%%cpp -d
#include <TMath.h>
#include <TCanvas.h>
#include <TRandom3.h>
#include <TFitter.h>
#include <TF1.h>
#include <TStyle.h>
#include <TVector.h>
#include <TGraph.h>
#include "TUnfold.h"


TRandom *rnd=nullptr;

%%cpp -d
Double_t GenerateEvent(Double_t bgr, // relative fraction of background
                       Double_t mass, // peak position
                       Double_t gamma) // peak width
{
  Double_t t;
  if(rnd->Rndm()>bgr) {
    // generate signal event
    // with positive mass
    do {
      do {
        t=rnd->Rndm();
      } while(t>=1.0);
      t=TMath::Tan((t-0.5)*TMath::Pi())*gamma+mass;
    } while(t<=0.0);
    return t;
  } else {
    // generate background event
    // generate events following a power-law distribution
    //   f(E) = K * TMath::power((E0+E),N0)
    static Double_t const E0=2.4;
    static Double_t const N0=2.9;
    do {
      do {
        t=rnd->Rndm();
      } while(t>=1.0);
      // the mass is returned negative
      // In our example a convenient way to indicate it is a background event.
      t= -(TMath::Power(1.-t,1./(1.-N0))-1.0)*E0;
    } while(t>=0.0);
    return t;
  }
}

%%cpp -d
Double_t DetectorEvent(Double_t mTrue) {
  // smear by double-gaussian
  static Double_t frac=0.1;
  static Double_t wideBias=0.03;
  static Double_t wideSigma=0.5;
  static Double_t smallBias=0.0;
  static Double_t smallSigma=0.1;
  if(rnd->Rndm()>frac) {
    return rnd->Gaus(mTrue+smallBias,smallSigma);
  } else {
    return rnd->Gaus(mTrue+wideBias,wideSigma);
  }
}

TH1::SetDefaultSumw2();

rnd=new TRandom3();

Double_t const luminosityData=100000;
Double_t const luminosityMC=1000000;
Double_t const crossSection=1.0;

Int_t const nDet=250;
Int_t const nGen=100;
Double_t const xminDet=0.0;
Double_t const xmaxDet=10.0;
Double_t const xminGen=0.0;
Double_t const xmaxGen=10.0;

TH1D *histMgenMC=new TH1D("MgenMC",";mass(gen)",nGen,xminGen,xmaxGen);
TH1D *histMdetMC=new TH1D("MdetMC",";mass(det)",nDet,xminDet,xmaxDet);
TH2D *histMdetGenMC=new TH2D("MdetgenMC",";mass(det);mass(gen)",nDet,xminDet,xmaxDet,
                            nGen,xminGen,xmaxGen);
Int_t neventMC=rnd->Poisson(luminosityMC*crossSection);
for(Int_t i=0;i<neventMC;i++) {
  Double_t mGen=GenerateEvent(0.3, // relative fraction of background
                              4.0, // peak position in MC
                              0.2); // peak width in MC
  Double_t mDet=DetectorEvent(TMath::Abs(mGen));
  // the generated mass is negative for background
  // and positive for signal
  // so it will be filled in the underflow bin
  // this is very convenient for the unfolding:
  // the unfolded result will contain the number of background
  // events in the underflow bin

  // generated MC distribution (for comparison only)
  histMgenMC->Fill(mGen,luminosityData/luminosityMC);
  // reconstructed MC distribution (for comparison only)
  histMdetMC->Fill(mDet,luminosityData/luminosityMC);

  // matrix describing how the generator input migrates to the
  // reconstructed level. Unfolding input.
  // NOTE on underflow/overflow bins:
  //  (1) the detector level under/overflow bins are used for
  //       normalisation ("efficiency" correction)
  //       in our toy example, these bins are populated from tails
  //       of the initial MC distribution.
  //  (2) the generator level underflow/overflow bins are
  //       unfolded. In this example:
  //       underflow bin: background events reconstructed in the detector
  //       overflow bin: signal events generated at masses > xmaxDet
  // for the unfolded result these bins will be filled
  //  -> the background normalisation will be contained in the underflow bin
  histMdetGenMC->Fill(mDet,mGen,luminosityData/luminosityMC);
}

