{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import rootnotes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from ROOT import TCanvas, TFormula, TF1\n",
      "from ROOT import gROOT, gObjectTable\n",
      "c1 = rootnotes.default_canvas()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We create a formula object and compute the value of this formula for two different values of the x variable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "form1 = TFormula( 'form1', 'sqrt(abs(x))' )\n",
      "form1.Eval( 2 )\n",
      "form1.Eval( -45 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "6.708203932499369"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a one dimensional function and draw it"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fun1 = TF1( 'fun1', 'abs(sin(x)/x)', 0, 10 )\n",
      "c1.SetGridx()\n",
      "c1.SetGridy()\n",
      "fun1.Draw()\n",
      "c1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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TVRi+lAAAoImiqhhNLX+4rpLuA0uHN/J7k84vHd7I70o6fECx+nICfUgWAQEASyEw7WZF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\nABlQVAEAAGRAUQUAAJABRRUAAEAG/x8gKHsBI9p1NwAAAABJRU5ErkJggg==\n",
       "prompt_number": 4,
       "text": [
        "<ROOT.TCanvas object (\"icanvas\") at 0x4151050>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "canvas = TCanvas(\"icanvas\", \"icanvas\", 800, 600)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: icanvas\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}