Grabbing World Bank Data with the wbdata module and plotting it

wbdata-plotting-example.json

{
 "metadata": {
  "name": "Grabbing World Bank Data with the wbdata module and plotting it"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Grabbing World Bank Data with the wbdata module and plotting it\nby [Tariq Khokhar](http://twitter.com/tkb)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#I'm running pylab but if you're not, don't forget to import pandas and matplotlib too!\n\nimport wbdata",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Let's start by setting the countries and indicators we want - in this case, I'm after the GNI per capita for Chile, Uruguay and Hungary. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "countries = [\"CL\",\"UY\",\"HU\"]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "indicators = {'NY.GNP.PCAP.CD':'GNI per Capita'}",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "OK, we now use wbdata's \"get_dataframe\" function to grab the data and put it into a pandas dataframe"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = wbdata.get_dataframe(indicators, country=countries, convert_date=False)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Let's just check what we've got there"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>GNI per Capita</th>\n    </tr>\n    <tr>\n      <th>country</th>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">Chile</th>\n      <th>2012</th>\n      <td> 14280</td>\n    </tr>\n    <tr>\n      <th>2011</th>\n      <td> 12270</td>\n    </tr>\n    <tr>\n      <th>2010</th>\n      <td> 10720</td>\n    </tr>\n    <tr>\n      <th>2009</th>\n      <td>  9940</td>\n    </tr>\n    <tr>\n      <th>2008</th>\n      <td> 10020</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "              GNI per Capita\ncountry date                \nChile   2012           14280\n        2011           12270\n        2010           10720\n        2009            9940\n        2008           10020"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.tail()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>GNI per Capita</th>\n    </tr>\n    <tr>\n      <th>country</th>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">Uruguay</th>\n      <th>1964</th>\n      <td> 660</td>\n    </tr>\n    <tr>\n      <th>1963</th>\n      <td> 610</td>\n    </tr>\n    <tr>\n      <th>1962</th>\n      <td> 580</td>\n    </tr>\n    <tr>\n      <th>1961</th>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1960</th>\n      <td> NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 6,
       "text": "              GNI per Capita\ncountry date                \nUruguay 1964             660\n        1963             610\n        1962             580\n        1961             NaN\n        1960             NaN"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "OK, that looks like the data we want but it's currently \"pivoted\" which is no good for plotting. Luckily, the pandas unstack() function has us covered:"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dfu = df.unstack(level=0)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dfu.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">GNI per Capita</th>\n    </tr>\n    <tr>\n      <th>country</th>\n      <th>Chile</th>\n      <th>Hungary</th>\n      <th>Uruguay</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1960</th>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1961</th>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1962</th>\n      <td> 600</td>\n      <td>NaN</td>\n      <td> 580</td>\n    </tr>\n    <tr>\n      <th>1963</th>\n      <td> 640</td>\n      <td>NaN</td>\n      <td> 610</td>\n    </tr>\n    <tr>\n      <th>1964</th>\n      <td> 660</td>\n      <td>NaN</td>\n      <td> 660</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 8,
       "text": "         GNI per Capita                  \ncountry           Chile  Hungary  Uruguay\ndate                                     \n1960                NaN      NaN      NaN\n1961                NaN      NaN      NaN\n1962                600      NaN      580\n1963                640      NaN      610\n1964                660      NaN      660"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "That's ready to plot, we could get rid of the missing values with dropna() but let's leave it for now - a one-liner with matplotlib should get us there:"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure(); dfu.plot(); plt.legend(loc='best'); plt.title(\"GNI Per Capita ($USD, Atlas Method)\"); plt.xlabel('Date'); plt.ylabel('GNI Per Capita ($USD, Atlas Method');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0x922cdd0>"
