{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require(['base/js/utils'],\n",
       "function(utils) {\n",
       "    utils.load_extensions('usability/python-markdown/main');\n",
       "    utils.load_extensions('usability/codefolding/main');\n",
       "});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "require(['base/js/utils'],\n",
    "function(utils) {\n",
    "    utils.load_extensions('usability/python-markdown/main');\n",
    "    utils.load_extensions('usability/codefolding/main');\n",
    "});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Code Cell\n",
    "This section tests the ability to run Python inline using the a code cell.  The code cell displays the Python code in the resulting document and makes the results of the Python analysis available for later reference as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x1 = 1\n",
    "x2 = 1\n",
    "y = x1 + x2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "variables": {
     "x1": "1",
     "x2": "1",
     "y": "2"
    }
   },
   "source": [
    "The above code demonstrates that {{x1}} + {{x2}}  = {{y}}."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "# Test Codefolding\n",
    "This section tests the ability to run Python inline using the codefolding extension.  The codefolding extenstion hides the content of code cells that start with a comment, thereby running the Python code without displaying the source in the resulting document.  This feature makes the results of the Python analysis available for later reference as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hidden code\n",
    "x3 = 2\n",
    "x4 = 2\n",
    "y2 = x3 + x4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [
     0
    ],
    "variables": {
     "x3": "2",
     "x4": "2",
     "y2": "4"
    }
   },
   "source": [
    "The hidden code demonstrates that {{x3}} + {{x4}}  = {{y2}}."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test MatPlotLin Integration and Inline Plotting\n",
    "This section tests the ability to use plotting methods to create a basic plot and then include it in the document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6f5d516c88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YHbyf7hzCEg0O9NLYOFC2QX+0FPhFRFIqfT/d0VCqR0QkkizP0A2cBhxD/n66fX3lNWEr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\nB0zwHvVteGoyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f769492e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from numpy import *\n",
    "from pylab import *\n",
    "  \n",
    "# define the function to evaluate\n",
    "def f(x):\n",
    "  return x**2 +2*x + 3\n",
    "  \n",
    "range_begin = -5\n",
    "range_end = 5 \n",
    "x = linspace(range_begin, range_end, 1000)\n",
    "  \n",
    "clf()    \n",
    "plot(x, f(x), 'bo')\n",
    "xlabel('x')\n",
    "ylabel('f(x)')\n",
    "title('Demonstration Quadratic Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "variables": {
     "range_begin": "-5",
     "range_end": "5"
    }
   },
   "source": [
    "The code above calculates values for the quadratic equation: $$f(x) = x^2 +2x + 3$$ \n",
    "\n",
    "The code then runs this equation for the integer values from {{range_begin}} to {{range_end}} and plots these values in a newly generated plot above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "source": [
    "---\n",
    "This document is a work of Mach 30 and is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}