{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Using Jupyter Notebooks on Crane\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### HCC Fall Kickstart 2018 - October 16, 2018\n",
    "\n",
    "##### Carrie Brown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What is Jupyter Notebook?\n",
    "\n",
    "Jupyter Notebook is a web-based application used to create documents that contain code, visualizations and narrative text. Can we used interactively, or to create static reports.\n",
    "\n",
    "**These slides were created using a Jupyter Notebook**\n",
    "\n",
    "You can find this notebook in `job-examples/jupyter/jupyter_overview.ipynb`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Layout of a Notebook\n",
    "\n",
    "Notebooks are built of cells which can be executed individually. Cells can either contain code or text.\n",
    "\n",
    "Text cells are rendered using Markdown, a lightweight markup language that can be used to create basic formatting or even including more complicated elements such as LaTeX. Similar in concept but simplier than HTML."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "With markdown, we can insert:\n",
    "\n",
    "### Headlines:\n",
    "\n",
    "# Headline 1\n",
    "## Headline 2\n",
    "### Headine 3\n",
    "#### Headline 4\n",
    "##### Headline 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Bulletted nested lists:\n",
    " - item 1\n",
    "     - subitem A\n",
    "         - subsubitem a\n",
    "     - subitem B\n",
    " - item 2\n",
    " - item 3\n",
    " \n",
    "### Numbered nested lists:\n",
    " 1. item 1\n",
    "     1. subitem A\n",
    "         1. subsubitem a\n",
    "     1. subitem B\n",
    " 1. item 2\n",
    " 1. item 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Links:\n",
    "[Holland Computing Center](http://hcc.unl.edu)\n",
    "\n",
    "### Tables:\n",
    "|         | col1   | col2   | col3   |\n",
    "|---------|--------|--------|--------|\n",
    "| row1    | data11 | data12 | data13 |\n",
    "| row2    | data21 | data22 | data23 |\n",
    "| row3    | data31 | data32 | data33 |\n",
    "\n",
    "### Even LaTeX equations\n",
    "Both inline: $e^{i\\pi} + 1 = 0$ and displayed:\n",
    "\n",
    "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Kernels at HCC\n",
    "\n",
    "The ‘kernel’ is a program that runs and introspects the user’s code. We have several popular kernels available for Jupyter Notebooks at HCC.\n",
    " - Python 2 and 3\n",
    " - R\n",
    " - MATLAB\n",
    " - SAS\n",
    " - Julia\n",
    " - TensorFlow (Python 2 and 3)\n",
    " \n",
    "If you need an additional kernel, let us know by completing a software installation request: https://hcc.unl.edu/software-installation-request"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using libraries and packages in Jupyter Notebook\n",
    "\n",
    "HCC has many of the commonly used packages and libraries already installed such as SciPy and the core tidyverse packages in R.\n",
    "\n",
    "If you need custom environments, you can build them yourself using Anaconda. For information on how to use Anaconda with Jupyter Notebooks, check out our documentation at https://hcc-docs.unl.edu/display/HCCDOC/Using+Anaconda+Package+Manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Accessing JupyterHub on Crane\n",
    "\n",
    "Login using your HCC credentials at http://crane.unl.edu\n",
    "\n",
    "Select one of the Notebook options from the dropdown menu and click \n",
    "\n",
    "Once your server starts up, open a new notebook by clicking the **New** button in the top right corner."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Example Notebook\n",
    "\n",
    "## Iris Data Set in R\n",
    "\n",
    "### Load the data\n",
    "\n",
    "We can load the built-in iris dataset in R by using the `data()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>5.1   </td><td>3.5   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.9   </td><td>3.0   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.7   </td><td>3.2   </td><td>1.3   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.6   </td><td>3.1   </td><td>1.5   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0   </td><td>3.6   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4   </td><td>3.9   </td><td>1.7   </td><td>0.4   </td><td>setosa</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       "\\hline\n",
       "\t 5.1    & 3.5    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 4.9    & 3.0    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 4.7    & 3.2    & 1.3    & 0.2    & setosa\\\\\n",
       "\t 4.6    & 3.1    & 1.5    & 0.2    & setosa\\\\\n",
       "\t 5.0    & 3.6    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 5.4    & 3.9    & 1.7    & 0.4    & setosa\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | \n",
       "|---|---|---|---|---|---|\n",
       "| 5.1    | 3.5    | 1.4    | 0.2    | setosa | \n",
       "| 4.9    | 3.0    | 1.4    | 0.2    | setosa | \n",
       "| 4.7    | 3.2    | 1.3    | 0.2    | setosa | \n",
       "| 4.6    | 3.1    | 1.5    | 0.2    | setosa | \n",
       "| 5.0    | 3.6    | 1.4    | 0.2    | setosa | \n",
       "| 5.4    | 3.9    | 1.7    | 0.4    | setosa | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
       "1 5.1          3.5         1.4          0.2         setosa \n",
       "2 4.9          3.0         1.4          0.2         setosa \n",
       "3 4.7          3.2         1.3          0.2         setosa \n",
       "4 4.6          3.1         1.5          0.2         setosa \n",
       "5 5.0          3.6         1.4          0.2         setosa \n",
       "6 5.4          3.9         1.7          0.4         setosa "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(iris)\n",
