{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03a2fadf",
   "metadata": {},
   "source": [
    "# testUnfold4\n",
    "Test program for the class TUnfoldSys.\n",
    "\n",
    "Simple toy tests of the TUnfold package\n",
    "\n",
    "Pseudo data (5000 events) are unfolded into three components\n",
    "The unfolding is performed once without and once with area constraint\n",
    "\n",
    "Ideally, the pulls may show that the result is biased if no constraint\n",
    "is applied. This is expected because the true data errors are not known,\n",
    "and instead the sqrt(data) errors are used.\n",
    "\n",
    "\n",
    " **Version 17.6, in parallel to changes in TUnfold**\n",
    "\n",
    "#### History:\n",
    " - Version 17.5, in parallel to changes in TUnfold\n",
    " - Version 17.4, in parallel to changes in TUnfold\n",
    " - Version 17.3, in parallel to changes in TUnfold\n",
    " - Version 17.2, in parallel to changes in TUnfold\n",
    " - Version 17.1, in parallel to changes in TUnfold\n",
    " - Version 16.1, parallel to changes in TUnfold\n",
    " - Version 16.0, parallel to changes in TUnfold\n",
    " - Version 15, use L-curve scan to scan the average correlation\n",
    "\n",
    " This file is part of TUnfold.\n",
    "\n",
    " TUnfold is free software: you can redistribute it and/or modify\n",
    " it under the terms of the GNU General Public License as published by\n",
    " the Free Software Foundation, either version 3 of the License, or\n",
    " (at your option) any later version.\n",
    "\n",
    " TUnfold is distributed in the hope that it will be useful,\n",
    " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    " GNU General Public License for more details.\n",
    "\n",
    " You should have received a copy of the GNU General Public License\n",
    " along with TUnfold.  If not, see <http://www.gnu.org/licenses/>.\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Stefan Schmitt DESY, 14.10.2008  \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, May 19, 2026 at 08:11 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26217aa2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:23.360537Z",
     "iopub.status.busy": "2026-05-19T20:11:23.360416Z",
     "iopub.status.idle": "2026-05-19T20:11:23.368346Z",
     "shell.execute_reply": "2026-05-19T20:11:23.367863Z"
    }
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "#include <TMath.h>\n",
    "#include <TCanvas.h>\n",
    "#include <TRandom3.h>\n",
    "#include <TF1.h>\n",
    "#include <TStyle.h>\n",
    "#include <TVector.h>\n",
    "#include <TGraph.h>\n",
    "#include <TError.h>\n",
    "\n",
    "#include \"TUnfoldDensity.h\"\n",
    "\n",
    "using std::cout;\n",
    "\n",
    "TRandom *rnd=nullptr;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ce9aa5",
   "metadata": {},
   "source": [
    " Definition of a helper function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3100c2e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:23.370180Z",
     "iopub.status.busy": "2026-05-19T20:11:23.370058Z",
     "iopub.status.idle": "2026-05-19T20:11:23.374935Z",
     "shell.execute_reply": "2026-05-19T20:11:23.374371Z"
    }
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "Int_t GenerateGenEvent(Int_t nmax,const Double_t *probability) {\n",
    "   // choose an integer random number in the range [0,nmax]\n",
    "   //    (the generator level bin)\n",
    "   // depending on the probabilities\n",
    "   //   probability[0],probability[1],...probability[nmax-1]\n",
    "   Double_t f=rnd->Rndm();\n",
    "   Int_t r=0;\n",
    "   while((r<nmax)&&(f>=probability[r])) {\n",
    "      f -= probability[r];\n",
    "      r++;\n",
    "   }\n",
    "   return r;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "643a11a9",
   "metadata": {},
   "source": [
    " Definition of a helper function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f9a54c2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:23.376568Z",
     "iopub.status.busy": "2026-05-19T20:11:23.376448Z",
     "iopub.status.idle": "2026-05-19T20:11:23.379581Z",
