ch08_20170526.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(12345)\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'C:\\\\Users\\\\Administrator\\\\pydata-book-master'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Error 2] : u'../book_scripts/'\n",
      "C:\\Users\\Administrator\\pydata-book-master\n"
     ]
    }
   ],
   "source": [
    "%cd ../book_scripts/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A brief matplotlib API primer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures and Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax1 = fig.add_subplot(2, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "ax3 = fig.add_subplot(2, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8a88198>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YsKHZodid5ORkIiMjuffee6latarZ4QhhF+TFq1JITk7mzz//LHM9Y8eOZciQIRaIyPkc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\ntNaDtdZ3a61baK0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VUgeVUgeUUqNzjzt035VS5ZVSO5VS+3P7PTX3uEP3+zqllKtSaq9S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x88c00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy.random import randn\n",
    "plt.plot(randn(50).cumsum(), 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x89fd470>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)\n",
    "ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000008AD8630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000008DC9240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000008E43CF8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000008F3A630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000904B080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000009130898>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGdRJREFUeJzt3V+IXOd9//H355f1XtR1HTtSnTC7Bg8j1mjNJmRHsjEm\ndW8iWSgrCqasKQlELWJDdVswvnDw3ULvUrVdRBEmhXhvWmdFbK/bUEygIVmPgu1oaVXtSkp3h1DL\ndqNgx+x61e/vYo7s0Xr/nPmznj/P5wUHzXPO88w8Rx/Z3z07548iAjMzS9f/6/QEzMyss1wIzMwS\n50JgZpY4FwIzs8S5EJiZJc6FwMwscbsWAknnJL0t6eI22yXpe5KWJL0l6at1245KupRte7qdE7fW\nONf+5WytUXmOCJ4Hju6w/QngQLacAv4eQNLngL/Nth8EnpJ0sJXJWls9j3PtV8/jbK0BuxaCiPgJ\n8N4OXU4A34+anwGfl/Ql4DCwFBFXImIdmM36Whdwrv3L2VqjBtrwHgVgpa69mq3bav3D272JpFPU\nfjrhzjvvHH/wwQfbMDXbyUMPPcTS0hLlcvlTl5fffffd3Lhx40+BF7JVzrWH7JQtsA78Y1274Wyd\na/e5cOHCOxGxv5mx7SgEbRERZ4GzAOVyOSqVSodn1P+uXbvG8ePH2erv+vjx47z00kv/0+pnONfO\n2ClbSR+2+v7OtftI+lWzY9tRCKrAcF17KFt3xzbrrQcUCgWAwbpVzrV/fIT/m7U67Th99DzwrexM\nhEeAGxHxa+B14ICkByQNApNZX+sBExMTAF9wrn3pN/i/Wauz6xGBpBeAx4F9klaB71L7yYGImAFe\nBo4BS8DvgG9n2zYknQZeBT4HnIuIxT3YB2vCU089xWuvvcY777zD0NAQzz33HB999BEAU1NTHDt2\nDGAN59pzdssWuAFcwdlaRt14G2r/zrE7SLoQEeV2vZ9z7Q7OtT+1kquvLDYzS5wLgZlZ4lwIzMwS\n50JgZpY4FwIzs8S5EJiZJc6FwMwscS4EZmaJcyEwM0ucC4GZWeJcCMzMEudCYGaWOBcCM7PEuRCY\nmSXOhcDMLHEuBGZmictVCCQdlXRJ0pKkp7fY/leS3siWi5JuSro323ZN0i+zbX56RReZn59nZGSE\nUqnE9PT0Vl3uc669x7lawyJix4XaI+uWgSK1h5m/CRzcof83gH+ra18D9u32OfXL+Ph42N7a2NiI\nYrEYy8vLsba2FmNjY7G4uHhbH6ASzrWnONd01efa6JLniOAwsBQRVyJiHZgFTuzQ/ynghVxVyDpm\nYWGBUqlEsVhkcHCQyclJ5ubmdhriXHuAc7Vm5CkEBWClrr2arfsUSb8HHAX+qW51AD+WdEHSqe0+\nRNIpSRVJlevXr+eYlrWiWq0yPDz8cXtoaIhqtbplX+faO5yrNaPdXxZ/A/j3iHivbt1jEfEV4Ang\nLyV9bauBEXE2IsoRUd6/f3+bp2Utcq79ybkakK8QVIHhuvZQtm4rk2w6zIyIavbn28CL1H7VZB1W\nKBRYWfnkQG91dZVCYcsDPXCuPcO5WjPyFILXgQOSHpA0SO0fz/nNnSTdDfwRMFe37k5Jd916DXwd\nuNiOiVtrDh06xOXLl7l69Srr6+vMzs4yMTHxqX7Otbc4V2vGwG4dImJD0mngVWpnEJ2LiEVJU9n2\nmazrnwD/EhEf1A2/D3hR0q3P+kFEzLdzB6w5AwMDnDlzhiNHjnDz5k1OnjzJ6OgoMzO1OKempm51\nda49xLlaM1Q766i7lMvlqFR8CnOnSboQEeV2vZ9z7Q7OtT+1kquvLDYzS5wLgZlZ4lwIzMwS50Jg\nZpY4FwIzs8S5EJiZJc6FwMwscS4EZmaJcyEwM0ucC4GZWeJcCMzMEudCYGaWOBcCM7PEuRCYmSXO\nhcDMLHEuBGZmictVCCQdlXRJ0pKkp7fY/rikG5LeyJZn8461zpmfn2dkZIRSqcT09PRWXe5yrr3H\nuVrDImLHhdrjKZeBIjAIvAkc3NTnceBHzYzdahkfHw/bWxsbG1EsFmN5eTnW1tZibGwsFhcXb+sD\nXHKuvcW5pguoxC5ZbbfkOSI4DCxFxJWIWAdmgRM560wrY20PLSwsUCqVKBaLDA4OMjk5ydzc3O4D\na5xrl3Ku1ow8haAArNS1V7N1mz0q6S1Jr0gabXAskk5JqkiqXL9+Pce0rBXVapXh4eGP20NDQ1Sr\n1a26Otce4lytGe36svgXwP0RMQb8DfDDRt8gIs5GRDkiyvv372/TtKxFH+Bc+5FztdvkKQRVYLiu\nPZSt+1hE/DYi3s9evwzcIWlfnrHWGYVCgZWVT374W11dpVD41A9//+dce4tztWbkKQSvAwckPSBp\nEJgEztd3kPRFScpeH87e9908Y60zDh06xOXLl7l69Srr6+vMzs4yMTGxuduAc+0tztWaMbBbh4jY\nkHQaeJXaWQXnImJR0lS2fQZ4EviOpA3gQ2Ay+xZ7y7F7tC/WgIGBAc6cOcORI0e4efMmJ0+eZHR0\nlJmZGQCmpqYA7gEuOtfe4VytGarl313K5XJUKpVOTyN5ki5ERLld7+dcu4Nz7U+t5Oori83MEudC\nYGaWOBcCM7PEuRCYmSXOhcDMLHEuBGZmiXMhMDNLnAuBmVniXAjMzBLnQmBmljgXAjOzxLkQmJkl\nzoXAzCxxLgRmZolzITAzS1yuQiDpqKRLkpYkPb3F9j/LHoT9S0k/lfTlum3XsvVvSPJNy7vI/Pw8\nIyMjlEolpqent+pyr3PtPc7VGhYROy7UnlS0DBSBQeBN4OCmPo8C92SvnwB+XrftGrBvt8+pX8bH\nx8P21sbGRhSLxVheXo61tbUYGxuLxcXF2/oA/+Fce4tzTRdQiQZyq1/yHBEcBpYi4kpErAOzwIlN\nxeSnEfG/WfNn1B56bV1sYWGBUqlEsVhkcHCQyclJ5ubmNnf7wLn2FudqzchTCArASl17NVu3nT8H\nXqlrB/BjSRckndpukKRTkiqSKtevX88xLWtFtVpleHj44/bQ0BDVanWnIc61BzhXa8auD69vhKQ/\npvYP67G61Y9FRFXSHwL/Kuk/I+Inm8dGxFngLNSegdrOeVlrnGt/cq52S54jgiowXNceytbdRtIY\n8A/AiYh499b6iKhmf74NvEjtV03WYYVCgZWVTw70VldXKRQ+faDnXHuLc7Vm5CkErwMHJD0gaRCY\nBM7Xd5B0P/DPwDcj4r/q1t8p6a5br4GvAxfbNXlr3qFDh7h8+TJXr15lfX2d2dlZJiYmNncbxLn2\nFOdqzdj1V0MRsSHpNPAqtTOIzkXEoqSpbPsM8CzwBeDvJAFsREQZuA94MVs3APwgIub3ZE+sIQMD\nA5w5c4YjR45w8+ZNTp48yejoKDMzMwBMTU0BfIlabs61RzhXa4ZqZx11l3K5HJWKT2HuNEkXsv9B\ntIVz7Q7OtT+1kquvLDYzS5wLgZlZ4lwIzMwS50JgZpY4FwIzs8S5EJiZJc6FwMwscS4EZmaJcyEw\nM0ucC4GZWeJcCMzMEudCYGaWOBcCM7PEuRCYmSXOhcDMLHEuBGZmictVCCQdlXRJ0pKkp7fYLknf\ny7a/Jemrecda58zPzzMyMkKpVGJ6enrLPs619zhXa1hE7LhQezzlMlCk9qzTN4GDm/ocA14BBDwC\n/Dzv2K2W8fHxsL21sbERxWIxlpeXY21tLcbGxmJxcfG2PsBl59pbnGu6gErsktV2S54jgsPAUkRc\niYh1YBY4sanPCeD72Xx+Bnxe0pdyjrUOWFhYoFQqUSwWGRwcZHJykrm5uc3dPo9z7SnO1Zqx68Pr\ngQKwUtdeBR7O0aeQcywAkk4Bp7LmmqSLOebWzfYB73R6Eju4B/gDSb/K2vcCv//MM8/8d12fr+Bc\nN3OuONcuNdLswDyF4DMREWeBswCSKtHGh2t3Qrfvg6QngaMR8RdZ+5vAwxFxuq7Pb1r9HOf62XKu\nzemXfWh2bJ5CUAWG69pD2bo8fe7IMdY6I0+uH23Tx7l2L+dqDcvzHcHrwAFJD0gaBCaB85v6nAe+\nlZ099AhwIyJ+nXOsdUaebH6Dc+01ztUatusRQURsSDoNvErtrIJzEbEoaSrbPgO8TO3MoSXgd8C3\ndxqbY15nm9mZLtPV+5Az178GvoxzrdfV++Bcm5b0Pqh21pGZmaXKVxabmSXOhcDMLHEdKwSt3Lai\nW+TYh8cl3ZD0RrY824l57kTSOUlvb3ceeKM59EOu0PvZtjvXbEzPZ9vrucLeZNvU5citLrRw24pu\nWXLuw+PAjzo9113242vAV4GL22zPnUM/5Nov2bYz137Jth9y3YtsI/LdYmIvtHLbim7RF5fjR8RP\ngPd26NJIDv2QK/RBtm3OFfoj257PFfYk244Vgu1uSdFon07KO79Hs8OzVySNfjZTa6tGcuiHXCGN\nbBvNoR+yTSFXaCKHrrnFRJ/6BXB/RLwv6RjwQ+BAh+dk7eFs+1OSuXbqiKCV21Z0i13nFxG/jYj3\ns9cvA3dI2vfZTbEtGsmhH3KFNLJtNId+yDaFXKGJHDpVCFq5bUW32HUfJH1RkrLXh6n9fb/7mc+0\nNY3k0A+5QhrZNppDP2SbQq7QRA4d+dVQtHDbim6Rcx+eBL4jaQP4EJiM7Gv9biHpBWpnSuyTtAp8\nl9rNxxrOoR9yhf7Itp25ZmN6Ptt+yBXany34FhNmZsnb9VdDrVy8ID//tGs51/7lbK1Reb4jeB44\nusP2J6h9q36A2hOL/h5A0ueAv822HwSeknSwlclaWz2Pc+1Xz+NsrQG7FoIWLl7oi4s3+pVz7V/O\n1hrVji+LW35eMdz+DNQ777xz/MEHH2zD1GwnDz30EEtLS5TL5U99UXT33Xdz48aNPwVeyFY51x6y\nU7bAOvCPde2WnlnsXLvDhQsX3omI/c2M7ZoLyqLuGajlcjkqlaYfv2k5Xbt2jePHj7PV3/Xx48d5\n6aWX/qfVz3CunbFTtpI+bPX9nWv3kfSrZse2oxD4ecV9qFAoQO3GXLc41/7hZxbbbdpxQZmfV9yH\nJiYmAL7gXPuSn1lst9n1iKDZixe2u3hjD/bBmvDUU0/x2muv8c477zA0NMRzzz3HRx99BMDU1BTH\njh0DWMO59pzdsgVuAFdwtpbpygvK/DvH7iDpQkSU2/V+zrU7ONf+1EquflSlmVniXAjMzBLnQmBm\nljgXAjOzxLkQmJklzoXAzCxxLgRmZolzITAzS5wLgZlZ4lwIzMwS50JgZpY4FwIzs8S5EJiZJc6F\nwMwscS4EZmaJcyEwM0tcrkIg6aikS5KWJD29xfa/kvRGtlyUdFPSvdm2a5J+mW3z0yu6yPz8PCMj\nI5RKJaanp7fqcp9z7T3O1RoWETsu1B5ZtwwUqT3M/E3g4A79vwH8W137GrBvt8+pX8bHx8P21sbG\nRhSLxVheXo61tbUYGxuLxcXF2/oAlXCuPcW5pqs+10aXPEcEh4GliLgSEevALHBih/5PAS/kqkLW\nMQsLC5RKJYrFIoODg0xOTjI3N7fTEOfaA5yrNSNPISgAK3Xt1Wzdp0j6PeAo8E91qwP4saQLkk5t\n9yGSTkmqSKpcv349x7SsFdVqleHh4Y/bQ0NDVKvVLfs6197hXK0Z7f6y+BvAv0fEe3XrHouIrwBP\nAH8p6WtbDYyIsxFRjojy/v372zwta5Fz7U/O1YB8haAKDNe1h7J1W5lk02FmRFSzP98GXqT2qybr\nsEKhwMrKJwd6q6urFApbHuiBc+0ZztWakacQvA4ckPSApEFq/3jOb+4k6W7gj4C5unV3Srrr1mvg\n68DFdkzcWnPo0CEuX77M1atXWV9fZ3Z2lomJiU/1c669xblaMwZ26xARG5JOA69SO4PoXEQsSprK\nts9kXf8E+JeI+KBu+H3Ai5JufdYPImK+nTtgzRkYGODMmTMcOXKEmzdvcvLkSUZHR5mZqcU5NTV1\nq6tz7SHO1Zqh2llH3aVcLkel4lOYO03ShYgot+v9nGt3cK79qZVcfWWxmVniXAjMzBLnQmBmljgX\nAjOzxLkQmJklzoXAzCxxLgRmZolzITAzS5wLgZlZ4lwIzMwS50JgZpY4FwIzs8S5EJiZJc6FwMws\ncS4EZmaJy1UIJB2VdEnSkqSnt9j+uKQbkt7IlmfzjrXOmZ+fZ2RkhFKpxPT09FZd7nKuvce5WsMi\nYseF2lPJloEiMAi8CRzc1Odx4EfNjN1qGR8fD9tbGxsbUSwWY3l5OdbW1mJsbCwWFxdv6wNccq69\nxbmmC6jELlltt+Q5IjgMLEXElYhYB2aBEznrTCtjbQ8tLCxQKpUoFosMDg4yOTnJ3Nzc7gNrnGuX\ncq7WjDyFoACs1LVXs3WbPSrpLUmvSBptcCySTkmqSKpcv349x7SsFdVqleHh4Y/bQ0NDVKvVrbo6\n1x7iXK0Z7fqy+BfA/RExBvwN8MNG3yAizkZEOSLK+/fvb9O0rEUf4Fz7kXO12+QpBFVguK49lK37\nWET8NiLez16/DNwhaV+esdYZhUKBlZVPfvhbXV2lUPjUD3//51x7i3O1ZuQpBK8DByQ9IGkQmATO\n13eQ9EVJyl4fzt733TxjrTMOHTrE5cuXuXr1Kuvr68zOzjIxMbG524Bz7S3O1ZoxsFuHiNiQdBp4\nldpZBeciYlHSVLZ9BngS+I6kDeBDYDL7FnvLsXu0L9aAgYEBzpw5w5EjR7h58yYnT55kdHSUmZkZ\nAKampgDuAS46197hXK0ZquXfXcrlclQqlU5PI3mSLkREuV3v51y7g3PtT63k6iuLzcwS50JgZpY4\nFwIzs8S5EJiZJc6FwMwscS4EZmaJcyEwM0ucC4GZWeJcCMzMEudCYGaWOBcCM7PEuRCYmSXOhcDM\nLHEuBGZmiXMhMDNLnAuBmVnichUCSUclXZK0JOnpLbb/maS3JP1S0k8lfblu27Vs/RuS/PSKLjI/\nP8/IyAilUonp6emtutzrXHuPc7WGRcSOC7VH1i0DRWAQeBM4uKnPo8A92esngJ/XbbsG7Nvtc+qX\n8fHxsL21sbERxWIxlpeXY21tLcbGxmJxcfG2PsB/ONfe4lzTBVSigdzqlzxHBIeBpYi4EhHrwCxw\nYlMx+WlE/G/W/BkwlL8UWScsLCxQKpUoFosMDg4yOTnJ3Nzc5m4fONfe4lytGXkKQQFYqWuvZuu2\n8+fAK3XtAH4s6YKkU9sNknRKUkVS5fr16zmmZa2oVqsMDw9/3B4aGqJare40xLn2AOdqzRho55tJ\n+mNq/7Aeq1v9WERUJf0h8K+S/jMifrJ5bEScBc5C7WHY7ZyXtca59ifnarfkOSKoAsN17aFs3W0k\njQH/AJyIiHdvrY+Iavbn28CL1H7VZB1WKBRYWfnkQG91dZVC4dMHes61tzhXa0aeQvA6cEDSA5IG\ngUngfH0HSfcD/wx8MyL+q279nZLuuvUa+DpwsV2Tt+YdOnSIy5cvc/XqVdbX15mdnWViYmJzt0Gc\na09xrtaMXX81FBEbkk4Dr1I7g+hcRCxKmsq2zwDPAl8A/k4SwEZElIH7gBezdQPADyJifk/2xBoy\nMDDAmTNnOHLkCDdv3uTkyZOMjo4yMzMDwNTUFMCXqOXmXHuEc7VmqHbWUXcpl8tRqfgU5k6TdCH7\nH0RbONfu4Fz7Uyu5+spiM7PEuRCYmSXOhcDMLHEuBGZmiXMhMDNLnAuBmVniXAjMzBLnQmBmljgX\nAjOzxLkQmJklzoXAzCxxLgRmZolzITAzS5wLgZlZ4lwIzMwS50JgZpa4XIVA0lFJlyQtSXp6i+2S\n9L1s+1uSvpp3rHXO/Pw8IyMjlEolpqent+zjXHuPc7WGRcSOC7XHUy4DRWrPOn0TOLipzzHgFUDA\nI8DP847dahkfHw/bWxsbG1EsFmN5eTnW1tZibGwsFhcXb+sDXHauvcW5pguoxC5ZbbfkOSI4DCxF\nxJWIWAdmgROb+pwAvp/N52fA5yV9KedY64CFhQVKpRLFYpHBwUEmJyeZm5vb3O3zONee4lytGbs+\nvB4oACt17VXg4Rx9CjnHAiDpFHAqa65Juphjbt1sH/BOpyexg3uAP5D0q6x9L/D7zzzzzH/X9fkK\nznUz54pz7VIjzQ7MUwg+ExFxFjgLIKkSbXy4did0+z5IehI4GhF/kbW/CTwcEafr+vym1c9xrp8t\n59qcftmHZsfmKQRVYLiuPZSty9PnjhxjrTPy5PrRNn2ca/dyrtawPN8RvA4ckPSApEFgEji/qc95\n4FvZ2UOPADci4tc5x1pn5MnmNzjXXuNcrWG7HhFExIak08Cr1M4qOBcRi5Kmsu0zwMvUzhxaAn4H\nfHunsTnmdbaZnekyXb0POXP9a+DLONd6Xb0PzrVpSe+DamcdmZlZqnxlsZlZ4lwIzMwS17FC0Mpt\nK7pFjn14XNINSW9ky7OdmOdOJJ2T9PZ254E3mkM/5Aq9n227c83G9Hy2vZ4r7E22TV2O3OpCC7et\n6JYl5z48Dvyo03PdZT++BnwVuLjN9tw59EOu/ZJtO3Ptl2z7Ide9yDYi3y0m9kIrt63oFn1xOX5E\n/AR4b4cujeTQD7lCH2Tb5lyhP7Lt+VxhT7LtWCHY7pYUjfbppLzzezQ7PHtF0uhnM7W2aiSHfsgV\n0si20Rz6IdsUcoUmcuiaW0z0qV8A90fE+5KOAT8EDnR4TtYezrY/JZlrp44IWrltRbfYdX4R8duI\neD97/TJwh6R9n90U26KRHPohV0gj20Zz6IdsU8gVmsihU4WgldtWdItd90HSFyUpe32Y2t/3u5/5\nTFvTSA79kCukkW2jOfRDtinkCk3k0JFfDUULt63oFjn34UngO5I2gA+Byci+1u8Wkl6gdqbEPkmr\nwHep3Xys4Rz6IVfoj2zbmWs2puez7Ydcof3Zgm8xYWaWPF9ZbGaWOBcCM7PEuRCYmSXOhcDMLHEu\nBGZmiXMhMDNLnAuBmVni/j+QqnKVrD+zPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8aa1ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 3)\n",
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adjusting the spacing around subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9349940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None,\n",