      },
      {
       "output_type": "display_data",
       "png": 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RYQ4dHR1mkJrFYsHPzw/Tp0+HoqIiIiMj+b5vUfe/YoBbVVUVwcHB2Lt3L9/6oMpU/i30\n798fampqTHrla6dPn0ZZWRkcHR2hrq6OZcuWMTP1Ro8eDTabDQ0NjSprQiq3U1Ff9+7dceLECRw+\nfBh2dnYwMTHBtm3bmHt/9epV3Lx5E3369EG7du0wYMAApp5Pnz7h0KFDWLhwYbXttDZYNJATRVL4\n6aefkJqayowrNDSFhYUwNTVFXFwcn5vl4MGD6NatGzO9tTExMzPDtWvXoKio2OhtSzInTpzAxYsX\nERQU1NSiNAsarMfh5eUFVVVV9O7dmy99//79MDIyQs+ePbFkyRImfefOndDX14exsTHfDKDExET0\n7dsX3bt355s+WlZWhunTp0NbWxv29vZ4/fp1Q6lCaSE09juQrKwsUlNTq/jmp02b1iRGAwDu379P\njUYDMGnSJGo0KtFghsPT07PKVMa4uDjs3bsXZ86cQXx8PBYtWgQAePPmDXbv3o3Q0FAEBATwdb19\nfHywZMkSREdH4/r164z/9dSpU8jLy0NiYiKcnJywdu3ahlKF0kKoz+0mKBSKYIRPc6kDtra2zD49\nFVy8eBHTp0+Hvr4+AN7MEYA36OTk5AQOhwMOhwNCCLOfTnJyMpydnQHw9ouJjIxEv379EBkZCTc3\nN3To0AGzZs3C8OHDG0oVSgth1apVTS0ChdIqaNTB8cuXLyMuLg79+vXDjBkzmNWuUVFRMDIyYvIZ\nGBggMjISqampUFFRYdKNjY2ZbRCioqKYKYhKSkrIysqqMsOGQqFQKPVPg/U4quPDhw/IyclBREQE\nrl69iu+++w7Xrl2r1jddncuBEMKkf7nyV5B/m7ouKBQKpXYIeq42ao/D2toazs7OaN++PUaNGoWk\npCR8+PABVlZWfHvtJCUlwcLCAnp6enzL+xMSEpjFXJXL5OTkQFVVFe3atau23QojI4nHqlWrmlwG\nqh/Vr7Xp1hr0E0ajGg4bGxtcvHgRhBBERkZCV1cXMjIysLS0REhICNLT05m9+isWNBkaGuLo0aPI\nzs7GqVOn+AxHcHAwioqKsHfv3mo3ZGsNfDmOJGlQ/VoukqwbIPn6CaPBXFUVAX7evXsHLS0t/Pzz\nz5g6dSouX74MY2NjGBoaMotvVFVV8e2332Lw4MGQlpbm2zl1y5YtcHNzw7Jly+Di4oJ+/foBAMaN\nG4dLly7ByMgI3bt3F7jQi0KhUCj1i8QvAGSxWCK7XS0ZLpcLe3v7phajwaD6tVwkWTdA8vUT9uyk\nhoNCoVAoVRD27GzUWVXNCSUlJb6dWykUCg9FRUW+kK61RdLfyCVdP2G0WsORm5tLeyIUSjXQKewU\nUbRaVxV1YVEo1UP/GxRA+O+AbqtOoVAolBpBDQeFQmkQuFxuU4vQoEi6fsKghqMZs2bNGvzyyy9N\nLUaL4c8//2zSzS7t7e0RGBhY7bX09HTIyckxXX9heStTUlICXV1d5Ofn16usFEpdoIajmVJUVIR9\n+/Zh1qxZTNrHjx+xatUq9OnTB/Ly8uBwOJg0aRKioqKYPGw2GyYmJny+SV9fX3h6egLgrXZls9k1\nitVc31y8eBF2dnbo0qUL1NTUMGLECNy8ebPO9bq6uiIkJIQ5Z7PZQuN115Ty8nJs27YN5ubm6NSp\nE3R0dDB9+nQmfKywbd05HA4KCgqY6+JuAd++fXtMmDABAQEB9aZHYyHpM44kXT9hUMPRTAkMDISj\noyOUlJQA8PbbGjp0KM6fP4+5c+ciMzMTMTExGDt2LE6fPs1X9tWrV3wr6Ztiloyg/W727dsHV1dX\nWFtb486dO0hJSYGXlxeOHTvWYHLUF+7u7tizZw+mTp2K58+f4+bNmzA0NMSVK1fqrY3qmDt3Lvz9\n/cWOrU2hNDhEwhGkYnNXfcyYMWTPnj3M+eHDhwmbzSYvX74UWo7FYpFNmzYRfX198unTJ0IIIb6+\nvsTDw4MQQkhaWhphsVjk8+fP1Za3s7Mja9euJQ4ODkRDQ4Ns2LCBFBYWMtdTU1PJjz/+SDgcDpkx\nYwaJj4/nK7tu3ToydOhQIicnR548ecJXd35+PlFUVCTr168XKH9kZCSxtrYmCgoKxNramuzatYuU\nlZXx6bd//35iYmJCjI2NyZEjR0h5eTkhhJD9+/eTgQMHEkIIsbW1JSwWi3Ts2JHIysqS48ePk9zc\nXDJy5EjSpUsXoqenR/z8/EhWVpbQ+1nBjRs3CIvFIrdu3RKYx97enmzYsIEMGzaMqKmpkQULFpDc\n3FxCSNX7bm9vTwIDA5myERER5JtvviE6Ojpk9erV5O3bt3x1q6qqktjYWLFkrSv19d8ICwurl3qa\nK5Kun7DfAe1xNFOSkpKgq6vLnEdERMDc3Bzq6uoiy44bNw7y8vI4cOAAgJq/df/6669YsGABrl+/\njqtXrzLRFT9//oz+/fvD2NgYcXFxsLW1rTKm8Ntvv2H+/PnIyckBh8PhuxYXF4f3799j9OjRAttu\n06YN/P39kZ2dja1bt2LLli1VBiH37t2L4OBgBAQEYNmyZbh8+XKVesLDwwEADx8+REFBASZNmoTy\n8nJMnz4d6enpuHTpEqKiorBz506x7kl4eDhUVFRgY2MjMA8hBLt378bixYsRExODGzdu4OTJkyLr\nfvjwIaZMmQJPT0/cu3cP7969w/z58/ny6Onp8e0gTaE0JdRwCIHFqvtRWzIyMtC1a1fmPDMzk++h\nFRsbC0VFRXTq1AmGhoZ8ZdlsNtasWYM1a9agrKyshjqzMHToUIwaNQq6urpYvHgxzp07BwC4du0a\n+vTpAw8PD8jJyWHq1KlQVlZGdHQ0U97JyQkjR45EmzZt0KYN//rSjIwMyMnJoWfPngLb79u3Lywt\nLSElJYX+/fvDzc0N//77L1+eGTNmoHfv3hg0aBCmTJnCyCcKJSUljBs3DjIyMtDV1cWiRYuq1C2I\njIwMWFpaCs3DYrEwduxYODo6QkNDA+PHjxfLjXXs2DF8++23GDJkCBQVFbFq1SpcvnyZzzWlqamJ\n9PR0sWRtLkj6GICk6ycMajiEQEjdj9qira2NFy9eMOdaWlq4ceMGc25qaorc3Fz8888/1UY+/Oqr\nr6CpqYnff/+9xmMcpqamzGczMzPEx8ejsLAQV69eRUREBBQVFZkjNTWVebtnsVjMtvfVoaWlhYKC\nAsTHxwvM8+LFC8yZMwcmJiaQl5fH9u3b8fDhQ6Hy3b59Wyy9CCFYvnw5bG1toaCggAkTJiAhIUGs\nHpmWlhYiIyNF5qssm5qaGt93KIirV69iw4YNzD3V09NDcXEx7t+/z+TJzMyEtra2yLoolMaAGo5m\nipGREZ48ecKcDxw4EPfv36/yIBL20Fu3bh3Wr1+P4uLiGrVd+YF179499OzZE7Kyshg8eDDs7e2R\nm5vLHAUFBfDx8WHyf9nLqEyvXr2gqKiIM2fOCMyzdu1alJWV4cKFC8jLy8OCBQuqzAD7Ur7+/ftX\nWxebzea7P8ePH8f58+exf/9+ZGdn4+TJk2IFrQEAOzs7vH37VmwjVRMGDx4MX19fvvtaVFQECwsL\nJk9qaipfeOWWgKSvc5B0/YRBDUczZfDgwXxvuK6urujfvz/GjBmD33//Hbm5ufjw4QNiYmIE9ijs\n7OzQq1cvHDx4UOx2CSEIDQ3F+fPn8fTpU2zZsgWjRo0CAAwZMgSPHj3CoUOHmPa5XC6fMRP2EJaT\nk8PGjRuxdetWLF++HE+ePEF+fj5OnTrF+PRfvnwJJSUldO7cGVwuF4cOHapST1BQEOLi4hAREYFj\nx47h66+/rrY9c3NzxMTEMOcvX76EgoIClJWV8fjxY2zcuJEv/+rVq+Hg4FBtXf3798c333wDT09P\n7Nq1C7m5uXj9+jW2bt2Kffv2iaW