docs/geoms/geom_step.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ggplot import *\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>breeder</th>\n",
       "      <th>pigeon</th>\n",
       "      <th>name</th>\n",
       "      <th>color</th>\n",
       "      <th>sex</th>\n",
       "      <th>ent</th>\n",
       "      <th>arrival</th>\n",
       "      <th>speed</th>\n",
       "      <th>to_win</th>\n",
       "      <th>eligible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Texas Outlaws</td>\n",
       "      <td>19633-AU15-FOYS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BCWF</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>42:14.0</td>\n",
       "      <td>172.155</td>\n",
       "      <td>0:00:00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Junior Juanich</td>\n",
       "      <td>0402-AU15-JRL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SIWF</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>47:36.0</td>\n",
       "      <td>163.569</td>\n",
       "      <td>0:05:21</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jerry Allensworth</td>\n",
       "      <td>0404-AU15-VITA</td>\n",
       "      <td>Perch Potato</td>\n",
       "      <td>BB</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>47:41.0</td>\n",
       "      <td>163.442</td>\n",
       "      <td>0:05:27</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alias-Alias</td>\n",
       "      <td>2013-AU15-ALIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BBSP</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>47:43.0</td>\n",
       "      <td>163.392</td>\n",
       "      <td>0:05:28</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Greg Glazier</td>\n",
       "      <td>5749-AU15-SLI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BC</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>47:44.0</td>\n",
       "      <td>163.366</td>\n",
       "      <td>0:05:30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pos            breeder           pigeon          name color sex  ent  \\\n",
       "0    1      Texas Outlaws  19633-AU15-FOYS           NaN  BCWF   H    1   \n",
       "1    2     Junior Juanich    0402-AU15-JRL           NaN  SIWF   H    1   \n",
       "2    3  Jerry Allensworth   0404-AU15-VITA  Perch Potato    BB   H    1   \n",
       "3    4        Alias-Alias   2013-AU15-ALIA           NaN  BBSP   H    1   \n",
       "4    5       Greg Glazier    5749-AU15-SLI           NaN    BC   H    1   \n",
       "\n",
       "   arrival    speed   to_win eligible  \n",
       "0  42:14.0  172.155  0:00:00      Yes  \n",
       "1  47:36.0  163.569  0:05:21      Yes  \n",
       "2  47:41.0  163.442  0:05:27      Yes  \n",
       "3  47:43.0  163.392  0:05:28      Yes  \n",
       "4  47:44.0  163.366  0:05:30      Yes  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pigeons.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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AAYGxjYBzamzzdu4AAMDsGNsIOLGxsQoNDdXu3buNjgIAAHBWjG0EJG4BCAAAAgFjGwGJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130d6f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (288454005)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(pigeons, aes(x='pos', y='speed')) + geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KUtH7ucsxs7PhxrwkMrMrFAo5PcKsomwDADAHJhbviXu6eaASKE2UbQAA5thk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113355dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (288579529)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(pigeons, aes(x='pos', y='speed')) + geom_step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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27GmdFwCAamuoK9vHsmfPnuzcuTPr169Pa2trVq5cmd7e3rzjHe/Ili1b8hd/8RcZGRnJ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150a24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (290497257)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(pigeons.head(25), aes(x='pos', y='speed')) + geom_step(direction='hv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aqrbnxK6r4uieZ4qGi+2xsbEJ7y9evDg7duzIeeedl1deeSVHjhzJKaeckttu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\ntgEAoBCxDQAAhYhtAAAo5P8DOKcemmX8SqIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b23710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (293747037)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(pigeons.head(25), aes(x='pos', y='speed')) + geom_step(direction='vh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y\n",
       "0  0 -1\n",
       "1  1 -2\n",
       "2  2 -1\n",
       "3  3 -2\n",
       "4  4 -1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_walk = pd.DataFrame(dict(\n",
    "    x=np.arange(100),\n",
    "    y=np.random.choice([-1, 1], 100).cumsum()\n",
    "))\n",