    "head(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Create a plot\n",
    "\n",
    "We can use popular libraries right in JupyterHub, such as `ggplot2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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uv3w3QbPfPDt5/ZsHCTZZ9LtRmjQ7sHRvXuiHGs40eJI3LHpPqFm617GlSzITqk\nW3BsH3/bm6XgkmFNZYWhVXtMnqDc1RE+o9xXbGe6ExwFKdNnUL7zzjsvvvhiQuONN954wQUX\nPPjggy6X6/HHHxdCaJr2yiuvLFq0KBqNjhs37pprrmnlzhNer7flFMftV1hYmDAPC1oqKioK\nh8Mcim0dh2JTETsU6/P5jttDsSkym82yLGfiTU9PYodi3W53xxyKzV0citW9jAe7TCDYZRHB\nLhUEu1QQ7FJEsEsFwS5FBDvd64hbigEAAKADEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATB\nDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAA\nQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgE2q2CwAAHNE+l7pzlyGqST0daq+C\ncMLSr7ZaNtQYTIo2smegomsw9W7DUem7/cZaj5JvjpzYNWg2aGmtunNp9MsbDhg9QalbXuSE\nkqAkZbsgIJMIdgDQSf1zrW3RZsv3z4yje/svHdEUiyXhsPjjJ4WuwKGjLiv3mMpLQtef2phK\ntzVNyotVeXVeJfbUbopOH+suKwylufrOYcVu0z9W2oORQ2mud0F45qmNFl0HWRznOBQLAJ3R\nmr2mZqlOCCGW7zR/tdUce/zif5zxVBezqdqw8PD1k9KEeHW5I57qhBBNAfnV5Y5gWIcDWbUe\n5Y1mqU4IsbNefXu1PYslAZlGsAOAzujrXaZkjYeC3fY6Q8ul/9lmbrPb/S51b2PisZpGn7yp\nJkmHuW71HmMokhhYV+81haI6TLFADMEOADojbzBJ+PB83xhNdizR3yLEpNitEMIb0uHHQdIf\nKhIV/hDBDrqlw/9kANCBYnukZWOp41CjUUmS7Aot0Ta7LbInT38lyV4u1yXdhzajZje2vaOA\nHEWwA4DOaNJAn1E9LL2psjblBG/s8Znl3sQNJHHx8KY2u3Wao6f29Sc0Duoa7K3HiydG9gzE\no3DclAoPF8ZCxwh2ANAZFdkiM09xdc07NMVJF1tk+lh3fMaTSQN94wf45O8DilHVrhjh7ulM\nnA8lqQuHeCYO8KmyJoSQZTGmt//yEW5dRh2Dol13iuuEkkOHn61GrfIkz2ktci2gJ5Km5d5V\n316v1+tt8W213QoLC+vq6tLerc4UFRWFw+GGhoZsF9KpKYpis9lcLle2C+nUDFlfzT0AACAA\nSURBVAaD0+n0+XwejyfbtXRqEcmiCVnVku+l3Y2qRY12sR31scVoVDT4Fac5qsi59ynQksVi\nsdlsbrc7EAi0XBoMS56glG+JMlZnt9sDgUAodCwDtEVFRWmvB2nHPHYA0KnZTJosa0f6Mpvi\nKF1LsiwKrTo8ry4po6olHNcG9IpDsQAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7\nAAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAA\nnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAIOOqvcEv9rq9oXDSpQc98taD\nhmj0qLvVhKj3KZ5gR7+TxwoOH6HgpoBc7z2WkqKaqPMqvpB0DNsGw+KASxyppPao98ruAJ+V\nyBlqtgsAAD37ttYzZ2mBHCoSQrwrhOrYc/94xWo49N777T7jvG8cgbAkhJCEOKlH4OrR7hR7\nXrHbtOBbWyxzdHeGfzysqVdB8uCYRhuqjX9f7ohlL0mIwd2D08e44ku31xneXGXf71KEEPmW\naOVJniHdAin2/PkWy8cbrP6QJIQYUBy6eGhTsT2SyobeoPzOGtvKPSZNE6rsOLWveu6JXoOs\nHfXP1sK3+4zvfWuPhdRSR+TiYU39uoTa3y2QUXwLAYBM8Ycis6tK5VBevCXs7vF/Fx8aVjro\nkf+2LC+W6oQQmhCr95j+scqeSs8bqo2vfe2IjyTtbVRfXOJs8GX2Lb3RL89ekhcfUdOE+Hav\nce43jtjTgx7lpaq8WKoTQjT45P9d6th60JBKz0u2m9/71ub/vufNNYaXlvywZ1qhCfH3rx0r\ndps0TQghwlHx+RbLgm9tR/mTJbGjzvC/S/PiQ48H3MpLS/JqmpT29wxkFMEOADLlpbVuJWpO\naIw0dd/dFBBCvLPaHm0xrrRsR+L6SX283prQ4g1KX2yxHGOhqXk3WcErdpliQXXRZou/RRRr\nWWdLmhD/arHaQY+yfKepzW23HzRsrE7Mjku2mRv97f10+3hDYknBsLRwU2b3MNB+BDsAyJRq\nlzFp+4oanxCi1pNk+CeqCX+47XfmpNtWZ3g8qcabpH9NiEaf3J6S/CGpKdlJbLWetk8WSvqi\nmhC17d4VNe4kJTFih86PYAcAmWIxJT8lq6ddFULYjEnOA5OEMKptXwJgMSRZx2bMwLUDzftP\nVpgkhMMQFUJYj7Ukk6KpyU6Js6awbdIXTfF1W2czJSnJnqwR6FQIdgCQKT+pMGoiMQpEFM+I\nYrsQYvwAX8tNuuWlMF4nxJjeSS5KGJ2sMY0mDvC1POut2B5R1SO+etI6E8iyGNEzcTWDog3v\n0fa25cWhfEtihutVEC7NS+nCi1aM7uVv2TgqWSPQqRDsACBT+jstFf22NM92USk07dTa2OOh\n3QOjegWaRyWbUbthvEuk4IwB3qHdf8g9iqxdMNjTvyiz12xWdA2OLTss2VgM2s9Obzy0tDQ4\npcKrNvtUGdUrMK5fkvDaUuVJnr7NLjg1qtpPhqd0VaxR1a4a7XaYf8h2xfbIlaPcxzJjyuFO\n7ecf0/uwH3byQO/gbsF2dwxklqRpuTew7PV6vV5v2rstLCysq6tLe7c6U1RUFA6HGxoasl1I\np6Yois1mc7lS+oQ+bhkMBqfT6fP5PB5PtmvJrG0u/5sbAm6foasjeO3QPJNy2Dfq3fXqV9st\n3pA0oDg0rm+SGGQ2m2VZTvqmt6NO3dVgMMjagOJQF1t7x6hStKdR/WqrxROQ+haFxg9IvBC3\npknZUmuIRKU+haGe+Ucx/YomxOYaw75G1WrUTigJNs9qbQqEpS31dk/IlGf09S/0qOkbstjT\nqG6vMyiS1q9LqMTRQXs4o+x2eyAQCIWO5TtAUVFR2utB2hHsfkCwSwXBLhUEu1QcP8GunVoJ\ndoizWCw2m83tdgcCmT0enesIdrrHoVgAAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmC\nHQAAgE4Q7AAAAHSCYAcAAKATarYLOBaSJCmKkomeM9St/rCjWqcoSub+SnVDlmWRyX9n3ZBl\nmb3UptifkyzL7KjWxXZRNHoUN/ZAbsnJO08Eg8HY/3B6qaoaDh/FDXCOT6qqapoWiejh1jqZ\nI0mSLMvspdbFwko0GuUzpnWxtzv2UutkWY790+Xih1pHkmVZ07Rj20uqmpODQcebnPwlhcPh\nDN1SjDtltamoqCgSibCjWsctxVIRu6VYIBDglmKt45ZiqYjdUszr9XJLsdZxSzHd4xw7AAAA\nnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAncjJ\nO08AQNo1+uXPNlp31asmVasoDY7r51fkLN+cyhP1P33gH195vo2I6BhLxa2lPylQHdktCUAn\nR7ADAFHvU55cmO8NSrGnW2oN6w8Yrz+tUZayVpIvGjhv453rfDtiT5e6v3u74fPPTphVqOZl\nrSYAnR6HYgFAvLPaFk91MVtqDct2mLNVjxDi6QP/iKe6mD3Bmt/veyVb9QDICQQ7ABBbaw0t\nG7ccTNLYYb5q+rZl45dNazq+EgA5hGAHAEIkO+SavcOwRyR1xqIAdCIEOwAQA4pCSRqLkzR2\nmHGOoS0bxzuGdXwlAHIIwQ4AxNQhTTbjYdfAlheHRvfyZ6seIcTNpT8ebOnbvKWXseTerldn\nqx4AOYGrYgFAFFijt59Z/++Nll0NqkkVFaXBU8t8UlYPe5okwwcDH/mf6re/8q4Ni8gY8wm/\nLP2xU7FlsyYAnR7BDgCEEMJhilae5Ml2FYexyKbbu17+a7NZlmWv15vtcgDkAA7FAgAA6ATB\nDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAA\nQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcI\ndgAAADpBsAMAANAJNdsFAEBn8bl71QrvJodineAY1t/UI/UNfdHAB43/2R7Y18tYcp7zFLti\nyVyRzVU1rV3uWW+VTRMcw8vNPTvmRTNEE2LDAeO+RsVq1E4oDeZbotmuCMhJBDsAEMFoaMa2\n33/iWh57apTUX3efdlPJJalsu86/46otD+0KVseedjUUvtz3vpG2gZmqVQghRFiLzNz+x/cb\nlsSeGiX1rm5X/qr00oy+aOb4w9KLVc4ddYc+kgyKdvHQptG9A9mtCshFHIoFAPHIgdfiqU4I\nEdTCD+6ZvaRpbZsbRrTo9dv+HE91Qoj9obrrt//ZrwUzUuj3Zh14PZ7qhBBBLfzw3v9d7F6V\n0RfNnHdW2+KpTggRikhvrbJXu5UslgTkKIIdAIh5Bz9r2fh6/cI2N1zl3bzBvzOhcWfwQFUK\nobA95h38tEWbNr8uyU/R+UWiYuUeU0JjKCqtatEIoE0EOwAQ9WF3y8a6kKvtDSNJNhRC1IXb\n3rY9GqJNLdqkumQ/RecXjEiRqNSy3RviEwo4avzbAIAYYE5yqcRAS69j21AIMdDc9rbtMcCU\n5FKJTL9ohpgNmsOU5FKJYnu444sBch3BDgDEr7tPS2gpUQuuL57a5oZ9jF2v6jIloXFq/ukn\nWfqlrbhk7ul2VUJLoZL3s5KLMvqiGSIJce6J3oTGYntkdC8ungCOGsEOAMSUvDH/0+eOboYu\nsadjbYNeL3+oSHWmsu0fet54Y3GlUVKFEAZJvabovCd735LBWoUQQkx0DH+x7z09jcWxp6Ns\nJ7w+4KGuhsJMv26GjOntv+ikJptRE0JIQpxQErzuFJdR1bJdF5B7JE3Lvf8cr9fr9SZ+vWu/\nwsLCurq6tHerM0VFReFwuKGhIduFdGqKothsNpcrs2dZ5TqDweB0On0+n8fjyXYtP9gXOmiV\nzU7FdrQbhrTw/lBdqVpglA3pLclsNsuyfKQ3vf2hOrNkzFft6X3RrNCEaPTJVoN2DJHOYrHY\nbDa32x0IMM7XGrvdHggEQqHQMWxbVFSU9nqQdsxjBwA/iA/aHS2DpPYylqS3mFTk7ihdS5IQ\nzEsMtBOHYgEAAHSCYAcAAKATBDsAAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAA\ngE4Q7AAAAHSCYAcAAKATBDsAAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q\n7AAAAHSCYAcAAKATaoe9UiQSmT59+jPPPFNQUNBy6Ztvvjlnzpz4U1mW33777Q6rDQAAQAc6\nKNgFg8G5c+e63e4jrVBdXT1s2LDKysrYU0mSOqYwALmoKeKrCTf0MpaokpLGbkMiNGvf64Ms\nfS7MP73l0rAW2R2q6aLkORRry6VBLbwreKCHsdgsGY/2dbcH9u0N1Y61DlLlJO/JLr+kCcmU\nbENNiAafYlQ0mzF6tC8KQJc6Iti99957s2fPDofDraxTXV1dUVExZsyYDqgHQO7aE6y5Z/df\nPmz8jxDCIpt+WXLJHV2vUKQ0nFUyeM306nB9/OlFznEv9Ls79jiqRWcdeP3JA294o34hxJS8\nMX/q9bNexpLYUm/U/9DeOS/XfhjWIookX1545kM9fupUbKm86AcNS27a8YQ76hVCSEK6KP/0\nv/a9O750S63hrdX2arcihCiwmi46qenErsH40hW7TQu+tbkDshCiuzP842FNvQpae5sFcDzo\niHPsJkyY8MQTT9x1112trHPgwIHi4uIOKAZA7gpGQzO2/T6W6oQQvmjgkf2vPXFgfvt7Pm39\nz5qnOiHEO41fvFr/UezxM9Vv/mHf32KpTgjxsWvZ1Vv+b0ALxZ7evfv5F2v+GdYiQoiIFv37\nwU9u2TkrlRfdEThw7fY/xlKdEEIT2tsNX9y565nY02q3MntJXizVCSHqvfLfljl21B36Nr6h\n2vja145YqhNC7G1UX1zibPBx2jRwvOuIETun0+l0OkOh0JFW0DSturp6+fLl8+bN8/v9gwYN\nuu6667p37x5fIRKJbNy4Mf7U4XDY7fZMlKqqHXfSYe6SJIkd1TpZltlLbVIURQghy3LqO2pB\nXdUq7+aExif3v35r90stctJjlana5NvbsvG/dzw/o/iCQDT0+P55CYu+82//p6vqsi5nbgvs\nm3vw04Sl7zcsWRPYOsI2sPUXvXfrXyJaJKHx1dqPZ/X9lRBi0RZrMHLYSSnhqPTpRtsN4zxC\niE82JI4IeoPSV9tslUN9rb+oXsmyLI7yz+n4JMuyoiiapmW7EGRKp/gHcLlcgUAgHA7fcsst\nkUhk3rx5991337PPPmu1WuMrTJs2Lb7+DTfccMMNN2Sikvz8/Ex0qzOKorCjUsFeSoXJZDKZ\nUs1ke+oPtmz0a8EGk6+brbR9hST5nAtpkfz8/O2+fZ7vx+qa2yVq8/Pz99euS9rdXrl+Ult/\nADvCB1o2hkVU2NV81V6f5DVFrdcQ+7uq9SRZWuc35ee3K+DmOpstpSPgxzmj8ahPA0UO6RTB\nzm63v/zyy06nM/aVq7y8/Nprr62qqpo8eXJsBbPZPGPGjPj6Q4YM8fnS/63UbDb7/cneStGM\nxWKJRqOBQCDbhXRqkiQZDIZgMNj2qscxWZZNJlM4HG5lOD+BUyT52JaEZI+Y2/ueICWJdrKQ\nfD6fJWKQJTmqJV6d4BRWn8/n0CxJ+8vTLG2WlC8nOfIgCckQknwhn0U1CpF4aYjNGPX5AkII\nq9HsCSReZGY1RHy+4/SvTlXV2D9dJJI4CIrmDAZDJBKJRo/lahuLJflfOzqVThHsFEVpPgeK\nw+EoKSmpra2Nt1gslptvvjn+1Ov1ejzJvq62j8lkykS3OhMLduyo1imKIssye6l1BoPBZDKF\nQqHUd9SZluH5qr0h3NS8cYpztCWoeoLt2ttGyRDUEvPlBMdwj8ejCHF+3ikLGr9qvsihWKdY\nRnk8nnLRfZC5zzr/juZLy4xdRxrK2/y5flZcudT9XULjieY+Po9PCDGiR2jNnryEpSN7eD0e\nvxBiVE/tw3WJF+cO69bk8aSaknXGYrEYDIZAIMDXztbZ7fZAIJD6t6nmCHY5oVOcabtq1ao7\n77wzPhmK1+utqanp1atXdqsC0NmUqAXP9bk9X/1hoGuotf+s3re0v+cVJ74oicMGwPIU6/wB\nv409fqzPTSOs5fFFTsX2bJ/behiLhRCKJP+173/3MXaNL+1hLH6h790mydDmi051nn554ZnN\nWwpUx9sn/CH2eEi34JkDDxvzO7mP/5S+h44qnDHAO7T7DwlGkbULBnv6Fx2nqQ5AnNRhZ1Bu\n3rz59ttvf/nll+ODcx999FEgEKisrAwEAj/96U/79es3depUk8k0f/78urq6J5988kjnwHq9\nXq/Xm/YKCwsL6+rq0t6tzhQVFYXD4YaGhmwX0qkpimKz2VwuV7YL6dQMBoPT6fT5fEc7tFkX\ncX3a+PWBUH2FpfeZjpFyOuY6ibls82+We9YbFXVmlwvu6nZl80VRLbqwaeU63/YiNf+svNFd\n1MPG0gJa6GPXsm3+fb1NpVPyRltlc+ovusKz8aXaDxoi7vGOYTcUT01YesCt7HZZI5rU0+Ht\n7kyczWRHnbqrwWCQtQHFoS624/oQpMVisdlsbrebEbvWtWfErqioKO31IO2yeSi2qqrK5XJV\nVlaaTKbHHnvshRdeePLJJyVJGj58+O23386VTQCSKlTyLi2clIme40N0LcmSfKZj5JmOkUmX\nmiTDhc7ThPNYXnSEbeDTR75+ttQR6VMckmXZ600yR12fwnCfQuauA/CDjhuxSyNG7LKIEbtU\nMGKXimMesTvemM1mWZYz8aanJ4zYpahzjtj5/f7nn3/+73//+5YtWyKRSP/+/S+88MLbb7/d\n6Tymb0spGz16tN1uX7hwYUZfpYN1inPsAADA8SkUCk2ePPm2227Ly8v75S9/ecstt3Tt2vWh\nhx4aMWJEY2NjRl/aYrHo74oQDncCAICs+etf//rVV189+uijd9xxR7zxrbfeuuSSS37zm9/M\nmpXSfVyOzeeff565zrOFETsAAJA1VVVVQoiE+w5cfPHFJ5100hdffJGlonIYwQ4AAGRN7Fz/\nVatWJbR/+OGHr7/+uhAiHA5LkvT888/PmzdvwoQJeXl5p5xyyksvvdR85Z07d1555ZVlZWUO\nh2PcuHFvvfVW86VLliw555xzunTp0q9fv2nTph04cOimL+PGjTvjjDNS6aSxsfGuu+7q37+/\n2Wzu27fvbbfd1mnPoibYAQCArLn88suFEJMmTZo2bdqiRYvi9+zp3r17375946vNnTv3mmuu\nGThw4M0339zU1DRz5syHHnootmj9+vXDhw9fvHjxFVdccccddzQ0NFxyySVPP/10bOmCBQvG\njx+/a9euX/7ylxdffPG77747cuTI6urqhDJa7+SKK6544oknhg0b9utf/3rw4MGzZs266aab\nMrpbjhlXxf6Aq2JTwVWxqeCq2FRwVWyKuCo2FVwVm6LOeVXsK6+88n/+z//Zvn27EMJms51x\nxhnnnnvuVVddFZv4NhwOGwwGIcSHH354zjnnCCG8Xu9ZZ521cuXKrVu3du3atbKycvXq1StW\nrIitHwqFpkyZsnTp0r1799psthNPPNFkMi1ZssRutwshPvvss8mTJz/yyCN33nnnuHHjVFWN\nXRXbSifRaLRLly633HLLk08+GSv4qquuWrZs2YYNGyQp8c5+WceIHQAAyKZp06Zt2bLlm2++\neeSRR8aNG/fpp5/efPPNvXv3XrBgQXydU089NZbqhBBWq/WBBx7w+Xwff/yxx+N57733rr76\nakmSGhoaGhoaPB7PjBkzfD5fVVXVN998s3nz5ltvvTWW6oQQkyZNeu6550aPHt28gNY7MZlM\niqIsXLhw8+bNsfVfffXVjRs3dsJUJ1K/KrahoeHOO+/87LPPkn5r3L9/f1qrAgAAxxFZlkeM\nGDFixIg777yztrb22Wef/dOf/nTVVVdt3bo1Npvd0KFDm68/YsQIIcSWLVs2bdokhHj44Ycf\nfvjhhD5ramoOHjwohBgyZEi8UZKkn//85wlrtt6JzWZ79tlnf/WrX5WXl48cOXL8+PGVlZWT\nJk3K7WB3++23z549e+zYsYMHD5ZlxvkAAEB7NTY2zpgx40c/+tE111wTbywqKvrNb36jqur9\n99//5Zdfnn/++S03VBRFCBEOh8PhsBDinnvuiY/nxQ0cOPCjjz6Kr9yK1jsRQtx4440XX3zx\nggULPv3009dee+3JJ588++yzFyxYEDtG3KmkGuwWLFhw1lln/etf/+qc+RQAAOQch8Px4Ycf\nejye5sEuxmq1CiHi95dfvXp186UrVqwQQpSXl5eXlwshVFVtfn3r+vXrly9fPmrUqNjSdevW\njR07Nr705ptvHjBgwK233hpvab2TgwcPbtu2rby8/LrrrrvuuutCodB99933yCOPvP/++xdd\ndFEa9kJapTr2pmnaRRddRKoDAADpIsvyFVdc8cknnzz++OPRaDTeXltb+9xzzzmdzvjJcFVV\nVbHhNyGEz+d76KGHTCbT5MmTnU7nxIkTn3/++Q0bNsSWBgKB6dOn33333VardeTIkd26dXvi\niSfiJ5J9+eWXzzzzTMJ5Za13snbt2jFjxjz++OOxRQaD4eSTTxZCdM5QlOqI3cknnxz/aQEA\nANLiT3/6U1VV1R133DF79uyRI0cWFRXt2bPn/fffd7vd77zzjsViiR0n7dGjR2Vl5YwZM0pK\nSt55551vv/32N7/5Tc+ePYUQjz/++Pjx408//fTLL7+8R48er7/++sqVK+fOnasoitVqfeyx\nx6666qqxY8f++Mc/9vv9f/nLX3r06PGzn/0soYxWOhkzZszAgQMffvjh7du3Dxo0aPXq1QsW\nLOjXr1/z4b3OI9Vg99RTT02YMGHIkCEzZ85UVW5EBgAA0qC0tHT58uWzZs16880333rrrUgk\n0rt374suuuiOO+4YPnx4fLWrr7560KBBTz/99IYNGyoqKv7617/OnDkztmjkyJErVqy4++67\n33nnHbfbPXTo0Pfff/+8886LLf2v//qvkpKShx9++KmnnrJYLOeee+4f//jH+BHeuFY6sVgs\nH3zwwf333//pp5/OnTu3e/ful19++QMPPJCXl9che+jotDaPXcLFwHv37t23b5/NZisrKzOb\nzc0XLV++PFMFJsM8dlnEPHapYB67VDCPXYqYxy4VzGOXos45j13rYvPY3X333X/84x+zUkBu\naW3sLeFXWFRUlHCxMQAAADqP1oLdhx9+2GF1AAAAoJ1SvSr2iiuu+O6771q2f/bZZzfeeGNa\nSwIAAMCxaOMyiNra2tiDefPmxU4/bL40Go1+8MEHr7zyyl/+8pdMFQgAAI5jqqrm4n3ts6WN\nYFdcXBx//KMf/SjpOpMmTUpnRQAAADgmbQS7J554Ivbgtttuu+mmmwYMGJCwgsFgmDp1akZK\nA5DLdgdrnjgwf5V3c55im5I3+qfFFxqkLM+UtC948Kfb//idf4fQtBMsff5adncvY3Hbmwkh\nhFjn2/F09T/W+3YUqc5LCideXnimJH6Ym/QvNe88c+CthrDbqdivK7ng9tLL4os0ob1d//m8\nus8OhOrKzb1+WXLJUGv/NP9gnUYwIi3abNlcYwhHpbLC0JkDfTZjtO3NAKRPa9OdNHfGGWfM\nmjWr+YwyWcR0J1nEdCepYLqTrYG9kzf8qinii7dMyhs5t99vZOmH83o7eLqT6nDD8G+vDWnh\neIsqqctP/H