docs/scales/scale_fill_identity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_fill_identity`\n",
    "`scale_fill_identity` applies the fill values in your data to your ggplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "      <th>ze-color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>MediumAquaMarine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Peru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Tomato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>#f8b195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>#ffb6c1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>#933835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Bisque</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y          ze-color\n",
       "0  0  0              blue\n",
       "1  1  1               red\n",
       "2  2  2             green\n",
       "3  3  3  MediumAquaMarine\n",
       "4  4  4              Peru\n",
       "5  5  5            Tomato\n",
       "6  6  6           #f8b195\n",
       "7  7  7           #ffb6c1\n",
       "8  8  8           #933835\n",
       "9  9  9            Bisque"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ze_colors = [\n",
    "    \"blue\",\n",
    "    \"red\",\n",
    "    \"green\",\n",
    "    \"MediumAquaMarine\",\n",
    "    \"Peru\",\n",
    "    \"Tomato\",\n",
    "    \"#f8b195\",\n",
    "    \"#ffb6c1\",\n",
    "    \"#933835\",\n",
    "    \"Bisque\"\n",
    "]\n",
    "df = pd.DataFrame({\n",
    "        \"x\": range(10),\n",
    "        \"y\": range(10),\n",
    "        \"ze-color\": ze_colors\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAIACAYAAADACICKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4VOXd//HPJDOEAEkTaJRVrCxBCUIWQCAkIWFxQdbY\nWiwPQsXWKvRxgdpaL4u1D1UBWx+tlWoroqJIwQUQUrYg4iMGWUQkBLQUAyJLYAgESMj8/vDHmEkm\nN5PJZCZneL+uq5eZM+fc5/vNfWaaD+ecGZvL5XIJAAAAAGoREeoCAAAAADRuhAYAAAAARoQGAAAA\nAEaEBgAAAABGhAYAAAAARoQGAAAAAEb2UBfgr7ffflu7d+9W8+bN9Ytf/MLrOsuXL9eePXvkcDg0\natQotWnTJshVAgAAANZn2TMNvXr10k9+8pNany8qKlJJSYmmTp2qm2++WUuXLg1idQAAAED4sGxo\n6Nixo6Kjo2t9fteuXerZs6ckqX379jp79qxKS0uDVR4AAAAQNiwbGi7m5MmTio2NdT+OiYmR0+kM\nYUUAAACANVn2nob6cjqdNc48tGjRwiNoAAAAAAjj0FD9zILT6fQIBJs3b1Z+fr7HNk8//WuVlHz3\n+PLLpa+/bvBSQ6t1a+nQoe8eXwJNt57VWodOfdfz5c0v19cPhHfP07a/I2fFWffjWHuUnrx2RAgr\nanjb/zxaFadKPJbZm8fr2l8uCVFFQXDfjyWnZ8+KjZfmLAhNPUFwduU/pLNlngujohU1bGJoCgqG\n1R9K58o9lzVxSDn9QlNPELx40/UqO3bMY1l0y5b66bIVIaooCA5ukiqrzXO7AaGpBZDFQ4PL5ar1\nucTERH388cdKSkrS/v371bRpU7Vo0cL9fGpqqhITEz22mTEjyuPxoUPS4cOH61Wj3W5XfHy8SkpK\nVFFRUa+x6iIqKkpnz5696HoJVQODJB06VOeeQ9Gjr/15UzUwXHhs6rmxz6EvqgaGC4+r9my1OfRF\n9cBwYVk49ViV3W5XfPXAIEnOknq/j9UmmP1J3o/T2OqBQZLOlgWs56DPoQ+vw4TqgUGSzpX73XNj\n7LG66oHhwjJferZCf94kVA8MQIhZNjQsWrRI//73v1VWVqY5c+Zo0KBBOn/+vCQpLS1NXbt2VVFR\nkf785z+rSZMmGjlypMf2sbGxPl2KVF4emBdtRUVFwMbyhd1u93t//m4XzB7r0583voxlpTn0hbex\nrTyHvroUeqyuoWpozHMYqLpC0aO/x6i/dVqpx+p8GcPK/QGNiWVDQ25u7kXXuemmm4JQCQAAABDe\nwvbTkwAAAAAEBqEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAA\ngBGhAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACA\nEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIAR\noQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAgBGh\nAