MuScleFitUtils.cc

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// Some notes:
// - M(Z) after simulation needs to be extracted as a function of |y_Z| in order to be
//   a better reference point for calibration. In fact, the variation of PDF with y_Z
//   in production is sizable <---- need to check though.
// - ResHalfWidth needs to be optimized - this depends on the level of background.
// - Background parametrization still to be worked on, so far only a constant (type=1, and
//   parameter 2 fixed to 0) works.
// - weights have to be assigned to dimuon mass values in regions where different resonances
//   overlap, and one has to decide which resonance mass to assign the event to - this until
//   we implement in the fitter a sum of probabilities of an event to belong to different
//   resonances. The weight has to depend on the mass and has relative cross sections of
//   Y(1S), 2S, 3S as parameters. Some overlap is also expected in the J/psi-Psi(2S) region
//   when reconstructing masses with Standalone muons.
//
//   MODS 7/7/08 TD:
//   - changed parametrization of resolution in Pt: from sigma_pt = a*Pt + b*|eta| to
//                                                       sigma_pt = (a*Pt + b*|eta|)*Pt
//                                                  which is more correct (I hope)
//   - changed parametrization of resolution in cotgth: from sigma_cotgth = f(eta) to f(cotgth)
// --------------------------------------------------------------------------------------------
 
#include "MuScleFitUtils.h"
#include "DataFormats/HepMCCandidate/interface/GenParticle.h"
#include "SimDataFormats/Track/interface/SimTrack.h"
#include "DataFormats/Candidate/interface/LeafCandidate.h"
#include "DataFormats/Math/interface/LorentzVector.h"
#include "TString.h"
#include "TFile.h"
#include "TTree.h"
#include "TCanvas.h"
#include "TH2F.h"
#include "TF1.h"
#include "TF2.h"
#include <iostream>
#include <fstream>
#include <memory>  // to use the auto_ptr
 
// Includes the definitions of all the bias and scale functions
// These functions are selected in the constructor according
// to the input parameters.
#include "MuonAnalysis/MomentumScaleCalibration/interface/Functions.h"
 
// To use callgrind for code profiling uncomment also the following define.
//#define USE_CALLGRIND
#ifdef USE_CALLGRIND
#include "valgrind/callgrind.h"
#endif
 
// Lorenzian Peak function
// -----------------------
Double_t lorentzianPeak(Double_t *x, Double_t *par) {
  return (0.5 * par[0] * par[1] / TMath::Pi()) /
         TMath::Max(1.e-10, (x[0] - par[2]) * (x[0] - par[2]) + .25 * par[1] * par[1]);
}
 
// Gaussian function
// -----------------
Double_t Gaussian(Double_t *x, Double_t *par) {
  return par[0] * exp(-0.5 * ((x[0] - par[1]) / par[2]) * ((x[0] - par[1]) / par[2]));
}
 
// Array with number of parameters in the fitting functions
// (not currently in use)
// --------------------------------------------------------
//const int nparsResol[2] = {6, 4};
//const int nparsScale[13] = {2, 2, 2, 3, 3, 3, 4, 4, 2, 3, 4, 6, 8};
//const int nparsBgr[3] = {1, 2, 3};
 
// Quantities used for h-value computation
// ---------------------------------------
double mzsum;
double isum;
double f[11][100];
double g[11][100];
 
// Lorentzian convoluted with a gaussian:
// --------------------------------------
TF1 *GL = new TF1(
    "GL", "0.5/3.1415926*[0]/(pow(x-[1],2)+pow(0.5*[0],2))*exp(-0.5*pow((x-[2])/[3],2))/([3]*sqrt(6.283185))", 0, 1000);
 
TF2 *GL2 = new TF2("GL2",
                   "0.5/3.1415926*[0]/(pow(x-[1],2)+pow(0.5*[0],2))*exp(-0.5*pow((x-y)/[2],2))/([2]*sqrt(6.283185))",
                   0,
                   200,
                   0,
                   200);
 
// // Lorentzian convoluted with a gaussian over a linear background:
// // ---------------------------------------------------------------
// TF1 * GLBL = new TF1 ("GLBL",
//   "0.5/3.1415926*[0]/(pow(x-[1],2)+pow(0.5*[0],2))*exp(-0.5*pow((x-[2])/[3],2))/([3]*sqrt(6.283185))+[4]+[5]*x",
//   0, 1000);
 
// // Lorentzian convoluted with a gaussian over an exponential background:
// // ---------------------------------------------------------------
// TF1 * GLBE = new TF1 ("GLBE",
//   "0.5/3.1415926*[0]/(pow(x-[1],2)+pow(0.5*[0],2))*exp(-0.5*pow((x-[2])/[3],2))/([3]*sqrt(6.283185))+exp([4]+[5]*x)",
//   0, 1000);
 
std::vector<int> MuScleFitUtils::doResolFit;
std::vector<int> MuScleFitUtils::doScaleFit;
std::vector<int> MuScleFitUtils::doCrossSectionFit;
std::vector<int> MuScleFitUtils::doBackgroundFit;
 
int MuScleFitUtils::minuitLoop_ = 0;
TH1D *MuScleFitUtils::likelihoodInLoop_ = nullptr;
TH1D *MuScleFitUtils::signalProb_ = nullptr;
TH1D *MuScleFitUtils::backgroundProb_ = nullptr;
 
bool MuScleFitUtils::duringMinos_ = false;
 
const int MuScleFitUtils::totalResNum = 6;
 
int MuScleFitUtils::SmearType = 0;
smearFunctionBase *MuScleFitUtils::smearFunction = nullptr;
int MuScleFitUtils::BiasType = 0;
// No error, we take functions from the same group for bias and scale.
scaleFunctionBase<std::vector<double> > *MuScleFitUtils::biasFunction = nullptr;
int MuScleFitUtils::ResolFitType = 0;
resolutionFunctionBase<double *> *MuScleFitUtils::resolutionFunction = nullptr;
resolutionFunctionBase<std::vector<double> > *MuScleFitUtils::resolutionFunctionForVec = nullptr;
int MuScleFitUtils::ScaleFitType = 0;
scaleFunctionBase<double *> *MuScleFitUtils::scaleFunction = nullptr;
scaleFunctionBase<std::vector<double> > *MuScleFitUtils::scaleFunctionForVec = nullptr;
int MuScleFitUtils::BgrFitType = 0;
 
CrossSectionHandler *MuScleFitUtils::crossSectionHandler;
// const int MuScleFitUtils::backgroundFunctionsRegions = 3;
// backgroundFunctionBase * MuScleFitUtils::backgroundFunctionForRegion[MuScleFitUtils::backgroundFunctionsRegions];
// backgroundFunctionBase * MuScleFitUtils::backgroundFunction[MuScleFitUtils::totalResNum];
BackgroundHandler *MuScleFitUtils::backgroundHandler;
std::vector<double> MuScleFitUtils::parBias;
std::vector<double> MuScleFitUtils::parSmear;
std::vector<double> MuScleFitUtils::parResol;
std::vector<double> MuScleFitUtils::parResolStep;
std::vector<double> MuScleFitUtils::parResolMin;
std::vector<double> MuScleFitUtils::parResolMax;
std::vector<double> MuScleFitUtils::parScale;
std::vector<double> MuScleFitUtils::parScaleStep;
std::vector<double> MuScleFitUtils::parScaleMin;
std::vector<double> MuScleFitUtils::parScaleMax;
std::vector<double> MuScleFitUtils::parCrossSection;
std::vector<double> MuScleFitUtils::parBgr;
std::vector<int> MuScleFitUtils::parResolFix;
std::vector<int> MuScleFitUtils::parScaleFix;
std::vector<int> MuScleFitUtils::parCrossSectionFix;
std::vector<int> MuScleFitUtils::parBgrFix;
std::vector<int> MuScleFitUtils::parResolOrder;
std::vector<int> MuScleFitUtils::parScaleOrder;
std::vector<int> MuScleFitUtils::parCrossSectionOrder;
std::vector<int> MuScleFitUtils::parBgrOrder;
 
std::vector<int> MuScleFitUtils::resfind;
int MuScleFitUtils::debug = 0;
 
bool MuScleFitUtils::ResFound = false;
int MuScleFitUtils::goodmuon = 0;
int MuScleFitUtils::counter_resprob = 0;
 
std::vector<std::vector<double> > MuScleFitUtils::parvalue;
 
int MuScleFitUtils::FitStrategy = 1;   // Strategy in likelihood fit (1 or 2)
bool MuScleFitUtils::speedup = false;  // Whether to cut corners (no sim study, fewer histos)
 
std::vector<std::pair<lorentzVector, lorentzVector> >
    MuScleFitUtils::SavedPair;  // Pairs of reconstructed muons making resonances
std::vector<std::pair<lorentzVector, lorentzVector> >
    MuScleFitUtils::ReducedSavedPair;  // Pairs of reconstructed muons making resonances inside smaller windows
std::vector<std::pair<lorentzVector, lorentzVector> >
    MuScleFitUtils::genPair;  // Pairs of generated muons making resonances
std::vector<std::pair<MuScleFitMuon, MuScleFitMuon> >
    MuScleFitUtils::SavedPairMuScleFitMuons;  // Pairs of reconstructed muons making resonances
std::vector<std::pair<MuScleFitMuon, MuScleFitMuon> >
    MuScleFitUtils::genMuscleFitPair;  // Pairs of generated muons making resonances
//std::vector<GenMuonPair> MuScleFitUtils::genPairMuScleMuons; // Pairs of generated muons making resonances
std::vector<std::pair<lorentzVector, lorentzVector> >
    MuScleFitUtils::simPair;  // Pairs of simulated muons making resonances
 
// Smearing parameters
// -------------------
double MuScleFitUtils::x[][10000];
 
// Probability matrices and normalization values
// ---------------------------------------------
int MuScleFitUtils::nbins = 1000;
double MuScleFitUtils::GLZValue[][1001][1001];
double MuScleFitUtils::GLZNorm[][1001];
double MuScleFitUtils::GLValue[][1001][1001];
double MuScleFitUtils::GLNorm[][1001];
double MuScleFitUtils::ResMaxSigma[];
 
// Masses and widths from PDG 2006, half widths to be revised
// NB in particular, halfwidths have to be made a function of muonType
// -------------------------------------------------------------------
const double MuScleFitUtils::mMu2 = 0.011163612;
const double MuScleFitUtils::muMass = 0.105658;
double MuScleFitUtils::ResHalfWidth[];
double MuScleFitUtils::massWindowHalfWidth[][6];
int MuScleFitUtils::MuonType;
int MuScleFitUtils::MuonTypeForCheckMassWindow;
 
double MuScleFitUtils::ResGamma[] = {2.4952, 0.000020, 0.000032, 0.000054, 0.000317, 0.0000932};
// ATTENTION:
// This is left because the values are used by the BackgroundHandler to define the center of the regions windows,
// but the values used in the code are read computed using the probability histograms ranges.
// The histograms are read after the initialization of the BackgroundHandler (this can be improved so that
// the background handler too could use the new values).
// At this time the values are consistent.
double MuScleFitUtils::ResMinMass[] = {-99, -99, -99, -99, -99, -99};
double MuScleFitUtils::ResMass[] = {91.1876, 10.3552, 10.0233, 9.4603, 3.68609, 3.0969};
// From Summer08 generator production TWiki: https://twiki.cern.ch/twiki/bin/view/CMS/ProductionSummer2008
// - Z->mumu              1.233 nb
// - Upsilon3S->mumu      0.82  nb
// - Upsilon2S->mumu      6.33  nb
// - Upsilon1S->mumu     13.9   nb
// - Prompt Psi2S->mumu   2.169 nb
// - Prompt J/Psi->mumu 127.2   nb
// double MuScleFitUtils::crossSection[] = {1.233, 0.82, 6.33, 13.9, 2.169, 127.2};
// double MuScleFitUtils::crossSection[] = {1.233, 2.07, 6.33, 13.9, 2.169, 127.2};
 
unsigned int MuScleFitUtils::loopCounter = 5;
 
// According to the pythia manual, there is only a code for the Upsilon and Upsilon'. It does not distinguish
// between Upsilon(2S) and Upsilon(3S)
const unsigned int MuScleFitUtils::motherPdgIdArray[] = {23, 100553, 100553, 553, 100443, 443};
 
// double MuScleFitUtils::leftWindowFactor = 1.;
// double MuScleFitUtils::rightWindowFactor = 1.;
 
// double MuScleFitUtils::internalLeftWindowFactor = 1.;
// double MuScleFitUtils::internalRightWindowFactor = 1.;
 
// int MuScleFitUtils::backgroundWindowEvents_ = 0;
// int MuScleFitUtils::resonanceWindowEvents_ = 0;
 
// double MuScleFitUtils::oldEventsOutInRatio_ = 0.;
 
bool MuScleFitUtils::scaleFitNotDone_ = true;
 
bool MuScleFitUtils::sherpa_ = false;
 
bool MuScleFitUtils::useProbsFile_ = true;
 
bool MuScleFitUtils::rapidityBinsForZ_ = true;
 
bool MuScleFitUtils::separateRanges_ = true;
double MuScleFitUtils::minMuonPt_ = 0.;
double MuScleFitUtils::maxMuonPt_ = 100000000.;
double MuScleFitUtils::minMuonEtaFirstRange_ = -6.;
double MuScleFitUtils::maxMuonEtaFirstRange_ = 6.;
double MuScleFitUtils::minMuonEtaSecondRange_ = -100.;
double MuScleFitUtils::maxMuonEtaSecondRange_ = 100.;
double MuScleFitUtils::deltaPhiMinCut_ = -100.;
double MuScleFitUtils::deltaPhiMaxCut_ = 100.;
 
bool MuScleFitUtils::debugMassResol_;
MuScleFitUtils::massResolComponentsStruct MuScleFitUtils::massResolComponents;
 
bool MuScleFitUtils::normalizeLikelihoodByEventNumber_ = true;
TMinuit *MuScleFitUtils::rminPtr_ = nullptr;
double MuScleFitUtils::oldNormalization_ = 0.;
unsigned int MuScleFitUtils::normalizationChanged_ = 0;
 
bool MuScleFitUtils::startWithSimplex_;
bool MuScleFitUtils::computeMinosErrors_;
bool MuScleFitUtils::minimumShapePlots_;
 
int MuScleFitUtils::iev_ = 0;
 
