{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# CDS-102: Lab 9 Workbook\n",
    "## Helena Gray\n",
    "### March 30, 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading tidyverse: ggplot2\n",
      "Loading tidyverse: tibble\n",
      "Loading tidyverse: tidyr\n",
      "Loading tidyverse: readr\n",
      "Loading tidyverse: purrr\n",
      "Loading tidyverse: dplyr\n",
      "Conflicts with tidy packages ---------------------------------------------------\n",
      "filter(): dplyr, stats\n",
      "lag():    dplyr, stats\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>month</th><th scope=col>day</th><th scope=col>year</th><th scope=col>t.avg</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1   </td><td>1   </td><td>1995</td><td>40.6</td></tr>\n",
       "\t<tr><td>1   </td><td>2   </td><td>1995</td><td>39.8</td></tr>\n",
       "\t<tr><td>1   </td><td>3   </td><td>1995</td><td>29.3</td></tr>\n",
       "\t<tr><td>1   </td><td>4   </td><td>1995</td><td>33.0</td></tr>\n",
       "\t<tr><td>1   </td><td>5   </td><td>1995</td><td>20.9</td></tr>\n",
       "\t<tr><td>1   </td><td>6   </td><td>1995</td><td>27.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "execution_count": 2,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this code block to load the Tidyverse package\n",
    ".libPaths(new = \"~/Rlibs\")\n",
    "library(tidyverse)\n",
    "# The dataset is in the file \"MDWASHDC_JAN1995_DEC2016.csv\"\n",
    "dc.temps<-read.csv(\"MDWASHDC_JAN1995_DEC2016.csv\")\n",
    "head(dc.temps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 1##\n",
    "The code below generates a summary statistics (mean(), median(), min(), max(), and sd()) report of the\n",
    "average temperature grouped by month using the summarise() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>month</th><th scope=col>mean</th><th scope=col>max</th><th scope=col>min</th><th scope=col>med</th><th scope=col>sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1       </td><td>36.19589 </td><td>63.1     </td><td>-99.0    </td><td>36.10    </td><td>11.903005</td></tr>\n",
       "\t<tr><td> 2       </td><td>38.54678 </td><td>62.8     </td><td> 11.9    </td><td>38.55    </td><td> 8.666396</td></tr>\n",
       "\t<tr><td> 3       </td><td>46.83534 </td><td>76.4     </td><td>-99.0    </td><td>46.60    </td><td>10.634093</td></tr>\n",
       "\t<tr><td> 4       </td><td>56.95727 </td><td>79.8     </td><td>-99.0    </td><td>56.90    </td><td>10.009018</td></tr>\n",
       "\t<tr><td> 5       </td><td>66.27434 </td><td>85.5     </td><td> 50.2    </td><td>65.95    </td><td> 7.144027</td></tr>\n",
       "\t<tr><td> 6       </td><td>74.23167 </td><td>89.6     </td><td>-99.0    </td><td>75.55    </td><td>14.716578</td></tr>\n",
       "\t<tr><td> 7       </td><td>79.70176 </td><td>92.8     </td><td> 65.5    </td><td>79.80    </td><td> 4.813713</td></tr>\n",
       "\t<tr><td> 8       </td><td>77.95806 </td><td>91.0     </td><td>-99.0    </td><td>78.20    </td><td>10.525838</td></tr>\n",
       "\t<tr><td> 9       </td><td>71.11848 </td><td>86.1     </td><td>-99.0    </td><td>72.10    </td><td>11.103055</td></tr>\n",
       "\t<tr><td>10       </td><td>59.88578 </td><td>79.5     </td><td> 40.4    </td><td>59.95    </td><td> 7.446588</td></tr>\n",
       "\t<tr><td>11       </td><td>49.30621 </td><td>70.7     </td><td> 28.2    </td><td>49.30    </td><td> 7.769721</td></tr>\n",
       "\t<tr><td>12       </td><td>40.26364 </td><td>65.9     </td><td>-99.0    </td><td>40.75    </td><td>13.539830</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "execution_count": 4,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "by_month<-group_by(dc.temps,month)\n",
    "\n",
    "temps.table<-summarise(by_month,\n",
    " mean=mean(t.avg), max=max(t.avg),min=min(t.avg),med=median(t.avg), sd=sd(t.avg))\n",
    "temps.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 2##\n",
    "The code below plots the Probability Mass Function (PMF) histogram of the average daily temperatures in\n",
    "the full dataset for each month of the year using the geom_histogram() and ggplot() functions. It uses the\n",
    "facet_wrap() function to create this as a 12 panel plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TEUJut3vSpEkLFy5U/mns2LG5ORwAAAAAALQxIOGIx+P19fWLFi2aO3cuQqi8vHzFihWtra3quxEaba6//nql2XvvvRcKhX74wx8ihDo7OxP+FQAAAACAYYIB73C0t7f39PTMmTMHL44bN668vLypqYm0jSiKL7744vLly61WK0LI7XaPHj06GAx2dXWNTDUzAAAAABi2GHCHAyvMlJWV4UWGYcrKyhJkZ/S0efXVVysrK10uF0JIlmW3233kyJFt27bJsmy325csWYLvjmCOHTu2b98+ZXHFihXKxhFCPM8jhDiOG/wqiQYsyzIMQ2SiPDbCyipE+7JarYIg6DfBUg0mk4mohxzHsSxL6gf8RzIxnyHBrrDb7UTZIbYym80a34gn9F/xudVqNZvN+vfFcZzFYiHSPcQDSVGI0r8jirGH/ygoKCBNrynGHkJIEAQicV7sClI/IIRSxlTCMKML3nTGOcWgtdlsRD7HrhAEQdmpHnieJ52RdPo8ARxThYWFFD/tsLabRn+GDF6sTqZ/L3jCJI13lNsJk8LndBMm6TxmMplkWSYdewghjuO0rwIGJBxY3Q/flsDYbLYEyb+UbXw+3759+9asWaMshsPh8ePHr1y50mKx7N+/f8OGDaNGjZoxYwZu0N7e/vrrryvm999//+CxyDAM0QDFUJgghCjke3mexyeVCHwxI7WiOygKK6IUSkHbFQzDDLnZnPmcZdmcDSQKB9JJR1MPP1KTZKdPQRCEwdLmeenzfA1ejYNKdvbB55hsTJjJoAtebbcbkHDgtE4URcV3oiiq7zfoaXPw4MGxY8dWVVXhRYfDob6BsXjx4uPHjzc0NCgJxyWXXPKNb3xDaWA2m9VSrzgGZFkm0g5nWdZkMhFJxirhFI1GiX7xCIJAKk5qMplYlpUkiUhTGSfsRJLA2A8IIQpXRCIRooQdh2gsFtM4KFmW1T3Jvc/j8TiRA/EcRzH2EEIUDqTwA8MwsViMyBV4giMdezzP4zDUmIsTHIWDNy99Thq8+A4HqR/wmcpl8GoMpGTBS7ovrHZP5PP8njBJ5zFZlknHHr6GRqPR4VVLBSvqezweXOECIdTb24ufjOhsI8vywYMHb7vttmS7YBimoqJC/Qjm2muvvfbaa5VFr9errrxgt9utVqskSaT1LBwOB5EJx3E4fgKBANHpLCkpEUWRaIDiEhikdT3wgxuigzKbzTh+/H4/kbR5aWlpIBAgigQ8EYfDYY1LRTweT6ilgrX9SYvRFBcXh0IhogIiDoeDoq6HxWKxWCykpUAUnxPNqmazmWLs4WszUY0JfGeVdOzhhMPn82kkHAnDrLCwkOM40uDFNUSITHieLy4uRghRDFrSuh7FxcUUhZDsdjvLsqRjDz+VI7LCMeXz+SgufqIoarhCkiT1ISsTZjAYpJgwieKdesI0mUx0EyaRVToTJuk8VlhYGI/HSccevob6/X6LxZKsmQEvjVZUVDidzubmZrzY2dnpdrtnzpypv82pU6fOnTv31a9+VWnf2Nh4xx13nD17Fi/KstzW1lZZWZn1gwEAAAAAQAcG3OHgOK66unrnzp04q9iyZUtVVRX+JvbQoUPd3d21tbUabRBCTU1No0ePVteTnDFjht1uX7t27fz5851O58GDB7u7u6urq3N/dAAAAAAADMYY4a8FCxbEYrGtW7cGAgGXy7Vs2TL8FlhjY+Pp06dra2s12iCEmpubp02bpt6gIAhr1qzZunXrxo0bEUJTp06tq6srLS3N+ZEBAAAAADAExiQcDMPU1NTU1NQkrF+1alXKNgihtWvXDl45evRotTkAAAAAAMMHKN4GAAAAAEDWgYQDAAAAAICsAwkHAAAAAABZx5h3OAwnQbQVv47KMAyRthreCJGJ0hhrpOg3ZBiGZVmifeGDIrWiOCjFmURSuEr39JuobQdrTapR91/dPVIH0vk8BwNJfVDarhjSNgcHha3oDkrbyvDgpRi0OfM5nR8Q1ZkincQUW40Rm9B/tc+HbfDmxufpTJiKKz7o7f3NBx8ghFa4XK7kH1VQDCTFe9o9ZEZmnTNJkih0W4HhQzAYtNlsQ/5TNBqlUzUGhgmyLGtck7T/FRj++P3+ZBU3IHizyuL6+ldOnUII1UyduiO5cmY6aF9bR+gdDp/Pp9Zrs9vtFoslFov19/fr3wgu8+PxePSbcByHxQr7+/uJhPOKi4uDwSCRcJ7D4cDK6xTCeQmlbbQRBAFL0Xs8HiKlUafT2dfXRySc53Q6GYYJBAKhUChZwiFJkrr/LMtiyZaBgQEitceioqJQKESk7lpYWIjlh0nVHs1mM+nYczgcCCGv10ukNFpaWko69kpKSliWDQaDoijqt7Lb7QghIrFCq9WKi5x5vV6Nb9q9Xq96mBUUFGDtcNJBa7PZEupBasPzPBY+phi0Pp+PVGmU4zhRFInUXW02G8dxpGMP1wMjmsdwTBHFO0IIn1O/3x8Oh5MlHLFYTH0eqSfMkpKSQCBAMWGGQiHSQUs6YZrNZnz4vb29+q3whEka7+oJEyHkPx/CflHU2HthYaEkSaRjDyuN9vX1aQTvCE04ZFlWh4ryN1H84MYUJoM7QGpOZJLtg6LeF52JYqjzX5P9nZEdZcRqmPucLjooTHTuKFn/c9m9Yevz3OxIGbHZmJGSzcykPczS3JL+jtIJKDor3MM9bW1HOjr0bId6RkrpCnhpFAAAAADynDUnTgRJ7hJlA0g4AAAAACDPkUiexWQJSDgAAAAAAMg6xrzDIcvy7t27Dx8+7PP5XC7X0qVL8VuHetocOHBg06ZN6pZ1dXVTpkzRs00AAIARTovHs+mjjx68/PLpTqfRfQFGFsYkHHv37t2xY8d9993ndDq3b9++evXqurq6hE/dkrVxu92TJk1auHCh0nLs2LE6twkAQDJaPJ4XW1r6IhGEULEgLJ8+HS5Iecn6lpZ9bW2RePw/brrJ6L4AueD9np71zc3RYfBIxYCEIx6P19fXL1q0aO7cuQih8vLyFStWtLa2KtXntdt0dnZOmTLl+uuvJ90mQA2+FCGE4CKUx+DrkLIYRwguSHkJvvAMh8sPkBueP3ly95kzFt74j1INeIejvb29p6dnzpw5eHHcuHHl5eVNTU0627jd7tGjRweDwa6uLuULHD3bBKhZ39Kyp61tT1vb+pYWo/sCZIUWj6exq0u9Bi5IAJAfRLBszDAQ+TQg5cFiO2VlZXiRYZiysrIEBZ5kbWRZdrvdR44c2bZtmyzLdrt9yZIlc+fOTbnNt956a8eOHcriqlWrysvLlUWsjMZxHBb20QnWfyU1wX8UFBQQfeXMsqzNZrNYLPpNeJ5HCAmCQNRDlmVZlk0wkc+r1b7X3b38nXf+71VXzbjoIrUJ/gNLURGRTP8nGdiBFotFQ/1mcP8xWJpG/744jrNarWazmcgEqRSidDKkz7VRBhLFi0rK2DvZ3f3v772HEPq/V1218dSps/+sd6QcBT6/ZrOZSAISu4LUDwghhmG0B1JhYaH6UakhPicKXoZhsLiWfhN8UGazmSf5VarT53ibgXj8x8eO/eSqq2YXFJDOYxiKeEcI2Ww2DVckTMJpTphE8U49YZJ6T5kwKXxON2H+r/SnKmq040WJKf07Uq6h2jOSAQkHFmVTDwWbzZag1Jasjc/nC4fD48ePX7lypcVi2b9//4YNG0aNGoXF9TS22dnZ2djYqCyGw+HBsyfDMBSqunRCvETnEsNxHIUcO55YSa2Ug2ru6qprbDxx/rfvl37/K62tiGWHlMXNmfe0XZHsPF4oPs+2leKHf29qeqW1FSGEWDY2aDZnWVa9cWpXkJqkDEOTyTT43axhHrwUJoh2IA1pggMZIVQ9aRIO5797PG+2t8cQwrGcy+DVOKgMBq/hPk9Jznw+OF4SontI6IJX2+0GJBw4AxJFURAEvEYUReXmhHYbh8Oxb98+pdnixYuPHz/e0NBw0003aW9z4sSJCxYsUBatViuWesXwPM/zfDweJ5LCZVlWEAT1dlLCMAz+xRyJRIgUas1mcywWIxJUFgSBZVlJkog0lXmeZ1lW8cMz77676x//SGgjRiLqo+Y4Do9dIlcghCwWSzgcJvrhYjabGYaJRqMaOseyLKt7Qu1zQRAkSSLyuclk4jiO1Of4Wk4x9hBCpA60WCyKH8TzexSH2rUkSdiN2OexWIxIWxoPCdKxx/M8Pn0av00TxOaxz0mDl+M4nueJdOsz4nOd0Pmc53kcHYP/SQnktzs6vvT5EEK+cBghFDrfmGIeo4h3hFA0GtWIqUwFL1a7J4339CdMPeRywsQ+H+wHJbqHxGQyybJMOvZw8IbD4WRFJ5AhCQcubOHxeJRbOr29vS6Xi7QNQohhmIqKir6+vpTtZ82aNWvWLGXR6/WqK4zY7XaccBCVHTGZTCaTiciE4zgcP8FgkHT6Jq3rUVRUxLJsNBolraUiCIJikuxSpN6mcrM9EAjoDwaGYSwWSzAYJLqiK7OPRoQnnEeWZbFVKBQimheKi4vD4TDRpOBwOHDCQeRzi8VisViITARBwBe/QCBANKtin+Oxp3j+Pbd7wqD7q8pR4Ct6JBIhqq2A7/2Sjj08ZwUCAY2EI2GYFRYWUvjcbDbbbDYiE57nsc8pBq0oihS5VzQaJarrYbfbWZYd8qCUQI6e7zn2IR48siwTuQLHFFG8o/MXv1AopOEKSZLUh5zmhEkU79QTJulVQJkwiayoJ0yGYf53ilCdLO14KSwsjMfjpGOP53lcgUUj4TDgpdGKigqn09nc3IwXOzs73W73zJkz9bRpbGy84447zp49i9fLstzW1lZZWalnmwAADMnZYPAMSfUpAAAuFJq7upq6uxFC6t8lAySpWAYx4A4Hx3HV1dU7d+7EWcKWLVuqqqrw96uHDh3q7u6ura1N1iYajdrt9rVr186fP9/pdB48eLC7u7u6ulpjmwAApISujhRwwdE9qOQvfr0jHA7DR+95yXONjR1+P/pnaXOjfmAY82HuggULYrHY1q1bA4GAy+VatmwZfqulsbHx9OnTtbW1ydoIgrBmzZqtW7du3LgRITR16tS6ujpcDDfZNgEASEn/oF88zd3dDzQ0LJ8+/caSEkO6BGQDaVBm+Vxj4yunTiFQXslTIkM9gokb9APDmISDYZiampqampqE9atWrUrZZvTo0epmKdsD2QDUkfMMcdAD8rPB4J62tjhCN06caEiXgKwSiccRQn9zuy8//4k7KK/kGS0ez38cO3bc7caLg3PN3GO89BhwwdHc3X3XG2986feDOnLeAxehfAX/xu0SRcHrNbovQFZI0A4eDkDCARBz9vzXCnA1AoALHaPurgPZRmN+9oTDNYcO5b5kEiQcAAAAADCCiEjSGx0dKOcv7ozQhIPjOPUrpYqmMoWYK5GJot1GqoLHMAzLskRW+ABJrbBSr2Ki/eItbqkcFJESpaK5S/Fur7YaYMJ5VLZP4UA6n5MOpASf60FxtbZuo7btgA5lCKzfj6i8R31QKcQKeV79TU3OfK40phi0HMeRqqGjjI5YPR3W7xA85BJOhE5SKo2q+6Ae50R7YRgGa7sRmSByn+PBkI1xPmT3iMaenpYxWR7cDZZl5aHWa6BcQ7XP1AhNOOx2+2C/cBxXXFxMuikKE0QuiY8QstvtFDtSFKKIUA4qpci0+vApXEFXjsFqtWrMdCzLDtkTCp/bbDYNEZtkJLhFJ3QDiaIcg1LsoM3nS9nYZDLhqQSrk5Hui2LspSxx4nA4Bs+kPM/nzOc5qBmEMZvNRKV8MEMelEYge89L25GOW4qxhxCy2WwaUnXJJmGKmkGGTJjZtiIae3p00JOddJPJRBHvHMdp93CEJhwDAwNqvTa73W61WmOxWEINOW1MJpPD4ejt7dVvwnEcFkXt6+sjEs4rKSkJBoOkSqNYbo9CabS/vx8vakv1RSKRnp4es9mMp4Pe3l4ipdHS0lKv10sknFdaWsowTCAQEEUxWSogSZLSf4QQywb8wBYAACAASURBVLJOpxMhNDAwQKo0GgqFSJVGBUGIRCIDJJ+542s50dgTBAEHtsfjIVIaxUUNY7FYi8fTM0iSYTCRSESSJI7jgsFgDpRG7XZ7PB73eDwJtQ7UeDyeBKVRrGOtPukpwUqjXpL3JZWchmLQDgwMECmNFhcX8zwviiKF0qhvqDxSY+QrHyjhcNazIxxTRPGOzhfXxPWwkmVgsVhMfR7TmTADgQCp0ijdhGkymYjiXZkwdXobQzFh6jn8IU86ndKocg3VCN4RmnAAGrzS2rrq6NFJRUU1kyadJAkJ4AJifUtLiGQGBwAASBNIOIBEVr79drvf/4Xf/0l/fwdJkgtcQMAXRgAA5BgDaqkAw5zY+UsR/AIGAAAAMgUkHEBSApBwAMCI4WRPT4vHY3QvgJzyjttdc+jQAw0NuTn1xjxSkWV59+7dhw8f9vl8Lpdr6dKlg19CTtYmGAz+4Q9/aGxs9Pl848ePv+OOO6688kqE0IEDBzZt2qTeQl1dHdRvS4coyZtxQF5yNhC488ABhmFWuFyTyT/YAYYDLR5Ps46XsToCgadOnNj5zW/moEvAMMEbDudSkMOYhGPv3r07duy47777nE7n9u3bV69eXVdXl/CpW7I2mzdvPnny5A9/+MPS0tLXX3/95z//+a9+9auqqiq32z1p0qSFCxcqWxg7dmzOjyyvGA7a+4CxnOrrw9eqqCRtvP56o7sD0LC+peWsvpexTpF8KgXkE7l5qcuAhCMej9fX1y9atGju3LkIofLy8hUrVrS2tqrvRiRrU1lZeeTIkR//+Mc33HADQuiyyy47ffr0wYMHq6qqOjs7p0yZcj3MiQCQOZTvHoesOQlcEOi/llAIeQGAfgx4h6O9vb2np2fOnDl4cdy4ceXl5U1NTXra9Pb2XnrppVdccQVezzCM0+nEH9O73e7Ro0cHg8Guri4IGwAAAAAYVhhwhwMLHCnaIAzDYDEiPW0uvvjidevWKc3a29tbWlruvPNOWZbdbveRI0e2bdsmy7Ldbl+yZAm+O4I5fPjwSy+9pCw+88wzY8aMURaxliKp0igWbyY1wX8UFhYSZUUsy9psNqvVqt8ES6kKgkDUQyz5rLPx+729K95995E5c2YXFiIq5UGHw0Eh+UynNGq324lkQzmOs9lsRHJ72Oekio3Y53QDiUL1Eo89PSqEeE/4/8m8mgwcUxQmpEqjSoUBUgeSHpHa56SDtqCggMgEH5TZbNZ7mhBCyQeS/o14I5E7jxwpsVj+71VXzThftj4Z1EqjGv+aMAmnM2FSxDuinTDpBhKd0qh+PxANHvWshZX4ScceGp5Ko1iUTX3ttNlsCUptKdvIsnzs2LEXX3xx0qRJt956K1avGz9+/MqVKy0Wy/79+zds2DBq1KgZM2bg9h6P5+OPP1bMo9HoYKF4Ukl8DIUJIi8NQGeCUpUdSZMv/f4dn3wiM8yO225DVK7IxkElO48U+8KXJQqrnA0kap+TVgOhOyi6sZeylsrgzkPwKgw5s+m0Dcdir33+OUJICWqiHelBuxpIBoPXWJ9nyUrnQTV3dT359tvvnj2rf8uDS1DRTX3DrpYK/thEFEVFsl4UxQQxVO02PT09L7744gcffDBv3rzFixdj9ft9+/Yp5osXLz5+/HhDQ4OScEycOHHBggVKA6vVqpas5nme5/l4PE4khcuyrCAIRNLXDMPg4giRSIRIkdpsNsdiMSJBZUEQWJaVJIlIU5nnedLHUaHz2ydyBULIYrGEw2GiHy5ms5lhmGg0qqFzLMuyuifUPhcEQZIkIp+bTCaO40h9znEcx3EUYw8hROpAi8USiUSau7oaz53T0175UinBqynBP49Ixx6uBxYKhTRu5iUI/GOfkwYvruxFVCsgTZ+TxjvDMLFYjEjPG6dig31ONIYxYiSicbpxTFHEO0IoGo1q9CdTwYvV7knjnW7CZFmWdOzh6MjehPnMu+/u//RToo1LkhRSVdWRZZl07OHgDYfDGjeWDEg4sDa+x+NR7sj19va6XC6dbT799NNVq1ZNmDDht7/97ejRo4fcBcMwFRUV6sc0s2bNmjVrlrLo9XrVgvl2ux0nHEQq+iaTyWQyEZlwHIfjJxgMEp1OLPJPWkuFZdloNEpaGgARvgGjRHUgECCqpWKxWILBINFsqMw+GhGecB5ZlsVWoVCItJZKOBwmraWCEw4in+NaKkQmSompQCBANKtinz9x9GiHvt0pXyqRRgddLRU8ZwUCAY2EI2GYFRYWUvgc11IhMuF5HvucYtCKokiRe0WjUYpaKoMPiiLh0PYnjimieEfnE45QKKThCkmS1Iec5oRJWkuFbsIkvQooT8qIrPRPmC0ezzsk9zYw6jNOV0uF53lJkoLBoEbCYcBLoxUVFU6ns7m5GS92dna63e6ZM2fqaSNJ0jPPPHP11VevWbNGnW00NjbecccdZ897WZbltra2ysrKnBxQ/tDi8Sw6cMBDmHc3d3c3d3VlqUtANoCvHwFtzgYCPzp6FHTALkT0fwWdewy4w8FxXHV19c6dO3FWsWXLlqqqKvxN7KFDh7q7u2tra5O1OXnyZFdX17x5844fP65s0Ol0zpgxw263r127dv78+U6n8+DBg93d3dXV1bk/ugua9S0tf/38c1KrL/3+eXv37ps/fxzVQ1MgN7R4PC+2tJjN5gcvuww+4xpR9JP80Mdg/ZVIPJ4DMSggswznMknGCH8tWLAgFott3bo1EAi4XK5ly5bh14gaGxtPnz5dW1ubrE1HRwdC6He/+516a9ddd93jjz++Zs2arVu3bty4ESE0derUurq60tJSIw7uAqaX8N6Gwhc+33ONjRu+8pXM9gfIIOtbWva1tSGE3v7yyz7yKxBw4XKGpHg6Bqekw/nSBWSW5u7uFo9nutOZ1b0Yk3AwDFNTU1NTU5OwftWqVdptbrvtttuSvDs9evRotTlAAcXEpADCUMMc5eLRTvLkGMgD4IbWyEGnjP1gzgaDtx04cO2oUf/f7NnXDao0kimgeBvw/4inMTEdP3cOnvgCwLCixeOpOXSoWxRJDSPxODr/qzcL/QKyRTovcARjsTc6Ota3tGS2S2og4RjptHg8DzQ0pF8t8Eu/P6sjFTCWY+fOzd61a09bm9EdAQh46sSJNzo6KIoi4d8eZ4NBCOoLi/SfgmX1OZoxj1SA4YPyaL/h3Lk+ks9uB/M3t/uBhobl06dn+0EgkHu6RBEhtObEiQWXXmp0XwAt8NvBCKHl06en/zkSvMYBZJARmnBgtRZlEf/NMAyRmCvWZaPQf0VJ1BI1wApuRPvC22dZVtsqdv7XD/Ubowpng8E9bW2IZbd9/es6u5dwInSClbI0tqw+ZMXPFA6k8znpQMLai6Qm+A8sXJGy/Qe9vSepnuwmEIrFlh49ihD6P1de6dJ8KRufVrqD0rbCqkTKoiE+Jx20pJqSOoM3AayS+eJHH/3vjSiWTf/tjSG9pJxciu1zHKctE6zendrnpBOmztBQmyByn+PDycY4H7J7KcceqYLwkFugCF6d19ARmnBYrVb1FQufpJQ68INhGIaimAVCyG63k+7IarUS1VLBByUIQsrpm6gnKZFZVr9PsDyUfvBBmc1mDfUbNkkHiAoroPNKO0S1VHD3TCZTzgZSob7XuzYcPdqRiU/zg5K068wZhBBnMr18660aLRVXkO6CZVntg8LyVgk74nk+Zz6nGLQUYw8hZDabFall/VbyeefImShroDGYdY69BKxWq4ZUXbJJePhMmEMa0g0kCquUYy/9+RwLWiKEKMYex3HaZ2qEJhw+n099xbLb7VarNRaL9ZHcgcSh2Nvbq9+E4zgsotrf308knFdSUhIMBkmVRrHcnraeHZEYnx4ikYgenzAMU1pa2tfXRySDWFpayjBMMBgURTHZyJYkqb+/X1lkWdbpdCKEfD4fqdJoKBQiVRoVBCESiQyQfO+D0xqisScIAp6tvF6vHqVRP/lrg0MSOT9o/aKofZbplEbtdns8Hvd4PAm1DtT09fUlKI1iHWv1SU8JVhrFhaZ1otSHoxi0AwMDREqjxcXFPM+LokihNKoM8r91dHjSju4ev3/xvn0PXn65+jkpjimPx0N0CwGfU7/fHw6Hk104Y7GY+jymM2EGAgFSpVE9E2YCWGmUKN7NZjPO1YiuHXomzBaP52/kGqMJRCKRcDhMoTSqXEM1gneEJhxA9jjZ07Onre21L75ACMH7HHkGxeuHgFGcDQbT38ipvr6j586943Zv/5d/gVge5mREY/SNL7+sO3Hi497eH06ZkvEzDgkHkGE6AoE1J07gUh1xhECpcDhAoTUJAAghXzSKEPoyELjr8GHIOYYt+E3hxkyUmBAl6dnjx4PRaDASyfjsDZ/FAplHOn+TH15xN5wWj+dHR49+kuniKaDQMFI4f0/ry0AAPpEdtqxvadnT1papEirxrOnMQsIxomnxeDLy8UIymru701f4ANJhfUvLzjNnfCQvEOgBFBqGJye7u+mEJpOhvurA74dhy4Vyaox5pCLL8u7duw8fPuzz+Vwu19KlSwe/8JysDel6QIP1LS0Z+XghAfH8a034Q1l4sGIgeCaKZkF4vrGzMwfFFwAi/r2pKbOVQuGtneEPtZx57jHmDsfevXt37Ngxb968hx56qL29ffXq1YPfdk7WhnS9Ht7v6amtr//+X/4yQn6LY3XRmkOHGs6dy8b2+//5a5oLJfvOG9TqsQORCMrOZQNucgwTlNP9fk9PdyZeFE3G2UAA7+gDks8rgGyT8Xr04VgMIfR6e3vNoUOZvSYacIcjHo/X19cvWrRo7ty5CKHy8vIVK1a0trbiCvXabSZPnky0Xr1NDf69qWnXP/6BEApl4TWZYYiiLgrkH3va2n7yzjvBaBQh1HDunD/TD1PUHPj88+k7d66eMwfkRw1ECWeW5/+R6Zd11OCa9QghmWG+Nnly9nakprmrq66xMRKJ/OiKK+B2WgItHs/TJ06829mZ2c3iXyfhePyNjo5/9Pf//utfz5TnDbjD0d7e3tPTM2fOHLw4bty48vLypqYmPW1I1+vsklLpdIT8Fs/xYcILhrlkzYkTwfNJRm8oFM5mFd9IPO4OBtecOJG9XaTkZHf3iLo9mUCLx6N8m1Df1kZRp00/yjzZI4q19fX3v/lmDnz+XGPjy6dO7f70U7idNpj1LS1vdHSIJAolpLRntEiWAXc4sMCRog3CMExZWVmC6lGyNqTrlQ0eOHBg8+bNyuL69esrKiqURUV19HBHx2WvvJLBgx2eZPVX72DOBoO3HDhQQCWBZ2JZ10UX/fKrX51x0UXovJ6d1WrVeF6miAUpNHd1rTx69P2urpGQUHrTlqgn5VwgQB016vOraEtjfa1kFBUVqfWbn//LX145dQoh9Oqnn9KNsQuaQDSqXG9C2bzwINWDuXc6O6Vz5xBCB9rbNXyeELyYFEqUPK8OXoZhlCznr+3tI2FyJiI3M/mBL74Y0vPq84s1bTmOKyoq0tiUAQkHFmVTi87abLYEpbZkbUjXK4uBQKCjo0NZlCRJLW0unP87FItlO2hHJuk49lwgUG6zbb/lFmUNm0qzOaHSyvPvvfdXeISUNeKy3JPGD+vB51ejUA46XwZl8HoI3pyhfPee0udDBq92vY+Esw+Ts+FEJClZgCecX6UOSzIMSDjwxyOiKCpS7aIoJoihJmtDul7Z4NSpU++++25l0Wq1iioPPjJ7dn84/H5XV4Tw/jPDMKTli/633gG5Fd2OtPcVk+VQLCZwXESSrDwvIxSKxQSej8Ri/2+R4yKSlNiGxIRLMr/oOSgTy04vK1s2fTo+XxaLhWGYaDSqIRQty7L65DIM85OrruoWxZNdXUQfa2TJ55nd12Croc+pnpOo0cZkQrIsJjEZ8hTj5ZSHpD6/PM/jemCiKGoUHxFFUX3F+ulVV+Vt8JKY/e955/lI8tM05IlGDCNGo3pM1CeaInjxD8JIJKIhUh6Px9XBy7LsY1df3R8O5y54Cc1w5UDDB5JG1CcEr66x8c/zebI5XH1+TSYTz/PxeDwUCmncxDIg4cB3zDwej3Lvpbe31+Vy6WlDul7Z4PTp06dPn64ser1etVD85cXFf1m4MJe1VPr6+oZJLZUErFarIAikZSmU0gD6QwiXBvB6vTrLUuDzhUupRSIRjYQjoQoAy7Izy8v/snDhwMBA/tVS8Xg8emqpKOBHjaRjj+O4YDAYJPkCgrSWSiAQwGUpZFkOBoPaCYd6mF1RUvKXhQtzWUtF/6DF5LiWis/n029isVgKCgpkWSaax3AtFf3xjo8CJxzhcFg74VAfMsdxOHgpBu3wr6XSQ/ItK+mEicHFp/x+P9E8VlhYSFRLJRAI2O12nHBoVLlChrw0WlFR4XQ6m5ub8WJnZ6fb7Z45c6aeNqTrc3VMAAAAAABoYcAdDo7jqqurd+7cibOELVu2VFVV4e9XDx061N3dXVtbm6wNwzBE63V2ST4P6bEQ/bhU9kVqgs73MNsmudwRonJFPB5nWZaihxS7o3ZFznZEZIKhG7F0PSTdERrePke0B5VLn+cm5BGVK3I2S9BZUV8FcumKCz0MiR8+ZQRZlnfu3PnGG28EAgGXy7Vs2TJ8A/aXv/zl6dOn//M//1OjDel6AAAAAAAMx5iEAwAAAACAEQUUbwMAAAAAIOtAwgEAAAAAQNaBhAMAAAAAgKwDCQcAAAAAAFkHEg4AAAAAALIOJBwAAAAAAGQdSDgAAAAAAMg6BiiNDge++OILotIGwDBk4sSJQ64fGBjo7u7OcWeAzJLs5CKE2traKLQ7gWFFsvPb19dHVNUFGIZoBO8ITTji8ThRCRzgAkKWZTi5eQwEbx4DJze/gUcqAAAAAABkHUg4AAAAAADIOpBwAAAAAACQdSDhAAAAAAAg60DCAQAAAABA1oGEAwAAAACArAMJBwAAAAAAWQcSDgAAAAAAss4IFf7KV1599dUXXnhBvWbDhg3Tpk0zqj9AxnnnnXdefvnlM2fOXHLJJcuWLbvyyiuN7hGQAd5+++0nn3wyYeWoUaN27NhhSH+AjBMMBrdu3frWW2+Jojht2rSlS5deeumlRncq10DCkVecPXt2ypQptbW1ypqKigoD+wNklmPHjv3sZz+7++6777rrrvr6+scff/yll14aNWqU0f0C0mXq1KmrV69Wr/n9738/depUo/oDZJz169cfP378Rz/6UUlJyR//+Mef/vSnv//97+12u9H9yimQcOQV586du+yyy2688UajOwJkhT/+8Y/z58///ve/jxC64oornnjiiY8//hgSjjygrKxMHbbHjh0LhUI/+tGPDOwSkEEkSWpoaHjwwQe//vWvI4QqKysXLlz44YcfXnPNNUZ3LafAOxx5xdmzZy+++OJAINDZ2QkFrvKMrq6ujz766F/+5V/wotVqfeGFF772ta8Z2ikg8wSDwbVr1z7yyCM2m83ovgAZg2VZk8mE/xYEASHEMIyhPTIAuMORP8iyfO7cuddee23jxo3xeLygoOD++++vrq42ul9AZujq6sL/Xbdu3WeffTZu3Lh77rnn2muvNbpfQIbZtWvXhAkTZs6caXRHgIzBcdwtt9zyxz/+ccyYMcXFxS+99FJlZeUVV1xhdL9yDSQc+cPAwEA4HJ4wYcLTTz9tsVj27t3761//esyYMVdddZXRXQMygMfjQQht2LDhnnvuGTNmzBtvvLFy5crf/va38KQ/nxgYGNi5c+fatWuN7giQYZYsWXL06NGHH34YL77wwgsj8A4WJBz5Q1FR0euvv64s3n333ceOHTt8+DAkHPkBvh/70EMPXXfddQghl8v197//vb6+HhKOfGL//v0XX3wxnNM8QxTF+++//8orr7z33nvtdntDQ8Pjjz/+7LPPzpgxw+iu5RR4hyNvYRhm3LhxXq/X6I4AmaG0tBQhNHHiRLzIMMyll16Kn7MA+YEsy/v37//Wt75ldEeADNPY2NjZ2fnwww+Xl5fb7fZbbrll9uzZ+/fvN7pfuQYSjvzhnXfe+e53v9vR0YEX4/H46dOnR+Cn3vlKZWVlQUHBxx9/jBfx+R03bpyxvQIyyEcffdTR0XHzzTcb3REg88Tj8XA4rCwGAgF4aRS4gJk9e3ZBQcFTTz31ve99r7S09MCBA11dXQsXLjS6X0BmMJvN8+bNW7duXSQSGTNmzP79+zs6Op5++mmj+wVkjMbGxrFjxzqdTqM7AmSYq666asyYMatWrbrrrrtsNtubb775wQcf/PrXvza6X7kGEo78wWw219XV/fa3v33++ecRQpdffvmGDRvKysqM7heQMe655x6WZf/whz94PJ7JkyevW7du7NixRncKyBjHjx8fgV8ujATsdvuvf/3rLVu2/Nu//VsoFJowYcJzzz03AmWCGVmWje6DAXz22WeRSMToXgBpUVVVNeT6/v7+zs7OHHcGyCzJTi5C6MyZM5Ik5bIzQMZJdn49Hk9PT0+OOwNkFo3ghXc4AAAAAADIOpBwAAAAAACQdSDhAAAAAAAg60DCAQAAAABA1oGEAwAAAACArAMJBwAAAAAAWQcSDgAAAAAAsg4kHAAAAAAAZJ0RmnCAcFAeE41Gje4CkEVGplbhCAGCN78ZodLmTqdTnXPY7Xar1RqLxfr6+vRvxGQyORyO3t5e/SYcx5WUlCCE+vr6YrGYfsOSkpJgMKiu/ZOSoqIik8kUCoX8fr9+K6vVKghCf3+/fhOz2VxYWIgQ6u3t1X8xYBimtLTU6/USJX+lpaUMwwQCAVEUk7UpKChQL7Isi4tTDAwMEMnLFhcXh0KhUCik38ThcAiCEIlEBgYG9FtZLBaLxUI09gRBcDgcCCGPxxOPx/UblpWVUYw9juOCwWAwGNRvhc8C6diz2+3xeNzj8Wj3Rz3MCgsLzWZzNBolHbQ2m42oljLP88XFxQghikE7MDBAdCktLi7meV4UxUAgoN/KbrezLOvz+fSbWCyWgoICWZaJ5jEcU0TxjhDCZRZ8Pp/GPOZwODiOUxbTmTADgQBRvFNPmCaTiSjelQmTSFM1nQnT7/cTzWOFhYXxeJx07Om5ho7QOxwAAAAAAOSSEXqHAwAAAACAIXm/p6eusREhtHz69OmZK19sTMIhy/Lu3bsPHz7s8/lcLtfSpUvxLaYh+clPfvKDH/xg+vTp2rZE2wQAAAAAYEj+valpT1sbQiiO0H/cdFOmNmvMI5W9e/fu2LFj3rx5Dz30UHt7++rVq4d8FihJUn19/ZkzZ/TY6twmAAAAAAAaRM6/KRIleUUsJQbc4YjH4/X19YsWLZo7dy5CqLy8fMWKFa2trVOmTFE3O3To0ObNmxPe+klmO3nyZD3bBAAAAADAEAxIONrb23t6eubMmYMXx40bV15e3tTUlJAcXHPNNVVVVX6//4knnkhpa7FY9GwTAAAAAAANTnZ3n+jqysaWDUg48Gcz+BMphBDDMPhTvYRmDofD4XAkfG6UzDblNo8dO7Zv3z5lccWKFUpjhBDP8wghjuOIXvtgWZZhGCIThmHwH/jzP6J94Q9W9Zvgr8tMJhNRDzmOY1mW1A/4j4TvUbXBrrDb7URPvrCV2WzW+DYsof+Kz61Wq9ls1r8vjuMsFovJZNJvggcSz/OkPqcYe/gP/E2jfkNENfYQQoIgqL9XTAl2BakfEEIpYyphmNEFbzrjnGLQ2mw2Ip9jVwiCoOxUDzzPk85IOn2eAI6pwsJCisfWVqtVwxUJ51EJXpvNRrQvPGGSxjvK7YRJ4XO6CZN0Hvv1u+92nP82WOdspoSh9lXAgIQD5xBWq1VZY7PZdH7HnMw25Tbb29tff/11ZfH+++8fPBYZhiEaoBgKE4QQ0enH8DyPTyoR+GJGakV3UBRWRCmUgrYrGIYZcrM58znLsjkbSBQOpPADSmP4kZokO30KgiAo1yGFvPR5vgavxkElO/vgc0w2JszBqN/bIIoshmG03W5AwoHTJVEUFd+Joqi+30Bhm3Kbo0aNuvrqq5VFrBSkLOIsVZZlIm0ZhmF4nifS88EmCKFYLEaUqPI8H4/HiX4k4Z878XicSCiGZVmWZYn8wLIsDlFSlUCTyURhghCSJEnjoGRZTtgstsqBz/FAyoHPlYFE4UAKPzAMI0kSxW90Uj9wHIdPn8bEGo1G1QkHXfCm43NSB1L7nHQgUfsc5TZ4NQZSQvCmM2FKkpQDn+P73Be0zwej9ls8HtezU3UYamRsBiQcWDnO4/EUFRXhNb29vS6XKx3blNu88cYbb7zxRmXR6/WqdQmxSpokSURihVhplMhEEc7z+/25URqNRCI5UxodGBggVRr1+/0UwnmhUCgcDie70RePx9WHrCiNBoPB3CiNxmIxCqVRIp8rSqM+n49UaZRi7HEcFw6Hc6M0KsvywMCAxi8Qn883WGk0FotRKI0SmShKoz6fj3TQBgIBCqXRcDicM6VRIlfgmCKKd3T+kbd2GEqSpD7kNCdMCqVRigmTWmmUyOfpTJiiKBLNY+rTqjOylGuoz+fTuCNiwGexFRUVTqezubkZL3Z2drrd7pkzZ6Zjm842AQAAAADA9JP8siXCgDscHMdVV1fv3LkTZwlbtmypqqrCn5McOnSou7u7traW1JZhmGTbBAAAALJBi8fzYksLyrQeJWAs/yCp60SEMUqjCxYsiMViW7duDQQCLpdr2bJl+KFsY2Pj6dOnNRIODdtk6wEAAIBssL6lZV8W9CgBY4lnTTPTmISDYZiampqampqE9atWrUpY43A4/vznP+uxTbYeAAAAyAbK5wyZ1aME8hWoFgsAAAAAQNaBhAMAAAAAgKwDCQcAAAAAAFnHmHc4DIdhGPUrpcrfRO+Z4sYUJoM7QGpOZJLtg0qwJd1RNvY15MlNaUWxo4xYpelzUkO6IYGy7z2dO0r2r7ns3rD1eW52xDBMc1fXG9S41QAAIABJREFUL99+u7mnZ/Cm0uxhspmZtIdZmlvS31E6lxtSK8UknU8o9NumPrkjs4a7JEkU+rXA8CEYDNpstiH/KRqN0qkaA8MEWZY1pi3tfwVyw+L6+ldOnVIW50+evOe739Vp6/f7k1XcgOAdDly8adPZ8+pnFQUFf16wYGZ5uU5b7WvrCL3DkaAVaLfbLRYLqVghLvPj8Xj0m3Ach8UK+/v7iYTziouLSYXzHA6HyWQKh8PZFs4TBAEL53k8HiKlUafT2dfXRySc53Q6GYYJBAKhUChZwiFJkrr/LMtiscKBgQEitceioiIsaarfpLCwUBCESCRCqvZoNpspVG4RQl6vl0hptLS0lHTslZSUsCwbDAZFUdRvZbfbEUJEQplWqxUXOfN6vaWlpcmaeb1e9TArKCjAlQpIB63NZhtcM1IDnuexkDHFoPX5fKRKoxzHiaJIpO5qs9k4jiMde1jdlWgeY1k28s8eiEQivb292lb4nPr9/nA4nCzhSFDppZ4wS0pKAoEAxYQZCoVIBy2F0ig+/JQeU4MnTNJ4V0+Y+q3U8fWl3//U22+n/ObZZrNhpdG+vj6N4NWbcFx55ZV33XVXbW3tmDFjdJoMZ2RZVvtU+Zvofg9uTGEyuAOk5kQm2T4o6n3RmSiGOv812d8Z2VFGrIa5z+mig8JE546S9T+X3Ru2Ps/NjtLxf8oeJpuZSXuYpbkl/R2lE1B0VtSuwER0V6VJuSO9L4329fU98sgjFRUV3/72t19++WWivBsAAAAAgBGO3oSjra3trbfeuu+++xobG2tra0ePHn3vvfc2NDQQ3d4BAAAAAGBkojfhYFn2hhtu2LRp07lz51599dVvf/vb//Vf//W1r31twoQJTz75ZGtra1Z7CQDABUSLx3P3a6/d/dprLSRvBgAAkN8QvzQqCMJ3vvOd6urqY8eO3XvvvR9//PHTTz/99NNPX3PNNQ8//PDtt9+u5wVyWZZ37959+PBhn8/ncrmWLl06uNT4kG2OHTv2zDPPJLQsLy/fsmXLgQMHNm3apF5fV1cH9dsAIPcoJTYisRiU2AAAAEOWcMiy/P777+/atWvXrl2ffPIJQuiaa665/fbbPR7Ptm3bampqPvnkkyeffDLldvbu3btjx4777rvP6XRu37599erVdXV1CZnKkG0mT5782GOPqZu98sorkydPRgi53e5JkyYtXLhQ+aexY8cSHR0AABlBqazRGwo90NCAoJooAAD6E47Gxsbdu3fv2rXr008/RQhdffXVa9euXbRoUWVlJW6wcuXKb3zjG88//3zKhCMej9fX1y9atGju3LkIofLy8hUrVrS2tqrvRmi0uf7665Vm7733XigU+uEPf4gQ6uzsTPhXAACM5czAwNFz5xBUE813Tvb0tHg8kFMC2uhNOK655hqE0Jw5cx588MHbb799/PjxCQ3sdvusWbO6urpSbqq9vb2np2fOnDl4cdy4ceXl5U1NTeqEQ08bURRffPHFhx9+2Gq1IoTcbve0adOCwaDf77/oootAGggADEepcw3VRPObjkBgfUsL5JSANnoTjueee+7222+/9NJLNdps2LBBz6aw2E5ZWRleZBimrKwsQYFHT5tXX321srLS5XIhhGRZdrvdR44c2bZtmyzLdrt9yZIl+O4I5q233tqxY4eyuGrVqnKVdBpWRuM4Dgv76ATLuJKa4D8KCgqIPoxmWdZms1ksFv0mPM8jhARBIOohy7Isy5Ka4D+wFBURyfR/koEdaLFYND6PStZ/LE2jf18cx1mtVrPZTGSCVApROqHwuTKQBr/8lBKKsYcQMpvN+iUg8dhDqn7q8QneEcMw2gOpsLBQ/XPCEJ8TOZBhGCyupd8EH5TZbFY8qd+KInhJ57EhkfX502azabgiYRJOc8IkinfqCZPUe8qESeFzugmTdB5D//xzXU9wKddQ7RlJ72jes2fPDTfcMDjh2Ldv3+bNm//yl7/o3A5CCIuyqYeCzWZLUGpL2cbn8+3bt2/NmjXKYjgcHj9+/MqVKy0Wy/79+zds2DBq1KgZM2bgBp2dnY2NjYp5OBwePHsyDEOhqksnxEs0j2A4jqOQY8cTK6kV3UHlzHvarkh2HsHnGAo/IEJXKIevXDBYltXZ1ZRhaDKZBt+/HObBS+dzuoFEYYJoXZGwXz0b4ThOo4cZDN689Hk2JszBJETXic7OD71ePermDMNouz3FKTl1Xi3/b3/724cffoglohXi8fh///d/Hz16NGU/1OAMSBRFQRDwGlEUlZsZOtscPHhw7NixVVVVeNHhcOzbt0/518WLFx8/fryhoUFJOC655JJvfOMbSgOz2ayWrOZ5nuM4WZaJpHBxgBFJXzMMg48oGo0SSZgIgiBJEpGgsslkYllWkiQiSWA8HRDJMCsTDYUrIpEI0Q8XnKfHYjGNg5JlWd2T3Ps8Ho8TORBPBxRjDyFE4UAKPzAME4vFdLriZHf3e243/lvpWzweTzk8OI7jeR6HocYPsgRH4eDNS5+TBi/P8wzDkPoBXyFIg3fwypSnWAlejYGULHhJfS4IQiwWI/J5fk+YRPNYwg6+9PufPXbsD6onBoNRrqHRaJS+lspll12m/H3//fcP2ebb3/629kYSwFmLx+NR7tL09vbiJyM628iyfPDgwdtuuy3ZLhiGqaioUD+Cufbaa6+99lpl0ev1qisO2O12rANPVIYA17MgMuE4DsdPIBAgLQ0giiLRAC0qKsKRQFpLRRAEooNSbrb7/X79wcAwTGlpaSAQIIoEPBGHw2GNS0U8HlcfMsuyTqcTISSKItEFpri4OBQKEdUgcDgceKYjrWdhsViITARBUHxONKuazWaKsYevzTr1hf/tb39rP38synjQ4xOr1YoTDp/Pp5FwJAyzwsJCjuNIg9dsNttsNiITnudxXQ+KQRsMBklrqfA8H4lEiOp62O12lmVJxx5+WkFk9aHXe/x8TqmQ8hTjcyqKooYrJElSH7IyYQaDQYoJkyjeqSdMk8lEN2ESWaUzYZLOY2jQNB4Mh7V7q1xD/X6/xqP/FAnHxo0b8R9Lly5dvnz55ZdfntDAbDbfeuut2htJoKKiwul0Njc34wc0nZ2dbrd75syZ+tucOnXq3LlzX/3qV5X2jY2N69atq6urw5/CyrLc1tY2e/Zsoo4BAJA+8H5o3rO+peVLkoslAGBSJBwPPvgg/mPHjh3f//73r7766vR3yXFcdXX1zp07cVaxZcuWqqoq/PnJoUOHuru7a2trNdoghJqamkaPHq1+vjNjxgy73b527dr58+c7nc6DBw92d3dXV1en31sAAABATWSoH9n9JLcTgJFJioSjp6cHIVRWVvbmm29mcK8LFiyIxWJbt24NBAIul2vZsmX4oWBjY+Pp06dra2s12iCEmpubp02bpt6gIAhr1qzZunUrviUzderUuro6jSK5AAAAQAY5Q1KiHRiZpEg4LrroIrPZHAqFBgtvqPnss8+I9sowTE1NTU1NTcL6VatWpWyDEFq7du3glaNHj1abAwBgOPCrd+SQTgF0YISQIuGorKzEb/oon3sAAADoRCR50Q8AAGNp8XhebGnpI/k6gYgUCYdy60L90SkAAAAAqOkLh0Hd/EJHKbuYJQjkSuLxOC6kghA6e/bso48++thjj7W0tGSnYwAAAMAFQ0iS1sPl4AIn25+Y6dVi+/LLL7/zne+0trb6/f5oNPrNb37z73//O0Jow4YNR48eTfiodfiTIAyHX0dlGIZIjg1vhMhEaYw1UvQbMgzDsiyZWhzD4E5m+6AUZxLJ7Snd02+ittWulaPuv7p7pA6k83kOBpL6oEjLBmX7oAaGUlnQY64clHZLw4OXTokyBwMJxwXdQKLo3mBisqznLGuM2IT+q30+bIM3lz6nmzD1uyLZqUl5jIr3tHuoN+FYtWpVS0vLww8/jBA6cuTI3//+97q6ugULFtxwww3PPvvszp07dW5nmFBQUDDYfRzHJUip6oHCBFGVwLDb7Xa7ndRKEARFrVU/dAdFYUVXwcFms2n8a7LzSFqGAO9Ie19DYjKZcjaQsBQVERQlbxBCVqtVZ2WKtqEUGvT7hGVZ7YMqLi4ePC3yPJ8zn1MMWop4R+cV4UitKOKdYRgiVyQT2NZzlrXLyiQL3hzUacKYzWaysiMIoQthwtQ5jyU7sx/09n4WjaZUN09Zj0xvwvH666/feuutzz33HELotddeKygoWL58ucVimTt37uHDh3VuZPgwMDCg1mvDKmmxWCyhPpw2WGm0t7dXv4kSTn19faTCecFgkFRp1GQyhUIhCqXR/v5+/SZmsxnPp729veqpBL9/hBBaPn364Ce7WDjP6/USCeeVlpYyDBMIBERRTBZCkiSp+68ojQ4MDORGaTQSiQyQfCKIrytEY08QBDwFezweIqVRXASRQmk0GAzqVBqNDXVCI5EI/sZeA6vVarfb4/G4x+NJqHWgxuPxJCiNYu1w0kFrs9m8Xq9+E0VplGLQDgwMUCiNiqKYM6VRonksWRxpn2V8TnHdq2TZQCwWU5/HdCbMQCBAqjRKN2GaTCaieFcmzJQRoSadCdPv9+ucx5J57Aufb83RoxrVgNXXUI3g1Xt/xuPxKDKjR48e/cpXvoJT76qqqs7OTp0bAUYU61ta9rS17Wlrgye7ADASaO7ubvF4jO4FkBUy8nqH3oSjsrLy2LFjCKEzZ84cP378m9/8Jl7/ySefjB49Ov1+APmHMkBB6xoARgJng0H4dQFooDfhqK2tfeONN+68885bbrmF47j58+f39fU9/vjj27dv/9rXvpbNHgIXPAORyAMNDQ80NMCvHwDIb+DXRb6SERE/ve9wPPLII5988snLL78sy/Kzzz47adKkkydPPvfcc1OnTv3FL36Rfj+APObMwMDRc+cQQnGENJ4CAgAAAMOTjEjX6004bDbbn/70p9/97ncIIfytxLhx4xoaGubMmaPz3XU1sizv3r378OHDPp/P5XItXbp08FvcydocOHBg06ZN6pZ1dXVTpkzRs00gZ7R4PM3nX4mKn3/Fr7Gz84GGhiHfIQXyG/x0H847AFygZES6Xm/Cgffn9XrVb/CWl5d//vnnCKEJEyYQfYu1d+/eHTt23HfffU6nc/v27atXr66rq0v41C1ZG7fbPWnSpIULFyotcUl6PdsEcsb6lpazg16wPxsM7mlrg/scIxD8dB/Oe34wACVyACr0JhxnzpyZN2/ehx9+OOS/ut3uUaNG6dxUPB6vr69ftGjR3LlzEULl5eUrVqxobW1Vqs9rt+ns7JwyZcr1119Puk0gl2g8yoWnvCMTOO95AxSGBejQm3A8/PDD7e3tTzzxxCWXXDL4toHGd7eDaW9v7+npmTNnDl4cN25ceXl5U1OTOjnQaON2u6dNmxYMBv1+/0UXXYQ7o2ebgFFkrxQQAAC5Jw6FYQEq9CYcb7311pYtWxYtWpT+LrHAkZKjMAyDxYj0tJFl2e12HzlyZNu2bbIs2+32JUuWzJ07N+U2Dx8+/NJLLymLzzzzzJgxY5RFReeYSLcRCwmTmuA/CgsLiR6JsSxrs9mIXpfBUqqCIBD1ECv10h2UWmNOrVgX+melGpPJpGzf4XCQSrwjhKxWq4ZVMqlKu91OJBvKcZzNZiOSesQ+Vx+gHtLxOYUCI8XYQwhZLBadj02ZJNrGKd2Cd8SyrLZYocPhUP/mwT5XVLl0gjWYqX1OOmixuJZ+E3xQZrM5mfLjkFAMJOxz4uGXXL465VlOKROsNk9nwqSId5TbCZNOJphiwtQ/j2mMN0YzXpRrqPaMpDfhkCTpyiuv1NlYGyzKpr522my2BKW2ZG2wSt348eNXrlxpsVj279+/YcOGUaNGYXE9jW16PJ6PP/5YWYxGozyfeOwMwwxemRIKE0Sook9tghBiWZZCfp/uoNRW2uUSlJbZOKhk55FiXylLAxB1QJv0fa6TbA+kZCdev1u0m/E8P3h0QfAq5GAgabwZl/JEcBync3JIsNLfPWoTNIx9jsl68GqcGh0dTllyRe8Bf+tb32poaJg8ebLO9hrgj0dEUVR+MImimPBQJlkbh8Oxb98+pdnixYuPHz/e0NBw0003aW9z4sSJCxYsUBatVqta6pXneZ7n4/E4kRQuy7KCIBBJXzMMg4X6I5EIkSK12WyOxWJEoraCILAsK0kSkaYyz/MsyxL5geM4nBerXaHRVUmScEuLxRIOh4kSdrPZzDBMNBrV0DmWZVndE2qfC4IgSRKRz00mE8dxpD7nOI7jOIqxhxAidaDFYqEYewzDxGIxndrSyfqjnPdk4DDEp0/jZl6CwD/2OWnwchzH8zxRrYDh7HMMTsVIx97g4E2JxuFrnGX8IzsajWrEVKaCF6vdk8a7gROmHigmTD0+V6P/1CSgBG84HNa4saQ34Vi/fv13vvMdk8m0ePFiito2arA2vsfjUW6c9vb2ulwu0jYIIYZhKioq+vr6UrafNWvWrFmzlMWEz23sdjtOOIhU9E0mk8lkIjLhOA57LxgMEk0lWOSftJYKy7LRaJSilgqRiXLjNxAIKMGgMXDfc7vf/eILV2mpxWIJBoNEV3Rl9tGI8ITzyLIstgqFQqS1VMLhMGktFZxwEDkQ11IhMlFq8gUCAaJZFfucdOzhfEhnLRWNhEP7GK1WK56zAoGARsKhHmYIocLCQgqf41oqRCY8z2OfUwxaURRJL2M8z0ejUYpaKqRjz2QyybKs36rF4/Emn4g0TgS++IVCIQ1XSJKkPuQ0J0zSWip0EybpVUCZMImsGIahmzAZhtE/j2knHBodxtdQSZKCwaBGwqH33tH3vve9aDS6ZMkSm8128cUXj/9ndG4EU1FR4XQ6m5ub8WJnZ6fb7U4ocJ+sTWNj4x133HH27Fm8Xpbltra2yspKPdsEhgmgfwwAFy7rW1pCJNd+AFDQe4ejrKysrKzs0ksvTX+XHMdVV1fv3LkTZwlbtmypqqrCn5McOnSou7u7trY2WZtoNGq329euXTt//nyn03nw4MHu7u7q6mqNbQLDEPhCMi9p8XiePnGiWxSN7giQRSB4AWr0JhzqNyfSZ8GCBbFYbOvWrYFAwOVyLVu2DL+r0tjYePr06dra2mRtBEFYs2bN1q1bN27ciBCaOnVqXV1daWmpxjYBAMgN61ta3ujoMLoXAAAMU8jekv2f//mfAwcOdHZ2rlixory8vL+/f+rUqRR7ZRimpqampqYmYf2qVatSthk9erS6Wcr2AADkhh7Cl+AAABhR6E04ZFleunTp5s2b8eKiRYu8Xu/NN9+8YsWK559/nu5bHQAA8gkQoAQAQAO9L43+7ne/27x589KlS9va2vCa2bNnP/TQQ7/5zW+2bduWte4B+Ulzd/f9b77Z3NVldEeATJKR8k4AAOQrehOOjRs3fuUrX9mwYYPyTUphYeELL7xw3XXXJdRuBYCU4CpuzzU2Gt0RAAAAIEfofaTS2tr6yCOPDH4N8+abb37hhRcy3ausk6B2p+j7Ekm/KZrK+k0UuTdSjTkseUmmBsgweI+kPST1g3JQ6idret7YjUiStuygxh71K40q26dwIJ3PKRxIN/bwH6TCiKQPQOkG0pDbSalBif9IqTSqvpWSM5+r5XFJBy3HcRSK1DkYsTp9PrhvGv+a8izrD171ONfZPWU7WNuNyASR+xwPhmz7XOke3YSp3xXaG9fYiHINzYzS6NixY3t7ewev93q9o0eP1rmR4YPNZht8deQ4TruIw2AYhiE1wdjtdtIdEdUFQOcPShAEonIMKI2DwlbNXV1rjx8/OdRoGQyWlCXqG0LIYrFoqF0lK8ZB4XOr1UpUvwZ3z2Qy5WwgUdRSKSgoIGqPD8psNqdU/NNOfXS6hWVZ7YMqKChQ7wh3j+f5nPmcYtBSjD2EkNls1lm/Rm1FcVBErtCeT1KeZavVqhG8ySZhikF7oUyYRFBPmOnXUtEucqRcQ7V7qDfhuO666/70pz89+uijlZWVysrW1tbt27d/97vf1bmR4YPP51NLqtntdqvVGovFEmrIaWMymRwOx5B5WDI4jsOiqP39/UTCeSUlJf9/e2cfH0V17/8zDzv7lMfdsESMARQCqKyEAFIQ9fZBqsItT9dEaKu1ooKl6vX2aqFeBR+qBqpQVFDg3tJqgV4e9AJaELmUWmiABFgfMBBSCYE87eZhszv7NDu/P85lfuMmO5kzm93ZJN/3H7wywzlnzvme8z3znbMzn+P3+0mVRrHcngal0fb2dvVZjEYjHmQej0cUxef/+tddV1706ZG2tjYi4Ty73U5RlN/v53k+3iQuCIK8/jRN22w2hJDX6yVVGg0EAqRKoxzHhUKhDpLXJ/F0QDT2OI7Dd+XW1lYipdG8vDwNY49hGJ7ne1QaVa5JKBRSdhaz2Wy1WqPRqMfjUdiAGm/iKB1mZmZiHWvSQWuxWFpbW9VnkfaH0zBoOzo6iJRGc3JyWJbleV6D0ijeWEolJpMJbyynfh5TdqK/19f/79mzY222rv+F+7SzszMYDMYLICKRiLwfE5kwfT4fqdKotgnTYDAQ+bs0YRLdOyiKstvt2iZMn8+nch5TVnBWqLD8HqrgvGoXY19++WWGYUpKSv7t3/4NIbR9+/aHHnpo3LhxBoPh5ZdfVlkIMBAAXSAAGLCAjnB/pS0YfPjQIZfHk0ghagOOIUOGHD16dPLkyatWrUIIbdiwYcOGDXfccceRI0cKCgoSqQEwYDnR0HCaJMYH+jRVzc0JzlaA7rg8nqqWFuU08MjRd2mPv8IREIQdtbUJRpMEr5uNHDly9+7dPp+vqqrq888/9/v9u3btAvlwQDMXvN7Vp0/rXQsgRcCzbz9gjct1ieRXHqBv0aOUToLRpNI7HMpy5tXV1fgPh8MxZcqURCoBDFjgYWhAAd3d14Ee7N8kW0pHKeCYPXu2miKmT5/+0UcfEV1VFMXt27cfOHDA6/U6nc5FixZ1fbU1Xhq/3//73/++oqLC6/UOGzZswYIFN910E0Jo7969MYog5eXlsACTYtSsuAIAAAADEKWA4+DBg9LfgiA8+uijzc3NjzzySHFxMcMwp06devPNN8eOHfvee++RXnXnzp1btmxZuHChzWbbvHnz8uXLy8vLY74Ajpdm/fr1J0+efPDBB+12+8cff/zss8+++uqrRUVFDQ0NI0aMmDt3rlTCkCFDSCsGJAjpiqvCT4ZA/wO/xtHtJwxAvwF6GYiHUsBx++23S3//6le/crvdlZWV0mexs2fPXrhw4fjx41999VWiD1Wi0eju3bvnzZs3ffp0hJDD4ViyZEl1dbV8NSJemqFDhx48ePDxxx+fNm0aQmjMmDHnzp3bt29fUVFRY2PjqFGjpk6dStB6oLchXXGtIfmUEejr4Nc43rntNr0rAiQR6GUgHmpfGt25c+c999wjF+FACF199dVlZWUffPAB0SXr6upaWlomTpyIDwsLCx0OR2VlpZo0brd7+PDhN954Iz5PUZTNZsMf0zc0NOTn5/v9/qamJtjTQS9IVyyi0FMDDHgJYCAAvQx0i1rhr4sXL3YrWUpR1KVLl4guiQWOJG0QiqLy8vJiVI/ipbn66qtXr14tJaurq3O5XD/84Q9FUWxoaDh48OCmTZtEUbRarffffz9eHcHs3btX2uoWIbRmzRr517xYuFASmVEJRVEURRFlkcjKyiKKihiGsVqtRNp5uFFGo5FIOA/nIrUD/iMnJ6eWRDMHIdQeCn0diYwbNIjoWmazWcF68foRCxyprxtN0xaLhUhpFFvPYDAkeyBJNtcgVqhh7CGETCZTt0qjJ5ubVx47hhCaOWKEmnBTwTiStjTW14pHdnZ2130JWJYlNSBN09psTqruSlFUZmYm6dhDCJlMJiKlUc3Oq374qZxMFHpZWXQ1ph/lNicdtBr8HaV2wtRw78jOztagka9+HqN62ichXs9K91DlGUltwOF0Onfs2PHMM88Mkt0bWlpatm/fXlxcrLIQDBZlk7ffYrHEKLX1mEYUxaNHj65du3bEiBF333231+sNBoPDhg1bunSpyWTas2fPG2+8MXjw4HHjxuH0Pp+vvr5eyi4IQtf4qUcd+G7RkAX1JALdW1lQahtFumEEQoiPROa8//7Uq6/+xaRJxQ6HylzKe6mgOPXXYEAN2xaglNucNIu2gRQvV/nx41u/+goh9LfLl3kVWpBqjKOcoNvtJPqlzdOwUSo94nhDw2m3u1uPxrvYkNakv06YpFl613m70mPvKtsHx/EK2dUGHL/4xS9+8IMffOtb33r66adLSkpEUayqqnr55ZcvXrwoXzlQA/7YhOd5KXjneT5GDFU5TUtLy9q1a0+fPj1r1qyysjKO4ziOk3/EW1ZWduzYsUOHDkkBx+jRo++77z4pgdls5nleOjQYDCzLRqNRIu1wmqaNRqO8nB6hKApr2geDQSJFapPJFA6HiURtjUYjTdOCIBDp+7IsyzAMkR0YhsHdxPO8hh+zLni9F86cCQvC5jvu6DGxyWSiKCocDisIRYuiKO8UyeahUIjUgJFIhCgLx3EMw5DaHG+tpM3mgUCAyOZms1nD2MM271Zbmr/S0rA6QwmCEM9lWJY1GAy4+xQW83iel9+xsM1JnZdhGKxjrT4L9neUEptj541EIkSC6AaDgaIoUn/HD/Rq5rFTLS0Vly+rKfZiZ+dLR47EeDR+gAyFQgoi5dFoVF4TyeYaBm0oFEqBzVmWpWma1N+lCVN9LoSQ2WzWMPYQQvGctys9Fh7Pf6V7aCAQUFjEUhtw/PM///O6deuefvrphQsXSiftdvs777xz1113qSwEgxdkPB6PtPbidrudTqfKNOfPn1+2bNm111775ptvxts3jqKogoIC+c80Y8eOHTt2rHTY2toq36TAarViYxHtXGAwGDiOI8rCMAy++fE8T7Q1AMdxwWCQaEpzovPgAAAgAElEQVTFnhAOh4lqiPdSIcoibTHl9/s1vz3Dh0JqLiqFDgqTQkw/0jSNcwUCAaJ5wWAwhEIhonsSwzA44CAyoMlkommaKAsOshFCfr+faFbFoTbp2GMYJhwOd7uXihSQtaozlIJx8LYUoij6/X7lgEM+zGia1mBzo9HIMAxRFpZl8c2P53miMNRkMgUCAdLQQYPz4r1USMcetrmaXK9UVNSr/s20q0fjm18wGFQOOOS5GIaRbK5hwiSNvXDAQTphGgwGbRMmUS68l6SGsUdRVDAYVDmPqQk4uq22dA9V2OUKqQ84EEIPP/zwv/zLvxw+fLi6utpgMIwYMeLWW2/VsFllQUGBzWarqqoaPnw4QqixsbGhoSHmd5l4aQRBeOmllyZNmvTzn/9cvrBTUVGxevXq8vJy/CmsKIq1tbUlJSWkdQP0Aj6l6+sE1M2DHfAhdJ8FXgUFTra0JDJREwQcCCGbzZb43rAMw8ycOXPbtm04qtiwYUNRURH+Jnb//v3Nzc3z58+Pl+bkyZNNTU2zZs06duyYvFbjxo2zWq0rV66cPXu2zWbbt29fc3PzzJkzE6wqkDLgU7oBQo/ayUDaAqo5/RuXx9PWUxfX+3yJTNRkAUdvMWfOnEgksnHjRp/P53Q6Fy9ejH+UraioOHfu3Pz58+OlwS9+vv322/LSpkyZ8vTTT69YsWLjxo1vvfUWQmj06NHl5eV2u12PxgEageengYAnEIClrD4KBIv9mzUuV0DF71YtJL8yx6BPwEFRVGlpaWlpacz5ZcuWKaeZMWPGjBkzui0zPz9fnh0AgDQkIAiwlNVHAX2j/o3KR75E4k4t39gAAABoBpayACDdUL8NViJxJwQcAAAAADCgId0GSxsQcAAAAAC9DHyO1LdI5M0M9ejzDofu4E+upUP8N0VRRKK2LMsi1Vq/8gvhvEQqlljfjehaklA0US4s40iaBf+BDaIZ9dfFchcqy5HsrMGA2myuwYCJ2Jx0hVPD2ENxBtJpt/ukumXYmAK7bazUKGVTYN2ImOql2Oakgo+krqHNebECr7ZG9ZjrtNtN9JVKTUdHvF5WsF5Mp8htTjpoSV1D84SZPJt3rZ6GsYfUTX3nSd7M6FqaynvoAA04sNSSdIj/pmk6IyNDfSF4CwyiLBIWi4VU5z/eZhYKWRBCBoOBSEBXQ6OkiSAjI6NHKX4FWJbt8br4WhzHKagAxetH5R1Yui1HkuhRnwWpa4icRGyuvDNFt2gYewghjuO6TiVr//KXevJl2Hj2kWZ85UZZLJaue6ngvTPU1wFrMGuzOdGWRuiKZJM2mxNFKjiXhkapGX5r//IXNdL1EmKcMk0mk4J0VUw/ym3eLydM9bkwGsYekkmNKSCqjueo7hxHuocq13CABhydnZ3yQW+1Ws1msyAIMXvIKWMwGLKysvBetSqR9hXr6OggEs7Lzc31+/1ESqPZ2dkGgyEYDHaSbKiGlUbbSXaNNxqNWIr+8PnzKuUmuyUcDvdoTLvdTlEUz/OBQCCeuwqCIK8/TdM2mw0h1NnZSaQ8mJOTEwgEiJRGs7KyOI4Lh8MdJI8LJpPJZDIRjT2O47DmXnt7O5HSaF5enoaxxzBMIBDoqjTq09Td8TrabDZbrdZoNNrW1haz14Gc9vZ2+b0nMzMTi9CTDlqLxULkvCzL4l3lOjo6iNQe7Xa71+slUhrNyclhWTYQCGhQGvV6veqzmEwmvMlZj6Yg7WsxGo0pE/epz+cLBoN4xuhKjNBnIhOmz+cj8nfNE6bBYCDyd2nCJBp+FEXZ7XYNY4+iKL/frzyPuTwe9VN3K8//paYm5st26R7a3t6u4LzwDgfQO1Q1Nf3owAE1n3ED/QCXx1O6f//HFy/qXREgTWkLBl0ej961AHrG5fH8+JNP1E/d+Mt2bdeCgAPoBaqammbt2nWRcG/62EKam2GG6iuscbk+qa8PkjxsSXxy8eKO2tperxKQViRyWwJSyRqXi3Tq1vxlOwQcQC/wSkXFhYRVCLG6ea/UJ4aqpqb5u3f/9MABCGh6i0S0NHhBePzTT6Ev+j0guNInSGU3QcAB9AJN3e0jqoEkDf1XKir+eObMf9fUwCNXmsBHItAXADDQ0OelUVEUt2/ffuDAAa/X63Q6Fy1a1PUdonhpSM8DKeAsydtPqSd0ZeUfHrnSB+iLPsSO2tqD9fWkuT65eLF0//5flZTA1jn9DM2be+uzwrFz584tW7bMmjXrscceq6urW758eddPnuKlIT0PpIBoL5kaxIL6CrBx6IBixYkTfvL3wXlB+KS+PsGlLPx76IOffAK/wSUD9Yrmci75/T/W9Au1Disc0Wh09+7d8+bNmz59OkLI4XAsWbKkuroa71CvnGbkyJFE5+VlAkniVEtLIl/Dyqloapr/8ce/HD8eHonSE5fH8+uqqrZgsLK5OcGi8Kujc4YP75WKAUliR23tiydOtPC85hISXMp6paJi65kzCCFBFGHPv17E5fGsdbk+bWxs1PSD+EVN+9TrsMJRV1fX0tIyceJEfFhYWOhwOCorK9WkIT2fqjYNaF4/dYpIEUiBoCDsv3gRft1PW9a4XPvr6o41NQkJr2nxgrDixIleqRWQPFacOHGhs1Pb50i9AvwemiTWuFw7amu1RRsYDT2iwwoHFjiStEEoisrLy4tRPYqXhvS8VOD777+/evVq6XDTpk1Dhw6VDk82Ny/du/dUUxOMaQ10kigaqWHvhQtjtm7Ffxto+qZBg16cNq3Y4UBXhPOUxewYhrHb7d3+14H6eqlkQAMdvdrXl30+55/+JO9fJBNqi0dOTo5cJvhkc/PSw4fBeZNE4ouXHMfJ/TEjI0NBpJxlWXDe1JD4vH2wvv5H//u/2Hkl5XUsjhcPHQIOLMpmNpulMxaLJUapLV4a0vPSYSgUkh9Go1H5oH/t+PGPQBggbQgJgnwJ97LPN8hi2XzXXdIZLCesUELM/3JXtIoDkQhIk6UPUVG87PN1278KuWiaBuftQ3AMI++vHrdEAeftK/gjkY/+8Q8i59Uh4MAfj/A8L6m78zwfI4YaLw3pealAp9O5ZMkS6dBqtcoFdJeMG9fM86eamkKEK4cURZG+mor7Q0MubRcivpKmawkI8eGwiWURQoFIxMAwYUHA/8aclA45hgkJgpllxe6ysFdGrYGmx9rtj9xwA+4vvI9GKBQKhULx1jmi0ai8cymKemrSpPZg8GRjI9FDcJrbPMUDCeeKiGIgElHT0WaWRQjxXfo9JouZYaT+NRgMHMeJouj3+xW2U4lRWP95cTE47//lQgghpGHsKVwK93i3HY1dOJ6DGxhGEATnoEE/HzsW+yPu02AwqCBSHuO8NE2D8yb1Qt16dI8929V58V5L0WiU53mFPWJ0CDiwNr7H48nOzsZn3G630+lUk4b0vFTgqFGj5C+Qtra28rJn6Btzcz+cOzcSiWjYS8XtdqvPIm0N0NbWlpq9VAKBQMr2UnG73er9AW8N0NraqnJrANxfOMgIh8MK1hNFUd65NE0XOxwfzp3b0dGRmr1UYpbTeiSRvVQ8Hg/pXioaxh7DMH6/v+teKgrgSUf92MNdhgMOnucVAo5AICAfZmNttg/nzg2HwynbS0X9oMXgLTA07KXC83zK9lIhmsfwz17q/R13Lu7TUCikHHDInZdhGOy8Ggattr1UNEyYmvdSaSH5PIR0wsTgvVQ6OzuJ5rHMzMyYyE8ZnufxlrnRaFRhlyuky0ujBQUFNputqqoKHzY2NjY0NBQXF6tJQ3o+VW0CAAAAAEAJHVY4GIaZOXPmtm3bcJSwYcOGoqIivPywf//+5ubm+fPnx0tDURTReZVVEkVREASiJ0UMUbCpOQtCKBqNki6mRaNRQRBIc4miSGoHbD1EvkKooXqCIGj5xUdT9TTbXIMB09nmuG4acvX4a30MuFFpa3Ok1XlT1igNLZIGEimap740d95UTpikpNJ5NYw9NSOWeO7uFURR3LZt2yeffOLz+ZxO5+LFi/EizIsvvnju3Ln//M//VEhDeh4AAAAAAN3RJ+AAAAAAAGBAAZu3AQAAAACQdCDgAAAAAAAg6UDAAQAAAABA0oGAAwAAAACApAMBBwAAAAAASQcCDgAAAAAAkg4EHAAAAAAAJB0dlEbTgX/84x9EMvtAGlJUVNTt+fb29sbGxhRXBuhd4nUuQqimpkabUCOQPsTrX4/HQ7TDCJCGKDgvrHAAAAAAAJB0IOAAAAAAACDpQMABAAAAAEDSgYADAAAAAICkAwEHAAAAAABJBwIOAAAAAACSDgQcAAAAAAAkHQg4AAAAAABIOhBw9Cs6Ozt/85vf3HPPPbNnz3722Wc9Ho/eNQJ6jYcffvjUqVPSoSiK77777o9+9KNZs2YtX77c6/XqWDcgQWI6t8fzQN8iph99Pt+aNWvKysruuuuun/3sZydOnNCxbqkEAo7+QzQaXbZs2alTpx5//PFf/epXra2tjz32GGgy9gMEQdixY0d1dbX85NatWzdv3lxaWvrUU099/fXX//7v/y6Kol41BDTTbecqnAf6Ft3245o1aw4dOvTQQw+98sorhYWFTz311JdffqlXDVPJAJU275d89tlnp0+f3rRp0/DhwxFCRUVF99xzz5EjR2655Ra9qwZoZ+/evatXr45R4sez2IIFC2bMmIEQys/Pf+CBB7788svrr79ep2oCWui2cxXOA32LbvuR5/l9+/b98pe//Pa3v40QuuGGG7766qs9e/aMGTNGp2qmDgg4+g8XLlxgGGbYsGH4MDMzs7Cw8PTp0xBw9GmmTp06ZswYr9f72GOPSScvXLjQ3Nw8efJkfDhs2LDBgwcfO3YMAo6+Rbedq3Ae6Ft0248tLS3XXXfdTTfdhA9pmrbb7QPk528IOPoPgwYNEgTh8uXLQ4YMQQjxPH/p0qVrrrlG73oBCZGdnZ2dnd3e3i4/iaenQYMG4UOKohwOxwCZs/oT3Xauwnmgb9FtP15zzTUbNmyQDr/++uuTJ0/+9Kc/TXntdAACjv5DcXFxQUHB888//8ADD7As++677/I8HwwG9a4X0Pt0dHQghCwWi3TGYrHA/QkA+hCiKB4+fHjlypWjRo2aNWuW3tVJBRBw9B84jisvL1+7du2KFSsyMjLuvvvuSCRit9v1rhfQ+2RmZiKEeJ43Go34DM/zDodD10oBAKCWpqamlStXVlVV3XPPPffddx/HcXrXKBVAwNGvyM/Pf+GFF/Dfoiju3bv35ptv1rdKQDLAcaTb7c7JycFnWlpaxo8fr2ulAABQxdmzZ//1X//1uuuu+93vfod/AR8gwGex/Qev17tkyZKTJ0/iw6+++qqhoeG2227Tt1ZAMigsLLTb7ceOHcOHly9fvnTp0oQJE/StFQAAPSIIwjPPPDNlypRVq1YNqGgDwQpHfyIzM5NhmFWrVi1cuJCiqDfffPN73/veQBvQAwSGYebOnfuHP/yhsLAwLy9v7dq1Y8aMgU9UACD9qaysbGxsHDVq1JEjR6STeXl5o0eP1rFWqQECjn7Ff/zHf6xevfrVV1/Nycm5/fbbH3zwQb1rBCSLsrKySCTy5ptvdnZ2FhcXP/nkkxRF6V0pAAB6oK6uDiH029/+Vn7ytttue+655/SpUAqhBqY64T/+8Q8Q1enrFBUVdXu+vb29sbExxZUBepd4nYsQqqmpAf3cvk68/vV4PC0tLSmuDNC7KDgvvMMBAAAAAEDSgYADAAAAAICkAwEHAAAAAABJBwIOAAAAAACSDgQcAAAAAAAkHQg4AAAAAABIOhBwAAAAAACQdCDgAAAAAAAg6UDAAfQ3BqaWHQD0A8B5+zcDVNo8OztbLlZotVrNZnMkEmlra1NfiMFgyMrKcrvd6rMwDJObm4sQamtri0Qi6jPm5ub6/f5gMKg+S3Z2tsFgCAQCnZ2d6nOZzWaO49rb29VnMRqNeLd0t9utfr6gKMput7e2thKpRtrtdoqifD4fz/Px0phMJpvNJh3SNI0POzo6iORlc3JyAoFAIBBQnyUrK4vjuFAo1NHRoT6XyWQymUxEY4/juKysLISQx+OJRqPqM+bl5WkYewzD+P1+v9+vPldGRgZCiHTsWa3WaDTq8XgUkuXk5MiHWWZmptFoDIfDpIPWYrG0traqz8KyLN6bV8Og7ejoCIfD6rPk5OSwLMvzvM/nU5/LarXSNO31etVnMZlMGRkZoigSzWPYp4j8HSGUl5eHEPJ6vQrzmMVikZeZyITp8/mI/F3zhGkwGIj8XZowiTRVE5kwOzs7ieaxzMzMaDRKOvbU3ENhhQMAAAAAgKQDAQcAAAAAAEkHAg4AAAAAAJKOPu9wiKK4ffv2AwcOeL1ep9O5aNEi/JtWtzzxxBMPPPDA2LFjlfMSlQkAAAAAQCrRZ4Vj586dW7ZsmTVr1mOPPVZXV7d8+fJuXz4SBGH37t01NTVq8qosEwAAAACA1KPDCkc0Gt29e/e8efOmT5+OEHI4HEuWLKmurh41apQ82f79+9evXx/zmnG8vCNHjlRTJgAAAAAAuqBDwFFXV9fS0jJx4kR8WFhY6HA4KisrY4KDm2++uaioqLOz85e//GWPeU0mk5oyAQAAAADQBR0CDvydLv4mGyFEURTWBohJlpWVlZWVFfN9c7y8PZZ59OjRXbt2SYdLliyREiOEWJZFCDEMQ/TaB03TFEURZaEoCv+B9QaIroUVMtRnYRgGIWQwGIhqyDAMTdOkdsB/YOkFlWBTWK1Wol++cC6j0ajwMXpM/SWbm81mo9Go/loMw5hMJoPBoD4LHkgsy5LaXMPYw39gEQX1GZGmsYcQ4jgOjyiVYFOQ2gEh1KNPxQwzbc6byDjXMGgtFguRzbEpOI6TLqoGlmVJZySVNo8B+1RmZqaGn63NZrOCKWL6UXLeGH2OHsETJqm/o9ROmBpsrm3CJJ3HDAaDKIqkYw8hxDCM8l1Ah4ADxxBms1k6Y7FYVAqnxMvbY5l1dXUff/yxdPjQQw91HYsURRENUIyGLAghou7HsCyLO5UIfDMjzaWtURpyEYVQEsqmoCiq22JTZnOaplM2kDQYUIMdUALDjzRLvO6T4DhOug9J9Eub91fnVWhUvN4Hm2OSMWHGQ5vzKptdh4ADh3U8z0u243levt6gIW+PZV5zzTXf/e53pUOj0ShXu8M+IIoikTIdTdMGg4FI/VNyp3A4TPTEw3GcIAhEGnMGg4GmaUEQiBT6cMBOpIqI7YAQ0mCKUChEFLBjF41EIgqNEkVRXpPU2zwajRIZEM9xGsYeQkiDATXYgaKoSCRCZAo8wZGOPZZlsRsqzMUxhsLO2y9tTuq8eIWD1A64p1LpvAoDKZ7zkl6L47hIJEJk8/49YZLOY6Ioko49fA8Nh8MKkYoOAQeWqvV4PNnZ2fiM2+12Op2J5O2xzMmTJ0+ePFk6bG1tlQsAY1lWQRCIVIGxtDlRFoZhsP/4fD5SpV6e50mlzbEnaJA2J2qU0WjE/tPZ2Ukqbe7z+Yg8AU/EwWBQ4VYRjUblTZakzXmeT420eSQSIZWXNplMRFk4jpNsTjSrGo1GDWMP35tTIG2OAw6v16sQcMQMs8zMTIZhSJ0XS5sTZZGkzTUMWr/fr0HaPBQKpUzanCgX9imv16vh5sfzvIIpBEGQN1maMP1+v4YJk1TaXNuEaTAYtE2YRLkSmTBJ5zHN0uaCIHR2dppMpnjJdPgstqCgwGazVVVV4cPGxsaGhobi4uJE8iZSJgAAAAAAyUaHFQ6GYWbOnLlt2zYcJWzYsKGoqAh/TrJ///7m5ub58+eT5qUoKl6ZAAAAAADojj5Ko3PmzIlEIhs3bvT5fE6nc/HixfgtsIqKinPnzikEHAp5450HAAAAAEB39Ak4KIoqLS0tLS2NOb9s2bKYM1lZWR988IGavPHOAwAAAACgO7B5GwAAAAAASQcCDgAAAAAAkg4EHAAAAAAAJB193uHQnRjRVvx6KUVRRNpquBCiLFJirJGiPiNFUTRNE10LN4o0l4ZGScYkksKVqqc+izyv8hvB8vrLq0dqQG02T8FAkjeK9OXo1DQK59LWKOVcujuvhkGbMptrswPS1FOkk5iUV2HExtRfbvO0dd7U2DyRCVODKbT5Oza7UrKBuYe7IAgadFuB9MHv91sslm7/KxwOa1M1BtIEURQV7knK/wukP52dnfF23ADn7eso31sH6AqH1+uV67VZrVaTyRSJRNrb29UXgrf58Xg86rMwDIPFCtvb24mE83Jycvx+P5FwXlZWFlZe1yCcp3JrGwzHcVha3uPxECmN2my2trY2IuE8m81GUZTP5wsEAvECDkEQ5PWnaRoL0XZ0dBCpPWZnZwcCASJ118zMTCw/TKr2aDQaScdeVlYWQqi1tZVIadRut5OOvdzcXJqm/X4/z/Pqc1mtVoQQkVih2WzGm5y1trba7fZ4yVpbW+XDLCMjA2uHkw5ai8XSdc9IBViWxULGGgat1+slVRplGIbneSJ1V4vFwjAM6djD+4ERzWPYp4j8HSGE+7SzszMYDMYLOCKRiLwfNU+Yubm5Pp9Pw4QZCARIBy3phGk0GnHz3W63+lx4wiT1d/mEqT5XZmamIAikYw8rjba1tSk47wANOERRlLuK9DeR/+DEGrJ0rQBpdqIsyW6U5mtpyyJlVPm/8f7ulQv1Sq40t7k279CQReWF4tU/ldVLW5un5kLSiE3GjBRvZiatYZLmlsQvlIhDacuVsrtAjxeCl0YBAAAAAEg6EHAAAAAAAJB0BuhPKoC+uDyetS6X0Wh8ePToG3Jy9K4OAAAAkHT0CThEUdy+ffuBAwe8Xq/T6Vy0aBF+67DHNEePHn3ppZdiUjocjg0bNuzdu3fdunXy8+Xl5bB/W3qyxuXaVVuLEAqEQm/feqve1QEAAACSjj4Bx86dO7ds2bJw4UKbzbZ58+bly5eXl5fHfOrWbZqRI0c+9dRT8mRbt24dOXIkQqihoWHEiBFz586V/mvIkCGpaQ5ASvjKi9ZhkjeuAQAAgL6LDgFHNBrdvXv3vHnzpk+fjhByOBxLliyprq6Wr0YopJk6daqU7Pjx44FA4MEHH0QINTY2xvwvkJ64PJ6TLS161wIAAABIKTq8NFpXV9fS0jJx4kR8WFhY6HA4KisrSdPwPL927dqf/exnZrMZIdTQ0JCfn+/3+5uamgammllfYY3LVU/ypTsAAADQD9BhhQOL7eTl5eFDiqLy8vJiFHjUpHn//feHDh3qdDoRQqIoNjQ0HDx4cNOmTaIoWq3W+++/H6+OYP7yl79s2bJFOly2bJnD4ZAOsTIawzBY2EclWP+VNAv+IyMjgygqomnaYrGYTCb1WViWRQhxHEdUQ5qmaZomzYL/wFJUPSLKtG812BwhZDKZFNRv4tUfS9OovxbDMGaz2Wg0EmVBMoUolWiwuTSQur781CMaxh5CyGg0EklAYlNoGEgURSkPpMzMTPnPr7rYnMiAFEVhcS31WXCjjEYj9mKiXNpsTpQLo9LfY7BYLAqmiJkQEpwwifxd84RJaj1pwtRg83iCafHABtQ8j5FmYRhGeUbSIeDAomzyoWCxWGKU2npM4/V6d+3atWLFCukwGAwOGzZs6dKlJpNpz549b7zxxuDBg8eNG4cTNDY2VlRUSNmDwWDX2ZOiKA2qutqEeIn6EsMwjAY5djyxkubS1iiVuTpkkos0TWu4lrIp4vXjQLa5HA12QAmYgjRLj25oMBi6SpunufNqs7m2gaRtu42UWY9hGIUa9qLzgs0lUum8ymbXIeDAERDP8xzH4TM8z0uLGSrT7Nu3b8iQIUVFRfgwKytr165d0v+WlZUdO3bs0KFDUsBx3XXXzZkzR0pgNpvlUq8sy7IsG41GiaRwaZrmOI5IMpaiKBxphkIhIoVao9EYiUSIBJU5jqNpWhAEIk1llmVpmiayA8Mw2A1UmqJaJqIcjUaJDGg0GimKCofDCjrHoijKy9Rsc47jBEEgsrnBYGAYhtTmeDrQMPYQQsFgkOjJz2QyaRh7FEVFIhEibWk8JEjHHsuyuPsUnk1jxOaxzUmdl2EYlmWJdOvT3+Ysy2LvUJ+F1Hkx2KeIsiCE8AJtOBxW8Knecl6sdk/q7+k5YUqYTCYNYw/1ZPOuGAwGURRJxx523mAwGG/TCaRLwIE3tvB4PNKCktvtxr+MqEwjiuK+fftmzJgR7xIURRUUFMh/ghk/fvz48eOlw9bWVvkOI1arFQccRNuOGAwGg8FAlIVhGOw/fr+fdPom3dcjOzubpulwOEy6lwrHcURZpMV2n8+nxhkE2SwgCALptRBCoVBIwcNj+pGmaZwrEAgQzQs5OTnBYJBoUsjKysIBB1GjTCaTyWQiysJxHL75+Xw+olnVZDJpGHs4HiLaWwGv/ZKOPTxn+Xw+hYAjZphlZmZqsLnRaLRYLERZWJbFNvf7/UTTt9Fo5HleQ+wVDoeJ9vWwWq00TZOOPXx3IcqFfUqlv8uvhRAKBAIKphAEQd7kBCdMIn/XPGGS3gWkCZMoF0VR2HlJxx5FUaTzWGZmZjQaJR17LMviHVgUAg4dXhotKCiw2WxVVVX4sLGxsaGhobi4WH2aM2fOXL58+ZZbbpHSV1RULFiw4NKlS/hQFMXa2tqhQ4cmvTEAAAAAAKhAhxUOhmFmzpy5bds2HFVs2LChqKgIfxO7f//+5ubm+fPnK6RBCFVWVubn5+NVEMy4ceOsVuvKlStnz55ts9n27dvX3Nw8c+bM1LcOAAAAAICu6CP8NWfOnEgksnHjRp/P53Q6Fy9ejN8Cq6ioOHfu3Pz58xXSIISqqqquv/56eYEcx61YsWLjxo1vvfUWQmj06NHl5eUKm+QCAAAAAJBK9Ak4KIoqLS0tLS2NOb9s2bIe0yCEVq5c2fVkfn6+PDsAAAAAAOkD7BYLAAAAAEDSgYADAAAAAICkAwEHoMJ5CiUAACAASURBVCftJN+tAQAAAH0Xfd7h0B2GYeRihZK+rwYxV6IsknYbqQoeRVE0TRPlwg0kzYWVerU1SoOYXU1HhwZBQGU1wJj6Sx2twYDabK7BgNrGHupJt1E5r0q0DSQs/K+tUT2IFbKsXP4hZTaXEsfMHmpgGIZUDR2lZMSqtHkMeMjFdIT6K6p3Xvk4J7oKRVFY240oCyK3OR4MKbA5rp6GsYeuyNypT0/TtCiKGoYENrtCMmpg7nMmCIKGuyPQK1y9bt2lK4o3QzIy6h95REMhCvIy4XBYmwAwkCaIoqgwqyr/L5D+dHZ2xtsTBJxXG1VNTeUVFQihX0yaVCzbJiz1KN9bB+gKR0dHh1yvzWq1ms3mSCQSsz+cMgaDISsry+12q8/CMAyWD2lrayMSzsvNzfX7/aRKo1huT4PSaHt7u/osRqMRS9G73W418atcGTMajbaQbFVvt9spivL5fDzPxws4BEGQ15+maZvNhhDq6OggVRoNBAKkSqMcx4VCoZi9gZTBSqNEY4/jOLx1lsfjIVIaxZsgko49hmH8fn8KlEatVms0GvV4PDF7HcjxeDwxSqNYx5p00FosltbWVvVZWJbNyclBCLW2thKpPdrt9o6ODiKl0ZycHJZleZ7XoDTq9XrVZzGZTHhfNKJ5DPuUSn+XwH2K972KF3BEIhF5PyYyYfp8PlKlUW0TpsFgIPJ3acIkmvooirLb7fHG3orDh3fV1iKE+GDwndtuk87jCbOzszMFSqPSPVTBeeEdDgAANOLyeB49fNgl2xwHAIDUE77y1BEmefxIPRBwAACgkTUu17aamjUul94VAQCgDwABBwAAGsGPU2n+UAUAQJowQN/hAPTC5fG0waewfR+Xx7PW5aoi+REaSB9w9yGE/rWkZGqc1ykAoNfRJ+AQRXH79u0HDhzwer1Op3PRokX4JRo1afbu3btu3Tp5yvLy8lGjRqkpE9ARl8ez7vPPmwOBAMnLX0B6ssblwi+pAX0Rqfsohpk6bFhVU9OLf/0rQuhnY8eOtdn0rh2gnarm5ocPHUrbftQn4Ni5c+eWLVsWLlxos9k2b968fPny8vLymE/d4qVpaGgYMWLE3LlzpZRDhgxRWSagC/hxqqK5+WJn51VWq97VAbTj8nh+XVUlRKNnSL6pAdIN6Vewo5cvz9+92x0I7PvHPxBCUYTk3zgAfY5Lfv+O2tq07UcdAo5oNLp79+558+ZNnz4dIeRwOJYsWVJdXS3tPq+cprGxcdSoUVOnTiUtE9AL+dPwwNR96Tescbn219XpXQug16j3+f545kzBlcVgeB2nf5C2/ajDS6N1dXUtLS0TJ07Eh4WFhQ6Ho7KyUmWahoaG/Px8v9/f1NQk3b3UlAnoRdqOfoAU6Mp+iZvn9a4C0JtUNTen58fqOqxwYIEjSRuEoigsRqQmjSiKDQ0NBw8e3LRpkyiKVqv1/vvvnz59eo9lHjhw4L/+67+kw5deeumqq66SDrEsK8MwWNhHJVi8mTQL/iMzM5PoWZ+maYvFYjab1WfBcm8cxxHVEEs+a2tUdnZ2twnk0oHUN1WN20OhJUeOPDlhwrhBg9Rfy2w2K1iPpulu62+1WuNphXULwzAWi8VkMhFlQQgZDIaU2RzLfxGhYewhhEwmE8dx3apAdttenIvUDvjfeAMJk5WVJf+pVNphgNSA8caJQhapAqQ65VhcS30W3Cij0Ugku6l+IMUUy195rcofjaq3iXI3xUPZB2Mm4UQmTA3+jlI7YRLlwsQbezEdesnvX/fVV+/eeSe+loZ5TBRF0rGHMyrPSDoEHFiUTX7vtFgsMUpt8dJglbphw4YtXbrUZDLt2bPnjTfeGDx4MBbXUyjT4/F8+eWX0mE4HO4qFE8qiY/RkAVp2nZEmxa78rYj8dDWqHi55LcHzzefpfhIZMtXX4kUtWXGDPUXItpLRUKDAfFtSUOulA0kDbm0DaRTLS3lFRXHGxu7/tfxhgaXx9OtoLIG6yEVe6l0fTcLnFdCTaPivdx2tq1NvU00W0/h1bpedN50s3mv5JI3Si5n3tWk4WhUKl9DozRPfcpm1yHgwB+P8DzPcRw+w/N8jBhqvDRZWVm7du2SkpWVlR07duzQoUO33XabcpmjR4++7777pEOz2czL7nwGg4Fl2Wg0SqQdTtO00WjkSVYjKYrCkWYwGCRSpDaZTOFwmEhQ2Wg00jQtCAKRvi/LsgzDENmBYRhs9nimkFc72F0T+FBIpRlNJhNFUeFwWEEoWhRFeWmSzUOhEKkBI5EIURaO4xiGIbU53lpJm80DgQDRk5/ZbNYw9iiK+vXRo3+qru42wcXOzpeOHNl8xx3yk/jxiEjPm2VZg8GAu0/h2ZTnefn0im1O6rwMw2Ada/VZsL+jlNgcO28kEiEyoMFgoChKzdiLN6qFaFSNJ2KfIpr60JUHwlAopCBSHv1mBSSbaxi0oVAoBTZnWZamaVJ/V54w42E2m+Vj76UjR/777FmEUFgQunaoIAg8z2Obh8NhImF4juNEUSQde/geGggErPG/DNAh4MDa+B6PR1qRc7vdTqeTNA1CiKKogoKCtra2HtOPHTt27Nix0mFra6tcKN5qtWJjEanHGwwGjuOIsjAMg29+PM+TjoBgMEg0pWJPCIfDRDXEe6kQZTEajdh//H5/txNxj/dsQRBUXlEKHRScIaYfaZrGuQKBANG8YDAYQqEQ0T2JYRgccBAZ0GQy0TRNlIXjOMnmRLMqDrVJxx7DMEHFLHwoFFN/vFkG6djDAYfCznwIIZ7n5cOMpmkNNjcajQzDEGVhWRbf/HieJwpDTSZTIBAgnb41OC/eS0VNlnj1F0VRTXbsU/H8PR745hcMBpUDDnkFGIaRbK5hwiR91sIBh4ZBq23CJMpFURR2Xqnv+CutO1RX19nFONgj8NNCMBgkja017KWC76E8zysEHDq8NFpQUGCz2aqqqvBhY2NjQ0NDcXGxmjQVFRULFiy4dOkSPi+KYm1t7dChQ9WUCQAAAAD9jOZAgO8ScKTne6M6rHAwDDNz5sxt27bhKGHDhg1FRUX4+9X9+/c3NzfPnz8/XppwOGy1WleuXDl79mybzbZv377m5uaZM2cqlAkAAABIuDwekIjt91zy+9e4XLePHKl3Rb6BPsJfc+bMiUQiGzdu9Pl8Tqdz8eLF+EfZioqKc+fOzZ8/P14ajuNWrFixcePGt956CyE0evTo8vJyu92uUCYAAAAgscblukSyWg70USoaG6uamsYPHqx3Rf4/+gQcFEWVlpaWlpbGnF+2bFmPafLz8+XJekwPAAAASICYygDhkt//6rFjRB8AJhvYvA0AgETpCIUePnQIwWYc6Q1suTfQCJG83ZwCIOAAACBRjjc349fW0nYTBwDBlnuA3kDAAejPyZYWl8cDT8ZpiMvjefvIEYqi2hU/yZZekofl+nSmpadvI9uCQfDEPoHL4znZB1eqBmjAgT+5lg7x3xRFEYm5Yh03DfqvKI5aogJYwY3oWrh8mqaJcmEdQNIs+A81SqPdUu/zrf38803f/rb6Kyro2cXUX7q6BgNqs7kGAyZic9L98IjG3trPP99RU4MQujojQ016qSF4qGtrlHIurNUhvyJKuc1JRRhJNSW1OS8WlFTIcv6bgs5dCQiCGk+UOlfDXowMwyjLBMvrL7c56YRJ6hqaJ0wNWfAfGuYWaeyt/fzzetVv/pLOYxqcV+U9dIAGHGazWX7Hwn3Zow58VyiK0rCZBUJIQRol3oXMZjPRXiq4UfH2v1DOqK1R8XL5VDz1tobDai6KG2U0GhWUl2ia7rYooo0V0BU5RaI9CHD1DAZDygZS5pV9PtVDNPbEK/cGlTO31HbJFKTVo2lauVFY3ko6lCbilNk8Q13sJb+QhrGHZApRRLmUGqXini3GcZ+uaBh7CCGz2awgVRdvEh5QE6YC0tgTSUJebfOYhrHHMIxyTw3QgMPr9crvWFar1Ww2RyKRmD3klMFzq9vtVp+FYRgsitre3k4knJebm+v3+4mURrOzs7F4c2dnp/pcWGm0vb1dfRaj0YinHo/H0+0jxRkVJjrjdquxpN1upyjK7/cr6NkJgiCvP03TNpsNIeT1eomUB3NycgKBAJFCX1ZWFsdxoVCoo6dHSTl4OiAaexzH4dmqtbWVSGk0Ly+PaOxJFlP5sBgKhXA/4pmRdOxZrdZoNOrxeGL2OpCDN3GUDjMzM41GYzgcJh20FoultbVVfRZpf7i2tjYipVG73d7R0UGkNJqTk8OyLM/zGpRG8cZS3aJmqEg9qAD2qXj+Hg/cp52dncFgMF7QFolE5P2YyITp8/mI/F3zhGkwGIj8XZowie4dFEXZ7XZp7BE1zefzEc1jmZmZGpRGpXuogvMO0IADSCVqZiUNa7MAACQD/DELgm+OgN4GAg4AAADg/yN9zALfHAG9iw57qQAAAABpi/SpEXxzBPQusMIBAAAA/B+fXLyYQfK2IKALHSTvcKQP+gQcoihu3779wIEDXq/X6XQuWrSo6wvP8dL4/f7f//73FRUVXq932LBhCxYsuOmmmxBCe/fuXbdunbyE8vJy2L9Nd1weT1vf9A1AjofkpTMgDVHpibwg8DyfgvoAiVBD8ppq+qBPwLFz584tW7YsXLjQZrNt3rx5+fLl5eXlMZ9Zx0uzfv36kydPPvjgg3a7/eOPP3722WdfffXVoqKihoaGESNGzJ07VyphyJAhKW8ZEMsalytA8no5kJ4E00wjGSAFPLE/Ee2bb9nrEHBEo9Hdu3fPmzdv+vTpCCGHw7FkyZLq6mr5akS8NEOHDj148ODjjz8+bdo0hNCYMWPOnTu3b9++oqKixsbGUaNGTZ06NfUtAhSAn4EBIB0ATwR0R4eXRuvq6lpaWiZOnIgPCwsLHQ5HZWWlmjRut3v48OE33ngjPk9RlM1mwx/TNzQ05Ofn+/3+pqYm+Mayz9EWDD586JDL49G7IgAAAP2EtmBw/u7dD3z8cZpMrTqscGCBI0kbhKKovLy8GNWjeGmuvvrq1atXS8nq6upcLtcPf/hDURQbGhoOHjy4adMmURStVuv999+PV0cwe/fuXb9+vXS4Zs2agoIC6RALF0oiMyqhKIqiKKIsEllZWURREVZwI9IrxI0yGo0aFGpJ7YD/wLJIMai8ekAQdtTWshz33l139Xgts9msYL14/ZiRkUFkc5qmLRYLkVihJAmc7IEk2Tw7O1t9LgzR2CNVXZTarkEdWdKW7nYgSWRnZ8t/fsUXYlmW1IA0TWuzuQZJ08zMTNKxhxAymUxEao/KzqtB9TXeSMamUO6mePSgRPnNfpTbnHTC1ODvKLUTpoZ7x/lgcOWxYwghQV3Tzra2HrxwASFEMYzy1CoHN0rD2GMYRnlG0iHgwKJs8nncYrHEKLX1mEYUxaNHj65du3bEiBF333231+sNBoPDhg1bunSpyWTas2fPG2+8MXjw4HHjxuH0Pp+vvr5eyi4IQtfNOPDeGaTN0ZAFyTZVSWoWlNpGdZuLaAeEcDSq5tJ4zwjSmmgwIFHl5bn0tbkyRHYgtUBM25PRKLwNivJ1e+VCvZgrHZxXw2BWroDmqU+5Jr3lvOlg817PVX78+NavvkIIMep6033l/V+VU2uC1cNxvEICHQIO/LEJz/NSAMXzfIwYqnKalpaWtWvXnj59etasWWVlZRzHcRy3a9cuKXtZWdmxY8cOHTokBRxOp3PJkiVSAqvVKtdtxfr50WiU6PVshmGMRqPf71efhaZpHEXxPE+kSG02m8PhMJG4r8lkYhgmEokQCaIbDAaGYYh0cFmWNRqNCKFupXCJRKAFQVDW07VYLBRFhUKhUCgUb70nRpRX2skiEAgQVQYr9RIpUmu2OdaxVp+FYRi8OYLf7yd6jLNarURjj8hiSNaDeEiQ2oHjOFEU/X6/wnNwjMcZjUaWZQVBIB20BoOByOaanddisQSDQdKxR9N0OBwmUrDmOI6iqG5tfqqlpeLyZfVFYeL5I/YpDWMPIRQMBhXmsRjnHYATZjywzfkr40HlCoe0jXOPU6sco9EoiiLp2JPuoQqbDekQcOB1JI/HI629uN1up9OpMs358+eXLVt27bXXvvnmm/n5+d1egqKogoIC+c80o0aNkr+U2traKp9r8HZ/pAGHwWAwGo2k9wnsP8pe1xWTyRQKhYg8geM47D+kH7lRFEWUxWg0Yv8JBAJdJyDSgEP50jh0UJ5KRFGM6VycC4cp6itjNBpDoRDRVIJnH9KBJIoiTdNEWTiOwwFHIBAgmoitVivR2NMQcOCG4Mcj0rGHAw6FjXJQl2HGsizLsqQ2x2EKURaWZbHzkkauOOAgilyNRiNN06TOi1f+YrJgnfKK5uZ6ki1CMMcbGirq67uqm2Of4nleQ8ARCoWUAw55/ROZMIPBIOktU9uEiQjHuTRhEuXCAQeRs8vpcWqVo8GhpHtoIBBQCDh0eGm0oKDAZrNVVVXhw8bGxoaGhuLiYjVpBEF46aWXJk2atGLFCnm0UVFRsWDBgkuXLuFDURRra2uHDh2akgYBvUYfVbPpl7g8nkcPH24n7JGTLS1p8noagFnjcu2orb1IHm0ghC75/Wtcrl6vEpB6qpqb08ExdVjhYBhm5syZ27Ztw1HFhg0bioqK8PLD/v37m5ub58+fHy/NyZMnm5qaZs2adezYMalAm802btw4q9W6cuXK2bNn22y2ffv2NTc3z5w5M/WtAxKhj6rZ9EvwhhpXEW4LXu/zrXG5YAOO9CHBr2HhY9r+AY4ddXdMfYS/5syZE4lENm7c6PP5nE7n4sWL8WtEFRUV586dmz9/frw0+MXPt99+W17alClTnn766RUrVmzcuPGtt95CCI0ePbq8vNxut+vROOD/wGu5VS0t6rP0UTWbfgm+02j4wrwFNEkBIP1wBwKPHj78yA036LgDsD4BB0VRpaWlpaWlMeeXLVumnGbGjBkzZszotsz8/Hx5dkB3pD0ngQEFLFMBQBpS09Fx+PLlUDSq4zoH7BYLJAtYjO0HaOjE1kAgHX4tBgBADl4/1ndahoADAIC4+Mh33wgIwo8PHDhF8lMaAAADAQg4gKTg8ngqmpr0rgWgHfzFUFDTdl8Xfb6VJ070do0AAEiUjlDo0cOH9VqD1OcdDt3BUkvSIdYMoGkafx6tEpyLKIukwoa/+VafkaIoeYXVXwurk6nPxbIsqR2kimGVtlMtLatPnvx7Y2Ozhk2uKUrNpVmWVbAe9c1CJFlDg8FAJLaIba6hfzUYkFLXcHkW/AcWrlCfEakeezVeL0JI80u8Ry9fdnk8ozMz1WfBjaIoSllTOeZ/U2ZzyWgcx5HKIRgMBiLhS23OyzBMVztoE9yUONnScsbrvembwoySCriGd4oNBoOCokZMpyQyYZL6e8omTEk6nSiXJPyvPkvXIhBCNV7v4cuXBYr6z+98J15CLOarwTtomlZ23gEacGBdHekQ/y1JUqoEb4FBlEXCZDKR6vwbjUYN4vYsyya7UZJX41y//eyz/66pUZ9dTnswWO3zjRs0SPlaBoNBYb8DSekrBg0GxPJ5RFkQQgzDpMzmRFu9YFSOvQS/F7rY2bmqsvLdO+9UnwU3qkdTmM3mrnupxOt0hWtpyIL/wJJrRBlJ/V0a50R3WWyKmEZpU92WqPf5fvvZZ91uw6Fh7CGEOI5T0EBjGEbucXKb99cJk4hEAg58XWxEQfHqNE0zDKNh7FEUpTwqBmjA0dnZKdcKtFqtZrNZEISYPeSUMRgMWVlZeK9alUj7inV0dBAJ5+Xm5vr9fiKl0ezsbIPBEAwGO0k0f8xmM8dx7e3t6rMYjUYsRd/W1iaKoi+BryL5SOSFTz9VeInabrdjIVQFPTtBEOT1p2naZrMhhDo7O4mUB3NycgKBAJHSaFZWFp5PO0i+1DCZTCaTiWjscRyHtxBrb28netrOy8tTOfbEhF8u40MhIu8wm81WqzUajba1tcXsdSCnvb1dfu/JzMw0Go2RSIR00FosFqLqsSyLtyvr6OggUhq12+1er5dIaTQnJ4dl2UAgQKR+bbVaaZr2er3yk0TX7RZfIBBjKOxT2N/Vl4P71OfzBYPBzDhLX5FIRN7kRCZMn89H5O+aJ0yDwUDk79KESTT8KIq6IAga9OklsEfjf8PhsMLVMzMzYzTme0S6h7a3tys4L7zDAaQX8G1LOuDyeNpA9bXv4/J4Tib89u4nFy+W7t8PXx7pi8vjmbVr1yWSICCGNpLn1SQBAQeQXoA2djqwxuUKaHpdVE67rq+nAQih50+cqE/gFoXhBeGT+nrQONeXFcePX0hM4SYgCAgh/God6ZYFvQUEHEB6gbWx9a7FQKdX1pnOtbVtq6mB3tQFl8fz8KFDRxsbe6tAWHrUl69IfnJVAG8zq5c63wB9hwNIZ2Bq6x80+HwIlM51ot/o/FY1NZVXVIRCoUdvvFFHTW7d6d1tHzR8XtQr6BNwiKK4ffv2AwcOeL1ep9O5aNGiru8QxUtDeh7oc+i13AdgXB4P0Q448dD3WWqA02+i9lcqKraeOYMQEkRR973HdMHl8bxw4oQWlYH4hKPRhw8dQghNLyw8cPFiyjZY0ecnlZ07d27ZsmXWrFmPPfZYXV3d8uXLuwZc8dKQnlfDqZaW+bt3/+jDD+H35gTplXsV3KJ0AS/CP3zo0PMnTiTybloMTX4/vHKYYnbU1h6sr+/dMvXa3zx05YOgfhNCkbLG5fqkvl7o1TWJtmBwR23tjtraJz79NJW/e+qwwhGNRnfv3j1v3rzp06cjhBwOx5IlS6qrq/EO9cppRo4cSXReXqYCqyor//vsWYRQIBQamEF0guBlz2AweMHnS/xehTfjGMjLp7ogLcIzJHJJPSKI4if19VkcB56VAk42Ny89fPjQ5ctCb9+eL/n9Tx09Ojwz85Ebbrgp/nePQG+BFzY+93hak7DiK4Uv/kgEpfB3Tx1WOOrq6lpaWiZOnIgPCwsLHQ5HZWWlmjSk51VWCYLoBHmlouKPZ87sqK39vDeegQKCAG8aph5p8Pfus1RM4UBSWVVZ+Ul9fa9HG5jPPB54Czhl4IWNRp4PkYi+aCNli8o6rHBggSNJG4SiqLy8vBjVo3hpSM9LBb7//vurV6+WDjdt2jR06FDpUJJUO1BfP2br1l5s7ACh84q4UOLfUmI+qqvDHWGg6ZsGDXpx2rRihwNdEctTVuhjGMZut8vPVDU1LT18+FRTE9z2FOhMWCFKgXieFdO/SCbUFo+cnBy53iJ+pofOxSS1E7F3771wYdQf/6gmfdfORQhlZGQoKI6zLBvjvBIDbXJOalfG0Oj3a7CtvH9xn0riePHQIeDAomxyAVSLxRKj1BYvDel56TAUCskPo9GofNBzVwKOQCTSW7fMgUlvPRrLO+KyzzfIYtksE1fGcsIK2WP+97Xjxz/qF2/s910UPKvb/lUoiqZpeQLo3JSBvTskCC2qX2Ak7dyuCWByTgFRUVTfp3JI+1eHgAN/PMLzvCR0z/N8jBhqvDSk56UCS0pKli5dKh1arVa5fu2T48e3B4OnmppIF68oiiL9vuj/BO3Jc2m7kIbvnxJpVEQUA5GIgWHCgoD/NbEsQkg6GXNoZllRdiilYaUd12j6Rrv94euvx/1ltVopigoGg6FQKN46RzQalXcuRVFPTJjQzPMnGxuJHoK12AEhRFEptnnvXgj3YGw3GQyirGdj/jdezyKE+EhE3ptdkfcvx3F4LzqfzxdPtx4hFKO4/PPi4maeB+dFePghJMbpxG57zcyyiKL4cFiNt8rdU02jYpwX92kgEFBQW49xXpqmn5o0qT0YTIXzap4wyR+0iAaSfFKNN2EihTn2m84bL4vcSaWBpIy8f41Go8FgiEajfr9f4ftQHQIOrI3v8Xiys7PxGbfb7XQ61aQhPS8VeO2111577bXSYWtrq3yPjBtzcz+cOzcSiWjYS8XtdqvPIm0N0NbWlpq9VAKBQMr2UnG73eq9laIou93e2tqqclsK3F9WqxUhFIlEFKwniqK8c2maLnY4Ppw7t6OjIzV7qcQsp/VIInupeDwe0r1UNIw9hmH8fr/f71efC99d1I+9QCCA94nF3acQcASDQfkwG2uzfTh3bjgcTtleKuoHLcZut3d0dGjYS4Xn+cT3UlHGZDJlZGSIokg0j+GfvdT7O3Yi3KfhcFjBetFoVO5xDMNg59UwaLXtpaJhwtS8l0oLyQd9pBMmBm8+1dnZSTSPke6lEggE8K570WhUYaMcpMtLowUFBTabraqqCh82NjY2NDQUFxerSUN6PlVtAgAAAABACR1WOBiGmTlz5rZt23CUsGHDhqKiIvz96v79+5ubm+fPnx8vDUVRROdVVqm9vb25uZlhGKPRSNQWomATIRSJRC5cuIAQMplMRBsNR6NR0qW+5ubmSCSivJN7V0RRJHpiRgj5/X78pBizb3iPCIJA2qiLFy+Komg0GtVvnRyNRrHNSaunweZutzscDqfA5oFAAK+IpMDmly9fjkajHMexLMF0EfOalBq8Xq/b7aZpmmj/d4/HEw6HWZYl2otcg83D4bC2gaTB5o2NjYIgkA4k0hYhhHw+n8fj6XFX8a6QTn0IIWw9IueVbE6UC2ly3qamJg02T2TCJN2ePjUTJkIoGo2SNqqtrU3VPVTUg2g0umXLloceemjBggWvvPKK1+vF51944YX7779fOQ3peTX85je/KSkpuffee3uvid3z9ddfl5SUlJSUnD59OtnXeuSRR0pKSp577rlkX+jPf/4zbhRe604q//RP/1RSUvLee++pz+J2u3H1/va3vyWvYpgnn3yypKTkF7/4RbIv9Omnn+JG4WXtpDJz5sySkpK333472RfatGlTSUnJnXfeSZTr2WefLSkpWbRoUZJqJXHq1Cls86+//jrZ1yorKyspKXn99deTfaFtG8zaLQAADCRJREFU27aVlJRMmzYt2RcSBAFbb8+ePepz1dTU4FxffPFF8uqGefDBB0tKSl544YVkX2jv3r24UTiASCrTpk0rKSnZtm1bsi/0+uuvl5SUlJWVKSfTR9qcoqjS0tLS0tKY88uWLesxDel5AAAAAAB0B3aLBQAAAAAg6UDAAQAAAABA0mGee+45veugP1ar9YYbbpgyZcrw4cOTeiGGYYYMGTJt2jSn00n6lhYp2dnZJSUlkyZNGjJkSFIvZDQar7322mnTpl1//fWk7wmSYrfbp0yZUlJSoixGKYem6fz8/GnTpo0bNw5/VZs8srKyxo0bd/PNNxcUFCT1QhzHDRs2bNq0aTfccAPRu5wasNlskydPnjBhQl6Sd9CwWq2jRo2aOnXqyJEj1efKzMx0Op2TJ08uLCxMXt0QQgaD4Zprrpk2bdqNN95I9IKqBnJzcydNmjRhwoTBgwcn9UJms3nkyJG33HKL+lfsNTNo0CDshpJ4QY8wDHP11VfjCZPoVWIN5OTklJSUTJw48aqrrkrqhUwm03XXXTdt2rQxY8akYMKcOnXq+PHjsRxD8pDuoXL5ia4Qq6MAAAAAAACQAj+pAAAAAACQdCDgAAAAAAAg6ejzWWw68MQTTzzwwANjx47Fh6Iobt++/cCBA16v1+l0Llq0COuzxjtPyt69e9etWyc/U15ePmrUqN4qX04yypSTmrYk2EGp7N9Udm5v1TkeKWtLIh0EnasZcF5dDCIxwJ13IL7DIQjChx9++Pbbb7/44ouSyXbs2PHee+8tXLjQZrNt3rzZaDSWl5dTFBXvPOlFN23a9Nlnn82dO1c643Q6MzMze6t8OckoU06y25JgB7388ssfffRRKvs3lZ2r0JYEi8WkoC0J9u9tt932zjvvQOdqA5w3xQaJYYA774Bb4di/f//69etjNvWJRqO7d++eN2/e9OnTEUIOh2PJkiXV1dUjR47s9ryGN7obGxvxG/hqrpvIG+PJKDOGpLYl8Q4qLS2N2Sgr2f2bss5NXrESyW5LIv1bU1Pz3nvvVVdXa8gLnYsB502lQWIYyM6Lzw+4dzhuvvnmVatW/frXv5afrKura2lpmThxIj4sLCx0OByVlZXxzmu4bkNDQ35+vt/vb2pqklaVerH8HtuSSJkxJLUtCXZQXl7ed7/73RT3b8o6N3nFSiS7LYn075133mmz2b7zne9oyAudiwHnjaE/9W86Oy8+P+BWOLKysrKysmJ2E8b7YEkyAxRF4V28450nvagoig0NDQcPHty0aZMoilar9f77758+fXpvla+mLYmUKSfZbUmwgxwOB0Lommuu0ZZdQ51T2bkKbUmwWEwK2pJI/2ZnZ+fn58cUCJ2rHnDeGPpT/6a58+LzAy7g6BZsQbkSl8Vi6ejoiHeetHyv1xsMBocNG7Z06VKTybRnz5433nhj8ODBXq+3V8pX05ZEypSTyrZIJNhBSe3fFBskqf2rS+ciwg7CldGWdyB3LgLn7UJ/6t8+4bz9POA4evToSy+9hP9+5plnpOWdGPArtTzPSwKCPM/n5eXFO6/hurt27ZL+t6ys7NixY4cOHbrtttu0la+A5jqrJCsrK2VtkYjXqIsXLyKE5s2bh67YuduL9m7/6ti5Cm1JsFiMLp2L4jRKEITy8nKE0Lx58yTn5Xk+RnwTOlc94Lz9uH/TynnjdVA/f4dj/Pjxv7vCTTfdFC8Zln31eDzSGbfbnZubG+98gtelKKqgoKCtrU1z+Qoko0wFktoWiXiF45vQ888/L9m524v2bv/q2LkKbUmw2G5JTeeiOI0aOXLkiy++iBB6/vnnJTu73e6cnJwe80LnqgGcN+Z/+1P/6uu88TqonwccHMflXkFh+4OCggKbzVZVVYUPGxsbGxoaiouL450nvW5FRcWCBQsuXbqE/1cUxdra2qFDh2ouX4FklCknlW2RiFf48OHDbTZbTU0NtnO8i/Zu/+rYuQptSbBYjC6di+I0asKECddffz3uX+y8+Lz0MZ5CXujcbgHn7cf9m1bOG6+DBujmbcFgcMeOHd/5znfw8ixN04Ig/OlPfyosLOR5/s0338zNzb333nvjnSf9gnnQoEEff/zx8ePHLRZLZ2fnu+++W11d/cQTT1it1l4pX05v1VnftiTYQaFQKGX9m8rOVWhLr/RvytqSSP/Onj17586d0LkaAOfVxSAS4Lz9/B0O9cyZMycSiWzcuNHn8zmdzsWLF+P+iHeeCI7jVqxYsXHjxrfeegshNHr06PLycrvd3lvlq2xLr5Ditkgk2EHJ69/UGyR5dtarcxXK73o+Go1qzjuQOxeB83ahP/Vvn3Degag0CgAAAABAiunn73AAAAAAAJAOQMABAAAAAEDSgYADAAAAAICkAwEHAAAAAABJBwIOAAAAAACSDgQcAAAAAAAkHQg4AAAAAABIOhBwAAAAAACQdCDgAAAAAAAg6UDAAQAAAABA0oGAAwCAFLFu3TqKos6cOaN3RQAA0AEIOAAAAAAASDoQcAAAAAAAkHQg4AAAIBXcfvvtixYtQgiNGTNm8uTJ3ab54IMPbr311ry8PIvFMnr06Oeeey4UCiGEFixYQFHU119/LU9cUlJit9uDwSBC6PDhw9/73vdyc3PHjh373HPPffzxxxRFHT9+PPnNAgBALRBwAACQCl599dWf/exnCKG33377tdde65pg3759s2bNamxsfOihhx599NGsrKzly5cvXboUIXTvvfcihHbs2CElPnv2bGVl5fz5841G4wcffPDtb3+7pqbmwQcfvOWWW15//fWf/vSnqWoWAABqoURR1LsOAAAMCNatW7do0aIvv/xy9OjRXf93wYIF//M//3PhwoWcnByEkCiKEydO9Pv9X3zxRSgUys/Pv/766//617/ixC+88MIzzzxz4sSJG2+8ccyYMQzD/P3vf8/NzUUIff7558XFxeFw+NixYxMmTEhlAwEAUABWOAAASAtee+21s2fP4mgDISQIAs/zfr8fIcRx3Ny5c//2t79dvnwZ/+/WrVudTmdxcfHf//738+fPP/744zjaQAjdcMMNc+fO1aUJAAAoAAEHAABpgcPh4Hn+j3/849KlS++66668vLwvvvhC+t+ysjJRFHfu3IkQ+vzzzz/77LOf/OQnFEVVV1cjhMaOHSsvKuYQAIB0AAIOAADSgnXr1o0aNerhhx+urq6+44479uzZM336dOl/b7/99sGDB2/fvh0htGXLFpZlFyxYgBAKh8MIIYqi5EUxDJPaugMA0DOs3hUAAABAfr//iSeeuPPOO7dt28ZxHD4pCIKUgGGYe+6554033mhubt66deuMGTMGDRqEEBo1ahRC6IsvvrjlllukxPKlEQAA0gRY4QAAIKVEo9GuJy9cuBAIBIqLi6Vo48svv/z000/licvKyqLR6LPPPnv27Nmf/OQn+OSkSZOuuuqq1157raOjA5+pqanZtm1bkhsBAAAxEHAAAJAijEYjQmjVqlW7du1CCP3hD3/IyMh47LHHEEIjRowYPnz4b37zmyeeeGL9+vWPP/74rbfeeu2119bV1T355JOBQAAh9K1vfauwsPCtt95yOBx33nknLtNqta5ateqrr76aMGHC0qVLn3jiicmTJ99xxx0IIbPZrFtTAQDoAgQcAACkiLvvvvv73//+1q1b169fjxCKRCI+nw8rd7Es++GHH06bNu13v/vdc889d/78+T//+c/79+//wQ9+8Omnn0YiEYQQRVFlZWUIoR/96EcGg0Eq9t577/3www8HDx68du3aI0eOvP766z/+8Y8RQoMHD9annQAAdAfocAAA0IcRBKG1tdVisVgsFunkq6++umzZsmAwSNPwTAUA6QJ4IwAAfZhIJFJYWHjfffdJZ4LB4DvvvPP9738fog0ASCvgKxUAAPowRqNx8eLFq1atmjNnzh133BGJRDZs2HD+/PkNGzboXTUAAL4B/KQCAEDfJhKJ/Pa3v924ceP58+ezs7NHjx794osvTpkyRe96AQDwDSDgAAAAAAAg6cBvnAAAAAAAJB0IOAAAAAAASDoQcAAAAAAAkHQg4AAAAAAAIOlAwAEAAAAAQNKBgAMAAAAAgKQDAQcAAAAAAEkHAg4AAAAAAJIOBBwAAAAAACSd/wfWcBWQC3xLugAAAABJRU5ErkJggg=="
