kjordahl/doc.ipynb

## doc.ipynb
{
 "metadata": {
  "name": "doc.ipynb"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 10\n",
      "y = 30"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Now let's print the result:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print x, y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 30\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "now let $x = \\frac{\\pi}{10}$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = pi / 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "theta = arange(0, 6 * pi, x)\n",
      "plot(theta, sin(theta))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10b9beed0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qmX55OS0jHcHJvl6opNH16zQz5CW1vVFJI0CPD+ag3+ZNTU2I/HviMzIyEk2D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\nF/H/J7iv0UT6WNUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b97c2d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "doc.ipynb"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"x = 10\n",
	"y = 30"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 8
	},
	{
	"cell_type": "heading",
	"level": 4,
	"metadata": {},
	"source": [
	"Now let's print the result:"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"print x, y"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"10 30\n"
	]
	}
	],
	"prompt_number": 9
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"now let $x = \\frac{\\pi}{10}$"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"x = pi / 10"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 12
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"theta = arange(0, 6 * pi, x)\n",
	"plot(theta, sin(theta))\n"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 13,
	"text": [
	"[<matplotlib.lines.Line2D at 0x10b9beed0>]"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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qmX55OS0jHcHJvl6opNH16zQz5CW1vVFJI0CPD+ag3+ZNTU2I/HviMzIyEk2D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\nF/H/J7iv0UT6WNUAAAAASUVORK5CYII=\n",
	"text": [
	"<matplotlib.figure.Figure at 0x10b97c2d0>"
	]
	}
	],
	"prompt_number": 13
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}