// Find the best simulated resonance from a vector of simulated muons (SimTracks)
// and return its decay muons
// ------------------------------------------------------------------------------
std::pair<SimTrack, SimTrack> MuScleFitUtils::findBestSimuRes(const std::vector<SimTrack> &simMuons) {
  std::pair<SimTrack, SimTrack> simMuFromBestRes;
  double maxprob = -0.1;
 
  // Double loop on muons
  // --------------------
  for (std::vector<SimTrack>::const_iterator simMu1 = simMuons.begin(); simMu1 != simMuons.end(); simMu1++) {
    for (std::vector<SimTrack>::const_iterator simMu2 = simMu1 + 1; simMu2 != simMuons.end(); simMu2++) {
      if (((*simMu1).charge() * (*simMu2).charge()) > 0) {
        continue;  // this also gets rid of simMu1==simMu2...
      }
      // Choose the best resonance using its mass. Check Z, Y(3S,2S,1S), Psi(2S), J/Psi in order
      // ---------------------------------------------------------------------------------------
      double mcomb = ((*simMu1).momentum() + (*simMu2).momentum()).mass();
      double Y = ((*simMu1).momentum() + (*simMu2).momentum()).Rapidity();
      for (int ires = 0; ires < 6; ires++) {
        if (resfind[ires] > 0) {
          double prob = massProb(mcomb, Y, ires, 0.);
          if (prob > maxprob) {
            simMuFromBestRes.first = (*simMu1);
            simMuFromBestRes.second = (*simMu2);
            maxprob = prob;
          }
        }
      }
    }
  }
 
  // Return most likely combination of muons making a resonance
  // ----------------------------------------------------------
  return simMuFromBestRes;
}
 
// Find the best reconstructed resonance from a collection of reconstructed muons
// (MuonCollection) and return its decay muons
// ------------------------------------------------------------------------------
std::pair<MuScleFitMuon, MuScleFitMuon> MuScleFitUtils::findBestRecoRes(const std::vector<MuScleFitMuon> &muons) {
  // NB this routine returns the resonance, but it also sets the ResFound flag, which
  // is used in MuScleFit to decide whether to use the event or not.
  // --------------------------------------------------------------------------------
  if (debug > 0)
    std::cout << "In findBestRecoRes" << std::endl;
  ResFound = false;
  std::pair<MuScleFitMuon, MuScleFitMuon> recMuFromBestRes;
 
  // Choose the best resonance using its mass probability
  // ----------------------------------------------------
  double maxprob = -0.1;
  double minDeltaMass = 999999;
  std::pair<MuScleFitMuon, MuScleFitMuon> bestMassMuons;
  for (std::vector<MuScleFitMuon>::const_iterator Muon1 = muons.begin(); Muon1 != muons.end(); ++Muon1) {
    //rc2010
    if (debug > 0)
      std::cout << "muon_1_charge:" << (*Muon1).charge() << std::endl;
    for (std::vector<MuScleFitMuon>::const_iterator Muon2 = Muon1 + 1; Muon2 != muons.end(); ++Muon2) {
      //rc2010
      if (debug > 0)
        std::cout << "after_2" << std::endl;
      if ((((*Muon1).charge()) * ((*Muon2).charge())) > 0) {
        continue;  // This also gets rid of Muon1==Muon2...
      }
      // To allow the selection of ranges at negative and positive eta independently we define two
      // ranges of eta: (minMuonEtaFirstRange_, maxMuonEtaFirstRange_) and (minMuonEtaSecondRange_, maxMuonEtaSecondRange_).
      double ch1 = (*Muon1).charge();
      double ch2 = (*Muon2).charge();
      double pt1 = (*Muon1).Pt();
      double pt2 = (*Muon2).Pt();
      double eta1 = (*Muon1).Eta();
      double eta2 = (*Muon2).Eta();
      if (pt1 >= minMuonPt_ && pt1 < maxMuonPt_ && pt2 >= minMuonPt_ && pt2 < maxMuonPt_ &&
          ((eta1 >= minMuonEtaFirstRange_ && eta1 < maxMuonEtaFirstRange_ && eta2 >= minMuonEtaSecondRange_ &&
            eta2 < maxMuonEtaSecondRange_ && ch1 >= ch2  // In the configuration file, MuonOne==MuonPlus
            ) ||
           (eta1 >= minMuonEtaSecondRange_ && eta1 < maxMuonEtaSecondRange_ && eta2 >= minMuonEtaFirstRange_ &&
            eta2 < maxMuonEtaFirstRange_ && ch1 < ch2))) {
        double mcomb = ((*Muon1).p4() + (*Muon2).p4()).mass();
        double Y = ((*Muon1).p4() + (*Muon2).p4()).Rapidity();
        if (debug > 1) {
          std::cout << "muon1 " << (*Muon1).p4().Px() << ", " << (*Muon1).p4().Py() << ", " << (*Muon1).p4().Pz()
                    << ", " << (*Muon1).p4().E() << ", " << (*Muon1).charge() << std::endl;
          std::cout << "muon2 " << (*Muon2).p4().Px() << ", " << (*Muon2).p4().Py() << ", " << (*Muon2).p4().Pz()
                    << ", " << (*Muon2).p4().E() << ", " << (*Muon2).charge() << std::endl;
          std::cout << "mcomb " << mcomb << std::endl;
        }
        double massResol = 0.;
        if (useProbsFile_) {
          massResol = massResolution((*Muon1).p4(), (*Muon2).p4(), parResol);
        }
        double prob = 0;
        for (int ires = 0; ires < 6; ires++) {
          if (resfind[ires] > 0) {
            if (useProbsFile_) {
              prob = massProb(mcomb, Y, ires, massResol);
            }
            if (prob > maxprob) {
              if (ch1 < 0) {  // store first the mu minus and then the mu plus
                recMuFromBestRes.first = (*Muon1);
                recMuFromBestRes.second = (*Muon2);
              } else {
                recMuFromBestRes.first = (*Muon2);
                recMuFromBestRes.second = (*Muon1);
              }
              if (debug > 0)
                std::cout << "muon_1_charge (after swapping):" << recMuFromBestRes.first.charge() << std::endl;
              ResFound = true;  // NNBB we accept "resonances" even outside mass bounds
              maxprob = prob;
            }
            // if( ResMass[ires] == 0 ) {
            //   std::cout << "Error: ResMass["<<ires<<"] = " << ResMass[ires] << std::endl;
            //   exit(1);
            // }
            double deltaMass = std::abs(mcomb - ResMass[ires]) / ResMass[ires];
            if (deltaMass < minDeltaMass) {
              bestMassMuons = std::make_pair((*Muon1), (*Muon2));
              minDeltaMass = deltaMass;
            }
          }
        }
      }
    }
  }
  //If outside mass window (maxprob==0) then take the two muons with best invariant mass
  //(anyway they will not be used in the likelihood calculation, only to fill plots)
  if (!maxprob) {
    if (bestMassMuons.first.charge() < 0) {
      recMuFromBestRes.first = bestMassMuons.first;
      recMuFromBestRes.second = bestMassMuons.second;
    } else {
      recMuFromBestRes.second = bestMassMuons.first;
      recMuFromBestRes.first = bestMassMuons.second;
    }
  }
  return recMuFromBestRes;
}
 
// Resolution smearing function called to worsen muon Pt resolution at start
// -------------------------------------------------------------------------
lorentzVector MuScleFitUtils::applySmearing(const lorentzVector &muon) {
  double pt = muon.Pt();
  double eta = muon.Eta();
  double phi = muon.Phi();
  double E = muon.E();
 
  double y[7] = {};
  for (int i = 0; i < SmearType + 3; i++) {
    y[i] = x[i][goodmuon % 10000];
  }
 
  // Use the smear function selected in the constructor
  smearFunction->smear(pt, eta, phi, y, parSmear);
 
  if (debug > 9) {
    std::cout << "Smearing Pt,eta,phi = " << pt << " " << eta << " " << phi << "; x = ";
    for (int i = 0; i < SmearType + 3; i++) {
      std::cout << y[i];
    }
    std::cout << std::endl;
  }
 
  double ptEtaPhiE[4] = {pt, eta, phi, E};
  return (fromPtEtaPhiToPxPyPz(ptEtaPhiE));
}
 
// Biasing function called to modify muon Pt scale at the start.
// -------------------------------------------------------------
lorentzVector MuScleFitUtils::applyBias(const lorentzVector &muon, const int chg) {
  double ptEtaPhiE[4] = {muon.Pt(), muon.Eta(), muon.Phi(), muon.E()};
 
  if (MuScleFitUtils::debug > 1)
    std::cout << "pt before bias = " << ptEtaPhiE[0] << std::endl;
 
  // Use functors (although not with the () operator)
  // Note that we always pass pt, eta and phi, but internally only the needed
  // values are used.
  // The functors used are takend from the same group used for the scaling
  // thus the name of the method used is "scale".
  ptEtaPhiE[0] = biasFunction->scale(ptEtaPhiE[0], ptEtaPhiE[1], ptEtaPhiE[2], chg, MuScleFitUtils::parBias);
 
  if (MuScleFitUtils::debug > 1)
    std::cout << "pt after bias = " << ptEtaPhiE[0] << std::endl;
 
  return (fromPtEtaPhiToPxPyPz(ptEtaPhiE));
}
 
// Version of applyScale accepting a std::vector<double> of parameters
// --------------------------------------------------------------
lorentzVector MuScleFitUtils::applyScale(const lorentzVector &muon, const std::vector<double> &parval, const int chg) {
  double *p = new double[(int)(parval.size())];
  // Replaced by auto_ptr, which handles delete at the end
  // std::unique_ptr<double> p(new double[(int)(parval.size())]);
  // Removed auto_ptr, check massResolution for an explanation.
  int id = 0;
  for (std::vector<double>::const_iterator it = parval.begin(); it != parval.end(); ++it, ++id) {
    //(&*p)[id] = *it;
    // Also ok would be (p.get())[id] = *it;
    p[id] = *it;
  }
  lorentzVector tempScaleVec(applyScale(muon, p, chg));
  delete[] p;
  return tempScaleVec;
}
 
// This is called by the likelihood to "taste" different values for additional corrections
// ---------------------------------------------------------------------------------------
lorentzVector MuScleFitUtils::applyScale(const lorentzVector &muon, double *parval, const int chg) {
  double ptEtaPhiE[4] = {muon.Pt(), muon.Eta(), muon.Phi(), muon.E()};
  int shift = parResol.size();
 
  if (MuScleFitUtils::debug > 1)
    std::cout << "pt before scale = " << ptEtaPhiE[0] << std::endl;
 
  // the address of parval[shift] is passed as pointer to double. Internally it is used as a normal array, thus:
  // array[0] = parval[shift], array[1] = parval[shift+1], ...
  ptEtaPhiE[0] = scaleFunction->scale(ptEtaPhiE[0], ptEtaPhiE[1], ptEtaPhiE[2], chg, &(parval[shift]));
 
  if (MuScleFitUtils::debug > 1)
    std::cout << "pt after scale = " << ptEtaPhiE[0] << std::endl;
 
  return (fromPtEtaPhiToPxPyPz(ptEtaPhiE));
}
 
// Useful function to convert 4-vector coordinates
// -----------------------------------------------
lorentzVector MuScleFitUtils::fromPtEtaPhiToPxPyPz(const double *ptEtaPhiE) {
  double px = ptEtaPhiE[0] * cos(ptEtaPhiE[2]);
  double py = ptEtaPhiE[0] * sin(ptEtaPhiE[2]);
  double tmp = 2 * atan(exp(-ptEtaPhiE[1]));
  double pz = ptEtaPhiE[0] * cos(tmp) / sin(tmp);
  double E = sqrt(px * px + py * py + pz * pz + muMass * muMass);
 
  // lorentzVector corrMu(px,py,pz,E);
  // To fix memory leaks, this is to be substituted with
  // std::unique_ptr<lorentzVector> corrMu(new lorentzVector(px, py, pz, E));
 
  return lorentzVector(px, py, pz, E);
}
 
// Dimuon mass
// -----------
inline double MuScleFitUtils::invDimuonMass(const lorentzVector &mu1, const lorentzVector &mu2) {
  return (mu1 + mu2).mass();
}
 
// Mass resolution - version accepting a std::vector<double> parval
// -----------------------------------------------------------
double MuScleFitUtils::massResolution(const lorentzVector &mu1,
                                      const lorentzVector &mu2,
                                      const std::vector<double> &parval) {
  // double * p = new double[(int)(parval.size())];
  // Replaced by auto_ptr, which handles delete at the end
  // --------- //
  // ATTENTION //
  // --------- //
  // auto_ptr calls delete, not delete[] and thus it must
  // not be used with arrays. There are alternatives see
  // e.g.: http://www.gotw.ca/gotw/042.htm. The best
  // alternative seems to be to switch to vector though.
  // std::unique_ptr<double> p(new double[(int)(parval.size())]);
 
  double *p = new double[(int)(parval.size())];
  std::vector<double>::const_iterator it = parval.begin();
  int id = 0;
  for (; it != parval.end(); ++it, ++id) {
    // (&*p)[id] = *it;
    p[id] = *it;
  }
  double massRes = massResolution(mu1, mu2, p);
  delete[] p;
  return massRes;
}
 
double MuScleFitUtils::massResolution(const lorentzVector &mu1, const lorentzVector &mu2, double *parval) {
  double mass = (mu1 + mu2).mass();
  double pt1 = mu1.Pt();
  double phi1 = mu1.Phi();
  double eta1 = mu1.Eta();
  double theta1 = 2 * atan(exp(-eta1));
  double pt2 = mu2.Pt();
  double phi2 = mu2.Phi();
  double eta2 = mu2.Eta();
  double theta2 = 2 * atan(exp(-eta2));
 
  double dmdpt1 = (pt1 / std::pow(sin(theta1), 2) *
                       sqrt((std::pow(pt2 / sin(theta2), 2) + mMu2) / (std::pow(pt1 / sin(theta1), 2) + mMu2)) -
                   pt2 * (cos(phi1 - phi2) + cos(theta1) * cos(theta2) / (sin(theta1) * sin(theta2)))) /
                  mass;
  double dmdpt2 = (pt2 / std::pow(sin(theta2), 2) *
                       sqrt((std::pow(pt1 / sin(theta1), 2) + mMu2) / (std::pow(pt2 / sin(theta2), 2) + mMu2)) -
                   pt1 * (cos(phi2 - phi1) + cos(theta2) * cos(theta1) / (sin(theta2) * sin(theta1)))) /
                  mass;
  double dmdphi1 = pt1 * pt2 / mass * sin(phi1 - phi2);
  double dmdphi2 = pt2 * pt1 / mass * sin(phi2 - phi1);
  double dmdcotgth1 = (pt1 * pt1 * cos(theta1) / sin(theta1) *
                           sqrt((std::pow(pt2 / sin(theta2), 2) + mMu2) / (std::pow(pt1 / sin(theta1), 2) + mMu2)) -
                       pt1 * pt2 * cos(theta2) / sin(theta2)) /
                      mass;
  double dmdcotgth2 = (pt2 * pt2 * cos(theta2) / sin(theta2) *
                           sqrt((std::pow(pt1 / sin(theta1), 2) + mMu2) / (std::pow(pt2 / sin(theta2), 2) + mMu2)) -
                       pt2 * pt1 * cos(theta1) / sin(theta1)) /
                      mass;
 
  if (debugMassResol_) {
    massResolComponents.dmdpt1 = dmdpt1;
    massResolComponents.dmdpt2 = dmdpt2;
    massResolComponents.dmdphi1 = dmdphi1;
    massResolComponents.dmdphi2 = dmdphi2;
    massResolComponents.dmdcotgth1 = dmdcotgth1;
    massResolComponents.dmdcotgth2 = dmdcotgth2;
  }
 
  // Resolution parameters:
  // ----------------------
  double sigma_pt1 = resolutionFunction->sigmaPt(pt1, eta1, parval);
  double sigma_pt2 = resolutionFunction->sigmaPt(pt2, eta2, parval);
  double sigma_phi1 = resolutionFunction->sigmaPhi(pt1, eta1, parval);
  double sigma_phi2 = resolutionFunction->sigmaPhi(pt2, eta2, parval);
  double sigma_cotgth1 = resolutionFunction->sigmaCotgTh(pt1, eta1, parval);
  double sigma_cotgth2 = resolutionFunction->sigmaCotgTh(pt2, eta2, parval);
  double cov_pt1pt2 = resolutionFunction->covPt1Pt2(pt1, eta1, pt2, eta2, parval);
 
  // Sigma_Pt is defined as a relative sigmaPt/Pt for this reason we need to
  // multiply it by pt.
  double mass_res = sqrt(std::pow(dmdpt1 * sigma_pt1 * pt1, 2) + std::pow(dmdpt2 * sigma_pt2 * pt2, 2) +
                         std::pow(dmdphi1 * sigma_phi1, 2) + std::pow(dmdphi2 * sigma_phi2, 2) +
                         std::pow(dmdcotgth1 * sigma_cotgth1, 2) + std::pow(dmdcotgth2 * sigma_cotgth2, 2) +
                         2 * dmdpt1 * dmdpt2 * cov_pt1pt2 * sigma_pt1 * sigma_pt2);
 
  if (debug > 19) {
    std::cout << "  Pt1=" << pt1 << " phi1=" << phi1 << " cotgth1=" << cos(theta1) / sin(theta1) << " - Pt2=" << pt2
              << " phi2=" << phi2 << " cotgth2=" << cos(theta2) / sin(theta2) << std::endl;
    std::cout << " P[0]=" << parval[0] << " P[1]=" << parval[1] << "P[2]=" << parval[2] << " P[3]=" << parval[3]
              << std::endl;
    std::cout << "  Dmdpt1= " << dmdpt1 << " dmdpt2= " << dmdpt2 << " sigma_pt1=" << sigma_pt1
              << " sigma_pt2=" << sigma_pt2 << std::endl;
    std::cout << "  Dmdphi1= " << dmdphi1 << " dmdphi2= " << dmdphi2 << " sigma_phi1=" << sigma_phi1
              << " sigma_phi2=" << sigma_phi2 << std::endl;
    std::cout << "  Dmdcotgth1= " << dmdcotgth1 << " dmdcotgth2= " << dmdcotgth2 << " sigma_cotgth1=" << sigma_cotgth1
              << " sigma_cotgth2=" << sigma_cotgth2 << std::endl;
    std::cout << "  Mass resolution (pval) for muons of Pt = " << pt1 << " " << pt2 << " : " << mass << " +- "
              << mass_res << std::endl;
  }
 
  // Debug std::cout
  // ----------
  bool didit = false;
  for (int ires = 0; ires < 6; ires++) {
    if (!didit && resfind[ires] > 0 && std::abs(mass - ResMass[ires]) < ResHalfWidth[ires]) {
      if (mass_res > ResMaxSigma[ires] && counter_resprob < 100) {
        counter_resprob++;
        LogDebug("MuScleFitUtils") << "RESOLUTION PROBLEM: ires=" << ires << std::endl;
        didit = true;
      }
    }
  }
 
  return mass_res;
}
 
double MuScleFitUtils::massResolution(const lorentzVector &mu1,
                                      const lorentzVector &mu2,
                                      const ResolutionFunction &resolFunc) {
  double mass = (mu1 + mu2).mass();
  double pt1 = mu1.Pt();
  double phi1 = mu1.Phi();
  double eta1 = mu1.Eta();
  double theta1 = 2 * atan(exp(-eta1));
  double pt2 = mu2.Pt();
  double phi2 = mu2.Phi();
  double eta2 = mu2.Eta();
  double theta2 = 2 * atan(exp(-eta2));
 
  double dmdpt1 = (pt1 / std::pow(sin(theta1), 2) *
                       sqrt((std::pow(pt2 / sin(theta2), 2) + mMu2) / (std::pow(pt1 / sin(theta1), 2) + mMu2)) -
                   pt2 * (cos(phi1 - phi2) + cos(theta1) * cos(theta2) / (sin(theta1) * sin(theta2)))) /
                  mass;
  double dmdpt2 = (pt2 / std::pow(sin(theta2), 2) *
                       sqrt((std::pow(pt1 / sin(theta1), 2) + mMu2) / (std::pow(pt2 / sin(theta2), 2) + mMu2)) -
                   pt1 * (cos(phi2 - phi1) + cos(theta2) * cos(theta1) / (sin(theta2) * sin(theta1)))) /
                  mass;
  double dmdphi1 = pt1 * pt2 / mass * sin(phi1 - phi2);
  double dmdphi2 = pt2 * pt1 / mass * sin(phi2 - phi1);
  double dmdcotgth1 = (pt1 * pt1 * cos(theta1) / sin(theta1) *
                           sqrt((std::pow(pt2 / sin(theta2), 2) + mMu2) / (std::pow(pt1 / sin(theta1), 2) + mMu2)) -
                       pt1 * pt2 * cos(theta2) / sin(theta2)) /
                      mass;
  double dmdcotgth2 = (pt2 * pt2 * cos(theta2) / sin(theta2) *
                           sqrt((std::pow(pt1 / sin(theta1), 2) + mMu2) / (std::pow(pt2 / sin(theta2), 2) + mMu2)) -
                       pt2 * pt1 * cos(theta1) / sin(theta1)) /
                      mass;
 
  // Resolution parameters:
  // ----------------------
  double sigma_pt1 = resolFunc.sigmaPt(mu1);
  double sigma_pt2 = resolFunc.sigmaPt(mu2);
  double sigma_phi1 = resolFunc.sigmaPhi(mu1);
  double sigma_phi2 = resolFunc.sigmaPhi(mu2);
  double sigma_cotgth1 = resolFunc.sigmaCotgTh(mu1);
  double sigma_cotgth2 = resolFunc.sigmaCotgTh(mu2);
 
  // Sigma_Pt is defined as a relative sigmaPt/Pt for this reason we need to
  // multiply it by pt.
  double mass_res = sqrt(std::pow(dmdpt1 * sigma_pt1 * pt1, 2) + std::pow(dmdpt2 * sigma_pt2 * pt2, 2) +
                         std::pow(dmdphi1 * sigma_phi1, 2) + std::pow(dmdphi2 * sigma_phi2, 2) +
                         std::pow(dmdcotgth1 * sigma_cotgth1, 2) + std::pow(dmdcotgth2 * sigma_cotgth2, 2));
 
  return mass_res;
}
 
// Mass probability - version with linear background included, accepts std::vector<double> parval
// -----------------------------------------------------------------------------------------
double MuScleFitUtils::massProb(const double &mass,
                                const double &resEta,
                                const double &rapidity,
                                const double &massResol,
                                const std::vector<double> &parval,
                                const bool doUseBkgrWindow,
                                const double &eta1,
                                const double &eta2) {
#ifdef USE_CALLGRIND
  CALLGRIND_START_INSTRUMENTATION;
#endif
 
  double *p = new double[(int)(parval.size())];
  // Replaced by auto_ptr, which handles delete at the end
  // Removed auto_ptr, check massResolution for an explanation.
  // std::unique_ptr<double> p(new double[(int)(parval.size())]);
 
  std::vector<double>::const_iterator it = parval.begin();
  int id = 0;
  for (; it != parval.end(); ++it, ++id) {
    // (&*p)[id] = *it;
    p[id] = *it;
  }
  // p must be passed by value as below:
  double massProbability = massProb(mass, resEta, rapidity, massResol, p, doUseBkgrWindow, eta1, eta2);
  delete[] p;
 
#ifdef USE_CALLGRIND
  CALLGRIND_STOP_INSTRUMENTATION;
  CALLGRIND_DUMP_STATS;
#endif
 
  return massProbability;
}
 
double MuScleFitUtils::probability(const double &mass,
                                   const double &massResol,
                                   const double GLvalue[][1001][1001],
                                   const double GLnorm[][1001],
                                   const int iRes,
                                   const int iY) {
  if (iRes == 0 && iY > 23) {
    std::cout << "WARNING: rapidity bin selected = " << iY << " but there are only histograms for the first 24 bins"
              << std::endl;
  }
 
  double PS = 0.;
  bool insideProbMassWindow = true;
  // Interpolate the four values of GLZValue[] in the
  // grid square within which the (mass,sigma) values lay
  // ----------------------------------------------------
  // This must be done with respect to the width used in the computation of the probability distribution,
  // so that the bin 0 really matches the bin 0 of that distribution.
  //  double fracMass = (mass-(ResMass[iRes]-ResHalfWidth[iRes]))/(2*ResHalfWidth[iRes]);
  double fracMass = (mass - ResMinMass[iRes]) / (2 * ResHalfWidth[iRes]);
  if (debug > 1)
    std::cout << std::setprecision(9) << "mass ResMinMass[iRes] ResHalfWidth[iRes] ResHalfWidth[iRes]" << mass << " "
              << ResMinMass[iRes] << " " << ResHalfWidth[iRes] << " " << ResHalfWidth[iRes] << std::endl;
  int iMassLeft = (int)(fracMass * (double)nbins);
  int iMassRight = iMassLeft + 1;
  double fracMassStep = (double)nbins * (fracMass - (double)iMassLeft / (double)nbins);
  if (debug > 1)
    std::cout << "nbins iMassLeft fracMass " << nbins << " " << iMassLeft << " " << fracMass << std::endl;
 
  // Simple protections for the time being: the region where we fit should not include
  // values outside the boundaries set by ResMass-ResHalfWidth : ResMass+ResHalfWidth
  // ---------------------------------------------------------------------------------
  if (iMassLeft < 0) {
    edm::LogInfo("probability") << "WARNING: fracMass=" << fracMass << ", iMassLeft=" << iMassLeft
                                << "; mass = " << mass << " and bounds are " << ResMinMass[iRes] << ":"
                                << ResMinMass[iRes] + 2 * ResHalfWidth[iRes] << " - iMassLeft set to 0" << std::endl;
    iMassLeft = 0;
    iMassRight = 1;
    insideProbMassWindow = false;
  }
  if (iMassRight > nbins) {
    edm::LogInfo("probability") << "WARNING: fracMass=" << fracMass << ", iMassRight=" << iMassRight
                                << "; mass = " << mass << " and bounds are " << ResMinMass[iRes] << ":"
                                << ResMass[iRes] + 2 * ResHalfWidth[iRes] << " - iMassRight set to " << nbins - 1
                                << std::endl;
    iMassLeft = nbins - 1;
    iMassRight = nbins;
    insideProbMassWindow = false;
  }
  double fracSigma = (massResol / ResMaxSigma[iRes]);
  int iSigmaLeft = (int)(fracSigma * (double)nbins);
  int iSigmaRight = iSigmaLeft + 1;
  double fracSigmaStep = (double)nbins * (fracSigma - (double)iSigmaLeft / (double)nbins);
  // std::cout << "massResol = " << massResol << std::endl;
  // std::cout << "ResMaxSigma["<<iRes<<"] = " << ResMaxSigma[iRes] << std::endl;
  // std::cout << "fracSigma = " << fracSigma << std::endl;
  // std::cout << "nbins = " << nbins << std::endl;
  // std::cout << "ISIGMALEFT = " << iSigmaLeft << std::endl;
  // std::cout << "ISIGMARIGHT = " << iSigmaRight << std::endl;
  // std::cout << "fracSigmaStep = " << fracSigmaStep << std::endl;
 
  // Simple protections for the time being: they should not affect convergence, since
  // ResMaxSigma is set to very large values, and if massResol exceeds them the fit
  // should not get any prize for that (for large sigma, the prob. distr. becomes flat)
  // ----------------------------------------------------------------------------------
  if (iSigmaLeft < 0) {
    edm::LogInfo("probability") << "WARNING: fracSigma = " << fracSigma << ", iSigmaLeft=" << iSigmaLeft
                                << ", with massResol = " << massResol
                                << " and ResMaxSigma[iRes] = " << ResMaxSigma[iRes] << " -  iSigmaLeft set to 0"
                                << std::endl;
    iSigmaLeft = 0;
    iSigmaRight = 1;
  }
  if (iSigmaRight > nbins) {
    if (counter_resprob < 100)
      edm::LogInfo("probability") << "WARNING: fracSigma = " << fracSigma << ", iSigmaRight=" << iSigmaRight
                                  << ", with massResol = " << massResol
                                  << " and ResMaxSigma[iRes] = " << ResMaxSigma[iRes] << " -  iSigmaRight set to "
                                  << nbins - 1 << std::endl;
    iSigmaLeft = nbins - 1;
    iSigmaRight = nbins;
  }
 
