{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ratioplot\n",
    "Display two histograms and their ratio.\n",
    "\n",
    "This program illustrates how to plot two histograms and their\n",
    "ratio on the same canvas. Original macro by Olivier Couet.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Michael Moran  \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 Tuesday, August 16, 2022 at 09:39 AM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.27/01\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TColor, TGaxis, TH1F, TPad\n",
    "from ROOT import kBlack, kBlue, kRed\n",
    "\n",
    "\n",
    "def createH1():\n",
    "    h1 = TH1F(\"h1\", (\"Two gaussian plots and their ratio; x title; h1 and h2\"\n",
    "                \" histograms\"), 100, -5, 5)\n",
    "    h1.SetLineColor(kBlue+1)\n",
    "    h1.SetLineWidth(2)\n",
    "    h1.FillRandom(\"gaus\")\n",
    "    h1.GetYaxis().SetTitleSize(20)\n",
    "    h1.GetYaxis().SetTitleFont(43)\n",
    "    h1.GetYaxis().SetTitleOffset(1.55)\n",
    "    h1.SetStats(0)\n",
    "    return h1\n",
    "\n",
    "\n",
    "def createH2():\n",
    "    h2 = TH1F(\"h2\", \"h2\", 100, -5, 5)\n",
    "    h2.FillRandom(\"gaus\")\n",
    "    h2.SetLineColor(kRed)\n",
    "    h2.SetLineWidth(2)\n",
    "    return h2\n",
    "\n",
    "\n",
    "def createRatio(h1, h2):\n",
    "    h3 = h1.Clone(\"h3\")\n",
    "    h3.SetLineColor(kBlack)\n",
    "    h3.SetMarkerStyle(21)\n",
    "    h3.SetTitle(\"\")\n",
    "    h3.SetMinimum(0.8)\n",
    "    h3.SetMaximum(1.35)\n",
    "    # Set up plot for markers and errors\n",
    "    h3.Sumw2()\n",
    "    h3.SetStats(0)\n",
    "    h3.Divide(h2)\n",
    "\n",
    "    # Adjust y-axis settings\n",
    "    y = h3.GetYaxis()\n",
    "    y.SetTitle(\"ratio h1/h2 \")\n",
    "    y.SetNdivisions(505)\n",
    "    y.SetTitleSize(20)\n",
    "    y.SetTitleFont(43)\n",
    "    y.SetTitleOffset(1.55)\n",
    "    y.SetLabelFont(43)\n",
    "    y.SetLabelSize(15)\n",
    "\n",
    "    # Adjust x-axis settings\n",
    "    x = h3.GetXaxis()\n",
    "    x.SetTitleSize(20)\n",
    "    x.SetTitleFont(43)\n",
    "    x.SetTitleOffset(4.0)\n",
    "    x.SetLabelFont(43)\n",
    "    x.SetLabelSize(15)\n",
    "\n",
    "    return h3\n",
    "\n",
    "\n",
    "def createCanvasPads():\n",
    "    c = TCanvas(\"c\", \"canvas\", 800, 800)\n",
    "    # Upper histogram plot is pad1\n",
    "    pad1 = TPad(\"pad1\", \"pad1\", 0, 0.3, 1, 1.0)\n",
    "    pad1.SetBottomMargin(0)  # joins upper and lower plot\n",
    "    pad1.SetGridx()\n",
    "    pad1.Draw()\n",
    "    # Lower ratio plot is pad2\n",
    "    c.cd()  # returns to main canvas before defining pad2\n",
    "    pad2 = TPad(\"pad2\", \"pad2\", 0, 0.05, 1, 0.3)\n",
    "    pad2.SetTopMargin(0)  # joins upper and lower plot\n",
    "    pad2.SetBottomMargin(0.2)\n",
    "    pad2.SetGridx()\n",
    "    pad2.Draw()\n",
    "\n",
    "    return c, pad1, pad2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create required parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h1 = createH1()\n",
    "h2 = createH2()\n",
    "h3 = createRatio(h1, h2)\n",
    "c, pad1, pad2 = createCanvasPads()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "draw everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pad1.cd()\n",
    "h1.Draw()\n",
    "h2.Draw(\"same\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to avoid clipping the bottom zero, redraw a small axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h1.GetYaxis().SetLabelSize(0.0)\n",
    "axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, \"\")\n",
    "axis.SetLabelFont(43)\n",
    "axis.SetLabelSize(15)\n",
    "axis.Draw()\n",
    "pad2.cd()\n",
    "h3.Draw(\"ep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To hold window open when running from command line\n",
    " text = raw_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}