RooWorkspace* makeInvertedANFit(TTree* tree, float forceSigma=-1, bool constrainMu=false, float forceMu=-1) {
RooWorkspace *ws = new RooWorkspace("ws","");
std::vector< TString (*)(TString, RooRealVar&, RooWorkspace&) > bkgPdfList;
bkgPdfList.push_back(makeSingleExp);
bkgPdfList.push_back(makeDoubleExp);
#if DEBUG==0
//bkgPdfList.push_back(makeTripleExp);
bkgPdfList.push_back(makeModExp);
bkgPdfList.push_back(makeSinglePow);
bkgPdfList.push_back(makeDoublePow);
bkgPdfList.push_back(makePoly2);
bkgPdfList.push_back(makePoly3);
#endif
RooRealVar mgg("mgg","m_{#gamma#gamma}",103,160,"GeV");
mgg.setBins(38);
mgg.setRange("sideband_low", 103,120);
mgg.setRange("sideband_high",131,160);
mgg.setRange("signal",120,131);
RooRealVar MR("MR","",0,3000,"GeV");
MR.setBins(60);
RooRealVar Rsq("t1Rsq","",0,1,"GeV");
Rsq.setBins(20);
RooRealVar hem1_M("hem1_M","",-1,2000,"GeV");
hem1_M.setBins(40);
RooRealVar hem2_M("hem2_M","",-1,2000,"GeV");
hem2_M.setBins(40);
RooRealVar ptgg("ptgg","p_{T}^{#gamma#gamma}",0,500,"GeV");
ptgg.setBins(50);
RooDataSet data("data","",tree,RooArgSet(mgg,MR,Rsq,hem1_M,hem2_M,ptgg));
RooDataSet* blind_data = (RooDataSet*)data.reduce("mgg<121 || mgg>130");
std::vector<TString> tags;
//fit many different background models
for(auto func = bkgPdfList.begin(); func != bkgPdfList.end(); func++) {
TString tag = (*func)("bonly",mgg,*ws);
tags.push_back(tag);
ws->pdf("bonly_"+tag+"_ext")->fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
RooFitResult* bres = ws->pdf("bonly_"+tag+"_ext")->fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
bres->SetName(tag+"_bonly_fitres");
ws->import(*bres);
//make blinded fit
RooPlot *fmgg_b = mgg.frame();
blind_data->plotOn(fmgg_b,RooFit::Range("sideband_low,sideband_high"));
TBox blindBox(121,fmgg_b->GetMinimum()-(fmgg_b->GetMaximum()-fmgg_b->GetMinimum())*0.015,130,fmgg_b->GetMaximum());
blindBox.SetFillColor(kGray);
fmgg_b->addObject(&blindBox);
ws->pdf("bonly_"+tag+"_ext")->plotOn(fmgg_b,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("sideband_low,sideband_high"));
fmgg_b->SetName(tag+"_blinded_frame");
ws->import(*fmgg_b);
delete fmgg_b;
//set all the parameters constant
RooArgSet* vars = ws->pdf("bonly_"+tag)->getVariables();
RooFIter iter = vars->fwdIterator();
RooAbsArg* a;
while( (a = iter.next()) ){
if(string(a->GetName()).compare("mgg")==0) continue;
static_cast<RooRealVar*>(a)->setConstant(kTRUE);
}
//make the background portion of the s+b fit
(*func)("b",mgg,*ws);
RooRealVar sigma(tag+"_s_sigma","",5,0,100);
if(forceSigma!=-1) {
sigma.setVal(forceSigma);
sigma.setConstant(true);
}
RooRealVar mu(tag+"_s_mu","",126,120,132);
if(forceMu!=-1) {
mu.setVal(forceMu);
mu.setConstant(true);
}
RooGaussian sig(tag+"_sig_model","",mgg,mu,sigma);
RooRealVar Nsig(tag+"_sb_Ns","",5,0,100);
RooRealVar Nbkg(tag+"_sb_Nb","",100,0,100000);
RooRealVar HiggsMass("HiggsMass","",125.1);
RooRealVar HiggsMassError("HiggsMassError","",0.24);
RooGaussian HiggsMassConstraint("HiggsMassConstraint","",mu,HiggsMass,HiggsMassError);
RooAddPdf fitModel(tag+"_sb_model","",RooArgList( *ws->pdf("b_"+tag), sig ),RooArgList(Nbkg,Nsig));
RooFitResult* sbres;
RooAbsReal* nll;
