anaderi/gist:6149882

## gistfile1.txt
{
 "metadata": {
  "name": "gerrors"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import rootnotes",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from ROOT import TCanvas, TGraphErrors\nfrom ROOT import gROOT\nfrom array import array",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "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": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: c1\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "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,  0.8,  0.5,  0.6,  0.7,  0.8  ] )\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "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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UdedkZXZooO/d3xnom19Y6u64AhDuqhHNIqJZJBzNqacLK96OwobBfj/dMFmZrfrRUGoa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\n",
       "prompt_number": 10,
       "text": "<ROOT.TCanvas object (\"c1\") at 0x2ce2120>"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "gerrors"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "import rootnotes",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 6
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "from ROOT import TCanvas, TGraphErrors\nfrom ROOT import gROOT\nfrom array import array",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 7
	},
	{
	"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": [
	{
	"output_type": "stream",
	"stream": "stderr",
	"text": "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: c1\n"
	}
	],
	"prompt_number": 8
	},
	{
	"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, 0.8, 0.5, 0.6, 0.7, 0.8 ] )\n\n",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 9
	},
	{
	"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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UdedkZXZooO/d3xnom19Y6u64AhDuqhHNIqJZJBzNqacLK96OwobBfj/dMFmZrfrRUGoa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\nSDiaU08XFoUCAADIRKFgg3B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	"prompt_number": 10,
	"text": "<ROOT.TCanvas object (\"c1\") at 0x2ce2120>"
	}
	],
	"prompt_number": 10
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 10
	}
	],
	"metadata": {}
	}
	]
	}