    "                wspace=None, hspace=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w/u6xJGmFJnboVfWhHZ46vuBYJElz8NT/XXLMoD7yWGz/NsFT/yWpGXbo1+AZlJL2Ezt0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\nNcKCLkmNsKBLUiMs6JLUCAu6JDXiXwEXJlWJE7Ji9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x933cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ihNWK9Wpmabk8Cdyc5O1Jfh/4EHC2n7AkSdPadYVeVa8muQf4GnAAeKCqnuktMknSVGY6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\nS1IjTOiS1AgTuiQ14v8B+elzEDfGJI8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x933c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors, markers, and line styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x930fd30>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x930fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa0612b0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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np6Nz586q/v8rJ07sKpaWlob8/HwEBAQoHYpTmDFjBgoLCxWZYqC6uhpGoxHd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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x875fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(randn(30).cumsum(), 'ko--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x9e506d8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kpTyKiGw1xkQVpWxxdPX0I59uHhG5CzhljDEicg/ZnzDOFcM5lRu5cuUKDz74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\nK6WUl9HEr5RSXkYTv1JKeZn/DxqGZWkWW/F0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9fd2358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = randn(30).cumsum()\n",
    "plt.plot(data, 'k--', label='Default')\n",
    "plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ticks, labels, and legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting the title, axis labels, ticks, and ticklabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xa0bbe80>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aAKb9di4by9puvaiUaksDfopyT82E1kJrXZWeJnjSe//PyFmBC3D1E0t6/f2V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\nS3g0yhoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa13b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(randn(1000).cumsum())\n",
    "\n",
    "ticks = ax.set_xticks([0, 250, 500, 750, 1000])\n",
    "labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],\n",
    "                            rotation=30, fontsize='small')\n",
    "ax.set_title('My first matplotlib plot')\n",
    "ax.set_xlabel('Stages')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding legends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x9df4ba8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MnhMcHAwPDw80atQI6enpkmQoRYUKFSrgwYMHBgum5sJUaObE89dHEATcuHGD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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a956a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(randn(1000).cumsum(), 'k', label='one')\n",
    "ax.plot(randn(1000).cumsum(), 'k--', label='two')\n",
    "ax.plot(randn(1000).cumsum(), 'k.', label='three')\n",
    "\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotations and drawing on a subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xbb66208>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rr78kOTlZ2rdvbz/0b1VJWErqP//5j4SEhMjp06clMTFRSpcuLdHR0U7nGh8fL6GhoXY6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\nknAcx3EK4knCcRzHKcj/A6Kzv0wln53tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbecaa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "data = pd.read_csv('ch08/spx.csv', index_col=0, parse_dates=True)\n",
    "spx = data['SPX']\n",
    "\n",
    "spx.plot(ax=ax, style='k-')\n",
    "\n",
    "crisis_data = [\n",
    "    (datetime(2007, 10, 11), 'Peak of bull market'),\n",
    "    (datetime(2008, 3, 12), 'Bear Stearns Fails'),\n",
    "    (datetime(2008, 9, 15), 'Lehman Bankruptcy')\n",
    "]\n",
    "\n",
    "for date, label in crisis_data:\n",
    "    ax.annotate(label, xy=(date, spx.asof(date) + 60),\n",
    "                xytext=(date, spx.asof(date) + 200),\n",
    "                arrowprops=dict(facecolor='black'),\n",
    "                horizontalalignment='left', verticalalignment='botton')\n",
    "\n",
    "# Zoom in on 2007-2010\n",
    "ax.set_xlim(['1/1/2007', '1/1/2011'])\n",
    "ax.set_ylim([600, 1800])\n",
    "\n",
    "ax.set_title('Important dates in 2008-2009 financial crisis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Polygon at 0xc0a2e10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OB11EhtBYjNNYjNNYjDtrqt+Y1tYKzrkngCcAzGyXc25dOl8/U2ksxmksxmks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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2a3128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)\n",
    "circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)\n",
    "pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],\n",
    "                   color='g', alpha=0.5)\n",
    "\n",
    "ax.add_patch(rect)\n",
    "ax.add_patch(circ)\n",
    "ax.add_patch(pgon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving plots to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OB11EhtBYjNNYjNNYjDtrqt+Y1tYKzrkngCcAzGyXc25dOl8/U2ksxmksxmks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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2a3128>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('figpath.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('figpath.png', dpi=400, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb396ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from io import BytesIO\n",
    "buffer = BytesIO()\n",
    "plt.savefig(buffer)\n",
    "plot_data = buffer.getvalue()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('figure', figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting functions in pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb39f1d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N+frRx2eqx9+nrwxM6//jhn3q8XNXLICRIYjZrCg7VfFxDga7ImRWb2/UkvvX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\nMREREZGQ/D8bYVKhgvAHKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc140f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))\n",
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc374630>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aETpOgQ3vweG50Gi0cbEkRMEfvSD8nHaouXw742LJSqr10rZSt03SbhZ6T3rs\nLSazyoErd1l7KoRNAWHEJqZSOE8uXqhXGp/a7tQrUyhdK1H96pdm1fEQpm48R7sqxSiaL3fm/zti\nb8PSwZCnqHbuyiFjjYZF2hy9Hsm6U6G82bY8JQs66zq2JFhC5CS3z0LEeeiSg+oHpVX9EXDRH7Z+\nCp4tjWl+nRgDi/pAWIB2gLtCe+vHkJU1e0ur9L7ve638SN2hqKrKyaAo/E6GsuHMLSJik8ib24EO\n1YrhU8udZuWL4GifsfteiqIwtVcNuvywly/Wn+XHgXUyF39qklalPj4SRmzRWgQJizGbVT5fd5bi\n+Z14tXU53ceXBEuInCTQFxQ7qNrD6Ehsj6KAz0z4uYlW5X3kduuuHiTFwuK+WvmMfr9DpU7WmzuD\nToSfIJ9jPsoXspFWS4qiHQiPDkJd/w4rLsHMG6W5GRlPLgc72lZyw6e2O20ru+HkaK/LlOXd8jKm\ndTl+2H6J3nVL0rqSW8YGUlVtBTXoEPT9LWdV6DfIquPBnAmJ5j/9a+GSS/90SBIsYZNUVcWkmjCr\nZkyqCZPZ9Mivzar5sdce/vX/X/v79Yd//eBZs2omVU195phm1Uyq+V/v+deYT3qPWTVTq2gtepTv\ngZ1iI9VQVFUrLurRAvJm8JtAdpevmFZfaekg2DUVvD+zzrxJcbD4BQg+Ci8sgMpdrTNvJvhd9mPi\n/omUK1gOXx/baEEbFBmvnam68wpfpl6i89kPOFF8Bm+0bUbH6sXJ7+RokXnHtinHutOhTFgTwJZ3\nWmbsm/XhOXDiD2jxfs6uT2clcUmpfON/gdqlC9KjVkmLzCEJlrCYFHMKXx36iqO3jz4xqXlW8mRW\nzUaH/1z2ij12it0/H+3s///vqqqy6tIq1lxew6Qmk/Aq6GV0uBB2WruR1vQNoyOxbZW7Qt2hsG8G\nlG8PHs0sO1/yffizPwQdhr7zba9UxBOsuLiCLw58QSGnQly6d4mL9y5SsVBFQ2KJiE1i45lb+J0M\n4fjNKADqlS1EQOt5VDw2lK8Sp0ClbWCh5Aogt4M9X/WqQf85B/lh26X0H5a+ugs2j4dKXaDNJxaJ\nUTzqvzsvExGbxJwh9fS/Afo3SbCERaSYUhi3Zxzbb26nValWuDi4YGenJSP/Tkwc7Byemqj8+z0P\nXnvSe/792v/nsrN77PWHX3NQHpr/STHa/es9f8fxrBtGqqrid8WPb49+S591fRhVYxQja4wkl72B\nB1YDfEFlHq/mAAAgAElEQVSx13qZiWfr+BVc2wurX4Ux+8CpgGXmSY7XqnTf3K/dZMwChV8Xn1vM\ntMPTaFGyBRObTKTTqk5svLqRivWsl2DFJKbgHxDG2lOh/HX5DmYVKhfPxwedKtG9pjulC//dQ67y\ncviti5bADtsIuR4vt6CXRl6u9K9fmnn7ruFT251q7mn8PRN5Tbv9WKQC9JotbXCs4ObdeObvvUbv\nOiWpU6aQxeZRVAO6kdevX189evSo1ecV1pFiSuG93e+xM2gnHzX8iMFVBhsdkmHuJtxl+tHpbLi6\nAc8CnkxsPJH6xetbPxBVhR9qgWt5GGIb2zk2L+gI/NoRaryglQDQW0qillxd3aV9Y63VX/85dLYg\nYAHfHfuOdmXaMb3ldBztHRmzbQxXo66yqc8mi26HJ6aY2HE+HL+TIey8EEFyqpkyhV3wqeWOT213\nKhZ7SrHPC5u1+lgVO2kXB+z0OXv1JFHxyXh/v5uSBZ3xHdsM++etjCTFwrz2EHsLRu/UanoJi3v1\nj2PsvhjBzvdbU7xA2mqmKYpyTFXVdP3lLamy0FWKKYV3d7/LzqCdfNzo4xydXAG4OrsyrcU0fvb+\nmWRTMsP8h/HZ/s+IToq2biChxyHqhpztSI/SDaDlODi9FAJW6Tt2ahIsG6wlVz1nZYnkavap2Xx3\n7Ds6eXRieistuQLo4tmF0PuhnAw/qfucKSYzuy6E8+7yk9Sfso2xi49z/GYUgxuVYfXYpuwe15r3\nO1Z6enIF2mWBTtPgwkbYMkH3GB9W0CUXn3aryqngaP44cP3ZbzabtRXSOxe0c3eSXFnFgSt32RwY\nxtjW5dKcXGWUbBEK3SSbknlv13vsCt7FJ40+YUDlAUaHZDOal2yOr48vP5/6mT/O/sGuoF181Ogj\nOpbt+MytRt0E+IKdY5Y4PG1TWr6vVXlf/w6UbgwFdDgMm5oEy4Zo4/r8BLUHZX5MC1JVlZ9O/MTc\nM3PxKefD5KaTsX9oFahdmXY42Tux8dpG6harm+n5zGaVYzfvsfbvsgqR95PJ5+RA1xol8KntTmMv\n1+evDP1bo1e0rbiDs6CQp0XrnPnUcmflsWCm+1+gQ7XiuD+tttLuaXB+vbYdXa6NxeIR/zCZVSav\nP0vJgs6Mamn5hFZWsIQukk3JvLPrHXYF7+LTxp9KcvUELo4uvFf/PZZ0XUKxPMUYt3scr+94nVtx\ntyw7sdkMgWu0gpXOljtvkC3ZO0LvOWBK1SqEmzN5+SI1GVYMg0v+0G2GdpjehqmqyndHv2Pumbn0\nqdCHL5p98UhyBdrv6zal2+B/3Z8Uc0qG5wkMjearTedo/vUOXvjlACuOBdG0nCtzhtTj6ARvvu5b\nk2bli6Q/uXqg45dQqSts/lDbNrQQRVH4smcNTKrKpLWBT37TWT/Y/TXUHgyNx1gsFvGoZUeCOHcr\nhvFdKutWpuNZJMESmZZkSuLtnW+zJ3gPE5tMpF+lfkaHZNOquFZhcZfFjKs/jiNhR+jh14NFZxdh\nMpssM2HwEYgJzhIHqG2SaznoNBWu7YZDP2d8HFMKrBoOFzZohV7rD9MvRgswq2amHprKwrMLGVh5\nIBObTHzqGauuXl2JSoriQOiBdM1x/c59ftx+Ce/vd9P1x33M33uNyiXyM6N/bY5NaM/MQXXpUK04\nuR10+GZoZw995kKJWrByGITqv6X5QBlXF972rsjWs7fZHBD26CfDzmhbg6UaQLf/SBscK4lJTOG7\nLRdo6FGYrjWs08VCDrmLTEkyJfHWzrf4K+QvPmvyGX0q9jE6pCwlNC6ULw5+wb6QfVRzrcZnTT+j\ncmGdK4hv+hCO/gbjLoNTfn3HzilUVauNdXkbjN4Fxaql73lTKqwaAWfXQKevofGrlohSN2bVzOQD\nk1l1aRUvVX2J9+q/98yt7BRTCm1WtKGZezO+bvn1M8e+HZPIulOhrDsVyqlg7SxiQ8/C9KjtTufq\nJSicx8I3bWNvw7x2WsI7ajsUKGWRaVJMZrr/tI+o+BS2vtuSfE6OcP8OzGkD5lTtULutNBbPAb7c\ncJZ5+66x7vXmVC+Z/lvBcshdWFViaiJv7niT/SH7mdx0siRXGeCe151Z7WYxveV0bt2/xYD1A/j+\n2PckpCboM8GD7cEK7SW5ygxF0c5LORWEVaO0G4BpZUqF1aO15KrjVJtPrlLNqUzYN4FVl1Yxuubo\n5yZXAI72jnQo24GdQTuJT4l/7PNR8cksOXyTgXMO0vir7UzZcA6TqvJJlyocGN+W5a80YXCjspZP\nrkArJjtoOaTEw+J+WnsiC3C0t2Nan5rcjk3kW/8LWkK3/CWIuw0DFklyZUXX7txnwf7rvFCvVIaS\nq4ySBEtkyIPk6kDoAT5v+jm9Ksj2U0YpikInz06s7bmWnuV78lvAb/Ty68X+kP2ZH/zmAYgLk+1B\nPeQpAj3+C+GBsOOLtD1jNoHfWO0WYvvJ0OQ1y8aYSSnmFMbvHc+6q+t4vfbrvFHnjTRfwujq1ZWE\n1AR2Bu0EID45Fb+TIYxceIQGX25jvO8Zbsck8la7Cux4rxXr32jBqJZelCigb4PdNClWFfot1G7w\nrXhZS34soHbpggxtXJbfD94gYsXbcGMf9JgJJetZZD7xZF9uOEtuB3ve71jJqvPKLUKRbgmpCby5\n400O3TrEF82+oEd56WunhwK5C/BZ08/o6tWVyQcm88q2V+jm1Y1xDcZR2KlwxgYN9AUHZ60GkMi8\nih20ptAHZkKFDuDV6unvNZvB73U4vQzaTdQaEduwZFMy43aPY0fQDt6r9x4vV385Xc/XcatDMZfi\n/H56Nf6H3Nl69jYJKSaK53diWDNPfGq5U809v3VuzaZFubbaGai1b8DG97VLBxaI7f2OlXA+/TtF\nzy/C1OQN7GvKGVVr2nMxgm3nwvmoc2Xc8lm2LMO/SYIl0iUhNYE3tr/B4bDDTGk+BZ9yUhVcbw2K\nN2Clz0rmnZnHvDPz2Buyl3H1x+FTzid935xMqdptpYodIXdeywWc03SYAtf2aLcKx/z15JuZZjOs\nexNO/am1PmnxnvXjTIckUxLv7HyHvSF7Gd9wPIOqpL10hMmscujaXdadCuX2rSqE5d/NheBr9K5b\nAZ9a7jTwKGyxViSZVneoVr5h3/da+Ybmb+s+Rb6ww3xgns9OUy0u5h7KK7rPIJ4m1WTmi/VnKevq\nwrBmHlafX7YIRZrFp8Tz+vbXOXL7CF82/1KSKwvKbZ+b12q/xsruK/Eq4MWEvyYwassobsbcTPsg\nN/bB/QgpLqq3XC5a6Ya427D+Xe0A/MPMZtjwjta4t9WH0OoDY+JMowd/rveF7GNik4npSq7+PHST\nptO2M2juIfxOhtKwSDsUxcwHfZL5slcNGnm52m5y9UDbT6Fab9g2CQJX6zt21E1YPhS7wp6s8ZrM\nf7Zf4ebdx8+oCctYfOgml8Lj+LhLFX1uoqaTJFgiTeJT4nlt+2scvX2UL5t/Sfdy3Y0OKUcoV7Ac\nCzot4NPGnxJ4N5Dea3sz78y8tNUbCvAFxzxaw2Khr5J1ofVH2hbsmRX/vK6qsGkcHFugrVq1Hm9Y\niGlxP+U+Y7eP5XDYYb5o9gUvVHwhzc+GxyTy2dpA3As6M3NQHY5NaM+8gT6UL1iezdc3WjBqndnZ\nQc+ftUKyvq9oTbf1kHxfu3lqSoaBS/iodyMc7Oz4ZM0ZjLi9n9NExSfzn20XaVrOlQ5VixkSgyRY\n4rniU+IZu30sx8OP81Xzr+jm1c3okHIUO8WOfpX64dfTj5alWvLD8R/ov74/pyJOPf0hUwqcWwuV\nOmsrLkJ/zd/VvilveF9bqVBV2PwRHJmnnbdq+6lN1ziKSY5h9NbRnAw/ybQW09J9lnLhgeukmM38\np19tutV0xzmXPYqi0NWrKyfCTxASF2KZwC3B0QkG/An53bX+kJHXMjeeqsKasRAWAH3mQ5EKlCjg\nzPsdKrL30h3WngrVJ27xVDO2XSImIYWJ3asadu5PEiwrSEg2MWT+Icb7njY6lHS7n3KfMdvGcDL8\nJF+3+JouXl2MDinHcnNx4/vW3/Njmx+JTopmyMYhTD00lbjkuMfffG03JNyT7UFLsrPXmkCrf/eU\n8/8EDv0CTV4H789tOrmKTopm1JZRnL17lu9afUdnz87pev5+UiqLDt6kY9XieBTJ88jnHoy16dom\n3eK1ijyuMHil9vVc/ALER2Z8rL3famU5vD/TLkb8bUgTD2qVLsjkdWeJik/OdMjiyS7djuWPgzcY\n2LAMlYsbV55GEiwLSzWZeWPJcfZeusOSw0FsP3fb6JDS7EFydSriFNNaTqOTp9xEswVtyrTBr4cf\nAysPZOn5pfTw68GOmzsefVPAasidH8p7GxNkTlHIAzp/DTf+goP/hUavaofgbTi5ikyMZLj/cC7f\nu8wPbX6gXdl26R5jxdEgohNSntjPrWTektRxq8OGqxv0CNe6ipTXVrKibmj9IlOT0j/G+Y2wYwrU\n6PfYzVF7O4WvetUgKiGFrzae1ylo8TBVVfliwzlcctnzbvuKhsYiCZYFqarKxLWBbDsXzoSuVajg\nlpeJfoHEJ6caHdpzxSXH8erWVzkdcZpvWn5DJw9JrmxJ3lx5Gd9oPIu6LKJA7gK8tfMt3tn5DuHx\n4Vq/u/PrtMbODrmNDjX7qz1IW7VqOQ46TbPp5CoiPoLhm4dzM+YmP7X7iZalWqZ7jFSTmXn7rlG/\nbCHqlX1yb8sunl24HHWZi/cuZjZk6yvbFHrM0i6JrH3z8UsMzxJ+DnxHgXsd8Pnxib8XqrrnZ2Rz\nT5YdDeLQ1bs6Bi4Adl4IZ8/FCN5qVwHXvMb+/ScJlgXN2nWFPw/d5NVW5RjZwoupvWsQEpXAjG2X\njA7tmeKS43h126sE3AlgeqvpdPDo8PyHhCFqFq3Jsm7LeKvuW+wN2UuPNT1Y9tcUzInR2s0oYXmK\nojUSbjvBppOrsPthDPMfRuj9UGZ5z6Kpe9MMjbM5MIzgewlPXL16oINHB+wV+6y5igVQ8wVoMwFO\nL4Vd09L2THwkLBkIji7QfzE4Pr2A6lveFShVyJnxq8+QlGqhHqQ5UHKqmSnrz+FVJA9Dm3gYHY4k\nWJay6lgw0/0v0LO2Ox/8XT22gUdhBjYszfx91wgMjTY4wieLTY7llW2vEHgnkG9bfUv7snIDzdY5\n2jkyssZIfH18qVakGlOur+alku5cLlza6NCEjQiODeblzS9zN+Euc9rPoUHxBhkaR1VV5u65imeR\nPHhXefrNrMJOhWnq3pRN1zZhVs0ZDdtYLd+H2oNh9zQ4ueTZ7zWlag2kY0JgwGIoUPKZb3fJ5cCU\nntW5GnGfWTuv6Bh0zvb7getcvXOfCd2qkMvB+PTG+Aiyob2XIvhw1WmalnPlm761HqkD81GnKhRy\nceRj3zOYzLZ1VTcmOYZXtr6iHXxt/V2GzmYI45TJX4a5rX/iy8j7XM/txAubBvHTiZ9IMmXgHInI\nNm7E3GCY/zBik2OZ12Eetd1qZ3isw9ciORUczYjmntg/p75VF68u3Lp/i5PhJzM8n6EURavu7tlS\nq/Z+be/T37tlAlzdpVWGL90wTcO3ruSGTy13ft51hcvhT7ioItLlblwSP2y/RKuKRWlTyc3ocABJ\nsHQXGBrNmEXHKe+Wl1+G1Hssiy7g4sin3apyKjiaRQdvGBTl42KSY3hlyyucizzH962+p22ZtkaH\nJDJAubIdn+i7+DX4jM4enZlzeg591/blSNgRo0MTBrgadZVhm4eRlJrE/I7zqVakWqbGm7PnKoXz\n5KJvvVLPfW/b0m1xdnDOutuEAA65oN8fUNgLlg2GiCecKTuxCA79DI3GQJ0X0zX8p92q4uRox8er\nz2C2sR+4s5rvtl4kPtnEp92q2Ew7JkmwdBR8L56XfztCPicHfhvWgPxOjk98n08td1pUKMJ0/wuE\nRSdaOcrHRSdFM3rLaC7cu8CM1jNoU6aN0SGJjAr0BRdXClfsytQWU5ndfjap5lSG+w9n0v5JRCfZ\n5ta00N+FyAsM8x+GWTXza8dfqVy4cqbGuxwey/bz4QxtUhYnx+dXxXZxdKF16db43/AnxULNlK3C\nuSAMXgH2uWBxX4iL+OdzQYdh/Tvg1Vq7PZpORfPl5uMuVTh8LZIVx4J0CzmnOXcrhqWHbzKkcVnK\nu+UzOpz/kwRLJ1Hxybz82xESU0wsHN7wmR3iFUVhSs/qpJjMfL4u0IpRPu5BPZyL9y4yo80MWpV+\nRvNaYduS4+HCZqjiA/Zam9Gm7k3x7eHL8OrD8bvsh88aHzZd2ySVpLO5s3fPMmLLCBzsHFjQaQHl\nC5XP9Jjz9l4jt4MdQxqXTfMzXT27Ep0Uzf7Q/Zme31CFysLAZRAXDksHQkoCxITCshe14qR9f/v/\nn7n06le/NA09CjN143nuxMl2fnqpqsrkdWfJ7+zI294VjA7nEZJg6SAxxcTo349x8248c4bUp2Kx\n52fQZV3z8Ga7CmwKCGPbWWNqYz1Iri5HXWZGmxkZurItbMglf0i5/1hxUWcHZ96p9w7Lui3DPY87\nH+z5gLHbx2atStsizU5HnGak/0jyOORhQacFeBTwyPSY4bGJ+B4PoW+9Uum6+t60ZFMK5i7IhmtZ\neJvwgVL1oM9cCD6qlWJYOkhrhzNwKbgUzvCwdnYKU3tXJz45lS/Wn9Ux4JzBP/A2B67e5d32FSno\nksvocB4hCVYmmc0q7y4/yeHrkXzbrxZNyrmm+dlRLbyoWCwvk9YGcj/JurWxohKjGLllJFeirvBj\n2x8lucoOAnwhjxuUbfbET1cqXIlFXRbxYYMPOXb7GL38erEwcCGpZtuvyybS5tjtY4zaMoqCTgVZ\n0GkBpfPpc5P09/03SDGbGdni6aUZnsTRzpEOZTuwK2gX8SnZoMlxle7aVuC5dRB6Qmv67VYl08OW\nd8vHmNbl8TsZyu6LEc9/QACQlGpi6sZzVCyWl0ENyxgdzmMkwcoErWLsWTaeCeOTLlXwqeWerudz\nOdjx1d+1sf6z1XoF+e4l3mPklpFcjbrKj21/pHnJ5labW1hIUixc2gJVe2gtXJ7C3s6eF6u+iF8P\nPxoWb8i3R79l0IZBnL0rPzlndQdvHWTMtjEUy1OMBZ0WUCJvCV3GjU9O5Y+DN+hQtRie/2qLkxZd\nvbqSkJrAjqAdz39zVtDkNWg/GXxmasV8dTK2dTm8iuZhwpozJCRLbay0+HXfdW5GxvNpt6o42Nte\nOmN7EWUh8/dd47e/rjOsmQcjW3hmaIx6ZQszqFEZfv3rGgEhlj+AHJkYyYgtI7gec52f2v5Es5JP\nXu0QWcyFzZCamObegyXyluCntj/xbatvCY8PZ+CGgXx75NvsscqQA+0N3str216jZN6S/NrxV9xc\n9LumvuJoMNEJKYx+RmHRZ6ntVpsSeUqw8epG3WIylKJoLXDqDtF1WCdHe6b2qkFQZAIztmfBCvhW\nFh6byMwdl/Cu4kaLCkWNDueJJMHKoHWnQpmy4RydqxdnQtfMdev+sGNlCufJzcerLVsb627CXUb4\nj9DaZLT9iaYlM1bJWdigwNWQzx1KN07zI4qi0NGjI349/ehdoTcLzy6kl18v9oXss2CgQm87bu7g\nrZ1vUa5gOX7t+CtFnIvoNrbJrDJv31XqlilIvbIZO2dkp9jR2bMz+0P3E5mYiQbKOUBjL1f61S/F\nvL3XOBsaY3Q4Nu1b/wskm8x80rWq0aE8lSRYGXDgyl3eW36KBh6F+E//2s8tuPc8BVwcmdi9KqeD\no/njwHVdYvy3uwl3GbllJMGxwcxsN5Mm7k0sMo8wQGI0XN4K1XqCXfr/SBfIXYBJTSaxoNMCcjvk\nZsy2MXyw5wPuJNyxQLBCT/7X/Xlv13tULlyZuR3mUsjpyb0BM2pzQBhBkQmMblkuU+N09eqKSTWx\n5foWnSLLvj7uUoWCzo6Mt/AP3FlZQEg0K44F83JTjwxtW1uLJFjpdPF2LKP/OEoZVxfmDq2fpnow\nadG9ZglaVizKdP8L3IpO0GXMB+4k3GGE/wiCY4P5b7v/0rhE2lc5RBZwfiOYkjPde7BesXqs7L6S\nsbXGsu3GNnqs6cHqS6ulpIONWndlHR/s+YAaRWswp/0cCuQuoOv4qqoyZ88VPFxdaF/16W1x0qJi\noYqUL1iejdeyyTahBRV0yaUVow6Ksqli1LZCVVU+XxdIYZdcvNHOtsoy/JskWOkQFp3IS78exsnR\nngXDGuh6JVRRFKb0qI5JVflsrX61sR4kVw8avDYskbY2DiILCfSFAqWhVP1MD5XLPhdjao9hZfeV\nlC9Ynon7J2pn9qKvZz5OoZvVl1bzyb5PqF+sPr94/0LeXHl1n+PI9XtaW5wWXplepQdtFetE+Akp\nD5IGPWrbVjFqW7L+9C2OXL/H+x0rPbWYt62QBCuNYhJTePm3w8QkpLBgWANKFXLRfY4yri681a4i\n/oG32RIYlunxIuIjGO4/nFv3bzGr3awMN3gVNiw+Eq7s0LYHdWwP4VXQi986/cakJpM4f/c8fdb2\nYfap2Vm7Inc2sfT8Uibun0jTkk35b7v/4uKo/99F8FBbnLrPb4uTFp09OwOw6domXcbLzh4uRj1p\nbYDR4diMxBQT0zadp0qJ/PSrb/vN7CXBSoPkVDOv/nGMy+Fx/DKkHtXc9V2Kf9jIFp5UKpaPSWsD\nictEbawHyVXY/TB+9v6Z+sUzv7ohbND59WBOzfT24JPYKXb0rdgXv55+tCnThpknZ9Jvfb+s27w3\nG/g98He+PPQlrUu15sc2P+Lk4GSReS6Hx7Ht3G2GNC6Lcy59jkGUzFuSOm512HB1g2w7p0FZ1zy8\n7a39wO2vww/c2cGcPVcJiUpgUvequqyqWpokWM9hNqt8sPIU+6/c5es+NS1+HdTR3o6pvWtwKzox\nw7WxwuPDGe4/nPD4cH7x/oV6xerpHKWwGYGroZAHuNex2BRFXYrybatvmdl2JnEpcQzdNJQpB6cQ\nmxxrsTnF4+admcf0o9NpX7Y937f+nlz2lqtaPX/fVa0tTpO0t8VJi66eXbkcdZmL96QMQVqMbOFJ\n5eL5mOQXSGxizl49DotO5OddV+hcvTiNvdJe0NtIkmA9xzf+F1hzMpT3O1SkTxo6yOuhXtlCDG5U\nht8yUBvr9v3bDPcfTkRCBLPbz6ZusboWilIY7v4duLpbW72yQvf4VqVb4dfDj8FVBrPi4gp6runJ\n9hvbLT5vTqeqKv89+V9+OP4DXTy78E3Lb3C0t9zZk4jYJFYdD6FPvVIUSUdbnLTo4NEBB8VBDrun\nkaO9Voz6dmwi323J2Unp15vPY1JVPu6S+cr51iIJ1jP8fuA6v+y+wqBGZXitTeabpabHB5202ljj\nfc+QajKn6Zmw+2EM9x/OnYQ7/OL9C7Xdals4SmGoc2tBNaW5uKgeXBxd+LDhhyzusphCToV4e9fb\nvLXjLcLuyxaGJaiqyozjM/jl1C/0LN+Tqc2n4mCXsabCafXHgeukmMyMbJ6x4snPUsipEE1LNmXj\ntY2Y1bT9vZbT1SlTiCGNy7LwwHVOBkUZHY4hjt+8x+oTIYxs7knpwpY5c2gJkmA9hX9gGJPWBuJd\nxY3JPtUyVUg0Iwo4OzKpe1XOhETz+4HnX9V9kFxFJkYyu/1sSa5yggBfcK0AxapbferqRaqzpNsS\n3q33LvtD99PTrydLzi/BZJYWH3pRVZVvjnzDrwG/0q9iPz5v+jn2z2iDpIf45FR+P3iD9lWK4VVU\n/5uJAF08uxB2P4wT4ScsMn52NK5jJdzyaT9wp6TxB+7swmxWmbzuLEXz5WaslRc6MksSrCc4diOS\nN5ecoGapgvw4sI5hPY661SxB60pF+W7LBUKjnl4b61bcLYZtHsa9xHvMbj+bWkVrWTFKYYjY23Dj\nL6jWyyrbg0/iaOfIsOrD8O3hS80iNZl6aCpDNw/l2O1jsjqRSWbVzJSDU1h0bhEvVnmRCY0nYKdY\n/u+hlceCiYrPeFuctGhTug3ODs5suLrBYnNkN/mcHPncpxrnbsXw675rRodjVX6nQjgZFMUHHSuR\nN7dlV2/1JgnWv1yJiGPEwqOUKODEry/VxyWXcV9QRVH44jm1sULjQhnmP4zopGjmtJ9DzaI1rRyl\nMMRZP1DNVt0efJrS+Uozu/1svmrxFUExQby8+WW8V3gz5eAUDt06RKo547dhcyKT2cTEvyay/OJy\nRlQfwQcNPrDKCrrJrDJv7zXqlClIvbL6VoR/mIujC21Kt2HLjS1S9iMdOlYrjneVYvxn20WCInNG\nz9D45FS+3nSBmqUK0EenciHWJAnWQ8JjE3n5t8PYKwoLhzfEVecDnhlRurALb3tXZMvZx6/qhsSF\nMNx/ODHJMcztMJcaRWsYFKWwusDVULQKuNnGgU9FUejm1Y2NvTcyrcU0ahWthd9lP0ZuGUmb5W2Y\n+NdE9gTvIdmUbHSoNi3VnMr4fePxu+LH2FpjeavuW1Y7nrAlMIybkfGMbuFl8Tm7enUlOima/aH7\nLTpPdqIoCpN7VMNeUfhkTUCOKHXxy64rhMUkMql7VeyyQFmGf8ta620WdD8plRELjnInNpkloxtT\n1tV2+huNaO7JmhMhfLY2kGbli5A3twPBscGM8B9BXEocczvMpZprNaPDFNYSEwo3D0Cbj42O5DF5\nc+Wlq1dXunp1JSE1gb9C/mLrja1svbGV1ZdXk8cxDy1LtaR92fY0c29msSKZWVGKKYUP937I1htb\neavuW4ysMdJqc6uqyuw9Vynr6kKHasUtPl8T9yYUzF2QDVc30Kp0K4vPl124F3Tm/Y6V+HzdWdae\nCqVH7ZJGh2Qxwffimb3nKj613DPcaNxokmABKSYzYxcfJzA0mrlD61O7dEGjQ3qEo70dX/aqQd9f\n9vPdlgsMb52fEf4juJ9yn7kd5lLV1Xa7iQsLCFwDqBYpLqonZwdnvMt6413Wm2RTMgdvHWT7ze3s\nuDKcZv8AACAASURBVLmDTdc24WTvRLOSzfAu602rUq3Ilyuf0SEbJsmUxHu73mN38G4+aPABQ6oO\nser8R2/c42RQFF/0qGaVAo6Odo509OiI32U/4lPiJdFOh6FNPFhzIoQv1p+lVcWiurZssyVfbTqP\nosBHnSsbHUqG5fgtQlVV+WT1GXZfjGBKzxq0q5K5pqaW8qA21u9HjjFkw8vEp8Yzr8M8Sa5yokBf\nKF4DimSdGzW57HPRslRLPm/6OTv77WR+h/n0LN+TMxFnGL93PC2XtWTMtjGsuriKyMRIo8O1qoTU\nBN7c8Sa7g3czodEEqydXoFXILuTiSN961ms/0sWzC4mmRLbflFpq6WFvpzC1dw3uxacwbdN5o8Ox\niMPXItlw+havtCyHe0Fno8PJsByfYM3YdonlR4N5o215BjUqY3Q4zzSoeR5cys7lXsJ9ZnvPpYqr\nbZy/EVYUdROCj2i3B7MoBzsHGpZoyCeNP2HrC1v5o/MfvFjlRa5FX+OzA5/RZnkbhvsP589zf3L7\n/m2jw7Wo+JR4Xt/+OgdCDzC56WT6V+5v9RiuROjfFictarvVxj2PuxQdzYBq7gUY0dyTpUeCOHwt\ne/1AYjarTF4fSIkCTrzaqpzR4WRKjk6wlh25yQ/bL9G3XinebV/R6HCe6WbMTd7YOYo8Tiqx10dy\n8LxlepAJGxe4Wvto49uDaWWn2FHbrTbv1X+PTb03saL7CkbWGElkQiRfHf4K75XeDN44mN8CfiMo\nNsjocHUVmxzLq9te5ejto0xtMZVeFYxJmuftvYajvR1Dm3pYdV47xY7Onp05EHogx61a6uFt7wqU\nLOjMeN/TJKVmn/pzK48FExASw0edK1s14beEHJtg7TwfzserA2hZsShf9a5h9UKi6XEj5gbDNg8j\nxZTCws7zaeVR87m1sUQ2FeCr9R0srH+VbaMpikLlwpV5o84brOm5Br+efrxZ501STCl8f+x7uvh2\noe/avvxy6hcu37ucpW9RRSdFM3rLaM5EnGF6y+l08+pmSBx34pJYdTyYPnX1b4uTFl29umJSTfhf\n97f63FmdSy4HpvSqzpWI+/yy66rR4egiNjGFb/wvULdMQXxquRsdTqblyATrdHAUYxcfp3LxfMwa\nXBdHgwqJpsW16GsM2zyMVDWV+R3nU6lwJSb3qI5ZVZnoF5ilv8mIdIq8CrdOZpvVq+fxKuDFqJqj\nWN59OZv7bOb9+u/j4ujCrJOz6LW2Fz5rfPjh+A8E3s1afw7uJd5j5JaRXLh3gf+xd9/hUZVpH8e/\nJ703CAlpJCEBktB77zUBKYqKiDRBXdTdVdd3LWtf3XWtq1iQKqIoCiKEKh2kF0mjhFQIpEJ6nTnv\nH1FXFCSQmZwp9+e6csGSzJkfK8nc8zzPue+3h7zNyNCRmmX5dH9m/VicAdoU7JHekUR6R7IhTbYJ\nb8WQti0Y1ymA+TtSSc0r0zpOo83fcY6CsmqeH9f001OMwXQrCyPJKqxg1tLD+Lg6sGRGD5PuDJtW\nnMbszbPRqToWjVxEpHckUN8b66/D2/B9Si6bkyz7jIr4lV+2B833/NWtCnQLZHrMdD4d8ynbJm/j\n2V7P4ufqx5LEJdy9/m5GfzOa1w+/zrHcYybdRb6gsoBZm2eRXpzOf4f+l8HBgzXLUlmjY/n+DIZH\n+dHaSGNxGiI2LJYT+Sc4X3peswzm7Lmx0TjZ2/DMmgSzeqPxW5mF5Szem86kroF0MrE7+W+VVRVY\nReU1TF9yiDq9yrJZPWnhYbrnmNKu1BdXelXP4lGLifC++o6xWf3DaOfvzgvfJVFaJd2QrULiGgjq\nCV5Nd6eXKfJ18eWudnexcORCdt65k5f6vkSkdyQrT61k+qbpDFs1jJf3v8z+nP3U6k3neyO3PJeZ\nm2ZyoewC84fNp39gf03zfH00m8tGHovTELFhsQBsTN+oaQ5z5evuyFOxURxML2LVEfMtUl/dkIKd\nrcL/jTbftgy/ZTUFVmWNjtnLDnPhSiUL7+tORAvt3rHdyLkr55i1eRaqqrJ41GJae/3+Tgp7Wxte\nm9SB3NIq3txyRoOUokkVnIXcBKtcvfojXk5eTIycyPvD3mf3Xbt5feDrdGnRhXVp65i7dS5DvhrC\ns3ufZWf2Tqp11ZrlzCnLYcamGeRX5vPR8I/o1bKXZlngp7E4e9PpHOxFdyOOxWmIALcAurboSnxa\nvFmvwGjpru7B9Aj15p8bUigo0+7f+a36IbWAzUm5zBsSgZ8JL3zcLKsosHR6lUdXHudE9hX+e3dn\nuoeablfY1MupzNo8C0VRWDx6MeFe13932SXEm2m9W7FsfwY/Zl9pupCi6SWuBhSImaB1EpPl5uDG\nmLAxvDX4LXbdtYt3hrzDgMABbM/aziPbH2HgyoE8uetJNmdspqK26Wa5/Tyf8ed5oV39ujbZc1/P\n1uRLZBZWMHeg8cfiNERceBznis9x5rK8WbwVNjYKr03qQEVNHa+sT9Y6zk2p0+l5aX0yQd7OzO5v\nWTfvWHyBpf40KHlrci7Pj41mdPuWWke6rrOXzzJ7y2xsFVsWj1pMuOeNl+6fGNUWXzdHnlqdQJ3O\ndM+eiEZKWg0hfcDD/O+saQrOds4MCxnGawNeY9ddu/hw+IeMCRvDwUsHeWLXEwz8ciCPbn+UdefW\nUVxdbLQc6cXpzNj0U2PgUQtNZhj7gt1phPi4MKoJxuI0xIhWI7BT7IhPj9c6itmKaOHOQ4Na8+2J\nHPaczdc6ToOtPJzNqUulPB0bhZO9ebdl+C2LL7A+3HWO5QcyeWBgODP6mW51fObyGWZvno2dYsfi\nUYsJ82xYVg8ne164LYbkiyUs/SHDuCGFNvJSIP8UtLeOuwcNzd7Wnv6B/Xmh7wtsn7ydxaMWc3vk\n7SQVJvH03qcZ/OVgHtj6AKvOrKKwstBgz3v28tlf7gBePGqxyUxdOJJRxLGsK9w/IKxJxuI0hLeT\nN30D+7IxfaNJ36Rg6v40JILw5q48syaRyhrT741VXFnLW1vP0DPMhzHtTaPYNySLLrDWHD/P65tO\nM65TgEkfnDtddJrZm2djb2vP4tGLCfUMvanHj2nvz9B2LXhzyxnOX266rQ/RRBJXg2ID0eO1TmL2\nbG1s6eHfg6d6PcXWO7ayInYF02KmkV2azUv7X2LoqqHM2DSDFSkruFR+6ZafJ6UwhVmbZ2Gj2LBk\n1BLaeJtOI+MFu9PwcrHnjm5BWke5SlxYHJfKL3Es95jWUcyWk70t/5zYgayiCt7ddlbrODf0321n\nuVxRw3Njo01iq9rQDFJgKYqyWFGUPEVREg1xPUPYl1rAk1+fpE94M96Y3BEbE3mn9luni05z/5b7\ncbR1ZMmoJbTyaHXT11AUhZfGxwDwvPTGsiyqWr89GNof3Fponcai2Cg2dPTtyGPdHiN+Yjxfj/ua\nuR3nUlxdzL8O/YsRX4/gnvh7WJSwiKySrAZfN7EgkdlbZuNk58TS0Uv/8BxlU0vLL2PrT2NxXBxM\nq0XN4ODBONs5y+icRurTuhmTuwXxyZ40Ui6WaB3nus7ll7Hshwzu6h5M+0BPreMYhaFWsJYCow10\nrUZLzinhgeVHCW/uxkfTuuFoZ5r7uqeKTv3yg3jJqCWEeNz6LMQgbxceG9GGbafy2Jx06++8RePp\n9Sof7TrHuh9zGn+xSwlQmCp3DxqZoii09WnLvM7zWDN+DesmrOPPXf+MXtXzzrF3iFsTx6TvJvHh\niQ85c/nMdd/EHM87zv1b7sfDwYOlo5c26nvaGBbt/WksTp9QraP8jou9C0NDhrIlcwu1OtNpr2GO\nno6NwtPZnqdWJ6DTm+Yb7n/Gp+Bkb8vjI9tqHcVoDFJgqaq6GzCJYVIXrlQyc+kh3BztWDKzB57O\n9lpHuqbkwmRmb56Ni50Li0ctJtij8b2NZvYLJaqlB89LbyzN1On0/O3rk/xr4ymeXp1AcWUj/zsk\nrQbFFqJke7AphXqGcn+H+1k5diVbbt/Ckz2exN3enQ9//JDbv7udcd+O4+2jb5NYkPhLsXX40mEe\n2PoAzZ2bs3T0UgLdAjX+W1ytoKyar4+e5/augfi6N/1YnIaIDYuluLqYfTn7tI5i1rxdHfjH2ChO\nZF9hxcFMreP8zq4z+Ww/lccjQyNM9t+iIVjUGaziilpmLD5ERbWOpbN6EODlrHWka0oqTGLOljm4\n2bvVF1fuhmkcafdTb6y80mre2HzaINcUDVdVq+NPK47xzbHz3Nk9iNLqOpbvz7j1C6pq/fmr8EHg\n2sxQMcVNaunWkmnR01g2Zhnb79zOP3r/gwDXAD5N+pQp8VMY+c1IXvjhBR76/iECXANYMmoJ/q6m\nd2B3+f5Mquv0zO5vOluWv9UnoA/ejt7Ep8ndhI01oXMgAyKb8/qm01wqrtI6zi9qdXpeXp9Mq2Yu\nzOgXqnUco2qyAktRlLmKohxRFOVIfr7hbyGtqtUxZ/kRMgrL+fi+brTz9zD4cxhCUkF9ceXu4M7i\n0YsJcjfsQdPOwV7c17sVnx7I5IT0xmoyZdV1zFp6mC3Jubx4Wwyv39GJIW19WbQ3nYqaulu7aM4x\nuJJpNbMHzUFz5+bc2fZOFoxcwM67dvJKv1do59OOdefWEe4ZzuLRi/F18dU65u9U1uhYfiCT4VEt\nTLrJsr2NPSNDR7IzeyflteVaxzFriqLwyoT21Or0vPBdktZxfrHiQCapeWU8Extlssd3DKXJCixV\nVReoqtpdVdXuvr6G/QGk16s8vupHDqUX8cbkTvRt3dyg1zeUxIJE5myZg4eDB4tHLTbaFsITo9rS\nwl16YzWVy+U1TF14kIPpRbx9Vyem9w0F4OGhEVyuqOXzgw0/IH2VpDVgYw9RYw0XVhiMp6Mn4yPG\n897Q99h/z36+HPslPk6m2cT4m2PnKSqvYe7A30+FMDVx4XFU6arYnrVd6yhmr1UzV/48PJJNSZfY\nYgJncy+X1/D292fpH9GcEdF+WscxOovYInx1QwrxJy/y1Jh2jO9sWucedHodl8ovsT1rO3O2zMHT\n0ZMlo5YQ4Ga8hpHuTva8eFsMKRdLWLIvw2jPIyC3pIq7Fuwn5WIJH93bjYld/rci2a2VD33Cm/HJ\nnjSqam+yJ42qQtK30HooOGs7ykTcmIOtg8neZq7Tqyzck0anYC96hJr+v6XOvp0JdAuUpqMGMmdA\nOO383Xn+uyTKqm9xNd1A3v7+DKVVtfzDQtsy/JZB7tNVFOULYDDQXFGU88DzqqouMsS1b2TR3nQW\n7k1nRt9QTYaWqqpKYVUhF8oucKH0Qv2vv/q4WH6ROn39P+pg92AWj1rcJOczRsX4MzyqBW9tPcOY\nDv4EebsY/TmtTWZhOfcuOkhRWQ1LZ/a45srpw0MjmLrwIF8fPc+9vW+iBcf5w1CcDUOfNWBiYY22\nJueSUVjB/FHtzOJFTVEUxoSNYUniEgorC2nmLOcPG8Pe1oZXJ3Xg9g9/4I3Np3nhthhNcpzJLWXF\nwSym9mpFW393TTI0NYMUWKqqTjHEdW5W/MmLvBKfzKgYP6NVxKqqUlxdzIXy3xdQOWU55JTlUKW7\n+gChj5MPQW5BxDSLYWSrkQS6BxLoGkhH3464OTTN+QdFUXhxfHtGvLWL59YmsWh6d7P44WouTl0q\nYdqiQ9Tp9Hwxtzcdg7yu+XV9Wzejc7AXH+06x109grG3beCiceJqsHWAtmMMmFpYo0/2pBHs48yo\nGPPZkokNi2VhwkI2Z2zmnqh7tI5j9rqGeHNvr/q5tRO7BNIp+No/r4xFVVVeXp+Mq4Mtfx1hOk13\njc20Os3dhINphfz1yxN0C/Hm3bu7NGrkQ3ltOedLz/9SNF0ou8D5svO//P63hy09HDwIdAsk3DOc\nAYEDCHALIMg9iEC3QFq6tsTF3jRWiwK9nHlsRBteiU9hY+IlYjuY7hxGc3Is6zIzlxzG2d6Wzx/o\nQ6Tf9d+NKYrCw0MiuP/TI3x3IofbG9I9W6+H5G8hYgQ4WWYDPtE0jmYWcTTzMi/eFoNdQ4t7ExDp\nHUkb7zZsSN8gBZaB/G10WzYnXeKp1Ql893C/Jv33sC0ljz1nC3hubDQ+rg5N9rxaM8sC62xuKXM+\nPUKQjzOf3Nf9hgMiq+qqyCnLuapo+vXHb4e9Ots5E+gWSJBbEN39uhPoFli/CuVW/+HuYD7LmzP6\nhrL62AVe+C6J/pHN8XAyzb5g5mLv2QLmLj9CC3dHls/uRbDPjYvpYVEtaOfvzgc7U5nYJfDGUwWy\n9kPpRZk9KBptwe40PJ3tmdzdtMbiNERsWCzvHHuH7NJsg7WysWYeP53NfWjFMRbvS2+yGx5q6vT8\nc0MKrX1dmdbn5ieVmDOzK7ByS6qYseQwjva2LJvZE29XB2p1tVwsv/i7wunnc1GFVVcPcHWwcSDA\nLYBAt0DaN2tPoHtg/SqUW/0qlJejl8Vsp/3cG2viB/t4Y/NpXhrfXutIZmtT4kUe/eIE4b6ufDq7\nJy3cnRr0OEVReHhoBA9/fpxNSQ1YSUxaA3bO0MZkhiMIM5ReUM6W5FzmDY4wubE4DfFzgbUxfSNz\nO87VOo5FGN2+/mzu21vPMqZ9ywa9QWysZT9kkF5QzpKZPRp+RMJCmMV3nU6vI7cil9SiLJ6N302x\n40UGxtjx7MHl5JTnkFeRd9UEdlvFFn9Xf4LcghgUPIgA1wAC3etXpALcAmju3BwbxXr+Q3cK9uK+\nPqG/7L93CTH9O4lMzVdHsvn7NyfpEuLN4uk98HS5uZXAMe1bEu57hve2pzKmvf/1C3i9DpLXQpuR\n4Gi6/YqE6Vu0Nw17Gxvu62ueqwYt3VrStUVX4tPimdNhjsW86dVS/dza+rO5z36byNKZPYz6/2tB\nWTX/3XaWwW19GdLW+mapmkSBpVf1FFYW/u7s08+Hyi+VX6JO/en2UhewcVE4U9yCQLdAevj1uGr7\nLtAtkBYuLbCzMYm/msl4fGQbNiXW77+ve6S/1b2TaIyFe9J4JT6FAZHN+Xhat1taDbC1UXhoUGv+\n9vVJdpzOY2i76xw4ztgL5XnSXFQ0SmFZNauOnGdS18AGr7SaorjwOF4+8DKnL5+mnU87reNYhAAv\nZx4f2ZaX1iez7uRFbutkvJZBb245Q2Wtjmfjoo32HKZMkyqkoLKAl/e//MudeTllOdToa676mmZO\nzQh0C6RD8w6MCh3FD6f0HE2z4cmhfZjZuwsOttZzUM4Q3J3seeG2GB787CiL96bzwCDTbzioNVVV\neXvrGf67PZW4Di15665Ojeo8PKFLIO98f5b3t6cypG2La79zTFoN9q4QObIRyYW1W36gfizO/QPC\ntI7SKCNbjeS1g6+xIW2DFFgGNL1vKN+euMBL65IYFOl70yvyDZGcU8KXh7OY0TfMpKcHGJMmBVZu\nRS6bMjYR6BZIpHckg4IGXbUKFeAWgLPd/+YI/mfzKQ4lnOOxEW14oF+kFpEtwqgYP4ZH+fH292eI\n7dA0++/mSq9XeXFdEsv2Z3JX92BendShUXeqQn0/mgcHhfOPtUnsTyv8fd8sXS0kfwdtR4OD/LcR\nt6aqVsen+zMZ1q4FES3M54aca/Fy8qJfYD82pG/gL93+YlVHO4zJ1kbh1YkdGD9/H//alMJrkzoa\n9PqqqvLS+iQ8ne358zDrfc3WpMCK8oli35SGTUv/7EAm83ecY0rPYB4ZGmHkZJatvjdWzE+9sRJZ\nPMO4++/mqlan58mvT7Lm+AXmDgznqTGGa9A4uXsw/92eyvwdqb8vsNJ3QWWRbA+KRvl5LM4cDRov\nG0NsWCy7zu/iaO5Revj30DqOxWgf6MmsfqF8siediV2C6BlmuDFPmxIvcSCtiJcntDfK6pi50OTt\nQEPfhWxJusRzaxMZ2q4FL49vL8WAAfzcG2vH6Xw2JGg/m8rUVNXqeOizo6w5foG/jWpr0OIKwMne\nlrkDwtmXWsixrMtXfzJxDTh6QMRwgz2fsC56vcrCPel0CvKklwFfMLU0OHgwznbObEjfoHUUi/PX\nEW0I9HLm6TUJVNfd5Div66iq1fHPDSm09XNnSg/rbq9hsuutx7Iu8+jK43QI9OT9e7qYVZM8Uzej\nbyjtAz14YV0SJVW1WscxGaVVtUxffIhtp/J4eUJ75g2JMEpRf0+vELxc7Jm/PfV/f1hXA6fWQdtY\nsDffQ8lCW1tTckkvKGfOwHCLeUPqYu/C0JChbMnYQq1Ofl4ZkouDHa9MaE9qXhkf70ozyDUX7U3n\n/OVKnhsXbfWv2yb5t08vKOf+ZUfw83Bi0YweZtnDxZTZ2drw2sSOFJZV8/qmU1rHMQlF5TVMXXiQ\no5mXeeeuzky7mbmBN8nV0Y5Z/cLYdiqP5JyS+j9M2wFVxdJcVDTKJ7vTCPJ2ZnSM8eedNqW4sDhK\nakrYe2Gv1lEszpB2LRjbsSXvb0/lXH5Zo66VV1LF/B2pjIj2o1/E72ezWhuTK7DyS6uZvvgQAMtm\n9qS5m6PGiSxThyBPpvcNZcXBLI5mXr7xAyzYxeJK7vx4P6cvlbLgvm6M7xxo9Oec3icUN0c75u/8\naRUrcXX9WJzwIUZ/bmGZjmZe5kjmZWb3D7O4lYPeAb3xdvSWbUIjeW5cNE72NjyzJgFVVW/5Oq9v\nPk2tTs8zsVEGTGe+TOq7sKKmjtnLDpNXWsWi6d0Jbe6qdSSL9vjItvi5O/HMmgRqdfobP8ACpReU\nc8eH+8ktruLTWT2v35/KwDxd7JnWpxUbEi6SdrEATsVDu3FgJ+1HxK1ZuKd+LM6d3S3v3Iu9jT0j\nQ0eyM3vn72bDisZr4e7E38dEcSCtiFVHz9/SNU6ev8LXR88zq1+YvHb/xGQKrDqdnnkrjpF4oZj3\np3SVbuNNwM3RjhfHx3DqUimL9qZrHafJJeeUMPmj/VTW6vhibm96hTdr0uef3T8MRzsbdm1YCTWl\n0H5ikz6/sBwZBeVsSrrEvb1DcHW0zCMVY8PHUqWrYnvWdq2jWKS7ewTTvZU3r25IobCs+qYeq6oq\nL61LprmbAw/L3f6/MIkCS1VVnv02kR2n83l5QnuGRzfNKoKAUTH+jIj2453vz5BdVKF1nCZzNLOI\nuxfsx8FW4asH+tA+0LPJMzR3c2RKzxCaZ6xH5+QNYYOaPIOwDIv2pmNvY8P0PqFaRzGaTr6dCHQL\nJD4tXusoFsnGRuG1SR0or67jlfiUm3rsupMXOZJ5mSdGtsXdyXrbMvyWSRRY721PZeXhbOYNac3U\nXuY5N8ucvXhbDLaKwrPfJjZq/91c7DqTz9SFB2nu5siqh/pq2mV4bh9/htoc45jrALCVH0zi5hWV\n17DqaDYTugTQwsNy70BVFIXYsFgOXDxAQWWB1nEsUqSfOw8Oas2a4xfYcza/QY+prNHxrw0pxAR4\nMNkCt6cbQ/MC66sj2by19QyTugbyxMi2WsexSj/Pptp1Jp/1Jy9qHceo4k9e5P5lhwlv7sZXD/Yh\n0Mv5xg8yopa5e3BVqnkvryN5JVWaZhHm6bMDmVTV6pkzwDIai/6R2LBYdKqOLRlbtI5iseYNiSCs\nuSvPfptIVe2Ne2Mt2J1GTnEVz4+LafS0C0ujaYG183QeT61OYEBkc/41qaPF9G0xR9P7htIh0JMX\n1yVTXGmZvWZWHsrikS+O0TnYi5UP9DaNO1STVqNzbs7+urZ8sscwfWiE9aiq1bHshwyGtmtBpJ95\nj8VpiAjvCNp4tyE+XbYJjcXJ3pZ/TmxPZmEF/9129g+/NudKJR/uqp/VashO8JZCswIr8UIxf1px\njLZ+7nwwtSsOdpovplm1n2dTFZVbZm+sj3ed4++rExjYxpdPZ/XCwxTOCVSXwZkt2LafwNjOIaw4\nmEVRec2NHyfET1Yfu0BheY1VrF79LC48jpP5J8kuzdY6isXq27o5d3QLYsHuNE5dKrnu1/170yn0\nKvx9jAzivhZNqpqaOj0zlhzG28WBJTN7yKE4E9EhyJMZfcMsqjeWqqq8vukUr208xdiOLVkwrTvO\nDrZax6p3ZhPUVULMJP40uDUVNTqW7LO+uznFrakfi5NGh0BPeodbz+rBmNAxAGxIk55YxvRMbBQe\nzvY8tToBvf73Z3OPZl5m7Ykc5g4IJ9hHhtNfiyYFVkZhObU6Pctm9cDPgg9lmqPHR7YhwNOJp1eb\nf28svb7+7tQPdp7jnl4hvHt3F9NaKU1cDe4tIaQPkX7ujI7xZ+kPGTK+SDTItlN5pBWUM9eCxuI0\nREu3lnTz60Z8erxV3JSjFW9XB56Ni+J41hVWHMy86nN6vcpL65Jo4e7IQ4Nba5TQ9Gm2grVwenci\nWlj+mQFz4+pox4vj23M6t9SszwTV6vT85csTrDiYxUODW/PPCe1N6wBmVQmkboXoCWBT/204b0gE\npVV1LN+feYMHCwELdp8j0MuZMe0tayxOQ8SGxZJenM7py6e1jmLRJnYJpF9EM17fdJrcX92Es+b4\nBX48X8z/jW5nsX3XDEGTAivYx4UeodazpG1uRkT7MSrGj3e/P0tWofn1xqqs0TH30yN892MOfx/T\njv8b3c703uGf3gC6mqtmD3YI8mRQG18W702nssYwk+2FZTqWdZnDGZY5FqchRrYaiZ1iJz2xjExR\nFP45oQM1Oj0vfJcEQHl1Hf/edIpOwV5M7GL8sWLmTJPvTE9nOXNl6l64LQY7G4Vn15pXb6ySqlqm\nLz7EzjP5vDapAw8OMtHl68TV4BkMQT2u+uNHhkZQWF7DF4eyNAomzMHCPWl4ONlxVw/r7Dvk5eRF\n/8D+bEjfgF4176MMpi60uSuPDotkY+Ilvk/O5cOd58grrea5sdHYmNKugAmyvrc+okFaejrzxKi2\n7D6Tzzoz6Y1VWFbNlAUHOJ59mfemdGFKzxCtI11b5WU4tx2ix8NvVta6h/rQK8yHj3efo7pOVrHE\n72UWlrMp8RL39m5l1dszseGx5FXkcTT3qNZRLN6cAeG09XPnmW8TWLAnjfGdA+jWSsbZ3YgUOHzc\nHwAAIABJREFUWOK67usTSscgT15al0xxhWkfvL5wpZLJH+/nXH4Zn9zXnbEdA7SOdH0p60Ffe9X2\n4K89PDSC3JJqvjl6oYmDCXOwaG86tjYKM/qGah1FU4ODB+Ns5yzbhE3Awc6GVyd1IK+0GltFkbYM\nDSQFlriuX/fG+vdm0+2NdS6/jMkf/kB+aTWfze7F4LYttI70x5JWg3coBHS95qf7RzSnU5AnH+06\nR52Z38kpDOtyeQ1fHclmQudAix6L0xDOds4MCxnG1syt1Oikf5yxdWvlzasTO/DWnZ1o6antBAxz\nIQWW+EPtAz2Z1S+Mzw9mcSSjSOs4v5N4oZg7P9pPjU7Pyrm96W7qN0+UF0DaLoiZ+LvtwZ8pisK8\nIRFkFVWw7mROEwcUpuyXsTgDraex6B+JDYulpKaEvRf2ah3FKkzpGcKYDi21jmE2pMASN/TXET/1\nxlqTQE2d6ayoHEovYsqCAzjZ2/LVA32ICfDUOtKNpawDVQcx194e/NnwKD/a+rkzf8e5azb5E9an\nqlbHsv0ZDGnrSxsrGIvTEL0DeuPj5MOGdGk6KkyPFFjihlwd7XhpfHvO5JaZTG+sHafymLboIC08\nHPn6oT6E+7ppHalhklZDswjw7/CHX2ZjozBvaASpeWVsTrrUROGEKVtz/AIFZTWyevUr9jb2jGw1\nkp3ZOymrKdM6jhBXkQJLNMjwaD9Gx/jz321nySws1zTLdz/mMOfTI7Txc+erB/qYz3mAsjzI2PuH\n24O/FtehJWHNXXl/R6pZtcoQhqfXq3yyJ432gR70CW+mdRyTEhceR7Wumu3Z27WOIsRVpMASDfbC\nbTHY29rw7Lfa9cZacTCTP688TtdW3nw+pxfN3Bw1yXFLkteCqr/h9uDPbG0UHhrUmqScEnaeyTdy\nOGHKtp/KIy2/nLkDW5te01yNdfLtRKBboMwmFCZHCizRYP6eTvxtVFv2nC3gux+b/vD1BztTeWZN\nIkPbtuDTWT3Nb0h44mrwbQd+0Q1+yIQugQR4OjF/u6xiWbMFe9II9HIm1grH4tyIoijEhsWy/+J+\nCioLtI4jxC+kwBI35d7eregU5MnL65O5UtE0t0arqsprG1N4fdNpJnQO4KNp3XCyt22S5zaYkhzI\n2t/g1aufOdjZ8MCg1hzJvMzBdNO7i1MY34nsKxxKL2KWlY7FaYi48Dj0qp7NGZu1jiLEL+S7VdwU\nWxuFVyd14HJFLf/eZPzeWDq9ytNrEvh4Vxr39WnFW3d2xt4cX2SS1wLqdZuL/pG7egTT3M2R97en\nGj6XMHmf7E7D3YrH4jREa6/WtPVuK9uEwqSY4SuV0FpMgCez+oXyxaFsDhuxN1ZNnZ5HvzjOF4ey\neWRoBC/eFmO+s68SV4NfB2geedMPdbK3Zc6AMPamFnAi+4oRwglTlVVYwcbEi9zbuxVuVjwWpyHi\nwuM4WXCS7JJsraMIAUiBJW7RX4a3IdDLmadXG6c3VkVNHfd/eoT4hIs8GxfF4yPbmu/h3ivZcP4Q\nxEy45UtM7d0KT2d7WcWyMov3yVichhoTNgZAemIJkyEFlrgl9b2xYjibZ/jeWMWVtdy36BB7z+bz\n+u0duX+Amff9SVpT/+stbA/+zM3Rjpn9Qvk+JZdTl0oMFEyYssvlNXx5OJvxnQPxs/KxOA3h7+pP\nN79uxKfHyw0hwiRIgSVu2bAoP2I7+PPutrNkFBimN1Z+aTV3LzjAj+evMP+ertxpCedOklZDy87g\n07hCcUbfUFwdbJm/45yBgglTtuJgJpW1OuaY+xuMJhQXHkd6cTqnikx3dqqwHlJgiUZ5flwMDgbq\njXX+cgWTP/qBjIJyFs/oYRkzr4rSIOd4o1avfubl4sC0PqHEn8whLV+6VluyqlodS3/IZHBbX9r6\ny1ichhrZaiR2NnbEp8VrHUUIKbBE4/h5OPHk6LbsTS1g7Ylb742VmlfKHR/up6i8hs/u78WASF8D\nptTQz9uDMRMNcrnZ/cOwt7Xhw52yimXJ1p64QEFZNXNl9eqmeDp60j+wPxvTN6LT67SOI6ycFFii\n0ab2akWnYK9r98bS66Gu+g8fn3C+mDs/PkCdXuXLB/rQrZW3EdM2saQ1ENQDvEIMcjlfd0em9Axh\nzfELnL9cYZBrCtOi16ss2J1GTIAHfVrLWJybFRcWR15lHsfyjmkdRVg5ue9XNJqtjcKrE9tz2/v7\n+NfGU/zr9o71n6ithGXj4PxhcHADFx9waXbVR1aVM2uOFxPr6M3Dsb3wtzkPpc3qv9bWzDq1/1ZB\nKlxKgFGvGfSycweGs+JgJgt2p/HS+PYGvbbQ3o7TeZzLL+fduzub752zGhoUPAgXOxfi0+Lp4d9D\n6zjCikmBJQwiJsCT2f3DWLA7jUldg+gZ5gMbn6wvrvo+Ur+SVVH4v4+Cs9SVFRBSV85zClADfPub\nizp6/qYo8/nNr7/5cPYGGxPq8J60uv7X6PEGvWyAlzOTugSx8nA2Dw+NoIW73GFmSRbs/mksjiWc\nQdSAs50zw0KGsSVzC0/3ehoHWwetIwkrJQWWMJi/DI8k/uRFnl6TwMYhOdgf+xQGPA7Dnvvd1357\n/AKPr/qRzi2dWXRna7zUkv8VX5VFUFF0dUFWdgnykut/X3u9rTEFnDyvUXxdqyD76c+cvMDGSDvl\niashpA94Bhr80g8Nbs2qo9ks2pPOU7FRBr++0MaP2Vc4mF7Es3FR5jmxwETEhseyLm0dey7sYVjI\nMK3jCCslBZYwGBcHO16Z0J5Xln0L6/4BrfrB4Kd/93Wf7s/gubVJ9AlvxifTu998h+qaimsUYb8p\nyCoKofg8XPyx/ve665wDU2zqV76uV5A5X2MFzckTbrR1k5cC+Skw5j8393droNDmrozrFMDyA5k8\nOKg13q7yLt0SLNhTPxbn7p6GObNnrXq37I2Pkw8b0jZIgSU0IwWWMKgh4a60dZ9PSbUD5UPeI8T2\nf//EVFXl/e2pvLn1DCOi/XhvSpdbG9rs4FL/4RnUsK9X1fpVr6sKsGsUZBVF9W0Vzh+p/9/62mtf\nz8buN4XXbwu0ZpC+u754M/D24K/9aXAEa0/ksOSHDB4b0cZozyOaRnZRBRsTLjJnYLiMxWkkOxs7\nRoWOYvXZ1ZTVlOHm4KZ1JGGF5LtYGNaGJ2lZk8kDPEP59/l8NjscRVFQVZV/xqewcG86k7oG8vrt\nHbFrqi0QRQEH1/qPht7Np6pQXfoHxdivtjILzkLFgfrfq7+6NTx8MLj7GeNvBEBbf3dGRvuxdF86\ncwaE4e5k5jcFWLlFe+vH4szsG6Z1FIsQGxbLF6e+YFvWNsZHGO+NjhDXIwWWMJwTn8OJz1AGPskA\n58n8Y20S3564wLiOATy9JoGvjpxnRt9QnhsbbfpDmxUFnDzqP3wa+IKn10N18f8KsmYRxs0IPDw0\ngi3JuXx2IIuHBrc2+vMJ47hSUcNXR7K5rVMg/p5y04IhdPLtRKBbIBvSN0iBJTQhpyiFYeSlwPrH\nIHQADP479/RqRedgL15en8JDK47x1ZHz/HlYJM+PM4Pi6lbZ/HSeq1lrCO5Zf2bLyDoGeTEgsjmL\n9qZRWSONFc3VioNZVNTomDNQVq8MRVEUYsNiOXDxAAWVBVrHEVZICizReNVl8NV0cHSH2xeBjS22\nNgqvTepAcWUtW5NzeW5sNH8d0Ub6+hjBI0MjKSirYeXhLK2jiFtQXadjyb4MBrbxpZ2/h9ZxLMrY\n8LHoVT2bMzZrHUVYISmwROOoKsQ/DoVn4faFV505imrpwbt3d2bBtG7M6i/vzI2lZ5gPPUN9WLA7\njZo6vdZxxE1aezyHgrJqHhgoY3EMLdwrnHY+7diQtkHrKMIKSYElGuf4cji5Egb9HcIH/e7TYzsG\nMDLGX4Ng1mXe0AguFlex+th5raOIm6DXqyzYk0Z0Sw/6ylgco4gNi+VkwUmySmSFVzQtKbDErbuU\nCBv+Vn+33MAntE5j1QZGNqdDoCcf7jpHnU5WsczFzjN5pOaVMXdguGyfG8mYsDEoKGxIl1Us0bSk\nwBK3proUVk2vb7o56RPTGlFjhRRFYd6QCDILK4hPuKh1HNFAC3anEeDpRFxHGYtjLP6u/nTz60Z8\nWjyqqmodR1gRKbDEzVNVWPeX+qacdywGtxZaJxLAyGg/2vi58f72VPR6eSExdSfPX+FAWhGz+ofJ\nWBwjiwuPI6Mkg5SiFK2jCCsi39Xi5h1dColfw5CnIbS/1mnET2xs6lexzuaVsSU5V+s44gY+2ZOO\nu6Mdd/UI1jqKxRvRagR2NnZy2F00KSmwxM25eBI2/h+0Hgb9H9c6jfiNuA4tadXMhfk7UmU7xIRl\nF1WwIeEi9/QKkQ78TcDT0ZP+gf3ZmL4RnV76xYmmIQWWaLiqkvpzVy7NYNKC+saawqTY2drw0KDW\nJFwoZvdZaa5oqhbvS0cBZvQL1TqK1YgLjyOvMo+juUe1jiKshLxCioZRVVj3KFzOrD935dpc60Ti\nOiZ1DaKlpxPzt6dqHUVcQ3FFLV8ezua2zgG09HTWOo7VGBQ0CBc7F+LT47WOIqyEFFiiYQ4vhKQ1\nMPRZaNVH6zTiDzjY2fDAwHAOZRRxMK1Q6zjiN1YcyqwfizNAGos2JWc7Z4a3Gs7WjK3U6Gq0jiOs\ngBRY4sZyjsPmpyFyJPT7i9ZpRAPc3TOE5m4OvL9DVrFMyc9jcQZENieqpYzFaWqxYbGU1pay58Ie\nraMIKyAFlvhjVcWwaga4+sKEj+TclZlwsrdldv9w9pwt4MfsK1rHET9ZeyKH/NJq5spYHE30atkL\nHycf4tNkm1AYn7xaiutTVVj7MBSfhzuWgKuM8jAn9/YOwcPJjvmyimUSVFXlk91pRLX0oH+EnGHU\ngp2NHaNDR7MrexdlNWVaxxEWTgoscX2HFkDKdzDseQjppXUacZPcneyZ0S+MLcm5nL5UqnUcq7fz\nTD5n88qYOzBMxuJoKDY8lhp9Dd9nfa91FGHhDFJgKYoyWlGU04qipCqK8ndDXFNo7MJR2PwMtBkD\nfR/ROo24RTP7huLiYMsHO2UVS2sLdqXh7+HE2I4BWkexah2bdyTILUiajgqja3SBpSiKLTAfGANE\nA1MURYlu7HWFhiov15+7cveHCR+AvNs2W96uDkzr3Yp1P+aQUVCudRyrlXC+mP1phczqHypjcTSm\nKAqx4bEcvHSQgkrpFSeMxxDf6T2BVFVV01RVrQFWAuMNcF2hBVWFb+dBSU79uSsXH60TiUaaPSAM\nO1sbPtx5TusoVuuTPWm4O9oxpWeI1lEEEBcWh17Vsyl9k9ZRhAUzRIEVCGT/6n+f/+nPrqIoylxF\nUY4oinIkPz/fAE8rjOLAB3A6Hka8BME9tE4jDKCFuxN39whm9fHz5Fyp1DqO1Tl/uYL4hItMkbE4\nJiPcK5wonyg2pMs2oTCeJlurVlV1gaqq3VVV7e7r69tUTytuxvkjsPU5aDcWev9J6zTCgB4Y1BpV\nhQW707SOYnUW782oH4vTN1TrKOJXYsNiSShIILMkU+sowkIZosC6APx6HHzQT38mzElFUf25K48A\nGP++nLuyMIFezkzsEsgXh7LIL63WOo7VKK6oZeXhLMZ1CiDAS8bimJLRYaNRUGQVSxiNIQqsw0Ck\noihhiqI4AHcD3xnguqKpqCp8+ycovQSTl4Kzt9aJhBE8NLg1tTo9C/fKKlZT+fxQlozFMVH+rv50\n9+/OhrQNqKqqdRxhgRpdYKmqWgc8DGwGUoCvVFVNaux1RRP64T04sxFG/RMCu2mdRhhJuK8bcR0D\n+Gx/JlcqZBabsdXU6VmyL50Bkc2JDpCxOKYoNiyWjJIMkouStY4iLJBBzmCpqrpBVdU2qqq2VlX1\nn4a4pmgiWQfh+xcg6jboOVfrNMLI5g1pTXmNjqU/ZGgdxeKtPXGBvNJqWb0yYSNajcDOxk56Ygmj\nkIYs1qy8EL6eCV7Bcu7KSrTz92B4lB9L9mVQVl2ndRyLpaoqn+xJo52/OwMiZSyOqfJ09GRA4AA2\npm9Ep9dpHUdYGCmwrJVeD2segPJ8mLwMnDy1TiSayMNDIyiurGXFAbl7ylh2ncn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VyCTRZYAA5+ftRaEovB05OEQYMpPnlS70iimsnbvp20ufPw7toF3z59qvz8rrfeQq3ly9HKy4nv\nF0XRoUNVnkEIYb3uC7mPet71WHFghSxRZIVstsACcAwOptaSWAASBg6k9OxZnROJ6qLk9GnOjBqN\nc6NG1HzjDZRSuuRwaXQTtVcsRzk7E99/AIV//qlLDiGE9VFK0a9JPw5mHOT35N