if(constrainMu) {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
} else {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE));
}
sbres->SetName(tag+"_sb_fitres");
ws->import(*sbres);
ws->import(fitModel);
RooPlot *fmgg = mgg.frame();
data.plotOn(fmgg);
fitModel.plotOn(fmgg);
ws->pdf("b_"+tag+"_ext")->plotOn(fmgg,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("Full"));
fmgg->SetName(tag+"_frame");
ws->import(*fmgg);
delete fmgg;
RooMinuit(*nll).migrad();
RooPlot *fNs = Nsig.frame(0,25);
fNs->SetName(tag+"_Nsig_pll");
RooAbsReal *pll = nll->createProfile(Nsig);
//nll->plotOn(fNs,RooFit::ShiftToZero(),RooFit::LineColor(kRed));
pll->plotOn(fNs);
ws->import(*fNs);
delete fNs;
RooPlot *fmu = mu.frame(125,132);
fmu->SetName(tag+"_mu_pll");
RooAbsReal *pll_mu = nll->createProfile(mu);
pll_mu->plotOn(fmu);
ws->import(*fmu);
delete fmu;
}
RooArgSet weights("weights");
RooArgSet pdfs_bonly("pdfs_bonly");
RooArgSet pdfs_b("pdfs_b");
RooRealVar minAIC("minAIC","",1E10);
//compute AIC stuff
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooAbsPdf *p_bonly = ws->pdf("bonly_"+*t);
RooAbsPdf *p_b = ws->pdf("b_"+*t);
RooFitResult *sb = (RooFitResult*)ws->obj(*t+"_bonly_fitres");
RooRealVar k(*t+"_b_k","",p_bonly->getParameters(RooArgSet(mgg))->getSize());
RooRealVar nll(*t+"_b_minNll","",sb->minNll());
RooRealVar Npts(*t+"_b_N","",blind_data->sumEntries());
RooFormulaVar AIC(*t+"_b_AIC","2*@0+2*@1+2*@1*(@1+1)/(@2-@1-1)",RooArgSet(nll,k,Npts));
ws->import(AIC);
if(AIC.getVal() < minAIC.getVal()) {
minAIC.setVal(AIC.getVal());
}
//aicExpSum+=TMath::Exp(-0.5*AIC.getVal()); //we will need this precomputed for the next step
pdfs_bonly.add(*p_bonly);
pdfs_b.add(*p_b);
}
ws->import(minAIC);
//compute the AIC weight
float aicExpSum=0;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
aicExpSum+=TMath::Exp(-0.5*(AIC->getVal()-minAIC.getVal())); //we will need this precomputed for the next step
}
std::cout << "aicExpSum: " << aicExpSum << std::endl;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
RooRealVar *AICw = new RooRealVar(*t+"_b_AICWeight","",TMath::Exp(-0.5*(AIC->getVal()-minAIC.getVal()))/aicExpSum);
if( TMath::IsNaN(AICw->getVal()) ) {AICw->setVal(0);}
ws->import(*AICw);
std::cout << *t << ": " << AIC->getVal()-minAIC.getVal() << " " << AICw->getVal() << std::endl;
weights.add(*AICw);
}
RooAddPdf bonly_AIC("bonly_AIC","",pdfs_bonly,weights);
RooAddPdf b_AIC("b_AIC","",pdfs_b,weights);
//b_AIC.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
//RooFitResult* bres = b_AIC.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
//bres->SetName("AIC_b_fitres");
//ws->import(*bres);
//make blinded fit
RooPlot *fmgg_b = mgg.frame(RooFit::Range("sideband_low,sideband_high"));
blind_data->plotOn(fmgg_b,RooFit::Range("sideband_low,sideband_high"));
TBox blindBox(121,fmgg_b->GetMinimum()-(fmgg_b->GetMaximum()-fmgg_b->GetMinimum())*0.015,130,fmgg_b->GetMaximum());
blindBox.SetFillColor(kGray);
fmgg_b->addObject(&blindBox);
bonly_AIC.plotOn(fmgg_b,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("sideband_low,sideband_high"));
fmgg_b->SetName("AIC_blinded_frame");
ws->import(*fmgg_b);