     "shell.execute_reply": "2026-05-19T20:11:23.379164Z"
    }
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "Double_t GenerateRecEvent(const Double_t *shapeParm) {\n",
    "   // return a coordinate (the reconstructed variable)\n",
    "   // depending on shapeParm[]\n",
    "   //  shapeParm[0]: fraction of events with Gaussian distribution\n",
    "   //  shapeParm[1]: mean of Gaussian\n",
    "   //  shapeParm[2]: width of Gaussian\n",
    "   //  (1-shapeParm[0]): fraction of events with flat distribution\n",
    "   //  shapeParm[3]: minimum of flat component\n",
    "   //  shapeParm[4]: maximum of flat component\n",
    "   Double_t f=rnd->Rndm();\n",
    "   Double_t r;\n",
    "   if(f<shapeParm[0]) {\n",
    "      r=rnd->Gaus(shapeParm[1],shapeParm[2]);\n",
    "   } else {\n",
    "      r=rnd->Rndm()*(shapeParm[4]-shapeParm[3])+shapeParm[3];\n",
    "   }\n",
    "   return r;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8831df08",
   "metadata": {},
   "source": [
    " Arguments are defined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6cb15ed4",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:23.381336Z",
     "iopub.status.busy": "2026-05-19T20:11:23.381213Z",
     "iopub.status.idle": "2026-05-19T20:11:23.710268Z",
     "shell.execute_reply": "2026-05-19T20:11:23.709911Z"
    }
   },
   "outputs": [],
   "source": [
    "bool printInfo = false;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c736d38",
   "metadata": {},
   "source": [
    "switch off printing Info messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "487275a5",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:23.721883Z",
     "iopub.status.busy": "2026-05-19T20:11:23.721752Z",
     "iopub.status.idle": "2026-05-19T20:11:23.925044Z",
     "shell.execute_reply": "2026-05-19T20:11:23.924684Z"
    }
   },
   "outputs": [],
   "source": [
    " if (!printInfo) gErrorIgnoreLevel = kWarning;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06555492",
   "metadata": {},
   "source": [
    "switch on histogram errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41843014",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:23.931418Z",
     "iopub.status.busy": "2026-05-19T20:11:23.931292Z",
     "iopub.status.idle": "2026-05-19T20:11:24.134782Z",
     "shell.execute_reply": "2026-05-19T20:11:24.134126Z"
    }
   },
   "outputs": [],
   "source": [
    "TH1::SetDefaultSumw2();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edbd9eb2",
   "metadata": {},
   "source": [
    "random generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b20436b3",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:24.136739Z",
     "iopub.status.busy": "2026-05-19T20:11:24.136581Z",
     "iopub.status.idle": "2026-05-19T20:11:24.339848Z",
     "shell.execute_reply": "2026-05-19T20:11:24.339430Z"
    }
   },
   "outputs": [],
   "source": [
    "rnd=new TRandom3();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a438a29",
   "metadata": {},
   "source": [
    "data and MC number of events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a0a6eb5",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:24.341743Z",
     "iopub.status.busy": "2026-05-19T20:11:24.341593Z",
     "iopub.status.idle": "2026-05-19T20:11:24.544989Z",
     "shell.execute_reply": "2026-05-19T20:11:24.544454Z"
    }
   },
   "outputs": [],
   "source": [
    "Double_t const nData0=    500.0;\n",
    "Double_t const nMC0  =  50000.0;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f215a3",
   "metadata": {},
   "source": [
    "Binning\n",
    "reconstructed variable (0-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41e030c0",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:24.546835Z",
     "iopub.status.busy": "2026-05-19T20:11:24.546710Z",
     "iopub.status.idle": "2026-05-19T20:11:24.750040Z",
     "shell.execute_reply": "2026-05-19T20:11:24.749541Z"
    }
   },
   "outputs": [],
   "source": [
    "Int_t const nDet=15;\n",
    "Double_t const xminDet=0.0;\n",
    "Double_t const xmaxDet=15.0;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66624747",
