void fit_MC_norm(std::string input_file = "/afs/cern.ch/work/a/apmorris/private/cern/ntuples/new_tuples/normalisation_samples/reduced_Lb2JpsipK_MC_2011_2012_norm.root", std::string out_file_mass = "~/cern/plots/fitting/Lb2JpsipK_MC_2011_2012_cut_mass_fit.png"){
//
gROOT->ProcessLine(".L ~/cern/scripts/lhcbStyle.C");
//lhcbStyle();
const std::string filename(input_file.c_str());
const std::string treename = "DecayTree";
TFile* file = TFile::Open( filename.c_str() );
if( !file ) std::cout << "file " << filename << " does not exist" << std::endl;
TTree* tree = (TTree*)file->Get( treename.c_str() );
if( !tree ) std::cout << "tree " << treename << " does not exist" << std::endl;
// -- signal, mass shape
RooRealVar Lambda_b0_DTF_MASS_constr1("Lambda_b0_DTF_MASS_constr1","m(J/#psi pK^{-})", 5450., 5850., "MeV/c^{2}");
RooRealVar Jpsi_M("Jpsi_M","m(#mu#mu)", 3000., 3200., "MeV/c^{2}");
//RooRealVar chi_c_M("chi_c_M","m(J/#psi#gamma)", 3350., 3750., "MeV/c^{2}");
RooRealVar mean("mean","mean", 5620., 5595., 5650.);
RooRealVar sigma1("sigma1","sigma1", 10., 1., 100.);
RooRealVar sigma2("sigma2","sigma2", 100., 1., 1000.);
RooRealVar alpha1("alpha1","alpha1", 1.0, 0.5, 5.0);
RooRealVar n1("n1","n1", 1.8, 0.2, 15.0);
RooRealVar alpha2("alpha2","alpha2", -0.5, -5.5, 0.0);
RooRealVar n2("n2","n2", 0.7, 0.2, 10.0);
//RooRealVar bkgcat_chic("bkgcat_chic","bkgcat_chic", 0, 100);
RooGaussian gauss1("gauss1","gauss1", Lambda_b0_DTF_MASS_constr1, mean, sigma1);
RooGaussian gauss2("gauss2","gauss2", Lambda_b0_DTF_MASS_constr1, mean, sigma2);
RooCBShape cb1("cb1","cb1", Lambda_b0_DTF_MASS_constr1, mean, sigma1, alpha1, n1);
RooCBShape cb2("cb2","cb2", Lambda_b0_DTF_MASS_constr1, mean, sigma2, alpha2, n2);
/*
// the chi_c2 component
RooRealVar mean3("mean3","mean3", 5570., 5520., 5580.);
RooRealVar sigma3("sigma3","sigma3", 10., 1., 20.);
RooGaussian gauss3("gauss3","gauss3", Lambda_b0_DTF_MASS_constr1, mean3, sigma3);
*/
RooRealVar cbRatio("cbRatio","cbRatio", 0.8, 0.1, 1.0);
RooRealVar frac2("frac2","frac2", 0.3, 0., 1.);
/*
alpha1.setVal( 2.1 );
alpha2.setVal( -4.9 );
n1.setVal( 3.2 );
n2.setVal( 7.9 );
cbRatio.setVal( 0.6808 );
alpha1.setConstant( true );
alpha2.setConstant( true );
cbRatio.setConstant( true );
n1.setConstant( true );
n2.setConstant( true );
*/
// -- add signal & bg
//RooAddPdf pdf("pdf", "pdf", RooArgList(gauss1, gauss2), RooArgList( frac2 ));
RooAddPdf pdf("pdf", "pdf", RooArgList(cb1, cb2), RooArgList( cbRatio ));
RooArgSet obs;
obs.add(Lambda_b0_DTF_MASS_constr1);
obs.add(Jpsi_M);
//obs.add(chi_c_M);
//obs.add(bkgcat_chic);
RooDataSet ds("ds","ds", obs, RooFit::Import(*tree));
//RooFit::Cut("Lambda_b0_DTF_MASS_constr1 > 5580")
RooPlot* plot = Lambda_b0_DTF_MASS_constr1.frame();
plot->SetAxisRange(5500., 5750.);
pdf.fitTo( ds );
ds.plotOn( plot, RooFit::Binning(200) );
pdf.plotOn( plot );
//gauss3.plotOn( plot );
RooPlot* plotPullMass = Lambda_b0_DTF_MASS_constr1.frame();
plotPullMass->addPlotable( plot->pullHist() );
//plotPullMass->SetMinimum();
//plotPullMass->SetMaximum();
plotPullMass->SetAxisRange(5500., 5750.);
TCanvas* c = new TCanvas();
c->cd();
TPad* pad1 = new TPad("pad1","pad1", 0, 0.3, 1, 1.0);
pad1->SetBottomMargin(0.1);
pad1->SetTopMargin(0.1);
pad1->Draw();
//TPad* pad2 = new TPad("pad2","pad2", 0, 0.05, 1, 0.4);
TPad* pad2 = new TPad("pad2","pad2", 0, 0, 1, 0.3);
pad2->SetBottomMargin(0.1);
pad2->SetTopMargin(0.0);
pad2->Draw();
pdf.plotOn( plot, RooFit::Components( cb1 ), RooFit::LineColor( kRed ), RooFit::LineStyle(kDashed) );