/INzd3fH777/j8uXLKC0tRV5eHk6cOMFcf/bsGVgsFoyNjWtc\nN4XSEFDD0Uzx8vLCtWvXmF1KWSwWQkNDMWLECOzevRs6OjowNDTE3bt3cfz4cabcl0Zk7dq1yMnJ\n4UsX5rpisVjw9vbGtm3bYGtrCwcHB6xYsQIAICUlBS6Xi+TkZJibm4PD4WDr1q18D0tRbrEZM2Yg\nODgYN2/ehJWVFXr06IGgoCBMmTIFAO/hHRsbC01NTWzevBnfffddlTpnzZoFV1dXzJ49G2vXrsXQ\noUOZtivnXbRoEbZs2QJFRUX8/fff8PLygoaGBnr06AF3d3d4eXnx5c/IyMDAgQMFyn748GHMnj0b\nBw4cgI6ODqysrJCcnIyvvvqqWv2/lEfQvTE2NsbBgwdx/PhxaGpqonfv3nzrUXbv3o0FCxagbdu2\nQu9tc0PSxwAkXT9h0E0OmzHr1q2DlJQUli5d2mhtOjg4MA/V5gibzUZqaiq6d+9e73WbmZnh2rVr\nUFRUrPe6a8uHDx/Qs2dPxMbGMuGUG5qW8N+gNDw0HkcLpeJNv7FprQ+NymMnzQUZGRm+sa6WhKTH\nq5B0/YRBXVWUKjTneAzNWTYKpTqePgWETDZskQg0HLKyspCTk6v2kJeXF1mxl5cXVFVV0bt37yrX\ntm7dCjabzRdlbOfOndDX14exsTHftNPExET07dsX3bt353sDLysrw/Tp06GtrQ17e3u8fv1abKUp\nggkLC2u2biqAtwixIdxUlPpH0t/GxdXv7l1ATa1hZWlsBBqOwsJCFBQUYNmyZViwYAEePXqER48e\nwcfHB8uWLRNZsaenJy5dulQlPSMjA1euXOGbk/7mzRvs3r0boaGhCAgIwLx585hrPj4+WLJkCaKj\no3H9+nVmlsypU6eQl5eHxMREODk5MaubKRQKpTlx9y5gbt7UUtQvIl1Vhw4dwk8//QQdHR3o6Ohg\n5cqVYk3vtLW1rXaQceHChdi0aRNfWmRkJJycnMDhcGBnZwdCCAoLCwEAycnJcHZ2RufOnTF+/Hhm\nimpkZCTc3NzQoUMHzJo1S6zFWRQKpfGQ9HUO4urXKg3HgAEDsGXLFrx79w7Z2dnYvn07BgwYUKvG\n/v33X2hqasLExIQvPSoqim9xk4GBASIjI5GamgoVFRUm3djYGHfu3GHKVMxrV1JSQlZWVrUrqCkU\nCqWpIEQyDYfIWVUVwYQqVufW1i1UXFyM9evX8+3dUzF7R9B02S8hhDDpX674FTYTyMPDAzo6OgAA\nBQUFvm0hKBSKYCreqiv8+TU5t7e3r1P55n4ujn5Hj3LBZgNqak0vr6hzLpfLbIxa8bwURIOu43j2\n7BlGjRrFjI8MGTIEHTp0AMDbe0dDQwORkZGIiorC1atX4e/vD4C3309ERATk5OTQvXt3JhjP1q1b\nISMjA29vb/j4+GDgwIEYN24ccnJyMGzYML5VwoyCLXgdB4XSFND/Rv3x99/AoUOAkF12mi3Cfgci\nXVVZWVlYsmQJjI2NYWxsjKVLl+LNmzc1FqJ3797IyspCWloa0tLSoKmpiXv37kFVVRWWlpYICQlB\neno6uFwu2Gw2s9jJ0NAQR48eRXZ2Nk6dOsVsomdlZYXg4GAUFRVh7969sLa2rrFMzR0aOrZmNHXo\n2JaEt7d3jbaiqQ10jEMy3VQAREds+eGHH8j69etJVlYWycrKIhs2bCA//PCDyCAgLi4uRF1dnUhL\nSxNNTU0SFBTEd71bt27k3bt3zPmOHTuIrq4uMTIyIuHh4Ux6fHw8MTMzIzo6OmTp0qVMemlpKfH0\n9CRaWlrEzs6OvHr1qlo5BKkohupNSmFhIdHS0uK7Rx8+fCArV64kJiYmRE5OjmhpaZGJEyeSyMhI\nJg+LxSK9e/dmghsRQsiKFSvEDuTUGFy4cIEMGjSIKCsrE1VVVfLVV1+RGzdu1Hs7LBarSjCp2iLo\nvk2bNo34+vrWSxuNSXx8POnZs2e11+rrvyHpgY7E0W/oUELOnm14WRoCYb8Dkb8QExMTvvPPnz9X\nSWvOtFTD4e/vzzzsCSGkvLyc2NraEnNzc7Jnzx6Sl5dHsrKySHBwMFm2bBmTj8ViEWVlZfLXX38x\naTWJAFhflJeX8xmvCvbu3UsUFRXJ4sWLSWpqKsnPzycnTpwg33//fb3LwGKxSGpqar3UJei+eXh4\nED8/v3ppo74QdO+/xM7Ojpw5c6ZKenP/b7QUyssJUVIiRETQzmaLsN+BSFeVvb09Nm/ezDerStIX\n9jQHrl1ZfodoAAAgAElEQVS7xud++/PPP3Hz5k2cPXsWs2fPhry8PFRUVODq6or169fzlV28eDFW\nrVqFz58/17hde3t7rFu3DoMHD4ampiZ++eUXFBUVMdefPHmCxYsXQ1tbGzNnzuSLSmdvb4/169dj\n2LBh6NSpE9LS0vjqLigowJIlS/Djjz9i48aN0NXVhZycHCZOnMhE4ouKioKNjQ0UFRVhY2ODX3/9\nlS+gEZvNxoEDB9CnTx/07NkTR48eZfywBw4cgK2tLQBg0KBBAIA+ffpATk4OJ06cwPv37/H1119D\nRUUF+vr6WLlyZa3crl9S0T6Xy4WWlhbfNR0dHVy7dg0AbwPHKVOm4LvvvoOamhomT56MxMREJu/z\n588xa9YsqKmpYcaMGXBzc4Ofnx8AIDc3V6jsX977rVu3ol+/fnyybNu2DWPHjmXOra2tGdko9c/z\n50C7doB6l09ASkpTi1OviDQcS5YswatXrzBw4EDY2tri5cuXjbrpXmuFho5tXqFjK/jyXoq6t1/O\nDvznn3/Qp08fJCYmolOnTnxGf8KECVBQUEBcXBx69uyJEydO8M0iFCV75Xs/b948pKWlISkpibl+\n+PBhTJs2jTlv6HC0rX2MgxnfePQIGDOmUWRqLEQajq5du2Lbtm1ITExEYmIitm7dKtbDSxJg/cSq\n81FbaOjY5hU6tgJlZWW+CIhHjhyp0f5ZBgYGmDlzJhQVFTF9+nRcvXoVAG8SSnx8PH7++WcoKytj\nwYIFUKu0T4Uo2VksFt+9l5aWxuTJkxEcHAwAiI+Px/Pnz/lil2hoaOD58+c10p8iPnfvAn37Arhz\nBxASq74lInIdR05ODs6dO4fbt2/jw4cPAHg/0qCgoAYXrqkhq5puSmJF6NiKRY6CQseGhoZixowZ\nVco3dOjYCj59+oTw8HBYWFjUKHSsIOPx4sULrFmzBrdu3cKzZ8/w+fPnKi6XL+X7MiCTIAghWLFi\nBSIiIvDo0SNmhwJSaX2QKN69ewc2+//vW56enjXq0fXp04f5rKamhqysLJSXlyMqKgr6+vqQkZFh\nrvft25f5XF5eDl9fX6Gyf3nvp02bhm+++QZr167F4cOH4ezszBfTo6HD0Uq6S1uUfvfuAXPnAvj7\nNvCfC1VSENnj+O677xAREYFBgwZh5MiRzEFpWGjo2OYVOlYcNDQ0kJOTw4wtZWdnIzMzU6yyFhYW\nSElJYV7OAJ5uFZw4cUKk7F/ee2tra0hLSyM8PBxHjhyBu7s73/XU1FQaVbCB4Fsxfvu2xPU4RBqO\nBw8eYN++fZgyZQomTpyIiRMnYsKECY0hW6uGho5tXqFjBVFZX319fSgrK2P//v14+/YtVq1aJXZP\nRk1NDT179sTq1auRnZ0Nf39/vh2fRcn+pSwVuLu747vvvoO0tHQVAxsVFYXBgweLq2qNac1jHBkZ\ngJQU0FU6G3jzBmhh8eJFIdJwuLi4IDAwkO9NiNLw0NCxzS90bHW6fdlmQEAAgoKCYGlpCRMTE2hq\nagrM+2WdJ06cQHZ2Nnr27IlHjx5h5MiR6NSpEwCIlF2QfO7u7oiPj4ebmxtfekJCAt68ecO8FFDq\nl4reBivyDmBpybMiEoTALUdkZWWZH2JRURHatGmDdu3a8QqxWMjPz288KetAS95yhIaOrUprCR1L\nCIG6ujouXLjAN9ZRU4qLi6Gmpob79+/zzdLz9vaGlZUVpk6dWqVMS/hvNHd8fQE2G/i5vOLDz00t\nUo2pVejYim3NKU0HDR3buDR16Njw8HD06NEDbdu2xa+//ory8vI6GQ0A2LFjB4YOHcpnNADe1F2K\nYN4UvcHtjNu4lXkLD14/gKmaKb7u8TVsNG0gxRbde7h7F5gzB8DO20ClMUBJQeSsKkdHR4SGhopM\no0gOzTk8a3OWra4kJydj8uTJKCsrw8SJExESElKn+nR0dKCkpISjR4/Wk4Q1o6XF5E54m4ANNzbg\ndsZtvCt5BysNK/TX6o85/eYg5mUMvC9442XBS3yl9xVG9RgFPAMGDuJ3bbJZbKh0VOW5qkw/A