delete fmgg_b;
#if 1
RooRealVar sigma("AIC_s_sigma","",5,0,100);
if(forceSigma!=-1) {
sigma.setVal(forceSigma);
sigma.setConstant(true);
}
RooRealVar mu("AIC_s_mu","",126,120,132);
if(forceMu!=-1) {
mu.setVal(forceMu);
mu.setConstant(true);
}
RooGaussian sig("AIC_sig_model","",mgg,mu,sigma);
RooRealVar Nsig("AIC_sb_Ns","",5,0,100);
RooRealVar Nbkg("AIC_sb_Nb","",100,0,100000);
RooRealVar HiggsMass("HiggsMass","",125.1);
RooRealVar HiggsMassError("HiggsMassError","",0.24);
RooGaussian HiggsMassConstraint("HiggsMassConstraint","",mu,HiggsMass,HiggsMassError);
RooAddPdf fitModel("AIC_sb_model","",RooArgList( b_AIC, sig ),RooArgList(Nbkg,Nsig));
RooFitResult* sbres;
RooAbsReal *nll;
if(constrainMu) {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
} else {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE));
}
assert(nll!=0);
sbres->SetName("AIC_sb_fitres");
ws->import(*sbres);
ws->import(fitModel);
RooPlot *fmgg = mgg.frame();
data.plotOn(fmgg);
fitModel.plotOn(fmgg);
ws->pdf("b_AIC")->plotOn(fmgg,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("Full"));
fmgg->SetName("AIC_frame");
ws->import(*fmgg);
delete fmgg;
RooMinuit(*nll).migrad();
RooPlot *fNs = Nsig.frame(0,25);
fNs->SetName("AIC_Nsig_pll");
RooAbsReal *pll = nll->createProfile(Nsig);
//nll->plotOn(fNs,RooFit::ShiftToZero(),RooFit::LineColor(kRed));
pll->plotOn(fNs);
ws->import(*fNs);
delete fNs;
RooPlot *fmu = mu.frame(125,132);
fmu->SetName("AIC_mu_pll");
RooAbsReal *pll_mu = nll->createProfile(mu);
pll_mu->plotOn(fmu);
ws->import(*fmu);
delete fmu;
std::cout << "min AIC: " << minAIC.getVal() << std::endl;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
RooRealVar *AICw = ws->var(*t+"_b_AICWeight");
RooRealVar* k = ws->var(*t+"_b_k");
printf("%s & %0.0f & %0.2f & %0.2f \\\\\n",t->Data(),k->getVal(),AIC->getVal()-minAIC.getVal(),AICw->getVal());
//std::cout << k->getVal() << " " << AIC->getVal()-minAIC.getVal() << " " << AICw->getVal() << std::endl;
}
#endif
return ws;
}
void doPseudoFits(const TString infilename, // input file
const TString binname, // name
const TString binfile, // file with bin info
const Int_t sigModPass, // signal extraction method for PASS sample
const Int_t bkgModPass, // background model for PASS sample
const Int_t sigModFail, // signal extraction method for FAIL sample
const Int_t bkgModFail, // background model for FAIL sample
const TString outputDir, // output directory
const Int_t charge, // 0 (no charge requirement), -1, +1
const TString mcfilename="") // ROOT file containing MC events to generate templates from
{
gBenchmark->Start("plotEff");
//--------------------------------------------------------------------------------------------------------------
// Settings
//==============================================================================================================
// signal extraction mass region
const Double_t massLo = 60;
const Double_t massHi = 120;
// fit mass region
const Double_t fitMassLo = massLo;
const Double_t fitMassHi = massHi;
gSystem->mkdir(outputDir,kTRUE);
//
// parse pseudodata file
//
TH1D* histPass = new TH1D("histPass","",Int_t(fitMassHi-fitMassLo)/BIN_SIZE_PASS,fitMassLo,fitMassHi);
