{
 "metadata": {
  "name": "gerrors"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import rootnotes",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from ROOT import TCanvas, TGraphErrors\nfrom ROOT import gROOT\nfrom array import array",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "gROOT.Reset()\nc1 = TCanvas( 'c1', 'A Simple Graph with error bars', 200, 10, 700, 500 )\nc1.SetFillColor( 42 )\nc1.SetGrid()\nc1.GetFrame().SetFillColor( 21 )\nc1.GetFrame().SetBorderSize( 12 )",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "n = 10;\nx  = array( 'f', [ -0.22, 0.05, 0.25, 0.35,  0.5, 0.61,  0.7, 0.85, 0.89, 0.95 ] )\nex = array( 'f', [  0.05,  0.1, 0.07, 0.07, 0.04, 0.05, 0.06, 0.07, 0.08, 0.05 ] )\ny  = array( 'f', [     1,  2.9,  5.6,  7.4,  9.0,  9.6,  8.7,  6.3,  4.5,    1 ] )\ney = array( 'f', [  0.8,  0.7,  0.6,  0.5,  0.4,  0.4,  0.5,  0.6,  0.7,  0.8  ] )\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "gr = TGraphErrors( n, x, y, ex, ey )\ngr.SetTitle( 'TGraphErrors Example' )\ngr.SetMarkerColor( 4 )\ngr.SetMarkerStyle( 21 )\ngr.Draw( 'ALP' )\n\nc1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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S7dFyRLOJaBYJR3Pq6cKyVyiMjW6eP3bSP55bWJo/dnKs7nZMesQ6stKIZhHRLBKO5tTT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\ni4hmkXA0p54uLAoFG4R71Yh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       "prompt_number": 5,
       "text": "<ROOT.TCanvas object (\"c1\") at 0x49ae1e0>"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}