float getDLL(RooWorkspace* w, TString tag) {
//RooFitResult::sexp_b_fitres
RooFitResult *b = (RooFitResult*)w->obj(tag+"_b_fitres");
RooFitResult *sb = (RooFitResult*)w->obj(tag+"_sb_fitres");
return -2*(sb->minNll()-b->minNll());
}
void fit(Int_t i, Double_t va=-0.43, Double_t vb=0.){
sprintf(namestr,"%d_%.2f_%.2f",i,va,vb);
Double_t g1, g2;
g1 = 0.1*r.Rndm() - 0.05;
g2 = r.Rndm() - 0.5;
//a->setVal(va);
//b->setVal(vb);
//G001->setVal(g1);
//G002->setVal(g2);
//RooRealVar * G000 = new RooRealVar("G000", "G000", 0.5);
//RooRealVar * G001 = new RooRealVar("G001", "G001", g1, -5., 5.);
//RooRealVar * G002 = new RooRealVar("G002", "G002", g2, -5., 5.);
//RooRealVar * G003 = new RooRealVar("G003", "G003", 0.0);//, -1., 1.);
//RooRealVar * G004 = new RooRealVar("G004", "G004", 0.0);//, -1., 1.);
//RooRealVar * a = new RooRealVar("a", "a", va);
//RooRealVar * b = new RooRealVar("b", "b", vb);
//RooRealVar * n = new RooRealVar("n", "n", 1000., -100., 5000.);
RooRealVar G000("G000", "G000", 0.5);
RooRealVar G001("G001", "G001", g1, -5., 5.);
RooRealVar G002("G002", "G002", g2, -5., 5.);
RooRealVar G003("G003", "G003", 0.0);//, -1., 1.);
RooRealVar G004("G004", "G004", 0.0);//, -1., 1.);
RooRealVar a("a", "a", va);
RooRealVar b("b", "b", vb);
RooRealVar n("n", "n", 1000., -100., 5000.);
RooB2Kll pdf("pdf", "pdf", *cosTheta, G000, G001, G002, G003, G004, a, b, n);
RooFitResult * fitresult = pdf.fitTo(*data,Save(kTRUE), Minos(kFALSE), NumCPU(4), SumW2Error(kTRUE));
RooPlot * frame = cosTheta->frame();
data->plotOn(frame);
pdf.plotOn(frame);
if(fitresult->minNll() == fitresult->minNll() && fitresult->minNll()>0 ) {
fout << i << "\t" << a.getVal() << "\t" << b.getVal() << "\t" << fitresult->minNll() << "\t" << fitresult->status() << "\t"
<< G000.getVal() << "\t" << G001.getVal() << "\t" << G002.getVal() << "\t" << G003.getVal() << "\t" << G004.getVal() << endl;
}
gROOT->ProcessLine(".x ~/lhcb/lhcbStyle.C");
TCanvas c("fit","fit", 800, 800);
frame->Draw();
c.SaveAs("fits/fit"+TString(namestr)+".png");
c.SaveAs("fits/fit"+TString(namestr)+".pdf");
}
void runFit(RooAbsPdf *pdf, RooDataSet *data, double *NLL, int *stat_t, int MaxTries, int mhLow, int mhHigh){
int ntries=0;
int stat=1;
double minnll=10e8;
while (stat!=0){
if (ntries>=MaxTries) break;
RooFitResult *fitTest = pdf->fitTo(*data,RooFit::Save(1),Range(mhLow,mhHigh));
//RooFitResult *fitTest = pdf->fitTo(*data,RooFit::Save(1),Range(85,110));
//RooFitResult *fitTest = pdf->fitTo(*data,RooFit::Save(1),SumW2Error(kTRUE)
stat = fitTest->status();
minnll = fitTest->minNll();
ntries++;
}
*stat_t = stat;
*NLL = minnll;
}
void FitBias(TString CAT,TString CUT,float SIG,float BKG,int NTOYS)
{
gROOT->ForceStyle();
RooMsgService::instance().setSilentMode(kTRUE);
RooMsgService::instance().setStreamStatus(0,kFALSE);
RooMsgService::instance().setStreamStatus(1,kFALSE);
// -----------------------------------------
TFile *fTemplates = TFile::Open("templates_"+CUT+"_"+CAT+"_workspace.root");
RooWorkspace *wTemplates = (RooWorkspace*)fTemplates->Get("w");
RooRealVar *x = (RooRealVar*)wTemplates->var("mTop");
RooAbsPdf *pdf_signal = (RooAbsPdf*)wTemplates->pdf("ttbar_pdf_Nominal");
RooAbsPdf *pdf_bkg = (RooAbsPdf*)wTemplates->pdf("qcdCor_pdf");
TRandom *rnd = new TRandom();
rnd->SetSeed(0);
x->setBins(250);
RooPlot *frame;
TFile *outf;
if (NTOYS > 1) {
outf = TFile::Open("FitBiasToys_"+CUT+"_"+CAT+".root","RECREATE");
}
float nSigInj,nBkgInj,nSigFit,nBkgFit,eSigFit,eBkgFit,nll;
TTree *tr = new TTree("toys","toys");
tr->Branch("nSigInj",&nSigInj,"nSigInj/F");
tr->Branch("nSigFit",&nSigFit,"nSigFit/F");
tr->Branch("nBkgInj",&nBkgInj,"nBkgInj/F");
tr->Branch("nBkgFit",&nBkgFit,"nBkgFit/F");
tr->Branch("eSigFit",&eSigFit,"eSigFit/F");
tr->Branch("eBkgFit",&eBkgFit,"eBkgFit/F");
tr->Branch("nll" ,&nll ,"nll/F");
for(int itoy=0;itoy<NTOYS;itoy++) {
// generate pseudodataset
nSigInj = rnd->Poisson(SIG);
nBkgInj = rnd->Poisson(BKG);
RooRealVar *nSig = new RooRealVar("nSig","nSig",nSigInj);
RooRealVar *nBkg = new RooRealVar("nBkg","nBkg",nBkgInj);
RooAddPdf *model = new RooAddPdf("model","model",RooArgList(*pdf_signal,*pdf_bkg),RooArgList(*nSig,*nBkg));
RooDataSet *data = model->generate(*x,nSigInj+nBkgInj);
RooDataHist *roohist = new RooDataHist("roohist","roohist",RooArgList(*x),*data);
// build fit model
RooRealVar *nFitSig = new RooRealVar("nFitSig","nFitSig",SIG,0,10*SIG);
RooRealVar *nFitBkg = new RooRealVar("nFitBkg","nFitBkg",BKG,0,10*BKG);
RooAddPdf *modelFit = new RooAddPdf("modelFit","modelFit",RooArgList(*pdf_signal,*pdf_bkg),RooArgList(*nFitSig,*nFitBkg));
// fit the pseudo dataset
RooFitResult *res = modelFit->fitTo(*roohist,RooFit::Save(),RooFit::Extended(kTRUE));
//res->Print();
nSigFit = nFitSig->getVal();
nBkgFit = nFitBkg->getVal();
eSigFit = nFitSig->getError();
eBkgFit = nFitBkg->getError();
nll = res->minNll();
tr->Fill();
if (itoy % 100 == 0) {
cout<<"Toy #"<<itoy<<": injected = "<<nSigInj<<", fitted = "<<nSigFit<<", error = "<<eSigFit<<endl;
}
if (NTOYS == 1) {
frame = x->frame();
roohist->plotOn(frame);
model->plotOn(frame);
}
}
if (NTOYS == 1) {
TCanvas *can = new TCanvas("Toy","Toy",900,600);
frame->Draw();
}
else {
outf->cd();
tr->Write();
outf->Close();
fTemplates->Close();
}
}
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;
}
///
/// Find the global minimum in a more thorough way.
/// First fit with external start parameters, then
/// for each parameter that starts with "d" or "r" (typically angles and ratios):
/// - at upper scan range, rest at start parameters
/// - at lower scan range, rest at start parameters
/// This amounts to a maximum of 1+2^n fits, where n is the number
/// of parameters to be varied.
///
/// \param w Workspace holding the pdf.
/// \param name Name of the pdf without leading "pdf_".
/// \param forceVariables Apply the force method for these variables only. Format
/// "var1,var2,var3," (list must end with comma). Default is to apply for all angles,
/// all ratios except rD_k3pi and rD_kpi, and the k3pi coherence factor.
///
RooFitResult* Utils::fitToMinForce(RooWorkspace *w, TString name, TString forceVariables)
{
bool debug = true;
TString parsName = "par_"+name;
TString obsName = "obs_"+name;
TString pdfName = "pdf_"+name;
RooFitResult *r = 0;
int printlevel = -1;
RooMsgService::instance().setGlobalKillBelow(ERROR);
// save start parameters
if ( !w->set(parsName) ){
cout << "MethodProbScan::scan2d() : ERROR : parsName not found: " << parsName << endl;
exit(1);
}
RooDataSet *startPars = new RooDataSet("startParsForce", "startParsForce", *w->set(parsName));
startPars->add(*w->set(parsName));
// set up parameters and ranges
RooArgList *varyPars = new RooArgList();
TIterator* it = w->set(parsName)->createIterator();
while ( RooRealVar* p = (RooRealVar*)it->Next() )
{
if ( p->isConstant() ) continue;
if ( forceVariables=="" && ( false
|| TString(p->GetName()).BeginsWith("d") ///< use these variables
// || TString(p->GetName()).BeginsWith("r")
|| TString(p->GetName()).BeginsWith("k")
|| TString(p->GetName()) == "g"
) && ! (
TString(p->GetName()) == "rD_k3pi" ///< don't use these
|| TString(p->GetName()) == "rD_kpi"
// || TString(p->GetName()) == "dD_kpi"
|| TString(p->GetName()) == "d_dk"
|| TString(p->GetName()) == "d_dsk"
))
{
varyPars->add(*p);
}
else if ( forceVariables.Contains(TString(p->GetName())+",") )
{
varyPars->add(*p);
}
}
delete it;
int nPars = varyPars->getSize();
if ( debug ) cout << "Utils::fitToMinForce() : nPars = " << nPars << " => " << pow(2.,nPars) << " fits" << endl;
if ( debug ) cout << "Utils::fitToMinForce() : varying ";
if ( debug ) varyPars->Print();
//////////
r = fitToMinBringBackAngles(w->pdf(pdfName), false, printlevel);
//////////
int nErrors = 0;
// We define a binary mask where each bit corresponds
// to parameter at max or at min.
for ( int i=0; i<pow(2.,nPars); i++ )
{
if ( debug ) cout << "Utils::fitToMinForce() : fit " << i << " \r" << flush;
setParameters(w, parsName, startPars->get(0));
for ( int ip=0; ip<nPars; ip++ )
{
RooRealVar *p = (RooRealVar*)varyPars->at(ip);
float oldMin = p->getMin();
float oldMax = p->getMax();
setLimit(w, p->GetName(), "force");
if ( i/(int)pow(2.,ip) % 2==0 ) { p->setVal(p->getMin()); }
if ( i/(int)pow(2.,ip) % 2==1 ) { p->setVal(p->getMax()); }
p->setRange(oldMin, oldMax);
}
// check if start parameters are sensible, skip if they're not
double startParChi2 = getChi2(w->pdf(pdfName));
if ( startParChi2>2000 ){
nErrors += 1;
continue;
}
// refit
RooFitResult *r2 = fitToMinBringBackAngles(w->pdf(pdfName), false, printlevel);
// In case the initial fit failed, accept the second one.