    "random_walk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tyDmmPfg+0gratmyf68knn4xrr712zW2zs7Nx6tSpNbf19PREsZiPSDo7OyPLsmaPUZOV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\nxGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150a2f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (294112189)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_walk, aes(x='x', y='y')) + geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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33nlnuUeqWhcuXIhDhw7FpUuXoq6uLm6//fbYs2dPjI+Pxze/+c24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\nGwAAElG2AQAgEWUbAAAS+X8//QcAwNYiGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11905e7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (290513349)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_walk, aes(x='x', y='y')) + geom_point() + geom_step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z38zSFqB6/7m6OdvtalzfAiFFgSyxoIWI0oW9dWfDLKz1TciI6i6ky6JqvVgL\nRGHQOy0nR6SGOXf3L2Mg4dH6OKsYbHsYzH/z6xlr55Zg63lorYrz/wAFQkwlIaK04apxM9KekOkU\n3ovaoW8btcG8WaJZGNWNrcNaXAGA7vohnS5t5GCw7WEw/00GuB0iy9sQx7vdf/sVCLH9HxGlSXfh\ni8Vi3ENJhd52fuZSEbZHJ5LL7VkkSbPnl68NAFCUvpiGF9z9GGx7Gch/C3I7xChtdw90QT5ocqBd\nIBHRPOvOarMTyUw4C9U454gg5xRO4FAYghY79nZl4wqy/Rhsj9Cb/xYkB7E3LSRosM2cbSJKC+Zr\nz5axNuY5aHULon4U9rAoY4IuUiXXdrppTJzZ7ifiHkBYvvzlL+P555+HpmlYW1vDj/7ojyKfz4/1\nGrK8jYXD6wBeAbPo3YnkcvsdLD3/VQA/GOiqziyU2Y2EiFLDrxcvjceZKewELwH+rkKHWSgDzZAH\nRpmhnJ9AuTh1Plc+jNIO8t/9GwDfz+PAgNTObN9111345Cc/iU984hNYW1vDX/zFX4z9GrK8g/yL\n33D+o2q+24+dr6SovkUHRETzwq/lKY3HXFmDet4CEKxuCMDIxdeIxiU6rTwV/3Cxt10lP4f9Uh1s\nq+1Wfbfffjvq9frYr2GUtyHqtwJvb+cLsIUOAIELhHj1R0RpISq7PMnOkqJClrYAALK8FegpPKfQ\nLDn93YNd6FlLq1AsE4ATD9Gl1AbbvZ555hm8+tWvHvt5cnUL9piFPt1ZnYDP4ywQEaWBctaEIo2R\nC1/Q+DrnCFsPlgbJcwrN0lgrwioKP38e5jpn+7HHHkOzOZyc9o53vAOvec1rAAB//ud/Dk3T8PrX\nv75vm3q9PvTcQqEAIXr+JLoOs7gBNAFd113HMPi4tX4V2A++vX3ldgDnntu7yb3wNZy/+t5At3X8\naJo21nvPu87+7dvPGcF9PbncC0/h/NV/L/BFtP7ys5BrV2EvhdQCzzSw8OLf4uKu+1x/7LavF779\nNC5e8QZAC+ezrx/egrl2FfqCe3u6MD97af5eW+u3Ad8Jfk7Bxh0A9lL7XU/zvh4lruP3Qm0fp3e+\nLvDnz1y/Ctwa4/PqIW37V7Ht9nI/KfTMM8/g61//Oj784Q8P7bg/+7M/wxNPPNH32AMPPIAHH3yw\n/0WefxrX/ugWmmdy6PULeR1/8Mvv7H/w4DquffY/onluBtv+eBfX/o+/QhPDJyjX7Y1z4J/+V7i2\n9N8Efw+Xtg1wAAAgAElEQVQimtxpC/hnD+Ha0seCf+c+/U9w7ehtaBrDLzeT7+j1v8O13/07NJFz\n/fHQe9g28L98ANfEh9C8sMIZ09cex7Uvn6NpDte38Lg0heoBrv3O08H328U5rv2zfx/8s0E0yq9/\nFNfkteDHvr0Xce2xZxmfDEjXpUOPF154AU8++SQefvhh1yuk++67rzv73VEoFFCpVCBlT2BdvhOP\nfOROz/c5PDzsf0BZxCMPu882uW5vqfi3yr/B0cP/K+yF/tuEDz3y1ND22uFLWION5rmJz3/8/qHX\nd3vOKLlcDufn54G3n3dCCJTL5eH9nAHc1xO+zt63UQaCf+dsG+sH19G0MJPvqJv8d5xA+1//9Kth\nraz5vofaOMK6cYamZYU2puXrz6Npvsr19QGXY98Mpft7reCRn/lh15/kcjnXv+sXlr+E2j/672CW\nhvO8Z7Gv45Tufe0tjuO3cnGK9ZMamrb7sQ9w+V5ry+PFQB46+zktUhtsf+lLX4JpmnjssccAOEWS\n733ve7s/LxaLKBaHb/EeHh7CMFymo0IkV7dgH74EY/MVQz8bHIs4vO75M7/H3QghIv99k0BKmbnf\nm/t6MvrhS91/B/nOqc0KVOMM0GbzHXWzeOtlAHcBh9dh5Fd83yN3MN7vMAnt6AaAV8X6Gcva99rr\nO22UtoGD6zCWhy/EgOn3dRJwX4dPP3wJxuo2UE3HZyZOqQ22f/7nfz7uIQRmtBfPcQu2B4nKHmTx\nCtAKf1xENP53Tq/shv4dddpr3QVR2cP5HT/ov301ijFxNdykMDoLrL0y7pHQPHNWhHWCbZpOJrqR\nJJ0sb3dXXfIjqns4fdW9IY+IiDpEZXes75yo7OH89tcBQGiLVunVve57BR1TmMcN5fwEinEW2uvT\neGR5B6LK1YlpOkFXjiR/DLYTwDkwBjtp6pVdnL3ijc5/rOGCGSKaLVHZw9kr29+5APXkoroLY+1q\n+9+zn+1VLk67QbweMKDSK3s4v+N13efPmqjscYGuBHEmcBhs03S6M9s0NQbbCWCULlddGkka0JoV\nGOu3AQC0RvAFd4hofMrFKdSzJoz1OwAAasv/fmrvkuVhBDyistsNbMXxrv8FgG07aWrtGaow0j24\nTHuyyNIWtOoBJ2RoKr3HDZoOg+0EMItXoJ3UoRijK41F7QByZb3bJ5czF0ThEtX2jG17NVrf75xt\nt2eDwgxsL2ebbKFDPamN3F5tVWELvbuiW6AL+zHxpJwstp6HtVTkhAxNTDHOoJ42YK5ciXsoqcBg\nOwlUDXJ1E6K6P3KzwZWcWJBEFK7B26h+3zn1pAZbE7BCDmw7xwFZ3vG9ANCre33HDT2E1BYx8B4U\nP1na5jmCJiaq+zBLm92JBpoO/4oJYZT9U0mck2bviZ8z20RhGiwQ8is6G8xdDiPYEdW97ixykIBq\ncNY5jOOG3pPaQsnAvG2ahqjswSjxAnpWGGwnhGy3/xtFHA+eNDlrQRQmMZCL7DuLPBCcq2fNmRck\niuPLMRlBjhsDv8Osg+1OwabpsrgOxSfIZ4PIy+Bxg6bDYDshnNvBPjNU1YGTZm2fBTBEIRKVveEL\n3BEFic4s8uV3VJa2Z9qRpFOwaRbWndcPckessgfZM0OlnjahXMyuTV93Nl/h6SRJxulyRTSIbf9m\ni0fHhJDl7dG3qE0J0TiGXN3sPmTll6E1jyMYHVH2uBUI2ZoYWZA4mLvsBMOzC3hEdb+vYFOWd5wc\nbK8LgHYnErl2OSaztOVbHzLWmDgDlkjOhd4+YHNChsY3ONFA02GwnRCyuAGtVQPkhevPRe0AsrAG\naPrlc3ibkCg0bgVCI3OkbXtoNihIAeNYYxoIbK3FFdiKCvW07rq9elqHrWrdgk2gXR8ywwVPBgsw\nKRnshTys3DK0BidkaDyKvIB2UoNZZCeSWWGwnRSqBlm84jnj5DZ7JEssgCEKi1uB0KjgWT2tw4bS\nF9jOemZbd1lkYlQqiZPfPfA7lHZmO9te2YXB4shECpJmRDRIq+5DFjcAVYt7KKnBYDtBRp3I9crw\n7JEzs82cPKIwuF3gjuoa1G0TqCg928/27tNg+0+gc9HtfhzQqy4X6TPuUjGYpkLJEaQWiGgQ87Vn\nj8F2ghgjZsGc4sidge13Zno7mIguuZ1wRhWduQXC5soVqKcNKMZsChJ72/51jAro3fIuZ3kBoFyc\nQT29LNikZJE8R9AEBgu9aXoMthNk9Il8b+jD371FzQIYoplzC1S7d59cChJdc5dVFWbJf8GqIBTj\nHNpJHeZKf2A79gVAwBVrg3Dy2re48EVCBVm/gWjQ4GJeND0eIRPEMwfbMiHqt/o6kQCAvbAIO7cE\nrVmJaIRE2eBVIGTlC7ChQD1tDD3H7YIYAIwZ5UiL6r5zDBjIo/TMC7ftdtu/gTGpGuTqhtM6dOox\ncQYsyboFvZyQoTGw6Hn2GGwniFzddFr5SaPvcVE7hLlcAsTC0HOMEmcuiGZNVPfcC4QUxbPozG0W\nGZhdkZrXrV1rsQjFtoYuADr/txZXhsdU2oE4nn5M+vFuXw9vShY7twR7YZETMhScNKA1j53jH80M\ng+0k0QTkyhWI2kHfw4PLLfdikSTR7AmXguQOt5ab6mkDim3BWiy6bj+LgkSvYB6K4pqH7eR39xds\ndhhrs1nwRFT3WByZcAbPETQGUduHLF4BNBH3UFKFwXbCuM2COQtluN+qnXUfXyIaXSDUXUimb/s9\np/2dW2A7o2DHrSNRd0wu/b89g/Pu9jOabWfbv0Tjegw0jsEVZ2k2GGwnjPuJfMRJkwUwRDM3qkDI\nrWuQW7egDqcgsQbFY8GqwGNyaePX4XbR7daTu3/76S4AugWbXPgi0Wbd6pHSjSvChoPBdsIYpe2h\nXEp9xIffaf+3771cMxGNbVSfWbeZQv14xAlK1SCLG1OlbSjyAlqr5plH6XpHrLI7tChPd/vVDWit\nqueKtUE4BZtc+CLpZHl7JilDlA2j0lZpcgy2E0auDfRFtUyI2uFwR4E2O7cEW89BbVUjGiFRyskL\naK3KUPefDreCROFTvT9tbYXfim6GS/u/kYvN+KxYGwRPyvOh2w2HEzIUwKh6FZocg+2EkaubEI0j\nwJQAAK1+C+bSKmyXTiTd55S3oVduRjVEolQTtQOnQMhrxlZR2l2ALoNbv8Bz2n7Ho+5uAYC1tArF\nlFDPmpfDtEzXgs0OWd4eSlkbh98FBiWDnV+GLRY4IUP+TAOieeTcsaKZYrCdNJoOs7DW7UiijyiO\n7Bg88RPR5IIUCA2uzKdIA9bS6ojtp/uOevXwvhyAMvQesrzjWrDZ/fmU/b9FZdfzjhslC/O2KQhR\nO4QsrAOaHvdQUofBdgL1tvESx/63atn+j2h2ghQIyfI29J7aCt/A1qXweawxBZhFdtIFLsfkt9jM\n9LPtnNmeF2z/R0GwODI8DLYTqLdTwKgOBJfbb0OvctaCaBaC5CIPLpHuF9jK4obrglVB6QFmkQeL\nJIPMzk882ykvoLWqvN08JwbvxBC5GVUYTtNhsJ1ATvW4c2AMMntklNurwbEAhmhqQQqEBheR8T1B\nacIpSJxkifTOim4eBZu9Y+idPfdbbKa7Yq05/gWAqO6PzmunRGEaCQXB4sjwMNhOoN4cbK26D1na\nGrm9nS/AFjrUk1oUwyNKr4AFQtbSKhRpQGkXJAa59SpLk/W2FrUDyBX/Fd0GUwV8F5vRBOTK+tCK\ntUEEqSWh5OimGnJChkYQVe/FvGg6DLYTSJa2IOqHAABrcQW2ng/wHM5cEE0rcIGQovSlewWZDZp0\nAaqgeZTWcslZaKZz7Fgu+Y9pwiJJwVXm5oqVL8BWNU7IkDfLhKjf8r2DRpNhsJ1EYgFm+0QZdPaI\nS/ISTW+cAiGjvI2F/e8AAMzlcoDtJ/uOBs6jVBTI0hYWv/NM9/9+Jk0vGLWcPSXTVDn6lHqiduAc\nx0a0GabJMdhOqE6BVtD8KVabE01vnAIhWd5B/rvjBLaTzyIHXTymb0wBhH4BQInBrlU0ChepCheD\n7YTqnMiCn2Sna+NFROMVCMnyNhYOrwd+bbm6AdE8Gn9MAToSdRjlnfHGNEkAZgYr2KRkmbbVI6Wb\nc+zj3aqwMNhOqE6br3HSSKbp40tE4xUIjT2zq+lOPvg4TAnROAoc2I57snRWrL011nOcgs1134JN\nSpbBdpVEvdg3P1wMthOq86EPukKblS/Ahv+tbKKsUC7OgNoYgaQp2wVCo7v/dDdfLsPSc2ONadxg\nWNQOYBbWAq/oNvbJUjgr1o71lONdFkfOIRbR0yhc0CZcnJrocXZ2Bl3XIUQC/ixXX4WCdoiPfO5Z\n1x8v5wQWFxf7HrOu3IbCkYKHHx3O2VzOCTz60fuHHldVdeh10kxRFJycnCRnP0coa/s6/8yf4tpf\n5tC0hvez2/dBPbqB9+EDaP7uf3B9Pbfv3PkPvwfL39A8v3NDf+/NO1C4aQX+juqtI/zUyXvQDPr6\n+as4u/cfYPlZEXhM1pXbUDhD4DHlm7fw0M17g48pAln9Xo/1nc7nAUXF8oI61jkiabivQ2CZEPVD\nfOxPqmidD6e6xfG9VgLUwcyT7HxSA8jn82g0GjCMyVZ5m7Xf/Mh9I39+enra9/+F4ib+1fdV0brn\nvxja9uFHnxnaHgAWFxddH08rXddRKpXQarUSs5+jkrV9nb/1EprWa/H5j98/tK/dvg/5vRfRxAI+\n87Nv8nzNweec/tDfx2/9kPcYhv7ehQ18/rZnUPmR/3poW7cxif3raFpXPcfktj9Pf/gf4dH/3Htf\nD73HyiY+c89NNO/7h4HGlD98CU1za6wxhS2r3+txv9NLpS186r48Lm57zdDPvM4RScN9PXtadR/m\n0ipaJ2Zivte6Huxu3rxgGkmKsP0f0SVx3P4umDLY9hF0ajB6VocNIooltp32f8F/d3a0mF88R5Ab\nnSkkoWOwnSLOiZwnQiLYNrR2UKFVgy2RHkU+q1zdgqjfGuMCIPwxjdX+z5RjF1RScsjyNgvpacg4\n7UVpMgy2U0SWd5zZPC7JSxmnntQAxTm8dWe4fUQyY9suSOysEDtSe0W3sHVXrLVM3227BZs0lybt\nq07pJtg3P3QMtlPEWixCgQ31rBn3UIhiJSp7kGtXAQBa5ab/E9oFQlEwSsH6HYvaYaCVKafWXrE2\nyO8vqpwBm2dc2Ibc6GP08qfJMNhOE0UJfCInSjO9ugtzzQkKtQAz21r9Fsyl1bCHBaCzAJV/wBPl\nkuiyFCwIE5XdwO1IKXmsxSIUy4R62oh7KJQUlgWtegBZCtbylCbDYDtlOHNB1L8SZJCLzygLhGR5\nJ1BBoqhGt8hE0NUFda4yN98UhecI6qM1bsFaXIGt5+MeSqox2E4Zp7MAZ7Yp20RlF2YnjaTmn4/s\nzCJHFdgGy5vVj6PLowzapSLKvxOFg8u2Uy+d+dqRYLCdMmztRJln284JpB1sm4UyRG10PrKIcKli\nM2BBoogwjzLQbHs7r523m+ebs695jiCH4N2qSDDYThmjvMP2f5RpTj6qAntxBQBgrvlfgEZZIGSL\nBZhLq9BGdRqxTIjaYWT50bK0BVE7GHkBIOqHMJdLgFiIZEwUDslzBPXg3apoMNhOGWtpFYo02JGE\nMsvpmLENtJf7NctXRwcXMRQI+aV7OQWbRdgRBba2noO5VITWGF6quUNU9iBLPCnPOxbRUy+nNoQz\n22FjsJ023QIYHkwpm8Txzb4ZYbm2A31ERxKtcQvWUjHSAiG/vO048ij90gtEZRfGGoPteWctl6BI\nAwonZMi2IKr7vIiOAIPtFDJYbU4Zpg908TDXdkYue67H0M7OryNEHCu6SZ8ZT7b9S4n2hEyQjjiU\nblrjGFZuGfYCO5GEjcF2CklWm1OGDQaqsrTtFEh65CPHUSDkLJs9IrCNYZEJw6f/tx5hESmFix1J\nCOisHMkL6Cgw2E4h50TOWQvKpqETiJ4bWZAYR4GQLG1Bqx4AluX6cz2G/OiRs+2WCa3GhS/SQpa3\nWSRJvICOEIPtFOKiBZRV6mkDimXCGlgNclRBYpRt/zpsPQ9rqQit4XIBYFnQqvuQ5WgDW1lqB2Au\nFwBavZPXnot0TBQOWWIaCXXuoDHYjgKD7RQyl8tQjHMo5ydxD4UoUt2UkHYnkg7POgbLcqrxY8hF\nliX3C4C4VnSzF/KwFgvQmsMdSQbz4Gm+yQDtMCn9nLt6TCOJAoPtNFIUyNIWFy6gzBFV95QQp7ew\nS2DbPIaVL8RSICQ9cqTjXGTC664YiyPTxZmQOeOETJbZVrudJ7/XUWCwnVJMJaEs8uoF7aSReASR\nMbWz82r/F+csslf7P1HZY9u/NFGUdvcZniOySmtWYOeWYOeW4h5KJjDYTilWm1MW6R7V9U5B4v5Q\nPnIcbf+6Y/KaRT6+GduKbl4Lnjh/JwbbaeLXEYfSTVT2YHBWOzIMtlOKC9tQFnl1FrH1PKzFlaGC\nxDiKIzu8ChLjXNHN9QKgU7DJTiSpIks7ECMWe6J0EzEsnJVlDLZTSpZ32P6PMkU5a0KRBqzlkuvP\n3QJJJ8c7nsDWXsjDyhegNY97Huys6BZXsN2+ALAvLwC05hGsxXjy2ik8xtoO2/9lmNddQAoHg+2U\nMgtlKOcnUC5O4x4KUST0qnsnko6h/vOdAqEYZ3cGu0Jcrui2GMt47IVF2LklaM1K97G4/0YUDq9u\nOJQNgh2GIsVgO60UlQUwlCl+lfWDBYndAqGYAlugE/BcfkfjTCHpGPw76bzdnErmyhonZLLKtpmz\nHTEG2ynGZdspSwaXaR80+H0Qld3YTzaDtRX6cfyBrSwl7+9EIVBUyNIWU0kySG1VYYsF2PnluIeS\nGQy2U8yrjRdRGvnNwMrSDkR1v5uPnIT0CKO83df/21nRLf4LgL7Z9gT8nSgcsswiySxivnb0GGyn\nmFFmAQxlh/A5gdgLeVi5ZWgNpyAxCekR3aLNnguAuNr+dQymkSQhtYXCwUL6bErCcSZrGGynGNNI\nKCs6uadmoTxyu263DSQjiBwsSIxr6fhezt9oH7BtAIg9r53C49VXndLNuYPGYDtKDLZTzCysQz1t\nQrk4i3soRKHqBs7K6EOaLO9Ab982T0qBUG/AY+v52Fd0s3NLsPUctJZzAcAZsPRyJmQ4s501eiX+\niYasYbCdZqp6uXAGUYoFTQmR5Z1ujnRSCoR6+38nZbap965Y3DPtFB5z5QrU0wYUgxMymWHbXNAm\nBgy2U46pJJQFTts//5OH0TOTJ9eScbLpLWROyiyy0XsBkJC/E4VAVWGWtiAq+3GPhCKintRgawJW\nvhD3UDIl9cH2k08+iV/91V/FyclJ3EOJhSz39/ElSiNnmXb/GdjeOz1JSCEB+vPIk3Jrt7dHvxHg\nIobm12BHHEo3v/UIKBypDrZrtRq+/e1vo1RyX745CyQ7klAGBG1P5+QjO8uOJ+U2qtET2CYm2C7v\nQK/cbP87GWOicMjSDvO2M0RncWQsUh1sP/7443jnO98Z9zBiNdjGiyhtlItTqOctmCtrgbbvnGiS\nEkTa+WXYYgFAcmaRjfI29MPrABB7wSaFS65xPYYsEcfB7gLSbKU22H722WdRLBaxtbUV91BiZa6s\nQzupxz0MotA47fK2fDuRdHTyopOSHw1c5kUnoWATAOx8AVZCxkLhYvu/bHE6NyXn2JcVIuiGv/iL\nv4gPf/jDeOMb3xjmeMby2GOPodlsDj3+9re/HV/5ylfwoQ99yPO59Xp96LmFQgFCBP6TzAkdZmkL\nqAK6rg/9VNO04cdtC7AsQBvjb2HK8baPSWf/pm8/+3Pd1+Oa5LMRslz9ENba1aHfzWtf21ducx5f\nce/JPfXfaALm2m3Anvd7jzumWexrc+3qTMcUpqx+r2fynV7f6U7IJGmfeuG+noJtO3cxNu5I/Pc6\nbfs38G9jmibe9a53YWNjAx/84Afx0z/907j99tvDHJsvr2B6f38f1WoVv/3bvw3ACax/53d+Bx/9\n6EdRKDgVuE8//TSeeOKJvuc98MADePDBB8MddBx2XoFCC3jokaeGflTI6/iDXx5ItXnm/8a1L7XQ\nNLVg29du4dq/+Cs0seD69q7PiVm5PHrxE/Lwza/g2r+roGkF/GxE4Zkqrn3vHjQ9Pt9D+/oHfhiF\nJ//G8/uwsbER1ki9ve7vofD8rWSN6QfuR+FISdaYfPB7PaH1qyg01ODniATgvp5As4pr8hqajz3r\n+uOkfq/TQLHt9jJhAZimiS996Uv4vd/7PXzxi1/Em9/8ZnzoQx/CT/zET3SD2CT6jd/4DXzsYx/D\n4uLlKmheM9umaUJKGfUQQ7X011+EIi/Q+k9+YuhnDz3yFD7/8fv7Hlv+89/Hj//HVw097rX9wot/\ni5/8kzP83od/APbiSqDnxEUIgXK5jEqlkrr97CeXy+H8/Hyq11h+8g/w4397R+DPRhSKX/wX+C9f\nfsvQe3NfT7ev50lW9/Ws9vPK45/CxStej/PXvGXoZ0k6fgPc19PQX34WP/XFeqL2p5fOfk6Lsebp\nNU3De9/7Xrz3ve/Ft771LXzgAx/ARz7yEXzyk5/E+9//fvzar/0abrvttrDGOpXBa4pisYhisTi0\n3eHhIQzDiGpYkThf3cLSc3/l+XsNPq4dvQzgVTDOzwF1OA92cPvc4csArgAHL8G4eneg94iblDJx\nYwqbEGLq31k9ugHgDhhnJ4A2fLsxjr+pdnRz5HtzX2dH1vb1rPbzRWkbyq2XYbwq2DkiCbivx6cf\nvgRgNVN/t6QYq0CyXq/j0UcfxYMPPoi3ve1tePOb34yvfOUr+Lu/+zsUCgW8+93vDmucU/mFX/gF\nLC1lt6JejtlHtdMGSmseBdu+/drs1Zp+nUIqUTuMeSQOxTiDetqIexhEc02ySDITdLYBjk3gme33\nve99ePzxx/G2t70NH//4x/FjP/ZjyOVy3Z//+q//OlZXV0MZJE1HFjegtWqAvACEe151h3LWgiIv\nADhBt1n0z99ygvMfYq/WlFOMc6jtQipR2YVcuxrziABR3YdZ2gSqcY+EaH45fdV5/E475xz9mriH\nkUmBg+23vOUt+K3f+i1sb7v3Z1RVFfv7XPI1kVQNsngForoPeeWOkZvq1T2n//ARoFd2cf59PzT6\ntW27u2gOe7Wmm6jtw1zdBGqAXtnDWdwDgnPyMEo7DLaJpiBXN6C1qoEmZGh+8e5FfAKnkfzSL/2S\nZ6DdkeVUjaRzZi78v2iisttdyjXIF1NrHndX5OMXOd16F0NIyr4WFa6GRjS1ngkZSif1tAHFtuIe\nRmaldlEb6meUtwOleYjKHoz2AhuBt28HO4o0oJwN9z2ndOhdDCEp+fl6ZTcxK0ESzTNZ3mZOb4o5\ndwF5rIwLg+2MkOWdbrrHKM7Mdieg2nMWMRm1fXW3Z/nrHR6sU6x3FlnUbwGWGfOI+i/2iGhysrTD\nupsU6z1XU/QYbGdE0DSSbs42ADu3BK1ZGbm9qFxuH3T2nOaT3rOvzeVy7B1JFHkB7aQGs3gl1nEQ\npYGxtpOY9DCaPf2YdwHjxGA7I2RxwwmcpXd/TeX8BMr5CcyC00jeCNAOqvc2viyzfVRqyQtorSpk\nuzuNc2F1M9Yhieoe5OoGoA6vZklE45GlbRa5p1hvGiBFj8F2VmgCcmUdonbguYnozGorzsdCln1m\nOmz7shsEnNuQbB+VTqK6D1m80g1snc9GvPtaVPa6KU9ENB25ugmteQyYXPAkjURllyl3MWKwnSF+\nM89OmsBOz/ajg2e1VYUtFmDnl53teRsytfTqeJ+NKDgnD94WJZqJABMyNJ+UsyYUacBa4loocWGw\nnSF+BYy9xZHO9j7BeU9+N+Dk8SrGGZTzk9kMmBLDKUS83NfjrkoaBlHhbVGiWUrCHSuave5EmqLE\nPZTMYrCdIUZ5B+LYO0AanCk0StvtjiS2+/bHA7elFMVZ9pcdSVJnsJ+1LG07BZIxdiQZnG0noukE\nLaSn+cK7gPFjsJ0hfrORgzOFdn4ZtshBa7l3JHFaCfV/gWWZRTZppPcsdgQAtliAuVSEVr8Vz4Ck\nAa1ZcQokiWgmghTF0/xhcWT8GGxniFzdhGgcAaYc+plycQr1vAVzZa3/OSPysAdzvIH27DlvQ6aL\n2QlsN/sejnMWTNT2IYvr7ERCNENMI0mn3ratFA8G21mi6ZAF9wIYUd2HLG11O5F0ODMdLgdf23Zd\nKtu3gwnNHVE7gFxZBzTR93icF1ZuF3pENB1Z8p6Qofnldq6maDHYzhivokcxkCZwub178Kye1GCr\nGqx8oX97r+Cc5pbwmBWJs0hysJiXiGZA02EWyhD1eBesotlRzk+gGGcwl8txDyXTGGxnjFfLNt3j\nytfJwR7e3qsThLmyBvW8BeXidDYDpth5fjZKMaaRsOCHKBQG706mirMewTY7kcSMwXbGeM1GOq3d\nhgOq7oF3oCOJXvW4LaWo7EiSMl6LIcjyFrTqAWBZMYyJaSREYZAskkwVz3M1RYrBdsZ45dmKqnuq\ngJ0vwBY61JNa//YDfZd7OakqDLbTwiuNxNbzsBZXoDUi7khiGhDN46GCTSKanlyLf8Eqmh2nRS/v\nAsaNwXbGyNVNiPqtvv7IinEG9aQOc+WK+3NKw+38RhVcsFdripgSonHkGdjGcWElaoeQK2tDBZtE\nND3PoniaS2z7lwwMtrNGLMBcLjkLknQequ7DLG0CqvvHYahI0raH+i73Yq/W9BC1A5iFMqDprj+P\n48KKxZFE4ZGlLadAMsYFq2h2vGpuKFoMtjPIGOhI4pWvfbl9f+qJetqADQXW4orr9k5vbs6MpIGo\nBvlsRBtsOycP3hYlCoVYgLm8yo4kKaBcnEI5P3EmTChWDLYzSJZ3+goY/WYKB9sFdvO7PaqbzcI6\n1NMGFONsdoOmWPj1Z3UKbiNOI/G5OCSi6XBxsnS47ETCUC9u3AMZJMs70I8vg2e/mcJuqkC7I4k4\nvvlBZY4AACAASURBVDn6tpSqwixtQVT3ZzZmioffymNO55l9wI6uI4lgdT1RqLg4WToI3gVMDAbb\nGTTY/s9vptDKF2ArKtTTOgBA9+hc0stgR5JUEJU9GB65+QBgLyzCzi1BaxxHMyDLhKh7F2wS0fS4\nOFk66CyOTAwG2xkkS9tOgWS7AEY7qcEsunciAQAoSjsP2wnQg9zG58xIClgmRP0QsrQ1crMo87a7\nBZvCvWCTiKbHjlLp4LVGAkWPwXYG2WIB5tIqtLrTH1mubgCqNvI5Tvs/Z6bDL48X4ME6DUT9EOZy\nCRALI7fzWpU0lDHx5EEUOlnehlY7YEeSOcc0kuRgsJ1RsrwNvV3YFqSNWu9MtWKZsBaLI7dnr9b5\nN2rhol5eq5KGoVvwQ0ShscUCrKUitMZR3EOhCSkXZ1BPmzAL63EPhcBgO7OM8g5Eu0gySEDV2y5Q\nlnc8O5F0mMUrUE9qUOTF9IOlWAS5gwEARim6zgV+BZtENBu8OznfnPUztjzXz6BocS9kVO9sZJCA\nqjdVINBtfFWDuboZeVs4mp2gwXaU7f+YRkIUDcnFyeaaqNzksTJBGGxnVG/wHCSgctJG7Pb2wWYW\n2ZFkvgWdRbZzS7D1fPgDCliwSUTTM7g42VzjXcBkYbCdUbKnD7Zc3fB/gqJ0W8AFbSXEjiRzzDKh\n1Q4C50dH0V5Kq9+CubzqW7BJRNOTEaaH0ewJtv1LFAbbGWXreZid5dZ9OpF0dL64o/ou923PIsm5\npTWOYC0VYQcMbIPk/U9L91nplIhmR5a3IGpcmGxeicpu4HM1hY/BdoaNe9Xb2d5aLgXbfo0FNvNK\nD5iv3RHFDIqo7MJYY7BNFAVbz8PKF+IeBk1AMc6hntRHr59BkWKwnWHGlTvG3P525x8+nUg6ZHED\nWqs67rDGkv/uN1D4xp+G+h5Jo8gLrH/xfwdsO7T3EJXdsVrsjZ0baNtY+/e/CeXidIwxse0fUZTG\n/V47x+MvB95ePamh/OVPjTussRS+8afIf/cbob7HuFb//Pcgjm+E9vqiug8zwPoZFB0R9wCS5Ozs\nDLquQ4hs/FnkW69h+bmn8fCjzwz9bDknsLi42P/gK38Iy7mngm8PwCptYvlE8XzOox+9f/JfAMDi\nwXfw09+6Hc2ng72+oig4OTmZ6/2sHRzgA3v3o/mv3E8gXn9XVVVd95GbXP0QH3jxB9H8VrB9rWy/\nEgX8deD9rLRqyN94Fj/3+8+hZQxfNLg9Z6G2j4duvAmtrwQcUwr29aTG2ddpkNV9Hfp+3rgDhX0r\n8Pd63OOx2HseD11/PZour+/1nHH39eLNZ/H+b5TR/H/COQdNYvF738SHvvsDaMqDwGMaZ18vtI