pdf.plotOn( plot, RooFit::Components( cb2 ), RooFit::LineColor( kOrange ), RooFit::LineStyle(kDotted) );
//pdf.plotOn( plot, RooFit::Components( bgPdf ), RooFit::LineColor( kBlue ), RooFit::LineStyle(kDashDotted) );
pad1->cd();
//pad1->SetLogy();
plot->Draw();
pad2->cd();
plotPullMass->Draw("AP");
c->SaveAs(out_file_mass.c_str());
}
double FitInvMassBkg_v3(TH1D* histo, TH1D* histo_bkg, TString signal = "CBxBW",TString _bkg = "Cheb", TString option = ""){
//Set Style
setTDRStyle();
if(option.Contains("nentries")){return histo->GetEntries()-histo_bkg->GetEntries();}
else{
//Getting info about the histogram to fit
int n = histo->GetEntries();
double w = histo->GetXaxis()->GetBinWidth(1);
int ndf;
double hmin0 = histo->GetXaxis()->GetXmin();
double hmax0 = histo->GetXaxis()->GetXmax();
histo->GetXaxis()->SetRangeUser(hmin0,hmax0);
//Declare observable x
//Try to rebin using this. Doesn't work for now
//RooBinning xbins = Rebin2(histo);
RooRealVar x("x","x",hmin0,hmax0) ;
RooDataHist dh("dh","dh",x,Import(*histo)) ;
//Define the frame
RooPlot* frame;
frame = x.frame();
dh.plotOn(frame,DataError(RooAbsData::SumW2), MarkerColor(1),MarkerSize(0.9),MarkerStyle(7)); //this will show histogram data points on canvas
//x.setRange("R0",0,200) ;
x.setRange("R1",55,200) ;
x.setRange("D",55,120) ;
/////////////////////
//Define fit function
/////////////////////
cout<<"Debug4"<<endl;
//fsig for adding two funciton i.e. F(x) = fsig*sig(x) + (1-fsig)*bkg(x)
//RooRealVar fsig("fsig","sigal fraction",0.5, 0., 1.);
RooRealVar nsig("nsig","signal events",histo->GetEntries()/2., 1,histo->GetEntries());
RooRealVar nbkg("nbkg","background events",histo_bkg->GetEntries()/2.,1,histo_bkg->GetEntries());
RooArgList pdfval(nsig,nbkg);
//Various parameters
//True mean
RooRealVar mean("mean","PDG mean of Z",91.186);//, 70.0, 120.0);
//For the BW
RooRealVar width("width","PDG width of Z",2.4952);//, 0., 5.);
//For the Gauss and the CB alone
RooRealVar sigma("sigma","sigma",1, 0., 10.);
RooRealVar alpha("alpha","alpha",0.7, 0., 7);
RooRealVar ncb("ncb","ncb",7, 0, 150);
//For the CB used for convolution, i.e. CBxBW
RooRealVar cb_bias("cb_bias","bias",0, -3.,3.);
RooRealVar cb_sigma("cb_sigma","response",1, 0.,5);
RooRealVar cb_alpha("cb_alpha","alpha",1.,0.,7);
RooRealVar cb_ncb("cb_ncb","ncb",2, 0, 10);
//Gauss used for convolution
RooRealVar gau_bias("gau_bias","alpha",0, -3., 3.);
RooRealVar gau_sigma("gau_sigma","bias",1, 0., 7.);
cout<<"Debug5"<<endl;
mean.setRange(88,94);
width.setRange(0,20);
sigma.setRange(0.5,10);
//fsig.setConstant(kTRUE);
//alpha.setConstant(kTRUE);
RooVoigtian sig_voigtian("sig_voigtian","Voigtian",x,mean,width,sigma);
RooBreitWigner sig_bw("sig_bw","BW",x,mean,width);
//RooGaussian sig_gau("sig_gau","gauss",x,mean,sigma);
RooCBShape sig_cb("sig_cb", "Crystal Ball",x,mean,sigma,alpha,ncb);
RooCBShape sig_cb_resp("sig_cb_resp", "Crystal Ball for conv.",x,cb_bias,cb_sigma,cb_alpha,cb_ncb);
RooGaussian sig_gau_resp("sig_gau_resp", "Gaussian for conv.",x,gau_bias,gau_sigma);
x.setBins(10000,"cache");
RooFFTConvPdf sig_cbbw("sig_cbbw","CBxBW",x,sig_cb_resp,sig_bw);
RooFFTConvPdf sig_bwgau("sig_bwgau","BWxGau",x,sig_bw,sig_gau_resp);
//NB: The CrystalBall shape is Gaussian that is 'connected' to an exponential taill at 'alpha' sigma of the Gaussian. The sign determines if it happens on the left or right side. The 'n' parameter control the slope of the exponential part.