// If both failed, still select the second one and hope the
// next fit succeeds.
if ( !(r->edm()<1 && r->covQual()==3) ){
delete r;
r = r2;
}
else if ( r2->edm()<1 && r2->covQual()==3 && r2->minNll()<r->minNll() ){
// better minimum found!
delete r;
r = r2;
}
else{
delete r2;
}
}
if ( debug ) cout << endl;
if ( debug ) cout << "Utils::fitToMinForce() : nErrors = " << nErrors << endl;
RooMsgService::instance().setGlobalKillBelow(INFO);
// (re)set to best parameters
setParameters(w, parsName, r);
delete startPars;
return r;
}
int main(int argc, char *argv[]){
OptionParser(argc,argv);
TStopwatch sw;
sw.Start();
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
RooMsgService::instance().setSilentMode(true);
system("mkdir -p plots/fTest");
vector<string> procs;
split(procs,procString_,boost::is_any_of(","));
TFile *inFile = TFile::Open(filename_.c_str());
RooWorkspace *inWS = (RooWorkspace*)inFile->Get("cms_hgg_workspace");
RooRealVar *mass = (RooRealVar*)inWS->var("CMS_hgg_mass");
//mass->setBins(320);
//mass->setRange(mass_-10,mass_+10);
//mass->setBins(20);
RooRealVar *MH = new RooRealVar("MH","MH",mass_);
MH->setVal(mass_);
MH->setConstant(true);
map<string,pair<int,int> > choices;
map<string,vector<RooPlot*> > plotsRV;
map<string,vector<RooPlot*> > plotsWV;
for (unsigned int p=0; p<procs.size(); p++){
vector<RooPlot*> tempRV;
vector<RooPlot*> tempWV;
for (int cat=0; cat<ncats_; cat++){
RooPlot *plotRV = mass->frame(Range(mass_-10,mass_+10));
plotRV->SetTitle(Form("%s_cat%d_RV",procs[p].c_str(),cat));
tempRV.push_back(plotRV);
RooPlot *plotWV = mass->frame(Range(mass_-10,mass_+10));
plotWV->SetTitle(Form("%s_cat%d_WV",procs[p].c_str(),cat));
tempWV.push_back(plotWV);
}
plotsRV.insert(pair<string,vector<RooPlot*> >(procs[p],tempRV));
plotsWV.insert(pair<string,vector<RooPlot*> >(procs[p],tempWV));
}
vector<int> colors;
colors.push_back(kBlue);
colors.push_back(kRed);
colors.push_back(kGreen+2);
colors.push_back(kMagenta+1);
for (int cat=0; cat<ncats_; cat++){
for (unsigned int p=0; p<procs.size(); p++){
string proc = procs[p];
RooDataSet *dataRV = (RooDataSet*)inWS->data(Form("sig_%s_mass_m%d_rv_cat%d",proc.c_str(),mass_,cat));
RooDataSet *dataWV = (RooDataSet*)inWS->data(Form("sig_%s_mass_m%d_wv_cat%d",proc.c_str(),mass_,cat));
//mass->setBins(160);
//RooDataHist *dataRV = dataRVtemp->binnedClone();
//RooDataHist *dataWV = dataWVtemp->binnedClone();
//RooDataSet *dataRVw = (RooDataSet*)dataRVtemp->reduce(Form("CMS_hgg_mass>=%3d && CMS_hgg_mass<=%3d",mass_-10,mass_+10));
//RooDataSet *dataWVw = (RooDataSet*)dataWVtemp->reduce(Form("CMS_hgg_mass>=%3d && CMS_hgg_mass<=%3d",mass_-10,mass_+10));
//RooDataHist *dataRV = new RooDataHist(Form("roohist_%s",dataRVtemp->GetName()),Form("roohist_%s",dataRVtemp->GetName()),RooArgSet(*mass),*dataRVtemp);
//RooDataHist *dataWV = new RooDataHist(Form("roohist_%s",dataWVtemp->GetName()),Form("roohist_%s",dataWVtemp->GetName()),RooArgSet(*mass),*dataWVtemp);
//RooDataSet *dataRV = stripWeights(dataRVweight,mass);
//RooDataSet *dataWV = stripWeights(dataWVweight,mass);
//RooDataSet *data = (RooDataSet*)inWS->data(Form("sig_%s_mass_m%d_cat%d",proc.c_str(),mass_,cat));
int rvChoice=0;
int wvChoice=0;
// right vertex
int order=1;
int prev_order=0;
int cache_order=0;
double thisNll=0.;
double prevNll=1.e6;
double chi2=0.;
double prob=0.;
dataRV->plotOn(plotsRV[proc][cat]);
//while (prob<0.8) {
while (order<5) {
RooAddPdf *pdf = buildSumOfGaussians(Form("cat%d_g%d",cat,order),mass,MH,order);
RooFitResult *fitRes = pdf->fitTo(*dataRV,Save(true),SumW2Error(true));//,Range(mass_-10,mass_+10));
double myNll=0.;
thisNll = fitRes->minNll();
//double myNll = getMyNLL(mass,pdf,dataRV);
//thisNll = getMyNLL(mass,pdf,dataRV);
//RooAbsReal *nll = pdf->createNLL(*dataRV);
//RooMinuit m(*nll);
//m.migrad();
//thisNll = nll->getVal();
//plot(Form("plots/fTest/%s_cat%d_g%d_rv",proc.c_str(),cat,order),mass_,mass,dataRV,pdf);
pdf->plotOn(plotsRV[proc][cat],LineColor(colors[order-1]));
chi2 = 2.*(prevNll-thisNll);
//if (chi2<0. && order>1) chi2=0.;
int diffInDof = (2*order+(order-1))-(2*prev_order+(prev_order-1));
prob = TMath::Prob(chi2,diffInDof);
cout << "\t RV: " << cat << " " << order << " " << diffInDof << " " << prevNll << " " << thisNll << " " << myNll << " " << chi2 << " " << prob << endl;
prevNll=thisNll;
cache_order=prev_order;
prev_order=order;
order++;
}
rvChoice=cache_order;
// wrong vertex
order=1;
prev_order=0;
cache_order=0;
thisNll=0.;
prevNll=1.e6;
chi2=0.;
prob=0.;
dataWV->plotOn(plotsWV[proc][cat]);
while (order<4) {
//while (prob<0.8) {
RooAddPdf *pdf = buildSumOfGaussians(Form("cat%d_g%d",cat,order),mass,MH,order);
RooFitResult *fitRes = pdf->fitTo(*dataWV,Save(true),SumW2Error(true));//,Range(mass_-10,mass_+10));
double myNll=0.;
thisNll = fitRes->minNll();
//double myNll = getMyNLL(mass,pdf,dataRV);
//thisNll = getMyNLL(mass,pdf,dataRV);
//RooAbsReal *nll = pdf->createNLL(*dataWV);
//RooMinuit m(*nll);
//m.migrad();
//thisNll = nll->getVal();
//plot(Form("plots/fTest/%s_cat%d_g%d_wv",proc.c_str(),cat,order),mass_,mass,dataWV,pdf);
pdf->plotOn(plotsWV[proc][cat],LineColor(colors[order-1]));
chi2 = 2.*(prevNll-thisNll);
//if (chi2<0. && order>1) chi2=0.;
int diffInDof = (2*order+(order-1))-(2*prev_order+(prev_order-1));
prob = TMath::Prob(chi2,diffInDof);
cout << "\t WV: " << cat << " " << order << " " << diffInDof << " " << prevNll << " " << thisNll << " " << myNll << " " << chi2 << " " << prob << endl;
prevNll=thisNll;
cache_order=prev_order;
prev_order=order;
order++;
}
wvChoice=cache_order;
choices.insert(pair<string,pair<int,int> >(Form("%s_cat%d",proc.c_str(),cat),make_pair(rvChoice,wvChoice)));
}
}
TLegend *leg = new TLegend(0.6,0.6,0.89,0.89);
leg->SetFillColor(0);
leg->SetLineColor(0);
TH1F *h1 = new TH1F("h1","",1,0,1);
h1->SetLineColor(colors[0]);
leg->AddEntry(h1,"1st order","L");
TH1F *h2 = new TH1F("h2","",1,0,1);
h2->SetLineColor(colors[1]);
leg->AddEntry(h2,"2nd order","L");
TH1F *h3 = new TH1F("h3","",1,0,1);
h3->SetLineColor(colors[2]);
leg->AddEntry(h3,"3rd order","L");
TH1F *h4 = new TH1F("h4","",1,0,1);
h4->SetLineColor(colors[3]);
leg->AddEntry(h4,"4th order","L");
TCanvas *canv = new TCanvas();
for (map<string,vector<RooPlot*> >::iterator plotIt=plotsRV.begin(); plotIt!=plotsRV.end(); plotIt++){
string proc = plotIt->first;
for (int cat=0; cat<ncats_; cat++){
RooPlot *plot = plotIt->second.at(cat);
plot->Draw();
leg->Draw();
canv->Print(Form("plots/fTest/rv_%s_cat%d.pdf",proc.c_str(),cat));
}
}
for (map<string,vector<RooPlot*> >::iterator plotIt=plotsWV.begin(); plotIt!=plotsWV.end(); plotIt++){
string proc = plotIt->first;
for (int cat=0; cat<ncats_; cat++){
RooPlot *plot = plotIt->second.at(cat);
plot->Draw();
leg->Draw();
canv->Print(Form("plots/fTest/wv_%s_cat%d.pdf",proc.c_str(),cat));
}
}
delete canv;
cout << "Recommended options" << endl;
for (map<string,pair<int,int> >::iterator it=choices.begin(); it!=choices.end(); it++){
cout << "\t " << it->first << " - " << it->second.first << " " << it->second.second << endl;
}
inFile->Close();
return 0;
}
void constrained_scan( const char* wsfile = "outputfiles/ws.root",
const char* new_poi_name="mu_bg_4b_msig_met1",
double constraintWidth=1.5,
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 10.0,
double ymax = 9.,
int verbLevel=1 ) {
TString outputdir("outputfiles") ;
gStyle->SetOptStat(0) ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "hbb_observed_rds" ) ;
cout << "\n\n\n ===== RooDataSet ====================\n\n" << endl ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooRealVar* rv_sig_strength = ws->var("sig_strength") ;
if ( rv_sig_strength == 0x0 ) { printf("\n\n *** can't find sig_strength in workspace.\n\n" ) ; return ; }
RooAbsPdf* likelihood = ws->pdf("likelihood") ;
if ( likelihood == 0x0 ) { printf("\n\n *** can't find likelihood in workspace.\n\n" ) ; return ; }
printf("\n\n Likelihood:\n") ;
likelihood -> Print() ;
/////rv_sig_strength -> setConstant( kFALSE ) ;
rv_sig_strength -> setVal(0.) ;
rv_sig_strength -> setConstant( kTRUE ) ;
likelihood->fitTo( *rds, Save(false), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//double minNllSusyFloat = fitResult->minNll() ;
//double susy_ss_atMinNll = rv_sig_strength -> getVal() ;
RooMsgService::instance().getStream(1).removeTopic(Minimization) ;
RooMsgService::instance().getStream(1).removeTopic(Fitting) ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
} else {
printf("\n Found it.\n\n") ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
double startPoiVal = new_poi_rar->getVal() ;
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
} // constrained_scan.
///
/// Perform the 1d Prob scan.
