void twoChannels()
{
// open log file
BCLog::OpenLog("log.txt", BCLog::detail, BCLog::detail);
// ---- read histograms from a file ---- //
// open file
std::string fname = "templates.root";
TFile* file = TFile::Open(fname.c_str(), "READ");
// check if file is open
if (!file || !file->IsOpen()) {
BCLog::OutError(Form("Could not open file %s.", fname.c_str()));
BCLog::OutError("Run macro CreateHistograms.C in Root to create the file.");
return;
}
// read histograms
TH1D* hist_bkg1 = (TH1D*)file->Get("hist_bkg1"); // background template for channel 1
TH1D* hist_bkg2 = (TH1D*)file->Get("hist_bkg2"); // background template for channel 2
TH1D* hist_sgn1 = (TH1D*)file->Get("hist_sgn1"); // signal template for channel 1
TH1D* hist_sgn2 = (TH1D*)file->Get("hist_sgn2"); // signal template for channel 2
TH1D* hist_data1 = (TH1D*)file->Get("hist_data1"); // data for channel 1
TH1D* hist_data2 = (TH1D*)file->Get("hist_data2"); // data for channel 2
if (!hist_bkg1 || !hist_bkg2 || !hist_sgn1 || !hist_sgn2 || !hist_data1 || !hist_data2) {
BCLog::OutError("Could not find data histograms.");
return;
}
// ---- perform fitting ---- //
// create new fitter object
BCMTF* m = new BCMTF();
// set precision
m->SetPrecision(BCEngineMCMC::kQuick);
// add channels
m->AddChannel("channel1");
m->AddChannel("channel2");
// add processes
m->AddProcess("background_channel1", 700., 900.);
m->AddProcess("background_channel2", 300., 700.);
m->AddProcess("signal", 0., 400.);
// set data
m->SetData("channel1", *hist_data1);
m->SetData("channel2", *hist_data2);
// set template and histograms
// note: the process "background_channel2" is ignored in channel 1
m->SetTemplate("channel1", "signal", *hist_sgn1, 0.5);
m->SetTemplate("channel1", "background_channel1", *hist_bkg1, 1.0);
// note: the process "background_channel1" is ignored in channel 2
m->SetTemplate("channel2", "signal", *hist_sgn2, 1.0);
m->SetTemplate("channel2", "background_channel2", *hist_bkg2, 1.0);
// set priors
m->GetParameter("background_channel1").SetPrior(new BCGaussianPrior(800., 10.));
m->GetParameter("background_channel2").SetPrior(new BCGaussianPrior(500., 50.));
m->GetParameter("signal").SetPriorConstant();
// marginalize
m->MarginalizeAll();
// find global mode
m->FindMode( m->GetBestFitParameters() );
// print all marginalized distributions
m->PrintAllMarginalized("marginalized.pdf");
// print results of the analysis into a text file
m->PrintSummary();
// print templates and stacks
for (int i = 0; i < m->GetNChannels(); ++i) {
BCMTFChannel* channel = m->GetChannel(i);
channel->PrintTemplates(channel->GetSafeName() + "_templates.pdf");
m->PrintStack(i, m->GetBestFitParameters(), channel->GetSafeName() + "_stack.pdf");
}
// ---- clean up ---- //
// free memory
delete m;
}
void ensembleTest()
{
// open log file
BCLog::OpenLog("log.txt", BCLog::detail, BCLog::detail);
// ---- read histograms from a file ---- //
// open file
std::string fname = "templates.root";
TFile* file = TFile::Open(fname.data(), "READ");
// check if file is open
if (!file || !file->IsOpen()) {
BCLog::OutError(Form("Could not open file %s.", fname.c_str()));
BCLog::OutError("Run macro CreateHistograms.C in Root to create the file.");
return;
}
// read histograms
TH1D* hist_bkg1 = (TH1D*)file->Get("hist_bkg1"); // background template for channel 1
TH1D* hist_bkg2 = (TH1D*)file->Get("hist_bkg2"); // background template for channel 2
TH1D* hist_sgn1 = (TH1D*)file->Get("hist_sgn1"); // signal template for channel 1
TH1D* hist_sgn2 = (TH1D*)file->Get("hist_sgn2"); // signal template for channel 2
TH1D* hist_data1 = (TH1D*)file->Get("hist_data1"); // data for channel 1
TH1D* hist_data2 = (TH1D*)file->Get("hist_data2"); // data for channel 2
if (!hist_bkg1 || !hist_bkg2 || !hist_sgn1 || !hist_sgn2 || !hist_data1 || !hist_data2) {
BCLog::OutError("Could not find data histograms");
return;
}
// ---- perform fitting ---- //
// create new fitter object
BCMTF* m = new BCMTF("MyModel");
// set Metropolis as marginalization method
m->SetMarginalizationMethod(BCIntegrate::kMargMetropolis);
// set the required precision of the MCMC (kLow, kQuick, kMedium, kHigh)
// the higher the precision the longer the MCMC run
m->SetPrecision(BCEngineMCMC::kQuick);
// add channels
m->AddChannel("channel1");
m->AddChannel("channel2");
// add processes
m->AddProcess("background_channel1", 700., 900.);
m->AddProcess("background_channel2", 300., 700.);
m->AddProcess("signal", 0., 400.);
// set data
m->SetData("channel1", *hist_data1);
m->SetData("channel2", *hist_data2);
// set template and histograms
// note: the process "background_channel2" is ignored in channel 1
m->SetTemplate("channel1", "signal", *hist_sgn1, 0.5);
m->SetTemplate("channel1", "background_channel1", *hist_bkg1, 1.0);
// m->SetTemplate("channel1", "background_channel2", *hist_bkg1, 0.0);
// note: the process "background_channel1" is ignored in channel 2
m->SetTemplate("channel2", "signal", *hist_sgn2, 1.0);
m->SetTemplate("channel2", "background_channel2", *hist_bkg2, 1.0);
// m->SetTemplate("channel2", "background_channel1", *hist_bkg2, 0.0);
// set priors
m->GetParameter("background_channel1").SetPrior(new BCGaussianPrior(800, 10));
m->GetParameter("background_channel2").SetPrior(new BCGaussianPrior(500, 50));
m->GetParameter("signal").SetPriorConstant();
// run MCMC
m->MarginalizeAll();
// find global mode
m->FindMode( m->GetBestFitParameters() );
// print all marginalized distributions
m->PrintAllMarginalized("marginalized.pdf");
// print results of the analysis into a text file
m->PrintSummary();
// print templates and stacks
for (int i = 0; i < m->GetNChannels(); ++i) {
BCMTFChannel* channel = m->GetChannel(i);
channel->PrintTemplates(channel->GetName() + "_templates.pdf");
m->PrintStack(i, m->GetBestFitParameters(), channel->GetName() + "_stack.pdf");
}
// ---- perform ensemble tests ---- //
// create new analysis facility
BCMTFAnalysisFacility* facility = new BCMTFAnalysisFacility(m);
// settings
facility->SetFlagMarginalize(false);
// open new file
file = TFile::Open("ensembles.root", "RECREATE");
file->cd();
// create ensembles
TTree* tree = facility->BuildEnsembles( m->GetBestFitParameters(), 2000 );
// run ensemble test
TTree* tree_out = facility->PerformEnsembleTest(tree, 2000);
// write trees into file
tree->Write();
tree_out->Write();
// close file
file->Close();
// free memory
delete file;
// ---- clean up ---- //
// free memory
delete m;
}