  // If f11,f12,f21,f22 are the values at the four corners, one finds by linear interpolation the
  // formula below for PS
  // --------------------------------------------------------------------------------------------
  if (insideProbMassWindow) {
    double f11 = 0.;
    if (GLnorm[iY][iSigmaLeft] > 0)
      f11 = GLvalue[iY][iMassLeft][iSigmaLeft] / GLnorm[iY][iSigmaLeft];
    double f12 = 0.;
    if (GLnorm[iY][iSigmaRight] > 0)
      f12 = GLvalue[iY][iMassLeft][iSigmaRight] / GLnorm[iY][iSigmaRight];
    double f21 = 0.;
    if (GLnorm[iY][iSigmaLeft] > 0)
      f21 = GLvalue[iY][iMassRight][iSigmaLeft] / GLnorm[iY][iSigmaLeft];
    double f22 = 0.;
    if (GLnorm[iY][iSigmaRight] > 0)
      f22 = GLvalue[iY][iMassRight][iSigmaRight] / GLnorm[iY][iSigmaRight];
    PS = f11 + (f12 - f11) * fracSigmaStep + (f21 - f11) * fracMassStep +
         (f22 - f21 - f12 + f11) * fracMassStep * fracSigmaStep;
    if (PS > 0.1 || debug > 1)
      LogDebug("MuScleFitUtils") << "iRes = " << iRes << " PS=" << PS << " f11,f12,f21,f22=" << f11 << " " << f12 << " "
                                 << f21 << " " << f22 << " "
                                 << " fSS=" << fracSigmaStep << " fMS=" << fracMassStep << " iSL, iSR=" << iSigmaLeft
                                 << " " << iSigmaRight << " GLvalue[" << iY << "][" << iMassLeft
                                 << "] = " << GLvalue[iY][iMassLeft][iSigmaLeft] << " GLnorm[" << iY << "]["
                                 << iSigmaLeft << "] = " << GLnorm[iY][iSigmaLeft] << std::endl;
 
    //     if (PS>0.1) std::cout << "iRes = " << iRes << " PS=" << PS << " f11,f12,f21,f22="
    //                      << f11 << " " << f12 << " " << f21 << " " << f22 << " "
    //                      << " fSS=" << fracSigmaStep << " fMS=" << fracMassStep << " iSL, iSR="
    //                      << iSigmaLeft << " " << iSigmaRight << " GLV,GLN="
    //                      << GLvalue[iY][iMassLeft][iSigmaLeft]
    //                      << " " << GLnorm[iY][iSigmaLeft] << std::endl;
 
  } else {
    edm::LogInfo("probability") << "outside mass probability window. Setting PS[" << iRes << "] = 0" << std::endl;
  }
 
  //   if( PS != PS ) {
  //     std::cout << "ERROR: PS = " << PS << " for iRes = " << iRes << std::endl;
 
  //     std::cout << "mass = " << mass << ", massResol = " << massResol << std::endl;
  //     std::cout << "fracMass = " << fracMass << ", iMassLeft = " << iMassLeft
  //          << ", iMassRight = " << iMassRight << ", fracMassStep = " << fracMassStep << std::endl;
  //     std::cout << "fracSigma = " << fracSigma << ", iSigmaLeft = " << iSigmaLeft
  //          << ", iSigmaRight = " << iSigmaRight << ", fracSigmaStep = " << fracSigmaStep << std::endl;
  //     std::cout << "ResMaxSigma["<<iRes<<"] = " << ResMaxSigma[iRes] << std::endl;
 
  //     std::cout << "GLvalue["<<iY<<"]["<<iMassLeft<<"] = " << GLvalue[iY][iMassLeft][iSigmaLeft]
  //          << " GLnorm["<<iY<<"]["<<iSigmaLeft<<"] = " << GLnorm[iY][iSigmaLeft] << std::endl;
  //   }
 
  return PS;
}
 
// Mass probability - version with linear background included
// ----------------------------------------------------------
double MuScleFitUtils::massProb(const double &mass,
                                const double &resEta,
                                const double &rapidity,
                                const double &massResol,
                                double *parval,
                                const bool doUseBkgrWindow,
                                const double &eta1,
                                const double &eta2) {
  // This routine computes the likelihood that a given measured mass "measMass" is
  // the result of a reference mass ResMass[] if the resolution
  // expected for the two muons is massResol.
  // This version includes two parameters (the last two in parval, by default)
  // to size up the background fraction and its relative normalization with respect
  // to the signal shape.
  //
  // We model the signal probability with a Lorentz L(M,H) of resonance mass M and natural width H
  // convoluted with a gaussian G(m,s) of measured mass m and expected mass resolution s,
  // by integrating over the intersection of the supports of L and G (which can be made to coincide with
  // the region where L is non-zero, given that H<<s in most cases) the product L(M,H)*G(m,s) dx as follows:
  //
  //   GL(m,s) = Int(M-10H,M+10H) [ L(x-M,H) * G(x-m,s) ] dx
  //
  // The above convolution is computed numerically by an independent root macro, Probs.C, which outputs
  // the values in six 1001x1001 grids, one per resonance.
  //
  // NB THe following block of explanations for background models is outdated, see detailed
  // explanations where the code computes PB.
  // +++++++++++++++++++++++
  //   For the background, instead, we have two choices: a linear and an exponential model.
  //     * For the linear model, we choose a one-parameter form whereby the line is automatically normalized
  //       in the support [x1,x2] where we defined our "signal region", as follows:
  //
  //         B(x;b) = 1/(x2-x1) + {x - (x2+x1)/2} * b
  //
  //       Defined as above, B(x) is a line passing for the point of coordinates (x1+x2)/2, 1/(x2-x1),
  //       whose slope b has as support the interval ( -2/[(x1-x2)*(x1+x2)], 2/[(x1-x2)*(x1+x2)] )
  //       so that B(x) is always positive-definite in [x1,x2].
  //
  //     * For the exponential model, we define B(x;b) as
  //
  //         B(x;b) = b * { exp(-b*x) / [exp(-b*x1)-exp(-b*x2)] }
  //
  //       This one-parameter definition is automatically normalized to unity in [x1,x2], with a parameter
  //       b which has to be positive in order for the slope to be negative.
  //       Please note that this model is not useful in most circumstances; a more useful form would be one
  //       which included a linear component.
  // ++++++++++++++++++++++
  //
  // Once GL(m,s) and B(x;b) are defined, we introduce a further parameter a, such that we can have the
  // likelihood control the relative fraction of signal and background. We first normalize GL(m,s) for
  // any given s by taking the integral
  //
  //   Int(x1,x2) GL(m,s) dm = K_s
  //
  // We then define the probability as
  //
  //   P(m,s,a,b) = GL(m,s)/K_s * a  +  B(x,b) * (1-a)
  //
  // with a taking on values in the interval [0,1].
  // Defined as above, the probability is well-behaved, in the sense that it has a value between 0 and 1,
  // and the four parameters m,s,a,b fully control its shape.
  //
  // It is to be noted that the formulation above requires the computation of two rather time-consuming
  // integrals. The one defining GL(m,s) can be stored in a TH2D and loaded by the constructor from a
  // file suitably prepared, and this will save loads of computing time.
  // ----------------------------------------------------------------------------------------------------
 
  double P = 0.;
  int crossSectionParShift = parResol.size() + parScale.size();
  // Take the relative cross sections
  std::vector<double> relativeCrossSections =
      crossSectionHandler->relativeCrossSections(&(parval[crossSectionParShift]), resfind);
  //   for( unsigned int i=0; i<relativeCrossSections.size(); ++i ) {
  //     std::cout << "relativeCrossSections["<<i<<"] = " << relativeCrossSections[i] << std::endl;
  //     std::cout << "parval["<<crossSectionParShift+i<<"] = " << parval[crossSectionParShift+i] << std::endl;
  //   }
 
  // int bgrParShift = crossSectionParShift + parCrossSection.size();
  int bgrParShift = crossSectionParShift + crossSectionHandler->parNum();
  double Bgrp1 = 0.;
  //   double Bgrp2 = 0.;
  //   double Bgrp3 = 0.;
 
  // NB defined as below, P is a non-rigorous "probability" that we observe
  // a dimuon mass "mass", given ResMass[], gamma, and massResol. It is what we need for the
  // fit which finds the best resolution parameters, though. A definition which is
  // more properly a probability is given below (in massProb2()), where the result
  // cannot be used to fit resolution parameters because the fit would always prefer
  // to set the res parameters to the minimum possible value (best resolution),
  // to have a probability close to one of observing any mass.
  // -------------------------------------------------------------------------------
 
  // Determine what resonance(s) we have to deal with
  // NB for now we assume equal xs for each resonance
  // so we do not assign them different weights
  // ------------------------------------------------
  double PS[6] = {0.};
  double PB = 0.;
  double PStot[6] = {0.};
 
  // Should be removed because it is not used
  bool resConsidered[6] = {false};
 
  bool useBackgroundWindow = (doBackgroundFit[loopCounter] || doUseBkgrWindow);
  // bool useBackgroundWindow = (doBackgroundFit[loopCounter]);
 
  // First check the Z, which is divided in 24 rapidity bins
  // NB max value of Z rapidity to be considered is 2.4 here
  // -------------------------------------------------------
 
  // Do this only if we want to use the rapidity bins for the Z
  if (MuScleFitUtils::rapidityBinsForZ_) {
    // ATTENTION: cut on Z rapidity at 2.4 since we only have histograms up to that value
    // std::pair<double, double> windowFactors = backgroundHandler->windowFactors( useBackgroundWindow, 0 );
    std::pair<double, double> windowBorders = backgroundHandler->windowBorders(useBackgroundWindow, 0);
    if (resfind[0] > 0
        // && checkMassWindow( mass, 0,
        //                     backgroundHandler->resMass( useBackgroundWindow, 0 ),
        //                     windowFactors.first, windowFactors.second )
        && checkMassWindow(mass, windowBorders.first, windowBorders.second)
        // && std::abs(rapidity)<2.4
    ) {
      int iY = (int)(std::abs(rapidity) * 10.);
      if (iY > 23)
        iY = 23;
 
      if (MuScleFitUtils::debug > 1)
        std::cout << "massProb:resFound = 0, rapidity bin =" << iY << std::endl;
 
      // In this case the last value is the rapidity bin
      PS[0] = probability(mass, massResol, GLZValue, GLZNorm, 0, iY);
 
      if (PS[0] != PS[0]) {
        std::cout << "ERROR: PS[0] = nan, setting it to 0" << std::endl;
        PS[0] = 0;
      }
 
      // std::pair<double, double> bgrResult = backgroundHandler->backgroundFunction( doBackgroundFit[loopCounter],
      //                                           &(parval[bgrParShift]), MuScleFitUtils::totalResNum, 0,
      //                                           resConsidered, ResMass, ResHalfWidth, MuonType, mass, resEta );
 
      std::pair<double, double> bgrResult = backgroundHandler->backgroundFunction(doBackgroundFit[loopCounter],
                                                                                  &(parval[bgrParShift]),
                                                                                  MuScleFitUtils::totalResNum,
                                                                                  0,
                                                                                  resConsidered,
                                                                                  ResMass,
                                                                                  ResHalfWidth,
                                                                                  MuonType,
                                                                                  mass,
                                                                                  eta1,
                                                                                  eta2);
 
      Bgrp1 = bgrResult.first;
      // When fitting the background we have only one Bgrp1
      // When not fitting the background we have many only in a superposition region and this case is treated
      // separately after this loop
      PB = bgrResult.second;
      if (PB != PB)
        PB = 0;
      PStot[0] = (1 - Bgrp1) * PS[0] + Bgrp1 * PB;
 
      // PStot[0] *= crossSectionHandler->crossSection(0);
      // PStot[0] *= parval[crossSectionParShift];
      PStot[0] *= relativeCrossSections[0];
      if (MuScleFitUtils::debug > 0)
        std::cout << "PStot[" << 0 << "] = "
                  << "(1-" << Bgrp1 << ")*" << PS[0] << " + " << Bgrp1 << "*" << PB << " = " << PStot[0]
                  << " (reletiveXS = )" << relativeCrossSections[0] << std::endl;
    } else {
      if (debug > 0) {
        std::cout << "Mass = " << mass << " outside range with rapidity = " << rapidity << std::endl;
        std::cout << "with resMass = " << backgroundHandler->resMass(useBackgroundWindow, 0)
                  << " and left border = " << windowBorders.first << " right border = " << windowBorders.second
                  << std::endl;
      }
    }
  }
  // Next check the other resonances
  // -------------------------------
  int firstRes = 1;
  if (!MuScleFitUtils::rapidityBinsForZ_)
    firstRes = 0;
  for (int ires = firstRes; ires < 6; ++ires) {
    if (resfind[ires] > 0) {
      // First is left, second is right (returns (1,1) in the case of resonances, it could be improved avoiding the call in this case)
      // std::pair<double, double> windowFactor = backgroundHandler->windowFactors( useBackgroundWindow, ires );
      std::pair<double, double> windowBorder = backgroundHandler->windowBorders(useBackgroundWindow, ires);
      if (checkMassWindow(mass, windowBorder.first, windowBorder.second)) {
        if (MuScleFitUtils::debug > 1)
          std::cout << "massProb:resFound = " << ires << std::endl;
 
        // In this case the rapidity value is instead the resonance index again.
        PS[ires] = probability(mass, massResol, GLValue, GLNorm, ires, ires);
 
        std::pair<double, double> bgrResult =
            backgroundHandler->backgroundFunction(doBackgroundFit[loopCounter],
                                                  &(parval[bgrParShift]),
                                                  MuScleFitUtils::totalResNum,
                                                  ires,
                                                  // resConsidered, ResMass, ResHalfWidth, MuonType, mass, resEta );
                                                  resConsidered,
                                                  ResMass,
                                                  ResHalfWidth,
                                                  MuonType,
                                                  mass,
                                                  eta1,
                                                  eta2);
        Bgrp1 = bgrResult.first;
        PB = bgrResult.second;
 
        if (PB != PB)
          PB = 0;
        PStot[ires] = (1 - Bgrp1) * PS[ires] + Bgrp1 * PB;
        if (MuScleFitUtils::debug > 0)
          std::cout << "PStot[" << ires << "] = "
                    << "(1-" << Bgrp1 << ")*" << PS[ires] << " + " << Bgrp1 << "*" << PB << " = " << PStot[ires]
                    << std::endl;
 