/// Saves chi2 values and the prob-Scan p-values in a root tree
/// For the datasets stuff, we do not yet have a MethodDatasetsProbScan class, so we do it all in
/// MethodDatasetsProbScan
/// \param nRun Part of the root tree file name to facilitate parallel production.
///
int MethodDatasetsProbScan::scan1d(bool fast, bool reverse)
{
if (fast) return 0; // tmp
if ( arg->debug ) cout << "MethodDatasetsProbScan::scan1d() : starting ... " << endl;
// Set limit to all parameters.
this->loadParameterLimits(); /// Default is "free", if not changed by cmd-line parameter
// Define scan parameter and scan range.
RooRealVar *parameterToScan = w->var(scanVar1);
float parameterToScan_min = hCL->GetXaxis()->GetXmin();
float parameterToScan_max = hCL->GetXaxis()->GetXmax();
// do a free fit
RooFitResult *result = this->loadAndFit(this->pdf); // fit on data
assert(result);
RooSlimFitResult *slimresult = new RooSlimFitResult(result,true);
slimresult->setConfirmed(true);
solutions.push_back(slimresult);
double freeDataFitValue = w->var(scanVar1)->getVal();
// Define outputfile
system("mkdir -p root");
TString probResName = Form("root/scan1dDatasetsProb_" + this->pdf->getName() + "_%ip" + "_" + scanVar1 + ".root", arg->npoints1d);
TFile* outputFile = new TFile(probResName, "RECREATE");
// Set up toy root tree
this->probScanTree = new ToyTree(this->pdf, arg);
this->probScanTree->init();
this->probScanTree->nrun = -999; //\todo: why does this branch even exist in the output tree of the prob scan?
// Save parameter values that were active at function
// call. We'll reset them at the end to be transparent
// to the outside.
RooDataSet* parsFunctionCall = new RooDataSet("parsFunctionCall", "parsFunctionCall", *w->set(pdf->getParName()));
parsFunctionCall->add(*w->set(pdf->getParName()));
// start scan
cout << "MethodDatasetsProbScan::scan1d_prob() : starting ... with " << nPoints1d << " scanpoints..." << endl;
ProgressBar progressBar(arg, nPoints1d);
for ( int i = 0; i < nPoints1d; i++ )
{
progressBar.progress();
// scanpoint is calculated using min, max, which are the hCL x-Axis limits set in this->initScan()
// this uses the "scan" range, as expected
// don't add half the bin size. try to solve this within plotting method
float scanpoint = parameterToScan_min + (parameterToScan_max - parameterToScan_min) * (double)i / ((double)nPoints1d - 1);
if (arg->debug) cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() " << scanpoint << " " << parameterToScan_min << " " << parameterToScan_max << endl;
this->probScanTree->scanpoint = scanpoint;
if (arg->debug) cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() - scanpoint in step " << i << " : " << scanpoint << endl;
// don't scan in unphysical region
// by default this means checking against "free" range
if ( scanpoint < parameterToScan->getMin() || scanpoint > parameterToScan->getMax() + 2e-13 ) {
cout << "it seems we are scanning in an unphysical region: " << scanpoint << " < " << parameterToScan->getMin() << " or " << scanpoint << " > " << parameterToScan->getMax() + 2e-13 << endl;
exit(EXIT_FAILURE);
}
// FIT TO REAL DATA WITH FIXED HYPOTHESIS(=SCANPOINT).
// THIS GIVES THE NUMERATOR FOR THE PROFILE LIKELIHOOD AT THE GIVEN HYPOTHESIS
// THE RESULTING NUISANCE PARAMETERS TOGETHER WITH THE GIVEN HYPOTHESIS ARE ALSO
// USED WHEN SIMULATING THE TOY DATA FOR THE FELDMAN-COUSINS METHOD FOR THIS HYPOTHESIS(=SCANPOINT)
// Here the scanvar has to be fixed -> this is done once per scanpoint
// and provides the scanner with the DeltaChi2 for the data as reference
// additionally the nuisances are set to the resulting fit values
parameterToScan->setVal(scanpoint);
parameterToScan->setConstant(true);
RooFitResult *result = this->loadAndFit(this->pdf); // fit on data
assert(result);
if (arg->debug) {
cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() - minNll data scan at scan point " << scanpoint << " : " << 2 * result->minNll() << ": "<< 2 * pdf->getMinNll() << endl;
}
this->probScanTree->statusScanData = result->status();
// set chi2 of fixed fit: scan fit on data
// CAVEAT: chi2min from fitresult gives incompatible results to chi2min from pdf
// this->probScanTree->chi2min = 2 * result->minNll();
this->probScanTree->chi2min = 2 * pdf->getMinNll();
this->probScanTree->covQualScanData = result->covQual();
this->probScanTree->scanbest = freeDataFitValue;
// After doing the fit with the parameter of interest constrained to the scanpoint,
// we are now saving the fit values of the nuisance parameters. These values will be
// used to generate toys according to the PLUGIN method.
this->probScanTree->storeParsScan(); // \todo : figure out which one of these is semantically the right one
this->pdf->deleteNLL();
// also save the chi2 of the free data fit to the tree:
this->probScanTree->chi2minGlobal = this->getChi2minGlobal();
this->probScanTree->chi2minBkg = this->getChi2minBkg();
this->probScanTree->genericProbPValue = this->getPValueTTestStatistic(this->probScanTree->chi2min - this->probScanTree->chi2minGlobal);
this->probScanTree->fill();
if(arg->debug && pdf->getBkgPdf())
{
float pval_cls = this->getPValueTTestStatistic(this->probScanTree->chi2min - this->probScanTree->chi2minBkg, true);
cout << "DEBUG in MethodDatasetsProbScan::scan1d() - p value CLs: " << pval_cls << endl;
}
// reset
setParameters(w, pdf->getParName(), parsFunctionCall->get(0));
//setParameters(w, pdf->getObsName(), obsDataset->get(0));
} // End of npoints loop
probScanTree->writeToFile();
if (bkgOnlyFitResult) bkgOnlyFitResult->Write();
if (dataFreeFitResult) dataFreeFitResult->Write();
outputFile->Close();
std::cout << "Wrote ToyTree to file" << std::endl;
delete parsFunctionCall;
// This is kind of a hack. The effect is supposed to be the same as callincg
// this->sethCLFromProbScanTree(); here, but the latter gives a segfault somehow....
// \todo: use this->sethCLFromProbScanTree() directly after figuring out the cause of the segfault.
this->loadScanFromFile();
return 0;
}
void ws_constrained_profile3D( const char* wsfile = "rootfiles/ws-data-unblind.root",
const char* new_poi_name = "n_M234_H4_3b",
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 20.,
double constraintWidth = 1.5,
double ymax = 10.,
int verbLevel=0 ) {
gStyle->SetOptStat(0) ;
//--- make output directory.
char command[10000] ;
sprintf( command, "basename %s", wsfile ) ;
TString wsfilenopath = gSystem->GetFromPipe( command ) ;
wsfilenopath.ReplaceAll(".root","") ;
char outputdirstr[1000] ;
sprintf( outputdirstr, "outputfiles/scans-%s", wsfilenopath.Data() ) ;
TString outputdir( outputdirstr ) ;
printf("\n\n Creating output directory: %s\n\n", outputdir.Data() ) ;
sprintf(command, "mkdir -p %s", outputdir.Data() ) ;
gSystem->Exec( command ) ;
//--- Tell RooFit to shut up about anything less important than an ERROR.
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;
if ( verbLevel > 0 ) { printf("\n\n Verbose level : %d\n\n", verbLevel) ; }
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
if ( verbLevel > 0 ) { ws->Print() ; }
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
if ( verbLevel > 0 ) {
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
}
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_all0lep = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_all0lep == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
}
//-- do BG only.
rrv_mu_susy_all0lep->setVal(0.) ;
rrv_mu_susy_all0lep->setConstant( kTRUE ) ;
//-- do a prefit.
printf("\n\n\n ====== Pre fit with unmodified nll var.\n\n") ;
RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true),Hesse(false),Minos(false),Strategy(1),PrintLevel(verbLevel));
int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
if ( dataSusyFixedFitCovQual < 2 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;
if ( verbLevel > 0 ) {
dataFitResultSusyFixed->Print("v") ;
}
printf("\n\n Nll value, from fit result : %.3f\n\n", dataFitSusyFixedNll ) ;
delete dataFitResultSusyFixed ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
if ( npoiPoints <=0 ) {
printf("\n\n Quitting now.\n\n" ) ;
return ;
}
double startPoiVal = new_poi_rar->getVal() ;
//--- The RooNLLVar is NOT equivalent to what minuit uses.
// RooNLLVar* nll = new RooNLLVar("nll","nll", *likelihood, *rds ) ;
// printf("\n\n Nll value, from construction : %.3f\n\n", nll->getVal() ) ;
//--- output of createNLL IS what minuit uses, so use that.
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
/// rrv_poiValue->setVal( poiMinVal ) ;
/// rrv_poiValue->setConstant(kTRUE) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
if ( verbLevel > 0 ) {
printf("\n\n ======= debug likelihood print\n\n") ;
likelihood->Print("v") ;
printf("\n\n ======= debug nll print\n\n") ;
nll->Print("v") ;
}
//----------------------------------------------------------------------------------------------
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
//-------
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
//----------------------------------------------------------------------------------------------
//-- If POI range is -1 to -1, automatically determine the range using the set value.
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
}
///
/// Perform 1d Prob scan.
///
/// - Scan range defined through limit "scan".
/// - Will fill the hCL histogram with the 1-CL curve.
/// - Start at a scan value that is in the middle of the allowed
/// range, preferably a solution, and scan up and down from there.
/// - use the "probforce" command line flag to enable force minimum finding
///
/// \param fast This will scan each scanpoint only once.
/// \param reverse This will scan in reverse direction.
/// When using the drag mode, this can sometimes make a difference.
/// \return status: 2 = new global minimum found, 1 = error
///
int MethodProbScan::scan1d(bool fast, bool reverse)
{
if ( arg->debug ) cout << "MethodProbScan::scan1d() : starting ... " << endl;
nScansDone++;
// The "improve" method doesn't need multiple scans.
if ( arg->probforce || arg->probimprove ) fast = true;
if ( arg->probforce ) scanDisableDragMode = true;
// Save parameter values that were active at function call.
if ( startPars ) delete startPars;
startPars = new RooDataSet("startPars", "startPars", *w->set(parsName));
startPars->add(*w->set(parsName));
// // start scan from global minimum (not always a good idea as we need to set from other places as well)
// setParameters(w, parsName, globalMin);
// load scan parameter and scan range
setLimit(w, scanVar1, "scan");
RooRealVar *par = w->var(scanVar1);
assert(par);
float min = hCL->GetXaxis()->GetXmin();
float max = hCL->GetXaxis()->GetXmax();
if ( fabs(par->getMin()-min)>1e-6 || fabs(par->getMax()-max)>1e-6 ){
cout << "MethodProbScan::scan1d() : WARNING : Scan range was changed after initScan()" << endl;
cout << " was called so the old range will be used." << endl;
}
if ( arg->verbose ){
cout << "\nProb configuration:" << endl;
cout << " combination : " << title << endl;
cout << " scan variable : " << scanVar1 << endl;
cout << " scan range : " << min << " ... " << max << endl;
cout << " scan steps : " << nPoints1d << endl;
cout << " fast mode : " << fast << endl;
cout << endl;
}
// Set limit to all parameters.
combiner->loadParameterLimits();
// fix scan parameter
par->setConstant(true);
// j =
// 0 : start value -> upper limit
// 1 : upper limit -> start value
// 2 : start value -> lower limit
// 3 : lower limit -> start value
float startValue = par->getVal();
bool scanUp;
// for the status bar
float nTotalSteps = nPoints1d;
nTotalSteps *= fast ? 1 : 2;
float nStep = 0;
float printFreq = nTotalSteps>15 ? 10 : nTotalSteps;
// Report on the smallest new minimum we come across while scanning.