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEB\nAAAAgJE91AU0dtHR0fXa3maz6fTp03I4HLLbg/frjoiI8Lv2um4Xih7r0583prGsOIe+qDp2OMyh\nr8K1R5vNVutzDVVDsOewLsdpoOoK9hzW53Xob51W6rE6X+q2bH+nA1MTECiEhosoKyur1/YOh0Nx\ncXE6deqUysvLA1TVxUVHR/tUe7yXZXXtORQ9+tqfr0xjNfY59FfVscNhDn1VXl4elj06HI5an2uo\nGoI9h96O02a1rBuouoI9h768Dr29b0v+99wYe/SVL3Vbtb/a5hkIFS5PAgAAAGBEaAAAAABgRGgA\nAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAA\nAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAA\nAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAA\nYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABg\nRGgAAAAAYERoAAAAAGBkD3UB/ioqKtKKFSvkcrmUkpKi9PR0j+fPnDmjxYsX68SJE3K5XOrXr5+S\nk5NDVC0AAABgXZYMDZWVlVq+fLkmTJigmJgYzZ07V4mJiUpISHCv8/HHH+uyyy7TuHHjdOrUKT3z\nzDO69tprFRkZGcLKAQAAAOux5OVJxcXFatWqleLi4hQZGamkpCQVFhbWWO/s2bOSpHPnzik6OprA\nAAAAAPjBkmcaTp48qdjYWPfj2NhYFRcXe6zTp08fLViwQLNmzdK5c+d0yy23BLtMAAAAICxYMjT4\nYu/evWrTpo1uv/12HTt2TC+//LLuuusuRUVFSZKcTqdKS0urbdW2xjgOh6Neddjtdo//BktkZKTf\ntdd1u1D0WJ/+vDGNZcU59EXVscNhDn0Vrj2a+mqoGoI9h3U5TgNVVyjm0N9j1N86rdRjdb7Ubdn+\nztV/CCCQLBkaYmJidOLECfdjp9PpceZBkrZs2aKBAwdKklq2bKn4+HgdOXJE7dq1kyRt3rxZ+fn5\n1Ub+XY19Vb1Poj7i4+MDMk4w+NuzlXqszpeerdyf9tdc5K1nS/dYjZeWJYVXj74K1PtYY1F1Ds/W\nso6Ve774Mfq516VW6jlQr8PG2nNA+iveXf8xgACyZGho166djh07puPHj6tFixbasWOHcnNzPdaJ\ni4vTF198oSuuuEKlpaU6evSox4s4NTVViYmJHtvMmFFzX4cPH65XrXa7XfHx8SopKVFFRUW9xqqL\nqKgo9z0dJt7ebuvacyh69LU/X5l6buxz6K+qPYfDHPoqXHu02+2q7c+U+r6P1SbYc+jtOI2tZd1A\n9Rz0OfThdVjbn8n+9twYe/SVLz1btb/GGYdwKbNkaIiIiNCNN96o+fPny+VyKTk5WQkJCSooKJAk\npaWlKSMjQ2+99Zb+8pe/SJKGDBmiZs2auceIjY2tcXbCm/Ly8oDUXFFREbCxfGG32/3en7/bBbPH\n+vTnjS9jWWkOfeFtbCvPoa8uhR6ra6gaGvMcBqquUPTo7zHqb51W6rE6X8awcn9AY2LJ0CBJXbp0\nUZcuXTyWpaWluX+OiYnR+PHjg10WAAAAEHYs+ZGrAAAAAIKH0AAAAADAiNAAAAAAwIjQAAAAAMCI\n0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQ\nAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAA\nAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAA\nAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAA\nAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAyB7qAhq76Ojoem1vs9l0+vRpORwO2e3B+3VH\nRET4XXtdtwtFj/XpzxvTWFacQ19UHTsc5tBX4dqjzWar9bmGqiHYc1iX4zRQdQV7DuvzOvS3Tiv1\nWJ0vdVu2v9OBqQkIFELDRZSVldVre4fDobi4OJ06dUrl5eUBqurioqOjfao93suyuvYcih597c9X\nprEa+xz6q+rY4TCHviovLw/LHh0OR63PNVQNwZ5Db8dps1rWDVRdwZ5DX16H3t63Jf97bow9+sqX\nuq3aX23zDIQKlycBAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAA\nMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAA4JLWu3fvUJfQ\n6BEaAAAAcEmz2Ww+redyuRq4ksaL0AAAAABL+Pvf/65BgwZp0KBBatasmd59911lZGQoPT1db7zx\nRo319+7dq8GDBys7O1vTpk2TJM2ZM0f9+/dXRkaGtm7dKum7MFBcXKwhQ4YoKytLU6dOlSTNmzdP\nP/7xjzVy5EitXLkySJ02PvZQFwAAAAD4YtKkSZo0aZKef/55ZWRk6PHHH9e6desUERGhjIwM/fCH\nP/Q4azB9+nTNmjVLvXr1kiQdOnRI77zzjjZu3Kh9+/Zp8uTJysvLc2/zxz/+UdOnT9eQIUM0efJk\nbdiwQZLUpEkTLViwIPgNNyKcaQAAAIBlrF69WuvXr9c999yj3bt3a+jQocrJyZHT6dThw4c1bNgw\nZWdna8eOHfrqq6/cgUGS/v3vf6tnz56SpI4dO+rEiRMeY+/Zs0dpaWmSpLS0NBUVFUningeJMw0A\nAACwiKKiIj3++ON655131KRJE1199dXKy8uT3W5XRUWF7Ha7xyVEHTp00JYtW5ScnCyXy6Urr7xS\nW7dulcvl0r59+xQXFyfpu8uTunTpoo8++kjXX3+9Pv74Y91+++3au3evIiL4d3ZCAwAAACzh8ccf\n19dff60bbrhBNptNDzzwgAYPHqyIiAhddtllev3112usP3nyZElSamqqnnzySY0YMUL9+/dXZGSk\nnnnmGUnf3Qg9ffp0TZgwQTNnzlRSUpLS09O1d+/e4DbZSBEaAAAAYAkvvPBCjWU333xzret