        PStot[ires] *= relativeCrossSections[ires];
      }
    }
  }
 
  for (int i = 0; i < 6; ++i) {
    P += PStot[i];
  }
 
  if (MuScleFitUtils::signalProb_ != nullptr && MuScleFitUtils::backgroundProb_ != nullptr) {
    double PStotTemp = 0.;
    for (int i = 0; i < 6; ++i) {
      PStotTemp += PS[i] * relativeCrossSections[i];
    }
    if (PStotTemp != PStotTemp) {
      std::cout << "ERROR: PStotTemp = nan!!!!!!!!!" << std::endl;
      int parnumber = (int)(parResol.size() + parScale.size() + crossSectionHandler->parNum() + parBgr.size());
      for (int i = 0; i < 6; ++i) {
        std::cout << "PS[i] = " << PS[i] << std::endl;
        if (PS[i] != PS[i]) {
          std::cout << "mass = " << mass << std::endl;
          std::cout << "massResol = " << massResol << std::endl;
          for (int j = 0; j < parnumber; ++j) {
            std::cout << "parval[" << j << "] = " << parval[j] << std::endl;
          }
        }
      }
    }
    if (PStotTemp == PStotTemp) {
      MuScleFitUtils::signalProb_->SetBinContent(
          MuScleFitUtils::minuitLoop_,
          MuScleFitUtils::signalProb_->GetBinContent(MuScleFitUtils::minuitLoop_) + PStotTemp);
    }
    if (debug > 0)
      std::cout << "mass = " << mass << ", P = " << P << ", PStot = " << PStotTemp << ", PB = " << PB
                << ", bgrp1 = " << Bgrp1 << std::endl;
 
    MuScleFitUtils::backgroundProb_->SetBinContent(
        MuScleFitUtils::minuitLoop_, MuScleFitUtils::backgroundProb_->GetBinContent(MuScleFitUtils::minuitLoop_) + PB);
  }
  return P;
}
 
// Method to check if the mass value is within the mass window of the i-th resonance.
// inline bool MuScleFitUtils::checkMassWindow( const double & mass, const int ires, const double & resMass, const double & leftFactor, const double & rightFactor )
// {
//   return( mass-resMass > -leftFactor*massWindowHalfWidth[MuonTypeForCheckMassWindow][ires]
//           && mass-resMass < rightFactor*massWindowHalfWidth[MuonTypeForCheckMassWindow][ires] );
// }
inline bool MuScleFitUtils::checkMassWindow(const double &mass, const double &leftBorder, const double &rightBorder) {
  return ((mass > leftBorder) && (mass < rightBorder));
}
 
// Function that returns the weight for a muon pair
// ------------------------------------------------
double MuScleFitUtils::computeWeight(const double &mass, const int iev, const bool doUseBkgrWindow) {
  // Compute weight for this event
  // -----------------------------
  double weight = 0.;
 
  // Take the highest-mass resonance within bounds
  // NB this must be revised once credible estimates of the relative xs of Y(1S), (2S), and (3S)
  // are made. Those are priors in the decision of which resonance to assign to an in-between event.
  // -----------------------------------------------------------------------------------------------
 
  if (doUseBkgrWindow && (debug > 0))
    std::cout << "using backgrond window for mass = " << mass << std::endl;
  // bool useBackgroundWindow = (doBackgroundFit[loopCounter] || doUseBkgrWindow);
  bool useBackgroundWindow = (doBackgroundFit[loopCounter]);
 
  for (int ires = 0; ires < 6; ires++) {
    if (resfind[ires] > 0 && weight == 0.) {
      // std::pair<double, double> windowFactor = backgroundHandler->windowFactors( useBackgroundWindow, ires );
      std::pair<double, double> windowBorder = backgroundHandler->windowBorders(useBackgroundWindow, ires);
      // if( checkMassWindow(mass, ires, backgroundHandler->resMass( useBackgroundWindow, ires ),
      //                     windowFactor.first, windowFactor.second) ) {
      if (checkMassWindow(mass, windowBorder.first, windowBorder.second)) {
        weight = 1.0;
        if (doUseBkgrWindow && (debug > 0))
          std::cout << "setting weight to = " << weight << std::endl;
      }
    }
  }
 
  return weight;
}
 
// Likelihood minimization routine
// -------------------------------
void MuScleFitUtils::minimizeLikelihood() {
  // Output file with fit parameters resulting from minimization
  // -----------------------------------------------------------
  std::ofstream FitParametersFile;
  FitParametersFile.open("FitParameters.txt", std::ios::app);
  FitParametersFile << "Fitting with resolution, scale, bgr function # " << ResolFitType << " " << ScaleFitType << " "
                    << BgrFitType << " - Iteration " << loopCounter << std::endl;
 
  // Fill parvalue and other vectors needed for the fitting
  // ------------------------------------------------------
 
  // ----- //
  // FIXME //
  // ----- //
  // this was changed to verify the possibility that fixed parameters influence the errors.
  // It must be 0 otherwise the parameters for resonances will not be passed by minuit (will be always 0).
  // Should be removed.
  int parForResonanceWindows = 0;
  // int parnumber = (int)(parResol.size()+parScale.size()+parCrossSection.size()+parBgr.size() - parForResonanceWindows);
  int parnumber =
      (int)(parResol.size() + parScale.size() + crossSectionHandler->parNum() + parBgr.size() - parForResonanceWindows);
 
  int parnumberAll = (int)(parResol.size() + parScale.size() + crossSectionHandler->parNum() + parBgr.size());
 
  // parvalue is a std::vector<std::vector<double> > storing all the parameters from all the loops
  parvalue.push_back(parResol);
  std::vector<double> *tmpVec = &(parvalue.back());
 
  // If this is not the first loop we want to start from neutral values
  // Otherwise the scale will start with values correcting again a bias
  // that is already corrected.
  if (scaleFitNotDone_) {
    tmpVec->insert(tmpVec->end(), parScale.begin(), parScale.end());
    std::cout << "scaleFitNotDone: tmpVec->size() = " << tmpVec->size() << std::endl;
  } else {
    scaleFunction->resetParameters(tmpVec);
    std::cout << "scaleFitDone: tmpVec->size() = " << tmpVec->size() << std::endl;
  }
  tmpVec->insert(tmpVec->end(), parCrossSection.begin(), parCrossSection.end());
  tmpVec->insert(tmpVec->end(), parBgr.begin(), parBgr.end());
  int i = 0;
  std::vector<double>::const_iterator it = tmpVec->begin();
  for (; it != tmpVec->end(); ++it, ++i) {
    std::cout << "tmpVec[" << i << "] = " << *it << std::endl;
  }
 
  // Empty vector of size = number of cross section fitted parameters. Note that the cross section
  // fit works in a different way than the others and it uses ratios of the paramters passed via cfg.
  // We use this empty vector for compatibility with the rest of the structure.
  std::vector<int> crossSectionParNumSizeVec(MuScleFitUtils::crossSectionHandler->parNum(), 0);
 
  std::vector<int> parfix(parResolFix);
  parfix.insert(parfix.end(), parScaleFix.begin(), parScaleFix.end());
  parfix.insert(parfix.end(), crossSectionParNumSizeVec.begin(), crossSectionParNumSizeVec.end());
  parfix.insert(parfix.end(), parBgrFix.begin(), parBgrFix.end());
 
  std::vector<int> parorder(parResolOrder);
  parorder.insert(parorder.end(), parScaleOrder.begin(), parScaleOrder.end());
  parorder.insert(parorder.end(), crossSectionParNumSizeVec.begin(), crossSectionParNumSizeVec.end());
  parorder.insert(parorder.end(), parBgrOrder.begin(), parBgrOrder.end());
 
  // This is filled later
  std::vector<double> parerr(3 * parnumberAll, 0.);
 
  if (debug > 19) {
    std::cout << "[MuScleFitUtils-minimizeLikelihood]: Parameters before likelihood " << std::endl;
    for (unsigned int i = 0; i < (unsigned int)parnumberAll; i++) {
      std::cout << "  Par # " << i << " = " << parvalue[loopCounter][i] << " : free = " << parfix[i]
                << "; order = " << parorder[i] << std::endl;
    }
  }
 
  //   // Background rescaling from regions to resonances
  //   // -----------------------------------------------
  //   // If we are in a loop > 0 and we are not fitting the background, but we have fitted it in the previous iteration
  //   if( loopCounter > 0 && !(doBackgroundFit[loopCounter]) && doBackgroundFit[loopCounter-1] ) {
  //     // This rescales from regions to resonances
  //     int localMuonType = MuonType;
  //     if( MuonType > 2 ) localMuonType = 2;
  //     backgroundHandler->rescale( parBgr, ResMass, massWindowHalfWidth[localMuonType],
  //                                 MuScleFitUtils::SavedPair);
  //   }
 
  // Init Minuit
  // -----------
  TMinuit rmin(parnumber);
  rminPtr_ = &rmin;
  rmin.SetFCN(likelihood);  // Unbinned likelihood
  // Standard initialization of minuit parameters:
  // sets input to be $stdin, output to be $stdout
  // and saving to a file.
  rmin.mninit(5, 6, 7);
  int ierror = 0;
  int istat;
  double arglis[4];
  arglis[0] = FitStrategy;  // Strategy 1 or 2
  // 1 standard
  // 2 try to improve minimum (slower)
  rmin.mnexcm("SET STR", arglis, 1, ierror);
 
  arglis[0] = 10001;
  // Set the random seed for the generator used in SEEk to a fixed value for reproducibility
  rmin.mnexcm("SET RAN", arglis, 1, ierror);
 
  // Set fit parameters
  // ------------------
  double *Start = new double[parnumberAll];
  double *Step = new double[parnumberAll];
  double *Mini = new double[parnumberAll];
  double *Maxi = new double[parnumberAll];
  int *ind = new int[parnumberAll];  // Order of release of parameters
  TString *parname = new TString[parnumberAll];
 
  if (!parResolStep.empty() && !parResolMin.empty() && !parResolMax.empty()) {
    MuScleFitUtils::resolutionFunctionForVec->setParameters(
        Start, Step, Mini, Maxi, ind, parname, parResol, parResolOrder, parResolStep, parResolMin, parResolMax, MuonType);
  } else {
    MuScleFitUtils::resolutionFunctionForVec->setParameters(
        Start, Step, Mini, Maxi, ind, parname, parResol, parResolOrder, MuonType);
  }
 
  // Take the number of parameters in the resolutionFunction and displace the arrays passed to the scaleFunction
  int resParNum = MuScleFitUtils::resolutionFunctionForVec->parNum();
 
  if (!parScaleStep.empty() && !parScaleMin.empty() && !parScaleMax.empty()) {
    MuScleFitUtils::scaleFunctionForVec->setParameters(&(Start[resParNum]),
                                                       &(Step[resParNum]),
                                                       &(Mini[resParNum]),
                                                       &(Maxi[resParNum]),
                                                       &(ind[resParNum]),
                                                       &(parname[resParNum]),
                                                       parScale,
                                                       parScaleOrder,
                                                       parScaleStep,
                                                       parScaleMin,
                                                       parScaleMax,
                                                       MuonType);
  } else {
    MuScleFitUtils::scaleFunctionForVec->setParameters(&(Start[resParNum]),
                                                       &(Step[resParNum]),
                                                       &(Mini[resParNum]),
                                                       &(Maxi[resParNum]),
                                                       &(ind[resParNum]),
                                                       &(parname[resParNum]),
                                                       parScale,
                                                       parScaleOrder,
                                                       MuonType);
  }
 
  // Initialize cross section parameters
  int crossSectionParShift = resParNum + MuScleFitUtils::scaleFunctionForVec->parNum();
  MuScleFitUtils::crossSectionHandler->setParameters(&(Start[crossSectionParShift]),
                                                     &(Step[crossSectionParShift]),
                                                     &(Mini[crossSectionParShift]),
                                                     &(Maxi[crossSectionParShift]),
                                                     &(ind[crossSectionParShift]),
                                                     &(parname[crossSectionParShift]),
                                                     parCrossSection,
                                                     parCrossSectionOrder,
                                                     resfind);
 
  // Initialize background parameters
  int bgrParShift = crossSectionParShift + crossSectionHandler->parNum();
  MuScleFitUtils::backgroundHandler->setParameters(&(Start[bgrParShift]),
                                                   &(Step[bgrParShift]),
                                                   &(Mini[bgrParShift]),
                                                   &(Maxi[bgrParShift]),
                                                   &(ind[bgrParShift]),
                                                   &(parname[bgrParShift]),
                                                   parBgr,
                                                   parBgrOrder,
                                                   MuonType);
 
  for (int ipar = 0; ipar < parnumber; ++ipar) {
    std::cout << "parname[" << ipar << "] = " << parname[ipar] << std::endl;
    std::cout << "Start[" << ipar << "] = " << Start[ipar] << std::endl;
    std::cout << "Step[" << ipar << "] = " << Step[ipar] << std::endl;
    std::cout << "Mini[" << ipar << "] = " << Mini[ipar] << std::endl;
    std::cout << "Maxi[" << ipar << "] = " << Maxi[ipar] << std::endl;
 
    rmin.mnparm(ipar, parname[ipar], Start[ipar], Step[ipar], Mini[ipar], Maxi[ipar], ierror);
 
    // Testing without limits
    // rmin.mnparm( ipar, parname[ipar], Start[ipar], Step[ipar], 0, 0, ierror );
  }
 
  // Do minimization
  // ---------------
  if (debug > 19)
    std::cout << "[MuScleFitUtils-minimizeLikelihood]: Starting minimization" << std::endl;
  double fmin;
  double fdem;
  double errdef;
  int npari;
  int nparx;
  rmin.mnexcm("CALL FCN", arglis, 1, ierror);
 
  // First, fix all parameters
  // -------------------------
  if (debug > 19)
    std::cout << "[MuScleFitUtils-minimizeLikelihood]: First fix all parameters ...";
  for (int ipar = 0; ipar < parnumber; ipar++) {
    rmin.FixParameter(ipar);
  }
 
  // Then release them in the specified order and refit
  // --------------------------------------------------
  if (debug > 19)
    std::cout << " Then release them in order..." << std::endl;
 
  TString name;
  double pval;
  double pmin;
  double pmax;
  double errp;
  double errl;
  double errh;
  int ivar;
  double erro;
  double cglo;
  int n_times = 0;
  // n_times = number of loops required to unlock all parameters.
 