// Sometimes the scan doesn't find the minimum
// that was found before. Warn if this happens.
double bestMinOld = chi2minGlobal;
double bestMinFoundInScan = 100.;
for ( int jj=0; jj<4; jj++ )
{
int j = jj;
if ( reverse ) switch(jj)
{
case 0: j = 2; break;
case 1: j = 3; break;
case 2: j = 0; break;
case 3: j = 1; break;
}
float scanStart, scanStop;
switch(j)
{
case 0:
// UP
setParameters(w, parsName, startPars->get(0));
scanStart = startValue;
scanStop = par->getMax();
scanUp = true;
break;
case 1:
// DOWN
scanStart = par->getMax();
scanStop = startValue;
scanUp = false;
break;
case 2:
// DOWN
setParameters(w, parsName, startPars->get(0));
scanStart = startValue;
scanStop = par->getMin();
scanUp = false;
break;
case 3:
// UP
scanStart = par->getMin();
scanStop = startValue;
scanUp = true;
break;
}
if ( fast && ( j==1 || j==3 ) ) continue;
for ( int i=0; i<nPoints1d; i++ )
{
float scanvalue;
if ( scanUp )
{
scanvalue = min + (max-min)*(double)i/(double)nPoints1d + hCL->GetBinWidth(1)/2.;
if ( scanvalue < scanStart ) continue;
if ( scanvalue > scanStop ) break;
}
else
{
scanvalue = max - (max-min)*(double)(i+1)/(double)nPoints1d + hCL->GetBinWidth(1)/2.;
if ( scanvalue > scanStart ) continue;
if ( scanvalue < scanStop ) break;
}
// disable drag mode
// (the improve method doesn't work with drag mode as parameter run
// at their limits)
if ( scanDisableDragMode ) setParameters(w, parsName, startPars->get(0));
// set the parameter of interest to the scan point
par->setVal(scanvalue);
// don't scan in unphysical region
if ( scanvalue < par->getMin() || scanvalue > par->getMax() ) continue;
// status bar
if ( (((int)nStep % (int)(nTotalSteps/printFreq)) == 0))
cout << "MethodProbScan::scan1d() : scanning " << (float)nStep/(float)nTotalSteps*100. << "% \r" << flush;
// fit!
RooFitResult *fr = 0;
if ( arg->probforce ) fr = fitToMinForce(w, combiner->getPdfName());
else if ( arg->probimprove ) fr = fitToMinImprove(w, combiner->getPdfName());
else fr = fitToMinBringBackAngles(w->pdf(pdfName), false, -1);
double chi2minScan = fr->minNll();
if ( std::isinf(chi2minScan) ) chi2minScan=1e4; // else the toys in PDF_testConstraint don't work
RooSlimFitResult *r = new RooSlimFitResult(fr); // try to save memory by using the slim fit result
delete fr;
allResults.push_back(r);
bestMinFoundInScan = TMath::Min((double)chi2minScan, (double)bestMinFoundInScan);
TString warningChi2Neg;
if ( chi2minScan < 0 ){
float newChi2minScan = chi2minGlobal + 25.; // 5sigma more than best point
warningChi2Neg = "MethodProbScan::scan1d() : WARNING : " + title;
warningChi2Neg += TString(Form(" chi2 negative for scan point %i: %f",i,chi2minScan));
warningChi2Neg += " setting to: " + TString(Form("%f",newChi2minScan));
//cout << warningChi2Neg << "\r" << flush;
cout << warningChi2Neg << endl;
chi2minScan = newChi2minScan;
}
// If we find a minimum smaller than the old "global" minimum, this means that all
// previous 1-CL values are too high.
if ( chi2minScan<chi2minGlobal ){
if ( arg->verbose ) cout << "MethodProbScan::scan1d() : WARNING : '" << title << "' new global minimum found! "
<< " chi2minScan=" << chi2minScan << endl;
chi2minGlobal = chi2minScan;
// recompute previous 1-CL values
for ( int k=1; k<=hCL->GetNbinsX(); k++ ){
hCL->SetBinContent(k, TMath::Prob(hChi2min->GetBinContent(k)-chi2minGlobal, 1));
}
}
double deltaChi2 = chi2minScan - chi2minGlobal;
double oneMinusCL = TMath::Prob(deltaChi2, 1);
// Save the 1-CL value and the corresponding fit result.
// But only if better than before!
if ( hCL->GetBinContent(hCL->FindBin(scanvalue)) <= oneMinusCL ){
hCL->SetBinContent(hCL->FindBin(scanvalue), oneMinusCL);
hChi2min->SetBinContent(hCL->FindBin(scanvalue), chi2minScan);
int iRes = hCL->FindBin(scanvalue)-1;
curveResults[iRes] = r;
}
nStep++;
}
}
cout << "MethodProbScan::scan1d() : scan done. " << endl;
if ( bestMinFoundInScan-bestMinOld > 0.01 ){
cout << "MethodProbScan::scan1d() : WARNING: Scan didn't find similar minimum to what was found before!" << endl;
cout << "MethodProbScan::scan1d() : Too strict parameter limits? Too coarse scan steps? Didn't load global minimum?" << endl;
cout << "MethodProbScan::scan1d() : chi2 bestMinFoundInScan=" << bestMinFoundInScan << ", bestMinOld=" << bestMinOld << endl;
}
// attempt to correct for undercoverage
if (pvalueCorrectorSet) {
for ( int k=1; k<=hCL->GetNbinsX(); k++ ){
double pvalueProb = hCL->GetBinContent(k);
pvalueProb = pvalueCorrector->transform(pvalueProb);
hCL->SetBinContent(k, pvalueProb);
}
}
setParameters(w, parsName, startPars->get(0));
saveSolutions();
if (arg->confirmsols) confirmSolutions();
if ( (bestMinFoundInScan-bestMinOld)/bestMinOld > 0.01 ) return 1;
return 0;
}
///
/// Perform a 2d Prob scan.
/// Scan range defined through limit "scan".
/// Fills the hCL2d histogram with the 1-CL curve.
/// Saves all encountered fit results to allResults.
/// Saves the fit results that make it into the 1-CL curve into curveResults2d.
/// Scan strategy: Spiral out!
///
int MethodProbScan::scan2d()
{
if ( arg->debug ) cout << "MethodProbScan::scan2d() : starting ..." << endl;
nScansDone++;
sanityChecks();
if ( startPars ) delete startPars;
// Define whether the 2d contours in hCL are "1D sigma" (ndof=1) or "2D sigma" (ndof=2).
// Leave this at 1 for now, as the "2D sigma" contours are computed from hChi2min2d, not hCL.
int ndof = 1;
// Set up storage for fit results of this particular
// scan. This is used for the drag start parameters.
// We cannot use the curveResults2d member because that
// only holds better results.
vector<vector<RooSlimFitResult*> > mycurveResults2d;
for ( int i=0; i<nPoints2dx; i++ ){
vector<RooSlimFitResult*> tmp;
for ( int j=0; j<nPoints2dy; j++ ) tmp.push_back(0);
mycurveResults2d.push_back(tmp);
}
// store start parameters so we can reset them later
startPars = new RooDataSet("startPars", "startPars", *w->set(parsName));
startPars->add(*w->set(parsName));
// // start scan from global minimum (not always a good idea as we need to set from other places as well)
// setParameters(w, parsName, globalMin);
// Define scan parameters and scan range:
RooRealVar *par1 = w->var(scanVar1);
RooRealVar *par2 = w->var(scanVar2);
// Set limit to all parameters.
combiner->loadParameterLimits();
// fix scan parameters
par1->setConstant(true);
par2->setConstant(true);
// Report on the smallest new minimum we come across while scanning.
// Sometimes the scan doesn't find the minimum
// that was found before. Warn if this happens.
double bestMinOld = chi2minGlobal;
double bestMinFoundInScan = 100.;
// for the status bar
int nSteps = 0;
float nTotalSteps = nPoints2dx*nPoints2dy;
float printFreq = nTotalSteps>100 && !arg->probforce ? 100 : nTotalSteps; ///< number of messages
// initialize some control plots
gStyle->SetOptTitle(1);
TCanvas *cDbg = newNoWarnTCanvas(getUniqueRootName(), Form("DeltaChi2 for 2D scan %i",nScansDone));
cDbg->SetMargin(0.1,0.15,0.1,0.1);
float hChi2min2dMin = hChi2min2d->GetMinimum();
bool firstScanDone = hChi2min2dMin<1e5;
TH2F *hDbgChi2min2d = histHardCopy(hChi2min2d, firstScanDone);
hDbgChi2min2d->SetTitle(Form("#Delta#chi^{2} for scan %i, %s",nScansDone,title.Data()));
if ( firstScanDone ) hDbgChi2min2d->GetZaxis()->SetRangeUser(hChi2min2dMin,hChi2min2dMin+25);
hDbgChi2min2d->GetXaxis()->SetTitle(par1->GetTitle());
hDbgChi2min2d->GetYaxis()->SetTitle(par2->GetTitle());
hDbgChi2min2d->GetZaxis()->SetTitle("#Delta#chi^{2}");
TH2F *hDbgStart = histHardCopy(hChi2min2d, false);
// start coordinates
// don't allow the under/overflow bins
int iStart = min(hCL2d->GetXaxis()->FindBin(par1->getVal()), hCL2d->GetNbinsX());
int jStart = min(hCL2d->GetYaxis()->FindBin(par2->getVal()), hCL2d->GetNbinsY());
iStart = max(iStart, 1);
jStart = max(jStart, 1);
hDbgStart->SetBinContent(iStart, jStart, 500.);
TMarker *startpointmark = new TMarker(par1->getVal(),par2->getVal(),3);
// timer
TStopwatch tFit;
TStopwatch tSlimResult;
TStopwatch tScan;
TStopwatch tMemory;
// set up the scan spiral
int X = 2*nPoints2dx;
int Y = 2*nPoints2dy;
int x,y,dx,dy;
x = y = dx = 0;
dy = -1;
int t = std::max(X,Y);
int maxI = t*t;
for ( int spiralstep=0; spiralstep<maxI; spiralstep++ )
{
if ((-X/2 <= x) && (x <= X/2) && (-Y/2 <= y) && (y <= Y/2))
{
int i = x+iStart;
int j = y+jStart;
if ( i>0 && i<=nPoints2dx && j>0 && j<=nPoints2dy )
{
tScan.Start(false);
// status bar
if (((int)nSteps % (int)(nTotalSteps/printFreq)) == 0){
cout << Form("MethodProbScan::scan2d() : scanning %3.0f%%", (float)nSteps/(float)nTotalSteps*100.)