89Ush2S5rW/njz/cFm207rcs7jvX8Ze3zLjqdeq/u4+frnOU/53373xR4/uGf2\ns9VvNl86t/+Dk/NGpV55rkx3EolKTy927mlU44HXYY7edkaD3dQR2Y7pTlLUCac7CQQCkUgk\njR2aTKY2bw6rY61dPFHbzBtvvNGzZ8/aI+iwcgHkhLt3P9881Qkh/u365vX6hVkqRwghpm/9\nXfNUJ4QIa+Grt/4ulW1v3flU8PBtXzn40ZKmtUKIXcGa1+o+SVj/g8Ylq3ybhRDf+rYlpLpY\nb2EtnR9jncTizea9zVKdEMLtl9//zpa1gpAjIumW7R8oy1o7MtL8BLvWcVYjgDhNaF+517Rs\n/9K95vLCMzu+npj1vp0tGzcHdre54b7Qwa2BvS3bv2hac4p98NsNi4VI8gb4j7pFw3oMWOJZ\n23LRgVDdZv/uCkufFKrOJVsPGlo2bqlN0gggc1oLdo888kj8saZpzz333I4dO84///yhQ4dK\nkrR69ep//vOf48ePv/feezNfJwC0i5QsfqW2YfL7fMda5SMc92j93uCd897hGcHXfqBjtRbs\n7rzzzvjjp59+uqampqqq6uSTT443Ll26dNKkSZs2bTr33HMzWCOAnCIJaZx96GfubxLaJziG\nZaWemBMtZUs96xMaTzD3bnPDrobCAeYem/17EtrHO4YJIX6UP+63e19qmV4uLZgkhDjNPqRl\nh90MXQaYeqZceM7oXxTaUG1MaBxQfCzncgE4ZqlOUDx79uwZM2Y0T3VCiLFjx86YMWPOnDnp\nrwtALvtT7587lcNOrjorb/SPCydmqx4hxMv97jNK8cOCmhDCIKmv9L0/lW2f6v0r4+GX9F5X\nfMFY2yAhRA9j8dVdEr/ZVuaPG2LtJ4Q40Vx2a+mlzRcZJfXJ3rcoUqrvvTlkXH9fr4LDzkR0\nWqLnD+7s13wAOpPq7ANHGpZzOp2bN29Oa0kAcl6Zsevng559av8bq3yb7Yr1nLwxM4rOO9Ix\nzY5RpOavGPLSDdv//K13qya0Ey39/l+fO7sZu6Sy7RhbxaKKZ56ufmO9f2eR6rykYOIlBRPi\nSx/vddNQS79ZB16vD7udqv3G4sqbSi6OL72/+/ThtgGv1/17X/DgQHOvdFHAjAAAIABJREFU\nX5RefKK5LO0/XWegyuJn4xq/2GzeXGsMRURZl/AZA7xWI4digQ6V6nQnp5566sGDB7/++muH\nwxFvdLlco0ePLi0t/fzzzzNWYRJMd5JFTHeSCqY7SUUHT3eSu3JlupPsYrqTFHXC6U68Xm96\nL2W1Wq1Md9K2m2++edOmTePHj58/f/6mTZs2bdo0f/788ePHb9q06ZZbbsloiQAAAB1jzpw5\nX3zxRbarOHapHoq98sor9+3b9+CDD15++eXxRqfT+eSTT1566aWtbAgAANBekYjokHG4OXPm\nnHvuuePGjeuA18qEo7jDzx133DF9+vRFixZt2rRJVdX+/ftPmjSpoKAgc8UBAIDjnLx+rbL4\nU6m2RjOatEGDw5POFlYmvj6io7syq7i4+Cc/+cm999571113XXLJJaQ6AACQOfL6teobf5eq\nD4hoVPL75BXL1ddeFkd5Tt6bb745bNgwi8XSp0+fRx99NNbo9XpvvfXWPn36OByO8847b926\ndUKI0aNHL1q06N577z3rrLOEEDU1NVdddVVpaWm3bt2uuuqqmpqaVjrcsGHDhRdeWFxc7HA4\nxo8f/803iVM+dYzWRuzOOOMMi8XywQcfxB63subChQvTWhUAADjuaZryr38mtMn79sjfrowO\nS/WGy9u2bbvssstuu+22v/71r4sXL77rrrtOOeWUcePGzZgxY9++fXPmzLFYLH/+858nTpy4\nfv36JUuWTJ48+eyzz77nnns0Tbvwwguj0ejcuXMlSbr77rvPP//8pUuXbt++PWmHF110Ubdu\n3ebOnSuE+O1vf3v99dd//fXXad4hKWgt2DU1NcUvVAmHw62sCQAAkF5SwC+5GpO0798nUp7v\nfOvWrZFI5IYbbigvLx8zZsygQYN69uy5YcOGt956a9++fbG7p86dO7dXr15ffPFFZWWlJEmK\noiiKsnjx4uXLl2/durVPnz5CiHnz5vXv33/x4sXhcLhlh9Fo9Gc/+1llZWW/fv2EEHv37r3t\nttvStiOORmvBbtmyZfH73uT0FSIAACDnaKoqZFlEo4kLTKbUOzn99NOnTp06bNiw8847b/Lk\nyVdffXVeXt4bb7wRiUTKy8vjq7nd7oR5edetW9e3b99YqhNClJWV9enTZ926dddcc03LDoUQ\nP//5zxcvXvzmm29+/fXX//rXv7J158DWzrHr2rXrZZdd9swzz6xevTracrcCAABkjmqIDjih\nZbNWMTj1Psxm87vvvrt69eqRI0e++uqrvXr1evfdd8PhcFFR0cpmtmzZcu2117belSzL4XA4\naYdNTU2nn376nXfeGQqFbrrppueee+7oftL0aS3YuVyu119//eabbx42bFiXLl0qKysfffTR\npUuXHtvEhgAAAEclfP5FWkFh85bI5HOjXbun3sNnn33261//un///vfdd9+XX3559tlnz549\n+8QTT6ytrfX5fGVlZWVlZRaL5cYbb9y9e3fzDSsqKrZv375z587Y0x07dmzbtm3w4MFJO1y4\ncOG6deuWLVt27733jhs3Lr1TLh+V1g7FNjY2rl69+j/fe++999577z0hhNVqPf300ydMmDBh\nwoSxY8eazeaOqhYAABxP7I7QjbfIa1bJB/ZpVmt04Ila125H1YGiKH/4wx+cTuc555yzbt26\nqqqqG264YejQoeecc87UqVOfeOIJs9n8+9//fv/+/RUVFUIIWZY3b968f//+8ePHjxw58tJL\nL/3Tn/4khPjv//7vkSNHTpw48fPPP2/ZYV5entfr/dvf/nbuuecuW7bs/vvv93g8e/bs6dGj\nR0Z2y5GleksxIURdXd3SpUtjIW/p0qUHDx4UQhiNxrFjx3JLseMHtxRLBbcUSwW3FEsRtxRL\nBbcUS9HxeUuxp556atasWXv27CktLb3iiit+97vfGY1Gl8t1xx13vP/++x6P54wzzpg1a1ZZ\nWZkQ4uWXX77jjjvGjRv39ttv19TU3HrrrZ9++qkQ4qyzzpo1a1bsYouWHRoMhoceeui5554L\nh8OTJ0/+3e9+d8kll1it1qVLl6bxR0vFUQS7OE3T1q5d+/zzz7/wwguxf6Fj6KQ9CHZZRLBL\nBcEuFQS7FBHsUkGwS9HxGeyOK0dx54m9e/d+8sknn3zyyccff7x//34hRNeuXc8666zYJH4A\nAADIrjaCndvtXrx48ccff/zJJ5+sXbtWCGG1WidOnDhlypQpU6YMHjw4W1fzAgAAIEFrwe7/\ns3ff8VVU+f/Hz8zcmtz0hA4JJYBIE5FeVKSIEBUVIigg1rWjuPayruzXgoq67Fd/q191LWhs\nICqwrqighlVWQVS6FOklpN1+78zvj2GvIbnc3CT35ibD6/mHjzvnzJz53JGEN1POjBgxori4\nOBAIyLI8YMCA++6775xzzhk8eLDFYmm0+gAAABClSMFOfyQiPT39tttumzRpUo8ePTg/BwAA\n0GRFmsfu7bffvuaaa7Kysh544IGePXu2bt166tSpL7300vbt2xutPgAAAEQp0hm7yZMnT548\nWQixc+fOzz777LPPPluxYsXChQuFEHl5eaNGjTr77LPPPvvsVq1aNVKxAAAAOLG6TXeiadqG\nDRv0kPfFF1+UlZUJIXr06KE/V9FomO4kgZjuJBpMdxINpjuJEtOdRIPpTqLEdCeGV4fpToQQ\nkiR17drV6/Xqf2+99957Xq/3l19+iVNxAADA2Ox2e2wHPMmfB4gq2B04cGD16tWrV68uLi7+\n7rvv9H84Kopy+umnjxgxYuTIkXEuEkAcHfCXfFq+5rC/rLu9w+jUMxQp0q23sVIZdN+049GN\n3p05SsY9rS4b5OhRtfdwoPTxfQu3efZ0sbe7o1Vhtim9au9Gz84FBxbtDxzpbetyR+tCm3zc\nc/orK9a9ceTTsmDl8JQ+N7S4sNp+f3RtK3b+rGnaIEePvkn51XpXVaz7wbUlRUkakdKns/W4\nFwEFNfWf5d9ucv+WbU4bk3ZGC1NGQw8BACHESZ/DYi7Spdi//vWvxcXFxcXFoacl9BeI6WFu\nyJAhDoejseo8DpdiE4hLsdFoRpdil5R+ffOuZyqDbn2xd1Lnos4PZ5lS47rTL8t/uGTbg5r4\n/ZfPBRnD/573R/3zeyVf3rDrqaCm6ouKJP8t7/ZJ6SP0xb/se23+/ndC29oky9KuT/RM6qQv\nXrH9fz4q/SY0bKYp9btT/54qJ+mLd//2wouHPwr1zsge90T76yUhCSF8qn/G9r/8q3yN3mWR\nTPe0ufyGFpP0xSOB8ou33veT+9hvQodify731glpQ2J1QCLjUmw0uBQbpSZ4Kdbtdsf2Uqzd\nbj+ZL8VGCnZ6iLbb7YMGDRo5cuSIESMGDRoU81Om9UCwSyCCXTSaS7D7zXdw+IYbnKqnauN5\n6YNf6XhPXPfb5ocL/SJQrbGoy5/PSunrDLrz10/1a8f1miXTll5vJiv2H92/nrPx1qqJUAiR\nZUrb2Ot1IcQbRz69ddez1Ybtk9TlX92eFkK8U/L59Tufqtb7dIebLssaI4SYu+8f8/e/U613\nSf6jgxynCiFmbJ/7Senqql0Oxb6q+4J2lpy6fO96IthFg2AXpSYY7LjHLrYiXXP5y1/+8tVX\nX5WWlq5YseLBBx8866yzmkKqAxArn5StrpbqhBBLy/5dFozj0wyrK3+pmeqEEP+z9x9CiIUl\nn1VLdUIIvxZ4o+RfQogFB96vluqEEEcCZZs8vwkhXjy0pOawP7q26R+Kjn5es/etI5/pH94+\nsqJm7ztHvxBClAWdy8qqv8a7Muj+uKy45iYAkFiR7rG7++67G60OAI2vJBDmnKKqqeVBZ5qS\nHKedbnb/Fra9VK0UQuz1HQ7bu89/WAhxJFgWtvc378FutvblaphzWprQAmrAJJuOBipq9h4N\nHmsM21viLxdClAed6n+vCx/XG+7oAUBiNfQu6UcfffShhx6KRSUAGlu+tV3NxhQlqWU8nwwY\nldYvbHt3W64QYmDKKWF79acreto7hu3tn9JNCJFnDjOnpkUym2STEKLL8Q9D6Lra2usfutjC\n9drbCyFamjIcSpiLFaFtAaDpiEGw+9Of/hSTUgA0soKMYTWj0h2tLrXI5vjttK0lp6OtdbVG\nWZL/2uFWIcTY1IEdrC2r9XawthybOlAI8cdWU5MVW7XeM1NPS5cdQognOlyv1Piddm1Ogf5h\ndqvJdtlatcsmWW5vVah/vqfN5dU2bGHKuDpnohDCIpvvaHVptd5e9k4T04dG/qYA0PgaGuye\neuqp//3f/41JKQAamUUyvd7p/nPTBsmSLIRIU5IfbHPFdS3Oj/d+v+z614621uK/UxzYZGtR\np4dS/3vxd2nXeT3seaGVe9jzlnadp39Okm0f5j/W2pSlL0pCOjP1tIUdH9AXO1nb/F/Hu0Pj\nyJJ8WdaYB9rO1Be72Tq82emB7vZcfTHf1u61TveFcu3o1DP+N/f21uZjIw9IPuWd/IezTWn6\n4h9aXPBAm5n6yLIkj08f9Hqn+y1S3eYBBYBGULc3TzQRPBWbQDwVG43m8lRsiFv1HgmUtzFn\nyY0yiZ1Ok7VVwZ/7Wrqk+qufhBNCuFTPetevvZI6JclhekvVys3u3/rZ8/XLrNXs8R066D96\nWnLXsPstCZarmhYKbdXs8x9Jkm1hbzFUNXWv/0iWKbXamb9446nYaPBUbJR4KtbwGu+XOIAm\nyy5b21lyGjPVCSEsimVSzpmtLVlhe5Nk20BHj7CpTgiRLjsGJJ8SNtUJIdpack6U6oQQmUrq\niVKdEKK1OetED47IktzOktPIqQ5AU+PxeCRJWrNmTV03lCTpq6++ikdJVUW6lDBo0KAoR1m9\nenXtKwEAANRdRdC1xbM705SSa20liQS/qUJRlFtuuaVFixaJLeNEIgU7my38v5UBAAAagaqp\nj+x+9bl973pVvxCiT3KXBZ1u753cOYElmc3m+fPnJ7CAyCJdefkiao1VLQAAOIk8u+/deXsW\n6qlOCLHOubVw84NhJ548kfPPP/+CCy4ILS5YsCA7O9vn87lcrltuuSU3NzclJeXcc8/dsGGD\nvoLJZFq9evXo0aP1rd5///0+ffrY7fbc3Nx58+YJIQKBQOhS7MGDB6dMmZKdnZ2Xlzdnzhyf\nzyeEOHTo0LRp01q2bNm6detp06YdOnSoWkknWqHaruunobfULF26dPTo0Q0cBAAAoBpVU5/a\n+1a1xt3egwsP/yv6QSZPnrx8+XKn89gLdYqKii699FKLxTJjxoz//Oc/r7zyyqeffmq320eO\nHBl6gHLWrFmDBw+eM2fO9u3bJ0+ePGbMmC+//PKmm2664447qt4kFwwGR48eXVJSsnjx4kce\neeS1116bO3eupmkTJkzYvHnzW2+9tXDhwq1bt44fP77qg6qRVwjtuh6HSxft4/qapr366quf\nf/551QeONE1bvXo1jyABAICYKw1WlgYqa7b/6t4T/SAFBQWapi1fvnzSpEl79+5dtWrVvHnz\nNm3a9MEHH+zbty8nJ0cI8dZbb7Vv3/6rr74qKCgQQowdO/bhhx8WQnz22WfBYPCaa67Jz88/\n44wzTjnllHbtfp/XfdmyZZs3b16xYkVWVtbQoUMDgcCaNWtWrVq1Zs2aX3/9NTc3Vwjx9ttv\nd+7ceeXKlSNHjtS3irxCaNf1Fm2we/bZZ2+99daUlJRAIOB2uzt06BAMBvfs2dO6deunn366\nIRUAAADUlKIk2WWrW61+/qjVCZ6mDz9ISsr48eM/+OCDSZMmvffee926devfv/97770XDAbz\n8/NDq1VUVGzdulX/PHz4cP3D0KFDJ06c2KdPn3PPPXfUqFGXXXZZampqIHDsfdbr16/v0aNH\nVtaxYmbOnDlz5swXXnihY8eOemgTQuTl5eXm5m7YsCEU7DZs2BBhhdCu6y3aS7F///vfe/fu\nfejQoa1bt8qy/NVXX+3evbu4uDgYDA4bNqyBRQAAAFRjlkyX54w9vk1LUZImZ59dp3GmTJny\n0Ucf+f3+d955Z/r06ZIkBQKB7OzstVVs27btiiuu0NfPzMzUP9hstg8//PDHH3/s16/fG2+8\n0b59+w8//DA0rN/vN5lqP0Emy3IoC9a6QmjX9RZtsNu+ffu4ceOsVmubNm1OP/30b7/9Vggx\naNCgwsLCO++8s4FFAAAA1PTn3KvHZwwOLWaZ0l/sclfNFw9Gdt5553m93jfffPObb76ZNm2a\nEKJHjx6HDx92u915eXl5eXl2u/3aa6/dvXt3tQ1XrFhxzz33dO7c+d577/3666/HjBnz8ssv\nh3p79Ojx888/h2bsf/7550877bTu3bvv2LFj165deuPOnTu3b99+6qmnhraqdYUGivZSrN1u\nV1VV/3zaaaetWrXqoosuEkIMGDDg7rvvjlU1UZJl2W4P807uBpIkKR7DGk+cjr+RyLKsKApH\nKTJZloUQJpOJAxWZyWTit1OtzGazEMJiseh/rnAi+hmmaM4zNRF22fpWtz+tc25d79qWaUod\nmtr7RFOIR+BwOCZMmHDbbbeNHDmyQ4cOQojevXuPHTt24sSJTz/9tM1m+8tf/rJ///7u3btX\n21BRlP/5n/9JS0sbO3bshg0biouLr7nmmlBvQUFB27ZtCwsLH3jggR07djz88MNTpkwZPnx4\nv379Lrnkkscee0wI8cc//rFfv36h67BCiFpXaKBofwC6d+++bNky/TmJPn36LFq0SH+CY+PG\njc3ovUkAAKDZ6ZPc5bKcseMzBtcj1ekmT55cUlIyffr0UEtRUdFZZ5113XXXXXLJJWlpaUuX\nLtX/eVDVyJEjn3nmmRdeeGHgwIF33nnn1KlT77rrrlCv2Wz+7LPP7Hb7hAkTbr/99osvvnju\n3LmyLH/88cedO3eeMmXKlClT8vPzP/nkk6r/3qh1hQaK9l2xixYtuvDCC9u0abNp06Zff/21\nb9++s2bN6tmz59y5c/v377906dJYFRQN3hWbQLwrNhrN7l2xCWE2m9PS0txud2gaAoTFu2Kj\nwbtio8S7Yg0v2pOx559//t///veioiJN03r37j137twHH3zQ7/d36NDhySefjGuJAAAAiEa0\nZ+xqqqio2LFjR7du3SwWS2xrqhVn7BKIM3bR4IxdNDhjFyXO2EWDM3ZR4oyd4UV7TbewsPCX\nX36p2pKSktKrV6+vvvrq2muvjUNhAAAAqJtagt3h/3r77be3bNly+HgHDx5cunTpa6+91ji1\nAgAAIIJa7rHTX7WhO9Erac8666xYVgQAAIB6qSXYhV4XNnv27BtuuKFLly7VVjCbzRMnToxL\naQAAAKiLWoLdrbfeqn9YtGjRVVdd1bdv3/iXBAAAgPqIdrqTL774QghRWlpaXFx86NChc845\nJyUl5SR/8AQAADRQUlJSokswlDrMdPzss8+2bt16/PjxM2bM2Lx58+LFi3Nzc4uKiuJXHIBm\nTdXUnb79JYHw0774tcAW12/lgfrMdVIRdP3q3evXIr1X+0T2+Y/s8R2qx4aReTX/Vs8ej+aL\n+ciAsWmapsZUor9QgkV7xu7999+/5ZZbRo4cOWPGjFmzZgkh+vfv37p16ylTpqSmpo4bNy6e\nRQJofv5xeNncfa/pqa5/cvcnO9zQw5andwU19Yn9CxcceF+PQePSBj7W7ro2lqimyNrjO3Tn\n7ueXl30rhLBJlhtbTprT6lJFiurfqJ+Xf3/n7ue3e/cJIXItrR5tf+05qf3r9eWO41Q9D+99\n5R+HlwW0oCzJUzLO/nO7q+r94iPgZON2u5nHLoainaB4yJAhFRUV33//vdPpzMjI+Pzzz888\n80yPxzNo0KDU1NSVK1fGu9CqmKA4gZigOBpMULzo6KqrdzxetaWVOfOL7s9lmVKFEI/ve/OJ\n/Qur9vZNyv84/zGLXP1FjdX4VP/4LX9c59patfGPrafe0erSWkva4Nk5ZuNtVc+oWSXzx10f\n75NU/Zmwurpx59Nvl6yo2nJu2qBXO90jCamBI+uYoDgaTFAcJSYoNrxoL8X++OOPF198cbX3\n49pstksuueTHH3+MQ2EAmrFH971RrWW/v+SVw58IIVyq59kD71brXevasqz821qHXVb+bbVU\nJ4R4Zv87brX2v8uf3l9U7TqpV/M9deDtWjeMbIdvf7VUJ4RYWra6Zp0A0AiiDXZZWVkej6dm\n+549e1JSUmJaEoDmTRPadt++mu1bvXuEEHt8h71amLMF27x7ah057Dpezf+b72C9tpW2emrf\naS3DnmCEho8MAPUQbbAbPHjw66+/fvTo0aqNmzZtKioqGjBgQBwKA9BcSUJKlx0127OUVCFE\npik17FZ6b2SZ4daRhJRlSqt127DrZJ2gmOid8Os0eGQAqIdog91jjz1WWVnZr1+/efPmCSGW\nL19+//33Dxs2zOPxPProo/GsEEDzMzX7nGotVsl8SeZZQogsU+qYtDOq9WaYUsalD6x12HPT\nB6abqkfGMWlnRJOiLs0cFabOrNG1bhhZb3vn0EMhIXmWVkNSejVwZACoh2iDXW5ubnFxcc+e\nPefOnSuEePTRRx955JGePXuuXLkyPz8/nhUCaH7ubn356NTf05tNsjzS7urQYwrzO9zcO6lz\nqDfDlPK/ube3MGXUOmwLU8b/5t6eYfr99o8+SV3md7g5mpIuzBhxfYsLq7ZclT2hMFzaqxNF\nkv9fxzvyLK1CLW0tOS92vNMq1fIgCADEQ7RPxYY4nc4tW7YEAoH8/Py0tNovf8QDT8UmEE/F\nRoOnYnX/dv6y1rXVIdtHpvRtZ8mp2qVq6peudbvEoUyRMtR66okuaIZVEihfUf79wcDRbrYO\nZ6WcJkc314lug3vnaufPqqYOTulZ80xbvfm0wD/Lvt3h3d/B2nJ0an+7bI3VyIKnYqPDU7FR\n4qlYw6s92Llcrs2bNx86dCg/P79Dhw6yXIffoXFCsEsggl00CHbRMJvNaWlpbrfb6azPHMUn\nD4JdNAh2USLY6Twej91u/+677/r3j2oyy1rXr+uA8RMppWmaNm/evJYtW5522mljxozp2LHj\n8OHDN2/e3GjFAQAAbD+sfL3NvPY3k9MXm+khFUW55ZZbWrRoEav16zpg/EQ6Y/fGG29cdtll\n6enpU6ZMycrKKi4u/vzzzzt37rxu3brk5EROqs4ZuwTijF00OGMXDc7YRYkzdtHgjF2Umt0Z\nO19Q+r+vbRv2H3tRVpJFK+zv6dMu0usET/JLsZHO2D3zzDMZGRnr169//vnn586du2LFirvv\nvnvbtm1LlixptPoAAMBJa9FaayjVCSFcPun1b21HnHW4K+z888+/4IILQosLFizIzs52uVyS\nJK1Zs0YIYTKZVq9ePXr0aH213bt3FxQUZGRk9O/ff9WqVQ6H46effgoEAqH1bTZbcXHxpEmT\nMjIyOnfu/O677wohqq5w8ODBKVOmZGdn5+XlzZkzx+fzCSE2bdo0YcKEnJyclJSU4cOHf//9\n97E5QDVEOjQbNmy48MIL27VrF2q54YYbhBAbN26MUzUAAAC6oCq+3VH9AXNfQFqzM9o33Qsh\nJk+evHz58tCVgaKioksvvdRisVRdZ9asWYMHD54zZ47f7x81apTf7//444/vvffeK664wu12\n1xzzuuuuKywsXLVq1RlnnHHZZZdVXScYDI4ePbqkpGTx4sWPPPLIa6+9pk8ncv755zudzrfe\nemvRokWSJF199dXRf4U6iXRoKisrW7VqVbWlTZs2QohAINIpUAAAgIbzBCR/uIu0FZ463GlX\nUFCgadry5csnTZq0d+/eVatW6TPyVjV27NiHH35YCPHuu+/u3bv322+/1ef9KC8vnzlzZs0x\nL7jggsmTJwsh/vSnP7399tt79uzJy8vTu5YtW7Z58+YVK1ZkZWUNHTo0EAisWbNGVdXrrruu\noKCgU6dOQoi9e/fOnj07+q9QJ7VkXkmSIiwCAADESZJZS7ZoNR+YaJFSh5naUlJSxo8f/8EH\nH0yaNOm9997r1q1b//79q93VN3z4cP3DTz/91KdPn9BsbkOGDAk7ZujR16ysrGpd69ev79Gj\nR6h95syZejT8wx/+sHLlyvfff/8///nPP//5z/gFqsTPXQIAAFCTJIkxPXzVGjOS1DPy6vbw\nx5QpUz766CO/3//OO+9Mnz69ZqjKzMzUP+i3yoXaTzTFm91uP9G+/H6/yVT9rFllZeXQoUP1\nS7033HDD3/72tzrVXye1nLFbt27dK6+8Umtj2BOVAAAADTGyq8/jF59ttPqCQgiRlxUs7O+x\nm+v2boXzzjvP6/W