  if (debug > 19)
    std::cout << "Before scale parNum" << std::endl;
  int scaleParNum = scaleFunction->parNum();
  if (debug > 19)
    std::cout << "After scale parNum" << std::endl;
  // edm::LogInfo("minimizeLikelihood") << "number of parameters for scaleFunction = " << scaleParNum << std::endl;
  // edm::LogInfo("minimizeLikelihood") << "number of parameters for resolutionFunction = " << resParNum << std::endl;
  // edm::LogInfo("minimizeLikelihood") << "number of parameters for cross section = " << crossSectionHandler->parNum() << std::endl;
  // edm::LogInfo("minimizeLikelihood") << "number of parameters for backgroundFunction = " << parBgr.size() << std::endl;
  std::cout << "number of parameters for scaleFunction = " << scaleParNum << std::endl;
  std::cout << "number of parameters for resolutionFunction = " << resParNum << std::endl;
  std::cout << "number of parameters for cross section = " << crossSectionHandler->parNum() << std::endl;
  std::cout << "number of parameters for backgroundFunction = " << parBgr.size() << std::endl;
  // edm::LogInfo("minimizeLikelihood") << "number of parameters for backgroundFunction = " << backgroundFunction->parNum() << std::endl;
 
  for (int i = 0; i < parnumber; i++) {
    // NB ind[] has been set as parorder[] previously
    if (n_times < ind[i]) {
      edm::LogInfo("minimizeLikelihood") << "n_times = " << n_times << ", ind[" << i << "] = " << ind[i]
                                         << ", scaleParNum = " << scaleParNum << ", doScaleFit[" << loopCounter
                                         << "] = " << doScaleFit[loopCounter] << std::endl;
      // Set the n_times only if we will do the fit
      if (i < resParNum) {
        if (doResolFit[loopCounter])
          n_times = ind[i];
      } else if (i < resParNum + scaleParNum) {
        if (doScaleFit[loopCounter])
          n_times = ind[i];
      } else if (doBackgroundFit[loopCounter])
        n_times = ind[i];
    }
  }
  for (int iorder = 0; iorder < n_times + 1; iorder++) {  // Repeat fit n_times times
    std::cout << "Starting minimization " << iorder << " of " << n_times << std::endl;
 
    bool somethingtodo = false;
 
    // Use parameters from cfg to select which fit to do
    // -------------------------------------------------
    if (doResolFit[loopCounter]) {
      // Release resolution parameters and fit them
      // ------------------------------------------
      for (unsigned int ipar = 0; ipar < parResol.size(); ++ipar) {
        if (parfix[ipar] == 0 && ind[ipar] == iorder) {
          rmin.Release(ipar);
          somethingtodo = true;
        }
      }
    }
    if (doScaleFit[loopCounter]) {
      // Release scale parameters and fit them
      // -------------------------------------
      for (unsigned int ipar = parResol.size(); ipar < parResol.size() + parScale.size(); ++ipar) {
        if (parfix[ipar] == 0 && ind[ipar] == iorder) {  // parfix=0 means parameter is free
          rmin.Release(ipar);
          somethingtodo = true;
        }
      }
      scaleFitNotDone_ = false;
    }
    unsigned int crossSectionParShift = parResol.size() + parScale.size();
    if (doCrossSectionFit[loopCounter]) {
      // Release cross section parameters and fit them
      // ---------------------------------------------
      // Note that only cross sections of resonances that are being fitted are released
      bool doCrossSection =
          crossSectionHandler->releaseParameters(rmin, resfind, parfix, ind, iorder, crossSectionParShift);
      if (doCrossSection)
        somethingtodo = true;
    }
    if (doBackgroundFit[loopCounter]) {
      // Release background parameters and fit them
      // ------------------------------------------
      // for( int ipar=parResol.size()+parScale.size(); ipar<parnumber; ++ipar ) {
      // Free only the parameters for the regions, as the resonances intervals are never used to fit the background
      unsigned int bgrParShift = crossSectionParShift + crossSectionHandler->parNum();
      for (unsigned int ipar = bgrParShift; ipar < bgrParShift + backgroundHandler->regionsParNum(); ++ipar) {
        // Release only those parameters for the resonances we are fitting
        if (parfix[ipar] == 0 && ind[ipar] == iorder &&
            backgroundHandler->unlockParameter(resfind, ipar - bgrParShift)) {
          rmin.Release(ipar);
          somethingtodo = true;
        }
      }
    }
 
    // OK, now do minimization if some parameter has been released
    // -----------------------------------------------------------
    if (somethingtodo) {
      // #ifdef DEBUG
 
      std::stringstream fileNum;
      fileNum << loopCounter;
 
      minuitLoop_ = 0;
      char name[50];
      sprintf(name, "likelihoodInLoop_%d_%d", loopCounter, iorder);
      TH1D *tempLikelihoodInLoop = new TH1D(name, "likelihood value in minuit loop", 10000, 0, 10000);
      likelihoodInLoop_ = tempLikelihoodInLoop;
      char signalProbName[50];
      sprintf(signalProbName, "signalProb_%d_%d", loopCounter, iorder);
      TH1D *tempSignalProb = new TH1D(signalProbName, "signal probability", 10000, 0, 10000);
      signalProb_ = tempSignalProb;
      char backgroundProbName[50];
      sprintf(backgroundProbName, "backgroundProb_%d_%d", loopCounter, iorder);
      TH1D *tempBackgroundProb = new TH1D(backgroundProbName, "background probability", 10000, 0, 10000);
      backgroundProb_ = tempBackgroundProb;
      // #endif
 
      // Before we start the minimization we create a list of events with only the events inside a smaller
      // window than the one in which the probability is != 0. We will compute the probability for all those
      // events and hopefully the margin will avoid them to get a probability = 0 (which causes a discontinuity
      // in the likelihood function). The width of this smaller window must be optimized, but we can start using
      // an 90% of the normalization window.
      double protectionFactor = 0.9;
 
      MuScleFitUtils::ReducedSavedPair.clear();
      for (unsigned int nev = 0; nev < MuScleFitUtils::SavedPair.size(); ++nev) {
        const lorentzVector *recMu1 = &(MuScleFitUtils::SavedPair[nev].first);
        const lorentzVector *recMu2 = &(MuScleFitUtils::SavedPair[nev].second);
        double mass = MuScleFitUtils::invDimuonMass(*recMu1, *recMu2);
        // Test all resonances to see if the mass is inside at least one of the windows
        bool check = false;
        for (int ires = 0; ires < 6; ++ires) {
          // std::pair<double, double> windowFactor = backgroundHandler->windowFactors( doBackgroundFit[loopCounter], ires );
          std::pair<double, double> windowBorder = backgroundHandler->windowBorders(doBackgroundFit[loopCounter], ires);
          // if( resfind[ires] && checkMassWindow( mass, ires, backgroundHandler->resMass( doBackgroundFit[loopCounter], ires ),
          //                                       0.9*windowFactor.first, 0.9*windowFactor.second ) ) {
          // double resMassValue = backgroundHandler->resMass( doBackgroundFit[loopCounter], ires );
          // double windowBorderLeft = resMassValue - protectionFactor*(resMassValue - windowBorder.first);
          // double windowBorderRight = resMassValue + protectionFactor*(windowBorder.second - resMassValue);
          double windowBorderShift = (windowBorder.second - windowBorder.first) * (1 - protectionFactor) / 2.;
          double windowBorderLeft = windowBorder.first + windowBorderShift;
          double windowBorderRight = windowBorder.second - windowBorderShift;
          if (resfind[ires] && checkMassWindow(mass, windowBorderLeft, windowBorderRight)) {
            check = true;
          }
        }
        if (check) {
          MuScleFitUtils::ReducedSavedPair.push_back(std::make_pair(*recMu1, *recMu2));
        }
      }
      std::cout << "Fitting with " << MuScleFitUtils::ReducedSavedPair.size() << " events" << std::endl;
 
      // rmin.SetMaxIterations(500*parnumber);
 
      //Print some informations
      std::cout << "MINUIT is starting the minimization for the iteration number " << loopCounter << std::endl;
 
      //Try to set iterations
      //      rmin.SetMaxIterations(100000);
 
      std::cout << "maxNumberOfIterations (just set) = " << rmin.GetMaxIterations() << std::endl;
 
      MuScleFitUtils::normalizationChanged_ = 0;
 
      // Maximum number of iterations
      arglis[0] = 100000;
      // tolerance
      arglis[1] = 0.1;
 
      // Run simplex first to get an initial estimate of the minimum
      if (startWithSimplex_) {
        rmin.mnexcm("SIMPLEX", arglis, 0, ierror);
      }
 
      rmin.mnexcm("MIGRAD", arglis, 2, ierror);
 
      // #ifdef DEBUG
      likelihoodInLoop_->Write();
      signalProb_->Write();
      backgroundProb_->Write();
      delete tempLikelihoodInLoop;
      delete tempSignalProb;
      delete tempBackgroundProb;
      likelihoodInLoop_ = nullptr;
      signalProb_ = nullptr;
      backgroundProb_ = nullptr;
      // #endif
 
      // Compute again the error matrix
      rmin.mnexcm("HESSE", arglis, 0, ierror);
 
      // Peform minos error analysis.
      if (computeMinosErrors_) {
        duringMinos_ = true;
        rmin.mnexcm("MINOS", arglis, 0, ierror);
        duringMinos_ = false;
      }
 
      if (normalizationChanged_ > 1) {
        std::cout << "WARNING: normalization changed during fit meaning that events exited from the mass window. This "
                     "causes a discontinuity in the likelihood function. Please check the scan of the likelihood as a "
                     "function of the parameters to see if there are discontinuities around the minimum."
                  << std::endl;
      }
    }
 
    // bool notWritten = true;
    for (int ipar = 0; ipar < parnumber; ipar++) {
      rmin.mnpout(ipar, name, pval, erro, pmin, pmax, ivar);
      // Save parameters in parvalue[] vector
      // ------------------------------------
      if (ierror != 0 && debug > 0) {
        std::cout << "[MuScleFitUtils-minimizeLikelihood]: ierror!=0, bogus pars" << std::endl;
      }
      //     for (int ipar=0; ipar<parnumber; ipar++) {
      //       rmin.mnpout (ipar, name, pval, erro, pmin, pmax, ivar);
      parvalue[loopCounter][ipar] = pval;
      //     }
 
      // int ilax2 = 0;
      // Double_t val2pl, val2mi;
      // rmin.mnmnot (ipar+1, ilax2, val2pl, val2mi);
      rmin.mnerrs(ipar, errh, errl, errp, cglo);
 
      // Set error on params
      // -------------------
      if (errp != 0) {
        parerr[3 * ipar] = errp;
      } else {
        parerr[3 * ipar] = (((errh) > (std::abs(errl))) ? (errh) : (std::abs(errl)));
      }
      parerr[3 * ipar + 1] = errl;
      parerr[3 * ipar + 2] = errh;
 
      if (ipar == 0) {
        FitParametersFile << " Resolution fit parameters:" << std::endl;
      }
      if (ipar == int(parResol.size())) {
        FitParametersFile << " Scale fit parameters:" << std::endl;
      }
      if (ipar == int(parResol.size() + parScale.size())) {
        FitParametersFile << " Cross section fit parameters:" << std::endl;
      }
      if (ipar == int(parResol.size() + parScale.size() + crossSectionHandler->parNum())) {
        FitParametersFile << " Background fit parameters:" << std::endl;
      }
      //       if( ipar >= int(parResol.size()+parScale.size()) && ipar < int(parResol.size()+parScale.size()+crossSectionHandler->parNum()) && notWritted ) {
 
      //         std::vector<double> relativeCrossSections = crossSectionHandler->relativeCrossSections(&(parvalue[loopCounter][parResol.size()+parScale.size()]));
      //         std::vector<double>::const_iterator it = relativeCrossSections.begin();
      //         for( ; it != relativeCrossSections.end(); ++it ) {
      //           FitParametersFile << "  Results of the fit: parameter " << ipar << " has value "
      //                             << *it << "+-" << 0
      //                             << " + " << 0 << " - " << 0
      //                             << " /t/t (" << 0 << ")" << std::endl;
      //         }
 
      //         notWritten = false;
      //       }
      //       else {
      FitParametersFile << "  Results of the fit: parameter " << ipar << " has value " << pval << "+-"
                        << parerr[3 * ipar] << " + " << parerr[3 * ipar + 1] << " - "
                        << parerr[3 * ipar + 2]
                        // << " \t\t (" << parname[ipar] << ")"
                        << std::endl;
    }
    rmin.mnstat(fmin, fdem, errdef, npari, nparx, istat);  // NNBB Commented for a check!
    FitParametersFile << std::endl;
 
    if (minimumShapePlots_) {
      // Create plots of the minimum vs parameters
      // -----------------------------------------
      // Keep this after the parameters filling because it recomputes the values and it can compromise the fit results.
      if (somethingtodo) {
        std::stringstream iorderString;
        iorderString << iorder;
        std::stringstream iLoopString;
        iLoopString << loopCounter;
 
        for (int ipar = 0; ipar < parnumber; ipar++) {
          if (parfix[ipar] == 1)
            continue;
          std::cout << "plotting parameter = " << ipar + 1 << std::endl;
          std::stringstream iparString;
          iparString << ipar + 1;
          std::stringstream iparStringName;
          iparStringName << ipar;
          rmin.mncomd(("scan " + iparString.str()).c_str(), ierror);
          if (ierror == 0) {
            TCanvas *canvas = new TCanvas(("likelihoodCanvas_loop_" + iLoopString.str() + "_oder_" +
                                           iorderString.str() + "_par_" + iparStringName.str())
                                              .c_str(),
                                          ("likelihood_" + iparStringName.str()).c_str(),
                                          1000,
                                          800);
            canvas->cd();
            // arglis[0] = ipar;
            // rmin.mnexcm( "SCA", arglis, 0, ierror );
            TGraph *graph = (TGraph *)rmin.GetPlot();
            graph->Draw("AP");
            // graph->SetTitle(("parvalue["+iparStringName.str()+"]").c_str());
            graph->SetTitle(parname[ipar]);
            // graph->Write();
 
            canvas->Write();
          }
        }
 
        //       // Draw contours of the fit
        //       TCanvas * canvas = new TCanvas(("contourCanvas_oder_"+iorderString.str()).c_str(), "contour", 1000, 800);
        //       canvas->cd();
        //       TGraph * contourGraph = (TGraph*)rmin.Contour(4, 2, 4);
        //       if( (rmin.GetStatus() == 0) || (rmin.GetStatus() >= 3) ) {
        //         contourGraph->Draw("AP");
        //       }
        //       else {
        //         std::cout << "Contour graph error: status = " << rmin.GetStatus() << std::endl;
        //       }
        //       canvas->Write();
      }
    }
 