     },
     "execution_count": 10,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all.months.temps<-ggplot(dc.temps) +\n",
    "geom_histogram(mapping = aes(x =t.avg, y = ..density..),\n",
    "binwidth = 1, fill = \"cyan3\", color = \"cyan4\") + facet_wrap(~month)\n",
    "\n",
    "ggsave(\"all.months.temps.png\", plot = all.months.temps, device=\"png\", scale=1, width=5, height=4)\n",
    "all.months.temps\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 3##\n",
    "\n",
    "The code below creates the normal distribution model for the month of June (all years) using the summary\n",
    "statistics computed in task 1 by generating the Probability Density Function (PDF). It then stores\n",
    "the computed values of the model in a new two-column tibble named jun.model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "74.2316666666667"
      ]
     },
     "execution_count": 15,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "14.7165782544198"
      ]
     },
     "execution_count": 15,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.temps.june<-filter(dc.temps,month==6)\n",
    "\n",
    "jun.pdf<-dnorm(x = dc.temps.june$t.avg, mean =74.23167, sd = 14.716578)\n",
    "\n",
    "jun.model<-tibble(temps=dc.temps.june$t.avg,PDF=jun.pdf)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 4##\n",
    "\n",
    "The code below creates a new plot containing the average daily temperature PMF histogram and the normal\n",
    "distribution model for June (all years). Note whether or not the model visually agrees with\n",
    "the histogram.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 23,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jun.ggplot<-ggplot(data=dc.temps.june)  + geom_histogram(binwidth = .5, mapping = aes(x=t.avg), alpha=.5)\n",
    "\n",
    "data.ggplot.full <- ggplot_build(jun.ggplot)\n",
    "data.ggplot.table <- data.ggplot.full$data[[1]]\n",
    "histogram.table <- tibble(x = data.ggplot.table$x, density = data.ggplot.table$density, frequency = data.ggplot.table$count)\n",
    "\n",
    "mean.june<-mean(dc.temps.june$t.avg)\n",
    "sd.june<-sd(dc.temps.june$t.avg)\n",
    "\n",
    "\n",
    "options(repr.plot.width = 6, repr.plot.height = 4)\n",
    "data.ggplot.june<-ggplot(data=histogram.table)  + geom_col(mapping = aes(x=x, y=density), alpha=.5) + stat_function(fun=dnorm, args=list(mean=mean.june,sd=sd.june), color= \"red\")\n",
    "\n",
    "\n",
    "ggsave(\"data.ggplot.june.png\", plot = data.ggplot.june, device=\"png\", scale=1, width=5, height=4)\n",
    "data.ggplot.june"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 5##\n",
    "The code below creates a qqplot for the average temperature distribution in June. A theoretical line is\n",
    "computed and included for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 21,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the 1st and 3rd quartiles (0.25 and 0.75 percentiles)\n",
    "qq_y <- quantile(dc.temps.june$t.avg, c(0.25, 0.75))\n",
    "# Find the matching normal values on the x-axis\n",
    "qq_x <- qnorm(c(0.25, 0.75))\n",
    "# Compute line slope\n",
    "qq_slope <- diff(qq_y) / diff(qq_x)\n",
    "# Compute line intercept\n",
    "qq_int <- qq_y[1] - qq_slope * qq_x[1]\n",
    "\n",
    "\n",
    "qqplot.june<-ggplot(dc.temps.june) +\n",
    "geom_qq(aes(sample = t.avg), color = \"cyan3\") +\n",
    "geom_abline(intercept = qq_int, slope = qq_slope, color = \"black\")\n",
    "\n",
    "ggsave(\"qqplot.june.png\", plot = qqplot.june, device=\"png\", scale=1, width=5, height=4)\n",
    "qqplot.june"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 6##\n",
    "The code below creates a 12 panel series of qqplots (without theoretical lines) for each month (all years)\n",
    "using facet_wrap(). Note whether the trend for June applies to the other\n",
    "months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 22,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all.months.qqplot<-ggplot(dc.temps) + geom_qq(aes(sample = t.avg), color = \"cyan3\") + facet_wrap(~month)\n",
    "ggsave(\"all.months.qqplot.png\",plot = all.months.qqplot, device=\"png\", scale=1, width=5, height=4)\n",
    "all.months.qqplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 7##\n",
    "The normal distribution model is used to compute the temperature of the 0.10 percentile for the\n",
    "month of June (all years) using the qnorm() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "55.3716164246408"
      ]
     },
     "execution_count": 18,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The top 90% of temperatures are the temperatures in the 10th percentile or higher\n",
    "\n",
    "june.mean =74.23167\n",
    "june.sd = 14.716578\n",
    "june.p10 <- qnorm(p = 0.10, mean = june.mean, sd = june.sd)\n",
    "june.p10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lab Task 8##\n",
    "The normal distribution model is used to compute the percentile of the temperature 83◦F for the\n",
    "month of June (all years) using the pnorm() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.72434995525201"
      ]
     },
     "execution_count": 24,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnorm(q = 83, mean = june.mean, sd = june.sd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Key Questions##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "For the month of June, what is the probability that any given day will have a temperature\n",
    "of 83◦F or higher? The code below uses the pnorm() function to find this probability.\n",
    "\n",
    "How cold are the coldest 10% of days? The qnorm() function is used to find this average temperature of the 10% coldest days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.27565004474799"
      ]
     },
     "execution_count": 26,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "55.3716164246408"
      ]
     },
     "execution_count": 26,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnorm(83, mean=june.mean, sd=june.sd, lower.tail=FALSE)\n",
    "qnorm(0.1, mean=june.mean, sd=june.sd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Report the mean for the month of March with a 68% and a 95% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 95% confidence interval for the unfiltered dataset is  74.23167 +- 29.43316 \n",
      "The 68% confidence interval for the unfiltered dataset is  74.23167 +- 14.71658"
     ]
    },
    {
     "data": {
      "text/html": [
       "103.664826"
      ]
     },
     "execution_count": 27,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "44.798514"
      ]
     },
     "execution_count": 27,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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       "88.948248"
      ]
     },
     "execution_count": 27,
     "metadata": {
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     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
       "59.515092"
      ]
     },
     "execution_count": 27,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci.95<- 2* june.sd\n",
    "\n",
    "cat(\"The 95% confidence interval for the unfiltered dataset is \", june.mean, \"+-\",ci.95,\"\\n\")\n",
    "cat(\"The 68% confidence interval for the unfiltered dataset is \", june.mean, \"+-\",june.sd)\n",
    "\n",
    "june.mean+ci.95\n",
    "june.mean-ci.95\n",
    "\n",
    "june.mean+june.sd\n",
    "june.mean-june.sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
   ],
   "source": [
   ]
  }
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