TH1D *histMgenData=new TH1D("MgenData",";mass(gen)",nGen,xminGen,xmaxGen);
TH1D *histMdetData=new TH1D("MdetData",";mass(det)",nDet,xminDet,xmaxDet);
Int_t neventData=rnd->Poisson(luminosityData*crossSection);
for(Int_t i=0;i<neventData;i++) {
  Double_t mGen=GenerateEvent(0.4, // relative fraction of background
                              3.8, // peak position
                              0.15); // peak width
  Double_t mDet=DetectorEvent(TMath::Abs(mGen));
  // generated data mass for comparison plots
  // for real data, we do not have this histogram
  histMgenData->Fill(mGen);

  // reconstructed mass, unfolding input
  histMdetData->Fill(mDet);
}

TUnfold unfold(histMdetGenMC,TUnfold::kHistMapOutputVert,
               TUnfold::kRegModeNone);

Double_t estimatedPeakPosition=3.8;
Int_t nPeek=3;
TUnfold::ERegMode regMode=TUnfold::kRegModeCurvature;

Int_t iPeek=(Int_t)(nGen*(estimatedPeakPosition-xminGen)/(xmaxGen-xminGen)
                    // offset 1.5
                    // accounts for start bin 1
                    // and rounding errors +0.5
                    +1.5);

unfold.RegularizeBins(1,1,iPeek-nPeek,regMode);

unfold.RegularizeBins(iPeek+nPeek,1,nGen-(iPeek+nPeek),regMode);

if(unfold.SetInput(histMdetData,0.0)>=10000) {
  std::cout<<"Unfolding result may be wrong\n";
}

Double_t tauMin=0.0;
Double_t tauMax=0.0;
Int_t nScan=30;
Int_t iBest;
TSpline *logTauX,*logTauY;
TGraph *lCurve;

iBest=unfold.ScanLcurve(nScan,tauMin,tauMax,&lCurve,&logTauX,&logTauY);
std::cout<<"tau="<<unfold.GetTau()<<"\n";
std::cout<<"chi**2="<<unfold.GetChi2A()<<"+"<<unfold.GetChi2L()
         <<" / "<<unfold.GetNdf()<<"\n";

Double_t t[1],x[1],y[1];
logTauX->GetKnot(iBest,t[0],x[0]);
logTauY->GetKnot(iBest,t[0],y[0]);
TGraph *bestLcurve=new TGraph(1,x,y);
TGraph *bestLogTauX=new TGraph(1,t,x);

Int_t *binMap=new Int_t[nGen+2];
for(Int_t i=1;i<=nGen;i++) binMap[i]=i;
binMap[0]=-1;
binMap[nGen+1]=-1;

TH1D *histMunfold=new TH1D("Unfolded",";mass(gen)",nGen,xminGen,xmaxGen);
unfold.GetOutput(histMunfold,binMap);
TH1D *histMdetFold=new TH1D("FoldedBack","mass(det)",nDet,xminDet,xmaxDet);
unfold.GetFoldedOutput(histMdetFold);

TH1D *histRhoi=new TH1D("rho_I","mass",nGen,xminGen,xmaxGen);
unfold.GetRhoI(histRhoi,binMap);

delete[] binMap;
binMap=nullptr;

TCanvas output;

output.Divide(3,2);

output.cd(1);
histMdetGenMC->Draw("BOX");

output.cd(2);
histMunfold->SetLineColor(kBlue);
histMunfold->Draw();
histMgenData->SetLineColor(kRed);
histMgenData->Draw("SAME");
histMgenMC->Draw("SAME HIST");

output.cd(3);
histMdetFold->SetLineColor(kBlue);
histMdetFold->Draw();
histMdetData->SetLineColor(kRed);
histMdetData->Draw("SAME");
histMdetMC->Draw("SAME HIST");

output.cd(4);
histRhoi->Draw();

output.cd(5);
logTauX->Draw();
bestLogTauX->SetMarkerColor(kRed);
bestLogTauX->Draw("*");

output.cd(6);
lCurve->Draw("AL");
bestLcurve->SetMarkerColor(kRed);
bestLcurve->Draw("*");

output.SaveAs("testUnfold2.ps");
return 0;

gROOT->GetListOfCanvases()->Draw()