/1jiP+waYLLADn\nunWpFbMYU04uCQMHUZaRoXckYeVMhYUkDhsOShE2ZzYGV1dd8zjXrUudlSsw+viQ8PQz5P/6q655\nhBDWo1O9Tng7e8uSDVbI5gssAJcmTQhfuIDSM2c4PXiILOQo/pWmaZx74w2KDx8m9P33cAoP1zsS\nAI6hodResQKH4GBODx5C3o4dekcSQlgBVwdXet7Uk28TvuV0rtwQY03sosACcIuIIHTWTIoOHzZv\nSVJUpHckYYUyV60i+4sNBLz4Ah4PPqh3nL9xDAqk9orlONWvx+nnXyDn66/1jiSEsAK9b+6NURlZ\ndXCV3lHEReymwALwbNWKkHfeoWDnTs689B+0Urm1VfylYNcfJL/9Dh6tWhHw3HN6x7kkBz8/ai9b\nhmvTppwZ+RLZGzfqHUkIobNAt0AeqfsI64+tJ69EOjTWwq4KLADvTo9Rc8J48uLiSHrtNTSTSe9I\nwgqUpaZyZsQIHENCCHnvXZTBen80jF5e1IqNMS+o+8qrZH72md6RhBA6i2oSRX5pPuuOrqu0c8jv\ny2tjvb9FKpHvk09SY+QIcjZsJHnq23J7q53TSktJfOklynNzCZszG6OXl96Rrsjg7k74ooW4P/gA\n5yZMJOPDD/WOJITQUVP/prQIbMGqQ6soN5Vb/PiappHw1NOc6Nadwv37LX58W2SXBRaA/9Ch+D31\nFJkrV5I2d57ecYSOUj74gMKdvxP85mRcGjXSO85VM7i4ED5nDp7t25P89jukLVggfywIYceimkRx\nJu8McafjLH7s/B9/pODXXyk5dYpTPXuRMmsWWkmJxc9jS+y2wFJKEfjqK3g/3oO0efPIWL5c70hC\nB9mbNpPx4XJ8+/XDu3NnveNcM+XkROj0aXh37ULqrNmkTp8hRZYQdqp1eGtCPUJZcWCFxY+dHr0Y\nh8BAGnzzNd6dO5O+YCEnn4iU2azLsNsCC8xFVvCkSXi2a0fy1LfJ+vxzvSOJKlR0+Ahnx4/HtUUL\ngl4ZrXec66YcHAh++218evciffFikt+aItdKCGGHjAYjfW7uw66UXexPs1zhU/DHHxT88gt+zzyN\nQ0AAIe+8TdiC+ZRnZnKqV29SZ8+R2axLsOsCC8y/nEKmfYDbPXdz9rXXyf3f//SOJKpAeU4OicOH\nYfBwJ3TmDJSTk96RbogyGKg5cSJ+Tz9N5kcfcfb18Wjllr8OQwhh3Xo07IG7ozsrDlpuFis9ejFG\nHx98IyMvPObZujX1Nm3E+7GOpM2fz8mevSg6eNBi57QFdl9gARicnAifOxeXW8y3vuf//LPekUQl\n0kwmkl4dQ+mZJMJmzsQxMFDvSBahlCLwldEEvPAC2evWkTR6tCxFIoSd8XDyoHuD7nx18iuS85Nv\n+HhFh4+QFxeHb/8oDO7uf/uc0dubkHffJWz+fMrS0zgZ2ZPUOXNlNquCFFgVDO7uhC9ciFOd2iQ+\n/wKFe/fqHUlUkvRFi8iLiyPolVdwu+MOveNYlFKKGsNeJHD0aHK2/JfE4SMwFRfrHUsIUYX6NO5D\nuVbOp4c/veFjpS9ejMHNDb++ff/1OZ5tWlN/40a8Oj5K2rx55tks2ZxeCqyLOfj6Eh4Ti9HPj9OD\nh1B87JjekYSF5e3YQersOXh16oRvVD+941Qa/4HPUHPiBPLi4kh87jlMBQV6RxJCVJFwz3Da1GrD\nZ0c+o7Cs8LqPU5KQQM6WLfg82Rujt/dln2v08SH0vfcImzeXsrQ0Tj4RSerceXY9iy4F1j84BgVS\na0ksODqQMHAQJYln9I4kLKQkMZEzo0bj3LAhwZMnoZTSO1Kl8n3ySYLffpv8n38hYdBgynNz9Y4k\nhKgi/Rr3I7s4m00nNl33MdJjYlEODvgNGHDVr/Fs25Z6Gzfg1aEDaXPncrJXL4oOH77uDNWZFFiX\n4FSrFrViYjEVFpIw8BnK0tL0jiRukKmoiMThw8FkImzObAxubnpHqhI+3bsROn0ahXv2kPDU05Rl\nZuodSQhRBe4IuoPGfo1ZeWDldS3dUpqcQvb69Xj36H7N16k6+PoS+sH7hM2dQ1lyink2a/58u5vN\nkgLrX7g0uonwRQspS0k1//Wfk6N3JHGdNE3j3KTJFB84SMh77+JUu7bekaqUV4cOhM2dQ/HRoyT0\nH0BZaqrekYQQlUwpRVSTKE5kn+CHpB+u+fUZy5ahmUz4Dxx43Rk8H36Yeps24tW+PWmz53CqV2+K\nDh+57uNVN1JgXYZb8+aEzZlD8fHjnB76rFzHUk1lffop2evXE/D883i2bq13HF14tmpFePQiSs6c\nIb5fFKVJSXpHEkJUsg51OlDDtQYrD6y8pteVZWaS+emneD3WEafw8BvK4ODrS+i0DwidPYvS5GRO\nPvGEedcJO5jNkgLrCjzuv4/Q99+n8M8/SRwxUm4/rWYKd+/m3JSpuD/4AAEvvqB3HF253303tWJi\nKMvI4FS/fpTEx+sdSQhRiRyNjvS+uTc/JP3A8azjV/26zI9WoRUUEDB4sMWyeLVvb57NavcwqbNm\nm2ezjtj2bJYUWFfBq8Mj1Jz0Bvk7dpA0Zows4FhNlKWlkThiJI5BQYS+9x7KIN/ubi2aU2vZUrSC\nQuL7RcmdskLYuMibInE2OrPy4NXNYpXn5ZOxYgUebdvi3LChRbM4+PoSOn06obNmUXruHCcff4K0\nhQvRysoseh5rIb9xrpJvZCSBo0eRs+W/nJv8puz3ZuW0sjLO/OdlyrOyCJszG6OPj96RrIZr06bU\nXrEcDY34qP6yl5gQNszXxZdO9Tqx8fhGMouufJNL1mefYcrOJmCI5Wav/snrEfNslufDbUmdOYtT\nvZ+k+OjRSjufXm6owFJKva+UOqSU2qOUWq+UsunfYv4DB+I/ZAhZn35K6oyZescRl5EybToFv/5K\n8ORJuDRurHccq+PcsCF1Vq5EubqQ8NTTFPzxh96RhBCVJKpJFMXlxaw+svqyzzOVlJCxdClu99yN\n6+23V2omBz8/wmbMIHTmTErPnOFkj8dJWxRtU7NZNzqD9TVwi6ZptwFHgLE3Hsm61XhpJD69epEe\nHU16bKzeccQl5Pz3v2QsXYpvnz54d+2qdxyr5VS7NnVWrsTo50vCwEHk//yL3pGEEJWgvk997g25\nl08OfUJp+b9fXJ69/nPKUlMJGDKkyrJ5dXiEeps34dG2LakzZnDqyT42c+nCDRVYmqZt1TTtfLn5\nMxB245Gsm1KKmhPG49XxUVLe/4DM1Zf/i0BUreKjR0l67XVcmzUjaMyresexeo4hIdResQKn0BBO\nDx1K3nff6R1JCFEJoppEkVqYypenvrzk57WyMtJjYnC57Tbc7r67SrM5+PkRNnMGoTOmU5qYyMnu\nPUiLXlztZ7MseQ3WM8B//+2TSqkhSqmdSqmdqdV8HR5lNBLyzju4P/AA5yZMJOfLS3/DiqpVnptL\n4rDhGFxdCZ01E+XkpHekasExMJBay5fjXL8+p18cRs6XX+kdSQhhYfeG3Etd77qsOLDiktcQ5/z3\nS0pPnyZg6BDddrnwevRR6m3aiEfr1qROn86pPn0pPn71dz9amysWWEqpb5RS+y7x1vWi57wGlAEf\n/dtxNE2L1jQtQtO0iBo1algmvY6UkxNhs2fh2rw5Z0a/Qt73176Qm7AczWQiaexYSk6fJnTGdByD\ngvSOVK04+PpS68NluN5yC2f+8x+yPv9c70hCCAsyKAP9GvfjYMZBdqXs+tvnNJOJ9OhonBrUx0Pn\ntQId/P0JnTWT0OnTKE1I4GT3HqTHxFTLu/evWGBpmvawpmm3XOLtCwCl1FNAJ6CvZme31hlcXQlf\nuADn+vVJHDZMLhTWUfriGPK++R+Bo0fhfuedeseployentSKjcHtrjs5O2YsmZ98onckIYQFda7f\nGW9nb1YcWPG3x/O2fUfx0aMEDBliFcvZKKXw6tjRPJv10EOkfDCNU336UHzihN7RrsmN3kXYAXgF\n6KJpml0uc2708qJWzGIcAmtweuizdruppZ7yfviB1Fmz8Or46DVtSir+P4ObG+ELF+LRqhXn3phE\n+pKlekcSQliIq4MrkTdF8m3Ct5zOPQ2YtxJLW7QQx9BQvDp21Dnh3zkEBBA6exYh0z6g9FQ8J7t1\nJz02ttrMZt1oqToX8AS+VkrtVkottECmaschIIBasUswuLqSMGgQJQkJekeyG6VnzpD08iic69cj\n+M03dbt2wJYYnJ0JmzMbz0c7kPLee6TOnSfrvglhI3o36o1RGVl1cBUABb/8StGfe/AfPAjl4KBz\nuv9PKYX3Y4+Z7zR86EFS3v+A+L79KD5xUu9oV3SjdxE20DQtXNO0ZhVvz1oqWHXjFBZKrdgYKC0j\n4ZmBlCan6B3J5pmKi0kcPgKtrIywOXMwuLvrHclmKEdHQj/4AO/u3UmbO5eU9z+QIksIGxDkHkT7\nOu1Zf2w9eSV5pEdHY6wRgHf37npHuyzzbNZsQt5/n5KTJznZvTvpS5Za9WyW/s1WG+LcoAHhi6Mp\nz8jg9KCBlGVeedVccX00TePc5MkU7d9PyHvv4lSnjt6RbI4yGgme8ha+ffqQsWQJ5yZPRjOZ9I4l\nhLhB/Zv0J780n6/+O4/8H3/E/6mnMDg76x3ripRSeHfuRL1NG3G//35S3nvPvOXXSeuczZICy8Jc\nb72VsPnzKYlP4PTQZzHl5+sdySZlrV5N9tp1+D87FM82bfSOY7OUwUDQ+NfxHzSQrI8/4ezYcdV+\nbRoh7F3TgKa0CGxB2YefYvDywqdXb70jXROHGjUImzuHkPffo/jECfO1WUuXWd1slhRYlcD97rsI\nnTGdov37Of3ii5hKSvSOZFMK9+wh+c23cL//fmoMG6Z3HJunlKLGyy8TMHwY2V98wZmXR6HJ97QQ\n1dpT7m257UAh2Z3uxehR/S6vMM9mdabexg2433svKe++S3xUf6uazZICq5J4tm1L8JS3KPjpZ5Je\nHiV/9VtIWUYGiSNG4hAYSMj776GMRr0j2QWlFDWef57AV18l96uvOD1sGKaiIr1jCSGu002b91Ps\nqFjSOFnvKDfEMTCQsPnzCHn3HYqPHTPPZi2zjtksKbAqkU+3bgSNG0vu119zdsJEuUj4BmllZZz5\nz8uUZ2QQOnsWDr6+ekeyO/5PP0XNSZPI375DWuBCVFMliWfI2bSZzEci+KFgL/vT9+sd6YYopfDu\n2pV6Gzfifs89pLzzLvH9B1ASH69rLimwKplf//4EvPAC2evWcWb4cAp++00KreuUOnMmBT//TM2J\nE3Ft2lTvOHbLt1dPQt59h4LffiNh4CDKc3L0jiSEuAYZS2LBYKD5iIm4Obix8sBKvSNZhGNQIGEL\n5hP8ztsUHz3Kia7dyFi+XLebc6TAqgIBL75AwPBh5P/0M/FR/TnxaEfSY2MpS0/XO1q1kfPVVtJj\nYvHp3QufHtZ9O7E98O7ShdCZMyjcv5/4p56SO2aFqCbKUlPJWrMWn25d8QmvT4+GPfjy5JekFNjG\n0kJKKXy6dTNfm3XXXSRPfZv4/v11mc2SAqsKnL9+peH27wh++22Mfn6kvP8BRx9qReLwEeTt+N4q\n+sXWqvj4cc6OHYvL7bcRNG6c3nFEBa/27QmfN5eS4yeIj4qStd+EqAYyli9HKyvDf+BAAPrc3Idy\nrZxPDtnW1liOQUGELVxA8NSpFB8+UjGbtaJKZ7OUHu2qiIgIbefOnVV+XmtSfOwYWWvWkv3555Rn\nZeEQEozP44/j06MHjsHBesezGuV5+Zzq2ZPyrCzqrluLY82aekcS/5D/y6+cfu45HAICqL10CY6h\noXpHEkJcQnlODsdat8HjoYcInT7twuMjvh3BrpRdbH1iK64OrjomrBylycmcHVS7sG8AACAASURB\nVD+e/O07cIuIIHjqFJxq1bqmYyilftc0LeJaXiMzWDpxbtCAoDGv0mD7d4TOmI5znTqkzZnLsbYP\nkzB0KDlff41WWqp3TF1pmsbZceMoOXWK0OnTpbiyUu533UntJbGUZ2Vxql8UJadO6R1JCHEJmatW\nYcrPx3/I4L89HtUkiqziLDad2KRTssrlGBRE+KJFBE+ZQtGhQ+bZrBUrK302S2awrEhJYiJZa9aQ\nvW49ZSkpGGsE4NOtOz5PPI5T7dp6x6ty6TExpHwwjcDRo/Ef+IzeccQVFB08SMIzAzH6+FB3/ToM\nLi56RxJCVDAVFHCs7cO43nYb4Yv+vm2wpmn02tSL4vJiPu/6uU3v6Vp67hxnx08gf8cO3Fq2NM9m\nhYdf8XUyg1XNOYWFEThyJA2+/R9h8+fjesutpMfGcvyRDsQPeIrsjZswFRfrHbNK5P/0EynTZ+DZ\noQN+zzytdxxxFVwaNybkA/M+Yalz5ugdRwhxkaw1ayjPzMR/6ND/9zmlFFFNojiRfYIfk37UIV3V\ncaxZk/DoRQRPeYuigwfNs1kffVQps1kyg2XlSpOTyV6/nqw1aylNTMTg7Y13ly74RD6By0036R2v\nUpQmJXHy8Scw+vlR59NPq+Uqw/bs7ISJZK1ZQ51VH+HarJnecYSwe1pJCcfaP4JTWBi1V6645HNK\ny0tpv7Y9jXwbsbDdwks+x9aUnj1rns36/nvc7rzTPJsVFnbJ58oMlg1yDAoi4Nlnqb/1K2oticXj\nvnvJ+uQTTnbpyslevchas8amFns0FReTOGIkWkkJYXPmSHFVDQW+MhqHoCCSxo6T1d6FsALZGzdS\ndu7cJWevznM0OtK7UW9+SPqB41nHqzCdfhyDgwlfHE3NNydTtH8/J7p0JWPVKovNZkmBVU0ogwH3\ne+8ldPp0Gmz/jsAxr2LKy+fs6+M5+sCDnB0/gcK9e6v9IqbJb02haO9egt95G+d6dfWOI66D0cOD\n4DffpOTkSdLmztU7jhB2TSsvJz16MS5NmuB+/32XfW5ko0icjc6sPGgbC49eDaUUvpGR1Nu4Abdm\nzUie/CYJTz9DSeKZGz62FFjVkIOvL/5PPUW9TRupvWoVno88QvbGjZyK7MnJbt3JWPkR5dnZese8\nZllr1pC1ejX+gwfj1a6d3nHEDfC4/z58IiNJX7KUwt279Y4jhN3K3bqVkvh4/IcOveLF634ufnSq\n14mNxzeSVZRVRQmtg2NICOGxMdScPImiffs40aULmR9/fEOzWXINlo0oz80lZ/Nmsj5bTdGBAyhn\nZzwfaY/PE0/g1rKl1d8VUrh3H/F9++IWcQfhixfLJs42oDwvjxOdu2BwdTXfVejsrHckIeyKpmmc\n7PE4WlER9TZvQhmuPKdyLPMY3Td0Z3jz4Qy+bfAVn2+LSpOSOPv66+T/+BNu99xNyFtv4RQWJtdg\n2Sujpye+vXtTd91a6qxdg3eP7uR9G0dC/wGc6PAo6TExlKWl6R3zksoyM0kcMRxjgD8h06ZJcWUj\nLrQKT5wgTe4qFKLK5e/YQfHBg/gPHnxVxRVAA98G3BN8Dx8f+pjScvtci9E8mxVLzUmTKPpzDyc6\nd7mu40iBZYNcmzYleOJEGu7Ybt6aJyCAlA+mcbRV64qteXZYzdY8Wnk5SS+/THlaOmGzZuPg66t3\nJGFB5lbhE+ZW4Z9/6h1HCLuStigah5BgvDt3uqbXRTWJIrUwla/iv6qkZNZPKYVvr57U27gBl9tv\nu75jSIvQPhQfP/7X1jyZmeateXo8js/j+m7NkzJ9BunR0QS/9SY+TzyhWw5Recpzc82tQnd36q5b\nK61CIapAwc6dxPeLIui11/CL6ndNrzVpJrp90Q1XB1c+eewTq7/EpLJpmobBYJAWobg05/r1CXr1\nFRp8t43QmTNwrlOXtLlzOdamLQlDhuiyNU/uN9+QHh2NT2SkFFc2zOjpaW4VHj8udxUKUUXSoqMx\n+vnh88Tj1/xagzLQr3E/DqQfYFfKrkpIV71cb4EpBZadMTg54dWhA7WWxFL/m6/xf3YoxYcOc2bY\ncI62bkPKtGmUxMdXeo7iEydJenUMLrfeStD41yv9fEJfHg/cj/cTj5Meu4TCPXv0jiOETSs6cID8\n7TvwGzAAg+v1bd7cuX5nvJ29WXnAfpZssDQpsOyYU1gYgSNGmLfmWTAf19tuI33JUvPWPP0HVNrW\nPKb8fBKHD0M5ORE2exYGJyeLn0NYn6BXX8UhMNC8AKmdbPkkhB7Sohdj8PDAt8+T130MVwdXIm+K\n5NvT35KYm2jBdPZDCiyBcnDAs3VrwufPo8G331Jj5EhKk5JIGj2aow8+xLkpUyk6fMQi59I0jaTX\nXqfkxElCp0/T9fovUbXMrcLJFa3CeXrHEcImFZ88Se5XX+Hbpw9GT88bOlbvRr0xYGDVoVUWSmdf\npMASf+MYFEjAs0PNW/MsXYLHffeZt+bpat6aJ3P16hvamidj6TJyv/ySGi+NxP2eeyyYXFQHHg88\ngPfjPUiPjaVw71694whhc9JjYlBOTvgN6H/DxwpyD6J9nfasO7qOvJI8C6SzL1JgiUtSBgPu99xD\n6PRpf9ua59z4CRVb84yncM+ea9qaJ/+XX0mZNg3Pdu3wHzSoEtMLaxY0ZkxFq3CstAqFsKDSpCSy\nv9iAT2QkDv7+FjlmVJMo8kvzWX9svUWOZ0+kwBJX9P+25unQgexNmznVs5d5a54VK6+4NU/puXOc\neeklnGrXJvjtqXZ/2689M3p6Ejx5EiXHjpM2b77ecYSwGelLlwHg/8zTFjvmLQG30DywOR8d/Ihy\nk3Wsn1hdSIElrppSCrcWzQmZOoWG27+j5htvoBwcSJ4yhaMPPMiZ0a+Q/+uv/29Wy1RSQuKIEWhF\nRYTNmY3Rw0OnEQhr4fHgg+ZWYUyMtAqFsICyjAyyVq/Gu3NnHENCLHrsqCZRnMk7w7bT2yx6XFsn\nBZa4LuateXpRd+0a6q5bi88Tj5O3bdslt+ZJnjqVoj/3EDx1Ks716+ucXFiLoFdfxaFGDXOrsKRE\n7zhCVGsZy5ejFRfjP9jyl1+0Dm9NiHsIKw6usPixbZkUWOKGuTRpQs0JE2i4/TuC3/n71jzxUf3J\n+uRT/AY+g1eHR/SOKqyI0cvLfFehtAqFuCHleXlkfrQKz/btca5Xz+LHdzA40KdxH35P/p0D6Qcs\nfnxbJQWWsBiDqys+3bpR56OV1NuyGb+oKIqPHcP9vvsIfOklveMJK+Tx4IN49zjfKtyndxwhqqXM\njz/GlJuL/5DBlXaOHg174Obgxhs/vsHulN2Vdh5bInsRikqlmUwAV72Tu7A/5Tk5nOjcBaOXJ3XW\nrpWFZ4W4BqaiIo61fRiXxo2pFbO4Us+19dRWpv4ylfSidNrWasvwFsOp5235GTNrpJSSvQiFdVEG\ngxRX4rKMXl4ET55E8dFj0ioU4hplrV1LeXo6AUOHVPq52tdpz5YeW3ih2Qv8lPQTPb7owRs/vkFK\nQUqln7s6kt98QgjdeTz0EN7du0urUIhroJWWkh4bi2vz5rhGXNPkynVzc3Tj2dufZUuPLfS+uTdf\nHP+Cx9Y9xqxds8gpyamSDNWFFFhCCKsQNHYMDv7+nB03Tu4qFOIqZG/eTFnSWfyHDqnytQX9Xf0Z\nc+cYNnTbQJtabYjZG0PHdR35cP+HlJTLzy9IgSWEsBJGLy9qTp5E8dGjpM2XVqEQl6OZTKRHL8a5\nUSM8HnpItxzhnuG8++C7fNbpM5r6N+WDnR/QeX1nNh7faPcLk0qBJYSwGp6tWuHdrRvpi2Mo3Ldf\n7zhCWK3cb76h5MQJAnSYvbqUxv6NWdRuEdHtovF29mbc9+PouaknOxJ3XNOWarZE7iIUQliV8uxs\n812F3t7UWbtG7ioU4h80TeNUZE/Kc3Oov2ULymjUO9LfmDQTW09tZdauWSTmJdKyZkteavESt9a4\nVe9o103uIhRCVHtGb++/WoULFugdRwirk//jjxTt24f/oEFWV1wBGJSBDnU7sKHbBsbdNY7jWcfp\ns6UP/9n2H05ln9I7XpWRAksIYXU8W7XCu2tX0qMXU7hfWoVCXCx9UTQOQUF4d+2qd5TLcjQ68uTN\nT7Klxxaev/15vj/zPd2+6MabP71JakGq3vEqnRRYQgirFDRuLA5+fpwdOw5N7ioUAoCCP/6g4Ndf\n8Xv6qWrTPnd3dOe5Zs+xpccWejbqybqj63hs/WPM+WMOeSV5eserNFJgCSGs0oVW4ZEjpC1cqHcc\nIaxCevRijD4++EZG6h3lmgW4BjDurnF80e0LHgp7iOg90XRc15GVB1ba5NIOUmAJIayWZ+vWeHft\nStqiaGkVCrtXdPgweXFx+PaPwuDurnec61bLqxbvP/Q+nzz2CTf53sS7v71Ll8+7sOnEJkyaSe94\nFiMFlhDCqkmrUAiz9OjFGNzc8OvbV+8oFtE0oCmL2y9m0cOL8HTyZOyOsfTc2JMfzvxgE0s7SIEl\nhLBqRm9vak463ypcpHccIXRRkpBAzn//i8+TvTF6e+sdx2KUUtwbei+fdvqUdx54h7zSPJ795lkG\nbx3MvrTqvW2WFFhCCKvn2aY13l27kBYdTdGBA3rHEaLKpcfEohwc8BswQO8olcKgDDxW7zE2dtvI\nmDvHcCTzCE9ufpJR340iISdB73jXRQosIUS1EDR2LEZfH5KkVSjsTGlyMtnr1+P9eA8cAwP1jlOp\nHI2O9G3cly09tjD0tqFsT9xO18+78tbPb5FWmKZ3vGsiBZYQolow+vgQPGkyxYcPS6tQ2JWMpcvQ\nTCb8Bw7UO0qV8XDy4MXmL7KlxxYev+lx1hxZQ8d1HZm3ex75pfl6x7sqUmAJIaoNzzat8erSWVqF\nwm6UZWaS+dlneD3WEaewML3jVLkA1wBev/t1Pu/6OQ+EPsDCPxfScV1HPjr4EaXlpXrHuywpsIQQ\n1UrNcePMrcJxr0mrUNi8zJUfoRUUEDB4sN5RdFXHuw7TWk1jVcdV1Pepzzu/vkOXz7uw5cQWq13a\nQQosIUS1Ym4VTqL40CHSFkXrHUeISlOel0/GypV4tG2Lc8OGesexCrfWuJXY9rEseHgBbo5uvLrj\nVXpv6s2PST/qHe3/kQJLCFHteLZpg1fnzqQtWkTRwYN6xxGiUmR99hmm7GwChtj37NU/KaW4P/R+\nVndezdT7p5JdnM3Qr4cyeOtg9qdbz4LESo/FvCIiIrSdO3dW+XmFELajPCuL45064xAQQN3PPkVV\nk33ZhLgapuJijj/cDqcG9am9dKnecaxaSXkJnx7+lOg90WQVZ/FonUcZ1nwY4V7hFjuHUup3TdMi\nruU1MoMlhKiWzK3CN8ytwujFescRwqKy139OWWoqAUOH6h3F6jkZnYhqEsWWHlsYfOtg4k7H0eXz\nLkz9ZSrphem65ZICSwhRbXm2bYtXp06kLVxI0aFDescRwiK0sjLSY2Jwue023O66S+841YankyfD\nWwxnc4/NdG/Ync8Of0bHdR1ZsHuBLks7SIElhKjWgl4bh9GnYgHSUuu+bVuIq5Hz3y8pTUwkYOgQ\nlFJ6x6l2At0CmXDPBNZ3Xc99ofcx/8/5dFzXkY8PfVylSztIgSWEqNYcfH0JfmMixQcPkhYtdxWK\n6k0zmUiPjsa5YQM8WrfWO061Vte7LtNbTWdlx5XU9a7L1F+m0vWLrnx58ssqWdpBCiwhRLXn+fDD\n5lbhAmkViuotb9s2io8exX/wYJRBfkVbwu01bmfpI0uZ13YezkZnRm8fzZObn+Tnsz9X