7w\n/sY7Xf+uXufqpFECTurNi+wclQLI5/NoNBowDCPuoUTm0Y/ej9NT99lFt8d/66HXe76W2/b51U08\n+gaJ01f/8NDPHn70Gc/3Dmrp8CU07VfiMz/7pkCvr+s6SqUSWq3W3O7nxb0X0VTy+NfvXMD5HT84\n9HOvv+vi4mLgv/fyrRtoyntc/66A277W8IWF/wuHP/5PYBXKvuNZ2HsRANAy7GD7zrKwWtlDC+7b\nu40pDft6UuPs6zTI6r4OfT8XruBz3/ctVB/80NCP3L7XS7deHut4vLz/PTSVK/jcj21Crt8W6Dnj\n7uvi0Q00TS3wmMKmnjagnjbQ1JSxxjTOvhYH19G07hzj+J08uq7HPYSZYhoJhcqpaA8vb7tTgKmc\ntUJ7j6Tp/D1DKz61rIkKo8ZZBEMc34StBD/8aI1bsJZGr1pKRLM1bkepzkJpylkz0Pad40VYtT3K\nWRNqws4Noro31rFvovdgrVTiMNimUDm9WsM6kLa6K1TqES0XngSXwXY4v7PWPJqoMMooB28Vplf3\ncH71+wO/9rgFm0Q0ve6CVbblu61zPD4HgO4aDn46x4uwjmV6ZQ/Gxp3O+M5PQnmPcYnK7ljHvkmw\nMUHyMNimUIXZ/k+v7nYLeLLUYrBzIgt6QhuXmHAxhN5VSYO8x9mr7h1vTCyOJIqUvbAIO7cErVnx\n3VavXn5HAwXPtt1deTas47ez4uxVZ3wJCUBFZQ/nd7wOQEgXAPICWqs2+9elqTDYplDJ1U2I5jFg\nzj6PsjcAy0ywLS+gtZwTn6jshtKRZNJZZFnaGWNGaxdnnRNOgI4kzkmTwTZR1IyAqSTORbpz3OgE\n0aNorUp35dmgF+njchZ2SdY5Qq/sQq45FwBB/k7jEtV9SLb8SxwG2xQuTUCurEHUhquupyUqezA6\nB/eEzFqETdQOugdSW+hQT2Y/gzHpYgiykzLkcwGgnjagWGa3X3tnJdORY+JqaESxCHp3sveCOMhF\nt+i5qBeNo1AmZHqXLE/KOUJU9rrH1zDuTupVLtOeRAy2KXRyjFzecTgzsNvdf2dB7yqK4xQkjkNU\n9iAnWDzGyhdgq5rvBUB3Bqzd2sn3JGhbzmwN00iIIhd0cbK+4Dng9p3gXBbWIWqH0w10BmMKW6fO\nqDvREMbx+3i3OwlFycFgm0IXXlB4eSBVjLPEFMCEqfdWbdDbu2OxLWcWecLANsiFlaj2nwz8Phta\n4xhWfhn2Qn6iMRHR5IyAHaX0nrtPinHuezzuPZaN2/UkCOX8BIpxDnO53B1f3Jw6ozEmGibAu4DJ\nxGCbQmeEUCSpnJ9AuTiF2e7pLEvboeS/JU3fbFB59n9XrVmBnVuCvTDZogeyvO3bGWawANM3OGcn\nEqLYOIXP+yPTw5SLUyjnJz3H4y3f73Vviocsb888pcKp6dnqBradc0acnNTH4Me+Sehjrv5L0WCw\nTaELY9aiW/jS7lcaxsE6ifpmg0LoYe4E85MHtkHa/w0WYPpdJOnsREIUGzu3BFvPQW1VPbfpFqt3\nj8c7oy+6bbuvNiSMc0R3FrktzM5YQQ1ONKhnzdleAJgGtGYFcnVzdq9JM8Fgm0InS5vtAhg5s9cc\nDNjCXjwnEUwDonkEuboBoHOBMduOJNPOIgc5aQ4WYKondSjG2ejtObNNFBsnFfCm588Hj8dGebu7\nwI0btVWFLXTY7X7+xhhtQ4NycpcvjzNhpTOOo7fmBpj9HVlRPYBcWQc0Lg6eNAy2KXyaDrNQhqjP\nrgDGmUnpObiHuHhOUoha50DqLGNrLa44BYmn9Zm9h16ZrpJdlkZfAChnTSjS6BYIAYBZ2hzZkURU\nmUZCFCfDJ2VN9BSrA+2L7hFBpD6QVyxXtyDqR4BlzmbAGM5ddtLu4j1HiMpuX/H5rFMBB/cDJQeD\nbYrErIv5BvsuJ+EWYdgGLzCA2d9+dWadJw9srcUVAArU04brz7sn2XYeJdApwPLYd7Y18SI7RDQb\nfsdXUd3ru/vkN4s8dLdKtCdkZtiRZHi2ffQFQNicgs2zbsEmMPvZ9sGLGEoOBtsUCedgPcugsP+g\nYq6sQT1vxV4AEybdZdZiprnq7RXdpgpsFaV9S9h9TIMFQsDoGadpCzaJaHp+OdiDM6pmoTyyINHt\nAtoppPdOVRnHYMEmEP/MdrfLU89EQxgz25OskUDhY7BNkZhlUKhcnEI9b8FcWet5UHUq4FPckcQt\nn9qY4UVMZ0U3O7c01evI8g7EsftJ0+0256jZHZ48iOLXTSNxSQ9TLs6gnrVgFtZ7HlRHzoYP5i4D\nsw08Bws2AcAsrEM9a0G58K4PCZPbZEkYd3w5s51MDLYpEkG6VATlzBBs9R1IgfSnkrjPCs/uYD2r\nA7XT/s/rJOuWCuO93wbvYBBR9Ox8AbYmXBesEtU9yNUtQB04HnvNJLc7kbhfdM8q2HbJXVZVJzc8\npgkZtzRAc+UKtNPGyALxwEwJ0TiCLLETSRIx2KZIyNKWUyA5gwIY4dEKTq5djb3aPDSmhGjcck5q\nPbonqBl0JHEL5icxKgd7sEAIAGRxA9pJDYq8GNp+MO+SiOLhdQfKqyjPST0ZPg6op3XYqtau77jk\n5FTP5vjtlbscZyqJqOzCGFyZV1Wdbl0jCsQDv37tAGZhrVtAT8nCYJuiIRZgLq/OpCOJVwAWxuI5\nSSHqh86BVPQfSDsnLPWsOf17zGgxBOnRGcatQAgAoGpOwO1ywpk6h5yIZsLrzqFXByOvFAmvO2hm\nacspkJzJhIx7u9A412PwmiQaWSA+zusPFKlSsjDYpsjIWR1UKu4HFTmiMG/eeeYudwoSZzBbo88o\nZcNaLEKxzKGOJJd5lMrQc7o9w3vZtue+JqJojRs8e80iu+UuA4AtFmAurUKr35p6rF4djPxaEobF\ntc6oO6bZHL9nNVlC4WCwTZGZVS9sr9uW5soV3wVS5tWo/qkz6fTSWdFtFrPIitLOJe8/qTkrurm/\nvtsFg9qqwtZzUxdsEtH0vOpDvGZUzcI61NPmUEGiqOx5Fj27XnSPSTHOoJ41+ws222ZdkBiUV50R\nMLv2f7pLih4lB4NtiswsgkLFOIN62oC5cmX4h6rq3IqcQf5b0oyadZZr0xcWDa7oNi23FeFGzVK7\nBudcoIEoMbqFzz31IYpxDu2kDnNlOLB18pG3h47HTt3GVdf3mEUwLCr7rgWbAGCurEM7qUMxzqd6\nj/HH5H38nlXzgFEXMRQ/BtsUmVlUm4vqPszSpuuBFPBf6WxejQpUjRksVT/rxRDcgudR3U7ciql4\n8iBKDmtxBbai9q1YK6r7kKubgKq5PkcOXnTbdrsjkdfM9vSBpxhxBw2qBrk6m4LEcYwq9DaLVzwL\nxAOzTIj6oTN7TonEYJsiI0tbELWDqQpg/HJ45QwCz8RpH0hNjwPpLHLVZ53v53ZrVB/xHrK4Aa15\nDEijf0zM1yZKjMH8Yq9CxN7t9ePL7Tt1HIOdSPq2n7Ijid+Ks3GkkoycOBhRIB749euHMJdLgFiY\n+DUoXAy2KTK2noO5VITWOJr4NYTLYgi9ZpHzlzSifghzaRW2x4HUWlqFYsqpOpLMuhBxcLGd7opu\nLgVCAABNQK5cgahdnnBG5XgTUfRkqb+bx6haEmC4INHJ73YvknZefwta9QCwrInH6NcuNI72f/5/\np+nOW+J49HmR4sdgmyI1bTGIXx7vLBfPSQrfhV26BYnT/l1nd7C2lktQpAGlfQFwuVSx9yFnMM2I\nnUiIkmVwVtivg9Hg9n53q2w9D2upCK0xeUcS/9n22S2eE8TIOqO2aWfbRXVvuIc3JQqDbYrUtEWS\nfgHYTPLfEiZIl5CpctU9VnSbSvsCoJOH7XdrFxiecbI1MbOCTSKa3vBM9ejA1lxZb3eIcgoSvXpy\n971HafJZXsU4h+pVsNk2y8VzgnDqjNwLNjumXaqebf+Sj8E2RWqaoFCRF9BOajCL3jMETgHMdPlv\nSRNkyfJpctW9VnSbVu++DjJzPnhXgicPomQZDAq1lv/x2Fzd7KaHOesF+BzLpjhHiNo+zBEFm0B7\nQqZVAyKakAmyMq/XaptBzWqNBAoPg22K1DTV5qK6B7m6MfJACjiBZ5rytoO0wJsm52/ms9ptvTPV\nnovyeGzv/J8nD6IksRaLUGyrW+goi/7HY6O8DXF8eRzwO9ZMk1IhjgOsFdAuSIyqI4lfnRHgXiAe\nmGVC1A7YiSThGGxTpJy+q3sTFcA4KxD6B2CzWjwnEboH0iAnqMkuYmadr93hXPT0pJH45BTK1U2I\n5hFgOicc5msTJYyi9AXDQS7Se1NPFMuEtbTqv/00EzIBjhtRFtIHWmxGE5DF/gLxoLT6LZhLRdh6\nbsIRUhQYbFOk7IU8rMUCtOb4HUlEZTdQEYiTF56OIkmtcRToQOoUJF5AOWuN/R5h9bOWPRc96mnD\ndUW3PpoOWViHqB06z2cnEqLE6T2+Brkgln3B+Y5nJ5K+1594QibYxEGUhfRBj6+9kxPjmPUaCRQO\nBtsUuUlnLsSIxRAGX3+a/LckGdWbuo+iXK7wNqaw+lmby2UohrNUs1+BUEdvKglPIETJ09tRKtjM\n9mW7QN8UD7QnZPIFJ61iTEEKMDtjmnZtgiAC1Rn1jmmC2XYWR84HEfcAwvTVr34Vf/3Xfw1VVXH3\n3XfjR37kR+IeEqG32nxjrOfp1T00gsykrG5Aa1YmHF2yBOni0XE547QU/A06K7qFMYusKM5J4DjY\nSRZwTuS5m88DeC0sdiIhShxZ3kb+xb8B8P3BUjaKG9BaVeffAXtBTxR4ygtoraqTR+77+tHU9WjV\n/UB1RoAz27