TH1D* histFail = new TH1D("histFail","",Int_t(fitMassHi-fitMassLo)/BIN_SIZE_FAIL,fitMassLo,fitMassHi);
TString readInFile = infilename+"/"+binname;
cout << readInFile << endl;
ifstream ifs;
ifs.open(readInFile.Data());
string line;
while(getline(ifs,line)) {
if(line[0]=='#') continue;
if(line[0]=='%') continue;
Double_t mass;
Int_t state;
stringstream ss(line);
ss >> mass >> state;
if (state == 1) histPass->Fill(mass);
else if (state == 2) histFail->Fill(mass);
}
ifs.close();
cout << "histPass has " << histPass->GetEntries() << endl;
cout << "histFail has " << histFail->GetEntries() << endl;
cout << "total we have " << histPass->GetEntries()+histFail->GetEntries() << endl;
//
// get binning info
//
TFile *f = new TFile(binfile);
TTree *intree = (TTree*)f->Get("Bin");
BinInfo bin;
intree->SetBranchAddress("Bin",&bin);
intree->GetEntry(0);
cout << "we should have " << bin.nEvents << endl;
cout << bin.ptLo << " " << bin.ptHi << " " << bin.etaLo << " " << bin.etaHi << " " << bin.phiLo << " " << bin.phiHi << " " << bin.npvLo << " " << bin.npvHi << " " << bin.absEta << endl;
//
// Generate histogram templates from MC if necessary
//
if(sigModPass==2 || sigModFail==2) {
generateHistTemplates(mcfilename, bin.ptLo, bin.ptHi, bin.etaLo, bin.etaHi, bin.phiLo, bin.phiHi, bin.npvLo, bin.npvHi, bin.absEta, 0, bin.iBin);
}
RooRealVar m("m","mass",fitMassLo,fitMassHi);
m.setBins(10000);
Int_t nflpass=0, nflfail=0;
TFile *histfile = 0;
if(sigModPass==2 || sigModFail==2) {
histfile = new TFile("histTemplates.root");
assert(histfile);
}
RooCategory sample("sample","");
sample.defineType("Pass",1);
sample.defineType("Fail",2);
RooAbsData *dataPass=0;
RooAbsData *dataFail=0;
RooAbsData *dataCombined=0;
dataPass = new RooDataHist("dataPass","dataPass",RooArgSet(m),histPass);
dataFail = new RooDataHist("dataFail","dataFail",RooArgSet(m),histFail);
dataCombined = new RooDataHist("dataCombined","dataCombined",RooArgList(m),
RooFit::Index(sample),
RooFit::Import("Pass",*((RooDataHist*)dataPass)),
RooFit::Import("Fail",*((RooDataHist*)dataFail)));
// Define signal and background models
CSignalModel *sigPass = 0;
CBackgroundModel *bkgPass = 0;
CSignalModel *sigFail = 0;
CBackgroundModel *bkgFail = 0;
char hname[50];
sprintf(hname,"pass_%i",bin.iBin);
TH1D *h = (TH1D*)histfile->Get(hname);
assert(h);
sigPass = new CMCTemplateConvGaussian(m,h,kTRUE);
//((CMCTemplateConvGaussian*)sigPass)->mean->setVal(-0.1);
//((CMCTemplateConvGaussian*)sigPass)->sigma->setVal(0.015);
//((CMCTemplateConvGaussian*)sigPass)->sigma->setMax(0.2);
nflpass += 2;
if (bkgModPass == 1) {
bkgPass = new CExponential(m,kTRUE);
nflpass += 1;
}
else if (bkgModPass == 2) {
bkgPass = new CErfExpo(m,kTRUE);
nflfail += 3;
//((CErfExpo*)bkgPass)->alfa->setVal(64.);
//((CErfExpo*)bkgPass)->alfa->setMax(80.);
//((CErfExpo*)bkgPass)->beta->setVal(1.0);
//((CErfExpo*)bkgPass)->beta->setMax(3.0);
//((CErfExpo*)bkgPass)->gamma->setVal(1.0);
//((CErfExpo*)bkgPass)->gamma->setMax(3.0);
}
else {
cout << "trying to use bkg model that's not implemented!!" << endl;
}
char hname2[50];
sprintf(hname2,"fail_%i",bin.iBin);
TH1D *h2 = (TH1D*)histfile->Get(hname2);