++eab33zzzeuvvx5hzR49ejz77LPl5eWpqalCiG+++aauBffo0eOxxx4r\nLS1NT08XQjz//PMvvPDCn//85w0bNhw9elS/t+/NN9+s67DRqyXYffTRRx999FGtjQQ7AAAQ\nc5IQ4071jeruO1ghJ1u09KS6RTqdw+GYMGHCbbfdNnLkyA4dOkRYc9KkSffdd9+ll1563333\nHThw4NFHHxVC1GnylIKCgrZt2xYWFj7wwAM7dux4+OGHp0yZkpqa6nK5Xn/99XHjxn333Xf3\n3Xef0+ncs2dP27Zt6/F1IosU7N55552Y7w8AAKBOzIpom642ZITJkyfr12Ejr2az2T799NMb\nbrhh3LhxvXv3fvbZZ88555yaN9JFKtVs/uyzz2666aYJEyZYrdZLLrlk7ty5drv9oYceuvvu\nu++4445Ro0YtW7Zs0qRJF1544bffftuQLxVWnd8V2xQwQXECMUFxNJigOBpMUBwlJiiOBhMU\nR6nZTVBcDw2ZoHjXrl1Lliy55pprzGazEOLzzz8fN26cy+VqRjMe12EmGAAAAAOz2+133nnn\nli1bbrrpJqfT+cc//nH69OnNKNUJnooFAADQ5eTkfPTRR8XFxb169SooKBg4cOBTTz2V6KLq\nhjN2AAAAx5x55pn//ve/E11F/XHGDgAAwCAIdgAAAAZBsAMAADAXEeUCAAAgAElEQVQI7rED\nAAAJI0nSid7chXog2AEAgISJ8N5V1AMZGQAAwCA4YwfEUlBTXz/yzyWlX5dpzm6W9re0vCTf\n1i7RRcVLaaDymYPvrK78RRJiqKPXzS0vTlGSQr2/+Q4+vb9onXtrmuIYndr/yuzzLLI5gdUC\nwMmAYAfE0o27nn635Av981qxZdHRVR/mP9ovuWtCi4qLsqBz1KZbd/kO6IvfOTcuKf3ms+7z\nk2WbEGKbd885m2ZXBt1676qKdZ9X/PBWpwdliasEABBH/JIFYubzih9CqU7n1fy37no2QeXE\n1+P73gylOt02754n97+lf75r9wuhVKf7vPz7opLPG68+ADgpEeyAmPmmYn3Nxg2enSWB8sYv\nJt6+qQzzZb+p/EkIoQkt7KH4OtwmAIAYItgBcScJKdElxF7YLxX5m8qSAY8DADQpBDsgZoan\n9KnZ2MveKcOU0vjFxNuwlN5hGh29hBCSkIY5wvQOd4Q5PgCAGCLYATEzIqXPpZnnVG2xSZb5\nHW5OVD1xdUerSztZ21Rt6W7Pnd1qsv758Q5/SFOSq/aOTj3josyRjVcfAJyUeCoWiKVncm8e\nmtJrSdnXpaqzm6X9TS0vyrO0SnRRcZGiJP2r29MLDn6wuvJnSZKGOnpd3+KCJNmm9+ZaWn11\nyt+ePfDuWtcWh2wflzZwevY4Q16SBoAmRdI0LdE11JnL5XK5XDEfNjMzs6SkJObDGkx2dnYg\nECgtLU10IU2aoijJycnl5QZ8ZiKGzGZzWlqa2+12Op2JrqVJs9lssizH45eekdjt9uTk5IqK\nCq/Xm+hamjSHw+H1ev1+fz22zc7Ojnk9iDkuxQIAABgEwQ4AAMAgCHYAAAAGQbADAAAwCIId\nAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACA\nQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgCHYAAAAG0XjBLhgMTps27ejRo2F7\nVVV94403rrzyypkzZ7744ovBYLDRCgMAADAGU+PsxufzvfXWWxUVFSdaoaio6OOPP77xxhtN\nJtOCBQtkWZ41a1bj1AY0C6pQ5x94d3XFzxmmlJnZ5w52nBr9tvt8R5448NYO795uttw7WhVm\nmlKj33aLZ/fKirVO1dMvqeuwlN51Lzy8gBZcdvTbnSUHM0XKWfa+2aa0qr1O1fNJafEu34Fc\nS6vx6YOSZFus9gsAxtYYwW7JkiUvv/xyIBA40QqBQOCTTz65/PLLhwwZIoTwer0LFiyYOnWq\nzcZvc0AIIQ4HSgf+cm150KUvvn/0y4syznw+7/Zotn3jyKe3/fZXVVOFEKsqfnz58Mcvdbzr\nvLTB0Wz73IH3Ht3/hk/164ujU894pePdFtlcry/xu/3+ksnbHtjg3qkvpirJC3Jnj0sbqC/+\n6Np22a9/3uc/oi+22Zf9Wsf7eid1buBOAeBk0BiXYkeMGPH000/fcccdJ1ph9+7dpaWl/fr1\n0xf79evncrm2bdvWCLUBzcIFW+4NpTrde0e/WHT0q1o3LFUr5/y2QE91uqCmXrP9CY/qq3Xb\n4sqfH977SijVCSE+Lf/uiQML61J4eDfvnB9KdUKI8qDzxp1P7/eXCCF8qv/qHY+HUp0QYq/v\n8NU7Hq9aBgDgRBrjjF1aWlpaWprff8LfyyUlJUKIrKwsfTE5Odlms5WWloZWcLvdL774Ymjx\n9NNPP+2002JepyRJycnJMR/WeGRZ5kBFJkmSoiixOkqqUDd7dtVs/39HPpzWbmzkbRfsXhzQ\nqt+x6tP8S1zfzGx5XuRtF+0LExyLSj7/S5c/RN4wskP+0s8rfqjWWBZ0fu5Ze1X6xO/L1v3q\n3Vut91fv3p/UncNT+jRkv82Xoij8dqqVyWQSQlitVv0DTsRkMkmSZLFYEl0I4qVJ/ACUl5eb\nzeaqP41JSUllZWWhRY/H8+qrr4YWrVarftE25ux2ezyGNRhZljlQ0YjVUSoPuLRw7WWqs9Zd\nHA6WhW0/qJbVum2Z6qzZWBIob+D3cqkHw7aXC5fdbndWeMP2Vsqek/xPndnc0CvgJwPySjTI\nvsbWJP7vOhwOv98fDAYVRdFbXC6Xw+EIrZCamvraa6+FFlNSUqqez4uV1NTU8vLymA9rMOnp\n6cFgMMJzMBD/zb5OZ5hgVD8mSal54q2TuXWtPwi9TR3Dtp9u7lLrtnlKy5qNXW3tG/jTl6ba\nbJLFo1W/FtxB5JSWlrZRM8Ju1VbNjMdPfbNgsVhkWfZ4PIkupEmzWq36D12Eq0MQQiQlJfl8\nvgh3vUeQnp4e83oQc00i2GVkZAghSkpKcnJyhBBut9vj8eiNOkVRTjnllNCiy+VyuVw1x2m4\n+v1ZP9lomsaBikxRlNgepenZ4/7v0MdVW0yS8mjba2vdxYXpwx80vbQvcKRqY2db20H2U2vd\n9srs8/5xaNmhwHFx6u7WlzXwe5mFclvrKX/Z+1rVxtOTu53jOD0QCHQ2tbk488x3S76o2ntx\n5pmdTW1O2j91+vmVk/brR0k/o6mqKgcqMlVVg8EgR8nAmsQExXl5eWlpaWvXrtUX165da7fb\n8/PzE1sV0HQ81u66yZlnyULSF9MVxxsdH2hryYlm2+Xdnuxqax9a7J3U+ZP8x6PZMMeU/k6X\nh89I7q4vtrFkv5B3xzmp/etYexg3t7j4njaXOxS7EEKR5InpQ1/teI9ZOvbvzHntb7gqZ4L+\n7K1FNl+VM2Fe+xsavlMAOBlImhb27p3Y27p162233fbqq6+GTsUtX77c6/UWFBQIIRYuXLhs\n2bI5c+bIsvz0008PHjz4yiuvPNFQcTpjl5mZqT/GgQiys7MDgcBJe1EsSvqTE/G4sv8f56Y2\n5uzWlqy6bugMun9yb++V1Kkec8KVBZ0u1dPaXOedRqaYlHKbNyVoU91hTh74tcB+f0krc2Yo\n8J20bDabLMtxukxhGHa7PTk5uaKiwusNf5smdA6Hw+v11u+CdXZ2dszrQcwl8jdmcXFxeXm5\nHuwKCwv9fv/8+fNVVR02bNjMmTMTWBjQZJ2e3K1+GyYr9oGOHvXbNk1JTlNi/0imLMm5tlZu\nt9spwgQ7s2Rqb2kR850CgLE13hm7GOKMXQJxxi4a8TtjZyRmszktLc3tdsfwKRND4oxdNDhj\nFyXO2Blek7jHDgAAAA1HsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATB\nDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAA\nwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAI\ndgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAA\nAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZB\nsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMA\nADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAI\ngh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAISdO0\nRNdQZz6fT5ZjH0lNJlMgEIj5sAZjMpk0TQsGg4kupEmTJEmWZY5SZJIkKYqiqqqqqomupUnT\nf91xlCKTZVn/oWuOf6k1JlmWNU2r31EymUwxrwcx1yz/JwUCAbfbHfNhMzIyysrKYj6swWRl\nZQWDQQ5UZIqiJCcnl5eXJ7qQJs1kMqWlpfl8PqfTmehamjSbzSZJUjx+6RmJ3W5PSkpyuVw+\nny/RtTRpDofD6/X6/f56bJuVlRXzehBzzTLYCSHi9G8y/qkXJQ5UZNp/JbqQZoADVStN0ySp\nWV5daUyh48OBiozfTobHPXYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg\n2AEAABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEA\nABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgE\nwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4A\nAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEu+ZKCgbkksNSwJ/oQgAAQFNh\nSnQBqDPJ77N+8S/zj98LVRWS5D+1t/esMZrNnui6AABAgnHGrvmxfbrUvHaNUFUhhNA080/r\nbEs/FJqW6LoAAECCEeyaGbn0qOnnddUaTVs3KQf2JaQeAADQdBDsmhn5aEnYdqnkcCNXAgAA\nmhqCXTOj2cPfS6fZkxu5EgAA0NQQ7JqZYMvWwZyW1RrV9Ay1Q25C6gEAAE0Hwa65kSRPwUVq\nemaoQUtN8xRcrCk84AwAwMmONND8qJnZrll/ULZtkcuOamnpgU5dNJM50UUBAIDEI9g1S5qi\nBLp2T3QVAACgaeFSLAAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAA\nBkGwAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGw\nAwAAMAiCHQAAgEEQ7AAAAAzC1Aj7UFV14cKFK1asCAaDw4YNu+KKKxRFqbbO+++//8orr4QW\nZVletGhRI9QGAABgGI0R7IqKij7++OMbb7zRZDItWLBAluVZs2ZVW+fgwYN9+vQpKCjQFyVJ\naoTCAAAAjCTuwS4QCHzyySeXX375kCFDhBBer3fBggVTp0612WxVVzt48GD37t3POOOMeNdj\nfJpm3viz6ad1krNCzcz2DxgSbNUm0TUBAIDGEPd77Hbv3l1aWtqvXz99sV+/fi6Xa9u2bdVW\nO3DgQE5OTryLORlYv/yX7aP3TTu2KYcOmjf9kvTai6ZtmxNdFAAAaAxxP2NXUlIihMjKytIX\nk5OTbTZbaWlp1XU0TTt48OCaNWvefvttj8dzyimnzJo1q00bzjPVmXLogOW74mqNtmVLKq+7\nVdS4rxEAABhM3INdeXm52Ww2mX7fUVJSUllZWbV1vF5vIBC4+eabg8Hg22+/fe+99y5YsCAp\nKUlfobS0dNKkSaH1Z8yYMX369JiXKklSKIA2U8FNPwdrNEouZ6YakFq0iNVeTCZTcz9QjcAA\nf5wah91ur3ZjBsKy2+2JLqEZcDgcDocj0VU0dVarNdElII7iHuwcDoff7w8Gg6EnYV0uV7Uf\nPIfD8eqrr6alpcmyLITIz8+/4ooriouLR40apa8gy3JKSkpofYvFoqpqzEtVFCUewzYm7UTt\nmtBi9NUURdE0rbkfqHjTn/7hKEUmSZIkSfxxqpV+oDhKkYX+OGnaiX4RQgghZFmu91GqOaMF\nmqC4B7uMjAwhRElJiX4Lndvt9ng8emOIoihVW1JSUlq0aHH48OFQS2pq6uLFi0OLLpfr6NGj\nMS81MzMzHsM2JjkrJ7lGo+ZIOWoyixh9tezs7GAwWO1iOqpRFCU5Obm8vDzRhTRpZrM5LS3N\n4/E4nc5E19Kk2Ww2WZZdLleiC2nS7HZ7cnKy0+n0er2JrqVJczgcXq/X7/fXY9vs7OyY14OY\ni/vDE3l5eWlpaWvXrtUX165da7fb8/Pzq66zbt26OXPmVFRU6Isul+vQoUPt27ePd23Go2a3\n8A4eflyTorjHFQiZmagBADC+uJ+xUxRl/Pjxr7/+eqtWrWRZfumll8aMGaPfUrN8+XKv11tQ\nUNC9e/cDBw7Mmzdv4sSJVqu1qKioRYsWAwYMiHdthuQbdpbaqo355x/lynI1M9t3xuBgdszu\nrgMAAE1ZY0xQXFhY6Pf758+fr6rqsGHDZs6cqbcXFxeXl5cXFBRYrdYnn3zyxRdffOaZZyRJ\n6tu372233Vb1eQvUSaBLt0CXbomuAgAANDapOd5n6nK54nG7SWZmpj45CyLIzs4OBALcYxcZ\n99hFQ7/Hzu12c49dZNxjFw39HruKigrusYuMe+wMj1uvAAAADIJgBwAAYBAEOwAAAIMg2AEA\nABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgE\nwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMwJboAg1MO7FN+\n2yk0LdiuQ7B12zps6fPZP16sHNqvJid7h4wMduxctVMK+JXNG5Wyo8GUtGB+d81qPa7X7TJt\n3SRXVqiZ2f4u3YSi1KHggweUXduFqgbatlfbtq9DwQAAINEIdnFk+9dS8w/fhRb9vU7zjJ0g\nJKnWDZXdu5IWviqEJoRQyo4mvftGsFO+66JLj/UePmh/b6FUXqYvais/c10wWW3TTl80bd9q\n++gDyePWFy2ZWa6Lp2lp6dEUbP3iU8t3xcc+CxE4tbf73POjKRgAADQFXIqNF9PPP1ZNdUII\n8/ofzD/+EM229nde11PdMZpQft1i2rxBCCFU1bbkvVCqE0JIzsqkj96XAn4hhOR22T9eFEp1\nQgi55Ij9k0XR7NS86ZdQqgt9BcvxXwEAADRlBLt4sWxYX7PR/PO6WjdUjhyUAoHjmiQhhLB+\n/YUQQjmwTz58qNomUlmp8ttOIYSybYtwu6oPuHuXXCUInojplzAFm36qvWAAANBEEOzixu2u\n2SaFa6zu8OHw7V6vEELyeCLsTvaEH1+qkfaiXCeaDQEAQBNBsIsXNTOr6tXUY43Z2bVuGMzr\nLMLd1aZmZh8bNhwtK1sIoWaFG19R1PSMWverj1+9MSun1g0BAEATQbCLF9+g4ZrFXLVFM5l8\ng0fWvqXVqma1qNamCck78SIhhJqW7u/dr1qvv+spwZathRCB3E7B9rnVer0DhmhWWxQFD9Us\nluN2qph8Q6MoGAAANA0Eu3hRs7LdF01Vc1r8vnjhlGCLltFs65x+tZqZHTrfp5kV90WFqt2u\nL3pHjfX1H6QpJiGEUBR/3/7ecQXHVpVld8HF/lN6CVkWQmhmi2/oSN+QqMKZmp7pvnhaqEI1\nI9NzweS6TdECAAASStK0GtcLmzyXy+Vyxf7er8zMzJKSkpgPK9wuSQjNnlTnDYNB5dctonXr\noCMtTK+qShXlmiMl7DR1UjAonJWaI0VPeHUiud1CU7Wk5LC92dnZgUCgtLS0rsOeVBRFSU5O\nLi8vT3QhTZrZbE5LS3O73U6nM9G1NGk2m02W5Xj80jMSu92enJxcUVHh9XoTXUuT5nA4vF6v\n3++vx7bZUdxNhIRjHrv4syfVMzsrSjC/+wl7ZTnC7HSaoojUcHEwCtp/Tw0CAIDmhUuxAAAA\nBkGwAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGw\nAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAA\nMAiCHQAAgEEQ7OJO3r9X3rs7fF8waNr4i7J714l65aNHJI+nHjuVvF655IgIBuuxLQAAaKZM\niS7AyCyrv7J+/YVQVSGEkGXf4OHeISNDvUkLX1V279Q/a5LkHzLi915Ns65eZV79tRTwCyEC\nHTt7x0xQU9Oi2alUWWH7bKlp80YhhKYo/v6DvUNHCkWJ5RcDAABNEmfs4sW8ZYN11YpjqU4I\noaqWr1eaf16nL9k/fCeU6oQQkqZZvv5S2b5NX7R8+7Xlqy/0VCeEMG3fZntvoRQI1L5XVbUv\nfkdPdUIIKRi0/Psr61dfxOQbAQCAJo5gFy+WFf+s0aZZv/xM/6Rs3lBzE9uyxUIIEQxair+q\n1qUcPqhs/LnWnZp2bFNqXPa1rCmWPO5oagYAAM0awS5eJJcrTKP7WMCStHCbeN1CCKmyQvL7\navbKR4/UulO5JNw6qiqXlda6LQAAaO4IdnFjNodr/O9NjVK4TRSzEELY7EIO9/8lKbnWfar2\npDq1AwAAIyHYxUvglJ41G/1dT9E/qCnpNXt9vU8XQmhWqz+/e7UuzWqr2Rhmp526aDXyXzCv\nkxbdgxcAAKBZI9jFi2fUuGDrtlVbgi1aeMYV6J+dV1ynHX9KT81p4Rs5Sv/sHX2e2ub3bTWb\nzTP+/KjCmT3JPXGSVuX8XDCnpXvc+fX9EgAAoDmRNC3c3V5Nm8vlcoW7g62BMjMzS0pKYjum\naetG08afhZCC+T383aqfcrOsXGH+datmNvvOGBT478m8YzRN2fGrcuSQmpQU7NhFq8u1VMnj\nMe3YJlWUq1nZgbzO4S/s1ld2dnYgECgt5aa9SBRFSU5OLi8vT3QhTZrZbE5LS3O73U6nM9G1\nNGk2m02W5Xj80jMSu92enJxcUVHh9XoTXUuT5nA4vF6v3++vx7bZ2dkxrwcxxzx28RXo0j3Q\n5YSXUH0jzvaNODt8nyQFO3YOduxcj51qNpu/+6n12BAAADRrXIoFAAAwCIIdAACAQRDsAAAA\nDIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgCHYAAAAG0SwnKJYkSVGUeIwcp2GNhwMVmaIo\n8ftTahiyLIt4/jgbhizLHKVa6X+cZFnmQEWmHyJVVRNdCOKlWb5SzOfzSZIU82FNJlMgEIj5\nsAZjNps1TeNARab/NcxRikySJJPJpKpqMBhMdC1Nmh5Z+Js4Mj2vBINBDlRkeqqr31/95uNf\ncY6mqVmesQsEAnF6V2xZWVnMhzWY7OzsYDDIgYqMd8VGQ39XrNfr5V2xkfGu2Gjo74p1uVy8\nKzYy3hVreNxjBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDs\nAAAADIJgBwAAYBDN8s0TMads32b54buAs9LuSPH17R/s2DnaLf1++4fvKnt+E8GglprmHjtB\nbdch1Gna95vt7Tclv1dIQjNbPBcVBtrlhXqt/1xiWfeDJiRJaEIW7mkzAq1yQ73JRa9KO3dJ\nQhNCUk2y8/o5wmoN9drffNm05zchhBCSmpPjnHnd7yVpmnnjz6af1knOCjUz2z9gSLBVmyi/\njeTzmf/9lWnXDqGqwXYdfIOGa3Z7tIcCAAAkWrN8V6zL5Yrh23XMP3xn+9fSqi2ec871n3ZG\n7VuqquNvT0pud9U29+RpgdzOQgjTkYP2/3u+2hbuS2cF2rUTQtg/+kDZsL7a+24rrrlFpKUJ\nIZJe/KtytOT4Tqnijvv1T8l/fUp2Vx7/HSwVt96lf7R+8anlu+LjdjqpMNC5a63fRgr4k157\nST58MNSipaQ6Z1xbLdtlZ2cHAoHS0tJaBzyZ8UqxaOivFHO73bxSLDJeKRYN/ZViFRUVvFIs\nMl4pZngn+6VYye2yfv5ptUbr559Krtr/prF++km1VCeEsC15/9iH11+qsYVmf/dV/ZNpw081\nB3S89Jz+oUaqE0JoSX9/Tghh3rWteqoTQvh95v+sFkIohw5US3VCCNuyJSKKl6yb//1N1VQn\nhJAqyq0rP6t1QwAA0ESc7MFO2bdHCgaqNUrBgLJ3d63bmn7bWbNRcruFqgohJH/1YYUmCf+x\ngKUJTareLURQFUJYvgyfpZSyo0II08ovw/Za//OdEEIOW5LLqRw9Ev47VGHaHWZbJdyAAACg\naTrZg50QYfKVEEJIJ2iPXs1r3NENqZlPdOOjJIQQyok6pd//G0UtJxw/ijYAANA0nezBLtC6\njWY2V2vUTOZgm/a1b5vbsWajZk8WsiyE0Ko861B15GOfwiYwRRZC+IeMDLu7YHqWECIwZFTY\nsOUdNEQIEWyfW7NLc6SoWbXfGBHskBemMbdTrRsCAIAm4mQPdsKe5B01rlqbd9S4aJ4G9Y4a\npyUlV23RhOS+4BL9s2f6lTXPd3mmXal/8PfuW3PAyuvn6B8COS1r9rquul4I4c/NVR2Oal2a\nzerv1U8IoWa38A4eflyforjHFehZMzLfgMHBFq2qtqhp6d5hZ9a6IQAAaCKUhx56KNE11Jnf\n76/fEz1hqS1bB3M7ioDfnJTka93OO/q8QH73qLaUJF+/AXLZUdlVKclyMDvHfck0tXXbY8Pa\nktTuPc0b1otAUEhCs1k9064OtGih9wY7dwuqAdPu3yQhhCQ0Saq88kaRkqL3Bvr2V0oOyIf/\ne2Oc2VJx4xxhOnaJ1t9/sLx/r1x6VJKEJkSwbXvXVTeGigp26Ki2bCWpqrBag7mdPOPPD7at\n/eyjEELIcqBnH81sliShpab7e/T0nneBZqsecJOSklRV9Xg8UY15spJl2WKx8HReZIqi2Gy2\nQCAQwx9nQzKZTJIkcZQiM5vNFovF5/MFo3hW7GRmsViCwaCqqvXYNikpKeb1IOaY7uR3mZmZ\nJSU1H0fFcZjuJBpMdxINpjuJEtOdRIPpTqLEdCeGd9JfigUAADAKgh0AAIBBEOwAAAAMgmAH\nAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABg\nEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEKZE\nF9AMSM5K07YtsrMymJUd6NJNyDFLw+Zvi827flUtlkCvvoGO+cf1BQLWr75UDuxVU1J8pw9U\nW