//____________________________________
void DoSPlot(RooWorkspace* ws){
std::cout << "Calculate sWeights" << std::endl;
// get what we need out of the workspace to do the fit
RooAbsPdf* model = ws->pdf("model");
RooRealVar* zYield = ws->var("zYield");
RooRealVar* qcdYield = ws->var("qcdYield");
RooDataSet* data = (RooDataSet*) ws->data("data");
// fit the model to the data.
model->fitTo(*data, Extended() );
// The sPlot technique requires that we fix the parameters
// of the model that are not yields after doing the fit.
RooRealVar* sigmaZ = ws->var("sigmaZ");
RooRealVar* qcdMassDecayConst = ws->var("qcdMassDecayConst");
sigmaZ->setConstant();
qcdMassDecayConst->setConstant();
RooMsgService::instance().setSilentMode(true);
// Now we use the SPlot class to add SWeights to our data set
// based on our model and our yield variables
RooStats::SPlot* sData = new RooStats::SPlot("sData","An SPlot",
*data, model, RooArgList(*zYield,*qcdYield) );
// Check that our weights have the desired properties
std::cout << "Check SWeights:" << std::endl;
std::cout << std::endl << "Yield of Z is "
<< zYield->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("zYield") << std::endl;
std::cout << "Yield of QCD is "
<< qcdYield->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("qcdYield") << std::endl
<< std::endl;
for(Int_t i=0; i < 10; i++)
{
std::cout << "z Weight " << sData->GetSWeight(i,"zYield")
<< " qcd Weight " << sData->GetSWeight(i,"qcdYield")
<< " Total Weight " << sData->GetSumOfEventSWeight(i)
<< std::endl;
}
std::cout << std::endl;
// import this new dataset with sWeights
std::cout << "import new dataset with sWeights" << std::endl;
ws->import(*data, Rename("dataWithSWeights"));
}
//____________________________________
RooStats::SPlot* DoSPlot(RooWorkspace* ws){
//Users will have to edit this functioon to account for changes made for their own model
//This will mainly involve matching the variable names to those defined in AddModel and AddData
std::cout << "Calculate sWeights" << std::endl;
// get what we need out of the workspace to do the fit
RooAbsPdf* model = ws->pdf(pdfName);
RooDataSet* data = (RooDataSet*) ws->data(dataSetName);
// fit the model to the data.
model->fitTo(*data, Extended() );
// The sPlot technique requires that we fix the parameters
// of the model that are not yields after doing the fit.
ws->var(TString("SigMean"))->setConstant();
ws->var(TString("SigWidth"))->setConstant();
ws->var(TString("l0"))->setConstant();
ws->var(TString("l1"))->setConstant();
ws->var(TString("l2"))->setConstant();
//The only 2 free parameters will now be the signal and background yields
//Note, if we have more than 2 types of event we would need to get additional yeilds here
RooRealVar* s_Yield = ws->var(TString("SigYield"));
RooRealVar* b_Yield = ws->var(TString("BckYield"));
RooMsgService::instance().setSilentMode(true);
// Now we use the SPlot class to add SWeights to our data set
// based on our model and our yield variables
RooStats::SPlot* sData = new RooStats::SPlot(pdfName+"SW","An SPlot",
*data, model, RooArgList(*s_Yield,*b_Yield) );
//Check that our weights have the desired properties
std::cout << "Check SWeights:" << std::endl;
std::cout << std::endl << "Yield of Signal is "
<< s_Yield->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight(TString("SigYield")) << std::endl;
std::cout << "Yield of Background is "
<< b_Yield->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight(TString("BckYield")) << std::endl
<< std::endl;
for(Int_t i=0; i < 10; i++)
{
std::cout << "signal Weight " << sData->GetSWeight(i,TString("SigYield"))
<< " background Weight " << sData->GetSWeight(i,TString("BckYield"))
<< " Total Weight " << sData->GetSumOfEventSWeight(i)
<< std::endl;
}
std::cout << std::endl;
//Now plot the fit resluts for checking
//Users will have to edit this for the variables they are using
RooRealVar* vML = ws->var("Mmiss");
RooAbsPdf* b_Model = ws->pdf("BackPDF");
RooPlot* frame = vML->frame() ;
data->plotOn(frame, DataError(RooAbsData::SumW2) ) ; //plot the data
model->plotOn(frame,LineStyle(kDashed), LineColor(kRed)) ; //model = signal + back fit result
model->plotOn(frame,Components(*b_Model),LineStyle(kDashed),LineColor(kGreen)) ; //just the back fit result
model->paramOn(frame,
FillColor(kRed),
Label("Global Fit parameters:"),
Layout(0.1, 0.4, 0.9),
Format("NEU",AutoPrecision(3)),
ShowConstants());
frame->SetTitle("M#Lambda distribution fit");
frame->Draw() ;
canvas->Modified();
canvas->Update();
cin.get();//wait here until users ready to continue
//Write fit plot canvas to output file
TDirectory* savedir=gDirectory;
outPlots->cd();
canvas->SetName(pdfName);
canvas->Write();
savedir->cd();
//return the finished RooStats::sPlot object
return sData;
}