assert(h2);
sigFail = new CMCTemplateConvGaussian(m,h2,kFALSE);
//((CMCTemplateConvGaussian*)sigFail)->mean->setVal(-0.28);
//((CMCTemplateConvGaussian*)sigFail)->sigma->setVal(0.2);
//((CMCTemplateConvGaussian*)sigFail)->sigma->setMax(2.0);
nflfail += 2;
if (bkgModFail == 1) {
bkgFail = new CExponential(m,kFALSE);
nflfail += 1;
}
else if (bkgModFail == 2) {
bkgFail = new CErfExpo(m,kFALSE);
nflfail += 3;
//((CErfExpo*)bkgFail)->alfa->setVal(60.);
//((CErfExpo*)bkgFail)->alfa->setMax(80.);
//((CErfExpo*)bkgFail)->beta->setVal(0.07);
//((CErfExpo*)bkgFail)->beta->setMax(0.5);
//((CErfExpo*)bkgFail)->gamma->setVal(0.02);
//((CErfExpo*)bkgFail)->gamma->setMax(1.0);
}
else {
cout << "trying to use bkg model that's not implemented!!" << endl;
}
// Define free parameters
Double_t NsigMax = histPass->Integral()+histFail->Integral();
cout << "NsigMax " << NsigMax << endl;
Double_t NbkgFailMax = histFail->Integral();
cout << "NbkgFailMax " << NbkgFailMax << endl;
Double_t NbkgPassMax = histPass->Integral();
RooRealVar Nsig("Nsig","Signal Yield",0.80*NsigMax,0,NsigMax);
RooRealVar eff("eff","Efficiency",0.9,0,1.0);
//cout << "got here" << endl;
RooRealVar NbkgPass("NbkgPass","Background count in PASS sample",10,0,NbkgPassMax);
//cout << "chicken" << endl;
RooRealVar NbkgFail("NbkgFail","Background count in FAIL sample",10,0.01,NbkgFailMax);
//cout << "frog" << endl;
RooFormulaVar NsigPass("NsigPass","eff*Nsig",RooArgList(eff,Nsig));
RooFormulaVar NsigFail("NsigFail","(1.0-eff)*Nsig",RooArgList(eff,Nsig));
RooAddPdf *modelPass=0, *modelFail=0;
RooExtendPdf *esignalPass=0, *ebackgroundPass=0, *esignalFail=0, *ebackgroundFail=0;
if(massLo!=fitMassLo || massHi!=fitMassHi) {
m.setRange("signalRange",massLo,massHi);
esignalPass = new RooExtendPdf("esignalPass","esignalPass",*(sigPass->model),NsigPass,"signalRange");
ebackgroundPass = new RooExtendPdf("ebackgroundPass","ebackgroundPass",(bkgModPass>0) ? *(bkgPass->model) : *(sigPass->model),NbkgPass,"signalRange");
modelPass = new RooAddPdf("modelPass","Model for PASS sample",(bkgModPass>0) ? RooArgList(*esignalPass,*ebackgroundPass) : RooArgList(*esignalPass));
esignalFail = new RooExtendPdf("esignalFail","esignalFail",*(sigFail->model),NsigFail,"signalRange");
ebackgroundFail = new RooExtendPdf("ebackgroundFail","ebackgroundFail",*(bkgFail->model),NbkgFail,"signalRange");
modelFail = new RooAddPdf("modelFail","Model for FAIL sample", (bkgModFail>0) ? RooArgList(*esignalFail,*ebackgroundFail) : RooArgList(*esignalFail));
} else {
modelPass = new RooAddPdf("modelPass","Model for PASS sample",
RooArgList(*(sigPass->model),*(bkgPass->model)),
RooArgList(NsigPass,NbkgPass));
modelFail = new RooAddPdf("modelFail","Model for FAIL sample",
RooArgList(*(sigFail->model),*(bkgFail->model)),
RooArgList(NsigFail,NbkgFail));
}
cout << "whale?" << endl;
RooSimultaneous totalPdf("totalPdf","totalPdf",sample);
totalPdf.addPdf(*modelPass,"Pass");
totalPdf.addPdf(*modelFail,"Fail");
RooFitResult *fitResult=0;
fitResult = totalPdf.fitTo(*dataCombined,
RooFit::Extended(),
RooFit::Strategy(1),
//RooFit::Minos(RooArgSet(eff)),
RooFit::Save());
// Refit w/o MINOS if MINOS errors are strange...