747a8DuH2s9xCVPZzf9poJR0hRSW2w/d3vfhfPPfccPvGJT0DTNLRa499ep3AY5R3k\ndl/AWMG2NKA1K86By4+qQRbXgbr/pkknKruBD6SXuZR3BX79yxXdipMMz5csO8H2OCfZ4pP/FsBr\nfW83E1H0jPIO9OMbABAosHWOx1eAevDUsMtgOPg5QlT3nfcJENg6BYnhT8joAYojOyad2dYru2jd\n8+DYz6NopTaN5Gtf+xre+ta3QtOcL97y8nLMI6KOSdJIRC34gbTzHmkQpO1fxyQL2/it6DatTk6n\nb4FQz/baFCthElG4rKVVwLad/2jB5us6x7CgRc+TnCPGyl3WhDMhE7KgdUZAT4H4OCzLmT1nJ5LE\nS22wfXR0hO9973v49Kc/jc9+9rO4ceNG3EOitkny5cZNdUjF0rWWdbnyYgCT5GyHne/XPckG/R1W\nN2FzRpsoudr1IePopJFZy6XA24+78EyQfta9ojhHBK0zAtAtEB+H1jyCtViAvZCfYHQUpblOI3ns\nscfQbA7Pgr397W+HZVn/f3v3H9xWee95/HOkI/+UZSmOfyX8SAlbk0uAJrkUOgOlgfY2DEP5kTLT\nS7OZspSSodMZpp07u3/tdPbPzk67f2wZpjPd7mVbegtc2gQ6KWXvLQG27dCG0Da5TcqtCYEkju1Y\ntixbds6Rzv4hW7H1y7KtoyPpvF8znSFHRzqP9VTP+erR9/k+mpub02OPPaazZ8/q+eef15NPPpk7\nJ5FIFDw3HA7LNBv6LVm1YDCoUChU24uGQnJaO6Q5VXztlsSoMhs2lzw//7iz8QpJmYLji/3bCP0c\nmBqT0x6W2Vl8s5mC9yLWJ+NSqvhjKt7XrVOjSm+s/H1dtd4rJQ0r0DOoQIlfJZZdIxRSOtIrJUtf\nu9I2NVJfV5snn2sP+bWvverndM9mabzyz6iz8UpJaYVaWio63+i9UqH4haKPlerrlqkLmr/25orb\nlOnZLJ2pfDwx5pJqP35Es397T0XnS9nZ9lTvFato06aSY1+xvm5JjCmzYZN747eHmu2z3NB/zf79\n+0s+9vvf/17btm2TJG3evFmGYWh2dlYdHdnFY0ePHtWRI0eWPeeOO+7Q7t3kPtVE/9UKnze07+m3\nCh4Kt4X0z//wd8sPJse19/SNSh4rfn5vb15uX+ZvFP7Xtyt//Xo0cVp75+9TssTfUPA3S1LvlQrH\nA5X/3dNj2vvX/6Dkb1ZxjdXo7VW49X3t+97Rog8XvcZ//K8K/+BEyb9htW2KxWKrOh+Ni76ukbv/\nk8Lvv1X5ZzR2l8Jv/MvqPtPtnQqng5WPZVMX9PCbhpL/UuE1tgwp/IeLlb/+qdPa+/uIkr+v8Hzr\nkjQzqccOjSg592Flbdq8VeGzTuVtOjmlvSO3rO4eAU80dLBdznXXXaf33ntPW7Zs0fj4uDKZTC7Q\nlqRdu3ZpaGj5wrNwOKx4PC7btmvdXM+0trZqfn6+5tft7Nqof+yfUGrnZwse2/f0WxobG1t2LDZy\nWknrBv3wwMeLvl7++XJa9ILzY40/9j+k4OVv96Zp6gv/89cN0c/tp08qmdlQ+d8sqSvSq/89NKe5\nv7mt4LFi72vP6BklL31iVddYracf2VX28cJrtOjpL+1YxfnFmaapWCzWEH1dbV59rr3i1772sp+f\n/tLOko8V+4yWGweKnd8d7df/urFFl7bcsOx40TE8bWnj5JiSSlc8lgXNsH4S/pHiD/+3gnOLjZXt\np08qqQ169otblenqWfH84PgHikQ2KjllV9ym1raonr3ibSX2HKioTV0f/ruS6e2ujt9eWfxMN4um\nDbZ37NihgwcP6qmnnlIwGNQDDzyw7PFIJKJIpLACw9jYmCxrDVumNijTND35ey9F+tVyYbjktZcd\nT1sKTk8UHl+B3bVBzvg52Rs2FT5m23Xfz53jZyVtWFU7L0X6FRj/sKL3NZCaltJ2wfFm0wh9XW1e\nfa695re+buZ+vtQ9II1/KGvzdUUfX9rX5sRZ2V090nTlY5nVGVMscVHWXKroQs/81+m8mB2PNfaB\nrLbC2CH/fHPsQ1nRfmmq8jY5kV61T5yr7L4oKXDxnKTtTfv/gWbStMF2MBjUgw8+6HUzUIIdG1DH\nyf9X0bnm1Jjsrg3S9CqvEc1W5ygWbDeC7AKhG1Y8bykrNqDO83+p8PUXFp2Or6FxAOAiOzaglgvv\nVXRubj+C1dwjgiHZ4Q0yp0Yrukdkq6PckK1rfeX1FZy/UPbv/cqbZHf3yUyMS5n0ypW3nExNNuZB\ndTRtNRLUNys2mB0oFktIlWGuolbpUtmdy9zfuMAVTmZNu2zasUGZE5X9zWb8vKxmqNoCoOmspu50\naI274FZ8jSWBbaVtMuPnV1UdJfukFqU7YzKnRlc8NTg9kS00gIZAsA1POK0dckJtCs6svLFAKH6+\n4jrNS1lr3Ba+HgST8TUNpOmuDQrMz+SqkpSzmhreAFBLVnR1EzKV1vBeKrt5zsr3iGAyLieULa9X\nyfnZNq1tfLUq3C8hu0cC43ejINiGZyqdVTDjIxXXaV7++oOrrtVaL7Kzzmuof20EZEf7ZU5eWPHU\n7GyQezW2AWCtnLZOOWarAgtbvZdjrnIfhkWV3iOyNbwHF/77/MpfANKWzOSE7O6+NbRpoKJJIrf3\nSEB1EWzDM1a0wkFlco0/EXb3yUxczOa/NZj1zDrb0VV8iWFmBECdqigVMG3LnL64psDWqjSwXXIP\ncsyQArNT5c9fXGdU4Q6bS1U62x7il8mGQrANz1S0vXjalplY20AqM6R0OCZzqvHKH601B1FaHKzL\nv6/G3IwM+1LFO7oBQK1VklJhTo0qHY4tK/FaKbu7//KCxDJCE5d/BbSjK38BWOs6I2kVaSRrTJ2B\nNwi24ZlKvsGbibHsQGqubSes7MB1bk3P9ZK5jhSPSnLVQ5MLr8/W6ADqlF3BWLau3OUKJ2RylZtU\n2STRWtcZSVI62i8zMVb+C4DjLGsT6h/BNjxjLeZsl8l/W++3d7vCVJW6sjCQrilnWws5fyuUhDLj\nI+T7AahrdmxAoRVyqs11/AooLaYzlrmG4yysG8peo5LJjLWuM5Ikx2xRuqNbwUTpmqzBmeyCTaqR\nNA6CbXjGaQuvmP+23qAwm/PXWMF2YGZSjtkqp61zTc9Pd21UYDYhwyq9sxz52gDqXW5mu8yETGiN\niyMvX6P85ER2PG7JjceVLOxf6zqjpW0ql6qynl8+4Q2CbXhqpfzi9Q6kVgNWJFnPT5CSpEBA6Whf\n2YokDNYA6l2mLSwnaK44IbPWWWRp5bSQUF66hh0bVKhcScJMeu3rjBasNHvOZEnjIdiGp1aqnLHe\nNJJ0tD+bj9dAFUnWXPZvCSu6wg1knT+9AkAtlF2QmEnLTIzJjvav/fVXmkWeWH4PyrR3yTECCqQS\nxc9fXLC5xnVG2TaVH78p+9d4CLbhqbLf4KswkDpmi9Kd5fPf6k01NpspN1gb87MyLqWyNwQAqGPl\nFkmaiTGlO6OS2bL2148OlJ2QKZYSUi6VpBpVQlbKVafsX+Mh2Ianyg1awcS40p3d6xpIJcmOrlwK\nr57kKoWsQ7lc9dwqdoOPP4D6ZpUNbEdWvyV6npUWJBZLZczOtpcYX6uw+NyO9is4OSplMkUa7Kxt\nK3h4irstPFUu/y20jlqlS1W6cUFdWFz5vs5Zi3K56qH4SFXeVwBwW3aXx1KBbXXS4bIzyUWusRDY\nFs5sl/7lcL3rjCTJCbUp0xFRcLrwC0BgdkqOGZLTFl7XNVBbBNvwVLn8t2p9e69o85w6EZidkhM0\n1z2QpiMbFZyZkmFfKniMxZEAGkVuEX2RCZlq5S5bsUGZE4X3iMDslJxAUJm88bjcxjPV2mymVK46\n620aE8E2PFcqlaQaucvZ1x9csVZrvQhVa+FLICi7u1fBIhVJ2HkMQKPItIXlyCg6IROKj6yvctMC\nu8QvgaXuQbk0vfwvAFVYZ7T0GsV+kWVxZGMi2IbnSuW/hdZZq/Ty65fJf6sz1fqCIZXOVa/mNQDA\nVYZRPPDMpBWcGq1K4FlqjUupe1CmPSLJUWAuuex4tdYZSaVnzyn715gItuG5ooNKJqPg5GhVZgic\nUGvJ/Ld6U81Z52K56sallAJzSaXDG6pyDQBwW7FUwGBiXJmOiJwqBLbZCZkLBRMyJRdgGkbRnSer\ntc5IKp2rXq08ddQWwTY8V3QgnV4YSEOt1blGg2zbbk6uf3HNomLpOebkwkr5AB99AI2h2OZnwYlz\nVQs6nVCbMu1dBRMy5QLbYvetalYJsaMD2Y3JnCVfABxnIWebNJJGwx0XniuW/1btRSAr7VRZF3ID\nabVmRjYVrLA3q7BSHgBqySqypXr+ZjPrVVDPe3E8LpGmkr2nFBtfq/QFoKVNmdZOBacncscCqUR2\nwWZ7V1Wugdoh2IbniuW/VaNW6VLlarXWi0AqIUdGwcr3tbK7exVMxiXbyh1jJTuARlNsY5tglWd4\n88v/BVLT2fG4RGBb7JfDaq0zKnUNKkk1LoJteM8wCvK2q120v1yt1nphLq6sN4zqvGAgKDvSI3Nq\ndPk1GKwBNJBMe0SGk1EgNZ07Fpw4V9X9AgruQbnNv4qPx1b+PaWK64wW5c+es3Nk4yLYRl2wo/nf\n4Ks7qBTNf6szbpR0yq9IQtk/AA2nyILE7PqTaga2ea+/Qk54pqNbRtrO/SJb7XVG2TYVmYSi7F9D\nIthGXcj/Bm9OXahq4LmY/xZIXKzaa1abGyke+TeQYGpa6a6NVb0GALgtP5Uk09Ylp6Wteq+fDGzK\nIAAAFwJJREFUNyETWmmxeq4