36tRJ\na9as8Vh2//336/777/dYtmnTJklS+/bttXr1ao/nJkyY4G+5YYVzLQAAAACMCA0AAAAAjAgNAAAA\nAIwIDQAAAACMCA0AAAAAjAgNAAAAAIwIDQAAALCUQ4cOaerUqfrwww/13HPPqbKyUuPHj1dOTo7u\nuOMOVVZWSpKmTJmirKws9e3bV4sXL5YkrVixQunp6crIyNCkSZPcYz700EPq16+f+vfvr40bN0qS\n/vCHPygzM1N9+/Z1f6dDfn6+rrjiCmVnZ2vIkCFB7jx0+J4GAAAA+O2Zfn0CNtY9H27yab2tW7cq\nJSVFBQUF6t27t5YsWaKrrrpK8+fP16xZs7R48WLl5ubqqaeekt1u16lTp5Senq4xY8Zo8ODBuv76\n6yVJEydO1MaNG3X11Vdr7dq1+vDDD/XVV1/ppz/9qVauXKlf/epXeuihh1RZWakePXro7rvvliTd\neuuteuKJJwLWtxUQGgAAAGAZ99xzj95//3116NBBhYWFSk5OVo8ePdSrVy9JUnJyst5++23l5ubK\nbv/2T93S0lJ1795dktzLXC6XJOnKK69UTEyMWrVqpYqKCh07dkwJCQke65aVlalz587ub45etGiR\nPvroI40dO1ZTp04NXvMhxOVJAAAAsIxnnnlGSUlJWrp0qXr37q2FCxcqOTnZ/c3Pq1atUklJiXv9\nH//4x+rVq5eGDRvmXjZv3jx1797dHRDsdruuvfZade3aVTfccIOmTZvmXvfee+9VYmKirrvuOklS\n7969tXv3bq1evVorVqzQli1bgtR5aBEaAAAAYAmrVq1SZmam1q1bp379+mnjxo3Kzc3V8OHDFRUV\npcGDB+v06dNq3bq1e5sFCxZo165deuyxx9zLJkyYoJ07d6pDhw5asmSJCgsLtWnTJn3xxRfatGmT\n/vu//9u97lNPPaUvvvhCixcv1qFDh9SsWTPZ7XbZ7XbdfPPN2rZtW1B/B6HC5UkAAACwhMGDBysh\nIUF5eXnq2LGjmjRpolGjRkmSZs2aJUmaMWOGcnJyJEnnzp1TkyZN1LRpU8XGxnosk6TY2Fg1a9ZM\nLpdL8fHx7mWnTp3yWNfhcKh58+Zq2rSpTp48qZiYGEnShg0bdNdddwXvFxBChAYAAAD4zdeblwNl\n8+bNSklJUV5enqZMmSLp209T+vGPf6zIyEjl5OQoPT1dkvSjH/1Ix48fV3l5uX77299Kkv7xj3/o\n9ddflyR17dpVw4cPlyS1adNG6enpqqio0MMPPyxJ+uUvf6ldu3apvLxc48eP1/e+9z29+OKLmjt3\nrhwOhwYMGODeV7gjNAAAAMAyLnxM6oWzCZJ0+eWXu+9pqGrJkiU1lv3sZz/Tz372sxrL//znP9dY\n9txzz9VY9tOf/lQ//elP61RzOOCeBgAAAABGhAYAAAAARoQGAAAAAEaWvaehqKhIK1askMvlUkpK\nitebUL788kutXLlS58+fV/PmzXX77bcHv1AAAADA4iwZGiorK7V8+XJNmDBBMTExmjt3rhITE93f\n3idJZ86c0fLlyzV+/HiPj84CAAAAUDeWvDypuLhYrVq1UlxcnCIjI5WUlKTCwkKPdT799FNdffXV\n7s/kbd68eShKBQAAACzPkqHh5MmT7jAgffslHE6n02Odo0ePqqysTC+99JLmzp17yXxbHwAAQLg7\ndOiQpk6dqg8//ND9sagjR45UTk6OvvnmG02cOFEZGRnKyMjQ7t27JUm9e/euMc6GDRuUlJSktm3b\neiy///77lZmZqVtuucV9tcrEiRPVt29fZWdna/bs2Q3cYeNjycuTfFFZWamDBw9qwoQJKi8v1wsv\nvKD27durVatWkiSn06nS0tJqW7WtMY7D4ahXHXa73eO/wRIZGel37XXdLhQ91qc/b0xjWXEOfVF1\n7HCYQ1+Fa4+mvhqqhmDPYV2O00DVFYo59PcY9bdOK/VYnS91W7a/c/UfIljOvvOXgI0VNeIXPq23\ndetWpaSkqKCgQH369NHBgwdls9m0evVqbdmyRefOndP69eu1YcMGzZkzR3/9619ls9lqjNOzZ08V\nFBQoIyPDvWzz5s06cuSI8vPz9eabb+ovf/mLpk2bJunbL4a75pprAtOsxVgyNMTExOjEiRPux06n\n0+PMg/Td14I7HA45HA517NhRhw4dcoeGzZs3Kz8/v9rIv6uxr0Bc1nT69GlFRUUpKiqq3mPVhb9v\nWv70HIoeA/nH38V6ttoc+qJ6z1afQ19dCj1W15CXZwa7P1+P00D2HMwe6/M6rE/PVumxOl97tmR/\nFgoNwXbPPffo/fffV4cOHVRYWKjk5GQdOXJEO3bsUG5urv7617/K5XJJko4dO6bvf//7kqTz58/r\nZz/7mXbs2KExY8bo/vvvV0xMTI3x9+7dq169ekmSUlJSNH/+fE2bNk02m0133HGHYmJi9OSTT+ra\na68NXtONQOj/38wP7dq107Fjx3T8+HG1aNHCfZBUlZiYqPfee0+VlZWqqKhQcXGx+vXr534+NTVV\niYmJHtvMmFFzX/W9gdputys+Pl4lJSWqqKio11h1ERUVpbNnz150vWZeltW151D06Gt/vjL13Njn\n0F9Vew6HOfTV2bNnw7JHu93u9fUs1f99rDbBnkNvx2lsLesGqudgz6Evr8NAz3Nj7NFXvvRs1f5q\nm2dIzzzzjG677Ta9+uqrGjdunF577TXt27dP06ZN08KFC+VyuWS329WtWzedPXtWH3zwgSSppKRE\n06ZNU6dOnZSVlaUJEya4A0VV11xzjV599VXde++9+te//qWSkhJJ0uzZsxUfH6/CwkJNmDBB//d/\n/xfUvkPNkqEhIiJCN954o+bPny+Xy6Xk5GQlJCSooKBAkpSWlqaEhAR16tRJzz33nGw2m1JTU3XZ\nZZe5x4iNja1xdsKb8vLygNRcUVERsLF8Ybfb/d6fv9sFs8f69OeNL2NZaQ594W1sK8+hry6FHqtr\nqBoa8xwGqq5Q9OjvMepvnVbqsTpfxrByf6hp1apV+v3vf689e/aoX79+OnjwoHJzcz3uMcjLy5PD\n4dCuXbv0ySef6L777tPrr7+uFi1aqHPnzpK+vSzpyy+/9BoakpKSNHDgQGVnZ6tPnz5q3bq1JCk+\nPl7St/8wbbPZ5HK5vF