6nnlLkIh\nhE0oy8zkRKfOOAQGmu8qdHTUO5IQ10TTNE717k15Wjr1v/oS5eCgdySbU24qZ/PJzcz9Yy5n889y\nX8h9jLxjJDf73XzZ18ldhEIIu+Xg60vN863CxXJXoah+Cn75laI/9+A/eJAUV5XEaDDSpX4XNnbf\nyKiIUexL30fkxkjG7BhDYm6iRc8lBZYQwmZ4tWuH12OPmVuFhw/rHUeIa5IevQhjjQC8u3fXO4rN\nczY6M6DpALb02MLAWwbyTfw3dP68M+/++i4ZRRkWOYcUWEIImxL0+msYvbxIGjtW7ioU1Ubh3r3k\n//gT/k89hcHZWe84dsPLyYuRd4xkc/fNdK3flVWHVtFxXUcW/bmIgtKCGzq2FFhCCJtyoVV4QFqF\novpIj47G4OWFT6/eekexS0HuQbxx7xus77Keu2rexdzdc+m4riOfHf6MUtP1/aEmBZYQwuZ4tWuH\nV8eO0ioU1ULxsWPkfv0Nfv36YfRw1zuOXavnU49ZbWax4tEV1PaqzZs/v0n3L66vZXtDBZZS6k2l\n1B6l1G6l1FalVMiNHE8IISwlaPzrGL28OCsLkAorl754McrVFd+ofnpHERWaBTZjWYdlzGkzBwd1\nfTcc3OgM1vuapt2maVozYBMw4QaPJ4QQFuHg60vNiRMoOnCA9JgYveMIcUkliWfI3rQZ3549cfD1\n1TuOuIhSilbhrVjbZe11vf6GCixN03Iu+tAdqPpFtYQQ4l94tW+PV8dHSZ2/gKLDR/SOI8T/k7Ek\nFgwG/J55Wu8o4l8YDcbret0NX4OllJqilDoN9EVmsIQQViZo/HiMnp6clbsKhZUpS00la81afLp1\nxTEoSO84wsKuWGAppb5RSu27xFtXAE3TXtM0LRz4CHjxMscZopTaqZTamZqaarkRCCHEZZhbhRPN\nrcLYWL3jCHFBxocfopWV4T9woN5RRCWw2FY5SqlawBZN02650nNlqxwhRFVLfOklcr/5H3XXrsHl\nppv0jiPsXHl2NsfatMXjoYcInT5N7zjiCqp8qxylVMOLPuwKyC6rQgirVPNCq1DuKhT6y1y1ClN+\nPv5Dh+gdRVSSG70G652KduEeoD0wwgKZhBDC4hz8/Kg5YQJF+/eTHrtE7zjCjpkKCsj4cDkeDz2E\nS6NGescRleSGdpPUNO1xSwURQojK5tXhEXIe7UDqvHl4tGktrUKhi6w1ayjPysJ/6FC9o4hKJCu5\nCyHsSs3x4zF6eJhbhWVlescRdkYrKSE9dgluLVvi1qK53nFEJZICSwhhVxz8/MwLkEqrUOgge8MG\nypKT8R8i117ZOimwhBB2x6tDBzw7dCBt7lyKjsgCpKJqaOXlpC+OwaVJE9zvv0/vOKKSSYElhLBL\nNce/jsHDg7PjXpNWoagSuVu3UhIfj//QoSil9I4jKpkUWEIIu+Tg729uFe7bJ61CUek0TSNtUTRO\n9erh2e5hveOIKiAFlhDCbnl16IDnI4+QNncuxUeP6h1H2LD87dspPnQI/0GDUAb51WsP5KsshLBr\nNSeMx+DhQZK0CkUlSotejENIMN6dO+kdRVQRKbCEEHbNwd+fmhPGU7R3L+lLluodR9iggp07Kfz9\nd/yfGYhydNQ7jqgiUmAJIeyeZ4cOeLZvT9qcORQfO6Z3HGFj0hZFY/Tzw+fxHnpHEVVICiwhhN1T\nSlFz4gQM7u7SKhQWVbh/P/k7duA3YAAGV1e944gqJAWWEEJwUatwzx7Sl0qrUFhG+uIYDB4e+PZ5\nUu8ooopJgSWEEBU8H30Uz3btSJstrUJx44pPnCT3q6/w7dsXo6en3nFEFZMCSwghKkirUFhSekwM\nyskJv/5RekcROpACSwghLuIQEEDQ+Ncp2rOHjGXL9I4jqqnSpCSyN2zAJzISB39/veMIHUiBJYQQ\n/+DVsSOe7dqROnsOxceP6x3HKmnl5RQdPkxJQoLeUaxS+tJlAPg/87S+QYRuHPQOIIQQ1uZ8q/DE\nY51IGjeOOqtWoYxGvWPpSisro+jAAQp+20nBb79RsGsXppwcAJzq1cOjdSs8W7XCtXlzlIN9/2op\nS08na/VqvDt3xjEkRO84Qif2/VMghBD/wtwqHE/SqFFkLFuG/8CBekeqUqaSEor27jUXU7/tpOCP\nP9AKCgBwqlMHr0fa43rHHZhycsnbFkfG8hVkxC7B6O2N+4MP4tm6Fe4PPGCXF3dnLF+BVlyM/+BB\nekcROpICSwgh/oXXYx3J/epLUmfNxqNVK5zr19c7UqUxFRZS+OefFPz6m3nl8T//RCsuBsC5YUN8\nunXFrWVLXO+4A8fAwL+91q9/FOV5eeR//wN5cXHkffcdORs3goMDbhEReLZuhUfr1jjVqqXH0KpU\neW4umatW4dm+Pc716ukdR+hIaZpW5SeNiIjQdu7cWeXnFUKIa1WWmsqJTp1xrFPbplqF5Xl5FP7x\nx18F1b59UFoKBgMuN9+MW8uICwWVg6/vNR1bKy+n8M8/yYuLIzcujpJj5uvYLrQSW7fGtVkzm2wl\npkUvJnX6dOqsXYNr06Z6xxEWopT6XdO0iGt6jRRYQghxedmbNpM0ahSBo0fjP/AZveNcl/KsLAp+\n//3CNVRFBw+CyQQODrg2bYrbnS1xi4jAtUULi7f1Sk6fJi9uG3nb4sj/bSeUlppbiQ89iGfr1rjf\nf79NtBJNRUUca/swLo0bUytmsd5xhAVJgSWEEJVA0zQShw0jf/sO6n6+vlq0fsrS0ijYufNCQVV8\n5AgAyskJ19tu+6ugatYMg5tbleUytxK/r2glbqc8K8tmWokZH31E8ptvUXvFctxattQ7jrAgKbCE\nEKKSnG8VOtWpQ+1VH1ldq7D03Lm/Lkj/7TdKTp4EQLm64ta8ubnlFxGBy223YXB21jmt2b+2EuvX\nNxdbrVpVm1aiVlrKsUcewbFmMLU/WolSSu9IwoKkwBJCiEqUvXETSaNH694q1DSN0sTEC9dPFfz2\nG6WJiQAYPDxwu+OOC9dQuTRpgnJ01C3rtTC3EuPI27at2rUSs9Z/ztmxYwlbuADPVq30jiMsTAos\nIYSoRJqmkfjiMPJ3VG2rUNM0Sk6c+GsNqp07KUtOBsDo43NhdsqtZUucGzWyutm161Gem0v+Dz/8\n/1Ziywg8W1lXK1EzmTjRqTPK0ZG6n6+X2SsbJAWWEEJUsrLUVI536oxz3brmVlAlFDOayUTxkSN/\nK6jKMzIAMNYIwL1lS1wjInBv2RKn+vVRBtvelEMrL6dw927ytm27dCuxdWtcb79dt1ZiztatnBk+\ngtDp0/Dq2FGXDKJySYElhBBVIHvjRpJGv0LgK69YZCsUrayMooMH/yqofv/9wirpjiEhF9p9bhER\nONaubfczJCUJCReKrYLfdkJZmW6tRE3TOPVEJOV5udTfssUmZg/F/ycFlhBCVAFN00h84UXyf/iB\nuuvX41yv7jW93lRSQtG+fX+tQbVrF6bzq6TXro1rS/PslFtEBI6hoZUxBJtx2VZi69bmVmJ4eKWd\nP+/7Hzg9aBA135yMb2RkpZ1H6EsKLCGEqCKlKSmc6NzlqlqF5lXS91xo9xXu3n3RKukNLrT7XO+I\nwDEo8F+PIy7vQisxLo7cuG2UVGzU7dSg/oXrtlybNbPoLFN8/wGUxMdT/+utGJycLHZcYV2kwBJC\niCqUvWEDSa+8SuCrr+L/9FMXHi/Pyzevkn6+oNq717xKulI4N775wjVUbnfcgYOfn34DsHElCQnm\nYmvbtr9aiT4+eDz0IB7nW4keHtd9/IJdfxDfpw9BY8fgN2CABZMLayMFlhBCVKGLW4U133ij4sL0\nilXSy8vBaMTllqZ/FVQtWmD08tI7tl0qz80l//vvyY2LI/+77ZRnZ4OjI24Rd1x3K/H0s89RuHs3\nDb79X5Uu1iqqnhRYQghRxc63Ck3Z2ShHR1xuv+3CBeluzZphcHfXO6L4B62s7KK7Ev/RSqwotlxv\nv/2yrcSiw4c52bUbAcOHUeP556squtCJFFhCCKGD4mPHKM/MtKpV0sXVu9BKjNtGwc6rayWeeXkU\neXFxNIj7FqO3t07JRVWRAksIIYS4Af/WSnRvGYFHq9Z4tG4FJhPHH+2I39NPETR6tN6RRRWQAksI\nIYSwkPOtxNy4OPK2fXehlWjw9EQrLqb+N1/jGCh3fdqD6ymwrH8HTSGEEEIHysHBfC1dRARBo0dT\nEh9P3rZt5H23Hbd77pbiSlyWzGAJIYQQQlzG9cxg2fYGVkIIIYQQOpACSwghhBDCwqTAEkIIIYSw\nMCmwhBBCCCEsTAosIYQQQggLkwJLCCGEEMLCpMASQgghhLAwKbCEEEIIISxMCiwhhBBCCAuTAksI\nIYQQwsKkwBJCCCGEsDApsIQQQgghLEwKLCGEEEIIC5MCSwghhBDCwqTAEkIIIYSwMCmwhBBCCCEs\nTAosIYQQQggLkwJLCCGEEMLCpMASQgghhLAwKbCEEEIIISxMCiwhhBBCCAuTAksIIYQQwsKkwBJC\nCCGEsDClaVrVn1SpXOBwlZ9YfwFAmt4hdCDjti8ybvsi47Yv9jruRpqmeV7LCxwqK8kVHNY0LUKn\nc+tGKbVTxm0/ZNz2RcZtX2Tc9kUptfNaXyMtQiGEEEIIC5MCSwghhBDCwvQqsKJ1Oq/eZNz2RcZt\nX2Tc9kXGbV+uedy6XOQuhBBCCGHLpEUohBBCCGFhVVpgKaU6KKUOK6WOKaXGVOW5q5pSaolSKkUp\nte+ix/yUUl8rpY5W/OurZ0ZLU0qFK6XilFIHlFL7lVIjKh639XG7KKV+VUr9WTHuSRWP2/S4z1NK\nGZVSfyilNlV8bPPjVkqdUkrtVUrtPn93kZ2M20cptUYpdUgpdVApdY+tj1sp1aji63z+LUcpNdLW\nxw2glHqp4v+0fUqpjyv+r7OHcY+oGPN+pdTIiseuedxVVmAppYzAPOBRoAnwpFKqSVWdXwfLgA7/\neGwM8D9N0xoC/6v42JaUAS9rmtYEuBt4oeJrbOvjLgbaaJp2O9AM6KCUuhvbH/d5I4CDF31sL+Nu\nrWlas4tuWbeHcc8CvtQ07Wbgdsxfd5set6Zphyu+zs2AO4ACYD02Pm6lVCgwHIjQNO0WwAj0xvbH\nfQswGLgT8/d4J6VUA65n3JqmVckbcA/w1UUfjwXGVtX59XgD6gD7Lvr4hjiraQAAA6BJREFUMBBc\n8X4w5vXAdM9ZieP/AmhnT+MG3IBdwF32MG4grOI/mzbAporH7GHcp4CAfzxm0+MGvIGTVFy7ay/j\n/sdY2wM/2MO4gVDgNOCHec3MTRXjt/VxRwKxF308HnjlesZdlS3C81+s8xIrHrMnQZqmna14/xwQ\npGeYyqSUqgM0B37BDsZd0SbbDaQAX2uaZhfjBmZi/s/HdNFj9jBuDfhGKfW7UmpIxWO2Pu66QCqw\ntKIlHKOUcsf2x32x3sDHFe/b9Lg1TTsDfAAkAGeBbE3TtmLj4wb2AQ8opfyVUm5ARyCc6xi3XOSu\nE81cBtvkLZxKKQ9gLTBS07Sciz9nq+PWNK1cM7cQwoA7K6aZL/68zY1bKdUJSNE07fd/e44tjrvC\n/RVf70cxt8IfvPiTNjpuB6AFsEDTtOZAPv9ok9jouAFQSjkBXYDV//ycLY674hqjrpgL6xDAXSnV\n7+Ln2OK4NU07CLwLbAW+BHYD5f94zlWNuyoLrDOYq8DzwioesyfJSqlggIp/U3TOY3FKKUfMxdVH\nmqatq3jY5sd9nqZpWUAc5uvvbH3c9wFdlFKngE+ANkqpldj+uM//dY+maSmYr8e5E9sfdyKQWDE7\nC7AGc8Fl6+M+71Fgl6ZpyRUf2/q4HwZOapqWqmlaKbAOuBfbHzeapsVqmnaHpmkPApnAEa5j3FVZ\nYP0GNFRK1a34S6A3sKEKz28NNgADKt4fgPkaJZuhlFJALHBQ07TpF33K1sddQynlU/G+K+brzg5h\n4+PWNG2spmlhmqbVwfzz/K2maf2w8XErpdyVUp7n38d8Xco+bHzcmqadA04rpRpVPNQWOICNj/si\nT/JXexBsf9wJwN1KKbeK/9vbYr6pwdbHjVIqsOLfWkAPYBXXMe4qXWhUKdUR8zUbRmCJpmlTquzk\nVUwp9THQCvPO48nAROBz4DOgFhAP9NQ0LUOvjJamlLof2AHs5a9rcsZhvg7Llsd9G/Ah5u9rA/CZ\npmmTlVL+2PC4L6aUagWM0jStk62PWylVD/OsFZjbZqs0TZti6+MGUEo1A2IAJ+AE8DQV3/PY9rjd\nMRcc9TRNy654zB6+3pOAXpjvEP8DGAR4YPvj3gH4A6XAfzRN+9/1fL1lJXchhBBCCAuTi9yFEEII\nISxMCiwhhBBCCAuTAksIIYQQwsKkwBJCCCGEsDApsIQQQgghLEwKLCGEEEIIC5MCSwghhBDCwqTA\nEkIIIYSwsP8DLwUwRBCkBtIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf4fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = DataFrame(np.random.randn(10, 4).cumsum(0),\n",
    "               columns=['A', 'B', 'C', 'D'],\n",
    "               index=np.arange(0, 100, 10))\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc231400>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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51/Czk6dPTfKcPLAsx0aST3T3jy+qlqma3p7kviQv7O6nTwLnB7r7OQsa/6eT/FSSp2R3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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5f03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "data = Series(np.random.rand(16), index=list('abcdefghijklmnop'))\n",
    "data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)\n",
    "data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1607bef0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OMa6Y6WDLWFX9SuausfpGVf2nzL3ub1yuR6y8K3Aya+cvumORVNU7k+yX5KuZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130ed470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = DataFrame(np.random.rand(6, 4),\n",
    "               index=['one', 'two', 'three', 'four', 'five', 'six'],\n",
    "               columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))\n",
    "df\n",
    "df.plot(kind='bar', alpha= 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc647c88>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc647c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152028d0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAJCCAYAAACIzRDTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12116320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='barh', stacked=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>1708</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  1708\n",
       "day        \n",
       "Fri      19\n",
       "Sat      87\n",
       "Sun      76\n",
       "Thur     62"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = pd.read_csv('ch08/tips.csv')\n",
    "party_counts = pd.crosstab(tips.day, tips.size)\n",
    "party_counts\n",
    "# Not many 1- and 6-person parties\n",
    "#party_counts = party_counts.ix[:, 2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [Fri, Sat, Sun, Thur]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize to sum to 1\n",
    "party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis=0)\n",
    "party_pcts\n",
    "\n",
    "#party_pcts.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms and density plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc26d8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc26d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xebd01d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qUkzd0Lv5xhgPjzG+NMZ4PMkvJHnhjVwPAMCqu6GYqqpjV528O8mD1zsWAOBmdv3nR2aq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\nxBQAQIOYAgBoEFMAAA1iCgCg4f8DdtJxM/UlzGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc2c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips['tip_pct'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc2d68>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xed6a278>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vf0GN8PUC1tpPW2sPWmsPTkxMOCwHCE6uWHF+J6JnpC+lfKmqcrXuy+sBAC7PWdiy1p6V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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa58f898>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa58f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa73f780>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y839BTwcAABDlTlXWfiept7V245EJ1toNkvpIuimYwQAAAHDqshZvrf3PsRMLr1uLD04k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\nZmYZ5s6amZmZWYa5s2ZmZmaWYf8DHoSoNO6y6ksAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeaebcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp1 = np.random.normal(0, 1, size=200)  # N(0, 1)\n",
    "comp2 = np.random.normal(10, 2, size=200)  # N(10, 4)\n",
    "values = Series(np.concatenate([comp1, comp2]))\n",
    "values.hist(bins=100, alpha=0.3, color='k', normed=True)\n",
    "values.plot(kind='kde', style='k--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.007904</td>\n",
       "      <td>0.045361</td>\n",
       "      <td>-0.396881</td>\n",
       "      <td>0.105361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.021979</td>\n",
       "      <td>0.066753</td>\n",
       "      <td>-2.277267</td>\n",
       "      <td>0.139762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.002340</td>\n",
       "      <td>0.010286</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>0.160343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.037461</td>\n",
       "      <td>-0.200671</td>\n",
       "      <td>0.127339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.008894</td>\n",
       "      <td>0.012202</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>0.042560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cpi        m1  tbilrate     unemp\n",
       "198 -0.007904  0.045361 -0.396881  0.105361\n",
       "199 -0.021979  0.066753 -2.277267  0.139762\n",
       "200  0.002340  0.010286  0.606136  0.160343\n",
       "201  0.008419  0.037461 -0.200671  0.127339\n",
       "202  0.008894  0.012202 -0.405465  0.042560"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv('ch08/macrodata.csv')\n",