7aO1U4BAIBREexqYdq6yfbJIsnr1RfV7BauS6ZpjpSGjhwfo9sAAA/FSURBVOv3O/7fM5LL\nJYRQhGbetCHYsYvr4ql6p3zkUNI//i4FAkIIRQjzzz/6+w/ynDWmoTsFAACGxqXYSCRnpW3p\n4lCqE0LIhw/al33Y8JHt77yupzp9P0IIZftW83++1ZeTil7XU90xmjCvWa3s+a3h+wUAAAZG\nsIvEtHWT5PFUa1S2b5OclQ0ded+emo2Wtd8JIeSKMqmy4rgOSQghLN9+08CdAgAAYyPYRSK5\n3XVqrwNVDTOs3yuEEGVl4Tdxu8K3AwAACCEIdpGpmdk1GzWTSUtPb+DImslcszGYmiGEUFu2\n1k/RVS8mp2UDdwoAAIyNYBdJoEtXtW37ao3+wSPCxrI68Q8aXr1JkrxjJwghhNkc6NytWqdm\nNvtHntPAnQIAAGMj2EUky67zL/F366FPcaJZrd7hZ3sHDm34wN7Bw/ynD/x95hSr1TPxEjUr\nR19yn39JoHNX7b8n7rQUh6twhmqxNHy/AADAwJjupBZassNTcLE3GBTOSi0lVUjhrpLWi+fs\nsZ6zx8qHDgirXU1NPa5Plt2TCoWqygf3i/RM1WaL1U4BAICBEeyioimKSE2Lx8iR7pyTZbVV\nm3jsFAAAGBKXYgEAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAAMAiC\nHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAAMAiCHQAA\ngEEQ7AAAAAyCYAcAAGAQpkbYh6qqCxcuXLFiRTAYHDZs2BVXXKEoSj3WAQAAQASNEeyKioo+\n/vjjG2+80WQyLViwQJblWbNm1WMdAAAARBD3YBcIBD755JPLL798yJAhQgiv17tgwYKpU6fa\nbLY6rQMAAIDI4n6P3e7du0tLS/v166cv9uvXz+Vybdu2ra7rAAAAILK4n7ErKSkRQmRlZemL\nycnJNputtLS0Tuv4/f4ffvghtJidnR1aOYYkSTKbzTEf1ng4ULVSFEWWZY5SZPp9tByoWvHH\nKRr6HydFUThQkcmybDI1xl1YSJS4/98tLy83m81V/xglJSWVlZXVaZ3Kysrrr78+tHjNNddc\nc8018ag2LS0tHsMajKIoHKhocJSiYbVarVZroqtoBjhK0UhKSkp0Cc2AxWJJdAmIo7gHO4fD\n4ff7g8Fg6ClXl8vlcDjqtI7NZpsxY0ZosWfPnm63O+al2mw2j8cT82ENxm63q6rq9XoTXUiT\npp/U9Pl8iS6kSZNl2Wq1BgIBv9+f6FqaNEVRJEkKBAKJLqRJM5lM+g9dMBhMdC1NmtlsDgaD\nqqrWY1u73R7zehBzcQ92GRkZQoiSkpKcnBwhhNvt9ng8emP069jt9ptuuim06HK5nE5nzEu1\nWq3xGNZg9GDHgYpMv3bGUYrMbDZbrVa/38+Bisxms8my7HK5El1Ik2a3281ms9fr5Z+dkTkc\nDq/XW79/TRHsmoW4PzyRl5eXlpa2du1afXHt2rV2uz0/P7+u6wAAACCyuJ+xUxRl/Pjxr7/+\neqtWrWRZfumll8aMGaPPY7J8+XKv11tQUBBhHQAAAESpMR6NKSws9Pv98+fPV1V12LBhM2fO\n1NuLi4vLy8sLCgoirAMAAIAoSZqmJbqGOnO5XPG43cRut8fjmQyDWbRoUWZm5ogRIxJdSJMm\ny7LFYuFZnMhKSkpWrlyZn59/6qmnJrqWJs1kMkmSxCMmkW3btm39+vUDBw5s3bp1omtp0vQn\nlur3iEl2dnbM60HMNcvJbJKSkuL0THtycnI8hjWSF1544dRTT500aVKiC2kGqj39jWr27t37\n/PPPT5s2beTIkYmuBc3eJ5988vzzz3fq1KlXr16JrgVIpLg/PAEAAIDGQbADAAAwCIIdAACA\nQTTLhyeQQOXl5SaTiff2oOGCwaDT6eSVYogJn8/n8Xj0aYoTXQuQSAQ7AAAAg+BSLAAAgEEQ\n7AAAAAyiWc5jh4Tw+Xwvv/zy999/X1ZW1r1796uvvrpt27aJLgrN3saNG++8885XXnklIyMj\n0bWguVq1atXixYt37drVrVu36667jl9NOJlxxg7RevLJJ1evXj19+vT7779f07T7778/Hu//\nwEnF4/E89dRT3OmLhli5cuUzzzwzevToe+65JxAIPPLII6qqJrooIGEIdohKSUlJcXHx9ddf\nP3To0FNPPfWuu+6qqKj47rvvEl0XmreXXnqJR2LREJqmFRUVFRYWjh07tm/fvjfddFNqauq+\nffsSXReQMAQ7RKW8vLxLly5du3bVF202m9VqLSkpSWxVaNa+/fbbNWvWXHnllYkuBM3Y3r17\nd+3aNWzYMH2xTZs2jz32GJdicTLjHjtEJS8v76mnngotfv311+Xl5aecckoCS0KzVlZW9txz\nz82ePZs36qIhjhw5IoTYsWPH448/vn///vz8/Kuuuqp9+/aJrgtIGM7YoW6CweCiRYvmzZs3\nduzY7t27J7ocNEuapj333HNDhgzp169fomtB81ZaWiqEePnlly+66KJ7771XUZR7772X239x\nMuOMHepg586dTz755P79+6+88soJEyYkuhw0VytWrNi1a9ecOXMSXQiaPYvFIoS48cYbe/Xq\nJYTo1KnT9OnTV69effbZZye6NCAxCHaI1vr16x966KHTTjvtT3/6EzNToCE2bdq0f//+wsJC\nIYT+SOzMmTNHjRp18803J7o0NDP676K8vDx90W635+TkHDp0KJE1AQlFsENU/H7/E088MWbM\nmGuuuUaSpESXg+ZtypQp5513nv55165dTzzxxCOPPNK6devEVoXmKDc3NykpacuWLfpl/crK\nyoMHD7Zr1y7RdQEJQ7BDVNatW1daWpqfn79mzZpQY4cOHVq2bJnAqtBMZWVlZWVl6Z/9fr8Q\nol27dpwGRj3YbLbx48f/9a9/vfrqq1NTUxcuXJiTkzNgwIBE1wUkDMEOUdmzZ48QYv78+VUb\nr7322tB5FwBIiMsvv1ySpJdfftnpdPbq1Wv27NlmsznRRQEJIzHnOwAAgDEw3QkAAIBBEOwA\nAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh1gWO+++650vNTU\n1IEDBy5cuDAeM5OfeeaZgwYNCtt11113SZL0008/xXyndTJu3Lj+/fsntgYAiCteKQYY3IQJ\nE/r06SOE0DRt7969ixcvnjp16t69e2+//fZat50/f/7s2bMPHTqUnZ0d/0pjr7nXDwB1RbAD\nDO6iiy6aOXNmaHH37t29evV66KGH/vCHPyQlJSWuLgBA7HEpFji5tGvXbsqUKZWVlZs3b050\nLQCAGCPYASedrKwsIcShQ4f0xV27dk2dOjUvLy8lJWXYsGEffPCB3n7mmWfOnj1bCJGTk1NY\nWKg3Ll68ePjw4Tk5OQ6Ho1evXs8++2xMbtc7UQ1CiHHjxl1wwQUbN24cPXp0cnJyq1atrrrq\nqrKystAKy5cvP/PMM9PS0gYPHlxUVDRv3jybzXai+oUQv/7668SJE3NycmoOBQDNHcEOOOkU\nFxcLIfLz84UQGzdu7Nu378qVKwsLC2+//fbS0tJJkyY999xzQoj58+ffcMMNQohFixY98MAD\nQogXX3zxggsuOHr06IwZM66//npZlm+55ZZ//OMfDawnQg26ffv2nXXWWfn5+fPnzx85cuRL\nL71022236V1FRUXjx48vKSmZPXt23759Z86c+e677+pdNesXQuzdu3fEiBG5ubkPPvhgz549\nX3rppTlz5jSwfgBoQjQABvXOO+8IIV5++WV9UVXVvXv33nvvvUKIIUOGBINBTdMmTpyYm5tb\nUlKir+Pz+UaOHGm3248ePapp2tNPPy2EOHTokN47evToNm3auN1ufdHj8aSkpEyfPl1fHDly\n5MCBA8NWcueddwoh1q9fH7Y3cg1jx44VQjzzzDOhbzFgwID27dvrBbRv3/60005zOp1679Kl\nS4UQVqtVX6xWvz7U888/HxrqjDPO6NixYx2OKQA0bZyxAwzuiiuu0Oc6kWW5TZs2c+fO7dy5\n88svvyzLstPpXLJkyWWXXSZJUmlpaWlpqdPpnDFjhtvt1s/qVfPe/2/nXkLS2eI4gJ8ps4Vk\nGykqRloZhD2wJ8hg1qIHFNGmXQsLW7loIBRatBo0QukJgdFasD/URqJtBRKGWVBKi6LHpsVA\nCeWkde9iYBgspHu94HX8flbx6zh+cfVlzsz58yeRSIgbnYQQnuczmYwgCPnE+00GtVo9Ozsr\n/k1RVFtb29vbGyEkHA4/PDywLCu9BTI4OGg0GnN8nUajsdls0qWMRqN4KQAAZcBbsQAKJx13\nQghRqVRNTU1jY2MajYYQcnNzQwjhOI7juKxPSU/gyVVVVV1cXJycnMRisWg0enZ29vn5mWe8\n32TQ6/WVlZXSvKysTP5ZeZOjKKq5uVmc/6ixsbGiouL7pQAAlAHFDkDhso47kctkMoQQl8sl\n7lHKGQyG7+vdbvfCwgJN0+Pj4/Pz893d3QzD5BnvNxnkrU4unU5/H+buatLtRgAARUKxAyhd\n4vsTKpWqr69PGsbj8Ugk0tHRkbU4mUwuLi7abDa/309RlDjM/47dP8qQRWx+V1dX7e3t0vD6\n+jrPSAAAxQvbEAClq7q62mKxbG1tJRIJcSIIwtTUlNPplJ9d/PX1RQi5u7tLp9MtLS1SqwuH\nw09PT3/ld9zJLzP8qKurS6fTeb3e9/d3cXJ4eBiLxbKWifkBAEoB7tgBlDSfz8cwjNlsnpyc\nbGhoCAaD5+fngUCgvLycECI+jra8vDwyMmI2m/V6PcdxPM8bDIbT09NAIFBXVxcOhw8ODoaG\nhuSX3dnZYVnW5XK5XC5puLa2VlNTI1+m1+vtdnvuDDlotVqPxzMzM9Pb2zsxMfH8/BwMBjs7\nO+PxuLhAnt9qtf4XPxgAwP8aih1ASTOZTNFo1Ol07u/vJ5PJ1tbWUCg0PDws/nd0dHR3d3dz\nc/P19dVqtYZCIZZlV1ZWtFotwzCRSOTo6Ghubs7r9WYVu4+Pj5eXl1QqJR/6/f6sbzebzXa7\nPXeG3Kanp3U6ndvt9vl8JpNpb29vY2OD5/kf8//L3wgAoHhQeW6jAAAUiiAIl5eXNE3X1tZK\nQ6vVmkqlfjyuBQBA8fCMHQAUK4qiLBaLePqx6P7+/vj4uL+/v4CpAAAKCFuxAFCs1Gq1w+FY\nWlpSqVQDAwOPj4+rq6sajcbhcBQ6GgBAYWArFgCKWCaTWV9f397evr29ra+v7+np8Xg8NE0X\nOhcAQGGg2AEAAAAoBJ6xAwAAAFAIFDsAAAAAhUCxAwAAAFAIFDsAAAAAhUCxAwAAAFAIFDsA\nAAAAhUCxAwAAAFAIFDsAAAAAhUCxAwAAAFCIvwFvvfo9Wym+nwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "\n",
    "ggplot(iris, aes(x=Petal.Length, y=Petal.Width, color=Species)) + geom_point()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "R 3.4",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}