   "metadata": {},
   "source": [
    "signal binning (three shapes: 0,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "adb1d9ae",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:24.751761Z",
     "iopub.status.busy": "2026-05-19T20:11:24.751627Z",
     "iopub.status.idle": "2026-05-19T20:11:24.955066Z",
     "shell.execute_reply": "2026-05-19T20:11:24.954381Z"
    }
   },
   "outputs": [],
   "source": [
    "Int_t const nGen=3;\n",
    "Double_t const xminGen=-0.5;\n",
    "Double_t const xmaxGen= 2.5;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2c8d68",
   "metadata": {},
   "source": [
    "parameters\n",
    "fraction of events per signal shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bdcd0f87",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:24.968844Z",
     "iopub.status.busy": "2026-05-19T20:11:24.968684Z",
     "iopub.status.idle": "2026-05-19T20:11:25.180427Z",
     "shell.execute_reply": "2026-05-19T20:11:25.177917Z"
    }
   },
   "outputs": [],
   "source": [
    "static const Double_t genFrac[]={0.3,0.6,0.1};"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5706c1",
   "metadata": {},
   "source": [
    "signal shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e07963b7",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:25.182124Z",
     "iopub.status.busy": "2026-05-19T20:11:25.181965Z",
     "iopub.status.idle": "2026-05-19T20:11:25.385721Z",
     "shell.execute_reply": "2026-05-19T20:11:25.384703Z"
    }
   },
   "outputs": [],
   "source": [
    "static const Double_t genShape[][5]=\n",
    "   {{1.0,2.0,1.5,0.,15.},\n",
    "    {1.0,7.0,2.5,0.,15.},\n",
    "    {0.0,0.0,0.0,0.,15.}};"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1422eb2f",
   "metadata": {},
   "source": [
    "define DATA histograms\n",
    "observed data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55e277db",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:25.387247Z",
     "iopub.status.busy": "2026-05-19T20:11:25.387123Z",
     "iopub.status.idle": "2026-05-19T20:11:25.590567Z",
     "shell.execute_reply": "2026-05-19T20:11:25.590133Z"
    }
   },
   "outputs": [],
   "source": [
    "TH1D *histDetDATA=new TH1D(\"Yrec\",\";DATA(Yrec)\",nDet,xminDet,xmaxDet);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1545c4c",
   "metadata": {},
   "source": [
    "define MC histograms\n",
    "matrix of migrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b07a7e1c",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:25.612450Z",
     "iopub.status.busy": "2026-05-19T20:11:25.612299Z",
     "iopub.status.idle": "2026-05-19T20:11:25.814648Z",
     "shell.execute_reply": "2026-05-19T20:11:25.813858Z"
    }
   },
   "outputs": [],
   "source": [
    "TH2D *histGenDetMC=new TH2D(\"Yrec%Xgen\",\"MC(Xgen,Yrec)\",\n",
    "                            nGen,xminGen,xmaxGen,nDet,xminDet,xmaxDet);\n",
    "\n",
    "TH1D *histUnfold=new TH1D(\"Xgen\",\";DATA(Xgen)\",nGen,xminGen,xmaxGen);\n",
    "\n",
    "TH1D **histPullNC=new TH1D* [nGen];\n",
    "TH1D **histPullArea=new TH1D* [nGen];\n",
    "for(int i=0;i<nGen;i++) {\n",
    "   histPullNC[i]=new TH1D(TString::Format(\"PullNC%d\",i),\"pull\",15,-3.,3.);\n",
    "   histPullArea[i]=new TH1D(TString::Format(\"PullArea%d\",i),\"pull\",15,-3.,3.);\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "719491c4",
   "metadata": {},
   "source": [
    "this method is new in version 16 of TUnfold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "feeaaf2e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:25.816997Z",
     "iopub.status.busy": "2026-05-19T20:11:25.816817Z",
     "iopub.status.idle": "2026-05-19T20:11:30.959392Z",
     "shell.execute_reply": "2026-05-19T20:11:30.958620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779221490860\" style=\"width: 700px; height: 500px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779221490860() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       "Core.unzipJSON(85921,'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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221490860', obj, '');\n",
