvqIL7/8kueff564\nuLiqDkdEqqEf9x7jgSUbOJpzItl+4E9NuLNjA9+Z0QF2foc581aMnLT85x8/ht3ycgiJ8mPEIiLH\nHd6COf8+2PcLhXsrGwiq24qoQW/jbNihqqITEanxamQiHhERAUBWVvG/yGdmZvrs90epqamMHTuW\nzp0707dv38oJUkQC2ocbDvLk8i24rfxJ2YKDDMZe3pIrW9QpuvPWrzBnD8HIy+977NBYrMFzlYSL\nSJUx37oaIzvVp8wOr0tI39eI6nBdFUUlIlJ71MhEvFGjRhiGwf79+4vdvnfvXgCaNm1a7PaXXnqJ\nY8eOcf/99/sc49ixYwCkpKSwf/9+4uLiikwGJyI1m23bTFy9mzd/2O0tqxPq5D/XteacM4q5NGnj\nMsy5d2F4cvLrhydgDfkAzmjnr5BFRIqwLn2IoGVPAmA7w8ns/nciLrmPqDCd14iI+EONTMTDwsJo\n3bo1ycnJ5OTkEBwc7N3m8Xj4+eefqV+/Pg0aNCi2/qpVq8jLy+P2228vdvsDDzwAwLvvvkuXLl0q\n/g2ISLWU47Z4cvlmPt502FvWPC6U169rS4Oo4KIV1i7ETPwLhpV/6bod1QDr9kSo09xfIYtIbWdZ\n8N2b0OlucBRKsv80DHvNLDxndedwt78TGxZMVHCNPC0UEamWamyP269fP5577jlmz57NHXfc4S1f\ntGgRhw8fZuTIkd6yLVu24HK5vEuZjR07luzs7CLHXLlyJdOnT+ehhx6iVatWtGrVqvLfiIhUCylZ\neTz48QZ+3pfmLbuocTQvX9WKyGJOXo1f5mAs/CuGbQFgxzbDun0+xDTxW8wiUsv9Mgdz6T8wso9i\nHdyI3Wecz2Zr2FekZbsJNSE8OAhXUI1cTEdEpFqqsYn4LbfcwuLFi3nppZfYu3cv7du3Z/PmzUyb\nNo1WrVpx9913e/e99tprOfPMM1m6dClAiUuVHTlyBIDzzjtPI+Eitci2I1mM+HAdu4/leMv6n30G\noy89E4dpFNnf+OEdzI/+z/vcjm+JNWQ+RNX3S7wiUstt/wYzaSTG0RO30BhrZmFfOQZCYrxluR6L\nXMsmPsRBpKvGnhKKiFRLNbbXdTqdvP3224wfP55ly5YxY8YM4uLi6N+/PyNHjvQuYSYiUprvdh/l\noaUbSMvxAGAAD3dryuBz6xedGb1ASDS2YWLYFvYZZ2MNmQfhCf4LWkRqJ8uN+W5f2LGiyEzo1GkB\n2Wk+iXh6roeIYJMIl4OgYn5UFBGRymPYtm1XdRA11cGDaSffqYrkuC2yTIOMXIugPPfJK0itorWA\n8yWt+51nvtzqnRk9xGHy4hUt6XHWyZc0NNbMwvjxXazbZkJobGWH6jdqG1IatY8qdGw/5pReGOm/\n+xTbEWdg9XkNWvbyKc/M85DjsagT5iIhzFnyD4sVJFDWAxb/U9uQkgRK29A64iIiFcSybcav2sXU\nn/Z4yxLCnIy/rg3t6ha/7OEf2efdit2hP5jqZkWkkm1fgfn+AO/qDAC2Kxzriqfgwj8X2d2ybTJz\nPUSHOogKDqr0JFxERIrSGaKISCHZbg+Pf7aFZVtOzIzeuk4Y43u3oV5EMTOj2xbGt+Oxzx8CYX8Y\nKVcSLiKV7acZmIsfxODEBY52/XOx7lriO0t6IRm5HoIdJuEuByGOIH9FKiIihWh6TBGR4w5n5nJ3\nUrJPEn5p0xjeubl98Um45cFY9ADm589hvj8Aso/5MVoRESC+hfehDVgdB2EN/azEJNxt2WS7LcJd\nQUS6lISLiFQVJeIiIsDmw5kM+uA3fj2Q7i277Zx6vHptG8KLO1n15GEk/gVzzSwAjH2/YHw3xV/h\niojka9IF69oXsI0grOtexu7zaqm7p+e6CXcFEeFy4NRyZSIiVUbXTYpIrffd7qM8+PEG0nPzZ0Y3\nDXjk4mbc1qGE5cbc2Zgf3IuxYam3yDrvVuxLHvBHuCJSm7lzi452d7ob65z+EBJVatUct4XHgvDQ\nICKCNRouIlKV9FOoiNRq3+0+yv0frvMm4WFOk/9c26bkJDwvE3PWEN8kvNPd+aNQpk5sRaQSbfoU\n859nwqo3i247SRJu2zbpuW4igvNHw01N0CYiUqWUiItIrfXD3mOM/Gg9OZ78SY7qhruYfnN7Lm1W\nwnJjOWmY7w/E2Pqlt8jqNhL7mhfAUHcqIpXov69gzrwNw5OL+ckTsH1Fmapn5lkEmQbhrqDib7cR\nERG/0qXpIlIr/bzvGCMWryPbbQH5Sfi0m86mcXRI8RWyjmDOuAVjz0/eIqv7Y9iXPgQaWRKRymJZ\nGHNux9j4CSd6Ght2roRmF53SITyWTZbbQ2yIk0iXTv1ERKoD9cYiUuv8sj+NYYvXkXU8CU8IczL1\nxnYlJ+EZhzDf64dxYK23yLryGew/DfNHuCJSW2Ufw5xyOUbKNm+RbQRh3fQ6nNP3lA+Tkesh1GES\n5goi2KGrd0REqgMl4iJSq/x2IJ1hi9eRmZefhNcJdfLWjWfTNCa05ErOUHCFA2BjYF/3MvaFd/gj\nXBGprQ6sxXz7OozcDG+R7YrAuusjOKPdKR8mz2ORa9nEhziICtZpn4hIdaGfRUWk1kg+mM59i5O9\nE7PFhjp468Z2nBlbShIO4ArHum0WdsMLsG+coCRcRCrXr/Mx3+zpm4THnYn14C9lSsIB0nI9RLhM\nIlwOgkzdRiMiUl3op1ERqRU2HMpg6MJk0nLyk/CYEAdTbmhH87iwUztASFT+SJRmRheRyrTsScyV\nb3jvB7cBu/U12APeAbNs4ydZeR4MIMzl0ARtIiLVjEbERaTG23Q4k6ELkzl2PAmPCg5icp92tKoT\nXnyFA8kYP71XtFxJuIhUMiP9kE8SbvV4DPuWd8uchFu2TUauh4jgICJdQRiaVFJEpFrRiLiI1Ghb\nUjK5Z+FajmS7AYh05SfhbRJKSML3/Iz5/gCM7FQsw8TuOMiP0YpIbWffPBH7wFo4tAnrlvegZa/T\nOk5mngeXwyDMGUSoUz8iiohUN0rERaTG2nYki3uSkjmSlZ+ER7iCmNSnLe3qRhRfYecqzBm3YuSm\nA2Asewq7zXUQGuOvkEVEsO79BNIPQkzj06rvtmyy8izqhDk1QZuISDWlS9NFpEbamZrFvQvXcjgr\nD4Awp8kb17flnDMii6+w5UvM9wZ4k3A7NBZryAdKwkWk8nw/DfOlVpB+wLfcEXLaSThAeq6bMJdJ\nuCsIZ5BO9UREqiP9TCoiNc7uo9ncvTCZ3zPyk/BQh8nE3m05t14JSfjGZZhz78Tw5AJghydg3T4f\n6rb1V8giUssYC/+KsWYWBmC+2QvrwTVgnvppmWXbeCwbywaPbWPZNpZV8BhiQxxEajRcRKTaUg8t\nIjXKnmPZ3J20lgPp+Ul1iMPk9d5tOL9BVPEVtnzhm4RHNcC6PRHqNPdXyCJSm7izMadeh7H/fyfK\n0n+Hnauh2UUA2MeTacu28xNrC9zHk22PlV8OYBoGQSYEGQamCS6HgWmaOAyDyBAHpiZoExGptpSI\ni0iNsT8th3sWJrPveBIeHGTwn2vbcGHD6OIr7FiBOfuOE0l4TFOsOxIhpom/QhaR2iR1F+bkyzGy\nUrxFtiOY9Fvn4q7XGU9W3vHk+3iSbeRPlh5kGDhMA9M0CDKO/5nG8UQ8f78g80R5wX9FRKT6UiIu\nIjXCgfQc7k5ay55jOQC4ggxeu7YNXRuXkITv/hFz5m0Y7iwA7KiGWHcsKNd9mSIif1SwjJixZTlR\n8/+MYeV5t3kiziDtz8swo+riMvJHs4M4nmT/IcEuPPpdkISLiEjgUiIuIgHvYEYu9yxMZtfxJNxp\nGoy7ujUXNSl5ojUjdSfkZQNgR9Q9PhKuJFxEKo5t26Rmu4n4bjwh/30Bn9S5SVccdy7kDKfTO+p9\nIulG636LiNRwSsRFJKAdzsxPwnek5ifVDtPgX1e34pJmsaXWs9vfhB3kxPx4NNbgeRB3lj/CFZFa\nJC3XQ9SHw3ElJ/kk4SGXDCfyhn9WWVwiIlL1lIiLSMBKycrjnoXJbDuSf3l5kAEvX9WS7mfGndoB\n2vbGatETnGGVGKWI1EZZeR7yMlJxbVhyIgk3goi45Q1CL7ilKkMTEZFqQItLikhASs3OY+jCZLak\nnEjCX7yyFb3OqlN8haN7IOtI0XIl4SJSwfI8Fhl5HqJj4ggb+RU4Q8EVRuyDXysJFxERQCPiIhKA\njmW7GbpwHRsPZwJgGjD28pZc2aKEJDxtP+a7N4EzDGvIPAhP8GO0IlKbWLbNsRw3Ea4gIoMdRES3\nI+SRHwBwxGoeChERyacRcREJKMdy3Ny3OJn1hzIAMIBne7Xg2lbxxVfIOIT5Xj+MlG0YB9Zivtcf\nLI//AhaRWiXt8F5cQSYRwQ6igvPHOxyxjZWEi4iIDyXiIhIw0nPdDFu8jrW/Z3jLnu7ZnOtblzDC\nnZWK+X5/jIMbALCNIKzuj4IZ5I9wRaSWyflpLrGvdyTqy6eJDXFq5nMRESmREnERCQgZuR6GL17P\nrwfSvWVPdj+LG9vWLb5CTjrmjFsw9v8GgG2Y2De/AW2u8Ue4IlLLuNcvI3Tx/RjYmKsmkZn4YFWH\nJCIi1ZgScRGp9jLzPIz4cB1r9qd5y/5+6Zn0O/uM4ivkZWLOug1jz4/eIrvPq9jtb6rsUEWkFvJs\nX4lr7hAM7OMlBq5WPao0JhERqd6UiItItZaV52HkR+v5ad+JJPzRi5txyzn1iq/gzsGccwfGjpXe\nIuvaF7HPu7WyQxWRWsje/xvO927GsC1vWUT/1wjucEMVRiUiItWdEnERqbay3R5GLdnA93uOecse\n7taUQefWL76CJw/zg3swtnzpLbKueAq7012VHKmI1Eop2wh662oMy+0tCr/uWUK7/LnqYhIRkYCg\nRFxEqqUct8WDH29k1e6j3rIH/tSE289rUGId4+tXMTYs9T63uj+KfdGISo1TRGqp9AOYk7pjeHK8\nRaE9/0ZYj1FVGJSIiAQKJeIiUu3keSz+tnQD3+5M9Zbd36Uxd53fsNR69p+GYTe9CADropHYl/6t\nUuMUkVoqOxXz9Ysx8jK9RSFd7yLi2jFVGJSIiAQSR1UHICJSWGaeh4eXbuSbQkn4Xzo1YuiFjU5e\nOTgCa9AsjF/mYl9wB2jpIBGpaJaVn4Rnn+ijXOfeRGS/V6swKBERCTQaEReRauNwZh73JK31ScLv\nuaAhwzqdQhJewBmGfeGflYSLSOUwTXKbXOSdH93ZsgfRQ6ZXaUgiIhJ4NCIuItXCrqPZDFu8jp1H\ns71l913YkOGdG2OUkFQbX74EEXXzE28RET/IzPWQ0/t14sLjcBz4lah7F1R1SCIiEoCUiItIlVv7\nezrDP1zHkaz8mYdNI3+d8AHtS1iiDDC++Q/mf18GwMrLxv7TX/wSq4jUXrkei4w8D3GhTqL7vkKI\nI6iqQxIRkQClS9NFpEp9s+MIdy1Y603Cg4MM/n1169KT8O+mYH7+7InnW78Ay1PpsYpI7WR8/Hc8\nqfs4lu0mKsRBdIhDSbiIiJSLEnERqTIL1//OX5dsIMttARAVHMTkG9rR86y4EusYP72PufTv3ud2\ns4uxBrwDpk6KRaTiGYnDMVdPwTGxK2Fp24kMdhARrAsKRUSkfJSIi4jf2bbNlB9288TnW3Bb+VMe\n1Y9w8e7N7elYP6rEesav8zEWP3TiOI06Yd36HjhDKz1mEal9jI//jvHrPADMvEwi5t9BTIiScBER\nKT99m4iIX3ksm39+vY05vx3wlrWuE8br17elbrir5IrrPsJYMALj+FzFdv0OWINmgyuiskMWkdro\nixcxVk+hYKpI2xlK1L0LMbUig4iIVAAl4iLiN9luD6M/3cznW1O8ZZ0bRjHumtZElnap56bPMD+4\nF8POvw/crtsWa/A8CCl59FxE5LStehPzq1dOJOFBLkLv/y8hdRpXaVgiIlJzKBEXEb84mp3HX5ds\n4Od9ad6ya1rW4dleLXAFlXKXzLZvMOfeiWHlAWDXaY41ZB6ElXwfuYjIaftlDuYnj59Iws0gHEOX\nENmwTZWGJSIiNYsScRGpdPvSchi2eB1bj2R5y24/rz4PXdT05Jd5Zqd6Z0S3Y5pg3Z4IEWdUZrgi\nUlttWIqZNPJEEm6YGHfMJ/asTlUaloiI1DyarE1EKtXGQxkMmf+rTxL+f92a8nC3Zqd2r2Xb3li3\nTMeOOxPr9vkQ1aASoxWRWmv7N5hz7jgxDwUGVv+pxLbtjqH7wkVEpIJpRFxEKs3q3Ud54OMNpOfm\nj2g7TYOxl7fg6pbxZTtQyyuwzuoOQc4Kj1FEBMCc/xcMO38pRRvw9HmN2PNvxGFqzEJERCqevl1E\npFIs3XSIYYvXeZPwCFcQb1zf9uRJ+JYv4eDGouVKwkWkEll3fYTtDMcG8i5/hogugwlxBFV1WCIi\nUkNpRFxEKtx7a/by8rc7vM/rhjuZ2LstreLDS61n/PAOxpLHILoR1j1LIbyMI+ciIqfJjmnC4Xu/\nJXzzEiIuua/0lRxERETKSSPiIlJhLNvmlW+2+yThZ8WG8l7fc0pPwi0PxidPYH70fxi2ByN1B8bH\nj/khYhGptSzL52l6rgcjsi4h3YYSE6IkXEREKpcScRGpELkei8c+3cS7v+zzlnWsH8n0m8+mfmRw\nKRXTMefcgblqkrfIrn8u9lXPVWa4IlKb5WZijuuAsfhvAGS7PeR6bKJDHMSEOE5tIkkREZFy0E++\nIlJuaTluHvh4A9/vOeYt63VWHC9c0aL0eyyP7cWcNQhj/2/eIrvNdVg3vQ6u0i9jFxE5Le5czInd\nMNIPYPz0Lp6sVNJ7v0F0iIOoYCeuII1RiIhI5VMiLiLl8ntGLsMXr2Pj4Uxv2cD2Z/DYJWcSZJYy\nqrT3l/wkPP2At8jqNhK71+Ng6ERYRCrBvv/l9ztp+71FntTdhLuCiAx2EO7S5GwiIuIfSsRF5LRt\nTclk2OJ17EvP9Zb9tWsT7j6/Qenr7q5fgpk4DCMvP3m3TQd271ewOw6q7JBFpDbKPobxwb0YW5ZT\nuGdyx7cmY8giYl0OojU5m4iI+JG+dUTktPy87xgjP1rPsZz85ckcpsGYHmdxQ5u6pVfcsRJzzp8x\nsAGwQ6KxBrwDZ15cyRGLSK30xYuY37yKYbl9ij11WnLkz58SF+oiNtRR+o+HIiIiFUyJuIiU2fKt\nKTy6bCM5nvxkOtRh8q+rW3Fx09iTV27SBdr1huTF2LHNsG6bBfEtKjliEal1tnyBmTgcI/OQT7Ed\n5MLdfTSHOw4lLtRJTKgTh6nbYURExL+UiItImcz5bT8vfLUNKz8HJy7Uyeu923B23YhTO4BhYt04\nASM8Abv7IxBWp/KCFZFay1z6D58k3Abstr1x3ziRI3lBRIU4iApxlD6hpIiISCVRIi4ip8S2bSZ8\nt4spP+7xljWJDuGN69vSODqk5IpHd0NkPTALdTfOMOxrX6zEaEWktrP6v435xiX5T+q0wBo4HTu+\nJUez3YS6DCKDHUTqvnAREaki+gYSkZPK81g88+VWFq4/6C1rXzec8de1pU6Ys+SKO1ZizrkDu/1N\nSrxFpPL8+gHUP8/3Npe6bbD+NAzOOAfO7Q/Asew8HCZEBjuJCdEpkIiIVB19C4lIqTJyPfztkw2s\n2HnUW3ZJ0xhevqoVYc6SL+k0fpmLsegBDCsP4/u3seJbYne+xx8hi0htcXAj5tw/w6FNEN8aa8Q3\nvtuvfMb78FiOGwuIC3ESF+rA1ORsIiJShZSIi0iJfs/I5f4P17H+0Ik1wm9qm8AT3ZvjKGmNcNvC\n+OJFzK//faIoPAG7QcfKDldEagt3NkbiMIx1H3qXI7MPbYBf58M5fYvsnpnrwW3ZxIU6iNXkbCIi\nUg0oEReRYm34PZ07PviV/YXWCL/vwoYM79y45GV+8rIwFv4Vc22St8hOaIN12wyIaVLZIYtIbbBi\nIubysRieXN/ysHiIPKPI7tluD5lui9gQBzGhTlxBSsJFRKTqKREXkSJWbE/hvvm/kZaTv+5ukAFP\ndm/OTe1KWSM8/XfMOXdg7P7BW2Q374HV7y0IiarskEWkptv5HeYH92Ck7fcptk0H1sUPQI9Hi1TJ\n9Vik53qIOZ6Ea4Z0ERGpLpSIi4iPDzccZMzyLeQdX58szGnyr6tb061JTMmVfl+HOXMQxtFd3iLr\nwjuxr3ned7Z0EZGyyjyCMfdOjB3fUvhaHBuwm/fE7jel2B/73JZNWo6byGAHUcFOwl1KwkVEpPrQ\nGbKIAPnLk035cQ8TvjuRTNcNdzLhura0SQgvueL2bzFnDcbITc8/jmFiX/Usdud7QZMhiUh5fTMO\nc8e3PkV2TBOsAdOgfodiq3gsm6PZeYS7gogKzl8vXEREpDrRN5OIkOexGPvVNhKTf/eWtU4IZ/w1\nrakXGVx65ehG4AiB3HRsVzhW38nQ6spKjlhEao3Ln8L+6X2MnDRsRwjWlc9ApztL3N22bY7muAlx\nmkQGO7RMmYiIVEv6dhKp5TJyPTz8yUa+3ZnqLbuoaSxv9G2Pcfwe8VLFNsW6ZTpm0v1Y/d+Geu0r\nMVoRqdFSd8GBtdD66hNlpol1/b8xNnyC3ec1cLhKrF6QhDtMiAp2EhvqLHlySRERkSqkRFykFitu\nebLrWyfwrxvPxhVkcqy4RNy2i15y3rgz1ogVuh9cRE6POxdj8YMY/5sHQU6sRzaBK+zE9rNvxD77\nxpMeJi3XA0B0iJNYrRUuIiLVmNbwEKmlNh/OZPAHv/ok4fdd2JDnejUveXmfo3sw37oKdqwsuk1J\nuIicjuRFmC+1wPzfXAxsDE8uRuJfynyYjONrhceEaK1wERGp/vQtJVILrd59lDsSf/OuER5kwFM9\nzmJElyYlX8a5dw3mW1di7P0Zc86fIWWr/wIWkZpp94+Y8+7ByMvyFtlGEHZCqzIdJivPQ5bb8i5T\nprXCRUSkutMQlkgt8+GGgzy5fAvusixPtmEp5gdDMdzHT5ZzjsH+3yDuLD9ELCI1Um465nt9Mcjv\ni2yAphdhDXgbwuqc+mGOrxUeG+ogOkRrhYuISGBQIi5SSxS3PFlCmJPXe59kebLkxZjzh2JY+feL\n2yExWAPfgWbdKjliEanJzGnXY+RmAPlJuHXNC9D5njIdI89jcSzHTfTxJFxrhYuISKBQIi5SC7gt\nm7H/3cr8QsuTNY8LZWLvttQvbXmytQsx59+HYedPgGTHNsMaNBvqNK/skEWkJvvkCYz9v3mf2i2v\nLHMS7rFsjuW4iTi+VnhksE5pREQkcOhbS6SGy8j1Dmu+UwAAIABJREFU8H+fbOSbQsuTdWoYxbhr\nWhNVyomr++d5vkl4neZYdyRBZL1Kj1lEarBNn2OumuR9akecgX3Le2U6xB/XCo9WEi4iIgFG31wi\nNdjBjFxGfLie9YcyvGW9W8fzdI/mOEuZzMj94xxyZt6LYVsA2PEtsW5PVBIuIuVmLhpFwZSQtunA\nuvtjKMMM5wVJuNM0tFa4iIgELE0rKlJDnVie7EQSPvTChozt1aLUJJy9a8iZeS8UJOEJrTUSLiIV\nxrp3GXZk/fz7wm+cADGNy1S/YK3wqBAHcaFOrRUuIiIBSYm4SA1UsDzZvkLLk43pcRb3l7Y8WYH6\n5+K4ZBgAdt12WHcsgIi6lR2yiNQWUQ2wHliDNeAdOKdvmar+ca3wIFNJuIiIBCZdmi5Sw3y04SBP\n/GF5sleuasXFTWNP7QCGgeuGf2JGNyCrTd8yLSMkInJKTBPaXlemKgVrhceFaq1wEREJfPoWE6kh\nbNtmyg+7Gf3ZZm8SnhDmZNpN7U+ehNu2z1PDMHD2GKUkXETK7+geWDamXIcoWCs8JiRIa4WLiEiN\nUKNHxFNTU5kwYQKff/45Bw8eJCYmhssuu4xRo0ZRt27pl9rats2iRYuYO3cumzdvJjMzk/r169Or\nVy+GDx9OZGSkn96FyMmd9vJkgPH927BrNfaNE8Cs0V2CiPibZWG+dRVG+gHszcux7voIQqLKdAit\nFS4iIjVRjT3rzs7OZsiQIWzbto1BgwbRvn17duzYwdSpU1m1ahWJiYlER0eXWP+FF15g+vTpXHTR\nRTz44IMEBQXx5Zdf8vbbb7Ny5UrmzZuH0+n04zsSKV5mroeHT2N5MgBj9VuYH48GwALsG18HUye5\nIlIxjDm3Y6QfyH98cD2smgTdHznl+lorXEREaqoa+402ffp0Nm7cyJNPPsmgQYO85W3atGHEiBFM\nnDiR0aNHF1s3OTmZ6dOnc9lllzF58mRvef/+/Rk+fDiff/45X375JVdccUWlvw+R0hzMyOX+j9az\n7mCh5claxfN0z9KXJwMwVk3C/OSJE89TtmHnZUFwRKXFKyK1yPfTMDZ+4n1qn9GuTEm4pbXCRUSk\nBqux94gnJSURFhZG//79fcp79epFvXr1WLRoEfYf7ost4HK5eOihh7j//vuLbOvWrRsAe/furfig\nRcpgS0r+8mSFk/B7L2jI2MtPsjwZYKyY6JOE2406YQ2epyRcRCrG7+sxP37sxHrhzvD8y9JPkW3n\nj4RrrXAREampauTPy+np6WzdupULL7wQl8vls80wDDp06MCyZcvYvXs3jRsXXb+0RYsWtGjRothj\nb926FYDWrVtXfOAip+iHPUcZtWSDdz3dIAP+cdlZ9Dv7jJPWNb4dj/nZM97nduPOWIPmKAkXkQph\nuXMx37kew7YAsDGwhswD16n3MVorXEREaroamYjv2bMHgHr16hW7vX79+gDs2rWr2ES8sNzcXLKy\nsjh48CALFy5kxowZ3HTTTXTt2vWkccTEhJUxcv/JcXvIzczF4bGJDAut6nCkDJau/52/Ll5Prif/\nJDfMGcTrN7enR/OTz3Ce+9kr5BVKws2zLiLknvkYIb6TDzqOj6hHRattiC+1DSmNI8gk/fXrMbJO\nzFnhvPofBLe/9JSPkZ7jJszpIC7UQUJE8Emv8JHA4HDk/ztW53MjqRpqG1KSmt42amQinpGRf6lu\nSEhIsdtDQ0N99ivNhx9+6L2XPDY2lmeffbbI5e4lcTqr76RXTmcQEaGuk+8o1c593c7kvm5nnl7l\ngWPy/0REKsuT31R1BFKNVedzI6laahtSkpraNmpkIl6RLrnkEqZPn05KSgpff/01TzzxBN988w0v\nvvhiiYm+iIiIiIiISElqZCIeEZF/H1pWVlax2zMzM332K01CQgIJCQkAXHvttbRp04bnn3+eVq1a\nMWLEiAqKWERERERERGqLGnnjVaNGjTAMg/379xe7vWDG86ZNm5b52AWXpX/99denH6CIiIiIiIjU\nWjUyEQ8LC6N169YkJyeTk5Pjs83j8fDzzz9Tv359GjRoUGz9N954gy5durBy5coi29LS0rzHERER\nERERESmrGpmIA/Tr14+srCxmz57tU75o0SIOHz5Mv379vGVbtmxh165d3uctW7YkNTWV6dOnFzlu\nUlISAOeff34lRS4iIiIiIiI1mWHbtl3VQVSGvLw8Bg0axNq1axk8eDDt27dn8+bNTJs2jaZNmzJ3\n7lzv7OmtW7fmzDPPZOnSpQDYts2wYcP44osv6NSpE1dffTWhoaF8//33JCUlER8fz/z58znjjJOv\n2SwiIiIiIiJSWI1NxAHS09MZP348y5Yt4+DBg8TFxXHFFVcwcuRIYmJivPv9MRGH/EvPZ8yYQVJS\nElu3bsXtdlO3bl0uvfRShg0bpiRcRERERERETkuNTsRrutTUVCZMmMDnn3/OwYMHiYmJ4bLLLmPU\nqFHUrVu31LqtW7cudfv3339PVFSU9/nmzZv5z3/+w+rVq0lPT6dhw4Zcf/31DB06FJdL65FXN/5q\nGz179mTPnj0l7puUlETbtm3L/gakUpWnfQDk5uYyefJkFi1axL59+4iNjaV79+488MADxMXF+eyr\nviOw+KttqO8IPKfbNhITExk9enSpx+7cuTPvvfee97n6jcDir7ahfiMwlfd7ZeHChcyePZv169eT\nl5dHgwYN6N69O8OGDSM2NtZn30DrO2rk8mW1QXZ2NkOGDGHbtm0MGjSI9u3bs2PHDqZOncqqVatI\nTEwkOjq61GO0aNGCkSNHFrut4LJ9gE2bNnHLLbcQEhLCXXfdRb169Vi9ejUTJkwgOTmZiRMnVuh7\nk/LxZ9sAiIuLY8yYMcXu26hRo9N7E1Jpyts+3G43Q4cO5fvvv2fQoEGcffbZ/Pbbb8yYMYMff/yR\nBQsWeL/s1HcEFn+2DVDfEUjK0za6dOnCa6+9Vuy2/fv388ILL9CiRQtvmfqNwOLPtgHqNwJNeb9X\n/v3vf/Pmm2/SoUMHHnroIcLCwvj55595//33+fLLL0lMTPQuRx2QfYctAWnSpEl2q1at7Pfff9+n\n/NNPP7VbtWplP//886XWb9WqlT148OBTeq277rrLbtOmjb1+/Xqf8ueee85u1aqV/dlnn5UteKlU\n/mwbPXr0sHv06HHasYr/lbd9vPfee3arVq3sBQsW+JS//vrrds+ePe3vv//eW6a+I7D4s22o7wgs\n5W0bJRk2bJjduXNnOyUlxVumfiOw+LNtqN8IPOVpH0eOHLHbtWtn9+jRw87JyfHZ9sorr9itWrWy\n33nnHW9ZIPYdNXbW9JouKSmJsLAw77rmBXr16kW9evVYtGgRdgXcdfD777/z7bff0rVr1yKXLA8e\nPBjIv2REqg9/tQ0JTOVtHzNmzKBZs2bccMMNPuXDhw/n888/58ILLwTUdwQif7UNCTyV8b3y6aef\n8vnnn/Pwww97Ly9VvxF4/NU2JDCVp33s27cPt9tNhw4dilxWXvB9UnCrQqD2HUrEA1B6ejpbt26l\nXbt2RRqmYRh06NCBlJQUdu/efUrHs22bzMzMYrf99ttv2LbNeeedV2Rb06ZNiYmJ4X//+1/Z34RU\nCn+2jeJkZWUpya/Gyts+9u/fz9atW+nWrRuGYQCQk5NT7L+5+o7A4s+2URz1HdVXRX+vQP5cAmPH\njqVDhw4+y8mq3wgs/mwbxVG/Ub2Vt300atQIl8vFjh07imwrSMBbtmwJBG7foUQ8ABU0vnr16hW7\nvX79+gA+a6MX58iRIzzyyCOcf/75dOzYkfPPP59HHnmEAwcOlOm1Cn6xkqrnz7ZRIDs7m+eee45O\nnTpx3nnnce655zJ8+HC2bNlSzncjFa287WPr1q0ANGnShOnTp9OzZ086dOhAhw4dGD58uM+XpfqO\nwOLPtlFAfUdgqKjvlcLmzp3Lvn37+L//+z/vDzen+lrqN6oPf7aNAuo3Akd520dkZCTDhw8nOTmZ\nZ599lp07d3L48GG++OILJk2aRNu2benTp88pv1Z17Ds0WVsAysjIACAkJKTY7QWTaRXsV5LNmzfT\nrl07Xn75ZdxuN1988QVJSUmsXr2axMRE4uLiyvRaJ5sATCqfP9tGgcOHD7N7926efvppXC4Xq1at\nYubMmaxevZp58+Zx5plnVtC7k/Iqb/tITU0FYMGCBeTl5fGXv/yFOnXqsHLlSmbMmMGaNWtISkqi\nbt266jsCjD/bRgH1HYGhor5XChTMrN+pUyc6d+582q+lfqPq+bNtFFC/ETgqon0MGzaM+Ph4nn32\nWd5//31veY8ePXjxxRcJDg4u82tVp75DiXgtNWXKFOLi4mjfvr237Oqrr6ZevXpMmjSJadOm8be/\n/a0KI5SqUpa28c9//hPTNH3u/bz88stp3bo1jz/+OOPHj+ff//6339+DVI68vDwg/0Ro8eLF3nv3\nevXqRXx8POPGjWPatGk8+uijVRmmVIGytg31HbVXYmIiBw4c4IknnqjqUKSaOVnbUL9R+8ycOZOx\nY8fSrVs3rrvuOuLi4vjll1+YOnUqQ4cOZcqUKT7LLQcaXZoegAqm6c/Kyip2e8E9vQX7FefSSy/1\nSbQK3HbbbQCsWLGiTK8VHh5+KqFLJfNn24D89T2Lm4Cpb9++BAcH++wrVa+87aPg//OePXsWmUCn\n4F6+7777rkyvpb6jevBn2wD1HYGkIr5XCps3bx4xMTF07979tF9L/Ub14M+2Aeo3Ak1528fWrVsZ\nO3YsXbt2ZfLkydxwww1ccskl3H///bz88susWbOGSZMmlem1qlvfoUQ8ADVq1AjDMNi/f3+x2/fu\n3QvkT05QVnFxcRiGQXp6OgCNGzcGKPW1GjVqhMOhiyuqA3+2jdKYpklsbOwp7Sv+U9720bBhQwA8\nHk+RbbGxsRiG4b08TH1HYPFn2yiN+o7qpyK/V3bv3s1vv/1G9+7dcTqdRbar3wgs/mwbpVG/UT2V\nt32sWrUKt9vNlVdeWWTbpZdeimEY3h94A7XvUCIegMLCwmjdujXJycnk5OT4bPN4PPz888/Ur1+f\nBg0aFFt/w4YNzJ492/s/QGE7duzAtm1v3XPOOQeHw8FPP/1UZN+NGzdy7NgxLrjgggp4V1IR/Nk2\ndu3axbx589i4cWORfTMyMjhw4ECJryNVo7zto3nz5kRGRrJu3boi2/bt24dt25xxxhmA+o5A48+2\nob4jsJS3bRT2zTffANC1a9dit6vfCCz+bBvqNwJPedtHwej2H+tC/nwCtm2Tm5sLBG7foUQ8QPXr\n14+srCxmz57tU75o0SIOHz7ss+TDli1bfGYk3LRpE2PGjOH1118vctw333wTgCuuuALIHwXt2bMn\nq1evJjk52WffadOmARRZG1Cqlr/axqFDh3j88cd54YUXiiwfMnnyZGzb9u4r1Ud52ofL5aJ3796s\nXbuW5cuX+9SfMWMGkH9pMqjvCET+ahvqOwJPedpGYWvXrgVOLDn0R+o3Ao+/2ob6jcBUnvbRsWNH\nAJYsWVLk33zp0qU++wRq32HYWoAvIOXl5TFo0CDWrl3L4MGDad++PZs3b2batGk0bdqUuXPnemcI\nbN26NWeeeaa30brdbu69915WrFhBr169uOyyy/B4PHz66aesWLGCiy66iClTpngv39i1axf9+/fH\nMAzuuusu6taty9dff83ixYvp168fY8eOrbLPQYryZ9sYPXo0iYmJdOrUiWuuuQaXy8XXX3/NJ598\nQqtWrZg1a9Yp3xsm/lGe9gGQkpLCgAEDOHDgAEOHDqVhw4asWrWKhQsX0rZtW2bPnu2dtVR9R2Dx\nZ9tQ3xFYyts2CgwZMoTVq1ezatWqInMJFFC/EVj82TbUbwSe8raPUaNGsXTpUjp27Mg111xDXFwc\nv/76KzNnziQ6Opp58+b5XKkZaH2HEvEAlp6ezvjx41m2bBkHDx4kLi6OK664gpEjRxITE+Pdr7iG\nnZOTw/vvv8/8+fPZtWsXpmnSrFkz+vTpw+23317k/pzt27czbtw4Vq1aRUZGBk2aNKFfv37ccccd\nBAUF+e09y6nxV9vweDwkJiYyc+ZMtm7dimVZNGrUiKuuuop77rlHX4jVVHnaB+QnXK+++irLly8n\nNTWVhIQErrrqKkaMGEFkZKTPvuo7Aou/2ob6jsBT3rYB0KdPHzZs2MCvv/6Ky+Uq8bXUbwQWf7UN\n9RuBqTztw+PxMGvWLBITE9m2bRt5eXnUrVuXiy++mBEjRnhveSoQaH2HEnERERERERERP9I94iIi\nIiIiIiJ+pERcRERERERExI+UiIuIiIiIiIj4kRJxERERERERET9SIi4iIiIiIiLiR0rERURERERE\nRPxIibiIiIiIiIiIHykRFxEREREREfEjJeIiIiIiIiIifqREXERERERERMSPlIiLiIiIiIiI+JES\ncRERERERERE/UiIuIiIiIiIi4kdKxEVERCSgJSUlMX78eHbv3l3VoYiIiJwSR1UHICIiIlIeL730\nEocPH6Zz5840atSoqsMRERE5KY2Ii4iISMDasmULhw8fruowREREykSJuIiIiASsH3/8sapDEBER\nKTPDtm27qoMQEREJRD179mTPnj20adOGhQsXkpOTw/z581mwYAGbN28mNzeX6Ohozj77bAYOHMjl\nl19e4rFyc3P58MMP+fTTT1m7di0pKSmEhIQQHx9Ply5duOmmmzjvvPNOGtOBAweYOXMmK1euZNeu\nXaSlpREWFkbdunW54IIL6NevH+ecc85Jj7Np0yaSkpL4+uuv2b9/P1lZWcTHx9OxY0cGDBhA165d\nS62/detW5syZw+rVq9m7dy/p6elERETQoEEDOnfuzMCBAznrrLOKrTt+/HgmTJgA5N//XadOHcaN\nG8fy5ctJT0/npZdeYuvWrd59ivPCCy9w8803+5Sd7mc8b948Hn/8cSD/3/yNN94o8XVffvll3nrr\nLQCGDBnirSciIlKY7hEXERGpACkpKdx3333873//8yk/fPgwX331FV999RXDhw9n1KhRRepu2bKF\nESNGsG3bNp/yvLw80tLS2LZtG3PmzGHAgAGMGTOGoKCgYmOYPXs2Y8eOJTc316f86NGjHD16lE2b\nNjF79mwGDhxY6nHGjRvH5MmTsSzLp3zv3r3s3buXjz76iJtvvplnn30Wh6PoqcR//vMfJk2ahMfj\n8SlPTU0lNTWV5ORk3nvvPYYPH879999fbAwFcnNzGTp0KOvWrfOW5eTklFqnOOX5jPv378/y5cu9\nf0uXLuXqq68u8hrr1q3jnXfeAaB58+Y8/PDDZY5TRERqB42Ii4iInKaCEfHWrVsTHx/PL7/8wn33\n3ccVV1xBXFwchw8fZtGiRUyaNAnbtjEMg48++ojmzZt7j3HgwAH69u3LwYMHAejbty8333wzzZo1\nw+Px8PPPPzNp0iRvInrLLbfw9NNPF4klKSmJRx99FICIiAjuu+8+evbsSUJCAunp6fzwww9MmDCB\nnTt3AjB48GCeeOKJIseZOHEir732GgDt2rVj5MiRtG/fHsuyWLduHa+99po3lttuu40xY8aUWD8h\nIYHhw4fTrVs3YmJiSE1N5dtvv2XChAne+7offfRR7rrrLp9jFB4RHzhwIAsXLuSRRx7hiiuuICgo\nCKfTSUhICHl5eYwZM4bFixcDMHnyZC688EIAgoODvT8SVMRnfPjwYXr37k1KSgoJCQksWbKEqKgo\n73bLshgwYAC//vorTqeT2bNn0759+yKfr4iICCgRFxEROW0FibhhGDidTmbNmlVs8vWPf/yDDz74\nAIBRo0YxfPhw77a//vWvfPLJJwA8+eSTDBo0qEj9nJwcBg8e7B1tnzVrFueff753e3p6Ot27dyct\nLQ2Hw8GsWbPo0KFDkeMcOnSIG264gUOHDmEYBgsWLKBt27be7bt27eKaa64hLy+PFi1aMG/ePMLC\nwnyOkZaWxo033sju3bsxDIMlS5Z4LzHfuXMn11xzDW63m6ioKBITE2ncuHGROLZs2cLNN99MdnY2\nwcHBLF++nPj4eO/2wol4cHAwzzzzDDfeeGOR4wA89thjLFiwAIB3332XLl26FNmnIj5jgM8++4wR\nI0YA+aPkzz33nHfbO++8wwsvvADAAw88wLBhw4qNV0REBDRZm4iISLnZts3gwYNLHAG98sorvY83\nbtzofbxr1y6WLVsGQMeOHYtNECE/GX3ssce8z+fMmeOzfdGiRaSlpQH5o73FJeEA8fHxDB061Btz\nwY8DhY+bl5cHwF/+8pciSThAZGQkAwYMwDRNoqOjWblypU99t9sNwD333FNsEg75l20PHDgQyE+A\nFy1aVOx+AHXq1KFPnz4lbj+ZivqMAS6//HL69u0LwAcffMB3330H5F+yX3AVQMeOHb2fsYiISEmU\niIuIiFSAa665psRtDRs29D4+duyY9/HXX39NwYVp1157banHv+CCC0hISADgq6++8tlWOBkuLQ6A\nXr16eR+vXr3aZ9vXX3/tfXzJJZeUeIy7776btWvX8t133/kkthUVR2Fdu3bFNE//dKWiPuMCf//7\n32nUqBG2bfPkk0+Sk5PD008/TWZmJmFhYbz00ksl3nsvIiJSQIm4iIhIBSh83/cfhYSEeB8XjDgD\nrF271vs4Pj6ejIyMUv8KXiMlJcVn7ezCE5m1bt261DgbNWrkHeneunWrd0I1j8fDli1bAIiJiSEm\nJqbEYzgcjiLJscfj8Y72h4WFlTgaXqBly5bex4WvEvijBg0alHqck6moz7hAREQEL774IqZpsn37\ndu68806+/PJLAEaPHk2TJk3KFa+IiNQOmjVdRESkAoSHh5e5TkpKivfxgw8+WKa6v//+O3Xq1AHg\nyJEjAJimSWxs7EnrxsbGkpmZidvtJi0tjZiYGI4ePer9kSA6OrpMsUD+feoF9WNjYzEM46QxGIaB\nbdukpqaWuF9cXFyZYymsoj7jwi688ELuvvtupkyZ4l3HvEePHgwYMKBcsYqISO2hEXEREZEqkpWV\nddp1MzIyihzH5XKdNAGG/PuhCxQsBVZ4ybPTubS68HspfAVASQzDwOVy+cRQnNDQ0DLHUlJcZVX4\nM/6jwvf9A1x22WWn/ToiIlL7aERcRESkihQeRS9ulu5TFRYWRlpaGjk5Od5l0kqTnZ3tUxd8k+fC\n97GXJYbijl8Sy7K8yX9xk8JVlIr6jAvLzc3lH//4h0/ZK6+8wqWXXuozH4CIiEhJNCIuIiJSRQpf\n9lz4EuqyKrh827btYu9rLqzwpeBOp5OIiAgAoqKivCPlpV0qXpKIiAicTieQ/14syyp1/yNHjngn\nUSvv5eelqajPuLBXX33Ve1/7448/TkhICOnp6Tz22GMnfd8iIiKgRFxERKTKnH322d7Hv/3222kf\np127dt7HycnJpe67c+dOMjMzAWjTpo139Nw0TVq0aAGA2+1m+/btJR4jKyuLNWvWsGbNGu9+pml6\n1yTPyspi27ZtpcaxYcMG7+M2bdqUum95VNRnXOCHH35g2rRpANx0000MGTKEUaNGAfmzv7/99tvl\nfg0REan5lIiLiIhUkUsuucQ7+/hHH33kXYO7JNOnT2fFihU+93MDdOvWzft46dKlpR7jk08+KbYe\nwKWXXup9XLD2dnGWLFnCwIEDGThwIAsWLCh3HBdffHGp+56qghH2wirqM4b8e8YfffRRLMsiISGB\n0aNHA3DHHXd4125/7bXXWL9+fXnfioiI1HBKxEVERKpIgwYNuPzyy4H8kerx48eXuO/8+fN5/vnn\nufPOO/nXv/7ls+3666/3Xt69cOFCfv3112KPsX//fqZOnQrkX5Y+cOBAn+19+/b1TqA2depU9u3b\nV+QYWVlZvPvuu0D+hGvXXXedd9utt97qrT9t2jT27NlTbBzr169n/vz5QP5Sab179y7xfZ9M4Xvb\nDxw4UGR7RX3GAC+88AK7d+8G4KmnnvLOLh8UFMTzzz+P0+kkNzeXRx55pNhEXkREpEDQU0899VRV\nByEiIhKIpk+fTlpaGgAjR44scb9jx455k9eGDRty8803e7ddcMEFJCUlkZ2dzQ8//MCmTZuIi4sj\nNDSUY8eOkZyczIQJE3j99dcBSEhI4F//+pfPbOIOh4OmTZuyZMkSLMti6dKluFwuYmJiME2TAwcO\nsGzZMh555BEOHToEwCOPPOIzAg4nli377rvvyM7OZunSpcTFxRETE0NGRgbff/89jz32mPfy91tu\nuYV+/fp560dERBAWFsY333xDbm4uH3/8MZGRkd7j7t69m0WLFvH3v//de3n8Sy+9VOTS9NWrV7N6\n9WoALr/8cu8l78XZvHkzK1asAGD79u00bNiQQ4cOsWfPHu/EaRXxGX/xxRe89NJLAFx77bUMHz7c\nJ446depgWRarV6/m8OHD5OTkVNhIv4iI1DyGXdx1XCIiInJSPXv29I76Fr7n+Y92795Nr169AOjc\nuTPvvfeez/YtW7YwfPjwUu/LBmjWrBkTJkygZcuWxW5PSkpizJgxpc5a7nQ6GTVqFPfee2+J+4wb\nN47JkyeXOvHYTTfdxHPPPYfDUXQBljfffJPXXnsNj8dTYv3Q0FCeeeYZ+vTpU2Tb+PHjmTBhApA/\nCl34h4s/2r9/P1dddVWR99yrVy8mTpzofV6ezzglJYU+ffpw8OBBYmNjWbJkSbETzOXl5dG3b182\nbNiAaZq8++67dOrUqdTXExGR2knLl4mIiFSx5s2b8+GHH7Jo0SI+/fRTkpOTOXLkCJA/o3jbtm25\n6qqr6N27t3dm8uLceOONXHTRRcyYMYMVK1awc+dO0tPTiYiIoGHDhnTt2pVbb72Vxo0blxrPgw8+\nyFVXXcXcuXNZtWoVBw8eJCcnh/j4eC644AIGDBhAly5dSqx/3333cfXVVzNz5kxWrVrF3r17yczM\nJDIykmbNmnHxxRdz6623+sxofrrq1avH1Klflbm7AAAA6klEQVRTeeWVV9iwYQO2bVOnTh3OOecc\nn/3K8xk/9dRTHDx4EIAnnniixFnenU4nY8eOZeDAgXg8Hh599FEWLVrknZleRESkgEbERURERERE\nRPxIk7WJiIiIiIiI+JEScRERERERERE/UiIuIiIiIiIi4kdKxEXk/9uvYwEAAACAQf7Ws9hVFgEA\nACMRBwAAgJGIAwAAwEjEAQAAYCTiAAAAMBJxAAAAGIk4AAAAjEQcAAAARiIOAAAAIxEHAACAkYgD\nAADASMQBAABgJOIAAAAwEnEAAAAYiTgAAACMRBwAAABGIg4AAACjAIsNlj/m1v5vAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53e97458d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 376,
       "width": 497
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.plot(neocortex, post_pred['kcal'].mean(0), ls='--', color='C2')\n",
    "hpd_post_pred = pm.hpd(post_pred['kcal'])\n",
    "plt.plot(neocortex,hpd_post_pred[:,0], ls='--', color='C2')\n",
    "plt.plot(neocortex,hpd_post_pred[:,], ls='--', color='C2')\n",
    "\n",
    "plt.plot(neocortex, milk_ensemble['kcal'].mean(0), color='C0')\n",
    "hpd_av = pm.hpd(milk_ensemble['kcal'])\n",
    "plt.fill_between(neocortex, hpd_av[:,0], hpd_av[:,1], alpha=0.1, color='C0')\n",
    "\n",
    "plt.scatter(d['neocortex'], d['kcal.per.g'], facecolor='None', edgecolors='C0')\n",
    "\n",
    "plt.ylim(0.3, 1)\n",
    "plt.xlabel('neocortex', fontsize=16)\n",
    "plt.ylabel('kcal.per.g', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was createad on a computer x86_64 running debian stretch/sid and using:\n",
      "Python 3.5.4\n",
      "IPython 4.1.2\n",
      "PyMC3 3.2\n",
      "NumPy 1.13.3\n",
      "Pandas 0.21.0\n",
      "SciPy 1.0.0\n",
      "Matplotlib 2.0.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sys, IPython, scipy, matplotlib, platform\n",
    "print(\"This notebook was createad on a computer %s running %s and using:\\nPython %s\\nIPython %s\\nPyMC3 %s\\nNumPy %s\\nPandas %s\\nSciPy %s\\nMatplotlib %s\\n\" % (platform.machine(), ' '.join(platform.linux_distribution()[:2]), sys.version[:5], IPython.__version__, pm.__version__, np.__version__, pd.__version__, scipy.__version__, matplotlib.__version__))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}