gistfile1.txt

{
 "metadata": {
  "name": "formula1"
 },
 "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\nfrom ROOT import gROOT, gObjectTable\nc1 = 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))' )\nform1.Eval( 2 )\nform1.Eval( -45 )",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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 )\nc1.SetGridx()\nc1.SetGridy()\nfun1.Draw()\nc1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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80uGN/K6kwwcUqy+XSx+SK4ICAALIZdgdC50qD5G2MAAAgqCocnVkX1e6Dywd3sjvTTq/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\nQ6JpYjOAtKn24JRAIAPRhl0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AQA8oqgAAAHpAUQUAANADiioAAIAeUFQBAAD04P8DbS0r\niZoCB9MAAAAASUVORK5CYII=\n",
       "prompt_number": 3,
       "text": "<ROOT.TCanvas object (\"icanvas\") at 0x4705ee0>"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}