       "});\n",
       "\n",
       "      }\n",
       "      const servers = ['/static/', 'https://root.cern/js/7.11.0/', 'https://jsroot.gsi.de/7.11.0/'],\n",
       "            path = 'build/jsroot';\n",
       "      if (typeof JSROOT !== 'undefined')\n",
       "         execCode(JSROOT);\n",
       "      else if (typeof requirejs !== 'undefined') {\n",
       "         servers.forEach((s,i) => { servers[i] = s + path; });\n",
       "         requirejs.config({ paths: { 'jsroot' : servers } })(['jsroot'],  execCode);\n",
       "      } else {\n",
       "         const config = document.getElementById('jupyter-config-data');\n",
       "         if (config)\n",
       "            servers[0] = (JSON.parse(config.innerHTML || '{}')?.baseUrl || '/') + 'static/';\n",
       "         else\n",
       "            servers.shift();\n",
       "         function loadJsroot() {\n",
       "            return !servers.length ? 0 : import(servers.shift() + path + '.js').catch(loadJsroot).then(() => execCode(JSROOT));\n",
       "         }\n",
       "         loadJsroot();\n",
       "      }\n",
       "   }\n",
       "   process_root_plot_1779221490860();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TUnfold version is V17.9\n",
      "toy iteration: 0\n",
      "toy iteration: 10\n",
      "toy iteration: 20\n",
      "toy iteration: 30\n",
      "toy iteration: 40\n",
      "toy iteration: 50\n",
      "toy iteration: 60\n",
      "toy iteration: 70\n",
      "toy iteration: 80\n",
      "toy iteration: 90\n",
      "toy iteration: 100\n",
      "toy iteration: 110\n",
      "toy iteration: 120\n",
      "toy iteration: 130\n",
      "toy iteration: 140\n",
      "toy iteration: 150\n",
      "toy iteration: 160\n",
      "toy iteration: 170\n",
      "toy iteration: 180\n",
      "toy iteration: 190\n",
      "toy iteration: 200\n",
      "toy iteration: 210\n",
      "toy iteration: 220\n",
      "toy iteration: 230\n",
      "toy iteration: 240\n",
      "toy iteration: 250\n",
      "toy iteration: 260\n",
      "toy iteration: 270\n",
      "toy iteration: 280\n",
      "toy iteration: 290\n",
      "toy iteration: 300\n",
      "toy iteration: 310\n",
      "toy iteration: 320\n",
      "toy iteration: 330\n",
      "toy iteration: 340\n",
      "toy iteration: 350\n",
      "toy iteration: 360\n",
      "toy iteration: 370\n",
      "toy iteration: 380\n",
      "toy iteration: 390\n",
      "toy iteration: 400\n",
      "toy iteration: 410\n",
      "toy iteration: 420\n",
      "toy iteration: 430\n",
      "toy iteration: 440\n",
      "toy iteration: 450\n",
      "toy iteration: 460\n",
      "toy iteration: 470\n",
      "toy iteration: 480\n",
      "toy iteration: 490\n",
      "toy iteration: 500\n",
      "toy iteration: 510\n",
      "toy iteration: 520\n",
      "toy iteration: 530\n",
      "toy iteration: 540\n",
      "toy iteration: 550\n",
      "toy iteration: 560\n",
      "toy iteration: 570\n",
      "toy iteration: 580\n",
      "toy iteration: 590\n",
      "toy iteration: 600\n",
      "toy iteration: 610\n",
      "toy iteration: 620\n",
      "toy iteration: 630\n",
      "toy iteration: 640\n",
      "toy iteration: 650\n",
      "toy iteration: 660\n",
      "toy iteration: 670\n",
      "toy iteration: 680\n",
      "toy iteration: 690\n",
      "toy iteration: 700\n",
      "toy iteration: 710\n",
      "toy iteration: 720\n",
      "toy iteration: 730\n",
      "toy iteration: 740\n",
      "toy iteration: 750\n",
      "toy iteration: 760\n",
      "toy iteration: 770\n",
      "toy iteration: 780\n",
      "toy iteration: 790\n",
      "toy iteration: 800\n",
      "toy iteration: 810\n",
      "toy iteration: 820\n",
      "toy iteration: 830\n",
      "toy iteration: 840\n",
      "toy iteration: 850\n",
      "toy iteration: 860\n",
      "toy iteration: 870\n",
      "toy iteration: 880\n",
      "toy iteration: 890\n",
      "toy iteration: 900\n",
      "toy iteration: 910\n",
      "toy iteration: 920\n",
      "toy iteration: 930\n",
      "toy iteration: 940\n",
      "toy iteration: 950\n",