9HR\nkhc3FkIMR0lJCYqLi/H27Vu+EK9v3rxBQUFBowhHaXzCwsKaWgSh1GY1fEth5syZmDlzZr3V9+zZ\ns3qrqzXwy41f0KldJ5yZcgaGyoZgs/4/BDzWcCzWDl6L5++f43zKeQTFBiHqZhSkk6T56igqLYKH\n0TywWGuh8T4eUFcHOndubFUaHIFjHDt27IC/vz9evnzJtxBNW1sbs2bNwjfffNNoQtaFljzGQaE0\nBa3xv/G5/DPUtqohZmYMtBVqv7blbdFb9N5pDZXElXg44ANvKu5/Ozi0NIT9DkTG49i5cydfDPCW\nBjUcFErNaI3/jcjMSEw/Mx1xc+PqXNe3fkk4LGWH1Pi+UBsyFpg9ux4kbHzqFI9j9uzZOHbsGLy9\nvQEAKSkpYm/xQKFQWi8taR3HhdQLGKE/okZlBOn3/K4hFuv+hYLrV/DMUDK3ZxJpOFatWoV79+4x\nN6lr165NNtuHQqFQGoILKTU3HNVRsWJ8uqkZtIvbwinWB++K39WDhM0LkYYjLCwMGzduhLQ0bxCo\nY8eOra4bS6FQak5LmVGVVZiFlHcpGKA1oEblqtPvxQue8eiaEQlpSxuMNh6H8cfHo/RzaT1J2zwQ\naTgMDAyQl5fHnN+5cwdmZmYNKhSFBw0dWzNo6FjxKCkpga6ubotZxNvQXEy9iCHdh6CtVFvRmUXA\nrBi/w9uf6pchv0CpvRJmnZ0lUS/cIg3H999/j7FjxyIzMxMODg7w8vJq0YPlLYWioiLs27cPs2bN\nYtI+fvyIVatWoU+fPpCXlweHw8GkSZMQFRXF5GGz2TAxMeH7kfr6+sLT0xMAb4omm82usnFgY3Lx\n4kXY2dmhS5cuUFNTw4gRI3Dz5s061+vq6sq39oHNZuPp06d1rheoPkgTwHvrDAwMrJc2Gov27dtj\nwoQJCAgIaNB2WsoYx4WUCxipX/ONW6vTj9nY8L+t1NksNoLHBcNa07pK3paMSMNhYWGBsLAwXLp0\nCZs2bUJCQgLMJTL6evMiMDAQjo6OUFJSAsBbzT106FCcP38ec+fORWZmJmJiYjB27FicPn2ar+yr\nV6/4Fn01xaI5QbvO7tu3D66urrC2tsadO3eQkpICLy8vHDt2rMHkaEiq23+qgua85mTu3Lnw9/fn\ni67YGin7XIYrT6/ASc+pXuqLiQH6mpYDUVHMwr+O0h0xp98cyVq8Kiim7N27d8ndu3dJTEwM87ny\nIQpPT0+ioqJCevXqxaQtWrSIGBoaEjMzMzJ//nxSXFzMXPP39yd6enrEyMiIREREMOkJCQnEzMyM\ndOvWjSxfvpxJLy0tJV5eXoTD4RA7Ozvy6tWrauUQpKIQ1ZsFY8aMIXv27GHODx8+TNhsNnkpIoAx\ni8UimzZtIvr6+uTTp0+EkJrFHLezsyNr164lDg4ORENDg2zYsIEUFhYy11NTU8mPP/5IOBwOmTFj\nBomPj+cru27dOjJ06FAiJydHnjx5wld3fn4+UVRUJOvXrxcof2RkJLG2tiYKCgrE2tqa7Nq1i5SV\nlfHpt3//fmJiYkKMjY3JkSNHmPja+/fvJwMHDiSEEGJra0tYLBbp2LEjkZWVJcePHye5ublk5MiR\npEuXLkRPT4/4+fmRrKwsofezgrCwMKKpqVkl3d7engQGBhJCCFm1ahVxcXEhc+bMIWpqauSPP/4g\n06ZNI76+vgLrefbsGZk5cyZRVVUl06dPJ66urkz+yvpU1r/ivp47d46YmpoSeXl5MmTIEHLw4EEm\n34gRI8iuXbv4yvbu3ZucPn2aOVdVVSWxsbFVdGru/4365Pqz66Tv733rpa7SUkLk5Ql5Fx5HiJ5e\nvdTZlAj7HQjscfTr1w8eHh5YtGgRfHx8qhyi8PT0xKVLl/jShg0bhvj4eMTExKCoqAh//fUXAN5q\n9N27dyM0NBQBAQF8rjAfHx8sWbIE0dHRuH79OrNN9qlTp5CXl4fExEQ4OTkx4U0lBRo6tnmGjhXF\nyZMnYWxsjLS0NLi6ugrtkQDCw8WKQlZWFsHBwcjJycGiRYvw3XffITU1FQDg4eHBRP8DeOERXr58\nyRdLp6FDx7YE6ms2FcBzU+noAErJtwFryXJNfYlAw7Ft2zbIycmhQ4cO8PT0xJkzZxAWFsYcorC1\nta2yy+jQoUPBZrPBZrMxfPhwXL9+HQAQGRkJJycncDgc2NnZMdHNAN7+Pc7OzujcuTPGjx/PxKiI\njIyEm5sbOnTogFmzZvHFrqg3WKy6H7WEho5tnqFjRaGlpYXvv/8eMjIyTDQ/QYZbVLhYUdjZ2aFn\nz56QkpLC8OHDMWbMGEafUaNG4fHjx0wwsMOHD8PFxYXvO9HU1ER6enptVRVJSxjjOJ9yHiP0amc4\nvtTv2jVg8GBIZOCmLxFoOH744QfcvHkTO3fuRGZmJhwdHTFp0iTExsbWS8P79u1jYgFERUXBqFKg\nEwMDA0RGRiI1NRUqKipMurGxMe7cucOUqYhepqSkhKysLHz8+LHatjw8PLB69WqsXr0aO3bsEP8H\nTUjdj1pSETq2AkGhY//5559q9W7o0LEVR2pqKvN2X5PQsYJ48eIF5syZAxMTE8jLy2P79u14+PCh\nUPlu374tll6EECxfvhy2trZQUFDAhAkTkJCQIFaPrEOHDigqKqqSXlhYiA4dOjDnwvT/ElHhYkUR\nHx8PT09PGBgYoFOnTvj777+ZeyUjI4PJkyfj8OHDIITg6NGjVSIAihM6lsvl8v1fJOk8PS8d6bHp\nKEkpqZf6rl0DVFS44IaGMoajOekr6pzL5cLDw4N5XgpFHF9XXFwcWbFiBdHW1iZHjx4V20eWlpbG\nN8ZRwU8//UQmTJjAnK9YsYLPn+/s7ExCQ0NJSkoKsba2ZtIvXLhA3N3dCSGEDBgwgCQlJTHXNDU1\nyYcPH6q0JUhFMVVvMsaPH08CAgKY88OHDxMWi0UyMzP58l25coXo6Ogw55V94Fwul6irq5OFCxeK\nPcZhb29P3NzcmPNLly4x3+GlS5eIk5OTQJkr+/urIz8/nygpKQkd45gzZw7x8vIiGRkZpLy8nKxY\nsYLPz89iscgff/zBnC9dupR8//33hJCqYwJSUlIkNTWVOT969CgxMTEhKSkppKysjFy5ckXovajM\np0+fiLKyMomLi+PTR0ZGhmRnZxNCCFm9ejXfvSOE99ueOXMmc75t2zZmjOPVq1dERkaGlJSUMNc5\nHA7x8/MjhBBy+fJloq+vz1y7d+8e3/fr5OREfH19mfE9V1dX5v9BCCG3bt0ienp6VeqpoLWPceyJ\n3kNcT7rWS10lJYTIyhKS9zyX96HSuFxLRdjvQGCP48mTJ1i3bh0sLS2ZKaCJiYlwdnYWbolEcODA\nAYSEhPD5X62srPh8rUlJSbCwsICenh6ysrKY9ISEBOaNrnKZnJwcqKqqMoGmJAEaOrZ5hY6VkpLC\nqFGjsGbNGiQkJCA9PR1+fn7o378/Ov+3+2l1ujs6OuLKlStISUlBTEwM33chKlzswIEDkZGRgcuX\nLyMjIwObNm3iq/vly5dQVlZGp06dcObMmSqx3G1sbMBisbBo0aIqAZuePXsGFovVqmOO12abEUHc\nuQP07AnIJ0Xx5uO2ERmxokUj0HDo6+vj+PHj+Oqrr2BjY4P09HQEBARg69at2LZtW60au3TpEjZv\n3owzZ87wdc8tLS0REhKC9PR0cLlcsNlsyMnJAQAMDQ1x9OhRZGdn49SpU3yGIzg4GEVFRdi7dy+s\nJWwwioaObX6hYzdu3AhLS0tMmzYNw4cPR+fOnZkJCNW1D/Ae/m5ubnB0dMT8+fPh7e0tdrjY9u3b\nY+/evVi0aBGGDx8OFxcXvrJbt27F8ePHweFwcOTIEcyZM6eKzFOnTsWjR4+qhI7dvXs3FixYgLZt\n677oTRCVXSLNjY+fPiIsLQzDdWu/YLSyfnzjGxL2LKoWQV2RVatWkdWrVws8ROHi4kLU1dVJ27Zt\niaamJgkMDCR6enqEw+EQU1NTYmpqSr799lsm/44dO4iuri4xMjIi4eHhTHp8fDwxMzMjOjo6ZOnS\npUx6aWkp8fT0JFpaWhI5HZcQQtauXUs2bNjQqG2Kcjc1NZVdNfWNqakpycnJaZC6xaG8vJyoqqqK\nNd1dXA4ePEhsbW350kpKSkj37t1Jfn5+tWXq678RFhZWL/U0BCGpIcTmD5s61VFZvwEDCLlyhRAy\nbBghlaY8t2SE/Q5Ebqve0qHbqtcMBwcHuLm5Yfr06U0tSrU0ZMzxpuDLcLG//fYb3rx5Uy91FxcX\nY8CAAfj5558Zd6M4tIb/xg+XfoByB2X4DvKtc12FhYCaGvDmRRk6aHUGnj0D/lu425Kp07bqlNZH\nc17h2pxlqw3JyckwNTVFjx498PLlyzqHi60gJCQEXbt2Rd++fWtkNFoLtd1mpDoiIoB+/YAOiXeB\n7t0lwmiIgvY4KBQKH/X13+A205jjuyJ3Yd+9fXgw50GdXkQq9PvxR0BODljZdgPw5g2wfXs9Stt0\n0B4HhUKhADj/+Dw23NiAM1PO1FvvlRkY53KBZmgoGwKRhmPHjh3MtupLlizB0KFDmUV4FAqFIojm\n1tt4mPUQHv964OTkk9BR0Klzffb29sjJAVJSAEvTUt6MqkGD6i5oC0Ck4QgKCkKnTp1w69YtxMbG\n4ueff4afn19jyEahUCj1wquCVxh1ZBR+/epX2GjV33Yg168D/fsD0g9jAD094IttliQVkatUKuZ5\nHzp0CLNmzYKNjQ2ys7MbXLCGRlFRUeIGWimU+uDLPeZqS3MZ4yguK8boo6Mxs+9MOPeq2wLmynC5\nXFy7Zg9HR7QqNxUgRo9j6NChGDRoEG7cuIExY8YgPz8fbHbLHxrJyclhYka05CMsLKzJZaD6SZZ+\nFYtOJYFyUg73U+4wUjbCCtsV9V4/M74RFgYI2HVAEhFrVtXTp0+hqakJaWlpvHv3Di9evICJiUlj\nyFdn6OwpCqX1sub6Glx5egVX3K+gXZv63ZLo9WvA2Bh4+6IUUiqdgYwMQEGhXttoSoQ9O8XaUKV7\n9+549OgRcnNz61UwCoVCaSiyi7Ox/c52xM6JrXejAfA6GXZ2gNTdKMDAQKKMhihE+pxOnTqFvn37\nwtbWFvPnz4e9vb3EBU1qyTTn/YDqA6pfy6WpdfOP9MdE44ngdOKIzlwL/vyT2+qm4VYg0nDs2rUL\nXC4XWlpauH//PiIiIphN2CgUCqU58v7DewREB2DpwKUN1sa9e61v/UYFIsc4zM3NcffuXTg6OuLE\niRNQUlKCkZEREhMTG0vGOkHHOCiU1sea62vwJPcJDow90CD1P3sGWFkBr59/BKuLMpCZCUjYC3Wd\nxjg4HA5yc3MxceJE2Nvbo0uXLnwhTCkUCqU5UfCxADujduKm180Ga+PQIeDrrwFWdBRgaChxRkMU\nNdqr6unTp3j58qXQmAXNDUnvcTSXufINBdWv5dJUum28sRGxWbE4MuFIg9Sfmwvo6wP+/ly4Pgnn\nbY/7RZAtSaBWPY7q5nIrKChAQUEBOTk5UGoFO0BSKJSWRVFpEbbf2Y6rU682WBtbtwJjxwIaGgAC\nucCiRQ3WVnNFYI9DR0dH6MrqtLS0BhOqPpH0HgeFQvk/O+7sQER6BE5OPtkg9Wdn82be3rsHaKt+\nALp0AV68AOTlG6S9pqRWPY5nz541lDwUCoVS73z49AGbb23GuSnnGqyNTZsAZ2dAWxvA9UjeCkAJ\nNBqiEDkd19HRUaw0StPQ1HPlGxqqX8ulsXULuh+Evup9YaZu1iD1v34N/PEHsOK/nUu4Bw60umm4\nFQjscZSUlKC4uBhv377lG+948+YNCgoKGkU4CoVCEYfSz6XYeHMjjk883mBtbNgATJv239gGANy/\nD/zyS4O116whAti+fTvR0dEh0tLSREdHhzns7OzIn3/+KagYg6enJ1FRUSG9evVi0vLz88no0aOJ\nlpYWGTNmDCkoKGCu+fv7Ez09PWJkZEQiIiKY9ISEBGJmZka6detGli9fzqSXlpYSLy8vwuFwiJ2d\nHXn16lW1cghRkUKhSAgXUy4Smz9sGqz+9HRClJQIef36v4SSEkI6diQkP7/B2mxqhD07Bbqqfvjh\nB6SlpWHz5s1IS0tjDi6XK5arytPTE5cuXeJLCwgIAIfDQUpKCjQ1NbFnzx4AvF7M7t27ERoaioCA\nAMybN48p4+PjgyVLliA6OhrXr19HTEwMAN5WKHl5eUhMTISTkxPdBoVCacVEpEfAsXvDudDXrQNm\nzABUVf9LuHMH6NWLFzO2FSJyjKPiIf7+/Xv88ccfcHR0hJmZaB+ira1tlX39o6KiMH36dLRr1w5e\nXl6IjIwEAERGRsLJyQkcDgd2dnYghKCwsBAAkJycDGdnZ3Tu3Bnjx4/nK+Pm5oYOHTpg1qxZTHpr\nQ5J95ADVryXTmLpFPI+ALce2TnUkJQH79gF37wKlpf9PT0sDTpwAFi+ulDksDFxd3Tq115IRunK8\nuLgY//77L44cOYLY2Fjk5+fj9OnTsLWt3RcUHR0NQ0NDAIChoSGioqIA8IyAkZERk8/AwACRkZHQ\n1taGiooKk25sbIw///wT3t7eiIqKwuzZswEASkpKyMrKwsePH9GuXdVdMD08PKCjowOAtxbF1NSU\nWZhU8eNuqeexsbHNSh6qH9Wvsc/LPpfh3qt7sNG0qVN9mzYBkZFclJQAr1/bw9gYUFfn4tUrwNvb\nHp07V8rP5QJff90s9K+vcy6XiwMHDgAA87wUiCAflouLC+nWrRuZPXs2CQ0NJZ8+fSI6Ojo18pGl\npaXxjXFoaWmRkpISQgghRUVFhMPhEEIIWbFiBdmzZw+Tz9nZmYSGhpKUlBRibW3NpF+4cIG4u7sT\nQggZMGAASUpKYq5pamqSDx8+VJFBiIoUCkUCiHgeQcx/N69THeXlhKirE5KSwjsvKiLk1i1Cdu0i\nxNubkNzcSpmLi3njG5XGaCURYc9OgT2OxMREqKiowMjICEZGRpCSkhJugcTAwsICiYmJMDMzQ2Ji\nIiwsLAAAVlZWuHr1/ys9k5KSYGFhATk5OWRlZTHpCQkJsLKyYsokJCTAwMAAOTk5UFVVrba3QaFQ\nJJuI5xGw1a6bm+rRI6BDB17YcID32caGd1Th9m3AxASQla1Tmy0ZgWMcsbGx2L9/P969ewcHBwfY\n2tqioKAAr1+/rnVjVlZWCAoKQklJCYKCgmBtbQ0AsLS0REhICNLT08HlcsFmsyH336CToaEhjh49\niuzsbJw6dYrPcAQHB6OoqAh79+5l6mptVHQ1JRWqX8ulsXSLSK/7+MalS4CTk5iZ/9tGXZK/O5GI\n222Jjo4mCxcuJFpaWsTGRvS0NxcXF6Kurk6kpaWJpqYmCQoKEjodd8eOHURXV5cYGRmR8PBwJj0+\nPp6YmZkRHR0dsnTpUia9tLSUeHp6Ei0trVY9HTcsLKypRWhQqH4tl8bQ7dPnT6TThk4kqzCrTvU4\nOBBy9qyYmQcOJOTyZYn+7ggR/uys0e64AFBeXo6IiAjY2dk1jCWrZ+heVRSK5BL7OhYuf7sg6buk\nWtdRUAB07cpbGd6xo4jMxcWAigqQlSVG5paNsGenyOm4VQqw2S3GaFAoFMmmPsY3wsIAa2sx7cCt\nW0CfPhJvNERRY8NBaV5Iup+V6tdyaQzd6mN84+LFGo5vODj895Fbp3ZbMtRwUCiUFgkhpM6Gg5Da\nDYy3dmo8xnH69Gmoq6szs5uaO3SMg0KRTFJzUmF/wB4ZCzKExg4SRnIyMGQIkJ4OiKyiqIi358ib\nN7z5uhJOnWKOf0lkZCTi4uJQVlZWZS8qCoVCaSwqxjdqazSA//c2xKri1i3AzKxVGA1R1NhVtWHD\nBpw9e5YajWaCpPtZqX4tl4bWrdHXb4SF8bmpJPm7E4XQHkdRURH+/PNPJCcng8ViwdDQEN988w06\nUItLoVCamIj0CCywXlDr8iUlwI0bwJEjYhbgcgG6CzcAIWMcz549g62tLZSUlDBq1Ch8/vwZ586d\nQ15eHiIiIqCtrd3YstYKOsZBoUgerwtfw/g3Y2QvzgabVbs5PiEhPDsQESFG5sJCQE0NePsWaN++\nVu21NGq1jsPX1xfff/89Hjx4gLVr12LDhg149OgR5s2bhxUVsRMpFAqlCYh4HoEBnAG1NhpADd1U\nN28C5uatxmiIQuBdv3PnDlxdXaukT5kyBbdv325QoSjiI+l+Vqpfy6UhdYtIj8BArYF1qqOu03Al\n+bsThUDDISsrCw0muO7/0dDQYDYgpFAolKYgIr1uK8afPQNycniTpMTii4Hx1o7AMQ5NTU0sXLiw\nWh/X9u3bkZmZ2eDC1Qd0jINCkSzyPuRBY5sGcpbkQFpKulZ17NnDm1176JAYmQsKAHV1IDsbkJGp\nVXstkVqt45gxYwYKCgqqpBNCMHPmzPqTjkKhUGrArYxb6Ne1X62NBsBzU02eLGbmmzeBfv1aldEQ\nhUDDsXr16kYUg1JbuFwuEwZSEqH6tVwaSrcbGTfq5KYqLeV5nvbtE7OAADeVJH93ohA4xrF37148\nfvyYOV+6dCm0tLTg7OyMpKTab2FMoVAodeFG+o06LfwLDweMjIAuXcQscO0as7EhhYfAMY6ePXvi\n/v37kJaWBpfLxZw5c3Dp0iXcuHEDly9fxiGxnINNDx3joFAkh8/ln6GwUQHpP6RDsb1irer44QdA\nWRnw9RUjc3Q0MGECkJoKSNfeNdYSqdU6jjZt2kD6vxu1d+9ezJw5Ezo6OnBzc0NcXFzDSEqhUChC\nSH6XDNWOqrU2GoQAZ88Co0aJWWDdOuDHH1ud0RCFQMOhra2N5ORkFBYWIiQkBJMmTWKuFRcXN4pw\nFNFI+lxyql/LpSF0u/vyLvp17Vfr8klJQFkZYGIiRuZHj4DISGDGjGovS/J3JwqBg+MLFy6Es7Mz\nsr90uPUAACAASURBVLKyMGfOHHA4HAC8m6Wrq9toAlIoFEoFd1/dhbm6ea3LnzsHfP21mLvhrl8P\nLFhAV4tXh6iA5cXFxXznhYWFpKCgoAYhz6uyd+9eYmNjQ/r27Uvmz59PCCEkPz+fjB49mmhpaZEx\nY8bwteHv70/09PSIkZERiYiIYNITEhKImZkZ6datG1m+fHm1bYmhIoVCaSEMCBxAQp+G1rq8rS0h\n58+LkfHxY0KUlQnJz691Wy0dYc9OgYPjJ0+e5NvnnsViwdTUFN26dauTocrJyYG5uTni4uLQvn17\nfP3115g/fz4ePHiAjIwMbNmyBT4+PtDR0cGiRYvw5s0bDBo0CJcvX0ZaWhoWLFiAe/fuAQBGjBiB\nadOmYciQIRgzZgx27NiBfv34u7F0cJxCkQwqBsYzFmRAQUahxuVzcgAdHV4cJpFLMqZPB7S0gFa8\nLKFWg+Nnz57lO/799184Ozujb9++uHv3bq2Fad++PQghyMvLQ0lJCYqLi6GgoICoqChMnz4d7dq1\ng5eXFyIjIwHwAkc5OTmBw+HAzs4OhBAUFhYCAJKTk+Hs7IzOnTtj/PjxTJnWhKT7Wal+LZf61i0p\nOwlqsmq1MhoAL7a4g4MYRuP5c+D0aWDePKHZJPm7E4XAMY4DBw5Umx4TE4Ndu3YJvC6K9u3bIyAg\nADo6OmjXrh3mzZsHKysrREdHw9DQEABgaGiIqKgoADzDYWRkxJQ3MDBAZGQktLW1oaKiwqQbGxvj\nzz//hLe3d5U2PTw8oKOjAwBQUFCAqakps3Cn4stvqeexsbHNSh6qH9Wvoc7vvroLTi4H3EoL72pS\n/tw5QF+fC95+hULy+/vDfsYMQEmpWenf0OdcLpd5rlc8LwVSG99Xnz59alOMEELImzdviLa2NklJ\nSSHZ2dnEwcGBnD17lmhpaZGSkhJCCCFFRUWEw+EQQghZsWIF2bNnD1Pe2dmZhIaGkpSUFGJtbc2k\nX7hwgbi5uVVpr5YqUiiUZsb3F74nm29urlXZ0lJCFBUJefFCRMZXr3gZX7+uVTuShLBnZ403s+dy\nuVXGEWpCVFQUrK2toaenh86dO2PSpEmIiIiAhYUFEhMTAQCJiYmwsLAAAFhZWSEhIYEpn5SUBAsL\nC+jp6SErK4tJT0hIgLW1da3lolAozZu7r2o/FffmTaB7d6BrVxEZt24F3NwAVdVatdNaEGg4Ro0a\nxXd8/fXXMDAwwMqVKzF//vxaN2hra4uYmBjk5OTg48ePuHjxIoYNGwYrKysEBQWhpKQEQUFBjBGw\ntLRESEgI0tPTweVywWazmW3dDQ0NcfToUWRnZ+PUqVOwsrKqtVwtlYqupqRC9Wu51Kdun8o/4cHr\nBzBTE3cfdH7OnRNj0d+7d0BgIG/BnxhI8ncnCoFjHD4+PnznbDYbxsbGUFZWrlOD8vLy8PX1xbhx\n41BcXAwnJyc4ODjA0tISbm5uMDAwQN++fbFx40YAgKqqKr799lsMHjwY0tLS+P3335m6tmzZAjc3\nNyxbtgwuLi516glRKJTmS1J2EjTkNdBJplOtyp89C/z1l4hMf/wBjB3Lm01FEYrA6biSAp2OS6G0\nfA7GHkTIkxD8NUHU078qjx/zZlNlZopY+GdtzQtCPmRI7QWVIGo1HRcAjh49iidPngDgTX0dNGgQ\nzMzMcOXKlfqXkkKhUAQQ8yqm1ivGz50DRo4UYTRevwaSkwE7u9oJ2MoQajg2b94MTU1NAMDWrVsx\nc+ZMHD9+HL/88kujCEcRjaT7Wal+LZf61K0ue1SJNb5x9iwvAHnbtmLXK8nfnSiEBnJ6+fIlNm7c\niE+fPuHUqVNQU1PD06dP8eTJE/z0008AgFWrVjWasBQKpfXxqfwTHmQ9gJl6zQfG378HYmIAR0cR\nGc+cAb75pnYCtkKEjnFMmTIFAwcOxLt37/Du3Tv4+/sDAPr3749bt241mpB1gY5xUCgtm0dZjzDp\nxCQkfVfzAHLHjvHiip8/LyRTUREvpvjz54Bi7bZrl0RqPcaxbt06JCcnIy8vDytWrADAWy8xcuTI\n+peSQqE0b86c4Q0WDBwI9OnDWxihogJ07AiMGwc0UJyemJcxMO9au/GNU6eA0aNFZLp6FbCwoEaj\nJjTCAsQmRdJVDAsLa2oRGhSqXzMgJ4cQd3dCdHUJOXqUkPBwQu7fJyQ1lZCsLEJycwnZto0QFRVe\nvidPCCH1p5v3eW+y7da2Gpd7+5aQTp144gvFy4uQHTtqXH+L+O7qgLBnp8AeR0BAAAoKCgQanPz8\nfAQEBDSAKaNQKM2GixeB3r0BeXngwQPA2RmwtQVMTQFdXV6PQ0GBF7ciJQXQ0wMsLYG5c3kL6uqB\nu6/u1qrHcegQr7chtCPx+TNv9Fxkt4RSGYFjHIGBgQgMDISGhgaMjIygo6MDQgiePXuGpKQkZGZm\nYubMmfDy8mpsmWsEHeP4X3vnHRfl8fzxD6CCBUUBCwocBmk2UJoVNLGgWL6WoEYjEaOiRk00iSXl\nZ4zG3qOIxqhY0GgwaMSGnlgCImiQItgABYM0pbe7+f2xeoJyBTjaue/X617keW53n5ncuXO7szPD\n4VSCrCzgq6/YNs5vvyngXS5FWhqwZg2buW/eBIyNKy1GibgELVa3wH8L/4O2prbC/YgAKyvA25vZ\nOancuAHMmgVERFRaRlVF1twp9VSVh4cHPDw8EBISgtu3b0vyRXXq1Amurq7vZXoPDue9oKQE6NMH\ncHBgE2rz5hXrr6cHrFsH6OsDn3wCCIVAA6lTjUyiU6Nh1MKoQkYDYLmpAOaOkYm/P19tVAK5n6aD\ngwM3EnWY0immVRGuXy3g68v2d3bvVrDGavkIbW3hfPEisGIF8Or4fkW5lXyrUvEb3t7A558rIL6/\nP1DJEhF18rOrISqcHZfD4agwIhFLu/HDD1UyGgAAdXW2XeXtDVy5UqkhKlNjPDOT2YNPP5XT8P59\n1pjnuKswPFcVh8N5w5EjwPbtwLVrVTccrwkIAGbOBG7fBnR1K9TVYY8D1g9aj37GshwVZdm2jbku\njhyR03DjRuDePWbYOO9Q6TgODofzHiESsW0lZaw2SuPiAowfD0yfzrzWClIsKkbk88gKRYwTsR22\nzz9XoPFff3H/RiVRyHDExsZi69atWL58OX766Sf89NNP1S0XR0FUPV8O168GOXGCOcIHD1bKcGV0\nW7UKSEwEKnCEPzo1GsYtjNGsUTOF+9y8CeTlAXJdD+npbAVUkdNib1GnPrsaRq5zfNWqVQgODkZ4\neDjGjx+Pv/76C8OGDasJ2TgcTk0hFrPVxpo1yl1tvEZTkznde/dm52O7dpXb5caTG7Bvb1+hx7x2\niqvL+0l85gwzGo0bV2h8DkOuj8POzg7BwcHo1q0boqKikJSUBDc3N1y7dq2mZKwS3MfB4SjAiRPA\n6tXsJ3t1GI7XrF3L0pf/9pvcpq6HXTGl2xS4dXFTaOisLBYycu+eApVfx48Hhg0DPvtMobHfR6rk\n41BTU4OGhgYsLCwQGRmJFi1aICMjQ+lCcjicWkIsBn76Sfm+jfIYM4b92heLZTbLK85DUEIQhpgO\nUXjoI0eAgQMVMBr5+cCFCyzvFqdSyDUcrq6uyMzMxKxZszBu3DiYm5vD09OzJmTjKICq77Ny/WoA\nf39AQwNwdVXqsOXqZmrK/Ci3b8vsG/goELYGttDR0lH4ed7ewIwZCjQ8fpxV+2vdWuGxy6NOfHa1\nhFwfxzfffAMtLS0MGjQIMTExKCwsrAm5OBxOTUBUc6uN17i6svxQPaXHZ5y+fxojzORVX3pDeDjz\ndw8apEBjb2+WToVTaeSuOHr37i35bzU1NWhpaZW5Vxlyc3MxdepUmJmZwcrKCiEhIcjOzsaoUaNg\nZGSE0aNHIycnR9J+69at6NSpE6ysrMr4VmJiYtCjRw907NhRkvb9fUPVI1e5ftWMvz/bNho1SulD\nS9Vt+HCZBTKICKfjTsPVTPEV0J49gIeHAk7x6GjgwQOlrK5q/bOrRaT+b3727BnCwsKQl5eH8PBw\nhIWFITw8HGfPnoWmpmaVHvrjjz/CyMgIERERiIiIgIWFBXbu3AkjIyPcv38fHTp0gJeXFwDg+fPn\n2LFjBwIDA7Fz507MmzdPMs7ChQvx7bffIjQ0FFeuXMGtW7eqJBeH816RkwPMn88c1jW12gBYAqm4\nOCAlpdy3b/93G80aNUMn3U4KDZebyw5sKeTn9vYGpk2rUIlYzrtINRznzp3DokWLkJSUhIULF2LR\nokVYuHAhfH19sWLFiio99OLFi1i6dCm0tLTQoEEDtGjRAjdv3oSHhwc0NTUxbdo0hISEAABCQkIw\ndOhQGBkZwcnJCUQkWY3ExsbCzc0Nurq6GDNmjKTP+4Sq77Ny/aqRH39kR2OVFLfxNlJ1a9SI7SkF\nBJT79qnYUxXapjp+nJ3y7dBBTsP8fODgQRaIqARU/bspC6k+Dnd3d7i7u+PEiRMYO3as0h749OlT\nFBQUwNPTEzExMRgzZgzmzZuH0NBQWFhYAAAsLCxw8+ZNAMxwWFpaSvqbm5sjJCQExsbGaF3KuWVl\nZYVDhw5hzpw55eoiEAgAADo6OrC2tpYsM19/+PX1+s6dO3VKHq5fPdGvWTPg4EEId+0CSiXrq7Hn\nDx8OnD4N4at/l6XfP3L6CLzmeik83vr1wIoVCjz/+HEIO3YEEhLgbGJSs/rWg2uhUIh9rxI+vp4v\npSE1jsPHxwdTpkzBhg0boFZqGUtEUFNTw1eVdC49ePAAZmZm+Ouvv/DRRx9h5syZ+PDDD/H9998j\nLi4OWlpayMvLg6WlJRISEvDdd9/B0NAQM2fOBABMmDABM2bMgJGREaZMmYJ//vkHABAQEIDDhw/D\nx8enrII8joPDKUtJCSuV+uWXCmQCrCZSUgBzc+D5c7YCeUVydjK67OiClEUpaKghfzspJoYdwU1M\nVGD3qV8/5hT/3/+qKPz7QaXiOPLy8gAA2dnZ5b4qi6mpKczNzTFixAg0btwYEydOxNmzZ2FnZ4eY\nmBgAzOltZ2cHgKV1f10LBADu3bsHOzs7mJqaIqXUHml0dDQcHR0rLReH896waROrmTFlSu3J0KYN\nMxxvBRKfuX8GQ0yHKGQ0ABZH6O6ugNGIigIePlT6keP3lmoqVyuTESNGUHBwMIlEIpozZw7t2bOH\n1qxZQ3PnzqW8vDyaPXs2rVu3joiI/vvvPzI3N6eEhAS6fPky2djYSMZxcXGhI0eOUGpqKvXp04dC\nQ0PfeVYtqVhjqHrdY66fknn4kEhXl9ULr2bk6rZ8OdGXX5a5NerIKDr470GFxi8oINLXJ7p/X4HG\n8+cTLVum0LiKourfTVlzp9zjuElJSfjiiy9gbm4Oc3NzzJs3D8nJyVUyVuvXr8f8+fPRo0cPaGlp\nYcKECfD09ERiYiLMzc2RlJSEWbNmAQDatGkDT09PDBw4ELNnz8aWLVvKjLN27VrY2dmhX79+sOV5\n9Tkc6RCxMqnffMPqhdc2bx3LLSgpwOX4y3Dp5KJQd39/oEsXFlMoEyU7xTkK5Kpyd3dH586d4e7u\nDgA4cOAA7t69K3Gi1HW4j4PDecXBg8D69UBoaN04jioWs6NQQUGAqSkC7gfgl2u/IOizIIW6Dx7M\ntqkmTZLT0McHOHxY6ikuTvnImjvlGg5LS0tER0dLHOQikQhdunSR+CPqOtxwcDgAnjxhDvFTp9jf\nusL06SxT7vz5mP33bJjomODrPl/L7fb4MVPj6VNAS0tOY+4UrxRVSnI4YcIELFy4ELdv30Z4eDi+\n+eYbTJgwARkZGTzZYR3g9XE6VYXrpwTy8lhk+MKFNWo0FNLt1bFcqmC0+N69wCefKGA0qtEprurf\nTVnIzVX1+++/Q01NDX5+fmXu79u3D2pqanj06FG1CcfhcKoIEYuU7twZWLRIoS4FBSygvE0bYOxY\noFu3agws/+gjYOpURD78Bw01GsJCz0Jul5IS4PffFdx5+vVXFlJeF7bmVAhec5zDUWV++QX480/m\nR1CgaJFIBLi5MfdDx46sTEeDBsyAjB0L2NpWgxEZPBh/9NfHdVt9bB66WW7z06eBn38GgoPlNIyI\nYIYpKgrQ11eOrO8RsuZOuSsOgCUlvHTpEjIzMyX3Pq2twCEOh6MYp04B27ez4kwKGA0iFhOYng6c\nPcuK9q1bxzLPnjgBTJ4MNGkCnDzJCiYpjeHDofnHSrhOOqxQcy8vBWqKi8WApyerasiNhvKRd5bX\n29ubHBwcSF9fn0aPHk3a2to0adKkqh4RrjEUULFeo+pnybl+lSQqigU5/POPwl3WriXq0oUoM7P8\n98Viok2biNq1IwoOlj+eorodPvkzPW+uQYVF+XLbnjtHJBAQ5ebKabh3L5GdHVFJiUIyVAZV/27K\nmjvlOsd///13BAUFQV9fH35+frh16xZSU1Or36JxOJzKkZnJnOFr17KCRQpw+DBbnAQEADpSaiep\nqQELFrBf/K6uwB9/VF3UqwlXMT9uC7QF5mh08bLMtoWFwNy5wNatbOUjlYwMYMkSYMcOVqCKo3zk\nWR1bW1siIho2bBg9ffqUSkpKyMLCQnlmrZpRQEUOR7X4+GOiefMUbn7xIlHr1kSRkYo/IjycqEMH\nolWr2EqkMsRnxlPb9W3p7P2zRPv3E330kcz2P/9MNGKEAgPPmkXk6Vk5oTgSZM2dcn0cdnZ2yMzM\nxNSpU9GvXz80bNhQqdlyORyOErlyBQgJYdn/FCAyEpg4ka0eOndW/DE2Nsw5PXIkEBvL3AlFRexV\nWMj+ammxBISlchhKyC3Kxeijo7Go1yJWV9yoCFi8GLh7l8V1vEV8PEuxFRoqR7DQUMDPT2H9OZWj\nQqeqsrOz8eLFCxgaGlanTEpF1U9VCUulxFZFuH4VQCRi5ViXLQPGj5fbvKiIhXUsWKBgEaRyyM0F\n5sxh83SjRuylqcn+PnwoxPPnznB3Z87s16lBiAhux92g1UAL+0fvf5N9e9UqVp1v7953njNqFGBv\nz1STikjEtubmzGEh5dWMqn83K3Wq6uzZs0hLS8PkyZMl97S1teHv74/WrVtjkELFfTkcTo2xZw9z\nUIwbp1DzNWtYxo+qzLFNmwLSsg8JhYCBAbB7Nyu01K0bMGMGcE9/FRJeJuCK+5UyJRswcyazLqtW\nAW3bSm6fPg3cuwccOyZHmN27mdXiJz6rH2l7WPb29pSUlPTO/eTkZHJwcKjy/llNIUNFDkd1yMhg\njorbtxVqHhlJpKdHlJhYzXK9oqCAaJdPKnWcvI40vm5Pm39LKv/A06xZRN9/L7nMyyMyMSE6f17O\nA1JS2Cmyf/9VqtzvM7LmTqmnqrKzs2FgYPDO/Xbt2lWpHgeHw6kGli9nuZisreU2LSlhW1MrVwLV\nveucXZiNgxEHMebEcHz99APYjw7DngEBOP67Abp2ZbGJZXZDFiwAdu1iGW3B4hdtbVmlWakQMSfL\nZ5+xZQ2n+pFmUczNzSmynGMWUVFRZGZmphyTVgPIUFElUPWz5Fw/BYiKYsuH588Var5uHdHAgZU/\nDSWPjLwMOhxxmJz/z5la/NKChh8aTociDlF2YbakjVhMdOYMkbU1ka0t0V9/Ed24weI0knu60jV3\nb9q4kan19KmcBx46RGRlRZQvPw5Emaj6d1PW3CnVxzFlyhQsWbIEq1evhpWVFQAgKioKy5YtK+P3\n4HA4tQgR+5X+3XcKRUjHxQGrV7NgcmWmDolLj8PpuNM4FXcKYclhcBI4oWe7njg++Th0m+i+015N\nDXBxAYYMYVHpa9Yw37a2NtCr8VeY6TcHRxp7YN8+dbRvL+PByclM/4AABTIecpSF1FNVYrEY27dv\nx/Hjx5GYmAgAMDIywrhx4zBnzhxo1JPAGlU/VcV5z/H3Z8dY//1XbiI/sRhwdma+83nzKv/IjPwM\nhD8Lx63kWwh7FoZbybdQWFIIVzNXjDAbgQ87fogmDWVF6MmBiJ33/eUXZl1ktXN1ZUfD/u//Kv88\nTrlUqR4HAPz3338AgLalTjrUF7jh4KgsBQWsBN6vv7Kf7nL49VfgyBGW71C9lHezoKQAiS8TEf8i\nXvJKfJmI3OJcFJYUokhUhEIR+/s89znS89Jh3dYatga26NmuJ3oa9ISZrhnU1eQmolCcAwdY4anz\n56W3+e03plRICM9+Ww1U2XDUZ1TdcKj6WXKunww8PVl6jaNH5TZNTmZ+4+vXAZ32KbiScAXCeCGE\n8UI8zHwIw+aGEOgIJC+jFkbQbqQNzQaaaKTRCI00GkFTQxMtG7eEaStThYxElXQrKgIEAuDcuXID\nApGQwLzmly6V/34NoOrfzSpnx+VwOHWMgweBwEDg1i2Fms//MR4Cz43438WLeJbzDP2N+8PZ2Bmf\n9/gc3dp0g4Z6Hdt6btSI7ad9/DELbR88mG1JaWiwPbdp01hhqloyGu89srzqYrGYEqvpoHdJSQlZ\nW1uTq6srERFlZWXRyJEjydDQkEaNGkXZ2W9OYGzZsoVMTU3J0tKSrl69KrkfHR1NNjY2ZGJiQkuX\nLi33OXJU5HDqH3fvsuNGERFym2bkZdAUn4WktrgVLTqzjMKTw6lEVH0ZY5VKSQkL4Fi0iKhbN6KW\nLYnGjSP6/HMiR0ei4uLallClkTV3yl1vDhs2rFoM1pYtW2BlZSWJHN25cyeMjIxw//59dOjQAV5e\nXgCA58+fY8eOHQgMDMTOnTsxr5RXb+HChfj2228RGhqKK1eu4JaCv744nHpLVharqLRhg8xf24Ul\nhdj4z0aYbzfHxatZWG0YiXUuP8OmnU3dW11IQ0ODBXCsW8ec/1FRwIgR7L6PD6swxakVZBoONTU1\n9OrVC3/99ZdSH/r06VOcOXMG06dPl+yh3bx5Ex4eHtDU1MS0adMQEhICAAgJCcHQoUNhZGQEJycn\nEBFycnIAALGxsXBzc4Ouri7GjBkj6fM+oep1j7l+pSACPDzY0SgZaTUC7gfA8ldLXI6/jO8NL6PV\ndW98NaNdlWWtKEr/7Nq1Y3rv3Pkm8VUtourfTVnINdlXr17Fnj17oKurKzlVpaamhoiIiEo/9Msv\nv8S6deuQlZUluRcaGgoLC1Zv2MLCAjdv3gTADIelpaWknbm5OUJCQmBsbIzWrVtL7ltZWeHQoUOY\nM2fOO89zd3eHQCAAAOjo6MDa2lri1Hr94dfX6zt37tQpebh+1ajfli0Q/vsvsH072Lvvtl+8ZzF2\nh+/GsUXH0K/Dh+jYUYj584Vo0KBu6Muv6+61UCjEvleJx17Pl1KRt8/1+PHjcl+V5dSpUzR79mwi\nYpGXr30choaGlP8q8jM3N5eMjIyIiGjZsmXk5eUl6e/m5kaBgYF0//59cnR0lNw/c+YMTZ48+Z3n\nKaAih1P3uX6d5WJ69Ehqkw03NpDRJiO6l3qPiFi1PheXmhKQo2rImjvl+jgEAgE0NTVx/fp1CAQC\nNG3atErHW2/cuAF/f3+YmJhg4sSJuHTpEqZMmQI7OzvEvMqhHxMTAzs7OwCAg4MDoqOjJf3v3bsH\nOzs7mJqaIiUlRXI/OjoajgpWO+Nw6hV5eazg9549gInJO28TEZYGLsXu8N249tk1mOuZIyODJZld\nv74W5OWoPHINh7e3NyZOnIjly5cDAIqKiqqUcmTVqlV48uQJHj9+DF9fXwwcOBA+Pj5wcHDA3r17\nkZ+fj71790qMgL29Pc6dO4fExEQIhUKoq6tDW1sbANvS8vX1RVpaGvz8/ODg4FBpueorr5eaqgrX\nD8DPPwMODqxq0luIxCLM+nsWLjy6gCD3IBi2YFkLf/qJRYi/yhZUK/DPTnWRazh8fHxw/vx5NG3a\nFADQvn17pWbHfX2qytPTE4mJiTA3N0dSUhJmzZoFAGjTpg08PT0xcOBAzJ49G1u2bJH0Xb9+Pdau\nXQs7Ozv069cPtra2SpOLU0d5+ZJNpAUFtS1JzRAdzepMbNz4zlvFomJMPDER99Pv49Knl6DflOWq\niotjYR48Cwen2pC3zzV8+HAqLi4ma2trIiJKSEggl3q0caqAipz6xA8/EOnqEvXvT5SZWdvSVC9i\nMZGTE9G2beW+7Xnak4YeHEr5xW+ywt66RWRuTrRxYw3JyFFZZM2dclccU6dOxSeffIIXL15g+fLl\ncHV1xfTp06vfonE4b5OeznITBQezuhP9+wNJSbUtVfXh4wPk5LDUIm+x69YuXI6/DN+xvtBqoAWR\niNXXcHFhK40vv6x5cTnvEYpYnvj4eFq/fj2tXbu22iLJqwsFVay3qHpNgDL6LV5MNGMG+2+xmGj1\naiJjY6Lo6NoQTSlI/fzS04natGFLiLe4En+FWq9rTXFpcURE9PAhUe/erMZGXfrn+V59N1UQWXOn\n1DiOhIQEbNu2DdeuXcOQIUMwe/ZstGnTpuYsGodTmufPAW9v4FXcA9TUgG+/ZbWpBwwA/PyAXr1q\nV0ZlsngxMH480LNnmdsJLxLgdtwNPv/zgWmrTvj9d+Cbb4AlS1hZCnUlJqjlcKQhNTvu1KlTYWZm\nhiFDhuDo0aPQ0NDA6tWra1q+KqPq2XHfG776CiguBrZte/e9gAAWUXziBNu+qu/cuMGMRnQ00KKF\n5HZuUS767O2DT7t/ipndvsKsWcyOHj7Mc/1xlE+l0qp3794d//77LwCguLgYvXv3RmhoaPVJWU1w\nw6ECJCezuhNRUSztRHns3w8cOwb8/XfNyqZsSkrYKmPJEmDCBMltIoLbcTc0btgYSy33Ydw4NfTo\nwbJvNKlCzSQORxqy5k6pC9vCwkKEh4cjPDwcERERyM7ORnh4OMLCwhAeHl5twnIqhqqfJRcKhSyS\nbdo06UYDYEEL168DpYJC6wNlPr+CAmDqVKB9e8DNrUy7n678hMSXiRgm2oV+/dQwdy6wb1/dNhrv\nxXfzPUWqj6Nt27ZYuHCh1OvLly9Xr2QcDsAMwZEjwL17sts1bcoC5Hx9gfnza0Y2ZZKaCvzv1QEp\nvwAAHQVJREFUf4CBAdtyK1UQfN31dfCJ8MHgpKv49qgW/v6blabgcGoLXgGQU7f5/HNAX5+tOuRx\n4QJzKoeFVb9cyuTePWD4cLY1tWJFGQ/3uuvrsCtsF4wvC9EwvwMOHQJ0dWtRVs57Ay8dq9oqqi4P\nH7JUG3FxQKtW8tuLRICRETMgtZlroyIEBgKTJgFr1gDu7mXeem00RmUKES7sgAsXeAkKTs1RKR8H\np36g0vusy5dD6OqqmNEAWIGfSZNY4Fwd5UXBC/wZ8ycWHJuG1W7t8ffoYfh36zKIp5atr/HaaPxs\nKsSRXR1w+HD9Mxoq/d2E6usni3r2VeS8N4SHs5XDnj0V6zdlCuDqysKoazmoQUxiPHn5BLHpsQiO\nv47Ey34wDonF/xKbwPVpPnLtrPHzgr64kLEHmZvXYUKXCZjYZSICHwViV9guHHURYoRTB/j4yD4X\nwOHUNFK3qsLCwiQJCMujR48e1SaUMuFbVfUQIuDDD4GPPwZeJbusENbWwKZNLDCwBnlZ8BKbgjch\n8nkkYtNjkfzfA4xJaAK3uIboFfkS4nZt0XjYSDQYOozFmzRuLOkb+TwSRyKP4PDdw2io3hDnP7kE\n9zEdMGAA8OOPNaoGhwOgkj4OZ2dnmYajvpyq4oajHvL338CiRcDdu+/sz2RmAsePA9Onlzl4VJYN\nG1jMx9691S/rKyJSIjD22Fh8pO+AKU90YRUUjRZBN6FmZ8dqhI8cyY7ZyoGIQCD88L06QkKAs2fZ\nDhyHU9PInDuVnuCkjqHqKqpcvpziYiJLS6JTp4iorH4ZGUS2tkR6ekTLlskYIzmZSEeHKDe3emV9\nxYE7B0hvrR4F/vo1e66LC9GePUSpqXL7lvf5nT1L1L490X//VYOwNYjKfTffQtX1kzV3SvVxBAUF\nybRG/VUhtQOn7rFnD9vQHz68zO3MTGDwYKBvX+DMGaBPH6BDByk7We3aAfb2gL9/mehrZVNYUogF\n5xYg8FEgQtv8AMGiFUy4KuTMiotjh6t8fQGeGo5TV5G6VeXq6lruVlVERASePn0KkUhU7cIpA75V\nVY/IygLMzdnka2MjuV3aaGzcyLaoHj4E+vVjKTdGjSpnrIMHWeBgNaUgSXyZiPF/jEeH5h3gIx6N\nJvMXAadPVzoyr6iIlXnduJGdzPXwULLAHE4FUcpW1bVr12jIkCHk4OB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       "text": "<matplotlib.figure.Figure at 0x922c190>"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You can spend more time styling your plot than I did - [matplotlib's documentation](http://matplotlib.org/contents.html) is good and you can even go [all XKCD](http://nbviewer.ipython.org/url/jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb) on your charts!"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}