My failed attempt at making a nice histogram with IPython.

HistogramFail

{
 "metadata": {
  "name": "HistogramFail"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import random\nimport numpy\nfrom matplotlib import pyplot, axes\nimport pylab\nimport math\n\npylab.rcParams['figure.figsize'] = (14.0, 8.0)\n\nbins = numpy.logspace(-64, 64, base=2)\n\ndef bin_iter(bins):\n    for i in range(len(bins) - 1):\n        yield (bins[i], bins[i + 1])\n\nx = [long(high - low) for low, high in bin_iter(bins)]\ny = [1 for _ in bin_iter(bins)]\n\nfig = pyplot.figure()\nplot = fig.add_subplot(111)\nplot.set_yscale(\"log\", basey=2)\nplot.set_xscale(\"log\", basex=2)\nplot.hist(x, bins, alpha=0.6, label='x', cumulative=True, histtype='stepfilled')\nplot.legend(loc='upper right')\npyplot.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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o6RzAL4wVAACAKHP8+Oeqr9+jrVsX68wzzzCdA/iNsQIAABBF6uvdOnbsfm3Z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\nPXuqqKgoVF0AAAAAbK7NsfLrX/9aF11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       "text": "<matplotlib.figure.Figure at 0x7fe5b5c07290>"
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nimport matplotlib.pyplot as plt\nmu, sigma = 100, 15\nfig, ax = plt.subplots()\nx = mu + sigma * np.random.randn(10000)\nax.set_yscale('log', basey=2)\nn, bins, histpatches = ax.hist(x, 50, facecolor='green', alpha=0.75)\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IfPjDH46LLroovvrVry7GlHmDk63fbbfdFl1dXXHJJZfENddcEy+++OL0r1m/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\n+AAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x7fe5b609fdd0>"
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}