  }  // end loop on iorder
  FitParametersFile.close();
 
  std::cout << "[MuScleFitUtils-minimizeLikelihood]: Parameters after likelihood " << std::endl;
  for (unsigned int ipar = 0; ipar < (unsigned int)parnumber; ipar++) {
    std::cout << ipar << " " << parvalue[loopCounter][ipar] << " : free = " << parfix[ipar]
              << "; order = " << parorder[ipar] << std::endl;
  }
 
  // Put back parvalue into parResol, parScale, parCrossSection, parBgr
  // ------------------------------------------------------------------
  for (int i = 0; i < (int)(parResol.size()); ++i) {
    parResol[i] = parvalue[loopCounter][i];
  }
  for (int i = 0; i < (int)(parScale.size()); ++i) {
    parScale[i] = parvalue[loopCounter][i + parResol.size()];
  }
  parCrossSection =
      crossSectionHandler->relativeCrossSections(&(parvalue[loopCounter][parResol.size() + parScale.size()]), resfind);
  for (unsigned int i = 0; i < parCrossSection.size(); ++i) {
    //   parCrossSection[i] = parvalue[loopCounter][i+parResol.size()+parScale.size()];
    std::cout << "relative cross section[" << i << "] = " << parCrossSection[i] << std::endl;
  }
  // Save only the fitted background parameters
  for (unsigned int i = 0; i < (parBgr.size() - parForResonanceWindows); ++i) {
    parBgr[i] = parvalue[loopCounter][i + parResol.size() + parScale.size() + crossSectionHandler->parNum()];
  }
 
  // Background rescaling from regions to resonances
  // -----------------------------------------------
  // Only if we fitted the background
  if (doBackgroundFit[loopCounter]) {
    // This rescales from regions to resonances
    int localMuonType = MuonType;
    if (MuonType > 2)
      localMuonType = 2;
    backgroundHandler->rescale(parBgr, ResMass, massWindowHalfWidth[localMuonType], MuScleFitUtils::ReducedSavedPair);
  }
 
  // Delete the arrays used to set some parameters
  delete[] Start;
  delete[] Step;
  delete[] Mini;
  delete[] Maxi;
  delete[] ind;
  delete[] parname;
}
 
// Likelihood function
// -------------------
extern "C" void likelihood(int &npar, double *grad, double &fval, double *xval, int flag) {
  if (MuScleFitUtils::debug > 19)
    std::cout << "[MuScleFitUtils-likelihood]: In likelihood function" << std::endl;
 
  const lorentzVector *recMu1;
  const lorentzVector *recMu2;
  lorentzVector corrMu1;
  lorentzVector corrMu2;
 
  //   if (MuScleFitUtils::debug>19) {
  //     int parnumber = (int)(MuScleFitUtils::parResol.size()+MuScleFitUtils::parScale.size()+
  //                           MuScleFitUtils::parCrossSection.size()+MuScleFitUtils::parBgr.size());
  //     std::cout << "[MuScleFitUtils-likelihood]: Looping on tree with ";
  //     for (int ipar=0; ipar<parnumber; ipar++) {
  //       std::cout << "Parameter #" << ipar << " with value " << xval[ipar] << " ";
  //     }
  //     std::cout << std::endl;
  //   }
 
  // Loop on the tree
  // ----------------
  double flike = 0;
  int evtsinlik = 0;
  int evtsoutlik = 0;
  // std::cout << "SavedPair.size() = " << MuScleFitUtils::SavedPair.size() << std::endl;
  if (MuScleFitUtils::debug > 0) {
    std::cout << "SavedPair.size() = " << MuScleFitUtils::SavedPair.size() << std::endl;
    std::cout << "ReducedSavedPair.size() = " << MuScleFitUtils::ReducedSavedPair.size() << std::endl;
  }
  // for( unsigned int nev=0; nev<MuScleFitUtils::SavedPair.size(); ++nev ) {
  for (unsigned int nev = 0; nev < MuScleFitUtils::ReducedSavedPair.size(); ++nev) {
    //     recMu1 = &(MuScleFitUtils::SavedPair[nev].first);
    //     recMu2 = &(MuScleFitUtils::SavedPair[nev].second);
    recMu1 = &(MuScleFitUtils::ReducedSavedPair[nev].first);
    recMu2 = &(MuScleFitUtils::ReducedSavedPair[nev].second);
 
    // Compute original mass
    // ---------------------
    double mass = MuScleFitUtils::invDimuonMass(*recMu1, *recMu2);
 
    // Compute weight and reference mass (from original mass)
    // ------------------------------------------------------
    double weight = MuScleFitUtils::computeWeight(mass, MuScleFitUtils::iev_);
    if (weight != 0.) {
      // Compute corrected mass (from previous biases) only if we are currently fitting the scale
      // ----------------------------------------------------------------------------------------
      if (MuScleFitUtils::doScaleFit[MuScleFitUtils::loopCounter]) {
        //  std::cout << "Original pt1 = " << corrMu1.Pt() << std::endl;
        //  std::cout << "Original pt2 = " << corrMu2.Pt() << std::endl;
        corrMu1 = MuScleFitUtils::applyScale(*recMu1, xval, -1);
        corrMu2 = MuScleFitUtils::applyScale(*recMu2, xval, 1);
 
        //         if( (corrMu1.Pt() != corrMu1.Pt()) || (corrMu2.Pt() != corrMu2.Pt()) ) {
        //           std::cout << "Rescaled pt1 = " << corrMu1.Pt() << std::endl;
        //           std::cout << "Rescaled pt2 = " << corrMu2.Pt() << std::endl;
        //         }
        //  std::cout << "Rescaled pt1 = " << corrMu1.Pt() << std::endl;
        //  std::cout << "Rescaled pt2 = " << corrMu2.Pt() << std::endl;
      } else {
        corrMu1 = *recMu1;
        corrMu2 = *recMu2;
 
        //         if( (corrMu1.Pt() != corrMu1.Pt()) || (corrMu2.Pt() != corrMu2.Pt()) ) {
        //           std::cout << "Not rescaled pt1 = " << corrMu1.Pt() << std::endl;
        //           std::cout << "Not rescaled pt2 = " << corrMu2.Pt() << std::endl;
        //         }
      }
      double corrMass = MuScleFitUtils::invDimuonMass(corrMu1, corrMu2);
      double Y = (corrMu1 + corrMu2).Rapidity();
      double resEta = (corrMu1 + corrMu2).Eta();
      if (MuScleFitUtils::debug > 19) {
        std::cout << "[MuScleFitUtils-likelihood]: Original/Corrected resonance mass = " << mass << " / " << corrMass
                  << std::endl;
      }
 
      // Compute mass resolution
      // -----------------------
      double massResol = MuScleFitUtils::massResolution(corrMu1, corrMu2, xval);
      if (MuScleFitUtils::debug > 19)
        std::cout << "[MuScleFitUtils-likelihood]: Resolution is " << massResol << std::endl;
 
      // Compute probability of this mass value including background modeling
      // --------------------------------------------------------------------
      if (MuScleFitUtils::debug > 1)
        std::cout << "calling massProb inside likelihood function" << std::endl;
 
      // double prob = MuScleFitUtils::massProb( corrMass, resEta, Y, massResol, xval );
      double prob = MuScleFitUtils::massProb(corrMass, resEta, Y, massResol, xval, false, corrMu1.eta(), corrMu2.eta());
      if (MuScleFitUtils::debug > 1)
        std::cout << "likelihood:massProb = " << prob << std::endl;
 
      // Compute likelihood
      // ------------------
      if (prob > 0) {
        // flike += log(prob*10000)*weight; // NNBB! x10000 to see if we can recover the problem of boundary
        flike += log(prob) * weight;
        evtsinlik += 1;  // NNBB test: see if likelihood per event is smarter (boundary problem)
      } else {
        if (MuScleFitUtils::debug > 0) {
          std::cout << "WARNING: corrMass = " << corrMass
                    << " outside window, this will cause a discontinuity in the likelihood. Consider increasing the "
                       "safety bands which are now set to 90% of the normalization window to avoid this problem"
                    << std::endl;
          std::cout << "Original mass was = " << mass << std::endl;
          std::cout << "WARNING: massResol = " << massResol << " outside window" << std::endl;
        }
        evtsoutlik += 1;
      }
      if (MuScleFitUtils::debug > 19)
        std::cout << "[MuScleFitUtils-likelihood]: Mass probability = " << prob << std::endl;
    }  // weight!=0
 
  }  // End of loop on tree events
 
  //   // Protection for low statistic. If the likelihood manages to throw out all the signal
  //   // events and stays with ~ 10 events in the resonance window it could have a better likelihood
  //   // because of ~ uniformly distributed events (a random combination could be good and spoil the fit).
  //   // We require that the number of events included in the fit does not change more than 5% in each minuit loop.
  //   bool lowStatPenalty = false;
  //   if( MuScleFitUtils::minuitLoop_ > 0 ) {
  //     double newEventsOutInRatio = double(evtsinlik);
  //     // double newEventsOutInRatio = double(evtsoutlik)/double(evtsinlik);
  //     double ratio = newEventsOutInRatio/MuScleFitUtils::oldEventsOutInRatio_;
  //     MuScleFitUtils::oldEventsOutInRatio_ = newEventsOutInRatio;
  //     if( ratio < 0.8 || ratio > 1.2 ) {
  //       std::cout << "Warning: too much change from oldEventsInLikelihood to newEventsInLikelihood, ratio is = " << ratio << std::endl;
  //       std::cout << "oldEventsInLikelihood = " << MuScleFitUtils::oldEventsOutInRatio_ << ", newEventsInLikelihood = " << newEventsOutInRatio << std::endl;
  //       lowStatPenalty = true;
  //     }
  //   }
 
  // It is a product of probabilities, we compare the sqrt_N of them. Thus N becomes a denominator of the logarithm.
  if (evtsinlik != 0) {
    if (MuScleFitUtils::normalizeLikelihoodByEventNumber_) {
      // && !(MuScleFitUtils::duringMinos_) ) {
      if (MuScleFitUtils::rminPtr_ == nullptr) {
        std::cout << "ERROR: rminPtr_ = " << MuScleFitUtils::rminPtr_ << ", code will crash" << std::endl;
      }
      double normalizationArg[] = {1 / double(evtsinlik)};
      // Reset the normalizationArg only if it changed
      if (MuScleFitUtils::oldNormalization_ != normalizationArg[0]) {
        int ierror = 0;
        //         if( MuScleFitUtils::likelihoodInLoop_ != 0 ) {
        //           // This condition is set only when minimizing. Later calls of hesse and minos will not change the value
        //           // This is done to avoid minos being confused by changing the UP parameter during its computation.
        //           MuScleFitUtils::rminPtr_->mnexcm("SET ERR", normalizationArg, 1, ierror);
        //         }
        MuScleFitUtils::rminPtr_->mnexcm("SET ERR", normalizationArg, 1, ierror);
        std::cout << "oldNormalization = " << MuScleFitUtils::oldNormalization_ << " new = " << normalizationArg[0]
                  << std::endl;
        MuScleFitUtils::oldNormalization_ = normalizationArg[0];
        MuScleFitUtils::normalizationChanged_ += 1;
      }
      fval = -2. * flike / double(evtsinlik);
      // fval = -2.*flike;
      //     if( lowStatPenalty ) {
      //       fval *= 100;
      //     }
    } else {
      fval = -2. * flike;
    }
  } else {
    std::cout << "Problem: Events in likelihood = " << evtsinlik << std::endl;
    fval = 999999999.;
  }
  // fval = -2.*flike;
  if (MuScleFitUtils::debug > 19)
    std::cout << "[MuScleFitUtils-likelihood]: End tree loop with likelihood value = " << fval << std::endl;
 
  //  #ifdef DEBUG
 
  //  if( MuScleFitUtils::minuitLoop_ < 10000 ) {
  if (MuScleFitUtils::likelihoodInLoop_ != nullptr) {
    ++MuScleFitUtils::minuitLoop_;
    MuScleFitUtils::likelihoodInLoop_->SetBinContent(MuScleFitUtils::minuitLoop_, fval);
  }
  //  }
  // else std::cout << "minuitLoop over 10000. Not filling histogram" << std::endl;
 
  std::cout << "MINUIT loop number " << MuScleFitUtils::minuitLoop_ << ", likelihood = " << fval << std::endl;
 
  if (MuScleFitUtils::debug > 0) {
    //     if( MuScleFitUtils::duringMinos_ ) {
    //       int parnumber = (int)(MuScleFitUtils::parResol.size()+MuScleFitUtils::parScale.size()+
    //                             MuScleFitUtils::parCrossSection.size()+MuScleFitUtils::parBgr.size());
    //       std::cout << "[MuScleFitUtils-likelihood]: Looping on tree with ";
    //       for (int ipar=0; ipar<parnumber; ipar++) {
    //         std::cout << "Parameter #" << ipar << " with value " << xval[ipar] << " ";
    //       }
    //       std::cout << std::endl;
    //       std::cout << "[MuScleFitUtils-likelihood]: likelihood value = " << fval << std::endl;
    //     }
    std::cout << "Events in likelihood = " << evtsinlik << std::endl;
    std::cout << "Events out likelihood = " << evtsoutlik << std::endl;
  }
 
  //  #endif
}
 
// Mass fitting routine
// --------------------
std::vector<TGraphErrors *> MuScleFitUtils::fitMass(TH2F *histo) {
  if (MuScleFitUtils::debug > 0)
    std::cout << "Fitting " << histo->GetName() << std::endl;
 
  std::vector<TGraphErrors *> results;
 
  // Results of the fit
  // ------------------
  std::vector<double> Ftop;
  std::vector<double> Fwidth;
  std::vector<double> Fmass;
  std::vector<double> Etop;
  std::vector<double> Ewidth;
  std::vector<double> Emass;
  std::vector<double> Fchi2;
  // X bin center and width
  // ----------------------
  std::vector<double> Xcenter;
  std::vector<double> Ex;
 
  // Fit with lorentzian peak
  // ------------------------
  TF1 *fitFcn = new TF1("fitFcn", lorentzianPeak, 70, 110, 3);
  fitFcn->SetParameters(100, 3, 91);
  fitFcn->SetParNames("Ftop", "Fwidth", "Fmass");
  fitFcn->SetLineWidth(2);
 