kYXZ8rdoeCUtkc9Xz00iYLGlEBNuoC/lBYaats6oDqSTZG+p7J0lz\nskSZqXWw8m5QdrSPSiQAGk52If3l8dvesKmqr++0tC9bkFhJPWsrenl8dWP/glwVrcUvAKQBNizu\nuqgL+flvbnx7t6MDCk6cq/rrVosrg3XezmhWFXMcAaBW8tfdpKuwc2ThNS5XPalkFnlpmke11xlJ\nktPaIae1I7vQfUG2oAAaDcE26kJ+/psb29EWq4taLwKpaRlOpuoDqd3dK3P6opTOViRhVgRAI8pf\nwJh2Y0ImNqjQRPYaRia94ni8dLbdrZ15l+aqW2UWbKK+EWyjPuTlv7kyaMXqd2Y7t/Cl2gNpMCQ7\n3CNzakySO+8rALgt09Etw7ZkLFQkqXYaibQ4s73kHrTCeLw0Ta/a64wut+nyNdx4fdQGwTbqxtL8\nNzcqZtixQZkT9Vn+z61ZEWn5jD6VSAA0JMNYGMOzEyZupJEsDZ4rGSsznVEZ9qXsf7uwzkhaPnvO\nZEnjIthG3cj+hHdu4b+r/w0+uwCmveqvWw1ubjZjxwbUMnZGkpSOUIkEQGOyYoNqOXNCUnY8r7Zs\nRZKFWeRK7kELv8hmz3dxsmTSvUko1AbBNuqGHRtQ64f/JsmdgVRyZzakGsyVykytgx0bVNvpd7L/\nCARduQYAuM2ODaj134+69vpOa4ecUNvCtSq7VyymdriV4mEtViQRa24aGcE26oYVG5SZGHf1Gnas\n+nl+1eDmZjNWbMD19xUA3GbHBhRMjLl8jew4XOnmMYvjtlsz205bpxyzRVI2bx2NiWAbdSPTGVUm\nVP2ct6XSLiyqWa/AXFJG2nZtILW7++Wwgh1Ag7NqMFmyGDxnOqMVnb842+xmioe9+Iss43jDIthG\n/ViS/+YWuw7TSMzFjQrcGkjNkNKRXndeGwBqJDshU73t0IvJ3YMqHI/t3My2e/cutmhvfKbXDagn\nc3NzCoVCMk3/vC2BQEDt7fWzaHD+tofUeTihR75/rOCxzlZz3W0N9F2lsM5r39NvFX397z/28XW9\n/lq0zFzU3098SkmX/mZJSt3xsDr/b8q197UeGYah2dlZ332mpfr7XLvNr33tt36WpNRnv6zwv6ZL\njuHrfj+GPq7w7/6t8rGybbPC5hl96f+cLPpy1WhTesenFR7+wHfjdzMxHMdxvG5EPRkbG5NlWV43\no2ba29uVSqW8bkbNhEIh9f7jf9bFvf9F863hZY898v1j+sGjO2repsivn9feP1/r+rV92de9vb77\nTEv0tV/4rZ8l+tovFvu5WZBGAv/pvaKuNrcJ1emulgAAYP0ItuE/vVflNkaoB4tlnQAAQPMh2Ib/\n9F6hYJ3MJhvzszKsea+bAQAAXEKwDf/pvbJuZrbN+IjsaL/XzQAAAC4h2Ib/9F6p4MR5qQ7WBru5\nTTsAAPAewTb8pzMiBU0FZqe8bomrO0cCAADvEWzDl+wNg3WxMNGcHHF9Ix8AAOAdgm34Ujq2qS5K\n7pFGAgBAcyPYhi9lZ7a9DbaNSykZ87NKh2OetgMAALiHYBu+lN6wSeakt2kkZnwhhcTgYwgAQLPi\nLg9fsmOD2TQSDyuSmPHzsqOkkAAA0MwItuFLTnuXHBkKpBKetSHE4kgAAJoewTb8yTBkxwY8rUhC\n2T8AAJofwTZ8y455u0jSpBIJAABNj2AbvmXHBhXyaJGkcWlOgbkZpcMbPLk+AACoDYJt+JYVG/Bs\nZtucvCC7u18K8BEEAKCZcaeHb2XTSLyZ2TYnz7M4EgAAHyDYhm9l2iMyMmkFUtM1v3ZogmAbAAA/\nINiGfxmGLI9mt83JERZHAgDgAwTb8DWvKpJQ9g8AAH8g2Iav2bGB7E6SNWRY8wrMJpTu6qnpdQEA\nQO0RbMPX7NigzBqX/zMnLyjd3ScFgjW9LgAAqD2Cbfia5UEaCSkkAAD4B8E2fC3T0S3DtmTMJWt2\nzeziSCqRAADgBwTb8DfDyO4kWcOKJGzTDgCAfxBsw/dqvZNkKM7MNgAAfkGwDd+r6SJJ+5KCM5Oy\nI721uR4AAPAUwTZ8r5bl/8zJC9lAm0okAAD4AsE2fM+u4S6SIRZHAgDgKwTb8L10Z0yGNVeTa5nx\nEVkE2wAA+IbpdQPcMjIyopdfflm2bSsQCOiee+7R5s2bvW4W6pFhyI4OSBPuX8qMn1fq2r91/0IA\nAKAuNO3M9quvvqpPfepTOnDggHbv3q1XX33V6yahjtWqFF+Isn8AAPhK0wbbhmFofn5ekjQ3N6eu\nri6PW4R6VpPUjrSlYDJOJRIAAHykadNIPvvZz+qHP/yhXnnlFUnSo48+6nGLUM+ys82Wq9cwp0Zl\nd/VIwab92AEAgDwNfdd/5plnlEwWbrN91113aXh4WHv27NG2bdt04sQJHTx4UPv378+dk0gkCp4b\nDodlmg39lqxaMBhUKBTyuhk1s9i/Bf3ce6Wk4VW9F4GpUWXCGyoOnlsTo8r0bCp5Dbf7gb72D/ra\nH/zWzxJ97RfN1r8N/dcsDZ7zvfjii7r77rslSddff70OHTq07PGjR4/qyJEjy47dcccd2r17d/Ub\niroTi8WWH+jpUVh/1r6n3yp6frgtpH/+h79bfvBn/117L96hpOVUdv6fprT3w48pWeQa4baQentJ\nL3FDQV+jadHX/kFfo5E0dLBdTiQS0enTp7VlyxYNDw+rp6dn2eO7du3S0NDQsmPhcFjxeFy2bdey\nqZ5qbW3N5bb7gWmaisViRfv5nza8ruQn/172wDUFz9v39FsaGxu7fMBx1DP6vpIZRz888PGVz5cU\nOftXJe2dRc+XVHB+tdHX/kFf+4Pf+lmir/1isZ+bRdMG2/fee68OHz4sx3FkmqbuvffeZY9HIhFF\nIpGC542Njcmy3M3drSemafrq711k23bB321F+6WxD2T1XFn0OUvPDyTjCljzUlAl37/844GL5yTt\n9Oz9pq/9g772B7/2s0Rfo7E0bbB91VVX6fHHH/e6GWggdmxQofh5pSo4NxQ/p3RbuPI1lWlb5vTF\n9TQPAAA0oKYt/QeslhUblDlZ2bbtZnxEc1tuzP4jk175/KlR2eEN62keAABoQATbwAI7NiAzfr6i\nc834iKyNV0mSgonxlc+fHJHNNu0AAPgOwTawIB3uUSCVlHFpbsVzQ5OXd4IMVRCgm+wcCQCALxFs\nA4sCAaWj/TInL5Q/z3GyM9sLM9VmfOXUk+w27cxsAwDgNwTbwBJWbFBm/FzZcwKzU3KCppy2sCRV\nlHpixkdkRQm2AQDwG4JtYAk7NqDQCjPVobyUkNDkCsF2Ji0zMS472l+NJgIAgAZCsA0sYVdQkcSM\nj8heMksdnByVMpnS5yfGlO6MSmZL1doJAAAaA8E2sIQVXbkiiRk/L2vJzHamI6LgdOmKJObE+Vx+\nNwAA8BeCbWCJdGSjArMJGVbpbXHNvMWOdnSgbEWSbNk/KpEAAOBHBNvAUoGg0t19pSuSOI5CecFz\ntj536dQTyv4BAOBfBNtAHqvM5jaBVEKOEVCmvWvJ+YNlU08o+wcAgH8RbAN5yi2SNOOFO0GWXVSZ\nScucGlu2oBIAAPgHwTaQx44NKjRRfKY6FD9fEDjb0YFs2olTWJEkmBhXuiMih0okAAD4EsE2kMeO\nDcgsUTu7WP6109KmTGungtMTBefn53cDAAB/IdgG8tiRXgVnpiT7UsFj5uTIsrJ/uedsKJ63nS37\nR7ANAIBfEWwD+QJB2ZHeohVJiuVsS4vl/wrzts1JFkcCAOBnBNtAEdlt25fPVAdS0zKcjDLtkSLn\nF5/ZDsVHZEeZ2QYAwK8ItoEirCK1s834eVnRAckwip+fn+edySg4eUF2rN/NpgIAgDpGsA0UUayc\nXzaFpPgsdXZme2RZRZLg9Lgy7V1yQm2uthUAANQvgm2gCDs2WJBGEiqzE6TT0i6ntUPBZDx3rFR+\nNwAA8A+CbaAIO9KbDZzTVu6YOVk+eLaiy1NPKPsHAAAItoFigqbsrh6Zk6O5Q2a8fBm//EWSuRxv\nAADgWwTbQAl2bGBZ8GykbWU6usucvzz1xIyfl72BmW0AAPyMYBsowY4NKrRkkaQdK16JZOnjSxdV\nmpMXCrZ2BwAA/kKwDZRg5aWFWCvUy87lbDuOJMlp7ZDT0u5qGwEAQH0j2AZKyE8jWamyiNPWKcds\nUWBmUpLYph0AABBsA6XY3X0ypy9KaTv77wqCZ3vD5bxtKpEAAACCbaCUYEh2uEfmVLYiSSUz1UvL\n/1FjGwAAEGwDZdixAbVcGJYkZTqjFZw/mNu2faUcbwAA0PwItoEy7Nig2oaPZf9RphLJ5fMHFJo4\nn/tvAADgbwTbQBl2bECt5/6yivMHFRp7X1K2GgkAAPA3gm2gDCs2KMPJVHx+pi2sTFuniy0CAACN\nhGAbKMPu7pNTQfrIsuewkQ0AAFhget2AejI3N6dQKCTT9M/bEggE1N7un41XDMPQ7OzsKvq5XdY1\nH1PnuaAe+f6xgkc7W82C9y99zU0Kx1Xx+bVCX/sHfe0Pfutnib72C2OVk1z1znCche3uIEkaGxuT\nZVleN6Nm2tvblUqlvG5GzYRCIfX29vqunyX62k/oa3/wWz9L9LVfLPZzsyCNBAAAAHAJwTYAAADg\nEoJtAAAAwCUE2wAAAIBLCLYBAAAAlxBsAwAAAC4h2AYAAABcQrANAAAAuIRgGwAAAHAJwTYAAADg\nEoJtAAAAwCUE2wAAAIBLCLYBAAAAlxBsAwAAAC4h2AYAAABcQrANAAAAuIRgGwAAAHAJwTYAAADg\nEoJtAAAAwCUE2wAAAIBLCLYBAAAAlxBsAwAAAC4h2AYAAABcQrANAAAAuIRgGwAAAHAJwTYAAADg\nEoJtAAAAwCUE2wAAAIBLCLYBAAAAlxBsAwAAAC4h2AYAAABcYnrdgPU6ceKEXnvtNY2Pj+uxxx7T\npk2bco+98cYbOnbsmAKBgPbs2aNrr73Ww5YCAADAbxp+Zru/v19f+MIXdPXVVy87PjY2phMnTuir\nX/2qvvjFL+rnP/+5HMfxqJUAAADwo4YPtjdu3Kienp6C4ydPntT27dsVDAYVi8XU09Ojs2fPetBC\nAAAA+FXDB9ulTE9PKxKJ5P7d1dWlRCLhYYsAAADgNw2Rs/3MM88omUwWHL/rrrs0NDS0ptdMJBIF\nrxkOh2WaDfGWVE0wGFQoFPK6GTWz2L9+62eJvvYT+tof/NbPEn3tF83Wvw3x1+zfv3/Vz8mfyU4k\nEstmuo8ePaojR44se87VV1+tvXv3KhaLrb2xqGuJREK/+tWvtGvXLvq5ydHX/kFf+wd97Q9L+3lp\n7NaomjaNZGhoSMePH5dt24rH45qYmNDmzZtzj+/atUtf+cpXcv974IEH9P777xedQUfzSCaTOnLk\nCP3sA/S1f9DX/kFf+0Oz9XNDzGyX8+c//1mHDx/W7Oysnn32WQ0MDGjfvn3q6+vT9ddfr+9+97sK\nBoO65557ZBhG7nmRSKQpvi0BAACgfjV8sL1t2zZt27at6GO33367br/99hq3CAAAAMhq2jQSAAAA\nwGvBb37zm9/0uhH1wHEctbS0aMuWLWptbfW6OXAJ/ewf9LV/0Nf+QV/7Q7P1s+GwraLeffdd/eIX\nv5DjONq5c6duu+02r5uEKpmamtJPf/pTzczMyDAM7dy5U7feeqtSqZSef/55TU1NKRqN6qGHHlJb\nW5vXzcU6ZTIZfe9731MkEtHDDz9MPzepubk5HTp0SKOjozIMQ/fdd596enro6yb0m9/8Rm+//bYM\nw1B/f7/uu+8+WZZFXzeBgwcP6i9/+Ys6Ozv1xBNPSFLZMfuNN97QsWPHFAgEtGfPHl177bVeNn9V\nfD+znclk9KMf/Uj79+/XbbfdpsOHD2vLli3q7Oz0ummoAsuydNVVV+nOO+/UjTfeqJdeeknXXHON\n3nrrLfX19emhhx7S9PS0/vrXv2rr1q1eNxfr9Nvf/laZTEbpdFo33HCDXnvtNfq5Cb300kvaunWr\n7rvvPu3atUttbW1688036esmk0gk9PLLL+uJJ57QLbfcohMnTiidTuvkyZP0dRNob2/Xjh07dPLk\nSd18882SVHLMHh0d1euvv64DBw5oaGhIL7zwgm655ZZlhS/qme9zts+ePauenh5Fo1EFg0Ft375d\np06d8rpZqJKuri4NDg5KklpbW7Vx40YlEgmdPHlSH/vYxyRJN910k06ePOllM1EFU1NTevfdd7Vz\n587cMfq5+czNzenMmTPasWOHpOxmH21tbfR1k3IcR5ZlKZ1Oy7IsdXV10ddN4uqrr1Z7e/uyY6X6\n9tSpU9q+fbuCwaBisZh6enp09uzZmrd5rRq+Gsl65W/rHolEGqoDUbl4PK6RkRFdccUVmpmZUTgc\nlpQNyGdmZjxuHdbrlVde0Wc+8xnNz8/njtHPzWdyclIdHR362c9+ppGREW3atEl79uyhr5tQJBLR\nJz7xCX3nO99RKBTS1q1btXXrVvq6iZXq2+npaV1xxRW58/I3Lqx3vp/Zhj/Mz8/rueee09133110\nsUWj/BSF4hbz/gYHB1VuGQr93PgymYzOnz+vm2++WQcOHFBLS4vefPPNgvPo68aXSqV06tQpPfnk\nk/rGN76hS5cu6Y9//GPBefR182qWvvX9zHZXV5empqZy/87f1h2NL51O67nnntNNN92k6667TpIU\nDoeVTCYVDoc1PT1Njn6DO3PmjE6dOqV3331Xtm1rfn5eL774Iv3chBY3JFvcEXjbtm1688036esm\nNDw8rFgspo6ODknZvv7ggw/o6yZWqm/zZ7IbLVbz/cz25s2bNTExocnJSdm2rePHj2toaMjrZqGK\nDh48qN7eXt166625Y0NDQ3rnnXckSX/4wx/o8wb36U9/Wl//+tf15JNP6vOf/7w+8pGP6MEHH9RH\nP/pR+rnJhMNhdXd3a3x8XJL03nvvqa+vj890E+ru7taHH34oy7LkOI6Gh4fV29tLXzeR/F8iS/Xt\n0NCQjh8/Ltu2FY/HNTExkfvC3Qgo/aflpf927NjBrpNN5MyZM/rBD36gvr6+3M9Rd911lzZv3qzn\nn39eiURC3d3deuihhwoWaqAxnT59Wr/+9a/18MMPa3Z2ln5uQiMjIzp06JDS6bRisZjuv/9+ZTIZ\n+roJvfbaazp+/LgCgYAGBwf1uc99TvPz8/R1E3jhhRd0+vRppVIpdXZ2avfu3bruuuv03HPPFe3b\nN954Q2+//baCwWDDlf4j2AYAAABc4vs0EgAAAMAtBNsAAACASwi2AQAAAJcQbAMAAAAuIdgGAAAA\nXEKwDQAAALiEYBsAAABwCcE2AAAA4BKCbQAAAMAlBNsAAACASwi2AQAAAJcQbAMAAAAuIdgGAAAA\nXEKwDQAAALiEYBsAAABwCcE2AAAA4BKCbQAAAMAlBNsAAACASwi2AQAAAJcQbAMAAAAuIdgGAAAA\nXEKwDQANYnh4WD09PXrnnXckSefOnVNfX59ef/11j1sGACiFYBsAGsQ111yjb33rW9q3b59SqZQe\neeQRPfLII/rkJz/pddMAACUYjuM4XjcCAFC5+++/X8PDwwoEAvrd736nUCjkdZMAACUwsw0ADebL\nX/6yTpw4oa997WsE2gBQ55jZBoAGMjMzo5tuukl33nmnDh8+rD/96U+KRqNeNwsAUALBNgA0kEcf\nfVSpVErPPvusHn/8cU1OTuonP/mJ180CAJRAGgkANIhDhw7pl7/8pZ566ilJ0re//W0dO3ZMP/7x\njz1uGQCgFGa2AQAAAJcwsw0AAAC4hGAbAAAAcAnBNgAAAOASgm0AAADAJQTbAAAAgEsItgEAAACX\nEGwDAAAALiHYBgAAAFxCsA0AAAC45P8DexmNngOnw60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b08ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (295363765)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_walk, aes(x='x', y='y')) + geom_line(color='coral') + geom_step(color='steelblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zalpha</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zalpha</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  project  x  y\n",
       "0  Zalpha  0 -1\n",
       "1   Alpha  1  0\n",
       "2  Zalpha  2  1\n",
       "3   Alpha  3  0\n",
       "4   Alpha  4 -1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_walk = pd.DataFrame(dict(\n",
    "    x=np.arange(250),\n",
    "    y=np.random.choice([-1, 1], 250).cumsum(),\n",
    "    project=np.random.choice([\"Alpha\", \"Zalpha\"], 250)\n",
    "))\n",
    "random_walk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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35XK5VFlZqauuukpe74lvpdixY4f27dsnp9Opyy+/XGeeeWaRqwUAAIBdWf6O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\nAAAM+P8B7bzQCot3730AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119dbe810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (295523493)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_walk, aes(x='x', y='y', color='project')) + geom_step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TUFjPINU7d8gYDlgdBgAAgCNRWM8gZd1dqtr1uNVhAAAAOBKFtQOka21eNhGOX/ZYd4Gj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\nbATBdMiTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b9b0150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (297382089)>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_walk, aes(x='x', y='y', color='project')) + geom_step(size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}