<< " \r" << flush;
}
nSteps++;
// status histogram
if ( spiralstep>0 ) hDbgStart->SetBinContent(i, j, 500./*firstScan ? 1. : hChi2min2dMin+36*/);
// set start parameters from inner turn of the spiral
int xStartPars, yStartPars;
computeInnerTurnCoords(iStart, jStart, i, j, xStartPars, yStartPars, 1);
RooSlimFitResult *rStartPars = mycurveResults2d[xStartPars-1][yStartPars-1];
if ( rStartPars ) setParameters(w, parsName, rStartPars);
// memory management:
tMemory.Start(false);
// delete old, inner fit results, that we don't need for start parameters anymore
// for this we take the second-inner-most turn.
int iOld, jOld;
bool innerTurnExists = computeInnerTurnCoords(iStart, jStart, i, j, iOld, jOld, 2);
if ( innerTurnExists ){
deleteIfNotInCurveResults2d(mycurveResults2d[iOld-1][jOld-1]);
mycurveResults2d[iOld-1][jOld-1] = 0;
}
tMemory.Stop();
// alternative choice for start parameters: always from what we found at function call
// setParameters(w, parsName, startPars->get(0));
// set scan point
float scanvalue1 = hCL2d->GetXaxis()->GetBinCenter(i);
float scanvalue2 = hCL2d->GetYaxis()->GetBinCenter(j);
par1->setVal(scanvalue1);
par2->setVal(scanvalue2);
// fit!
tFit.Start(false);
RooFitResult *fr;
if ( !arg->probforce ) fr = fitToMinBringBackAngles(w->pdf(pdfName), false, -1);
else fr = fitToMinForce(w, combiner->getPdfName());
double chi2minScan = fr->minNll();
tFit.Stop();
tSlimResult.Start(false);
RooSlimFitResult *r = new RooSlimFitResult(fr); // try to save memory by using the slim fit result
tSlimResult.Stop();
delete fr;
allResults.push_back(r);
bestMinFoundInScan = TMath::Min((double)chi2minScan, (double)bestMinFoundInScan);
mycurveResults2d[i-1][j-1] = r;
// If we find a new global minumum, this means that all
// previous 1-CL values are too high. We'll save the new possible solution, adjust the global
// minimum, return a status code, and stop.
if ( chi2minScan > -500 && chi2minScan<chi2minGlobal ){
// warn only if there was a significant improvement
if ( arg->debug || chi2minScan<chi2minGlobal-1e-2 ){
if ( arg->verbose ) cout << "MethodProbScan::scan2d() : WARNING : '" << title << "' new global minimum found! chi2minGlobal="
<< chi2minGlobal << " chi2minScan=" << chi2minScan << endl;
}
chi2minGlobal = chi2minScan;
// recompute previous 1-CL values
for ( int k=1; k<=hCL2d->GetNbinsX(); k++ )
for ( int l=1; l<=hCL2d->GetNbinsY(); l++ ){
hCL2d->SetBinContent(k, l, TMath::Prob(hChi2min2d->GetBinContent(k,l)-chi2minGlobal, ndof));
}
}
double deltaChi2 = chi2minScan - chi2minGlobal;
double oneMinusCL = TMath::Prob(deltaChi2, ndof);
// Save the 1-CL value. But only if better than before!
if ( hCL2d->GetBinContent(i, j) < oneMinusCL ){
hCL2d->SetBinContent(i, j, oneMinusCL);
hChi2min2d->SetBinContent(i, j, chi2minScan);
hDbgChi2min2d->SetBinContent(i, j, chi2minScan);
curveResults2d[i-1][j-1] = r;
}
// draw/update histograms - doing only every 10th update saves
// a lot of time for small combinations
if ( ( arg->interactive && ((int)nSteps % 10 == 0) ) || nSteps==nTotalSteps ){
hDbgChi2min2d->Draw("colz");
hDbgStart->Draw("boxsame");
startpointmark->Draw();
cDbg->Update();
cDbg->Modified();
}
tScan.Stop();
}
}
// spiral stuff:
if( (x == y) || ((x < 0) && (x == -y)) || ((x > 0) && (x == 1-y)))
{
t = dx;
dx = -dy;
dy = t;
}
x += dx;
y += dy;
}
cout << "MethodProbScan::scan2d() : scan done. " << endl;
if ( arg->debug ){
cout << "MethodProbScan::scan2d() : full scan time: "; tScan.Print();
cout << "MethodProbScan::scan2d() : - fitting: "; tFit.Print();
cout << "MethodProbScan::scan2d() : - create RooSlimFitResults: "; tSlimResult.Print();
cout << "MethodProbScan::scan2d() : - memory management: "; tMemory.Print();
}
setParameters(w, parsName, startPars->get(0));
saveSolutions2d();
if ( arg->debug ) printLocalMinima();
if ( arg->confirmsols ) confirmSolutions();
// clean all fit results that didn't make it into the final result
for ( int i=0; i<allResults.size(); i++ ){
deleteIfNotInCurveResults2d(allResults[i]);
}
if ( bestMinFoundInScan-bestMinOld > 0.1 )
{
cout << "MethodProbScan::scan2d() : WARNING: Scan didn't find minimum that was found before!" << endl;
cout << "MethodProbScan::scan2d() : Are you using too strict parameter limits?" << endl;
cout << "MethodProbScan::scan2d() : min chi2 found in scan: " << bestMinFoundInScan << ", old min chi2: " << bestMinOld << endl;
return 1;
}
return 0;
}
int fitHisto(int siteNum,string dataVer)
{
//===>initialize variable
bool anaLiHe=true;//He8/Li9 bg
int ADNumOfSite[3]={0};
int daqHistNum=5;
if( dataVer.find("13")!=-1 || dataVer.find("14")!=-1 || dataVer.find("15")!=-1 || dataVer.find("16")!=-1)
{
ADNumOfSite[0]=2;
ADNumOfSite[1]=2;
ADNumOfSite[2]=4;
daqHistNum=5;
} else
{
if( dataVer.find("11")!=-1 || dataVer.find("12")!=-1 )
{
ADNumOfSite[0]=2;
ADNumOfSite[1]=1;
ADNumOfSite[2]=3;
daqHistNum=4;
}
}
string site;
if( siteNum==1 )
{
site="EH1";
} else if(siteNum==2)
{
site="EH2";
}else if (siteNum==3)
{
site="EH3";
}else
{
site="EH1";
}
//===>initialize histograms
//livetime
string runnum;
TH1F* h[daqHistNum];//the fifth is for daqtime
Double_t totalTime[5]={0.};
//Li9 He8
double n98total=0.;
//double in98total=0.;
double ifitn98total=0.;
double n98Rate=0.;
double in98Rate=0.;
double tlivetime=0.;
TH1F* showermuonNum[6];
TH1F* time2lastshowermuon[6];
TH1F* hh[6];
//===>get histograms from .root file for analyse
string filename;
filename=site;
filename+="mergedHist_";
filename+=dataVer;
filename+=".root";
TFile* f=new TFile(filename.c_str());
//livetime
for( int i=0 ; i<daqHistNum ; i++ )
{
stringstream hname;
if( i==daqHistNum-1 )
{
hname<<"LiveTime/DaqTime";
} else
{
hname<<"LiveTime/AD";
hname<<i+1;
hname<<"LiveTime";
}
h[i] = (TH1F*)f->Get(hname.str().c_str());
if( !(h[i]) )
{
cout<<"Can not get Hist : "<<hname.str()<<" from "<<site<<"/"<<runnum<<" ."<<endl;
//return true;
continue;
}
}
//Li9 He8
if(anaLiHe)
{
for( int i=0 ; i<6 ; i++ )
{
TString hnameLi;
hnameLi="lidj/showermuonNum";
hnameLi+=i+1;
showermuonNum[i]=(TH1F*)f->Get(hnameLi);
hnameLi="lidj/time2lastshowermuon";
hnameLi+=i+1;
hnameLi+="4Li";
time2lastshowermuon[i]=(TH1F*)f->Get(hnameLi);
}
}
//===>analyse
TAxis *xaxis = h[0]->GetXaxis();
int binNum = xaxis->GetNbins();
int BinBound=h[0]->FindBin(1346428800);//2012.9.1 0:0:0
//livetime
if( BinBound!=0 )
{
if( site=="EH2" )
{
for(int j=1 ; j<=BinBound ; j++ )
{
h[1]->SetBinContent(j,0);
}
}
if( site=="EH3" )
{
for(int j=1 ; j<=BinBound ; j++ )
{
h[3]->SetBinContent(j,0);
}
}
}
double totalTimeSpecical=0.;
for( int j=BinBound ; j<=binNum ; j++ )
{
totalTimeSpecical+=h[4]->GetBinContent(j);
}
for( int i=0 ; i<5 ; i++ )
{
totalTime[i]=0.;
for( int j=1 ; j<=binNum ; j++ )
{
totalTime[i]+=h[i]->GetBinContent(j);
}
}
double totalDaqtime=0.;
if( site=="EH2" )
{
totalDaqtime=totalTime[4]+totalTimeSpecical;
}else if( site=="EH3" )
{
totalDaqtime=totalTime[4]*3+totalTimeSpecical;
} else
{
totalDaqtime=totalTime[4]*2;
}
//Li He
std::cout<<"begin to analyse Li "<<endl;
if(anaLiHe)
{
int hnum=time2lastshowermuon[0]->FindBin(100);
//std::cout<<"0.001 : "<<time2lastshowermuon[0]->FindBin(0.001)<<endl;
//std::cout<<"100 : "<<time2lastshowermuon[0]->FindBin(100)<<endl;
//std::cout<<"hnum : "<<hnum<<endl;
for( int k=0 ; k<6 ; k++ )
{
TString hhname="slice";
hhname+=k+1;
hh[k]=new TH1F(hhname,"hh",hnum-2,0.001,100);
for( int i=1 ; i<hnum-2 ; i++ )
{
hh[k]->SetBinContent(i,time2lastshowermuon[k]->GetBinContent(i+1));
}
hh[k]->SetOption("E1");
}
double showerTh[7] = {0.02, 0.5, 1.5, 2.5, 3.5, 4.5, 5.0};
TH1F* LiResult[5];
LiResult[0]= new TH1F("LiResult", "All", 6, showerTh);
LiResult[1]= new TH1F("LiResult", "AD1", 6, showerTh);
LiResult[2]= new TH1F("LiResult", "AD2", 6, showerTh);
LiResult[3]= new TH1F("LiResult", "AD3", 6, showerTh);
LiResult[4]= new TH1F("LiResult", "AD4", 6, showerTh);
double NumMuon[6]={0.};
double RateMuon[6]={0.};
double NumIbd[6]={0.};
RooRealVar x("x","x",0.001,40., "s");
RooRealVar tauLi9("tauLi9", "tauLi9", -0.257, "s");
RooRealVar tauHe8("tauHe8", "tauHe8", -0.172, "s");
RooRealVar rateMu("rateMu","rate of showermuon",-0.1,-10., 0.,"Hz");
RooRealVar Li9Frac("Li9Frac","Li9's Frac", 0.0);//R
RooRealVar N98("N98","total number of Li9 and He8",500.,0.,1.e5);
//RooRealVar N98("N98","total number of Li9 and He8",3e2, 1e1, 2.e5);
RooFormulaVar lambdaLi9("lambdaLi9","lambdaLi9","1/@0 + @1",RooArgList(tauLi9, rateMu));
RooFormulaVar lambdaHe8("lambdaHe8","lambdaHe8","1/@0 + @1",RooArgList(tauHe8, rateMu));
RooFormulaVar coeLi9("coeLi9","coeLi9","@0 * @1 ",RooArgList(N98,Li9Frac));
RooFormulaVar coeHe8("coeHe8","coeHe8","@0 * ( 1 - @1 )",RooArgList(N98,Li9Frac));
RooRealVar NIbd("NIbd","number of background",3e2, 1e1, 2.e6);
RooExponential expLi9("expLi9", "Li9 distribution", x, lambdaLi9);
RooExponential expHe8("expHe8", "He8 distribution", x, lambdaHe8);
double Ibdrate[3]={0.0076,0.0067,0.00082};
RooRealVar rateIbd("rateIbd","rateIbd",-Ibdrate[siteNum-1],-10,0.,"Hz");
RooFormulaVar lambdaIbd("lambdaIbd","lambdaIbd","@0 + @1",RooArgList(rateIbd, rateMu));
RooExponential expIbd("expIbd","Ibd distribution",x,rateMu);
//RooExponential expIbd("expIbd","Ibd distribution",x,lambdaIbd);
RooFitResult* fitres;
RooAddPdf* sum;
RooDataHist* data;
RooPlot* mesframe[6];
double n98[41]={0.};
double in98[41]={0.};
double nIbd[41]={0.};
double inIbd[41]={0.};
double minNLL[41]={0.};
double rateMuValue[41]={0.};
double rateMuErr[41]={0.};
//double minNl[6]={0.};
int minindex[6]={0};
double n98fit[6]={0.};
double in98fit[6]={0.};
double rateMufit[6]={0.};
double binwidth=0.;
TH1F* lh[5][6];
TString lname[5]={"","AD1","AD2","AD3","AD4"};
int lcolor[5]={4,3,2,6,5};
bool draw6slice=false;
TCanvas* c;
TString xTitle[6]={"slice 1 : 0.02~0.5GeV","slice 2 : 0.5~1.5GeV","slice 3 : 1.5~2.5GeV","slice 4 : 2.5~3.5GeV","slice 5 : 3.5~4.5GeV","slice 6 : >4.5GeV"};
if( draw6slice )
{
c = new TCanvas("c","c",2000,800) ;
c->Divide(3,2);
gStyle->SetEndErrorSize(5.0);
gStyle->SetMarkerSize(0.1);
//gStyle->SetHistLineWidth(1);
if( 0 )
{
// it's strange ,if you can't draw following six figs in the big 3*2 pad,you can fist use these code,run once,don't exit the ROOT,then you can draw six figs!!!