  // Fit slices projected along Y from bins in X
  // -------------------------------------------
  double cont_min = 20;  // Minimum number of entries
  Int_t binx = histo->GetXaxis()->GetNbins();
  // TFile *f= new TFile("prova.root", "recreate");
  // histo->Write();
  for (int i = 1; i <= binx; i++) {
    TH1 *histoY = histo->ProjectionY("", i, i);
    // histoY->Write();
    double cont = histoY->GetEntries();
    if (cont > cont_min) {
      histoY->Fit("fitFcn", "0", "", 70, 110);
      double *par = fitFcn->GetParameters();
      const double *err = fitFcn->GetParErrors();
 
      Ftop.push_back(par[0]);
      Fwidth.push_back(par[1]);
      Fmass.push_back(par[2]);
      Etop.push_back(err[0]);
      Ewidth.push_back(err[1]);
      Emass.push_back(err[2]);
 
      double chi2 = fitFcn->GetChisquare();
      Fchi2.push_back(chi2);
 
      double xx = histo->GetXaxis()->GetBinCenter(i);
      Xcenter.push_back(xx);
      double ex = 0;  // FIXME: you can use the bin width
      Ex.push_back(ex);
    }
  }
  // f->Close();
 
  // Put the fit results in arrays for TGraphErrors
  // ----------------------------------------------
  const int nn = Fmass.size();
  double *x = new double[nn];
  double *ym = new double[nn];
  double *e = new double[nn];
  double *eym = new double[nn];
  double *yw = new double[nn];
  double *eyw = new double[nn];
  double *yc = new double[nn];
 
  for (int j = 0; j < nn; j++) {
    x[j] = Xcenter[j];
    ym[j] = Fmass[j];
    eym[j] = Emass[j];
    yw[j] = Fwidth[j];
    eyw[j] = Ewidth[j];
    yc[j] = Fchi2[j];
    e[j] = Ex[j];
  }
 
  // Create TGraphErrors
  // -------------------
  TString name = histo->GetName();
  TGraphErrors *grM = new TGraphErrors(nn, x, ym, e, eym);
  grM->SetTitle(name + "_M");
  grM->SetName(name + "_M");
  TGraphErrors *grW = new TGraphErrors(nn, x, yw, e, eyw);
  grW->SetTitle(name + "_W");
  grW->SetName(name + "_W");
  TGraphErrors *grC = new TGraphErrors(nn, x, yc, e, e);
  grC->SetTitle(name + "_chi2");
  grC->SetName(name + "_chi2");
 
  // Cleanup
  // -------
  delete[] x;
  delete[] ym;
  delete[] eym;
  delete[] yw;
  delete[] eyw;
  delete[] yc;
  delete[] e;
  delete fitFcn;
 
  results.push_back(grM);
  results.push_back(grW);
  results.push_back(grC);
  return results;
}
 
// Resolution fitting routine
// --------------------------
std::vector<TGraphErrors *> MuScleFitUtils::fitReso(TH2F *histo) {
  std::cout << "Fitting " << histo->GetName() << std::endl;
  std::vector<TGraphErrors *> results;
 
  // Results from fit
  // ----------------
  std::vector<double> maxs;
  std::vector<double> means;
  std::vector<double> sigmas;
  std::vector<double> chi2s;
  std::vector<double> Emaxs;
  std::vector<double> Emeans;
  std::vector<double> Esigmas;
 
  // X bin center and width
  // ----------------------
  std::vector<double> Xcenter;
  std::vector<double> Ex;
 
  // Fit with a gaussian
  // -------------------
  TF1 *fitFcn = new TF1("fitFunc", Gaussian, -0.2, 0.2, 3);
  fitFcn->SetParameters(100, 0, 0.02);
  fitFcn->SetParNames("max", "mean", "sigma");
  fitFcn->SetLineWidth(2);
 
  // Fit slices projected along Y from bins in X
  // -------------------------------------------
  double cont_min = 20;  // Minimum number of entries
  Int_t binx = histo->GetXaxis()->GetNbins();
  for (int i = 1; i <= binx; i++) {
    TH1 *histoY = histo->ProjectionY("", i, i);
    double cont = histoY->GetEntries();
    if (cont > cont_min) {
      histoY->Fit("fitFunc", "0", "", -0.2, 0.2);
      double *par = fitFcn->GetParameters();
      const double *err = fitFcn->GetParErrors();
 
      maxs.push_back(par[0]);
      means.push_back(par[1]);
      sigmas.push_back(par[2]);
      Emaxs.push_back(err[0]);
      Emeans.push_back(err[1]);
      Esigmas.push_back(err[2]);
 
      double chi2 = fitFcn->GetChisquare();
      chi2s.push_back(chi2);
 
      double xx = histo->GetXaxis()->GetBinCenter(i);
      Xcenter.push_back(xx);
      double ex = 0;  // FIXME: you can use the bin width
      Ex.push_back(ex);
    }
  }
 
  // Put the fit results in arrays for TGraphErrors
  // ----------------------------------------------
  const int nn = means.size();
  double *x = new double[nn];
  double *ym = new double[nn];
  double *e = new double[nn];
  double *eym = new double[nn];
  double *yw = new double[nn];
  double *eyw = new double[nn];
  double *yc = new double[nn];
 
  for (int j = 0; j < nn; j++) {
    x[j] = Xcenter[j];
    ym[j] = means[j];
    eym[j] = Emeans[j];
    // yw[j]  = maxs[j];
    // eyw[j] = Emaxs[j];
    yw[j] = sigmas[j];
    eyw[j] = Esigmas[j];
    yc[j] = chi2s[j];
    e[j] = Ex[j];
  }
 
  // Create TGraphErrors
  // -------------------
  TString name = histo->GetName();
  TGraphErrors *grM = new TGraphErrors(nn, x, ym, e, eym);
  grM->SetTitle(name + "_mean");
  grM->SetName(name + "_mean");
  TGraphErrors *grW = new TGraphErrors(nn, x, yw, e, eyw);
  grW->SetTitle(name + "_sigma");
  grW->SetName(name + "_sigma");
  TGraphErrors *grC = new TGraphErrors(nn, x, yc, e, e);
  grC->SetTitle(name + "_chi2");
  grC->SetName(name + "_chi2");
 
  // Cleanup
  // -------
  delete[] x;
  delete[] ym;
  delete[] eym;
  delete[] yw;
  delete[] eyw;
  delete[] yc;
  delete[] e;
  delete fitFcn;
 
  results.push_back(grM);
  results.push_back(grW);
  results.push_back(grC);
  return results;
}
 
// Mass probability for likelihood computation - no-background version (not used anymore)
// --------------------------------------------------------------------------------------
double MuScleFitUtils::massProb(const double &mass, const double &rapidity, const int ires, const double &massResol) {
  // This routine computes the likelihood that a given measured mass "measMass" is
  // the result of resonance #ires if the resolution expected for the two muons is massResol
  // ---------------------------------------------------------------------------------------
 
  double P = 0.;
 
  // Return Lorentz value for zero resolution cases (like MC)
  // --------------------------------------------------------
  if (massResol == 0.) {
    if (debug > 9)
      std::cout << "Mass, gamma , mref, width, P: " << mass << " " << ResGamma[ires] << " " << ResMass[ires] << " "
                << massResol << " : used Lorentz P-value" << std::endl;
    return (0.5 * ResGamma[ires] / TMath::Pi()) /
           ((mass - ResMass[ires]) * (mass - ResMass[ires]) + .25 * ResGamma[ires] * ResGamma[ires]);
  }
 
  // NB defined as below, P is not a "probability" but a likelihood that we observe
  // a dimuon mass "mass", given mRef, gamma, and massResol. It is what we need for the
  // fit which finds the best resolution parameters, though. A definition which is
  // more properly a probability is given below (in massProb2()), where the result
  // cannot be used to fit resolution parameters because the fit would always prefer
  // to set the res parameters to the minimum possible value (best resolution),
  // to have a probability close to one of observing any mass.
  // -------------------------------------------------------------------------------
  // NNBB the following two lines have been replaced with the six following them,
  // which provide an improvement of a factor 9 in speed of execution at a
  // negligible price in precision.
  // ----------------------------------------------------------------------------
  // GL->SetParameters(gamma,mRef,mass,massResol);
  // P = GL->Integral(mass-5*massResol, mass+5*massResol);
 
  Int_t np = 100;
  double *x = new double[np];
  double *w = new double[np];
  GL->SetParameters(ResGamma[ires], ResMass[ires], mass, massResol);
  GL->CalcGaussLegendreSamplingPoints(np, x, w, 0.1e-15);
  P = GL->IntegralFast(np, x, w, ResMass[ires] - 10 * ResGamma[ires], ResMass[ires] + 10 * ResGamma[ires]);
  delete[] x;
  delete[] w;
 
  // If we are too far away we set P to epsilon and forget about this event
  // ----------------------------------------------------------------------
  if (P < 1.0e-12) {
    P = 1.0e-12;
    if (debug > 9)
      std::cout << "Mass, gamma , mref, width, P: " << mass << " " << ResGamma[ires] << " " << ResMass[ires] << " "
                << massResol << ": used epsilon" << std::endl;
    return P;
  }
 
  if (debug > 9)
    std::cout << "Mass, gamma , mref, width, P: " << mass << " " << ResGamma[ires] << " " << ResMass[ires] << " "
              << massResol << " " << P << std::endl;
  return P;
}
 
std::pair<lorentzVector, lorentzVector> MuScleFitUtils::findSimMuFromRes(
    const edm::Handle<edm::HepMCProduct> &evtMC, const edm::Handle<edm::SimTrackContainer> &simTracks) {
  //Loop on simulated tracks
  std::pair<lorentzVector, lorentzVector> simMuFromRes;
  for (edm::SimTrackContainer::const_iterator simTrack = simTracks->begin(); simTrack != simTracks->end(); ++simTrack) {
    //Chose muons
    if (std::abs((*simTrack).type()) == 13) {
      //If tracks from IP than find mother
      if ((*simTrack).genpartIndex() > 0) {
        HepMC::GenParticle *gp = evtMC->GetEvent()->barcode_to_particle((*simTrack).genpartIndex());
        if (gp != nullptr) {
          for (HepMC::GenVertex::particle_iterator mother = gp->production_vertex()->particles_begin(HepMC::ancestors);
               mother != gp->production_vertex()->particles_end(HepMC::ancestors);
               ++mother) {
            bool fromRes = false;
            unsigned int motherPdgId = (*mother)->pdg_id();
            for (int ires = 0; ires < 6; ++ires) {
              if (motherPdgId == motherPdgIdArray[ires] && resfind[ires])
                fromRes = true;
            }
            if (fromRes) {
              if (gp->pdg_id() == 13)
                simMuFromRes.first = lorentzVector(simTrack->momentum().px(),
                                                   simTrack->momentum().py(),
                                                   simTrack->momentum().pz(),
                                                   simTrack->momentum().e());
              else
                simMuFromRes.second = lorentzVector(simTrack->momentum().px(),
                                                    simTrack->momentum().py(),
                                                    simTrack->momentum().pz(),
                                                    simTrack->momentum().e());
            }
          }
        }
        // else LogDebug("MuScleFitUtils") << "WARNING: no matching genParticle found for simTrack" << std::endl;
      }
    }
  }
  return simMuFromRes;
}
 
std::pair<lorentzVector, lorentzVector> MuScleFitUtils::findGenMuFromRes(const edm::HepMCProduct *evtMC) {
  const HepMC::GenEvent *Evt = evtMC->GetEvent();
  std::pair<lorentzVector, lorentzVector> muFromRes;
  //Loop on generated particles
  for (HepMC::GenEvent::particle_const_iterator part = Evt->particles_begin(); part != Evt->particles_end(); part++) {
    if (std::abs((*part)->pdg_id()) == 13 && (*part)->status() == 1) {
      bool fromRes = false;
      for (HepMC::GenVertex::particle_iterator mother = (*part)->production_vertex()->particles_begin(HepMC::ancestors);
           mother != (*part)->production_vertex()->particles_end(HepMC::ancestors);
           ++mother) {
        unsigned int motherPdgId = (*mother)->pdg_id();
 
        // For sherpa the resonance is not saved. The muons from the resonance can be identified
        // by having as mother a muon of status 3.
        if (sherpa_) {
          if (motherPdgId == 13 && (*mother)->status() == 3)
            fromRes = true;
        } else {
          for (int ires = 0; ires < 6; ++ires) {
            if (motherPdgId == motherPdgIdArray[ires] && resfind[ires])
              fromRes = true;
          }
        }
      }
      if (fromRes) {
        if ((*part)->pdg_id() == 13)
          //   muFromRes.first = (*part)->momentum();
          muFromRes.first = (lorentzVector(
              (*part)->momentum().px(), (*part)->momentum().py(), (*part)->momentum().pz(), (*part)->momentum().e()));
        else
          muFromRes.second = (lorentzVector(
              (*part)->momentum().px(), (*part)->momentum().py(), (*part)->momentum().pz(), (*part)->momentum().e()));
      }
    }
  }
  return muFromRes;
}
 
std::pair<lorentzVector, lorentzVector> MuScleFitUtils::findGenMuFromRes(
    const reco::GenParticleCollection *genParticles) {
  std::pair<lorentzVector, lorentzVector> muFromRes;
 
  //Loop on generated particles
  if (debug > 0)
    std::cout << "Starting loop on " << genParticles->size() << " genParticles" << std::endl;
  for (reco::GenParticleCollection::const_iterator part = genParticles->begin(); part != genParticles->end(); ++part) {
    if (std::abs(part->pdgId()) == 13 && part->status() == 1) {
      bool fromRes = false;
      unsigned int motherPdgId = part->mother()->pdgId();
      if (debug > 0) {
        std::cout << "Found a muon with mother: " << motherPdgId << std::endl;
      }
      for (int ires = 0; ires < 6; ++ires) {
        if (motherPdgId == motherPdgIdArray[ires] && resfind[ires])
          fromRes = true;
      }
      if (fromRes) {
        if (part->pdgId() == 13) {
          muFromRes.first = part->p4();
          if (debug > 0)
            std::cout << "Found a genMuon + : " << muFromRes.first << std::endl;
          //      muFromRes.first = (lorentzVector(part->p4().px(),part->p4().py(),
          //                       part->p4().pz(),part->p4().e()));
        } else {
          muFromRes.second = part->p4();
          if (debug > 0)
            std::cout << "Found a genMuon - : " << muFromRes.second << std::endl;
          //      muFromRes.second = (lorentzVector(part->p4().px(),part->p4().py(),
          //                        part->p4().pz(),part->p4().e()));
        }
      }
    }
  }
  return muFromRes;
}