if((fabs(eff.getErrorLo())<5e-5) || (eff.getErrorHi()<5e-5))
fitResult = totalPdf.fitTo(*dataCombined, RooFit::Extended(), RooFit::Strategy(1), RooFit::Save());
cout << eff.getVal() << " " << fabs(eff.getErrorLo()) << " " << eff.getErrorHi() << endl;
/*
RooPlot *mframePass = m.frame(Bins(Int_t(fitMassHi-fitMassLo)/BIN_SIZE_PASS));
dataPass->plotOn(mframePass,MarkerStyle(kFullCircle),MarkerSize(0.8),DrawOption("ZP"));
modelPass->plotOn(mframePass);
modelPass->plotOn(mframePass,Components("backgroundPass"),LineStyle(kDashed),LineColor(kRed));
RooPlot *mframeFail = m.frame(Bins(Int_t(fitMassHi-fitMassLo)/BIN_SIZE_FAIL));
dataFail->plotOn(mframeFail,MarkerStyle(kFullCircle),MarkerSize(0.8),DrawOption("ZP"));
modelFail->plotOn(mframeFail);
modelFail->plotOn(mframeFail,Components("backgroundFail"),LineStyle(kDashed),LineColor(kRed));
TCanvas *cpass = MakeCanvas("cpass","cpass",720,540);
cpass->SetWindowPosition(cpass->GetWindowTopX()+cpass->GetBorderSize()+800,0);
TCanvas *cfail = MakeCanvas("cfail","cfail",720,540);
cfail->SetWindowPosition(cfail->GetWindowTopX()+cfail->GetBorderSize()+800,cpass->GetWindowTopX()+cfail->GetBorderSize()+540);
char ylabel[50];
//
// Plot passing probes
//
sprintf(ylabel,"Events / %.1f GeV/c^{2}",(Double_t)BIN_SIZE_PASS);
CPlot plotPass("pass",mframePass,"Passing probes","tag-probe mass [GeV/c^{2}]",ylabel);
plotPass.Draw(cpass,kTRUE,"png");
//
// Plot failing probes
//
sprintf(ylabel,"Events / %.1f GeV/c^{2}",(Double_t)BIN_SIZE_FAIL);
CPlot plotFail("fail",mframeFail,"Failing probes","tag-probe mass [GeV/c^{2}]",ylabel);
plotFail.Draw(cfail,kTRUE,"png");
*/
//
// Write fit results
//
TObjString* temp=(TObjString*)readInFile.Tokenize("/.")->At(4);
ofstream txtfile;
char txtfname[100];
sprintf(txtfname,"%s/%s.output",outputDir.Data(),temp->GetString().Data());
txtfile.open(txtfname);
assert(txtfile.is_open());
fitResult->printStream(txtfile,RooPrintable::kValue,RooPrintable::kVerbose);
txtfile.close();
}