cout<<"Debug6"<<endl;
RooAbsPdf* sig;
if(signal == "Vo"){sig = &sig_bwgau;}
else if(signal == "BW"){sig = &sig_bw;}
//else if(signal == "Gau"){sig = &sig_gau;}
else if(signal == "CB"){sig = &sig_cb;}
else if(signal == "CBxBW"){ sig = &sig_cbbw;}
else if(signal == "BWxGau"){ sig = &sig_bwgau;}
else{ cout<<"Wrong signal function name"<<endl;
return 1;
}
/////////////////////////////
//Background fitting function
/////////////////////////////
//Get the initial parameter of the background
//
cout<<"Debug7"<<endl;
vector<double> vec = FitBkg(histo_bkg,_bkg);
//Chebychev
RooRealVar a0("a0","a0",vec[0],-5.,0.) ;
RooRealVar a1("a1","a1",vec[1],-2.5,1.2) ;
RooRealVar a2("a2","a2",vec[2],-1.5,1.) ;
RooRealVar a3("a3","a3",vec[3],-3,1.) ;
RooRealVar a4("a4","a4",vec[4],-1.5,1.) ;
RooRealVar a5("a5","a5",vec[5],-1.,1.) ;
RooRealVar a6("a6","a6",vec[6],-1.,1.) ;
RooChebychev bkg_cheb("bkg","Background",x,RooArgSet(a0,a1,a2,a3,a4,a5,a6));
//Novo
RooRealVar peak_bkg("peak_bkg","peak",vec[0],0,250);
RooRealVar width_bkg("width_bkg","width",vec[1],0,1) ;
RooRealVar tail_bkg("tail_bkg","tail",vec[2],0,10) ;
RooNovosibirsk bkg_nov("bkg","Background",x,peak_bkg,width_bkg,tail_bkg);
RooAbsPdf* bkg;
if(_bkg == "Cheb"){bkg = &bkg_cheb;}
if(_bkg == "Novo"){bkg = &bkg_nov;}
//////////////////////////
//Adding the two functions
//////////////////////////
//RooAddPdf model("model","Signal+Background", RooArgList(*sig,*bkg),fsig);
RooAddPdf model("model","Signal+Background", RooArgList(*sig,*bkg),pdfval);
//Perform the fit
RooAbsPdf* fit_func;
fit_func = &model;
RooFitResult* filters = fit_func->fitTo(dh,Range("R1"),"qr");
fit_func->plotOn(frame);
fit_func->plotOn(frame,Components(*sig),LineStyle(kDashed),LineColor(kRed));
fit_func->plotOn(frame,Components(*bkg),LineStyle(kDashed),LineColor(kGreen));
frame->SetAxisRange(50,120);
frame->Draw();
fit_func->paramOn(frame);
dh.statOn(frame); //this will display hist stat on canvas
frame->SetTitle(histo->GetTitle());
frame->GetXaxis()->SetTitle("m (in GeV/c^{2})");
frame->GetXaxis()->SetTitleOffset(1.2);
float binsize = histo->GetBinWidth(1);
//Store result in .root file
frame->SetName(histo->GetName());
frame->Write();
///////////////////////
//Plot the efficiency//
///////////////////////
//Old stuff
//ofstream myfile;
//myfile.open("/Users/GLP/Desktop/integrals.txt");
//This one doesn't take the normalisation into account !
//myfile<<"integral 1 "<<histo->GetName()<<endl;
////Integral of sig
//RooAbsReal* integral_sig = sig->createIntegral(x,x,"D") ;
//myfile<<fsig.getVal()*integral_sig->getVal()<<endl;
//Integral of sig+bkg
//RooAbsReal* total = fit_func->createIntegral(x, NormSet(x), Range("D")) ;
//Integral of sig only
//RooAbsReal* background = bkg->createIntegral(x, NormSet(x), Range("D"));
//cout<<"The total integral is"<<n*total->getVal();
//cout<<"The bkg integral is"<<n*bkg->getVal();
//cout<<"The bkg with the fsig is"<<fsig.getVal()*n*bkg->getVal();
//cout<<"The returned value is"<<n*(total->getVal()-fsig.getVal()*background->getVal());
//myfile<<"integral 2 "<<endl;
//myfile<<n*(total->getVal()-(1-fsig.getVal())*background->getVal())<<endl;
//myfile.close();
//return n*(total->getVal()-(1-fsig.getVal())*background->getVal());
return nsig.getVal();
}
}
