Really cool nobel prize wining notebooking.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:21e6d887e2b3f6dab3bb4d9da4a16ddc5d890bc9300d80831c21e6b274343492"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"hello world\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hello world\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we solve an equation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1> NO REALLY</h1>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This one $\\Sigma = i^{23} \\int$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src = 'http://scollis.github.io/open_source_radar_short_course/images/rhi.png'>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "%matplotlib inline "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(0, 100, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print x.max(), x.mean()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100.0 50.0\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = x**2.0 "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize = [10,8])\n",
      "plt.plot(x,y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[<matplotlib.lines.Line2D at 0x105f2d6d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAHfCAYAAAAVw3+UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105e7db10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}