7yFK4sGRokqUuXLurSpYvHsrS0NI/HAwYM0IABA4JZFgAAABrI4MGDlZCQ\noLy8PHXs2FFNmjTRqFGjtG/fPvc6LpfLfTl6y5Yt3R+WU1paqr179+qqq67S9u3bdeWVV3psU9UD\nDzygBx54QPPmzVNSUpKkbz+IJyYmRt98843Ky8svqcAgWTg0AAAAIPR8vXk5UDZv3qyUlBTl5eVp\nypQpNZ4fMmSIXnrpJWVlZencuXN66qmnJH0bIP70pz+poKBAY8eOVUJCgnbt2qUpU6aoqKhIQ4cO\n1ZNPPqmePXtq0KBBstvtuvbaazVr1ixJ0k9+8hMdO3ZMlZWV7mWXEkIDAAAALGPSpEmSpJycHPey\njh07auHChZK+/cSs119/vcZ2Fy5jr6pbt27617/+VWP52rVrayx7++23/a45HFjyexoAAAAABA+h\nAQAAAIARoQEAAACAEaEBAAAAgBGhAQAAAIARoQEAAACAEaEBAAAAlnLo0CFNnTpVH374oZ577jlJ\n0siRI5WTk6OjR49q1KhRysnJ0aJFizRt2rQa2584cULjxo3T4MGDddddd9W6n1//+tdq166dpk+f\n3mC9WAXf0wAAAAD/vbc+cGPdkOHTalu3blVKSooKCgrUp08fHTx4UDabTatXr/b4OT8/3+s3Nz/y\nyCP61a9+pZ49exr3c++99+r666/XsmXL/GonnBAaAAAAYBn33HOP3n//fXXo0EGFhYVKTk7WkSNH\n9Nlnnyk3N1eRkZH64IMPlJubqylTpmjbtm0aMWKEDh06pL///e/q3r27tmzZotOnT2vPnj365S9/\nqZEjR+rIkSOaOHGiSktL1b59e82fP1+XXXaZPv/881C33ChweRIAAAAs45lnnlFSUpKWLl2q3r17\na+HChfrHP/6hzMxMLVq0SE888YSysrK0aNEiSVJZWZneeecdzZs3T7/5zW8kSR999JF+/vOf6913\n39Wjjz6qc+fOaebMmZo0aZLWrl2r+fPnh7LFRonQAAAAAEtYtWqVMjMztW7dOvXr108bN25Ubm6u\ncZvk5GRJUrdu3fT1119Lkq644gqlpKSoefPm6tatm7766it9/vnnyszMbPAerIrLkwAAAGAJgwcP\nVkJCgvLy8tSxY0c1adJEo0aN0r59+2rdZsuWLZKkwsJCtWnTRpLUs2dPffHFF+rYsaP27t2rtm3b\n6pprrlF+fr5Gjx4tl8vlcS+Ey+Vq2MYsgNAAAAAA//l483KgbN68WSkpKcrLy9OUKVMuun5cXJxu\nvvlmffPNN3rxxRclSX/4wx90xx136MyZM5o8ebKaNm2qBx98ULfffruefvpp9z0NTz/9tF5++WUd\nPXpUBw4c0KuvvtrQ7TVahAYAAABYxqRJkyRJOTk57mUdO3bUwoULa/ycmZnp9ZKjrl27as2aNR7L\nvv/972vp0qUey6ZOnaqpU6cGtH6r4p4GAAAAAEaEBgAAAABGhAYAAAAARoQGAAAAAEaEBgAAAABG\nhAYAAAAARnzkKgAAACxj37596t27t5KSknTq1Cndd999iouL05kzZzRy5MhQlxe2CA0AAADwX/EH\ngRur3QCfVsvKytLChQt19uxZDRgwQAUFBYGrAV5xeRIAAAAsxeVySZJOnTql5s2ba968eXr22WdV\nUlKiQYMGKScnR6NHj5Ykbdu2TX369NGIESM0bNgwrV+/Xvn5+Zo2bZok6bPPPtPEiRMlSStXrlRG\nRobS09P1xhtvhKa5RoozDQAAALCU/Px8ZWdnq6ioSL/97W8lSTabTVu2bFHfvn31xz/+0b3uww8/\nrNdee02dOnXSwIED3cttNluNn3//+99r3bp1ioiIUEZGhn74wx96rHcp40wDAAAALCUrK0tr1qzR\nv//9b73yyisqLi52L2/evLnGjx+vOXPmSJK+/vprde7cWTabTSkpKZI8A8OFsxaHDx/W7t27NXTo\nUOXk5MjpdOrw4cNB7qzx4kwDAAAALOXCH/qRkZGKiopSbGysJKm8vFwPP/ywJGnYsGG65ZZb1Lp1\na+3Zs0edOnXSJ598otzcXMXHx2v//v2Svr18SZK+//3v6+qrr1ZeXp7sdrsqKipkt/On8gX8JgAA\nAOA/H29eDqT169crOztbZ86cUZ8+fRQbG6vS0lJt2rRJDz30kCIiItShQwd16NBBjz76qMaNG6fL\nL79cLVu2lCT16NFDp0+f1tChQ9W9e3dJ3559eOihhzR48GBFRETosssu0+uvvx703horQgMAAAAs\no2PHjjp06FCtz69fv97jca9evbRp0yZJct/8LElvv/12jW2HDh2qoUOHBqjS8MI9DQAAALgkcFOz\n/zjTAAAAgEvCE088EeoSLIszDQAAAACMCA0AAAAAjAgNAAAAAIwIDQAAAACMCA0AAACwjH379iki\nIkL5+fmSvv1Ct5YtW+ovf/nLRbe95ZZb9J///EcrV670+pGrdXHy5Ek1a9ZMGzZsqNc4VQ0aNEiT\nJk1yP7777rvVu3dvn7e/6667AlZLdXx6EgAAAPz2s0/eDNhYz6fc4tN6aWlpWrx4sTIzM7Vq1Sp1\n7dq1TvsZNmyYP+V5eOeddzRp0iS9+eabSk9Pr/d4Fxw4cEDnz59XRESEiouLff6YWJfLpeeeey5g\ndVTHmQYAAABYSseOHfWf//xHkrRkyRKNGTNGkjRv3jxlZGQoPT1d69atkyStWrVKqampGjt2rA4c\nOOBe78KZiar/kn/h54kTJ+rnP/+5srOzNXnyZD322GPq37+/x5fDLVmyRI8++qg+//xz97Kq++rf\nv7/+85//eOxr2bJlevTRRyVJt912mwYNGqSMjAx99dVX7jFycnK0evVqbdiwQQMGfPdt26+88ooG\nDRqktLQ0vfrqq5KkGTNmaOLEiRo+fLi2b9/uUf9dd92loUOHun83kjR16lTl5ORo6NCh7t+Fr3wO\nDffee68cu58GAAAgAElEQVS2bt1ap8EBAACAhtCvXz+tX79eR44cUevWrXXkyBG98cYbWr9+vfLy\n8jRjxgxJ0sMPP6w1a9ZowYIFXv9Qrvov+VV/zszM1Jo1a7Rz50716tVLGzduVH5+vs6fP6+TJ0/q\n/PnzatmypTIyMvTBBx/U2NfBgweN9b/44otau3at7rvvPj3//PPu5aNHj9aSJUu0ePFijR071r08\nNzdXa9eu1YYNGzRnzhz38iuuuEJLly5Vz549PeofMGCA8vLyFBUVpR07dmjZsmVq2bKlVq9ercce\ne0wzZ8709VctqQ6XJ50/f17Dhg1TQkKCxo8fr9tuu03t27ev086sKDo6ul7b22w2nT59Wg6HQ3Z7\n8K4Gi4iI8Lv2um4Xih7r0583prGsOIe+qDp2OMyhr8K1R9Pp64aqIdhzWJfjNFB1BXsO6/M69LdO\nK/VYnS91W7a/04GpKVzZbDaNHTtWP/zhDzVhwgS5XC7ZbDbt2LFD2dnZcrlcOnr0qKRv/4b93ve+\nJ0nq0aNHjbFcLpfXn6+99lpJUtu2bd3btW7dWk6nU++9956++OIL3XjjjTp9+rSOHj2qAQMGeN1X\n1ffnC+NXVlZq2rRp+vTTT3X69GmPddu2batDhw6poqJCV111lXvb9957T08//bRcLpf27t3rXl7b\nPQ/JycmSpPbt26ukpEQ7d+7U4sWLtX79erlcLnXo0OHiv+gqfD6in376aT311FN677339Oqrr+qx\nxx5T37599V//9V8aM2aMWrRoUacdW0VZWVm9tnc4HIqLi9OpU6dUXl4eoKouLjo62qfa470sq2vP\noejR1/58ZRqrsc+hv6qOHQ5z6Kvy8vKw7NHhcNT6XEPVEOw59HacNqtl3UDVFew59OV16O19W/K/\n58bYo698qduq/dU2z/hOp06dNHDgQOXm5upf//qXIiIi1LNnT7377ruSvg0LkmS323XixAk1bdpU\nn376aY1xzp49K5fLpf3796ukpMS93NsZCJfLpcrKSi1atEhr1qxRq1atJElDhw6tdV/x8fHuS5i2\nbdsmSdq6dauOHz+udevWafHixVq6dKl7fEkaP368zpw541HnH/7wB73//vvu3i+IiPB+4VD1sHL1\n1VfrRz/6kR566CGP34+v6hSDIyMjNXz4cA0fPlyfffaZxo0bp9tvv12/+MUvdOutt2rGjBlq165d\nnQoAAACAdfl683JD+NOf/uT+OT4+XrfeeqsyMzMVGRmpa6+9Vn/60580Y8YMZWdn6wc/+IE6duxY\nY4xx48apX79+GjBggOLjv41rtV2yZLPZVFZWpoMHD7oDgyQlJibqgw8+0KOPPlpjX4MHD9asWbN0\n0003qV27dmrfvr26deumffv2adiwYerWrVuNfY0ePbpGnWPGjNHAgQOVnJysli1bGn8v3uofPny4\nVq9erezsbEVEROi2227TxIkTjeN4jOmqeh7mIpxOp95880298sor2r59u8aOHasJEyboiiuu0OzZ\ns7VmzRpt377d5503Nt7O7hcX1+0mkeocDocSEhJ0+PDhRvmv1G29hLwDxcV12lcoeqzPvxy1+1vN\nnosn195zY59DX8z4+oMayx5p/d3NVVabQ198/dI4r8s7TH4zbHqsyuFwKOHX470+d+DRvzfIPkNx\npqH6cdqq4C2v6x5NGxWQfQZ9Dn14Hbbdtsfr8gM9O/u138bYY3WLx3qfzzH/9D7/VVmhP2/aur6s\nubDdgJrL0Gjdcsstmj17tq644opQlxIQPp9pyM3N1cqVK5WRkaGf//znGjVqlKKiotzPz5kzx30N\nFwAAAHAp8/WjUq3C59Bw3XXX6ZlnnlHr1q29Ph8REaFDhw4FrDAAAADAqhYuXBjqEgLK59DwwAMP\nXHSdZs1quyUNAAAAgFXx5W4AAAAAjAgNAAAAAIwIDQAAAACMCA0AAACwjH379umyyy5Tdna2rrvu\nOm3evDnUJV0S6v8d7gAAALhkffI/WQEbK+U363xaLysrSwsXLtSmTZv0m9/8RitXrqx1XZfLFXYf\nfxoKhAYAAABYUq9evbR//37NnDnTHRyeffZZde/eXampqcrIyNCRI0c0ePBglZaW6u6779ayZctU\nUFCgRx55JMTVWwuXJwEAAMBSXC6XJGndunXq1q2bdu/erXXr1mnBggV66KGHJEklJSWaOnWq5s+f\nL8nzy9Y481B3nGkAAACApeTn5ys7O1stWrRQRkaG5s6dq+zsbLlcLjkcDklSfHy8fvCDH0jyDAkX\nAgfqhtAAAAAAS7lwT4Mkffrpp9q9e7fmzp0rSTp//rwkKSLiuwtq4uPj9fnnn0uStm3bFuRqwwOh\nAQAAAH7z9eblhtKjRw917txZWVlZioyM1JAhQ/Tggw96nF0YPHiwZs2apZtuuknt2rVT+/btQ1ix\nNREaAAAAYBkdO3Z0n2W4YPr06Zo+fbrHsk2bNrl/jo6OVn5+flDqC1fcCA0AAADAiNAAAAAAwIjQ\nAAAAAMCI0AAAAADAiNAAAAAAwIjQAAAAAMCI0AAAAABLOHPmjAYNGqRBgwYpNjZW2dnZys7O1vHj\nxwMy/t/+9reAjBOO+J4GAAAA+O+O6wM31gsrjE83bdpUa9eulST16dNHa9asCdy+Jc2dO1eTJ08O\n6JjhgjMNAAAAsByXyyVJOn/+vG677TZlZWVp5MiRcjqd2rt3rwYOHKgf/ehH6tGjh958800NHz5c\nKSkp+vLLLyVJt956qwYNGqTMzEwdOHBA//znP1VYWKjs7Gy9+eab2r59uwYOHKj09HQ9+eSToWy1\nUSA0AAAAwHJsNpskadGiRerUqZPWrVunMWPG6Nlnn5UknThxQm+88YaeeOIJzZkzR0uXLtVvf/tb\nvfrqq5Kkl156SWvXrtWUKVP0t7/9TWPHjlW3bt20Zs0a3XLLLfr1r3+tl156SRs2bNDKlStVXFwc\nsl4bA0IDAAAALGvPnj3q3bu3JCktLU179uyRJHXv3l2S1LZtWyUlJUmS2rVrp5KSEp0/f1733Xef\nsrKy9MQTT+jAgQOSvjt7IUmHDx9Wp06dJEnJycn64osvgtZTY0RoAAAAgOVc+AO/c+fO+uijjyRJ\nH3/8sbp06VJj3QtnJS5st3nzZpWVlWndunV64IEH3GNVXS8hIUF79uyRy+XSli1bdNVVVzVkO40e\nN0IDAADAfxe5ebmhXPgDf+zYsXrrrbeUlZWlmJgYvfLKKzpy5IhHAKiue/fu2rNnj66//nqPkDFw\n4ECNHj1ad9xxh/7nf/5HEyZMkM1m04gRI9SuXbsG76kxIzQAAADAcjZt2iRJstvtWrBggcdz3/ve\n9/Taa69Jknr27Km5c+dKkvr27au+fftKkt5///0aY86ePdvj8QcffBDwuq2Ky5MAAAAAGBEaAAAA\nABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAABgCfn5+Zo2bZrHsgvfBo2Gxfc0AAAA\nwG+G71Crs///xcwX2Z/N+BgNw7JnGoqKivS///u/evrpp7Vhw4Za1ysuLtajjz6qnTt3BrE6AAAA\nNIRt27ZpxIgR6tu3r3bs2OFePmPGDC1fvlyS9Oyzz+rll1+WJM2cOVNZWVnKysrSZ599FpKaw4El\nzzRUVlZq+fLlmjBhgmJiYjR37lwlJiYqISGhxnqrVq1Sp06dQlQpAAAAAqmsrEwrV65UYWGhpk+f\nblz3s88+U2FhodatW6eDBw/qrrvu0ltvvRWkSsOLJUNDcXGxWrVqpbi4OElSUlKSCgsLa4SGTZs2\n6ZprrlFxcXEoygQAAECAJScnS5ISExN18OBB9/Kqlym5/v91Tjt37tTGjRuVnZ0tSbLbLfmnb6Ng\nycuTTp48qdjYWPfj2NhYOZ1Oj3WcTqd27drFzTEAAABhZMuWLZKkwsJCtWnTxr08Pj5e+/fvl/Tt\nJUyS1K1bN2VlZWnNmjVas2aN3nvvveAXHCbCNm6tWLFCgwcPrvV5p9Op0tLSakvb1ljP4XDUq44L\niTbYyTYyMtLv2uu6XSh6rE9/3pjGsuIc+qLq2OEwh74K1x5NfTVUDcGew7ocp4GqKxRz6O8x6m+d\nVuqxOl/qtmx/5+o/RLD4cvNyIMXFxenmm2/WN998oxdffFGTJk2SJOXm5mrEiBFatmyZ+x+Xe/To\noc6dOysrK0uRkZEaMmSIHnzwweAWHCYsGRpiYmJ04sQJ92On0+lx5kGSDhw4oEWLFkmSTp8+raKi\nIkVERKhbt26SpM2bNys/P7/ayL+rsa/qlzz5Kz4+PiDjBIO/PVupx+p86dnK/Wl/zUXeerZ0j9V4\naVlSePXoq0C9jzUWVefwbC3rWLnnix+jn3tdaqWeA/U6bKw9B6S/4t31HyMMZWZmKjMz02PZpk2b\nJElt2rTRxx9/XGOb6dOnX/TeB1ycJUNDu3btdOzYMR0/flwtWrTQjh07lJub67HOf//3f7t/fuut\nt9S1a1d3YJCk1NRUJSYmemwzY0bNfR0+fLhetdrtdsXHx6ukpEQVFRX1GqsuoqKidPZsbf93+h1v\nb7d17TkUPfran69MPTf2OfRX1Z7DYQ59Fa492u121fZnSn3fx2oT7Dn0dpzG1rJuoHoO+hz68Dqs\n7c9kf3tujD36ypeerdpf44xDuJRZMjREREToxhtv1Pz58+VyuZScnKyEhAQVFBRIktLS0i46Rmxs\nbI2zE96Ul5fXu15JqqioCNhYvrDb7X7vz9/tgtljffrzxpexrDSHvvA2tpXn0FeXQo/VNVQNjXkO\nA1VXKHr09xj1t04r9VidL2NYuT+gMbFkaJCkLl26qEuXLh7LagsLo0aNCkZJAAAAQFiy5KcnAQAA\nAAgeQgMAAAAAI0IDAAAAACNCAwAAAAAjy94IDQAAgNCzzbAFbCzXI/5/U5zL5ZLNFrha4InQAAAA\nAEs4f/68br31Vp04cUJdu3bVqVOntH37dg0cOFBHjx7VCy+8oDvuuEMHDx5UixYt9Morr6hFixaa\nOXOmVq5cKUl69tln1b17d6Wmpqp///76+OOPNWbMGL4A7iK4PAkAAACW8NZbbykxMVF5eXnq2bOn\nJOn48eP65S9/qfnz5+uFF15QTk6OVq1apXHjxun555/XZ599psLCQq1bt04LFizQQw895N7uV7/6\nlTZu3Kj58+eHsi1L4EwDAAAALGHPnj1KTU2VJKWmpmrjxo2Kj4/XD37wA0nSzp07VVBQoJdfflnl\n5eUaOHCgdu7cqY0bNyo7O1sul0sOh0OSFB8fr/bt20uSoqOjQ9OQhRAaAAAAYAmdO3fWJ598otGj\nR2vLli2SpIiI7y6cufrqq9W/f3/ddtttkr69nGnnzp3KysrS3Llz3cskedz/4HL5fy/FpYLQAAAA\nAL/V5+bluho1apTeeOMNDRkyRFdddZX7rMEFkydP1p133qm///3vstlsuv/++3XDDTeoc+fOysrK\nUmRkpIYMGaIHH3zQIzRwA/XFERoAAABgCZGRkXrttddkt9v1t7/9TcePH3efQZCkqKgozZs3r8Z2\n06dPr3Gj86ZNm7z+DO8IDQAAALCMkSNHqrS0VE2bNtUbb7wR6nIuGYQGAAAAWMayZctCXcIliY9c\nBQAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAEDYefbZZ/Xyyy+HuoywwacnAQAAwH+B/GK0\nOnwzs8vl4kvZgojQAAAAAEvIz8/X7Nmz5XA4dN111+m9997T+fPnNXLkSN1333366quvNG7cOMXE\nxKhJkyYaPXp0qEsOG1yeBAAAAMtwOp365z//qby8PC1ZskT5+flat26dvvnmGz3++ON65JFHtGzZ\nMjVt2jTUpYYVzjQAAADAMtLS0iRJ27dv1+jRo+VyuXTixAl99dVX2rNnj1JSUiRJvXv3DmWZYYfQ\nAAAAAMuIiPj2QplevXpp0aJFiomJcd/f0KVLF33yySfKyclRQUGBrr/++hBXGz4IDQAAAPBfHW5e\nDqSZM2dq9OjRqqysVNOmTbVkyRJNmzZN48aN0+zZsxUbGxuSusIVoQEAAACWkJmZqczMTElSSkqK\nVq1a5fF8hw4d9P7774eitLDHjdAAAAAAjAgNAAAAAIwIDQAAAACMCA0AAAAAjAgNAAAAAIwIDQAA\nAACMCA0AAAAAjAgNAAAAAIwIDQAAAACMCA0AAAAAjAgNAAAAAIwIDQAAAACMCA0AAAAAjAgNAAAA\nAIwIDQAAAACMCA0AAAAAjOyhLqCxi46Ortf2NptNp0+flsPhkN0evF93RESE37XXdbtQ9Fif/rwx\njWXFOfRF1bHDYQ59Fa492my2Wp9rqBqCPYd1OU4DVVew57A+r0N/67RSj9X5Urdl+zsdmJqAQCE0\nXERZWVm9tnc4HIqLi9OpU6dUXl4eoKouLjo62qfa470sq2vPoejR1/58ZRqrsc+hv6qOHQ5z6Kvy\n8vKw7NHhcNT6XEPVEOw59HacNqtl3UDVFew59OV16O19W/K/58bYo698qduq/dU2z0CocHkSAAAA\nACNCAwAAAAAjQgMAAAAAI0IDAAAAACNCAwAAAAAjQgMAAAAAI0IDAAAAACNCAwAAAAAjQgMAAAAA\nI0IDAAAAACNCAwAAAAAjQgMAAAAAI0IDAAAAACNCAwAAAAAjQgMAAAAAI0IDAAAAACNCAwAAAAAj\nQgMAAAAAI0IDAAAAACNCAwAAAAAjQgMAAAAAI0IDAAAAACNCAwAAAAAjQgMAAAAAI0IDAAAAACNC\nAwAAAAAjQgMAAAAAI0IDAAAAACNCAwAAAAAjQgMAAAAAI0IDAAAAACNCAwAAAAAjQgMAAAAAI0ID\nAAAAACNCAwAAAAAjQgMAAAAAI0IDAAAAACN7qAvwV1FRkVasWCGXy6WUlBSlp6d7PL99+3Z98MEH\nkqQmTZpo+PDhuvzyy0NRKgAAAGBplgwNlZWVWr58uSZMmKCYmBjNnTtXiYmJSkhIcK8THx+viRMn\nqmnTpioqKtI777yjyZMnh7BqAAAAwJoseXlScXGxWrVqpbi4OEVGRiopKUmFhYUe63To0EFNmzaV\nJLVv314nT54MRakAAACA5VkyNJw8eVKxsbHux7GxsXI6nbWu/8knn6hz587BKA0AAAAIO5a8PKku\nvvzyS23dulWTJk3yWO50OlVaWlpt7bY1tnc4HPXav91u9/hvsERGRvpde123C0WP9enPG9NYVpxD\nX1QdOxzm0Ffh2qOpr4aqIdhzWJfjNFB1hWIO/T1G/a3TSj1W50vdlu3vXP2HAALJkqEhJiZGJ06c\ncD92Op0eZx4u+Prrr/Xuu+/qJz/5iaKjoz2e27x5s/Lz86tt8bsaY1S9T6I+4uPjAzJOMPjbs5V6\nrM6Xnq3cn/bXXOStZ0v3WI2XliWFV4++CtT7WGNRdQ7P1rKOlXu++DH6udelVuo5UK/DxtpzQPor\n3l3/MYAAsmRoaNeunY4dO6bjx4+rRYsW2rFjh3Jzcz3WOX78uBYuXKjRo0erZcuWNcZITU1VYmKi\nx7IZM2ru6/Dhw/Wq1W63Kz4+XiUlJaqoqKjXWHURFRWls2dr+7/T73h7u61rz6Ho0df+fGXqubHP\nob+q9hwOc+ircO3Rbrertj9T6vs+Vptgz6G347TmPxd9K1A9B30OfXgd1vZnsr89N8YefeVLz1bt\nr3HGIVzKLBkaIiIidOONN2r+/PlyuVxKTk5WQkKCCgoKJElpaWlav369ysrKtGzZMvc2d955p3uM\n2NhYr2cnqisvLw9IzRUVFQEbyxd2u93v/fm7XTB7rE9/3vgylpXm0BfexrbyHPrqUuixuoaqoTHP\nYaDqCkWP/h6j/tZppR6r82UMK/cHNCaWDA2S1KVLF3Xp0sVjWVpamvvnESNGaMSIEcEuCwAAAAg7\nlvz0JAAAAADBQ2gAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAA\nYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABg\nRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBE\naAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERo\nAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgA\nAAAAYGQPdQGNXXR0dL22t9lsOn36tBwOh+z24P26IyIi/K69rtuFosf69OeNaSwrzqEvqo4dDnPo\nq3Dt0Waz1fpcQ9UQ7Dmsy3EaqLqCPYf1eR36W6eVeqzOl7ot29/pwNQEBAqh4SLKysrqtb3D4VBc\nXJxOnTql8vLyAFV1cdHR0T7VHu9lWV17DkWPvvbnK9NYjX0O/VV17HCYQ1+Vl5eHZY8Oh6PW5xqq\nhmDPobfjtFkt6waqrmDPoS+vQ2/v25L/PTfGHn3lS91W7a+2eQZChcuTAAAAABgRGgAAAAAYERoA\nAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAA\nAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAA\nABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAA\nGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAY\nERoAAAAAGBEaAAAAABgRGgAAAAAY2UNdgL+Kioq0YsUKuVwupaSkKD09vcY6y5cv1549e+RwODRq\n1Ci1adMmBJUCAAAA1mbJMw2VlZVavny5xo8fr7vvvluffvqpDh8+7LFOUVGRSkpKNHXqVN18881a\nunRpiKoFAAAArM2SoaG4uFitWrVSXFycIiMjlZSUpMLCQo91du3apZ49e0qS2rdvr7Nnz6q0tDQU\n5QIAAACWZsnQcPLkScXGxrofx8bGyul0GteJiYmpsQ4AAACAi7PsPQ315XQ6vZx5aFtjPYfDUa/9\n2O12j/8GS2RkpN+113W7UPRYn/68MY1lxTn0RdWxw2EOfRWuPZr6aqgagj2HdTlOA1VXKObQ32PU\n3zqt1GN1vtRt2f7O1X8IIJAsGRpiYmJ04sQJ92On0+lxVuHCOlXPLFRfZ/PmzcrPz/fYZs2aTA0a\nNKja3hLqVavT6dTatWuVmpqq+Pj4eo3VIFyuGovq2nGj77Ea1yM1ezaxWn/ePJ9wi/H5cOixuoTf\nrPN47HQ6tXnzZkVGRoZNj1U5nU6tve1XSk1NrfF+WL93scbD63E64hde17Vizz6/Dm/w3p0Vevb3\nveaeDzc1YFWBE9j3UivMKC4llrw8qV27djp27JiOHz+uiooK7dixQ4mJiR7rJCYmatu2bZKk/fv3\nq2nTpmrRooX7+dTUVN15550e/0tNTQ14raWlpcrPzw/r+ynCvcdw70+ix3AQ7v1J4d9juPcnhX+P\n4d4fLm2WPNMQERGhG2+8UfPnz5fL5VJycrISEhJUUFAgSUpLS1PXrl1VVFSkP//5z2rSpIlGjhzp\nMUZsbGyNf40DAAAAUJMlQ4MkdenSRV26dPFYlpaW5vH4pptuCmZJAAAAQFiy5OVJAAAAAIIn8ne/\n+93vQl1EOHO5XGrSpImuvPJKRUVFhbqcBhHuPYZ7fxI9hoNw708K/x7DvT8p/HsM9/5wabO5XF4+\nPgcBU1RUpBUrVsjlciklJUXp6emhLimg3n77be3evVvNmzfXL37h/VNMrOzEiRNasmSJTp06JZvN\nppSUFF133XWhLitgKioq9I9//EPnz59XZWWlrrnmGmVlZYW6rAZRWVmpuXPnKjY2VuPGjQt1OQH3\n1FNPqWnTprLZbIqIiNCdd94Z6pIC6syZM3rnnXf0zTffyGazaeTI/9fe3b1E1e5hHL9mfMm0ybdx\nYlIQH01TNLVACCKwjNCIJKKjwMQoDwqkP6MDO4sSOrEoNdHqICmogyhKAotUZDDHsCwx8bVhHMeZ\neQ5CYe8Ni2ez17S2q+/nbM4uWbjWXPO773WfUl5entWxTDM3N6fe3t7NzwsLC6qtrbXV/ebNmzca\nGhqSw+HQrl27dOrUqd/+Kut4e/v2rYaGhiTJds8LwF7/rf9notGonjx5oqamJrlcLnV0dKikpEQ5\nOfZ5jVpVVZVqamrU399vdZS4cDqdOn78uLxer0KhkDo6OlRYWGiba5iYmKimpiYlJycrGo3q9u3b\nKioqstWXsQ2Dg4PKyclRKBSyOkpcOBwOnT9/Xtu3b7c6SlwMDAxoz549Onv2rCKRiMLhsNWRTOV2\nu9Xa2irp17Ojvb1dpaWlFqcyz/LysgYHB3X58mUlJibqwYMHGhkZUVVVldXRTDM7O6uhoSFdvHhR\nTqdTd+/eVXFxsbKysqyOBpiCPQ1xND09rezsbGVkZCghIUHl5eXy+XxWxzJVfn6+bb+kSL/O+/B6\nvZKkbdu2ye12a2VlxeJU5kpOTpb0a+oQjUblcDgsTmS+paUljY+Pa//+/VZHiSu7Do5XV1c1NTWl\n6upqSb8O60pJSbE4Vfz4/X5lZWUpPT3d6iimisViCofDm6XP5XJZHclUP378UF5enhITE+V0OpWf\nn6+xsTGrYwGmYdIQRysrK//yWtedO3dqenrawkT4XywsLGhmZka5ublWRzHVxrKd+fl51dTU2O7v\nk6SnT5/q2LFjtp0ybOjs7JTT6dSBAwficu6MVRYXF5WamqqHDx9qZmZGu3fvVn19vSUniv8Oo6Oj\nKjSSLdIAAASYSURBVC8vtzqGqXbu3KmDBw/q+vXrSkpKUmFhoQoLC62OZSqPx6MXL14oGAwqISFB\n4+Pjtryf4s9FaQD+gVAopJ6eHtXX19tuc5vT6VRra6tWV1fV1dWl2dlZeTweq2OZZmPPjdfr1eTk\npNVx4qalpUUul0uBQECdnZ1yu93Kz8+3OpYpotGovn//roaGBuXm5mpgYECvXr1SbW2t1dFMF4lE\n5PP5VFdXZ3UUUwWDQfl8PrW1tSklJUU9PT36+PGj9u3bZ3U00+Tk5OjQoUPq7OxUcnKyvF6vLSe3\n+HNRGuLI5XJpaWlp8/Py8jIHym1BkUhEPT09qqys1N69e62OEzcpKSkqKCjQp0+fbFUapqam5PP5\nND4+rvX1dYVCIfX19en06dNWRzPVxlKPtLQ0lZaWanp62jalYeMwzo1fbcvKyvT69WuLU8XH+Pi4\nvF6v0tLSrI5iKr/fr8zMTKWmpkqSSktL9eXLF1uVBkmqrq7eXEb3/PlznvmwFfY0xFFubq7m5+e1\nuLio9fV1jYyMqKSkxOpYprPrOuoNjx49Uk5Oji3fghEIBLS6uipJCofDmpiYkNvttjiVuerq6nT1\n6lW1tbXpzJkzKigosF1hWFtb21x6tba2pomJCVsVvx07dig9PV1zc3OSpMnJSdu8jODfjYyMqKKi\nwuoYpktPT9fXr18VDocVi8Xk9/tteQ0DgYCkX0vqxsbGbHkt8edi0hBHTqdTDQ0NunPnjmKxmKqr\nq213k+zt7dXnz58VDAbV3t6u2trazV9Z7GBqakrDw8PyeDy6efOmJOno0aP/cRr5VvXz50/19/cr\nFospFoupvLxcxcXFVsfCfykQCKirq0sOh0PRaFQVFRUqKiqyOpap6uvr1dfXp0gkoszMTDU2Nlod\nyXRra2vy+/06efKk1VFMl5eXp7KyMt26dUtOp1Ner9dW+242dHd3b+5pOHHihK037OPPwzkNAAAA\nAAyxPAkAAACAIUoDAAAAAEOUBgAAAACGKA0AAAAADFEaAAAAABiiNAAAAAAwRGkAAAAAYIjSAAAA\nAMAQpQEAAACAIUoDAAAAAEOUBgAAAACGKA0AAAAADFEaAAAAABiiNAAAAAAwRGkAAAAAYIjSAAAA\nAMAQpQEAAACAIUoDAAAAAEOUBgAAAACGKA0AAAAADFEaAAAAABiiNADAFuH3+5Wdna0PHz5Ikr59\n+yaPx6OXL19anAwAYHeUBgDYIv766y9du3ZN586dUzAYVHNzs5qbm3X48GGrowEAbM4Ri8ViVocA\nAPxzjY2N8vv9cjqdevfunZKSkqyOBACwOSYNALDFXLhwQaOjo7py5QqFAQDwWzBpAIAtJBAIqLKy\nUkeOHNHAwICGh4eVkZFhdSwAgM1RGgBgC2lpaVEwGNS9e/d06dIlLS4uqru72+pYAACbY3kSAGwR\njx8/1rNnz3Tjxg1JUnt7u96/f6/79+9bnAwAYHdMGgAAAAAYYtIAAAAAwBClAQAAAIAhSgMAAAAA\nQ5QGAAAAAIYoDQAAAAAMURoAAAAAGKI0AAAAADBEaQAAAABgiNIAAAAAwNDfBzYTTM+qjXQAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c264d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (274640201)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df, aes(x='x', y='y', fill='ze-color')) + \\\n",
    "    geom_bar() + \\\n",
    "    scale_fill_identity()"
   ]
  },
  {
   "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
}