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 /** See header file for a class description
  *
  *  \author S. Bolognesi - INFN Torino / T. Dorigo, M. De Mattia - INFN Padova
  * Revised S. Casasso, E. Migliore - UniTo & INFN Torino
  */
 // 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;
 }
 
 /**
  * We use the following formula: <br>
  *
  * M = sqrt ( (E1+E2)^2 - (P1+P2)^2 ) <br>
  *
  * where we express E and P as a function of Pt, phi, and theta: <br>
  *
  * E  = sqrt ( Pt^2*(1+cotg(theta)^2) + M_mu^2 ) <br>
  * Px = Pt*cos(phi), Py = Pt*sin(phi), Pz = Pt*cotg(theta) <br>
  *
  * from which we find <br>
  *
  * M = sqrt( 2*M_mu^2 + 2*sqrt(Pt1^2/sin(theta1)^2 + M_mu^2)*sqrt(Pt2^2/sin(theta2)^2 + M_mu^2) -
  *           2*Pt1*Pt2* ( cos(phi1-phi2) + cotg(theta1)*cotg(theta2) ) )
  *
  * and derive WRT Pt1, Pt2, phi1, phi2, theta1, theta2 to get the resolution.
  */
 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;
 }
 
 /**
  * This method can be used outside MuScleFit. It gets the ResolutionFunction that must have been built with the parameters. <br>
  * TO-DO: this method duplicates the code in the previous method. It should be changed to avoid the duplication.
  */
 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;
 }
 
 /**
  * After the introduction of the rapidity bins for the Z the probability method works in the following way:
  * - if passing iRes == 0, iY is used to select the rapidity bin
  * - if passing iRes != 0, iY is used to select the resonance
  */
 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;
 }