      "toy iteration: 960\n",
      "toy iteration: 970\n",
      "toy iteration: 980\n",
      "toy iteration: 990\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      17.3145\n",
      "NDf                       =           12\n",
      "Edm                       =  7.81966e-06\n",
      "NCalls                    =           52\n",
      "Constant                  =      153.648   +/-   6.05711     \n",
      "Mean                      =    -0.121307   +/-   0.0338681   \n",
      "Sigma                     =       1.0209   +/-   0.0243688    \t (limited)\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      8.19808\n",
      "NDf                       =           12\n",
      "Edm                       =  2.68037e-06\n",
      "NCalls                    =           53\n",
      "Constant                  =      149.692   +/-   5.79204     \n",
      "Mean                      =    -0.259587   +/-   0.0346952   \n",
      "Sigma                     =      1.05667   +/-   0.0243581    \t (limited)\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      59.2962\n",
      "NDf                       =           12\n",
      "Edm                       =   3.6247e-07\n",
      "NCalls                    =           61\n",
      "Constant                  =       127.75   +/-   5.41128     \n",
      "Mean                      =    -0.482115   +/-   0.0490259   \n",
      "Sigma                     =      1.09744   +/-   0.0308576    \t (limited)\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      24.0114\n",
      "NDf                       =           12\n",
      "Edm                       =  3.60094e-06\n",
      "NCalls                    =           53\n",
      "Constant                  =      147.382   +/-   5.80116     \n",
      "Mean                      =     0.189905   +/-   0.0351528   \n",
      "Sigma                     =      1.05774   +/-   0.0251621    \t (limited)\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      17.3474\n",
      "NDf                       =           12\n",
      "Edm                       =  7.55094e-06\n",
      "NCalls                    =           53\n",
      "Constant                  =      149.977   +/-   5.77475     \n",
      "Mean                      =     0.201466   +/-   0.034281    \n",
      "Sigma                     =      1.04379   +/-   0.0232952    \t (limited)\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      66.0535\n",
      "NDf                       =           12\n",
      "Edm                       =  1.27624e-06\n",
      "NCalls                    =           61\n",
      "Constant                  =      124.004   +/-   5.38123     \n",
      "Mean                      =    -0.412836   +/-   0.0498374   \n",
      "Sigma                     =      1.12618   +/-   0.0336756    \t (limited)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n",
      "Warning in <TUnfoldSys::SetInput>: One input bin has zero error, 1/error set to 1.000000.\n"
     ]
    }
   ],
   "source": [
    "cout<<\"TUnfold version is \"<<TUnfold::GetTUnfoldVersion()<<\"\\n\";\n",
    "\n",
    "for(int itoy=0;itoy<1000;itoy++) {\n",
    "   if(!(itoy %10)) cout<<\"toy iteration: \"<<itoy<<\"\\n\";\n",
    "   histDetDATA->Reset();\n",
    "   histGenDetMC->Reset();\n",
    "\n",
    "   Int_t nData=rnd->Poisson(nData0);\n",
    "   for(Int_t i=0;i<nData;i++) {\n",
    "      Int_t iGen=GenerateGenEvent(nGen,genFrac);\n",
    "      Double_t yObs=GenerateRecEvent(genShape[iGen]);\n",
    "      histDetDATA->Fill(yObs);\n",
    "   }\n",
    "\n",
    "   Int_t nMC=rnd->Poisson(nMC0);\n",
    "   for(Int_t i=0;i<nMC;i++) {\n",
    "      Int_t iGen=GenerateGenEvent(nGen,genFrac);\n",
    "      Double_t yObs=GenerateRecEvent(genShape[iGen]);\n",
    "      histGenDetMC->Fill(iGen,yObs);\n",
    "   }\n",
    "   /* for(Int_t ix=0;ix<=histGenDetMC->GetNbinsX()+1;ix++) {\n",
    "      for(Int_t iy=0;iy<=histGenDetMC->GetNbinsY()+1;iy++) {\n",
    "         cout<<ix<<iy<<\" : \"<<histGenDetMC->GetBinContent(ix,iy)<<\"\\n\";\n",
    "      }\n",
    "      } */\n",