    "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]\n",
    "trans_data = np.log(data).diff().dropna()\n",
    "trans_data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc2550>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1505ca20>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AlWi+BAAACAA1ZUAgZucbNBECwBgjlAEBYIJWAADNl0AAuk3QCgAYD4QyIABM0AoAIJQB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\nBIBQBgAAEID/H5+5W48xCWvQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c118d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(trans_data['m1'], trans_data['unemp'])\n",
    "plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011851208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000128DA780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011F0A4A8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001175A9E8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011D844E0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011998358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000117B8E80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000123C36D8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000120624E0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000010BEA828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000115EA978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000110D7240>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011008D68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011035710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011632DA0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001151A7F0>]], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sY/FnxqMo7WoUqnRMVoiGIdEDEFaHU28UH9/MUY2a4lymZkPccPnX5f03A+E9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\nFAqFQqFQKCKA/w/7bNpjflpIkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11841d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(trans_data, diagonal='kde', c='k', alpha=0.3)  #要把color 改成c，否者会报错！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Maps: Visualizing Haiti Earthquake Crisis data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3593 entries, 0 to 3592\n",
      "Data columns (total 10 columns):\n",
      "Serial            3593 non-null int64\n",
      "INCIDENT TITLE    3593 non-null object\n",
      "INCIDENT DATE     3593 non-null object\n",
      "LOCATION          3592 non-null object\n",
      "DESCRIPTION       3593 non-null object\n",
      "CATEGORY          3587 non-null object\n",
      "LATITUDE          3593 non-null float64\n",
      "LONGITUDE         3593 non-null float64\n",
      "APPROVED          3593 non-null object\n",
      "VERIFIED          3593 non-null object\n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 280.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('ch08/Haiti.csv')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCIDENT DATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05/07/2010 17:26</td>\n",
       "      <td>18.233333</td>\n",
       "      <td>-72.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28/06/2010 23:06</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>5.729886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/06/2010 16:21</td>\n",
       "      <td>22.278381</td>\n",
       "      <td>114.174287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20/06/2010 21:59</td>\n",
       "      <td>44.407062</td>\n",
       "      <td>8.933989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18/05/2010 16:26</td>\n",
       "      <td>18.571084</td>\n",
       "      <td>-72.334671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26/04/2010 13:14</td>\n",
       "      <td>18.593707</td>\n",
       "      <td>-72.310079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26/04/2010 14:19</td>\n",
       "      <td>18.482800</td>\n",
       "      <td>-73.638800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26/04/2010 14:27</td>\n",
       "      <td>18.415000</td>\n",
       "      <td>-73.195000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15/03/2010 10:58</td>\n",
       "      <td>18.517443</td>\n",
       "      <td>-72.236841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15/03/2010 11:00</td>\n",
       "      <td>18.547790</td>\n",
       "      <td>-72.410010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      INCIDENT DATE   LATITUDE   LONGITUDE\n",
       "0  05/07/2010 17:26  18.233333  -72.533333\n",
       "1  28/06/2010 23:06  50.226029    5.729886\n",
       "2  24/06/2010 16:21  22.278381  114.174287\n",
       "3  20/06/2010 21:59  44.407062    8.933989\n",
       "4  18/05/2010 16:26  18.571084  -72.334671\n",
       "5  26/04/2010 13:14  18.593707  -72.310079\n",
       "6  26/04/2010 14:19  18.482800  -73.638800\n",
       "7  26/04/2010 14:27  18.415000  -73.195000\n",
       "8  15/03/2010 10:58  18.517443  -72.236841\n",
       "9  15/03/2010 11:00  18.547790  -72.410010"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1. Urgences | Emergency, 3. Public Health, \n",
       "1    1. Urgences | Emergency, 2. Urgences logistiqu...\n",
       "2    2. Urgences logistiques | Vital Lines, 8. Autr...\n",
       "3                            1. Urgences | Emergency, \n",
       "4                            1. Urgences | Emergency, \n",
       "5                       5e. Communication lines down, \n",
       "Name: CATEGORY, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CATEGORY'][:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3593.000000</td>\n",
       "      <td>3593.000000</td>\n",
       "      <td>3593.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2080.277484</td>\n",
       "      <td>18.611495</td>\n",
       "      <td>-72.322680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1171.100360</td>\n",
       "      <td>0.738572</td>\n",
       "      <td>3.650776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.041313</td>\n",
       "      <td>-74.452757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1074.000000</td>\n",
       "      <td>18.524070</td>\n",
       "      <td>-72.417500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2163.000000</td>\n",
       "      <td>18.539269</td>\n",
       "      <td>-72.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3088.000000</td>\n",
       "      <td>18.561820</td>\n",
       "      <td>-72.293570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4052.000000</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>114.174287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Serial     LATITUDE    LONGITUDE\n",
       "count  3593.000000  3593.000000  3593.000000\n",
       "mean   2080.277484    18.611495   -72.322680\n",
       "std    1171.100360     0.738572     3.650776\n",
       "min       4.000000    18.041313   -74.452757\n",
       "25%    1074.000000    18.524070   -72.417500\n",
       "50%    2163.000000    18.539269   -72.335000\n",
       "75%    3088.000000    18.561820   -72.293570\n",
       "max    4052.000000    50.226029   114.174287"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) &\n",
    "            (data.LONGITUDE > -75) & (data.LONGITUDE < -70)\n",
    "            & data.CATEGORY.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cat_list(catstr):\n",
    "    stripped = (x.strip() for x in catstr.split(','))\n",
    "    return [x for x in stripped if x]\n",
    "\n",
    "def get_all_categories(cat_series):\n",
    "    cat_sets = (set(to_cat_list(x)) for x in cat_series)\n",
    "    return sorted(set.union(*cat_sets))\n",
    "\n",
    "def get_english(cat):\n",
    "    code, names = cat.split('.')\n",
    "    if '|' in names:\n",
    "        names = names.split(' | ')[1]\n",
    "    return code, names.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2', 'Vital Lines')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_english('2. Urgences logistiques | Vital Lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Earthquake and aftershocks'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cats = get_all_categories(data.CATEGORY)\n",
    "# Generator expression\n",
    "english_mapping = dict(get_english(x) for x in all_cats)\n",
    "english_mapping['2a']\n",
    "english_mapping['6c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_code(seq):\n",
    "    return [x.split('.')[0] for x in seq if x]\n",
    "\n",
    "all_codes = get_code(all_cats)\n",
    "code_index = pd.Index(np.unique(all_codes))\n",
    "dummy_frame = DataFrame(np.zeros((len(data), len(code_index))),\n",
    "                        index=data.index, columns=code_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3569 entries, 0 to 3592\n",
      "Data columns (total 6 columns):\n",
      "1     3569 non-null float64\n",
      "1a    3569 non-null float64\n",
      "1b    3569 non-null float64\n",
      "1c    3569 non-null float64\n",
      "1d    3569 non-null float64\n",
      "2     3569 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 195.2 KB\n"
     ]
    }
   ],
   "source": [
    "dummy_frame.ix[:, :6].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row, cat in zip(data.index, data.CATEGORY):\n",
    "    codes = get_code(to_cat_list(cat))\n",
    "    dummy_frame.ix[row, codes] = 1\n",
    "\n",
    "data = data.join(dummy_frame.add_prefix('category_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3569 entries, 0 to 3592\n",
      "Data columns (total 5 columns):\n",
      "category_1     3569 non-null float64\n",
      "category_1a    3569 non-null float64\n",
      "category_1b    3569 non-null float64\n",
      "category_1c    3569 non-null float64\n",
      "category_1d    3569 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 167.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.ix[:, 10:15].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named basemap",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-68-ec31ba3e955e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbasemap\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mBasemap\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,\n\u001b[0;32m      5\u001b[0m                     lllon=-75, urlon=-71):\n",
      "\u001b[1;31mImportError\u001b[0m: No module named basemap"
     ]
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap            #首先要安装basemap插件，它是matplotlib的插件，http://matplotlib.org/basemap/\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,\n",
    "                    lllon=-75, urlon=-71):\n",
    "    # create polar stereographic Basemap instance.\n",
    "    m = Basemap(ax=ax, projection='stere',\n",
    "                lon_0=(urlon + lllon) / 2,\n",
    "                lat_0=(urlat + lllat) / 2,\n",
    "                llcrnrlat=lllat, urcrnrlat=urlat,\n",
    "                llcrnrlon=lllon, urcrnrlon=urlon,\n",
    "                resolution='f')\n",
    "    # draw coastlines, state and country boundaries, edge of map.\n",
    "    m.drawcoastlines()\n",
    "    m.drawstates()\n",
