void GenericModel::ConstructPseudoPDF(Bool_t)
{
// Build a parameterised pseudo-PDF from all the imported model datasets.
// Each dataset is transformed into a RooNDKeysPdf kernel estimation p.d.f.
// This p.d.f. models the distribution of an arbitrary input dataset as a
// superposition of Gaussian kernels, one for each data point, each
// contributing 1/N to the total integral of the p.d.f.
//
// If debug=kTRUE, each dataset and kernel PDF will be displayed in a canvas.
// This can help to decide if the kernel smoothing parameter needs attention.
if (!GetNumberOfDataSets()) {
Error("ConstructPseudoPDF", "import model datasets with ImportModelData() first");
return;
}
if (fModelPseudoPDF) {
delete fModelPseudoPDF;
fModelPseudoPDF = 0;
fNDKeys.Delete();
fFractions.removeAll();
}
//generate kernels
RooArgList kernels;
Double_t initialWeight = 1. / GetNumberOfDataSets();
for (int i = 0; i < GetNumberOfDataSets(); i++) {
Info("ConstructPseudoPDF", "Kernel estimation of dataset#%d...", i);
RooNDKeysPdf* p = new RooNDKeysPdf(Form("NDK%d", i),
Form("Kernel estimation of dataset#%d", i),
GetObservables(),
*((RooDataSet*)fDataSets[i]),
"am", fSmoothing);
fNDKeys.Add(p);
kernels.add(*p);
if (i < (GetNumberOfDataSets() - 1))
fFractions.addClone(RooRealVar(Form("W%d", i), Form("fractional weight of kernel-PDF #%d", i), initialWeight, 0., 1.));
}
fModelPseudoPDF = new RooAddPdf("Model", "Pseudo-PDF constructed from kernels for model datasets", kernels, fFractions, kTRUE);
}
void GenericModel::ImportModelData(Int_t parameter_num, RooArgList* plist)
{
// Import all model datasets corresponding to the defined parameters, ranges & binnings
RooArgList PLIST;
if (plist) PLIST.addClone(*plist);
RooRealVar* par = dynamic_cast<RooRealVar*>(fParameters.at(parameter_num));
RooAbsBinning& bins = par->getBinning();
Int_t N = bins.numBins();
RooRealVar* par_in_list = (RooRealVar*)PLIST.find(par->GetName());
if (!par_in_list) {
PLIST.addClone(RooRealVar(par->GetName(), par->GetTitle(), 0.));
par_in_list = (RooRealVar*)PLIST.find(par->GetName());
}
for (int i = 0; i < N; i++) {
par_in_list->setMax(bins.binHigh(i));
par_in_list->setMin(bins.binLow(i));
par_in_list->setVal(bins.binCenter(i));
if ((parameter_num + 1) < GetNumberOfParameters())
ImportModelData(parameter_num + 1, &PLIST);
else {
AddModelData(PLIST, GetModelDataSet(PLIST));
}
}
}
void fitTF1(TCanvas *canvas, TH1F h, double XMIN_, double XMAX_, double dX_, double params[], Color_t LC_=kBlack) { //double& FWHM_, double& x0_, double& x1_, double& x2_, double& y0_, double& y1_, double& INT_, double& YIELD_) {
//TCanvas* fitTF1(TH1F h, double HSCALE_, double XMIN_, double XMAX_) {
//TF1* fitTF1(TH1F h, double HSCALE_, double XMIN_, double XMAX_) {
gROOT->ForceStyle();
RooMsgService::instance().setSilentMode(kTRUE);
for(int i=0;i<2;i++) RooMsgService::instance().setStreamStatus(i,kFALSE);
//TCanvas *canvas = new TCanvas(TString::Format("canvas_%s",h.GetName()),TString::Format("canvas_%s",h.GetName()),1800,1200);
RooDataHist *RooHistFit = 0;
RooAddPdf *model = 0;
RooWorkspace w = RooWorkspace("w","workspace");
RooRealVar x("mbbReg","mbbReg",XMIN_,XMAX_);
RooRealVar kJES("CMS_scale_j","CMS_scale_j",1,0.9,1.1);
RooRealVar kJER("CMS_res_j","CMS_res_j",1,0.8,1.2);
kJES.setConstant(kTRUE);
kJER.setConstant(kTRUE);
TString hname = h.GetName();
RooHistFit = new RooDataHist("fit_"+hname,"fit_"+hname,x,&h);