data = new RooDataHist("data", "data", x, time2lastshowermuon[0]);
sum = new RooAddPdf("sum","sum",RooArgList(expLi9, expHe8, expIbd),RooArgList(coeLi9, coeHe8, NIbd));
RooPlot* mesframe = x.frame() ;
data->plotOn(mesframe) ;
c->cd(1);
mesframe->Draw();
return true;
}
}
for( int ihist=0 ; ihist<1 ; ihist++ )
{
double minNl[6]={0.};
for( int j=0 ; j<6 ; j++ )
{
std::cout<<"now is "<< j+1<<endl;
NumIbd[j]=hh[j]->Integral(1,hh[j]->FindBin(40));
hh[j]->Rebin(100);
data = new RooDataHist("data", "data", x, hh[j]);
sum = new RooAddPdf("sum","sum",RooArgList(expLi9, expHe8, expIbd),RooArgList(coeLi9, coeHe8, NIbd));
for( int i=0 ; i<41 ; i++ )
{
Li9Frac.setVal(i*0.025);
fitres = sum->fitTo((*data),Save(),PrintLevel(-1),Extended(kTRUE));
//fitres->Print();
n98[i]=N98.getVal(0);
in98[i]=N98.getError();
nIbd[i]=NIbd.getVal(0);
inIbd[i]=NIbd.getError();
rateMuValue[i]=rateMu.getVal(0);
rateMuErr[i]=rateMu.getError();
minNLL[i] = fitres->minNll();
if( minNLL[i]<minNl[j] )
{
minNl[j]=minNLL[i];
minindex[j]=i;
}
}
n98fit[j]=n98[minindex[j]];
in98fit[j]=in98[minindex[j]];
rateMufit[j]=rateMuValue[minindex[j]];
if( draw6slice )
{
//for drawing fit figure
Li9Frac.setVal(minindex[j]*0.025);
fitres = sum->fitTo((*data),Save(),PrintLevel(-1),Extended(kTRUE));
//fitres->Print();
mesframe[j] = x.frame() ;
data->plotOn(mesframe[j]) ;
sum->plotOn(mesframe[j]);
sum->plotOn(mesframe[j],Components(expIbd),LineStyle(kDashed),LineColor(kGreen)) ;
sum->plotOn(mesframe[j],Components(RooArgSet(expLi9,expHe8)),LineStyle(kDashed),LineColor(kRed)) ;
xTitle[j]+=" time since last muon (s)";
mesframe[j]->GetXaxis()->SetTitle(xTitle[j]);
mesframe[j]->GetYaxis()->SetTitle("Entries");
c->cd(j+1);
mesframe[j]->Draw();
//gPad->SetLogy();
}
}
n98total=(n98fit[0]+n98fit[1]+n98fit[2]+(n98fit[3]+n98fit[4]+n98fit[5])*exp(-1/0.257))/0.678;
ifitn98total=sqrt(in98fit[0]*in98fit[0]+in98fit[1]*in98fit[1]+in98fit[2]*in98fit[2]+((in98fit[3]+in98fit[4]+in98fit[5])*exp(-1/0.257)*(in98fit[3]+in98fit[4]+in98fit[5])*exp(-1/0.257)))/0.678;
if( ihist==0 )
{
for( int i=0 ; i<ADNumOfSite[siteNum-1] ; i++ )
{
tlivetime+=totalTime[i];
}
}else
{
tlivetime=totalTime[ihist-1];
}
n98Rate=n98total/(tlivetime/(24*3600));
in98Rate=sqrt((ifitn98total/(tlivetime/(24*3600)))*(ifitn98total/(tlivetime/(24*3600)))+(n98total/(tlivetime/(24*3600))/2)*(n98total/(tlivetime/(24*3600))/2));
for( int j=0 ; j<6 ; j++ )
{
LiResult[ihist]->SetBinContent(j+1,n98fit[j]);
LiResult[ihist]->SetBinError(j+1,in98fit[j]);
for( int jbin=0 ; jbin<binNum ; jbin++ )
{
NumMuon[j]+=showermuonNum[j]->GetBinContent(jbin);
}
RateMuon[j]=NumMuon[j]/totalDaqtime;
//std::cout<<"RateMuon[j] : "<<RateMuon[j]<<endl;
//std::cout<<"RateMuon0[j] : "<<RateMuon0[j]<<endl;
std::cout<<" R "<<minindex[j]*0.025<<" n98["<<minindex[j]<<"] "<<n98fit[j]<<" in98["<<minindex[j]<<"] "<< in98fit[j]<<" fitRateMu["<<minindex[j]<<"] "<<rateMufit[j]<<" realRateMu "<<RateMuon[j]<<" NumTol "<<NumIbd[j]<<endl;
}
std::cout<<"n98Num : "<<n98total<<" +- "<<ifitn98total<<" Rate : "<<n98Rate<<" +- "<< in98Rate<<endl;
}
/*
//TCanvas* c4 = new TCanvas("c4", "c4", 600, 400);
//gStyle->SetEndErrorSize(0.0);
LiResult[0]->SetMarkerStyle(20);
LiResult[0]->SetMarkerColor(lcolor[0]);
LiResult[0]->SetLineColor(lcolor[0]);
LiResult[0]->SetMarkerSize(1.0);
LiResult[0]->SetMinimum(0);
LiResult[0]->SetMaximum(LiResult[0]->GetMaximum()*1.5);
LiResult[0]->GetXaxis()->SetTitle("Muon visible energy (GeV)");
LiResult[0]->GetYaxis()->SetTitle("Fitted ^{9}Li events");
LiResult[0]->SetTitle("");
LiResult[0]->SetStats(kFALSE);
LiResult[0]->Draw("EP");
TLegend *legend=new TLegend(.6,.65,.79,.89);
TString legendLabel=dataVer+" "+site+" "+"All";
legend->AddEntry(LiResult[0],legendLabel,"lp");
for( int ihist=1 ; ihist<ADNumOfSite[siteNum-1]+1; ihist++ )
{
LiResult[ihist]->SetMarkerStyle(20);
LiResult[ihist]->SetMarkerColor(lcolor[ihist]);
LiResult[ihist]->SetLineColor(lcolor[ihist]);
LiResult[ihist]->SetMarkerSize(1.0);
LiResult[ihist]->SetStats(kFALSE);
LiResult[ihist]->Draw("EPsame");
legend->AddEntry(LiResult[ihist],lname[ihist],"lp");
}
legend->SetFillColor(0);
legend->Draw();
*/
}
//f->Close();
//===>print result
std::cout<<""<<endl;
std::cout<<site <<"'s infomation : "<<endl;
std::cout<<""<<endl;
for( int i=0 ; i<ADNumOfSite[siteNum-1] ; i++ )
{
std::cout<<"Total AD"<<i+1<<"LiveTime : "<<totalTime[i]/(24*3600)<<endl;
}
std::cout<<"Total DaqTime : "<<totalTime[daqHistNum-1]/(24*3600)<<" day" <<endl;
std::cout<<""<<endl;
if(anaLiHe)
{
std::cout<<" "<<endl;
/*
std::cout<<"Li9/He8 "<<endl;
std::cout<<"n98total : "<<n98total <<" +- "<<in98total <<" Rate:"<<n98total/totalTime[0]*24*3600 <<" +- "<<in98total/totalTime[0]*24*3600 <<endl;
std::cout<<"n98total0(without rpc) : "<<n98total0 <<" +- "<<in98total0 <<" Rate:"<<n98total0/totalTime0[0]*24*3600 <<" +- "<<in98total0/totalTime0[0]*24*3600 <<endl;
*/
}
//===>write into .root file
string rootname=site;
rootname+="FitResult_"+dataVer+".root";
TFile* file = new TFile(rootname.c_str(),"RECREATE");
file->cd();
for( int i=0 ; i<daqHistNum ; i++ )
{
h[i]->Write();
}
if( anaLiHe )
{
//for( int i=0 ; i<ADNumOfSite[siteNum-1] ; i++ )
for( int i=0 ; i<6 ; i++ )
{
hh[i]->Write();
}
}
file->Close();
f->Close();
return 0;
}
float ComputeTestStat(TString wsfile, double mu_susy_sig_val) {
gROOT->Reset();
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
modelConfig->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_sig == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
} else {
printf(" current value is : %8.3f\n", rrv_mu_susy_sig->getVal() ) ; cout << flush ;
rrv_mu_susy_sig->setConstant(kFALSE) ;
}
/*
// check the impact of varying the qcd normalization:
RooRealVar *rrv_qcd_0lepLDP_ratioH1 = ws->var("qcd_0lepLDP_ratio_H1");
RooRealVar *rrv_qcd_0lepLDP_ratioH2 = ws->var("qcd_0lepLDP_ratio_H2");
RooRealVar *rrv_qcd_0lepLDP_ratioH3 = ws->var("qcd_0lepLDP_ratio_H3");
rrv_qcd_0lepLDP_ratioH1->setVal(0.3);
rrv_qcd_0lepLDP_ratioH2->setVal(0.3);
rrv_qcd_0lepLDP_ratioH3->setVal(0.3);
rrv_qcd_0lepLDP_ratioH1->setConstant(kTRUE);
rrv_qcd_0lepLDP_ratioH2->setConstant(kTRUE);
rrv_qcd_0lepLDP_ratioH3->setConstant(kTRUE);
*/
printf("\n\n\n ===== Doing a fit with SUSY component floating ====================\n\n") ;
RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
double logLikelihoodSusyFloat = fitResult->minNll() ;
double logLikelihoodSusyFixed(0.) ;
double testStatVal(-1.) ;
if ( mu_susy_sig_val >= 0. ) {
printf("\n\n\n ===== Doing a fit with SUSY fixed ====================\n\n") ;
printf(" fixing mu_susy_sig to %8.2f.\n", mu_susy_sig_val ) ;
rrv_mu_susy_sig->setVal( mu_susy_sig_val ) ;
rrv_mu_susy_sig->setConstant(kTRUE) ;
fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
logLikelihoodSusyFixed = fitResult->minNll() ;
testStatVal = 2.*(logLikelihoodSusyFixed - logLikelihoodSusyFloat) ;
printf("\n\n\n ======= test statistic : -2 * ln (L_fixed / ln L_max) = %8.3f\n\n\n", testStatVal ) ;
}
return testStatVal ;
}
void ws_cls_hybrid1_ag( const char* wsfile = "output-files/expected-ws-lm9-2BL.root", bool isBgonlyStudy=false, double poiVal = 150.0, int nToys=100, bool makeTtree=true, int verbLevel=0 ) {
TTree* toytt(0x0) ;
TFile* ttfile(0x0) ;
int tt_gen_Nsig ;
int tt_gen_Nsb ;
int tt_gen_Nsig_sl ;
int tt_gen_Nsb_sl ;
int tt_gen_Nsig_ldp ;
int tt_gen_Nsb_ldp ;
int tt_gen_Nsig_ee ;
int tt_gen_Nsb_ee ;
int tt_gen_Nsig_mm ;
int tt_gen_Nsb_mm ;
double tt_testStat ;
double tt_dataTestStat ;
double tt_hypo_mu_susy_sig ;
char ttname[1000] ;
char tttitle[1000] ;
if ( makeTtree ) {
ttfile = gDirectory->GetFile() ;
if ( ttfile == 0x0 ) { printf("\n\n\n *** asked for a ttree but no open file???\n\n") ; return ; }