    "   //========================\n",
    "   // unfolding\n",
    "\n",
    "   TUnfoldSys unfold(histGenDetMC,TUnfold::kHistMapOutputHoriz,\n",
    "                     TUnfold::kRegModeSize,TUnfold::kEConstraintNone);\n",
    "   // define the input vector (the measured data distribution)\n",
    "   unfold.SetInput(histDetDATA,0.0,1.0);\n",
    "\n",
    "   // run the unfolding\n",
    "   unfold.ScanLcurve(50,0.,0.,nullptr,nullptr,nullptr);\n",
    "\n",
    "   // fill pull distributions without constraint\n",
    "   unfold.GetOutput(histUnfold);\n",
    "\n",
    "   for(int i=0;i<nGen;i++) {\n",
    "      histPullNC[i]->Fill((histUnfold->GetBinContent(i+1)-genFrac[i]*nData0)/\n",
    "                          histUnfold->GetBinError(i+1));\n",
    "   }\n",
    "\n",
    "   // repeat unfolding on the same data, now with Area constraint\n",
    "   unfold.SetConstraint(TUnfold::kEConstraintArea);\n",
    "\n",
    "   // run the unfolding\n",
    "   unfold.ScanLcurve(50,0.,0.,nullptr,nullptr,nullptr);\n",
    "\n",
    "   // fill pull distributions with constraint\n",
    "   unfold.GetOutput(histUnfold);\n",
    "\n",
    "   for(int i=0;i<nGen;i++) {\n",
    "      histPullArea[i]->Fill((histUnfold->GetBinContent(i+1)-genFrac[i]*nData0)/\n",
    "                          histUnfold->GetBinError(i+1));\n",
    "   }\n",
    "\n",
    "}\n",
    "TCanvas output;\n",
    "output.Divide(3,2);\n",
    "\n",
    "gStyle->SetOptFit(1111);\n",
    "\n",
    "for(int i=0;i<nGen;i++) {\n",
    "   output.cd(i+1);\n",
    "   histPullNC[i]->Fit(\"gaus\");\n",
    "   histPullNC[i]->Draw();\n",
    "}\n",
    "for(int i=0;i<nGen;i++) {\n",
    "   output.cd(i+4);\n",
    "   histPullArea[i]->Fit(\"gaus\");\n",
    "   histPullArea[i]->Draw();\n",
    "}\n",
    "output.SaveAs(\"testUnfold4.ps\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40644455",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f1a0d3a",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:30.961072Z",
     "iopub.status.busy": "2026-05-19T20:11:30.960960Z",
     "iopub.status.idle": "2026-05-19T20:11:31.165084Z",
     "shell.execute_reply": "2026-05-19T20:11:31.164068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779221491075\" style=\"width: 700px; height: 500px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779221491075() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       "Core.unzipJSON(85921,'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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221491075', obj, '');\n",
       "});\n",
       "\n",
       "      }\n",
       "      const servers = ['/static/', 'https://root.cern/js/7.11.0/', 'https://jsroot.gsi.de/7.11.0/'],\n",
       "            path = 'build/jsroot';\n",
       "      if (typeof JSROOT !== 'undefined')\n",
       "         execCode(JSROOT);\n",
       "      else if (typeof requirejs !== 'undefined') {\n",
       "         servers.forEach((s,i) => { servers[i] = s + path; });\n",
       "         requirejs.config({ paths: { 'jsroot' : servers } })(['jsroot'],  execCode);\n",
       "      } else {\n",
       "         const config = document.getElementById('jupyter-config-data');\n",
       "         if (config)\n",
       "            servers[0] = (JSON.parse(config.innerHTML || '{}')?.baseUrl || '/') + 'static/';\n",
       "         else\n",
       "            servers.shift();\n",
       "         function loadJsroot() {\n",
       "            return !servers.length ? 0 : import(servers.shift() + path + '.js').catch(loadJsroot).then(() => execCode(JSROOT));\n",
       "         }\n",
       "         loadJsroot();\n",
       "      }\n",
       "   }\n",
       "   process_root_plot_1779221491075();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gROOT->GetListOfCanvases()->Draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT C++",
   "language": "c++",
   "name": "root"
  },
  "language_info": {
   "codemirror_mode": "text/x-c++src",
   "file_extension": ".C",
   "mimetype": " text/x-c++src",
   "name": "c++"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}