tunfold/1-backgrounds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TUnfold: Basic Usage with Backgrounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.10/09\n"
     ]
    }
   ],
   "source": [
    "import ROOT\n",
    "import utils\n",
    "\n",
    "# Set the ROOT style\n",
    "ROOT.gROOT.Macro(\"style.C\")\n",
    "utils.setTDRStyle()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supply the input distribution to be unfolded as a 1-dimensional list for N bins, with each entry corresponding to the bin content.\n",
    "Supply the response matrix where each row corresponds to a truth level bin.\n",
    "The normalization of each row must be the acceptance efficiency of the corresponding bin (e.g. the normalization is 1 for resolution only unfolding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load histograms from ROOT file\n",
    "f = ROOT.TFile.Open(\"../data/example.root\")\n",
    "data = f.Get(\"data\")\n",
    "response = f.Get(\"resmat\")\n",
    "bckg1 = f.Get(\"bckg1\")\n",
    "bckg2 = f.Get(\"bckg2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s7Pzs7FwpdXZ2fu/e/cePXz948O3WTcOphBToks7mPklz95CclZpFk+O9pU6ABF1cfPPo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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%jsroot off\n",
    "c1 = ROOT.TCanvas('c1','c1',500,400)\n",
    "\n",
    "stack = ROOT.THStack(\"hs\",\"hs\")\n",
    "\n",
    "signal = data.Clone()\n",
    "signal.SetFillColor(ROOT.kBlue-9)\n",
    "bckg1.SetFillColor(ROOT.kRed-7)\n",
    "bckg2.SetFillColor(ROOT.kGreen+2)\n",
    "\n",
    "stack.Add(bckg1)\n",
    "stack.Add(bckg2)\n",
    "stack.Add(signal)\n",
    "stack.Draw(\"hist\")\n",
    "stack.SetMaximum(220) # adjust the range of the plot\n",
    "\n",
    "data.Draw(\"same\")\n",
    "\n",
    "leg = ROOT.TLegend(0.2,0.7,0.35,0.9)\n",
    "leg.AddEntry(data,\"Data\")\n",
    "leg.AddEntry(signal,\"Signal\",\"f\")\n",
    "leg.AddEntry(bckg1,\"bckg1\",\"f\")\n",
    "leg.AddEntry(bckg2,\"bckg2\",\"f\")\n",
    "leg.Draw(\"same\")\n",
    "c1.Update()\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JYnnISEVBrLUOTw/JmDCpl1ug8lv5Xymat23rnJO29F6g7+6mKACsSWLaex+e2h9bSdAY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\n53aOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c2 = ROOT.TCanvas('c2','c2',500,400)\n",
    "response.Draw(\"COLZ\")\n",
    "c2.SetRightMargin(0.13);\n",
    "c2.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create TUnfold instance using the response matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TUnfold::SetConstraint>: fConstraint=1\n",
      "Info in <TUnfold::TUnfold>: underflow and overflow bin do not depend on the input data\n",
      "Info in <TUnfold::TUnfold>: 4 input bins and 2 output bins\n",
      "Info in <TUnfoldDensity::TUnfold>: *NOT* unfolding bin #0 (yaxis:[ufl])\n",
      "Info in <TUnfoldDensity::TUnfold>: *NOT* unfolding bin #3 (yaxis:[ofl])\n",
      "Info in <TUnfoldDensity::RegularizeOneDistribution>: regularizing yaxis regMode=3 densityMode=1 axisSteering=*[UOB]\n",
      "Info in <TUnfoldDensity::SubtractBackground>: Background subtraction prior to setting input data\n",
      "Info in <TUnfoldDensity::SubtractBackground>: Background subtraction prior to setting input data\n"
     ]
    }
   ],
   "source": [
    "#tu = ROOT.TUnfoldDensity(response,ROOT.TUnfold.kHistMapOutputVert)\n",
    "tu = ROOT.TUnfoldDensity(response,ROOT.TUnfold.kHistMapOutputVert, \n",
    "                         ROOT.TUnfold.kRegModeCurvature, \n",
    "                         ROOT.TUnfold.kEConstraintArea, \n",
    "                         ROOT.TUnfoldDensity.kDensityModeBinWidth)\n",
    "for bckg in [bckg1,bckg2]:\n",
    "    tu.SubtractBackground(bckg,bckg.GetName(),1.0,0.0)\n",
    "\n",
    "if tu.SetInput(data)>=10000: \n",
    "    print \" Something went wrong! \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the unfolding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4764972652613041"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tu.DoUnfold(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve the unfolded N-dimensional distribution in the form of a histogram.  Retrieve the total error matrix as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hUnfold = tu.GetOutput(\"Unfolded\")\n",
    "hErrMatTotal = tu.GetEmatrixTotal(\"EmatTotal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[204.44228287378138, 206.55771712621865]\n",
      "[0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "print [hUnfold.GetBinContent(i) for i in range(1,hUnfold.GetNbinsX()+1)]\n",
    "print [hErrMatTotal.GetBinContent(i) for i in range(1,hErrMatTotal.GetNbinsX()+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "unfolding-hats",
   "language": "python",
   "name": "unfolding-hats"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}