if ( isBgonlyStudy ) {
sprintf( ttname, "toytt_%.0f_bgo", poiVal ) ;
sprintf( tttitle, "Toy study for background only, mu_susy_sig = %.0f", poiVal ) ;
} else {
sprintf( ttname, "toytt_%.0f_spb", poiVal ) ;
sprintf( tttitle, "Toy study for signal+background, mu_susy_sig = %.0f", poiVal ) ;
}
printf("\n\n Creating TTree : %s : %s\n\n", ttname, tttitle ) ;
gDirectory->pwd() ;
gDirectory->ls() ;
toytt = new TTree( ttname, tttitle ) ;
gDirectory->ls() ;
toytt -> Branch( "gen_Nsig" , &tt_gen_Nsig , "gen_Nsig/I" ) ;
toytt -> Branch( "gen_Nsb" , &tt_gen_Nsb , "gen_Nsb/I" ) ;
toytt -> Branch( "gen_Nsig_sl" , &tt_gen_Nsig_sl , "gen_Nsig_sl/I" ) ;
toytt -> Branch( "gen_Nsb_sl" , &tt_gen_Nsb_sl , "gen_Nsb_sl/I" ) ;
toytt -> Branch( "gen_Nsig_ldp" , &tt_gen_Nsig_ldp , "gen_Nsig_ldp/I" ) ;
toytt -> Branch( "gen_Nsb_ldp" , &tt_gen_Nsb_ldp , "gen_Nsb_ldp/I" ) ;
toytt -> Branch( "gen_Nsig_ee" , &tt_gen_Nsig_ee , "gen_Nsig_ee/I" ) ;
toytt -> Branch( "gen_Nsb_ee" , &tt_gen_Nsb_ee , "gen_Nsb_ee/I" ) ;
toytt -> Branch( "gen_Nsig_mm" , &tt_gen_Nsig_mm , "gen_Nsig_mm/I" ) ;
toytt -> Branch( "gen_Nsb_mm" , &tt_gen_Nsb_mm , "gen_Nsb_mm/I" ) ;
toytt -> Branch( "testStat" , &tt_testStat , "testStat/D" ) ;
toytt -> Branch( "dataTestStat" , &tt_dataTestStat , "dataTestStat/D" ) ;
toytt -> Branch( "hypo_mu_susy_sig" , &tt_hypo_mu_susy_sig , "hypo_mu_susy_sig/D" ) ;
}
//--- Tell RooFit to shut up about anything less important than an ERROR.
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;
random_ng = new TRandom2(12345) ;
/// char sel[100] ;
/// if ( strstr( wsfile, "1BL" ) != 0 ) {
/// sprintf( sel, "1BL" ) ;
/// } else if ( strstr( wsfile, "2BL" ) != 0 ) {
/// sprintf( sel, "2BL" ) ;
/// } else if ( strstr( wsfile, "3B" ) != 0 ) {
/// sprintf( sel, "3B" ) ;
/// } else if ( strstr( wsfile, "1BT" ) != 0 ) {
/// sprintf( sel, "1BT" ) ;
/// } else if ( strstr( wsfile, "2BT" ) != 0 ) {
/// sprintf( sel, "2BT" ) ;
/// } else {
/// printf("\n\n\n *** can't figure out which selection this is. I quit.\n\n" ) ;
/// return ;
/// }
/// printf("\n\n selection is %s\n\n", sel ) ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
const RooArgSet* nuisanceParameters = modelConfig->GetNuisanceParameters() ;
RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_sig") ;
if ( rrv_mu_susy_sig == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_sig in workspace. Quitting.\n\n\n") ;
return ;
}
//// printf("\n\n\n ===== Doing a fit ====================\n\n") ;
//// RooFitResult* preFitResult = likelihood->fitTo( *rds, Save(true) ) ;
//// const RooArgList preFitFloatVals = preFitResult->floatParsFinal() ;
//// {
//// TIterator* parIter = preFitFloatVals.createIterator() ;
//// while ( RooRealVar* par = (RooRealVar*) parIter->Next() ) {
//// printf(" %20s : %8.2f\n", par->GetName(), par->getVal() ) ;
//// }
//// }
//--- Get pointers to the model predictions of the observables.
rfv_n_sig = ws->function("n_sig") ;
rfv_n_sb = ws->function("n_sb") ;
rfv_n_sig_sl = ws->function("n_sig_sl") ;
rfv_n_sb_sl = ws->function("n_sb_sl") ;
rfv_n_sig_ldp = ws->function("n_sig_ldp") ;
rfv_n_sb_ldp = ws->function("n_sb_ldp") ;
rfv_n_sig_ee = ws->function("n_sig_ee") ;
rfv_n_sb_ee = ws->function("n_sb_ee") ;
rfv_n_sig_mm = ws->function("n_sig_mm") ;
rfv_n_sb_mm = ws->function("n_sb_mm") ;
if ( rfv_n_sig == 0x0 ) { printf("\n\n\n *** can't find n_sig in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb == 0x0 ) { printf("\n\n\n *** can't find n_sb in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_sl == 0x0 ) { printf("\n\n\n *** can't find n_sig_sl in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_sl == 0x0 ) { printf("\n\n\n *** can't find n_sb_sl in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sig_ldp in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sb_ldp in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_ee == 0x0 ) { printf("\n\n\n *** can't find n_sig_ee in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_ee == 0x0 ) { printf("\n\n\n *** can't find n_sb_ee in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_mm == 0x0 ) { printf("\n\n\n *** can't find n_sig_mm in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_mm == 0x0 ) { printf("\n\n\n *** can't find n_sb_mm in workspace. Quitting.\n\n\n") ; return ; }
//--- Get pointers to the observables.
const RooArgSet* dsras = rds->get() ;
TIterator* obsIter = dsras->createIterator() ;
while ( RooRealVar* obs = (RooRealVar*) obsIter->Next() ) {
if ( strcmp( obs->GetName(), "Nsig" ) == 0 ) { rrv_Nsig = obs ; }
if ( strcmp( obs->GetName(), "Nsb" ) == 0 ) { rrv_Nsb = obs ; }
if ( strcmp( obs->GetName(), "Nsig_sl" ) == 0 ) { rrv_Nsig_sl = obs ; }
if ( strcmp( obs->GetName(), "Nsb_sl" ) == 0 ) { rrv_Nsb_sl = obs ; }
if ( strcmp( obs->GetName(), "Nsig_ldp" ) == 0 ) { rrv_Nsig_ldp = obs ; }
if ( strcmp( obs->GetName(), "Nsb_ldp" ) == 0 ) { rrv_Nsb_ldp = obs ; }
if ( strcmp( obs->GetName(), "Nsig_ee" ) == 0 ) { rrv_Nsig_ee = obs ; }
if ( strcmp( obs->GetName(), "Nsb_ee" ) == 0 ) { rrv_Nsb_ee = obs ; }
if ( strcmp( obs->GetName(), "Nsig_mm" ) == 0 ) { rrv_Nsig_mm = obs ; }
if ( strcmp( obs->GetName(), "Nsb_mm" ) == 0 ) { rrv_Nsb_mm = obs ; }
}
if ( rrv_Nsig == 0x0 ) { printf("\n\n\n *** can't find Nsig in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb == 0x0 ) { printf("\n\n\n *** can't find Nsb in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_sl == 0x0 ) { printf("\n\n\n *** can't find Nsig_sl in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_sl == 0x0 ) { printf("\n\n\n *** can't find Nsb_sl in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsig_ldp in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsb_ldp in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_ee == 0x0 ) { printf("\n\n\n *** can't find Nsig_ee in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_ee == 0x0 ) { printf("\n\n\n *** can't find Nsb_ee in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_mm == 0x0 ) { printf("\n\n\n *** can't find Nsig_mm in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_mm == 0x0 ) { printf("\n\n\n *** can't find Nsb_mm in dataset. Quitting.\n\n\n") ; return ; }
printf("\n\n\n === Model values for observables\n\n") ;
printObservables() ;
//--- save the actual values of the observables.
saveObservables() ;
//--- evaluate the test stat on the data: fit with susy floating.
rrv_mu_susy_sig->setVal( poiVal ) ;
rrv_mu_susy_sig->setConstant( kTRUE ) ;
printf("\n\n\n ====== Fitting the data with susy fixed.\n\n") ;
RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true));
int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
if ( dataSusyFixedFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;
rrv_mu_susy_sig->setVal( 0.0 ) ;
rrv_mu_susy_sig->setConstant( kFALSE ) ;
printf("\n\n\n ====== Fitting the data with susy floating.\n\n") ;
RooFitResult* dataFitResultSusyFloat = likelihood->fitTo(*rds, Save(true));
int dataSusyFloatFitCovQual = dataFitResultSusyFloat->covQual() ;
if ( dataSusyFloatFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFloatFitCovQual ) ; return ; }
double dataFitSusyFloatNll = dataFitResultSusyFloat->minNll() ;
double dataTestStat = 2.*( dataFitSusyFixedNll - dataFitSusyFloatNll) ;
printf("\n\n\n Data value of test stat : %8.2f\n", dataTestStat ) ;
printf("\n\n\n === Nuisance parameters\n\n") ;
{
int npi(0) ;
TIterator* npIter = nuisanceParameters->createIterator() ;
while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) {
np_initial_val[npi] = np_rrv->getVal() ; //--- I am assuming that the order of the NPs in the iterator does not change.