RooRealVar YieldVBF = RooRealVar("yield_"+hname,"yield_"+hname,h.Integral());
RooRealVar m("mean_"+hname,"mean_"+hname,125,100,150);
RooRealVar s("sigma_"+hname,"sigma_"+hname,12,3,30);
RooFormulaVar mShift("mShift_"+hname,"@0*@1",RooArgList(m,kJES));
RooFormulaVar sShift("sShift_"+hname,"@0*@1",RooArgList(m,kJER));
RooRealVar a("alpha_"+hname,"alpha_"+hname,1,-10,10);
RooRealVar n("exp_"+hname,"exp_"+hname,1,0,100);
RooRealVar b0("b0_"+hname,"b0_"+hname,0.5,0.,1.);
RooRealVar b1("b1_"+hname,"b1_"+hname,0.5,0.,1.);
RooRealVar b2("b2_"+hname,"b2_"+hname,0.5,0.,1.);
RooRealVar b3("b3_"+hname,"b3_"+hname,0.5,0.,1.);
RooBernstein bkg("signal_bkg_"+hname,"signal_bkg_"+hname,x,RooArgSet(b0,b1,b2));
RooRealVar fsig("fsig_"+hname,"fsig_"+hname,0.7,0.0,1.0);
RooCBShape sig("signal_gauss_"+hname,"signal_gauss_"+hname,x,mShift,sShift,a,n);
model = new RooAddPdf("signal_model_"+hname,"signal_model_"+hname,RooArgList(sig,bkg),fsig);
//RooFitResult *res = model->fitTo(*RooHistFit,RooFit::Save(),RooFit::SumW2Error(kFALSE),"q");
model->fitTo(*RooHistFit,RooFit::Save(),RooFit::SumW2Error(kFALSE),"q");
//res->Print();
//model->Print();
canvas->cd();
canvas->SetTopMargin(0.1);
RooPlot *frame = x.frame();
// no scale
RooHistFit->plotOn(frame);
model->plotOn(frame,RooFit::LineColor(LC_),RooFit::LineWidth(2));//,RooFit::LineStyle(kDotted));
model->plotOn(frame,RooFit::Components(bkg),RooFit::LineColor(LC_),RooFit::LineWidth(2),RooFit::LineStyle(kDashed));
// with scale
// RooHistFit->plotOn(frame,RooFit::Normalization(HSCALE_,RooAbsReal::NumEvent));
// model->plotOn(frame,RooFit::Normalization(HSCALE_,RooAbsReal::NumEvent),RooFit::LineWidth(1));
// model->plotOn(frame,RooFit::Components(bkg),RooFit::LineColor(kBlue),RooFit::LineWidth(1),RooFit::LineStyle(kDashed),RooFit::Normalization(HSCALE_,RooAbsReal::NumEvent));
frame->GetXaxis()->SetLimits(50,200);
frame->GetXaxis()->SetNdivisions(505);
frame->GetXaxis()->SetTitle("M_{b#bar{b}} (GeV)");
frame->GetYaxis()->SetTitle("Events");
frame->Draw();
h.SetFillColor(kGray);
h.Draw("hist,same");
frame->Draw("same");
gPad->RedrawAxis();
TF1 *tmp = model->asTF(x,fsig,x);
//tmp->Print();
double y0_ = tmp->GetMaximum();
double x0_ = tmp->GetMaximumX();
double x1_ = tmp->GetX(y0_/2.,XMIN_,x0_);
double x2_ = tmp->GetX(y0_/2.,x0_,XMAX_);
double FWHM_ = x2_-x1_;
double INT_ = tmp->Integral(XMIN_,XMAX_);
double YIELD_= YieldVBF.getVal();
double y1_ = dX_*0.5*y0_*(YieldVBF.getVal()/tmp->Integral(XMIN_,XMAX_));
params[0] = x0_;
params[1] = x1_;
params[2] = x2_;
params[3] = y0_;
params[4] = y1_;
params[5] = FWHM_;
params[6] = INT_;
params[7] = YIELD_;
//cout<<"Int = "<<tmp->Integral(XMIN_,XMAX_)<<", Yield = "<<YieldVBF.getVal()<<", y0 = "<<y0_<<", y1 = "<<y1_ <<", x0 = "<< x0_ << ", x1 = "<<x1_<<", x2 = "<<x2_<<", FWHM = "<<FWHM_<<endl;
TLine ln = TLine(x1_,y1_,x2_,y1_);
ln.SetLineColor(kMagenta+3);
ln.SetLineStyle(7);
ln.SetLineWidth(2);
ln.Draw();
canvas->Update();
canvas->SaveAs("testC.png");
// tmp->Delete();
// frame->Delete();
// res->Delete();
// TF1 *f1 = model->asTF(x,fsig,x);
// return f1;
////tmp->Delete();
////ln->Delete();
////model->Delete();
////RooHistFit->Delete();
////w->Delete();
////YieldVBF->Delete();
////frame->Delete();
////res->Delete();
//delete tmp;
//delete ln;
//delete model;
//delete RooHistFit;
//delete w;
//delete YieldVBF;
//delete frame;
//delete res;
// return canvas;
}