    "    m.drawcountries()\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'basic_haiti_map' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-69-395ceffee373>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_plot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m     m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,\n\u001b[0m\u001b[0;32m     10\u001b[0m                         lllon=lllon, urlon=urlon)\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'basic_haiti_map' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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BAKBDWQYAgA5lGQAAOpRlAADoUJYBAKBDWQYAgA5lGQAAOpRlAADoUJYBAKBDWQYAgA5l\nGQAAOpRlAADoUJYBAKBDWQYAgA5lGQAAOpRlAADoUJYBAKBDWQYAgA5lGQAAOpRlAADoUJYBAKBD\nWQYAgA5lGQAAOpRlAADoUJYBAKBDWQYAgA5lGQAAOpRlAADoUJYBAKBDWQYAgA5lGQAAOkYqy1V1\nb1W9WFXnqurRq+yvqvqT4f5nq+oDmz9VgMly4sSJ7NmzJ7Ozszl06NAV+2UrwPhNbTSgqnYkeSzJ\nPUmWkpyuquOttRfWDLsvyR3Djw8l+cLwvwBcxcrKSg4ePJiTJ09mMBhkfn4++/fvXz9MtgKM2Shn\nlu9Ocq619lJr7WKSo0keWDfmgSR/2lY9k+QdVfVLmzxXgIlx6tSpzM7OZvfu3dm5c2cWFhZy7Nix\n9cNkK8CYjVKWp5NcWLO9NHzsescAMLS8vJyZmZnXtweDQZaXl9cPk60AY7bhZRibqaoeTvLwcPNH\nVfXN7Tz+TepdSV4d9yTGzBpYg8veSuvwziRv/9KXvvTd4fY/S/KzSfZc7xeSrVd4K/0cXYt1sAaJ\nNbjsurP1slHK8nKSmTXbg+Fj1zsmrbUjSY4kSVUtttbmrmu2E8g6WIPEGlz2VlqHqvqXSf5ja23f\ncPv3h7v+/ZphsvUGWINV1sEaJNbgsqpavNHnjnIZxukkd1TV7VW1M8lCkuPrxhxP8unhO7c/nOQf\nW2t/f6OTAngLkK0At4ANzyy31i5V1SNJnkqyI8kTrbXnq+rAcP/jSb6S5P4k55L8U5IHt27KALe+\na2Trrqo6IFsBbg4jXbPcWvtKVkN77WOPr/m8JTl4ncc+cp3jJ5V1sAaJNbjsLbUOV8vWJP9peFmF\nbL1x1mCVdbAGiTW47IbXoVazGAAAWM/trgEAoGPLy7JbZY+0Br85/N6fq6qvVdX7xzHPrbTRGqwZ\nN19Vl6rqE9s5v+0yyjpU1Uer6utV9XxV/c12z3GrjfD78PNV9VdV9Y3hGkzcdbpV9URVfa/3J95G\nyUXZKlsT2XqZbJWtyeZk61W11rbsI6tvWvlfSXYn2ZnkG0n+xbox9yf56ySV5MNJ/sdWzmm7P0Zc\ng3+V5J3Dz+97K67BmnH/PavXcH5i3PMe08/CO5K8kOS24fa7xz3vMazBf0jyueHnu5L8IMnOcc99\nk9fhXyf5QJJvdvZfMxdlq2wddQ3WjJOtslW23mAubvWZZbfKHmENWmtfa639w3Dzmaz+LdVJMsrP\nQZL8bpK/SPK97ZzcNhplHX4jyZOttZeTpLU2aWsxyhq0JD9XVZXVm3T8IMml7Z3m1mqtfTWr31fP\nRrkoW2VrIlsvk62yNcmmZOtVbViW3+QpbbfKvv7v77ez+q+eSbLhGlTVdJJfT/KFbZzXdhvlZ+G9\nSd5ZVU9X1Zmq+vS2zW57jLIGh5PcleTvkjyX5Pdaa69tz/S21f+d5M5Otk4nuXA5W5P8cpJ/u37/\nmm3ZKlsT2XqZbH1rZ+u13FAujnJm+ctJ7r3G/vuS3DH8eDiT/Qu5parq17Ia6J8Z91zG4I+TfOYt\n+Iu73lSSDyb5P5PsS/J/VdV7xzulbbcvydezWhB/Ncnhqnr7eKe0Jf48yXeusf/DeSNb/2eS37/G\nWK5BtsrWyNbkrZOtm26Um5J8tarec40hr5/STvJMVb2jqn6prd5latNulX0LG+n7q6r3Jflikvta\na9/fprltl1HWYC7J0dVXh/KuJPdX1aXW2l9uzxS3xSjrsJTk+621Hyb5YVV9Ncn7k3x7e6a45UZZ\ngweTHBpmyrmqOp/kziSntmeK2+ZUkpXOvuWsnqT4UmutDf+HtkO2/gTZKlsvk62ydVQ3lIubcc3y\ntU5pu53rCGtQVbcleTLJb7XWJuUXd60N16C1dntr7T2ttfdk9Yzb70xYmCej/T4cS/KRqpqqqp9J\n8qEk39rmeW6lUdbg5SQfS5Kq+sUke5K8tK2zHL/jSd6X1UsxPpzkH5N8N7J1LdkqWy+TrbJ1VDeU\niyPdlGR4Zvm/ttZ+5Sr7/mtW/6Xy/w23/5+svuSzONy+P6svA+1I8mxWG/2ut73tbbfdeeedI35v\nAJPnRz/6Uc6dO5e9e/e+/tiZM2deTfLuJOeT/FRW36zyYJLPRbYC3JA12Xo4q6/c/VOSBy9n6rWM\ndLvrDVzzlHa7+u1cMzc31xYXN5wfwMT6zne+k49//ONZm4VV9d3hpRdPJXm6tfZnw8dlK8ANupyt\nSQ5e73M34zKMSX+pD2AcZCvATWDDM8tV9WdJPprkXVW1lOQPs/rSYFprj2f1zMb9Sc5leEp7qyYL\nMCk+9alP5emnn86rr76awWCQz372s/nxj3+crN4sIJGtADeFka5Z3gpeKgS4UlWdaa3N3ejzZSvA\nld5Mtm71HfwAAOCWpSwDAECHsgwAAB3KMgAAdCjLAADQoSwDAECHsgwAAB3KMgAAdCjLAADQoSwD\nAECHsgwAAB3KMgAAdCjLAADQoSwDAECHsgwAAB3KMgAAdCjLAADQoSwDAECHsgwAAB3KMgAAdCjL\nAADQoSwDAECHsgwAAB3KMgAAdCjLAADQoSwDAECHsgwAAB3KMgAAdCjLAADQoSwDAECHsgwAAB3K\nMgAAdCjLAADQoSwDAECHsgwAAB3KMgAAdCjLAADQoSwDAECHsgwAAB3KMgAAdCjLAADQoSwDAECH\nsgwAAB3KMgAAdIxUlqvq3qp6sarOVdWjV9n/81X1V1X1jap6vqoe3PypAkyWEydOZM+ePZmdnc2h\nQ4eu2C9bAcZvaqMBVbUjyWNJ7kmylOR0VR1vrb2wZtjBJC+01v5dVe1K8mJV/efW2sUtmTXALW5l\nZSUHDx7MyZMnMxgMMj8/n/37968fJlsBxmyUM8t3JznXWntpGNBHkzywbkxL8nNVVUl+NskPklza\n1JkCTJBTp05ldnY2u3fvzs6dO7OwsJBjx46tHyZbAcZslLI8neTCmu2l4WNrHU5yV5K/S/Jckt9r\nrb22KTMEmEDLy8uZmZl5fXswGGR5eXn9MNkKMGab9Qa/fUm+nuSXk/xqksNV9fb1g6rq4aparKrF\nV155ZZMODTCxZCvAmI1SlpeTzKzZHgwfW+vBJE+2VeeSnE9y5/ov1Fo70lqba63N7dq160bnDHDL\nm56ezoULb7xot7S0lOnp9S/ayVaAcRulLJ9OckdV3V5VO5MsJDm+bszLST6WJFX1i0n2JHlpMycK\nMEnm5+dz9uzZnD9/PhcvXszRo0ev9gY/2QowZhv+NYzW2qWqeiTJU0l2JHmitfZ8VR0Y7n88yR8l\n+XJVPZekknymtfbqFs4b4JY2NTWVw4cPZ9++fVlZWclDDz2UvXv3JsmuqjogWwFuDtVaG8uB5+bm\n2uLi4liODXCzqqozrbW5G32+bAW40pvJVnfwAwCADmUZAAA6lGUAAOhQlgEAoENZBgCADmUZAAA6\nlGUAAOhQlgEAoENZBgCADmUZAAA6lGUAAOhQlgEAoENZBgCADmUZAAA6lGUAAOhQlgEAoENZBgCA\nDmUZAAA6lGUAAOhQlgEAoENZBgCADmUZAAA6lGUAAOhQlgEAoENZBgCADmUZAAA6lGUAAOhQlgEA\noENZBgCADmUZAAA6lGUAAOhQlgEAoENZBgCADmUZAAA6lGUAAOhQlgEAoENZBgCADmUZAAA6lGUA\nAOhQlgEAoENZBgCADmUZAAA6lGUAAOhQlgEAoGOkslxV91bVi1V1rqoe7Yz5aFV9vaqer6q/2dxp\nAkyeEydOZM+ePZmdnc2hQ4euOka2AozX1EYDqmpHkseS3JNkKcnpqjreWnthzZh3JPl8kntbay9X\n1bu3asIAk2BlZSUHDx7MyZMnMxgMMj8/n/379//EGNkKMH6jnFm+O8m51tpLrbWLSY4meWDdmN9I\n8mRr7eUkaa19b3OnCTBZTp06ldnZ2ezevTs7d+7MwsJCjh07tn6YbAUYs1HK8nSSC2u2l4aPrfXe\nJO+sqqer6kxVfXqzJggwiZaXlzMzM/P69mAwyPLy8vphshVgzDa8DOM6vs4Hk3wsyU8n+duqeqa1\n9u21g6rq4SQPJ8ltt922SYcGmFiyFWDMRjmzvJxkZs32YPjYWktJnmqt/bC19mqSryZ5//ov1Fo7\n0lqba63N7dq160bnDHDLm56ezoULb7xot7S0lOnp9S/ayVaAcRulLJ9OckdV3V5VO5MsJDm+bsyx\nJB+pqqmq+pkkH0ryrc2dKsDkmJ+fz9mzZ3P+/PlcvHgxR48eveINfpGtAGO34WUYrbVLVfVIkqeS\n7EjyRGvt+ao6MNz/eGvtW1V1IsmzSV5L8sXW2je3cuIAt7KpqakcPnw4+/bty8rKSh566KHs3bs3\nSXZV1QHZCnBzqNbaWA48NzfXFhcXx3JsgJtVVZ1prc3d6PNlK8CV3ky2uoMfAAB0KMsAANChLAMA\nQIeyDAAAHcoyAAB0KMsAANChLAMAQIeyDAAAHcoyAAB0KMsAANChLAMAQIeyDAAAHcoyAAB0KMsA\nANChLAMAQIeyDAAAHcoyAAB0KMsAANChLAMAQIeyDAAAHcoyAAB0KMsAANChLAMAQIeyDAAAHcoy\nAAB0KMsAANChLAMAQIeyDAAAHcoyAAB0KMsAANChLAMAQIeyDAAAHcoyAAB0KMsAANChLAMAQIey\nDAAAHcoyAAB0KMsAANChLAMAQIeyDAAAHcoyAAB0KMsAANChLAMAQIeyDAAAHSOV5aq6t6perKpz\nVfXoNcbNV9WlqvrE5k0RYDKdOHEie/bsyezsbA4dOtQdJ1sBxmdqowFVtSPJY0nuSbKU5HRVHW+t\nvXCVcZ9L8t+2YqIAk2RlZSUHDx7MyZMnMxgMMj8/n/37918xTrYCjNcoZ5bvTnKutfZSa+1ikqNJ\nHrjKuN9N8hdJvreJ8wOYSKdOncrs7Gx2796dnTt3ZmFhIceOHbvaUNkKMEajlOXpJBfWbC8NH3td\nVU0n+fUkX9i8qQFMruXl5czMzLy+PRgMsry8/BNjZCvA+G3WG/z+OMlnWmuvXWtQVT1cVYtVtfjK\nK69s0qEBJpZsBRizDa9ZTrKcZGbN9mD42FpzSY5WVZK8K8n9VXWptfaXawe11o4kOZIkc3Nz7UYn\nDXCrm56ezoULb7xot7S0lOnp6fXDZCvAmI1yZvl0kjuq6vaq2plkIcnxtQNaa7e31t7TWntPkj9P\n8jvrwxyAN8zPz+fs2bM5f/58Ll68mKNHj17xBj/ZCjB+G5bl1tqlJI8keSrJt5L8l9ba81V1oKoO\nbPUEASbR1NRUDh8+nH379uWuu+7KJz/5yezduzdJdslWgJtHtTaeV+zm5uba4uLiWI4NcLOqqjOt\ntbkbfb5sBbjSm8lWd/ADAIAOZRkAADqUZQAA6FCWAQCgQ1kGAIAOZRkAADqUZQAA6FCWAQCgQ1kG\nAIAOZRkAADqUZQAA6FCWAQCgQ1kGAIAOZRkAADqUZQAA6FCWAQCgQ1kGAIAOZRkAADqUZQAA6FCW\nAQCgQ1kGAIAOZRkAADqUZQAA6FCWAQCgQ1kGAIAOZRkAADqUZQAA6FCWAQCgQ1kGAIAOZRkAADqU\nZQAA6FCWAQCgQ1kGAIAOZRkAADqUZQAA6FCWAQCgQ1kGAIAOZRkAADqUZQAA6FCWAQCgQ1kGAIAO\nZRkAADqUZQAA6FCWAQCgY6SyXFX3VtWLVXWuqh69yv7frKpnq+q5qvpaVb1/86cKMFlOnDiRPXv2\nZHZ2NocOHbpiv2wFGL+pjQZU1Y4kjyW5J8lSktNVdby19sKaYeeT/JvW2j9U1X1JjiT50FZMGGAS\nrKys5ODBgzl58mQGg0Hm5+ezf//+9cNkK8CYbViWk9yd5Fxr7aUkqaqjSR5I8npZbq19bc34Z5IM\nNnOSAJPm1KlTmZ2dze7du5MkCwsLOXbs2E+Mka0A4zfKZRjTSS6s2V4aPtbz20n++s1MCmDSLS8v\nZ2Zm5vXtwWCQ5eXlaz1FtgKMwShnlkdWVb+W1UD/SGf/w0keTpLbbrttMw8NMLFkK8D4jHJmeTnJ\nzJrtwfCxn1BV70vyxSQPtNa+f7Uv1Fo70lqba63N7dq160bmCzARpqenc+HCGy/aLS0tZXr6yhft\nZCvAeI3jFZWqAAAHKklEQVRSlk8nuaOqbq+qnUkWkhxfO6CqbkvyZJLfaq19e/OnCTBZ5ufnc/bs\n2Zw/fz4XL17M0aNHr3iDn2wFGL8NL8NorV2qqkeSPJVkR5InWmvPV9WB4f7Hk/xBkl9I8vmqSpJL\nrbW5rZs2wK1tamoqhw8fzr59+7KyspKHHnooe/fuTZJdVXVAtgLcHKq1NpYDz83NtcXFxbEcG+Bm\nVVVn3kwhlq0AV3oz2eoOfgAA0KEsAwBAh7IMAAAdyjIAAHQoywAA0KEsAwBAh7IMAAAdyjIAAHQo\nywAA0KEsAwBAh7IMAAAdyjIAAHQoywAA0KEsAwBAh7IMAAAdyjIAAHQoywAA0KEsAwBAh7IMAAAd\nyjIAAHQoywAA0KEsAwBAh7IMAAAdyjIAAHQoywAA0KEsAwBAh7IMAAAdyjIAAHQoywAA0KEsAwBA\nh7IMAAAdyjIAAHQoywAA0KEsAwBAh7IMAAAdyjIAAHQoywAA0KEsAwBAh7IMAAAdyjIAAHQoywAA\n0KEsAwBAh7IMAAAdyjIAAHSMVJar6t6qerGqzlXVo1fZX1X1J8P9z1bVBzZ/qgCT5cSJE9mzZ09m\nZ2dz6NChK/bLVoDxm9poQFXtSPJYknuSLCU5XVXHW2svrBl2X5I7hh8fSvKF4X8BuIqVlZUcPHgw\nJ0+ezGAwyPz8fPbv379+mGwFGLNRzizfneRca+2l1trFJEeTPLBuzANJ/rSteibJO6rqlzZ5rgAT\n49SpU5mdnc3u3buzc+fOLCws5NixY+uHyVaAMRulLE8nubBme2n42PWOAWBoeXk5MzMzr28PBoMs\nLy+vHyZbAcZsw8swNlNVPZzk4eHmj6rqm9t5/JvUu5K8Ou5JjJk1sAaXvZXW4Z1J3v6lL33pu8Pt\nf5bkZ5Psud4vJFuv8Fb6OboW62ANEmtw2XVn62WjlOXlJDNrtgfDx653TFprR5IcSZKqWmytzV3X\nbCeQdbAGiTW47K20DlX1L5P8x9bavuH27w93/fs1w2TrDbAGq6yDNUiswWVVtXijzx3lMozTSe6o\nqturameShSTH1405nuTTw3dufzjJP7bW/v5GJwXwFiBbAW4BG55Zbq1dqqpHkjyVZEeSJ1prz1fV\ngeH+x5N8Jcn9Sc4l+ackD27dlAFufdfI1l1VdUC2AtwcRrpmubX2layG9trHHl/zeUty8DqPfeQ6\nx08q62ANEmtw2VtqHa6WrUn+0/CyCtl646zBKutgDRJrcNkNr0OtZjEAALCe210DAEDHlpdlt8oe\naQ1+c/i9P1dVX6uq949jnltpozVYM26+qi5V1Se2c37bZZR1qKqPVtXXq+r5qvqb7Z7jVhvh9+Hn\nq+qvquobwzWYuOt0q+qJqvpe70+8jZKLslW2JrL1MtkqW5PNydaraq1t2UdW37Tyv5LsTrIzyTeS\n/It1Y+5P8tdJKsmHk/yPrZzTdn+MuAb/Ksk7h5/f91ZcgzXj/ntWr+H8xLjnPaafhXckeSHJbcPt\nd4973mNYg/+Q5HPDz3cl+UGSneOe+yavw79O8oEk3+zsv2YuylbZOuoarBknW2WrbL3BXNzqM8tu\nlT3CGrTWvtZa+4fh5jNZ/Vuqk2SUn4Mk+d0kf5Hke9s5uW00yjr8RpInW2svJ0lrbdLWYpQ1aEl+\nrqoqqzfp+EGSS9s7za3VWvtqVr+vno1yUbbK1kS2XiZbZWuSTcnWq9rqsuxW2df//f12Vv/VM0k2\nXIOqmk7y60m+sI3z2m6j/Cy8N8k7q+rpqjpTVZ/ettltj1HW4HCSu5L8XZLnkvxea+217ZneTWOj\ndZKtsjWRrZfJVtk6qhvKxW293TXXVlW/ltVA/8i45zIGf5zkM62111b/0fuWNZXkg0k+luSnk/xt\nVT3TWvv2eKe1rfYl+XqSf5vknyc5WVX/b2vtf493WtyqZKtsjWxNZOsN2+qyvGm3yr6FjfT9VdX7\nknwxyX2tte9v09y2yyhrMJfk6DDM35Xk/qq61Fr7y+2Z4rYYZR2Wkny/tfbDJD+sqq8meX+SSQn0\nUdbgwSSH2uoFZueq6nySO5Oc2p4p3hQ2WifZKlsT2XqZbJWto7qhXNzqyzDcznWENaiq25I8meS3\nJvRfuRuuQWvt9tbae1pr70ny50l+Z8LCPBnt9+FYko9U1VRV/UySDyX51jbPcyuNsgYvZ/XsT6rq\nF5PsSfLSts5y/DbKRdkqWxPZeplsla2juqFc3NIzy82tskddgz9I8gtJPj/81/+l1trcuOa82UZc\ng4k3yjq01r5VVSeSPJvktSRfbK1d9U/g3IpG/Fn4oyRfrqrnsvqO5c+01l4d26S3QFX9WZKPJnlX\nVS0l+cMkP5WMlouyVbYmsvUy2SpbL3uz2dr9uqtn4wEAgPXcwQ8AADqUZQAA6FCWAQCgQ1kGAIAO\nZRkAADqUZQAA6FCWAQCgQ1kGAICO/x9rLLf8FVQBQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121a5940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
    "fig.subplots_adjust(hspace=0.05, wspace=0.05)\n",
    "\n",
    "to_plot = ['2a', '1', '3c', '7a']\n",
    "\n",
    "lllat=17.25; urlat=20.25; lllon=-75; urlon=-71\n",
    "\n",
    "for code, ax in zip(to_plot, axes.flat):\n",
    "    m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,\n",
    "                        lllon=lllon, urlon=urlon)\n",
    "\n",
    "    cat_data = data[data['category_%s' % code] == 1]\n",
    "\n",
    "    # compute map proj coordinates.\n",
    "    x, y = m(cat_data.LONGITUDE.values, cat_data.LATITUDE.values)\n",
    "\n",
    "    m.plot(x, y, 'k.', alpha=0.5)\n",
    "    ax.set_title('%s: %s' % (code, english_mapping[code]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAKBDWQYAgA5lGQAAOpRlAADoUJYBAKBDWQYAgA5lGQAAOpRlAADoUJYBAKBDWQYAgA5l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\nZRkAADqUZQAA6FCWAQCgQ1kGAICO/x9rLLf8FVQBQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e33828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
    "fig.subplots_adjust(hspace=0.05, wspace=0.05)\n",
    "\n",
    "to_plot = ['2a', '1', '3c', '7a']\n",
    "\n",
    "lllat=17.25; urlat=20.25; lllon=-75; urlon=-71\n",
    "\n",
    "def make_plot():\n",
    "\n",
    "    for i, code in enumerate(to_plot):\n",
    "        cat_data = data[data['category_%s' % code] == 1]\n",
    "        lons, lats = cat_data.LONGITUDE, cat_data.LATITUDE\n",
    "\n",
    "        ax = axes.flat[i]\n",
    "        m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,\n",
    "                            lllon=lllon, urlon=urlon)\n",
    "\n",
    "        # compute map proj coordinates.\n",
    "        x, y = m(lons.values, lats.values)\n",
    "\n",
    "        m.plot(x, y, 'k.', alpha=0.5)\n",
    "        ax.set_title('%s: %s' % (code, english_mapping[code]))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'basic_haiti_map' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-84a404958358>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmake_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-70-34382dfa5e94>\u001b[0m in \u001b[0;36mmake_plot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,\n\u001b[0m\u001b[0;32m     16\u001b[0m                             lllon=lllon, urlon=urlon)\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: global name 'basic_haiti_map' is not defined"
     ]
    }
   ],
   "source": [
    "make_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'm' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-72-748ee28b5fa0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mshapefile_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'ch08/PortAuPrince_Roads/PortAuPrince_Roads'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadshapefile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshapefile_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'roads'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'm' is not defined"
     ]
    }
   ],
   "source": [
    "shapefile_path = 'ch08/PortAuPrince_Roads/PortAuPrince_Roads'\n",
    "m.readshapefile(shapefile_path, 'roads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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