{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "np.random.seed(12)\n",
      "\n",
      "fig, ax = ppl.subplots(1)\n",
      "\n",
      "ndatapoints = 100\n",
      "nitems = 5\n",
      "data = np.random.randn(ndatapoints, nitems)\n",
      "\n",
      "ppl.hist(data)\n",
      "fig.savefig('hist_multiple_bars_prettyplotlib_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEACAYAAAB8nvebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngIQobwBIiPIGgIQobwBIiPIGgIT+D0X1cXok1+tyAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f436950>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}