{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph\n",
    "A Simple Graph Example\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Wim Lavrijsen  \n",
    "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Wednesday, March 03, 2021 at 09:40 AM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.23/01\n",
      " i 0 0.000000 1.986693 \n",
      " i 1 0.100000 2.955202 \n",
      " i 2 0.200000 3.894183 \n",
      " i 3 0.300000 4.794255 \n",
      " i 4 0.400000 5.646425 \n",
      " i 5 0.500000 6.442177 \n",
      " i 6 0.600000 7.173561 \n",
      " i 7 0.700000 7.833269 \n",
      " i 8 0.800000 8.414710 \n",
      " i 9 0.900000 8.912074 \n",
      " i 10 1.000000 9.320391 \n",
      " i 11 1.100000 9.635582 \n",
      " i 12 1.200000 9.854497 \n",
      " i 13 1.300000 9.974950 \n",
      " i 14 1.400000 9.995736 \n",
      " i 15 1.500000 9.916648 \n",
      " i 16 1.600000 9.738476 \n",
      " i 17 1.700000 9.463001 \n",
      " i 18 1.800000 9.092974 \n",
      " i 19 1.900000 8.632094 \n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from ROOT import TCanvas, TGraph\n",
    "from ROOT import gROOT\n",
    "from math import sin\n",
    "from array import array\n",
    "\n",
    "\n",
    "c1 = TCanvas( 'c1', 'A Simple Graph Example', 200, 10, 700, 500 )\n",
    "\n",
    "c1.SetFillColor( 42 )\n",
    "c1.SetGrid()\n",
    "\n",
    "n = 20\n",
    "x, y = array( 'd' ), array( 'd' )\n",
    "\n",
    "for i in range( n ):\n",
    "   x.append( 0.1*i )\n",
    "   y.append( 10*sin( x[i]+0.2 ) )\n",
    "   print(' i %i %f %f ' % (i,x[i],y[i]))\n",
    "\n",
    "gr = TGraph( n, x, y )\n",
    "gr.SetLineColor( 2 )\n",
    "gr.SetLineWidth( 4 )\n",
    "gr.SetMarkerColor( 4 )\n",
    "gr.SetMarkerStyle( 21 )\n",
    "gr.SetTitle( 'a simple graph' )\n",
    "gr.GetXaxis().SetTitle( 'X title' )\n",
    "gr.GetYaxis().SetTitle( 'Y title' )\n",
    "gr.Draw( 'ACP' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TCanvas.Update() draws the frame, after which one can change it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c1.Update()\n",
    "c1.GetFrame().SetFillColor( 21 )\n",
    "c1.GetFrame().SetBorderSize( 12 )\n",
    "c1.Modified()\n",
    "c1.Update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the graph does not appear, try using the \"i\" flag, e.g. \"python3 -i graph.py\"\n",
    "This will access the interactive mode after executing the script, and thereby persist\n",
    "long enough for the graph to appear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ROOT import gROOT \n",
    "gROOT.GetListOfCanvases().Draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}