TString npname( np_rrv->GetName() ) ;
npname.ReplaceAll("_prim","") ;
RooAbsReal* np_rfv = ws->function( npname ) ;
TString pdfname( np_rrv->GetName() ) ;
pdfname.ReplaceAll("_prim","") ;
pdfname.Prepend("pdf_") ;
RooAbsPdf* np_pdf = ws->pdf( pdfname ) ;
if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; }
if ( np_rfv != 0x0 ) {
printf(" %20s : %8.2f , %20s, %8.2f\n", np_rrv->GetName(), np_rrv->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ;
} else {
printf(" %20s : %8.2f\n", np_rrv->GetName(), np_rrv->getVal() ) ;
}
npi++ ;
} // np_rrv iterator.
np_count = npi ;
}
tt_dataTestStat = dataTestStat ;
tt_hypo_mu_susy_sig = poiVal ;
printf("\n\n\n === Doing the toys\n\n") ;
int nToyOK(0) ;
int nToyWorseThanData(0) ;
for ( int ti=0; ti<nToys; ti++ ) {
printf("\n\n\n ======= Toy %4d\n\n\n", ti ) ;
//--- 1) pick values for the nuisance parameters from the PDFs and fix them.
{
TIterator* npIter = nuisanceParameters->createIterator() ;
while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) {
TString pdfname( np_rrv->GetName() ) ;
pdfname.ReplaceAll("_prim","") ;
pdfname.Prepend("pdf_") ;
RooAbsPdf* np_pdf = ws->pdf( pdfname ) ;
if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; return ; }
RooDataSet* nprds = np_pdf->generate( RooArgSet(*np_rrv) ,1) ;
const RooArgSet* npdsras = nprds->get() ;
TIterator* valIter = npdsras->createIterator() ;
RooRealVar* val = (RooRealVar*) valIter->Next() ;
//--- reset the value of the nuisance parameter and fix it for the toy model definition fit.
np_rrv->setVal( val->getVal() ) ;
np_rrv->setConstant( kTRUE ) ;
TString npname( np_rrv->GetName() ) ;
npname.ReplaceAll("_prim","") ;
RooAbsReal* np_rfv = ws->function( npname ) ;
if ( verbLevel > 0 ) {
if ( np_rfv != 0x0 ) {
printf(" %20s : %8.2f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ;
} else if ( strstr( npname.Data(), "eff_sf" ) != 0 ) {
np_rfv = ws->function( "eff_sf_sig" ) ;
RooAbsReal* np_rfv2 = ws->function( "eff_sf_sb" ) ;
printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ;
} else if ( strstr( npname.Data(), "sf_ll" ) != 0 ) {
np_rfv = ws->function( "sf_ee" ) ;
RooAbsReal* np_rfv2 = ws->function( "sf_mm" ) ;
printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ;
} else {
printf(" %20s : %8.2f\n", val->GetName(), val->getVal() ) ;
}
}
delete nprds ;
} // np_rrv iterator
}
//--- 2) Fit the dataset with these values for the nuisance parameters.
if ( isBgonlyStudy ) {
//-- fit with susy yield fixed to zero.
rrv_mu_susy_sig -> setVal( 0. ) ;
if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to zero.\n\n") ; }
} else {
//-- fit with susy yield fixed to predicted value.
rrv_mu_susy_sig -> setVal( poiVal ) ;
if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to %8.1f.\n\n", poiVal) ; }
}
rrv_mu_susy_sig->setConstant( kTRUE ) ;
if ( verbLevel > 0 ) {
printf("\n\n") ;
printf(" Fitting with these values for the observables to define the model for toy generation.\n") ;
rds->printMultiline(cout, 1, kTRUE, "") ;
printf("\n\n") ;
}
RooFitResult* toyModelDefinitionFitResult(0x0) ;
if ( verbLevel < 2 ) {
toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1));
} else {
toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true));
}
int toyModelDefFitCovQual = toyModelDefinitionFitResult->covQual() ;
if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d\n\n", toyModelDefFitCovQual ) ; }
if ( toyModelDefFitCovQual != 3 ) {
printf("\n\n\n *** Bad toy model definition fit. Cov qual %d. Aborting this toy.\n\n\n", toyModelDefFitCovQual ) ;
continue ;
}
delete toyModelDefinitionFitResult ;
if ( verbLevel > 0 ) {
printf("\n\n\n === Model values for observables. These will be used to generate the toy dataset.\n\n") ;
printObservables() ;
}
//--- 3) Generate a new set of observables based on this model.
generateObservables() ;
printf("\n\n\n Generated dataset\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
//--- Apparently, I need to make a new RooDataSet... Resetting the
// values in the old one doesn't stick. If you do likelihood->fitTo(*rds), it
// uses the original values, not the reset ones, in the fit.
RooArgSet toyFitobservedParametersList ;
toyFitobservedParametersList.add( *rrv_Nsig ) ;
toyFitobservedParametersList.add( *rrv_Nsb ) ;
toyFitobservedParametersList.add( *rrv_Nsig_sl ) ;
toyFitobservedParametersList.add( *rrv_Nsb_sl ) ;
toyFitobservedParametersList.add( *rrv_Nsig_ldp ) ;
toyFitobservedParametersList.add( *rrv_Nsb_ldp ) ;
toyFitobservedParametersList.add( *rrv_Nsig_ee ) ;
toyFitobservedParametersList.add( *rrv_Nsb_ee ) ;
toyFitobservedParametersList.add( *rrv_Nsig_mm ) ;
toyFitobservedParametersList.add( *rrv_Nsb_mm ) ;
RooDataSet* toyFitdsObserved = new RooDataSet("toyfit_ra2b_observed_rds", "RA2b toy observed data values",
toyFitobservedParametersList ) ;
toyFitdsObserved->add( toyFitobservedParametersList ) ;
//--- 4) Reset and free the nuisance parameters.
{
if ( verbLevel > 0 ) { printf("\n\n") ; }
int npi(0) ;
TIterator* npIter = nuisanceParameters->createIterator() ;
while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) {
np_rrv -> setVal( np_initial_val[npi] ) ; // assuming that the order in the iterator does not change.
np_rrv -> setConstant( kFALSE ) ;
npi++ ;
if ( verbLevel > 0 ) { printf(" reset %20s to %8.2f and freed it.\n", np_rrv->GetName() , np_rrv->getVal() ) ; }
} // np_rrv iterator.
if ( verbLevel > 0 ) { printf("\n\n") ; }
}
//--- 5a) Evaluate the test statistic: Fit with susy yield floating to get the absolute maximum log likelihood.
if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy floating.\n\n") ; }
rrv_mu_susy_sig->setVal( 0.0 ) ;
rrv_mu_susy_sig->setConstant( kFALSE ) ;
if ( verbLevel > 0 ) {
printf("\n toy dataset\n\n") ;
toyFitdsObserved->printMultiline(cout, 1, kTRUE, "") ;
}
/////---- nfg. Need to create a new dataset ----------
/////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1));
/////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true));
/////--------------
RooFitResult* maxLikelihoodFitResult(0x0) ;
if ( verbLevel < 2 ) {
maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1));
} else {
maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true));
}
if ( verbLevel > 0 ) { printObservables() ; }
int mlFitCovQual = maxLikelihoodFitResult->covQual() ;
if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", mlFitCovQual, maxLikelihoodFitResult->minNll() ) ; }
if ( mlFitCovQual != 3 ) {
printf("\n\n\n *** Bad maximum likelihood fit (susy floating). Cov qual %d. Aborting this toy.\n\n\n", mlFitCovQual ) ;
continue ;
}
double maxL_susyFloat = maxLikelihoodFitResult->minNll() ;
double maxL_mu_susy_sig = rrv_mu_susy_sig->getVal() ;
delete maxLikelihoodFitResult ;
//--- 5b) Evaluate the test statistic: Fit with susy yield fixed to hypothesis value.
// This is only necessary if the maximum likelihood fit value of the susy yield
// is less than the hypothesis value (to get a one-sided limit).
double testStat(0.0) ;
double maxL_susyFixed(0.0) ;
if ( maxL_mu_susy_sig < poiVal ) {
if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy fixed to %8.2f.\n\n", poiVal ) ; }
rrv_mu_susy_sig->setVal( poiVal ) ;
rrv_mu_susy_sig->setConstant( kTRUE ) ;
if ( verbLevel > 0 ) {
printf("\n toy dataset\n\n") ;
rds->printMultiline(cout, 1, kTRUE, "") ;
}
////--------- nfg. need to make a new dataset ---------------
////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1));
////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true));
////-----------------------------
RooFitResult* susyFixedFitResult(0x0) ;
if ( verbLevel < 2 ) {
susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1));
} else {
susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true));
}
if ( verbLevel > 0 ) { printObservables() ; }
int susyFixedFitCovQual = susyFixedFitResult->covQual() ;
if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", susyFixedFitCovQual, susyFixedFitResult->minNll() ) ; }
if ( susyFixedFitCovQual != 3 ) {
printf("\n\n\n *** Bad maximum likelihood fit (susy fixed). Cov qual %d. Aborting this toy.\n\n\n", susyFixedFitCovQual ) ;
continue ;
}
maxL_susyFixed = susyFixedFitResult->minNll() ;
testStat = 2. * (maxL_susyFixed - maxL_susyFloat) ;
delete susyFixedFitResult ;
} else {
if ( verbLevel > 0 ) { printf("\n\n Floating value of susy yield greater than hypo value (%8.2f > %8.2f). Setting test stat to zero.\n\n", maxL_mu_susy_sig, poiVal ) ; }
testStat = 0.0 ;
}
printf(" --- test stat for toy %4d : %8.2f\n", ti, testStat ) ;
nToyOK++ ;
if ( testStat > dataTestStat ) { nToyWorseThanData++ ; }
if ( makeTtree ) {
tt_testStat = testStat ;
tt_gen_Nsig = rrv_Nsig->getVal() ;
tt_gen_Nsb = rrv_Nsb->getVal() ;
tt_gen_Nsig_sl = rrv_Nsig_sl->getVal() ;
tt_gen_Nsb_sl = rrv_Nsb_sl->getVal() ;
tt_gen_Nsig_ldp = rrv_Nsig_ldp->getVal() ;
tt_gen_Nsb_ldp = rrv_Nsb_ldp->getVal() ;
tt_gen_Nsig_ee = rrv_Nsig_ee->getVal() ;
tt_gen_Nsb_ee = rrv_Nsb_ee->getVal() ;
tt_gen_Nsig_mm = rrv_Nsig_mm->getVal() ;
tt_gen_Nsb_mm = rrv_Nsb_mm->getVal() ;
toytt->Fill() ;
}
//--- *) reset things for the next toy.
resetObservables() ;
delete toyFitdsObserved ;
} // ti.
wstf->Close() ;
printf("\n\n\n") ;
if ( nToyOK == 0 ) { printf("\n\n\n *** All toys bad !?!?!\n\n\n") ; return ; }
double pValue = (1.0*nToyWorseThanData) / (1.0*nToyOK) ;
if ( isBgonlyStudy ) {
printf("\n\n\n p-value result, BG-only , poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ;
} else {
printf("\n\n\n p-value result, S-plus-B, poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ;
}
if ( makeTtree ) {
printf("\n\n Writing TTree : %s : %s\n\n", ttname, tttitle ) ;
ttfile->cd() ;
toytt->Write() ;
}
} // ws_cls_hybrid1
